esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Biochars in Terms of Vitamin E Adsorption Capacity
## 2 Materials and Methods
### Analysis of Mesopore Distribution  
The analyses of the surface area and the pore size distribution were performed at AdFiS products GmbH (Teterow, Germany), according to the protocols of [PERSON] [46]. To determine the Brunauer-Emmet-Teller (BET) surface area and porosity, a surface area and porosity analyser (Gemini VII 2390p, Micromeritics, Unterschieshheim, Germany) was used, measuring the adsorption isotherm of nitrogen at 77 K. Therefore, 0.2 to 0.5 g was degassed under a nitrogen flow before analysis: firstly, for 10 min at room temperature; secondly, for  
\begin{table}
\begin{tabular}{c l l l} \hline
**Biochar** & **Pyrolysis Process** & **Biomass** & **Wood Type** \\ \hline A & Kiln & Mostly beech, some oak and acacia (with very little bark) & Hard \\ B & PYREG & Sieved fresh wood chips (mainly trunk wood of poplars) & Probably hard \\ C & Pyrolysis, N/A & Beech & Hard \\ D & Pyrolysis, N/A & Beech, spruce & Hard, soft (spruce) \\ E & PYREG & Wood chips & N/A \\ F & Kiln & Beech, larch, spruce, oak, herbal extracts & Hard, soft (spruce) \\ G & PYREG & Beech, oak, ash, maple, hornbeam & Hard \\ H & PYREG & Wood chips, beech wood chips & Hard \\ I & PYREG & Spruce, pine & Soft \\ J & PYREG & Mostly beech, some oak & Hard \\ \hline \end{tabular} N/A indicates that no further information was provided.  
\end{table}
Table 1: An overview of the pyrolysis processes and biomasses of the analysed biochars. The biomasses were sorted by wood type according to Wiedenhoeft [45].  
1 h at 300 \({}^{\circ}\)C; and thirdly, cooled to room temperature under nitrogen after degassing [46]. The surface area was deduced from the multipoint BET calculation method using the BET expression in the linear form [47] in the relative pressure range of p/p\({}_{0}\) = 0.05-0.20. To classify the mesopore sizes, despite the IUPAC classification, where mesopores are \(>\)2 nm and \(<\)50 nm [19; 26], mesopores were redefined from \(>\) 2.6 nm to \(<\) 20 nm, divided into six classes: 2.58-3.22, 3.22-4.03, 4.04-5.16, 5.16-6.92, 6.92-10.26, and 10.26-20.01 nm [46]. The volume in each class was determined by the adsorption of nitrogen and calculated according to the Barrett-Joyner-Halenda (BJH) method [48]. The analysis was carried out in duplicate (\(n\) = 2).
# A Dyna-Q-Based Solution for UAV Networks Against Smart Jamming Attacks
## 2 Related Work
### Cyber Threats Against UAV Networks  
Recently, there were some attempts to adopt the idea of machine learning into UAV network defense. [PERSON] et al. [11] evaluated the behavior of attackers targeting UAV nodes, especially automatic dependent surveillance--broadcast (ADS-B) attack and false data injection attack. They designed a set of rules to identify the normal behavior of UAVs, but they did not evaluate the performance of their methods in terms of detection accuracy and resource overhead. [PERSON] et al. [12] summarized the attacks targeting ADS-B components, such as eavesdropping, jamming, and false data injection, but they did not validate their countermeasures via simulation. [PERSON] [12] and [PERSON] [13] claimed that ADS-B is a component that is vulnerable to cyberattacks because it does not have built-in security mechanisms. They investigated a communication scheme based on confidentiality to protect the privacy of messages broadcasted by ADS-B components. Unfortunately, they did not offer a solution to prevent the detected attacks. [PERSON] et al. [14] believe that GPS (Global Positioning System) spoofing attacks are the most lethal attacks to UAV networks. They proposed an improved scheme to identify GPS spoofing attacks. [PERSON] et al. [15] systematically reviewed the security risks and security solutions of ADS-B, and they divided the security solutions into ten categories, namely lightweight PKI (Public Key Infrastructure), message authentication code, \(\mu\) TESLA (Timed Efficient Stream Loss-tolerant Authentication), multilateration, fingerprinting, spread spectrum, distance bounding, Kalman filtering, data fusion, and traffic modelling. However, they did not consider the situation of air-to-air interference, so their review only focused on interference to ground stations. [PERSON] et al. [16] proposed a method for escorting detected malicious UAVs by transforming the formation of the UAV swarm. However, if the malicious UAV suddenly launches a jamming attack, the large number of UAVs near this malicious UAV will suffer disastrous consequences. [PERSON] et al. [17] proposed two algorithms including a centralized deployment algorithm and a distributed motion control algorithm for UAV airborne networks, and the functions of these algorithms are to realize on-demand coverage when a disaster occurs; however, looking at it from another angle, the jamming UAV can use these algorithms to achieve maximum jamming efficiency.
