esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Data Valuation for Collaborative Edge Intelligence  
The data valuation problem has been a central topic in the realm of collaborative ML for a long time. Fairness of data valuation has been recognized as the key to trustworthy AI [22]. As a line of research, Shapley values have been extensively investigated as an approach in data valuation [23]. Specifically, the data Shapley value measures the contribution of a single data point to a learning task [24]. The distributional Shapley value extends the concept to arbitrary data distributions and provides stability guarantees [25]. The contribution index extends the data Shapley value to FL by gradient-based model reconstructions [26]. The authors of [26] investigated the profit allocation problem for FL, wherein a diminishingvaluation factor was introduced to encourage participants in early rounds. The federated Shapley value is determined from local model updates in each training iteration, avoiding the need for retraining the model [27]. Proof-of-Shapley opens opportunities for Shapley-value-based blockchain consensus without additional need for trust [28]. However, it is often challenging to identify a viable, well-defined and regulated source of financial compensation for data contributors in the public domain. Apart from Shapley value, self-reported valuation requests the data owners to submit their bids, which consist of information about the combination of resources, local accuracy and costs [29]. Additionally, permutation-based data valuation is for identifying the training points (a labeled data sample) that are most responsible for a given prediction [30]. The method depends on computing influence functions of data samples. Furthermore, the reinforcement-learning-based method adaptively learns the contribution of each data point towards the learned predictor model [31]. Unlike the existing literature, we aim to explore blockchain's potential for improving the QoS of edge intelligence via decentralized, time-dependent and token-independent data valuation.
## 3 Experimental Results and Analysis
### SPEED Datasets Analysis  
SPEED [13; 41] has 12,000 synthetic images in its training dataset, 2998 synthetic images in the test dataset, and 5 labeled real images. The image size of this dataset is 1920 \(\times\) 1200 pixels. In this dataset, a few real images were captured through the semi-physical simulation platform of the \"Tango\" satellite model, and the synthetic images were rendered using OpenGL. The post-processing technique eliminates the relevant background and randomly adds the Earth's background to some of the images, which enriches the information of the dataset and provides the reliability and robustness of the dataset. Due to the large range of pose distribution, some satellites occupy fewer pixels in the imaging process, which brings great challenges to pose estimation. The camera parameters used to capture SPEED images are shown in Table 2. The camera parameters determine the internal matrix of the camera, and the camera distortion is ignored here.
# Channel and Bit Adaptive Power Control Strategy for Uplink NOMA VLC Systems
## 3 Adaptive Power Control Strategy for Uplink NOMA VLC
### Channel and Bit Adaptive Power Control  
In NOMA-based systems, different power values are allocated to the users and the power control strategy is a key factor affecting the system's performance. In [28], we proposed an optimal power control strategy for two-user multi-access VLC system under the assumption that the two users' transmission bit rates are identical. In this paper, for multiuser communication scenarios with different transmission bit rate of each user, we develop an efficient channel and bit adaptive power control strategy by jointly considering the effect of channel state information and transmission bit rate. The efficient adaptive power control strategy we proposed is given in the following algorithm.  
**Algorithm 1**.: _(Channel and Bit Adaptive Power Control) Let \(\check{\gamma}_{i},i=1,2,...,N\) denote the channel and bit adaptive power control coefficient. Then, it can be determined in the following._  
\[\left\{\begin{array}{l}\check{\gamma}_{1}=\frac{1}{\max_{2\leq i\leq N}\{1, 2^{\sum_{j=1}^{i-1}K_{j}\frac{h_{1}}{h_{i}}}\}},\\ \check{\gamma}_{i}=2^{\sum_{j=1}^{i-1}K_{j}\frac{h_{1}}{h_{i}}\gamma_{1}}, \quad i=2,3,\cdots,N.\end{array}\right. \tag{5}\]  
This algorithm indicates that the adaptive power control strategy we proposed is essentially channel and bit adaptive, it enables each LED transmitter to transmit its own sub-constellation with amplitude being adaptively adjusted, and it has the following favorable property.  
