esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### SSG Current and Beam Neutralization  
The electric current measured between the facility ground and SSG is a good indicator of the degree of disturbance introduced by probes when they enter the ion beam. Without the disturbing probes, the value of SSG current follows the value of the first grid current, as can be seen in Figure 5. The amplitude of the disturbance depends on many factors, such as the size of the disturber (number of probes inserted into the beam), position of the disturber (closer to or further from the thruster) and the heating state of the thermionic cathode (ability to compensate for a certain amount of the electric current).  
The quantification of the self-neutralization disturbance by the SSG current is illustrated in Figure 13, where the changes to the SSG current during the radial movement of  
Figure 12: Comparison of electron density profiles for non-compensated and partially compensated beams at \(\Delta x=50\) mm.  
the probes are shown. The diagram on the left was taken when the first probe was located \(25\,\mathrm{mm}\) from the thruster, while the diagram on the right was taken when the first probe was located \(5\,\mathrm{mm}\) from the thruster. The number of probes and the heating power of the thermionic cathodes were the same. The data in the left panel show that the SSG current behaves differently when the partial compensation is switched on or off. While for the completely uncompensated beam, the change in the SSG current is \(0.5\,\mathrm{mA}\), and the partial compensation reduces it almost to zero. However, the data in the right panel indicate that when the disturber is located closer to the thruster, the partially compensated beam still shows a similar change in the SSG current of \(0.5\,\mathrm{mA}\). The situation is more severe when the thermionic cathode is not powered at all. In that case, the current of the SSG can change by as much as \(2\,\mathrm{mA}\).  
The situation with the cold thermionic current is very different from that with the hot thermionic cathode, even if they are kept floating. In the former case, the insertion of the probes into the ion beam is accompanied by the appearance of numerous sporadic sparks on the surfaces of the vacuum chamber. In addition, the VCC traces of the probes suffer from sudden jumps in the measured current values. This is not the case when the thermionic cathode is hot, regardless of its sourcing status. A possible explanation could be the limited ability of the measurement device to keep the cathode floating. While the average DC current from the cathode is zero, the rapid changes in current at the micro- or nanosecond scale cannot be compensated for by the device. Thus, the electron cloud of the hot cathode serves as a source of readily available electrons for any sudden changes in the space charge of the ion beam. When the cathode is cold, such changes should be compensated for by the SEE, leading to increased levels of secondary electron emission from the vacuum facility walls, which are occasionally accompanied by surface discharges.
## 4 Experimental Results
### Quantitative Evaluation  
As shown in Table 4, compared with CGAN, Pix2 Pix and CycleGAN for three different input sequence images based on the four evaluation metrics, our method obtained the best performance in 10 cases, indicating the effectiveness of the proposed method. From Table 4, the texture of the synthesized image was the best (i.e., the value of FID, KID, and LPIPS are highest) when the number of the sequence is 48 (i.e., 48 s). The contour of the synthesized image was optimal (i.e., DSC is the best) when the number of the sequence was 64 (i.e., 64 s). From analysis of the experimental results, in terms of the accuracy of the contour of the final synthesized image, when the sequence length was longer, the more accurate was the contour of the generated image. In terms of the realism of the generated image texture, using a sequence length that was too long or too short made the texture details of the generated image poor, and an appropriate sequence length (i.e., 48) should be chosen. For a sequence length of 32, less information was contained about the overall structure of the vessel, and the synthesized image was poorer in terms of contour and detailed texture. For a more direct display of the results, we give a line plot of the comparison results, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Model/Evaluation Metrics** & **FID** & **KID** & **LPIPS** & **DSC** \\ \hline
1 & CGAN\_64s & 101.459 & 0.1172 & 0.180 & 0.865 \\ \hline
2 & Pix2 Pix\_64s & 85.045 & 0.097 & **0.174 *** & 0.867 \\ \hline
3 & CycleGAN\_64s & 245.226 & 0.307 & 0.449 & 0.599 \\ \hline
4 & **MTv2l\_64s (Ours)** & 79.295 * & 0.086 * & 0.177 & **0.872 * \\ \hline
5 & CGAN\_48s & 98.485 & 0.1168 & 0.186 & 0.863 \\ \hline
6 & Pix2 Pix\_48s & 111.586 & 0.136 & 0.192 & 0.856 \\ \hline
7 & CycleGAN\_48s & 195.270 & 0.234 & 0.422 & 0.614 \\ \hline
8 & **MTv2l\_48s (Ours)** & **61.900 *** & **0.062 *** & 0.181 * & 0.865 * \\ \hline
9 & CGAN\_32s & 109.561 & 0.129 & 0.194 & 0.855 \\ \hline
10 & Pix2 Pix\_32s & 107.912 & 0.124 & 0.186 * & 0.833 \\ \hline
11 & CycleGAN\_32s & 297.366 & 0.344 & 0.515 & 0.464 \\ \hline
12 & **MTv2l\_32s (Ours)** & 85.992 * & 0.094 * & 0.187 & 0.858 * \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons of all methods. * indicates the best evaluated metric in the current round of training, bold font indicates that the best flat cabinet value was obtained in all experiments.
