esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Track Geometry  
Figure 7 shows the track-geometrical measurement results, which are expressed as the rail level relative to the level of the base point. As can be seen in the figure, in the mid-span of the three arches, large downward deflections in winter and upward deflections in summer occurred. The differences of the vertical rail levels in winter and summer are as large as 25 mm in the center arch and 15 to 18 mm in the side arches. This phenomenon was almost the same in both the south- and north-bound lines. On the other hand, the seasonal differences of vertical rail levels were relatively small in the supports. The deformed shape was almost symmetrical in winter and summer, and exactly matched the shape of the main arch ribs. Also, it could be seen, in the plot of the peak temperature of the arch ribs and the maximum rail levels shown in Figure 8, that the vertical deformation of the bridge increased proportionally to the temperature of the member, and that the vertical deformation of the center arch with the longer span was larger than that of the side arch with the shorter span. Therefore, it could be confirmed that the main cause of the change of rail levels is the contraction and expansion of the main arch ribs due to temperature change.  
Figure 7: \(Cont.\)By contrast, the changes in the lateral alignment of the rails from winter to summer were not large in either the south- or north-bound line. The effect of deformation due to main-arch temperature change on the rails' lateral alignment, then, was very limited. The singular point of the lateral alignment of the rails at the position of approximately 100 m in Figure 7b,d was due to FACOP, which is a kind of kinematical gauge optimization system for reducing wear on the tongue rail in the turnouts [24]; it was not related to track-geometrical errors.  
Figure 7: Measured vertical and lateral track irregularities: (**a**) vertical track irregularities of south-bound line; (**b**) lateral track irregularities of south-bound line; (**c**) vertical track irregularities of north-bound line; (**d**) lateral track irregularities of north-bound line.
# An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network
## 2 Preliminaries
### Network Energy-Consumption Model  
Much literature has proposed node energy-consumption models for UASNs [18; 19]. This paper uses the same energy-consumption model as [PERSON] and [PERSON] (2006) [19]. The energy consumed by the transmitter to send a package of \(m\) bits is  
\[E_{TX}\left(m,d\right)=mE_{elec}+mT_{b}CHde^{a\left(f\right)d} \tag{3}\]  
Similarly, the energy consumption of the receiver is  
\[E_{RX}\left(m,d\right)=mE_{elec} \tag{4}\]  
where \(E_{elec}\) is the energy consumed by the node circuit to process one bit of information. \(C\) is the coefficient when the transmitter is at the minimum transmit power derived from the sonar equation in [19], and its value is \(2\pi\times 0.67\times 10^{-9.5}\). \(H\) is the average depth of the node, and \(d\) is the transmission distance. \(T_{b}\) is the duration of one bit, which can be expressed as [20]  
\[T_{b}=\frac{M^{\prime}}{S_{v}} \tag{5}\]  
Here, \(M^{\prime}\) is the size of the packet to be sent, \(S_{v}\) is the data transmission rate, and \(a(f)\) is the absorption coefficient that depend on frequency [18; 21]:  
\[a\left(f\right)=\frac{0.11f^{2}}{1+f^{2}}+\frac{44f^{2}}{4100+f^{2}}+2.75\times 1 0^{-4}f^{2}+0.003 \tag{6}\]  
where \(f\) is the transmission frequency of the signal in kHz and the absorption coefficient \(a(f)\) has the unit dB/m, and it is the frequency correlation coefficient. The determination of Equation (6) can be done based on [PERSON]'s model [22].
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 4 Big Data Collection in LS-WSNs
### Architecture Based on Mobility  
In order to maximize network lifetime and reduce latency in WSNs, several works [86; 87; 48; 88] have introduced mobility into WSNs. Mobility in WSNs is useful since it has a number of advantages that include good connectivity, reduced deployment cost, reliability, and energy efficiency [89; 90; 84; 91]. Although mobility brings significant benefits in terms of increased network lifetime, it presents several challenges for sensor networks. These challenges (see Figure 16) are, among others, contact detection, mobility-oriented power management, reliable data transfer, QoS, location, and energy consumption [92; 93; 94].  
