esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Particle Size Distribution  
Determining the particle size distribution in powder products is essential for various reasons. It affects not only the solubility and texture but also the stability, flowability, and packing properties of the product. A well-defined particle size can optimize product performance, enhance processing efficiency, and ensure consistent quality. Furthermore, it plays a significant role in storage and conservation, as finer particles may have a higher surface area, making them more susceptible to moisture absorption, oxidation, or microbial growth. Therefore, understanding and controlling particle size is crucial for improving the usability, shelf life, and safety of powder-based products [51; 52]. Additionally, variation in particle size distribution can result in a heterogeneous mixture of granules, directly affecting the textural properties of the final product. Such variability may lead to irregular textures and inconsistencies during both the production process and consumer experience, compromising product quality and uniformity. The particle size distribution obtained for the 35 samples of hydrated cassava starch is shown in Table 4.  
For better visualization, principal component analysis was performed, with sieve openings considered as variables and the hydrated cassava starch samples as observations. As shown in Figure 1, 84.96% of the data variability was explained by the first two components (F1 and F2). Samples closer to a specific sieve opening are primarily characterized by that opening, and vice versa. Furthermore, samples that are closer together indicate a similarity in their distribution. Thus, once again, the lack of standardization is evident, this time regarding the granulometric characterization of the hydrated cassava starch.  
Several samples, especially with lower moisture contents, were predominantly characterized by intermediate sieve openings, 0.833 mm (Tyler 20), 0.589 mm (Tyler 28), and 0.495 mm (Tyler 32). Additionally, it is noteworthy that most samples with moisture content higher than 39% were primarily characterized by larger sieve openings, namely 1.397 mm (Tyler 12) and 1.168 mm (Tyler 14). In other words, the higher moisture content in the sample tends to result in a distribution with larger particles [52], as moisture can act as a binder, facilitating the formation of agglomerates, which can have impacts during the processing or storage of the product [53; 54].
# REISCH: Incorporating Lightweight and Reliable Algorithms into Healthcare Applications of WSNs
## 1 Introduction
### Our Contributions  
We propose Reliable and Efficient Integrity Scheme for Data Collection in HWSN (REISCH) to ensure that patient data is transferred/stored to the \(LS/BS\) securely and efficiently. The REISCH is characterized as follows:  
* REISCH applies the Elliptic Curve Digital Signature Algorithm (ECDSA) with BLAKE2 bp instead of ECDSA with Secure Hash Algorithm 1 (SHA1) to improve HWSN lifetime and prevent intruders from altering/changing patients' data.
* REISCH used the homomorphic mechanism with \(CH\)s to reduce energy consumption when aggregating patient data from sensors.
* REISCH hides the sensor's identification (SID) and location (SL) by using random pseudonyms. This mechanism prevents intruders from detecting sensors information transmitted between network terminals.
* Formal security analysis in REISCH is simulated by an automated validation of Internet security protocols and applications (AVISPA). This tool is dramatically accepted as an effective way to validate threat models in HWSN. AVISPA is used to check that our scheme is secure against both passive and active attacks.
## 6 Results and Discussion
### Vehicle Measurement Data
To assist with comprehension, please find a brief description of the recorded data below:  
* The route identification number (Route ID) is used to distinguish between different routes. * The distance of journeys is calculated from GNSS data and is presented in the form of a trip distance. * The mean velocity of a vehicle is defined as the mean speed of the vehicle over the course of a given route. * The mean consumption data is retrieved from the vehicle board computer, which has calculated the aforementioned data. * The term \"Trip duration\" is defined as the length of time that a journey takes to complete. * The term \"average ambient temperature\" is used to describe the temperature that is measured in the environment. In consideration of the brief duration of the trips, it was sufficient to measure the temperature at the beginning and end of each trip. In all three instances, the initial and final temperatures were identical. Therefore, it can be concluded that the ambient temperature can be considered as constant throughout the course of the test. * Finally, we present the battery SOC at the point of departure and arrival.
## 2 Project Profile
### Tunnel Monitoring Information  
As depicted in Figure 2, the construction excavation's area of impact extends to within twice the excavation depth of the foundation pit. Based on the design drawings of the tunnel structure and the main foundation pit of the metro station, it has been decided to designate both the excavated section of the foundation pit and the tunnels on both the left and right sides within a 50 m radius as the monitoring area. A schematic diagram of the tunnel structure displacement measurement points is shown in Figure 3.  
