esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Geophysical Data
#### 4.1.2 GPR Survey  
In June 2015, [PERSON] et al. (2017) [33] completed eight GPR sections (labelled from G1 to G8, Figure 2). The GPR survey covered all the geological formations and coarsely followed the trace of the MASW sections to (1) disambiguate the boundary of geological structures having similar VS, such as the pairs t1-g1 and t2-g2; (2) deduce thickness of the shallowest anthropogenic filling; and (3) delineate transient hydrogeological features such as water table, capillary fringe, and seawater-freshwater interface. Basic data for all GPR sections were 50-Hz T-rate, 150-ns range, 120 scans per second, 40 scans per meter, 512 samples per scan, length from 88 m (section G3) to 652 m (section G6), and 4.5-m prospecting depth. Radargrams were topographically corrected and hydrogeological data were superimposed to refine shallow hydraulic features and geometry of the geological formations.  
Three GPR sections (G2, G3, and G4) of special interest to deduce the hydrogeological functioning of the NSUS are displayed in Figure 5. In Adra, steep topography determines sub-horizontal reflections for Pleistocene sedimentation in the NSUS and horizontal ones for Holocene sedimentation in the SFUS. The southern sector of section G2 (Figure 5b) and the central sector of section G4 (Figure 5c) show how t2 produces stronger reflections than g2 due to cementation, thus permitting its identification. Outside the displayed sectors, the pair t1-g1 shows the same behavior. Bedrock and WB produce similar strong reflections,thus limiting its boundary definition in some cases. Some shallow hydrogeological features are identified. An easily detected reflector inside the clay-rich g1 and g2 in G2 (Figure 5b) and G4 (Figure 5c) is capillary fringe, which narrowly follows piezometry measured in wells W1, W3, and W10 (Figure 2). This means that t1, t2, and the uppermost part of the WB together form an unconfined aquifer whose upward groundwater flow determines the capillary fringe position inside the clay-rich g1 and g2, which form an aquitard.  
In the SFUS, section G3 (Figure 5a) and the southern sector of section G4 (Figure 5c) show how the Holocene ARD Formation produces strong reflectors. In these coarse-grained sediments, the first strong reflection associated to the saturated media detection is stronger than the observed one in g1 and g2 and very closer to the water table, as deduced from piezometry measured in wells W2 and W7 (Figure 2), thus corroborating the unconfined behavior of this hydrogeological formation. In section G3 (Figure 5a), the VE signal loss below the sea level is attributed to the seawater-freshwater interface.
## 4 Discussion
### Lipid Oxidation Analyses  
The TBARS method is commonly used as a marker of lipid peroxidation to control lipidic changes during the processing of fermented sausages. At the beginning of the study up to Day 20, there were no differences between the sausages on TBARS values. Moreover, TBARS values did not alter in both control and sausages with black cumin throughout the study. Thus, the incorporation of black cumin did not affect lipid peroxidation. The benefits of chia seeds, which are an excellent source of antioxidants, are well-known. However, the incorporation of chia seeds into dry fermented sausages led to a significant increase in malondialdehydedehyde