esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Results
### Validating Tests
#### 5.5.3 Validating Tests in the RPE Environment  
In Figure 15, G-QMIX outperforms MASA-QMIX and acquires the optimal _evaluating average makespan_. In both Figure 15a,b, G-QMIX converges faster than MASA-QMIX and the variance of G-QMIX is also smaller than that of MASA-QMIX. Especially in the more complex eight-agent scenario in Figure 15b, the learning curve of MASA-QMIX is very tortuous and eventually manages to converge to the optimal processing procedure at the end of the training. The effectiveness of the GAPO method in the RPE environment proves its application ability in real-world scenarios.  
Figure 14: Results for MAMuJoCo validating tests using policy-based algorithms.  
Figure 12: Results of reducing \(\lambda_{I}\) in MPE\(-\)LS\(-\)V2 with value-based algorithms.  
Figure 13: Results of reducing \(\lambda_{I}\) in MPE\(-\)LS\(-\)V2 with policy-based algorithms.
# Image Splicing Location Based on Illumination Maps and Cluster Region Proposal Network
## 2 Related Works  
Illumination maps can establish the illumination source, which are generally categorized into two classes: statistics-based and physics-based methods. Since the pristine and tampered regions are from different images, the inconsistency in illumination maps is a critical clue for splicing detection. The illumination color [24] and transformed spaces [25] are applied to detect image forgery, which can distinguish original and manipulated images in image level. Instead of traditional methods, the illumination maps with CNN can detect the splicing forgery at the pixel level [26; 27], where the first step is to classify the pristine or fake image, and the second step is to locate the tampered region. However, the precision in locating the region is low. In order to make full use of illumination maps, we consider GreyEdge (GE) [28] and Inverse-Intensity Chromaticity (IIC) [29] as the illumination stream, which can extract rich features with the RGB image stream.  
Multiple scale feature fusion always consists of merging a low-level feature with a high-level feature map, which can capture global information. U-Net exploited lateral/skip connections that associate low-level feature maps across resolutions and semantic levels [30]. HyperNet concatenated features of multiple layers before predictions, but it brings additional calculations [31]. The Feature Pyramid Networks (FPN) designed horizontal connections to fuse the feature maps in multiple scale in bottom-up and top-down stages, which can obtain more robust semantic information [32]. Since the above multiple scale feature fusion methods are applied in single-stream framework, we propose a Multiple Feature Pyramid Network (MFPN) for the dual-stream feature fusion.
# Cyclic Consistent Image Style Transformation: From Model to System
## 2 Related Work
### Generation of Sample Diversity  
The issue of insufficient sample diversity is another significant challenge faced by GANs. Mode collapse results in the generator producing samples with limited variability, lacking sufficient diversity [7]. This problem arises primarily because traditional GAN objective functions focus too much on generating realistic samples without encouraging diversity [8; 9]. Additionally, the adversarial nature of the game between the generator and discriminator can lead to mode collapse, particularly when the discriminator is excessively powerful. To improve sample diversity, researchers have proposed several effective strategies. One approach is to introduce diversity rewards, which maximize the mutual information between samples to encourage the generator to produce more diverse outputs. Conditional GANs [17] guide the generator by incorporating conditional information during training, resulting in samples with higher diversity and relevance. Models combining Variational Autoencoders (VAEs) with GANs (e.g., VAE-GANs) [27] utilize the latent space modeling capabilities of VAEs along with the generative capabilities of GANs to enhance both sample diversity and quality. Furthermore, gradient penalty and other regularization techniques are used during training to constrain the generator's outputs, reducing mode collapse [28; 29]. These improvements offer effective solutions for enhancing GAN performance in generating diverse samples.
## 4 Optimization of Microseismic Monitoring Network Design Based on Particle Swarm Optimization
### Design Process of Microseismic Monitoring Network Based on IPSO  
The microseismic network optimization layout system consists of two modules, namely, the station optimal scheme selection of Module I and the station scheme evaluation of Module II. According to the algorithm flow in Figure 1, in Module I, the improved particle swarm algorithm proposed (IPSO) in this paper is used for microseismic network layout optimization following the principle of not only taking care of the current mining area but also considering the mining scheme in a certain period in the future, according to the delineation of the dangerous area of the mine and geological factors, etc. In Module II, the positioning error and sensitivity of the current network are evaluated. The specific process is shown in Figure 1.  
