esa-sceva/satcom-chunk-collection huggingface dataset:
# Arithmetic Study about Efficiency in Network Topologies for Data Centers
## 6 Discussion about the Results Obtained
In this sense, it might be used as a tie-breaker to choose among some designs offering different strengths, such as throughput or robustness. It is to be reminded that the scope ofsmall data centers isusually local, which implies that they are used by a limited number of end users; hence, the number of nodes involved  
Figure 31: Coefficient for topologies commonly used in data centers. Figure 32: Coefficient for topologies with many nodes. to serve such users may usually be farlower than in large data centers located in the cloud. In spite of that, some of the commonly used data center network architectures presented in Table 7 for edge deployments have been extended to involve far more users, which makes them ready to be employed in larger cloud data center scenarios. Those results have been exposed in Table 8, where the outcome is higher than that viewed in Table 7, which was expected because coefficient \(\eta\) grows with the number of nodes, as was seen in the first tables. However, the figures obtained may be seen as coherent with the ones exhibited in the previous tables as values obtainedseem to increase steadily as the number of nodes rises with a relatively small slope. Hence, it may be concluded that the coefficient \(\eta\) has been established for data center network topologies, offering a balance between performance, measured in average number of links between any pair of nodes, and simplicity, measured in the average number of links per device within the topology. It was initially thought of forsmall data centers because of itslower number of nodes compared to a normal cloud data center scenario, and the tests carried out in Tables 1 to 7 proved that \(\eta\) offers an increasing value on average as the number of nodes in a network topology grows. However, further tests have been undertaken for larger data centers, such as those being deployed in normal cloud data center scenarios, shown in Table 8, where results proved that \(\eta\) offers coherent results with those obtained for smaller data centers. Therefore, it may be said that this coefficient \(\eta\) may be used in data center network topologies of any size, where the greater the number of nodes, the higher the value obtained for \(\eta\). Apart from the \(\eta\) values obtained in order to measure the efficiency of a certain topology, other considerations could be taken into account in further research, such as the need for redundancy or the requirement for steady values of latency in real-time implementations, where the former is more likely attained with topologies incorporating more links to get extra paths, and the latter is more easily done by using tree-like designs.
# The Standardization Method and Application of the BIM Model for Interchanges
## 5 Creation of a BIM Component Library for Interchanges
### The Significance of Creating a Component Library  
The creation of a BIM component library for interchange interchanges is of great significance to the standardization of components, which is reflected in the following three aspects.  
1. Standardization of BIM components for interchanges is a process that includes several elements, such as component classification and coding and component information depth levels. The interchange BIM component library is an important carrier for the application of interchange BIM component standardization results to actual projects.
2. The interchange BIM component library is the final product of interchange standardization. It integrates all types of BIM components from the standardization process of interchange BIM components and enables a high degree of sharing and reuse during the whole life cycle of interchange design, construction, operation, and maintenance so that the advantages of the standardization work can be fully utilized.
3. The creation of the interchange BIM component library facilitates the management of BIM components, which is important for maintaining, updating, and improving the standardized results of interchange BIM components.
## 3 Materials and Methods  
### Study Area and Data Used  
The Gaza Strip is one of the administrative regions of Palestine, located on the southwestern Mediterranean coast. Geographically, it is located between longitudes 34\({}^{\circ}\)13\({}^{\prime\prime}\) E and 34\({}^{\circ}\)34\({}^{\prime\prime}\) E and latitudes 31\({}^{\circ}\)13\({}^{\prime\prime}\) N and 31\({}^{\circ}\)59\({}^{\prime\prime}\) N with an approximate area of about 365 km2 and an altitude ranging between 0 m and 90 m above mean sea level (MSL) [53]. The population of the Gaza Strip is reported to be about 2 million people (5315 people  
Figure 1: Neural network (MLP) model.  
per square kilometer) according to the Palestinian Central Bureau of Statistics [54]. The Gaza Strip is bordered in the east by the Sinai Peninsula, Egypt in the south, and Israeli settlements in the east and north (Figure 2). In terms of climate, the Gaza Strip belongs to the temperate Mediterranean climate and the arid climate of the Negev and Sinai deserts' climatic zones with annual rainfall ranges of about 335 mm/y and temperature between 27 \({}^{\circ}\)C and 13 \({}^{\circ}\)C [55]. The Gaza Strip is made up of five administrative regions, namely, the Gaza Governorate, which is the capital of the Gaza Strip and accounts for about 34.3% of the population of the Gaza Strip [56], the North Governorate, the Middle Governorate, Khan Yunis, and the Rafah Governorates in the south.  
