esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Current Status and Identified Needs
### Level 2 Bio-Geophysical Land Products
#### 4.3.3 CEOS-LPV Supersites  
CEOS-LPV sub-group has identified a new set of sites, so-called supersites, useful for the validation of satellite land products [155]. The CEOS-LPV supersites are super charac terised sites (canopy structure and bio-geophysical variables) following well established protocols, useful for the validation of multiple satellite land geophysical variables (at least 3) and for 3-D radiative transfer modelling approaches. The need for measuring multiple variables was specifically driven by the requirement to assess physical inter-consistency within ECVs.  
The supersites were selected primarily from well-known and established networks (e.g., ICOS, NEON). All proposed sites were evaluated for their suitability by ranking them first based on the availability of data (active site) and their spatial representativeness. The sites were additionally classified, based on how many key variables could be validated within a given location, whether structural 3D information was available for the site and if atmospheric and other properties were measured. This process resulted in the selection of 55 globally spread supersites, which were endorsed at CEOS-LPV level [155].
## 2 Materials and Methods
### Analysis
#### 2.3.3 Minor Volatile Compounds  
Minor volatile aroma compounds were extracted via HS-SPME, using a DVB-CAR-PDMS fiber (StableFlex, 50/30 \(\upmu\)m, 2 cm; Supelco, Sigma Aldrich, Milan, Italy). The samples were injected in splitless mode by a Gerstel MPS autosampler (GERSTEL GmbH & Co. KG, Mulheim an der Ruhr, Germany) and analyzed via GC\(\times\)GC/TOF-MS, using an Agilent 7890N GC (Agilent Technologies) connected to a LECO Pegasus IV time-of-flight MS (TOF-MS) (Leco Corporation, St. Joseph, MI, USA). The system was equipped with two columns of different dimensions and polarity connected by a modulator. The first-dimension column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m d.f. VF-WAXms) (Agilent Technologies) was held at 40 \({}^{\circ}\)C for 4 min, then increased to 250 \({}^{\circ}\)C at 6 \({}^{\circ}\)C/min, and then maintained at 250 \({}^{\circ}\)C for 5 min. The second-dimension column (1.5 m \(\times\) 0.15 mm \(\times\) 0.15 \(\upmu\)m Rxi 17 Sil MS) (Restek) was maintainedat temperatures of 5 \({}^{\circ}\)C higher than those applied for the first-dimension column throughout the analysis. Helium carrier gas flow rate was 1.2 mL/min. To acquire mass spectra in the 40-350 \(m/z\) range, EI mode with 70 eV was used. Baseline correction, chromatogram deconvolution, and peak alignment were conducted by LECO ChromaTOF software version 4.32 (Leco Corporation). Other operation conditions and identification and quantification parameters were reported previously by [PERSON] et al. [24] and [PERSON] et al. [23].
