esa-sceva/satcom-chunk-collection huggingface dataset:
# Physicochemical and Sensory Evaluation of Yanggaeng Treated with Roasted Guateman Coffee Extracts
## 3 Results and Discussion  
### Proximate Compositions of Yanggaeng  
Table 3 shows the proximate analysis results for Yanggaeng samples prepared with varying concentrations and roasting levels of Guatem coffee extract. Moisture content was the highest in Yanggaeng samples containing L coffee extract compared to those with M and D roasts, irrespective of the coffee extract concentration (\(p<0.05\)). Within the L group, the moisture content did not differ significantly across the different extract concentrations. In contrast, the moisture content decreased with the addition of coffee extract in both the M and D groups when compared to the CON group. The M6 sample exhibited the lowest moisture content among all samples, while within the D group, the D4 sample had the lowest moisture content (\(p<0.05\)).  
The crude ash content was lower in group L than in groups M and D (\(p<0.05\)). No significant variations in crude ash content were observed between the L and M groups at different coffee extract concentrations. The D4 sample exhibited the lowest crude ash content (0.12%). Regarding crude fat content, in the L group, the L6 sample demonstrated the highest crude fat content among the roasting levels (\(p<0.05\)), whereas the D6 sample had the lowest crude fat content across all samples (\(p<0.05\)).  
Crude protein content remained consistent across different roasting levels, showing no overall significant differences. However, in the L group, the L2 sample had the highest crude protein content (\(p<0.05\)). In groups M and D, the crude protein content did not vary significantly with different coffee extract concentrations. The carbohydrate content did not differ significantly across the extract concentrations in the L group. However, in the M group, the carbohydrate content increased with the addition of coffee extract compared to that in the control sample, regardless of the concentration (\(p<0.05\)). In group D, only D4 showed a higher carbohydrate content than the other samples.  
These findings demonstrate that both the roasting degree and concentration of the Guatem coffee extract significantly affect the proximate composition of Yanggaeng. Variations were observed in the moisture, ash, fat, protein, and carbohydrate content, indicating that the incorporation of coffee extract altered the nutritional profile of Yanggaeng depending on the roasting level and extract concentration.
## 3 Proposed Calibration Process
### Step 1: Image Capture  
As our goal is to analyze the distribution of the luminance in the cyclorama (understanding luminance as the amount of light energy that arrives to an area per second [25]), the reference captured images must encode the higher dynamic range. HDR images allow the encoding of a wider range of colors, giving more detailed information of the differences between the lighted and dark areas than the traditional digital techniques [26]. This makes possible to represent in a more precise way the huge range of luminance levels that go, for example, from the direct sun light to a completely dark area. These kinds of images are obtained from several photographs of the scene (taken from the exact same point of view) with different shutter speeds in order to capture the different intensities of lighting [27].  
The ideal configuration for this step is to have a panoramic camera with the capacity to obtain HDR photographs. In this case, the camera must have a lens with enough amplitude to capture the whole cyclorama surface in a virtual TV set. This implies capturing a 180\({}^{\circ}\) image. This hardware eases and speeds up the capture process but it is not indispensable. We used a Canon EOS 600D reflex camera and a Canon EF-S 18-55 mm lens. In this case, both the HDR images and the panoramic composition must be created by software. To capture the HDR images, we used a tripod to take three images with different shutter speeds: ideal, lower and higher speed (for example 1/13, 1/3, 1/50) (Figure 3). These three images are composed in a 32 bit and three channels HDR image using the tool HDR Pro included in the software Adobe Photoshop [28]. The panoramic view was obtained using four different positions (four different HDR images) with a 45\({}^{\circ}\) rotation (Figure 4a). For the final composition we used Photomerge, also included in Photoshop (Figure 4b). Once the final image is obtained it is clipped so that it includes only the cyclorama (Figure 4c).  
Figure 4: Panorama construction process. Set of four original images (**a**). Panorama result of joining the images (**b**). Cropped final image (**c**).  
Figure 3: Cyclorama photographs captured using three different shutter speeds.
