esa-sceva/satcom-chunk-collection huggingface dataset:
# Modelling of ML-Enablers in 5G Radio Access Network-Conceptual Proposal of Computational Framework
## 3 Applying AI/ML into 5G RAN: Proposed Methodology
### Data processing/Feedback  
The role of the feedback function in the ML algorithm located in RAN is to process the input data collection toward a prediction. For that purpose, the actor may associate each parameter with a target value. For the analyzed example scenario, for RAN coverage optimization, the network may set an observation rule: the \"\(RSRP\)\" parameter does not drop below the \"target threshold\". In the undertaken optimization tasks, if the observation is performed for a single user, the drop may indicate a \"Feedback: Bad coverage\". The feedback forecasts the associated prediction and typically implies an action. However, in practice, a single indication in a radio network on a signal drop should not trigger a prediction that would result in network parameters changes. The remedy for that can be a cell change for the user (i.e., trigger mobility), by setting mobility parameters in a way the user switches to a neighboring cell (overlaying radio layer).  
Before making a decision on the following action, a more detailed observation can rely on a different weighting setting to the RAN optimization parameters. For instance, an iteration on joined feedback on the \(RSRP+SINR\) may be required, where the complementary parameter \(SINR\), can indicate whether a drop in signal is caused by noise interferences or purely weak coverage. In this case, the measurement configuration should be followed by the updated configuration with a request to provide a more complete dataset based on the received feedback.  
If the user feedback is repetitively provided for an observation period, it becomes more reliable, leading to a certain set of decisions, such as change cell, adjust radio parameters, adjust the antenna, change scheduling policy, release resources for \(eMBB\) users, increase modulation scheme, MIMO layers, and add serving carriers (set carrier aggregation).
## 2 Materials and Methods
### ERT Methodology  
The ERT method investigated subsurface cavities, offering a reliable approach to mapping underground features and detecting lithological changes. This method is particularly well-suited for identifying variations in subsurface resistivity that correlate with geological transitions, water-saturated zones, and potential voids. The survey involved three 2-D ERT profiles with line lengths of 527 m, designed to provide comprehensive coverage of the study area and target key subsurface features (Figure 8). A 17 m electrode spacing was chosen, enabling data acquisition at depths of approximately 50 m, with a maximum survey depth of about 105 m at the center of the lines. The configuration and command files for the measurements were prepared using the Zond 2D software package (Version 7.0), a versatile commercial tool that supports geophysical modeling, inversion, and advanced data visualization. Wenner-Schlumberger arrays were employed for data collection and are known for their balanced sensitivity and depth coverage, allowing for the detection of both shallow and deep subsurface features with high reliability.  
The survey was conducted using a multi-electrode system manufactured by Pasi Ltd. (Turin, Italy), capable of simultaneously operating up to 32 electrodes, ensuring efficient data acquisition across the long survey lines. To optimize the quality of the measurements, meticulous preparation was undertaken at each electrode site. The ground surface was cleared of obstructions, such as stones, to ensure proper electrode placement and was wetted with a saltwater solution to enhance galvanic contact between the electrodes and the soil. This step minimized contact resistance, improving the accuracy of resistivity measurements, particularly in areas with dry or compacted soils. The comprehensive dataset from the survey encompassed 9912 individual resistivity readings, providing a robust foundation for analyzing the subsurface environment. The high density of data points enabled detailed imaging of the lithological changes and potential cavities, supporting the  
Figure 8: The 2023 subsidence location is marked by a yellow star, indicating the affected area. Light blue lines represent the 527 m ERT measurement profiles overlaid on a satellite image of the region for enhanced spatial understanding.  
interpretation of structural features and the development of targeted mitigation strategies for subsidence risks.
# A Multi-Model Proposal for Classification and Detection of DDoS Attacks on SCADA Systems
## 3 Literature Studies on SCADA Security
In the literature, studies that detect attacks on SCADA systems have been examined and it has been seen that machine learning algorithms such as random forest, decision tree, logistic regression, Naive Bayes, KNN, and SVM are used more frequently than other algorithms for detection. In addition to these, there are models in which neural networks and deep learning algorithms such as LSTM are used. In this study, deep learning-based models (CNN, LSTM, LSTM-CNN hybrid) and machine learning-based models (13 models such as KNN, LogitBoost, Naive Bayes and decision tree) were proposed. The attack detection accuracy rates of the examined studies and this study are placed in the table in the Section 5.2. Thus, a general review and comparison is provided for the studies.
