esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Thermal and Thermal Physical Procedures--Optimistic Solutions in the Winemaking Industry
## 2 The Use of Non-Thermal Techniques in Winemaking Optimization
### Cold Plasma Technology  
Non-thermal procedures like cold plasma are easy to use and inexpensive. Cold plasma is usually used for the inactivation of microorganisms and oxidative enzymes in food and beverages, and it is effective in food preservation and pesticides elimination [36; 37; 38; 39; 40]. This treatment has been shown to have a minor impact on the physicochemical parameters of wine, but it leads to important changes in some chromatic parameters, such as lower hue and increased color intensity and luminosity [41]. [PERSON] et al. [42] reported the inactivation of yeast on grapes using plasma-activated water, without presenting specific results regarding dilution of the final samples. Similar to these results, [PERSON] et al. [2] obtained wine with an intense color, lower tonality, and higher content in phenolic  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Analyzed Varieties** & **Origin** & **Winemaking Phase** & **Duration (min)** & **Frequences (kHz)** & **Amplitudes (\%)** & **Analyzed Parameters** & **Results** & **References** \\ \hline \multirow{6}{*}{\begin{tabular}{} \end{tabular} } & \multirow{6}{*}{\begin{tabular}{} \end{tabular} } & \multirow{6}{*}{\begin{tabular}{} \end{tabular} } & \multirow{6}{*}{\begin{tabular}{} \end{tabular} } & \multirow{6}{*}{\
\begin{tabular}{} \end{tabular} } & total polyphenol content & \multirow{6}{*}{
\begin{tabular}{} \end{tabular} } \\  & & & & & anthocyanins & \\  & & & & content & & \\  & & & & & & & \\  &  
compounds and anthocyanins, when atmospheric pressure cold plasma was involved. [PERSON] et al. [40] postulated that cold plasma influences grape juice quality through yeast inactivation and non-enzymatic browning. It also changes the total phenolic, flavonoid, and flavonoid concentrations.  
[PERSON] et al. [43] examined the impact of cold plasma as a preservation method on the physicochemical and biological properties of red wine compared to those commonly used, in accordance with International Organization of Vine and Wine regulations. Color changes were least noticeable in samples treated with combined and cold plasma methods, while traditional methods showed significant color alterations. A reduction in phenolic compounds and antioxidant activity was observed in stored samples. Cold plasma treatment resulted in less microorganisms in wine samples compared to traditional methods. The authors suggest that cold plasma in a helium or nitrogen medium may reduce the use of sulfur dioxide in winemaking.  
Although cold plasma technology is being increasingly studied for the food industry, there are limited studies pertaining to its application on winemaking. The removal of pesticides and allergens is part of food and beverages safety, but the studies on these fields are just starting and there is not enough data to consider a comprehensive and detailed description of the process.
# Two-Channel OTDM System for Data-Center Interconnects: A Review
## 2 Two-Channel OTDM System with Constant-Phase Pulse
### OTDM System Using the Symbol-Spaced MIMO Equalizer  
#### 2.2.1 Transmission Experiments  
Figure 2 shows the experimental setup used to transmit a 150 Gb/s OTDM signal generated using the sinusoidal optical pulse [21]. For pulse generation, the output of a 1550 nm laser was modulated using a commercial LiNbO\({}_{3}\) MZM1 (3-dB bandwidth = 17.2 GHz). The MZM was biased at the quadrature point and driven by a 25 GHz electrical clock. The pulse with a 7.7-dB ER was modulated using an MZM2 to generate the 75 Gb/s RZ-PAM-8 signal. The electrical PAM-8 signal was generated by a 3-bit DAC and three outputs from a pulse-pattern generator (PPG). The MZM2 was driven through a push-pull operation using 75 Gb/s differential pair signals from the DAC. Instead of SOAs, erbium-doped fiber amplifiers (EDFAs) were used to compensate for the insertion losses of MZMs. The RZ signal was split into two parts using a polarization beam splitter (PBS) and combined again using a polarization beam combiner (PBC). A proper delay line was inserted in one of the paths to realize decorrelation and accurate timing alignment (for multiplexing). The generated signal was transmitted over a 1.9 km standard single-mode fiber (SSMF) and detected by a receiver consisting of a PIN PD (3 dB bandwidth = 70 GHz) and an electrical amplifier (3-dB bandwidth = 50 GHz). After the signal was captured using a real-time oscilloscope (RTO; sampling rate = 160 GSample/s), it was demultiplexed and equalized using a 2 \(\times\) 2 MIMO equalizer consisting of 7-tap symbol-spaced feed-forward equalizers (FFEs).  
