esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Methods
#### 3.4.1 MobileNet  
MobileNetV2, developed by Google, is a deep c convolutional neural network (DCNN) with enhanced performance and efficiency. The key feature of MobileNetV2 is the introduction of a new structure called inverted residuals [29]. Deep neural networks face the problem of destroying information in non-linear layers in convolutional blocks. The inverted residual bottleneck layers are used to address this problem by preserving all the useful information. The name \"MobileNet\" suggests its usability in mobile applications. The inverted residual bottleneck layers are particularly memory-efficient, which makes this the best fit for mobile applications.  
The initial convolutional layer of MobileNetV2 consists of 32 filters, followed by 19 bottleneck layers, resulting in an increase in the number of filters from 32 to 1280. In order to prevent overfitting, the model uses dropout and batch normalization during training and has a kernel size of 3 \(\times\) 3. The dimensions of the input images were resized to 224 \(\times\) 224 pixels before being passed to the network with the help of the image data generator class available in the Keras library. The pictorial overview of convolutional blocks in MobileNetV2 is presented in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Categories** & **Training** & **Testing** & **Validation** & **Total** \\ \hline Non-over-volume goods carriers & 839 & 106 & 104 & 1049 \\ \hline Over-volume goods carriers & 804 & 101 & 100 & 1005 \\ \hline Non-goods carriers & 800 & 100 & 100 & 1000 \\ \hline  & 2443 & 307 & 304 & 3054 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed description of the dataset consisting of three different classes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline Non-over-volume goods carrier & Over-volume goods carrier & Non-good carrier \\ \hline \hline \end{tabular}
\end{table}
Table 3: Preview of images belonging to their respective classes.
## 3 Results and Discussion
### Comparison between Commercial Blocks (AFCB and ACCB) and AFCB Developed with Agate Waste  
Table 4 shows the commercial blocks (AFCB and ACCB) and the AFCB of present study characteristics. The difference in the compressive strength between the blocks (commercial and AFCB with RP) is due to density. In the specific case of compressive strength and the density of AFCB, it is believed that production processes, grain size, type of cure, and, mainly, the foam production are factors decisive for its improvement.  
An economic viability estimate was made by analyzing the production cost of 1 m\({}^{3}\) of commercial AFCB and AFCB with RP. The Table 5 shows the comparative cost between commercial AFCB and AFCB with RP production. In this estimation, the same transportation, electrical energy, and labor costs were considered.  
The use of the agate wastes, completely replacing the natural sand, can generate an 18% saving in the AFCB fabrication. This result shows to be promising to use agate wastes in the development of building materials, but an optimization with respect to compressive strength should be performed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Blocks**} & **Compressive Strength (MPa)** & **Density** & **Description of Manufacturing Process** \\  & **Strength (MPa)** & **(kg/m\({}^{3}\))** & **Description of Manufacturing Process** \\ \hline \multirow{4}{*}{Commercial AFCB} & \multirow{4}{*}{2.14} & \multirow{4}{*}{830} & water/cement rate of 0.82; 25 kg of natural sand; 25 kg of cement Portland (V-ARI); 20 kg of water; \\  & & & The materials were added in a mixer and stirred for 10 min. Sequentially, 50 liters of foam were added \\  & & & and mixed for another 5 min. The concrete curing \\ \hline \multirow{2}{*}{Commercial ACCB} & \multirow{2}{*}{4.50} & \multirow{2}{*}{600} & Autoclave curing process. Other information not \\  & & & provided by the manufacturer. \\ \hline \multirow{2}{*}{Commercial ACCB} & \multirow{2}{*}{1.20} & \multirow{2}{*}{400} & Autoclave curing process. Other information not \\  & & & provided by the manufacturer. \\ \hline \multirow{4}{*}{AFCB with RP} & \multirow{4}{*}{1.07} & \multirow{4}{*}{430} & water/cement rate of 1.28; 500 g of RP; 500 g of \\  & & & cement Portland (V-ARI); 400 g of water; \\ \cline{1-1}  & & & The materials were added in a mixer and stirred for 10 min. Sequentially, 230 g of foam were added and \\ \cline{1-1}  & & & mixed for another 5 min. The concrete curing was of 28 days in open environment. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Characteristics of concrete blocks.