## 4 Discussion
### Low-Load Blood Flow Restriction Training and Muscle Volume  
The ten articles that assessed muscle volume reported improvements in this outcome after the LLBERT intervention [33; 40; 41; 42; 43; 44; 45; 46; 48; 50]. These results align with those obtained in other studies not included here. [PERSON] et al. [31], [PERSON] et al. [60], and [PERSON] et al. [61], all of whom studied knee surgery, demonstrated the beneficial effect of LLBERT on muscle volume during post-surgical trauma rehabilitation. However, other studies have failed to show the positive effects of LLBERT on muscle size, refuting its efficacy in the recent literature. For example, [PERSON] et al. [56] found small and non-significant increases in thigh circumference (15 cm above the patella) after 18 LLBERT sessions as a post-surgery rehabilitation therapy. [PERSON] et al. [62] reported a \(13.8\pm 1.1\%\) loss of quadriceps muscle area in the LLBERT group after two weeks of intervention.  
According to our meta-analysis, only four studies (less than 50% of the total cases) showed positive effects in favor of the LLBERT intervention groups [33; 45; 46; 48]. In fact, the non-significant overall effect was also in favor of controls, which calls into question the effectiveness of LLBERT on hypertrophy, especially in thigh muscles. Nevertheless, regarding upper limb muscles, [PERSON] et al. [46] showed greater increases in wrist and forearm circumferences in participants who performed a LLBERT rehabilitation program than those who participated as controls.  
Considering the heterogeneity observed in our analysis, the lack of an LLBERT effect on muscle size could also be explained from different perspectives. First, the duration of early postoperative rehabilitation programs based on LLBERT therapy seems to be a key factor for inducing hypertrophy, since the loads used (<30% RM) are not the most suitable for stimulating muscle growth. Thus, the number of sessions, especially their frequency (sessions per week), could be a determinant. Accordingly, two of the selected studies in which a greater effect of LLBERT on muscle size was observed reported both a greater number of sessions and a higher weekly frequency. Moreover, the total number of exercises included in the program could be decisive, since a greater number and diversity of exercises could limit the focus on the muscles that are finally evaluated. Lastly, according to the basis of blood flow restriction, the AOP level could influence the effects of LLBERT on muscle size. As confirmed in previous studies, metabolic stress related to blood flow restriction promotes the response of anabolic hormones, myogenic stem cell proliferation, and protein synthesis [24], some of the physiological pathways that stimulate muscle growth. In our analysis, LLBERT-related ischemia was induced mainly by 80% AOP, as only two studies used a lower AOP [46; 48]. Nevertheless, contrary to the expected results, these two studies reported the greatest hypertrophic effects, making it difficult to draw consistent conclusions.
###### Abstract
Further, construction consumes over 44% of the life-cycle energy, with maintenance processes accounting for most of the rest. [PERSON] et al. [7] review the accuracy of truck weighing techniques, both static and weigh-in-motion (WIM). Vehicle weight data are widely used for the design of pavements and for the assessment of bridge safety. [PERSON] et al. analyze data that have been weighed using four different technologies: weigh station, portable scales, bridge WIM and pavement (strip sensor) WIM. The conclusion is that all four systems produce results within the required and expected accuracy. [PERSON] et al. [8] report on a study where the bridge WIM concept was extended to the problem of weighing trains. Four trains were statically weighed to test the results. It was found that, unlike trucks, the train speed cannot be assumed to remain constant during the bridge crossing event. When the system was corrected to allow for variable speed, the accuracy was found to be very good. Two papers consider the topic of multi-objective decision support methods. [PERSON] [9] reviews the literature to determine what approaches are used for Civil Engineering applications. He concludes that, while many methods are available, a small number dominate usage in issues relating to sustainability, environment, risk, safety and project management. The Analytic Hierarchy Process seems to be particularly popular. When he widened his search to other topics, such as bridge design and transportation, the results were quite different, with a more complete range of supporting software available. [PERSON] et al. [10] consider the application of one of these methods of multi-objective decision-making to a railway network maintenance problem. In the context of slope stability, they propose an optimal approach to management, considering the competing objectives of user cost (asset availability), asset owner cost and structural reliability. Multi-attribute utility theory is used to balance the priorities of the competing objectives in a harmonious way, giving a holistic means of ranking assets and ultimately prioritizing maintenance activities. It can be seen that this special anniversary issue covers a very wide range of topics. In addition to traditional fields such as Structures and Geotechnics, Civil Engineers are often concerned with 'big picture' issues such as environmental implications and sustainability. They can also find themselves in unexpected fields, such as weighing trucks or trains, which have implications for Civil Engineering infrastructure. This Special Issue reflects the wide diversity in this branch of Engineering. This research has received no external funding. The author declares no conflict of interest.
