esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Background and Hypotheses
### Government Regulation and Policies
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Code** & **Factor** & **Description** \\ \hline V3F3 & Resource Allocation & This factor examines the allocation of financial and human resources to support the adoption and utilization of green logistics technologies. It includes factors such as budget allocation, staffing, and training programs [19]. \\ \hline V3F4 & Compatibility with Existing Systems & This factor measures the compatibility of green logistics technologies with the organization’s existing systems and processes. It evaluates the ease of integration and potential challenges in aligning these technologies with the current infrastructure [19]. \\ \hline V3F5 & Data Management and Security & This factor focuses on the organization’s capability to effectively manage and secure the data generated by green logistics technologies. It includes considerations of data storage, privacy, and cybersecurity measures [17; 25]. \\ \hline V3F6 & Scalability and Flexibility & This factor assesses the scalability and flexibility of the organization’s technological infrastructure to accommodate future growth and changes in green logistics technologies [25]. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._implementation. The study evaluates various aspects of government regulation and its impact on adopting green logistics technologies. It examines the extent to which regulatory frameworks influence compliance with environmental laws, adherence to sustainability standards, and the effects of regulatory incentives or penalties on organizational behavior. The role of industry and international environmental standards, such as ISO 14001, is assessed to determine their impact on guiding the adoption and implementation of green logistics technologies. The study also evaluates the support provided by governmental and non-governmental policies, including subsidies, tax incentives, grants, and other instruments designed to encourage sustainable technology adoption. Furthermore, it explores the extent and effectiveness of collaborations and partnerships aimed at advancing green logistics initiatives, considering joint ventures with technology providers, participation in sustainability consortia, and partnerships with environmental organizations. Additionally, the study assesses efforts to raise awareness and communicate about green logistics practices, including internal and external communication strategies, educational programs, and marketing initiatives related to sustainability.
## 4 Interaction between Periodontitis and AMI  
Periodontitis is associated with AMI in several populations [52]. Moreover, periodontitis and AMI share common risk factors, such as smoking and diabetes [14]. This is especially important because of the close link between diabetes and AMI, since the latter is the most frequent cause of mortality and morbidity in diabetic populations [15]. Patients with periodontitis tend to have an increased frequency of being overweight and experiencing endothelial dysfunction, hypertension, platelet hyperreactivity, dyslipidemia, and a prothrombotic state [16], which are known risk factors for AMI [64].  
Local/systemic inflammation caused by periodontitis also contributes to the risk of CVD [65; 66]. This is supported by a large number of studies that show an increase in the circulation of inflammatory mediators in patients with periodontal diseases [66], such as C-reactive protein (CRP), interleukin-6, fibrinogen, and platelet-activating factor, among others, compared to healthy controls [66; 67]. Moreover, an increased serum leptin concentration has been reported due to periodontal disease, which promotes atherosclerosis by enhancing platelet aggregation, the production of proinflammatory cytokines such as interleukin-2 and interleukin-6, and arterial wall calcification [65; 68]. In addition, the translocation of periodontitis-causing microorganisms into the bloodstream and their accumulation in atheroma plaques may contribute to plaque instability and reduce the risk of AMI development [16].  
Evidence has confirmed the presence of bacterial DNA in atheromatous plaques [69]. The bacterial species most commonly found were _Porphyromonas gingivalis_, _Actinobacillus actinomycetemontinus_, _Tamerella forythia_, _Prerotella intermedia_[70], _Fusobacterium nucleatum_, and _Campylobacter rectus_[71], suggesting the migration of these oral pathogens to distant body sites [69].  
The mechanisms by which periodontitis can participate in AMI development include bacteremia and associated systemic inflammatory sequelae, including elevations in CRP and oxidative stress [52]. These mechanisms favor atherosclerotic plaque formation and instability [65; 66]. Several authors have proposed that periodontitis may be a non-traditional modifiable risk factor for AMI [52].
## 3 Results and Discussions
### FT-IR Analysis  
Figure 2 depicts FT-IR data in the range of 400-4000 cm\({}^{-1}\) for the \(\alpha\)-MnO\({}_{2}\) nanowires (black and brown) synthesized using the hydrothermal method. The bands at 3429 and 1635 cm\({}^{-1}\) are due to the stretching and bending vibrations of water molecules or hydroxyl groups, while the bands at 2836 and 2920 cm\({}^{-1}\) are attributed to C-H stretching and bending vibrations. The narrowband at 1376 cm\({}^{-1}\) is assigned to C-O vibrational mode.  
