esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks
## 5 Results and Discussions
### Permutance-Based Feature Importance Analysis for \(R_{a}\)  
In literature, studies have used only a few variables to assess retro-reflectivity degradation of traffic signs deployed along highways. An accurate prediction of traffic sign retro-reflectivity for different sign sheeting under various circumstances and field conditions is very important. It can give clear indication of effective sign management practices for the respective signs. Additionally, it is established that retro-reflectivity performance for different sheeting is entirely different. It further depends on the height of the vehicle headlight (which is simulated by the observation angle for GR3), sheeting directions, and temperature, precipitation, and snow condition in an area. It is, therefore, imperative to establish individual variable importance for retro-reflectivity output. For our study, we had a dataset containing field retro-reflectivity measurement for different sheeting brands, three sheeting grades, different sheeting orientation, instrument observation angles, and sign age. We designed a permutation-based feature importance test on the data to evaluate the effect of each feature on the retro-reflection value. In this test, we measured the reduction in the performance of the model when a single feature is shuffled randomly. To perform this test, we divided our data into training and testing and fitted the training data on a random forest regression model and evaluated the permutation feature importance with the test data. The test was performed in several steps, i.e., permute a single feature, perform prediction, and calculate the importance score based on the difference in error between the original data and the permuted data. The process was applied 10 times, and each time the shuffling was performed with different seeds. The results for the feature importance tests are shown in Figure 6.  
It may be noted from the figure that the parameter orientation has a score close to zero, which confirms the previous studies, suggesting that it has a very weak correlation with the degradation of retro-reflection. It can also be observed from this figure that sheeting color and observation angle have a higher score, indicating that they have a significant bearing on the ultimate retro-reflection values. The brand of sheeting also has a low score, which suggests that the difference in the performance of the same sheeting type and color between the different brands is small. As shown in the analysis above, we  
Figure 5: Regression plot for actual values and predicted values using one-hot encoding.  
using all the features to identify the best model. This was followed by experimenting with the effect of each feature by removing it from the model. This will help to further evaluate the effect of different features on prediction performance since some features can be determined to be not important in one model but can be important in another model that is able to capture complex relationships.
## 2 State of the Art
### Heterogeneous Routing Protocols
#### 2.2.4 Swarm-Based Routing Protocols  
Swarm-based routing protocols are inspired by the behavior of animals and nature. Thus, for example, as swarm-based routing protocols that are created for FANETs, we highlight the BeeAdHoc [65] algorithm that bases its operation on the behavior of bees, as well as AntHocNet [66], and APAR [67] algorithms, both inspired by the behavior of ants.
## 4 Conclusions  
In this work, an IoT system capable of monitoring sensors located in places with little or no coverage was presented. The proposed approach integrates a beacon with different sensors and two communication modules. First, a Bluetooth link allows the connection to external sensors, while a LoRa module is associated with the communication to the UAV. Furthermore, a home-developed electronic nose has been connected to the beacon to support the detection of compounds in the air. To support data transfer between the remote beacons and the system, a UAV with a LoRa communication link was proposed. Furthermore, a Java-based application was also developed to retrieve and store data handled by the beacon and transmitted through the UAV. A multilayer perceptron neural network with a backpropagation learning algorithm was deployed and trained for data processing purposes. To this end, two different air compounds were considered (ethanol and hydrogen peroxide), while clean air was used as the reference gas. Finally, different tests were performed considering manual and autonomous flights to validate the proposed system. These tests allowed the verification of the connection between the beacon and external sensors, the correct data transferring from the beacon to the central system through a UAV, and the identification of air compounds. Although this work focused on supporting communication for one beacon, the expansion to several beacons forming a heterogeneous network of sensors is simple: it would be enough to place new beacons in different positions and plan UAV paths that pass through the positions of the beacons.  
Figure 10: Flight tests: (**a**) manual flight test; (**b**) autonomous flight test.  
The main conclusion drawn from this work is that the proposed system solves some of the problems identified in traditional IoT systems. More specifically, the proposed system focuses on unfavorable environments, where communication coverage is not guaranteed. The combination of UAVs and LoRa communication provides a real solution in this environment requiring low-cost budgets.  
