esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation and Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Nodes**} & \multicolumn{3}{c}{**Objective Function Values (ms)**} \\ \cline{2-4}  & \(MB-NO\) & ACO & ABC \\ \hline
10 & 0.4 & 0.3 & 0.2 \\ \hline
15 & 0.5 & 0.5 & 0.3 \\ \hline
20 & 0.7 & 0.6 & 0.4 \\ \hline
25 & 0.95 & 0.8 & 0.56 \\ \hline
30 & 1.12 & 0.9 & 0.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Objective function estimation for an H-shaped network with optimized sensor node. Figure 7: Objective function estimation for an H-shaped network with optimized nodes. In Figure 9, the number of iterations is increased with the sensor nodes in the node optimization. The \(MB-NO\) algorithm proposes maximum energy conservation, as shown in Table 7; the maximum conservation is obtained with the energy measured, assuming that the other algorithms propose the values with the least energy level equilibrium. In Figure 10, the number of iterations is increased with the sensor nodes in the node optimization. The number of iterations is measured with the network lifetime process; hence, the network may be compared in a higher lifetime using the \(MB-NO\) algorithm. Hence, the energy parameters, node optimization and the increased secure transmission through Q learning assumed substantial outcomes. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Nodes**} & \multicolumn{3}{c}{**Objective Function Values (ms)**} \\ \cline{2-4}  & \(MB-NO\) & **ACO** & **ABC** \\ \hline
10 & 0.5 & 0.4 & 0.3 \\ \hline
15 & 0.6 & 0.5 & 0.4 \\ \hline
20 & 0.8 & 0.7 & 0.6 \\ \hline
25 & 0.98 & 0.8 & 0.7 \\ \hline
30 & \(1.18\) & 1.08 & 1.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Objective function estimation for an S-shaped network with optimized sensor nodes. Figure 8: Objective function estimation for an S-shaped network with optimized nodes. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Iterations**} & \(MB-NO\) & **ACO (J)** & **ABC (J)** \\ \hline
1000 & 60 & 65 & 70 \\ \hline
2000 & 55 & 60 & 68 \\ \hline
3000 & 45 & 48 & 55 \\ \hline
4000 & 40 & 45 & 56 \\ \hline
5000 & 35 & 40 & 50 \\ \hline
6000 & 25 & 35 & 45 \\ \hline
7000 & 8 & 15 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Energy conservation using Bat Algorithm in various iterations.
## 5 LSTM-Based Model  
### Basic Model with 12 Features  
One of the straightforward ways to build the acceleration prediction model is to treat 12 basic features as the input of the model. The \"encoder-decoder\" architecture proposed for trajectory prediction in SS-LSTM [15] is a suitable architecture for the acceleration prediction problem as the acceleration curve is a trajectory based on past experiences. While SS-LSTM uses different input information such as the occupancy map, the 12 basic features are packed into one single input channel in our proposed basic model. These features are fed into an \"encoder\" module and this \"encoder\" extracts the key information from input features and generates an intermediate result, or more precisely, a latent representation of these features. The intermediate result is then forwarded into a \"decoder\" module, which decodes the representation and outputs the acceleration prediction. The figure of this architecture is shown as Figure 4.  
However, the limitation of information provided by the 12 input features cannot be ignored. It is difficult for the model to capture and analyze the complex surrounding environment for the AV by these simple and limited numerical features, and the model trained on these features may fail to give a satisfying prediction. High-quality features with more key information are needed to improve the model capacity.
