esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Indirect Tensile Strength  
Following the same procedure adopted in the UCS study, the performance of all four algorithms in ITS prediction was compared based on REC curves, as depicted in Figure 6. As previously discussed and shown in Table 3, it is also clear here that the superior performance of ANN algorithm on ITS prediction and the weak response of a linear approach (MR.ITS model). Concerning RF.ITS and SVM.ITS, both present a very similar response on ITS prediction.  
Looking in detail to ANN.ITS model, it is observed that around 96% of all records can be predicted with an absolute deviation lower than 100 kPa. Moreover, even for a tighter tolerance, such as an absolute deviation around 50 kPa, ANN.ITS presents an accuracy higher than 85%, showing its good performance.  
Figure 7 validates the high performance of both ANN.ITS (Figure 7a) and RF.ITS (Figure 7b) models on ITS prediction. As shown, particularly according to the ANN.ITS model, all predictions are close to the experimental values (diagonal line).  
Figure 5: A comparison of the relative importance of each input variable based on a GSA in the UCS prediction of soil-binder-water mixtures reinforced with fibers.  
Concerning to model's interpretability, Figure 8 compares the relative importance of each model attribute. As for the UCS study, with the ANN.ITS model taken as a reference, the dosage of the binder (\(D_{\text{\emph{kg/m3}}}\)) was identified as the most relevant variable in ITS prediction with a relative influence close to 16%. A higher influence of the fibers is also observed, which was considered by the ANN.ITS model through \(E_{\text{\emph{fiber}}}\) (7.2%), \(F_{\text{\emph{kg/m3}}}\) (7.1%) and \(T_{\text{\emph{fibre}}}\) (7.0%), which ranks in the five most relevant variables. This higher influence of the fibers on the ITS prediction, when compared to the UCS study, is in agreement with some empirical studies [10; 14; 16]. In fact, when the composite material is subject to indirect tensile through a splitting failure mechanism, there is an effective mobilization of the tensile strength of the fibers that cross the vertical failure plane imposed by the ITS test, and consequently, the tensile strength is directly related to the fibers' characteristics. According to RF.ITS, once again, an influence above 40% is observed for \(\omega_{0}\)/\(a_{w}\), which demonstrates the coherence of the algorithm.  
Figure 6: A comparison of ANN.ITS, SVM.ITS, RF.ITS and MR.ITS models performance in ITS prediction of soil-binder-water mixtures reinforced with fibers based on REC curves.  
Figure 7: The relationship between ITS experimental versus predicted values of soil-binder-water mixtures reinforced with fibers according to: **(a)** the ANN.ITS model; **(b)** the RF.ITS model.
# Design of a Generic Wearable-Robot Monitoring System (GWRMS) for Patients with Lower Limb Paralysis
## 1 Introduction  
Robots comprise a collection of embedded systems whose performance is sometimes limited by the requirement for high portability and mobility [1]. In addition, in some cases, embedded systems need to guarantee real-time performance. To address these requirements, previous studies have demonstrated methods such as kernel design to guarantee real-time performance [2, 3]. Improvement of the performance of embedded systems can also be achieved through code analysis and optimisation.  
The objectives of this study were to optimise the code for wearable robots and to predict their status by developing a generic wearable-robot monitoring system (GWRMS). The robot described in this paper assists with walking in patients with lower-body palsy. Since such patients can walk only by using exoskeleton robots, software optimisation is critically important. This can be achieved by monitoring the system resources of the wearable robot.  
The GWRMS also works with any operating system that meets Portable Operating System Interface (POSIX) [4] standards. POSIX is a set of operating system application programming interface (API) standards established by the IEEE for the purpose of developing highly portable UNIX applications by organising common APIs for different UNIX operating systems. Most UNIX-like operating systems, with the exception of Windows, also follow POSIX standards.  
In addition, the GWRMS can monitor the current status of the robot by analysing various data generated by the robot and storing the data on a cloud server for later analysis. The GWRMS also provides an interface through which developers of wearable robots can efficiently change component parameters.  
The resource-monitoring function of the GWRMS allows monitoring of embedded systems and optimisation of each program operating in the robot. In addition, the resource-monitoring system uses the resources of embedded systems effectively, allowing on-demand operations through TCP-socket communication.  
