esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Method
#### 2.3.2 Microstructure Test  
The microscopic parameters of the soil can be obtained by image acquisition equipment. The image acquisition equipment adopts the soil microstructure test system independently developed by Hohai University. This system is composed of a long-distance microscope, three-dimensional control platform, charge couple device (CCD) camera, coaxial cold light source, built-in image acquisition card, and computer, as shown in Figure 3. The long-distance microscope is connected to a CCD camera, and the fine structure image captured by the CCD camera is transmitted to the computer. The three-dimensional control platform can precisely adjust the test position of the sample with an accuracy of 0.01 mm. The digital image acquisition card is the Matrox Meteor II/Standard type, and the image acquisition rate is greater than 25 frames/second. The CCD camera is a Uniq UM-201 type with an asynchronous acquisition function, and the signal-to-noise ratio is 56 dB.  
Figure 2: Direct shear test. (**a**) Schematic diagram of the direct shear test. (**b**) Ten percent grouting silty clay. (**c**) Silty clay without grouting. (**d**) Muddy clay without grouting.  
Specimens after direct shear failure were used for microstructure testing. With the help of finger force, the sheared sample is broken apart to expose the fresh surface, representative small pieces are selected, and the size of the sample is adjusted and controlled for microstructure testing. The specimen was placed on the three-dimensional control platform in front of the camera, and the focal length was roughly adjusted to make the image more clearly displayed on the computer screen. Then, the focal length is finely adjusted to achieve the best display effect of the image accuracy. Different magnifications do not have obvious effects on the soil sample structural parameters. The image data collected by the test system were saved to the computer, and the mesoscopic images were binarized by the image segmentation method. The binarized image is used to analyze the change of soil structure characteristic parameters. In the binarized image, white represents soil particles, and black represents pores, as shown in Figure 3.
## 3 Results and Discussions
### Current Retrieval and Validation
To diagnose the potential uncertainty, we divided the data points into two groups: one with a collocation time within 1 h and the other with a collocation time longer than 1 h. The respective RMSEs of the retrieved current velocity component for these two groups are 0.28 m/s and 0.33 m/s, implying the impact of collocation time. Note that the drifter current measurements are available every 6 h, which may also induce additional uncertainties.
# A Novel Capsule Network Based on Wide Convolution and Multi-Scale Convolution for Fault Diagnosis
## 3 Proposed WMSCCN for Fault Diagnosis
### Capsule Network Structure  
After the multi-scale convolutional layer, we access the capsule network, which includes the primary capsule layer and the digit capsule layer. Among them, the primary capsule layer converts the extracted fault features into capsule form and transmits them to the digit capsule through dynamic routing. Each digit capsule represents a specific fault type and its length represents the probability of the specific fault type.  
Specifically, first of all, two convolutional layers with a size of \(3\times 1\) are used to transform fault features into primary capsules with dimensions of 8. Further, the primary capsule \(i\) is transmitted to the digit capsule \(j\) through the dynamic routing mechanism, and this process retains all the spatial information of the primary capsule. In the model of this paper, the dimension of the digit capsule is 10. The number of iterations of dynamic routing is consistent with the original literature [24], which is 3.  
The activation function in traditional neural networks is usually used to non-linearly activate the output of the network layer and only works on scalars. In the capsule network, the squashing function is a special activation function to normalize vectors. This activation function shrinks short vectors to almost zero length and long vectors to lengths slightly less than 1 and keeps its orientation unchanged.  
It is worth noting that the length of the vector mentioned here refers to its L2 norm. The formula is as follows.  
\[\mathbf{v}_{j}=\frac{\|\mathbf{s}_{j}\|^{2}}{1+\|\mathbf{s}_{j}\|^{2}}\frac{ \mathbf{s}_{j}}{\|\mathbf{s}_{j}\|}, \tag{4}\]  
where \(\mathbf{v}_{j}\) is the vector output of capsule \(j\) and \(\mathbf{s}_{j}\) is its total input.
