esa-sceva/satcom-chunk-collection huggingface dataset:
# Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication
## 6 Simulation Results  
### Effects of Density and Group Connectivity  
In public safety situations, the aim is to guarantee reachability between participating users during the rescue mission. Group connectivity is defined as the capacity of all group users to continue to be inter-connected and reachable by other members of the same group. However, the consistency of group connectivity is not usually guaranteed, especially when a limited number of users are dispersed in large areas with different rescue missions and should move in opposite directions. We define three levels of group connectivity: level_1, when all users participating in the mission are reachable by forming a single group; level_2, when two groups of users are formed; and level_3, when three groups or more are formed. Figure 14 depicts the progression of the three levels of connectivity according to the number of UEs using the simulation parameters presented in Table 1. For each selected value of UE,from 5 to 70, placed on a square area of 500 m\({}^{2}\), with a random speed that varies from 1 to 4 m/s, we calculate the average value of 10 simulations of 7200 s (2 h).  
During these simulations, a verification is processed every second to count the time duration corresponding to every formed group's connectivity type. For better presentation, we express the resulting time as a percentage of the total simulation time duration. From Figure 15, it is possible to conclude that with fewer than 20 UEs (80 UEs/km\({}^{2}\)), the probability of having a single group (or even two) is very low. With 50 UEs (200 UEs/km\({}^{2}\)), the probability of having level_3 becomes very minimal. As soon as 70 users (280 users/km\({}^{2}\)) are reached, the uniqueness (level_1) of the group is confirmed, and the decomposition of this group occurs for less than 2% of the total time, i.e., up to 2 to 3 min every 2 h.
# Comparing Monitoring Networks to Assess Urban Heat Islands in Smart Cities
## 3 Choice of System Components and Programming
### Citizen Weather Observer Networks
#### 3.3.2 Centralized Network Data Processing System  
Given that the centralized network data processing system used in CWON is proprietary and common to all the AWSs, its components are defined, and its programmed functions are fixed and therefore cannot be personalized. Furthermore, the complete information concerning these questions is not available to the user.  
In Nt, the centralized system carries out its functions automatically. Data acquisition is performed via wireless Wi-Fi. The data are then processed and stored in the manufacturer's database, connecting the AWS to the internet via Wi-Fi. Finally, they are available to the user for extraction or visualization. The data can be downloaded thanks to the available API using Python scripts to do so [87]. As for visualization in real time, it is possible to publicly access the measurements from all the stations installed via the visor web of Weathermap [88], or individually and privately from the mobile application, where the historical registers are also available. Alerts are also created.  
The centralized system of the WU collects the data registered by the different AWSs via the Internet. To do so, devices that work as network bridges are used, connecting the AWSs to the Internet and thus allowing direct communication with the system. WU offerstwo options: AirBridge [89; 90], which is wireless, and WeatherBridge [91], which connects to the AWS through a USB connection to the router via an Ethernet cable or Wi-Fi. However, WeatherBridge allows the configuration of the AWS and establishes system function alerts. In addition, it automatically reinitiates if there is a power cut, as all the configurations are stored. Once all the data have been collected, it processes them in centralized servers, stores them in historical archives, and provides access to them. Data visualization is publicly performed using panels and graphics through the visor web [92] or the WU app [93], where all the AWSs in the network can be seen in an interactive map using high-resolution satellite images, or historical archives using tables or complete graphics of each variable and AWS. Similarly, the data can be seen privately from the app of the commercial company.
## 4 Conclusions  
In the present study, carriers made of hybrid materials were fabricated based on an organic core that was used as template for the production of Ce\({}_{x}\)Ti\({}_{y}\)O\({}_{x}\) shells. The successful fabrication of the hybrid material was confirmed by characterizations based on morphology, structure and composition. The produced hollow Ce\({}_{x}\)Ti\({}_{y}\)O\({}_{x}\) nanocontainers were loaded with the organic inhibitor ATT and  
Figure 11: Schematic representation of the thiol and thion forms of ATT.  
