esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Latency Evaluation Using a Transatlantic Connection and an Edge VPN  
The final network scenario studied in this paper connected the collaborating researchers at the VTT Technical Research Centre of Finland to the Advanced Telecommunication lab in Lincoln, Nebraska, USA, as shown in Figure 6. For this test, both VTT and the University network utilized high-speed connections, with the major network challenge arising from the inclusion of a transatlantic Internet connection and the continued use of the Edge VPN.
# rStaple: A Robust Complementary Learning Method for Real-Time Object Tracking
## 5 Experimental Results  
We implemented rStaple in the MATLAB 2018b environment on a standard PC with i5-8400 and 16 GB RAM. The proposed tracking algorithm was evaluated on video sequences from OTB2013 [10] and OTB2015 [11] datasets and compared with 12 state-of-the-art trackers, namely, KCF [20], DSST [21], fDSST [22], Staple [4], SRDCF [23], dual linear structured support vector machine (DLSSVM) [45], compressive tracking (CT) [46], CSK [19], long-term correlation tracking (LCT) [24], SiamFC [29], CFNet [30] and ACFN [31]. Of these, SiamFC, CFNet, and ACFN are deep learning-based algorithms. KCF, DSST, fDSST, Staple, SRDCF, LCT, CT, and CSK are correlation filter -based trackers. To show the results clearly and avoid overly dense result curves, the proposed tracker is compared with nine trackers without deep learning on OTB2013. In addition, the deep learning-based algorithms and several trackers that perform well on OTB2013 are compared with our proposed tracker on OTB2015.
## 5 Example Analysis
### Analysis of the Simulation Results
#### 5.2.2 Voltage Deviation Analysis of the Distribution Network Comprising the Micro Energy Grid  
The average voltage deviations before and after the grid connection of the micro energy grid, on a typical day in the winter (a), and in the summer (b), are shown in Figure 10. When the proposed model was used, the average reduction in the average voltage deviation was 6.4% in the winter, and 2.7% in the summer. The voltage fluctuation after the grid connection of the micro energy grid was significantly reduced when compared with that before the grid connection. The micro-energy-grid level supported the voltage of the distribution-network level, and improved the stability of the system voltage.  
Figure 9: Comparison of the network’s active power loss before and after the grid connection of the micro energy grid on a typical day in the winter (a), and in the summer (b).
## 4 Experiments and Evaluations
### Optimizing the Experimental Results  
The adversarial samples were marked as malicious samples of XSS attacks, and the DeepXSS model was retrained. The experimental results of 10 rounds of alternate training between the detection model and the adversarial model are shown in Figure 7.  
The experimental results showed that the detection rate of DeepXSS was continuously increased, and the escape rate was continuously reduced. Finally, the escape rate of the DeepXSS model was reduced to less than 1%. This proved that the ability of the defense adversarial attack of the detection model can be improved by alternate training of the detection model and adversarial model.  
Figure 6: Examples of generic XSS escape strategies for XSSChop.  
Figure 7: Optimizing experiment results.  
In summary, RLXSS can effectively mine adversarial samples. By marking adversarial samples as malicious samples, the detection model was retrained. The adversarial sample space of the detection model was compressed, so that the detection rate was improved and the escape rate reduced. Thus, the ability of the detection model to defend against adversarial attack was effectively optimized.
# Privacy-Preserving Federated Singular Value Decomposition
###### Abstract  
Singular value decomposition (SVD) is a fundamental technique widely used in various applications, such as recommendation systems and principal component analyses. In recent years, the need for privacy-preserving computations has been increasing constantly, which concerns SVD as well. Federated SVD has emerged as a promising approach that enables collaborative SVD computation without sharing raw data. However, existing federated approaches still need improvements regarding privacy guarantees and utility preservation. This paper moves a step further towards these directions: we propose two enhanced federated SVD schemes focusing on utility and privacy, respectively. Using a recommendation system use-case with real-world data, we demonstrate that our schemes outperform the state-of-the-art federated SVD solution. Our utility-enhanced scheme (utilizing secure aggregation) improves the final utility and the convergence speed by more than 2.5 times compared with the existing state-of-the-art approach. In contrast, our privacy-enhancing scheme (utilizing differential privacy) provides more robust privacy protection while improving the same aspect by more than 25%.  
