esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Sensory Attributes
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \begin{tabular}{c} **Sensory** \\ **Descriptor** \\ \end{tabular} & _Sc-Td 1_ & _Sc-Mp 2_ & _Sc 3_ & _p-Value 4_ & **CS 5_ & **GT 6** & _p-Value_ & \begin{tabular}{c} **Interactions** \\ **(_p-Value_) \\ \end{tabular} \\ \hline \begin{tabular}{c} Scent intensity \\ Red fruit \\ Black fruit \\ \end{tabular} & 6.0b & 6.2 ab & 6.6a & * & 6.4 & 6.1 & ns & ** \\ \begin{tabular}{c} Red fruit \\ Black fruit \\ \end{tabular} & 1.35b & 1.97a & 2.01a & *** & 1.90 & 1.66 & ns & ns \\ \begin{tabular}{c} Black fruit \\ Citrus fruit \\ \end{tabular} & 1.47b & 1.99a & 2.03a & * & 2.95a & 0.71b & *** & *** \\ \begin{tabular}{c} Citrus fruit \\ Tree fruit \\ \end{tabular} & 0.86b & 1.65a & 1.84a & *** & 1.00b & 1.90a & *** & ** \\ \begin{tabular}{c} Tree fruit \\ \end{tabular} & 1.10b & 1.78a & 2.12a & *** & 1.58 & 1.75 & ns & ** \\ \begin{tabular}{c} Taste intensity \\ \end{tabular} & 5.8 & 5.5 & 5.6 & ns & 5.9a & 5.4b & ** & ns \\ \begin{tabular}{c} Acidity \\ Alcohol \\ \end{tabular} & 4.5b & 4.9a & 5.0a & ** & 4.6b & 5.0a & * & ns \\ \begin{tabular}{c} Alcohol \\ Complexity \\ \end{tabular} & 4.7b & 5.0a & 4.9a & ** & 4.8 & 4.9 & ns & ns \\ \begin{tabular}{c} Complexity \\ Balance \\ \end{tabular} & 4.1b & 4.6a & 4.7a & *** & 4.6a & 4.3b & * & ns \\ \
\begin{tabular}{c} Balance \\ Persistence \\ \end{tabular} & 4.1c & 5.0b & 5.7a & *** & 4.5b & 5.3a & *** & ** \\
\begin{tabular}{c} Persistence \\ \end{tabular} & 5.1a & 4.2b & 4.3b & *** & 4.7 & 4.4 & ns & * \\ \hline \end{tabular}  
* Sc-Td: wines fermented with sequential inoculation of _Toralusgom delbrueckii_ and _Saccharomyces cerevisiae. 2_ Sc-Mp: wines fermented with sequential inoculation of _Metchnikoin pulcherrina_ and _Saccharomyces cerevisiae. 3_ Sc: wines fermented with monoculture of _Saccharomyces cerevisiae. 4_ p-value from the two-way ANOVA. ns: not significant,
* : 0.05 \(>\) p-value > 0.01, *
* : 0.01 \(>\) p-value > 0.001, and **
* : _p-value_ < 0.001. Mean values with different letters differ significantly.5 CS: Cabherent sauvignon. 6 GT: Garacha Tinta.  
\end{table} 
Table 2: Two-way analysis of variance (ANOVA) performed on the sensory scores of the rose wines from different fermentation strategies and grape varieties.
## 6 Connection-Aware Scheduling Heuristics
### Connection-Aware Reinforcement Learning Agent
#### 6.4.2 Problem Definition: Job Scheduling in Dew Computing  
We tackle the problem of distributing jobs in a dew environment. The dew environment consists of devices connected to a local network. Some of these devices can be \(IoT\)devices_ that have an unlimited power supply (e.g., Raspberry Pis and personal computers), and others are _mobile devices_ with a limited battery (e.g., tablets and smartphones). In addition, one node in the local network is assigned as the _scheduler_. The scheduler's purpose is to receive and distribute jobs among the devices.  
