esa-sceva/satcom-chunk-collection huggingface dataset:
# Chemical Analysis of Gunpowder and Gunshot Residues
## 2 Main Components of Gunpowder and Gunshot Residue
### Potential Sources of SG and GSR Compounds  
The widespread existence of GSR-like particles in the environment imposes a serious problem in forensic investigations as they may be confused with GSR particles [32; 38]. These GSR-like particles can originate from fireworks [44; 63; 64], stud guns [38; 65], some industrial tools [66], paints [38] or car repair and maintenance products [38]. Police officers' equipment and vehicles are also susceptible to contaminating suspects and evidence with GSR particles [32; 67; 68]. However, this contamination was shown to be negligible [68], and the officers' implementation of simple tasks, such as hand washing or hand sanitising with alcoholic gels, can further mitigate this risk [67].  
Several elements commonly found in IGSR, such as Sb, Ba, and Pb, can also be found in the environment. For example, Pb still exists in soils close to major highways and roads due to its long-term use in car fuels. In addition, Pb is still used in solder for plumbing materials, glasses, paints, and battery plates [16]. Sb and derivates (oxidates) exist in some alloys used as fire retardants in cotton and polyester blends [16]. Ba is present in car grease and paints [16]. Nonetheless, despite the multiple sources from which these compounds can originate, their combined presence is a good indication of GSR since there are no environmental sources for this mixture, as mentioned previously [69; 70]. Some compounds commonly found in OGSR can also be found in the environment. For example, DPA, the most used stabiliser, can be found on the surface of apples and in outer garments, pesticides, solid rocket fuel, tires, dyes, and veterinary medicine [69; 70].  
However, due to the removal of the signature elements from the primer, there is a need for the selection of new target analytes. Because the compounds now used as replacement are relatively common in the environment, it is necessary to adopt different analytical methodologies suitable for the determination of these compounds [15; 16].
## 4 EMD and DCNN Partial Discharge Type Recognition
### Analysis of Effect  
The original signal of the input three different defects is decomposed by EMD, and the decomposition map of the 15 kV pollution pipe sleeve waveform is shown in Figure 15. According to the obtained IMF information, the network training is performed according to the set model parameters, and the obtained test results are shown in Figure 11. The overall defect recognition rate of the sample in the test set reached 95.3%. The recognition rates of insulation scratch, casing fouling, and joint loosening were 94%, 96%, and 94%, respectively. The ratios of wrongly considered joint loosening and insulation scratch were 9.4% and 4.1%, respectively. No defect was wrongly considered as casing fouling.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network Layer** & \begin{tabular}{c} **Number of** \\ **Convolutional** \\ **Kernels** \\ \end{tabular} & \begin{tabular}{c} **Convolution** \\ **Kernel Height** \\ \end{tabular} & \begin{tabular}{c} **Output Size** \\ **Width** \\ \end{tabular} & \begin{tabular}{c} **Parameter Ma-** \\ **trix/Number of** \\ **Weights** \\ \end{tabular} & \
\begin{tabular}{c} **Step Size** \\ **Step Size** \\ \end{tabular} &
\begin{tabular}{c} **Activation** \\ **Function** \\ \end{tabular} \\ \hline \hline Input layer & & & & & & \\ Convolution layer-1 & 4 & \(9\times 1\) & \(20\times 1\times 4096\) & \(4\times 1\times 9/4\) & 1 & ReLU \\ Pooling layer-1 & & \(2\times 2\) & \(20\times 1\times 2045\) & & 2 & \\ Convolution layer-2 & 8 & \(9\times 1\) & \(20\times 8\times 2038\) & \(8\times 4\times 9/8\) & 1 & ReLU \\ Pooling layer-2 & & \(2\times 2\) & \(20\times 8\times 1019\) & & 2 & \\ Convolution layer-3 & 16 & \(9\times 1\) & \(20\times 16\times 1013\) & \(16\times 8\times 9/16\) & 1 & ReLU \\ Pooling layer-3 & & \(2\times 2\) & \(20\times 32\times 506\) & & 2 & \\ Convolution layer-4 & 32 & \(9\times 1\) & \(20\times 32\times 500\) & \(32\times 16\times 9/32\) & 1 & ReLU \\ Pooling layer-4 & & \(2\times 2\) & \(20\times 32\times 250\) & & 2 & \\ Fully connected layer & & & \(100\times 1\) & \(100\times 8000/100\) & & ReLU \\ Output layer & & & \(4\times 1\) & \(4\times 100/4\) & & Softmax \\ Other parameters & & Learning rate: 0.001; the maximum number of iterations: 320 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: DCNN parameter settings.  
