esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Ensemble Classification  
The method combines multiple base classifiers for ensemble learning to improve accuracy. This method can solve the same problem and produce much higher prediction results in stability and accuracy. The main reason ensemble classifiers are commonly used is their ability to improve the accuracy and performance of a given project. Another reason ensemble learning is commonly used is insufficient training data. This can lead to a weak or erroneous hypothesis. In this case, the individual classifier will spend significant time developing a reasonable hypothesis.  
There are traditionally three categories of ensemble learning: voting, bagging, and boosting. The vote and bagging methods are similar in that they combine multiple algorithms to determine the final result, but they differ in how data are sampled. The boosting method differs slightly from the voting and bagging methods in ensemble learning. In the boosting method, models are trained sequentially, one after another.  
Voting is the most popular method used in ensemble learning to improve classification performance by combining multiple classifiers' advantages into one model. It is widely used to build various models. Ensemble methods, such as intrusion detection, can often improve classification accuracy in security applications. Voting is more suitable for heterogeneous learners' ensembles (ET, KNN, RF) with lower computational complexity and less time overhead. ET has been widely used in anomaly detection among decision tree algorithms due to its high efficiency and superficial characteristics. The main advantage of KNN is that it can be applied to various programming problems, such as quadratic programming. This allows the current optimal solution to be continuously renewed. Random forest, on the other hand, is the most representative algorithm used in ensemble learning techniques. It is typically more reliable and can achieve better results than individual decision trees. As a result, ET, KNN, and random forest were chosen to build the ensemble for multi-class intrusion detection.
# Condition Monitoring of Wind Turbine Blades Using Active and Passive Thermography
## 2 Experimental Procedure  
### Materials  
All samples used in this experiment came from a 50 m long wind turbine blade made of fiberglass composite obtained after it had been damaged in transit to a wind farm. The blade was never installed or operated. Figure 1a shows the 3 m long blade section with significant surface damage that was used for the passive thermography experiments. The laminate thickness in the damaged section was 14 mm. The chord length was approximately 1 m. The yellow/orange regions in Figure 1a are the exposed sandwich core on the rear section of the suction surface where there is very little laminate. Some patches of glue on the suction side resulted in different effusivity than the background and therefore generated spots with different brightnesses on the thermograms. The upper side of the blade section contained a crack which was visible to the naked eye.  
A \"defect plate\" with dimensions of 170 mm \(\times\) 195 mm \(\times\) 8 mm was cut from the laminate skin of another section of the blade closer to the tip where the laminate skin was thinner. Flat-bottomed holes with different diameters and depths were drilled from the rear to produce a range of known \"defects\". The holes had diameters ranging from 4 mm to 20 mm with depths between 0.5 mm and 3 mm. Figure 1c is a schematic of the plate that illustrates the geometry and pattern of the defects. No holes penetrated the outer surface of the plate which corresponded to the outer surface of a blade and all thermograms were of the outer surface. The defect plate was attached to the surface of the damaged blade section during passive thermography experimentation. It was also the only blade material tested with active thermography. The holes were used to evaluate the minimum defect that can be detected using passive and active thermography.
## 2 Antenna Array Design
### Radiating Element Design  
In this subsection, the antenna radiating element structure will be described. The radiating element, which is shown in Figure 4, is based on double-stacked circular patches, a 2.7 mm diameter driven patch (L1) and a 2.3 mm diameter parasitic patch (L3), which are surrounded by an artificial metallic wall formed by twenty vias (L1 to L3) whose diameter is 200 \(\upmu\)m. The stacked patches technique is used to enhance the impedance bandwidth, while cavity backing is employed for surface wave suppression.  
This element works at Ka-band frequencies, from 27.5 to 31 GHz, with a double circular polarization, switchable by means of electronic control circuitry. To achieve this, the driven patch is fed by electromagnetic coupling in two orthogonal points through both H-shaped slots. The aperture-coupled feeding technique has advantages such as no  
Figure 4: Three-dimensional diagram of the radiating element structure.  
physical contact between the feed and radiator, wider bandwidths, and better isolation between feeding points.  
