esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Results for Redundancy, Overhead, and Delay
#### 4.5.4 Key Technical Insights  
* The multi-profile approach (Proposed Method B) significantly reduces Redundancy and Overhead due to its ability to contextually select decision parameters relevant to real-time network states, effectively preventing redundant broadcasts at the source.
* Reinforcement learning-driven optimization in Proposed Method A consistently manages the BC/DNB ratio, offering improvements in Overhead and Delay by adaptively suppressing unnecessary broadcasts, although slightly less granularly compared to multi-profile methods.
* Baseline methods face elevated Redundancy, Overhead, and Delays primarily due to static or inadequately adaptive decision-making mechanisms that fail to effectively account for rapidly evolving network dynamics and topology-specific constraints.  
This detailed performance analysis elucidates that superior adaptability through dynamic parameter selection and specialized fuzzy profiles is essential for achieving optimal network efficiency in broadcasting schemes within dynamic MANET environments.  
In Table 3, we summarize the end-to-end Delay (in milliseconds) for different node densities. This metric measures how quickly the broadcast reaches the last node in the network.
## 3 Results
### Intermodulation Distortion  
When a system is excited by a two-tone stimulus, intermodulation distortion may appear at the sum and difference of the two tones and their harmonics. If one of the tones \((f_{1})\) is significantly lower than the other tone \((f_{2})\), the intermodulation components are concentrated around the highest frequency, at frequencies \(f_{2}\pm nf_{1}\) (where \(n\) is a natural number). According to the IEC Standard 60268-21 [26], the total intermodulation distortion (\(IMD_{T}\)), in percentage and considering only intermodulation components up to 3 rd order, can be expressed as:  
\[IMD_{T}(f_{1},f_{2})=\frac{\sum_{k=1}^{2}\overline{p}(f_{2}-kf_{1})+\overline{ p}(f_{2}+kf_{1})}{\overline{p}(f_{2})}100\%, \tag{11}\]  
where \(\overline{p}(f_{2}\pm(n-1)f_{1})\) is the RMS value of the nth-order intermodulation component at the sum and difference of the two tones, and \(\overline{p}(f_{2})\) is the RMS value of the fundamental component at the excitation frequency \(f_{2}\).  
To analyze this parameter along the entire frequency range, the calculation has been divided into two parts: variation of the low-frequency tone \(f_{1}\) from 20 Hz to 1 kHz while keeping the high-frequency  
Figure 6: Comparison of \(L_{THD}\) levels for each modulation technique (modulation index m = 1).  
tone \(f_{2}\) at a constant frequency of 7 kHz, and variation of the high-frequency tone \(f_{2}\) from 500 Hz to 20 kHz while keeping the high-frequency tone \(f_{1}\) at a constant frequency of 60 Hz.  
The intermodulation distortion is shown in Figure 7. The graph shows that the lowest values of the intermodulation distortions were obtained with SRAM and MAM modulations. Single sideband modulations obtained the worst results. It is also observed how the \(IMD_{T}\) measured with the lower single lateral band (LSBAM) had a peak around 6.5 kHz caused again by the minimum that the parametric array had in the ultrasonic response.  
All measurements were repeated systematically for different modulation indices varying from 0.1 to 1 at steps of 0.1. The results are shown in Supplementary Figure S5. In this case, the MAM and SRAM techniques provide the lowest distortion even at large modulation indices. The rest of techniques are more sensitive to changes in the modulation index.
# Research on the Robustness of Interdependent Networks under Localized Attack
## 3 Results and Analysis  
To study the robustness of interdependent networks under PDLA, we use four kinds of network models to create interdependent networks, three of them are artificial networks, namely the WS small world network, BA scale-free network, and NN nearest-neighbor coupled network. The forth one is an infrastructure network, the western states power grid of the United States [47]. Then we construct WS-WS, BA-BA, NN-NN, and power-BA interdependent networks using the methods proposed in [4].  
Figure 3: Illustration of the failure probability degradation model of a localized attack. The failure probability of a node within the area affected by a disaster decreases with its distance from the attack center.  
