esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Study Limitations  
Nevertheless, some limitations must be acknowledged. The low variability of layer thicknesses limits the understanding of the effects of this parameter. In addition, external influences--such as powder structure, environmental conditions or material-specific properties--were not considered in the modelling phase, as emphasised in more extensive AM reviews [40]. Future research should include a wider range of process parameters and evaluate the ability of the model to predict other characteristics such as microhardness and wear resistance, which are similarly affected by LPBF conditions [39].  
The measured density range (89.29-99.975%) indicates optimised LPBF conditions; however, lower densities may occur in practise due to defects such as porosity. The accuracy of the XGBoost model (R\({}^{2}\): 0.835) may decrease at lower density ranges (<89%) due to  
Figure 8: Predicted vs. actual relative density and scatter plots for (**a**) the training set and (**b**) the predictions of the test set.  
insufficient training data in this range. In future experiments, parameter adjustments leading to lower densities (e.g., higher scanning speeds or lower laser power) should be made and added to the training data to improve the robustness of the model.  
The lack of a structured design of experiments (DOE) approach weakens the statistical validity of the findings, especially concerning interaction effects and the optimisation of parameter combinations. The grid-based method, although organised, might not investigate the multidimensional parameter space in full or consider the non-linear interactions among variables. Moreover, because of limitations in machine time and material access, the variety of test conditions was restricted, which could hinder the applicability of the findings.
###### Abstract  
Substantial advancements have been achieved in hyperspectral image (HSI) classification through contemporary deep learning techniques. Nevertheless, the incorporation of an excessive number of irrelevant tokens in large-scale remote sensing data results in inefficient long-range modeling. To overcome this hurdle, this study introduces the Group-Sensitive Selective Perception Transformer (GSAT) framework, which builds upon the Vision Transformer (ViT) to enhance HSI classification outcomes. The innovation of the GSAT architecture is primarily evident in several key aspects. Firstly, the GSAT incorporates a Group-Sensitive Pixel Group Mapping (FGM) module, which organizes pixels into distinct groups. This allows the global self-attention mechanism to function within these groupings, effectively capturing local interdependencies within spectral channels. This grouping tactic not only boosts the model's spatial awareness but also lessens computational complexity, enhancing overall efficiency. Secondly, the GSAT addresses the detrimental effects of superfluous tokens on model efficacy by introducing the Sensitivity Selection Framework (SSF) module. This module selectively identifies the most pertinent tokens for classification purposes, thereby minimizing distractions from extraneous information and bolstering the model's representational strength. Furthermore, the SSF refines local representation through multi-scale feature selection, enabling the model to more effectively encapsulate feature data across various scales. Additionally, the GSAT architecture adeptly represents both global and local features of HSI data by merging global self-attention with local feature extraction. This integration strategy not only elevates classification precision but also enhances the model's versatility in navigating complex scenes, particularly in urban mapping scenarios where it significantly outclasses previous deep learning methods. The advent of the GSAT architecture not only rectifies the inefficiencies of traditional deep learning approaches in processing extensive remote sensing imagery but also markedly enhances the performance of HSI classification tasks through the deployment of group-sensitive and selective perception mechanisms. It presents a novel viewpoint within the domain of hyperspectral image classification and is poised to propel further advancements in the field. Empirical testing on six standard HSI datasets confirms the superior performance of the proposed GSAT method in HSI classification, especially within urban mapping contexts, where it exceeds the capabilities of prior deep learning techniques. In essence, the GSAT architecture markedly refines HSI classification by pioneering group-sensitive pixel group mapping and selective perception mechanisms, heralding a significant breakthrough in hyperspectral image processing.  
