esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Compressive Strength Variations  
Compressive strength can be defined as the capacity of concrete to withstand loads before failure. Of the many tests applied to the concrete, the compressive strength test is the most important, as it gives an idea about the characteristics of the concrete. Compressive stress is the stress on materials that leads to a smaller volume. When compressive stress is applied to brittle materials, they fracture as there is a sudden release of the stored energy. When the compressive stress is applied to the ductile materials, they compress, and there is no failure. Hence, the results of the test gain importance. The results of compressive strength tests are presented in Figure 7a,b.  
Figure 7a,b shows that as the exclay ratio in the samples increases, the compressive strength of the samples decreases. For example, in samples produced with 2-4 mm diameter of exclay aggregates without resin, the compressive strength decreases by 39.42% as the exclay ratio increases. In samples produced with exclay aggregates with a diameter of 4-6 mm, compressive strength decreases by approximately 43.64% as the exclay ratio increases. In samples containing resin, these reduction values are around 37.5% in samples made with 2-4 mm diameter aggregates and 34.43% in samples made with 4-6 mm diameter aggregates. It can be concluded that by means of compressive strength, adding expanded clay as an aggregate into the concrete mixture diminishes the strength of the concrete. Since compressive strength is the ability of the material or structure to carry the loads on its surface without any crack or deflection, the samples with high exclay ratios cannot be recommended in buildings under high compression.  
Figure 6: Thermal conductivities with respect to exclay ratios: (a) 2–4 mm, (b) 4–6 mm.  
If the effect of cherry tree resin on the compressive strength is analyzed, it will be seen that, whether the concrete includes exclay with 2-4 mm diameter or the exclay with 4-6 mm diameter, the compressive strength decreases with the addition of resin. This shows that the porous structure formed by the resin reduces the strength of the material. As the resin ratio in the samples increases, the compressive strength value naturally decreases.  
The compressive strengths of the samples with diameters of 2-4 mm and 4-6 mm were, respectively, 15.64-25.82 MPa and 14.55-25.82 MPa.  
With the test results, the usage areas of the prepared samples needed to be designated clearly. According to these outcomes, it may be suggested that concretes prepared by using exclays be utilized as columns and building beam concretes in multistore buildings instead of tall buildings. Moreover, low-density concretes with exclay aggregates may be recommended for use as panel walls, bricks, concrete biquettes, internal and external plasters, and concrete partitioning components to minimize building weight.
## 5 Key 6G Technologies
### Improved Wireless Interface
#### 5.2.4 Ultra-Massive MIMO: Enhancing 6G Network Capabilities  
The concept of massive MIMO, initially introduced in 2009 by [PERSON] [125], has been a pivotal technology for 5G due to its remarkable enhancement of spectral efficiency (SE). Ultra-massive MIMO is the next development in 6G, which is defined by the deployment of hundreds or thousands of antennas to achieve greater energy efficiency (EE) and SE, more flexible and wide-ranging network coverage, and improved positioning precision across a wide frequency range. This advancement holds promising applications, including improved multiplexing capability, interference suppression, energy efficiency, and non-terrestrial coverage. The unique spatial resolution of ultra-massive MIMO enables precise positioning in complex wireless communication environments, offering accurate 3D positioning capabilities.  
Regarding near-field and wideband effects, which result in channel sparsity in the angle and delay domains, there are a number of important trends and issues in ultra-massive MIMO [126]. Research efforts have focused on estimation, channel modeling, codebook design, beam assignment, and beam training to address these effects [127; 128; 129]. As ultra-massive MIMO spreads into wider frequency bands like THz and mmWave, current research focuses on modulation strategies, channel characteristics, and integrated circuit design [130]. Implementation approaches are diversifying, with the exploration of reflective intelligent surfaces (RISs) as an alternative to traditional active antennas, contributing to enhanced multi-user capacity, network coverage, and signal strength [131; 132; 133]. Distributed ultra-massive antenna systems, deploying numerous antennas across a wide area, offer benefits such as high SE, consistent user experience, and reduced energy consumption [134; 135; 136].  
Artificial intelligence (AI) brings intelligence to several areas of ultra-massive MIMO, such as beam management, sounding, channel estimation, and user identification. However, challenges remain in meeting real-time requirements and acquiring sufficient training data [137; 138; 139]. Furthermore, there is hope for improved performance in broader application scenarios, including skywave communications [140], underwater acoustic communications [141], and satellite communications [142] when ultra-massive MIMO is considered with a combination of space, air, ground, and sea networks.
# State-Sensitive Black-Box Web Application Scanning for Cross-Site Scripting Vulnerability Detection
## 4 Evaluation
### Experimental Setup  
We evaluate our approach on a set of eight different real-world web applications and answer the following questions.  
