esa-sceva/satcom-chunk-collection huggingface dataset:
# Laser Technology in Photonic Applications for Space
## 3 Lasers in Space
### Fiber Optic Sensing  
For any space mission to be successful, spacecraft monitoring of several parameter is paramount, in particular temperature and pressure. Several sensors are typically used to monitor the spacecraft health from the fabrication stage up to the actual space mission lifetime. Fiber optic sensors use bidirectional optical signals propagated in an optical fiber from the source to the optical sensor and back. This allows the interrogation electronics to be installed remotely from critical areas of the spacecraft, minimizing electromagnetic interferences and avoiding safety issues related to the proximity to explosive materials from the propulsion system. Other advantages include flexibility, low weight, compactness, and lower power consumption, high sensor capacity, and high measurement resolution of the sensor response.  
The Fiber Sensor Demonstrator (FSD) for ESA's Proba-2 satellite, launched in November 2009, was the first demonstration of a full fiber-optic sensor network in the space environment. The central interrogation module was used together with Fiber Bragg Gratings (FBG) based sensors to monitor the temperature in the propulsion system and the spacecraft bus between -60 \({}^{\circ}\)C and +120 \({}^{\circ}\)C. High-temperature sensor were also installed to monitor the temperatures above 350 \({}^{\circ}\)C in the thrusters, whilst other sensors provided both temperature and pressure measurements of the propellant tank. The interrogation sensor used the first fiber laser used in space, tuneable between 1520 and 1560 nm [21].  
ESA has also been investigating the application of fiber sensors for other applications, such as the ISS furnace temperature measurements, strain measurement in solar cells, or temperature monitoring during the re-entry of a spacecraft in the atmosphere [117], for instance, in the frame of ARTES 5.2 and Future Launchers Preparatory Programme (FLPP) phase 3 programs, in which a fiber ring tunable laser emission is swept between 1500 nm and 1600 nm. Several types of lasers have been considered,for instance, a semiconductor optical amplifier has been used as an active medium [118] and a tunable laser-based optical interrogator has been designed with semiconductor modulated grating Y-branch laser diode (MG-Y) type laser [119].  
NASA also reported similar works on fiber sensing technologies, in particular a multi-point fiber optic hydrogen micro-sensor system for leak detection for launch vehicles. The system contains chemically reactive coatings whose reflection behaviour changes in proportion to the hydrogen concentration and are used as leak detectors [120].  
Fiber optic sensors show several advantages that can motivate their mainstream adoption in space applications although several challenges are still open, such as resistance to extreme conditions and the impact of microgravity on the sensors, as well as resistance to temperatures above 1000 \({}^{\circ}\)C [121].
# CRCSI: A Generic Block Interleaver for the Next Generation Terrestrial Broadcast Systems
## 2 System Model  
### Block Interleaving  
In a terrestrial broadcast system, deep fading and impulse noise typically introduce burst errors beyond the capability of forward FEC codes. As a solution, block interleaver is introduced to convert burst errors into random-like ones, spreading burst errors over multiple codewords within the correction capability of the FEC codes.  
The interleaving operation \(\pi\) (the mapping rule) [4] can be generally expressed as  
\[\pi(i)=(p\cdot i+\mathbf{s}(i))\bmod K, \tag{1}\]  
where \(i=0,1,\ldots,K-1\) is the index of the input data, \(\pi(i)\) is the index of the output data, \(p\) denotes the increment reading interval and \(\mathbf{s}\) is the predefined shift vector with a period of disorder cycle \(Q\). In addition, \(\mathbf{s}(l)\in[0,Q-1],l\in 0,1,\ldots,K-1\), and mod denotes the modulo operation.  
For block interleaving, the input data are divided into blocks of the size \(MN\), and fed into the interleaver sequentially [17]. This operation is given by  
\[\Pi(j)=k_{1}\cdot\pi_{1}\bigg{(}\lfloor\frac{j}{M}\rfloor\bigg{)}+k_{2}\cdot \pi_{2}(j\bmod M), \tag{2}\]  
where \(j=0,1,\ldots,MN-1\) is the index of one input block, \(\Pi(j)\) is the index of the output block, and \(\lfloor\cdot\rfloor\) denotes the round down function. Note that \(k_{1}=1,k_{2}=N\) and \(k_{1}=M,k_{2}=1\) denote the row-wise and column-wise reading, respectively. Here, \(\pi_{1}\) and \(\pi_{2}\) follow the mapping rules defined in (1).  
