esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Setup and Results  
The simulation of the chain reliability-based RBD proposed, is conducted utilizing MATLAB R2022a software, on an Intel(R) Xeon(R) W-2145 CPU @3.70 GHz 3.70 GHz computer processor. In this section, we present the simulation results for our proposed work, in order to determine its performance by considering the average reliability of 20 runs. In this section, two types of chain-based stationary CH and mobile CH are evaluated. Single-chain, multi-chain/single-hop, and multi-chain/multi-hop models are evaluated, for both CH cases and both chain models (Q-Chain and D-Chain). Chain formation of 100 nodes, based on \(Q\) and \(D\) for a single chain, is presented in Figure 16, while chain formation of 100 nodes and 4 chains, based on \(Q\) and \(D\) for a multi-chain/single-hop and a multi-chain/multi-hop, are presented in Figures 17 and 18, respectively.
# A Review of Deep Learning Based Methods for Acoustic Scene Classification
## 2 Signal Representations
### Learnable Signal Transformations  
Three different approaches have been used to the best of our knowledge in ASC systems to avoid fixed pre-defined signal transformations. The first approach is to apply end-to-end learning where neural networks directly process raw audio samples. Examples of such network architectures are \(\mathrm{AclNet}\) and \(\mathrm{AclSincNet}\)[26], as well as SoundNet [27]. As a potential advantage against spectrogram based methods, the signal phase is not discarded.  
The second approach is to interpret the signal transformation step as a learnable function, commonly denoted as \"front-end\", which can be jointly trained with the classification back-end [28]. The third approach is to use unsupervised learning to derive semantically meaningful signal representations. [PERSON] et al. combined representations learned using a deep convolutional generative adversarial network (DCGAN) and using a recurrent sequence to sequence autoencoder (S2 SAE) [29]. Similarly, environmental audio recordings can be decomposed into suitable basis functions using well-established matrix factorization techniques such non-negative matrix factorization (NMF) [30] and shift-invariant probabilistic latent component analysis (SIPLCA) [31]. Up to this point, the best-performing ASC algorithms of the recent DCASE challenges still focus on fixed spectrogram based signal representations instead of learnable signal transformations.
## 5 Evaluation Protocol and Results
### Ablation Study
#### 5.2.2 Brain Regions  
In order to investigate which brain region can better detect schizophrenic, we tested the effect of different brain lobes. Table 5 shows the results of testing different regions of the brain on the best model using 10-fold cross-validation.  
By testing each brain lobe individually, Table 5 shows that the temporal brain lobe has the highest accuracy (80.59%) among other lobes. Based on these results, temporal region channels were selected again to perform further experiments with the other regions. The accuracy scores significantly improved to 94.64% using temporal and frontal regions and 92.38% using temporal and occipital regions. Moreover, we tested the channels of the brain's right and left sides. The right side has better results than the left one, achieving 88.81% accuracy. The best results were obtained when we examined all channels together, which gives 99.88% accuracy.  
Our analysis revealed significant variability in the model's sensitivity to different brain regions, with the fronto-temporal areas emerging as particularly critical for distinguishing between people with schizophrenia and healthy controls. This observation aligns with neuroscientific findings that link these areas to key functions disrupted in schizophrenia. The model parameters were optimized individually for each region to capture their unique contributions effectively.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 5 Experiments and Results
### Experiment of Carrierless PPM Demodulation Using Conversion to Quasi-FM-PWM Signal  
For the experiment involving the demodulation of PPM signals without a carrier by converting them into a quasi-FM-PWM signal, a monostable circuit and an RC lowpass filter are used, as shown in Figure 21. For this experiment, PPM modulation without a carrier under the same conditions is experimented like those conducted for the first two demodulation methods. The experimental results are shown in Figure 22.  
Figure 21: Demodulation circuit for carrierless PPM using a monostable multivibrator circuit and an RC lowpass filter.  
Figure 20: Demodulation results using the PLL circuit: (**a**) Output of the PLL demodulation with sine wave and triangle wave input from conventional PPM modulation signal; (**b**) Output of the PLL demodulation with sine wave and triangle wave input from proposed PPM modulation signal. (CH1) Information signal (upper line); (CH2) Demodulated output signal using the PLL circuit (lower line).
