esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
Thus, gestures to browse movies and play/pause a video are desired. Gesture \(x\) (_index and middle fingers scroll left/right with the pronated forearm_) was assigned to show the previous or next item. For the command _play/pause_, gesture \(j\) (_index finger taps on the table with the forearm in pronated position_) was the most popular gesture among users. The same gesture was proposed in another study to play the selected channel while watching TV [39]. From the proposed microgesture-command sets, we can acknowledge that tapping and swiping microgestures were popular among participants which was discovered while designing gestures to control mobile devices [43,50]. The preference for tapping and swiping gestures may be caused by the users' past experience with touch screens. The preference for microgestures revealed that understanding the users' interaction habits is vital to implementing an interface based on microgestures for VR or AR systems. The proposed 3D microgestures are not limited to commands investigated in this study (Table 1). The microgestures designed to manipulate an object could be used to set parameters for the virtual scene. For example, microgestures with the purpose of enlarging or shrinking an object could be performed to zoom in or out of a virtual scene while no object was selected. Similarly, microgestures used to translate or rotate an object could control the coordinate systems in a VE. Although the proposed 3D microgesture set is designed for VR/AR systems specifically, the use of it can be extended to other platforms using similar context-based commands. Interfaces based on hand gestures have been developed to complete secondary tasks while driving a car so that drivers can pay attention to the road safely without distraction while manipulating a control panel through a touchscreen or keypad [44,51]. However, another study [52] found that an interface based on mid-air gestures takes longer to complete a task and takes a higher workload compared to a touch-based system. Perhaps, performing 3D microgestures with the hands resting on the steering wheel or one forearm resting on an armrest could allow drivers to reduce muscle load, cognitive workloads, and the duration to complete a task when performing secondary tasks. Similar to feedback provided to users when resting their arms on a table, drivers may receive feedback when resting their hands on the steering wheel when compared to mid-air gestures.
# Detection of Volatiles by HS-SPME-GC/MS and Biological Effect Evaluation of Buddha's Hand Fruit
## 2 Materials and Methods
### Cell Culturing and Cytotoxicity Test (MTT)  
The cellular metabolic activity, as an indicator of cell viability of HL60 cells treated with extracts, EO, and related Hy of _C. medica_, was investigated by thiazolyl blue tetrazolium bromide assay (MTT assay), both in a dose- and time-dependent manner. The cells were maintained in a 75 cm\({}^{2}\) flask containing RPMI1640 culture medium supplemented with 10% of FBS (fetal bovine serum), 1% glutamine and 1% penicillin/streptomycin, and maintained in an incubator to control temperature and %CO\({}_{2}\) (37 \({}^{\circ}\)C and 5% CO\({}_{2}\)). Cells were seeded (2 \(\times\) 10\({}^{4}\) cells/well) in a 96-well plate 24 h before being treated. EO was dissolved in DMSO (50% \(v/v\)). Twelve two-fold diluted concentrations were applied from 0.5 to 0.01 mg/mL for extracts, from 1 \(\times\) 10\({}^{-1}\)% to 2 \(\times\) 10\({}^{-4}\)% \(v/v\) for the EO and from 50% to 1 \(\times\) 10\({}^{-1}\)% \(v/v\) for the HY; the DMSO (0.05% final concentration) and ddH\({}_{2}\)O were used as solvent controls. As positive control, vinblastine sulfate (Merck KGaA, Darmstadt, Germany) was used. After treatments for 72 h, the medium has been removed and MTT solution (0.5 mg/mL) was added. Incubation was carried out for 3 h in a dark condition at 37 \({}^{\circ}\)C and, after formazan crystals solubilization by DMSO, the absorbance was read by a Tecan Sunrise(tm) UV-vis spectrophotometer at 595 nm. The obtained values were used to calculate the cell viability percentage (AAT Bioquest EC50 Calculator) [28], and to obtain the concentration at which the treatments exerted half of the maximal response values (EC50). Means \(\pm\) SD were calculated by repeating the assay three times.
