esa-sceva/satcom-chunk-collection huggingface dataset:
# Explainable Artificial Intelligence for State of Charge Estimation of Lithium-Ion Batteries
## 2 Related Work  
### Battery Testing  
There exist many forms of battery testing. Within this work, we focus on battery cycles in particular, which also consist of different state estimation categories: State of X (SoX), including State of Power (SoP), State of Health (SoH), and SoC.  
SoP focuses on the distribution of power; it measures this by examining the power output during various different conditions [9]. SoH focuses on the lifespan of a battery; it measures the battery's performance in terms of capacity and internal resistances [15]. This can be achieved by using different signal profiles such as voltage and current [16]. SoC focuses on representing the remaining energy within a battery; it measures this by calculating the charge via methods such as Coulomb Counting [5; 7], or via model-based estimations using different signal profiles [5; 10]. SoC is important for state estimation of batteries, as most battery characteristics, e.g., open-circuit voltage, resistance, time constants, etc., strongly depend on SoC. For example, in order to estimate SoH accurately, the capacity of the battery is required, which is directly related to SoC.  
Many SoC methods in the literature [17] are only applicable with experiments that have been specifically designed to be conducted within a laboratory, e.g., constant-current discharging. In most real-world scenarios, this is not feasible. The simplest approach to SoC estimation is Coulomb counting [5; 7]; however, it suffers from cumulative measurement errors and sampling rates. This calls for model-based methods, such as equivalent circuits [9] used in conjunction with the Kalman filter and its variations [6], which adjust errors based on a battery model. With the rise of AI, there has also been an increassee in data-driven methods to estimate SoC [8; 18], which have shown comparable results with the results of model-based methods. Within this work, we explore the feasibility of utilizing a data-driven approach when estimating SoC, and provide its achieved benchmark.
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 4 The RST Approach  
The RST algorithm is an automatic multi-temporal change-detection method that has been extensively tested for detecting various environmental hazards [29; 50]. The method is based on the preliminary identification of \"normal\" (i.e., unperturbed) values of the investigated signal that should be compared with the signal at hand for the detection of any significant, from a statistical point of view, anomalies. Such an analysis is performed at the pixel level and requires the preliminary generation of a homogeneous long-term satellite database. Homogeneity in the spatial domain is ensured by always considering the same area (and pixels), whereas homogeneity in the temporal domain is ensured by collecting data belonging to the same calendar month and acquired at the same time of day (or in the same orbit). The change detection step is performed by implementing the Absolutely Local Index of Change of Environment (ALICE) index (1), as in the following [50]:  
\[\otimes_{V}(x,y,t)=\frac{V(x,y,t)-\mu_{V}(x,y)}{\sigma_{V}(x,y)} \tag{1}\]  
where \(V(x,y,t)\) is the signal measured at time t for each pixel (\(x,y\)) of the analyzed satellite scene, and \(\mu_{V}(x,y)\) and \(\sigma_{V}(x,y)\) represent the temporal mean and standard deviation, respectively, which are computed by processing the above-mentioned homogeneous multi-temporal dataset of the collected satellite data [29]. The Alice index, by its inherent design, is intended to be a standardized variable that exhibits Gaussian behavior, with a mean close to 0 and a standard deviation close to 1. Consequently, a higher absolute value of the index indicates a lower probability of its occurrence [50].  
In its first implementation on SAR data, performed in this work, the signal under investigation is the backscatter measured by Sentinel 1, with the prescriptions for the used data indicated as follows. It is worth mentioning that the backscatter intensities are influenced by the characteristics of the surface, including its roughness, composition, and presence of water. In the absence of flooded areas, these characteristics can be considered constant if the same pixel is viewed with the same geometry during the same period of the year. Therefore, in the absence of any other perturbations, signal anomalies, if detected, can be related to the presence of flooded areas. Considering the incidence angle, its similarity along the temporal series accounts for shadowing and layover effects, enabling accurate differentiation between flooded and non-flooded regions [51]. As already mentioned, the RST approach implementation requires computational capability to increase on the basis of the spatiotemporal and resolution of the investigated data, as well as on the length of the historical series analyzed. These factors, together with the limited accessibility of radar data, have prevented the application of RST to raw SAR data. This study represents the first attempt to analyze a historical series of backscatter values following RST prescriptions and exploit its inherent advantages.