Figure 14: The influence of the number of iterations on the network.
## 2 Materials and Methods
### Numerical Model
* No flow boundary conditions were applied to the heater-bentonite interface for water and air, and Dirichlet-type boundary conditions were applied for the bentonite-granite interface (\(P_{G}=P_{L}=0.1\) MPa). * No flux boundary condition was applied on the bottom model boundary representing the heater-bentonite interface for the transport of dissolved species and CO\({}_{2}\). * A fixed concentration boundary condition was applied on the upper boundary, representing the bentonite-granite interface for the transport of dissolved species. * Initially, after the equilibration of predefined porewater composition, the initial ion exchange concentration and minerals resulted in log(pCO\({}_{2}\)) = -2 with the ThermoChimie database. * A fixed partial CO\({}_{2}\) pressure (log(pCO\({}_{2}\)) = \(-\)4) boundary condition was applied on the top boundary, representing the bentonite-granite interface for CO\({}_{2}\) transport following CO\({}_{2}\) concentration in granite porewater (Case C1). * A fixed partial CO\({}_{2}\) pressure (log(pCO\({}_{2}\)) = \(-\)1) boundary condition was applied on the top boundary, representing the bentonite-granite interface for CO\({}_{2}\) transport (Case C2). \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Selectivity Constant KNa-cation [13]**} & \multicolumn{1}{c}{**Log K**} \\ \hline Cation exchange reactions on montmorillonite & & \\ Na\({}^{+}\) + X-K \(\leftrightarrow\) K\({}^{+}\) + X-Na & 0.138 & \\ Na\({}^{+}\) + 0.5 X\({}_{2}\)-Ca \(\Leftrightarrow\) 0.5 Ca\({}^{2+}\) + X-Na & 0.294 & \\ Na\({}^{+}\) + 0.5 X\({}_{2}\)-Mg \(\Leftrightarrow\) 0.5 Mg\({}^{2+}\) + X-Na & 0.288 & \\ Or expressed with half reactions: & & \\ X\({}^{-}\) = X\({}^{-}\) & & 0.0 \\ K\({}^{+}\) + X\({}^{-}\) = KX & & 0.86 \\ Ca\({}^{2+}\) + 2X\({}^{-}\) = CaX\({}_{2}\) & & 1.064 \\ Mg\({}^{2+}\) + 2X\({}^{-}\) = MgX\({}_{2}\) & & 1.082 \\ \hline Surface complexation & & [23] \\ \(\equiv S^{3}OH_{2}^{+}\)\(\leftrightarrow\)\(\equiv S^{4}OH+H^{+}\) & & \(-\)4.5 \\ \(\equiv S^{5}O^{-}+H^{+}\)\(\leftrightarrow\)\(\equiv S^{5}OH\) & & 7.9 \\ \(\equiv S^{5}nOH_{2}^{+}\)\(\leftrightarrow\)\(\equiv S^{5}nOH+H^{+}\) & & \(-\)4.5 \\ \(\equiv S^{5}nO^{-}+H^{+}\)\(\leftrightarrow\)\(\equiv S^{5}nOH\) & & 7.9 \\ \(\equiv S^{5}n^{2}OH_{2}^{+}\)\(\leftrightarrow\)\(\equiv S^{5}n^{2}OH+H^{+}\) & & \(-\)6.0 \\ \(\equiv S^{5}n^{2}O^{-}+H^{+}\)\(\leftrightarrow\)\(\equiv S^{5}n^{2}OH\) & & \(-\)10.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Data for cation exchange and surface complexation reactions.
