esa-sceva/satcom-chunk-collection huggingface dataset:
# DGA Domain Name Classification Method Based on Long Short-Term Memory with Attention Mechanism
## 3 Theoretical Basis
### Long Short-Term Memory (LSTM)  
However, limited by structure, it is difficult for the original RNN to learn valid data in long-term dependent sequence data. Inputs that are far from the current moment cannot contribute to the update of the current time model parameters, the so-called gradient disappearance problem. The length of the DGA domain name is usually very long. For example, the switch domain name of Wannacry is 41 characters long, and, in practice, DGA domain names longer than 70 are encountered often. The most popular solution to the problem of RNN gradient disappearance is using the LSTM [19] structure instead of using the sigmoid activation function in the original RNN.  
The LSTM is a special artificial RNN architecture [19] used in the field of deep learning. LSTM has proven to be more effective than traditional RNN models in dealing with long-term dependency problems. LSTM and RNN are similar in timing, but the way to calculate the state of hidden layer neurons is different. Each memory unit of the LSTM includes four main elements: input gate, forgetting gate, output gate, and self-looping connected units. Thus, the output value is controlled between 0 and 1, responsible for describing how much is passed. At time \(t\), \(x_{t}\) represents the input; \(i_{t}\) is the activation value of the input gate; \(i_{t}\) is the activation  
The state of the memory cell at time \(t\) is as follows,  
\[\begin{split} C_{t}=\sigma(W_{i}x_{t}+U_{i}h_{t-1}+b_{i})\times tan(W _{c}x_{t}+U_{c}h_{t-1}+b_{c})\\ +\sigma(W_{f}x_{t}+U_{f}h_{t-1}+b_{f})\times C_{t-1}\end{split} \tag{3}\]  
The output of the t-memory unit is  
\[H_{t}=\sigma(W_{o}x_{t}+U_{o}h_{t-1}+b_{o})\times tan(C_{t}) \tag{4}\]  
This mechanism of the LSTM memory unit allows long-term storage and access to sequence information, thereby reducing the problem of gradient disappearance. It is suitable for building DGA domain name detection and classification models. [PERSON] et al. [16] used LSTM constructed DGA domain name detection and classification model to obtain Good results.
## 3 Application to Reflectarray for Compact Antenna Test Range  
The algorithm proposed in the previous section will now be applied to the optimization of a reflectarray array for its use as a probe in a CATR.
## 2 Data
### HidroMORE Model  
The real or actual evapotranspiration (\(ET_{a}\)) during 2014 is obtained from the HidroMORE hydrological model [42; 43]. It is an operational distributed model based on the water balance equation [50]. HidroMORE provides daily estimates of deep percolation (DP), water storage (WS) and ET after considering effective precipitation (P), surface runoff (RO) and irrigation (I):  
\[P+I-RO-ET-DP=\pm\Delta WS \tag{1}\]  
where \(\pm\Delta WS\) is a residual of the balance and expresses the rate of change in soil moisture content or water storage [mm]. The calculation in Equation (1) accounts for the effective root zone.  
The rationale of the \(ET_{a}\) estimation is based on the dual crop coefficient-reference ET methodology [50]:  
\[ET_{a}=ET_{0}(K_{s}K_{cb}+K_{e}) \tag{2}\]  
where \(ET_{0}\) stands for the reference or potential evapotranspiration, \(K_{s}\) is the water stress coefficient, \(K_{cb}\) is the basal crop coefficient, and \(K_{e}\) is the soil evaporation coefficient. The \(K_{cb}\) is calculated on the basis of the daily Normalized Vegetation Difference Index (NDVI), retrieved from temporal series of optical imagery (NDVI-\(K_{cb}\) approach), \(K_{e}\) is computed from the Food and Agriculture Organization (FAO) water balance procedure, and water stress conditions are applied to estimate \(K_{s}\).  
Figure 1: Land use map of the REMEDHUS network (**left**) at the river Duero basin in Spain, including the location of the 21 stations for measuring SM and LST (black triangles) and the 4 weather stations (white dartboards). Time-averaged 1-km MODIS LST Aqua day (**top right**) and 25-km SMOS BEC L3 SM (**bottom right**) maps during the study period.  
