esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Volatile Compounds  
A total of 81 volatile compounds were detected: 17 acids, 17 alcohols, 16 ketones, 7 aromatic compounds, 8 esters, 5 aliphatic hydrocarbons, 2 sulphur compounds, 1 terpene, 3 aldehydes, and 5 lactones. Acids, alcohols, and ketones comprised almost 95% of the volatile compounds in both kinds of cheeses (FC and RC). However, acids were the most abundant family (39.7%) followed by alcohols (29.7%) in FC, while alcohols were the predominant compounds in RC (48.0%) followed by ketones (33.2%). Although both kinds of cheeses showed the same number of aromatic hydrocarbons, their concentration was higher in FC (3.0%) than in RC (1.2%). FC exhibited a greater quantity and variety of ester compounds and aliphatic hydrocarbons than RC (2.1% vs. 0.6%). Just one sulphur compound was detected in FC, but its concentration was much higher than the two detected in RC combined (0.15% vs. 0.06%). Only aldehydes and lactones were more abundant and varied in RC (0.21% and 0.31% respectively) than in FC (0.09% and 0.03%). Acetic and 3-methylbutanoic acids were the most abundant short-chain FAs detected in FC, while hexanoic and octanoic acids predominated in RC. Ethanol was one of the predominant alcohols in both FC and RC samples. Moreover, 3-methyl-1-butan-ol and 2-butanol were present in high concentrations in FC and RC, respectively; the latter was the most concentrated volatile compound detected in RC. Butan-2-one-3-hydroxy and butan-2-one were the most abundant ketones identified in FC and RC, respectively. The former was also the predominant volatile organic compound in FC, while the latter was not present in this type of cheese. Benzene-ethanol, toluene, and \(p\)-xylene were the most abundant aromatic compounds that were detected in both kinds of cheese. Regarding ester compounds, ethyl acetate was the most concentrated in FC, while ethyl hexanoate had the highest concentration in RC. Lactones such as \(\delta\)-octalactone and \(\delta\)-decalactone were detected in both kinds of cheese, albeit in low quantities. Decanal was the only aldehyde identified in FC, while 4-heptanal was the most abundant in RC. Other compounds belonging to various minority families in this kind of product such as pentadecane, 2-ethylthio-ethanol, and farnesol were detected in both kinds of cheese. The PCA was performed using the most discriminating volatile organic compounds present in both kinds of cheeses. The first two components (PC1 and PC2) accounted for 67.3% of the total variance, with PC1 and PC2 contributing 57.62% and 9.64%, respectively (Figure 1). FC and RC were clearly separated by PC1. Acids, alcohols, one aldehyde, and one ketone contributed positively to PC1, while alcohols, one ester, and one aromatic compound contributed negatively to PC1.
## 6 Conclusions and Future Trends
Additionally, further studies may be conducted on improving the prediction accuracy by using different model combinations or ensemble methods. The other important issue that can be examined in more detail is the performance of deep learning techniques on more complex, larger datasets. Particularly, it is important to understand how techniques such as Bi-LSTM, a deep learning model, perform on larger, more complex datasets in predicting the state of health of the battery. Additionally, investigating the potential advantages of new machine learning models, which were not examined in this study, or less explored in the field of the SoH of battery prediction, could be an exciting area for further research. More comprehensive studies need to be conducted on the role and impact of new-generation artificial intelligence techniques and deep learning methods in SoH prediction. In conclusion, this study presented an evaluation of the usability and effectiveness of machine learning techniques and deep learning approaches for the SoH of battery prediction. Future research might be expected to focus on further developing and optimizing the applications of these technologies in the battery industry. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14062306/s1](https://www.mdpi.com/article/10.3390/app14062306/s1)]([https://www.mdpi.com/article/10.3390/app14062306/s1](https://www.mdpi.com/article/10.3390/app14062306/s1)), Table S1. Parameters used in regression-based ML models; Table S2. Parameters of DT-based ML models; Table S3. Parameters of SVM-based ML models; Table S4. Hyperparameters of ANN-based models. Conceptualization, S.O. and E.B. ; methodology, S.O. ; software, S.O. ; validation, F.D., A.A. and E.B. ; formal analysis, E.B. ; investigation, S.O. ; resources, A.A. and F.D. ; data curation, E.B. ; writing--original draft preparation, S.O. ; writing--review and editing, E.B. and F.D. ; visualization, S.O. and F.D. ; supervision, E.B. ; project administration, A.A.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript. This research was funded by the European Union's Horizon Europe Research and Innovation Program under the \"Next Generation of Multifunctional, Modular and Scalable Solid State Batteries System\" (EXTENDED), grant number 101102278. Not applicable. The data presented in this study are available on request from the corresponding author. The research was conducted collaboratively by the MOBILERS team at Sivas Cumhuriyet University and the Battery Research Group at Sivas University of Science and Technology. The authors also acknowledge [PERSON] for his English proofreading and editing assistance. The authors declare no conflicts of interest.
# Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers
## 2 Background
### Space-Time Block Coding and OFDM
An OSTBC example of coding matrix for \(M_{T}=4\)[44] is given by  
\[\mathbf{OSTBC_{4,8}}= \begin{bmatrix}s[1]&-s[2]&-s[3]&-s[4]&s[1]^{*}&-s[2]^{*}& -s[3]^{*}&-s[4]^{*}\\ s[2]&s[1]&s[4]&-s[3]&s[2]^{*}&s[1]^{*}&s[4]^{*}&-s[3]^{*}\\ s[3]&-s[4]&s[1]&s[2]&s[3]^{*}&-s[4]^{*}&s[1]^{*}&s[2]^{*}\\ s[4]&s[3]&-s[2]&s[1]&s[4]^{*}&s[3]^{*}&-s[2]^{*}&s[1]^{*}\end{bmatrix} , \tag{3}\]  
Figure 1: Coding scheme for a MIMO-OFDM system in which \(k\) is the index of the \(k\)th MIMO-STBC-encoded symbol matrix \(\mathbf{X}[k]\) along the OFDM symbol [11]. Figure 2: Detailed MIMO-OFDM coding system. \(\{\cdot\}^{*}\) denotes the conjugate operator. in which \(s[m_{s}]\) is the transmitted signal in the discrete symbol index \(m_{s}\). Notice that, as proved by [PERSON] et al. [54], the inner product of any two distinct rows of this matrix is equal to zero (i.e., the matrix is orthogonal) and of full-rank, yielding full-diversity [11]. One of the disadvantages of OSTBC is the code rate. Let \(P\) represent the number of time samples to convey one block of coded symbols and \(M_{s}\) represent the number of symbols transmitted per block. The space-time block code rate is defined as the ratio between the number of symbols that the encoder receives at its input and the number of space-time coded symbols transmitted from each antenna, given by \(R=M_{s}/P\). This implies that Equation (3) has a code rate \(R=1/2\), which consequently reduces the spectral efficiency. Supplementary to the diversity gain, the OSTBC leads to a secondary linear coding gain \(G_{c}=10\log(R)\) at the receiver due to the coherent detection of multiple copies of the signal over time. Furthermore, the multi-antenna system, as presented in Figure 1, will lead to an array gain \(G_{a}=10\log(M_{R})\) due to the coherent combination of multiple received signals over the receiving antennas [11].
# A Double Rate Localization Algorithm with One Anchor for Multi-Hop Underwater Acoustic Networks
## 4 Performance Analysis and Simulation Results
### The Localization Performance
#### 4.2.2 Test2: The Relationship between Nodes' Number and Localization Error  
In this test, we analyze the impact of nodes' number on the localization performance. Fix the Gaussian noise variance \(\sigma^{2}\) to be 5 m and increase the number of nodes from 20 to 200. Figure 6 shows the impact of nodes' number on localization error of DRL, MDS-MAP, MSL and the Double Rate Scheme with no reference nodes.  
From the simulation result, we can see that the proposed DRL algorithm and the Double Rate Scheme with no reference nodes algorithm are less sensitive to the nodes' number. MSL and MDS-MAP are affected by the number of nodes because the increase of nodes' number will improve both the communication connectivity and enlarge the error accumulation. The localization results for MDS-MAP and MSL are irregular.
## 3 The Proposed Approaches
### HHHOWOA1 PSO and HHHOWOA2 PSO Approaches  
The HHHOWOA1 PSO and HHHOWOA2 PSO approaches were created with further improvements over the HHHOWOA approaches. So, in addition to HHHOWOA1 and HHHOWOA2, modified PSO equations are used in the last part of the HHO algorithm. The main goal is to use PSO's velocity update mechanism to reduce the likelihood of HHO getting stuck in local optima. While HHO is effective at navigating around local optima through its hunting strategies, there is still a risk of entrapment in local optima in the later stages. PSO, on the other hand, can overcome this by focusing on the globally best solution. By incorporating PSO into the final stage of HHO, the proposed hybrid methods, HHHOWOA1 PSO and HHHOWOA2 PSO, enable a broader search when HHO encounters local optima, thereby enhancing the likelihood of reaching a global optimum.  
