esa-sceva/satcom-chunk-collection huggingface dataset:
# A Strategic Pathway from Cell to Pack-Level Battery Lifetime Model Development
## 5 Pack-Level Electro-Thermal Modeling
### Lifetime Model Extension to a Pack-Level Use Case  
The electrical model is expected to deliver the predictions of electrical parameters of terminal voltage, SoC, overall internal resistances, and conclusively power loss under various environmental and operational conditions. For this work, the authors have used their 2 nd order EC reported in [35], and the characterization results can be found in [32]. The already validated model development is not repeated to avoid redundancy; however, the governing equations can be found as the following.  
\[V_{bat}=OCV-R_{0}I_{bat}-V_{p1}-V_{p2} \tag{4}\]  
\[P_{loss}=(R_{0}+R_{1}+R_{2})*I_{bat}^{2} \tag{5}\]  
The open-circuit voltage \(OCV\) of the cell is assumed as an ideal DC voltage source, connected to an ohmic resistance \(R_{0}\), and followed by multiple \(RC\) loops for simulating battery polarization effect [45]. For this study, two branches are considered including \(R_{1}\), \(R_{2}\), \(C_{1}\), and \(C_{2}\). The Kirchhoff Voltage Law gives the battery terminal voltage \(V_{bat}\), where \(I_{bat}\) is the charge/discharge current, and \(V_{p1}\) and \(V_{p2}\) are voltage drops across each \(RC\) branch. All the beforementioned parameters are a function of instantaneous \(I_{bat}\) (also referred to as C-rate), cell temperature \(T_{bat}\), SoC, and number of cycles the battery has undergone, i.e., aged. In which the latter is the focus of this study. Coulomb counting method is utilized to estimate SoC as in [46]. The other important parameter to be achieved through the electrical model is the overall Ohmic power loss \(P_{loss}\) induced by internal resistances of the battery as stated in Equation (5). It is used as volumetric heat input to the 3D model described in the next section.  
Once all the required look-up tables are achieved from experimental results, they are loaded to the battery module within the AVL CRUISET(tm) M commercial platform. Interpolation and extrapolation techniques are used to achieve consistent data series of the parameters within or outside the testing points. Once the cell model is implemented and validated (RMSE = 0.09), it gets upscaled into pack-level by series or parallel connection of multiple individual cells. The considered battery pack at its beginning of life (BoL) state, can store 7.62 kWh energy, and outputs 331 V nominal voltage at its terminals.
# A Study on a Scenario-Based Security Incident Prediction System for Cybersecurity
## 1 Introduction  
The integration of advanced technologies such as artificial intelligence (AI), big data, and machine learning in the Fourth Industrial Revolution has led to significant advancements across various industries by seamlessly connecting them to cyberspace. However, this technological convergence has also introduced increasingly sophisticated cybersecurity threats. Existing security measures, including heuristic and signature-based detection methods, primarily operate reactively, addressing threats only after incidents occur. This reactive approach leaves organizations vulnerable to novel and evolving attack vectors, creating an urgent need for proactive and comprehensive threat management systems [1].  
This study aims to address these challenges by proposing a scenario-based security incident prediction system that leverages the MITRE ATT&CK framework. The system integrates diverse security logs, simulates adversarial tactics and techniques, and quantifies the likelihood of potential security incidents. By automating the profiling of security incidents and enhancing predictive capabilities, the proposed system establishes a structured and proactive threat management framework [2].  
The contributions of this research are threefold. First, the study develops an automated framework for generating scenario-based security incident simulations, enabling detailed profiling of potential attack vectors. Second, it enhances predictive capabilities by integrating unstructured and structured security logs, allowing for a comprehensive analysis of security incidents. Third, the research introduces a novel methodology for seamlessly integrating legacy systems with modern threat intelligence platforms, thereby streamlining proactive security management.  
By addressing the limitations of traditional detection-based security products, this research lays the groundwork for advanced security systems capable of mitigating emergingcyber threats. The findings provide actionable insights and robust defense mechanisms for organizations operating in increasingly complex and interconnected digital environments. This study not only contributes to the theoretical advancement of threat modeling and prediction but also offers practical solutions to improve organizational cybersecurity posture [3].
# Research on Image Denoising in Edge Detection Based on Wavelet Transform
## 3 Materials and Methods
### Data Processing
#### 3.2.1 Selecting Wavelet Function and Number of Levels  
The wavelet functions sym5, db5, coif5 and fk6 were chosen to decompose the target image with three decomposition levels. A hard threshold function was used for denoising. The \(PSNR\) and \(MSE\) of the reconstructed results were calculated.  
