esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Rainfall Patterns in Harare  
Although the linear trend in annual precipitation was positive, indicating an increase with time (slope = 2.16 mm/year), the change was not statistically significant (\(p\) = 0.381). [PERSON] [36] also observed an insignificant trend in rainfall in most places in Zimbabwe.  
Based on the Standardized Precipitation Evapotranspiration Index (SPEI), years with negative annual rainfall anomalies decreased between 1984 and 2020 (Figure 2). This yielded a positive linear trend in SPEI (0.001/year), which, however, was not statistically significant (\(p\) > 0.05).  
There was also an insignificant (\(p\) > 0.05) and minor increase (0.2 days/year) in the number of consecutive days with precipitation less than 1 mm in a year. This shows that the effect of dry spells is not noticeable in the city. Harare is in agro-ecological region 2a, which is distinguished from region 2b by a low risk of prolonged mid-season dry spells.  
The number of consecutive wet days in a year also increased at a statistically insignificant rate between 1984 and 2020 (slope = 0.2 days/year and \(p\) > 0.05).  
A significant trend (\(p\) = 0.001--red line) was observed in the number of days when precipitation exceeded 30 mm (Figure 3). Although the number was increasing, the frequency of such amounts in a year remained low.  
The trend (red line) in extreme and very rare rainfall events (greater than the 99 th percentile) further confirms that extreme amounts of high rainfall are increasing from 1985 to 2020 (Figure 4). They increased at a rate of 1.702 events/year, which was statistically significant (\(p\) = 0.001).  
Figure 3: Trend in extreme high rainfall intensities in Harare.  
Figure 2: Standardized Precipitation Evapotranspiration Index (SPEI) rainfall anomalies between 1984 and 2020 in Harare.
# A High-Precision Energy-Efficient GPS Time-Sync Method for High-Density Seismic Surveys
## 3 Performance Evaluation and Field Measurement for Validation
### Field Measurement I  
In this section, we did a field measurement to verify the time synchronization performance of the acquisition nodes. We put 12 sensor nodes on the specific point together and then hit the ground with a hammer source about 5 m away from the center of the collection node cluster. We used a base plate on the ground to make the seismic wave spread uniformly. During the experiment, the host computer is used to set the sampling rate of 1000 Hz for GEIWSR-III through the wireless monitoring system. Other parameters of the GEIWSR-III are shown in Table 2. The sensor nodes are deployed in one field experiment, which is shown in Figure 14a, and the waveform of the recorded seismic data is shown in Figure 14b. Note that the origin of the coordinates is not the starting point of the test time, but the starting point of the time period when we use data processing software for time interception. It can be seen from the figure that the first arrival of the seismic waves in the 12 sensor nodes is strictly aligned on the same horizontal line.  
Figure 12: The schematic of the time synchronization stability test.  
Figure 13: The results of the synchronization stability test with (**a**) the time synchronization stability varying in different nodes and (**b**) the time synchronization stability varying with time.
# Optimization of Storage Medium for Hematological Reference Samples in External Quality Assessment
## 2 Materials and Methods
### Screening of Affecting Factors  
The reference samples for hematology diagnostics need to have long shelf life to provide enough time for laboratories to perform the tests, including distribution time. The concentrations of the cells were similar to those in normal blood samples, which were 3.5-4.5 \(\times\) 10\({}^{12}\) RBCs/L, 4.5-8.5 \(\times\) 10\({}^{9}\) WBCs/L, and 150-250 \(\times\) 10\({}^{9}\) PLTs/L [8].  
A Plackett-Burman design was used to evaluate the level of influence of each factor on the concentrations of blood cells with two levels: low (\(-\)1) and high (\(+\)1). The factors chosen for this design were the concentrations of serum, neomycin sulfate, chloramphenicol, sodium azide, and glycerol [12]. The concentration ranges of neomycin sulfate and chloramphenicol concentrations were extended to increase the possibility of finding optimal concentrations. Sodium azide and glycerol were added to enhance the antifungal activity and viscosity, and to mitigate the hemolysis in storage media (Table S1) [13; 14].