Figure 3a shows the bit-error rate (BER) curves of the 150 Gb/s OTDM signal depending on the received optical power [21]. The receiver sensitivities (measured at a BER  
Figure 1: Schematic diagram of two-channel OTDM system (MOD: modulator, DEMUX: demultiplexer).  
Figure 2: (**a**) Experimental setup for the transmission of a 150 Gb/s OTDM-PAM-8 signal. Eye diagrams of (**b**) a 75 Gb/s electrical PAM-8 signal, (**c**) a 75 Gb/s optical RZ-PAM-8 signal, and (**d**) a 150 Gb/s OTDM-PAM-8 signal. (TLD: tunable laser diode; OBPF: optical band-pass filter; PC: polarization controller; PS: phase shifter; EA: electrical amplifier; LPF: low-pass filter).  
of \(3.8\times 10^{-3}\) determined by a forward error correction (FEC)) of the two demultiplexed signals were \(-5.5\) dBm and \(-5.1\) dBm, respectively. A penalty of approximately \(2.0\)\(\sim\)\(2.9\) dB was observed compared with the \(75\) Gb/s PAM-8 signal generated by a single MZM and equalized by a 6-tap symbol-spaced FFE on the receiver side due to the doubled bit rate and the decreased signal-to-noise ratio (SNR) owing to the finite ER of the pulse. Figure 3b shows the BERs of the OTDM-PAM-8 signal depending on the bit rate. The number of taps for each FFE constituting the MIMO equalizer was increased to 11. The resulting signals exhibited speeds up to \(174\) Gb/s and could reach an FEC limit of BER = \(3.8\times 10^{-3}\). Notably, the bandwidth of the MZMs was only \(17.2\) GHz.
## 3 System Model Design
### Using the Fuzzy Inference System to Compute the Transmission Preference  
Node similarity measures the special relationships between nodes that are conducive to the data transmission process in opportunistic social networks, such as the distance between nodes, the probability of the future meeting between nodes, or the community division of nodes. However, the unstable factors between nodes may cause changes in their similarity. In addition, if the priority of data transmission between nodes is measured through the variational information of node similarity, the node in the network may obtain inaccurate statistics about the state of the network. Therefore, we have tried using the node similarity to obtain a transmission metric representing the fuzzy level of message delivery between nodes, which effectively avoids the influence of uncertain node similarity. We creatively employ the fuzzy inference system to determine the grade of mobile and social similarities and to implement the calculation of transmission preferences between nodes in our proposed algorithm. Moreover, we adopt the Mamdani fuzzy system [17] as our fuzzy inference system because of its extensive applicability. The fuzzy system consists of three components: the fuzzifier, fuzzy inference, and defuzzifier. In the following sections, we will describe the implementation of each component of the fuzzy inference system in detail.
## 1 Introduction
### State of the Art
The authors rely on any generic encryption and hash function, including Zero-Knowledge Proofs, for transparency, security and privacy. They mention using codified language, such as smart contracts, for the regulation aspect. To maintain efficiency, they suggest the usage of Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (SNARK) or Merkle tree over boolean circuits to analyze encrypted data. The work is amore theoretical discussion rather than a concrete implementation with particular work on its challenges. The uncovered inconveniences are global fairness issues where fairness in a larger context cannot be guaranteed if more participants of varying interests participate in the ecosystem. Another issue is the inefficiency of predicate checking in encrypted data for arbitrary logic. The incentivization mechanism is not theoretically evaluated, and capturing practical constraints in the framework is complicated. Also, data duplication is quite a problem, and the authors suggest using convergent encryption without revealing any data information. A proof-of-concept work for creating a novel secure and fair reseller market is proposed in work [10]. It creates a level of trust between trading parties and it avoids the possibility of counterfeit goods. The authors use non-fungible tokens for authenticating the items as well as product ownership leveraging Web3.
