esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The WADGPS Master Station Processes
### Satellite Ephemeris and Clock Corrections
Equation (24) shows the clock error measurements for the \(j^{th}\) satellite from the \(i^{th}\) RS [12]:  
\[z_{c,i}^{j}=\Delta\hat{R}^{j}\cdot I_{i}^{j}-\Delta\tilde{\rho}_{i}^{j}=\Delta B ^{j}+n_{i}^{j} \tag{24}\]  
Then, the Equation (24) is re-written in matrix forms as follows:  
\[z_{c}=H_{c}\Delta B^{j}+n_{c} \tag{25}\]  
where:  
the subscript \(c\) denotes clock,  
\(H_{c}\) is a column vector with all 1's,  
\(n_{c}\) is the measurement noise with covariance matrix \(W_{c}\). In Equation (26), a weighted least-square method is used to derive the satellite clock error [12]. \[\Delta\hat{B}_{WLS}^{j}=(H^{T}{}_{c}W^{-1}{}_{c}H_{c})^{-1}H^{T}{}_{c}W^{-1}{} _{c}z_{c} \tag{26}\]  
Finally, to bound and indicate the uncertainty of the satellite ephemeris and clock corrected pseudorange, UDRE is calculated for each visible satellite as in Equation (27) [17]:  
\[P_{UDRE}=R+H\hat{P}H^{T} \tag{27}\]  
where:  
\(R\) is the measurement covariance matrix of the synchronized pseudorange residuals,  
\(\hat{P}\) is the covariance of the estimated ephemeris and clock errors, and  
\(H\) is the design matrix composed by unit length line of sight vectors and satellites clock term, and the line of sight vectors cover all users inside the reference network [12]. The UDRE value is calculated in Equation (29):  
\[\sigma^{2}_{UDRE}=\left(\sum_{i=1}^{i=m}\frac{1}{P_{UDRE,ii}}\right)^{-1} \tag{28}\]  
\[UDRE=3.29\times\sigma^{2}_{UDRE} \tag{29}\]  
where \(P_{UDRE,ii}\) is the \(i^{th}\) diagonal element of the \(P_{UDRE}\). When the WADGPS users receive the satellite ephemeris and clock corrections, the corrections need to be converted to the pseudorange domain. Equation (30) shows the pseudorange correction error for satellite \(i\) which is corrected by satellite ephemeris and clock errors, and this pseudorange correction error has to be bounded by the combined UDRE and pseudorange sigma values [12]:  
\[\rho_{corrected}^{i}=PR^{i}-\Delta\hat{R}^{i}\cdot l^{i}+\Delta\hat{B}^{i} \tag{30}\]  
where:  
\(PR^{i}\) is pseudorange from the \(i^{th}\) visible satellite,  
\(\Delta R^{i}\) is satellite ephemeris corrections,  
\(\hat{l^{i}}\) is line of sight vector from the user to the satellite, and  
\(\Delta\hat{B}^{i}\) is clock corrections.
## 5 Conclusions  
Herein, we considered the optimization of an access point (AP) beamformer and IRS phase adjustment in a multicast network with an intelligent reflecting surface (IRS) assist. Two computationally efficient AP beamforming schemes and two IRS phase adjustment algorithms were proposed, comprising four combinations of joint optimization schemes. Semi-definite relaxation (SDR) and least squares (LS) equalization were considered for the AP beamformer and the LS equalization, and grouping methods were suggested for the IRS phase adjustment. From these two sets, four combinations of joint optimization were built and their performances were compared to discuss their associated strengths and weaknesses. All combinations utilized the increased transmit power and the increased number of IRS reflecting elements in their performance enhancement, but their efficiency differed. Computationally competitive LS-based combination showed comparable performance that matched that of the SDR-based scheme, which had a relatively heavy computational load that increased as the transmit power or the IRS reflecting element number grew. On the other hand, the LS and the grouping of reflecting elements combination showed merit in the case of heavily loaded users with small numbers of reflecting antennas.  
[PERSON] proposed the beamforming principles and associated algorithms for the MC NOMA wireless networks; [PERSON] and [PERSON] helped to improve the algorithms, and derived and analyzed all simulation results. Additionally, [PERSON]-[PERSON] provided the experimental materials for better computational simulation and revised critical errors of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2022-2018-0-01423) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation), by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2021R1F1A1047271).  
Not applicable.  
The authors declare no conflict of interest.
