esa-sceva/satcom-chunk-collection huggingface dataset:
# Roadmapping of Nanoelectronics for the New Electronics Industry
## 11 Data Centers  
A continuous increase of gigantic clusters of servers and memory banks, called \"data centers\" (Figure 13), has been observed due to the exponential demand of data transfer and information. The dramatic increase of their power consumption has reached a range of hundreds of megawatts, needing complex cooling techniques. Fiber optics allow communications with low attenuation specially for long distance, but the traffic within data centers is a bottleneck (Figure 14).  
Figure 12: Metal pitch and contacted gate pitch scaling vs. time horizons.  
The advent of multicore processors was used to \"host\" different operating systems in each of the cores, which was useful to address different applications leading to a substantial increase in efficiency. The data transfer in these data centers moved from 1 Gigabit Ethernet (GbE) links out of a server rack a decade ago to 100 GbE today and 400 GbE very soon, needing single mode fibers to satisfy these requirements.
## 5 Experimental Setup and Database  
### Database  
The experiments are performed on the AGORA database, which contains audio recordings of 34 TV shows from Catalan broadcast TV3 [43] (in total 68 audios of approximately 38 min each). These audios contain segments from 871 adult Catalan and 157 adult Spanish speakers. For all the experiments in this work, we selected 38 audio files for testing and 30 audios are used as background data. The background data were used to train the Universal Background Model (UBM) and Total Variability (T) matrix for the baseline i-vector system. For the proposed system, the background data were used to train the URBM and PCA. We manually extracted 2631 speaker segments from the test audios, according to ground truth rich transcription. These segments were used in the speaker clustering experiments. In the testing audios, 414 different speakers appear which were used as target speakers for the tracking experiments. For an audio file, all the speakers are considered as possible target speakers. A priori knowledge is required to enroll the target speakers in the system. Thus, the target speakers are enrolled using i-vectors and RBM vector approaches for the baseline and proposed systems, respectively. The target speakers are enrolled with 30 s of utterances. These enrollment utterances of target speakers are manually selected from the corresponding audio file (in which they appear) according to the ground truth rich transcription. It is worth noting that each target speaker appears in at least one of the test segments.
# Thermal Analysis of Radiation Heat Transfer of Improved Fractal Solar Collectors
## 2 Design and Analysis
### Numerical Setup  
In the proposed study, a parabolic dish was placed so that the thermal solar collector in the fractal array could benefit more from the reflected light. The radiative heat transfer analysis results of the 10 mm diameter 3-row, 4-row, and 5-row toroidal absorbers designed on the parabolic concentrator were compared in this study. The Spacecalaim program was used while modeling the geometry of the design. The dimeric and upper section views of the model-designed 3-row, 4-row, and 5-row structures are given in Figure 3.  
ANSYS Fluent software was used to solve radiation heat transfer within the scope of numerical analysis. It is known that ANSYS Fluent software offers various radiation models to simulate radiation heat transfer, including Discrete Transfer Radiation (DTR), P-1, Rosseland, Surface to Surface (S2S), and the DO Radiation Model. While the DO Radiation model was preferred for the analysis due to its capabilities and advantages, the turbulent terms of the flow equations were solved using the k-omega SST viscous model. In the modeling, solar radiation values were automatically used according to the conditions in which the experiments were conducted. As previously stated, the experiments were conducted in the climatic conditions of 1 March 2023, at 68.23\({}^{\circ}\) latitude and 43.3051\({}^{\circ}\) longitude of Kazakhstan. Thermophysical properties of the materials used in the analysis are given in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measurement** & **Test (\({}^{\circ}\)C)** & **Retest 1 (\({}^{\circ}\)C)** & **Retest 2 (\({}^{\circ}\)C)** \\ \hline \(1\) & 323.15 & 323.18 & 323.17 \\ \hline \(2\) & 323.20 & 323.24 & 323.22 \\ \hline \(3\) & 323.10 & 323.14 & 323.12 \\ \hline \(4\) & 323.18 & 323.22 & 323.20 \\ \hline \(5\) & 323.12 & 323.16 & 323.14 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Temperature measurements for the test–retest reliability analysis of a 3-row toroidal fractal solar collector.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Property**} & **Density** & **Heat Capacity** & **Thermal Conductivity** & **Viscosity** \\ \cline{2-5}  & \(\rho\) **(kg/m\({}^{3}\))** & \(Cp\) **(J/kg.K)** & \(k\) **(W/m.K)** & \(\mu\) **(Pa.s)** \\ \hline Air & - & 1006.43 & 0.0242 & \(1.79\times 10^{-5}\) \\ \hline Water-liquid & 998.2 & 4182 & 0.60 & 0.001003 \\ \hline Galvanized & 7950 & 486 & 55 & - \\ \hline Pipe-material & 2719 & 871 & \(1\times 10^{-6}\) & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of the materials [43].