## 3. Freestanding Position-Based Routing Protocol
### Location Service  
The FPBR protocol invokes the location service (LS) when a packet arrives for a vehicle with unknown geographic position. The LS is performed with broadcast packets. Therefore, in order to eliminate the broadcast storm problem, a suppression broadcast technique is included in FPBR. Additionally, a redundancy strategy is also introduced in order to improve the LS robustness. The proposed algorithm for the LS is detailed in the next subsections:
## 3 Temperature and Humidity Characteristics of [PERSON]'s Tomb and Their Variation Patterns  
### Annual Variation Patterns of the Temperature and Humidity in the Burial Chamber  
#### 3.1.1 Temperature Variations  
As shown in Figure 3a, the temperatures inside and outside the chamber of [PERSON]'s tomb varied throughout the year. No data were collected from April to early May due to equipment upgrades. The temperature outside the chamber was highly responsive to seasonal variations, and the season variation was high. The temperature outside the chamber reached an annual maximum value of 37.8 \({}^{\circ}\)C in August and then began to decrease, reaching an annual minimum value of \(-\)1.4 \({}^{\circ}\)C in January, thus yielding a yearly temperature difference of 39.2 \({}^{\circ}\)C. The temperature inside the chamber was less responsive to seasonal variations, and the seasonal variation magnitude was small. The temperature inside the chamber gradually increased from February to August, reaching a maximum value of 26.5 \({}^{\circ}\)C in August, after which it began to decrease, reaching an annual minimum value of 4.8 \({}^{\circ}\)C in January, thus yielding a temperature difference of 21.7 \({}^{\circ}\)C throughout the year. In summer, gas exchange inside and outside the chamber was not intense, and heat transfer was reduced. Therefore, the temperature variations inside the chamber remained more stable in the summer months.  
The average monthly temperatures inside and outside the chamber (Figure 3b) were analyzed. From the perspective of the monthly average temperature, the temperatures both inside and outside the tomb reached maximum values in summer and minimum values in winter, with obvious seasonal characteristics. However, due to the insulating effect of the subterranean system of the chamber, the temperature inside the chamber was less sensitive to seasonal variations, and the temperature peaks inside the chamber exhibited a certain lag. The minimum monthly mean temperature inside the chamber was 3.45 \({}^{\circ}\)C higher than that outside the chamber, indicating that the underground system of the chamber was well insulated. In summer, the temperature outside the chamber was higher than that inside the chamber, and heat was transferred from outside to inside. In winter, the temperature outside the chamber was lower than that inside the chamber, and heat was transferred from inside to outside.
# Non-Volatile Memory Forensic Analysis in Windows 10 IoT Core
## 5 Forensic Evidence in Windows 10 IoT Core
### NTFS Filesystem  
Two of the partitions into which the storage is divided, and which are the most relevant ones forensically speaking, use the NTFS filesystem. Among its features, it contains multiple files that define and organize the filesystem, from which relevant information for an investigation can be recovered. In Table 4 a description of the purpose of each one of the files is provided.  
In Figure 6, the content of the \"$MFT\" file of the \"Data\" partition is shown graphically using the MFTExplorer tool.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Directory** & **Description** \\ \hline \$Extend & Folder that contains metadata and optional extensions regarding the NTFS filesystem. \\ \hline \multirow{2}{*}{CrashDump} & Keeps the information stored in memory when the system or an application crashes. \\ \hline \multirow{4}{*}{Logfiles} & Directory in which the logs from different applications are stored. \\  & Complements the namesake directory that is available in the “MainOS” partition, although in this analysis only information about the Windows Management Instrumentation (WMI) has been found. \\ \hline \multirow{4}{*}{ProgramData} & Same purpose as the directory in the system root partition. In this folder there is more information than in the aforementioned one, and the data regarding the SSH service is especially relevant. Also, the packages that have been installed for the different applications in the system can be found here. \\ \hline \multirow{4}{*}{Programs} & Contains directories for the different applications that have been installed on the system as well as a folder with the deleted ones. Each folder contains the resources needed for that application to run. \\ \hline SharedData & Folder designed for shared storage. \\ \hline System Volume Information & Folder used by the system to store its information and the restore points. \\ \hline \multirow{2}{*}{SystemData} & Contains a directory for the Event Tracing for Windows (ETW) logs, a different one for the non-ETW ones and a “Temp” folder. \\ \hline \multirow{2}{*}{test} & Used for the Windows Driver Test Framework (WDTF) for developing and running tests on the system. \\ \hline \multirow{4}{*}{Users} & Local information for the users of the system are stored in this directory. \\  & The most relevant user is the “administrator” as this is the one that is logged on automatically when the system boots, therefore being the one who executes all the actions that are performed by a user on the device. \\ \hline \multirow{4}{*}{Windows} & Little data can be found in this directory, as the relevant “Windows” folder is the one stored in the “MainOS” partition. Some packages are stored in this partition as well as the system registry files, which are almost empty. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Directories in the data partition.
# Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images
## 4 Experiments
### Experimental Results and Analysis  
To ascertain the viability of the model enhancements and their efficacy in mudslide target detection, this research incorporated a series of comparative experiments. These included an exhaustive evaluation of six fundamental models: YOLOv3, YOLOv4, YOLOv5, YOLOv7, YOLOv8, and YOLOX, with a focus on aspects such as detection accuracy, model parameter size, and inference speed. Additionally, a comparative analysis was conducted between the enhanced RS-YOLOX-Nano model and the standard YOLOX-Nano, assessing metrics like model detection _Precision_, _Recall_ rate, \(F1\) score, \(mAP\) value, and actual detection outcomes. Furthermore, ablation studies were undertaken to gauge the effects of varied enhancement strategies on the \(mAP\) value in mudslide target detection, providing comprehensive insights into the overall performance and improvements of the models.
## 3 Results
### Parameter Uncertainty Analysis based on Sensitivity Analysis  
To analyze the sources of uncertainty in the parameters, two experiments were designed for comparison. Based on the sensitivity analysis, four parameters \(f_{ci}\), \(V_{m}^{25}\), \(\alpha_{q}\), and \(\mathrm{E}_{\mathrm{V}m}\) had the highest sensitivity when the model's output was the GPP. We designed two parameter estimation experiments to analyze uncertainty in the model based on the sensitivity analysis. Experiment I estimated four sensitive parameters and experiment II estimated 16 based on MCMC. The results are shown in Figure 5.  
The results of GPP optimization are shown in Figure 5. The results obtained using the four most sensitive parameters through MCMC were almost identical to those obtained using 16 parameters. The RMSE values were 1.657 and 1.496, respectively, and the bias values of the simulated GPP were \(-0.03\) and \(-0.055\), respectively.
### Traditional Methods of F0 Range Estimation  
There are different methods of estimating a speaker's F0 range, depending on the type of definition of F0 range, and on the phonetic or phonological point of view.  
The maximum phonational F0 range (MPFR), which is often used in clinical studies, is usually obtained by a 'tone sweep' method that starts from the speaker's comfortable F0 level and then sweeps up or down to reach his/her highest or lowest sustainable F0s [3; 15; 26].  
In linguistic/paralinguistic studies and speech technologies, estimation of the underlying speaking F0 range (for short, henceforth the underlying F0 range) is more meaningful, and thus is the aim of the present study. The simplest method to estimate the underlying F0 range, however, is a direct analysis of the surface F0 range. There are both acoustic-phone and phonological approaches to do this.  
The most common acoustic-phonetic approach is the Long-Term Distributional (LTD) method, which calculates the robust maximum and minimum F0 values from a lengthy speech sample--the surface F0 range derived this way was then used to estimate the underlying F0 range. As for the length of speech to validate the LTD method, a review in [15] suggested that an English speech of 14 s or two to four sentences long resulted in an error of estimate below 3 Hz, while a longer speech sample did not give a significantly higher accuracy.  
