esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Testing Procedures  
### Static Setup  
The first procedure is a measurement called static measurement. In this measurement, data were collected during 30-min periods inside a building. In the first test, both devices were placed next to the window. Once the 30 min had elapsed, the measurement was repeated, placing the devices away from the window toward the inside of the building. The purpose of this was to check how each device was affected by being more conditioned inside the building in order to simulate whether the geolocation could be obtained within the building. For this measurement, the average of the CNo, the average of the satellites used during this period, and the average of the accuracy were used, as illustrated next.
## 3 Automatic Recognition Algorithm of VLPT
### The Predetermined Number of Clusters, K  
In the K-means algorithm, \(K\) refers to the number of clusters to be formed, also known as the predetermined number of clusters. The appropriate value of \(K\) is crucial for the effectiveness of the K-means algorithm and the quality of clustering results. A common method of determining the value of \(K\) is experimentation and adjustment based on data characteristics and domain knowledge. There are also quantitative methods such as the elbow method, silhouette coefficient, and within-cluster sum of squares (WCSS) to assist in this determination. The elbow method involves plotting the curve of the sum of squared errors (SSE) of clusters under different \(K\) values and selecting the \(K\) value at the \"elbow\" point where the curve begins to level off. The silhouette coefficient, on the other hand, evaluates the compactness and separation of clustering results based on similarity and dissimilarity among samples as shown as Figure 6.  
After dimensionality reduction, the data about Figure 5 is fed into the K-means++ model for training. The curve of the sum of squared errors (SSE) at different values of \(K\) is plotted, and the value of \(K\) is chosen based on the location where the curve exhibits an elbow bend. It is observed that the optimal number of clusters corresponds to the inflection point, which in this case is \(K=2\). This result is further supported by calculating the silhouette coefficient, also yielding \(K=2\). Furthermore, considering the practical requirements of the experiment to divide the data into two categories: straight lines and non-straight lines, \(K=2\) is appropriate based on the actual application.  
Therefore, in this paper, we set \(K=2\), representing two categories: straight lines and non-straight lines. The clustering operation is performed on each column as the clustering element. After clustering, each row is assigned a label representing either a straight line or a non-straight line. The visualization of the labeled straight lines after training the model is shown in Figure 7.
## 1 Introduction
However, this algorithm necessitates that the original training sequence be pre-stored at the receiving end, which introduces system training overhead [24]. The time-domain 4 th power algorithm [22] is straightforward to implement, but exhibits poor compatibility and is impractical for high-order QAM systems. The traditional QPSK partitioning algorithm [25] addresses the limitations of the time-domain 4 th power approach for QAM signals, but overlooks the time interval between the selected signals, leading to reduced estimation accuracy [26; 27; 28]. To address this issue, some researchers have modified the traditional QPSK partitioning algorithm, proposing a generalized carrier FOE algorithm [26]. However, implementing this modified algorithm in hardware presents significant challenges [27]. One FOE method discussed in [28] incorporates considerations for the time interval, but it results in high algorithmic complexity. To address the aforementioned issues, we propose a TTQP FOE scheme and apply it to the PM 16 QAM FSO spatial diversity reception simulation platform. Figure 1: Block diagram of digital signal processing (DSP).