# MPH-M, AODV-M and DSR-M Performance Evaluation under Jamming Attacks
## 5 Anomaly-Based Model
### CSMA/CA Retries  
Before a node starts transmitting a packet, the MAC layer listens the channel, if the channel is busy since there is an ongoing transmission, then it waits for a random time, otherwise, it transmits the packet. In order to reduce the probability of collision of other nodes that have been waiting for an idle channel, a backoff algorithm generates another random waiting time, with a uniform distribution probability defined over an interval named as contention window. After this time finishes, then the node sense the channel again, and this loop can be repeated if the channel is busy. The number of times the loop is done is referred in this work as retries. The standard IEEE 802.15.4 [2] limits the number of retries to five, and therefore we use such maximum number of retries.  
Figures 7-9 show that for AODV, DSR and MPH, the implementation of the average of the number of retries is also a good metric to decide if the node is under attack. It is observed that most of the nodes of the network increase their number of retries under the presence of a jammer for AODV and DSR; particularly those nodes located in the jamming area show a higher number of retries, which can be related to an anomaly behavior.  
Figure 8: Average number of retries (MAC algorithm) for the nodes from Figure 3 for DSR (Dynamic Source Routing) under normal (blue) and jamming (red) conditions.  
Figure 7: Average number of retries (MAC algorithm) for the nodes from Figure 3 for AODV (Ad hoc On Demand Distance Vector) under normal (blue) and jamming (red) conditions.
## 4 Experiments and Results
### Speech Corpus and Feature Extraction  
An Isarn speech corpus contains 4700 utterances uttered by one male native Isarn speaker (five hours of speech) [5]. The speech corpus was carefully uttered in the reading style by using text gathered from many sources, such as news articles, web pages, and web boards. The statistical information of utterances in the speech corpus is given in Table 2. The total number of syllables for each tone is shown in Table 3. We divided the speech data into three subsets: 3980 utterances for training, 300 utterances for validation, and 420 utterances for testing. We used a sampling rate of 32,000 Hz instead of the 16,000 Hz used in other studies because this does not degrade the quality of the synthetic speech and is equivalent to using higher sampling rates [53]. These acoustic features were extracted using the WORLD vocoder [54] with a 5 ms frame shift. The acoustic features consisted of three parts: The Mel-cepstral coefficients, band aperiodicity, and log \(F_{0}\).
## 4 Discussion
### Spectral Signature and Chl Concentration  
Leaf spectral features in the visible and NIR regions of the spectrum have been associated to pigment concentration (e.g., chlorophyll) and leaf structure, respectively [77]. Chl peaks of absorbance are located in the blue and red regions of the spectrum. Since carotenoid absorbance also occurs in the blue region, typically the red spectral bands are used to estimate Chl [45]. Low reflectance in the red part of the spectrum is then related to the presence of Chl. In this study, _R. mangle_ had the lowest reflectance in the red spectral bands, followed by _L. racemosa_, _A. germinans_, and finally _C. erectus_ with the highest reflectance of the four species. Accordingly, the average CC followed the same gradient; it was highest for _R. mangle_ and lowest for C. erectus, no statistical difference was found between _L. racemosa_ and _A. germinans_ and _R. mangle_ and _L. racemosa_. Likewise, [PERSON], [PERSON] and [PERSON] [22, 32] reported similar CC ranges per species; the highest CC in _R. mangle_ and lowest for _L. racemosa_ and _A. germinans_ was in the Mexican Pacific. In Brazil, [PERSON] and [PERSON] [78] reported the highest reflectance in the visible bands for _C. erectus_ and lowest for _R. mangle_, confirming our results. However, they did not measure pigment concentration.  
The NIR reflectance, in contrast, is known to be affected by leaf anatomical structure such as leaf thickness, cell walls, and intracellular air spaces. We did not carry out leaf anatomical measurements; however, the low NIR reflectance of _R. mangle_ and significantly higher reflectance of _A. germinans_ would suggest differences in leaf morphological characteristics. [PERSON] and [PERSON] [78] also reported higher reflectance in the NIR for _A. germinans_. In addition, [PERSON] _et al._[79] reported significantly lower palisade, spongy parenchyma, and total leaf thickness for _R. mangle_. Furthermore, physical gradients such as waterlogging [80] and salinity [81, 82, 83] act on mangrove leaf morphology and pigment concentration and this, in turn, could affect the visible and NIR leaf reflectance.
