esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
However, most of these _SIF_ datasets have a coarse resolution of \(0.05^{\lx@math@degree}\) which is not sufficient for drought monitoring in smaller regions and at a fine scale, especially for agricultural applications. The main challenge to further resolution improvements of _SIF_ products is the lack of finer spatiotemporal satellite SIF. Moreover, existing downscaled _SIF_ or reconstructed _SIF_ have one or more issues that limit their applications. First, _SIF_ datasets are not updated frequently, making it difficult to obtain new SIF. Second, for some _SIF_ products (e.g., GOSIF), global samples are needed to train a universal global model, which is a model that uses all data for training without time and space stratification [1]. Third, the _SIF_ derived from very coarse satellites, such as GOME-2, is not suitable for generating very high-resolution downscaled SIF. Fourth, for local models, such as \(\overline{SIF}_{\text{coo2},005}\), it is impossible to train local models in small regions due to the sparse sampling strategy and long revisit cycle. Fortunately, TROPOMI provides an alternative _SIF_ data source (TROPOSIF) which has a spatially contiguous _SIF_ with a 16-day revisit and 7 km \(\times\) 3.5 km resolution at nadir (5.5 km \(\times\) 3.5 km since 2019) and nearly daily global coverage, making it an ideal data source for _SIF_ downscaling at a small regional scale and high spatial resolution. The idea of using time or space constraints to train a local model has been widely applied in LST downscaling studies [16; 17]. Some researchers have also introduced such an idea for _SIF_ downscaling [11; 14; 15]. For example, [PERSON] [14] built local models by stratifying biomes and times for training and predictions and showed that local models performed better than a universal model, but further discussion was not performed. The use of _SIF_ for drought monitoring has been widely explored [18; 19; 20; 21]. There is wide evidence that _SIF_ at coarse resolution (e.g., \(0.05^{\lx@math@degree}\)) is a promising drought indicator. However, using _SIF_ at finer spatial resolution (e.g., \(500\) m) for drought monitoring has not been widely explored because of the lack of high-resolution _SIF_ products. In this paper, we presented an operational downscaling method of TROPOSIF. We used TROPOSIF data and the Moderate Resolution Imaging Spectroradiometer (MODIS) data in the same area and date for training and prediction of local models. We then examined our local models' performance by comparing our downscaled TSIF with TROPOSIF. Moreover, we tested our local models' robustness against overfitting. To further validate our downscaled TSIF, we made a comparison with CASIF and Tower GPP in California. Finally, we applied our TSIF to monitor the Henan drought event in July 2019. Our results show that our method will benefit regional drought monitoring at a fine scale.
# GCN-Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting
## 2 Methodology
### Overall Framework  
Our goal is to make the model learn better representations by making full use of local features.  
In Figure 1, we can see how the GCN-Informer model is structured.  
The GCN-Informer has two parts, the encoder layer and the decoder layer. In the encoder layer, this paper use ProbSparse self-attention to simplify the way the attention is calculated. The specific implementation is described in detail in Section 2.2.1. In the next down-sampling part, the input sequence is reduced to the original 1/2 through the self-attention distilling mechanism, which greatly improves the efficiency. The specific implementation process will be described in detail in Section 2.2.2. The main task of the encoder layer is to obtain a hidden layer representation of the input data and to ensure consistent dimensions. As shown in Figure 2, the EncoderStack mentioned in the paper is actually composed of multiple encoder and distillation layers. Each horizontal stack represents a separate encoder module. The upper stack is the main stack, which receives the entire input sequence, while the second stack takes half of the input, and the second stack throws away a layer of self-attention to the number of distillation layers, making the output dimensions of the two stacks aligned. Finally, the feature map connecting the two stacks is used as the output of the encoder. In the decoder layer, this paper uses the GCN module to extract the characteristics of adjacent contacts and the use of multi-output output together to solve the efficiency of long sequence prediction. Detailed description of the embodiments will be described in detail in Sections 2.2.3 and 2.3.
