esa-sceva/satcom-chunk-collection huggingface dataset:
# Congestion Control in CoAP Observe Group Communication
## 3 CoCo-RED: Congestion Control Random Early Detection
When the client has obtained the CON message from the Server, it calculates the \(AvgQ\) via the exponential weighted moving average (EWMA) [26], which can be seen in Formula (1) (below). This operation conforms to three principles: 1. If the \(AvgQ\) is lower than \(Threshold_{min}\), the incoming packet is accepted or placed in the queue. 2. If the \(AvgQ\) is between \(Threshold_{min}\) and \(Threshold_{max}\), the incoming packet is dropped in accordance with the dropping probability in Formula 2. Then, the client waits for a notification with the same MID which arises from the server's retransmission. 3. If the \(AvgQ\) is equal to or beyond \(Threshold_{max}\), the incoming packet is dropped in accordance with the exponential dropping probability in Formula 3. Similar to the second principle, the client waits for a notification with the same MID which arises from the server's retransmission. **Algorithm 1** Fibonacci Pre-Increment Backoff  
Figure 3: Backoff method durations for one message transmission and four message retransmissions, starting with \(RTO_{init}\) = 2 s.  
In Formula (2) to Formula (3), dropping the packet when the \(AvgQ\) in the buffer is greater than the threshold can help to decrease the traffic congestion in a particular network and lessen the communication burdens on the client's side. Thus, when the packet is dropped, the server still waits for the backoff during the retransmission before resending the notification. Figure 4: Congestion Control Random Early Detection (CoCo-RED) thresholds in the buffer queue. Figure 5: CoCo-RED timing diagram. FPB: Fibonacci Pre-Increment Backoff. \[AvgQ\ \leftarrow\ (1-w_{q})\ AvgQ+w_{q}q \tag{1}\]  
\[p_{d}(AvgQ)=\frac{AvgQ-Threshold_{min}}{Threshold_{max}-Threshold_{min}}\times max_{p} \tag{2}\]  
From the original exponential formula:  
\[y=ab^{x}\]  
where the limit of the buffer size is \(K\) and the max probability is 1 in order of the (_x,y_) value a and value b is expressed as  
\[a=\frac{max_{p}}{(e^{K-Threshold_{max}})}\]  
\[b=(e^{\frac{ln\ (1/max_{p})}{K-Threshold_{max}}}).\]  
Therefore, for given \(a\) and \(b\) values, the actual probability of an arrived packet being dropped is computed as  
Figure 3.0  
In this regard, the \(AvgQ\) is the average queue length, \(q\) is the current queue size, \(w_{q}\) represents the exponentially weighted moving average (equal to 0.002), and \(K\) denotes the limit of the buffer size. Figure 6 shows the packet drop probability function for CoCo-RED supposing that \(K=100\) bytes, \(max_{p}=0.1\), \(Threshold_{min}=10\) bytes, and \(Threshold_{max}=60\) bytes. Figure 7 exhibits an overview of the RTO used to maintain and update the RTO state information for a destination endpoint in CoCo-RED. Figure 3.1 Packet drop probability function for CoCo-RED.
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 4 Imaging Fusion Algorithms
### Pyramid Method  
The principle of the pyramid method is to decompose individual images within the fusion into a multiscale pyramid image sequence (i.e., reduce the resolution of the image in a pyramid sequence shown in Figure 1). The low-resolution image is in the upper layer and high-resolution image in the lower layer, with the upper layer image being 1/4 of the previous layer image size. The pyramid of all the images is fused to the corresponding layer using a specific rule. The synthetic pyramid obtained is reconstructed according to the inverse process of pyramid generation. Based on this theory, multiple pyramid fusion algorithms (e.g., Gaussian pyramid and Laplacian Pyramid) have been proposed with different pyramid decomposition structures, fusion rules and reconstruction methods. In the medical field, the pyramid method has been applied in fusing multimodal medical images, such as MRI/CT, PET/MRI and SPECT/MRI [57; 58; 59].
