esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Optimization of Sand Control Methods in Hydrate Reservoirs
### Evaluation and Optimization of Sand Control Methods  
The common sand control methods and approaches in the oil and gas industry are divided into three categories: mechanical, chemical and composite sand control. The mechanical mainly refers to suspending the pipe string and auxiliary tools with sand filtering and functions in the sand-producing layer of the wellbore. The pipe string with sand filtering and functions can allow fluids to pass through, but can prevent the formation of sand. It is mainly divided into two categories. One type does not require fillers, and only the sand control strings such as wire-wrapped screen pipes, slotted liner pipes, double-layer or multi-layer screen pipes need to be run into the wellbore. Since the slots and pores of the sand control pipe string are easily plugged by fine sand particles produced from the reservoirs, the sand control effect is not good. The second category is mechanical sand control with fillers. That is, in accordance with the sand control target, the targeted fillers are added to avoid the blockage of pores or slots. It is the most widely used mechanical sand control process at present. Chemical sand control refers to the injection of chemical agents into the formation without running mechanical devices. Through the reaction of  
Figure 1: Schematic diagram of the sand production mechanism of the argillaceous fine silt gas hydrate reservoirs.  
chemical agents, some substances are generated to stabilize the formation or form an artificial wellbore with a certain strength and permeability, and to achieve the purpose of preventing sand production with the formation's flowing sand consolidated.  
The composite sand control refers to the combination of mechanical and chemical methods. It requires not only the development of pipe strings with excellent performance but also the availability of materials to various approaches. It has a high degree of innovation, complex processes, and huge difficulties, but the effect is relatively ideal. In view of the sand production mechanisms and characteristics of the argillaceous fine silt gas hydrate analyzed previously, and taking into account the maturity of the technology, the application and promotion of the sand control screen pipe, it is recommended to select the mechanical sand control method, which is relatively applicable for sand control in argillaceous fine silt gas hydrate reservoirs.
# A Self-Scrutinized Backoff Mechanism for IEEE 802.11 ax in 5G Unlicensed Networks
## 5 Performance Evaluation
### Normalized Throughput and Average Delay  
Figure (a)a describes the normalized throughput for various numbers of WEs in an indoor HEW system. A monotonic decrease can be observed for the performance of BEB and EIED. The reason  
for performance degradation of BEB is resetting its contention window value to minimum value after successful transmission, which causes higher collision with the increases in the number of WEs. In spite of increased throughput, the performance of EIED also degrades with the increase of WEs due to a blind decrease of the contention window. In the figure, ECA performs better until \(n<15\), where the number of contenders is less than the deterministic cycle length \(W_{min}/2\) due to the collision-free deterministic environment. In the beginning, COSB also has a curved performance (increase and decrease) in normalized throughput. The curved performance of COSB shows that after observing the channel, it adjusts the contention window adaptively and results in increased normalized throughput. The performance degradation of COSB is slower than the compared protocols when the number of the WE increases. However, COSB provides the best throughput (Figure 6a) and average delay (Figure 6b) in a high density network as compared to the BEB, ECA and EIED. ECA has the best performance at low density, but the throughput decreases drastically in high density. COSB has acceptable performance at low density. This performance enhancement of COSB comes from the adaptive channel observation-based scaling of W. Figure 6 shows that the analytical model is accurate because analytical results (COSB-ana) match with the simulation results (COSB-sim) in both normalized throughput (Figure 6a) and average delay (Figure 6b).
## 4 Optimization Method
### Case Study Descriptions and Simulation Results  
The test system is the IEEE 30-bus test system, which has 6 generation units, 4 transformers, and 41 branches. The initial settings of the generators' voltage magnitude and transformers tap settings are obtained from [30]. Figure 1 shows the single line diagram of the IEEE-30-bus test system. Also, both the output active and reactive power of generators are set according to [41]. The loads' data and line data are available in [42]. It is assumed that there is not any VAR source in the case study.  