## 4 Results and Discussion
### Sensitivity Analysis of Predictor Variables  
Owing to the possible hazards of the PPV that are caused by blasting operations, it is indispensable to clarify the major factor affecting the PPV. Therefore, in this section, further analysis is conducted to identify the significant correlation of predictor variables that are used to predict the PPV. As previously stated, six variables, i.e., BS, HD, ST, PF, MC, and DI, were used to develop the RF models in this study. Based on the attribute of the RF model, a significant correlation of each input variable can be obtained. The (normalized) total decrease in the criterion brought by a feature is used to calculate the input variable's relevance with the target variable, which is also known as the Gini importance [56, 57]; the higher it is, the more significant the input variable. In this way, the significant correlation of the input variables with PPV can be determined, as shown in Figure 14. It can be revealed that the most significant predictor variable is the PF, followed by the BS, DI, and HD. The least significant parameters include ST and MC. Accordingly, we will focus more on the relationship between the PF and PPV.  
Figure 12: Measured and predicted PPV of the RF-WOA model on the training set.  
Figure 13: Measured and predicted PPV of the RF-WOA model on the testing set.  
To obtain a clearer picture of how the PF affects the PPV, partial dependency plots (PDPs) are utilized to achieve this goal. The PDPs illustrate the intuitive relationship between the target response and an input feature of interest and simultaneously marginalize the values of other input features. The PDPs visualize the average influence of the input feature on the target response, which can reveal a homogeneous connection between the feature and the target response [63]. Figure 15 shows the partial dependence between the PPV and PF. From an overall perspective, as the PF gradually increased from 0.23 to 0.94, the PPV gradually increased from 3.13 to 6.94. The process of the PF causing the PPV changes can be divided into three stages. The first stage is that when PF increases from 0.23 to 0.46, there is no significant change in the PPV, and the PPV reaches a minimum value when the PF equals 0.46. The second stage is that when the PF increases from 0.46 to 0.63, the PPV shows a dramatic increase from 3.01 to 6.27 in an exponential-like trend. The third stage is when the PF increases from 0.63 to 0.94, and the PPV increases from 6.27 to 6.94, but the amplification is significantly smaller compared with that in the second stage. From this, managing the PPV through curbing the size of the PF is a potentially effective means.  
Figure 14: Significant correlation of the input variables with PPV.  
Figure 15: The partial dependency between the PPV and PF.
## 4 Grating Coupler
### Parameter Optimization
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \(\mathbf{\Lambda}\) & \multirow{2}{*}{\(\mathbf{\mu}\)} & \multirow{2}{*}{\(\mathbf{\mu}\)} & \multirow{2}{*}{\(\mathbf{\mu}\)} & \multirow{2}{*}{\(\mathbf{\theta}\)} & \multirow{2}{*}{\(\mathbf{\mu}\)} & \multirow{2}{*}{\(\mathbf{\mu}\)} \\  & (\(\upmu\)m) & & & & & \\ \hline Original & 0.423 & 0.500 & 0.211 & 0.212 & 20 & 0 \\ Final & 0.420 & 0.476 & 0.200 & 0.220 & 20 & 3.4 & 3.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the grating part. Figure 8: (**a**) Simulation model of the grating part. (**b**) Influence of the width \(b\) and length \(L_{\text{t}}\) on the transmission efficiency \(\eta_{\text{g2}}\). the mode in the SMF is 3 \(\upmu\)m. Once width \(b\) is chosen to be 6 \(\upmu\)m, transmission efficiency of the taper part is nearly close to 1 when length \(L_{\mathrm{t}}\) is 300 \(\upmu\)m.  