## 4 Discussion
Notably, a linear model was the best fit for coastal waters (Figure 6). The most optimal overall fit was achieved when the database was segmented based on climatic conditions. The El Nino condition presents a more intricate data distribution compared to La Nina and normal conditions. During El Nino events, Chlorophyll-\(a\) was lower than during the transitional and La Nina conditions. When analyzing the interannual variations associated with ENSO, ref. [40] found an increase in the mesotrophic area in the IMECOCAL region during these warm events. This coincides with the fact that for the IMECOCAL area, the dispersion in \(\mathfrak{R}\) values is less during an El Nino event. Ref. [41] found that during the cold event of 2008, the productivity rates increased. This coincides with the high Chlorophyll-\(a\) observed in the IMECOCAL data during La Nina and normal conditions during El Nino. During La Nina events, the colder waters and intensified upwelling [17] enhance nutrient availability for phytoplankton, thereby elevating mChl-\(a\) levels, predominantly in coastal and transitional stations. Such events alter the region's physical, biological, and chemical attributes, promoting microhyptoplankton growth. The consequential shift in the phytoplankton community, as reflected in the algorithms, underscores the climatic conditions' significance for the region.
## 3 LDAF Background and Overview
### LDAF Architecture
#### 3.1.2 Connections  
A LDAF connection determines how end devices connect to the LDAF server. There can be various options implemented to meet the needs of the constrained devices. Current implementation supports WebSockets (over TCP/IP), HTTP (over TCP/IP) and integration with the LoRaWAN and The Things Network [41]. In a similar way, CoAP (over UDP/IP) or MQTT can be added.  
A connection receives and forwards messages from a device to the corresponding Service. During the connection setup, service definitions at the LDAF client and at the LDAF server are compared. If they do not match (e.g., client requesting unavailable services), the connection is not established. This assures that the server can provide all the required services, that the message types are correctly interpreted, and that the message payload is correctly decoded.
# Coordinating Entrainment Phenomena: Robot Conversation Strategy for Object Recognition
## 5 Experiment 1
### Experiment Settings
#### 5.1.2 Conditions  
To verify the hypotheses, we controlled the robot's confirmation behavior and the arrangement of objects in the environment.  
The confirmation factor was a within-participants design and had two levels: minimum attributes and all attributes. In the minimum attributes level, the robot confirms the indicated object with minimum information for identifying the indicated object among objects. In the all information level, the robot confirmed the objects with all the information, it gave every attribute of an object and pointed during the confirmations. The speech format of the confirmations was the sequence of the object attributes. For example, the robot said, \"That circle and red book?\" or \"That triangle, B and blue book?\" In this experimental design, in the confirmation factor, we did not separate the speech from the pointing gesture control as a level. This is because the content of robot's speech is calculated depending on the result of pointing gesture control, and strict separation of speech and pointing gesture control is difficult. In addition, even if the speech level is fixed to the minimum information level, it is difficult to distinguish the effects of a robot's gesture itself from the effects of combination of a speech and a robot's gesture. Therefore, in this experimental design, we did not separate the speech control from the pointing gesture control and treated both controls together as the minimum information level.  
The arrangement factor was a within-participants design and had three levels: sparse set, two groups and congestion. We set this factor considering the influence of the arrangement of objects on the performance of the indicated object recognition because the arrangement may affect what kinds of lexical expressions and pointing gestures are chosen by people. For example, if objects were sparsely arranged, humans would refer to an object by pointing gestures and a deictic. On the other hand, if objects were densely arranged, humans would refer to an object by a speech that is composed of many object's attributes because only use of pointing gestures is not enough to identify the object. Hence, not only the robot's confirmation behavior but the arrangement of objects would have influence on humans' reference behavior, and the performance of the indicated object recognition would change depending on the arrangement.  
In the experiment, we asked participants to select objects and arrange them freely under these three conditions. For example, we instructed them to \"Arrange the books close to each other,\" \"Arrange the books into two groups,\" and \"Separate each book from the others\" in the congestion, two groups, and sparse set conditions, respectively. Figure 10 shows the examples of objects arrangement by a participant.