In this study, the process of assessing site suitability for locating a hospital in the Gaza Strip involves a number of datasets on the geodemographics, the environment, and the topography, as well as remote sensing imagery obtained from the Ministry of Local Government, the United Nation Relief and Work Agency (UNRWA), and the United States Geological Survey (USGS) data archive. The Gaza Strip and its governorates' boundary data (in shapefile), neighborhood data, and land-use base map were provided by the Ministry  
Figure 2: Map of the study area showing Palestine (**a**) and the Gaza Strip (**b**).  
of Local Government; the geographic position of the hospitals was provided by the health ministry; the population census data of 2018 was obtained from the Ministry of Interior, while the no-go zone, aerial photo, and digital elevation model (DEM) were obtained from the UNRWA. In addition to these sources, Landsat 8 imagery acquired on June 2016, and downloaded from the USGS data depository on April 2018 ([[http://earthexplorer.usgs.gov](http://earthexplorer.usgs.gov)]([http://earthexplorer.usgs.gov](http://earthexplorer.usgs.gov)), accessed on 4 November 2021) was utilized to update the land-use map. From these datasets, 15 conditioning factors were obtained: population number, population density, distance from road, distance from main road, distance from river, distance from residential areas, distance from agricultural land, distance from refugee camps, slope, altitude, plan curvature, topographic wetness index, topographic roughness index, and stream power index. Based on the literature sources [12; 13; 57], the conditioning factors are identified as important criteria for determining hospital locations.
###### Abstract  
Real-time tool condition monitoring (TCM) for corner milling often poses significant challenges. On one hand, corner milling requires configuring complex milling paths, leading to the failure of conventional feature extraction methods to characterize tool conditions. On the other hand, it is costly to obtain sufficient test data on corner milling for most of the current pattern recognition methods, which are based on the supervised method. In this work, we propose a time-frequency intrinsic feature extraction strategy of acoustic emission signal (AEs) to construct a cutting condition-independent method for tool wear monitoring. The proposed new feature-extraction strategy is used to obtain the tool wear conditions through the intrinsic information of the time-frequency image of AEs. In addition, an unsupervised tool condition recognition framework, including the unsupervised feature selection, the clustering based on adjacent grids searching (CAGS) and the density factor based on CAGS, is proposed to determine the relationship between tool wear values and AE features. To test the effectiveness of the monitoring system, the experiment is conducted through the corner milling of a titanium alloy workpiece. Five metrics, _PUR, CSM, NMI, CluCE_ and _CluCE_, are used to evaluate the effectiveness of the recognition results. Compared with the state-of-the-art supervised methods, our method provides commensurate monitoring effectiveness but requires much fewer test data to build the model, which greatly reduces the operating cost of the TCM system.  
tool wear monitoring; corner-milling; unsupervised Article
## 4 Experiments
### Experimental Setup  
We evaluate a model by utterance-level accuracy:  
\[\text{Accuracy}=\frac{H}{N}\times 100\ [\%] \tag{2}\]  
where \(H\) and \(N\) are the number of correctly recognized utterances and the total number of utterances, respectively. In addition, we also evaluate our model performance by the F1 score. An F1 score can be computed as:  
\[\text{F1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{3}\]  
where  
\[\text{Precision}=\frac{T_{P}}{T_{P}+F_{P}}\quad,\quad\text{Recall}=\frac{T_{ P}}{T_{P}+F_{N}} \tag{4}\]  
In Equation (4), \(T_{P}\) is the number of correctly classified utterances. \(F_{P}\) and \(F_{N}\) indicate false positives and false negatives, respectively. We calculate the score in each class.  
