esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed FGPQ Scheme
### Privacy-Preserving LBS Query
#### 4.2.1 LBS Query Request Generation  
When a mobile user wants to search for a POI within a certain range, e.g., a hospital within 1000 m of the current location, he should send the query request that contains the searching content \(m_{*}\) (a hospital), the current location \((X_{*},Y_{*})\) and the searching spatial range \(T_{*}\) (within 1000 m), to a local fog node (denoted as \(id_{j}\)). In order to ensure the privacy, he encrypts the request information \((m_{*},(X_{*},Y_{*}),T_{*})\) firstly, and then sends the encrypted query request to the fog node \(id_{j}\). The details are described as follows.  
1. For the searching content \(m_{*}\), the user chooses a random number \(r_{*}\in\mathbb{Z}_{p}^{*}\), and generates the ciphertext as \[E(m_{*})=(g^{{{{\mathit{a}}}{{\mathit{h}}}(m_{*})}r_{*}},g^{r_{*}}).\]
2. For the location information \((X_{*},Y_{*})\) and the searching spatial range \(T_{*}\), the user first generates a 7-dimensional vector as \(L=(X_{*}^{2},Y_{*}^{2},1,1,X_{*},Y_{*},T_{*}^{2})\), and extends it to a \(d^{\prime}\)-dimensional vector \(\widehat{L}\) where the first seven dimensions are copied from \(L\), and for \(i=8\) to \(d^{\prime}\), set \(\widehat{L}[i]=\omega_{i}\), i.e., \(\widehat{L}=(X_{*}^{2},Y_{*}^{2},1,1,X_{*},Y_{*},T_{*}^{2},\omega_{8},\omega_ {9},\ldots,\omega_{d^{\prime}})\). Then, the user chooses a random large positive number \(\beta\) to confuse \(\widehat{L}\), and computes \(\widehat{L}^{\prime}=\beta\cdot\widehat{L}\). After that, the user encrypts \(\widehat{L}^{\prime}\) with the private key \((S,M_{1}^{-1},M_{2}^{-1})\) by means of the query encryption function \(E_{Q}(\cdot)\) (the Equation (2)) as \[E_{Q}(\widehat{L}^{\prime})=(M_{1}^{-1}\cdot\widehat{L}_{a}^{\prime},\ M_{2}^{-1} \cdot\widehat{L}_{b}^{\prime}),\] where \(\widehat{L}_{a}^{\prime}\) and \(\widehat{L}_{b}^{\prime}\) are two column vectors split from \(\widehat{L}^{\prime}\).  
Finally, the user sends the encrypted query request \(\{E(m_{*}),E_{Q}(\widehat{L}^{\prime})\}\) to the fog node \(id_{j}\).
# Autonomous Load Regulation Based Energy Balanced Routing in Reshargeable Wireless Sensor Networks
## 4 Energy Replenishment Strategy
### Charging Time and Charging Power Allocation  
The MCV replenishes the energy of nodes with the objective of maximizing the network lifetime. With the given sequence of charging cells, we can formulate the sojourn time and charging power allocation problem into the following optimization problem:  
\[\max_{(P_{i}t_{c_{m}})}\sum_{m\in CV}\sum_{i\in c_{m}}\log(1+\frac{\eta_{i}P_{i }t_{c_{m}}}{E_{i}}) \tag{6}\]  
subject to  
\[\sum_{i\in c_{m}}P_{i}\leq P \tag{7}\]  
\[\eta_{i}P_{i}t_{c_{m}}+E_{i}\leq E_{o},i\in c_{m} \tag{8}\]  
\[t_{c_{m}}/ce_{c_{m}}=t_{c_{k}}/ce_{c_{k}}\ \forall m,k\in CV \tag{9}\]  
\[\sum_{c_{m}\in CV}t_{c_{m}}\leq T-t_{sp} \tag{10}\]  
where \(P_{i}\) is the charging power for node \(v_{i}\). \(t_{c_{m}}\) and \(t_{c_{k}}\) are the charging times for cell \(c_{m}\) and cell \(c_{k}\), respectively. \(T\) is the time of one round. \(t_{sp}\) is the minimum time that it takes for the MCV to visit all cells in \(CV\), and we use the TSP algorithm [25] to find the shortest distance among their centers.  
The charging power constraint (7) states that the sum of charging power allocated for all nodes in the same cell cannot exceed the maximum power of the MCV. The battery capacity constraint (8) states that the sum of the supplementary energy and residual energy for any node in a round cannot exceed the battery capacity of the sensors. These constraints ensure that the MCV can reasonably use its own charging capability without any energy wasting behavior.  
