esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Method
### Data Fusion in MDI-CNN  
Due to the impact of the environment noise, a raw signal needs to be processed before it can be entered into the traditional diagnostic model. However, diagnostic results are dependent on the selection of spectrum analysis method. In view of that, the MDI-CNN model extracts and fuses fault information of different dimensions through multiple input layers. The MDI-CNN is mainly composed of multidimensional matrix construction, initial feature extraction, and fusion model import.  
In the first stage, a series of samples from different input layers are fed into the network model and then combined into a multidimensional sample matrix. After the convolution operations are performed in each dimension of the sample matrix, the multidimensional feature map matrix is fed to the next stage. Finally, the feature values at the same position in each dimension are fused through the fusion model. There are many types of data fusion methods, such as the algebraic method, principal component transformation (PCT), Bayes estimation method, and others. In this paper, the weighted fusion model based on the algebraic method is used for data fusion. The fusion model is defined as:  
\[F=q_{1}F_{1}+q_{2}F_{2}+\cdots+q_{n}F_{n} \tag{5}\]  
where \(F_{n}\) denotes the \(n^{th}\) feature value, and \(q_{n}\) denotes the \(n^{th}\) weight coefficient.  
The fusion model has three advantages. First, the original signal can be processed by a variety of signal processing methods, and then all the results can be entered into the model together, which enhances model generalization ability. Second, instead of a single input layer, the MDI-CNN has multiple input layers, which not only enhances the ability to extract features, but also reduces the computation burden. Third, the model convergence speed is accelerated, due to the use of processed data that has a strong clustering compared to raw data. The general steps of the fusion model are described as follows.  
Step 1: For an input layer \(\{i,L_{i}\}(i=1,2,\cdots,n)\), a multidimensional data matrix is defined as \(L=\{L_{1},L_{2},\cdots,L_{n}\}\), where \(L_{i}\) denotes the \(i^{th}\) layer of input data, and \(n\) denotes the number of model input layers.  
Step 2: Enter the \(i^{th}\) data matrix \(L_{i}\) into the initial convolution layer of the CNN model. After the convolution operation, the feature map \(F_{i}\) is obtained.  
Step 3: Add a weight coefficient to each feature map. In the output position of the convolution layer, the final input of the feature map is defined as \(F=q_{1}F_{1}+q_{2}F_{2}+\cdots+q_{n}F_{n}\).
###### Abstract  
The creation of as-built Building Information Models requires the acquisition of the as-is state of existing buildings. Laser scanners are widely used to achieve this goal since they permit to collect information about object geometry in form of point clouds and provide a large amount of accurate data in a very fast way and with a high level of details. Unfortunately, the scan-to-BIM (Building Information Model) process remains currently largely a manual process which is time consuming and error-prone. In this paper, a semi-automatic approach is presented for the 3D reconstruction of indoors of existing buildings from point clouds. Several segmentations are performed so that point clouds corresponding to grounds, ceilings and walls are extracted. Based on these point clouds, walls and slabs of buildings are reconstructed and described in the IFC format in order to be integrated into BIM software. The assessment of the approach is proposed thanks to two datasets. The evaluation items are the degree of automation, the transferability of the approach and the geometric quality of results of the 3D reconstruction. Additionally, quality indexes are introduced to inspect the results in order to be able to detect potential errors of reconstruction.  
as-built BIM; building indoor environments; Terrestrial Laser Scanning; point clouds; 3D reconstruction; assessment; automation; transferability; quality Article
# Variable Structure PID Controller for Satellite Attitude Control Considering Actuator Failure
## 4 Discussion
### Actuator Failure Situation  
In this section, the simulation of the fault tolerant controller proposed in Section 3.3 is given. First, assume that, during 20-50 s, the control actuator failure happens, and the system actuator failure is set as follows  
\[\begin{split}\mathbf{u}=\mathbf{\rho}\mathbf{u}_{d}\\ \mathbf{\rho}=diag(0.5,0.7,0.6)\end{split} \tag{45}\]  
It could be found that the actual actuator could only output part of the desired torque in the three axes. Only 50%, 70%, and 60% torque calculated by the control algorithm could be generated by the actuator in the \(x,y,z\) axes, respectively. The simulation would be made under this condition for both the controller of the comparing group in Section 4.1 and the controller proposed in this paper.  
The fault detection factor \(\overline{\delta}\) is set as follows:  
\[\overline{\delta}=5\times 10^{-3} \tag{46}\]  
First, in order to demonstrate the effect of the actuator on the system performance, the PID and finite time controller in Section 4.1 under the same actuator as Equation (45) is given.  
