esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Effect of Smaller Training Dataset Size
For all the different training dataset sizes, it can be seen that as we transition from one subfigure to the next (left to right direction) the data points become closer to the 45-degree line. This observation is another indication of the accuracy improvement attained using the assisted approach scenarios. It can also be observed from the figure that, as we transition from one subfigure to the one below, the data points tend to get farther from the 45-degree line. This observation gives further verification that the accuracy decreases as the training dataset size becomes smaller. It can also be seen from the figure that there is no noticeable difference in accuracy for the different panel configurations (unstiffened, stiffened, etc.) since all the data are clustered close to the 45-degrees line. The coefficient of determination (R\({}^{2}\)) values for the training, validation, and testing datasets are also shown in each of the subfigures. It can be noticed that the coefficient of determination for the training datasets are clearly higher than those for the validation and training datasets. In fact, this trend is quite normal since the training data are already \"seen\" by the ANN (since they are used for training); therefore,  
Figure 6: The experimental residual strength values along with the predictions obtained by the ANN model built using the reduced size training dataset (reduced 3) for the traditional and assisted approaches (assisted 3 and 4 only) for one of the testing datasets (**top**), together with the residual strength prediction errors (**bottom**). The shown ANN predictions are for the same testing dataset used in Figure 4.  
the ANN can predict the residual strength for the training datasets more accurately that the \"unseen\" validation and testing datasets.
## 2 Materials and Methods
### Subjective Evaluation Measurement  
During the experiment, a questionnaire was used to guide the participants. The questionnaire included two attributes to assess the impact of noise stimuli: acoustic comfort and noise disturbance [40].  
The first attribute was noise disturbance. Work disturbance is a significant issue caused by noise in office spaces [41]. In this study, the participants were asked to perform a letter retrieval task to simulate the situation in an office environment. After each noise stimulus, the participants were asked to evaluate the level of disturbance caused by the noise using a five-point verbal scale. The verbal marks were: (1) \"Not disturbing at all\", (2) \"slightly disturbing\", (3) \"moderately disturbing\", (4) \"very disturbing\", and (5) \"Extremely disturbing\". A higher value in the noise disturbance evaluation indicates that the participant was more disturbed during the experiment.  
In addition to noise disturbance, acoustic comfort was also used as a metric to assess the quality of the overall sound environment [12]. As suggested by [PERSON] et al., acoustic comfort is a complex aspect that can provide a comprehensive understanding of how people perceive the acoustic environment [42]. During the experiment, participants were instructed to evaluate the overall experience of the acoustic environment rather than the response to a single noise source. A five-point scale with verbal descriptors suggested by [PERSON] and [PERSON] was used [43]: (1) \"very uncomfortable\", (2) \"uncomfortable\", (3) \"neither comfortable nor uncomfortable\", (4) \"comfortable\", and (5) \"very comfortable\". Thus, a higher value of the acoustic comfort evaluation indicates that the participant felt more comfortable in the office environment.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & **Noise** & **Vehicle** & **Vehicle** & \multirow{2}{*}{\(\mathbf{L_{aug}}\)/dBA} & \multirow{2}{*}{\(\mathbf{L_{Atm}}\)/dBA} & \multirow{2}{*}{\(\mathbf{L_{Au}}\)-\(\mathbf{L_{Au}}\)/dBA} \\  & **Source** & **Speed Km/h** & & & Length/m & \\ \hline \multirow{2}{*}{Interior noise} & AC & – & – & 40 & 40.33 & 0.76 \\  & IS & – & – & 40 & 43.04 & 5.41 \\ \hline \multirow{6}{*}{Traffic noise} & R & \textless{}60 & – & 40/50 & 45.7/56.3 & 2.63 \\  & Ma & \textless{}100 & 90 & 40/50 & 52.0/62.4 & 17.5 \\ \cline{1-1}  & T & \textless{}30 & 32 & 40/50 & 53.5/63.6 & 21.9 \\ \cline{1-1}  & Me & \textless{}80 & 120-150 & 40/50 & 51.9/61.3 & 22.6 \\ \cline{1-1}  & C & \textless{}120 & 200–300 & 40/50 & 56.2/66.0 & 22.4 \\ \cline{1-1}  & H & \textless{}350 & 200 & 40/50 & 51.5/61.3 & 23.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Acoustic characteristics of single sound clips in the experiment.  