# Optimizing Foreign Language Learning in Virtual Reality:
## 3 Theoretical Foundations
### Cognitive Theories in Language Learning
#### 3.1.3 Cognitive Load and Language Processing  
Cognitive burden denotes the mental strain necessitated to assimilate data and execute endeavors. Within the realm of linguistic edification, it signifies the cerebral capital utilized by scholars during the assimilation and digestion of nascent linguistic components. Numerous variables such as the intricacy of the linguistic content, the scholar's antecedent wisdom, and the pedagogical blueprint can sway this burden [43].  
Three cardinal cognitive burdens are recognized: inherent, superfluous, and pertinent. The inherent burden is indelibly linked to the material under study and is gauged by its convolution juxtaposition with the scholar's preexisting acumen. As an illustration, grappling with the intricate facets of an esoteric grammatical paradigm in an unfamiliar tongue might inflict a lofty inherent strain upon novices.  
The superfluous burden is not quintessential for the edification trajectory but emanates from how the data are showcased or chores are delineated. Haphazardly structured content or nebulous directives can gratuitously augment this burden, sidetracking cerebral assets from genuine edification.  
Pertinent burden pertains to cerebral undertakings that bolster the edifice of enduring cognition such as schema formulation. Quintessential pedagogical blueprints strive to curtail superfluous burdens while amplifying pertinent ones, ensuring the judicious utilization of the scholars' cerebral capital for substantive edification.  
Within VR linguistic edification terrains, supervising cognitive burden is paramount. VR's engulfing essence can proffer sumptuous, contextually apt episodes but concurrently risks inundating scholars with a surfeit of sensory data. Thus, it becomes cardinal to concoct VR voyages that harmonize the unveiling of nascent linguistic content with interactive facets, safeguarding that those scholars remain captivated without succumbing to saturation. This equilibrium underpins proficient linguistic digestion, solidifying emergent wisdom and competencies.
# 5G Cross-Border Operation for Connected and Automated Mobility: Challenges and Solutions
###### Abstract  
The vision of cooperative, connected and automated mobility (CCAM) across Europe can only be realized when harmonized solutions that support cross-border traffic exist. The possibility of providing CCAM services along different countries when vehicles drive across various national borders has a huge innovative business potential. However, the seamless provision of connectivity and the uninterrupted delivery of services along borders also poses interesting technical challenges. The situation is particularly innovative given the multi-country, multi-operator, multi-telco-vendor, and multi-car-manufacturer scenario of any cross-border layout. This paper introduces the challenges associated to a cross-border deployment of communication technologies through the analysis of three use cases: tele-operated driving, high-definition map generation and distribution for autonomous vehicles, and anticipated cooperative collision avoidance. Furthermore, a set of 5G solutions have been identified to ensure that CCAM services can be supported efficiently in cross-border scenarios. Faster handover of a data connection from one operator to another, generalized inter-mobile edge computing (MEC) coordination, and quality of service (QoS) prediction are some of the solutions that have been introduced to reduce the uncertainties of a real 5G cross-border deployment.  
V2X; 5G; wireless communication; cross-border; trials +
Footnote †: journal: Future Internet  
03390/612010005
## 5 Introduction to the Artificial Bee Colony (ABC) Algorithm  
The ABC algorithm is inspired by the behavior of bee colonies, which is an optimization method mimicking the behavior of bees, proposed by the [PERSON] team in 2005 to optimize the algebra problem [14]. It is an application of cluster intelligence concepts. Through the local optimization of artificial bees, the global optimal value emerges. In the ABC algorithm, the colony of artificial bees contains three groups of bees: worker bees, onlookers and scouts. A bee waiting on the dance area for making decision to choose a food source, is called an onlooker and a bee going to the food source visited by itself previously is named a worker bee, a bee carrying out random search is called a scout. The position of a food source represents a possible solution of the optimization problem and the amount of nectar of a food source corresponds to the quality (fitness) of the associated solution. In the first step, the ABC generates a randomly distributed initial population of \(SN\) solutions, where \(SN\) denotes the size of the population. Each solution \(X_{i}\) (\(i\) = 1, 2,..., \(SN\)) is a D-dimensional vector. Here, D is the number of optimization parameters. In order to produce an initial food position, the ABC uses the following expression:  
\[x_{i,j}=x_{\min,j}+rand(0,1)(x_{\max,j}-x_{\min,j}) \tag{6}\]  
where \(x_{\max}\) and \(x_{\min}\) are the upper and lower bounds of search space.  
