esa-sceva/satcom-chunk-collection huggingface dataset:
# Forward Fall Detection Using Inertial Data and Machine Learning
## 3 Development of the Fall Detection Models
### Training the KNN Classifier  
After generating the dataset using the digital model, with Matlab programming tools, we imported the data into the classifiers. To train the data, the KNN classifier was chosen [29]. By changing the k-fold cross-validation to 5, each method was trained independently on the imported data.  
Figure 15: Training performance of the trained LSTM network (**a**) Confusion matrix; (**b**) True positive (TPR) and false negative rates (FNR).  
The model type was set to a Fine KNN with 3 neighbors, using the City block distance metric and squared inverse distance weight. Additionally, all features were used in the model, and PCA was disabled. The Fine KNN in MATLAB refers to a K-Nearest Neighbors (KNN) classifier model that uses a relatively small number of neighbors (typically K = 1) and employs a fine granularity in its distance metric for classification. We employed 5-fold cross-validation to ensure reliable and unbiased performance evaluation across different subsets of data. Several trials were made by considering 2-fold to 10-fold cross-validation. After training, 5-fold exhibited the best performance and computational efficiency.  
A model validation technique called cross-validation was used to calculate the model's performance. True positive, false positive, true negative, and false negative values are documented in the confusion matrix that was produced and shown in Figure 16.  
Figure 16 illustrates the performance of the Fine KNN classifier on the raw normalized acceleration data, which was also used for training and as a test set in the form of a confusion matrix. Of the 17,268 test sample pairs, for the damped fall class, the accuracy was 98%, with 0.3% predicted as forward fall and 1.6% as normal activity. For the forward fall class, 99.1% accuracy was obtained, with 0.9% being wrong predictions from which 0.3% were classified as normal activities. The results show that the proposed classifier was very sensitive to falls and had high recognition accuracy, achieving a True Positive Rate (TPR) of 98% for damped falls, 99.1% for forward falls, and 92.6% for normal activities.  
From the confusion matrix, the results obtained for the trained KNN network were Accuracy: 97.33%, Precision: 97.11%, Recall: 97.5% and F1 Score: 97.3%, comparable to the results obtained after training the LSTM network.
# Post-Earthquake Damage Inspection of Wood-Frame Buildings by a Polarimetric GB-SAR System
## 2 Materials and Methods
### Signal Processing
#### 2.3.3. Bistatic SAR Processing  
One of the common SAR (migration) processing algorithms in time domain, namely back propagation, is applied to collapse the hyperbolic diffraction and to increase the azimuth resolution. Our targets are in the near range, relative to the dominant wavelength and the antenna separation. Therefore, we adopt a bistatic model for the SAR processing algorithm, as shown in Figure 5. The antennas are simplified as point sources suspending at a height of \(z_{a}\) from the ground surface. The algorithm is expressed as  
\[S_{out}(x,y,z)=\sum_{i=1}^{\text{M}}\sum_{j=1}^{\text{N}}S_{in}(x_{i,j},y_{i,j },(r_{1}+r_{2})/v), \tag{2}\]  
where \(S_{in}\) is the acquired raw data, \(S_{out}\)is the reconstructed image, \(r_{1}\) is the ray path from the apparent phase center of the transmitting antenna at \((x_{i,j},y_{i,j},z_{a})\) to the imaging point at \((x,y,z)\), \(r_{2}\) is the ray path from the imaging point back to the apparent phase center of the receiving antenna at \((x_{i,j}+d,y_{i,j},z_{a})\), \(d\) is the antenna separation, \(v\) is the propagation velocity of electromagnetic waves, M and N are the number of measurement points in \(x\) and \(y\) directions, respectively. Most of the volume inside the wall is occupied by heat-isolation material, whose dielectric properties are close to those of air. Thus, we ignore the velocity variation of electromagnetic waves in air, wood, and other media, and use the speed of light in the SAR processing. The expressions of \(r_{1}\) and \(r_{2}\) are given by  
\[r_{1}=\sqrt{\left(x_{i,j}-x\right)^{2}+\left(y_{i,j}-y\right)^{2}+\left(z_{a}+ z\right)^{2}} \tag{3}\]  
\[r_{2}=\sqrt{\left(x_{i,j}+d-x\right)^{2}+\left(y_{i,j}-y\right)^{2}+\left(z_{a }+z\right)^{2}} \tag{4}\]  
where \(z_{a}\) is the height of the apparent antenna phase center above the surface of wooden wall under investigation.  
