esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Plane to Points Histogram (P2 PH)
### Analysis Method  
In order to determine the roughness of the three-dimensional point cloud, Plane to Points histogram (P2 PH) method was proposed that a two-dimensional fitting plane representing the point cloud of the failure surface is compared with the points of the failure surface. In order to evaluate the roughness of the point cloud of each pile sample, the point cloud of the failure surface was cut into five parts with a rectangle. The size of the gravel is 20 mm so that the fitting plane size was set to 20 mm in width and 50 mm in length in order to reflect the curvature of the gravel in 3D roughness. If the size of the fitting plane becomes larger, the overall curvature is reflected in the analysis result rather than the roughness of the grouting or sand sample. The point cloud in cutting area can fit a plane which is closest to the point clouds among all planes that exist on three dimensions. It means that a rectangular mesh that is most similar to the shape of the cut point cloud can be obtained by calculating the average distance between a plane and points. The fitting plane can be a reference mesh for calculating the distance between the fitting plane and each point, as shown in Figure 5(a). Since the fitting surface is recognized as a mesh, Cloud to Mesh (C2M) comparison method was used that calculates the distance between the point cloud and a fitting plane. Points close to the fitting plane are represented in blue, and points far from the fitting plane are represented in red. The distribution of points according to the C2M distance can be expressed as shown in Figure 5(b). Figure 5(c) shows three cases of failure planes: flat failure surface, slightly curved failure surface, and severely curved failure surface. The C2M distance can be varied depending on the roughness of the failure surface. The point cloud of a flat failure surface is distributed close to a fitting plane. However, if the failure surface is rougher, the points are spread further from a fitting plane. Therefore, if the distance between points and a fitting plane is calculated by the C2M method, the C2M distance increases with the increase of roughness. Figure 5(d) shows a schematic diagram of the histogram of the expected C2M distance with different roughness. Most of the points are densely distributed at a small range of C2M distance in the flat failure surface. However, as the failure surface becomes rougher, the range of C2M distance can be wider. In this paper, the histogram was used as a criterion to assess the roughness, and the point cloud analysis of the failure surface of pile samples was conducted.  
Figure 5: Three-dimensional point clouds of pile samples.
# Optical Lensless-Camera Communications Aided by Neural Network
## 2 Proposed Scheme
### Adaptive Training Sequence Adjustment Mechanism  
Because the classification effect of the BP neural network is related to the size of the training set, we had to control the training sequence length to ensure the accuracy of classification without incurring too much data overhead.  
In indoor working conditions, the difference between the \"on\" and \"off\" states of the LED images varies due to the change of the background scene; e.g., at night, the difference between these two types of images is obvious, but at daytime it becomes vague. The length of the training sequence should be adaptive and ensure the recognition effect of the neural network. When system works outdoors, the communication channel is more complicated due to ambient light intensity variation or bad weather (snow or rain). In this paper, an adaptive training sequence adjustment mechanism is proposed to adapt to time-varying environments.
# Human Activity Recognition Using CSI Information with Nexmon
###### Abstract  
Using Wi-Fi IEEE 802.11 standard, radio frequency waves are mainly used for communication on various devices such as mobile phones, laptops, and smart televisions. Apart from communication applications, the recent research in wireless technology has turned Wi-Fi into other exploration possibilities such as human activity recognition (HAR). HAR is a field of study that aims to predict motion and movement made by a person or even several people. There are numerous possibilities to use the Wi-Fi-based HAR solution for human-centric applications in intelligent surveillance, such as human fall detection in the health care sector or for elderly people nursing homes, smart homes for temperature control, a light control application, and motion detection applications. This paper's focal point is to classify human activities such as _EMPTY_, _LYING_, _SIT_, _SIT-DOWN_, _STAND_, _STAND_, _STAND_-_UP_, _WALK_, and _FALL_ with deep neural networks, such as long-term short memory (LSTM) and support vector machines (SVM). Special care was taken to address practical issues such as using available commodity hardware. Therefore, the open-source tool Nexmon was used for the channel state information (CSI) extraction on inexpensive hardware (Raspberry Pi 3B+, Pi 4B, and Asus RT-AC86U routers). We conducted three different types of experiments using different algorithms, which all demonstrated a similar accuracy in prediction for HAR with an accuracy between 97% and 99.7% (Raspberry Pi) and 96.2% and 100% (Asus RT-AC86U), for the best models, which is superior to previously published results. We also provide the acquired datasets and disclose details about the experimental setups.  
