esa-sceva/satcom-chunk-collection huggingface dataset:
# A Packet Content-Oriented Remote Code Execution Attack Payload Detection Model
## 5 Experimental Results and Analysis
### Evaluation of the ELi and IDSER Detector
#### 5.2.1 Evaluation of ELi Detection  
We evaluate the effect of the ELi and IDSER detector in detecting ELi. 5-fold cross-validation is used to obtain machine learning models' evaluation metrics and ROC (Receiver Operating Characteristic) curves. The evaluation results are shown in Table 3, with the ROC curves shown in Figure 8. In terms of evaluation metrics, the decision tree and random forest models exhibit the highest precision and F1 score, while the SVM model achieves the highest recall. Upon analyzing the confusion matrix, it is observed that decision tree, logistic regression, random forest, and XGBoost models achieve the lowest false positives. However, the logistic regression and XGBoost models show the highest false negatives. In contrast, the SVM model achieves the highest true positive and the lowest false negative. Regarding the ROC curves, the naive Bayes model demonstrates the highest AUC (Area Under Curve) value. In general, the decision tree and random forest models are more suitable for detecting ELi attacks.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **TP** & **TN** & **FP** & **FN** & **Precision** & **Recall** & **F1** \\  & (\(\
earrow\)) & (\(\
earrow\)) \\ \hline Naive Bayes & 109 & 220 & 2 & 9 & 0.98 & 0.92 & 0.95 \\ Decision Tree & 110 & **221** & **1** & 8 & **0.99** & 0.93 & **0.96** \\ Logistic Regression & 106 & **221** & **1** & 12 & **0.99** & 0.90 & 0.94 \\ Random Forest & 110 & **221** & **1** & 8 & **0.99** & 0.93 & **0.96** \\ SVM & **111** & 219 & 3 & **7** & 0.97 & **0.94** & **0.96** \\ XGBoost & 106 & **221** & **1** & 12 & **0.99** & 0.90 & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation results of ELi attack detection. The higher (\(\
earrow\)) or lower (\(\searrow\)) the value is, the better the model is. The best scores are shown in **bold**.  
Figure 8: ROC curves of ELi detection. (**a**) ROC curves of naive Bayes; (**b**) ROC curves of decision tree; (**c**) ROC curves of logistic regression; (**d**) ROC curves of random forest; (**e**) ROC curves of SVM; (**f**) ROC curves of XGBoost.
###### Abstract  
A diffusion tensor models the covariance of the Brownian motion of water at a voxel and is required to be symmetric and positive semi-definite. Therefore, image processing approaches, designed for linear entities, are not effective for diffusion tensor data manipulation, and the existence of artefacts in diffusion tensor imaging acquisition makes diffusion tensor data segmentation even more challenging. In this study, we develop a spatial fuzzy c-means clustering method for diffusion tensor data that effectively segments diffusion tensor images by accounting for the noise, partial voluming, magnetic field inhomogeneity, and other imaging artefacts. To retain the symmetry and positive semi-definiteness of diffusion tensors, the log and root Euclidean metrics are used to estimate the mean diffusion tensor for each cluster. The method exploits spatial contextual information and provides uncertainty information in segmentation decisions by calculating the membership values for assigning a diffusion tensor at one voxel to different clusters. A regularisation model that allows the user to integrate their prior knowledge into the segmentation scheme or to highlight and segment local structures is also proposed. Experiments on simulated images and real brain datasets from healthy and Spinocerebellar ataxia 2 subjects showed that the new method was more effective than conventional segmentation methods.  
Keywords:diffusion tensor; fuzzy c-means; non-euclidean metrics; K-means; Corpus Callosum +
Footnote †: journal: Applied Sciences
### Contents and Contribution of This Paper  
A large power grid has been adopted in time-of-use pricing programs to adjust the urban electricity demand in China. With the gradual opening of the electricity market (2018), a second electricity supply role appears between the large power grid and the consumers, namely, the integrated energy service providers, which can provide cooling, heating, and power for designated areas. Generally, the integrated energy service provider appears in the form of a PIES. The location and function of the PIES in the urban energy system are illustrated in Figure 1. As can be seen from Figure 1, the main function of PIES is to supply cooling, heating, and electricity to consumers in specific regions by considering the local energy resources, time-of-use electricity prices of the large power grid, and various energy conversion equipment comprehensively. Particularly, the PIES can formulate the selling price of cooling, heating, and electricity under the supervision of the relevant departments. In addition, it is difficult to consider the energy demand characteristics of different parks when time-of-use tariffs are formulated for the large power grid, which leaves some margin for the PIES. From the perspective of the PIES, earning greater profits is the goal. In terms of consumers, paying smaller electricity bills is the only criterion for selecting the electricity supplier, as power can be purchased from the large power grid or the PIES. Therefore, how to formulate the energy selling price of the PIES is the focus of this study, in order for consumers to spend less money while the PIES obtains more benefits. Through the analysis in Section 1.1, it can be concluded that changes in the selling price of electricity cause changes in demand, which in turn leads to fluctuations in the configuration capacity. Furthermore, this fluctuation leads to changes in the cost of energy production, which ultimately leads to changes in the selling price. The challenge in solving this problem is the consideration of this coupling cycle in the selling pricing of the PIES.  