# Novel Chitosan Derivatives and Their Multifaceted Biological Applications
## 3 Biological Applications of ChDs
### Antioxidant Applications  
A new class of ChD-possessing thiourea salts has been reported that could successfully enhance the solubility and the antioxidant activity. Their antioxidant properties were tested and an increase in thiourea salt groups in ChDs showed enhanced scavenging effect. The scavenging activity of the ChDs against DPPH-radical and superoxide-radical was more than 90% at 1.6 mg/mL. No cytotoxicity was observed for the L929 cells at all testing concentrations [114]. Quentized N-pyridylurea ChDs were prepared and the antioxidant activity of the synthesized ChDs was assessed in vitro. Enhanced antioxidant capacity compared with N-pyridylurea ChDs was observed and high biocompatibility with L929 cells demonstrated [113].  
A group of novel chitosan quaternary ammonium derivatives containing pyridine or amino-pyridine were successfully synthesized. This showed improved antifungal activity and better water solubility and stronger antioxidant activity than chitosan. The synergistic effect of the amino group and pyridine improved the antioxidant activity of ChDs [110]. A new class of ChDs possessing 1,2,3-triazoleolium charged units by \"click reaction\" with efficient 1,2,3-triazoleo quaternization were synthesized and possessed enhanced radical-scavenging activity [112].
# PFSegIris: Precise and Fast Segmentation Algorithm for Multi-Source Heterogeneous Iris
###### Abstract  
Current segmentation methods have limitations for multi-source heterogeneous iris segmentation since differences of acquisition devices and acquisition environment conditions lead to images of greatly varying quality from different iris datasets. Thus, different segmentation algorithms are generally applied to distinct datasets. Meanwhile, deep-learning-based iris segmentation models occupy more space and take a long time. Therefore, a lightweight, precise, and fast segmentation network model, PFSegIris, aimed at the multi-source heterogeneous iris is proposed by us. First, the iris feature extraction modules designed were used to fully extract heterogeneous iris feature information, reducing the number of parameters, computation, and the loss of information. Then, an efficient parallel attention mechanism was introduced only once between the encoder and the decoder to capture semantic information, suppress noise interference, and enhance the discriminability of iris region pixels. Finally, we added a skip connection from low-level features to catch more detailed information. Experiments on four near-infrared datasets and three visible datasets show that the segmentation precision is better than that of existing algorithms, and the number of parameters and storage space are only 1.86 M and 0.007 GB, respectively. The average prediction time is less than 0.10 s. The proposed algorithm can segment multi-source heterogeneous iris images more precisely and quicker than other algorithms.  
Keywords:iris segmentation; heterogeneous iris; fast segmentation; iris recognition +
Footnote †: journal:
## 3 Results and Discussion
### Effect of Coating Technology on Mechanical Performance  
The mechanical properties of coating films are shown in Table 5. Coating technology had little effect on the hardness and shock resistance of the coating film, the hardness of the coating film was 2H-3H, and the shock resistance of the coating film was about 10.0 kg-cm. The adhesion grade of No. 5 and No. 7 experimental samples was grade 1, which was because microcapsules were added in the topcoat, so the coating film surface was rough and the adhesion declined. Compared with the coating film without microcapsules, the fracture elongation of the coating film with microcapsules was obviously increased, which was because the microcapsules break under the action of tensile force, and the outflow of core material inhibits the coating film, thus improving the toughness of the coating film. The method of adding microcapsules had little effect on the fracture elongation of the coating film, and the fracture elongation of No. 2 and No. 7 was higher. According to all the mechanical test results, the mechanical properties of No. 2 coating film were the best, that was, the coating film prepared by two coats of primer, three coats of topcoat, and microcapsules added in primer. This coating film has better mechanical properties,  
Figure 4: Chromatic aberration of coating film under different coating technologies.  
Figure 5: Glossiness of coating film under different coating technologies.  
with hardness of 3H, adhesion of grade 0, shock resistance of 12.0 kg:cm, and fracture elongation of 26.3%.