# Secure Transmission for Buffer-Aided Relay Networks in the Internet of Things
## 1 Introduction
further examined the maximization of the secrecy sum rate for the downlink IoT systems with a novel relay-aided secure transmission scheme [26]. In recent years, some works have studied the physical layer security of buffer-aided relay networks [27; 28; 29]. In [27], [PERSON] et al. proposed a novel max-ratio relay selection scheme to enhance the physical layer security for buffer-aided DF networks. For multi-relay multiple-input multiple-output (MIMO) cooperative networks, a buffer-aided joint transmit antenna and relay selection (JTARS) scheme was proposed in [28], and then the expression of the secrecy outage probability in closed-form was derived to assess the impact of different parameters on the secrecy performance. The closed-form expression of the secrecy outage probability was also derived in [29] to understand the secrecy performance of a buffer-aided underlay cognitive relay network. However, the secure transmission of buffer-aided relay network in IoT is an open issue to study. To the best of our knowledge, the design of secure transmission schemes for buffer-aided relay IoT networks has not been examined. Inspired by these observations above, we investigate the secure transmission for buffer-aided relay IoT networks. To enhance the secrecy performance of the considered system, we adopt the max-link relay selection policy and propose three secure transmission schemes. The main contributions of this paper are summarized as follows:  
* We propose three secure transmission schemes, i.e., non-jamming (NJ), source cooperative jamming (SCJ) and source cooperative jamming with optimal power allocation (SCJ-OPA), to enhance the secrecy performance for buffer-aided relay networks in IoT scenarios. * By modeling the dynamic buffer state transition as a Markov chain, we derive the closed-form expressions of the secrecy outage probability, the average secrecy throughput and the end-to-end delay under the non-colluding eavesdroppers (NCE) and colluding eavesdroppers (CE) scenarios, respectively. Based on these expressions, the impacts of different parameters on the secrecy performance can be evaluated effectively. * Our findings highlight that although the NJ and the SCJ schemes can achieve good secrecy performance when the total transmit power is small or large, respectively, the SCJ-OPA scheme outperforms the other two schemes across the whole transmit power range of interest, which can make up the defects of the other two schemes. Table 1 provides a list of the fundamental symbols throughout this paper. The remainder of the paper is organized as follows. In Section 2, we introduce the considered system model and the relay selection policy. Section 3 presents three transmission schemes. In Section 4, we investigate the several key performance metrics of the system, respectively. Section 5 provides simulation results. Finally, the conclusion is given in Section 6.
# Response of Grassland Vegetation Growth to Drought in Inner Mongolia of China from 2002 to 2020
## 4 Discussion
### NDVI, EVI, GOSIF, and GPP Response under Different Drought Severities  
This study also quantified the grassland NDVI, EVI, GOSIF, and GPP losses under different drought severity levels, and reductions varied with grassland type within the context of the same drought level, which is critical for identifying grasslands most sensitive to severe and extreme drought events (Table 2 and Figure 12). Our findings confirmed that NDVI, EVI, GOSIF, and GPP all exhibited a substantial decrease as drought severity increased, which is consistent with previous research [10; 25]. For different grassland types, TS ecosystems were the most vulnerable to severe and extreme droughts in Inner Mongolia. TS regions, primarily studied in the semi-arid region of central Inner Mongolia (Figure 1b),  
Figure 13: Reductions in NDVI, EVI, GOSIF, and GPP across TS in June (**a**) and July (**b**) 2017.  
displayed the highest reduction rates. This heightened vulnerability can be attributed to the unique characteristics of semi-arid ecosystems, which tend to exhibit the most pronounced physiological and functional responses to drought stress [67; 68]. These findings align with similar observations in other semi-arid regions, such as southeastern Australia and northern China [69; 70].  
The TSD ecosystems exhibited the second-largest reduction in response to drought. TSD regions are mostly found in the arid western part of Inner Mongolia (Figure 1). While TSD ecosystems have adapted to living in arid conditions, extreme or severe droughts can still have detrimental effects. These conditions can lead to structural changes and reduced photosynthesis, as observed in our study and supported by previous research [25; 67].
