esa-sceva/satcom-chunk-collection huggingface dataset:
# AOA-Based Three-Dimensional Positioning and Tracking Using the Factor Graph Technique
## 5 ACRB Derivation  
In this section, the proposed ACRB for the DOA-based 3D location detector is derived. Based on the result in [16], the conventional CRB is given by:  
\[\text{CRB}=\sqrt{\text{trace}\left[FIM^{-1}\left(\mathbf{s}\right)\right]}, \tag{60}\]  
where \(FIM\) represents the Fisher information matrix and \(s\) denotes the real target position. Given the PDF of the random variable \(\hat{A}\), denoted by \(p(\cdot)\), the Fisher information matrix can be expressed by:  
\[F\left(s\right)=E\left[\left(\frac{\partial}{\partial s}\ln p\left(\hat{A} \right)\right)^{2}\right]. \tag{61}\]  
where \(\hat{A}\) represents the measured DOAs with \(L\) samples. Then, the CRB of the DOA-based 3D location detector can be expressed by:  
Figure 3: Proposed FG structure for EKF.  
\[\text{CRB}=\sqrt{\text{trace}\left[\left(\mathbf{J}^{T}\Sigma_{A}^{-1}\mathbf{J} \right)L\right]^{-1}}, \tag{62}\]  
where \(\mathbf{J}\) represents the Jacobian function, which can be further expressed by:  
\[\mathbf{J}=\begin{bmatrix}\frac{\Delta y_{1}}{\Delta xy_{1}^{2}}&-\frac{ \Delta x_{1}}{\Delta xy_{1}^{2}}&0\\ \frac{\Delta y_{2}}{\Delta xy_{2}}^{2}&-\frac{\Delta x_{2}}{\Delta xy_{2}}&0\\ \vdots&\vdots&\vdots\\ \frac{\Delta x_{1}\Delta z_{1}}{\Delta xy_{2}\Delta xy_{1}}&\frac{\Delta y_{1} \Delta z_{1}}{\Delta xy_{1}^{2}\Delta xy_{1}}&-\frac{\Delta xy_{1}}{\Delta xy_ {1}^{2}}\\ \frac{\Delta x_{2}\Delta z_{2}}{\Delta xy_{2}}&\frac{\Delta y_{2}\Delta z_{2}} {\Delta xy_{2}^{2}\Delta xy_{2}}&-\frac{\Delta xy_{2}}{\Delta xy_{2}z^{2}}\\ \vdots&\vdots&\vdots\end{bmatrix}, \tag{63}\]  
where \(\Delta x_{n}=X_{n}-x\), \(\Delta y_{n}=Y_{n}-y\) and \(\Delta z_{n}=Z_{n}-z\), \(\Delta xy_{n}=\sqrt{\left(X_{n}-x\right)^{2}+\left(Y_{n}-y\right)^{2}}\), and \(\Delta xyz_{n}=\sqrt{\left(X_{n}-x\right)^{2}+\left(Y_{n}-y\right)^{2}+\left( Z_{n}-z\right)^{2}}\). Finally, the ACRB at time index \(k\) can be expressed by:  
\[\text{ACRB}=\sqrt{\text{trace}\left[\left(\mathbf{J}_{\mathbf{k}|\mathbf{k}- \mathbf{1}}^{T}\Sigma_{A}^{-1}\mathbf{J}_{\mathbf{k}|\mathbf{k}-\mathbf{1}} \right)L\right]^{-1}}, \tag{64}\]  
where the expression of \(\mathbf{J}_{\mathbf{k}|\mathbf{k}-\mathbf{1}}\) is equal to \(\mathbf{J}\), but with \(\Delta x_{n}=X_{n}-x_{k|k-1}\), \(\Delta y_{n}=Y_{n}-y_{k|k-1}\), \(\Delta z_{n}=Z_{n}-z_{k|k-1}\), \(\Delta xy_{n}=\sqrt{\left(X_{n}-x_{k|k-1}\right)^{2}+\left(Y_{n}-y_{k|k-1} \right)^{2}}\), and \(\Delta xyz_{n}=\sqrt{\left(X_{n}-x_{k|k-1}\right)^{2}+\left(Y_{n}-y_{k|k-1} \right)^{2}+\left(Z_{n}-z_{k|k-1}\right)^{2}}\).
