esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of Testing Methods  
The pre-embedded sensor method and the impact-echo technique have their respective advantages and limitations in evaluating the grout filling degree of sleeve connections, as summarized in Table 3.  
The pre-embedded sensor method has the advantage of convenient and continuous monitoring, as the sensors are pre-embedded in the sleeve and do not require special access for testing [33; 34]. However, the method has limited accuracy in evaluating the grout filling degree, particularly when the filling degree is low, due to the air exposure of the sensor.  
The impact-echo technique, on the other hand, demonstrates high accuracy in assessing the grout filling degree across different filling levels, as it is based on the principle of impact elastic wave propagation and can effectively detect the internal density/impedance changes [35; 36; 37]. However, the technique requires access to the exposed reinforcement ends for excitation, which may not be feasible in some field conditions.  
To leverage the strengths of both testing methods and improve the overall accuracy of grout filling quality assessment, a collaborative sensing approach has been developed, as discussed in the following section.  
Figure 10: Deviation between set value and calculated value.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristics** & **Pre-Embedded Sensor Method** & **Impact-Echo Technique** \\ \hline Principle & Vibration energy attenuation & Impact elastic wave propagation \\ Evaluation target & Vibration energy value & Density/Impedance \\ Applicability & Embedded sleeve & Exposed reinforcement end \\ Accessibility & Convenient, no special access required & Requires access to reinforcement end \\ Test duration & Short, continuous monitoring & Relatively longer, point-by-point testing \\ Accuracy & Limited at low filling degrees & High accuracy across filling degrees \\ Limitations & Air exposure of sensor, limited range & Requires access to reinforcement end \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of pre-embedded sensor method and impact-echo technique.
## 3 Results and Discussion
### Determination of Antioxidants in Seeds
#### 3.2.2 Total Flavonoid Content (TFC)  
The colorimetric method used to estimate the TFC of _O. focus-indica_ seeds was based on the reaction between flavonoids and aluminum (Al\({}^{3+}\)) chloride. The C-4 keto group and either the C-3 or C-5 hydroxyl group of flavones and flavonoids can chelate Al\({}^{3+}\), and the products formed showed a maximum absorption of UV-vis light at 432 nm. The UV-vis absorption was proportional to the concentration of flavonoids present in the extracts, and could be determined spectrophotometrically [15; 37].  
Results of TFC determination in seeds of the three cultivars of _O. focus-indica_ from Algeria are reported in Table 3. In this case, these values are in line with those reported by other authors in seeds of the same plant species [35], although a significant variability is shown in the literature (e.g., [36]). It should be noted that the TFC is often expressed in equivalents of different standards (qurcetin, rutin, catechin) and the nature of the standard used could influence the final result, together with the factors mentioned above. Our results show a TFC range comprised between 1.15 \(\pm\) 0.06 mg GAE/100 g of seeds (green cultivar) and 2.97 \(\pm\) 0.11 mg GAE/100 g (orange cultivar).
# Effects of the Hybrid CRITIC-VIKOR Method on Product Aspect Ranking in Customer Reviews
## 2 Literature Review and Theory
### Types of Criteria Weighting Tasks in MCDM
Recently, the research study of [32] drew attention to the fact that both subjective and objective methods ignore the significance of criterion value and introduced SODOSM, which combines subjective and objective approaches to overcome this drawback. Subjective weighting methods based on human assessment of the importance of the criteria may generate inaccurate values [33] because they depend on the knowledge and experience of decision makers while processing the decision-making problem. Moreover, the vagueness of the judgments of decision makers complicates the adoption of decisions in real-life situations [34]. The ranked criteria in subjective weighting take the form of benefit criteria (i.e., better performance is preferred, but the criteria must be mutually dependent), which creates a conflict for the decision maker when attempting to decide the relative importance of the criteria. The analysis presented above underscores the suitability of mathematically determining criteria weights through objective approaches. Objective methods offer a more robust and reliable means of establishing the importance of criteria. In this study, the CRITIC weighting method, representing an objective approach, was investigated alongside the VIKOR approach to prioritize the most important product aspects for potential customers. By employing objective approaches, this research seeks to overcome the limitations of subjective weighting and provide a more accurate and unbiased ranking of product aspects.
