esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Feature Extraction in Multiple Domains
### Feature Extraction in the Frequency Domain  
Spectrum analysis is applied to the raw signals for feature extraction in the frequency domain. In this process, the Fast Fourier Transform (FFT) algorithm is used, which breaks the signal down into all its frequencies by computing the Discrete Fourier transform of a sequence. Let \(X_{0}\), \(X_{1}\),..., \(X_{\text{N-1}}\) be complex numbers. The Discrete Fourier transform is defined by the formula:  
\[X_{k}=\sum_{n=0}^{N-1}x_{n}e^{-2\pi kn/N}\,k=\ 0,\ \ldots\,N{-}1, \tag{2}\]  
where \(e^{2\pi/N}\) is a primitive \(N^{\text{th}}\) root of 1.  
Besides the FFT, Envelope Spectrum Analysis (ESA) is also proved to be effective in extracting periodic impacts from the vibration signal. It can extract impacts with very low energy and even those hidden by other vibration signals, thus it is widely used to detect and diagnose rolling element bearing faults [35], where the vibration becomes amplitude-modulated due to periodic changes in the forces when a fault develops. To obtain the envelope spectrum, the Hilbert Transform (HT) [36], one of the most frequently used methods for envelope analysis, is first performed on the signal. For signal \(s(t)\), its Hilbert Transform \(\delta(t)\) is defined as  
\[\hat{s}(t)=\text{H}[s(t)]=\frac{1}{\pi}p\int_{-\infty}^{\infty}\frac{s(\eta)}{ t-\eta}d\eta, \tag{3}\]  
Then, the analytic signal of \(s(t)\) can be expressed as  
\[s_{A}(t)=s(t)+j\hat{s}(t)=A(t)e^{i\psi(t)}, \tag{4}\]  
\(s_{A}(t)\) is the analytic signal of \(s(t)\), which is complex-valued. \(A(t)\) is the instantaneous amplitude namely envelope and \(\psi(t)\) is the instantaneous phase. Then, FFT is employed on the envelope to obtain the envelope spectrum.  
Figure 1: The process of continuous statistical analysis.
# Client-Aware Negotiation for Secure and Efficient Data Transmission
## 3 Methodology
### CAN Based on Renegotiation
Because transmission requiring the highest security tends to have the highest transmission overhead, the scheme described here sets the default encryption scheme to the maximum security, so that clients that need the highest security can reduce the overhead by avoiding the overhead of renegotiation and a full handshake. When a client does not need the highest security, the renegotiation will be needed. The message sequence diagram is shown in Figure 3, in which there are four parts. Part 1 is a full handshake of two-way authentication, in which the server gets the client certificate and sets the encryption scheme. Then, in Part 2, a renegotiation is executed. Part 3 is also a full handshake, in which the server uses the new encryption scheme to negotiates with the client. Finally, Part 4 shows the data security transmission after the negotiation. Figure 3: A message sequence diagram of client-aware negotiation based on renegotiation. The messages with “*” are for two-way authentication.
## 2 Materials and Methods
### RT-PCR, DNA Extraction from Bacteria, and 16s rRNA Gene Sequencing  
Total RNA was extracted from RAW264.7, Caco-2 cells, and colon tissues using TRIzol (Thermo Fisher) and RNeasy mini kit (QIAGEN, Hilden, Germany) according to the manufacturers' instructions. The concentration was determined using a spectrophotometer (Nabi, Seongnam-si, Gyeonggi-do, Republic of Korea), 1 \(\upmu\)g of RNA was reverse transcribed into cDNA using the iScript cDNA synthesis kit (Bio-Rad, Hercules, CA, USA), and gene amplification was detected using the CFX Connect Real-Time PCR Detection System (Bio-Rad). The primer designs are listed in Supplementary Table S1 (Macrogen, Seoul, Republic of Korea). The relative gene expression was normalized to that of \(\beta\)-actin. The composition of the gut microbiota was determined as follows. Microbial DNA was extracted from mouse feces using a PowerFecal Pro DNA Kit (QIAGEN) according to the manufacturer's instructions. DNA from two mice (\(n\) = 3-4/group) was mixed in equal proportions for 16S rRNA sequencing using the Illumina iSeq platform (Illumina, San Diego, CA, USA). The following primers were designed for the v4 region of 16s metagenomic sequencing: forward primer: TCGTCGCGACGCGTCAGATGTGATAAGAGCACAGGCCTACGGGNGGCWGGCAG and reverse primer: GTCTGGGGCTTGATGGTATAAGAGACAGGACTACHVGGGTATCTAATCC. Trimmomatic (version 0.39) was utilized to eliminate the adaptor sequence, and the results were grouped into operational taxonomic units (OTUs). \(\alpha\)- and \(\beta\)-diversity analyses were performed using QIIME2 (version 1.9.5) ([[https://qime2.org](https://qime2.org)]([https://qime2.org](https://qime2.org)), accessed on 30 September 2022) [38]. To analyze the composition of specific intestinal microorganisms, 2 \(\upmu\)g of DNA extracted from bacteria was used for quantitative PCR and amplified using primers (Supplementary Table S2, Macrogen, Seoul, Republic of Korea). The CFX Connect Real-Time PCR Detection System (Bio-Rad) was used for quantitative PCR. The relative abundance of bacterial groups in each fecal sample was calculated as the percentage of total bacteria (F341/R518) [47].
