esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling
### Nonlinear Dynamic Model
\begin{array}{l}m_{4}\ddot{X}_{4}+c_{4x}\dot{X}_{4}+k_{4x}X_{4}=-F_{4 6}\cos\alpha-F_{47}\cos\alpha\\ m_{4}\ddot{Y}_{4}+c_{4y}\dot{Y}_{4}+k_{4y}Y_{4}=F_{46}\sin\alpha-F_{47}\sin \alpha\\ f_{4}\ddot{\theta}_{4}=F_{46}r_{4}\cos\alpha-F_{47}r_{4}\cos\alpha\end{array} \right. \tag{29}\]  
The differential equation for the vibration of the tail gear is:  
\[\left\{ \begin{array}{l}m_{5}\ddot{X}_{5}+c_{5x}\dot{X}_{5}+k_{5x}X_{5}=-F_{ 56}\cos\alpha-F_{57}\cos\alpha\\ m_{5}\ddot{Y}_{5}+c_{5y}\dot{Y}_{5}+k_{5y}Y_{5}=F_{56}\sin\alpha-F_{57}\sin \alpha\\ f_{5}\ddot{\theta}_{5}=F_{56}r_{5}\cos\alpha-F_{57}r_{5}\cos\alpha-T_{5}\end{array} \right. \tag{30}\]  
The differential equation for the vibration of the lower face gear is:  
\[\left\{ \begin{array}{l}m_{6}\ddot{X}_{6}+c_{6x}\dot{X}_{6}+k_{6x}X_{6}= \begin{pmatrix}-F_{16}\cos\beta_{1}-F_{26}\cos\beta_{2}\\ +F_{36}\cos\beta_{3}+F_{46}\cos\beta_{4}+F_{56}\cos\beta_{5}\end{pmatrix}\cos \alpha\\ m_{6}\ddot{T}_{6}+c_{6y}\dot{Y}_{6}+k_{6y}Y_{6}=\begin{pmatrix}F_{16}\sin \beta_{1}-F_{26}\sin\beta_{2}-F_{36}\sin\beta_{3}\\ -F_{46}\sin\beta_{4}-F_{56}\sin\beta_{5}\end{pmatrix}\cos\alpha\\ m_{6}\ddot{Z}_{6}+c_{6z}\ddot{Z}_{6}+k_{6z}Z_{6}=(-F_{16}-F_{26}-F_{36}-F_{46}-F_{ 56})\sin\alpha\\ J_{6x}\ddot{\theta}_{6x}+c_{6x}\dot{\theta}_{6x}+k_{66x}\theta_{6x}=\begin{pmatrix} F_{16}\cos\beta_{1}+F_{26}\cos\beta_{2}+F_{36}\cos\beta_{0}\\ +F_{46}\cos\beta_{4}+F_{56}\cos\beta_{5}\end{pmatrix}r_{f}\cos\theta_{6x}\sin \alpha\\ J_{6y}\ddot{\theta}_{6y}+c_{6y\dot{\theta}}\dot{\theta}_{6y}+k_{6y}\theta_{6y} =\begin{pmatrix}-F_{16}\sin\beta_{1}+F_{26}\sin\beta_{2}\\ -F_{36}\sin\beta_{0}-F_{46}\sin\beta_{4}-F_{56}\sin\beta_{5}\end{pmatrix}r_{f} \cos\theta_{6y}\sin\alpha\\ J_{6z}\ddot{\theta}_{6z}=(F_{16}+F_{26}-F_{36}-F_{46}-F_{56})r_{f}\cos\alpha \end{array} \right. \tag{31}\]  
The differential equation for the vibration of the upper face gear is:\[\left\{ \begin{array}{l}m_{7}\ddot{X}_{7}+c_{7x}\dot{X}_{7}+k_{7x}X_{7}=\begin{pmatrix} F_{17}\cos\beta_{1}+F_{27}\cos\beta_{2}\\ +F_{37}\cos\beta_{3}+F_{47}\cos\beta_{4}+F_{57}\cos\beta_{5}\end{pmatrix}\cos \alpha\\ m_{7}\ddot{Y}_{7}+c_{7y}\dot{Y}_{7}+k_{7y}\gamma_{7}=\begin{pmatrix}-F_{17} \sin\beta_{1}+F_{27}\sin\beta_{2}\\ -F_{37}\sin\beta_{3}-F_{47}\sin\beta_{4}-F_{57}\sin\beta_{5}\end{pmatrix}\cos \alpha\\ m_{7}\ddot{Z}_{7}+c_{7z}\dot{Z}_{7}+k_{7z}Z_{7}=F_{17}+F_{27}+F_{37}+F_{47}+F_{57 }\end{pmatrix}\sin\alpha\\ f_{7x}\ddot{\theta}_{7x}+c_{67x}\dot{\theta}_{7x}+k_{67x}\theta_{7x}= \begin{pmatrix}-F_{17}\cos\beta_{1}-F_{27}\cos\beta_{2}\\ -F_{37}\cos\beta_{0}-F_{47}\cos\beta_{4}-F_{57}\cos\beta_{5}\end{pmatrix}r_{f} \cos\theta_{7x}\sin\alpha\\ f_{7y}\ddot{\theta}_{7y}+c_{67y}\dot{\theta}_{7y}+k_{67y}\theta_{7y}= \begin{pmatrix}F_{17}\sin\beta_{1}-F_{27}\sin\beta_{2}\\ +F_{37}\sin\beta_{0}+F_{47}\sin\beta_{4}+F_{57}\sin\beta_{5}\end{pmatrix}r_{f} \cos\theta_{7y}\sin\alpha\\ f_{7z}\ddot{\theta}_{7z}=(F_{17}+F_{27}+F_{37}+F_{47}+F_{57}-T_{7})r_{f}\cos \alpha\end{array} \right. \tag{32}\]
