esa-sceva/satcom-chunk-collection huggingface dataset:
and decelerating over short distances [22]. Therefore, analysis of goalkeepers' physical demands usually requires additional quantification of dives, jumps, acceleration frequencies, and explosive efforts [21].  
For successful dealing with high physical demands of contemporary football [23], an appropriate distribution of external training load (ETL) is essential [24; 25; 26; 27]. Previous research investigating ETLs of football players has mostly involved the outfield players [28; 29], while studies analyzing goalkeepers' ETL are scarce. Briefly, [PERSON] et al. and [PERSON] et al. analyzed the ETL of goalkeepers according to the differing training types without information on daily/weekly demands in the English Premier League and the German fifth league, respectively [21; 30]. On the other hand, [PERSON] et al. and [PERSON] et al. gave information on weekly demands by comparing ETLs of starter and nonstarter goalkeepers from the U23 English Premier League and the Spanish second division, respectively [22; 31]. Furthermore, [PERSON] et al. and [PERSON] et al. examined the relationship between ETL and subjective wellness in the English Premier League and top Dutch league, respectively [32; 33].  
Although these studies provide valuable information on goalkeepers' ETL, daily demands of training within a weekly microcycle are rarely presented [22]. Furthermore, studies have mostly investigated subelite goalkeepers and/or have not utilized goalkeeping-specific indicators of ETL [21; 22; 32] or considered goalkeepers' loads during specific actions. In addition, no studies thus far have investigated differences in goalkeepers' daily demands utilizing multivariate techniques, which can be particularly helpful in the better understanding of ETL [34]. As a consequence, comprehensive knowledge on daily physical demands placed on elite-level goalkeepers is currently limited, and additional research is warranted [22]. The findings from such research could serve football practitioners in goalkeeping-specific training prescription processes. Therefore, this study aimed to evaluate the ETL of elite-level goalkeepers during the competitive microcycle. As empirical evidence suggests that goalkeepers' ETLs may vary considering their playing status in subsequent matches [32], ETL was additionally evaluated for starting and nonstarting goalkeepers (i.e., starters and nonstarters).
# On Self-Interference Cancellation and Non-Idealities Suppression in Full-Duplex Radio Transceivers
### Organization of the Paper  
The rest of the paper is organized as follows: the proposed system model and techniques for SIC and the linearization of non-idealities of the transceiver's components are described in Section 2. The simulations, results and discussion for the proposed system and techniques along with comparison of the proposed work with the previous works are described in Section 3. Finally, Section 4 presents the conclusion of our work.
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 1 Introduction
### Terminology
According to seemingly normative reference material published by the National Electronic Manufacturing's Association (NEMA) [22], the concept of volt/var optimization (\"VVO\") depends on real-time information and modeling to effect coordinated control of voltage and current fluctuations. This process depends on in-line voltage regulators, LTC transformers, and shunt capacitor banks whose interactions with the distribution grid occur via an established model, built based on circuit assumptions, and operated via long-term feedback loops. In other words, conventional VVO is not \"dynamic\" and does not adjust the distribution circuit to changing conditions because of the time-lag in implementation as well as the inaccuracies of the modeling approach. According to NEMA, CVR is atype of VVO which leverages a \"flexible, AC transmission system\" (FACTS) to flatten voltage profiles and reduce system demand by controlling the feeder and lowering the distribution line voltage. With this definition, the topic of concern here is \"CVR\". Regardless, we define an alternative term to ensure clarity. The terminology used here is \"Dynamic Voltage Optimization\" (\(\overline{\text{DVO}}\) ) which refers to the process of modulating the feeder voltage at the substation as well as along the distribution circuit to provide optimal supply voltage at the customer's endpoint. This concept is enabled by real-time, on-wire communication between control and monitor devices which may be deployed at various points in the distribution grid. Using this description, the term \(\overline{\text{DVO}}\) used herein is similar to a combination of terms, including \"DVO\" \"CVR\", \"LDC\", and \"VSR\" and \"DVR\" which are defined/used in the literature. The objective of the discussion here is not to standardize terminology or present an architecture for voltage optimization which has not been previously explored. Rather, the objective is to describe the intersection between areas of technology which may have valuable implementation considerations in the ongoing efforts to optimize distribution grid efficiency.
