esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Principles of MDF  
The scientific origin of modern MDF can be traced to a classic paper written by [PERSON] from Russia in the early 1990s, and a special deformation method with extensive strain for preparing bulk ultrafine-grained metals was proposed [26]. Subsequently, MDF has attracted more and more attention.  
The principles of modern MDF are shown in Figure 1. The whole process is that the deformation force was sequentially applied to X, Y and Z planes to achieve repeated compression. Compared with other SPD technologies, MDF can provide the rheological stress behaviors of deformation materials, and the relationship between rheological stress and microstructure evolution can be obtained [25].  
Figure 1: Schematic diagram of MDF [26].  
When a bulk sample is processed by MDF, the true strain, \(\varepsilon\), for each MDF cycle is given by the following equation [24].  
\[\varepsilon=\ln\frac{H_{X0}}{H_{X}}+\ln\frac{H_{Y0}}{H_{Y}}+\ln\frac{H_{Z0}}{H_{ Z}} \tag{1}\]  
where \(H_{X0}\), \(H_{Y0}\) and \(H_{Z0}\) are the initial height of the bulk sample. \(H_{X}\), \(H_{Y}\) and \(H_{Z}\) represent the height of the bulk sample after MDF in a specific direction.  
Thus far, MDF has been regarded not only as an SPD technology in metallurgy but also as an important scientific method in various fields of science and engineering. Compared with traditional thermomechanical processing technologies, such as forging, extrusion and rolling, and SPD methods, such as ECAP and HPT, MDF has the following characteristics: (1) The whole process is simple with low cost in equipment and can be expected to achieve industrial production. (2) The multi-directional variation of loading force makes the rheological behavior more complex, thereby having a significant impact on microstructure evolution. (3) The variation of loading force can eliminate the \"deformation dead zones\" and fabricate samples with homogeneous microstructure and uniform mechanical properties [27; 28; 29; 30].
## 3 Results and Discussion
### Physicochemical Properties
After cooking, all samples exhibited an increase in L* values, with the control sample showing the highest increase. The a* value decreased in the control, SS1, and SS2 groups whereas increased in the SS3 group after cooking. The b* values increased in all samples after cooking, with the highest increase observed in the SS3 group, followed by the SS2, SS1, and control groups. The main conclusion about the color change is that the addition of CSS at increasing rates darkens the color of the meathalls and reduces redness and yellowness. This change may be undesirable in chicken meathalls, but when the results of the color values in the sensory analysis were examined, it was observed that the difference between the 2% CSS added sample and the control sample was not statistically significant (\(p>0.05\)). This result shows that meathalls with 2% CSS added are acceptable to the consumer in terms of color. Figure 5: Changes in color properties of chicken patties during the production stages (Control: 12% fat, SS1: 10% fat + 2% silverskin, SS2: 8% fat + 4% silverskin, SS3: 6% fat + 6% silverskin). a–d: Means marked with different letters in different formulations at the same production stage are statistically different from each other (\(p<0.05\)). (a) L*: represents the lightness values, (b) a*: represents the redness/greenness values, and (c) b*: represents the yellowness/blueness values.
## 1 Introduction
### Research Questions  
RQ1: What is the role of feature extraction techniques in the effectiveness of OLD models?  
Feature extraction techniques significantly influence the performance of OLD models. The choice of features, such as TF-IDF (Term Frequency-Inverse Document Frequency), Word2 Vec, FKRA (Flesch-Kincaid Readability Assessment)/FRE (Flesch Reading Ease), and VADER sentiment scores, can impact the model's ability to accurately identify offensive content.  
RQ2: How does the dimensionality of GloVe word embeddings affect the performance of DL models in OLD?  
The dimensionality of GloVe embeddings, whether it is 50d, 100d, 200d, or 300d, plays a critical role in the effectiveness of DL models.  
RQ3: How does the combination of various transformer models such as encoders and preprocessors with neural networks, MLPs (Multi-Layer Perceptrons), and 1D CNNs affect the performance of OLD classification?This approach combines the strengths of pre-trained models, local pattern detection, and feature aggregation to effectively identify OL. It leverages the capabilities of pre-trained models to understand the context of text and employs convolutional layers to capture relevant patterns, making it a robust approach for the detection of OL.  
