esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Muscular Demands
#### 3.1.2 Effect of Assistance during Retraction Portion  
Over the retraction task portion, slight benefits were seen in the LES, LBF, and RBF muscles but not in the RES muscle, where the BSIE led to \(\sim\)5% increased demands (\(p<0.01\)) with a normalized amplitude of 0.64. However, benefits in the leg muscles of \(\sim\)22% (\(p<0.01\)) were seen in both leg muscles with the BSIE. Looking at the postures, asymmetry decreased the demands in the LES by 20-25% but increased the demands by about the same amount in the RES under both the with-assistance/without-assistance conditions. The highest activity of 0.71 (SD: 0.26) occurred in the RES with the BSIE during the asymmetric postures. During retraction, the activity in the LBF was higher in the asymmetric vs. symmetric posture under the with (8% difference)- and without (13% difference)-assistance conditions (Figure 5). Considering the temporal differences (Figure 6), the use of the BSIE led to 12% and 11% increases in the LES and RES with time, while the increases were \(\sim\)10% and 5% without the BSIE, respectively (\(p<0.01\)).  
Figure 6: Muscle demands in left/right erector spinae (LES/RES) and left/right bicep femoris muscles compared between without assistance (N) and with assistance (E) across time of experiment as beginning (B) and end (ED) and categorized according to task portion as bending (BD), retraction (RT), and transition (TS) (Note: dissimilar letters/symbols within the same task portion denote statistical significance).
## 3 Results and Discussion
### Volatile Aroma Compounds
#### 3.2.8 Sulfur-Containing Compounds
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Co.**} & \multicolumn{1}{c}{**Volatile Aroma Compounds**} & \multicolumn{1}{c}{**ID**} & \multicolumn{1}{c}{**LR\({}_{\text{exp}}\)**} & \multicolumn{1}{c}{**LR\({}_{\text{lit}}\)**} & \multicolumn{1}{c}{**F-Ratio**} & \multicolumn{2}{c}{**Treatment**} \\ \hline \multicolumn{1}{c}{} & & & & & & \multicolumn{1}{c}{_SCE_} & \multicolumn{1}{c}{_LEV_} \\ \hline SU1 & 2-Thiophenecarboxaldehyde & MS, LRI & 1704 & 1701 & 109.241 & 0.273 \(\pm\) 0.027 \({}^{\text{a}}\) & 0.105 \(\pm\) 0.004 \({}^{\text{b}}\) \\ SU2 & Ethly1-3(methylthio)propionate & MS, LRI & 1570 & 1571 & 95.263 & 2.72 \(\pm\) 0.22 \({}^{\text{a}}\) & 1.45 \(\pm\) 0.07 \({}^{\text{b}}\) \\ SU3 & Dihydro-2-methyl-3(2H)-thiophenone & MS, LRI & 1512 & 1506 & 92.128 & 2.82 \(\pm\) 0.08 \({}^{\text{b}}\) & 3.31 \(\pm\) 0.03 \({}^{\text{a}}\) \\ SU4 & 3-Hydroxyethyl 2-hydroxypropyl sulfide I & MS & 1779 & - & 75.423 & 0.21 \(\pm\) 0.18 \({}^{\text{b}}\) & 1.71 \(\pm\) 0.24 \({}^{\text{a}}\) \\ SU5 & 3-Hydroxyethyl 2-hydroxypropyl sulfide I & MS & 1822 & - & 69.285 & 0.076 \(\pm\) 0.010 \({}^{\text{b}}\) & 0.297 \(\pm\) 0.045 \({}^{\text{a}}\) \\ SU6 & Methionol & S, MS, LRI & 1722 & 1717 & 21.853 & 14.56 \(\pm\) 1.21 \({}^{\text{a}}\) & 10.50 \(\pm\) 0.89 \({}^{\text{b}}\) \\ SU7 & Ethyl methanesulfonate & MS & 1691 & - & 8.972 & 2.53 \(\pm\) 0.88 \({}^{\text{a}}\) & 0.97 \(\pm\) 0.18 \({}^{\text{b}}\) \\ SU8 & 3-Methionyl acetate & MS, LRI & 1635 & 1627 & 7.876 & 2.67 \(\pm\) 0.16 \({}^{\text{b}}\) & 3.23 \(\pm\) 0.31 \({}^{\text{a}}\) \\ SU9 & Benzodiazep & MS, LRI & 1962 & 1962 & 5.833 & 0.710 \(\pm\) 0.026 & 0.609 \(\pm\) 0.067 \\ SU10 & Sulfur & MS, LRI & 2305 & 2302 & 4.756 & 0.446 \(\pm\) 0.083 & 0.301 \(\pm\) 0.079 \\ SU11 & 4-(Methylthio)-1-butanol & MS, LRI & 1841 & 1812 & 4.753 & 0.450 \(\pm\) 0.107 & 0.314 \(\pm\) 0.016 \\ SU12 & Isothiocyanatocyclohexane & MS, LRI & 1679 & 1670 & 4.142 & 0.793 \(\pm\) 0.088 & 0.661 \(\pm\) 0.071 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Concentrations (\(\mu\)g/L) of sulfur containing compounds found in Malvazija istarska white wines produced using different yeasts determined by targeted one-dimensional gas chromatography/mass spectrometry (GC/MS) ‡ and untargeted two-dimensional gas chromatography with time-of-flight mass spectrometry (GC\(\times\)GC/TOF-MS) sorted by decreasing Fisher’s \(F\)-ratio.
