esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of Actual Data Measured by Wearable ECG Device  
Actual ECG data were measured by a developed wearable ECG patch with a 250 Hz sampling rate, as shown in Figure 19a, where the ECG patch was developed by TriBell-Lab and ETRI (Electronics and Telecommunications Research Institute) in South Korea. The ECG signals measured by the ECG  
Figure 17: Unused R point in noise region for noisy Record 234 contaminated by MA and BW.  
Figure 18: Comparison of beats per minute (BPM), RR interval trend, histogram of RR interval, and Poincaré plot for original Record 234 and noisy Record 234.  
patch are stored in the hospital DB via Bluetooth, Gateway (Bluetooth to WiFi), AP, and the hospital internal network, and are analyzed in real-time using the proposed algorithm. Ten minutes of ECG data at a fixed posture and walking for a 36-year-old man without heart disease were measured. Figure 19b,c show the 1 min length for two situations. Also, the R points on the measured ECG signals were detected by the proposed method.  
Figure 20 shows the comparison of BPM, RR interval trend, histogram of RR interval, and Poincare plot for the measured ECG signal with a fixed posture and walking. The average BPM, average RR interval, RMSSD, and width and length in the Poincare plot are 86.75 bpm, 0.69 s, 0.08 s, 0.05, and 0.09 for the measured ECG signal in a fixed posture and 97.12 bpm, 0.62 s, 0.08 s, 0.06, and 0.09 for the measured ECG signal when walking, respectively. The BPM is higher when walking than with a fixed posture, but the other parameter values are very similar. It can be seen that the histogram distribution of the RR intervals for walking is a little more irregular than for a fixed posture. Also, the Poincare plot is more clean when walking than in a fixed position. Through simple experiments, it can be seen that the proposed method can induce reliable analysis results in other parameters except BPM even in the presence of some movement.  
Figure 19: (**a**) Developed wearable ECG patch; R point detection result with (**b**) a fixed posture and (**c**) walking.
## 2 Materials and Methods
### Acquisition of Image of Sweet Potatoes  
Macroscopic digital images were acquired using a test rig under constant exposure conditions at the Department of Dairy and Process Engineering. The correct uniformity of illumination of the test sample inside the chamber was verified using a lux meter in accordance with PN-EN 12464-1:2004. Digital images were taken using a NIKON D5100 camera (Kabushiki-gaisha Nikon, Bangkok, Thailand) with a 16.2 megapixel sensor (CMOS sensor 23.1 \(\times\) 15.4 mm--DX format) and a NIKKOR 28 mm fixed-telephoto lens. The test samples were placed in a shadowless 0.5 \(\times\) 0.5 \(\times\) 0.5 m tent, illuminated by visible light(VIS) with a cold white color temperature of 6500 Kelvin (K), in a room with no natural lighting [37]. To ensure reproducibility of results, the following image exposure parameters were used: aperture--F9, exposure time --125, and sensitivity--IS0-125. The light source used in the test and at the measurement station consisted of 6 lamps: 3 \(\times\) 70 W (600 lm) and 3 \(\times\) 60 W (806 lm). Digital image acquisition produced 180 original 24-bit images with a resolution of 4928 \(\times\) 3264. Image processing resulted in the segmentation of primary images to a frame that included the entire sweet potato discs and a uniformly colored background. Finally, the 600 \(\times\) 400 secondary images were saved in JPEG format and RGB color space.  
Images belonging to each class formed equidistant subsets. The subsets were created by pre-extracting features by removing the background and replacing it with a uniform black. Furthermore, data augmentation was applied by rotating each image by 90 degrees to obtain 4 derived images. In addition, data augmentation also consisted of zooming in on a random section or shift of each epoch of the learning sessions [38]. Training, test, and validation sets accounted for 60%, 20%, and 20% of the total number of instances, respectively. Gray-Level Co-Occurrence Matrix (GLCM) discriminants were calculated according to the usage given in the documentation of the Python and MATLAB language function libraries and modules used [15; 32; 39; 40].
