esa-sceva/satcom-chunk-collection huggingface dataset:
# Predictive Maintenance on the Machining Process and Machine Tool
## 1 Introduction
### Predictive Maintenance  
Predictive Maintenance (PdM) is a maintenance type that occurs before breakdown happens. It is based on precise formulas in addition to sensor measurements, and maintenance is performed with the analysis of the measured parameters. The premise of this maintenance is to ensure the maximum interval between repairs and to minimize the cost and number of scheduled maintenance operations ([PERSON] [8]).  
A predictive maintenance program consists of three steps (see Figure 1):  
* Data acquisition.
* Data processing.
* Machine decision making.  
Although there are monitoring systems dedicated to predicting errors in industrial processes such as [PERSON] et al. [9] exposed for metro doors, the techniques of data analysis and automatic learning have not yet been exploited completely. In this article, some of these techniques will be applied to develop a predictive maintenance. The obtained results will be shown with data extracted from a real process in production. Anticipating how much time is allowed before a failure occurs, which is commonly called the Remaining Useful Life (RUL), is an important task due to the important costs associated with the early replacement of tools [10]. Results show that RUL prediction provides a better accuracy and enables each tool to machine more pieces. Furthermore, the RUL algorithm developed has been deployed with a user visual interface for industrial maintenance purposes, achieving good results also in terms of generalization for other processes. The methodology in this use case is replicable  
Figure 1: Steps followed to develop a predictive maintenance.  
in most of the processes with a serial production of pieces where the machining tool is changed with a certain frequency. Therefore, in this kind of process, the current preventive strategy can be changed by a predictive strategy where the number of pieces for the next tool substitution is predicted.  
Section 2 describes the data acquisition step. Section 3 exposes some preprocessing methods. Section 4 presents the machine decision making, where diagnostics and prognostics are explained. Section 5 exposes the particular problem and the developed methodology. Finally, Section 6 concludes about the results and exposes some future works.
## 2 Preliminaries
### System Overview  
**System Model.** The setting considered is one where \(N\) parties each locally hold a partition of a global dataset wishing to collaboratively perform statistical analysis securely. For the Welch's \(t\)-test, we assume that the dataset is partitioned vertically whereas for the \(\chi^{2}\)-test, we assume that the dataset is partitioned horizontally. Different types of partitioning for the two statistical tests were focused so as to capture real world use cases as explained in Section 4 and additionally to compare the scalability of the secure analytics solutions with respect to the size of the dataset. For example, if the horizontal federated setting was considered for the Welch's \(t\)-test, then each party can simply compute the mean and standard deviation on their local datasets and combine the results securely, thus making the size of the dataset completely irrelevant in the performance analysis.  
**Threat Model.** We consider the semi-honest setting with a dishonest majority where a majority of parties can collude to share information and try to extract information about the other parties.
# A Deep Learning Approach for Maximum Activity Links in D2D Communications
## 5 Deep Learning-Based Link Scheduling  
In real networks, users' positions, channel conditions, and data requests vary frequently, so any effective scheduling algorithm must be able to make decisions in real time. In addition, the potential number of D2D links increases exponentially with the number of D2D devices \(M\) operating within the same cell. In scenarios characterized by large \(M\) values, selecting all the feasible D2D links in order to offload cellular communications becomes extremely time consuming. To develop a time-efficient algorithm, we designed a machine learning-based solution aimed at supporting and simplifying the resolution of the aforementioned _MaxL_ problem, based on properly training a DNN.
## 5 Connected Vehicles
### Benefits and Business Opportunities
#### 5.2.1 Advantages in EVs  
Connectivity is not a technology unique to EVs; it is beneficial to all types of powertrains. This technology can provide vehicle owners with real-time service information, keep the in-vehicle software system up-to-date, and facilitate real-time monitoring of vehicle operating status and handling of traffic accidents and insurance issues, preventing costly recalls and repair charges. Vehicle connectivity plays a greater role and has greater advantages in EVs than other power systems. First, the IoV system can monitor and manage the operating parameters of EVs in real-time through the wireless communication network, which will be an important technical means and the most practical resource to improve the safety of EVs, especially the safety of onboard batteries [89]. Second, the energy efficiency of EVs will be continuously optimized by employing Big Data technology to improve the economy of EV operation. Third, easy access to charging piles and smart charging management can be realized through the IoV technology, thereby increasing consumer attractiveness. Finally, unlike the traditional ICE systems, the operation of EVs is highly dependent on software control, and it is difficult to obtain sufficient accident information through mechanical parts after a car accident. The IoV technology provides abundant maintenance and insurance information acquisition and processing convenience for EVs after an accident.