# Hybrid Multi-Antenna Techniques for V2X Communications--Prototyping and Experimentation
## 1 Introduction
Nevertheless, the integration of multiple antennas in real-world mobile radio transceivers imposes significant challenges rising from the variety of hardware and signal processing constraints as well as the peculiarities of the wireless medium. In this article, we present a novel hybrid diversity scheme that simultaneously supports multi-mode services, and has been developed and tested under real-world conditions in the context of H2020-ROADART project. The scheme exploits the capabilities of electronically switched parasitic array radiator (ESPAR) antennas, which demonstrate considerably reduced complexity and size, compared to conventional multi-antenna solutions. Capitalizing on the additional degrees of freedom offered by the ESPAR antennas, the design of a new diversity engine is presented. The engine implements a flexible, hybrid scheme that combines conventional diversity, that is, a maximal ratio combiner (MRC) and a pattern selection technique built on low-cost, compact ESPAR antennas. Finally, the new technique was integrated in a real-world testbed for truck-to-truck (T2T) communications and the results from various experimentation trials are presented. It is noteworthy that the new diversity engine is compatible with all current V2X technologies (NR, LTE or WiFi-based solutions) and new features can be easily integrated on it as new standards and technologies emerge. The paper is organized as follows. In Section 2, the basic principles of the ESPAR antennas, the diversity concept as well as the main steps that were followed for their configuration are presented. Moreover, in Section 3, a detailed description for the hybrid diversity engine is presented, in which the operational algorithm and the reconfiguration procedure are analyzed. In Section 4, various performance results are given, based on a real world vehicle-to-vehicle (V2V) communication testbed that has been built. Finally, in Section 5, the concluding remarks are provided.
# Techniques for Calculating Software Product Metrics Threshold Values: A Systematic Mapping Study
## 6 Threat to Validity and Limitations  
Five well-known electronic databases have been investigated to reach papers that focus on threshold-related studies. These libraries are IEEE Explore, Science direct, ACMDigital Library, Springer, and Wiley. There are variations of systematic mapping and experimental studies that can have limitations on our study.  
The papers searched in this work are limited to well-known digital libraries. Therefore, there is a possibility of missing relevant papers. The authors have focused on well-known databases. The authors used synonyms related to threshold values of object-oriented metrics. Threshold calculation papers are usually related to fault prediction papers, and there is no complete separation between threshold papers from quality-related papers.  
Bias over publications: It is a human tendency to report more favorable outcomes over adverse outcomes in every publication. If any work is reported, negative results have low chances of getting accepted and vice versa.  
We have not limited to search analysis and selection techniques. We have searched all types of work related to this review, irrespective of their specific data or method. The selected studies for the review process from different periods when various institutions/practitioners utilized such methods. The broad acceptance of such methods leads to influence the researchers to use such methods.  
