esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Traffic Multi-Maps Generation Based on Disjoint Path Flows
### Traffic Model
It is measured as edge occupancy (or traffic density) \([p_{e}]\)[62]. The routing agent \(v_{a}\) chooses a single path between all the paths \(\{p_{i}^{d}\}\in P^{d}\) that will be referred as route \(\mathrm{r_{a}}\) for simplicity (\(r_{a}=\left[p_{i}^{d}\right]\)). Routing functions implement criteria such as bounded rationality or selfish behaviors [7; 65] to implement traffic equilibrium principles at the user and system levels. Traffic assignment relies on these routing functions and how equilibrium conditions are applied. Other valuable metrics associated with the route \(r_{a}\) are the route length \(L_{a}\), the number of traversed edges \(E_{a}\), and the number of traversed nodes \(N_{a}\). Any routing agents \(v_{a}\) and \(v_{b}\) belonging to the same flow \(x^{d}\) may select different routes. According to this, the traffic flow \(x^{d}\) can be divided in sub-flows \(\{x_{i}^{d}\}\) called path flows, containing each one of the vehicles that have selected the same route (9):  
\[x^{d}=\{x_{i}^{d}\},\forall\,v_{a},v_{b}\in x_{i}^{d},r_{a}=r_{b} \tag{9}\]  
Recent activity-based traffic models (ABM) [44] consider individual utility functions and driving constraints. The vehicles may also be grouped in \(Q\) sets called fleets that reflect their common requirements, such as electric cars, regular buses, scholar transport, diesel cars, etc. Traffic flows \(X=\{x^{d}\},\forall q\in Q\) are also classified depending on the activity model. Table 1 shows an example of traffic flow decomposition for five commodities and three fleets.
# Instability and Drift Phenomena in Switching RF-MEMS Microsystems
## 5 Instabilities Due to Fabrication Uncertainties and Material Wear
As the contact deteriorates, the difference of capacitance values in the on and off states becomes less sharply defined, and the RF performances deteriorate up to the point where it is not possible to clearly define an electrical actuation [37]. The quality of the contact [26] and the contact resistance vary randomly with time, and, again, the switch becomes highly unstable and therefore useless.
## 1 Introduction  
Visual monitoring, including people detection, tracking, recognition and activity interpretation [1], is a key component of intelligent video surveillance systems [2, 3]. The contribution of a camera to the observation of a scene depends on its viewpoint and on the scene configuration. This is a dynamic property, as the scene content is subject to change over time. There are two major types of security cameras when setting up a security system: visual-light (or color) and thermal infrared sensors. Recently, the robustness of a new thermal infrared pedestrian detection system has been investigated under different outdoor environmental conditions [4]. This paper offers a step forward towards enhancing the performance of visual monitoring systems by adding a visual-light sensor.  
After studying different visual sensors, it is commonly concluded that the advantages and disadvantages are complementary in using visual-light and thermal infrared spectra. On the one hand, although the information obtained from an infrared camera is useful for detecting humans in nocturnal environments, it presents severe problems in other environments [5]. This is the case for hot or thermally homogeneous environments. Moreover, visible-light yields good results when conducting human detection in well-lit environments, but it is problematic in dark environments or in areas of the scene that present shadows or have low visibility in general. In order to enhance the performance of people monitoring, some researchers are performing image fusion by using visible and infrared images together [6]. However, the fusion of thermal infrared and visible images is not trivial [7]. This is why there has been a growing interest in visual surveillance using multimodal sensors, such as thermal and visible cameras, in both civilian and military applications [8].  
This paper introduces a proposal based on confidence measures in a bimodal visual sensor setup for automatically selecting visible-light or thermal infrared in response to natural environmental changes. The purpose of the setup is to robustly detect people in dynamic outdoor ambiences. The rest of the article is organized as follows. Section 2 describes the work related to robust people detection in visible-light and thermal infrared video. Then, Section 3 introduces our proposal based on confidence measures to automatically select between visible-light and thermal infrared sensor. After that, two efficient segmentation algorithms, one for the visible-light spectrum and another one for the thermal infrared spectrum, are introduced in Section 4. In Section 5, the segmentation algorithms are applied to five different video sequences recorded under very different environmental conditions in a bimodal sensor setup. The results of the segmentation in both spectra allow one to establish the most suited confidence thresholds and values. Finally, some conclusions are provided in Section 6.
