esa-sceva/satcom-chunk-collection huggingface dataset:
# Texture and Materials Image Classification Based on Wavelet Pooling Layer in CNN
## 4 Proposed Method  
The design of an effective model for texture and material classification considers several issues: CNN architecture, dataset, regularization methods, model accuracy, and information pooling. The proposed wavelet pooling method mainly focuses on improving the model's classification performance. Moreover, the wavelet pooling method reduces the artifacts that result during a dimension reduction in feature maps. Our approach preserves the significant features that traditional methods cannot retain. To evaluate our approach (DWTP) and to have the effect of each pooling method concerning the dataset, we outline the main steps below:  
1. We decided to involve digital images containing mainly textures and materials for the CNN training. Textures and materials are key features for evaluating the pooling method against a loss of information with repetitive patterns.
2. Each dataset being evaluated is divided into three parts: training, validation, and test. A higher distribution percentage for the training set and the remaining percentages for the validation and test sets are similar. This is a good practice in state-of-the-art CNNs [35].
3. An approximate version of the VGG16 architecture is used in the CNN design but with only three convolutional blocks. In addition, a classification block is proposed for our research case. The training hyperparameters are described in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Classes** & **Images per Class** & **Training** & **Validation** & **Test** \\ \hline CIFAR-10 & 10 & 10,000 & 40,000 & 10,000 & 10,000 \\ DTD & 47 & 120 & 3931 & 863 & 846 \\ FMD & 10 & 100 & 700 & 150 & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of images per class.  
4. The configuration for pooling inside each convolutional block of the CNN (Block.CX) permits a reduction in the feature map. This initial configuration depends on the selection of the pooling method. Therefore, we have at our disposal Ave and Max Pooling, the proposed DWTP method, and the complementary versions DWTaP and DWTdP.
5. The evaluation stage includes the analysis of the classifier with the accuracy metric because it allows us to evaluate the performance of the model and its learning behavior.
6. Finally, we use regularization methods to improve the performance of the model.  
The main contribution is to perform pooling (as a layer) inside the CNN using a level-based decomposition approach. Hence, the proposed approach (DWTP) concatenates the sub-images \(x_{LL}\), \(x_{LH}\), \(x_{HL}\) and \(x_{HH}\), given Equation (3). From this approach, we obtain two configurations. The first configuration (DWTaP) uses only the first level approximation sub-band \(x_{LL}\), and the second approach (DWTdP) uses all the first level detail sub-bands. The traditional methods (AveP-MaxP) are implemented with the Keras and TensorFlow methods. The diagram of the proposed methodology is presented in Figure 2.
## 2 Results and Discussion
### AMP Resistance  
Between January 2020 and September 2023, five publications relating to AMP resistance were published, relating to four different biofilm-forming bacteria. Specifically, a mutation of the 'biofilm-regulating _Francisella_ protein Regulator' (_bftrp_) gene resulted in phenotypic changes indicating that bftrp is a positive regulator of AMP resistance (AMP sheep AMP SMAP-29 and human cathelicidin peptide LL-37) and a negative regulator of biofilm formation in _Francisella novocida_[45]. [PERSON] et al. investigated the induction of the two-component system pcfreg-pcfRK, the primary mechanism for nisin resistance in _Streptococcus_ sp. A12. The results indicated that a novel set of genes (e.g., dipeptidase-PrsW-like protease, Ccma ABC transporter, and Man-PTS) was responsible for nisin resistance [46]. With respect to _Listeria monocytogenes_, a transposon library was used to identify transposants with significantly altered biofilm production levels when compared to a wild-type strain. Inactivation of the phosphatidylinositol lysyltransferase gene _mprF_ led to enhanced biofilm production but increased sensitivity to gallidermin (a type-A-I Antibiotic from _Staphylococcus galliumarum_ with some structural similarity to nisin) [47]. Interestingly, [PERSON] et al. showed that polyunsaturated fatty acids (PUFAs) could generate modest protection or vulnerability to polymyxin B and colistin in _Aeronomas salmonicida_. They concluded that diverse, strain-specific responses to exogenous PUFAs may be an adaptive survival strategy in bacteria that fluctuate between fish and environmental niches [48]. Finally, _Bordetella pertussis_ polysaccharide (Bps) was shown to resist AMP (LL-37, polymyxin B, HNP-1, and HNP-2) killing, helping'shield' non-pathogenic K12 _E. coli_ and increase in vivo bacterial survival in the respiratory tract [49].