## 5 Evaluation
### PER and Signal Selection  
We conducted 5 PER evaluation trials each for considering LED 1 or LED 2 as the desired signal. We collected traces from these trials and determined the PER through offline calculations. In each trial we transmit a continuous stream of packets of size 2096 bits, where each packet has a random stream of bits as payload and a 13 bits header. The header served as the unique ID for each LED. The header would be the same for all packets from a specific transmitter. For LED 1 we chose a 13 bits sampled from barker sequences as ID, and for LED 2 we chose a 11 bits sampled from barker sequence plus a 2 bit (11) padding. We chose the time slot (\(T_{s}\)) duration to be 2 seconds for each iteration over a pixel. We recorded the received and decoded bits from packets in each iteration of pixel OPEN cycles. We collected the received signal traces from each iteration of each STEP of the automated shutter control protocol (Algorithm 1). We iterated over 1 cycle of each pixel OPENING and then choosing either of the pixels that corresponds to the desired signal. We can observe from Table 4 that the PER is around 3%-6% which is comparable to typical PERs observed in traditional multiple access wireless communication systems. We estimate the theoretical throughput considering a PER range of 3% to 6%, and error control code rate of 1/2 as 0.94-0.97 Mbps, where \(Throughput\approx(1-PER)*packets/sec*bits/packet*errorcontrolcoderate\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency (Hz)** & **Configuration 1** & **Configuration 2** & **Configuration 3** \\ \hline
500 kHz & 0.015 & 0.039 & 0.21 \\
1 MHz & 0.015 & 0.035 & 0.21 \\
2 MHz & 0.015 & 0.030 & 0.21 \\ \hline \hline \end{tabular}
\end{table}
Table 3: BER at different shutter pixel configurations. **Configuration 1:** ONLY pixel-1 is OPEN.  
**Configuration 2:** ONLY pixel-2 is OPEN. **Configuration 3:** BOTH, pixel-1 and pixel-2 are OPEN.
# Blockchain-Based Federated Learning: A Survey and New Perspectives
## 4 Blockchain-Based Federated Learning: State-of-the-Art
### Model Classification for Blockchain-Based Federated Learning
#### 4.1.4 Hash Algorithm  
Transactions between each node in the blockchain are processed by a hashing algorithm. At the same time, SHA-256 is used to ensure the invariance of the blockchain, that is, the data in the blockchain cannot be modified or deleted. The hash indicator concatenates each block to the next block and preserves the previous hash data. Also, encrypted hash tables are often used in security protocols and applications to ensure the integrity of authentication files in a connection.  
Using hashing algorithms has many benefits for federated learning. In blockchain-based federated learning, federated learning can prevent malicious actors from tampering with model information to protect the global model from being damaged by utilizing the immutability of blockchain [54]. In addition, the hash algorithm is used to encrypt the federated learning node data to protect data privacy [72].  
Unlike traditional federated learning, the proposed BAFL [83] uses an asynchronous federated learning strategy that allows each device to upload a local model when global aggregation requires rapid convergence of global models. Blockchain prevents the failure of a single central server and ensures decentralized and secure data storage. Blockchain also incentivizes devices by rewarding them for participating in federated learning.
# Lithium Battery SOH Estimation Based on Manifold Learning and LightGBM
## 2 Algorithm Principle
### LightGBM Algorithm  
#### 2.3.1 Gradient Boosting Algorithm (GBDT)  
Gradient boosting is a machine learning algorithm of the boosting class. GBDT is based on the principle of addition, which accumulates the results of multiple regression trees to obtain the final result, and effectively avoids the problem of easy overfitting of a single tree by controlling the weights of the regression tree results in each round of iteration [23].  
The steps of the GBDT algorithm are as follows:  
Step 1: Input the training dataset, as follows:  
\[T=\{(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{n},y_{n})\} \tag{8}\]  
where \(x_{i}\in X\subseteq R^{n},y_{i}\in Y\subseteq R^{n}\).  