To allocate appropriately the VAR compensation devices, firstly, the \(L\)-index should be determined for the load buses. Then, the load buses with high values of \(L\)-index are taken into account as the candidate buses for the VAR compensation devices installation. It is observed that after implementing the proposed methodology, the load at bus 24, bus 25, bus 26, bus 29, and bus 30 obtain the higher values of \(L\)-index than the other load buses. As a result, the VAR compensator buses are found. After the allocation of VAR compensation devices, it is supposed that the capacities of the VAR compensators can be set to zero. The initial conditions of the system considering the full-load and 1-year planning are stated in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Generator Voltage Magnitude**} \\ \hline \(V_{g1}\) (p.u.) & 1.050 \\ \(V_{g2}\) (p.u.) & 1.044 \\ \(V_{g5}\) (p.u.) & 1.023 \\ \(V_{g8}\) (p.u.) & 1.025 \\ \(V_{g11}\) (p.u.) & 1.050 \\ \(V_{g13}\) (p.u.) & 1.050 \\ \hline \multicolumn{1}{c}{**Transformer Tap Settings**} \\ \hline \(t_{b-9}\) (p.u.) & 0.950 \\ \(t_{b-10}\) (p.u.) & 1.100 \\ \(t_{b-12}\) (p.u.) & 1.025 \\ \(t_{b2-27}\) (p.u.) & 1.050 \\ \hline \multicolumn{3}{c}{**VAR Compensator**} \\ \hline \(\Omega_{24}\) (MVAR) & 0.000 \\ \(\Omega_{25}\) (MVAR) & 0.000 \\ \(\Omega_{26}\) (MVAR) & 0.000 \\ \(\Omega_{27}\) (MVAR) & 0.000 \\ \(\Omega_{28}\) (MVAR) & 0.000 \\ \hline \multicolumn{3}{c}{**Objective**} \\ \hline \(P_{\text{loss}}\) (MW) & 5.4970 \\ \(f_{1}\) (S) & \(2.8892\times 10^{6}\) \\ \(f_{2}\) & 0.1635 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Control variables and objectives under initial conditions.  
Figure 1: Single line diagram of the IEEE-30 bus test system [43].  
The control variables limits are expressed in Table 3. The per-unit energy cost is equal to 0.06 ($/h) [3], the fixed installation cost (\(e_{i}\)) for all VAR sources is 1000 ($), and the variable installation cost (\(C_{Ci}\)) for all VAR sources is 3.0 ($/kVAR) [44].  
The voltage magnitude at the load buses, which are considered as the state variables, must be limited between 0.95 (p.u.) and 1.05 (p.u.). To show the effectiveness of the proposed method, two deterministic and three probabilistic cases are considered in the following subsections.
## 2 Formulation of the Hybrid Method  
As shown in Figure 1, the whole solution domain consists of two parts: the exact solution region (DGTD region) and the asymptotic domain (TDPO region). In the first region, a multi-scale and multi-object complex region is defined. As a result of the complex structure and requirements of higher precision in the first region, the DGTD method is adopted. TDPO is used to calculate the scattering field generated by asymptotic region itself and the transient excitation of DGTD region in the second region, which includes an electrically large plate or rough sea surface.  
The whole computational domain is illuminated through a transient source \(\overset{\rightarrow}{E}_{\text{inc}}(r,t)\). The scattered field of whole region in far-field area is defined as \(\overset{\rightarrow}{E}_{\text{total}}(r,t)\), and approximately given by the following components:  
\[\overset{\rightarrow}{E}_{\text{total}}^{\text{scat}}(r,t)\cong\overset{ \rightarrow}{E}_{\text{DGTD}}(r,t)+\overset{\rightarrow}{E}_{\text{TDPO}}^{ \text{scat}}(r,t)+\overset{\rightarrow}{E}_{\text{DGTD}\rightarrow\text{ TDPO}}^{\text{scat}}(r,t) \tag{1}\]  
The first two terms \(\overset{\rightarrow}{E}_{\text{DGTD}}(r,t)\) and \(\overset{\rightarrow}{E}_{\text{TDPO}}^{\text{scat}}(r,t)\) are the primary scattered fields generated from DGTD region and TDPO region, respectively. Nevertheless, both terms do not contain the multiple reflections between two regions. These interactions are included in secondary scattered fields denoted as \(\overset{\rightarrow}{E}_{\text{DGTD}\rightarrow\text{TDPO}}^{\text{scat}}(r,t)\) and \(\overset{\rightarrow}{E}_{\text{TDPO}\rightarrow\text{DGTD}}^{\text{scat}}(r,t)\). \(\overset{\rightarrow}{E}_{\text{DGTD}\rightarrow\text{TDPO}}^{\text{scat}}(r,t)\) represents the scattered field through the asymptotic region when illuminated through the field generated by the DGTD region. Meanwhile, \(\overset{\rightarrow}{E}_{\text{TDPO}\rightarrow\text{DGTD}}^{\text{scat}}(r,t)\) is defined as the inverse process of \(\overset{\rightarrow}{E}_{\text{DGTD}\rightarrow\text{TDPO}}^{\text{scat}}(r,t)\). Additionally, we assume that the asymptotic region is significantly larger than the DGTD region. At the same time, because  
Figure 1: Combined contributions of the two regions.  
with the increase of the distance between two regions, the calculation accuracy will be improved [16], the distance between the two regions has to be large enough.