1. Module I: Station optimal scheme selection: 1 According to the mining technical factors and geological factors related to the risk of rock burst on site, select the microseismic monitoring area, divide the structural area into a geographic grid, and determine the range of candidate points for the station. 2 According to the on-site investigation or numerical analysis method, determine the microseismic probability factor \(F_{e}\), the microseismic importance factor \(F_{Q}\), and the effective range factor \(F_{V}\) to form a set of station candidate points. 3 According to the \(D\)-value optimization theory, establish the station optimization objective function of Equation (9). 4 Use the improved particle swarm algorithm (IPSO) proposed in this paper to select the optimal station scheme. 2. Module II: Station Scheme Evaluation: 1 According to Equation (10), the sensitivity contour map and the positioning error map of the current network are calculated to preliminarily evaluate the feasibility of the network layout. 2 Carry out the field test of percussion or blasting in the mine to obtain the positioning accuracy and stability of the current network layout scheme and further evaluate the pros and cons of the network layout. 3 According to the above results, judge whether the performance of the station network can meet the needs of on-site microseismic monitoring.  
Figure 1: Flow chart of the microseismic network layout optimization.
# Estimation of the Achievable Performance of Mobile Ad Hoc Networks with Optimal Link State Routing
## 3 Results
### Network Parameters Determined before Starting Work
#### 3.1.1 Physical Layer Parameters  
The physical layer parameters were chosen based on the prevalence of devices with such characteristics and their relatively low cost:  
Transmitter power: 14 dBm; receiver sensitivity: \(-80\) dBm  
Data transmission rates for modulation and coding scheme (MCS index) number 7, the number of spatial streams (SS = 1), and the Guard Interval between symbols (GI) equal to 400 ns in accordance with the 802.11n and 802.11 ac standards are shown in Table 2[12].  
The main difference when using the 802.11 ac standard with the same modulation and coding scheme is that it provides the capability to expand the channel by four and eight times (up to 80 and 160 MHz, respectively).
# Recurrent Deep Learning for Beam Pattern Synthesis in Optimized Antenna Arrays
## 3 Numerical Experiments
### Data Arrangement and Pre-Processing for DNN  
A total of 7623 radiation patterns were generated for training and arranged into a matrix \(X\in\mathbb{R}^{7623\times 180}\), 32 patterns for validation \(X\in\mathbb{R}^{32\times 180}\), and 92 patterns for testing \(X\in\mathbb{R}^{92\times 180}\), where each radiation pattern was composed of 180 features and each feature represents a point of the normalized power ranging from 1 to 180 degrees; see Figure 3. Conversely, labels for training were organized in a matrix \(Y\in\mathbb{R}^{7623\times 16}\), for validation \(Y\in\mathbb{R}^{32\times 16}\), and for testing \(Y\in\mathbb{R}^{92\times 16}\), where the first eight features belonged to the amplitude of the radiation patterns and the other eight to the phase features. Note that the amplitude and phase information is embedded in the array factor that generates the radiation pattern.  
Figure 3: Example of normalized power pattern ranging from 1 to 180 degrees.  
Figure 2: Uniform linear array of 8 antenna elements used as a case study.
## 3 Controller Design  
### Barrier Function for Sliding Mode Controller  
Let us suppose that first order system dynamics are given as:  
\[x(t)=u(t)+d(t) \tag{42}\]  
where system's disturbance is \(d(t)\) with known value, i.e., \(|d(t)|\leq d_{m}\), where \(d_{m}\) is a finite positive integer. Control input of the system's state can be denoted as \(u(t)\in\mathbb{R}\). By assuming that the bounds of disturbances acting on the system are unknown. A first-order sliding mode controller is required to stabilize the system, and is given as:  
\[u(t)=\ -K(t)sign\ (x) \tag{43}\]  
However, there are two major concerns regarding the first-order sliding mode controller, firstly chattering and secondly gain selection of optimum control. Thus, higher-order SMC is considered an efficient approach to resolve the chattering, and adaptive SMC to mitigate the gain section of optimum control. Additionally, barrier function-based adaptive SMC can be used to tackle both the issues mentioned above. It is evident from Equation (43) that control law is a function of the system's state, and it varies with time. There are two different approaches assumed to define the barrier functions in this paper [46].  