In summary, when parameters of the grating part are final values in Table 1, and width \(b\) and length \(L_{\mathrm{t}}\) are 6 \(\upmu\)m and 300 \(\upmu\)m respectively, coupling efficiency \(\eta_{\mathrm{g1}}\) reaches its maximum 0.35, and transmission efficiency \(\eta_{\mathrm{g2}}\) is approximately equal to 1. Therefore, maximum coupling efficiency of the grating coupler (\(\eta_{\mathrm{g}}\)) is 0.35.
# Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics
## 4 Experimental Results
### Limitations of Word2 Vec and Recommendations  
The memory consumption predictions for the Word2 Vec model displayed inconsistencies, as evidenced by a lower \(R^{2}\) value (0.06). This discrepancy can be attributed to the complexity of the algorithm and the irregular memory allocation patterns caused by processing unstructured textual data.  
To address this, we recommend integrating advanced memory profiling tools and developing enhanced feature engineering techniques such as the following:  
* Incorporating word frequency distributions and text complexity metrics as additional input features for predictive modeling.
* Adopting hierarchical or hybrid modeling approaches to capture the nonlinear behavior inherent in text-based algorithms.  
These strategies aim to improve the accuracy of memory usage predictions for Word2 Vec, thereby enhancing its applicability in large-scale distributed systems.
# An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures
## 3 Allocating Resources in Heterogeneous Cloud Infrastructures
### Tier 2: Data Placement in Heterogeneous Storage  
Intelligent algorithms are widely used for resource allocation in CC [23]. In general, evolutionary algorithms are used, such as the particle swarm method and artificial neural networks. This section focuses on improving data placement for heterogeneous storage in order to obtain better performance.  
In the following, we formulate the problem to be solved by our data placement algorithm, which represents an improved version of our solution presented in [9]. In the present work, we have integrated an additional type of storage (ARCHIVE) that can be handled by our algorithm. This type of storage is provided as part of a multicloud arrangement. Figure 3 illustrates the architecture and the types of storage considered in this problem.  
One of the most widely used file systems in CC is HDFS. This file system supports heterogeneous types of storage, and to improve the performance of the applications running on it, we can optimize the way in which it uses these different types of storage. For this problem, we need to store \(N\) datasets in \(M\) different types of storage. We can formulate this problem as an integer linear programming (ILP) problem using the objective function given below:  
\[\sum_{j=1}^{M}\sum_{i=1}^{N}\frac{DSS_{j}}{B_{j}}\times X_{ij} \tag{7}\]  
And the following constraints:  
\[\sum_{j=1}^{M}X_{ij}=1,\quad\forall i \tag{8}\]  
\[\sum_{i=1}^{N}DSS_{i}\times X_{ij}\leq C_{j},\quad\forall j \tag{9}\]  
Figure 3: Hadoop cluster architecture and public cloud.  
In this problem, we have one variable that relates to the files, i.e., the dataset size (DSS), and several that are related to the types of storage, i.e., the capacity (C) and the data write rate (B). The solution we are looking for is the best data placement (X) for each dataset (DS). In this case, we propose the use of a genetic algorithm (GA) to solve the described ILP, in order to optimize the use of the heterogeneous storage using the ARCHIVE storage type. Algorithm 1 shows the GA that is used to find the best data placement on heterogeneous types of storage. For a more detailed explanation of the GA used for data placement, the reader is referred to our previous paper, in which this was developed [9]. In this work, we use the GA to integrate ARCHIVE into a public cloud, and a third party cluster is responsible for allocating the data.  
