esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Assessment Tool for 3D Computer-Aided Design Models
## 2 State of the Art and Related Works
### Comparison Tools in Commercial CAD Systems  
Computer-aided design (CAD) systems and models are key tools in engineering, design projects, and education. CAD systems are software applications that enable users to create 2D or 3D digital models or prototypes of physical structures or products. These models may be manipulated, edited, and enhanced before the final design is fully developed. These systems include a number of features and tools to automatically perform certain tasks, such as taking accurate measurements, generating BOMs, and running simulations. Commercially available software tools indeed play a critical role in identifying differences between 3D CAD models, known as Model Difference Identification (MDI) tools [13].  
The Comparison Feature is one of the features generally available in 3D CAD systems. According to their documentation, the main goal of 3D Computer Aided Design systems in providing model comparison capabilities is to assist users to compare different versionsof a model and, ultimately, to manage modifications to 3D CAD models. Notably, 3D CAD systems are the only software that can compare the procedural representations of models, i.e., the sequencing of the modeling operations with respect to their corresponding parameters (also called feature manager, modeling histories, etc.). Although comparing imported models is also possible, it is highly dependent on the capabilities of the 3D CAD system to import and regenerate external models, which are likely to introduce a degree of data degradation during the import process (e.g., the loss of model features, topological changes, etc.) [6].  
Comparing CAD models using commercial CAD software is a common industry practice. Advanced features for comparing and evaluating differences between different CAD models are available in commercial CAD software. These tools allow engineers and designers to identify design discrepancies, errors, or inconsistencies between different versions of CAD models, whether they are geometric or dimensional differences or changes in material properties. Furthermore, commercial CAD software can provide detailed reporting on differences, performing quantitative measurement to assess the extent of variation between models.  
Each piece of commercial CAD software existing on the market has its own specific comparison capabilities. Comparison tools can vary in their ability to detect differences, ease of use, and file formats supported.  
CAD model comparison tools in commercial software can have limitations, particularly in detecting subtle differences, handling complex modifications, handling a variety of file formats, automating the comparison process, and integrating with other analysis tools. It is very important to be conscious of these limitations (Table 1).
## 3 Materials and Methods  
### Overview  
E-APTDetect builds on the cyber attack impact assessment methodology developed in [13]. Accordingly, we presume an attester agent that triggers the attestation procedure against the attestee, which, in our case, denotes the physical process controlled by the SCADA system. The attester selects one or more control variables, which would attest a particular sub-set of components of the SCADA system. The behavior of the attested components is measured by sensors, which are collected by regular agents. The regular agents also perform fundamental computations, and send the calculated behavior to the attester.  
The developed attestation scheme of SCADA systems is based on the fundamental laws of physics, where changes in control parameter values have a particular impact on measured parameters, hereinafter called observed variables. As an example illustration of this concept, let us assume an industrial installation such as the boiling water power plant shown in Figure 1. This showcases the plant model published by [PERSON] and Astrom in [36]. The model represents a 160 MW oil-fired electric power plant based on the Sydsvenska Kraft AB plant in Malmo, Sweden.  
In the context of this process, a slightly opened fuel valve, for example, will trigger an increase in the water temperature, which will subsequently increase the pressure in the boiler. In case other valves do not change their position, the increased pressure will yield an increase in the rotation of the turbine, which, subsequently, will increase the generated power. According to this procedure, one may attest the correct behavior of several subsystems that are physically and, perhaps, digitally interconnected.  
Figure 1: Example process of a boiling water power plant including control and measurement parameters.
# Emotion Estimation Using Noncontact Environmental Sensing with Machine and Deep Learning Models +
## 3 Materials and Methods
### Model Building  
In this experiment, RF and GBDT models were adopted as machine learning models for emotion estimation. These models were chosen in the initial phase of the experiment because of their high versatility and accuracy potential in handling regression tasks involving multiple features. RF is an ensemble learning method known for its robustness against feature dependency and overfitting, and it enables the evaluation of feature importance, which helps clarify the contribution of each environmental data point to emotion estimation. The CBDT model captures complex nonlinear relationships within the data, offering the potential for improved accuracy compared to the RF model.  
In addition, we adopted the CNN-LSTM model as a deep learning model. This CNN component captures data features, while the LSTM components learns temporal patterns. Specifically, by inputting multiple time steps of data, we can incorporate a temporal range, while the LSTM component enables the consideration of past data's influence. Since emotions are thought to change continuously over time, processing them as time-series data in this way allows a better capture of these ongoing emotional changes, with an anticipated improvement in accuracy.
