esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Secure FSO Link Experimental Demonstration
### Experiment Results  
The experiment tested system resilience against eavesdropping attacks in the presence of [PERSON]. Similar to Section 3.5, [PERSON] generated a private encryption key for the public decryption at the receiver. As shown in Figure 12, the decryption fails even if [PERSON] can intercept the message and decode the header and packet data. This matches the results from the simulation and is the expected result due to the nature of the encryption. It can be concluded that the FSO link is reliable and secure against eavesdropping attacks.  
The experiment results show that a distance of 250 mm between the transmitter and receiver achieves a minimum received electrical amplitude of 27.5 mV required at the Red Pitaya SDR to process the received signal. This achieves a PDR of 92%, as shown in Figure 13a. While Figure 13b shows that reducing the distance to 130 mm increases the received electrical amplitude to 38.5 mV and the PDR to 96%.  
Although communication speed is not the driving factor of this experiment, analysis was carried out to find the maximum achievable data rate. The communication speed can be changed by changing the sampling rate of the Red Pitaya, and taking fewer samples, which means a faster overall message speed. When the Red Pitaya samples the transmitter at 1.25 Mb/s, and since each byte sampled is made up of eight bits (from 'init_u8 vector' function during message formation), the achievable Red Pitaya transmission rate is 10 Mbit/s.  
Figure 12: FSO link output under eavesdropping attacks.  
Figure 13: FSO link output at (a) 250 mm and (b) 130 mm distance.
## 3 Results and Discussion
### Global Seasonal Distribution
For further comparison with the amplitude scintillation in the top ionosphere, Figure 6 shows the S4c index calculated from the carrier-to-noise ratio. The occurrence rate of amplitude scintillation was obviously different from the RODI and ROTI. The occurrence rate of amplitude scintillation (S4c) was higher at low and high latitudes, was lowest  
Figure 5: Same as Figure 2 but for ROTI after midnight. The panels from top to bottom show results for Swarm A (F107 \(<\) 100, (**a-d**)), Swarm A (F107 \(>\) 100, (**e-h**)), Swarm B (F107 \(<\) 100, (**i-l**)), and Swarm B (F107 \(>\) 100, (**m-p**)), from left to right show results at March equinox, June solstice, September equinox, and December solstice. at middle latitudes, and increased with the increasing solar activity. The S4c index also displayed a clear longitudinal variation of a higher occurrence rate over the low latitude region of the Asian sector under low solar activity. The amplitude scintillation of the top ionosphere calculated from C/N was very weak (an amplitude scintillation index greater than 0.3 in the output of a general ground-based scintillation receiver is considered to be a strong scintillation), and showed no seasonal dependence.
## 3 Shearer Positioning Technology  
The shearer positioning method includes three components, the shearer inertial navigation system, the measurement of longwall retreat and creep displacements, and the backward calibration of the shearer trajectory. Researchers in the LASC and CUMT have been focusing on those three aspects and have acquired good results.
# Multi-Pedestrian Tracking Based on KC-YOLO Detection and Identity Validity Discrimination Module
## 4 Experiments and Analyses
### Experimental Dataset and Evaluation Index  
#### 4.2.1 Experimental Data  
We opted for the MOT series datasets, CrowdHuman dataset, and MIX datasets, commonly utilized in pedestrian tracking tasks, to conduct our experiments. This choice enhances the credibility of our proposed method's effectiveness. Below is an introduction to the three datasets:  
* MOT series datasets: These are datasets on the Open Data Lab platform and are mainly targeted at pedestrian tracking tasks in dense scenes.
* CrowdHuman dataset [35]: It is for pedestrian detection. Unlike other mainstream human detection datasets, the pedestrian targets in the CrowdHuman dataset are much denser, more crowded, and even have serious overlaps. According to the data provided in the citation, the CrowdHuman dataset has an average of 22.64 figures per image, which is far more than other human detection datasets.
