esa-sceva/satcom-chunk-collection huggingface dataset:
# Location Optimization of Emergency Bell Based on Coverage Analysis for Crime Prevention
## 3 Materials and Methods
### GIS (Geographic Information System)  
In this section, the GIS software, tools used for coordinate extraction, and the method employed to calculate the surveillance range of CCTV and emergency bells are introduced.  
In spatial data analysis, geocoding refers to the process of extracting latitude and longitude coordinates from address data. Particularly, the extracted data enable improved visualization and analysis. Python offers numerous geocoding techniques, such as Geocoder and GeoPy. Additionally, the use of the Pandas module also facilitates geocoding processing [29].  
In this study, geocoding was used to convert the address information provided in the five major crime datasets into coordinates. More precisely, the GIS program, QGIS (Ver 3.16), was utilized to integrate the datasets for analysis based on the grid from the geographic information of the administrative dong. As a result, a 50 m buffer was realized based on the obtained coordinates of the CCTV cameras and emergency bells. Subsequently, the area of the buffer that fell within the 100 \(\times\) 100 (m\({}^{2}\)) grid was calculated to determine the surveillance area.
# Fast Simulation of Laser Heating Processes on Thin Metal Plates with FFT Using CPU/GPU Hardware
## 5 Conclusions and Future Work  
This manuscript presents four different schemes for the solution of the laser heating problem on thin metal plates using the DST and the FFT. The presented methods reduce the computational complexity of the problem from \(\mathcal{O}(M^{2}N^{2})\) to \(\mathcal{O}(MN\log(MN))\) (with \(M\times N\) being the discretization size of the metal plate). It is worth noting that forced convection and radiative heat transfer are not present in the solution considered in this manuscript. The inclusion of those effects in the simulation implies a significant change in the structure of the mathematical model, which is out of the scope of this research. Overcoming of such a limitation is left as future work.  
These schemes are implemented in both CPU and GPU architectures using available optimized FFT libraries. Mathematical and numerical proofs of the correctness of the schemes are presented and the numerical error is measured below \(10^{-10}\) K (and independent of the laser trajectory).  
The performance evaluation shows that the minimum achievable computation time varies in function of the used library, specially for big input sizes. Furthermore, the obtained results improve the state of the art [25] in both CPU and GPU platforms for all the proposed schemes. Specifically, using GPU hardware, the computation times for the temperature evaluation are reduced from 1 s to 0.01 s (100\(\times\) faster), measured in an NVIDIA GeForce GTX 960 (GPU).  
With more modern GPU hardware (GeForce RTX2060) even faster results can be acquired which shows the potential of the proposed algorithms towards real-time laser heating/cutting simulations and flexible manufacturing scenarios that require fast tool-planning capability and laser parameter optimization to easily adapt to customer order changes.  
Future work includes (1) the inclusion of thermal/stress models for structural analysis of the plate after the generated high temperature gradients, (2) analysis of non-rectangular plate geometries, and (3) consideration of non-linear interactions such as temperature-dependent thermal properties, forced convection, radiation heat transfer and phase changes.  
[PERSON]-[PERSON], [PERSON] and [PERSON] conceived, designed and implemented the algorithms and performed the simulations. [PERSON]-[PERSON] and [PERSON] supervised the Computational Geometry, Heat Transfer and Spectral Methods research. [PERSON]-[PERSON] supervised the Parallel Computing, Data Structures and High Performance Programming aspects of this research. All the authors contributed to the writing of this manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:
## 4 Results: The Usage of CARIMO
### Usage Data Logging for CARIMO
#### 4.2.3 Determination of Logging Capacity and Bandwidth  
The logging capacity calculations considered two dimensions: the number of users (more than 100), and the duration of the trial (eight months + extension options). Thus, in addition to a monthly bandwidth of approximately 70 to 80 MB per user, for the CARIMO app, we calculated 5 MB for the logging data per user and month.
# RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images
## 2 Related Work
### Attention Mechanisms in Vision Tasks  
The principle of the attention mechanism is to imitate the attentional thinking of human beings. It can shift the attention of the model learning to important informative regions and ignore irrelevant information. Attention algorithms used in computer vision include spatial domain attention, channel domain attention, and mixed domain attention.  
Spatial domain attention obtains attention weights by computing the correlation of each position in the feature map with all other positions. For example, STNs (Spatial Transformer Networks) [25] learn the spatial transformation parameters by performing reverse spatial transformation on the data, so as to reduce the influence of the deformation of the target on the picture, thereby making the recognition of the classification network more efficient.  