Figure 2: The yellow nodes represent attack center, the red, blue, green and black nodes denote nodes whose shortest distance to attack center is 1, 2, 3, 4, respectively. (**a**) Schematic illustration of a localized attack in partially-coupling interdependent networks; and (**b**) schematic illustration of a localized attack in fully-coupling interdependent lattice networks.  
In order to compare the effect of PDLA, we also show the result of a targeted attack, a random attack, and a traditional localized attack.
# On the Use of LoRa Technology for Logic Selectivity in MV Distribution Networks
## 7 Experimental Tests  
The experimental tests were conducted with the aim of demonstrating the feasibility of LoRa technology to fulfill logic selectivity on medium-voltage distribution lines.  
The designed demonstrator was used with two different types of fault. With regard to the case studies, the authors referred to Table 9, taken from the Italian Standard [42] that regulates the connection of users to the MV network. In particular, this standard specifies the maximum fault clearing time that the user's protection must comply with. Depending on the type of fault, the protection is identified by a unique code. The two cases taken into consideration were: 50.S3 poly-phase short circuit (with current threshold of 600 A) and 67N.S1 single-phase to earth fault with compensated neutral. The fault case 50.S3 is the worst case since the time required to clear the fault is equal to 120 ms, while this time for fault case 67N.S1 is equal to 450 ms.  
For each considered fault, two conditions were emulated: a fault between the protection of the primary substation SR and the first protection PR1, and fault in the line section between PR2 and PR3.
# AUV-Aided Optical--Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning
## 5 Proposed DRL-Based Multi-Modal Data Collection Scheme
### Multi-Modal Steering Angle Optimization Algorithm
Furthermore, if \(d_{seg_{i}}\) is less than \(d_{op}\), i.e., the AUV crosses the UOC range of \(c_{i}\), then UOC is selected directly as the communication option. This is due to the superiority of UOC over UAC in terms of energy consumption and transmission time for the same AUV trajectory. The data collection hover point and steering angle of the AUV are similar to the method in Case 2. Based on the above discussion, we obtain the MSAO algorithm that is shown in Algorithm 1. Figure 2: The AUV multi-modal steering angle diagram. (a) The trajectory of the AUV from the current coordinates \(p_{a,t}\) towards the next target \(c_{j}\) will not sail through the communication region \(C_{m}\) of \(c_{i}\). (b) The trajectory of the AUV from the current coordinates \(p_{a,t}\) towards the next target \(c_{j}\) will sail through the communication region \(C_{m}\). ```
0: Coordinate of the AUV \(p_{a,t}\), coordinates of the current target CH \(c_{i}\), coordinates of the next target CH \(c_{j}\), UAC communication radius \(d_{ac}\) and UOC communication radius \(d_{op}\). 1:if\(d_{seg_{i}}>d_{ac}\) and \(m=ac\)then
2: Calculate the data collection hover position \(p_{c_{i},ac}\) by (22). 3: Calculate steering angle \(\Psi_{ac,t}\) by (23). 4:elseif\(d_{seg_{i}}\leq d_{ac}\) and \(d_{seg_{i}}>d_{op}\) and \(m=ac\)then
5: The data collection hover position is the vertical foot \(p_{r_{i},ac}\) from \(c_{i}\) to the segment \(\overline{p_{a,t}c_{j}}\). 6: Calculate steering angle \(\Psi_{ac,t}\) by (23). 7:elseif\(d_{seg_{i}}>d_{op}\) and \(m=op\)then
8: Calculate the data collection hover position \(p_{r_{i},op}\) by (22). 9: Calculate steering angle \(\Psi_{op,t}\) by (23). 10:elseif\(d_{seg_{i}}\leq d_{op}\)then
11: The data collection hover position is the vertical foot \(p_{r_{i},op}\) from \(c_{i}\) to the segment \(\overline{p_{a,t}c_{j}}\). 12: Calculate steering angle \(\Psi_{op,t}\) by (23). 13:endif