Figure 4: The apparent antenna phase center. The dotted curves denote wavefront radiated from the apparent phase center at three different range distances.  
Figure 5: Geometry of the bistatic model for the SAR processing.
###### Abstract  
This study aimed to investigate the antibacterial and antib biofilm activities of the essential oils of _Cuminum cyminum_ (_CEO_) and _Foeniculum vulgare_ (_FEO_) on the clinically isolated _Porphyromonas gingivalis_ (_P. gingivalis_) and _Prevotella intermedia_ (_P. intermedia_). Subgingival plaque samples were collected from periodontal pockets (\(\geq\)6 mm) of patients with generalized severe periodontitis. Microbiological and molecular tests were performed to isolate and confirm _P. intermedia_. A previously isolated _P. gingivalis_ was used. The hydrodistillation method was used to extract the _CEO_ and _FEO_. The antibacterial activity of the examined oils against the confirmed clinical strains was determined using the disc diffusion and broth dilution methods to determine the minimum inhibitory (MIC) and bactericidal concentrations (MBC). The antibofilm properties of _CEO_ and _FEO_ were determined using the tube method. The _t_-test was used for comparisons of inhibition zones. Both _CEO_ and _FEO_ presented inhibition zones against both clinically isolated bacteria. The _CEO_ and _FEO_ revealed antibacterial activity with MIC values of 3.125 \(\mu\)L/mL and MBC of 6.25 \(\mu\)L/mL against _P. gingivalis_, respectively. In addition, their MIC and MBC against _P. intermedia_ were 0.195 \(\mu\)L/mL and 3.125 \(\mu\)L/mL, respectively. Finally, the essential oils showed moderate to strong antib biofilm activities against the clinically isolated _P. gingivalis_ and _P. intermedia_, respectively. This study supports the antibacterial and antib Biofilm properties of _CEO_ and _FEO_ against clinically isolated _P. gingivalis_ and _P. intermedia_. Further studies should focus on using these essential oils as an adjunct to periodontal therapy.  
Cuminum cyminum; Foeniculum vulgare; Porphyromonas gingivalis; Prevotella intermedia; antibacterial activity; periodontal disease; dentistry
## 4 Results and Discussion
### Results
#### 4.1.5 Results on the Dimension of Feature Representation  
In time-series feature representation, the dimension of generated features is an important hyperparameter. If dimensions are too small, they cannot adequately convey the inherent characteristics of crop growth, whereas too-high dimensions could increase the amount of computation required for classification. In the Dijon study area, the contrastive learning framework produced features with dimensions of T/8, T/4, T/2, T, 2T, 3T, 4T, 5T, 6T, 7T, 8T, 9T, 10T, 15T, and 20T (T is the number of timestamps of raw time-series features, which is 48 in the Dijon study area). These features were used for time-series crop classification. Figure 8 illustrates classification accuracies (in OA and F1 scores) utilizing feature representations of varying dimensions.
# Integrated Components and Solutions for High-Speed Short-Reach Data Transmission
## 2 Transmitter for Short-Reach Transmission Systems
### Vertical-Cavity Surface-Emitting Laser
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Tx** & **A** & **MF** & **BW** & **Rate** & **Reach** & **Fiber** & **FEC** & **Ref.** \\
**Device** & **(nm)** & **MF** & **(GHz)** & **(Gbit/s)** & **(m)** & **Type** & **Limit** & **Ref.** \\ \hline MM-VCSEL & 850 & MultiCAP & -20 & 107.5 & 10 & OM4-MMF & \(3.8\times 10^{-3}\) & [31] \\ MM-VCSEL & 850 & PAM-8 & – & 108 & 25 & OM3-MMF & \(3.8\times 10^{-3}\) & [32] \\ MM-VCSEL & 850 & MultiCAP & 25 & 112 & 150 & OM4-MMF & \(3.8\times 10^{-3}\) & [33] \\ \hline \end{tabular}
\end{table} 
Table 1: Incomplete summary of SM-/MM-VCSEL applied in beyond 100-Gbit/s per lane data transmission.