Since DNN-based model performance slightly varies depending on the probabilistic gradient descent algorithm, which is a common model training approach, we repeat the same experiment three times and the mean accuracy is calculated. In terms of DNN hyperparameters, we choose a cross-entropy function as a loss function and Adam as an optimizer. Batch size, epochs and learning rate are set to 32, 50 and 0.001, respectively. We carry out our experiments using NVIDIA GEFORCE RTX 2080 Ti.
# Effect of Compressed Sensing Rates and Video Resolutions on a PoseNet Model in an AIoT System
## 4 Conclusions  
In this paper, we proposed an AIoT system consisting of an IoT-sensing device and an AIoT edge server. The IoT-sensing device exploited a random sampling function of compressed sensing for data traffic mitigation, and the AIoT edge server fulfilled the CS recovery and domain transform functions of compressed sensing and a PoseNet model based on the MobileNet v1 architecture for pose estimation. To investigate the trade-off between data traffic mitigation and pose estimation accuracy (i.e. pose score), we analyzed the effects of CS rates and video resolutions on pose estimation in the AIoT edge servers in terms of pose score. In the meaningful range of CS rates from 100% to 50%, the higher the video resolution, the lower the pose score. At the CS rate of 80%, we could reduce data traffic by 20%, but the pose score decreased less than about 0.03 for all video resolutions. When the range of CS rates from 50% to 10%, the quality of the recovered image is very low, so it is difficult to properly estimate human pose and the pose score decreases sharply.  
Figure 5: Outputs of the PoseNet model for video resolution of \(480\times 360\): (**a**) original image, (**b**) CS rate of 90%, (**c**) CS rate of 70%, (**d**) CS rate of 50%, (**e**) CS rate of 30% and (**f**) CS rate of 10%  
Figure 6: Pose scores of the PoseNet model according to CS rates and video resolutions.  
Therefore, when the CS rate is 80% and the video resolution is 1280 \(\times\) 720, we can achieve the most effective data traffic mitigation and pose score.  
Writing--original draft, H.-M.K.; Writing--review & editing, J.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-00959, Fast Intelligence Analysis HW/SW Engine Exploiting IoT Platform for Boosting On-device AI in 5G Environment).  
Not applicable.  
Data available in a publicly accessible repository The data presented in this study are openly available in reference number [43].  
The authors declare no conflict of interest.
# Comparative Study on Load Monitoring Approaches
## 1 Introduction
Therefore, the implementation of electricity control and automation measures in households can provide significant benefits to both consumers and energy providers. This study compares intrusive and non-intrusive approaches to load monitoring by evaluating the respective capabilities and limitations of each approach in monitoring and analyzing electricity consumption in residential environments. By comparing the performance of ILM and NILM, researchers can better understand which approach is more suitable for specific applications, such as residential energy management. The majority of studies that have addressed load monitoring issues address energy disaggregation, with appliances varying from household to household. Datasets have the ability to substitute for data collected in situ, and thus play a critical role in the development of NILM. However, the public accessibility of these datasets, despite their plurality (REDD, UK-DALE, PLAID) is not sufficiently representative of the diversity of appliances and devices that might be found in different types of households or buildings. Furthermore, despite the growing interest in ILM and NILM, there are still weaknesses in the literature that need to be addressed. An example is the lack of standardized evaluation measures to compare the performance of ILM and NILM algorithms. This makes it difficult to compare the results of different studies and establish a baseline for the accuracy and reliability of these methods. Thus, further research is needed on the cost-effectiveness and practicality of implementing ILM and NILM in real-world settings, as these factors may affect the adoption and sustainability of these approaches. In addition, it is also critical to focus on the privacy and security issues that NILM may have on its users. The contribution of this article is based on presenting in detail the different approaches to load monitoring, highlighting some aspects that other authors do not present when discussing the different approaches to load monitoring in their work. In this way, readers are given a range of information to form their own ideas about each of the approaches presented. This document is organized into several sections. The first section highlights the causes of energy growth in the residential environment and presents the reasons for energy conservation. The second section presents the concept of load monitoring, as well as the different approaches that comprise it, and an overview of the concept of load monitoring, as well as the different variations that comprise it. Section three discusses the strengths and weaknesses of the approaches presented. Section four discusses the cybersecurity risks faced by the different approaches to load monitoring. Section five provides a summary of what was presented in Section three.
