esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Data Analysis
#### 2.3.5 Convolution-GRU Network  
The structure of the Convolution-GRU network used in this study is shown in the Figure 4. The entire network consists of four 2-dimensional convolutional layers with convolution kernels of different sizes (\(3\times 6\), \(5\times 6\), \(7\times 6\), \(9\times 6\)), a LeakyReLU layer, 2 gate recurrent units (GRU), a global average pooling layer and 2 fully connected layers.  
First, in order to extract the spatial features in different time periods from the data (the extraction of temporal features is mainly carried out in GRU), 4 different scale convolution kernels are selected, in order to maintain the same length of the time series, padding is done in the direction of the time axis. After convolution, the data pass through the LeakyRelu layer and are concatenated together to form a 7 (TRs) * 320 (features) feature map, which will then be input into the GRU.  
RNN has become one of the most commonly used time series artificial neural networks with its excellent time feature extraction ability. As a variant of LSTM network, GRU has similar performance to LSTM network, but it is easier to calculate than LSTM. In this study, two GRU models [14] with different hidden nodes connected in a feed-forward manner are used to extract high-dimensional time features from IC data. The feature map obtained in the previous step is first input into a GRU network with 180 hidden nodes, then the output of this GRU is concatenated with the previous feature map as the input of the GRU network with 64 hidden nodes.  
Considering that the classifier used in this study is to classify the tasks performed by the subjects, the features of the brain activity of the subjects during the tasks are more valuable than the features at a single time point. Therefore, the output of the GRU network will go through a global average pooling layer to average the output on the entire time axis, instead of extracting only the output at the last time point to obtain a 64-dimensional feature vector. Finally, the feature vector passes through two fully connected layers to obtain the final binary classification result.  
Figure 4: The basic structure of the convolution-GRU model is to combine the spatial features obtained by convolution at four different time scales and send them to the GRU to learn the temporal features. Considering that negative values may appear in the process of convolution, the LeakyRelu layer is selected to ensure the learning of parameters. It should be noted that the number before the @ sign in this figure means the number of the channels of the convolution kernel.
## 3 The Variety of Effects the Broken Cloud Arrays Could Have on a Light Field  
Based on the aforementioned theoretical approaches, the UniSky tool [20] has been linked to the HOLIGILM tool [25]. The UniSky solution provides the zenith-normalized spectral sky radiance or luminance distributions, accepting the optical properties of a local atmosphere. Some of these properties vary only slightly over time, but others depend strongly on time and place. The HOLIGILM is used for modeling the light transmission through the tubular light guide with a hemispherical top dome and allows for different types of diffusers. Using this tool we can test various tube adjustments by altering the tube radius, length and inner surface reflectance. In addition, the optical properties of the cover dome and diffuser can be varied, taking into account different positions of the sun and different luminance patterns under arbitrary atmospheric conditions. We have analyzed the most typical atmospheric conditions aiming to demonstrate the role broken cloud arrays play in forming the illuminance patterns below a straight light pipe. It is ideal to keep the bottom interface of a light tube open in order to illustrate the effects the clouds could have on the illumination scene in the interior. Note that the Lambertian diffusers distribute the light homogeneously in all directions, thus efficiently suppressing daylight dynamics that is typical for natural conditions outdoors.  
Among many others, the UniSky simulator also uses a set of parameters to model light scattering in a turbid atmosphere. The most important ones are shortly introduced below. The total optical thickness of a cloud-free atmosphere, \(\tau_{0}\), is due to the trivial superposition of the Mie and Rayleigh optical depths. The asymmetry factor \(g\prime\) and the single scattering albedo \(\omega_{0}\) are key parameters for modeling the light scattering in a cloud free atmosphere. The parameter \(g\prime\) is used to characterize the scattering function, while \(\omega_{0}\) is the ratio of the scattering efficiency to the extinction efficiency. Unless it is stated otherwise, the most typical values for the Central European region (\(\tau_{0}=0.3\); \(g\prime=0.65\); \(\omega_{0}=0.85\)) are used in our simulations. Other parameters particularly important for further computations are as follows: The altitude of a cloud base \(H_{C}\), cloud radius \(R_{C}\), cloud albedo \(\rho_{C}\), and cloud fraction \(C_{F}\). The cloud fraction varies from zero to one, where zero refers to a cloudless sky and one is for a completely overcast sky. Input parameters to the HOLIGILM tool are as follows: Tube length \(l\), tube radius \(r\), inner reflectance of the tube \(\rho\), optical transparency of the top dome \(t_{C}\), diffuser type and room dimension \((x,y)\). To be consistent with our previous studies we use the following: \(H=1.5\) m, \(r=0.26\) m, \(\rho=0.934\), \(t_{C}=0.92\), \(x=6\) m and \(y=4\) m. The position of the light guide center with respect to the center of the room coordinate system is \(x\prime=1.5\) m and \(y\prime=2\) m. The vertical separation of the room ceiling and work plane is 3 m.
