esa-sceva/satcom-chunk-collection huggingface dataset:
# Current Status and Consideration of Support/Care Robots for Stand-Up Motion
## 3 Results
### Functions and Effects
#### 3.1.1 Partial Assistance
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Year** & **First Author** & **Assist System** & **Assist Level** & **Function** & **Effect** & **Reference** \\ \hline  & & & & Use visual feedback to & & \\
2013 & Shiraishi & C & P & reduce the difference in & Increased floor reaction force & [12] \\  & & & & utilization between & on the affected lower limb. & [12] \\  & & & & both legs. & & \\ \hline  & & & & Motion estimation with & & \\
2012 & CAO & S & P & two ropes. & Floor reaction force & \\  & & & STS trajectory guidance and & decreased compared to & [13] \\  & & & reduction of lower & self-movement. & & \\ \hline  & & & & & \\
2018 & Takeda & L/W & P & Estimate motion with the & The estimated time of & \\  & & & minimum number of sensors & movement was 0.005 s. & \\  & & & to reduce the burden on the & The average estimation error & [16] \\  & & & lower limbs. & was 0.145 s. & \\ \hline  & & & Lateral mobile armrests & & \\
2015 & Eto & H & P & reduce the burden on the & Rectus femoris activity & \\  & & & & lower limbs. & & \\ \hline  & & & Reduction of lower limb & It was judged that the & \\
2015 & Hoang & L/W & P & burden by manipulator that & optimum trajectory can be & \\  & & & & draws the optimum & realized by the force of the & [39] \\  & & & & trajectory. & & \\ \hline  & & & & Imitate professional & & \\  & & & & assistance skills. & & \\
2015 & Tsusaka & H & P & Posture guidance in the & & \\  & & & horizontal direction and & & appropriate assistance is & \\  & & & & lower limb burden. & possible under hybrid & \\  & & & & assistance in the vertical & & control. & \\  & & & & direction & \\ \hline  & & & & Imitate professional & & \\
2012 & [PERSON] & L/W & P & assistance skills. & The maximum error of the & \\  & & & STS assistance coordinated & estimate was about 0.04 m. & [41] \\  & & & & with user posture estimation. & & \\ \hline  & & & Reduction of lower limb & The maximum power of the & \\
2015 & Asker & L/W & P & burden by 3 DOF & power unit is 63\% of the & [42] \\  & & & & manipulator. & body weight. & \\ \hline  & & & & & A steering wheel trajectory & \\
2011 & Saint- & & L/W & P & STS assistance with 2 DOF & that does not cause & \\  & & & manipulator. & discomfort to the user was & \\  & & & & guided. & \\ \hline  & & & & STS assistance by changing & & \\
2013 & Yuk & L/W & P & the angle and height of the & ND & [44] \\  & & & armrest. & & \\ \hline  & & & Robot towed by rails placed & & \\
2010 & Carrera & S & P & on the ceiling. & ND & [45] \\  & & & STS assist by prism joint. & & \\ \hline  & & & & Seat-off assist by the seat & The generated floor reaction & \\
2010 & NANGO & C & P & follows the thigh angle. & force has decreased. & [46] \\ \hline  & & & & Emulate professional & & \\
2012 & Morita & L/W & P & assistance skills. & Knee joint load reduced by & \\  & & & Manipulator reduces STS & 0.5 Nm/kg. & [47] \\  & & & lower limb burden. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Assist functions and effects (Non-wearable type).