# Robustness of Double-Layer Group-Dependent Combat Network with Cascading Failure
## 3 Cascading Failure Model for DGCN
### Conditional Group-Dependent Failure  
There is a default rule in asymmetric dependent failure, that is, the dependent failure will not happen as long as the dependent relation of nodes is maintained, such as node \(G_{2}\) in Figure 3. Based on this, a conditional group-dependent failure model is proposed. This model can tolerate the partial dependent failure of nodes, which is shown in Figure 4. \(a\) and \(b\) are two nodes in network A which depend on network B, the solid lines are the edges still alive, and the dashed lines are the edges that are about to fail.  
As we can see in the figure, network A is unidirectionally dependent on network B, and the scale of the dependency can be different. If the node \(x\) has depended on a number \(n\) communication nodes, the capacity of the node \(v_{x}\) is \(C_{x}\), the capacity of the depended node is \(C_{x}^{i}\), and the communication connectivity index of node \(x\) is expressed as follows:  
\[\zeta_{x}=\frac{1}{nC_{x}}\sum_{i=1}^{n}C_{x}^{i} \tag{3}\]  
When the depended communication nodes fail, the sum elements of \(\zeta_{x}\) will decrease correspondingly while \(n\) will remain the same. If we set the upper limit of the communication connectivity index as \(\zeta\), then the normalized connectivity index is \(\zeta=\zeta/\zeta\). In our conditional group-dependent failure model, the communication connectivity index will decrease as the depended communication nodes are destroyed. When the index is lower than a threshold \(\tau\), the communication network cannot ensure that the functional nodes complete the flow of information (the default threshold is \(\tau=0.6\)). Therefore, the functional node will conditionally fail. For the node \(a\) in Figure 4, its dependent nodes reserve \(\frac{2}{3}\), and the connectivity index is \(\zeta_{a}>\tau\), so \(a\) still remains normal, while node \(b\) will fail as it only has \(\frac{1}{4}\) dependent nodes left and the connectivity index is \(\zeta_{b}<\tau\).
###### Abstract  
The age of the Internet of Things (IoT) and smart cities calls for low-power wireless communication networks, for which the Long-Range (LoRa) is a rising star. Efficient network engineering requires the accurate prediction of the Received Signal Strength Indicator (RSSI) spatial distribution. However, the most commonly used models either lack the physical accurateness, resolution, or versatility for cityscape real-world building distribution-based RSSI predictions. For this purpose, we apply the 2D electric field wave-propagation Oscillator Finite-Difference Time-Domain (O-FDTD) method, using the complex dielectric permittivity to model reflection and absorption effects by concrete walls and the receiver sensitivity as the threshold to obtain a simulated coverage area in a 600 \(\times\) 600 m\({}^{2}\) square. Further, we report a simple and low-cost method to experimentally determine the signal coverage area based on mapping communication response-time delays. The simulations show a strong building influence on the RSSI, compared against the Free-Space Path (FSPL) model. We obtain a spatial overlap of 84% between the O-FDTD simulated and experimental signal coverage maps. Our proof-of-concept approach is thoroughly discussed compared to previous works, outlining error sources and possible future improvements. O-FDTD is demonstrated to be most promising for both indoors and outdoors applications and presents a powerful tool for IoT and smart city planners.  
LoRa; RSSI; RF propagation; O-FDTD simulations; Internet of Things (IoT); smart city +
Footnote †: journal: Sensors  
0000-0001-998-2217-1000/00001-998-2217
## 3 Motivation
### Impact of Sparsity on Redundancy Removal  
Due to the huge size of raw visual information, images and video sequences are compressed before transmission. We present an example to show the impact of DCS on the removal of redundancy. Consider a length-\(N\), real-valued signal of one dimension indexed as \(x\), its coefficient \(\theta\) is sparse in wavelet basic \(\psi\), i.e., \(x=\psi\theta\), where \(\theta\) is an \(N\times 1\) column vector with \(k\) non-zero elements and \(k\) is the sparsity of \(\theta\).  
