esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### DisA May Protect Cells from Chromosome Breaks  
DNase I footprinting studies suggested that DisA might shield branched intermediates so that it could have protecting activity in vivo, thus avoiding the degradation of the reversed fork. We then tested whether DisA protects DNA from degradation during rapid growth in LB medium in unperturbed conditions or upon MMS-induced replicative stress, quantifying the accumulation of chromosomal fragments in \(wt\) and \(\Delta\)_disA_ cells by pulsed-field gel electrophoresis (PFGE) (Figure 2F). It is worth noting that circular DNA and chromosome-sized branched replication intermediates are unable to migrate into pulsed-field gels and remain in the well [20; 53].  
In the absence of DNA damage, a modest but significative increase in the amount of degraded DNA was observed in \(\Delta\)_disA_ cells when compared to the \(wt\) control (\(p<0.01\)) (Figure 2 FG). To test whether this degradation is due to RuvAB-RecU-mediated cleavage of the reversed fork as observed in _E. coli_[10] or during canonical DSB repair [37], we tested chromosomal degradation in the \(\Delta\)_recU_ context (Figure S3). In the absence of the RecU HJ resolvase, no significative decrease in the amount of degraded DNA was observed (\(p>0.5\)) (Figure S3). It is likely that in the absence of DisA, prolonged replication stress may cause replication fork collapse and degradation but not in the absence of RecU.  
Then, DNA replication was perturbed by the addition of MMS. MMS, which mainly methylates purines, causes replication fork slowing and fork stalling [20]. In the presence of 10 mM MMS, which reduces \(\sim\)3-fold the survival of \(wt\) cells [23], an increase in DNA fragmentation was observed when compared to the absence of MMS (\(p<0.05\)) (Figure 2F). The quantitative analysis of three independent experiments also revealed the increase of chromosomal fragmentation in \(\Delta\)_disA_ cells upon treatment with MMS. The levels of chromosomal degradation in the \(\Delta\)_disA_ context were higher than in the _wt_ control (Figure 2G). These results suggest that: (i) DisA may contribute to protect paused or stalled forks from degradation in unperturbed replicating cells, and (ii) MMS induces chromosomal fragmentation in both _wt_ and \(\Delta\)_disA_ cells.
# Wireless Sensor Networks for Enabling Smart Production Lines in Industry 4.0
## 4 Results and Discussion
### Performance Analysis
#### 4.2.2 Network Bandwidth  
The client's throughput depends on the received power, which depends on PL and TX power. With higher received power, a higher MCS setting can be used. Apart from the maximum throughput of the wireless link, real-life throughput also depends on the bandwidth available at the AP, that is, the uplink data stream is shared by all clients connected to the AP. Furthermore, the number of simultaneously connected clients is often limited.  
By using lower TX power, more APs are needed to cover the complete floor plan. However, this also increases the total network capacity. Considering that most commercial WiFi APs have a Gigabit Ethernet link, and can connect up to 20 clients at full download speed, the total network capacity of the factory warehouse increases from 12 Gbps when the APs operate at full TX power, with a total of 460 clients, up to 53 Gbps when the APs use a default TX power of 15 dBm. For the latter, more than 1000 clients can connect.  
Figure 10: Network coverage as a function of the maximum transmit power of the access points.  
Figure 9: Updated floor plan of the simulation environments, with numbers indicating the order in which racks are added or modified. (**a**) Factory warehouse (112 m \(\times\) 131 m). (**b**) Processing facility (138 m \(\times\) 175 m).
# A Lyapunov-Optimized Dynamic Task Offloading Strategy for Satellite Edge Computing
## 5 Simulation and Analysis  
### Parameters Setting  
The system has been constructed using the Iridium Next constellation [25]. To establish the specific simulation parameters of the DOALH algorithm, we referred to existing studies [1, 11] and outlined them in Table 1. To validate the efficacy of the proposed algorithm, we present the following alternative approaches for comparison:  
1. Local Computing (Local): All tasks are processed solely within the receiving satellite, without any computational offloading.
2. Greedy Offloading (Greedy): In each time slot, the task with the highest number of pending tasks is offloaded to the satellite with the lowest task queue for processing.
