esa-sceva/satcom-chunk-collection huggingface dataset:
# Short Text Classification with Tolerance-Based Soft Computing Method
## 1 Introduction
We have also demonstrated that with transformer-based vectors, our proposed TSC outperforms five well-known machine learning algorithms on four datasets, and it is comparable with all other datasets based on the weighted F1, Precision and Recall scores. The highest AUC-ROC score was obtained in two datasets and comparable in six other datasets. The highest ROC-PRC score was obtained in one dataset and comparable in four other datasets. Additionally, significant differences were observed in most comparisons when examining the statistical difference between the weighted F1-score of TSC and other classifiers using a Wilcoxon signed-ranks test. The proposed sentiment/text classification pipeline is given in Figure 1 where feature vectors are generated using two pre-trained deep learning models BERT [26] and SBERT [27] shown in step 2. In step 3, a cosine distance matrix using the training set is created. This distance matrix is used to create tolerance classes in step 4. In step 5, a mean vector for each of the tolerance classes is created. This vector represents a _prototype class_. In step 6, each of these prototype classes are then labeled using the majority class of their respective tolerance class members. In step 7, for each test example (in the testing set), the cosine distance is computed from every prototype class. In step 8, the test example that has the smallest distance value to the prototype class is selected. In step 9, the label of this prototype class is then assigned to the test example. In the final step, the predicted label is then checked with original label. This paper is organized as follows. In Section 2, we introduce formal definitions for text-based tolerance relation and an illustration of sample tolerance classes. In Section 3, we present the datasets used in this work as well as the proposed supervised TCS algorithm. In Section 4, we discuss our findings in terms of the weighted F1-score measure using both TF-IDF and transformer-based vectors on all the ten datasets followed by the concluding remarks in Section 5.
## 3 Results and Discussion
### Damage Prediction Based on Impact Parameters
However, when the steel surface was covered with rubber, drop heights could be increased to 25 cm before damage occurred. Statistical models linking the readings of the PRT-200 electronic device, biological conditions, and seasonality with the discoloration of potato tuber tissues were the result of research by [PERSON] et al. [4]. These models are a source of information on the relationship between the energy recorded by the device and the actual tissue damage, taking into account the sensitivity of the product. Due to the limited number of factors in the models and the not very high determination coefficients, the areas of damage prediction are quite broad, which limits the statistical power of these models. Taking into account the formulated mathematical model between the potato tuber damage index and the peak acceleration and changes in the impact velocity of the IRD 400 device, this electronic device can be used to assess the work of the combine harvester or potato processing line working units under the conditions of bench or field tests. This will help to minimize quantitative and qualitative losses of tubers during harvesting and preparation for further processing. Concerning potato harvesting and processing conditions, the model may be less precise when stones appear in the harvested material. Further research should be conducted in actual field conditions to fully evaluate the model in a broader scope, considering dynamic interactions. Applying the developed empirical mathematical model of the tuber damage indicator in the context of Agriculture 4.0 will require further work on measuring the impact parameters in real time. Using the damage indicator model, automatic harvesters equipped with sensors and actuators can then dynamically adjust working speeds and control mechanisms based on the predicted impact parameters. This will reduce tuber damage caused by working units. By adapting appropriate control systems in processing lines, the model can be used to optimize the settings of the sorting line (e.g., conveyor speed, impact force) depending on the types of soil and the potato variety. Further research should be conducted to continuously monitor impact parameters and provide immediate feedback to operators or automated systems. This will enable ongoing adjustments to maintenance processes, minimizing damage as it occurs. A continuous flow of measurement data will improve the accuracy and reliability of the real-time damage index model.
# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 10 Conclusions  
In this paper, we address existing gaps in misbehavior detection by introducing a scheme that concentrates on cluster segmentation and hybrid trust computation specifically designed for dynamic VANETs. Our primary concerns include appropriate cluster-based detection scenarios, and reliable trust computing to enhance node-centric detection. To achieve this goal, this study utilizes a hypergraph model to integrate association relationship weights into mixed trust value calculations within vehicular networks. The impact of these weights and vehicle behaviors on trust assessments is examined through a three-layer'vehicle-vehicle cluster-RSU' architecture. Trust values are calculated at various network layers, which are then used for collaborative misbehavior vehicle detection. Through simulation experiments, we demonstrate the method's effectiveness in scenarios with high-density traffic and varied misbehavior vehicles, confirming its suitability for fostering multi-vehicle collaboration in vehicular networks.  
