esa-sceva/satcom-chunk-collection huggingface dataset:
# MODIS Retrieval of Aerosol Optical Depth over Turboid Coastal Water
## 2 Data
### AERONET Data  
AOD measurements from the ground-based AERONET sun photometers are commonly used for validating MODIS retrievals. Here we use data from six coastal water sites for this study (Table 1), and their locations are marked with green dots in global map in Figure 1f. All AERONET sun photometers (SP) measure direct solar radiation at 0.44 \(\upmu\)m, 0.67 \(\upmu\)m, 0.87 \(\upmu\)m, 0.94 \(\upmu\)m and 1.02 \(\upmu\)m, and these measurements are used to infer AOD through Beer-Lambert-Bouguer law with quality at Level 1.0 (unscreened), Level 1.5 (cloud screened), and Level 2.0 (cloud-screened and quality-assured) [13; 14]. We evaluate MODIS AOD (both MxD04 product and retrieval of this study) at 0.55 \(\upmu\)m against an AERONET counterpart that is derived through linearly interpolating AERONET AOD at 0.44 and 0.67 \(\upmu\)m in the logarithm domain.  
Dalma and MVCO are two sites over the ocean, with distance to coastline being 48 km and 29 km, respectively, while the rest of the sites are over land within 6 km from the coastline. According to our analysis, most AERONET sites are more than 10 km away from the coast lines, and lack dedicated long-term continuous measurements of AOD over the turbid coastal water. Here, we use the SP AOD data from August 2015, December 2015, May 2016, August 2004, March 2014, and September 2004 at MVCO Bhola, Anmyon, Dalma, Karachi, and MAARCO sites, respectively, because these time periods have the most measurements available at their corresponding sites. AERONET Level 2.0 data are used for most sites except Bhola and Anmyon where only Level 1.5 data are available and utilized here. The monthly mean AERONET 0.44-0.87 \(\upmu\)m Angstrom exponent ranges from 0.597 to 1.842 over these six sites. Hence, these sites represent a wide range of atmospheric conditions, ranging from coarse mode dominated to fine mode dominated cases [15].
# On Cross-Layer Interactions for Congestion Control in the Internet
## 3 TCP Variants
### TCP New Reno with SACK  
TCP New Reno is implemented in most operating systems, along with SACK (Selective Acknowledgment) support. It is the default version used by Windows 10 and Windows Server 2016 operating systems and FreeBSD.  
TCP New Reno is an improved version of TCP Reno [18]. It modifies only the Fast Recovery phase, while other phases are identical to TCP Reno, which is based on one of the earliest versions of TCP, i.e., TCP Tahoe proposed by [PERSON] [19]. The congestion window of TCP New Reno increases exponentially in the Slow Start phase and linearly in the Congestion Avoidance phase. When duplicated ACKs are received, the congestion is detected, the congestion window is reduced by half, and the Fast Recovery phase is started.  
TCP New Reno overcomes the problem of multiple losses, which causes reducing the congestion window multiple times. TCP New Reno remains in the Fast Recovery phase until all outstanding segments, at the time it enters the Fast Recovery, are acknowledged. During this phase, it may retransmit multiple loss segments before returning to the Congestion Avoidance phase.  
Without SACK, New Reno requires one RTT to detect each packet loss. SACK allows acknowledging the received packets selectively; therefore, it improves the recovery from multiple losses. Each ACK has a block that describes which segments have already been received and do not require retransmission.  
The main flaw of TCP New Reno is its poor bandwidth utilization in high-speed environments with moderate or high RTTs, e.g., in a network having 10 Gbps capacity, 100 ms RTT, and a maximum packet size of 1500 bytes, it would take almost two hours of error-free transmission to get to a theoretical upper bound of transfer rate [20].
## 4 Conclusions  
It was demonstrated that the research can be continued in the qualitative evaluation of sweet potatoes during convective drying. The designed and generated artificial neural networks, especially with the use of deep learning, made it possible to assess the quality, state, and color parameters of dried sweet potatoes based on images (indirectly via the GLCM matrix). Based on tests and analysis of data obtained, we can conclude that there are differences in color and texture between samples of sweet potato convective dried at 60, 70, 80, and 90 \({}^{\circ}\)C. The color expressed in the CIE L\({}^{*}\) a\({}^{*}\) b\({}^{*}\) system makes it possible to distinguish sweet potatoes dried at 90 \({}^{\circ}\)C from other classes, but involves the possibility of erroneously assigning a sample that does not belong to this class. At the same time, the color expressed in RGB space calculated from digital images does not make it possible to distinguish effectively between the samples tested and, when analyzed using artificial neural networks, makes classification difficult. The positive aspect of using color as a discriminator is that the identification of sweet potato discs dried at 90 \({}^{\circ}\)C is subject to the error of falsely assigning the test sample to this class.  