# Towards a Hybrid Approach to Context Reasoning for Underwater Robots
## 5 The Proof of Concept: A Case Study on Oil Spill Detection
### Context Reasoning in the Case Study
#### 5.3.1 Ontological Reasoning  
The ontology model is found to be consistent and accurate after applying ontological reasoning. The specific ontological reasoner employed is the Pellet reasoner. In addition, more high-level context information can be inferred. For instance, an implicit context, SeverityLevel, depends on Currents and Weather and can be obtained through the following ontological reasoning capability.  
(?dependsOn rdf:type owl:TransitiveProperty) \(\bigwedge\) (?SeverityLevel?dependsOn?SpreadSpeed) \(\bigwedge\) (?SpreadSpeed?dependsOn?Currents) \(\Rightarrow\) (?SeverityLevel?dependsOn?Currents) (?dependsOn rdf:type owl:TransitiveProperty) \(\bigwedge\) (?SeverityLevel?dependsOn?SpreadSpeed) \(\bigwedge\) (?SpreadSpeed?dependsOn?Weather) \(\Rightarrow\) (?SeverityLevel?dependsOn?Weather)  
Similarly, other high-level information that is more meaningful for usage can also be deduced based on ontological reasoning.
## 4 Discussion of Inversion Results and Accuracy Evaluation  
### The Influence of Observation Band on Accuracy  
Based on the optimal estimation algorithm combined with Tikhonov regularization constructed in the third section, the three bands within \(A\)-band night glow are inversed, respectively. Through the forward model simulation, the observed radiance spectra of the three bands covering 80-120 km and the vertical resolution better than 2 km are obtained. In order to carry out the synchronous retrieval of the atmospheric temperature in all the tangent heights, the radiance spectra in each field of view are integrated as observation vectors. The synchronous retrieval of temperature through the observation vector is carried out by the optimal estimation method, and the most estimated value of temperature is obtained by taking the minimization of the cost function as the convergence basis. As shown in Figure 15 below, the different color profiles represent the retrieved temperature from bands 1-3, respectively, and the mean absolute bias between the retrieved temperature and the actual temperature from each band is shown in Figure 16.  
Figure 15: Retrieval of atmospheric temperature in MLT region based on different bands.  
Comparing the retrieved temperature from the three bands and the mean absolute bias, it is found that the difference among the mean absolute bias is slight, and Band 2 can be used for the retrieval of temperature to obtain the highest accuracy. In the altitude range of 80-120 km, the mean absolute bias at 3.4 K can be obtained, which is slightly better than the 3.6 K from other bands. Therefore, the pre-selection scheme of observation bands in the third section is further verified.