Searching and selecting of studies: Our strategy was designed to find as many articles as possible. We built a wide range of search strings over digital databases. Although the search outcomes included many irrelevant articles, we decided to keep the search string fixed, as such, we do not miss any relevant papers. We considered regulating the search string to diverse our search results. We applied the inclusion and exclusion principle to choose relevant papers. Three researchers examined the principle of selection and discarded criteria. Therefore, the possibility to exclude the paper incorrectly was minimized.  
Quality assessment: Usually, quality assessment is conducted by one researcher and validated by another researcher. However, in our review, the second researcher performed data extraction and quality assessment. Most biases were expected in quality assessment because research questions may be hard to justify. The score of articles may vary from one researcher to another.
## 3 The State-of-the-Art Review in Tunnel Blasting Evaluation
### Introduction to Half Porosity in Tunnel Blasting  
Half porosity is a critical factor in evaluating the effectiveness of tunnel blasting, significantly influencing rock fragmentation and overall excavation quality. The presence of half porosity can affect the structural integrity of tunnel walls, impact the efficiency of blasting operations, and pose safety risks during construction. Recent advancements in the assessment and monitoring of half porosity have leveraged innovative techniques, particularly in the realm of three-dimensional image reconstruction and analysis.  
[PERSON] et al. (2018) were pioneers in developing a digital identification technique for detecting half porosity in tunnel blasting. Utilizing 3D point cloud data extraction, their work provided a reliable framework for assessing the impacts of blasting on rock structures [15]. This method not only improved the accuracy of half porosity detection but also facilitated better monitoring of blasting outcomes, allowing for timely interventions if structural issues arose. The integration of advanced imaging techniques has thus proven essential in enhancing the understanding of how half porosity affects tunnel integrity.  
Furthermore, [PERSON] et al. (2019) proposed a method that combines machine vision with digital imaging to analyze the rock surface characteristics of tunnel excavation faces. This approach underscores the importance of understanding surface features and their correlation with half porosity, thereby contributing to more informed decision-making in blasting strategies [17]. By integrating visual analysis with structural assessments, researchers can better predict how half porosity may impact excavation efficiency and safety.  
The ongoing research in this field emphasizes the need for accurate monitoring and assessment methods. For instance, [PERSON] et al. (2020) developed techniques for detecting over and underdigging, which are critical in contexts where half porosity may affect excavation outcomes [18]. These methodologies contribute to a more comprehensive understanding of the interplay between blasting practices and rock characteristics, ensuring that potential risks associated with half porosity are effectively managed.  
As a final point, the study of half porosity in tunnel blasting is gaining momentum, driven by advancements in imaging technology and analytical methods. The integration of 3D reconstruction and machine learning techniques presents new opportunities for accurately assessing the implications of half porosity on tunneling projects, ultimately leading to enhanced safety and efficiency in tunnel construction. As research progresses, it will be essential to continue exploring these innovative approaches to mitigate risks and optimize blasting outcomes.
## 3 Earth Surface Effects on GNSS-R Relevant to Land Applications
### Regional Scale
#### 3.2.2 Asia: Arid Deserts and Boreal Forests  
The evaluation over the Northern Asia region is useful to study separately the effects of SWI and AGB (Figures 14-16). The comparison between Figures 14 and 15 shows the effects of higher AGB on the waveforms' parameters, for relatively homogeneous SWI conditions at latitudes higher than \(\sim\)55\({}^{\circ}\) (Figure 15g). SWI data from ASCAT could have some limitations for high vegetation density because measurements are derived using a radar operating at a high frequency \(\sim\)5.2 GHz (C-band). The trailing edge width is lower (Figure 15e,f) over regions with higher canopy height (Figure 14), and rapidly increases over the Gobi Desert or over the highest latitudes in Siberia. On the other hand, the highest PR values (Figure 15a,b) and SNR values (Figure 15c,d) are associated to regions with larger values of the trailing edge width (Figure 15e,f). As is shown in Figure 15g\(-\)j, NDVI is not associated with SWI over Northern Asia in Winter or Summer. Canopy height (Figure 14) is a key parameter. The spreading of the waveforms, the PR, and SNR values show approximately a similar trend in Summer (Figure 15a,c,e) and Winter (Figure 15b,d,f), but different absolute values. The main difference is the smaller waveforms' spreading in areas with higher values of AGB. Additionally, the SNR is lower in Winter (Figure 16), particularly over areas with lower AGB values, because of the effect of snow on the surface at these latitudes. This additional signal attenuation explains the lower spreadingof the signals in Winter, since a smaller region around the nominal specular point contributes to the power waveforms. Finally, it is found that PR is lower in Summer over Northern Siberia (in agreement with Figure 12) because of the high soil moisture level in Summer (Figure 15g) and snow conditions in Winter (Figure 15h).