# One-Photon-Interference Quantum Secure Direct Communication
## 4 Performance Analysis
### Effect of Light Intensity  
The effects of light intensity \(u\) on the OPI-QSDC protocol have been explored separately. The data in Table 1 are utilized to determine the parameters of the numerical simulations in this subsection.  
In terms of light intensity, there are two aspects to consider. Firstly, as the light intensity increases, the mean photon number in the channel also increases, leading to a corresponding increase in the gain \(Q_{u}\). Secondly, the even-photon-number component in the channel becomes more prominent, resulting in a higher phase-error rate \(E_{u}^{Z}\). As illustrated in Figure 3, the OPI-QSDC protocol demonstrates its optimal performance when the light intensity is set to \(u=0.046\). With these parameters, our OPI-QSDC protocol achieves a maximum transmission distance of 443.5 km. This finding underscores the significance of employing a relatively weaker coherent state light source to enhance the protocol's performance in practical applications. However, it is worth noting that an excessively weak light source may also lead to a low transmission distance.  
Figure 3: **The longest transmission distance versus the light intensity \(u\).** The red-solid line indicates the longest distance that can be transmitted by OPI-QSDC protocol under different light intensities. The blue-dashed line \(d=443.5\) km and the pink-dashed line \(u=0.046\) mark the maximum distance and corresponding light intensity that can be transmitted by our protocol.
# Collecting Sensed Data with Opportunistic Networks: The Case of Contact Information Overhead
## 1 Introduction
### Authors' Contribution  
To avoid confusing this overhead with others used in OppNet evaluation, we employ the term \"contact information overhead\" which we define as the overhead incurred in maintaining, exchanging and updating contact information. In this regard, the following are questions are yet to be answered,which will give insight on the feasibility of existing routing approaches and take them a step closer to real-world implementation:  
1. How can contact information overhead be modelled and what is its impact on data exchange success and the energy consumption on portable handheld devices?
2. How can contact information overhead be minimized without compromising throughput?  
We address these questions in the following sections and contribute by: (i) modelling contact information overhead and investigating its impact on data exchange success and energy consumption on portable handheld devices; and (ii) proposing a forwarding algorithm namely Point-of-interest Forwarding (PoiFord) that can be incorporated into existing encounter-based routing protocols to reduce contact information overhead without compromising throughput.
# Decellularized Scaffolds for Skin Repair and Regeneration
## 3 Decellularization Methods
### Complementary Agents
#### Organic Solvents  
Organic solvents are used to remove lipid content, an essential step for the preparation of acellular matrices using SIS and mesothelium, two fat-rich tissues.  
A protocol combining methanol and chloroform (1/1, v/v, 12-h immersion) has been reported with porcine SIS, following manual elimination of the tunica serosa and tunica muscularis and prior intensive washings and SDS decellularization [81; 82]. A degreasing step did not elicit notable changes in ECM architecture, and no remaining fat cells were detected in the final acellular matrix. The decrease in GFs reported in the final scaffold has been attributed to the manual removal of some compartments of the native tissue, not to organic solvent treatment.  
Isopropanol (IPL) exposure is another method to remove lipids [76; 93]. Two protocols have been compared with porcine skin: a single final exposure of the tissue to 100% IPL, and two 70%IPL exposures distributed during the whole process, followed by a final immersion in 100% IPL [93]. The single exposure was associated with the removal of half of the total lipid content, whereas a decrease of 90% was estimated for repeated treatments. Regarding nucleic acid removal, no significant variation in remaining DNA levels was detected between the two methods, confirming that IPL is not a main decellularizing agent. A combination of IPL and acetone is also mentioned in the patent of the commercial Oasis(r) matrix derived from porcine SIS [84].  
Use of acetone/hexane (40/60, v/v) and pure acetone with porcine mesothelium was reported [94]. Lipid amounts were not assessed, and only a visual evaluation suggested better elimination of fat using the acetone/hexane combination.
