esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment Results  
### Experiment Setup  
**Dataset.** The experiment uses a dataset collected from the casting process to evaluate the proposed framework and compare experiment results with other methods. Part of the data in this dataset is shown in Figure 7. The dataset contains information about liquid levels in mold (measured in cm) captured by the sensor.  
**Architecture.** In the experiment, the forecasting network is a GRU-AE model. The encoder part is a 20-dimensional latent space GRU with two hidden layers, followed by a linear function as a Fully Connected (FC) layer. The decoder part is a one-to-many GRU, followed by an FC layer that combines static and temporal features. No dropout is applied. The error extraction network uses a Seq2 Seq model with a 20-dimensional latent space and two hidden layers. The sliding window size \(s_{w}\) is set to 10 and \(s_{w_{p}}\) is set to 4.  
**Baselines.** The experiment compares the proposed framework with several popular and state-of-the-art methods as the baseline.  
* GRU-AE [25]: GRU trains faster and performs better than LSTM on a smaller dataset. [26]. The decoder's and encoder's GRU layers both have two hidden layers. Encoder GRU outputs 10-D-encoded data and then passes it through an FC layer. The decoding process takes the encoded 10-D data and passes it through the decoder GRU and an FC layer. Drop-out is applied for both encoder and decoder GRU;
* RAE [20]: Use LSTM-AE as an anomaly detector. The hyperparameter \(\lambda\) is set to 0.1, which has the best experiment result in their practice. A sliding window of size 10 is used to evaluate the anomaly score;
* TadGAN [23]: A sliding window of size 10 is used to calculate the area difference for reconstruction error;
* LSTM-AE-Advanced (LSTM-AE-ADV) [22]: use a sliding window size of 4 to perform the enrichment time series process.  
All methods above use a fixed threshold group {0.3, 0.2, 0.1, 0.09, 0.07, 0.05, 0.03, and 0.01}. For every baseline method, a score is calculated for each threshold. The highest score is recorded in the process. A sliding window of size 10 is used to calculate F1 scores. Except for RAE, all other methods are implemented with sliding windows of size 10. The evaluation is also using a sliding window of the same size.  
Figure 7: An example of actual data of the liquid level in the mold during the manual control phase.  
All baseline methods are implemented in Python 3.7 with the Pytorch 1.7.1 library [27] and CUDA 11.0. The Adam optimizer [28] is used to update the frameworks.
## 2 Materials and Methods
### Cross-Validation Test  
While using a cross-validation test, the complete dataset should be broken down into k subsets. In our study, one of the k-folds was applied in training and hyper-parameter selection; however, the remaining fold was utilized for testing the model. The reported results are the average of all five folds. The benchmark dataset shown in Equation (1) was used to train the first-stage classifier; similarly, the predictor was trained on the benchmark dataset, which is stated in (2) for the second-stage classifier. Table 4 and Figure 4 show the results obtained using the five-fold cross-validation on the benchmark dataset for both the first and second layers. Results from the cross validation show that the proposed model acquired a sensitivity of 86.99%, specificity of 88.54%, accuracy of 87.77%, MCC of 75.96%, and AUC of 94.01% for the first phase of the classification. For the second phase, the proposed predictor attained a sensitivity of 83.57%, specificity of 78.16%, accuracy of 76.7%, MCC of 62.05%, and AUC of 83.28%.
## 3 Case Study
### Modal Test
When measuring the frequency response functions, the measured result of the damping ratio is larger than the actual result due to the effect of accelerometer weighting functions. The first four order damping ratios under different pre-tightening forces were obtained, and are all shown in Table 5. Furthermore, the relationship between the experimental damping ratio and the pre-tightening torque was obtained as is shown in Figure 8. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Pre-Tightening Torque**} & \multicolumn{4}{c}{**Damping Ratio (\%)**} \\ \cline{2-5}  & **Mode 1** & **Mode 2** & **Mode 3** & **Mode 4** \\ \hline
2 & 0.44 & 0.07 & 0.99 & 0.72 \\
4 & 0.49 & 0.03 & 0.80 & 0.98 \\
6 & 0.20 & 0.11 & 1.46 & 0.53 \\
8 & 0.01 & 0.50 & 1.78 & 0.48 \\
10 & 0.48 & 0.24 & 1.23 & 0.41 \\
12 & 1.80 & 0.14 & 0.84 & 1.11 \\
14 & 0.09 & 0.78 & 1.32 & 0.54 \\
16 & 0.06 & 0.18 & 1.77 & 0.44 \\
18 & 0.18 & 0.12 & 1.77 & 0.42 \\
20 & 0.17 & 0.04 & 1.48 & 0.93 \\
22 & 0.93 & 0.11 & 1.05 & 0.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental damping ratio under different pre-tightening torque. Figure 7: The relationship between the experimental frequency and the pre-tightening torque. As shown in Figure 8, the relationship between the damping ratio of the connection structure and the pre-tightening torque is generally non-linear, and there is no rule to be found. Meanwhile, the relationship between the first four order experimental modal shapes and the pre-tightening torque is shown in Figure 9.  
