esa-sceva/satcom-chunk-collection huggingface dataset:
# Battery Management System Hardware Concepts: An Overview
## 1 Introduction  
Nowadays, lithium-ion batteries are used in various applications, ranging from personal electronic devices, like cell phones, to the emerging class of electric vehicles. Because of the fragile nature of these types of batteries, when compared to lead-acid, or NiCd batteries, a comparatively advanced monitoring is necessary for safe operation.  
The complexity of a battery management system (BMS) strongly depends on the individual application. In simple cases, like single cell batteries in mobile phones, or e-book readers, a simple \"fuel gauge\" Integrated Circuit (IC), like e.g., [1] or [2] can be sufficient. These ICs usually are able to measure voltage, temperature and current and use simple methods to estimate the battery's current State of Charge (SOC). In more complex devices, like electric cars, the BMS has to fulfill more sophisticated tasks. In addition, the basic parameters like cell voltage, cell temperature and current have to be measured. Nevertheless, advanced algorithms are needed, as e.g., the available energy has to be determined in order to reliably calculate the cruising range.  
This work focuses on the hardware aspects of battery management systems for lithium-ion batteries, which provide the described functions. The first section introduces a set of requirements on the hardware portion of a BMS, taking into account measurement of needed values and electromagneticinterference, as well as galvanic isolation, contactors and redundancy aspects. Afterwards, in Section 3, an overview of possible BMS topologies is presented. In that section of the paper, differences between simple use cases, as portable electronic devices and more complex ones, as e.g., electric vehicles (EVs) or aeroplanes are illuminated. The section closes with the description of four real-world examples for battery packs taken from electric vehicles. Section 4 explains in detail how the requirements regarding measurement of physical values can be fulfilled and lists some common pitfalls in doing so. Afterwards, in Section 5, the important topic of balancing is discussed. There, different approaches for charge equalization are presented and compared to each other. Finally, Section 6 focuses on safety and reliability aspects. The main risks that are associated with operating high voltage lithium-ion battery packs and possible countermeasures are shown. In the last part, a short overview of different methods of insulation measurement and some relevant standards are given.
# New Landslide Disaster Monitoring System: Case Study of Pingding Village
## 2 Material and Methods  
### Study Area: The Village of Pingding  
Pingding is situated in the township of Linnei in the county of Yunlin, Taiwan, and its geographical coordinates are 23 deg45'36'' N and 120 deg36'38'' E (Figure 1).  
Linnei is divided into mountainous areas and plains by the Taiwan 3 rd Line Highway. With mountains on its southeastern side and plains in the northwest, Linnei's altitude is 70-320 m above sea level, and its topography is a slope extending roughly from the southeast to the northwest. Pingding is situated on a mesa-type hill, with an altitude of 325 m above sea level. With a tropical and humid climate, the village has a yearly rainfall of 1500-2000 mm, which is concentrated in the summer. The exposed geological formation is the Toukoshan Formation Huoyanshan Conglomerate Section; the gravel components of the conglomerate are of less than 20 cm in size and are unevenly distributed. The sandstone and mudstone, which are sometimes mixed in the conglomerate, are mainly composed of sandstone and quartz sandstone. The exposed parts of the conglomerate often form cliffs or precipices and gorges if there are any valleys. The Pingding Collapse Zone is located within Pingding  
Figure 1: Flowchart of the research procedure.  
in the mountains east of Linnei. Linnei, which is located in the northeast of Yunlin county (one of the counties in Taiwan), adjoins Pingding, Pingding Mountain, and the township of Zhushan, Nantou.  
In terms of its geographical location, Pingding is located between Shexi and Gaoxikou in the township of Gaoshu (Figure 1). The study site is flat, tilting north and west, and the west is similar to a valley (Figure 2).  
According to the \"Central Taiwan Geological Map--From Zhushan to Jiayi\" document from the fourth issue of the Central Geological Survey Article Collection and Annual Report of Taiwan Council of Agriculture by the Soil and Water Conservation Bureau, the exposed strata near the Pingding Collapse Zone include the Huben Stratum and the Sanxing Stratum of the Pleistocene Age, as well as the Accretion of the Holocene. Judging from the positions and patterns of these strata, the Pingding Collapse Zone is located in the range of the Huben Stratum and the Sanxing Stratum, while both of the shores of the Pingding sub-stream are in accretion. According to the on-site geological surveys and the six (BH-1-BH-6) (geographical surveying site from BH-1 to BH-6) 12-30 m deep geological drilling holes, the strata can be further divided into reddish brown silty clay, brown sandy silty soil in the colluvial rocks, brown silty soil in the gravel stratum, and brown or grey siltstone.