A deep artificial neural network of the MobileNet type accepting a greyscale bitmap as input proved to be the most effective tool for dividing dried sweet potato slices by drying temperature. The use of the aforementioned architecture enables a 77% accuracy of the four-class classification. At the same time, binary classification using the MobileNet architecture for greyscale data makes it possible to distinguish with 89% accuracy between sweet potato discs dried at 60 and 70 \({}^{\circ}\)C and those dried at 80-90 \({}^{\circ}\)C. The use of image analysis and deep artificial neural networks proved to be a more effective method of dividing samples by drying temperature relative to data from a colorimeter or mechanical texture analyzer. This justifies further research into the use of image analysis to evaluate the quality of convective-dried products in online mode. Describing the mechanism of shape and texture change in convective-dried sweet potatoes at different temperatures remains a topic for further research.  
Conceptualization, K.P.; methodology, F.A., K.P., J.W. and J.S.; software, F.A. and K.P.; validation, F.A. and K.P.; formal analysis, K.P. and J.W.; investigation, K.P. and J.W.; resources, F.A., J.S. and K.P.; data curation, F.A. and K.P.; writing--original draft preparation, K.P., F.A. and J.W.; writing--review and editing, K.P., J.W., F.A., M.G.-W. and J.S.; visualization, F.A., K.P., D.K. and M.G.-W.; supervision, M.G.-W., D.K. and K.P.; project administration, K.P.; funding acquisition, D.K. and K.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
The use of the reflected Global Navigation Satellite Systems' (GNSS) signals in Earth observation applications, referred to as GNSS reflectometry (GNSS-R), has been already studied for more than two decades. However, the estimation precision that can be achieved by GNSS-R sensors in some particular scenarios is still not fully understood yet. In an effort to partially fill this gap, in this paper, we compute the Cramer-Rao bound (CRB) for the specific case of static ground-based GNSS-R receivers and scenarios where the coherent component of the reflected signal is dominant. We compute the CRB for GNSS signals with different modulations, GPS L1 C/A and GPS L5 I/Q, which use binary phase-shift keying, and Galileo E1 B/C and E5, using the binary offset carrier. The CRB for these signals is evaluated as a function of the receiver bandwidth and different scenario parameters, such as the height of the receiver or the properties of the reflection surface. The CRB computation presented considers observation times of up to several tens of seconds, in which the satellite elevation angle observed changes significantly. Finally, the results obtained show the theoretical benefit of using modern GNSS signals with GNSS-R techniques using long observation times, such as the interference pattern technique.  
GNSS-R; Cramer-Rao bound; CRB; coherent reflection; L-band; soil moisture estimation; altimetry; interference pattern technique; IPT +
Footnote †: journal: Sensors
## 3 Results  
### Phase 1--GNSS and Behavioural Recordings of Alpacas and Ewes  
#### 3.1.1 Distances Travelled, Both Species  
There were no significant differences in relation to distance travelled over the 41-day testing period between the two alpacas (F\({}_{1,78}\) = 0.07, \(p\) = 0.79); with Alpaca 1 traveling an average of 6.6 \(\pm\) 1.3 km a day and Alpaca 2 travelling 6.5 \(\pm\) 1.3 km (Figure 2). On average, the alpacas travelled a greater distance (2.8 km) than the ewes over the testing period (F\({}_{1,78}\) = 107.7, \(p\) < 0.001). However, there was a correlation between species in relation to distances travelled (r = 0.8, \(n\) = 16, \(p\) < 0.001), with both alpacas and ewes decreasing the distance they travelled over the observation period of 41 days (Figure 3). This shows that although the alpacas travelled greater distances, the two species shared similar movement trends in relation to distance travelled per day over the 41 days.  
Figure 2: Daily distances (m) travelled by each alpaca over the 41-day testing period (day 20 shows no data due to collar change). Yellow shaded area highlights the 10-day observation period.
