esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Determination of Shelf-Life and Usability of Fresh and Dried Herbs via E-Nose  
Freshness evaluation of a substance is of great interest in food industry. E-noses of various sensing materials have already been used in this regard [25]. Substantial achievements were also obtained in this field by using MIP coated QCM based e-nose for determination of volatile constituents compared to the classical, bulky and expensive chromatographic methods [26]. Aroma contents of herbaceous plants can be used to assess their freshness and shelf-life. The sensor signals recorded as a function of time from the emanated terpenes is the powerful tool to determine the shelf-life of fresh and dried herbs. In fresh rosmary and sage maximum values were achieved earlier, which indicated high amounts of terpenes, especially \(\alpha\)-pinene, limonene, estragole _etc._  
Figure 8: Comparison of maximum and minimum responses.  
Figure 8 shows a comparison for fresh and dried species of herbs from maximum to minimum sensor signal. In case of fresh rosemary high sensor responses for selected terpen _i.e._, 1183 Hz for \(\alpha\)-pinene, 533 Hz for limonene, 445 Hz for eucalypt, 74 Hz for \(\beta\)-pinene, 170 Hz for pertinene and 149 Hz for estragole were observed in between 40 hours, whereas the minimal sensor signals (9-157 Hz) were observed till the 150 th hour. This suggests that the best time of its use under usual environmental conditions is within first 36 hours after being removed from the garden.  
While considering the behavior of dried rosemary, maximum emanation (12-111 Hz) occurred in first 15 hours and lead to lower sensor signals (04-48 Hz) till 72 h. Its higher signal values are equivalent to lower values of fresh rosemary indicating the dryness of herb due to emanation of volatile constituents. Dried rosemary can be used for longer time as microbial activity will be lower due to negligible amount of moisture. A similar kind of trend is observed from fresh and dried sage. Fresh basil in contrast showed its maximum sensor signals (37-281 Hz) for all terpen after three hours of measurement and then lead to similar sensor signals, especially for \(\alpha\)-pinene (219 Hz) and limonene (56 Hz), till the end of measurement. These identical sensor signals from high to low _i.e._, (11-183 Hz) and (10-161 Hz) for all emanated terpen were also observed in dried basil, including \(\alpha\)-pinene and limonene. Focusing on shelf life, fresh herbs have shorter life compared to dry herbs. This study encompasses the efficacy of the designed e-nose in developing artificial receptors for real life applications.
# Malicious JavaScript Detection Based on Bidirectional LSTM Model
## 2 Related Work
### Dynamic Analysis of Malicious JavaScript  
Dynamic analysis can capture the runtime behavior of JavaScript programs, which is important for detecting runtime attack vectors.  
[PERSON] et al. [15] proposed a system for detecting and analyzing malicious JavaScript code, which uses ten characteristics to describe the entire life cycle of a vulnerability attack, from the initial request to the actual exploitation of the vulnerable component. During the detection process, the system can identify the abnormal JavaScript code by simulating the behavior of the abnormal JavaScript code and comparing it with the established profiles.  
Simulating the behavior of JavaScript may lead to distortion, and hence some researchers used JavaScript engines to capture dynamic features. [PERSON] et al. [16] proposed JavaScript lightweight execution engine J-Force. When using the engine to execute and traverse code execution paths that may hide malicious programs, the J-Force records and selects different branches to form new paths during execution. The function call parameters, tracked objects, and Document Object Model (DOM) node content are also used to detect malicious and DOM injection attacks. Because J1-Force simulates all possible execution paths of JavaScript code, the detection time of a complex JavaScript with multi-branch and multi-path increases exponentially, so the detection speed is less than that of other methods.  
Similarly, [PERSON] et al. [17] proposed a method for dynamically monitoring web browsers to generate byte streams, which can detect potential drive-by download attacks in the web browser environment.  
Considering the JavaScript's support for Android, a method for detecting attacks in cross-platform applications was proposed by [PERSON] et al. [18]. This method extracts runtime information from the application and uses function-level execution information to distinguish benign and malicious behavior.  
In conclusion, dynamic analysis plays an important role in detecting malicious JavaScript. However, in order to obtain higher accuracy, dynamic analysis requires more time and resources (e.g., memory or CPU). Sometimes, dynamic analysis fails to obtain test results in an acceptable time.