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 3 Electromagnetic Irradiation
### X-ray Irradiation  
There are few reports on X-ray irradiation of MoS\({}_{2}\). Only one paper [108] reported that MoS\({}_{2}\) mono-layers were irradiated with 10 keV X-rays with varying total ionizing doses. It was reported that the MoS\({}_{2}\) mono-layers were robust to X-ray radiation, withstanding doses of up to 6 Mrad doses without any noticeable degradation of optical properties. It is generally accepted that X-rays do not affect MoS\({}_{2}\) few-layers.  
[PERSON] et al. [135] fabricated single-layer MoS\({}_{2}\) FETs and irradiated them under 10 keV X-ray exposure. At room temperature in air, the drain current of the devices decreased significantly under X-ray irradiation up to 10 Mrad. Effective threshold voltage and mobility were degraded with X-ray irradiation dose. It stated that the degradation was consistent with the generation of negatively charged surface states during the X-ray exposure.
## 4 Results and Discussion
### Techniques in Modeling GWL Changes
#### 4.2.2 MM Approaches and Performance Metrics  
Mathematical and statistical models accounted for 39.32 percent (46 papers) of the primary investigations (117 articles) used (Figure 8). The most prevalent approaches were the Boussinesq equation, Biot's model, rheological model, cross-correlation analysis, Laplace transform (LT), Laplace homotopy perturbation model (LHPM), and Bayesian model averaging. The Glover-Dumm mathematical model was also used in primary investigations that took into account MM methods. The Glover-Dumm MM technique, on the other hand, was coupled with other models such as frequency ratio (FR) by the researchers to produce a proposed model. The most widely used performance indicators are the coefficient of determination (CD), the modeling efficiency (EF), the maximal error (EF), coefficient of residual mass (CRM), and the root mean square error (RMSE). The MM  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Metric** & **Number of Times Employed** & **Percentage (\%)** \\ \hline RMSE & 32 & 30.8 \\ MSE & 7 & 6.73 \\ MAE & 11 & 10.6 \\ NSE & 11 & 10.6 \\ MAPE & 4 & 3.8 \\ R & 11 & 10.6 \\ Monte Carlo & 2 & 1.9 \\ NMSE & 1 & 0.96 \\ Shannon Entropy & 1 & 0.96 \\ AUC & 1 & 0.96 \\ AIC & 1 & 0.96 \\ R\({}^{2}\) & 11 & 10.6 \\ PBias & 4 & 3.83 \\ PR & 2 & 1.9 \\ ASE & 1 & 0.96 \\ AUROC & 1 & 0.96 \\ KGE & 1 & 0.96 \\ SI & 1 & 0.96 \\ MARE & 1 & 0.96 \\ Total & 104 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance metrics and their proportions.  
Figure 9: Number of times ML approaches were employed in studies.  
techniques and assessment criteria utilized to estimate groundwater level changes derived from our primary investigations are shown in Table 7.  
The majority of the MM techniques were clearly deployed between the years 2000 and 2011. Between 2012 and 2022, few MM methods were used.
###### Abstract  
Current research highlights the use of natural products or phytochemicals as drugs and functional additives to treat obesity with few side effects. _Sargassum horneri_ (SH) and _Ulva australis_ (UA) are marine waste resources on Jeju Island, Republic of Korea. In this study, we analyzed their antioxidant and anti-obesity efficacies to confirm their potential as functional additives. We prepared SH and UA extracts using 80% ethanol and observed that free radical scavenging activity and total phenol content were high in SH extracts, and total flavonoid content was elevated in UA extracts. Additionally, 3T3-L1 cells were treated with SH and UA extracts, and the ability of the extracts to inhibit adipocyte differentiation was examined using Oil Red O staining and analysis of neutral fat content. We confirmed that the mRNA expression of the C/EBP\(\alpha\), PPAR\(\gamma\), and SREBP1c genes that act on adipocyte differentiation, and of FAS, a fatty acid synthase, was suppressed. Experiments in a mouse model of obesity showed that 12-week administration of a high-fat diet with 1% extract added to drinking water resulted in lower weight gain compared to the high-fat diet alone. These results suggest that SH and UA extracts have antioxidant properties and are effective in obesity prevention. Therefore, the two marine waste resources are potential functional additive candidates for preventing obesity.  