## 3 Experimental Result Analysis
### Deformation Characteristics
#### 3.3.1 Elastic Modulus
Between 25 \({}^{\circ}\)C and 200 \({}^{\circ}\)C, mineral particles experience volume expansion and intergranular compaction due to thermal and mechanical influences, leading to an initial rise in elastic modulus. However, further temperature increases promote crack propagation and damage, gradually reducing the elastic modulus. The elastic modulus of granite specimens decreases significantly at 600 \({}^{\circ}\)C compared to 25-400 \({}^{\circ}\)C under both cycling methods. This may be due to the phase transition of quartz from \(\alpha\)-phase to \(\beta\)-phase near 573 \({}^{\circ}\)C [39], which induces additional thermal microcracks. Additionally, a comparison of the number of cycles in the fatigue degradation stage between the two cycling methods reveals that the degradation of elastic modulus is significantly slower in the variable lower limit cycle. Once the crack of the sample develops to a certain level under constant lower limit cycle loading and unloading, it rapidly experiences damage. In contrast, the specimen subjected to variable lower limit cycle loading enters a damage accumulation stage, where fatigue damage occurs once irreversible deformation reaches a certain level. The number of cycles of the specimen will be gradually shortened with increasing temperature, highlighting the significant impact of temperature on the fatigue properties of rock materials. These variations suggest that both temperature and cycling methods influence the elastic modulus and the physical and mechanical properties of the specimen.
## 4 Description of Different Deployed VPN Solutions
### OpenVPN Road Warriors Scenario  
OpenVPN, a project managed by the \"OpenVPN community Team\" allows the creation of IP network tunnels using virtual ethernet adapters on TCP/UDP ports. The encryption, authentication, and certification functions are provided by default by the OpenSSL library or by the Gnu-TLS library, and this guarantees a high standard of protection of private network traffic on the public network. OpenVPN allows two authentication modes, one with a static key (PSK) and the other through SSL / TLS certificates that regulate authentication and key exchange using a customized security protocol. Figure 12 shows a comparison of the average throughput and standard deviation in the OpenVPN Road Warriors scenario, wired (on the left) and wireless (on the right). As can be seen from the figure, OpenWRT as an OpenVPN client turns out to be the best performing, followed by Windows 10, which uses a third-party application to allow the connection. Mikrotik only supports TCP-based connections, but guarantees decent throughput. When it comes to wireless clients, Android falls between the two iPhone generations.
# Energy Efficient Dynamic Symmetric Key Based Protocol for Secure Traffic Exchanges in Smart Homes
## 7 Conclusions  
Over the recent past, there have been numerous security protocols developed to address security and privacy threats in smart homes. Unfortunately, the majority of these security solutions have performance challenges or still have some security and privacy weaknesses. In this paper, a symmetric key-based authentication protocol is presented. The formal security analysis of the proposed protocol has been accomplished using BAN logic. Based on this formal analysis, it has been demonstrated that the communicating parties negotiate shared session keys for traffic protection over the public internet. In addition, the protocol has been informally analyzed to demonstrate its resilience against common smart home attacks such as packet replays, impersonation, eavesdropping, man-in-the-middle, denial of service, and session hijacking. It has also been demonstrated to provide backward key secrecy, mutual authentication, secret key agreement, device anonymity, and forward key secrecy. Moreover, its performance evaluation has shown that it reduces communication and computation complexities by 16.7% and 57.7%, respectively. Future work lies in the exploration of other cryptographic primitives that can further reduce the reported computation overheads. There is also a need for other innovative ways of assuring the same level of security and privacy using very few message exchanges to reduce communication and storage overheads. The current work was limited to performance evaluation using computation, storage, and communication overheads. There is, therefore, a need to evaluate this protocol using other metrics that were not within the scope of the current work.  
V.O.N. and Z.A.A.; methodology, conceptualization, and writing--original draft preparation, K.A.-A.M.; software, and data curation, IQ.A. and D.G.H.; validation, and writing--review and editing, J.M.; formal analysis, investigation, supervision, project administration, and funding  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Metric** & **Achieved by** & **Best Performance** & **Proposed Protocol Score** & **Improvement** \\ \hline Computation Costs & [PERSON] et al. [47] & 1.0130 ms & 0.4285 ms & 57.7\% \\ \hline Communication Costs & [PERSON] et al. [39] & 1728 & 1440 & 16.7\% \\ \hline Storage overheads & [PERSON] et al. [40] & 384 & 384 & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 14: Percentage improvements.  
acquisition, A.J.Y.A.; resources, and visualization. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Natural Science Foundation of Top Talent of SZTU (grant No. 20211061010016).  
