esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 2 Methods  
### Quad-Polarimetric SAR Mode  
For quad-pol SAR data, the 2 \(\times\) 2 scattering matrix is measured on the traditional linearly horizontal and vertical bases, which can be described by [30]:  
\[S=\left(\begin{array}{cc}S_{HH}&S_{HV}\\ S_{VH}&S_{VV}\end{array}\right) \tag{1}\]where the subscript \(H\) and \(V\) describes the transmitted and received polarization, respectively, with \(H\) denoting horizontal and \(V\) denoting vertical directions. For the monostatic case, the reciprocity always holds, which means that the two cross-polarized terms are identical, i.e., \(S_{HV}=S_{VH}\).  
The covariance matrix can be derived by:  
\[\mathbf{C}=\left(\begin{array}{cc}\left\langle S_{HH}^{2}\right\rangle& \left\langle\sqrt{2}S_{HH}S_{HV}^{*}\right\rangle&\left\langle S_{HH}S_{VV}^{ *}\right\rangle\\ \left\langle\sqrt{2}S_{HV}S_{HH}^{*}\right\rangle&\left\langle 2S_{HV}^{2} \right\rangle&\left\langle\sqrt{2}S_{HV}S_{VV}^{*}\right\rangle\\ \left\langle S_{VV}S_{HH}^{*}\right\rangle&\left\langle\sqrt{2}S_{VV}S_{HV}^{ *}\right\rangle&\left\langle S_{VV}^{2}\right\rangle\end{array}\right) \tag{2}\]  
where * is the symbol of conjugate and \"\(<\) -\" stands for multilook by using an averaging window (\(5\times 5\) in this study). This \(5\times 5\) averaging window is important to obtain the statistical property of the compound target's polarization status and reduce the effect of speckle noise.
## 2 Materials and Methods
### Modelling the Reactive Power with ANFIS  
Numerous experiments have demonstrated success when neural networks and fuzzy systems are applied to railway supply systems. The accuracy of the mathematical models proposed in [45] for the modelling of electric power consumption in railway facilities may be significantly improved by using artificial neural networks, fuzzy neural networks, and support vector machines. The paper [46] describes a program that employs neural network-based prediction algorithms and optimizes the reactive power mode. The study [47] describes how the adaptive neuro-fuzzy inference system (ANFIS), support vector machines (SVM), and artificial neural networks were used to determine the rail voltage for a 1500 V DC-fed rail system (ANN). The discussion of a detailed method for compensating for negative sequence and harmonic currents using PI and fuzzy controllers can be seen in [48,49]. ANFIS and MRAS-PI controller-based adaptive-UPQC for power quality enhancement applications are presented in [50].  
Figure 2: Current and voltage variation: (**a**) When the locomotive is at a distance of over 5 km from the station; (**b**) When the locomotive is at the station.  
The data shown in Figure 3b were utilized to train ANFIS in modelling the reactive power produced by electric locomotives. Understanding the phenomena related to the transportation of non-linear loads, such as electric locomotives, will be aided by the modelling results.  
An ANFIS was trained and tested using the data sets. An ANFIS network [50; 51; 52; 53; 54] can be used to model complex systems that do not limit themselves to classical modelling. It is such a complex system in the case discussed in this paper, because of the fluctuations in the power consumption of the electric locomotives' power station. Since currents and voltages are distorted, the device is difficult to mathematically model. As a result, intelligent techniques are a more suitable approach in this situation. The collected data for power generation were used to build this model of neuro fuzzy networks.
