esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvement of Electrical Performance in Heterostructure Junctions TFET Based on Dual Material Gate
## 3 Results and Discussion
### The Output Characteristics  
Figure 5a indicates the output characteristics of JLTFET, HJLTFET and DMMG-HJLTFET at Vgs = 1 V, it can be seen clearly that JLTFET shows worse saturation performance under this simulation condition, meanwhile, the maximum ON-state current of JLTFET is only \(2.35\times 10^{-10}\) A/\(\upmu\)m. While HJLTFET and DMMG-HJLTFET have better saturation performance, and the maximum ON-state currents of HJLTFET and DMMG-HJLTFET are \(3.01\times 10^{-8}\) A/\(\upmu\)m and \(8.32\times 10^{-7}\) A/\(\upmu\)m, respectively.  
\begin{table}
\begin{tabular}{l c c c c c} \hline  & **Ion (A/\(\upmu\)m)** & **Ioff (A/\(\upmu\)m)** & **Ion/Ioff** & **g\({}_{\text{m}}\) (S/\(\upmu\)m)** & **SS (mV/Dec)** \\ \hline JLTFET & \(7.21\times 10^{-7}\) & \(1.36\times 10^{-15}\) & \(5.3\times 10^{8}\) & \(1.22\times 10^{-6}\) & 122 \\ HJLTFET & \(3.82\times 10^{-6}\) & \(2.84\times 10^{-15}\) & \(1.34\times 10^{9}\) & \(5.76\times 10^{-6}\) & 88.2 \\ DMMG-HJLTFET & \(1.01\times 10^{-5}\) & \(6.5\times 10^{-19}\) & \(1.56\times 10^{13}\) & \(1.26\times 10^{-5}\) & 52 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of parameters of JLTFET, HJLTFET and DMMG-HJLTFET.  
Figure 4: (**a**) The log scale and linear scale transfer characteristics of JLTFET, HJLTFET, and DMMG-HJLTFET; (**b**) transconductance of JLTFET, HJLTFET and DMMG-HJLTFET.  
The output transconductance(g\({}_{\text{ds}}\)) is also an important parameter to evaluate the analog performance of devices, and it can be calculated by Equation (3):  
\[g_{\text{ds}}=\frac{\text{d}\text{I}_{\text{ds}}}{\text{dV}_{\text{DS}}} \tag{3}\]  
Figure 5b shows the output conductance (g\({}_{\text{ds}}\)) characteristics of JLTFET, HJLTFET and DMMG-HJLTFET at Vgs = 1 V. As can be seen from the Figure 5b, output conductance of DMMG-HJLTFET is greater than that of JLTFET and HJLTFET from 0.2 V to 1.0V Vds, and maximum output conductance of DMMG-HJLTFET is \(1.99\times 10^{-6}\) S/\(\upmu\)m when Vds = 0.74 V. However, the maximum output conductance of JLTFET is \(1.73\times 10^{-9}\) S/\(\upmu\)m when Vds = 0.6 V, and the maximum output conductance of HJLTFET is \(9.86\times 10^{-8}\) S/\(\upmu\)m when Vds = 0.52 V.
## 2 Methodology and Modeling
### Passive Solution Tests
#### 2.6.7 Natural Ventilation  
Uniform ventilation maintains a constant airflow rate throughout the year. This approach was applied across all locations to assess the effects of steady ventilation on the thermal environment. Ventilation rates were increased progressively in each alternative, and the impact on thermal loads and the environment was evaluated. The air changes per hour (ACH) values considered are presented in Table 11.  
The value of 0.3 ACH is the base simulation, and 0.5 ACH is the minimum required by Portuguese legislation for residential buildings. The values employed for Portugal were also applied to Brazil. In the case of Turkey, where an exterior anteroom facilitates safer ventilation, higher ACH values were selected.  
