esa-sceva/satcom-chunk-collection huggingface dataset:
# The Local Integrity Approach for Urban Contexts:
## 3 Experimental Validation and Data Analysis
### Measurement Setup and Vehicular Data Collections  
A GNSS measurement setup, suitable to perform vehicular field tests and data collections, was designed and assembled at ISMB [11]. As shown in Figure 3a,b, this setup includes seven GNSS devices under test. They are from different manufacturers and representative of different receiver categories.  
Figure 3: Measurement setup for vehicular data collections: (**a**) Experimental setup; (**b**) Devices under test; (**c**) Example of logged positions and speeds on a selected path, shown in Google Maps\({}^{\copyright.}\).  
In detail, two survey-grade GNSS receivers were used as references during these tests: AsteRx2 eH PRO (Sepentriro N.V., Leuven, Belgium) and FlexPackG2-V2-RT2-G (Novatel Inc., Calgary, AB, Canada) and five consumer-grade GPS/EGNOS receivers were compared: LEA-4T (uBlox, Thalwil, Switzerland), uBlox AMY-5M, uBlox EVK-6T, uBlox 6, and IT600 MVK (formerly Fastrax Oy, Espoo, Finland, acquired by uBlox in 2012).  
This setup was used for carrying out multiple vehicular measurement campaigns, grabbing pseudorange residuals measurements in an urban area (in Torino, Italy). Figure 3c reports a rectangular path, selected as a preliminary test route. Both the position and speed information logged during multiple consecutive loops by one receiver under test (uBlox EVK-6T [16]) are displayed. Note that the speed of the car in each straight road segment was similar along all passes (reaching a maximum of 33.6 km/h): this fact makes reasonable to combine the outputs of the receivers during successive loops in order to compute an ensemble average.  
Preliminary analyses of these measurements were anticipated in [11], where pseudorange residuals were validated as an indicator of local degradations of GNSS signals. In addition, the repeatability and a remarkable spatial/temporal correlation of such measurements were demonstrated in this urban scenario. For this reason, 15 m and 5 min have been identified as reasonable space and time granularities, respectively, for combining these residual measurements (_i.e.,_ the resolution for estimating the \(\vartheta_{\text{UERER},eff}\) parameter). This result will be used in the next section for building an effective UER data base and then for extracting a simplified model of local GNSS degradations.
## 3 Simulation and Analysis
### Double Lane Change Scenario  
The double lane change scenario simulates common driving situations such as overtaking and obstacle avoidance. After debugging, the first PID layer was configured with \(k_{p1}=20\), \(k_{i1}=10\), and \(k_{d1}=1\), while the second PID layer was set with \(k_{p2}=0.1\), \(k_{i2}=0.1\), and \(k_{d2}=0\). With these parameters, the motorcycle successfully stabilizes its body roll  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & **Unit** \\ \hline Mass & \(m\) & 165 & kg \\ Wheelbase & \(L\) & 1.53 & m \\ Distance from center of gravity to the & \(l_{f}\) & 0.85 & m \\ \multirow{2}{*}{Height of the center of gravity Rolling radius of the front/ rear tire} & \(h\) & 0.53 & m \\  & \(R_{f}/R_{r}\) & 0.31/0.32 & m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters.  
motion and maintains balance through steering control. Figure 6 displays the motorcycle's performance at 72 km/h in the BikeSim phantom simulation. Figure 7a-d illustrate the trajectory, lateral deviation, roll angle, and lateral acceleration of the motorcycle at this speed, while Figure 7e,f present the steering torque and front-wheel steering angle, respectively. The simulation results indicate that the maximum lateral deviation from the tracking path does not exceed 0.6 m, and the maximum roll angle remains below 15\({}^{\circ}\). This shows that the motorcycle can follow the predetermined trajectory within the lane while maintaining balance. Additionally, the lateral acceleration stabilizes towards the end of the maneuver. There is a clear correlation between the front-wheel steering angle and steering torque, indicating that torque compensates for changes in the front-wheel steering angle during operation. By employing torque compensation, the system effectively achieves trajectory tracking and balance, resulting in excellent control performance during steering maneuvers.