# The Analysis of Experimental Deployment of IGLUNA 2019 Trans-Ice Longwave System
## 2 Transmitting Antenna Design and Parameters
The calculated \(\mu_{r}\) values vary in the same way as the inductances and range from 0.835 to 0.880--this indicates a diamagnetic environment, which is consistent with the geological composition of the Alps in this region [16] and a large (yet still smaller than the wavelength) presence of ice. This calculation could be adapted in a system for remote surveying the glacier- and mountain's state by analyzing the magnetic permeabilities of wire loops laid in different regions and positions.
## 3 Experimental Results and Analysis
### Shape Search Parameter  
After many experiments, the required parameters in the search strategy are determined. The value of each parameter is suitable when searching for shapes (Table 1). Therefore, in the actual search  
Figure 13: Results of subregion searching of shape feature region for image registration.  
strategy, the parameter in Table 1 is taken as the initial value of the parameter if the condition is not found under this condition. The appropriate parameter limits of the shape can be further relaxed.  
In the experiment, the time required to search for the best shape region in the printed image with size of \(1604\times 888\) pixel is 4 s. In an actual printing defect detection system, searching the feature region in the detected reference template image is set offline. Therefore, this speed of 4 s can meet the needs of the printing defect detection industry.
# Impedance Modeling Based Method for Sub/Supsynchronous Oscillation Analysis of D-PMSG Wind Farm
## 1 Introduction  
Recently, renewable energy generation has been developing rapidly all around the world due to the depletion of traditional energy and serious environmental pollution. Wind and solar energy with the benefits of cleanness, short construction cycle, and low running cost, have become the promising energy source. For practical power grids, the doubly fed induction generator (DFIG) and the direct-drive wind generator (direct drive permanent magnet synchronous generator (D-PMSG)) are the most applied generators for wind turbines due to the advantages of variable speed-based operation and independent control of active and reactive power [1]. Compared to DFIG, D-PMSG uses full power converters and has lower maintenance cost. The back-to-back pulse width modulation (PWM) converter isolates the generator from the grid, which improves the low voltage ride through ability. Because of this, D-PMSG based wind farms get a wide range of applications.  
Due to the reverse distribution of the energy and power load in China, the renewable energy, mainly based on the large-scale of wind and solar power, long-distance high voltage direct current (HVDC) transmission system and series compensation transmission lines are widely utilized in nowadays power grid. However, previous researches pointed out that subsynchronous oscillation (SSO) is a potential problem in this series-compensated and HVDC transmission system [2; 3]. On the other hand, with the increase of the wind energy, the short circuit ratio (SCR) at the point of common coupling (PCC) will decrease since the wind farm cannot provide enough support to the power grid, which will worsen the stability of power grid.  
The SSO of grid-connected wind farm has been detected worldwide, for instance, in the wind farms of USA (Texas, 2009), China (GU yuan, Hebei Province, 2011) [4; 5]. The above events are all dueto the series compensation or HVDC transmission, which has already been analyzed and classified as subsynchronous resonance (SSR), subsynchronous control interaction (SSCI) [6; 7; 8]. Theoretical research and simulation studies have been conducted to investigate the mechanism and characteristics of these phenomena. Various countermeasures have also been proposed to solve the SSR problem [9; 10; 11].  
A new type of sub/supsynchronous oscillation which had no relation with series compensation was detected the D-PMSG based full-converter wind farm in Xinjiang, China, in July 2015 [12; 13]. The continuous oscillation of power grid initiated shafting torsional vibration of synchronous generators outside 200 km, which finally resulted in the decrease of HVDC power. The analysis of the accident shows that the operating adjustments of HVDC have no relation with this type of SSO. Besides, since the natural torsional frequency of shafting vibration in direct drive permanent magnet synchronous generator is very low (1-10 Hz), and the generator and power system are isolated by back-to-back PWM converter, subsynchronous torsional interactions (SSTI) will not occur in D-PMSG based wind farm.
