esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 A Two-Stage Robust Optimization Model  
### Mathematical Model of Microgrid System  
#### 2.1.1 New Energy and Load Uncertainty Model  
In the robust optimization model of this article, the uncertain parameters are the output of wind turbines and distributed photovoltaic power plants, as well as the demand for loads. In this article, \(R\) represents the range of uncertain parameters as a bounded closed box type uncertain set. At the same time, to effectively improve the conservatism of the robust optimization, this paper introduces robustness indicators, \(\Gamma_{\text{wt}}\), \(\Gamma_{\text{pv}}\), and \(\Gamma_{\text{load}}\) measures, to characterize the volatility of uncertain variables [25]. Uncertain set U is represented by Equations (1)-(3):  
\[\left\{\begin{array}{l}P_{\text{wt},t}=P_{\text{wt},t}^{0}+r_{\text{wt},t }\cdot\Delta P_{\text{wt},t}^{max}\\ -1\leq r_{\text{wt},t}\leq 1\\ \frac{r_{\text{wt},t}\cdot\Delta P_{\text{wt},t}^{\text{max}}}{P_{\text{wt}, t}}\leq\Gamma_{\text{wt}}\end{array}\right. \tag{1}\]  
\[\left\{\begin{array}{l}P_{\text{pv},t}=P_{\text{pv},t}^{0}+r_{\text{pv},t} \cdot\Delta P_{\text{pv},t}^{\text{max}}\\ -1\leq r_{\text{pv},t}\leq 1\\ \frac{r_{\text{pv},t}\cdot\Delta P_{\text{pv},t}^{\text{max}}}{P_{\text{pv},t }}\leq\Gamma_{\text{pv}}\end{array}\right. \tag{2}\]  
\[\left\{\begin{array}{l}P_{\text{load},t}=P_{\text{load},t}^{0}+r_{\text{load },t}\cdot\Delta P_{\text{load},t}^{\text{max}}\\ -1\leq r_{\text{load},t}\leq 1\\ \frac{r_{\text{load},t}\cdot\Delta P_{\text{load},t}^{\text{max}}}{P_{\text{ load},t}}\leq\Gamma_{\text{load}}\end{array}\right. \tag{3}\]  
where \(P_{\text{wt},t}^{0}\), \(P_{\text{pv},t}^{0}\), and \(P_{\text{load},t}^{0}\) are the predicted output values and load demand values of wind turbines and distributed photovoltaic power plants during time period \(t\). \(P_{\text{pv},t}\), \(P_{\text{pv},t}\), and \(P_{\text{load},t}\) are the actual output value and the load demand value of the wind turbine and the distributed photovoltaic power station in the t period. \(\Delta P^{\max}_{\text{wt,t}}\), \(\Delta P^{\max}_{\text{pv,t}}\), and \(\Delta P^{\max}_{\text{load,t}}\) are the maximum fluctuation of the wind turbine, distributed photovoltaic power station, and load in the \(t\) period. \(r_{\text{wt,t}}\), \(r_{\text{pv,t}}\), and \(r_{\text{load,t}}\) are the auxiliary variables characterizing the upper and lower limits of the fluctuations of the uncertain variables.  