seaweed; extract; antioxidant; obesity; hyperlipidemia Article
# Extreme Learning Machine Enhanced Gradient Boosting for Credit Scoring
## 2 Methodology
### Gradient Boosted Decision Tree  
Given a training set \(\{(\mathbf{x}_{1},y_{1}),(\mathbf{x}_{2},y_{2}),\cdots,(\mathbf{x}_{N},y_{N})\}\), where \(\mathbf{x}_{i}\) is the feature of \(i\)-th sample and \(y_{i}\in\{0,1\}\) denotes the label of the \(i\)-th sample. ML algorithms realize credit scoring by designing a function \(F(\mathbf{x}_{i})\) to minimize the loss function \(L(y_{i},F(\mathbf{x}_{i}))\):  
\[F^{*}=\operatorname*{arg\,min}_{F}\sum_{i=1}^{N}L(y_{i},F(\mathbf{x}_{i})). \tag{8}\]  
Gradient boosting algorithms realize Equation (8) in an additive integration way:  
\[F(\mathbf{x})=\sum_{t=1}^{T}f_{t}(\mathbf{x}), \tag{9}\]  
where \(T\) is the number of iterations. It can be seen from Equation (9) that \(F(\mathbf{x}_{i})\) is incrementally integrated in an additive manner. In the \(t\)-th iteration, \(f_{t}\) realizes the further optimization of the overall loss of the previously formed ensemble \(\{f_{j}\}_{j=1}^{t-1}\). In the implementation of GBDT, each function \(f\) is implemented by a DT that can be regarded as a base learner. Therefore, \(f\) can be embodied as \(f(\mathbf{\alpha};\mathbf{x})\), \(\mathbf{\alpha}\) is the structural parameters of each DT, which determines the feature and splitting threshold at each internal splitting node in the decision tree.  
Since the \(t\)-th iteration realizes the further optimization of the loss function, the loss function can be expressed as \(L(y_{i},F_{t-1}(\mathbf{x}_{i})+f_{t}(\mathbf{x}_{i}))\). According to Taylor expansion, the loss function can be approximated as:  
\[\begin{split} L(y_{i},F_{t-1}(\mathbf{x}_{i})+f_{t}(\mathbf{x}_{i}) )\\ \approx& L(y_{i},F_{t-1}(\mathbf{x}_{i}))+g_{i}f_{t} (\mathbf{x}_{i})+\frac{1}{2}f_{t}(\mathbf{x}_{i})^{2},\end{split} \tag{10}\]  
where \(g_{i}\) is the first derivative of the loss function, which can be calculated as:\[g_{i}=\left[\frac{\partial L(y_{i},F_{\mathbf{(x}_{i})})}{\partial F(\mathbf{x}_{i })}\right]_{F(\mathbf{x}_{i})=F_{\mathrm{f}_{\mathrm{i}-1}(\mathbf{x}_{i})}}. \tag{11}\]  
Therefore, Equation (11) can be transformed into an optimization problem:  
\[f_{t}^{*}=\operatorname*{arg\,min}_{f_{t}}\sum_{i=1}^{N}\frac{1}{2}(f_{t}( \mathbf{x}_{i})-g_{i})^{2}. \tag{12}\]  
It can be seen from Equation (12) that the fitting target of \(f_{t}\) is the negative gradient of the loss function. Therefore, before training each tree in GBDT, we update the training target of each tree as \(\{y_{i}\}_{i=1}^{N}=-\left[\frac{\partial L(y_{i},F_{\mathbf{(x)}_{i}})}{ \partial F(\mathbf{x}_{i})}\right]_{F(\mathbf{x}_{i})=F_{\mathrm{f}_{\mathrm{ i}-1}(\mathbf{x}_{i})}}.\)
## 2 Case Study: The Acuto Field Laboratory
All sensors were connected via low-noise cables to an HBM SomatXR MX1601B-R signal amplifier coupled with a SomatXR CX23-R digital data controller, and set to a sampling frequency of 2400 Hz. The piezoelectric micro-accelerometers were selected due to their optimized design for low-frequency and low-level vibration measurements, as witnessed by their high sensitivity (2500 mV/g) and flat frequency response from 5 to 2 kHz. The power supply was constantly guaranteed by means of external backup batteries and one solar panel; however, several gaps exist in the monitoring dataset due to frequent system malfunctions. Two different long-term monitoring campaigns were carried out from February 2018 to November 2019. These campaigns are mainly differentiated because two distinct array configurations were designed and implemented. During the first campaign (February 2018-May 2018), six one-component micro-accelerometers were deployed in a linear array on the back face of the rock block--where the vertical open joint partially detaches the rock block from the rock wall (Figure 2b)--with regular 15 cm spacing. During the second campaign (November 2018-October 2019), a different array configuration was designed, and the micro-accelerometers were deployed in a \"composite\" three-component setting to investigate potential differences between the dynamic behavior of the rock wall and the rock block (Figure 2c).
