esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 SAFE Method for Solving Vibration Mode Data of a Rail
### Solving the Vibration Mode Information  
The material properties of the CHN60 rail are as follows: Density is 7800 kg/m\({}^{3}\); modulus of elasticity is 210 GPa; Poisson's ratio is 0.3. The eigenvalues, frequencies, and eigenvectors are calculated by solving Equation (5). The eigenvalues reflect the dispersion characteristics of ultrasound guided waves and the eigenvectors represent the displacement of all nodes in the cross-section. At the frequency of 200 Hz, there are four ultrasonic guided modes in the CHN60 rail, as shown in Figure 4.  
Figure 4: CHN60 rail’s dispersion curves at low frequency.  
Figure 3: Phase velocity dispersion curves of the rail.  
The phase velocities of the four modes are 403 m/s, 634 m/s, 847 m/s, and 5263 m/s, respectively. Each mode shape is plotted according to the eigenvector, \(\hat{\mathbf{U}}\).  
The eigenvalue, \(\xi\), and the eigenvector, \(\hat{\mathbf{U}}\), are obtained by substituting \(K_{1},\hat{K}_{2},K_{3},\omega\), and \(\mathbf{M}\) into Equation (5) at the frequency, \(f=200\) Hz. The eigenvector, \(\hat{\mathbf{U}}\), represents the displacements of all nodes of the discrete rail cross-section. Figure 5a-d illustrates the mode shapes of the four modes, respectively. The colorized rail cross-section is the original size, and the red one is the mode shape.  
The vibration forms of each mode are able to be distinguished through an analysis of the mode shapes. Specifically, in the case of mode 1, there is no relative displacement inside the node, and the whole cross-section rotates along the vertical axis, thus the mode is the flexural horizontal mode; in mode 2, the cross-section is turning back and forth along the edge of the rail base, thus the mode is the flexural vertical mode. Mode 3 is the torsional mode, and mode 4 is the extensional mode. The three-dimensional vibration diagrams of all modes are shown in Figure 6.  
Figure 5: Mode shapes at 200 Hz. (**a**) The phase velocity is 403 m/s; (**b**) the phase velocity is 634 m/s; (**c**) the phase velocity is 847 m/s; (**d**) the phase velocity is 5263 m/s.  
Figure 6: CHN60 rail’s modes at low frequency. (**a**) Flexural horizontal mode; (**b**) flexural vertical mode; (**c**) torsional mode; (**d**) extensional mode.  
The vibration characteristics of each mode can be known by analyzing the mode diagrams. It provides a data basis for the subsequent selection of guided wave modes suitable for long-distance propagation.
## 3 The Proposed IoMT Model
### Updating the Shared Key in the Proposed IoMT Model  
Updating the shared secret key is performed by the HC. Each time a patient, doctor, or GHC registers/leaves the network, the shared secret key has to change. The proposed scheme ensures that the ssk is updated before a new client is added to the network and after an existing client in the IoMT network leaves the network using Equation (1).  
\[\text{ssk}=H_{HC}\;(P_{id})\;\oplus\;\text{ssk} \tag{1}\]  
If a client \(P_{id}/GHC_{id}\) joins the network, the HC adds its identification number to the nList and updates the ssk. Algorithm 2 outlines the steps necessary for a new client _Ni_ to join the IoMT.  
```
Input: \(id_{Ni}\), \(H_{HC}\), ssk Output: Updated ssk, snp and nList
1. Start
2. HC generate new \(id_{Ni}\) for the new client
3. HC update key ssk \(=H_{s}\;(id_{Ni})\oplus\text{ssk}\);
4. HC update shared point snp = ssk \(\times\) G;
5. HC update its nList
6. Stop
```  
**Algorithm 2**Procedure for updating ssk, SNP, and nList when a client joins the HC network  
HC removes the \(P_{id}\) or \(GHC_{id}\) of the client departing the network, updates the ssk using Equation (1), and removes the related hashed ID from its node list. Algorithm 3 provides instructions for carrying out a client's departure operation.
