esa-sceva/satcom-chunk-collection huggingface dataset:
# Enabling Autonomous Agents for Mobile Wireless Sensor Networks
## 5 Implementation
### Algorithms  
This paper, as previously pointed out, improves our existing work (as in [42; 43]). Apart from refactoring some formulations in the negotiation part and porting the software to the ROS 2 environment, we additionally propose several assignment algorithms to be used with the Agents approach. These assignment algorithms are focused on determining which agent is to take care of a robot in need during the negotiation operation. In that sense, every algorithm takes place when a negotiation round is triggered. While the negotiation round is active, an agent or potentially a set of them is asking for help. However, based on their willingness, each agent has a different urgency to be replaced.  
The first algorithm, 'the algorithm 0' (_ALG0_), recreates a distributed scenario where every agent that needs help is randomly picked up and the most adequate replacement is chosen. This approach, although straightforward to distribute, presents an inconvenience due to randomness. The disadvantage in this case is that it cannot be guaranteed that the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**SPF**} & \multicolumn{3}{c}{**Agents**} \\ \hline  & **Deployment** & **Adjustment** & **Deployment** &
\begin{tabular}{c} **Replacement and** \\ **Retrieval** \\ \end{tabular} & **Adjustment** \\ \hline Obstacles, \(fr_{1}(d_{i,j})\) & ✓ & ✓ & ✓ & ✓ & ✓ \\ Robots, \(fr_{2}(d_{ij})\) & ✓ & ✓ & ✓ & ✗ & ✓ \\ Attraction, \(fa_{1}(d_{i,p})\) & ✗ & ✗ & ✗ & ✓ & ✗ \\ Cohesion, \(f_{c}(t)\) & ✗ & ✓ & ✗ & ✗ & ✓/ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Forces according to strategy and network phase.  
agent with the most urgency is attended in the first place, which could lead to sub-optimal network utility.  
In contrast, ALG1 sorts out the willingness of the agents in need and takes care of them in a logical order, prioritizing the ones with a more negative \(w(t)\). Although this ensures the order in which help requests are attended, it represents a more challenging scenario to implement the ALG1 in a distributed manner.  
The last algorithm proposed, ALG2, attempts to achieve optimal network utility. This is achieved by taking a slightly different approach. Contrary to the previous algorithms, ALG2 focuses on the robots that are willing to provide help. ALG2 sorts the agents with positive \(w(t)\) in descending order and picks the robot in need in which the combined utility \(W(t)\) is greater. Thus, robots with high willingness are maximizing network utility. The disadvantage this approach presents is the amount of information that needs to be exchanged, which complicates the implementation in a distributed form.  
In order to conduct empirical comparison between ALG0, ALG1, and ALG2 against our base approach, the SPF-only strategy is effectuated in Section 6.
## 3 Automated Negotiation Techniques for Channel Selection
### Interaction Protocol  
There are many interaction protocols for negotiations, from the classical alternating offers model [35] to auction-based protocols [36]. Accepting the hypothesis that the negotiation scenarios coming from the channel assignment problem in WSSNs will be highly nonlinear and according to the discussion in [37], we have chosen a simple text mediation protocol [38]. In its simplest version, the negotiation protocol will be as follows:  
1. It starts with a randomly-generated candidate contract (\(S_{0}^{c}\)). In our case, this means assigning a random channel to each access point.
2. In each iteration \(t\), the mediator proposes a contract \(S_{t}^{c}\) to the rest of agents.
3. Each agent either accepts or rejects the contract proposed by the mediator.
4. The mediator generates a new contract \(S_{t+1}^{c}\) from the previous contracts and from the votes received from the agents, and the process moves to Step 2.  
This process goes on until either a maximum number of iterations is reached or another stop condition is met. The protocol, as defined, is rather generic and must be completed with the definition of the decision mechanism or strategies to be used by the negotiating agents and the mediator.