## 3 Proposed Analog RF Beamforming
### Relay Analog RF Beamforming
#### 3.3.3 Relay Selection Criteria  
When multiple relays are deployed between the source and destination for the selection of best available channel to maximize the spectral efficiency then, multi-relay transmission in parallel suffers from severe interference with each other. To address this problem, a relay selection technique can be exploited to leverage spatial diversity gain in practical systems while minimizing the effect of interference at the destination. This problem can be addressed by selecting the best relay for the transmission of data streams while the other relays are kept silent.  
Mainly, there are two relay selection schemes. The first scheme is opportunistic relay selection, where a decision is taken by considering the maximum end-to-end SNR. The other relay selection strategy is partial, where some suitable performance metric is chosen (e.g., SNR, SVD etc.) either in the first-hop or the second-hop. It is worth noting that opportunistic relay selection achieves diversity gain equal to the number of relay nodes, whereas the diversity gain for partial relay selection is one [46; 47]. Therefore, the proposed hybrid beamforming algorithm adopts opportunistic relay selection to attain better system performance by maximizing SNR from the source to the end user. The index \(k^{*}\) of the selected relay can be determined by  
\[k^{*}=\arg\max_{k=\{1,\ldots,K\}}\Bigl{(}\|\mathbf{H}_{eff,k}\|_{F}+\|\mathbf{G}_{eff,k }\|_{F}\Bigr{)}\]  
where \(\mathbf{H}_{eff,k}=\mathbf{H}_{k}\mathbf{F}_{opt},~{}\mathbf{G}_{eff,k}=\mathbf{W}_{opt}^{H}\mathbf{G} _{k}\).
## 2 Methodology
### Undifferenced Model  
For the satellite \(s\) observed by the receiver \(r\), the GPS dual-frequency IF undifferenced observation equations in units of length can be written as follows:  
\[\left\{\begin{array}{l}P_{3}=\rho+c(dt_{r}-dt^{s})+T+d_{r,3}-d_{3}^{s}\\ L_{3}=\rho+c(dt_{r}-dt^{s})+T+A_{3}+b_{r,3}-b_{3}^{s}\end{array}\right. \tag{1}\]  
where \(P_{3}\) and \(L_{3}\) denote the ionosphere-free code and phase observables, respectively; \(\rho\) is the receiver-satellite geometric distance; \(c\) is the speed of light in vacuum; \(dt_{r}\) and \(dt^{s}\) are the clock offsets of receiver and satellite, respectively; \(T\) is the slant tropospheric delay; \(A_{3}\) denotes the ambiguity parameter in the unit of length; \(d_{r,3}\) and \(d_{3}^{s}\) are the ionosphere-free code biases at the receiver and satellite, respectively, while \(b_{r,3}\) and \(b_{3}^{s}\) are the ionosphere-free phase biases. Phase center offsets (PCOs) and variations (PCVs), phase windup [26], relativistic effect, earth rotation effects and tide loading are assumed to be precisely corrected, and random noise is ignored here for brevity.  
Generally, code and phase biases are assimilated by clocks to eliminate the rank deficiency of Equation (1). Considering that the ambiguity is estimated as a constant, the PPP model with full rank after reparameterization can be expressed as follows [27]:  
\[\left\{\begin{array}{l}P_{3}=\rho+c\left(d\overline{t}_{r}-d\overline{t}^{ s}\right)+T+\delta d_{r,3}-\delta d_{3}^{s}-\delta b_{r,3}+\delta b_{3}^{s}\\ L_{3}=\rho+c\left(d\overline{t}_{r}-d\overline{t}^{s}\right)+T+\overline{A}_ {3}\end{array}\right. \tag{2}\]  
where \(\Delta\) and \(\delta\) are the symbols representing the time constant and time-varying portions, \(d\overline{t}_{r}\) is the receiver clock offset, \(d\overline{t}^{s}\) is the International GNSS Service (IGS) legacy satellite clock offset and \(\overline{A}_{3}\) is the ambiguity parameter in the unit of length. Their specific forms are as follows:  
\[\begin{split} d\overline{t}_{r}&=dt_{r}+\frac{\Delta d _{r,3}+\delta b_{r,3}}{c}\\ d\overline{t}^{s}&=dt^{s}+\frac{\Delta d_{3}^{s}+ \delta b_{s}^{s}}{c}\\ \overline{A}_{3}&=A_{3}+\Delta b_{r,3}-\Delta b_{3}^ {s}-\Delta d_{r,3}+\Delta d_{3}^{s}\end{split} \tag{3}\]  
Usually, a network is needed for satellite clock estimation. Additionally, Equation (2) represents a rank deficient system for the linear correlation between receiver and satellite clocks. In this study, a zero-mean condition (ZMC) imposed on all satellite clocks has been adopted to define the clock datum, which can be expressed as follows [28]:  
\[\sum_{i=1}^{m}d\overline{t}^{s_{i}}=0 \tag{4}\]  
where \(m\) is the number of satellites observed in the global network.  