## 3 Results
### Artificial Intelligence (Al)-Supported Analysis
#### 3.2.1 AI-Supported Measurement of Epidermal Thickness and Stratum
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **V0 Median (IQR)** & **V1 Median (IQR)** & **V2 Median (IQR)** & \begin{tabular}{l} **\(p\)-Value (Friedman** \\ **Test)** \\ \end{tabular} \\ \hline ET (SP) & 254 \(\mu\)m (201–272 \(\mu\)m) & 200 \(\mu\)m (139–243 \(\mu\)m) & 124 \(\mu\)m (92–166 \(\mu\)m) & \(p=0.006\) \\ \hline SCT (SP) & 62 \(\mu\)m (40–71 \(\mu\)m) & 36 \(\mu\)m (27–47 \(\mu\)m) & 25 \(\mu\)m (20–39 \(\mu\)m) & \(p<0.001\) \\ \hline ET (CoA) & 70 \(\mu\)m (61–81 \(\mu\)m) & 71 \(\mu\)m (67–80 \(\mu\)m) & 73 \(\mu\)m (67–82 \(\mu\)m) & \(p=0.339\) \\ \hline SCT (CoA) & 14 \(\mu\)m (12–19 \(\mu\)m) & 16 \(\mu\)m (14–19 \(\mu\)m) & 18 \(\mu\)m (15–21 \(\mu\)m) & \(p=0.105\) \\ \hline \multicolumn{4}{l}{\begin{tabular}{l} Percentage difference \\ of ET (SP, AI vs. \\ subjective scoring) \\ \end{tabular} } & \(-\)17.6\% (\(-\)23.2—13.0\%) & \(-\)15.4\% (\(-\)17.6—5.0\%) & \(-\)0.35\% (\(-\)13.0—9.6\%) & \begin{tabular}{l} Pearson \\ coefficient = 0.97, \\ Spearman = 0.91 \\ \end{tabular} \\ \hline \multicolumn{4}{l}{\begin{tabular}{l} Percentage difference \\ of SCT (SP, AI vs. \\ subjective scoring) \\ \end{tabular} } & \(-\)23.6\% (\(-\)42.3—6.8\%) & \(-\)20.8\% (\(-\)29.7—6.9\%) & 37.4\% (6.5—84.1\%) & \begin{tabular}{l} Pearson \\ coefficient = 0.84 \\ Spearman = 0.81 \\ \end{tabular} \\ \hline \multicolumn{4}{l}{\begin{tabular}{l} Difference in ET (SP) \\ and ET (CoA) \\ \end{tabular} } & 181 \(\mu\)m (141–194 \(\mu\)m) & 117 \(\mu\)m (77–150 \(\mu\)m) & 56 \(\mu\)m (25–77 \(\mu\)m) & \(p=0.004\) \\ \hline \multicolumn{4}{l}{\begin{tabular}{l} Difference SCT (SP) \\ and SCT (CoA) \\ \end{tabular} } & 39 \(\mu\)m (24–59 \(\mu\)m) & 18 \(\mu\)m (11–30 \(\mu\)m) & 9 \(\mu\)m (3–16 \(\mu\)m) & \(p=0.001\) \\ \hline \multicolumn{4}{l}{\
\begin{tabular}{l} DEJ undulation (SP \\ and CoA) \\ \end{tabular} } & 75.9\% (52.7–134.6\%) & 48.8\% (36.4–68.0\%) & 27.0 (14.0–79.0\%) & \(p=0.148\) \\ \hline \multicolumn{4}{l}{
\begin{tabular}{l} Difference in DEJ undulation (SP \\ and CoA) \\ \end{tabular} } & 65.5\% (20.0–126.1\%) & 46.4\% (21.9–57.1\%) & 19.3\% (3.2–49.6\%) & \(p=0.103\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Artificial intelligence (AI)-supported measurements of epidermal thickness (ET) and stratum corneum thickness (SCT), respective \(p\)-values in the study plaque (SP) and control area (CoA), and differences in the ET and SCT comparing the SP and CoA. \(\mu\)m = micrometer; IQR = interquartile range, 25–75% percentile; DEJ = demo-epidermal junction. Figure 7: **(a)** AI model measurement of epidermal thickness/acanthosis (in \(\mu\)m) in the SP (orange) and CoA (blue) before (V0) and under therapy (V1 after 4 weeks, V2 after 12 weeks); **(b)** AI-model measurement of stratum corneum/hyperkeratosis (in \(\mu\)m) in the SP (orange) and CoA (blue) before (V0) and under therapy (V1 after 4 weeks, V2 after 12 weeks). SP = study plaque, CoA = control area; \(\mu\)m = micrometer.