s +
Footnote †: journal: Medical Image Analysis  
1
# A Cloud-Based Real-Time Mechanism to Protect End Hosts against Malware
## 2 Background
### VirusTotal  
The prototype of Skywalker uses VirusTotal [8] as its cloud-based scanner. However, Skywalker can switch to any other cloud-based scanner that provides similar functionality. VirusTotal aims to help users to check the legitimacy of web pages or files up to 128 KB (32 MB for developers using public API). A user uploads an image file or provides a Uniform Resource Locator (URL) to VirusTotal first. Then VirusTotal analyzes the file or the web page pointed by the URL using 56 antivirus engines or website scanners to scan and identify Trojans, viruses, worms, and other malicious content in the file or the web page. According to the official website [12] of VirusTotal, VirusTotal live updates its infection signature data sources and blacklists. Additionally, VirusTotal supplies comprehensive results from each scanner. Moreover, VirusTotal can supply detail information from each scanner. Furthermore, VirusTotal provides public [13] and private APIs for developers to use its services. The public API has a request limitation and private API does not have a request limitation, but needs to pay to use it. The prototype of Skywalker uses public API due to limited budget. Uploading files and getting responses from VirusTotal are quite time-consuming. Users may need to wait for an irregular time to retrieve the scanning results from VirusTotal due to the transmit limitations for developers. Table 1 shows some limitations of VirusTotal public API.  
The scanning precedence of VirusTotal is determined by the API that a user utilizes. The private API has a higher priority than the public API. Two tips are strongly recommended by VirusTotal to save time and bandwidth efficiently. First, use hash values of files to retrieve file scan reports that already exist in the VirusTotal database. Second, rescan files that have already been sent by others in the past, because these files have already existed in VirusTotal, so these files do not need to be uploaded again, but time to wait for scanning these files is still needed. Thus, retrieving file scan reports before rescanning files that have already been sent is the most efficient way to save time and bandwidth. By using both tips, developers can retrieve file scan reports first, and then determine whether to rescan files or not in accordance with the report dates. If those reports are recent ones, files that have been rescanned can be ignored. Hence, time and bandwidth can be saved simultaneously.
## 3 Proposed Method  
### Improved Quantum Rotation Gate  
The magnitude of the rotation angle of the QRG significantly affects the convergence speed. A relatively large amplitude leads to premature convergence. Conversely, smaller angles lead to slower convergence. In particular, the rotation angle of the original quantum rotation gate is fixed, which is not conducive to the balance between exploration and exploitation. Based on this, we propose a new dynamic adaptation strategy to adjust the rotation angle of the quantum rotation gate. In the early exploration stage, the value of \(\theta\) should be increased when the current individual is far from the best. In the exploitation stage, the value of \(\theta\) should be decreased. This method allows the search process to adapt to different solutions and is more conducive to searching for the global optimum. In detail, this improved method determines the value of the rotation angle by the difference between the current individual's fitness and the best fitness that has been obtained so far. The rotation angle \(\theta\) is defined as  
\[\Delta\theta=\theta_{\text{min}}+\gamma_{i}\cdot(\theta_{\text{max}}-\theta_{ \text{min}}) \tag{13}\]  
where \(\theta_{\text{max}}\) and \(\theta_{\text{min}}\) are the maximum and minimum values of the range of \(\Delta\theta\), respectively. The maximum and minimum values take \(0.035\pi\) and \(0.001\pi\), respectively. \(\gamma\) is defined as:  
\[\gamma_{i}=1-e^{-4(\frac{RF-S(i)}{RF-air})^{2}} \tag{14}\]  
The pseudo-code of DQRG (Algorithm 2) is as follows:  
```
Input: position \(X_{i}\), fitness values of \(X_{i}\)\(fitness(i)\), the best fitness value \(bF\), \(dim\) Output: updated position \(X\).
1 Initialize the parameters \(\alpha\), \(\beta\), \(s\); while\(i<dim\)do
2 Update \(\alpha\), \(\beta\);
3 Compare the \(fitness(i)\) and \(bF\);
4 Update \(s\) according to Table 1;
5 Update \(\Delta\theta\) by Equation (13);
6 Perform DQRG by Equation (10);
7\(i=i+1\);
8
9 end while return\(X\)
```  
**Algorithm 2**Pseudo-code of the quantum rotation gate (DQRG).
### Technical Motivation Based on MAE
#### 1.1.4 Practical Impact of the Proposed Technique on Li-Fi Transmission  
Increasing Channel Capacity Without New Hardware  
Li-Fi's bandwidth limitations arise from LED response constraints. Our approach circumvents these constraints by truly compressing the waveform in time.  
