esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Evaluation of Load Distribution between Piles in Case of Block Failure
###### Abstract  
In this study, the load distribution of the piles in the pile group was investigated in the case of the pile raft foundations failure under the effect of vertical loads in cohesive soils. For this purpose, loading tests were carried out on model pile groups in the laboratory. In addition, the effects of these conditions on the load distribution between the piles were examined by changing the pile spacing and soil properties. As a result of the experiments, it was determined that the piles in the corner region carried more load than the ones in the center region in small settlement amounts as in the literature. However, it has been determined that as the amount of settlement in the pile foundation increases, the soil between piles behaves like a block and approaches failure, and the piles in the center region of the pile group carry more load. As a result of excessive settlement, a fracture surface formed around the block and therefore the corner piles of the block began to carry less load. It has been determined that failure analyses should be performed considering the load carried by the corner piles after failure due to excessive settlement.  
pile group; clayey soil; model tests; pile load distribution; consolidation pressure Article
## 2 Related Work
### Noise Reduction Methods for Anomaly Detection
#### 2.2.1 Decision Tree  
The decision tree model is an intuitive methodology for classifying data using a top-down technique. This model is less affected by the quality of the data and is effective for analyzing large amounts of network traffic data because the learning rate of the data is relatively fast. In addition, since the judgment result can be interpreted through the white-box model, it is suitable for detecting attacks sensitive to False Alarm and analyzing attack traffic data containing the attacker's intentions [21]. In addition, the decision tree model has a point that it can effectively calculate the impurity of the data by analyzing the entropy \(\sum_{i=1}^{C}-f_{i}\log f_{i}\) and the Gini coefficient \(\sum_{i=1}^{C}f_{i}(1-f_{i})\) for the frequency \(f_{i}\) of the data label \(i\) and unique label set \(C\). This function is very effective in reducing noise [22].
# Addressable Refraction and Curved Soliton Waveguides Using Electric Interfaces
## 3 Results
### Waveguiding and Propagation Losses  
#### 3.2.1 Straight Propagation  
Focusing the attention on the signal beam, the numerical simulator allows us to calculate the propagation losses by comparing the input and output power of the IR beam confined inside the soliton waveguides. In Figure 6a, we have reported the input (red) and output (black) profiles of the signal beam within self-confined structures. In this case, uniform biases are applied (no electric interfaces are present), and straight propagations are obtained consequently. The self-confinement conditions depend on the applied biases, as expected: as previously mentioned, the solitonic regime is considered to be reached at 27 kV/cm, for which a uniform writing beam is obtained (see Figure 1). By integrating the input and output power distributions of the signal beam, the propagation losses have been calculated, as shown in Figure 6b.  
These losses strongly depend on the applied bias, which influences the refractive index contrast as well. The larger the electric bias, the larger the refractive index contrast and the higher the capacity of the refractive structure written by the soliton to confine a signal beam. At the solitonic regime (27 kV/cm), losses reach the lowest limit of 0.07 dB/cm, value already found previously [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21]. Such value seems to be the reference limit for all cases.
# A Review of Reactor Designs for Hydrogen Storage in Clathrate Hydrates
## 2 Hydrate-Formation Technologies
### Stirred-Tank Reactor  
Continuous mixing of gas and water to improve both heat and mass transfer can be achieved by stirring. In a hydrate-formation, shortened induction, accelerated formation, and increased storage capacity are observed as the result of applied stirring [23; 24]. In this system, high-pressure gas is introduced on the top of the liquid phase and mixed with water by an impeller. During the formation process the reactor temperature controls by the cooling system and the pressure and temperature of the liquid/gas will be logged into a computer and will be used to for further analysis (Figure 3). This set-up is made by four main components (1) gas supply, (2) high pressure reactor, (3) cooling and (4) data logging systems. Due to the simplicity in the design and operation factors, for the most fundamental studies such as thermodynamics and kinetics of gas hydrate formation/dissociation this family of the reactors have been widely used [23]. However, the use of stirring reactors involves the following main disadvantages:  
* As slurry thickens the energy cost of stirring increases [24].
* Additional work required for hydrate/slurry separation, for hydrate packing in a storage vessel.
* Considerable amounts of interstitial water will remain between hydrate grains.
* Hindered scale-up. The required agitation power increases with the 5 th power of the impeller size [25].  
Figure 2: Developed hydrate formation techniques, categorised based on phase continuity.