# Electromagnetic Soil Characterization for Undergrounded RFID System Implementation
## 3 Link Budget Analysis of the Soil  
Now, the collected data in the previous section are used to arrive at an approximated link budget. The obtained link budget model can be employed to calculate the total losses due to the wave propagation inside the soil. To achieve this goal, a MATLAB code is developed assuming the following parameters:  
* Reader antenna gain of 7 dBi
* Tag antenna gain of 2 dBi
* Total radiated power of the reader (_Pt_) is 30 dBm
* Both reader and tag antennas are \(-20\) dB matched
* The first order reflection from the soil layer is considered
* Full homogenous soil material  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Vellow Sand**} \\ \hline  & \multicolumn{4}{c}{**915 MHz**} & \multicolumn{4}{c}{**2.45 GHz**} \\ \cline{2-7}  & **Dry** & **\% water** & **10\% water** & **Dry** & **5\% water** & **10\% water** \\ \hline \(\epsilon_{\text{s}}\) (Real) & 2.9143 & 4.2973 & 4.942 & 2.9356 & 4.0599 & 4.6842 \\ \(\epsilon_{\text{s}}\) (Image3) & 0.0732 & 0.1728 & 0.184 & 0.0642 & 0.2026 & 0.3269 \\ _Conductivity to (5 MM)_ & 0.0037 & 0.0088 & 0.0094 & 0.0088 & 0.0276 & 0.0446 \\ _Tau (8)_ & 0.0251 & 0.0402 & 0.0372 & 0.0219 & 0.0499 & 0.0698 \\ \hline  & \multicolumn{4}{c}{**Red Sand**} \\ \hline  & \multicolumn{4}{c}{**915 MHz**} & \multicolumn{4}{c}{**2.45 GHz**} \\ \cline{2-7}  & **Dry** & **\% water** & **10\% water** & **Dry** & **5\% water** & **10\% water** \\ \hline \(\epsilon_{\text{s}}\) (Real) & 2.5346 & 3.4321 & 3.7533 & 2.6658 & 3.5885 & 4.2652 \\ \(\epsilon_{\text{s}}\) (Image3) & 0.0749 & 0.1574 & 0.  
The attenuation coefficient due to wave propagation inside the soil \(\alpha\) can be estimated using Equation (1), while the total received power \(P_{r}\) can be obtained using Equation (2)  
\[\gamma=\alpha+j\beta=j\omega\sqrt{\mu\epsilon}\sqrt{1-j\frac{\sigma}{\omega \epsilon}} \tag{1}\]  
\[\frac{P_{r}}{P_{t}}=G_{t}G_{r}\bigg{(}\frac{\lambda}{4\pi R}\bigg{)}^{2}\Big{(} 1-|\Gamma_{t}|^{2}\Big{)}\Big{(}1-|\Gamma_{r}|^{2}\Big{)}e^{-\alpha R} \tag{2}\]  
where \(\gamma\) is the propagation constant, \(\beta\) is the phase constant, \(\omega\) is the radial frequency, \(\sigma\) is the electrical conductivity, \(\epsilon\) is the soil permittivity, \(G_{t}\) and \(G_{r}\) are reader antenna and the tag antenna gain, respectively, \(\Gamma_{t}\) and \(\Gamma_{r}\)\(r\) are the reader antenna and the tag antenna reflection coefficient, respectively and \(R\) is the soil thickness in meters.  
Figure 5 shows the calculated link budget analysis graphs for different soil types (dry soil) over two operating bands, from 850 to 950 MHz, and from 1 to 5 GHz. The graphs are plotted for different soil thicknesses ranged from 0.5 m up to 4 m. A smooth variation in the total path-loss in the lower band can be noticed, while there are some up-rising peaks and fast variations in the second band. This phenomenon can be attributed to the soil inhomogeneity and the presence of some big stones compared with the wavelength which cause this kind of discrepancy.  
