esa-sceva/satcom-chunk-collection huggingface dataset:
# Temporal Changes in Land Use, Vegetation, and Productivity in Southwest China
## 4 Discussion
### Spatiotemporal Variation in Land Types and NDVI and GPP
In addition, studies have shown that the interaction of temperature, precipitation, and solar radiation has different effects on vegetation greening [63; 64; 65], and sustained warming and decreased precipitation are key factors in limiting vegetation growth [66]. In the past 20 years, the overall climate has been dry and warm, with a significant upward trend in temperature (0.42 \({}^{\circ}\)C/10 year) but no significant change in precipitation in southwest China [36; 48]. The occurrence of extreme weather events and natural disasters hinders the growth of vegetation, resulting in a decline in regional vegetation coverage [13; 67]. In recent decades, severe droughts have occurred frequently in southwest China [68; 69; 70; 71], which have had a significant impact on grassland and cropland. The average NDVI in the vegetation growing season has shown a decreasing trend (Figure 3b). However, the area of forest in this study region was about 2.4 times that of cropland, and the increase in the greening effect on the whole region compensated for the decreasing trend of NDVI. The productivity levels of different LULC types had different responses to influencing factors. For example, cropland and grassland were vulnerable to extreme climate disasters, but the positive effects of human activities on the effective management of cropland weakened the negative effects of drought. From 2000 to 2019, the area of cropland decreased, but the average GPP value in the growing season showed an increasing trend in southwest China (\(p<0.01\)), which may be related to the improvement in the productivity of cropland by agricultural management measures in recent years [24]. The forest ecosystem was relatively stable, and human activities, such as deforestation and afforestation, had a greater impact on the productivity of the forest ecosystem than the impact of extreme climates [72]. The average GPP of forest in the growing season showed a significant increasing trend, which was related to the increase in forest area and vegetation restoration in this region [73]. Studies have shown that vegetation productivity exhibits different growth patterns at different stages of forest age [74; 75]. Due to the conversion of grassland to other land types, the amount of grassland has been decreasing continuously for 20 years. However, with the influence of ecosystem protection policies in recent years, the GPP of grassland still showed a fluctuating growth trend (Figure 4a). These results indicate that the overall carbon sequestration capacity of the southwest region is gradually increasing, and the environmental quality of the ecosystem is gradually improving, which corresponds to the significant increase in the vegetation coverage in southwest China in recent years.
## 3 Performance Analysis
### BER Source Analysis
Firstly, a pilot signal \(P(t)\) needs to be inserted before each frame of TDOM symbols, which can be expressed as follows:  
\[P(t)=\sum_{n=0}^{N}sin[2\pi f_{n}(t-\tau_{n})]. \tag{20}\]  
Then, at the receiver side, the received pilot \(P_{r}(t)\) is firstly separated from the communication symbol \(r(t)\), and fast Fourier transform (FFT) is conducted on \(P_{r}(t)\) and \(P(t)\) to obtain the channel state information \(H(f)\), i.e.,  
\[H(f)=\frac{FFT(P_{r}(t))}{FFT(P(t))}, \tag{21}\]  
where \(H(f)\) represents the frequency response of the channel, and \(FFT\) (\(\cdot\)) is the fast Fourier transformation operator. Then, an FFT operation is performed on the received signal \(r(t)\), and channel equalization is completed as follows,  
\[R(f)=\frac{FFT(r(t))}{H(f)}. \tag{22}\]  
Then, the received signal in the time domain is achieved by an inverse fast Fourier transform (IFFT) operation, as follows:  
\[r(t)=IFFT(R(f)). \tag{23}\]  
where \(IFFT(\cdot)\) is the IFFT operator. Finally, \(r^{\prime}(t)\) is sent to the receiver to demodulate the transmitted information. In addition, to cope with inter-symbol interference (ISI), a zero prefix (ZP) is inserted between two TDOM communication symbols and its duration is made to be longer than the maximum delay caused by a multipath channel.