# Trade-Off Analysis for Array Configurations of Chipless RFID Sensor Tag Designs
## 8 Conclusions  
Our research studied the performance and trade-offs of chipless RFID tags using linear and planar array configurations. We created a Pi-shaped resonator with a high Q-factor to design six different array configurations, including three linear arrays (\(1\times 2\), \(1\times 3\), and \(1\times 4\)) and three planar arrays (\(2\times 2\), \(3\times 3\), and \(4\times 4\)). Our simulations showed that planar arrays outperformed linear arrays in terms of the RCS and Q-factor. Through experimental validation and analysis of fabricated planar arrays, we found that the simulated and measured RCS values were in close agreement, indicating potential for reliable tag detection using radar systems. We also discovered that the trade-offs between linear detectability and spherical detectability were influenced by the array size. While larger arrays (e.g., \(4\times 4\) array) offered better linear detectability, smaller arrays (e.g., unit cell and \(2\times 2\) array) provided enhanced spherical detectability. Moreover, we highlighted the impact of the array configuration on the Q-factor, revealing the trade-offs between resonance quality and scattering efficiency. Understanding and optimising these trade-offs is crucial for tailoring chipless RFID systems to meet specific application requirements. Our  
Figure 26: Trade-off summary of studied parameters of chipless RFID planar array configurations.  
study laid the foundation for developing more efficient and reliable chipless RFID systems customised to specific application needs by elucidating the trade-offs among performance parameters such as detectability, the RCS, and the Q-factor.  
Conceptualisation, L.L.; methodology, L.L.; software, L.L.; validation, L.L.; formal analysis, L.L.; investigation, L.L.; resources, N.K.; data curation, L.L.; writing--original draft preparation, L.L.; writing--review and editing, B.R. and N.K.; visualisation, L.L.; supervision, B.R. and N.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All relevant data are within the paper.  
The authors declare that there are no conflicts of interest regarding the publication of this manuscript.
# Prototypical Graph Contrastive Learning for Recommendation
## 2 Preliminaries  
As a foundational method of recommender systems [4; 16], graph-based collaborative filtering can recommend items to users with potential preferences based on observed feedback. Specifically, let us define a user set \(\mathcal{U}=\{u_{1},u_{2},...,u_{M}\}\) consisting of \(M\) users and an item set \(\mathcal{I}=\{i_{1},i_{2},...,i_{N}\}\) consisting of \(N\) items. To represent the observed feedback between users and items, we define the user-item interaction matrix \(\mathbf{R}\in\mathbb{R}^{M\times N}\), where \(\mathbf{R}_{ij}=1\) denotes feedback between user \(u_{i}\) and item \(v_{j}\), while \(\mathbf{R}_{ij}=0\) indicates no feedback. Then, the adjacency matrix \(\mathbf{A}\in\mathbb{R}^{(M+N)\times(M+N)}\) can be represented as  
\[\mathbf{A}=\begin{bmatrix}\mathbf{0}&\mathbf{R}\\ \mathbf{R^{T}}&\mathbf{0}\end{bmatrix} \tag{1}\]  
In GNN-based recommendation, the initial embeddings of users and items, denoted as \(\mathbf{E}^{(\mathbf{0})}\in\mathbb{R}^{(M+N)\times d}\), are typically generated using the Xavier method. The exploration of latent preference between users and items based on the observed interaction graph is achieved by iteratively applying the function \(f_{propagate}(\cdot)\) for \(L\) layers to aggregate neighborhood information between nodes, and the function \(f_{readout}(\cdot)\) to summarize and generate the final embedding representations. The two-stage functions for aggregating information can be specifically expressed as  
\[\begin{split}&\mathbf{E}^{(\mathbf{I})}=f_{propagate}( \overline{\mathbf{A}},\mathbf{E}^{(\mathbf{I-1})})\\ &\mathbf{E}=f_{readout}([\mathbf{E}^{(\mathbf{0})},\mathbf{E}^{( \mathbf{1})},...,\mathbf{E}^{(\mathbf{L})}])\end{split} \tag{2}\]  
where \(\overline{\mathbf{A}}=\mathbf{D}^{-\frac{1}{2}}(\mathbf{A}+\mathbf{I})\mathbf{ D}^{-\frac{1}{2}}\) represents the symmetrically normalized adjacency matrix. \((\mathbf{A}+\mathbf{I})\) denotes the adjacency matrix \(\mathbf{A}\) added to the identity matrix \(\mathbf{I}\) to achieve self-connections and maintain self-information during propagation. \(\mathbf{D}\) is the diagonal matrix, where \(\mathbf{D}_{ii}=\sum_{j}(\mathbf{A}+\mathbf{I})_{ij}\) represents the non-zero instance counts in row \(i\).