3. DQN Offloading (DQN): In a similar study, the authors modeled the computational offloading process as an MDP and used Deep Q-Networks (DQN) to determine the optimal offloading strategy. The reward function is set as \(P2\)[5].  
\begin{table}
\begin{tabular}{c c} \hline Satellites computational capacity \(C_{k}\) (Mb/s) & [10, 12] \\ \hline Number of satellites in the constellation \(N\) & \(6\times 11\) \\ \hline The bound of random & [10, 15] \\ distribution \([\lambda_{min},\lambda_{max}]\)(Mb) & \\ \hline Satellites transmission power \(\mathit{p}^{trans}\) (J/s) & 400 \\ \hline Satellites computational power \(\mathit{p}^{com}\) (J/s) & [100, 200] \\ \hline Length of one time slot \(\tau\) (s) & 10 \\ \hline Simulation time slots \(T\) & 360 \\ \hline \end{tabular}
\end{table}
Table 1: System parameters.
## 3 Results
### Eye Metric Classification
#### 3.3.1 Classification of Eye Movements  
Research by [PERSON] et al. addresses the rising prevalence of Computer Vision Syndrom (CVS) through a deep learning model integrating a convolutional neural network (CNN) and Long Short-Term Memory (LSTM) network for real-time blink detection [14]. The CNN identifies eye states (open or closed), while the LSTM captures temporal blink patterns from video streams. The model demonstrated high accuracy--94.2% on non-RGB eye patch images and 91.4% on RGB facial images--showcasing its robustness across input types. This approach distinguishes itself by tailoring the CNN-LSTM model for live stream analysis, overcoming challenges such as computational inefficiency in prior methods.  
Two studies demonstrate the effectiveness of hybrid 1D-CNN and BLSTM models for eye movement classification, achieving F1 scores of approximately 0.8 across datasets. The first study, trained on the GazeCom and Nystrom-Andersson datasets, outperformed 12 state-of-the-art algorithms, particularly excelling in smooth pursuit detection through features like speed and acceleration across temporal scales [15]. The second study, targeting real-time classification for virtual workshops, showed improved accuracy, F1 scores, and Kappa coefficients over CNN-only and traditional methods [16], highlighting the robustness of hybrid models for eye movement analysis.  
Several studies have focused on addressing the challenges of efficient and accurate eye-tracking on resource-constrained mobile devices, proposing hybrid approaches to balance computational efficiency and accuracy. One approach combines an SVM with Histogram of Oriented Gradients (HOG) features for region proposals and a lightweight LeNet-5 CNN for classification, enabling real-time blinking detection with a precision of 94.4% and a processing rate of 22 FPS on commodity smartphones [17]. Another study introduces GazeHFR, a biologically inspired framework that classifies eye movements into saccade and smooth pursuit phases by leveraging MobileNet [18]. The framework achieves a 7\(\times\) improvement in efficiency and a 15% boost in accuracy compared to prior methods. Both approaches demonstrate the feasibility of lightweight, real-time models for mobile eye-tracking applications, even under the computational constraints of high-frame-rate mobile scenarios.
# Synchronous Remote Calibration for Electricity Meters: Application and Optimization
## 3 Methods
### Algorithm
#### 3.2.3 Strategy Comparison
```
0:\(\mathcal{N}_{K}\): Set of detection points, \(\mathcal{N}_{G}\): Set of central laboratory nodes, \(\mathcal{N}_{X}\): Set of all tasks
0: Fitness value \(fitness\)
1:for\(m=1\) to \(|\mathcal{N}_{C}|\)do
2:\(C_{m}^{et}\gets 0\)
3:for\(n=1\) to \(|\mathcal{N}_{S}|\)do
4:\(S_{n}^{et}\gets 0\)
5:while P do
6: Select task \(X_{t}\) from set \(P\)
7: Identify corresponding client \(X_{t}^{C_{m}}\) and server \(X_{t}^{S_{n}}\)
8:\(X_{t}^{et}\leftarrow\max(C_{m}^{et},S_{n}^{et})\)
9: Update \(C_{m}^{et},S_{n}^{et},X_{t}^{et}\gets X_{t}^{st}+X_{t}^{\tau}\)
10:\(T_{1}\leftarrow\max(X_{t}^{et})\)
11:\(T_{2}\leftarrow\max(\overline{C^{et}},\overline{S^{et}})\)
12: Initialize: \(succ\leftarrow 0\)