## 5 Improved SAC (iSAC) Algorithm
### Algorithm Implementation  
First, the environment, deep neural network, and experience pool are initialized. Hyperparameters are set, and the deep neural network adopts He initialization for parameters. When the experience buffer is empty, the default priority of experiences is set to 1.0. Before the experience pool reaches the batch size, random actions are obtained using the actor network for decision making and states, actions, rewards, and the next states are stored in the experience pool. Once the experience pool reaches the batch size, every task will sample n experiences based on the priority of the samples in the experience pool and then train the samples, updating the policy network and value network. Finally, the temperature coefficient \(\alpha\) and the priority of the sampled n experiences are updated. Algorithm 1 presents the pseudocode of PER-iSAC.  
```
1: discount factor \(\gamma\), temperature coefficient \(\alpha\), soft update coefficient \(\tau\), batch size \(n\), learning rate
2: policy
3: Initialize Actor and Q Net 1, Q Net 2, Target Q Net 1, Target Q Net 2, hyperparameters
4: Initialize Replay Buffer
5:for each \(episode\in[1,M]\)do
6: Initialize environment
7:for each \(t\in[1,T]\)do
8:\(vector=Actor(t)\)
9:\(p(t)=softmax(vector)\)
10:\(a(t)\) = sampling from action space based on \(p(t)\)
11:\(s(t+1),r(t)\) = environment exec \(a(t)\)
12: save \((s(t),a(t),r(t),s(t+1))\) in Replay Buffer
13: compute the priority \(p\) in Replay Buffer
14:if \(len(ReplayBuffer)>n\)then
15: sample \(n\) samples from Replay Buffer according to \(p\)
16: train with \(n\) samples
17: update Q Net 1, Q Net 2, Actor with \(\alpha\)
18: update Target Q Net 1, Target Q Net 2
19: update \(\alpha\), \(p\)
20:endif
21:endfor
22:endfor
```  
**Algorithm 1** PER-iSAC
## 5 Numerical Tests and Comparisons  
### Experimental Setup  
To test the performance of the PHPSO algorithm, a computational simulation is carried out with some well-studied benchmarks. In this paper, 120 problem instances that were contributed by the Taillard dataset are selected. The Taillard benchmark dataset is composed of 12 groups containing the problems of size ranging from 20 jobs and five machines to 500 jobs and 20 machines with 10 instances of each problem size. Further, these subsets are denoted as 20 \(\times\) 5 (ta001-ta010), 20 \(\times\) 10 (ta011-ta020), 20 \(\times\) 20 (ta021-ta030), 50 \(\times\) 5 (ta031-ta040), 50 \(\times\) 10 (ta041-ta050), 50 \(\times\) 20 (ta051-ta060), 100 \(\times\) 5 (ta061-ta070), 100 \(\times\) 10 (ta071-ta080), 100 \(\times\) 20 (ta081-ta090), 200 \(\times\) 10 (ta091-ta100), 200 \(\times\) 20 (ta101-ta110), and 500 \(\times\) 20 (ta111-ta120) representing the number of jobs and machines respectively. In this paper, we used this dataset to test our PHPSO algorithm, and this test bed is treated for NWFSSP with TFT as the optimization criterion. PHPSO is coded in Java and run on Intel Core i5, 8 GB RAM, 2.20 GHz PC.
## 3 Results
### Microbial Community Structure: Diversity and Function  
The analysis of relative abundance within the microbial community across different treatments at the taxonomic level of the family showed trends in microbial community structure within the three treatments compared to the control (Figure 3, Table S1). The families of _Clostridiaceae_ and _Erysipelotrichaceae_ in the phyla Firmicutes were consistent across all treatments (X\({}^{2}\) = 2.05 \(p\) = 0.56 and X\({}^{2}\) = 1.21 and \(p\) = 0.75), although both families were slightly higher (7-10%) in sediments containing SBR.  
_Desulfocapsaceae_ was slightly, but not significantly, greater than the control in PET treatments (X\({}^{2}\) = 3.07, \(p\) = 0.38).  
