esa-sceva/satcom-chunk-collection huggingface dataset:
# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
## 4 FOXS-GSC Performance Evaluation
### Network Evaluation  
#### 4.1.1 Methodology  
This evaluation considered an area of 1 km\({}^{2}\) from New York City, USA. The scenario configuration used vehicle densities of 50, 150, and 500 vehicles/km\({}^{2}\). We used one RSU to obtain partial scenario coverage, allowing us to evaluate the efficiency of the proposed multi-hop solution.  
The network parameters for all simulations were set to 18 Mbit/s at the MAC layer and a transmission power of 2.2 mW, resulting in coverage of approximately 300 m under a two-ray ground propagation model [36]. In the case where the simulation is stable (i.e., all vehicles are in the scenario), all vehicles periodically send a message to the nearest cloudlet; when a cloudlet receives a message, it sends a reply to the vehicle that sent the message. To prevent all vehicles from sending messages at the same time, a period is added using a random variable. This experiment simulates the behavior of the proposed service when a route is requested from the cloudlet, which then responds with a suggested route.  
Table 3 shows the simulation parameters and values used in our evaluation.  
The following metrics were considered to evaluate the performance of the proposed multi-hop data communication protocol:  
* Total number of packets re-transmitted: displays the number of retransmissions required to reach the destination.
* Number of collisions per vehicle: the total number of collisions per vehicle in the system.
* Receive coverage: the percentage of vehicles that receive an answer at least once during the simulation time.
* Percentage of messages received: the percentage of messages sent that were answered.
## 3 Results
### Feature Selection  
From the wide variety of available features, each study makes a selection of features to use for their model. Consumer-grade wearables differ in the amount and sophistication of sensors they can provide, which is the first limiting factor researchers encounter in which features they select. However, sometimes, pre-existing hypotheses about the underlying mechanisms of depression also influence feature selection. For example, [PERSON] et al. [50] and [PERSON] et al. [55] chose to look specifically at features involving the circadian rhythm. Alternatively, data-driven approaches can be applied for selection. An often necessary first step in this process is to remove features that are heavily correlated with each other and thus provide redundant information [57; 58; 59]. Another data-driven approach is to create several model iterations using different selections of features and choosing the selection that results in the best prediction accuracy. This method was used, for example, by [PERSON] et al. [52], to choose how many features to include in their model. After identifying the features with the highest importance, they created models using different numbers of features (top 5, top 10, etc.). As determined by cross-validation, the model using the top10 features performed best, letting them predict symptom severity with up to 82% accuracy. Similarly, [PERSON] et al. [60] made models using different groups of features (smartphone only, wearable only, or both) to estimate HDRS scores. They then used two cross-validation approaches. The first simulated the tracking of single patients by using the first weeks for training and the last weeks for testing. The second focused on generalizability, splitting the participants into a training and test group. They found the smartphone-only model to be more accurate for the more personalized approach, whereas the wearable-only model allowed for better generalizability.  
It is important to also realize how feature selection can lead to overfitting when conducted incorrectly. For example, [PERSON] et al. [54] first classified their population as either depressed or not depressed, then compared these two groups and selected the features that were most predictive for classifying individuals. Since it is not reported otherwise, it must be assumed they made this comparison without first splitting the data into a training and test group. While these features may be particularly relevant within this population, it is impossible to know whether this would be the same in a new and unseen population. In a similar manner, [PERSON] et al. [61] explicitly state they based their feature selection on the statistical associations found over the entire sample. This again greatly compromises generalizability to new populations.
# A Deep Evolution Policy-Based Approach for RIS-Enhanced Communication System
## 3 Algorithm Description  
### Overview of DRL  
In a typical RL process, the agent incrementally determines the optimal action through interactions with the environment. The agent can observe instant rewards and state transitions in the environment by executing actions. Specifically, RL can be formulated as an MDP, which encompasses several fundamental components that comprehensively characterize the RL learning process: the state, the action, and the instant reward.  
**State**: observations of the environment. The state \(s^{(t)}\in\mathcal{S}\) denotes the observation at the time step t.  
**Action**: choices of the agent. The agent takes one action step by step during the learning process. Once the agent takes an action \(a^{(t)}\in\mathcal{A}\) at time instant \(t\) following a policy \(\pi\), the state of the environment will transit from the current state \(s^{(t)}\) to the next state \(s^{(t+1)}\).  
