esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of VoIP QoS Performance in Wireless Mesh Networks
## 3 Experimental Setup
#### Configuration Parameters  
The technologies and standards configuration details are important for understanding how these simulations scenarios were set up and configured. In most cases the default parameters have been used; we have only configured specific parameters in scenarios where they are required for the technology and protocol to work. The configured parameters for IEEE 802.11g/n standards are given in Table 4.  
The configured parameters for IEEE 802.11, 802.11e and 802.11s are described in Table 5. The IEEE 802.11 MAC is the generic MAC for IEEE 802.11g. IEEE 802.11e is configured due to the constraint of the simulation tool, since we are only allowed to use IEEE 802.11e for the 802.11n PHY to work; however, the parameters for IEEE 802.11e MAC used in our simulation scenarios are the same as the IEEE 802.11 generic MAC given in Table 5. Furthermore, the VoIP-only traffic is simulated without any background traffic in all scenarios including the IEEE 802.11n/e scenarios in order to compare it with the IEEE 802.11g/s VoIP-only scenarios, since this is the focus of our work. Thus no different access categories were configured. On the other hand, IEEE 802.11s parameters are different and the HWMP parameters are also described under the 802.11s column since HWMP is configured as part of the IEEE 802.11s MAC protocol feature in on-demand mode. The configured parameters for the AODV routing protocol are given in Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Values**} & \multirow{2}{*}{**Remarks**} \\ \cline{2-2} \cline{4-4}  & **802.11s** & & **802.11n** \\ \hline Phy-model & Phy802.11g & Phy802.11n & Common 802.11 standards \\ Transmit power (dBm) & 20 & 20 & Max transmit power for a node \\ Phy802.11-frequency-band & 2.4 & 2.4 & Used instead of 5 GHz, better coverage \\ Phy802.11-20 MHz-channel-index & 6 & 6 & Operating channel \\ \hline \hline \end{tabular}
\end{table}
Table 4: IEEE 802.11g/n physical configuration parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{4}{c}{**Values**} & \multirow{2}{*}{**Remarks**} \\ \cline{2-2} \cline{4-4}  & **802.11** & & **802.11e** & **802.11s** \\ \hline
**MAC-PROTOCOL** & **MACDOT11** & **802.11e** & **MACDOT11** \\ \hline MAC-dot11-short-packet-transmit-limit & 7 & 7 & 7 & Default \\ MAC-dot11-long-packet-transmit-limit & 4 & 4 & 4 & Default \\ MAC-dot11-RTS-threshold & 0 & 0 & 0 & No limit \\ MAC-dot11-stop-receiving-after-header-mode & No & No & No & Default \\ MAC-dot11-association & None & None & Dynamic & Required for 802.11s \\ MAC-dot11-directional-antenna-mode & No & No & No & Not used \\ MAC-dot11-ssid & N/A & N/A & meeran & Required for 802.11s \\ \hline \hline \end{tabular}
\end{table}
Table 5: IEEE 802.11/e/s and HWMP MAC configuration parameters.
## 3 Genetic NCPs
### Small Heat Shock Proteins
dHMNs are genetically heterogenous characterized by degeneration of distal lower motor neurons, resulting in muscle weakness and atrophy [75]. However, in many forms of dHMN there may also be minor sensory abnormalities and/or a significant upper-motor-neuron involvement. For this reason, there is often an overlap not only with axonal forms of CMT, i.e., CMT2, but also with juvenile forms of amyotrophic lateral sclerosis, [PERSON]'s disease, spinal muscular atrophy, and hereditary spastic paraplegia [76]. Various types of chaperopathies can occur, as follows: CMT type 2L is caused by mutations in _HSPB8_ and CMT type 2F is caused by mutations in _HSPB1_, while dHMN type IIA is caused by mutations in _HSPB8_ and dHMN type IIB is caused by mutations in _HSPB1_[27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44]. Often, a single mutation in _HSPB1_ or _HSPB8_ gives rise to one of these conditions. However, in many cases the same mutation was found associated with both diseases, such as the p.(Lys141 Asn) substitution in HSPB8 [28; 31], and the p.(Ser135 Phe) [38; 54] and p.(Arg127 Trp) substitutions in HSPB1 [36; 37; 77]. Moreover, there are also examples of mutations found in patients with an unclear phenotype, as shown in Table 1.  