Likewise, the input feeding network which excites each aperture is made up of a 50 \(\Omega\) ground coplanar waveguide (GCPW) trace, a quasi-coax vertical transition, and a stripline to the H-slot. It is especially important to use the coaxial-like structures to provide reliable and high-performance vertical transitions between layers L5 and L13, where eight vias allocated in a 500 \(\upmu\)m radius circle are used to surround the vertical transition to create a pseudocoaxial waveguide.
# Deep Fake Image Detection Based on Pairwise Learning
## 4 Experimental Results
### Fake Face Image Detection
#### 4.1.2 Experimental Settings  
In the training of the proposed CFFN and fake face detector, we set the learning rate to =\(1e-3\), and the total number of epochs to 15. The threshold value m of the contrastive loss was set to 0.5. Adam optimizer [25] was used for both the first- and second-step learning. The number of epochs of the first-step learning of the CFFN was set to 2, and the number of epochs of the classification learning of the classification sub-network was set to 13. The batch size was 88 for all the learning tasks. The parameters settings were as in [5; 9; 11].  
In the experiments, we used the conventional image forgery method based on the sensor pattern noise [8] for performance comparison. The Baseline-I method was the jointly learning method based on the contrastive loss and binary cross-entropy loss without two-step learning. In the Baseline-II method, instead of the CFFN structure, the DenseNet [19] with two-step learning of the contrastive and binary cross-entropy loss functions was adopted.  
We compared the performance regarding the precision \(P\) and recall \(R\), which were defined as:  
\[P=\frac{TP}{TP+FP} \tag{4}\] \[R=\frac{TP}{TP+FN}, \tag{5}\]  
where \(TP\) denoted the number of true positives, indicating that a real image was recognized as a real, \(FP\) denoted the number of false positives, denoting that a fake image was detected as a real image, and \(FN\) denoted the number of false negatives, showing that a real image was recognized as a fake one.
# A Survey of the Applications of Text Mining for the Food Domain
## 3 Text Mining Overview
### Information Extraction
Various neural network models can autonomously learn semantic features from text, enabling more intelligent and automated information-extraction processes [44; 45; 46]. In the field of NLP, information-extraction technology has consistently been a key focus in the domain of food. From the evolution of traditional machine learning to the advent of deep learning, this technology has not only driven advancements in information extraction, but has also spurred the evolution of related NLP techniques. Given the crucial applications of information extraction in extracting food-related information and monitoring safety issues, it holds significant value in the food domain. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Algorithms** & **Comments** & **Papers** \\ \hline \multirow{3}{*}{Rules-Based} & \multirow{3}{*}{Pattern Matching Algorithm, Regular Expressions, and so on.} & \begin{tabular}{c} Easily understood and adapted, \\ but difficult to adapt to complex \\ linguistic structures and \\ uncertainty of change. \\ \end{tabular} & \begin{tabular}{c} [PERSON] et al. (2012) [38], \\ [PERSON] et al. (2012) [39], \\ [PERSON] et al. (2014) [40] \\ \end{tabular} \\ \hline \multirow{3}{*}{Machine Learning} & \multirow{3}{*}{SVM, CRF, HMM, and so on.} & \begin{tabular}{c} Ability to learn models from large \\ amounts of labeled training data, \\ adaptable, but highly dependent \\ on the quality and diversity of \\ training data for \\ model performance. \\ \end{tabular} & \begin{tabular}{c} [PERSON] et al. (2012) [41], \\ [PERSON] et al. (2021) [42] \\ \end{tabular} \\ \hline \multirow{3}{*}{Deep Learning} & \multirow{3}{*}{CNN, RNN, LSTM, and so on.} & \
\begin{tabular}{c} Capacity of learning more complex \\ semantics and feature \\ representations, but requires \\ many computational resources \\ and labeled data, poor \\ model interpretability. \\ \end{tabular} &
\begin{tabular}{c} [PERSON] et al. (2021) [43], \\ [PERSON] et al. (2022) [44], \\ [PERSON] et al. (2022) [45], \\ [PERSON] et al. (2022) [46] \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: General methods of information extraction.