## 2 Theoretical Background
### Calculation Method of Fractal Dimension  
There are several formal mathematical definitions of fractal dimension that build on the basic concept of change in detail with change in scale [30]. The box-counting dimension is the most widely used one [31]. In fractal geometry, the box-counting dimension, also known as Minkowski dimension, is a way of determining the fractal dimension of a set \(S\) in a Euclidean space \(\mathbb{R}^{n}\), or more generally in a metric space (\(X\), \(d\)).  
To calculate the dimension for a fractal \(S\), imagine this fractal lying on an evenly spaced grid, and count how many boxes are required to cover the set. The box-counting dimension is calculated by  
Figure 1: The conceptual diagram of Tunnel-lining Crack Index (TCI).  
seeing how this number changes as we make the grid finer by applying a box-counting algorithm. Suppose that \(N(\varepsilon)\) is the number of boxes of side length \(\varepsilon\) required to cover the set. Then the box-counting dimension is defined as:  
\[\mathrm{D}=\underset{\varepsilon\to 0}{\lim}\frac{\log N(\varepsilon)}{\log(1/\varepsilon)} \tag{4}\]  
Roughly speaking, the fractal dimension is the slope of the fitting line obtained by fitting a set of points (\(\log N(\varepsilon)\), \(\log\,\varepsilon\)) [32]. In practical, the differential equation is used to estimate fractal dimension as shown in Equation (5). This calculation is easy to achieve through a MATLAB program.  
\[\mathrm{D}\approx-\frac{d\log N(\varepsilon)}{d\log\varepsilon} \tag{5}\]  
If the above limit does not exist, one may still take the limit superior and limit inferior, which respectively define the upper box dimension and lower box dimension.  
**3. Feasibility Analysis of Fractal Dimension as a New Health Assessment Index**  
To confirm the feasibility of fractal dimension as a new health assessment index, the influence factors, such as the density, the width, and the intersection of cracks, on the fractal dimension of tunnel lining cracks was studied in this part.
By addressing current challenges and proposing potential solutions, this work aims to contribute new insights to the field. For this survey, a comprehensive literature review was conducted using multiple academic databases, including Google Scholar, IEEE Xplore Digital Library, and Web of Science. A targeted search strategy (e.g., (\"Time Synchronization\" OR \"Time Source\" OR \"Time Distribution\") AND \"Smart Grid\") was employed to initially identify 15,500 relevant publications from the year 1990 to 2025, encompassing research articles, review papers, and book chapters. After an initial screening based on relevance and scope, a subset of 391 publications was selected for further evaluation. Following a thorough review of the content, studies less aligned with the focus of this paper were excluded, resulting in a final selection of 112 references that best support the survey's objectives. The structure of this paper is as follows: Section 2 introduces time-dependent applications within the power grid and their time requirements. Section 3 covers time sources and their performance. Section 4 discusses the characteristics of various time distribution systems. Section 5 addresses challenges and future research directions, and Section 6 concludes the survey.
## 4 Experiment Results
### Parameter Experiment  
In Section 2.2, the input image is compressed at the scale of \(r^{c}\). We evaluate the different \(r^{c}\) by using TPR and the number of possible objection locations in D-Textureless dataset, which consists of 9 models and each model has 54 images. The possible object locations are generated and TPR represents the ratio of possible object locations which contain the correct target object to all positive  
Figure 7: Block-diagram of OCM-DRW algorithm.  
samples in the 486 test images. In the first stage, we consider \(r^{c}\) which has higher TPR as better choice, because the possible object locations where negative samples are incorrectly detected as the positive samples will be removed in the second stage. From Figure 8, it can be seen that \(r^{c}=6\) has higher TPR.  