Figure 12: Schematic representation of the ATT connections onto the mild steel surface resulting in the formation of the protective film.  
evaluated for their corrosion inhibition effectiveness onto mild steel using saline water as corrosive environment. According to the obtained results, it can be assumed that the inhibitor ATT provides effective corrosion protection to mild steel. It is mentioned that the calculated values of \(\eta\)(%) derived after 24 and 144 h of mild steel panel exposure to 3.5 wt.% NaCl solution, in the presence or absence of Ce\({}_{\mathrm{x}}\)Ti\({}_{\mathrm{y}}\)O\({}_{\mathrm{z}}\) nanocontainers loaded with ATT were 99.77% and 98.96%, respectively.  
Taking into account that ATT includes polar groups sulfur and nitrogen, there is an increase in the electron density at the reaction center resulting in the formation of chemical bonds between the ATT and the metal. Thus, ATT is adsorbed on the surface of mild steel creating monolayers of ATT compounds on the surface of mild steel due to the lone pair of electrons of the N and S atoms, isolating the metal from aggressive ions. Estimating the morphology, spectroscopy and electrochemical characterizations, the steel corrosion protection mechanisms can be assigned to the formation of protective layers onto the metal surface because of the inhibitor presence that prevents the penetration of chlorides. Furthermore, it was observed that the fabricated Ce\({}_{\mathrm{x}}\)Ti\({}_{\mathrm{y}}\)O\({}_{\mathrm{z}}\) nanocontainers can be utilized as carriers of the ATT corrosion inhibitor. Therefore, the system Ce\({}_{\mathrm{x}}\)Ti\({}_{\mathrm{y}}\)O\({}_{\mathrm{z}}\) nanocontainers loaded with ATT can be used as potential additives into the industrial conveying API 5L X42 pipeline steels in order to provide a corrosion protection up to 144 h. Our future studies will include the investigation of the aforementioned corrosion inhibitor release from either the Ce\({}_{\mathrm{x}}\)Ti\({}_{\mathrm{y}}\)O\({}_{\mathrm{z}}\) nanocontainers or other cerium based nanocontainers for extended periods of exposure to corrosive environments up to 3 months.  
[PERSON] and [PERSON] conceived and designed the experiments. [PERSON] conducted the experiments. [PERSON] performed the characterization and evaluation of the data. [PERSON] and [PERSON] discussed the data and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to thank [PERSON] for the XRD analysis.  
The authors declare no conflict of interest.
## 4 Proposed Estimation Scheme
### Packet Selection  
The packet error degrades its accuracy in the signal recovery of the proposed estimation; thus, the packet selection is considered.  
Figure 5 illustrates the flow of the considered packet selection, where PEP is the process flow of the proposed estimation illustrated in Figure 4 and the estimated \(\eta_{I,A}\) is omitted.  
The receiver confirms any error bit in the tentative decoded packet using the cyclic redundancy check (CRC). If the CRC is OK, the tentative decoded packet is adopted to estimate the desired power, CCI power, and COR in the final estimation. If not, the tentative decoded packet is moved to the next step. For simplicity, CRC OK and CRC NG entail that the decoded packet does not include any error bits and includes one or more error bits, respectively. Subsequently, the probability distribution functions of the noise power and CCI plus noise power are estimated by the EM algorithm using only the tentative decoded packets with the CRC OK. After the subtraction, we obtain the average noise power, \(\widehat{P_{n}}^{\prec}\)average CCI power, \(\widehat{P_{l}}^{*}\), and variance of the CCI power, \(\sigma_{\widehat{l}}^{2*}\). Note that to distinguish the tentative and conclusive estimations, \(*\) is added to each tentatively estimated result.  
We subtract \(\widehat{P_{n}}^{*}\) from the combined power in the PUD from the packet with the CRC NG, \(\eta_{l,B}\), and obtain the estimated CCI power, \(\widehat{P_{l}}^{\prime}\). Note that we assume that the reason for the CRC NG is the occurrence of CCI; thus, \(\eta_{l,B}\) includes the CCI components. Subsequently, the following equation is confirmed:  
\[\widehat{P_{l}}>\widehat{P_{l}}^{*}-\sigma_{\widehat{P_{l}}}^{*} \tag{13}\]  
where \(\sigma_{\widehat{P_{l}}}^{*}\) denotes the deviation of \(\sigma_{\widehat{P_{l}}}^{2*}\). If Equation (13) is satisfied, the packet from which \(\eta_{l,B}\) is estimated is adopted to determine the desired power, CCI power, and COR in the final estimation.  
