esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Energy Conserving Solutions in Wireless Networking-Based IoT
### Energy Conserving Solutions for WLAN-Based IoT
#### 4.2.2 Solutions to Optimize IEEE 802.11 Based PSM  
The existing IEEE 802.11 PSM may not be adequate for battery-operated IoT devices and multi-hop IoT network settings. Authors in [66, 67] extended the existing PSM to a multi-hop PSM in which traffic announcements are propagated along multi-hop wireless links and all intermediate devices should stay awake to forward received data with minimum latency. The scheme can improve end-to-end delay and enlarge device sleep time. [PERSON] _et al._[68] suggested mobility supported PSM that considers both user mobility and traffic conditions for making sleep/wakeup schedules. Based on channel condition estimation, APs buffer packets and mobile stations adjust their sleep interval to reduce wake-up overhead. However, we think that consideration for delay aspects in sleep interval adjustment is needed for delay-sensitive IoT. [PERSON] _et al._[84] examined the usage of low-power WiFi modules for providing IP connectivity for sensors deployed in various IoT scenarios. Particularly, battery lifetime depends heavily on scenarios, e.g., frequent wake-up to receive timely command messages, periodic data transmission, connection maintenance, _etc._
# Combining Keyframes and Image Classification for Violent Behavior Recognition
## 4 Experiments
### Keyframes  
We counted the data distribution of frames in violent videos and found that the number of video frames varies greatly between datasets (Table 5). Moreover, the data distributions of different violent videos are different (Figure 11); as a result, the important violent frames (such as Figure 11a) cannot be extracted from consecutive frames. Therefore, using a fixed number of frames is an unreliable approach to identifying violent behavior. We instead recommend dynamically adjusting the number of frames to be identified based on the total number of frames in the video. We select keyframes in a given window length that are most different from other frames. This allows our system to control the number of keyframes. Therefore, the number of keyframes can be used to control the recognition efficiency and robustness of the system. Specifically, a sliding window is established containing \(N\) frames, and the local maximum frame in every continuous \(K\) frames is selected as the keyframe. In this way, \(N/K\) keyframes for judgment can be obtained. The setting of \(N\) can prevent short-duration behaviors that are superficially similar to violent behaviors (such as friendly play-fighting hugs, and handshakes) from causing false alarms. Moreover, \(K\) can make uniform sampling, which can prevent camera movement and lighting changes from causing many redundant frames being extracted. In summary, extracting the keyframe can more effectively accumulate the recognition results of a single frame and improve the overall recognition accuracy.  
\begin{table}
\begin{tabular}{l} \# Define the reconstruction loss function \\ def recon\_loss(recon\_x,x): \\ loss = torch\_sum((recon\_seg—recon\_unseg)**2).mean() \\ return loss \\ \# Define segmented-original image pair hidden layer distance \\ def Distance(latent\_seg,latent\_unseg): \\ D = torch\_sqrt(latent\_seg—latent\_unseg)**2).mean() \\ Return D \\ \#model training \\ for seg, unseg in emulate (trainloader): \\ latent\_seg = Encoder(seg) \\ recon\_seg = Decoder(latent\_seg) \\ \#compute the segmented image reconstruction loss \\ recon\_loss\_seg = recon\_loss(seg, recon\_seg) \\ latent\_unseg = Encoder(unseg) \\ recon\_unseg = Decoder(latent\_unseg) \\ \#compute the original image reconstruction loss \\ recon\_loss\_unseg = recon\_loss(unseg, recon\_unseg) \\ \#compute the distance of segmented-original image pair in the hidden layer \\ Distance = Distance(latent\_seg, latent\_unseg) \\ Loss = recon\_loss\_seg + recon\_unseg\_loss+ 0.001*Distance \\ Loss.backward() \\ optimizer() \\ \end{tabular}
\end{table}
Table 4: Implementation of the model.  
Figure 10: Reconstruction effect of the encoder–decoder network.
## 6 Conclusions  
The end-to-end-planning control model based on DRL, proposed in this study, demonstrated significant efficacy in automatic path planning and yarn-spool-assembly tasks for textile robotic arms. The introduction of the CFRS effectively enhanced learning efficiency, accelerated convergence, and showcased remarkable robustness. These achievements not only provide strong support for the automation process in the textile industry but also demonstrate the immense potential of DRL in handling complex and highly uncertain tasks. Overall, this research not only advances the frontier in robotic arm control algorithms but also provides empirical evidence for the broad application of DRL in automation and robotics, indicating its vast potential in real-world industrial environments.  