# State of Industry 5.0--Analysis and Identification of Current Research Trends
## 4 Conclusions  
Enabled by the capabilities of text mining techniques, in this paper an attempt to understand and classify Industry 5.0 based on the published research articles with the time frame of when the term was first coined i.e., 2016 to the year 2022 is portrayed. Addressing the objective of the paper, term extraction technique was used to identify the most often occurring terms in the abstract text data gathered on Industry 5.0 related publications. The terms artificial intelligence, big data, supply chain, digital transformation, machine learning were observed to be most referred to. This coincides with the fact that Industry 5.0 is seen to facilitate repetitive tasks with the use of artificial intelligence and machine learning technologies, parallelly assisting humans for cognitive support. Further, big data and digital transformation are foreseen to provide an information space rich with data that can be used for resource planning and control in real time. Topic analysis technique was used to identify the thematic aspects of papers published addressing Industry 5.0. Five different thematic aspects were observed across the landscape, with most of them in the context of smart and sustainable manufacturing followed by human machine connectivity and co-existence. More specifically, the theme of Industry 5.0 as a gateway towards human machine connectivity and co-existence is observed to gain a lot of interest among the research community. Further a brief description of the top ten papers from the data defining the topics is provided for the audience to understand the perspective and relevance of topics.  
By examining the analysis provided future research directions can be identified and predicted on how Industry 5.0 will impact the manufacturing landscape in the years to come. The results obtained are limited to the data i.e., 196 abstracts extracted. It is to be noted that these the results are subject to change with increase in the number of abstracts used for analysis along with expanding the digital libraries used for extracting the data. As a future work, it is believed a comprehensive analysis along with integration of data crawling techniques will provide a better perspective on what Industry 5.0 is and how it is perceived among the research community.  
Conceptualization, A.A. and S.L.; methodology, A.A.; formal analysis, A.A., W.A. and S.L.; investigation, A.A., A.L. and I.E.; resources, A.A.; data curation, A.A., D.E. and W.A.; writing--original draft preparation, A.A. and D.E.; writing--review and editing, A.A. and A.L; visualization, A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# In Situ Assessment of Uplink Duty Cycles for 4G and 5G Wireless Communications
## 3 Results
### Duty Cycles  
We calculated the duty cycles according to Equation (2). Figure 4 shows the boxplots of the calculated duty cycles for 5G NR, 5G LTE (to distinguish it from LTE in the case of 4G connectivity, we define 5G LTE as LTE in the case of the 5G NSA), and 4G LTE at the investigated positions. The measurement positions were ordered as a function of their horizontal distance from the base-station antenna (see Table 1, where for each position, we mention the distance and the type of path). We observed that data-intensive applications, as expected, resulted in the largest duty cycles: for 5G-NR, the FTP upload showed a duty cycle of 19.99% for all measurement positions and iPerf also showed duty cycles larger than 19.9% for all positions, except at Pos 6 (18.6%) and Pos 9 (8.2%). The duty cycles for Ping and Twitch were below 1.6% and 8%, respectively. For WhatsApp, we observed duty cycles between 9.7% (Pos 10) and 11.9% (Pos 7). For 5G-LTE, the uplink duty cycle for FTP and iPerf exceeded 90%, except at Pos 6 (FTP upload: 86.9%; iPerf: 87.7%); the duty cycle for Ping and WhatsApp was below 2% for all positions; and for Twitch, the duty cycle ranged between 0.8% (Pos 1) and 1.9% (Pos 7). We note that the maximum duty cycle for 5G NR was 20%, based on the used TDD slot format DDDSU. For 4G-LTE, the uplink duty cycle for FTP and iPerf exceeded 90%, except at Pos 6 (FTP upload: 87.9%; iPerf: 88.8%); Ping showed duty cycles around 50%, except at Pos 4 and Pos 10, where a duty cycle above 95% was observed; Twitch showed duty cycles below 10%; the duty cycles for the WhatsApp (VoIP) and VoLTE calls followed a similar trend, with duty cycles below 10% except at Pos 4 (WhatsApp: 20.4%; Call: 21.6%), Pos 6 (WhatsApp: 15.0%; Call: 19.7%), and Pos 10 (WhatsApp: 18.6%; Call: 14.8%).  
Figure 3: The average number of TBs per second (Equation (1)) at position 2 during our 2-min QP campaign involving a continuous FTP upload of a huge file (4G LTE and 5G NR).  
