esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### XRD Analysis  
The XRD patterns depicted in Figure 2b illustrate the XRD analyses of the POA polymer, PPA polymer, TiO\({}_{2}\) nanoparticles, and PPA/TiO\({}_{2}\), POA/TiO\({}_{2}\) nanocomposites. The XRD pattern of PPA and POA conducting polymers exhibits a broad peak around 20 (20\({}^{\circ}\)-30\({}^{\circ}\)). This specific value indicates an amorphous nature [30], possibly attributed to polymer chains scattering at interplanar spacings [23].  
The XRD of both the POA@TiO\({}_{2}\) and PPA@TiO\({}_{2}\) nanocomposites was compared to those of the polymers (PPA and POA) and revealed distinct features. Notably, prominent peaks were observed at 26 values of 25.28\({}^{\circ}\), 36.9\({}^{\circ}\), 37.81\({}^{\circ}\), 38.57\({}^{\circ}\), 48.05\({}^{\circ}\), 53.85\({}^{\circ}\), 55.03\({}^{\circ}\), 62.12\({}^{\circ}\), 62.69\({}^{\circ}\), and 68.76\({}^{\circ}\) and were ascribed to the (101), (103), (004), (112), (200), (105), (211), (204), and (116) crystal planes of anatase TiO, respectively [28, 32]. These findings decisively confirm the presence of TiO\({}_{2}\) within both POA and PPA matrices. The XRD analysis also indicated that TiO\({}_{2}\) retains its structural integrity even after dispersion within the polymer matrix following the polymerization process [28]. Additionally, a small, low-intensity peak around 32\({}^{\circ}\), observed specifically in the hybrid material, likely corresponds to the presence of polymer@TiO\({}_{2}\) materials via a chemical oxidation method [21], further supporting the successful integration and structural integrity of the hybrid materials.
## 2 Preliminaries and Related Work
### A Multi-Objective Hyper-Heuristic Approach  
Hyper-heuristics have lately increased in use by researchers, and various papers on hyper-heuristics are still mainly limited to single-objective optimization. The use of hyper-heuristics for multi-objective optimization problems is a new area of research in evolutionary computation and operational research [6; 7]. The multi-objective hyper-heuristic evolutionary algorithm (MHypEA) [20] has some features that provide excellent solutions for a wide range of optimization problems such as a generic form of low-level heuristics without combining any domain-specific knowledge, selection of low-level heuristics based on support learning linked with roulette-wheel selection, dynamic adaptation of the weights assigned to low-level heuristics based on the performance of the heuristics in directing the search towards the right areas and acceptable exploitation and exploration of the search space. Studies have been identified that deal with hyper-heuristics for multi-objective problems.  
A paper entitled [1] Fast Multi-objective Hyper-heuristic Genetic Algorithm for Multi-Objective Software Module Cluster is presented. Six test problems were solved by using the two-archive multi-objective evolutionary algorithm. The second paper [21] presented a solution to the multi-objective software module clustering problem based on a hyper-heuristic approach using the multi-objective hyper-heuristic evolutionary algorithm (MHypEA). The problem was studied using two multi-objective approaches to clustering--the equal-size cluster approach (ECA) and the maximizing cluster approach (MCA). The MHypEA can be used to recommend suitable modular structures for weighted MDGs. In the third paper [1], a multi-objective hyper-heuristic genetic algorithm (MHypGA) is applied to solve the multi-objective software module clustering problem using selection, crossover, and mutation operations of evolutionary algorithms.  
Figure 1: The framework of module clustering optimization prototype.  
In the scientific literature, many studies use SBSE approaches to address software model clustering problems. In this article, we present the main ideas of SBSE techniques, including meta-heuristics and hyper-heuristics designed to solve multi-objective optimization problems. There are only a few studies on using MOHH to solve module clustering problems, which highlights the need for scientific research in the area of solving module clusters using multi-objective evolutionary algorithms and combining meta-heuristics to yield improved performance, rather than focusing on solving only one problem.