After initialization, the worker bee will start the neighborhood search. It generates a new candidate position based on the local information in its memory and checks the amount of nectar in the new location. If the new location is better than the original position, the bee remembers the new location and forgets the original location. The new candidate position equation is:  
\[v_{i,j}=x_{i,j}+\phi_{i,j}(x_{i,j}-x_{k,j}) \tag{7}\]  
The above is called the ABC algorithm search equation, where \(k\in\{1,2,\ldots,BN\}\), \(j\in\{1,2,\ldots,D\}\) are randomly chosen indexes. Although \(k\) is determined randomly, it has to be different from \(i\). \(\phi_{i,j}\) is a random number between \([-1,1]\).  
After the worker bees complete the search process, they will share the memory of the nectar with onlookers through the dance. An onlooker bee chooses a food source depending on the probability \(p_{i}\) associated with that food source, \(p_{i}\) is calculated by the following expression:  
\[p_{i}=\frac{fit_{i}}{\sum\limits_{j=1}^{SN}fit_{j}} \tag{8}\]  
where \(fit_{i}\) is the fitness value of the solution \(i\) evaluated by its employed bee. When a nectar source is selected, the onlooker bee will use Equation (8) to produce a new position. By checking the amount of nectar in the new position, if the position is better than the one in memory, it will be replaced, otherwise the original position is left. In order to prevent the algorithm from falling into a local optimum, if the amount of nectar does not improve after the limit cycle, the colony will abandon the position, whereby the worker bee becomes a scout and randomly generates a new nectar according to Equation (7).
# Performance Analysis of Rate Splitting in Massive MIMO Systems with Low Resolution ADCs/DACs
## 5 Simulation Results
The optimal \(\xi\) improves the SE performance especially in the high-SNR region, where the multiuser interference is dominant. With low-resolution ADCs/DACs, the QN is considered in deriving the optimal regularization parameter which shows an increase in SE for \(b_{A}(b_{D})=1\) and \(b_{A}(b_{D})=2\). Figure 9: SE versus number of BS antennas for different quantization bits (\(K=2\), \(P_{p}=2\) dB, \(\mathrm{SNR}=20\) dB, and \(\tau=200\)). Figure 11: SINR of private message versus regularization parameter (\(M=100\), \(K=10\), \(P_{p}=2\) dB, and \(\tau=200\)). Figure 10: SE versus quantization bits for different number of users (\(M=100\), \(P_{p}=2\) dB, SNR = 30 dB, and \(\tau=200\)). Figure 13 presents the EE versus the number of quantization bits. From the plot, the EE first increases and then decreases as the quantization bits increases. This is due to the fact the higher quantization bits increases the power consumption, which in turn decreases the EE performance. There exists an optimal value of the quantization bits where the EE of the system is maximum. In the figure, maximum EE is achieved when \(b_{A}(b_{D})=4\) for the RS scheme irrespective of the number of BS antennas, \(M\). For the non-RS scheme, maximum EE is achieved at \(b_{A}(b_{D})=3\). The EE of the RS scheme is improved compared to that of the non-RS scheme. Furthermore, the BS with a higher number of antennas shows a lower EE since the power consumption increases with the number of BS antennas. Figure 14 presents the EE/SE trade-off performance by plotting the EE against the SE for different number of quantization bits. The SE and EE are evaluated for the quantization bits from \(b_{A}(b_{D})=1\) to \(b_{A}(b_{D})=10\). From the figure, we observe that the system with lower number of BS antennas (i.e., \(M=50\)) shows the highest EE. However, this comes at the expense of lower SE. For \(M=100\), the EE decreases due to increased power consumption of the additional BS antennas. Moreover, the RS scheme performance better than the non-RS scheme in terms of EE and SE. For \(M=100\), a comparison of the SE and EE is provided in Table 1.  
Figure 14: Energy efficiency versus spectral efficiency (\(K=2\), \(P_{p}=2\,\mathrm{dB}\), \(\mathrm{SNR}=30\) dB, and \(\tau=200\)). Figure 13: Energy efficiency versus quantization bits for different number of BS antennas (\(K=2\), \(P_{p}=2\,\mathrm{dB}\), \(\mathrm{SNR}=30\) dB, and \(\tau=200\)).