# Quantum PSO-Based Optimization of Secured IRS-Assisted Wireless-Powered IoT Networks
### Motivations and Main Contributions  
In this manuscript, we investigate quantum-inspired particle swarm optimization (QPSO) [46], a derivative of PSO that is shaped by principles of quantum mechanics. In contrast to conventional PSO, QPSO mitigates the challenge of slow convergence, and needs fewer iterations to attain the optimal solution, thereby markedly diminishing the processing duration within the proposed framework. In this paper, we also explore a jammer-IRS scheme to enhance the sum secrecy rate in wireless-powered networks. In this scheme, jamming signals are emitted to confuse an eavesdropper while the reflecting surface optimizes both EH and the efficiency of uplink information transmission. The key contributions of this paper are as follows:  
* We propose dual-layer protection for secure uplink communications by considering an EH jammer and an IRS-aided technique. Notably, we divide the time frame for the network into two phases (downlink and uplink), where the duration trade-off between DL and UL is adaptively balanced.
* In the WET phase, users and a friendly jammer harvest energy from a hybrid access point (H-AP) and in the subsequent WIT phase, users transmit data to the H-AP while an eavesdropper attempts to intercept the communication. To counter this, the jammer utilizes the energy harvested during the WET phase to disrupt the eavesdropper's efforts. The overall secrecy rate can be maximized by jointly optimizing the phase-shift matrix and the time allocation between DL and UL phases.
* We employ the AO approach and decompose the difficult non-convex optimization problem into sub-problems. QPSO is used to optimize the IRS phase-shift matrix, and the bisection method is used to optimize downlink and uplink times.
* As a comparative scheme, we consider PSO, QPSO (phase-shift optimal with a fixed time), and fixed time and random phase techniques, and we evaluate the performance of the proposed scheme via simulations that demonstrate that the proposed IRS-assisted scheme has better performance than other conventional scenarios and PSO techniques.  
The remainder of this paper is organized as follows. In Section 2, we discuss the system model and problem formulation for sum secrecy throughput maximization. Section 3 describes the proposed solution for the AO method, which consists of QPSO and the bisection method. Section 4 explains the numerical results and provides a discussion. Section 5 describes the challenges and future directions. Finally, the conclusions are presented in Section 6.
## 5 Experimental Results  
### Experimental Platform  
This study took an STM32 32-bit chip (TI Corporation, Dallas, TX, USA) as a vehicle controller and an electrical steering wheel (Lianshi Co., Ltd., Shanghai, China) as an alternative to the previous manned wheel, together with the angle sensor (Tianhaike Co., Ltd., Beijing, China), to measure the front steering angle. The satellite positioning system (Beidou Xingtong Technology Co., Ltd., Beijing, China) and MTI-300 inertial navigation system (XSENS, Enschede, The Netherlands) also made contributions [29]. Figure 6 portrays the experimental prototype. Two sets of BDS RTK positioning systems were installed in the tractor and trailer, respectively, which could record the position and heading information. In addition, a PC was used as the navigation controller, which was designed based on the Qt environment [30].