## 3 Results and Discussion
### Temporal Variations of Air Pollutants in the Various Micro-Environments  
#### 3.3.1 Daily Variations  
In the summer monitoring period, weather conditions were consistent except on 28 August 2017, when it rained in the evening. The first 4 days of the winter monitoring period were impacted by a cold wave, with consistently low concentrations of air pollutants (cold wave case). Following the cold wave event, the PM\({}_{2.5}\) concentration increased sharply from the evening of 13 January 2018, and a severe PM\({}_{2.5}\) pollution event with a daily mean concentration of 76 \(\upmu\)g\(\cdot\)m\({}^{-3}\) occurred during 16-17 January 2018 (PM pollution case) (Figure 5h).  
As expected, the air pollutant levels were higher in winter (except for the cold wave period), with a wider temporal variation than in the summer season (Figure 5). In summer, the daily mean concentrations of CO, NO\({}_{2}\), and PM\({}_{2.5}\) ranged from 370 to 780 ppb, 10 to 52 ppb, and 8 to 17 \(\upmu\)g\(\cdot\)m\({}^{-3}\), respectively, whereas in winter they ranged from 400 to 1370 ppb, 20 to 70 ppb, and 13 to 82 \(\upmu\cdot\)m\({}^{-3}\), respectively. Daily mean ozone concentrations were in a similar range in summer and winter, with values from near zero to 32 ppb. There were usually very low ozone concentrations at night due to chemical loss, with the NO\({}_{\mathrm{X}}\) concentrations showing reciprocal variations between NO\({}_{2}\) and ozone.  
The levels of air pollutants (except PM\({}_{2.5}\)) differed noticeably in each of the adjacent micro-environments. However, the daily variations of primary pollutants (CO and NO\({}_{2}\)) and O\({}_{3}\) in each micro-built environment were similar to those recorded by the AQMS, where the direct emissions had a less significant impact. This implies that even with different emission strengths, the daily variations of pollutant levels in all micro-environments were controlled by larger-scale (regional) meteorological conditions in both monitoring periods (Figure 5), and the relative pollutant concentrations were influenced by local emissions from the adjacent roadways.
## 3 Renewal and Revocation Protocols
### Renewing Symmetric Keys  
Figure 3 presents protocol RSK, which allows an initiator node \(I\) to renew a session key with its neighbor \(R\). The protocol consists of sending the new session key \(K^{\prime}(I,R)\) encrypted with the previous session key \(K(I,R)\) in order to confirm to \(R\) that \(I\) has the previous session key. Then, the message is encrypted again with the public key of \(R\). Notice that an intruder should obtain \(K(I,R)\) and \(sk(R)\) in order to learn the new session key \(K^{\prime}(I,R)\). This part of the protocol takes more execution time due to the extra public key encryption (see Table 2). Finally, the new key \(K^{\prime}(I,R)\) is used as a nonce by \(R\) to confirm the reception by sending back to \(I\) the message \(\{K^{\prime}(I,R)\}_{K^{\prime}(I,R)}\).  
Figure 2: Key revocation protocol (KR): revocation of \(k\) malicious neighbor nodes of node \(I\).
# Cascade or Direct Speech Translation? A Case Study +
## 6 Advanced End-to-End Models
### Models
#### 6.1.1 Architectural Variants  
The first variant (CNN.11e-4d.trf) was based on the description in [24] and differs from the S-Transformer architecture by first removing the 2D attention layers and modifying the number of layers, with a stack of eleven SA encoder layers and four SA decoder layers; the model preserves the CNN layers to reduce the dimensionality of the input audio data. This architecture was selected as representative of models that have closed the gap with cascade models in the aforementioned study.  
The second variant (linear.trf) replaces both the CNN and the 2D SA layers of the S-Transformer with a simple linear layer as the first encoder layer, following [18]. This architecture was mainly designed to support knowledge distillation, as it combines a Transformer model for ASR and another one for MT (see Section 6.1.3 for more details). We included it as a variant to provide KD results with the architecture as described in [18], using only the output of the teacher model as it provided the best results in the aforementioned work. We also extended the use of KD to the other ST models described in this section, as it can be straightforwardly applied in all cases.  
