esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 MAC Protocol Design
### Adaptive Slot Allocation Method
#### 4.4.1 Dynamic Slot Borrowing  
As mentioned earlier, a roll-call access is adopted in the TDMA period. As shown in Figure 9, we assume that there are several nodes in the sleep mode and their slots are in idle. The last idle slot in the TDMA period (closest to the first sub-beacon) is marked as \(m_{3}\).  
As shown in Figure 9, in the TDMA period, slots \(m_{1-4}\) are idle. At the same time, in the CSMA period, nodes \(n_{1-4}\) generate large amount of data packets. Due to the intense contention, packets are queued in their buffer zone and recorded in the queue counter. When the data packets are received by the host node, queue counters are extracted. Let \(q_{i}\) denote the list of the queue counter value, the node \(n_{2}\) that with the largest queue counter will be chosen, and then the idle slot \(m_{3}\), the slot closest to the sub-beacon, will be 'lent' to it. In other word, the node \(n_{2}\) temporarily becomes a node in the TDMA  
Figure 8: Data packet structure of ISWN.  
period. To avoid unnecessary control overhead, the available slots are limited, let \(N_{\mathit{borrow\_max}}\) be the maximum of the slots number that can be borrowed.
# Analysis of Spatiotemporal Gait Variables before and after Unilateral Total Knee Arthroplasty
###### Abstract  
This study aimed (a) to evaluate the spatiotemporal gait variables of total knee arthroplasty (TKA) before (pre-) and after the procedure (post-), and (b) to investigate the influence of the surgical side on these variables. Twenty-one volunteers (13 females and 8 males) participated, undergoing assessments pre-surgery and nine to 12 months post-surgery. Clinical tests indicated significant reductions in knee pain and improvements in active and passive extension post-surgery. TKA resulted in decreased pain, extension deficits, and functional assessments, with lower scores on the WOMAC questionnaire. A gait analysis showed post-surgery improvements in gait speed (5.8%), cycle time (\(-\)4.8%), step time (4.4%), double limb support time (\(-\)11.1%), step (4.4%) and stride (6.3%) lengths, and step (5.1%) and stride (5.0%) cadences. Comparisons between surgical side and limb dominance indicated significant differences in gait speed, stance, swing and step times, double limb support time, step and stride lengths, and step cadence. The non-dominant limb demonstrated greater improvements across most parameters compared to the dominant limb. These findings emphasize the importance of considering the limb dominance of patients with knee osteoarthritis when evaluating post-TKA function. These conclusions can be helpful for personalized rehabilitation programs, allowing tailored interventions for individuals undergoing knee surgery.  
knee; arthroplasty; gait analysis; biomechanics 2024 14 8901. [[https://doi.org/10.3390/app1498901](https://doi.org/10.3390/app1498901)]([https://doi.org/10.3390/app1498901](https://doi.org/10.3390/app1498901))
# Influence of Plastic Waste on the Workability and Mechanical Behaviour of Asphal Concrete
## 3 Results
### Water Sensitivity  
The indirect tensile strength (ITS) results for the dry and wet specimens and the ITS ratios are presented in Figure 9 (average of three cylindrical specimens). There is no evidence that both plastic type and content influence the ITS values of asphalt mixtures. HDPE and LDPE(uw) increased the ITS values measured for the reference mixture on non-conditioned specimens, whereas ABS only increased ITS to 4% and 6% of plastic content. For wet conditions, the added plastic waste did not improve ITS in dry conditions. Because the reference mixture has a good ITSR result (ITSR around 110%), improvements in resistance to water were hard to achieve with the incorporation of plastic waste polymers. As occurs in this case, when the moisture resistance is excellent, the normal variation of the ITS results may deliver values of ITSR above 100%, mainly if the testing temperature is 25 \({}^{\circ}\)C. Only for the case of 6% HDPE500, the value of ITSR was under the typical requirements of 80%. Although the asphalt mixtures with plastic waste had good water sensitivity performance compared with the typical requirements, in the case of ABS and HDPE500, plastic contents above 6% are likely to decrease the ITS values.  
Figure 9: Indirect tensile strength (ITS) in dry conditions and ratio between ITS measured on wet and dry specimens (ITSR) as a function of plastic waste content.