# Internet of Things in Marine Environment Monitoring: A Review
## 2 Overview of IoT in Marine Environment Monitoring
### A General Marine Environment Monitoring Sensor Node  
The general architecture of a marine environment monitoring sensor node is shown in Figure 4. It typically has a buoy device to protect electronic devices against water, and consists of the following four main modules [28]: A sensing module, a microcontroller, a wireless transceiver module, and a power supply module.  
Figure 4: General architecture of a marine environment monitoring sensor node.  
Figure 3: Typical wireless sensor network topologies.  
The sensing module obtains the environmental and equipment status (with associated amplifiers and analog-to-digital (A/D) converters). The microcontroller receives the data from the sensor and processes the data accordingly. A wireless transceiver module includes a radio frequency (RF) transceiver and an antenna. A power supply module includes energy storage devices (like rechargeable batteries), and a power management system with energy harvesting devices, which can be a solar panel, a wind energy harvesting device, a tidal power generator, or a seawater power generator. The buoy has an anchor to prevent it from moving.
# Deceptive Jamming Detection for SAR Based on Cross-Track Interferometry
## 4 Deceptive Jamming Extraction
### Phase Filtering  
By complex conjugate multiplication of two co-registered images, the InSAR phase with deceptive jamming is obtained. In practice, the quality of interferograms is limited by phase noise induced by co-registration errors, thermal noise, temporal decorrelation, baseline decorrelation and so on [28; 29]. To reduce the deception extraction difficulty caused by phase noise, a slope-compensated mean filter [30; 31] is applied before frequency detection.  
First, the local fringe frequency estimation is realized by the maximum likelihood (ML) method [32; 33]. Subsequently, to preserve the fringes of real targets and strengthen the feature of the false part, the estimated fringe frequency in each filtering window is removed from the original noisy phase. Then, the residual phase part is smoothed based on InSAR multi-look filter processing. Finally, the removed fringe frequencies and the low-noise residual phase are combined to generate the filtered interferogram. The filtering procedure removes most of the phase noise while still preserving the false phase well.
## 3 Results and Discussion
### Optimization of USM Processing Parameters
#### 3.2.2 Shape and Morphology: SEM Analysis  
Figure 3 compares the shapes and surface morphologies of P3 HB specimens fabricated using conventional microinjection molding and USM technology, both employing the same configurable mold. Both methods produced samples with smooth surfaces and uniform shapes, with no visible macroscopic defects such as pores observed in the cross-sections. However, magnified images revealed notable differences between the two processing methods.  
The cross-section of the USM specimen demonstrated a more uniform and defect-free surface compared to that of the conventional injected specimen (Figure 3a,b). High-magnification SEM micrographs of the USM-processed P3 HB samples revealed the presence of distinct spherulitic aggregates with radial lamellae structures, highlighted by dashed ellipsoids in Figure 3c. These crystalline features were absent in specimens produced via conventional microinjection molding.  
The observed differences can be attributed to variations in the cooling and solidification processes inherent to each technique. The rapid and localized heating followed by efficient cooling in USM likely promotes the formation of spherulitic crystalline domains. By contrast, the prolonged thermal exposure and slower cooling rates in conventional microinjection may hinder the development of such structures. These differences suggest that USM not only enhances surface uniformity but also facilitates a unique crystallization behavior in P3 HB, which may influence its thermal and mechanical properties.
## 3 Results and Discussion
### GITT--Chemical Diffusion Coefficient
A closer look to the titration steps (Figure 6) enables us to distinguish dissimilarities in the voltage changes of each cell. At \(x\) = 0.96 and at a close value of the determined diffusion coefficient for each cathode material, the voltage drop in the reference-cell is significantly higher than in the cell with laser structured electrode. This is also noticed in the hexagonal phase at \(x\) = 0.63, however with a slight smaller effect. The equilibrium potential behaviour at this point clearly indicates a different diffusion-rate, which yields a four times higher diffusion coefficient. The voltage loss \(\Delta E_{i}\) as function of SOC was determined in order compare the influence of each specific cell design characteristics. Immediately after the current was interrupted a respective steep  
Figure 6: Measured current pulses during GITT from cells with structured (open symbols) and unstructured (solid symbols) electrodes at different state of charge, i.e., different lithium content \(x\) in cathode material (black: \(x\) = 0.96, blue \(x\) = 0.63). drop in voltage was measured (insert graph in Figure 4b). This instantaneous drop is mainly due to the ohmic resistance of the cell (ohmic drop), to which a contribution of charge transfer and polarization effect is added. Since the ohmic drop is a very fast process, it could not be separated here. The instantaneous cell impedance, calculated as the ratio of the instantaneous voltage drop and current, of reference-cell is factor 3 higher than of the cell with laser structured electrode (Figure 7). As the cell impedance is dependent on current, this is expected to increase with increasing current densities.