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 4 Network Resource Allocation Algorithm Design for the HSN  
In this paper, we propose a network scheduling strategy that leverages traffic types to optimize resource allocation. Our approach involves a systematic classification of traffic, considering factors such as the generation rate, time sensitivity, and frequency. We then employ multi-prioritization and timeout preemption policies to determine the execution order of traffic. Furthermore, we introduce an evaluation algorithm that combines traffic requirements with the dynamic operational status of the network to select the most suitable transmission channel for each traffic. By adopting this scheduling approach, our goal is to enhance traffic quality and improve the user experience by efficiently allocating network resources based on the specific demands of different traffic types.
## 3 Proposed Method  
In this section, each part of the proposed SSD7-FFAM in Figure 2 is illustrated in detail. First, we introduce the specific structure of the entire network in Section 3.1. Then, the feature fusion module is described in Section 3.2. Next, we illustrate the attention module combined with feature fusion in Section 3.3. Immediately afterward, Section 3.4 presents the prediction layers. Finally, in Section 3.5, the loss function during training is explained.  
Figure 2: The specific structure of the proposed lightweight real-time object detection network SSD7-Feature Fusion and Attention Mechanism (FFAM) using a feature fusion and attention mechanism. Two feature maps from different convolutional layers firstly pass the feature fusion module to form a new feature map and then pass the attention module to generate a feature map for prediction and input to the prediction layers.
# Intelligent Data-Enabled Task Offloading for Vehicular Fog Computing
## 4 Proposed Technique
### Performance Metrics  
The following four types of error score indicators are used as performance metrics for the evaluation of the ANN prediction model. These include root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute bias error (MABE) and the coefficient of determination (\(R^{2}\)). The following equations describe the mathematical formulae used to evaluate these four types of errors:  
\[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{(Z_{i}-Z_{i}^{\prime})^{2}}}} \tag{18}\]  
\[MAPE=\frac{1}{n}{\sum\limits_{i=1}^{n}{\frac{|Z_{i}-Z_{i}^{\prime}|}{Z_{i}}}} \times 100 \tag{19}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ANN Model** & **Features Used** & **Data Range** \\ \hline \multirow{3}{*}{Computation Available Time} & Vehicle speed & 30–120 km/h \\ \cline{2-3}  & Distance between source and computing vehicles & 5–100 m \\ \cline{2-3}  & Channel gain & Rayleigh distribution \\ \hline \multirow{3}{*}{Link Connectivity Time} & Number of tasks & Poisson distribution \\ \cline{2-3}  & Task size & 0.5 MB-16 MB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of the dataset generated to train the ANN model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Task size & 0.5 MB–16 MB \\ \hline \(C_{b}\) of vehicles’ CPUs & 500 cycles/bit \\ \hline \(C_{b}\) of fog node’s CPU & 2000 cycles/bit \\ \hline Frequency \(C\) of vehicles’ CPUs & 2G cycles/s \\ \hline Frequency \(C\) of fog node’s CPU & 8G cycles/s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters and values used.  
\[MABE=\sum_{i=1}^{n}\frac{|Z_{i}-Z_{i}^{\prime}|}{n} \tag{20}\] \[R^{2}=1-\frac{\sum_{i=1}^{n}\left(Z_{i}-Z_{i}^{\prime}\right)^{2}}{ \sum_{i=1}^{n}Z_{i}} \tag{21}\]  
where \(n\) is the total number of dataset samples and \(Z_{i}\) and \(Z_{i}^{\prime}\) are the actual and estimated values, respectively.  
RMSE is the first indicator employed to examine model performance, which has a value of zero in the ideal case and positive or non-zero values in non-ideal cases. MAPE is another indicator of the accuracy of ANN models and has low values for accurate models. Similarly, the MABE error score can be used to evaluate the precision of ANN models. Lastly, \(R^{2}\) shows how the fitness of the model and its value lie in the range of \(0-1\), with \(1\) being an indicator of the most accurate models.
# Underground Mine Road Detection Using Deep Learning Technique
## Materials and Methods
### STDC Backbone Network
#### 2.1.3 STDC Network  
STDCMNet (short term dense concatenate network) is a lightweight semantic segmentation network proposed by [PERSON], which is an upgrade and improvement based on BiSeNet. STDCNet has two main contributions. On the one hand, it improves the backbone of the backbone network and changes the structure of the dense concatenate module. In the same STDC module, the number of channels output by each ConvX gradually decreases with the increase in the receptive field, and finally concatenate together, so it contains more feature scale information. On the other hand, it is a multi-branch low-level detail information assisted training structure. The detail information guidance structure is only used during training. After the network training is completed, it can be directly discarded. This method can reduce the amount of calculation during reasoning compared to the previous BiSeNnet. The network structure organized from the perspective of network output is shown in Figure 5.  