# Novel Equalization Techniques for Space Division Multiplexing Based on Stokes Space Update Rule
## 5 Results
### Frequency Offset  
The effect of the linewidth for the FD-SSA and FD-LMS was already studied in [8] for the 6 \(\times\) 6 case. In this paper, we study the effect of the frequency offset. Figure 8 shows the required OSNR (ROSNR) for BER \(10^{-3}\) as a function of the frequency offset. Due to the block nature, LMS with larger number of taps is more sensitive to frequency offset and requires higher OSNR for achieving the same BER performance as SSA.  
Figure 8 also confirms that the frequency offset introduces a slightly higher penalty for the 6 \(\times\) 6 equalization compared to the 2 \(\times\) 2 for the FD-LMS equalizer. The FD-SSA is insensitive to frequency offsets, which is reflected by constant ROSNR, independent of the equalizer filter length.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **64 Taps** & **512 Taps** & **1024 Taps** \\ \hline
2 \(\times\) 2 LMS & 8 & 38 & 76 \\
2 \(\times\) 2 SSA & 10 & 76 & 180 \\
6 \(\times\) 6 LMS & 12 & 100 & 128 \\
6 \(\times\) 6 SSA & 32 & 250 & 500 \\ \hline \end{tabular}
\end{table}
Table 2: Number of symbols required (\(\times\) 10\({}^{-3}\)) for convergence (approx.).  
Figure 8: Required OSNR for BER = 10\({}^{-3}\) as a function of the frequency offset. (**a**) FD 2 \(\times\) 2 LMS and SSA; (**b**) FD 6 \(\times\) 6 LMS and SSA.  
Figure 7: Convergence of the equalizers as a function of the number of symbols employed. (**a**) Convergence of the FD 2 \(\times\) 2 LMS and SSA; (**b**) convergence of the FD 6 \(\times\) 6 LMS and SSA.
# Opportunistic Network Algorithms for Internet Traffic Offloading in Music Festival Scenarios
## 3 A Festival Mobility Model
### Wi-Fi Direct vs. Bluetooth  
Choosing one technology over the other implies making different sacrifices. Although Bluetooth has a better power consumption, this comes at the cost of a lower transfer speed. Moreover, Wi-Fi Direct has a larger range of transmission given optimal conditions, but the crowd present at a mass event such as a festival is far from optimal.  
Wi-Fi experiences a considerable drop in performance in the context of crowded events [2]. The authors have named this phenomenon \"the effect of dense crowds\". They state that, because the Wi-Fi signals are mainly transmitted in the 2.4 GHz range (which happens to be around the resonance frequency of water), people might affect the transmission of these signals. Experiments have established that the human body can attenuate the signal, and the authors' conclusions were that the Wi-Fi signal strength loss caused by the human body is stronger when a person is closer to an access point than further away [15].  
Furthermore, the Wi-Fi signal drops when receiving the signal from behind a person than receiving it in front, and smartphones carried inside trouser pockets are still able to communicate over a distance of up to 50 m, but they can sporadically lose their connection when the subjects move further away [2]. Although these tests were not performed on Bluetooth, it is worth taking into account that Bluetooth also operates in the 2.4 GHz range, so there may also be some attenuation of signal in this case.  
In light of these findings, the maximum ranges should be set lower than their current values. It is safe to assume a range of 5 m for Bluetooth devices and 30 m for Wi-Fi Direct devices. Although MobEmu nodes do not run over an actual network stack implementation, the comparison between Bluetooth and Wi-Fi Direct can be useful when generating contacts between people in a crowd. For the simulation to be more realistic, when using Bluetooth as  
Figure 2: Location popularity and hourly attendees for day 1 of the Sonar dataset.  
a means of communication, a node should have an average of 5-7 contacts at a certain point in time, while Wi-Fi Direct allows for a higher number of contacts within a larger radius.