As can be seen from Figure 9, the modal shapes are generally smooth. On the one hand, the connection stiffness is enough to provide good linearity to the structure; on the other hand, it is impossible to measure the detailed characteristics of the modal shapes in the connecting part due to the limited test precision. At the same time, it can be found that the mechanical properties of the bolt connection tend to be stable with the increase of the pre-tightening force. Figure 8: Experimental damping ratio depends on the pre-tightening torque. Figure 9: The first four order experimental modal shapes under different pre-tightening torque.
# Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree
## 4 Methodology
### Path Smoothing  
To solve the problems of redundant turns and frequent oscillations in path planning, a prominent path-smoothing strategy has been incorporated. This strategy uses an iterative optimization method to gradually optimize the original path through continuous iterations. This phase weights both local geometric contains and overarching path optimization indicators.  
In the path-smoothing procedure, two non-adjacent nodes from the original path nodes are chosen. An attempt is then made to directly connect these two nodes to construct a potential new path to replace the path segment between these two nodes in the original path. This procedure must ensure that the fresh path segment does not intersect with any obstacles, preserving the path's validity. Only when the new path segment meets this criterion is it approved to replace the original path section.  
Path smoothing is a key part of this research, aiming to improve the initial path generated by the search algorithm, making it more continuous, smooth, and efficient while meeting the requirements of feasibility and safety. This method helps to optimize the algorithm by directly connecting non-adjacent nodes, eliminating redundant bends in the original path, and finally generating a relatively smooth and high-quality path. The process continues until it reaches a predetermined number of iterations or the path converges. This method aims to gradually achieve global path optimization by adjusting the non-optimized parts of the path, thereby improving the navigation efficiency of robots in complex environments.
# Thermochromic Yams' Application in Fever Detection for Young Children
## 3 Results and Discussion
### RQ2: Consumer Perceptions, Concerns, and Suggestions
#### 3.2.3 Suggestions  
The word co-occurrence for question 3-b, \"What are your suggestions to the developer?\", as illustrated in Figure 7b, summarized the suggestions provided by parents into two categories: functional values (such as \"use,\" \"soft,\" \"material,\" \"comfortable,\" \"accurate,\" \"size,\" \"fabric,\" \"safe,\" and \"test\") and aesthetic values (such as \"attractive,\" \"design,\" \"cuter,\" \"style,\" \"fashionable,\" and \"appeal\"). This aligns with the F and A components of the Functional (F), Expressive (E), and Aesthetic (A) Consumer Need Model that has been widely utilized in apparel design [27].  
Regarding functional values, parents expressed the need for the product to prioritize safety (e.g., \"Maybe something that can't suffocate a baby\"), undergo rigorous testing before being sold (e.g., \"Test test test and prove it works and prove it's safe\"), and demonstrate consistent performance across various conditions (e.g., \"Make sure it detects fever and not just a hot forehead. I live in South GA and it's hot here all the time\"). These functional features were consistent with the identified concerns in question 3a. Besides, it is also essential to use a lightweight, soft, and comfortable material for the hat. Additionally, it is important to create a product with a long life cycle that can be used throughout the year. Furthermore, proper fit options such as adjustable straps and various sizes for children of different ages should be provided.  
Although aesthetics is not explicitly recognized as a primary consideration, parents recommend it to make the product more attractive to a wider range of buyers. Parents desire greater diversity in the product's kind, colour, and style. For instance, they suggest making the product more appealing by incorporating headbands in various colours and designs (i.e., \"I would be more likely to buy if it was a headband and if it was decorated with kid-friendly patterns\"). This aligns with the findings obtained from preference question 2a. Besides, several parents proposed incorporating the concept into a sticker instead of a hat due to the sticker's advantages of affordability, user-friendliness, and entertainment value for children. Although expressive values did not become a prominent subject, a parent proposed inviting more mothers into the product creation process, expressing a desire for their opinions to be considered by the designers.