## 4 Results and Discussion
### Flexural Strength
The improvement in bending strength can be explained by the fact that GO has a large surface area, so the contact area between GO and cement increases, helping to improve transmission efficiency. The mortar sample containing 0.03 wt% GO and varying amounts of fly ash had the highest flexural strength among all of the other samples. When 15 wt% fly ash was added, the maximum value of flexural strength at 28 days was 8.69 MPa (an increase of 30.09% more than the control sample), while, when using zero fly ash, it only reached 8.16 MPa. In contrast, when a fly ash content of up to 30 wt% was used in the mixture, the flexural strength declined to only 7.06 MPa (a slight increase of 5.69% compared to the control sample). With an extensive surface area, adding GO to the mortar mixture has dramatically improved the bond interface. The result was the increased strength of the mortar after curing. However, the reasonable rate of using GO content in many studies around the world is not the same. Within the scope of this study, it shows that the amount of GO used at 0.03 wt% gave the most reasonable results. When the amount of GO employed was increased to 0.05 wt%, the strength that existed in the mortar showed a tendency to decline, possibly because of the aggregation of GO. However, when the fly ash content was too much, precisely 30 wt% in this study, the strength development trend decreased clearly compared to samples with the same amount of GO.
# Novel Materials for Heavy Metal Removal in Capacitive Deionization
## 3 Materials for Heavy Metal Removing Using CDI
### Metal Oxide Supported Carbon Composites
GO-TiO\({}_{2}\) nanotube composites have high specific electrosorption capacities of 253.25 mg g\({}^{-1}\) and 241.65 mg g\({}^{-1}\) for Cu\({}^{2+}\) and Pb\({}^{2+}\), respectively [120]. This microwave-assisted fabricated composite releases little during desorption at zero voltage caused by induced oxygen, and \(\pi\) electrons lead to strong interactions with Pb\({}^{2+}\) and Cu\({}^{2+}\). Metal oxide/carbonaceous gel hybrid materials derived from natural and renewable biomass exhibit excellent flexibility and high electrochemical reactivity (capacitance of 120.4 F g\({}^{-1}\) in 50 mg L\({}^{-1}\) CuCl\({}_{2}\) solution), primarily in the area of CDI decontamination applications. Inone study, Cu\({}^{2+}\) was nearly completely removed (removal capacity of 57.3 mg g\({}^{-1}\)) and the as-assembled CDI testing apparatus showed durability after 1000 cycles [121]. Activated carbon cloth coated with zinc oxide nanoparticles has been used for electrode inflow via a CDI system. Upon incorporation with ZnO NPs, electrosorption efficiency was enhanced from 17% to 33% for Pb\({}^{2+}\), from 21% to 29% for Cd\({}^{2+}\), and from 21% to 35% for mixed Pb\({}^{2+}\) and Cd\({}^{2+}\) ions [122]. These results together illustrate the promising applications of metal-oxide/carbon electrodes for heavy metal removal.
## 2 Related Works
### Transport Layer Related Works  
The traditional transport protocols that are currently used for the Internet (i.e., UDP and TCP) cannot be directly implemented for WSNs. The TCP based protocols suffers from retransmission overhead, delays and high energy consumption [21; 22]. For real-time applications like streaming media, the User Datagram Protocol (UDP) is preferred to TCP since timeliness is more important than reliability. UDP does not provide required reliability which is needed for many sensor applications. Also, it does not offer flow and congestion control. Therefore, using it leads to packet loss and unnecessary energy consumption.  
With regards to constrains of UDP, recently new proposals using multi-path transmission have been introduced such as COngestion Detection and Avoidance (CODA) [23] and Multi-flow Real-time Transport Protocol (MRTP) [24]. CODA has considerable delay since it decides on the basis of the status of the intermediate nodes and MRTP does not regard energy efficiency in WMSN [2].  
Some other transport protocols have been designed recently for scalar WSNs with the aim of decreasing energy consumption, providing reliability, and controlling congestion. These protocols did not fulfill major factors of QoS, such as high bandwidth and real-time communication, which are required for multimedia communication in sensor networks, e.g. RMST [25], RBC [26], and STCP [21] do not support real-time communication while providing reliability. Also, Fusion is another transport protocol that is not compatible with the limited energy sources of video sensor nodes [27].