## 2 Selected Wireless Sensor Networks Standards: State-of-the-art
### Network Elements  
WirelessHART networks are composed of different devices as illustrated in Fig. 4, including field devices, gateways, network and security managers. The _field devices_ are organized in either star or mesh topology. However, the star topology is not recommended by [18]. The _gateway_ is a bridge between the field device network and the host application. The gateway is configured by the _network manager_ using HART commands and allows buffering large sensor data, event notifications, diagnostics, and command  
\begin{table}
\begin{tabular}{c|c|c|c|c|c}  & WISA & WirelessHART & ISA100.11a & ZigBee & 802.15.4e MAC \\ \hline \hline PHY/MAC layers & 802.15.1-based & 802.15.4-based & 802.15.4-based & 802.15.4-based & 802.15.4-based \\ \hline NET/TRANS & unspecified & specified & specified & specified & unspecified \\ APP layers & & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Scope of the selected technologies.  
Figure 4: WirelessHART network elements [19].  
responses. In addition to the gateway configuration, the network manager configures the remaining devices and maintains the whole network. WirelessHART networks may include several network managers for redundancy reasons, however only one should be active at a time. The active network manager also schedules communication, manages routing tables, and monitors network health. In addition, the network manager can receive input from the host application and also queries the field devices about particular information via the gateway. The _security manager_ collaborates with the network manager and prevents intrusion and attacks against the network by generating session keys, joint keys and network keys. Each security manager can collaborate with several network managers responsible for the key distribution to the concerned field devices. Additionally to these devices required by the standard, _adapters_ and _handheld devices_ may be added. The adapters connect HART devices to WirelessHART networks, whereas handheld devices configure and maintain WirelessHART compliant devices.
# Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis
## 5 Discussion
### Support Vector Machine (SVM)  
The SVM model demonstrated a robust ability to distinguish between spam and non-spam instances, achieving an AUC of 0.91. This indicates a high probability that the model will rank a randomly chosen positive instance (spam) higher than a negative one (non-spam). The confusion matrix reveals a true positive (TP) rate of 37% and a true negative (TN) rate of 47%, suggesting balanced performance in identifying both classes. The SVM model performed better with a high AUC when a linear kernel was utilized, indicating good discrimination between spam and ham images.  
However, the false positive (FP) rate of 9% and false negative (FN) rate of 7% indicate that the model occasionally misclassifies non-spam as spam and misses some spam  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **TP (\%)** & **FN (\%)** & **TN (\%)** & **FP (\%)** & **AUC** & **Variance** & **95\% Conf. Int.** \\ \hline SVM & 37 & 7 & 47 & 9 & 0.91 & 0.000277 & [0.872, 0.939] \\ LR & 38 & 6 & 49 & 8 & 0.94 & 0.000171 & [0.909, 0.961] \\ XGBoost & 40 & 3 & 52 & 4 & 0.97 & 0.000049 & [0.960, 0.987] \\ LightGBM & 36 & 8 & 47 & 9 & 0.91 & 0.000215 & [0.880, 0.937] \\ ResNet50 & 42 & 1 & 53 & 3 & 0.99 & 0.000009 & [0.985, 0.997] \\ VGG16 & 42 & 1 & 44 & 13 & 0.93 & 0.000167 & [0.907, 0.956] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix components and AUC scores (as % of total dataset).  
Figure 12: Training and validation loss curves.  
instances. The number of false positives and false negatives suggest the model can be further optimized, though it demonstrated robust performance overall. The AUC variance of 0.000277 reflects that its performance is consistent.
# Feature Deep Continuous Aggregation for 3D Vehicle Detection
## 3 Approach
### Seven-Channel BEV Map  
Considering the disorder and sparsity of a point cloud, it cannot be directly processed by 2D convolution layers. Therefore, References [6; 7; 8; 19; 20; 21] project the raw point cloud as a BEV map to output the detection results using a 2D detector. In this paper, a seven-channel BEV map was generated, which contained five local height channels, one global height channel, and one density channel.  
There were about 120 K points in each frame of the point cloud, which is large and only some of them were in the area of the image plane. Therefore, the point cloud was first cropped to give a fixed region to ensure that the reserved points (about 20 K) were included in the field of view of the camera. Then, the reserved points were divided into \(m\times n\times k\) voxel grids with a 0.1-m resolution.  