# Identification and Allelopathy of Green Garlic
## 2 Results  
### Identification of Volatile Allelochemicals in Green Garlic  
Identification of the effective components of VOCs collected from green garlic is the basis to reveal its mechanism in scavenging ROS. For the VOCs collected from the green garlic segments that were contained in the SPME vial, only two compounds including DADS and diallyl sulfide (DAS) were identified (Figure 1A and Table 1). The retention time and relative peak area of DADS were 13.83 min and 99.52%, respectively, while, DAS had a retention time of 7.04 min and a relative peak area of 0.48%. Among the VOCs collected using green garlic segments that were put in a sealed desiccator, 17 compounds were detected (Table S1), in which there were three sulfur compounds including DADS, methyl propenyl disulfide and DAS, with relative peak areas of 86.33%, 1.53% and 0.35%, respectively (Figure 1B and Table 1). All the other compounds were heterocyclic compounds and long chain hydrocarbon compounds, which are impurities in air. There were 42 compounds identified in the VOCs obtained from the whole green garlics that were placed in a sealed desiccator (Table S2). Among the 42 detected compounds there was only one sulfur compound, which was DADS with a relative peak area of 15.3% (Figure 1C and Table 1), while the others were also heterocyclic compounds and long chain hydrocarbon compounds. The qualitative identification result showed that DADS is the main volatile compound of green garlic. Compared with the whole green garlic, the cut green garlic segments produced more DADS with about a 5-fold difference.  
Figure 1: Identification of the collected volatiles from green garlic volatiles by GC-MS. (**A**) Cut (SPME vial); (**B**) Cut (sealed desiccator); (**C**) Whole (sealed desiccator); (**D**) MS spectrum and compound structure of DADS. \(n=3\), three biological replicates.  
The result of an external standard method for quantitative analysis of volatiles showed that the linear relation between DADS concentration (mmol/L) (y) and peak area (x) fits a simple linear regression equation (Equation (1)):  
\[\text{y}=5\text{E}+09\text{x}+3\text{E}+07,\text{R}^{2}=0.9903 \tag{1}\]  
Based on this equation, the DADS that released by green garlics has a concentration of about 0.08 mg/g.  
Our biological tests showed that the green garlic treatment had a consistent effect with the DADS treatment (Figure 2). When the concentration of DADS was higher than 1 mmol/L, cucumber leaves turned yellowish (Figure 2). Inversely, when the green garlic treatment was less than 2 g or the concentration of DADS was lower than 1 mmol/L, the cucumber leaves had no morphology changes (Figure 2). The amount of DADS volatilized by 2 g of green garlic approached 1 mmol/L DADS treatment based on Equation (1). In addition, higher concentrations of green garlic volatiles and DADS can cause cucumber leaves turn to soft and rot (Figure 2). These results confirmed that DADS is the main allelochemical of green garlic volatiles.
## 2 Study Area and Data Processing  
### Study Area  
The Chindwin River basin is located in the northwestern part of Myanmar within the latitude of 21.6\({}^{\circ}\)-27.4\({}^{\circ}\)N and longitude of 93.5\({}^{\circ}\)-97.3\({}^{\circ}\)E (Figure 1). The river has a drainage area of 110,350 kmand a length of 985 km. The Chindwin River basin is mainly characterized by a complex mountainous terrain (the Arakan Mountain Range) in the north and west, and the wide Chindwin Valley at its central and southern parts (Figure 1). The basin is situated in subtropical and tropical climate regions and has distinct humid and dry seasons controlled by monsoon systems. The southwest monsoon season begins in June and ends in September, with a rainy and hot summer. The northeast monsoon prevails from December to April, leading to the dry and cool season. Precipitation is highly spatially uneven due to the effects of complex topography and the southwest monsoon. The mean annual precipitation tends to decline sharply from the north (approximately 3700 mm) to the south (approximately 750 mm) [47]. The basin suffers from severe floods every year, and floods occur most frequently and seriously in July and August, contributing 72% of the total number of flood events because of the high rainfall intensities with considerable spatiotemporal variations in the southwest monsoon season [47; 59; 60; 61; 62]. Severe flood disasters cause enormous damage to the local society, leading to serious threats to people's lives, property, and safety. Thus, predicting and monitoring flood risks and employing effective prevention measures are urgent and necessary. However, the local rainfall-monitoring network is sparse, with a density of 0.63 rain gauge per 10,000 km\({}^{2}\), which hampers the accuracy of flood forecasting. Therefore, adopting SPPs on fine spatiotemporal resolutions as supplementary precipitation data sources is a promising approach for effective flood simulation and timely flood forecasting.