Future works will deal with issues such as the detection of new air compounds detected by the electronic nose and the application of a UAV swarm that can be intelligently organized to cover a large area in the shortest time.  
[PERSON] participated in the conceptualization, defining work methodology and formal analysis tasks. [PERSON] performed investigation, writing--original draft preparation, writing--review and editing, and visualization tasks. [PERSON] was in charge of the supervision, project administration, and funding acquisition tasks. Finally, [PERSON] developed software and validated the system. All authors have read and agreed to the published version of the manuscript.  
Authors want to thank Spanish Ministry of Science, Innovation and Universities for supporting the NEOGAS project (PID2019-107697 RB-C44).  
The authors declare no conflict of interest.
## 3 Fundamentals
### The Influence of the AAC Basicity  
#### 3.1.1 Factor #1: Molecular Structure of a Surfactant  
The subject of research was a building mortar based on an AAC with M\({}_{\rm b}\) = 2.4 (Mix 1, see Table 1) (reference composition). The criteria for evaluating the effectiveness of a surfactant were the consistency (flow) of fresh mortar and the compressive strength of mortar.  
The effectiveness of plasticizing surfactants based on PCE--PCE1 (\"JK-04 PP\"), PCE2 (\"Vinavil fluxes\"), and modified polyethylene glycol--PEG-M (\"Melflux PP100F\") was evaluated. These PCE-based anionic surfactants were chosen as the most effective for use in AAC with a minimal required content of the alkaline component [58]. The surfactant content was 0.5% by mass.  
The action effect of a surfactant was evaluated by the changes in consistency of the mortar mix within the range of (150 \(\pm\) 15)-(200 \(\pm\) 15) mm as W/C decreased from 0.65 to 0.45-0.47 (Figure 1), as well as by the changes in strength of the AAC building mortar at 1 day and 28 days (Figure 2).  
Among three tested admixtures, the addition of PEG-M produced the greatest effect on the compressive strength values at 1 day (Figure 2a) and 28 days (Figure 2b) at the W/C values from 0.65 to 0.47 and lower to 0.45.  
A conclusion can be drawn that the addition of a copolymer of _polyethylene glycol_ and acrylic acid (PEG-M) exhibits the highest effectiveness in the materials based on AAC with M\({}_{\rm b}\) = 2.4 (Mix 1) compared to those of PCE1 and PCE2.  
Figure 1: Consistency (flow) of the fresh mortar based on the AAC with M\({}_{\rm b}\) = 2.4 (Mix 1) vs. the admixture type (PCE1—blue, PCE2—red, PEG–M—yellow, and without admixture (reference)—black), and W/C (0.45, 0.47, and 0.65).
## 2 Optimization Model for Network Reconfiguration Problem of Ship Power System
### Mathematical Description of Network Reconfiguration for Ship Power Systems
#### 2.2.3 Objective of Maximizing Load Distribution Uniformity  
Upon the completion of the power grid reconfiguration, the topology of the network undergoes alterations, resulting in unavoidable fluctuations in transmission lines and subsequent network losses [22]. To enhance the load-carrying capacity of the reconfigured ship's power grid, it is essential to ensure a balanced distribution of loads across the network. Therefore, a description of the uniformity in load distribution along the reconfigured lines is necessary. The formula is established as Equation (8):  
\[\min\!f_{3}=\sum_{i=1}^{Bsum}{(I_{Li}/I_{Lb})}^{2}, \tag{8}\]  
where \(Bsum\) is the total number of lines, \(I_{Li}\) is the current of line \(i\), \(I_{Lb}\) is the total current of the line loads, and the smaller the value, the more evenly the loads are distributed. Equation (8) is normalized to Equation (9):  
\[U(f_{3})=\frac{1-f_{3}}{1-1/Bsum}, \tag{9}\]
## 6 Active Transparent Antenna Design  
In this section, we will discuss active transparent antennas by substituting the metal strip with active MA4 AGP907 diodes, namely, D1, D2, and D3, as shown in Figure 9a. This will enable the antenna to switch between states electronically. Note that the performance of the passive antenna (with metal strip) obtained in the previous sections highlights the losses brought about by the diodes for the active antenna. The three PIN diodes are connected in series in order to use only two DC supply voltages (V1 and V2) for the different states (the configuration is displayed in Table 1). Biasing lines, 200 \(\upmu\)m in width, are positioned perpendicularly to minimize their impact on the radiation pattern. Each DC biasing line in the system includes an inductor (L = 6 nH) and resistors (R = 100 \(\Omega\)) in order to block the RF current and provide protection for the diodes.  