## 2 Materials and Methods
### Effectiveness Outcomes  
The effectiveness of the Armeeo Spring device in improving the performance of ADL was analyzed by means of these outcomes.  
* Upper Extremity Motor Score (UEMS) [30]. The UEMS consists of measurement of the strength of 5 key muscle groups in each UE between the range of 0 (absence of function) and 5 (normal function). In the present study, only the UEMS over the treated UE was taken into account, with a score range of between 0 and 25 points;
* Spinal Cord Independence Measure (SCIM) [32]. The SCIM is a disability scale developed specifically for patients with spinal cord lesions in order to make the functional assessments of these patients more sensitive to changes. In this research, the self-care subscale and the total score were taken into account. A higher score corresponds to a higher degree of independence;
* Capabilities of Upper Extremity (CUE) Questionnaire [33]. The CUE Questionnaire is a clinical scale designed to measure UE functional limitations in subjects with cervical SCI, representing the self-perceived difficulty in actions such as reaching or grasping. Therefore, the minimum score is 32 (worse UE function) and the maximum is 224 (complete UE function).
* Jebsen-Taylor Hand Function (JTHF) [34]. The JTHF test was designed to provide a short, objective test of hand functions commonly used in ADL, specifically designed for a concrete pathology measuring the time in seconds of reaching each task. A higher spent time is a worse result;
* Kinematic indices related for the ADL of drinking from a glass. Several kinematic indices were computed for characterizing the UL movement pattern during the performance of the drinking task in a real environment. Following the experimental protocol described in our previous study, the subjects were instrumented with the active markers of the photogrammetry system Codamotion (Charnwood Dynamics, Ltd., Rothley, UK) for measuring their UE kinematics during the performance of the drinking task in a real environment [35]. For each participant, this assessment was unilateral over the treated UL chosen in the context of this clinical study, analyzing the movement of the trunk, arm, forearm, and hand body segments. Then, the kinematic indices of accuracy, efficiency, agility, coordination, and smoothness were computed as measures of UE ability and dexterity [36; 37]. Thus, accuracy and efficiency indices were computed from the hand trajectory during the movement; agility and smoothness indices from the analysis of the hand velocity profile; and coordination index from the joint kinematic data of the shoulder and elbow. The results related to the kinematic indices could be graphically expressed for each participant (Figure 3).  
Moreover, as a measure of effectiveness, subjects' evolution in terms of the gravity-compensation level in the Armeo Spring arm and forearm levers was registered.
# Deep Learning-Based Consistent Object Detection in Distance on Highly Perspective Region
## Inverse Perspective Transform (IPT)  
### Concept and Application of IPT  
In this study, the introduced technique referred to as IPT involves the geometric transformation [18] of an image by modifying the arrangement structure of pixels based on a principle. The IPT technique represents one of the image transformation methods primarily employed to correct and mitigate perspective issues within a given image [19; 20; 21; 22]. As mentioned in the introduction, IPT has found applications in various fields.  
In the field of autonomous driving, a number of studies have been conducted utilizing IPT to identify lane information through lane detection [23; 24] and road sign detection [25] from images acquired after applying the IPT technique. Furthermore, in various domains, IPT has been employed for generating top-view images from rearview cameras during parking maneuvers [26], road orthophoto mapping [27], and real-time detection of cut-in violation vehicles based on CCTV [28]. It is noted that in this general autonomous driving area, imaging points are moving with the vehicle body, the and target distance of concern is relatively near to the points.  
Apart from the concern above, this study is focused on tunnel environments where any imaging points (CCTV) are fixed at law height and law brightness, and any incident for detection can take place at any distance from the points.
# Amplitude Modeling of Specular Multipath Components for Robust Indoor Localization
## 4 Analysis of the GPR-Based Channel Model
### Energy Capture  
We analyze the EC of the SMCs, which is defined as the energy ratio between one individual or a few SMCs over the total energy of the CIR [52]. Figure 4 plots the EC of individual SMCs, while Figure 5 shows the combined ECs of different sets of SMCs. The LOS component captures between 20% to 80% of the total received signal power; the closer the agent to the anchor, the higher the EC. Individual first-order SMCs capture up to 20% of the total received power, while the accumulated EC of all first-order SMCs is between 5% to 35%. For second-order SMCs, the ECs are between 0.1% to 5%, except for VA 8 and 10 where the maximum EC is 25% and 20% respectively. Their accumulated EC is mostly between 2% to 20%.  