Despite the notable performance of our model in path planning and yarn-spool-assembly tasks for textile robotic arms, it has certain limitations. Specifically, the model may face challenges when dealing with extreme or unforeseen environmental conditions. For instance, the current model may not adequately account for extreme variations in the environment, such as sudden mechanical failures or unexpected operational errors. Future work could involve applying the model to a wider range of scenarios, such as various types of automated robotic arm tasks, to validate and enhance its generalizability and applicability.  
Figure 12: Robotic arm’s path-planning process in real environments.  
**Author Contributions**: D.Z.: conceptualization, methodology, resources, project administration. Z.D.: software development, validation, formal analysis; W.L.: investigation, visualization, data curation; S.Z.: writing--original draft, writing--review and editing; Y.D.: supervision, funding acquisition. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This work was supported in part by Tianjin Science and Technology Bureau under Grant 20 YDTPJC00740, and in part by Ministry of Education of the People's Republic of China under Grant 220603032241503.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The datasets generated or analyzed during this study are available from the corresponding author on reasonable request.  
**Conflicts of Interest**: The authors have no relevant financial or non-financial interests to disclose.
## 4 Results and Analysis
### Single Passage Model
#### 4.1.3. Blade to Blade Surface  
It is shown in Figure 11 that the relative Mach number distribution for three different sections such as span wise 90% (Figure 10(a)), 50% (Figure 10(b)), and 10% (Figure 10(c)) at the design point. For the LL impeller, the maximum Mach number is about 0.45, which is the largest among four impellers, and the region of high Mach number is also the biggest as shown in the Figure 11, which indicates the biggest flowing loss at the design point. Meanwhile, the Mach number near outlet region of the LL impeller appears to be the highest, which indicates lower static pressure ratio as is shown in Figure 7b.  
Figure 11. Relative Mach number distribution on blade to blade plane at design point.  
Figure 10. Aerodynamic parameters distribution on meridional plane at the 0.85 flowrate.  
The relative Mach number distribution at 0.85 design flow rate is shown in Figure 12. The highest Mach number can be observed near outlet region in the prototype, which is very different from the performance at the design point. This phenomenon means that the flowing situation of the prototype is closing to its stall point, while the LL impeller still has excellent stall tolerance capability. So, the LL impeller presents the better pressure ratio at the low flow rate than the prototype, which can also be obtained in Figure 6.
# Chemical and Sensorial Characterization of Scented and Non-Scented Alstroemeria Hybrids
## 4 Discussion
### Monoterpenes  
Significantly higher values of absolute peak areas of the monoterpenes \(\beta\)-pinene, D-limonene and eucalypt were detected in the scented hybrids in comparison with thenon-scented plants, suggesting that perhaps those compounds are part of the floral scent detected through the sensorial analysis. These compounds have been previously detected in Alstroemeria caryophyllaea Jacq. [16; 24] and alstroemeria hybrids [25]. \(\beta\)-pinene is one of the most common monoterpenes found in floral scents [31], and has been detected as the major VOC in species like Magnolia grandiflora L. [40], chrysanthemum [17] and Citrus grandis Osbeck [41]. Moreover, this monoterpene has been associated with a pleasant odour perceived in orange juice [42]. D-limonene is one of the most common terpen found in nature. It is mainly associated with citrus oils [43] and detected through sensorial evaluation of orange juice [44]. Eucalyptustol, known also as 1,8-cineole, is a cyclic monoterpene has been detected previously as a major compound in flowers of oriental \(\times\) trumpet hybrid lilies [45] and _Laurus nobilis_ L. [46] and as an important aroma constituent of strawberry and lemon guava (_Sidium cattleianum_ [PERSON]) juice [47]. Both D-limonene and eucalypttol have been identified as major VOCs in some tulip varieties clustered in the group 'herbal' according to their floral scent [48]. Thus, these three monoterpenes, detected with significantly higher values of absolute peak areas in the scented hybrids, possibly contribute to the floral scent of alstroemeria perceived in sensorial analysis, and, considering previous reports and the low ODT these VOCs normally present, particularly eucalypttol and D-limonene [48; 49].