Conceptualization--X.X. and X.F.; Funding acquisition--W.Z. and L.Y.; Methodology--X.X. and W.Z.; Modeling--L.Y., G.Y. and W.Y.; Software and Test--G.Y. and L.J.; Verification and Validation of related Model--X.F. and W.Z.; Writing--original draft, X.X.; Writing--review and editing, L.J. and W.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets used during the current study are available from the corresponding author on reasonable request. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
## 4 Numerical Simulation
### Controller Simulation  
For our proposed adaptive neural network control law, we chose 16 RBFNN nodes, the centers for \(\Delta_{i}(Z)\) were \([-1,1]^{4}\), and the RBFNN weights were initialized from zero. The gain matrix was set as \(\mathrm{K}=diag[20,20]\). The output constraints were designed as \(\underline{x}_{c}=\overline{x}_{c}=0.3\). The position tracking performance is shown in Figures 3 and 4. From Figures 3 and 4, it can be seen that the output of the exoskeleton can follow the expected trajectory, and the tracking error can converge to a small area near zero. The RMSEs (Root Mean Square Errors) of the position tracking errors of joint 1 and joint 2 are 0.0597 (rad) and 0.0373 (rad), respectively. Moreover, the output constraint rule is not violated. Figure 5 shows the velocity tracking performance of the controller. The RMSEs of the velocity tracking errors of joint 1 and joint 2 are 0.0792 (rad/s) and 0.0353 (rad/s), respectively. The robustness of the controller against external disturbances is tested and depicted in Figures 6 and 7. These figures demonstrate that the proposed controller exhibits strong robustness to external  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(m_{1}\) & Mass of link 1 & 10 kg \\ \(m_{2}\) & Mass of link 2 & 5 kg \\ \(l_{1}\) & Length of link 1 & 0.5 m \\ \(l_{2}\) & Length of link 2 & 0.4 m \\ \(l_{1}\) & Inertia of link 1 & 1.2 m\({}^{2}\cdot\) kg \\ \(l_{2}\) & Inertia of link 2 & 1 m\({}^{2}\cdot\) kg \\ \hline \hline \end{tabular}
\end{table}
Table 1: Exoskeleton system parameters.  
disturbances. Although some fluctuations appear when the disturbances occur, the actual tracking curves consistently remain within the constraint boundaries. Figures 8 and 9 indicate that the output torque and human-exoskeleton interaction torque of the controller are regularly bounded.  
Figure 5: Velocity tracking of exoskeleton.  
Figure 6: Position tracking of exoskeleton with disturbance.  
Figure 8: Controller output torque.  
Figure 7: Position tracking error with disturbance.
# Digital Shearography for NDT: Phase Measurement Technique and Recent Developments
## 2 Brief Review of Digital Shearography
### Interpretation of Fringes in Digital Snarography  
As can be seen from the previous section, the relative phase change of snarography is directly related to the deformation derivative. This section describes an interpretation of digital snarographic results from another perspective. As it is well known how to interpret holography results [7], a comparison between holographic and snarographic measurements are presented. Figure 2 shows a comparison of the results of holographic and snarographic NDT. Assuming a delamination exists in a sample, the surface of the sample is slightly curved by thermal or vacuum loading. As shown, holography discovers the flaw by detecting the deformation anomalies caused by the flaw, and the anomaly area exhibits a circular fringe pattern. Shearography detects it by looking for strain anomalies, and the anomaly area presents a butterfly pattern.  
In order to demonstrate the concept above, a laminated plate with three debonds is simultaneously detected using holography and snarography. The measurement results are shown in Figure 3. The results of holography, which appears as circular fringes, are shown in Figure 3a, while Figure 3b shows the snarographic measurement results, which appear as butterfly patterns. It should be noted that the snarographic measurement results were obtained using the phase-shift technique, which will be introduced in the next section.  