In Section 3.1, the weight value is computed by counting the number of different quantized orientations over a \((2r^{w}+1)\times(2r^{w}+1)\) neighborhood in the template image. The value of \(r^{w}\) adjusts adaptively according to the scale of template in the training stage. In the process of computing the weight in D-Textureless dataset, template images are scaled in the range of [60%, 90%] with 5% increment and the value of \(r^{w}\) equals to its initial value multiplied by the scale of the templates. Figure 9 reports object detection results attained by an approach of multi-scale operation with different initial values \(r^{w}\). From the Figure 9, it can be seen that \(r^{w}=6\) offers the best region weighted.
# Research on Performance Metrics and Environmental Conditions for 5G MIMO OTA
## 3 Experimental Results and Analysis
### Comparison between TRMS and MARSS  
TRMS and MARSS results under both UMi and UMa channel models are compared in Figures 6 and 7.  
Figure 5: A comparison between Variance and Peak-Null for (**a**) the UMi and (**b**) the UMa channel models for MARSS under CTIA interference-limited environmental condition.  
Figure 6: A comparison between TRMS and MARSS for the UMa channel model.  
Under 3 GPP UE noise-limited condition, different channel models present nearly the same UE performance trends. TRMS-UMa (blue curves) in Figure 6 always presents worse MIMO OTA performance with a consistent performance degradation around 5 dB with respect to TRMS-UMi (blue curves) in Figure 7. In addition, both TRMS-UMa and TRMS-UMi results show a similar performance variation of around 5 dB across all DUTs at all three thresholds (i.e., TP@95%, TP@90%, and TP@70%). Therefore, if TRMS under 3 GPP UE noise-limited condition is adopted for NR FR1 MIMO OTA, selecting only one threshold of TP@70% is sufficient to distinguish between UEs' performance. TP@95% should also be considered as a criteria for checking whether the UE can pass 11 of total 12 positions.  
Under CTIA interference-limited condition, MARSS results show nearly the same UE performance trends at all thresholds under the same channel model. However, different channel models present very different DUT performance trends. Meanwhile, the performance degradation for MARSS-UMa (orange curves) in Figure 6 with respect to MARSS-UMi (orange curves) in Figure 7 is not consistent. The offset is between 5 to 9 dB across DUTs. The maximum UE performance variation across DUTs is around 4.2 dB at the threshold of TP@95% with CTIA testing method (MARSS-UMa in Figure 6), which is similar to TRMS results at the threshold of TP@95% under 3 GPP UE noise-limited condition. However, the maximum variation of MARSS-UMa (orange curves) in Figure 6 at the threshold of TP@70% is 2.6 dB, much smaller than the maximum offset at the threshold of TP@95%. We can conclude that, for MARSS-UMa under CTIA interference-limited condition, different thresholds present different performance variations among DUTs. Furthermore, for MARSS-UMi, the maximum performance variation is ~1.1 dB under all thresholds, which is much smaller than that of MARSS-UMa. Therefore, it can be concluded that MARSS performance is highly dependent on channel models.  
In summary, TRMS under UE noise-limited condition has a stronger ability to distinguish between good and bad performing DUTs than MARSS under CTIA interference-limited condition. If MARSS under CTIA interference-limited condition is adopted for NR FR1 MIMO OTA, a proper channel model shall be carefully selected to differentiate UE performance. Meanwhile, both thresholds of TP@70% and TP@95% shall be defined.