# Magnetooptics in Cylindrical Structures
## 2 Maxwell Equations  
For the time, \(t\), dependence of harmonic waves propagating with the angular frequency, \(\omega\), and described by a factor \(\exp\left(\mathrm{j}\omega t\right)\), the Maxwell equations in a linear medium \((i)\) characterized by the electric permittivity tensor, \(\tilde{\mathbf{\varepsilon}}^{(i)}\), and magnetic permeability tensor, \(\tilde{\mathbf{\mu}}^{(i)}\), become  
\[\
abla\ \times\ \mathbf{E}^{(i)} = -\mathrm{j}\omega\tilde{\mathbf{\mu}}^{(i)}\mathbf{H}^{(i)}, \tag{1a}\] \[\
abla\ \times\ \mathbf{H}^{(i)} = \mathrm{j}\omega\tilde{\mathbf{\varepsilon}}^{(i)}\mathbf{E},\] (1b) \[\
abla\cdot\left(\tilde{\mathbf{\varepsilon}}^{(i)}\mathbf{E}^{ (i)}\right) = 0,\] (1c) \[\
abla\cdot\left(\tilde{\mathbf{\mu}}^{(i)}\mathbf{H}^{(i)}\right) = 0. \tag{1d}\]  
A linear homogeneous originally isotropic region \((i)\) magnetically ordered parallel to the \(z\)-axis of a Cartesian coordinate system is characterized by the electric permittivity tensor  
\[\tilde{\mathbf{\varepsilon}}^{(i)} = \left( \begin{array}{ccc}\varepsilon_{0}^{(i)}&-\mathrm{j} \varepsilon_{1}^{(i)}&0\\ \mathrm{j}\varepsilon_{1}^{(i)}&\varepsilon_{0}^{(i)}&0\\ 0&0&\varepsilon_{z}^{(i)}\end{array} \right)\] (2a) and by the magnetic permeability tensor \[\tilde{\mathbf{\mu}}^{(i)} = \left( \begin{array}{ccc}\mu_{0}^{(i)}&-\mathrm{j}\mu_{1}^{(i)} &0\\ \mathrm{j}\mu_{1}^{(i)}&\mu_{0}^{(i)}&0\\ 0&0&\mu_{z}^{(i)}\end{array}
# Effect of Plant Biostimulants on Nutritional and Chemical Profiles of Almond and Hazelnut
## 3 Results and Discussion
### Linear Discriminant Analysis  
To extend the characterization of differences observed in all individual parameters described in the former sections, the totality of results was evaluated simultaneously to analyze the overall effects of the studied biostimulants on almond and hazelnut profiles. This study was made using linear discriminant analysis (LDA), measuring the correlations among different biostimulants (categorical dependent variables) and the obtained results (quantitative independent variables). All significant independent variables were selected from the stepwise method of LDA, using the Wilks' \(\lambda\) test criterion. The obtained statistical models maintained only the variables with a statistically significant classification performance (\(p<0.050\)). Concerning almonds, most of the results' variation was included in function 1 (function 1:68.3%; function 2:24.1%; function 3:6.1%; fourth function:1.5%); the first three functions are represented in Figure 1. Among all studied variables, \(\gamma\)-tocopherol, sucrose, tocopherol, \(\beta\)-tocopherol, protein, water, and C18:2n6c were selected as having discriminant ability (i.e., the ones showing the highest variation in result of using different biostimulants). Considering the correlations among discriminant functions and selected variables, function 1 was more correlated with \(\gamma\)-tocopherol, \(\beta\)-tocopherol, and water, mostly separating samples treated with phytoplankton (which showed the maximum values in \(\gamma\)-tocopherol, \(\beta\)-tocopherol, and water) and Tradebor (which showed the minimum values in \(\gamma\)-tocopherol, \(\beta\)-tocopherol, and water). In turn, function 2 had the highest correlation coefficients with sucrose, grouping samples treated with phytoplankton or Tradebor (higher sucrose contents) in one cluster. On its side, function 3 was mostly correlated with total tocopherols, and its most observable effect was the separation of markers corresponding to samples treated with Borax (lowertocopherol contents). Overall, the most pronounced effect in almond were obtained when using phytoplankton, mainly due to the increase in \(\gamma\)-tocopherol, \(\beta\)-tocopherol. In this LDA, the classification performance was 91.7% accurate for the original grouped cases and 89.4% for the cross-validated grouped ones.
