esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Collection and Preparation
### Data Preparation  
It is known that the UCS values depend on a large number of parameters. In this study, factors that are important in the UCS of the soil-cement mixture determination were selected. The soil type (denoted as S) seem to influence the UCS of the mixture as it determines the soil's grain composition [22]. Moisture content of soil samples (denoted as Mc) also play an important role in the UCS detecting [23; 24]. The wet density of soil (denoted as We) should also be considered as it determines the natural state of the soil prior to mixing [24]. The soil sampling depth (denoted as D) should also be taken into account when it affects the soil condition [25]. We cannot fail to mention the amount of cement (denoted as Ac) used for mixing when it determines the adhesion between soil particles [26]. Factors related to the sample after mixing such as: specimen diameter (denoted as Di); specimen length (denoted as L); specimen area (denoted as A); specimen volume (denoted as V); mass of specimen (denoted as M) and density of specimen (denoted as De) might also affect much to the prediction [27; 28]. In addition, a number of other important factors to consider were the curing condition (denoted as Cc) and the curing period (denoted as Cp)  
Figure 4: Axial stress–strain relation.  
Figure 3: Unconfined compression test equipment.  
that affect the rate of strength development of the cement. Finally, the type of cement (denoted as T) was also taken into consideration when the quality of cement in Vietnam appears to be uneven among each other. The UCS of the cement-soil mixture (denoted as q\({}_{\rm u}\)) was the single output. Detailed statistics of the parameters used in the study are presented in Table 1. An example of this data set is given in Appendix A (Table A1).
## 4 Results and Analysis
### Test Environment and Experimental Design  
#### 4.1.1 Test Environment  
The experiments were conducted on two x86 servers, each equipped with dual Intel Xeon Gold 2650 CPUs. Both servers ran the Ubuntu 22.04 LTS operating system with kernel version 6.2.0-33-generic. Each server was fitted with a 100 Gbps Mellanox ConnectX-5 InfiniBand NIC and connected via 100 Gbps fiber.
## 1 Introduction
Firstly, it increases the overall cost and size of the device, making it less practical for certain applications. Also, the system's complexity increases as multiple antennas require additional components and circuitry to function properly. Moreover, the usage of multiple antennas creates coupling issues, which can adversely affect the performance of the antennas. Coupling leads to interference and reduced efficiency, resulting in degraded signal quality and overall system performance. To overcome these challenges, several alternative approaches to achieving multiband operations without relying on multiple antennas have been developed; this includes the development of wideband antennas that can cover a broad frequency range while maintaining a compact form factor [8]. However, a notable issue with this design is the introduction of notch bands in the antenna structure. In an effort to enhance the bandwidth of microstrip patch antennas, these advancements aim to meet the increasing demands of communication systems while simultaneously reducing costs, size, and complexity. This study suggests the utilization of fractal geometry to reduce the size of the radiating patch and enhance the antenna's gain when operating in the UWB bandwidth. This research introduces the design and analysis of the second-order iterated circular Minkowski fractal antenna (CMFA) on the design presented in [9] tailored for ultra-wideband applications. The proposed CMFA offers several appealing features, including high gain, a small form factor, and the ability to operate across the entire UWB frequency spectrum. The antenna features a microstrip feedline and employs Minkowski fractal geometry to optimize its gain across the UWB frequency spectrum. The design integrates a circular groove on the ground plane along with an arc slot at the periphery of the radiating element. The modified UWB CMFA antenna utilizes a circular patch and a single microstrip feedline, while adopting the cost-effective FR-4 substrate and copper for the radiating component. The design and simulation of this UWB CMFA antenna are carried out using the time-domain solver within the Computer Simulation Technology (CST) Studio Suite 2020 by Dassault Systemes. To validate the real-world usability of the proposed fractal antenna, it is fabricated and subjected to measurements of S-parameters and radiation characteristics. These measurements are performed using the Rhodes and Schwarz ZVL vector network analyzer (VNA) and a radio frequency (RF) anechoic chamber, respectively. The remaining sections of the paper are organized as follows: Section 2 presents the design and geometry of the circular Minkowski fractal antenna, and Section 3 presents the characteristics of the antenna. The fabricated measured results and validation of the measured results with simulated results are presented in Section 4, and Section 5 presents the conclusion of the work.