In the HHHOWOA1 PSO and HHHOWOA2 PSO approaches, the PSO algorithm is employed with the inertia weight (\(w\)), as specified in Equation (29), in place of the conventional PSO equation for calculating the velocity vector. In the proposed HHHOWOA1 PSO and HHHOWOA2 PSO algorithms, Equation (30) is applied to derive the new position vector by merging the position vectors from HHHOWOA1 and HHHOWOA2 with the velocity vector derived in PSO. The HHHOWOA1 PSO and HHHOWOA2 PSO approaches' pseudocode is given in Figure 4.  
\[v_{i}^{k+1}=wv_{i}^{k}+c_{1}r_{1}(Pbest_{i}^{k}-x_{i}^{k})+c_{2}r_{2}(gbest-x_{i} ^{k}) \tag{29}\]  
\[x_{i}^{k+1}=W_{c}x_{i}^{k}+(1-W_{c})v_{i}^{k+1} \tag{30}\]  
Here, \(W_{c}\) is the weight coefficient and determines the combination ratio of the velocity component with the position vector obtained from HHHOWOA1 and HHHOWOA2. Both \(W_{c}\) and \(w\) coefficients were tested for different values. The optimal coefficients were determined through a parametric analysis. Initially, the impact of the \(w\) coefficient was examined without using the \(W_{c}\) coefficient, starting from a value of 0.1 and increasing by  
Figure 3: HHHOWOA1 and HHHOWOA2 approaches’ pseudocode.  
0.1. The results indicated that \(w\) = 0.1 and \(w\) = 0.3 yielded better performance. Subsequently, for each of these values of \(w\), the \(w\) coefficient was fixed, and the effect of the \(W_{c}\) coefficient was examined. Better results were obtained with \(W_{c}\) values of 0.8, 0.87, and 0.99. The personal best and global best in Equation (29) have been taken as the same, and these values have been assigned as the rabbit's best-known position up to that point. \(c_{1}\) and \(c_{2}\) have been taken as the same, and these values have been assigned 0.5, while \(r_{1}\) and \(r_{2}\) have been taken as random values in (0, 1).  
In addition, the computational complexity of the HHHOWOA1 PSO and HHHOWOA2 PSO algorithms is approximately the same as the HHHOWOA1 and HHHOWOA2 algorithms, which is \(O\) (\(N\times(T+TD+1)\)).
# Management of Obstructive Sleep Apnea in Hospitalized Patients
## 5 Management of the Perioperative Patient  
OSA is an established risk factor for a difficult airway during intubation [77]. OSA patients have a higher risk of respiratory complications, including respiratory failure, hypoxemic episodes, and reintubation. In one study, OSA patients were 61% more likely to develop postoperative pneumonia [38]. From an inpatient physician's perspective, the post-operative management of these patients is important as they are transferred from recovery rooms to hospital units under the care of house staff and hospitalists.  
High-risk patients should be screened for OSA using screening tools, and consideration should be given to empirically treating positive airway pressure therapy in high-risk patients in the perioperative setting [37]. In a meta-analysis of 26 studies with 50,592 patients, a higher STOP-BANG score was associated with postoperative respiratory and neurologic complications and in-hospital mortality [78]. Although CPAP use has not been shown to reduce cardiovascular complications other than improving blood pressure directly, there was a reduced risk of respiratory and cardiac complications in the postoperative setting in patients using CPAP [38]. Patients with undiagnosed SDB have a higher risk of transfer to the ICU with opioid use [79]. In a study of patients undergoing joint replacement, the risk of postoperative complications was higher in patients with OSA [80]. A second study showedthat using CPAP reduced respiratory and cardiac complications' risk in the postoperative setting [38].  
We recommend that inpatient providers consider the empiric use of auto-titrating CPAP in patients with hypoxia or apneic events at high risk for OSA in perioperative settings. Other options include HFNC, which has been shown to improve the AHI [74; 75; 76]. In a randomized control trial of post-operative obese patients, CPAP vs. HFNC at 20-40 L/min was equal in keeping desaturation events <4%, with HFNC being tolerated better than CPAP [81]. Another small study reported improvement in AHI when HFNC was coupled with a 30\({}^{\circ}\) upper body position [59].