## 2 System and Failure Mode Analysis  
### CDC System and Operation Principle  
The CDC system optimizes the vehicle's riding comfort and steering stability by changing the damping force in real time using the acceleration sensor and the vehicle's internal signals. It receives input from the vehicle's vertical acceleration sensor, calculates the target damping force to control the vehicle's behavior, and changes the damping force by changing the solenoid current inside the variable damper. Figure 1 shows the system configuration.  
The control logic that generates the necessary target damping force by analyzing the vehicle's behavior based on the acceleration sensor and internal vehicle signals is schematized in Figure 2. Based on the vehicle acceleration sensor, skyhook control theory is applied to perform real-time damping force proportional control, which controls vehicle ride comfort [13; 14; 15; 16]. [PERSON] and [PERSON] explored similar control approaches in semi-active suspension systems using skyhook and groundhook controllers, providing additional insights into control strategies [17]. Additionally, during an event such as a lane change, braking, or acceleration event, control is performed to control vehicle behavior. By integrating the damping force command value based on each vehicle behavior state, proportional solenoid control for changing the damper's damping force is performed in real time, and the appropriate change in damping force affects vehicle performance [18; 19].  
Figure 3 provides an example of how changes in damping force affect vehicle control performance. When the vehicle's behavior is influenced by the road surface, the system's damping force control is instrumental in minimizing this impact or bringing it close to an appropriate target physical quantity. This adaptive control is crucial in ensuring optimal vehicle performance, as it allows for constant changes in damping force to be generated, thereby improving vehicle performance. It is important to monitor these changes in damping force performance and major failures while driving to ensure the system's effectiveness.  
Figure 1: Continuous damping control system configuration.  
Figure 2: Brief CDC system control functions.
# Methods for Assessing Human-Machine Performance under Fuzzy Conditions
## 4 The Center of Gravity Defuzzification Technique  
The process of solving a problem with principles and methods of FL involves the following phases:  
* _Choose_ the suitable universal set.
* _Fuzzify_ the given data by defining suitable membership functions for the FSs involved in this procedure.
* _Elaborate_ the fuzzy data with FL techniques for expressing the solution of the given problem in the form of a _unique_ FS.
* _Defuzzify_ the above FS by representing it with a real numerical value to \"translate\" the problem's solution into the natural language.  
There are more than 30 defuzzification methods in use, but two of them are the most popular. In the _Maximum method_, the crisp value representing the solution's output is one of the values at which the corresponding FS has its maximum truth (e.g., [15] (Chapter 4, pp. 97-99)). On the other hand, in the _Center of Gravity (COG) technique_, the representative crisp value is obtained by calculating the coordinates of the COG of the level's area S between the graph of the membership function of the corresponding FS and the OX axis [28].  
[PERSON] [29] developed in 1999 a fuzzy model for representing mathematically the process of learning a subject matter in the classroom and later, considering the class as a fuzzy system, he calculated its existing uncertainty (see Section 3) for assessing the student mean performance [30]. [PERSON] et al. [31], based on the [PERSON]'s model, adapted properly the COG defuzzification technique for use as an assessment method of student learning skills. Since then, [PERSON] and [PERSON] applied jointly or separately the COG method, termed as _Rectangular Assessment Model (RFAM)_, in many other types of assessment problems (e.g., see [15] (Chapter 6)). Here, we present the headlines of RFAM and we compare it to the traditional assessment method of calculating the _Grade Point Average (GPA)_ index.