Channel domain attention is to apply attention weights on channels to capture the dependencies between channels, a typical representative is SENet (Squeeze-and-Excitation Networks) [26], which uses global average pooling to process feature maps to generate per-channel statistics, then uses two fully connected layers to learn weighted values for each channel, and finally gets a weighted feature map as the next layer network input.  
Both spatial domain attention and channel domain attention algorithms are theoretically flawed. The former treats the features of each channel the same, ignoring the correlation between channels, while the latter performs global average pooling on each channel, and local information is ignored. Therefore, some researchers proposed a mixed domain attention algorithm, which combines the attention of the spatial domain and the channel domain. For example, in CBAM [27], which uses a serial method, the feature map first passes through the channel attention module, and then passes through the spatial attention module.  
All of the above three attention algorithms can be integrated with convolutional neural networks to complete visual tasks such as classification, detection, and segmentation. For example, [PERSON] et al. [28] fused channel attention with YOLOv5 to achieve disease detection in maize leaves, and [PERSON] et al. [29] introduced mixed attention into two-stream convolution network to realize human action recognition. [PERSON] et al. [30] proposed a new mixed attention algorithm and used it in the U-Net network to achieve cell segmentation. It can be known from these studies that the proposal of the attention mechanism promotes the development of computer vision models to have higher accuracy.
# A Survey of of Side-Channel Attacks and Mitigation for Processor Interconnects
## 6 Layout-Based Interconnect Side-Channel
### Challenges in Mitigating Heat-Channel Attacks  
Heat-channel-based side-channel and covert-channel attacks rely entirely on temperature information exposed to software, making the natural solution to this issue limiting access to temperature sensors on the system. However, this requirement contradicts the original intent of thermal management and its design flexibility for pursuing optimal performance. Moreover, centralized monitoring and control of processor thermal states have become increasingly challenging to adapt to the growing number of processor cores in modern commercial processors. Modern commercial processors can now contain hundreds of cores, each hosting numerous autonomous processes. For instance, Intel's SCC platform [85], a research prototype, allows subsets of cores to independently manage their frequency and voltage to enhance power efficiency, making it impossible to hide temperature information, a crucial input in the decision-making process, within this architecture. Balancing performance and security poses a challenge for all processor microarchitecture optimization measures, including thermal management.  
In addition to temperature sensors, many other parameters may still leak information about the system's thermal state, such as clock skew, fan speed, and even processor frequency in systems that allow dynamic frequency scaling. Since all these parameters are  
Figure 6: Heat propagation from a neighbouring core.  
typically shared among cores or subsets of cores within the processor chip, even if access to temperature sensors is restricted or prohibited, these parameters can still be used to implement side-channel and covert-channel attacks.
# Image Quality for Near-Eye Display Based on Holographic Waveguides
## 2 Materials and Methods
### Diffraction Analysis for the Waveguide Gratings
**  
The resulting data represents the performances of diffraction efficiencies in \(-1\) order (\(\eta_{-1}\)) depending on grating depth \(h\) in the range from 0 to the grating period. To maximize the diffraction efficiency across the angular field of view it was decided to use gratings with a profile height of 100 to 150 nm. In the following experiments, surface relief was about 120 nm. This condition, firstly, is consistent with the equalization of diffraction efficiency for different incidence angles. Secondly, at such a profile height the property of dependence of diffraction efficiency on polarization is less apparent, as illustrated by Figure 3. Thirdly, when holographic diffraction gratings are not designed to maximize diffraction efficiency, various interference artifacts at the recording stage have less effect on the quality of the diffracted wavefront across the field. Experimental confirmation of these statements is described in the following sections.
# Analysis of Wafer Warpage in Diamond Wire Saw Slicing Sapphire Crystal
## 6 Conclusions  
This paper establishes a finite element model for thermal analysis of sapphire crystal during diamond wire saw cutting, and analyzes the wafer temperature field distribution and the variation law and internal relationship of node thermal deformation displacement field during the sawing process. Based on the thermal deformation displacement field of wafer surface nodes, a calculation and analysis model for the sapphire wafer warpage in wire sawing was established. After the rationality of the finite element model was verified through sawing experiments, the influence of wafer thickness, diamond wire diameter, and sawing process parameters on the warpage of sapphire wafers was studied based on the finite element simulation model. The following conclusion was drawn:  
1. The temperature and deformation at the sawing position is the largest. As the cutting depth increases during sawing, the temperature of the wafer gradually increases and tends to stabilize.
2. The thickness of sapphire wafers cut by the diamond wire saw decreases, and the smaller the diameter of the diamond wire used, the greater the warpage of the cut wafers.