Figure 2: Comparison between the NDT results of holography and snarography.
# Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network
## 3 Challenges and Problem Statement
### Faults in WSNs
#### 3.1.2 Offset Fault  
Sensing circuits with sensors may have errors in them. They are found by the calibration process. This error is rectified by addition/subtraction with a displacement value in the real sensed data. The offset fault is represented as below:  
\[x^{J}=\alpha+x+\eta \tag{3}\]  
where \(\alpha\)--the offset value to be added in the actual reading.
# Numerical Simulation of Rock Mass Structure Effect on Tunnel Smooth Blasting Quality: A Case Study
## 4 Conclusions  
(1) With Zigaojian tunnel as background, the rock mass structure effect of smooth blasting quality in tunnels was analyzed. Onsite information collection, field monitoring, rock mass structure reconstruction in software, numerical verification and orthogonal numerical tests were performed to evaluate the rock mass structure effect.  
(2) The rock mass structure effect influenced the smooth blasting quality of a tunnel obviously.  
(3) The tunnel smooth blasting quality decreased with the decrease in rock mass integrity. The influencing factors of rock mass structure effect depended on the rock mass structure types.  
(4) The peripheral hole spacing was the most important factor affecting the smooth blasting quality of massive integrity structure rock mass and cataclastic structure rock mass. Strictly controlling the peripheral hole spacing can reduce the overbreak rate of smooth blasting.  
(5) The angle between structural planes was the most important factor affecting the smooth blasting quality of massive structure rock mass, and the average spacing of structural planes was the main factor affecting the smooth blasting quality of layered structure rock mass, which was much greater than the peripheral hole spacing.  
Figure 14: Overbreak or underbreak rate of smooth blasting under different levels of each factor in the cataclastic structure rock mass.  
(6) The rock mass structure effect of smooth blasting quality in the tunnel can provide a reference for the rock mass structure effect of other similar cases.  
Conceptualization, J.W.; funding acquisition, H.W.; investigation, X.L., H.L., and Y.S.; software, J.L.; writing - original draft, A.C. and J.W.; validation, Y.L. and F.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shanghai Municipal Science and Technology Project (18 DZ1201301; 19 DZ1200900); Xiamen Road and Bridge Group (XM2017-TZ0151; XM2017-TZ0117); the project of Key Laboratory of Impact and Safety Engineering (Ningbo University), Ministry of Education (CJ202101); Shanghai Municipal Science and Technology Major Project (2021 SHZDZX0100) and the Fundamental Research Funds for the Central Universities; and the Key Laboratory of Land Subsidence Monitoring and Prevention, Ministry of Natural Resources of the People's Republic of China (No. KLLSMP202101), Suzhou Rail Transit Line 1 Co., Ltd., China Railway 15 Bureau Group Co., Ltd.  
The authors declare no conflict of interest.
# MALDI-TOF Mass Spectrometry Applications for Food Fraud Detection
## 2 MALDI Applications
### Fruits and Vegetables
[107] in a differential proteomic study on wheat grain, grown under normal CO\({}_{2}\) concentration and Free Air CO\({}_{2}\) Enrichment, respectively. It was found that a decrease of 9% in the protein content, in particular glutenin high molecular weight subunits associated with lower flour rheological properties and bread quality, occurred at higher CO\({}_{2}\) concentrations. Since soybean is a known source of allergens that can also be hidden in other food commodities, a MALDI-TOF/TOF study was developed [108] to identify potential markers of its presence. Soybean protein extracts were digested with trypsin, separated using an LC system and eventually subjected to MS/MS analysis. Peptides arising from G1 glycinin and \(\beta\)-conglycinin were identified and proposed as potential markers for the detection of soybean protein traces in processed foods. Furthermore, these peptides were found to be stable when subjected to simulated food processing reactions, such as denaturation, Maillard reaction, and oxidation. Rapid, simple, and reliable MALDI-TOF methods has been also proposed for cultivar characterization of hazelnut kernels through proteomic analysis [109], for geographical origin discrimination (coupled to multivariate analysis) of fermented salted vegetables comparing the relevant mass fingerprints [110] and to understand the effect of organic and inorganic crop nonwithstanding on the nutritional quality of yardlong bean through two-dimensional proteome profiling [111]. MALDI-TOF/TOF was used by [PERSON] at al. [112] for the untargeted analysis of dark chocolate from cocoa beans of different geographical areas to characterize the relevant metabolites (including sucrose, choline, sphingolipids, phospholipids, peptide material, and polyphenols) based on molecular weights and fragmentation patterns.