Based on Figures 7 and 8, it could be easily found that the system performance is worse than that in Section 4.1, the convergence rate slows down, and the steady accuracy drops. This proves that actuator failure would harm the system performance when it is not properly solved.  
The simulation results of the fault tolerant controller (34) in Section 3.3 are given as follows.  
Based on Figures 9 and 10, it could be found that the system is still stable when the actuator failure happens, and this proves the effectiveness of the method proposed in this paper. The fault detection factor could judge whether the actuator failure happens and when it happens, and the fault tolerant controller could ensure system stability. It could also be found that the system convergence rate is about 80 s, which is slower than the controller in Section 3.2, but still better than the standard PD controller. The system convergence  
Figure 7: Curve of the system state under the standard PID controller with actuator failure.  
rate is about \(1\times 10^{-3}\) rad/s of angular velocity and \(3\times 10^{-3}\) of attitude quaternion, i.e., 0.15 deg of the Euler angle, which means that when actuator failure is relieved, the system steady accuracy could still be maintained at a high level. Based on Figure 11 (blue curve is the actual torque and red curve is the expected torque), it could be found that the effect of the actuator is the drop of output torque, and based on the simulation results, it could be found that the proposed method is robust to this type of failure.  
Figure 8: Curve of the system state under the finite time controller from reference [20] with actuator failure.  
Figure 9: Curve of angular velocity under the controller proposed in this paper with actuator failure.
# Adsorption of Ni2+ and Cd2+ from Water by Calcium Alginate/Spent Coffee Grounds Composite Beads
## 3 Results and Discussion
### Analysis of Adsorption Isotherms
In the current study, Cd\({}^{2+}\) adsorption values were higher than the Ni\({}^{2+}\) adsorption values, and are comparable with values obtained in other studies. It is worth mentioning that the adsorption by CA-SCGs beads in this study did not fit well with the Langmuir model of adsorption as was discussed earlier in this manuscript. The Sips model, however, showed a better correlation.
## 3 Results and Discussion
### Effect of Reuse of Pretreatment Solution on Soluble Protein Removal in Corncob
#### 3.1.2 Effect of Reuse Round of Pretreatment Liquid on Soluble Protein Removal in Corncob  
Figure 2 shows that the increments of soluble proteins in the solution for three reuse rounds of the pretreatment liquid are in negligible difference comparing to the increment of the soluble proteins in the solution after pretreatment of the corncob with the new added liquid. Therefore,  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Reuse of** & **Volume of** & **Volume of** & **Solution** & **Concentration of** & **Increment of** \\
**Pretreatment** & **Previous Used** & **Deionized** & **pH after** & **Protein** & **the Soluble** \\
**Liquid (n)** & **Liquid (mL)** & **Water (mL)** & **Pretreatment** & **(mg-L\({}^{-1}\))** & **Protein** \\ \hline
0 & 0 & 300 & 3.1 & 228 & 68.4 \\
1 & 225 & 75 & 3.2 & 395 & 67.2 \\
2 & 228 & 72 & 3.1 & 532 & 69.5 \\
3 & 226 & 74 & 3.1 & 628 & 68.2 \\ \hline \end{tabular}
\end{table}
Table 1: Concentration increment of soluble protein in solution after corncobs being pretreated.  
Figure 1: Scheme on the experimental process round of the pretreatment liquid applied for corncob pretreatment.  
the corncob pretreatment liquid can be continuously reused for effectively removing soluble proteins from the corncob.  
Note that as long as the increment of soluble proteins in the pretreatment liquid was significantly reduced and/or the soluble proteins in the pretreatment liquid was saturated, the pretreatment liquid was no longer applicable. At this point, we believe that the pretreatment liquid has attained a threshold processing round with respect to effective removal of soluble proteins in the corncob.
# Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics
## 5 Discussion
#### Real-World Applications and Limitations  
* Real-World Scenarios: Applications: The predictive models in this study have direct application in several industrial settings. To further extend this application, the models perform the optimization of resource allocation themselves for quality control processes, including defect detection via the application of machine learning, improving efficiency and eliminating downtime while increasing production throughput in the manufacturing industry, for example. For healthcare analytics, predictive models  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Operator** & **Best Model** & **Total Time \(R^{2}\)** & **Memory Usage \(R^{2}\)** \\ \hline K-Means & Gradient Boosting Regressor & 0.922 & 0.98 \\ \hline Random Forest & Random Forest Regressor & 0.942 & 0.76 \\ \hline Word2 Vec & Linear Regressor & 0.972 & 0.06 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Best models and corresponding metrics for each operator.  
can be used to plan costs and generate timeliness of the results in priorities such as medical image processing and patient outcome predictions.  