# Incorporating Multi-Hypotheses as Soft-Templates in Neural Headline Generation
## 4 Results
### Effect of Basic Model Architecture  
The selection of the basic model architecture plays a significant role in determining the system performance. Within this subsection, we explore diverse architectures in order to ascertain the most optimal configuration.  
The Transformer [30] architecture is employed due to its notable proficiency in neural encoder-decoder functionality. To leverage the success of pre-trained models in natural language processing tasks, we utilize them as encoders and connect the output of the last layer to the Transformer decoder. Specifically, we employ the base versions of BERT [22] and ELECTRA [31] as our pre-trained language models.  
For the Transformer, we leverage the base version consisting of a six-layer encoder and a six-layer decoder with a 512 embedding/hidden size and a 2048 feed-forward filter size. We set the max sentence lengths to 60 and 20 for the source and target sentences, respectively. Adam [32] with \(\beta 1=0.9\), \(\beta 2=0.999\) is used for the optimization. The learning rate is \(1\times 10^{-8}\). The dropout rate is 0.1. The weight decay is 0.01. The batch size is 64. We train our models on four GeForce RTX 2080 GPU cards. The training takes approximately 4 h for 10,000 steps. We inherit the huggingface's PyTorch implementation for all experiments.  
Table 2 shows the experimental results of each model structure. Aside from the ROUGE metrics, we also utilize BERTScore to perform the evaluation. BERTScore mainly focuses on solving the ROUGE problem, an evaluation metric based on the exact N-Gram matching, where semantically similar words would be considered incorrect. BERT + Transformer achieved higher ROUGE scores than the basic Transformer. The Electra + Transformer decoder outperformed both the basic Transformer and BERT + Transformer decoder. The BERTScore evaluation also presented the same results. BERT is a widely used bidirectional (or non-directional) pre-trained language model that reuses the encoder block from the Transformer and adopts self-supervised learning to learn the deep meaning of words and contexts. BERT is effective when fine-tuning on downstream tasks. In our experiments, incorporating BERT as the encoder in the NHG model could also bring improvements. ELECTRA comprises a generator and a discriminator, essentially two BERTs, with the generator focusing on masked language modeling and the discriminator specializing in token replacement detection. The training objective of the discriminator aligns more closely with headline generation, involving the replacement of words in a source document to produce a succinct headline. As a result, the Electra + Transformer decoder architecture outperformed the other two systems, leading us to adopt it as the foundational model structure for subsequent experiments.
## 2 Materials and Methods
### Experimental Data  
#### 2.5.1 Real Data  
Similar to the method proposed by [PERSON] et al. to synchronously label change-points with the help of a high-performance gaming mouse to obtain EMG signals with labels [9], this paper used a high-precision gaming steering wheel to locate the change-points. Specifically, fifteen healthy participants (10 males and 5 females, 24-32 years old) with right-handedness participated in the experiment, completing one signal acquisition per day for five days. During each signal acquisition, the electrodes were fixed on the extensor carpi radialis longus (ECRL) and flexor carpi radialis (FCR) of the right limb of participants after skin treatment. Participants operated the game steering wheel to the left and right 30 times, respectively, in their most natural state without limiting speed or strength. The control signal collected by the game steering wheel can be regarded as the human motion data to segment the active and baseline segments of the EMG signals (including the onset and offset time points). The raw real EMG signals are illustrated in Figure 5b.  