## 1 Introduction
reported around 30 mg/L [22], and [PERSON] and [PERSON] found that 200 mg/L was necessary to achieve 76% turbidity and 85% COD removal [23]. One of the main objective of this study is to reduce the dosage of the chemical coagulant (FeCl\({}_{3}\)) by incorporating a natural coagulant (PSE) in order to achieve an efficient and more environmentally sustainable treatment of wastewater. To this end, the study was carried out in two main stages. The first stage involved the physicochemical characterization of the PSE bio-coagulant using (FTIR) and (SEM). The second stage focused on the optimization and modeling of the coagulation-flocculation process using response surface methodology (RSM). In this part, the (BBD) was applied to evaluate the influence of three variables: pH, FeCl\({}_{3}\) dosage, and PSE dosage on the removal efficiency of turbidity, COD, UV 254 absorbance, and phosphate. The novelty of this study lies in several aspects. Firstly, it proposes the development of predictive mathematical models that could be directly used in wastewater treatment plants to determine optimal operating conditions without requiring a preliminary laboratory-scale optimization step. Secondly, it contributes to the valorization of agro-industrial waste specifically, _Pumpkin seed_ residues after essential oil extraction by reusing them as a functional bio-coagulant. Lastly, the combination of an iron-based chemical coagulant with an organic coagulant offers a dual advantage: the effective purification of contaminated water in accordance with Algerian discharge standards, and the complete recovery of waste within a circular economy approach.
# A Green Computing Business Aggregation Strategy for Low Earth Orbit Satellite Networks
## 6 Conclusions  
This paper proposes a business aggregation strategy for Low Earth Orbit (LEO) Satellite Networks (LSNs) to address energy constraints by incorporating green computing principles. The strategy dynamically adjusts satellite operations based on business volume, enabling some satellites to enter a sleep state to conserve energy. A three-layer architecture, including control satellites, service satellites, and user devices, is utilized, with optimization achieved through a Markov Decision Process (MDP) and a Double Deep Q-Network (Double DQN).  
The simulation results demonstrate a 47.87% energy reduction under low business volume and a 4.36% reduction under high business volume, showcasing its effectiveness, especially in low-traffic scenarios.  
This research provides practical insights into energy-efficient management for LEO satellite networks and related energy-sensitive fields, such as data centers and smart grids. The proposed strategy reduces energy consumption, enhances resource efficiency, and supports the broader adoption of green computing and sustainable development globally. Moreover, one of the key visions of 6G is ubiquitous connectivity, which is intrinsically linked to the advancement of LEO satellite networks. As these networks evolve, energy optimization will become an inevitable consideration. Therefore, the strategy proposed in this paper offers valuable reference and guidance for the future development of LEO satellite networks, supporting their role in achieving the goals of 6G.  
Methodology, D.H.; Software, Y.F.; Data Curation, Z.L.; Writing--Original Draft, B.W. and J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Director Fund of the Key Laboratory of Cognitive Radio and Information Processing (Grant No. CRKL210109) and the Guangxi Natural Science Foundation for Youths (Grant No. 2022 GXNSFBA035645). We also thank the ImageNet team for providing the dataset used in this research.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 4 Validation and Comparison
### Predicted Results

Table 14: Error statistics of different methods in the testing phase. Figure 12: Comparison of prediction results for Height III and Position E.  
To deeply analyze the predictive effect of the models in a single carriage, the data at the height III position in the first carriage is selected to show the comparison between the original data and the predicted value of the path loss. Figure 13a shows the distribution of the path loss data in the block obtained by applying WI simulation. Figure 13b shows the distribution of path loss predictions generated by the integrated neural network prediction method proposed in this paper. Based on the prediction results in Figure 13b, Figure 13c uses a linear interpolation method to interpolate the signal path loss across the entire compartment plane, which is used to predict the path loss distribution over a broader range within the compartment. In Figure 13, the horizontal axis represents the number of seat rows in the compartment, the vertical axis represents the position, and the color shade directly reflects the magnitude of the predicted or actual path loss. The predicted values of the path loss of the model in this paper are consistent with the original data in terms of distribution, indicating that the model has a high accuracy in predicting the path loss at the height III position of a single compartment. The feasibility of the model in predicting the field strength distribution in the compartment is demonstrated.