## 4 Case Study  
In this study, the proposed optimization configuration method for ESSs mainly consists of two parts. Firstly, a method is presented for deriving frequency stability based on the NFDC, which clearly indicates the distribution of inertia resources in the power system such as SGs and GFM-ESSs. Secondly, an optimization configuration method for ESSs considering the distribution of inertia resources is proposed. The focus of this study lies in identifying the weak links in the inertia level of the power system based on the NFDC and quantifying the inertia resource requirements of different nodes in the power system. This study verifies the effectiveness of the proposed stability index and analyzes the changes in the inertia distribution characteristics of the nodes in the power system under different operating scenarios. Aiming to minimize both the operation cost of generators in the power system and the construction cost of ESSs, an optimization configuration method for GFL-ESSs and GFM-ESSs has been devised.  
The NFDC evaluation method and the optimization configuration model utilize the CPLEX solver to conduct effective solution analysis. The configuration model is formulated as a linear programming model, and both the power flow constraints and inertia constraints of the power system have been linearized to ensure the accuracy and efficiency of the model solution.
## 3 Materials and Research Design
### Materials  
The source texts used in this study comprised simulated dialogs situated within a business context, depicting interactions between Chinese and British speakers. These dialogs were displayed onscreen and deliberately constructed to align with the study's research parameters. Both the English and Chinese texts were informal, conversational in tone, and free from poetic elements, complex sentence structures, or specialized terminology. The aim was to mirror authentic professional communication, thereby enhancing the relevance of the materials for translation tasks in business environments.  
The English text consisted of 125 words, while the Chinese version contained 151 words, with both comprising 9 sentences, labeled S1-S9 for analytical consistency. Each sentence was rigorously evaluated and aligned at multiple linguistic levels--including syllables, words, and sentence structures--to ensure cross-linguistic equivalence. Alignment criteria included word count, stylistic features, genre, sentence type and structure, sentence length, word frequency, lexical difficulty, and character or word length.  
This meticulous standardization of linguistic and contextual variables ensured equivalency between the two texts, thereby supporting valid comparisons in cognitive resource allocation during translation. Additionally, the simulated healthcare dialog incorporated into the design enhances the study's applicability, particularly in translation scenarios relevant to medical and professional interpretation contexts.
# Risk Assessment of UAV Cyber Range Based on Bayesian-Nash Equilibrium
## 6 Application and Analysis
In the Bayesian equilibrium method of the incomplete-information game introduced in this paper, the UAV cyber security's benefits can be greatly improved by about 70% only when the information demand of both sides is expanded from 1 to 2. However, when the information demand is further expanded, the security benefit is only improved by about 10% at most. Figure 5: Defense policy income average and threat value. In this paper, the uncertainty of information between attack and defense is increased, and the incomplete-information game is adopted to be closer to the actual UAV application scenario. FAHP can deal with the risk assessment of complex systems, especially in the multi-criteria decision problems. It allows decision-makers to consider the impact of multiple factors and is able to deal with ambiguous and uncertain information. Compared with the Minimax Theorem and Nash equilibrium, the attack time cost is higher. The Minimax Theorem can deal with the decision problem under incomplete information, and provides a framework for decision making under an uncertain environment. However, compared with FAHP, Nash equilibrium and the method in this paper, it is too conservative in decision-making and misses potential opportunities. Nash equilibrium is too idealized and may not exist or be difficult to achieve in actual situations. Therefore, this method introduces an incomplete-information game, optimizes Nash equilibrium method, and provides a specific threat value calculation formula and threat assessment algorithm steps, which can better complete UAV risk assessment. In this experiment, the attacker launched 20 attacks on the UAV flight system in the UAV cyber range, counted the results of the security benefit changes in the 20 attacks and defense games, and compared the security benefit results of the traditional method and the Bayesian game method with incomplete information. As can be seen from Figure 7, using the risk evaluation algorithm based on an incomplete-information game, the security benefits obtained by the UAV system and the attacker after 17 attacks are greatly improved, and the bargaining process of the attack and defense parties is simulated to better demonstrate the interaction between the attack and defense parties. Figure 6: Comparison of methods.