Summer Ventilation (Portugal and Turkey)  
Summer ventilation adjusts the airflow rates based on seasonal air temperature variations. For Portugal and Turkey, summer ventilation was prioritized to address the significant seasonal variation in air temperature. During the colder months (October to March), ventilation was set at 0.5 ACH. In the warmer months (April to September), ventilation rates were increased, depending on the location and on the alternative. This approach optimizes cooling in summer while minimizing heat loss during the winter and it is summarized in Table 12.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Alternative** & **Shading Type** \\ \hline Alt. 1\_Shading & No shading \\ Alt. 2\_Shading & Internal shading \\ Alt. 3\_Shading & Overhang \\ Alt. 4\_Shading & Vertical fins \\ \hline \hline \end{tabular}
\end{table}
Table 10: Tested shading solutions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Alternative** & **ACH, BR** & **ACH, PT** & **ACH, TK** \\ \hline Alt. 1\_YearVent & 0.3 & 0.3 & 0.3 \\ Alt. 2\_YearVent & 0.5 & 0.5 & 1 \\ Alt. 3\_YearVent & 1 & 1 & 2 \\ Alt. 4\_YearVent & 2 & 2 & 4 \\ Alt. 5\_YearVent & 4 & 4 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Infiltration values for uniform ventilation and for each location: Brazil (BR), Portugal (PT), and Turkey (TK).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Alternative** & **Summer, PT** & **Winter, PT** & **Summer, TK** & **Winter, TK** \\  & **April–September** & **October–March** & **April–September** & **October–March** \\ \hline Alt. 1\_SumVent & 0.5 & 0.5 & 1 & 0.5 \\ Alt. 2\_SumVent & 1 & 0.5 & 2 & 0.5 \\ Alt. 3\_SumVent & 2 & 0.5 & 4 & 0.5 \\ Alt. 4\_SumVent & 4 & 0.5 & 6 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Natural ventilation values, in ACH, for summer ventilation and for the locations of Portugal (PT) and Turkey (TK).
###### Abstract  
During the construction of coal mine shafts through Cretaceous water-rich stratum using the freezing method, the frozen shaft lining can break and lose stability. Hence, it is necessary to examine the mechanical properties and constitutive relationship of Cretaceous water-rich sandstone under the effect of surrounding rocks. To this end, in this work, the mechanical properties of red sandstone at different confining pressures and freezing temperatures were examined by using a ZTCR-2000 low-temperature triaxial testing system, wherein the 415-418 m deep red sandstone in the Lijagou air-return shaft of Wenjapo Mine was taken as the research object. The test results indicated that the stress-strain curves of rock under triaxial compression and uniaxial compression presented four stages: pore compaction, elastic compression, plastic yield, and post-peak deformation. The difference between the two cases was that the post-peak curve of the former was abrupt, while the latter exhibited a post-peak strain softening section. As the freezing temperature was constant, with the raise in the confining pressure, the elastic modulus and peak strength of the rock rose linearly, while the Poisson's ratio decreased quadratically. As the control confining pressure was constant, the elastic modulus and rock's peak strength increased with the decrease in the temperature, and under the condition of negative temperature, the two parameters were linearly correlated with the temperature, while the Poisson's ratio showed the opposite trend. The two-part Hooke's model and the statistical damage model based on Drucker-Prager (D-P) yield criterion were used to establish the stress-strain relationship models before and after the rock yield point, optimize the model parameters, and optimize the junction of the two models. The results revealed that the optimized model curve was in good agreement with the experimental curve, which suggests that the proposed model can accurately describe the stress-strain characteristics of rock under three-dimensional stress. This verified the feasibility and rationality of the proposed model for examining the constitutive relationship of rocks.  
Cretaceous red sandstone; triaxial compression; low-temperature freezing; mechanical properties; constitutive model +
Footnote †: journal: Applied Sciences
## 7 Module of Maneuver Activation  
The principle of the system is noninterference and minimizing interference while simultaneously achieving collision avoidance. To realize this principle, the system should activate the maneuver as late as possible. The Figure 4 is the relative motion of aircraft A and B; aircraft B is resting relatively, while aircraft A is moving toward B relatively. The isolation sphere is the safety separation zone for aircraft B. Before entering the isolation sphere, aircraft A can activate a maneuver to avoid collision.  
Figure 3: Maneuver evaluation.  
The earlier aircraft B maneuvers, the more available options aircraft B can choose, and the less likely a collision will occur. There exists a final moment when activation is too late; no matter what maneuver is taken under the restriction of performance; aircraft A cannot avoid entering the isolation sphere of aircraft B and the collision between two UAVs cannot be prevented. If UAV A activates maneuvers at point 1, no matter which maneuver is taken, it can avoid collision, but if the activation time is too early, it will seriously interfere with the normal flight of the UAV. If UAV A activates at point 3, no matter which maneuver UAV A takes, it cannot avoid collision with UAV B for the restriction of performance. At point 3, only one maneuver can avoid collision, the planned trajectory is tangent to the isolation spheres, and the special maneuver is the preferred maneuver for aircraft A. In summary, the moment at point 2 is the last moment when collision can be avoided and is the best moment that satisfies the noninterference principle. The radius of the sphere can be modified to increase or decrease the early warning time.  