## 3 Deep Learning Techniques for Cardiovascular Disease Prediction
### Role of Deep Belief Network for Cardiovascular Disease Prediction  
Deep Belief Network (DBN) is a popular DL model. The main reason for its popularity is its deep architecture. DBN is basically an alternate to a DNN in which there are multiple input, output, and hidden layers. Each layer is interconnected with the other. The connections between these layers have weights that are drived from the input. An unsupervised DBN is used for feature detection, while supervised DBN is used for classification. DBN has many real time applications including object detection, computer vision, NLP, and more [34]. The design of the generative unsupervised algorithm, DBN, includes a stack of RBMs. The input of the second layer's is the output of first layer, and so on. RBM training is conducted using input data. The visible layer (v) and hidden layer (h) of RBM are its two layers. While the hidden layer captures features, the visible layer holds input data. There is no connection between the nodes in a single layer, which is the primary distinction between Boltzmann and RBM. In order to discover significant characteristics in raw data, RBM is utilized. The DBN model functions in the following two phases.  
Step 1: Training In DBN, each RBM is trained, individually. The top layer RBMs are trained first. They capture the least important or low-level features from the data. After one RBM layer is trained, it becomes visible for the next RBM layer.  
Step 2: Fine-Tune  
Once the entire DBN model is trained, it is backpropagated to minimize the error in the model by updating the weights:  
\[P(x,h^{1},...,h^{l})=(\prod_{k=0}^{l-2}P(h^{k}|h^{k+1})P(h^{l-1},h^{l})) \tag{1}\]  
where \(x\) is the input and \(h\) are the hidden layers. DBN can be implemented using RBM and an autoencoder. We have used RBM in DBN layers because of the following reason.  
Vanishing is one of the biggest challenges in Deep Neural Network (DNN). During training, weights are updated using gradient, and if gradient is very small, vanishing gradient can occur. During pretraining, RBM learns data representation that does not change with minor changes in the weights. This means that the accuracy of the model will increase because the gradient used to update weights is significantly larger [35]. On the other hand, in an autoencoder, decoders and encoders have their own gradients due to which vanishing gradient can be more worse leading to poor accuracy of model and poor performance.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 4 Evolution of Communications Technologies for Telemedicine
### Communication Network Links for Telemedicine  
How exactly are telemedicine technologies made available to users, and what kinds of technological advancements make it possible to establish digital links between a healthcare physician at a large hospital and a patient residing in a remote rural home? Because of the proliferation of the internet, the manner in which telemedicine is provided has evolved in many ways. Patients who live in rural or otherwise inaccessible places can now take part in at least some forms of telemedicine thanks to the widespread availability of affordable high-speed internet connections. This subsection discusses the two communication network links that can be used to drive telemedicine, namely, point-to-point and point-to-multipoint communication links.
## 2 Methods
### Source Activity Analysis  
Since EEG has a high temporal resolution, it has been considered to be the most suitable neuroimaging tool to investigate rapid changes in brain activity. However, EEG has some intrinsic limitations. Firstly, sensor-level EEG has low spatial resolution due to volume conduction. In other words, the brain signals may not reflect brain activity directly below the recording electrodes [27, 28]. Secondly, EEG data can be contaminated with various noises and artifacts, so it can have a poor signal-to-noise ratio [29, 30]. Using EEG source-imaging methods is one of the cost-effective options to improve the spatial resolution. For example, mapping the potential distribution of the scalp onto the cortical source space below can improve the spatial resolution. Standardized low-resolution brain electromagnetic tomography (sLORETA) is also a well-established source-imaging method [31]; it has calculated brain source signals comparable to those of intracranial recording [32].  
Particularly, sLORETA has been used to calculate the cortical distribution of the standardized source current density. This is a representative source-imaging method for solving the EEG inverse problem [31], assuming that the source activation of a voxel is similar to the source activity of the surrounding voxels to compute a particular solution. It also applies an appropriate standardization of the current density. The lead field matrix was computed using a realistic head model that was segmented on the basis of the Montreal Neurological Institute (MNI) 152 standard template, wherein the 3D solution space is restricted only to the cortical gray matter and hippocampus [33]. The solution space was composed of 6239 voxels, with a 5 mm resolution. Anatomical structures, such as the Brodmann areas, were labelled using an appropriate transformation from MNI to Talairach space [34].  
This EEG source activity analysis method was similar to that of our previous study [35].
