esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Influencing Factors
### Effect of Receiving Angle  
Keeping all other conditions the same, Figure 10 shows the inversion results of profiles C6-C5 before grouting for the unrestricted receiving angle of, from left to right, 60\({}^{\circ}\), 45\({}^{\circ}\), and 30\({}^{\circ}\), respectively. As can be seen from the figure, when the receiving angle was unrestricted, although the data information was most comprehensive at this time, the larger receiving angle indicated the longer path of elastic wave propagation and more error information; when the receiving angle was 60\({}^{\circ}\), the two typical stratigraphic interfaces of 15 m and 33 m could be roughly judged. Additionally, the cloud map did not express the other stratigraphic interfaces well, and most of the area did not show the stratigraphic characteristics. This indicates that the receiving angle was still too large, which contains more error information. The inversion result was better when the receiving angle was 45\({}^{\circ}\), which can better delineate most stratigraphic interfaces compared to the previous geotechnical investigation data. When the receiving angle was 30\({}^{\circ}\), although it can reflect the main stratigraphic information, the delineation of strata was too simple and the information contained was relatively rough. Therefore, it is more reasonable to set the receiving angle to 45\({}^{\circ}\), which can utilize enough elastic wave information and effectively reduce the errors in the information.
###### Abstract  
The Zhuang ethnic minority in China possesses its own ethnic language and no ethnic script. Cultural exchange and transmission encounter hurdles as the Zhuang rely exclusively on oral communication. An online cloud-based platform was required to enhance linguistic communication. First, a database of 200 h of annotated Zhuang speech was created by collecting standard Zhuang speeches and improving database quality by removing transcription inconsistencies and text normalization. Second, SAformerNet, a more efficient and accurate transformer-based automatic speech recognition (ASR) network, is achieved by inserting additional downsampling modules. Subsequently, a Neural Machine Translation (NMT) model for translating Zhuang into other languages is constructed by fine-tuning the BART model and corpus filtering strategy. Finally, for the network's responsiveness to real-world needs, edge-computing techniques are applied to relieve network bandwidth pressure. An edge-computing private cloud system based on FPGA acceleration is proposed to improve model operation efficiency. Experiments show that the most critical metric of the system, model accuracy, is above 93%, and inference time is reduced by 29%. The computational delay for multi-head self-attention (MHSA) and feed-forward network (FFN) modules has been reduced by 7.1 and 1.9 times, respectively, and terminal response time is accelerated by 20% on average. Generally, the scheme provides a prototype tool for small-scale Zhuang remote natural language tasks in mountainous areas.  
automatic speech recognition; natural language processing; neural machine translation; transformer; cloud edge computing; network programming Article
# GCARe: Mitigating Subgroup Unfairness in Graph Condensation through Adversarial Regularization
## 5 Experiments
### Main Results and Analysis
#### 5.2.1 Fairness  
The results from all four datasets demonstrated that GCARe can generate condensed graphs that accommodate fair GNN training. Note that all the fairness metrics reported here are better the lower they are.  
Impressively, our technique outperforms both GCond and DosCond across all datasets. When evaluating degree bias, the data from the Cora and Ogbn-arxiv datasets revealed that GCARe achieves lower \(\Delta_{acc}\) and \(\sigma_{acc}\) compared to both GCond and DosCond. Similarly, in terms of attribute bias, GCARe improves the \(\Delta_{SP}\) and \(\Delta_{EO}\) from the Credit-defaulter and Recidivism datasets. It is worth noting that when our method is combined with GCond (GCond + Ours), the \(\Delta_{SP}\) value was reduced from 4.47 to 3.98, and the \(\Delta_{EO}\) decreased from 3.40 to 2.52 on the Credit-defaulter dataset. This equates to improvements of 10.96% and 25.88%, respectively. Variants utilizing statistical parity regularization also exhibit significant improvements over GCond and DosCond, although they are generally not as effective as GCARe. These findings underscore the ability of our method to effectively enhance the fairness of GNNs that are trained on condensed graphs.
