esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Achievement Test (16 multiple-choice questions)  
1. What era does the Chichawan Creek site belong to? (1) The Metal Age (2) Paleolithic Age (3) Neolithic Age (4) Iron Age
2. What is the traditional tool of the Atayal tribe for bounding grains? (1) Pestle and mortar (2) Crushing tool (3) Grinding bowl (4) Fishing tool
3. What skill is used to assess Atayal women's social status and talent? (1) Planting (2) Hunting (3) Weaving (4) Harvesting
4. What is the traditional name for Atayal houses constructed with bamboo? (1) Bamboo house (2) Main house (no inaan) (3) Treasure house (4) Thatched house
5. How is the Atayal granary constructed to prevent dampness and avoid rats? (1) Watchtower of Atayal (2) Elevated construction (3) Half-underground house (4) Stone slab house
6. What color is typically predominant in Atayal tribe clothing? (1) Green (2) Red (3) Yellow (4) Blue
7. What does facial tattooing signify for Atayal women? (1) Hunting (2) Weaving (3) Cooking (4) Fishing
8. Which tribe is located near Rahaw Creek? (1) Nanshan Tribe (2) Shanoh Tribe (3) Four Seasons Tribe (4) Sqoyaw Tribe
9. Which water environment is known as the \"hometown of Taiwan Masu Salmon\"? (1) Chichawan Creek (2) Jhuoshuei River (3) Lanyang River (4) Zengwun River
10. What type of fish is the Taiwan Masu Salmon? (1) Warmwater fish (2) Coldwater fish (3) Hotwater fish (4) Icy water fish
11. Where do Cherry Salmon lay their eggs? (1) Drifting with the current in the water (2) Shallow riverbeds with good water flow on gravel ground (3) Spawn on land (4) In crevices of rocky reefs
12. How many years does Taiwan Masu Salmon typically live? (1) 3-4 years (2) 1-2 years (3) 5-6 years (4) 7-8 years
13. What is the object in the picture? (1) Orange pearl (2) Plankton (3) Fish eggs (4) Fish oil 14 What bird species is shown in the picture below?  
(1) Swinhoe's Pheasant (2) Sparrow (3) Chinese Bulbul (4) Mikado Pheasant  
15 What animal is shown in the picture below?  
(1) Cow (2) Reeve's Muntjac (3) Formosan Sambar Deer (4) Dog  
16 What plant is shown in the picture below?  
(1) Kuban Royal Palm (2) Taiwan Red Pine (3) Taiwan Box (4) Palimara Alstonia
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 4 Design of Control Strategies
### Design of the 3-DoF Robot  
To observe and analyze the robot's motion, it is necessary to use the associated dynamic model. Currently, the MATLAB/Simulink V2024 simulation software offers the Simscape  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Controller** & **Weaknesses** & **Strengths** & **Complementary Characteristics** \\ \hline Fuzzy Control & \begin{tabular}{c} Cannot eliminate steady-state error. \\ \end{tabular} & \begin{tabular}{c} Provides better performance in terms of settling \\ time, response time, overshoot, and robustness. \\ Stable control. High precision. \\ Able to adapt to different operating conditions. \\ \end{tabular} & \begin{tabular}{c} Applicable to other \\ robot types. \\ \end{tabular} \\ \hline Model Predictive & \begin{tabular}{c} Requires knowledge of a \\ sufficiently accurate dynamic \\ system model. \\ \end{tabular} & \begin{tabular}{c} Executes constraints and can be used with \\ Multiple-Input Multiple-Output \\ (MIMO) systems. \\ Time-domain formulation, flexible and open. \\ Allows handling of linear and nonlinear systems, \\ Single-Variable and Multi-Variable using the \\ same controller formulation. \\ Control law responds to optimal criteria. \\ Allows incorporation of constraints in \\ controller synthesis. \\ \end{tabular} & \begin{tabular}{c} Executes constraints and can be used with \\ Multiple-Input Multiple-Output \\ (MIMO) systems. \\ Time-domain formulation, flexible and open. \\ Allows handling of linear and nonlinear systems, \\ Robot types. \\ \end{tabular} \\ \hline PID & \begin{tabular}{c} Insufficient for the nonlinear \\ control system. \\ Low precision. \\ Unable to adapt to different \\ operating conditions. \\ \end{tabular} & \
\begin{tabular}{c} Stable control. \\ Easy to apply. \\ \end{tabular} &
\begin{tabular}{c} Used at industrial \\ level. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 5: Control scheme.  