## 4 Performance Analysis
### Outage Probability  
The OP is defined as the probability that the end-to-end instantaneous SNR falls below a predefined threshold \(\gamma_{T}\) and can be mathematically expressed as  
\[P_{\text{out}}=\Pr[\gamma_{o}\leq\gamma_{T}]=F_{\gamma_{o}}(\gamma_{T}). \tag{24}\]  
Since a DF relay protocol has been assumed, the CDF of the received SNR \(\gamma_{o}\) can be expressed as  
\[F_{\gamma_{o}}(\gamma_{T})=F_{\gamma_{1}}(\gamma_{T})+F_{\gamma_{t}}(\gamma_ {T})-F_{\gamma_{1}}(\gamma_{T})F_{\gamma_{t}}(\gamma_{T}), \tag{25}\]  
where \(F_{\gamma_{1}}(\gamma_{T})\) is given by (5) and \(F_{\gamma_{t}}(\gamma_{T})\) is given by (17).  
High SNR Analysis  
In the high SNR regime, asymptotic and easy-to-evaluate expressions can be derived that can be used to provide insights into the behavior of a system as the SNR increases without needing to rely on detailed numerical evaluations. This helps researchers gain a deeper understanding of how the system behaves in the high SNR limit. In the high SNR regime, i.e., \(\hat{\gamma}_{1},\hat{\gamma}_{2}\rightarrow\infty\), simpler expressions for (5) and (15) can be obtained. More specifically, using ([33] [Equation (07.34.06.0006.01)]) in (5) and after some mathematical simplifications, the following asymptotic closed-form expression is obtained for the CDF of \(\gamma_{1}\)  
\[\begin{split} F_{\gamma_{1}}(\gamma)\approx\sum_{k=1}^{3b}\frac {\mathcal{A}_{i}\prod\limits_{j=1}^{3b}\Gamma\bigl{(}\mathcal{B}_{j}- \mathcal{B}_{k}\bigr{)}\Gamma(1-\mathcal{D}_{1}+\mathcal{B}_{k})}{\prod \limits_{j=2}^{b+1}\Gamma\bigl{(}\mathcal{D}_{j}-\mathcal{B}_{k}\bigr{)} \Gamma(1-\mathcal{B}_{3b+1}+\mathcal{B}_{k}\bigr{)}}\Bigg{(}\frac{D_{i}^{b} \gamma}{b^{2b}\hat{\gamma}_{i}^{f}}\Bigg{)}^{\mathcal{B}_{k}},\end{split} \tag{26}\]  
where \(\mathcal{D}_{1}=1,\mathcal{D}_{2}=\Delta_{b,\hat{\gamma}_{i}^{2}+1},\mathcal{ B}_{1}=\Delta_{b,\hat{\gamma}_{i}^{2}},\mathcal{B}_{2}=\Delta_{b,\hat{\alpha}_{i}}, \mathcal{B}_{3}=\Delta_{b,\hat{\beta}_{i}},\mathcal{B}_{4}=0\). From the above expression, it becomes evident that the diversity gain (\(G_{d}\)) for FSO links, given by \((\gamma_{i}^{f})^{-G_{d}}\) as \(\hat{\gamma}_{i}^{f}\rightarrow\infty\), depends on the small- and large-scale turbulence parameters as well as the pointing error coefficient.  
As far as the RF link is concerned, by following the same procedure for (15), the corresponding expression is given by  
\[F_{\gamma_{2}}(\gamma)\approx\mathcal{S}_{1}\sum_{i=1}^{2}\frac{\Gamma(m_{3-i }-m_{i})\Gamma(m_{i}+\alpha_{2})\Gamma(m_{i}+\alpha_{1})\Gamma(m_{i})}{\Gamma (m_{i}+1)}\left(\frac{m_{1}m_{2}\gamma}{\hat{\gamma}_{2}}\right)^{m_{i}}. \tag{27}\]Following the same approach used for the FSO link, it can be shown that diversity gain for the RF link depends only on the small-scale fading parameters.
# Blind Spoofing GNSS Constellation Detection Using a Multi-Antenna Snapshot Receiver +
## 4 System of Detections  
In the previous section, single detections between satellite similarities were done. In this section, three strategies to combine the matrix of results to improve spoofing detection are presented. These strategies result in different metrics and features which can later be used for detection or further statistical approaches.  
Figure 4: CDF of spatial angle \(\theta\) vs. the steering vector angle \(\xi_{m,n}\) for a UCA. ©IEEE. Reprinted, with permission, from [22].