# Enhanced Feature Selection via Hierarchical Concept Modeling
## 4 Research Methodology  
### Dataset Description and Tools  
This study obtained all required datasets from the UCI machine learning repository [29]. These datasets belong to many different fields, such as engineering, social science, and business. Among them, we selected \(11\) datasets covering many areas for our evaluation. Table 2 shows the different samples of data used. We also provide the number of attributes, instances, and classes. Moreover, in the final column, we describe the attribute data type in each dataset. These data are used to demonstrate the applicability and performance of feature selection. Afterwards, classification is used to evaluate the accuracy when each dataset is randomly split into training and test sets.  
This study used R for data management and analysis, specifically R-3.5.1 for Windows and RStudio Version 1.1.456. They were used to classify using the DT, SVM, and ANN classifiers. We also used Weka to provide the previous feature selection algorithms compared to our models. The feature selection methods tested were filter methods (information gain  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **No. Attributes** & **No. Instances** & **No. Classes** & **Data Type** \\ \hline Dermatology & 33 & 366 & 6 & Integer \\ \hline Glass & 10 & 214 & 7 & real \\ \hline Iris & 4 & 150 & 3 & real \\ \hline Lung-cancer & 56 & 32 & 3 & Integer \\ \hline Movement & 91 & 360 & 15 & Integer \\ \hline Pageblocks & 10 & 5473 & 5 & Integer, real \\ \hline Segmentation & 19 & 2310 & 7 & Integer, real \\ \hline Soybean & 35 & 307 & 19 & Integer \\ \hline Tunadromd & 242 & 4465 & 2 & Integer \\ \hline Wine & 13 & 178 & 3 & Integer, real \\ \hline Zoo & 17 & 101 & 7 & Nominal, Integer \\ \hline \hline \end{tabular}
\end{table}
Table 2: Datasets used in this study.  
and chi-square), wrapper methods, and forward selection. In addition, in the proposed model using FCA for feature selection, we applied Concept Explorer (ConExp) [30] to generate the hierarchical structure for FCA. ConExp has the functionality to create and edit contexts, generating lattices from contexts and finding the relative attribute that actually occurs in the context and the basic relationship that is true in the context.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 5 Future Research Directions
### Hybrid Localization Systems  
Future research should focus on developing hybrid localization systems that combine multiple technologies such as Wi-Fi, BLE, UWB, RFID, and ZigBee. By leveraging the strengths of each technology, hybrid systems can overcome individual limitations related to accuracy, signal interference, and power consumption. For example, integrating UWB with BLE can provide high precision while maintaining energy efficiency, making it suitable for wearable devices in healthcare and military applications [17].
## 5 Discussion
### Application Areas  
It is still not well understood if the capture of an individual is dangerous to the habitat; in any case, the health of the birds is paramount. Therefore, non-invasive, passive methods are to be preferred (to avoid the common problems of capture such as the stress or death of the individuals) [5; 9]. On the other hand, as mentioned in the Introduction, obtaining a large amount (and high quality) of vocalizations from an individual to apply the statistical methods efficiently is nowadays a complex challenge. In this context, the proposed algorithm can provide an identification evaluation by only analyzing two samples from individuals (a limited data condition), implying a time-cost reduction and a new easy way to monitor biodiversity. Although the proposed method does not extract the same features as those from a captured individual, it is a non-invasive method for individual identification that, in addition to the application in counting, could be used to determine the dispersion of the habitats and the mapping of the territories.  