Once a job is assigned to a device, the time it takes to complete (and how much battery it consumes) depends on the job's features and the device's current state. For instance, since some devices have limited battery life, a job might not be completed if assigned to a device with a low battery level. Users might also interact with the devices, and the local network might have congestion issues. The scheduler must consider all these factors to distribute jobs effectively.  
In more detail, assigning a job to a device adds that job to the device's job queue. Then, the device will keep running the jobs in its queue, one by one, until they are completed or running out of battery. Once a device runs out of battery, all the jobs in its current queue are discarded. If a job is assigned to a device that has no battery, that job is also discarded. In our experiments, a job is not reassigned to a different device when the job is discarded. This forces the scheduler to be extra careful when assigning a job to a battery-dependent device.  
To evaluate the effectiveness of a given job distribution, we use the _giga-instructions per second (GIPS)_ that are completed in the dew environment:  
\[\text{GIPS}(t_{0},t)=\frac{\sum_{j\in I_{c}}j\text{.ops}}{t-t_{0}}, \tag{6}\]  
where \(J_{c}\subseteq J\) is the subset of jobs that were completed within the time interval \([t_{0},t]\) and \(j\).ops is the number of giga-operations of job \(j\in J_{c}\). Intuitively, GIPS measures how many operations are completed in a unit of time. The scheduler aims to distribute the jobs to maximize the GIPS in the dew environment.  
In this study, we postulate that the dew computing environment is subject to a limitation regarding the maximum number of devices that can be integrated into the network at various intervals. This constraint results from the restrictions imposed by the size of the RL model, which is crucial for optimizing network performance and decision making and cannot accommodate an undefined number of devices. Understanding the implications of this restriction is essential for managing the network's scalability and ensuring that the dew environment can efficiently handle the dynamic addition of devices over time.
## 3 Issues in Implementing URLLC
### URLLC Scheduling  
The scheduling of an unexpected packet generation by URLLC is one of the most significant issues. When user data arrives, it is stored in a user-specific transmission buffer, as illustrated in Figure 5. The transmission of each packet takes no less than one TTI. However, radio channel conditions, payload size, and availability of resources may force scheduling to increase the TTI of a packet [20].  
The NR defined by 3 GPP for 5G has proposed two scheduling schemes, instant scheduling and reservation-based scheduling, to handle URLLC packets [20]. The instant scheduling approach proposes to facilitate URLLC packets whenever they are generated. Therefore, this scheduling can interrupt the ongoing data transmission. Consequently, this approach can result in a drastic degradation of other services. The reservation-based scheduling is further divided into two types, semi-static and dynamic reservation, for effective handling of packets. Both approaches use a reservation-based frame for URLLC, which results in overheads in the control signaling. In the case of no URLLC data, the reserved slot may be wasted.
## 5 Conclusions  
In this paper, we proposed a novel strongest AoA estimation algorithm for hybrid mmWave communication equipped with 1-bit A/Ds at transceivers. The proposed algorithm aims to estimate the strongest AoA components while reducing the number of detection repetitions as much as possible. We conducted Monte Carlo simulation experiments to obtain appropriate quantization thresholds for different SNRs and different number of antennas M. With appropriately designed quantization thresholds, we further investigated the RMSE performance of the strongest AoA estimation of our proposed algorithm and compared to different schemes via simulations, e.g., 1-bit FFT, 1-bit CRLB, and the exhaustive search algorithm. Comprehensive simulation results verified that our proposed algorithm can significantly reduce the estimation overheads while maintaining the estimation performance of strongest AoA components in low SNR regimes.  