One monitoring section is established every 10-15 m along the length of the south tunnel, and one monitoring section is established every 15-20 m along the length of the north tunnel. There are 39 monitoring sections for horizontal displacement of the tunnel structure, with one to two measurement points established for each section, totaling 51 monitoring points, each positioned in the side wall of the tunnel structure at a distance of 0.5 m from the ground. There are 39 monitoring sections for tunnel structure settlement,  
Figure 1: Schematic diagram of foundation pit excavation.  
Figure 3: Schematic diagram of tunnel structure displacement measurement points.  
Figure 2: Schematic diagram of the tunnel monitoring area.  
with one to two measurement points established for each section, totaling 51 monitoring points, of which 24 points are shared with differential settlement. There are six tunnel deformation joints within the monitoring range, with each deformation joint having a pair of settlement monitoring points, arranged on both sides of the tunnel at the end of the bottom plate, 1 m from the deformation joints, totaling 24 differential settlement monitoring points. The tunnel joins are marked at their original positions. The specific locations of each monitoring point in the tunnel are shown in Figure 4. The structural deformation warning and control values for the existing tunnels are shown in Table 1.
## 1 Introduction
The decomposition results correspond to the natural mode determined by the signal itself, different from wavelet transform that acquires modes inside certain filter bands. Mode mixing sometimes appears and reduces the decomposition effectiveness, and in order to overcome this disadvantage, ensemble empirical mode decomposition (EEMD) was developed utilizing a white-noise-assisted method [24]. In this paper, an EEMD-based approach is developed to analyze GPR data for the novel application in identifying unstable subsurface rock structures. The remainder of this paper is organized as follows: Section 2 introduces the developed signal processing approach for interpreting GPR radargrams; Section 3 presents the results of both numerically simulated and field-measured GPR signals; Section 4 discusses and concludes this paper.
# Autonomous Navigation Based on the Earth-Shadow Observation near the Sun-Earth L2 Point
## 3 Design of the Navigation Filter Algorithm  
### Fifth-Order Spherical-Radial Rule  
In order to solve the integral problem of a nonlinear function \(f(x)\) in the n-dimensional Cartesian coordinate system, the integral problem is described as  
\[I(f)=\int_{\mathbb{R}^{n}}f(x)e^{-x^{\top}x}\;\mathrm{d}x. \tag{26}\]  
Define \(\mathbf{x}=r\mathbf{y}\), and \(\mathbf{y}\) is the direction vector of the state space that satisfies \(\mathbf{y}^{\mathrm{T}}\mathbf{y}=1\). We construct a unit sphere \(U_{n}=\{\mathbf{y}\in R^{n}\mid\mathbf{y}^{\mathrm{T}}\mathbf{y}=1\}\) with the radius \(r\). \(I(f)\) can be decomposed into a spherical integral \(S(r)\) and radial integral \(R\),  
\[S(r)=\int_{U_{n}}f(r\mathbf{y})d\sigma(\mathbf{y}) \tag{27}\]  
\[R=\int_{0}^{+\infty}S(r)r^{n-1}e^{-\gamma^{2}}\;\mathrm{d}r. \tag{28}\]  
In general, the integrals above cannot be solved analytically. It is advisable to use the Gauss-Hermite rule and the Spherical-Radial rule to approximate the integral, given by  
\[I(f)\approx\sum_{j=1}^{n_{s}}\sum_{i=1}^{n_{R}}w_{R,i}w_{S,j}f\big{(}r_{i}\mathbf{ y}_{j}\big{)}, \tag{29}\]  
where \(w_{R,i}\) and \(w_{S,j}\) are weights for the Gauss-Hermite integration of point \(n_{R}\) and point \(n_{s}\), respectively.  