Toolbox, which allows for designing a robot using blocks to visualize its movements. For the design of the 3-DoF robot, the Simscape Multibody and Robotics System Toolboxes of MATLAB/Simulink are used.  
Scorbot Robot Model  
Similarly, Figure 6 shows a schematic of a 3-DoF robot similar to the Scorbot model.
## 2 Materials and Methods
### Study Design  
Panoramic and lateral cephalometric radiographs were analyzed. Since all patients had been previously diagnosed with PDC, no additional radiation exposure was required for the purposes of this study. Panoramic radiographs were obtained using the ORTOPHOS apparatus (Sirona Dental Systems GmbH, Bensheim, Germany). Lateral cephalometric radiographs were acquired with a Wehmer cephalostat (BF Wehmer Co., Lombard, IL, USA) and a Sedecal generator (Sociedad Espanola de Electromedicina y Calidad S.A., Algete, Madrid, Spain). Both types of radiographs were digitally processed using AGFA DX-S systems and specific software (version STR_1505, Agfa HealthCare, Mortsel, Belgium).  
The presence of agenesis, supernumerary teeth, or other inclusions was diagnosed through panoramic radiographs. A cephalometric analysis was performed on each lateral cephalogram to determine the skeletal class and growth pattern. Cephalometric tracings were conducted on a personal computer using DBF-Ceph software (Gioconda OrthoCeph, Diagnostico Bucofacial, Spain), which provided [PERSON], Steiner, Jarabak, and McNamara cephalometric analyses.  
A detailed clinical history and examination were also carried out, focusing on dental anomalies such as a family history of canine impaction or agenesis, presence of conoid or microdontic lateral incisors, generalized microdontia, crossbite, and upper dental midline deviation. The clinical diagnosis of dental anomalies, radiographic analysis, and cephalometric tracings were all performed by a single clinician--an orthodontist with 12 years of experience.
# Influence of Polypropylene Fiber on Tensile Property of a Cement-Polymer Based Thin Spray-On Liner
## 4 Test Results and Discussions
### Tensile Strength  
The average tensile strength was determined by the valid specimens. The mean values as well as standard deviation associated with different fiber contents and different curing periods are listed in Table 2.  
As shown in Table 2, fiber incorporation resulted reinforcement to the tensile strength of specimens at all curing periods. Moreover, specimens with 2% fiber addition exhibited higher tensile strength than that of 1% fiber addition at the same curing period.  
Figure 10 shows the relationship between the tensile strength and curing time of each group. As shown in the figure, the strength depended strongly on time. For all three groups, the tensile strength increased rapidly during the 1-day to 7-day curing period, then after the 7-day period, showing a similar tendency with that of water loss.
## 4 Results and Discussion  
### Experimental Procedure  
In this study, bridge BIM models are selected as examples of transportation infrastructure to calculate the semantic similarity of concepts in the BIM model. The following steps were taken:  
Step 1: Collecting Data. The specific method involves selecting 300 pairs of concepts related to bridges from the _Classification and Coding Standard for Road Traffic InfrastructureUnit Information ModelThe reference similarity of these 300 concept pairs is obtained based on expert opinions. Bridge BIM models used in several engineering projects are selected, and the data from these models are used for ontology mapping.  
Step 2: Establishing Semantic NetworkThe specific method involves following the approach described in [10]. A bridge ontology model corresponding to the bridge models is constructed in Protege, and a semantic network is built in Python based on the ontology model's tree-like structure.  
Step 3: Training Calculation ModelCalculating Edge-Counting similarity, Feature-based similarity, and Information-Content similarity between concepts in the Semantic Network. Similarity data are then collected to form training and testing samples, and the calculation model is trained using the training samples.  
Step 4: Testing and Analyzing ResultUsing the trained model to calculate the comprehensive ontology-based semantic similarity for test samples. The results are compared and analyzed against results obtained from other methods.
