esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Combustion Characteristics  
#### 3.1.1 In-Cylinder Pressure  
Figure 2 illustrates the in-cylinder pressure curves for the diesel-ethanol binary blended fuels at different engine loads. In order to demonstrate the differences of in-cylinder pressures for different blended fuels, the figures present parts of indicator diagrams for selected parts from 40\({}^{\circ}\) CA before top dead center (bTDC) to 40\({}^{\circ}\) CA after top dead center (aTDC). As shown in Figure 2, it can be clearly observed that the in-cylinder pressure of all test fuels increases with the increase of engine load, and the combustion starts earlier in terms of the crank angle under high load. This is because the increase of engine load causes more air and fuel to be injected into the combustion chamber. After fuel combustion, the temperature and pressure in the cylinder are increased, and the physical delay of fuel is reduced. In addition, the air flow intensity is also improved, which reduces the retention of excessive residual gas in the cylinder. In a word, the increase of load significantly reduces the ignition delay. A similar trend was also found by [PERSON] et al. [25]. On the other hand, with the increase of engine load, the position of the maximum pressure in the cylinder gradually shifts towards aTDC. In Figure 2a, the peak in-cylinder pressure of D100 at 0 Nm, 15 Nm, and 30 Nm is 60.1 bar, 66.1 bar, and 74.3 bar, which occurs at 10\({}^{\circ}\) CA aTDC, 12\({}^{\circ}\) CA aTDC, and 13\({}^{\circ}\) CA aTDC, respectively. In Figure 2b, the peak in-cylinder pressure of D95E5 at 0 Nm, 15 Nm, and 30 Nm is 59.7 bar, 65.7 bar, and 71.7 bar, which occurs at 10\({}^{\circ}\) CA aTDC, 12\({}^{\circ}\) CA aTDC, and 13\({}^{\circ}\) CA aTDC, respectively. In Figure 2c, the peak in-cylinder pressure of D90E10 at 0 Nm, 15 Nm, and 30 Nm is 59.5 bar, 65.9 bar, and 73.7 bar, which occurs at 10\({}^{\circ}\) CA aTDC, 12\({}^{\circ}\) CA aTDC, and 14\({}^{\circ}\) CA aTDC, respectively. In Figure 2d, the peak in-cylinder pressure of D85E15 at 0 Nm, 15 Nm, and 30 Nm is 59.6 bar, 65.4 bar, and 72.3 bar, which occurs at 11\({}^{\circ}\) CA aTDC, 12\({}^{\circ}\) CA aTDC, and 14\({}^{\circ}\) CA aTDC, respectively. This is because the pressure and position in the cylinder mainly depend on the engine operating conditions and fuel properties. The increase of load leads to more fuel injection and the increase of combustion duration, which is the main reason for the shift of the position of the maximum in-cylinder pressure to aTDC.
## 2 Background Information
### Bacterial Foraging Optimization (BFO)  
The bacterial foraging algorithm (BFO) is a novel swarm intelligence algorithm proposed by [PERSON] in 2002 [PERSON] (2002), based on the competitive cooperative mechanism of _E. coli_ in the process of searching for food in the human intestine. By simulating the foraging behavior of _E. coli_, the BFO mainly consists of four behaviors: chemotaxis, swarming, reproduction, and elimination-dispersal.  
1. Chemotaxis: Chemotaxis operation is the core of the algorithm, which simulates the foraging behavior of _E. coli_ moving and tumbling. In poorer areas, the bacteria tumble more frequently, while bacteria move in areas where food is more abundant. The chemotaxis operation of the \(i\)th bacterium can be represented as \[\begin{split}&\theta^{i}(j+1,k,l)=\theta^{i}(j,k,l)+C(i)*det_{i}\\ & dct_{i}=\frac{\Delta(i)}{\sqrt{\Delta^{\mathrm{T}}(i)\Delta(i)}} \end{split}\] (5) where the \(\theta^{i}(j,k,l)\) represents the \(i\)th bacterium at the \(j\)th chemotactic, \(k\)th reproductive, and \(l\)th elimination-dispersal steps. \(C(i)\) is the trend step length of bacteria \(i\) in a random direction (\(det_{i}\)). \(\Delta\) is a random vector between \(-1\) and \(1\).