This study attempts to address this challenge through the following methods. Using the established energy cost price sub-model (C-P sub-model), the production costs of cooling, heating, and electricity are obtained under different peak load transfer quantities. Then, the function relationship between the energy cost price and load transfer quantities is determined by regression analysis. On this basis, the time-sharing price is obtained by using an established selling price sub-model (S-P sub-model) that is beneficial to the PIES and consumers.  
The rest of this paper is organized as follows. Section 2 clarifies the research problem and provides the relevant assumptions. Section 3 describes the modeling process of the C-P sub-model and S-P sub-model. On this basis, the characteristics of the changes in the cost price and selling price are analyzed. Section 4 unfolds the numerical study based on the specific price pattern and demand function. Finally, Section 5 presents the conclusion of this study.
# Trace Elements and Mineralogy of Upper Permian (Zechstein)
## 2 Geological Setting  
The Upper Permian (Zechstein) has evaporites up to 1.8 km thick [54], occupying over 60% of the Poland area (Figure 1B) and infilling the eastern part (Polish Permian Basin) of the giant epicontinental Southern Permian Basin (Figure 1A). Evaporites compose ca 75% of all the Zechstein deposits, with an estimated thickness at over 2.3 km [54; 55].  
Figure 1: Studied Zechstein potash-bearing deposits in Poland. (**A**) Extent of the European Permian Basin with the area studied (after [56]). (**B**) Sites of analysed potash-bearing deposits.  
The dominant evaporite rocks (salts and sulphates) together with subordinate clastics and carbonates formed distinct lithost stratigraphic units, grouped into four cyclothem successions (Table 1) equivalent with ones defined in the German portion of the basin (e.g., [55; 57; 58; 59; 60; 61; 62; 63; 64; 65]).  
Discussed potash-bearing deposits occur in the successions of the first three Zechstein cyclothems: Werra (PZ1; Z1 in Germany), Stassfurt (PZ2; Z2), and Leine (PZ3; Z3). Indicator \"P\" (polish) in the cyclothem standard symbol was based on some differences in evolution of the Upper Permian basin in the Poland area compared with the German portion [55; 63; 64; 65].  
A stratigraphy of the selected evaporite Zechstein rocks with studied potash-bearing deposits is presented in Table 1.  
The potash-bearing rocks belong to all the Zechstein K-Mg evaporites, including both chloride salts (main minerals: bischofite, carnallite, sylvitee, and tachydrite) and sulphate salts (main minerals: polyhalite, kainite, kieserite, langbeinite, and hexahydrate).  
The best recognised and analysed potash-bearing deposits--due to sampling accessibility in the underground mines and borehole cores located in the salt domes in central Poland (Figure 1)--represent two rock complexes occurring within the PZ2 and PZ3 cyclothems in Poland. These complexes include the following lithost stratigraphic units:  
(a) In PZ2 cyclothem: the Transitional beds Na2+K2, composed of rock and K-Mg salt interbeds and the Older Potash (K2) unit with dominant K-Mg salts.  
(b) In PZ3 cyclothem: the Transitional beds Na3+K3 and the Younger Potash (K3) unit of the same composition for older deposits.  
Sulphate K-Mg deposits (polyhalites), occurring within sulphates of the Lower Anhydrite (A1d) unit in northern Poland, westward from the Puck Bay, compose there (Figure 1) the four potash deposits with registered resources (e.g., [67; 68; 69; 70]).
# Data Rate Selection Strategies for Periodic Transmission of Safety Messages in VANET
## 2 Background Review
### Power- and Message Rate-Based Approaches
[54], the authors use a Q-learning approach to leverage the trade-off between transmission rate and power to balance the channel load and improve the packet delivery ratio. In Ref. [55], the authors present two DCC mechanisms that adapt message rate and data rate combined with the transmit power control mechanism to improve channel utilization and load.