* MIX datasets: They are diverse and comprehensive, covering different types of pedestrian detection and tracking scenarios. This comprehensiveness allows researchers to test the robustness and effectiveness of algorithms in a variety of real-world situations. Using these datasets, researchers can conduct multimodal data studies, explore commonalities and differences between different datasets, and lay the foundation for improving multi-target tracking and pedestrian detection algorithms.  
Figure 8: Percentage of tracking failures for different levels of occlusion: (**a**) Experiment on MOT16 dataset; (**b**) Experiment on MOT17 dataset; (**c**) Experiment on MOT20 dataset.
# Notes on Bus User Assignment Problem Using Section Network Representation Method
## 2 Background
In [54], the MNL approach includes the frequency-sharing concept. The assumption was that, for each O/D pair, the traveler looks first for transfer-free path options, where the frequency sharing rule is implemented. Using the MNL share function, users were divided among paths with transfers (up to three transfers) if no direct choices were available. Some research concentrated on the design of the network lines without addressing the assignment issue. The assessment conditions might include a path time relative to the shortest route time (i.e., path directness) [55], network demand coverage [56], and required transfers [4; 52; 53; 57]. The construct costs might also be evaluated if the system is specified to be buried [52; 58]. Conversely, in [59; 60], the mathematical functions included the non-equilibrium assignment models to maximize the concurrent bus line design and passenger line assignment. Notably, the majority of non-equilibrium-based BNDP research employed capacity-free assignment [61]. It was claimed that the bus network architecture intends to determine the capacities of the routes based on the total number of prospective boarding passengers without limitation. Moreover, it has been shown that capacity-free assigning algorithms are convenient and rapid in complex systems [62; 63]. To this end, equilibrium assignment algorithms have been used in a few BNDP studies. In the bulk number of studies, it is evident that the prerequisites for the uniqueness and existence of equilibrium in FB approaches exclude their implementation in suggested solution methods. This study will shed light on section network representation as an efficient assignment tool for BNDP solution schemes. This study provides the needed information to give the reader a full assessment of the selected transit assignment model. The model's graphical representation and solution technique are described in depth in addition to the constraints that will be integrated into the BNDP solution approaches. It is aimed to regain focus on this approach for the following BNDP research since it is infrequently used in BNDP solution frameworks, as previously discussed. In addition, the most popular transit assignment models have weak fundamental assumptions compared to the model under consideration.
## 5 Measurements and Experimental Results
### Experimental Results of PHOG/HOG with SVM  
Compared with the deep learning and BOW methods, the accuracies of PHOG and HOG are far lower (less than 45%) as shown in Table 4.  
Although PHOG is an extension of HOG, the time required to generate PHOG results is far less than that of HOG. Also, the accuracy of applying PHOG is better than HOG, including for the results with and without normalization.  
The accuracy of PHOG without normalization is approximately 39.77%, which is 5% higher than that of HOG. Furthermore, after normalization, the accuracy of PHOG is approximately 44%, which is approximately 7% higher than that of HOG. In addition, the time spent on PHOG is 15 times higher than that of HOG. As seen in Table 5, the greatest accuracy was achieved using SVM based on the PHOG features. Compared to the BOG features, when using the same five machine-learning methods, the accuracy of the PHOG features is lower.
## 3 Results and Discussion  
### The Operational Meteorological Forecast  
The meteorological forecast obtained using the operational framework based on the aforementioned model WRF-OL setup showed a very good predictive capability for rainfall timing and location, especially over eastern Sicily. Figure 4 shows the observed (merging radar and rain gauges [25]) in panel A and modeled cumulated rainfall (panel B) for the 48 h between 24 October 2021 at 00 UTC and 26 October 2021 at 00 UTC, uploaded on the MyDewetra platform to provide an easy and immediate situation view during the forecasting phase.  