## 1 Introduction
Despite the availability of sparse attempts in the literature to join heterogeneous data [4; 5; 13], there is a lack of publicly available systems for efficient integration of mobility, pollution, and meteorological data at scale with QoS guarantees. QoS is at the core of the design principles of our system. According to International Telecommunication Union, for example regarding the QoS regulations (specifically ITU-T Supp. 9 of E.800 Series), QoS is defined as \"Totality of characteristics of a telecommunications service that bear on its ability to satisfy stated and implied needs of the user of the service\" [14]. Based on that, big data management systems typically incorporate the notion of Quality-of-Service (QoS) by introducing a variety of QoS metrics. QoS metrics can be classified into either time-based or accuracy-based (i.e., quality-based, or content-based) metrics. Time-based QoS metrics concern the ability of a big data processing system to adapt the processing speed to fluctuations in the size of the data and its arrival rate in data streams. Metrics used in this category include delay (i.e., end-end latency) and throughput. On the other hand, accuracy-based QoS metrics focus the challenges caused by the volume of the data and the accuracy of query results obtained in case of relying on approximation instead deterministic solutions. Examples include approximation and data mining quality. The target of design characteristics of big data processing systems is to guarantee achieving QoS goals prespecified in a Service Level Agreement (SLA). Having said that, in this paper, we are closing this gap by presenting a novel system that we have designed for this purpose. Specifically, we present a novel system that term as EMDI (for Environmental Mobility Data Integrator). EMDI is a system for integrating human and vehicle mobility data with environmental data such as pollution, climatological and meteorological data. Our system is adept in unifying data from such heterogeneous sources and presenting the unification in a simplified data structure with appropriate multi-dimensional access structures, such as spatial indexes. It also offers over-the-counter API that simplifies creating geo-statistical and spatial aggregation queries on the unified mobility-environment view with QoS guarantees. The focus of our novel system is on the enrichment of mobility data with meteorological, environmental, and weather data on a granular scale (e.g., one square meter). This is so because such enrichment information provides a capacity for understanding the way human mobility trajectories and patterns are affected by the context around us [5]. The remaining sections of the paper are divided as follows. We first review the related literature. Then, we briefly discuss the theoretical foundations that are appropriate for subsequent discussion. We afterwards showcase the design, functionalities, and realization of our EMDI system. Thereafter, we show and discuss the results of performance testing. In what follows, we close the paper by a conclusion, challenges, and recommendations for future research.
# Evaluating Packaging Design Relative Feature Importance Using an Artificial Neural Network (ANN)
## 2 Methods
### Methods for Evaluating Input Feature Importance
#### 2.2.2 Gradient-Based Method  
The gradient-based method is a key technique for evaluating how a model's outputs are influenced by its input features. It involves calculating the partial derivative of the output with respect to the input, which measures sensitivity. This approach is applicable in deep neural networks (DNNs), a specialized type of ANN [39]. Examining how variations in input features influence the predicted output can reveal insights into feature importance within ANN models. The gradient's magnitude indicates the extent of change in the predicted output due to an infinitesimal alteration in the input feature [40]. The gradient of output \(\hat{y}\) with respect to input \(X\) is represented in Equation (2),  
\[\
abla\hat{y}=\
abla F(X)=\left[\frac{\partial F(X)}{\partial x_{1}}\dots \frac{\partial F(X)}{\partial x_{d}}\right]^{T} \tag{2}\]  
The differentiability of deep neural networks is determined by the activation function used. Activation functions like sigmoid, ReLU, and Tanh are differentiable almost everywhere. In this study, where the sigmoid function is utilized, a central difference method was applied to numerically approximate the gradient of \(F(X)\) at \(X\), as defined in Equation (3).  