# Study About the Influence of Diatoms on the Durability of Monumental Limestone
## 3 Results and Discussions
### Results of Water Absorption Test at Atmospheric Pressure  
Figure 13(a),b illustrate the mass increase of the samples at 24 h intervals. The average values indicate that the diatom-treated samples absorbed more water than the control samples during the first two 24 h intervals. In the third and fourth intervals, the absorption  
\begin{table}
\begin{tabular}{c c} \hline
**Average Open Porosity Control Samples** & **Average Open Porosity Diatom Samples (\%)** \\ \hline
7.69 & 8.66 \\ \hline \end{tabular}
\end{table}
Table 3: Average of the results obtained from open porosity.  
\begin{table}
\begin{tabular}{c c} \hline
**Average Volume of Open Pores in Control** & **Average Volume of Open Pores in** \\
**Control Samples (mL)** & **Diatom Samples (mL)** \\ \hline
9.89 & 11.37 \\ \hline \end{tabular}
\end{table}
Table 4: Average volume of open pores in control and diatom samples.  
levels of both groups converged. Thereafter, the diatom samples exhibited a reduced absorption rate, ultimately absorbing less water than the controls. This decrease may be attributed to a potential pore-blocking effect in the rock matrix, which could hinder further water penetration.  
Figure 15 shows the cumulative average water absorption of both the control and diatom-treated samples throughout the experiment. Analysis of the average values reveals that all samples reached saturation after 120 h of immersion. Moreover, at every stage of the test, the diatom-treated samples consistently absorbed greater amounts of water compared with the control samples. After 240 h of immersion, the diatom samples absorbed an average of 14.10 g of water, while the control samples absorbed an average of 11.17 g,  
Figure 14: Average of mass of water absorbed in each 24 h interval of the absorption test at atmospheric pressure: (**a**) full graph and (**b**) enlarged graph for better readability.  
Consequently, the diatom-treated samples absorbed approximately 2.93 g more water than the controls.  
It can be verified from the average values in Table 6 that, as expected, water absorption in the diatom-treated samples is 1.19% higher than that in the control samples. This result confirms that the diatom-treated samples absorb more water and at a faster rate during the initial 24 and 48 h intervals. Although their absorption rate subsequently slows, it is not sufficient to prevent them from ultimately reaching a higher total water uptake. This behavior aligns with the increased open porosity observed in these samples.  
However, it cannot yet be conclusively determined whether diatoms specifically improve or reduce water absorption on the upper face of the treated samples. As in the test above, this procedure involves complete immersion of the samples, preventing the evaluation of individual surfaces in isolation.
# The Identification and Assessment of Rail Corrugation Based on Computer Vision
## 3 Rail Corrugation Identification Based on Improved SPM Model
### Identification Experiments and Results
#### 3.5.2 Performance Evaluation for Corrugation Identification  
Corrugation identification is one of the critical parts of this work and this subsection will evaluate the performance of the proposed method for corrugation identification. The proposed identification method is compared with three baselines including Gabor+SVM [13], Accumulate Energy Thresholding (AET) [14] and Maximum Energy (ME)+SVM [15]. More precisely, the global 8-dimensional Gabor filtering features and SVM classifier are adopted in Gabor+SVM. AET uses the local dominant frequency features and fixed thresholding. And the local dominant frequency features and SVM classifier are applied in ME+SVM. In addition, all parameters are consistent with the original literature and 10-fold cross validation method for SVM training and prediction is used.  
In this subsection, to assess the proposed method quantitatively, indicators such as precision (\(P\)), recall (\(R\)) and accuracy (\(ACC\)) are used, which are widely adopted in many research fields [37]. The formula for calculating these indicators as follows:  
\[P=\frac{TP}{TP+FP} \tag{12}\]  
\[R=\frac{TP}{TP+FN} \tag{13}\]  
\[ACC=\frac{TP+TN}{TP+FP+TN+FN} \tag{14}\]  
where \(TP\) represents the number of corrugation images that are correctly detected. \(FN\) indicates the number of corrugation images for missed inspection. \(FP\) is the number of images that are falsely reported as corrugation. \(TN\) stands for the number of non-corrugation images that are correctly excluded.  