# Multi-Tier Heterogeneous Beam Management for Future Indoor FSO Networks
## 3 MTH Beam Management Protocol
### Multi-Tier Beam Refinement  
A high-level depiction of the beam refinement and link selection protocol for a single UD is shown in Figure 3. The three-tier beam refinement begins with the UD connecting to the RFSC and progresses through the course resolution directional tier (e.g., mmWave) and the fine-resolution indoor FSO tier. Prior to beginning the acquisition process for a higher resolution tier, the UD assesses its current mobility traits and application requirements (e.g., reliability, security, etc.) in order to make a decision about moving to the next tier. For example, a quasi-static UD that is currently in motion is likely to postpone transferring to a higher resolution tier. Similarly, a UD running a latency-constrained application may opt to remain in Tier 2 if the Tier 3 connection has intermittent outages--even if the link capacity is higher in Tier 3. Outages are more likely in Tier 3 due to the resolution of the FSO link and the fact that optical wireless communications have a higher potential for occlusions.
###### Abstract  
Logging continues to rank among the most lethal occupations in the United States. Though the hazards associated with fatalities are well-documented and safe distances from hazards is a common theme in safety education, positional relationships between workers and hazards have not been quantified previously. Using GNSS-RF (Global Navigation Satellite System-Radio Frequency) transponders that allow real-time monitoring of personnel, we collected positioning data for rigging crew workers and three common cable logging hazards: a log loader, skyline carriage, and snag. We summarized distances between all ground workers and each hazard on three active operations and estimated the proportion of time crew occupied higher-risk areas, as represented by geofences. We then assessed the extent to which positioning error associated with different stand conditions affected perceived worker safety status by applying error sampled in a separate, controlled field experiment to the operational data. Root mean squared error was estimated at 11.08 m in mature stands and 3.37 m in clearcuts. Simulated error expected for mature stands altered safety status in six of nine treatment combinations, whereas error expected for clearcuts affected only one. Our results show that canopy-associated GNSS error affects real-time geofence safety applications when using single-constellation American Global Positioning System transponders.  
GNSS-RF; GPS; GNSS positioning error; logging safety; geofences +
Footnote †: journal: _forests_  
_Article_
## 2 Methods for Determination of Lipophilicity
### Indirect Experimental Determination of Lipophilicity
#### 2.3.2 Reversed-Phase High-Performance Liquid Chromatography  
Reversed-phase high-performance liquid chromatography (RP-HPLC) is the most widely used indirect method to experimentally determine the lipophilicity [58; 64] and the standard procedure for measuring log P is officially recommended by OECD [30]. The wide use of RP-HPLC is justified by the advances in the elucidation of solute interactions, the availability of well-characterized stationary phases, and the ubiquitous presence of automated HPLC systems [2]. Table 1 provides a list of reported RP-HPLC methods to illustrate the versatility and large applicability of this technique, including for each example the aim of the study, the compounds under analysis, and the chromatographic method and conditions used. In addition, Table 1 also describes other indirect methods coupled with LC, which will be discussed in the following subchapters. The bibliographic research of assembling Table 1 was conducted using Web of Science and Scopus without any temporal restriction. The keywords used were \"lipophilicity AND chromatography\", \"lipophilicity AND chromatography\", \"partition AND reversed-phase high-pressure liquid chromatography, \"micellar liquid chromatography\", and \"immobilized artificial membrane\". Figure 3: Octyl (**a**) and octadecyl (**b**) silica-based stationary phases. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Analytes** & **Aim** & **Chromatographic Method** & **Chromatographic Conditions** & **Ref.
# Experimental Study on the Strength and Durability for Slag Cement Mortar with Bentonite
## 2 Materials and Proportioning Design
### Proportional Design of Specimens  
The base materials of the mixture of cement mortar were similar to that of a traditional cement mortar, which includes Type-I cement, sand as the fine aggregate, and blast-furnace slag. All materials other than the sand, namely, cement, slag, and bentonite, are binding materials that are hydrated with water. The water-binder (w/b) ratios were designed as 0.445, 0.485, and 0.550 and the corresponding proportion of the fine aggregate, sand remained the same. Due to the fact that bentonite has good moisture absorption capacity, the proposed water-binder ratio was based on a range above the water-binder ratio of a standard proportion.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{6}{c}{**Physical Properties**} \\ \cline{2-7}  & **Specific** & **Sieve \#325** & **Specific Gravity** & \multicolumn{2}{c}{**Reactive Index (\%)**} & **Air Content** \\  & **Surface** & **(Retained \%)** & & **7-day** & **28-day** & **(\%)** \\ \hline Cement \({}^{1}\) & - & 5.90 & 3.14 & - & - & - \\ Slag & 440 & 1.10 & 2.89 & 88.1 & 119.2 & 3.23 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the slag and cement used in this study.