Conceptualization, R.L., L.Z., C.L. and M.B.; methodology, R.L., L.Z., C.L. and M.B.; software, R.L., L.Z., C.L. and M.B.; validation, R.L.; formal analysis, R.L., L.Z., C.L. and M.B.; investigation, R.L., L.Z., C.L. and M.B.; resources, R.L., L.Z., C.L. and M.B.; data curation, R.L., L.Z., C.L. and M.B.; writing--original draft preparation, R.L.; writing--review and editing, L.Z., C.L. and M.B.; visualization, R.L., L.Z., C.L. and M.B.; supervision, L.Z., C.L. and M.B.; project administration, R.L., L.Z., C.L. and M.B.; funding acquisition, L.Z., C.L.. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by Zhejiang Provincial Natural Science Foundation of China under Grant No. LZ22F010001, and the National Natural Science Foundation of China under Grant No. 42176190.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Result and Discussion  
### Simulation Results  
The effects of the positions and forces of drawbacks were analyzed through FEA. Figure 8 shows a graph that analyzed the correlation between wrinkles and cracks based on the positions and forces of drawbacks. The tendencies of wrinkles and cracks were different depending on the drawback position. The wrinkles decreased as the drawback forces of no. 2, 3, 6, 8, and 10 increased, and they tended to increase and then decrease in other cases. When the correlations for wrinkles were calculated, negative correlations were obtained in the order of beads 6, 2, 8, and 10. Cracks tended to increase as the drawback forces increased for beads 1, 3, 4, 6, and 12, and they tended to increase and then decrease for bead 8. In particular, when the correlations were analyzed, bead 12 showed the largest impact on crack occurrence, followed by beads 6, 3, and 1. Beads 1, 3, and 6 were located in the straight line of the geometry and bead 12 in the curved part. These beads were found to be similar to areas where wrinkles occurred inside the product when drawbacks were not applied, which are shown in Figure 5b. The influence of cracks appears insignificant because the number of cracks was relatively smaller than that of wrinkles; however, they were selected as important variables through sensitivity analysis in Section 3.2. It appears that there was no relatively significant difference, owing to the narrow comparison range between the variables. This confirmed that drawbacks should properly control the flow of the material while preventing wrinkles, and excessive control may cause cracks. Figure 9a shows the result with no cracks and the smallest number of wrinkles in the analysis case, and Figure 9b shows the result with the smallest sum of wrinkles and cracks. In the case of Figure 9a, no cracks occurred, but wrinkles penetrated the geometry. In Figure 9b, no wrinkles occurred inside the geometry; however, cracks occurred in the main corner. These two results confirmed the necessity of minimizing wrinkles with no cracks in the forming process.  
Figure 7: Prediction of wrinkles and cracks by drawbacks from the results of sheet metal forming simulation.
# Deep Learning Optimisation of Static Malware Detection with Grid Search and Covering Arrays
## 6 Results and Discussion
### Choice of Optimisers  
The significance of choosing the appropriate optimiser cannot be overstated. After thorough consideration, we opted to compare the performance of two optimisation techniques: SGD and Adam. Each optimiser has its unique set of dependencies and challenges. For instance, with SGD, determining the optimal learning rate requires experimental trials, as determining its value beforehand can be challenging, as demonstrated in prior studies [1]. Our experiments indicated that both SGD and Adam optimisers can yield high accuracy scores for our model. Our experimentation with SGD showed that the learning rate (LR) hyperparameter had a significant impact on the model's accuracy. Thus, we conducted a grid search with a learning rate range from 0.0001 to 0.5. Our findings revealed that a learning rate above 0.2 negatively impacted the model's accuracy score, with a value of 0.4 yielding an accuracy of 65%. Based on these findings, we refined our search space for the learning rate, as reflected in Table 3. On the other hand, the accuracy of the Adam optimiser was found to be dependent on the number of epochs and batch size.  
It is worth noting that while previous studies on deep learning for static malware detection have explored the use of optimisers such as SGD and Adam [45; 46], few have investigated the impact of hyperparameters on their performance. Our study provides valuable insights into the effects of hyperparameter tuning on the efficacy of these optimisers, thereby contributing to the development of more robust deep learning models for static malware detection. Furthermore, our comparison of the performance of SGD and Adam in our study highlights the importance of careful consideration of optimiser selection in deep learning models, particularly when it comes to static malware detection.
## 3 Results
### Device Structure and Environment Variation
#### 3.3.2 Copper Electrode Thickness Variation  
Although it has been confirmed that the difference in the optimum carrier concentration between the material ZT and the device power density is influenced by the thermal conductivity, it is necessary to confirm the influence of changes of the device structure. The copper pad in the device is used as an interconnection between the thermoelectric materials, and when the copper pad is too thin, it degrades the ZT of the device [16]. As shown in Figure 6, when the copper is thickened, the copper resistance decreases and the power density of the device increases slightly.  