The reason for using Equation (13) as the selection criterion is as follows: As explained, the dominant factor of the packet error is the CCI. In particular, as the CCI power increases, there is a higher occurrence of the probability of the packet error. If the estimated CCI power exceeds the threshold, which is as large as the average CCI power minus the deviation of the CCI power, we assume that the estimated CCI is most likely true.  
Finally, the probability density functions of the noise and CCI plus noise are estimated by the EM algorithm from the packets with the CRC OK and those satisfied by the criteria in Equation (13). After some subtractive processes, we estimate the noise power, \(P_{n}\), CCI power, \(P_{l}\), and COR, \(\rho\). In addition, the power of the desired signal power, \(\widehat{P_{D}}\), is also estimated from the packets with CRC OK and those satisfied by Equation (13).  
Figure 5: Flow for packet selection in proposed estimation scheme.
###### Abstract  
The recent advances in 5G and beyond wireless networking have enabled the possibility of using the cellular network as the infrastructure for wireless sensor networks, due to the high bandwidth availability and the reduced cost per data unit. In this paper, we perform an evaluation of the 5G infrastructure for sensor networks in order to quantify the performance in terms of energy efficiency and bandwidth within a testing environment. We used an ESP32 sensor with BLE-connected sensing devices for environmental conditions, and a Raspberry Pi with the Waveshare SIM8200 EA-M2 5G module for cellular connectivity. We measured the power usage of each component of the system, in real conditions, as well as the power consumption for different bandwidth usage scenarios, and the end-to-end delay of the system. The results showed that the system is capable of achieving the required delay and bandwidth; however, the energy efficiency of the specific setup leaves room for improvement.  
Internet of Things (IoT); 5G networks; Bluetooth Low Energy (BLE); sensor monitoring; ESP32; Raspberry Pi; real-time data processing; data visualization Article
## 1 Introduction
[20] compared the rock-breaking mechanisms of flat- and arcing-blade cutters under hard rock, summarizing the characteristics of rock-breaking load, rock chips, rock-breaking efficiency, and stress distribution. [PERSON] et al. [21] simulated the crack propagation induced by the arcing-blade cutter. The above studies have a preliminary understanding of the action mechanism of the arcing-blade cutter, but lack research on its performance under different cutting parameters, such as cutter spacing and penetration, which cannot effectively guide the arrangement and construction of the arcing-blade cutter. As a comprehensive approach, numerical simulation has proven to be an effective method for studying the rock-cutting process of TBM cutters [22; 23]. Consequently, this paper establishes a linear cutting model of the arcing-blade cutter based on the finite element method, analyzes the influence of different  
Figure 1: Two types of cutter: (**a**) flat-blade cutter and (**b**) arcing-blade cutter. cutter spacings and penetrations on its performance, and provides valuable insights for optimizing the parameters and cutter arrangement of the arcing-blade cutter. It should be noted that the rock surface simulated in this paper is smooth and intact, which is slightly different from the rock surfaces cut by TBM cutters in actual TBM engineering. Meanwhile, this paper only studied the cutting process of two TBM cutters, which differs from the actual sequential cutting of multiple TBM cutters.
## 3 Optical Error Analysis
### Optical Errors of FTIS
#### 3.2.1 Collimation Error
\[I_{2-dive}(\Delta) =\int_{0}^{2\pi}d\psi\int_{0}^{\theta_{0}}I_{0}(\Delta\cos\theta)*FT \left[a_{0}+\sum\limits_{m=1}^{\infty}2a_{m}\cos(m\cdot 2\pi\
u\Delta_{FPI}\cos\theta) \right]\sin\theta d\theta\Big{/}\int_{0}^{2\pi}d\psi\int_{0}^{\omega_{0}}\sin \theta d\theta\] \[=I_{0-dive}(\Delta)*\left[a_{0}\delta(\Delta)+\sum\limits_{m=1}^ {\infty}a_{m}\delta(\Delta\pm m\Delta_{FPI})\right]\] \[=a_{0}I_{0-dive}(\Delta)+\sum\limits_{m=1}^{\infty}a_{m}I_{0-dive }[(\Delta\pm m\Delta_{FPI})]\]  
Compared to Equation (6), \(I_{0}(\Delta)\) in this equation becomes \(I_{0-dive}(\Delta)=\int I_{0}(\Delta\iota)du/(1-\cos\theta_{0})\). The frequency-shifted component response function \(a_{m}\xi(\Delta\pm m\Delta_{FPI})\) remains unchanged, and the linear relationship formed by the FPI does not cause any additional effects. Therefore, the target in Equation (8) also changes from \(I_{0}(\Delta)\) to \(I_{0-dive}(\Delta)\), while the establishment and solution of the linear equation system remain unaffected. This indicates that the large optical path difference interferogram \(I_{0}(\Delta)\) obtained using MJI-HI and the interferogram obtained using FTIS with the same maximum OPD are affected by the divergence angle in the same way. Therefore, the effect of the divergence angle on the MJI-HI with double super-resolution is the same as that on the FTIS with a maximum OPD of \(2L\). According to Equation (21), the maximum divergence angle for FTIS can be used to derive the maximum divergence angle for MJI-HI as \(\theta_{0}=(\lambda_{\rm min}/2L)\)\({}^{1/2}\).