Finally, we prepared a third variant (wav2 vec-st), with wav2 vec 2.0 [43] set as the encoder in a Transformer-based model [63]. This architecture is of notable interest in its leverage of speech data via self-supervised methods, as one of the main limitations for the development of end-to-end ST models is the lack of parallel audio-text data. This ST model features initial linear and CNN layers, followed by twenty-four SA encoder layers and six SA decoder layers. After preliminary experiments, training on the mintzai-ST corpus was performed by first freezing the encoder layers for the first 20% of the epochs, then allowing updates for all parameters, except the wav2 vec 2.0 CNN, during the remaining epochs. With this architecture,and contrary to the results obtained with S-Transformer variants, preliminary experiments indicated that character-level decoding outperformed bpe-based decoding. We thus maintained the original configuration and parameters in the fairseq-st toolkit used to train the wav2 vec-st models, as provided at: [[https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text)]([https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text)) (accessed on 18 October 2021).
## 5 Results
### Flow Characteristic Analysis  
Transient flow simulations revealed critical deviations between parametric configurations (Figure 9) and theoretical predictions (\(Q_{t\_{\text{max}}}\) = 40.0316 kg/s): steady-phase flow rates exhibited \(<\)6% deviation, while valve transition periods showed 113.4-114.1% discrepancies due to kinematic hysteresis (valve lag time: 0.04-0.05 s). Maximum simulated flow rates exceeded theoretical values by 6.3 \(\pm\) 0.1%, correlating with amplified pressure differentials (\(\Delta p_{\text{max}}\) = 39.6 MPa) during delayed valve actuation. Valve disc velocity fluctuations synchronized with flow perturbations (Figure 6). The characteristics of the flow change when the pump valve is closed (magnified insets) show that, when the valve disc falls back to the seat, the valve flap movement oscillates due to the cushioning effect of the wave springs, resulting in a flow disturbance.  
A cylindrical helical spring stiffness increase of 36% reduced peak flow by 1.5%. A wave spring stiffness boost of 24% decreased peak flow by 7.03%. A preload augmentation of 36% (110 N\(\rightarrow\)150 N) suppressed peak flow variations by 2.54%. Optimized configurations achieved stabilized flow characteristics while maintaining volumetric efficiency across cycles.  
Transient flow simulations elucidated the non-linear relationship between peak flow and stroke frequency (Figure 10). The parametric analysis shows that the peak flow rate in the suction/discharge phase increases simultaneously by more than 30% when the operating revolutions increase from 90 rpm to 120 rpm. This parametric response validates the active displacement control strategy presented in [1,2], which identifies stroke number as the main operating determinant for flow optimization.  
Figure 9: Transient flow rate profiles with theoretical comparison.
## 3 SDN-Enabled Edge Cloud Architecture
### Mobile Edge Cloud  
Mobile edge cloud (MEC) is an idea of network architecture that brings cloud-like functionality closer to the edge of the network to improve the performance of high-throughput applications and reduce the latency. Being closer to end users, MEC enhances service delivery, but it also introduces challenges related to resource allocation and load balancing. These challenges arise due to factors like user mobility and network load. Virtualization, through the use of virtual machines and lightweight containers, has paved new ways for dynamic service migration to handle user mobility and MEC system loads. Dynamic resource transfer techniques are essential to achieve the goals of load balancing, fault tolerance, and system maintenance. Container migration, in particular, is emerging as a promising solution to enable efficient migration of resources across virtualized networks and MEC environments [9; 25]. The conceptual architecture of mobile edge cloud is shown in Figure 4.