Relative abundance of _Rhodocyclaceae_ was also 52% higher in sediments containing PVC compared to the control and was the overall highest among the three treatments. While there were few differences among the individual families across treatments, the trends in alpha diversity were more clear.  
Alpha diversity was reduced to some extent in all three polymer treatments relative to the control (Figure 4 and Table 4). Total observed features were 28 % to 43 % higher in the control compared to all plastic treatments, with sediments containing PET having the lowest value (\(F\) = 4.76, \(p\) < 0.01). While there was no difference in  
Figure 3: Bubble graph depicting relative abundances of the top 14 bacterial families among the four treatments. The size of the bubble is proportional to the relative abundance of each bacterial family, indicated by the relative abundance legend.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Component 1** & **Component 2** \\ \hline NEM & 0.42 & 0.28 \\ GPP & \(-\)0.07 & 0.44 \\ Oxide Depth & 0.45 & 0.33 \\ Daily NO\({}_{3}^{-}\) & 0.23 & 0.30 \\ Daily NH\({}_{4}^{+}\) & 0.38 & \(-\)0.37 \\ Daily PO\({}_{4}^{\frac{\beta}{3}-}\) & 0.23 & \(-\)0.38 \\ PDNF & 0.18 & \(-\)0.20 \\ Chl \(a\) & \(-\)0.18 & 0.45 \\ Worm Count & 0.54 & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Factor loading for the first two Principal Components.  
across treatments, Faith's phylogenetic diversity was 16-23% greater in the control than SBR and PET, but not different from PVC (\(F\) = 4.76, \(p\) < 0.05) (Figure 4, Table S2).  
Differences in specific metabolic functions may further reflect changes in community structure (Figure 5). Sediments containing SBR had the most notable differences in metabolic functions when compared to the control (\(p\) < 0.05), including greater sulfate reduction, methanothrophy, hydrocarbon degradation, respiration of sulfur compounds, methylotrophy, fermentation, and anaerobic chemoheterotrophy. PET treatments also demonstrated higher anaerobic chemoheterotrophy than the control (\(p\) < 0.05). PVC was slightly higher for anaerobic chemoheterotrophy and methylotrophy (\(p\) < 0.1).
# SC-FDE Layer for Sensor Networks in Remote Areas Using NVIS Communications
## 2 Background Study
### HF Communications Evolution  
HF communications started out as point-to-point and shortwave, and were mostly used by radio amateurs. Technical evolution and continuous research have led to these communications being used over long distances with several generations of advances. The first generation (1G) focused on narrowband systems with amplitude modulations for voice transmission with a 3 kHz channel (current standards as STANAG and MIL-STD keeps this channel bandwidth). Subsequently, attempts were made to improve performance with different modulations such as FSK, PSK, or QAM. The main drawback was the choice of frequency, as it was variant during the day. In the second generation (2G), microprocessors were introduced, and progress was made towards digital communications and signal processing. With this, point-to-multipoint communications were achieved. Finally, an interesting development was the Automatic Link Establishment (ALE), which allowed not to be in constant analysis of frequencies, but had a high cost of computing and very poor signal processing. The third generation (3G) improved the links, being faster and with a lower SNR. It also supported larger networks, and improved the efficiency of the entire network by separating channels for link establishment and traffic communications. The fourth generation (4G) already involves advances in artificial intelligence and broadband, and seeks the integration of systems with high data volumes and throughput [28; 29].