## 3 Results and Discussions
### SEM Analysis  
To determine the surface morphology and distribution of the prepared TiO\({}_{2}\) photocatalyst, scanning electron microscopy (SEM) images were collected at different magnifications as shown in Figure 2. The SEM images show that the TiO\({}_{2}\) photocatalyst exhibits a non-uniform distribution composed of irregularly shaped and agglomerated cubic structures.
## 5 Results
### Results of SEM
#### 5.1.1 Results of the Measurement Model  
The measurement model analysis was conducted to evaluate reliability and validity, with the results presented in Tables 5 and 6. All items demonstrated factor loadings above 0.6, indicating satisfactory reliability [115]. Additionally, composite reliability and Cronbach's alpha values exceeded the recommended threshold of 0.7 for all constructs [116; 117], further supporting reliability. Convergent validity was confirmed as average variance extracted (AVE) values surpassed 0.5 for all constructs [116; 117]. Discriminant validity was established by ensuring that the square root of the AVE for each construct exceeded its correlation coefficients with other constructs [56; 116]. Table 6 presents the square root AVE values and the correlation matrix.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Construct** & **Item** & **Loading** & **CR** & **AVE** & **Cronbach’s Alpha** \\ \hline \multirow{3}{*}{Complexity} & Complexity1 & 0.829 & 0.816 & 0.598 & 0.813 \\  & Complexity2 & 0.798 & & & \\  & Complexity3 & 0.686 & & & \\ \hline \multirow{3}{*}{Compatibility} & Compty1 & 0.770 & 0.812 & 0.592 & 0.807 \\  & Compty2 & 0.856 & & & \\  & Compty3 & 0.671 & & & \\ \hline \multirow{3}{*}{Top management support} & TMS1 & 0.845 & 0.865 & 0.681 & 0.861 \\  & TMS2 & 0.855 & & & \\ \cline{1-1}  & TMS3 & 0.773 & & & \\ \hline \multirow{3}{*}{Technology competence} & TechComp1 & 0.854 & 0.892 & 0.673 & 0.862 \\ \cline{1-1}  & TechComp2 & 0.876 & & & \\ \cline{1-1}  & TechComp3 & 0.782 & & & \\ \cline{1-1}  & TechComp4 & 0.765 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Validity and reliability indicators of the constructs.
## 8 Anonymity and Security Analysis
### Data Privacy  
All the data is encrypted before transmission and encrypted again at every hop along the route to the BS. A message digest will authenticate data. The only time data is not protected when the sensors are physically compromised. The compromised nodes are able to inject data in the network. If ADV uses the compromised nodes actively, a good IDS can detect the falsified data. The framework provides a secure facility to remove compromised sensors and to add valid sensors, if needed, to the WSN.
# Value of Information Based Data Retrieval in UWSNs
## 3 Value of Information in Underwater Sensor Networks  
### Value of Information  
Assume there is a situation of interest that is being monitored so that it can be kept under control. Information is knowledge that helps in developing and updating a model of such a situation. It is based on this model that certain steps are taken to keep the situation in check. Value of Information (VoI) is the valuation of an information segment that is relevant to and can be added to this model.  
Consider a marine environment that is being monitored by an underwater sensor network for reporting any catastrophic event. An example could be an offshore oil-rig and pipeline system that is under surveillance for events such as potential oil spills. The sensor nodes have video cameras which record information that can be processed to ascertain aging, rust, accidental damage or oil leaks. The higher the increase in the risk of an oil spill, as concluded from a model after adding an information segment, proportional is the increase in valuation of the information segment that leads to that conclusion.  
From the perspective of multi-agent systems, VoI is the price a player would pay to acquire a piece of information in a game theoretic setting. This description is consistent with the usage of the term VoI in [62; 63; 64; 65].  
To have a more abstract view, let us consider a classic control theory scenario where, based on feedback, an agent has a certain degree of observability and controllability over a system. In other words, the agent can take specific actions in response to various observations so as to bring the state of the system to a rather more desirable one. The actions taken are such that some measure of fiscal profit or loss is incurred. These profits or losses are assessed from the current and future states of the system. The observations are part of the information based on which the future discourse of steps is decided by the agent. Therefore, information is a data report that can aid in building a more accurate model of a system such that the newer model aids in alleviating e.g., fiscal consequences.
