esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Antioxidant Activity
For instance, lycoposamine significantly improved locomory function and reduced apoptotic cell death following spinal cord injury in rats. Additionally, lycoposamine decreased the expression of tumor necrosis factor-\(\alpha\) and upregulated the expression of interleukin-10 [36]. Thus, lycoposamine derivatives may be useful in the treatment of certain disorders, but more investigations are required. The high biological activities of hydroxycinnamates and flavonoids are well known. In particular, 5-O-caffeoylquinic acid inhibits the invasion of non-small cell lung cancer cells through the inactivation of p7056K and Akt activity [37], suppresses P-selectin expression on platelets by inhibiting cyclooxygenase enzymes [38], and possesses antimicrobial activity [39]. Quercetin-3-O-rutinoside or rutin has shown a number of pharmacological properties, including cytoprotective, anticarcinogenic, neuroprotective, and cardioprotective activities [40; 41; 42; 43]. Rosmarinic acid is a prospective therapeutic agent against a wide range of lifestyle-related diseases. Many anti-cancer mechanisms of action of rosmarinic acid have been confirmed: prevention of tumor formation development, such as reduction in lipid peroxidation byproducts [44]; inhibition of transcription factor HIF-1\(\alpha\) expression [45]; and apoptosis induction [46]. Additionally, cardioprotective [47], antidiabetic [48], anti-inflammatory [49], and antidepressant [50] activities have been established for this compound. Thus, using HPLC-PDA-ESI-IQ-MS analysis, we found twenty-six phenolic compounds, including hydroxycinnamates and flavonoids. Some flavonoids and rosmarinic acid were previously found in other _Mertensia_ species while the remaining hydroxycinnamates, derivatives of caffeic and quinic acids, and kaempfer-3-O-glucose were found in this plant genus for the first time.
## 4 Results
To better understand the spatial variability of selected elements within the Vigonzano mining waste, we calculated experimental variograms [22] (direct and cross-variograms). Direct experimental variograms were obtained and modelled for the two main parameters: target elements (chromium, copper, cobalt, cerium, niobium, rubidium, lanthanum, and iron) and band ratios (Band Ratio 4/2 and Band Ratio 4/11). In order to analyze the spatial correlation between band ratios and selected elements, we calculated the cross-variograms and the co-regionalization models were fitted. Therefore, the Co-Kriging estimation method [22] could be used to map the variability of geochemical elements in Vigonzano mining wastes. On the basis of the high correlation coefficient (\(\geq\)0.6) between the element's concentration and the band ratios (Table 1), we used the multivariate approach (Co-Kriging estimation) to map the major and trace elements (Figures 5 and 6). Final maps have a 10 \(\times\) 10 m grid, equal to the Sentinel-2 data spatial resolution. The obtained maps for the major elements (Co, Cr, Fe\({}_{2}\)O\({}_{3}\), and Cu) showed the higher concentration inside the mining wastes in comparison with the boundaries. For Co and Cr, similar spatial variability showed the highest concentration in the south and south-east parts of the mining wastes, while for Fe\({}_{2}\)O\({}_{3}\) and Cu, the central parts had the highest concentration. For trace elements (La, Ce, Nb, and Rb), the concentration was highest near the boundaries, with similar spatial pattern for the four elements. The depleted area was from the north-east into the south-west. Figure 5: Composition maps of Co, Cr, Fe\({}_{2}\)O\({}_{3}\), and Cu within the Vigonzano mining waste obtained by Co-Kriging of samples and the band ratio (4/2).
## 3 Proposed Deployment Methodology  
The IIoT is essential to making mining operations safer and controllable. The effectiveness of IIoT systems in supporting long-term planning in underground mines is highly dependent on how they are deployed. The coverage and connectivity parameters should be incorporated and evaluated from the initial deployment. The contribution of this paper is a two-step methodology using a proposed software tool to deploy an IIoT node system to assist long-term planning, attending to safety regulations related to the monitoring of gases in underground mining, as shown in Figure 2. As mentioned, the methodology combines deterministic and random deployment because it can be the most effective and efficient deployment strategy for large-scale and mission-critical systems [47]. It is observed that the deterministic deployment needs to insert the number of nodes necessary to meet the coverage and connectivity requirements demanded for this type of system. This is due to changes in the inclination, distance between nodes, and length of the tunnels. Therefore, nodes are included following a random distribution. With this second step, random deployment, the aim is to meet the requirements to connect the nodes to the outside of the mine and cover the most significant area. In this way, robustness and reliability in the WSN are guaranteed.  