## 2 Materials and Methods
### Proximity Matrix and Network Clustering  
To complement the CD and CCD assessments of supply-demand coordination, a Revealed Comparative Advantage (RCA)-based proximity matrix was constructed to identify and classify districts with disproportionately high GSI demand [41]. Unlike the CD/CCD framework, which evaluates the internal and external alignment between supply and demand systems, RCA is used here to diagnose demand-driven imbalance at the district level by comparing each indicator's intensity to the regional average. Specifically, RCA scores were computed to determine whether a given district exhibited relative dominance in any particular vulnerability dimension (e.g., flood exposure or socio-demographic sensitivity). A district was considered to have a comparative demand advantage if its RCA score for an indicator exceeded 1. These scores formed the basis for calculating conditional proximity values, which quantify the likelihood of similarity between districts based on their vulnerability profiles. Pairwise proximity was then encoded in a matrix and converted into a proximity network using a maximum spanning tree algorithm [42; 43]. The full modeling workflow is provided in Supplementary Materials--Modeling Workflow. This approach ensures that all nodes (districts) are connected via the most representative pathswhile minimizing redundancy. A total of 399 districts were included in this analysis. The proximity between districts \(c\) and \(d\) was calculated as follows:  
\[RCA_{i_{c}}=\frac{x_{i_{c}}\,c/\sum_{i}x_{i_{c}}}{\sum_{c}x_{i_{c}}\,c/\sum_{i} \sum_{c}x_{i_{c}}\,c} \tag{6}\]  
\[Pr(c|d)=\frac{\sum_{i}RCA_{i_{c}}\,c\cdot RCA_{i_{c}}\,d}{\sqrt{\sum_{i}RCA_{i_{ c}}^{2}\,c}\cdot\sqrt{\sum_{i}RCA_{i_{c}}^{2}\,d}} \tag{7}\]  
\[Proximity_{c,\,d}=Pr(c|d) \tag{8}\]  
where \(x_{i_{c}}\) is the value of demand indicator \(i\) in district \(c\) and the denominator of Equation (6) represents the regional average of that indicator. These equations compute comparative vulnerability and mutual proximity, which inform the subsequent network clustering analysis for demand-driven GSI optimization.  
The Fast-Newman modularity optimization method was used to detect community structures, identifying clusters of districts with similar vulnerability profiles. These clusters formed the basis for prioritizing GSI investment, offering spatial references and benchmarks for demand-based planning [44], see Equation (9):  
\[Q=\frac{1}{2m}\sum_{c,d}\bigg{[}B_{cd}-\frac{k_{c}k_{d}}{2m}\bigg{]}\delta(h_ {c},h_{d}) \tag{9}\]  
where \(Q\) indicates the modularity of the system; \(B_{cd}\) represents the weighting of the edge between district \(c\) and district \(d\); \(k_{c}\) is the sum of the weightings of the edges connected to the district \(c\); and \(h_{c}\) denotes the cluster of district \(c\).
## 3 Results and Discussion
### Analysis of Electrical Results
#### 3.2.2 Comparison of Real and Imaginary Permititivities  
The material with the greatest real permittivity is polyamide (see Figure 9a), which without additives of GTR presents values of 2.5, and it is deduced that the addition of GTR affects the imaginary permittivity in this case (see Figure 9b). Thus the materials with lower real permittivity are PP, HDPE, EVA, and PS (between 1 and 2 for T\({}^{*}\): 30 \({}^{\circ}\)C), followed by PVC and ABS (3.27 and 3.34 for T\({}^{*}\): 30 \({}^{\circ}\)C), and finally with greater real permittivity is the PA (4.34). For increases in the proportion of GTR, this relationship changes, tending to converge all polymers and with very few variations with the increase in temperature, with the exception of PVC, PA, and EVA. In low concentrations of GTR, the \(\epsilon^{\prime}\) is heterogeneous and as GTR is added, the behavior begins to be assimilated. In general, the \(\epsilon^{\prime}\) oscillates between 2 and 20 for almost all the polymers; the addition of GTR does increase an order of magnitude. The PVC has a variable behavior, up to two orders of magnitude from 0% GTR to 50% addition of GTR.  
The material with the highest imaginary permittivity is polyamide (see Figure 9b), which without additives of GTR presents values of 1, and it is deduced that the addition of GTR does not affect the imaginary permittivity in this case. Thus, the materials with lower imaginary permittivity are PP, HDPE, EVA, and PS (0.000375, 0.00718, 0.000322 for T\({}^{*}\): 30 \({}^{\circ}\)C), followed by PVS and ABS (close to 0.1), and finally with greater imaginary permittivity is the PA (close to the unit); for increases in the proportion of GTR this ratio changes. In low concentrations of GTR, the \(\epsilon^{\prime\prime}\) is heterogeneous and as GTR is added, the behavior begins to assimilate. In general, the \(\epsilon^{\prime\prime}\) ranges from 1 to 10\({}^{-1}\) for almost all polymers; the addition of GTR increases by an order of magnitude. PVC has a variable behavior of \(\epsilon^{\prime\prime}\) of up to two orders of magnitude from the addition of GTR (0.49 to 22.96).
# Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models
## 4 Methodology
### Preprocessing
#### 4.1.3 Normalization of Attributes  
Normalization is a critical preprocessing step that rescales numerical attributes to a standardized range, ensuring uniformity in feature magnitudes. In this study, we employ **Min-Max scaling**[36], which transforms each attribute into a range of [0, 1] using the following formula:  
\[X_{\text{normalized}}=\frac{X-X_{\text{min}}}{X_{\text{max}}-X_{\text{min}}}\]  
where \(X\) is the original value, and \(X_{\text{min}}\) and \(X_{\text{max}}\) are the minimum and maximum values of the attribute, respectively. This approach ensures that all attributes contribute equally to the analysis, preventing features with larger scales from dominating the model.  
Normalization is particularly beneficial for algorithms that rely on distance metrics or gradient-based optimization, such as Support Vector Machines (SVMs) and k-Nearest Neighbors (k-NN). By eliminating scale-related biases, normalization enhances model performance and stability [37]. For example, in our cardiovascular dataset, attributes like age (ranging from 0 to 100) and cholesterol levels (ranging from 100 to 600 mg/dL) were normalized to ensure consistent scaling.
# LDPC Decoder Design Using Compensation Scheme of Group Comparison for 5G Communication Systems
## 2 Low-Density Parity-Check Codes  
LDPC is a type of simultaneous equation that consists of a sparse matrix \(\mathbf{H}\). Each row of the \(\mathbf{H}\) matrix represents a check node (CN) and each column represents a variable node (VN). The 1s elements in the \(\mathbf{H}\) matrix are dependent on the CNs and VNs. The 0s elements in the \(\mathbf{H}\) matrix are ignored. The data transmission relationship between the CNs and VNs can be expressed using the Tanner graph [12].
## 4 Bionanosystems
### Bionanosystems for RA
#### 4.1.1. Bionanosystems Based on Organic Carriers
In another example, nanoemulsion containing RA dispersed in hydrogel made of hydroxyethyl cellulose was tested for topical application. The hydrogel was used as a thickening agent to increase the intrinsic low viscosity of nanoemulsions and improve their stability by reducing the surface and interfacial tension. Hydrogel of RA-loaded nanoemulsions were prepared by using 2.5% _w/v_ of egg-lecithin, 6% _w/v_ of medium chain triglyceride (MCT), water 1% _w/v_ of Tween(r) 80 (polysorbate 80) a nonionic surfactant and dispersed in hydroxyethyl cellulose. In-vitro RA release studies performed in a Franz type diffusion cell showed that about 60% of RA is released in 8 h and skin retention studies evidenced that 0.65 \(\pm\) 0.08 \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{cm}^{2}\) of RA were retained in total skin [127]. \begin{table}
\begin{tabular}{c c c c} \hline
**Type of Carrier** & **Host-Guest System** & **Main Features** & **Refs.** \\ \hline Chitosan (CS) & RA-loaded CS & • & Slower RA release in oil from & \\ microparticles & microparticles & • & modified CS microparticles; & [91] \\ \hline  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\ \hline Chitosan (CS) & Water-insoluble & • & High amount of RA adsorbed on & [100; 102] \\ powder & RA-CS complexes & • & CS powder. & [100; 102] \\ \hline Chitosan (CS) & RA-loaded CS & • & High in vitro antibacterial activity & \\ nanoparticles & encapsulated graphene & • & and efficacy for wound & [103] \\ and graphene & nanoparticles & & healing treatment. & \\ \hline Chitosan (CS)-zinc oxide & RA-loaded CS-ZnO & • & Preconcentration and recovering of & \\ nanoparticles & nanoparticles & nanoparticles & RA in complicated matrices by & [104] \\  & & & dispersive micro-solid & \\  & & & phase extraction. & \\ \hline Cyclodext & RA-CD complexes & • & Water-soluble and photo-stable; & [108; 109; 111] \\  & & & increased antioxidant capacity; & \\ \hline  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\ \end{tabular}
\end{table} 
Table 1: Encapsulation of RA in organic nano and microsystems.