13:for\(X_{t}=1\) to \(|\mathcal{N}_{X}|\)do
14: Initialize: \(pr\_succ,cty\_succ,cty\_succ,d\_succ\gets 0\)
15:if client \(X_{t}^{C_{m}}\) can process task type \(X_{t}^{Q_{yy}}\)then
16:\(cty\_succ\gets 1\)
17:if\(severX_{t}^{S_{n}}\) can process task type \(X_{t}^{Q_{yy}}\)then
18:\(sty\_succ\gets 1\)
19:if\(X_{t}^{C_{m}}\) is within detection points of \(X_{t}^{P_{T}}\)then
20:\(pr\_succ\gets 1\)
21:if\(\text{distance}(X_{t}^{C_{m}},X_{t}^{S_{n}})<\text{over}(X_{t}^{S_{n}})\)then
22:\(d\_succ\gets 1\)
23:if\(pr\_succ\wedge cty\_succ\wedge sty\_succ\wedge d\_succ\)then
24:\(succ\gets 1\)
25:else
26:\(succ\gets 0\)
27:break
28:if\(succ\)then
29:\(fitness\leftarrow\frac{\alpha}{T_{1}+\gamma T_{2}}\)
30:else
31:\(fitness\leftarrow\frac{\beta}{T_{1}+\gamma T_{2}}\)
32:returnfitness
```  
**Algorithm 1** Fitness Calculation Algorithm
# The Vibration Response Characteristics of Neighboring Tunnels Induced by Shield Construction
## 5 Discussion
### Validation of Theoretical Models Through Numerical Simulation  
Numerical simulation results using PFC3D further confirm the reliability and accuracy of the theoretical models. The model's input damping ratio was set to 0.5, while the reversecalculated damping ratio in the simulation was 0.59, showing a high degree of consistency. This agreement not only reinforces the appropriateness of the simulation model parameters but also provides valuable insights for both theoretical analysis and practical applications. Moreover, the simulated attenuation trend of vibration energy closely matches theoretical predictions and on-site monitoring data, validating the accuracy of the energy distribution patterns and establishing a strong foundation for predicting and assessing the vibration impact of shield tunneling construction.  
The simulation also reveals localized anomalies in vibration propagation. Between 2 and 3 m from the vibration source, the acceleration power density shows an unexpected decrease, indicating a unique energy dissipation mechanism. This phenomenon could be linked to the propagation of localized microcracks in concrete segments or material nonlinear behaviors that dissipate energy. The presence of cracks not only weakens vibration energy transmission but may also alter the local stress field, resulting in more complex vibration response patterns. Additionally, soil heterogeneity and the effects of vibration wave reflection and scattering at the concrete-soil interface may contribute to this anomaly.  
These findings underscore that localized vibration anomalies could pose potential risks during shield construction, highlighting the need for further investigation. Optimizing the crack resistance of segment materials and improving structural design can reduce the impact of crack propagation on energy dissipation. Additionally, monitoring localized vibration anomalies with high-precision sensors and dynamically adjusting construction parameters through numerical simulations can help minimize risks to structural safety.
# Training-Free Acoustic-Based Hand Gesture Tracking on Smart Speakers
## 3 Materials and Methods
### Hand Tracking
#### 3.4.3 Kalman Filter  
For model \(i\) of the \(k^{th}\) chirp, the predicted state \(\mathbf{\hat{x}}_{k|k-1}\) can be calculated by  
\[\mathbf{\hat{x}}_{k|k-1}^{i}=F^{i}\mathbf{\hat{x}}_{k-1|k-1}^{i}, \tag{25}\]  
where \(F^{i}\) is the transition matrix. And the predicted error covariance matrix can be calculated by  
\[P_{k|k-1}^{i}=Q_{k-1}^{i}+F^{i}P_{k-1|k-1}^{i}(F^{i})^{T}, \tag{26}\]  
where \(Q_{k-1}^{i}\) is the covariance of the process noise. Therefore, the state estimation and covariance matrix after the Kalman Filter can be calculated by  
\[\mathbf{\hat{x}}_{k|k}^{i}=\mathbf{\hat{x}}_{k|k-1}^{i}+K_{k}^{i}\Big{(} \mathbf{z}_{k}^{i}-H^{i}\mathbf{\hat{x}}_{k|k-1}^{i}\Big{)}, \tag{27}\]  
\[P_{k|k}^{i}=P_{k|k-1}^{i}-K_{k}^{i}H^{i}P_{k|k-1}^{i}, \tag{28}\]  
where \(H^{i}\) is the measurement matrix, and \(K_{k}^{i}=P_{k|k-1}^{i}\big{(}H^{i}\big{)}^{T}\Big{(}H^{i}P_{k|k-1}^{i}\big{(} H^{i}\big{)}^{T}+Q_{k}^{i}\Big{)}^{-1}\) is the Kalman gain.