singular value decomposition; federated learning; secure aggregation; differential privacy +
Footnote †: journal: Applied
# Toward Efficient Mobile Electric Vehicle Charging under Heterogeneous Battery Switching Technology
## 1 Introduction  
EV refers to a vehicle that is powered by electric energy. Normally, an EV can be charged from an external energy source and also discharged to an external energy source via a wall socket. Following the advancement of technology in the automobile industry and sustainable energy development, an EV can be compared to an Internal Combustion Engine Vehicle (\(ICEV\)) with the benefits of less emissions and air pollution [1]. As such, a lot of countries have pledged millions and billions of dollars to fund the development of EV to replace conventional vehicles with EVs. According to studies from the Center of Solar Energy and Hydrogen Research, the total number of EVs in the global market was 740,000 in early 2015. It is predicted that in the coming 50 years, the number of operational EV will reach 2.5 billion [2].  
The proliferation and development of EV has derived both challenges and opportunities for the power grid [3]. On one hand, heavy load generation caused by EV integration into the power grid raises some issues such as operating cost, frequency excursion, and voltage stability at both (transmission and generation) side [4, 5]. On the other hand, bi-directional charging and discharging have an extensive application of EV into the power grid, including peak shaving, frequency fluctuation mitigation, load flattening, and improving the integration of renewable sources [6]. The charging management of EV has been investigated in parking mode and in an on-the-move mode of charging. In the parking mode, an EV is parked at home/Charging Station (CS). On the other hand, EV's positionis non-stationary in on-the-move mode. Without regard to modes, a major concern is to determine the time and place to charge EV [7].
## 3 Results and Discussion
### Establishment of LC-MS/MS MRM Conditions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Pesticides**} & \multirow{2}{*}{**RT (min)**} & \multicolumn{4}{c}{**Precursor \textgreater{} Product Ion (CE, eV)**} \\ \cline{3-6}  & & & \multicolumn{2}{c}{**Quantiifier Ion**} & \multicolumn{2}{c}{**Qualifier Ion**} \\ \hline Cyhalothrin-1 & 13.196 & 197.00 \(>\) 141.10 & 12 & 197.00 \(>\) 161.10 & 6 \\ Cyhalothrin-2 & 13.365 & 197.00 \(>\) 141.10 & 12 & 197.00 \(>\) 161.10 & 6 \\ Cyermethrin-1 & 14.938 & 163.00 \(>\) 127.10 & 9 & 163.00 \(>\) 109.00 & 24 \\ Cyermethrin-2 & 15.042 & 163.00 \(>\) 127.10 & 9 & 163.00 \(>\) 109.00 & 24 \\ Cyermethrin-3 & 15.092 & 163.00 \(>\) 127.10 & 9 & 163.00 \(>\) 109.00 & 24 \\ Cyermethrin-4 & 15.138 & 163.00 \(>\) 127.10 & 9 & 163.00 \(>\) 109.00 & 24 \\ Cyprodil & 9.527 & 224.00 \(>\) 208.10 & 21 & 225.00 \(>\) 210.10 & 18 \\ DDD-p,p\({}^{\prime}\) & 11.121 & 235.00 \(>\) 165.10 & 24 & 235.00 \(>\) 199.00 & 18 \\ DDE-p,p\({}^{\prime}\) & 10.466 & 246.00 \(>\) 176.10 & 27 & 318.00 \(>\) 246.00 & 21 \\ DDT-o,p\({}^{\prime}\) & 11.12 & 235.00 \(>\) 165.10 & 24 & 235.00 \(>\) 199.10 & 18 \\ DDT-p,p\({}^{\prime}\) & 11.702 & 235.00 \(>\) 165.10 & 24 & 235.00 \(>\) 199.00 & 18 \\ Deltamethrin & 16.511 & 253.00 \(>\) 172.00 & 0 & 253.00 \(>\) 174.00 & 0 \\ Dicofop-methyl & 11.925 & 253.00 \(>\) 162.10 & 21 & 340.00 \(>\) 253.00 & 18 \\ Dicloran & 7.704 & 206.00 \(>\) 176.00 & 12 & 206.00 \(>\) 124.10 & 27 \\ Dicofol & 9.289 & 139.00 \(>\) 111.10 & 15 & 250.00 \(>\) 139.10 & 15 \\ Dieldrin & 10.565 & 279.00 \(>\) 206.90 & 27 & 263.00 \(>\) 192.90 &
# Multi-Source Data Fusion for Vehicle Maintenance Project Prediction
## 3 Method
### Multi-Source Data Deep Fusion Network
#### 3.4.1 Mileage Fusion Representation  
Mileage is an important facet of vehicle condition. In general, higher mileage corresponds to an increase in the type and frequency of maintenance projects required.  