To simplify a mine's structure, it is modeled like a set of tunnels, considering the distance and the tilt using the graphs theory. A graph is a collection of entities known as vertices (or nodes) and a choice of pairs of vertices, referred to as edges, that may or may not possess directionality. Traditionally, a graph is depicted by a sequence of points (the vertices) linked by strokes (the edges). In the proposed approach, the vertices symbolize the intersection of tunnels, and the edges represent the tunnels.
# Data Traffic Modeling in RPAS/UAV Networks with Different Architectures
## 2 Related Works
In the new technology, the pilot can control the UAV through secure communication with an end device without any information about network architectures, types of network address translation, and version of the Internet protocol. Network traversal with mobility offers secure communications for UAVs over the public network infrastructure. An effective technology to improve wireless coverage for remote and disaster-affected areas is UAV radio access, which will be key for providing improved and sustainable coverage in heterogeneous 5G cellular networks. Paper [29] studied edge caching for multiple  
Figure 1: International Civil Aviation Organization (ICAO) aeronautical Remotely Piloted Air System (RPAS) communication links [23]. UAV-enabled radio access networks (UAV-RANs) and investigated the improvement in overall spectral efficiency (SE) through efficient edge caching. UAV base stations may not serve directly nearby users, but serve users based on requested content. Based on the SE analysis achieved by the content-oriented UAV-RAN, a hybrid caching strategy is proposed for further improvement the SE. RPAS communication channels based on IEEE 802.11a, 802.11b, and 802.16 and LTE standards are modeled in our works [30; 31; 32; 33; 34]. The articles considered above do not contain quantitative information on the impact of traffic parameters on the operation of RPAS/UAV communication channels. It has not been investigated how quantitatively the channel load can change with an increase in the duration of transactions. How does the bandwidth affect the channel load at the given traffic parameters? How does the increase in bit errors affect? There are no answers to these questions in the literature.
## 3 Results and Discussion
### Rotatable Central Composite Design
[53] obtained yield values of 0.04 and 0.58 for lactic acid and 1,3-propanediol, respectively, in a fed-batch fermentation by _K. pneumoniae_. According to previous study developed by [PERSON] et al. [54], _K. pneumoniae_ produced about 12 g\(\cdot\)L\({}^{-1}\) of lactic acid in bioreactors. There was also the production of 1,3-propanediol, 2,3-butanediol, 3-hydroxypropionic acid and acetic acid. In the present study, it was possible to produce only lactic acid and 1,3-propanediol, aiding in future purifications. Figure 10: Surface plot for glycerol consumption for rotatable central composite design (**A**) experiments and contour plot (**B**) for glycerol consumption for experiment inoculum concentration versus pH. Solvent extraction is a technique normally used to purify lactic acid. [PERSON] et al. [8] studied the recovery of the L-(+)-lactic acid obtained by fermentation. A multiple-pass distillation was performed after the solvent extraction to recover L-lactic acid with a purity of 91.3% and a yield of 74.63%. The experiment with the highest yield was the one that verified the evaluation of the inoculum concentration and agitation, performed in 15 mL flasks (Y\({}_{\text{LA/Gly}}\) = 0.81). Thus, it is possible to deduce that higher yields can be achieved from changes in fermentation conditions. This study can be continued in bioreactors to obtain even more promising results, starting from a wild-type bacterium and analyzing other process variables, such as substrate feed, temperature control, and nitrogen addition.
