esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Results of Sensory Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Sensory** \\ **Attribute** \\ \end{tabular} & **Control + Salt** & \
\begin{tabular}{c} **Thyme HAB** \\ **0.75 mL** \\ \end{tabular} & **Ajwain HAB** & **Ajwain OT** & **Thyme OT** & **Ajwain OT** \\ \hline
\begin{tabular}{c} General \\ pleasantness of \\ the taste \\ \end{tabular} & 48.33 (17.48) \({}^{\text{a}}\) & 42.53 (23.82) \({}^{\text{a}}\) & 44.47 (22.11) \({}^{\text{a}}\) & 56.53 (19.59) \({}^{\text{a}}\) & 48.53 (26.02) \({}^{\text{a}}\) & 50.53 (22.38) \({}^{\text{a}}\) \\ General & & & & & & \\ intensity of the & 39.73 (19.98) \({}^{\text{a}}\) & 52.40 (16.96) \({}^{\text{ab}}\) & 56.33 & & & \\ taste & & & & & & \\ Thymol taste & 23.40 (19.79) \({}^{\text{a}}\) & 55.27 (16.68) \({}^{\text{b}}\) & 56.27 (19.69) \({}^{\text{b}}\) & 57.33 (17.83) \({}^{\text{b}}\) & 51.97 (17.22) \({}^{\text{b}}\) & 47.00 (16.96) \({}^{\text{b}}\) \\ Salty & 41.40 (16.72) \({}^{\text{a}}\) & 29.13 (16.15) \({}^{\text{a}}\) & 31.93 (22.18) \({}^{\text{a}}\) & 34.53 (19.04) \({}^{\text{a}}\) & 37.13 (12.51) \({}^{\text{a}}\) & 35.30 (17.48) \({}^{\text{a}}\) \\ Bitter & 12.00 (15.15) \({}^{\text{a}}\) & 31.60 (27.23) \({}^{\text{ab}}\) & 33.00 (23.03)\({}^{\text{ab}}\) & 23.33 (21.47) \({}^{\text{ab}}\) & 39.20 (28.27) \({}^{\text{b}}\) & 29.63 (24.12) \({}^{\text{ab}}\) \\ Astringent & 18.40 (16.37) \({}^{\text{a}}\) & 28.20 (25.72) \({}^{\text{a}}\) & 29.23 (27.14) \({}^{\text{a}}\) & 21.73 (20.38) \({}^{\text{a}}\) & 39.33 (30.32) \({}^{\text{a}}\) & 28.73 (26.26) \({}^{\text{a}}\) \\ Pungent & 18.73 (15.16) \({}^{\text{a}}\) & 28.53 (24.27) \({}^{\text{a}}\) & 28.06 (27.06) \({}^{\text{a}}\) & 26.23 (21.84) \({}^{\text{a}}\) & 27.43 (24.85) \({}^{\text{a}}\) & 23.33 (21.66) \({}^{\text{a}}\) \\ Overall & 50.70 (17.19) \({}^{\text{a}}\) & 46.60 (19.60) \({}^{\text{a}}\) & 45.07 (24.51) \({}^{\text{a}}\) & 52.80 (17.92) \({}^{\text{a}}\) & 48.80 (25.15) \({}^{\text{a}}\) & 54.33 (17.55) \({}^{\text{a}}\) \\ the sample & & & & & & \\ \hline \hline \end{tabular} Note: Means in the same row followed by different superscript letters are significantly different (\(p<0.05\)). For the evaluation, unstructured graphical 100 mm linear scales were used. The overall evaluation and the general pleasantness of the taste (0% = very bad and 100% = excellent). General intensity of taste and intensity of partial tastes: thyme taste, salty, bitter, as stringent, and pungent (0% = imperceptible and 100% = very strong).  
\end{table} 
Table 6: Mean [%] (standard deviation) sensory trait scores for taste of dried meat sample. In the end, the ajwain OT 0.75 mL and 1.5 mL samples achieved superior scores in the overall evaluation of the sample.
## 3 Dataset Analysis and Description
### Statistical Figures on Seriousness and Non-Seriousness
#### 3.2.3 Histogram on Number of Active Substances in Drugs  
To understand the distribution of active substances present in drugs and their correlation with adverse events, as shown in Figure 5, a histogram was plotted. The histogram's x-axis represents the number of active substances present in a drug, while the y-axis indicates the count of drugs. Each bar in the histogram showcases how many drugs contain a particular number of active substances. For instance, if a bar corresponding to '2' on the x-axis reaches '500' on the y-axis, it suggests that 500 drugs in the dataset contain two active substances. This visual representation can shed light on multiple facets:  
* Predominant Composition: If the taller bars of the histogram are centered around a specific number, it indicates that most drugs in the dataset contain that particular number of active substances.