The \(K_{cb}\) approach has been previously applied with different formulations (linear and exponential) and also using different indirect growing parameters, such as the Leaf Area Index (LAI), the Fractional Vegetation Cover (FVC) or the NDVI. Results show that the linear approach is adequate for SM estimation at the root zone, which is denoted by \(\pm\Delta WS\) in Equation (1). The \(ET_{a}\) differences produced by the indirect growing parameter are lower than 3 mm of the total annual \(ET_{a}\)) [51]. Ground-based SM measurements from REMEDHUS have also been used for validating these results [52]. The higher correlations obtained between in situ SM and HidroMORE SM at the root-zone (R \(\approx\) 0.78 to 0.95) indicate that the model was well calibrated and working correctly [51].
# An Explainable Deep Semantic Coding for Binary-Classification-Oriented Communication
## 3 Deep Variational Semantic Coding
### Parameterized Chernoff Information
#### 3.2.2 Estimation of Chernoff Information  
By comparing Equations (8) and (20), we notice that when \(0<\alpha<1\), Chernoff information can be expressed in terms of Renyi divergence as  
\[C(P_{1},P_{2})=-\min_{0<\alpha<1}(\alpha-1)\cdot R_{\alpha}(P_{1}\|P_{2}). \tag{24}\]  
Based on the variational formulation of Renyi divergence in Equation (21), the optimization of Chernoff information involves jointly training the discriminator network \(T_{\psi}\) and the Chernoff coefficient \(\alpha\). Specifically, after obtaining the optimized discriminator \(T_{\psi^{*}}\), \(\alpha\) is trained with respect the following objective:  
\[\alpha^{*}=\arg\min_{0<\alpha<1}\left[\log\sum_{x}P_{1}(x)e^{(\alpha-1)T_{ \psi^{*}}(x)}-\frac{\alpha-1}{\alpha}\log\sum_{x}P_{2}(x)e^{\alpha T_{\psi^{*} }(x)}\right]. \tag{25}\]  
With both \(T_{\psi}\) and \(\alpha\) optimized, the Chernoff information can be estimated as:  
\[C_{\psi^{*},\alpha^{*}}(P_{1},P_{2})=-\log\sum_{x}P_{1}(x)e^{(\alpha^{*}-1)T_ {\psi^{*}}(x)}+\frac{\alpha^{*}-1}{\alpha^{*}}\log\sum_{x}P_{2}(x)e^{\alpha^{* }T_{\psi^{*}}(x)}. \tag{26}\]  
In practice, the optimal coefficient \(\alpha^{*}\) in Chernoff information rarely attains an extreme value at \(\alpha=0\) or \(\alpha=1\). Therefore, this numerical approximation over \(0<\alpha<1\) remains effective in most cases.  
Overall, the estimation of Chernoff information in our framework involves two stages: a variational approximation of Renyi divergence and a numerical minimization over the coefficient \(\alpha\in(0,1)\). The first stage is theoretically supported by the Donsker-Varadhan variational formulation [34] using discriminator networks. The second stage is implemented via gradient-based optimization to update the parameter \(\alpha\). Our experimental results demonstrate stable convergence and effective estimation of Chernoff information throughout training.
## 2 Related Work
### State of the Art of Hardware Metrics in the Estimation of Link Quality in Wireless Sensor Networks
In [32], the impact of humidity and temperature on the RSSI in indoor WSNs was explored; the 868 MHz Panstamp NRG 2.0 wireless modules were disposed of, where different distances showed various values of the interaction of the RSSI according to the humidity and temperature. In [33], the effects of ambient temperature and humidity on the radio signal strength of Atmel ZigBit 2.4 GHz wireless modules were explored in an outdoor WSN. The experimental results demonstrated that changes in weather conditions influenced the received signal strength. The temperature seemed to have a more significant negative impact on the signal strength in general, while high relative humidity may have had some effect on it, particularly below 0 \({}^{\circ}\)C.  