* **RQ1.** How effective is the filtering method in the crawler?
* **RQ2.** Is the ranking method beneficial for vulnerability detection?
* **RQ3.** How effective is SSBW in vulnerability detection?  
To make the comparison as fair as possible, we repeated all experiments three times. As in previous studies, we imposed a maximum time limit of eight hours for each scanner to perform a single scan. We take the average of the experimental results for code coverage and the union set of the experimental results for XSS vulnerabilities. Experiments were conducted on a desktop with a 16-core Intel Core i7-10700 CPU 2.90 GHz and 32 GB of RAM.  
**Web Applications:** The eight applications we selected are sourced from previous studies [8; 35], including reference applications with known vulnerabilities: WackoPicko (2018), phpbB (2.0.23) and Vanilla (2.0.17.10); and modern production-grade applications: osCommerce (2.3.4.1), Joomla (3.9.6), WordPress (5.1), PrestaShop (1.7.5.1), and ImpressCMS (1.4.4).  
**Code coverage:** To compare the crawling capabilities of the scanner, we calculate the Lines of Code (LoC) executed by web applications during scanning as a metric to measure code coverage. We implement this by enabling Xdebug [36] in each application. During the crawling process, we add functionalities such as version upgrade, user deletion, and password modification to the crawler's blacklist. This ensures the normal operation of the crawler and the correct deployment of the application. Although these pages may also contain vulnerabilities, they should be scanned separately due to their greater impact on crawlers and applications.  
**Vulnerability detection:** Our rank method aims to crawl more attack surfaces in a shorter time, so we also compare the scanner's ability in vulnerability detection. To be consistent with the terminology used in previous works [7; 8; 19], we also define an XSS vulnerability as any injected JavaScript code that results in execution. Unlike the previous clustering method of XSS vulnerabilities, we believe that independent injection points are a unique vulnerability, while previous research has clustered the injections with the same SQL query into a set of vulnerabilities, since developers only need to change one line of server code to fix the vulnerability. Our method is based on the fact that injection points from different pages may call the same SQL query encapsulated in the program; however, clustering them together cannot accurately evaluate the vulnerability detection ability of scanners. Believing that different injection points are a unique vulnerability is more conducive to a fair comparison of the scanner's vulnerability detection capabilities.
# Enhancing Cloud Security--Proactive Threat Monitoring and Detection Using a SIEM-Based Approach
## 3 Materials and Methods
### Virtual Network Design
#### 3.1.1 Application Gateway/Web Application Firewall  
An Azure instance that serves as a public web traffic load balancer and as a WAF to protect against web application vulnerabilities. It supports TLS/SSL traffic encryption between users and an application gateway and between application servers and an application gateway [35]. In this experiment, the application gateway serves as a gateway to the web servers running in the back-end pool. The servers are accessible using the public IP address of an application gateway, and a listener routes requests to a back-end pool of servers following a configured HTTP rule.  
The WAF component examines incoming requests before they reach the listener and employs the Open Web Application Security Project (OWASP) core rules to detect potential threats [27]. The OWASP defines a collection of rules known as the core rule set, which covers common security threats such as remote file inclusion, HTTP request smuggling, cross-site scripting (XSS), SQL injection, command injection, HTTP response splitting, and various types of bots and scanners. The Azure WAF currently supports four core rule  
Figure 1: Overall deployment architecture model.  
Figure 2: Logical virtual network design.  
sets: CRS 3.2, 3.1, 3.0, and 2.2.9. In our experiment, we utilized CRS 3.1. By default, the WAF policy consists of managed rules, but it is also possible to incorporate customized rules, for example, to restrict remote access to a cloud infrastructure based on geo-location. Additionally, the WAF policy can operate in either detection or prevention mode. A snippet of the policy settings and rules is depicted in Figure 3.
# Identification of Deformation Stage and Crack Initiation in TC11 Alloys Using Acoustic Emission
## 4 Analysis of Experimental Results
### AE Signal Preprocessing Based on Variational Modal Decomposition  
Based on the above analysis, the VMD was used to preprocess the AE signals collected at each stage. The VMD algorithm can obtain the optimal solution of the variational model by iterative optimization in the frequency domain, making it easy to determine the frequency center and bandwidth of each modal component [28].  