## 1 Introduction
### Cell Organelles, Oxidative Stress, and Apoptosis  
Mitochondria and peroxisomes are dynamic organelles that have intricate structural and functional relationships [42; 43]. Both of these organelles play a key role in ROS production, which is important for cell signaling and ROS scavenging [44]. They also show high plasticity and easily adapt in response to developmental, metabolic and environmental alterations [42]. Peroxisomal function has long been linked to oxygen metabolism due to their high concentration of H\({}_{2}\)O\({}_{2}\) generating oxidases and have several antioxidant systems, such as catalase, superoxide dismutase, peroxisomal membrane protein 20 and glutathione peroxidase [45]. Mitochondria also possess enzymes and non-enzymatic antioxidant systems [46]. Mitochondria are quantitatively the most important source of ROS, which maintain the steady-state concentration at non-toxic levels by a variety of antioxidant defenses and repair enzymes [47]. Oxidative stress is an expression used to describe various deleterious processes resulting from an imbalance between the excessive formation of ROS and limited antioxidant defenses.  
Oxidative stress associated with cell death in the developing limb is due to mitochondrial production of ROS [48]. Mitochondria play a key role in apoptotic processes. The mitochondrial permeability transition pore (mPTP) is a large conductance channel that opens in the mitochondrial membrane in response to high calcium (Ca\({}^{2+}\)), low ATP, and oxidative stress [49; 50; 51; 52], and opening of the mPTP causes abrupt mitochondrial depolarization. APP treatment is associated with an alteration in morphology of mitochondria, a decrease in mitochondrial membrane potential, mitochondrial enzyme activity and respiration rate in cancer cells [18; 53].  
Ca\({}^{2+}\), one of the most ubiquitous cellular second messengers, is also known to be involved in cell death by apoptosis, necrosis and autophagy [54]. Mitochondria are the paradigm of the double-edged sword effect of Ca\({}^{2+}\) on cell survival and death [55]. Ca\({}^{2+}\) overload could be detrimental to the cells as it can lead to mitochondrial depolarization, cytochrome c release, lipid peroxidation, transcription factor activation and DNA damage, resulting in apoptotic and non-apoptotic cell death [56]. Peroxisomes also play an important role in Ca\({}^{2+}\) homeostasis.
# Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT
## 5 Result and Analysis
### Packet Loss ratio  
The packet drop ratio discusses the proportion of data packets lost or dropped during transmission. It is an inverse proportion to PDR. Different factors can trigger the dropping of a packet, together with the large size of the data packet. If the packet is small, the chance of dropping is reduced. Furthermore, data packets with large sizes are more prone to being dropped. As in case some data bits are corrupted, leading to the entire message being dropped. If the bandwidth is low, the network can become congested due to large-sized data packets, which in turn starts a higher packet loss. The EE-FDAS has more compressed data, when the size is reduced it lowers the chances of being dropped. As shown in Figure 6b, the proposed scheme retains a lower drop ratio because of smaller data packets. It eases successful data transmission, letting packets be transmitted even when the bandwidth is low. Additionally, only critical data is transmitted in its full form while normal data is sent in Boolean digits. This plays an important role in reducing the drop ratio. In case of the threshold based scenario, the packet size grows only for the a few packets sent after a certain count. In a similar context, EHDA and FDAS also hold low drop rates, while DICA and DICA_EXTENSION experience a higher dropping ratio due to the large-sized packets required to pass through several hops before reaching the parent node. The packet drop ratio remains 73%, 87%, 80%, 53% and 43% in FDAS, DICA, DICA_EXTENSION, EHDA, and EE-FDAS when the attribute count is 12.
