esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### PEF Economics  
Given the present results, PEF can increase the added value of white grape vniification, in particular Arinto vniification, either by juice yield increase or by increasing product quality and therefore value and allowing the reduction in production factors, indirectly contributing also to cost reduction.  
Direct costs, as described by [PERSON] and [PERSON], 2012 [(67)], include energy and water consumption, chemicals, labor, consumables and wine losses. Given the inherent design of the PEF installation in the winery, particularly its inline configuration and little maintenance needs, there are no requirements for process modifications: thus, labor and water consumption alterations can be disregarded, as well as consumables. Concerning wine losses, as previously mentioned in this paper, PEF-treated wines presented lower lees production, very close to being statistically significant (\(p\) = 0.058). This suggests that PEF treatment can potentially reduce wine loss directly while also potentially contributing to an increase in cucee wine yield. For comparison purposes, we can examine the direct costs, which are virtually only associated with energy consumption. For instance, in the present assay, with an applied \(W\)s = 2 kJ/kg at a rate of 4 ton/h, this process requires an energetic input of 2.22 kW per hour. Considering an average energy cost of 0.22 EUR/kWh, the application of this protocol represented a cost of 0.49 EUR/h and 0.12 EUR/ton. In contrast, the cost of the commercial-grade enzyme, recommended at a minimum dosage of 15 g/ton, amounts to 1.80 EUR/ton of processed grapes. Consequently, a direct cost saving of 94% can be observed.  
In addition, it is important to note that PEF treatment demonstrated a positive effect in the tartaric stability of the wine under study; thus, it is possible to hypothesize that it can also indirectly promote the economic sustainability of the product by avoiding the need to employ other techniques, such as the usage of stabilizing additives that prevent the formation of crystals, such as mannoproteins (direct costs (DC) = 3.0 EUR/hL), metataturatic acid (DC = 0.07 EUR/hL) and Carboxymethylcellulose (CMC) (DC = 0.7 EUR/h), or processes such as cold stabilization (DC = 0.76-3.74 EUR/hL), electrodialysis (DC = 0.56-3.1 EUR/hL) and ion exchange (0.07 EUR/hL) [(67; 72)].
## 3 Results and Discussion
### Differences in Volatile Compounds of Large Yellow Croaker Under Different Storage Conditions
#### 3.5.1 Thermal Reaction Volatile Compounds Pathway  
Although previous studies have proposed that thermal reactions may contribute to the formation of specific volatile compounds in fish muscle, their relevance under cool storage conditions remains questionable. In the volatiles during the storage of large yellow croaker, only 3,4-dimethylpyrimidine and benzoin aldehyde were detected, while the other glycolytic compounds were notably absent. This suggests that changes in heat treatment might have rendered these compounds non-significant. In addition, 3,4-dimethylpyrimidine and benzoin aldehyde, volatiles extracted from nitrogen-containing compounds during storage of large yellow croaker, were identified as contributing a'smoky' and 'almondy' aroma, respectively [26]. In this study, while the thermal reaction pathway cannot be overlooked, its contribution to the overall volatile profile is minimal (Table 2). The reason for this is that the volatiles produced by large yellow croaker during storage are mainly aliphatic volatile compounds, with relatively high levels of alcohols and aldehydes (Table 2). The reason why thermal reactions are not important in this study is because it is performed under cool storage. Given that the major volatiles identified were aliphatic alcohols and aldehydes (Table 2), lipid oxidation and amino acid degradation are more likely to be the predominant pathways contributing to the odor changes observed.
# Ionospheric Error Models for Satellite-Based Navigation--Paving the Road towards LEO-PNT Solutions
## 2 Related Work
### Ionospheric Models for LEO Satellites  
LEO satellites do not have proprietary models to predict the ionospheric delay as GPS and GALILEO satellites have. Due to the orbital height difference between GNSS and LEO satellites, the ionospheric models used for terrestrial receivers need to be modified before they can be applied to LEO satellites. Researchers in [49] used ionospheric data from the IRI model and developed a scale factor to measure the ionospheric delay above an LEO satellite in order to model the ionospheric delay for a GNSS receiver on board an LEO satellite. The authors of [50] used the NeQuick model to calculate the ionospheric delay from a GNSS satellite to a GNSS receiver on board an LEO satellite after they used a scale factor to scale the result from a ground-based to a space-based result. The authors of [51] presented a new communication channel model between an LEO satellite and a ground receiver that takes into account the ionospheric scintillations to analyze the ionospheric effects on bit error rates or channel capacity for satellite communication purposes. However, to the best of the authors' knowledge, no research so far has tackled the ionospheric delay problem between LEO satellites and the Earth for LEO-PNT applications.