We address several critical challenges in offensive language detection:  
1. Problem: Offensive language is highly context-dependent, making it difficult for existing models to capture nuances and make accurate predictions.  
Our solution: We propose a multi-architecture approach combining feature concatenation (TF-IDF, Word2 Vec, FKRA/FRE, sentiments), advanced transformer models (BERT, ALBERT, ELECTRA), and neural network architectures.
2. Problem: The huge amount of social media data generated through smartphones, laptops, and tablets make processing, analyzing, and classifying OL from this unstructured data inefficient.  
Our solution: Efficient feature extraction techniques, scalable architecture design, and optimized processing pipeline are proposed.
3. Problem: Limited accuracy in existing approaches for distinguishing between hate speech, offensive language, and neutral content.  
Our solution: Novel feature fusion approach, context-aware transformer models, and multiple classification architectures are used.
## 2 Modeling Method  
### Base Algorithm  
Our modeling of \(f_{0}\)F\({}_{2}\) is based on the EOF and combines harmonic function theory and two-fold regression analysis. This EOF analysis method was proposed by [PERSON] [35] and first introduced into empirical modeling of the ionospheric parameters by [PERSON] [36]. It involves a mathematical procedure that transforms a dataset into a number of uncorrelated components called principal components, any two components of which are orthogonal to each other [23]. The EOF analysis method not only considerably reduces the number of modeling parameters but also provides significant rapid convergence with high calculation accuracy. Compared with other methods of expansion, the EOF model provides optimum convergence in the representation of ionospheric characteristics [36; 37]. The underlying physical meaning is that the variation of the physical field variable is mainly controlled by some independent processes that can be separated [34]. Therefore, we separated \(f_{0}\)F\({}_{2}\) into temporal and spatial variables. According to the above-mentioned theory and analysis, the \(f_{0}\)F\({}_{2}\) characteristics can be retained from a numerical mapping technique based on the orthogonal temporal and spatial functions. Specifically, the \(f_{0}\)F\({}_{2}\) numerical map function is simply modeled as an orthogonal harmonic function series:  
\[f_{0}F_{2}(\lambda,\varphi,F_{12},R_{12},m,UT)=\sum_{n=1}^{N}w_{n}(\lambda, \varphi)\cdot\hat{f_{0}}F_{2}(F_{12},R_{12},m,UT), \tag{1}\]  
where \(\lambda\) is the geographic latitude, \(\varphi\) is the geographic longitude, \(F_{12}\) is the twelve-month running mean value of the monthly flux of solar radio waves at 10.7 cm, \(R_{12}\) is the twelve-month running mean value of the monthly sunspot numbers, \(m\) is the month, \(UT\) is the given universal time, \(N\) is the maximum number of measured stations used for interpolation, \(\hat{f_{0}}F_{2}(F_{12},R_{12},m,UT)\) is the temporal reconstruction function and is determined in Section 3.1, \(W=[w_{1}\), \(w_{2}\), \(\ldots\), \(w_{N}]\) are the weights calculated by the spatial reconstruction equations in Section 3.2.  
The flow chart of the model reconstruction and verification is shown in Figure 1. In the following sections, the new modeling approach and the comparison between measured value and different models will be described and analyzed.
## 7 Discussion
### Collaborations with 6G Key-Enabler Technologies  
#### 7.2.1 Integrated Sensing and Communication (ISAC)  
With the rapid evolution of advanced communication technologies, including 6G, integrated sensing and communication (ISAC) with wireless localization systems has become a focal point for cutting-edge research. The high data rates, low latency, and massive device connectivity provided by advanced communication networks present new opportunities for enhancing the capabilities of integrated sensing and communication systems. As a result, ISAC systems play a crucial role in leveraging advanced communication technologies for enhanced localization capabilities. The integration of sensing and communication components allows for real-time data exchange and collaborative decision-making, particularly in dynamic and complex environments. If such systems are applied to comprehensive localization techniques, such as hybrid technologies, it is anticipated that faster and more precise localization will be achievable. In addition to these advancements, it is expected that further progress can be achieved through the integration of AI [51]. For example, a predictive model can be implemented by incorporating machine learning algorithms within the ISAC framework to predict and compensate for dynamic changes in a user's location. Moreover, contextual information obtained through ISAC can enhance the understanding of a user's environment and use it to enable more precise localization. These are particularly relevant in scenarios where traditional positioning systems may face challenges due to obstructions or signal interference.