# Parallel Eclipse-Aware Routing on FPGA for SpaceWire-Based OBC in LEO Satellite Networks
## 4 Experimental Results
### Results with SpaceWire-Based Devices  
In this study, we utilize SpaceWire, a high-speed serial communication standard established by the European Space Agency (ESA). We verify the dynamic routing performance of the proposed algorithm through the STAR Dundee's USB-based SpaceWire interface device, Brick MK4. First, we construct a network topology based on the satellite locations predicted from the artificially generated TLE. After finding K paths from origin to destination through the KSP algorithm, the EAR algorithm identifies the satellites located in the solar eclipse zone from K paths, calculates the expected energy consumption of each satellite, and compares it with the highest energy consumption of each path to select the path with the lowest energy consumption. Then, the RMAP function of Brick MK4 updates the routing table of BrickMK4, which is not directly connected to the PC connected to the FPGA, by sending the command to update the routing table generated by the PC to the SpaceWire network. Experimental results show that the proposed technique successfully explores the path that is optimal and minimizes energy consumption, unlike the path chosen by the traditional KSP algorithm. This shows that it provides significant benefits for energy balance optimization, battery degradation prevention, and optimal path selection in the LEO satellite network. Figure 7 illustrates the internal architecture of the Brick MK4 and the experimental scenario. In the test case defined in Figure 5 (source = node 38, destination = node 98), a standalone KSP search would select node 39 (channel 2) and route through port 5, whereas the EAR-enhanced algorithm is designed to choose node 37 (channel 1) and route through port 4. Figure 8 is a screenshot from the PC attached to the receive-side Brick MK4; the routing table was remotely updated by the FPGA-host PC so that the link was redirected to node 37, exactly as prescribed by the EAR result. These observations confirm that the proposed algorithm executes correctly on the FPGA and that the Brick MK4 routing table can be updated in real time over the network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **PS (ms)** & **PS + PL (ms)** \\ \hline
160 edge data/Departure 38/Destination 98 & 8.893 & 2.621 \\ \hline
160 edge data/Departure 2/Destination 98 & 71.098 & 19.090 \\ \hline
300 edge data/Departure 38/Destination 98 & 226.378 & 59.304 \\ \hline
300 edge data/Departure 2/Destination 98 & 2450.603 & 638.854 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison KSP Algorithm results between only PS and PS + PL.  
Figure 7: SpaceWire Brick MK4 inner structure and experimental scenarios.
## 5 Simulation Results
### Velocity-Level Algorithm Results  
#### 5.1.1 Trajectory Tracking Results  
Figure 7 shows the behavior of the system when controlled through the velocity-level inverse kinematics algorithm in an open-loop scenario (meaning without the feedback term introduced in Section 3). All the disturbances and possible variations in the internal parameters previously described in Section 4 act on the system. It is worth noticing that, even though the use of the Generalized Jacobian adapted to UAMs should predict and compensate for base motions, this method fails to track the desired trajectory because of the presence of disturbances.  
By setting a feedback loop gain of \(K_{P}=100\), it can be noticed from Figure 8 that the system can track the trajectory with good accuracy. The sensors noise causes the trajectory to oscillate at a high frequency around the desired one, but the error norm still stays below 5 mm most of the times.  
Figure 8: System response in the case of wind disturbances and sensors noise. The system is controlled by a closed-loop velocity-level inverse kinematics algorithm (\(K_{P}=100\)). The inverse kinematics is solved using the maximum positive variation in all the inertial parameters, see Table 5. (**left**) Task-space error 2-norm, \(\|\mathbf{e}(t)\|\), as a function of time. From the instant \(t=T_{\text{path}}\), indicated with the black vertical line, the end-effector tries to remain still at the trajectory final point. (**right**) Graphical output of the simulator in the vertical (x–z) plane; the blue star represents the UAM CoG.  
Figure 7: System response in the case of wind disturbance and sensors noise. The system is controlled by an open-loop velocity-level inverse kinematics algorithm (\(K_{P}=0\)). The inverse kinematics is solved using the maximum positive variation in all the inertial parameters, see Table 5. (**left**) Task-space error 2-norm, \(\|\mathbf{e}(t)\|\), as a function of time. From the instant \(t=T_{\text{path}}\), indicated with the black vertical line, the end-effector tries to remain still at the trajectory final point. (**right**) Graphical output of the simulator in the vertical (x–z) plane; the blue star represents the UAM CoG.  
Figure 9 shows the performance of the algorithm in controlling an aerial manipulator equipped with a three-DOF arm. Since the orientation of the end-effector is not controlled, the arm is kinematically redundant for this tracking task. The reference velocities of the joints, \(\dot{\mathbf{q}}\), are obtained from Equation (19) using the pseudoinverse of the Generalized Jacobian. As in the previous case, \(K_{P}=100\).
# Implicit Algorithm of the SBSP-R Model for Predicting the Non-Unique Critical State of Soils
## 2 SBSP-R Model
### Loading Surface and Mapping Rule  
According to the bounding surface plasticity theory, the loading surface should be similar to the bounding surface [31]:  
\[f=q^{2}-M^{2}(p^{\prime}+p^{\prime}_{\ s})(p^{\prime}_{\ 0}-p^{\prime})=0 \tag{4}\]  
where \((p^{\prime},q)\) is a current stress that is the stress state on the loading surface; \(p^{\prime}_{\ 0}\) charges the size of the loading surface. The current stress and the image stress follow a radial mapping rule (Figure 3).  