## 3 Results and Discussion
### Identification of Thermoduric Indigenous Milk LAB  
Twenty-one presumptive LAB (Gram positive and catalase negative) isolated from pasteurized milk were purified and analysed by RAPD-PCR in order to perform their strain typing. The last analysis, commonly used to discriminate LAB strains isolated from food matrices [46], showed the presence of four different strains (Figure 3).  
These strains were identified at the species level by sequencing of the 16S rRNA gene, indicating that the thermoduric indigenous milk LAB included species within the genera _Enterococcus_ and _Lactobacillus_. The last genus included two strains belonging to the species _Lacticaseibacillus casei_ (PBM19) and _Lacticaseibacillus paracasei_ (PBM7) (named _Lactobacillus casei_ and _Lactobacillus paracasei_, respectively, before reclassification of [PERSON] et al. [47]). Regarding the _Enterococcus_ genus, one strain was identified as _Enterococcus faecalis_ (PBM1), while the strain PBM16 could not be identified at the species level due to the different results of the BLAST and EzTaxon searches. This strain was further analyzed by a species-specific multiplex PCR assay and classified as _Enterococcus faccium_. All the species identified are components of the nonstarter LAB (NSLAB) community implicated in cheese maturation [39], commonly associated with raw milk [48] and cheeses [44; 49]. However, their presence in pasteurized milk is mainly due to postpasteurization contamination [50] or to their ability to survive the pasteurization process [51; 52].
## 3 Results and Discussion
### Analysis of the pH Changes  
Figure 6 shows the change in pH of hFOB 1.19 cells after exposure with different concentrations (0, 1, 5, 10, and 50 mM) of MgO NPs. Results show that with the increasing concentrations of MgO NP exposures in hFOB 1.19 cells, the pH was more alkaline (mean pH values: 1 mM = 7.83, 5 mM = 8.13, 10 mM = 8.33, 50 mM = 8.83) when compared to the control group (0 mM = 7.53).  
Figure 4: Real-time electric cell-substrate impedance sensing (ECIS) impedance results measuring cytotoxicity in hFOB 1.19 cells in response to different concentrations of MgO NPs.  
Figure 5: Real-time ECIS impedance results measuring cytotoxicity in hFOB 1.19 cells in response to different concentrations of Mg(OH)\({}_{2}\) NPs.  
Figure 7 shows that with the increasing concentrations of Mg(OH)\({}_{2}\) NPs exposure in hFOB 1.19 cells, the pH was more alkaline (mean pH values: 1 mM = 8.23, 5 mM = 8.33, 10 mM = 8.57, 50 mM = 9.07) when compared to the control group (0 mM = 7.57).  
Figure 6: Analysis of the change in pH in hFOB 1.19 cells in response to the different concentrations of MgO NPs. Data are obtained as means \(\pm\) SEM, \(n=3\); \({}^{***}\)\(p\)-value \(\leq\) 0.0001 when compared to the control group.  
Figure 7: Analysis of the change in pH in hFOB 1.19 cells in response to the different concentrations of Mg(OH)\({}_{2}\) NPs. Data are obtained as means \(\pm\) SEM, \(n=3\); \({}^{***}\)\(p\)-value \(\leq\) 0.0001 when compared to the control group.