## 2 Materials and Methods
### Constitutive Equations  
The constitutive equations of the orthogonal anisotropy laminates can be simplified as Equation (9).  
\[\begin{bmatrix}\sigma_{xx}\\ \sigma_{yy}\\ \gamma_{yz}\\ \tau_{zx}\\ \tau_{xy}\end{bmatrix}=\begin{bmatrix}\bar{Q}_{11}&\bar{Q}_{12}&0&0&\bar{Q} _{16}\\ \bar{Q}_{12}&\bar{Q}_{22}&0&0&\bar{Q}_{26}\\ 0&0&\bar{Q}_{44}&\bar{Q}_{45}&0\\ 0&0&\bar{Q}_{45}&\bar{Q}_{55}&0\\ \bar{Q}_{16}&\bar{Q}_{26}&0&0&\bar{Q}_{66}\end{bmatrix}\begin{bmatrix} \varepsilon_{xx}\\ \varepsilon_{yy}\\ 2\gamma_{yz}\\ 2\gamma_{xz}\\ 2\gamma_{xy}\end{bmatrix}=\bar{Q}\mathbf{e}, \tag{9}\]  
\(\sigma=\begin{bmatrix}\sigma_{xx}&\sigma_{yy}&\tau_{yz}&\tau_{zx}&\tau_{xy} \end{bmatrix}^{T}\) is a stress vector; \(\bar{Q}\) is the simplified elastic matrix in the primary material coordinate; \(\mathbf{\varepsilon}=\begin{bmatrix}\varepsilon_{xx}&\varepsilon_{yy}&\gamma_{yz}& \gamma_{zx}&\tau_{xy}\end{bmatrix}^{T}\) is a strain vector. A generalized constitutive equation of a 3-D piezoelectric beam in a local primary coordinate can be written as  
\[\begin{bmatrix}\sigma_{xx}^{\prime}\\ \tau_{xy}^{\prime}\\ \tau_{xz}^{\prime}\\ \tau_{yzz}^{\prime}\end{bmatrix}=\begin{bmatrix}\bar{Q}_{11}^{\prime}&0&0&0\\ 0&\bar{Q}_{66}&\bar{Q}_{65}&0\\ 0&\bar{Q}_{65}&\bar{Q}_{55}&0\\ 0&0&0&\bar{Q}_{\rho}\end{bmatrix}\begin{bmatrix}\varepsilon_{xx}^{\prime}\\ 2\gamma_{xy}^{\prime}\\ 2\gamma_{xz}^{\prime}\\ \kappa_{x}^{\prime}\end{bmatrix}+\begin{bmatrix}\varepsilon_{11}^{\prime}&0& \bar{\varepsilon}_{31}^{\prime}\\ 0&0&\bar{\varepsilon}_{36}^{\prime}\\ \varepsilon_{15}^{\prime}&\bar{\varepsilon}_{25}^{\prime}\\ 0&0&0\end{bmatrix}\begin{bmatrix}\varepsilon_{xx}^{\prime}\\ \varepsilon_{y}^{\prime}\\ \varepsilon_{z}^{\prime}\end{bmatrix}=\bar{Q}^{\prime}\mathbf{\varepsilon}^{ \prime}+\bar{\mathbf{e}}^{\prime}\mathbf{E}^{\prime}, \tag{10}\]\[\begin{bmatrix}D_{x}^{\prime}\\ D_{y}^{\prime}\\ D_{z}^{\prime}\end{bmatrix}=\begin{bmatrix}\tilde{e}_{11}^{\prime}&0&\tilde{e}_{15}^ {\prime}&0\\ 0&0&\tilde{e}_{25}^{\prime}&0\\ \tilde{e}_{31}^{\prime}&\tilde{e}_{36}^{\prime}&0&0\end{bmatrix}\begin{bmatrix} \tilde{e}_{xx}^{\prime}\\ 2\gamma_{xy}^{\prime}\\ 2\gamma_{xz}^{\prime}\\ x_{x}^{\prime}\end{bmatrix}+\begin{bmatrix}\tilde{\chi}_{11}^{\prime}&0&0\\ 0&\tilde{\chi}_{22}^{\prime}&0\\ 0&0&\tilde{\chi}_{33}^{\prime}\end{bmatrix}\begin{bmatrix}\tilde{E}_{y}^{ \prime}\\ E_{y}^{\prime}\\ E_{z}^{\prime}\end{bmatrix}=\tilde{e}^{\prime}\tilde{e}^{\prime}+\tilde{ \chi}^{\prime}\tilde{E}^{\prime}. \tag{11}\]  
Equation (10) is the mechanical equation and Equation (11) is the electric equation. \(\tilde{\mathbf{Q}}^{\prime}\) is the simplified elastic matrix of the beam; \(\tilde{e}^{\prime}\) and \(\tilde{\chi}^{\prime}\) are the simplified piezoelectric constant matrix and dielectric constant matrix, respectively. The actuation mode of the PZT actuator used in this study is the d11 type; therefore, in matrices \(\mathbf{e}^{\prime}\) and \(\tilde{\chi}^{\prime}\), only \(\tilde{e}_{31}\
eq 0\) and \(\tilde{\chi}_{11}\
eq 0\).