Figure 5 also plots the number of visible VAs at each trajectory point. For first-order and especially second-order SMCs, the accumulated EC correlates with the number of visible VAs, as shown in Figure 5a,b. It could be because for second-order VAs, the energy are relatively the same, as shown in Figure 4b. For first-order VAs, the energies for different VAs are quite different.  
Figure 5c plots the EC of the LOS signal versus the total number of visible VAs. It is shown that the closer the PA to the anchor, the higher the EC and the fewer the number of visible VAs, which are both understandable.  
Figure 5d illustrates the EC of all SMCs versus the number of visible VAs. It is seen that, for our data set, between 50% and 90% of the received power can be modeled by means of SMCs. Correspondingly, between 50% and 90% of the received power can be predicted accurately by means of a GPR amplitude model. A beam selection and beam searching algorithm can select strong first and second-order SMCs as candidate beams, in case the LOS gets blocked (e.g., by the user itself).  
Figure 4: EC of individual SMC: (**a**) VA 1 to VA 6, (**b**) VA 7 to VA 19.
## 5 Experiment and Results
### Comparison of the Original Data and Data after Extending Function
Here, \(n\) represents the time length of the signal segment, \(c\) denotes the number of channels, and scale signifies the scaling factor for down-sampling. \(X\) and \(X^{\prime}\) represent different electromyographic signal segments before and after SPS processing. In the table, \(\dagger\) indicates whether a feature participates. After SPS processing, the time length and number of channels of the signal segment are reduced to \(\frac{n}{scale}\) and \(c\times scale\), respectively. The shape of \(X\), untreated by SPS, after feature extraction through TD, mDWT, and ICC is \((c\times 5)\), \((c\times 3)\), and \((c\times c)\), respectively. On the other hand, the shapes of \(X\), post SPS processing, after feature extraction through TD, mDWT, and ICC, are \(((scale\times c)\times 5)\), \(((scale\times c)\times 3)\), and \(((scale\times c)\times(scale\times c))\), respectively. Bold indicates the best result under the current method.
## 4 Discussion
### Uncertainties and Limitations
As a result, there will be more certainty in the evaluation of drought risks and their effects on WUE. To clearly explain this variation, more research on the effects of drought timing and duration on the monthly and seasonal scales is required. Therefore, additional investigation should examine how droughts, in addition to anthropogenic activities and fire events, affect terrestrial ecosystems. To comprehend the potential dynamics affecting the spatial-temporal changes in the GPP and AET in the study area, those studies can take into account soil moisture-based drought indices. Additionally, they can take into account the significance of identifying the immediate and delayed effects of droughts.
## 1 Introduction
### The State of the Art and Analyses
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**References**} & \multirow{2}{*}{\(\mathbf{f_{f}}\) (GHz)} & \multicolumn{2}{c}{**Size**} & \multirow{2}{*}{**Gain (dBi)**} & \multirow{2}{*}{**Polarization**} & \multicolumn{2}{c}{**Efficiency**} & \multirow{2}{*}{**Antenna Type**} & \multirow{2}{*}{**Application**} \\  & & & **(mm \(\times\) mm)** & & & & \\ \hline
[10] & 2 & 12 \(\times\) 12 & 3 & N/A & N/A & Patch antenna & CubeSat \\ \hline
[11] & 2.3, 2.4, and 2.45 & 6 \(\times\) 26.30 & 4.39 & N/A & N/A & Fractal antenna & CubeSat \\ \hline
[12] & 2.4 & 55 \(\times\) 55 & 3.49 & CP & N/A & Planar & Pico satellite \\ \hline
[13] & 2.5 & N/A & 5.03 & N/A & N/A & Dipole & CubeSats \\ \hline
[14] & 7.89–10.49 & N/A & 35 & N/A & N/A & Microstrip patch &
\begin{tabular}{c} Space communication \\ \end{tabular} \\ \hline
[15] & 10.47–11.4 & 20 \(\times\) 17.2 & \(<\)5 & N/A & \(<\)97 & Microstrip patch &