These models can help in logistics and supply chain management where real time analytics workflows such as route planning and inventory control incur delays in processing large datasets and can cause financial inefficiencies. In such dynamic environments, it provides the ability to carry out proactive scheduling and resource management, i.e., the ability to forecast and execute time and memory usage.
* Challenges and limitations: There are a number of limitations. During training, the predictive models have computational overhead, and at a sufficient scale, this overhead could rigorously outweigh the resource savings provided during execution. Moreover, implementing these models within existing Apache Spark workflows with architectural modifications is needed. A limitation of this work is dependency on hyperparameter tuning, due to which it may be challenging to utilize in a resource-constrained or expertise-scarce environment.
* Future Directions: To address these limitations, future research could be based on using hybrid modeling techniques to make a predictive task scalable and robust. Furthermore, by embedding automated hyperparameter optimization frameworks within the predictive modeling process, we can simplify the deployment of these tools for broader industrial use.
# Knowledge-Aware Enhanced Network Combining Neighborhood Information for Recommendations
## 4 Experiments
### Experimental Parameter Analysis
#### 4.6.5 Impact of User Propagation Depth  
In this part, we observe the changes in KCNR's performance, by varying the depth of user perception propagation. Table 8 shows the AUC evaluation metrics for the three  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{k}\) & \(\mathbf{2}\) & \(\mathbf{4}\) & \(\mathbf{6}\) & \(\mathbf{8}\) & \(\mathbf{16}\) & \(\mathbf{32}\) \\ \hline MovieLens-1M & 0.923 & 0.923 & **0.926** & 0.924 & 0.923 & 0.923 \\ Book-Crossing & **0.750** & 0.744 & 0.743 & 0.742 & 0.734 \\ Last.FM & 0.805 & 0.807 & 0.808 & **0.811** & 0.808 & 0.807 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of AUC on three datasets with different item neighbor sampling sizes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{d}\) & \(\mathbf{4}\) & \(\mathbf{8}\) & \(\mathbf{16}\) & \(\mathbf{32}\) & \(\mathbf{64}\) & \(\mathbf{128}\) \\ \hline MovieLens-1M & 0.918 & 0.924 & 0.925 & **0.926** & 0.925 & 0.922 \\ Book-Crossing & 0.743 & 0.744 & **0.750** & 0.742 & 0.743 & 0.738 \\ Last.FM & **0.811** & 0.808 & 0.808 & 0.807 & 0.806 & 0.803 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of AUC on three datasets with different embedding dimensions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{h}\) & \(\mathbf{1}\) & \(\mathbf{2}\) & \(\mathbf{3}\) & \(\mathbf{4}\) \\ \hline MovieLens-1M & 0.924 & **0.926** & 0.923 & 0.923 \\ Book-Crossing & 0.742 & **0.750** & 0.740 & 0.735 \\ Last.FM & 0.807 & 0.808 & **0.811** & 0.806 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of AUC on three datasets with different item perception depths.  
datasets. From the table, we can see that the model performance is optimal at a propagation depth of two for the MovieLens-1M dataset, three for the Book-Crossing dataset, and one for the Last.FM dataset. This is because propagating too far in the KG, in addition to bringing in more supplementary information to enhance the user representation, may bring in more noise. Therefore, a reasonable propagation depth can be set, to obtain the optimal model prediction capability in different recommendation scenarios.
# An IoT System for Remote Monitoring of Patients at Home
## 7 Experiments
### Experiment on the Protocol Conversion Process  
When a PHD wants to connect to the IoT server, the PHD sends the server an ISO/IEEE 11073 Association Request message for connection. A gateway, located between the PHD and the server, needs to perform the protocol conversion process mentioned in Section 5, because the server does not support the ISO/IEEE 11073 protocol. Figures 15 and 16 show screen captures of the results of the protocol conversion execution for connection establishment. The upper screen in Figure 15 shows the content of the ISO/IEEE 11073 Association Request/Response message, while the lower screen shows that of the oneM2M Operation (Retrieve) primitive Request/Response message. An explanation on the figures is omitted because the same example was explained in detail in Figure 6. The content of the oneM2M message is represented in XML.  
