esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Baseline RANS Model
The production \(P\) and destruction \(D\) terms in the turbulence model are computed as follows:  
\[P=c_{b1}\tilde{S}\tilde{v} \tag{8}\]  
\[D=c_{w1}f_{w}\left(\frac{\tilde{v}}{d}\right)^{2} \tag{9}\]  
where \(f_{w}\) is given by:  
\[f_{w}=g\left(\frac{1+c_{w3}^{6}}{g^{6}+c_{w3}^{6}}\right)^{1/6} \tag{10}\]  
\[\frac{\partial\rho\mathbf{u}}{\partial t}+\
abla\cdot(\rho\mathbf{u}\mathbf{u })=-\
abla p+\
abla\cdot\boldsymbol{\tau} \tag{2}\]0\[\frac{\partial\rho\mathbf{u}}{\partial t}+\
abla\cdot(\rho\mathbf{u}\mathbf{u })=-\
abla p+\
abla\cdot\boldsymbol{\tau} \tag{2}\]1  
The term \(\tilde{S}\) represents the modified vorticity magnitude defined as:  
\[\tilde{S}= \begin{cases}S+\overline{S}&:\overline{S}\geq-c_{v2}S\\ S+\frac{S(c_{v2}S+c_{v2}\overline{S})}{(c_{v2}-c_{v2})S-\overline{S}}&: \overline{S}<-c_{v2}S\end{cases}  \tag{13}\]  
where \(S\) is the vorticity magnitude and \(\overline{S}\) is:  
\[\overline{S}=\frac{\tilde{v}}{k^{2}d^{2}}f_{v2} \tag{14}\]  
The functions \(f_{v1}\) and \(f_{v2}\) depend on the viscosity ratio \(\chi=\frac{\tilde{v}}{v}\):  
\[f_{v1}=\frac{\chi^{3}}{\chi^{3}+c_{v1}^{3}} \tag{15}\]  
\[f_{v2}=1-\frac{1}{1+\chi f_{v1}} \tag{16}\]  
In the case of the negative model, the production \(P_{n}\) and destruction \(D_{n}\) term are written as follows:  
\[P_{n}=c_{v1}(1-c_{v3})S\tilde{v} \tag{17}\]  
\[D_{n}=-c_{v1}\bigg{(}\frac{\tilde{v}}{d}\bigg{)}^{2} \tag{18}\]  
and \(f_{n}\) is a modification to the diffusion coefficient defined as follows:  
\[f_{n}=\frac{c_{n1}+\chi^{3}}{c_{n1}-\chi^{3}} \tag{19}\]  
The constants \(\sigma\), \(c_{b1}\), \(c_{b2}\), \(c_{v1}\), and \(c_{v1}\), \(c_{v3}\) and \(c_{n1}\) are defined in [27]. Finally, the heat flux \(\mathbf{q}\) is described by [PERSON]'s law:  
\[\mathbf{q}=-\bigg{(}\frac{c_{p}\mu}{\text{Pr}}+\frac{c_{p}\mu\tilde{v}}{\text{ Pr}_{t}}\bigg{)}\
abla T \tag{20}\]  
where \(T\) denotes the temperature, \(c_{p}\) the constant pressure specific heat capacity, \(Pr\) the Prandtl number, and \(Pr_{t}\) the turbulent Prandtl number. For the test cases considered, corresponding to experiments with air, the following values are assumed: \(\gamma=1.4\), \(Pr=0.72\), and \(Pr_{t}=0.9\).
# A Precise and Stable Space-Based Time System for Navigation in Smart Cities
###### Abstract
The analysis results show that for MEO satellites, the accuracy of the satellite clock offset fitting residual obtained by the centralized clock offset estimation parameter sequence is higher than that of the satellite-ground inter-satellite joint method, which has a 50% accuracy increment. The clock offset monitoring arc coverage is much higher than the satellite clock offset obtained by the direct observation of the satellite and the anchor station. Thus, our proposed method can be used for future satellite clock offset modeling and centralized clock offset prediction accuracy assessment.
