esa-sceva/satcom-chunk-collection huggingface dataset:
# Applying Vision-Based Pose Estimation in a Telerehabilitation Application
## 2 Materials and Methods
### ExerPlayer Module
#### 2.2.1 ExerPlayer Calibration Mode  
One of the main problems in vision systems is usually due to changes in the conditions of the environment (lighting, distances, camera orientation, etc.). For ExerCam, these problems are taken into account, and that is why in each session, both for performing tasks and for playing games, a previous calibration process should be carried out. ExerCam automatically launches an obligatory static calibration before executing a task or game.  
In calibration mode, a static posture evaluation (SPE) [42] is performed for the patient. For this, the patient must adopt the neutral or zero position (look forward, arms hanging at the side of the body, thumbs directed forward and lower limbs side by side with knees in full extension), which will allow the application to calibrate the exact measurements and characteristics of the patient. Mainly, the distances between joints are recorded in the system (the connection between two joints or joint. This connection is known as pair or limb) and possible malformations of the patient (for example, lack of extremities). This information is sent encapsulated in JavaScript Object Notation (JSON) (JavaScript Object Notation) format to the DB ExerCam Database via the REpresentational State Transfer (REST) API [43].  
To simplify the calibration, the application has visual aids to help patients place themselves in the right position and make sure that the camera is oriented in the right direction (see Figure 3a). This aid consists of a rectangle on the screen. Patients can place their body inside the rectangle, and in the case that patients move from their original position during the exercise, they can move back to it. In this way, the camera will have a different distance for each patient. It is also recommended that the camera is oriented orthogonally to the patient, although the system works correctly even with the camera oriented with a certain inclination angle. Once the system is calibrated and a task or game session is started, the camera orientation cannot be changed, neither can the distance between the patient and the camera (in the initial or resting position, during the game, they can move). For future works, a dynamic calibration is proposed, as opposed to static calibration, to avoid possible maladjustment in the measurements in the case of changes in the environment.
# Recent Progress on Novel DSP Techniques for Mode Division Multiplexing Systems: A Review
## 1 Introduction
It is a fundamental limitation for a desirable data-rate or transmission distance. MDL causes the difference in optical signal to noise ratio (OSNR) degradations among fiber modes, which finally reduce the system capacity [24]. Thus, to suppress MDL effectively, specific optical devices such as mode scramblers and strongly-coupled fibers are utilized to mix different modes and balance the differences between them [25; 26]. MDL-induced penalty is also found can be mitigated by advanced DSP techniques such as receiver-side maximum-likelihood (ML) detection and interference cancellation (IC) as well as transmitter-side space-time coding (STC) techniques. Another fundamental limitation for SDM systems is Kerr-effect-caused nonlinear signal distortions. Because of the inevitable mode overlap, nonlinear effects will not only happen between different frequencies in the same eigen mode (intramodal nonlinearity), but also between different eigen modes (intermodal nonlinearity) [27; 28; 29]. Intermodal nonlinear coupling will further degrade the signal quality. The digital back-propagation (DBP) algorithm is the most common DSP method to compensate the fiber nonlinearities in single mode fiber. And fortunately, the extension of DBP based single-mode split-step Fourier method (SSFM) has recently been proposed to solve the multimode propagation problem in FMFs [30; 31; 32]. Recently, artificial intelligence (AI) technologies have also attracted great interest in optical fiber communication, for its high nonlinear representation ability. Many neural network models are provided by deep learning to handle various optical communication problems. We will also review the recent applications of AI in optical communication. The outline of this review article is organized as follows: Section 2 sets forth the principles of MIMO equalizers, the main challenges and applications of MIMO equalizers. Section 3 introduces the significance and principle of RLS techniques in MIMO equalizers, and the tracking performance in MDM TV channels based on frequency-domain variable forgetting factor RLS algorithm (FD-VFF-RLS). In Section 4, the mainstream DSP solutions for mitigating MDL effects are reviewed. Then, Section 5 explores the novel DSP schemes to compensate nonlinear effects in MDM systems and enlightens the advanced AI techniques that could be introduced in MDM systems. A brief conclusion and some perspectives will be provided in the last section.