For both _HSPB8_ and _HSPB1_, the identified mutations in most cases occur in the highly conserved \(\alpha\)-crystallin domain, likely destabilizing the proteins structurally and functionally [78]. To elucidate the molecular mechanisms responsible for the association between mutated forms of HSPB8 and HSPB1 and neuromuscular disorders, in vitro studies have been performed. For instance, it was reported that the expression of HSPB8 mutants p.(Lys141 Asn) and p.(Lys141 Glu) in motor neurons resulted in a reduction in average length and number of neurites per cell, without inducing cell death. In contrast, these abnormalities were very moderate in sensory neurons, and absent in cortical neurons and glial cells [79]. Thus, motor neurons appear to be more sensitive to HSPB8 dysfunction compared to sensory neurons, as indicated by the predominant motor neuron phenotype in dHMN and CMT2L. Moreover, both HSPB8 mutant forms tend to associate with HSPB1 mutant proteins, forming intracellular aggregates [28]. Studies in vitro with HSPB1 mutants demonstrated that some of the substitutions affecting the \(\alpha\)-crystallin domain reduced motor neuron viability, impaired neurofilament assembly, destabilized microtubules, and disrupted the axonal transport of specific cellular cargoes and of mitochondria [36; 80; 81; 82; 83; 84], events that can be responsible for the premature axonal degeneration, typical of both CMT and dHMN [85]. In addition, in vivo studies have shown that overexpression of HSPB1 mutants in neurons is sufficient to cause pathological and electrophysiological changes typically observed in patients with motor neuropathy [86].
# Phonocardiography Signals Compression with Deep Convolutional Autoencoder for Telecare Applications
## 2 Related Work  
### Sound Compression  
For heart sound compression problems, the conventional performance metrics are compress ratio (CR) and percent root-mean-square difference (PRD) [11]. CR is defined, as follows:  
\[CR=\frac{B_{0}}{B_{1}} \tag{1}\]  
where \(B_{0}\) and \(B_{1}\) are the data size before and after compression, respectively. PRD can be calculated, as follows:  
\[PRD=\sqrt{\frac{\sum_{i}^{N}\left(x_{i}-\hat{x}_{i}\right)^{2}}{\sum_{i}^{N} \left(x_{i}-\mu_{x}\right)^{2}}}\times 100 \tag{2}\]  
where \(x_{i}\) is the \(i\)-th sample in the original signal, and \(\hat{x}_{i}\) denotes the corresponding reconstructed sample. \(\mu_{x}\) is the sample mean of the \(N\) data samples of the original signals. Note that the mean value of the original signals must be removed to obtain unbiased PRD [12]. Audio compression can be lossless or lossy. It has been shown that lossless compression algorithms rarely obtain a CR larger than 3, while lossy compression algorithms allow attainable CR up to 12 and higher [13]. For example, the value of CR for free lossless audio codec (FLAC) compression is only about 1.94 [14]; and, the value of CR for lossless ECG compression is 2.56 [15]. Additionally, it has been reported that a medical professional felt the necessity of a high CR and can tolerate a PRD as high as 5% [16]. Thus, our design aims to attain a high CR at the values of PRD are less than 5%.  
The pioneering work on PCG signals compression has been reported in [17]. The authors proposed using wavelet transform or wavelet packet transform methods in order to compress the original heart sound signals by coefficient thresholding. Their method can further be combined with some lossless compression methods, such as Huffman coding, to increase the compression ratio. The thresholding method based on wavelet energy was reported to reduce the loss caused by compression. At the PRD values of 5.82, the average values of CR are about 40 using the wavelet transform [18]. In [19], the authors proposed using a better wavelet transform method with tunable Q-factor [20]. The optimal value of \(Q\) can be found by using the genetic algorithm. In [16], the authors exploited the repetition patterns that were embedded in the PCG signals in order to eliminate their redundancy. After decomposing the PCG signals into the time-frequency domain, the authors proposed clustering the decomposed data to build a dictionary during the training phase. This dictionary is then used to obtain the optimal codebook. They achieved the averaged CR of 17.4 at the averaged PRD 4.87 for PCG signals. For the fetal PCG signals, the authors had reported that the achievable CR was less than 7.5 when the required PRD was less than two by using the compression techniques based on Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) [21].