Different kinds of mobility in WSNs have been proposed: static-sink architecture and moving sensor nodes, architectures with mobile and static-sink sensor nodes, architectures with mobile sinks and mobile sensor nodes, architectures with sensors, and multiple static-sink architectures with sensors and multiple mobile sink and hybrid architectures.
## 2 Materials and Methods
### Parallel Lattice Grid Method  
In order to calculate the intersection points between rays and facets, we need to first determine whether a facet is likely to intersect with a ray. To achieve this, triangular facets are organized as lattice grids data structure. The projection plane is divided into a series of lattice grids as shown in Figure 4. If a facet is overlapped with a grid, the facet will be marked by a specific grid index. Thus, for rays in a lattice grid, only the triangles which are marked by current grid index need to be tested. Triangles that are not marked by the current grid do not participate in the current step, which greatly reduces the calculation times of intersection between rays and triangular facets. In addition, the process of index marking can be performed in parallel, which eliminates the extra time consumption caused by lattice construction. An edge function [31] is used to test the overlap in projection plane. As Figure 5 shown, assuming that the facet's normal vector is \(\mathbf{n}\), \(\mathbf{e_{i}}\) is an edge of facet, and \(\mathbf{v_{0}}\), \(\mathbf{v_{1}}\) and \(\mathbf{v_{2}}\) are vertexes of  
Figure 3: Cartesian mesh lines distribution and ray generation based on ray casting method.  
facet. According to Equations (13) and (14), \(\mathbf{n}_{ei}\) and \(d_{ei}\) are computed. Then, for all three edges, test whether Equation (15) is true. If Equation (15) is true, the facet overlaps with grids.  
\[\mathbf{n}_{ei}=(-e_{i,y},e_{i,x})^{T}\cdot\begin{cases}1,&n_{z}\geq 0\\ -1,&n_{z}<0\end{cases} \tag{13}\]  
\[d_{ei}=-\langle\mathbf{n}_{ei},\mathbf{v}_{i}\rangle+max\{0,\mathbf{\Delta p}_{x}n_{ei,x} \}+max\{0,\mathbf{\Delta p}_{y}n_{ei,y}\} \tag{14}\]  
\[\bigcap_{i=0}^{2}\left(\langle\mathbf{n}_{ei},\mathbf{p}_{min}\rangle+d_{ei}>0\right) \tag{15}\]  
where \(\mathbf{\Delta p}=\mathbf{p}_{max}-\mathbf{p}_{min}\), an important point needed to be noticed here is that the \(\mathbf{p}_{min}\) and \(\mathbf{p}_{max}\) are the coordinate of the first and end ray starting point in the current lattice grid (sub-bounding box), respectively. The point participating to calculation is the mesh cell center point.  
Figure 4: Lattice grid method for primitive searching.  
Figure 5: Overlap test between triangle and grid in projection plane (\(s_{x}\) and \(s_{y}\) are the cell size in \(X\) and \(Y\) dimension, respectively).
# Methodology for Simulating 5G and GNSS High-Accuracy Positioning
## 2 Scenario Definition
### Network of Cellular Transmitters
Since the building height is the main limiting factor of the GNSS availability, our approach is to characterize the GNSS satellite visibility or elevation mask according to the density of BSs, as introduced in the following section.