## 3 Results and Discussion  
### Immobilized Lactiaseibacillus rhamnosus OXLAL-1 Cultures  
At first, free and immobilized _L. rhamnosus_ OXLAL-1 cells on cereals (oat and wheat flakes) and fruits (apple and banana pieces) were produced in both wet and freeze-dried form, and, subsequently, viable _L. rhamnosus_ OXLAL-1 cell counts were determined (Figure 2). The levels of wet immobilized cultures on oat and wheat flakes ranged from 9.08 to 9.19 log CFU/g, while the corresponding levels of apple and banana pieces were > 8 log CFU/g. After freeze-drying, the levels of freeze-dried immobilized cells on oat and wheat flakes were >9 log CFU/g (dry cell mass) and >8 log CFU/g (wet cell mass), while the corresponding levels in the apple and banana pieces were >8 log CFU/g (dry cell mass) and >7 log CFU/g (wet cell mass). Notably, the daily minimum recommended cell concentration [26,27], in order to deliver beneficial health effects on the host, was achieved in all immobilized freeze-dried cell cultures on natural carriers.  
Freeze-drying led to a significant (\(p<0.05\)) reduction in cell viability when comparing the initial populations of wet free or immobilized _L. rhamnosus_ cells to the corresponding rehydrated freeze-dried cultures (wet cell mass); however, in all cases, high levels of immobilized cells (>7 log CFU/g) were determined. Despite this reduction, the freeze  
Figure 2: Levels (log CFU/g) of free or immobilized _L. rhamnosus_ OXLAL-1 cells on cereals and fruit pieces before and after freeze-drying. “Dry cell mass” refers to the concentration of cells after the freeze-drying process, while “wet cell mass” refers to the concentration of freeze-dried cells in their rehydrated state. \({}^{*}p<0.05\) compared to initial wet culture (non-freeze-dried) levels.  
drying process is considered necessary due to the long-term stability it offers, not only at the cellular level [28], but also for overall product safety, quality and convenience during handling and processing [29]. Similarly, high levels of dried immobilized lactic acid bacteria (LAB) on natural supports have been recorded in previous studies. In particular, [PERSON] et al. [30] studied the effect of cell immobilization and freeze-drying on three different LAB strains (_Lactiplantiobacillus plantarum_ B282, _Pediococcus acidilactic_ SK and _Lactiplantiobacillus plantarum_ F4). Freeze-dried immobilized LAB cells on zea (_Zea mays_) flakes, pistachio nuts and raisins were recorded at >7 log CFU/g and remained at >6 log CFU/g when stored at room temperature for 40 days. Additionally, [PERSON] et al. [17] also observed high levels of freeze-dried immobilized _L. rhamns_ cells on apple pieces, with levels exceeding 9 log CFU/g.  
Along with the microbiological analysis, cell immobilization was verified by scanning electron microscopy (Figure 3). Interestingly, _L. rhamns_ OLXAL-1 cells exhibited adhesion to the food matrices and also formed aggregates, previously described as \"cell matting\" [31].
## 5 Conclusions
### Limitations and Future Recommendations  
Despite these strengths, our study has several limitations:  
* We modeled manufacturing dynamics in SimPy, which abstracted away low-level physics (e.g., robot kinematics, network jitter) and assumed perfect sensor telemetry.
* All robots shared identical dynamics and capacity distributions. Real factories often deploy heterogeneous fleets with different speeds, charging profiles, and failure modes.
* Our scenario considered one production cell network. Large plants feature multiple cells with cross-cell resource sharing and traffic conflicts not captured here.
* In this work, we held sensor noise (\(\sigma=0.03\)) and one-way communication latency (50 ms) fixed to isolate the impact of our DRLCC framework. In practice, both quan  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metrics** & **PCP** & **DRL** & **DRL\_I** & **PI** & **EDF** & **EDF\_SRP** \\ \hline Total completion time & 24.27\% & 1.51\% & 6.21\% & 9.60\% & 80.75\% & 78.45\% \\ \hline High-priority delay & 4.86\% & 2.18\% & 5.22\% & 6.65\% & 60.25\% & 18.83\% \\ \hline Priority inversion count & 0 & 47.36\% & 74.86\% & 84.87\% & 79.51\% & 63.81\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Percentage improvement of the proposed DRLCC framework over six baseline scheduling algorithms—PCP, DRL, DRL_I (DRL + Inheritance), Priority Inheritance (PI), EDF, and EDF_SRP—measured on three key metrics: total completion time, high-priority delay, and priority-inversion count.  
tities can vary substantially higher. \(\sigma\) would introduce noisier state observations (slowing convergence and increasing reward variance), and greater latency would risk decisions acting on stale state (increasing idle times, conflicts, and inversion penalties). A full sensitivity sweep over \(\sigma\) and latency is left to future work to delineate the robust envelope of DRLCC.  
