esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In summary, a comprehensive characterization of key differential VFCs and odorants in WRTs from four different cultivars was performed using GC-O/MS, chemometrics, and multivariate statistical analysis. Sixteen aroma-active compounds were identified as the characteristic key odorants among the four types of WRTs, especially methyl salicate, _p_-cymene, 2,5-dimethylpyrazine, and 1-furfurfurylyrrole in DHP; phenylethyl alcohol, phenethyl acetate, indole, and (_E_)-\(\beta\)-famesene in RG; linalool, phenethyl butyrate, hexyl hexanoate, and dihydroactinidiolide in JF; and naphthalene in SX. The obtained results have enriched the theoretical knowledge of tea biochemistry and provide a fundamental basis for distinguishing tea cultivars, recombination, and simulation of the WRT aroma.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/foods11244109/s1](https://www.mdpi.com/article/10.3390/foods11244109/s1)]([https://www.mdpi.com/article/10.3390/foods11244109/s1](https://www.mdpi.com/article/10.3390/foods11244109/s1)), Table S1: The peak area of the identified aroma components in WRTs with different cultivars; Table S2: The detailed panel performance on the GC-O results of DHP; Table S3: The detailed panel performance on the GC-O results of RG; Table S4: The detailed panel performance on the GC-O results of SX; Table S5: The detailed panel performance on the GC-O results of JF.  
**Author Contributions:** Data curation, visualization, methodology, writing--original draft, writing--review & editing, Y.Z. ([PERSON]); investigation, methodology, formal analysis, software, S.K.; methodology, formal analysis, H.Y.; methodology, D.X. and Q.C.; methodology, mupervision, H.L.; resources, project administration, funding acquisition, writing--review & editing, Z.L.; conceptualization, methodology, funding acquisition, writing--review & editing, Y.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Zhejiang Province Public Welfare Technology Application Research Project (LGN20C160007), the National Natural Science Foundation of China (U21A20231,32172631), the Special Funds for China Agriculture Research System of MOF and MARA (CARS-19) and the Science and Technology Innovation Project of the Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2014-TRICAAS).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data will be made available on request.  
**Conflicts of Interest:** The authors from Tea Research Institute, Chinese Academy of Agricultural Sciences, and Key Laboratory of Tea Processing Engineering of Zhejiang Province. declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Efficient and Secure Federated Learning for Financial Applications
## 2 Related Works
### Secure Aggregation  
The secure aggregation framework utilizes the Diffie-Hellman (DH) protocol for key exchange [27]. Multiple participants exchange public keys and upon completion of local training, the gradient is not directly transmitted. Instead, a mask matrix, equal in size to the gradient matrix, is generated according to the DH protocol's public key. This random mask is then added to the original gradient to mask the actual gradient information [28; 29]. As a result, the server cannot obtain the participants' gradient information. However, the secure aggregation framework faces communication bottlenecks since the gradient sparsification method cannot be directly applied to protect user privacy.  
The inherent shortcomings of these methods motivated us to find a better way to combine gradient sparsification with the secure aggregation algorithm. In general, there are two ways to achieve this goal, including sparsing the encrypted gradient and encrypting the spared gradient.
# Grid-Connected PV Systems Controlled by Sliding via Wireless Communication
## 2 The Grid-Connected PV System Configuration
### The DC/AC Converter  
After the DC stage, a DC/AC inverter is used to inject the extracted photovoltaic power to the electrical AC grid, converting the direct current to alternating current by means of the transistors' commutation. In this work, a single-phase DC/AC converter is used. Figure 5 shows the inverter power block topology including four insulated-gate bipolar transistors (IGBT) branches with anti-parallel diodes. The IGBTs are IRGB4062 DPB devices whereas the diodes are driven by IR2106 devices.  
The AC output is connected to the grid through a reactance of 20 mH to implement the current control. The output current measurements are compared with the reference ones, in phase with the load voltages to inject only active power to the AC system, and a PI control calculates the adequate IGBT trigger signals.  
Figure 4: DC/DC converter input and output voltages for some \(D\) values.