## 3 Protocol Specification
### Protocol Overview  
Informally, our hybrid routing protocol based on AFSA and the ACO routing algorithm, named FSACO, works as follows:  
1. First, AFSA is initialised for each AF to perform preying, swarming and following behaviours from the source node to the sink node.  
After several iterations, a selection of satisfactory routes to the sink node is obtained through the AFSA algorithm.
* Initialise the pheromone information of the ACO routing algorithm using the satisfactory routes obtained by AFSA.
* At some variable interval, a Forward Ant (FANT) is launched from the source node toward the sink node.
* Each FANT chooses the next node according to a probabilistic state transition rule integrated with the crowd factor in AFSA.
* Once FANT reaches the sink node, a Backward Ant (BANT) is created, which moves back along the route that the FANT had previously traversed.
* During the BANT travel, it will modify the amount of pheromone on the path by applying the pheromone updating rule.
* Once a BANT arrives at the source node, the next iteration will begin after some interval.
# Use of Phase Change Materials for Food Applications--State of the Art in 2022
## 2 Food Applications of PCM
### Food Storage and Transport
#### 2.2.11. Compartments That Are Fixed on a Train  
A very informative article on transportation of food by train is [77]. To transport perishable freight at specific temperatures so-called refrigerator cars (also called reefer car, refrigerated boxcar... ) were developed, which do not just rely on ventilation, but instead have their own way of cold supply. As early as 1851, butter was transported in the US by purpose-built freight cars, using ice for cooling. A few years later transport of beef started, then fruits... To avoid direct contact between food and ice, the ice was placed in separate bins. After about 400-600 km, new ice had to be supplied. By 1920, thousands of ice-cooled rail cars were in operation. In the second half of the 20 th century, mechanical refrigeration started to replace the use of ice. Similarly to the US, in Japan refrigerated transport of fish in cars equipped with ice bunkers started in the early 1900s, in parallel to fish in smaller boxes with ice. By the 1960s, refrigerated trucks began to displace rail transport, in the late 1980s also reefer containers. Mechanically cooled cars never became widespread in Japan. In the UK, the needs to refrigerate food during transport on trains was minimized by using nonstop express trains, and the generally shorter distances in the UK. Nevertheless, some ice cooling as well as mechanical cooling on cars was used. Summarizing, cooling by ice and mechanical refrigeration were used on a large scale, while however dry ice (frozen carbon dioxide) was tested but not economic. In any case, nowadays many railways around the world transport in specialized refrigerated containers, the trains now taking back the role of long-distance transport by trucks between transport hubs to reduce CO\({}_{2}\) emissions.
## 4 Results from the Literature Review
### Importance of Recent Solutions in Transport in Case of Global Crises
In addition, the use of renewable electricity or locally sourced hydrogen can lessen dependency on imports, ensuring transport even when access to traditional fuels is disrupted [109]. Electric vehicles also contribute to the development of more accessible transport systems, which is essential for maintaining mobility in crisis situations [107,110]. Their quiet operation is an additional advantage, especially in military operations that require discretion [111].