## 1 Introduction  
A considerable number of different rice (_Oryza sativa_ L.) cultivars are grown throughout the World. Japanese consumers seem to prefer the more bland non-scented cultivars, whereas in South East Asia, India, and Middle Eastern countries a number of scented cultivars of rice are highly favored.  
Determination of volatile compounds present in cooked samples of various rice cultivars has been the topic of a number of investigations, and more than 100 volatiles have been reported [1,2]. However, only a few compounds have been found to have an impact on the aroma of cooked rice [3, 4, 5, 6, 7, 8]. In scented rice, the strong popcorn-like smell of 2-acetyl-1-pyrroline was identified by [PERSON] _et al_. [5,6]. [PERSON] _et al_. [9] determined the odorants in cooked brown rice of four cultivars using aroma extract dilution analysis (AEDA). More recent work by [PERSON] _et al_. [10] reported that 35 volatile compounds were identified by gas chromatography-mass spectrometry (GC-MS) using a dynamic headspace system with Tenax trapping in cooked black rice, and 25 compounds emanating from black rice were characterized as odor-active based upon gas chromatography-olfactometry (GC-O).  
Solid-phase microextraction (SPME) is an interesting and promising technique for the extraction and concentration or enrichment of volatile compounds from different sample matrices [11, 12, 13, 14]. It is a growing sample preparation technique, and an attractive alternative to conventional extraction methods, that reduces solvent usage and exposure, disposal costs and extraction time for sample separation and concentration purposes. This technique has been used in studies on the flavor indicators of rice [4,15,16]. [PERSON] _et al_. [17,18] investigated the volatile microbial metabolites gathered in rice based on the SPME technique.  
In our previous studies, a modified headspace SPME method was developed for the analysis of flavor volatiles of rice and a number of volatile components have been identified in the headspace of rice during cooking [19]. By using this method, the flavor volatiles of freshly cooked rice could be determined [20]. However, the chemical definition of the aroma of non-scented rice has not been adequately achieved. This study was undertaken to determine the strong, sweet, coconut-like odorant in freshly cooked non-scented rice samples using the modified headspace SPME method coupled with GC-O and GC-MS. Comparisons between the spectra of GC-O and GC-MS of the flavor volatiles in the headspace above the freshly cooked rice samples were used to determine which component was associated with a strong, sweet, coconut-like aroma.
## 1 Introduction
Subsequently, in each time slot, it employs a Stackelberg game strategy to jointly optimize the transmission power for I2V and V2V. Additionally, vehicles achieve sensing domain sharing through V2V communication, utilizing sensing information from other vehicles to switch target vehicles and reduce the path loss caused by communication distance or obstruction. For clarity, the contribution of this work is summarized as follows:  
* To address the challenge of environmental instability, this study employed the multi-agent reinforcement learning algorithm MAPPO to optimize V2V vehicle beam direction and frequency band allocation, reducing interference and enhancing overall sensing-communication performance. * We separated the joint power allocation of I2V and V2V from the reinforcement learning policy and used Stackelberg game theory and iterative optimization to obtain an approximate optimal solution. This method reduces the action space size and improves the learning efficiency of the agents. * V2V communication enables the sharing of sensing domains between vehicles, utilizing the sensing information from other vehicles to switch to closer target vehicles, effectively reducing the path loss caused by communication distance and obstructions. This paper is structured as follows: Section 2 introduces the system model, Section 3 presents the beam allocation algorithm, Section 4 proposes the simulation results, and Section 5 concludes the paper. _Notations_: A matrix is denoted by a bold uppercase letter (e.g., **A**), a vector is denoted by a bold lowercase letter (e.g., **a**), and a scalar is denoted by an ordinary letter (e.g., _a_). The subscript indicates the index of the vehicle and the time slot (e.g., \(\theta_{k,n}\) indicates the beam direction of vehicle \(k\) at the \(n\)th time slot).