## 4 Multimedia-Based Cross-Layer Optimization in VSNs
### Video-Based Cross-Layer Optimization  
Video transmission on visual sensor networks is even more challenging than the image-based transmission. In fact, video requires more bandwidth than image does, and the amount of data to be coded is much larger when source nodes are streaming video.  
The coding process of collected video may follow different approaches, achieving high or low compression. For example, many techniques exploit information of the scene, such as motion and variation of luminosity, for higher compression. The expected resolution and number of frames per second (fps) are also relevant for the coding process. Whatever the chosen approach, the nature of resource-constrained wireless sensor networks will often bound the quality of the coded video, imposing low transmission rate and low resolution (typically CIF - 352 \(\times\)288 - or lower).  
In video-based cross-layer optimization, protocols and algorithms in transport, network, MAC and even physical layers may exploit information of the coded data (application layer) to reduce the transmission rate, to decrease the end-to-end delay, to protect more relevant data against bit errors and packet dropping and to save energy in source nodes and throughout the network.
## 3 Experiments and Discussion
### Test on the WTB with Known Defects  
Experimental investigations with application of the proposed method on a WTB sample are described in this section. The blade sample to be inspected and the loading heat flow gun are shown in Figure 8. There were two known delamination defects on the WTB sample. These are circled red and marked as 1 and 2 in Figure 8a. The heat gun used (Figure 8b) had an output power of 2200 W with an air flow of 150-500 L/min. The temperature went from 80 \({}^{\circ}\)C to 630 \({}^{\circ}\)C with nine stages of temperature settings. In this study, the seventh stage temperature setting was used with the heating time set to 15 s for a certain area. This was to make sure that the area is heated up evenly and the temperature reached effectively. A 532 nm laser with 300 mW power (Class 3R) was illuminated on the inspected area. The FLIR CMOS camera with micro-polarisation image sensor matrix, comprising 5 MPs and 2048*2448 for the image sensor, was used in this system.  
The inspection test for the two defects on the WTB sample was used to verify and consolidate the feasibility and function of the proposed method for actual defect inspection. The two known defects to be inspected were at different shapes and conditions. Both defects were delaminations in the subsurface layer, but defect 1 was an oval-like delamination, while defect 2 was made by small defects in the circled area and a had more irregular shape than defect 1. The diameters of each of the two circular areas were \(\sim\)50 mm.  
According to the proposed algorithm, the WTB surface was heated up using heat gun and the inspection data was recorded. Figure 9 shows the change of blade surface mean temperature over time for the surrounding area of the defects after heating up. During the increment of the surface temperature, accompanied by rise in surface, stress concentration has subsequently formed at the spot where the subsurface defect exists. Displacement at those spots could be seen microscopically but invisible in macroscopic. The task of the shearography system is to reveal the strain by dint of phase change before and after the thermal loading and display the phase change. In the process of cooling down, the surface strain and stress concentration at defect spots are restored to the original status.  
Figure 7: (**a–c**) Development of fringe patterns at time points 10 th, 40 th, and 70 th frame in SPS-DS system; (**d–f**) Retrieved wrapped phase map at time points 10 th (\(\Delta\phi_{1_{0}}\)), 40 th (\(\Delta\phi_{1_{0}}\)), and 70 th (\(\Delta\phi_{1_{0}}\)) frame in SPS-DS system.  
The reason for using the cooling process is to avoid the temperature turbulence caused by the heat gun and the residual surrounding heat when heating up the surface. Also, the restoration of the displacement at these defects varies smoothly, which is easy to see in step 6 of the algorithm.
# An Optimal Scheduling Model for Connected Automated Vehicles at an Unsignalized Intersection
## 4 Numerical Experiment
### Sensitive Analysis
#### 4.3.1 Optimized Time Window  
To investigate the impact of the optimized time window length on the model solution results, the optimized time window length is set to values ranging from 10 to 30 s [2; 6; 13; 14],  
\begin{table}
\begin{tabular}{c} **Average** \\ **Delays** \\ \end{tabular} \\ \hline
1 & 116 & 139.61 & 1.33 & 83.96 & 0.72 & 82.34 & 0.71 \\
2 & 132 & 325.44 & 2.46 & 129.22 & 0.98 & 125.55 & 0.95 \\
3 & 166 & 3593.00 & 21.65 & 967.22 & 5.83 & NA & NA \\
4 & 200 & 5264.33 & 26.32 & 1252 & 6.26 & NA & NA \\ \hline \hline \end{tabular} Note: NA indicates that the optimal solution has not been solved within 12 h.  