14: The steering angle of the AUV: \(\Psi_{m,t}\). ```  
**Algorithm 1** Proposed MSAO Algorithm
# Research on Coal and Gangue Recognition Model Based on CAM-Hardswish with EfficientNetV2
## 3 Image Data Preprocessing  
### Acquisition of Image Data  
There are 200 coal and 200 gangue samples in the image data collected for this experiment. The samples were captured using an industrial camera, as shown in Figure 6. Coal and gangue display distinct physical characteristics. The coal samples exhibit deeper color, rounder contours, and reflective areas, as visually demonstrated in Figure 6a. On the other hand, the collected gangue samples are grayish-white, have rougher textures, and lack reflections, as demonstrated in Figure 6b.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stage** & **Block** & **Channels** & **Stride** & **Layers** \\ \hline
0 & Conv3\(\times\)3 & 24 & 2 & 1 \\
1 & Fused-MBConv3\(\times\)3 & 24 & 1 & 2 \\
2 & Fused-MBConv3\(\times\)3 & 48 & 2 & 2 \\
3 & Fused-MBConv3\(\times\)3 & 64 & 2 & 2 \\
4 & MBConv3\(\times\)3 & 128 & 2 & 3 \\
5 & MBConv3\(\times\)3 & 160 & 1 & 6 \\
6 & MBConv3\(\times\)3 & 256 & 2 & 10 \\
7 & Conv1\(\times\)1\&Pooling\&FC & 1280 & 1 or 2 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural parameters of EfficientNetV2 network after improving network training speed.  
Figure 6: Partial sample images (figure (**a**) is a partial sample of coal and figure (**b**) is a partial sample of gangue).  
The accuracy of the deep recognition network model is heavily influenced by the quality of the dataset used. As there are no public datasets for coal and gangue images, the image data have to be acquired manually. Preprocessing of the image data has resulted in a coal and gangue dataset with rich feature information.
# Evaluating the Energy-Saving Potential of Solar Dedicated Ventilation Systems Based on ADRC
## 3 Accurate Control and Analysis Based on ADRC
### Design of ADRC for This Research
Parameter adjustment of nonlinear TD: When the ADRC has an effect in a closed-loop system, the TD simply gives a differential signal to the input signal and the tracking signal adjusts the parameters \(r\), \(h\), \(h_{0}\) in the open-loop system. Parameter adjustment of ESO: The three parameters \(k_{1}\), \(k_{2}\), \(k_{3}\) of the extended state observer have a great influence on the closed-loop system. When the system has a large inertia coefficient and a long-time lag, the parameters \(k_{1}\), \(k_{2}\), \(k_{3}\) get larger. When the system overshoots too much, the overshoot can be suppressed by appropriately decreasing \(k_{3}\). Parameter adjustment of NSEF: The parameters have an explicit physical meaning. The parameter \(\beta_{01}\) represents the proportional gain. The parameter \(\beta_{02}\) represents the differential gain, and the parameter tuning can use the proportion (P) and differentiation (D) setting theory in the PD controller. The function of NSEF generates a nonlinear control law. When the control speed is fast, the control purpose is achieved by decreasing \(\beta_{01}\), otherwise \(\beta_{01}\) will increase. Acceleration of the adjustment speed may increase the overshoot, which can be suppressed by increasing \(\beta_{02}\) to get smaller oscillations. The ADRC also has a special parameter \(b\), which is related to the control quantity u and the state quantity \(z_{3}\) of the state observer. The selection of different \(b\) is equivalent to changing the total disturbance value in different ranges, and the compensation component can also be changed in response. The selection principle of \(b\) is that a larger \(b\) can effectively compensate the disturbance and uncertainty of the model when the delay time of the object is longer. According to the calculation and analysis of the ADRC, the proposed solar dedicated ventilation system can be controlled within a precise range.