## 3 Materials and Methods
### Model Development  
The pre-trained Google CNN architecture Inception v3 is implemented in this work (Mountain View, CA, USA). This CNN used 1.28 million images and 1000 classes for its pre-training, achieving an accuracy of 93.33% on the 2014 ImageNet Challenge [27].  
For this approach, the CNN is trained using a \"transfer learning\" technique [28], where the final classification layer from the network presented in Figure 3 was retrained for 5000 epochs with the dataset collected in this research [29; 30] and optimized to detect the presence of cellphones while driving. The rest of the layers are fine-tuned using the original learning parameters [27]. Inception CNN was chosen due the fact that such CNN can be exported for low cost hardware such as Raspberry Pi and can be deployed in Android platforms; furthermore, the CNN architecture acted as multiple convolution filters that were then applied to the same input; the results were then concatenated and passed forward. This approach allowed the model to take advantage of multi-level feature extraction.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & & & \multicolumn{2}{c}{**Day**} & \multicolumn{2}{c}{**Night**} \\ \hline
**Dataset** & **Vehicle** & **Subject** & **Cellphone Model** & **Distracted** & **Non** & **Distracted** & **Non** & **Distracted** & **Total** \\ \hline ID & Honda HR-V 2017 & Az & Xiaomi redmi 5a & 1707 & 5451 & 2755 & 4394 & 14,307 \\ ID1 & Honda HR-V 2017 & Fa & Motorola X Play & 1885 & 5260 & 1952 & 5239 & 14,336 \\ ID1 & Honda HR-V 2017 & Je & Alcatel pop 5 & 1312 & 5763 & 1236 & 5833 & 14,144 \\ ID1 & Honda HR-V 2017 & Jo & Huawei P20 Light & 2453 & 4446 & 683 & 6471 & 14,053 \\ ID2 & VW Golf 2016 & Ja & Moto G5 plus & 1222 & 5975 & 1057 & 5842 & 14,096 \\ ID2 & VW Golf 2016 & va & Motorola moto g5 plus & 2006 & 5207 & 2355 & 4897 & 14,465 \\ \hline \multicolumn{1}{c}{Number of captured images.} & & & & & Total = & 85,401 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data distribution.  
The CNN Inception V3 is based on a pattern recognition network, and it is designed to use minimal amounts of image pre-processing. Each of the proposed CNN layer reinforces key features; the first layer detects edges, and the second tackles the overall design, among others [29].  
The final CNN is shown in Figure 3. For this stage, using Python (version 2.7), the Google TensorFlow deep learning framework is used to retrain the CNN; the experimental parameters were set to 5000 epochs, with two binary classes (distracted i.e., \"texting and driving\", non distracted i.e., \"driving\") using the transfer learning technique presented by [28].
# SPEKS: Forward Private SGX-Based Public Key Encryption with Keyword Search
## 3 SPEKS Overview and Definitions
### Algorithms and Security Definitions
The data receiver returns search token_ \(t_{w}\) _to_ \(\mathcal{A}\) _after calculating_ \(t_{w}\gets Trapoor(K_{u},w,sc[u])\)_._ \(\mathcal{A}\) _can use_ \(\mathcal{R}\) _and the returned tokens at any time to make a query to the trusted hardware. The trusted hardware answers the query by running_ \((F/\perp)\leftarrow\text{Search}(SK_{u},\mathcal{R},t_{w})\)_. If the query matches, then the search counter is incremented and_ \(\mathcal{A}\) _returns a bit_ \(\mathsf{b}\) _as a result of the experiment._
* \(\mathbf{Ideal}_{\mathcal{A},\mathcal{S}}(\lambda)\)_: The adversary_ \(\mathcal{A}\) _outputs search counter_ \(sc\) _to the data sender. Using_ \(\mathcal{L}_{PEKS}\)_, the data sender creates_ \(\mathcal{R}\) _and sends it to_ \(\mathcal{A}\)_. The simulator_ \(\mathcal{S}\) _creates search token_ \(t_{w}\) _and passes it to_ \(\mathcal{A}\)_._ \(\mathcal{A}\) _can use_ \(\mathcal{R}\) _and search token_ \(t_{w}\) _to make queries to_ \(\mathcal{S}\)_, who simulates the trusted hardware. Next, with the given_ \(\mathcal{L}_{hw}\)_,_ \(\mathcal{S}\) _returns the search result. At last, the adversary_ \(\mathcal{A}\) _returns an output bit_ \(\mathsf{b}\) _of the experiment._  