Figure 8: Measured return losses of the transparent passive antenna.  
Figure 7: Passive transparent antenna (example of the antenna in state 3).  
State 1 is achieved when D1 and D2 are in the ON state, functioning as a short circuit, while D3 is in the OFF state, acting as an open circuit. State 2 is attained by turning D2 and D3 ON and keeping D1 OFF. Similarly, state 3 is achieved by activating D1 and D3 while keeping D2 OFF. A summary of these states is provided in Table 2.
# Clinical Trial Classification of SNS24 Calls with Neural Networks
## 4 Experiments
### Fine-Tuning of the Transformers-Based Models
#### 4.3.2 Optimization Algorithm Choosing  
The optimization algorithm (a.k.a. optimizer) is one main approach used to minimize the error when training neural network models. A number of optimizers have been researched and generally used ones include Stochastic Gradient Descent (SGD), Momentum Based Gradient Descent, Mini-Batch Gradient Descent, Nesterov Accelerated Gradient (NAG), Adaptive Gradient Algorithm (AdaGrad), Adaptive Moment Estimation (Adam), etc. [34; 35; 36; 37]. When choosing an optimizer for fine-tuning the neural network models, the speed of convergence and the generalization performance on the new data are usually considered.  
In our work, we mainly take into account two widely used optimizers in our experiments, Adaptive Moment Estimation (Adam) [34] and Stochastic Gradient Descent (SGD) [37] since these two optimizers present better performances than the others in many NLP tasks. When training neural network models, Adam is one of the most practical optimizer. Adam is an algorithm for gradient-based optimization and combines the advantages of two SGD extensions: RMSProp and Adagrad [34]. Adam can compute adaptive learning rates for different parameters individually. As a variant of Gradient Descent (GD), SGD is a computationally efficient optimization method on large-scale datasets. When doing experiments on a large-scale dataset, computations over the whole dataset are usually redundant and ineffective. SGD can do computations on a small or a randomly selected subset instead of the whole dataset [37].
# Interference Mitigation for Metro Train Location Based on Karhunen-Loeve Decomposition
## 4 Experimental and Analysis of Results
### Positioning Accuracy  
We will extend the experiment to the post terminal correlation stage. The JSNR is set to 45 dB, and the three jamming signal parameters refer to the settings in the previous summary. We collect 20 s of data to facilitate the positioning results output by the receiver, and finally count the positioning accuracy in the horizontal direction. Figure 14 shows the comparison of the positioning accuracy of the receiver after implementing different interference suppression algorithms in three interference scenarios and the positioning accuracy when there is no interference signal is obtained. In different interference scenarios, the median value of the KL algorithm is smaller than other algorithms. The average median value of positioning error of the KL algorithm is 0.35 m, the average median value of error of WCT algorithm is 0.61 m, and the average median value of error of the STFT algorithm is 0.76 m. The interference suppression effect is the best. In addition, the corresponding frame of the KL algorithm is smaller than other algorithms, which also shows that the KL algorithm has better performance than the other two algorithms. It can be seen from the figure that the positioning accuracy of the KL algorithm is close to that without interference.  
Figure 12: Performance of different L in KL algorithm.  
Figure 13: MSE of different interference mitigation algorithms under chip signal interference.