The module of maneuver activation calculates the minimum allowed separation distance (MASD), which is the sum of the wing span (WS) of two aircrafts, plus the desired separation distance (DSD). The predicted minimum range (PMR) is the minimum distance between the conical region.  
\[MASD=DSD+\sum WS \tag{9}\]  
\[MR=\min(|P_{A}(t+\Delta t)-P_{B}(t+\Delta t)|+UD_{A}(\Delta t)+UD_{B}(\Delta t)),\Delta t\subseteq(0,T) \tag{10}\]  
\[\text{UD}(\Delta t)=0.425*(\Delta t)^{2}+1.19*\Delta t \tag{11}\]  
In the module of the maneuver activation, only the planned trajectories corresponding to preferred maneuver are considered to determine the activation. As shown in Figure 5, as the risk of collision becomes more likely, the cones of the two aircraft will converge in time and space when the predicted  
Figure 4: The moment to activate the maneuver.  
Figure 5: Diagram of maneuver activation.  
minimum range (PMR) is less than the permitted minimum allowed separation distance (MASD); then the maneuver is activated, and the participants take the preferred maneuver, and the UAV will fly in the planned trajectory. Indeed, the desired separation distance (DSD) is a fixed value that is input by the system in advance.
# Weak and Dynamic GNSS Signal Tracking Strategies for Flight Missions in the Space Service Volume
## 1 Introduction
The remainder of the paper is organized as follows: Section 2 establishes a reference assumption PLL structure as the baseline of state-of-art; in Section 3, a four-state KF-based signal tracking method is presented which can adjust the equivalent LNBW adaptively; Section 4 depicts the architecture of the INS-assisted open loop design, and further proposes the non-coherent MLE algorithm applied in the architecture. Simulation experiments are carried out to test the effectiveness and feasibility of our strategies in Section 5; and finally, the paper is concluded by outlining the distinctive benefits in Section 6.
# Conditional Knowledge Extraction Using Contextual Information Enhancement
## 5 Experiment
### Experimental Setup  
#### 5.1.1 Dataset Construction  
The Chinese dataset \"cpd-cn\" is first separated into four components: title, content, table, and picture. The table and pictures are further processed to match the document's structure. The \"python-doc\" tools identify and extract headings, body text, and tables, providing structural and content information. The content section is split into sentence-level information using special symbols. For vector extraction, position marks such as \"<e1>, \" \"</e1>,\" \"<e2>,\" \"</e2>,\" \"<e3>,\" and \"</e3>\" are inserted at the beginning and end of the entities and conditional phrases. For Chinese and English, the process of inserting labels is the same, and the specific algorithm process is as follows:  
1. Take out the entity of the sentence in the output of the named entity recognition result;
2. Find the position of the entity in the sentence;
3. Insert the label corresponding to the entity before and after the position in step 2;
4. Input the sentence obtained in step 3 into the relationship extraction model;
5. Take out the sentence with the relationship in step 4, and perform conditional phrasing Extraction;
6. For the sentence in step 5 where the conditional phrase exists, find the position of the conditional phrase and perform label insertion.  
Since BERT's glossary does not recognize these marks, they are converted to \"$, \"#,\" and \"$.\" Due to various structural problems in the document, some headings need to be recognized, and manual help is needed for the modification. A rule-based plus manual annotation approach [26] extracts the corresponding conditions from the text. The process begins with manual annotation to build a condition corpus. The conditions extracted with rules are then matched with all the data in the condition corpus using text similarity. A threshold is set, and any data exceeding this threshold is considered the conditions for that text.  
The English dataset \"cpd-en\" pairs factual triplets extracted from the published dataset in [17] with conditional triplets. The data are processed to clean up any factual and conditional-triplet noise, and pairwise labels are assigned. The resulting data include fact triplets, conditional triplets, and pairwise labels.  
The cpd-cn and cpd-en datasets were divided into training, validation, and test sets using specific ratios, as outlined in Table 1, respectively. The experiment was repeated five times with small validation and testing sets to ensure accuracy, and the average results were reported.