## 2 Materials and Methods
### Band Identification  
The identified bands were combined into six bands because the multispectral sensor MCA 6 (Tetracam Inc., California (USA)) can only be fitted with six bands; therefore, in this paper, only six bands are shown. The best six-band combinations were chosen from the eight bands (from the currently available MCA 6 (Tetracam Inc., California (USA)), with another six bands from the 20 significant bands (Table 2). The MCA 6 multispectral camera was used to collect the aerial images in the sorghum field using UAV. For the MCA 6 camera, only six bands were used during the flight (because the sensor could only be fitted with six bands). The combination of the eight bands was used because produced more combinations; thus, to find the best six bands, the band combinations were run using discriminant analysis to obtain the highest classification accuracy. Almost all spectral regions resembled those from MCA 6 and the six bands from the 20 significant bands (using linear discriminant analysis (LDA)); however, for the red-edge region, MCA 6 highlighted bands at 750, 730, 720, and 710 nm, while LDA only highlighted bands at 720 and 710 nm.
## 4 Discussion
### Simulation Scenario Construction  
OPNET modeler was used to build our simulation system. First, we import satellite orbit parameters from STK to construct the network scenario model, as shown in Figure 6. After that, we build the node model of the satellite, as shown in Figure 7. The src module is used as the data source to simulate the flow distribution of the user terminal. The proc module serves as the CPU of the satellite and is used to build the network protocol and the algorithm proposed in this paper. The four signal receiving and transmitting terminals serve as laser terminals to establish ISLs with adjacent satellites.  
In the proc module, we use the finite state mechanism to build the network protocol and algorithm, as shown in the Figure 8. The main functions of proc module include initialization, packet sending and receiving processing, signaling generation and broadcasting, and routing algorithm implementation.  
Figure 6: The scenario of the simulation.  
Figure 7: Satellite model.
## 2 Materials and Methods
### Data Acquisition  
#### 2.2.1 Observation of Environmental Factors  
An automatic microclimate observation system was set up near the experimental site to continuously observe common parameters. Ta and relative humidity (RH) were recorded using temperature/humidity probes (HMP155, Vaisala Inc., Vantaa, Finland). The total radiation referred to the downward short-wave radiation was measured using a radiation transducer (CNR-4, Kipp & Zonen Inc., Delft, The Netherlands). PAR was recorded using photosynthetic photon sensor (LI-190 SB, LI-COR Inc., Lincoln, NE, USA). Wind speed (Ws) was measured using an ultrasonic anemometer (Gill Windsonic, Gill Inc., Hampshire, UK). Soil moisture (SM) was measured using time-domain reflectometry (CS650, Campbell Scientific Inc., Logan, UT, USA). Precipitation was measured using a rainfall recorder (TE525 MM, TEXAS ELECTRONICS Inc., Dallas, TX, USA). All environmental data were collected every minute and the 10 min average was recorded in the datalogger (CR1000, Campbell Scientific Inc., Logan, UT, USA). VPD refers to the difference between the saturated vapor pressure and the actual vapor pressure in the air at a certain temperature [54]. Daily environmental factors values were calculated as the mean of the observations between 6:00 and 19:30. The VPD was calculated as follows:  
\[\mathrm{VPD}=(1-\mathrm{RH})\times 0.611\times\mathrm{e}^{\frac{17.27\times \mathrm{Ta}}{23.7\times\mathrm{Ta}}} \tag{1}\]
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 5 Food Safety and Traceability in the Wine Industry
### Detecting and Managing Microbiological Risks  
Microbiological risks are a significant concern in the wine industry, as they can affect both the safety and quality of the wine. AI plays a crucial role in detecting and managing these risks, focusing on the identification of mycotoxins, bacteria, wild yeasts, and other contaminants that can compromise the integrity of wine products. By leveraging advanced technologies such as machine learning, spectroscopy, and biosensors, AI systems provide winemakers with powerful tools to ensure the safety and quality of their wines.
