esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Master Curve  
The evidence that all flocculated particles tend to distribute uniformly around an average size, suggests to normalize each sample to the final height, \(H_{\infty}\), of the solid-rich phase.  
Therefore, we plot as a function of time, for each flocculated sample, the relative collapse of the particle-rich phase  
\[Z(t)=\frac{H(t)-H_{0}}{H_{\infty}-H_{0}} \tag{8}\]  
Interestingly, the curves can be all be scaled together onto a single master curve. The shape of the curve does not vary for different solids concentration, the scaling behaviour remains robust even on a very large time scale. Indeed, Figure 4 shows the behavior of \(Z(t)\) in an interval of 12,000 min. We can interpret this result as an effect of the flocculant which induces an attractive interaction between the particles creating a density mismatch between the particles and the suspending water. An analytical form of the function was obtained by means of a non-linear fitting procedure with two adjustable parameters, to all the experimental data.  
\[Z(t)=0.90\frac{\frac{t}{64.1}}{1+\frac{t}{64.1}} \tag{9}\]  
where the value 64.1 min has to be considered a typical lifetime [14] of this material. Nonetheless the \(Z(t)\) calculation is strongly dependent on the parameter \(H_{\infty}\), the height for completed settling process, i.e., at infinite time. This term depends on several factors so that it can be hardly deduced _a-priori_. First all, sedimentation measurements were carried out at very long times (3-4 days) in order to consider the settling process completely finished. The height of the interface measured under these conditions is \(H_{\infty}\) (red point in Figure 5). However, it is not practical to do so because the experimental procedure is time consuming. Furthermore, due to the compaction of the clayey material it is not always possible to carry out such measurements. A more useful alternate technique, therefore, is needed for calculation of \(H_{\infty}\) in dispersions. Such a technique is obtained by observing that \(H_{\infty}\) is defined by  
\[\lim_{t\rightarrow\infty}H(t)=H_{\infty} \tag{10}\]  
which can be rewritten as  
\[\lim_{\frac{t}{t}\to 0}H\bigg{(}\frac{1}{t}\bigg{)}=H_{\infty} \tag{11}\]  
In addition, it is experimentally verified that a plot \(H(t)\) vs. \(1/t\) exhibits a linear trend for a large range of variable \(t\). Accordingly, \(H_{\infty}\) can readily be computed by a linear fit. The procedure is illustrated in Figure 5, where the value measured after four days is also shown for comparison. The maximum deviation between measured and extrapolated value is within 5%.
## 5 Results and Discussion
It also implies that the parallel implementation is adept at effectively distributing and managing the workload across its parallel processing cores, thereby better controlling the rise in computational time. Insights into the scalability and efficiency gains of the proposed parallelization strategy can be derived from an analysis of the speedup achieved during the transition from sequential to parallel GPU algorithms. Across all test scenarios, the speedup lines (bottom plots in Figures 3-9) exhibit a clear upward trend, indicating the proficient parallelization of the algorithms in the GPU-based implementation tested in both the RTX 3090 and A100 GPU. Furthermore, with the increase in problem dimension, the speedup consistently rises, emphasizing the effectiveness of the parallelization strategy in managing larger computational workloads and highlighting its scalability. With the RTX 3090 GPU, variations in problem dimensions, as observed in the Jaya, BWP, and MaGI GPU-based algorithms, sometimes result in more jagged speedup lines, while the A100 GPU tends to consistently produce smoother speedup results. This observation suggests that the former GPU is more sensitive to variations in computational  
Figure 9: Mean computational times (**top**) and speedup (**bottom**) for the MaGI algorithm, categorized by problem and dimension. demands. While the speedup lines obtained with both GPUs exhibit positive growth for all algorithms across all problem dimensions, specific algorithms such as EJAYA, Rao-2, and Rao-3 display a concave downward shape in the speedups achieved with the RTX 3090, indicating a slowdown in speedup growth at higher dimensions. In contrast, the speedup lines resulting from the A100 GPU mostly show a nearly linear or concave upward shape, signifying that the parallelization strategy effectively leverages this GPU hardware to achieve superior processing capabilities. A noteworthy observation unfolds when examining the speedup performance across different problems. Problems 9 and 10 generally demonstrate remarkable speedup achievements with the A100 GPU. In contrast, these same problems tend to rank among the worst speedup results when executed on the RTX 3090 GPU. This disparity in speedup outcomes between the two GPUs underscores the nuanced influence of problem characteristics on the effectiveness of parallelization strategies. A more comprehensive analysis aiming to further investigate the adaptation capabilities of the parallelization strategy across various GPU hardware configurations is presented in Table 4. The provided data include the average times of all test problems per problem dimension and are grouped by algorithm. The computations encompass results for the sequential implementation (in the column labeled 'CPU') and the GPU-based implementation executed on four different GPUs, along with the achieved speedup gains.