The energy balance constraint (9) states that the charging time allocated for each cell is positively correlated with its own charging efficiency, and the cell with the high charging efficiency can get more charging time. The charging time constraint (10) states that charging time is limited by the mobile time and round time.  
The optimization problem calculates the charging effects of different schemes through the objective function (6). According to the optimal solution, the MCV allocates charging time for each cell and charging power for each node in the same cell based on multi-point charging technology. Nodes with low energy and high energy consumption will get higher energy supplement and the balance of the network energy distribution can be ensured.
# Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion
## 3 Framework for System Design and Experimentation
### Gas Sensors  
Gas Sensors detect the presence of gas by converting the chemical information to electrical information. Metal Oxide Semiconductor (MQ) gas sensors are appropriate as they are compact, have fast response speed, and long service life [34; 35]. Each sensor consists of a heating element that produces the analog output voltage proportional to the gas concentration. The performance of Gas sensor depends on various sensor characteristics like sensitivity, selectivity, detection limit, response time, etc. [36]. Different gas sensors namely MQ2, MQ3, MQ5, MQ6, MQ7, MQ8 and MQ135 are used in the present work. These sensors are sensitive to various gases like Methane, Butane, LPG, Alcohol, Smoke, Natural Gas, Carbon Monoxide, Air Quality etc. (Table 1).  
Figure 1: Process of Data Collection.  
Figure 3: Network Testing Process.  
Figure 2: Network Training Process.
## 5 Results
### Safety Tests
#### 5.3.3. Operating Condition Tests  
With the aim of demonstrating the reliability of the developed platform, Hygrobot was also tested under a wide range of temperature, humidity, and charging/discharging rates to validate the correct operation of the hydrogen-battery power system. Table 8 and Figure 21 show the operating condition tests (temperature and humidity) and polarization curves obtained from the PEM fuel cell and battery subsystem.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Power System** & **Parameter** & **Test** & **Conditions** \\ \hline \multirow{8}{*}{PEM fuel cell} & & Test 1 & 15 \({}^{\circ}\)C/60\% RH/1 atm \\  & Temperature & Test 2 & 25 \({}^{\circ}\)C/60\% RH/1 atm \\  & (a) & Test 3 & 35 \({}^{\circ}\)C/60\% RH/1 atm \\  & & Test 4 & 50 \({}^{\circ}\)C/60\% RH/1 atm \\ \cline{2-4}  & Humidity & Test 5 & 50\% RH/25 \({}^{\circ}\)C/1 atm \\  & (b) & Test 6 & 60\% RH/25 \({}^{\circ}\)C/1 atm \\  & (b) & Test 7 & 70\% RH/25 \({}^{\circ}\)C/1 atm \\  & & Test 8 & 80\% RH/25 \({}^{\circ}\)C/1 atm \\ \hline \multirow{8}{*}{LiPo battery subsystem} & & Test 9 & 5 \({}^{\circ}\)C/1 C-rate \\  & Temperature & Test 10 & 15 \({}^{\circ}\)C/1 C-rate \\  & (c) & Test 11 & 25 \({}^{\circ}\)C/1 C-rate \\  & & Test 12 & 35 \({}^{\circ}\)C/1 C-rate \\  & & Test 13 & 50 \({}^{\circ}\)C/1 C-rate \\ \cline{2-4}  & Discharging Rate & Test 14 & 0.5 C rate/25 \({}^{\circ}\)C \\  & (d) & Test 15 & 1.0 C rate/25 \({}^{\circ}\)C \\ \cline{2-4}  & & Test 16 & 1.5 C rate/25 \({}^{\circ}\)C \\ \hline \end{tabular}
\end{table}
Table 8: Hygrobot operating condition tests for the PEM fuel cell and LiPo battery subsystem.  
Figure 21: Safety test: hydrogen supply failure test, showing (**upper**) Hygrobot power consumption and (**lower**) DC bus voltage.  
From the results shown in Figure 22, it can be seen that the temperature within the range studied increases the performance of the PEM fuel cell and battery subsystem without compromising their integrity, allowing the power/energy ratio between the PEM fuel cell and battery subsystem to remain constant. Conversely, an increase in ambient humidity also improves the performance of the PEM fuel cell, although this is not a necessary condition for operation due to the internal design of the PEM fuel cell, which makes it self-humidifying. Finally, it can be observed that the energy capable of being stored by the battery subsystem is higher at lower discharge coefficients, although the subsystem shows correct performance in the whole range of discharge rates studied.