We claim that \(\mathcal{D}\) is \((\mathcal{L}_{PEKS},\mathcal{L}_{hw})\)-secure against adaptive chosen-keyword attacks if for any probabilistic, polynomial-time algorithms \(\mathcal{A}\), there exists a probabilistic, polynomial-time \(\mathcal{S}\) such that:  
\[\left|\mathsf{Pr}\left[\mathbf{Real}_{\mathcal{A}}\left(\lambda\right)=1\right] -\mathsf{Pr}\left[\mathbf{Ideal}_{\mathcal{A},\mathcal{S}}(\lambda)=1\right] \right|\leq\mathsf{negl}\left(\lambda\right).\]
# Design of a Cargo-Carrying Analysis System for Mountain Orchard Transporters Based on RGB-D Data
## 2 Materials and Methods  
### Overview of the Cargo-Carrying Analysis System  
To classify and determine the load volume of the cargo carried by a monorail transporter in citrus transportation, a cargo-carrying analysis system was developed based on RGB-D data, as shown in Figure 2. The scheme had two parts: a deep-learning-based object detection algorithm for classification and localization, and a carried cargo height measurement model based on 3D vision technology.  
During citrus transportation, the depth camera captured RGB-D images in the front of the loading platform, and the improved YOLOv7-tiny object detection algorithm was used to detect the RGB images to classify the objects (\"basket\", \"orange\", and \"fullbasket\") and obtain 2D coordinate information. Second, the 2D coordinate information of the predicted box was converted into the region of interest (ROI) for height measurement. By combining it with a depth map, we randomly sampled the points in the ROI and used the spatial point height measurement model to calculate the height of the cargo to determine the number of layers where the carried cargo was located. Finally, the classification and load volume were obtained.  
Figure 2: Overview of the system.
## 3 Beamforming Techniques
### Robust Beamforming with FDL Technique  
Basically, the FDL technique works by adding a fixed value diagonally with the correlation matrix to minimize the signal cancellation due to the look direction error. The equation of the correlation matrix is shown in Equation (2).  
\[R_{new}=R+(\varepsilon\times I) \tag{2}\]  
Here, \(\varepsilon\) represents the fixed diagonal loading factor used to equalize with \(10{\sigma_{n}}^{2}\) where \({\sigma_{n}}^{2}\) refers to the background noise of the corresponding system. \(I\) symbolizes the identity matrix.
## 3 Proposed Methodology
### Dynamic Assessment Strategy  
In expensive problems, due to limited computational resources, it is necessary to conserve computational resources by evaluating candidate solutions using surrogate models. The reliability of the surrogate model varies for different stages of the algorithm. When the reliability of the surrogate model is low, the objective values and constraint violations obtained by evaluating the candidate solutions using the surrogate model may have large errors with the real values, and it is difficult to truly reflect the superiority of a candidate solution among all the offspring solutions, which leads to the algorithm selecting a solution that cannot help the population to advance to the CPF, which results in a waste of real evaluation resources. Therefore, when the reliability of the surrogate model is low, the number of surrogate evaluations of the surrogate model should be reduced to avoid misleading the direction of population evolution. To this end, this paper dynamically adjusts the number of model evaluations according to the mean square error (MSE) of the surrogate model prediction results and the results of the real evaluation with the evaluation errors. The error bounds in Kriging models are determined by the MSE and the correlation index. The MSE is used to evaluate the prediction accuracy of the model, with a lower MSE indicating a better fit [25]. When the prediction error rate is high, the reliability of the surrogate model is considered to be low. At this point, the number of surrogate model evaluations is reduced, and when the surrogate model evaluation error rate is low, the number of surrogate model evaluations is increased. In [26], [PERSON] et al. proved that the change of \(w_{\text{max}}\) will greatly affect the performance of the surrogate-assisted evolutionary algorithm.  