It should be noted that, due to the antagonistic characteristics of the muscles, the active muscles (spontaneous muscles) of the right forearm are different when the steering wheel is operated to the left or right, which results in half of the real EMG signals acquired in this paper not being accurately detected at the change-points of the active segments and needing to be discarded.
## 3 Methods and Materials
### Overall Architecture  
To fully exploit the complementarity between different modalities, we design a dual-branch learning structure to process the zero-filled auxiliary modality \(\hat{x}_{aux}\) and the target modality \(\hat{x}_{tar}\) of the input, respectively. The dual-branch network uses a unified structural design, with an encoding-decoding structure enhanced by residual learning and multi-attention. As shown in Figure 3, to enhance the network's recognition and emphasis on key features, we integrate a triple attention block (TAB, as shown in the right part of Figure 3) consisting of three types of attention into the basic modules of the encoder and decoder. At the same time, to improve the feature expression ability and optimize the information flow, the TAB is also used to replace the traditional linear operation at the bottleneck. The TAB consists of channel attention, spatial attention, and gate mechanism. Its detailed structure is shown in Figure 4. Combined with local residual learning, the network can automatically focus on key multi-scale features at different hierarchical levels to achieve more efficient feature processing and information transfer. In addition, replacing traditional skip connections with residual learning can alleviate the gradient vanishing problem in deep networks and improve generalization ability.  
Figure 3: Detailed architecture of the proposed encoder–decoder and detailed construction of triple attention.  
As shown in Figure 2, in the dual-branch learning architecture, the top branch accepts the input of the zero-filled auxiliary modality \(\hat{x}_{max}\in\mathbb{R}^{\text{H}\times\text{W}\times 2}\) (e.g., T1 WI or PDs), and the bottom branch processes the zero-padded target modality \(\hat{x}_{tar}\in\mathbb{R}^{\text{H}\times\text{W}\times 2}\) (e.g., T2 WI or PDFSs), where H and W are the height and width of the image, and 2 are the real and imaginary parts. Then, during the forward propagation, we can obtain feature maps at different hierarchical levels. In the decoder stage, we interact with the feature maps of the auxiliary modality \(I^{i}_{aux}(i=1,2,\cdots,\text{N})\) at each hierarchical with the feature maps of the target modality \(I^{i}_{aux}\) through the proposed MMFIM, where \(i\) represents the output feature map of the layer, and N is set to 4 in this paper. After all features in the encoder complete the interaction, we introduce the feature maps of the same modality into the SMFFM to further realize the fusion of feature maps of the same modality at different scales. In addition, we introduce a data consistency layer (DC) to fully utilize the k-space data to enhance the realism of the reconstructed images. Finally, after processing by the global residual learning module, we obtain the restored high-fidelity auxiliary modality \(x^{\prime}_{aux}\) and target modality \(x^{\prime}_{tar}\).
# A Performance Benchmark for the PostgreSQL and MySQL Databases
## 4 Experiments and Results
### Results
#### 4.3.2 Complex Experimental Results  
In production environments requiring handling multiple connections and requests simultaneously, the following steps must be taken:  
1. Select Query While Performing Insert Operations: In continuous authentication scenarios requiring simultaneous insert and select operations every few seconds, this scenario evaluates select operation performance when inserts occur simultaneously. Figure 14 shows MySQL's select operation performance while executing simultaneous inserts; the execution time varies between 7 ms and 13 ms compared to PostgreSQL's range of 0.7 ms to 0.9 ms as shown in Figure 15. Figure 16 and Table 9 compare MySQL's degraded performance due to increasing record numbers against PostgreSQL's stable performance despite changes.  
2. Select Query with Condition While Performing Insert Operations: This case evaluates a select operation with a where condition while executing parallel insert queries without adding additional records for the username 'clair'. Figures 17-19 show PostgreSQL outperforming MySQL again, with execution times ranging from 1 ms to 1.5 ms for MySQL compared to PostgreSQL's range of 0.07 ms to 0.13 ms. Table 10 displays statistics confirming PostgreSQL's superior performance by roughly nine times over MySQL.  
Figure 19: Select operation with where query comparison of MySQL and PostgreSQL with insert operation in parallel.  
Figure 17: Select operation with where query execution time of MySQL with insert operation in parallel.  