## 2 Review Methodology
* Use of open remote sensing data (Sentinel and/or Landsat). * Application of key methodologies (AI, GIS, UAV, or GNSS validation). * Thematic and methodological relevance. Articles below the citation barrier, or not aligned with the theme or methodologically sound, were not considered. This might preclude a few potentially useful but extremely new studies, but it does guarantee a close examination of highly influential research. We recognize that some articles published in late March 2024 could be too new for citations and thus are underrepresented in this overview. These would be considered in future reviews. To facilitate a holistic, multi-disciplinary overview, the 58 articles were allocated into seven theme-based sections, each highlighting a unique technical or methodological feature of remote sensing in coastal monitoring. The sections vary from specialized methods (e.g., DSA, NDWI) through integrated approaches (e.g., UAV-satellite fusion, AI models) to encompass both traditional and new techniques. Several sections reflect cross-disciplinary interfaces, e.g., between machine learning and GIS platforms. The screening of articles was made possible through thematic keyword filters within article titles and metadata. Remarkably, articles were not excluded during this stage based merely on geographic area or area of application--guaranteeing methodological diversity. Hence, the current research undertakes a structured and directed overview of influential methods employed in studies of coastal erosion, in this instance, through open-access Sentinel and Landsat satellite imaging. From Figure 1, 103 initial articles were extracted from the Web of Science Core Collection, from which 58 were kept following title, keyword, DOI, and language-based filtering. These were then divided by thematic relevance as well as citation impact, ensuring a good reflection of both innovation and practical applicability. For a few cases involving article duplication, additional studies of relevance were incorporated to maintain scope based on the same criteria. In future iterations, we intend to extend the scope of this review to include the Scopus database, which will further enrich the dataset, especially in fields such as coastal engineering, geophysics, and marine infrastructure monitoring.
## 5 The Experiment and Analysis
#### 5.1.3 Experimental Results under Variable Condition  
To prove the effectiveness of KMVMD-PGMCKD under variable working conditions, the experiments are carried out under the conditions of 2 HP motor load and 1748 r/min rotating speed. According to the changed conditions, the fault frequency is about 137.4 Hz, and the rotating frequency is about 29.1 Hz. The experimental results are shown in Figure 6.  
Figure 4: Power spectrum of the reconstructed signal by MCKD.  
Figure 5: Power spectrum of the reconstructed signal by MCKD based on Shannon entropy.  
From Figure 6, the fault frequency \(f_{0}\) (134.8 Hz) and \(7f_{0}\) (943.4 Hz) are obvious. Therefore, the experiment results proved that the KMVMD-PGMCKD still can extract the fault features under variable working conditions.
## 2 System Model
### Network and Channel Model  
As shown in Figure 1, a multi-MEC server and multi-IoT device cellular network is considered, which consists of a high-performance PMS, \(M\) SMSs with relative weak computing powers, and \(K\) IoT devices. The computing power of the PMS is much stronger than the SMSs. The PMS is deployed at the BS, and the SMSs are deployed in the APs, which  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Work** & **Objective** & **Method** & **Environments** \\ \hline Our work & Weighted sum of computing & DRL & Dynamic \\  & pressure on the PMS, energy & & \\  & consumption, and task & & \\  & dropping cost & & \\ \hline
[6] & Computation rate of the wireless & Convex optimization & Static \\  & devices & & \\ \hline
[7] & Energy consumption of the AP & Convex optimization & Static \\ \hline
[8] & Number of serviced devices & Convex optimization & Static \\ \hline
[9] & Total energy consumption of the devices & & Convex optimization & Static \\ \hline
[10] & System utility & Game theory & Static \\ \hline
[11] & Latency & Convex optimization & Static \\ \hline
[15] & Long-term average delay of the & DRL & Dynamic \\  & tasks & & \\ \hline
[16] & Cache bit rate & DRL & Dynamic \\ \hline
[17] & Average completion time of tasks & DRL & Dynamic \\ \hline
[18] & Energy consumption of the & DRL & Dynamic \\  & system & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of relevant works.  
are distributed in different locations within the coverage area of the BS. A specific scenario of this network is a UAV-assisted MEC system, in which the PMS is the BS and the SMSs can be the UAVs equipped with limited computing power MEC servers. To avoid repetition, the notations used below do not distinguish between PMS and BS, the SMS and the AP. We assume each SMS's cost is low and can be easily deployed and removed according to the requirements of the MEC system. Let \(\mathcal{M}=\{0,1,2,\ldots,M\}\) denote the set of the MEC servers, where 0 denotes the PMS and others denote the SMSs. Let \(\mathcal{K}=\{1,2,\ldots,K\}\) denote the set of the IoT devices. We assume that all of the IoT devices and MEC servers are equipped with one single antenna. By adopting multi-antenna technologies, our work can be extended to multi-antenna scenarios [7; 9; 22; 23].  