Step 2: Initialize the weak learner \(f_{0}(x)\), and define the prediction function of the model \(f(x)\), as follows:  
\[f_{0}(x)=Arg\ \text{min}_{c}\sum_{i=1}^{m}L(y_{i},c) \tag{9}\]  
\[f(x)=\sum_{i=0}^{N}f_{i} \tag{10}\]  
Step 3: Construct a weak learner for each round in the iteration number \(t=1,2,\ldots,T\) with the training sample \(D=\{x_{i},y_{i}\}_{1}^{N}\) and the corresponding loss function \(L(y_{i},f(x_{i}))=\left(y_{i}-f(x_{i})\right)^{2}\) to solve for the negative gradient, as follows:  
\[r_{ti}=-[\frac{-\partial L(y_{i},f(x_{i}))}{\partial f(x_{i})}]_{f(x)=f_{t-i}( x)} \tag{11}\]  
Step 4: Using the solved negative gradients \(r_{ti}\) and \(x_{i}(i=1,2,\ldots,N)\), a CART regression tree is fitted according to the squared error minimization training decision tree to obtain the \(t\)th regression tree, whose corresponding leaf region is \(R_{ti}(j=1,2,\ldots,J)\), where \(j\) is the number of leaf nodes of the regression tree \(t\), as in the following Equation (12):  
\[C_{tj}=Arg\ \text{min}_{c}\sum_{x_{i}\in R_{tj}}L(y_{i},f_{t-1}(x_{i})+c) \tag{12}\]  
Step 5: Update the strong learner, as follows:  
\[f_{t}(x)=f_{t-1}(x)+\sum_{j=1}^{J}C_{tj}I(x\in R_{tj}) \tag{13}\]  
If the corresponding decision function satisfies the convergence condition, the iteration stops and the expression of the final strong learner \(f(x)\) is as follows:  
\[f(x)=f_{0}(x)+\sum_{t=1}^{T}\sum_{j=1}^{J}C_{tj}I(x\in R_{tj}) \tag{14}\]
# Measurement of Regional Electric Vehicle Adoption Using Multiagent Deep Reinforcement Learning
## 3 Materials and Methods  
### Study Area and Current \(EV\) Adoption  
Our study focuses on Austin, TX, including three counties: Hays, Travis, and Williamson. The geographic unit of analysis used to establish the state in the reinforcement learning environment is based on census block groups. Figure 1 presents the sociodemographic characteristics in Austin, derived from the American Community Survey (ACS) 5-year estimates (2015-2020). The I-35 highway visibly divides the city. West Austin is primarily populated by wealthier, White residents, while low-income communities and communities of color, including Non-White Hispanic and African-American populations, mainly reside in East Austin.  
We acquired EV registration data from the Dallas-Fort Worth Clean Cities [27]. The dataset provides the count of registered EVs from various manufacturers, categorized by different Texas zip codes and updated monthly. As of April 2023, it has collected records from 141 distinct companies. We organized this data into two primary categories, PHEV and BEV, utilizing twenty months of records from September 2021. The organized data were spatially interpolated into census block groups based on population density. Figure 2 visualizes the spatial distribution of EV registration records as of April 2023, supplemented by a histogram. The \"total count\" signifies the combined sum of PHEV and BEV registrations. Notably, all distributions lean towards the left. Downtown Austin in Travis County and West Austin generally exhibit higher numbers of registered EVs than East Austin. This suggests that wealthier, predominantly White communities may own a more significant proportion of EVs.  
Figure 3 demonstrates that the growth rate of BEVs in Austin has outpaced that of PHEVs. The average monthly increase in registration records is approximately 3.6%. PHEVs show a monthly growth rate of 2.3%, while BEVs display a higher rate of 4.1%. By April 2023, Austin had 183,076 registered EVs, accounting for nearly 7.7% of the total vehicles. Should the current average growth rate persist, it is forecasted that the COA will accomplish its goal of electrifying 40% of its vehicles by September 2026. This timeframe is four years earlier than the original target, indicating a faster adoption rate of EVs than initially expected.  
Figure 1: Demarcated socio-demographic characteristics in Austin, TX, USA. The legend in this figure depicts data categories for a thematic map, broken down by quartiles.
# Feasibility Study on Grouting Compactness Detection in Sleeves Using Piezoelectric Transducers
## 2 Detection Method of Piezoceramic Transducer  
### Piezoelectric Effect  
Crystal materials having a piezoelectric effect are known as piezoelectric materials. When piezoelectric materials are subjected to an external load, the positive and negative charges inside the materials do not coincide and are expressed as charges on the surface of the materials. This phenomenon is called the direct piezoelectric effect and is illustrated in Figure 1a. The opposite effect is called the inverse piezoelectric effect [25], as shown in Figure 1b.
