esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### 3a/ Soft or Eco-Friendly Photopolymerization of Synthetic Monomers
#### 3.1.3 Development of PISs for Soft Irradiation Conditions  
Household devices, such as halogen lamps, fluorescent bulbs and LED bulbs, deliver low intensity visible light and are used in organic synthesis. They have been recently introduced for the photopolymerization of low viscosity monomers under air [51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76] (see, again, the polymerization profiles under a red LED bulb exposure in Figure 6). Today, many PIS allow FRP, CP and FRPCP in these irradiations conditions: e.g., Figure 7 shows an efficient polymerization of EPOX under halogen lamp exposure. It is obvious that all the work on the design of PISs carried out in this area should be very helpful for potential and promising applications with more energetic light sources in both laboratory scale devices and industrial lines.  
Figure 6: (**A**) Polymerization profiles of TMPTA upon Xe-Hg lamp irradiation (\(\lambda>390\) nm) in laminated conditions in the presence of (**1**) Napht (0.5% _w/w_); (**2**) Napht/Ethyl-dimethylaminobenzzoate (EDB) (0.5%/4.5% _w/w_); (**3**) Napht/EDB/phenoxyyl bromide (0.5%/4.5%/3% _w/w_). (**B**) Compared polymerization profiles of EPOX under air upon a red LED bulb irradiation in the presence of: (**1**) Pent/Ph\({}_{2}\)l\({}^{+}\) (0.5%/ 2% _w/w_) and (**2**) Pent/TTMSS/Ph\({}_{2}\)l\({}^{+}\) (0.5%/3%/ 2% _w/w_). Insert: emission spectra of the used LED bulbs (2–12 mW/cm\({}^{2}\)); different photoinitiators recently proposed [75; 76].  
Metal-based, as well as metal-free photoinitiator catalysts operating on the basis on a photoredox catalysis can efficiently initiate a radical or a cationic photopolymerization or a radical/cationic hybrid curing [75, 76, 105]. Figure 8 shows the achieved performance when using a Ru complex as a photocatalyst for the FRPCP of EPOX under a household fluorescent bulb exposure. Ir-, Pt- or Zn-based complexes also lead to interesting results under soft visible irradiation; interpenetrated radical/cationic networks can be formed (see, e.g., [51, 52, 53, 54, 55, 56, 105]).
## 3 Proposed Methods and Model Architecture
### Interval Sampling Weighted Module  
Pooling and convolution with a step size of two are usually used for downsampling in object detection, but these two traditional downsampling methods introduce information loss because they directly discard part of the information. In contrast, interval sampling avoids the risk of feature loss through selectively decreasing the sampling rate. More importantly, the downsampling mode of interval sampling is more in line with the characteristics of deep features, so can retain more comprehensive spatial information, alleviate the spatial mismatch problem, and help capture the overall structure and global context of the target. The neck region has more abundant contextual and semantic information and usually contains object information of distinct scales. The interval sampling downsampling method can not only be used to balance the training importance of the target dense region and the target sparse region but also can better adapt to the objects of these distinct scales to more precisely retain and transfer the relevant information in the feature fusion process.  
Figure 3: The SPPF network structure composition. \(1\times 1\) is the convolution layer, and \(5\times 5\) is the maximum pooling operation.  
According to the above viewpoint, this paper proposes an interval sampling weighting (ISW) module. Combined with the module framework chart illustrated in Figure 4 and the following equation, the input feature tensor is set as \(\mathbf{F}\in\mathbb{R}^{S\times S\times C_{1}}\). Firstly, the original tensor is split into four portions of the spatial dimension information \(\mathbf{F}_{\mathbf{x}}^{{}^{\prime}}\in\mathbb{R}^{\frac{S}{2}\times\frac{S}{2 }\times C_{1}}(x\in 1,2,3,4)\). employing interval sampling, and then the channel dimension information is merged in the direction of the channel to comprehensively obtain abstract semantic features. In addition, the use of multi-imensional collaborative attention before \(1\times 1\) convolution dimensionality reduction to dynamically adjust sample weights and suppress irrelevant or redundant features makes the model focus on the features that are more challenging for network training and generalization. At the same time, the noise in the \(1\times 1\) convolutional input is weakened, which is beneficial for the target detection task with large context dependence and elevates the precision of detecting targets. The final output feature layer is \(\mathbf{Y}\in\mathbb{R}^{\frac{S}{2}\times\frac{S}{2}\times C_{2}}\).  