Even with standard optics and the same nominal 10 MHz bandwidth, we achieve up to 1.85\(\times\) capacity by transmitting fewer samples per symbol interval and reconstructing them later.  
Robustness Under Realistic Conditions  
As we showed experimentally (with 1 m free-space Li-Fi, indoor illumination levels of 300-440 lux), the MAE-based approach remains feasible and robust.  
A high masking ratio (75-85%) still yields acceptable EVM under the firstfirst-generation GFEC threshold.  
In summary, the motivation for using a masked autoencoder (MAE) in our Li-Fi DMT system is that it uniquely enables:  
* Actual time-domain compression (masking) and subsequent self-supervised reconstruction,
* Improved generalization through forced global pattern learning, and Real capacity gains without hardware changes, going beyond mere channel equalization.  
These benefits are not readily realized by standard autoencoders, CNNs, or purely end-to-end supervised networks. Hence, we designed our system around the MAE paradigm to capitalize on its inherent ability to inpaint missing signals and ultimately boost Li-Fi transmission capacity.  
The process of implementing the proposed technique can be summarized as follows. The entire DMT signal waveform is divided into N sub-waveforms, and K of these sub-waveforms are randomly selected and masked (deleted) for encoding. As a result, the encoded DMT signal has a reduced length compared to the original signal. After the encoded DMT signal is transmitted through the Li-Fi wireless link, the decoding process involves learning from the information of the adjacent unmasked waveforms in order to recover the K deleted sub-waveforms without requiring a high level of understanding. To the best of our knowledge, this is the first time that the MAE-based technique has been proposed to increase the channel capacity of DMT signal by reducing (masking) its length and then recovering it. We aim to more clearly convey the key contribution of our proposed technique by presenting in greater detail the differences between our approach and previously proposed techniques for improving the transmission performance of optical signals, as discussed in Section 1.2.
## 5 Performance Evaluation of Our Routing Protocol  
We evaluate the performance of JRCA using the _network simulator-2 (ns2)_[49]. The simulator is significantly modified at the physical and MAC layer to model the cumulative interference and carrier sensing based on cumulative received power. The simulator is also extended to equip each node with multiple radios that can switch between multiple orthogonal channels. We consider a static network consisting on 30 nodes that are placed in a uniform grid. Two of these nodes are chosen as gateways. The sources are selected randomly that generate UDP flows, each of them remain alive for 200 s. We assume the channel switching delay to be 0 for simplicity. The key parameters used in our simulations are listed in Table 3.  
We compare JRCA with the single channel scheme as well as with a random channel selection based multi-channel scheme. We evaluate four key performance metrics: (a) the average network throughput, (b) packet delivery ratio, (c) end-to-end packet delay and (d) jitter. The results are shown in Figures 9-11.