Table 1 shows the comparison result between the traditional methods and the proposed method. Note that the size of the two visual codebooks is adaptively set to \(patchNum\_sift*0.7\) and \(patchNum\_lbp*0.7\), respectively ( \(patchNum\_sift\) is the number of patches extracted according to dense_SIFT and \(patchNum\_lbp\) refers to the number of patches extracted according to Uniform LBP). It can be seen that the algorithm proposed in this paper achieves a sound performance, especially the recall rate is significantly improved. The recall rate is the most critical indicator among the three indicators and the high recall rate means that the number of undetected corrugation images is small. In summary, in the light of the most distinguishable texture features and spatial structure relationship of the corrugation, the multi-features extracted in this paper can represent the corrugation image more comprehensively than those mentioned in the baselines. Moreover, the results demonstrate that the LLC method can facilitate precise and efficient encoding of the corrugation image features, which benefits from the degradation of image feature reconstruction error and the effective organization of image feature vectors.
# Optical accelerometers for Detecting Low-Frequency Micro-Vibrations
## 2 Principle and Requirements
### Requirements of Low Frequency Accelerometers
#### 2.2.2 Structural Health Monitoring  
Bridges vibrate when vehicles pass over them, and high buildings sway when strong winds blow around them. If the vibration of large buildings exceeds the threshold value, catastrophic losses may occur. Accelerometers can measure the structural vibration of these buildings to determine their structural health and provide early warning for potential damage. Structural vibrations in large buildings are typically low-frequency vibrations below 100 Hz, and they require a multipoint arrangement for monitoring. Therefore, accelerometers must achieve high accuracy with a small size and meet the requirements for long-distance measurement [10]. An optical sensor-based accelerometer has high measurement accuracy while enabling long-distance and distributed measurements; thus, it can meet the structural health monitoring needs of large buildings [11].
## 3 Experimental Results
### Taguchi Experimental Results  
The proposed T-MCCFNN model included a CNN and TSK-type compensatory FNN. The CNN, which used the scale factor to adjust the convolution kernel size of the two remaining convolutional branches, comprised three convolutional layers and two maximum pooling layers. The TSK-type compensatory FNN comprised a fuzzy layer, a compensatory rule layer, and an output layer. The initial parameters of the proposed T-MCCFNN model are depicted in Table 3. The model architecture included many tunable hyperparameters. The Taguchi method was used to find the optimal parameter combination through the use of L27(3\({}^{7}\)), as shown in Table 2. The subsequently generated OA reduced the number of experiments required. In our Taguchi experiment, we selected [PERSON] as our optimizer, a learning rate of 0.001, a batch size of 256, and 50 training epochs. To improve the stability of the Taguchi experiment, we conducted ten experiments for each set of parameter combinations and calculated the average. The results of OA are shown in Table 4.  
The \(S/N\) ratio of each control factor was calculated and used to analyze the contributions of each parameter to model performance. As shown in Table 5, the optimal parameter combinations of Conv1_filters (A), Conv1_kernel_size (B), Conv1_strides(C), Scale (D), Conv2_filters (E), Conv2_kernel_size (F), and Conv2_strides (G) values were 64, 50, 4, 4, 256, 9, and 1, respectively. The control factors with the highest contribution to the model performance were Conv2_strides (G), Conv2_filters (E), and Conv1_strides (C). Thus, changes in stride size tended to affect the performance of the T-MCCFNN model. Figure 3 depicts the main effects on the \(S/N\) ratio. In particular, a smaller stride size was associated with a higher \(S/N\) ratio for both Conv1_strides (C) and Conv2_strides (G).  
Figure 2: Fpz-Cz channel location.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Channel** & **Kernel Size** & **Strides** & **Other Parameters** \\ \hline Conv layer 1 & 64 & 100 & 6 & - \\ MaxPool layer 1 & - & 8 & 8 & - \\ Conv layer 2-1 & 128 & 8 & 1 & - \\ Conv layer 2-2 & 128 & 8 & 1 & - \\ MaxPool layer 2 & - & 4 & 4 & - \\ Scale & - & - & - & Scale = 4 \\ Concatenate layer & - & - & - & Mode = Depthwise \\ GlobalMaxPool layer & - & - & - & - \\ Compensatory fuzzy & - & - & - & Rule\_node = 64 \\  & & & & \\ Output layer & 5 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Initial parameters of the proposed T-MCCFNN model.