# Adaptive Software Defined Equalization Techniques for Indoor Visible Light Communication
## 6 Measurements
### Configuration, Scenario, and Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**256-QAM**} \\ \hline \multicolumn{10}{c}{**Without EQ**} \\ \hline
**Distance** & **E\({}_{\text{b}}\)/N\({}_{\text{0}}\)** & **EVM** & **BER** & **MER** & **Distance** & **E\({}_{\text{b}}\)/N\({}_{\text{0}}\)** & **EVM** & **BER** & **MER** \\ \hline
**(cm)** & **(dB)** & **(\%)** & **(-)** & **(dB)** & **(cm)** & **(dB)** & **(\%)** & **(-)** & **(dB)** \\ \hline
0 & 23.98 & 5.32 & 3.87\({}^{-2}\) & 21.21 & 0 & 32.69 & 1.90 & 1.00\({}^{-3}\) & 30.18 \\
50 & — & — & — & — & — & 50 & 30.09 & 3.00 & 3.03\({}^{-2}\) & 27.50 \\
100 & — & — & — & — & — & 100 & 29.49 & 2.78 & 6.38\({}^{-3}\) & 26.90 \\
150 & — & — & — & — & 150 & 23.25 & 6.16 & 9.16\({}^{-2}\) & 20.39 \\
200 & — & — & — & — & 200 & — & — & — & — \\
250 & — & — & — & — & 250 & — & — & — & — \\
300 & — & — & — & — & 300 & — & — & — & — \\
350 & — & — & — & — & 350 & — & — & — & — \\ \hline \hline \multicolumn{10}{c}{**256-QAM**} \\ \hline
**NLMS** & \multicolumn{10}{c}{**QR-RLS**} \\ \hline
**Distance** & **E\({}_{\text{b}}\)/N\({}_{\text{0}}\)** & **EVM** & **BER** & **MER** & **Distance** & **E\({}_{\text{b}}\)/N\({}_{\text{0}}\)** & **EVM** & **BER** & **MER** \\ \hline
0 & 32.10 & 1.99 & 1.48\({}^{-3}\) & 29.78 & 0 & 40.62 & 0.79 & 0 & 37.81 \\
50 & 30.60 & 2.42 & 3.74\({}^{-3}\) & 28.12 & 50 & 40.96 & 0.75 & 0 & 38.28 \\
100 & 27.76 & 3.42 & 1.15\({}^{-2}\) & 25.15 & 100 & 38.91 & 0.94 & 1.70\({}^{-5}\) & 36.33 \\
150 & 22.86 & 6.39 & 6.71\({}^{-2}\) & 20.03 & 150 & 35.55 & 1.40 & 1.15\({}^{-4}\) & 32.88 \\
200 & — & — & — & — & 200 & 28.00 & 3.66 & 5.70\({}^{-2}\) & 25.18 \\
250 & — & — & — & — & 250 & 20.70 & 8.53 & 1.57\({}^{-1}\) & 17.83 \\
350 & — & — & — & — & 300 & — & — & — & — \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Table of measured parameters of the ceiling light, 256-QAM modulation format, bandwidth 1 MHz. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**4-QAM**} \\ \hline \multicolumn{10}{c}{**Without EQ**} \\ \hline
**Distance** & **E\({}_{\text{b}}\)/N\({}_{\text{0}}\)** & **EVM** & **BER** & **MER** & **Distance** & **E\({}_{\text{b}}\)/N\({}_{\text{0}}\)** & **EVM** & **BER** & **MER** \\ \hline
**(cm)** & **(dB)** & **(\%)** & **(-)** & **(dB)** & **(cm)** & **(dB)** & **(\%)** & **(-)** & **(dB)**
## 3 Results
### Seasonal Dynamics of the Carbon Cycle, WUE, Tower-Measured GPP, and Remotely Sensed GPP  
Carbon fluxes (GPP, Reco, and NEE) and WUE are presented in Figure 4. The seasonality of these parameters is evident, as well as a relative coupling with observed rainfall pulses. GPP peaked in April 2014 and the entire period monthly mean was of 1.89 \(\pm\) 0.87 gC m\({}^{-2}\) month\({}^{-1}\). MODIS GPP presented a similar behavior, but with slightly higher GPP values, with a maximum of 10.0 \(\pm\) 0.84 gC m\({}^{-2}\) month\({}^{-1}\) in April 2014. Monthly mean GPP ranged from 1.0 to 3.7 gC m\({}^{-2}\) month\({}^{-1}\). On average, accumulated GPP during the wet season (163.9 gC m\({}^{-2}\) month\({}^{-1}\)) was 126% higher than in the dry season (72.5 gC m\({}^{-2}\) month\({}^{-1}\)). The lowest carbon fluxes occurred in the dry season, when rainfall accounts for roughly 17% of the total annual expected amount of precipitation. Total accumulated ecosystem productivity (estimated through GPP) was 414.7 gC m\({}^{-2}\) yr\({}^{-1}\) in 2014 and 334.0 gC m\({}^{-2}\) yr\({}^{-1}\) in 2015. The seasonal pattern of NEE was also modulated by local hydmeteorological conditions, and the Caatinga site behaved as a carbon sink (NEE \(<\) 0). Higher carbon uptake was observed during the wetter months (Figure 4a). With  
Figure 3: Relationship between: (**a**) vegetation indices (NDVI, EVI); (**b**) energy fluxes measured at the flux tower (daily means corresponding to the exact dates of satellite data). The shaded area indicates the wet season.  