2. Swarming: In the chemotactic of bacteria to the foraging process, in addition to searching for food in their own way, there is both gravitation and repulsion among the individual bacteria. Bacteria will generate attractive information to allow individual bacteria to travel to the center of the population, bringing them together; at the same time, individual bacteria are kept at a distance based on their respective repulsion information.
3. Reproduction: According to the natural mechanism of survival of the fittest, after some time, bacteria with weak ability to seek food will eventually be eliminated, and bacteria with strong feeding ability will breed offspring to maintain the size of the population. By simulating this phenomenon, a reproduction operation is proposed. In \(S\)-sized populations, \(S/2\) bacteria with poor fitness were eliminated and \(S/2\) individuals with higher fitness self-replicated after the bacteria performed the chemotaxis operator. After the execution of the reproduction operation, the offspring will inherit the fine characteristics of the parent completely, protect the good individuals, and greatly accelerate the speed towards the global optimal solution.
4. Elimination-Dispersal: In the process of bacterial foraging, do not rule out the occurrence of unexpected conditions leading to the death of bacteria or causing them to migrate to another new area. By simulating this phenomenon, an elimination-dispersal operation has been proposed. This operation occurs with a certain probability _Ped_. When the bacterial individual satisfies the probability _Ped_, then the individual of the bacterial dies and randomly generates a new individual anywhere in the solution space. This bacterium may be different from the original bacterial, which helps to jump out of the local optimal solution and promote the search for the global optimal solution.
# Reflections on the Limited Pervasiveness of Augmented Reality in Industrial Sectors
## 2 Investigating the Causes of the Lack of Pervasiveness
### Industry Survey Based on AR Applications  
To avoid possible bias during the evaluation process caused by the interviewer pre-assumptions, firstly the participants were asked to brainstorm about all the difficulties, downside, and pitfalls of the applications and of the process. Figure 3 shows the methodological steps used during the survey for identifying the main factors that are reducing AR penetration on the market.  
Firstly, the interviewees had to reflect on the AR application as a whole: from the very starting moment of the chosen operation, to the development, until the real test. After the brainstorming session, the interviewer collected and clustered in 11 main factors, all the focus points mentioned by the groups, and the interviewees were asked to rank them using a Likert scale ranging from 0 to 5, where 0 represents the less critical value. In total, fifty-six employees that tested the AR solutions were interviewed: Twenty-four in the manufacturing sector, twenty-four in the automotive sector, and eight in the railway sector.  
Figure 3: Survey Methodological Steps.  
Figure 2: (**A**): overlapping of digital information on the real brake system. (**B**): digital instructions to disassembly the component. (**C**): digital information for the train driver.  
Figure 4 offers the graphical representation through radar plots of the results of the survey.  
Figure 4: Results of the survey performed for ranking lack of AR Pervasiveness factors.  
After the brainstorming session, several limitations and pitfalls were identified by the three different groups, and they were defined or named in slightly different ways. As previously explained, once these sessions terminated, the interviewer clustered all the points mentioned by the interviewees, in order to avoid repetitions that could affect the validity of the next step of the survey. The 11 main factors identified and used for the ranking are the following:  
* Department/Unit not available to handle AR projects;
* Skills not present in the company to handle AR projects;
* IT Infrastructures;
* Financial resources;
* Ergonomics;
* Corrective Lenses/Glasses;
* Distraction;
* Image Recognition Method;
* Internet Connection;
* Digitalization Time;
* Responsiveness.  
The results acquired with the three surveys, based on the Likert Scale evaluation show comparable concerns and AR limitation motivations regardless of the industrial sector. The values are expressed in percentage points. Together with the mean value and the standard deviation (sd), they represent the possible contribution (according to the interviewees' considerations) of a specific factor to the overall lack of AR pervasiveness.
# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 2 Materials and Methods
### R50A3-LWBENet Model Components
#### 2.2.2 SE-ResNet  
Our proposed SE-ResNet, which refers to the method of fusing the SE block, a channel-based attention mechanism with stronger feature extraction capability and deeper network layers, into the residual module of ResNet attempts to perform feature extraction in the channel dimension, so that it can better learn the feature information of the water body in the image, and improve the accuracy of the tasks such as image classification and target detection to a certain extent [27; 28]. The structure of SE-ResNet network is shown in Figure 2.  
The original ResNet residual module is shown in Figure 2a, and the SE-ResNet residual module is shown in Figure 2b. As can be seen in Figure 2b, the biggest difference between the two models is that the feature maps are re-weighted by the SENet Layer for each interconnecting channel of the feature maps after passing through the two layers of the ordinary convolution layer of the ResNet residual module, which makes it possible to emphasize the channel features throughout the whole encoding process, so as to strengthen the ability to pay attention to the part of the water body.
## 3 Incremental Redundancy
### Chase Combining  
Let \(r^{(0)}=b+n^{(0)}\) and \(r^{(1)}=b+n^{(1)}\) be the received values corresponding to two transmissions of the same bit \(b\) with different \(SNR_{0}\) and \(SNR_{1}\), respectively. With Chase Combining, the receiver can combine both values into \(r^{(0)}+r^{(1)}=2b+n^{(0)}+n^{(1)}\) resulting in an effective SNR of  
\[\text{SNR}_{\text{CC}}=\frac{4}{\frac{1}{\text{SNR}_{0}}+\frac{1}{\text{SNR}_{ \text{IR}}}}, \tag{11}\]  
for the retransmitted bits. Since \(p(1|r^{(0)},r^{(1)})\) is proportional to \(p(1|r^{(0)})p(0|r^{(1)})\) (the same applies for \(b=0\)), the decoder can just add the LLRs from the individual transmissions.
# Modeling of Fuzzy Systems Based on the Competitive Neural Network
## 3 Proposed Method
### Design Process of the Fuzzy Inference Systems
#### 3.2.4 Fuzzy Rules  
The fuzzy rules for the proposed method will be automatically generated. Specifically, the rules depend on both the number of input data and the quantity of features in each dataset.  
In an FIS, the knowledge base or fuzzy rules depend on the antecedents (input variables) and the partition of these variables (number of MF). It is essential for researchers to have the option of using either the total number of fuzzy rules or, in specific cases, employing fewer rules than the total possible number.  
The fuzzy rule generation process for the proposed CNNT1 FL method is detailed below:  
1. IF (\(Input_{1}\) is \(Cluster_{1}\)) and (\(Input_{2}\) is \(Cluster_{1}\)) and,..., (\(Input_{n}\) is \(Cluster_{1}\)), THEN (\(Output\) is \(Class_{1}\)).
2. IF (\(Input_{1}\) is \(Cluster_{2}\)) and (\(Input_{2}\) is \(Cluster_{2}\)) and,..., (\(Input_{n}\) is \(Cluster_{2}\)), THEN (\(OutputsClass_{2}\)).
3. IF (\(Input_{1}\) is \(Cluster_{3}\)) and (\(Input_{2}\) is \(Cluster_{3}\)) and,..., (\(Input_{n}\) is \(Cluster_{3}\)), THEN (\(Output\) is \(Class_{3}\)).  
\(\vdots\)
2. IF (\(Input_{1}\) is \(Cluster_{k}\)) and (\(Input_{2}\) is \(Cluster_{k}\)) and,..., (\(Input_{n}\) is \(Cluster_{k}\)), THEN (\(Output\) is \(Class_{k}\)).  
where \(m\) denotes the maximum number of fuzzy rules, \(n\) represents the number of input data, and \(k\) denotes the events to evaluate.