## 3 System Implementation in MATLAB
### Lossless System  
The process of transmitting information for the lossless PDCCH initiates on the transmitter side with the transmission of 140 bits of information [4; 5]. The following processes are then executed: DCI encoding, PDCCH encoding, mapping, and the OFDM transmission technique [4; 5]. The information is transmitted through the additive white Gaussian noise (AWGN) channel. On the receiver side, the inverse processes are carried out: OFDM transmission technique, demapping, PDCCH decoding, and DCI decoding [4; 5]. These processes lead to the retrieval of the transmitted 140 bits, which are then used for BER calculation.
# Design Dimensions of Co-Located Multi-Device Audio Experiences
## 7 Discussion
### Relationship with Audio-Augmented Reality  
Another point of interest is the apparent application overlap between co-located, multi-device audio experiences and audio-augmented reality (AAR) experiences, when comparing with [PERSON]'s six categories of AAR [61]. Specifically, experiences in the _Augmented broadcasting_ and _Social games_ experience groups illustrate AAR through two forms. _Augmented broadcasting_ experiences tend to exhibit AAR through the provision of extra context-relevant audio content. In contrast, _Social games_ use an enchanted objects approach to AAR by adding extra purposeful sound to typically silent objects. Given the understanding that AAR can be defined as real-world experiences that are accompanied by additional layers of sound under either form mentioned above, there is an inherent requirement for two or more sound sources, where either, some, or all of those sources could be audio-capable devices. The question then is: When are co-located multi-device audio experiences considered to be AAR experiences?  
A good starting point is to evaluate the _Organisation_ dimension, to consider the roles of the devices in the experience. In most 'non-hierarchical' experiences, where each device contributes to the audio equally, the experience content can be considered to be a sum of the output of all the devices. Another perspective is that the experience cannot be 'complete' without the array of devices, so there is not augmentation but equal complementation. Conversely, in many 'hierarchical' experiences, the core experience can be listened to on a single device, and the addition of new devices can introduce enhancements such as spatialised audio and additional audio content, which is consistent with AAR characteristics. An example of this is demonstrated in the BBC trial 'Immersive Six Nations' where the original program can be viewed on a main device, but additional devices grant access to extra audio assets such as on-pitch sound effects [22]. This is applicable for _Augmented Broadcasting_ use-cases but is less useful for _Social games_, as is illustrated by Soundwear [47], where the device organisation is 'non-hierarchical', yet the purpose of the devices is to add additional audio to an otherwise ordinary outdoor play experience. Thus, when trying to characterise new experiences, it is proposed that the _Organisation_ dimension should be used as a guide to determine whether a co-located, multi-device audio experience is an AAR experience. Consider, however the roles of the devices on an individual experience basis, as well as evaluating which form of AAR is exhibited in the experience.
# A Survey on Shortest Unique Substring Queries
## 5 Approximate-SUS Queries
### GPU Based Approach  
All the works that have been discussed on \(k\)-mismatch SUS query were focusing on improving the time and space complexity for SUS computation in the sequential CPU model. In 2018, [PERSON] and [PERSON] [15] presented the first parallel approach for \(k\)-mismatch SUS problem in the shared-memory model, particularly leveraging on the massive multi-threading GPU technology. Obviously, this approach is experimentally faster than the CPU solutions. Their experimental results on a mid-end GPU show that the GPU approach is at least six times faster than the CPU approach in the case of \(k=0\) (exact match), and at least 23 times faster in the general case (\(k>0\)). This is essential when the input string is massively long as genomic sequences are studied. In terms of memory usage, this approach is almost the same as the sequential CPU approaches. Their algorithms totally happen on GPU except a transferring input string to GPU, which is done by the CPU host. When there are no mismatches (\(k=0\)), they implicitly use the efficient data structures like suffix array and lcp array. However, when \(k>0\), using these data structures would be problematic since they do not have enough information such as \(k\)-mismatch LCPs. To solve this issue, [PERSON] and [PERSON] [15] designed a method that can be parallelized in the GPU architecture.  
In 2019, they published an extension of their paper [16], providing more experimental results on exact SUS and approximate SUS computation. They show the speedup gained by the GPU-based approach against the sequential solution, including or excluding suffix array construction. It is important to observe the difference of speedup in case of whether the suffix array is given or not. Corresponding to their results, almost 50% of the time of the sequential approach is to spend on suffix array construction. Thus, if the suffix array is given, it can be copied to the GPU memory directly to achieve a better speedup.