A quantitative verification of the meteorological forecast is performed using the MODE tool [34; 35] by comparing the observed accumulated rainfall field with the forecast fields of the other runs. The main advantage of such a validation is that the forecast is not only evaluated point-wise but also at feature level, thus overcoming the so-called \"double-penalty\" issue [36]. MODE identifies precipitation structures above given thresholds in both the forecast and the observed fields and performs a spatial evaluation of the model capability of reproducing the identified objects [37]. Especially for high-resolution observations and  
Figure 4: Observed (Panel (A)) and modeled (WRF-OL, Panel (B)) 48 h cumulated rainfall between 24–26 October 2021. In Panel A, black dots with labels represent the rain gauges cited in Section 2.1, namely Lentini (LE), Linguaglossa (LI), Catania (CA), and Fabrizia (FA).  
cloud-resolving meteorological forecasts during deep convective events, it is preferable to use feature-based verification techniques, such as MODE, because traditional methods cannot provide a measure of spatial and temporal match between observed and forecast fields. The MODE analysis aims to verify the model capability in reproducing the pattern of the considered atmospheric field through the evaluation of indices (for further description refer to [11; 12]) such as centroid distance (CEN DIST), angle difference (ANG DIFF), area ratio (obtained with forecast area--FCST AREA divided by OBS AREA, symmetric difference (SYMM DIFF), intesection area (INTERS AREA), union area (UNION AREA), and percentile intensity within object (FCST INT and OBS INT considering 50 and 90 percentile) above a fixed threshold (150 mm and 200 mm in this case). All the above-mentioned attributes are then summarized in a single index, called Total Interest, through a fuzzy logic algorithm (Equation (1) of [38] and Section 4 of [39]). In this work, the quantitative object comparison and some of the calculated indices, including the Total Interest (ranging between 0 and 1), are reported in Table 2. The Total Interest for both thresholds is above 0.9 (up to 0.95 for the 150 mm threshold), close to 1 (best score); this means that the forecast pattern is very close to the observed pattern, despite a slight overestimation of rainfall peaks and patterns, as also visible in Figure 4.
# Wave Polarization Control in Anisotropic Locally Resonant Materials
## 4 Transmission Analyses
### Eccentricity with \(\vartheta=0\)  
As a first example, we consider an array of 40 cells with horizontally eccentric fibers (\(\vartheta=0\)), as shown in Figure 6a. The medium is excited by a unit displacement applied on the left end in the \(\mathbf{e}_{1}\) direction, which coincides with the principal mass direction \(\mathbf{e}_{1}\) of the metamaterial. Figure 6c shows the response of the system, in terms of the displacement \(u_{1}\) evaluated on the right end of the cell array, as a function of the reduced frequency \(f^{*}\). As it is possible to observe, elastic waves, which are polarized in the \(\mathbf{e}_{1}\) direction, are damped only in the blue shaded interval, i.e., where \(\rho_{1}^{0}<0\).  
The absorption is higher at the opening of the polarization band, i.e., where the negative mass \(\rho_{1}^{0}\rightarrow-\infty\), and becomes smaller as the frequency increases. This is also visualized through the contours of the displacement \(u_{1}\) of the matrix, shown in Figure 6d for \(f^{*}=11\) m/s (point A of Figure 6c) and \(f^{*}=13\) m/s (point B of Figure 6c), for the first twenty cells of the metamaterials. For the lowest frequency (A), fifteen cells are enough to completely damp out the propagating wave, whilst for the highest one (B), more than twenty cells are required.  
In the same figure, on the left, the contour of the horizontal displacement on a whole unit cell is shown as well with a different color scale in order to appreciate the variation of the \(u_{1}\) field inside the cell leading to a locally resonant mechanism.  
Figure 6: Transmission analysis with \(\vartheta=0\): **(a)** model for the \(\mathbf{e}_{1}-\) transmission; **(b)** principal values of the homogenized mass; **(c)**\(u_{1}-\)transmission as a function of frequency; **(d)** contour of \(u_{1}\) in the first twenty cells for: (A) \(f^{*}=11\) m/s and (B) \(f^{*}=13\) m/s.  