# Analysis and Design of Integrated Blocks for a 6.25 GHz Spacefibre PLL
## 4 Simulation's Results
### PLL Testbench
#### 4.3.1 Locking Process  
The locking process in the typical technology corner at 27 \({}^{\circ}\)C is shown in Figure 20. The lock time is 1 \(\upmu\)s, which is twice that expected from the ADS behavioral model. This is mainly due to two factors: (1) the non-linearity of the VCO's characteristic, and (2) the cycle slipping. In the ADS model, the VCO was approximated to have a linear characteristic with 2 GHz/V gain, but this is true only in a small range of frequencies. For the other frequencies, a smaller gain and consequently, a smaller loop bandwidth and damping factor results, which leads to a higher lock time. Moreover, as can be seen from the locking process, at the beginning of the process, two cycle slips are seen. This is due to the RESET state of the PFD necessary to cancel the dead zone. When the phase difference between the two PFD's inputs is high enough that the new edge of the input ahead comes while the PFD is in its RESET state, this edge is not seen, and the PFD goes into the wrong state with respect to the ideal one. From that, it recovers the correct state when the cycle slip happens, but this leads to an enlargement of the lock time.
# Design of an Imaging Optical System for Large-Sized Stepped Shaft Diameter Detection
## 2 Optical Imaging System Principles for Large-Size Stepped Shaft Diameter Detection  
### Object-Space Telecentric Optical Path Design and Optimization  
Telecentric optical paths can be classified into object-side telecentric, image-side telecentric, and dual-telecentric paths. The object-side telecentric path features a simple structure and a larger working distance range. However, compared to the dual-telecentric path, it exhibits greater distortion and aberration in the edge field of view. On the other hand, the image-side telecentric path provides high image quality with fewer secondary errors. Nevertheless, relative to the dual-telecentric path, it typically has a smaller working distance range and requires higher lens quality and manufacturing precision. Dual-telecentric optical systems combine the advantages of both object-side and image-side telecentric paths while avoiding the errors introduced by these paths during detection [10]. The schematic diagram is shown in Figure 1.  
Dual-telecentric optical systems typically adopt a symmetric structure, with the aperture stop located at the center of the system, ensuring that the chief ray is incident and emergent parallel to the optical axis. The incident of parallel rays guarantees a sufficient depth of field in the system. The emergence of parallel rays ensures that the magnification remains constant when the working distance changes within the depth of field range, and the position of the chief ray remains unchanged. Therefore, the system has no parallax [11; 12; 13; 14].
## 4 Results  
### Sample Compositions of SCB and SW  
The sample compositions of SCB and SW are shown in Table 2. It can be observed that both fibers were primarily composed of cellulose, with lignin and hemicellulose present in lesser content. SCB appeared to have a higher hemicellulose content, but lower lignin content compared with SW, with approximately a 20% difference for each. This trend in SCB composition aligns with the previous literature [22; 23; 24; 25]. It is also known that the composition of fibers can vary depending on factors such as the origin of a plant, harvesting period, a domain of the plant, and the aging process [1; 3].