# Therapeutic Potential and Challenges of Pioglitazone in Cancer Treatment
## 3 Therapeutic Use of Pioglitazone
### Anticancer Properties
#### 3.5.2 Molecular Pharmacology
\begin{table}
\begin{tabular}{l l l} \hline
**Process** & **Mechanism** & **Cancer Type** \\ \hline \multirow{7}{*}{Reduced cell proliferation} & Increased expression of excitatory amino acid transporter 2 [44]. & \\ \cline{2-3}  & Increased activity of p-Akt and p-GSK-3\(\beta\)[44]. & Neuroblastoma \\ \cline{2-3}  & Redifferentiation of tumor associated adipocytes [44]. & \\ \cline{2-3}  & Inhibited cell growth via mTOR and STAT5 pathway tampering & \\ \cline{2-3}  & by retinoid X receptor agonists [45]. & \\ \cline{2-3}  & Inhibited expression of estrogen receptor and aromatase via & \\ \cline{2-3}  & PGE2 and BRCA1 pathways [46]. & \\ \cline{2-3}  & Inhibition of JAK2/STAT3 pathway [47]. & \\ \cline{2-3}  & Increased expression of p21 and MAPK activity [48]. & \\ \cline{2-3}  & Inhibited CSC proliferation due to decreased STAT5 and HIF-2\(\alpha\) levels [49]. & Chronic myeloid \\ \cline{2-3}  & Downregulated _MAPK_, _RAS_, _MYC_ gene expression and & \\ \cline{2-3}  & phosphorylation of MAPK pathway proteins [50]. & Non-small cell lung \\ \hline \multirow{7}{*}{Increased apoptosis} & Downregulation of BCL2 and SCD1 [44]. & \\ \cline{2-3}  & Reduced expression phosphorylation of MEK1 and ERK & \\ \cline{2-3}  & phosphorylation [51]. & \\ \cline{2-3}  & Downregulation of STAT3 with ERK1/2, NF-\(\kappa\beta\) and p38 MAPK & \\ \cline{2-3}  & molecules unaffected [49; 52]. & \\ \cline{2-3}  & Reduced expression of Survivin [49; 52]. & \\ \cline{2-3}  & Increased expression of TRAIL death ligand and apoptosis & \\ \cline{2-3}  & inducing factor [49; 53]. & \\ \cline{2-3}  & Downregulation of BCLXL/BCL2 in a PPAR-\(\gamma\) and & Prostate cancer, \\ \cline{2-3}  & caspase-independent manner [51]. & \\ \cline{2-3}  & Induction of apoptosis in a caspase-dependent manner assisted & \\ \cline{2-3}  & by downregulation of c-FLIP, leading to BCL2 downregulation and instability [54]. & Caki cells \\ \cline{2-3}  & Downregulation of X-linked inhibitor of apoptosis (XIAP) and & \\ \cline{2-3}  & cyclooxygenase-2 (COX-2) [55]. & \\ \cline{2-3}  & Upregulation of cyclinB1, CDC2, p21 and alteration of & \\ \cline{2-3}  & BAX/BCL2 ratio [55]. & \\ \hline \multirow{7}{*}{Reduced angiogenesis} & Reduced expression of matrix metalloproteinase 2 (MMP2), & \\ \cline{2-3}  & vascular endothelial growth factor (VEGF), COX-2. & \\ \cline{2-3}  & Inhibition of fibroblast growth factor 2 (FGF-2) and urokinase & \\ \cline{2-3}  & plasminogen activator. & \\ \cline{2-3}  & Apoptosis of endothelial cells. & \\ \cline{2-3}  & Combined downregulation of COX-2 and VEGF when coupled & \\ \cline{2-3}  & with clofibric acid [45]. & \\ \cline{2-3}  & Reduction of bFGF- and VEGF-initiated angiogenesis [59]. & Chick chorioallantoic \\ \cline{2-3}  & membrane model & \\ \hline \end{tabular}
\end{table} 
Table 1: Molecular mechanisms of pioglitazone.
# Low-Complexity QRD-\(M\) with Path Eliminations in MIMO-OFDM Systems
## 2 System Model  
In this paper, a MIMO-OFDM system which has \(N_{t}\) transmit antennas and \(N_{r}\) receive antennas is considered where \(N_{r}\) is greater than or equal to \(N_{t}\). Figure 1 shows the general structure of the MIMO-OFDM transceiver. Input data is demultiplexed into \(N_{t}\) substreams at the transmitter. Then, \(N_{t}\) substreams go through quadrature amplitude modulation (QAM) mapping, inverse fast Fourier transform (IFFT) processors. Finally, cyclic prefix (CP) is added to decrease the effect of inter-symbol interference (ISI) and inter-carrier interference (ICI). At the receiver, MIMO received vector \(\mathbf{Y}=\left[\begin{array}{ccc}Y_{1}&Y_{2}&\cdots&Y_{N_{t}}\end{array} \right]^{T}\) after the fast Fourier transform (FFT) is as follows,  
\[\mathbf{Y} =\mathbf{H}\mathbf{X}+\mathbf{W} \tag{1}\] \[=\left[\begin{array}{cccc}H_{11}&H_{12}&\cdots&H_{1N_{t}}\\ H_{21}&H_{22}&\cdots&H_{2N_{t}}\\ \vdots&\vdots&\ddots&\vdots\\ H_{N_{t}1}&H_{N_{t}2}&\cdots&H_{N_{t}N_{t}}\end{array}\right]\left[\begin{array} []{c}X_{1}\\ X_{2}\\ \vdots\\ X_{N_{t}}\end{array}\right]+\left[\begin{array}{c}W_{1}\\ W_{2}\\ \vdots\\ W_{N_{t}}\end{array}\right],\]  
where \(\mathbf{X}\) denotes \(N_{t}\times 1\) MIMO transmit vector, \(\mathbf{H}\) denotes \(N_{r}\times N_{t}\) rich scattering complex Rayleigh channel matrix where the element \(H_{ij}\), \(i=1,2,\cdots,N_{r},j=1,2,\cdots,N_{t}\) denotes channel components from the \(j\)-th transmit antenna to the \(i\)-th receive antenna, and \(\mathbf{W}\) denotes \(N_{r}\times 1\) complex additive white Gaussian noise (AWGN) vector.  