# An FPGA-Based Four-Channel 128k-Point FFT Processor Suitable for Spaceborne SAR
## 2 Fast Fourier Transform (FFT) Operation Analysis and Principle Introduction
### Introduction of Mixed-Radix Algorithm  
Considering the high real-time performance of the processor, we adopted a mixed-radix algorithm to achieve a four-channel FFT structure, which could theoretically shorten the processing time by about four times. The mixed-radix algorithm refers to the decomposition of DFT according to at least two decomposition methods, and the time domain index and frequency domain index in Equation (2) are decomposed as follows:  
\[\left\{\begin{array}{l}n=L\cdot m+l\;m=0,1,\ldots,M-1;\;l=0,1,\ldots,L-1\\ k=i+M\cdot j\;i=0,1,\ldots,M-1;\;j=0,1,\ldots,L-1\\ N=L\cdot M\;M=2^{k}\end{array}\right. \tag{6}\]  
In Equation (6), L is regarded as the number of channels, and M is regarded as the number of FFT points calculated in a single channel. When k is an integral multiple of 3, the radix-2\({}^{3}\) algorithm can be used. Substituting Equation (6) into Equation (3) results in the following equation:  
\[\begin{array}{rl}X(i+M\cdot j)&=\sum\limits_{m=0}^{M-1}\sum\limits_{l=0}^{L- 1}x(L\cdot m+l)W_{N}^{(L\cdot m+l)\cdot(i+M\cdot j)}\\ &=\sum\limits_{m=0}^{M-1}\sum\limits_{l=0}^{L-1}x(L\cdot m+l)W_{N}^{il}\cdot W _{M}^{mi}\cdot W_{L}^{lj}\\ &=\sum\limits_{l=0}^{L-1}X_{l}(i)W_{N}^{il}\cdot W_{L}^{lj}\end{array} \tag{7}\]  
In Equation (7), there are the following results:  
\[X_{l}(i)=\sum\limits_{m=0}^{M-1}x(L\cdot m+l)W_{M}^{mi} \tag{8}\]  
From Equations (7) and (8), the mixed-radix algorithm can be summarized as the process of decomposing an N-point FFT into a \(L\times M\)-point FFT. The number of FFT processor points, designed in this study, is 128k, and the decomposition method of \(4\times 32\)k is adopted. Where \(M=32\)k, which is realized by the five-stage radix-2\({}^{3}\) algorithm, \(L=4\), which can be implemented by the radix-4 butterfly unit. Substituting numbers into Equations (7) and (8), the following results can be obtained:  
\[X(i+32k\cdot j)=\sum\limits_{l=0}^{3}\left[X_{l}(i)\cdot W_{128k}^{il}\right] \cdot W_{4}^{li} \tag{9}\]  
\[X_{l}(i)=\sum\limits_{m=0}^{32k-1}x(4\cdot m+l)W  
function of the four-channel and mixed-radix algorithm of the FFT processor designed in this article, the \"radix-4 algorithm\" instead of the \"radix-2\({}^{2}\) algorithm\" is used afterwards.  
It can be clearly seen from Figure 7 that the four-channel FFT parallel structure can save three-quarters of the time as compared with the single-channel FFT algorithm. The radix-4 structure can be implemented through combinational logic circuits without introducing new processing delays. Therefore, the four-channel design can make full use of hardware resources, increase the number of FFT calculation points, and greatly reduce the FFT calculation time. However, an increase in the number of channels means that the processing bandwidth is doubled; therefore, the number of channels is limited by the processing bandwidth and data transmission bandwidth of the FPGA.