# A Temperature-Robust Envelope Detector Receiving OOK-Modulated Signals for Low-Power Applications
## 3 ED and PTAT Block Description
### ED Model and Receiving Chain Sensitivity Optimization
Since the diodes have no flicker noise, the noise power at the output of the ED is \(V_{N,ED}^{2}(f)f_{N}=4k_{B}TN_{DS}f_{N}=4k_{B}TN^{2}R_{in}f_{N}\), where \(k_{B}\) is the Boltzmann constant and \(f_{N}\) is the ED noise bandwidth, which is ideally equal to bitrate \(f_{S}\). Then, the SNR at the ED output is  
\[SNR_{ED,\rho}=\frac{V_{ED}^{2}}{V_{N,ED}^{2}(f)f_{N}}=\frac{A_{v}^{4}P_{RF,ant }^{2}R_{S}^{2}}{\left(4 nV_{T}\right)^{2}k_{B}TR_{in}f_{N}}. \tag{6}\]  
Thus, the SNR ultimately does not depend on \(N\). The ED is followed by a baseband amplifier with Noise Factor \(NF\), which can be expressed as  
\[NF=1+\frac{N_{amp,\rho}}{\int G(f)^{2}V_{N,ED}^{2}(f)df}, \tag{7}\]  
where \(N_{amp,\rho}\) is the integrated noise at the amplifier output due to the amplifier itself, \(G\) is the amplifier voltage gain and \(V_{N,ED}^{2}\) is as defined previously. Assuming \(SNR_{req}\) to be the minimum required SNR at the output of the baseband amplifier, the overall chain sensitivity can be calculated as  
\[P_{SEN}=\sqrt{\frac{SNR_{req}NF(4 nV_{T})^{2}k_{B}TR_{in}f_{N}}{A_{v}^{4}R_{S} ^{2}}}, \tag{8}\]  
where \(A_{v}\) depends on \(R_{in}\) through (5). This expression as a function of \(R_{in}\) has a minimum [12], and the optimum \(\overline{R_{in}}\) must be chosen accordingly in order to optimize chain sensitivity. As mentioned, \(SNR_{ED,\rho}\) in (6) does not depend on \(N\), whereas a big value for \(N\) is desirable for a small \(NF\) (7), as \(NF\) directly degrades sensitivity (8). This poses a trade off between sensitivity and bitrate, which is closely linked to the propagation delay the ED introduces \(\tau\), as in (4). Moreover, a big value for \(N\) also results in a high \(C_{in}\), thus degrading \(A_{v}\). Therefore, \(N\) becomes an important design parameter in a complicated trade-off, as it affects \(NF\), \(A_{v}\), \(R_{in}\) and \(\tau\)[1]. This leads to the choice of an optimum \(r_{DS}\), \(\overline{r_{DS}}=N\overline{R_{in}}\). Thanks to the possibility of setting the \(V_{GS}\) of the diodes, \(\overline{r_{DS}}\) can be found through the following formula:  
\[\overline{r_{DS}}=\frac{1}{\overline{g_{DS}}}=\left[\frac{\partial i_{DS}}{ \partial v_{DS}}\right]^{-1}\simeq\frac{V_{T}}{I_{S}\left(\frac{W}{L}\right)_ {MD}}e^{-\frac{\overline{V_{GS}}}{nV_{T}}}, \tag{9}\]  
given that \(V_{DS}\sim 0\)[12] and where \(MD\) are the diodes. Ultimately, by setting the appropriate diode \(\overline{V_{GS}}\), it is possible to set the correct \(R_{in}\) for chain sensitivity optimization. All useful formulas for sensitivity optimization are reported in \[I_{S}=I_{S0}e^{-\frac{V_{TH}}{nT}}, \tag{2}\]0
# Blind Deconvolution with Scale Ambiguity
## 3 The Proposed Approach
### Optimization
#### 3.2.1 Blind Kernel Estimation  
**Intermediate Image Estimation**. With the blur kernel \(k\) output from the previous iteration, the intermediate latent image \(u\) is estimated by  
\[\min_{u}\|u*k-f\|_{2}^{2}+\lambda\|k\|_{p}^{0.5}\|\
abla u\|_{0.5}. \tag{5}\]  
We use the iterative reweighed least squares (IRLS) [42] method to solve (5). At the \(t\)-th iteration, we need to solve the following quadratic problem:  
\[u^{[t+1]}=arg\min_{u}\sum_{\mathbf{x}}\big{\{}|(u*k-f)_{\mathbf{x}}|^{2}+\lambda\|k\|_{ p}^{0.5}(\omega_{h}^{u}|(\partial_{h}u)_{\mathbf{x}}|^{2}+\omega_  
**Kernel Estimation.** Given a fixed intermediate latent image \(u\), the blur kernel \(k\) can be obtained by solving the following model  
\[\min_{k}\|u*k-f\|_{2}^{2}+\lambda\|k\|_{p}^{0.5}\|\
abla u\|_{0.5}. \tag{7}\]  
According to the analysis in [9], the kernel estimation methods in gradient domain can be more accurate. Thus, we replace the image intensity with the image derivatives in the data fidelity. That is, the blur kernel \(k\) is estimated by  
\[\min_{k}\|\
abla u*k-\
abla f\|_{2}^{2}+\lambda\|k\|_{p}^{0.5}\|\
abla u\|_{0.5}. \tag{8}\]  
Similar to (6), the IRLS method is utilized to solve (8) by iteratively optimizing  
\[k^{[l+1]}=arg\min_{k}\big{\{}\|\
abla u*k-\
abla f\|_{2}^{2}+\lambda\sum_{i}\| \
abla u\|_{0.5}(\omega^{k}|k_{i}|^{2})\big{\}}, \tag{9}\]  
where \(\omega^{k}=0.5\|k^{[t]}\|_{p}^{0.5-p}|k_{i}^{[t]}|^{p-2}\).  