# Multi-View Multi-Attention Graph Neural Network for Traffic Flow Forecasting
## 4 Experiments
### Experimental Settings  
All figures were generated and executed on a desktop computer with an AMD Ryzen 7-5800H with Radeon Graphics, and an NVIDIA GeForce RTX 3070. The model presented in this paper was deployed and experimented with on Google Colaboratory using Pytorch version 1.12.0. We use two layers of GCN layer in the multi-graph convolution module, the number of heads in the temporal self-attention module was set to 3, the number of heads in the spatial self-attention module was set to 2, and we set the kernel size in the temporal convolution module to 3. Dropout = 0.2 was set up in both the multi-graph convolution module and the spatial-temporal self-attention module. In addition, the historical time step was set to [5,15], and the batch-size was set (4, 8, 16, 32, 64, 128, 256) in this experiment; the testing results are shown in Figures 4 and 5. Notably, during the training phase, the model was trained with 200 iterations and an early stop was set as 40 to preserve the optimal model, with a learning rate of \(1\times 10^{-3}\). MAE was used as a loss function for the training process, and the optimizer used Adam optimization. We divided the Hangzhou datasets according to the rules of 70% training, 10% validation, and 20% testing. The result evaluation was conducted after the predicted results were rescaled to their original scale range.
## 2 Background and Related Work  
This section describes the background and related work of the simulation-based V2X security evaluation methods. First, we will describe the background of the analysis and security evaluation methods for the SAE J2735 standard, which defines the message sets used for V2X communication. We also analyze the major V2X simulations suggested by academia and industry and present problems in performing security evaluations in conventional simulations.
# Study of the Process of Packet Arrival at a Multiservice Node
## 2 Mathematical Model of a Multiservice Node
### Three Numerical Examples
They are shown in Table 3. As in the previous table, the point where the last reading is located is denoted by the letter \(m\). Both tables illustrate the fact that the last reading remains an important quantity for assessing the CDF \(A(t)\). It cannot be neglected. The correctness of replacing several readings located at a distance from their main mass by the value of the histogram located at the \(m\tau\) point requires a detailed discussion. Another very important conclusion that can be drawn from the data of both tables is that, for small values of the last reading, \(A^{(1)}\) increases not so noticeably, but \(C_{A}\) increases faster. Another important issue should be the definition of that range of values of the studied distribution, \(t>t_{x}\), which can be considered as a tail. If the CDF \(A(t)\) is obtained as a result of measurements, then it is not difficult to set the value of the sought function at this point, which is denoted below as \(R_{x}\), for the value of \(t_{x}\). In mathematical statistics, the correlation of a part of the distribution of a random variable with the concept of \"tail\" is carried out subjectively. Let us take the following definition as an initial hypothesis: the tail is the part of the distribution that includes all values of \(t>t_{x}\), for which \(A(t)>R_{x}\), along the abscissa axis. The \(R_{x}\) threshold is usually 0.90, 0.95, or 0.99. If the distribution under investigation is given analytically, then the value of \(t_{x}\) is usually found numerically by solving the equation \(A(t_{x})>R_{x}\). In rare cases, the value of \(t_{x}\) can be found explicitly after solving this equation.
# CNN-BiLSTM-DNN-Based Modulation Recognition Algorithm at Low SNR
## 3 AMR Framework
### Cnn  
As a feedforward neural network, the CNN [17] is composed of multi-layer neural networks, and its neurons can respond to the partial coverage of surrounding units, which has obvious advantages in local feature extraction. Convolutional layers and pooling layers make up the majority of CNNs. The core component of the CNN, the convolutional layer, uses a convolutional filter to extract the spatial aspects of the signal from the input data to learn the features and convolve with the learnable kernel. The following is a representation of the convolution operation:  
\[X(i,j)=\sum_{m}\sum_{n}x(m+i,n+i)\omega(m,n)+b \tag{4}\]  
where \(X(i,j)\) represents the convolution output, \(x\) is the input matrix, \(\omega\) is the weight matrix of size \(m\times n\), and \(b\) is the bias.  