In this paper, we consider two types of typical block interleavers: RCI applied in DVB-T2 and TBI also known as diagonal interleaver in ATSC 3.0. For RCI, we have \(p=1\), \(Q=K=N\) and \(\mathbf{s}(l)=0,\forall l\) in \(\pi_{1}\), and \(p=1\), \(Q=K=M\) and \(\mathbf{s}(l)=0\) in \(\pi_{2}\). For TBI, we have \(p=1\), \(Q=K=N\) and \(\mathbf{s}(l)=l\bmod Q\) in \(\pi_{1}\), and \(p=1\), \(Q=K=M\) and \(\mathbf{s}(l)=0\) in \(\pi_{2}\).
# Metallurgical Aspects in the Welding of Clad Pipelines--A Global Outlook
## 3 Microstructure Evolution
### Heat-Affected Zone
#### 3.4.2 HAZ of Welded 316L Stainless Steel Clad  
The HAZ of austenitic stainless steels (e.g., 304L/309/316L) consists of an austenitic matrix after solidification with almost negligible grain growth when compared to carbon steels. The grain size of the 316L SS base metal is \(\sim\)45-75 \(\upmu\)m, where the variation highly depends on manufacturing route [78]. When compared to fine-grained carbon steel having grain size smaller than 10 \(\upmu\)m [79], there is a much lower driving force for grain growth [80]. In addition, upon cooling, some delta ferrite may form at the HAZ grain boundaries, suppressing grain growth [57]. This results in a minor hardness increase near the FL [63]. However, despite the low susceptibility for grain growth, high heat input should be avoided, especially when the BM has smaller grains that can cause significant grain growth even in austenitic steels with subsequent softening [81]. In addition, due to high heat input, there is the formation of chromium carbides on the grain boundaries of austenite. As a result, the area around the carbides has lower chromium content compared to the bulk material. This can lead to the sensitization phenomenon, which reduces corrosion resistance significantly [57].  
Due to dissolved precipitates, some carbides and nitrides can form upon cooling along grain boundaries in the HAZ or at the ferrite-austenite interface, especially when the stainless steel contains  
Figure 17: CCT (continuous cooling transformation) diagram for X70 steel [69].  
Ti and Nb. This can cause grain boundary liquation by lowering the melting temperature, in addition to the segregation of impurities (primarily P and S), causing liquid films upon cooling. Notably, between the FZ and the HAZ, there is the partially melted zone (PMZ) due to temperature gradients, which has low mechanical properties [82]. Grain growth in this zone is not significant and the grain size is similar to the as-received BM [83]. The thickness of the UMZ (unmixed zone) (<0.5 mm) can be reduced by applying mechanical vibrations during welding [84].