## 5 Discussion
### Output Analysis
#### 5.1.2 Parameter Influence Analysis  
For step 2, compatibility and advertisement are implemented for the value of 1, as shown in Figure 15. Though it shows the value of 4G diffusion has been changed by each of the parameters, in most of the experiments, the impact of compatibility has been much higher than that of advertisement in increasing the diffusion of 4G. All the results of steps 1 and 2 show that the compatibility parameter is not only necessary for 4G diffusion, but also has a much higher impact on 4G technology diffusion over time. Therefore, the compatibility value must never be zero if CT strategists are decided to scale up the diffusion of 4G technology.
## Materials and Methods
### Materials Characterization  
The crystalline structure of the as-synthesized samples was characterized by X-ray diffraction (XRD, Bruker D8 ADVANCE, Cu k\(\alpha\) source), and the microstructures and morphology were observed by scanning electron microscope (SEM, JSM-5610 LV, JEOL, Akshima, Japan), and transmission electron microscope (TEM, JSM-2100F, 200 kV, Hitachinaka, Naka, Japan) and high-resolution transmission electron microscope (HRTEM, FEI, TecnaiG2 F30, 200 KV, Hitachinaka, Naka, Japan). The sample of cross-linked binder (CMC-FA) was prepared by the method same as preparing electrode without active materials and conductive materials, Fourier transform infrared (FT-IR) spectroscopy of CMC,FA and CMC-FA samples were carried out as diffuse reflectance measurements using a Bruker IFS 66/S FT-IR spectrometer and Spectra-Tech Collector II DRIFTS accessory. The specific surface area of the sample was calculate based on Brunauer-Emmett-Teller (BET) method through nitrogen gas adsorption isotherms measured by using a BEL mini instrument.
# A Survey on Secure WiFi Sensing Technology: Attacks and Defenses
## 2 Background
### Concept and Scope  
Secure WiFi sensing is a technology that uses WiFi signals to perform sensing tasks (e.g., location estimation, health monitoring, and activity recognition) while ensuring data privacy and security. In the traditional WiFi sensing process, WiFi signals are emitted by the transmitter (Tx) and then propagate through the wireless channel so that the signal properties (e.g., amplitude, phase, frequency, and polarization) are changed due to the existence or movement of sensing targets. A WiFi receiver (Rx) can receive these WiFi signals and employ different sensing algorithms to extract the sensing information of the sensing target. However, there might be malicious attackers who try to attack the WiFi sensing systems, for example, by means of jamming or eavesdropping. Secure WiFi sensing aims to explore adequate security strategies to defend against these attacks, protecting user privacy and ensuring reliable data interpretation.  
The scope of this survey contains the attack techniques and defense strategies related to WiFi sensing technology.
## 3 Materials and Methods
### Gas Chromatography-Mass Spectrometry (GC-MS) Analysis  
Analyses were carried out using Agilent 7890A (Agilent Technologies, Santa Clara, CA, USA) coupled to a mass spectrometer (Agilent MSD 5975C, Agilent Technologies, Santa Clara, CA, USA).  
The separation of compounds was performed on DB-1 ms capillary column (30m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent J&W, Agilent Technologies Santa Clara, CA, USA). The Topaz 0.75 mm ID SPME inlet liner (Restec) for Agilent GCs was used. Injections were performed in the splitless mode. As a carrier gas, helium was used with a flow rate of 1.1 mL/min. The oven temperature was as follows: 30 \({}^{\circ}\)C for 6 min, raising to 130 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min, then to 220 \({}^{\circ}\)C at a rate of 20 \({}^{\circ}\)C/min, where it was held for 2 min. The temperatures of the MS ion source, transfer line, and quadrupole analyzer were 230, 250, and 150 \({}^{\circ}\)C, respectively. The electron impact energy was 70 eV. The mass spectrometer was operating in the full scan mode. Qualification of VSCs in the real samples was performed by comparison of obtained spectra with the reference mass spectra from NIST/EPA/NIH mass spectra library (2012; Version 2.0 g.), with the retention times of authentic standards and confirmed with use of the deconvolution procedure. Deconvolution was done using MassHunter Workstation Software (Agilent). Quantification of VSCs in the real samples was performed using selected ion monitoring (SIM) mode. The ions monitored in SIM mode (Table S1) were selected based on data gathered in NIST/EPA/NIH Mass spectra library (2012; Version 2.0 g.). For high selectivity, ions of VSC were grouped and monitored in the time segments. The total dwell time ranged between 60 and 120 ms, with an acquisition rate higher than 6 cycles/sec. Agilent MassHunter software (Agilent Technologies, Santa Clara, CA, USA) was used for data processing.  