## 5 Ablation Study
### Effectiveness with Different Kernel Sizes of the MLB  
We performed experiments with different sizes of convolutional kernels in the MLB and evaluated the performance on the same datasets. As shown in Table 5, the PSNR and SSIM showed an upward trend for our design as the convolutional kernel became larger. Such observations are consistent with our assumption that the receptive field increases as the convolutional kernel increases, capturing more contextual information. As the number of network parameters increases, the generalization ability also increases. The performance begins to saturate and decay when the convolutional kernel is further expanded. Therefore, we considered using kernels of sizes \(7\times 7\) and \(9\times 9\) as the backbone of the MLB. We also visualize the convergence speed and performance of different sizes of convolutional kernels during training in Figure 11. We can observe that: compared with other configurations, when the convolutional kernels were \(7\times 7\) and \(9\times 9\), the convergence speed was faster, and the performance increase was stable, which shows that the MLB under this configuration can extract rich attention context features with cross-dimensional interactions to capture good detail information and restore degraded image details.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Setting** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **MSE\(\downarrow\)** \\ \hline (a) Base & 19.412 & 0.863 & 0.127 \\ (b) Base+DAEB(IN\(>\)BN) & 22.110 & 0.894 & 0.087 \\ (c) Base+DAEB(w/o SA) & 22.338 & 0.898 & 0.085 \\ (c) Base+DAEB(w/o CA) & 22.558 & 0.903 & 0.082 \\ \hline Ours & 22.651 & 0.904 & 0.072 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study on the Dual-Attention Color Enhancement block. Among them, w/o SA and w/o CA refer to removing the Spatial Attention and Channel Attention, respectively. \(\uparrow\) represents that higher is better, and \(\downarrow\) represents that lower is better. Underlines indicate the best results.  
Figure 10: Color correction results of degenerated underwater image by DAEB.
## 5 Discussion
### Comparison of the Results  
The number of vacant houses was low in the core area and high in the outer suburbs based on the HVR of the main urban area of Xi'an city, which is consistent with the findings of many existing vacant case cities such as Guiyang [40], Yichun [41], and Detroit [42]. The results of this study show that the HVR was the same as the distance from the city center. First, the distance from the city center had the most obvious impact on the HVR, which is consistent with [PERSON]'s [43] research based on Hangzhou. The closer you get to the city center, the stronger the integrity and sharing of commercial infrastructure in driving and aggregating urban real estate development. Secondly, areas with significant employment locational advantages, good public service facilities, such as education and medical care, and high advantages in subway resources are more likely to meet the commuting needs of residents and enhance their willingness to live there, resulting in ideal housing use and a low HVR [40,44]. In addition, from the results of this study, it can be seen that population density had a certain impact on urban vacancy rates. The POI data represent the degree of population aggregation to a certain extent; however, this is rarely mentioned in existing studies, indicating that population density is also a direction that should be paid more attention to in the study of HVRs. The higher the data resolution, the more accurate the estimation results, and the estimation method should be more novel and innovative.  
This paper uses POI as auxiliary data to calculate the HVR in the main urban area of Xi'an city by combining Luojia-1. Compared with the results of estimating the HVR using DMSP/OLS and NPP-VIIRS, the evaluation results of HVRs herein are more accurate because the spatial resolution of Luojia-1 data used in this paper is much higher than that of DMSP/OLS [45,46] and NPP-VIIRS [12,47]. The higher the resolution of the results, the easier for us to see the details of housing vacancies. We can get a true picture of housing use  
Figure 10: Results validation in the community.  
at a small regional scale, which is more conducive to regional planning and development. When using Luojia-1 [31] to calculate the HVR, the impact of non-residential areas such as roads is not taken into account, resulting in a large difference between the results and the real situation. We use auxiliary data (POI) to avoid the impact of non-residential areas. When using Jilin 1-03 [8], although the spatial resolution is better than that of Luojia-1, the data acquisition is much more difficult than other data (including POI), thus it is not suitable for all researchers. Considering the above factors, the results of this paper have certain advantages in terms of resolution and accuracy.
## 3 Results and Discussion  
### Hyper-Parameters Tuning  
To improve model performance, regression indicator R was used as the objective function of PSO optimization to tune the hyper-parameters. The hyper-parameters of the models with \(K_{\text{c}}\), \(K_{\text{h}}\), \(K_{\text{m}}\), and \(K_{\text{v}}\) as target variables were tuned 5 times, and the average performance R represented by the solid line was the final performance of the model. As shown in Figure 7, the CV training performance gradually improved with the iteration of PSO, and it was mainly realized in the first 10 iterations. Taking the \(K_{\text{c}}\) dataset as an example, the average R-value increased from 0.982 to 0.987 after hyper-parameter tuning and then remained unchanged, demonstrating the efficiency of PSO in fine-tuning the DT's hyper-parameters.  