## 3 Results and Discussion
### Variations in ARGs and LRGs After Remediation via Multiple Methods
#### 3.1.3 Variations in LRGs  
At least one of the four LRGs (viz., _Znt_A, _Pbr_T, _Pbr_A, and _Cad_A) occurred in each treatment group. _Znt_A encoded a metal efflux protein crucial for heavy metal detoxification [27]. _Pbr_T was responsible for Pb\({}^{2+}\) uptake, while _Pbr_A and _Cad_A were involved in Pb\({}^{2+}\) efflux [28]. _Cad_A and _Pbr_T were most abundant in RS, with negligible levels in PS2.5, PS5, and PS10 (Figure 3d). Specifically, _Pbr_A reached its peak abundance in PS5 (6 \(\times\) 10\({}^{-5}\)), and _Znt_A was most abundant in PS10 (6.4 \(\times\) 10\({}^{-4}\)). The remediation strategies differentially affected LRGs abundances. _Pbr_T levels were higher in Imvite and MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\) compared to PS2.5, indicating reduced Pb toxicity [15]. In contrast, _Znt_A abundance was lower in Imvite than in PS2.5, likely due to montmorillonite's adsorption, which restricted Pb entry into bacterial cells. As _Znt_A, a P1B-ATPase transporter, functions to expel Pb\({}^{2+}\) actively, its relative abundance decreased with reduced Pb influx [29]. In the BES system, _Pbr_T abundance peaked at 3.4 \(\times\) 10\({}^{-3}\) but decreased to 2.1 \(\times\) 10\({}^{-4}\) in BES-Imvite-MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\), likely due to reduced Pb solubility from composite remediation. _Znt_A abundance fell from 6 \(\times\) 10\({}^{-5}\) in BES to 5 \(\times\) 10\({}^{-6}\) in Imvite-MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\), attributed to increased pH following remediation [15]. Pb concentration also influenced LRGs levels; _Pbr_T was more abundant in BES-Imvite-MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\) compared to BES5-Imvite-MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\) and BES10-Imvite-MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\).
## 2 Machine Learning and Defect Size: Algorithms
### Defects in AM Components  
The main reason for the lower fatigue response of AM parts, if compared to that of traditionally built components, is the formation of large manufacturing defects during the AM process. Defects are well-known to significantly affect the fatigue response, since they act as stress concentrators and enhance the crack nucleation process. For this reason, a damage-tolerant approach should be employed when AM components are to be designed, with the influence of defects that cannot be neglected to ensure a safe design. Figure 1 shows the most common AM defects, pores (Figure 1a) and lack of fusion defects (Figure 1b).  
In order to properly apply the damage-tolerant approaches developed in the literature, the defect size in the component should be known or reliably predicted. According to [4], the defect size, rather than the defect type or morphology, mainly affects the fatigue response. In particular, the area of the defect projected in a direction perpendicular to the maximum applied stress can be considered as the characteristic defect size, \(\sqrt{a_{c}}\), and for the analysis of the fatigue response. It is of utmost importance, therefore, to properly measure or predict the \(\sqrt{a_{c}}\) parameter in components. Moreover, according to [4], an equivalent defect size can be estimated for defects with complex shapes, by considering the fracture mechanics approach. In the following, therefore, the \(\sqrt{a_{c}}\) parameter is considered as the characteristic defect size and will be employed for the developed ML algorithms.  
This work and, in general, design methodologies against the fatigue failure of AM parts, focus on the most critical defect, or the killer defect, which is the one originating the fatigue failure. The most effective procedure for assessing the population of critical defects is by analyzing the defects originating the fatigue failure in specimens subjected to fatigue tests. The population of defects can be also measured through micro-CT inspections, which provide reliable information on the defect population, but do not allow an assessment of the most critical defect in the inspected specimen or part. In this case, the largest defect can be considered the most critical, even if, according to [22], the largest defect may not necessarily be the killer defect.  
In the following, according to [4], \(\sqrt{a_{c}}\) is assumed to follow a LEVD distribution, whose cumulative distribution function (cdf) is given by:  
\[F_{\sqrt{A_{c}}}=e^{-e^{-\frac{(E-\sqrt{a_{c}})}{\sigma}}} \tag{1}\]  
where \(\mu\) and \(\sigma\) are the location and the scale parameters. Equation (1) has been proven to properly work for AM defects [5] and allows us to reliably compute the probability associated with a defect with an equivalent size equal to \(\sqrt{a_{c}}\). The location and the scale parameters \(\mu\) and \(\sigma\) should be estimated experimentally and are strongly dependent on the process parameters, as discussed in the following Section.