Possible Treatment of Severe Bone Dehiscences Based on 3D Bone Reconstruction--A Description of Treatment Methodology  
[PERSON]  
1 Department of Oral Surgery, Wroclaw Medical University, Krakowska 26, 50-425 Wroclaw, Poland; [EMAIL_ADDRESS] (M.D.); [EMAIL_ADDRESS] (C.O.); [EMAIL_ADDRESS] (S.D.)  
[PERSON]  
2 Department of Maxillofacial Surgery, Wroclaw Medical University, Borowskaloss. At the same time, there is also a lack of adequate dimensions of the avascular base to conduct the subsequent implantation [5].  
The regeneration procedures known to date are based on the use of autogenous bone, or its allogeneic, xenogeneic, or alloplastic substitutes, which exhibit different osteogenesis potentials. Biomaterials are available in the form of blocks or crushed chips and granules [6,7]. Autogenous grafts provide optimal osteeconductive, osteoinductive, and osteogenic properties. However, donor site morbidity, costs, and the fact that stable blocks of autogenous bone are not always sufficient for regenerating an existing defect prompt one to look for alternative biomaterials. Alloplastic and xenogenous materials are mainly osteoconductive without the intrinsic potential for osteogenesis or osteoinduction. Although they are readily available commercially, they do not constitute a \"gold standard\". Therefore, an ideal compromise seems to be an allosteric material that exerts both an osteoconductive and osteoinductive potential [7, 8, 9].  
Therefore, in regenerative surgery, the use of techniques aimed to obtain three-dimensional structures of porous scaffolds is gaining increased popularity [10,11]. The use of volumetric tomography scans of bone defects allows for designing and creating a bone block that fits the recipient's site and adheres to it with its entire surface. It is desirable that not only the macrostructure of the scaffold fits the defect, but also that this microstructure should favor angiogenesis, cell migration, and the diffusion of nutrients. This so-called biomimetism then leads to regeneration within the graft through the adhesion, proliferation, and differentiation of cells [11].  
Modern methods enable to obtain an adequate volume of bone tissue within the edentulous alveolar bone. They are described in the literature with reports of their high effectiveness during long-term observation [12, 13, 14].  
However, no effective procedure for three-dimensional bone reconstruction for cases in which there is dentition with recessions has been described to date, especially with regards to the bone's vertical dimension.  
The aim of this case report was to describe the patented method of the three-dimensional bone reconstruction of the anterior mandible with preserved dentition when using an allogeneic bone block. The additional goal was to report the results of the method presenting a case with a 2-year follow-up as an example.
## 4 Wireless Body Area Networks (WBANs)
### Requirements for Wireless Medical Sensors in WBANs
#### 4.1.1 Wearability  
To achieve non-invasive and unobtrusive continuous monitoring of health, wireless medical sensors must be lightweight and small. Size and weight of sensors are mainly determined by the size and weight of batteries [47,48]. But, a battery's capacity is directly proportional to its size. We can expect that further development of technology and advances in miniaturization of integrated circuits and batteries will help developers to improve medical sensor wearability and the user's level of comfort.
# A Semantic Framework for Decision Making in Forest Fire Emergencies
## 2 Background and Related Work
### Ontologies and Ontology-Based Systems for Emergencies
Several classes, includingincident, impact, location, dataset, mission, etc., are efficiently reused in the ONTO-SAFE ontology. The evaluation of this ontology was conducted via OOPS! (Ontology Pitfall Scanner), an online tool for identifying the most common pitfalls in ontologies [35], in order to assess the consistency and overall quality of the ontology. Furthermore, competency questions (CQs) were translated into SPARQL queries to evaluate whether the retrieved results satisfy the requirements of the end users.