## 2 Materials and Methods
### Theory
#### 2.1.6 Pairwise Lag-One Estimation  
The lag-one estimation algorithm described above is versatile enough to be used with either compounded or precompounded images, with the latter being advantageous from a Nyquist standpoint, as it allows for a shorter sampling interval. However, using the lag-one estimation algorithm on the LRIs may result in artifacts due to the rotation of the PSF. By repeating each angle immediately after transmission, we can eliminate these artifacts while simultaneously maintaining the short slow-time sampling interval characteristic of the LRIs. This leads to a variant of the lag-one estimation algorithm which we shall call pairwise lag-one estimation. In pairwise lag-one estimation, we form the short-time autocorrelation estimates only on adjacent LRI pairs which share the same transmission angle (and therefore the same PSF). For notational purposes, let us define the slow-time complex analytic signal corresponding to angle \(\phi_{i}\), repeat channel \(m\), and slow-time sample \(n\) as  
\[s_{a}(\vec{r},m,\phi_{i},n)=s_{a}(\vec{r},t(m,i,n)) \tag{10}\]  
where  
\[t(m,i,n) =(nN_{T}+2i+m)\Delta t \tag{11}\] \[i \in\{0:N_{\phi}-1\}\] (12) \[m \in\{0:1\}\] (13) \[n \in\mathbb{Z}\] (14) \[\phi_{i} =i\Delta\phi+\phi_{0}\] (15) \[N_{T} =2N_{\phi} \tag{16}\]  
and \(N_{\phi}\) is the number of angles used in the transmission sequence.  
Using this convention, we may now define a slow-time lag-one autocorrelation estimate for each angle via conjugate product such that  
\[\hat{R}_{2}(\vec{r},t=(2i+nN_{T})\Delta t,\tau=\Delta t)=s_{a}^{*}(\vec{r},0, \phi_{i},n)s_{a}(\vec{r},1,\phi_{i},n). \tag{17}\]  
By defining the autocorrelation this way, adjacent autocorrelation estimates are now temporally indexed at half the LRI sampling rate, but are still lagged by one LRI slow-time sample. This effectively allows for phase estimation at the LRI frame rate, but without any motion artifacts due to the rotation of the PSF. At this point, averaging may be performed at this half sample rate via a convolution with a rectangular filter yielding  
\[R_{2}(\vec{r},t,\Delta t)=\text{rect}\left(\frac{t}{T}\right)*\hat{R}_{2}( \vec{r},t,\Delta t). \tag{18}\]  
From this point, the velocity estimation process is the same as given in Equations (5)-(7).
# Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System
## 5 Experimental Tests
### System Prototype  
Figure 10 illustrates the hardware employed for the system prototype. The SR and tag prototypes (Figure 10a,b, respectively) have been designed based on the CC2510 chip [60] offered by Texas Instruments (TI). These commercial chips integrate diverse functionalities to operate as transceivers and also to capture the RSS. All related communication protocols and middleware were programmed in the C language through the CC2510 DK (development kit), also offered by TI. The tag prototype incorporates an integrated PCB slot antenna with vertical polarization and a quasi-omnidirectional pattern in the \(XY\) plane (assuming a vertical orientation of the tag). Both devices operate in the ISM band (2.45 GHz).  
Each SR device has an RP-SMA (Reverse-Polarity SubMiniature version A) connector allowing it to have an external antenna plugged in. In this case, each SR can be connected to either an external omnidirectional or directional antenna. Two commercial antennas from TP-Link have been acquired for our investigations. In this case, the TL-ANT2408C omnidirectional antenna (OA) with an 8 dBi gain (Figure 10c) and the TL-ANT2406A directional antenna (DA) with a 120\({}^{\circ}\) horizontal half-power beam width and a 6 dBi gain (Figure 10d) were chosen. In Figure 11, the set of tripods with the SR antennas installed in order to locate the antennas in the scenario for the experiments is shown. These  
Figure 10: Hardware devices: (**a**) tag and (**b**) four sensor reader prototypes based on TI CC2510 modules. Employed commercial antennas (TP-Link): (**c**) omnidirectional TL-ANT2408C antenna and (**d**) directional TL-ANT2406A antenna with associated \(XY\)-plane radiation patterns (vertical polarization).  
tripods are prepared with a support, which is able to hold up to four antennas: one omnidirectional antenna to cover 360\({}^{\circ}\) and three oriented directional antennas to cover sectors of approximately 120\({}^{\circ}\). The tripods are two meters in height. Each tripod has a box encapsulating the SR prototypes, which are connected to the corresponding antennas via RP-SMA. In this first prototype, USB connectors have been used for the SRs to communicate with the server. Furthermore, the USB feeds the SRs hardware by its 5-V output. Future versions are being developed to employ Power over Ethernet (PoE) technology in order to get longer, less expensive and faster connection between SRs and the server. These tripods are located with the indicated antennas, according to the planning phase, at the locations depicted in Figure 9.