Due to the particular skills of experienced observers, it is recommendable that only these qualified observers participate in biodiversity monitoring programs (to guarantee reliability on the data) [5]. Although this is the desired condition, there is often a lack of available qualified observers mainly because of the amount of time consumed by the identifications [7]. On the other hand, training new observers requires from two to three weeks in an intensive program (depending on the number of species to be identified) [9]. The acoustic identification of birds has shown greater efficiency than visual identification, especially because acoustic identification maximizes the visual observations [7]. For that reason, to increase the efficiency of identifications, it is recommended to combine visual and acoustic identification techniques. In this context, the proposed semi-automatic identification algorithm could be used as a valuable tool that can be applied together with traditional counting techniques. Since it is based on the identification of patterns in the spectrogram, the methodology could be used for the training of new observers and to help experienced observers with the reliability of avian counts through the search methodologies.
## 2 Current Status of Development of Geophysical Exploration Techniques
### Exploration Techniques Based on Dielectric Constant  
The physical basis of the electromagnetic method of exploration is the difference in the electromagnetism and dielectric constant between earth and rock-fill dam hazards and normal media. The potential leakage area of earth and rock dams will change the dielectric constant. The electromagnetic exploration technology detects abnormalities in the electrical parameters of earth and stone embankment dams, and the detection and diagnosis of the potential leakage inside the earth and rock dams can be realized [48]. The greater the difference in the dielectric constant between media, the better the detection effect, and the water content is one of the main factors affecting the dielectric constant [49]. Currently, the electromagnetic-type physical detection methods mainly include the ground-penetrating radar method, transient electromagnetic method, electromagnetic wave CT method, and magnetic resonance method.
## 4 Reduction of Computational Costs with Data-Driven Sparse Sampling
If the \(r\) mode reconstruction is considered, then \(\mathbf{\tilde{U}}\) and \(\mathbf{\tilde{V}}\) contain the first \(r\ll n\) columns of \(\mathbf{U}\) and \(\mathbf{V}\), respectively, and \(\mathbf{\tilde{\Sigma}}\) contains the first \(r\times r\) block of \(\mathbf{\Sigma}\). Subsequently, the sampling point candidate matrix becomes the spatial POD modes \(\mathbf{\tilde{U}}\), and the latent state vector becomes the mode coefficients \(\mathbf{\tilde{\Sigma}}\mathbf{\tilde{V}}^{*}\) of the data matrix, respectively. In addition, \(\mathbf{H}\) is obtained using the greedy method [17], so that the determinant of the Fisher information matrix is maximized by considering \(\mathbf{H}\mathbf{\tilde{U}}\) as a sampling-point candidate matrix. See ref. [17] for the detailed explanation and refs. [14; 21; 22; 23; 24; 25; 26; 27; 28] for relevant techniques. Thus, the dominant modes and optimum \(p\) sampling points are determined based on the training data. Once \(\mathbf{H}\) and \(\mathbf{\tilde{U}}\) are constructed, a test condition of microphone position and wind velocity from the range that is shown in Table 1 is chosen. Here, the test conditions are not included in the training data. The observation vector \(\mathbf{Y}\) is constructed by calculating the propagation time or the attenuation rate of \(p\) sampling points under the test condition. Subsequently, \(\mathbf{\hat{X}}\) and \(\mathbf{\hat{P}}\) are quickly obtained by using Equations (27) and (29). For evaluation of the proposed method, the estimated data matrix \(\mathbf{\hat{P}}\) is compared with the reference matrix, in which all of the points are exhaustively calculated by the Amiet method [5]. The reproducibility is verified by taking the relative error and calculating the average of each row.
# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
## 4 Discussion
### Performance Evaluation of LOF with Different Distance Metrics  
This study investigates the impact of distance metrics on the LOF algorithm for anomaly detection, evaluating the performance of six distance metrics: Euclidean, Manhattan, Chebyshev, Cosine, Canberra, and Bray-Curtis. As shown in Figure 5, Euclidean distance achieves the highest AUC (93.41%), followed closely by Manhattan and Chebyshev distances. While slight differences in AUC and pAUC are observed across the metrics, the overall results were remarkably consistent, with most metrics achieving AUC values around 93% and pAUC values around 83%. This consistency highlights the robustness of the LOF algorithm to different distance metrics, suggesting that, for this dataset, the choice of metric does not significantly impact detection performance. However, the relatively lower performance of the Canberra distance (AUC: 90.36%, pAUC: 81.74%) demonstrates that not all metrics are equally effective, underscoring the importance of tailoring the selection of distance metrics to the specific characteristics of the dataset.  