## 1 Introduction  
Information and Communication Technology (ICT) is an extended term that is usually employed in place of Information Technology (IT) [1]. It describes an extensive industrial spectrum of services concerning information technology, information systems, computer science, e-business, and software engineering. It embraces both soft and hard skills in programming development and systems, together with interpersonal communication skills [2; 3]. It is a sector that comprises the distribution of information technology, telecommunication products, manufacturing of information technology, maintenance, and communication system installation.  
In the ICT sector, the opportunities that favored the de novo entry were the rapid technological advancements that started in the early 1980s [4]. Over the last few decades, these advancements have gradually spread across many countries, reflecting the ICT components' increasing export rate in the 1990s. Consequently, this growth accounted for a considerable proportion of the total trade in ICT products, including telecom equipment, semiconductors, office machines, and IT products. Currently, it is commonly seen that the use of ICT enhances the competitiveness of organizational enterprises [5].
# AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector
## 4 Methodology
### Data Preprocessing
#### 4.1.3 Feature Scaling (Standardization and Normalization as Appropriate)
While not essential for tree-based models, normalization can be particularly beneficial when integrating these features into dashboards, distance-based visualizations, or ensemble models with scale-sensitive learners. The DSG Ratio represents the frequency of DSG gearbox-related issues and is inherently bounded between 0 and 1 with low variance (mean \(\approx\) 0.12, std \(\approx\) 0.05). In the left panel of Figure 14, the original distribution shows a concentrated range around the mean. The middle plot of Figure 14 applies Z-score standardization, stretching the distribution to have unit variance. While this transformation is mathematically valid, it may over-amplify noise for low-variance features. The right panel of Figure 14 uses Min-Max normalization, which preserves the bounded nature of the feature and is generally more appropriate for ratio-based or percentage-type variables. This approach ensures interpretability and compatibility, especially when features are combined or visualized. The variable Number of Service Visits (User) represents the total number of services visits a customer has made with their vehicle. As a discrete variable with low cardinality (values range from 1 to 12), its original distribution of Figure 15 (left panel) is inherently grouped and resembles a categorical pattern. The middle plot of Figure 15 applies Z-score standardization, which mathematically centers the values but has limited interpretive benefit due to the ordinal and low-range nature of the feature. In the right panel of Figure 15, Min-Max normalization maps the values to the [0,1] range while preserving the ordinal structure. Although scaling is not strictly necessary  
Figure 14: Effect of feature scaling on ’DSG Ratio’. Figure 15: Effect of feature scaling on ’Number of Service Visits (User)’. for tree-based models like XGBoost or Random Forest, normalization may aid interpretability and visualization in exploratory data analysis or when integrating with other scaled features.