## 3 Remote-Sensing System Framework and Problem Defined
### The GPS, IMU, and AHRS Model  
The location information in the aerial SWIR map construction system can be captured from a 5-Hz GPS, IMU, and AHRS. The GPS provides the location information of drones, such as the current time, latitude, longitude, and height, whereas AHRS shows the current flight state, such as the roll, pitch, and heading of the drone for navigation. IMU measures a body's specific force, angular rate, and orientation of the body. The onboard computer captures data from the GPS, IMU, and AHRS through the universal asynchronous receiver-transmitter interface and then writes it into the exchangeable image file (EXIF) of each aerial image.
# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 2 Technology Overview
### Transmission Parameters  
LoRa end nodes are set up utilizing distinct SF, BW, CR and TP settings, resulting in several permutation possibilities. The LoRaWAN network performance vastly depends on the configuration of these parameters [20]. Determining the settings that curtail the cost of transmission energy whilst sustaining the communication performance requirements is challenging. Configuring these parameters helps to optimize the communication performance as these parameters have an effect on energy utilization in end nodes. Therefore, it is vital that the battery powered end nodes select transmission  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristic** & **Description** \\ \hline Modulation & CSS \\ Frequency & Sub-GHz ISM: EU868, EU430, US918, AS433 \\ Bandwidth & 125 kHz and 250 kHz \\ Data rate & 0.3–5 kops \\ Range & 5 km (urban), 20 km (rural) \\ Maximum payload & 250 bytes \\ Error correction & FEC \\ Data transmission & Half-duplex \\ Topology & Star \\ Standardization & LoRa-Alliance \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of LoRaWAN [18]. CSS: Chirp Spread Spectrum, FEC: Forward Error Correction.  
parameters that are appropriate in a LoRa network. Poor choices could cause a hundred-fold briefer end node lifetime, rendering numerous commercial applications unfeasible as a consequence [21]. Algorithms that can find each node's optimum transmission parameter configuration are required for the network. Achieving configurations that are optimum involves investigating links with varied settings. LoRaWAN implementations employ static transmission parameter settings with high reliability.
## 1 Introduction
Meanwhile, from the experiments of data quality analysis (Section 2), it was found that the smaller cycle-slip ratio, especially for GPS, in the iGMAS tracking data was apparent. Thus, research on the optimal iGMAS observations is vital for the BDS-3 orbit determination. To control the GNSS observation quality, the effective cycle-slip detection and repair algorithm plays a key role [23]. When triple-frequency observations of BDS and GPS are received, the linear combinations of triple-frequencies make it easy to realize cycle-slip detection and repair in each frequency [24]. However, most of stations for tracking BDS-3 signals are still double frequencies. In [25], the Turboedit algorithm was proposed, which has been widely used in the field of navigationand positioning. However, it should be noted that the observation noise and poor data quality might impose restrictions on this algorithm, especially for omitting the observations with several cycle slips that reduces the data availability of the network directly. Therefore, an accurate cycle-slip detection and repair algorithm in BDS-3 orbit determination should be proposed to improve data availability given the limited data sources and the poor quality of iGMAS data. Furthermore, to increase the data availability, the estimation of parameters based on the normal equation stacking (NES) of several networks is another method, thereby improving the parameter's strength in BDS-3 orbit determination. Since the iGMAS network can receive BDS-3 signals (B1 and B3) with a few stations but not consistent with BDS-2 signals (mainly B1 and B2) tracked by the MGEX network. The relevant studies about the NES were discussed based on the single-day into multi-day solutions [26; 27; 28]. These served as references for improving the parameter estimation in BDS-3 orbit determination in this study. To determine four new-generation BeiDou (BDS-3) experimental satellites (C31, C32, C33, C34; C35 could not be tracked by iGMAS) orbits with the given limited data availability and poor data quality of iGMAS data, this study mainly proposes an improved cycle-slip detection and repair algorithm and two optimized methods based on iGMAS and MGEX networks. In Section 2, the quality of iGMAS observations is briefly analyzed. Then, an improved cycle-slip detection and repair algorithm for iGMAS is proposed to solve the poor data quality and low data availability. In Section 3, two orbit determination methods are discussed based on the combination of MGEX and iGMAS observations. In Section 4, orbit accuracy analysis is conducted by overlapping arc errors and SLR residuals, respectively.