At the same time, to ensure the stable operation of the system, constraints are imposed on the fluctuation range of uncertain variables [26], as shown below:  
\[\left\{\begin{array}{l}P^{0}_{\text{wt,t}}-\Delta P^{\max}_{\text{wt,t}}\leq P _{\text{wt,t}}\leq P^{0}_{\text{wt,t}}+\Delta P^{\max}_{\text{wt,t}}\\ P^{0}_{\text{pv,t}}-\Delta P^{\max}_{\text{pv,t}}\leq P_{\text{pv,t}}\leq P^{0} _{\text{pv,t}}+\Delta P^{\max}_{\text{pv,t}}\\ P^{0}_{\text{load,t}}-\Delta P^{\max}_{\text{load,t}}\leq P_{\text{load,t}}\leq P ^{0}_{\text{load,t}}+\Delta P^{\max}_{\text{load,t}}\end{array}\right. \tag{4}\]
# Feasibility of a 3D Surgical Guide Technique for
## Materials and Methods  
### Development of the 3D Printed Simulation Model and Surgical Guide for ST  
The 3D printed simulation model for ST was designed following human anatomy data in primary dentition through a digital workflow (Figure 1). A laboratory scanner (T500, Medit, Seoul, Korea) was used to yield digital scans of the primary dentition model (Dentoform, Nissin, Kyoto, Japan). All scans were saved as an STL file and imported into the 3D modeling software (Meshmixer, Autodesk, San Rafael, CA, USA). We created a digitalized 3D tooth model with 3 mm of uniform thickness, using scanned data of the primary dentition model. In the digitalized 3D tooth model, an impacted ST and adjacent permanent tooth germs were embedded to imitate the natural positional relationship in the primary dentition (Figure 2).  
Figure 1: Digital workflow of the development of the 3D printed simulation model and surgical guide for a supernumerary tooth. (**A**) A 3-dimensional digitalized tooth model was obtained by laboratory scan of a primary dentition model. (**B**) A 3D localization of a supernumerary tooth and adjacent permanent tooth germs were determined to imitate the natural positional relationship in the primary dentition. The asterisks refer to the supernumerary tooth. (**C**) A surgical guide was digitally designed to provide direct access to the 3D position of supernumerary tooth. The bars refer to the 3D predetermined path to access the supernumerary tooth. (**D**) A 3D printed simulation model and surgical guide were finally fabricated.  
A digitalized surgical guide was made to guide surgical drilling along the 3D position of the impacted ST. The orientation of drilling was designed to avoid damage to the adjacent permanent tooth germs and provide direct access to the impacted ST, minimizing the amount of material removed during surgery.  
The 3D printing process was performed at DIO (Dio Co., Busan, Korea). The STL files of the final digitalized 3D tooth model and surgical guide for ST were imported into the 3D printer (DIO PROBO (Dio Co., Busan, Korea)) for 3D printing. Optically cured resin material (DIOnavi-SG, Dio Co., Busan, Korea) was used as the 3D printing material.
# Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges
## 1 Introduction
Fog computing and edge computing being extended form of cloud computing gives solutions to the challenges faced by cloud computing that is attractive for IIoT real-time applications. The terms fog computing and edge computing are often used by industry interchangeably. Both these computing technologies bring computing and processing capabilities near the vicinity where data originates. Edge computing complements fog computing by bringing computation to one of the devices of a network. This device is named as E-node and is close to the data. E-node has more power, computation capabilities and intelligent controllers, such as programmable automation controllers (PAC). Presence of E-node in edge computing improves latency, reliability, security and privacy issues [24; 25]. E-node acts as an interface/bridge between the data sources and the cloud. The basic architecture for fog network is given in Figure 1 depicting fog cloud serving as a middle layer between the cloud server and smart end-devices. Figure 1 demonstrates a basic idea of cloud, fog and edge computing promoting different IIoT application domains. Fog is a relatively new paradigm that brings new challenges in terms of efficient and scalable network architecture. It is expected that it will gradually develop over the next few years for realizing the Industry 4.0. Challenges, such as energy conservation, real-time communication, efficient spectrum use, cache memory on edge devices and optimized allocation of resources are open issues that need to be addressed for future automation. Without such considerations, guaranteed QoS requirements of IoT devices may not be fulfilled. In the future, solutions to these challenges must be provided by researchers for the development of the industrial revolution. This paper is written with an aim to give a summarized version of existing solutions using fog computing acting as an enabler for IIoT applications. The paper is organized as follows; Section 2 briefly introduces IIoT. Benefits of IIoT applications in daily life and their critical requirements are briefly explained in Section 3. Section 4 presents protocol/solution proposed by various researchers promoting fog computing as an enabling technology for IIoT development. Section 5 describes challenges and solutions in communication and networking proposed in the literature to use fog computing in IIoT. Section 6 lists down several open research issues in fog computing. Finally, the paper is concluded in Section 7. The flow of this survey paper is shown in Figure 2.  