# Can IMU Provide an Accurate Vertical Jump Height Estimate?
## 2 Materials and Methods
### Measurement Equipment  
For the purposes of this research, we used an FP (AMTI, USA Inc. Watertown, MA; sampling frequency 1000 Hz) and a custom IMU-based system previously used in [16; 28; 29]. The IMU-based system consists of 6 degrees of freedom (DOF) LSM6 DS33 3 Daccelerometer/gyroscope [30] mounted on an Adafruit Feather M0 WiFi micro-controller with a built-in WiFi module [31], all powered by a LiPo battery. The overall size of the unit is 50 mm \(\times\) 24 mm \(\times\) 10 mm. For the purposes of this research, only accelerometer data was used. The LSM6 DS33 accelerometer detection range is up to \(\pm\)16 \(g_{0}\). The system sampling frequency was 200 Hz. IMU data was sent via UDP to a laptop running the main LabView application (LabView 2019, National Instruments, Austin, TX, USA) used for signal processing. Both the signal acquired from the FP and the signal acquired from IMU were filtered using a low-pass Butterworth filter (order = 5, \(t_{\text{cof}}\) = 40 Hz). The calculation of the jump height from IMU data was performed using the FT method [13; 22; 27] by Equation (I).  
\[h=\frac{{t_{F}}^{2}\cdot g_{0}}{8} \tag{1}\]  
Where \(h\) is the height of the jump, \(t\) is the flight time and \(g_{0}\) is gravity acceleration (9.81 ms\({}^{-2}\)). The same method of jump height calculation was used for the FP data [11]. The FP ground-reaction force threshold value of zero (\(\pm\)5 N) [14] and IMU absolute acceleration threshold value of 5 \(g_{0}\) were used for detection of the start/end of the jump flight phase.
## 3 Comparative Analysis of FEM-Calculated and ART-Measured Eigenfrequencies
### Strategy for Adapting the Virtual Model to the Real Part  
As mentioned, we selected a more pragmatic approach than a physical-based model update in order to generate virtual ART data that reflect measurement data in a trustful way. For the connecting rods, in detail, correction factors were determined for each vibration mode separately from the measured eigenfrequencies of the 5 reference parts and corresponding simulated frequencies. Regarding Mode-1, for example, we observed an averaged frequency value of approximately 1807.9 Hz from ART. The corresponding frequency value from FEM simulation is approximately 1760.1 Hz. Dividing the measured value by the calculated value leads to a quotient of about 1.0272, which is the correcting factor regarding Mode-1 since the factor's product with the simulated eigenfrequency results in a valid estimation for the measured frequency value. Regarding all 18 vibration modes, correction factor values in the range of 1.0073 to 1.0272 were found by fitting simulated eigenfrequencies to measurement. Only as a reminder, for FEM simulations, our standard modeling regarding sample properties as described in Section 2 was used, that is, among others the geometric model with in each case 8 individually updated geometric values and material properties from literature were assumed.  
Such a simple adaptation of simulation data to measurement is assumed to be suitable for the application example and possibly also for practical applications. Besides this, other statistical methods or models could be used depending on requirements and circumstances, of course. The advantages of such a pragmatic strategy are obvious. There is no need for complicated physical modeling and effects like batchwise changes can be considered without extensive numerical re-calculations.
# Comparative Study of Movie Shot Classification Based on Semantic Segmentation
## 2 Related Works  
### Shot Type Classification  
The various studies that have classified shot types can be largely categorized into two methods: Methods that use the face, and methods that use the CNN. The first method classifies shot types based on the proportion of the face that occupies the screen. In this type of study, although the accuracy of movie shot classification is high when there is a face in the frame, as shown in the two frames of Figure 2a, there is a disadvantage in that the accuracy of shot type classification is low when there is no face in the frame, as shown in the frame for Figure 2b. Also, if no person is on-screen, the shot type cannot be classified [5; 6; 7; 8].  
Figure 1: Types of shots in the movie _Veteran_: (**a**) Close-up shot; (**b**) Medium shot; (**c**) Long shot.  
The second type of study uses a CNN to classify close-up shots, medium shots, and long shots [1]. In one study using 400,000 frames extracted from 120 movies as data, learning was done using CNNs with AlexNet, GoogLeNet, and VGG-16 structures to check and compare accuracy [9; 10; 11]. Figure 3 is a confusion matrix of the experiment results in which the shot types were classified at 74% accuracy with AlexNet, 75% accuracy with GoogLeNet, and 94% with VGG-16. Unlike the first study, this study can classify shot types even if there is no person on-screen. VGG-16, with the highest accuracy, is a structure using 16 network layers among VGGNets. The VGGNet has a disadvantage, however, in that its performance decreases as the network layers increase, and ResNet solves this disadvantage [12].  