3. The sawing process parameters mainly affect the wafer warpage by influencing the heat density and power during sawing. The warpage of sapphire wafers increases with the increase of the saw wire speed and feed rate. How to achieve effective lubrication and cooling of a narrow kerf in sawing processing is the key to obtaining low warpage wafers.  
Y.L.: writing--original draft preparation, data curation, visualization. D.C.: data curation, visualization. G.L.: writing--original draft preparation. Y.G.: conceptualization, methodology, writing--review and editing, project administration. All authors have read and agreed to the published version of the manuscript.  
The work is financially supported by Opening Foundation of Key Lab. of Manufacturing Equipment of Shaanxi Province (No. JZZZB-2022-01) and Natural Science Foundation of Shandong Province (No. ZR2023 ME145).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
## 3 Research Methodology
### Data Sourcing and Sampling  
Data sourcing and sampling is a pivotal process, as the validity of the model outcomes depends on the dataset provided to the model. Illustrated in Figure 4, the process of data sourcing and sampling commenced with the identification and review of journal articles and reports from companies with published inventories of KPIs from respective ecosystems. Subsequently, companies that produced real-world KPI data were identified and requested to make data available where possible. These data were statistically analysed to select only correlated data. Subsequently, a scientific method was applied to create more correlated data that would be sufficient to develop the model.  
Figure 4: Process for data sourcing and sampling.  
Figure 3: High-level algorithm of the model.  
Data sampling involved determining and selecting only KPIs that were correlated with other KPIs in the whole dataset. This was achieved by calculating correlation coefficients of the KPIs through Equation (1). KPIs that had negative (<0) or positive (>0) correlations with other KPIs were selected, while those KPIs with correlation values of zero (0) were not selected, as they indicated that they were not correlated with other KPIs in the whole dataset.  
\[c=\frac{\text{cov}\left(kpi_{i},kpi_{j}\right)}{\sigma_{kpi_{i}}\sigma_{kpi_{j}}} \tag{1}\]  
where \(c\) is the correlation coefficient, \(\text{cov}\left(kpi_{i},kpi_{j}\right)\) is the covariance of \(i\)th and \(j\)th KPIs, \(\sigma_{kpi_{i}}\) is the standard deviation of the \(i\)th KPI, and \(\sigma_{kpi_{i}}\) is the standard deviation of the \(j\)th KPI.  
Subsequent to successful data sampling, a scientific method was applied to create more data (correlated random KPI values) from the correlated data sample through Equation (2). Creating correlated random KPI values ensured that there are adequate data to build the model.  
\[x(kpi_{i})=random(\leq(kpi_{i}),\geq(kpi_{i}),\overline{kpi_{i}},\sigma(kpi_ {i})) \tag{2}\]  
where \(x(kpi_{i})\) is the expected random value for the \(i\)th KPI, \(\leq(kpi_{i})\) is the minimum value of the \(i\)th KPI, \(\geq(kpi_{i})\) is the maximum value of the \(i\)th KPI, \(kpi_{i}\) is the value of the \(i\)th KPI in the distribution, \(\overline{kpi_{i}}\) is the mean of the \(i\)th KPI, and \(\sigma(kpi_{i})\) is the standard deviation of the \(i\)th KPI. The final KPI dataset needed to be validated for its statistical suitability and adequacy to build the model, as discussed in the next section.
# Control Strategy of Multiple Battery Energy Storage Stations for Power Grid Peak Shaving
## 1 Introduction
For example, by utilizing time-of-use pricing mechanisms, the energy storage station can charge during off-peak hours and discharge during peak hours to maximize economic benefits [17], thereby indirectly achieving peak-valley regulation. Related economic models include the BESS revenue model [18], which considers the interests of the generation side, grid side, and user side [19], and the investment economic model [20], which analyzes the economic conditions for large-scale energy storage applications from an investment return perspective. In summary, although existing strategies for BESSs to participate in peak shaving can smooth load fluctuations, most of them are still limited to a single BESS, and their effectiveness is restricted. Single BESSs are limited by their rated power and capacity constraints, making it difficult for their regulation capabilities to match continuously changing load curves. To overcome this inherent limitation and improve peak-shaving effectiveness, this paper proposes a multiple BESS coordinated variable-power control strategy. The main contributions of this paper are as follows:  
This paper proposes a control strategy of multiple battery energy storage stations (BESSs) for power-grid peak shaving. Firstly, the working principle of the variable-powercontrol strategy for multiple BESSs is proposed. Secondly, an optimization model is constructed incorporating load forecasting, safety constraints, and evaluations for peak-shaving effectiveness. Thirdly, the collaborative algorithm based on a continuous time-period search and phased control is used to obtain the optimal control strategy. Finally, a typical daily case is employed to quantitatively analyze the improvement effects on peak shaving. The structure of this paper is as follows: Section 2 explains the design principles of the cooperative variable-power mechanism and constructs an optimization model incorporating load forecasting, safety constraints, and evaluation metrics. Section 3 conducts a detailed analysis of the peak-shaving effects of the cooperative variable-power strategy and the single constant power strategy through a comparison of typical daily cases, quantifying the improvement effects on key metrics. Section 4 summarizes the work of this paper and outlines future research directions.