## 2 Force Tracking System
### Quantification Protocol of Force Control Capabilities  
To accurately quantify force control capabilities, the analysis period of the measured force data was set as 30 s after the time to stably reach a target force. Since the first time to reach a target force is often subject to artifacts, including the initial status of the participant, the force data measured after the first achievement of target force was included in the analysis.  
Force control capabilities were quantified in (1) initiation time, (2) development time, (3) maintenance error, (4) termination time, and (5) tracking error, as illustrated in Figure 2. The initiation time (IT, unit: second) was the elapsed time between the increase of a target force and the beginning of increase force exertion. The development time (DT, unit: second) was the elapsed time between the end of the initiation phase and the time to reach a designated target force. The maintenance error (ME, unit: N) was the average absolute difference between a designated target force and corresponding exerted forces during the maintenance phase. The termination time (TT, unit: second) was the elapsed time between the decrease of a target force and the time to reach a designated target force. Lastly, the tracking error (TE, unit: N) was the average absolute difference between target forces and exerting forces during all the test phases.  
Figure 1: Force tracking system: (**a**) system layout; (**b**) computer screen.
# Bioelectrical Signals for the Diagnosis and Therapy of Functional Gastrointestinal Disorders
## 2 Oropharynx and Esophagus  
Swallowing is a combination of voluntary and reflex actions and involves passing food from the mouth through the pharynx and into the esophagus. Safe swallowing requires precise and complex coordination of both voluntary and involuntary motor activities. Failure to coordinate these activities leads to disturbances in swallowing known as dysphagia. Dysphagia can be a consequence of central and peripheral sensorimotor deficits related to a broad spectrum of diseases, including stroke, neurodegenerative diseases, e.g., Parkinson's, amyotrophic lateral sclerosis (ALS) and Alzheimer's disease, or tissue trauma as a consequence of head and neck cancer [15]. Dysphagia can lead to serious health problems, including respiratory complications, aspiration pneumonia, nutritional compromise, dehydration, reduced quality of life and death [16].  
Dysphagia management practices are imprecise, in part due to a limited number of non-specific diagnostic tests and medical therapies. The majority of diagnostic techniques focus on detailing general swallowing biomechanics through visualization modalities such as fluoroscopy, endoscopy, and ultrasonography [17; 18]. Without further analysis of the underlying muscle physiology, the primary level of impairment is often unidentified [19]. Pharyngeal and esophageal manometry offers quantifiable investigation of pressure patterns and insights into temporal characteristics that are inadequately captured with visualization modalities. Recently, high-resolution manometry has provided increased spatial resolution [20; 21]; however, manometry continues to assess primarily the biomechanics of pharyngeal and esophageal motility.
## 5 Future Directions
### Ethical Dimension  
With the growth of LLMs, ethical considerations have gone beyond data biases to include environmental responsibility as well. The high energy consumption of these models has significant environmental consequences that may exacerbate technological and biological inequalities. The future of ethically grounded LLM development must focus on designing architectures that are aligned with ethical principles and sustainability from the outset. Transparency, accountability, and the reduction in negative impacts will be key principles along this path [227].  
One of the clearest examples of how technical challenges intersect with ethical priorities is the issue of energy optimization during the training and deployment of large LLMs. Given that one of the major challenges facing LLMs across all domains is their high computational cost and substantial energy use, achieving energy sustainability has become a serious concern for both researchers and policymakers [228]. This concern is especially critical as LLMs are increasingly applied in sensitive domains such as healthcare and clinical data analysis, where the environmental consequences of training and inference processes such as excessive carbon emissions, high electricity consumption, and water usage raise pressing ethical and practical questions. Training a single advanced model can result in the emission of hundreds of tons of carbon and may demand infrastructure that is simply not available in many medical systems, particularly in resource-limited settings.  