\begin{tabular}{c} X-band \\ applications \\ \end{tabular} \\ \hline
[16] & 6.0350, 6.7950, 7.46, 8.5050, 11.45, 13.35, 15.06, 17.24, and 19.05 & 22 \(\times\) 27 & 14.4 & Linear & N/A & Microstrip patch & C- and X-band applications \\ \hline
[17] & 2.4–2.63 & 95 \(\times\) 95 & 10.5 & N/A & N/A & Microstrip patch & S/X-bands \\ \hline
[18] & 2.5 and 8 & 140 \(\times\) 140 & 11.5 & N/A & N/A & Microstrip patch &
\begin{tabular}{c} S/X- \\ communications \\ \end{tabular} \\ \hline
[19] & 2.4 & 90 \(\times\) 51.5 & 5 & N/A & N/A & Planar Yagi–Uda &
\begin{tabular}{c} PicoSat and \\ CubeSat \\ \end{tabular} \\ \hline
[20] & 23.2–23.5 and 24.8–25.2 & 20 \(\times\) 12 & 8 & LP & 87 & LWA & CubeSat \\ \hline
[21] & 27.4–37.3 & 107 \(\times\) 17 & 17.9 & CP & 89 & LWA & CubeSat \\ \hline
[22] & 7.99–9.02 & 80 \(\times\) 80 & 15.73 & CP & N/A &
\begin{tabular}{c} Fabry–Perot \\ cavity \\ \end{tabular} & CubeSat \\ \hline
[23] & 2.45 & 90 \(\times\) 90 & 9.71 & CP & N/A &
\begin{tabular}{c} CPW–fed slot \\ antenna \\ \end{tabular} & CubeSat \\ \hline
[24] & 12.5 & N/A & N/A & N/A & N/A & \
\begin{tabular}{c} Metasurface \\ antenna \\ \end{tabular} &
\begin{tabular}{c} Frequency \\ energy \\ harvesting \\ \end{tabular} \\ \hline
[25] & 10 & 229 \(\times\) 229 & \(<\)10 & N/A & 11 &
\begin{tabular}{c} Metasurface \\ antenna \\ \end{tabular} & N/A \\ \hline
[26] & 26.5–27 & 26 \(\times\) 26 & N/A & N/A & N/A &
\begin{tabular}{c} Metasurface \\ antenna \\ \end{tabular} & N/A \\ \hline
[27] & 3.6 & 80 \(\times\) 80 & 8 & Orthogonal & \(>\)80 &
\begin{tabular}{c} Reconfigurable \\ antenna \\ \end{tabular} & Beam-scanning \\ \hline
[28] & sub-6 GHz & 400 \(\times\) 441 & N/A & N/A & N/A &
\begin{tabular}{c} Reflectarray \\ antenna \\ \end{tabular} & Beamsteering \\ \hline
[29] & 39 & 37 \(\times\) 30.96 & 16 & N/A & 53 & 3D-printed lens &
\begin{tabular}{c} Ka-band \\ CubeSat \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The state of the art of recent antennas designed for CubeSat communications.
## 3 Results and Discussion
### Soxhlet Extraction  
After Soxhlet extraction with methylene chloride, the samples cured exclusively in a convection oven exhibited, on average, 17 wt % of soluble material, while samples cured under microwaves exhibit, on average, 19 wt % of soluble material. These numbers are in agreement with previously published data on similar tung oil-based resins [38]. The percentage of soluble material extracted is inversely proportional to the crosslink density of the polymer, therefore, it can be concluded that samples cured under microwaves are only slightly less cross-linked than those cured exclusively in a convection oven.  
The soluble material recovered after Soxhlet extraction from each sample was analyzed by \({}^{1}\)H NMR spectroscopy (Figure 10) and the results reveal that the extracts correspond to partially reacted tung oil and/or tung oil oligomers. Figure 10C shows a standard \({}^{1}\)H NMR spectrum of a conjugated triglyceride with peaks at 4.00-4.40 ppm, corresponding to the glycerol methylenes, and the series of multiplets at 5.20-6.50 ppm, corresponding to the protons of conjugated sp\({}^{2}\) hybridized carbons. In Figure 10A,B, the spectra match very closely that of tung oil, with the exception of peaks \(>\)5.5 ppm, denoting that at least some of the carbon-carbon double bonds in tung oil have reacted, most likely forming soluble oligomers.  
Figure 9: Thermogravimetric analysis (TGA) curves of tung oil-based resins, cured in a convection oven for 6 h at 140 \({}^{\circ}\)C, reinforced with (A) 0.00 wt % CNTs; and (B) 0.25 wt % CNTs, and tung oil-based resins, cured under microwaves at 100 W for 10 min, followed by 200 W for 10 min, and a 2 h 10 min post-cure step at 140 \({}^{\circ}\)C, in a convection oven, reinforced with (C) 0.00 wt % CNTs; (D) 0.02 wt % CNTs; (E) 0.10 wt % CNTs; (F) 0.20 wt % CNTs; and (G) 0.25 wt % CNTs.