# Characteristic Length and Time Scales of the Highly Forward Scattering of Photons in Random Media
## 2 Materials and Methods
### Time-Domain Photon Transport Models
#### 2.1.2 Henyey-Greenstein Phase Function and Anisotropy Factor  
For a formulation of \(p(\mathbf{\Omega},\mathbf{\Omega}^{\prime})\), the Henyey-Greenstein (HG) phase function [26] is widely employed in biomedical optics to model the forward scattering of photons:  
\[p_{HG}(\mathbf{\Omega}\cdot\mathbf{\Omega}^{\prime})=\frac{1}{4\pi}\frac{1-g^{2}}{ \left(1+g^{2}-2g\mathbf{\Omega}\cdot\mathbf{\Omega}^{\prime}\right)^{3/2}}, \tag{2}\]  
where \(g\in\)[\(-1\), 1] is the anisotropy factor, defined by \(\int_{\mathbb{S}^{2}}d\mathbf{\Omega}^{\prime}(\mathbf{\Omega}\cdot\mathbf{\Omega}^{ \prime})p(\mathbf{\Omega},\mathbf{\Omega}^{\prime})\). Mostly, \(g\)-values of biological tissue volumes are larger than 0.8 [27], meaning the highly forward scattering. For examples, the \(g\)-value of muscle or bone is considered as 0.9; and the \(g\)-values of the human liver and of blood vessels are 0.955 [28] and 0.992 [29], respectively. Meanwhile, there are void regions in the human body such as the trachea in the human neck, whose \(g\)-value is almost zero, meaning the isotropic scattering. Figure 1 shows the HG phase function (Equation (2)) as a function of \(\mathbf{\Omega}\cdot\mathbf{\Omega}^{\prime}\in[-1,1]\) in a logarithmic scale. At \(g=0.0\) (isotropic scattering), the phase function is constant over the whole region of \(\mathbf{\Omega}\cdot\mathbf{\Omega}^{\prime}\). Meanwhile, as the \(g\)-value approaches to unity from zero (enhancement of the highly forward scattering), the exponential change of \(p_{HG}\) with respect to \(\mathbf{\Omega}\cdot\mathbf{\Omega}^{\prime}\) becomes large and the peak of \(p_{HG}\) around \(\mathbf{\Omega}\cdot\mathbf{\Omega}^{\prime}=1.0\) becomes sharp.  
The HG phase function is expanded in the infinite series of (unassociated) Legendre polynomials \(P_{l}\) (\(l=0,1,\cdots,\infty\)):  
\[p_{HG}(\mathbf{\Omega}\cdot\mathbf{\Omega}^{\prime})=\sum_{l=0}^{\infty}\frac{2l+1}{4\pi} g^{l}P_{l}(\mathbf{\Omega}\cdot\mathbf{\Omega}^{\prime}), \tag{3}\]  
where \(g^{l}\) is the \(l\)-th order expansion coefficient.
# Experimental Study of Low Temperature Performance of Porous Asphalt Mixture
###### Abstract
[PERSON] et al. [45] analyzed the effect of aging on the low temperature performance of porous asphalt mixture through the Thermal Stress Restrained Specim Test (TSRST), and the results showed that the low temperature performance of porous asphalt mixture decreased after aging, while it was not much different from that of dense graded asphalt mixture. However, other factors such as porosity, modifier content, and test temperature also had effects on the low temperature performance of porous asphalt mixture. From literature review, the effects of these factors had not been adequately investigated, thus further research is needed. In this paper, the low temperature performance of porous asphalt mixture is studied by means of the low temperature bending test. The mineral aggregate gradation of porous asphalt mixture is designed based on Bailey method [46; 47], and the effects of porosity, modifier content, aging condition, and test temperature on the low temperature performance of porous asphalt mixture are assessed through orthogonal experimental design. The works have enabled the development of porous asphalt mixture with desirable engineering properties and resistance to low temperature. The porous asphalt mixture developed in this research has been successfully applied to the viaduct bridge pavement of Liangdang County to Hui County section of G316 National Expressway in China. G316 National Expressway is a major highway from southeast part to northwest part of China, connecting Fuzhou City in Fujian Province and Lanzhou City in Gansu Province. The total length of the expressway is 2915 km. The section from Liangdang County to Hui County is located in Gansu Province in northern part of China and has a length of 53.4 km. Its major construction works took place from year 2016 to 2018. The porous asphalt surface layer has a thickness of 40 mm and has been testified to have good drainage performance as well as good overall performance in the cold environment of northern China.