Figure 1: The general structure of the MIMO-OFDM (multiple input multiple output system in combination with orthogonal frequency division multiplexing) transceiver. CP: cyclic prefix; FFT: fast Fourier transform; IFFT: inverse FFT; QAM: quadrature amplitude modulation.
## 4 Simulation Results and Discussion
### Simulation of Flight Cruising in a Real Wind Field
#### 4.2.2 Comparison of Energy Generation
2. The RC configuration optimizes the distribution of irradiance by combining adjacent column components in reverse order. This approach reduces the differences in irradiance among the row components of the array, thereby decreasing mismatch losses. Simulation results demonstrate that the RC configuration significantly enhances the output power of the PV array and eliminates localized MPP on the P-V curve. 3. The performance of the RC configuration surpasses that of the SP, HTCT, and VTCT configurations. It achieves the highest output power during the entire day's operation. The net energy accumulated on the four days is 10.6% higher than the suboptimal HTCT configuration. Implementing this configuration can alleviate to some extent the energy shortage issue during weak irradiance flight conditions. For airships with frequent variations in attitude angles, the performance of the RC configuration surpasses that of the TCT and SP configurations. However, the increased complexity of the RC configuration system and the additional mass of cables and structural attachments it brings have not been evaluated. In future work, we would like to conduct ground-scale experiments to validate the effectiveness of the proposed RC configuration and assess the cost of additional weight. Additionally, we intend to extend this configuration to other solar-powered aerial vehicles, such as solar drones and high-altitude balloons. Conceptualization, C.S. and K.S. ; methodology, C.S. ; software, C.S. ; validation, C.S., D.C. and X.J. ; formal analysis, J.G. ; investigation, T.Z. ; resources, K.S. ; data curation, D.C.; writing--original draft preparation, C.S. ; writing--review and editing, C.S. ; visualization, X.J. ; supervision, K.S. ; project administration, J.G. and T.Z. ; funding acquisition, K.S. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant No. 51775021), the Fundamental Research Funds for the Central Universities (Grant No. YWF-23-JC-01, YWF-23-JC-02, YWF-23-JC-03, YWF-23-JC-04, YWF-23-JC-09). Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy. The authors declare no conflict of interest.
# Uniform vs. Lognormal Kinematics in Robots: Perceptual Preferences for Robotic Movements
## 3 Robotic Arm Modelling
### Pose of an Object
#### 3.1.1 Forward Kinematic  
The forward kinematics of a robot calculates the pose of the tool located at its end referred to the CF located at its base, as a function of its joints' position.  
Forward kinematics is calculated by applying the procedure described by [PERSON] (DH) [17] and which is widely used in robotics. In broad strokes, the procedure is as follows:  
As shown in Figure 4, a CF is associated with the base of the robot \(\{s_{0}\}\), one with each joint in the corresponding link and one at the end of the tool \(\{s_{6}\}\). Therefore, in the six-degrees-of-freedom robot analyzed, there will be seven CFs. These coordinate frames were located following the DH rules.  