We note that the nuisance term \((\delta d_{r,3}-\delta b_{r,3}-\delta d_{3}^{s}+\delta b_{3}^{s})\) of Equation (2) is part of the code residuals. In fact, the nuisance term is not driven into all residuals, resulting in the deviation of other parameters [25]. Additionally, the biased satellite clocks caused by satellite time-varying code biases will be discussed in Section 3.
# Joint Content Recommendation and Delivery in Mobile Wireless Networks with Outage Management
## 6 Theoretical Performance Bounds with Simple Models
### Upper Limit of Mean Outage  
Let us assume that the number of files is \(F\), and the file size is subject to a uniform distribution on \([b,b+g]\). We further assume that user's interest is independent of the file size. In this case, each user's recommended list is an independent sample on the content set \(L\).  
According to [43; 44], we can see that if \(X_{i}\) is uniformly distributed on \([0,g_{i}]\), the PDF of \(S_{X}=X_{1}+X_{2}+\ldots+X_{U}\) is:  
\[\begin{split} f_{S_{X}}(x;U;g_{i})&=\frac{1}{A_{U} (U-1)!}\{x^{U-1}\\ &+\sum_{k=1}^{U}(-1)^{k}\binom{U}{k}[(x-\sum_{l=1}^{k}g_{l})_{+} ]^{U-1}\}\end{split} \tag{37}\]  
where \(A_{U}=\prod_{k=1}^{U}g_{k}\), \(x_{+}=\max(0,x)\). If \(Y_{i}\) follows a uniform distribution on \([b_{i},b_{i}+g_{i}]\), \(S_{Y}=\sum_{i=1}^{U}Y_{i}=\sum_{i=1}^{U}x_{i}+\sum_{i=1}^{U}b_{i}\), and the PDF of \(S_{Y}\) is given by:  
\[f_{S_{Y}}(s;U;b_{i};g_{i})=f_{S_{X}}(\sum_{i=1}^{U}Y_{i}-\sum_{i=1}^{U}b_{i}; U;g_{i}). \tag{38}\]  
Correspondingly, the CDF of \(S_{Y}\) is:  
\[F_{S_{Y}}(s;U;b_{i};g_{i})=\int_{-\infty}^{s}f_{S_{Y}}(t;U;b_{i};g_{i})\;\text {d}t. \tag{39}\]  
When the system capacity \(C_{sys}\) is given, the outage is estimated as:  
\[\hat{\eta}=1-F_{S_{Y}}(C_{sys}*T_{s};U;b_{i};g_{i}). \tag{40}\]  
In Figure 3, we compare the numerical CDF calculated by Equation (40) with the empirical CDF obtained via Monte Carlo simulations. The numbers of contents and users are set to be 500 and 10, respectively, and the length of the recommendation list is set to be 50. It can be observed that the numerical and empirical CDFs agree well in all cases with different file size distributions. This validates the correctness of Equation (40).
## 2 Related Work
Although the existing works are tremendous in terms of healthcare security through federated learning and integrating blockchain, they merely focus on individual aspects like privacy preservation or energy efficiency and cannot achieve a holistic solution in IoMT environments. Most approaches usually miss critical points related to data quality assessment and reliability of devices and, most importantly, come up with lightweight security mechanisms tailored for resource-constrained medical devices. EdgeGuard has addressed this by designing an integrated framework that contains blockchain-secured federated learning and IoMT-specific optimizations. Our solution uniquely integrates adaptive aggregation based on data quality and device reliability, a lightweight consensus mechanism designed for medical devices, and comprehensive security measures that maintain HIPAA compliance while enabling efficient collaborative learning. The comparative analysis in Table 1 demonstrates the current work addresses particular aspects of privacy and security for health networks but not in totality. As such, EdgeGuard clearly distinguishes itself by combining privacy preservation, lightweight blockchain security, resource optimization, data quality assessment, and device reliability monitoring within the proposed healthcare-specific framework.