It is assumed that the system is operated in a time-slotted manner with time-slot length \(\Delta\). In this paper, we concern about the long-term return during \(T\) consecutive time-slots. The set of the time-slots is denoted as \(\mathcal{T}=\{1,2,\ldots,T\}\). Let \(h_{k,m,t}\) denote the channel power gain between the IoT device \(k\) and the MEC server \(m\) at time-slot \(t\). Similar to [7; 24], we assume that the wireless channels between the IoT devices and the MEC servers remain unchanged at each time-slot and vary at different time-slots. Motivated by the works in [25; 26], we adopt a \(Z_{k}\)-element channel power gain state set to capture the time-varying characteristics of the \(h_{k,m,t}\), denoted as \(\mathcal{H}_{k,m}=\left\{h_{1,m}^{(k)},h_{2,m}^{(k)},\ldots,h_{Z_{k},m}^{(k)}\right\}\), i.e., \(h_{k,m,t}\in H_{k,m}\).
# Principles and Characteristics of Different EDM Processes in Machining Tool and Die Steels
## EDM Process Details
### Various EDM Process Variants
#### 2.4.3 Micro-EDM  
Micro-EDM follows the same principle as sinking EDM and wire EDM; however, it removes material at a micro-scale for components smaller than 100 \(\upmu\)m. This process cuts workpieces of micro-scale, including micro-holes, micro-shafts, and any 3D micro-cavities [96]. The MRR will be in nanometers because the machined part is at the micro level. Characteristically, micro-EDM works on a very small pulse energy and requires voltage and current several orders lower than those used in normal EDM processes. This process can produce hole or shaft diameters on the order of 5 \(\upmu\)m [47]. EDM in the micro-scale is available in four configurations, namely, die sinking micro-EDM, micro-wire EDM, micro-EDM drilling, and micro-EDM milling. In micro-wire EDM, a wire with a diameter less than 20 \(\upmu\)m is used [24].
# Multi-Objective Optimization of Drilling Trajectory Considering Buckling Risk
## 1 Introduction
In this paper, we present an optimization method that minimizes the risk of buckling and the total length of the drilling trajectory by minimizing the drag and torque, which are the loads on the drilling pipe. To this end, the objective function is defined in terms of the total length of the drilling trajectory and the load acting on the drilling pipe, and the design variables include the azimuth, inclination, dogleg severity, and kickoff point. In addition, the particle swarm optimization (PSO) algorithm is used for optimization. The PSO algorithm is suitable for multi-objective optimization related to buckling [14; 15]. To verify the method proposed in this paper, it was compared with earlier results of the optimization of the drilling trajectory, including the total length of the drilling trajectory, load acting on the drilling pipe, and occurrence of buckling. If the orbit is designed according to the method presented in this paper, the length of the drilling orbit can be reduced to save time and money consumed in drilling. In addition, the risk related to buckling can be reduced, improving the drilling safety.
# Moving Towards Body-to-Body Sensor Networks for Ubiquitous Applications: A Survey
## 2 From WBANs to BBNs
### BBN Challenges  
Compared to single WBANs, BBNs present further challenging features. First, the energy problem become more constraining when the WBAN coordinator plays also the role of a gateway that transmits the sensing data to the neighboring WBANs within the BBN. Furthermore, the aforementioned standards used for BBN communications are not optimal and do not provide a secure communication. Thus, implementing a security system that helps to protect and limit the access to the data transmitted between the WBAN and the destination is necessary. Indeed, people may consider BBNs as a threat on their personal data and their own privacy, their approval is the key of the achievement of BBNs. Then, a third issue consists of the routing of sensor data while supporting WBANs mobility and ensuring  
Figure 2: Body-to-body network for the performance monitoring of a group of athletes © [2019] IEEE.  