\[\mathbf{F}_{\mathbf{x}}^{{}^{\prime}}=\mathit{IntervalSampling}(\mathbf{F}) \tag{13}\]  
\[\mathbf{Y}=\mathit{Conv}(\mathit{MCA}(\mathit{Concat}(\mathbf{F}_{\mathbf{1} }^{{}^{\prime}},\mathbf{F}_{\mathbf{2}}^{{}^{\prime}},\mathbf{F}_{\mathbf{3}}^ {{}^{\prime}},\mathbf{F}_{\mathbf{4}}^{{}^{\prime}}))) \tag{14}\]
## 4 Enabling Technologies for FSO Communication
### Hybrid FSO Systems
#### 4.11.1 Overview  
FSO, mmWave, and THz communications have proven to be effective solutions for high-data-rate wireless transmission over short distances. They offer a promising response to the increasing demand for higher data rates amid the growing scarcity of RF spectrum, positioning them as strong candidates for joint deployment to ensure reliable, high-data-rate wireless backhaul [428]. Nevertheless, these technologies fall short of achieving the desired carrier-grade availability of 99.999% due to their related transmission issues. A possible solution to enhance the FSO link's availability and reliability is to integrate it with the RF (or mmWave/THz) link (hereinafter referred to as an alternative link), which can reach data rates similar to those of an FSO link, to create a hybrid FSO/RF (or mmWave/THz) communication system (hereinafter referred to as a hybrid system) [2; 365; 429; 362]. As a result, a hybrid system is better equipped to maintain connectivity compared to a purely FSO system. By integrating the advantages of various technologies and addressing the limitations of each, the hybrid system greatly improves network performance. It enhances network resilience and offers a backup solution in difficult atmospheric conditions, thereby contributing to overall network reliability [2; 365].
# A Novel Base-Station Selection Strategy for Cellular Vehicle-to-Everything (C-V2X) Communications
## 6 Markov Decision Policy Topology
### Transition Probability  
Transition probability only depends on the current state rather than the former. Due to the small transmit power of the femtocell base station, the number of connected users is limited. To avoid the femtocell base station reaching its upper use limit, we used the redundancy probability used by the femtocell base station as the transition probability.  
The \(n\) vehicles are sorted, and the base stations are selected in order. To ensure that the usage rate of the femtocell base station is not equal to zero, the first vehicle selects the femtocell base station. The utilization rate of the femtocell base station in the \(i\)-th (\(i\in n\)) choice is \(P_{i}\), and the redundancy use probability is \(1-P_{i}\). Then, the selection probability of the \((i+1)\)-th choice of the macrocell base station is \(P_{i}\), and the selection probability of the femtocell base station is \(1-P_{i}\). Therefore, usage rate \(P_{i}\) of the cellular base station can be expressed as:  
\[P_{n}=\frac{f_{i}}{F} \tag{10}\]  
where \(f_{i}\) indicates the number of users connected to the femtocell base station in the \(i\)-th choice and \(F\) indicates the maximum number of vehicles connected to the femtocell base station.  
If the vehicle selects the macrocell base station in the \(i\)-th choice, then in the \((i+1)\)-th choice, the probability of selecting the macrocell base station is \(P_{i+1}=P_{i}\). Therefore, the probability of selecting the femtocell base station is still \(1-P_{i}\). If the vehicle selection is the femtocell base station in the \(i\)-th choice, the probability of selecting the macrocell base station is \(P_{i+1}=\frac{f_{i-1}+1}{F}\) in the \((i+1)\)-th choice, and the probability of selecting the femtocell base station is \(1-\frac{f_{i-1}+1}{F}\).