# TFBN: A Cost Effective High Performance Hierarchical Interconnection Network
## 2 Interconnection of the TFBN
### Higher Level Networks of the TFBN  
Higher levels of TFBN are created by repeatedly connecting \((2^{2m}\) lower networks in a 2D-torus network composed of \(2^{m}\) rows and \(2^{m}\) columns like the basic module. Therefore, we can connect 16 Level-1 networks by considering \((m=2)\) to construct Level-2 of TFBN. In addition, connecting 16 Level-2 in a 2D-torus network will participate in creating Level-3 of TFBN and so on. The structure  
Figure 1: A \(4\times 4\) basic module of a TFBN.  
of the upper-level networks of a TFBN demonstrated in Figure 2. \(4\times 2^{q}=2^{(q+2)}\) free wires and its associated port in each BM used for higher-level connections. For vertical connection, we used \(2(2^{q})\) links and for the horizontal connection, we used \(2(2^{q})\), where inter-level connectivity represented by \(q\), \(q\in 0,1,\ldots,m\), when \(q=0\) represents the minimal inter-level connectivity, and, when \(q=m\), it will be the maximum inter-level connectivity. In Figure 2, there is a \((4\times 4)\) BM with \(2^{(2+2)}=16\) free ports, by considering \(m=2\). By considering \(q=0\), we will have four free ports for every upper-level connection divided into two for horizontal and two for vertical interconnections. Tied incoming and outgoing links will form a bidirectional link to connect two adjacent BMs of higher-levels of TFBN. Vertical in and vertical out wires tied to create vertical links and horizontal in and horizontal out wires connected to create horizontal links.  
TFBN represented by the value of \(m\) to decide the size of a BM, the level of hierarchy \((L)\), and the inter-level connectivity \((q)\) as TFBN \((m,L,q)\). To guarantee better granularity, here we have considered \(m=2\). \(L_{max}\) is the maximum hierarchical level can be created from a \((2^{m}\times 2^{m})\) BM and it is \(L_{max}=2^{(m-q)}+1\). As an example, with \(q=0\) and \(m=2\), the highest level of hierarchy is \(L_{m}ax=2^{(2-0)}+1=5\). This implies that we obtained Level-5 as a maximum possible level of TFBN by connecting \((4\times 4)\) BM hierarchically. The number of nodes in each level \((L)\) of a TFBN is \(N=2^{2 mL}\). Therefore, the number of nodes can be obtained of connecting TFBN\((m,L,q)\), where \(L=5\), \(m=2\), \(q=0\) is \(N=2^{2m(2^{(m-q)}+1)}\). Thus, 1,048,576 nodes could be interconnected together to create a massively parallel computer system based on using TFBN network topology.
## 5 Conclusions  
In this paper, the contribution and novelty are that we propose a novel map-assisted SINS/OD train location system that is assisted by a digital track map, an SINS/OD navigation that is built on OD velocity in the inner circle. Then, we utilized a map-matching algorithm that takes into account vertical projection and heading weighting. Once we obtained matching results from the outer circle, we replaced the positions acquired from SINS/OD with the matching positions for a Kalman filter measurement update. The data processing results from comparisons between SINS/OD and SINS/OD/MM show that our digital track map-assisted SINS/OD algorithm significantly reduces train position error accumulation. The algorithm's performance is validated through field testing. The position error after nearly 80 km of navigation is 24 m, with relative mileage accuracy equal to or less than 0.03% DT.  
In future studies, low-accuracy IMUs can be used to validate the algorithms in this paper in order to obtain the best balance between optimal navigation accuracy and IMU cost.  