## 3 Proposed Credibility Evaluation Methodology
### Definitions of Scenarios and Parameters
In the integrated assessment scenarios (E and F), the lateral and longitudinal metrics were combined to ensure a holistic assessment framework for analyzing system performance under complex maneuvering conditions. \begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{1}{c}{**Signal**} & **Frame** & **Unit** & **Maneuver (Scenario)** \\ \hline Lateral acceleration (\(a_{y}\)) & Vehicle & \(\frac{\mathrm{m}}{\mathrm{s}^{2}}\) & \\ \hline Yaw rate (\(\gamma\)) & Vehicle & \(\frac{\mathrm{deg}}{\mathrm{s}}\) & \\ \hline Desired steering wheel angle (\(\delta_{des}\)) & Vehicle & \(\circ\) & Lateral \\ \hline Longitudinal position (\(X\)) & Global & m & (A, B) \\ \hline Lateral position (\(Y\)) & Global & m & \\ \hline Yaw angle (\(\psi\)) & Global & \(\circ\) & Integrated \\ \hline Longitudinal acceleration (\(a_{x}\)) & Vehicle & \(\frac{\mathrm{m}}{\mathrm{s}^{2}}\) & (E, F) \\ \hline Velocity (\(V_{e}\)) & Vehicle & \(\frac{\mathrm{m}}{\mathrm{s}}\) & \\ \hline Desired longitudinal acceleration (\(a_{x,des}\)) & Vehicle & \(\frac{\mathrm{m}}{\mathrm{s}^{2}}\) & Longitudinal \\ \hline \multirow{3}{*}{Object state} & Relative longitudinal distance (\(RD_{x}\)) & Vehicle & m \\ \cline{2-4}  & Relative lateral distance (\(RD_{y}\)) & Vehicle & m \\ \cline{1-1} \cline{2-4}  & Relative velocity (\(RV\)) & Vehicle & \(\frac{\mathrm{m}}{\mathrm{s}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of key test parameters based on maneuver (scenario) types. The simulations in this study were performed in a VILS environment built using IPG CarMaker HIL 10.2, a high-fidelity autonomous driving and vehicle dynamics simulation software program. A realistic test environment was implemented using a high-fidelity vehicle dynamics model and an object list-based perception sensor model. Specifically, we focused on evaluating the accuracy of the relative distance and relative velocity to analyze the effects of sensor noise and uncertainty on the performance of the ADS [35; 36; 37]. This approach contributed to improving the similarity between simulated and actual driving and enhancing the credibility of the evaluation results.
## 3 Results
### NDVI and Rainfall MK Trends
#### 3.1.2 Rainfall MK Trends  
Figure 4 below shows temporal MK trend for rainfall over the past 30 years. The dots shown in the figure represent rainfall values for wet season rainfall weighted by the number of days acquired, while the grey-shaded area represents the values that mainly contribute to the rainfall trend. Analysis of Sen's rainfall trend shows an overall decrease of \(-3.083\), Kendall's Tau of \(-0.14\) and an insignificant trend with a \(p\)-value of \(0.27\).  
The rainfall trend shows that rainfall in the district varied greatly due to periods of high and low rainfall, hence the \(p\)-values are not significant.  
The spatial MK test and Sen's slope show that rainfall is decreasing in the whole district as all the Sen's slope values are negative (Figure 5a). The declining magnitude of the rainfall was much higher than that of vegetation, which indicates that rainfall has decreased more than vegetation in the district. This could mean that there are indigenous vegetation species resistant to drought or the growth of alien invasive species that cause bush encroachment. However, the decrease in rainfall is not significant as the \(p\)-values are above \(0.05\) (Figure 5b).  
The spatial Mann-Kendall trend shows that the north-eastern part of the district (Fetakgomo Tubatse local municipality with a record of \(-3.61\) Sen's slope) has the largest share of the decrease in rainfall, including the south-western part of the district. The north-western and southern parts of the district also show that rainfall is decreasing but the magnitude is lower than in the north-eastern and south-western parts of the district (Figure 5a).  
Figure 4: Wet season Rainfall trend magnitude (Sen’s Slope) between 1990–2019.