## 4 Validation and Discussion  
### Construction of the Test Platform  
#### 4.1.1 Server Software Design  
The most important feature of DT is the two-way communication between physical and virtual, and it is realized by establishing a cloud server based on TCP. In this process, the data acquisition card collects sensor signals and sends them to the host computer through a USB interface. The host computer then sends the data to the cloud server through TCP and the cloud server saves the data as data files. Finally, the data of the whole vehicle are read, updated, analyzed and calculated in real time by MATLAB to realize the information interaction between the virtual entity and the physical entity, real-time energy consumption calculation, data monitoring and other functions. The data transmission principle of the cloud platform is shown in Figure 8.  
The cloud server uses the Windows system; hence, this system uses socket programming under Windows to establish a server in the cloud to receive data. Socket is the handle of a communication chain to describe the IP address and port, and applications of different computers can send requests to the network or answer network requests through socket. Socket is a network communication unit supporting TCP/IP and contains five basic information for conducting network communication: the protocol used for connection (TCP, UDP), the local host IP address (server intranet IP), the local remote protocol port (0\(-\)65535), the IP address of the remote host (client extranet IP), and the protocol port of the remote process. The communication model is shown in Figure 9.  
Figure 8: Cloud platform data transmission principle.  
Figure 7: Simulink simulation model of the whole vehicle.
## 3 Findings for Each of the Three Layers
### Technologies Layer
#### 3.3.2 Demand Side Flexibility  
Technical flexibility stresses two main aspects: controllability (e.g., on/off modes, shiftability, modulation, and so on) and characteristics (e.g., minimum and maximum power, reaction time, etc.). Apart from these general characterizations of resources, metrics can be introduced to specify a certain resource with respect to its capability, or 'characteristics'. In [55], different approaches for defining flexibility in terms of characteristics are discussed, in which the three main aspects identified are ramp magnitude, ramp frequency, and response time. Figure 4 shows a classification of the residual load for the stakeholders group 'prosumer', in terms of controllability. The base load represents the consumption in the prosumer premises, without any degree of freedom for flexibility or controllability. Loads providing controllability can be distinguished along with their ability to curtail, shift or store energy. As a concrete example, we can mention smart washing machines or dishwashers, which can provide a controllability, albeit within limits [56], to the smart grid for it to activate before the latest run time defined by the user [17]. In the case where a user programs the schedule of the appliance for economic reasons, the smart grid will optimize this according to the three layers: stakeholder (objective), market layer (price), technology (demand shifting).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Category** & **Example** \\ \hline  & - Electric vehicles (Battery) \\  & - Heat pumps \\  & - Air conditioners \\  & - Dish washers \\  & - Washing machines \\ Responsive appliances & - Clothes dryers \\  & - Freezer/refrigerator \\  & - Battery operated home appliances robots (vacuum cleaners, \\  & kitchen appliances) \\  & - 3D printers, robot arms \\  & - Close-in boilers \\ \hline  & - Electricity meters (frequency ranges from seconds to \\  & day intervals) \\ Smart/digital meters & - Gas meters \\  & - Meters that allow for breakdown to appliance level (usually part \\  & of a monitoring and control system) \\ \hline Energy monitoring and & - Sensors and energy monitoring systems, ranging from household \\ control systems & aggregate to breakdown to appliance level \\  & - Gas measurement, often combined with a smart thermostat \\ \hline  & - Energy services gateways \\  & - Apps \\ Home automation for & - Steering of deferrable load (smart appliances) \\ smart energy use & - Home automation and control \\  & - Internet of things \\  & - Smart plugs and smart battery chargers (lighting, USB grids, etc.) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cont.