## 4 Simulation Results
### The Localization Error of ACFL and PCFL under Different Parameters
#### 4.1.5 Total Energy Consumption  
In Figure 9 the threshold is set to 0.0142 and the number of samples is set to 400. The energy consumption which is based on the energy consumption model referred by [22,23] can be expressed by:  
\[\alpha(f)=\frac{0.1f^{2}}{1+f^{2}}+\frac{40f^{2}}{4100+f^{2}}+2.75\times 10^{-4}f^{ 2}+0.003 \tag{30}\]  
\[E=p_{0}\times d_{i^{\prime}}^{t}\times 10^{(\alpha(f)\cdot 10)};d_{i^{\prime}}^{t}=p _{i^{\prime}}^{t}\;\;or\;\;d_{i^{\prime}}^{t}=d^{t}(n_{i},n_{j}) \tag{31}\]  
where \(p_{0}\) as a constant is the least energy consumption level. \(f\) denotes the frequency and its unit is \(\mathrm{Hz}\), \(\alpha(f)\) represents the absorption coefficient relying on the frequency value, underwater temperature and underwater salinity, its unit is dB/km. and \(d_{i^{\prime}}^{t}\) denotes the distance between the mobile node and the anchor (or the anchor' projection).  
It is obvious that total energy consumption for either PCFL or ACFL is increasing with the density of anchors. Energy consumption for ACFL is more than that for PCFL. This also verifies the high energy efficiency of PCFL.  
Figure 8: The original and estimated coordinates.  
Figure 9: Total energy consumption.
## 2 Materials and Methods
### Microstructure and Surface Feature Analysis  
To characterize the individual particles to build up a distribution, the static imaging method was applied in this study. The Ti64 powders were uniformly immobilized on coverslip, and then the assembled chamber was placed on the microscope stage. High resolution bright field images of Ti64 powders were obtained with an inverted microscope (TE2000U, Nikon, Japan) equipped with an objective lens (Plan Apo 60\(\times\)/1.40 NA oil, Nikon) and an EMCCD camera (Luca\({}^{\text{EM}}\) DL6581, Andor). These images were then processed using ImageJ software to assess the particle size distribution.  
Surface morphological characteristics of the untreated and heat-treated Ti64 specimens were observed by a JEOL JSM-6500F scanning electron microscope (SEM; Tokyo, Japan) at an operating voltage of 5 kV. The chemical compositions of SLM-produced Ti64 specimens were analyzed using SEM-EDS. The result showed that the compositions in wt.% for SLM Ti64 specimens comprised 90.46% titanium, 5.94% aluminum, and 3.60% vanadium, indicating that the alloying element compositions were not affected by SLM.  
Untreated and heat-treated Ti64 specimens were further characterized under a JadeSurf laser confocal 3D profiler equipped with a 50x/0.75 NA objective lens (Southport, Taipei, Taiwan); five different interrogation areas with a square field of view (FOV) of 146.52 \(\times\) 146.52 \(\upmu\)m were chosen and scanned to obtain a 3D surface profile. Here, the surface height was measured point- by- point with 295 nm data spacing.  
According to ISO 25178-3, surface roughness measurement usually requires removing the waviness portion from the measured surface to focus on the surface features of interest. Because waviness is mostly more or less periodic irregularities of a workpiece surface with spacings greater than the spacings of its roughness, the recommended minimum cut-off value is 0.1 mm for surface texture. In this study, to assess the effect of unmelted and partially melted Ti64 metal powder particles on surface texture parameters, a Gaussian high-pass filter with a cut-off value of 0.1 mm was used to suppress the longer wavelength components for determining the surface features. This allowed us to compare the areal surface texture parameters via the measured surface in the absence and filtering presence. Both construction of a Gaussian high-pass filter and analysis of 3D surface texture parameters, including the S\({}_{\text{A}}\), S\({}_{\text{Q}}\), S\({}_{\text{S}k}\), and S\({}_{\text{K}u}\), were conducted with Matlab software.
# Technique Evolutions for Microorganism Detection in Complex Samples: A Review
## 2 Current Detection Methods
### Methods Based on Growth Monitoring
#### 2.1.1 Measurement of Gas Production  
Microorganisms in an active multiplication and metabolism phase can produce or consume gas. One of the possible approaches is to monitor changes in the composition of the gaseous state in closed culture flasks, using pressure transducers that react to the production (e.g., CO\({}_{2}\)) or to the consumption of a gas (e.g., O\({}_{2}\)). Blood culture is an example of application of this method and is the reference method for microorganism detection in patient blood samples [12]; it is widely used at the hospital in sepsis diagnosis, but also for all sorts of biomedical culture such as cell therapy products [7]. The blood culture technique only allows to detect the presence or absence of a pathogen through the release of CO2. A false negative can occur if the bottle is not filled with enough medium. The method only works for culturable microorganisms. The detection of viable but non-culturable (VBNC) pathogens [13], as well as non-viable ones, requires a complementary method [14]. While non-viable microorganisms are less hazardous than viable ones, they can be associated with the presence of pathogenic metabolites, toxins, or membrane debris.