# Faudistic Platooning: Subdivide Platoons, Unite Networks, and Conquer Efficiency and Reliability
## 5 Results
### Validation and Impact
Taking into account the observations described and the data relations expressed in Figures 8-15, the key advantages of _Feudalistic Platooning_ are clearly indicated:  
1. Relieve networks: * Ad hoc network Communication that uses the ad hoc network is limited to one vassalage. On the one hand, a vassalage may agree on a specific communication channel that would ensure no interference for neighboring vassalages. On the other hand, transmission power may be reduced as broadcasted information is only of interest within the vassalage and is therefore short-ranged. This enables earlier frequency reuse. * LTE network Only one single vehicle per vassalage, namely the lord, accesses the cellular network using LTE communication. This decreases the communication overhead including the number of communication requests and simplifies the resource scheduling within the cellular network. This does not, however, reduce the net payload. 2. Improved operability: Operability does not depend on the quality of the network: once in coverage, the platoon may be updated downstream and upstream, e.g., about new platoon members. All platoon members are aware of each other. Out of coverage, this reduces transmissions by design to a bare minimum. Here, communication is limited to a single vassalage and C-V2X updates from the preceding vassalage. However, control policy should be selected appropriately: required data may come from the current or preceding vassalage. 3. Increased efficiency: Except for the knight of each vassalage, vehicles of single vassalages benefit from the short inter-vehicle distances, which increase platoon efficiency. Furthermore, negative influences from the surrounding traffic are limited as the vassalages are inseparable. 4. Improved coexistence: Large inter-vehicle distance between two vassalages reserve room for the surrounding traffic and unforeseeable maneuvers and create credit for emergencies. 5. Improved modularity: Vassalages may be built up on a situational basis and according to different indicators such as a common destination or driver availability. Furthermore, a quicker integration of entire vassalages into other platoons is enabled, instead of exiting and rejoining the individual vehicles. 6. Low overhead: Once _Feudalistic Platooning_ is established, communication is purely data based and no additional management data are required. 7. Circumvent handover problem: Due to the fact that only one vehicle per vassalage is connected via LTE to the cellular network, the data dependency of different cells is not given. Whatever cell the lord is part of, its whole vassalage is also part of, even though technically they would already have switched cell.
## 4 Simulation and Discussion
### Reconstruction Performance versus the Measurement Length
#### 4.4.2 Reconstruction Time  
Figure 9a,b depict reconstruction time versus the measurement length \(M\) for synthetic and real signals. Similarly, in this experiment, the greedy algorithms in the first category were omitted because that they took considerably longer time. The horizontal axis represents different \(M\), and the vertical axis shows the corresponding reconstruction time (in seconds). For better comparison, the measurement lengths for synthetic signals were drawn from [75; 95], while those were assigned to [90; 110] for real signals. To ensure similar success rates obtained by FRSVssMP, the step size was set to 4 for SAMP, and the appropriate parameters were selected for CBMP.  
As can be seen from Figure 9, for both synthetic and real sparse signals, reconstruction time achieved by FRSVssMP is less than that of SAMP and CBMP for different measurement lengths. This implies that, FRSVssMP is more efficient than the competing algorithms with the same \(M\) because of the initial sparsity level estimation approach and sparsity variable step-size adaptive mechanism. In addition, FRSVssMP shows superiority in terms of robustness as the range of the reconstruction time for it is less than those for the competing methods.  