## 5 Conclusions  
As a micro-ecosystem supporting vegetation, variations in soil hydrothermal conditions inevitably lead to corresponding changes in the overlying vegetation. Previous studies have predominantly treated soil hydrothermal conditions as a collective factor or solely focused on the impact of water availability on vegetation, with limited exploration of the differential effects across soil profiles. This study aims to address this gap by examining the influence of soil hydrothermal conditions across different layers on GPP in the Qilian Mountains area, utilizing MODIS GPP data and the ERA5-Land reanalysis dataset. Analytical methods such as trend tests, correlation analysis, and machine learning algorithms are employed to investigate these influence effects. The findings yield several key conclusions:  
1. Deeper soil is wetter than shallower soil, proving that the deep soil possesses stronger water retention characteristics than other profiles. Conversely, the interannual varia  
Figure 11: Influence degree threshold analysis of soil temperature values in different dominant vegetation areas: (**a–c**) vegetation zone, (**d–f**) grassland, (**g–i**) shrub, and (**j–l**) alpine.  
tions in soil temperature do not exhibit significant divergence with soil depth. Moreover, the deep soil moisture demonstrates a distinct and significant interannual increasing trend, while soil temperature values show a similar fluctuating upward trend across three soil layers. GPP values exhibit a spatial distribution pattern characterized by low values in the northwest and high values in the southeast. Furthermore, GPP values generally demonstrate a significant increasing trend throughout the study period, confirming the persistent vegetation carbon sequestration capacity of the semi-arid region.
2. The correlation analyses have confirmed that GPP values exhibit a stronger correlation with deep soil moisture, while the correlations with soil temperature do not show significant differentiation across layers. This pattern is consistently observed in different dominant vegetation areas. Additionally, the magnitude of the PCCs between GPP values and soil moisture of the same layer aligns with the vegetation cover area occupancy.
3. The Random Forest feature importance extraction provides evidence that the prominent feature variables influencing GPP are the deep soil moisture and the surface soil temperature. These results suggest that soil temperature may exert a non-linear influence on GPP while also confirming the stronger correlation between deep soil moisture and GPP.  
D.W., Conceptualization, Investigation, Writing--original draft; Y.Z. ([PERSON]), Data curation, Investigation; Y.L., Validation; Y.Z. ([PERSON]), Writing--reviewing and editing; B.W., Supervision. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Inference Acceleration with Adaptive Distributed DNN Partition over Dynamic Video Stream
## 2 Related Works
### D2D Inference  
DNN collaboration among devices is divided into interlayer cooperation and intralayer collaboration. The devices in an interlayer cooperation must follow the original structure of the DNN and calculate successively. AAIoT [7] divided the DNN model under a multi-layer IoT architecture with hierarchical constraint relations to achieve model collaborative inference for multiple devices. [PERSON] et al. [8] proposed a partitioning algorithm that simultaneously performs on the data and model. [PERSON] et al. [9] designed a fusion search strategy based on dynamic planning, which adaptively performed layer fusion and thus dynamically distributed the workload according to the heterogeneity of resources to minimize the end-to-end inference latency. Based on the compatibility between the multilayer structure of a DNN and mobile edge computing (MEC), [PERSON] et al. [10] deployed the model to multiple devices in a layered structure by optimizing the device resource occupancy and realized the distributed inference of DNN. The data transmission delay, processing delay, and queuing delay were integrated and optimized.  
CoEdge [11] considered computational and communication resources and used layer fusion combined with a workload dynamic adjustment mechanism to achieve load balancing on heterogeneous devices. [PERSON] et al. [12] proposed a collaborative approach in order to fully utilize 5G resources, which could perform fine-grained DNN elastic computational partitioning, and deployed the model on multiple end devices, as well as on the cloud and edge, to achieve collaborative inference, and finally developed a mobile Web AR application to verify the effectiveness of the method. EdgeSP [13] designed a multiple-fused-layer-block parallelization strategy to reduce the communication overhead between devices during parallel inference, effectively reducing the average task inference delay, and improving resource utilization by adding early exit branches. [PERSON] et al. [14], in order to achieve inference of complex DNN models by unmanned aerial vehicles (UAVs) while avoiding air- and ground-generated additional communication delays, proposed a method for a dynamic collaborative DNN model inference by UAV air-to-air communication, which improved the real-time DNN inference while effectively utilizing the storage and computational resources of UAVs. [PERSON] [15] proposed a distributed edge inference framework to partition the model and perform distributed inference on resource-constrained devices, effectively reducing the device energy consumption.  