## 4 Performance Degradation of MICP-Treated Soil Under Wet-Dry Cycling
### Degradation of Mechanical Properties
Importantly, even after cyclic wet-dry exposure, the treated soil maintains mechanical properties substantially superior to those of untreated expansive soil. To further analyze the degradation patterns of the various mechanical parameters, the concept of a damage factor was introduced. The damage factor (\(D_{V}\)) was calculated using Equation (1), based on the initial and post-wet-dry cycling mechanical property  
Figure 11: Degradation of CBR results. Figure 10: Degradation of shear strength results. values. The total degradation (\(D_{\text{S}}\)) and degradation rate (\(V_{\text{S}}\)) were then determined using Equations (2) and (3), respectively, as suggested in the literature. The damage factor curves for the different mechanical properties are presented in Figure 12.  
\[D_{V}=1-\frac{S}{S_{0}}, \tag{1}\]  
\[D_{\text{s}}=\int\limits_{0}^{a}\sqrt{f(x)}dx \tag{2}\]  
\[V_{\text{s}}=\frac{\int\limits_{0}^{a}\sqrt{1+\left[f^{\prime}(x)\right]^{2}} dx}{a} \tag{3}\]  
where \(D_{V}\) represents the damage factor; \(S\) denotes the mechanical property value after wet-dry cycling; \(S_{0}\) indicates the initial mechanical property value; \(D_{\text{S}}\) signifies total degradation; \(V_{\text{S}}\) refers to the degradation rate; \(a\) is the number of wet-dry cycles; and \(f(x)\) represents the degradation curve for various parameters. The total degradation areas for the UCS, angle of internal friction, cohesion, and CBR were calculated to be 710.02, 413.95, 279.24, and 198.89, respectively. The corresponding degradation rates were 3.12, 2.84, 2.49, and 1.97, respectively. These results indicate that the wet-dry cycling had the most significant impact on the unconfined compressive strength of the MICP-treated soil, followed by the CBR, while the shear strength parameters were the least affected.
## 4 The Geometries of the Proposed Transmitters
### SNR Performances of Proposed Transmitters
With this change, our largest LED array, the honeycomb hexagonal array, covers an area of 5 mm \(\times\) 5 mm, which is even smaller than Samsung's PixCell LED module. As shown in Figure 24, these results are similar to those in Figure 19 for \(s=1\) cm. The communication ranges for each transmitter are provided in Table 9 for \(s=1\) mm. We observe that the honeycomb hexagonal array transmitters offer a 16% improvement in system performance, which remains robust even with such a compact light-emitting surface. Therefore, our design offers flexibility in the choice of spacing \(s\), as it mainly affects the orientation vector angles, while system performance remains robust even with very small values of \(s\). These results remain consistent as long as \(d_{0}\) is kept constant. This allows  
Figure 24: An SNR vs. distance plot for the proposed transmitters with the value of \(s\) decreased to 1 mm. \begin{table}
\begin{tabular}{l l} \hline \hline
**Transmitter Type** & **Range** \\ \hline Single-LED & 78.4 m \\ \(3\times 3\) square array & 87.4 m \\ Single hexagonal array & 87.3 m \\ Octagonal array & 80.7 m \\
5 \(\times\) 5 square array & 89.0 m \\ Honeycomb hexagonal array & 93.0 m \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The distances at which the bit error rate (BER) reaches \(10^{-6}\) for the proposed transmitter configurations. \begin{table}
\begin{tabular}{l l} \hline \hline
**Transmitter Type** & **Range** \\ \hline Single-LED & 70.5 m \\ \(3\times 3\) square array & 78.0 m \\ Single hexagonal array & 78.0 m \\ Octagonal array & 71.7 m \\
5 \(\times\) 5 square array & 78.7 m \\ Honeycomb hexagonal array & 81.9 m \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The communication ranges of the proposed transmitter configurations with \(s\) decreased to 1 mm, evaluated at a 15 dB SNR threshold. automotive manufacturers to adjust the design of lighting equipment to meet specific size or aesthetic requirements.