## 3 Mobile Network Security Evolution and the Evolution of Threat Landscape  
### Evolution from 1G to 4G  
Mobile network security has evolved significantly from 1G to 5G networks. Each generation presented its unique security challenges influenced by technological advancements, changing user needs, and evolving threat patterns. Understanding this progression is crucial for addressing the demands of future 5G networks. In the 1980s, the 1G networks, providing analog voice services, had minimal security concerns [46]. However, with 2G's introduction of digital communication, the security landscape shifted. The digital nature of 2G made data vulnerable to interception and manipulation, prompting the introduction of encryption algorithms like A5/1, although some vulnerabilities like the A5/1 attacks still arose [47]. A graphical representation of mobile networks evolution is shown in Figure 4.  
The 3G networks, which integrated broadband data services and IP-based services, opened the door to a wider range of threats, such as malware and phishing. To counteract these, robust encryption and mutual user-network authentication became necessary [48]. While, the fourth generation further expanded capabilities but also vulnerabilities, particularly with its all-IP network architecture [49]. This led to the development of sophisticated security measures, including advanced encryption and secure IP-based protocols. Thus, the evolution from 1G to 4G shows the interplay between technological evolution and escalating security focus.  
In addition, 3G's integration with IP-based services brought both enhanced functionality and increased threats, prompting the use of more robust security measures. Further, 4G, emphasizing all-IP network architecture, brought even more vulnerabilities [50], requiring stronger countermeasures like advanced encryption and robust user authentication.
## 4 Experimental Results and Analyses
### Ablation Experiment
#### 4.3.2 Ablation Experiments Introducing EMA and EEMA into the Backbone Network

Table 8: Experimental results of applying EMA and EEMA in the backbone network on the ShanghaiTech PartA dataset and the DTH dataset. some background noise persists. However, the heat map generated using EEMA almost entirely avoids such interference, concentrating the model's attention on the head area. These observations highlight EEMA's clear advantages in suppressing background noise and enhancing the model's ability to focus on relevant features.
# An Automatic Participant Detection Framework for Event Tracking on Twitter
## 4 Results
### Evaluation Set-up  
#### 4.1.1 Datasets  
Like many specified event detection approaches, we base our evaluation on sports events [1; 3; 13; 29]. We iterate that neither APD nor its framework are limited to sports events, and both could be applied in other domains. However, just like sports events are popular in TDT evaluations due to their rigid structures, they are also suited to and facilitate APD's analyses. To demonstrate the portability of our APD algorithm, in this evaluation we follow football matches and basketball games. Both sports are immensely popular, with easily-enumerable participants and widely-available ground truth.  
Since we were unable to find appropriate corpora for APD's evaluation and Twitter restricts data sharing, we collected our own datasets using the Tweepy ([[https://www.tweepy.org/](https://www.tweepy.org/)]([https://www.tweepy.org/](https://www.tweepy.org/)), last accessed on 17 February 2021) library. The event queries, along with statistics from the datasets, are available in Table 1; the tweet IDs from these corpora are available on GitHub [24]. In all cases, we collected English tweets before the events started, at a time when we expected more discussion about the match and its participants.  
The datasets cover different scenarios; some events oppose equally-popular teams, like the match between Arsenal and Liverpool, while others are mismatched, like the match between Aston Villa and Manchester United. The dataset sizes vary as well, which allows us to understand how the number of tweets before an event influences APD's ability to identify its participants. While not all tweets have the same quality, we leave the analysis of filtering strategies for future work.