**Reward**: return of the action. By taking action \(a^{(t)}\) in a given state \(s^{(t)}\), the agent will obtain a reward \(r^{(t)}\), which is a performance metric to evaluate how good the action is.  
The MDP can be represented as a tuple \((\mathcal{S},\mathcal{A},P,\gamma)\), where \(P\) is the transition dynamics such that \(s^{(t+1)},r\sim P(s^{(t)},a^{(t)})\) and \(\gamma\in[0,1]\) is a discount factor which prioritizes the short-term rewards. The purpose of RL is to find the optimal policy \(\pi\) that can maximize the total reward defined by \(V_{t}=\sum_{i=0}^{\infty}\gamma^{i}r_{i+1}\). The policy can be regarded as stochastic \(\pi:\mathcal{S}\to p(\mathcal{A})\) or deterministic \(\pi:\mathcal{S}\to\mathcal{A}\). By adopting the Q-function as the state-action value function, the Bellman equation can be expressed as follows:  
\[\mathcal{Q}^{\pi}(s^{(t)},a^{(t)})=\mathbb{E}_{r^{(t)},s^{(t+1)}\sim P_{\mathbf{ \beta}}a^{(t+1)}\sim\pi}[r^{(t)}+\gamma Q^{\pi}(s^{(t+1)},a^{(t+1)})], \tag{4}\]  
where \(a^{(t+1)}\) is the action selected by the policy on the next state \(s^{(t+1)}\).  
Figure 1: The considered RIS-aided MU-MISO communication system comprised of an \(M\)-antenna BS simultaneously serving in the downlink \(K\) single-antenna users, and the transmit signal propagates to the users via the RIS assistance.  
In a typical DRL, the critic is usually approximated by a deep neural network \(Q_{\theta}\), where \(\theta\) is the parameters of the DNN. For training \(Q_{\theta}\), a temporal-difference (TD) algorithm is adopted to minimize the loss \(J(\theta)\) with TD error \(\delta\), which is the difference between the output of \(Q_{\theta}\) and the learning target \(y\).  
\[y^{(t)} \triangleq r^{(t)}+\gamma Q_{\theta^{(t+1)}}(s^{(t+1)},a^{(t+1)}), \tag{5}\] \[\delta \triangleq y^{(t)}-Q_{\theta}^{(t)}(s^{(t)},a^{(t)}),\] (6) \[\theta \leftarrow\theta-\eta\
abla_{\theta}J(\theta), \tag{7}\]  
where \(J(\theta)=|\delta|^{2}\), \(\
abla_{\theta}J(\theta)\) is the gradient of the loss \(J(\theta)\) with respect to \(\theta\), and \(\eta\) is the learning rate.
###### Abstract  
Effectively classifying each pixel in an image is an important research topic in semantic segmentation. The Existing methods typically require the network to directly generate a feature map of the same size as the original image and classify each pixel, which makes it difficult for the network to fully leverage the representations from the backbone. To handle this challenge, this paper proposes a method named mask classification combined with an adaptive dilated convolution network (MCDCNet). Firstly, a Vision Transformer (ViT)-based module is employed to capture contextual features as the backbone. Secondly, the Spatial Extraction Module (SEM) is proposed to extract multi-scale spatial information through adaptive dilated convolution while preserving the original feature size. This spatial information is then integrated into the corresponding contextual features to enhance the representation. Finally, a novel inference process is proposed that incorporates the instance activation map (IAM)-based decoder for semantic segmentation, thereby enhancing the network's capability to capture and comprehend semantic features. The experimental results demonstrate that our network significantly outperforms other per-pixel classification networks across several semantic segmentation datasets. In particular, on Cityscapes, MCDCNet achieves 80.3 mIoU with 11.8 M Params, demonstrating that the network is able to deliver a strong segmentation performance while maintaining a relatively low parameter count.  