Not applicable.  
All individuals included in this section have consented to the acknowledgment.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Procedures  
Data collection took place in a biomechanical laboratory, where each researcher performed identical tasks to avoid inter-rater errors. Before data collection, anthropometric measurements, such as body mass and height, were taken for each participant. To evaluate scapulothoracic motion (including scapular abduction, elevation, upward rotation, and winging), scapulohumeral rhythm, and movement quality (such as trunk lateral flexion, rotation or flexion/extension compensation, time-to-peak acceleration, and smoothness), inertial sensors were attached to several body parts. These included the thorax (at the flat anterior aspect of the sternum [3; 51], using double-sided tape) and unilaterally, using Velcro straps, to the acromion (positioned superiorly over the flattest posterolateral surface [61]), humerus (laterally, above the elbow), forearm (on its lateral surface), and the back of the hand, following manufacturer recommendations. System calibration, crucial for accurate measures up to 120\({}^{\circ}\) of shoulder elevation (a range less affected by skin artifacts) [62], involved participants standing straight with arms close to the body, elbows flexed at 90\({}^{\circ}\), and palms of the hands facing each other, as advised by the software manufacturer. To record EMG signals, the skin was previously prepared (shaved, abraded, and cleaned with isopropyl alcohol 70%) to reduce impedance to values lower than or equal to 5 K\(\Omega\). EMG sensors were positioned parallel to the muscle fiber orientation, at the muscle belly of the trapezius and serratus anterior portions, as well as LS, as detailed in Table 2. These sensors were attached to the skin by a 4-slot sensor adhesive interface (Delsys, Inc.; Natick, MA, USA). Additionally, a sensor aligned with the inertial sensor and with a Velcro strap was placed at the back of the hand to collect acceleration data. Before the recordings,the quality of the raw signals was checked to confirm the correct electrode placement and exclude possible interference or noise. To allow for the comparison between groups, recordings were conducted during shoulder elevation and lowering in the frontal plane, as well as during a daily activity task (drinking). Participants were seated on a bench with adjustable height to maintain their knees and hips at 90\({}^{\circ}\) angles, and their feet were parallel to hip width, flat on the floor. Additionally, the hand of the limb not being tested was supported on the respective thigh [32]. After participants' familiarization with the movements and task to be evaluated, the upper limb affected by chronic shoulder pain (or of the most painful shoulder in symptomatic subjects) or the upper limb matched with the symptomatic subjects according to side and dominance, in asymptomatic subjects, was assessed. Participants started
# Amplitude and Phase Information Interaction for Speech Enhancement Method
## 4 Experimental Setup  
### Dataset  
The dataset used in this experiment is the Interspeech 2021 Deep Noise Suppression Challenge dataset [20]. It includes clean speech, test sets, noise, and reverberation. The clean speech consists of 500 h of speech segments from 2150 speakers, while the noise comprises 150 classes with over 180 h of sound segments. The processing of these sound segments aligns with the training set processing method described in [18].  