## 3 Results  
The present section describes the application of the interferometric satellite data to identify subsidence effects due to the excavation of the T3 section of Line C subway in Rome's urban center. Firstly, the assessment is performed on a territorial scale of observation. Secondly, the entropy-energy representation is adopted to focus on the effects on a single structural system and to detect points with anomalous signals. In the third part, a comparison is discussed and, finally, the approach is applied to the Colosseum case study.
## 2 Materials and Methods
### Inversion Model Construction, Accuracy Verification, and Salinity Visualization
#### 2.8.4 Accuracy Verification  
To verify the accuracy of the models developed in this study, 61 samples were used for model development and an independent validation dataset of 20 samples to assess the models' performance. The evaluation metrics included root mean square error (RMSE), mean absolute error (MAE), standard deviation (SD), mean squared error (MSE), coefficient of determination (R\({}^{2}\)), with smaller values of RMSE, MAE, SD, and MSE indicating better model performance, while a larger R\({}^{2}\) indicates better performance. The formulas for these five kinds of evaluation metrics are as follows [41]:  
RMSE:  
MAE:  
\[MAE=\frac{1}{n}{\sum\limits_{i=1}^{n}}|y_{i}-\hat{y}_{i}|\]  
SD:  
MSE:  
\[MSE=\frac{1}{n}{\sum\limits_{i=1}^{n}}(y_{i}-\hat{y}_{i})^{2}\]  
R\({}^{2}\):  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}}{(y_{i}-\hat{y}_{i})^{2}}{\sum\limits_{i=1 }^{n}}\]  
where \(\hat{y_{i}}\) is the predicted value; \(\overline{y}\) is the mean value; \(y_{i}\) is the true value; and \(n\) is the sample size.
###### Abstract
The molecules and ions that are retained on the membrane create osmotic pressure and concentration polarization effect on the surface. This requires the use of high pressures (up to 60 MPa or higher) to establish a permeate flux [13]. Described membrane characteristic makes them applicable for wine concentration. Water, ethanol, acetic acid and several low molecular weight compounds pass through the membrane, but high percentage of valuable bioactive compounds is retained on it. Therefore, RO and NF processes can be used for wine dealcohlisation with minimized organoleptic changes [14; 15; 16; 17], for grape juice or wine concentration in order to increase or correct the content of sugars, polyphenols and aroma compounds [17; 18; 19; 20; 21], for acetic acid correction [22; 23; 24] or for eliminating \"bad\" aroma compounds [25; 26; 27]. The aim of this study was to obtain wine concentrates with enriched and corrected chemical composition and aroma profile, and to examine the influence of different membrane type and operating conditions on final product. For that purpose, membranes for reverse osmosis and nanofiltration process were used, applying four pressures (2.5, 3.5, 4.5 and 5.5 MPa) and two temperature regimes (with and without cooling) for both processes. During concentration, permeate flux and retentate temperature were measured. In obtained samples, sugars, acids, sulphur dioxide and ethanol were determined. Aroma compounds were analysed by gas chromatograph with mass spectrometer in obtained retentates and initial wine. In our previous study Cabernet Sauvignon was subjected to process of reverse osmosis and nanofiltration at different pressures and temperature regimes to determine the influence of RO and NF membranes on phenolics and colour compounds retention [28].
# Standing Wave Field Distribution in Graded-Index Antireflection Coatings
## 4 Discussion  
Strictly speaking, the index distribution of idea graded-index coating is continuous. From Figures 1d, 2d and 3d, we find that refractive index distributions for the divisions at best thicknesses \(d\) (green lines) are nearly continuous and quite close to the idea index distribution which are described by Equations (3)-(5). So the results of the divisions at best thicknesses \(d\) (Figure 1b,c, Figure 2b,c and Figure 3b,c) stand for the results of idea graded-index coatings.  