An experiment demonstrated a linear decrease of 8 dB in signal strength when the temperature rose from 25 \({}^{\circ}\)C to 65 \({}^{\circ}\)C using a TI CC2420 radio chip on a Tmote Sky node. They also showed the implications of the experiment on different communication ranges [34]. In a study [35], a sensor network comprising 16 TelosB sensor nodes was manipulated in outdoor measurement over a year, revealing that the RSSI had a negative correlation with temperature. In [9], the variation in RSSI was investigated for different rooms and floors of a building using different hardware technologies for the amplifier in a WuRx, e.g., bipolar junction transistor (BJT) and transimpedance amplifier (TIA) technologies. The highest RSSI was achieved when both WuRx and wake-up transmitter (WuTx) were on the same floor and near each other. At a distance of more than 4 m, the RSSI abruptly dropped. In [36], an experiment to measure the RSSI using a CC2420 2.4 GHz radio chip was conducted in outdoor and indoor environments within the line of sight. The experiment curve showed that the deviation in the RSSI value was different from one environment to another. In indoor environments, the noise is higher due to the presence of obstacles with a higher probability of the reflection of the waves. The physical study of the software and hardware link quality metrics in this article proves the important role of network reliability optimization in many studies. Additionally, multiple strategies can be adopted in future work for more investigation into the scalability process of networks, such as [37], who proposed a simulation for a proposed approach for improved routing, or the [38], who cited the optimization of some real traffic or communication networks to control congestion.
# AI Algorithms in the Agrifood Industry: Application Potential in the Spanish Agrifood Context
## 1 Introduction  
Artificial intelligence (AI) is increasingly acknowledged as a pivotal catalyst within the agrifood sector, addressing imperative issues related to productivity enhancement, resource optimization, and environmental stewardship. Specifically, AI algorithms, encompassing machine learning, deep learning, and optimization techniques, are being used to refine agricultural practices such as crop yield prediction, disease identification, and water resource management. In the context of Spain, where agriculture plays a substantial role in the national economy and is influenced by a variety of climatic conditions, the integration of AI presents significant opportunities for enhancing both operational efficiency and sustainability within the sector.  
This systematic analysis, underpinned by AI, big data, and Data Mining methodologies, examines the prospective applications of AI algorithms in the Spanish agrifood industry. The paper delivers an exhaustive review of AI's current and future contributions to this domain. It begins with a comprehensive overview of cutting-edge AI algorithms that are currently used or have the potential for application in the global agrifood industry. This highlights methodologies such as supervised and unsupervised learning, neural networks, and reinforcement learning, among others. Subsequently, the methodology section elucidates the data acquisition and analytical tools used. For that purpose, it emphasizes the bibliometric approach and big data techniques used to research AI trends and patterns within the agrifood sector from 2020 to 2024.  
Section 4 offers an in-depth analysis of AI applications within Spain's agricultural landscape, concentrating on areas such as remote sensing, crop monitoring, and precision agriculture. Additionally, the study identifies prominent trends in AI adoption across various agricultural sub-sectors, underscoring the leading role of larger enterprises in driving AI-driven innovations in contrast to smaller farms, which frequently encounter barriers to implementing these technologies. The discussion segment addresses the inherent limitations and obstacles associated with AI integration in Spain. In particular, emphasis is made on the need for enhanced digital infrastructure, improved data integration, and greater accessibility for small to medium-sized agricultural enterprises.  
Ultimately, the paper outlines future research pathways aimed at fostering the adoption of AI for agriculture in Spain. This includes the development of scalable AI solutions tailored to Spain's diverse agricultural environments and contributions toward achieving sustainability goals. The insights and recommendations provided are intended to guide policymakers, researchers, and industry stakeholders in accelerating AI integration within Spanish agriculture, thereby ensuring its extensive impact on the sector's productivity and environmental management.