## 2 Results  
### Volatile Compounds in Young Wines  
A total of 53 volatile compounds have been identified and quantified in wine samples (Table 1), including five alcohols, 3 C\({}_{6}\) alcohols, 10 esters, three acids, 18 terpenes, seven norisoprenoids, seven benzenoids. The analysis of the variance (ANOVA) made between the three modalities, showed statistically significant (\(p<0.05\)) differences for 35 compounds. Wines from withered grapes in fruttaio and on-vine were characterized by higher content in terpinen-4-ol, \(\beta\)-citronellol, 1,4-cineole, 3-oxo-\(\alpha\)-ionol, vinylguiaicol, ethyl acetate, benzyl alcohol and ethyl vanillate. At the same time, samples fruttaio and on-vine compared to control showed lower amounts of 1-pentanol, 1-hexanol, _cis_-3-hexenol, _trans_-3-hexenol, isoamyl acetate, \(n\)-hexyl acetate, phenylethyl acetate ethyl 3-methylbutanoate, ethyl hexanoate, ethyl octanoate, hexanoic acid, octanoic acid, _cis_-linalool oxide and \(\beta\)-damascenone. Compared to each other, statistical differences of the two withering techniques were observed for 19 volatile compounds, among them 1-butanol, 1-hexanol, _trans_-3-hexenol, ethyl butanoate, \(\beta\)-citronellol, limonene, \(\alpha\)-phellandrene, terpinolene, benzyl alcohol and methyl vanillate were found in higher concentration in fruttaio samples, while on-vine samples showed higher content of 2-butanol, ethyl acetate, octanoic acid, \(\beta\)-damascenone, TDN and 4-vinyl guiaicol. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & **Control** & & **Frutialo** & & **On-Vine** & & \\ \hline \multirow{3}{*}{**Compounds**} & **Odor** & & & & & & & \\  & **Thread 1** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & \(p\)**-Value** \\ \hline \multicolumn{2}{c}{**Alcohols**} & & & & & & & & \\  2-Butanol & & 3283.8 2 2 207.9 & 2203.1 2 & \(\pm\)80.3 & 3467.7 2 2 20 & \(\pm\)93.1 2 & \(<\)0.0001 \\  1-Butanol & & 2697.9 2 2 207.9 2 203.1 2 207.9 2 209.1 2 209.1 2 209.1 209. Eighteen glycosylated bound compounds have been quantified (Table 2). The ANOVA showed significant differences for eight of these compounds. Compared to control, fruttaio samples and on-vine showed significantly lower concentrations in _cis_-3-hexenol, benzyl alcohol precursor and higher content of methyl vanillate and ethyl vanillate precursors. Comparing fruttaio and on-vine samples, statistically significant differences have been observed for six glycosylated bound compounds. Fruttaio samples were richer in 1-hexanol, _trans_-3-hexenol, geraniol and vanillin precursors, while on-vine samples showed higher concentration only for the phenylethyl alcohol glycosidically bound precursor.