## 1 Introduction  
Intensive farming techniques require the use of plant protection chemicals to prevent the spread of pests, diseases, and weeds that reduce crop yields. These chemicals can also be toxic to humans and animals; therefore, they should be applied only when necessary [1, 2, 3, 4, 5], particularly because their residues can persist in various parts of protected plants for long periods of time. Crop-spraying operations should involve equipment that is in good technical condition, and chemicals should be applied in minimal effective doses [6, 7, 8]. To achieve this goal, the applied liquid should be uniformly distributed over the target crops [9, 10, 11]. A uniform spray distribution is also one of the requirements of sustainable agriculture, which aims to minimize the environmental impact of farming operations through the rational use of natural resources and agricultural inputs without compromising farming profits [12, 13, 14]. The position of spray nozzles above the crop stand should be optimized to achieve the above goal and to minimize spray drift [15, 16]. In most commercial field sprayers, the boom is raised to a certain height above the crop stand and the nozzles are positioned on the boom at equal intervals. The nozzles should be spaced 50 cm apart and the boom should be positioned around 50 cm above the ground to ensure that the spray cones overlap and that the liquid is applied uniformly onto the target crops [16].  
In row crops, chemicals should not be applied across the entire spray band, especially in the early stages of plant development, to minimize their environmental impact. Over-row sprayers are better suited for these types of production systems because the nozzles can be positioned at various locations on the boom, and their configuration can be adjusted to the cropping pattern. In over-row sprayers, the delivered dose can be precisely controlled and reduced by even 20% to 70% [2; 17; 18; 19; 20; 21]. The effectiveness of spray treatments can be improved through the use of a secondary air blower (SAB) to ensure that the liquid is broadcast evenly onto all plants and on all sides of the stand [14; 22; 23; 24; 25]. However, the effect of the auxiliary air stream on the horizontal distribution of liquid in an over-row sprayer has never been investigated to date. Therefore, the aim of this study was to determine the influence of boom height above the crop stand and the spacing between nozzles and diffusers in an over-row sprayer on the uniformity of the horizontal spray distribution and the uniformity of the air velocity distribution.
# Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information
## 2 Research Methods and Data
### Data
#### 2.2.5 Density of Underground Pipelines  
Previous studies have shown that the density of underground pipelines is highly correlated with ground subsidence [11,13]. A high underground pipeline density implies that numerous ground excavations were undertaken for pipeline construction, which is likely to result in an increased level of ground disturbance. Therefore, the density of underground pipelines was considered a significant factor and was incorporated into the dataset. The density was extracted by using linear density analysis in ArcGIS to calculate the length of the pipeline corresponding to a unit area.
# Advances in the Assembly Model of Bacterial Type IVB Secretion Systems
## 2 Gene Composition of T4 BSS  
A typical T4 BSS consists of proteins encoded by \(\sim\)30 genes, which are named _dot_ (defect in organelle trafficking) or _icm_ (intracellular multiplication), as shown in Figure 1. The coding genes for the Dot/Icm system are mostly found in plasmids except the genes for T4 BSS in the genera _Legionella_, _Coxiella_ and _Rickettsiella_ are in the chromosome [11]. The similarity of sequence of the genes suggests that these genes may have originated from a common ancestor (for instance between the tra system in R64 and Dot/Icm system) [12]. The genes encoding the T4 BSS proteins tend to form different gene clusters: (a) the _dotD-dotC-dotB_ gene cluster; (b) the _dotM/icmP-dotL/icmO_ gene cluster; and (c) the _dotI/icmL-dotH/icmK-dotG/icmE_ gene cluster [11]. Compared with other T4 SSs (Figure 2b) [1; 7; 13], only _dotL_ (_icmO_), _dotG_ (_icmE_), and _dotO_ (_icmB_) have homology with the corresponding genes _virD4_, _virB10_ and _virB4_ in T4 ASS in sequence level.  