# Protograph LDPC Code Design for Asynchronous Random Access +
## 3 Code Design for the Asynchronous Random Access Channel
### Code Design for Gaussian Interference  
Two different code designs for the surrogate channel with Gaussian interference, as defined in Section 3.3.1, are presented next. The designs are exemplified for a code rate of \(R_{c}=1/2\), QPSK modulation and a target \(E_{s}/N_{0}=6\) dB, typical parameters for an asynchronous RA scheme [25; 26]. However, the code design can be easily performed for any other parameter set.
###### Abstract
The stator had four contact points that were located at the inner junctions of the cantilevers. The elliptical motion of the contact points was generated at off-resonance operation mode. The results of numerical and experimental investigations showed that the rotation speed of 100 RPM and torque of 26 gf-cm were obtained at the excitation frequency of 80.7 kHz while the voltage amplitude of 24 V was applied. [PERSON] and et al. reported on small size piezoelectric rotary motor with a volume of 0.49 mm\({}^{3}\)[16]. The design of the motor was based on three cantilever oscillators, which form a cross-shaped stator. The shaft and rotor were placed at the center of the stator, and a magnetic field of permanent magnet was used as a preload force. The simple design of the motor makes it simple to scale and adapt it to the special demands of the target application. The motor operation was based on longitudinal and flexural vibrations of cantilever-based oscillators. Synchronization of these vibrations allows one to obtain an elliptical motion of contact points. Experimental investigations of the motor showed that maximum rotation speed reached 1800 RPM at an excitation voltage of 14 V\({}_{\text{P-P}}\) when a preload of 1.6 MPa was applied. This paper represents numerical and experimental investigations of a new flat cross-shaped piezoelectric rotary motor. The main advantages of the proposed motor are high rotation speed, simple and scalable design, and the small space required for motor mounting. Moreover, the proposed piezoelectric motor can be mounted directly to the printed circuit board or in the systems where mounting volume and surface are critical parameters.
# A Real Case of Implementation of the Future 5G City
## 3 Future 5G City Use Cases
### The 5G Project Use Cases
#### 3.2.9 Smart Safety for Smart Cities  
Another objective is to ensure a greater level of safety for citizens by using innovative technologies that allow security forces to immediately determine the emergency status of an event and to simplify/optimize coordination and intervention activities. Security agents will be equipped with smart glasses connected to the 5G network and remote-controlled drones equipped with cameras for a top view, in order to offer the best support for effective decision making in the management of citizens' safety, in emergency and danger situations as well as in daily operations.
## 3 Large-Scale Path Loss Models  
The path loss is reflected in the decay of the signal power as it travels through the communications channel from a transmitter to the receiver. As a result, the coverage area and the transmission rate are significantly affected. In this sense, the propagation models based on experimental measurements receive great attention since they allow the calculation of the propagation characteristics of wireless channels.  
The value of the path loss can be calculated by knowing the transmission power, the gain of the transmission and reception antennas, and the received power level obtained by the measurements campaign. It can be shown according to the following expression:  
\[PL(d)\big{[}\mathrm{dB}\big{]}=P_{Tx}+G_{Tx}+G_{Rx}-P_{Rx}(d), \tag{1}\]  
where \(P_{Tx}\) is the transmitted power (in dBm); \(G_{Tx}\) and \(G_{Rx}\) are the transmit and receive antennas gain (in dB), respectively; and \(P_{Rx}(d)\) is the received power level (in dBm). \(P_{Rx}(d)\)  
Figure 4: Front view of the corridor environment. The Rx and Tx positions have been indicated.  
Figure 3: Propagation environment where the measurements were carried out.  
was calculated from the average of several received signal levels detected by the time channel sounder used in the measurements campaign.  