The phonological approach associates the maximum and minimum F0s with the phonological pitch targets, including sentence-initial peaks (H), accent peaks (M), post-accent valleys (L) and sentence-final lows (Fmin) [27; 28; 29]. The F0 range is then derived from the F0 values of these phonological targets: F0 level is represented by the F0 value of Fmin, and F0 span is denoted by the F0 difference between H and L.  
Although the phonological approach was reported to correlate better with human perceptual characteristics than the LTD method [28], it requires annotations based on intonational phonology, and hence is more difficult to implement than the LTD method. Therefore, LTD is more widely used in linguistic/paralinguistic and clinical studies, and various algorithms have been proposed to improve the robustness of LTD [10; 11; 12].
# Working State of ECC Link Slabs Used in Continuous Bridge Decks
## 3 Experimental Program
### Experimental Continuous Bridge Deck Models  
The experiment on ECC link slabs for continuous bridge deck was conducted with three different types: Type I ECC link slab without reinforcement, Type II ECC link slab strengthened by carbon fiber textile (CFT) grid, and Type III ECC link slab strengthened by carbon fiber reinforced polymer (CFRP) bars [26]. The experimental continuous bridge deck model with ECC link slab is illustrated in Figure 1. Each test model was composed of two half-span steel girders covered with steel reinforced concrete deck (600 mm wide and 200 mm thick). Shear studs were soldered to the top of steel girder which could firmly anchor the concrete bridge deck. The two adjacent spans were connected by the ECC link slab (1600 mm by 600 mm by 100 mm) with a debond zone of 1000 mm long. CFT grids and CFRP bars were embedded in the tension zone of ECC link slabs.
## 4 Results and Discussion
### Fluctuating Wind Characteristics
#### 4.2.1 Turbulence Intensity
This result indicates that with the variation in wind speed, the turbulence energy spectrum may change, with the energy of high-frequency turbulence components relatively reducing, leading to an overall decrease in turbulence intensity.
# Development of Wearable Textile MIMO Antenna for Sub-6 GHz Band New Radio 5G Applications
## 7 Conclusions  
This paper introduces an innovative MIMO antenna system designed to deliver superior isolation and stable pattern characteristics suitable for wearable applications. This antenna features a single conducting layer in the shape of an irregular octagon and can be seamlessly integrated into wearable jeans. The design process is employed in an FEM-based HFSS full-wave simulator. The compact MIMO antenna, measuring 50 mm \(\times\) 40 mm, was physically fabricated on 1 mm\({}^{2}\) of jeans cloth material. This antenna system operates across a wide bandwidth in sub-6 GHz, offering a fractional bandwidth of 45.6%, and has been demonstrated to withstand bending and body movement without compromising its performance. The antenna system achieves gain, radiation efficiency, and SAR values of 6.7 dB, 96%, and 1.93, respectively. The gains achieved by the proposed antenna system ensure robust and dependable communication links for wearable devices operating within the sub-6 GHz band, thus providing seamless connectivity for diverse applications for the N79, Wi-FiF-5, Wi-Fi-6, and V2X/DSRC bands.  
Conceptualization, P.P. and M.M.B.; methodology, P.P., S.G. and J.S.; validation, S.G., M.M.B. and P.P.; formal analysis, M.M.B. and J.S.; investigation, P.P. and M.M.B.; writing--original draft preparation, P.P., S.G. and M.M.B.; writing--review and editing, S.G. and J.S.; visualization, P.P. and J.S.; supervision, J.S.; project administration, P.P. and J.S.; funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Deanship of Research and Graduate Studies of King Khalid University under Large Research Project, grant number RGP 2/386/45.  
The data will be provided on the request basis.  
The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/386/45.  
The authors declare no conflicts of interest.