# Determining Effective Threshold Range of Image Pixel Values for Municipal Waste-Contaminated Clay
## 3 Results
### Determining Effective Threshold Range of Image Pixel Values for Contaminated Clay  
When the clay microstructure is investigated, clay pores and particles are the main subjects. Pores and clay particles should be considered when determining the ideal threshold range of image pixel values. The effective threshold range of image pixel values in this investigation was 110-140 when the apparent void ratio of the contaminated clay samples was accounted for. The effective threshold range of the pixel values was also 110-140 when the fractal dimension of the contaminated clay samples was accounted for. As a result, 110-140 is the ideal threshold range of the pixel values based on the microscopic structure of municipal waste-contaminated clay.
# Damage Identification of Stay Cables Based on a Small Amount of Deflection Monitoring Data
## 2 Theory Related to the Reduction in Measurement Points
### Characteristic Analysis of Beam Deflection Difference Distribution Curve under Concentrated Force
#### 2.3.2 Analysis on Distribution Curve of Deflection Difference before and after Double-Cable Damage  
It is assumed that the damage to both cables is different (see Table 1) when the concentrated force acts on the anchorage point of cable M19. Figure 8 is the comparison between the deflection distribution curve obtained after the reduction in the measurement points and the (actual) deflection distribution curve before the reduction in the measurement points when the double cable is damaged in the working condition listed in Table 1. Figure 8 is a comparison between the deflection distribution curve obtained after the reduction in the measurement points and the (actual) deflection difference distribution curve before the reduction in the measurement points when the double cable is damaged in the working condition listed in Table 1.  
As can be seen from Figure 8, when the double cables are damaged under the action of concentrated force, the girder deflection distribution curve after the reduction in the measuring points almost coincides with that before the reduction in the measurement points (the actual deflection distribution curve), with a maximum of only 2.51%.  
As can be seen from Figure 9, the deflection difference distribution curve of girder before and after the reduction in the measurement points is essentially the same, with no difference, and the maximum is only 3.41% (located at the anchorage point of cable M7). After the damage of the double cable (M7 and M16), the deflection difference distribution curve of the side girder where the damage cable is located is convex downward in the area  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Damage Type** & **Damage Cable** & **Damage Degree** & **Index of Damage** \\ \hline Case 1 & M7, M16 & 10\%, 15\% & 0.9, 0.85 \\ Case 2 & M7, M16 & 20\%, 15\% & 0.8, 0.85 \\ Case 3 & M7, M16 & 30\%, 15\% & 0.7, 0.85 \\ Case 4 & M7, M16 & 40\%, 15\% & 0.6, 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Double cable damage case.  
Figure 8: Comparison of beam deflection distribution curves before and after the reduction in the measurement points in the case of double-cable damage (different damage conditions).  
near the anchorage point of the damage cable (M7 and M16), and a peak (point) appears. When the damage degree of one cable (such as M16) remains unchanged, while the damage degree of the other cable (such as M7) increases, the peak value corresponding to the beam deflection difference distribution curve at the anchorage point of M16 is essentially unchanged, while the peak value corresponding to the anchorage point of M7 increases with the increase in the damage degree.  
Damage Location Identification Method of Stay Cable Based on First Derivative Wavelet Transform of the Girder Deflection Difference
## 4 Methods for Mitigating Jamming
### Scenario 2  
When the jammer jams the IRS, we assume that the jamming optical path can effectively align with the legitimate optical path unit of the IRS. As a result, we can obtain the received signal for the n-th path as follows  
\[y_{n-IRS}=Ra_{n}h_{in}\sqrt{P_{s}}s+R\sqrt{N_{J}}b_{n}h_{In}\Lambda s_{J}+n_{n} \tag{33}\]  
where \(b_{n}\) represents the weighting value of the nth interfering branch, similarly, we assume \(b_{n}=\frac{1}{N}\).  