## 3 Multi-Source Data in the Construction of Smart City
### Characteristics and Challenges of the Data  
In the construction of smart cities, the spatiotemporal characteristics of multi-source data are its core attributes, which are of great significance to real estate management and urban optimization [16]. Spatiotemporal characteristics reflect the changing patterns of data in time and space dimensions. Through the analysis of time series data, the patterns of urban development can be mined and provide a decision-making basis for real estate management. At the same time, data in the time dimension need to fully consider real-time nature in order to grasp urban dynamics accurately [17]. In the spatial dimension, multi-source data show significant geographical distribution characteristics. Analysis of spatial data can reveal the laws of urban land use, traffic conditions, environmental pollution, etc., and provide a scientific basis for urban planning and optimization [18]. In addition, the coupling relationship between spatiotemporal characteristics is also an issue that cannot be ignored, requiring that the interconnection between time and space be fully considered when processing data.  
At the same time, the fusion and analysis of multi-source data are the keys to improving management efficiency and decision-making quality in smart city construction, but data quality and consistency issues are major challenges in achieving the goal [19]. Data quality involves accuracy, completeness, reliability, timeliness, and availability, while data consistency is related to the unified standards and compatibility of data from different sources, formats, and types during the fusion process.  
In addition, in the construction of smart cities, multi-source data fusion and analysis also face severe challenges in data privacy and security. With the increasing number of data sources, it is crucial to ensure that data security and personal privacy are not violated. In the data collection stage, unauthorized data collection may infringe on personal privacy. In the data storage and transmission stage, there are risks such as hacker attacks and data leakage. Sensitive information may also be accidentally leaked during the data analysis process [20].
## 3 Methods and Simulation
### Geometry of Measurement System and \(\alpha\)-Detector  
The measurement system consists of a silicon detector that is an active element and a metallic chamber, of with different shapes and dimensions, in which the detector is mounted. Since high voltage is applied to the chamber walls, the detector and the chamber are electrically isolated by means of a thin polyethylene layer wrapping the detector non active surfaces [20]. Silicon detectors are known to trap \(\alpha\)-radiation and to measure the effect of incident charged particles. They provide high-resolution; the electrostatic transport allows high-efficiency alpha spectroscopy and very low background.  
From the simulation point of view, to design the system geometry and define the materials in Geant4, the _Construct()_ method is used by the _G4 RunManager_ class. Materials, the 'world' environment and all components must be defined. Firstly, the creation of the 'world' environment is required as physical volume that represents the experimental area and contains all other components. Inside the 'world', the shape of the system with the detector can be defined, by _G4 VSolid_ class. This class has several preset shapes already implemented, such as parallelepiped, hemisphere and cylinder with _G4 Box_, _G4 Sphere_ and _G4 Tubs_ respectively. The volume inside the system, where the investigated radionuclides move, is filled by air. The _G4 Material Data-Base_ allows choosing the material to be assigned to the system components. The silicon detector uses _G4_Si_ material; the air volume uses _G4_AIR_, which is surrounded by a metal grid made of _G4_Fe_ material. The insulator, made up by _G4_POLYETHYLENE_, is placed between detector and metal grid. The 3D-coordinates of the system within the 'world' are defined by _G4 VPhysicalVolume_. The silicon detector is made sensitive to \(\alpha\)-radiation by _SensitiveDetector+HitsCollection_ tools.
# Mapping Subtidal Marine Forests in the Mediterranean Sea Using Copernicus Contributing Mission
## 2 Materials and Methods  
### Study Area  
The study area is located in the Central Aegean Sea, around Skyros Island (Figure 1). This site was selected for its extensive intact _Cystoseira_ forests, now rare in the eastern Mediterranean Sea [34]. A suitable location has been identified on the eastern side of the island [35], where an extensive reef system is present across 2667.2 hectares. The seascape is a complex mosaic of shallow rocky reefs, partly covered by _Cystoseira_ s.l. (see Figure 2), surrounded by sandy bottoms of varying depths and Posidonia oceanica _Seagrass_ beds. The study area reaches a maximum depth of 30 m.  
Figure 1: In the red polygon is Skyros Island (upper left) in the North Aegean Sea, and in the red lined polygon is the case study area.  
Figure 2: Elusive _Cystoseira_ forests as recorded during fieldwork in September 2024. Photo: [PERSON].