# Smart Classification Paradigms for Protein Samples from 1-D Electrophoresis Gel +
## 4 Materials and Methods
### Electrophoresis  
Electrophoresis is an analytical technique used by professionals in the area of industrial chemistry to obtain protein profiles and the analysis of proteins. Its objective is to separate the proteins by means of an electric field in a chamber device (see Figure 2). This technique is carried out in polyacrylamide gel (PAGE), in which the protein samples are placed and then, the electric field is applied. Sodium dodecylsulfate (SDS) is also used in the natural charge of the proteins, so they can be computed and separated on the basis of their mass. Therefore, SDS-PAGE electrophoresis evaluates the purity and estimates the atomic weight of the proteins [1].  
The electrophoresis process begins with the placement of the samples in each lane of the gel. Then, the gel is poured into the electrophoresis chamber, which allows the migration of the proteins. The speed of migration of the proteins is proportional to the percentage of the pore of the material and its mass, those that are of greater weight, show resistance, and migrate slowly. Those with less weight are those that end up until the end of it, meaning that they will migrate faster.  
Figure 2: Electrophoresis chamber.  
The analysis of electrophoresis uses a standard of measurement of molecular weights, called a marker, its value is around $300 US, and its function is precisely to measure the molecular weight of the proteins in question, or the sample in turn. The weight is measured in kilodaltons (kD). There are different types of markers, the standard is two colors, the standard of five colors that belong to the standards of the brand _kaleidoscope_, where the ranges of the markers vary between 10-250 kD. In commercial markers, the colors indicate the range and type of protein contained in the electrophoresis analysis.
# A Predictive Reliability Model to Assess the Performance of Photovoltaic Systems
## 2 Materials and Methods
### Factors Design Experiments  
In this major step, an experiment was designed to study the significance of the inputs and device factors were selected as variables affecting the output and system responses and hence its quality. This gives an overview of the impact of each individual variable of which field orientation allows for the best utilization of global irradiation.  
To achieve this task, we applied the robust Taguchi Orthogonal Array (Taguchi OA) for factorial design.  
The Taguchi OA technique is a full factorial design (FFD) of experiments. This highly fractional design can consider a selected subset of factor (including inputs and device factors) runs at different levels of values. The balanced performance of the orthogonal arrays ensures equal consideration of all factors at all levels. Thus, individual factors can be evaluated independently of each other regardless of the design fractionality [19].  
In this work, the significance and effects of the four factors (\(T_{A}\), \(V_{w}\), \(G_{eff}\), \(Y_{a}\)) on the output and the two system responses (\(E_{prod}\), \(\eta\), \(PR\)) were estimated using the full factorial Taguchi OA design. The reason for using these four factors is to consider all factors that affect the system performance. In more detail, the weather-related factors and device factors that affect the system performance must be considered, but redundancy should be avoided. \(T_{A}\), \(V_{w}\) and \(G_{i}\) are the weather-related factors that affect performance. \(T_{A}\) and \(V_{w}\) are explicitly considered. The reason for using \(G_{eff}\) is that it implicitly includes the \(G_{i}\) as well as the effect of optical losses. The same concept was utilized when using \(Y_{a}\), as it implicitly includes other device factors \(APR\) and \(Y_{r}\) as per the definitions mentioned in section number 1.  
The software DOE++ is used for a 2-level full factorial Taguchi OA. The settings used for the 6 experiments (3 field orientations and 2 datasets) are shown in Tables 1 and 2 for near Cairo and near Riyadh, respectively. In these tables, L12 means the number of runs equal to 12 (as the length of data collected over the 12 months of the year). The value 2\(\lx@math@degree\)10 indicates that the Taguchi OA algorithm was applied for 2 levels (low and high) with 10 output terms in the equations describing the responses. The 10 output terms are related to the 4 factors and their 6 combinations for 2 locations.