###### Abstract  
Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RIS) are the promising emerging technologies proposed for the 6 th-Generation (6G) network to improve the capacity, reliability, and coverage of wireless communications. By integrating the UAV with RIS (RIS-UAV), the three-dimensional (3D) mobility of the UAV can be leveraged to establish strong line-of-sight links with the ground nodes, while the RIS intelligently reflect the signals toward the desired directions. However, the existing literature on RIS-UAV systems mainly assumes the use of passive elements, which suffers from the double path-loss problem. The use of active elements in RIS, which could improve the reflected link performance at the cost of increased energy consumption, has not been considered for the RIS-UAV system. Further, the energy efficiency of a RIS-UAV with active elements remains as an open direction because there is a need to investigate the feasibility of either an active or hybrid RIS-UAV implementation. This paper proposes active and hybrid RIS-UAVs and investigates the energy efficiencies of active and hybrid RIS-UAVs in comparison with existing passive RIS-UAVs and conventional UAV relays. The numerical results reveal that the proposed hybrid and active RIS-UAV relaying schemes can provide up to 14 times and 26 times improvement as compared to the passive RIS-UAV, respectively. As opposed to the active RIS-UAV that requires a larger power budget, half-duplex UAV relays that have a lower spectral efficiency, and full-duplex UAV relays that suffer from self-interference, the hybrid RIS-UAV emerges as a promising option to assist the ground communication system.  
U
# Extracellular Vesicles for Regenerative Medicine Applications
## 3 Pre-Clinical Applications of EVs in Tissue Engineering and Regenerative Medicine
### Cardiovascular System  
The cardiovascular system, specifically the heart, represents a challenge in the field of TERM, as the inherent regenerative capacity of the heart cannot compensate for extensive cardiomyocyte damage and death [76; 77]. Therefore, there is great interest in developing novel treatment strategies to promote cardiac tissue regeneration and repair. One specific approach is the implementation of EV-based therapies for cardiac regeneration.  
There has been notable success in implementing EV-based therapies in animal models of cardiac injury, including myocardial infarction (MI), ischemia-reperfusion injury, and heart transplantation. Exosomes derived from mesenchymal stem cells (MSCs) have been shown to reduce disease progression in models of myocardial infarction and ischemia-reperfusion injury, with an end result of decreased cardiac fibrosis and increased blood flow to the site of injury [8; 78; 79; 80; 81; 82]. In a separate study, placental MSC-derived exosomes were shown to promote angiogenesis in a model of an ischemic cerebral vascular accident [83]. In addition to unaltered, MSC-derived exosomes, exosomes released from CD34+ human cells engineered to contain Sonic Hedgehog (Shh), a potent mediator of angiogenesis, have shown great promise in reducing infarct size, increasing capillary density, and improving cardiac function in a model of MI [84].  
In addition to applications related to MI and ischemia-reperfusion injury, EV-based therapies for promoting tissue transplantation have also been investigated. Specifically,local delivery of MBVs containing IL-33 in a rodent model of cardiac transplantation have been shown as a promising chronic graft rejection prophylaxis via the immunomododulation of pro-inflammatory macrophages towards an anti-inflammatory phenotype [85].
# GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks
## 6 Performance Evaluation
### Performance Analysis
#### 6.2.1 Network Lifetime  
Three paths are denoted by \(p_{1}\), \(p_{2}\), and \(p_{3}\), fixed \(FTR\) (or \(f\)), and a constant key ratio (\(t\)) (i.e., total keys over total nodes en-route) as in Figure 9a. In greedy routing, the forwarding node is determined by distance only. This results in a single or fixed path which would be used unless it is broken by depletion of a node on the path or a session is expired.  
Considering \(p_{2}\) as the shortest path, intuitively disconnect first. In contrast, the proposed scheme has multiple paths to select from since remaining energy varies with time, therefore resulting in alternative path selection, thus resulting in more events reported to the \(BS\) from source \(CH\). Thus, if lifetime is defined by first node depletion, the proposed scheme results in extended network lifetime.  