Figure 18: Select operation with where query execution time of PostgreSQL with insert operation in parallel.  
3. Insert New Records While Performing Select Operations: This scenario assesses insert operation performance during simultaneous select query execution. Figures 20 and 21 show performances where MySQL varies between 0.00020 ms and 0.00043 ms compared with PostgreSQL's range of 0.00010 ms to 0.00017 ms--demonstrating better performance by PostgreSQL during concurrent operations. Figure 22 compares the execution time results, showing clear superiority of PostgreSQL over MySQL, as confirmed by Table 11, showing median values of MySQL at 0.00020 ms versus 0.00010 ms for PostgreSQL.  
Also noteworthy is that these results can be attributed partly to PostgreSQL's robust support hierarchical data models, such as the adjacency list model, allowing efficient management insertion of complex tree-like data structures through its capability to handle recursive queries and optimize write-heavy operations, as evidenced by benchmark studies demonstrating superior write operation performance compared with MySQL [29].
## 4 Discussion
### Recommendations for Initial Support Design Criteria  
In contrast to conventional practices that rely on increasing burial depth or reducing overburden thickness to address engineering safety and economic concerns, a more rational determination of burial depth for interval tunnels can be achieved by integrating strata distribution with software analysis. Furthermore, the simulation analysis reveals a positive correlation between the bending moment and axial force of the initial support with the burial depth of the tunnel, displaying a similar pattern of change. However, the growth rate of internal forces decreases after reaching a certain depth. Considering the geological conditions specific to this project, the impact of burial depth on the internal forces of the initial support structure is concentrated in the range of 33-35%, whereas the impact of soil bulk density is concentrated in the range of 44-45%. This indicates that, within a fixed soil layer, the bulk density factor has a greater influence on the internal forces of the initial support structure compared to the burial depth factor. Therefore, when determining the tunnel path, it is crucial to control the burial depth within the range of approximately 22 kN/m\({}^{3}\), where bulk density undergoes significant changes. This approach ensures the optimal utilization of the structure's load-bearing capacity under substantial burial depths while considering the impact of soil bulk density on the initial support structure in this project.  
Moreover, the soil Poisson's ratio \(\mu\) has a distinct impact on axial force changes, differing from other factors. In this project, the range of \(\mu\) variation falls within 0.25-0.3, which corresponds to the declining section of the internal forces in the initial support. Thus, it has minimal detrimental effects on this structure. Regarding the structural dimensions, it is notable that the lining thickness experiences a sudden change in the internal forces of the initial support within the range of 300-350 mm. Therefore, a thickness of 300 mm can be selected for the initial support in this project, allowing for the full utilization of the structure's load-bearing capacity while ensuring an economical choice of materials.
# Ground Displacement in East Azerbaijan Province, Iran, Revealed by L-band and C-band InSAR Analyses
## 5 Discussion  
The correlation coefficient of the coherence between time spans 1 and 2 is 0.72, between time spans 1 and 3 is 0.64, and between time spans 1 and 4 is 0.62 (Figure 6). The high values for time spans 1 and 2 and low values for time spans 1 and 4 are not surprising, because as the temporal gap increases, the correlation coefficient decreases, mainly because of urban or vegetation growth, land use changes, etc. Although the PALSAR-2 with longer wavelengths can preserve the phase correlation in larger timespans, the standard deviation of the above-mentioned time spans is not small. The results of L-band (SBAS) and C-band (DInSAR) both identified two subsidence patterns in Shabestar and Tabriz basins. The mean subsidence rate of the DInSAR method is higher than that of the SBAS method. The velocities deduced from the C-band dataset show that there are higher subsidence rates in central, eastern, and western parts of the study area (dashed circles in Figure 7b), while these parts are almost stable in L-band results (Figure 7a). The strong displacement (overestimation) in these parts is related to the ambiguous nature of the phase [55] in pure displacement, atmospheric component, and remaining topographic phase, etc. For this reason, establishment of GPS stations in different parts of the study area for continuous measurement is essential for accuracy evaluation of DInSAR and SBAS results in future studies.  