This work analyzes path loss propagation in an indoor corridor environment using the CI and the FI path loss prediction models. Their parameters can be obtained from the measured data through the Minimum Mean Square Error (MMSE) approach. These models were considered in [7], like single-frequency path loss models. The path loss (in dB) given by the CI model is:  
\[PL^{CI}(d)=FSPL(f_{c},1\ \mathrm{m})+10 nlog_{10}(d)+\chi^{CI}_{\sigma}. \tag{2}\]  
The term \(FSPL(f_{c},1\ \mathrm{m})=10 log_{10}(4\pi f_{c}/c_{0})^{2}\) being the free space path loss (FSPL) for a Tx-Rx separation distance equal to 1 m, (43.32 dB at \(f_{c}=3.5\) GHz and 31.03 dB at \(f_{c}=850\) MHz). \(c_{0}\) is the speed of light; \(n\) is the path loss exponent (PLE) associated with the characteristics of the propagation environment; \(d\) is the Tx-Rx separation distance; and \(\chi^{CI}_{\sigma}\) is the shadowing factor (SF) term, which is a Gaussian distributed random variable with zero mean and standard deviation \(\sigma\) (in dB). In addition, the SF term describes the statistical variation in the distant-dependent mean path loss. In the FI model, the path loss (in dB) is shown through:  
\[PL^{FI}(d)=\beta+10\alpha log_{10}(d)+\chi^{FI}_{\sigma}, \tag{3}\]  
where \(\beta\) is the offset parameter (in dB), \(\alpha\) is the PLE, and \(\chi^{FI}_{\sigma}\) is SF, with the standard deviation \(\sigma\).
# Mo(VI) Complexes Immobilized on SBA-15 as an Efficient Catalyst for 1-Octene Epoxidation
## 3 Materials and Methods  
### Synthesis of Mo(VI) Containing-SBA-15 Catalyst  
Pure silica SBA-15 used as support for the Mo-Schiff base catalyst was synthesized according to the procedure described by [PERSON] et al. (2016). Pluronic P123 (PEO = 20, PPO = 70, PEO = 20, Mn = 5800, Aldrich) was used as a surfactant and tetraethyl orthosilicate (TEOS, Aldrich, St. Louis, MO, USA) as the silicon precursor. Dried silica was outgassed under vacuum conditions overnight at 150 \({}^{\circ}\)C. Before silica functionalization, the previously dried SBA-15 material was treated by azeotropic distillation with ethylbenzene in a Dean-Stark apparatus to ensure complete removal of water molecules. Then, sample was functionalized with (3-glycidyloxypropyl) trimethoxysilane (GPTMS, Aldrich) in under reflux and inert conditions for 8 h by using a molar GPTMS/silica ratio = 0.1. Next, the solid was recovered by filtration and intensively washed with toluene. Epoxy rings were reacted with 2-aminomethyl pyridine (AMP, 2-picolylamine, Aldrich). This stage was carried out in tetrahydrofuran under reflux and inert conditions for 24 h by using an AMP/GPTMS molar ratio = 2. Excess of AMP was removed by washing the sample with ethanol. By means of this procedure, glycolyl functionalities react with 2-picoline to form the (hydroxypropyl)-2-aminomethyl pyridine (HPAMP) ligands, which are the anchoring points to ligate and stabilize molybdenum species (see Scheme 1). The molybdenum centers were immobilized on silica surface by reaction of the HPAMP/SBA-15 material with bis(acetylacetonate)dioxomolybdenum(VI) (MoO\({}_{2}\)(acac)\({}_{2}\), Aldrich). Typically, the HPAMP/SBA-15 material support was treated for 24 h in ethanol under reflux with one equivalent of MoO\({}_{2}\)(acac)\({}_{2}\), relative to the amount of the previously attached HPAMP ligand. The molybdenum-containing material was then filtered off and washed with ethanol prior to vacuum drying overnight ([PERSON] et al., 2016). Scheme 1 summarizes the different functionalization steps carried out for the synthesis of Mo(VI) containing-SBA-15 catalyst.