## Appendix A Analytical Symbol-Error-Rate Formulation
### Analytical Symbol-Error-Rate of NU in S\({}_{2}\)  
In a similar manner, we can also consider the estimation of any transmitted \(M\)-PSK/\(M\)-QAM symbol at NU in S\({}_{2}\) as a special case of \(1\times A_{\text{NU}}\) MRC detection [12] due to the exploitation of SM. Now the error estimation of any transmitted \(M\)-PSK data symbol at NU during \(P_{1}\) in S\({}_{2}\) can be obtained by using [57] (Equation (41)).  
\[P_{\text{SER,NU}}^{S_{2},P_{1}}=\frac{1}{\pi}\int_{0}^{\frac{(M_{1}-1)\pi}{M_{ 1}}}\prod_{i=1}^{A_{\text{NU}}}\biggl{(}1+\frac{\rho_{\text{SRU,1}}}{\sin^{2} \phi}\biggr{)}^{-1}d\phi \tag{46}\]  
where \(P_{\text{SER,NU}}^{S_{2},P_{1}}\) denotes SER at NU during \(P_{1}\) and \(g_{\text{NU,1}}=\sin^{2}(\frac{\phi}{M_{1}})\). The average SNR, \(\rho\) at the receiving side is denoted by \(\rho\triangleq\frac{P_{\text{BS}}}{\sigma^{2}}\), where \(\sigma^{2}\) is the noise variance.  
Again by using [57] (Equation (41)), the error estimation of any transmitted \(M\)-QAM data symbol at NU during \(P_{1}\) in \(S_{2}\) can be obtained as  
\[\begin{array}{ll}P_{\text{SER,NU}}^{S_{2},P_{1}}&=\frac{4}{\pi}\left(1-\frac{1} {\sqrt{M_{1}}}\right)\int_{0}^{\frac{\pi}{4}}\prod\limits_{i=1}^{A_{\text{NU}} }\left(1+\frac{\rho_{SQAM,1}}{\sin^{2}\phi}\right)^{-1}\!\!d\phi\\ &-\frac{4}{\pi}\left(1-\frac{1}{\sqrt{M_{1}}}\right)^{2}\int_{0}^{\frac{\pi}{4 }}\prod\limits_{i=1}^{A_{\text{NU}}}\left(1+\frac{\rho_{SQAM,1}}{\sin^{2}\phi }\right)^{-1}\!\!d\phi\end{array}\] (A6)  
where \(g_{QAM,1}=\frac{3}{2(M_{1}-1)}\) and \(P_{\text{SER,NU}}^{S_{2},P_{1}}\) denotes SER at NU during \(P_{1}\).
# A Modified Artificial Bee Colony Algorithm Based on the Self-Learning Mechanism
## 4 Experiments and Results  
### Experimental Setup  
To investigate the performance of the SLABC algorithm, 9 benchmark functions shown in Table 1 were used, including four unimodal functions (\(f_{1}-f_{4}\)) and five multimodal functions (\(f_{5}-f_{9}\)). In Table 1, \(D\) denotes the dimensions of the solution space and 30, 60, and 100 dimensions are used in the present paper. The unimodal functions can be used to analyse the convergence rate of the algorithms. The multimodal functions are commonly used to show whether the algorithms can escape from the local optimum effectively.  
The effectiveness of the proposed SLABC algorithm was evaluated by comparing its results with other related algorithms, such as ABC [2], GABC [20], MABC [21], and distABC [24]. To make a fair comparison among ABCs, all the algorithms were tested using the same parameters: the population size \(N=100\), \(limit=100\), the maximum number of iterations, \(Max.FE=2000\). Additionally, other specific parameters of each comparison algorithm are same as in ABC [2], GABC [20], MABC [21], and distABC [24]. In order to ensure the experiment results stability, we repeated each algorithm for 20 times and average the results.
# Body-to-Body Cooperation in Internet of Medical Things: Toward Energy Efficiency Improvement
## 6 Conclusions  
Recent advances in the design of Internet of Medical Things (IoMT) technologies are encouraging the development of smart systems to support and improve healthcare. In this paper, we have designed a new system of the IoMT. The proposed IoMT consists of four tiers, starting from human body sensors and ending with the health cloud. Along with the proposed design, we proposed a new protocol known as IWC-IoMT. The proposed protocol would allow two bodies to cooperate with each other to deliver data efficiently.  
In this work, two important metrics were considered: outage probability and energy efficiency. The results show that the proposed protocol achieved better performance when compared to non-inter-WBAN cooperation for a symmetric transmission rate. On the other hand, the performance of the proposed protocol degraded when the asymmetric transmission rates were utilised.  