concentration after 20 days, pointing to these sausages as the most oxidized. Even in the sausages with higher chia content, the oxidation was greater. This could be explained due to the higher concentration of polyunsaturated fatty acids in chia. Additionally, malondialdehydedehyde is mainly formed from linolenic acid oxidation, a compound present in chia seeds. Furthermore, aldehydes from sugars and phenolic compounds may interfere on TBARS [41]. Nonetheless, these findings are in contradiction  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Tyramine** & **Putrescine** & **Cadaverine** & **Spermidine** & **Agmatine** & **Spermine** \\ \hline SK & 0.094 \(\pm\) 0.066 a & 0.048 \(\pm\) 0.023 a & 0.099 \(\pm\) 0.051 a & 0.010 \(\pm\) 0.003 a & 0.105 \(\pm\) 0.012 & 0.089 \(\pm\) 0.036 c \\ \hline SCH1 & 0.194 \(\pm\) 0.080 ab & 0.114 \(\pm\) 0.058 c & 0.103 \(\pm\) 0.107 a & 0.010 \(\pm\) 0.005 a & 0.055 \(\pm\) 0.028 abc \\ \hline SCH2 & 0.223 \(\pm\) 0.053 b & 0.147 \(\pm\) 0.009 c & 0.144 \(\pm\) 0.043 a & 0.016 \(\pm\) 0.001 b & 0.070 \(\pm\) 0.003 b \\ \hline SBC1 & 0.147 \(\pm\) 0.028 ab & 0.104 \(\pm\) 0.005 b & 0.120 \(\pm\) 0.046 a & 1.010 \(\pm\) 0.001 a & 0.060 \(\pm\) 0.003 ab \\ \hline SBC2 & 0.166 \(\pm\) 0.056 ab & 0.108 \(\pm\) 0.004 b & 0.148 \(\pm\) 0.066 a & 0.011 \(\pm\) 0.001 a & 0.059 \(\pm\) 0.003 a \\ \hline \end{tabular}
\begin{tabular}{c} nd—not detected; SK—control sample; SCH1—sample with 1% addition of chia seed; SCH2—sample with 2% addition of chia seed; SBC1—sample with 1% addition of black cumin seed; SBC2—sample with 2% addition of black cumin seed; means with the same letter a—c do not differ significantly (\(p>0.05\)) within the variable (column). \\ \end{tabular}
\end{table}
Table 3: The biogenic amines content at 30 day of manufacturing process of dry fermented sausages [mg g\({}^{-1}\)].  
with other authors, who found that chia seeds retard oxidation. Specifically, sausages with 3% chia suffered less oxidation than the control (roughly 1.6 less) [24]. The same trend was observed in dry-cured sausages (around 1.2 times lower, Day 3) [38] as well as in camel burgers (abound 1.3 times lower, Day 12) [28].
## 4 Results and Discussion
### State-of-the-Art Comparison
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **Params** & **Synth. Data** & **AP\({}^{50}\)** & **Metric** & **mAP** \\ \hline \hline \multicolumn{5}{c}{**Full Dataset**} \\ \hline Faster-RCNN * [4] & 41.5 M & & 76.7 & 49.3 \\ Cascade-RCNN * [4] & 69.2 M & & 79.2 & 51.6 \\ Faster-RCNN * [8] & 41.5 M & & 80.2 & 56.0 \\ AttnPAFPN * [8] & 32.8 M & & 96.3 & 68.2 \\ YOLOv8 [48] & 3.2 M & & 93.7 & 64.9 \\ \hline \multicolumn{5}{c}{**500 real images**} \\ \hline Faster-RCNN * [8] & 41.5 M & & 78.8 & 53.4 \\ AttnPAFPN * [8] & 32.8 M & & 92.3 & 62.9 \\ YOLOv8 [48] & 3.2 M & & 84.7 & 53.0 \\ YOLOv8 + Ours & 3.2 M & & 89.9 & 57.5 \\ \hline \multicolumn{5}{c}{**100 real images**} \\ \hline Faster-RCNN * [3] & 41.5 M & ✓ & - & 40.1 \\ Cascade-RCNN * [3] & 69.2 M & ✓ & - & 41.6 \\ YOLOv8 [48] & 3.2 M & & 73.4 & 44.6 \\ YOLOv8 + Ours & 3.2 M & ✓ & 84.9 & 52.6 \\ \hline \multicolumn{5}{c}{**50 real images**} \\ \hline Faster-RCNN * [8] & 41.5 M & & 70.6 & 41.2 \\ AttnPAFPN * [8] & 32.8 M & & 72.5 & 42.8 \\ YOLOv8 [48] & 3.2 M & & 60.8 & 36.0 \\ YOLOv8 + Ours & 3.2 M & ✓ & 79.2 & 48.1 \\ \hline \hline \end{tabular}  
* Results are taken from the original publication.  