## 3 Quality of Service
### Evaluation of the Selected Specifications
#### Priority Management
To optimize the transmission, dedicated as well as shared timeslots can be combined to support fluctuations of the traffic load. Such hybrid modes can be envisaged in most of the considered technologies except for the WISA standard, which only support dedicated timeslots. * Tuning the timeslot length is a second key feature that allows to support application-specific traffic as well as optimize the real-time support. Except for the WISA and the WirelessHART standards, all standards offer this option. However, the timeslot length is common to all timeslots within a superframe, which limits the adaptation to variations of traffic as well as real-time requirements. Although this property may be useful for applications with various length of data to send, it would introduce additional overhead and complexity and may therefore unnecessary. * Superframe management is an additional means to maintain real-time communication in case of very high traffic load. However, the superframe optimization proposed by WirelessHART and ISA100.11a remains a minor contribution to the real-time support in comparison to the aforementioned dedicated timeslots and adaptable timeslot lengths. * Priority mechanisms are provided by the WirelessHART and the ISA100.11a standards. Both standards support message-based priority, while the ISA100.11a standard offers contract-based priority additionally. Although the proposed contracts are promising in terms of QoS guarantees, the overhead and the complexity introduced by their management as well as the additional resulting traffic may limit their practical feasibility. Even if message-based priorities may be less efficient than contracts, their utilization allows to reach a balance between overhead and efficiency and provides a complementary method to support real-time traffic.
## 4 Evaluation and Results' Discussion
### Protocol Implementation  
We implemented our protocol in C++ on a Linux platform and compared it with the related protocol [28]. In our experiments, we set the false probability \(P_{err}\) of the Bloom filter to \(2^{-30}\) and set the length \(m\) of the Bloom filter to \(-size\cdot log_{2}e\cdot log_{2}P_{err}\), as well as the number of hash functions \(k\) to \(-log_{2}P_{err}\), where \(size\) is the server's set size. For the ElGamal encryption used in the protocol, we used the security parameter of \(k=1024\) bit.
# A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance +
## 2 Model Overview
### Our Proposed Scheme
Then, motivated by the generalized MPTCP analysis in [23], we specify the following fluid model:  
\[\lambda_{i}=k_{i}(\mathbf{\lambda})(\phi_{i}(\mathbf{\lambda})-\mathbf{p})_{\lambda_{i}}^{ +};\ \ \dot{p}_{i}=\gamma_{i}(\lambda_{i}-\theta_{i})_{\dot{p}_{i}}^{+} \tag{4}\]  
for analyzing MPTCP (Equations (3) and (4) in [23]). Here, \(\gamma_{i}>0\) is a positive gain, \(k_{i}(\mathbf{\lambda})\) is a vector of positive gains determining the dynamic properties of the system, and \(\phi_{i}(\mathbf{\lambda})\) determines the equilibrium properties. Our objective is to find a _pseudo-price_ that is globally fixed per unit of resource across all network paths. The equilibrium in Equation (4), along with this special _pseudo-price_, is known as a _normalized_ equilibrium for MPTCP dynamics over multiple networks (Section 2 in [13]). Therefore, a successful idea developed recently by [PERSON] et al. [13] can be used to analyze the existence and uniqueness of the equilibrium \((\mathbf{\lambda}^{\star},\mathbf{p}^{\star})\). Moreover, starting from any initial point \((\mathbf{\lambda}(0),\mathbf{p}(0))\), the trajectory \((\mathbf{\lambda}(t),\mathbf{p}(t))\) generated by the cooperative MPTCP algorithm converges to a unique and globally stable equilibrium \((\mathbf{\lambda}^{\star},\mathbf{p}^{\star})\) (see Lemma 2 later in the paper). In summary, the approach in our model is as follows. We use an existing fluid modeling approach for MPTCP to quantify its interactions over the network paths as a dynamic system [23]. We then implement the Lagrangian method to solve the dynamic system. However, for the Lagrangian approach to succeed, we require the existence of a well-defined utility function with certain properties to guarantee the stability. Therefore, we establish a utility function that satisfies these properties by adopting the idea from [PERSON]'s rent-seeking game [8; 13], which we describe in the next section.