* Complexity of Drugs: If the histogram has a broad spread, it suggests that the dataset includes drugs with a wide range of complexity in terms of active substance count.
* Potential Correlations with Adverse Events: By juxtaposing this histogram with the serious vs. non-serious statistics, researchers might discern patterns. For example, if drugs with a higher count of active substances tend to have more serious adverse events, it could indicate potential interactions between substances leading to severe reactions.  
Together, the bar graph, word cloud, and histogram collectively provide a multifaceted perspective on the FAERS dataset. These visual tools, juxtaposing quantitative figures with qualitative representations, deliver profound insights into the seriousness of adverse events and the complexity of active substances in drugs. Such comprehensive visualization aids are essential for researchers and stakeholders, enabling them to grasp the depth and nuances of drug-related adverse events within the FAERS dataset.
# Hierarchical Transmission Tower Detection from High-Resolution SAR Image
## 5 Conclusions  
The main contribution of this paper is to provide a new method for hierarchical detection of transmission towers based on SAR images.  
The method is simple, fast, it has high precision, and fully combines various characteristics of the transmission tower to achieve high-precision detection results. Compared with the other known and proven techniques, the proposed algorithm has the following advantages. (1) The idea of combining high SCR with geometric features is fully utilized to hierarchical detection, without relying too much on any one step. (2) There is no need to do much preprocessing work and to consider a priori knowledge. (3) The proposed algorithm can better demonstrate the imaging morphology of transmission towers within SAR images. In addition, comparative experiments are conducted to verify the superiority of the proposed algorithm. Although the proposed algorithm is effective in distinguishing transmission and non-transmission towers, it has some shortcomings. For example, a large number of small targets with similar geometric and scattering characteristics to transmission towers within the image are not accurately detected and classified. In future work, the study will focus on detecting targets with characteristics similar to transmission towers and perform more accurate analysis.  
Conceptualization, J.L. and Y.L.; methodology, J.L.; validation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, Y.L., H.J. and Q.Z.; visualization, J.L. and H.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Project for Key Scientific Issues from Education Department of Liaoning (LJ2020 ZD003) and the Youth Foundation of National Natural Science Foundation of China (42001286).  
Not applicable.  
We gratefully thank the anonymous reviewers for their critical comments and constructive suggestions on the manuscript.  
The authors declare no conflict of interest[PERSON]
## 4 Results and Discussion
### Rayleigh Wave Dispersion Curves  
The dispersion curves generated for each array measurement site using the SPAC method are presented in Figure 7 (panel a). The aforementioned curves are illustrated within the theoretical wavenumber limits (kmin, kmax), which were obtained by introducing the coordinates of each of the seven sensors into the WARANGPS module (from Geopsy package, release 2.10.1) [58], encompassing a frequency range of 3.5 to 12 Hz and a velocity range extending from 400 to 680 m/s. The Rayleigh wave phase velocity values are within the same range with those obtained previously in a similar investigation in the Middle Cheliff Basin [33]. In their research, the same geometry was employed, as well as a 30 m aperture, resulting in reported frequency values within the range of 5 to 12 Hz. Furthermore, in another study [45] carried out in the city of Oran, 80 km NE of Ain T\(\hat{\text{{\em{T}}}}\)mouchent, array measurements were performed using the same geometry but with apertures of 30 and 50 m. The dispersion curves were obtained within frequency ranges between 5 and 12 Hz (for apertures of 30 m) and between 2.7 and 14 Hz (for apertures of 50 m). The variation in frequency range is mainly due to local geological conditions between the two cities. It also could be attributed to different apertures.  
Figure 6: Variation in the vulnerability index (\(Kg\)) for the city of Ain T\(\hat{\text{{\em{T}}}}\)mouchent.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 4 Ecosystem
### Application Areas
#### 4.2.1 IoT  
Image encryption is a necessary requirement in many modern IoT systems, yet the requirements of said algorithms are very steep and their efficiency must be proven. One such algorithm proposed by [PERSON] et al. [167] was simulated in MATLAB and contrasted against existing algorithms to prove its efficiency.  
Another novel algorithm was proposed by [PERSON] et al. [168], where hardware acceleration was used to meet the speed requirements of real-time IoT applications. For a \(512\times 512\) image, the algorithm achieved a maximum frequency of \(135\) MHz and \(256\) fps.