## 3 Results
### 3D Flat Model  
The 3D flat model considered is modified from the COMMEMI 3D-2 test model, as shown in Figure 4. Frequencies used in these simulations are \(10^{3}\), \(2\times 10^{3}\), and \(5\times 10^{3}\) Hz. The model domain is discretized with a \(60\times 60\times 58\) cell mesh (including eight air layers) with the smallest cell size \(100\times 100\times 50\) m\({}^{3}\). A finer discretization and an addition of more air layers does not improve the solution further (to avoid errors caused by the numerical discretization). Since the background conductivity can be considered as \(10^{-4}\) S/m, we use conductivity of \(10^{-8}\) S/m for the air for the quasi-static approximation case. We compare modeling results for cases using the complete wavenumber (zero conductivity in the air) and using the quasi-static approximation. Modeling grids are the same for the two situations, but can be different for different frequencies.  
The comparison between solutions by treating the air conductivity value as constant (i.e., \(10^{-8}\) S/m) and those using the complete wavenumber is shown in Figure 5 for different frequencies. The relative errors with respect to the solution based on the use of the complete wavenumber are also calculated. As shown in Figure 5 a-d, at a frequency of \(10^{3}\) Hz, the modeling results for both real and imaginary parts of the \(x\)-component electrical field based on the use of the quasi-static approximation and the complete wavenumber are in good agreement. As the frequency increases, the deviation between them becomes larger. At a frequency of \(2\times 10^{3}\) Hz, the relative errors for both the real and imaginary parts are exclusively greater than \(1\%\). When the frequency reaches \(5\times 10^{3}\) Hz, all the relative errors increase to greater than \(7\%\) and greater than \(9\%\) for the real and imaginary parts, respectively.  
The results for the flat model are very similar to those for model I. It can be expected that the frequency at which the quasi-static approximation can produce accurate EM modeling results can be different for different overall earth conductivities. For typical conductivities (greater than \(10^{-4}\) S/m), the quasi-static approximation can be valid for frequencies greater than \(2\times 10^{3}\) Hz.  
Figure 4: COMMEMI 3D-2 test model: (**a**) \(x\)-\(z\) section view along \(y=0\), (**b**) \(x\)-\(y\) plan view at \(z=0\).  
Figure 5: Comparison of the \(E_{x}\) numerical solutions from the case based on the quasi-static approximation (upward-pointing triangle lines) with 10\({}^{-8}\) S/m for air conductivity, and the one with the complete wavenumber (circle lines) at frequencies of (**a**–**d) 10\({}^{3}\), (**e**–**h**) 2\(\times\)10\({}^{3}\), and (**i**–**l**) 5\(\times\)10\({}^{3}\) Hz for the 3D flat model. The relative errors are referenced to the results based on the use of the complete wavenumber.
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
## 4 Proposed Scheme
### Encryption and Digital Signature Algorithm
#### 4.2.2 Security Level of SPHINCS-256  
As mentioned above, [PERSON] et al. [13] claim that SPHINCS-256 provides \(2^{128}\) security against the quantum computing attack. However, [PERSON] et al. [18] proposed an efficient quantum algorithm that claims to trim the post-quantum security from \(2^{128}\) to \(2^{119.6}\). Moreover, [PERSON] et al. [42] prove a practical attack exploiting the greedy algorithm on the HORS signature scheme. To mitigate such quantum threats, we need to increase the security of the post-quantum SPHINCS-256 scheme. In our proposed algorithm, we infuse the laws of quantum physics in SPHINCS-256, by using a quantum random number generator and a quantum key management algorithm.
## 2 Experiments
### Test Method and Procedure  
Wettability measurement:  
The wettability of the samples was measured using an OCA 20 contact angle system (Databases Instruments GmbH, Filderstadt, Germany) at an ambient temperature by placing 5-7 \(\upmu\)L drops on the surface of the sample. In order to reduce the error, each sample was measured at 5 points and averaged.  
Comparison test steps of corrosion resistance of different samples:  
(1) Sample preparation: choose 3D-printed, polished, 3D-printed + modified, and 3D-printed + corroded (corrosion for 15 h, standing for 15 days) samples as the test objects, and use deionized water to ultrasonically clean the samples for 10 min. Set aside after drying.  
(2) Configure simulated seawater solution: take a 250 mL clean beaker, add 200 mL boiled and cooled deionized water to it, then weigh 7 g of sodium chloride (Dingshengxin Chemical Industry Co., Ltd., Tianjin, China) and use a magnetic stirrer to stir the solution until the sodium chloride is completely dissolved.  