Take Figure 2 as an example of sparsity coefficients of signals observed by three different sensors, where the signals are processed under three situations, i.e., non-compression, CS and DCS. The horizontal direction (equivalent to \(x\)-axis) represents different locations of sparsity coefficients observed by different sensors; the vertical directions (equivalent to \(y\)-axis) represents different sparsity coefficients (i.e., \(\theta_{1}\), \(\theta_{1}\) and \(\theta_{3}\)). The locations of the sparsity coefficient are the same for different coefficients, but sparsity coefficients of noise are different. Consider a simple case where there is no encoder to process these three signals. If the length of a signal is 40, 120 signal samples need to be transmitted for the three signals. Next, consider a simple case where there is an encoder to process these three signals by CS. Based upon the CS machinery, \(M=c\cdot k\) (\(c=4\)) measurements are required to reconstruct the signal \(x\). Accordingly, we need to transmit 60 signal samples for three signals. If the encoder processes these three signals by DCS, \(M=c\cdot(K+\sum_{i}k_{i})\) measurements are expected to reconstruct the signal \(x\), where \(K\) represents the common sparsity of three coefficients and \(k_{i}\) represents the unique sparsity of coefficient \(\theta_{i}\). As a result, only 36 signal samples are transmitted for these three signals.  
The above analysis indicates that DCS can mainly reduce unnecessary redundancy of data transmission compared with compressed sensing. However, if \(m\) sensors can realize the application requirement, there is no need to let all sensors that observe the object of interest compress signal. By applying SCS [32], \(m\) sensors can be selected out of \(n\) sensors to take part in DCS, which can make the reconstruction error below the defined threshold and further reduce the communication load.  
Figure 1: An interest object.
## 2 Materials and Methods
### GNSS Tracking Methods and Conditions  
In order to determine the actual workload of key performance indicators (KPIs) using GNSS technology, an analysis of 4 matches of the international tournament was carried out. The position of goalkeeper has been removed due to his special role and rules of the game, which makes the GK stay in the penalty box of strictly reduced dimensions. The study only takes into account the actual playing time of individual players on the pitch, including all timeouts requested by the coach, additional time and the timeout between halves. The parameters characterizing the external loads were: total distance (TD) [m], distance in bands: 1.0-3.0 m/s; 3.0-5.0 m/s; 5.0-7.0 m/s; 7.0-9.0 m/s in meters, session time [min], activity time [min], peak acceleration [m/s\({}^{2}\)], peak deceleration [m/s\({}^{2}\)], maximum speed [m/s], sprint total distance (STD) [m], mean sprint distance [m], mean sprint duration [s], GNSS load and inertial load. Descriptions of all parameters can be found in Appendix A.  
In addition to the sampling frequency of the device (10 Hz), the accuracy of positioning measurement and its derivatives is influenced by other factors. The most important of these include the number of satellites connected and their horizontal dilution of precision (HDOP) (Han, 2022; Han, 2023). HDOP values >1 are defined as ideal, while values >2 are defined as excellent (Han, 2023). In this study, the average number of satellites was 16.2 \(\pm\) 3.19, and HDOP was 1.44 \(\pm\) 0.32 during the first match and 16.75 \(\pm\) 1.85, respectively, for HDOP 1.17 \(\pm\) 0.13 in the second match; on the second day, 18.66 \(\pm\) 1.88 for HDOP 1.22 \(\pm\) 0.10 in the first match and 20 \(\pm\) 4.65 for HDOP 0.95 \(\pm\) 0.27 in the second game.  
The research used a GNSS sensor from Integrated Bionics Inc., 2020 (Houston, TX, USA) called TITAN2 (technical parameters are provided in Appendix B). The data was initially prepared using the manufacturer's web platform (export to Excel) and then subjected to statistical analysis. All device and software settings were original and were not modified for this research.