Figure 1: CNN-BiLSTM-DNN model frame diagram.  
Two 1D convolutional layers (Conv2 and Conv3) and three 2D convolutional layers (Conv1, Conv4, and Conv5) make up the spatial feature extraction module, which refines features for BiLSTM by reducing noise and abstracting input data at a higher level. Initially, the I/Q multiplex signal is preprocessed and split into distinct I-channel and Q-channel data streams. These three data streams are then individually processed by Conv1, Conv2, and Conv3 to capture both multi-channel and single-channel characteristics of the I/Q signal. To maintain data integrity during modeling, Conv2 and Conv3 utilize zero padding. The outputs are then combined in Concatenate2 before being fed into Conv5 for spatial feature extraction. This multi-channel input structure effectively captures representation features at various scales and maximizes the utilization of information from I-channel, Q-channel, and I/Q multi-channel data.
# Evaluating the Capacity Coordination in the Urban Multimodal Transport Network
## 2 Methodology  
### Evaluation Process of Capacity Coordination  
The evaluation process of the capacity coordination in an urban multimodal transport network consists of the following steps.  
* Step 1. Obtain the information of urban multimodal transport network (transport facilities, transit operation information, etc.).
* Step 2. Construct the network topology of urban multimodal transport.
* Step 3. Establish the impedance function to calculate the impedance of links and paths.
* Step 4. Quantify the passenger flow of each sub-transport network using SUE assignment model.
* Step 5. Evaluate the capacity coordination via the resulting passenger distribution and the evaluation functions.
## 2 Materials and Methods
### Experimental Indicators and Methods
#### 2.2.1 Phenological Monitoring  
Growth stages were quantified using the Zadoks decimal scale [29], with stage transitions defined as the Julian date when 50% of plants reached each developmental milestone: emergence (VE), first square (V5), first flower (R1), first open bolt (R4), and harvest maturity (R8).  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline
**Treatment** & **Total Fertilizer** & **Seedling Stage** & **Bud Stage** & **Boll Stage 1** & **Boll Stage 2** & **Boll Stage 3** & **Boll Stage 4** & **Boll Stage 5** \\ \hline T1\_2023 & 253.35 & 16.84 & 34.57 & 54.72 & 87.56 & 23.87 & 23.87 & 11.92 \\ T2\_2023 & 228.02 & 15.16 & 31.12 & 49.25 & 78.80 & 21.48 & 21.48 & 10.72 \\ T3\_2023 & 202.68 & 13.47 & 27.66 & 43.78 & 70.05 & 19.09 & 19.09 & 9.53 \\ T1\_2024 & 253.35 & 16.84 & 34.57 & 54.72 & 87.56 & 23.87 & 23.87 & 11.92 \\ T2\_2024 & 228.02 & 15.16 & 31.12 & 49.25 & 78.80 & 21.48 & 21.48 & 10.72 \\ T3\_2024 & 202.68 & 13.47 & 27.66 & 43.78 & 70.05 & 19.09 & 19.09 & 9.53 \\ \hline \end{tabular}  
* Notes: (1) Treatment nomenclature: T1–T3 represent different fertilizer gradients (high, medium, low, respectively), with swiftes indicating experimental years (2023–2024). (2) Growth stage definitions: Seedling stage (emergence to 5 leaves), Bud stage (6–9 leaves to first flower), Boll stages 1–5 correspond to sequential poll development phases from flowering cessation to bull maturation. (3) Identical application rates in 2023–2024 reflect experimental controls for interannual comparison. (4) Fertilizer splits align with critical growth phases: 6.7% at seedling, 13.6% at bud, and 79.7% distributed across five poll stages (21.6% at Boll 1, 34.6% at Boll 2, with decreasing allocations thereafter). (5) Gradient design follows progressive reduction patterns: T2 (10% reduction) and T3 (20% reduction) relative to conventional practice (T1). (6) Application timing references cotton nutrient uptake patterns documented in [27] and split fertilization strategies in [28].  