###### Abstract  
With the construction and development of the BeiDou navigation satellite system (BDS), the precise point positioning (PPP) performance of the BDS is worthy of research. In this study, observational data from 17 stations around the world across 20 days are used to comprehensively evaluate the PPP performance of BDS B1c/B2a signals. For greater understanding, the results are also compared with the Global Positioning System (GPS) and BDS PPP performance of different signals and system combinations. The evaluation found root mean square (RMS) values of the static PPP in the north (N), east (E), and upward (U) components, based on the B1c/B2a frequency of BDS-3, to be 6.9 mm, 4.7 mm, and 26.6 mm, respectively. Similar to the static positioning, the RMS values of kinematic PPP in the three directions of N, E, and U are 2.6 cm, 6.0 cm, and 8.5 cm, respectively. Besides this, the static PPP of BDS-3 (B1 cB2a) and BDS-2 + BDS-3 (B1 IB3l) have obvious system bias. Compared with static PPP, kinematic PPP is more sensitive to the number of satellites, and the coordinate accuracy in three dimensions can be increased by 27% with the combination of GPS (L1L2) and BDS. Compared with BDS-2+BDS-3 (B1 IB3l), the convergence time of BDS-3 (B1 CB2a) performs better in both static and kinematic modes. The antenna model does not show a significant difference in terms of the effect of the convergence speed, though the number of satellites observed has a certain influence on the convergence time.  
BDS-3; B1C/B2a signal; precise point positioning; performance evaluation; positioning accuracy +
Footnote †: journal: Sensors
###### Abstract
It offers distinctive benefits by improving the efficacy of medications and radiation therapy while minimizing the potential side effects [8]. It is noteworthy that a total of 573 agents were approved by the United States Food and Drug Administration (USFDA) from January 2020 to October 2022 for several oncological applications, such as targeted drugs (48%), biological therapies (43%), and cytotoxic treatments (9%) [9]. One such advantage is the use of multifunctional nanocarriers, which capitalize on the distinction between tumor and normal tissues to preferentially deliver therapeutic pharmaceuticals, improving medication permeability and retention. Moreover, nanotechnology leverages the specific physical and chemical characteristics of the tumor microenvironment, such as a lower environment, weak acidity, overexpressed proteins and enzymes, hypoxia, and anomalous temperature gradients. The drug delivery rate of loaded medications from nanocarriers can be accurately controlled due to these unique features. Nanoparticles possess a size that is substantial enough to accommodate numerous tiny molecules and yet remain comparatively small in relation to cells. The relatively large surface area of nanoparticles can also be functionalized by ligands such as DNA or RNA strands, aptamers, peptides, and antibodies [5]. These ligands can serve as therapeutic measures or be utilized to regulate the in vivo behavior of nanoparticles [5]. These features enable multimodal therapy, diagnostic action, and the administration of several medications. Similar to the application of laser ablation, nanoparticles can utilize their physical properties of energy absorption and reradiation when applied to diseased tissues. Hence, the aim of this review is to explore various innovative approaches to combat breast cancer through the application of nanotechnology. It covers an overview of the classification of breast cancer, traditional breast cancer treatments, the significant properties of conventional breast cancer drugs, and nanomedicine as a potential alternative drug delivery candidate for breast cancer therapy.
## 5 Simulations and Analyses
### Algorithm Simulation
#### 5.2.2 Force Tracking Control Simulation
(**a**) Comparison of constant force tracking control; (**b**) comparison of variable force tracking control; (**c**) comparison of constant force position tracking control. comprehensive quantitative and visual comparison of force tracking errors for these three algorithms is conducted, and the results are presented in Table 3.  
According to the data in Table 3, similarly, taking MSE as a quantifiable perspective, during the force tracking control on the complex surface, FOAIC achieves an improvement of 40.3% in force tracking accuracy compared to AIC, and an improvement of 58.6% compared to VIC. This indicates that in more complex environments, FOAIC demonstrates greater enhancement in force tracking accuracy, highlighting its advantage. Therefore, even in uncertain and complex environments, it maintains excellent force tracking control performance. In summary, based on the simulation results of the four scenarios mentioned above, it can be observed that all four algorithms maintain satisfactory force tracking performance in a flat environment. Although CIC exhibits force steady-state error in the inclined plane environment, it still manages to maintain force tracking. AIC and VIC are able to perform force tracking stably but show overshoot, with longer adjustment times compared to FOAIC. In the sinusoidal and complex surface environments, CIC struggles to achieve stable force tracking, and both AIC and VIC experience increased overshoot. FOAIC, on the other hand, nearly eliminates overshoot, exhibits faster adjustment times, and, according to MSE quantification data, demonstrates superior force tracking accuracy compared to AIC and VIC, with even greater improvement in the complex surface environment. Hence, the proposed FOAIC is better suited for robot interactions in complex and dynamically changing environments, offering shorter adjustment times, minimal overshoot, and stable and reliable force tracking performance.