# Numerical Analysis of Flow in Building Arrangement: Computational Domain Discretization
## 1 Introduction
### Building Arrangements Analysis
Additionally, the flow and pressure fields are dominated by large-scale motions generated by ground objects. Selection of an appropriate method of numerical solution concerns, first of all, the choice of methodology for numerical analysis. The most obvious choices are the steady Reynolds-Averaged Navier Stokes (RANS) or unsteady RANS (uRANS) which are used most often since Large Eddy Simulation (LES) or hybrid URANS/LES techniques require much more computational time [15]. Apart from the selection and parameterization of a turbulence model, the discretization of the computational domain is a particularly important task in the case of flow modelling in built-up areas because it influences the CPU time, as well as the accuracy of the results [16; 17; 18]. Therefore an innovative approach to discretization method of the analysed domain representing the array of two buildings located in an urban environment is addressed in the carried out research.
# Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
## 3 RFF Identification Scheme
### Existing DCTF-Based CNN Structures
#### 3.1.1 Single-Channel DCTF-Based CNN Structure  
The DCTF extracted from the PRACH signal was fed into a CNN following the structure of LeNet-5 [35]. Originally proposed for high-performance handwritten digit identification, the application of LeNet-5 is similar to our experiment, where we needed to recognize six 5G mobile phones. Utilizing more complex network structures would significantly increase the computational complexity, with limited potential for performance improvement. The specific layers are illustrated in Figure 7, consisting of six layers. Convolutional layers capture the local features of the RFF, with the convolution kernel sizes set to \([3\times 3]\). Hidden layers following the convolutional layers included BatchNorm to enhance the training speed and generalization capability and ReLU to introduce nonlinearity and alleviate the vanishing gradient problem. Additionally, pooling layers were employed to reduce the size of the parameter matrices and the number of parameters in the fully connected layers, speed up computation and prevent overfitting. In this stage, max-pooling layers with a stride of 2 and kernel size of \([2\times 2]\) were used.
## 4 Location Estimation-Based Congestion-Aware Routing Protocol (LECAR)--Detailed Proposal
### Routing Decision Process
#### 4.3.5 Calculating the Forwarding Cost  
To make the routing decision (i.e., to select the next custodian responsible for forwarding the packet), LECAR considers the estimated distance to the destination and the buffer occupancy. We demonstrated how to calculate the normalized distance and average buffer occupancy. Next, to combine these two, we introduced a parameter, the forwarding cost (\(FC\)), which is calculated as follows:  
\[\begin{split} FC=(2-\Psi)\times\Pi+(\Psi-1)\times\varphi\\ \text{where, }\Psi=\left\{\begin{array}{ll}e^{\varphi^{10}} \text{when }\varphi<0.965\\ 2&\text{when }\varphi\geq 0.965\end{array}\right.\end{split} \tag{11}\]  
where \(e\) is an exponential constant and has a value of approximately 2.718. We incorporated this value following a previous study because buffer utilization is consistently low when the buffer occupancy is below 60% [28].
# FPGA Implementation of a Deep Learning Acceleration Core Architecture for Image Target Detection
## 2 Proposed Method
### Computational Scheduling Kernel Pipeline Design
#### 2.2.1 Convolutional Layer, Pooling, Fully Connected Layer Pipelining Methods
Loop level 3 represents the traversal of the number of output channels. Loop levels 4 and 5 represent the traversal of the convolution kernel tensor rows and columns, respectively. Loop level 6 represents the traversal of the convolution kernel input channels. Loops 7, 8, and 9 are loops of pixel parallelism, input channel parallelism, and output channel parallelism, respectively. Loops 1-6 use the pipeline constraint, which means that these loops will be combined into one big loop, and the upper limit of the big loop is the product of the upper limit of all small loops. Loops 7-9 below the pipeline constraint are constrained to be unrolled loops. Within a convolutional operation, the computation scheduling kernel iterates through all the elements of the output tensor by loops 7-9, computes one of the element values by loops 4-6, and unrolls the convolutional operation in parallel by loops 1-3 to improve the computational efficiency and finally obtain the result of a whole convolutional layer. As shown in Algorithm 2, the pooling layer does not need to consider input channel parallelism compared to the convolutional layer since the number of input channels is the same as the number of output channels. Only the output channels need to be parallelized. Loop level 1 represents the traversal of the input tensor row direction. Loop level 2 represents the traversal of the input tensor column direction. Loop level 3 is the traversal of the output channels. Loop level 4 is the traversal of the pooling range in the row direction. Loop 5 is the traversal of the pooling range in the column direction. Loops 6 and 7 are for pixel and output channel parallelism, respectively. Loops 1 and 2 in the pooling layer traverse the output tensor, and each element of the output tensor corresponds to the maximum value of the pooling range in the input tensor; loops 4 and 5 are the traversal of the pooling range, and loops 6 and 7 are parallel acceleration. As shown in Algorithm 3, Considering TinySolo's 17 th fully connected layer, with an input tensor of 1 \(\times\) 1 \(\times\) 50,176 and an output tensor of 1 \(\times\) 1 \(\times\) 256, the fully connected layer parameter of 1 \(\times\) 1 \(\times\) 50,176 \(\times\) 256 is the most significant layer in the entire network. Suppose the output channels are parallelized like the convolutional layer, the fully connected layer must load at least 50,176 times the number of output channels in parallel with 16-bit fixed-point parameters, which takes up a lot of on-chip BRAM storage resources. Therefore, in this paper, the fully connected layer includes only two dimensions: input channel and pixel parallelism. The loop level 1 of the fully connected layer traverses the input tensor channels. Loop level 2 traverses the output tensor channels. Loop levels 3 and 4 are pixel parallelism and input channel parallelism, respectively.