# General Total Least Squares Theory for Geodetic Coordinate Transformations
## 5 Numerical Examples
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Shift Parameters**} & \multicolumn{3}{c}{**The Transformation Matrix**} \\ \hline \(\Delta\hat{x}\) & \(-293.3670\) & \(1.000010668\) & \(0.000021228\) & -0.000010763 \\ \(\Delta\hat{y}\) & \(40.7974\) & \(-0.000021228\) & \(1.000010668\) & \(0.000018196\) \\ \(\Delta\hat{z}\) & \(354.7273\) & \(0.000010763\) & \(-0.000018196\) & \(1.000010668\) \\ \hline \multicolumn{2}{c}{**Objective Function**} & \multicolumn{3}{c}{115.2651} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Estimates of the 3D similarity transformation parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Shift Parameters**} & \multicolumn{3}{c}{**The Transformation Matrix**} \\ \hline \(\Delta\hat{x}\) & \(-238.3801\) & \(1.00000000\) & \(0.000021228\) & -0.000010763 \\ \(\Delta\hat{y}\) & \(49.9133\) & \(-0.000021228\) & \(1.00000000\) & \(0.000018196\) \\ \(\Delta\hat{z}\) & \(393.5986\) & \(0.000010763\) & \(-0.000018196\) & \(1.00000000\) \\ \hline \multicolumn{2}{c}{**Objective Function**} & \multicolumn{3}{c}{123.4189} \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Estimates of the 3D rigid transformation parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Shift Parameters**} & \multicolumn{3}{c}{**The Transformation Matrix**} \\ \hline \(\Delta\hat{x}\) & \(-238.3801\) & \(1.00000000\) & \(0.000021228\) & -0.000010763 \\ \(\Delta\hat{y}\) & \(49.9133\) & \(-0.000021228\) & \(1.00000000\) & \(0.000018196\) \\ \(\Delta\hat{z}\) & \(393.5986\) & \(0.000010763\) & \(-0.000018196\) & \(1.00000000\) \\ \hline \multicolumn{2}{c}{**Objective Function**} & \multicolumn{3}{c}{115.2651} \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Estimates of the 3D rigid transformation parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Shift Parameters**} & \multicolumn{3}{c}{**The Transformation Matrix**} \\ \hline \(\Delta\hat{x}\) & \(4274.5307\) & \(0.999438051\) & \(-0.000101814\) & \(-0.000425541\) \\ \(\Delta\hat{y}\) & \(-5094.8874\) & \(0.000622535\) & \(1.000112976\) & \(0.000493015\) \\ \(\Delta\hat{z}\) & \(-17\),\(013.5695\) & \(0.002199299\) & \(0.000407742\) & \(1.001581580\) \\ \hline \multicolumn{2}{c}{**Objective Function**} & \multicolumn{3}{c}{58.5720} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Estimates of the 3D affine transformation parameters.
## 3 System Model
### Data Filter  
Indoor localization involves the collection of a large amount of data, including invalid data which need to be filtered. The data filter procedure consists of reading data.  
The process of reading data consists of opening a specified file, reading the contents of the file line by line and storing them in a list. Lines in the data file starting with a specific identifier are recognized as position and WiFi data and are converted to a list of floating-point numbers or strings accordingly. Finally, the data are converted into an array and stored in a dictionary.
# RRT*-Fuzzy Dynamic Window Approach (RRT*-FDWA) for Collision-Free Path Planning
## 5 Experimental Results
### Experimental Comparison
The general comparison of the four algorithms is given in Table 4. The RRT*-FDWA algorithm proposed in this paper considers both global and local optimality and can successfully plan a collision-free optimal path in both dynamic and static environments. In Table 4, E means Exist, N means None, L means Low, and H means High.
# Study on a Landslide Segmentation Algorithm Based on Improved High-Resolution Networks
## 4 Results and Discussion  
### Results of Ablation Experiment  
The performance of the DHRECA-Net exhibited a significant enhancement in the segmentation accuracy on the Loess Plateau dataset, as evidenced by the results from the conducted ablation experiments presented in Table 3. Specifically, the results indicate that the MIOU, MPA, and recall improved with the ECARes-Net and ECA-ASPP compared to the HR-Net model. Although the precision decreased, the F1 value improved for all metrics. This suggests that the inclusion of these two modules enhances the overall performance of the network. The network's performance significantly improved when all three modules were added in pairs. The DHRECA-Net achieved outstanding metrics by incorporating all three modules, with an MIOU of 88.37%, MPA of 94.55%, and F1-score of 93.85%. From these results, it outperformed the baseline HR-Net model, demonstrating an increase of 4.82% in the MIOU, 4.42% in the MPA, and 3.15% in the F1-score. These findings highlight a substantial enhancement in the segmentation performance.  