## 2 Materials and Methods
### Non-Negative CCA and Estimation of Coefficients
Then, \(\hat{\beta}^{t+1}\) is considered a fixed constant, and the coefficient \(\hat{\omega}^{t+1}\) is determined to be similar to that in Equations (3) and (4), respectively. The procedure is iteratively continued until the coefficients \(\omega\) and \(\beta\) converge. Figure 1: Schematic overview of the method. The two input datasets for non-negative CCA were composed of identical samples. A and M are matrices of the cell type abundance and the mutational process, respectively, and \(\omega\) and \(\beta\) are coefficient vectors for the linear mapping. To apply a penalty term at the regression coefficients for regularization, Equation (3) is modified as follows:  
\[\hat{\beta}=\underset{\beta}{\text{argmin}}(||\hat{\omega}\text{A}-\beta\text{M} ||^{2}+\lambda_{1}\sum_{g=1}^{\text{G}}||\beta_{g}||^{2}), \tag{5}\]  
where \(\beta_{g}\) is the coefficient of the \(g\)th component of CCA. Similarly, an optimal \(\omega\) vector can be determined by adding the constraint as follows:  
\[\hat{\omega}=\underset{\omega}{\text{argmin}}(||\hat{\beta}\text{M}-\omega \text{A}||^{2}+\lambda_{2}\sum_{h=1}^{H}||\omega_{h}||^{2}), \tag{6}\]  
where \(\omega_{h}\) is the coefficient of the \(h\)th component of CCA represented as the \(\omega\) vector. \(\lambda_{1}\) and \(\lambda_{2}\) are the shrinkage parameters for regularization in Equations (5) and (6). A high coefficient for a mutational signature indicates that it is highly linked to the immune cell infiltration. In the experiments, many of the mutational signatures were observed as zero; therefore, mutational signatures with the number of non-zero values exceeding 20% of the total samples of the tumor type were used. The glmnet() function's parameter alpha was specified as zero for Ridge regression, while the other parameters were left as defaults.
## 3 Results
### Solution Accuracy
The 2D models captured the longitudinal tensile PS distribution in the region near the substrate, but they overestimated the tensile PS magnitude in the region close to the rolled surface. Nevertheless, the 2D modelling results for the longitudinal tensile RS distribution near the rolled surface were consistent with the prediction by the 3D control model. However, there was marked discrepancy in the compressive RS distribution near the substrate between the predictions by the 2D and 3D models. Figure 12: Transverse displacement distribution on the inspection plane: (**a**) large scale implicit transient model (control model); (**b**) short implicit transient model; (**c**) short explicit transient model; (**d**) Eulerian steady-state model. Figure 13: Longitudinal PS distributions on inspection planes. Figures 13 and 14 also demonstrate a minor effect of elastic deformation of the roller on the predicted longitudinal PS and RS distributions under steady-state rolling. Figure 15 confirms such a minor effect on the longitudinal RS when the full-field stress distributions are compared. The minor effect of the elastic deformation of the roller found here was consistent with the findings of [PERSON] et al. (2018) using a 3D rolling model. However, it should be noted that a typical rolling load of 50 kN for WAAM was used in this study; the difference in results between elastic and rigid roller models could be considerable at significantly larger rolling loads. The use of an elastic roller in the 2D model doubled the number of elements involved in the analysis and increased the wall clock time by 48% (Table 1). Given the minor difference in results between the elastic and rigid roller models, it can be confirmed that the  
Figure 14: Longitudinal RS distributions on inspection planes. Figure 15: Longitudinal RS predicted by 2D short implicit transient models with elastic roller (**a**) and analytic rigid roller (**b**). analytic rigid roller in the rolling model allowed significant computational savings with almost equivalent accuracy of predicted results.
## 2 Materials and Methods
### Health Risk Assessment of Trace Elements in Soil Using RSL of USEPA  
USEPA has introduced health risk parameters to determine the probabilistic non-carcinogenic and carcinogenic risks to the public from long-term exposure to heavy metals. The aim of utilizing the RSL calculator to analyze a site is to pinpoint the regions, pollutants,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ei Value** & **Interpretation** & **RI Value** & **Interpretation** \\ \hline Ei < 40 & Low & RI \textless{} 95 & Low \\
40 \(\leq\) Ei < 80 & Moderate & 95 \(\leq\) RI \textless{} 190 & Moderate \\
80 \(\leq\) Ei < 160 & Considerable & 190 \(\leq\) RI \textless{} 380 & Considerable \\
160 \(\leq\) Ei < 320 & High & 380 \(\geq\) RI & Very High \\
320 \(\geq\) Ei & Very High & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ei and RI classification.  
and circumstances that need special attention at a certain place [36]. The different routes considered here are inhalation, ingestion, and the cutaneous route. An in-depth assessment of the potential risks must be carried out when the screening level (SL) is exceeded at a site. The SLR, target Hazard Quotient (HQ), or target risk can be calculated using Equations (6) and (7) [37].  