Figure 4: One-second-averaged duty cycles as a function of distance to the base-station antenna per use case and per technology: (**a**) 5G-NR, (**b**) 5G-LTE, and (**c**) 4G-LTE. Remark that for 5G NR, there was an upper duty cycle limit of 20%, considering the used TDD slot format DDDSU. The color of the labels on the horizontal axis indicates the path type, i.e., red, orange, and blue indicates NLOS (buildings), NLOS (vegetation), and NLOS (veg./build.), respectively.  
Based on this limited set of ten measurement positions, no trend in the uplink duty cycles could be determined as a function of distance to the base-station antenna. Comparing the uplink duty cycles for the different path types, only Pos 7 indicated an increase in the uplink duty cycle for Twitch of about 77% (comparing Pos 7 with Pos 8) for 5G-NR. The relation between the DCs and other parameters will be discussed in the next section.
## 3 Actual Platform Benefits
### Discussion and Limitations  
The digital health platform helps Keralty organization with closing the gaps between multiple datasets, improving clinical benefits, improving patient's lives, supporting better decision-making to manage larger populations, and improving overall health outcomes. However, the need for algorithms with high accuracy in medical diagnosis is still a challenge that needs to be improved precisely and efficiently [28]. The increasing complexity of building end-to-end platforms to integrate disparate systems and to apply machine learning techniques in specific areas such as computer vision, natural language processing, reinforcement learning, and other generalized methods present many challenges when forming the interdisciplinary team needed and the set of technological components used for the implementation.  
Some challenges should be considered in the design and implementation of machine learning projects for healthcare. One of the most critical challenges requires algorithms that can answer causal questions. These questions are beyond classical machine learning algorithms because they require a formal model of interventions [29]. To address this type of question from the analytical component of the platform, we need to learn from data differently and to gain knowledge in causal models to understand how machine learning algorithms need to be trained. Another challenge is to create reliable outcomes from heterogeneous data sources with the participation of SME (Subject Matter Experts) who understand the disease; the machine learning predictive accuracy and correct clinical interpretation depend on the criteria and context of the disease. Providers and machine learning engineers should work together on model interpretability and applicability. Machine learning implementation is not an easy task; the selection of predictive features and optimization of hyperparameters is another challenge that needs to be mastered to implement models that provide useful insights [30]. The success and meaningful use of these algorithms, and their integration into the platform depends on the accuracy of the models and their interpretability.
# Effect of the Transition Points Mismatch on Quanta Image Sensors
## 3 Imaging Models for the Effect of the Transition Points Mismatch
### Multi-bit QIS
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \(m^{2}\) & \(\mathbf{1^{2}}\) & \(\mathbf{2^{2}}\) & \(\mathbf{4^{2}}\) & \(\mathbf{8^{2}}\) & \(\mathbf{16^{2}}\) & \(\mathbf{32^{2}}\) \\ \hline \multirow{4}{*}{GSI (LSB)} & 2-bit & 9.51 & 5.18 & 4.00 & 2.98 & 2.18 & 1.56 \\  & 3-bit & 3.87 & 2.70 & 1.93 & 1.45 & 1.16 & - \\ \cline{1-1}  & 4-bit & 2.06 & 1.51 & 1.13 & 0.76 & 0.68 & - \\ \cline{1-1}  & 5-bit & 1.15 & 0.89 & 0.59 & 0.54 & - & - \\ \cline{1-1}  & 6-bit & 0.62 & 0.47 & 0.42 & 0.37 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The GSI values for different bit depths and spatial oversampling factors with \(U_{thn}=0.21\).
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 3 Network Model
### Adversary Model  
We consider an adversary that aims to manipulate trust scores, disrupt network operations, or degrade trust evaluation accuracy. The following attack scenarios are considered:  
* Sybil Attack: Malicious entities create multiple fake identities (Sybil nodes) to overwhelm the network, manipulate trust scores, and gain an unfair advantage. This leads to trust dilution and misleading reputation values.
* Self-Promotion Attack: A malicious node falsely increases its own trust score by injecting fake transactions or repeatedly reattaching its own transactions to the Tangle. This can compromise decision-making in the trust evaluation process.