Comparing the results of this image processing from Table 1, the performances of the four wavelet functions show little difference from each other. Generally speaking, sym5 corresponds to a \(PSNR\) and \(MSE\) of 23.48 dB and 299.49, which are the maximum and minimum values in the results, respectively. Therefore, sym5 performs slightly better than the other wavelet functions overall. That is, sym5 will be a better choice for noise removal.  
Now, the sym5 wavelet function is used to decompose the image into different levels for denoising, and the \(PSNR\) and \(MSE\) of the processing results are calculated again.  
Comparing the \(PSNR\) and \(MSE\) in the results, we can see that when the number of decomposition levels is three, the \(PSNR\) is the largest and the \(MSE\) is the smallest, at 23.48 dB and 299.49, respectively, which means that the denoising effect on the image is more obvious.  
Considering Tables 1 and 2 together, when the sym5 wavelet function is chosen to perform a three-level decomposition on the original image, the result shows the largest \(PSNR\), the smallest \(MSE\) and the best image quality. Therefore, the combination of sym5 wavelet function and three-level decomposition will be adopted in the following edge detection in this paper.
## 3 Results and Discussion
### Calibration
The daily cumulative sums by year of \(LE\), \(H\), and \(NEE\) (Figures 8b, 10b and 12b) show that the calibration of the model substantially improved the representation of these variables. Especially for simulated LE in 2016 and 2017, the accumulated differences did not exceed 10%. On the other hand, despite the noticeable improvement in the accumulated annual NEE computation compared to the default simulation, it is noted that the model still greatly overestimates the carbon assimilation in the Pampa. Figure 11: Daily average of NEE observed (\(NEE.obs\)), simulated by the model before calibration (\(NEE.def\)), and simulated after the calibration (\(NEE.clb\)). Figure 10: (**a**) Average diurnal cycle of sensible heat flux observed (\(H.obs\)), simulated by the model before calibration (\(H.def\)), and simulated after the calibration (\(H.clb\)); (**b**) daily \(H\) cumulative sum by year; (**c**) scatter plot of hourly \(H\) observed and simulated by the calibrated model. Figure 13 shows the cumulative distribution function (CDF) of daily differences observed between the simulations of the default and calibrated parameters in relation to observed values of variables \(Rn\), \(LE\), \(H\) and \(NEE\). This result reinforces the improvement that the model made after calibration. In particular, underestimates of 8 \(\upmu\)mol CO\({}_{2}\) m\({}^{-2}\)s\({}^{-1}\) or more accounted for approximately 35% of the \(NEE.def\) and for 0% of the \(NEE.clb\). That is, the calibration of the model parameters related to the calculation of CO\({}_{2}\) flux (mainly \(V_{max}\), \(clitls\), \(clitrs\), \(clitrs\) and \(m\)) was able to substantially reduce \(NEE\) estimation errors. Figure 12: (**a**) Average diurnal cycle of the net ecosystem exchange of CO\({}_{2}\) observed (\(NEE.obs\)), simulated by the model before calibration (\(NEE.def\)), and simulated after the calibration (\(NEE.clb\)); (**b**) daily \(NEE\) cumulative sum by year; (**c**) scatter plot of hourly \(NEE\) observed and simulated by the calibrated model. Figure 13: Cumulative distribution function (CDF) of daily differences between the simulations using the default and calibrated parameters in relation to the observed values of the variables \(Rn\) (**a**), \(LE\) (**b**), \(H\) (**c**), and \(NEE\) (**d**).
# Deep Contrastive Learning-Based Model for ECG Biometrics
## 2 Materials and Methods
### Proposed Method
#### 2.2.2 Contrastive Learning for Effective Leveraging of Cluster Distinction  
Recently, the contrastive learning strategy has produced significant improvements in self-supervised representation learning [35]. The key idea in the contrastive learning technique is to attract an anchor to the same class sample called a positive sample in the embedding space and repel apart this anchor from the different class samples, called negative samples.  
For a given input batch of data, and to apply a contrastive representation learning which is intuitively considered as learning by contrast or comparison of the anchor with a positive sample and a negative one, we need to create negative and positive pairs for the contrastive learning process.  