Figure 6: Setup of the combined noise stimuli.
# Biosynthesis of Polyhydroxyalkanoates from Defatted _Chlorella_ Biomass as an Inexpensive Substrate
## 4 Conclusions  
In this work, DCB-hydrolysate-derived sugars were successfully converted into PHA by _Cupriavidus necator_ KCTC 2649, _Bacillus megaterium_ ALA2, and _Haloferax mediterranei_ DSM 1411. _C. _nc reactor_ exhibited a maximum PHA production of 7.51 \(\pm\) 0.20 g/L (75.4% of DCW) at 120 h of cultivation. Along with PHA accumulation, a maximum carotenoid content of 1.80 \(\pm\) 0.16 mg/L was produced by _H. mediterranei_ DSM 1411 at 120 h of cultivation in shake flasks. PHA and carotenoid production respectively increased by 1.45- and 1.37-fold when HCl DCB hydrolysate biotransformation was upscaled to a 1 L working volume fermenter. Taking into account the overall results of this study, DCB can be investigated as a sustainable substrate to reduce the high production cost related to bioplastic production, along with limiting environmental problems arising from waste disposal.  
Methodology and investigation, C.K. and H.A.; formal analysis and data curation, C.K.; writing--original draft and data analysis, H.A.; conceptualization, supervision, and writing--review & editing, B.S.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea (NRF-2019R1I1A3A02058523).  
Not applicable.  
The data presented in this study are available on request from the corresponding authors.  
We would like to thank [PERSON] at National Center for Agricultural Utilization Research, ARS, USDA for supplying _Bacillus megaterium_ ALA2 strain.  
The authors declare no conflict of interest.  
Figure 6: Time profile of OD at 600 nm, DCW, PHA accumulation, carotenoid production, and substrate utilization by _Haloferax mediterranei_ DSM 1411 using 0.3 N HCl DCB hydrolysate as the carbon source in the fermenter. Data are shown as mean values \(\pm\) standard deviation (n = 3).
###### Abstract  
Laser vibrometers are known for their high precision, long-range capabilities, and non-contact measurement characteristics. However, in long-range applications, spike noise often arises, primarily due to the laser speckle effect induced by rough targets. To address this challenge, this paper develops a light field transmission model for laser vibrometers. By exploiting the differences in speckle patterns formed by lasers of different wavelengths on the same rough target, a dual-wavelength laser vibrometry technique utilizing wavelength division multiplexing devices is proposed, along with a dual-channel signal enhancement method based on orthogonal demodulation. This approach effectively reduces the likelihood of spike noise and enhances the system's velocity measurement resolution. The experimental results demonstrate that, compared to the single-wavelength system, the dual-wavelength system significantly suppresses laser speckle noise, mitigates measurement spike noise, and improves the stability of micro-vibration measurements. Additionally, the system's velocity resolution improves from 0.165 \(\upmu\)m/s/Hz\({}^{1/2}\) in the single-wavelength system to 0.122 \(\upmu\)m/s/Hz\({}^{1/2}\) in the dual-wavelength system, thereby enhancing the system's sensitivity to micro-vibrations. In engineering applications, the dual-wavelength approach provides higher stability and resolution for micro-vibration signal measurements.  
laser Doppler effect; light field transmission; speckle; optical fiber coupling +
Footnote †: journal: Applied sciences  
0000-0002-3300-3300-3300-33000][PERSON]  
1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; [EMAIL_ADDRESS] (X.J.); [EMAIL_ADDRESS] (Y.S.); [EMAIL_ADDRESS] (Y.W.); [EMAIL_ADDRESS] (X.K.)