## 1 Introduction
In the proposed approach, we use deep learning architectures including a type of DNN: the Feed-Forward Neural Network (FFNN) and three types of Recurrent Neural Network (RNN): the Layer Recurrent Network (LRN), the Nonlinear AutoRegressive network with eXogenous inputs (NARX), and Long Short-Term Memory (LSTM). To the best of our knowledge, this is the first attempt to apply deep learning technology based on the RNN for transmit power control in the SWIPT system. * Through our proposed approach, the transmit power of transmitters, and the PS ratio of the receivers can be obtained with lower complexity and less computation time, compared to conventional iterative approaches. Simulation results show that the deep learning-based approach is a great potential tool for approximating iterative optimization algorithms. The rest of this paper is arranged as follows. Section 2 shows the system model, and the problem formulation and solution for the SWIPT system. The deep learning-based proposed approaches are described in Section 3. The numerical results and discussions are provided in Section 4. Finally, Section 5 provides a conclusion.
# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 3 Results
### BOSS Algorithm Tests
#### 3.3.1 Sidereal Pointing Camera and Different Exposure Times  
This test aims to investigate the BOSS algorithm behaviour against an increasing stray light noise source. Stray light noise is due to external light sources which cause a flare all over the image. It can be experienced when an optical-space-based sensor has a boresight direction close to the Sun, the Moon, or another high luminosity source. This stray light can have a uniform intensity or uniform gradient. If it is too strong, it can hide the useful signal associated to stars and RSOs and make them difficult or even impossible to be segmented and detected. A change in the stray light intensity during the mission may cause the need for continuous sensor calibration. This could be avoided with a calibration-less algorithm such as the U-Net-based proposal in this work. This noise can increase with the increase of the sensor exposure time as the amount of collected photons proportionally increases with this time interval. In this test, images acquired with a night sky simulator facility (NSSF) (Figure 7) were used. This facility is made of a darkroom where a ZWO ASI 120 MM-S camera points towards a screen. Here, Stellarium software is used to simulate the sky as seen by a specific camera with a selected FOV and optics (2.8 mm of focal length, f/1.4). All the simulated stars have a magnitude lower than 6.5 in Figures 8 and 9. With this software and the real camera, it is possible to simulate the working of a camera pointing at the night sky both for stars and RSO segmentation and detection for preliminary results. Images' noise sources are the real camera noises plus a further stray light source due to the screen technology (backlit screen).  
A sidereal pointing was considered in this test just to see the effect of the image stray light noise increasing with the increase of the camera exposure time. Here 100, 200, 500 and 1000 ms of exposure were considered, and the original images and segmented ones are reported below (Figure 8). From this figure, it is possible to assess that the segmentation algorithm removes the stray light and correctly segments the stars. As the noise increases, the foreground pixels increase because of the increasing magnitude of the targets.  
Figure 7: NSSF at ARCALab, School of Aerospace Engineering, Sapienza University of Rome.  
Figure 8: Left column: images from NSSF; right column: segmented images by the BOSS algorithm; from top to bottom: 100, 200, 500 and 1000 ms of exposure time.
## 3 Discussion of Results  
### Effect of Glass on the Setting Time of Mortar  
Glass powder has a high proportion of silica, thereby causing the Ca/Si ratio to be reduced. It is also noted that the presence of glass powder prompted the reduction of the initial and final setting time of the mortar due to the simultaneous reaction of tricalcium aluminate (C\({}_{3}\)A) with gypsum (CaSO\({}_{4}\)\(\cdot\)2H\({}_{2}\)O) on one hand, and the frictional effect of glass and cement particles on the other hand [6]. The formation of calcium-silicate-hydrate in cement compounds is formed together with hydrated glass particles that occupy the capillary pores, thereby causing early hardening [1]. From Figure 4, it can be seen that the control sample has an initial setting time of 169 min, which decreased by 17.16%, 27.21% and 34.91%, while the final setting time of 240 min was reduced by 12.1%, 25% and 31.25% for the addition of glass powder at 5%, 10% and 15%, respectively. However, when the glass content increased to 20 wt.%, the initial and final setting time reduced by only 13.01% and 10.41%, respectively. This implies that excessive glass content beyond the capillary pore spaces could lead to a dilution effect that affects both setting and strength development. Therefore, early setting could not necessarily lead to early strength development in glass blended mortar.