# Experimental Study on Repairing Corroded Cracks by Electrophoretic Deposition
###### Abstract  
The corrosion of reinforced concrete could reduce its mechanical properties and cause concrete cracking. Electrophoretic deposition is a new method for repairing rust-cracked reinforced concrete. In this study, we observed the crack surface morphology, mass growth, epoxy resin film thickness, ultrasonic rate, and epoxy resin filling depth of rust-cracked reinforced mortar specimens during the repair process and investigated internal microstructure and phase composition of the repaired mortar, and clarified the principle behind the electrophoretic deposition of cationic epoxy resin and curing agent molecules in the repair solution. The result of research shows that longer repair times decreased the carbonization depth and water absorption of the specimens. After repair, the porosity of the surface mortar decreased, owing to the filling of epoxy resin, and the newly generated epoxy resin film had a dense structure. Thus, the carbonization resistance and waterproof performance of the repaired specimens improved. The repair process of electrophoretic deposition can be divided into the immersion, repair, and curing stages, and the physical and chemical principles of each stage were explained.  
corrosion; electrophoretic deposition; repair principle; anti-carbonation; waterproofing Article
## 3 GeoLife Dataset  
The GeoLife dataset [16; 17], which was collected from 2009 to 2012, consists of more than 16 thousand trajectories with a total of 18 million GPS points. A total of 95% of trajectories were logged in a dense representation, e.g., every 1-8 s or every 5-10 m by point. In order to tackle data inconsistencies, we performed several cleaning operations. For each trajectory, we only considered the GPS points with a speed between 0 and 100 m/s. This filtering operation can be implemented by calculating the instantaneous speed of each point. Detected errors correspond to events where the GPS device stopped working properly, not recording the elapsed time.  
We chose to analyze the trajectories inside the Beijing metropolitan area, corresponding to the following range of longitude and latitude: \([116.1,39.7]\), \([116.7,40.13]\). Since in this zone 0.0009 degree of latitude and 0.00118 degree of longitude correspond approximately to 100 m, the selected rectangular area is 51 long and 48 km wide. Figure 1 shows the filtered trajectories on a map of this area.  
In this dataset some paths appear broken, probably due to the presence of buildings or tunnels that disturb the GPS signal. We have decided not to tackle this problem. We have divided the dataset into 6 time slots of 4 hours each in order to analyze flows of trajectories in different scenarios.
## 6 Analysis of Optimization Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Type of Longitudinal** \\ **Connection** \\ \end{tabular} } & \multirow{2}{*}{**Conditions**} & \multicolumn{2}{c}{**Uncertain Variables**} & \multicolumn{2}{c}{**Evaluation Criteria**} \\ \cline{3-6}  & & \(u_{1}\) & \(u_{2}\) & \(u_{3}\) & \(\alpha\) & \(DLSC\) \\ \hline \multirow{3}{*}{Type 1} & Condition 1 & 6 & 10 & 3735 & 0.21827 & 0.03467 \\  & Condition 2 & 8 & 30 & 5595 & 0.28506 & 0.05775 \\  & Condition 3 & 10 & 50 & 7455 & 0.60039 & 0.07671 \\ \hline \multirow{3}{*}{Type 2} & Condition 1 & 6 & 10 & 3735 & 0.20694 & 0.02883 \\  & Condition 2 & 8 & 30 & 5595 & 0.27991 & 0.05379 \\  & Condition 3 & 10 & 50 & 7455 & 0.55921 & 0.0696 \\ \hline \multirow{3}{*}{Type 3} & Condition 1 & 6 & 10 & 3735 & 0.18811 & 0.02836 \\  & Condition 2 & 8 & 30 & 5595 & 0.26783 & 0.05096 \\ \cline{1-1}  & Condition 3 & 10 & 50 & 7455 & 0.48512 & 0.0651 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparisons among the three types of connections at the spring height of \(H_{2}\). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Type of Longitudinal** \\ **Connection** \\ \end{tabular} } & \multirow{2}{*}{**Conditions**} & \multicolumn{2}{c}{**Uncertain Variables**} & \multicolumn{2}{c}{**Evaluation Criteria**} \\ \cline{3-6}  & & \(u_{1}\) & \(u_{2}\) & \(u_{3}\) & \(\alpha\) & \(DLSC\) \\ \hline \multirow{3}{*}{Type 1} & Condition 1 & 1 & 10 & 0 & 0.08172 & 0.02151 \\  & Condition 2 & 5.5 & 30 & 1870 & 0.34561 & 0.09851 \\  & Condition 3 & 10 & 50 & 3735 & 1.16307 & 0.14593 \\ \hline \multirow{3}{*}{Type 2} & Condition 1 & 1 & 10 & 0 & 0.0811 & 0.02023 \\  & Condition 2 & 5.5 & 30 & 1870 & 0.33927 & 0.10141 \\  & Condition 3 & 10 & 50 & 3735 & 1.10252 & 0.14496 \\ \hline \multirow{3}{*}{Type 3} & Condition 1 & 1 & 10 & 0 & 0.07933 & 0.02014 \\  & Condition 2 & 5.5 & 30 & 1870 & 0.32748 & 0.09953 \\ \cline{1-1}  & Condition 3 & 10 & 50 & 3735 & 0.99083 & 0.13814 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparisons among the three types of connections at the spring height of \(H_{0}\). From an engineering point of view, favourable dynamic load-sharing (low _DLSC_) can ensure the equalization of the axle group load across all wheels/axles, which has at least two advantages: (a) it reduces the possibility of a tire bursting as well as enhancing the maneuverability and stability of the vehicle; and (b) it alleviates the rutting and fatigue that contributes to pavement damage. Desirable ride comfort (low \(\alpha\)) decreases the possibility of damage to goods during transportation and thus reduces transportation costs.