Normally, characteristic bands at 707, 518, and 449 cm\({}^{-1}\) correspond to the Mn-O stretching modes of the octahedral in the \(\alpha\)-MnO\({}_{2}\) structure, while in this case, the peak at 518 cm\({}^{-1}\) exhibits the Mn-O stretching mode, and the band at 707 cm\({}^{-1}\) is due to O-O stretching mode, demonstrating the presence of MnO\({}_{6}\)[36; 37].
# Proactive Caching at the Edge Leveraging Influential User Detection in Cellular D2D Networks
## 6 Conclusions and Future Directions  
In this paper, we present a proactive caching approach for D2D in small cellular communication networks. We exploit the social and spatial structure of social networks and represent the D2D cellular network as a social graph. In order to find the influential users and clusters formation, we use an eigenvector centrality measure and CC-GA clustering algorithms, and cache the content of influential users of each community. Based on the experimental results, we conclude that caching content of influential users at the user cache can reduce a significant amount of backhaul load and increase the user satisfaction rate, compared to reactive caching or no caching. The key advantage of the proposed scheme is the simplicity of its popular content identification process, which is a key high complexity challenge in both reactive and proactive caching.  
This work can be extended in the following directions:  
* _Event-driven influential users detection:_ When there is a change in the network being detected, it is possible that current influential users may not be available or as influential as they were before the change. In such scenarios, the system should be able to dynamically detect the influential users through re-computing the centrality scores.  
Figure 5: Proactive vs. Reactive caching showing satisfied requests vs. no. of requests (**left**) and cache size (**right**).  
Figure 6: Proactive vs. Reactive caching showing backhaul load vs. no. of requests (**left**) and cache size (**right**).  
* _Joint optimization techniques for D2D enabled caching:_ Another interesting future work would be to investigate joint optimization of proactive content caching, scheduling techniques, and interference management.
* _Machine learning for network optimization:_ For optimizing D2D caching, effective machine learning approaches can be exploited for clustering the network users and detection of influential users.
* _Mobility aware proactive caching:_ Most of the work discussed in the literature focuses on cache placement and delivery strategy while the user is stationary. Caching becomes challenging when a user is moving. For example, when a user with a live streaming session moves from the connectivity of one influential user to another, the user will not be able to stream if the content is not available at the new influential user cache. In such scenarios, deep learning and big data analytics are essential tools that can be leveraged.  
[PERSON], [PERSON] and [PERSON] discussed and confirmed the idea. [PERSON] and [PERSON] carried out the experiments and wrote the main paper. [PERSON], [PERSON], [PERSON] and [PERSON] provided the technical guidance for proactive caching in D2D small cellular networks with proofreading the paper. All authors provided comments on the manuscript at all stages.  
The authors would like to thank [PERSON] for the insights provided and [PERSON] for her help in reviewing the manuscript.  
The authors declare that there is no conflict of interests regarding the publication of this paper.
# Battery Management System Hardware Concepts: An Overview
## 6 Safety and Reliability
### Insulation Measurement
[PERSON] and [PERSON] [80] are presenting a good comparison of three common insulation measurement devices with slightly different measurement techniques. One device each from Eaton (Dublin, Ireland), from Siemens (Berlin/Munchen, Germany) and from Bender (Grunberg, Germany) is tested. The Eaton only applies a constant DC voltage; thus, this device is not able to measure insulation faults behind a rectifier. The second device, the Siemens, applies an altering voltage so it works in the same way as mentioned before. The last one, the Bender, measures with an altering voltage as well and in addition is capable of adapting the measuring time depending on the time constant of \(R\) and \(C\). The insulation measurement is also standardized in a few norms. Distinguished in _Measurement Method_ and recommended _Minimum Insulation Resistance_, they will be named in the following subsections.