## 3 Experimental Setup
### Photon Counting with PCM  
The PCM includes an analog photomultiplier (PMT) and a discriminator. Due to the photocathode being sensitive to a single photon, a PCM can be used for single-photon detection and photon counting [49]. In PCM, the PMT is used to convert the incident photons to high-amplitude electric pulses, and the discriminator set at some threshold level is used to analyze the PMT output. Once the electrical output exceeds the set threshold, a detection occurs. By setting an appropriate threshold, most spikes of current consistent with the amplification of thermal electrons that escaped from the dynodes of PMT (especially those dynodes that are closer to the anode) may be filtered out. One important issue is that the electrical current at the output of the PCM is no longer a continuous high voltage  
Figure 6: The results of the test and simulation: (**a**) the measured transmittance of the tested water, (**b**) Normalized power loss, and 3 dB underwater channel bandwidths for Jerlov II.  
Figure 7: The test results of turbulence: (**a**) the measured and normalized power variation as a function of time, (**b**) the histogram of the measured data with the PDF of normalized power and scintillation.  
output when a photon is incident on the photocathode, and the output electrical pulse has a finite width and a fixed amplitude. In this experiment, an off-the-shelf photon-counting module (HAMAMATSU, H10682, 40 \(\times\) 22 \(\times\) 36 mm) was selected as the device. Figure 8 demonstrates the internal structure of the device. The diameter of the effective area is 8 mm. As shown in Figure 8c, the yellow curve is the output waveform of the conventional analog photodetector, and the green curve shows the output waveform of the PCM. Since the operating characteristics of the PCM are dependent on wavelength, we tested the performance parameters including the output pulse width, the dead time, the dark, and the distribution of photocounts of the PCM used in this experiment at 450.1 nm.
###### Abstract  
Early prediction of students' learning performance and analysis of student behavior in a virtual learning environment (VLE) are crucial to minimize the high failure rate in online courses during the COVID-19 pandemic. Nevertheless, traditional machine learning models fail to predict student performance in the early weeks due to the lack of students' activities' data in a week-wise timely manner (i.e., spatiotemporal feature issues). Furthermore, the imbalanced data distribution in the VLE impacts the prediction model performance. Thus, there are severe challenges in handling spatiotemporal features, imbalanced data sets, and a lack of explainability for enhancing the confidence of the prediction system. Therefore, an intelligent framework for explainable student performance prediction (ESPP) is proposed in this study in order to provide the interpretability of the prediction results. First, this framework utilized a time-series weekly student activity data set and dealt with the VLE imbalanced data distribution using a hybrid data sampling method. Then, a combination of convolutional neural network (CNN) and long short-term memory (LSTM) was employed to extract the spatiotemporal features and develop the early prediction deep learning (DL) model. Finally, the DL model was explained by visualizing and analyzing typical predictions, students' activities' maps, and feature importance. The numerical results of cross-validation showed that the proposed new DL model (i.e., the combined CNN-LSTM and ConvLSTM), in the early prediction cases, performed better than the baseline models of LSTM, support vector machine (SVM), and logistic regression (LR) models.  