Our simulation uses idealized AMR models (identical robots, perfect sensing, zero communication loss), which may not fully capture real-world variability. While our SimPy experiments demonstrate DRLCC's potential under controlled conditions, translating these results to real AMR fleets involves nontrivial system integration and resource investment. We therefore identify physical testbed deployment together with corresponding hardware-in-the-loop evaluations as an important future research direction.  
Future work may focus on employing more advanced and realistic simulation environments to model generic and more complex manufacturing scenarios, potentially involving varying production layouts, dynamic AMR behavior, or disruptions. Additionally, further research can explore integrating federated learning techniques to support distributed training across multiple digital twin agents and extending the system to support heterogeneous AMRs and multi-agent coordination at larger production scales.
## 4 Results  
The fire risk maps for September 2019 and September 2021, with spatial resolution of 25 m \(\times\) 25 m, were calculated using Equation (3), and they are presented in Figure 12, along with the relative fire risk scale value. The risk class for most areas was unchanged, which is expected. The parameters used in our model remained relatively invariant for long periods of time. Therefore our risk map represents the baseline fire risk in the area [24].  
Figure 12: Fire risk in the National Park Forest of Dadia-Lefkimi-Soufli (**a**) for September 2019 and (**b**) for September 2021.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**n** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline RI & 0 & 0 & 0.58 & 0.9 & 1.12 & 1.24 & 1.32 \\ \hline \end{tabular}
\end{table}
Table 9: Values of the random consistency index (RI).  
The average fire risk in the National Park Forest of Dadia-Lefkimi-Soufli was high for both years. Specifically, 50% of the total 42,481 ha of our study area for 2019 was considered to be at high fire risk. Similarly, 48% of the total area was considered to be at high fire risk for 2021. Moreover, for both years, 5% of the National Park was at extremely high risk of fire.  
The extreme fire risk areas form clusters. The most noticeable ones are located near the center of the National Park, spreading along the line that connects the points with coordinates 41\({}^{\circ}\)10\({}^{\prime}\) N, 26\({}^{\circ}\)7\({}^{\prime}\) E and 41\({}^{\circ}\)6\({}^{\prime}\) N, 26\({}^{\circ}\)16\({}^{\prime}\) E. Two additional extremely high risk areas were detected. The first one is located in the northwest of the National Park, near the settlement of Giannouli, and the second one is located in the southeast of the park, near the settlement of Lefkimi. The overall distribution of fire risk for September 2019 and September 2021 in the National Park is presented in Table 10.  
Within the National Park Forest of Dadia-Lefkimi-Soufli, two major fire incidents took place between September 2019 and September 2021. The first one was recorded in October 2020, and the second one in July 2021 [46]. In order to validate our results and measure the impact of change in the land cover on the fire risk, due to the fires, each fire incident was examined separately as follows.
## 3 Results
### Study Characteristics
#### 3.2.2 Outcome Measures  
Two-dimensional cephalometry was used to assess craniocortical posture and mandible position in seven studies [12; 24; 25; 26; 27; 28; 29]. Regarding analyzed parameters, craniocortical posture included OPT-SN and CVT-SN angles. As for mandible position evaluation, SNB angle was used in five [12; 26; 27; 28; 29], and SN-RL angle in two [24; 25] studies. Mean values for variables were given in seven papers [12; 24; 25; 26; 27; 28; 29], while the correlation coefficient was given in two [24; 28]. No methods were required to prepare the data for presentation and synthesis. The characteristics of the studies included in this systematic review are shown in Tables 3 and 4 [1; 30].  
\begin{table}
\begin{tabular}{c c} \hline
**Reference Line/Point** & **Description** \\ \hline SN & \begin{tabular}{c} Nasion-sella line. Line through Nasion and Sella points \\ \end{tabular} \\ \hline OPT & \begin{tabular}{c} Odontoid process tangent. Posterior tangent to the odontoid process through the most \\ posterior and inferior point on the corpus of the second cervical vertebra \\ \end{tabular} \\ \hline CVT & \begin{tabular}{c} Cervical tangent. Posterior tangent to the most posterior and inferior point on the \\ corpus of the second and fourth cervical vertebra \\ \end{tabular} \\ \hline RL & \
\begin{tabular}{c} Ramus line. Tangent to the posterior border of the mandibular ramus \\ \end{tabular} \\ \hline B &
\begin{tabular}{c} The deepest point on the anterior contour of the mandible \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 3: Parameters used to assess craniocortical posture and mandible position.