In the evaluation of vehicle maintenance tasks, it is imperative for maintenance personnel to first grasp the present state of the vehicle, encompassing its historical maintenance projects and mileage. Drawing on this information, maintenance staff can make initial inferences about the ongoing maintenance projects necessary for the vehicle. Yet, due to distinct driving patterns exhibited by different vehicles, the impact of mileage on maintenanceprojects varies across vehicles. To tackle this challenge, we propose a mileage-aware key query attention mechanism that discerns pivotal mileage thresholds in the development of vehicle maintenance projects. Herein, the correlation representation result of each project serves as the query vector, while the mileage of each maintenance episode forms the key and value vectors. Notably, the raw mileage \(\{M_{t}\}_{t=1,2,\ldots,T}\) and the correlation representation results of the maintenance project \(\{R_{t}\}_{t=1,2,\ldots,T}\) are not in the same potential space, and it becomes imperative to map \(\{M_{t}\}_{t=1,2,\ldots,T}\) to the same potential space as \(\{R_{t}\}_{t=1,2,\ldots,T}\) to obtain the mapping result \(\{N_{t}\}_{t=1,2,\ldots,T}\). This is achieved by \(F_{2}(M_{t})\)[25], as illustrated in Equation (3).  
\[F_{2}(M_{t})=W_{2}\left[1-\tanh\left(\left(W_{1}\frac{M_{t}}{180}+b_{1}\right) ^{2}\right)+b_{2}\right] \tag{3}\]  
where \(W_{1}\in\mathbb{R}^{|P|}\), \(b_{1}\in\mathbb{R}^{|P|}\), \(W_{2}\in\mathbb{R}^{|P|\times|P|}\), and \(b_{2}\in\mathbb{R}^{|P|}\) are parameters. Once we have obtained the potential space result \(\{N_{t}\}_{t=1,2,\ldots,T}\) that aligns with \(\{R_{t}\}_{t=1,2,\ldots,T}\), we input \(\{N_{t}\}_{t=1,2,\ldots,T}\) as the query vector and \(\{R_{t}\}_{t=1,2,\ldots,T}\) as the key and value vectors into the attention mechanism one by one. The specific implementation is illustrated in Equation (4).  
\[O_{t}=\text{Atten}(N_{t},R_{t},R_{t}) \tag{4}\]  
The attention mechanism [12] in the above equation is defined as shown in Equation (5):  
\[\text{Atten}(Q,K,V)=\text{softmax}\left(\frac{QW_{q}(KW_{k})^{T}}{\sqrt{d}} \right)VW_{v} \tag{5}\]  
Attention weights are defined as shown in Equation (6):  
\[\text{Atten-weight}(Q,K,V)=\text{softmax}\left(\frac{QW_{q}(KW_{k})^{T}}{\sqrt {d}}\right) \tag{6}\]  
where \(d\) is the dimension of attention and \(W_{q},W_{k}\in\mathbb{R}^{|P|\times d},W_{v}\in\mathbb{R}^{|P|\times|P|}\) are attention weights. For each maintenance project and mileage of the vehicle, the attention mechanism is fused one by one and merged to obtain the result of the historical mileage-fused attention representation of the vehicle \(E=[O_{1};O_{2};\ldots;O_{T}]\).
# Research on Agricultural Product Price Prediction Based on Improved PSO-GA
## 3 Materials and Methods
### Methodological Framework of the Experiment  
In summary, the combined forecasting framework based on the improved PSO-GA weight assignment method proposed in this study is shown in Figure 3, below, which includes four parts: data preprocessing, single forecasting, combined forecasting, and determining combined model weights. First, the original dataset is decomposed and denoised using the three techniques of SSA, EMD, and VMD to filter the external noise of the original price series and obtain the reconstructed price series. Second, the reconstructed prices are used as input sequences for a single model, which are combined with the three techniques, ARIMA, LSTM, and GRUs, respectively, to generate diverse base models for selection. Finally, all the base models are divided into different combinations under the three combination strategies of isomorphism, heteromorphism, and semi-heteromorphism, and the improved PSO-GA, a weight optimization approach, is used to combine the individual base models in the group to form the prediction results of each combination. This method has the advantages of better scalability and easy implementation, while improving the prediction accuracy and robustness.  