## 4 Results
### Comparison of DEM Heights to dGNSS Measurements  
We compared SPOT6 DEM heights to dGNSS field measurements collected during field campaigns in 2015, 2016, 2017, and 2019. The validation of DEM heights with n \(\approx\) 75,000 dGNSS points (accuracy \(<\)0.1 m horizontally and vertically) indicates that DEMs generated with large CKs are closer to the ground control (narrower distribution) than smaller CKs  
Figure 3: Comparing the map-projected SPOT6 PAN B-scene (**A**) to the resulting stereo DEMs (Table 1) derived from AC pair; (**B–D**) hillshaded DEMs generated using the ASP BM algorithm at increasing CK sizes gridded to a 3 m resolution; (**E**) local topography with an elevation range of \(\sim\)500 m, taken from the DEM generated using the ASP default parameters and gridded to a 6 m resolution (4\(\times\) input-image resolution); (**F–H**) the hillshaded DEMs based on the MGM algorithm with different CK sizes. See Figure 2A for location in study area. Green and purple frame colors correspond to colors in Figures 8 and 9.  
(Figure S3). For the BM algorithm, the normalized mean absolute deviation (NMAD), which measures the accuracy of the DEM elevations [43], ranges from 1.4 m (15 \(\times\) 15 pixel CK) to 1.2 m (35 \(\times\) 35 pixel CK); for MGM from 1.9 m (5 \(\times\) 5 pixel CK) to 1.3 m (9 \(\times\) 9 pixel CK). However, we do not consider the dGNSS difference as our primary indicator for DEM quality, as the absolute height difference does not reflect on the sharpness or smoothness of a landscape [15; 21]. Furthermore, these reference points are limited to flat and easy-to-access parts of the study area (i.e., roads; Figure S2). Particularly flat areas are difficult to match without sufficient image texture. In these regions, a larger CK size is beneficial to reduce the number of false matches and artifacts. We suggest that the elevation comparison with dGNSS data reflects precisely this: in areas of low relief, a larger CK size producing a continuous smooth surface will better approximate ground truth. These observations, however, cannot be extrapolated to the steeper parts of the landscape, where larger CKs can blur complex features and average estimated 3D positions.
## 4 Experimental Simulation and Results Analysis  
### Experimental Environment and Parameter Settings  
The proposed MSECHOA algorithm was tested in a simulation environment based on an Intel (R) Core (TM) i7-8300H CPU @ 2.30 GHz processor, 16 GB RAM, NVIDIA GeForce RTX 3050 Ti, and Windows 10 Professional (64-bit) operating system. The programming software used was Matlab R2021a. The experimental settings were as follows: search space dimension of 30, population size of 30, maximum iterations of 1000, and 30 independent runs.  
Table 1 lists the basic information for 11 benchmark test functions, including five unimodal functions (F1 to F5), four nonlinear multimodal functions (F6 to F9), and two fixed-dimension multimodal functions (F10 and F11). The functions F1 to F11 cover a range of optimization problems, from simple to complex, from unimodal to multimodal, and from variable dimensions to fixed dimensions. Detailed information and the search ranges for each function are as follows:
## 4 Performance of SERENA and TDMA/CA  
To evaluate the performance of TDMA/CA with SERENA, we proceed by simulation and consider various network configurations. Each network configuration is characterized by a number of nodes and a density (_i.e._, the average number of neighbors per node +1). For each configuration, we perform 20 simulation runs and evaluate for each the four following performance criteria:  
* _the number of colors_: The difference between the node number and the color number gives the number of slots saved by our algorithm in each cycle compared with classical TDMA where the slot number is equal to the node number.