# Applied Engineering Using Schumann Resonance for Earthquakes Monitoring
## 1 Introduction
These data showed a tsunami signal during the across the Indian Ocean [24]. The number of publications based on earthquake monitoring increases significantly and becomes a topic of increasing interest in the last decade [25; 26]. Some authors suggest that a probabilistic approach might be a method for assessing the risk posed by seismic phenomena for a wide range of magnitudes [27]. Due to the relationship between earthquakes and tsunamis, both the recurrence allocation of events in time and the delivery of earthquake or landslide sizes can be used to calculate the tsunami probability. In coastal locations with a broad register of tsunami is the distribution scope that is similar to the ones from other natural damage such as landslides, forest fires, and earthquakes [1], which could be described as a power law [28]. But apart from this approach, methods for early detection are needed. Sensors can collect data to detect seismic phenomena that can lead to major natural disasters by means of a suitable seismic system and a correct treatment of the information. In addition, these systems could help with the early detection of a disaster, supporting the decisions to minimize human and environmental damage [12]. In the literature there are several types of sensor systems that measure different environmental responses after an earthquake and could determine the material damage caused. An example is the sensor Structural Health Monitoring (SHM), which measures the acceleration of the response movement of a building after an earthquake [29]; other sensors measure post-seismic deformations in horizontal structures [30] or in vertical structures [31]. Early detection of this type of seismic events can help save many lives. One such method of early detection could be the presence of ELF wave's observatories, because of the relationship between Very Low Frequency/Extremely Low Frequency (VLF/ELF) waves and seismic phenomena [32; 33]. During the eruption of the in the Kelud volcano eruption, seismic signals showing peaks at 3.7 mHz, 4.8 mHz, 5.7 mHz, and 6.8 mHz were observed in Indonesia. This fact suggests that an ionosphere-atmosphere coupling phenomenon was present along with the lithosphere-atmosphere coupling [34]. In addition, mentioning that such electromagnetic phenomena might be used to predict earthquakes, an example of this is shown in the precursors of the Loma-Prieta earthquake [35], or for the Great East Japan earthquake of 2011 (or Tohoku earthquake) [36]. The aim of this paper is to represent the major worldwide earthquakes and tsunamis of twentieth and twenty-first centuries, and to show the location of ELF wave's observatories existing in the world accounting for the relationship between the two phenomena. This allows us to visualize the zones high probability of seismic phenomena. Taking into consideration the presence of seismic phenomena and their possible detection, different areas of the world will be shown which are suited to this kind of observatories.
# SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention
## 3 The Proposed Method
### Classification and Regression
#### 3.3.2 Regression Branch  
The regression branch is responsible for predicting the bounding box regression map. In the regression branch, this paper employs L\({}_{\text{GIoU}}\) as the regression loss function. Compared to the traditional IoU metric, L\({}_{\text{GIoU}}\) extends the overlap evaluation between the predicted and actual boxes from rectangular boxes to shapes of any form. Therefore, \(L_{GIoU}\) is considered a more comprehensive target box evaluation metric, providing a more accurate regression loss for object tracking tasks. The regression loss function is expressed as shown in Formula (6) [36]:  
\[L_{reg}=L_{GIoU}(b,\hat{b}) \tag{6}\]  
The SSTrack's final classification and regression loss function is expressed as shown in Formula (7):  
\[L_{loss}=\lambda_{cls}L_{vfl}\ (p,IoU(b,\hat{b}))+\lambda_{reg}L_{GIoU}(b,\hat{b}) \tag{7}\]  
where \(\lambda_{cls}\) and \(\lambda_{reg}\) represent the weight parameters for the classification loss and regression loss, with values of 1.2 and 1.2.
# Design and Analysis of the High-Speed Underwater Glider with a Bladder-Type Buoyancy Engine
###### Abstract  
This study entailed the design and analysis of a 400 m class underwater glider operated by a bladder-type buoyancy engine. The underwater glider was designed for high-speed movement with a maximum velocity of 2 knots. The shape of the hull was designed to reduce water resistance using the Myring hull profile equation. The reliability was verified by performing simulations using resistance coefficients. The relationship between the control value of the ballast discharged from the buoyancy engine and the glider's speed according to the path angle was analyzed. Further, the relationship between the optimal glide angle and the design control value of the ballast was derived, and the optimal glider speed was estimated accordingly. Based on the analysis results, a bladder-type buoyancy engine was developed, and the maximum speed of the tested underwater glider was measured via sea trials.  
underwater glider; buoyancy engine; resistance coefficients; horizontal speed; optimal glide angle +
Footnote †: journal:
## 2 Results and Discussion  
To evaluate the potential for SCC distillation to be used to remediate the sensory impact of smoke taint, two wines with elevated concentrations of free and glycosylated volatile phenols, and perceivable smoke-related sensory characteristics, were processed using an industrial scale SCC system. The composition of wine, and stripped wine and condensate fractions collected after 1%, 13-14% and 29% stripping (i.e., removal of 1%, 13-14% and 29% of the initial wine volume, as a volatile extract), were compared, along with the sensory profiles of wine and stripped wine. Since SCC distillation had to be performed on an industrial scale, it was not possible to replicate treatment of individual wines. Instead, SCC treatment was undertaken on two different smoke-tainted wines: a 2020 Shiraz Sangiovese (ShS) and a 2020 Petit Verdot Sangiovese (PVS).