## 3 Results and Discussion
### Optimization for the Removal of the FD&C Red 40 Dye in an Aqueous Solution
#### 3.2.1 Titanium Dioxide Amount Effect  
Upon the addition of TiO\({}_{2}\) nanoparticles, oxygen atoms are added across the surface of the CS beads [14]. These can be chemically transformed into oxonium ions in acidic media, which become active adsorption sites due to the positive charge density they bring to the material's surface [28].  
The contribution of TiO\({}_{2}\) nanoparticles to the removal efficiency of FD&C Red 40 dye is hardly noticeable. This low contribution is because the efficiency of the adsorption mechanism of FD&C Red 40 dye involves, to a greater extent, the amino groups of the CS, which are hindrance by the interaction between them and the titanium (IV) cation (Ti\({}^{4+}\)) [14]. Moreover, it tends to decrease as more TiO\({}_{2}\) is added in the processing of the beads.  
On the other hand, [PERSON] et al. [14] found that optimal removal of the active dye Red 120 (anionic in nature) using CS-TiO\({}_{2}\)-GLA beads is not necessarily obtained by  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Factors** & **Sum of Squares** & **d\({}_{\text{f}}\)a** & **Mean Square** & **F** & **Value-\(p\)b** \\ \hline A: TiO\({}_{2}\) & 332.35 & 1 & 332.35 & 1.40 & 0.25 \\ B: Red 40 & 1719.46 & 1 & 1719.46 & 7.25 & 0.01 \\ C: pH & 8333.46 & 1 & 8333.46 & 35.12 & 0.00 \\ AA & 244.94 & 1 & 244.94 & 1.03 & 0.32 \\ AB & 111.09 & 1 & 111.09 & 0.47 & 0.50 \\ AC & 276.90 & 1 & 276.90 & 1.17 & 0.30 \\ BB & 1003.32 & 1 & 1003.32 & 4.23 & 0.05 \\ BC & 2578.10 & 1 & 2578.10 & 10.86 & 0.00 \\ CC & 61.87 & 1 & 61.87 & 0.26 & 0.61 \\ Blocks & 13.06 & 1 & 13.06 & 0.26 & 0.82 \\ Total error & 4983.58 & 21 & 237.31 & & \\ Total & 20.385.40 & 31 & & & \\ \hline R2 & 0.75 & & & & \\ \hline D2 adj & 0.64 & & & & \\ \hline Adequate Precision & 8.56 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: ANOVA results for the optimization of the removal efficiency of FD&C Red 40 dye with CS-TiO\({}_{2}\)-GLA beads.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Run** & **TiO\({}_{2}\) (mg)** & **Initial Dye Concentration (mgL\({}^{-1}\))** & **pH** & \multicolumn{2}{c}{**Removal Efficiency (\%)**} \\ \cline{4-5}
1 & 100 & 15 & 3.0 & 100.0 & 100.0 \\
2 & 200 & 4.8 & 5.0 & 100.0 & 100.0 \\
3 & 100 & 15 & 7.0 & 90.0 & 83.6 \\
4 & 200 & 30 & 5.0 & 72.0 & 77.2 \\
5 & 200 & 30 & 8.36 & 76.8 & 70.7 \\
6 & 300 & 45 & 7.0 & 17.3 & 20.  
incorporating more metal oxide into the beads. There must be a balance between materials' surface area and the available amino groups for removing the dye, variables that depend on the dose of TiO\({}_{2}\) used to elaborate the material.
# Cause Analysis and Preventive Measures against False Bottoms in Echosounder Data
## 1 Introduction  
In 1906, [PERSON] invented the first sonar receiving device for the purpose of detecting icebergs [1]. With the outbreak of World War I, sonar technology garnered significant interest from researchers. After nearly a century of progress, sonar technology has become increasingly comprehensive [2, 3], and numerous detection devices based on sonar technology have emerged and are now widely utilized in diverse fields, such as scientific research, fisheries, and military operations [4, 5, 6, 7, 8, 9]. The advancement of sonar technology not only enhances productivity in activities like aquaculture and fishing [10, 11], but also enriches research methodologies for studying aquatic organisms and conducting geological exploration [12, 13]. With the increasing prevalence of sonar technology, research in this field is also expanding, incorporating successful applicationsfalse bottoms were limited in terms of their data sources and comprehensiveness. These studies relied solely on echosounder data from a single water body, failing to provide a comprehensive summary of the common occurrences of false bottoms. Subsequently, several relevant articles have addressed the phenomenon of false bottoms [18]; however, none have conducted a thorough analysis of the various situations in which false bottoms can arise.  