# Digital Health Technology for Stroke Rehabilitation in Canada: A Scoping Review
## 4 Discussion
### Interpretations of Study Characteristics on the Use of DHTs in Stroke Rehabilitation in Canada
Various studies review neurotechnology, including computerized cognitive training and brain-computer interfaces, noting their promise but emphasizing the need for further research to integrate these tools into clinical practice [47; 48]. It appears that Canadian research places comparatively less emphasis on non-upper extremity outcomes than international studies, highlighting a need to align more closely with global efforts that address LE function, cognition, and ADLs. Broadening the range of targeted outcomes would support a more comprehensive approach to stroke rehabilitation and significantly enhance the overall quality of life for stroke survivors. We also examined the contribution of DHTs at each phase of stroke. As expected, most studies focused on the chronic phase, where technology is primarily used for remote rehabilitation. However, while remote rehabilitation is more feasible in the chronic stage, its role remains somewhat niche. In the acute stage, rehabilitation should begin as soon as the patient is clinically stable, with a focus on preventing secondary complications such as pneumonia, pressure ulcers, and contractures through early mobilization and functional training. During this phase, in-person intervention is generally recommended over remote rehabilitation [49; 50]. As patients stabilize, more intensive therapies can be introduced, including new technologies such as functional electrical stimulation, robot-assisted therapy, and VR-based cognitive therapy [51]. While the effectiveness of rehabilitation in the acute stage of stroke is well established, further research is needed to evaluate the role of technology in this phase [52]. In line with international developments, future Canadian research could prioritize the use of rehabilitation technologies not only during the chronic phase but also in the early stages of stroke recovery. Focusing on the acute and subacute phases--critical periods for rehabilitation--could better support patients during the transition from hospital to home and enhance early rehabilitation outcomes. Addressing these earlier phases may also help prevent further disability and promote improved long-term recovery [49]. As this review highlighted, most studies on rehabilitation technology for stroke patients in Canada focus on home-based interventions. This aligns with another research, which indicates that telemabilization for stroke patients has gained traction in Canada, offering a promising alternative to traditional face-to-face rehabilitation [53]. Furthermore, as previously discussed, technology-based stroke rehabilitation is more commonly implemented during the chronic stage, typically delivered in a home setting. However, since some global studies have begun focusing on the use of telemabilization during the acute phase, this could represent an emerging area of research for Canada.
## 3 Method
### Parameters  
The data measured from the edge device are converted into 5 types of parameters, which are \(\theta\), \(SVM\), differential \(SVM\) (\(DSVM\)), and gravity-weighted \(SVM\) (\(GDSVM\)), and gravity-weighted \(DSVM\) (\(GDSVM\)). They are calculated as follows [17]:  
\[\theta(i)=\tan^{-1}\left(\frac{\sqrt{m_{y}^{2}(i)+m_{z}^{2}(i)}}{m_{x}(i)} \right)\times\frac{180}{\pi}, \tag{1}\]  
\[SVM(i)=\sqrt{m_{x}^{2}(i)+m_{y}^{2}(i)+m_{z}^{2}(i)}, \tag{2}\]  
\[DSVM(i)=\sqrt{\left(m_{x}(i)-m_{x}(i-1)\right)^{2}+\left(m_{y}(i)-m_{y}(i-1) \right)^{2}+\left(m_{z}(i)-m_{z}(i-1)\right)^{2}}, \tag{3}\]  
\[GSVM(i)=\frac{\theta(i)}{90}\times SVM(i), \tag{4}\]  
\[GDSVM(i)=\frac{\theta(i)}{90}\times DSVM(i), \tag{5}\]  
where \(i\) represents the sampling number, and \(m_{x}(i)\), \(m_{y}(i)\), and \(m_{z}(i)\) represent the \(x\)-axial, \(y\)-axial, and \(z\)-axial acceleration of the \(i\)th sampling, respectively. For STM, \(SVM\) and \(\theta\) are used. The parameters are finally used as the average value using the sliding window method [39] in which the modified \(n\)th parameter is calculated as the average from \(n\)-69 to \(n\) parameters as follow as one example (\(\theta\)):  
\[\overline{\theta}(n)=\frac{1}{70}\sum_{i=n-69}^{n}\theta(i). \tag{6}\]  
For the input data of LSTM in the fall detection system, single and multiple parameters are used. The single parameters, \(P_{\theta}\), \(P_{S}\), \(P_{D}\), \(P_{G}\), and \(P_{GD}\) represent the average values of 70 \(\theta\)s, 70 \(SVM\)s, 70 \(DSVMs\), 70 \(GSVMs\), and 70 \(GDSVMs\), respectively, as expressed in Equation (6). The multiple parameters, \(P_{\theta S}\), \(P_{BD}\), \(P_{\theta G}\), \(P_{\theta GD}\), and \(P_{ALL}\), represent the combination of \(P_{\theta}\) and \(P_{S}\), \(P_{\theta}\) and \(P_{D}\), \(P_{\theta}\) and \(P_{G}\), \(P_{\theta}\), and \(P_{GD}\), and \(P_{\theta}\), \(P_{S}\), \(P_{D}\), \(P_{G}\), and \(P_{GD}\), respectively, as shown in Table 2.