## 5 ML Intersection with 6G Enabling Technologies
### ML and Non-Orthogonal Multiple Access (NOMA) Convergence for Optimization in 6G Networks  
One way to meet the enormous requirements of the future 6G network is to integrate the existing enabling technologies for effective performance. As connectivity demands peak, the NOMA approach is evolving into an adequate access point optimization tool for network communication [140]. While the transition into NGMA is imminent, the NOMA has shown to be effective for critical networking issues such as spectrum management, prevention of interference between clusters, etc. [140, 141], and integration with ML will provide improved performance [15, 140]. ML was efficiently integrated with NOMA for the optimization of power allocation in a NOMA-based communication network [142]. The authors proved that effective spectrum sharing can be actualized by developing an ML-enhanced NOMA system. Improvements have been achieved in signal detection by applying DL to NOMA communication [143]. The authors compared the performance of an LSTM-based DL approach to the conventional successive interference cancelation (SIC) and reported the former to outperform the SIC-based approach. Other successful convergence applications have been reported for improved energy optimization in NOMA communication [144]. The proposed EE-CSL algorithm enabled the improvement in the learning performance of the overall system. Other methods involving the convergence of ML and NOMA system for the optimization of the 6G communication network include a Double DQN (DDQL) approach for optimized transmission power [145], a federated learning (DREAM-FL) system for client selection [146], and an LSTM-based DL algorithm for optimal channel coefficients prediction [147] and for optimized NOMA-based relay-aided D2D transmissions [148]. Similar successes have been reported for MIMO-NOMA systems [149, 150], and ML proved to be effective for actualizing optimal performance. Table 5 provides a comprehensive summary of ML integration for improved NOMA systems.  
Figure 4: The performance of AHC algorithms in terms of sum data rates against an increasing number of users. Adapted from [135].
# Indole-3-Acetic Acid Action in Outdoor and Indoor Cultures of _Spirulina_ in Open Raceway Reactors
## 1 Introduction
In addition, the comparison between indoor and outdoor microalgae cultivation is crucial, as outdoor cultivation in raceway-type reactors offers the advantage of preventing the necessity for artificial lighting and aeration. This strategic decision aligns with sustainability principles and decreases energy consumption, making microalgae cultivation economically feasible and environmentally sustainable [3]. Reactors like the open raceway reactors utilized in this study, designed for outdoor operation, are extensively employed in commercial microalgae production due to their cost-effectiveness in construction and operation compared to enclosed systems [26]. In light of this context, a critical investigation into the influence of phytohormones in cultures conducted under uncontrolled environmental conditions becomes imperative. Such research could offer valuable insights to optimize cultivation parameters and maximize targeted biomolecule production. Additionally, this cultivation approach considers energy efficiency, sustainability, and economic feasibility and provides pivotal guidance for implementing large-scale production strategies. Consequently, this study aims to assess the cost-effectiveness and productivity of _Spirulina_ sp. LEB 18 supplemented with indole-3-acetic acid (IAA) when the cultivation is conducted within raceway bioreactors under indoor and outdoor conditions.
# PVPBC: Privacy and Verifiability Preserving E-Voting Based on Permissioned Blockchain
## 7 Security Analysis
### Malicious-Attack Analysis
#### 7.2.1 Malware  
Malware, which includes Trojan horses, worms, spyware, and ransomware, typically targets the integrity of an election by preventing the voter's vote from being recorded as intended [35; 36]. Distributed denial-of-service (DDOS) attacks can have a significant negative impact on network routing and performance metrics [37]. A DDOS attack aims to disrupt the voting process by slowing down communication in the network, including vote casting, tallying, and auditing. Regarding PVPBC's resilience to these attacks, vote integrity is achieved as the vote is cast through the front-end system which is controlled and run by the election authority as well as the trustees. We assume that there is a secure channel between the voter and the front-end which reduces the likelihood that adversaries can intercept the votes and alter them during the election phase. Regarding intrusions that intensify DDOS attacks, PVPBC offers a high level of resilience as a result of its use of permissioned blockchain. A consequence of this design choice is that participants are pre-defined and authenticated before joining the network. In addition, every DL node maintains a copy of the voting information (e.g., encrypted votes and verification proofs, etc). This helps PVPBC to stay alive; consequently, it is available for voters if a particular node fails due to a DDOS attack. When the node goes live again, it synchronises with othernodes and updates its DL ledger. In summary, PVPBC achieves a high level of resilience due to its use of blockchain technology.