## 3 Materials and Methods
### Dataset  
Preprocessing studies were carried out on the data first. Patients with a null value were excluded from the evaluation. The features in the dataset were normalized and the standard scalar distribution was applied so that the weights would not lose their effectiveness during the classification process. After this stage, the data were first divided into training and test data for training the models. Since the dataset used in this study was limited to training machine learning models, 90% of the data were allocated for training and 10% for testing. Demographic and clinical characteristics including age, gender, body mass index, smoking status, alcohol consumption, comorbidities (diabetes, hypertension, coronary artery disease, and hyperlipidemia), physical examination findings, and medications were recorded. Investigations were performed during the attack, and patients with at least two attacks were included in this study. The criteria for gouty arthritis are based on the work of [33]. Laboratory values included complete blood count (hemoglobin, neutrophil count, lymphocyte count, and platelet count), biochemical parameters (urea, creatinine, glucose, alanine aminotransferase (ALT), aspartate aminotransferase (AST), triglyceride (TG), total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), lactic dehydrogenase (LDH), uric acid, total protein, and albumin), erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP). Patients were divided into two groups: GFR < 60 mL/min/1.73 m\({}^{2}\) (CKD) and GFR \(\geq\) 60 mL/min/1.73 m\({}^{2}\). This study included 276 patients. Of these patients, 97 (35.1%) were female and 179 (64.9%) were male. The mean age of the patients was 66.85 \(\pm\) 12.17 years, and the mean age was found to be significantly higher in female than in male patients with GFR \(\geq\) 60 mL/min/1.73 m\({}^{2}\) compared to GFR < 60 mL/min/1.73 m\({}^{2}\). The mean GFR of the patients was 65.31 \(\pm\) 20.97 mL/min/1.73 m\({}^{2}\), 162 (58.7%) had a GFR \(\geq\) 60 mL/min/1.73 m\({}^{2}\), and 114 (41.3%) had a GFR below 60 mL/min/1.73 m\({}^{2}\). The mean GFR and the proportion with GFR \(\geq\) 60 mL/min/1.73 m\({}^{2}\) were higher in males than in females. Table 1 shows the summary of the information presented.
# High-Speed GaN-Based Superluminescent Diode for 4.57 Gbps Visible Light Communication  
[PERSON]  
1 Key Laboratory for Information Science of Electromagnetic Waves (MoE), School of Information Science and Technology, Fudan University, Shanghai 200438, China; [EMAIL_ADDRESS] (D.L.); [EMAIL_ADDRESS] (C.M.); [EMAIL_ADDRESS] (I.W.); [EMAIL_ADDRESS] (C.M.); [EMAIL_ADDRESS] (C.M.); [EMAIL_ADDRESS] (J.H.); [EMAIL_ADDRESS] (S.V.); [EMAIL_ADDRESS] (M.J.;[EMAIL_ADDRESS] (J.S.); [EMAIL_ADDRESS] (J.Z.); [EMAIL_ADDRESS] (Z.L.); [EMAIL_ADDRESS] (N.C.)  
2 Shanghai Engineering Research Center of Low-Earth-Orbit Satellite Communication and Applications, Shanghai 200438, China  
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Superluminescent diodes (SLDs) have the advantages of both LEDs and LDs, and have lately been studied as the transmitter in VLC systems with high-speed, droop-free and low speckle noise characteristics [5; 6]. In this work, a blue SLD was fabricated using a tilted facet configuration with a tilting angle of 8.3\({}^{\circ}\) on the front facet. We experimentally demonstrate a VLC system based on this blue SLD, which has reached a record data rate of up to 4.57 Gbps.
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 4 Security Analysis of the Proposed Algorithm
### Mathematical Analysis through BAN Logic
#### 4.1.2 Proof of Concept  
When applied to the rules, all the messages mentioned above should ensure freshness, confidentiality, origin, and non-repudiation. Hence, we define a new formula based on the following rules:  
1. Origin Rule: * \(S\) receives \(GSverRep\) from Admin and believes that \(\binom{\sigma_{GS}}{\gamma}C_{i}\). When \(S\) sees \(Update\) is signed with \(\sigma_{GS}\), it believes that this message was sent by \(C_{i}\): \[\frac{S|\equiv A|\sim^{\sigma_{GS}}C_{i}\wedge S\triangleleft Enc(Sign(Nc, \omega_{i})_{\sigma_{GS}})_{S_{pk}}}{S|\equiv C_{i}|\sim Enc(Sign(Nc,\omega_{ i})_{\sigma_{GS}})_{S_{pk}}}.\] * \(S\) believes that \(\binom{A_{pk}}{\gamma}A\), hence it also believes that \(A\) sent \(Enc(Nd,\sigma GS)_{A_{pk}}\), verifying origin of the message from \(A\): \[\frac{S|\equiv\binom{A_{pk}}{\gamma}A\wedge S\triangleleft(Enc(Nd,\sigma GS)_ {A_{pk}}\right)}{S|\equiv A|\sim\left(Enc(Nd,\sigma GS)_{A_{pk}}\right)}.\]
2. Non-Repudiation Rule * Because of above origin rules, \(C_{i}\) cannot deny sending \(M_{3}\), and \(A\) cannot deny sending \(M_{4}\).