\[w_{\text{max}}=\lambda\left(\frac{1}{\sum_{i=1}^{S}\text{MSE}_{i}}\right) \tag{13}\]  
where \(w_{\text{max}}\) is the maximum number of evaluations of the surrogate model, \(S\) is the number of candidate solutions, and \(\lambda\) is the scaling factor.
# An Evolutionary-Based MPPT Algorithm for Photovoltaic Systems under Dynamic Partial Shading
## 4 Test Case Simulations and Results
### Results: Rectangular Shape  
Table 3 reports the efficiency calculated for all the dynamical shading scenarios characterized by a rectangular shape's objects; the results obtained both from simulations and from preliminary experimental tests are reported. In the latter case, the plane of array global irradiance and the PV cell temperature during each test were recorded by the weather station installed in the SolarTechLab, and the maximum harvestable energy was estimated accordingly. A stepper motor moves the carriage with the shading object at constant speed along a rail and the signals from two optical sensors on the rail, synchronized with the shadow produced by the shape on the PV module, trigger the beginning and the ending of the partial shading process [24]. Figure 7 shows, as example, the diagrams of the harvested power (solid blue line) and maximum harvestable power (dotted red line): these are obtained in case of dynamical shading caused by the rectangular \(2\times 1\) shading object and controlling the off grid PV system, with the best performing traditional MPPT algorithm (P&O--B) and the proposed PSO-based MPPT algorithm characterized by three, four, and five agents respectively. The comparison with these diagrams highlight the benefits and drawbacks of the proposed PSO-based MPPT algorithm under a quite general case of a dynamic partial shading scenario. The PSO-based MPPT algorithm reacts immediately to any variation in the PV output power caused by dynamic shading, while Variable Step Perturb and Observe requires quite a long time to move to another peak, and during this process could get stuck in a specific (local) peak. The latter characteristics, that are a common feature of the hill-climbing methods, can be both a benefit or a drawback, depending on how the shade moves on the surface of the PV module. On the other hand, the PSO-based MPPT algorithm always tracks the GMPP, but the search of the GMPP requires quite a long time interval in which the PV generator is operated at different power values, even far from the GMPP, thus reducing conversion efficiency. Moreover, the PSO-based MPPT algorithm is not suitable for tracking the GMPP when its power changes during scan. The efficiency gap between the best PSO-based MPPT algorithm and the best hill climbing technique varies from 2.71% (3 \(\times\) 1, P&O--A vs. PSO--3p) to 5.95% (4 \(\times\) 1, P&O--A vs. PSO--5p). This behavior is strictly related to the long time required to track the GMPP, also combined with the inadequacy of the PSO-based MPPT method when the power of the GMPP changes during the scan phase. The comparison among the results generated by the PSO-based MPPT algorithm with a variable number of agents shows that there is no specific rule to determine the optimum number of particles. As the number of particles increases, there is a tendency to reduce the number of iterations required to track the GMPP, but each iteration requires more time to evaluate the fitness function.