\end{table} 
Table 4: Performance comparison of various object detection models [3, 4, 8, 48] on different sizes of the AGAR dataset [4], showcasing the impact of synthetic data. The results are categorized by the size of the real image datasets, illustrating how model performance varies with the amount of available real data and the incorporation of synthetic data. In summary, this table illustrates the significant impact of synthetic data on enhancing model performance, particularly in scenarios with limited real data. YOLOv8 with our augmentation strategy consistently demonstrated strong performance across different dataset sizes, showcasing its robustness and efficiency. Meanwhile, the AttnPAFPN model, although the top performer on the full dataset, encountered challenges when real data were scarce, underscoring the importance of synthetic data in achieving reliable object detection outcomes.
## 3 Results  
### Controlled Experiment: Estimating Atlas PT Positioning Error  
Plot-level Root Mean Squared Error (RMSE) calculated for all four Atlas PT units within each stand (Equation (2)) ranged from 2.64 m to 4.09 m in clearcuts, with the best accuracy achieved in Stand 531. By contrast, RMSE ranged from 8.56 m to 14.34 m in mature stands, with the lowest accuracy occurring in Stand 524 (see Table 2 for RMSE by stand and unit). The RMSE of all mature stands combined was 11.08 m, while the overall RMSE of clearcuts was 3.37 m. With the exception of one unit (B in Stand 58), RMSE values in clearcut conditions are under the 5 m accuracy expected for Atlas PTs 90% of the time. However, none of the devices in mature stands achieved this level of accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Cover**} & \multirow{2}{*}{**Stand**} & \multicolumn{5}{c}{**Unit RMSE (m)**} \\ \cline{3-6}  & & **A** & **B** & **C** & **D** & **All Units** \\ \hline \multirow{4}{*}{MATURE} & 260 & 13.07 & 8.29 & 12.52 & 4.79 & 10.34 \\  & 290 & 11.68 & 6.91 & 7.48 & 7.28 & 8.56 \\  & 139 & 11.23 & 7.68 & 12.78 & 7.90 & 10.14 \\  & 524 & 18.17 & 11.16 & 11.59 & 15.30 & 14.34 \\ \hline \multirow{4}{*}{CLERCT} & 58 & 4.33 & 5.36 & 2.98 & 1.46 & 3.81 \\  & 531 & 2.42 & 1.67 & 3.16 & 3.01 & 2.64 \\ \cline{1-1}  & 345 & 1.38 & 1.49 & 3.09 & 3.66 & 2.67 \\ \cline{1-1}  & 262 & 3.96 & 3.69 & 4.52 & 4.07 & 4.09 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Root mean square error (RMSE) of Atlas PT GNSS horizontal positioning error, in meters, at each unit position (A, B, C, and D) and across all units (last column).  
Actual error calculated for each second of sampling (Equation (1)) varied significantly by stand (F-statistic = 4735, \(p\)-value \(<\) 2 \(\times\) 10\({}^{-16}\)), cover (F-statistic = 25,390, \(p\)-value \(<\) 2 \(\times\) 10\({}^{-16}\)), and individual transponder unit (F-statistic = 337.3, \(p\)-value \(<\) 2 \(\times\) 10\({}^{-16}\)). The Bonferroni multiple comparison test comparing all stands indicated that only two stands did not differ significantly from one another (clearcut units 345 and 531, with \(p\) = 1.00). Multiple comparison indicated that all transponder units differed significantly from one other (\(p\)-values less than 2 \(\times\) 10\({}^{-16}\)) except for units B and D (\(p\) = 0.089). Figure 4 illustrates actual error variation across stands of different cover types.  
Figure 5 illustrates the distribution of Atlas PT GNSS positions collected over each 30-min sampling period compared to the single coordinates recorded by the EOS Arrow 100 at each Atlas PT location. The largest actual error observed for an Atlas PT in a mature stand was 81.5 m, and the largest error observed in a clearcut stand was 12.6 m.  