## 2 Materials and Methods
### Methods
#### 2.2.5 Kinetic and Adsorption Studies of 4-Ethylphenol and 4-Ethylguaiacol  
In an Erlenmeyer, 10 mg of AC were weighed, to which 25 mL of an aqueous solution containing 4-EP or 4-EG was added with concentrations varying between 0.05 and 1.5 mmol L\({}^{-1}\). The range in concentrations of 4-EP and 4-EG used allowed the remaining amount in the solution (after removal by ACs) to fall below the limits of the perception thresholds in wine. The Erlenmeyer flasks, with a stopper, were placed under continuous agitation (150 revolutions per minute) in a thermostated shaker bath, at room temperature, for different times, until a maximum of 48 h. The quantity of phenolic compounds adsorbed on the ACs was obtained from the difference between the initial and measured concentration, after an equilibrium time.  
To simulate real wine, acidic solutions (pH = 3) containing 4-EG, 4-EP, and 13% \(v/v\) of ethanol were prepared. The adsorption capacity of the ACs for 4-EP and 4-EG, in the presence of 13% \(v/v\) of ethanol, was evaluated following the procedure described above. The first quantification tests were conducted by UV/VIS spectrophotometry on a UV-Visible Nicolet Evolution 300 apparatus, from Thermo Electron Corporation. Furthermore, the 4-EP was quantified at 221 and 271 nm and the 4-EG at 234 and 294 nm. Spectrophotometric analysis is commonly used because it is environmentally friendly, simple, rapid, and low-cost. However, for more accurate results, and when both compounds were present in the mixture, their quantification was achieved using high-performance liquid chromatography (HPLC) equipment. This was an Agilent 1100 system, from Agilent Technologies, Germany, equipped with a UV detector and a manual injector Rheodyne 7725i, controlled by HP Chemstation software. The experimental conditions used on the HPLC were optimised. Three flow rates were tested (0.8, 1.2 and 1.5 mL min\({}^{-1}\)) of acetonitrile and acidified water, varying between 25 and 75%. The 4-EP and 4-EG quantification was performed using an external pattern, presenting an excellent correlation coefficient (R > 0.999), for concentrations ranging between 0 and 1.5 mmol L\({}^{-1}\). The best results were obtained at 278 nm, with an isocratic elution, with a mobile phase comprised of acetonitrile/ultrapure water/phosphoric acid (750/240/1, \(v/v\)). The assays were made in triplicate and under these conditions, the retention times were 7.8 and 8.5 min, for 4-EG and 4-EP, respectively. The method used in HPLC was validated for its selectivity, linearity range, detection and quantification limits, reproducibility, precision, and accuracy.
# Evaluation of Machine Learning Assisted Phase Behavior Modelling of Surfactant-Oil-Water Systems
## 2 Methodology and Materials
### Machine Learning Model Development  
We applied a range of machine learning algorithms to the generated dataset to identify the model best suited for predicting surfactant phase behavior. The primary goal was to develop a model that can accurately predict critical outputs, including the number of phases, phase compositions, and whether the system exhibited multiphase behavior (up to three phases). The dataset, which consisted of 5000 data points representing various combinations of salinity, oil composition, surfactant concentration, and other factors, served as the input for training and validation.  
The machine learning algorithms considered in this study include support vector machine (SVM), decision tree, k-nearest neighbor (KNN), and boosted tree models. We applied both fine and medium decision tree models. Decision trees are well-suited for this task as they can handle non-linear relationships between variables and are interpretable, providing clear insights into the decision-making process for phase prediction. The fine tree model was designed to capture subtle variations in the data by creating a large number  
Figure 1: Illustration of input parameter range and model output types for machine learning algorithms.  
of branches, while the medium tree model struck a balance between complexity and generalization to avoid overfitting. SVM models are particularly effective in high-dimensional spaces where clear decision boundaries between phases are required. We applied both linear and cubic SVM models. The linear model served as a baseline, offering simplicity and fast computation, while the cubic model provided greater flexibility in capturing complex relationships within the compositional space, making it better suited for identifying transitions between single-phase, two-phase, and three-phase regions. We also tested k-nearest neighbors and boosted trees.  