## 3 Results
### Environmental Factors and GPP Annual Variation Characteristics
#### 3.1.2 Intra-Annual Variation Characteristics of Daily Average and Daily Cumulative Average of GPP  
In 2020, the daily average and daily cumulative average of GPP for various underlying surfaces exhibited an inverted \"U\" shape in their intra-annual variation (Figure 5). The peak values of both the daily average and daily cumulative average occurred in July and August. During the season when vegetation has not yet recovered (November to February), GPP does not show significant fluctuations and remains within a relatively low range. However, when vegetation begins to grow under sun radiation, GPP shows a clear fluctuation trend. The daily average and daily cumulative values are highest in farmland areas, followed by grassland areas, with the weakest values observed in desert vegetation areas. During the season when vegetation had not yet recovered (from November to February), GPP did not show significant fluctuations and remained within a relatively low range. However, when vegetation began to grow under sun radiation, GPP showed a clear fluctuation trend. The daily average and daily cumulative values were highest in farmland areas, followed by grassland areas, with the weakest values observed in desert vegetation areas.  
In addition, Table 5 shows the overall intra-annual variation in the time and magnitude of the daily average and daily cumulative average of GPP for different underlying surfaces. The timing of the trough (January) and inflection points (late March and mid-to-late October) in farmland and desert vegetation areas was generally the same. However, the timing of the trough and inflection points in grassland areas differed from that in farmland and desert vegetation areas. In grassland areas, the trough occurred in December, and the inflection points occurred in mid-to-late March and early November, respectively. Overall, the characteristics of attribute changes in all ecosystems were distinct. Compared to the other ecosystems, farmland areas showed the highest growth rate and peak values.  
Figure 5: Intra-annual variation characteristics of the daily average and daily cumulative average of GPP at each station.
# An Efficient Location Verification Scheme for Static Wireless Sensor Networks
## 4 The Proposed Scheme: Mutually-Shared Region-Based Location Verification  
MSRLV is the location verification algorithm that utilizes a mutually-shared region token (MSR token). The token can be calculated by legitimate sensors only. MSRLV focuses on the verification process, not the localization process. In order to verify the location claim of a sensor, the proposedscheme requires two participants and the MSR token between them for the location verification. In the following subsections, the MSR token and the detailed scheme will be described.
# Packed Bed Photoreactor for the Removal of Water Pollutants Using Visible Light Emitting Diodes
## 4 Conclusions  
In this work, a packed bed photoreactor at semi-pilot scale was developed, using a visible light active structured photocatalyst. The packed bed reactor was irradiated by visible LEDs for the evaluation of photocatalytic activity in the removal of MB dye, as well as CFX, PC, and CAF, which are considered pollutants of emerging concern.  
Photocatalytic tests driven in batch configuration showed that the N-TiO\({}_{2}\)/PS photocatalyst was active in the photocatalytic degradation of MB, reaching almost total MB degradation after 120 min of irradiation, while no significant decrease in MB concentration was observed during the overall irradiation time in the absence of the structured catalyst. No influence of liquid flow rate was observed confirming that the overall MB photocatalytic degradation rate is mainly controlled by kinetics. A mathematical model for the photocatalytic degradation of MB was developed neglecting the external mass transfer phenomena. In order to verify this last hypothesis, the apparent kinetic constant was estimated from the experimental data collected, using different liquid flow rates when the photoreactor operated in continuous mode and at steady-state condition. The accuracy of the obtained value for the kinetic constant was verified by performing another series of experiments in which the visible light intensity varied. Finally, the efficiency of packed bed photoreactor at semi-pilot scale was also evaluated in the removal of CFX, CAF, and PC. The obtained results showed that the almost total degradation of the pollutants with a TOC removal more than 80% was achieved after 180 min of visible light irradiation. As concluding remarks, the obtained results are an encouraging step forward in the possible application of photocatalytic technology based on the use of visible LEDs and visible light structured photocatalysts in the elimination of hazard environmental pollutants, especially for the degradation of emerging bio-recalcitrant organic pollutants.  
Figure 10: Schematic picture of the packed bed photoreactor configuration and the emission spectrum of the visible light LEDs strip (insert).  
**Author Contributions:** [PERSON] performed the experiments and wrote the manuscript. [PERSON] provided the concept and experimental design of the study and reviewed the paper prior to submission. All authors discussed the results, analyzed the data and commented on the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Auto-Encoder Learning-Based UAV Communications for Livestock Management
## 5 Conclusions  
This paper presents a communications system as an end-to-end optimization scheme using an auto-encoder to jointly learn UAV (transmitter) and GCS (receiver) signal processing implementations without prior knowledge. The auto-encoder was trained with fixed and varying SNR values and an input message modulated with different modulation schemes, such as BPSK, QPSK and 8 PSK, and various communication rates. We have seen from the results that the proposed learning-based communication framework can learn standard and distorted classical modulation techniques when trained with average and average power constraints, respectively. Comparisons with conventional baselines in various scenarios unveil a competitive BER performance against traditional communication techniques. From the results, we find that the proposed learning approach demonstrates its efficacy in terms of reliability in learning optimal communication schemes in a challenging environment or situations where the optimal strategies are not known. This could be used to design UAV communication links for reliable data transfer and efficient smart livestock farming. Future work should consider various channel types and communication rates for a more practical UAV communication system for different baseline schemes. An extension to a multiantenna UAV system with particular attention to beamforming, secrecy, channel interference and energy efficiency is appealing.  