The test set used in the experiment is the Interspeech 2021 test dataset, which is the same as the test set used in other comparative experiments.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 3 Realistic Scenarios and Modelling Issues
### Ways of Deriving Movement Models  
An alternative view of mobility models could be gained by considering the method by which a mobility model is generated. Broadly speaking, there are three possible methods that could be applied. Randomly generated models make use of a combination of a movement algorithms and random numbers to derive the node positions and movements. The most common of these is the previously discussed random waypoint model. This approach has the advantage of being able to generate multiple models based on the same set of parameters, such as node count, node maximum speed, and world size. The disadvantage is that the movements and distribution are not realistically matched to any particular real scenario. Some models have reportedly extracted behavioural patterns from trace data. Several examples are Seattle buses ([PERSON] et al., 2003, [143]); the urban pedestrian flows concept ([PERSON] et al., 2005, [144]), and the Shanghai taxi model ([PERSON] et al., 2012, [145]). The benefits of this method are that the resulting mobility models are realistic, and additionally, it is possible to generate multiple models from the extracted behaviour patterns. However, extracting a behaviour pattern that is sufficiently simple to model and using it to generate the movements is complex, because it could lead to over simplification and loss of realism ([PERSON] et al., 2012, [146]). Currently, there is limited number of movement models that are built entirely based on algorithms extracted from the defined movement patterns of nodes, followed by using them to generate movement files. The advantage of this approach is that it is easy to generate multiple models once the algorithm has been coded, but the limitation is the need for a clearly defined algorithm for the movement pattern at the onset of the model-building process. There are, however, scenarios where this is possible via the use of a toolbox approach for model generation. This approach is similar to applying a toolbox of mathematical methods to a problem in order to explore if a solution could be found. Each scenario will lend itself to one or more methods more effectively than to others.
# A Deep Learning-Based Course Recommender System for Sustainable Development in Education
## 4 Experiments
### Performance Evaluation  
#### 4.3.1 Impact of Predictive Factors Number  
In this section, we study the impact of changing the number of predictive factors on the model's recommendation performance. Like those in most previous recommender system studies, the collected real-world datasets in this study were highly sparse and high-dimensional. Therefore, we converted the input feature vector into a low-dimensional dense latent factor vector. To determine the optimal number of predictive factors, we followed the standard strategy used in previous studies by performing experiments, where the number of predictive factors was set to 8, 16, 32 and 64 [32]. For the MF, NCF, and DECOR models, the numbers of predictive factors equaled the numbers latent factors. Additionally, in the ItemKNN approach, we performed experiments with several neighborhood sizes and reported the best performance. Figure 3 shows the HR@10 and _NDCG@10_ performances of the models based on the number of predictive factors. Here, the HR metric indicates whether a given test item was in the top-10 list, and the _NDCG_ metric assigns a higher weight to a test item with a higher rank to describe its position.  
The results of the experimental findings are discussed as follows. First, we can see that the proposed DECOR model exhibited the best performance on real-world datasets, outperforming the state-of-the-art approaches. Moreover, we found that both metrics indicate the same changing pattern. When the number of predictive factors was 32, the DECOR model indicated the best performance, outperforming the MF and NCF approaches. Furthermore, NCF and ItemKNN were followed by MF, where MF performed slightly better than NCF. Although MF exhibited a performance drop due to overfitting when the number of predictive factors was large, the performance of MF was better than that of NCF with smaller numbers of predictive factors. The results show that the DECOR model indicated high performance when integrating the UBE layer, capturing the user behavior features, and the CAE layer, capturing the course attribute features. Second, we found that the performance improved as the number of predictors increased. The best DECOR performance was reached when the number of predictive factors was 32. Additionally, when the predictive factor increased, the capture power of the latent factor-based model improved. Simultaneously, however, there was much noise, the model suffered overfitting problems, and the performance started to drop. Therefore, we need to set a correct number of predictive factors to optimize the model.