## 2 Requirements Specifications and Measurements Gaps
### Iceberg Tracking  
Iceberg monitoring is important for climate studies and for navigation safety. In the time frame 2020-2030, the Copernicus EO infrastructure to support iceberg tracking will be SAR-C/Sentinel-1,SRAL/Sentinel-3 and SRAL/Sentinel-4 (JASON). Contributing Copernicus missions that are capable of this are ASCAT/MetOp and Scaterometer (SCA) on MetOp-SG. However, these missions are not enough to meet the user requirements, as iceberg tracking presents a gap in the revisit time (Table 2). Nowadays, the technologies measuring this parameter are the scatterometer, the SAR and the radar altimeter (in SAR processing). The main limitation of SAR and altimeters is their narrow swath, long revisit time and the long time to analyze the data (long latency). Scatterometer data are also valuable due to their coverage, but they present a coarse spatial resolution. The instruments that will be studied to cover the gaps in this measurement are SAR, SAR altimeters and microwave scatterometers.
## 5 Denoising Experiments
### Performance Analysis  
The existing literature generally uses signal-to-noise ratio and mean square error to measure the quality of the noise reduction effect [48]. In order to effectively evaluate the noise reduction effect of the algorithm proposed in this article and compare it with the existing algorithm, this article uses the following defined evaluation indicators. The output signal-to-noise ratio is defined as follows:  
\[SNR=10*\log_{10}(\frac{\sum_{i=1}^{i=N}\left[x(i)^{2}\right]}{\sum_{i=1}^{i=N} \left[x(i)-\widehat{x}(i)\right]^{2}}) \tag{30}\]  
The mean square error is defined as follows:  
\[MSE=\frac{1}{N}\sum_{i=1}^{i=N}\left[x(i)-\widehat{x}(i)\right]^{2} \tag{31}\]
# 2D-Unitary ESPRIT Based Multi-Target Joint Range and Velocity Estimation Algorithm for FMCW Radar
## 3 2D-Unitary ESPRIT Based Joint Range and Velocity Estimation Algorithm of Multiple Targets
### 2D-Unitary ESPRIT Algorithm for Joint Range and Velocity Estimation
Output : Range-velocity estimates of \(D\) targets. 1 Using 2D-spatial smoothing preprocessing, the signal matrix \(\mathbf{X}\in R^{M\times N}\) in (13) is reconstructed into a matrix \(\mathbf{Z}\in R^{M_{s}N_{s}\times m_{t}n_{t}}\) in (20);
2 Compute the signal matrix \(\mathbf{Y}\) in (37), calculate the covariance matrix \(\mathbf{R_{YY}}\) in (38), and perform EVD of \(\mathbf{R_{YY}}\) to obtain the signal subspace \(\mathbf{E_{s}}\);
3 Compute \(\mathbf{K}_{1}\), \(\mathbf{K}_{2}\), \(\mathbf{K}_{3}\), \(\mathbf{K}_{4}\), \(\mathbf{K}_{u1}\), \(\mathbf{K}_{u2}\), \(\mathbf{K}_{u1}\), \(\mathbf{K}_{u2}\);
4 Calculate \(\mathbf{\Psi}_{w}\), \(\mathbf{\Psi}_{u}\) in (39) and (40);
5 Calculate the largest \(D\) eigenvalues \(\lambda_{i}\) of the complex matrix \(\mathbf{\Psi}_{w}+j\mathbf{\Psi}_{u}\), where \(i=1,2,\cdots,D\);
6 Calculate the range-Doppler angular frequency estimates: \(w_{i}=2\arctan\{\text{Re}(\lambda_{i})\}\), \(u_{i}=2\arctan\{\text{Im}(\lambda_{i})\}\), where \(i=1,2,\cdots,D\);