# Grid Anchor Lane Detection Based on Attribute Correlation
## 3 Methods
### Network Design
Figure 7 describes the convolutional reordering upsampling method used in the model, which corresponds to the blue arrow in Figure 5. Given that the features obtained at different locations during upsampling may not be identical, the upsampling method does not use the nearest neighbor interpolation approach as in FPN. Instead, it combines convolution and reordering. Assuming the input format for upsampling is \(C\times H^{\prime}\times W^{\prime}\), after the 3 \(\times\) 3 convolution, the output data format is \(4C\times H^{\prime}\times W^{\prime}\), denoted as \(B\). Subsequently, after the reordering operation, the data are output in the format \(C\times 2H^{\prime}\times 2W^{\prime}\), denoted as \(R\). The reordering operation does not simply reorder by sequence position, but rather reorders by  
Figure 5: Illustration of the network architecture. The model takes FPN as the basic architecture, and the features passed across the layers include the features generated by upsampling and MLP. The features from different sources are concatenated in the channel layer dimension. Finally, the detection results of the corresponding dimension are outputted in different feature layers. Different colored arrows represent different types of data processing methods. Figure 6: Illustration of the method for constructing global features. By flattening the tensor, applying the MLP, and reshaping, global features are obtained to establish interconnections between features at different spatial locations in the high-level layers. The figure shows the MLP processing flow at the top layer. nearby positions. Equation (9) presents the calculation method for reordering, where % denotes the modulo operation, and \(\big\lfloor\rfloor\) represents the floor function. \[ \begin{array}{ll}B_{i,j,k}&\begin{array}{c}i\in\{0,\cdots,4C-1\},l\in\{0, \cdots,C-1\},\\ \rightarrow&\begin{array}{c}n=2k+((\beta 4)\%2)\\ \end{array}\\ \end{array}&R_{l,m,n,l}&\begin{array}{c}j\in\{0,\cdots,H^{\prime}-1\},m\in\{ 0,\cdots,2H^{\prime}-1\},\\ \in\{0,\cdots,W^{\prime}-1\},n\in\{0,\cdots,2W^{\prime}-1\}\end{array}\\ \end{array}  \tag{9}\]  
As a result, the outputs at the middle and lower layers not only include features from the corresponding positions but also include neighborhood features obtained through upsampling from the high layers and global features drawn by the MLP. During the forward propagation process, the model extracts features through downsampling for local feature fusion, decodes the lane semantics through lateral connections and upsampling, and establishes long-range feature associations through the MLP. Finally, the model outputs the corresponding grid anchor recognition results at three different feature map resolutions.
# Inertial Parameter Identification in Robotics: A Survey
## 2 Robot Modeling and Control in the Context of Parameter Identification
### Control Strategy  
Robots being double-integrator systems, they are naturally unstable in open-loop and must therefore be operated and identified in closed-loop. Since most electrically actuated robots use Direct Current (DC) or Brushless Direct Current (BLDC) motors, the control structure at the joint level usually consists of a cascade of PD or PID regulators, the position and velocity loops providing a reference for the current-torque loop (c.f. [37]). As pointed out in [12; 43], provided that the low-level current loop has a sufficient bandwidth, (typically, above 500 Hz, which is usually verified on most Direct current (DC) or Brushless Direct Current (BLDC) actuators since the torque reference is adjusted at the PWM frequency, i.e., 16-40 kHz) and considering the linear mapping between the current and the torque within DC and BLDC motors, its transfer function can be expressed as a static gain in the characteristic frequency range of the rigid robot dynamics (typically, less than 10 Hz for industrial robots as explained in [12; 43]). In the case of a PD controller, the resulting control torque applied to the robot can be then typically be computed as:  
\[\tau_{ctrl}=\mathbf{K}_{\tau}\mathbf{K}_{p}(\mathbf{q}_{d}-\mathbf{q})+\mathbf{K}_{\tau}\mathbf{K}_{d} (\mathbf{\dot{q}}_{d}-\mathbf{\dot{q}})+\mathbf{\tau}_{ff} \tag{16}\]  
where \(\mathbf{K}_{\tau}\), \(\mathbf{K}_{p}\) and \(\mathbf{K}_{d}\in\mathbb{R}^{n\times n}\) are the diagonal drive, position and velocity gain matrices and \(\mathbf{\tau}_{ff}\) is a feed-forward term (assumed to be zero in this work). Please note that the drive-gain matrix \(\mathbf{K}_{\tau}\) contains the combined influence of the static gains \(\mathbf{K}_{\bar{\
u}}\) of the current amplifiers, gear ratios \(\mathbf{K}_{\tau}\) and electromagnetic motor torque constants \(\mathbf{K}_{m}\), and can thus be written as \(\mathbf{K}_{\tau}=\mathbf{K}_{\bar{\
u}}\mathbf{K}_{\tau}\mathbf{K}_{m}\) (c.f. [43]).