As shown in Figure 6, excluding the input and output layers, the STDC network consists of 6 stages. Stages 1 to 5 downsample the input spatial resolution with a step size of 2, and the final feature map size is \(1/32\) of the input image. Stage 6 outputs the prediction logic using ConvX, a global average pooling layer, and two fully connected layers. Stages 1 and 2 use only one convolutional block to extract features, thus increasing efficiency.
# Voice Pathology Detection and Classification Using Convolutional Neural Network Model
## 3 Proposed Methodology
### Proposed Method
Following the proposed training methodology, experiments were performed using k-folds cross validation samples of SVD voice pathology as a ResNet34 training sample, although the reset samples were used for the testing. In the course of the training, 20 percent of the training set was chosen randomly and used as a validator set to test the model's general capacity and store the configuration of weights that gave the validation set the minimum error rate. The best model (based on hyper-parameters) used in the proposed Voice Pathology Detection System can be found in Table 2. To summarize, the principal steps in the proposed approach for training are as follows:  
1. Divide the SVD dataset into three separate sets: training, test and validation set. 2. Select initial hyper parameter values (e.g., learning rate, dynamic, and so on). 3. Train the ResNet34 utilizing the training group and the hyper-parameters group in step 2. 4. The validation set is used to evaluate ResNet34 performance during the training phase. 5. Reiterate phases 3 to 4 for 12 epochs. 6. Choose the best candidate typical with minimum validation error rate. 7. Through utilizing the test group, the best-trained model is identified. \begin{table}
\begin{tabular}{l c} \hline \hline
**Layer Name** & **ResNet34** \\ \hline Conv-1 & \(5\times 5\), 128 stride 2 \\ \hline Pool-1 & \(2\times 2\) Max-Pooling stride 2 \\ \hline Step-1 & \(\left[\begin{array}{cc}2\times 2,&32\\ 2\times 2,&32\end{array}\right]\times 2\) \\ \hline Step -2 & \(\left[\begin{array}{cc}2\times 2,&64\\ 2\times 2,&64\end{array}\right]\times 5\) \\ \hline Step -3 & \(\left[\begin{array}{cc}2\times 2,&128\\ 2\times 2,&128\end{array}\right]\times 8\) \\ \hline Step -4 & \(\left[\begin{array}{cc}2\times 2,&256\\ 2\times 2,&256\end{array}\right]\times 2\) \\ \hline Pool-2 & Average pooling, stride 1 \\ \hline Fc & 2-d, Softmax \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The ResNet34 architecture factors. Figure 2: The difference in a building block: (**a**) A regular block, and (**b**) A residual block.
## 3 Results and Discussions
### Analysis of Room Energy Consumption  
This research aims to find out the influence of room and opening parameters on room energy consumption. However, in the 25 schemes, the sizes of the rooms are different. So, room energy consumption per square meter was finally chosen as the analysis indicator.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Item** & **Level** & **Factor A** & **Factor B** & **Factor C** & **Factor D** \\ \hline \multirow{6}{*}{\(K\)*} & 1 & 48.12 & 54.92 & 53.67 & 53.51 \\  & 2 & 50.80 & 53.73 & 53.12 & 52.86 \\  & 3 & 53.05 & 52.52 & 52.57 & 52.63 \\  & 4 & 54.77 & 51.47 & 52.14 & 52.13 \\  & 5 & 56.36 & 50.46 & 51.60 & 51.98 \\  & 1 & 9.62 & 10.98 & 10.73 & 10.70 \\  & 2 & 10.16 & 10.75 & 10.62 & 10.57 \\ \(\overline{K}\)* & 3 & 10.61 & 10.50 & 10.51 & 10.53 \\  & 4 & 10.95 & 10.29 & 10.43 & 10.43 \\  & 5 & 11.27 & 10.09 & 10.32 & 10.40 \\  & \multicolumn{1}{c}{\(R\)-value *} & 1.65 & 0.89 & 0.41 & 0.31 \\ Rank & \multicolumn{5}{c}{A \textgreater{} B \textgreater{} C \textgreater{} D} \\ Optimal Level * & 5 & 1 & 1 & 1 \\ Optimal combination & \multicolumn{5}{c}{A\({}_{5}\)B\({}_{1}\)C\({}_{1}\)D\({}_{1}\)} \\ \hline \hline \end{tabular}  
* \(K\): The sum of the test results corresponding to a certain factor and a certain level,
* \(\overline{K}\): The average value of \(K\),
* \(R\)-value: The extreme value of the factor. The calculation method of this value is the maximum value of \(K\) minus the minimum value of \(K\), which can be combined with the extreme value of the factors to sort the factors,
* Optimal Level: The level number corresponding to the optimal \(\overline{K}\) value of a factor.  