Figure 2: Bounding surface in (**a**) principal stress space and (**b**) \(p^{\prime}\)-\(q\) plane.  
The mapping rule is essential for bounding surface plasticity models [22; 23]. A radial mapping rule is expressed as follows:  
\[\left\{\begin{array}{l}\overline{p}^{\prime}=\rho p^{\prime}\\ \overline{q}=\rho q\end{array}\right. \tag{5}\]  
where \(\rho\) (\(\rho\geq 1\)) is a scaler factor. If \(\rho=1\), the loading surface coincides with the structural bounding surface; if \(\rho>1\), the loading surface locates inside the structural bounding surface.
# A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network
## 3 Multi-Feature Fusion Neural Networks
### Model-Level Fusion Algorithm  
The Multi-Layer LSTM (ML-LSTM) method is a model-level fusion method that integrates different modal data at various stages of the network, enabling the acquisition of a joint feature representation of four types of characteristics from the pregnant woman's ECG signal for the recognition of fetal ECG QRS complexes [49].  
First, the time-domain features are fed into the first layer of the LSTM network, with an input dimension of 60 and a hidden unit size of 64. Then, the frequency-domain features are concatenated with the hidden state outputs of the first-layer LSTM (64 dimensions) and input into the second-layer LSTM, with an input dimension of 65 (\(64+1\)) and a hidden unit size of 64. Next, nonlinear features are concatenated with the hidden state outputs of the second-layer LSTM (64 dimensions) and introduced into the third-layer LSTM, with an input dimension of 65 (\(64+1\)) and a hidden unit size of 64. Finally, the hidden state of the third-layer LSTM (64 dimensions) is concatenated with the time-frequency domain features (24 dimensions) and fed into the fourth-layer LSTM, with an input dimension of 89 (\(64+25\)) and a hidden unit size of 64. In this way, the fourth-layer LSTM can simultaneously process information from the time domain, frequency domain, time-frequency domain, and nonlinear features. These features are then fed into a fully connected layer (FC layer) for the final classification task, generating the ultimate prediction results. The network architecture of the model-level fusion algorithm based on ML-LSTM is illustrated in Figure 6.  
By progressively combining different types of features at each LSTM layer, the ML-LSTM model efficiently captures the complex relationships between various signal characteristics. This layered fusion approach not only enhances classification accuracy but also optimizes resource allocation, ensuring that the computational load remains manageable.  
Figure 5: The network diagram for late fusion based on the cross-modal attention algorithm.
# VIIRS Day/Night Band--Correcting Striping and Nonuniformity over a Very Large Dynamic Range
## 3 Results
### Evaluating Causes of Striping and Nonuniformities
#### 3.2.5 Half-Angle Mirror Side Differences  
As described in the Introduction, the HAM rotates, alternating sides for every other scan. The reflectance of both sides of the HAM is measured before launch, and these measurements are used to produce a table used to compensate for the variation in reflectance across the scan and from one mirror side to the other. This correction is called Response Versus Scan (RVS). It is possible, however, for the reflectance of the HAM to change after characterization. This could happen during handling, during launch, or on-orbit. The main cause is expected to be contamination. If one mirror side becomes more contaminated than the other, then there would be a difference between one scan and the next that would appear as an alternating dark/light pattern with a 32 scan-line repetition rate.  
Figure 12 shows an image from 28 May 2014 taken by DNB. It is northwest of thunderstorms and depicts gravity waves modulating the intensity of airglow emissions in the upper atmosphere [3; 38]. This image has been destriped using the histogram matching method described below. It is obviously extremely noisy relative to any actual variation in the nightglow. However, there does appear to be some gravity waves propagating concentrically from the storms to the southeast. Destriping helps very much here, but on the right there is some modulation that is along-scan (horizontal) and has a 32 scan-line repetition rate that we believe to be due to changes to the RVS. This modulation was not visible before destriping, but is evident here. It could be removed with improvements to the destriping algorithm, and this will be discussed below. Also note the dark horizontal band about 3/4 ths of the way down on the image. This is the hysteresis striping that was discussed here previously. This is also not being corrected in the current algorithm but could be with improvements.  
It is worth noting here that the effects shown in this image are far below the radiance level for which the DNB was designed to detect. However, with improvements to calibration and to image processing such detection is possible. The gravity waves shown in Figure 12 have a peak-to-valley amplitude of less than 1 \(\times\) 10\({}^{-10}\) W\(\cdot\)cm\({}^{-2}\cdot\)str\({}^{-1}\), which is also about the magnitude of the RVS error and the hysteresis banding. The SNR here is less than 1, and the only reason that these gravity waves are even faintly visible is because the human mind aggregates the noise. It would be far better to do noise removal through image-processing, but before such noise filtering is done, these other subtle banding and striping errors must be removed.  