## 2 Theory, Requirements and Synthesis
### Practical Implementations  
We designed and built a prototype of the CLC system to establish the environment for an experimental evaluation of the proposed concept. The CLC prototype was built from several generic operational amplifiers (LM324), a CMOS switch (TL604), and other discrete components (capacitors, resistors, potentiometers, diodes, transistors). To enable our measurements, given the low optical powers in the system, we also assembled a sensitive PIN-FET receiver, and a voltage-controlled current source (VCCS) to drive the TEC element.
###### Abstract  
An indirect method for monitoring dynamic deflection of beam-like structures using strain responses measured by long-gauge fiber Bragg grating (FBG) sensors is proposed in this paper. Firstly, a theoretical derivation shows that structural deflection is in direct and linear relationship to long-gauge strain. Meanwhile, the method is suitable for structures with different boundary conditions and irrelevant to external loads. Secondly, the influence of boundary conditions, load type and sensor gauge length on the method is investigated by numerical simulation. Finally, an experiment of a simply supported beam subjected to dynamic loads was designed to verify the method. Experimental results show that both deflection time-history of arbitrary points of structures and deflection distribution along structures at a certain time can be obtained with high-precision. Therefore, the method presented can be a new alternative for the deflection evaluation and maintenance of engineering structures.  
deflection; fiber Bragg grating sensors; beam-like structures +
Footnote †: journal: Applied Mechanics and Applications
## 4 Performance Evaluation
### The Multiple Sink Setup  
In our earlier discussions, we suggested increasing either node density or transmission range to achieve a certain network connectivity requirement. In this subsection, we demonstrate the employment of a multiple sink setup as an alternative solution to meet the requirement.  
**Figure 7.** The simulation results of the network connectivity _versus_ transmission range for single and multiple sink setups.  
Assuming that a number of sensors are randomly deployed over a square area with the side length of 5,000 m, we consider two scenarios here: one is the scenario where there is only one sink located at the center of the square area, and the other is that there are four sinks individually placed at four different vertexes of the square. The node density \(\rho\) is set to either \(1\times 10^{-7}\) or \(2\times 10^{-7}\), and the transmission range varies from 1,000 m and 5,000 m. We evaluate the impact of multiple sinks on the connectivity, as shown in Figure 7. It is clear that the scenario with four sinks achieves higher connectivity than that with single sink.
## 3 Aggressive Control of Quadrotors with Extended Finite-Time Observer
### Extended Finite-Time Observer
According to Lemma 1, \(\epsilon\) will converge into the compact sphere within the finite-time \(T_{converge}\). \[T_{converge}\leq\frac{\sqrt{2}V^{\frac{1}{2}}(\epsilon(0))}{(\gamma-1) \underline{\lambda}\beta-\bar{\lambda}\rho}=\frac{\|M\vec{x}_{i}^{d}(0)\|}{( \gamma-1)\underline{\lambda}\beta-\bar{\lambda}\rho}\leq\frac{\|\vec{x}_{i}^{d }(0)\|}{(\gamma-1)\underline{\beta}\underline{\lambda}/\bar{\lambda}-\rho}\]  
The proof is completed. **Remark 2**. : _The observing error \(\vec{x}_{i}^{d}\) is ultimately bounded, since \(|(X_{i}^{d})^{(5)}|\) is bounded by \(\sqrt{2}\rho\). According to (26), the observing error will converge to the invariant set \(\|\epsilon\|\leq\frac{\bar{\lambda}\rho}{\gamma-1}\). Therefore, by increasing observer gains, which renders a greater \(\gamma\), the invariant set will become smaller. The observing error will smaller as well. Nevertheless, the gains cannot be chosen arbitrarily great in practice, since the sensor noise will become significant and lead to instability._  
**Remark 3**. : _We note that since a decoupling quadrotor dynamics control law is derived in (16), the observer for the derivatives of \(\Upsilon_{i}^{d}\) can be designed independent with \(X_{i}^{d}\)._  
The high-order derivatives are replaced by the estimated values using the observer (20). Then, the decoupled control law (17) becomes  
\[ \begin{split} v_{i1}=(\widehat{X}_{i}^{d})-k_{4X}(\dot{\theta}_{ i}g-(\widehat{X}_{i}^{d}))-k_{3X}(\theta_{i}g-(\hat{X}_{i}^{d}))-k_{2X}(\check{X}_{i} -\hat{\bar{X}}_{i}^{d})-k_{1X}(X_{i}-X_{i}^{d})\\ v_{i2}=(\widehat{\Upsilon}_{i}^{d})-k_{4Y}(-\dot{\phi}_{i}g-( \widehat{\Upsilon}_{i}^{d}))-k_{3Y}(-\dot{\phi}_{i}g-(\hat{\bar{Y}}_{i}^{d}))- k_{2Y}(\dot{Y}_{i}-\hat{\bar{Y}}_{i}^{d})-k_{1Y}(Y_{i}-Y_{i}^{d})\end{split}  \tag{27}\]  
where the scalars \(k_{iX}\) and \(k_{iY}\), \(i\in\{1,2,3,4\}\) represent some selected positive gains. In the following section, we will show how to find the \(X_{i}^{d}\) and \(Y_{i}^{d}\) in the guidance vector (since the control of the altitude and yaw is decoupled in double integrators, it is omitted in the following section).