Because of the benefit from the transformed deconvolution formulation (4), we are not required to impose \(L^{p}\) normalization on the blur kernel \(k\) practically. Similar to most existing methods, after obtaining \(k\), we set its negative elements to be 0, and divide it by its \(L^{1}\) norm so that the sum of its elements is 1.  
To get better results, the proposed kernel estimation process is carried out in a coarse-to-fine manner using an image pyramid. \(u\) and \(k\) are initialized as the blurred image and delta kernel in the coarsest level.
## 3 Results and Discussion
### Tank Deformation  
Both the aboveground and underground tanks have liquid levels at depths of 0 m and 41.6 m, respectively. The maximum relative horizontal displacement of the external tank wall (between RP13 and RP14) when subject to horizontal seismic activity is shown in Figure 13. For the aboveground empty tank, the max relative displacement is 122 mm, which is less than that of the full tank (136 mm). Meanwhile, for the empty underground tank, the max relative displacement is 41.5 mm, smaller than the aboveground tank's deformation but slightly higher than that of the full underground tank (37 mm). It can also be seen that the shaking frequency of the aboveground tank is lower than that of the underground tank. These results indicate that the soil constraint around the underground tank reduces its deformation and improves the safety of the tank during earthquakes.  
According to Figure 14, which illustrates the settlement nephogram of storage tanks, we can discern the following patterns: The maximum settlement of aboveground tanks is significantly higher when full (0.41 m) compared to when empty (0.23 m), indicating that the tank's own weight and the pressure of the liquid inside result in greater load on the foundation, leading to greater settlement. In contrast, the maximum settlement of underground tanks is notably lower than that of aboveground tanks, whether empty (0.078 m) or full (0.090 m). This is likely due to the underground structure being supported by the surrounding soil, which mitigates settlement. Additionally, the difference in settlement between the full and empty states of underground tanks is minimal, which may relate to the design and construction quality of underground tanks and reflects their stronger adaptability to load changes.  
Figure 11: Wave height monitoring curve of LNG in aboveground tanks.  
Figure 12: Wave height monitoring curve of LNG in underground tanks.  
Figure 14: Settlement nephogram of storage tanks.  
Figure 13: The temporal curve of the maximum horizontal displacement of the tank wall.  
Evaluating the results from the four diagrams, we can conclude that underground tanks have a clear advantage in seismic design, especially in controlling foundation settlement. Aboveground tanks exhibit greater settlement, which may necessitate stricter foundation treatment and structural reinforcement measures. With their lower settlement values, underground tanks demonstrate better stability and reliability, which is particularly important for tank design in earthquake-prone areas.
# On-Road Driver Emotion Recognition Using Facial Expression
## 3 Materials and Methods
### Face Detection Module (FD)  
The face detection module (FD) extracts the driver's face area from the input video frames and converts it to grayscale images. Face detection and alignment are essential to many applications in computer vision and many researches have proposed relevant algorithms. [PERSON] et al. [61; 62] proposed an algorithm based on template matching and [PERSON] et al. [63] proposed a Harr feature extraction and Adaboost classifier algorithm. With the development of deep learning, more and more novel algorithms have been proposed [64; 65; 66; 67], continuing improvement of face detection accuracy.  
The FD module proposed in this work is inspired by the deep cascaded multitask framework proposed by [PERSON] et al. [67]. The FD module adopts three-stage nets to generate the face window and alignment face landmark positions and extract the face pictures (resolution: 160 \(\times\) 160, RGB). Then, the extracted face pictures are converted into grayscale images as the output of the FD module. The resolution of the input video influences the performance of the FD module. For the on-road driver facial expression dataset collected in this work, the original dataset was edited and the driver scene (resolution: 1920 \(\times\) 1080) was taken as the input of FD module to boost the processing speed. The proposed method of this paper is aimed at the driver monitoring system (DMS) [68]. Therefore, the algorithm of the FD module performs largest face detection; when multiple faces appear in one image, only the largest face is considered to be the driver's face. Furthermore, the recorder's placement ensures that the driver's face is largest in the captured images.
# Novel Internet of Things Platform for In-Building Power Quality Submetering
## 1 Introduction
### Contributions  
In short, all previous developments are partial attempts to meet to the challenge in question. What is needed is an IoT low-cost solution (much cheaper than 6900 per node) that distributes monitors and controls critical variables inside the facility; this would be extremely beneficial for management of a building and thus ensure seamless business operations. Previously publications consider that the recorded PQ parameters which are in the Cloud system can help to reduce energy consumption in a building. However, those data can also be used to automatically detect PQ disturbances and even compare to standard values. An open platform that measures and compares values with standard limits has not been implemented to this end.  