The following are available online: Table S1: SIM ions used in the GC-MS analysis.  
Conceptualization, U.D.-K. and K.P.-P.; Formal analysis, U.D.-K. and K.P.-P.; Funding acquisition, U.D.-K.; Investigation, U.D.-K. and K.P.-P.; Methodology, U.D.-K. and K.P.-P.; Project administration, U.D.-K.; Resources, U.D.-K. and K.P.-P.; Supervision, M.B.; Validation, U.D.-K. and P.P.; Visualization, P.P.; Writing--original draft, U.D.-K. and K.P.-P.; Writing--review & editing, U.D.-K. and M.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Polish National Science Centre, grant number 2018/02/X/NZ9/02683.  
The authors declare no conflict of interest.
## 2 Method
### Drift Elimination
#### 2.2.3 Scaled Dot-Product Attention  
Capturing the long-term dependence and dynamics of the drifting signal is a prominent feature of the Transformer model, and this function is precisely realized by scaled dot-production attention, whose detailed structure is shown in Figure 3a. The inputs are mapped into three channels, namely, query, key, and value, and their lengths are _d\({}_{q}\)_, _d\({}_{k}\)_, and _d\({}_{v}\)_, respectively. The system's sampled voltage, denoted as _u\({}_{s}(t)\)_, serves as the input and is multiplied by distinct weight matrices _W\({}^{Q}\)_,_ W\({}^{K}\)_, and,_ W\({}^{V}\) _to obtain the query vector Q, key vector K, and value vector V, respectively. The specific formulas are shown in Equations (3)-(5):_  
\[Q =W^{Q}u_{s}(t) \tag{3}\] \[K =W^{K}u_{s}(t)\] (4) \[V =W^{V}u_{s}(t) \tag{5}\]  
The commonly used attention functions in attention mechanisms are additive attention [26] and dot-product attention. Additive attention employs a feedforward network with a single hidden layer to compute the compatibility function. In contrast, dot-product attention is faster and more space-efficient than additive attention because it can be implemented using a highly optimized matrix multiplication code. Thus, this paper opts for dot-product attention.  
Figure 3: (**a**) Structure of scaled dot-product attention (**b**) Structure of multi-head attention.  
The purpose of scaling dot-product attention is to apply the weights generated from query vectors and key vectors to the value vectors, thereby focusing on the more relevant locations in the input data. First, the degree of matching is determined by calculating the dot product between the query vector and the key vector. Then, the weights of the value vectors are obtained after appropriate scaling and processing by the SoftMax function. The scaling process involves dividing each dot product by \(\sqrt{d_{k}}\) to prevent gradient vanishing during training. Finally, the output of the attention mechanism is obtained by multiplying the weights with the value vector [36]. This can be expressed as  
\[Attention(Q,K,V)=softmax\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}}\bigg{)}V \tag{6}\]  
where SoftMax represents the SoftMax function, \(Q\) represents the query vector, \(K\) represents the key vector, \(V\) represents the value vector, and \(d_{k}\) represents the length of the key vector.
# Galileo: The Added Value for Integrity in
## 3 Integrity Algorithm
### Improvements with Respect to Classical Algorithms  
In harsh environments, such as urban canyon or mountainous areas, the hypothesis of a single outlier is not realistic; hence, the presence of multiple blunders should be considered. In order to identify and reject multiple erroneous measurements, quality monitoring algorithms can be performed iteratively. In this case, the algorithm performs recursive rejection of an observation until no additional outliers are present or the solution cannot be checked due to a geometric limit or due to the correlation among the measurements. In this research, the classical FB algorithm has been modified introducing a preliminary check to verify the robustness of the satellite geometry and a separability check to avoid erroneous rejections. The algorithm has been further enhanced considering different accuracies for GPS and Galileo pseudoranges [6; 21]. Moreover, a composite design matrix is used for the GPS/Galileo case. In this case, the presence of an inter-system bias is accounted for [7].