# Methods for Sampling Biogas and Biomethane on Adsorbent Tubes after Collection in Gas Bags
## 1 Introduction
The loss in concentration seems to occur to a greater extent during the initial storage period, during which the inner surfaces become saturated. The loss of concentration appears also to be related to the boiling point of the compounds [7]. Finally, transporting gas bags containing biogas and biomethane is also a challenge because they have to be treated as dangerous goods due to their flammability, which requires the consignments to be accompanied by transport documents complying with the regulations in place. Sorbents have also widely been used to collect biogas and biomethane [10; 13; 14; 18; 19; 20; 21]. The advantage is that only the compound(s) of interest are adsorbed while the matrix passes through. In this way, it creates an enrichment of the targeted compounds. Another advantage when using sorbent tubes is that they are easy to transport to the laboratory (small sizes, no gas to transport). The reliability of sorbents for many of the compounds, such as Tenax TA [7], has been assessed for biogas/biomethane and good storage stability (up to a week) has been demonstrated. The disadvantage is that it requires a known volume of gas to be collected at an adequate flow using additional equipment, such as flowmeters, which can only be performed by trained personnel. The optimum flow rate used to collect VOC samples on \(\frac{1}{4}\)-inch (6.35 mm) O.D. tubes is 50 mL/min [22] (for air, no value has yet been published for biogas or biomethane) and flow rates from 10 to 200 mL/min are typical. Higher sampling flow rates in excess of 200 mL/min are not recommended, unless for short term monitoring (e.g., 10 min). However, high sampling flow rates have been used for high boiling point materials, such as PCBs (polychlorobiphenyls) and PAHs (polycyclic aromatic hydrocarbons) [23]. In this study, we have combined the two methods, i.e., sampling in a gas bag before subsequent sampling on to tubes in order to demonstrate that this alternative can help remove the disadvantages associated with the two methods, while combining their advantages; with expected longer storage stability as well as easier sampling and transport. For this purpose, we have evaluated and compared three methods to sample real biogas and biomethane samples from a bag on to Tenax tubes; using a gas syringe, an easy-VOC grab sampler and a sampling pump combined with a flowmeter. The two first methods can be used directly onsite, as they do not require any electrical power.
# On Transform Domain Communication Systems under Spectrum Sensing Mismatch: A Deterministic Analysis
## 2 System Models and Modulation Schemes
### Receiver Side of the TDCS  
Traditionally, it is assumed that the TDCS receiver shares the same spectrum sensing result with the transmitter in order to simplify the analysis. The TDCS receiver is depicted in Figure 3 in a general diagram for both modulations. We assume the received signal is \(\mathbf{r}=\{r_{0},r_{1},...,r_{N-1}\}\) for both models. For CCSK signaling, this received signal is multiplied element-by-element with a replica of the spectral waveform \(\mathbf{B}\) to generate a periodic cyclic function (PCF):  
\[\mathbf{y}=\{y_{0},y_{1},...,y_{N-1}\}=\mathcal{F}^{-1}\big{\{}\left(\mathcal{ F}(\mathbf{r})\right)\cdot\left(\mathbf{B}\right)^{*}\big{\}} \tag{6}\]  
where \(\left(\cdot\right)^{*}\) denotes complex conjugate operation. Note that this PCF waveform should be like an impulse response shape [25] as depicted in Figure 4:  
\[y_{\tau}=\lambda\sum_{k\in\Omega^{C}}e^{(j2\pi k(\tau-S_{C})/N)_{N}}\underset {FFT}{\overset{FFT}{\leftarrow}}\delta(\left\langle\tau-S_{C}\right\rangle_{N}) \tag{7}\]  
where \(\left\langle m\right\rangle_{N}=m\text{mod}N\), and \(\delta(\tau)\) is the impulse response function. Thus, the estimated CCSK symbol is recovered by detecting the index of the maximum value in PCF vector (only the real part is considered):  
\[\left(\hat{S}_{C}\right)^{(CCSK)}=\underset{\tau}{\text{argmax}}\{\Re(y_{\tau} )\} \tag{8}\]  
where \(\Re(\cdot)\) denotes the operation of obtaining the real value of a complex quantity.  
In the QPSK-TDCS model, the operations of the receiver are pretty much the same except that one QPSK symbol message is spreaded over every spectral bin, and a conventional de-spread operation is needed to recover the estimated QPSK symbol:  
\[\big{(}\hat{S}_{Q}\big{)}^{(QPSK)}=\frac{1}{N}\sum\{(\mathcal{F}(\mathbf{r})) \cdot(\mathbf{B})^{*}\} \tag{9}\]