We also consider the transmission, on the same system, of a unit displacement in the \(\mathbf{e}_{2}\) direction, as illustrated in Figure 7a. The response of the array is evaluated in terms of the displacement \(u_{\text{II}}\) of the right edge of the array, and the transmission is plotted in Figure 7b. Since the propagating waves are polarized in the \(\mathbf{e}_{\text{II}}\) direction, the absorption is now present in the red polarization bands only, i.e., when \(\rho_{\text{II}}\) is negative.
# A Transfer Residual Neural Network Based on ResNet-50 for Detection of Steel Surface Defects
## 3 Results
### Rate of Convergence Analysis  
Figure 7 compares the accuracy and loss values of the four models in the test set. Although all models can converge within 100 cycles, their convergence time and accuracy significantly vary. During the training phase, TL-ResNet50 exhibits much better convergence than other models, reaching a total accuracy of 95% after only the first cycle, indicating that this model can rapidly recognize images. VGG19 exhibits the slowest convergence speed, achieving a somewhat stable but lower level of accuracy after 30 cycles. AlexNet displays the lowest accuracy and the slowest convergence rate. The ResNet50 model, which was not utilized for TL, has the highest loss value among the four models and converges considerably more slowly than the other TL models. In comparison, TL-ResNet50 shows the fastest reduction in loss value, remains at a lower level throughout the entire process, and consistently maintains higher accuracy than the other models.  
Figure 8 illustrates the accuracy and loss values of TL-ResNet50, ResNet50, VGG19, and AlexNet during the training phase. The K-fold cross-validation method was employed, with the mean of five training results presented in the figure. Notably, AlexNet displays significant variations during training, indicating its lowest level of stability. VGG19 exhibits the slowest convergence speed and the highest initial value. Regarding convergence and accuracy, ResNet50 without TL also performs worse than the TL-ResNet50 model. In comparison, the TL-ResNet50 model's performance can be fully demonstrated by mutually verifying the training set and test set curves.  
Figure 8: Comparison of the four models: (**a**) TL-ResNet50; (**b**) ResNet50; (**c**) VGG19; (**d**) AlexNet.
## 3 A Taxonomy for Planar FSS  
A review of the literature identified five fundamental planar unit cell designs commonly used in FSSs: cross, patch, patch with additional surrounding elements, loop or ring, and meander cell. These basic designs serve as the foundation for numerous variations, each tailored to achieve specific performance criteria. The following subsections will analyze these designs and their respective modifications in detail.
## 2 Background  
### Cluster-Tree Topologies  
Typical WSNs application deployments have multiple sensor nodes and one coordinator, generally located in the middle of the topology (star topology). A common way to achieve scalability is to connect multiple star networks, resulting in cluster-tree topologies that are complex peer-to-peer constructions, where sensor nodes are grouped into clusters. Each cluster is coordinated by a specific FFD node called coordinator or cluster-head (CH). All communication within the clusters is centralised under the control of the CH. The CH is responsible for building its own cluster, managing nodes' association and providing synchronisation mechanisms and intra-cluster communication.  
The first cluster of the network is built by a special node, called PAN coordinator. The PAN coordinator can be considered a special node with a powerful central processing unit, being responsible for all the network management activities and is usually the sink node of the network. The CHs (including the PAN coordinator) are interconnected by parent-child relationships, forming a hierarchical network structure (multicluster).  
Cluster-tree networks operate in a beacon-enabled mode, where a structure called _Superframe_ organises all communication rounds. A superframe is bounded by beacon frames, which are periodically transmitted by cluster-heads. Beacon frames are used to synchronise clusters and also to describe the superframe structure. This structure is described by two parameters: the _macBeaconOrder_ (BO) and _macSuperframeOrder_ (SO), where \(0\leq SO\leq BO\leq 14\). These parameters define the _Beacon Interval_ (BI) and the _Superframe Duration_ (SD), respectively. Figure 1 illustrates the superframe structure.  