Figure 6 shows the maximum power density according to the carrier concentration with copper thickness variation for the two materials A and D, presented in Figure 4, which have different thermal conductivities. In the case of Figure 6a, the power density is calculated by changing the copper thickness in the device using material D, which has high thermal conductivity. The optimum carrier concentration of maximum power density shifted more than that of material A, which has relatively low thermal conductivity, as shown in Figure 6b. In other words, when a material having a low thermal conductivity is used, the optimum point is close to ZT and the stability against copper pad thickness change is improved.  
Figure 5: (**a**) Carrier concentration at which the maximum power density is obtained for each material (A, B, C, D) and (**b**) power density at that time according to FF change.  
Figure 6: The power density distribution of (**a**) material D and (**b**) material A based TEG with variation of the copper thickness.
# Symbol Error-Rate Analytical Expressions for a Two-User PD-NOMA System with Square QAM
## 4 BER and SER for PD-NOMA Square QAM Signal
### Second Layer Decoding
Thus, we divide \(x_{i}+y\) by geometric position into three types (\(x_{i}+y_{left}\), \(x_{i}+y_{ins}\), \(x_{i}+y_{right}\)) with their detection areas (\(O_{y}^{left}\), \(O_{y}^{ins}\), \(O_{y}^{right}\)) and transform the equation for \(\rho\big{(}\Re_{s}\in O_{y}\big{)}\) into  
\[\rho(\Re_{s}\in O_{y})=\sum\limits_{i=1}^{\sqrt{Q_{1}}}\rho\Big{(}x_{i}+y_{ left}\Big{)}\rho\Big{(}\Re_{x_{i}+y_{left}}\in O_{y}^{left}\Big{)}+\sum\limits_{i=1 }^{\sqrt{Q_{1}}}\rho(x_{i}+y_{ins})\rho\Big{(}\Re_{x_{i}+y_{ins}}\in O_{y}^{ ins}\Big{)}+\sum\limits_{i=1}^{\sqrt{Q_{1}}}\rho\Big{(}x_{i}+y_{right} \Big{)}\rho\Big{(}\Re_{x_{i}+y_{right}}\in O_{y}^{right}\Big{)}\]  
The occurrence probabilities for each type are \(\rho\Big{(}x_{i}+y_{left}\Big{)}=1/\sqrt{Q_{2}}\), \(\rho\Big{(}x_{i}+y_{right}\Big{)}=1/\sqrt{Q_{2}}\), \(\rho(x_{i}+y_{ins})=1-2/\sqrt{Q_{2}}\), and by substituting them to the top equation, we obtain  
\[\rho(\Re_{s}\in O_{y})=\frac{1}{\sqrt{Q_{2}}}\sum\limits_{i=1}^{\sqrt{Q_{1}}} \rho\Big{(}\Re_{x_{i}+y_{left}}\in O_{y}^{left}\Big{)}+\Big{(}1-\frac{2}{ \sqrt{Q_{2}}}\sum\limits_{i=1}^{\sqrt{Q_{1}}}\rho\Big{(}\Re_{x_{i}+y_{ins}} \in O_{y}^{ins}\Big{)}+\frac{1}{\sqrt{Q_{2}}}\sum\limits_{i=1}^{\sqrt{Q_{1}}} \rho\Big{(}\Re_{x_{i}+y_{right}}\in O_{y}^{right}\Big{)} \tag{26}\]  
Now, we need obtain the probabilities for the symbol of each type falling into its own detection area. Firstly, let us focus on \(x_{i}+y_{left}\) and derive the expression for \(\rho(\Re_{x_{i}+y_{left}}\in O_{y}^{left}\in O_{y}^{left}\)). We start with discussing the example given in Figure 2. The symbol \(x_{1}+y_{left}\) is decoded correctly when \(\Re_{x_{1}+y_{left}}\in O_{y,local}^{left}(m)\forall m\in\{1,2,3,4\}\), marked by the blue color. It is important