\[\text{Non-carcinogenic risk}:\ \frac{THQ}{RSL}=\frac{HQ}{C} \tag{6}\]  
\[\text{Carcinogenic risk}:\ \frac{TR}{RSL}=\frac{Risk}{C} \tag{7}\]  
where C is the input concentration in site-specific mode [mg/kg; ug/m3; ug/L]; TR is the target risk provided by the user in site-specific mode; the Total Hazard Quotient (THQ) is the target HQ; HQ represents the potential non-carcinogenic risk for each TE and RSL is the regional screening level (mg/kg; ug/m3; ug/L). The risk associated with exposure to metals is evaluated using HI, which is the summation of the HQs. There are non-carcinogenic effects if HI is greater than 1 [38]. Using Equations (6) and (7), the RSL, the target risk or target quotient used to calculate the RSL, and a concentration entered by the user are sufficient to calculate the risk [37]. The New York State Department of Health (NYSDOH) has described carcinogenic risks as follows:  
* \(\leq\)10\({}^{-6}\): Low carcinogenic risk;
* 10\({}^{-4}\) a 10\({}^{-3}\): Moderate carcinogenic risk;
* 10\({}^{-3}\) a 10\({}^{-1}\): High carcinogenic risk;
* \(\geq\)10\({}^{-1}\): Very high carcinogenic risk.
## 3 Results
#### Test-Retest Reliability  
The test-retest reliability obtained between the initial measurement and the measurement recorded 5 days later was excellent for both the CST and 1 min-STST (Table 3). For the CST, MDC\({}_{90}\) was set at a change of 12% with respect to the initial measurement (16.96 steps), whereas MDC\({}_{95}\) was set at a change of 15% (20.15 steps). In the 1 min-STST, a lower change percentage was required to detect significant differences: MDC\({}_{90}\) at 8% (1.89 stands) and MDC\({}_{95}\) at 12% (2.71 stands). The descriptive statistics, ICC and 95% CI associated, SEM, MDC\({}_{90}\) and MDC\({}_{95}\) for the concordance between sessions are shown in Table 3.  
#### Test-Retest Reliability  
The test-retest reliability obtained between the initial measurement and the measurement recorded 5 days later was excellent for both the CST and 1 min-STST (Table 3). For the CST, MDC\({}_{90}\) was set at a change of 12% with respect to the initial measurement (16.96 steps), whereas MDC\({}_{95}\) was set at a change of 15% (20.15 steps). In the 1 min-STST, a lower change percentage was required to detect significant differences: MDC\({}_{90}\) at 8% (1.89 stands) and MDC\({}_{95}\) at 12% (2.71 stands). The descriptive statistics, ICC and 95% CI associated, SEM, MDC\({}_{90}\) and MDC\({}_{95}\) for the concordance between sessions are shown in Table 3. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Mean \(\pm\) SD** & **(95\% CI) Min–Max** \\ \hline Age (years) & \(51.6\pm 9.3\) & (50.6 to 57.0) 33–79 \\ Weight (kg) & \(84.0\pm 18.1\) & (78.6 to 89.6) 45.1–125.0 \\ Height (cm) & \(165.2\pm 9.4\) & (162.3 to 165.2) 145–186 \\ BMI (kg/m\({}^{2}\)) & \(30.5\pm 5.1\) & (29.0 to 32.2) 7.44–42.0 \\ Hospitalisation (days) & \(21.4\pm 39.0\) & (9.2 to 33.5) 10.0–191.0 \\ \hline \hline \end{tabular} Abbreviations: SD, standard deviation; CI, confidence interval.  
\end{table} 
Table 1: Descriptive data measurements for anthropometric variables, age, BMI and hospitalisation days.
## 5 Analysis of Interior Noise after Rail Corrugation Treatment
### Effect Evaluation of the Treatment Measures  
In order to compare the development of rail corrugation before and after treatments, the interior noise was tested five times. Test 1 and test 2 were conducted before the treatments. It is shown in Figure 18 that the interval between test 1 and test 2 was 4 months. Before the treatments of the test section, the track lateral stiffness of the test section was small and the rail was polished periodically. The rail grinding cycle is about 6 months.  
As can be shown in Figure 19, the interior noise of the section increases from 91.2 dB(A) to 95.6 dB(A) after four months of operation, and the noise value seriously exceeds the limit. The sound pressure value clearly increases at the frequency of 800 Hz. Combined with the conversion relationship between the corrugation and the corresponding frequency, this indicates that the rail corrugation with the wavelength of about 30 mm develops further.  