Table 4 summarizes the optimal hyper-parameters for four datasets. As shown, the optimal max_depth is in the range of 10 to 14, depending on the dataset. The min_samples _split was determined to be 3 for \(K_{\text{c}}\), while a constant value of 2 was determined for the remaining three datasets. For the other two hyper-parameters, the same optimal values were used for all datasets. Consistent with observations in a previous study [36], the optimized results suggest that the performance of a typical DT model is mainly controlled by the max_depth and is relatively insensitive to other hyper-parameters.  
Figure 7: The change of CV training performance R with PSO iterations on datasets (**a**) \(K_{\text{c}}\), (**b**) \(K_{\text{h}}\), (**c**) \(K_{\text{m}}\), and (**d**) \(K_{\text{v}}\). Each generation took about 2 h to calculate.
## 4 Experiments and Results
### Computational Cost, Scalability, and Deployment Considerations
For larger or highly dynamic networks, hierarchical learning or distributed simulation frameworks are recommended to maintain convergence speed and responsiveness.
## 3 Methods  
### Definitions  
A _directed graph_, also known as digraph, \(G=(V,E)\) consists of a set of nodes \(V\) and a set of directed edges \(E\subseteq V\times V\). An edge \((u,v)\in E\) of a digraph \(G=(V,E)\) is an incoming edge of the node \(v\) and an outgoing edge of the node \(u\); both nodes \(u\) and \(v\) are incident with the edge \((u,v)\).  
A rooted tree is a digraph \(T=(V,E)\) with one special tree node \(root(T)\), the root of \(T\), such that for every other node \(u\in V\setminus root(T)\) there is an unique path from \(root(T)\) to \(u\).  
From the above definition derives that every non-root node \(v\in V\setminus root(T)\) in a tree \(T=(V,E)\) has exactly one incoming edge \((u,v)\); \(u\) is called the parent of \(v\) (\(parent(v)\)); conversely, \(v\) is a child of \(u\) (\(child(u)\)). A node without children is a leaf, while nodes with descendants are referred to as internal nodes. The descendants of \(v\), \(desc(v)\), are all nodes \(u\) such that there is a path from \(v\) to \(u\). Conversely, a node \(u\) on the unique path from \(root(T)\) to \(v\) is said to be an ancestor of \(v\).  
The depth of \(v\), \(depth(v)\), is the number of edges making the path from \(root(T)\) to \(v\). This implies that \(depth(root(T))=0\). The depth of the entire tree \(T\) is defined as \(depth(T)=max_{v\in V}depth(v)\). The height of a node \(v\), \(height(v)\), is the depth of the subtree rooted at \(v\).  
A compound digraph \(D=(V,E,F)\) consists of nodes \(V\), directed inclusion edges \(E\), and directed adjacency edges \(F\). The inclusion digraph \(T=(V,E)\) is a rooted tree, and no adjacency edge connects a node to one of its descendants or ancestors, i.e., for every adjacency edge \((u,v)\in F\), \(u\) and \(v\) are unrelated in \(T\). If the adjacency edges are undirected, then \(D\) is called a compound graph.  
An additional constraint may impose that the adjacency edges only connect leaf nodes. In this case, adjacency connections between their ancestors are only meta-connections that represent the adjacency edges between their descendants. These meta-connections will also be named meta-edges. Analogously, nodes that are not leaves will be referred to as meta-nodes.
# Detecting Jamming in Smart Grid Communications via Deep Learning
## 4 Background
### Digital Modulation  
Digital modulation schemes are widely adopted in communication systems independently of the medium, i.e., wired or wireless, to exchange signals at high frequencies [41]. There are several modulation techniques available for transmitting information from a source to a receiver device, such as Amplitude Modulation (AM), Frequency Modulation (FM), and Phase Modulation (PM). Each of these techniques has advantages and disadvantages; for example, FM is used for simple data communication links, but it does not provide good spectral efficiency [42].  
Binary Phase-Shift Keying (BPSK) is the simplest and most noise-robust phase modulation technique, and it is largely used in PLC networks [43, 44]. According to this technique, the phase is shifted (modulated) at \(0^{\circ}\) and \(180^{\circ}\) to transmit bits with values 0 and 1, respectively. Figure 2 shows a schematic representation of BPSK, highlighting the main components. BPSK modulates two symbols \(\{0,1\}\) by changing the phase of the reference carrier; i.e., when \(b=1\), the phase \(\phi\) becomes \(\phi=\pi\) rad and \(\phi=0\) when bit \(b=0\) is being transmitted, as defined by Equation (2).  