_Example:_ For the sake of simplicity, let us assume that required energy for one \(T_{x}\) or \(R_{x}\) is 0.1 \(J\), whereas 1\(J\) the total energy of a node. Consequently, after five \(T_{x}\) and five \(R_{x}\), the fixed path \(p_{2}\), will be disconnected as the first node is depleted in ten communications. On the contrary, in the proposed scheme, energy aware and dynamic routing are employed using energy and attack information instead of distance only. Hence, it can alternatively traverse various paths among \(p_{1}\), \(p_{2}\), and \(p_{3}\).  
As the energy consumption for communication will be distributed among three paths, the energy consumption will be more balanced among these three routes. Therefore, theoretically, the lifetime would be prolonged up to three-fold if events are evenly using all three paths alternatively in GAFOR. Therefore, the proposed method can prolong the network lifetime regardless of the \(FTR\).
# A Review of Nanomaterials' Effect on Mechanical Performance and Aging of Asphal Mixtures
###### Abstract  
This review addresses the effects of the modifications with nanomaterials, particularly nanosilica, nanoclays, and nanioron, on the mechanical performance and aging resistance of asphalt mixtures. The desire for high-performance and long-lasting asphalt pavements significantly pushed the modification of the conventional paving asphalt binders. To cope with such demand, the use of nanomaterials for the asphalt binder modification seems promising, as with a small amount of modification an important enhancement of the asphalt mixture mechanical performance can be attained. Several studies already evaluated the effects of the modifications with nanomaterials, mostly focusing on the asphalt binder properties and rheology, and the positive findings encouraged the study of modified asphalt mixtures. This review focuses on the effects attained in the mechanical properties of the asphalt mixtures, under fresh and aged conditions. Generally, the effects of each nanomaterial were evaluated with the current state-of-art tests for the characterization of mechanical performance of asphalt mixtures, such as, permanent deformation, stiffness modulus, fatigue resistance, indirect tensile strength, and Marshall stability. Aging indicators, as the aging sensitivity, were used to evaluate the effects in the asphalt mixture's aging resistance. Finally, to present a better insight into the economic feasibility of the analyzed nanomaterials, a simple cost analysis is performed.  
modified bitumen; nanomaterials; nanosilica; nanoclay; nanoiron; asphalt mixtures; mechanical performance; aging sensitivity +
Footnote †: journal: Applied Sciences
## 5 Experiment and Analysis
### Computation and Results Analysis
#### 5.2.1 The Convolutional Neural Network
The training loss and testing loss converge quickly and eventually close to 0.0002. The training accuracy is consistent with the testing accuracy in the iterative process, as well as the training loss and testing loss. The testing results are as good as the training results, which shows that due to the regularization processing of the network, there is no overfitting phenomenon and the generalization performance is good. After 20 rounds, the training and test curves are relatively smooth, indicating that there is no need to iterate for 40 rounds to achieve a better effect. Meanwhile, the time consumption of each iteration is less (32 s/epoch, 10 ms/step). (c) Confusion matrix  
A confusion matrix is used to present the performance and the result of the CNN, as shown in Table 8 and Figure 11. The accuracy, precision, recall, and f1-score are all 1 for the 1125 prediction samples, and the prediction results of each category are exactly the same as the actual results (labels), indicating the good performance of the CNN algorithm. The CNN has a good prediction ability for the tail rope faults, can completely separate the nine kinds of tail rope states, and can accurately predict them. Therefore, the convolutional neural network for the health monitoring and fault diagnosis of hoisting system BTRs proposed in this paper presented a good performance, meeting the requirements of accuracy, real-time functioning, and generalization performance. Figure 11: CNN confusion matrices. (**a**) Without normalization; (**b**) normalized. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Matrix** & **Precision** & **Recall** & **f1-Score** & **Support** \\ \hline NM & 1 & 1 & 125 \\ DS & 1 & 1 & 1 & 122 \\ TR & 1 & 1 & 1 & 127 \\ BS-LR & 1 & 1 & 1 & 127 \\ BS-RR & 1 & 1 & 1 & 131 \\ BS-DR & 1 & 1 & 1 & 120 \\ L-BR & 1 & 1 & 1 & 129 \\ R-BR & 1 & 1 & 1 & 120 \\ D-BR & 1 & 1 & 1 & 124 \\ \hline Average/total & 1 & 1 & 1 & 1125 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The confusion matrix of the CNN.