Along the \(Z\)-axis, \(k\) voxel grid layers were divided into five equal slices to generate local height channels encoded with the maximum height in each voxel grid. To avoid destroying the local correlated between layers, every equal slice was globally normalized:\[BEV_{l}^{i}=\frac{P_{i}(h_{max})}{P(H)}\, \tag{1}\]  
where \(BEV_{l}^{i}\) represents the \(i\)**th** local height channel of the BEV map, \(P_{i}(h_{max})\) is the maximum height of points in the \(i\)**th** slice, and \(P(H)\) is the height of all selected point clouds.  
In References [6; 7], the global height feature of the point cloud was not considered. To solve this problem, a global height representation was generated and added into the BEV map as the sixth channel:  
\[BEV_{g}=\frac{P(h_{max})}{P(H)}, \tag{2}\]  
where \(P(h_{max})\) is the maximum height of points in each height pillar.  
The seventh BEV channel was a density channel that followed the procedure described in [PERSON] et al. [7]. The seven-channel BEV map is shown in Figure 2.
## 5 Limitations  
The present study, while shedding valuable light on the prevalence and patterns of knee injuries among youth male football players in Saudi Arabia, is subject to several limitations that warrant consideration. Firstly, the study's sample was drawn from specific regions of Saudi Arabia, potentially limiting the generalizability of its findings to the entire country. Given the diverse geographical and cultural landscape of Saudi Arabia, the injury patterns observed in this study may not fully represent those in other regions. Secondly, the focus exclusively on male youth football players restricts the applicability of the findings to other demographic groups, such as female players or individuals in different age brackets or skill levels. Consequently, the study's conclusions may not be directly extrapolated to these populations. Lastly, the study did not account for external factors that could influence injury patterns, such as changes in training methods, equipment, or coaching strategies over time. Considering these external factors could provide a more comprehensive understanding of the dynamics underlying knee injuries in youth football players. Addressing these limitations in future research endeavors would enhance the robustness and applicability of findings related to knee injuries among football players in Saudi Arabia. Future studies in this area could consider expanding the sample size to include a more diverse population from various regions of Saudi Arabia, thereby enhancing the generalizability of findings to the entire country. Additionally, investigating knee injury patterns among female football players and individuals across different age groups and skill levels would provide a more comprehensive understanding of the prevalence and risk factors associated with knee injuries in football. Moreover, longitudinal studies tracking changes in training methods, equipment, and coaching strategies over time could elucidate the impact of these external factors on injury patterns among youth football players in Saudi Arabia.
# Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling
## 4 Experimentation
### Ablation Experiment  
Taking YOLOv8 as the baseline model, the EB-YOLOv8 model is constructed by improving it. To more fully illustrate the effect of each structural change on the network properties, this paper conducts four sets of model ablation experiments on two datasets, including the original YOLOv8 model, the YOLOv8+E model, the YOLOv8+B model, and the EB-YOLOv8 model. The YOLOv8+E model adds EMA to the raw model; the YOLOv8+B model incorporates a new connection structure, Concat_BiFPN, into the network. The EB-YOLOv8 model is the integration of the YOLOv8-E model and the YOLOv8-B model. Tables 1 and 2 show the analytical results.  
From Table 1, it can be analyzed that the YOLOv8+E model increases the \(P\)-value by 3.6% and the \(mAP\) value by 0.4% in contrast to YOLOv8 model; the YOLOv8+B model increases the \(P\)-value by 1.9% and the \(mAP\)-value by 0.8% by integrating the BiFPN model with the Concat module compared to the YOLOv8 model; the EB-YOLOv8 model integrates YOLOv8+E with YOLOv8-B and the overall detection effect is 1.9% and 0.8%, respectively. The EB-YOLOv8 model integrates YOLOv8+E and YOLOv8+B, with an enhancement of 1.9% in terms of the \(mAP\) value. The EB-YOLOv8 model, comprising the integration of YOLOv8+E and YOLOv8+B, exhibits an enhancement in the overall detection effect, with an increase in the \(P\)-value from 68.8% to 77.5%. Since the higher the \(mAP\), the lower the \(R\), the \(R\) value changes from 74.6% to 73.8%, the \(mAP\) value increases from 77.1% to 80.2%, except for in Pa-type defects, whose accuracy remained unchanged, and In-type defects, which decreased by only 0.7%. The accuracy of all other defect types improved with varying degrees, especially Rs-type defects, which improved by 13.8%. This fully demonstrates the effectiveness of the EB-YOLOv8 model.  