# Robust Additive Value-Based Efficiency Analysis
## 2 Additive Value-Based Efficiency Analysis  
Let us consider a set of DMUs \(\mathcal{D}=\{D_{1},\ldots,D_{K}\}\). In value-based analysis, efficiency \(E_{o}\) of \(D\mathcal{M}U_{o}\in\mathcal{D}\) is defined using an additive value model:  
\[E_{o}=\sum_{q=1}^{Q}w_{q}\cdot u_{q}(DMU_{o}), \tag{1}\]  
where \(w_{q}\) is the weight of factor \(q\) (i.e., input \(x\in\mathbf{x}=\{x_{1},x_{2},\ldots,x_{m}\}\) or output \(y\in\mathbf{y}=\{y_{1},y_{2},\ldots,y_{n}\}\)), such that \(\sum_{q=1}^{Q}w_{q}=1\), and \(u_{q}\) is a monotonic marginal value function for \(q\). It is nondecreasing for outputs and nonincreasing for inputs. To verify if  
Figure 1: Paper’s main contributions.  
\(DMU_{o}\) is efficient, we solve the following Linear Programming (LP) model, minimizing the maximal distance of efficiency of \(DMU_{o}\) to any other unit:  
\[\text{Minimize }d_{o}\] (2) s.t. \[\left.\begin{array}{ll}\sum_{q=1}^{Q}w_{q}u_{q}(DMU_{k})-\sum_{q=1}^{Q}w_{q} u_{q}(DMU_{o})\leq d_{o},\text{ for }k=1,\ldots,K,\\ d_{o}\geq 0,\\ \sum_{q=1}^{Q}w_{q}=1,\\ w_{q}\geq 0,\text{ }q=1,\ldots,Q,\\ \mathbf{w}\in S_{w},\end{array}\right\}\]  
where \(S_{w}\) is the feasible weight vector space delimited by the linear constraints. The optimal value of \(d_{o}\), denoted by \(d_{o}^{*}\), represents the minimal distance of \(DMU_{o}\) from the efficient unit. If \(d_{o}^{*}=0\), \(DMU_{o}\) attains the greatest efficiency for at least one feasible weight vector, which implies that it is efficient. When \(d_{o}^{*}>0\), for all feasible weights, there is at least one unit with an efficiency greater than \(E_{o}\), denoting the lack of efficiency of \(DMU_{o}\).
# Overview of Active Ingredients Used in Deodorants and Antiperspirants Available on EU Market
## 2 Results
### Composition of Spray Deodorants (n = 50)  
In spray deodorants (n = 50), the most common antibacterial ingredient, present in 56% of products, was Alcohol. The most common deodorant components used for masking or absorbing odour were Triethyl Citrate (44%) and Ethylhexylglycerin (26%), while Aluminium Chlorohydrate (20%) was the most commonly used for inhibiting sweat secretion (Figure 1). A relatively popular antibacterial and deodorizing component was C12-15 Alkyl Benzoate (14%). The remaining active substances, i.e., Farnesol, Cappylyl Glycol, Silver Citrate, Zinc Sulfate, Cetrimonium Chloride, Aluminium Sesquichlorodrate, and Potassium Alum, were found in only one or two products (Figure 1). Interestingly, the examined spray deodorants also contained typical antiperspirant ingredients, such as Aluminium Chlorohydrate (20%), Aluminium Sesquichlorodrate (2%), and Potassium Alum (4%); however, their concentrations were so low that their role was most likely to increase the bacteriostatic properties of the product.  
In spray deodorants, the type and frequency of use of fragrance-giving substances were also determined. All substances with a content greater than or equal to 10% are  
Figure 1: Frequency of use of active ingredients in spray deodorants (n = 50).  
presented in Figure 2. The most popular fragrances, apart from Parfum (94% of products), were Limonene (48%), Linalool (48%), Citronellol (32%), Citral (26%), Geraniol (26%), and Coumarin (24%). Fragrances such as Eugenol, Benzyl Salicylate, Hydroxycitronellal, Alpha-Isomethyl Ionone, Benzyl Alcohol, Menthyl Lactate, Octyldodecanol, and Isopropyl Palmitate were present in 10-20% of the investigated spray deodorants.  