## 1 Introduction
Additionally, species-level studies, such as those examining divergence times within _Lagerstroemia_ and the diversification of _Agave_, offer more detailed insights into the evolutionary processes occurring within specific plant groups [34; 35]. These studies demonstrated that cpDNA is effective in explaining both large-scale and species-level diversification, thereby enhancing our understanding of plant evolution and diversification. The structural stability and evolutionary significance of the cp genome extend to the analysis of selective pressures acting on its genes [36]. The dN/dS ratio, which compares the rate of non-synonymous (dN) to synonymous (dS) substitutions, is commonly used to assess selective pressure [37; 38; 39; 40]. The dN/dS ratio can be used to identify genes under selective pressure at specific divergence points within a phylogenetic tree. By analyzing the dN/dS ratios of cp genes along particular branches of the tree, researchers can elucidate the direction and intensity of selection experienced by these genes during divergence events. A dN/dS ratio below 1 indicates purifying selection, in which deleterious mutations are removed to maintain gene function, which is a characteristic of evolutionary conservation [41; 42]. A dN/dS ratio close to 1 indicates neutral evolution, in which mutations neither benefit nor harm the organism. In contrast, a ratio above 1 suggests positive selection, indicating that beneficial mutations drive adaptive changes. In this study, we have described the original plant species morphology of Phytolaceae Radix in detail, analyzed the cp genome of _P. acinosa_, and performed a comprehensive comparative analysis of _Phylolaca_ species. Using Maximum Likelihood (ML) and Bayesian Inference (BI) methods, we constructed phylogenetic trees to elucidate evolutionary relationships within the genus. Additionally, we calculated dN/dS to evaluate the selective pressures on cp genes and estimated divergence times to trace species diversification. These phylogenetic and evolutionary insights are crucial for the safe use of _P. acinosa_ and _P. americana_ as Phytolaceae Radix, ensuring accurate species identification and reducing toxicity risks in medicinal applications. This genomic approach highlights the importance of precise species delimitation to enhance the efficacy and safety of herbal treatments derived from these plants.
## 5 User Study
### Measures
#### 5.3.2 Self-Reported Metrics--Post-Experimental Questionnaire  
Regarding the self-reported measure metrics, we used the UES-SF to assess user engagement. The UES-SF has demonstrated sufficient reliability and validity and is commonly applied in digital interaction studies [60; 61; 62]. The questionnaire comprises 12 items rated on a 5-point Likert scale, ranging from 1 (_strongly disagree_) to 5 (_strongly agree_). These items are evenly distributed across four dimensions: _Focused Attention_, _Perceived Usability_, _AestheticAppeal, and Reward Factor, with three items per category. Focused Attention refers to the degree to which users become absorbed in the interaction, often losing awareness of time or their surroundings. It reflects cognitive immersion and deep concentration during the task. Perceived Usability captures users' feelings of control, effort, and frustration during the interaction, where higher scores indicate a smoother and more intuitive experience. Aesthetic Appeal relates to users' appreciation of the system's visual and sensory design, reflecting how visually engaging and pleasant the interface is perceived. Finally, Reward Factor assesses how worthwhile, enjoyable, and personally valuable the experience is, encompassing aspects of intrinsic motivation and user satisfaction. In addition to the questionnaire, the participants were briefly interviewed about their overall experience. They were encouraged to provide honest and constructive feedback, which was noted for qualitative analysis.
## 3 The Proposed Approach
### Anomaly Detection and Data Mining Techniques
The hybrid detection method unfolds sequentially, starting with anomaly detection to screen for irregularities, followed by the in-depth analysis of flagged nodes using data mining techniques, and culminating in the cryptographic verification of suspected attackers. This comprehensive approach ensures the early detection, accurate identification, and effective mitigation of black hole and gray hole attacks in MANETs. This innovative detection method promises to significantly enhance the security landscape of MANETs by offering a robust solution capable of adapting to the dynamic nature of these networks. By integrating diverse detection techniques, our approach aims to establish a more resilient defense against sophisticated threats, ensuring the reliability and integrity of data transmission within these critical communication infrastructures.