_Pair generation_\(Pair(\cdot)\): For each feature sample \(w=ftr(x)\), we randomly generate a set of positive samples \(w^{+}=Pair(w)\) from the same class, and a set negative samples \(w^{-}=Pair(w)\) from the other classes.  
Encoder _network_\(Enc(\cdot)\): This network receives the generated pairs \(w^{+}/w^{-}\) separately and maps them to a couple of representation vectors \(r=Enc(w)\in\mathcal{R}^{512}\) normalized to the unit hypersphere in \(\mathcal{R}^{512}\).  
_Projection_ network \(Proj(\cdot)\): Used only in the contrastive training phase and discarded after, this multi-layer perceptron block maps the vector \(r\) to representation vector \(z=Proj(r)\in\mathcal{R}^{128}\). With the aim of measuring distances using inner product in the projection space, the output of the projection network is normalized to a unit hypersphere in \(\mathcal{R}^{128}\).  
Figure 5: The main block of EfficientNet models, which is the mobile inverted bottleneck layer. (BN—Batch normalization layer, FC—Fully connected layer).  
For pair of ECG input data \(\left\{x_{i},\ x_{j}\right\}\) and their representations \(\left\{z_{i},\ z_{j}\right\}\), We can define the contrastive loss function \(\mathcal{L}_{CL}\) as [36]:  
\[\mathcal{L}_{CL}\ \left(x_{i},\ x_{j}\right)=0.5\Big{[}y\cdot d\left(x_{i},\ x_{j} \right)^{2}+\left(1-y\right)\cdot max\left(m-d\left(x_{i},\ x_{j}\right),0 \right)^{2}\Big{]} \tag{1}\]  
where \(d\left(x_{i},\ x_{j}\right)\) is the distance between the representations of the input data \(x_{i}\) and \(x_{j}\) in the latent space as:  
\[d\left(x_{i},\ x_{j}\right)=\|z_{i}-z_{j}\|_{2} \tag{2}\]  
In Equation (1), the ground truth label \(y=1\) indicates that the pair of signals are similar (from the same class), and \(y=0\) for not-similar pair signals. The hyper-parameter \(m\) is the margin representing the threshold for the non-similar pair of signals, which is chosen by the user.
## 3 Materials and Methods
### Filed Test Regression Analysis
To avoid the confine from determination coefficient \(R^{2}\), an adjusted coefficient of determination Adj \(R^{2}\) is acquired. According to the results, the adjusted determination coefficient for the QRNDSM qualified rate \(y_{1}\) is 0.9494, showing that the model could explain 94.96% of the variation in QRNDSM \(y_{1}\). Similarly, the adjusted determination coefficient of the QRSRD \(y_{2}\) is 0.9391, which shows that the model could explain 93.91% of the variation in QRSRD \(y_{2}\). The adjusted determination coefficient of the QRSLS \(y_{3}\) is 0.9724, showing that the model could explain 97.24% of the variation in QRSLS \(y_{3}\). The statistic of Durbin-Watson meets the requirements of \(1<d<3\) simultaneously, indicating that the test data are highly reliable. Figure 12 displays the comparison between the curve of the test value and the curve of the fitted value from the three regression models. The blue line represents the test measured values, and the orange line represents the sample fitted values. The two values are quite closely matched, as can be seen. \begin{table}
\begin{tabular}{c c c c} \hline
**Inspection Items** & \(\mathbf{y_{1}}\) & \(\mathbf{y_{2}}\) & \(\mathbf{y_{3}}\) \\ \hline \(R\) & 0.9728 & 0.9672 & 0.9850 \\ \(R^{2}\) & 0.9464 & 0.9356 & 0.9703 \\ Adj \(R^{2}\) & 0.9496 & 0.9391 & 0.9724 \\ \(S\) & 0.5406 & 0.6290 & 0.4040 \\ Durbin-Watson & 2.6552 & 1.8228 & 2.9930 \\ Statistic & (\(1<d<3\)) & (\(1<d<3\)) & (\(1<d<3\)) \\ \hline \end{tabular}
\end{table} 
Table 7: Analysis of the significance of the regression coefficient on the test results.