QoS considerations. Finally, all data aggregated by the WBANs coordinators should be collected and processed seamlessly, without affecting the activity of the person. Patient records may be fragmented and transmitted through different nodes without any effort or knowledge from the person.
## 5 Performance and Security Analysis
### Performance Analysis  
**Storage Overhead.** Each sensor needs to store a Bloom filter, key, and prime number. They imply the constant storage overhead. At each round \(r\), each sensor \(s_{j}\) sends \(\bar{t}_{j}^{r}\) authentication materials, \(\langle j,P,E,Z\rangle\), to the randomly selected sensors. As a collection interval consists of \(v\) rounds, we therefore know that the storage overhead of each sensor in each collection interval is \(O(v\cdot\bar{t}_{j}^{r})\).  
**Communication and Computation Overhead.** As described in Section 5.1, the upper bound of the number of sensors involved in the authentication procedure for a single datum is \(\sum_{\sigma=1}^{\lambda}\frac{|N_{\sigma}|}{n}t\sigma\). Since each involved sensor performs either sending or receiving operation, the number of involved sensors can be used to estimate the communication and computation overhead of AAD. As the size of the authentication material is constant in terms of the network size, \(n\), the communication overhead of each sensor in AAD is \(O(\sum_{\sigma=1}^{\lambda}\frac{|N_{\sigma}|}{n}t\sigma)\). In addition, as the number of the operations needed to be performed is also a constant in terms of the network size, the communication overhead of each sensor in AAD is \(O(\sum_{\sigma=1}^{\lambda}\frac{|N_{\sigma}|}{n}t\sigma)\) as well.
## 3 Results
### Construction and Setup of a Measuring Device for Determining Plant Pigment Values
All screens have a \"Home\" button that returns the user to the \"Main Menu\". Figure 4: Electrical schematic of measuring device—general view. Figure 7 shows an overview of the developed measuring device. All the main elements are mounted in a box. The display and power button are mounted on its upper side. The sensor part, which includes the TCS230 and the LED diode, extends out of the device with extension cables in order to facilitate the measurement of plant leaves. The procedure for operating the proposed device is outlined below in Figure 8. This flowchart outlines the step-by-step procedure for operating the proposed device, starting from powering it on to accessing the recorded data and utilizing the device's features. Figure 5: Block diagram of the algorithm of the developed device. Figure 6: Graphical user interface of the proposed device: (**a**) main menu; (**b**) measurements; (**c**) raw RGB and spectral data; (**d**) help information. \[G_{c}=0.7305G_{m}+62.332 \tag{13}\]0 Procedure for operating the proposed device. \[G_{c}=0.7305G_{m}+62.332 \tag{13}\]1 Developed device—general view. By following this procedure, users can effectively utilize the proposed device to measure and record various parameters related to leaves. Firstly, the device is started by pressing the ON/OFF button. It is important to note that no leaf should be placed in the sensor cell during startup. From the main menu, the user navigates to the \"Measure\" screen. A leaf to be measured is then placed in the device, and after a 5 s interval, the device reads and records data related to chlorophyll, caroten, anthocyanins, and other relevant measurements. The recorded characteristics can be accessed from the RGB and spectral data screens, allowing for further analysis, such as calculating leaf colors and spectral characteristics. Additionally, if needed, the user can refer to the \"Help\" screen for additional guidance or information. The procedure described is relatively simple and clear, making it easy for users to understand and use the device. The steps are presented in a logical order, starting with turning on the device and progressing through the different screens for measurement and analysis.
## 4 Inference Rules for Matching Geographic Ontologies in Different Languages
### Conceptual Framework
#### 4.1.7 Matching Two Geographic Ontologies in Different Languages  
In this paper, we will examine two geographic ontologies respectively designed in different languages (\(\Omega_{1}\) and \(\Omega_{2}\)), for instance one in English and one in Spanish. Since concepts can be different or differently organized, how does one match them?  
From a mathematical point of view, we have two graphs in which nodes correspond to concepts and edges to relations. Matching ontologies means that:  
* types will be linked by homology relations;
* and ontological relations will also be linked via homology relations.