esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Source and Preprocessing of Data  
The data in this article are derived from historical operation and maintenance data of a certain energy storage station, which consists of seven battery packs connected in parallel, as shown in Figure 1.  
Cell monitoring technology is one of the core functions of a Battery Management System (BMS) that is used to collect the voltage and temperature of a battery cell as well as detect the current of the battery pack. These data are collected using sensors and stored in Log Files, which facilitate extraction when conducting a data analysis. A Power  
Figure 1: The collection process of the voltages and currents of battery cells in an energy storage station.  
Conversion System (PCS) is used to control the charging and discharging process of battery cells/battery packs, performing AC/DC conversion.  
The subject of this experiment is a large-scale lithium battery pack within the station, consisting of 238 series-connected battery cells. Table 1 lists the battery specifications.  
Voltage and current changes are monitored through voltage and current sensors with a sampling frequency of 10 s. The voltage and current of this battery pack on 30 June 2023 are as follows.  
The data are conducted to segment using the point at which a drop in current during the charging end leads to a change in the working conditions and an increase in the battery capacity as the threshold, and then a segment of charging data for battery cells (the red areas shown in Figure 2) is extracted, as shown in Figure 3. Due to the large number of battery cells, their labels are not displayed.  
Based on this set of data, the anomaly detection method for battery cells is introduced in Section 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Specification** & **Value** \\ \hline Battery type & LFP \\ Total voltage (V) & 761.6 \\ Battery charging termination voltage (V) & 3.65 \\ Battery discharge termination voltage (V) & 2.7 \\ Nominal voltage (V) & 3.2 \\ Nominal capacity (mAh) & 3000 \\ \hline \end{tabular}
\end{table}
Table 1: The battery specifications.  
Figure 2: June 30. (**a**) The voltage of the battery pack. (**b**) The current of the battery pack.
## 1 Introduction
Apart from this study, which investigated but not evaluated the MERRA-2 SSR trends, the interdecadal variation of various reanalysis SSR was computed and evaluated against ground-based stations over specific world regions, such as China (MERRA-2 by [65]; ERA5 by [66]; ERA-Interim and MERRA by [67];NCEP/NCAR, NCEP/DOE and MERRA-2 by [68]), the Tibetan Plateau (NCEP/NCAR and ERA-40 by [69]), the Iberian Peninsula (ERA-40 by [70]), the Euro-Mediterranean region (ERA5 by [71]), Brazil (ERA5, MERRA-2, JRA55 and CFSR by [72]) and Europe (20 CRv2c, 20 CRv3, ERA20C and CERA20C by [73]) or globally (ERA5 by [74]; ERA40 by [62]). Most of the previous studies evaluated SSR products of various reanalyses using a limited number of observations and for selected regions of our planet. In addition, fewer studies evaluated the reanalysis SSR, on a seasonal and regional basis [38; 39]. Even fewer studies have done so for SSR trends. Actually, no evaluation of MERRA-2 SSR trends, i.e., GDB, especially at global scale, has been made to date. This is attempted, for the first time to our knowledge, in the present study, which evaluates the 40-year (1980-2019) GDB based on MERRA version 2 (MERRA-2), the latest and improved available version of MERRA. Apart from the MERRA-2 SSR changes, the present study also thoroughly evaluates the MERRA-2 SSR over the 40-year period 1980-2019 against SSR measurements from two top-quality ground-based reference networks of stations distributed all over the globe, namely GEBA and the BSRN. It should be noted that apart from the novelty of providing a first-seen detailed evaluation of MERRA-2 Reanalysis GDB, the present study also has the originality of validating the MERRA-2 SSR fluxes against the absolute reference network of BSRN stations, apart from GEBA, whereas similar evaluation of MERRA-2 SSR has been done in the past only against GEBA [38]. Here, the parallel use of SSR measurements from both BSRN, which ensures the best reliability, and GEBA, which provides wider spatial coverage than BSRN, allows the derivation of more robust conclusions about the quality of MERRA-2 Reanalysis SSR and GDB. Moreover, in order to further strengthen the robustness of the derived conclusions and to achieve the maximum possible reliability of the computed MERRA-2 GDB, a columnar closure study was undertaken, in which the long-term changes of MERRA-2 reflected solar radiation fluxes at the Top of Atmosphere (TOA) were compared to the corresponding trends of TOA reflected solar radiation fluxes of CERES-EBAF, used as reference. The evaluation of MERRA-2 SSR and GDB is done using deseasonalized anomalies at various scales, ranging from the local (station-level) to global/hemispherical ones, and for temporal scales ranging from the entire 40-year study down to selected specific sub-periods. Section 2 describes the utilized data and the applied methodology, Section 3 presents and discusses the results of the attempted evaluation and finally the conclusions are drawn in Section 4.