## 6 Application Areas  
This section will explore agricultural applications where companies have implemented BCT. The application areas encompass small-scale farmers, processors, distributors, and retailers. This study aims to spotlight both the advantages and drawbacks of BCT for each participant. The examination of application areas will delve into how BCT can augment supply chain transparency, traceability, and food safety. Additionally, it will assess its potential to reduce food fraud and waste, enhance overall efficiency and profitability, and improve traceability in the agricultural sector.
## 3 Methodology
### Time-Series Feature Representation  
This study employed contrastive learning to transform spectral features into time-series feature representations. There are three advantages to this. First, feature representation contributes to learning the inherent time-series feature for classification. Second, it can generate complete and regular time-series features (without missing values). Thirdly, it can decrease the demand for a larger number of labeled samples in deep-learning-based applications. A general framework (known as TS2 Vec) was proposed for learning the representation of time series [39]. It consists of three main components: a representation framework, consistency augmentation, and loss functions. Feature representation was performed using the representation framework. Using consistency augmentation, augmented sample pairs were generated to train the framework. Loss functions ensured the discovery of consistent features from multiple augmented samples.  
This study attempted to improve the TS2 Vec model for supervised time-series crop classifications (named type-wise TS2 Vec) by incorporating prior-type information from labeled samples into contrastive learning. In general, we followed the architecture of the TS2 Vec model [39]. Further, the consistency augmentation and contrastive loss were enhanced. (1) When conducting consistency augmentation, we discarded original random cropping and developed novel type-wise random selection and random band-masking techniques. (2) When calculating multi-scale contrastive loss, type-wise contrastive loss was devised to replace instance-wise loss.
# The Effect of Pitch Surface on Match Running Performance in Women's Soccer
## 1 Introduction
Contrary to the results of previous studies, their research did not show any differences in low and high-intensity actions during the match. Evident inconsistencies in these findings are almost certainly the result of different methodologies utilized. Specifically, [PERSON] et al. [13] and [PERSON] et al. [24] used global positioning systems, while on the other hand [PERSON] et al. [25] used optical tracking systems. In addition, it should be noted that no study so far has compared physical demands exclusively among female football players on AT versus NG. As the physical demands of football differ significantly between the sexes [26; 27; 28], the findings from studies that investigated male players cannot be generalized to female players. Finally, studies rarely examined differences in acceleration and deceleration efforts on AT and NG, which are valid indicators of physical demands in soccer [29]. Understanding these nuances is not only critical for informing training practices but also for advancing surface engineering to better align with the unique physiological demands of athletes. For the above-mentioned reasons, we believe that new research analyzing MRP on AT versus NG among female professional soccer players is warranted. Such findings may assist football coaches in identifying the physical demands associated with different playing surfaces and inform players of the expected efforts in women's football. More importantly, the results could extend the understanding of how pitch conditions shape game dynamics and player efficiency, contributing to broader advancements in soccer performance science, such as tailoring surface-specific training protocols and optimizingplayer workload management. Beyond practical coaching applications, the findings could inform bioengineering efforts in surface design by identifying specific characteristics of AT that influence performance. Therefore, the aim of this study was to evaluate the MRP of female football players during matches played on AT and NG. We hypothesized that MRP would be significantly greater for players across all playing positions on AT than on NG.
# Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach
## 2 Methods
### Experiments and Experimental Setup
#### 2.2.4 Experimental Protocol  
The experimental protocol used in the present study was designed in such way to investigate the subjects' thermal and physiological responses to three different temperature (_low_, _normal_ and _high_) under two levels of physical activities (_low_ and _high_).  
The three predefined temperatures (_low_ = 5 \({}^{\circ}\)C, _normal_ = 24 \({}^{\circ}\)C and _high_ = 37 \({}^{\circ}\)C) were chosen based on the _thermal-comfort-chart_ of the ASHRAE-55 [32] and the effects on health according to the Wind Chill Chart for cold exposure (National Weather Service of the US) and for hot temperatures exposure according to [33]. The conducted experiments were consisted of two phases (Figure 4, upper graph), namely, low activity and high activity phases. During the first experimental phase, low activity phase, the test subjects (while being seated = low activity) were exposed, during 55 minutes, to three levels of temperatures in the following order: normal, low, high and normal again (Figure 4). During the high activity phase, the test subjects was exposed to a 15 minutes of light physical stress (80W of cycling on a fastened racing bicycle). During the course (75 minutes) of the active phase, each test subject was exposed to the predefined three temperature levels (Figure 4, lower graph). During each temperature level, starting from the normal level (24 \({}^{\circ}\)C), the test subjects are performed 15 minutes of cycling (with 80 W power) and followed 4 minutes of resting (seated). During the course of conducted experiments, the clothing insulation factor (_Col_) was kept constant at _Col_ = 0.34, which accounted for a cotton short and t-shirt as a standard clothing for all test subjects. The experimental protocol was approved by the SMEC (Sociaal-Maatschappelijke Ethische Comissie), on the 16 January 2019 with number G-2018 12 1464.