Conceptualization, A.M. and Y.A.S.; methodology, A.M., Y.A.S. and H.R.E.H.B.; software, A.M.; validation, Y.A.S., M.S.S. and H.R.E.H.B.; formal analysis, Y.A.S. and M.S.S.; investigation, A.M.; resources, A.M. and Y.A.S.; writing--original draft preparation, M.A.A., A.M., Y.A.S., H.R.E.H.B. and M.S.S.; writing--review and editing, M.A.A., A.M., Y.A.S., H.R.E.H.B. and M.S.S.; visualization, Y.A.S.; supervision, H.B. and M.A.A; project administration, M.A.A.; funding acquisition, M.A.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia, grant number IFP-A-201-2-1.  
Not applicable.  
Figure 12: BER vs. SNR for the auto-encoder test with different amounts of test samples against various benchmark communication techniques.  
**Acknowledgments:** The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project No IFP-A-201-2-1.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Discussion
PGE\({}_{2}\) plays a key role as a mediator in UC. In the current study, WG and GR inhibited the DSS-induced activation of PGE\({}_{2}\) production (Figure 5). As previously reported, the histopathological features of UC, such as ulceration, mucous membrane erosion, and loss of intestinal crypts, can be exhibited in DSS-induced colitis models. In the present study, WG 100 mg/kg and GR 100 mg/kg significantly reduced inflammatory cell infiltration versus the DSS-induced colitis model (Figure 6). Furthermore, bowel wall thickness associated with inflammation was suppressed by WG treatment (Figure 5a,c). Because of the original composition of the fecal microbiota between samples, which might be one reason for the variation, significant differences were not observed. However, the proportion of _Lactobacillus_ increased following the administration of WG and GR in the colitis model. In a previous report, the bacterial population of _Lactobacillus_ decreased upon the administration of DSS to induce colitis [45]. GR, including WG consumption, might act as a prebiotic to improve DSS-induced colitis through the modulation of probiotics such as _Lactobacillus_. Licorice is categorized as a potential prebiotic and one of the major compounds, liquiritin, showed selective antibacterial activity to reduce pathogenic bacteria and maintain probiotics such as _Bifidobacterium_ and _Lactobacillus_[46; 47]. Further studies on the relationship between the improvement of DSS-induced colitis and changes in the composition of the fecal microbiota are needed to confirm the effect of WG. In summary, the anti-inflammatory activities of WG and GR were equally effective as the positive control 5-ASA. WG inhibited inflammation by regulating inflammatory mediators such as IL-6, TNF-\(\alpha\), and PGE\({}_{2}\). WG may be a useful therapeutic agent to treat inflammatory diseases and utilized as an equivalent source of _Glycyrrhizae_ radix for medicine.
## 2 Basic Principles
### Model Evaluation  
To verify the accuracy and applicability of the model, it was necessary to evaluate the training and testing performance of the model. A comprehensive assessment was conducted using the coefficient of determination (\(R^{2}\)), root mean square error (_RMSE_), residual prediction deviation (_RPD_), and mean absolute error (_MAE_). \(R^{2}\) represents the degree of fit between predicted and actual values, ranging from 0 to 1, with higher values indicating better performance. _RPD_ is a common metric used to evaluate the predictive capability of a model, where a higher _RPD_ indicates better predictive performance; typically, an _RPD_ greater than 2 is considered a qualified model, while an _RPD_ greater than 3 is regarded as an excellent model. _RMSE_ measures the average deviation between predicted and actual values, with smaller values being preferable. _MAE_ is used to indicate the accuracy of the results, measuring the average absolute deviation between predicted and actual values, where smaller values are better. The calculation formulas are as follows:  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}{{{(y_{i}-\hat{y_{i}})}^{2}}}}{\sum\limits_{ i=1}^{n}{{{(y_{i}-\overline{y}_{i})}^{2}}}} \tag{24}\]  
\[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{{{(y_{i}-\hat{y_{i}})}^{2}}}}} \tag{25}\]  
\[RPD=\frac{\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{{{(y_{i}-\overline{y}_{i})}^{ 2}}}}}}{\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{{{(y_{i}-\hat{y_{i}})}^{2}}}}}} \tag{26}\]  
\[MAE=\frac{1}{n}{\sum\limits_{i=1}^{n}{{{|y_{i}-\hat{y_{i}}|}}}} \tag{27}\]  
where \(y_{i}\) is the observed value, \(\hat{y_{i}}\) is the predicted value, \(\overline{y}_{i}\) is the mean value, and \(n\) is the number of samples.