There are two main purposes for VMD decomposing. The first one is to decompose the AE signal into two frequency bands, 50-200 kHz and 200-500 khz, ensuring no useful information was lost. The second purpose is to filter out the noise that has a frequency less than 50 kHz. Figures 6-9 show the time- and frequency-domain plots of the various components of a typical AE signal in the four stages after six-layer VMD. It can be seen that the first component, u1, of all signals had an upper-limit frequency of less than 50 kHz and a peak frequency of about 3 kHz. Its amplitude and energy were both low, indicating a typical noise and irregular behavior. Of the five remained components, u2 and u3 were distributed between 50-200 kHz and had the characteristics of continuous signals, while  
Figure 5: Typical time- (left column) and frequency-domain (right column) AE signals induced by the deformation of TC11 titanium alloys: (**a**) Elastic-yield section; (**b**) Strengthening section; (**c**) Necking section; (**d**) Fracture section.  
u4-u6 were between 200-500 kHz and showed obvious burst-type characteristics. By comparing the amplitudes of each component at four stages, it can be found that they are completely consistent with the results described in the previous section.  
Figure 6: Time-and frequency-domain components of the typical AE signals after the Variable Mode Decomposition (VMD) in the elastic-yield stage.  
Figure 7: Time-and frequency-domain components of the typical AE signals after the VMD in the strengthening stage.
# A Semi-Explicit Multi-Step Method for Solving Incompressible Navier-Stokes Equations
## 1 Introduction
The above-mentioned semi-explicit finite volume and finite differences approaches rely on solving the pressure Poisson equation at every sub-step of the multi-step scheme, which is a computationally-intensive option. The idea of reducing the number of implicit steps of a semi-explicit scheme has been proposed in [25]. In the finite difference context, the authors proposed a semi-explicit third-order Runge-Kutta-based scheme where the convective term was advanced explicitly, while treating the viscous term implicitly. Most importantly, they suggested assuming that the pressure corresponding to the intermediate Runge-Kutta steps equals the historical one. Thus, the computationally-expensive pressure Poisson equation was solved only once per time step. In the present work, we develop a similar approach in the context of Galerkin finite element methods. In this work, we derive a scheme based on the combination of the fractional step technique with the fourth-order Runge-Kutta scheme, striving to minimize the computational cost corresponding to the implicit steps. We show that a certain assumption regarding the intermediate step pressures results in an efficient semi-explicit scheme that requires solving the pressure Poisson equation only once per time step. The paper is organized as follows. We first define the space-discrete problem considering linear velocity-pressure stabilized finite elements. Afterwards, time-discrete equations using the fourth-order Runge-Kutta scheme are derived. Next, assumptions for the pressure at intermediate Runge-Kutta steps are introduced, and the fractional split is applied. The resulting semi-explicit scheme is obtained. The paper concludes with a numerical verification example. Time accuracy and computational efficiency of the method are estimated.
## 3 Materials and Methods  
### Dataset  
The study presents an adapted dataset, created by combining and annotating the StaVer [20] and DDI-100 [21] datasets, as shown in Figure 1. The processes applied to the datasets are described in detail in Section 4.2. StaVer contains 400 images of scanned documents with the respective ground truths for the stamp locations. The documents refer to automatically generated invoice files that were printed, stamped, and scanned, containing stamps, texts, logos, and tables, as exemplified in Figure 1a. The ground truths are binarized images with masks representing the location of the stamps in the document. DDI-100 is a synthetic dataset based on 7000 pages of unique real documents and consists of 100,000 images augmented by the application of distortions. The documents are reports and book pages containing textual elements, figures, and graphs, and they include 99 different types of stamps, as exemplified in Figure 1b. The dataset contains text annotations that contain the locations of the stamps in pickle format, in addition to the mask files.  
The two integrated datasets include different scenarios and a wide variety of stamps in black, blue, green, red, and yellow colors, covering various shapes and styles, such as circular, triangular, rectangular, textual, and stamp designs, as illustrated in Figure 2. Additionally, the stamps appear at different angles and may be partially or fully overlapped with text or figures, as well as faded or blurred.
# Forklift Tracking: Industry 4.0 Implementation in Large-Scale Warehouses through UWB Sensor Fusion
## 4 Experimental Analysis
### Effect of Forklift Speed  
As expected, the forklift speed might impact the UKF performance. Therefore an error analysis with varying forklift speed was conducted also via experimental tests. By considering test case trajectories I-VI, the forklift speed assumes the values 0.75 m/s (test case I and test case II), 1.6 m/s (test case III and test case IV), and 2.75 m/s (test case V and test case VI), and the path length is around 100 m. Figure 14 shows the mean distance error \(\varepsilon_{d}\) when the forklift speed varies. Different from the analysis conducted with the numerical simulations (Figure 7), the error given by the UWB system varies with the speed, but it decreases until the forklift speed reaches 1.6 m/s (IV) and then rises again. This effect could be explained by considering that for low speeds the forklift spends more time by doing maneuvers and curves, in which the UWB system does not perform well. When the forklift travels fast, instead, as in test cases V and VI, the UWB system performs worse. The onboard kinematic sensors, however, perform better for low-speed trajectories, and their error increases with the forklift speed. Hence, by looking at Figure 14, it can be concluded that low forklift speeds are preferred by the proposed system, which is able anyway to reduce the localization error to values always lower than those guaranteed by the UWB system only, or by the kinematic sensors (IMU + OFS). The minimum obtained error with the UKF is around 1 m in for the test cases I and II.