# Magnetic Properties of Nanocomposites
## 3 Polymer Nanocomposites
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Polymer Nanocomposites**} \\ \hline
**Nancomposite** & \(M_{s}\) **(emu/g)** & \(M_{g}\) **(emu/g)** & \(H_{s}\) **(0e)** & \(H_{s}\) **(0e)** & \(M_{s}\) **of Starting** & \\  & & & **[300 K]** & **[10 K]** & **Nanopowder (emu/g)** & \(T_{\text{m refer}}\) **(0)** \\ \hline Fe\({}_{3}\)O\({}_{4}\) nanocomp & 1.06 & 0 & 0 & 42 & 56 & 23 \\ \hline Fe\({}_{3}\)O\({}_{4}\)@silica nanocomp & 0.88 & 0 & 0 & 135 & 56 & 25 \\ \hline Fe\({}_{3}\)O\({}_{4}\)@fTiO\({}_{2}\) nanocomp & 2.84 & \(\text{H}_{//}:0.025\) & \(\text{H}_{//}:2\) & \(\text{H}_{//}:164\) & 58 & \(\text{H}_{//}:168\) \\  & & & \(\text{H}_{\perp}:0.085\) & \(\text{H}_{\perp}:3.5\) & \(\text{H}_{\perp}:188\) & & \(\text{H}_{\perp}:157\) \\ \hline \hline \multicolumn{6}{c}{**Ceramic Nanocomposites**} \\ \hline
**Nancomposite** & \(M_{s}\) **(emu/g)** & \(M_{g}\) **(emu/g)** & \(H_{s}\) **(0e)** & \(H_{s}\) **(0e)** & \(M_{s}\) **of Starting** & \\  & & & **[300 K]** & **[10 K]** & **Nanopowder (emu/g)** & \(T_{\text{m refer}}\) **(K)** \\ \hline CoFeA750C\(-2\,\text{h}\) & 1.14 & 0.30 & 147 & - & - & - \\ \hline CoFeA800C\(-2\,\text{h}\) & 0.71 & 0.16 & 97 & - & - & - \\ \hline FeA800C\(-0\,\text{h}\) & 1.64 & 0.12 & 174 & - & - & - \\ \hline FeA750C\(-2\,\text{h}\) & 9.08 & 0.59 & 150 & - & - & - \\ \hline (Fe,H)A800C\(-0\,\text{min}\) & 4.19 & 0.46 & 45 & - & - & - \\ \hline (Fe,H)A600C\(-90\,\text{min}\) & 12.27 & 2.02 & 375 & - & - & - \\ \hline NiA735C\(-12\,\text{min}\) & 3.01 & 0.74 & 97 & 419 & - & \textgreater{}300 K \\ \hline NiA750C\(-0\,\text{min}\) & 2.82 & 0.55 & 70 & 426 & - & \textgreater{}300 K \\ \hline NiX735C\(-12\,\text{min}\) & 3.66 & 0.45 & 42 & 373 & - & \textgreater{}300 K \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Room temperature values of saturation magnetization, remanent magnetization, and coercive field of typical polymer/ceramic nanocomposites; coercive field at low temperature; saturation magnetization of the embedded nanoparticles at room temperature; maximum temperature of the zero-field-cooled (ZFC) curve (close to the blocking temperature of magnetic nanoparticles). Each nanocomposite is identified by the label referred to in the text. Ceramic nanocomposites were not obtained starting from a magnetic nanopowder. resulting in a comparatively lower surface energy and a better compatibility with the surrounding polymer matrix. In fact, this trend is common to the majority of the analyzed systems, regardless of the reactive solvent used. Instead, thermal or UV curing play a minor role on the quality of the particle dispersion.
## 3 Results and Discussion  
The aim of the study was to evaluate the effectiveness of MLP- and BT-based models to predict the Co, Cu and Pb in the soil. Data used in this study were gathered from  
Figure 2: Flowchart of the work carried out in this paper.  