Figure 4: Boxplot comparing Atlas PT actual GNSS error in the controlled experiment across eight stands: four mature (green) and four clearcut (tan).  
Figure 5: Visual comparison of Atlas PT coordinates (purple dots) and Arrow 100 coordinates (yellow stars) in clearcut versus mature stands of the controlled experiment. Scale is 1:1500.
# Two-Party Quantum Private Comparison Protocol Based on Rotational Encryption
## 5 Security Analysis
### Participant Attacks  
Two cases of participant attacks are considered as follows:  
**Case 1. Attacks from TP**  
In this context, the third party (TP) acts as a semi-honest intermediary. While TP is expected to follow the protocol and not allowed to collude with the participants, it may still attempt to glean additional insights from the data it processes. This dual nature raises potential security concerns, as TP could exploit its position to infer information about the participants' private data. Although the third party (TP) knows the encoded qubits containing the private information, TPcannot access [PERSON]'s (or [PERSON]'s) private data without knowing \(K_{AB}\) shared between them. From this perspective, the proposed protocol effectively safeguards the participants' privacy despite TP's access to the transmitted states.  
**Case 2. Attacks from [PERSON] ([PERSON])**  
In the proposed QPC protocol, the quantum sequence is only transmitted between [PERSON] ([PERSON]) and TP, with security ensured by utilizing decoy photon technology and rotational encryption. [PERSON]'s role is analogous to that of [PERSON]. Without the loss of generality, suppose [PERSON] intercepts \(SE_{B}\) and prepares a corresponding fake sequence to send to [PERSON]. When [PERSON] reveals the positions and bases of the decoy photons, [PERSON] can remove the decoy particles from \(SE_{B}\) using the information provided by [PERSON], even though this eavesdropping has been detected. However, since \(SE_{B}\) is encrypted with [PERSON]'s secret key \(K_{B}\), which is only known for him, [PERSON] has no chance of accessing [PERSON]'s private qubits without knowing \(K_{B}\) if she cannot bypass the eavesdropping detection. Similarly, in analyzing [PERSON]'s potential attack, he also has no chance of obtaining any information about [PERSON]'s private information. Therefore, attacks from [PERSON] ([PERSON]) are ineffective.
# COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques
## 3 Proposed Methodologies
### COVID-19 Infection Detection Using Deep Learning
#### 3.3.2 SoftMax Activation Function  
The SoftMax v 5.4 function is a widely used activation function in the output layer of deep learning models, including the Convolutional Neural Network (CNN) architecture [23]. It plays a crucial role in converting the final layer's raw scores into probability scores, allowing the model to make predictions based on class probabilities.  
The SoftMax activation function operates by calculating the probabilities of all the neurons in the output layer and producing a vector of probabilities. The sum of these probabilities across all classes in the vector is equal to 1, ensuring that the final output represents a valid probability distribution over the possible classes. The mathematical representation of the SoftMax function for the \(i\)-th element in the output vector is shown in Equation (2).  
\[S(x)_{i}=\frac{exp(x_{i})}{\sum_{x=1}^{n}exp(x_{j})} \tag{2}\]  
where \(x_{i}\) is the \(i\)-th input vector element, \(e^{x_{i}}\) is the standard exponential function, and \(\sum_{j=1}^{n}e^{x_{j}}\) is the normalization term.  
In the context of our COVID-19 detection model, the SoftMax activation function is applied to the output layer to obtain the probability scores for each class, i.e., COVID-19 positive and COVID-19 negative. The model then classifies the chest X-ray image as COVID-19 positive if the corresponding SoftMax probability score is higher than a certain threshold, and COVID-19 negative otherwise.  
By using SoftMax, the model can make confident and calibrated predictions, providing clear probabilities for each class and enabling more robust decision making. This activation function is especially crucial in the final layer of the CNN architecture, as it allows the model to produce meaningful and interpretable output probabilities, aiding medical professionals in making accurate diagnoses.