1. **Positive definite barrier functions (PBFs)**: Let assume some fixed \(\varepsilon>0\), which leads to a continuous even function \(K_{b}:x\in\big{]}-\varepsilon\), \(\varepsilon[\to K_{b}(x)\in[b,\infty[\), firmly cumulative on \([0,\varepsilon[\). So PBF will be: \[K_{PB}(x)=\frac{\varepsilon\overline{F}}{\varepsilon-|x|}\qquad\text{i.e., }K_{PB}(0)=\overline{F}>0\] (44)
2. **Positive semi-definite barrier functions (PSBFs)**: Following equation defines the PSBF as: \[K_{PSB}(x)=\frac{|x|}{\varepsilon-|x|}\qquad\text{i.e., }K_{PSB}(0)=0\] (45)  
The defined function in Equations (44) and (45) provides adaptive gains based on PBF and PSBF. Therefore, when \(\varepsilon\to 0\) then \(K\to 0\). If state lies in the vicinity of origin, i.e., \(\frac{|x|}{\varepsilon}<1\), then \(K\approx\frac{|x|}{\varepsilon}\), that certifies the convergence of state \(x\) to zero. Moreover, proof refers to the claim can be identified in [46].
# The Role of Renewable Energy Sources and Industry 4.0 Focus for Africa: A Review
## 3 Renewable Energy
### Biomass Generation  
Biomass generation or energy is energy produced using organic wastes produced by other operations. An example of this would be trash generated in wheat harvesting, offcuts, treetops, and branches left over from logging operations or offshoots of deforestation. It is seen as a renewable energy source due to organic biomass, cultivated almost indefinitely. The energy is produced using the same plants as traditional coal-fired power stations, except that the feedstock is organic biomass instead of coal. The plant combusts the biomass to produce heat, which is transferred to steam to spin a steam turbine and a generator. One of the significant drawbacks of this energy is the process. Carbon from biomass is still oxidized, forming CO\({}_{2}\) emissions. The biomass may also be host to other organic oils or contaminants, producing similar, if not more, harmful gasses.  
Secondly, organic biomass often has relatively high moisture contents, meaning the calorific value (LHV, or Lower Heating Value basis). Therefore, the energy density is comparatively low compared to other combustion-based technologies. Finally, the low energy density, plus the fact that fuel restocking is a long lead time process, significantly limits the size of a given plant. In South Africa, through the Department of Energy through the Renewable Energy Independent Power Producer Procurement Programme, the largest biomass power plant approved for construction has only 25 MW, which is a minimal addition to the 41 GW generation capacity existing in-country.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 8 Conclusions  
In this paper, we proposed routing protocols that are energy efficient, reliable and show improved network lifetime. The first scheme A-DBR reduced the void nodes' occurrence by adaptively adjusting the transmission range. Additionally, it achieved minimum energy consumption by avoiding data loss in the network. In the second scheme C-DBR, end-to-end delay is reduced at the cost of high energy consumption. The third scheme B-DBR minimized the void node occurrence probability along with a high packet delivery ratio. However, the cost of high accumulative propagation distance is paid. The fourth scheme CA-DBR experienced less energy consumption along with low end-to-end delay. Simulation results show the effectiveness of the proposed schemes in terms of average packet delivery ratio, average energy tax and average end-to-end delay.  
[PERSON], [PERSON] and [PERSON] proposed and implemented the novel schemes; [PERSON], [PERSON] and [PERSON] completed the mathematical modelling: All authors together refined the manuscript; Finally, [PERSON] and [PERSON] responded to the queries of the reviewers.  
The authors declare no conflict of interest.