Figure 1: Composition of different T4 SSs and homology comparison. Blocks of the same color (green, purple, red, orange, deep orange, celadon, cyan and yellow) represent homologous proteins; light grey blocks in T4 CSS represent proteins of unknown function.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Basis of Classification** & **Subtype** & **Example** & **Characteristics** \\ \hline \multirow{4}{*}{Plasmid incompatibility} & F-subtype & IncF, IncH, IncT, IncJ & Narrow range of secretory substrates, mainly including ssDNA. \\ \cline{2-4}  & P-subtype & IncF, IncN, IncW & Wide range of secretory substrates, including multiple virulence effectors. \\ \cline{2-4}  & I-subtype & IncIl plasmid R64 & Aid in both in liquid and solid-surface mating and secrete virulence effectors. \\ \hline \multirow{4}{*}{Functional classification} & Conjugal transfer & Self-transmissible plasmids or transposons & Mostly present in bacteria and archaea. \\ \cline{2-4}  & Effector translocators & Pertussis toxin secretion & Transport for virulence effectors in Gram-negative bacteria. \\ \cline{2-4}  & DNA release/uptake & ComB (_Heliobacter pylori_) & \\  & systems & system takes DNA from outside the cell & Similar to T2 SS. \\ \hline \multirow{4}{*}{Biochemical properties and structure} & T4 ASS & VirB/D (_A. tumefaciens_) & Contains pili, which assist in protein secretion. \\ \cline{2-4}  & T4 BSS & Dot/Icm (_Legionella_ & Secretes a large number of effectors and transfers nucleic acids to host cells. \\ \cline{2-4}  & T4 CSS & GI-type T4 SS-like system (_Streptococcus suis_) & Common in Gram-positive bacteria of the genus _Streptococcus_ \\ \cline{2-4}  & GI-type T4 SS & ICE _Hin_1056 (_Haemophilus_ & \\  & _influenza_) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of T4 SS.
## 3 SSA-CAE Based Abnormal Data Classification Techniques in Edge Intelligence Device
### Data Reconstruction Using Singular Spectrum Analysis  
As discussed in previous studies, SSA can be reclassified and reconstructed by decomposing the characteristics of data. It is impossible to apply all types of data generated within the smart factory. Processing data using vision sensors is not suitable for utilization purposes. In addition, if multivariate data are processed at the same time, this analysis technique will be inapplicable.  
Therefore, this paper focuses on data of one type of data collected in units of products. In Section 4, the experiment was conducted using vibration data. The role of SSA in the proposed architecture is to reduce noise in data collected in the field and to improve modeling performance through periodic component extraction and reconstruction.  
Owing to the different characteristics of analog data such as vibration data and voice data, data experts for each data type should analyze the SVD status and input reconstruction information for key components based on the data processed by EID. In general, it is decomposed into trends, predicted values, and noise values of data through the main components generated in the embedding stage of the Singular Spectrum. The rest of the trends and predictions except for noise values are reclassified and reconstructed into noise-reduced data to help the deep learning model analyze actual abnormal values.
# Fuzzy-Based Quality Adaptation Algorithm for Improving QoE from MPEG-DASH Video
## 5 Proposed Fuzzy-Based Adaptation Scheme
As the segment duration increases in an unstable environment, the risk of buffer underflow increases. Therefore, segment duration should be considered in the selection of \(B_{dang}\). However, with long segment durations and a small buffer size, it is not feasible to select \(B_{dang}\) based only on segment duration. For example, if the available segment is 10 s, and the buffer size is 20 s, setting \(B_{dang}\) equal to the segment duration means the client selects the video rate cautiously most of the time. Therefore, buffer size should also be considered in the selection of \(B_{dang}\). To this end, we set \(B_{dang}\) equal to the minimum segment duration plus 20% of the buffer size. If \(R_{s}\) is less than \(R_{prev}\) (line 9) and the predicted video rate is greater than 0.5 times the buffer size (line 11), the algorithm stays at the video rate selected for the previous segment. The reason is that there is enough buffer available to take the risk of staying at the current video rate. Otherwise, the algorithm reduces the video rate to \(R_{s}\).