## 4 Failure Mechanism of Soil-Rock Mixture at the Meso-Scale
### Failure Mechanism of Soil-Rock Mixture with Varying Soil-Rock Interface Strength  
The simulation results indicate that the formation of soil-rock interfaces is a key factor contributing to the high complexity of the failure characteristics of soil-rock mixture as rock content increases. As a unique virtual parameter of soil-rock mixture, the strength of the soil-rock interface is closely related to the mechanical properties of both the soil matrix and the rock blocks, making it difficult to control and characterize intuitively during testing. Therefore, to further analyze the influence of soil-rock interface strength on the failure mechanism, uniaxial compression simulation tests were conducted with varying contact strengths by adjusting the friction coefficient of the soil-rock contact model.  
Figure 12 shows the force chain distribution contours of soil-rock mixture models at the failure state with varying friction coefficients of soil-rock contacts. The results reveal that when the friction coefficient of soil-rock interfaces is high, the soil matrix, rock blocks, and interface collectively enhance the ability of the material to resist external deformation. The thickness of the internal force chains is relatively uniform and predominantly concentrated in the soil-rock contact areas. As the friction coefficient decreases, the strength provided by the soil-rock interface also diminishes, resulting in the force chain becoming predominantly concentrated in the middle and lower parts of the left side of the model. In comparison to the strong chain observed at the soil-rock interface in Figure 12a, the strong chains depicted in Figure 12c,d are predominantly located within the soil matrix. Furthermore, the uneven distribution of internal force chains within the soil-rock mixture becomes increasingly pronounced, resulting in a heightened probability of contact fractures at the weaker soil-rock interface.  
The force chain distributions of soil-rock mixture models with varying soil-rock interface strength suggest that under conditions of low friction coefficients, the soil matrix remains the primary contributor to the strength of soil-rock mixture. From a mesoscopic perspective, the reduction in strength at the soil-rock interface significantly diminishes its capacity to transfer bearing loads effectively. The improvement of the soil-rock contact failure probability leads to a macroscopic manifestation characterized by pronounced tensile cracking and rapid failure of the model. As the soil-rock contact strength increases, the soil-rock interface gradually becomes more influential on the strength of the whole soil-rock mixture, and the contact failure probabilities of the soil-soil, soil-rock, and the rock-rock contacts also become closer. The failure of the model is mainly through crack, and the ability to resist external force deformation is also stronger.
## 2 Experimental Apparatus and Procedure
### Experimental Apparatus and Methods
#### 2.2.2 Injection Rate Measurement  
There are two methods which can be used to measure the injection rate. The Zeuch method obtains the injection rate after injecting fuel into the pressure vessel. With the Bosch-Tube method, fuel is injected into the pipe to detect the pressure change in the injection rate meter. Then, the injection rate is measured. In this study, the Bosch-Tube method was used [5]. The Bosch-Tube measurement principle involves feeding the fuel into the pipe with a cross-sectional area A and selecting the fuel control volume in the pipe flowing at the speed of sound as c, when the fuel in the pipe moves at a speed of u. The characteristics of the fuel moving inside can be expressed as shown in Figure 3. By knowing the sonic velocity and fuel density in the pipe and ascertaining the pressure change in the chamber, the injection rate can be calculated.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Specification** \\ \hline Fuel & ULSD(Ultra-Low-Sulfur Diesel) \\ Injection duration (\(\upmu\)s) & 1000 \\ Injection pressure (MPa) & 30\(\sim\)180 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental conditions for maximum injection pressure measurement.  
Figure 4: Injection rate measurement; (**a**) Injection rate measuring device, (**b**) Schematic diagram.  
Figure 3: Control volume in the pipe to measure the injection rate.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Properties** & **Specification** \\ \hline Fuel & ULSD \\ Tube length (m) & 12.607 \\ Tube area (m\({}^{2}\)) & \(1.6409\times 10^{-5}\) \\ Injection pressure (MPa) & 30\(\sim\)180 \\ Duration (\(\upmu\)s) & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental conditions for measuring injection rate.  
As shown in Figure 4, the injection rate measurement system consists of a measuring tube with a length of about 12.6 m, needle valve, accumulator, pressure gauge, and regulator. The injector is installed on the adapter in the injection rate measuring device and is connected to the common-rail to supply high-pressure fuel. When fuel injection starts, the injection rate is measured by passing the fluid through the pressure sensor (Kistler, Model:6052c, Winterthur, Switzerland) that is mounted on the adapter. The experimental conditions for the injection rate are expressed in Table 2.