## 4 Result and Discussion
\begin{table}
\begin{tabular}{c l c c} \hline
**Dataset** & **Deep Learning Models** & **RMSE** & **MAE** \\ \hline \multirow{4}{*}{ABBANK} & CNN & 3.6829 & 3.4811 \\  & LSTM & 3.8222 & 3.7760 \\  & BiLSTM & 3.9660 & 3.9122 \\  & Proposed Stacking Ensemble of Neural Network & 0.6766 & 0.5116 \\ \hline \multirow{4}{*}{ACIBANK} & CNN & 2.3866 & 1.8764 \\  & LSTM & 1.2314 & 0.9824 \\  & BiLSTM & 0.2280 & 0.1931 \\  & Proposed Stacking Ensemble of Neural Network & 1.1880 & 0.8805 \\ \hline \multirow{4}{*}{APEXFOOT} & CNN & 5.4682 & 5.3320 \\  & LSTM & 4.7450 & 4.5975 \\  & BiLSTM & 1.3357 & 1.0584 \\  & Proposed Stacking Ensemble of Neural Network & 1.0634 & 0.9504 \\ \hline \multirow{4}{*}{BANKASIA} & CNN & 0.6713 & 0.5675 \\  & LSTM & 0.4191 & 0.3472 \\  & BiLSTM & 0.4956 & 6.8461 \\  & Proposed Stacking Ensemble of Neural Network & 0.8139 & 0.6806 \\ \hline \multirow{4}{*}{BATASHOE} & CNN & 6.3256 & 6.0763 \\  & LSTM & 7.7107 & 7.5957 \\  & BiLSTM & 1.5567 & 1.3306 \\  & Proposed Stacking Ensemble of Neural Network & 0.9859 & 0.7908 \\ \hline \multirow{4}{*}{BERGERPBL} & CNN & 5.4822 & 5.4256 \\  & LSTM & 5.4605 & 5.3972 \\  & BiLSTM & 4.4781 & 4.4417 \\  & Proposed Stacking Ensemble of Neural Network & 0.4721 & 0.3812 \\ \hline \multirow{4}{*}{EXIMCO} & CNN & 5.8887 & 5.8245 \\  & LSTM & 5.3503 & 5.3172 \\  & BiLSTM & 2.5656 & 2.5383 \\  & Proposed Stacking Ensemble of Neural Network & 0.6300 & 0.4795 \\ \hline \multirow{4}{*}{BRACBANK} & CNN & 4.3199 & 3.7514 \\  & LSTM & 2.7113 & 2.3612 \\  & BiLSTM & 4.0452 & 3.3964 \\  & Proposed Stacking Ensemble of Neural Network & 2.1705 & 2.4467 \\ \hline \multirow{4}{*}{CITYBANK} & CNN & 3.2923 & 3.1774 \\  & LSTM & 1.8324 & 1.7528 \\  & BiLSTM & 2.7964 & 2.7133 \\  & Proposed Stacking Ensemble of Neural Network & 0.7283 & 0.6993 \\ \hline \multirow{4}{*}{DESCO} & CNN & 1.0141 & 0.8770 \\  & LSTM & 0.9895 & 0.8442 \\  & BiLSTM & 0.7367 & 0.5795 \\  & Proposed Stacking Ensemble of Neural Network & 0.8349 & 0.8349 \\ \hline \multirow{4}{*}{DHAKABANK} & CNN & 1.5500 & 0.8770 \\  & LSTM & 1.0917 & 1.0176 \\ \cline{1-1}  & BiLSTM & 1.6444 & 1.4745 \\ \cline{1-1}  & Proposed Stacking Ensemble of Neural Network & 0.5206 & 0.5206 \\ \hline \end{tabular}
\end{table} 
Table 2: Experiment results obtained from 20 different companies’ stock price datasets of a frontier market using deep learning models such as CNN, LSTM, BiLSTM and our proposed Stacking Ensemble of Neural Network model. Table 3 provides the representation of RMSE and MAE error values obtained from 20 different datasets of a frontier market using machine learning ensemble models such as RandomForest, AdaBoost, GradientBoosting and ML stacking Ensemble Learning.
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 5 Sota Defense Mechanisms
### Pixel Backdoor  
In 2022, [PERSON] et al. proposed Pixel-Backdoor [19], a trigger synthesis-based defense approach. Unlike Neural Cleanse, which utilizes a mask to identify the smallest perturbations that can alter any samples into the target class, Pixel-Backdoor finds triggers without using any masks. It identifies smaller, more robust triggers with high ASRs. Pixel-Backdoor employs a _tanh_ function and optimization techniques to pinpoint the perturbation pixels within all available pixels of an input image. We utilized Pixel-Backdoor as a more recent approach for mitigating backdoors, in comparison to other existing methods.