# Literature Review on Hybrid Evolutionary Approaches for Feature Selection
## 3 A Brief Survey

Table 2: Introduction to the collected papers. Figure 3: Number of papers collected per year. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper** & **Year** & **Aim** & **Experimental Evaluation** & **Assessment Metrics** \\ \hline P2 [65] & 2020 & This study attempts to combine DE and Chaotic Dynamic Weight Particle Swarm Optimization (CHPSO) in an effort to enhance CHPSO. & According to the simulation outputs, CHPSO-DE performs better than other solutions at solving practically the FS challenge. & Average NFE. \\ \hline P3 [66] & 2020 & This study presented a novel hybrid FS model called RTHS, which is based on the HS metaheuristic and RTEA. & The RTHS approach was applied on 18 standard datasets from UCI Machine Learning Repository and it was contrasted to 10 popular evolutionary FS methods. The findings indicated that the RTHS approach appears to be more effective than the considered approaches. & CA, PR, RE, FSc, and AUROC. \\ \hline P4 [67] & 2022 & The aim of this research is to use a unique hybridised wrapper-based brain storm optimization-firefly algorithm (BSO-FA) strategy in order to enhance the FS technique and produce improved classification outcomes on standard UCI datasets, including a publicly available dataset with COVID-19 patient health data. & On 21 UCI datasets, the suggested approach is assessed and contrasted with 11 optimization techniques. The proposed approach is also used for a dataset related to coronavirus disease. The observed experimental findings support the robustness of the suggested hybrid model. In comparison to other methods in the literature, it effectively decreases and chooses the features and also produces better CA. \\ \hline P5 [68] & 2022 & The objective of this study is to apply a hybridised grey wolf optimization-heap-based optimizer (GWO-HBO) methodology as a wrapper for the FS phase of a fault-diagnosis system. & The suggested approach is validated on four separate datasets to ensure its efficiency. The proposed approach is compared to three methods, namely BGWO, BPSO, and GA, and the test results may attest to its predictability. \\ \hline P6 [69] & 2022 & This work aims at designing a hybrid optimization technique based on the CSA and the HHO for selecting features and mass categorization in digital mammograms. & This strategy was tested by using 651 mammograms. When compared with respect to conventional CSA and HHO methods employing experimental data, the new CSAHHO method was found to perform better. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
## 4 Implementation and Discussion
### The Security Analysis
Due to the integration of blockchain with VANETs within the suggested framework, both the sender and the recipient must follow several procedures to complete the transaction successful. It does not need the assistance of man-in-the-middle to carry out the steps, even though passing them is necessary. A smart contract contains the predetermined terms of the transaction. These terms are automatically evaluated and validated throughout the transaction. This mitigates the man-in-the-middle attack. The integration of Kerberos authentication in VANETs enables mutual authentication between vehicles and RSUs, meaning both parties authenticate each other through the Key Distribution Center (KDC). By ensuring that both sender and receiver are legitimate, Kerberos makes it more challenging for an attacker to impersonate either party. Moreover, we use AES-128 symmetric key encryption to generate a session key for each communication session. This session key is known only to the authenticated parties, reducing the risk of an attacker intercepting and decrypting messages. A consensus mechanism in a blockchain technology is a fault-tolerant mechanism that is advantageous to a single state of the network among the distributed multi-node systems in achieving the required agreement. The agreement is a list of rules and regulations for all the different participating nodes, which will eventually be helpful in deciding their contributions. Moreover, any transactions or events that take place in the system will be updated from time to time in the blockchain, and all the nodes will notify it. So, it is hardly possible to doubt the transparency of the transactions in a network that ultimately creates the trust among the nodes [39]. The Denial of Service (DoS) attack is an attack on the availability of the network. The main purpose of this attack is making the network unavailable to legitimate nodes. The attacker node generates a high volume of network traffic and consumes all the bandwidth of the network and makes it impossible for the RSU to manage such high-volume traffic, due to which the network become unavailable to the nodes [49]. Due to the characteristics of blockchain, Denial of Service (DoS) attacks do not seem to affect the system, as the consensus mechanism only allows legitimate nodes to participate in the network [39]. Kerberos relies on a Ticket Granting Ticket (TGT) to streamline session authentication. Once the initial authentication is complete, the TGT allows vehicles to access network services without contacting the AS each time. This minimizes bandwidth usage and decreases the number of authentication requests, therefore reducing the load on the network and limiting opportunities for DoS attacks.