Figure 8: Reconstruction success rates vs. the measurement length. (**a**) Synthetic signals; (**b**) Real signals.
# MA-Xnet: Mobile-Attention X-Network for Crack Detection
## 4 Experiment Study
### Experimental Results on MDTU
In terms of detection time, although the use of depth-wise separable convolution greatly reduces the number of parameters, the time is slightly higher than that of U-Net, due to the application of the dual attention module. Figures 8 and 9 provide a plot of the segmentation results for some different models as a way of showing their segmentation performance more visually. As can be seen from Figure 8, for the first, fifth, and sixth finer cracks, U-Net, MobileNetV2-UNet, and DANet show different degrees of fracture, while the proposed model does not show significant discontinuities or missed detections. For the second, third, and fourth cracks next to the dot-shaped cracks, the location and size of the proposed model are also most accurate in terms of segmentation results, compared with the other networks. Figure 9 shows a segmentation diagram after applying a blue box to zoom in \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Precision/\%** & **Recall/\%** & **F1/\%** & **mIoU/\%** & **Parameters** & **Time/(s)** \\ \hline U-Net & 95.08 & 68.62 & 79.21 & 68.95 & 36,968,449 & 0.012 \\ MobileNetV2-UNet & 95.54 & 76.16 & 85.22 & 73.26 & 6,504,227 & 0.007 \\ DANet & 91.48 & 79.47 & 86.19 & 75.27 & 71,727,307 & 0.024 \\ MA-Xnet & 92.34 & 82.57 & 90.53 & 81.32 & 6,533,527 & 0.015 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison experiment result table. Figure 8: Contrast experiment chart. on a detailed feature of Figure 8, aiming to show more clearly the segmentation ability of each model in detail. The first column is the ground truth and the second column is the enlarged ground truth, where the enlarged portion is marked with a blue box in the first column. It can be seen from Figure 9 that, compared with the other models, the proposed model is the best in recovering the detailed features of cracks, and the segmentation results are clearer and closest to ground truth, thereby proving that the proposed model makes more effective use of the crack feature information.
## 3 Results
### Diffuse Reflectance Spectroscopy
#### 3.2.2 Monte Carlo Outlier Elimination in Diffuse Reflectance Spectra  
Outlier detection was performed on the collected spectral data and SSC using the Monte Carlo outlier removal method. As shown in Figure 10, samples with larger means and standard deviations were identified as outliers. Consequently, three samples, labeled 22, 58, and 95, were removed based on these results. To evaluate the impact of outlier removal on model performance, PLSR modeling was conducted and compared both before and after the removal of outliers.  
Figure 10: Outliers in diffuse reflectance spectra.  
Figure 9: Diffuse reflectance raw spectra.  
As shown in Table 5, the removal of outlier samples resulted in significant improvements in model performance. The \(R_{p}^{2}\) improved from 0.81 to 0.84. Concurrently, the RMSEC decreased substantially from 0.54 to 0.51 \({}^{\circ}\)Brix, and the RMSEP was reduced from 0.93 to 0.92 \({}^{\circ}\)Brix. These results indicated a notable enhancement in model accuracy following outlier removal, with substantial reductions in both calibration and prediction RMSE. This improvement emphasized the importance of outlier removal in enhancing model performance during spectral data modeling. The Monte Carlo outlier removal method, by identifying and eliminating samples that deviate from the overall trend of the dataset, enabled the model to more accurately reflect the true relationships between samples, thereby improving its predictive capability and reliability, as illustrated in Figure 11.
# Privacy and Security in Federated Learning: A Survey
## 2 An Overview of Federated Learning
### Network Topology
#### 2.3.1 Centralized FL  
This approach is the standard one. The central server is the cornerstone of the architecture. It manages clients, centralizes their local models, and updates the global model. FL optimization is an iterative process wherein each iteration improves the global ML model. It consists of three main steps:  
* Model initialization: FL is based on an initial model generally prepared on the server side. Initial weights can rely on a pretraining step. The model parameters are then distributed to the participating clients and will be updated along the next steps in accordance with clients' feedback.