(3) Experimental measurement: the saturated calomel electrode is used as the reference electrode, the platinum sheet is used as the counter electrode, the simulated sea water solution is used as the electrolyte, and the CHI-660E electrochemical workstation (Chenhua Instrument Co., Ltd., Shanghai, China) is used to measure the open circuit potential, AC impedance and, Tafel polarization curve of the sample immersed in the electrolyte for different times.
### Descriptive Statistics by Conventional Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Test**} & \multicolumn{6}{c}{**Calibration**} & \multicolumn{6}{c}{**External Validation**} \\ \cline{2-13}  & & **N** & **Mean** & **Min** & **Max** & **SD** & **n** & **Mean** & **Min** & **Max** & **SD** \\ \hline IMF (g/100 g) & 1 & 119 & 4.26 & 1.30 & 12.42 & 2.24 & 34 & 4.10 & 1.63 & 6.22 & 1.17 \\  & & & & fatty acid composition & (g/100 g FAMEs) & & & & & & \\ C16:0 & 1 & 120 & 25.49 & 21.36 & 29.34 & 1.37 & 32 & 25.45 & 23.60 & 29.84 & 1.31 \\ C18:0 & 3 & 62 & 11.24 & 8.55 & 13.98 & 1.23 & 92 & 11.45 & 8.99 & 14.21 & 1.28 \\ C18:1 n-9 & 1 & 114 & 42.31 & 37.71 & 47.08 & 2.09 & 29 & 42.49 & 37.27 & 45.78 & 2.51 \\ C18:2 n-6 & 2 & 92 & 6.37 & 2.66 & 10.87 & 1.80 & 62 & 6.39 & 3.60 & 10.98 & 1.85 \\ C18:3 n-3 & 2 & 92 & 0.32 & 0.11 & 0.78 & 0.32 & 62 & 0.30 & 0.15 & 0.68 & 0.12 \\ SFA & 1 & 117 & 38.84 & 31.90 & 44.30 & 2.17 & 34 & 38.78 & 35.68 & 45.53 & 2.35 \\ MUFA & 1 & 117 & 52.38 & 44.56 & 58.45 & 2.88 & 34 & 52.53 & 46.15 & 57.99 & 3.19 \\ PUFA & 1 & 117 & 8.62 & 3.46 & 16.18 & 2.55 & 32 & 8.79 & 5.23 & 14.77 & 2.48 \\ PUFA n-6 & 1 & 116 & 7.87 & 3.24 & 14.44 & 2.32 & 31 & 8.18 & 4.57 & 13.65 & 2.28 \\ PUFA n-3 & 1 & 120 & 0.55 & 0.17 & 1.11 & 0.22 & 31 & 0.67 & 0.27 & 0.52 & 0.17 \\ \hline \hline \end{tabular}  
* Test 1: Calibration test composed of 80% of samples and 20% external validation test; Test 2: Calibration test composed of 60% of samples and 40% external validation test; Test 3: Calibration test composed of 40% of samples and 60% external validation test. Values are expressed as the mean \(\pm\) standard error. Number of samples (N). Standard deviation (SD). Fatty acid methylesters (FAMs). Sum of all saturated fatty acids (SFA) detected (C12:0 + C130 + C14:0 + C150 + C15:0 + C15:0 + C20:0 + C22:0). Sum of all monunsaturated fatty acids (MUEA) detected (C14:1 n-5 + C16:1 n-9 + C16:1 n-7 + C17:1 + C18:1 n-9 + C18:1 n-7 + C20:1 n-9 + C16:4 n-1). Sum of all polyunsaturated fatty acids (PUFA) detected (C182 n-6 + C18:3 n-4 + C18:3 n-3 + C20:2 n-6 + C20:3 n-6 + C20:4 n-6 + C20:3 n-3 + C20:5 n-3 + C22:4 n-6 + C22:5 n-3).  