\end{table}
Table 6: Range analysis of indoor air temperature.  
The room energy consumption for each scheme is simulated by EnergyPlus. The room energy consumption per square meter of each experimental combination is calculated according to the area of the room. After that, the 25 results are analyzed using ANOVA and range analysis, and each factor's influence degree on the energy consumption of the room and the optimal combination of factors are obtained.
## 5 Experimental Setup
### Metrics of Interest  
Based on previous works in the literature [12; 13; 14], we have used the following metrics in our performance evaluations:  
* **Delivery ratio--**it can be defined as the ratio of messages received by the destination nodes to those generated by the source nodes.
* **Average delivery delay--**it can be defined as the average time interval between the sending and receiving events for messages travelling along the network.
* **Average cost--**it can be defined as the average number of forwarding events per message delivered to the destination.
# Modeling a Wave Energy Harvester for Supplying Data Buoys
## 3 Results
### The Harvester Prototype
A high-speed camera was used to record videos of the moving cylinder, from whose frames it was possible to trace the time evolution of the cylinder's position. As an example, a frame acquired by the camera is shown in Figure 10. The time evolutions of the cylinder position in three cases are reported in Figure 11a,c,e. The parameters used for these tests are also reported in Figure 5 to highlight their relative position with respect to the boundary line between bang-bang and non-bang-bang regions of operation. It should be noted that the boundary line reported in Figure 5 for \(L=200\) mm and traced according to Equation (21) is able to correctly predict the mode of operation of the harvester. The mode of operation can also be easily deduced by measuring the voltage generated by the coils. In fact, in bang-bang operation, the voltage has a quite periodic and regular trend, whereas in different modes of operation, the voltage is highly irregular. This is the motivation for the practical interest in the bang-bang operation. The measured voltages of two pairs of coils for three parameter sets are reported in Figure 11b,d,f. The parameters used are reported also in Figure 5. Also, in these cases, Equation (21) and the boundary line in Figure 5 for \(L=200\) mm are able to correctly predict the mode of operation of the considered harvester. Figure 9: **(a,b)** Dynamics of the wave energy harvester in case \(\alpha_{M}=20^{\circ}\) and \(T=1.2\) s leading to \(L<L_{T/2,0}\). **(c,d)** Dynamics of the wave energy harvester in case \(\alpha_{M}=20^{\circ}\) and \(T=0.9\) s leading to \(L_{T/2,M}<L\). Results of numerical integration of the dynamics of Equations (4)–(8).
# Multimodal Natural Disaster Scene Recognition with Integrated Large Model and Mamba
## 3 Methods
### Overall Framework  
Conventional visual models typically establish a direct connection between ground-truth labels and raw image features. However, previous research has demonstrated that learning from multimodal information can significantly enhance models' performance, as seen in CLIP ([PERSON], et al., 2021) [33] and UNITER ([PERSON] et al., 2020) [34], both of which employ unified multimodal representations to tackle vision and language tasks. To leverage diverse modalities for image understanding, we utilize a popular pre-trained large model capable of comprehending and generating multimodal information, thereby incorporating new modalities beyond raw pixel data.  
Firstly, images and associated generation prompts are fed into Gemini1.5, which employs a question-answering approach to generate detailed textual descriptions and probability vectors for class predictions for each image in the dataset. Image features \(O_{I}\) are extracted using the image processing module (EfficientNet), text features \(O_{T}\) are derived via the text processing module (textCNN), and probability features \(O_{V}\) are obtained through the vector processing module (vecBlock). These three modality-specific features are concatenated along dimension 1 to form a multimodal feature tensor \(O\), which is subsequently aligned using the Mamba mechanism. The aligned multimodal tensor is flattened into a one-dimensional vector and concatenated with the original image features \(O_{I}\) to preserve image detail information. Finally, the fused features pass through a two-layer fully connected network, undergoing dimensionality reduction, activation, and classification to output predictions across 11 categories. Figure 1 presents the overall architecture of the proposed Mamba-MDRNet learning algorithm.