## 4 Results and Discussion
### Divergence of Attributes
#### 4.3.5 Archiving Information  
Two papers identified the ability to archive information after AR calls and that archiving the information exchanged was a benefit related to the application [38, 52]. However, [PERSON] et al. (2020) mentioned that archiving the outcomes of an AR call was difficult [36]. The reports were different and showed a variation in the outcomes. When examining the differences among these papers, we noticed that these three papers were conducted in different contexts. First, the study by [PERSON] et al. (2022) was conducted in an uncontrolled environment, while the other two studies were conducted in controlled environments [38]. Second, the information reported in the study by [PERSON] et al. (2020) were based on a finding reported by a different research study and not explored by the study itself [52]. Third, the findings by [PERSON] et al. (2020) were reported based on a series of exploratory workshops and questionnaires [36]. This might be the result of simply not being able to explore the application to its maximum since users were novice and not familiar with the application. Therefore, the ability to archive information without facing difficulties needs experienced practitioners and could be the subject of future research.
# Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
## 4 Radar Depth and Velocity Estimation
### Depth Estimation
expansion to be within the object boundary. They further clipped the association map close to the edge to get rid of imprecise boundary estimations. To compensate for height information, they directly used the height of the bounding box as a reference. Considering the complexity of the vehicle shape, they extended the detections to the lower third of its bounding box to capture the flat front surface of the vehicle. As the ground truth, LiDAR has some inherent defects, such as sparsity, limited range, and holes with no reflections. [PERSON] et al. [116] suggest to preprocess LiDAR points for better supervision. They accumulated multiple frames of LiDAR point clouds to improve density. Pixels with no LiDAR reaches are assigned zero values. Since LiDAR and the camera do not share the same FoV, the LiDAR points projected to the image view also have the occlusion problem. Therefore, the occluded points are filtered out by two criteria: one is the difference between visual optical flow and LiDAR scene flow, and the other is the difference between the segmentation mask and bounding boxes. [PERSON] et al. [117] suggest to use both the visual semantic mask and LiDAR as supervision signals. Visual semantic segmentation can detect smaller objects at a distance, thus compensating for the limited range of LiDAR. To extract better representations, they leveraged a shared decoder to learn depth estimation and semantic segmentation concurrently. Both the LiDAR measurement and the visual semantic mask annotations are used as supervision. Accordingly, the loss function consists of three parts: a depth loss with LiDAR points as the ground truth, a visual semantic segmentation loss, and a semantic guided regularisation term for smoothness. Projecting radar to the image view will lose the advantages of the see-through effect. Alternatively, [PERSON] et al. [118] leveraged radar RA maps for depth prediction. They used a short-range radar with a maximum range of 40 m. Because of the low angular resolution, the azimuth smearing effect is obvious, i.e., the detections are smeared as a blurry horizontal line in RA maps. It is expected that fusion of the image and RA map can mitigate this effect. Therefore, they used a two-branch encoder-decoder network with the radar RA map and image as inputs. A dense LiDAR depth map was used as the ground truth. Different from the above methods that align LiDAR to the image, they cropped, downsampled, and quantised LiDAR detections to match the radar's FoV and resolution. The proposed method was tested with their self-collected data. Although the effectiveness of the RA map and point cloud was not compared, it provides a new direction to explore radar in the depth estimation task.