## 4 Methodology and System Design
This ensures that election results are tallied securely without compromising individual voter privacy. Even administrators or validators cannot access or decrypt votes because they remain encrypted during the entire process. This prevents insider threats from individuals attempting to view or manipulate votes before the final tally is published. By utilizing these advanced cryptographic techniques, VoteChain ensures the confidentiality, integrity, and authenticity of votes while defending against common security threats such as Sybil attacks, MitM attacks, and insider manipulation. This combination of hashing, digital signatures, zero-knowledge proofs, and homomorphic encryption strengthens both the privacy of voters and the resilience of the system against multiple types of attacks.
## 2. Mature Technologies for Water and Wastewater Remediation
### Chlorination
#### 2.1.4 Advantages and Disadvantages of Chlorination  
Chlorination presents several advantages. It is inexpensive with a quick application procedure [12], with kits such as the SE200 Community Chlorine Maker capable of purifying 200 L of water in under 5 min [13]. Chlorination is also highly effective, with the ability to remove organics, inorganics and most pathogens [14].  
However, as mentioned previously, chlorination can form DBPs [15]. This includes trihalometanes (THMs) and halogenic acetic acids (HAAs). HAAs biologically decompose to eventually form THMs. The most notable of these THMs is trichloromethane (chloroform), an anaesthetic capable of damaging the liver and kidneys after chronic exposure. Furthermore, chlorination creates a smell in water, which may be unappealing or objectionable to some people. Chlorination utilises chlorine gas, a toxic chemical with inherent significant risk factors. The most crucial disadvantage is that chlorination cannot destroy protozoan cysts, as they possess a chlorine-resistant outer shell [14]. An example of a protozoan cyst is _Cryptosporidium_, a nefarious pathogen responsible for several outbreaks, such as the 2001 outbreak in northwest Saskatchewan, Canada [16].
###### Abstract  
Remote monitoring of a fall condition or activities and daily life (ADL) of elderly patients has become one of the essential purposes for modern telemedicine. Internet of Things (IoT) and artificial intelligence (AI) techniques, including machine and deep learning models, have been recently applied in the medical field to automate the diagnosis procedures of abnormal and diseased cases. They also have many other applications, including the real-time identification of fall accidents in elderly patients. The goal of this article is to review recent research whose focus is to develop AI algorithms and methods of fall detection systems (FDS) in the IoT environment. In addition, the usability of different sensor types, such as gyroscopes and accelerometers in smartwatches, is described and discussed with the current limitations and challenges for realizing successful FDSs. The availability problem of public fall datasets for evaluating the proposed detection algorithms are also addressed in this study. Finally, this article is concluded by proposing advanced techniques such as lightweight deep models as one of the solutions and prospects of futuristic smart IoT-enabled systems for accurate fall detection in the elderly.  
artificial intelligence; internet of things; fall detection; wearable sensors; old people +
Footnote †: journal:  
url
# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 5 Experiment and Results
### Results
#### 5.4.1 Analysis of SVLM-FE Forecasting Results
Other models did not make good directional judgements in the face of this rapidly changing load, with flatter curves that did not fit the small peaks and troughs well. Therefore, in the process of the SSA search for the key parameter set of VMD, the decomposition evaluation criteria proposed in this paper are used as the fitness function, which can effectively reduce the volatility of the load data and establish the foundation for the construction of a highly accurate short-term load forecasting model. At the same time, the component load forecasting values obtained in the first stage and the feature values obtained by feature selection are used to construct a feature vector, which is fed into the fully connected layer for forecasting. This completes the error correction for the first-stage forecasting, and the method is experimentally proven to be very effective. Figure 9: Load forecasting results of SVLM–FE and other models in Place 1.
## 5 Measurement Results
### Time-Based Sensors  
In addition to the DMA resistor and capacitor arrays, an array of time-based sensors has been fabricated. This array consists of five RC variants, each comprising four sensor units, resulting in a total of 20 sensors on a single chip.  
The _R-C_ variant operates at 500 MHz and consumes 16.2 mW from a 1.5 V supply. It is worth noted that the time-based sensors only need to be activated when an authentication verification is required, hence the power consumption is not a significant concern.  