To address this, future research should prioritize the development of lightweight and energy-efficient architectures that can significantly reduce power consumption without compromising model performance. Promising solutions in this area include quantization, model compression, domain-specific fine-tuning, and the adoption of high-efficiency hardware. Additionally, optimizing energy use during the inference phase particularly in real-time applications deserves special attention. These technological strategies, when combined with broader environmental policies such as the use of clean energy sources and the implementation of green frameworks in data centers [229], can support a more responsible and sustainable path forward for the deployment of LLMs. As artificial intelligence continues to advance, the need to balance computational capabilities with environmental responsibility must be treated as a core principle in the design, implementation, and regulation of this transformative technology.
## 3 Results
### Sustainable and Innovative Soil Improvement Technique: Potential and Practical Applications
\begin{table}
\begin{tabular}{c c} \hline \hline
**Chemical Materials** & **References** \\ \hline Fly ash & [4; 5; 15; 16; 22; 27; 43; 44; 45; 46; 47; 48] \\ Rice husk ash 1 & [4; 20; 32; 33; 44; 60; 61; 62] \\ Brick dust & [63] \\ Eggshell & [4; 64; 65; 66] \\ Silica fume & [4; 20] \\ Micro silica or silica fume (SF) & [33] \\ Copper slag (CS) & [33] \\ Bagasse ash 1 & [20; 67; 68; 69] \\ Sugarcane straw ash 1 & [4] \\ Blast furnace slag and GGBS & [4; 33; 70; 71] \\ Lime-induced calcite precipitation (LICP) & [72] \\ Sawdust ash & [5] \\ Coal bottom ash (CBA) & [73] \\ Municipal solid waste incineration (MSWI) ash & [26] \\ Kota stone sludge & [27] \\ Phosphogypsum (PG) & [33] \\ Nanomaterials & [29] \\ Recycled gypsum & [32; 62] \\ Recycled cement and concrete & [74] \\ Lignosulfonate (lignin-derived) & [75; 76; 77; 78; 79; 80] \\ Lignosulfonate-based composite (CA) admixture & [81] \\ Lime ash & [67] \\ Cement kiln ash (CKD) & [33] \\ Lime & [63; 65; 68; 79; 82; 83] \\ Cement slag & [69] \\ Calcined clay & [84; 85; 86; 87; 88] \\ Enzyme-induced carbonate precipitation (EICP) & [66; 89] \\ Basic oxygen furnace slag (BOF) & [90] \\ Banana leaf ash 1 & [91] \\ Coconut shell ash 1 & [20; 92] \\ Bamboo leaf ash 1 & [93] \\ Volcanic ash & [45] \\ Coal ash & [43] \\ Calcium carbide waste & [20] \\ Red mud & [20] \\ MSWI fly-ash & [94] \\ Magnesite mine waste & [95] \\ Recycled ground glass (PWG) & [96] \\ Cement kiln dust & [20] \\ Biochar derived from the invasive weed species _Prosopis juliflora_ & [58] \\ Biomass bottom ash & [97; 98] \\ Teff straw ash & [99] \\ Microorganism-facilitated calcium carbonate precipitation & [36] \\ EICP & [89; 100] \\ Digestate ash & [101] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Chemical materials used in sustainable and innovative expansive soil improvement techniques. \begin{table}
\begin{tabular}{c c} \hline \hline
**Biological Action Mechanism** & **References** \\ \hline Plant cellular compounds and microbial & [54] \\ products & [54] \\ Compost-treated topsoils (CMTs) & [123] \\ Guar gum biopolymer & [124; 125] \\ Enzymatic stabilization mechanism & [56] \\ Calcium carbonate precipitation facilitated & [57] \\ by microorganisms & [57] \\ Enzymatic stabilization (expansive soils & \\ subjected to moisture fluctuations) & [59] \\ Xanthan gum & [40] \\ Wool and banana fiber 1 & [59] \\ Molasses and vinasse residues 1 & [40] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Expansive soil improvement techniques with a biological action mechanism.