On the SSDD dataset (Table 2), under conditions of uneven defect type distribution, large data volume, and more complex lighting distribution, the \(P\)-values of both the YOLOv8+E model and the YOLOv8+B model increased compared to the original model, indicating improved reliability in model predictions. The \(mAP\) value of YOLOv8+E rose by 0.1%, while that of YOLOv8+B increased by 2.7% compared to the original model. Although the \(mAP\) values for Class-0 and Class-3 saw slight declines of 0.8% and 1.6%, respectively, Class-1 and Class-2 improved by 3.2% and 1.2%, respectively. Moreover, despite the incorporation of an attention mechanism and the replacement of the original Concat structure with the Concat_BiFPN structure in the improved model, the detection accuracy increased, while the number of parameters changed by only 62.  
In summary, EB-YOLOv8 achieved higher average precision without a significant increase in parameters. The improved model exhibits stronger robustness, faster convergence, enhanced feature extraction capability, and superior detection performance.
###### Abstract  
A reliable Headspace-Solid Phase Microextraction (HS-SPME) method was developed for the determination of polar volatile components of commercial nut-based milk alternative drinks prior to Gas Chromatography-Mass Spectrometry (GC-MS) analysis. Under the optimum extraction conditions, a divinylbenzene (DVB)/Carboxen(tm) CAR/polydimethylsiloxane (PDMS) fiber was used and 2 mL of sample was heated at 60 \({}^{\circ}\)C for 40 min under stirring, without salt addition. Ten compounds from different chemical classes (heptane, a-pinene, toluene, 2-methylpyrazine, 3-heptanone, heptanal, 2-octanone, 1-heptanol, benzaldehyde and 1-octanol) were chosen as model analytes for quantification. Limits of detection and limits of quantification were found to be 0.33-1.67 ng g\({}^{-1}\) and 1-5 ng g\({}^{-1}\), accordingly. Good linearity, precision and accuracy were obtained as well as a wide linear range. The proposed method was successfully applied to various beverages including almond milk, walnut milk, peanut milk and almond chocolate milk. More than 70 volatile compounds were detected in the different samples. Most of the detected volatiles were aldehydes, ketones and alcohols. This technique can be used for the determination of volatile compounds in nut-based beverages, to detect compositional changes during storage and technological treatment used for their production.  
HS-SPME; milk alternatives; nut-based drinks; almond milk; peanut milk; walnut milk; volatile compounds; gas chromatography; GC-MS Article
## 2 Related Works
### Method Based on Reinforcement Learning  
Reinforcement Learning (RL), as an effective method for time-varying dynamic systems, is gradually being applied to resource allocation in wireless networks. Ref. [35] studied the throughput and fairness issues in multi-channel centralized random access systems and proposed a distributed multi-channel access protocol based on multi-agent reinforcement learning (RL) to enhance throughput and fairness among active users. Addressing the high-concurrent-access problem in industrial wireless networks that meet strict real-time and reliable communication requirements, ref. [36] proposed a Deep Reinforcement Learning-based Dynamic Priority Multichannel Access (DRL-DPMCA) algorithm, which ensures the highest channel access probability and lowest queue delay for high-priority industrial equipment while minimizing access conflicts and achieving near 100% channel utilization. To tackle the problem of agents being unable to identify and explore useful information in sparse wireless environments, ref. [37] proposed a Cooperative Multi-Agent Exploration (CMAE) framework that projects the network state space into a low-dimensional space rather than learning strategies in a high-dimensional space, with nodes and BSs collaboratively exploring unexplored wireless network states to jointly learn channel access and signaling strategies, achieving good throughput performance and low collision rates.  