## 4 The Architecture of IoT-Enabled Smart City
### Communications in Multi-Tier Architectures
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & **Zights 002.15.6** & **WI-SUN 002.15.49** & **ULP 002.15.49** & **Wireless M-Bus** & **Z-Wave** & **Bluetooth Low Energy (BLE)** & **Wi-Fi Low Power (BLE)** \\ \hline Frequency bands & 866/715/2603 MHz & Sub-1 GHz, 2.4 GHz & 868/715/2603 MHz & 169/433/868 MHz & 968 MHz & 2.4 GHz & Sub-1 GHz \\ \hline Max T-wave & 250 kbps & 1 kbps & 100 kbps & 100 kbps & 1.3 Mbps & 7.8 Mbps \\ \hline TX power & 1-100 mW & 1-100 mW & 5-15 mW & 1-100 mW & 1-100 mW & 1-100 mW & 100 mW \\ \hline Max ramp (m) & 100 & 200 & 100 & 300 & 100 & 30 & 1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Short-range communication standards for multi-tier IoT architectures. increased from 150 kbps to 8 Mbps by using the OFDM waveform scheme in conjunction with Binary Phase Shift Keying (BPSK), Quadrature Phase-Shift Keying (QPSK), and Quadrature Amplitude Modulation (QAM) modulation. It is also possible to add more devices by using the MAC layer. It is a better protocol for various applications because it has a power conservation mode, a data optimization method, and an extended sleeping state. [PERSON] et al. [104] have reported a work that is based on Wi-Fi technology (IEEE 802.11 [105]). They found that Wi-Fi technology [106] has been extensively exploited in applications for smart cities. Due to the abundance of gadgets connecting, there is a severe interference issue. An anti-interference mechanism is essential to reduce the interference effect, and Wi-Fi can be more useful in IoT-based smart cities [107]. [PERSON] et al. [108] have reported a mechanism to measure Wi-Fi signal strength and developed smart medical health care for smart city applications. Every patient in a medical hospital carries a smartphone with Wi-Fi connectivity. The number of patients waiting in the waiting area, where they are located, and their real-time movements are all determined by the signal strength data gathered from their cell phones. The system's main benefit is that it can be implemented in the current infrastructure without any new infrastructure. The information gathered can be used to spot future medical problems, which is helpful for doctors.
## 2 Compact Model and Physics
### Discussion on Oxide Traps and Substrate Voltage  
In conventional theory, HCI degradation is usually considered to be caused by one type of interfacial trap, adhering to a simple single-stage power-law model or a self-saturation power-law model. However, a two-stage phenomenon under certain specific voltage conditions in HKMG devices has been found. One possible explanation is the existence of at least two different traps that contribute to HCI degradation, and they may each play a dominant role at different times of degradation. In the literature [6; 20], it can be observed that the subthreshold swing ratio (SS) decreases with time both in single-stage power-law phenomena and in two-stage power-law phenomena, implying that the production of interfacial traps saturates with time, accompanied by at least one type of oxide trap. Figure 4 demonstrates the distribution of the two traps in the oxide layer.  
The model of the generation of oxide traps can be described as  
\[N_{\text{ox}}=AR_{\text{ox}}\log(1+ct) \tag{11}\]  
The aging rate related to oxide traps \(AR_{ox}\) follows the same form as that of interface traps, but is strongly correlated with the gate impulse current and, therefore, is affected by the bias of the substrate. When reverse back-bias is applied to SOTB devices, energetic hot electrons generated by high drain bias are injected into the gate oxide layer under the enhanced vertical electric field \(E_{y}\), creating capture sites at the drain edge. As a result,  
Figure 4: The distribution of interface traps and oxide traps in the device.  
Figure 3: (**a**) The energy levels of Si-H bonding solutions in multi-vibrational excitations; (**b**) A schematic of carriers causing defects process in the SVE and MVE theories.  
electron capture is considered the dominant mechanism in reverse back-biasing [21]. The enhancement of HCI degradation due to substrate bias is related to impact ionization, and the ionization current can be written as \(I_{G}=I_{G_{\text{o},ii}}\exp(\alpha_{i}V_{b})\). So, the aging rate is  
\[AR_{\text{ox}}=B\big{(}V_{\text{gs}}-V_{T}\big{)}^{n_{2}}\exp\left(-\frac{H_{2} }{V_{ds}-\eta V_{\text{dast}}}\right)\exp\left(-\frac{E_{a}k}{k_{B}T}\right) \exp(\alpha_{i}V_{b}) \tag{12}\]
# Experimental Study on the Near-Red Flow Characteristics of Alluvial Channel with Seepage
## 1 Introduction
In order to decipher the consequences of seepage on turbulent parameters in the alluvial channel, the present work aims to simulate the experimental channel at moderate discharge, where sediment movement is minimal or has a negligible effect on channel morphology. Keeping this aspect for alluvial channels, this study investigates the turbulent flow characteristics in the near-bed flow of a bed with seepage. Experiments were carried out allowing downward seepage flow from the channel bed. The three-dimensional instantaneous velocity components of flow were measured by using an Acoustic Doppler Velocimeter (ADV).