Conceptualization, W.C. and Y.T.; methodology, W.C.; software, W.C.; validation, W.C. and Y.T.; formal analysis, Y.T.; investigation, Y.T.; resources, G.Y.; data curation, W.C.; writing--original draft preparation, W.C.; writing--review and editing, Y.T.; visualization, W.C.; supervision, G.Y.; project administration, G.Y.; funding acquisition, Y.T. and G.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fujian Province Young and Middle-aged Teacher Education Research Project (Science and Technology) Key Project (grant number JZ230027); China scholarship council (CSC), grant number 201906020135; and the National Key R&D Program of China, grant number 2021 YFB3201504.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to technical confidentiality.  
The authors declare no conflicts of interest.  
Figure 15: SINS/OD/MM navigation attitude angle estimation.
## 3 Results and Discussion
### Confirmatory Analysis Results
#### 3.2.2 Precision and Recovery  
A one-way ANOVA was performed on repeated analyses in order to verify the homoscedasticity of the data obtained on different days and at different levels of fortification. Table 3 shows the results of intra-day (relative standard deviation of repeatability (RSD\({}_{t}\))) and inter-day (relative standard deviation of reproducibility (RSD\({}_{\text{R}}\))) repeatability for the T-2 and HT-2 toxins using durum wheat samples spiked at three different contamination levels. While also considering the accuracy parameters reported in Table 4, it was possible to confirm the compliance of the validated method with the performance criteria reported for T-2 and HT-2 toxins in Regulation (EC) No. 519/2014 [17] and, more generally, in Decision No. 657/2002/EC [14].
## 5 Drift Support Analysis
### Monitoring Analysis  
The failure of the rock mass during the excavation of drift is an extremely complex process. The reasonableness of the support design and the stability of the rock mass can be evaluated by field monitoring [49]. The analysis in Section 5.3 showed that the failure mainly occurred at the location of the drift roof; therefore, the drift roof was monitored, and the effect of the support system was analyzed through the roof displacement data.  
Two displacement sensors with a spacing of 3 m were installed on the \(-960\) m drift roof for a period of one month (Figure 10). Sensors were installed immediately after the drift support. The monitoring results indicated that the displacement of monitoring site 1 increased rapidly in the first week after the excavation, then the deformation rate slowed down in the second week and was in a steady state after half a month with no obvious displacement; monitoring site 2 exhibited a near-linear and rapid increase in displacement on the eighth day, then the deformation rate slowed down from the eighth day to the seventeenth day and reached stability afterwards. The final displacement values of the two monitoring sites were 4.2 mm and 4.9 mm, respectively. Figure 11 reveals that the drift deformation occurred in three stages: fast deformation stage, slow deformation stage, and deformation steady stage. This indicates that the support system worked well to slow down the deformation of the drift and was able to effectively control the failure of the drift.
# Salp Swarm Algorithm Optimized A* Algorithm and Improved B-Spline Interpolation in Path Planning
## 2 Path Planning Algorithm
### Improvement of the A* Algorithm  
To address the limitations of the traditional A* algorithm and considering that its performance is highly dependent on the design of the heuristic function \(h(n)\), it is important to recognize that the heuristic function not only determines whether the algorithm can find the optimal solution, but also directly affects the search efficiency. The traditional A* algorithm imposes a strict requirement on the heuristic function \(h(n)\), which must satisfy \(h(n)\leq\)_the actual minimum cost_. This condition guarantees that the algorithm is capable of identifying the shortest possible path; however, it also leads to the expansion of a large number of nodes, resulting in longer computation times. In practical applications, once path planning is completed, the path is often smoothed, a process that may increase the path length. Therefore, during the initial phase of path planning, there is no need to strictly pursue the shortest path, but rather, it is more practical to seek a balance between path quality and computational efficiency.  
Based on this understanding, this chapter proposes an improvement strategy: by relaxing the strict requirements on the shortest path, we attempt to construct a new heuristic function, making the A* algorithm no longer focus solely on minimizing the distance. This approach significantly improves the algorithm's computational efficiency while ensuringthe path remains reasonable. This improvement not only reduces the number of nodes expanded by the algorithm and alleviates the computational burden, but also better accommodates the flexible requirements of path planning in real-world applications. To validate this hypothesis, we designed and implemented a new heuristic function and further improved it using the SSA. We refer to this algorithm as the SSA-A*.