## 2 Materials and Methods
### Methodology
#### 2.3.1 Geographically Weighted Regression Models
The CV method expression is as follows:  
\[\text{CV}=\frac{1}{\text{n}}\sum_{i=1}^{\text{n}}\big{(}y_{\text{i}}-\hat{y}_{ (\
eq i)}(\text{b})\big{)}^{2} \tag{6}\]  
where CV is the result of cross-validation, \(y_{\text{i}}\) represents the observed value of the dependent variable at spatial position i; \(\hat{y}_{(\
eq i)}(\text{b})\) represents that in the case of bandwidth b, when estimating regression parameters, the regression calculation is performed only based on the observation points around position i. The Gaussian function and the AICc method were selected in this paper. The GWR model downscaling algorithm assumes that the GWR relationship between precipitation and explanatory variables (CER, COT, and CWP) constructed at the original coarse resolution can be used to predict precipitation using the explanatory variables at higher resolutions. At the same time, due to the weak correlation between daily scale precipitation and environmental variables, we constructed a GWR model of cloud attributes and precipitation on the daily scale. Therefore, the specific steps of the downscaling algorithm in this study are as follows:  
According to the resolutions of the three kinds of satellite precipitation data (IMERG: 0.1\({}^{\circ}\), ERA5: 0.1\({}^{\circ}\), and CMORPH: 0.25\({}^{\circ}\)), the three cloud attributes (CER, COT, CWP: 1 km) are resampled to the corresponding resolutions (CER\({}^{0.25^{\circ}}\), COT\({}^{0.25^{\circ}}\), CWP\({}^{0.25^{\circ}}\) and CER\({}^{0.11^{\circ}}\), COT\({}^{0.1^{\circ}}\), CWP\({}^{0.1^{\circ}}\)) by performing bilinear interpolation. Taking the resampled cloud attributes as auxiliary variables, a daily scale GWR model is constructed at the original resolution scale, and its expression is as follows:  
\[\text{P}_{\text{i,d}}^{\text{original,SAT}}=\text{$\beta$}_{\text{i,d}}^{ \text{SAT,0}}+\text{$\beta$}_{\text{i,d}}^{\text{SAT,1}}\text{CER${}_{\text {i,d}}^{\text{SAT}}$}+\text{$\beta$}_{\text{i,d}}^{\text{SAT,2}}\text{COT${}_ {\text{i,d}}^{\text{SAT}}$}+\text{$\beta$}_{\text{i,d}}^{\text{SAT,3}}\text{ CWP${}_{\text{i,d}}^{\text{SAT}}$}+\text{$\epsilon$}_{\text{i,d}}^{\text{ original,SAT}} \tag{7}\]  
where \(\text{$\beta$}_{\text{i,d}}^{\text{SAT,0}}\), \(\text{$\beta$}_{\text{i,d}}^{\text{SAT,1}}\), \(\text{$\beta$}_{\text{i,d}}^{\text{SAT,2}}\) and \(\text{$\beta$}_{\text{i,d}}^{\text{SAT,3}}\) are the regression coefficients of day d at position i (d = 1,2,3... 365 in 2010-2014), and \(\text{$\epsilon$}_{\text{i,d}}^{\text{original,SAT}}\) represents the residual of day d at position i, respectively. CER\({}_{\text{i,d}}^{\text{SAT}}\), COT\({}_{\text{i,d}}^{\text{SAT}}\), and CWP\({}_{\text{i,d}}^{\text{SAT}}\) represent the corresponding resolution (0.1\({}^{\circ}\) and 0.25\({}^{\circ}\)) cloud attribute observations at position i, respectively, and \(\text{$\text{P}_{\text{i,d}}^{\text{original,SAT}}$}\) represents the observations of satellite precipitation data at the original resolution (0.1\({}^{\circ}\) and 0.25\({}^{\circ}\)).
# FPGA-Based Manchester Decoder for IEEE 802.15.7 Visible Light Communications
## 1 Introduction
### Our Contribution  
The weak point of the Manchester decoders described above is the very simple two-level front-end, which results in limited noise immunity. Input noise can easily surpass the threshold, leading to false commutations and data errors. Additionally, implementing delays through digital gates is not advisable, and overall tolerance to frequency errors is limited. In case of rates of Gb/s, this choice is related to the excessive complication and cost of a high-resolution and high velocity AD converter, together with the following digital data processing.  
On the other hand, for lower data rates, the improvement of the front-end will result in a performance gain, although at the expanse of complexity. Data produced by high-resolution AD converters, even in case of a relatively low rate, cannot by processed by a uC [27], and a more complicated FPGA system would be required. However, applications exist where the performance of a VLC link is more important than simplicity or cost. These include, for example, aerospace and automotive fields. It is feasible to imagine a situation placed in a near future where a reliable, low-latency data exchange between two cars equipped for VLC communication, one blocked and the other fast approaching in the same lane, can automatically trigger the breaks of the second car and be crucial in saving lives [28].  