Keywords:human activity recognition (HAR); channel state information (CSI); Nexmon; Raspberry Pi; machine learning; LSTM; SVM; neural networks; deep neural networks +
Footnote †: journal: _Sci._
## 4 Dynamic Load Balancing Modeling with ACO
### Pheromone Initialization  
In cloud computing, the physical resources allocated to each virtual node are not the same and usually changing dynamically. Due to of this characteristic, we use the physical resources of virtual machines to measure a node's initial pheromone, as described in [15, 16]. Five physical resources are involved in pheromone initialization here, that is, CPU (number of cores and MIPS for each core), internal storage, external storage, I/O interface, and bandwidth. The CPU capability can be calculated by:  
\[p_{CPU}=n\times p \tag{1}\]  
To facilitate the calculation, we set an upper limit for each parameter. When a parameter exceeds its upper limit, the limit value is chosen instead of the actual value. The limits are given in Equation (2).  
\[p_{CPU}\leq p_{\max}\,,m_{i}\leq m_{i\max}\,,m_{e}\leq m_{e\max}\,,p_{i/o}\leq p _{i/o\max}\,,p_{b}\leq p_{b\max} \tag{2}\]  
The capability definitions of physical resources of virtual machines are defined as blow.  
For CPU:  
\[\tau_{CPU}(0)=\frac{p_{CPU}}{p_{\max}}\times 100\% \tag{3}\]For internal storage:  
\[\tau_{mi}(0)=\frac{m_{i}}{m_{i\max}}\times 100\% \tag{4}\]  
For external storage:  
\[\tau_{me}(0)=\frac{m_{e}}{m_{e\max}}\times 100\% \tag{5}\]  
For I/O interface:  
\[\tau_{i/o}(0)=\frac{p_{i/o}}{p_{i/o\max}}\times 100\% \tag{6}\]  
For bandwidth:  
\[\tau_{b}(0)=\frac{p_{b}}{p_{b\max}}\times 100\% \tag{7}\]  
Therefore, we define the pheromone initialization for one slave node as:  
\[\tau_{i}=\psi_{1}\tau_{CPU}(0)+\psi_{2}\tau_{mi}(0)+\psi_{3}\tau_{me }(0)+\psi_{4}\tau_{i/o}(0)+\psi_{5}\tau_{b}(0)\] \[\sum_{n=1}^{5}\psi_{n}=1 \tag{8}\]  
where \(\psi_{n}\) is a weight coefficient, which is used to adjust the influence of the physical resources in cloud computing.
## 2 Electroadhesion Pad Release Test  
The EA pad design utilized in this work follows a concentric-comb pattern. It has an effective diameter of 56 mm with an electrode width and gap of 4 mm, a schematic diagram of which is shown in Figure 2a. The electrodes were made of off-the-shelf electrically conductive silicone sheet (0.5 mm thickness, 4.3 \(\Omega\)-cm volume resistivity, J-Flex, Coalville, UK). The electrode pattern was first designed in computer-aided design software (SOLIDWORKS, Dassault Systemes, Velizy-Villacoublay, France) and was fabricated via a computer-controlled cutter (Provo Craft & Novelty, South Jordan, UT, USA). The electrodes were bonded to the base materials via a thin layer of silicone adhesive (Sil-Poxy, Smooth-On, Macungie, PA, USA). Six different base materials were used: poly (methyl methacrylate) acrylic; medium-density fibreboard (MDF); silicone elastomer (Elastosil, Wacker Chemie AG); polyacrylate elastomer (VHB 4910, 3M); polyethylene terephthalate film (Mylar, Dupont, MI, USA); and polystyrene foam (Styrofoam, Dow Chemical, Michigan, USA), as illustrated in Figure 2b-g. Eight different object materials were used: balsa wood; polyethylene foam; card board; polyvinyl chloride (PVC) sheet; Mylar; paper tissue; paper sheet; and silicone film. The material properties, dimensions, and mass of both base and object materials can be found in Supplementary Table S1.  