# A Study on the Applicability of Waste Glass Wool and Waste Mineral Wool as Fiber Reinforcement
## 3 Methodology
### Chloride Ion Penetration Resistance  
The penetration of foreign substances inside concrete is mainly affected by the fine pore structure of concrete, construction site, curing time, etc. [38]. Materials such as water, chloride, and acid can reduce durability and strength when penetrating into concrete [39]. Among the above materials, chlorine ions not only adversely affect the internal matrix of concrete, but also induce corrosion rebar when contacting reinforced concrete, and when combined with freezing and thawing, the problem of durability accelerates. According to a related research case, fibers added to concrete can alter several physical properties of concrete, including voids and permeability [40]. The diameter of harmful pores defined by researchers varies, but in general, the pore structure inside concrete is known to directly affect the penetration of ions or moisture. The chloride ion penetration test was conducted according to ASTM C 1202 \"Electrical Indication of Concrete's Ability to Resistance Chloride Ion Penetration\" [41] to evaluate the resistance to chloride ion penetration of concrete reinforced with waste fibers. A side surface of specimens at the age of 28 days was coated with epoxy, cut to a thickness of 50 mm, and ion diffusion cells were attached to both ends thereof, and each cell was filled with calcium chloride solution and calcium hydroxide solution, and then a voltage of 30 V was energized for 8 h. Figure 8 shows the experiment foreground and schematic diagrams of the experiment, respectively. The current flowing through the specimens were collected through a data logger at 30 min intervals, and  
Figure 6: Freezing–thawing cycle.  
Figure 7: Experimental foreground for dynamic elastic modulus.  
the passing charge amount for each specimen was calculated, and the evaluation was performed according to the criteria specified in ASTM C 1202 as shown in Table 4.
## 6 Methodology
### Experimental Setup
#### 6.1.2 Scenario 2  
This scenario considers the same output powers and modems as Scenario 1, and was specifically designed to carry out experiments involving different PIR. The topology comprises 10 nodes placed along a straight line, equally spaced within a range of 15 m. As a consequence, each node has only communication with its nearest neighbors as depicted in Figure 8. Node 0 acts as sink, and all the generated traffic has this node as final destination. All the other nodes (from 1 to 9) behave as periodic message generators (with a configurable PIR) and also will route messages from further nodes in order to reach Node 0.  
A summary of the simulation parameters, describing physical and STANAG 5066 parameters is shown in Table 4.
## 3 Results and Discussion
### Bifacial Study on Two Different Electrolytes and Dyes
#### 3.2.3 The Incident Photon to Current Efficiency  
In Figure 4, the IPCE graph of C106 (Figure 4a) and of Y123 (Figure 4b) dyes with J8* and HSE electrolytes, rear and front side, respectively, are shown. As previously mentioned, the platinum/electrolyte influence in rear-side measurement is noticeable with both dyes and both electrolytes (Figure 4a,b). Indeed, rear-side measurements show a less photons absorbance from 450 to 550 nm in comparison to the front side (for C106, Y123, HSE, and J8* electrolytes) where photons directly reach the sensitized TiO\({}_{2}\) without crossing the electrolyte solution first. The C106-based device obtained higher PCE than Y123-based ones, consistently with absorption spectra of Figure 2b, where C106 absorbance is greater than Y123, although the latter has an higher molar extinction coefficient. C106 absorption spectra show a superior photon capture, as confirmed from electrical parameters that demonstrate C106's better performances, in terms of PCE, compared to Y123 for all measurement situations (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Electrolyte** & **Sample * & **PCE (\%)** &
\begin{tabular}{c} **Bifaciality** \\ **Factor (\%)** \\ \end{tabular} & **Jsc (mA/cm\({}^{2}\))** & **Voc (Volt)** & **FF (\%)** & **IPCE (\%)** \\ \hline \multirow{8}{*}{J8*} & B\_C106\_Front & 5.61 & 75 & 13.94 & 0.66 & 61 & 100 \\  & B\_C106\_Rear & 4.19 & 9.62 & 0.66 & 66 & 75 \\  & W\_C106\_Front & 8.11 & 93 & 21.07 & 0.7 & 55 & / \\  & W\_C106\_Rear & 7.58 & & 19.34 & 0.7 & 56 & / \\  & B\_Y123\_Front & 4.92 & & 9.78 & 0.7 & 72 & 85 \\  & B\_Y123\_Rear & 2.07 & & 3.93 & 0.7 & 75 & 40 \\  & W\_Y123\_Front & 6.48 & & 13.24 & 0.71 & 69 & / \\  & W\_Y123\_Rear & 4.67 & 72 & 9.15 & 0.71 & 72 & / \\  & B\_C106\_Front & 4.83 & & 11.99 & 0.66 & 61 & 84 \\  & B\_C106\_Rear & 2.95 & & 6.42 & 0.66 & 69 & 53 \\  & W\_C106\_Front & 6.74 & & 16.8 & 0.68 & 59 & / \\  & W\_C106\_Rear & 5.63 & & 13.76 & 0.66 & 62 & / \\  & B\_Y123\_Front & 3.77 & & 8.52 & 0.66 & 67 & 75 \\  & B\_Y123\_Rear & 2.17 & & 4.63 & 0.67 & 70 & 34 \\  & W\_Y123\_Front & 5.26 & & 12.28 & 0.68 & 63 & / \\  & W\_Y123\_Rear & 4.34 & & 9.64 & 0.68 & 66 & / \\ \hline \hline \end{tabular}
* B: black background; W: white background. A mask was applied on the device as a frame for the active area. TiO\({}_{2}\) film thickness is 7 \(\mu\)m.  