Figure 9 shows that the baseline HR-Net algorithm struggled to accurately delineate the boundaries of old landslides and tended to miss small-scale landslides in multiple areas of a single test image. In contrast, the DHRECA-Net algorithm exhibited a noticeable improvement in accurately delineating the boundaries between old landslides and the background, as well as providing more complete segmentation of small-scale landslides. The algorithm's performance was significantly enhanced and demonstrated greater accuracy in capturing details compared to the baseline model.  
\begin{table}
\begin{tabular}{c c} \hline
**Hyperparameter** & **Value** \\ \hline Input image size & [512; 512] \\ Optimizer & SGD \\ Initial learning rate & \(4\times 10^{-3}\) \\ Minimum learning rate & \(4\times 10^{-5}\) \\ Learning rate decay strategy & COS \\ Batch size & 16 \\ \hline \end{tabular}
\end{table}
Table 2: Model parameter configuration.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **A** & **B** & **C** & **MIOU/\%** & **MPA/\%** & **Precision/\%** & **Recall/\%** & **F1-Score/\%** \\ \hline \multirow{8}{*}{HR-Net} & \multirow{8}{*}{\(\surd\)} & & & 83.55 & 90.13 & 91.28 & 90.13 & 90.70 \\  & & & & 83.89 & 91.43 & 90.44 & 91.43 & 90.93 \\  & & \(\surd\) & & 85.57 & 91.39 & 92.56 & 91.39 & 91.97 \\ \cline{1-1}  & & \(\surd\) & & 84.49 & 91.83 & 90.80 & 91.83 & 91.31 \\ \cline{1-1}  & \(\surd\) & \(\surd\) & & 86.15 & 92.87 & 91.82 & 92.87 & 92.34 \\ \cline{1-1}  & \(\surd\) & & \(\surd\) & 84.05 & 90.82 & 91.22 & 90.82 & 91.01 \\ \cline{1-1}  & \(\surd\) & \(\surd\) & 86.18 & 92.02 & 92.03 & 92.02 & 92.02 \\ \cline{1-1}  & \(\surd\) & \(\surd\) & \(\surd\) & **88.37** & **94.55** & **93.17** & **94.55** & **93.85** \\ \hline \hline \end{tabular} A represents ECARes-Net, B the Stacked Feature Module, and C the ECA-ASPP. The best results are in bold.  
\end{table}
Table 3: Comparison of DHRECA-Net and HR-Net ablation experiments on the Loess Plateau landslide dataset.
# Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals
## 2 Communication System Model  
In this paper, we focus on the communication scenarios under the additive white Gaussian noise (AWGN) channel [33] with a low SNR. The AWGN channel is a widely used channel model between the transmitter and the receiver in wireless communication systems, in which the noise random variable obeys the zero-mean Gaussian distribution. We consider cooperative communication systems and non-cooperative communications systems and the modulation types of the systems are diversified. The system model is shown in Figure 1. At the transmitter, the source bit is modulated and sent to the channel, in which the modulation signal \(s(t)\) will be affected by AWGN.  
The received modulation signal \(s^{\prime}(t)\) can be expressed as Equation (1).  
\[s^{\prime}(t)=s(t)+n(t) \tag{1}\]  
where \(s(t)\) is the modulation signal with IQ components and generated at transmitter and \(n(t)\) represents zero-mean complex AWGN vector with variance \(\sigma^{2}\) in each signal dimension. When the noise interference is serious, the received signal quality is very poor, which will badly affect the demodulation performance. In this case, the noise reduction needs to be performed before subsequent processing. In the cooperative communication scenario, the modulation type is known in advance for the receiver, thus the denoised signal will be demodulated directly. For the application of non-cooperative communication, the receiver does not know the current modulation type, thus it needs to firstly perform modulation recognition, as shown in the dashed box in the Figure 1. We chose the automatic modulation classification (AMC) module proposed in [34] to finish the modulation recognition.  
This paper focuses on signal denoising based on deep learning, which corresponds to the denoising network module in Figure 1. We will develop a novel network architecture for noise reduction of multiple modulated signals. Based on the above analysis, eight modulated signals, which are in good agreement with the communication system model described in our paper, are generated using MATLAB software simulation.