imbalanced data distribution; explainable student performance prediction; feature importance +
Footnote †: journal: Applied Sciences
## 8 Performance Analysis
### Communication Overhead  
We first define that timestamp, identity, hash, random number, and ECC cryptosystem are 32, 160, 160, 160, and 320 bits, respectively. In our protocol, transmitted messages \(\{M_{1},M_{2},M_{3}\}\) and \(\{M_{4},M_{5},M_{6}\}\) require \((160+160+160=)\) 480 and \((160+160+160=)\) 480 bits, respectively. As a result, the proposed scheme has more efficient than related schemes [6; 20; 21; 28] because the total communication overhead of proposed protocol is very low compared with the others. Table 7 shows the analysis result of communication overhead compared to existing schemes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Schemes** & **Total Computation Cost** \\ \hline [PERSON]–[PERSON] [28] & \(7T_{mp}+T_{m}+5T_{h}+T_{s}+T_{cert}+T_{cert\_ver}\approx 528.91\) ms \\ [PERSON]–[PERSON] [20] & \(7T_{mp}+2T_{e}+2T_{b}+10T_{h}\approx 635.88\) ms \\ [PERSON] et al. [21] & \(5T_{mp}+2T_{e}+2T_{b}+12T_{h}\approx 505.72\) ms \\ [PERSON] et al. [6] & \(12T_{h}+4T_{ecm}\approx 268.40\) ms \\ Ours & \(16T_{h}\approx 11.05\) ms \\ \hline \hline \end{tabular}
\end{table}
Table 6: A comparative summary: computation overheads.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Entity** & \(T_{b}\) & \(T_{ecm}\) & \(T_{mp}\) & \(T_{e}\) & \(T_{h}\) \\ \hline Pentium IV & 3.16 ms & 1.17 ms & 1.17 ms & \(<\)1 ms & 0.01 ms \\ HiPerSmart Card & 0.38 s & 0.13 s & 0.13 s & \(<\)0.1 s & 0.001 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Various cryptographic operations based on execution time [21; 34].
## 3 Results
### In-Cylinder Pressure and Engine Performance  
Figure 9 shows the effect of SA and IP on the in-cylinder pressure of the engine. Figure 9a-c present the pressures and RoHRs inside the engine cylinder with various SAs in the 0.01, 0.015, and 0.02 m IP cases, respectively. Meanwhile, Figure 9d shows a summary of the in-cylinder peak pressure in all simulation cases.  
Although the difference in peak pressure among simulation cases was not significant, it can be observed that the peak pressure tended to increase slightly as IP increased at each SA in both diesel and DF modes. Among SA cases, the SA of 45 degrees produced the highest in-cylinder peak pressure in both diesel and DF modes. Considering the effects of both SA and IP on the in-cylinder pressure, Figure 9d shows that the SA of 45 degrees with an IP of 0.015 or 0.02 m produced the highest peak pressure in both diesel and DF modes.  
Figure 8: TKEs in the engine cylinder in all simulation cases: **(a)** IP = 0.001 m; **(b)** IP = 0.015 m; **(c)** IP = 0.02 m.  
Figure 10 shows the Indicated Mean Effective Pressure (IMEP) of the engine in all simulation cases. The simulation results showed an increasing tendency in the IMEP of the engine as the IP increased in both diesel and DF modes. Meanwhile, it tended to decrease as SA increased from 40 to 50 degrees. Among these cases, an IP of 0.02 m with an SA of 40 degrees produced the highest IMEP for the engine in both diesel and DF modes. Specifically, this set of injection parameters increased the IMEP of the engine by 11.15% and 6.08% compared to when using the original set of injection parameters (IP = 0.01/SA = 45) in the diesel and DF mode, respectively. It is thus considered to be the optimal parameter that should be set for the fuel injector to enhance engine performance in both diesel and DF modes. The fact that the engine reached the highest performance with this set of injection parameters might be because the target injection region of the injector was in the optimal combustion region of the engine in this case.
## 3 MDCAE Feature Extraction Model  
Traditional feature extraction methods and downsampling techniques, such as the convolutional auto-encoder (CAE), mainly focus on extracting local features around points and ignore the importance of global features, which may lead to the loss of some features during the extraction process. Therefore, the features extracted by this method have difficulty comprehensively representing the global attributes of the data. In this study, by integrating multi-scale convolution [33] and dilated convolution technology [34,35], the trade-off between the receptive field and resolution was addressed, and a new feature extraction model was proposed. This model is called Multi-scale fusion convolution and Dilated Convolution Auto-Encoder (MDCAE), effectively achieving comprehensive feature extraction.