the end of the wet season, uptake diminished, but the Caatinga still behaved as a carbon sink until the end of each year. Carbon uptake was different between the wet season of both studied years and this is probably not associated with rainfall differences between years since it was roughly 10%. The most plausible explanation is the difference in the number of rain days in each year (Figure 4b), with a 49% decrease from 2014 to 2015. This hypothesis agrees with the higher WUE observed in 2015 (1085.2 gC kg\({}^{-1}\) H\({}_{2}\)O) in relation to 2014 (827.3 gC kg\({}^{-1}\) H\({}_{2}\)O). Indeed, WUE represents the trade-off between carbon gains and water consumption during photosynthesis, and the ecosystem tends to shift its behavior towards a more efficient use of water in drier conditions (Figure 4b). In both years, WUE was inversely related to ET. The computation of WUE is important to characterize specific water cycles and the effect of drought on the water balance and carbon sequestration. Data show that WUE in the studied ecosystem decreased with the increase of rain days, and WUE values rose sharply with long sequences of consecutive dry days.
## 2 Data Augmentation  
In this study, we used noise addition as the data augmentation strategy. The main motivation for this type of data augmentation is to capture more acoustic variability of the data to improve the ASR system performance in noisy environments. The proposed approach is demonstrated in the block diagram shown in Figure 2. The input to the data augmentation procedure is clean adult speech taken from an existing large database, WSJCAM0 [44], which will be described in Section 4. Augmentation is conducted by corrupting the clean input signal with additive noise by varying the SNR from 0 dB to 15 dB with a step size of 5 dB and by using four different types of noise (babble, white,  
\begin{table}
\begin{tabular}{l l} \hline \hline ASR & automatic speech recognition \\ \hline TSM & time-scale modification \\ \hline DA & data augmentation \\ \hline \(F_{0}\)M & \(F_{0}\) modification \\ \hline SRM & speaking rate modification \\ \hline RTISI-LA & real-time iterative spectrogram inversion with look-ahead \\ \hline LP & linear prediction \\ \hline WER & word error rate \\ \hline DNN & deep neural network \\ \hline TDNN & time delay neural network \\ \hline VTLN & vocal tract length modification \\ \hline SRA & speaking rate adaptation \\ \hline MFCCs & mel-frequency cepstral coefficients \\ \hline GMM & Gaussian mixture model \\ \hline HMM & hidden Markov model \\ \hline fMLLR & feature-space maximum likelihood linear regression \\ \hline LM & language model \\ \hline LDA & linear discriminant analysis \\ \hline MLLT & maximum likelihood linear transform \\ \hline SAT & speaker adaptive training \\ \hline SNR & signal-to-noise ratio \\ \hline VMD & variational mode decomposition \\ \hline SMAC & spectral moment time-frequency distribution augmented by low-order cepstral \\ \hline CNN & convolutional neural network \\ \hline SFM & stochastic feature mapping \\ \hline GAN & generative adversarial network \\ \hline STFTM & short-time Fourier transform magnitude \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of abbreviations.  
factory, and volvo) extracted from the NOISEX-92 database [45]. It should be noted that the data augmentation approach is used in this study to generate new, noise-corrupted data for training, but the original clean adult speech taken from the WSJCAM0 database is not included in the generated training data.  
Data augmentation is performed in the ASR experiments of the current study using three scenarios: the \"same\" scenario, the \"different\" scenario, and the \"all\" scenario. The \"same\" scenario refers to testing the ASR system in circumstances where the test speech is corrupted by one type of noise (e.g., babble), and system training is based on using the same noise type in data augmentation. The \"different\" scenario refers to corrupting the test speech with one noise type and using the other three types in augmenting the training data (e.g., babble noise in testing, and factory, volvo as well as white noise in augmentation). The \"all\" scenario refers to testing with speech corrupted by one noise type (e.g., white) but using all four noise types in data augmentation.