# Transparent, Pliable, Antimicrobial Hydrogels for Ocular Wound Dressings
## 3 Results
### Carboxylated Agarose Retards Water Evaporation from Hydrogels
However, introduction of the carboxylic acid group in the agarose polymer backbone induces a switch in the secondary structure from an \(\alpha\)-helix to a \(\beta\)-sheet structure [14, 22]. This switch induces a disruption of the crosslinking points. We have previously demonstrated that this disruption of crosslinking points can also be achieved by blending NA with CA, inducing a lower shear modulus of the hydrogel [26]. Here, we showed that the introduction of CA into the NA matrix induced a decrease of the elastic modulus. Figure 3: (**A**) Rate of water evaporation (ER) from 3 mm thick hydrogel blends for 12 h at 50% humidity, with measurements taken every 10 min. The results are normalized and have units of mg/cm\({}^{2}\)/hour. Error bars show standard error for \(n=5\). (**B**) Hydrogel swelling for each formulation measured over 12 h.  
For ocular wound dressing applications, low elastic modulus is desirable, as a flexible dressing can better follow the contours of the eye and eye-socket to ensure contact between the dressing and the wound. In this regard, the blend with the highest concentration of CA (NA50 CA50) is preferred (Figure 4B). After ocular surgery, eye infection can lead to tremendous complications and longer hospitalization. Therefore, wound dressing that would release an antimicrobial molecule could help to reduce post-operative infections. As the mechanical properties of the NA/CA hydrogel blend are heavily influenced by the polysaccharide network structure, we tested if the loading of antimicrobial molecules in the hydrogel may affect the hydrogel's mechanical properties. We measured the elastic modulus of each formulation containing 3 mg/mL of the two selected antimicrobials (cefazolin and doxycycline). We observed that the antimicrobials did not induce a variation of the Young's modulus. This is likely due to the concentration of antimicrobials that is too low to have an impact on the hydrogel network formation (Figure 4C).
# A Capacity Enhancement Method for Frequency-Hopping Anti-Jamming Communication Systems
## 2 Materials and Methods
### FH Capacity Boost Signal Design
#### 2.2.3 FH Capacity Boost Signal Spectral Analysis
\[\mathrm{P}_{S_{\omega_{0}}}(\omega)=\frac{\omega_{S}}{4M}[|\mathrm{G}_{ \mathrm{ab}}(\omega-\omega_{0})|^{2}+|\mathrm{G}_{\mathrm{ab}}(\omega+\omega_ {0})|^{2}] \tag{14}\]  
\[\mathrm{P}_{S_{\omega_{M}}}(\omega)=\frac{\omega_{S}}{4M}[|\mathrm{G}_{ \mathrm{ab}}(\omega-\omega_{M})|^{2}+|\mathrm{G}_{\mathrm{ab}}(\omega+\omega_ {M})|^{2}] \tag{15}\]  
Thus, the power spectral density of \(s(t)\) is  
\[\mathrm{P}_{S}(\omega)=\sum_{m=0}^{M}\frac{\omega_{S}}{4M}[|\mathrm{G}_{ \mathrm{ab}}(\omega-\omega_{m})|^{2}+|\mathrm{G}_{\mathrm{ab}}(\omega+\omega_ {m})|^{2}] \tag{16}\]  
Taking eight frequency points as an example, when M = 8, the spectrum of the positive frequency point of the signal is shown in Figure 4. Each frequency point has equal power and the positive and negative frequency points are symmetrical about the position of the zero frequency point, which is consistent with Equation (16). The spectrum is characterized by the following three points:  
1. No steady state waves in the spectrum and no discrete carrier components, unlike FM signals;  
Figure 4: Schematic diagram of the frequency-hopping spectrum of frequency points. 2. The average power at each frequency point is 1/M of the total power, and as the number of frequency points increases, the average power of the signal at each frequency point decreases;
3. The signal power spectrum at each frequency point is the Fourier transform of the product of the time domain baseband waveforms of signals A and B.