3. Message Freshness Rule: * \(A\) believes that \(Na\) is fresh, and \(C_{i}\) once said \(\left(Enc(Na,x_{i})_{A_{pk}}\right)\). Therefore, \(A\) believes that \(GSReq\) message is fresh: \[\frac{A|\equiv\#Na\wedge A|\equiv C_{i}|\sim\left(Enc(Na,x_{i})_{A_{pk}}\right) }{A|\equiv C_{i}|\equiv\#(GSReq)}.\] * \(C_{i}\) believes that \(Nb\) is fresh, and it also believes that \(A\) once said \(\left(Enc(Nb,A_{i})_{x_{i}}\right)\), hence \(C_{i}\) believes that \(GSRep\) is fresh: \[\frac{C_{i}|\equiv\#Nb\wedge C_{i}|\equiv A|\sim\left(Enc(Nb,A_{i})_{x_{i}} \right)}{M_{2}|\equiv\#(GSRep)}.\] * \(S\) believes that \(Nc\) is fresh, and it sees that \(C_{i}\) has sent \(\left(Enc(Sign(Nc,\omega_{i})_{\sigma_{GS}})_{S_{pk}}\right)\). Therefore, it believes that the message is fresh: \[\frac{S|\equiv\#Nc\wedge S|\equiv\left(Enc(Sign(Nc,\omega_{i})_{\sigma_{GS}} )_{S_{pk}}\right)}{S|\equiv\#(Update)}.\]
4. Confidentiality Rule: * \(A_{pk}^{-1}\) is the corresponding private key of \(A_{pk}\), and it is only known to admin \(A\). Hence, only the admin can decrypt the message \(M_{1}\) and identify \(i\). In this way, confidentiality about \(i\) is maintained between sender \(C_{i}\) and admin.  
\[\frac{\xrightarrow{A_{\textit{pk}}}A\wedge(Na,(Enc(Na,i)_{A_{\textit{pk}}}))}{OnlyA \lessdot i}.\]
* Only the server knows \(S_{pk}\). Hence, only the server can decrypt \(M_{3}\) and see an update of client \(i\), i.e., \(\omega_{i}\). Hence, it is confidential between client \(C_{i}\) and the server. \[\xrightarrow{S_{pk}}S\wedge(Nc,(Enc(Sign(Nc,\omega_{i})_{e_{GS}})_{S_{pk}})) \over OnlyS\lessdot\omega_{i}}.\]
# Privacy-Preserving Outsourced Artificial Neural Network Training for Secure Image Classification
## 4 The Framework and Construction for Privacy-Preserving Image Classifier
### Description of Proposed Scheme  
We construct a scheme for the 3-layer perception which includes three phases: system initialization, user data encryption, and privacy-preserving training. In the system initialization phase, \(\mathcal{TPA}\) generates a master secret key and public key based on a given security parameter; in the data encryption phase, the user sends his samples blinded by a random mask matrix \(A\) to the \(\mathcal{S}_{\mathcal{AU}}\) and the inverse of the matrix \(A^{-1}\) to the \(\mathcal{S}_{\mathcal{NNN}}\), respectively, and transfer his encrypted sample attributes by the public key to the server \(\mathcal{S}_{\mathcal{NNN}}\); in the privacy-preserving training phase, the servers \(\mathcal{S}_{\mathcal{AU}}\) and \(\mathcal{S}_{\mathcal{NNN}}\) coordinate the neural network training. More specific details of each stage are described as follows.