## 3 Datasets and Features  
There is a high variety of available datasets gathering network traffic. In our study, we have chosen four of them. The choice was motivated by two principal factors. First, they should represent various types of traffic, including classic traffic, typical for business environments, and industrial IoT traffic. Secondly, all sets should have the same data format. As we focus on network features (not the raw traffic), they all should contain the same, unified set of traffic features. Our investigation uses four public datasets designed to train IDS systems, fulfilling the above requirements. Two of them, NB15 and IDS18, contain traffic circulating in typical enterprise networks. The remaining two, BoT and ToN, consist of traffic registered in the Internet of Things networks and address the growing need for robust cybersecurity solutions in IoT and IIoT environments, which are more complex and varied than traditional IT networks [65].  
All four datasets have been captured in controlled network environments specially designed to generate various types of traffic. They contain traffic samples belonging to various classes: single benign and multiple harmful. The composition of the latter depends on the datasets. Sets are also imbalanced--the number of samples in each class differs. The list of available classes and their share in the total number of samples of particular datasets is shown in Table 1.
# An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms
## 3 Nature-Inspired and Metaheuritcs-Based Image Registration  
As stated, NI&M is a specific computational approach within SC which has been extensively applied in addressing many real-world optimization problems as those within CV. The reader can find an extensive overview on the topic in references [11; 12; 18].  
Briefly, it is necessary to describe some key concepts of NI&M to achieve a suitable understanding of the inherent foundations of such an optimization approach. Specifically, EC proposes models of computation inspired in biological processes to evolve populations of solutions, thus providing key elements in the development of computer-based problem solving systems. On the other hand, Metaheuristics [19; 20] is a more general family of enhanced search optimization algorithms and, as EC, they constitute a very compelling alternative due to their capability to obtain good quality solutions when global optima solutions cannot be found within a reasonable time of computation.  
In particular, it has been proposed a variety of EC models referred as evolutionary algorithms (EAs) [21]. EAs is a family of Nature-inspired algorithms widely used for solving complex optimization problems. Among them, GAs are probably the most used EAs in the literature to face real-world optimization problems. Some other EAs have been proposed in the last few years improving the SoTA of this field by adopting more suitable optimization strategies: Evolution strategies (ES) [22], scatter search (SS) [23], differential evolution (DE) [24; 25], memetic algorithms (MAs) [26], particle swarm optimization (PSO) [27; 28], estimation distribution algorithms (EDAs) [29], membrane computing (MC) [30], and cellular automata (CA) [31], among others. Moreover, other EC models inpired by similar principles have also been recently proposed, e.g., bacterial foraging optimization algorithm (BFOA) [32] and artificial bee colony (ABC) [33].  
Specifically, the application of NI&M to the IR optimization process has caused an outstanding interest in the last decade. Unlike traditional ICP-based IR methods, those supported by NI&M need neither rough nor near-optimal pre-alignment of the images to proceed. The first attempt to tackle IR using NI&M can be found in the eighties, in which a binary-coded canonical GA was proposed for addressing medical scenarios of 2D angiography images [34]. Figure 3 depicts the evolution of the interest of the scientific community in the application of NI&M to IR in the last decade (The bar-graph in Figure 3 was directly obtained from [PERSON]'s Web of Science using the query TI = (image AND (registration OR alignment OR matching) AND (evolution* OR swarm OR optimization)), in which more than one hundred and fifty papers appeared.
## 2 Numerical Simulation Modeling of Plate-Fin Heat Exchanger
### Governing Equation  
The CFD solution of the heat exchanger model is performed using fluid-solid coupling based on the commercial software Fluent. To simplify the calculations, the following assumptions are made [27;28]:  
1. Since the thickness of the baffle in the heat exchanger is only 0.5 mm, the temperature difference in the baffle thickness direction is neglected.