## 2 Related Work
### ML-Based
Studies such as [54] also tested multiple algorithms (AdaBoost, Naive Bayes, and others) for classifying anomalous emails, although the dataset size was limited. In the IoT domain, [PERSON] et al. [55] compared ML algorithms, finding RF to be effective for anomaly detection despite its high computational overhead. [PERSON] et al. [56] explored a broader set of supervised models (RF, SVM, DT, Light Gradient Boosting Machine (LGBM), ExtraTrees, Gradient Boosting, Ada Boost, KNN, Multi-Layer Perceptron (MLP), Gaussian Naive Bayes, and LR), with SVM achieving the highest accuracy. Another study [57] used RF and deep learning models to classify network attacks into five types, although real-time adaptation was not addressed. [PERSON] and [PERSON] [58] employed different data processing techniques (Label Encoding, One-Hot Encoding, SMOTE) to enhance ML-based detection of data leakage incidents, showing that RF and DT performed best on the CERT dataset. [PERSON] et al. [59] compared classifiers for intrusion detection, with RF and Gradient Boost both performing well. [PERSON] et al. [60] combined Principal Component Analysis (PCA) and XGBoost for anomaly detection and achieved high accuracy on the UNSW-NB15 dataset, although continuous monitoring was not considered. Lastly, [PERSON] et al. [61] evaluated RF, KNN, and DT using balanced datasets, with RF achieving the highest accuracy, although instantaneous data analysis was missing. [PERSON] and [PERSON] [62] compared ML models such as SVM, DT, and KNN for detecting IoT network anomalies, achieving real-time detection; however, their study lacked user behavior analysis. Finally, a number of studies have reported exceptionally high detection accuracy. However, it is important to note that these results were derived from offline datasets rather than from real-time instantaneous data [63; 64; 65; 66]. Overall, ML-based approaches improve upon traditional methods by offering better predictive capabilities and reduced false positives. However, they often require significant computational resources and lack effective real-time classification features.
## 5 Conclusions  
In this study, remote sensing data from two different sensors, the new generation LiDAR GEDI and the land monitoring satellite Landsat 9, were combined to estimate the carbon storage of the _Quercus aquifolioides_ forest ecosystem in Shangri-La. Our study proposed that the parameters of GEDI footprints are to be interpolated to the surface via the Kriging interpolation to solve the problem of GEDI data discontinuity. The results show that the digital_elevation_model, modis_treecover, and fhd_normal provided by GEDI, and EVI and B2 offered by Landsat 9 had a high correlation with _Quercus aquifolioides_ carbon storage. And the random forest had the highest accuracy amongst the three models (R\({}^{2}\) = 0.81 and RMSE = 11.92 t/hm\({}^{2}\)). We discovered a consistent agreement between the evaluated results assessed by integrating GEDI and Landsat 9 (5,374,137.62 t) and the observed values calculated by the National Forestry Inventory (5,879,608.28 t). Through the analysis of the carbon storage of _Quercus aquifolioides_ in different aspects and slopes, the carbon storage distributed on the steep slopes (25-35\({}^{\circ}\)) and the sunny slopes (202.5-292.5\({}^{\circ}\)) were found to be the largest. Our study demonstrated that combining GEDI LiDAR and optical remote sensing Landsat 9 to estimate the carbon storage in Shangri-La can provide a reference for multi-source remote sensing inversion at the county scale by integrating multiple data.  
Conceptualization, L.X., Q.S. and J.Y.; methodology, L.X. and Q.S.; software, L.X.; validation, L.X., Q.S. and J.Y.; data curation, J.Y. and Y.G.; formal analysis, L.X. and J.Y.; investigation, L.X. and S.L.; writing--original draft preparation, L.X.; writing--review and editing, S.L., Q.S., J.Y., L.X., W.Z., H.L., S.W. and C.G.; funding acquisition, Q.S. and L.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Joint Agricultural Project of Yunnan Province, (No. 202301 BD070001-002), National Natural Science Foundation of China (Nos. 31860205 and 31460194), Yunnan Provincial Education Department Scientific Research Fund Project (No. 2023V0728), China, in 2023.  
Not applicable.  
All remote sensing data for our research are open source and free of charge. Landsat 9 data were obtained from GEE platform ([[https://code.eartheengine.google.com](https://code.eartheengine.google.com)]([https://code.eartheengine.google.com](https://code.eartheengine.google.com)), accessed on 7 May 2023); GEDI data were obtained through the NASA official website ([[https://search.earthdata.nasa.gov](https://search.earthdata.nasa.gov)]([https://search.earthdata.nasa.gov](https://search.earthdata.nasa.gov)), accessed on 7 November 2022).  
All authors are grateful to NASA and GEE for providing the GEDI and Landsat 9 data, and to the editors and reviewers for their valuable comments.  
The authors declare no conflict of interest.