## 3 Results
### Evaluation of Environmental Impacts  
Results of the LCA regarding environmental impacts of the developed P1 absorber's production are summarized in Table 3 and Figure 4. Table 3 shows environmental impacts of the P1 absorber divided per individual impact category, material, and process (in accordance with the production scheme in Figure 2). Figure 4 provides further insight into the numerical results by showing shares of the materials and processes relative to the sum of normalized environmental impacts. Both the table and the figure identify pigment production as the least environmentally demanding (less than a 0.1% share of the sum of normalized results in Figure 4). This is followed by transport. Although the sum of transport distances is close to 3000 km, this only accounts for 0.4% of the sum of normalized results. In contrast, PET and LDPE have the highest and second highest environmental impacts in most categories. Their shares of the sum of normalized results are 36% and 34%, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Solar** & **Thermal** & **Selectivity** & **Figure of Merit** \\  & **Absorbance \(\alpha_{\lambda}\)** & **Emissivity \(\varepsilon_{\lambda}\)** & \(f\) & **FM Based on [40]** \\  & **0.3–2.5 μm** & **2.5–18.0 μm** & \(\alpha_{\lambda}/\varepsilon_{\lambda}\)** & 0.39 \\ \hline S1 & 0.85 \(\pm\) 0.005 & 0.82 \(\pm\) 0.05 & 1.0 & 0.39 \\ S2 & 0.94 \(\pm\) 0.005 & 0.94 \(\pm\) 0.05 & 1.0 & 0.41 \\ S3 & 0.95 \(\pm\) 0.005 & 0.93 \(\pm\) 0.05 & 1.0 & 0.42 \\ R1 & 0.90 \(\pm\) 0.005 & 0.06 \(\pm\) 0.05 & 15.0 & 0.87 \\ R2 & 0.95 \(\pm\) 0.005 & 0.07 \(\pm\) 0.05 & 13.6 & 0.91 \\ R3 & 0.94 \(\pm\) 0.005 & 0.04 \(\pm\) 0.05 & 23.5 & 0.92 \\ P1 & 0.95 \(\pm\) 0.005 & 0.30 \(\pm\) 0.05 & 3.2 & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Optical parameters and figure of merit of the tested solar absorbers. Figure 3: Spectral optical parameters of absorbers: (**a**) shortwave spectrum; (**b**) longwave spectrum. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & \multicolumn{2}{c}{**MPET**} & \multicolumn{2}{c}{**Pigment**} & \multicolumn{2}{c}{**LDPE**} & \multicolumn{2}{c}{**Absorber**} \\ \cline{2-10}  & **Aluminum** & **PET** & **Processing** & **Transport** & **Mg. Ore** & **Processing** & **Transport** & **LDPE** & **Transport** & **Processing** \\ \hline Climate Change (CC) & \(2.81\times 10^{-3}\) & \(6.81\times 10^{-2}\) & \(1.43\times 10^{-2}  
Table 4 shows total environmental impacts of the P1 absorber, in addition to those of the R2 and R3 TiNOx absorbers and S3 synthetic coating. It shows that the P1 absorber has the lowest impact in 12 of the 13 categories. The difference is at least 98% compared to R2 and R3, and at least 83% compared to S3. The sums of normalized environmental impacts in Figure 5 further highlight the environmental efficiency of the P1 absorber, particularly when compared to TiNOx absorbers incorporating a comparatively high amount of metals.