In this work we propose a high-performance Manchester decoder implemented in a Field Programmable Gate Array (FPGA), specifically dedicated to demanding VLC applications that implements the IEEE 802.15.7 standard in real time. It accepts input data from a 12-bit analog-to-digital converter at 10 Msps, and it processes these data by performing a phase analysis over the samples acquired in a bit-time. In other words, the Manchester code is decoded like a special case of a Binary Phase-Shift Keying (BPSK) modulation [29]. The decoder calculates the phase by applying the discrete Fourier transform (DFT) to the single frequency component corresponding to the bit period. As will be clarified in the following, it compares to the existing methods such as those reported in the last row of Table 1: it achieves high noise immunity at the cost of a more demanding architecture.  
The architecture of the proposed decoder is detailed in Section 2. In Section 3 the proposed architecture is validated and shown more robust to noise with respect to the simple Manchester decoder typically employed. Then, in Section 4, the decoder is implemented in the FPGA of a system designed for VLC research [30], and tailored for the 100 kb/s rate provided in the IEEE 802.15.7 standard.  
In the experiments, reported in Section 5, the FPGA-based VLC system was connected to a 16 W white LED headlamp certified for automotive lighting. The tests showed that the decoder receives with no error when the signal amplitude is higher than \(-30\) dB with respect to the input dynamics, with a bit error rate (BER) [31] lower than \(10^{-3}\) as long as the signal is higher than \(-47\) dB.
# Validation of Copernicus Sea Level Altimetry Products in the Baltic Sea and Estonian Lakes
## 2 Material and Methods
### Study Sites  
The Baltic Sea is a semi-enclosed water body dotted with thousands of islands and islets. Our study area is in the eastern part of the Baltic Sea (Figure 2), including the Gulf of Finland and the Gulf of Riga. The topography of the southern coast of the Gulf of Finland varies. There is a 55 metre high cliff in eastern Kunda (see the tide gauge number 2 in Figure 2) and a flat seashore along the entire western coast. Steep terrain near the coast can cause peaky waveforms and hinder the performance of retracking algorithms. The Gulf of Riga has a gently sloping and sandy seashore. However, there are many islands in the northern part of the Gulf which can make the altimetry measurements challenging along the fragmental pass-overs.  
\begin{table}
\begin{tabular}{c c c} \hline
**Instrument/** & **Estimated Uncertainty** & **Error Source** \\ \hline Pressure sensor-based tide & & Time-dependent drift \\  & & Accuracy of the sensor \\ Pressure sensor-based tide & \(\pm\)0.01–0.02 & Connection to the height reference \\ gauge & & Stability of the height reference \\  & & (vertical land motion) \\ \hline Pressure sensor-based & \(\pm\)0.01–0.02 & Time-dependent drift \\ buoy & \(\pm\)0.01–0.02 & Accuracy of the sensor \\  & & Connection to the height reference \\ \hline  & & Accuracy and density of the \\  & & gravity data \\ EST-GEOID2017 & \(\pm\)0.005–0.03 & Accuracy and density of the \\  & & GNSS/levelling fitting points \\  & & Calculation method and accuracy \\  & & of the modelling \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: Study area with Sentinel-3A (S3A) and Sentinel-3B (S3B) ground tracks, tide gauges (red and blue squares), a buoy (green circle) and four GNSS tracks (magenta lines). The labelled Sentinel-3 ground tracks were used for validation. The tide gauges are operated by the Estonian Environmental Agency (EEA TG) and the Department of Marine Systems of Tallinn University of Technology (MSI TG). Estonian geoid model EST-GEOID2017 is laid over Estonia.  
Lake Peipus (Lake Peipus) and Lake Vortsjarv are the two largest lakes in Estonia (Figure 2), with a surface area of 3555 km\({}^{2}\) and 270 km\({}^{2}\), respectively. Lake Peipus is the fifth largest lake in Europe, with an average depth of 7.1 m and a max depth of 15.3 m. Vortsjarv is very shallow with an average depth of 2.7 m and a maximum depth of only 6 m.  
Sentinel-3 tracks for the GNSS vessel campaigns and buoy location were selected in such a way that in situ measurements could be matched with nearly simultaneous satellite observations in the selected waterbodies (Gulf of Finland, Gulf of Riga, Lake Peipus and Vortsjarv). This is shown in Figure 2. Additionally, three tracks for the geoid-based sea surface height validation were chosen. Thus, the following satellite data were used in our analysis: data from S3A pass 186, S3A pass 397 and S3B pass 397 (Gulf of Riga); data from S3B pass 625, S3A pass 186 (Gulf of Finland); data from S3B pass 321 (Lake Peipus); and data from S3B pass 528 (Lake Vortsjarv).