Figure 3: **(a)** Transmittance of polynomial index AR coatings at different searching thickness \(d\) of divided thin layers the black line presents \(d\) = 0.5 nm, the red line presents \(d\) = 1 nm and the blue line shows \(d\) = 1.2 nm; **(b)** The transmittance at best thickness \(d\) (d = 1.858 nm); **(c)** The intensity of standing wave field for optimum matching thickness of coating at wavelengths of 355 nm, 510 nm and 1064 nm; **(d)** Refractive index distributions for the division with best thickness \(d\) (green line) and the re-division with 42 layers (black bars); **(e)** Comparison of transmittance between division with best thickness \(d\) and the re-division with 42 layers.  
From Figures 1b, 2b and 3b, it can be found that transmittance are good at the designed wavelengths 200-1200 nm. It means that our simple searching method is working well.  
The results from Figures 1e, 2e and 3e show that the transmittance of linear index coating, exponential index coating and polynomial index coating are not sensitive to the re-divisions. Thus, these coatings are fabricable.  
As shown in Figures 1c, 2c and 3c, the intensities of standing wave field in the interface between coating and substrate for linear index, exponential index and polynomial index are bout 0.7, 0.9 and 0.7, respectively. We know that the absorption coefficient of interfaces between coating and substrate are several orders of magnitude larger than other parts of coating [25], so the lower intensity of standing wave field will be helpful for the resistance of laser induced damage. It means that linear index coating and polynomial index coating have large damage thresholds than exponential index coating. Moreover, as shown in Figures 1b, 2b and 3b, the transmittance of linear index coating and polynomial index coating are also better than exponential index coating at the designed wavelengths 200-1200 nm. The transmittance of linear index coating and polynomial index coating at designed wavelengths are all above 99.8%, but for exponential index coating, the transmittance at wavelengths from 1000 nm to 1200 nm are below 99.5%. So present results indicate that linear index coating and polynomial index coating are better than exponential index coating for the application in high energy laser system.
# A P2P Scheme for Debating and Voting with Unconditional Flexibility
## 3 Scheme
### Phase 1: Obtaining an Alias  
The objective in this stage is to provide users with an alias key signed by themselves. All the steps are depicted in Figure 1. For each user the steps are the same:  
1. Each user establishes a private channel with each of the other users. Transport Layer Security protocol (TLS) can provide it, but in the future there could be better solutions [40; 41]. In general, user \(U_{i}\) builds a channel with user \(U_{j}\) where \(j\in[1,n]\) and \(j\
eq i\). This makes a total of \(n-1\) channels for user \(U_{i}\). Basically, they use their digital certificates \(DC_{i}\) and \(DC_{j}\) to prove their identity by signature checking and negotiate a symmetric key.
2. They compare the message received from the initiator. If there is a mismatch, they check the initiator signature, determining who is being deceptive, whether the user (only one coherent signature) or the initiator (two different and coherent signatures). If initiator is being deceptive or the number of users trying to deceive is greater than \(\sqrt{n+1}-1\) the process ends, namely this user will warn the rest and will close connections. Otherwise the process continues.
3. Each user generates a pair of RSA keys. For example, user \(U_{i}\) generates the pair \(\{A_{i}^{-1},A_{i}\}\), from now on 'alias' keys. The hash of the public alias key, \(a_{i}=hash(A_{i})\), is then computed.
4. Each user \(U_{i}\) blinds their public alias key hash \(a_{i}\) with a random number \(r_{ij}\) and encrypts it with the public key of the other users. Therefore, user \(U_{i}\) computes a maximum of \(n-1\) blind messages, \(m_{ij}=Blind(a_{i},r_{ij},K_{j})\) according to Equation (1). These messages are then sent through the private channels to the corresponding peer (the user who owns the private key used in blinding in each message, that is, \(U_{j}\)).
5. Each user \(U_{j}\) receives these messages: blinded alias of the other users. Then each one shows others a table with just one received message per user. If there are any mistakes it can be solved publicly with a signature by the sender (any user can check what is sent by the signature and what is stored by the published table). Finally, everyone knows what messages have been sent to the others and that their own have been logged correctly.