# Anomaly Detection in 6G Networks Using Machine Learning Methods
## 6 Experimental Results and Discussion
### Restrictions  
To make the system more resistant to new threats, the major goal of the study was to differentiate between regular and aberrant actions. However, the study has the following drawbacks: When using dataset assaults, the HABBAs system performs admirably but ignores additional attacks initiated by external networks (when available). It is difficult to migrate the HABBAs system to identify new threats once the training phase is over due to the results of the training component.
# Research and Progress on Truing and Sharpening Process of Diamond Abrasive Grinding Tools
## 3 Dressing Method of Diamond Abrasive Grinding Tools
### Sharpening Technology of Diamond Abrasive Grinding Tools
#### 3.2.4 Laser Sharpening Method
The morphology of the diamond abrasive grains changed significantly, showing a sharp grinding edge. The removal rate of the bronze material was high, the height of the diamond abrasive grains protruding from the bronze bond was ideal, and a certain chip-holding space was reserved. When the laser power was increased to 3.36 \(\times\) 10\({}^{8}\) W/cm\({}^{2}\), as shown in Figure 18c, graphitization occurred on the surface of the diamond abrasive grains after dressing, and part of the surface had deep or shallow depressions and cracks. In the process of increasing the power density of the pulsed fiber laser from 1.68 \(\times\) 10\({}^{8}\) W/cm\({}^{2}\) to 3.36 \(\times\) 10\({}^{8}\) W/cm\({}^{2}\), the removal rate of the bronze material gradually increased. When the laser power density was 1.68 \(\times\) 10\({}^{8}\) W/cm\({}^{2}\), the bronze material could be removed; thus there was a suitable chip holding space between the diamond abrasive grains, and the ideal abrasive grain height could be obtained, and the sharpening of the bronze-bonded diamond abrasive grinding wheel could be realized. When the power density of the pulsed fiber laser was 2.52 \(\times\) 10\({}^{8}\) W/cm\({}^{2}\), the diamond abrasive tip was finely broken with appropriate shape accuracy, which could achieve the micro-shaping of the diamond abrasive grains and achieve the purpose of combining the truing and sharpening of the diamond abrasive grinding tools. However, when the power density of the pulsed fiber laser continued to increase to 3.36 \(\times\) 10\({}^{8}\) W/cm\({}^{2}\), the diamond abrasive grains were greatly deformed, the surface was graphitized, and some depressions appeared, which needs to be avoided as much as possible to obtain an ideal dressing effect. Therefore, for laser truing and sharpening diamond abrasive grinding tools, ideal laser process parameters should be selected. Among them, a pulsed fiber laser with a power density  
Figure 18: Topography of the bronze-bonded diamond abrasive grinding wheel after pulsed fiber laser dressing. (**a**) Laser power density: 1.68 \(\times\) 10\({}^{8}\) W/cm\({}^{2}\); (**b**) Laser power density: 2.52 \(\times\) 10\({}^{8}\) W/cm\({}^{2}\); (**c**) Laser power density: 3.36 \(\times\) 10\({}^{8}\) W/cm\({}^{2}\). of \(2.52\times 10^{8}\) W/cm\({}^{2}\)\(\sim\)3.36 \(\times\) 10\({}^{8}\) W/cm\({}^{2}\) is the most suitable for dressing the diamond abrasive grinding tools. It can not only properly remove the bronze bond materials, but also enable truing of the diamond abrasive grains in a small amount, and achieve truing and sharpening of the diamond abrasive grinding tools at the same time.
# Research on Optical System of Dim Target Simulator Based on Polarization Stray Light Suppression
## 3 Design of High-Image Quality Collimating Optical System  
Considering that the optical sensor is based on the energy centroid of the star point to realize the attitude positioning of the spacecraft, the collimating optical system of the simulation system requires small distortion, flat image field, and apochromatic image quality in the 380-760 nm band, and the field of view is large, which increases the difficulty of its design. At the same time, the exit pupil of the collimating optical system should coincide with the entrance pupil of the attitude sensor (D \(\
ot\)\(\times\) 18 mm) to ensure that the light emitted by the stars everywhere in the full field of view is the same as the total luminous flux of the entrance pupil of the attitude sensor. The focal length \(f^{\prime}\) of the optical system is determined according to the selected total effective size of LCOS. It is known that the size of a single LCOS image element is 8.4 \(\times\) 8.4 \(\mu\)m, the total effective size after splicing is 16.128 \(\times\) 16.128 mm, and the designed field of view of the optical system of the star simulator is 10.2 \(\times\) 10.2\({}^{\circ}\).  