After predicting the status of the wearable robot, data generated by the robot for analysis (e.g., upper-body tilt, plantar pressure, motor angle, etc.) are stored on a cloud server. Thus, it is possible to predict the state of the wearable robot and improve its accuracy simultaneously.  
As detailed adjustments to each motor and sensor are essential in the development of wearable robots, the GWRMS described in this paper supports both the display of system resources and the adjustment of component parameters. Adjusting component parameters can help wearable-robot developers to add or change functions.  
The remainder of this paper is organised as follows: Section 2 introduces related research; Section 3 presents each key component in the implementation of the GWRMS from the perspective of robots, servers, and clients; and Section 4 describes the merits of the GWRMS and its limitations.
## Appendix
9: extend\(star_{H_{ik}}\left((j,l)\right)\), \(stars\left(star_{H_{ik}}\right)\)
10: extend\(star_{H_{jl}}\left((i,k)\right)\), \(stars\left(star_{H_{jl}}\right)\)
11:endif
12:endif
13:endif
14:endfor
15:endfor
16:endfor
17:endfor
18:endfor
19:\(enlargedStars\gets stars:\deg(stars)\geq\max\left(\
u,\deg\left(stars_{old}\right)\right)\)\(\triangleright\) Collect all enlarged stars and new stars with central node degree of \(\
u\) or higher. 20:endprocedure
```  
**Algorithm 2** Pseudocode of Step 1 of EpitopeMatch algorithm covering the determination of stars, i.e. the seed matches. If several condition are fulfilled, including Inequality 2 in line 9, the star graph around atoms \(i\) of \(A\) and \(k\) of \(B\) is extended by an edge to atoms \(j\) of \(A\) and \(l\) of \(B\), and vice versa. Line 10 indicates that the star around \(i\) and \(k\) is addressed by hash key \(H_{ik}\). The corresponding hash table entry in \(stars\) is then also updated. Algorithms 2009, 2  
```
1:procedureDetermineClique(\(star,\delta_{2}\))
2:\(reducedStars\)\(\triangleright\) Declaration of hash table for reduced stars. 3:for\(m\gets 1,|star|\)do
4:for\(n\gets m+1,|star|\)do
5:\(i,k\gets star_{m}\)
6:\(j,l\gets star_{n}\)
7:if\(|D_{A,ij}-D_{B,kl}|\leq\delta_{2}\)then\(\triangleright\) Second distance condition. 8:extend\(reducedStar_{H_{ik}}\left(H_{jl}\right),reducedStars\left(reducedStar_{H_{ik}}\right)\)
9:extend\(reducedStar_{H_{jl}}\left(H_{ik}\right),reducedStars\left(reducedStar_{H_{jl}}\right)\)
10:endif
11:endfor
12:endfor
13:if\(|reducedStars|>0\)then
14:ReduceToClique(\(reducedStars\))
15:endif
16:endprocedure
17:procedureReduceToClique(\(reducedStars\))
18:for\(i\gets 1,|reducedStars|\)do
19:\(d_{i}\leftarrow\deg\left(reducedStars_{i}\right)\)\(\triangleright\) Degree of the central node. 20:for\(j\gets 1,|reducedStars|\)do
21:if\(j\
eq i\)then
22:\(d_{i}\gets d_{i}+\deg\left(reducedStars_{ij}\right)\)
23:endif
24:endfor
25:endfor
26:if\(\forall i,j:d_{i}==d_{j}\)then
27:extend\(cliques(reducedStars)\)\(\triangleright\)\(reducedStars\) is a new clique. 28:else
29:remove\(reducedStars_{i:\min(d_{i})}\)
30:ReduceToClique(\(reducedStars\))
31:endif
32:endprocedure
```  
**Algorithm 3** Determination of cliques. The non-central nodes of a previously determined star are central nodes of new stars (\(reducedStars\)) that have to be tested for fulfilment of the second distance condition (line 7). ReduceToClique then recursively reduces the set of these stars to a clique.