Figure 3: Optimal forecast combination based on PSO-GA approach.
## 3 Results and Discussion
### Adsorption Isotherms  
#### 3.4.1 Langmuir Isotherm  
It was found that the isotherms were linear over the entire range of tested concentrations, and \(R^{2}\) values equaled to 0.998, 0.991 and 0.990 for A24, A21 and A23, respectively (Table 4). The Langmuir equation represents a better fit of experimental data than the Freundlich equation (Figure 8). The values of Langmuir parameters largely depended on the type of anion exchanger matrix. The monolayer adsorption capacity of A24 was found to be 666.5 mg/g and was larger compared to A23 (\(Q_{0}\) = 284.3 mg/g). A high b value suggests a steep isotherm onset and indicates a high affinity of the anion exchanger for the  
Figure 7: The effects of (**a**) anionic SDS and (**b**) non-ionic TX-100 surfactants presence on DY50 adsorption on A21, A23 and A24 anion exchangers.  
dye [14]. The \(b\) constants for A24, A21 and A23 were 0.0834, 0.0460 and 0.0035, respectively. The highest value was obtained for polyacrylic Amberlyst A24 suggesting strong direct dye binding. The dimensionless separation factor \(R_{L}\) calculated from Equation (7) for the Langmuir type adsorption process were in the range 0 and 1 as can be seen in Figure 8.  
Research conducted by [PERSON] et al. [49] concerning DY50 removal using the polyacrylic (Amberlite IRA478, Amberlite IRA958) and polystyrene (Lewatit MonoPlus MP68, Amberlite IRA900) anion exchange resins of different basicity of functionalities as well as non-functionalized polymeric resins of polyacrylic and polystyrene matrices (Amberlite XAD7, Lewatit VPOC1064) confirm that this process depends on the composition and the structure of the anion exchanger. The highest sorption capacity towards DY50 was exhibited by the polyacrylic anion exchanger Amberlite IRA 958 of the quaternary ammonium functional groups and it was 534.8 mg/g. Moreover, in the case of adsorption of direct dyes on palacrylic anion exchangers, one can also notice much greater efficiency of the adsorption process using weakly basic resins compared to strongly basic ones, which was confirmed in paper [37] describing adsorption of C.I. Direct Red 75 on the weakly and strongly basic gel resins of polyacrylic skeletons such as Amberlite IRA 67 and Amberlite IRA 458.
# Towards a GDPR-Compliant Blockchain-Based COVID Vaccination Passport
## 5 Discussion
### Limitations  
This architecture is entirely a conceptual framework that can be developed with appropriate technical measures. Considering the stakeholder requirements in various organizations, the components inside the architecture and interaction among them can change. Moreover, real-time testing was not done, and the development phase is yet to be implemented.  
One limitation of the proposed architecture is that it provides a mechanism for storing vaccination data, however, in line with the EU recommendations COVID passports should also be able to store negative COVID test results and recovery information from COVID in case of the previous infection.  
Though the consortium blockchain uses pre-defined nodes for block validation, an insider attack is a possibility. If any node acts dishonestly it can hamper the validation process. One probable solution can be removing the dishonest nodes immediately upon finding out the malicious activity. Identifying the attacker is relatively easy in this architecture since the level of anonymity is much lower.  
Another limitation is related to the GDPR's right to be forgotten policy. This is an issue related to the blockchain characteristics. Blockchain can act as an enabler of GDPR compliance, but there remains an argument that, as the data cannot be deleted from blockchain, it contradicts Article 17. We have addressed this issue here by storing only the hash of personal data onto the blockchain. Moreover, the architecture performs logical deletion and the transactions remaining on the blockchain cannot identify a data subject. In addition, after the logical deletion, original data is also deleted from the off-chain.