* Bad-Mouthing Attack: Malicious nodes deliberately assign low trust scores to legitimate nodes to reduce their reputation and cause network disruption. This can result in honest nodes being falsely labeled as untrustworthy.  
Figure 1: Entities and flow of data in the proposed system model.  
* Collusion Attack: A group of compromised nodes coordinate to unfairly boost each other's trust scores while degrading others. This introduces bias in trust evaluation and allows attackers to manipulate network behavior.
* Resource Depletion Attack (DoS on Trust Evaluation): Attackers attempt to overload the trust evaluation process by flooding the network with excessive transaction verification requests. This increases computation and communication costs.
## 2 Previous Works
### Selecting Network Traffic Features
Two filter methods, ANOVA and chi-square test, were used in parallel to select the best features [27]. The first was analyzing the variances, whereas the second picked dependent features. Researchers used these methods on their own traffic. In their tests, the most important features were the number of bytes sent and received. Another research work used two feature selection methods, one after the other: an embedded method, naive Bayes, and a filter method, \(t\)-test [28]. The paper started with nine features and concluded that two features should be rejected. Researchers working on network traffic classification widely utilize different selection methods to create a combined ranking of features. Such a method is called an ensemble feature selection [29; 30]. A brief comparison of the ensemble feature selection concept proposed in this publication, along with methods from the literature review, is included in Table 1.  
In order to classify network traffic, the results of six feature selection methods: an embedded method (perceptron), four filter methods (chi-square, minimum redundancy, maximum relevance, and ReliefF) and a hybrid method (recursive feature elimination for SVM) were combined using SVM with a radial-basis function [29]. A combination of five feature selection methods--two wrapper methods (stability selection and recursive feature elimination), an embedded method (RF), and two filter methods (mean decrease impurity, and chi-square)--using the voting of selected classifiers was conducted [30]. The results of the five filter methods (information gain, gain ratio, chi square, symmetric uncertainty, and relief) were combined using summing [31]. The authors of [32] proposed combining two filter methods: information gain and symmetrical uncertainty. Due to the tests, the method achieved promising results. The idea of combining two filter methods was proposed while classifying Android traffic [33]. In the examination, both information gain and chi-square test showed that the initial set of 22 features should be minimized to only nine features. To enhance IDS systems, a feature ranking consisting of three selection methods was conducted [34]. For this purpose, two filter methods, the chi-square test and information gain, were used along with the extra trees embedded algorithm.
## 2 Background and Related Work
In this framework sensor networks can detect, localize, and quantify bursts, leaks, and other anomalies in pipelines. Acoustic wave propagation theory, distributed control, and statistical signal processing are used to analyze signals for defects detection and localization. All these methods can be also used with the sensor network architectures proposed in this paper. In another project, a wireless sensor network for a team of underwater collaborative autonomous agents has been developed [19]. This system was developed to locate and repair scale formations in tanks and pipeline within inaccessible areas such as underwater environments. We have previously developed a framework and protocols for monitoring above-ground long pipelines using wireless sensor networks [20]. In addition, we have developed a fault-tolerant wired/wireless sensor network architecture for monitoring above-ground pipeline infrastructures [21]. Although several of the mentioned projects are based on different network technologies, none of them studied the reliability issues of sensor networks for monitoring long underwater pipelines. Some companies have developed sensors for monitoring pipelines. Most of these commercial sensors are developed to provide individual sensing for some attributes. An example of these companies is vMonitor [22], which developed a set of wireless pressure and temperature sensors to remotely monitor pipelines. These sensors were designed mainly for above ground pipelines. Another example is from Westminster International LTD, which developed Diver Detection Sonar (DDS) that can be used underwater for the detection of divers and submerged swimmer delivery vehicles (SDVs) [23]. These sensors can be used to counter the threat of sabotage and terrorism for naval bases, commercial ports, as well as oil platforms by detecting unwanted divers, swimmer, and SDV. These commercial products can also be used as part of some of network architectures developed in this paper for monitoring underwater pipelines.