# A Specification-Based IDS for Detecting Attacks on RPL-Based Network Topology
## 3 A specification-Based IDS for RPL to Detect the Topology Attacks  
In this section, we introduce our specification-based IDS for RPL to detect the topology attacks as presented in the previous section. Our solution consists of two phases. In the first phase, we specify the RPL in the EFSM form using the ILP technique. In detail, we simulate the RPL network operation in the normal condition extensively to get the trace files. We then define all the states that relate to the network topology stability and analyze the transitions between those states based on similar algorithms presented in [12]. As discussed in Section 1, the approach of using the trace file to generate the operation rules has multiple advantages to compare with the manual approach of profiling a protocol from its documents. The generated module cannot only be improved by expert knowledge added from understanding the protocol, but also be used as a verification module for the detection. We also record the statistic of the states and their transitions as we know that the more transitions between the states, the more instable the network is. In the second phase, we translate the knowledge of the RPL profile of the detection algorithms that are implemented in the IDS agents. We discuss the process from IDS placement, how the monitoring data are collected, and how the IDS agents obtain the detections.
## 3 Results and Discussion
### Major and Trace Element Assessment in Soil
#### 3.2.1 Heavy Metals in Soil  
The average concentrations of Cd, Co, Mn, and Pb in agricultural soils did not go beyond Romanian standards [67] and are lower than the levels presented in Table 4. The results showed a pronounced impact of the steel industry (mainly in the downwind direction) and agricultural activities on the content of Cr, Cu, Ni, and Zn in the soil.  
In the three targeted ATUs, chromium exhibits the NV, and AT is even higher than the IT in several locations. Thus, the highest values were obtained for T-1a and V-2b, evidencing a high contamination. Most samples registered concentrations beyond the AT, and their Cr values ranged as follows: 102.0-175.5 mg kg\({}^{-1}\) (TUL), 183.5-215.4 mg kg\({}^{-1}\) (SEN), and 117.1-277.4 mg kg\({}^{-1}\) (VAD).  
For nickel, it can be noticed that all the investigated sites of the three ATUs are contaminated. The findings of our study reveal higher levels of Ni than NV, the concentrations, 50, 300...... being similar for TUL (39.6 mg kg\({}^{-1}\) for the first soil section and 38.8 mg kg\({}^{-1}\) for the second soil section) and SEN (39.1 mg kg\({}^{-1}\) for the first soil section and 37.0 mg kg\({}^{-1}\) for the second soil section) territories. In the case of VAD area, the values are higher (40.4 mg kg\({}^{-1}\) for the first section and 42.8 mg kg\({}^{-1}\) for the second one) than in SEN and TUL sites.  
The results obtained for copper indicate that levels exceed the NV in certain locations in TUL and SEN territories (T-1a,b, T-2a,b, T-3a, T-6a,b (TUL), S-2a (SEN)), while in VAD territory, they exhibit NVs in all the sampling sites. The Cu mean values are between 17.1 and 17.8 mg kg\({}^{-1}\) in TUL and between 18.9 and 19.8 mg kg\({}^{-1}\) in SEN in the non-polluted sites. In the sites where the content exhibits the NV, the values are ranging from 20.7 to 68.1 mg kg\({}^{-1}\) (TUL) and 22.9 mg kg\({}^{-1}\) (SEN). In VAD territory, where all the values are beyond the NV, Cu content ranges between 23.3 and 34.1 mg kg\({}^{-1}\).  