\[x(t)=cos(2\pi f_{c}t+\phi), \tag{2}\]  
where \(f_{c}\) is the carrier frequency of the signal, and \(x(t)\) represents the modulated PLC signal to be transmitted. The transmitted stream is translated into two orthogonal components, \(I\) (real) and \(Q\) (imaginary), represented as complex values \(I+jQ\). These orthogonal components are assembled at the receiver to recover the transmitted bit stream. A typical representation of the real and imaginary components of the signal is by resorting to the \(I-Q\) plane. We show an example of such a representation from real data in Figure 3, where the red crosses show the ideal values of the \(I-Q\) samples and the clouds of black dots represent the actual positions of the \(I-Q\) samples. The actual received samples are mapped to different positions due to internal (hardware) and external (environment) noise. At the receiver end, \(I-Q\) samples are converted into expected symbols (0 or 1) based on a decision threshold. Both external and internal noise factors are important for communication. Noise affects the received \(I-Q\) samples, and, thus, the chances of data corruption increase as noise increases (\(\uparrow\) BER). In contrast, in this work, we exploit the displacement of the \(I-Q\)  
Figure 1: Illustration of two reference scenarios in PLC jamming detection: Scenario 1 employs binary classification with both clear and jammed samples, while Scenario 2 leverages anomaly detection with thresholding based solely on clear samples.  
samples introduced by the hardware imperfections at the transmitter side to evaluate the feasibility of performing Radio Frequency Fingerprinting of the PLC channel.
# A Magnetic-Balanced Inductive Link for the Simultaneous Uplink Data and Power Telemetry
## Appendix A Modeling of Self- and Mutual Inductance
\[L_{\text{total}}\left(r,r_{\text{w}},N_{\text{a}},N_{\text{b}},w_ {\text{a}},w_{\text{b}}\right)=\sum_{a_{i}=1}^{N_{\text{a}}}\sum_{b_{i}=1}^{N _{\text{b}}}L\left(r+w_{\text{a}}\left(a_{i}-1\right),r_{\text{w}}\right)\\ +\sum_{a_{i}=1}^{N_{\text{a}}}\sum_{b_{i}=1}^{N_{\text{b}}}\sum_{ a_{j}=1}^{N_{\text{a}}}\sum_{b_{j}=1}^{N_{\text{b}}}M\left(r+w_{\text{a}} \left(a_{i}-1\right),r+w_{\text{a}}\left(a_{j}-1\right),0,w_{\text{b}}\left|b _{i}-b_{j}\right|\right)\left(1-\delta_{\left(a_{i},a_{j}\right),\left(b_{i}, b_{j}\right)}\right).\] (A14)  
\[M\left(r+w_{\text{a}}\left(a_{i}-1\right),r+w_{\text{a}}\left(a_{j}-1\right),0,0\right)=L\left(r+w_{\text{a}}\left(\max\left(a_{i},a_{j}\right)-1\right),w_ {\text{a}}\left|a_{i}-a_{j}\right|+r_{\text{w}}\right).\] (A15)  
For two non-coaxial coils with multiple turns, the total mutual inductance can be calculated as Equation (A16), where \(r_{1}\), \(N_{\text{a1}}\), \(N_{\text{b1}}\), \(w_{\text{a1}}\) and \(w_{\text{b1}}\) are the radius, the number of layers, the number of turns per layer, the distance between adjacent layers and the distance between adjacent turns of Coil #1, respectively, and \(r_{2}\), \(N_{\text{a2}}\), \(N_{\text{b2}}\), \(w_{\text{a2}}\) and \(w_{\text{b2}}\) are those of Coil #2, while \(d_{12}\) and \(d\) stand for the offset distance and the separation distance between the two coils. \[M_{total}\left(r_{1},r_{2},d_{12},d,N_{\text{a1}},N_{\text{b1}},w _{\text{a1}},w_{\text{b1}},N_{\text{a2}},N_{\text{b2}},w_{\text{a2}},w_{\text {b2}}\right)=\\ \sum_{a_{i}=1}^{N_{\text{a1}}}\sum_{b_{i}=1}^{N_{\text{b1}}}\sum _{a_{j}=1}^{N_{\text{a2}}}\sum_{b_{j}=1}^{N_{\text{b2}}}M\left(r_{1}+w_{\text {a1}}\left(a_{i}-1\right),r_{2}+w_{\text{a2}}\left(a_{j}-1\right),d_{12},d+w_{ \text{b1}}\left(b_{i}-1\right)+w_{\text{b2}}\left(b_{j}-1\right)\right).\] (A16)  
Finally, the coupling coefficient between the two coils with multiple turns can be calculated by using the total self-inductance and mutual inductance, as shown in Equation (A17). \[k=\frac{M_{\text{total}}}{\sqrt{L_{\text{total}}L_{\text{total}}}}.\] (A17)
## 3 Wind Speed Prediction Model Based on CEEMDAN-RIME-MHSA-BiLSTM
### MHSA-BiLSTM Model Structure
6:for each training epoch e = 1 to E do
7:for each \(IMF\)\(IMF_{i}\)\(IMF_{1}\) to \(IMFn\)do
8:for each time step \(t\) in \(IMF_{i}\)do
9:\(H_{1}^{f},H_{1}b\gets BiLSTM-1(IMF_{i}(t))\)