## 4 Experiments
### The Effect of Rough-Fine Classification  
In order to reduce the bone age assessment error caused by classification errors, we present a two-step classification flow. To illustrate the effect of our approach, Table 5 compares the accuracy and bone age assessment difference between one-step stage classification and two-step stage classification.  
It can be found that the effect of the two-step classification is much more accurate. This is because the difference between certain bone blocks in each stage is very small. If the classification is completed directly by one stage, it may cause a bone block that confuses the stage with other blocks. Moreover, Table 5 shows that if the maturity stage classification is wrong, the error of bone age will also be very large.  
We believe that multi-stage classification is beneficial to the overall model performance and, in the following, will explain why two-stage classification is chosen in the MSCS-CNN. Therefore, we experiment with adding a stage before and after these two stages, which we call three-stage classification config. 1 and 2, respectively. In three-stage classification config. 1, ABCD and CDEF classifiers are added before step 1 of the MSCS-CNN. In three-stage classification config. 2, the A-F maturity stages are separated after step 2 of the MSCS-CNN. In classification config. 1, it is not difficult to classify ABCD and CDEF, and for step 1 of the MSCS-CNN, AB and EF can be effectively classified, so adding this step will not help the overall bone age assessment. For three-stage classification config. 2, as we will explain later, it is not an easy task to classify the various maturity stages perfectly, so adding this layer is harmful to the overall bone age assessment. Therefore, we choose two-step classification as  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **MAE on Females** & **MAE on Males** \\ \hline Only step 3 & 1.225 & 1.421 \\ MSCS-CNN & 0.532 & 0.56 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparing bone age assessment performance with and without maturity stage classification.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Females** & **Males** \\ \hline One-stage acc. & 0.87 & 0.83 \\ MSCS-CNN (two-stage) acc. & 1.0 & 0.96 \\ One-stage MAE & 1.745 & 1.49 \\ MSCS-CNN (two-stage) MAE & 0.532 & 0.56 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of one- and two-stage classification performance.  
the most appropriate. The classification effects of the above two configurations are shown in Table 6.
# Continuous Space Wireless Communication Tower Placement by Hybrid Simulated Annealing
## 3 Problem Formulation  
Given an area where a new wireless communication network will be deployed, a set of users of the new wireless communication network in the area, and the number of wire communication towers and the coverage of the communication towers, the new continuous space communication tower placement problem is to find a placement of those wireless communication towers such that the total number of users that can be covered by the wireless communication network is maximal.  
This new continuous space wireless communication placement problem is formulated as follows:  
**Given**  
1. A two-dimensional continuous area, \(A\), and a set of \(n\) users in \(A\), \(U=\{u_{1},u_{2},\cdots,u_{n}\}\), where \(u_{i}=<x_{i}^{u},y_{i}^{u}>\) are the coordinates of user \(u_{i}\) and \(1\leq i\leq n\);
2. A set of \(m\) communication towers, \(C=\{c_{1},c_{2},\cdots,c_{m}\}\);
3. The coverage of the communication towers, \(d_{max}>0\);  
**find**  
a placement of the \(m\) wireless communication towers \(p=\{<x_{1}^{c},y_{1}^{c}>,<x_{2}^{c},y_{2}^{c}>,\cdots,<x_{i}^{c},y_{i}^{c}>, \cdots,<x_{m}^{c},y_{m}^{c}>\}\), where \(<x_{i}^{c},y_{i}^{c}>\) is the coordinates of the tower \(c_{i}\) and \(1\leq i\leq m\),  
**such that**  
\(|Cover(p)|\) is maximum, where \(Cover(p)\) is a set of users that can be covered by \(p\). A user \(u_{i}\) is said to be covered by \(p\) if there exists a communication tower \(r_{j}\) such that the Euler distance between \(u_{i}\) and \(r_{j}\) is less than or equals to \(d_{max}\), that is, \(\sqrt{(x_{i}^{u}-x_{j}^{r})^{2}+(y_{i}^{u}-y_{j}^{r})^{2}}\leq d_{max}\), where \(\langle x_{j}^{u},y_{j}^{u}\rangle\) are the coordinates of \(u_{i}\), \(\langle x_{j}^{r},y_{j}^{r}\rangle\) are the coordinates of \(r_{j}\) in \(p\), and \(d_{max}\) is the coverage of the communication tower.