# Analysis and Suppression of Self-Excited Oscillations in Pressure Servo Valve System
## 2 Dynamic Model of the PSVS
### Dynamic Model of the PSVS
\[p_{c}A_{v}-p_{b}A_{b}-p_{h}A_{h}=M_{v}\ddot{x}_{v}+B_{v}\dot{x}_{v}+\Big{(}K+K_ {f}\Big{)}x_{v}+Kx_{v0} \tag{7}\]  
(2) Load cylinder model. When the main valve spool moves along the positive direction, the oil enters the rod-less cavity of the hydraulic cylinder through the throttling window of the main valve spool. On the contrary, when the power stage spool moves in the negative direction, the oil in the rod-less chamber of the hydraulic cylinder enters the backpressure cavity. Therefore, the sign function (\(sign(x_{v})\)) needs to be used to establish the load flow Equation. \[Q_{L}=C_{d}Wx_{v}\sqrt{\frac{2}{\rho}\bigg{(}\frac{1+sign(x_{v})}{2}p_{s}+ \frac{-1+sign(x_{v})}{2}p_{b}-sign(x_{v})p_{h}\bigg{)}} \tag{8}\]  
Under the action of load flow and hydraulic cylinder leakage flow, the hydraulic cylinder piston will move along the positive or negative direction  
\[Q_{L}-C_{b}p_{b}=A_{g}\frac{\mathrm{d}y}{\mathrm{d}t}+\frac{V_{gb}}{\beta} \frac{\mathrm{d}p_{b}}{\mathrm{d}t} \tag{9}\]  
In the process, the hydraulic cylinder piston is in a dynamic force balance state under the action of the output pressure \(p_{b}\). \[T_{d}=K_{t}\Delta i+K_{m}\psi \tag{1}\]0  
(3) Dynamic backpressure model. The backpressure of PSVS is provided by a relief valve, and its working principle is shown in \[T_{d}=K_{t}\Delta i+K_{m}\psi \tag{1}\]1  
The channel P of the relief valve is connected to the port H of the pressure servo valve through the pipeline, and the backpressure acts on the control cavity through the internal orifice, forming a control pressure \(p_{cj}\) opposite to the pressure regulating spring. If the control pressure \(p_{cj}\) exceeds the setting value of the pressure regulating spring, the spool will move along the positive direction (\(x_{j}>0\)). At this time, the part of the load flow enters the oil return channel through the relief valve, and the change of backpressure can be calculated according to the flow continuity Equation which is shown below. \[T_{d}=K_{t}\Delta i+K_{m}\psi \tag{1}\]2  
The flow exiting through the relief valve can be calculated as follows [11]. \[T_{d}=K_{t}\Delta i+K_{m}\psi \tag{1}\]3  
The control pressure \(p_{cj}\) can be calculated according to the flow continuity equation of the control cavity. \[T_{d}=K_{t}\Delta i+K_{m}\psi \tag{1}\]4  
The spool is in a dynamic force balance state under the action of the control pressure \(p_{cj}\) and pressure regulating spring. \[T_{d}=K_{t}\Delta i+K_{m}\psi \tag{1}\]5  
According to the above formulas, the PSVS model is built in the Simulink software as shown in \[T_{d}=K_{t}\Delta i+K_{m}\psi \tag{1}\]6  
\[T_{d}=K_{t}\Delta i+K_{m}\psi \tag{1}\]7 The relief valve: (**a**) The structure of relief valve, (**b**) The simplified principle of relief valve.
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 4 Experimental Evaluation
### Security Analysis
#### 4.7.3 Formal Security Guarantees  
Theorem 1 (Post-Quantum Authenticated Encryption): If CRYSTALS-Kyber is an IND-CCA2 secure KEM and AES-256-GCM is a secure AEAD scheme, then the resulting hybrid encryption protocol provides authenticated encryption against quantum polynomial-time adversaries.  
Proof Sketch: The protocol uses a standard KEM-DEM construction. The IND-CCA2 security of KEM (Kyber) is used to securely establish a shared key. This key is then used by the AEAD scheme (using AES-GCM) to provide both confidentiality (IND-CPA) and integrity (INT-CTX) for the data. The security against quantum adversaries relies on the post-quantum hardness assumption for Kyber and the sufficient security margin of AES-256 against known quantum algorithms.  
Theorem 2 (Computational Privacy of Homomorphic Computation): Assuming the hardness of the Ring Learning with Errors (R-LWE) problem against quantum polynomial-time adversaries, our homomorphic encryption component (based on CKKS) provides IND-CPA security.  
Proof Sketch: The IND-CPA security of the CKKS scheme reduces directly to the hardness of the R-LWE problem. An adversary with access to the server's view observes only ciphertexts. The R-LWE assumption ensures that these ciphertexts are computationally indistinguishable from uniformly random values, thereby preventing the adversary from learning information about the encrypted plaintexts.