## 2 Materials and Methods
### Evaluation of Remote Sensing (RS) Approach
#### 2.2.2 Sample Spectra Acquisition and Processing  
Reflectance was measured by remote sensing (Sentinel-2). These data were downloaded from Copernicus data for the nine geo-referenced pixels in each experimental field. Two remote sensing indices were generated from different surface reflectance bands: the \(\text{NDVI}_{\text{RS}}\) (B4: 665 nm and B8: 842 nm, with a 10 m spatial resolution; see Equation (4) [27]) and the \(\text{NDVI}_{\text{RS}}\) (B8A: 865 nm and B11: 1610 nm, with a 20 m spatial resolution; see Equation (5) [28]). The \"Sen2 Cor 2.3\" processor (implemented on Sentinel Application Platform from European Space Agency) was used for atmospheric correction.  
\[\text{NDVI}_{\text{RS}}=\frac{\text{B8}-\text{B4}}{\text{B8}+\text{B4}} \tag{4}\]  
\[\text{NDVI}_{\text{RS}}=\frac{\text{B8A}-\text{B11}}{\text{B8A}+\text{B11}} \tag{5}\]  
These indices were extracted on the date without clouds closest to the corresponding pasture collection and were subject to a maximum deletion of 8 days between two dates (pasture sampling and Sentinel-2 data extraction).
## 3 Methodology
### The Proposed IRW System Design
#### 3.2.3 IoT Requirements  
Some equipment, such as microcontrollers and sensors, were used to build the proposed system, as shown below.  
1. ESP8266, a microcontroller  
This is a low-cost development board that combines GPIOs, I2C, UART, ADC, PWM, and WiFi for fast prototyping, as shown in Figure 8. The ESP8266 module, which is powered by a 3.3 V supply and includes a voltage regulator and a USB-to-serial converter,  
Figure 7: The main dashboard.  
was packaged as the ESP-12 module [51]. It was used to transfer the temperature readings from the DHT-11 sensor to the proposed system at all times.  
2 Temperature and Humidity Sensor (DHT-11)  
The DHT-11 sensor combines temperature and humidity sensors into a single unit, as shown in Figure 9. It contains a thermistor, a humidity-sensing component, and an integrated circuit for measuring the surrounding air. This sensor was used in the proposed system to measure the temperature, which helps to maintain blood bags' and blood components' temperature within an acceptable range from 2 to 6 \({}^{\circ}\)C (threshold degree) in the refrigerators and monitors them throughout the system in real time, 24/7. The temperature sensor has an accuracy of \(\pm\)2.0 \({}^{\circ}\)C [52]. Additionally, it was installed in the refrigerators and sends readings to the system through the ESP8266 board as a gateway [26].
## 3 Results
### The Coupling of GPP and ET  
Overall, GPP and ET were tightly coupled (\(R^{2}\) = 0.86), although the coupling was greater at MP (\(R^{2}\) = 0.86) than YP (\(R^{2}\) = 0.73, Figure 6b). The coupling was more robust during the non-growing season (\(R^{2}\)= 0.19 - \(R^{2}\) = 0.57), but it was slightly decoupled during the growing season (maximum \(\sim\)\(R^{2}\) = 0.35, Figure 6a).  
Figure 5: Regression curves between stand age and (**a**) evapotranspiration (ET), (**b**) gross primary productivity (GPP), and (**c**) water use efficiency (WUE) at YP and MP. Annual ET, GPP, and WUE were represented by circles colored according to site.
# Application of Image Recognition Methods to Determine Land Use Classes
## 5 Discussion
### Limitations of the Research  
However, several limitations remain to be considered. One of the primary challenges is in distinguishing visually similar land use types, particularly during seasonal transitions. Some land use classes, such as fallow fields and natural meadows, exhibit spectral similarities that pose difficulties in classification. Future research should explore the integration of temporal data analysis to mitigate classification errors caused by seasonal variations.  