## 2 Channel Reliability Modeling for 5G Communications
### Reliability Analysis of Wireless Channels  
The probability of successful signal transmission is defined as the probability that the SNR of the signal does not fall below the SNR threshold. Thus, the channel reliability between node n and node n + 1 can be defined as  
\[p_{\text{n}}^{succ}=\text{Pr}\bigg{\{}\frac{p_{\text{n}}^{t}h(d_{\text{n}})}{ n_{0}}>\gamma^{t}\bigg{\}}\forall\text{n}\in\{1,2,\cdots,\text{N}\} \tag{4}\]  
Substituting (2) into the above equation, we get  
\[p_{\text{n}}^{succ}=\Phi(\sqrt{2}\varphi_{\text{n}})=\frac{1}{2}(1+erf( \varphi_{\text{n}})) \tag{5}\]  
where the parameter \(\varphi_{\text{n}}\) can be expressed as  
\[\varphi_{\text{n}}=\frac{1}{\sqrt{2}\sigma_{\xi}}(\left[p_{\text{n}}^{t} \right]_{dB}+\left[G\right]_{dB}-\eta\left[\frac{d_{\text{n}}}{d_{ref}}\right] _{dB}-\left[\text{n}_{0}\right]_{dB}-\left[\gamma^{t}\right]_{dB}) \tag{6}\]The function (5) \(\text{erf}(x)\) in the formula represents the error function. The error function is defined as  
\[\text{erf}(x)=\frac{2}{\sqrt{\pi}}\int_{0}^{x}\exp(-t^{2})dt \tag{7}\]  
The function \(\phi(x)\) is the cumulative distribution function (CDF) of the standard normal distribution function, which is defined as  
\[\Phi(x)=\frac{1}{\sqrt{2\pi}}\int_{-\infty}^{x}\exp(-\frac{t^{2}}{2})dt. \tag{8}\]  
From the system model, it can be seen that to ensure the reliability of the signal reception, each packet is repeatedly sent \(K\) times, so the probability that node \(\text{n}+1\) successfully receives a packet from node \(\text{n}\) can be expressed as  
\[R_{\text{n}}^{\text{L}}=1-(1-P_{\text{n}}^{\text{succ}})^{K}=1-\frac{1}{2^{K} }[\text{erfc}(\varphi_{\text{n}})]^{K}\;\forall\text{n}\in\{0,1,\cdots,\text{ N}\}, \tag{9}\]  
The function \(\text{erfc}(x)\) represents the complementary error function, which is defined as  
\[\text{erfc}(x)=1-\text{erf}(x)=\frac{2}{\sqrt{\pi}}\int_{x}^{\infty}\exp(-t^{ 2})dt \tag{10}\]  
Furthermore, Formula (9) can be used to derive the link reliability of the entire wireless sensor network as  
\[R^{\text{L}}=\prod_{\text{n}=0}^{\text{N}}R_{\text{n}}^{\text{L}}=\prod_{ \text{n}=0}^{\text{N}}\biggl{\{}1-\frac{1}{2^{K}}[\text{erfc}(\varphi_{\text{ n}})]^{K}\biggr{\}}. \tag{11}\]  
3 Probability of Reliable Operation of Switching Elements Under the Influence of Information Systems
## 3 Results
### 4 HR Increased ER Stress  
To clarify the effect of 4 HR administration on ER stress, the expression levels of ER stress markers were evaluated. The administration of 4 HR increased ER stress markers such as phosphorylated protein kinase R-like endoplasmic reticulum kinase (p-PERK), phosphorylated eukaryotic initiation factor 2 (p-eIF2), activating transcription factor 4 (ATF4), and ATF6\(\alpha\) (Figure 2a and Supplementary Figure S4). The results of quantification are shown in Supplementary Figure S3. TUDCA is an inhibitor of ER stress [13], the administration of which decreased the expression of ER stress markers which were increased by 4 HR administration (Figure 2b and Supplementary Figure S5). TGF-\(\beta\)1 increases VEGF expression in HUVECs and helps differentiate them, and the administration of TUDCA decreased 4 HR-induced TGF-\(\beta\)1 expression (Figure 2c and Supplementary Figure S6).  