## 2 Materials and Methods
### Physical Vapor Deposition (PVD)  
All test samples were ultrasonically cleaned vigorously in alkaline solution and water-based degreasing soap solution followed by DI water rinsing and oven drying for 30 min at \(\sim\)80 \({}^{\circ}\)C. Samples were loaded into a cathodic arc physical vapor deposition (CA-PVD) vacuum chamber having multiple cathodes. The temperature of the substrates was held at approximately 400 \({}^{\circ}\)C inside the vacuum chamber. PVD was employed to prepare a TiN/AlTiN multilayer. The PVD coating was made by Surface Modification Technologies Pvt. Ltd. Vasai, India. After achieving a base vacuum of 8.0 \(\times\) 10\({}^{-3}\) Pa, the samples were etched (in situ cleanings) with Ti ions (60 A current) by applying bias voltage (450 V to 500 V) between the substrate and the chamber. This process was carried out in ultra-high purity argon atmosphere. After that, TiN layer was deposited at 150 V bias for 4 min to improve the adhesion. AlTi (67:33 %) composition cathodes (at 70 A current) were used in ultra-high pure N\({}_{2}\) gas to form the aluminum titanium nitride layer. The deposition time was 4 min. By switching the targets, TiN/AlTiN nano-layered coating was deposited. During the deposition process, the bias voltage was kept in a range of 100 V to 150 V at 1.3 Pa chamber pressure.
# Self-Adjustment Energy Efficient Redeployment Protocol for Underwater Sensor Networks
## 4 Description of 3D Distributed Virtual Forces Algorithm (3D-DVFA)  
The target of the 3D-DVFA [14] is to relocate sensors in the network area uniformly while maintaining a calculated target distance, \(D_{th}\), between neighbor nodes. In each iteration, each sensor will calculate its new position based on localized information. This information is about its position and the position of its sensor neighbors. The new position of a sensor node is calculated according to the sum of the forces exerted on it by its neighbors.  
An iteration is defined as follows:  
1. Step 1: Each node broadcasts a Hello-message each Hello-period to discover its neighbors.
2. Step 2: Each node computes the virtual forces exerted on it by its neighbors.
3. Step 3: Each node determines its new position by computing the resultant force. At each iteration, the algorithm computes for any neighbor sensor \(s_{i}\) located at \((x_{i},y_{i},z_{i})\), the force denoted \(\vec{F}_{ij}\), exerted by any sensor \(s_{j}\) located at \((x_{j},y_{j},z_{j})\). Let \(d_{ij}\) be the Euclidean distance between \(s_{i}\) and \(s_{j}\) and \(D_{th}\) the deployment target distance. \[\vec{F}_{ij}=\left\{\begin{array}{ll}K_{a}(d_{ij}-D_{th})\frac{x_{j}-x_{i}y_ {j}-y_{i}z_{i}-z_{j}}{d_{ij}}&\text{if $d_{ij}<D_{th}$}\\ K_{r}(D_{th}-d_{ij})\frac{x_{j}-x_{i}y_{j}-y_{i}-z_{j}-z_{i}}{d_{ij}}&\text{if $d_{ij}>D_{th}$}\\ Null&\text{if $d_{ij}=D_{th}$}\end{array}\right.\] (1) where \(K_{a}\) is a positive attractive coefficient and \(K_{r}\) is a positive repulsive coefficient. These coefficients are used to tune the attractive and repulsive forces. The resulting force exerted on the sensor \(s_{i}\) is \(\vec{F}_{i}\) \[\vec{F}_{t}=\sum_{j}\vec{F}_{ij}\] (2) where \(j\) represents the sensor \(s_{i}\)'s neighbors. The new position of the node \(s_{i}\) located at position \((x_{i},y_{i},z_{i})\) will be \((x^{\prime}_{i}=x_{i}+F_{ix}\), \(y^{\prime}_{i}=y_{i}+F_{iy}\), \(z^{\prime}_{i}=z_{i}+F_{iz})\).
4. Step 4: The node moves to its new position.  
Each iteration has a duration of one Hello period. The target of this algorithm is to spread sensor nodes in the whole area while ensuring network connectivity and area coverage. The result of the sensor network deployment using 3D-DFVA is very satisfactory with a good coverage rate and uniform density.