To our knowledge, there does not exist any low-cost IoT systems devoted to simultaneous PQ and energy monitoring. The paper introduces the first low-cost device able to analyze power quality parameters and upload them to an IoT cloud platform. This is the novelty of the paper: the ability to measure and send aggregated on-time PQ data to the cloud system, as well as process data and check with the standard compliance. The important point here is that this information is accessible by any device that has an Internet connection and a search engine. A mobile phone can access all data, without requiring any additional configuration or application. Voltage sags are on-time monitored through amplitude and duration, moreover categorized according to the SEMI F-47 curve. This is suitable for submetering industrial or commercial facilities as PQ sensor data can be an indication of the equipment performance regarding efficiency and accomplishment of PQ standards. In fact, users will be able to detect not only most PQ steady-state disturbances but events such as voltage sags which anticipate faults with early-stage alerts.  
The rest of the paper is organized as follows: Section 2 describes the system architecture, including the description of the hardware implemented. Section 3 resumes the PQ functionality implementedin the PQ sensor. The IoT PQ sensor cloud platform integration is included in Section 4. Results are included in Section 5. Section 6 puts forward several conclusions.
## 5 Field Measurement Analysis
### Existing Rail Deformation Measurement Analysis  
Considering that the vertical deformation of the left and right rails of the magnetic levitation viaduct is not significantly different, the vertical deformation monitoring data of the left rails are mainly analyzed. The vertical deformation monitoring points SJC-01-02, SJC-01-04, SJC-01-06, and SJC-01-08 of the upper left rail of peers 71#, 72#, 73#, and 74# and the vertical deformation monitoring point SJC-01-05 of the left rail of the viaduct are taken as typical points to draw the deformation time diagram, as shown in Figure 17.  
On the whole, the rails at each monitoring point show a settlement trend with the advancement of the construction stage, and the settlement trend is 'U'-shaped. The measured deformation trend is consistent with the numerical simulation deformation trend. The maximum cumulative vertical deformation occurs at the measuring point SJC-01-04-06, that is, the middle span of the viaduct; the maximum cumulative vertical deformation value of SJC-01-04 (left rail track on the upper part of pier 72#) is \(-1.19\) mm; the maximum cumulative vertical deformation of SJC-01-06 (the left rail at the top of pier 73#) is \(-1.12\) mm. The maximum cumulative deformation of SJC-01-02 and SJC-01-08 is only 0.29 mm and 0.37 mm, respectively.  
The time history deformation data of the vertical deformation monitoring points SJC-01-02, SJC-01-04, SJC-01-05, SJC-01-06, and SJC-01-08 of the rail row are analyzed. As shown in Figure 18, in the process of advancing the tunnel excavation face, the settlement deformation of the measuring point SJC-01-04 is first larger than that of other measuring points. With the excavation of the tunnel, and under the superposition of the excavation of the right line and the left line tunnel, the settlement deformation gap between the measuring points SJC-01-06 and SJC-01-04 gradually narrows. As the excavation face of the left line tunnel on the right line passes through the undercrossing point, the settlement rate of each monitoring point successively begins to decrease, and the settlement deformation gradually tends to be stable. The settlement law of SJC-01-02 and SJC-01-08 measuring points is similar to that of the above measuring points, but the settlement value is small.  
Figure 17: Monitoring values of the settlement curve of the existing left track.
## 4 Experiments and Discussions
### Image Acquisition Experiment  
To verify the representativeness of images collected by this system, this section divides the image acquisition experiment into two parts, which are image acquisition by using this system and image acquisition by manual. The camera used in the experiment was a Hikvision industrial area array camera, the model was MV-CA032-10 GM, the resolution was \(1920\times 1440\), the focal length of the lens was 25 mm, and 30 classes of fabrics were collected. The size of each fabric was 15 cm \(\times\) 30 cm, and the field of view of the camera was 3.5 cm \(\times\) 2.6 cm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Key Components** & **Model** & **Parameters** \\ \hline \multirow{4}{*}{Motion control} & Electric guide rail & JD45P & Travel: 300 mm \\  & Driver & DM442 & Two-phase stepping \\  & 8 axes motion control card & IMC408E & 8 axes \\ \hline \multirow{4}{*}{Illumination control} & Flat light source & HF-FX160 & DC12V, white light \\  & Ring light source, & YC-DR6836 WL & DC12V, white light \\ \cline{1-1}  & Light source Controller & CCS PD-3012-8 & Input AC100-240V, output: \\ \cline{1-1}  & Single chip Microcomputer & STM32F103 ZET6 & DC12V, power: 25 W \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Key components and related parameters of the system.  