# Stream Data Load Prediction for Resource Scaling Using Online Support Vector Regression
## 6 Results
### Estimation of Stream Data Load Prediction Algorithm
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Series** & **Data Set** & **Algorithm** & **MAE** & **NRME** & **Time** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{WordCup98} & online SVR & 0.082 & 0.455 & 0.135 \\  & & Improved online SVR & 0.001 & 0.106 & 0.124 \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{Poland Electric} & BPNN & 0.054 & 0.325 & 0.130 \\  & & online SVR & 0.112 & 0.452 & 0.263 \\  & & improved online SVR & 0.049 & 0.311 & 0.116 \\ \hline \multirow{3}{*}{3} & \multirow{2}{*}{Darwin Sea} & BPNN & 0.1336 & 0.006 & 0.200 \\  & & online SVR & 0.134 & 0.456 & 0.345 \\  & & improved online SVR & 0.0551 & 0.366 & 0.321 \\ \hline \multirow{3}{*}{4} & \multirow{3}{*}{Sunspot database} & BPNN & 0.123 & 0.426 & 0.369 \\  & & online SVR & 0.162 & 0.564 & 0.334 \\  & & improved online SVR & 0.124 & 0.521 & 0.316 \\ \hline \multicolumn{5}{c}{BPNN} & BPNN & 0.154 & 0.86 & 0.468 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Time series forecast results. After learning the model parameters, the model can be used to predict the MSTW of a greater number of virtual machines, and the MSTW prediction results of different number of virtual machines are shown in Figure 2. As can be seen from the diagram, the prediction accuracy of the MSTW model of Unique Visitor is better. The prediction of Page View has a larger deviation when m = 10, and the prediction accuracy is higher for the number of other virtual machines. However, when the model is predicted in Grep, the overall prediction value is lower than the actual value, but it still has a better fit for the trend of throughput. Though the effect is not as good as the other three in the prediction of the pair and Rolling Count, the prediction value is also close to the real value when m = 8.
# FLATSIM: The ForM@Ter LArge-Scale Multi-Temporal Sentinel-1 InterferoMetry Service
## 2 The NSBAS Processing Chain Used in the FLATSIM Service
### Coregistration and Interferometric Network Selection  
Image correlation is performed between the amplitude image of the primary acquisition and an amplitude image simulated from DEM and orbit information. Found offsets are fitted by a second-order polynomial in range and azimuth. This modeling and further adjustment serve to build lookup tables from radar to ground geometry and vice-versa. We then precisely coregister all secondary images with respect to the primary acquisition (Figure 2, top), as explained in [19]. A simulated topographic phase map (in radar coordinates) is calculated for each of the secondary images, using the precise orbital parameters of the secondary and the primary acquisitions. This simulation is used to predict the distortion in range between secondary and primary images, supplemented by an additional constant offset in range and an affine offset function in azimuth derived from amplitude image matching. The affine offset function depends linearly on range and azimuth. Prior to resampling, a deramping is performed, cancelling most of the azimuthal phase modulation pattern inherited from the TOPS mode pre-processing, taking the azimuth spectral properties of the primary image as a reference for the deramping function and taking into account the affine offset function in azimuth [40].  
The chosen network of interferograms includes all n/n + 1, n/n + 2, n/n + 3 couples independently of their perpendicular baselines, and systematic pairs with temporal separation  
Figure 1: Distribution of bursts as a function of acquisition dates (\(x\)-axis) and latitude (\(y\)-axis). Empty circles represent bursts that have not been kept, whereas blue circles correspond to bursts that have been selected for processing. Pink circles are bursts that have not been selected, but belong to the beginning or the end of a partially selected SAFE product (standard product produced by ESA that typically includes 9 bursts per sub-swath).  
of \(\sim\)2 to 3 months and \(\sim\)1 year with restricted perpendicular baselines (Figure 2, bottom). The inclusion of 2 to 3 months and 1 year interferograms is essential for the mitigation of bias due to fading signals [45]. The computed differential interferograms are multi-looked by a factor 8 \(\times\) 2 in range and azimuth.