2. Ignore the influence of radiation heat transfer.
3. No sliding at the fluid-solid interface.
4. The system is insulated.  
Based on the above assumptions, the governing equations for plate-fin heat exchangers are Mass conservation equations:  
\[\frac{\partial}{\partial x_{i}}(u_{i})\ =\ 0 \tag{4}\]  
Momentum conservation equation:  
\[\frac{\partial}{\partial x_{i}}(\rho u_{i}u_{k})\ =\ \frac{\partial}{ \partial x_{i}}\Big{(}\mu\frac{\partial u_{k}}{\partial x_{i}}\Big{)}-\frac{ \partial P}{\partial x_{k}} \tag{5}\]  
Energy conservation equation:  
\[\frac{\partial\big{(}\rho u_{i}u_{j}\big{)}}{\partial x_{j}}\ =\ -\frac{ \partial P}{\partial x_{i}}+\frac{\partial}{\partial x_{j}}\Big{(}\mu\frac{ \partial u_{i}}{\partial x_{j}}-\rho u_{i}u_{j}\Big{)} \tag{6}\]  
where \(x\) is the direction, \(u\) is the velocity of the fluid, \(\rho\) is the density of the fluid, \(\mu\) is the dynamic viscosity of the fluid, and \(P\) is the pressure.
# The Role of Smart Meters in Enabling Real-Time Energy Services for Households: The Italian Case
## 4 Services for Final Users
### Scheduling & Control  
In a more sophisticated environment (e.g., a smart home), some deferrable appliances or EMSs can schedule their activation, selecting the cheapest moment of the day. Customers could plug in their electric car and forget it there until the following day. With a ToU or RTP contract, the customer (or an EMS) can shift the load to select the best interval, thereby minimizing the recharging cost for the same final result (i.e., the vehicle is charged for the morning). Moreover, a smart home with a photovoltaic system coupled with a storage system can schedule a time that is more convenient to use local generation for storing energy (i.e., off-peak intervals) and when it is better to use stored energy to feed appliances, avoiding energy withdrawals during peak moments.
## 5 Experiment 2: Deployment of Biofeedback System during Online Academic Examinations
### Method
#### 5.3.2 Procedure  
The experiment followed a procedure consisting of two steps (Figure 6).  
**First Step:**  
The first step is the training phase, which is an attempt to apply a calibration procedure for the biofeedback device in order to create training biosignal sets for every participant. Thus, during this step, biosignal measurements were collected in order to train the regression algorithm for each participant. In parallel to the biosignal measurement collection procedure, real-time student anxiety levels were detected by the Neurosky Mindset (brain-wave measurements). Every detected anxiety level is used as a label for each corresponding galvanic skin response (GSR), heart rate (HR), and skin temperature (SKT) biosignal that is measured by the biofeedback device.  
Moreover, according to [PERSON] (2018), every person presents different areas of emotional and mental sensitivity when they try to interpret and respond to current situations by deploying their past experience. Thus, it is assumed that every participant has a unique human physical organization and personality, with its own specific biosignal measurements. For this reason, it was decided that a psychometric protocol, inducing and provoking mental stress, supervised by a psychologist supporting the research, should precede the scheduled examination (second step) and follow a specific experimental design. This design is very similar to the protocol followed in the first experiment. Nevertheless, there are two main differences between the two designs followed by the two experiments under examination. In the first experiment, we used the STAI questionnaire, which was not used in the second experiment. Moreover, we used the TRI questionnaire in the second experiment but not in the first experiment.  