semantic segmentation; mask classification; dilated convolution; deep learning Article
## 6 Results and Discussion
Slightly lower \(\rm V_{s}\) values were obtained with the equations suggested by [PERSON] et al. [54] and [PERSON] and [PERSON] [29] for 32 < \(\rm N_{60}\). Furthermore, the equations obtained by [PERSON] et al. [1] and [PERSON] et al. [7] provided similar results to the \(\rm V_{s}\) values obtained in this study for SPT-N blow counts less than 10 (Figure 13). The error criteria given in Table 3 were used to measure the estimation performance of the empirical correlations proposed in this study. The error criteria used were the mean square error (MSE), root-mean-square error (RMSE), mean absolute percent error (MAPE), and mean absolute error (MAE). The low results for the error criteria MSE, RMSE, MAPE, and MAE show a high performance, with the error results inversely proportional to the performance [57] (Table 4). MAPE is one of the most popular measures used to predict accuracy [58]. It measures the average magnitude of error produced by a model, or how far off predictions are on average. A MAPE value of 16% means that the average absolute percentage difference between the predictions and the actual values is 16% for the uncorrected \(\rm V_{S}\)-SPT-N correlation. The amount of deviation for the corrected \(\rm V_{S}\)-SPT-N(\(\rm N_{60}\)) correlation was found to be 20%. \begin{table}
\begin{tabular}{c c} \hline \hline
**Error Criteria** & **Error Formula** \\ \hline MSE & \(MSE=\frac{\sum_{s}\left(m_{i}-p_{i}\right)}{n}\) \\ RMSE & \(RMSE=\sqrt{\frac{\sum_{s}\left(m_{i}-p_{i}\right)^{2}}{n}}\) \\ \(\rm MAPE\) & \(MAPE=\frac{\sum_{s}\left(\frac{m_{i}-p_{i}}{n}\right)}{n}\times 100\) \\ MAE & \(MAE=\frac{\sum_{s}\left(m_{i}-p_{i}\right)}{n}\) \\ \hline \hline \end{tabular} Where \(m_{i}\) is the measured value, \(p_{i}\) is the predicted value, \(\overline{m}\) is the mean of the measured values, and \(n\) is the number of data in the database.  
\end{table} 
Table 3: The error criteria used in the performance measurement. Figure 13: Comparison of the correlations suggested in this study and those from previous studies for \(\rm V_{s}\) and SPT-\(\rm N_{60}\)[1; 7; 29; 54].
###### Abstract  
The Patras Gulf Pockmark field is located in shallow waters offshore Patras City (Greece) and is considered one of the most spectacular and best-documented fluid seepage activities in the Ionian Sea. The field has been under investigation since 1996, though surveying was partially sparse and fragmentary. This paper provides a complete mapping of the field and generates new knowledge regarding the fluid escape structures, the fluid pathways, their origin and the link with seismic activity. For this, data sets were acquired utilising high-resolution marine remote sensing techniques, including multibeam chosounders, side-scan sonars, sub-bottom profilers and remotely operated vehicles, and laboratory techniques focusing on the chemical composition of the escaping fluids. The examined morphometric parameters and spatial distribution patterns of the pockmarks are directly linked to tectonic structures. Acoustic anomalies related to the presence of gas in sediments and in the water column document the activity of the field at present and in the past. Methane is the main component of the fluids and is of microbial origin. Regional and local tectonsim, together with the Holocene sedimentary deposits, appear to be the main contributors to the growth of the field. The field preserves evidence that earthquake activity prompts the activation of the field.  
pockmarks; gas in marine sediments; gas seepage; earthquake; faults; microbial gas; Patras Gulf +
Footnote †: journal: Applied Sciences
## 4 Analyses of Numerical Simulation Results
### Compatibility of Negative-Curvature Ramp Under Off-Design Conditions
#### 4.2.1 Variation in Inlet Mach Number  
The total pressure loss coefficient distribution for the two cascades at a design static pressure ratio of 2.75 and different inlet Mach numbers is shown in Figure 20. It is clear that the overall aerodynamic performance of the improved cascade is superior to that of the baseline cascade at inlet Mach numbers ranging from 1.62 to 1.80, except for the range from 1.64 to 1.66. The analysis of the flow field characteristics of the two cascades based on the Mach number contours is shown in Figure 21. The shock impingement position at a designed inlet Mach number of 1.75 moves upstream or downstream with an increase or decrease in inlet flow velocity. At inlet \(M=1.64\), the first-passage shock impingement position moves to the smooth ramp profile, and the boundary layer separation of the improved cascade appears clearly. This can be attributed to the coupling between the compression wave generated by the ramp profile and the incident shockwave [24]. With a further decrease in inlet velocity, both cascades have an inlet Mach number of 1.62. The boundary layer separation caused by the normal shockwaves is as follows. The ramp section obstructs the evolution of separation fluids downstream within the boundary layer and reduces the flow loss in the improved cascade. On the contrary, the shock impingement position moves downstream to the plateau section at an inlet Mach number of 1.80. The local acceleration effect is formed on the plateau profile behind the compression waves, and the reflected shockwave is stronger, resulting in more shock loss.  