# Joint Cell Association and User Scheduling in Carrier Aggregated Heterogeneous Networks
###### Abstract  
This paper focuses on the network capacity maximization problem through joint cell association and user scheduling with multiple carrier aggregation (CA) in the heterogeneous networks (HetNets). For the downlink transmission, the proposed joint maximization problem is reformulated from single data flow into multiple data flow through carrier aggregated HetNets, in which the users could associate with BSs on more than one carrier band. Such a flexible joint maximization problem could be solved by convex optimization solutions with reasonable complexity. Numerical analysis has confirmed the performance advantages of the proposed multi-flow solution under different carrier aggregation deployment.  
Keywords:cell association; user scheduling; optimal resource allocation; HetNets; carrier aggregation +
Footnote †: journal: Information Information
## 3 Controller Design
### The Algorithm  
Steps to follow for design of such controller are summarized in the algorithm below:  
1. Measure current state vector \(\chi(k)\) of the plant (or estimate its value).
2. Take the vector \(\Delta\underline{u}(k-1)=\begin{bmatrix}\Delta u^{T}(k-1)&\dots&\Delta u^{T}(k+ n_{u-2})\end{bmatrix}^{T}\) calculated in previous iteration and remove the first element \(\Delta u^{T}(k-1)\), which has already been used in previous iteration for control. Using this vector get the future state predictions \(\underline{\chi}(k)=\begin{bmatrix}\chi^{T}(k)&\dots&\chi^{T}(k+n_{y})\end{bmatrix}^ {T}\).
3. Using the predictions \(\underline{\chi}(k)\) and known \(\chi(k)\) calculate the future matrix predictions \(\tilde{A}(k+i)\), \(\tilde{B}(k+i)\) and \(\tilde{C}(k+i)\) for \(i=0,...,n_{y}-1\) and finally obtain \(\phi(k)\), \(S(k)\) matrices.
4. From (32) calculate \(\Delta\underline{u}(k)\) and control \(u(k)=u(k-1)+\Delta\underline{u}(k)\).
5. From (34) calculate \(F(x(k))\) and \(d(k)\).
6. Using the parameterization in (35) and calculate the future vector predictions \(\underline{\chi}(k)\) and \(\Delta\underline{u}(k)\) by (41), (44) and (47).
7. Perform the minimization (15) using the future vector predictions obtained in Step 6 and implement \(u(k)=F(x(k))x(k)+\varepsilon^{*}(k)\), and move on to the next time step.
## 3 Results and Discussion
### Focus on 2019 and Link with Wildfires

Table 3: Number of fires and burnt areas reported by countries in 2019. A modified table from the “Forest Fires in Europe, Middle East and Africa 2019” [35]. The picture clearly shows how the entire Baltic catchment area is covered by regions with a very high wildfire outbreak probability. The reports [35] show that May and June 2019 were warm and dry in Finland; dryness contributed to wild forest fire outbreaks in July and August. There were two peaks of fires in Germany in 2019, one in April and the second one in the June-July period [35]. In Latvia, the wild forest fire peak season in 2019 ranged from April to August, and it coincided with the fire peak in Lithuania. The same pattern was reported for Poland, while in Romania, it was April. There were two fire outbreak peaks in Sweden, one in April and the second one in summer, which is similar to the Ukraine case [35]. Both peak periods coincide with high AOD values, which were measured at the selected AERONET stations around the Baltic Sea (Table 2). Between late June and late July 2019, there were two very distinct heat waves, which set all-time high temperature records in a number of European countries. The temperatures recorded in various European countries in late July 2019 are shown in Figure 10.  