In the upper screen of Figure 16, \"\(<\)responseStatusCode\(>\)\" is 4004 (on the third line), which means the PHD which requests connection has not been registered in the IoT server. Therefore, the PHD sends the IoT server its configuration in an ISO/IEEE 11073 Present (Notice Configuration) Request message for its registration to the server, as shown in Figure 17.  
Figure 16: Screen captures of connection establishment (Conversion of a oneM2M Retrieve primitive Response message into an ISO/IEEE 11073 Association Response message).  
Figure 15: Screen captures of connection establishment (Conversion of an ISO/IEEE 11073 Association Request message into a oneM2M Retrieve primitive Request message).  
When the registration process for the PHD finishes, the resource tree shown in Figure 18 is created by the IoT server (IN-CSE). \"<AE>\" tag node for the PHD is created in the resource tree. Measured biomedical values of three objects by the PHD can be stored in the resource tree later. \"content = \"Default\"\" means that the related measured value will be stored when a oneM2M Operation (Update) primitive Request message arrives later.  
Biomedical values measured by the PHD are delivered in an ISO/IEEE Present (Store Sensing Data) Request message, as shown in the upper screen of Figure 19. The explanation of Figure 19 is similar to what has been previously explained, and will be omitted here.  
Figure 17: Screen captures of registration (Conversion of an ISO/IEEE 11073 Present (Notice Configuration) Request message into a oneM2M Create primitive Request message).  
Figure 18: Resource tree created by the IoT server (IN-CSE) after the registration process.
## 4 Design-Dependent Loads
### Identify the Boundary Using the DRLSE Method  
In the DRLSE model, the boundary of the gray image is represented as the zero-level contour of the final level set function. The grayscale image (0-1) obtained in topology optimization is actually a 1-bit grayscale image. To use the DRLSE algorithm, the image must first be turned into an 8-bit grayscale image:  
\[I=255-255^{\star}\mathbf{x} \tag{30}\]  
where \(I\) is the 8-bit grayscale image data and \(x\) is the element node density matrix.  
Since the boundary of the grayscale image is not obvious, the DRLSE algorithm does not necessarily find a suitable loading surface, which also makes it difficult to control the expansion of the loading surface during the optimization process. To overcome these types of problems, a density threshold is introduced, and the density of elements that is less than the density threshold is reset to zero during the image search. In this way, the obtained grayscale image has a clear boundary, and the expansion of the loading surface can also be controlled by adjusting the density threshold. The setting of the density threshold is similar to that in reference [19].  
Assuming that the coordinates of the two intersection points of the pressure loading surface and the element boundary are \((x_{1},y_{1})\) and \((x_{2},y_{2})\), the equivalent nodal load matrix \(f_{e}\) acting on the element can be determined with the element shape function N and the load matrix \(\mathcal{F}\). The integral over the action boundary is obtained [19]. Then, the following expression is obtained:  
\[f_{e}=\int\limits_{L}N^{T}\mathcal{F}\mathrm{d}l=P\int_{x_{1}}^{x_{2}}N^{T} \begin{cases}-\frac{\Delta y}{\Delta x}\\ 1\end{cases}dx \tag{31}\]  
Converting the integral interval from \([x_{1},x_{2}]\) to \([-1,\,1]\), the above formula can be calculated using the Gaussian integral formula, and the following expression is obtained:  
\[\mathbf{f}_{e}=\frac{L}{2}\!\int_{-1}^{1}\mathbf{N}^{T}d\xi\!\left\{\!\!\begin{array}[ ]{l}-\Delta y/L\\ \Delta x/L\end{array}\!\right\}=\frac{1}{2}\!\sum_{i}\omega_{i}\mathbf{N}(\mathbf{g} _{i}^{\prime})^{T}\!\left\{\!\!\begin{array}{l}-\Delta y/L\\ \Delta x/L\end{array}\!\right\} \tag{32}\]  
where P represents the magnitude of the pressure load, and \(\omega_{i}\) and \(g_{i}\) are the \(i\)-th weight function and the corrected Gaussian quadrature point, respectively. Letting \(\Delta x=x_{1}-x_{2}\), \(\overline{x}=\frac{x_{2}-x_{1}}{2}\), and similarly for \(y\), then:  
\[\mathbf{g}_{i}^{\prime}=\infty\!\left\{\!\!\begin{array}{l}\frac{1}{2}g_{i} \Delta x+\overline{x}\\ \frac{1}{2}g_{i}\Delta y+\overline{y}\end{array}\!\right\} \tag{33}\]  
After the above calculation, the pressure on the loading surface in the element is equivalent to the nodal load, and finite element analysis can then be carried out.