# Hydrochemical Characteristics and Suitability Assessment of Groundwater Quality for Irrigation
###### Abstract  
The hydrochemical properties of groundwater play a crucial role in crop growth and soil health, as well as the maintenance of irrigation equipment. To ensure suitable water quality for irrigation, the present study was conducted to evaluate the hydrochemical properties of irrigation groundwater in a typical agricultural region of Serbia. Groundwater was sampled at three monitoring locations in the Srem region in Republic of Serbia between 2011 and 2020. Examined parameters included electrical conductivity (EC), total dissolved salts (TDS), pH values, and the concentrations of cations (sodium (Na\({}^{+}\)), calcium (Ca\({}^{2+}\)), magnesium (Mg\({}^{2+}\)) and potassium (K\({}^{+}\))) and anions (bicarbonate (HCO\({}_{3}\)\({}^{-}\)), chloride (Cl\({}^{-}\)), sulfate (SO\({}_{4}\)\({}^{-}\)), and nitrate (NO\({}_{3}\)\({}^{-}\))). Further assessment was done using three classification systems; Neigebauer's, the US USSL, and the FAO classification. To obtain a more detailed assessment, additional indices were included, such as Soluble Sodium Percentage (SSP), Sodium Adsorption Ratio (SAR), Residual Sodium Carbonate (RSC), Permeability Index (PI), Kelly's Ratio (KR), Magnesium Adsorption Ratio (MAR), and Total Hardness (TH). The PCA analysis detected that the concentration of K\({}^{+}\), Ca\({}^{2+}\), and Na\({}^{+}\) in the irrigation water were lower in recent years, while the concentration of Cl\({}^{-}\) was higher. The cluster analysis grouped the parameters into three clusters; I--electoconductivity, II--dry residue and HCO\({}_{3}\)\({}^{-}\), and III--other water parameters for all three monitoring localities. According to the applied classifications, the majority of underground water samples were suitable for irrigation. However, some samples fell out of the range of the first class, indicating the need for regular water quality monitoring. In light of climate changes that influence water deficiency, the urgent need for wise and sustainable water use implies the application of a comprehensive approach to irrigation water quality assessment, as shown in this study.  
hydrochemical characteristics; irrigation water quality; groundwater; Serbia Article
# Airborne GNSS Reflectometry for Water Body Detection
## 3 Airborne GNSS-R System  
We present in Figure 2 the GNSS-R system used in our airborne experimentation. The GNSS-R setup developed for this application consists of a typical GNSS-R sensor that uses an RHCP antenna and an LHCP antenna with a single mono-channel bit grabber for signal digitization. A delay line is used to separate the RHCP antenna signals and the LHCP antenna signals in time so that both signals can be tracked independently using a mono channel receiver with perfect phase and frequency synchronization. The specular points are localized as a function of the GPS time with the use of RINEX files and on-board drone card measurements. The raw data recorded by the GNSS-R receiver correspond to the samples of the direct and reflected GNSS signals at the output of the antennas (after frequency down conversion).  
After collecting the raw GNSS data, dedicated GNSS signal processing techniques are implemented in our self-built GNSS software receiver for the extraction of the required GNSS data. The processing of the GNSS signals adapts a master/slave configuration. The direct signals processing can be derived from the classical GNSS demultiplexing and demodulation processes in the form of Phase Lock Loop (PLL), Frequency Lock Loop  
Figure 1: Airborne GNSS-R Geometry.  
(FLL), and Delay Lock Loop (DLL) to obtain the corresponding GNSS data. The estimated code and frequency delay of the direct signals are used in the model aided tracking of the reflected GNSS signals. As a result, GNSS observations related to the direct and reflected signals (i.e., in-phase component of the signals, code delay, frequency delay and phase delay estimates) are derived. The message of navigation is extracted in the process and the GPS time of each data is derived after signal dating.  
The GNSS data provided by the direct and reflected signals tracking loops are used in order to obtain observations of the GNSS signals amplitudes. Then, the reflectivity measurements are derived from the amplitudes of the GNSS signals as a function of the GPS time. The GNSS signals are segmented into stationary parts based on the changes in reflectivity using the segmentation model developed in this work. These segments are associated with different areas of reflections represented by the specular point localization. For this purpose, the specular point coordinates and the segmented GNSS signals are linked using the GPS time provided by on-board sensors and the GPS time extracted from the digitized GNSS signals.