* Local model training: a selection of participating clients is defined. Each of them receives the global model and fine-tuning parameters, which rely on their local data for a set of training epochs. Then, the locally updated model weights are sent to the central server to update the global model.
* Aggregation: the central server collects the participating clients' updated models. Then, an aggregation of their parameters yields an updated general model. This step is critical and should integrate several factors, including client confidence and participation frequency, in order to reduce bias. Steps 2 and 3 constitute a single round that is repeated until a stop condition is reached.
# A Two-Stage Routing Protocol for Partitioned Underwater Wireless Sensor Networks
## 4 Results and Discussion  
Based on the operation of the proposed protocol, our protocol needs functionality only up to data link layer level. On the link level, for channel access we use the ALOHA protocol for dissemination of beacons and requests and DCF for data forwarding. Through our channel model, we acquire the required physical parameters for our work. We used Matlab R2016b to simulate our protocol. Table 1 shows simulation parameters.  
In all the following results, PH refers to the proposed scheme whereas No-PH refers to protocol 1.1. The results are averaged over 50 random topologies for all the different number of nodes.
###### Abstract  
To thoroughly investigate the distribution of heavy rain disaster risks in the Beijing-Tianjin-Hebei region, this paper analyzes the spatiotemporal evolution characteristics of heavy rain disaster-inducing factors. Based on disaster system theory, we constructed a heavy rain disaster risk assessment framework from four dimensions. We improved the application of model-agnostic meta-learning (MAML) in hyperparameter optimization for the random forest (RF) algorithm, thereby developing the MAML-RF heavy rain disaster risk assessment model. This model was compared with the SCV-RF model, which is based on random search and cross-validation (SCV), to determine which model had higher accuracy. Then we introduced the SHAP (Shapley additive explanations) interpretability algorithm to quantify the impact of each risk factor. The results indicate that (1) the annual characteristics of heavy rain days and rainfall amounts show a significant upward trend over the past 17 years; (2) the MAML-RF model improved the accuracy and precision of heavy rain disaster risk simulation by 4.44% and 3.71%, respectively, and reduced training time by 27.95% compared to the SCV-RF model; and (3) the SHAP interpretability algorithm results show that the top five influential factors are the number of heavy rain days, rainfall amount, slope, drainage pipe density, and impervious surface ratio.  
a _Article_  
1
## 3 Methods
### Pedestrian Trajectory Prediction
#### 3.5.1 The Generator  
The generator includes an encoder and a decoder. The encoder takes pedestrian trajectories \(\left(x_{i}^{t},y_{i}^{t}\right)\) as input to obtain the hidden status layer for each pedestrian \(H_{i}^{t}\). The decoder takes \(H_{i}^{t}\), the interaction vector \(C_{i}^{t}\), and the Gaussian noise as input to generate the future trajectory of the target pedestrian.  
The generator uses the LSTM as the structure of the encoder and the decoder to learn the neighbor vector of the historical trajectory, encode the historical trajectory, decode the neighbor vector, and, finally, obtain the predicted future trajectory.  
The calculation formula of the encoder can be expressed as  
\[S_{i}^{t}=F_{e}(C_{i}^{t},SC_{i}^{t},W_{Fe}) \tag{14}\]  
\[\tau_{i}=f_{1}\left(x_{i}^{t},y_{i}^{t};w_{f_{1}}\right) \tag{15}\]  
\[H_{i}^{t}=LSTM\left(H_{i}^{t-1},\tau_{i};\omega_{g_{1}}\right) \tag{16}\]  
where \(S_{i}^{t}\) represents the unification of pedestrian interaction information and environmental information in the embedding layer. \(\left(x_{i}^{t},y_{i}^{t}\right)\) is the position for pedestrians \(i\) at the moment \(t\), \(f_{1}\) is a fully connected network, \(w_{f_{1}}\) is the parameter of the fully connected network, \(\tau_{i}\) is the pedestrian track feature vector at the current time, \(H_{i}^{t}\) is the pedestrian track feature vector at the current time, and \(\omega_{g_{1}}\) is the weight parameter in the LSTM neural network layer.  