Figure 5: _Cont._
## 2 Materials and Methods
### Statistical Analysis  
The mean and the standard error of the VAS score at each time point (t0, t1, t2, and t3) were estimated for each study group. Data analysis was conducted using the Wilcoxon test for paired samples [17; 18] to assess for significant differences among the averages observed at t1, t2, and t3 compared to t0 in each study group. The mean of VAS scores obtained at t0 for each patient was compared among the three study groups applying a one-way ANOVA test. An unpaired t-test was applied to compare the averages of VAS scores for the pairs: LLLT vs. HPLT, LLLT vs. LHLT, and HPLT vs. LHLT.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Fiber** & **Wattage** & **Emission** & **First Stage** & **After 24 h** \\ \hline LLLT & 400 \(\upmu\)m & 0.2 & CW & 60 s & 60 s \\ \hline HPLT & 400 \(\upmu\)m & 2 & Pulse mode & 60 s & 60 s \\ \hline LHLT & 400 \(\upmu\)m & 0.2 & CW & 60 s & \\ \(400\)\(\upmu\)m & 2 & Pulse mode & & 60 s \\ \hline \end{tabular}
\end{table}
Table 1: Laser parameters used for each study group. LLLT: Low-Level Laser Therapy; HPLT: High-Power Laser Therapy; LHLT: Low-High combined Laser Therapy; CW: Continuous Wave.  
All the statistics used for the data analysis were implemented in GraphPad Prism V 6.0. Values of \(p\) < 0.05 were accepted as statistically significant.  
Moreover, in each study group, the mean residual pain (Pi) was estimated evaluating the mean VAS score at t1, t2, and t3 times against the mean VAS scores of t0. The Equation applied was  
\[\text{for i}=0,1,2,3\ \frac{(\text{Mean}\cdot\text{VAS}\cdot\text{score})\text{t}_{ \text{i}}}{(\text{Mean}\cdot\text{VAS}\cdot\text{score})\text{t}_{0}}\cdot X \cdot 100 \tag{1}\]  
The improvement of DH symptomatology was then obtained as the difference between 100, the best possible result, and the mean residual pain (P\({}_{\text{i}}\)) estimated at t1, t2, and t3. The formula applied was  
\[\text{for i}=0,1,2,3\ \text{I}_{\text{i}}=100-\text{P}_{\text{i}} \tag{2}\]  
A sample consisting of 90 NCCLs with DH needed a power of 80% to demonstrate an effect size of 30% with a Wilcoxon test for paired samples and with a first type error of 0.05.
## 5 Metasurface Antennas
The truncated metasurface, in contrast to artificial magnetic conductors (AMCs), also contributed to the radiation from the antenna thus increasing the gain and the front to back ratio [102]. Two-layered metasurfaces loaded with resistors have been demonstrated to reduce the radar cross section (RCS) of a stacked antenna array thus offering an alternative to radar absorbing material. The antenna was sandwiched between these two metasurfaces, the metafilm above the antenna reduced the RCS for out of band frequencies while the one below reduced the RCS for the in-band frequencies [103].
# A LiDAR-Based Backfill Monitoring System
## 4 Experiment
### Accuracy  
An accurate method to measure the progress of the backfill process is not available, so the precision can be evaluated by comparing it with other approaches, such as Monte Carlo-based volume estimation [42] and Hexahedrization [43].  
Figure 11: Results of steps for a set of LiDAR scans in different tunnels: (**a**) results of each step in a straight tunnel; (**b**) results of each step in a slightly curvy tunnel; (**c**) results of each step in a curvy tunnel.  
The Monte Carlo method can be used to estimate the volume of a point cloud by randomly sampling points in a defined bounding box and determining the proportion of points that fall within the point cloud. Here is a step-by-step outline of how it works:  
(1) Define the Bounding Box Volume: Enclose the point cloud within a simple geometric shape with a known volume.  
(2) Generate Random Points: Randomly generate a large number of points within the bounding volume.  
(3) Count Points Inside: Check how many of the random points fall within the point cloud.  
(4) Estimate Volume: Calculate the point cloud volume using Equation (5)  
\[V_{\text{TargetObject}}\,=\,\lim_{n\rightarrow\infty}\frac{n_{\text{InsidePoints }}}{N}\,\cdot\,V_{\text{Box}} \tag{5}\]  
The Hexahedrization method involves filling the model with a finite number of small hexahedra and then summing the volumes of all the hexahedra to obtain the volume of the model (Equation (6)). The steps are as follows:  
(1) Define the bounding box, then divide the entire bounding box into hexahedral cells according to the provided hexahedral element dimensions.  
(2) Perform planar cuts through the center of the bounding box along any axis and perpendicular to that axis, generating a series of cross-sections for the surface model.  