7 The paired \(f_{ri}\) and \(f_{vi}\) are calculated from \(w_{i}\) and \(u_{i}\) according to (14) and (15);
8\(\hat{R}_{i}\) and \(\hat{v}_{i}\) are calculated using (11) and (12). ```  
**Algorithm 1** 2D-Unitary ESPRIT based joint range and velocity estimation algorithm.
# Simulation and Design of Three 5G Antennas
## 2 Materials and Methods
### Theoretical Analysis of Microstrip Antenna Arrays
#### 2.1.1 Linear Array  
A linear array antenna consists of multiple antenna elements arranged in a straight line [16]. When each antenna element has the same structure and they are evenly spaced, it forms a uniform linear array antenna. As shown in Figure 1, this represents a uniform linear array antenna arranged along the x-axis.  
If the linear array antenna consists of N identical antenna elements, each spaced at a distance of 'd', without considering mutual coupling between the antenna elements, then, when excited with amplitudes of the same magnitude and a phase variation interval of '\(\alpha\)', the excitation current amplitude of the antenna elements can be represented by Equation (1).  
\[\dot{\mathrm{I}}_{\mathrm{n}}=\mathrm{I}_{\mathrm{n}}\mathrm{exp}(\mathrm{j} \mathrm{n}\alpha), \tag{1}\]  
By calculating the weighted direction function of the antenna elements, the array factor, representing the overall directionality of the antenna array, is obtained [17]. This can be represented using the expression S(\(\theta\), \(\varphi\)), as given in Equation (2).  
When the uniform array antenna forms a specific angle with the x-axis, we assume the angle is \(\beta\) and, in this case, \(\cos(\theta)\)\(\sin(\varphi)=\cos(\beta)\). Then, the above equation can be rewritten as Equation (3).  
\[S(\theta,\phi)=\sum_{n=0}^{N-1}I_{n}\exp[jn(kdcos\beta+\alpha)], \tag{3}\]  
Because all antenna elements are loaded with the same excitation current, we have \(I_{0}=I_{n}\), and after simplifying the above equation, we can obtain Equation (4).  
\[|S(\theta,\phi)|=I_{0}\left|\frac{\sin^{N(kdcos\beta+\alpha)}}{\sin\frac{(kdcos \beta+\alpha)}{2}}\right|, \tag{4}\]  
When \(u=kdcos(\beta)+\alpha=0\), the array factor of the antenna array is maximized, and the corresponding maximum radiation direction angle is as given in Equation (5).  
\[\beta_{max}=\arccos\left(-\frac{\alpha}{kd}\right), \tag{5}\]  
To sum up, within a uniform linear array, the spacing between antenna elements 'd' and the phase difference '\(\alpha\)' have the most pronounced influence on the maximum radiation direction. Consequently, when designing a practical uniform linear array antenna, the careful choice of antenna element spacing 'd' and phase difference '\(\alpha\)' is of paramount importance.
# Novel Common-Mode Current Suppression Method in Transformerless PV Grid-Connected System
## 5 Experimental Results  
To validate the dual CM internal loop to suppress the high frequent components in the CM current, the experiment platform of a 5 kW with two stages non-isolated grid-connected PV system is built. The experimental setup is shown in Figure 12. The Top Con voltage source is used to simulate the PV array. The inverter is two stages. The ZVT-Boost booster circuit is used in the first stage, and the H6 inverter topology is adopted at the second stage. The CM current is analyzed by the oscilloscope of Agilent DSO-X 4034A. We use the same parameters in Table 1 to verify this conclusion.  
In Figures 13-15, the experimental results of three different experiments are shown. In Figure 13, according to the CM current spectrum analysis without the CM loop, the CM current mainly includes the resonant frequency component, the Boost operating frequency component, the H6 bridge operating frequency component and the fundamental component. The peak value of CM currents is 161 mA, and the total harmonic distortion rate is 163.87%. Figure 14 shows the CM current and spectrum using the internal CM loop method. The Boost operating frequency segment component and the H6 bridge operating frequency component are greatly suppressed. The high frequency component in the CM current is substantially reduced, in which the peak-to-peak value is 146 mA and the distortion  
Figure 11: The common-mode current waveform and spectrum with double inner loops: (**a**) current waveform; and (**b**) current spectrum.  