**Second Step:**  
The second step is the examination phase. The students' test anxiety during this phase was measured using the biofeedback device (Figure 1) that can detect biosignal measurements and classify these measurements into anxiety levels. The Neurosky Mindset equipment was not used during this step, since the training set and the relevant anxiety prediction model for every participant had already been formulated. During the examination activity, every measured student had to answer certain multiple-choice or computational exercises, which were displayed one at a time (left part of the screen), while watching his/her anxiety measurements provided by biofeedback (right part of the screen).
## 2 Physical Modelling and Numerical Methodology
### Numerical Simulation
#### 2.1.2 Equations for the Dispersed Phase and Lagrangian Particle Tracking  
The discrete phase is solved by Lagrange tracking, where the motion of the particles is described by a set of ordinary differential equations for particle position \(\mathbf{x_{p}}\) and velocity \(\mathbf{u_{p}}\). Considering the drag force and particle inertia ([PERSON], 1977), these equations in vector form read as:  
\[\frac{d\mathbf{x_{p}}}{dt}=\mathbf{u_{p}} \tag{6}\]  
\[\frac{d\mathbf{u_{p}}}{dt}=\mathbf{g}-\frac{\Big{(}1+0.15 Ke_{p}^{0.687}\Big{)}}{\tau_ {p}}\big{(}\mathbf{u_{p}}-\mathbf{u}\big{)} \tag{7}\]  
where \(\mathbf{u}\) is the fluid velocity at the particle position, \(\mathbf{g}\) is the gravitational acceleration vector and \(\tau_{p}=\rho_{p}d_{p}^{2}/18\mu\) is the particle relaxation time (\(d_{p}\) and \(\mu\) being the diameter of the particle and the dynamic viscosity of the fluid, respectively). The Stokes drag coefficient is computed by using a standard non-linear correction required when the particle Reynolds number, \(\text{Re}_{p}=\big{|}\mathbf{u}-\mathbf{u_{p}}\big{|}d_{p}/\
u\) does not remain small.
## Abbreviations  
The following abbreviations are used in this manuscript:  
AcK acetate kinase  
AD adenine deaminase  
ADA adenosine deaminase  
ADP adenosine-5\({}^{\prime}\)-diphosphate  
_AjPPK_ Acinetobacter johnsonii PPK  
AMP adenosine-5\({}^{\prime}\)-monophosphate  
AMPDA AMP deaminase  
AprT adenine PRT  
_AtADK_ _Arabidopsis thaliana_ adenosine kinase  
ATP adenosine-5\({}^{\prime}\)-triphosphate  
B-RFAS 4-(D-ribofuranosyl)aminobenzene synthases  
5-BrU 5-bromo uracil  
5-Br-dUTP 5-bromo dUTP  
CD cytosine deaminase  
CDA cytidine deaminase  
CDP cytidine-5\({}^{\prime}\)-diphosphate  
CK creatine kinase  
6-Cl-P 6-chloro purine  
2,6-Cl-P 2,6-dichloropurine  
6-Cl-dPTP 6-chloro purine 5\({}^{\prime}\)-triphosphate  
2,6-Cl-dPTP 2,6-dichloropurine 5\({}^{\prime}\)-triphosphate  
CMP cytidine-5\({}^{\prime}\)-monophosphate  
CMPDA CMP deaminase  
CTP cytidine-5\({}^{\prime}\)-triphosphate  
CTP-S CTP synthase  
5-FU 5-fluoro uracil  
5-F-dUTP 5-fluoro dUTP  
2,6-D 2,6-diamino purine  
5\({}^{\prime}\)-deoxyNTP/dNTP deoxynucleoside-5\({}^{\prime}\)-triphosphate  
_DmdNK_ deoxynucleoside kinase of Drosophila melanogaster  
dNK deoxynucleoside kinase  
2,6-dPTP 2,6-diamino purine 5\({}^{\prime}\)-triphosphate  
_EcAPT_ _Escherichia coli_ adenine PRT  
_EcAPT_ _Escherichia coli_ hypoxanthine PRT  
GD guanine deaminase  
GDA guanosine deaminase  
GDP guanosine-5\({}^{\prime}\)-diphosphate