## 2 Instructional Methods and Best Practices
### Learning Resources
#### 2.3.2 Distance Learning Courses  
MetEd distance learning courses organize multiple lessons in a particular area of interest, making it easier for learners interested in that topic area to access all of the lessons related to that topic. Courses are self-paced and always available for open enrollment. When a learner passes the quizzes for all the core lessons in a course, he or she can receive a course completion certificate. As of early 2019, MetEd hosts several distance learning courses, which are shown in Table 2. COMET, in coordination with other training providers, also contributes individual lessons to larger courses designed to cover multiple topics that promote user readiness for next-generation operational satellite series, such as the GOES (geostationary) and JPSS (polar-orbiting) constellations. Several of COMET's most recent training deliveries are topic-focused case exercises that allow learners to work through an event to practice applying satellite products to specific forecast tasks or challenges. Since 2017, COMET has released four stand-alone case examples in the satellite topic area (Figure 8). Additional training case exercises are in development. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Satellite Constellations** & \(\bullet\) & GOES-R, Himawari, Meteosat Second Generation \\
**Satellite Constellations** & \(\bullet\) & JPSS, Metop, GCOM-W \\
**Satellite Constellations** & \(\bullet\) & Jason altimetry mission \\
**Satellite Constellations** & \(\bullet\) & COSMIC constellation \\ \hline
**Satellite Constellations** & \(\bullet\) & GEO vs. LEO orbits and observational coverage \\
**Satellite Constellations** & \(\bullet\) & Principles of radiation \\
**Satellite Constellations** & \(\bullet\) & Visible and infrared remote sensing \\
**Satellite Constellations** & \(\bullet\) & Microwave remote sensing \\
**Remote Sensing Topics** & \(\bullet\) & Hyperspectral sounding \\
**Satellite Constellations** & \(\bullet\) & Scatterometry for wind and wave estimates \\
**Satellite Constellations** & \(\bullet\) & Land and ocean surface properties \\
**Satellite Constellations** & \(\bullet\) & Radio occultation \\
**Satellite Constellations** & \(\bullet\) & Satellite altimetry \\ \hline
**Satellite Constellations** & \(\bullet\) & Satellite imagery interpretation \\
**Satellite Constellations** & \(\bullet\) & Multispectral satellite products and applications \\
**Satellite Constellations** & \(\bullet\) & Weather analysis, diagnosis, and prediction \\
**Satellite Constellations** & \(\bullet\) & Storm analysis \\
**Satellite Constellations** & \(\bullet\) & Monitoring and prediction of environmental hazards \\
**Satellite Constellations** & \(\bullet\) & Land and ocean analysis \\
**Satellite Constellations** & \(\bullet\) & Climate monitoring \\
**Satellite Constellations** & \(\bullet\) & Atmospheric chemistry \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sampling of MetEd satellite meteorology lesson categories.
# Privacy-Preserved Image Protection Supporting Different Access Rights
## 4 Experimental Results  
In this section, we conduct several experiments to evaluate the performance of our proposed scheme. The proposed scheme is implemented using python 3.10.4 and an open source pycryptodome 3.14.1 library on a PC with Intel Core i7-8750H 2.20 GHz CPU, 8 GB RAM, and 64-bits Windows 11 system. To make the process more secure and effective, an AES symmetric cryptosystem is used to encrypt the privacy-protected pixels with secret and derivative keys generated by RSA [28] and ElGamal [29]. The encrypted blocks can be decrypted by the derivative key and users' public keys according to the access policy. To demonstrate the feasibility and effectiveness of our scheme for privacy protection, portrait images are used in the analysis.
# Product Demand Prediction with Spatial Graph Neural Networks
## 4 Experiments
### Overall Performance (RQ1)  
Addressing Research Question 1, we assess the performance of our Spatial Graph Neural Network model in the realm of product demand prediction, setting it against a spectrum of modeling approaches, including linear, tree-based, and deep learning models. This evaluation leverages metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R\({}^{2}\)), providing a comprehensive view of each model's predictive accuracy. As delineated in Table 1, our analysis reveals a clear hierarchy in model performance. The data underscores that both tree-based models (XGBoost, LightGBM, and CatBoost) and deep learning models (encompassing LSTM, GRU, and CNN) surpass the Generalized Linear Model (GLM) in terms of predictive capability. This outcome hints at the limitations of traditional linear models in capturing the intricate and nonlinear relationships inherent in real-world product demand data. While deep learning models such as LSTM and GRU have demonstrated promise across various domains, their edge over sophisticated tree-based models in the context of product demand prediction appears marginal. This observation suggests a need for the further refinement and domain-specific tailoring of deep learning approaches to unlock their full potential in this area. Notably, the CNN model outperforms MLP, LSTM, and GRU, highlighting the pivotal role of spatial information in enhancing demand prediction accuracy. Amidst this competitive landscape, our SGNN model emerges as a standout performer, consistently outshining both the traditional tree-based and contemporary deep learning baselines. This remarkable performance, characterized by lower MAE, RMSE, and higher R\({}^{2}\) scores, underscores the efficacy of our approach in navigating the complexities of product demand prediction. The SGNN model's superior accuracy attests to its capacity to effectively leverage spatial relationships, offering a robust solution that could significantly benefit the online advertisement sector. The findings from our analysis not only validate the innovative design of the SGNN model but also illuminate the path for future advancements in the field of demand predicting. The compelling results achieved by our model on the Avito dataset underscore its potential as a transformative tool for marketers and strategists aiming to optimize their online presence and engagement strategies.