## 3 Comparison among Different AC Signal Analysis Methods  
### Processing Time  
Analysis time or processing time is a particularly principal factor that determines the speed of AC signal analysis. However, with today's progress, the comparison of the algorithms has become very practical and hardware/software-specific as hardware acceleration chips are being integrated into both generic computers and embedded systems to speed up analysis.  
Among existing hardware accelerations are data-level parallelism, such as Single-Instruction Multiple-Data, where a large amount of data is processed simultaneously. Alternatively, instructions are parallelized, to execute concurrently or interleaved depending on the microprocessor's architecture; For a single-core microprocessor, the instructions are executed interleaved to assure finishing the tasks at the same time. For multi-core multiprocessors, the instructions are split into different cores and are executed in parallel.  
These accelerations are typically determined by a combination of program code which should target these hardware optimizations, compiler settings, and hardware capabilities. In this section and as a first step, we focus on the theoretical processing time of each AC signal analysis for generic computing without hardware acceleration. Then, in the second part, we discuss the possible hardware acceleration for each AC method in detail.
## 3 Results and Discussions
### Element Analysis  
The EDS technique was used to investigate the chemical compositions of surfaces.  
Figure 3 shows the recorded EDS spectra of the as-received surface A_R (a) and the treated surfaces A_WS (b), A_NS (c), and A_FS (d). The EDS quantitative analysis of the A_R aluminum substrate reveals a composition rich in Al and confirms the presence of the alloy elements Si, Mg, Mn, and Fe. Small peaks of carbon and oxygen were also evidenced, due to contamination. However, the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples** & **Arithmetic Roughness Ra** & **Root-Mean-Square Roughness Rq** & **Skewness (Sku)** \\  & **(\(\upmu\)m)** & **(\(\upmu\)m)** & **Skewness (Sku)** \\ \hline A\_R & 0.254 & 0.301 & 0.256 \\ A\_WS & 0.297 & 0.382 & 1.063 \\ A\_NS & 4.151 & 4.800 & \(-\)0.783 \\ A\_FS & 1.427 & 1.722 & 0.243 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Roughness parameters of different prepared samples.  
Figure 2: Scheme of the as-received (A_R), A_WS, A_NS, and A_FS profiles.  
spectra of the treated surfaces reveal a great increase in the percentages of C and O, indicating that a silane layer was successfully grafted on the rough aluminum substrate [26; 36].  
Figure 4 shows FTIR spectra of A-WS. In this spectrum, the broad peak at 3450 cm\({}^{-1}\) is assigned to the stretching vibration of silanol (O-H group). The peaks at 950 and 800 cm\({}^{-1}\) are due to Si-O stretching and the deformation of Si-OH, respectively, in silanol. The peaks at 1100 and 470 cm\({}^{-1}\) are due to asymmetric and symmetric stretching of Si-O-Si in the siloxane group, respectively. This spectrum  
Figure 3: EDS spectra of A_R (**a**), A_WS sample (**b**), A_NS sample (**c**), and A_FS sample (**d**).  
also reveals the presence of two peaks of around 2930 and 2840 cm\({}^{-1}\), which are characteristic bands of silane corresponding to symmetric and antisymmetric stretching of the -CH\({}_{2}\) group, respectively. The presence of these bands could be attributed to the entanglements of hydrocarbon chains after the monomer due to the polymerization on a surface and also to the effect of neighboring electrons drawing a carbonyl group [37]. The small peak at 1450 cm\({}^{-1}\) corresponds to the Si-CH\({}_{2}\) bending vibration present in silane [38]. Thus, the presence of these characteristic bands of silane in the spectrum of A-WS confirms the formation of a self-assembled silane monolayer on the surface of the aluminum substrate.