Based on DMA results, the measured mean values of \(R_{metal}\) and \(C_{metal}\) deviated from the circuit simulation values, especially for the resistor values. To account for this deviation, adjustments are made to the \(R_{metal}\) and \(C_{metal}\) values in the time-based sensors' simulations using the mean values measured from the DMA. This modification led to closely matched results between simulations and measurements across the five variants, with a maximum error of 6.1%, as summarized in Table 6.  
Figure 14: Measurement results of DMA-capacitor array. (**a**,**c**,**e**) results are from the first wafer, and (**b**,**d**,**f**) from the second wafer.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cell** & **Simulation Results** &
\begin{tabular}{c} **Measurement** \\ **Results** \\ \end{tabular} & **Process Shift** \\ \hline \(hC\) & 0.73 pF & 0.74 pF & 1.4\% \\ \(C\) & 1.45 pF & 1.47 pF & 1.4\% \\ \(2C\) & 2.88 pF & 2.81 pF & \(-2.5\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of DMA-Capacitance array.  
Discrepancies between the simulations and the measurements can be attributed to within-die variation (DMA vs. time-based sensor) and variations in the parasitic capacitance values within the discharge path, which are not accounted for in the simulation results. These fluctuations can lead to different levels of charge redistribution, resulting in varying discharge cycles. It is important to emphasize that the observed maximum deviation of 6.1% does not solely reflect sensor measurement error but instead encompasses the cumulative impact of effects such as intra-die process variations and model limitations.  
For context, prior work by [PERSON] et al. [32] has shown that RC time constants in a 55 nm CMOS process can vary by up to \(\pm\)50% due to PVT effects. Compared to this baseline, the 6.1% deviation observed here reflects significantly higher measurement fidelity than the intrinsic variation within a single foundry. It therefore stands to reason that the proposed method is capable of distinguishing devices fabricated in different foundries, where variation is expected to be even greater.  
The distribution of the derived discharge cycles for \(R_{metal}C_{metal}\) is shown in Figure 15, with separate data for each wafer. Notably, the discharge cycles of the second wafer consistently exceed those of the first wafer, aligning with the DMA resistor array results. Similar to the histograms shown for the DMA, as the sample size increases, the distribution is expected to converge toward a Gaussian distribution.  
Figure 15: _Cont._
# Semi-Direct Point-Line Visual Inertial Odometry for MAVs
## 2 Mathematical Formulation
### Line Feature Projection  
The Plucker coordinates [29] is used for line parameterization. A 3D line \(\mathbf{L}\) representing the Plucker coordinates is constructed as \(\mathbf{L}=\left[\begin{array}{cc}\mathbf{n}^{T}&\mathbf{d}^{T}\end{array} \right]^{T}\), where \(\mathbf{n}\in\mathbb{R}^{3}\) represents the normal vector of the plane determined by the line and the coordinate origin, and \(\mathbf{d}\in\mathbb{R}^{3}\) represents the line direction vector. According to the description in [30], the transformation of a 3D line \(\mathbf{L}\) from the world coordinate \(\mathbf{L}_{w}\) to the camera coordinate \(\mathbf{L}_{c}\) can be defined as:  
\[\mathbf{L}_{c}=\left[\begin{array}{c}\mathbf{n}_{c}\\ \mathbf{d}_{c}\end{array}\right]=\left[\begin{array}{cc}\mathbf{R}_{w}^{c} &\lfloor\mathbf{p}_{w}^{c}\rfloor_{\times}\mathbf{R}_{w}^{c}\\ 0&\mathbf{R}_{w}^{c}\end{array}\right]\mathbf{L}_{w} \tag{5}\]  
and the projection from the camera coordinate to the camera image plane can be defined as:  
\[\mathbf{l}=\left[\begin{array}{c}l_{1}\\ l_{2}\\ l_{3}\end{array}\right]=\left[\begin{array}{ccc}f_{y}&0&0\\ 0&f_{x}&0\\ -f_{y}c_{x}&-f_{x}c_{y}&f_{x}f_{y}\end{array}\right]\mathbf{n}_{c}=\mathcal{K} \mathbf{n}_{c} \tag{6}\]  
where \(\mathcal{K}\) represents the projection matrix of line \(\mathbf{L}_{c}\).