Generally speaking, if the higher-order Spherical-Radial rule is selected, higher accuracy of the Gauss integral will be obtained. However, it will also lead to a sharp increase in computation. In consideration of this problem, the Spherical-Radial rule higher than the fifth degree is not usually used. Under the fifth-order Spherical-Radial rule \(n_{R}=2\), \(n_{s}=2n^{2}\), the expression is given directly as follows:  
\[\begin{split} I(f)&=\int_{\mathbb{K}^{n}}f(x)e^{-x ^{\top}x}\;\mathrm{d}x\\ &\approx\frac{1}{\pi^{n/2}}\sum_{j=1}^{n_{s}}\sum_{i=1}^{n_{R}}w _{R,i}w_{S,j}f\Big{(}\sqrt{2}r_{i}\mathbf{y}_{j}\Big{)}\\ &=\frac{2}{n+2}f(\mathbf{0})+\frac{1}{(n+2)^{2}}\sum_{j=1}^{n(n-1)/2 }\Bigl{[}f\Big{(}\sqrt{n+2}\cdot\mathbf{y}_{j}^{+}\Big{)}+f\Big{(}-\sqrt{n+2}\cdot \mathbf{y}_{j}^{+}\Big{)}\Bigr{]}\\ &+n\frac{1}{(n+2)^{2}}\sum_{j=1}^{n(n-1)/2}\Bigl{[}f\Big{(}\sqrt {n+2}\cdot\mathbf{y}_{j}^{-}\Big{)}+f\Big{(}-\sqrt{n+2}\cdot\mathbf{y}_{j}^{-}\Big{)} \Bigr{]}\\ &+n\frac{4-n}{2(n+2)^{2}}\sum_{j=1}^{n}\Bigl{[}f\Big{(}\sqrt{n+2 }\cdot\mathbf{e}_{j}\Big{)}+f\Big{(}-\sqrt{n+2}\cdot\mathbf{e}_{j}\Big{)}\Bigr{]}n2^{n },\end{split} \tag{30}\]  
where \(e_{k}\in R^{n}\) is the unit vector. \(\mathbf{y}_{j}^{+}\in\mathbf{S}^{+},\mathbf{y}_{j}^{-}\in\mathbf{S}^{-}\), and point sets \(\mathbf{S}^{+}\) and \(\mathbf{S}^{-}\) are given as follows:  
\[\begin{split}\mathbf{S}^{+}&=\bigg{\{}\sqrt{\frac{1}{2} }\big{(}e_{p}+e_{q}\big{)},p<q,p,q=1,2,\ldots,n\bigg{\}}\\ \mathbf{S}^{-}&=\bigg{\{}\sqrt{\frac{1}{2}}\big{(}e_{p}-e _{q}\big{)},p<q,p,q=1,2,\ldots,n\bigg{\}}.\end{split} \tag{31}\]
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 2 Processes
### In Situ Chemical Oxidation (ISCO)  
In situ chemical oxidation (ISCO) involves the injection of chemical oxidants into the subsurface to oxidise contaminants of emerging concern (COECs) such as chlorinated hydrocarbons, fuels, phenols, etc. This dynamic and aggressive process can be applied to both sorbed and dissolved hydrocarbons [284]. Several oxidants are being used or tested in practice today, such as Fenton, activated persulphate, permanganate, hydrogen peroxide, calcium peroxide, percarbonate, ozone, and peroxene. ISCO laboratory tests and field trials began in the 1990s. The results have been published in numerous articles, reports, and books, e.g., [285; 286; 287; 288; 289; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91]. Most ISCO studies focused on the use of ozone in the vadose zone [292; 293; 294; 295], permanganate, Fenton, and persulphate in the saturation zone [296; 297; 300; 301; 302; 303; 304; 305]. ISCO is a flexible cost-effective method that can be applied to light and dense recalcitrant hydrocarbons such as aromatic hydrocarbons [306; 307]. As with other remediation techniques, there are several challenges with ISCO, including optimal estimation of dose rate [290; 308], accurate injection [309; 310], ensuring safety [311; 312], and modelling, as few modelling tools are currently available [299; 313; 314]. ISCO was modelled using the computer code ISCO3D, which was adapted to simulate the oxidation of chlorinated hydrocarbons by permanganate [296], MITSU3D, and modified MIN3P to mimic the movement of permanganate in the 3D domain and under variable flux density [315]; DNAPL3D-RX simulated reactions between potassium permanganate and chlorinated ethenes [316], CDISCO, and CORT3D, and was developed as a decision support tool for permanganate oxidation planning [297; 299; 317].
# Extracting Weld Bead Shapes from Radiographic Testing Images with U-Net
## 2 Methods
### Contrast Limited Adaptive Histogram Equalization  
In weld images that have been captured by radiographic testing, there are cases where the weld zone cannot be seen clearly as the image is too dark, and it is difficult to extract the weld bead shape. Therefore, this study used a histogram equalization (HE) method as a preprocessing technique to improve the contrast of the image by changing the image's pixel distribution. HE adjusts the image's histogram so that the pixel intensities that are concentrated within a certain range are distributed evenly. However, because HE redistributes the pixels of the image using a single histogram, it can only be performed if the pixel distribution is the same throughout the entire image. Furthermore, because various bright and dark areas exist in the image, the image may become overly bright or distorted when HE is performed. To compensate for this, the contrast-limited adaptive histogram equalization (CLAHE) [12] was developed. CLAHE is a method that divides the image into several blocks of a fixed size and performs HE on each block to compensate for the challenges of the normal HE. As shown in Figure 1, it undergoes a process of redistributing the pixel intensities that are above a certain height in the histogram. A value that limits a certain height in the histogram is called clip limit. Clip limit depends on the normalization of the histogram and thereby on the size of the neighborhood region, and is usually set between 3 and 4.