## 2 Linear and Nonlinear Methods
### Time-Series Models  
Time-series models build a linear relationship between future values and previously observed values, and mainly refer to the ARMA model and its variants [34]. These models can be generally expressed as a sum of the autoregression (AR) part and the moving average (MA) part. The AR part presents a regression on its own lagged values, which shows the memory effect of \(p\) previously predicted values in the time series. The MA part describes the regression error as a linear combination of error terms that occurred in past periods. As the model depends on the orders of AR and MA parts, the model is often expressed as the ARMA(\(p\), \(q\)) model. Furthermore, if the data show evidence of non-stationarity in the sense of mean, an initial differencing step can be applied to eliminate the time-varying trend. Then, the difference between consecutive observations can be treated as wide-sense stationary. The series of differences can be characterized using the ARMA model, and the future value can be calculated from the predicted difference. These two consecutive steps form the ARIMA model. The expression of the ARIMA model is given as  
\[Y_{t}=\left(1-L\right)^{d}y_{t}, \tag{1}\]  
\[\left(1-\sum_{i=1}^{p}\varphi_{i}L^{i}\right)Y_{t}=\left(1+\sum_{i=1}^{q} \theta_{i}L^{i}\right)\varepsilon_{t}, \tag{2}\]  
where \(y_{t}\) represents the future value to be predicted at period \(t\); \(Y_{t}\) is the difference with the order \(d\); \(L\) is the lag operator; \(p\) and \(q\) are non-negative integers, referring to the orders of AR and MA parts, respectively; \(\varphi_{i}\) is the \(i\)th AR coefficient; \(\theta_{i}\) is the \(i\)th MA coefficient; and \(\varepsilon_{t}\) is the error term that follows a normal distribution with a zero mean. As the prediction depends on the order of differencing \(d\) apart from the AR order \(p\) and MA order \(q\), the ARIMA model is also named as the ARIMA(\(p\), \(d\), \(q\)) model. If the parameter \(d\) equals zero, the ARIMA model is reduced to the ARMA model.  
Generally, there are three indispensable steps to establish an ARIMA model, which can be summarized as follows [35]:  
1. Model order identification: an initial differencing step is applied to make the data stationary in the mean sense. Then, the autocorrelation function (ACF) and partial autocorrelation function (PACF) of differenced time series will be calculated for the order determination of AR and MA parts.
2. Estimation of parameters: the parameters to be determined are estimated using the maximum likelihood method or the least squares method.
3. Residual diagnostics: the goodness of fit of the given data is estimated by checking the prediction errors. The prediction errors of a good model correspond to white noise, and its ACF will be at a low level.
## 4 Discussion
Better results have been reported in patients treated with a single fraction in terms of LC, as in the study by [PERSON] et al. with a 1-year LC rate of 85%, which is in line with the patients treated by [PERSON] et al., where better LC rates were reported for single-fraction treatments compared to multi-session irradiations. Since most published series only report 1-year LC rates due to the poor overall survival of the study cohorts, the respective median follow-ups are around 1 year. Therefore, not much data are known about LC beyond that period after SBRT treatment. In this case, many patients were still alive after two years, with 73% LC at that time. After three years, the LC rate was 73%. Thus, despite the limited data available and the small sample size, SBRT can achieve satisfactory local control rates at 2 and 3 years [40]. We must acknowledge certain limitations in our study, such as that it is a retrospective, single-institutional study with a small number of patients. We did not use the Brief Pain Inventory (BPI), which quickly assesses pain severity and its impact on functioning. The use of the BPI by nursing staff is increasingly recommended. It is very important to offer more radical and effective treatments for disease and symptom control early on since patients' QoL is crucial until the end of their lives and since many patients with vertebral metastases are long survivors. Physicians and nursing staff should be trained in pain assessment, at least with the VAS, before and after treatments (at each follow-up visit) to determine the efficacy of treatments. In our opinion, it is also important to record the reduction of analgesic consumption, as it influences the quality of life of patients and the cost-effectiveness of health care. In our series, except for grade 3 radiodermatitis, there were no major toxicities. Toxicities of SBRT could be myelopathy (0.4%) and CVF (11-39%). Myelitis has not appeared in our series because the fractionations used were safe, are recommended in the literature (1 to 5 fractions), and all of them were dosed higher than 6 Gy. We could explain the absence of FVC by the short follow-up period in most of our patients.
# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
## 2 Materials
### Satellite Data
#### 2.3.2 Polarimetric Data Representation
Next, Sentinel-1 Back Geocoding operator was applied to each sub-swath (slave) in order to co-register it to the corresponding sub-swath of the master image (captured on 12 January 2018), using the SRTM 3 Sec HGT Digital Elevation Model (DEM), and create a stack of multi-temporal data [100]. Afterwards, TOPSAR Deburst module was used for joining separate successive bursts of each sub-swath into a single product. After debursting, separately processed sub-swaths were merged, creating a larger sub-scene. The following step was to reduce the original image size to the study area's extend in order to decrease processing time, using the \"Subset\" module. Subsequently, after deleting metadata baseline information from the image stack, Stack Split operator was used, to separate single date products under processing from the master image, and scattering matrix elements were exported from SNAP to the PolSARPro format. Each SAR image was then imported into the PolSARpro software and was multilooked (spatially averaged) with a window size of 4 \(\times\) 1 (in range and azimuth direction, respectively) pixels to create ground ranged squared pixels. After generating the multi-looked \(C_{2}\) matrix for each pixel, each SAR image was filtered by using a Refined Lee Filter [103] with a window size of 7 \(\times\) 7 pixels, in order to mitigate speckle noise. The last step of the pre-processing chain was the application of the \"Range Doppler Terrain Correction\" algorithm, by using a SRTM (Shuttle Radar Topography Mission) 1 arc-second Digital Elevation Model (DEM) and by employing a bilinear interpolation resampling method. As a result, SAR images' geometric distortions, due to sensor's varying viewing angle and topological variations, were corrected and a better geometric representation of Earth's surface was obtained [104].
## 2 Modeling of the DC Sub-Microgrid
### Global Modeling (Grid-Connected Mode)  
The proposed AC sub-grid system's global model comprises hybrid renewable sources, wind and PV systems, main grid, and AC loads. The global model, as described in Sections 2.1, 2.2, and 2.4 can be expressed as follows:  
\[\frac{dx_{1}}{dt}=\frac{V_{in}}{L_{w}}-\frac{R_{w}x_{1}}{L_{w}}-\frac{x_{5}(1- \mu_{1})}{L_{w}} \tag{32}\]  
\[\frac{dx_{2}}{dt}=\frac{I_{PV}}{C_{PV}}-\ \mu_{2}\frac{x_{3}}{C_{pv}} \tag{33}\]  
\[\frac{dx_{3}}{dt}=\ \mu_{2}x_{2}+(\mu_{2}-1)\frac{x_{5}}{L_{PV}} \tag{34}\]  
\[\frac{dx_{5}}{dt}=(1-\ \mu_{1})\frac{x_{1}}{C_{dc}}+(\ 1-\mu_{2})\frac{x_{3}}{C_{dc}}-\frac{I_{o}}{C_{dc}}-\frac{3}{2C_{dc}x_{5}}\ (x_{ 7}x_{9}+x_{6}x_{8}) \tag{35}\]  
\[\frac{dx_{6}}{dt}=\ -\frac{x_{8}}{L_{f}}-\frac{R_{f}x_{6}}{L_{f}}+w_{o}x_{7}+ \frac{U_{d}x_{5}}{L_{f}} \tag{36}\]  
\[\frac{dx_{7}}{dt}=\ -\frac{x_{9}}{L_{f}}-\frac{R_{f}x_{7}}{L_{f}}-w_{o}x_{6}+ \frac{U_{q}x_{5}}{L_{f}} \tag{37}\]  
\[\frac{dx_{8}}{dt}=\ -\frac{x_{8}}{RC_{f}}+w_{o}x_{9}+\frac{x_{6}}{C_{f}}- \frac{x_{10}}{C_{f}} \tag{38}\]  
\[\frac{dx_{9}}{dt}=\ -\frac{x_{9}}{RC_{f}}-\ w_{o}x_{8}+\frac{x_{7}}{C_{f}}- \frac{x_{11}}{C_{f}} \tag{39}\]  
\[\frac{dx_{10}}{dt}=\frac{x_{8}}{L_{g}}-\frac{R_{g}x_{10}}{L_{g}}+w_{o}x_{11}- \frac{V_{gd}}{L_{g}} \tag{40}\]\[\frac{dx_{11}}{dt}=\frac{x_{9}}{L_{g}}-\frac{R_{g}x_{11}}{L_{g}}-w_{o}x_{10}-\frac {V_{gq}}{L_{g}} \tag{41}\]  
where, \(x_{\theta}\), \(x_{7}\), \(x_{8}\), and \(x_{9}\) are the currents and voltages values of dq axis; \(x_{10}\) and \(x_{11}\) shows the values of utility grid dq axis currents, respectively; \(w_{o}\) is the angular frequency during the grid-connected mode of microgrid. In the next section, the controller's design is discussed to control the AC and DC bus voltages during the islanded mode and grid-connected mode.