# Damage Detection in Beam Bridges Using Quasi-static Displacement Influence Lines
## 1 Introduction
Additionally, if the sensing optical fiber is placed along the longitudinal direction of the bridge superstructure under quasi-static loading conditions, quasi-static strain ILs are obtained through the use of measurement points with a high spatial resolution and the long-distance measurement range of the BOTDA technique [26; 27]. Because the stain reflects the local performance of structures, the use of a sufficient number of quasi-static strain ILs can accurately locate damage in bridges. Unlike quasi-static strain ILs based on the BOTDA technique, quasi-static displacement ILs can be feasibly obtained for the bridge superstructure of actual bridges. Furthermore, only a few quasi-static displacement ILs need to be implemented for the load testing of actual bridges. However, because the displacement reflects the overall performance of a structure, it is not appropriate to attempt to locate bridge damage using only a few measured points. The displacement information helps determine if damage exists. In consideration of the above issues, on the basis of the previous method [25], an improved method is proposed to detect the damage of beam bridges using only a few quasi-static displacement ILs. While the reconstruction of the Hankel matrix of the damage feature is presented to establish the damage feature using a few quasi-static displacement ILs, the method used to obtain the metric for evaluating the damage feature is improved so that the efficiency of damage detection using quasi-static ILs is greatly increased. The proposed method is independent of the difference in the loading weight before and after damage and this performance is verified using numerical and experimental examples in this study. In addition, combining with structural health monitoring technique [28], the proposed method is applied to detect the damage of bridge under the load-moving excitation [29; 30]. The remainder of this paper is organized as follows. The method based on quasi-static displacement ILs is introduced in detail in the next section. The performance of the proposed method is discussed numerically in Section 3. Then, the effectiveness of the proposed method is identified in Section 4 using the measured data of a model bridge. Finally, conclusions are drawn in Section 5.
# A Scalable Parallel Architecture Based on Many-Core Processors for Generating HTTP Traffic
## 5 The High Concurrency Strategy
### Virtual users layer  
As a middle-level, the virtual users layer is mainly responsible for the management of virtual users, which needs both to handle the message that was sent by the control layer, and also deliver requests to the traffic generation layer. It is necessary to achieve flexible user management and resource allocation so as to make it available to multiple experimenters at the same time. According to the demand of experimenters, virtual users were divided into different users groups.  
The user management module is responsible for implementing the experimenter's experiments while, at the same time, involving more virtual users of the resource allocation, user behavior parameter configuration and scheduling. In each experiment, the experimenter usually needs to create a large number of virtual users and the corresponding user's group, while these resources must be free at the end of the operation. The frequent memory operations can lead to a lot of overhead, and are easy to cause memory leaks and other issues. By establishing a resource pool and a set of connections, the use management strategy can be effective to avoid frequent resource creation and release overhead. This module designs user_pool, group_pool and epm_pool based on the given object pool technology implemented.  
Figure 6: Parallel processing.  
Task processing: During the simulation task message processing, some modules must be initialized according to the contents of the message. Firstly, we initialize the free queue (free_q) and active queue (live_q), and then obtain the specified number of virtual users from user_pool and insert them into free_q; If the message contains scene parameters, the scene parameters are processed using the cubic spline interpolation algorithm described in Section 4.  
User scheduling: User scheduling, which is responsible for scheduling a large number of users to achieve and generate the request message to join the request queue, can be divided into two sub-processes: user activation and user number control. If the task queue is not empty, the scheduler can scan the task queue for a certain period of time and activate the users in each group with a certain frequency to join live_q. If the user group contains scene parameters, it will be processed by the user control process when the number of active users reaches the number of scene start points. It controls the number of active users based on the user profile. Otherwise, all users in the group are activated, and the user behavior module controls the behavior of each user.  
Load balancing: The numerous deployments of traffic generation engines offer the opportunity to exploit multiple accesses to the improvement of the concurrent performance. The load balancing module assigns the request queue messages to each traffic generation engine.