## 2 BDS-3 SAIM Monitoring Method
### Current Monitoring Methods
#### 2.1.3 Satellite Clock Frequency and Phase Hopping Monitoring  
The autonomous monitoring of the frequency hopping and phase hopping of the satellite clock is realized by a loop phase tracking system, which adjusts the frequency and phase of the local signal through negative feedback to track the reference signal. The phase shift introduced by the difference in frequency between the satellite clock and the local reference crystal oscillator can be eliminated by the secondary difference of the measured value of the adjacent epoch clock difference, and the phase hopping of the satellite clock can thus be monitored. The satellite clock frequency hopping can be derived from the phase hopping. If either the satellite clock phase or the frequency hopping exceeds the preset alarm threshold, SAIM will issue an alarm.
## 2 Background
### Forwarding Information
#### Wireless Sensor Network (WSN)  
WSN have been implemented in a variety of ways, but the architecture is quite similar across implementations. For example, WirelessHART was one of the first open standards available for use in the process control field. The architecture for a WirelessHART network is defined in its data sheet [25]. WirelessHART consists of three main elements, namely field devices, gateways, and network managers. The field devices form a mesh network, while all of these devices are then connected to the gateway. The gateway connects the mesh network to the network manager and the other servers. We are interested in the mesh network (or mesh nodes) and the gateway. The gateway is the only means for data to be sent in and out of the mesh network.
# Detection Model for 5G Core PFCP DDoS Attacks Based on Sin-Cos-bIAVOa
## 4 Experimental Results and Analysis
### Effectiveness Evaluation Experiment  
To assess the performance of the proposed method, this study utilizes a range of metrics, including accuracy, precision, recall, F-measure, specificity, and Feature Reduction Rate (FRR), as defined below.  
\[Accuracy=\frac{TN+TP}{TN+TP+FN+FP} \tag{6}\]  
\[Precision=\frac{TP}{TP+FP} \tag{7}\]  
\[Recall=\frac{TP}{TP+FN} \tag{8}\]  
\[F\text{-}measure=2\times\frac{Precision\times recall}{Precision+recall} \tag{9}\]  
\[Feature\ Reduction\ Rate=\frac{Number\ of\ Reduction\ Feature}{Total\ Features} \tag{10}\]  
In this context, TP denotes the count of DDoS attack records that have been accurately identified and TN represents the number of normal records that were correctly classified; conversely, FN and FP correspond to misclassified DDoS attack and normal records, respectively.  
The results of the algorithms on the datasets are provided in Tables 2-5. The tables present the detection results for the Deletion DDoS Attack, Establishment DDoS Attack, Modification DDoS Attack DROP, and Modification DDoS Attack DUPL datasets, which represent four distinct types of single-attack traffic. Each set of experimental data includes normal traffic and a single type of attack. The proposed algorithm demonstrates high detection accuracy across all five session scenarios with varying timeouts (15 s, 20 s, 60 s, 120 s, and 240 s).  
The experimental results demonstrate the proposed algorithm's comprehensive performance in detecting PFCP DDoS attacks against the 5G core network. By focusing particularly on the Deletion DDoS Attack, Establishment DDoS Attack, Modification DDoS Attack DROP, and Modification DDoS Attack DUPL datasets, each of which represents a unique type of single-attack traffic, the results provide an in-depth analysis of the algorithm's capabilities in identifying these distinct forms of cyber intrusions. Notably, the algorithm consistently demonstrated high detection accuracy across all five session scenarios, each with varying timeouts (15 s, 20 s, 60 s, 120 s, and 240 s). This array of timeout settings allows for a broad evaluation of the algorithm's performance under different conditions of urgency and computational load, further highlighting its robustness and efficiency in identifying cyber threats in real-time scenarios.