\end{table} 
Table 3: Descriptive statistics of intramuscular fat (IMF) content and fatty acid composition from minced _Longissimus thoracis et lumborum_ samples (calibration and validation sample sets). However, to our knowledge, there is no scientific literature that considers such diversity of pig breeds in the development of calibration models together with different feeding regimes, interacting with factors such as genetics or age at slaughter. Only [18] evaluated some proximate composition and fatty acid groups but considered only two of the native breeds from the present study: Krskopolje and Turopolje. Subsequently, it is difficult to compare these results with those from other studies, as the former are derived from the average values of all the breeds studied.
## 2 Study Area
### Land Use and Water Allocation  
In the area covered by the urban basins (Figure 1c), 1.29 km2 is occupied by marginal rainfed crops, scrublands, and bare bedrock in the northern peri-urban sector, 1.41 km2 is devoted to irrigation agriculture in greenhouses in the southern peri-urban sector, and 0.97 km2 is urban area (Figure 1c). Irrigation agriculture in greenhouses is the main economic driver [13], which has attracted a new population in recent years. As a consequence, new urbanizations have occupied the fertile plain of the ARD in the eastern and southern sectors of the town (Figure 2). The Spanish National Institute for Statistics [45] reported around 20,000 inhabitants in the main urban area in 2019.  
Water use is mostly devoted to urban and irrigation agriculture supply. Water allocation for urban supply is around 1.24 Mm3 per year; 0.97 Mm3 is intended to water supply at homes, 0.16 Mm3 to auxiliary industry and services, and 0.11 Mm3 to watering public gardens and urban cleaning. For 20,000 inhabitants, the average urban water endowment is 170 L per inhabitant and day [46]. Urban return from leakage in the sanitation network is around 0.20, after personal communication from the local water authority. Urban water allotment combines the ARDGW and the Fuente de Marbella spring from the Sierra de Gador Groundwater Body in the mid-valley ARB (Figure 1a) [15]. In the peri-urban area, average water allocation for irrigation agriculture is around 7850 m3 per hectare and year, from which 1800 m3 are devoted to soil disinfestation in June when intensive crops rotate. Greenhouses produce vegetables (pepper, eggplant, cucumber, and similar others) in the winter-spring season, and melon and watermelon in the autumn-winter season. Irrigation is done through drip systems, which reduce evaporation and infiltration losses. Average irrigation efficiency is 0.85, thus the average irrigation return is 0.15 [47]. Irrigation water comes from groundwater pumped in the lower-valley Adra River alluviums, which belongs to the ARDWB (Figure 1a). Urban supply and irrigation agriculture do not use groundwater from local aquifers in the NSUS, although some abandoned handmade open wells evidence the historical use of this resource [15].  
Figure 2: Satellite image of Adra town showing location of the MASW and GPR profiles, and selected groundwater observation points.
# Non-Thermal Technologies in Food Processing: Implications for Food Quality and Rheology
###### Abstract
Non-thermal processes can be employed for these foods as there is an established correlation between high-temperature treatment and the flow behaviour of foods, which may not be desirable. Furthermore, the non-thermal treatment of foods does not affect the structures and dynamics of the structural development of foods such as pastries and other sugary and sugar-free foods, making them a viable option in the growing trend within the food industry to reduce the presence of sugar and other sweeteners in food [44; 45; 46; 47; 48; 49]. They will be beneficial in closely matching the rheological behaviour and phase transitions that occur during baking and other processing that new food formulations receive [45]. In addition to utilising non-thermal technologies for foods with a complex rheology and improving the food quality, non-thermal processing technologies inactivate microorganisms that lead to the spoilage of enzymes and extend the shelf life of foods while maintaining their organoleptic, nutritional, and quality characteristics. Despite these developments, there are still some research gaps concerning the extent to which various non-thermal technologies affect the rheological characteristics of food matrices. The effects of each non-thermal method on complex food structures and their potential as substitutes for traditional thermal treatments without compromising the quality, especially in structured and semi-solid foods, are not well understood. Furthermore, while specific non-thermal technologies have been studied, how well they work in various foods have not yet been thoroughly compared, especially when it comes to microbial inactivation, texture preservation, and rheological attributes. Hence, this study intends to thoroughly review and compare various non-thermal technologies with respect to their impact on food rheology, assess their efficacy in microbial inactivation and quality retention across a range of food matrices, and identify major technological and industrial barriers that prevent their widespread use.