that in the real domain, \(O_{y,local}^{left}(1)\) is limited only on the right side by the neighboring inside symbol, and \(O_{y,local}^{left}(2)\), \(O_{y,local}^{left}(3)\), \(O_{y,local}^{left}(4)\) are limited on both sides. Hence, the complete detection area \(O_{y}^{left}\) is a compound of all local areas \(O_{y}^{left}=\cup\limits_{m=1}^{4}O_{y,local}^{left}(m)\). This conclusion is fair for \(x_{i}+y_{left}\forall i\in 1\ldots\sqrt{Q_{1}}\). In a general case, for \(Q_{1}\)-QAM, we have \(O_{y}^{left}=\cup\limits_{m=1}^{4}O_{y,local}^{left}(m)\) and obtain \(\rho(\Re_{x_{i}+y_{left}}\in O_{y}^{left})\) by using Gauss error function according to  
\[ \begin{array}{l}\rho(\Re_{x_{i}+y_{left}}\in O_{y}^{left})=1-\frac{1}{2} \sum\limits_{m=1}^{\sqrt{Q_{1}}-1}\bigg{[}erf\bigg{(}\frac{(\sqrt{Q_{1}}-i-m+ \frac{1}{2})D_{1}+(0.5\sqrt{Q_{2}}-\frac{1}{2})D_{2}}{\sqrt{N_{0}}}\bigg{)} \bigg{)}-erf\bigg{(}\frac{(\sqrt{Q_{1}}-i-m+1)D_{1}+\frac{1}{2}D_{2}}{\sqrt{N_ {0}}}\bigg{)}\bigg{]}\\ -\frac{1}{2}erf\bigg{(}\frac{(1-i)D_{1}+\frac{1}{2}D_{2}}{\sqrt{N_{0}}}\bigg{)} \end{array} \]  
Figure 2: The upper section of the PD-NOMA constellation shown in Figure 1.
## 1 Introduction
### Research Purpose  
Recently, a new technology for transmitting traffic signal information from a traffic management center (TMC) or a traffic signal controller installed at the individual intersections of an approaching vehicle through wireless communication has been developed, and it is entering into practical uses.  
The introduced mobility application using the traffic signal information transmitted to vehicles is simply intended to adjust their approaching speeds to minimize the chance for the vehicle to stop at the intersection, improve drivers' comfort, and reduce fuel consumption and GHG emission. This study highlights that such valuable information should be used for more diverse applications such as automatic vehicle idling control.  
This study aims to evaluate the environmental benefits of autonomous vehicles internally enhanced by an automatic vehicle idling control function, capable of reflecting the queue length and traffic signal information shared through C-ITS, specifically V2I, by employing a microscopic mobility and emission simulators, VISSIM and VISSIM COM (i.e., component object model) and MOVES (i.e., motor vehicle emission simulator), respectively.  
The remainder of this study is organized as follows: Section 2 provides an informative summary of related works. Section 3 describes the research methodology with the underlying concept and algorithm for the automatic vehicle idling control of autonomous vehicles under C-ITS. Section 4 presents the research methodology, conducted with VISSIM and VISSIM COM coupled with MOVES, and evaluates the proposed vehicle idling control function with a case study in Haeundae-gu in Busan, Korea. Lastly, Section 5 contains conclusions, research limitations, and future research issues.