\end{table}
Table 4: Vehicle delays in experimental results.  
\begin{table}
\begin{tabular}{c} **Average** \\ **Time** \\ \end{tabular} \\ \hline
1 & 0.0008 & 0.0005 & 4.66 & 0.35 & 20.66 & 1.28 \\
2 & 0.0006 & 0.0001 & 8.86 & 2.52 & 102.02 & 56.61 \\
3 & 0.0024 & 0.0023 & 415.38 & 48.89 & \textgreater{}43,200 & NA \\
4 & 0.0017 & 0.0021 & 816.77 & 166.72 & \textgreater{}43,200 & NA \\with a step size of 2 s, and an optimization time duration of 900 s, while keeping the other parameters unchanged, as detailed in Table 2. Through calculations, the average vehicle delay and total computation time under different optimized time window lengths are obtained, as shown in Figure 2.  
From Figure 2, it can be observed that as the optimized time window length increases, the average delay of vehicles generally shows a fluctuating downward trend. The analysis reveals that with the increase in the optimized time window length, the number of vehicles within a single time window increases, which means there is a larger optimization space. Therefore, the average vehicle delay exhibits an overall decreasing trend. However, certain fluctuations can be attributed to Equation (9) at the interval division boundaries. When the time window is short, this constraint does not take effect, whereas when the time window is long, the constraint becomes active, resulting in fluctuations. Furthermore, the average computation time in Figure 2 shows that the average computation time decreases and then increases with the optimized time window length. This indicates that in this scenario, an optimal time window exists in terms of computation time, and its value is 20 s.
# The Electric Vehicle Routing Problem with Time Windows, Partial Readers, and Parcel Lockers
## 1 Introduction
* We study the implementation of parcel lockers based on the concepts of EVRPTW-PR, called the Electric Vehicle Routing Problem with Time Windows, Partial Recharges, and Parcel Lockers (EVRPTW-PR-PL). * We formulate a mixed-integer programming (MIP) model and design an adaptive large neighborhood search (ALNS) for solving both EVRPTW-PR and EVRPTW-PR-PL. * The performance of the proposed ALNS is shown by solving the EVRPTW-PR benchmark instances. * We provide some managerial insights from the implementation of parcel lockers. The remaining parts of this study are structured as follows. Section 2 reviews papers related to EVRPTW-PR and the implementation of parcel lockers. Section 3 formulates MILP to solve EVRPTW-PR-PL along with a formal description. Section 4 presents an ALNS algorithm that incorporates operators designed for addressing parcel lockers in the network. Section 5 conducts numerical experiments related to ALNS performance and the implementation of parcel lockers. Section 6 discusses the effects of parcel lockers on the problems. Finally, we give some conclusions and potential research in Section 7.
## 5 Discussion
### Extrapolating this Study to Other Countries  
Several considerations should be considered regarding the extrapolation of this study to other countries. Firstly, differences in the language and terminologies used in job advertisements may require adaptations to natural language processing techniques and data extraction methods. For instance, cultural nuances and terminologies specific to a particular country may necessitate adjustments to the regex method to maintain its effectiveness. Secondly, variations in labor market structure and industry composition across countries could affect the applicability of the chosen clustering and dimensionality reduction methods. It would therefore be essential to assess the effectiveness of these methods in the context of the target country's labor market characteristics to ensure that the extracted job profiles are both accurate and reliable. Lastly, legal, and ethical considerations, such as data privacy regulations and consent requirements for using publicly available data, may differ across countries. Researchers must consider these factors when applying the methodology used in this study in other contexts. In conclusion, although this study successfully extracted job profiles within the Lithuanian context, researchers should be aware of the potential challenges and limitations when attempting to extrapolate the findings to other countries. This methodology may provide valuable insights into job profile requirements across various international contexts by addressing the language, labor market, and legal considerations.
# A High-Resolution, Wide-Swath SAR Imaging System Based on Tandem SAR Satellites
###### Abstract  
For the spaceborne synthetic aperture radar (SAR), it is difficult to obtain high resolution and wide width at the same time. This paper proposes a novel imaging system based on tandem SAR satellites, where one obtains coarse resolution and wide swath by the scanning mode, and the other obtains the undersampled echo from the same swath. The high resolution is achieved by associating the tandem SARS' echo and using the minimum-energy-based algorithm. Finally, a high-resolution wide-swath SAR system is designed, and its imaging performance is verified by simulated data and real airborne SAR data.  
synthetic aperture radar (SAR); high-resolution wide-swath; minimum-energy criterion 1