# Portable Smart Spectrometer Integrated with Blockchain and Big Data Technology
## 2 Structure of the Portable Smart Spectrometer
### Photoelectric Detection Module  
Photodetectors are employed for spectral imaging. The PSS-1.0 employs a TCD1304 DG linear CCD (Toshiba) as a photodetector. The TCD1304 DG is a low-priced, high-sensitivity, and low dark current CCD, with a spectral range of 400-1100 nm [13]. The CCD collects the spectral signals output by the spectroscopic module and converts them into analog voltage signals, and then outputs the analog voltage signals to the OS pin. Figure 4 shows the circuit diagram for driving the TCD1304 DG CCD with the STM32F407 microcontroller. The microcontroller increases the drive voltage from 3.3 to 5 V through the TC74 HC04P and provides an appropriate timing signal to drive the CCD, then uses the built-in analog to digital converter (ADC) to measure the analog signal output amplified by the 2 SA1015-Y from the OS pin. In detail, first, the microcontroller provides a default timing signal for the CCD. The CCD measures the optical signal and outputs an analog signal to the ADC of the microcontroller, which converts the analog signal to the corresponding digital signal. Then, the microcontroller adjusts the integration time of the CCD by changing the timing signal according to the digital signal, so that the integration time is as long as possible without saturating the CCD. Finally, the CCD remeasures the optical signal and the microcontroller obtains the appropriate digital signal as spectral data. That is to say, the photoelectric detection module finally outputs the spectral data.
# Ensemble Deep Learning Models for Forecasting Cryptocurrency Time-Series
## 6 Discussion
### Discussion of Results  
All compared ensemble models were evaluated considering both regression and classification problems, namely for the prediction of the cryptocurrency price on the following hour (regression) and also for the prediction if the price will increase or decrease on the following hour (classification). Our experiments revealed that the incorporation of deep learning models into ensemble learning framework improved the prediction accuracy in most cases, compared to a single deep learning model.  
Bagging exhibited the best overall score in terms of classification accuracy, closely followed by averaging and stacking(\({}^{kNN}\)); while stacking(\({}^{\text{LR}}\)) the best regression performance. The confusion matrices revealed that stacking(\({}^{\text{LR}}\)) as base learner was actually biased, since most of the instances were wrongly classified as \"Down\" while bagging and stacking(\({}^{kNN}\)) exhibited a balanced prediction distribution between \"Down\" or \"Up\" predictions. It is worth noticing that since bagging can be interpreted as a perturbation technique aiming at improving the robustness especially against outliers and highly volatile prices [37]. The numerical experiments demonstrated that averaging ensemble models trained on perturbed training dataset is a means to favor invariance to these perturbations and better capture the directional movements of the presented random walk processes. However, the ACF plots revealed that bagging ensemble models violate the assumption of no autocorrelation in the residuals, which implies that their predictions may be inefficient. In contrast, the ACF plots of stacking(\({}^{kNN}\)) revealed that the residuals have no or small (inconsiderable) autocorrelation. This is probably due to fact that the use of a meta-learner, which is trained on the errors of the base learners, is able to reduce the autocorrelation in the residuals and provide more reliable forecasts. Finally, it is worth mentioning that the increment of component learners had little or no effect to the regression performance of the ensemble algorithms, in most cases  
Summarizing, stacking utilizing advanced deep learning base learner and \(k\)NN as meta-learner may considered to be the best forecasting model for the problem of cryptocurrency price prediction and movement, based on our experimental analysis. Nevertheless, further research has to be performed in order to improve the prediction performance of our prediction framework by creating even more innovative and sophisticated algorithmic models. Moreover, additional experiments with respect to the trading-investment profit returns based on such prediction frameworks have to be also performed.