###### Abstract
On the other hand, calculating optical flow over a small area with a small number of features to track can run even on a mobile device--usually known for its limited resources. Therefore, the proposed system adopts optical flow. The optical flow of the ROI is calculated to track the movement of the object inside the ROI; the movement is utilized to adjust the ROI in the next frame, achieving efficient ROI management. Before encoding, we apply our devised scheme called XROI-DCT. With XROI-DCT in the proposed system, UAV tracking videos can achieve high transmission efficiency. The result shows that the accuracy of our approach is almost identical to that of YOLOv7 at a lower computation price [15]. The performance of the proposed system is evaluated by comparing it to YOLOv7 in various aspects. The contribution of this paper is presented as follows:  
* We present a UAV tracking system. It utilizes YOLOv7's real-time detection capabilities and optical flow for a lightweight and fast UAV tracking system for limited resource devices. In the evaluation section, experiment results show that the proposed scheme can perform UAV detection 315.86% faster than YOLOv7. * Our proposed system is devised as a surveillance/tracking system that considers video transmission. Due to XROI-DCT, the system is suitable for efficient video transmission. The file size of the transmitted video is significantly smaller (averaging 70% compression) than the file size of the same video processed by YOLOv7. * For predicting UAV models, our proposed system performs UAV identification based on a CNN model. Our UAV identifier can distinguish 16 UAV models with high accuracy, and its ROC-AUC score is 0.9869. The rest of the paper is organized as follows. In Section 2, we present the relative research regarding ROI-based video coding. In Section 3, an overall description of the system is presented. Section 4 presents the prototype implementation and performance evaluation of the implementation. The paper is concluded in Section 5 with a brief presentation of our future research.
## 2 Relative Work
### Attention Fusion Strategies  
Attention fusion strategies have been widely adopted in fields such as computer vision, natural language processing, and speech recognition. In the realm of computer vision, early fusion methods primarily involved Concatenation and Sum, which were widely used due to their fast computation speed and high interpretability. Subsequently, in order to capture both detailed information and high-level semantic information simultaneously, multi-scale feature fusion schemes [32; 33] were developed based on these methods. These schemes innovated the fusion mechanism significantly and made the fusion structure more flexible. However, simply aggregating multi-level information does not guarantee effective information propagation. To achieve better feature fusion, works like [34; 35; 36] utilize learned attention weights for different branches to aggregate multi-level information. Nevertheless, traditional attention paradigms based on the Transformer structure are often characterized by high complexity and large computational requirements, which are not suitable for lightweight model intentions. In recent years, with the introduction of [37], several attention calculation methods specifically for images have begun to gain attention. Many outstanding works have focused on enhancing the feature representation of specific branches before fusion, such as [38; 39], which emphasized exploring new feature enhancement methods with good portability using attention. However, this also leads to a fixed application scenario for the modules, making it difficult to tap into deeper performance capabilities. Other works focus on feature selection during fusion, such as [7; 40; 41], which carefully designs combinations of convolutions and nonlinear functions. However, these often concentrate on fully learning feature information while neglecting the astonishing capability of feature attention itself to enhance feature expression.
# New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System
## 3 Bridge Experiment
### Introduction
#### 3.1.1 Evidence of Existing Outliers  
A long-span bridge that is built for expanding trade, transport, and logistics, and which can be used by a number of different vehicles, often interferes with GNSS signals because of the usual amount of passing vehicles, the bridge structure itself, and environmental factors.  
Figures 4 and 5 graphically depict the results of vertical deflection, estimated from two GNSS receivers installed at both sides of the mid-span such that, in the lateral direction, the two receivers were installed at the same position. It is normal that no significant difference occurred in the amount of vertical deflection and its trend, as shown in the figures.
# PowerBridge: Covert Air-Gap Exfiltration/Infiltration via Smart Plug
## 6 Data Encoding and Decoding  
In this section, we will delve into the data encoding and decoding techniques employed in PowerBridge communication. It is worth noting that power line communication technology has been extensively researched in both academic and industrial domains, resulting in a plethora of proposed modulation, demodulation, encoding, and decoding schemes [42]. We present an overview of the modulation and encoding schemes utilized in PowerBridge communication while elucidating their distinctive characteristics.
## 4 Numerical Simulation
### Signal-to-Noise Ratio (SNR)  
SNR is one of the most widely used objective measurements that evaluates the power of the original voice signal. For the given original signal \(x(i)\) and the obtained encrypted speech signal \(y(i)\), the SNR is defined as:  
\[SNR=10 log_{10}(\frac{\sum x(i)^{2}}{\sum(x_{i}-y_{i})^{2}})\ \mathrm{dB} \tag{5}\]  
Suppose the value of SNR is positive, this indicates that the power of the encrypted signal covers the original signal and vice versa, for the negative value indicate that the encryption signal covers the original one [39]. In the context of security, higher SNR suggests that the encryption system is resilient to noise attacks, as any added noise has minimal impact on signal integrity.  