## 4 Augmented Reality Devices
### Headsets and Smart Glasses  
AR headsets are often larger, more immersive devices that provide a wider Field of View (FoV) and more advanced features. They typically include built-in sensors, cameras, and often depth sensors to map the surrounding environment and overlay 3D digital content seamlessly onto the real world [63]. A well-known example of an AR headset is the Microsoft HoloLens [51]. HoloLens features high-definition holograms integrated with the real world that provide a new way to see the world. It features sensors, a high-resolution 3D optical head-mounted display, and spatial audio capabilities to facilitate augmented reality experiences. Users can engage with an intuitive interface through eye movements, vocal commands, and hand motions [51].  
Figure 4: Types of augmented reality devices and illustrative examples [45; 46; 47; 48; 49; 50]. More details about some examples of AR devices whose technology is based on headsets and smart glasses are in [51; 52; 53], on handheld devices in [54; 55], on projection-based systems in [56], on SAR in [57], on HUD in [58], and on AR contact lenses in [59; 60].  
AR smart glasses are typically lighter, less immersive, and resemble a pair of standard eyeglasses. They often have a smaller FoV compared to AR headsets but are usually more comfortable to wear for extended periods. Smart glasses often provide simpler, heads-up display-style AR overlays, rather than the full 3D holograms seen in some AR headsets [52]. An example of AR smart glasses is Google Glass [53]. Google Glass presents data in a format similar to a smartphone but operates hands-free. Users interact with online content through voice commands in natural language. Another instance is the Vuzix Blade [53], offering a transparent display that enables the user to see both the physical environment and augmented reality elements at the same time. Other companies, such as Epson and Magic Leap, have also developed AR glasses and headsets, each with their unique features, strengths, and weaknesses. These devices allow for hands-free operation, which is particularly useful in fields like medicine, manufacturing, and maintenance where users need to access information while using their hands for other tasks [52]. Some of the sectors with implemented applications using this type of AR device include: education, healthcare, manufacturing, retail, tourism, and gaming [63].
## 5 Chain Synchronization
### Chain Synchronization Mechanism  
#### 5.2.1 General Chain Synchronization Mechanism  
In order to solve the problem of locating the error and re-request and verifying the message when the message hash chain verification or signature verification fails in the MHC method, we propose a communication synchronization mechanism, referred to as the chain synchronization mechanism. The MHC method uses this mechanism to maintain the consistency of the message hash chain of both parties. Two communicating nodes are communicating via the MHC method, A sending data stream \(P=p_{1},p_{2},\ldots,p_{n}\) to B. Note that the message hash chain constructed by the sender is \(HC\), and the chain constructed by the receiver is \(HC^{\prime}\). Whenever a message \(p_{\delta}=(m_{\delta},seq_{\delta},HC_{\delta})\) satisfy \(HC_{\delta}=h(h(m_{\delta})||HC^{\prime}(\delta-1))\) during verification, its content is wrong, and the receiver needs to re-request this message from the sender. On the contrary, the receiver can successfully verify the \(p_{\delta}\) and continue to verify \(p_{\delta+1}\).
# Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications
## 3 Simulation Results and Analysis
### Read Static Noise Margin  
During a read operation, the node storing a '0' is connected to the pre-charged bit line at VDD, allowing the read current to flow. This current can cause a voltage change at the node storing the '0', potentially altering the stored data. Therefore, read stability is a critical factor in SRAM design and is evaluated using the read static noise margin (RSNM). The RSNM is measured by applying a DC voltage sweep from 0 V to VDD to the main storage node and performing DC analysis to generate butterfly curves for the two main storage nodes. The RSNM is then derived from the largest square area that can be inserted within the butterfly curves measured at each storage node during the read operation [14]. RSNM is a critical parameter in SRAM design as it quantifies the cell's ability to maintain data integrity during read operations. A higher RSNM indicates improved resistance to soft errors, thereby enhancing the reliability of memory devices in radiation-prone environments. Consequently, optimizing RSNM is essential for ensuring stable performance in advanced semiconductor applications.  