The experimental procedure is summarized as follows, and a schematic diagram of the experimental equipment is shown in Figure 3. First, the EA pad was moved downward along a linear rail to enable contact with the substrate. A voltage of 3.5 kV was then applied across the electrodes for a total period of 10 s, and after 5 s of actuation, the EA pad, together with the object, was lifted upwards. The EA system was turned off after 10 s charging and the release time measurement began. The release time was defined as the time required to drop off the object from the EA pads after turning off the applied voltage. A high voltage amplifier (HVA) (Advanced Energy Industries, Fort Collins, CO, USA) was used to power the EA pads. A data acquisition device (National Instruments, Texas, USA) was utilized to control the HVA. A linear rail (Zaber Technologies, Vancouver, BC, Canada) was employed to position the EA grippers. The whole process was recorded by a camera at 60 frames-per-second and the release time in each set of experiments was determined from the video. All experiments were conducted in a controlled environment of 23.5 \(\pm\) 0.5 \({}^{\circ}\)C temperature and 45 \(\pm\) 2% humidity.  
Figure 1: (**a**) Schematic diagram showing a typical electroadhesion (EA) system with encapsulated electrodes and (**b**) exposed electrodes.
## 2 Materials and Methods
### Monitoring of the MSE Wall  
As mentioned above, the traditional monitoring method for MSE walls is to conduct onsite measurements of structural deformations using inclinometers, crack meters, and total stations at regular intervals, followed by internal office work management. However, the traditional monitoring method has limitations, such as the necessity of checking the structural deformation, which is the premise of the structure, due to the local measurement management of the MSE wall; restrictions with respect to measurement heights to ensure the stability of the operator; and the necessity of performing measurements, which are carried out by equipment experts. In particular, because many restricted-access and controlled areas in South Korea are located in mountainous and remote areas, the MSE walls are not accessible to experts on the site. The reliability of measurement results is also often challenged by the time constraints required to monitor the structure. Therefore, a method for monitoring the MSE walls by laypersons residing in the restricted-access and controlled areas is needed. This study employed digital image photogrammetry to assess the deformation of MSE walls. Figure 5 shows a comparison of the traditional monitoring and digital image photogrammetry procedures for MSE walls.  
Figure 4: Electrical resistivity survey location plan view at the MSE wall site.  
In this study, a total station is used for the traditional monitoring method, and it is generally applied to the measurement of structural surface displacements. A total station is an electronic device that can measure three-dimensional positions in space using light waves such as lasers. It requires a reflective target for the accurate measurement of a specific position and has high mechanical reliability. However, experts are required for measurement, and the reliability of the measurement results may vary depending on the expert. Digital image photogrammetry applied in this research can acquire images using imaging devices such as digital cameras and cellphone cameras, and it can measure the displacement of a required position in the image using a 3D analysis program.  
The digital image photogrammetry procedure based on commercial imaging devices is summarized as follows. First, the surface of the MSE wall is photographed using a digital camera or cellphone camera and sent to a remote expert. The expert then uses a 3D image  
Figure 5: Comparison of monitoring methods for MSE walls: (**a**) traditional monitoring procedure; (**b**) digital image photogrammetry procedure based on commercial imaging devices.  
conversion program (PhotoModeler) to check the wall displacement of the MSE wall. In comparison with the traditional monitoring method, this approach has advantages, such as time-saving qualities and high applicability, as the image can be taken by laypersons. In other words, it is a very simple monitoring method that allows laypersons in restricted-access and controlled areas to take multiple images of the target structure using commercial imaging devices, and they can send them to experts at a remote location.
## 3 Materials and Methods
### Signal Segmentation Technique  
As discussed in the literature review, signal segmentation is an important data analytics process used to improve the quality of the evaluated signal and detect operational state changes. Nonetheless, insights about the signal may be missed when evaluated holistically. For the presented study, a novel signal segmentation technique was applied to divide the collected vibration signal into smaller segments and evaluate them individually to detect areas of major change from the baseline. Upon detecting the anomalous segment, a further comparative analysis is conducted to determine its location, severity of the damage, and provide a repair decision. A flowchart of the proposed technique is illustrated in Figure 6.  