\[\frac{\partial F(X)}{\partial x_{k}}\triangleq\frac{F\left(X^{(k+)}\right)-F \left(X^{(k-)}\right)}{2\delta x} \tag{3}\]  
where  
\(X^{(k+)}\triangleq X+\delta x\cdot e_{k}\),  
\(X^{(k-)}\triangleq X-\delta x\cdot e_{k}\).  
\(\delta_{x}\in R\) is the step size, and for all \(k=1,\dots,d\), \(ek\in Rd\) is the standard basis vector. The terms \(F(X\) (\(k+\))) and \(F(X\) (\(k-\))) are obtained from two forward passes of the model. The importance of the kth feature is then defined as the absolute value of the partial derivative with respect to \(x_{k}\). This gradient vector provides the feature importance for a single test sample. To determine the global feature importance, the feature importances for all samples in the test set SN were averaged, where N represents the number of samples in the test set [40], as outlined in Equation (4),  
\[\begin{array}{c}\text{Sample gradient imp}\left.(x_{k}\right)\triangleq \left|\frac{\partial F(X)}{\partial x_{k}}\right|_{X}\\ \text{Global gradient imp}\left.(x_{k}\right)\triangleq\frac{1}{N}\sum_{X \in S_{N}}\left|\frac{\partial F(X)}{\partial x_{k}}\right|_{X}\end{array} \tag{4}\]  
In this study, gradients were computed using tf.GradientTape(), a Python 3.11.5 tool that allows for nesting to calculate higher-order derivatives.
# A Compact Monopole Wideband Antenna Based on DGS
## 4 Fabrication, Measurements, and Comparison
### Performance Comparison of the Antenna  
Table 3 summarizes the main performance of the proposed antenna with the parameters including size, impedance bandwidth, and gain. A comparison with other previous state-of-the-art wideband and UWB antennas is as shown. This antenna is compact in size and far better in bandwidth and gain. Thus, this broadband antenna is better in all parameters and appropriate for various wireless communication applications.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Reference**} & \multicolumn{2}{c}{**Antenna Size**} & \multicolumn{2}{c}{**Bandwidth**} & \multicolumn{1}{c}{**Gain**} & \multirow{2}{*}{**Year**} \\  & **(mm\({}^{3}\))** & **(GHz)** & **dB** & \\ \hline
[1] & 60 \(\times\) 60 \(\times\) 1.575 & 8.3–15.2 & 5–6.5 & 2020 \\ \hline
[2] & 36 \(\times\) 30 \(\times\) 1.6 & 1.73
# Measuring Ocean Surface Current in the Kuroshio Region Using Gaofen-3 SAR Data
## 2 Measurement of the Ocean Surface Current
### Measurement Method with Single Antenna SAR Data
#### 2.2.1 Sub-Aperture Processing  
Generally, the beam of SAR is perpendicular to the direction of flight, and the current velocity component in the range direction is obtained by Doppler centroid estimation with full-aperture data. However, through the above analysis, in order to retrieve the current vectors, it is necessary to derive the Doppler centroids in different directions, so the data under different azimuth angles can be processed through sub-aperture processing.  
In the basic theory of SAR, there is a linear relationship between Doppler frequency and azimuth time. By dividing Doppler frequency, the full-resolution image is decomposed into a series of low-resolution images formed by beams in different directions, which is sub-aperture processing technology [28].  