# Anticancer Effects of Seaweed-Derived Bioactive Compounds
## 7 Other Compounds (Quinones and Sterols)  
Figure 5 shows structures of quinones and sterols isolated from seaweeds. Deoxylapac chol (**54**) and 1,4-naphthoquinone derivatives isolated from _Landsurgia quereifolia_ demonstrated cytotoxicity against P-388 leukemia cells [80]. Bis-prenylated quinon, extracted from the brown alga _Perithalia capillaries_, showed antiproliferative effects against HL-60 cells (IC\({}_{50}\) 0.34 uM) [81]. Sargachromanol E (**55**), obtained from _Sargasum siliquastrum_, inhibited growth of HL-60 cells via caspase-3 dependent apoptosis induction. These results indicate that DNA fragmentation leads to the accumulation of cells in the sub-G1 phase. Furthermore, Sargachromanol E reduced Bcl-xL expression and enhanced Bax expression [82]. Tetraprenyltoluquinols (**56**) and (**57**) were found in _Cystoseira crimita_ brown algae [83; 84]. The compounds successfully inhibited the growth of HMO2, HepG2, and MCF7 cancer cell lines by inducing cell death [83].  
Cholesterol-5-en-3\(\beta\)-,7\(\alpha\)-diol (**58**), purified from the red algae _Laurencia tristicha_, exhibited cytotoxic effects against A549, BGC-823, Bel 7402, HCT-8, and HeLa cancer cell lines with IC\({}_{50}\) values of 16.8 uM, 5.1 uM, 0.5 uM, 0.5 uM, and 0.3 uM, respectively [13; 76]. Meanwhile, 3-keto-22-epi-28-nor-cathastore (**59**) and cholester-4-ene-3,6-dione (**60**), a brassinosteroid-related metabolite purified from the brown algae _Cystoseira myrica_, showed antiproliferative effects against HepG2 and HCT116 cancer cells [85]. Fucosterol (**61**) isolated from _Pelvetica siliquosa_, _Cystoseira feniculacea_, and _Sargassum angustifolium_ exhibited cytotoxic effects against various types of cancer cell lines, including HT-29 colon cancer cells [86], breast cancer [87; 88], prompyelocytic leukemia [86], lung cancer [89], and cervical cancer [90]. Furthermore, fucosterol treatment suppressed the growth of ES2 and OV90 human ovarian cancer cell lines by inducing mitochondrial dysfunction and endoplasmic reticulum stress [91].  
Figure 5: Chemical structures of quinones and sterols isolated from seaweeds.
# An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures
## 2 Background
### Optimization Methods for Cloud Resource Allocation
#### 2.3.1 Simplex  
In this method, the problem usually has some constraints that can be represented by a region called the feasible region. The point that can take on an objective function's maximum or minimum value is generally located in this region.  
The simplex method is based on the following assumption: if an objective function Z does not have a maximum/minimum value at a vertex A, there is then an edge from A to another vertex that improves the value of the objective function.  
This algorithm applies an iterative process that improves on the solution to the objective function in each iteration. The process finishes when this value stops changing, i.e., when the optimal solution has been found [13].  
The process can start at any point, and the value of this point in terms of the objective function is evaluated. At each iteration, the algorithm will find another point that improves on the previous solution. A central feature of this method is that these points form the vertices of the region determined by the problem constraints. A search is performed by moving along the edges of this area, moving from the current vertex to an adjacent one that gives a better value of the objective function. Whenever there is a feasible region, it will be possible to find a solution, as the numbers of vertices and edges are finite [13].  
This method only works when the constraints on the problem are inequalities of type \"\(\leq\)\" (less than or equal to). The independent coefficients of the constraints must also be greater than or equal to zero. This method is used to solve linear problems, meaning that the constraints must be standardized so that they comply with these requirements before the simplex algorithm is applied. If restrictions of type \"\(\geq\)\" (greater than or equal to) or \"=\" (equality) arise after this process, or if these cannot be changed, other methods must be applied to find a solution [13].  
Although linear problems can be solved using the simplex algorithm with a graphical method, this approach is limited by the number of decision variables in the formulated problem, since it is not possible to show more than three dimensions graphically. In this method, each vertices is evaluated using the objective function to select the best solution.