This technique was developed to localize a misalignment fault from the collected vibration signal of the monitored linear guide rail component of the testbed. Then, conduct a deeper analysis to determine the severity and impact on the system. The proposed technique segments the vibration signal by dividing it in half until it reaches a pre-determined number of segments, as shown in (1).  
\[n_{s}=2^{n} \tag{1}\]  
where \(n_{s}\) is the total number of segments and \(n\) is the user defined number of iterations. For instance, if the user selects \(n=2\) number of iterations, the system will divide the signal into 4 segments. Each of these segments are evaluated as individual signals. First, a time domain analysis where statistical features such as RMS, standard deviation, Peak-to-Peak, and kurtosis are calculated. For the next step, the evaluated features are compared against their baseline state to determine their percentage error. If the segment's percentage error is between 10-20%, the segment will be deemed as suspect and delineated yellow in the analysis. Any segment above 20% error will be deemed a faulty segment and depicted red in the analysis. Finally, to determine the location of the anomaly, the technique divides the total stroke distance travelled by the number of segments to determine the position and compares the anomalous segments by evaluating both stroke directions.
# EEG-Based Methods for Diagnosing Color Vision Deficiency: A Comprehensive Review
## 4 Comparing Conventional and EEG-Based Methods for Diagnosing CVD  
The value of EEG-based methods in diagnosing CVD can be seen when comparing them to conventional assessments. Firstly, conventional assessments require a behavioral response, limiting the pool of individuals who can undergo the assessment. For example, in the Ishihara test, individuals must verbally respond to or point out a specific direction containing the intended number, or in the anomaloscope test, active participation and movements are necessary. Secondly, because conventional tests rely on behavioral responses, they are subjective. When individuals being tested must verbally participate, their responses may be influenced by various factors related to individual perception, situation, and experience, making the assessments subjective and somewhat inaccurate. Thirdly, extensive training is required for some conventional tests, such as the anomaloscope. Individuals need clear steps to follow and precise instructions to perform the test accurately. This can pose challenges, especially when dealing with individuals who may find it difficult to comprehend complex instructions, such as children. Fourthly, conventional assessments can be manipulated. For instance, individuals might memorize the arrangements of the Ishihara plates before the test, compromising the credibility of the results [62]. However, the  
Figure 3: Illustration of the color vision pathway, showing how the eye processes specific colors, influencing EEG signals [61].  
most notable advantage of conventional methods is their speed, as they can be completed in a few minutes.  
In contrast, EEG-based methods rely on brain responses and do not necessitate behavioral interactions, making them a suitable option for individuals with disabilities or communication challenges, including those with neurological disorders, speech impairments, and hearing impairments. Additionally, EEG-based methods are objective as they depend on EEG signals rather than subjective interpretations of individual responses. Analyzing EEG signals can objectively determine how the brain reacts to different colors. Moreover, unlike conventional methods that may require training, EEG-based methods are easier to use, involving only sensor fixation to measure the required signals along with simple instructions like \"focus here\", making them easily understandable for all individuals, including children. Furthermore, unlike conventional assessments, EEG-based methods cannot be manipulated, as individuals cannot control their EEG signals. However, these methods are not as fast and require more time to conduct compared to conventional tests. Despite the time requirement, the numerous advantages of EEG-based methods give them an edge over conventional methods. Table 2 summarizes the key differences between conventional CVD diagnostic tests and EEG-based methods.
## 3 Experiments  
### Experimental Setting  
We have implemented the proposed SaDE-WNN method against the related methods--DEWNN [34] and Ada-DEWNN [8] for comparison in each scenario. The parameters used for the proposed SaDE-WNN classifier are the number of hidden nodes, learning rate, and momentum rate. To determine the optimum number of hidden nodes, we start with a small value of M and estimate the corresponding weight of the output layer, and compute the Normalized Root Mean Square Error (NRMSE). If the NRMSE is less than the predefined threshold value (4-10), we stop. Otherwise, increase M and repeat the same process. The value of \(n\) is set to be 50 with 500 training epochs after 100 generations. The maximum number of iterations \(G_{m}=50\); the new value of Mutation scale factor-F varies between [0.1, 1] and the CR varies between [0, 1]. The value of \(F_{max}\) and \(F_{min}\) have been selected as 0.9 and 0.2 respectively. The \(L_{min}^{factor}=0.00127\); the \(M_{min}^{factor}=0.8927\) and \(N_{min}^{hidden}=12\). The data set is divided into training, validation, and test set by ration 60:20:20. Five-fold cross-validation scheme has been utilized to avoid any over-fitting and/or uncertainty in the training of the classifier [42].  