## 2 Materials and Methods
### Patient Data Preparation  
In vivo respiratory-correlated planning 4 DCTs of the entire chest of six lung cancer patients were acquired at the department of radiotherapy. The 4 DCTs were acquired using a Siemens Sensation Open CT scanner (Siemens AG, Erlangen, Germany) at 120 kVp, 80 mA, and 361 ms. The source-to-detector and source-to-patient distances were 1040 and 570 mm, respectively. The 4D volumetric images were reconstructed for every 10% segment of the respiratory phase with an in-plane resolution of 0.97 \(\times\) 0.97 mm\({}^{2}\) and a slice thickness of 3 mm. The purpose of this study was to track tumors in CBCT projections taken from all directions, which should not be taken in all directions to minimize the radiation dose given to the patient. To simulate the CBCT projections in all directions around the patient, DRRs (i.e., a pseudo projection) were computed for every 10% segment of the respiratory phase with a normal respiration rate in adults at rest (12 breaths/min). Based on ray-casting through the planning 4 DCTs of the six patients, 360 CBCT projections were generated for an angular range of the gantry of 360\({}^{\circ}\)[35]. Figure 3a shows the selection of the respiratory phase bins to identify (boxed), and the respiratory phases to reference (underlined), from the planning 4 DCT projections. The respiratory phase bins to identify were assigned as a function of the gantry frame, as shown in Figure 3b. The correct phase of the projection images for the missing phases (i.e., the phase bins to identify) were detected using the existing respiratory phase data (i.e., the phases to reference).
# Intelligent Thermal Comfort Controlling System for Buildings Based on IoT and AI
## 2 Literature Review
### Feasibility Analysis Applied To Actual Projects  
With the continuous expansion of the building scale and the improvement of the new generation of information technology, more and more intelligent tools are used in the monitoring, control, and analysis of building energy consumption. People's requirements for the indoor living thermal environment are also getting higher and higher, so it is very important to adopt efficient energy consumption design schemes and reduce energy loss. In the process of energy analysis, more rigorous methods need to be adopted. In recent years, the use of building full energy consumption simulation methods to analyze and evaluate various indicators has become a mainstream trend. Creating a comfortable, energy-saving, and healthy living environment has been the focus of people's attention. Building a full energy consumption simulation is different from traditional methods. The new calculation method is based on a dynamic environment and relatively real-time data, and the accuracy of calculation results is greatly improved. But, at the same time, the data required for building full energy simulation is relatively higher. In addition to the typical meteorological year data, hourly data and more detailed thermal performance data are also needed. At present, the popular building full energy simulation software contains EnergyPlus, BLAST, DOE-2 by the United States, Esp-r by Europe, and DeST byTsinghua University in China. Each software has its own advantages and disadvantages, and the diversity of functions helps meet different needs in the energy analysis process. In addition, the full energy consumption simulation strives to have original functions in \"integration\". Under the current needs of architects and engineers, energy consumption analysis is not only purely mathematical analysis of numbers but also combined with other aspects of the building unit. And, the methodology must make judgments based on complexity thinking. The comfort of the indoor thermal environment is usually measured by Predicted Mean Vote (PMV) index. Previous scholars have conducted in-depth research on PMV. [PERSON] et al. firstly proposed a kind of control system and strategy with PMV as the core [27]. Then [PERSON] et al. proposed an optimization-based theory [28]. [PERSON] et al. proposed the concept of fuzzy control of indoor thermal environment [29] and indoor thermal comfort control based on PMV index.