Although Euclidean distance proves to be a robust and reliable choice in this study, future research should focus on exploring more flexible approaches. For instance, hybrid or adaptive distance metrics could combine the strengths of multiple metrics to better accommodate diverse datasets, while dimensionality reduction techniques may help address challenges posed by high-dimensional spaces. Furthermore, introducing more diverse and advanced distance metrics and evaluating their effectiveness across a broader range of datasets and anomaly detection tasks could provide deeper insights into their strengths and limitations. Such efforts would further enhance the adaptability and robustness of anomaly detection methods in various real-world scenarios.
# Evaluating the Performance of Lightweight Ciphers in Constrained Environments--The Case of Saturnin
## 3 Evaluating the Performance
### Methodology--The Testing Environment
For the Saturnin encryption we used the same portable C implementation that we used for the client application. Note that, for our experiments, we utilized the Saturnin-Short version, performing only encryption and not data authentication (since the AES-CTR also performs only encryption). Although the Saturnin-Short should not be used in practice for messages with sizes larger than 128 bits, this restriction, which exists for security reasons, does not affect our analysis since we focus only on its performance. Moreover, Saturnin-short does not require two passes of data (which has been considered as the main drawback of the full version of Saturnin, with respect to the 10 finalists of the NIST's competition), but uses only one call to Saturnin to provide confidentiality and integrity. Every Arduino application has two main functions:  
* void setup(): This function performs the initial setup. It is being used for the WiFi connection, the server initialization and a global variable setup. Then, it checks the correctness of the algorithms results and prints all the required messages to the terminal (see Figure 2). * void loop(): This is the main function, which is being executed continuously while a condition holds; otherwise, the program stops running (the board needs reset to run the program again). Here, the server \"listens\" to the port 2080 and for every request received from the client, it replies sending back the message, first unencrypted, then encrypted with AES and finally encrypted with Saturnin. It subsequently prints to the terminal the time needed for AES encryption and for Saturnin encryption. All times are shown in milliseconds. Figure 1: A screenshot from the client application, illustrating the responses that the client receives, and the messages that it prints regarding the time measurements. A serial terminal view after a successful answer to a client's request is shown in Figure 3.
## 3 Results
### CTHA--Templating in Two Planes--a.p. and Lateral View  
Preoperative templating in two planes (a.p. view and lateral \"Lauenstein\" view) of CTHA showed an exact match of the implanted stem size in 40.0% \(\pm\) 20.8% of cases. In 89.4% \(\pm\) 9.8% an implanted size within a deviation of +/\(-\)1 stem size was found. In 99.3% \(\pm\) 1.3% of our cases, we could predict the implanted stem size within a range of +/\(-\)2 sizes. The remaining 1.7% showed a deviation of +/\(-\)3 stem sizes (Table 1). In 25.5% an oversizing of the templated size was observed. In 34.5%, the templated stem size was smaller than the implanted stem size.  
Short Stem Total Hip Arthroplasty (STHA)--Conventional Templating in One Plane on the a.p. Pelvic Overview  
By using templating based on one radiological plane only, the rate of measurements matching the exact stem size was 36.2% \(\pm\) 16.9%. By a tolerance of +/\(-\)1 stem size, the predictability raised to 86.3% \(\pm\) 9.5%. A deviation of the implanted and templated stem size of up to +/\(-\)2 sizes was observed in 99.7% \(\pm\) 0.6% of the cases (Table 1)--the remaining 0.3% \(\pm\) 0.6% of the measurements showed a deviation of \(\pm\) 3 stem sizes. An oversizing of the templated implant size was found in 31.9%, and an undersizing also in 31.9% of the cases.