The following fragrance-giving ingredients were found in fewer than five products: Dimethyl Phenylbutanol, Dimethyl Phenylpropanol, Citronellyl Methylcrotonate, Cinnamyl Alcohol, Cocos Nucifera Oil/Coconut Oil, T-Butyl Alcohol, Levulinic Acid, Vanillyl Butyl Ether, Benzyl Benzoate, Ammonium Glycyrrhizate, and Isopropyl Myristate.  
The following fragment plant extracts and essential oils, which also have antibacterial and stringent effects on the skin, were found in the examined spray deodorants: Myristica Fragrans Kernel Oil, Helianthus Annuus Seed Oil, Calendula Officialis Flower Extract, Rosa Damascena Extract, Salvia Officialis Oil; Salvia Officialis Leaf Extract, Elettaria Cardamomom Seed Extract, Rosmarinus Officialis Leaf Extract, Rosa Moschate Leaf Extract and Equisetum Arvense Extract. These plant extracts and essential oils were found in approximately one-quarter of the analysed spray deodorants, and often several of them were found in the same single product.
# Pseudo-3D Receiver Deghosting of Seismic Streamer Data Based on \(l_{1}\) Norm Sparse Inversion
## 2 Methods
### Compressed Sensing-Based Sparse Inversion Method  
In recent years, compressed sensing-based inversion methods, which improve inversion accuracy by sparse representation, have been emerging. The basic idea is to discretize the seismic signal by a sampling matrix, then remove redundant information and retain fewer elements to represent the main characteristics of the signal. Because the signal becomes sparse, it does not easily fall into the local minimum during inversion. In this way, the signal becomes sparse and redundant information is not included during inversion, and it does not easily fall into the non-information local minimum during inversion [28].  
Sparse inversion requires the sparse representation (Figure 3) of the seismic signals \(y\in R^{m}\) using the sampling matrix \(\mathbf{A}\in R^{n\times m}\). The sparse representation can be described as a linear equation:  
\[y=\mathbf{A}x\ \ \ \text{s.t.}\min\lVert x\rVert_{0} \tag{9}\]  
where the sampling matrix \(\mathbf{A}\) can be expressed as  
\[\mathbf{A}=\left\{\Phi_{y}\in R^{n}\middle|\gamma\in\Gamma,\left\lVert\Phi_{y }\right\rVert=1\right\} \tag{10}\]  
where \(x\in R^{m}\) represents the coefficients in the transformed domain and in \(\left\lVert x\right\rVert_{0}\ll m\), \(\left\lVert x\right\rVert_{0}\) represents the \(l_{0}\) norm of \(x\), which measures the number of non-zero elements and represents the degree of matrix sparsity, and \(\Phi_{y}\) represents the vector of the sampling matrix \(\mathbf{A}\). Because the \(l_{0}\) norm is difficult to solve optimally (non-deterministic polynomial-hard  
Figure 2: Demonstration of a pseudo-3D data cube.  
problem, NP-hard), an \(l_{1}\) norm is usually used to replace the \(l_{0}\) norm as the constraint condition to simplify the optimization problem, as follows:  
\[y=\mathbf{A}x\ \ \ \text{s.t.}\ \text{min}\|x\|_{1} \tag{11}\]  
To demonstrate the sparsity change before and after the sparse representation of the seismic signal, we adopt a curvelet transform [29] to noisy synthetic data for sparse representation, where the noise distribution is \(N(\mu,\sigma)=N(0,0.0003)\). We select 5% as the curvelet and time-space coefficients, in order of amplitude, to reconstruct the data. From Figure 4, the sparsity of the curvelet coefficients is significantly better than that of the time-space coefficients.  
In the optimization inversion process, the objective function is generally minimized in the sense of least squares. However, the seismic signals not only contain useful signals, but also noise. The sparse representation of seismic signals regards the noise as redundant information, which means inversion in the transform domain can obtain more stable inversion results. Therefore, the sparse inversion methods are suitable for solving such problems.