* _the active period duration_: During the active period, each node is able to transmit during a slot. This does not mean that all nodes are active during this period. On the contrary, nodes sleep during the slots that correspond neither to their color, nor to the colors of their one-hop neighbors. The active period duration can be computed from the number of colors and the slot duration. Notice that if the active period is shortened, the application manager has the choice between:
* either increasing the inactive period during which all nodes sleep;
* or reducing the cycle comprising the active period followed by the inactive period. The active period duration is equal to the number of colors multiplied by the slot duration.
* _the average number of nodes sharing the same slot_. This criterion evaluates the bandwidth spatial reuse. It is equal to the number of nodes divided by the number of colors.
* _the error rate_. It shows that SERENA ensures the absence of collision, assuming an ideal environment.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline Node & slot0 & slot1 & slot2 & slot3 & slot4 & slot5 & slot6 \\ \hline A & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\ B & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\ C & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\ D & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\ E & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\ F & 0 & 0 & 0 & 0 & 0 & 1 & 0 \\ G & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\ H & 0 & 1 & 0 & 0 & 0 & 1 & 0 \\ I & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\ \hline \end{tabular}
\end{table}
Table 5: Slot schedule with SERENA tree or three-hop coloring.  
* _the end-to-end delay_. In the simulations reported in this paper, we consider two types of traffic. For general traffic, each node transmits data to a destination randomly chosen. For data gathering traffic, all nodes transmit data to the sink node, which is the PAN coordinator denoted by node \(0\).  
The result for a network configuration that is depicted in Figures 7 and 11 is the average of the 20 simulation runs corresponding to this configuration. In this section, we assume that all links are stable and symmetric. We will see what happens if this is not the case in Section 5. We first point out the necessity of using links of good quality only and then compare the performances of general coloring and tree-based coloring.
# Spatial and Temporal Variation of GPP and Its Response to Urban Environmental Changes in Beijing
## Data and Methods
### Methods
#### 2.3.4. Geographically Weighted Regression Method  
Geographically Weighted Regression enhances the traditional regression framework [22] by allowing the model's parameters to vary spatially rather than remaining constant across the entire dataset. So that the model can be expressed as  
\[Y_{i}=\beta_{0}(u_{i},v_{i})+\sum_{k}\beta_{k}(u_{i},v_{i})X_{ik}+\varepsilon_{i }\qquad i=1,\ldots,n \tag{7}\]  
where \((u_{i},v_{i})\) signifies the spatial coordinates of point \(i\), \(\beta_{0}(u_{i},v_{i})\) is the intercept value, and \(\beta_{k}(u_{i},v_{i})\) refers to the set of parameter values at point \(i\). In contrast to the global model, which employs constant coefficients across different regions, this model permits spatial variation in parameter estimates, thus potentially enabling a more accurate representation of regional-specific impacts.  
In order to calibrate the model, data near point \(i\) are assumed to more significantly influence the estimation of parameters \(\beta_{k}(u_{i},v_{i})\) than data that are more distant from the observation point \(i\):  
\[\hat{\beta}(u_{i},v_{i})=\left[X^{T}W(u_{i},v_{i})X\right]^{-1}X^{T}W(u_{i},v_ {i})Y \tag{8}\]  
where \(W(u_{i},v_{i})\) is an \(n\times n\) matrix with diagonal elements representing the geographic weights of the observation data for point \(i\), while the off-diagonal elements are zero. This weight matrix is determined for every point \(i\) where parameters are calculated.  
The weight matrix in Geographically Weighted Regression signifies the varying significance of each data observation for parameter estimation at location \(i\), with closer observations typically carrying more weight. Consequently, each point \(i\)'s estimate is associated with a distinct weight matrix.  
\[W_{ij}=\exp\left(-\frac{d_{ij}^{2}}{h^{2}}\right) \tag{9}\]  
\[d_{ij}=\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j}\right)^{2}} \tag{10}\]  
where \(h\) is a non-negative parameter called the bandwidth, which causes the influence to decay with distance. We utilized the Corrected Akaike information criterion [31] to identify its optimal value. \(d_{ij}\) is the distance between locations \(i\) and \(j\), and \((x_{i},y_{i})\) and \((x_{j},y_{j})\) denote the coordinates of the respective points.