\end{table}
Table 3: Detailed arrangement of cotton fertilization gradient experiments (Unit: kg/ha).
## 1 Introduction
In addition to the consideration of generation redispatch, additional useful mechanisms, such as energy storage and demand response strategies, have been added into this work, which makes the overall defense planning more comprehensive. The proposed algorithm is applied to actual isolated island power systems and the developed software is established in the dispatch center. In other words, the proposed algorithm should be easily applied to an actual system and the equations must be presented in a form where they are easily understandable. Nonetheless, the developed system has been applied to Penghu Island. In an Island system or micro grid, the penetration of a single online generating unit is high. Therefore, once a generator trips offline, the frequency drop will be a critical problem. That is, maintaining the frequency stability in such a system is significant. Several works have discussed this issue [22, 23, 24, 25, 26]. However, few research works consider various control methods, such as GR, BESS, DLC and EDRP, simultaneously. Additionally, few works calculated the economics value for each control method to identify the priority of various defense strategies. This work contributes to developing a complete underfrequency control strategy that includes four different frequency support methods to ensure system security and minimal operating cost. This paper is organized as follows. Section 2 introduces the proposed underfrequency preventive control strategy for an island or micro-grid power system. Section 3 describes the establishment of the database utilized in the control strategy. Section 4 describes the models that are used in the control strategy, including the frequency nadir forecasting module, frequency support capability forecasting module and their formulation relationship. Section 5 analyzes the cost for different frequency support methods. Section 6 introduces the testing system. Section 7 examines the accuracy of the forecasting modules. Section 8 presents the results of cost analysis. Section 9 analyzes the capacity requirements of different frequency support methods. Finally, Section 10 draws the conclusion.
## 5 Discussion
### The Effects of CSP and EH in Energy Services
#### 5.1.2 The Influences of CSP and EH on Renewables Curtailment  
Figure 8 represents the effects of CSP and EH on the renewable energy curtailment considering different probabilistic scenarios. Figure 8a shows the capacity of generators in different penetrations, and the bar graphs in each penetration represent Strategy 1 to Strategy 3 successively. Wind power is 10% of the total capacity, CSP and PV share the same proportion, and EH is 50% of CSP capacity. Figure 8b illustrates the renewable energy curtailment rate in corresponding penetrations. The power spillage includes the surplus wind power, PV power and CSP power. In three strategies, the dotted line is the weighted average of renewable energy curtailment in \(N_{s}\) typical scenarios, where the weight coefficient can be determined through scenario probability. The solid line in each strategy represents the maximum value of renewable energy curtailment in \(N_{s}\) typical scenarios.  
Comparing Strategy 1 with Strategy 2, it is clear that the average and maximum curtailment are significantly reduced owing to CSP station. For instance, when the renewables penetration is 20% in Strategy 1 (10% wind power and 10% PV), the curtailment reaches up to 36% (maximum) and 7.5% (average). However, when the renewable energy provides an additional 10% of the system energy in Strategy 2 (10% wind power, 10% PV and 10% CSP), the actual curtailment has dropped to less than 10% instead. Besides, CSP could effectively extend the feasible range of renewable energy penetration. In Strategy 1, without the CSP plant, 40% renewables penetration is the estimated upper limit of the scheduling model. In Strategy 2, although with the increasing renewables capacity, the ratio of renewable energy curtailment rises as well, the flexibility of the CSP makes the large-scale renewable energy penetration possible.  
When an EH is added to the CSP plant, the operation of EH to reuse the surplus electricity contributes to the further curtailment reduction of renewables. Even with 90% penetration, the curtailment could be controlled under 30% (maximum). Therefore, the incorporation of a CSP with TES/EH could realize the 100% renewables-dominated power system.  
Figure 8: Capacity and curtailment of renewable energy assuming an equal mix of PV and CSP in three strategies, including (**a**) the capacity of generators in different penetrations; (**b**) the renewable energy curtailment rate in different penetrations.