BI defines the periodicity at which cluster-heads must transmit their beacon frames. In turn, SD defines the communication period of the clusters. Each superframe can be composed of two parts: active and inactive periods. The inactive period exists only if the SO parameter is smaller than the BO parameter. During the active period, nodes can communicate with their cluster-heads. On the other hand, during inactive period, the coordinator and member nodes may enter in low-power (sleep) mode in order to save energy. The active part comprises two periods: _Contention Access Period_ (CAP), during which member nodes can communicate using a slotted _Carrier Sense Multiple Access with Collision Avoidance_ (CSMA-CA) mechanism to access the channel; and _Contention-Free Period_ (CFP), during which the coordinator can allocate up to seven _Guaranteed Time Slots_ (GTS) for specific devices to transmit data without contending for the channel access. The _aBaseSuperframeDuration_ parameter defines the minimum duration of a superframe (\(SD_{min}\)) when SO is 0 (by default, this parameter corresponds to 960 symbols, corresponding to a duration of 15.36 ms, considering a bit rate of 250 kbps, frequency band of 2.4 GHz, and one symbol as 4 bits).
# Parallel Image Completion with Edge and Color Map
## 4 Experimental Results and Analysis
### Quantitative Comparison
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Mask Size** & **FMM** & **GLCIC** & **DeepFill** & **GMCNN** & **DFNet** & **EdgeConnect** & **PIC-EC (Ours)** \\ \hline \multirow{8}{*}{\(\ell_{1}(\%)^{\ddagger}\)} & 70 & 0.76 & 0.59 & 0.78 & 0.62 & 0.49 & **0.48** & 0.50 \\  & 80 & 1.04 & 0.81 & 1.04 & 0.81 & 0.68 & **0.64** & 0.65 \\  & 90 & 1.41 & 1.11 & 1.36 & 1.02 & 0.92 & **0.85** & 0.87 \\  & 100 & 1.85 & 1 \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Mask Size** & **FMM** & **GMCNN** & **EdgeConnect** & **PIC-EC (Ours)** \\ \hline \multirow{8}{*}{\(\ell_{1}(\%)^{\ddagger}\)} & 70 & 0.69 & 0.65 & **0.55** & 0.60 \\  & 80 & 0.94 & 0.85 & **0.74** & 0.76 \\  & 90 & 1.21 & 1.08 & **0.96** & 1.06 \\  & 100 & 1.60 & 1.39 & **1.26** & 1.35 \\  & 110 & 1.99 & 1.73 & 1.57 & **1.48** \\  & 128 & 2.83 & 2.51 & 2.24 & **1.74** \\ \hline \multirow{8}{*}{PSNR (dB) \({}^{*}\)} & 70 & 30.061 & 31.685 & **32.06** & 29.05 \\  & 80 & 28.542 & 30.121 & **30.64** & 28.75 \\  & 90 & 27.283 & 28.773 & **29.36** & 28.30 \\  & 100 & 25.638 & 27.175 & **27.77** & 27.67 \\  & 110 & 24.556 & 25.955 & 26.61 & **27.12** \\  & 128 & 22.675 & 23.768 & 24.79 & **26.19** \\ \hline \multirow{8}{*}{SSIM \({}^{*}\)} & 70 & 0.956 & 0.960 & **0.961** & 0.945 \\  & 80 & 0.942 & 0.946 & **0.949** & 0.939 \\  & 90 & 0.926 & 0.930 & **0.934** & 0.930 \\  & 100 & 0.906 & 0.912 & 0.916 & **0.923** \\  & 110 & 0.886 & 0.891 & 0.895 & **0.912** \\  & 128 & 0.843 & 0.846 & 0.853 & **0.892** \\ \hline \multirow{8}{*}{FID \({}^{\ddagger}\)} & 70 & 26.027 & 4.736 & **4.482** & 6.931 \\  & 80 & 38.447 & 6.044 & **5.608** & 6.736 \\ \cline{1-1}  & 90 & 49.446 & 7.693 & **6.922** & 9.151 \\ \cline{1-1}  & 100 & 61.679 & 9.788 & **8.397** & 10.009 \\ \cline{1-1}  & 110 & 71.566 & 12.173 & 11.225 & **10.269** \\ \cline{1-1}  & 128 & 88.016 & 17.177 & 14.353 & **13.976** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative results over the Paris StreetView for six different masks with methods: FMM, GMCNN, EdgeConnect and PIC-EC. Boldface in each row indicates the best performance. \({}^{*}\) Higher is better. \({}^{\ddagger}\) Lower is better. Figure 8: Effect of different masks over CelebA-HQ. (**a**) results of \(\ell_{1}\) metric; (**b**) results of PSNR (Peak Signal-to-Noise Ratio) metric; (**c**) results of SSIM (Structural Similarity Index Metric) metric; (**d**) results of FID (Fréchet Inception Distance) metric.