## 4 Discussion
### Significance of Our Study  
This study offers a highly cost-effective, compatible, and low-complexity strategy for RR monitoring using WiFi CSI. The ESP32, a key component of our system, is known for its affordability, accessibility, and robust community-driven support, making it an ideal choice for this application. By utilizing this inexpensive and widely available hardware, we provide a practical solution that is within reach for a broad audience. Furthermore, we employed advanced filtering techniques to enhance the accuracy of measurements across different breathing patterns, including the slow breathing associated with sleep apnea. The involvement of multiple participants further validates the robustness and generalizability of our approach.
# Mapping Gaps in Sugarcane Fields in Unmanned Aerial Vehicle Imagery Using YOLOv5 and ImageJ
## 2 Literature Review
### Image] for Image Processing  
Imagej is a Java-based platform for image processing, developed by [PERSON] at the National Institutes of Health (NIH). Since its launch in 1997, it has been widely utilized in various image-processing applications [22].  
While object detection systems like YOLO are designed for identifying and counting objects in images, they lack the capability of detailed image manipulation provided by software like ImageJ. ImageJ offers a range of features, including measuring line lengths, calculating areas, and identifying and counting objects in images. It also supports advanced operations like filtering and performing mathematical operations on pixel values. For color images, these operations are carried out channel by channel, such as the red, green, and blue channels in RGB images [23].  
One notable feature of ImageJ is its ability to generate customizable reports that are tailored to specific applications. In this study, it was used to map planting failures in images. The location of an object within an image can be specified in pixels, such as the object's centroid or the top-left pixel. Additionally, pixel measurements can be converted to other units, such as meters or centimeters, provided that a standard scale is available [23].
# Analysis of the Near-Ground Wind Field Characteristics during Typhoon Soulik
## 3 Quantitative Analysis of Mean Wind Properties  
### Mean Velocity and Orientation of Wind  
The three-dimensional ultrasonic wind speed measures wind velocity components in each of the three directions, denoted as \(v_{x}\), \(v_{y}\), and \(v_{z}\). The average wind velocity \(U\) and wind direction angle \(\phi\) are computed using the vector decomposition method, leading to the following equations:  
\[U=\sqrt{\overline{v_{x}}^{2}+\overline{v_{y}}^{2}+\overline{v_{z}}^{2}} \tag{1}\]  
\[cos\phi=\frac{\overline{v_{x}}}{U} \tag{2}\]  
\[sin\phi=\frac{\overline{v_{y}}}{U} \tag{3}\]  
where \(\overline{v_{x}}\), \(\overline{v_{y}}\), and \(\overline{v_{z}}\) represent the mean wind velocities of \(v_{x}\), \(v_{y}\), and \(v_{z}\) over a duration of 10 min, respectively. The fluctuating wind speed can be calculated using the following formulas:  
\[u^{\prime}=v_{x}cos\phi+v_{y}sin\phi-U \tag{4}\]  
\[v^{\prime}=-v_{x}cos\phi+v_{y}sin\phi \tag{5}\]  
\[w^{\prime}=v_{z}-\overline{v_{z}} \tag{6}\]  
where \(u^{\prime}\), \(v^{\prime}\), and \(w^{\prime}\) stand for the fluctuating wind speeds in the longitudinal, transverse, and vertical directions, respectively.  
Figure 2 depicts the temporal variations of the 10 min average wind velocity and wind direction at various heights from 0:00 to 21:00 on 13 July. Throughout this time period,  
Figure 1: Typhoon Soulik: (a) Typhoon center track, and (b) Observation tower.  
the average wind velocity rose in correlation with the height of observation, peaking at its highest point around 13:10 on the 13 th. The average wind speeds at heights of 15, 27, 53, 67, and 82 m were recorded as 15.24 m/s, 16.67 m/s, 17.22 m/s, 17.72 m/s, and 18.24 m/s, respectively, with corresponding wind direction angles of 73.58\({}^{\circ}\), 83.23\({}^{\circ}\), 80.9\({}^{\circ}\), 74.21\({}^{\circ}\), and 76.40\({}^{\circ}\). Furthermore, the wind direction angle showed little variation with height and stabilized around 7:00 a.m. on 13 July 2013.