Figure 10: Maximum temperatures in Europe on 25 July 2019 [37]. Figure 9: EFFIS fire danger forecast on 25 June 2019 for Europe [36]. Again, it is clear that the areas located in the vicinity of the Baltic Sea and in the areas where the AERONET stations are located show high air temperatures, reaching extreme values in relation to multiannual averages. Such a situation led to many casualties and extreme dryness in vast European areas, hence, facilitating conditions for wildfire outbreaks. The biomass burning aerosol presence in the area of the Baltic is very well reflected in the high AOD values recorded at three stations around the Baltic between May and August 2019 (Figure 11). There are three AOD peak periods observed at all three stations: the first one in May, the second one in June, and the last one in August. The AOD values reach levels of up to 0.4 (Helsinki in June 2019), and there is a visible increasing trend towards summer with constantly high values of AOD for the period of late July and the start of August. That may indicate the increase and the accumulation of biomass burning pollution with time and subsequent fire outbreaks.
# MINC-NRL: An Information-Based Approach for Community Detection
## 6 Conclusions  
In this paper, an information-based approach for community detection MINC-NRL is proposed. The approach adopts network representation learning techniques to obtain the vectorial representation of each node of a network. Then, a community evolution process is generated with these vectors, through an agglomerative hierarchical clustering or overlapping hierarchical clustering, according to a non-overlapping/overlapping community detection task. Finally, the MINC index is used to figure out the optimum partition in the community evolution process. The experimental results show the effectiveness of MINC-NRL on both real-world and synthetic networks.  
Due to the definition of MINC, a limitation of the current approach is that it can only be applied in unweighted and undirected networks. In the future, we will try to further extend the definition of MINC to address this limitation. The approach achieves a high degree of accuracy on 10,000 nodes LFR synthetic networks, but it is still difficult to be applied to larger-scale networks. This is mainly due to the time complexity bottleneck of the clustering algorithms. We will continue our research on improving the accuracy and time efficiency of the clustering part and try to extend the algorithm to detect communities on larger-scale networks.  
Conceptualization, Y.C. and D.L.; methodology, Y.C.; software, Y.C.; validation, Y.C. and C.W.; formal analysis, C.W.; investigation, C.W.; resources, C.W.; data curation, C.W.; writing--original draft preparation, Y.C.; writing--review and editing, Y.C.; visualization, Y.C.; supervision, D.L.; project administration, D.L.; funding acquisition, D.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science and Technology Program of Guangzhou City (Grant No. 201707010052) and Natural Science Foundation of Guangdong Province, China (Grant No. 2020A1515010696).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} MINC & Mutual Information between Network and Community \\ NRL & Network Representation Learning \\ AHC & Agglomerative Hierarchical Clustering \\ OHC & Overlapping Hierarchical Clustering \\ \end{tabular}
# The Influence of Tilt Angle on the Aerodynamic Performance of a Wind Turbine
###### Abstract  
Aerodynamic performance of a wind turbine at different tilt angles was studied based on the commercial CFD software STAR-CCM+. Tilt angles of 0, 4, 8 and 12\({}^{\circ}\) were investigated based on uniform wind speed and wind shear. In CFD simulation, the rotating motion of blade was based on a sliding mesh. The thrust, power, lift and drag of the blade section airfoil at different tilt angles have been widely investigated herein. Meanwhile, the tip vortices and velocity profiles at different tilt angles were physically observed. In addition, the influence of the wind shear exponents and the expected value of turbulence intensity on the aerodynamic performance of the wind turbine is also further discussed. The results indicate that the change in tilt angle changes the angle of attack of the airfoil section of the wind turbine blade, which affects the thrust and power of the wind turbine. The aerodynamic performance of the wind turbine is better when the tilt angle is about 4\({}^{\circ}\). Wind shear will cause the thrust and power of the wind turbine to decrease, and the effect of the wind shear exponents on the aerodynamic performance of the wind turbine is significantly greater than the expected effect of the turbulence intensity. The main purpose of the paper was to study the effect of tilt angle on the aerodynamic performance of a fixed wind turbine.  