Figure 1: Schematic diagram of collaborative inference for video streaming tasks.
# Study of Global Navigation Satellite System Receivers' Accuracy for Unmanned Vehicles
## 2 Materials and Methods  
### System Design  
The study of navigation receiver performance was accomplished by preparing a PCB board using the widely used GNSS receivers, such as the NEO or MAX receiver series from U-blox AG company. Pin-compatible receivers have also been introduced by other companies, such as Minewemi Co., Ltd. (Shenzhen, China). The main positioning task was solved by the GNSS receiver (Figure 3). It is based on different GNSS modules depending on the required accuracy. If standard accuracy is required (\(\sigma\leq 2.5\) m), then modules like U-blox NEO-M9N or GP-01 GPS + BDS Compass ATGM332D may be used. If high-precision accuracy is required (sub-meter level), then a Minewemi ME32 GR01 module may be used, which is a multi-constellation (GPS, Beidou, GLONASS and Galileo), concurrent, simultaneous multi-constellation L1 + L5 positioning GNSS module. All the GNSS receivers mentioned are pin-compatible and support UART and I\({}^{2}\)C protocols, while U-blox modules additionally provide a USB connection, which allows high-speed data transmission or direct connection to a PC. As the board is powered by providing 5 V at Vin pin, a CMOS low-dropout (LDO) voltage regulator should be used, such as MCP1700T-3302, to convert the input voltage to 3.3 V as a GNSS-receiver power supply. The selection of this LDO regulator is set due to the very low drop-out voltage of only 178 mV@250 mA load; the LDO output is stable only when using 1 uF output capacitance. Simultaneously, the VDD_USB input at U-blox receivers is powered using another CMOS LDO, such as MCP1824, which is distinguished by enabling (EN) input.
## 7 Observations and Recommendations
### Recommendations and Research Challenges
#### Critical Infrastructure Applications  
Critical infrastructure applications employ cyber-physical systems to achieve their monitoring and control functionality. This improved functionality comes with numerous security challenges which need to be addressed because a compromised system could have disastrous real world consequences. Taking the previous discussions into consideration it is important to develop anomaly detection schemes that can fit into these complex environments in order to limit the damage of potential security breaches. In this context, it is not only important to detect the breach, but also to have recovery mechanisms in place to protect the underlying infrastructure once it has been detected. It is also paramount that these schemes be introduced while maintaining reliability and latency requirements of the system. These and the other requirements discussed in this paper have to be specifically considered for critical infrastructure applications. The implication of these schemes in this context should not be neglected in future work.
## 5 Discussion  
### The Application of the EHC-GCN Model in Human-Computer Interaction Scenarios  
Human-robot collaboration (HRC) is increasingly becoming a key element of manufacturing automation to meet the demands for higher productivity and flexibility. In assembly tasks, for instance, components may be placed at a location far from humans, and robots can assist in completing the assembly by performing operations such as picking up, placing, and tightening screws. HRC enables dynamic interaction between humans and robots in shared spaces, based on continuously changing real-time situations. Both humans and robots should be able to recognize each other's tasks and intentions. The central focus of this paper is on rapidly and effectively perceiving human behavior to implicitly help robots understand human intentions. After identifying human behavior, the robot uses a specific action as a trigger to predict the next behavior and take action, which can eliminate the need for workers to directly command the robot, making human-robot collaboration more natural and enjoyable. Currently, the deployment of human perception systems on robot platforms is not yet mature, due to the necessity of human detection, tracking, and action recognition models, all of which need to operate in real-time. Therefore, reducing model complexity is necessary, and our EHC-GCN model has achieved good performance  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Frames per pred** & **CPU (preds/s)** & **GPU (preds/s)** \\ \hline EfficientGCN-B0 [29] & 300 & 11.5 & 204.7 \\ EHC-GCN (3D Skeleton) & 300 & 12.1 & 210.2 \\ EHC-GCN (Pose) & 300 & 16.6 & 291.62 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Real-time efficiency on different hardware setups (CPU, GPU).  
in both recognition accuracy and efficiency, which is beneficial for its deployment on robot platforms.