## 2 Methodology
### Data Sources  
Primary data were sourced from direct measurements and equipment specifications at installations in the Indian Institute of Technology (IIT) Roorkee and Dayalbagh Educational Institute (DEI) Agra, capturing site-specific details such as regional solar irradiance, feedstock availability, and actual operating conditions. These datasets were supplemented with secondary information from peer-reviewed literature and standardized LCA databases, including Ecoinvent, to fill any knowledge gaps and ensure comprehensive modeling of upstream and downstream processes. Where possible, all data were validated through cross-referencing multiple sources and consulting technical experts, thereby improving the reliability of the life cycle inventory used in SimaPro.  
Figure 1: System boundaries and processes.  
Table 1 summarizes the key LCA parameters including component lifetimes, conversion efficiencies, emission factors, and allocation methods along with associated uncertainty bounds and source types.
## 2 Designing Nominal SAR Operational Parameters at Nominal Altitude  
Figure 2 shows a general design flow including an iteration process for SAR. The design flow starts from defining basic specifications, such as resolution, incident angle range, swath width, nominal orbit altitude, and so on. Available platform resources such as power consumption imposes another constraint to a mission design. Mission requirements are also used to define the SAR operational parameters such as Tx bandwidth, Tx duty cycle, azimuth integration time, acquisition time, pulse length, PRF and so on. As shown in Figure 2, the iteration process is used to determine PRF satisfying the requirements.  
Figure 1: Satellite altitude predicted on the WGS84.
# Burst Channel Error Reduction Based on Interleaving for Efficient High-Speed Wireless Communication
## 5 Simulation Results
### BER Performance Evaluation over AWGN Channel  
This experiment evaluates the BER performances of the FFT-OFDM and the DWT (Haar)-OFDM with and without helical and FRF interleavers over the AWGN channel. Figure 8 shows the BER vs. \(E_{b}\)/\(N_{0}\) for the helical (FFT/DWT)-OFDM and the FRF (FFT/DWT)-OFDM systems compared with the conventional (FFT/DWT)-OFDM over an AWGN channel. As shown in the results, there is no improvement, because there are no burst errors in the AWGN case. We can say that there is no need for the interleaver in this case.  
Figure 8: FFT\(-\)OFDM and DWT\(-\)OFDM BER performance over AWGN channel.  
Figure 7: BER performance comparisons of the DWT-OFDM and the FFT-OFDM systems over Rayleigh channel model using several wavelets.
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 5 Developing a Simple and Low-Cost CR-Based UAV Testbed  
One of the main obstacles when integrating CR into UAVs is the energy consumption. Devices such as USRP B200 [25] have a consumption around 4.1 Wh, whereas a commercial quad-rotor UAV like DJI Phantom 4 [89] has a LiPo 4S battery with 81.3 Wh of capacity, which ensure a max flight time up to 28 min. This issue has been subject of empirical evaluations in [PERSON] and [PERSON] [27], where the authors apply the Hope's RF RFM22B [50] to greatly reduce the energy utilization (0.3 Wh), although it likewise reduces the coverage range from USRP's 70 MHz-6 GHz Hope's 433-915 GHz.  
The employment of miniaturized SDR devices could also help to reduce the additional weight load of UAV. For instance, Phantom 4 weights 1380 g, whereas USRP B200 weights 350 g. In comparison, Hope's RF RFM22B weights only 1.8 g. The additional load could influence not solely the weight capacity of the aircraft, but also the energy consumption. Ultimately, the impact on the load capacity of the UAV poses as a major drawback on mobile manipulating UAVs [90].  