These parameters are placed in a table in which each row represents a joint and the four columns the respective values of each transformation. The following four transformations must be done on the \(\{S_{k-1}\}\) to match the CF \(\{S_{k}\}\):  
1. Rotate an angle \(\theta\) on the \(z\)_-axis_ of the CF \(\{S_{k-1}\}\) so that its \(x\)-axis is parallel to the \(x\)-axis of the CF \(\{S_{k}\}\).  
Figure 4: Coordinate frames associated with the robot.  
Figure 3: Example of CFs to define the pose of a mobile object.  
2. Displace the new CF \(\{S_{k-1}\}\) to a given distance \(d\) on its \(z\)-_axis_ so that its \(x\)-_axis_ coincides with the \(x\)-_axis_ of the CF \(\{S_{k}\}\).
3. Displace the new CF \(\{S_{k-1}\}\) to a given distance \(a\) on its \(x\)-_axis_ so that the positions of both CFs coincide.
4. Rotate the new CF \(\{S_{k-1}\}\) an angle \(\alpha\) on the new \(x\)-_axis_ so that the CFs match. With these parameters applied to the robot of Figure 4, Table 1 is obtained:  
Row \(k\) of Table 1 has the information needed to calculate the homogeneous transformation matrix that relates the CF \(\{S_{k}\}\) taking the CF \(\{S_{k-1}\}\) as a reference and is given by:  
\[{}^{k-1}T_{k}=\begin{pmatrix}c(\theta_{k})&-c(\alpha_{k})s(\theta_{k})&s( \alpha_{k})s(\theta_{k})&a_{k}c(\theta_{k})\\ s(\theta_{k})&c(\alpha_{k})c(\theta_{k})&s(\alpha_{k})c(\theta_{k})&a_{k}s( \theta_{k})\\ 0&-s(\alpha_{k})&c(\alpha_{k})&d_{k}\\ 0&0&0&1\end{pmatrix} \tag{5}\]  
where \(c()\) is \(cos()\) and \(s()\) is \(sin()\). Applying this equation to each of the rows in Table 1 and composing the matrices, we obtain the matrix that defines the pose of the tool located at the end of the robot with respect to CF of its base, depending on the angles of each of the joints.  
\[{}^{0}T_{6}=\ ^{0}T_{1}\ ^{1}T_{2}\ ^{2}T_{3}\ ^{3}T_{4}\ ^{4}T_{5}\ ^{5}T_{6} \tag{6}\]
# Application Layer Protocol Identification Method Based on ResNet
## 3 Materials and Methods
### Data Truncation  
Data truncation aims to extract network traffic data from PCAP format files and filter out packets transmitted using the TCP and UDP protocols. We utilize the Scapy library in Python to achieve this objective. Initially, the headers of the packets are removed, retaining only the payload data from the transport layer. Subsequently, following the method outlined in the reference [27], the length of the payload data is uniformly adjusted to 784 bytes, serving as the core for identification. If the payload data length exceeds 784 bytes, the first 784 bytes are extracted. Conversely, if the payload data length is less than 784 bytes, it is padded with \"\(\backslash\)x00\".
# Assimilation of Earth Observation Data Over Cropland and Grassland Sites into a Simple GPP Model
## Results
### Overall Agreement between Modelled and Observed GPP
Description of plots as Figure 7.  