# Effects of the 12 May 2021 Geomagnetic Storm on Georeferencing Precision
## 4 Discussion and Main Conclusions
### The 27 March 2017 and 5 August 2019 Geomagnetic Storms  
In Table 1, we present previous works where the positioning errors were studied. In this table, we can see two moderate geomagnetic storms in terms of the Dst and Kp classifications, the 7 January 2015 (Dst = \(-\)99 nT, Kp = 6) and 27 March (Dst = \(-\)74 nT, Kp = 6). The 2015 geomagnetic storm, unlike the 12 May 2021 storm, took place during the solar maximum period of the solar cycle 24. In addition, the January 2015 storm occurred during the summer season in the southern hemisphere. The 27 March storm occurred during the descent period of the solar cycle 24. Then, it had similar solar activity compared to the May 2021 storm. Moreover, the March 2017 storm, like the May 2021 storm, was on the fall season in the southern hemisphere, with the Sun moving from the (2.69\({}^{\circ}\)N, 91.35\({}^{\circ}\)W) to the (2.83\({}^{\circ}\)N,43.68\({}^{\circ}\)W) coordinates. For these reasons. we discarded the 7 January 2015 storm as a good comparison storm and selected 27 March 2017 as a good one. The main phase of the March 2017 geomagnetic storm was much longer than that of the May 2021 storm (6-15 UT).  
On the other hand, a recent study presented the case of a moderated geomagnetic storm occurring on 5 August 2019 (Dst peak = \(-\)53 nT, Kp = 5\({}^{+}\), AE \(\sim\)1000 nT) which produced a strong positive (decrease in electrons) ionospheric storm [61]. Although this geomagnetic storm was less intense than the May 2021 storm, its main phase was much longer (8-21 UT). During this storm, the southern hemisphere was in winter, then, like in May 2021, the Sun was over the northern hemisphere (from (17.01\({}^{\circ}\)N, 61.52\({}^{\circ}\)E) to (16.86\({}^{\circ}\)N, 133.50\({}^{\circ}\)W)). Thus, we used this storm as a comparison to the May 2021 storm. Unfortunately, the study of this storm did not analyze the positioning error caused by it. The study showed that the European sector was one of the most affected in terms of TEC variations. For this reason, the comparison was performed including a GNSS located in Europe (close to Madrid, Spain). The other two stations located in Chile were used, since that region was one of the most perturbed areas in terms of position during the May 2021 storm (see Figure 4g plot).
## 1 Introduction  
Fish fillets is a highly perishable product due to its high water activity and protein content, neutral pH, and presence of autolytic enzymes, resulting in deterioration of organoleptic properties and nutritional values, and eventually losing edibility [1]. Edible coating as an effective fresh-keeping method has been considered in aquatic products [2; 3; 4]. Rosarminic acid (RA) is a natural polyphenol present in Lamiaceae herbs like _Perilla frutescens_ with antioxidant, anti-inflammatory, and antimicrobial effect and has beneficial applications in improving shelf-life and imparting a desirable flavor to foodstuffs. \(\varepsilon\)-Polyllysine (\(\varepsilon\)-PL) as a natural antimicrobial polypeptide is recognized as safe preservative in foods [5]. Our previous studies have shown edible coating containing 30 mg/kg RA incorporated with 0.1% \(\varepsilon\)-PL could effectively maintain the quality of HTS fillets during refrigeration. In addition, modified atmosphere packaging (MAP) is the process of filling with specific proportions of CO\({}_{2}\), N\({}_{2}\), and O\({}_{2}\) around a food product and then sealing that product in an impermeable package. Many published works indicated that MAP could be very proficient in extending the shelf-life of perishable foods especially fish products [6; 7; 8].  
Different nondestructive analysis methods have been widely adopted to characterize the quality changes of raw fillets, such as near-infrared spectroscopy, X-ray computer tomography, hyperspectral imaging, frontface fluorescence spectroscopy, and so on [9]. However, low-field NMR (LF-NMR) and magnetic resonance imaging (MRI) techniques have more advantages in decreasing cost, saving time, and reducing damage to samples compared with the above instruments [4; 10; 11]. In addition, LF-NMR and MRI are able to assess the quality changes by providing the distribution as well as structural information of protons from water and lipid [12; 13; 14].  
Half-smooth tongue sole (HTS, _Cynoglossus semilaevis_ [PERSON]) is rich in high-quality protein and polyunsaturated free fatty acids, and is emerging as one of the most valuable marine aquatic species in Northern China [12]. With the development of electronic commerce, the convenient and fast consumption mode is pursued by residents, and fresh fish fillets will become a main marketing form of fish products [1]. However, HTS decompose easily by environmental factors due to nutrient-rich character, and eventually lose edibility. Therefore, the principal objectives of the present study were to evaluate the quality effects of composite edible coating incorporated with MAP on HTS fillets during cold storage, and then investigated the effectiveness of low field LF-NMR and MRI as fast monitoring techniques to estimate the qualities of half-smooth tongue sole fillets during refrigerated storage (4 \(\pm\) 0.5 \({}^{\circ}\)C).