wind turbine; tilt angle; unsteady aerodynamics; computational fluid dynamics Article
# Impact of Cytochrome P450 Enzymes on the Phase I Metabolism of Drugs
## 2 Examples of Drugs Metabolized by CYPs
### Atorvastatin  
Atorvastatin (Figure 16) belongs to the class of \"statins\" and acts as a 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase (HMGCR) inhibitor, producing an antihypertensive applicent activity. More recently, statins have been proposed, as well, as immunomodulatory and anti-inflammatory drugs [120; 121]. Atorvastatin is metabolized by CYP3A4 and CYP3A5 in the gut and liver [122] and its hydroxylated acid metabolites, 2-hydroxy-atorvastatin and 4-hydroxy-atorvastatin, inhibit HMGCR and, consequently, reduce the circulating low-density lipoprotein cholesterol levels, although 2-hydroxy-atorvastatin represents the most abundant metabolite [123; 124]. Lactone metabolites, formed from the acid forms of atorvastatin by uridine 5'-diphospho-glucuronosyltransferase (UGTs), do not determine the inhibition of HMGCR, but are unfortunately involved in statin-induced muscle toxicity [125]. In addition, atorvastatin was demonstrated as an inhibitor of CYP3A4 activity, by the means of the 4\(\beta\)-hydroxycholesterol to cholesterol ratio (4\(\beta\)HC: C) measurement, but these data are not in agreement with previous in vitro and in vivo studies [126]. Finally, a single nucleotide polymorphism in the CYP3A5 gene was associated with atorvastatin-induced adverse effects, highlighting the importance of genotyping for predicting its toxicity [127].  
Figure 15: The structure of quetiapine and its metabolites.
## 2 Material and Methods
### Recurrence Plots and Recurrence Quantification Analysis Measures  
Recurrence plots (RPs) make possible the visualization of the recurrence behavior or states of the phase-space trajectory of natural processes, considered as dynamical systems [30]. Periodic signals, such as the electrocardiogram, are examples of recurrence [31], in which patterns are repeated over time. Mathematically, RPs can be defined as  
\[R(i,j)=\Theta(\epsilon-\|\vec{x}(i)-\vec{x}(j)\|), \tag{3}\]  
where \(\Theta\) is the function \(\Theta\): \(\mathbb{R}\rightarrow(0,1)\) and \(\epsilon\) is a predefined distance.  
In this work, RPs were used to visualize recurrent patterns within 1, 2, and 4 s length sequences. The phase-space reconstruction from a time series \(u_{k}\) could be obtained following [PERSON] embedding theorem [32] by using an embedding dimension \(d\) and a time delay \(\tau\). For the study, we selected the embedding dimension and the time delay using the False Nearest Neighbors (FNN) [33] and the mutual information (MI) [34] strategies, respectively. FNN and MI were applied to every segment of the subsets analyzed. In particular, after applying FNN and MI to every CFAE segment, we chose to reconstruct the phase space of all segments of a given length using the most frequent values as embedding dimension \(d\) and the time delay \(\tau\). Thus, given a CFAE, considered as a time series, its trajectory \(\{s_{i}\}_{i=1}^{N}\), with \(N\) the number of considered states, was projected into a \(d\)-dimensional phase space in which the \(i\)-th point of the trajectory was represented as \(s(i)\). The vector of reconstructed state \(\vec{s(t)}\) in the phase space at the time \(t\) is defined as  
\[\vec{s(t)}=\vec{s}_{i}=(u_{i},u_{i+\tau},\ldots,u_{i+(d-1)\tau}),\qquad t=i \Delta t \tag{4}\]  
Next, the RP was produced too. As shown in equation (3), the RP was represented as an \(N\times N\) array, where each element \((i,j)\) is assigned a value of 1 if the distance between the point \(i\) and the point \(j\) of the trajectory \(\vec{s(t)}\) is less than a specified threshold \(\epsilon\). For the study, we set the threshold \(\epsilon\) as 10% of the mean of the phase-space diameter, and 0 otherwise, in accordance with previous works [35].  
The Recurrence Quantification Analysis (RQA) measure selected for this study is the determinism [30], which quantifies the sequence predictability by measuring the percentage of recurrence points that belong to diagonal lines of a minimum length \(l_{min}=50\).
## 5 Conclusions and Future Works
### Recommendation  
Based on the comprehensive analysis and performance metrics evaluated across different configurations, the Temporal Convolutional Network (TCN) in the four-sensor non-overlapping configuration emerges as the most suitable model for ITS applications. This recommendation is grounded in its superior accuracy, precision, recall, and F1 Score, as detailed in Table 20.  
The advantages of this configuration include:  
Enhanced Temporal Modeling: TCN's architecture effectively captures both short- and long-term dependencies in the data, even on unseen data, due to its use of dilated causal convolutions [28]. Improved Contextual Understanding: The use of multiple sensors provides richer context, enabling better interpretation of dynamic environments in real-world scenarios. Optimal Data Utilization: Non-overlapping data with multiple sensors avoids redundancy, enhancing generalization and preventing potential overfitting. Robustness and Scalability: High test set performance indicates that the model is robust and can generalize well, increasing adaptability to different ITS challenges. This recommendation aligns with the objectives of ITS, which require reliable and accurate real-time analysis of multi-sensor data to improve transportation efficiency and safety.