Figure 1: Generalized view: IIoT application domains with cloud, fog and edge computing. Figure 2: Flow of the paper.
# Advancements, Dynamics, and Future Directions in Rural Environmental Governance Research in China
## 3 Data Sources and Research Methodology
### Research Processes  
The primary focus of this article's research encompasses an analysis of publication trends, disciplinary distribution, citation trends, core authors, collaborative institutional networks, keyword co-occurrence network analysis, and timeline-based clustering. This investigation comprises six distinct steps: (1) Formulating research inquiries and delineating pivotal search terms; (2) Establishing criteria for article inclusion and exclusion, conducting screening, and curating data; (3) Employing two bibliometric tools for a comprehensive and essential data evaluation; (4) Summarizing and amalgamating the outcomes acquired. Figure 1 provides an illustrative depiction of the fundamental steps outlined in this article.  
The specific research inquiries addressed within this article encompass the following:  
* What are the distinctive attributes characterizing publication trends, disciplinary distribution, citation patterns, and prominence in high-impact journals within the realm of Chinese Resource and Environmental Management research?
* In what manner does Chinese REM research manifest specific traits in terms of keyword co-occurrence, clustering, and emerging focal points?
* What distinctive features arise concerning authorship and institutional collaborations in the realm of Chinese REM research?
## 3 The Proposed Method
### The LWAS Framework
#### 3.1.3 The Resource-Aware Search Algorithm  
To take both the effectiveness and resource consumption into consideration, we propose a resource regularization \(\mathcal{L}_{\text{Resource}}\) for LWAS. Combined with the training criterion of E2E ASR, the search objective function of NAS-SCAE can be described as:  
\[\mathcal{L}=\mathcal{L}_{\text{Performance}}+\eta\mathcal{L}_{\text{Resource}} \tag{4}\]  
where \(\mathcal{L}_{\text{Performance}}\) refers to the performance-related object, e.g., CTC, or CE, \(\mathcal{L}_{\text{Resource}}\) is the resource-related regularization, and \(\eta\) is a scaling factor. In this paper, we employ parameter numbers and FLOPs of candidate architectures as \(\mathcal{L}_{\text{Resource}}\), which can be obtained with a table-lookup method.
## 3 Methodology
Finally, the Evaluation block measures the performance of each model using standard classification metrics: Accuracy, Precision, Recall, and F1-score. Confusion matrices further aid in analyzing the model's behavior and error distribution. The entire architecture--modular, privacy preserving, and model-agnostic--supports a flexible and scalable framework for real-world fall-detection applications. Algorithm 1 summarizes federated and centralized training for fall detection models. Figure 1: Flowchart illustrating the proposed fall-detection methodology using federated deep learning. ```
1:Dataset D = {(x\({}_{i}\), y\({}_{i}\))}, number of clients K, Rounds, Epochs E
2:Global model M\({}_{\text{G}}\) with final weights \(\Theta^{G}\)
3:Function PREPAREDDATA(D)
4: Extract image-label pairs from YOLO annotations
5: Resize image to 128 \(\times\) 128
6: Convert labels: fall = 0, no-fall = 1
7: Split data into train, val, test (e.g., 80/10/10)
8: partition D\({}_{\text{train}}\) in to K shards: D\({}_{1}\), \(\ldots\), D\({}_{\text{K}}\)
9:endfunction
10:function INITIALIZEMODEL
11: Select base model M\(\in\){MobileNetV2,VGG16
12: Load pretrained weights \(\Theta^{G}\) from M