6. Each user \(U_{j}\) signs with her/his private key \(K_{j}^{-1}\) the received messages and gives back the results. That is, \(U_{j}\) sends \(Sign(m_{ij},K_{j}^{-1})\) back to \(U_{i}\). If any user \(U_{j}\) refuses to sign or sends a wrong signature, the affected \(U_{i}\) can claim publicly and force \(U_{j}\) to compute a right signature, because it can be checked by the other users.
7. Each user \(U_{i}\) removes the blinding factor \(r\) of the message and obtains his/her alias key signed by \(U_{j}\). Therefore, at the end of the process each user \(U_{i}\) has the hash of their alias public key signed by the other \(n-1\) users, which allows building \(n-1\) alias certificates \(AC_{ij}\).
## Appendix C Leading Angles of Phase Shifts Calculation  
The leading angles of phase shifts, which stand for the center of the laser beam aligned with the center of the PD under the current RIS coordinate and rotations status. As shown in Figure 11, the rotation angles can be expressed by \(\theta_{1}\) and \(\theta_{2}\), which stand for the rotations along the \(x\)-axis and \(y\)-axis with counterclockwise direction. Therefore, the inversion processing of the rotation matrix is proposed.  
First, the rotated normal unit vector \(\mathbf{u}_{\mathbf{R}}^{\star}\) can be solved by the specular reflection equation  
\[\mathbf{u}_{\mathbf{G}\mathbf{\prime}}=2(\mathbf{u}_{\mathbf{R}}^{\star}\cdot\mathbf{u}_{\mathbf{O}\mathbf{G}} )\mathbf{u}_{\mathbf{R}}^{\star}-\mathbf{u}_{\mathbf{O}\mathbf{G}}, \tag{13}\]where \(\mathbf{u}_{GS^{\prime}}\) is the laser unit vector from the center of the RIS to the beam projection point, the unit vector \(\mathbf{u}_{GS}\) starts from the center of the RIS to the center of the PD. By solving (A9) to find the solution \(\mathbf{u}_{R}^{\star}\), the problem is reversed to find the rotation angles as the solution from vector \(\mathbf{u}_{R}\) to vector \(\mathbf{u}_{R}^{\star}\).  
Second, we use the quaternion rotation method [54] to solve this problem. A quaternion is a 4-tuple written formally as \(q=q_{1}+q_{2}i+q_{3}j+q_{4}k\), where \(q_{m}\), \(m\in\{1,2,3,4\}\) are real numbers and \(i,j,k\) are imaginary parts, the axis-angle expression of rotations between \(\mathbf{u}_{R}\) and \(\mathbf{u}_{R}^{\star}\) can be denoted as the unit vector \(\mathbf{u}_{R}\) rotation angle \(\xi\) around the axis unit vector \(\mathbf{v}=\
u_{x}i+\
u_{y}j+\
u_{z}k\), which can be written as quaternion  
\[q=\cos\left(\frac{\xi}{2}\right)+\left(\
u_{x}i+\
u_{y}j+\
u_{z}k\right)\sin \left(\frac{\xi}{2}\right),\] (A10)  
where \(\xi=\arccos\left(\mathbf{u}_{R}\cdot\mathbf{u}_{R}^{\star}\right)\), and \(\mathbf{\
u}=\frac{\mathbf{u}_{R}\times\mathbf{u}_{R}^{\star}}{\left|\mathbf{u}_{R}\times\mathbf{ u}_{R}^{\star}\right|}\). Thus \(q_{m}\) can be extracted from the (A10).  
Third, after finding the elements \(q_{m}\) of the quaternion, the rotation phase shifts can be expressed by the quaternion elements  
\[\begin{split}\theta_{2}^{\star}&=-\arcsin\left(2(q _{2}q_{4}-q_{1}q_{3})\right),\\ \theta_{1}^{\star}&=\arctan\left(\frac{2(q_{3}q_{4}+ q_{1}q_{2})}{\cos\theta_{2}^{\star}},\frac{1-2q_{2}^{2}-2q_{3}^{2}}{\cos \theta_{2}^{\star}}\right).\end{split}\] (A11)