## 3 Artificial Neural Networks
### Artificial Neural Network Structure  
To properly understand how the developed model to evaluate the sideslip angle works, it is necessary to deep into the most important features of an artificial net. Every neuron in a neural network has multiple inputs that can be either the input signals of the problem or signals coming from previous neurons (Figure 3). These inputs are added up for each neuron based on the weight of their connections. During the training phase, the weights are adjusted to enhance the overall learning of the network. Additionally, every neuron in the network has a bias that helps in fine-tuning the optimal working point of the neuron. Another crucial aspect of the neuron is the activation function that determines its output based on the inputs, weights of the connections, and the bias. In brief, the neuron's ability to learn and adapt is governed by these key features.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Sensors for Input Channels**} \\ \hline
**Input Channels** & **Acquisition Devices** \\ \hline Longitudinal acceleration & Inertial platform \\ Lateral acceleration & Inertial platform \\ Steering angle & CAN-bus \\ Yaw rate & Inertial platform \\ Wheels speed for each corner & CAN-bus \\ Vehicle longitudinal velocity & Optical sensor \\ \hline \hline \end{tabular}
\end{table}
Table 2: NN input channels and their acquisition devices.  
Hence, the output of a neuron is returned by Equation (1).  
\[output_{i}=f\left(\sum_{j=1}^{p}\theta_{ji}input_{j}+\theta_{0i}\right) \tag{1}\]  
where  
\(\theta_{ji}\) are the weights of the i-th neuron;  
\(input_{j}\) are the inputs of the i-th neuron;  
\(bias\) is the bias of the i-th neuron;  
\(output_{i}\) is the output of the of the i-th neuron;  
\(f\) is the activation function.  
As mentioned, multiple neurons aggregate to create neural networks, complex structures organized into layers. These layers include an input layer, an output layer, and one or more intermediate layers, which are called hidden layers. The number of hidden layers and neurons in each layer varies based on specific application needs, with no fixed rules dictating their selection.
## Materials and Methods
### System Functioning and Components
SiPMs were biased by using a 30 V stabilized power supply; their output currents were measured by the voltage drop across a 1 k\(\Omega\) resistor mounted on the same board of the SiPM holder. All 13 SiPMs boards were connected to the analog inputs of the NI USB-6255 Multifunction device. The LEDs were connected to the 24 Transistor-Transistor Logic CMOS (TTL/CMOS) digital input/output (I/O) lines of the NI-6255 through 0-5 k\(\Omega\) resistive trimmers mounted on an auxiliary board. The operative range of the SiPMs is evaluated by measuring the photocurrent as a function of the optical intensity incident on the photodetector (Figure 6). The lower limit of the data points in Figure 6 for the incident optical power is approximately in correspondence with a SiPM photocurrent about equal to the dark current. Therefore, this is the lower limit of usable incident optical power. As far as the upper limit is concerned, taking as reference the case of 30 V SiPM bias, as shown in Figure 6, a saturation of the SiPM photocurrent at about 1 mA is evident. Therefore, the corresponding optical power, of the order of \(10^{-7}\) W/cm\({}^{2}\), represents the incident power upper limit. It should be noted, however, that such onset for the sub-linearity regime is not due to the above-mentioned SiPM avalanche pile-up effect, which takes place at a higher optical power for the used devices, but it is rather due to the use of the 1 k\(\Omega\) resistor put in series in our system scheme. That is, the used resistor causes a reduction of the linear range compared to the native linear range of the detectors. In fact, if both the value of the resistance and the value of the photocurrent are high, the voltage drop on the resistor causes a shift of the SiPM working point during illumination, and consequently non-linearity is generated. For example, under about \(5\times 10^{-7}\) W/cm\({}^{2}\) illumination, the 1 mA output current of the SiPM on 1 k\(\Omega\) resistor causes a reduction of the overvoltage of 1 V.  