The neighbor vector of the pedestrian at this moment and the state vector of the last moment of the trajectory data are spliced and input into the decoder to predict the trajectory of the pedestrian at the next moment.  
The calculation formula of the decoder can be expressed as  
\[Con_{i}^{t+1}=[f_{2}(S_{i}^{t},H_{i}^{t};W_{f_{2}}),z] \tag{17}\]  
\[H_{i}^{t+1}=LSTM\left(Con_{i}^{t},H_{i}^{t};W_{S_{2}}\right) \tag{18}\]  
\[\left(x_{i}^{t+1},y_{i}^{t+1}\right)=f_{3}\left(H_{i}^{t+1};W_{f_{3}}\right) \tag{19}\]  
where \(z\) is Gaussian noise, \(f_{2}\) and \(f_{3}\) are the fully connected networks, \(W_{f_{2}}\) and \(W_{f_{3}}\) are, respectively, the weight parameters of the fully connected networks, \(W_{g_{2}}\) is the weight parameter of the LSTM network, \(M\) is a fully connected network, \(W_{M}\) is the parameter of the fully connected network, \(H_{i}^{t}\) is the hidden state of the target pedestrian, \(H_{i}^{t}\) and the \(Con_{i}^{t}\) are input into the LSTM to obtain the next state \(H_{i}^{t+1}\) of the target pedestrian. Then, the \(H_{i}^{t+1}\) is input into the fully connected network to obtain the predicted trajectory coordinates \(\left(x_{i}^{t+1},y_{i}^{t+1}\right)\).
# Blood Pressure Monitoring Based on Flexible Encapsulated Sensors
###### Abstract  
Blood pressure monitoring is a significant concern in the field of healthcare, and the utilization of flexible encapsulated sensors presents a promising solution for achieving noninvasive and comfortable monitoring. This paper presents a study on the flexible encapsulation of MEMS pressure sensors and the development of an enhanced arterial tonometry method for blood pressure measurement, ultimately leading to the realization of a blood pressure monitoring system based on flexible encapsulated sensors. To improve wearer comfort and acquire reliable pulse signals, a flexible encapsulation sensor combining parylene and PDMS materials was fabricated. Additionally, to address the issue of low accuracy in blood pressure measurement, various machine learning algorithms were compared and analyzed, leading to the identification of the random forest model as the optimal regressor. Consequently, a blood pressure monitoring system based on the improved arterial tension method was designed and implemented. The experimental results demonstrate that the proposed system achieved a significant enhancement of 31.4% and 21% in the accuracy of systolic and diastolic blood pressure measurements, respectively, compared with the arterial tension method.  
continuous blood pressure measurement; MEMS pressure sensor; flexible packaging; machine learning 2023
## 4 Prospective
### Challenges of Existing Optimization Methodologies
#### 4.1.2 Local Optimum and Minimal Features  
TO is capable of optimizing the free-form irregular devices with thousands of DOF. However, inappropriate initial structures may limit the final optical performance to a local optimal. It takes trials and errors before finding an appropriate initial structure for a specific target. This issue could be solved by designing initial structure according to the physical laws or automatically adjusting initial structures based on optimization results. More interestingly, some researchers combine topology optimization with DNNs for global optimization for silicon metagratings [100], which could also be introduced to the design of integrated silicon photonics.  
Another emerging problem for free-form TO is that the optimized devices may have very small features which could be difficult for fabrication process. Apart from \"soft\" control methods such as filter-based or transform domain-based methods, \"hard\" control methods have also been proposed. For example, [PERSON] et al. confined the device structure to be QR-code with controllable minimal feature size [120]. However, these \"digital\" devices constrain the minimal feature size at the cost of DOF. More efficient methods to avoid local optimum and control minimal feature size are still desired for gradient-based freeform optimizations.