# Q-Pandora Unboxed: Characterizing Resilience of Quantum Error Correction Codes Under Biased Noise
## 1 Introduction  
Quantum computing uses quantum mechanics to perform tasks beyond classical computing's scope with applications in simulating molecules for drug discovery, enhancing finance optimization, accelerating machine learning, improving optimization tasks, and transforming supply chain management, to name a few [1, 2, 3, 4, 5]. These developments open the door to revolutionary commercial applications. Quantum computing, though advancing, faces challenges like qubit stability and quantum noise [6, 7]. Error correction, critical due to inevitable qubit noise, is being innovated via quantum error correction codes (QECCs) [8] to realize fault-tolerant quantum computing [9]. Classical error correction techniques [10] face hurdles in quantum computing due to the no-cloning theorem [11] and wavefunction collapse during qubit measurement [12]. Studies showcase quantum codes, such as the five-qubit code, Bacon-Shor code, topological code, surface code, color code, and heavy-hexagon code [13, 14, 15, 16, 17, 18], capable of rectifying single errors. Despite advances, one key question remains: Can enlarging the error-correcting code reduce logical error rates in realdevices? Theory suggests that higher code distances should lead to fewer logical errors. However, practical proof necessitates device scaling. QEC could significantly cut quantum processor error rates, but at the cost of time and qubit overhead [19].
# Recent Progress on Vegetation Remote Sensing Using Spaceborne GNSS-Reflectometry
## 4 Model Simulations and Discussion
### The Influence of Observation Geometry on Scattering Characteristics  
Figure 3a shows the relationship between the scattering characteristics and the specular scattering angle, and Figure 3b shows the relationship between the scattering characteristics and the scattering zenith angle, while the incident angle (\(\theta_{\text{i}}=30^{\circ}\)) and scattering azimuth angle (\(\varphi_{\text{s}}=120^{\circ}\)) are fixed. It can be seen that in different observation geometries, the scattering characteristics corresponding to various polarizations are obviously different. Figure 4 shows that in various polarizations, the scattering difference corresponding to the scattering azimuth angle also changes significantly. However, angle information is not well used in GNSS-R vegetation and soil moisture remote sensing. The normalization of angles or simple angle intervals for the characteristic analysis greatly limits the unique advantages of angle observation in GNSS-R. Due to the anisotropy of vegetation, how to effectively use its scattering characteristics under different observation angles to find the optimal observation geometry is a problem faced by GNSS-R vegetation remote sensing.
## 4 Results
### Disturbance Rejection
It is worth noticing that variations in \(V_{in}\) produce transient oscillations of smaller amplitude when compared with variations of \(V_{ref}\), which produces lower deviations of the error (see Figure 9B) that is kept below 10%. This is also reflected in a much faster evolution of the duty cycle towards the asymptotic value, as seen in Figure 9C. This phenomenon can be explained because changing the input voltage requires only the adjustment of the currents flowing trough the coils, while the capacitors' voltages are kept very close to the already desired value. On the contrary, when \(V_{ref}\) changes, it is necessary for all the states, including the voltages of the capacitors, to adjust their values. Finally, with the aim of further showing the robustness of the controller, we carried out a mixed perturbation test in which all three parameters were varied simultaneously. This scenario is shown in Figure 10, and the results support the initial findings, namely that the controller is robust to even large variations from the nominal values of the parameters, including transitions from step-up (for \(t<30\) ms) to step-down regimes (\(t>30\) ms). In this figure, it is possible to notice an additional feature of this converter that is not clearly observable in Figure 7, namely that the last transition, which corresponds to the largest value of \(R\), has a much faster transient response. Indeed, the transient time elapsed for the reference voltage \(V_{ref,2}=18\) V to \(V_{ref,3}=10\) V is significantly smaller than the initial transient taken to reach \(V_{ref,1}=10\) V (\(\approx\)5 ms vs. \(\approx\)10 ms, respectively). This is despite the fact that the initial errors are comparable (see Figure 10B,C). This result highlights the importance of understanding the particular application when tuning the parameters of the controller, especially if fast transient response is a requirement. Figure 9: Time trace of the ramp controlled sZETA converter under perturbations of the input: (**A**) voltage trace; (**B**) error percentage; (**C**) duty cycle. In all panels, \(V_{in}=10\) V for \(t<15\) ms, \(V_{in}=18\) V for \(15\) ms \(\leq t<30\) ms, and \(V_{in}=12\) V for \(t\geq 30\) ms.