In order to further disentangle the ability of the model in the spatial and temporal dimensions, we compared the mean annual GPP per site (spatial dimension Figure 10) and the site-level statistics of correlation between annual GPP time series (temporal dimension, Figure 11). Figure 10: Scatter plots of mean annual GPP per site: EC GPP versus GPP estimated by the Sim model (**left**) and MOD17 (**right**). Description of plots as Figure 9. Incomplete years (with partial EC measurements temporal cover) were excluded from this analysis. As a result, a subsample of 45 sites having at least one complete year was available. Figure 9: Scatter plots of annual GPP: EC GPP versus GPP estimated by the Sim model (**left**) and MOD17 (**right**). Circle and triangle symbols denote crop and grassland sites, respectively. Symbol color ranges from red to blue and indicated the site latitude. Crop symbols with green circle outline highlight site-year data points referring to maize. The dashed line is the 1:1 line while the continuous line is the ordinary least square linear regression. The coefficient of determination (R\({}^{2}\)), the Root Mean Square Error (RMSE), Mean Bias Error (MBE), and the number of data points (n) are reported for all sites together, only crop sites and only grassland sites. Incomplete years (with partial EC measurements temporal cover) were excluded from this analysis. The Sim model explains 39% of the spatial variability of GPP with a RMSE 0.34 kgC m\({}^{-2}\) y\({}^{-1}\) (Figure 10). MOD17 performances are lower (explain variability = 23%, RMSE = 0.52 kgC m\({}^{-2}\) y\({}^{-1}\)) and show consistent underestimate at mid to high annual GPP values. Inter-annual GPP variability is equally and relatively well tracked by both the Sim model and MOD17 (Figure 11). Despite this, examples of sites showing negative correlation are present both when comparing Sim model and MOD17 with EC over time. One example of such sites is the grassland site of IT-Amp reported in Figure 6 where, despite both the Sim model and MOD17 are able to track the intra-annual variation of the GPP, they both fail in modulating the inter-annual variation (r = \(-\)0.47 and \(-\)0.46 for Sim model and MOD17 respectively).
## 3 Control System Design
### Direct Yaw-Moment Control
#### 3.6.2 Sliding Mode Controller  
The linear sliding mode surface is used in this article [43; 44]. The sliding surface is defined as Equation (19). By differential transformation, it is easy to obtain Equation (20). The exponential reaching rate is selected to constrain the trajectory of the system, as shown in Equation (21). The additional yaw moment can be obtained as Equation (22). In order to smooth the signal, \(\operatorname{sgn}(s)\) is replaced by the saturation function \(\operatorname{sat}(s)\) as shown in Equation (23).  
\[s=(r-r_{ref})+\lambda(\beta-\beta_{ref}) \tag{19}\]  
\[\dot{s}=(\dot{r}-\dot{r}_{ref})+\lambda(\dot{\beta}-\dot{\beta}_{ref}) \tag{20}\]  
\[\dot{s}=-\operatorname{sgn}(s)-Ks \tag{21}\]  
\[\Delta M=-(l_{f}C_{f}-l_{r}C_{r})\beta-\frac{l_{f}^{2}C_{f}+l_{r}^{2}C_{r}}{V} +l_{f}C_{f}\delta_{f}-l_{r}C_{r}\delta_{r}-l_{z}(\operatorname{sgn}(s)+Ks)+ \dot{r}_{ref} \tag{22}\]  
\[\operatorname{sat}(s)=\begin{cases}1&s>\Delta t\\ \frac{s}{\Delta t}&|s|\leq\Delta t\\ -1&s<-\Delta t\end{cases} \tag{23}\]where \(\lambda\) represents the weight coefficient of the deviation of the state quantity, which is set to 10. \(\varepsilon\) and \(K\) are set to 1 and 0.1. The boundary layer thickness \(\Delta t\) is set to 0.05.
# Edge-Guided Dual-Stream U-Net for Secure Image Steganography
###### Abstract  
Steganography, a technique for concealing information, often faces challenges such as low decoding accuracy and inadequate extraction of edge and global features. To overcome these limitations, we propose a dual-stream U-Net framework with integrated edge enhancement for image steganography. Our main contributions include the adoption of a dual-stream U-Net structure in the encoder, integrating an edge-enhancement stream with the InceptionDMK module for multi-scale edge detail extraction, and incorporating a multi-scale median attention (MSMA) module into the original input stream to enhance feature representation. This dual-stream design promotes deep feature fusion, thereby improving the edge details and embedding capacity of stego images. Moreover, an iterative optimization strategy is employed to progressively refine the selection of cover images and the embedding process, achieving enhanced stego quality and decoding performance. Experiments show that our method produces high-quality stego images across multiple public datasets, achieving near-100% decoding accuracy. It also surpasses existing methods in visual quality metrics like PSNR and SSIM. This framework offers a promising approach for enhancing steganographic security in real-world applications such as secure communication and data protection.  
Keywords:steganography; information-hiding; dual-stream U-Net; edge enhancement; image security; iterative optimization +
Footnote †: journal: Applied sciences