## 3 Materials and Methods
### Headspace Solid-Phase Microextraction (HS-SPME)  
The fresh, frozen, and dried powdered fruits of the plant were used for HS-SPME coupled with GC-MS. The sample (5 g fresh or frozen, or 2.5 g dried) was placed in a 20 mL vial which was tightly capped with a PTFE/silicon septum. The analysis was performed using a PAL RTC 120 autosampler (CTC Analytics AG, Zwingen, Switzerland), which was able to yield strong analysis repeatability. The influence of the extraction temperature on the adsorption of the volatile compounds was first evaluated, and the extraction time was fixed at 30 min, in addition to equilibrium time of another 20 min before the extraction process. The incubation of the sample was performed at different temperatures (60 and 80 \({}^{\circ}\)C) under agitation (250 rpm, 5 s of on-time and 2 s of off-time), and extracted using two SPME fiber coatings (DVB/ CWR/PDS 80 \(\upmu\)m, and PDMS/DVB 65 \(\upmu\)m). Then, different extraction times (15 and 30 min) were examined at 60 \({}^{\circ}\)C (the optimum extraction temperature obtained from the previous analysis) using DVB/CWR/PDMS 80 \(\upmu\)m (the optimum SPME fiber obtained from the previous analysis). Lastly, the effect of NaCl addition was evaluated using the optimal extraction temperature (60 \({}^{\circ}\)C), fiber (DVB/CWR/PDMS 80 \(\upmu\)m), and time (30 min) obtained previously. The fibers were conditioned for 20 min at 250 \({}^{\circ}\)C and then inserted inside the headspace of sample vial with a speed of 20 mm\(\cdot\)s\({}^{-1}\) and a penetration depth of 40 mm. The extraction was performed and then the fiber was inserted into the injector port at a speed of 100 mm\(\cdot\)s\({}^{-1}\) and a penetration depth of 40 mm. The desorption occurred at 250 \({}^{\circ}\)C for 3 min. After desorption, the fiber was conditioned at 250 \({}^{\circ}\)C for 15 min.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Jam** & **Ingredients (100 g Jam Product)** \\ \hline Jam 1 (Coop Vivi verde jam) & 60 g strawberry, sugar, lemon juice, pectin, citric acid \\ Jam 2 (Coop jam) & 50 g strawberry, sugar, lemon juice, pectin, citric acid, \\  & elderberry juice \\ Jam 3 (Zuegg jam) & 50 g strawberry, sugar, lemon juice, pectin \\ Jam 4 (Alce Nero jam) & 100 g strawberry, lemon juice, grapes juice \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ingredients listed on the label of each strawberry jam used in this study.  
Figure 4: Appearance of dried strawberry fruits treated by different drying methods.
###### Abstract  
The kinematic source rupture process of the 2016 Meinong earthquake (M\({}_{\rm w}=6.4\)) in Taiwan was derived from apparent source time functions retrieved from teleseismic S-waves by using a refined homomorphic deconvolution method. The total duration of the rupture process was approximately 15 s, and one slip-concentrated area can be represented as the source model based on images representing static slip distribution. The rupture process began in a down-dip direction from the fault toward Tainan City, strongly suggesting that the rupture had a unilateral northwestern direction. The asperity with an area of approximately 15 \(\times\) 15 km\({}^{2}\) and the maximum slip of approximately 2 m were centered 12.8 km northwest of the hypocenter. Coseismic vertical deformation was calculated based on the source model. Compared with the results derived from InSAR (Interferometric Synthetic Aperture Radar) data, our results demonstrated that the location with maximum uplift was accurately well detected, but our maximum value was just approximately 0.4 times of the InSAR-derived value. It reveals that there are the other mechanisms to affect the vertical deformation, rather than only depending on the source model. At different depths, areas west, east, and north of the hypocenter maintained high values of Coulomb stress changes. This explains the mechanism behind aftershocks being triggered and provides a reference for predicting aftershock locations after a large earthquake. The estimated seismic spectral intensities, including spectral acceleration and velocity intensity (SIa and Slv), were derived. Source directivity effects caused damage to buildings, and we concluded that all damaged buildings were located within a SIa value of 400 gal. Destroyed buildings taller than seven floors were located in an area with a SIv value of 30 cm/s. These observations agree with those on damages caused by the 2010 Jiasian earthquake (ML 6.4) in Tainan, Taiwan.  