## 4 Discussion
### Relationship between Moisture Content and Unconfined Compressive Strength
[35] concluded that the relationship between the strength of the soil and the suction matric is composed of two parts: the first is linear, in which it presents an increase in the UCS test results, and the second is nonlinear, which indicates a point of maximum stress, and the cohesion of the matric decreases owing to the rapid rise in the suction matric, which leads to a decrease in cohesion. Additionally, in a study by [PERSON] et al. [36], it was observed that matric suction plays a vital role in the properties of unsaturated soils because the increase in strength with increasing suction is practically linear for SM soil but nonlinear for SC-SM soil. [PERSON] et al. [37] concluded that both particle distribution and mineralogy affect soil properties and that there are significant changes in water content with minor changes in the suction matric, producing hysteresis between the drying and wetting processes due to the bottleneck effect, contact angle effect, and air trapped between the pores, implying that the shear strength for a given matric suction may be different depending on whether the soil is dry or wet. Based on the above investigations and the results obtained, it can be assumed that the suction effect is involved in the soil strength, which may vary according to the MC or saturation. This relationship is where UCS and MC are compared, confirming the absence of a linear relationship (Figure 17). Based on the results obtained for the compressive strength of both types of soil, a significant decrease in the strength of the silty soils was evident after the 56 th day of testing of the sample. In contrast, while experiencing fluctuations in strength similar to loam soils, organic soils did not exhibit such a marked loss at 56 days. It is assumed that roots in organic soil could mitigate the loss of strength, as determined in previous studies on the effect of time and model prediction of the shear strength of rooted soil under dry-wet cycles. This analysis concluded that roots improve the compressive strength over time [38]. Additionally, according to [PERSON] et al. [39], it is challenging to correlate the compressive strength by considering only the MC and organic content without considering the presence of organic fibers.
## 6 Conclusions  
This study presents a flexible chassis coupling control methodology designed to reduce coupling errors and improve motion mode switching stability and handling performance. A coupling error model was established for linkage control of an off-center steering mechanism based coupling control, and Lyapunov stability theory. A fuzzy PID controller was designed to compensate for the coupling error and a fuzzy PI method was employed to reduce off-center steering mechanism tracking errors as well. The proposed control methodology was examined by simulation and validated experimentally on hard pavement. Compared to the conventional distribution control method, the proposed approach could effectively reduce coupling errors and guarantee MMS control stability while substantially shortening response times. The coupling control method proposed in this study demonstrated better effectiveness and feasibility than distribution control. The results of the study show that the DM and SM MMS performance with regard to coupling error was not as good as the CM and IR performance. Thus, we should pay attention to the reasons for this performance difference. It is important to devise a strategy to improve FC MMS control. Figure 17: Trajectories under (**a**) cross motion; (**b**) in-place rotation; (**c**) diagonal motion; (**d**) steering motion. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Motion Types**} & \multicolumn{2}{c}{**Longitudinal Acceleration**} & \multicolumn{2}{c}{**Lateral Acceleration**} \\ \cline{2-5}  & **Maximum** & **Average** & **Maximum** & **Average** \\  & **Value(m-s\({}^{-2}\))** & **Value(m-s\({}^{-2}\))** & **Value(m-s\({}^{-2}\))** & **Value(m-s\({}^{-2}\))** \\ \hline Cross motion & 0.057 & 0.007 & 0.056 & 0.005 \\ In-place rotation & 0.045 & 0.005 & 0.052 & 0.006 \\ Diagonal motion & 0.062 & 0.009 & 0.064 & 0.007 \\ Steering motion & 0.075 & 0.008 & 0.063 & 0.008 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Longitudinal and lateral acceleration of the flexible chassis centroid. **Author Contributions:** [PERSON] and [PERSON] proposed the original ideas and wrote the manuscript. [PERSON] developed the algorithm for differential steering coordinated control. [PERSON], [PERSON], and [PERSON] ran the simulations. [PERSON] developed the test. [PERSON] and [PERSON] directed the overall project. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by the General Program of the National Natural Science Foundation of China (51375401). **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**