# Bayesian Optimal Experimental Design for Race Tracking in Resin Transfer Moulding
## 4 Numerical Examples
### Model 1  
The first investigated example is illustrated in Figure 2. Resin is injected into the mould from a 5 cm injection gate in the lower left corner of the domain at a constant pressure of \(p_{\text{in}}=400\) kPa. The only exit vent is at the top right corner of the domain, which is again fixed at 5 cm. There are three areas of potential race tracking, with different permeabilities at the top and bottom of the domain, as well as around a central block. In reality, there will be potential for race tracking along the vertical walls, but this is ignored here for simplicity.  
As there are three race-tracking regions, the permeability vector is  
\[\mathbf{k}=[c_{1},c_{2},c_{3},\alpha_{1},\alpha_{2},\alpha_{3}]^{T}, \tag{32}\]  
where:  
\[\mathbf{K}_{i}=\exp(\alpha_{i})\mathbf{K}_{\text{bulk}}, \tag{33}\]  
and \(\mathbf{K}_{i}\) represents the permeability tensor for region \(i=1,2,3\), as shown in Figure 2.  
Here, \(\mathbf{k}_{\text{true}}\) was arbitrarily defined as follows:  
\[\mathbf{k}_{\text{true}}=[0,0,0,\ln(10.0),\ln(1.0),\ln(50.0)]^{T}. \tag{34}\]  
This represents a scenario where there is no race tracking along the bottom, 10 times higher permeability on the top, and 50 times higher permeability around the centre of the domain. All permeability values here have units of \(10^{-11}\) m\({}^{2}\).  
Error was added to the output of this model with the distribution \(\mathcal{N}(\mathbf{0},\mathbf{\Gamma}_{e})\). For this experiment, we set  
\[\mathbf{\Gamma}_{e}=\sigma_{e}^{2}\mathbf{I},\quad\sigma_{e}=0.01. \tag{35}\]  
As with the error distribution, the prior for this experiment is arbitrary and can be adjusted to the beliefs of the implementer but should, in general, be created broadly so as not to introduce bias into the prediction. This experiment used the following prior, which roughly approximated our prior beliefs in the race-tracking and permeability values.  
\[\mu_{k} =[0,0,-0.144,2,2,2]^{T}, \tag{36}\] \[\mathbf{\Gamma}_{k} =\text{diag}([0.25,1,0.25,2,2,2]). \tag{37}\]  
Figure 2: Domain for Model 1, representing a rectangular part with a square block at the centre. Race-tracking regions are indicated in blue and are numbered.  
The prior mean represents a permeability field that it is slightly more permeable in the x direction than in the y direction. Specifically, it represents the following tensors:  
\[\mathbf{K}_{\text{bulk}}=\begin{bmatrix}1&0\\ 0&\frac{3}{4}\end{bmatrix},\quad K_{1},K_{2},K_{3}=e^{2}\mathbf{K}_{\text{bulk}} \tag{38}\]
# Analytical-Numerical Model for Temperature Prediction of a Serpentine Belt Drive System
## 4 Results and Discussion  
To demonstrate the accuracy and reliability of this model, the experimentally obtained temperature and calculated temperature under various pulley radii, material types, and speeds, as described in Table 1, are compared. First, the reliability of \(\lambda_{pn}\) is validated by comparing the temperature distributions of the pulleys obtained using the numerical method with the experimental measurements. Second, the thermal model is validated by comparing the numerically and experimentally obtained temperatures of the two designed belt drives at different locations within the system. Finally, pulley temperature distribution results are validated by obtaining the experimental measurements on different locations of the pulleys.
