esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Smote  
Dealing with imbalanced datasets, where one class is significantly more represented than the other, is a challenging task in supervised learning as traditional classification algorithms are designed to work with balanced class distributions. One solution is oversampling, which artificially increases the number of samples in the minority class to balance the class distribution. One popular oversampling technique is SMOTE, which generates new synthetic samples of the minority class by interpolating the feature space between existing minority class samples and their closest neighbors [29]. This allows for a more balanced dataset and can improve the performance of machine learning models.  
There have been numerous studies that have proven that using SMOTE can significantly enhance the precision of classification. [PERSON] et al. [30] proposed a model that utilizes SMOTE to tackle the problem of imbalanced datasets, to be used as a tool by banks to evaluate enterprise credit. When tested using financial data from 552 Chinese listed companies, the proposed model outperformed traditional models. [PERSON] et al. [31] used various oversampling techniques to address imbalance issues in a financial dataset collected from Korean organizations between 2016 and 2017. The results showed that a combination of SMOTE and Edited Nearest Neighbor (SMOTE + ENN), as well as RF, achieved the highest accuracy in predicting bankruptcy. In another study, a method that combines SMOTE with SVM was proposed to enhance the prediction accuracy for identifying old banknotes [32]. The findings revealed that the proposed method could improve performance by as much as 20% compared to the standard SVM algorithm.
# SubvectorS_Geo: A Neural-Network-Based IPv6 Geolocation Algorithm
## 4 Experimental Results and Discussion
### Comparison and Verification  
For accuracy, in Table 3, the median error of the proposed method in the Shanghai dataset was improved by 16.4% compared to the optimal baseline. Compared to the optimal baseline, the maximum error and average error were improved by 3.0% and 18.8%, respectively. In the New York State dataset, the median error, maximum error, and average error values of this algorithm were improved by 6.8%, 1.6%, and 9.2%, respectively, compared to the optimal baseline. In the Tokyo dataset, the median error, maximum error, and average error of this algorithm compared to the optimal baseline were improved by 11.2%, 4.3%, and 12.1%, respectively.  
The errors of the four geolocation algorithms in the three different regions were analyzed. Figure 5 shows the cumulative distribution of the geolocation errors for their locatable entities. Furthermore, to show more clearly the geolocation performance of the algorithm at the street level (<10 km), we produced a map of the cumulative distribution of the geolocation errors for locatable entities within 10 km. In addition, the CDF images overlap at large values of the error distances. To solve this problem, we zoomed in on the CDF overlapping region images of each of the three regions. Combined with the overall CDF image in Figure 5, our IPv6 geolocation algorithm outperformed the existing state-of-the-art algorithms for geolocation between 10 km and 50 km.
# A Seabed Real-Time Sensing System for In-Situ Long-Term Multi-Parameter Observation Applications
## System Design of SRSS/ILMO  
### System Structure Design  
As shown in Figure 1, the proposed SRSS/ILMO system consists of a seabed observation system, a sea surface relay transmission buoy, and a remote monitoring system. The seabed observation system and the sea surface relay transmission buoy exchange information through underwater acoustic communication, and the sea surface relay transmission buoy is connected to the remote monitoring system through satellite communication. At the same time, the seabed observation system can also be directly connected to the ship-borne operation monitoring system through underwater acoustic communication.  
The SRSS/ILMO can also be used for networked observation in the future. As shown in Figure 2, the SRSS/ILMO can support a networked submarine observation system with multiple seabed observation systems, multiple surface relay transmission buoys, multiple remote monitoring systems, and their combination.  
Figure 1: System structure of the seabed real-time sensing system for in-situ long-term multi-parameter observation applications (SRSS/ILMO).