# Client-Aware Negotiation for Secure and Efficient Data Transmission
## 4 Result and Analysis
### Encryption Scheme and Security Analysis

Table 2: The ratio of the lowest security strength to the highest security strength encryption overhead in the experiment. The actual system deployed here uses the latest version of the software, so it is relatively safe in the current environment.
# Survey and Perspectives of Vehicular Wi-Fi versus Sidelink Cellular-V2X in the 5G Era
## 3 Simulation Tool and Settings
### Main Settings
#### 3.2.3. LTE-V2X Resource Allocation Algorithms  
In LTE-V2X, the 10 MHz channel corresponds to 50 resource block pairs (RBPs) per subframe. An adjacent allocation of the SCIs (i.e., control information adjacent to related data) is assumed, with a number of subchannels that is optimized based on the number of RBPs per packet and reported in the second column from the right of Table 3. The adopted resource allocation algorithms are as follows.  
**Mode 3 algorithm.** When the allocation is controlled by the network, the algorithm detailed in [38] is adopted. The network allocates the resources trying to maximize the distance between nodes using the same subchannels. More specifically, handling the vehicles in random order, it allocates to each of them the subframe used farther than its position (assumed known with a Gaussian error of less than 100 m in 95% of the cases) and, within that subframe, it reserves the subchannels used farther than its position. The allocation process is repeated every 2 s.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Application** & \\ \hline Beacon periodicity & 10 Hz \\ Beacon size & 300 B \\ \hline
**Common PHY layer** & \\ Channel bandwidth & 10 MHz \\ Transmission power & 23 dBm \\ Antenna gain (both tx and rx) & 3 dB \\ Noise figure & 9 dB \\ Propagation model & WINNER+, Scenario B1 \\ Shadowing variance & LOS 3 dB, NLOS 4 dB \\ Shadowing decorrelation distance & Highway 25 m, Urban 10 m \\ \hline
**Related to IEEE 802.11p** & \\ Duration of the initial interframe space & \(58\) μs \\ Random backoff & \([0\div 15]\times 13\) μs \\ Carrier sensing sensitivity & \(-85\) dBm \\ \hline
**Related to LTE-V2X** & \\ \hline
**Mode 3** & \\ Allocation interval & \(2\) μs \\ UTDOA positioning: 95 th-percentile of error & \(100\) m \\ \hline
**Mode 4** & \\ Probability to maintain the allocation (\(p_{\text{k}}\)) & \(0\) or \(0.8\) \\ Sensing threshold to assume the channel busy & \(-110\) dBm \\ \hline \hline \end{tabular}
\end{table}
Table 5: Main simulation parameters and settings.  
**Mode 4 algorithm.** The standard Mode 4 described in [39; 40] is adopted. It has been shown in [22; 23; 41] that its performance is significantly affected by the value of \(p_{\rm k}\), which allows a trade-off between reliability and latency (see Section 2.2). For this reason, both the minimum \(p_{\rm k}=0\) and the maximum \(p_{\rm k}=0.8\) are considered. In both cases, the initial threshold on the measured power to define the channel as busy is set to \(-110\) dBm as in [7].
## 3 Case Study
### Input Variable Determination  
Previous research results have shown that the choice of input variables has a significant influence on the model prediction results; hence, the causes of various deformation components should be analyzed. The seasonal component is affected by the periodic changes in the hydrostatic load and seasonal temperature, the trend component is affected by the combined action of the dam material degradation and inherent rheological property, and the residual part is due to uncertain aging factors such as geological changes and structural damage [34; 35; 36]. Most of the previous statistical models were developed on the basis of the HST model, whereas the HST model does not include the hysteresis of the water level and air temperature relative to the dam deformation, and its simulation of the temperature effect through a simple harmonic function cannot accurately reflect the actual  
Figure 7: Radial displacement changes at each measuring point.  
temperature changes [37]. In this study, the HSTT model was used to simulate the observed dam deformation.  