# Towards Online Estimation of Human Joint Muscular Torque with a Lower Limb Exoskeleton Robot
## 2 Methods
### Sensing System Design
#### 2.3.3 Hip and Knee Joint Kinematics Information Measurement  
The angles of the hip and knee joints are derived from the respective measurements of the cylinder displacement of the exoskeleton. The displacement signals (\(S_{H}\) and \(S_{K}\)) from LDSs are converted into hip and knee joint angles (\(\theta_{H}\) and \(\theta_{K}\)), according to the geometry of the joints, as shown in Figure 5. The angular velocities (\(\dot{\theta}_{H}\) and \(\dot{\theta}_{K}\)) are obtained via the first-order differences of \(\theta_{H}\) and \(\theta_{K}\). Similarly, the angular accelerations (\(\ddot{\theta}_{H}\) and \(\ddot{\theta}_{K}\)) are obtained from the first-order differential of \(\dot{\theta}_{H}\) and \(\dot{\theta}_{K}\).  
Figure 4: Trunk posture sensing with inertial measurement unit (IMU) and the limb posture definition.  
Figure 5: Exoskeleton joint geometry and angle measurements for (**a**) the hip joint and (**b**) the knee joint.  
The exoskeleton is design with its hip and ankle axes close to those of the user. The back frame and shoes are firmly connected to the user. So, we can assume that the coordinates of the exoskeleton ankle joints in the BCS are nearly the same as those of the human body. This can be expressed as follows:  
\[\left\{\begin{array}{l}x_{F}=l_{Te}sin(\theta_{He})-l_{Se}sin(\theta_{Ke}- \theta_{He})=l_{T}sin(\theta_{H})-l_{S}sin(\theta_{K}-\theta_{H})\\ \\ z_{F}=l_{Te}cos(\theta_{He})+l_{Se}cos(\theta_{Ke}-\theta_{He})=l_{T}cos( \theta_{H})+l_{S}cos(\theta_{K}-\theta_{H})\end{array}\right. \tag{10}\]  
where \(\theta_{He}\) and \(\theta_{Ke}\) are the hip and knee joint angles of the exoskeleton; \(l_{Te}\) and \(l_{Se}\) are the lengths of the thigh and shank of exoskeleton, which are \(450 mm\) and \(440 mm\), respectively; and \(l_{T}\) and \(l_{S}\) are the lengths of components \(T\) and \(S\) of the simplified human model, which are \(424 mm\) and \(422 mm\), respectively. Equation (10) allows us to derive the joint angles of the human body (\(\theta_{H}\) and \(\theta_{K}\)), as well as \(\theta_{H}\), \(\theta_{K}\), \(\dot{\theta}_{H}\), and \(\ddot{\theta}_{K}\).
controller via a communication network. The controller determines the control signal based on the sensor output, which is transmitted back to the plant using same communication channel. The control signal before being fed to the actuator section of the plant, is converted from digital to analog signal using the digital-analog (D/A) convertor. In this manner, the plant dynamics can be controlled from a remote location.
# An Improved Hilbert-Huang Transform for Vibration-Based Damage Detection of Utility Timber Poles
## 5 Results and Discussion
### Conventional HHT  
In this section, acceleration data from A90C1 of Serviceable Pole 1 is analysed using original HHT (using Equation (9)). Figure 13 demonstrates the Hilbert spectrum of the inspected circumferential component generated by original HHT. Colour bars in Figure 13 represent the instantaneous amplitude of the results. Hereinafter, similar colour bar, labels and notations will be used for all Hilbert Spectrums utilised in this study. From this figure, original HHT has produced 13 IMFs after EMD operation. An important observation is that the first five IMFs in the high frequency region contain too wide range of frequency. Consequently, these IMFs can be barely treated as mono-component or nearly mono component signal. It can be noted that most of frequency components present in the first five IMFs are above 2000 Hz which might be related to noise, since the induced frequency was up to 1500 Hz, as shown in Figure 9b. Hence, the original HHT fails to reveal the real frequency pattern of the analysed signal.
# Enhancing Far-Field Speech Recognition with Mixer: A Novel Data Augmentation Approach
###### Abstract
(d) Experimental results demonstrate that Mixer consistently outperforms existing methods such as SpecAugment across all versions of Whisper, achieving a relative word error rate (WER) reduction of 3.6% compared to the baseline. Moreover, Mixer-C further enhances the performance of Mixer, yielding an additional WER improvement of 2.2%.