Figure 17: Comparison of interior noise before and after the treatments of rail corrugation. (**a**) Time-varying sound pressure level, (**b**) Frequency domain.  
Therefore, only rail grinding cannot effectively restrain the development of rail corrugation. After rail grinding for a period of time, the interior noise increases significantly.  
In order to further determine the effect of the measures, six months after the implementation of the measures, this work carries out the tracking test on the test section. The results of rail irregularity are shown in Figure 20.  
The rail irregularity of the first curve section is almost unchanged. After the second curve section is renovated, the rail corrugation is improved. The peak-to-peak average value of rail irregularity is 12.1 \(\upmu\)m after six months, while it is 29.8 \(\upmu\)m before the treatments. Therefore, after six months of treatment, rail corrugation does not develop greatly, and the treatment measures are more effective than rail grinding alone.  
The comparison results of interior noise after 6 months of implementation of the measures are shown in Figure 21. After the treatments of rail corrugation, the interior  
Figure 19: Changes of interior noise before the treatments.  
Figure 20: Changes in rail irregularity value.  
Figure 18: Schematic diagram of test and treatment time.  
noise is 84.2 dB(A). The interior noise is 84.4 dB(A) after six months of operation, which is essentially consistent with the test results before six months, and the sound pressure level at each frequency is almost unchanged. Before the treatments, the interior noise increased by 4.4 dB(A) after four months of operation, and the sound pressure at the main frequency of 800 Hz increased significantly. The results of five tests are shown in Table 3. The rail corrugation tests were carried out in Test 2 and Test 5. The results indicate that the treatment measures can effectively reduce the interior noise, and the interior noise does not increase again for a long time.
# Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR
###### Abstract  
The Sentinel satellite constellation series, developed and operated by the European Space Agency, represents the dedicated space component of the European Copernicus program, committed to long-term operational services in environment, climate and security. We developed, tested and evaluated an algorithm for generating maps of snowmelt area from C-band synthetic aperture radar (SAR) data of the Sentinel-1 mission. For snowmelt classification, a change detection method is applied, using multitemporal dual-polarized SAR data acquired in Interferometric Wide swath (IW) mode, the basic operation mode over land surfaces. Of particular benefit for wet snow retrievals are the high instrument stability, the high spatial resolution across the 250 km wide swath, and the short revisit time. In order to study the impact of polarization, we generated maps of melting snow using data of the _VV_-polarized channel, the _VH_-polarized channel and a combined _VV_- and _VH_-based channel using a weighting function that accounts for effects of the local incidence angle. Comparisons are performed with snow maps derived from Landsat images over study areas in the Alps and in Iceland. The pixel-by-pixel comparisons show good agreement between the snow products of the two sensors, with the best performance for retrievals based on the combined (_VV_ and _VH_) channel and a minor decline for the _VH_-based product. The _VV_-based snowmelt extent product shows a drop-off in quality over areas with steep terrain because of the decreasing backscatter contrast of snow-covered _versus_ snow-free surfaces on fore-slopes. The investigations demonstrate the excellent capability of the Sentinel-1 mission for operational monitoring of snowmelt areas.  
Sentinel-1; SAR; snow extent; snowmelt; Alps; Iceland 1
# Kinematic Determination of the Aerial Phase in Ski Jumping
###### Abstract  
The purpose of this study was to find a generic method to determine the aerial phase of ski jumping in which the athlete is in a steady gliding condition, commonly known as the'stable flight' phase. The aerial phase of ski jumping was investigated from a physical point mass, rather than an athlete-action-centered perspective. An extensive data collection using a differential Global Navigation Satellite System (dGNSS) was carried out in four different hill sizes. A total of 93 jumps performed by 19 athletes of performance level, ranging from junior to World Cup, were measured. Based on our analysis, we propose a generic algorithm that identifies the stable flight based on steady glide aerodynamic conditions, independent of hill size and the performance level of the athletes. The steady gliding is defined as the condition in which the rate-of-change in the lift-to-drag-ratio (\(LD\)-ratio) varies within a narrow band-width described by a threshold \(\tau\). For this study using dGNSS, \(\tau\) amounted to \(0.01\,\mathrm{s}^{-1}\), regardless of hill size and performance level. While the absolute value of \(\tau\) may vary when measuring with other sensors, we argue that the methodology and algorithm proposed to find the start and end of a steady glide (stable flight) could be used in future studies as a generic definition and help clarify the communication of results and enable more precise comparisons between studies.  
ski jumping; dGNSS; \(LD\)-ratio; stable flight; steady glide +
Footnote †: journal: Sensors