## Appendix A
### Estimation of Signal to Noise Ratio (SNR) of the Four Systems  
Based on measurements of grey reference, the spectral signal to noise ratio (SNR) of the four systems were calculated [49] and shown in Figure 12.  
\[\mathrm{SNR}=\frac{S}{N}=\frac{R_{DN,total}-R_{DN,dark}}{\left(\sigma^{2} \Big{(}R_{DN,total}\Big{)}+\sigma^{2}\Big{(}R_{DN,dark}\Big{)}\right)^{1/2}} \tag{1}\]  
where \(R_{DN,total}\) and \(R_{DN,dark}\) represent total measured signal and dark current, respectively. DN means digital number, and \(\sigma\) is the standard deviation. The total signal refers to the average of repeated measurements of a grey reference panel (Spectralon, Labsphere, NH, USA) at noon under a relatively stable sky condition. Ambient light is ensured during the measurements to guarantee the relatively high radiance levels, which is usually present in naturally illuminated scenes.  
During the measurements (around 15 min), 10-20 spectra data for each system were acquired. Generally, SII has the highest SNR while SIV has the lowest SNR, probably due to more incident light input into the spectrometer of SII using 25 um slit than that of SIV using 5 um slit.  
### Estimation of Signal to Noise Ratio (SNR) of the Four Systems  
Similar to Figure 10, when downward sensors with bare fiber and with cosine corrector were kept at the same height, different field of view in another word, differences among diurnal patterns of ITs of the four systems showed the same features as those shown in Figure A5, when the bare fiber and cosine corrector were set at different heights. The IT of SII was always the shortest and was generally shorter  
Figure A4: Spectral signal to noise ratio of four systems.  
Figure A3: Radiance of four systems acquired under stable illumination condition. The dashed lines represent irradiance value of 150 mW/m\({}^{2}\)/nm.  
than 1 second, proving that using prism to switch between the downwelling and upwelling optic paths could reducing photons loss compared to using splitter fiber add TTL to switch between the two paths.
## 4 Derivation of Performance Indices and Stability Conditions
### Smoothing Performance Index  
An important function of a tracking filter is the reduction of random errors caused by measurement noise. One such performance index is the steady state error for a target undergoing the same motion as in the motion model, but taking into account sensor noise. We assume that \(x_{\rm o,}k\) contains noise with variance \(B_{\rm x}\), that \(v_{\rm o,}k\) contains noise with variance \(B_{\rm v}\) and that the target moves with constant acceleration. The variance of the predicted target position in a steady state is calculated from:  
\[\sigma_{\rm P}^{2}=\lim_{k\to\infty}E[(x_{\rm p,}k-x_{\rm ts,}k)^{2}], \tag{19}\]  
(see [11; 12]), where \(x_{\rm ts,}k\) is the true target position, used to evaluate the smoothing performance and \(E[\;]\) denotes the mean. The quantity \(\sigma_{\rm P}^{2}\) is called the smoothing performance index.  
The smoothing performance index for the \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter is derived as:  
\[\sigma_{\rm P}^{2}(\alpha,\beta,\eta,\theta)=\frac{g_{2}(\alpha,\beta,\eta, \theta)B_{\rm x}+g_{3}(\alpha,\beta,\eta,\theta)T^{2}B_{\rm v}}{g_{1}(\alpha, \beta,\eta,\theta)}, \tag{20}\]where:  
\[g_{1}(\alpha,\beta,\eta,\theta) =(\beta\eta-\alpha\theta-\beta)(\alpha\theta-\beta\eta-\alpha- \theta)(4-2\alpha-\beta-2\theta+\alpha\theta-\beta\eta), \tag{21}\] \[g_{2}(\alpha,\beta,\eta,\theta) =\alpha^{3}\theta(\theta-2)(\theta-1)+\alpha^{2}\beta(2-2\eta-2 \theta+6\eta\theta-3\eta\theta^{2})\] \[+\alpha^{2}\theta^{2}(2-\theta)+2\alpha\beta\theta(\eta-2)( \theta-2)\] \[+\alpha\beta^{2}(1+2\eta-\theta-3\eta^{2}+3\eta^{2}\theta)+\beta^ {3}\eta(1-\eta)^{2}\] \[+\beta^{2}\{\eta(2-\eta)(\theta-2)-\theta+2\},\] (22) \[g_{3}(\alpha,\beta,\eta,\theta) =\alpha\theta(2\eta^{2}+2\eta\theta+\theta^{2}-\theta)\] \[+\beta\eta(2\eta+2\theta-2\eta^{2}-\theta^{2}-2\eta\theta)+ \theta^{2}(2-\theta). \tag{23}\]  
The derivation of \(\sigma_{\rm P}^{2}(\alpha,\beta,\eta,\theta)\) is given in Appendix A. Note that when \(\eta=\theta=0\),  
\[\sigma_{\rm P}^{2}(\alpha,\beta,0,0) =\frac{2\alpha^{2}+2\beta+\alpha\beta}{\alpha(4-2\alpha-\beta)}B_ {\rm x}. \tag{24}\]  
This is the smoothing performance index of the conventional \(\alpha\)-\(\beta\) filter [12].