# Multi-Antenna Jammer-Assisted Secure Short Packet Communications in IoT Networks
## 2 System Model
Moreover, TAS is particularly well adapted to systems with computational constraints, since it has two main advantages: low cost and easy implementation. For the TAS scheme, the jammer selects an antenna, which minimizes the interference imposed on Bob, to transmit the jamming signal. Accordingly, the received SNRs at Bob and Eve of the TAS scheme, respectively, can be expressed as:  
\[\gamma_{b}^{TAS}=\frac{P_{a}|h_{ab}|^{2}}{P_{j}\underset{1\leq i\leq N_{j}}{ \min}\left|h_{jb}\right|^{2}+\sigma^{2}} \tag{7}\]  
and  
\[\gamma_{e}^{TAS}=\frac{P_{a}|h_{ae}|^{2}}{P_{j}|\left|h_{j\cdot e}\right|^{2}+ \sigma^{2}}, \tag{8}\]  
where \(h_{jb}\) is the Rayleigh channel coefficient for the \(i\)-th antenna of the jammer to Bob link, and \(h_{j\cdot e}\) is the Rayleigh channel coefficient for the selected antenna of the jammer to Eve link. According to [19], the maximal instantaneous secrecy rate of the IoT downlink short packet communications system with a cooperative jammer for a given information leakage probability \(\delta\), decoding error probability \(\epsilon\), and blocklength \(N\) can be approximated as:  
\[R_{s}^{\star}(N,\epsilon,\delta)=\left\{ \begin{array}{cc}C_{s}-\sqrt{\frac{V_ {s}^{\star}}{N}}\frac{Q^{-1}(\epsilon)}{\ln 2}-\sqrt{\frac{V_{s}^{\star}}{N}} \frac{Q^{-1}(\delta)}{\ln 2},&\gamma_{b}^{\star}>\gamma_{e}^{\star},\\ 0,&\gamma_{b}^{\star}\leq\gamma_{e}^{\star},\end{array} \right. \tag{9}\]  
where \(C_{s}=\log_{2}\left(1+\gamma_{b}^{\star}\right)-\log_{2}(1+\gamma_{e}^{\star})\) is the secrecy capacity for the infinite number of channel uses, \(V_{x}^{\star}=1-(1+\gamma_{x}^{\star})^{-2}\), \(x\in\{b,e\}\) is the channel dispersion, \(Q^{-1}(\cdot)\) is the inverse \(Q\)-function \(Q(x)=\int_{x}^{\infty}\frac{1}{\sqrt{2\pi}}e^{-\frac{\epsilon^{2}}{2}}dt\), and \(\star\in\{ZFB,NAN,TAS\}\). For the reader's convenience, we define \(\lambda_{a}=\frac{P_{a}}{\sigma^{2}}\) and \(\lambda_{j}=\frac{P_{j}}{\sigma^{2}}\).
Alternatively, the use of near infra-red spectroscopy (NIRS), now available in a portable format, provides a rapid, accurate, and inexpensive alternative for both total and individual carotenoid evaluation in cassava [15; 16; 17]. Generally, the deployment of NIRS requires the development of reliable calibration equations from a standard reference analytical method. So far, the current effort in developing calibration for carotenoids using portable NIRS has relied on the collaborative effort between low-resource and more advanced resource institutions [18; 16]. However, as most low-resource breeding programs continue to embrace the use of NIRS for carotenoid evaluations, most of the available reference values and genetic improvement studies were developed by the proxy iCheck and Chroma Meter protocols for TCC rather than HPLC values due to the lack of infrastructures and human capacity to run standard laboratories [19]. Therefore, for consistency and transferability of data, the need for alternative calibration using non-HPLC reference values becomes inevitable for the large-scale screening and breeding for TCC in cassava. Consequently, this study was in response to the need to develop a direct NIRS calibration equation for TCC on reference values derived from low-cost and simple sampling proxy methods for TCC on wider African cassava breeding populations. We compared calibration performances from two evaluation protocols: iCheck and Chroma Meter, which have been previously deployed for TCC quantification in cassava roots [13; 18]. The benefit of using a non-linear machine learning algorithm over the traditional partial least square (PLS) for carotenoids has been previously reported [16; 20; 21]; therefore, we compared calibrations from the random forest (RF) and PLS models. The effect of sample preparation on calibration performance has been reported where root mashing to obtain homogeneous samples was beneficial [16]. However, root mashing requires special devices and infrastructures which might not be available for off-site trials. Sample preparations for iCheck and Chroma Meter require relatively simple and low-cost chopping of roots with knives. Therefore, we equally developed calibration for root dry matter content (DMC) using the simple and cheap sampling method.