## Appendix B Proofs of Equations (26) and (27)  
The input distribution \(Q\) (\(\cdot\)) maximizing the differential entropy \(h(x)\) under the constraints of (10), (11), and (12), is with the following form [19] (Chapter 12)  
\[Q\left(x\right)=e^{\lambda_{0}+\lambda_{1}x+\lambda_{2}logx},\] (A8)  
where \(\lambda_{0}\), \(\lambda_{1}\), and \(\lambda_{2}\) are Lagrange factors needing to be optimized. Based on (10) and (12), and using the basic property of PDF function, we have  
\[\int_{0}^{P}Q(x)dx=1.\] (A9)  
Substituting (A8) into (A9), there is  
\[e^{\lambda_{0}}\int_{0}^{P}e^{\lambda_{1}x}.x^{\lambda_{2}}dx=1.\] (A10)  
By (11) and (A8), and using the integration by parts, we further get  
\[\int_{0}^{P}x\cdot Q(x)dx=e^{\lambda_{0}}\left(\frac{P^{\lambda_{2}+1}}{ \lambda_{1}}.e^{\lambda_{1}P}-\frac{\lambda_{2}+1}{\lambda_{1}}\int_{0}^{P}e^ {\lambda_{1}x}.x^{\lambda_{2}}dx\right)=\varepsilon.\] (A11)  
Combining with (A10), (A11) results in  
\[\frac{P^{\lambda_{2}+1}}{\lambda_{1}}.e^{\lambda_{0}+\lambda_{1}P}-\frac{ \lambda_{2}+1}{\lambda_{1}}=\varepsilon.\] (A12)  
Specially, by assuming \(\lambda_{1}=-\frac{\mu}{P}\) and \(\lambda_{2}=-\frac{1}{2}\), (A10) could be denoted as  
\[e^{\lambda_{0}}\int_{0}^{P}e^{-\frac{\mu}{P}x}.x^{-\frac{1}{2}}dx=1.\] (A13)  
Using the change of variable technique, that is, letting \(\sqrt{x}=t\), (A13) changes into  
\[2e^{\lambda_{0}}\int_{0}^{\sqrt{P}}e^{-\frac{\mu}{P}t^{2}}dt=1.\] (A14)Utilizing the indefinite integral formula (2.33.16) in [24], we have  
\[e^{\lambda_{0}}=\sqrt{\frac{\mu}{P\pi}}.\frac{1}{erf(\sqrt{\mu})}.\] (A15)  
Obviously, the fixed Lagrange factors \(\lambda_{1}=-\frac{\mu}{p}\), \(\lambda_{2}=-\frac{1}{2}\), and \(e^{\lambda_{0}}=\sqrt{\frac{\mu}{P\pi}}.\frac{1}{erf(\sqrt{\mu})}\) should satisfy (A12). Substituting these three parameters into (A8) and (A12), and after some necessary manipulations, we have (26) and the following equation  
\[\frac{P}{2\mu}-\frac{Pe^{-\mu}}{\sqrt{\mu}\sqrt{\pi erf}(\sqrt{\mu})}=\varepsilon,\] (A16)  
i.e., \(\mu\) is the solution to (A16).