Another limitation is the reliance on manually labeled training data. Although high-quality labeled datasets were sourced from governmental agencies, which represent exact land use, there remains potential for error when creating training and validation sets due to human error. The incorporation of semi-supervised or unsupervised learning techniques has the potential to enhance the system independency of labeled data and improve classification robustness.  
Furthermore, while cloud interpolation is an effective method for enhancing data availability, it is important to note that it may introduce artifacts when cloud coverage is extensive. This can affect classification accuracy, particularly in regions with persistent cloud cover. Future work should explore deep learning-based gap-filling techniques to further enhance interpolation accuracy.  
Another key consideration is computational efficiency. The proposed method is accurate, but it requires substantial computational resources due to the high resolution of the imagery and the large size of the dataset. Optimization strategies, such as model pruning and quantization, should be explored to improve the efficiency of classification algorithms without compromising accuracy.  
The present study is confined to Lithuania, a setting that is representative of the climate and land use characteristics of the Baltic region. However, it is expected that the methodological framework will be extended to other regions with similar environmental conditions. In Europe, beyond the immediate Baltic neighbors of Latvia and Estonia, countries such as Finland, Sweden, and even parts of Poland or the northern coastal areas of Germany have comparable transitional continental climates with significant seasonal variability. In North America, regions within Canada with analogous mid-latitude conditions further underline the potential transferability of the approach. In addition, several regionsin Asia, including northern Japan (particularly Hokkaido), parts of South Korea and parts of northeast China, exhibit environmental conditions similar to those observed in the Baltic region. While these findings are encouraging, further empirical validation is necessary to establish the robustness of the model across the wider range of climates, land use types and instances of persistent cloud cover found in these diverse but climatically analogous regions. All those works are left for further research.
## 5 Discussion
Combining both depthwise and pointwise operations, and substituting all the values, the total number of operations for depthwise separable convolution is  
\[\text{Operations}_{\text{depthwise separable}}=7,077,888+50,331,648=57,409,536\]  
To compare the efficiency, we can take the ratio of the operations required:  
\[\frac{\text{Operations}_{\text{depthwise separable}}}{\text{Operations}_{ \text{standard}}}=\frac{57,409,536}{151,165,440}\approx 0.38\]  
This indicates that depthwise separable convolution is approximately 62% more efficient than standard convolution in terms of operations. While existing studies have observed that single-thread depthwise separable convolution often underperforms in accuracy compared to conventional convolution [35], our findings demonstrate that incorporating dense connections can enhance its performance, achieving better results. Moreover, the computational and memory efficiency of our model is evident when comparing parameter counts and FLOPs with the original DeepLabV3+ [13]. Specifically, our model has a parameter count of 20,874,553 (20.87M), lower than DeepLabV3+ with ASPP, which has 28,314,137 (28.31M) parameters. In terms of FLOPs, our proposed model achieves 160,801,044,036 (160.80G), compared to the 181,132,742,326 (181.13G) required by DeepLabV3+ with ASPP. The reduction in FLOPs reflects a decrease in computational cost, while the smaller parameter count indicates improved memory efficiency. In summary, the utilization of depthwise separable dilated convolution in our DenseDDSPP module offers both computational and memory efficiency while also improving feature extraction capabilities. These advancements contribute to the better performance of our proposed model in road extraction tasks. The code implementation for the proposed model, along with the evaluation set for visualization and comparison, is available at [[https://github.com/amaha7984/Road-Extraction-with-Advanced-Deep-Learning-Model](https://github.com/amaha7984/Road-Extraction-with-Advanced-Deep-Learning-Model)]([https://github.com/amaha7984/Road-Extraction-with-Advanced-Deep-Learning-Model](https://github.com/amaha7984/Road-Extraction-with-Advanced-Deep-Learning-Model)) (accessed on 15 January 2025).