In future work, we will analyse the proposed protocol with an energy harvesting technique that allows the coordinator nodes to first harvest the energy and then cooperate based on a time switch.  
**Author Contributions:** Formal analysis: H.A.R.; funding acquisition: A.A.; methodology: A.A.; resources: D.A.H.; supervision: R.B.A.; validation: D.A.H.; writing--original draft: A.A.; writing--review and editing: H.A.R.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 8: Energy efficiency versus power consumption, \(P_{o}\). For all cases and links, the transmission rate is 2 b/s/Hz.  
Figure 9: Energy efficiency versus transmission rate, \(\beta_{o}\). For all cases and links, power consumption, \(P_{o}\), is 10 dBm.
## 3 Results
### Determination of relPICS for 248 nm  
Results for 248 nm measurements are shown in Figure 7. Hereby, no laser intensities higher than 1.2 \(\times\) 10\({}^{7}\) W/cm\({}^{2}\) were investigated. However, as expected, \(\sigma_{\mathrm{rel,tol}}\) of 248 nm and 266 nm showed significant differences in quantity, whereas similar intensity-dependent behaviors of \(\sigma_{\mathrm{rel,tol}}\) were observed. It has to be noted that \(\sigma_{\mathrm{rel,tol}}\) are influenced by the wavelength-dependent ionization behavior of the analyte as well as the reference substance. The \(\sigma_{\mathrm{rel,tol}}\) of benzene at 248 nm was increased by a factor of 7 compared to that at 266 nm. Much higher \(\sigma_{\mathrm{rel,tol}}\) were achieved for PAHs, such as fluorene, phenanthrene, and pyrene. In contrast, the \(\sigma_{\mathrm{rel,tol}}\) for \(p\)-xylene, 1,2,4-trimethylbenzene, guaiacol, and phenol were lower compared to that at 266 nm.  
An estimation of the differences in ionization efficiency between 266 nm and 248 nm can be accomplished by comparing the response factors of a single compound for both wavelengths (e.g., toluene is ionized three times better at 266 nm than at 248 nm). Therefore, it is possible to recalculate the relPICS at 248 nm, neglecting the influence of the wavelength-dependent ionization behavior of toluene. By doing this, benzene is ionized about 2.5 times better at 248 nm compared to 266 nm, whereas acenaphthene is ionized with approximately the same efficiency.
# Optimization of Signal Detection Using Deep CNN in Ultra-Massive MIMO
## 2 Materials and Methods  
For the construction of a system model, we developed a comprehensive communication framework emphasizing ultra-massive MIMO technology, with the objective of facilitating the computation of a diverse array of results. This will be explained in Section 2.1, including massive MIMO and ultra-massive MIMO.
## 2 Recent Evolution in Actor-Critic Frameworks
### Hybrid Structure-Based Frameworks
#### 2.3.2 Multi-Head Attention-Based  
This type of actor-critic framework employed multi-head attention mechanisms in both actor-critic networks to provide both self and cross-attention in multi-agent environments. In [10; 12], a joint optimization on trajectory planning and communication design in air-ground communication systems is investigated by adopting an actor-critic-based heterogeneous collaborative decision-making algorithm, considering UAV base stations and GUs as intelligent agents. Thus, the action space for UAV base stations includes trajectory planning and bandwidth allocation for G2U links to maximize long-term fair throughput. Similarly, the action space for GUs includes binary UAV-GUs association to compete for limited UAV resources in each timeslot to maximize their own long-term throughput. To quickly identify the correlation with other agent's complex observation-action space, as well as enhance the model adaptability and the decision-making process, they introduced a learnable multi-head attention mechanism in both the actor and critic network of all agents.  
In [49], UAVSNs are employed for a cooperative target search mission, with a multi-objective reward function designed to include four components: area coverage, exploration of unexplored regions, target discovery reward, reward for target discovery time, and avoidance of collisions with unknown threats and neighboring UAVs. This method utilizes a multi-head attention mechanism within the actor networks to focus on the most important features in observations from neighboring UAVs. Furthermore, to bridge the gap between simulation and real-world implementation, this approach leverages a digital twin of the physical environment to effectively train the agent UAVSNs in a high-fidelity simulation environment.