Based on the data of Figure 6, all the trimmers in series with the LEDs are regulated to get each SiPM output close to the upper limit of the linear range (i.e., \(\approx\)1 mA), considering the nearest neighbor SDS of the checkerboard, to obtain high sensitivity for all the source-detector separations and to avoid non-linearity effects to the measurements. In such a way, for each LED, the photocurrent value falls within the linear range of the SiPM. In fact, in our experiment, when the rod moves in the phantom, the SiPMs only undergo a decrease of the photocurrent, as shown in the next section.
## 5 Implementation of Multimedia Independent Multipath Routing Algorithms
### Algorithmic Implementation Details
#### 5.2.2 The Choice of the Order of Angle Distribution  
If the distribution of source nodes is not uniform, the angles assigned may vary in size. In order to balance the load, the paths need to be evenly distributed around sink nodes, so the order of angle allocation needs to be considered. The definition of coordinate system is that the angle of the horizontal line on the right side of the node is 0, clockwise from small to large. Consider the following scenarios: one sink, three sources, namely Src1, Src2, Src3. Assume that the weights of Src1 and Src3 are 1 and that of Src2 is 2. Then there are two considerations for the allocation scheme.  
1 Location allocation based on the source.  
The first allocation scheme is based on the location allocation of the source. The smaller the angle of Source relative to the sink, the earlier the allocation. Since the angle of Src1 is at 090, the angle range that should be allocated according to the weight calculation is as follows:  
\[Angle_{1}=\frac{Q_{1}}{Q_{total}}=1/(1+2+1)\times 360=90 \tag{32}\]  
With Src1 as the center, the distribution angle of Src1 is 45\({}^{\circ}\)-135\({}^{\circ}\). The weight of Src2 is 2 and its angle range is 180. Then the angle of distribution is followed by Src1, i.e., 135\({}^{\circ}\)-315\({}^{\circ}\). Src3 ranges from 315\({}^{\circ}\) to 45\({}^{\circ}\), as shown in Figure 7a. Although Src1 is located in the middle of its angle range, Src2 is located on one side of its angle range, while Src3 is completely out of its angle range.  
Distribution begins with the node with the largest weight.  
Considering that the larger the weight of the node, the larger the angle range of the allocation, in order to take care of the node with the largest weight, the node with the largest weight is assigned priority here. As shown in Figure 7b. The distribution range of Src2 is from 45\({}^{\circ}\) to 225\({}^{\circ}\) and occupies the range of Src1. The range of Src1 is from A to B, ranging from \(-\)45\({}^{\circ}\) to 45\({}^{\circ}\).
# Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning
## 2 Literature Review
### Feature Extraction  
Multiple studies are being exploited, focusing on applying machine learning to extract leaf features. [PERSON] [7] and [PERSON] [8] both emphasize the importance of stable and discriminative features, with [PERSON] further focusing on texture and shape features, leading to an effective recognition of leaves with varying texture, shape, size, and orientations to an acceptable degree. The authors [9] review the performance of various moment invariant techniques for a feature extraction process, concluding that a 100% classification rate can be achieved by using Total Mutual Information (TMI) and a General Regression Neural Network (GRNN). Another study [10] builds upon this to classify images of diseased leaves using color and shape attributes and several ML algorithms, including the Support VectorMachine, the K-Nearest Neighbor, and the Decision trees. The authors in [11] used image processing techniques to recognize plant species by identifying appropriate features for extraction and improving classification accuracy. They utilized feature extractions such as Contour-based and Region-based to achieve more precise results. Other studies [12; 13; 14; 15; 16; 17] use different implementations of deep learning algorithms to extract features of leaf plants, leading to classification or disease detection. The achieved accuracy is between 94.8% and over 99%, making the deep learning algorithms the best tool for feature extraction.
## Appendix F Description of Mechanisms for High Performance
### T-PBFT [479]  
To improve fault tolerance, reduce the probability of view changes, and reduce the protocol communication complexity, Gao et al. [479] introduce T-PBFT for permissioned systems. A performance gain over previous PBFT architectures is sought by narrowing down the number of consensus nodes according to an internal reputation system based on a performance metric. As a protocol targeting a permissioned environment, no Sybil attack resistance is provided.