## 2 Materials and Methods
### Eligibility Criteria  
The selection process involved several stages. First, duplicated papers were excluded. Second, the following eligibility criteria were considered: (a) full-length articles reporting empirical data and published in scientific journals, conference papers, and book chapters, thus excluding other works such as reviews, conference reviews, books, editorials, and surveys, and (b) papers not published in the English language were excluded. Searches were carried out without any timeframe constraints. In the following stage, papers with no full text available through the b-on consortium were excluded. Later, titles and abstracts were initially screened, and articles were excluded in three consecutive phases. First, exclusion was based on three specific criteria: (a) lack of relevance to the topic of mental workload, (b) focus on healthcare robots or health conditions, and (c) papers referred to social or cognitive robots. Next, a second screening was conducted to define the application scenarios, leading to the exclusion of studies related to virtual reality, teleoperations, search and rescue missions, unmanned aerial vehicles, extreme situations, and other scenarios not related to HRC in industry. After the authors completed their ratings, they gathered to finalize the list of which records to ultimately exclude and why. There was almost perfect agreement between the three raters in the assessment of the studies for exclusion based on their titles and abstracts. The inter-rater reliability, measured using the Kappa coefficient of Cohen, was 0.82 [31].  
The full content of all remaining papers was read after the initial screening by a single author (E.P.) to determine which ones, in light of the previously stated eligibility criteria,were the most pertinent. In this phase, papers lacking relevance to the application of physiological signals used to assess mental workload or containing marginal information were excluded from consideration. Finally, an analysis of the screened articles was conducted to identify different workload measurement types--namely performance, subjective, and objective measures--and their corresponding key indicators and specific features analyzed from signals to assess mental workload in HRC. Furthermore, data were also collected regarding the sample size employed in their studies, the type of tasks that were conducted, and the industry scenarios in which they can be categorized, as per the perspective of the authors of this review. At the end of this selection process, 25 papers were retained and deeply analyzed.  
Figure 1 presents the PRISMA flow diagram representing a schematic illustration of the strategy adopted, displaying the number of studies included at each phase of the selection process and the reasoning for the exclusion.
# Application of Deep Learning and Symmetrized Dot Pattern to Detect Surge Arrester Status
## 4 Methodology
### Symmetrized Dot Pattern  
For the PD phenomenon of surge arresters in the time domain waveform, it is difficult to directly identify different types of faults. Therefore, this study used the SDP method to map the PD signal of the surge arrester from the time domain waveform to the polar diagram. A snowflake-like symmetrical image (also known as a snowflake pattern) was drawn, and the image showed different snowflake petal patterns according to the variation of the signal waveform amplitude value and frequency value. This image was imported as a feature pattern into the CNN for image recognition.  
The SDP directly transformed the surge arrester's PD signal from the time domain waveform into the polar diagram. A schematic diagram is shown in Figure 15[22; 23], where \(\gamma(i)\) is the polar coordinate radius, \(\alpha_{cuv}(i)\) is the clockwise rotation angle of the initial line, and \(\alpha_{cuv}(i)\) is the counterclockwise rotation angle of the initial line. In the discrete sampling data sequence of the vibration signals, if the vibration value of time \(i\) is \(x_{i}\), the vibration value at time \(i+\Delta T\) will be \(x_{i+\Delta T}\). When this value is substituted in (6) to (8), the point in the transformed polar coordinate space is \(P(\gamma(i),\alpha_{cuv}(i),\alpha_{cuv}(i))\), and the vibration signals can be generated by changing the initial rotation angle to form the SDP.  
\[\gamma(i)=\frac{x_{i}-x_{min}}{x_{max}-x_{min}} \tag{6}\]  
\[\alpha_{cuv}=\psi-\frac{x_{i+\Delta T}-x_{min}}{x_{max}-x_{min}}k \tag{7}\]  
\[\alpha_{cuv}=\psi+\frac{x_{i+\Delta T}-x_{min}}{x_{max}-x_{min}}k \tag{8}\]  
where \(x_{min}\) is the minimum value of vibration value; \(x_{max}\) is the maximum value of the vibration value; \(\Delta T\) is the time interval (range value 1-10); \(\psi\) is the initial rotation angle; and \(k\) is the magnification of the rotation angle (generally smaller than the value of \(\psi\)). According to the test result, the feature extraction parameter values suitable for surge  
Figure 14: PD signal of salt damage to the ceramic insulator of a surge arrester after filtering.  
Figure 13: PD signal of a surge arrester with internal dampness after filtering.  
arraster fault prediction were \(\psi=60^{\circ},\ \Delta T=3,\ k=30\). The symbols and acronyms used in this section are presented in Table 2.