## 4 Conclusions  
The present study suggested the effectiveness of cell immobilization on cereals and fruits as delivery vehicles for _L. rhamns_ OXLAL-1 cells during GI transit. Notably, after in vitro digestion, all immobilized _L. rhamns_ OXLAL-1 cultures exhibited higher survival rates compared to free cells, with the highest survival rate recorded for the immobilizedcells on oat flakes (84.76%). Upon administration of both freeze-dried free and immobilized _L. rhamnus_ OXLXL-1 cells to BALB/c mice (groups ID and FD), a significant increase in lactobacilli populations was observed in the mice feces compared to baseline. Importantly, the ID group displayed a significantly higher lactobacilli count than the FD group (8.02 log CFU/g and 7.64 log CFU/g, respectively). Finally, the presence of _L. rhamnus_ cells at levels exceeding 6 log CFU/g was confirmed in the mice feces (groups ID and FD) using multiplex PCR analysis. These findings highlighted the successful survival of immobilized _L. rhamnus_ OXLXL-1 cells and suggested the potential for applications of this approach in the probiotic industry.  
Conceptualization, A.E.Y., N.K. and Y.K.; methodology, N.K., G.N., Y.K. and A.E.Y.; formal analysis, G.N., I.P., A.N., G.M., A.E.Y. and Y.K.; investigation, G.N., I.P., A.N. and G.M.; resources, N.K., Y.K. and A.E.Y.; writing--original draft preparation, G.N. and Y.K.; writing--review and editing, I.P., A.N., G.M., N.K., A.E.Y. and Y.K.; supervision, Y.K. and A.E.Y.; funding acquisition, Y.K. All authors have read and agreed to the published version of the manuscript.  
The research work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the \"First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant\" (Project Number: HFRI-FM17-2496, acronym: iFUNcultures).  
Animal experimentation was reviewed and approved by the Veterinary Directorate of the Athens Prefecture (Ref. Number 483166/30-05-2022) and conducted in compliance with the European Directive 2010/63.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to restrictions of the funding authorities.  
We would like to thank [PERSON] (Laboratory of Molecular Regulation and Diagnostic Technology Development, Department of Molecular Biology and Genetics of the Democritus University of Thrace) and [PERSON] for technical support on species-specific multiplex PCR analysis. We also wish to thank [PERSON] (Laboratory of Electron Microscopy and Microanalysis, School of Natural Sciences, University of Patras) for the SEM images.  
The authors declare no conflict of interest.
## 1 Introduction
Therefore, it is necessary to develop a new kind of instrument to test the bonding between the basalt fiber bundle and the asphalt mortar to better reveal the actual bonding status between the basalt fiber bundles and the asphalt mortar and also get an index that could quantitatively reflect the bonding between basalt fiber and asphalt material, because a quantitative index could be used to reflect the bonding ability between basalt fiber and asphalt material more clearly. This paper aims to introduce a new test instrument [20] and method to test the bonding ability between basalt fiber bundle and asphalt mortar using a new instrument called the fiber-asphalt pulling machine, and a new index reflecting the bonding between them called nominal bonding stress (NBS) was proposed in this paper. However, the reliability of this new test method and index need to be verified. The property of the asphalt or asphalt mortar are of great significance on the performance of the asphalt mixtures or the asphalt pavement [21]. Studies show that the rheological property of asphalt has a great correlation with the performance of asphalt mixture [22; 23; 24], and the rheological property indexes are much more accurate than the traditional evaluation indexes such as the softening point and the penetration value of asphalt [25]. When the basalt fiber type was fixed, the bonding ability between basalt fiber and asphalt mortar was mainly affected by the property of asphalt mortar. Therefore, the rheological property indexes of the asphalt mortar were chosen to study the correlation between them and the NBS index to prove the credibility of this new test method and the corresponding calculation equation of NBS index. The NBS index is a very important index and it can also represent the viscosity of the asphalt mortar to some extent when the fibers are the same. Because when the fibers are the same, the adhesion between the fiber and asphalt mortar will be affected all by the properties of the asphalt mortar, especially the viscosity of the asphalt mortar. In this paper, two kinds of continuous basalt fibers provided by two different manufacturers were adopted to test the NBS and the asphalt mortar under four kinds of different powder cement ratios. The reason for using two kinds of basalt fibers in the tests and analysis was to conduct the parallel testing to further verify the validity of this method and the NBS index. The rheological properties of the asphalt mortar under different powder cement ratios were also tested using the dynamic shear rheometer. The corresponding test data of the nominal bonding stress (NBS) obtained using the fibers provided by two different manufacturers were then used to study the correlation between them and the rheological property indexes of the asphalt mortar separately to verify the reliability of this new method.