## 2 Materials and Methods
### Classification Method  
A simple fuzzy-like classification method has been employed in the developed application. When a specific plant disease _Dis_ is examined, the feature \(X_{i}\) of the currently analyzed photograph is compared with the wide and narrow limits defined in _Dis_ signature. A grade \(G(X_{i})\) is given for this comparison which is zero if \(X_{i}\) is out of these ranges, a non-zero value if the feature is within the wide range and a higher grade if it is found within the narrow one. The weighted sums of these grades rank the likelihood that the currently examined disease is the one that has infected the plant. The application lists the top three diseases with the highest ranks. These comparisons can be formally described as:  
\[G(X_{i})=\left\{\begin{array}{l}G_{1},if\ X_{iWN}\leq X_{i}\leq X_{iWX}\\ G_{2},if\ X_{iN}\leq X_{i}\leq X_{iNX}\\ 0,\ otherwise\end{array}\right., \tag{6}\]  
where \(G(X_{i})\) is the grade for the feature \(X_{i}\) and \(X_{i}\) can be one of the 35 feature symbols listed in Table 1 (e.g., \(X_{1}=N_{s}\), \(X_{2}=A_{s}\), \(X_{3}=G_{s}\), etc.). \(G_{1}\) and \(G_{2}\) are the constant grades given if a feature value falls within the wide or narrow range of the specific plant disease signature respectively (\(G_{1}<G_{2}\)). This pair of constants can be the same for all features and diseases to avoid unnecessary complexity since the grade defined in Equation (6) is multiplied by a weight \(W_{i}\) as shown in Equation (7) below:  
\[R_{dis}=\sum\
olimits_{i=1}^{35}W_{i}G(X_{i}), \tag{7}\]  
the \(R_{Dis}\) is the rank that a specific disease _Dis_ has received. A different set of weights \(W_{i}\) can be defined for each disease. For example, the weights that correspond to the features related with the halo in diseases where no halo appears around the spots can be 0, the rest of the weights can have an identical value if the corresponding features have equivalent significance. A disease signature \(S_{Dis}\) is formally defined by the following set of values:  
\[S_{Dis}=\{W_{i},X_{iWN},X_{iWX},X_{iNN},X_{iNX}\},\ 1\leq i\leq 35, \tag{8}\]  
This classification method can be considered as fuzzy clustering since a photograph belongs to multiple diseases with different confidence rank \(R_{dis}\). A pure fuzzy clustering can be implemented by extracting average feature values from the training samples without using strict and loose limits. In this case, the disease signature would be based on the weights and the average feature values. A feature grade can be given during the analysis of a new photograph by examining the distance of the feature value from the expected average.
## 1 Introduction
By harnessing real historical futures price spread data, the study endeavors to ascertain the efficacy of the PSO Deep-ConvLSTM through a comprehensive comparative analysis against alternative forecasting models. In addressing the inherent challenges associated with the opaque decision-making of traditional ConvLSTM models, this research pioneers the utilization of the PSO algorithm for optimizing hyperparameters, with the overarching goal of enhancing the model's transparency and augmenting its performance. This systematic tuning process is designed to mitigate reliance on subjective expertise and historical precedents, thus transitioning toward an objective and replicable methodology capable of reliably guiding the network's learning process. Coupling the ConvLSTM's aptitude for capturing complex data patterns with PSO's strengths in parameter optimization, the proposed PSO Deep-ConvLSTM model promises to be a powerful tool in futures arbitrage. By advancing this synergy of methodologies, the framework aims to provide actionable insights, decision support, and investment strategies for market participants. Findings from this research are expected to offer a strong endorsement for the practical application of the PSO Deep-ConvLSTM model in arbitrage decision-making and risk management, thereby illuminating a pathway toward more sophisticated and precise financial market analyses. In short, we summarize the key contributions of this work as follows:  
1. We employ the ConvLSTM model to prognosticate multiple pertinent indicators in the future timeline of the futures price spread data. 2. By introducing the PSO algorithm, we optimize the ConvLSTM network. This approach rectifies the shortfall related to the inaccurate acquisition of initial connection weights and hyperparameters intrinsic to the ConvLSTM model. Consequently, it fortifies the objectivity of hyperparameter selection and enables a more accurate prediction of futures price spread data. 3. To evaluate the predictive performance of our PSO Deep-ConvLSTM model, we conducted comparative experiments with existing models, such as the FEDformer. Ourresearch findings demonstrate that our model exhibits marginally superior accuracy compared with state-of-the-art approaches. The remainder of this paper is structured to facilitate clear comprehension and logical flow. Section 2 delineates the problem and engages in an in-depth analysis of the dataset. It aims to articulate the objective function configured for our peculiar study needs and to confirm the empirical dataset's reliability and accessibility. In Section 3, we expound on both the COVLSTM and the innovative PSO Deep-ConvLSTM frameworks. Section 4 is devoted to a detailed experimental evaluation and analytical comparison between the PSO Deep-ConvLSTM and established benchmark models, focusing specifically on their applicability to forecasting inter-commodity spread. Section 5 concludes the paper and discusses future perspectives.