###### Abstract
In ML-based methods, the hyperparameter setting is the major issue that leads to inaccurate attack detection. For PUE detection, localization methods play a pivotal role [19]. The localization methods determine the accurate location of the signal source and then compare the location with the original location in order to detect the PUE attack accurately. In addition, trust-based methods are also proposed for detecting PUE attacks in CR-based networks [20]. However, slight variations or errors in the localization affect the entire detection process, while trust-based methods have low attack detection accuracy. Thus, PUE attack detection still needs better attention to improve accuracy. In mmWave-based CR-IoV, beamforming is the additional issue along with the security [21, 22]. A symmetry-based hybrid precoder and combiner is an efficient signal processing structure for Non-Orthogonal Multiple Access (NOMA) systems at millimeter wavelengths (mmWave) [23]. Though mmWave achieves better capacity and efficiency, it also demands optimal beamforming and alignment. In mmWave-based massive Multiple-Input-Multiple-Output (MIMO) systems, beamforming techniques make the signal from MIMO as focusing towards the particular receiving device, i.e., vehicles. For beamforming, the position of the receiver, i.e., the vehicle, is essential, which can be determined as 3D positioning [24]. Optimal beamforming results in a higher data rate and improved data transmission efficiency. In addition, optimal route selection also helps in improving data transmission efficiency in mmWave-assisted CR-IoV. Routing in IoV is performed by using conventional AODV, DSR, OLSR, etc. [25] and by using optimization algorithms [26], ML techniques [27] and so on. In all approaches, the main issue is the mobility of vehicles. Mostly, the vehicles dynamically move in any direction of the road, which causes frequent route changes. Therefore, in mmWave-assisted CR-IoV, we need to concentrate on three significant aspects: security, beamforming and routing. Optimizing IoV in these three aspects improves the overall performance of the system.
# Resource-Efficient Multicast URLLC Service in 5G Systems
## 4 Performance Evaluation
### Influence of the Maximum Number of Transmission Attempts  
Let us consider a 5G M-MIMO system with 64 antennas at the gNB. The precoder construction algorithm is SBFC. Figure 6 shows the results for various configurations of transmission parameters (i.e., the number of transmission attempts, the MCS selection algorithm, and the feedback scheme).  
Figure 5: Scenario with 3 kmph mobility and \(T_{SRS}=20\) ms: (**a**) downlink resource consumption, (**b**) scheduler execution time.  
Figure 6: Scenario with 3 kmph mobility and \(T_{SRS}=20\) ms: (**a**) downlink resource consumption, (**b**) overall resource consumption, (**c**) packet loss ratio, (**d**) average MCS.  
First, we can see that for all the considered configurations, strict URLLC reliability and latency requirements are satisfied. Specifically, according to Figure 6c, PLR is below \(PLR^{QoS}\). Note that the packet is assumed lost if it is not delivered within \(D^{QoS}\). Thus, the latency requirement is also satisfied.  
Second, let us analyze the influence of the number of transmission attempts. The main observation from the obtained results is that because of strict reliability requirements, with a single TX, the gNB uses too-low a MCS (see Figure 6d), which increases resource consumption too much. Though for a single TX, NACK-only feedback does not induce retransmissions, it allows dynamic MCS adjustment with the OLLA algorithm, which reduces resource consumption by up to 70% compared to the No feedback scheme with eOLLA MCS selection algorithm (see Section 3.6). Note that the curve '1 TX no feedback' is non-monotonic and non-smooth because eOLLA uses a discrete set of MCS adjustments \(\Delta(N)\). Thus, MCS adjustment changes significantly when the number of UEs changes. In contrast, for other curves corresponding to the OLLA algorithm, the MCS adjustment \(\Delta_{olla}\) is a real number, which changes smoothly. Switching from one TX to two TXs (i.e., the usage of multicast HARQ) reduces resource consumption up to three times. Note that this effect differs from that observed for loss-tolerant traffic (e.g., IPTV) [30], where multicast HARQs only negligibly reduce resource consumption compared with a single TX. So, in the case of URLLC with strict latency and reliability requirements, if the latency budget allows, the gNB shall enable multicast HARQ to reduce resource consumption.