# Latent Dimensions of Auto-Encoder as Robust Features for Inter-Conditional Bearing Fault Diagnosis
## 1 Introduction
In [13], the authors derived an entrogram from the time series data using Frequency Slice Wavelet Transform, which they analyzed to see if the bearing faults can be distinguished from the normal working condition. A state of the art (SOTA) survey on various methodologies used for bearing fault diagnosis is detailed in [14]. As can be observed from this article, there are numerous ways of detecting bearing faults. The results show that bearing fault classification is effective when sufficient data from different conditions is available. However, variability in the machine operating conditions poses a practical problem for condition-based monitoring. Furthermore, it is impractical to collect the data for all these variabilities (e.g., settings under which a machine operates, such as axial load, radial load, rotation speed, etc.). As we know, general features from the observations \(X_{1}\) and \(X_{2}\) of two different machine conditions do not always fall in a similar distribution. For example, data collected in a lab setting can be different to the data acquired from a similar setup in the field. Generic algorithms that are trained with the data from labs are then prone to transfer errors when used to infer the results from data measured in the field [15]. Similarly, the variation in the data can be caused by many parameters such as operating conditions, sensor displacement, variation in the sensor, etc., and it is currently a challenge to develop a model that is robust to these variations. In an attempt to generalize the classification across conditions, researchers have proposed several algorithms that transfer the learning from one condition to another [15], where the class distribution from one condition is known (source domain) and some information of another condition (target domain) to which the class information will be fitted and matched. To tackle the problem of needing data from all possible conditions, unlike the existing methods, we propose a new approach based on latent features of Auto-Encoders and nearest neighbor algorithms. This novel methodology considers any one condition's data and performs a robust classification across other conditions. For different open-source datasets, we then compared our method to the best performing source only methods and domain adaptation methods from the SOTA. This work is structured as follows. Section 2 gives the detailed problem description, which is followed up by Section 3, where we discuss some preliminaries necessary for understanding this work. The proposed methodology is then described in Section 4, succeeded by Experimental Setup, Results, and Conclusions in Sections 5-7, respectively.
## 3 Overview of Vitamin D  
### Vitamin D Physiology and Metabolism  
Vitamin D is a fat-soluble vitamin, which can be derived from dietary sources such as oily fish or fortified dairy products or from supplements in the form of ergocalciferol (vitamin D\({}_{2}\)) or cholecalciferol (vitamin D\({}_{3}\)). However, the primary source of vitamin D in humans is through skin exposure to ultraviolet-B (UVB) radiation [47]. The length of time needed to produce adequate vitamin D from skin exposure to sunlight depends on the strength of UVB rays (i.e., place of residence) and skin pigmentation [48].  
Upon sun exposure, vitamin D is photobiosynthetised in the epidermis by 7-dehydrocholesterol, a normal byproduct of cholesterol synthesis [7]. Bound to a vitamin D binding protein (DBP), and to a lesser extent to albumin, this biologically inert form of vitamin D is transported to the liver where it undergoes hydroxylation to form 25-hydroxyvitamin D\({}_{3}\) (25(OH)D; the main measure of vitamin D status). A second hydroxylation occurs in the kidneys by the enzyme 1\(\alpha\)-hydroxylase (CYP27B1) to form 1,25-dihydroxyvitamin D\({}_{3}\) (1,25(OH)\({}_{2}\)D\({}_{3}\)), the biologically active form [49]. This active hormonal form, 1,25(OH)\({}_{2}\)D\({}_{3}\), exerts pleiotropic biological functions by binding to a nuclear VDR, which is present in most human cells and tissues, including in the liver, kidney, ovary, pituitary, and endometrium, as well as in pancreatic \(\beta\)-cells [49]. The affinity of 1,25(OH)\({}_{2}\)D\({}_{3}\) to bind to the VDR is 1000 times that of 25(OH)D. However, the half-life of 25(OH)D is approximately 3 weeks, whereas 1,25(OH)\({}_{2}\)D\({}_{3}\) has a shorter half-life of 4-6 h [49]. Vitamin D controls calcium absorption in the small intestine and works with parathyroid hormone (PTH) to mediate skeletal mineralization and maintain calcium homeostasis in the bloodstream [50].