The Zn load in agricultural soil is in order VAD > TUL > SEN, having the following mean levels: 89.1 mg kg\({}^{-1}\) (VAD), 87.0 mg kg\({}^{-1}\) (TUL), and 74.4 mg kg\({}^{-1}\) (SEN) for the first soil section; 89.0 mg kg\({}^{-1}\) (VAD), 82.6 mg kg\({}^{-1}\) (TUL), and 67.3 mg kg\({}^{-1}\) (SEN) for the second soil section. The maximum concentration values for Zn in soil were found in TUL area (T-1a,b and T-2a) and in VAD area (V-2b and V-3a).
## 2 Materials and Methods
### Map  
The map area is found in the upper part of the software window (Figure 3).  
\begin{table}
\begin{tabular}{c c c} \hline
**DOP Value** & **Rating** & **Description** \\ \hline \multirow{2}{*}{\(<\)1} & \multirow{2}{*}{Ideal} & At this value level, measurements can be carried out in applications that require the highest possible precision at all times. \\ \hline
1–2 & \multirow{2}{*}{Excellent} & The measurements are accurate enough to satisfy the requirements imposed on the most critical applications. \\ \hline
2–5 & \multirow{2}{*}{Good} & This is the maximal DOP, for which accurate satellite measurements of high reliability can be carried out. \\ \hline
5–10 & \multirow{2}{*}{Moderate} & Under these conditions, measurements using a NSS can be carried out, but the positioning accuracy could be improved. A more open view of the sky is recommended. \\ \hline
10–20 & \multirow{2}{*}{Fair} & With this satellite constellation geometry, measurements should not be carried out using NSS systems, with the exception of determining a very approximate position. \\ \hline \multirow{2}{*}{\(>\)20} & \multirow{2}{*}{Poor} & Under such conditions, measurements are inaccurate and should not be carried out under any circumstances. \\ \hline \end{tabular}
\end{table}
Table 2: Qualitative assessment of the satellite signal depending on the DOP value [49; 50; 51].  
Internet access is required for the map to function correctly. The base map is downloaded on an ongoing basis from the OpenStreetMap project servers.  
After adding the trajectory, it is drawn on the map as a red broken curve, and the map area is zoomed in to display it in its entirety. After the planning has been initiated, blue points are added to illustrate the locations for which the algorithm will make calculations of the constellation status and the satellite visibility.  
After adding the DSM, a blue rectangle is drawn on the map with the selected data range, and the map area is zoomed in to display it in its entirety.
# Superpixel Segmentation Using Weighted Coplanar Feature Clustering on RGBD Images
## 5 Experimental Results and Analysis
### Undersegmentation Error  
Undersegmentation error (\(U\!E\)) is used to evaluate the ability to recover the ground truth object segments using a combination of superpixels. For this purpose, a significant penalty is applied to each superpixel that does not properly overlap with a ground truth segmentation. The computation of the \(U\!E\) metric is quantified as follows:  
\[U\!E=\frac{1}{N}\!\!\sum_{G\in G}\!\!\sum_{SP|SP\cap G\
eq\varnothing}\min(SP _{i},SP_{j}), \tag{10}\]  
where \(N\) is the number of pixels in an image, \(G\) is a ground truth segment, and \(SP_{i}\) as well as \(SP_{j}\) are superpixel segments of \(SP\) divided by \(G\). Therefore, a lower value is better. However, the \(BR\) measures the tightness of the superpixel boundary that overlaps with ground truth edges, and the \(U\!E\) measures what fraction of the ground truth edges falls within at least two pixels of a superpixel boundary, so a tighter superpixel boundary (a higher \(BR\)) is more likely to cause more \(U\!E\). Therefore, algorithms with a high boundary recall are usually subject to a high undersegmentation error.  
As done in the previous section, the \(U\!E\) is also calculated for the whole RGBD image. The results are shown in Figure 6 and indicate that the undersegmentation error of our method is slightly higher compared with DASP, which exhibits the lowest undersegmentation error, and is comparable to most other methods. Furthermore, it was found that, during the experiments, WCFC was the only one of the  
Figure 5: The boundary recall of the four superpixel methods.  
RGBD superpixel algorithms that could correctly process the inpainted RGBD images, which confirms that our WCFC works robustly against the noise from depth maps.