\end{table}
Table 1: PV parameters.
# CONCERTS: Coverage Competency-Based Target Search for Heterogeneous Robot Teams
## 2 Related Works
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Completion Time (s)** \\ \hline Guastella’s & 3591.8 \(\pm\) 0.00 \\ ScoPP & 196.53 \(\pm\) 14.53 \\ CONCERTS (ours) & 180.22 \(\pm\) 21.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative Test. CONCERTS provides a significant boost in efficiency when compared with the authors' previous work [20], which used a greedy sampling-based planner that resulted in overlapping and inefficient paths. The purpose of this manuscript is to improve completion time in multi-robot coverage and search tasks while maintaining online performance and achieving robustness to agent failure--a common scenario during real robot deployment. Reducing search time is critical for SAR or similar applications wherein small improvements in response time may make significant differences for the search target. The proposed method achieves this in three ways. First, by leveraging H-Clustering, each agent is assigned an appropriately sized search region that reflects the team heterogeneity. Second, the proposed method delivers high-quality candidate paths to the information-theoretic utility function using the solution to a TSP in two directions along the assigned waypoints as sample paths, as discussed further in Section 4.4. Finally, by incorporating each agent's TSP-based sample paths into a single information-theoretic utility function, final paths are selected as to minimize FoV overlap of agents to search areas faster. Furthermore, the proposed method manages this process while also maintaining robustness to agent failure.
## 2 Related Work  
Traffic modeling has been an important research topic for many years due to the changing nature of traffic (e.g., driven by newer and newer applications and various Internet access methods). In general, traffic is an important input in different research areas and in different types of networks. While temporal and spatial traffic distributions are widely covered for wireless networks (e.g., [7; 8]), this issue has been relatively unexplored for wired networks (particularly in the context of green core networking). This is first due to the fact that node mobility does not affect the amount and distribution of traffic exchanged among the nodes in core networks. In addition, while mobile networks often take into account variation of traffic in terms of packets, such analysis is challenging in the context of core networks, due to the extremely high amount of installed capacities in the devices.  
Focusing on traffic modeling, an analytical model covering spatio-temporal bursty traffic is proposed in [9] for communications networks in multicluster systems. Differently from [9], we focus on traffic demands between all node pairs in the network described by a set of traffic matrices covering a certain period of time.  
Focusing on variations of traffic over time, sinusoidal functions are usually used as a parametrized model [4; 5; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. Traffic variation in the context of saving energy is the focus only of [4; 5]. The authors propose a model of aggregated traffic taking the set of considered time periods (by default spanning 24 h) as well as maximum and minimum traffic volumes as parameters. Our model extends them with (sub)sets of time periods when traffic is high, low, increasing, and decreasing. To the best of our knowledge, no related work exists that provides a traffic model including parameters to change spatial traffic distribution over time in a controlled way. Some works consider a shift of traffic according to the time zone in which nodes generating traffic are located (see [23; 24; 25; 26; 27; 28; 29]). No variation of spatial traffic distribution is studied in these articles, though. We refer to Table VIII of [2] for a detailed overview of traffic data used in the works on green core networking.  
Summarizing, the contribution of this work w.r.t. previous works is the following: (i) a traffic model covering both temporal and spatial traffic variations; (ii) a sophisticated MILP formulation to design a realistic reference network (with single-shortest-path IP routing) used as input for the dynamic network operation; (iii) evaluation of the impact of controlled temporal and spatial traffic variations on both the energy consumed by LCs and their lifetime; (iv) assessment of the profitability as a difference between total electricity savings and total reparation costs in the network.