## 2 Experimental Program
### Material Properties
#### 2.2.2 GFRP Bars  
The GFRP bar for both 13M and 15M is Grade III type with a minimum tensile strength of 1000 MPa, modulus of elasticity of 60 GPa, ultimate tensile strain of 1.67%, and nominal cross-sectional area of 199 mm\({}^{2}\) per CSA-S807:18 Standard [67]. The use of headed-end GFRP bars was proposed in this research to allow for anchorage in concrete at a lower cost than the bend bars. The head of the 15M bar is 100 mm long. It begins with a wide disk of 38 mm diameter that transfers a portion of the load from the bar into the concrete. Beyond  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Specimen**} & \multicolumn{2}{c}{**Average Compressive Strength**} & \multirow{2}{*}{**Characteristic Value (MPa)**} \\ \cline{2-4}  & **Deck Slab** & & **Barrier Wall** & **Deck Slab** & **Barrier Wall** \\ \hline B-1 & & 46.65 & & 37.12 \\ \hline B-2 & & 52.46 & & 43.27 \\ \hline B-3 & & 46.39 & & 39.40 \\ \hline B-4 & & 51.80 & & 43.57 \\ \hline B-5 & 47.59 & 47.91 & 37.91 & 40.35 \\ \hline B-6 & 50.47 & 46.34 & 39.05 & 38.07 \\ \hline \end{tabular}
\end{table}
Table 1: Concrete compressive strength at the time of testing for all specimens.  
this disk, the head tapers in steps to the outer diameter of the bar as shown in Figure 5 to transfer load to the concrete by bearing. This geometry ensures optimal anchorage forces and minimal transverse splitting action in the vicinity of the head [68].  
Compared to traditional anchorage methods such as 180\({}^{\circ}\) hooks or bent GFRP bars, headed-end GFRP bars offer notable advantages in constructability and performance. Bent GFRP bars are susceptible to strength reduction at the curved regions due to fiber misalignment, and their fabrication requires off-site forming, increasing logistical complexity. In contrast, headed-end bars provide reliable anchorage through mechanical bearing, maintain fiber continuity, and simplify installation by reducing reinforcement congestion. These benefits make them particularly suitable for barrier-deck junctions and retrofit applications, where space constraints and anchorage reliability are critical. The findings of this study reinforce the structural and practical value of headed-end GFRP bars as a preferred anchorage solution.
## 3 Results  
### Model Training  
The finite-difference time-domain (FDTD) algorithm is used to simulate different underground targets, generating GPR profiles that serve as training datasets for network training. For synthetic datasets, the ricker waveform with a center frequency of 400 MHz is employed to simulate both random medium models and homogeneous mediums, each measuring 5.0 m \(\times\) 1.1 m. The size of our space grid was set to 0.005 m, and a total of 250 transmitting and receiving antennas were utilized to ensure adequate spatial resolution and coverage for our modeling process. The relative permittivity of the background is 9, and some small pores with a relative permittivity of 1 are distributed randomly in the random medium models. The model contains pipe anomalies of two materials: metal pipes and non-metallic pipes. The large pipe diameter is 0.2 m, the small pipe diameter is 0.15 m, and the pipe wall is 0.02 m. By varying the burial depth of the five pipes at the pipe position, a total of 500 datasets of simulated GPR pipe profiles were obtained under diverse conditions in two distinct media environments.  
Due to the interference from background random media and the difference in reflected energy from metal pipes and non-metallic pipes, it is difficult to obtain a satisfactory ROI-binarized hyperbola region. To achieve this, the forward profile under the same anomaly model is utilized to extract the ROI-binarized hyperbola region. Subsequently, a set of data pairs is formed between the profiles obtained under the random medium and those under the homogeneous medium for the network model. Part of the training data are shown in Figure 3.  
Figure 3: The training data: the random medium results (**a–d**), and the ground truth of ROI-binarized hyperbolic region from homogeneous medium (**e–h**).  
In this paper, the hyperparameters are set as follows: the learning rate \(\eta=0.001\) and momentum \(\gamma=0.9\). Additionally, the number of epochs is 500 times, and the batch size is 50. Figure 4 depicts the obtained loss function. It can be seen that the network has a quick convergence speed that eventually tends to be steady.