```
Generate random population solutions (Chromosomes) while\(generationCounter\leq maxGenerationValue\)do  evaluate each chromosome in the fitness function  add penalty factor to the fitness function value if condition for crossover is satisfied then  select parents with the best fitness value  choose crossover parameters  perform crossover endif if condition for mutation is satisfied then  choose mutation point  perform mutation endif endwhile
```  
**Algorithm 1** Genetic algorithm for heterogeneous storage.
## 11 Further Research Directions and Future Considerations
### Performance Optimization Issues of Short-Reach Transceivers
Moreover, it should be noted that most of the schemes such as nonlinear Volterra equalizations and decision feedback equalizations that are based on advanced DSP for handling the impairments are usually applied at the receiver, and they require relatively high computational complexity. On the other hand, training-based pre-equalization (Pre-EQ) and nonlinear look-up-table (LUT) predistortion (Pre-DT) can also be employed to address the issues [172]. These implementations offer a notable advantage regarding an effective reduction in the Rx-DSP complexity [103; 165].
## 4 Spectrum of Heterogenous Services for Transport Means and the Communication of Information
### The Information Flows and Modelling for the Context of Tracking
#### 4.3.2 Tracking the Cargo
Arrival to the customs office is identified in TIR and fulfilment of shipping instructions is presented in CMR, where carrier indicate any reservations about the goods or the shipping requirements. Finally, the information of road accidents is marked in TIR. Statistical Data Analysis of the Evaluation of e-Documents of Cargo Transportation by Application of TIR and CMR in Europe Countries  
The cargo (accompanying) transport document performs three main (statutory) functions: (a) prove the contract of carriage with liability, (b) prove ownership of the goods, and (c) description of the cargo and show transit route. The information which is retrieved covers such aspects of cargo transportation activity:  
* Process status. The consignor and the carrier (or their representatives) have agreed on cargo transportation and status reporting;
* Cargo information. All vital information about the load, such as its temperature setting (in the case of a temperature-controlled cargo), dangerous goods data, handling instructions, and pick up and drop off places (also known as pickup/delivery points); \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Extra Conditions**} & \multirow{2}{*}{**Result**} & \multicolumn{2}{c}{**e-Documents**} \\ \cline{3-5}  & & & **Data** & **Result** \\ \hline Loading and delivery & Network (available road & The route, incl. transit & & \\ places & infrastructure) & countries and successive & CMR & CMR \\ \hline Shipping instructions & & Assignment of road & & \\ (temperature, danger, & & transport means incl. vehicle & CMR & TIR, CMR \\ etc.) & & registration number & & \\ \hline Loading date & Tracking vehicle and cargo & Delivery on-time in-full & CMR, TIR & TIR \\ The time limit for & Traffic jump & according to shipping & TIR & CMR \\ transit & Accident & instructions & TIR & CMR \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The data needs for supporting the functionality of complex cargo transportation processes. * Delivery conditions. These conditions apply to the cargo shipment are specified in consignment note named as Contract for International Carriage of Goods by Road (CMR) and delivery terms called as Incoterms;
* Delivery payments. It proves the payment of import charges and other taxes which are paid during the carriage. Two main documents are used for cargo transportation: TIR and CMR. Each of them is presented below in detail.
## 4 Discussion
### Ethical Issues in the Use of Monitoring Technologies  
From a legal perspective, employee monitoring is governed by various laws, most notably the General Data Protection Regulation (GDPR) in the European Union (EU), which sets strict standards for the processing of personal data. In addition, national health and safety regulations require companies to ensure the physical and mental well-being of their employees. However, real-time monitoring raises legal issues that need to be carefully considered.  
The proposal suggests that employers should not be able to use consent (GDPR Art. 6(1)(a)) to justify data processing on the basis of an unequal relationship with employees. Furthermore, while health and safety legislation defines risk as the likelihood of injury in the event of an accident, artificial intelligence (AI) legislation also takes into account the severity of the injury. The key point is that the collection of data from workers and their limited capacity to consent poses risks that current legislation does not yet effectively address [89].  