## 3 Results
### Multifactorial Optimal Settings with Quadratic Effects
Additionally, to obtain the optimal setting for factor D,  
\[\frac{\partial COD}{\partial D}=0\ \Rightarrow\ D=\frac{-(-5.32)}{2\cdot(4.07)} \approx 0.65\]  
The optimal setting is within the second and third preset adjustment; it is closer to the third setting. Hence, for factor D(MWCO), the recommendation seems to be a setting of 100 kDa. Finally, in minimizing the cumulative flux decline response, factors A (TMP), B (CFV), C (temperature), and D (MWCO) may be simply set at 1.0 bar, 1.041 m/s, 15.0 \({}^{\circ}\)C, and 10 kDa, respectively. Based on this analysis, the optimal settings for the average permeate flux and COD rejection rate point in the same direction as expected. If the cumulative flux decline is to be taken in account, there will be agreement only on the CFV optimal setting for all three characteristics. However, it is the TMP and MWCO factors that will control the direction of concurrent optimization, depending on the weights that each characteristic response will receive in a realistic scenario. \begin{table}
\begin{tabular}{l r r r r r r} \hline \hline  & \multicolumn{4}{c}{**Quality Characteristic**} \\  & \multicolumn{2}{c}{**J\({}_{\rm P}\)**} & \multicolumn{2}{c}{**COD**} & \multicolumn{2}{c}{**SFD**} \\
**Coefficients** & **Estimate** & \(\mathbf{p}\)**-Value** & **Estimate** & \(\mathbf{p}\)**-Value** & **Estimate** & \(\mathbf{p}\)**-Value** \\ \hline
**Intercept** & 57.37 & \(<\)0.001 & 38.71 & \(<\)0.001 & 5.53 & \(<\)0.001 \\
**A** & 18.04 & \(<\)0.001 & 2.01 & \(<\)0.001 & 1.58 & \(<\)0.001 \\
**A\({}^{2}\)** & \(-\)8.83 & \(<\)0.001 & \(-\)0.4 & 0.461 & 0.54 & 0.014 \\
**B** & \(-\)0.18 & 0.886 & 3.29 & \(<\)0.001 & \(-\)0.51 & \(<\)0.001 \\
**B\({}^{2}\)** & \(-\)290 & 0.191 & 0.79 & 0.155 & \(-\)0.49 & 0.024 \\
**C** & \(-\)2.62 & 0.047 & \(-\)0.56 & 0.083 & 0.46 & \(<\)0.001 \\
**C\({}^{2}\)** & 2.85 & 0.198 & 3.58 & \(<\)0.001 & 0.11 & 0.591 \\
**D** & 15.81 & \(<\)0.001 & \(-\)5.32 & \(<\)0.001 & 1.7 & \(<\)0.001 \\
**D\({}^{2}\)** & 1.54 & 0.479 & 4.07 & \(<\)0.001 & 0.36 & 0.09 \\
**Multiple-Adjusted R\({}^{2}\)** & 0.94 & & 0.96 & & & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Regression coefficients (quadratic model) for the characteristic responses: (1) average permeate flux (J\({}_{\rm P}\)), (2) COD rejection rate (COD), and (3) cumulative flux decline (SFD).
# Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
## 2 Proposed Frequency-Based Haze and Rain Removal Network (FHRR-Net) Framework
### Frequency-Based Haze and Rain Removal Network (FHRR-Net) Architecture
#### 2.3.3 Loss Functions for Training  
Since we estimate the output through two encoder-decoder networks, we need to define two loss functions for high-frequency and low-frequency network. L1 error is known to produce better results in image restoration than L2 loss. This phenomenon is due to the convergence problem characteristics: L2 tends to converge into to the local minimum, but L1 converges well to the global minimum in image restoration [31]. Therefore, we train each encoder-decoder network using L1 errors to get better image restoration results from training dataset. Based on the introduced statement, the loss function of the high-frequency encoder-decoder is as follows:  
\[L_{H}=\sum_{x\in X}|F_{H}(O_{high}(x))+O(x)-I(x)|, \tag{7}\]  
where \(x\) denotes the pixel in image spatial domain \(X\), and \(F_{H}\) denotes proposed high frequency encoder-decoder network. \(O_{high}\) is the high-frequency region of the input image which is obtained from guided filter as mentioned. \(I\) denotes a ground truth image where only haze exists. Through the loss function, the high-frequency encoder-decoder network trains and outputs rain-removed images from the haze and rain images.  
Second, the loss function of the low-frequency encoder-decoder is as follows:  
\[L_{L}=\sum_{x\in X}|F_{L}(O_{low}(x))+O(x)-t(x)|, \tag{8}\]  
where \(t\) denotes transmission map of the input image, \(O_{low}\) is the low-frequency region of the input images, and \(F_{L}\) denotes proposed low frequency encoder-decoder network. Unlike the high-frequency encoder-decoder network, the ground truth of the low-frequency loss function is the transmission map. The other parameters of the loss function are the same as the parameters of the high-frequency loss function.  