10: Pass the output of the first BiLSTM layer \(H_{1}\) into the second BiLSTM layer:
11:\(H_{2}^{f},H_{2}^{b}\gets BiLSTM-2(H_{1})\)
12. Optimize BiLSTM parameters using RIME Algorithm:
13:\(\theta_{BiLSTM}\gets RIME(\theta_{BiLSTM})\)
14. Apply MHSA (Multi-Head Self-Attention) on BiLSTM outputs:
15:\(M_{i}=softmax(\frac{Q_{i}^{K}}{\sqrt{d}})V_{i}\)
16. Concatenate the attention outputs from all heads:
17:\(M=Concat(M_{1},M_{2},\cdots,M_{h})\)
18. endfor
19: Pass the concatenated matrix \(A_{t}=\left[A_{t}^{f},A_{t}^{b}\right]\) into the fully connected layer:
```  
**Algorithm 1**CEEMDAN-BiLSTM with RIME Optimization and MHSA
# Lower-Latency Screen Updates over QUIC with Forward Error Correction
## 2 Background and Related Work  
One way of communicating partial ordering requirements is using some notion of a dependency graph on the sender side and sending it to the receiver [1, 3]. The receiver then considers that graph when delivering the data. Although a dependency graph could express the ordering requirements precisely, it also adds complexity, and the sender must have the dependency knowledge in advance.  
Two other projects that support some notion of partial ordering and avoid total ordering of the TCP to solve the HOL blocking problem are the Stream Control Transmission Protocol (SCTP) [10] and QUIC [7]. Both the SCTP and QUIC use multi-streaming over a single connection to be able to relax the ordering of messages among different streams while supporting total ordering within each stream. However, not all workloads with partial ordering would fit in this scheme. For example, with the messages in Figure 1, using SCTP/QUIC does not allow us to send independent messages (e.g., \(M_{1}\), \(M_{2}\), and \(M_{3}\)) over different streams to relax ordering, because then there would not be no choice for \(M_{4}\), as it depends on both \(M_{1}\) and \(M_{2}\). For example, if we send \(M_{4}\) on the same stream as \(M_{1}\), then we can not make sure that its dependency on \(M_{2}\) will be satisfied, as there is no ordering among streams. Therefore, we can just use one stream for the whole DAG, which entails total ordering.
## 4 Experiments
### Results and Analysis
#### 4.3.3 The Impact Analysis of Feature Fusion Mode  
After extracting the corresponding features through the context semantic encoder and the affective interaction encoder, it is necessary to fuse the two features and input them into the full connection layer to achieve sentiment classification. In order to study the impact of different fusion methods on the experimental results of sentiment classification, this paper observes the change of F1 value under different sentiment categories by setting three fusion methods, i.e., sum, maximum pooling, and series connection. The experimental results are shown in Figure 5.  
The experimental results show that the series fusion method has the best F1 value in all sentiment categories because the series fusion method can retain more information than the other two fusion methods. In addition, the gap between the three fusion methods of maximum pooling, summing, and tandem was the smallest in the neutral sentiment category. The effect of the series fusion mode was significantly better than that of the other two fusion methods in the negative and positive sentiment categories. It indicates that the series method can better retain sentiment characteristics.
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 5 Discussion
### Limitations of the Study
#### 5.1.2 Limitations of the Selected Machine Learning Methods  
The study focused mainly on the Random Forest, Isolation Forest, and Autoencoder methods, leaving out other potentially useful ML methods. The study, while providing valuable information on the performance of the selected models, may have had some limitations. Focusing solely on the Random Forest, Isolation Forest, and Autoencoder methods may lead to conclusions that do not fully reflect the potential of other approaches. In other words, it means that the conclusions drawn from these three models may not be fully consistent with other methods, data characteristics, model settings, or computational constraints.