Figure 11: The multi-DOF automatic image acquisition system: (**a**) Hardware structure of the multi-DOF automatic image acquisition system; (**b**) The UI of the multi-DOF automatic image acquisition system.
## 4 Results
### Modeling Results
#### 4.2.1 Artificial Neural Networks  
All the tested ANNs have the same configuration; the input layer has four neurons (as many as the model's inputs), the internal layer has a varying number of neurons inside (this parameter is configurable), and the output layer has one neuron (as the model has only one output). The output layer neuron has a linear activation function, and the rest of the neurons in the ANN use a tan-sigmoid as their activation function. As it was said, the internal neuron number was varied to achieve the optimal one; 15 different models have been tested, each one with different neurons in the hidden layer.  
Table 2 shows the error distribution through the clusters. In this case, it presents the mean absolute error (MAE) calculated for ANN with seven neurons in the hidden layer. There are a total of 15 MAE tables for artificial neural networks, as 15 different configurations have been tested. The error values have been calculated using 10 k-fold cross validation; this implies 10 different models must be trained before the error is calculated. Moreover, four different error measurements have been calculated: MAE, MSE (mean squared error), MAPE (mean absolute percentage error), and NMSE (normalized mean squared error).  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Global**} & \multicolumn{8}{c}{**Hybrid Model (Local Models)**} \\ \cline{2-10}  & & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\ \hline Cl-1 & 24983 & 6875 & 5701 & 653 & 652 & 619 & 616 & 615 & 613 & 611 \\ \hline Cl-2 & & 18109 & 6988 & 5063 & 4287 & 2405 & 2316 & 1817 & 1463 & 1314 \\ \hline Cl-3 & & & 12295 & 7001 & 4928 & 2687 & 2349 & 2306 & 1899 & 1446 \\ \hline Cl-4 & & & & 12267 & 6498 & 4251 & 2646 & 2333 & 2195 & 2088 \\ \hline Cl-5 & & & & & 8617 & 6499 & 2668 & 2633 & 2271 & 2244 \\ \hline Cl-6 & & & & & 8522 & 6361 & 4379 & 2643 & 2246 \\ \hline Cl-7 & & & & & & 8029 & 4757 & 3636 & 2635 \\ \hline Cl-8 & & & & & & & 6144 & 4117 & 2708 \\ \hline Cl-9 & & & & & & & & 6146 & 4171 \\ \hline Cl-10 & & & & & & & & & 5520 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of samples in each created cluster.
## 2 Discrete-Event-Simulation-Based Method
### Interaction Mechanism Between the Simulation Model and NEH  
The NEHL algorithm proposed in Section 2.2 is encoded in a method object named \"NEH_L\". The NEH_L object is placed in the same structural layer as the simulation  
Figure 5: Pseudo-codes of NEH algorithm under simulation environment.  
environment established in Section 2.1. The discrete-event simulation procedure is illustrated in Figure 6, and a PFSP simulation meta-model is shown in Figure 7. The specific procedures and ideas of the simulation-optimisation mechanism presented in this paper can be described as follows:  
Step 1: Establish the initial model according to the number of machines, processing time, and other initial constraints, and selectively modify the model according to the actual situation to obtain the final simulation model.  
Step 2: Arrange the jobs in descending order of the PRL metric and select the first k jobs (starting at 3) as the current sequence. Insert the job k into the k possible slots of the current sequence to generate k job sub-sequences. Input these k job sub-sequences into the final simulation model.  
Step 3: Run the simulation model by calling the EventController in Plant Simulation software, and the absolute time of entry and departure of each job on each machine in the above k job sub-sequences can be obtained, which can be used to calculate the makespan and TBLJP1 metric of the corresponding sub-sequence. Then, select the optimal sequence among the above k job sub-sequences by TBLJP1's mechanism. Add k to 1 and assign the optimal sequence to the new current sequence and enter the next loop. End the loop and outputs the final job sequence when k = n.  
In summary, the automated dynamic discrete-event modelling method enables rapid modelling of real workshops with complex constraints, ensuring the practicality of the proposed method. The NEH algorithm provides a near-optimal solution to the sorting problem, ensuring the effectiveness of the proposed method.  
Figure 6: Simulation–optimisation procedure.