###### Abstract  
Vehicle to everything (V2X) has been deemed a promising technology due to its potential to achieve traffic safety and efficiency. This paper considers a V2X downlink system with a simultaneous wireless information and power transfer (SWIPT) system where the base station not only conveys data and energy to two types of wireless vehicular receivers, such as one hybrid power-splitting vehicular receiver, and multiple energy vehicular receivers, but also prevents information from being intercepted by the potential eavesdroppers (idle energy vehicular receivers). Both the base station and the energy vehicular receivers are equipped with multiple antennas, whereas the information vehicular receiver is equipped with a single antenna. In particular, the imperfect channel state information (CSI) and the practical nonlinear energy harvesting (EH) model are taken into account. The non-convex optimization problem is formulated to maximize the minimum harvested energy power among the energy vehicular receivers satisfying the lowest harvested energy power threshold at the information vehicular receiver and secure vehicular communication requirements. In light of the intractability of the optimization problem, the semidefinite relaxation (SDR) technique and variable substitutions are applied, and the optimal solution is proven to be tight. A number of results demonstrate that the proposed robust secure beamforming scheme has better performance than other schemes.  
SWIPT; nonlinear energy harvesting model; secure vehicular communication; semidefinite relaxation
## 4 Flow Characteristics and Optimization of the Coupler  
When hydraulic oil flows in the coupling system, frictional resistance and turbulent energy loss occur due to the complicated structure inside the coupler, which causes a pressure drop between the inlet and the outlet [39]. In order to improve the efficiency of hydraulic power transmission while ensuring the best flow characteristics in the NFFCS design, a series of numerical simulations, comparisons, discussions and design optimizations were carried out, which are presented in this section. Firstly, the internal flow characteristics of the CFFCS and the initial design of the NFFCS are compared to ensure that the pressure drops of the two coupling systems are at the same level. Then, for the initial design of the NFFCS, a series of geometric optimizations are proposed and the flow characteristics are discussed, leading to the best final design.
###### Abstract  
In this paper, degradation effects, such as self-heating effect (SHE) and drain-induced barrier lowering (DIBL) effect in 2D MoS\({}_{2}\)-based MOSFETs are investigated through simulations. The SHE is simulated based on the thermodynamic transport model. The dependence of the DIBL effect and the lattice temperature in the middle of the channel on the gate length is considered for transistors with different gate oxide and back oxide (BOX) materials. The effects of Al\({}_{2}\)O\({}_{3}\) and HfO\({}_{2}\) as gate oxide and SiO\({}_{2}\) and HfO\({}_{2}\) as BOX materials are compared. Transistors, in which the channel is fully and partially (i.e., just below the gate) covered by a gate oxide, are considered. It is shown that the transistors with Al\({}_{2}\)O\({}_{3}\) as gate oxide and SiO\({}_{2}\) as BOX materials have higher immunity to DIBL effect and transistors with HfO\({}_{2}\) as gate oxide and HfO\({}_{2}\) as BOX materials have higher immunity to SHE.  
self-heating effect; DIBL effect; lattice temperature; thermal conductivity; 2D MoS\({}_{2}\) ac  
in order to realize high-performance MOSFETs based on MoS\({}_{2}\) or other transition metal dichalcogenides, important problems, such as SHE, must be solved.  
As mentioned above, a 2D MoS\({}_{2}\)-based MOSFET has sufficient immunity against short channel effects, especially against the DIBL effect [2]. The DIBL effect is one of the noticeable effects in short channel transistors. It appears as an additional (however undesirable) control of the threshold voltage due to the lowering of the barrier by the drain voltage [11]. This effect depends on the electrical parameters of the channel material and the materials surrounding the channel, in particular on the dielectric constant of these materials [12]. However, thermal parameters of the same materials, namely thermal conductivity and heat capacitance, are responsible for thermal properties or thermal effects in the transistor. Thermal conductivity is mainly responsible for the rate of heat dissipation, while heat capacitance is the main parameter that determines the resulting temperature in the channel at a given heat generation or dissipation rate. Therefore, in order to study the transistor immunity to degradation effects, such as the DIBL effect and the self-heating effect, the combined influence of materials surrounding the channel on the above effects must be considered. In this context, this work considers the SHE and DIBL effects for 2D MoS\({}_{2}\)-based MOSFETs with the most commonly used oxide materials, such as SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), and HfO\({}_{2}\) as gate oxide and back oxide (BOX) in different combinations to compare them. Due to the small dielectric constant, SiO\({}_{2}\) is not considered as a gate oxide in this work.  
The rest of the paper is organized as follows. Section 2 describes both the simulation conditions and the parameterization of the transistor. The simulation results are presented in Section 3 and discussed thereafter. Finally, concluding remarks are formulated in Section 4.