The receiver combines the received electrical signals by using the EGC as follows  
\[y_{I-D}=\sum\limits_{n=1}^{N}y_{n-IRS}=R\sqrt{P_{s}}s\sum\limits_{n=1}^{N}a_{n}h _{in}+R\sqrt{N_{I}}\sum\limits_{n=1}^{N}b_{n}g_{In}\Delta s_{J}+\sum\limits_{n= 1}^{N}n_{n} \tag{34}\]  
From (34), we can calculate the SJR at the receiver as  
\[\gamma_{SJR-R}=\frac{P_{s}\bigg{(}\sum\limits_{n=1}^{N}a_{n}h_{in}\bigg{)}^{2} }{N_{I}\bigg{(}\sum\limits_{n=1}^{N}b_{n}h_{In}\bigg{)}^{2}}=\frac{\rho\overline {\gamma}_{JD}\bigg{(}\sum\limits_{n=1}^{N}a_{n}h_{in}\bigg{)}^{2}}{\bigg{(} \sum\limits_{n=1}^{N}b_{n}h_{In}\bigg{)}^{2}} \tag{35}\]
## 3 Results  
The original images are preprocessed using the methods described in Section 2.2.1. The Landsat image sequence is selected as the reference image sequence, and the HJ-1 B image sequence is used as the image sequence to be fused. Then, a fused image sequence is built based on the image fusion method described in Section 2.2.3. Finally, based on Mann-Kendall trend detection method and the Theil Sen slope analysis, the forest land in the study area is dynamically monitored and the accuracy of the results is evaluated.
# Application of Digital Twin in the Industry of Axial Hollow-Wall Pipes
## 4 Testing and Maintenance of Axial Hollow Pipe Products
### Establish a Digital Twin Model  
To validate the sealing performance of the produced pipeline interfaces, a leak tightness test under internal pressure and angular deviation was conducted. The test utilized the JJHBT pipe material static hydraulic testing machine manufactured by JJ-TEST for water pressure loading. Strain gauges, displacement sensors, and inclinometers were employed as monitoring equipment, connected to the CHHP-DJ dynamic signal testing and analysis system provided by NANJING HOPE TECHCO., LTD for data acquisition. The experimental procedure was as follows:  
The assembled pipeline was subjected to a water pressure of 0.4 MPa;  
Once the water pressure stabilized, angular displacement loading was applied to the pipe body incrementally, with each increment set at 0.5\({}^{\circ}\);  
After each loading step, the angular displacement was maintained at a constant value for 60 min to observe any signs of leakage;  
If leakage occurred during the observation period, the test was halted. Conversely, if no leakage was detected, the test continued.  
The experimental design is depicted in Figure 14.  
In numerical simulations, the pipe material is considered an elastoplastic material, and its elastoplastic behavior is determined based on the standards GB/T 8804-2003 and GB/T 1040.1-2018, as illustrated in Figure 15.  
The chosen mesh elements for the pipe material were 8-node linear brick-reduced integration elements (C3D8R). On the other hand, the sealing ring was composed of a hyper-elastic material, for which the Mooney-Rivlin material model was employed. The sealing ring mesh elements were 8-node linear brick hybrid-reduced integration elements (C3D8 RH), and a static general analysis approach was utilized for the simulation. The simulation consisted of three steps, as illustrated in Figure 16. Firstly, the straight pipe was inserted into the socket joint. Next, a uniform pressure of 0.4 MPa was applied to both the inner surface of the connection point of the pipe and the inner surface of the straight pipe. In the third step, an angular displacement was applied to the straight pipe.  
Figure 14: The leak tightness test under internal pressure and angular deviation: (**a**) Practical application, (**b**) Schematic diagram, a Deflection displacement loading point, b Displacement meter, c Inclinometer, d Welding point, e Hydrostatic loading point. (**c**) Photo of the test.  
Figure 15: Characteristic curve of Tensile test.