### Case Study No. 3: OpenPLC Linked with Node-RED Acting as a Software IIoT Gateway
#### 6.3.3 Creation of OPC UA Server in Node-RED  
In the local Node-RED, we first need to create an OPC UA server using the OPC UA server node (Figure 26) and the procedure already mentioned in the previous section. At the beginning, we create folders in the address space in order to have separate inputs and outputs in the address space. Next, we create 4 variables. These variables are the same as in OpenPLC and we named them **flo_Sensor_1_Q1-0**, **fo_Sensor_2_Q1-1**, **fo_Conveyor_1_Q0-0** and **fo_Conveyor_2_Q0-1**.
## 6 Results and Discussion
In this study, an energy simulation was conducted for one year, from January to December 2023, assuming Seoul to be the location where the shading structure will be installed. Consequently, the energy generation was the highest in June (3474 kWh\(\cdot\)month\({}^{-1}\)), and the lowest in December (1963 kWh\(\cdot\)month\({}^{-1}\)). The total energy produced through the solar panels in 2023 was 31,570 kWh\(\cdot\)year\({}^{-1}\). Assuming that LED street lights (100 W\(\cdot\)h\({}^{-1}\)) installed in outdoor spaces operate for 12 h every day, the amount of generated energy (31,570 kWh\(\cdot\)year\({}^{-1}\)) derived from this study is sufficient to supply power to 72 LED street lights. These results demonstrate that parametric design in the architectural field is highly efficient in terms of construction and renewable energy generation. As an empirical study related to kinetic facades, a building with a parametric design applied to the facade was completed in Germany in 2020. The optimal tilt angle for optimizing solar power generation was derived through a parametric simulation, and solar modules were placed at different angles on the south, southwest, and west sides of the building. As of 2022, the total renewable energy generated from building facades was equivalent to 14% of the total building energy consumed [49]. Solar panels at different an  
Figure 6: Virtual image of a shading structure with colored solar panels. gles were installed on the three building facades using the kinetic facade concept, increasing the energy independence of the building. The shading structure based on kinetic solar panels, whose positions change every month, as proposed in this study, can increase energy generation compared to solar panels fixed at optimal tilt angles. The generated renewable energy not only supplies power inside the building but can also power a number of external streetlights around the shading structure.
## 2 Related Works
### Gateway
Moreover, this protocol takes into consideration the aspects of the QoS requirements specified by the gateway requesters; furthermore, the protocol insures load balancing on the gateways as well as on the routes between gateways and gateway clients [11]. [PERSON] et al. proposed software defined networking (SDN) enabled 5G VANET. With proposed dual cluster head design and dynamic beamforming coverage, both trunk link communication quality and network robustness of vehicle clusters are significantly enhanced. Furthermore, an adaptive transmission scheme with selective modulation and power control is proposed to improve the capacity of the trunk link between the cluster head and base station. With cooperative communication between the mobile gateway candidates, the latency of traffic aggregation and distribution is also reduced [12]. [PERSON] et al. proposed a novel gateway discovery algorithm for VANETs, providing an efficient and adaptive location-aided and prompt gateway discovery mechanism (LAPGD). Here, all vehicles go across selected mobile gateways to access 3G networks instead of a direct connection. The algorithm aims to ensure every vehicle is capable of finding its optimal gateway, to minimize the total number of gateways selected in VANETs, and to guarantee the average delay of packets within an allowable range [13]. [PERSON] et al. proposed \"An Integrated Self-diagnosis System (ISS) for an Autonomous Vehicle based on an Internet of Things (IoT) Gateway and Deep Learning\". The ISS collects data from the  
Figure 1: The communication between the controller area network (CAN) and FlexRay. sensors of a vehicle, diagnoses the collected data, and informs the driver of the result. The ISS considers the influence between its parts by using deep learning when diagnosing the vehicle. By transferring the self-diagnosis information and by managing the time to replace the car parts of an autonomous driving vehicle safely, ISS reduces loss of life and overall cost [14]. They proposed \"A Lightweight In-Vehicle Edge Gateway (LI-VEG)\" for the self-diagnosis of an autonomous vehicle. LI-VEG supports a rapid and accurate communication between in-vehicle sensors and a self-diagnosis module and between in-vehicle protocols. The LI-VEG has higher compatibility and is more cost effective because it applies a software gateway to the OBD, compared to a hardware gateway. In addition, it can reduce the transmission error and overhead caused by message decomposition because of a lightweight message header [15].