13:endfunction
14:function LOCALTRAINING(\(\Theta^{G}\), D\({}_{\text{K}}\),E)
15: Fine-tune model M weight on D\({}_{\text{K}}\) for E epochs
16:return updated local weight \(\Theta^{G}_{k}\)
17:endfunction
18:function AGGREGATEWEIGHTS ({\(\Theta^{G}_{k}\)} centralized)
19:if centralized then
20:
21:\[\Theta^{G}\leftarrow\sum\limits_{k=1}^{k}\frac{n_{k}}{\sum\limits_{j=1}^{k}n_ {j}}.\Theta^{G}_{k}\]
22:else
23:
24:\[\Theta^{G}\leftarrow\frac{1}{k}\sum\limits_{k=1}^{k}\Theta^{G}_{k}\]
25:endif
26:return\(\Theta^{G}\)
27:endfunction
28:function MAIN
29:
30: PREPAREDDATA(D)
31: INITIALIZEMODEL
32:for r = 1 to R do
33: For k = 1 to K in parallel do
34:\(\Theta^{G}_{k}\leftarrow\) LOCALTRAINING(\(\Theta^{G}\), D\({}_{\text{K}}\),E)
35:endfor
36:\(\Theta^{G}\leftarrow\) AGGREGATEWEIGHTS ({\(\Theta^{G}_{k}\)} centralized)
37: Evaluate model M\({}_{\text{G}}\) on validation set
38:endfor
39: Evaluate final model M\({}_{\text{G}}\) on test set
40:return\(\Theta^{G}\) and test metrics
41:endfunction
```  
**Algorithm 1** Federated and Centralized Fall Detection with MobileNetV2 and VGG16
## 2 Pharmacology of Piperine: In Vitro and In Vivo Studies
### Anti-Cancer Effect of Piperine
#### 2.6.2 Lung Cancer  
A set of studies investigated the effect of piperine on lung cancer and found very promising results. [PERSON] et al. [53] found that piperine showed selective cytotoxicity toward lung cancer cell line (A549) by inducing apoptosis through arresting G2/M phase of the cell cycle and activating caspase-3 and caspase-9 cascades in cancer cells. It also decreased Bcl-2 protein expression and increased Bax protein, leading to higher Bax/Bcl-2 ratio. Benzo(a)pyrene induces lung carcinogenesis, by decreasing glutathione transferase (GST), quinone reductase (QR) and UDP-GT and increasing the hydrogen peroxide level. In the study on Swiss albino mice, piperine (50 mg/kg b.wt.) was orally given to mice together with benzo(a)pyrene (BaP) for 16 weeks. In animals treated with piperine, reduced levels of lipid peroxidation, protein carbonyls, nucleic acid content, and polyamine synthesis were recorded in comparison to the control groups, which demonstrated BaP-induced lung carcinogenesis-protective effect of this compound [54]. Piperine exerts BaP-induced cytotoxicity in V-79 lung fibroblast cells, due to a decrease in GST and UDP-GT [55]. Administration of piperine reduces DNA damage and DNA-protein cross-links in the lung cancer-bearing animals. In this study, mitochondrial enzymes (isocitrate dehydrogenase (ICDH), ketoglutate dehydrogenase (KDH), succinate dehydrogenase (SDH), malate dehydrogenase (MDH)), glutathione-metabolizing enzymes GPx, GR and glucose-6-phospho dehydrogenase(G6 PDH) were significantly reduced, while NADPH-cytochrome reductase (NADPH-C reductase), cytochrome P450 (cyt-p450) and cytochrome b5(cyt-b5) showed increased levels in mice administered with piperine [56]. Also, in these animals, ATPase enzymes in erythrocyte membrane and tissues were shown to be increased, while sodium/potassium/magnesium ATPase enzyme activities decreased, showing the chemopre effect of piperine [57]. Lung metastasis in C57 BL/6 mice induced by B16F-10 melanoma cells has been significantly inhibited when piperine was co-administered together with tumor induction. The results showed reduced lung collagen hydroxyproline, uronic acid, and hexosamine content, a significant decrease in tumor nodule formation and lung size and also a reduced serum sialic acid and serum v-glutamyl transpeptidase activity, pointing to very promising antimetastatic activity of piperine [58].