## 4 Application of the Concepts: Design of an In-Line Measuring Machine
### Architecture of In-Line Measuring Station
#### 4.2.1 Flexibility  
The proposed design includes a fast positioning system that uses linear motors and a laser sensor to perform the measuring actions. The use of the positioner allows the in-line machine to operate in a manner similar to a CMM, which can be adapted to variations in part size and geometry. In terms of the measurement functions that need to be performed, CMM are very flexible equipment. However, their flexibility comes at the expense of speed of measuring and cost of fixturing. Laser sensors are not as accurate and may be susceptible to variations in environmental conditions. On the other hand they are not hindered by the limitations of contact measurement sensors, which are inherently slow ([20,45]). The machine also includes a fixturing plate that holds the workpiece, and allows for quick part change. Parts of similar design may be measured on the same plate, and for parts of different designs, a new plate can be built.
# Data Fusion of Multivariate Time Series: Application to Noisy 12-Lead ECG Signals
## 4 Application of NDFA in 12-Lead ECG Signals
### Performance Comparison of Data Fusion Algorithms  
The MIT-BIH database is a source for standard ECG test available, and the database has been applied in the fundamental research. Each ECG recording in the database contains two leads, _MLII_ and _V5_, which are sampled at 360 Hz and recorded for 10 s.  
Here, the four data fusion algorithms, NDFA, MDF, the algorithm 1 [21], and the algorithm 2 [22], were applied in the MIT-BIH database. With the experimental results of the four data fusion algorithms compared, the advantages of NDFA were well manifeste. In the experiment, two lead ECG signals; _MLII_ and _V5_, were fused by the four algorithms, respectively. To show the advantage of NDFA, the similarity of the fused signals and the original ECG signals were calculated by the Dynamic Time Warping Algorithm (DTW), objectively. DTW is a well-known technique to describe the level of the similarity of the two warped time sequences. The algorithm has been successfully applied in automatic  
Figure 7: Reconstructed phase trajectories of ECG signals (No.106). (**a**) and (**b**) are reconstructed phase trajectories of lead _MLII_ and lead _V5_, respectively. (**c**) is reconstructed phase trajectory of final fused result via NDFA.  
speech recognition [37]. Here, the similarities of two lead ECG signals and the fused signals were calculated by DTW. Then the numerical values of the results were normalized further. The degree of the similarity between the original and fused signals were reflected by the normalized results, \(\mathrm{DDTW}\), and the smaller value of \(\mathrm{DDTW}\) implies that the morphology features of two signals were somewhat similar. Figure 8a shows the similarity between the fused signals by four algorithms and the lead _MLII_. The means and variances of the parameter \(\mathrm{DDTW}\) derived by NDFA, MDF, algorithm 1, and algorithm 2 were 0.0616, 0.0011; 0.8474, 0.0110; 0.2257, 0.0645; 0.2256, and 0.0644, respectively. Analogously, for fused signals and the lead _V5_, the means and variances of the parameter \(\mathrm{DDTW}\) based on NDFA and MDF were 0.0921, 0.0017; 0.8768, 0.0102; 0.1959, 0.0426; 0.1958, and 0.0425 in Figure 8b. As can be seen, via NDFA, the means and the variances of the parameter \(\mathrm{DDTW}\) were smaller, compared with the experiment results under the others. Figure 8 demonstrates that the characteristics of two leads ECG signals, _MLII_ and _V5_, can be well reserved on the fused signal yielded from NDFA, and reflects that the performance of our algorithm exceeds MDF, algorithms 1 and 2.  
In this section, NDFA is evaluated by the two databases of PhysioNet/Computing in Cardiology Challenge 2011 and MIT-BIH, respectively. Experimental results indicate that the fused trajectory can effectively inherit the quality characteristics of the realistic ECG signals.