However, the application of reinforcement learning algorithms in access control is still in the theoretical stage, and due to their issues pertaining to computational complexity, timeliness, and deployment, it is challenging to apply them to practical systems.
# Multi-Objective Optimisation of Welding Parameters for AZ91D/AA6082 Rotary Friction Welded Joints
## 2 Materials and Methods
### Sample Preparation  
Figure 1a shows the friction welding process on a continuous drive friction machine (ZT4-13 type). The aluminium alloy bar is rotated with the spindle during welding, and the magnesium bar is moved axially under the axial pressure. A detailed diagram of the welding process cycle is shown in Figure 1b. The samples are pressed under an axial force after reaching the desired rotational speed (RS). The heated contact surfaces rub against each other, and the metal slowly squeezes out as a flash (see Figure 1a). The rotation of the sample stops, and upsetting force (UF) is applied to consolidate the welding joint, as shown in Figure 1a. A set of reducers and compressed air valves performs the welding  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multicolumn{5}{c}{**Chemical Composition**} \\  & **Mg** & **Al** & **Zn** & **Cu** & **Si** & **Mn** \\ \hline AZ91D & Rest & 8.5–9.5 & 0.45–0.9 & 0.02 & 0.08 & 0.17–0.4 \\ AA6082 & 0.60–1.20 & Rest & 0.20 & 0.10 & 0.70–1.30 & 0.40–1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Alloying elements (wt.%) of parent materials.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multicolumn{5}{c}{**Mechanical Properties**} \\  & **Tensile Strength** & **Yield Strength** & **Elongation \(A_{5}\)** & **Hardness** \\  & **(MPa)** & **(MPa)** & **(\%)** & **(HB)** \\ \hline AZ91D & 280 & 160 & 7 & 49 \\ AA6082 & 310 & 260 & 8 & 95 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The mechanical properties of base materials.  
machine's work cycle. The air pressure during the friction and welding phases was set according to the graph shown in Figure 1c.  
The process parameters used in the welding process are shown in Table 3. The choice of parameters for welding was based on the previous reports [43; 44; 45; 46]. In the experiment, the rotational speed (RS) and upsetting time were constant and equal to 1450 rpm and 6 s, respectively.
# Combined Improved CEEMDAN and Wavelet Transform Sea Wave Interference Suppression
###### Abstract  
Cross water-air interface acoustic and electromagnetic integrated communication (AEIC) technology refers to an underwater speaker that excites the water surface micro-amplitude wave (WSAW) on the water's surface, and millimeter wave radar detects the vibrations of the WSAW to realize the transmission of information. The research on cross-media communication meets many challenges due to the large amplitude of the water surface disturbance and the small amplitude of the WSAW. In this paper, a novel sea wave interference suppression method based on improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet transform (WT) is presented. This method divides the phase change into different intrinsic mode functions (IMFs) and obtains a reconstructed scale of the WSAW signal through wavelet decomposition and correlation procession to separate the WSAW signal and the sea wave interference. It is proved to be better than the reference filtering method by experiment. By using this novel method, the bit error rate (BER) of the communication system can be reduced effectively.  
cross-media communication; sea wave interference suppression; improved CEEMDAN; wavelet transform 2023152007  
10 April 2023
# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
## 4 Experiments
### Quantitative Evaluation

Table 4: Quantitative evaluation of low-light image enhancement methods on single satellite images. hancement method proposed in this paper can improve details while avoiding excessive image distortion and information loss, and effectively suppresses noise and artifacts. These results demonstrate that our method can simultaneously enhance image contrast while mitigating halo artifacts and noise, achieving a well-balanced enhancement performance.
## 4 Experimentation and Results Analysis  
### Data Set and Data Preprocessing  
In this experiment using MATLAB R2018a, we utilize the charging infrastructure dataset provided by Statista including 41,742 users and 11,953 sites, with three types of charging behavior including 18,134 (slow), 25,572 (fast), and 1997 (ultra-fast) [30]. Preprocessing work is carried out on the above data to merge repeated interactions and filter out users and sites with fewer than 5 interactions.