Figure 21: Mach number contours with different inlet Mach numbers.  
Figure 20: Variation in total pressure loss coefficients with different inlet Mach numbers.
## 4 Results
### Dynamic Feature Monitoring of Glacier Movement Based on Sentinel-1
#### 4.2.1 Preseismic  
Sentinel-1A images from 12 September 2021 to 26 August 2022 were selected to calculate the glacier surface flow deformation rate based on the POT to reveal the long-term slow deformation of glaciers not triggered by earthquakes. Figure 4 shows the preseismic POT daily average deformation rate results. Negative values indicate glacier movement in the opposite direction along the radar LOS. From the monitoring results, the range displacement distribution before the earthquake generally showed movement along the track direction. The accumulation area of Hailuogou Glacier, the ice tongue of Mozigou Glacier, and the trailing edge of Yanzigou Glacier were the most obvious. The maximum velocity of the glaciers along the track direction was 0.94 m/d, and the velocity of the glaciers against the track direction was approximately \(-\)0.1 m/d. The maximum value for the glaciers along the track direction was obtained for Hailuogou Glacier No. 1. The  
Figure 3: Coseismic surface displacements in the LOS direction for the 2022 Luding earthquake.  
detailed names of each part of Hailuogou Glacier No. 1 are shown in Figure 5. The velocity difference between the glacier accumulation area and the ablation area was significant. There are three areas with higher flow velocities in the firm basin, and the average velocity is approximately 0.8 m/d. The three glacier tributaries developed from the firm basin converge in the connection area between the ice waterfall and the top of the ice tongue, with a flow velocity of approximately 0.7 m/d. In recent years, because of global warming, the melting of glaciers has accelerated, and their thickness has continued to decrease. The ice waterfall and ice tongue of Hailuogou Glacier No. 1 have been completely disconnected, forming a cliff area with a height difference of approximately 1000 m. The glaciers accumulated in the relatively wide and gentle firn basin concentrate downward from all directions and gather at the narrow and long valley section at the top of the ice waterfall. Due to the steep slope of the terrain and the exposed front edge of the glacier, the flow velocity here increases significantly. [PERSON] et al. used 38 PALSAR-1/2 images collected from 2007 to 2018 and POT to obtain glacier deformation velocities and found three areas with higher transport velocities in the firn basin of Hailuogou Glacier No. 1, which are consistent with the range obtained in this study.  
Figure 4: **(a)** The surface velocity of the Mt. Gongga glacier from 12 September 2021 to 26 August 2022 in the radar line-of-sight direction; **(b)** localized surface velocity of Yanzigou Glacier; **(c)** localized surface velocity of Mozigou Glacier; **(d)** localized surface velocity of Hailuogou Glacier No. 1.
## 3 Evaluation Experiments
### Objective Evaluation Experiments on Sound Image Perception  
In this experiment, we measured the DRR to evaluate the sound image perception, because the DRR is an important cue for human auditory distance perception [23]. The DRR can be calculated as  
\[\text{DRR}_{\text{pt}}(\mathbf{r})=10\log_{10}\left(\sum_{n=0}^{N_{\text{D}}-1}o_{ \text{pt},\mathbf{r}}^{2}[n]\right)-10\log_{10}\left(\sum_{n=N_{\text{D}}}^{N_{ \text{R}}-1}o_{\text{pt},\mathbf{r}}^{2}[n]\right), \tag{32}\]  
where \(N_{\text{D}}\) denotes the duration of the direct sound and \(N_{\text{R}}\) denotes the duration of the observed sound until the reverberation decays by 60 dB. Here, we set the duration of the direct sound as \(10^{-3}\) s; therefore, we have \(N_{\text{D}}=96\) samples. As in the evaluation on sound pressure in Section 3.2, we evaluated the transition of the DRR along the \(y\)-axis. The results are shown in Figure 11. Here, same as Figure 6, the gray zones in Figure 11 indicate the positions nnear to the real sound sources in 'Real' cases.  