Since the two-loss functions has same scale, the total loss is a made with addition operation.The total loss function can be expressed as follows:  
\[L_{T}=\alpha\cdot L_{L}+(1-\alpha)\cdot L_{H}, \tag{9}\]  
where \(L_{T}\) is the total loss function. The network is trained through optimizing function in (9). We empirically set the alpha to 0.7 for training and design our network structure, as described throughout this section.
# A New Passive Islanding Detection Solution Based on Accumulated Phase Angle Drift
###### Abstract
The overall performances on islanding detection of ROCOF relay and VVS relay depend on the condition of power imbalance. Moreover, mal-operations of VVS relay have been reported at the external system fault inception and the instant of fault clearance [17]. The ROCOF relay maybe become ineffective for islanding detection when the operation time delay is applied and the ROCOF value is not stable at the initial stage after islanding [18]. For VVS and ROCOF relays, there are user concerns on both sensitivity and reliability. There are obvious benefits if the existing passive methods can be improved to provide better sensitivity and reliability for islanding detection. This paper presents a composite passive islanding detection method which includes both VVS relay and ROCOF relay operating principles. The operation criterion is based on the accumulated phase angle drift. The dynamic response of inverter-interfaced DGs mostly depend on the control algorithms of the inverters. Normally, the inverter controllers will become unstable shortly after loss of the reference signal due to loss of main grid. Islanding detection is relatively easy for inverter-interfaced DGs and the inverter controller provides the islanding detection function. Besides, both ROCOF relay and VVS relay are applicable to inverter-interfaced DGs. The proposed solution is an improvement based on the existing ROCOF relay and VVS relay. To simplify the analysis, this proposed islanding detection solution is introduced in the presence of synchronous generator type DG, but it is also applicable for inverter-interfaced DGs. One similar passive islanding detection solution is presented in Reference [19]. Compared with Reference [19], this paper improves the solution, and provides more details on the application, full simulation tests, and prototype tests. This paper is organized as follows: Section 2 introduces the principles of ROCOF relay and VVS relay in the presence of synchronous generator type DG. Sections 3 and 4 introduces the principle and application guide of the proposed composite islanding detection method. Section 5 presents the verification results of the proposed method based on Matlab Simscape simulation tests. Section 6 presents the playback tests with the practical waveforms recorded from the engineering sites. Section 7 concludes this paper.
## 2 Methodology
### Simulation Model  
The data types discussed so far are input to, exchanged between, or output from modules consisting the entire simulation. Each module corresponds to one subsystem of a remote sensing satellite. In general, the following subsystems are of special interest: Optics subsystem for Earth observation; propulsion subsystem for stationkeeping; and guidance, navigation, and control (GNC) subsystem for determination of satellite orientation and location. These subsystems correspond to optics, propulsion, and astrodynamics modules in Figure 2, whose inputs and outputs are summarized in Table 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Objective** & **Description** & **Unit** \\ \hline _rom\_revisit_ & ROM revisit time & s \\ _gom\_coverage_ & GOM temporal coverage & \% \\ _reconfg\_time_ & Reconfiguration time between GOM and ROM & days \\ _const\_mass_ & Constellation total mass & kg \\ \hline \hline \end{tabular}
\end{table}
Table 6: Objectives of ReCon simulation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Constraint** & **Description** & **Value** & **Unit** \\ \hline _min\_alt_ & Minimum altitude & 350 & km \\ _max\_alt_ & Maximum altitude & 1200 & km \\ _max\_regard_ & Maximum field of regard & 50 & \({}^{\circ}\) \\ _max\_aperture_ & Maximum aperture diameter & 1.8 & m \\ _max\_prop\_frac_ & Maximum propellant mass fraction & 0.3 & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Constraints imposed in ReCon simulation.
## 3 Results
### Repeatability, Field of View, and Climate Tests
The successive images in Figure 6e-g, taken at t\({}_{1}\) < 30 ms, t\({}_{2}\) = 67 ms, and t\({}_{3}\) = 350 ms, document the progressive improvement in the visibility of the object. At the time t\({}_{3}\), which exceeds the device's response time, the center of the image displayed a minimal oversaturated spot, with the majority of the field remaining clear and detailed. This test effectively demonstrates the capability of the proposed NLC TN-based optical power limiter to provide substantial protection for CCD cameras against high-intensity laser beams. This confirms its operational efficacy in scenarios that closely resemble potential real-life applications.