## 2 Materials and Methods
### Wavelet Transform  
An alternative to band-pass filtering and the Hilbert transform is the use of a complex Morlet wavelet transform. The equivalent to the question of bandwidth choice here is the selection of the number of cycles in the wavelets [50]. PAC studies have employed wavelets for both phase and amplitude with 5 cycles [51; 52], or 7 cycles [24]. When this number is fixed for all the wavelet frequencies, the time resolution of the transformation increases with frequency, while the frequency resolution decreases [50; 53]. To improve the frequency resolution at the cost of some decrease in the temporal resolution at higher frequencies, the number of wavelet cycles can be set to increase with the frequency [50]. This approach has been implemented in the wavelet-based time-frequency decomposition in the EEGLAB function newtimef, which is used in the PAC calculation plugin PACTools for EEGLAB [54]. The numbers of cycles in this plugin are controlled by the variables 'cycles' for phase and 'cycles2' for amplitude, set by default to [3 0.5] and [10 0.5]. This means that, for phase, at the lowest frequency the number of cycles is 3, and at the highest frequency it is such that the wavelet standard deviation in the time domain is 0.5 of its value for the lowest frequency, and similarly for amplitude, starting from 10 cycles.  
In our calculations, one method variant had fixed numbers of cycles. For phase, we used 6 cycles, which we chose empirically as the smallest number producing few phase slips and reverses, which are a sign of the filter for phase being too wide [25]. For amplitude, we used 4 cycles to obtain approximately the smallest standard deviation in the frequency domain that satisfies the side-peak condition. In an alternative method, we used the adaptive cycle numbers from PACTools. The center frequencies for the wavelets were selected in the same way as for band-pass filters (previous section).
## 4 Analytical Discussion
### Keywords Assessment
The most popular keyword for \"yttrium iron garnet\" should be highlighted in YIG research, as it is given the greatest attention in the YIG study, as seen by the results displayed in Figures 6 and 7. However, \"spin-Hall effect\", \"magneto-optical\", and \"ferrimagnetic\" have the same frequency, with 11 occurrences. Figure 7: Popular keywords in the top 100 cited articles. After analyzing the information presented in Figures 6 and 7 about keywords, it is crucial to note that:  
* In a garnet structure, three yttrium ions occupy the dodecahedra (c) site, two iron ions occupy the octahedral (a) site, and three iron ions occupy the tetrahedral (d) site. Trivalent Y\({}^{3+}\) and Fe\({}^{3+}\) ions make YIG ideal for magnetic research. The most important interaction in this structure is the superexchange interaction of YIG, given by the Fe ion in the tetrahedral site. This interaction is caused by the antiferromagnetic superexchange interaction between the iron ions in the tetrahedral and octahedral sites through the intervening oxygen ion [32]. Two iron ions from the d-site have their magnetic moments canceled by two from the a-site. Thus, the garnet's magnetic moment is due to the d-excess site's iron ion. Composition and homogeneity control is essential in garnet synthesis, as stoichiometry and microstructure determine the magnetic characteristics [144]. * Yttrium iron garnet Y\({}_{3}\)Fe\({}_{5}\)O\({}_{12}\) (YIG) has the most significant role in understanding high-frequency magnetization dynamics. YIG has a high Curie temperature, low damping (lowest among all materials at ambient temperature), electrical insulation, good chemical stability, and facile single-crystal production [15]. * For YIG film, it is vital to minimize the YIG's thickness with preserving its magnetic characteristics [15]. Reduce the YIG film thickness below the exchange length (10 nm) to better understand spin momentum transfer at the YIG/metal contact. Submicrometer-thick YIG films have generally been generated via LPE; however, the final thickness is 200 nm. Other growth strategies can diminish thickness. PLD is the most versatile oxide film epitaxy technique [15]. PLD-grown YIG has been developed by several researchers in these most cited articles [8; 11; 13; 14; 15; 75; 76; 77]. In thin films, the temperature dependence of the longitudinal spin-Seebeck effect (LSSE) concerning YIG thickness has been studied [73]. * Other keywords that arise in the top-ranking papers, such as ferromagnetic, magnetism, spin-waves, pulse laser deposition, damping, spin-Hall effect, ferrimagnetic, magneto-optical, gadolinium gallium garnet, and microwave, are also significant as they play a remarkable role in assisting the researcher in tracking down and extracting data relevant to the YIG research. Upon this foundation of a summary of keyword analysis, researchers can obtain an overview of present research trends and potential future study directions in YIG research.