## 5 Agro-Climatic Mapping
### Input Data  
To perform the agro-climatic mapping, we needed data about elevation above sea level, monthly average temperature (\({}^{\circ}\)C), annual average precipitation (mm) and units of dominant soils (deaphology) for the entire region. For many countries, no information on the selected crops could be found; in these cases, the climatological conditions were estimated based on the data of neighboring countries or regions presenting similar climatic characteristics.  
The temperature and precipitation data were downloaded from the WorldClim's database, which offers a wide range of monthly precipitation data, as well as monthly minimum and maximum  
Figure 4: Schematic core process of agro-climatic mapping.  
average temperature data with a resolution of 30 s or 1 km approximately [34; 35]. The elevation data downloaded was recorded by the sensor Shuttle Radar Topography Mission (SRTM), which was on board the Endeavour shuttle [36]. The soil data was obtained from the FAO's database, which offers the Digital Soil Map of the World (DSMW) in shape format at a scale of 1:5,000,000 with a detailed report by global regions about types and soil mapping methods. Table 2 provides a brief summary of the characteristics of the input data used.
## 2 Materials and Methods
### Testing the Moisture Content of HIPS and ABS Flakes  
Moisture content measurement was performed using the moisture analyzer manufactured by RADWAG MA 210.X2.A (RADWAG, Warsaw, Poland) following the gravimetric principle (the moisture level was determined on the basis of the mass loss of the sample during heating). Before carrying out the test, we checked whether there were contaminants on the underside of the aluminum weighing pan. That levelling of the device was checked, and the ambient temperature and humidity level were examined using an EXTECH M029 hygrometer. A moisture content test was carried out under conditions of 19.90 \({}^{\circ}\)C and air humidity of 35.50%. After that, the device was started and the displayed drying parameters were set. The interval (time interval between recording measurements) for HIPS was 60 s, and for ABS, 1 s. The heating time lasted 3 min. The heating temperature for HIPS was 80 \({}^{\circ}\)C, and for ABS, 88 \({}^{\circ}\)C. A manual method of drying for samples sensitive to heat stress (mild profile) was used. This profile was used because in the areas where the material layer is thicker, the top layers of the sample can heat up too much and the inner layers too little, which can lead to burning or crust formation. The resulting crust makes it difficult to dry the bottom layer, which generates measurement errors. Moreover, the surface of plastics tends to heat up quickly. The drying temperatures were selected based on the production standards. Drying was completed manually when little or no change in the sample weight over time was observed. A sample consisting of randomly selected plastic flakes weighing approx. 13-14 g was placed on the pan in the thinnest and most even layer possible. This allowed it to be completely dried in the shortest possible time, leaving no undried areas. Once the sample was ready, drying began and the process was monitored on the device display. The dried samples were removed from the aluminum pan and their surface was immediately analyzed to exclude traces of burning or stickiness. The tests were carried out 5 times for each type of plastic. Moisture content calculations were made according to Formula (1):  
\[W_{1}=\frac{m_{1}-m_{2}}{m_{1}}\times 100\%, \tag{1}\]  
where m\({}_{1}\) is the initial sample weight [g] and m\({}_{2}\) is the sample weight after drying [g].