## 2 Theoretical Supports
### Probabilistic Seismic Hazard Analysis (PSHA)  
PSHA accounts for all surrounding \(i\) potential seismic sources to quantify seismic hazard at a site by \(\lambda_{i}\) and consider all possible aleatory uncertainties (magnitudes \(M\), distances \(R\), and epsilons \(\varepsilon\)), which affects the GMs (\(IM\)). The PSHA integrals the combination of these parameters to compute the total exceedance rate [1, 11, 37] of a given IM can be expressed by the following equation:  
\[\lambda\ (IM>x)=\sum_{i=1}^{N_{source}}\lambda_{i}\ (M_{i}>m_{min})\sum_{j=1}^{N_{M}} \sum_{k=1}^{N_{R}}P(IM>x|m_{j}r_{k})\ f_{M_{i}}\big{(}m_{j}\big{)}f_{R_{i}}(r_ {k}) \tag{10}\]  
where \(\lambda_{i}(M_{i}>m_{min})\) is the rate of occurrence of earthquakes greater than \(m_{min}\) and \(\lambda\ (IM>x)\) is the rate of \(IM>x\). \(f_{M_{i}}\big{(}m_{j}\big{)}\)_and_\(f_{R_{i}}(r_{k})\) are the probability density functions for the earthquake magnitude and the distance from the earthquake source to the site, respectively. \(P(IM>x|m_{j}r_{k})\) is the probability of \(IM>x\) under a condition for an earthquake event of magnitude m occurred at source-to-site distance \(r\), which results from a GMPE or attenuation equations.  
The rate of exceedance of a specific value of ground shaking \(x\) of the earthquake \(IM\) can be estimated by the PSHA approach. According to the process of PSHA described by [PERSON] [37] and [PERSON] [1], the probabilities distance or magnitude distribution varies in \(IM\) among multiple GMs with a common earthquake magnitude \(m\) and distance \(r\). This can be introduced as the number of standard deviations by which a given logarithmic spectral acceleration differs from the predicted mean logarithmic spectral acceleration, as shown in mathematical form in the following equation [40]:  
\[\varepsilon=\left(\frac{lnx-\overline{\ln PGA}}{\sigma_{\ln PGA}}\right) \tag{11}\]  
where \(\varepsilon\) is the standard normal distribution function and here for example \(\overline{\ln PGA}\) is the mean log peak ground acceleration (in units of \(g\)) proposed by [PERSON] et al. [22] and [PERSON] and [PERSON] [41].
## 3 Experimental Methods for Environmental Assessment  
The use of any waste in concrete manufacturing for a second cycle life implies, not only the technical feasibility of a material, but also its environmental risk assessment. Leaching tests are an essential tool as they allow analysis of the transport rate of a constituent (pollutant element) through a material, which depends on the physical factors of the concrete, such as its porosity and permeability. [PERSON] et al. [35] observed that physical factors, such as density, porosity, and absorption, are not as relevant to the release of metals as was expected, which confirms the relevant role of chemistry on the release of metals. It proves the difficulty of reproducing leaching phenomena and the complexity of analysing the complete processes that govern the pollutant release of the chemical species being studied. Testing can be classified as tests aimed at attaining equilibrium conditions at the end of the leaching experiment (generally based on batch-type leaching tests or with controlled pH) or tests aimed at the dynamic aspects of leaching (tests in which time is an important variable, such as diffusion tests for monolithic materials and column leaching tests for granular materials) [24].  
The experimental procedure performed on the FRC specimens was carried out according to the three different phases of study observed in Figure 3.  
* Phase 1: The Compliance Batch Test according to UNE-EN 12457-4:2003 for basic characterisation of leaching levels of a RPF sample. The data are compared with the legal limit established by Council Decision 2003/33/EC [31], which establishes limits for the release levels of 12 heavy metals (Ni, Cr, Sb, Se, Mn, Hg, As, Pb, Cd, Cu, Ba, and Zn) and three anions (sulphate, chloride, and fluoride), classifying the material as inert, non-hazardous and hazardous according to its pollutant potential.
* Phase 2: The availability test NEN 7341:2004 was performed in order to determine the maximum pollutant release from the tested materials. It was performed on ground samples of FRC2, FRC4, FRC6, C-REF, and FRC2-REF analysing three eluates per samples (at pH 4, pH 7, and the total, according to the standard).
* Phase 3: The standards conducted on monolithic samples (FRC2, FRC4, and FRC6) and the controls (C-REF, and FRC2-REF) were: the diffusion leaching tank test with periodic leachant renewal (UNE-EN 15863:2015) obtaining eight eluates per samples (40 samples in total) and the single stage batch leaching test (CEN-TS 15862:2012) analysing in ICP-MS one eluate per sample (five samples in total).  
Figure 2: Concrete specimens of FRC with RPF: FRC2, FRC4 and FRC6.