# Four-Channel Polarimetric-Spectral Intensity Modulation Imager
## 3 Effectiveness Validation for the FCPS Scheme
### Matrix Rank Calculation  
According to Section 2, we learned that the system matrix must be full of rank to solve Equation (13). Therefore, before system feasibility simulation verification, we first verify that the system matrix is always full rank. When there no errors in the FCPS, the rank of the system matrix is 4, as shown in Figure 2. In Figure 2, the x-coordinate represents the number of system matrix, and the y-coordinate represents the rank of system matrices. The number of system matrices is the same as the number of bands.  
When there is a \(\pm\)\(3^{\circ}\) angle error for the first phase retarder and there is no angle error for the second phase retarder, the rank of the system matrix is revealed, as in Figure 3. It can be seen from Figure 3 that the system matrix's rank is still 4. When the second phase retarder has an angle error of \(\pm\)\(3^{\circ}\) and the first phase retarder has no angle error, the rank of the system matrix is revealed, as in Figure 4. The rank of the system matrix also stays the same at 4. When both phase retarders have angle errors of \(\pm\)\(3^{\circ}\), the rank of the system matrix is shown in Figure 5. The system matrix's value is still 4. It can be seen from Figures 3-5 that when the phase retarder has angle errors, the rank of the system matrix remains unchanged and is always 4. Therefore, as long as we calibrate the system matrix after installing and adjusting FCPS, the demodulation of Stokes data can be completed.  
Figure 2: The rank of the system matrix.  
Figure 4: The rank of the system matrix with second phase retarder angle error.  
Figure 5: The rank of the system matrix with both phase retarder angle error. **(a)** First phase retarder angle error is \(-3^{\circ}\) and second phase retarder angle error ranges from \(-3^{\circ}\) to \(3^{\circ}\). **(b)** First phase retarder angle error is \(-2^{\circ}\) and second phase retarder angle error ranges from \(-3^{\circ}\) to \(3^{\circ}\). **(c)** First phase retarder angle error is \(-1^{\circ}\) and second phase retarder angle error ranges from \(-3^{\circ}\) to \(3^{\circ}\). **(d)** First phase retarder angle error is \(1^{\circ}\) and second phase retarder angle error ranges from \(-3^{\circ}\) to \(3^{\circ}\). **(e)** First phase retarder angle error is \(2^{\circ}\) and second phase retarder angle error ranges from \(-3^{\circ}\) to \(3^{\circ}\). **(e)** First phase retarder angle error is \(3^{\circ}\) and second phase retarder angle error ranges from \(-3^{\circ}\) to \(3^{\circ}\). **(f)** First phase retarder angle error is \(3^{\circ}\) and second phase retarder angle error ranges from \(-3^{\circ}\) to \(3^{\circ}\).  
Figure 3: The rank of the system matrix with first phase retarder angle error.
## 5 Experimental Results
### Computational Results on \(k\)-opt-LS and NewKLS_FVS  
#### 5.2.1 Impact of the Tabu Tenure  
In order to observe the impact of the tabu tenure of the algorithm, NewKLS_FVS, the algorithm for the tabu tenure, \(k\), is tested from 100 to 1,000 on four chosen graphs. Table 2 reports the best and worst solutions, respectively, for the algorithm, NewKLS_FVS, with different values of the parameter, \(k\).  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Instance** & \(\mathbf{k}\) & **Best** & **Worst** & **Average** \\ \hline inithx.i.1.col & 100 & 572 & 550 & 553 \\  & 200 & 575 & 552 & 561 \\  & 300 & 575 & 553 & 561 \\  & 400 & 575 & 552 & 562 \\  & 500 & 575 & 543 & 555 \\  & 600 & 572 & 548 & 558 \\  & 700 & 573 & 549 & 553 \\  & 800 & 569 & 546 & 553 \\  & 900 & 592 & 541 & 563 \\  & 1,000 & 570 & 546 & 556 \\ \hline le450\_25a.col & 100 & 157 & 154 & 155 \\  & 200 & 156 & 153 & 155 \\  & 300 & 157 & 155 & 155 \\  & 400 & 157 & 155 & 155 \\  & 500 & 156 & 155 & 155 \\  & 600 & 157 & 154 & 155 \\  & 700 & 157 & 154 & 155 \\  & 800 & 156 & 154 & 155 \\  & 900 & 157 & 155 & 155 \\  & 1,000 & 157 & 154 & 155 \\ \hline G(400,0.5) & 100 & 15 & 13 & 13 \\  & 200 & 15 & 12 & 13 \\  & 300 & 15 & 13 & 13 \\  & 400 & 15 & 13 & 13 \\  & 500 & 15 & 13 & 13 \\  & 600 & 15 & 13 & 14 \\  & 700 & 15 & 13 & 14 \\  & 800 & 15 & 13 & 14 \\  & 900 & 15 & 12 & 14 \\  & 1000 & 15 & 12 & 14 \\ \hline \end{tabular}
\end{table}
Table 2: Results with with different values of the parameter \(k\).  