spectral intensity; seismic hazard; Coulomb stress change +
Footnote †: journal: Applied Sciences  
doi:10.3390/app12010494
# A Connected and Autonomous Vehicle Reference Architecture for Attack Surface Analysis
## 3 A CAV Reference Architecture: Components and Related Attack Surfaces
### CAV Reference Architecture
#### 3.1.4 Sensors (Internal and External)  
Sensors are a key component of CAV systems. The vehicles will rely on their input to build a model of the world. Example sensors include: (i) Global Navigation Satellite System (GNSS) to be aware of a vehicle's position, (ii) wheel rotation sensors to be aware of velocity, (iii) LIDAR to be aware of the relative position of other vehicles, (iv) parking cameras to assist drivers and a variety of other sensors such as temperature, humidity and light. Sensors may also observe information _passively_ about the occupants in the vehicle. As sensors are a way for the vehicle to obtain the state of the environment around it, if that data can be maliciously manipulated, then the vehicle may make incorrect decisions based on the manipulated data. Alternately, an adversary may attempt to eliminate the vehicle being able to use certain sensors, such as by jamming GNSS signals or producing too much LIDAR interference for the data to be useful [10]. Another approach may be for an adversary to place additional sensors on the vehicle exterior or to subject the sensors to physical manipulation.  
In certain systems, the vehicle's sensors may wirelessly communicate their data to the car (such as when monitoring tire pressure [48]). Most sensors are expected to be hardwired to the system due to high reliability requirements. Wireless sensors pose a greater security threat as there is a larger attack surface for an adversary to take advantage of. For example, the Tire Pressure Monitoring System (TPMS) leaks identity information about the vehicle by including unencrypted identifiers in the packets it sends. Due to the lack of authentication and validation, the system also is vulnerable to spoofing and replay attacks, where the vehicle could easily be fooled into believing the tire is flat even if it was not.  
\begin{tabular}{l} \hline \hline
**Example Attacks** \\ \hline \(\bullet\) Induce misleading readings (Spoof, Replay, Delay) [10] \\ \(\bullet\) Blind, Jam [10] \\ \(\bullet\) Tamper (Disable, Replace) \\ \hline \hline \end{tabular}
## 3 Data Acquisition
### Landslide Conditioning Factors
#### 3.2.6 Normalized Difference Vegetation Index  
Normalized difference vegetation index (NDVI) provides a measure of vegetation within an area [57]. NDVI can be formulated as follows,  
\[\text{NDVI}=\frac{(\text{NIR}(\text{Band4})-\text{Red}(\text{Band3}))}{(\text{ NIR}(\text{Band4})+\text{Red}(\text{Band3}))} \tag{2}\]  
where Red and NIR are the red and near-infrared bands, respectively. The NDVI map was generated using Landsat 8 OLI from 2017. Our NDVI map is shown in Figure 3f.
# Effect of Hydrogen Ion Presence in Adsorbent and Solution to Enhance Phosphate Adsorption
## 3 Results and Discussion
### Isotherm Tests
Table 3 shows that all the values of \(n\) are greater than 1 and are in the range of 1.86-4.10. The physical adsorption process would have dominated the phosphate adsorption regardless of the pH of the solution. Figure 4: Langmuir isotherm for GAC and GACA with/without pH adjustment at initial pH of 4 (a), 7 (b), and 9 (c). Figure 5: Freundlich isotherm for GAC and GACA with/without pH adjustment at initial pH of 4 (a), 7 (b), and 9 (c). Although linearization from Equations (2) and (3) was attempted to define the higher model fitting using Equations (4) and (5) [32], none of the cases obtained relatively higher values (data not shown). \[\frac{1}{q_{e}}=\frac{1}{b_{L}}\frac{q_{e}}{C_{e}}+q_{mL} \tag{4}\]  
\[lnq_{e}=lnK_{F}+\frac{1}{n}lnC_{e}. \tag{5}\]  
This result clearly confirms that the transformation of the nonlinear equation to linear has a bias [27]. From the Langmuir coefficient (\(b\), L/mg), the adsorption thermodynamics can be estimated using the following Van 't Hoof Equations (6) and (7) [33; 34]:  
\[\Delta G^{\circ}=-RTlnK_{e} \tag{6}\]  
\[K_{e}=\frac{1000\times b\times M\times c^{\circ}}{\gamma}, \tag{7}\]  
where \(\Delta G^{\circ}\) is Gibbs energy change (kJ/mol), R is the universal gas constant (8.314 J/mol), T is the absolute temperature (K), \(K_{e}\) is thermodynamic constant (dimensionless), M is the molar mass of the adsorbate, \(c^{\circ}\) is the standard concentration of adrothe (1 mol/L), and \(\gamma\) is the dimensionless coefficient of activity, which is usually assumed to be (1). The value of \(\Delta G^{\circ}\) in Table 3 is negative for all cases and ranges from \(-21.6\) to \(-26.7\) kJ/mol. Therefore, adsorption is a spontaneous process.