## 2 Case Study
### General Features and Geometrical Description
Only beams located along the building's edges are presented in the tables provided in Appendix A. These tables, such as Tables A1-A5, utilise indicators like 'right-end (re)' and 'left-end (le)' to denote beams with varying reinforcements at their ends and semicolons (\";\") to differentiate between top and bottom side reinforcement. These tables use notation such as \"A, B_4\", which indicates the beam positioned between axes A and B in the direction of axis 4. Additionally, internal beams along the X direction measure 35 cm by 29 cm and are reinforced with 6\(\phi\)16 bars at the top and 5\(\phi\)12 bars at the bottom, while internal beams along the Y direction measure 50 cm by 29 cm with 7\(\phi\)16 bars at the top and 5\(\phi\)12 bars at the bottom. The floors consist of two-way joint cast-in-situ concrete slabs with a thickness of 19 cm, acting as diaphragms that distribute lateral loads to vertical resisting elements, such as columns and walls. Reinforced concrete (RC) walls (with the thickness of 300 mm), primarily located on the first floor, contribute substantial stiffness and strength to the structure, particularly in resisting lateral loads during seismic events. The building's frames are also infilled with masonry walls, which enhance the structure's stiffness and energy dissipation capacity, providing additional resistance to lateral forces and limiting inter-storey drifts. Figure 2 illustrates the three-storey building (block 14) with a rectangular plan and two asymmetric axes. This detailed information on dimensions, reinforcement specifications, and element locations is essential for accurate numerical modelling and seismic analysis.
## 2 Experiments
### Test Method
#### 2.4.6 EDTA  
In the drill core sampling procedure, cement materials were selected from five depth intervals: 0 to 5 mm, 5 to 10 mm, 10 to 15 mm, 15 to 20 mm, and 20 to 25 mm using a drill core sampler. The EDTA complex titration method was subsequently employed to determine the concentration of sulfate ions at various depths. The detection principle is as follows: BaCl\({}_{2}\) reacts with sulfate ions in the sample to form an insoluble white precipitate of BaSO\({}_{4}\), while excess barium ions are titrated with an EDTA standard solution to ascertain the sulfate ion content.
# Demand Prediction Using a Soft-Computing Approach: A Case Study of Automotive Industry
## 3 Proposed Model
### Expert Judements (Delphi Method)  
Gathering information by surveying experts in the area it is the first stage of the proposed methodology. This activity has as objective to generate information from the experts point of view, due to having a better perspective of the factors that affect the demand behavior. This activity is carried out through the application of surveys applying the Delphi methodology [42; 43].  
Information gathering by implementing this methodology is to conduct a series of successive surveys. The study consists of three stages. Through the application of this type of studies it seeks that opinions converge. A key characteristic is the feedback process by experts through clear tendencies examples. By means of graphs it manages to illustrate the behavior of the experts. In this way, the methodology seeks to provide feedback and contribute to the opinion convergence. The analysis of results should be performed by applying basic statistical tests such as average, median, and mode. It is noteworthy that the group of experts in research must be integrated between 7 and 30 persons [43; 44; 45].  
Before carrying out this step, it is important to identify the variables to study for experts to contribute to the selection of them. This is done by a preliminary survey that facilitates the collection of variables with the greatest impact on the demand behavior.  
Also, it has to be determined the features of the same instrument such as structure, elements to be analyzed, validation, testing, and Cronbach's alpha. This activity can be done by a specialist in the surveys preparation [46; 47]. By collecting data from experts, the variables are weighted. That is, it has to be established a range in each of the variables that must be weighted by the experts. Then, through the convergence level, the quality of the information is evaluated.  
Therefore, it is possible to reflect the behavior of the demand and this stage concludes with the weighting of variables. Subsequently, the analysis is performed through machine learning and fuzzy logic [48].  
Figure 1: Proposed model.
###### Abstract  
Dual-polarized sensitive arrays (DPSAs) with the space-time-polarization adaptive processing (STPAP) technique, which employs the polarization domain as well as the space domain and time domain to filter out interferences, can cancel a larger number of wideband interferences for GNSS receivers. However, the traditional STPAP beamforming algorithm, which requires a separate adaptive filter for each GNSS satellite, will make the process computationally intensive as there are multiple GNSS satellites in the field of view (FOV). In order to overcome the shortcoming, a novel STPAP beamforming algorithm based on the minimum variance distortionless response (MVDR) criterion is proposed. Compared with the traditional STPAP beamforming algorithm, the proposed STPAP beamforming algorithm can process multiple GNSS satellites at once using only one adaptive filter, which will greatly reduce the computational complexity. Moreover, the proposed algorithm will not lead to a sharp deterioration in the output carrier-to-noise density ratio (C/N\({}_{0}\)) performance if the number of GNSS satellites processed in the same adaptive filter is proper. Furthermore, to calculate weight vector iteratively, an adaptive algorithm based on the constrained least mean square (CLMS) method is derived for the proposed STPAP beamforming algorithm. Simulation results validate that the proposed algorithm is effective in mitigating interferences for GNSS receivers in the joint space-time-polarization domain and meanwhile has lower computational complexity when maintaining the output C/N\({}_{0}\) performance close to that of the traditional STPAP algorithm.  