The RHRSE-20T design separates the word line (WL) and the write word line (WWL), utilizing only two pairs of access transistors during the read operation. The use of more access transistors can degrade both read and write stability due to parasitic capacitance. Consequently, the RSNM of the RHRSE-20T is more than three times larger than that of the SCCS, which uses more than two pairs of access transistors, and more than ten times larger than that of the DNUCTM. Furthermore, the width (W) of transistors N4 and N5 is set to 300 nm, resulting in a higher cell ratio (CR) compared to other reference cells, thereby enhancing RSNM performance. The impact of CR on parasitic capacitance and area  
Figure 6: Multiple node upset recovery when SET occurs at nodes Q, QB, S0, and S3.  
cost is critical in determining the stability and performance of SRAM cells. A higher CR typically necessitates larger transistors, which leads to an increase in area cost. However, larger transistors reduce parasitic capacitance due to their improved ability to handle larger currents, thus enhancing noise margins and improving stability. In this design, the transistor width was optimized to achieve a balance between improved stability (RSNM) and minimized parasitic capacitance, ultimately enhancing both read and write operations. Nevertheless, the increase in transistor size associated with a higher CR result in a trade-off as it increases the overall area cost. While the larger CR in the RHRSE-20T design improves the RSNM and mitigates the effects of parasitic capacitance, it also requires more chip area compared to designs with a lower CR. The RSNM of various SRAM designs is compared in Figure 7, confirming that the proposed RHRSE-20T exhibits the best RSNM performance.
# Reducing System Load of Effective Video Using a Network Model
## 3 System Model or Proposal Method  
### Front on the Backaurd Frame  
Based on the PIP (Picture in Picture) method, we propose the FBF (Front on the Backward Frame) method to reduce system load without deteriorating QoE. First, a low-resolution image is transmitted in the background. After that, the FoV area is transmitted in high resolution to maintain the quality. In general, 360\({}^{\circ}\) video streams video with different resolutions according to the influence of network traffic, while the proposed method streams video to different areas depending on the FoV. Figure 10 shows the FBF algorithm configuration diagram.  
The server serves a low-quality background image to the client and the client receiving the FoV area outputs the high-definition area created by the transcoder using the conversion event. It is difficult to handle multiple clients at the same time, the reason being that a problem occurs in the conversion delay. Therefore, we solved the problem by pre-processing before streaming. Figure 11 shows the preprocessing of the FBF algorithm.  
Streaming using the FBF algorithm requires two preprocessing steps. The first step is to create a video streaming background. The background is outputted as a low-quality video. The second step requires region-division encoding because we have to stream FoV regions individually. The original video is segmented according to the number of target segmentation segments. The server receives the system information and then selects the video, so it creates a file containing the time information. Videos of different qualities  
Figure 11: Media pre-processing for the FBF algorithm.  
Figure 10: FBF network configuration diagram.  
generated by preprocessing are stored on the server and they are streamed in ways that are chosen by the client.  
Figure 12 shows the process of an FoV switching event as experienced by the client. The FoV transition event depends on the user's point of view. When viewing a 360\({}^{\circ}\) video in a PC environment, an event occurs through the mouse sensor, but in the case of a mobile device, it is operated through a gyroscope sensor. The FoV area changed by the environment according to the user's FoV is downloaded in the next buffering and is displayed as an image overlaid on the background image. In order to produce a video according to the proposed method, a transcoding process is needed.  
The transcoder converts the original image into three types of quality: low, medium, and high. The next step is to divide the number of divisions for the FoV area and store them according to the key frame. Thus, the user receives the background video while being presented with an image of the right quality for their viewing area. This method allows the video to be streamed to the viewing area including the FBF algorithm with real-time characteristics.