## 4 Engineering Application
### Numerical Simulation Model  
A numerical calculation model is established using FLAC\({}^{\text{3D}}\) finite difference software. In order to eliminate the influence of the boundary effect, the left and right boundary of the model are taken as more than two times the station span, the lower boundary is taken as two times the station span, the upper boundary is taken vertically to the surface, and the depth direction of the three-dimensional model is taken as 50 m, with an overall model size of 100 m \(\times\) 60 m \(\times\) 74.2 m, whose size scale is simulated according to the data given in the geological survey report. The model includes 200,853 nodes and 126,890 units. Ansys 15.0 software is used to establish and grid the model, as well as density the grid of main structures and surrounding rock areas. The numerical calculation and analysis are completed using FLAC\({}^{\text{3D}}\)3.0. The Mohr-Coulomb yield criterion is adopted in the analysis process of the model. A linear elastic model is used to simulate the primary support, secondary lining, backfill concrete, crown beam, and other structures. The boundary around the model is a normal constraint, the bottom boundary is a 3D fixed constraint, and the upper one is free. The input geotechnical parameters are based on Table 3. The numerical calculation model calculation model is shown in Figure 5. Note: the \"e\" in the  
Figure 4: The project location map.  
Figure 3: The longitudinal geological profile and the station cross-section: (**a**) the longitudinal geological profile; (**b**) the station cross-section.  
figure represents \"10\", and the number after it is his exponent. For example, 2.5000e-003 represents \(2.5000\times 10^{-003}\).  
The Mohr-Coulomb yield criterion is adopted to the station. The first layer is constructed with plain fill, the second layer consists of strongly weathered quartzite, and the third layer is made of moderately weathered quartzite (parameter layer to be inverted). The basic mechanical parameters are shown in Table 3 regarding field investigation and investigation data.
# BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets
## 3 Materials and Methods
### Metrics
#### 3.5.2 Verification  
Verification is a one-vs.-one comparison, where the system must check the subject's identity. Again, the provided result is probabilistic, but the only result is one probability instead of one value per class. This forces the establishment of a rule to determine when to consider if the probability is high enough to verify or not. Setting a threshold solves the problem but also implies false negative and false positive errors. Their corresponding measurements are called False Non-Match Rate (FNMR) and False Match Rate (FMR), respectively. Their cutting point represents the minimum possible error, named Equal Error Rate (EER). The EER is theoretically calculated when FNMR = FMR. However, values from these curves are not continuous in real case scenarios, so the EER has to be estimated from the available data, as represented in Figure 6. The threshold values \(th_{1}\) and \(th_{2}\) give the pairs of values FNMR and FMR that are the closest. These two pairs of points allow for the characterization of both FNMR and FMR as lines to calculate the crossing point that estimates the EER value.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Values** \\ \hline Batch size (\(b\)) & [20; 35; 50; 100] \\ Kernel size (\(k\)) & [3; 5; 7; 9] \\ Strides (\(s\)) & [3; 5; 7; 9] \\ Filters (\(f\)) & [8; 16; 32; 64] \\ Activation & ReLU \\ CNN layers & 1 \\ LSTM layers (\(L\)) & [1; 2; 3] \\ LSTM neurons (\(n\)) & [16; 32; 64; 128] \\ Epochs & 500 \\ Patience & 20 \\ Optimizer & Adam \\ Learning rate (\(\eta\)) & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Initial hyperparameter values for modeling. Single final values for \(k\), \(s\), and \(f\) were selected in Step 1 and specified in bold font. Values of \(b\), \(L\), and \(n\) were determined with Steps 2 and 3 depending on the \(d\) parameter.  