###### Abstract  
Natural polyphenol-rich plant resources, such as agricultural waste, were proven to diminish insulin resistance and weight gain in rats on a high-fat diet. To test whether date seed polyphenol pills (DSPPs) might lower adipose tissue accumulation by precisely affecting adipocytes, we explored the impacts of DSPPs on cell proliferation, differentiation, and lipolysis in 3T3-L1 cells. We utilized tablets made commercially from date seed polyphenols that were mostly composed of epicatechin (45.9 g/kg). The total polyphenol and antioxidant capacities of the digested and non-digested DSPPs were also evaluated. DSPPs at doses of 25, 50, and 100 ug/mL hindered the proliferation of both pre-confluent preadipocytes and mature post-confluent adipocytes. DSPPs decreased the quantity of viable cells in completely developed adipocytes. Treatment with 100 ug/mL of DSPPs decreased the basal lipolysis of completely differentiated adipocytes but modestly boosted epinephrine-induced lipolysis. A significant transcription factor for the adipogenic gene, the peroxisome proliferatorator-activated receptor (PPAR), was repressed by DSPPs, which significantly decreased lipid buildup. The total polyphenol and antioxidant capacities were also increased after digestion with a good bubble Pearson correlation between both. DSPPs may have anti-obesity and anti-diabetic characteristics by inhibiting adipocyte development and basal lipolysis, which could be commercially industrialized.  
agricultural waste; date seed polyphenols; industrial-scale pills; 3T3-L1 cells; obesity Article
# GPU-Accelerated CNN Inference for Onboard DQN-Based Routing in Dynamic LEO Satellite Networks
## 5 Experimental Results  
### Experimental Setups  
To accelerate the Dueling DQN routing algorithm for low-orbit satellites in an onboard environment, we used the NVIDIA Jetson Orin NX 16 GB embedded board to closely mimic the limited computational capacity of an onboard system across all phases of the application. This approach ensures consistency while utilizing state-of-the-art features. The technical specifications of this board are shown in Table 2. Based on the table, the NVIDIA Jetson Orin NX offers strong AI and GPU performance, allowing it to efficiently handle the high-density computations required by reinforcement learning algorithms like Dueling DQN. It also ensures that power consumption, which is critical for LEO satellites, remains within acceptable limits. The experimental environment provided a dynamic testing environment consisting of four channels: agent location, target location, obstacles, and boundaries, within a \(15\times 15\) grid world environment. This grid world introduced an additional layer of complexity, where disconnected regions autonomously moved one position to the left at every discrete time step, mimicking the dynamic challenges present in real-world environments. In this environment, we proposed a system that leverages GPU acceleration for CNN inference in the Dueling DQN routing scheme deployed on the embedded board. The kernel was implemented using the PyCUDA library to ensure smooth operation within the Python environment, and PyTorch v1.12.0 was selected as the deep learning framework for this research.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Specification** & **Detail** \\ \hline AI Performance & 100 TOPs \\ \hline GPU & 1024-core NVIDIA Ampere GPU featuring 32 Tensor Cores \\ \hline CPU & 8-core Arm Cortex-A78 AE v8.2 64-bit CPU \\ \hline Memory & 16 GB 128-bit LPDDR5 1024 GB/s \\ \hline Power & 10 W-25 W \\ \hline \hline \end{tabular}
\end{table}
Table 2: NVIDIA Jetson Orin NX 16 GB board technical specification.
# Multi-Technology Multi-Operator Site Sharing: Compliance Distance Analysis for EMF Exposure
## 1 Introduction
Passive site sharing raises concerns about the total EMF exposure radiated from the antennae because each operator has separate radio equipment and all of them transmit from the same location. In this study, we determined the total accumulated exposure by calculating and analyzing the CDs for the two types of site sharing at the macro site and the indoor IBS site. The merit of this analysis is in the calculations that consider the real operating setup (typical configurations) and involve the actual realistic transmitted power. Additionally, the goal was not to compare the levels of exposure to different technologies. Instead, it was to find out what the CDs are when different technologies and operators all transmit in the same place. This manuscript is structurally organized into three main sections: the exposure standard limits, CD calculations, and the results and discussion for site sharing at macro and indoor sites.