From the results above, it can be seen that the parameter, \(k\), is important for the algorithm. For example, for the graph, inithx.i.1.col (resp.le450_25a.col, G(400,0.5), G(800,0.5)), \(k=900\) is the most suitable one.
## 4 Experiments and Results
### Evaluation Metrics
#### 4.3.8 Comparison with Related Work  
Table 7 shows a comparison between our scheme and a recently proposed image encryption scheme; our schemes showed better performance in terms of NPCR and correlation values. This proves that the selection of the singular values to be encrypted is successful, and the encryption of those singular values resulted in high confusion and diffusion in the cipher.
# Kalman Filter Using a Third-Order Tensorial Decomposition of the Impulse Response
## 4 Simulation Results
### KF-NKP versus KF-TOT  
In the third set of experiments, the NKP-based version of the Kalman filter from [27] is involved in comparisons, namely, KF-NKP. This is also a decomposition-based algorithm, but exploits a second-order level, combining the solutions of two shorter filters of lengths \(P^{*}L_{1}^{*}\) and \(PL_{2}^{*}\), where the length of the global filter is factorized as \(L=L_{1}^{*}L_{2}^{*}\), while \(P^{*}<L_{2}^{*}\). In our scenario, since \(L=512\), we use the decomposition setup \(L_{1}^{*}=32\) and \(L_{2}^{*}=16\). In Figure 5, the input signal is a speech sequence, \(\text{SNR}=20\) dB, and different values of \(P^{*}\) and \(P\) are used for the KF-NKP and KF-TOT, respectively. As we can notice, the KF-TOT outperforms the KF-NKP for all the corresponding values of the decomposition parameters. In the case of the KF-NKP, the value of \(P^{*}\) is related to the rank of the corresponding matrix (of size \(L_{1}^{*}\times L_{2}^{*}\)) associated with the reshaped impulse response [27]. In the case of the impulse response used in the experiments, this rank is equal to 3, so that increasing the value of \(P^{*}\) beyond this value does not lead to any performance improvement.  
Figure 4: Normalized misalignment of the conventional KF and KF-TOT (with \(P=2\)). The input signal is an AR(1) process, the echo path changes after 3.75 s: (**a**) \(\text{SNR}=10\) dB and (**b**) \(\text{SNR}=0\) dB.  
Figure 5: Normalized misalignment of the KF-NKP [27] and KF-TOT, using different values of \(P^{*}\) and \(P\), respectively. The input signal is a speech sequence and \(\text{SNR}=20\) dB.  
These decomposition-based Kalman filters are further analyzed in Figure 6, using lower SNRs and considering a change in the echo path (after 3.75 s). In both scenarios (i.e., \(\text{SNR}=10\) dB and \(0\) dB), the proposed KF-TOT achieves better performance than the KF-NKP, in terms of both the tracking reaction and the misalignment level. Moreover, as supported in Figure 6b, the KF-TOT can operate with \(P<P^{*}\), while still outperforming its KF-NKP counterpart.