GNSS receiver; array antenna; dual-polarized sensitive array (DPSA); interference mitigation; space-time-polarization adaptive processing (STPAP); beamforming +
Footnote †: journal: Sensors
## 4 Discussion and Conclusions  
Our results about the properties of optimal allocations and envy-free prices are in agreement with those presented by [PERSON] et al. [6]: Each consumer who buys the item makes the purchase in their own location, and the price paid by a consumer is either the valuation in their own location or the price paid by another consumer plus its substitution cost for the location of that consumer, i.e., for each buyer \(1\leq i\leq n\), we have \(p_{i}=v_{i}\) or \(p_{i}=p_{j}+\delta_{ij}\) for some \(j\
eq i\) (more specifically, \(j<i\)), with \(p_{j}=v_{j}\).  
In order to justify this last statement, let us suppose that there is an \(i\) such that \(p_{i}=v_{j}+\delta_{ij}\) for some \(j<i\) with \(v_{j}\
eq p_{j}\). This implies that \(p_{j}<v_{j}\), and then \(p_{j}=v_{k}+\delta_{jk}\) for some \(k<j\). Since \(\delta_{ik}\leq\delta_{ij}+\delta_{jk}\), we would have \(v_{k}+\delta_{ik}\leq v_{k}+\delta_{ij}+\delta_{jk}=p_{j}+\delta_{ij}<v_{j}+ \delta_{ij}\), and then \(p_{i}\
eq\min_{1\leq f^{\prime}<i}\{v_{i},v_{i^{\prime}}+\delta_{ij^{\prime}}\}\), which is a contradiction.  
Our approach to the UDEFP with metric substitutability in terms of EFPM allowed, in addition to the presentation of alternative theoretical proofs, the reduction of the time complexity in the search for optimal solutions to the problem. Furthermore, we note that the symmetry property of the cost set was not required in order to find an optimal envy-free pricing. Thus, we can assume that \(\delta_{ij}\) does not necessarily have to be equal to \(\delta_{ji}\) (but positive) when \(i\
eq j\), which adds more subjectivity to the modeling of the problem, making it more realistic. In this way, the instances of the UDEFP with metric substitutability become networks with weights on nodes and arcs (see Figure 4).  
An interesting question that can be studied in future work is the verification of whether, by strengthening the triangular inequality and transforming the cost set into an ultrametric space, the UDEFP with (ultra)metric substitutability can be optimally solved in a time less than \(O(n^{2})\) in the competitive equilibrium scenario or less than \(O(n^{3})\) in general.  
[PERSON] contributed with the algorithms. [PERSON] contributed with the mathematical formalizations. [PERSON] contributed with the methodology and the organization of the text. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# The Impact of Freeze Drying on Bioactivity and Physical Properties of Food Products
## 2 Freeze Dry Technique  
Lyophilization, also known as freeze drying, is one of the widely used drying techniques for the preservation and production of foods in the food industry, especially in recent years, as well as for the production of nutraceuticals and pharmaceuticals in the pharmaceutical industry and medicine [32; 33]. Although FD is a technique with disadvantages, including high energy consumption and cost, it can preserve the biological and physical contents of various food products, especially vegetables, fruits, and even food waste and by-products [34; 35; 36]. This technique, which is used in the preservation and storage of seasonal fruits, vegetables, and perishable foods, also preserves the sensory properties of these products [37]. These features popularize the use of the technique and expand its application area. FD consists of three main steps: freezing, primary drying, and secondary drying [38]. However, this process and its results are improved by integrating different techniques and devices into the freeze-drying process. Depending on the product to be dried, freeze-drying is combined with various pretreatments, including pulsed electric field, ultrasound, blanching, and high hydrostatic pressure [39; 40; 41].  