The phosphorylation level of Sp1 is increased by mitochondrial and ER stress [27]. Dissociation of p-Sp1 from HDAC increases the expression level of TGF-\(\beta\)1 [20]. The administration of 4 HR increased Sp1 phosphorylation and TGF-\(\beta\)1 expression (Figure 3a). Mithramycin is an Sp1 inhibitor [28], the administration of which decreased 4 HR-induced TGF-\(\beta\)1 expression (Figure 3b). The transcription level of Sp1 was significantly increased following 100 uM 4 HR administration (\(p<0.05\); Figure 3c). Hyper-acetylated H3 binds to TGF-\(\beta\)1 promoter and increases the expression level of TGF-\(\beta\)1 [23]. The administration of 4 HR to HUVECs increased the expression level of acetylated proteins (Supplementary Figure S7). The administration of 4 HR increased H3 acetylation, too (Figure 3d). The data for the quantification of Western blot are shown in Supplementary Figure S7.  
Figure 2: The expression of ER stress-associated markers. (**a**) 4 HR administration increased ER stress markers such as p-PERK, p-eIF2, ATF4, and ATF6\(\alpha\). TUDCA is an inhibitor of the ER stress-mediated signaling pathway. (**b**) The expression of ER stress-associated markers was decreased by the administration of the inhibitor. (**c**) The administration of TUDCA decreased 4 HR-induced TGF-\(\beta\)1 expression. All blots were performed in triplicate, and the statistical analysis of the relative expression level is shown in Supplementary Figures S3–S5.
# PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks
## 1 Introduction
### Motivation and Contributions  
It is shown that [PERSON] et al.'s scheme [28] cannot provide secure mutual authentication and establish the session key, and it fails to resist offline password guessing attacks and user impersonation attacks. In [PERSON] et al.'s scheme [29], the session key between the user and the Onshore Base Station cannot achieve perfect forward secrecy and may suffer from ID guessing attacks. On the other hand, their scheme cannot resist sensor node capture attacks and does not establish the session key between the user and the sensor node. [PERSON] et al.'s scheme [30] cannot resist sensor node capture attacks and cannot achieve anonymity.  
Since almost all authentication protocols for UANs have one or more security flaws, designing a secure and effective lightweight authentication protocol for UANs is a challenge. Therefore, a secure and efficient lightweight authentication protocol for UANs is proposed, and the main contributions are as follows:  
(1) Based on the uniqueness and randomness of Physical Uncontrollable Functions (PUF) and the fast computation of chaotic maps, a secure and efficient authentication protocol for UANs is proposed.  
(2) The proposed scheme is proven secure under the random oracle model, which can achieve all known security properties, such as perfect forward secrecy, anonymity, and resistance to device capture attacks.  
(3) The proposed scheme is more secure and efficient compared with some other related schemes in terms of security and performance requirements, and the proposed scheme is suitable for UANs.  
The rest of this paper is constructed as follows: Section 2 provides the preliminaries and the threat model. The proposed authentication and key agreement scheme for UANs is presented in Section 3. Sections 4 and 5 provide corresponding formal and informal analyses of the proposed scheme. The security and performance comparisons between the proposed scheme and other resource-constrained schemes are presented in Section 6. Section 7 is the conclusion.
## 2 Experimental Section
### Vessel Mesh Generation  
Due to the natural round shape of the vessels, we segmented the vessels based on centerlines and diameter information. The filtered images were processed to create a distance map between the vessel centerlines and the vessel wall, where a zero level set corresponds to vessel walls. The fast marching algorithm was used to detect the network connectivity by solving the Eikonal equation [43]. The connected domain serves as the initial deformable model for the levelset geodesic active contour to compute the distances of each point to the nearest vessel wall. Then, the marching cubes algorithm is applied to track the physical coordinates of the vessel walls and to construct a connected surface mesh of the vascular network. Using the maximal inscribed spheres method [44], the centerline trajectory and the corresponding vessel radius are precisely tracked. The centerline space curve representation and diameters can be used for generation of unstructured volumetric meshes [6].  
Additionally, we developed an automatic parametric meshing algorithm to construct structured parametric volumetric meshes for the cerebral vasculature [45]. Our parametric meshing algorithm usesBezier splines for approximation and smoothing of the vessel centerline. The control points of the splines are then used to define the geometrical separation planes for creating a continuous and smooth volumetric mesh. The parameterized volumetric mesh allows us to define the vessel lumen and vessel walls of the cerebral vasculature for detailed hemodynamic simulation.