In this method, sub-aperture data are divided in the azimuth frequency domain. The divided beam is shown in Figure 3, where the solid line represents the beam center of  
Figure 2: Flow chart of ocean current field measurement using single antenna SAR data.  
different sub-aperture \(S\). The sub-aperture bandwidths are same, and the sub-aperture center frequency is \(f_{k}\), \(k=1\), \(2\), \(\cdots\), \(L\), where \(L\) is the number of sub-apertures. In the ideal side-looking condition, the estimated Doppler centroid should be zero when the full-aperture beam irradiates the stationary target. However, in practical systems, the full-aperture beam is not always perpendicular to the azimuth direction. The full-aperture azimuth angle is calculated from the data recorded by the Inertial Navigation System (INS) or estimated from the data in the stationary area, such as land. According to Equation (4), the azimuth angle \(\varphi_{k}\) of the sub-apertures beam center line can be expressed as  
\[\varphi_{k}=\arccos(\frac{\lambda f_{k}}{2v_{p}\sin\theta}). \tag{10}\]
###### Abstract  
This study investigated the effects of carbon fiber (CF) length, electrode spacing, and probe configuration on the electrical conductivity of cement composites. Accordingly, 57 different types of samples were prepared, considering three different CF lengths, five different CF contents, three different electrode spacings, and two different probe configurations. This research found that the influence of CF length on the electrical resistivity of cement composite depends electrode spacing. For the cement composite with wide electrode spacing of 40 mm, its resistivity decreased as increasing CF length as in the previous study. However, when the electrode spacing is 10 mm, which is narrow (10 mm), the resistivity of the cement composite rather increased with increasing CF length. The results implied that when an electrode is designed for the cement composite incorporating CF, the CF length should be short compared to the electrode spacing. The percolation threshold of CF measured by the two-probe configuration was 2% or more. This is higher than that measured by the four-probe configuration (1%). At a lower CF content than 2%, the two-probe configuration gave higher resistivity of the cement composite than the four-probe configuration. However, the difference coming from the different probe configurations was marginal as increasing the CF content.  
Carbon fiber; electrode spacing; cement composite; electrical resistivity; percolation threshold Article
# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
## 2 Related Theories and Methods
### The Temporal Convolutional Network (TCN)
#### 2.4.2 Dilated Casual Convolution  
The historical data captured by causal convolution are only linearly related to the depth of the network, so the older the historical information traced, the more hidden layers it leads to. Thus, using a simple causal convolution does not capture a long time series.  
Therefore, the dilated causal convolution technique is used in the TCN to improve efficiency. The dilated causal convolution injects holes into the standard causal convolution, which allows the dilated causal convolution to obtain a larger perceptual field without changing the size of the convolution kernel and reduces the depth of the causal convolution without increasing the number of parameters. The structure of the dilated convolution is shown in Figure 4.  
Assume that the one-dimensional sequence input to the temporal convolutional network is \(x\in R^{n}\) and the convolutional kernel is \(f:\{0,\ldots,k-1\}\to R\). Define the null causal convolution operation \(s\) on the elements \(F\) in the sequence as follows:  
\[F(s)=(x*_{d}f)(s)=\sum_{i=0}^{k-1}f(i)\cdot x_{s-d_{i}} \tag{23}\]  
where \(d_{i}\) is the dilation factor of the \(i\)-th layer convolution, \(k\) is the size of the convolution kernel, and \(s-d_{i}\) is the direction of the sequence past time. Usually, as the number of layers increases, the dilation factor \(d\) grows exponentially by 2. The larger \(d\) is, the larger the input range is, thus increasing the perceptual field of the convolutional network, and when \(d=1\), it is the standard causal convolution.