# An Anti-Interference Method Based on Energy Residual Searching in GNSS Positioning Applications
###### Abstract  
Addressing the issue of linear frequency modulation (LFM) interference in GNSS positioning, this paper proposes an interference suppression method based on energy residual searching, incorporating the fractional Fourier transform (FrFT) for improved performance. In GNSS systems, LFM interference can severely impair positioning accuracy and system robustness, potentially rendering the receiver incapable of performing accurate localization. To address this issue, an energy residual function is introduced to quantitatively assess the impact of interference on the original signal. This function combines differences in signal energy with signal quality after spectral line removal to achieve effective interference evaluation. By optimizing the parameters of the fractional Fourier transform to maximize the value of the energy residual function the proposed method significantly enhances interference suppression. Our experimental results demonstrate that the method performs exceptionally well on practical datasets, effectively removing interference and restoring relevant peaks. This approach mitigates the negative effects of LFM interference on GNSS positioning performance, substantially improving both positioning accuracy and system robustness.  
global navigation satellite system; interference detection and mitigation; fractional Fourier transform; signal acquisition +
Footnote †: journal: Electronics Electronics
## 3 Results and Discussion  
### Soil Chemical Properties  
The selected soils for comparing organic and conventional management systems of this study differ mainly by their pH and carbonates content. pH of acid soils ranges between 5.1 and 5.7, whereas pH of calcareous soils is very similar and slightly above neutrality (from 7.4 to 7.5) (Table 1). Carbonates are absent in the acid soils and variable between 36 to 118 g kg\({}^{-1}\) in the calcareous soils.  
Soil total organic C (TOC) does not differ so much in the four soils (from 17.1 to 24.6 g kg\({}^{-1}\) in acid soils and from 20.3 to 22.8 in calcareous soils), being generally lower than average values of Slovenian vineyard soils (i.e., 45 g kg\({}^{-1}\)) [45].  
Long-term studies in Europe and China show that organic farming leads to a general increase in soil organic matter [46], but in our case there was no significant difference in TOC content between organic and conventional calcareous soils. The main reason lies,probably, in the fact that all the vineyards in the study were inter-row cropped with grass for a long time. In vineyards with cover crops, the soil organic matter content is generally higher than vineyards with tilted conventional management systems. In Slovenia, for several reasons (e.g., soil organic matter depletion, soil erosion by wind or water, etc.), about 85% of vineyards were planted with cover crops in 2020 [47].  
Dissolved organic carbon (DOC) was significantly lower in the acid organic vineyard soil in comparison to other soils (Table 1) but on the same order of magnitude.  
Total Cu was similar in all four soils, ranging between 105 and 143 mg kg\({}^{-1}\), above the threshold at which soil is considered to be at risk for human health (100 mg kg\({}^{-1}\)) [48]. These values are also above the mean and median of European [3] and Italian [49] vineyard soils and well above the critical value of 60 mg kg\({}^{-1}\), respectively, stipulated by Slovenian legislation [50].  
Olsen P was very high in our soils with the exception of the calcareous conventional soil. Ammonium-N (NH\({}_{4}\)+-N) was significantly higher in the calcareous organic soil, whereas nitrate-N (NO\({}_{3}\)\({}^{-}\)-N) was significantly lower in the calcareous conventional soil than in other soils. Regarding textural composition, acid soils were richer in sand, whereas calcareous soils were richer in silt and clay.  
These four soils were selected to be as similar as possible in most chemical properties, TOC and total Cu in particular, with the exception of pH, in order to simplify comparisons of organic carbon dynamics and Cu behavior under climatic stresses (i.e., moisture, temperature, and combined stress).
# Uplink NOMA-MIMO Systems with Rayleigh Power Distribution
## 5 Conclusions  
In the article, based on the analysis of the capacity of a discrete-continuous MU-MIMO uplink channel, the possibility of such systems functioning in overload mode is proved. At the same time, multiuser systems with such a configuration can be considered as communication systems with NOMA technology with separation in power domain, and implemented naturally due to independent Rayleigh fading.  
Figure 5: Dependencies of FER from SNR for the MU-MIMO uplink system with different numbers of users using FEC in overload mode.  
It is shown that at high SNR ratios, the total channel rate of such systems increases proportionally to the total number of transmitting antennas of all users in the system \(N_{tx,\Sigma}\), even if it is greater than the number of receiving antennas at the base station \(N_{tx,\Sigma}>N_{rx}\), i.e., in overload mode. This conclusion is at odds with the well-established opinion, based on an analysis of the continuous channel capacity, that MIMO systems are inefficient under overload conditions.  
Conclusions on the possibility of functioning MU-MIMO uplink systems under overload conditions are confirmed by the simulation results.  
Conceptualization, V.K. and M.B.; methodology, V.K. and M.B.; software, T.B.R. and A.S.; formal analysis, D.P.; investigation, D.P.; writing--original draft preparation, T.B.R. and A.S.; writing--review and editing, D.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Institutional review board statement is not relevant for MTUCI university.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.