Figure 12: The experimental platform of common-mode current.  
is 79.51%. However, the resonance occurs in the circuit. Moreover, the resonant frequency segment harmonic increases, accounting for more than 10% of the fundamental component. Figure 15 shows the CM currents using the dual CM loop method. It can be clearly seen from the spectral analysis that the harmonic component is well suppressed, the peak-to-peak value is reduced to 101 mA, and the total harmonic distortion is 39.34%. The experimental results are in good agreement with the simulation results, which proves that the proposed method has obvious effect on the high frequency component suppression of CM current.  
Figure 14: The common-mode current and spectrum with single inner loop.  
Figure 13: The common-mode current and spectrum without the inner loop.  
Figure 15: The common-mode current and spectrum with double inner loops.
## 3 Results and Discussions
### Typical Samples of the Raw Data Collected During the Measurement Campaign
#### 3.2.1 Evaluated Measured Path Loss (MPL)  
Table 5 presents the evaluated mean measured path loss for the routes and seasons over 10 km from the base station. The mean values for both routes during the dry and wet season months were 106.186 and 114.555 dB, respectively, with an overall mean of 110.371 dB. Path loss during the wet season months was higher than the dry season months, which may be attributable to the effects of hydrometeors and wet vegetation compared to that of the dry season.
# Monitoring of Ground Movement and Groundwater Changes in London Using InSAR and GRACE
## 4 Results
### GRACE Results
The yearly data for groundwater abstraction and recharge for north London and groundwater abstraction data for the whole chalk aquifer of the London area is provided by EA [11]. The EA report suggests total groundwater abstraction of 30.480 MCM and a total recharge of 15.520 MCM between 2003 and 2010, which amounts to 50.91% recharge as compared to abstraction. The report also suggests an overall abstraction of 245.311 MCM groundwater from London chalk aquifer for our study period and, assuming the same recharge rate of 50.91% as north London, the total recharge for London chalk aquifer is estimated as 124.887 MCM, which results in a groundwater deficit of 120.423 MCM. The groundwater deficit as suggested by GRACE is 72.024 MCM, which is only \(\sim\)60% of the same as suggested by the groundwater monitoring estimated by EA. Thus, around 40% of the deficit estimated by EA is not sensed by GRACE. This shows that even though GRACE data is freely available, and it proves to be a suitable alternative to costly, spatially restricted microgravity surveys, it may not precisely quantify water variations for typical aquifer scales. Despite this, it gives a clear \"big picture\" indication of spatial variation for total groundwater induced mass variations, which is an appropriate input for setting broad regional or national policies [53], a view for which our study provides further evidence.
## 3 Dataset Description
### HFAVD Dataset Structure  
In this subsection, we present an overview of the models utilized for extracting physiological features used in the annotation of the videos. These models leverage deep learning, machine learning, and computer vision techniques to estimate specific physiological indicators based on facial video recordings of the subjects, as shown in Figure 2. Through these models, we were able to estimate 15 features at each minute of every video, capturing a comprehensive range of physiological information. The physiological features were extracted by using deep learning models with a sampling frequency of one second, as will be detailed in subsequent sections. However, many of these features exhibit minimal variation within a one-second interval, which can result in numerous identical or highly similar samples. This redundancy may compromise the reliability of the experimental outcomes. Consequently, we opted to compute the average of these indicators for each minute and label them with the corresponding mental performance value. This approach was selected to retain as many data as possible, ensuring they remained adequate for the machine learning models. Therefore, increasing the sampling interval from 1 min to 5, for example, could lead to insufficient data, adversely affecting performance, while decreasing it would result in duplicated samples, thereby rendering the experiments invalid and unreliable.  
The estimated physiological features were compiled and organized into comprehensive CSV files. These CSV files enhance the distinctiveness of the dataset compared with existing ones. They include almost every detectable or extractable feature from the subject while the latter performs various tasks, without the need for sensors or external devices apart from the widely available camera. This distinguishing characteristic contributes to the dataset's versatility and accessibility for researchers. These models are explained as follows.