Real-time workplace monitoring for health and safety is permitted if it complies with the GDPR principles of proportionality, transparency, and data minimisation. Companies must avoid intrusive practices, handle data securely, and ensure that data collection is justified and respects workers' fundamental rights. Monitoring technologies help identify risks, assess their severity, and analyse worker behaviour, enabling employers to prevent hazards through direct action or guidance. AI and the Internet of things (IoT) enhance this by enabling rapid responses to unsafe behaviour, thereby improving workplace safety. However, increased technological control also increases employers' safety obligations and reduces workers' autonomy. Once safety measures are in place, reckless behaviour becomes a breach of contract, emphasising the worker's duty to comply [90].
## 3 System Description
### Sensor Platform  
In order to meet the requirements presented in the previous section, Figure 1 shows the high level architecture for the hardware components of the system. It is composed of a microcontroller that controls different sensors. The system is powered by a battery and optionally by an energy harvesting module. The module is installed inside selected slab tracks to monitor different parameters as described in this section.  
The most viable option for the system prototype both from an economic point of view and for flexibility reasons was the use of the open-source computing platform known as Arduino. This platform offers a good balance between capabilities and energy consumption at a reasonable price. The MEGA 2560 model has been chosen. It provides more than one serial port which is necessary for some of the sensors selected. The platform integrates the ADXL345 accelerometer sensor, the SCA100T-D2 inclinometer sensor and the SRF05 distance sensor. In addition, a 1.5 W solar panel has been used as the energy harvesting mechanism in order to increase the life time of the battery that supplies energy to the board. Finally, the sensor platform has been equipped with the Xbee communication modules described in the following sections. A complete description of our selected sensor platform can be consulted in [7].
# A Fuzzy Inference System for Unsupervised Deblurring of Motion Blur in Electron Beam Calibration
## 2 Modeling and Implementation
### Deconvolution Artifact Residues  
Deconvolutions are performed using the Wiener inverse filtering process in Equation (4).  
\[\hat{F}_{i}=\frac{1}{H(\omega_{i})}\left[\frac{|H(\omega_{i})|^{2}}{|H(\omega_ {i})|^{2}+\frac{1}{SNR(\omega)}}\right]G(\omega), \tag{4}\]  
where in the frequency domain, \(\hat{F}_{i}\) is the restored ground truth signal and \(SNR\) is the signal-to-noise ratio. After the deconvolutions,\(\hat{f}_{i=1,2}\) has shorter lengths in spatial domain compared to \(g\). We first normalized \(g\) and both of the restorations (\(\hat{f}_{i=1,2}\)) between \([0,-1]\), \(g_{N}\) is then shifted so its minimum is matched with the minimums of each \(\hat{f}_{i}\) in the spatial domain to obtain \(\hat{g}_{N}\). Finally, every restoration residue (\(r_{i}\)) is quantified as in Equation (5).  
\[r_{i}=\frac{4}{\int g(x)dx}\cdot\int\hat{f}_{i}(\tau)d\tau\qquad\{\tau\in x| \hat{g}_{N}(\tau)>-0.05\}, \tag{5}\]  
Figure 1: Normalized power spectrum of \(G\) exhibits \(\omega_{1}\) and \(\omega_{2}\) at 0.12 and 0.165 MHz frequencies with their harmonics at higher frequencies.  
The deconvolution process using both of the extracted PSFs and their corresponding residues are showed in Figure 2. The deconvolution was performed with a Wiener inverse filter, where \(h\) is formulated in Equation (6).  
\[h_{\hat{L}_{i}}(x)=\left\{\begin{array}{ll}0&o.w\\ 1&|x|<\frac{L_{i}}{2}\end{array}\right., \tag{6}\]  
Two fuzzy sets (\(C_{low}\) and \(C_{high}\)) are defined with membership functions of \(\mu_{r}\) and \(\mu_{r}^{\prime}\) using zmf and smf respectively, where the overall shape of the functions is determined by \(a_{r}\) and \(c_{r}\). These functions are designed to assign a higher degree of membership to the \(\hat{L}_{i}\) that produces a smaller number of residues after restoration.