# On-Demand Scheduling of Command and Responses for Low-Power Multihop Wireless Networks +
## 5 Evaluation
### Evaluation Setup  
We use two protocol combinations for comparison to SCoRe: \"Flooding + RPL\" and \"Trickle + RPL\", where Flooding and Trickle are used for command dissemination, and basic RPL is used for response collection.  
\"Flooding\" is the most basic algorithm to disseminate information through a multihop wireless network. A node receiving a new (Sequence numbers are used to identify \"new\".) message forwards that to its neighbors \(M\) times with random jitter \(T_{C}\).  
\"Trickle\" [13] is an efficient data dissemination algorithm, designed to propagate information faster when inconsistency is detected but with less overhead otherwise. A node receiving a message schedules the next transmission time from the range \([T_{C}/2,T_{C})\). While waiting, the node counts the number of duplicates it receives. Then, when it is time for transmission, it transmits the packet if the number of duplicates is smaller than the specified suppression threshold \(K\). Otherwise, it keeps silent for energy efficiency. Trickle doubles \(T_{C}\) value after every retransmission, and resets to initial value when it receives a new packet or inconsistency is detected. Trickle algorithm has been adopted in various protocols for low-power wireless networks [7; 18; 22; 24; 48; 49; 50; 51].  
\"RPL\" is an IETF Internet standard IPv6 routing protocol for low-power and lossy networks (LLN) [24], and is used to collect data from nodes in multihop networks. In our application scenario, each RPL node sends its response packet with a random jitter \(T_{R}\) (at the app layer, not routing layer).  
All protocols are implemented using TinyOS 2.1.2 [39] on TelosB [32] platform (identical to Section 3) communicating over IEEE 802.15.4 links with CSMA. The network builds route topology according to the RPL protocol, and OF0 [52] is used as the objective function. The root starts transmitting commands 15 min after reset to allow for routes to stabilize, and generates 1000 commands every 5 s. Response packets are sent with a random time jitter in the range of \(T_{R}\) milliseconds to avoid synchronization effect.
## 6 Conclusions
\begin{tabular}{l l} \(E_{dst;d}\) & design value of destabilising actions \\ \(E_{st;bd}\) & design value of stabilising actions \\ \(E_{d}\) & design effect of actions \\ \(R_{d}\) & design resistance \\ \(T_{d}\) & design value of any stabilizing shear resistance of the ground or of structural elements \\ \(F_{k}\) & characteristic value of an action \\ \(V_{d}^{\prime}\) or \(N\) & design value of the effective vertical action \\ \(\delta_{k}\) or \(\phi^{*}\) & structure-ground interface friction angles \\ \(\Psi\) & factor for converting a characteristic value to a representative value \\ \(\gamma_{F}\) & partial factor for an action \\ \(\gamma_{R}\) & partial factor for resistance \\ \(\gamma_{M}\) & partial factor for a soil parameter \\ \(X_{k}\) & characteristic value of a material property \\ \(\gamma_{E}\) & partial factor for the effect of actions \\ \(a_{d}\) & design value of geometrical data \\ \(\gamma_{I}\) & factor of seismic importance \\ \(c^{\prime}\) or \(c_{k}^{\prime}\) & drained shear strength of soil \\ \(\gamma^{\prime}\) or \(\gamma_{K}\) & design effective weight density of the soil below the foundation level \\ \(B^{\prime}\) or \(\beta^{*}\) & effective foundation width \\ \(q^{\prime}\) or \(q_{0k}^{\prime}\) & design effective overburden pressure at the level of the foundation base \\ \(\Pi\) & height of the retaining wall \\ \(\gamma\) & unit weight of the material \\ \(\alpha\) & inclination angle to the horizontal plane \\ \(\beta\) & inclination angle of the backfill surface from the horizontal line \\ \(k_{h}\) & horizontal seismic coefficient \\ \(k_{p}\) & vertical seismic coefficient \\ \(a_{g}\) & acceleration of gravity \\ \(a_{gR}\) & reference peak ground acceleration \\ \(a_{b}\) & basic seismic acceleration \\ \(\rho\) & dimensionless risk coefficient \\ \(C\) & soil coefficient \\ \(S\) & soil factor \\ \(r\) & type of retaining structure factor \\ \(w_{E}\) or \(q_{e}\) & wind pressure acting on an external surface \\ \(q_{p}(z_{e})\) & peak velocity pressure at the reference height \\ \(q_{b}\) & basic velocity pressure \\ \(c_{e}(z_{e})\) & exposure factor at the reference height \\ \(c_{pe}\) or \(c_{p}\) & pressure coefficient \\ \(c_{prob}\) & probability factor \\ \(c_{dir}\) & directional factor \\ \(c_{eosom}\) & season factor \\ \(K\) & shape parameter depending on the coefficient of variation of the extreme-value distribution \\ \(\rho\) & air density \\ \(\mu\) & constant \\ \end{tabular}
# Design and Implementation of a Multi-Function Gripper for Grasping General Objects
## 4 Experimental Evaluation and Discussion  
To validate the design concept of this multi-function gripper, an aluminum prototype is manufactured to test its performance in the real world. A commercially available six-DOF robot arm is integrated into the experimental system. Different types of grasping modes, including parallel grasping, shape-adaptive grasping, combination of the suction and mechanical grippers, contact-based grasping, and grasping thin objects lying on a flat surface via scooping, are experimentally verified.
# Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN
## 5 Experimental Results
### Performance Comparison
#### 5.4.2 Node Data Overflow Rate  
In this section, we analyze the differences in node data overflow rates among different algorithms. In the MODDPG experiment, we designate the node with the highest data volume as the target node, and the experimental results are shown in Figure 8. In Figure 8a,b, with an increasing number of nodes, compared with MODDPG and random, the average decline rate of D-JEGDG in terms of node overflow rate is about 46.4% and 48.2%, respectively. With an increasing speed of the UAV, it is about 35.1% and 46%, respectively. This is because the dynamic routing protocol periodically selects CM nodes as CH nodes, allowing CH nodes to gather data from CM nodes and reduce node data overflow. However, for MODDPG and random, the one-to-one data collection approach leads to lower efficiency and inevitable node data overflow.
## 2 Materials and Methods
### Data
#### 2.2.2 Datasets for Auxiliary Analysis  
Meteorological data from five field observation stations in the GKM (Figure 1b) were available from China's meteorological data web [51]. Several daily meteorological parameters, including sunshine duration, average temperature, maximum temperature, minimum temperature, and precipitation, were used from 1982 to 2015, and then daily parameters were aggregated to determine seasonal averages for spring (March-May), summer (June-August), autumn (September-November), winter (December-February), and the growing season (April-October). Furthermore, with the inverse distance weighting (IDW) interpolation method, we obtained spatially distributed meteorological data that has the same spatial resolution as the GLASS-GPP products.  
Phenological parameters were acquired from the 8-km AVHRR NDVI-3g product with a 15-day interval from 1982 to 2015 that was produced by the Global Inventory Modeling and Mapping Studies (GIMMS) project [52]. The AVHRR NDVI-3g dataset was generated with a maximum value composite (MVC) per half-month and then corrected to minimize the influence of calibration loss, volcanic eruptions, and orbit drift [26]. To eliminate bare soil and sparsely vegetated pixels on the NDVI image, we excluded pixels with a mean NDVI \(<\) 0.05 in the growing season. Similarly, the S-G filter was used to smooth the original NDVI data. Finally, the dynamic threshold method [53] was used to obtain the phenological parameters, and the results were consistent with previous studies [45; 54].  
Similar to the GPP product, the Fraction of Photosynthetically Active Radiation (FPAR) dataset of GLASS was used. This kind of product was derived from leaf area index (LAI) via the LUT method [55] and had the same spatiotemporal resolution as the GPP product.