# Soil Erosion Assessment and Prediction in Urban Landscapes: A New G2 Model Approach
## 1 Introduction
The MPB is a part of the national urban spatial planning legislation as a long-term strategy that determines the basic projections of city development and spatial planning, the organization of construction, and the protection of urban spaces. One of the basic principles of urban planning is the preservation of land suitable for agriculture, natural values, and the environment of the urban whole, respecting the potentials and limitations of neighboring settlements and regions. The assessment of soil degradation in the study area was analyzed using the G2 model, which belongs to a group of empirical models. The model is applicable in a GIS environment and harmonized with standard geospatial input data from global and European databases. It was created as a result of the Geoland 2 project [27; 28] in cooperation with the Joint Research Centre (JRC) and the Laboratory of Forest Management and Remote Sensing of the Aristotle University of Thessaloniki. The model allows the estimation of soil loss in tons per hectare on a monthly or annual basis. The structure of the input data that are processed using this model shows certain similarity to the RUSLE method [29; 30] and the Erosion Potential Method (EPM) [31]. Since its inception, the model has been successfully applied to the Strimos River Basin, located between Greece and Bulgaria [27], and its further modifications were applied in Crete [28], watersheds in Albania [32; 33], Cyprus [34], Turkey [35], Poland [36], and Italy [37]. The model represents a modern tool created through a symbiosis of GIS with the application of remote sensing in the assessment of soil loss resulting from erosion. The application of the G2 model itself is very simple and fast, and so far it has proven to be a very flexible model with available databases and it has been used as a tool for supporting decision making in watersheds. Until now, research on the application of the G2 model has implied larger spatial dimensions and the use of global and pan-European data (for example, GlobCover, LC-CCI, MODIS products, CORINE), which do not have an adequate level of detail for the analysis of erosion processes of urban landscapes. The resolution of these databases is in the range of 100 to 500 m. The obtained soil losses are shown in a raster base of 30 m for two time periods. The first period is the year 2001, when the MPB entered into force. The second period is the present state from 2019 on the basis of available data. Although there are satellite images with a more detailed spatial resolution (Sentinel, SPOT, PROBA, etc. ), satellite images from the Landsat mission were used, since it is a rich database with half a century-long, continuous series of images enabling a unison display of land cover for 2001 and 2019.
## 6 Robustness Considerations  
There are a few drawbacks to the controller designs presented in Section 5. In particular, the control laws (26) and (34) require the knowledge of the inertia matrix \(J\), the damping matrix \(D\), the input-matrix \(B\), and the aerodynamic moment \(\Delta(v_{r},t)\). In this section, we focus on the control law (34) and state some properties regarding robustness to uncertainty in our model estimates. In addition, an adaptive version of (34) is presented, that provides integral action by estimating \(\Delta(v_{r},t)\) under a slowly time-varying assumption.  
The aerodynamic moment disturbance \(\Delta(v_{r},t)\) is slowly varying, satisfying \(\hat{\Delta}(v_{r},t)\approx 0\).
## 6 Analysis of Results
### Packet Delivery Ratio (PDR)  
The source nodes consistently generate the data packets and forward them to the destination. PDR is the ratio between the number of packets received at the destination to the packets created and sent by the source node. There can be more than one copy of a particular packet received, but we only consider one at the destination. The PDR is calculated as follows:  
\[PDR=\frac{Packets\;received\;at\;the\;centralized\;station}{Packets\;sent\;by\;the\;source\;node} \tag{17}\]  
In Figure 11, the simulation results of PDR are plotted by varying the number of nodes and the number of transmission ranges. For both protocols, the PDR increases when the number of nodes increases. It satisfies the fact that when the nodes increase, then in the transmission range of each node, there will be more numbers of nodes, which causes a reduction in the packet drops and increases the reliability. The probability of a void hole occurrence is reduced when the network changes from the sparse towards a denser one. As evident from Figure 11, the PDR of EESEVBF is greater than that of the ESEVBF because the former considers the second hop PFN while selecting the PFN at the first hop. There are two possibilities of void hole occurrence, one when there is no PFN and the second when there is PFN in the range of the source node, but it does not have sufficient energy to forward the packet. The proposed protocol avoids both cases by considering the holding time of the PFN at the second hop.  
For the transmission range of 500 m, the PDR of EESEVBF is slightly higher than ESEVBF; however, the difference is small compared to the 700 m transmission range, as shown in Figure 11. This is because, for small transmission range, the holding time of the proposed protocol is not effective compared to the large transmission, as shown in Equation (10). In this case, the selection of the node is purely based on the first hop as in ESEVBF; however, for large transmission range, the selection is prioritized from the combination of the first and the second hop. When the transmission range increases for the same number of nodes, the PDR is also increased. In this case, there is an increased probability of finding a more suitable node with respect to the holding time; however, for lower transmission range, it is difficult to find the next PFN and the packet cannot reach even at the fourth hop from the source node. Similarly, as shown in Figure 12, the difference between PDR of the two protocols is high when the number of nodes is 200 and the transmission range increases compared to the denser ones. The reason is that in a dense network, there are sufficient nodes in the transmission range for selecting the PFN, and therefore, the next forwarder of the PFN gets a negligible effect on the PDR. The converse is true for sparse networks.