Take the edge equal to 16.128 mm, and find the focal length from Equation (19):  
\[16.128/f^{\prime}=\tan 10.2 \tag{19}\]  
The solution is \(f^{\prime}=89.64\) mm.  
In order to meet the needs of the collimating optical system with the external exit pupil, large field of view, and low distortion, the non-distortion eyepiece is considered as the initial structure, and the focal length scaling and field of view setting make it meet the requirement of the index. According to the color difference of the system, the double-glued achromatic lens in the eyepiece structure is optimized, and the curvature radius  
Figure 13: Irradiation surface of illumination optical system.  
and thickness are optimized to be achromatic. Set aberration optimization target, increase spherical aberration, coma, field curvature and other optimization weights. The curvature of the first and fourth sides is set as a variable, and local optimization is started. In the optimization process, if there is interference between the thickness and size of the lens, the air interval is adjusted for automatic optimization. Determine whether the current optimization results meet the conditions by judging MTF and field curvature and distortion chart. If the conditions are met, the optimization is stopped; if not, the operands are reset to optimize again, and the optimal value is found. The two-dimensional structure diagram, spot diagrams, field curvature and distortion curve, and transfer function curve of the optimized collimating optical system are shown in Figure 14. The design results show that the optical system is composed of six lenses, and the RMS radius of the full field of view is less than 3.103 \(\upmu\)m. The system distortion is better than 0.04%, and the field curvature is better than 0.08 mm. At Nyquist frequency \(\
u\) = 68 lp/mm, full-field MTF is better than 0.7.
Different classification techniques have been developed; e.g., techniques based on optical data from Landsat missions [11] or a combination of optical and SAR missions [12]. [PERSON] et al. [13] developed an algorithm based on Moderate Resolution Imaging Spectroradiometer (MODIS) data and provided an overview of other existing methods. In addition to satellite imagery, other remote sensing techniques can also be used to extract surface water information; for example, passive microwave land surface emissivities or scatterometer responses [14] or altimeter radar echoes for water detection based on waveform classification [15]. In contrast to GRACE and satellite imagery, due to its measurement principle, satellite altimetry only delivers data along the satellites' ground tracks; i.e., so-called along-track data directly beneath the satellites. Depending on the active missions and their orbit configurations, some lakes are missed for which no height information can be derived. The focus of this study is to investigate the capability of different satellite altimetry configurations to measure basin-wide lake storage changes. As a study case, the Mississippi basin in North America is used. In order to allow for a comprehensive inventory of lakes and reservoirs covered by different satellite altimetry missions, an automatic tool for lake detection has been developed as part of the study. Model storage information from WGHM is used to assess the percentage of water volume changes missed due to the current insufficient data coverage of satellite altimetry as well as the improvements expected from the upcoming new Surface Water and Ocean Topography (SWOT) mission [16]. The paper is structured as follows: Section 2 describes the study area and the used input data sets. Afterwards, Section 3 introduces the used methods before Section 4 presents the results of the study, followed by a discussion in Section 5 and some concluding remarks in Section 6.
## 5 Simulation Results and Discussion
### Simulation Results and Discussion
We can see from Figure 7c that, after the number of active DPs exceeds 65, the throughput saturates. Figure 8 shows the spectral efficiency for varying D2D transmit power. We can observe from Figure 8a that the spectral efficiency DPs increases with increasing D2D transmit power. In contrast, we can see from Figure 8b that the spectral efficiency of the CUs decreases with increasing D2D user transmit power, which occurs because a higher D2D transmit power increases the uplink interference for CUs. Moreover, Figure 8c shows that our proposed scheme yields the highest spectral efficiency among the studied schemes. We can see from Figure 8c that the spectral efficiency increases promptly as the D2D transmit power increases. However, after the D2D transmit power exceeds 11 dBm, the spectral efficiency decreases. Figure 8: Spectral efficiency analysis with varying transmit power consumption of D2D for (**a**) DPs, (**b**) CUs, and (**c**) overall system. Figure 7: Average throughput analysis with varying numbers of available DPs for (**a**) DPs, (**b**) CUs, and (**c**) the overall system.