## 3 Results
### Research Area P4  
Research area P4, situated along District Road No. 2951L, was characterized by a high average noise level of 66.8 dB at measurement points directly adjacent to the road (Table 5). At a distance of 250 m from the road, an average decrease of 28.5 dB was noted, which aligns with the general trend of sound intensity diminishing with distance from the noise source. In measurement profiles A and C, the noise level decreased as the distance from the road increased. However, in profile B, similar to profiles B and C on surface P1, an increase in the sound level of approximately 9 dB was observed between points 250 m and 500 m away. The higher values in profile B at the most distant measuring point may result from other factors. In this profile, the midpoint is located on a slope, and the point farthest from the road is situated on a hill with an elevation of 299.3 m above sea level. At this location, the highest value was recorded at the greatest distance from the road across all test surfaces. This raises further questions about the validity of this point, which sits on the eastern side of the slope, suggesting that it may be somewhat isolated from the source of road noise. The elevated sound level is likely not attributable to spatial conditions.  
The analysis of the terrain revealed various spatial conditions. Profile B is characterized by the most significant differences in height (47.2 m), which could affect sound propagation. Profile A includes points situated in a slight depression between hills; however, no anomalies were found in the measurement results at this location. Profile C is almost flat, minimizing the influence of spatial factors on the test results. The apparent variability in sound intensity at different distances from the road, across different measurement profiles, may result from external factors, such as environmental interference, sound reflections, or irregularities in the sound source itself. The composition of the audiosphere at the P4 surface was similar to that recorded at other surfaces: anthropogenic sounds  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Profile**} & **Distance from** & **\(L_{AeqT}\)** & **\(L_{Aeq(mean)}\)** & **Height** & **Dominant** \\  & **Road (m)** & **(dB)** & **(dB)** & **a.s.l. (m)** & **Sounds** \\ \hline A & 5 & 68.1 & & 238.5 & \\ B & 5 & 71.2 & 68.4 \(\pm\) 5.2 & 245.0 & Anthropogenic \\ C & 5 & 61.0 & & 246.0 & \\ \hline A & 250 & 41.8 & & 259.9 & \\ B & 250 & 40.0 & 42.3 \(\pm\) 1.7 & 265.8 & Natural and anthropogenic \\ C & 250 & 43.9 & & 262.1 & \\ \hline A & 500 & 38.4 & & 265.2 & \\ B & 500 & 38.8 & 39.6 \(\pm\) 1.4 & 301.6 & Natural \\ C & 500 & 41.0 & & 263.0 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of equivalent sound level measurements in research area P3.  
dominated near the road, while natural sounds, such as birdsong, prevailed at points 500 m away, indicating a limited impact of road noise at a greater distance from the source.
## 2 System and Channel Models
### Link Selection Policy  
At the \(G\), a new link (or network) selection policy is adopted which offers reduced overhead in terms of channel monitoring operations and signaling exchanges. The proposed mode of operation of this policy is also depicted in Figure 1. In particular, two states are defined with regards to the communication system that has been employed, namely, the FSO (State 1) and the RF (State 2). State selection is performed on \(T_{p}\) time-based period. More specifically, in each \(T_{p}\) the received SNR at G of the previously selected state is examined if it exceeds a predefined switching threshold \(\gamma_{\text{th}}\). If this is the case, the system's algorithm selects to remain with that link; otherwise, it switches to the link (after examining both RF and FSO ones) that provides the highest SNR value at the G. Based on this approach, it is not necessary to continuously monitor the received SNR from both links in order to select the maximum, since in many cases the received SNR from one link will exceed \(\gamma_{\text{th}}\). As a result, a reduced number of links estimations are expected to be performed.  
Based on the above definitions, the end-to-end received instantaneous SNR at the G is given by  
\[\gamma_{o}=\text{min}\{\gamma_{1},\gamma_{t}\} \tag{4}\]  
Figure 1: HAPS-assisted hybrid FSO/RF satellite communications system: Proposed mode of operation that is based on a two-state Markov chain.  
where \(\gamma_{t}\) denotes the instantaneous received SNR at the G for the H-G link as a result of the mode of operation of the proposed policy. Next, the channel models assumed for both communication links are presented.