The effectiveness of the proposed CAD system is validated by the following indices: sensitivity, specificity, accuracy, F1-score (F1), and area under the ROC curve (AUC). The accuracy of diagnostic modality in terms of its trade-off between specificity and sensitivity is evaluated by receiver operating characteristics (ROC) analysis. Besides, the difference between the two feature sets is analyzed using the chi-square test. The AUC, the statistical significance of differences between ROC index and parameters is evaluated and compared by employing the z-test.  
\[Accuracy=\frac{TP+TN}{TP+FN+TN+FP} \tag{16}\]  
\[Sensitivity=\frac{TP}{TP+FN} \tag{17}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{18}\]  
\[F1=\frac{TP}{TP+\frac{1}{2}(FP+FN)} \tag{19}\]
# Analyzing Six Indices for Online Short-Term Voltage Stability Monitoring in Power Systems
## 2 Mathematical Preliminaries: SVS Indices
### Contingency Severity Indices (Csi)  
According to NERC/WECC Planing Standards, voltage dip should not exceed 25% at load buses and 30% at other buses; in addition, it should not be more than 20% during 20 cycles or more in \(N-1\) contingencies [21]. Based on this criteria, two severity indices are used in [15]. The first of them is \(SI_{v}\) that is used to quantify voltage limit violation and calculated for each bus at time \(t\) as follows:  
If \(D=\left\{v(t)|t\in[t_{cl},t_{f}]\right\}\) (\(t_{cl}\) is fault clearing time, and \(t_{f}\) is the time that is considered for short-time voltage stability study) and if there is a \(v_{dev}\) in \(D\) that is satisfied in relation (3):  
\[\left|v_{0}-v(t)\right|\leq\left|v_{0}-v_{dev}\right|\ \ \ \ \forall v(t)\in D \tag{3}\]  
\(SI_{v}(t_{f})\) for load buses is:  
\[SI_{v}(t_{f})=\left\{\begin{array}{cc}\left|v_{0}-v_{dev}\right|\!\!/_{v_{0 }}&\left|v_{0}-v_{dev}\right|\!\!/_{v_{0}}\geq 0.25\\ 0&o.w\end{array}\right. \tag{4}\]and similarly for non-load buses:  
\[SI_{v}(t_{f})=\left\{\begin{array}{cc}\left|v_{0}-v_{dev}\right|_{/\!\!\!/v_{0} }&\left|v_{0}-v_{dev}\right|_{/\!\!\!/v_{0}}\geq 0.3\\ 0&o.w\end{array}\right. \tag{5}\]  
\(v_{0}\) is initial voltage magnitude before disturbance.  
Another index that gives time continuance of unacceptable voltage deviation in each bus is \(SI_{t}\). \(SI_{t}\)'s calculation formulas are as follows:  
Let \(LV=\left\{t|v(t)=0.8v_{0},v(t)\in D\right\}\)  
1. For \(LV=\phi\), if there is at least a \(t\) in \([t_{cl}\,\ t_{f}]\) so that \(v(t)<0.8v_{0}\), then \(SI_{t}=1\); otherwise, \(SI_{t}=0\).
2. For \(t_{l}\) and \(t_{q}\) (\(t_{l}<t_{q}\)) in \([t_{cl}\,\ t_{f}]\), if, for all \(t\) in \((t_{l}\,\ t_{q})\), \(v(t)<0.8v_{0}\) is established, define \(\tau_{lq}=t_{q}-t_{l}\) else \(\tau_{lq}=0\). Consider \(L_{lq}=\left\{lq|\tau_{lq}\times f_{0}\geq\ 20 cycle\ \right\}\), \(f_{0}\) is nominal system frequency and equal to 50 or 60 Hz; now, \(SI_{t}\) will be computed as follows:  
\[SI_{t}(t_{f})=\sum_{lq\in L_{lq}}\tau_{lq}\Big{/}(t_{f}-t_{cl}) \tag{6}\]  
With summation of all individual \(SI_{t}\)s and \(SI_{t}\)s, Contingency Severity Index (\(CSI\)) will be obtained as follows:  
\[CSI(t_{f})=\sum_{i=1}^{n}\frac{w_{vi}SI_{vi}(t_{f})+w_{ti}SI_{ti}(t_{f})}{n} \tag{7}\]  
In Equation (7), \(n\) is the number of PMU buses and \(w_{v}\), \(w_{t}\) are weight factors which present sensitivity of each bus with respect to voltage dip and duration of low voltage, respectively.