As stated before, the PCA methodology helped us to find the trend of long-term and seasonal displacements based on the average velocity and the explained variance. PC1 explains \(\sim\)70% of the variance (Figure 12b) and the corresponding PC loading in Figure 12c gives insight into the correlation  
Figure 12: (**a**) SBAS time series of randomly selected points for PALSAR-2 dataset (only 250 points are shown), (**b**) scree plot of principal components (PCs), (**c**) three major components of principal component analysis (PCA), which explain about 96% of the variance, and (**d**) circle plot of variables for PC1 and PC2 loadings.  
between the uniform trend of subsidence in the study area. PC1 is well-explained by PC loading and the land subsidence is evident. But explanation of seasonal behavior is difficult, because PC2 and PC3 are similar. Further statistical tests may help to solve the complexity of the PCs, especially when the results are similar.
# Recent Updates on the Synthesis of Bioactive Quinoxaline-Containing Sulfonamides
## 2 Synthesis and Biological Activities
### Quinoxaline Sulfonamides with Antibacterial Activity
The morpholine moiety in analog 77 was responsible for the least antibacterial activity against Gram-positive and negative bacterial strains. (Figure 11) [131]. [PERSON] et al. described a new synthetic approach for the synthesis of libraries of quinoxaline sulfonamides and studied their antimicrobial activities. The starting 2,3-diphenylquinoxaline (**56**) was prepared in an excellent yield by condensation of \(o\)-phenylenediamine (OPD) (**34**) and diketone **78**. Then, quinoxaline **56** was treated with chlorosulfonic acid **47** at room temperature to afford the quinoxaline sulfonyl chloride **57** that reacted with primary and secondary amines to furnish quinoxaline sulfonamides **79** and **80** in good to excellent yield (Scheme 6) [132]. The prepared derivatives were tested against some Gram-positive and negative bacterial strains (Table 6). Among all these derivatives, the quinoxaline sulfonamide **81** exhibited an excellent antibacterial activity, with a ZOI of 30 mm and 24 mm against _P. vulgaris_ and _Enterobacteria_, respectively. A moderate activity was observed for **82**, with a ZOI value of 18 mm against _S. aureus_, 18 mm against _Enterobacteria_, 16 mm against _V. cholorie_, 16 mm against _E. coli_ and 16 mm against _P. vulgaris_. Compound **83** showed the least antibacterial activity, exhibiting a ZOI of 8 mm, 8 mm, 6 mm, 6 mm and 8 mm against _S. aureus_, _Enterobacteria_, _V. cholera_, _E. coli_ and _P. vulgaris_, respectively. The SAR showed that _ortho_-OH on the phenylsulfonamide moiety increased the antibacterial activity of the quinoxaline sulfonamide **81**, while the presence of a 2-chlorophenyl substituent was responsible for the moderate activity of the scaffold **82**. The introduction of a methoxyphenyl group into the quinoxaline sulfonamide derivative **83** decreased the antibacterial activity (Figure 12) [132]. Figure 11: Quinaxoline sulfonamide derivatives **74**–**77**. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Antibacterial Activity (20 mg/mL)**} \\ \hline \multirow{2}{*}{**Compound**} & \multicolumn{2}{c}{**Zone Diameter (mm)**} \\ \cline{2-3}  & _S. aureus_ & _E. coli_ \\ \hline
74 & 15 & 10 \\
75 & 14 & 8 \\
76 & 12 & 10 \\
77 & 10 & - \\ Sulfadiazine & 14 & 13 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Antibacterial activity of quinazoline sulfonamide derivatives **74**–**77**.
## 2 Jamming Classification and Mathematical Modeling  
Due to significant attenuation over long-distance transmission, GNSS signals arrive at the receiver with extremely low power. Although direct sequence spread spectrum (DSSS) techniques enhance signal concealment, the overall anti-jamming margin remains limited. In complex electromagnetic environments, GNSS receivers are highly susceptible to external interference, which may result in signal acquisition failure, loss of lock, or positioning drift. External interference can be categorized into two types based on its origin:  
1. Environmental interference, which arises from non-line-of-sight (NLOS) propagation and signal blockage or interruption caused by dense urban structures or complex terrain.
2. Man-made interference, which includes:  
Jamming, where energy-based disruptions degrade receiver performance. Typical forms include amplitude modulation (AM) jamming, frequency modulation (FM) jamming, chirp (swept-frequency) jamming, narrowband (NB) blocking, and pulsed interference, as shown in Figure 1.  
Spoofing, where counterfeit signals with code phase, carrier frequency, and Doppler parameters similar to the authentic signals are transmitted to mislead the receiver into producing erroneous navigation solutions.