The HSTT model is an improvement of the HST model, which adds an additional thermal term on the original basis to introduce a hysteresis description of the temperature effect. In the HSTT model, the reservoir level \(H\) is used to describe the hydraulic effect \(\delta(h)\). \(\delta(h)\) can be considered as a linear polynomial with \(H\), \(H^{2}\), \(H^{3}\), and \(H^{4}\) terms. The thermal effect consists of a seasonal component \(\delta(s)\) and an additional thermal term \(\delta(T)\). The seasonal component \(\delta(s)\) refers to the periodical variation in the dam deformation and can be modeled as \(\sin\frac{2\pi t}{365}\), \(\cos\frac{2\pi t}{365}\), \(\sin\frac{4\pi t}{365}\), and \(\cos\frac{4\pi t}{365}\). The additional thermal term \(\delta(T)\) introduced to improve the simulation of the temperature effect is the difference between the fitted periodic function of the seasonal component \(\delta(s)\) and the actual air temperature. The time-varying effect is the numerical response under the passage of time, which can be described with \(t\) and \(ln(t)\).  
In short, according to the mathematical description of the HSTT model, the 11 influencing factors need to be used as the input variables of the models to predict the radial deformation of the dam.
## 4 Results and Discussion
Figure 10 presents a comparative analysis of the sum-rate performance of the algorithms across varying SNR levels, assuming noisy measurements. Compared to DRL, SOMP, TS-HB, and CNN-MIMO [9], MARL demonstrates a superior performance. While SOMP was originally designed for single-user scenarios, it has been adapted for multi-user contexts in this proposed work. Both SOMP and TS-HB require the input of feasible sets F and W, which are the array response sets. Therefore, the precision of these feasible sets significantly affects the performance of SOMP and TS-HB, as it relies on the accuracy of both the channel matrices and array response sets. Corrupt channel and array response data can result in a reduction in the average sum rate at low SNR, as shown in Figure 10. While CNN-MIMO does not require feasible sets of beamforming vectors during the prediction stage, they are necessary during the training stage to acquire labels. Overall, these findings highlight the robustness of the proposed MARL-based approach for downlink RF beamforming codebook design in multi-beam and multi-user MIMO systems. To highlight the advantages and differences of each of the studied sachems over one another, a table is formed as given in Table 3. As shown in Table 3, the proposed MARL-based beamforming codebook design does not require any difficult-to-obtain or unavailable data during either the training or evaluation phases. The results discussed so far also demonstrate the robustness of MARL against noisy measurements.
###### Abstract  
The source-filter model is one of the main techniques applied to speech analysis and synthesis. Recent advances in voice production by means of three-dimensional (3D) source-filter models have overcome several limitations of classic one-dimensional techniques. Despite the development of preliminary attempts to improve the expressiveness of 3D-generated voices, they are still far from achieving realistic results. Towards this goal, this work analyses the contribution of both the the vocal tract (VT) and the glottal source spectral (GSS) cues in the generation of happy and aggressive speech through a GlottDNN-based analysis-by-synthesis methodology. Paired neutral expressive utterances are parameterised to generate different combinations of expressive vowels, applying the target expressive GSS and/or VT cues on the neutral vowels after transplanting the expressive prosody on these utterances. The conducted objective tests focused on Spanish \([\mathrm{a}]\), \([\mathrm{i}]\) and \([\mathrm{u}]\) vowels show that both GSS and VT cues significantly reduce the spectral distance to the expressive target. The results from the perceptual test show that VT cues make a statistically significant contribution in the expression of happy and aggressive emotions for \([\mathrm{a}]\) vowels, while the GSS contribution is significant in \([\mathrm{i}]\) and \([\mathrm{u}]\) vowels.  
expressive speech synthesis; emotional database; speech analysis; inverse filtering; glottal source; vocal tract; numerical voice production; GlottDNN +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).