(3) In each cross-section, calculate the volume of the hexahedra within the contour line of the model and sum them up.  
\[V=\sum_{i=0}^{n}v_{i} \tag{6}\]  
where \(v_{i}\) is the volume of the \(i\)-th hexahedron, and \(n\) is the total number of hexahedra.  
Given that the development of volume computation algorithms is relatively mature and the tunnel model is not particularly complex, the results from several methods are quite similar. Both of the results obtained using the methods described above show less than 1% difference compared to the result computed by the method in Section 3.3. On the premise of ignoring the errors generated by other steps, such as the low scanning accuracy of LiDAR and the low Poisson surface reconstruction accuracy, it can be seen that the accuracy of backfill monitor is high. By further upgrading each step in the backfill progress workflow, such as applying LiDAR sensors with a higher range and point rate, and optimizing the RANSAC and surface reconstruction algorithms, the overall accuracy could be improved.
# Perceptual Characteristics of Voice Identification in Noisy Environments
###### Abstract  
Auditory analysis is an essential method that is used to recognize voice identity in court investigations. However, noise will interfere with auditory perception. Based on this, we selected white noise, pink noise, and speech noise in order to design and conduct voice identity perception experiments. Meanwhile, we explored the impact of the noise type and frequency distribution on voice identity perception. The experimental results show the following: (1) in high signal-to-noise ratio (SNR) environments, there is no significant difference in the impact of noise types on voice identity perception; (2) in low SNR environments, the perceived result of speech noise is significantly different from that of white noise and pink noise, and the interference is more obvious; (3) in the speech noise with a low SNR (\(-\)8 dB), the voice information contained in the high-frequency band of 2930\(-\)6250 Hz is helpful for achieving accuracy in voice identity perception. These results show that voice identity perception in a better voice transmission environment is mainly based on the acoustic information provided by the low-frequency and medium-frequency bands, which concentrate most of the energy of the voice. As the SNR gradually decreases, a human's auditory mechanism will automatically expand the receiving frequency range to obtain more effective acoustic information from the high-frequency band. Consequently, the high-frequency information ignored in the objective algorithm may be more robust with respect to identity perception in our environment. The experimental studies not only evaluate the quality of the case voice and control the voice recording environment, but also predict the accuracy of voice identity perception under noise interference. This research provides the theoretical basis and data support for applying voice identity perception in forensic science.  
voice identity; noise; perceptual characteristics; frequency distribution Article
## 7 Fault Location Method Based on Similarity of Voltage Signals
### Discrimination between Different Cases of Direct Current (DC) Fault Current Rise  
Maximum fault resistance is characterized by minimum rate of change of current values, minimum voltage values and lowest peak values of MGV in the case of an internal fault. Morphological gradient voltage (MGV) value is an arithmetic difference of dilation and erosion of a signal obtained from the interaction of voltage sample and threshold value. Table 1 presents cases of rise of dc fault current in terms of MGV values.  
Reliability of protection algorithm is, therefore, dependent on correct evaluation of margin between minimum MGV peak value for internal fault and maximum MGV peak value for external fault. This nullifies inevitable measurement errors and probable signal distortion.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Serial Number** & **Fault Type** & **Discrimination/Classification of Fault** \\ \hline \multirow{2}{*}{1} & Internal fault & Maximum value of peaks of MGV in \\  & and external fault & external fault is smaller than minimum \\  & & value of peaks of MGV in internal fault \\ \hline \multirow{2}{*}{2} & Positive pole to ground fault and \\  & Negative pole to ground fault & MGV values are similar because of \\  & Negative pole to ground fault & symmetrical nature \\ \hline \multirow{2}{*}{3} & Healthy Pole and Faulted Pole & Rise in MGV values is more noticeable \\  & & at faulted pole \\ \hline \multirow{2}{*}{4} & Opening of dc circuit breaker and \\  & internal fault & MGV values in opening of dc circuit \\ \end{*} \\ \cline{1-1}  & internal fault & breakers are smaller than minimum \\ \cline{1-1}  & peaks values in internal fault \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cases of rise of dc fault current in terms of morphological gradient voltage (MGV) values.  
Figure 18: Proposed flow chart of Morphological gradient algorithm for fault classification in multi-terminal high-voltage direct current (MT–HVdc) transmission system.