# Evaluation of Coastal Sea Level Offshore Hong Kong from Jason-2 Altimetry
## 3 Data Sets
### Tide Gauge Data  
The Quarry Bay tide gauge is a float-type instrument. It provides sea level data with an accuracy of 1 cm for a single measurement and is regularly calibrated every other year [19]. The tide gauge is located at 114.22\({}^{\circ}\)E, 22.28\({}^{\circ}\)N, near the northern coast of the HK Island, separated from the Kowloon Peninsula by the Victoria Harbor (see Figure 2). Note that \(\sim\)95% of the Victoria Harbor shoreline is shaped by human activity [11]. Thus sea level on this area is likely influenced by anthropogenic local-scale factors, in addition to more regional and global ocean variations. Hourly tide gauge data were downloaded from the Sea Level Center of the University of Hawaii ([[https://uhslc.soest.hawaii.edu](https://uhslc.soest.hawaii.edu)]([https://uhslc.soest.hawaii.edu](https://uhslc.soest.hawaii.edu))).  
A harmonic analysis was first applied to the tide gauge data in order to compute and remove the tidal signals from the sea level time series. A time-averagedsea level value was also removed from the time series in order to be consistent with the altimetry sea level data. Finally, the hourly tide gauge data were interpolated to the time of the Jason-2 observations. Note that the dynamic atmospheric  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Retracker** & **Product** & **Idea** & **Sub-Waveform** & **Comments** \\ \hline MLE4 & SGDR 1 & Brown model & No & Official standard retracker. \\ MLE3 & SGDR 1 & Brown model & No & \\ OCE3 & PISTACH & Brown model & No & Same as MLE3 \\ RED3 & PISTACH & Brown model & Fixed: bins: t\({}_{0}\) + [\(-\)10:20] & Simplified version of ALES \\ ALES & ALES & Brown model & Adaptive to the SWH & Two-pass retracker \\ ICE1 & PISTACH & Modified threshold & No & \\ ICE3 & PISTACH & Modified threshold & Fixed: bins: t\({}_{0}\) + [\(-\)10:20] & \\ \hline \end{tabular}  
* 1: SGDR contains all GDR parameters, plus some supplementary items such as the retracker outputs and waveforms.  
\end{table}
Table 1: Overview of different retrackers applied in different altimetry products.  
correction was not removed from the tide gauge data. This will be discussed in Section 5.5. The tide gauge-based sea level time series interpolated to the closest Jason-2 observations is shown in Figure 4. We observe a large seasonal cycle, due to the monsoon, modulated by important high-frequency variations which can reach several tens of cm. In particular, a peak is observed at cycle 228. It is caused by a storm surge associated with the violent Typhoon Kalmaegi that sideswiped the HK coast before dawn on 16 September 2014. The Jason-2 altimeter flew over the HK area at 3.45 am (local time) on 16 September 2014, and the peak in the tide gauge sea level series is coincident with the typhoon event. Therefore, this peak was eliminated in our analysis as an outlier.
# Development of Korea Airport Pavement Condition Index for Panel Rating
## 3 Results
### Evaluation and Field Validation
#### 3.2.2 Comparison of PCI Calculation Results of ASTM D5340 and KPCI  
Figure 10 shows the scattered plots for comparison between the criteria of PCI calculation results. The x-axis of the graph represents the PCI calculated using ASTM D 5340 and the y-axis represents the PCI calculated using the KPCI. The farther away from the diagonal line, the greater is the difference. The KPCI evaluates the pavement condition to be satisfactory in comparison to the PCI of ASTM D 5340 if a point is located in an area above the reference line, and the opposite is the case if it is in an area below the reference line. As a higher PCI means a more satisfactory pavement condition, as a result of comparison from such a viewpoint, the asphalt pavements, shown in Figure 10(a), and the concrete pavements, shown in Figure 10(b), are evaluated to be mostly lower than ASTM D5340 when evaluated by the KPCI. The asphalt pavements were evaluated to be 4.6 points lower on average, and the concrete pavements 6.9 points lower on average. The trend of being evaluated low became greater with worsening pavement condition and most sensitively decreased at approximately 70.