Figure 11: S-NPP MGS gain ratio errors computed by on-orbit destriping algorithm.
## 3 Circuit Implementation
### Power Controller Circuit  
Figure 3 depicts the schematic of the power controller circuit. The power controller circuit consists of a power detector and comparators. The power detector detects the RF power signal and outputs a DC voltage, which enters the comparator with a given reference voltage to generate control signals for the power cells and capacitors.  
The power detector is composed of a two-stage amplifier. The RF differential signals enter M2P and M2N, whose W/L is half of M1. For small signals, the total current of the M2 branch does not change due to the differential inputs, and the output voltage Vpd is the same as Vgs due to the feedback. As the power increases, the total current of the M2 branch increases. Due to the tail current source, M9, the drain voltage of M2 increases, and the output DC voltage Vpd increases accordingly.  
The gain-bandwidth product (GBW) of the power detection circuit is 200 MHz and meticulously designed to suppress the carrier while ensuring minimal impact on the signal bandwidth of the wireless local area network (WLAN). Then, the output signal is directly compared with a predetermined reference voltage. Whenever the output voltage of the power detection circuit is higher than the reference voltage, the comparator generates a high-level signal, consequently activating the entire circuit to operate in a high-power mode. Conversely, if the output voltage falls below the reference voltage, the circuit switches to a low-power mode. The reference voltage is adjustable, allowing for the generation of control signals at different power levels.  
The overall time delay of the power controller circuits, from the moment the input signal enters the circuit until the digital control signal is generated, is approximately 3 ns. This time delay effectively satisfies the signal output requirements of the WLAN. Moreover, the circuit maintains a low current consumption level under 1.2 V supply voltage, thus minimizing its impact on the overall efficiency of the power amplifier.  
Figure 4 illustrates the simulated power detector voltage versus input power, which demonstrates good linearity over the entire power range.  
Figure 3: Schematic of the Power Controller.  
Then, the comparison between the output detector voltage Vpd and different adjustable reference voltages generates control signals. Figure 5 shows the simulated logic control voltages versus input power.
## 6 Experimental Results
### Use Cases
#### 6.2.2 Printed Text Recognition in Videos  
The objective of this application, which extracts printed text within video streams, is twofold:  
1. Identifying \"critical\" videos by analyzing and automatically interpreting the streams of a significant data sample; and
2. Defining new useful services in the context of monitoring proselytizing phenomena by terrorist groups (this is of utter importance, for example, according to the _Christchurch Call to Action_ ([[https://www.christchurchcall.com/](https://www.christchurchcall.com/)]([https://www.christchurchcall.com/](https://www.christchurchcall.com/)), accessed on 3 December 2021)).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **(a)** & **(b)** \\ \hline
**Message Broker** & **Throughput (MB/s)** & **Throughput (MB/s)** \\ \hline RabbitMQ & 38.9 & 44.2 \\ ActiveMQ (queue) & 37.5 & 40.5 \\ ActiveMQ (topic) & 43.5 & 43.5 \\ Kafka & 42.8 & 41.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison between the three considered message brokers: Message size 1 KB (a) and 1 MB (b).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Framework** & **Throughput (Images/s)** & **Throughput (KB/s)** \\ \hline Spark & 8.1 & 170.1 \\ Storm & 17.2 & 361.2 \\ Flink & _37.7_ & 791.7 \\ Samza & 11.4 & 239.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison between the four considered frameworks for the face recognition use case.  
In this case, key frames were extracted from a dataset of about 100 YouTube videos dealing with terrorism and proselytism. A pre-filtering is performed in the Receiver to eliminate superfluous frames, maintaining only those in which true information is present. This was obtained with a frame-to-frame analysis, eliminating frames that are too similar to each other. The summarization process exploits the functionalities of the SHIATSU video retrieval tools [16]. When the summary has been obtained, retained images are fed to Analyzers which analyze them by:  
1. Filtering each image to segment text and logos/symbols from the background;
2. Performing OCR by using the Tesseract library, ([[https://github.com/tesseract-ocr](https://github.com/tesseract-ocr)]([https://github.com/tesseract-ocr](https://github.com/tesseract-ocr)), accessed on 3 December 2021) and/or extracting logos by using the OpenIMA library ([[http://openimaj.org/](http://openimaj.org/)]([http://openimaj.org/](http://openimaj.org/)), accessed on 3 December 2021);
3. Once the text detection phase is over, extracted text and logos are compared with those included in the knowledge base, so as to recognize the ones that have to be considered critical.  
Table 3 reports experiments performed using this specific use case (average image size is about 61 kB).