The system's performance in function of the decision threshold provides graphs called Detection Error Trade-off (DET). DET graphs provide more information about how restrictive or permissive the system can be based on its purpose.
## 5 Discussion
### Uncertainty Analysis and Outlook  
In this research, optical image and SAR image metric composites were used as input in the RF classifier, and a high level of accuracy was realized in corn mapping. We used TOA reflectance as a substitute for surface reflectance (SR) in the Sentinel-2 data, and the effect of the atmosphere was not available until after this research was complete. The substitution may have affected the classification results. Although the acceptable accuracy shows a limited effect, the EVI obtained from the TOA reflectance will be higher than the true value [10]. A comparison between TOA reflectance and SR is recommended in the future to determine the effects. Additionally, the Sen2-Agri tool can be used to produce cloud-free SR composites [34].  
The corn map generated with only SAR image metric composites was subject to the'salt-and-pepper effect' (Figure 15a) due to the speckle perturbation in the SAR images [43]. Although this phenomenon can be eliminated (Figure 15b) when optical metric features are more than SAR metrics features, the'salt-and-pepper effect' will be acute when the approach introduced in this research is applied to the areas without sufficient cloud-free optical images, such as tropical regions.  
Figure 14: Number of good observations between June and October 2017 by masking opaque and cirrus clouds. The embedded histogram shows the distribution of Sentinel-2 imagery number with different color bars, and the color is the same as the map legends.  
With advancements in deep learning technology, more powerful machine learning methods can be introduced for crop identification. A long short-term memory (LSTM) neural network, which is a type of recurrent neural network, made important progress in speech recognition [75], which discriminates words in a sequence of acoustic signals. The time series image sequence for different crops is less complicated than the acoustic signal sequence [76]. Implementing LSTM to identify crops would be expected to generate crop-type maps with a higher level of accuracy.
## 1 Introduction
For ultralow-frequency flexible structures, many classical frequency domain identification methods are not applicable, so the time domain identification method has gradually become the main method in the identification of spacecraft dynamics parameters. The Eigensystem Realization Algorithm [31] (ERA) is convenient to determine the modal order and the recognition speed is faster due to the short sampling time, high identification accuracy and strong anti-noise ability. At the same time, it has a strong recognition ability for structures with ultralow-frequency modal characteristics. [PERSON] et al. [32] proposed a system identification method of the linear dynamics model of the multi-body mechanical system based on the ERA. Numerical examples of simple vehicle models are used to verify the effectiveness of the proposed recognition method. [PERSON] [33] proposed an improved method for the ERA, usually called the Eigensystem Realization Algorithm using data correlations (ERA/DC). Compared with the traditional ERA, the modal identification is carried out through the flexible truss structure, and the data correlation is used to reduce the influence of noise in the identification of modal parameters. This paper proposes an ultralow-frequency flexible structure modal identification method based on the DIC method. Taking the ultralow-frequency flexible structure as the research object, designing the ultralow-frequency flexible structure excitation system, and performing initial displacement excitation on the structure. The response signal of the ultralow-frequency flexible structure is obtained by the DIC method of the camera array, and the modal frequency is identified using the ERA, in which the first modal frequency is the ultralow-frequency less than 0.5 Hz. Establish an ultralow-frequency flexible structure ground test system based on the DIC method. First, carry out the modal test and identification of the traditional contact measurement and the DIC method of the solar wing array to verify the accuracy of the DIC method. Besides, based on the Initial Displacement Excitation Method and DIC method, the modal identification of the ultralow-frequency flexible structure of the cantilever beam is carried out. The theoretical results are compared to verify the feasibility and accuracy requirements of the ground modal test system for the dynamic characteristics of the flexible structure designed in this paper.