For quantitative evaluation, the slope of the DRR is defined as  
\[\Delta\text{DRR}_{\text{pt}}(y)=\text{DRR}_{\text{pt}}((0,y))-\text{DRR}_{\text {pt}}((0,y-\Delta y)). \tag{33}\]  
The error in DRR (EDRR) is defined as the root mean square error for the slope of the DRR, which can be given as  
\[\text{EDRR}_{\text{pt}}=\sqrt{\frac{1}{N_{y}}\sum_{y\in Y}\left(\Delta\text{ DRR}_{\text{pt}}(y)-\Delta\text{DRR}_{\text{Real}}(y)\right)^{2}}, \tag{34}\]  
where \(N_{y}\) denotes the number of observation points and \(\mathbf{Y}\) denotes the group of \(y\) for evaluation, which are defined as same as those in Section 3.2. A smaller value of EDRR suggests better distance perception over the entire sound field.  
The results on EDRR are given in Table 5. Values in bold font indicate the best performance among the comparison patterns. Table 5 shows that the proposed method achieves the smallest error of DRR slope overall. Moreover, the error does not change significantly when the VSS position changes; therefore, it can be concluded that the performance of the proposed method is robust against the position of VSSs.  
Figure 11: Results on direct-to-reverberant ratio (DRR) transition along \(y\)-axis.
# Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN
## 5 Conclusions  
This study attempted to obtain an accurate spectral model for evaluation of CLQ based on the GPP spectral indicator at four important growth stages of late rice phenology by comparison of PLSR, SVR, and GA-BPNN models using the measurements of CLQ from 294 training samples and the corresponding GPP data from MOD17 products. This study was conducted in the Zengcheng and Conghua district of Guangzhou City and led to the following conclusions: (1) The downscaled 30 m spatial resolution MODIS GPP data by the EBK interpolation with NRMSE of 1.59% were more reliable than the original 500 m resolution MODIS GPP products with NRMSE of 7.18%; (2) The GA-BPNN spectral model showed the strongest prediction ability for CLQ (RMSE = 60.39) compared to the PLSR and SVM models, indicating the existence of a nonlinear relationship of CLQ with GPP spectral indicators; (3) The NRMSE values of CLQ predictions from the GA-BPNN model for two validation areas were relatively small (12.14% and 18.39%), further implying that the GA-BPNN model based on rice phenological data could be applied to accurately mapping CLQ at the regional scale. This study is the first report to provide an effective means for CLQ evaluation using a crop growth stage GPP-driven spectral model with GA-BPNN.  
[PERSON] conceived and designed the experiments; [PERSON] and [PERSON] analyzed the data, created the tables and figures, and finished the first version of the paper; [PERSON], [PERSON], [PERSON], and [PERSON] contributed valuable opinions during the manuscript writing; [PERSON], [PERSON], and [PERSON] revised the whole manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (U1901601); National Key Research and Development Program of China (2018 YFD1100103); Guangdong Province Agricultural Science and Technology Innovation and Promotion Project (No. 2019 KJ102); and Guangdong Provincial Science and Technology Project of China (2017B030314155).  
We gratefully acknowledge the paper writing assistance of [PERSON] and [PERSON], as well as the experimental assistance of [PERSON] and [PERSON].  
The authors declare no conflict of interest.
## 4 System modeling
### Partial least square regression (PLSR)
#### 4.1.3 Linear discrimination analysis (LDA)  
LDA is a linear classifier that expects to find the best separating line or plane between two groups of samples. Figure 4 shows a schematic LDA based separation of two groups. Assuming a sensor output vector is **x**, a general equation for LDA can be expressed as:  
\[f(\textbf{x})=\textbf{w}^{T}\textbf{x}+w_{0} \tag{3}\]  
where **w** is a weight vector and \(w_{0}\) is a constant (threshold value). The decision rule for a two-class problem is based on the positivity or negativity of the function value of _f_(**x**).  
The key for designing a LDA classifier is to obtain the best separation line or plane (i.e., the best **w** and \(w_{0}\)) from the training samples. Fisher discrimination [56] is the most canonical strategy for finding the best parameters **w** and \(w_{0}\). Given the between- and within-covariance matrices ( \(S_{B}\) and \(S_{W}\) ) of two sample groups, Fisher discrimination is to maximize the ratio of the between-to-within variability through the following optimization problem.  
\[\max J(\textbf{w})=\frac{\textbf{w}^{T}S_{B}\textbf{w}}{\textbf{w}^{T}S_{W} \textbf{w}} \tag{4}\]  
Figure 4: A schematic illustration of a two-class linear separation.  
The above classification strategy can be extended to a multiple-class separation problem. Stepwise linear discrimination functions are a feasible tool for such an extension. LDA has also been extensively used in E-nose applications such as wine classification [57] and green tea grade identification [58].