Finally, there is no conclusive information referring to the maximum transmission distance of both USRP's and Hope's SDRs, as well as for other manufacturers. A limited transmission distance can be a tricky feature, potentially limiting the assimilation of CR into UAV Swarms. One probable solution to this shortcoming is the use of signal amplifiers; however, it is essential to note that additional weight load could result in less flight time, especially in swarms where the UAVs individual weight could reach 500 g in medium range devices or even less in short range devices.
# Gravity Field Recovery Using High-Precision, High-Low Inter-Satellite Links
## 2 Mission Concept
### Observation Geometry
These conditions connected with the repeat orbit lead to an altitude of about 10,000 km for the MEO satellites. The high-low tracking concept enables a multi-directional observation geometry with differing elevation angles from 3\({}^{\circ}\) (assumed minimum elevation angle of visible MEOs observed by the LEO) up to a near-radial direction. However, due to the observation geometry of the MEO-LEO satellite pairs and the changing satellite links from one to the other MEO, data gaps arise for every satellite pair, leading to a non-continuous measurement time series of these pairs. For the simulated MOBILE constellation, this results in a ranging window maximum of 45 min, and a maximum data gap of 18 min. The separation of the two MEO satellites of 180-degree mean anomaly is chosen to keep the time period of the data gap as small as possible. In Figure 2, the LEO ground track of the MOBILE concept is displayed for 1 day together with the corresponding elevation angles. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Satellite**} & **Altitude** & **Inclination** & **Revolutions/Nodal Days** & **Initial Mean** \\  & **(km)** & **(Degree)** & **in One Repeat Orbit** & **Anomaly (Degree)** \\ \hline LEO 1 & 358 & 90 & 476/30 & 0 \\ MEO 1 & 10.149 & 90 & 124/30 & 0 \\ MEO 2 & 10.149 & 90 & 124/30 & 180 \\ \hline Low-low 1 & 467 & 89 & 412/27 & 53.21 \\ Low-low 2 & 467 & 89 & 412/27 & 51.51 \\ \hline \end{tabular}
\end{table} 
Table 1: Orbit parameters for satellite constellations. Figure 2: One-day LEO ground track of the MOBILE mission constellation together with the elevation angle of visible MEOs observed by the LEO.
## 1 Introduction
Furthermore, flooding attacks are a potential hazard, whereby the attacker floods the network with a substantial volume of simultaneous communication requests, thus constituting a Denial-of-Service (DoS) attack [17]. By strengthening the signal from a certain side (i.e., a legitimate user) while weakening it from another (i.e., the adversary), the RIS can help mitigate the effect of such attacks. An effective way to accomplish this is to configure the RIS elements in a way that can destructively interfere in one direction, and constructively interfere in another. In summary, the main contributions of this work are as follows:  
* This study extends our previous work introduced in [5] by proposing a RIS-assisted key-extraction method that enhances the signal strength for the designated user's location while reducing that from the active attacker's location. Hence, the proposed method improves the key-extraction performance for designated users while mitigating the impact of DoS attacks within the network. * To accomplish this, a RIS configuration optimization algorithm is designed using the Hadamard matrix codebook. This algorithm optimizes the measurement quantization order based on the optimal configuration's average SNR estimate. * The proposed RIS optimization algorithm is practically implemented using a 1-bit RIS with \(64\times 64\) elements and two Universal Serial Radio Peripherals (USRPs) operating in the 5G communication frequency range (3.75 GHz). Finally, the statistical randomness of the extracted keys is measured to demonstrate the extracted key suitability for use as cryptographic keys. The structure of this paper is as follows: Section 2 provides an overview of the existing phase-based key-extraction method. Section 3 presents the preliminary concepts required for this research. Section 4 presents the proposed RIS-assisted key-extraction method. Section 5 analyses the hardware implementation of the method. Finally, Section 6 summarizes the findings and contributions of this work.
# Geometrical Degrees of Freedom for Cellular Structures Generation: A New Classification Paradigm
## 2 Design and Geometrical Consideration
### Overview of Representative Volume Elements
#### 2.2.2 Extruded 2D Cells  
Generally, 2D cellular structures can be described as CSs, where the repeated units are extrusions of polygons. The type of polygon that is extruded defines the type of extruded 2D CSs. Common extruded 2D CSs include Kagome, square, honeycomb [83], and triangular, as can be seen in Figure 2. The recent work from [PERSON] offers an in-depth review of common extruded 2D CSs, as well as their mechanical simulation [69].  
Furthermore, as it is shown by [PERSON] and [PERSON] [84], auxetic structures can also be made from extruded 2D cells. However, in that case, the expansion/contraction can only be done in the 2D plane perpendicular to the direction of the extrusion.