## 3 The Effect of Pulse Density on the Surface Structuring  
Figure 3 illustrates the impact of pulse density during the LSP process on the surface of the Al6061 T6 alloy. The SEM micrographs show the initial machined microstructure of the alloy (Figure 3), which clearly transforms into a wave-like microstructure following the LSP treatment (Figure 3). At first glance, a clear trend is observed in the microstructure as a function of pulse density, ranging from 900 to 2500 pulses/cm\({}^{2}\). Notably, at the highest pulse density, larger wave patterns emerge. This observation correlates well with the increase in average surface roughness of the LSP-treated alloys.  
Figure 3: The effect of the pulse density on the surface microstructure of the Al6061 T6 alloy after the LSP treatment.
# Xanthone Derivatives in the Fight against Glioblastoma and Other Cancers
## 2 Antineoplastic Activity of Xanthone Derivatives
### Mangostin Isomers and Derivatives
#### 2.1.2 Autophagy Phenomenon  
Some research shows that \(\alpha\)-Mangostin induces autophagic cell death but not apoptosis. This activity was proved with the use of autophagy inhibitors 3-methyladenine and bafilomycin or beclin-1 [48], which resulted in the suppression of \(\alpha\)-Mangostin-mediated cell death. A critical mediator of \(\alpha\)-Mangostin's inhibition of cell growth is the liver kinase B1 (LKB1)--AMP-activated protein kinase (AMPK). Activation of AMPK induces \(\alpha\)-Mangostin-mediated phosphorylation of raptor (RPTOR, the regulatory associated protein of mTORC1), which subsequently associates with 14-3-3y and results in the loss of mTOR1 activity. The phosphorylation of both downstream targets of mTOR1, p70 ribosomal protein S6 kinase (p7056 kinase), and 4E-BP1, is also diminished by the activation of AMPK [48]. Further evidence of the autophagy theory is in the inhibition of AMPK expression with shRNAs or the inhibitor of AMPK reduced \(\alpha\)-Mangostin-induced autophagy.  
**GBM8401** and **DBTRG-05 MG** are **human glioblastoma multiforme** (brain tumor) cell lines. The research showed that these cell lines are highly sensitive to \(\alpha\)-Mangostin both in vitro and in vivo (transplant glioblastoma in nude mice) tests. The mechanism of action of \(\alpha\)-Mangostin is associated with autophagic cell death, which has been proven by analysis as having influence on several autophagy-related pathways, e.g., liver kinase B1 (LKB1)/AMP-activated protein kinase (AMPK). This finding demonstrates that AMPK activation by \(\alpha\)-Mangostin could also inhibit mTOR1 activity by increasing raptor phosphorylation [48].
## 4 Discussion
### Comparison of Segments' Range of Motion between Groups  
The results of the ROM data showed that there was no significant difference between the two groups. To the best of our knowledge, limited studies investigated the spine range of motion during sit-to-stand or stand-to-sit, and there are contradictions in their results. [PERSON] et al. (2016) [28] and [PERSON] et al. (2005) [9] reported that ROM was limited in nonspecific CLBP people. [PERSON] et al. (2013) [39] reported no significant differences between LBP and healthy people. Additionally, [PERSON] et al. (2011) [40] and [PERSON] et al. (2011) [41] mentioned a decreased lumbar range of motion and a lower flexion peak. However, when the trunk's mobility is examined globally, the results are conflicting [9]. [PERSON] et al. (1994) [29] and [PERSON] discovered that there is a modified strategy for raising from a chair in participants with low back pain. In addition, the results of research by [PERSON] et al. (2019) [12] showed in patients with low back pain that ROM can be decreased because of the pain, muscle spasms, coactivation, or stiffness, while another study indicated that paraspinal muscle coactivation is utilized to stabilize the lumbar spine as a preventive technique to minimize pain provocation [42]. Furthermore, increased spine stiffness may damage patients with LBP by altering movement patterns and perhaps increasing the sensitivity of spinal and peripheral tissues, both of which may lead to pain chronicity [28; 43; 44]. Overall, considering the results of current and previous studies, it can be concluded that, despite the movement limitation due to the pain in low back pain patients, investigating the segments' ROM cannot only provide adequate information on the movement characteristics in people with and without nonspecific CLBP.