## 3 Results and Discussion
### Antibacterial Activity of Oregano Essential Oil  
The antibacterial activity of OEOs against the four main dairy pathogens (_E. coli_, _L. monocytogenes_, S. Enteritidis, and _St. aureus_) responsible for foodborne diseases associated with cheese consumption [53] and the two LAB (_L.c. lactis_) commonly used as fermentating agents in dairy production [54] is shown in Table 1.  
The high sensitivity of human pathogens to EOs extracted from different varieties of oregano cultivated in Sicily is well known [55]. However, OEOs tested in this study showed very high antibacterial activity against all pathogenic strains with a diameter of the inhibition area around the paper disc in the range 35.8-42.5 mm. Considering the strong activity shown by OEOs, it was also characterized in terms of an MIC that represents the lowest concentration of an active compound able to inhibit microbial growth [56]. MIC confirmed the strong activity of OEOs tested in this study with values of 2.50 \(\mu\)L/mL against _L. monocytogenes_, 1.25 \(\mu\)L/mL against _S. Enteritidis and _St. aureus_, and 0.625 \(\mu\)L/mL against _E. coli_. These results suggest that this natural product has great potential in food preservation to ensure for consumers a safe food supply. Interestingly, the development of the lactococci selected as starter cultures was not inhibited by OEOs, indicating their harmlessness against LAB, a basic condition for their application in cheese making. The resistance of LAB to the OEOs' components may be explained by the fact that bacterial susceptibility to antimicrobial agents is strain dependent [57].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Species** & **Strains** & **Inhibition (mm)** & **MIC (\(\mu\)L/mL)** \\ \hline Pro-technological & & & \\ _L. lactis_ & NT1 & - & n.d. \\ _L. lactis_ & NT5 & - & n.d. \\ Pathogenic & & & \\ _E. coli_ & ATCC25922 & 42.5 \(\pm\) 0.1 & 1.25 \\ _L. monocytogenes_ & ATCC19114 & 35.8 \(\pm\) 0.2 & 2.50 \\ _S. Enteritidis_ & ATCC13076 & 42.0 \(\pm\) 0.2 & 0.625 \\ _St. aureus_ & ATCC33862 & 38.2 \(\pm\) 0.1 & 1.25 \\ \hline \hline \end{tabular} Results indicate the mean value of three independent assays. Abbreviations: MIC, minimum inhibitory concentration; _L.c. Lactococcus_; _E. Escherichia_; _L., Listeria_; _S., Salmonella_; _St., Staphylococcus_; n.d., not determined. Symbols:—no inhibition found.  
\end{table}
Table 1: Antibacterial activity of oregano essential oil.
# Smart Routing for Sustainable Supply Chain Networks: An AI and Knowledge Graph Driven Approach
## 2 Theoretical Foundations and Research Gap
### Need for Further Investigation  
The synthesis of the recent literature shows a significant methodological gap: no existing framework dynamically integrates (a) generative AI with (b) graph-based modelling/optimization to (i) autonomously resolve incomplete transport routes and (ii) compute multimodal emissions. GDBs enhance LCA data interoperability [22; 23; 24; 25; 28] but lack generative capabilities to infer missing logistics connections. LLMs can effectively automate unstructured data parsing [26; 27; 29; 30] but cannot contextualize outputs within supply chain networks or perform optimization.  
Current applications exhibit three critical deficiencies: (1) existing GDB-centric approaches (e.g., [PERSON] et al. [28]) rely on static network representations that cannot dynamically detect missing or outdated logistic data; (2) most systems depend on narrow modal coverage, such as road-only APIs that are incompatible with global supply chains inherently intermodal nature [31; 32]; and (3) current architectures operate as open-loop systems where route generation, enrichment, and optimization remain disconnected, as demonstrated by hybrid methods like [PERSON]'s NLP-extracted emissions graph [29].  
This gap necessitates a unified system that integrates LLMs' generative reasoning with GDBs' analytical functionalities for sustainable optimization. The required application must enable generative route planning where LLMs suggest complete multimodal routes (e.g., \"Supplier A \(\rightarrow\) Port X (Road) \(\rightarrow\) Port Y (Sea) \(\rightarrow\) Plant B\"), intelligent data augmentation through LLMs combined with specialized tools for validation, network optimization via graph algorithms that identify optimal routes within the network, and dynamic network analytics.