# Benefit Analysis of Grid-Connected Floating Photovoltaic System on the Hydropower Reservoir
## 3 Economy-Financial Analysis
### National Economy Analysis of the Plant
#### 3.2.3 Other Effects  
The floating PV plant at Da Mi hydropower reservoir, Binh Thuan province will play an important role in the development of the region's electricity grid, as a basis for facilitating the development of other industries in the locality.  
In addition, the construction of a PV plant on a hydroelectric reservoir is intended to make the most of the plant's available resources to improve economic efficiency, and at the same time, to reduce the land fund used in the electricity production process. This also benefits the economy.  
When the plant is put into operation, the number of human resources used in the PV plant is minimal. However, the PV plant needs to use trained local human resources, and some jobs use local untrained workers. This also contributes to increasing the income and stabilizing the lives of workers.  
For state budget payment: will contribute to state budget revenue through taxes.
## 5 Discussion
### Comprehensive Comparison of Global and Local Interpretability  
The results indicate that SHAP demonstrates considerable advantages in global interpretability, offering complete, coherent, and auditable explanations, which are essential for ensuring algorithmic decision traceability in forensic and judicial scenarios, where transparency and legal defensibility are paramount [2]. This capability is reinforced when  
Figure 9: XAI metrics comparison: XGBoost vs. Tabnet.  
combined with tree-based models such as XGBoost, where SHAP not only achieves higher stability and fidelity, but also delivers robust behavior under perturbations, a critical element for the acceptability of explanations in forensic audits and digital examinations [3]. In contrast, LIME, although limited in fidelity and consistency, offers significant operational advantages in scenarios where agility is required, providing localized, instance-level explanations, which are particularly valuable in incident-response activities, alert triage, and rapid classification within security operations centers (SOCs). Nevertheless, its use should be restricted to exploratory or diagnostic functions, as its explanations present lower stability, especially when applied to deep learning models like TabNet, thereby increasing the risk of interpretive errors in highly complex scenarios [38].  
Our findings contrast with those of [5] who employed explainability techniques primarily for feature selection in IDS models, rather than for forensic analysis. While their approach improved model interpretability, it did not evaluate forensic auditability or explanation stability. In contrast, our work explicitly measures explanation fidelity and forensic relevance using both SHAP and LIME, providing a more targeted assessment for digital-evidence contexts. Similarly, [PERSON] et al. [6] focused on improving detection accuracy through ensemble deep learning methods, but without integrating explainable AI (XAI) components. As a result, their approach lacked the traceability and accountability required in forensic environments. Our study fills this gap by evaluating the forensic robustness of XAI-driven IDS models and quantifying their reliability in legally sensitive scenarios.  
These differences highlight the novelty and applied contribution of our work in bridging XAI and forensic cybersecurity.
###### Abstract  
This study aimed to evaluate the ability of a hydraulic resistance device (HRD) to assess the force and power output and force-velocity-power profile of short sprints, while examining the effects of hydraulic overload on these outcomes. Twenty-eight amateur athletes performed 20 m sprints under minimal (MiL), moderate (MoL), and high (HiL) overloads. Sprint velocity was measured with the HRD, while resistance force (F\({}_{\text{r}}\)) was assessed from the pressure via the HRD and from the reaction force via the force plate (FP). Using velocity and F\({}_{\text{r}}\) during the sprints, maximal velocity (v\({}_{\text{max}}\)), average horizontal force (F\({}_{\text{avg}}\)), average power (P\({}_{\text{avg}}\)), and FvP profile variables (F\({}_{0}\), v\({}_{0}\), and P\({}_{\text{max}}\)) were calculated. A two-way ANOVA analysed the effects of overload and calculation method. In addition, a correlation between the HRD and FP measurements was evaluated. For all variables, very high to excellent correlation between the HRD and FP was observed (r \(\geq\) 0.96). However, the F\({}_{\text{avg}}\), P\({}_{\text{avg}}\), F\({}_{0}\), and P\({}_{\text{max}}\) calculated by the HRD were lower than the FP across all overloads (\(\eta^{2}\geq\) 0.51; \(p\) < 0.001). Regardless of the method used, F\({}_{\text{avg}}\), P\({}_{\text{avg}}\), and F\({}_{0}\) were highest at HiL (\(\eta^{2}\geq\) 0.38; \(p\) < 0.001), and v\({}_{0}\) was highest at HiL (\(\eta^{2}\) = 0.35; \(p\) < 0.001), whereas overload had no significant effect on P\({}_{\text{max}}\) (\(\eta^{2}\) = 0.01; \(p\) = 0.770). The HRD is a feasible means for monitoring force and power output during hydraulic resisted sprints but should not be directly compared to other resistance devices. HiL produced the highest F\({}_{\text{avg}}\), P\({}_{\text{avg}}\), and F\({}_{0}\) and may be optimal for increasing power output and improving acceleration performance.  