This article addresses the limitations associated with single small-batch sampling data by providing a comprehensive summary of the various situations in which false bottoms occur. The study utilized a large dataset of echosounder data from different periods to analyze the problem of false bottoms from multiple perspectives and the analysis included an exploration of the factors contributing to their formation, their imaging characteristics, their impact, and potential solutions. Misleading seabed echoes are categorized into three distinct types, and the relationship is calculated between the maximum detection and the true depth of the seabed in each situation. Furthermore, the specific locations and intensities of each type of false bottom are determined, and preventive solutions based on sound transmission characteristics are proposed. By investigating false bottoms in echosounder imaging, we aim, in this study, to enhance the reliability and efficiency of echosounder detection, while facilitating its broader application in scientific research, fisheries, and related fields [17; 19].
# Hybrid Positioning for Smart Spaces: Proposal and Evaluation
## 3 Review of Indoor Positioning Technologies
Although the infrastructure can be already present for the case of WiFi, the high cost of the radio-map generation and maintenance should be also taken into account. * Dead Reckoning: Given that smartphones are usually provided with built-in gyroscopes and accelerometers, they can use orientation and acceleration data, respectively. A representative algorithm exploiting these capabilities is Dead Reckoning (DR). It consists of estimating the current position based on the last known position, movement direction and speed. The main disadvantage here is the accumulative error when computing new positions based on previous ones. This technique should be combined with another one that helps to eliminate the accumulated error and estimate and initial location. For instance, the work presented in [PERSON] et al. [20] is a good representative of this positioning method. We expect to introduce similar PDR technology in our solution soon. Emerging methods of measuring reference distances for indoor positioning are introduced in [21; 22], where the usage of OFDM signals in combination with the properties of the Zadoff-Chu sequences achieves promising results. Another example is the work in [23], which exploits millimeter-wave (mmWave) combined with Machine Learning (ML) procedures to obtain excellent results. With the deployment of new smart spaces crowded of sensors, cameras and services, new data sources for positioning are available. For example, in [PERSON] et al. [24] it is summarized some computer vision methods using cameras and QR codes. This is the line followed in the current paper: exploiting smart infrastructure to support indoor navigation. In particular, our implementation integrates an access control system based in ID cards to improve accuracy when card readers registered in the platform are used. In general, our work combines traditional radio-frequency positioning technologies with capabilities of smart environments and enrich fingerprinting knowledge with semantics about particular locations, to provide extra continuity, accuracy and support to reduce energy consumption of mobile devices using the system.
# Optimized Dissolved Oxygen Fuzzy Control for Recombinant _Escherichia coli_ Cultivations
## 3 Material and Methods
### Fuzzy Controller Implementation in SUIPERSYS_HCDC Program and E. coli Cultivation Test
#### 3.4.4 Bioreactor Cultivation  
The inoculum was prepared in a two-stage procedure. From a chemically-defined medium plate, single isolated colonies of _E. coli_ were inoculated into two 500 mL Erlenmeyer flasks with 50 mL of fresh HDF medium. The flasks were incubated in a shaker (SL-223 Solab) at 37 \({}^{\circ}\)C and 300 rpm for 12 h. After reaching an optical density of 2.0 at 600 nm (OD600 nm), guaranteeing that the cells were in the exponential growth phase, a 5 mL aliquot from this culture was transferred to two Erlenmeyer flasks (1 L volume) with 100 mL of fresh HDF medium. The two flasks were incubated in a shaker at 37 \({}^{\circ}\)C and 300 rpm, with growth monitoring every hour via optical density measurements. After reaching an optical density of 2.5 at 600 nm, the flasks were mixed together in a laminar flow cabinet and inoculated into the reactor (obtaining an initial OD600 nm of approximately 0.1) [21; 55; 55; 56].  
Batch cultivations were performed in a 5 L in house bioreactor. After calibrating the pH sensor, the bioreactor was sealed and autoclaved at 120 \({}^{\circ}\)C and 1.1 bar for 15 min. After cooling down, it was charged with 3.8 L of fresh medium, and DOC and biomass sensors were calibrated. Following the inoculation, cultivation conditions were set: pH controlled at 6.7, temperature set at 35 \({}^{\circ}\)C, and DOC kept at 30% of saturation [21; 55; 55; 56]. Online data acquisition, monitoring, and control of all instruments were performed by SUPERYS_HCDC software. Samples were taken periodically in order to assess cell growth and acetic acid production. With carbon source depletion, a 0.5 L glycerol pulse was injected in the bioreactor after approximately 13.5 h.