###### Abstract  
Ultra-wideband (UWB) antennas cover a frequency range of 3.1 to 10.6 GHz and have sparked a lot of research interest as an essential part of wireless communication systems as they provide high data transmission speeds, are less expensive, and consume less power. UWB antennas are widely used in radar imaging, radio frequency identification, public security, and other high-accuracy positioning devices such as altimetry. Some smart applications of UWB antennas are vehicular radar systems, surveillance systems, software-defined radios, spectrum analysis, proximity fuses, etc. Multiple-input-multiple-output (MIMO) is a multiplexing technology that adopts multiple antennas both at the transmitter and receiver, which can enhance the channel capacity. MIMO technology is extensively used in several applications, such as in portable devices, wireless body area networks (WBANs), vehicular communication, and satellite-terrestrial networks. Generally, the MIMO antennas are used to obtain high reliability, high capacity, high throughput, and high security. The UWB MIMO antennas (UMAs) are considered the best choice for wireless communication systems as they offer reliability and wide transmission capacity, in contrast to unit antenna elements (AEs), without increasing system bandwidth or transmission power. The present-day and future communications systems need higher throughput to meet the demands of users. The signal transfer rate can be improved by improving channel bandwidth or increasing the number of receiving antennas. However, the main issue in designing UMAs is to provide high isolation between AEs because mutual coupling interactions between them affect the generated radiation patterns, leading to worse performance and failing to meet the operative constraints and requirements. When introducing decoupling techniques (DTs), researchers experience numerous challenges, including an increase in antenna size, design complexity, and cross-polarization. This article offers an organized review and simulated study of the various DTs in UMAs. The simulated study has been carried out through the implementation of various types of DTs on the same two-port UMA, which consists of two microstrip-fed circular-shaped AEs with open-end slotted partial ground. In contrast with previously reported review articles, this article provides a detailed study of various types of DTs reported so far and a better understanding for selecting appropriate DTs, which help in designing UMAs with better performance.  
antenna; decoupling technique; isolation; MIMO; UWB 2023
## 3 Materials and Methods
### Vehicular CO Prediction Model
#### 3.4.3 Multilayer Perceptron (MLP) Neural Network  
The MLP algorithm is one of the ANN algorithms which results from adding hidden layers to the simple perceptron. In this algorithm, the structure of the NN is generally trained based on backpropagation algorithm and some related variants. Therefore, the models are designed based on the integration of the MLP algorithm and a backpropagation algorithm called backpropagation neural network [38; 39; 40; 41; 42; 43]. The multilayer perceptron algorithm was developed due to the computational limitations that resulted from single-layered perceptron models. According to the experiments, the multilayer perceptron algorithm has the ability to represent complex simulations and mapping and process high level non-linear problems. Also, the MLP algorithm has the ability to process the nonlinear features, thus allowing the representation of a continuous function of non-linear activation functions such as sigmoid functions [44], which has a clear analogy with the conventional representation of a periodic function such as a Fourier series (i.e., as the sum of simple sine waves). Therefore, the MLP can be considered as a universal functional approximation. Figure 3 shows the architecture of an MLP with several layers of  
\[\delta_{j}^{(1)}=\left\{\begin{array}{l}\rho^{\prime}\Big{(}S_{j}^{(l)}\Big{)}e_{ j}\;for\;l=L\\ \rho^{\prime}\Big{(}S_{j}^{(l)}\Big{)}.\sum_{i=1}^{n\;l+1}\omega_{i}^{(l+1)}. \delta_{i}^{(l+1)}\;for\;l=L-1,L-2,\ldots,1.\end{array}\right. \tag{3}\]  
Eventually, the weights of the MLP are reorganized at the \(k\)th instant as in Equation (4):  
\[\omega_{ji}^{(l)}(k+1)=\omega_{ji}^{(l)}(k)+\Delta_{ji}^{(l)}(k)\;and\;\Delta _{ji}^{(l)}(k)=\;\mu\delta_{i}^{(l)}x_{i}^{(l-1)}+\gamma\Delta_{ji}^{(l)}(k-1) \tag{4}\]  
where \(\mu\) is the learning rate and \(\gamma\) signifies the momentum rate hyper-parameters.  
The final proposed network architecture for traffic CO prediction is illustrated in Figure 5. The proposed network is designed based on the results of the best traffic predictors that resulted from the CFS model, optimization and hyper-parameter, which are used to select the best predictors. By using the open source machine learning software (Weka), different MLP structures have been used to select the optimal MLP neural network model for the traffic CO prediction model. The proposed methodology was designed by combining the correlation-based feature selection model and multilayer perceptron.