# Plane Detection Using Appearance-Based Pre-Processing and Convolutional Neural Network
## 3 Experiment and Performance Analysis  
### Dataset Configuration for Training  
In order to detect objects through deep learning-based convolutional neural networks, the sufficient collection of image datasets is essential for training the model. The number of datasets used in this study is shown in Table 2, divided into two classes: flame or non-flame. The total number of images used in the flame dataset was 10,153, the number of images in the non-flame dataset was 10,024, and the training dataset and test dataset were used in a ratio of 8:2, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline
**Layer** & **Kernel Size** & **Input Size** \\ \hline Convolution & \(3\times 3\) & \(299\times 299\times 3\) \\ Convolution & \(3\times 3\) & \(149\times 149\times 32\) \\ Convolution (Padded) & \(3\times 3\) & \(147\times 147\times 32\) \\ MaxPooling & \(3\times 3\) & \(147\times 147\times 64\) \\ Convolution & \(3\times 3\) & \(73\times 73\times 64\) \\ Convolution & \(3\times 3\) & \(73\times 73\times 80\) \\ MaxPooling & \(3\times 3\) & \(71\times 71\times 192\) \\ Inception A \(\times\) 3 & As in Figure 5a & \(35\times 35\times 192\) \\ Reduction & As in Figure 6 & \(35\times 35\times 228\) \\ Inception B \(\times\) 3 & As in Figure 5b & \(17\times 17\times 768\) \\ Reduction & As in Figure 6 & \(17\times 17\times 768\) \\ Inception C \(\times\) 3 & As in Figure 5c & \(8\times 8\times 1280\) \\ AveragePooling & \(3\times 3\) & \(8\times 8\times 2048\) \\ FC & - & \(1\times 2048\) \\ Sigmoid & - & - \\ \hline \end{tabular}
\end{table}
Table 1: Inception-V3 CNN parameter.  
Figure 6: Structure of the reduction module used to reduce feature size.
# Prioritization of Drugs for Fungal Keratitis Eye Infections: An In-Silico Analysis
## 2 Result and Discussion
### Model Validation  
The quality of the modelled structures was assessed through online quality evaluation tools such as PROCHECK, ERRAT, and Verify 3D, provided through the Structural Analysis and Verification Server (SAVES) of UCLA-DOE Lab. The phi and psi angles that explore the stereo-chemical parameters of the energy-minimized models were determined by using PROCHECK; the 1D-3D structure compatibility of the best models were determined by Verify 3D, and the regions of the modelled structure that can be rejected at 95% and 99% confidence intervals, were predicted through ERRAT programs. In general, \(>\)85% of residues in modelled structures that fall under the most favored regions of the RC plot are considered suitable for further study. Verify 3D calculates the amino acid propensity of each residue of the model with comparison to the PDB structures, and reveals the final score as a sum of the propensities of the individual residues in the built model [27,28]. Usually, the ERRAT scores are expressed as a percentage. The ERRAT scores of \(>\)95% were graded for high resolution structures, while low-resolution structures were graded at approximately 91%.  
The Ramachandran plot (PROCHECK) of the energy-minimized models of RNAP from _A. flavus_ (Figure 1b) and _A. fumigatus_ (Figure 2b) exhibited most of the residues in the most favorable region and 1.3% and 1.7% in the disallowed region, whereas the Ramachandran plot (RC plot) of the template  
Figure 2: Theoretical model of the RNAP structure from _Aspergillus fumigatus_ and model validation through the SAVES server. **(a)** The 3D structure of the built protein in a cartoon representation with the following structure colors: Pink (helices), Yellow (sheets), and blue (loop); **(b)** model validation by a Ramachandran plot; **(c)** ERRAT Plot showing the generated model at a good high resolution, as the regions of the modeled structure that can be rejected at 95% and 99% confidence is very low.  
(3h0g_B chain) exhibited 4.6% residues in the disallowed regions and 59.2% in the most favored regions. Significantly, this revealed that the generated models of RNAP are best, as they exhibited a greater number of residues (76.1% and 84.3%) in the most favorable regions and a low number of residues in the disallowed region of the RC plot. Further, the ERRAT plot values of the modelled RNAP from _A. flavus_ (Figure 1c) and _A. fumigatus_ (Figure 2c), and the Verify 3D measured quality factor values, suggested that the built models are relevant, reliable, and of good quality (Table 1).