field data. Figure 3 shows the correlation matrix of the dataset. Phosphorus and pH are positively correlated with Cu, Co and Pb, while Ca negatively correlates with these metals. Mg showed a moderate correlation with Cu, Co and Pb. Organic carbon also exhibits a moderate negative relationship with these elements. Figure 4 indicates the 3D plot of pH and P versus Co, Cu and Pb, respectively, dependent upon a set of three-dimensional points. These plots depict the relationship of phosphorus and pH with concentrations of Cu, Co and Pb. The effect of absolute variable can be examined using distinct plotting colours for the individual value of each variable. With the increasing concentration of phosphorus, the Co content increases, while the relationship of pH with Co shows that the concentration enhances with increasing values of pH, and maximum increase takes place at a pH value of 7.0. The maximum increase in Cu concentration occurs at phosphorus content 0.10 (mg kg\({}^{-1}\)) and pH value of 7.3, while in Pb, maximum enhancement occurs at 0.20 (mg kg\({}^{-1}\)) value of phosphorus and 7.0 value pH. Correlation coefficient and RMSE were used to assess the performance of developed models. The dataset consisted of 67 observations of studied variables where we can observe high levels of available P, and low levels of studied PTEs (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Variables** & **Units** & **Mean** & **Minimum** & **Median** & **Maximum** & **SD** & **CV** & **Skewness** \\ \hline pH & 7.76 & 6.70 & 7.88 & 8.80 & 0.41 & 0.05 & \(-\)0.21 \\ C & \% & 3.66 & 1.78 & 3.51 & 6.70 & 0.99 & 0.27 & 0.80 \\ P & mg kg\({}^{-1}\) & 128.97 & 7.00 & 132.85 & 355.20 & 74.94 & 0.58 & 0.44 \\ Ca & meq 100g\({}^{-1}\) & 0.19 & 0.05 & 0.14 & 0.93 & 0.15 & 0.79 & 3.68 \\ Mg & meq 100g\({}^{-1}\) & 0.17 & 0.02 & 0.15 & 0.90 & 0.13 & 0.74 & 3.37 \\ Co & mg kg\({}^{-1}\) & 0.10 & 0.01 & 0.11 & 0.36 & 0.09 & 0.91 & 0.37 \\ Cu & mg kg\({}^{-1}\) & 0.51 & 0.01 & 0.19 & 12.79 & 1.99 & 3.89 & 5.77 \\ Pb & mg kg\({}^{-1}\) & 0.23 & 0.01 & 0.15 & 5.83 & 0.70 & 2.98 & 7.68 \\ \hline \end{tabular} SD, Standard Deviation; C, Organic carbon; P, Available Phosphorus.  
\end{table}
Table 1: Descriptive statistics of studied variables (n = 67).  
Figure 3: Correlation of input and target variables.
# Seismic Response Analysis of Buried Nuclear Power Plant Structures in Non-Bedrock Sites
## 1 Introduction
### Operating Conditions  
To fully study the influence of structure embedment depth on the seismic response of buried nuclear island structures, five embedment depths were designed under average soft soil conditions. These designs are shown in Table 2, Cases 1-5. Under liquefiable interlayer conditions, three embedment cases with the same depth were designed with varying liquefiable layer positions. These designs are shown in Table 3, Cases 6-8.  
Figure 5: Viscoelastic boundary schematic.  
The design embedment depth is determined as 37.2 m, where the containment structure is fully aligned with the ground surface. Five operational cases are defined. In the deep embedment case, the ground surface is leveled with the lowest elevation of the plant roof. The design embedment case requires the ground surface to coincide with the uppermost elevation of the containment outer shell. For the semi-buried case, the ground surface is matched to the highest elevation of the containment internal structure. In the exposed case, the ground surface is positioned at the top elevation of the raft foundation. A shallow embedment case is introduced by increasing the embedment depth by 12.2 m relative to the exposed case, as the depth discrepancy between the exposed and semi-buried cases is deemed significant. The geometric configurations of these cases are systematically illustrated in Figure 6.  
Three distinct liquefiable layer-structure interaction cases were designed for the LT equivalent site conditions. The liquefiable layer thickness was maintained at 13 m across all configurations. In the upper position case, the liquefiable layer top was located 12.1 m below ground surface. The middle position case positioned the layer top at 29.2 m depth, corresponding to the structural mid-height. For the lower position case, the layer top was placed at 37.2 m depth, aligned with the raft foundation base. The spatial relationships between liquefiable layers and structural components are graphically shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Cases**} & \multicolumn{2}{c}{**Embedment Depth of Structural**} & \multirow{2}{*}{**Case Names**} \\  & **Embedment Depth** & \\ \hline
1 & 43.2 m & Deep Embedment Case \\
2 & 37.2 m & Design Embedment Case \\
3 & 22.6 m & Semi-embedded Case \\
4 & 12.2 m & Shallow Embedment Case \\
5 & 2.2 m & Exposed Case \\
6 & 37.2 m & Upper Case \\
7 & 37.2 m & Middle Case \\
8 & 37.2 m & Exposed Case \\ \hline \hline \end{tabular}
\end{table}
Table 3: Case design under the influence of embedment depth.  
Figure 6: Schematic diagram of structural embedment conditions.