## 3 The Conservative Maximum Power Estimation Procedure
* MPE estimation
* Calculation of the power of a Resource Element associated with the SSB with the highest power as \[P^{SSB}_{RE}=P^{SSB}/n_{RBW}\] (9)
* Calculation of the field amplitude \(E^{SSB}_{RE}\) as \[E^{SSB}_{RE}=\sqrt{\frac{P^{SSB}_{RE}Z_{in}}{\alpha}}\,AF\] (10) where \(Z_{in}\) (\(\Omega\)) is the input impedance of the instrument, \(\alpha\) is the power attenuation of the cable, and \(AF\) (1/m) is the Antenna Factor of the antenna connected to the measurement equipment;
* Calculation of the maximum level in the measurement location considering the REs of the broadcast beam \[B(\theta,\phi) =10^{B_{dB}(\theta,\phi)/10} \tag{3}\]0 (11) wherein \(E^{SSB}_{RE}\) is the power per RE of the signal used as a reference and transmitted along the broadcast beam, and \(E^{max\,SSB}\) is the maximum value of the field supposing a fully filled frame where all the REs are transmitted using a broadcast beam. * Identification of the angular range in which the measurement direction \((\theta_{0},\phi_{0})\) falls and the evaluation of \(F_{beam}(\theta_{0},\phi_{0})\) associated with this angular range is at the desired probability \(prob\); let \(F_{beam}^{prob}(\theta_{0},\phi_{0})\) be such a value. * Evaluation of the maximum EMF level in the measurement location with \(prob\) probability, as \[B(\theta,\phi) =10^{B_{dB}(\theta,\phi)/10} \tag{3}\]1 (12)  
It is understood that other procedures can be followed to obtain \(P_{SE}^{SSB}\). For example, if SS-RSRP is available, steps 2-3 can be substituted by the acquisition of SS-RSRP. Finally, the procedure has been described for one component of the electric field and must be repeated for each of the three components of the electric field [22]. To summarize, the procedure is simple and robust against many problems caused by the flexibility of the 5G standard.
## 3 Materials and Methods  
### ECG Databases  
All experiments were performed using selected records from public databases available in Physionet (2019). Each database has different files that correspond to the digitized signals, the information about the patients' annotations, and the labels for heartbeats in the records. The following databases were used:  
1. MIT-BIH Arrhythmia database (MITBIH), consisting of 48 half-hour signals recorded with two channels each one sampling at 360 samples per second with 11-bit resolution over 10 mV. The annotations for each heartbeat were made by cardiologists.
2. MIT-BIH Supraventricular Arrhythmia database (SUPRA), made up of 78 records, each 30 min long. Each record has two-lead signals, sampled at 120 Hz, with annotation files produced automatically first and then corrected by a medical student.
3. St. Petersburg Institute of Cardiological Technics (INCART) 12-lead Arrhythmia database, built up of 75 annotated records, each consisting of 12-lead signals sampled at 257 Hz for 30 min.  
Following the labels suggested by the AAMI and the subsequent classification proposed in [PERSON] and other works (2020, 2011, 2019), the heartbeats were relabeled to NB, SB, and VB. For the purpose of this first unsupervised work, the later labels were changed to their anatomic origin in the heart as SVBo and VBo.
###### Abstract  
Wireless communication advancements have significantly improved connectivity and user experience with each generation. The recent release of the framework M.2160 for the upcoming sixth generation (6G or IMT-2030) cellular wireless standard by ITU-R has significantly heightened expectations, particularly for Internet of Things (IoT) driven use cases. However, this progress introduces significant security risks, as technologies like O-RAN, terahertz communication, and native AI pose threats such as eavesdropping, supply chain vulnerabilities, model poisoning, and adversarial attacks. The increased exposure of sensitive data in 6G applications further intensifies these challenges. This necessitates a concerted effort from stakeholders including ITU-R, 3 GPP, ETSI, OEMs and researchers to embed security and resilience as core components of 6G. While research is advancing, establishing a comprehensive security framework remains a significant challenge. To address these evolving threats, our research proposes a dynamic security framework that emphasizes the integration of explainable AI (XAI) techniques like SHAP and LIME with advanced machine learning models to enhance decision-making transparency, improve security in complex 6G environments, and ensure effective detection and mitigation of emerging cyber threats. By refining model accuracy and ensuring alignment through recursive feature elimination and consistent cross-validation, our approach strengthens the overall security posture of the IoT-6G ecosystem, making it more resilient to adversarial attacks and other vulnerabilities.  
IoT security; artificial intelligence; 6G networks; XAI; SHAP; LIME; 6G security; machine learning; intrusion detection; network security +
Footnote †: This paper is an extended version of our paper published in [PERSON]; [PERSON] Enhancing IoT Security in 6G Environment with Transparent AI: Leveraging XGBoost, SHAP and LIME. In Proceedings of the 2024 IEEE 10 th International Conference on Network Softwarization (NetSoft), Saint Louis, MO, USA, 24-28 June 2024.