Additionally, the models were tested on their ability to map the compositional space accurately, ensuring they could correctly identify critical points, such as tie-line boundaries and phase transitions. Ultimately, the model that demonstrated the highest accuracy and consistency in predicting phase behavior was selected for further integration with the graphical equation-of-state model.
## 4 Experimental Results  
### Construction of Humeral Head SSM and Its Performance Evaluation  
This study involved a total of 22 subjects, as described earlier in the materials section. The signed distance was used to create the feature vector matrix. Eigenvector, mean vector, and PC scores were calculated using PCA. Figure 6 shows shape variations of the humeral heads synthesized by using the SSM. The horizontal direction of Figure 6 corresponds to the first PC, whereas the vertical direction corresponds to the second PC. Figure 7 shows the cumulative contribution rate (CCR) corresponding to the number of PCs. It shows that the SSM can represent around 95% of shape variation by using the first five PCs.  
Figure 6: Humeral head shape variation synthesized by the statistical shape model (SSM), where the horizontal direction corresponds to the first principle component (PC) score and vertical direction corresponds to the second PC score.  
Figure 7: Cumulative contribution rate (CCR) of the SSM corresponding to the number of principal components; the SSM can represent 95% shape variation from the first five PCs.  
An SSM can express the interindividual variation of the shape by the mean (i.e., average shape), the eigenvectors, and the PC scores (i.e., shape parameter). It can also reconstruct the original individual shape [5]. The shape of one subject was chosen as the test data, whereas the shapes of all other subjects were used to build an SSM. The shape parameter of the test data was calculated by PCA. The test data were reconstructed by using the SSM and the calculated parameter by Equation (8). Therefore, verification of the generalization ability [23] of the SSM was evaluated by comparing the test data shapes with the reconstructed one. The Sorensen-Dice coefficient (DSC) was used as an evaluation metric. Figure 8 shows a schematic diagram of the overall process.  
The leave-one-out cross validation (LOOCV) procedure [23] was used to validate the generalization ability of the SSM. As the total subjects were 22, LOOCV became a 22-fold cross validation process in this case. For every left-out subject (test subject), a new SSM was computed from the remaining training subjects. The parameters of the left-out subject were computed to reconstruct the subject. In this experiment, a total of 22 different evaluations were available. The average DSC was 0.918. The selected number of PCs was six because the cumulative contribution ratio of the PCs was 95% or more.
## 2 Materials and Methods  
### Soil Surface Image Acquisition  
Soil samples were prepared in molds, and digital images of the soil surface were acquired in an indoor studio. Four agricultural land soils collected in Korea were used for the study: sandy loam (SL), loam (L), silt loam (SiL), and silty clay loam (SiCL). Soil conditions for the soil sample were determined by considering general upland soil circumstances.  
Figure 1 shows the process of soil sample preparation. The soil molds were cylindrical acrylic molds, 150 mm in diameter and 50 mm in height. After oven (Changshin Science C-DOD3) drying at \(110\pm 5\)\({}^{\circ}\)C for 24 h, the WC of the oven dry soil was adjusted to 5, 10, 15, 20, and 25%. The wet soil was placed in a mold with a collar in a disturbed state. Each soil sample was compacted at four \(\rho_{dry}\) conditions of 1.1, 1.2, 1.3, and 1.4 t/m\({}^{3}\). The height of the sample after compaction was 50 mm, equal to the height of the mold.  
Digital images were taken under controlled lighting. The correlated color temperature (CCT) measured at the soil sample location was 6400 K. The focal length of the digital camera (Canon EOS 100d) was fixed at 35 mm. The size of the digital image was 5184 \(\times\) 3456 pixels. The photograph was taken in the vertical direction of the soil sample, and the distance between the soil surface and the image sensor of the camera was 240 mm. Under these conditions, one original image represented an area of 93.3 mm \(\times\) 62.2 mm with a resolution of 0.018 mm/pixel. Each soil sample was prepared three times under the same conditions. Therefore, 60 digital images were taken for one soil under 5 conditions of WC, 4 conditions of \(\rho_{dry}\) with 3 repetitions.