Shot type classification using a CNN does not semantically analyze and classify images. Instead, it classifies shot types based on image characteristics and patterns. Therefore, additional information is needed in order to access the images semantically, and this can be done through semantic segmentation, which distinguishes the boundary relationships among objects.
## 2 Materials and Methods
### Location of the Tomatoes and Their Peduncles  
During this stage (Figure 15a), the system provides the location of each ripe tomato in the XY plane of the image by computing the gravity center (c.g.) of the convex area of each tomato. In the text, we call this the \"center\". In addition, it also calculates the position of the tomato's peduncle in the XY plane of the image; this is because, later on, it will be necessary to indicate to the robot the place where the ripe tomato must be separated from the rest of the plant. To begin this stage, the image from the previous stage was used (Figure 13b), in which only the regions representing ripe tomatoes (one region per tomato) appear. Before calculating the positions of the tomatoes and their peduncles, it is necessary to compute a series of descriptors for these regions.  
The regions obtained for each ripe tomato after the detection stage may have gaps or \"holes\" inside. The first operation is to fill in the gaps (Figure 15b) in order to make the measurements carried out below more precise. After that, we computed the external gradient of the previous image (Figure 15c).  
For each of these regions, two sets of descriptors were obtained. The first set were:  
* Center X: x coordinate (in pixels) of the region's c.g.;
* Center Y: y coordinate (in pixels) of the region's c.g.;
* Height and width in pixels of the circumscribed rectangle;
* Minor axis in pixels of the equivalent Feret ellipse;
* Orientation: ellipse orientation in degrees.  
To obtain the second set of descriptors, we computed the external gradient of the regions without gaps. This operator returns a binary image with the external contour of the input image regions (Figure 15c). From this new image, we build the second set of descriptors, consisting of:  
* X center: x coordinate (in pixels) of the center of gravity of the region's external contour. To distinguish it from Center X of the first set, we will call it Center XGdExt;
* Y center: y coordinate (in pixels) of the center of gravity of the region's external contour. To distinguish it from the Y Center of the first set, we will call it Y Center YGdExt.  
After carrying out a large number of tests using different combinations of these and other features, they proved to be the ones that gave us the most accurate results when locating the peduncles.
# A Multi-Site Anti-Interference Neural Network for ASD Classification
## 2 Materials and Methods
### rs-fMRI Data Preprocessing  
The ABIDE dataset is derived from the abide dataset preprocessed by the preprocessed connectome project (PCP) [21]. A configurable pipeline for analysis of connectomes (CPAC) was selected for the preprocessing pipeline. The steps include slice timing correction, head motion correction, intensity normalization, nuisance signal removals, band-pass filtered (0.01-0.1 Hz), standard spatial registration. A pre-processed fMRI scan data is a 4D time series, including three-dimensional space dimension and one-dimensional time dimension. The time series used the mean time series signal or BOLD signal of voxels within the region of interest (ROI) in the brain map. The brain mapping template we selected was Craddock 200 (CC200), with 200 ROIs defined. Pearson correlation coefficients were used to evaluate the functional connections between the mean time series of each ROI pair, resulting in a 200 \(\times\) 200 functional connection for the CC200 brain map.
# Estimation of Mental Distress from Photoplethysmography
###### Abstract  
This paper introduces the design of a new wearable photoplethysmography (PPG) sensor and its assessment for mental distress estimation. In our design, a PPG sensor obtains blood volume information by means of an optical plethysmogram technique. A number of temporal, morphological and frequency markers are computed using time intervals between adjacent normal cardiac cycles to characterize pulse rate variability (PRV). In order to test the efficiency of the developed wearable for classifying distress versus calmness, the well-known International Affective Picture System has been used to induce different levels of arousal in forty-five healthy participants. The obtained results have shown that temporal features present a single discriminant power between emotional states of calm and stress, ranging from 67 to 72%. Moreover, a discriminant tree-based model is used to assess the possible underlying relationship among parameters. In this case, the combination of temporal parameters reaches 82.35% accuracy. Considering the low difficulty of metrics and methods used in this work, the algorithms are prepared to be embedded into a micro-controller device to work in real-time and in a long-term fashion.  
Keywords: distress estimation; wearable; heart rate variability; photoplethysmography +
Footnote †: journal: Applied Sciences