# Exploitation of Three Non-Conventional Yeast Species in the Brewing Process
###### Abstract  
Consumers require high-quality beers with specific enhanced flavor profiles and non-conventional yeasts could represent a large source of bioflavoring diversity to obtain new beer styles. In this work, we investigated the use of three different non-conventional yeasts belonging to _Lachancea thermotolerans_, _Wickerlamomyces anomulus_, and _Zygotorulaspora florentina_ species in pure and mixed fermentation with the _Saccharomyces cerevisiae_ commercial starter US-05. All three non-conventional yeasts were competitive in co-cultures with the _S. cerevisiae_, and they dominated fermentations with 1:20 ratio (_S. cerevisiae_/non-conventional yeasts ratios). Pure non-conventional yeasts and co-cultures affected significantly the beer aroma. A general reduction in acetaldehyde content in all mixed fermentations was found. _L. thermotolerans_ and _Z. florentina_ in mixed and _W. anomalies_ in pure cultures increased higher alcohols. _L. thermotolerans_ led to a large reduction in pH value, producing, in pure culture, a large amount of lactic acid (1.83 g/L) while showing an enhancement of ethyl butyrate and ethyl acetate in all pure and mixed fermentations. _W. anomalous_ decreased the main aroma compounds in comparison with the _S. cerevisiae_ but showed a significant increase in ethyl butyrate and ethyl acetate. Beers produced with _Z. florentina_ were characterized by an increase in the isoamyl acetate and \(\alpha\)-terpineol content.  
Keywords:non-conventional yeasts; yeast interactions; beer; biofavor +
Footnote †: journal: Microorganisms
# Flexible 5G New Radio LDPC Encoder Optimized for High Hardware Usage Efficiency
## 6 Results and Discussion
### Throughput-, Latency-, and Hardware-Usage Efficiency Results
The PCM matrix was also stored in the block RAM memories in the same form as for column parallel architecture. However, since a smaller number of shift values is necessary in parallel, the number of used BRAMs is also smaller. Core parity bits were also calculated from \(\mathbf{\lambda}\) vectors as in former designs. Implementation results and comparison are given in Table 3. Given resources are without input and output buffers. Therefore, utilized BRAMs are only used for PCM storage. The obtained maximum operating frequency differed depending on the chosen architecture as shown in Table 3. Although they are heavily pipelined, larger designs have smaller maximal operating frequencies due to routing congestion. However, in order to make a better architectural comparison, the information throughput was normalized with the operating frequency as follows:  
\[Thr_{norm}=\frac{Thr}{f_{CLK}}. \tag{35}\]  
Normalized throughput values are calculated for both BG1 and BG2 and for all lifting sizes. The average normalized throughput values and the average throughputs are shown in Table 3. Finally, the HUE is calculated as throughput, both normalized and achieved, divided by the number of utilized hardware resources. Results show that, on average, the proposed architecture provides by far the highest HUE. Finally, Figure 14 shows the normalized HUE depending on the lifting size for all the above architectures. It is obvious that highly parallel architectures are very inefficient, when compared to the serial and proposed partially parallel architecture. Considering the fact that the latency of the serial processing architecture and the proposed partially parallel architecture is lower than 1 \(\upmu\)s for all codeword lengths, and the fact that the air interface requirement for 5G ultra-reliable low-latency communication (URLLC) use case is 1 ms [47], which is 1000 times higher, there is no remaining reason to use highly parallel architectures for 5G LDPC encoding. By placing multiple encoders to work in parallel, the proposed architecture can be used for obtaining the throughput that is even higher than the throughput of the highly parallel architectures but with much fewer hardware resources.