# Deformation Law and Control Limit of CRTSIII Slab Track under Subgrade Frost Heave
## 5 Discussion on the Limits of the Subgrade's Frost Heave Control
### Limit of Frost Have Displacement Control of the Subgrade  
The tolerated deviation management value of subgrade frost heave, the critical values of seams between the self-compacting concrete and base plate, and the critical values of seams between the base plate and subgrade are summarized and analyzed. Finally, the control limit of subgrade frost heave based on static results is obtained, as shown in Figure 17. The critical values of seams between the base plate and subgrade are smaller than those between the self-compacting concrete and base plate under different frost heaves. Additionally, the difference between the control limit increases in response to the increase in the frost heave wavelength. It can be concluded that the critical values of the seams between the self-compacting concrete and base plate are taken as the main control index of the subgrade frost heave.  
When the subgrade frost heave amplitude reaches the tolerated deviation management value of the regular maintenance standard (Grade I), the corresponding subgrade frost heave wavelength limit is 12 m. This means that as long as the frost heave wavelength exceeds 12 m, no seams will occur in the Grade I standard. The comfortable limit (Grade II) corresponds to a wavelength limit of 16 m. The temporary repair standard (Grade III) corresponds to a wavelength limit of 19 m, and the speed limit standard (Grade IV) corresponds to a wavelength limit of 23 m. In addition, when the frost heave wavelength is less than 12 m, and the slab track irregularity standard of the high-speed railway (i.e., the subgrade frost heave tolerated deviation management value) is satisfied, the phenomenon of seam heights greater than 1.0 mm between the self-compacting concrete and the base plates may still occur. Therefore, it is necessary to pay attention to the state of the track structure when small wavelength frost heave occurs in the field.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Project** & \begin{tabular}{c} **Regular** \\ **Maintenance** \\ \end{tabular} & \begin{tabular}{c} **Comfortable** \\ **Level** \\ \end{tabular} & \
\begin{tabular}{c} **Temporary** \\ **Repair** \\ \end{tabular} &
\begin{tabular}{c} **Speed Limit** \\ **(200 km/h)** \\ \end{tabular} \\ \hline Grade & 1 & II & III & IV \\ Admissible deviation management value of & 4 & 6 & 8 & 10 \\ rail surface height(mm) & 3.6 & 5.5 & 7.3 & 9.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Admissible deviation management value of frost heave amplitudes.  
Figure 16: The peak and critical value of seams under different frost heave conditions. (**a**) Peak value between the self-compacting concrete and base plate; (**b**) peak value between the base plate and the subgrade’s surface layer; (**c**) critical value between the self-compacting concrete and base plate; (**d**) critical value between the base plate and the subgrade’s surface layer.
## 1 Introduction
A great deal of research has been carried out into the use of washing fines as a substitute for sand. The results of this research indicate that washing fines can be used as a partial substitute for fine aggregates (sand) in concrete, sometimes even improving certain properties of the material [26; 27; 28]. However, studies focusing specifically on their use as a partial cement substitute remain limited [29]. The main objective of this study is to assess the feasibility and potential benefits of using wash fines as SCMs in mortars. A series of extensive tests were carried out to examine the impact of incorporating these fines on various key mortar properties, such as compressive strength, flexural strength, porosity, bulk density, water absorption, shrinkage and fire resistance. These rigorous tests make it possible to determine the extent to which wash fines can improve or modify mortar characteristics, and thus to assess their viability as promising SCMs. The results of this research provide a detailed overview and in-depth understanding of the valorization of wash fines in cement-based construction materials. They demonstrate how more sustainable waste management can be seamlessly integrated into concrete production, contributing to a greener, more efficient construction industry. This study paves the way for innovative industrial practices that valorize waste while meeting the growing need for sustainability and superior performance in the construction sector. It represents an important step towards the transition to a more environmentally friendly circular economy in the concrete industry.