Article
# View Synthesis with Scene Recognition for Cross-View Image Localization
## 3 System Design
### Attribute Extractor  
In order to precisely select the patch images from the search engine to provide accurate weather and seasonal information, we selected an image classifier to extract the attributes associated with each image. This was accomplished by using Places365-CNN, a convolutional neural network (CNN) trained on a public dataset, Places365 [14], which contains 1.8 million images from 365 scene categories. Ref. [14] used a group of subjects to define the different scenic attributes which provided part of the training dataset for the CNN model. The CNN model trained on this dataset can effectively recognize the scene associated with it by showing the probability of each category. Therefore, we used the categories with high probabilities as the dominant category associated with the image. As a comparison, Table 2 shows the prediction results for both Figure 3a and Figure 3b, respectively. Clearly, we can easily remove images that do not qualify for the patch image from the search results.  
In general, we used \(A_{i}\) to denote a set of attributes extracted from the image \(I_{i}\), as expressed in Equation (1), where AE represents the Attribute Extractor.  
\[A_{i}=AE(I_{i})\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Figure 3a** & **Figure 3b** \\ \hline Type of environment & Outdoor & Indoor \\ \hline Scene categories & Beach, swamp, lagoon, coast & Alley \\ \hline \multirow{3}{*}{Scene attributes} & Natural light, open area, natural, & \\  & trees, foliage, sunny, leaves, faraway & Plaza, shopping mall/indoor, gym/indoor, \\ \cline{1-1}  & horizon, vegetation & atrium/public, construction site \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scene category and attribute outputs from Places365-CNN.  
Figure 3: Images were uploaded to Flickr by the author and retrieved on 7 February 2023, with the Flickr search API. (**a**) is an outdoor image tagged with a correct label. (**b**) is an indoor image but tagged as “outdoor”. The copyright of both images was obtained from the city of Virginia Beach.  
Additionally, the attribute extractor was also used in the performance evaluation, which measures the output attributes from the transferred images. A good transformation should contain the attributes from both patch images and reference images. A full list of attributes can be found in [14].
## 7 Experimental Results and Analysis
### Avalanche Effect Test
#### 7.2.1. Keep the Key Unchanged and Change One Plaintext  
We let the plaintext be \"planTextabcd\". Then, we kept the key of the conventional DES encryption unchanged, changed only one plaintext, and encrypted the plaintext changed by one bit using conventional DES. There is no key distribution problem for this chunking disorder algorithm, so the ciphertext length is guaranteed to be the same while keeping the number of iterations unchanged. On this basis, only one bit of plaintext was changed for encryption. In addition, in order to avoid experimental chance, it was necessary to ensure that the key of DES was unchanged when traditional DES encryption was used; chunked chaotic order encryption uses the encrypted strong random number generator (RNG) built in Java language to avoid the influence of pseudo-random numbers on the experimental results.  
The experimental results show that the traditional DES algorithm has the same \"avalanche effect\" as the chunking algorithm. When the plaintext is changed by one bit, the ciphertext is changed by more than half of the original ciphertext. The change ratio is basically maintained at 0.61 to 0.85, which has a good \"avalanche effect\" compared with the original ciphertext. Figure 14 illustrates \"avalanche effect\" of changing the plaintext test results.  
Furthermore, we conducted a comparison with other symmetric encryption algorithms, namely AES and RC4. During the encryption process, these algorithms keep the key unchanged and gradually change one bit of the plaintext, which is then encrypted in the same manner as described above. The experimental results demonstrate that the present chunking algorithm has the same \"avalanche effect\" as the AES and RC4 symmetric encryption algorithms. That is, when one bit of the plaintext is changed, the number of bits of the ciphertext is altered by more than half of the original ciphertext. The change ratio remains essentially in the range of 0.61-0.85. To some extent, the avalanche effect is more pronounced in this algorithm than in the AES and RC4 encryption algorithms. Figure 15 illustrates \"avalanche effect\" compared to AES and RC4 algorithm when changing the plaintext test results.  
Figure 13. Distribution of the same encryption length.