# Infrared Thermography to Evaluate Thermal Comfort under Controlled Ambient Conditions
## 4 Results of Phase II: Evaluation of Thermal Comfort
### Face Temperature vs. PMV  
For each tested scenario, the PMV value was calculated and its relationship with the average temperature for the six regions of the face was assessed. Figure 11 depicts the scatter plot of the results and Table 3 shows the linear regression analysis applied to each region. Both highlight the strong linear relation between the PMV and average temperature values in all subregions and for the face as a whole. This is confirmed by the goodness of fit of each linear model, R\({}^{2}\), which presents values higher than 89%. However, due to the greater variability in this subregion, the robustness of the linear relation between the PMV and average temperature on the nose is lower than in other regions. The best result occurs in the cheekbones, with a higher R\({}^{2}\) value, even if the chin and forehead have the lowest variations in temperature (Figure 10c). A strong relation was also found when the entire face was considered (R\({}^{2}\) = 93.3%), which is important, since the use of this region is easier to implement in practice. Therefore, the average facial temperature can be used as an indicator for the determination of thermal comfort, while an evaluation of the individual subregions is not necessary.
## 7 Limitations and Future Work  
The major limitation of the proposed work is the lack of device forensic analysis even though we extensively studied the encrypted network traffic of the Signal app and demonstrated significant detection of the app's behavior as supportive evidence for forensic investigation. Device forensic analysis helps to provide a complete solution in terms of investigation; therefore, device forensic can be targeted in the future. The second limitation consists of version dependency. Forensics analysis of applications is dependent on the specific version of the application. Hence, the proposed work is also version dependent. If a new version of the application is launched with extensive modification of the structure or network behavior, then its network traffic patterns may change and may conflict with the results presented in this work. In the future, we will work to provide a GUI-based solution for existing IM-based apps with configurable attributes including but not limited to ports and associated server IPs, etc., to generalize the proposed strategy and possibly make it portable to new versions of the application.
## 3 The Proposed OAM&WDM-PON Architecture Design with the P2 MP Topology  
In this section, a proposal of the WDM-PON architecture based on the OAM multiplexing is introduced with the P2 MP topology. In this design, multiple data streams are multiplexed by orthogonal OAM regimes on the same wavelength for achieving the effective utilization of the bandwidth resources. Experimental results confirm the high potential of the suggested network architecture for user service provisioning with flexibility and bandwidth efficiency in future ultra-high capacity WDM-PONs [7].  
In Figure 1, the access WDM-PON architecture based on the OAM multiplexing is displayed. This architecture consists of the OLT, the RN, and the ONT network elements. The interconnection between the OLT and RN is realized by the common optical fiber supporting the OAM transmission when compared with conventional P2 MP WDM-PON architectures [19; 20]. In the OLT, network data streams are first modulated on the optical carrier. The OLT can transmit multiple wavelength channels from various users or a single channel for multicasting, depending on the application's demands [7]. The interconnection between OAM TX/RX and WDM Mx/Dx is the main unique property of the proposed network architecture. The WDM channels' ridge in the given wavelength range with the appropriate channel spacing is generated using the CW (Continuous Wave) source. An optical beam from this CW source is phase- and intensity-modulated for the initial generation of the sideband and divided into even and odd channels by means of the WSS (Wavelength Selective Switch) element. The ridge is subsequently passed through the PDM (Polarization Division Multiplexing) emulator. Then, independent data channels are used for considered OAM regimes [21]. Each data channel is passed through a polarization controller and collimated. Each wavelength path is passed through a q-plate, which generates a polarization-dependent combination of OAM beams. After the WDM Mx/Dx element, data channels are jointly transmitted through the common optical fiber. In the RN, a multi-wavelength signal is split in the 1\(\times\)N power splitter and transmitted to interconnected ONT terminals. In the ONT, a user data channel on the assigned wavelength is selected using the tunable OBF (Optical Bandpass Filter) element from the received multi-wavelength signal. Then, the output is collimated and sent through a circular polarizer comprising a quarter-wave-plate and a linear polarizer [21]. The SLM (Spatial Light Modulator) element converts a detected OAM mode, after which the optical beam is sent to an optical modulator handling demodulation and error counting. Finally, the receiver receives the appropriate wavelength channel.  
The Simulation Model for the WDM and OAM Channel Allocation Methods in the Proposed OAM&WDM-PON Architecture