# A Survey on Big IoT Data Indexing: Potential Solutions, Recent Advancements, and Open Issues
## 8 Open Research Challenges
### Security and Privacy for 5G Data Indexing  
Several factors such as the hardware used, operating system settings, buffer sizes, page sizes, and datasets affect the performance of a particular data structure in indexing 5G data. Therefore, since the real identity of the data could potentially be disclosed in 5G data indexing, critical security questions can be identified as follows:  
* How to achieve efficient and privacy-preserving 5G data indexing?
* How to design an indexation protocol for achieving privacy-preserving priority classification on 5G Data?
* How to enhance trust management for 5G networks via data indexing in the era of big data?
* How to secure the multidimensional approaches in 5G data indexing (e.g., Pyramid, VA-file, \(k\)D-tree, X-tree, SR-tree, R\({}^{*}\)-tree, and R-tree)?
## 4 URI-Based Structural Design Optimization
### The Crisp Equivalent Programming Model for Solving the URBDO Model  
In many circumstances, the presented URBDO model (13) can be transformed into the crisp equivalent programming (CEP) calculation model. This crisp equivalent model is convenient for engineering structural designers.  
**Theorem 4**.: _Let the \(j\)th LSF \(g_{j}(\mathbf{d},\mathbf{\xi})\) of a structure contain independent uncertain variables \(\xi_{1},\xi_{2},\cdots,\xi_{n}\) with regular uncertainty distributions \(\Phi_{1}\), \(\Phi_{2},\cdots,\Phi_{n}\), respectively. Assume that \(g_{j}(\mathbf{d},\xi_{1},\xi_{2},\cdots,\xi_{n})\) is strictly increasing for \(\xi_{1},\xi_{2}\cdots,\xi_{k}\) and strictly decreasing for \(\xi_{k_{j}+1}\), \(\xi_{k_{j}+2}\cdots,\xi_{n}\). Moreover, assume that the \(i\)th objective function \(f_{i}(\mathbf{d};\xi_{1},\xi_{2},\cdots,\xi_{n})\) is strictly increasing for \(\xi_{1},\xi_{2}\cdots,\xi_{k_{i}}\) and strictly decreasing for \(\xi_{k_{i}+1},\xi_{k_{i}+2}\cdots,\xi_{n}\), \(j=1,2,\cdots,N_{\text{g}}\), and \(i=1,2,\cdots,N_{\text{t}}\). Then, the URBDO model (13) is converted into the following CEP model:_  
\[\begin{split}&\text{find }\mathbf{d},\mathbf{x}\\ &\min\sum\limits_{i=1}^{N_{\text{t}}}\omega_{i}f_{i}(\mathbf{d};\ \[PERSON]_{1}^{-1}(\alpha),\cdots,\[PERSON]_{k_{i}}^{-1}(\alpha),\[PERSON]_{k_{i}+1}^{-1}(1- \alpha),\cdots,\[PERSON]_{n}^{-1}(1-\alpha))\text{d}\alpha\\ &\text{s.t. }g_{j}(\mathbf{d};\ \[PERSON]_{1}^{-1}(1-R_{\text{U}_{j}}), \cdots,\[PERSON]_{k_{j}}^{-1}(1-R_{\text{U}_{j}}),\[PERSON]_{k_{j}+1}^{-1}(R_{\text{U}_ {j}}),\cdots,\[PERSON]_{n}^{-1}(R_{\text{U}_{j}}))\leq 0\\ &\mathbf{d}^{Lower}\leq\mathbf{d}\leq\mathbf{d}^{Upper},\mathbf{x}^{Lower} \leq\mathbf{x}\leq\mathbf{x}^{Upper},\ j=1,2,\cdots,N_{\text{g}}\end{split} \tag{14}\]  
**Proof of Theorem 4.** The proof follows directly from Theorem 2. \(\square\)  
After the general URBDO model is converted into a crisp equivalent model, structural engineers can solve it by employing any typical numerical methods or metaheuristic algorithms. Nevertheless, sometimes the LSF in practical structural problems is not a strictly monotonic function. In addition, when too many input factors and constraints are present in the design problem, Model (14) may not have an optimal solution. Therefore, a hybrid intelligent algorithm that combines US and genetic algorithm (USGA) is developed to solve the proposed URBDO model (13).