## 1 Introduction  
Compared with the radio-frequency satellite to ground communication link, the optical communication link has lower power consumption, higher transmission rate and greater security [1]. It has attracted significant attention for these advantages. One significant experiment is the LLCD (Lunar Laser Communication Demonstration) carried out by NASA [2]. However, as a result of the atmosphere channel, the received signal of the receiver on the ground is fluctuated and weak [1]. A number of studies are devoted to improve the sensitivity of the receiver and alleviate the signal fluctuation.  
Taking the advantage of the local oscillator laser, the receiver employing the coherent detection method is more sensitive than the intensity modulation direct detection (IM/ID) method [3; 4]. In particular, with the development of the digital signal processing technology, the frequency offset and the phase offset between the local laser and the received signal can be estimated and compensated in a coherent system [5; 6; 7; 8]. To alleviate the signal fluctuation, two important schemes can be used. The one is adopting a larger receiving aperture, and the other is employing a receiving array consisting of multiple apertures called spatial diversity [9; 10; 11; 12; 13; 14; 15]. The lager aperture method cannot only alleviate signal fluctuation, but can also receive more signal power by larger lens. So, employing coherent detection and aperture averaging together in the satellite-to-ground optical link, higher sensitivity, higher average SNR and lower scintillation index can be provided.  
At present, there is some study in terms of the satellite to ground link with on-off keying modulation scheme [16; 17] and point receiver model with which the ground receiver is assumed as a point [18]. The use of coherent detection and aperture averaging is considered together in this paper. The aperture averaging performance of a coherent satellite-to-ground optical communication system using BPSK scheme is analyzed. The BER, the outage probability and average capacity of this system with different receiving aperture diameters, different atmospheric conditions and different satellite angles are analyzed. The BER performance based on measuring data is given too.
# Efficient SMC Protocol Based on Multi-Bit Fully Homomorphic Encryption
## 3 Efficient Fully Homomorphic Encryption Scheme
Among them, \(e_{ij}\) is the \(j\)-th element of \(e_{i}\), and \(C_{ij}\) is the \(j\)-th element of \(C_{i}\). Let \(\mathbf{C}^{\prime}\) be a matrix formed by arraying \(m=(n+1)\cdot t\) ciphertexts as column vectors, whose dimension is \((n+1)\times m\). Output ciphertext \(\mathbf{C}=\text{mbFlatten}\left(\mathbf{\mu}\cdot\mathbf{I}_{N}+\text{mbDpt}\left(\mathbf{C} ^{\prime}\right)\right)\in\mathbb{Z}_{p}^{m\times m}\). 3. Decryption algorithm \(\text{NFHE.Decrypt}(\mathbf{sk},\mathbf{C})\). For ciphertext \(\mathbf{C}\in\mathbb{Z}_{p}^{m\times m}\) and private key \(\mathbf{sk}=\left( \begin{array}{c}-\mathbf{s}\\ 1\end{array} \right)\in\mathbb{Z}_{q}^{n+1}\), let \(\mathbf{s}^{\prime}=\text{pofmb}(\mathbf{sk})\), and calculate and output plain-text \(\mu=\left\lfloor\left\langle s^{\prime},C_{m-1}\right\rangle/(q/2p)+\frac{1}{2} \right\rfloor\text{mod}2\). 4. Homomorphic addition algorithm \(\text{NFHE.Add}(C_{1},C_{2})\). Input the ciphertext \(C_{1}\) and \(C_{2}\), and output the new ciphertext \(\mathbf{C}=\text{mbFlatten}(C_{1}+C_{2})\) obtained after homomorphic addition. Homomorphic multiplication algorithm \(\text{NFHE.Mult}(C_{1},C_{2})\). Input the cipher-text \(C_{1}\) and \(C_{2}\), and output the new ciphertext \(\mathbf{C}=\text{mbFlatten}(C_{1}\cdot C_{2})\) obtained after homomorphic multiplication.