In FD, ice crystals formed during the freezing process are directly converted to water vapor and then condensed and stored as a liquid in another area of the freeze dryer. Enthalpy was explained as the amount of energy required for the sublimation of ice crystals to water vapor in FD [42]. It is calculated to determine the drying time and final product quality. It provides the administration of the expense and energy depletion, which forbids the usage of FD most frequently. In addition to these, the computation of enthalpy specifies the appropriate energy sources, the sublimation process time for ice crystals, and the quality of the final product.
# Utilizing Energy Storage for Operational Adequacy of Wind-Integrated Bulk Power Systems
## 2 System Model
### Wind Generation and ESS Integrated Bulk Power System Model  
The set of system components required to operate in response to the demand will vary throughout the evaluation period. The transition from one operational set of components to the next is sequentially simulated for subsequent UCPs by modeling the start-up of standby units in the priority order, and the shutdown of the committed units that are no longer needed. The operational set of components vary from one UCP to the next but will remain the same for the time intervals within each UCP. The operational set of generators and transmission lines are modeled using two-state Markov processes with state enumeration. The number of committed conventional generators and wind farm, energy storage, and transmission lines are represented by \(N_{G}\), \(N_{W},N_{E}\), and \(N_{T}\), respectively. Therefore, the number of committed units, \(C^{u}=N_{G}+\ N_{W}+N_{E}\), and the total number of system states is \(2^{N_{W}+N_{E}+N_{G}+N_{T}}\). The system states with probabilities less than \(10^{-9}\) have an insignificant impact on the evaluation results. They are, therefore, ignored to reasonably limit the number of states for analysis in this work. Hence, the number of states considered is \(m\leq 2^{N_{W}+N_{E}+N_{G}+N_{T}}\). The complete set of these enumerated system states is given by Equation (11):  
\[Z_{k}=\left\{z^{1},z^{2},\ldots,z^{j},\ldots,\ z^{\rm m}\right\} \tag{11}\]  
Each system state \(\left\{z^{1},z^{2},\ldots,z^{j},\ldots,\ z^{\rm m}\right\}\) is further split into \(n_{w}=ws^{N_{w}}\) sub-states, resulting from the _w_s different capacity states of \(N_{w}\) number of wind farm and ESS combinations. The IPPT represented in Table 1 can be extended to \(n_{w}\) sub-states by associating each of the \(n_{w}\) wind generation capacity states with the corresponding SOC of the ESS. The ESS capacity, \(ES_{w}^{\rm P}\), is obtained from the SOC and the expected duration of service assuming ESS discharges until the end of the unit commitment period.  
The system status vector, \(z^{j}=\left[W_{1}\ ES_{1}\ G_{1}\ G_{2}\ \ldots G_{N_{G}}\ T_{1}\ T_{2}\ \ldots T_{N_{T}}\ \right]\), represents the \(j^{th}\) state of the m enumerated system states. The elements \(\ W_{1}\,ES_{1}\,\ (G_{1}\ G_{2}\ \ldots G_{N_{G}})\), and \((T_{1}\ T_{2}\ \ldots T_{N_{T}}\ )\) of \(z^{j}\) correspond to a wind farm represented by a generator, ESS modeled as a generator, conventional generators, and transmission lines, respectively. These elements can attain the values of 1 or 0 to represent the up and down state of the corresponding system components.
###### Abstract  
Various machine learning algorithms have been applied to network intrusion classification problems, including both binary and multi-class classifications. Despite the existence of numerous studies involving unbalanced network intrusion datasets, such as CIC-IDS2017, a prevalent approach is to address the issue by either merging the classes to optimize their numbers or retaining only the most dominant ones. However, there is no consistent trend showing that accuracy always decreases as the number of classes increases. Furthermore, it is essential for cybersecurity practitioners to recognize the specific type of attack and comprehend the causal factors that contribute to the resulting outcomes. This study focuses on tackling the challenges associated with evaluating the performance of multi-class classification for network intrusions using highly imbalanced raw data that encompasses the CIC-IDS2017 and CSE-CIC-IDS2018 datasets. The research concentrates on investigating diverse machine learning (ML) models, including Logistic Regression, Random Forest, Decision Trees, CNNs, and Artificial Neural Networks. Additionally, it explores the utilization of explainable AI (XAI) methods to interpret the obtained results. The results obtained indicated that decision trees using the CART algorithm performed best on the 28-class classification task, with an average macro \(F1\)-_score_ of 0.96878.  
intrusion; machine learning; XAI; imbalanced dataset; multi-class classification Article