Table 3 indicates the values of SNR for different values of N, where the absolute values of the SNR increase as the number of levels in the system increases, implying that the noise introduced by the encryption becomes more significant. This increased noise level contributes to making the encryption more robust against attacks, as it becomes increasingly difficult to recover the original signal without the decryption key.
# A Multi-Granularity Word Fusion Method for Chinese NER
## 4 Experiments
### Baselines  
To investigate the impact of our suggested method in Chinese NER, we compare it to other representative methods, such as including BiLSTM-CRF [31], Lattice-LSTM [16], FLAT [17], ZEN [17], BERT [1], and RoBERTa [2]. Meanwhile, the PLTE [20] model is chosen for comparison because it is a novel model with currently excellent results. A brief introduction of these models is given as follows.  
* BiLSTM-CRF [31]: a classic method, which uses bi-directional LSTM (BiLSTM) to model the language, obtain the left-to-right sentence encoding and right-to-left encoding then use crf to capture the dependencies between tags.
* Lattice LSTM [16]: a lattice-structured LSTM model for Chinese NER, which encodes a sequence of input characters as well as all potential words that match a lexicon.
* FLAT [17]: Flat-LAttice Transformer for Chinese NER converts the lattice structure into a flat structure consisting of spans. Each span corresponds to a character or latent word and its position in the original lattice.
* BERT [1]: BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right contexts in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.
* RoBERTa [2]: a replication study of BERT pre-training that carefully measures the impact of many key hyper-parameters and training data size.
* ZEN [5]: a BERT-based Chinese text encoder enhanced by N-gram representations. During training, different character combinations are examined, thus potential word or phrase boundaries are explicitly pre-trained and fine-tuned with the character encoder.
* PLTE [20]: PLTE augments self-attention with positional relation representations to incorporate lattice structure, and introduces a porous mechanism to augment localness modeling and maintain the strength of capturing the rich long-term dependencies.
* MECT [21]: MECT uses radical-level embeddings to combine the structural information into Chinese characters, and then uses multi-metadata embeddings in a two-stream Transformer to combine the features of Chinese characters with the radical-level embedding.
# Process Parameters Optimization of Thin-Wall Machining for Wire Arc Additive Manufactured Parts
###### Abstract  
Additive manufacturing (AM) is an arising production process due to the possibility to produce monolithic components with complex shapes with one single process and without the need for special tooling. AM-produced parts still often require a machining phase, since their surface finish is not compliant with the strict requirements of the most advanced markets, such as aerospace, energy, and defense. Since reduced weight is a key requirement for these parts, they feature thin walls and webs, usually characterized by low stiffness, requiring the usage of low productivity machining parameters. The idea of this paper is to set up an approach which is able to predict the dynamics of a thin-walled part produced using AM. The knowledge of the workpiece dynamics evolution throughout the machining process can be used to carry out cutting parameter optimization with different objectives (e.g., chatter avoidance, force vibrations reduction). The developed approach exploits finite element (FE) analysis to predict the workpiece dynamics during the machining process, updating its changing geometry. The developed solution can automatically optimize the toolpath for the machining operation, generated by any Computer Aided Manufacturing (CAM) software updating spindle speed in accordance with the selected optimization strategies. The developed approach was tested using as a test case an airfoil.  
additive manufacturing; thin walled machining; dynamics; machining cycle optimization Article
# Low-Thrust Nonlinear Orbit Control for Very Low Lunar Orbits
## 3 Nonlinear Orbit Control  
The aim of this section is to introduce a nonlinear feedback control law able to perform an initial orbit acquisition and keep the spacecraft along the desired orbit.
## 4 Experiments and Results
### Experimentation  
In this study, three experiments were performed with different numbers of nodes and time. Table 4 shows the different experiments performed with the number of deployed nodes, types of nodes, and runtime in each experiment.