###### Abstract  
Nowadays, the sensor community has become wireless, increasing their potential and applications. In particular, these emerging technologies are promising for vehicles' communications (V2V) to dramatically reduce the number of fatal roadway accidents by providing early warnings. The ECMA-368 wireless communication standard has been developed and used in wireless sensor networks and it is also proposed to be used in vehicular networks. It adopts Multiband Orthogonal Frequency Division Multiplexing (MB-OFDM) technology to transmit data. However, the large power envelope fluctuation of OFDM signals limits the power efficiency of the High Power Amplifier (HPA) due to nonlinear distortion. This is especially important for mobile broadband wireless and sensors in vehicular networks. Many algorithms have been proposed for solving this drawback. However, complexity and implementations are usually an issue in real developments. In this paper, the implementation of a novel architecture based on multilayer perceptron artificial neural networks on a Field Programmable Gate Array (FPGA) chip is evaluated and some guidelines are drawn suitable for vehicular communications. The proposed implementation improves performance in terms of Peak to Average Power Ratio (PAPR) reduction, distortion and Bit Error Rate (BER) with much lower complexity. Two different chips have been used, namely, Xilinx and Altera and a comparison is also provided. As a conclusion, the proposed implementation allows a minimal consumption of the resources jointly with a higher maximum frequency, higher performance and lower complexity.  
ECMA-368; peak to average power ratio; neural networks; FPGA implementation +
Footnote †: journal:  
Article
## 2 Dual-Port MIMO Antenna
### Antenna Measurements  
The testing setup employed for measuring the impedance and radiation performance is illustrated in Figures 6 and 7. The impedance characteristics (S\({}_{\text{XX}}\) and S\({}_{\text{XY}}\); X and Y values are from 1 to 2) are measured using Agilent N5247A: A.09.90.02 Vector network Analyzer (VNA) illustrated in Figure 6. To measure S\({}_{\text{XX}}\) (reflection) parameter, port X of the fabricated antenna is linked to port 1 of the VNA for signal excitation, and other ports are connected with 50-ohm terminations. For S\({}_{\text{XY}}\) (transmission) parameter measurement ports X and Y of the fabricated antenna are connected to ports 1 and 2 of the VNA.  
Figure 4: Parametric analysis (W1 = 12 mm to 16 mm).  
Figure 5: Fabricated antenna (**a**) top view (**b**) ground view.
## 5 Conclusions  
This paper has shown a proof-of-concept of a novel path planning based on remote imagery publicly available. The partial results, shown as intermediate illustrative real examples of each performed step, encourage reliance on satellite imagery and cadastral information to automatically generate the path planning. According to it, useful knowledge can be obtained from the combination of satellite imagery and information about the borders that divide the land into parcels. Some limitations have been also identified: some parameters (such as thresholds) are image dependent and have been manually set for the current set of satellite images; it can only be applied in the current configuration of citric trees in rows; and changes in the grove distribution are not immediately reflected in the satellite imagery. In our platform, this path planning is complemented by local imagery, which provides navigation centred in the path.  
We can conclude by remarking that the steps necessary to autonomously determine path planning in citric groves in the province of Castellon have been described in this paper and that the potential of this autonomous path planner has been shown with successful use cases. Firstly, a procedure to extract binary maps from a public remote imagery service has been described. Secondly, a novel methodology to automatically detect reference points from orange groves has been introduced using remote imagery. Some knowledge and global information about the grove can be extracted from those points. Thirdly, a procedure to determine a path has been designed for a stand-alone robotic vehicle. Moreover, it has been modified to be suitable for a team composed of two robotic systems. The first path planning provides useful information, and it may be considered the fast way to perform a maintenance task. This path is complemented by local navigation techniques (see Reference [21]) to obtain optimal results in the final implementation of the whole navigation module. Finally, although we introduced the system for orange groves, all the steps can be applied to other groves with a similar main structure (other citrus groves or some olive groves). Moreover, they could be also adapted for other crops or situations by changing the way they extract reference points, i.e., a hybrid team of multiple robots will require a specific planner and path strategy.  
Finally, this paper has introduced a proof-of-concept showing the feasibility of the proposed algorithms to automatically determine path planning inside a citric grove. The proposed algorithms can be used to ease the manual labelling of satellite imagery to create manual maps for each parcel. We will identify the full implementation in different contexts and experimental validation as further work.  
[PERSON] and P.N. equally contributed in the elaboration of this work. This research received no external funding. The authors declare no conflict of interest.