**Property 1**.: _The sum signal \(g=\sum_{i=1}^{N}h_{i}\check{\gamma}_{i}s_{i}\) constitutes a \(2^{\sum_{i=1}^{N}K_{i}}\)-ary equally-spaced UPAM constellation since \(g/h_{1}\check{\gamma}_{1}=(\sum_{i=1}^{N}h_{i}\check{\gamma}_{i}s_{i})/h_{1} \check{\gamma}_{1}=s_{1}+2^{K_{1}}s_{2}+\cdots+2^{\sum_{i=1}^{N-1}}s_{N}\in \{k\}_{k=0}^{k=2^{\sum_{i=1}^{N}-1}}\)._
# Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines
## 2 Materials and Methods
### Other Regression Methods
#### 2.4.1 Support Vector Machines  
Support vector machines have been widely used in wind farm condition-monitoring and failure detection [17]. They can be used for linear or non-linear classification or regression purposes and are able to find the optimal hyperplanes by keeping the maximum possible margin between samples of different classes. They use a parameter (slack variable) to control the margin violations that may appear (for example, when the database contains outliers). The most interesting idea behind SVMs is that they increase the dimension of the feature space using non-linear kernels, which can transform the problem into a linearly separable one in the new higher-dimensional space. Among the drawbacks of SVMs, we have to mention the large amount of possible kernels to use and their parameters, making them difficult to adjust, and also the longer time needed to train the models. In our case, we will focus on the capability of SVMs to perform linear/non-linear regression. The main parameters of the model for the experiments are as follows--Gaussian kernel, optimisation of the hyperparameter based on a 10 \(k\)-fold cross-validation (CV) with a limit of 5000 iterations. The parameter to be optimised is the minimisation of the \(log\) of CV loss.
# Channel Estimation Based on IOTA Filter in OFDM/OQPSK and OFDM/OQAM Systems
## 3 OFDM/OQPSK System Model  
### OFDM/OQPSK Symbols  
Figure 1 represents the real and imaginary values of OFDM/OQPSK symbols. The green triangle signature represents the OFDM/QPSK symbols in conventional CP-OFDM systems. The classical OFDM/QPSK symbols are real mapped into two parts (i.e., real and imaginary values in the OFDM/OQPSK-IOTA system), which are located in four quadrants. In Figure 1, the circular yellow signature represents the real value of OFDM/OQPSK symbols and the blue signature represents the imaginary value of OFDM/OQPSK symbols. \(\tau_{0}\) and \(v_{0}\) are the time-domain duration and frequency-domain spacing for OFDM/OQPSK symbols, respectively. \(\tau_{0}\) equals to half of the OFDM/QPSK symbol duration \(T_{\text{s}}\), that is, \(\tau_{0}=T_{\text{s}}/2\). The phases and dephases of the real and imaginary values of OFDM/OQPSK symbols are represented as \(\text{j}^{m+n}\) and \(\text{j}^{-(m+n)}\), respectively. If \((m+n)\%4=0\), then \(\text{j}^{m+n}=1\), \(\text{j}^{-(m+n)}=1\); if \((m+n)\%4=1\), then \(\text{j}^{m+n}=\text{j}\), \(\text{j}^{-(m+n)}=-\text{j}\); if \((m+n)\%4=2\), then \(\text{j}^{m+n}=-1\), \(\text{j}^{-(m+n)}=-1\); if \((m+n)\%4=3\), then \(\text{j}^{m+n}=-\text{j}\), \(\text{j}^{-(m+n)}=\text{j}\) where \(\%\) is the modulo operation.
# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
## 3 System Model
### Topology Optimization Using Cross-Layered Analysis  
The number of neurons per layer, denoted by \(N\), is a parameter that affects the communication systems performance (i.e., the MI) and the hardware implementation efficiency (power consumption and latency). Therefore, it is important to select \(N\) in a way to optimize the hardware implementation while not degrading the system-level performance. On the one hand, if we focus only on the system performance and forget about the impact of \(N\) on the hardware side, we are satisfied as long as \(N\) is providing similar bitwise MI as the conventional QAM techniques. On the other hand, if we focus on the hardware implementation, the number of multiply-accumulate (MAC) operations of the ANN grows quadratically with respect to \(N\). Thus, depending on the selected DOP, either the power consumption or the latency will increase.  
Since we are adopting a cross-layer approach for our implementation, we aim to select the best model by decreasing the neurons per layer up to the minimum that does not result in degradation of the MI. Therefore, we design a framework that continuously reduces the number of neurons, retrains the network for multiple SNRs and evaluates the bitwise MI for each trained model. Using this approach, we were able to reduce the number of neurons down to \(N=16\) while keeping the performance stable over the whole SNR range. This way, we can even achieve higher MI compared to 16-QAM for low SNRs while obtaining similar performance for higher SNRs, as show in Figure 3.  
Figure 2: Overview of the AE ANN structure.
# A Radiation-Hardened Low-Power SRAM with Enhanced Write Capability for Space Applications
## 2 Proposed EWS-16T SRAM Cell
### SEU Recovery Analysis
#### 2.2.2 SEU at Node S0  
If node S0, initially storing logic '0', is flipped to '1' due to an SEU, transistors N1 and N4 are turned on, while P2 and P4 are turned off. However, P5 and N6 remain off. Accordingly, the states of nodes Q and S1 do not change, and QB also retains its original value. As a result, P6 and N5 are turned on, and P3 remains off. Thus, node S0 also recovers its original value via the feedback path. As shown in Figure 5b, node S0 also exhibits self-recovery following a transient overshoot caused by the SEU, demonstrating the robustness of the proposed cell against single-node upsets.
## 3 System Architecture and Service Process
### System Services
#### 3.2.5 Average Statistics Service  
The average statistics service is a service to show users the sensing information incoming through environmental and soil sensors as a graphic form by compiling statistics into some constant units in order to understand the environmental properties of the planted crops. Crop production could be increased by better understanding the environmental properties of plantations and selecting crops suitable to the environment through this service.
## 4 Experiments and Results
### Results of the Grapheme and Phoneme Units  
We first compare the performance of the lexicons consisting of 52 graphemes and 40 phonemes. Table 3 shows the grapheme-based and the phoneme-based speech recognition performance of each evaluation dataset. Here, CI-GMM-HMM is an acoustic model (AM) using a context-independent (CI) unit, and CD-GMM-HMM is an acoustic model using CD units obtained by applying an implicit method [4]. CD-LSTM-HMM is an acoustic model using the LSTM classifier instead of GMM in _CD-GMM-HMM_. In experiments with the CI unit, the AM consists of a different number of parameters because each unit has a different number. However, in experiments with the CD unit, the AM has 8,033 equal parameters for each unit.  
In the News, Web, and SPT evaluation data, the phoneme unit performs 11.3%, 8.1%, and 1.9% better than the grapheme unit. Compared to other languages [19], there is little difference between Korean graphemes and phonemes. This is because Korean is a phonogramic language that basically represents a speech signal as a symbol. On the other hand, in the SPT evaluation data using spontaneous speech, the phoneme unit has similar performance to the grapheme unit since the inter-unit distance becomes close and the intra-unit variance increases [7].
# Planetary-Gearbox Fault Classification by Convolutional Neural Network and Recurrence Plot
## Experiments and Discussion  
### Planetary-Gearbox Test Rig  
In order to validate the validity of the proposed approach, an experiment with four conditions under three operating speeds was carried out on a planetary-gear test rig (with one 28-tooth sun gear, three 20-tooth planet gears, a 71-tooth fixed ring gear, and a carrier; the gear modulus was 2.25), shown in Figure 2a, where the output shaft was connected with the sun-gear shaft by a coupling, and the planetary carrier was the input. The kinematical schema of the experiment planetary gearbox can be found in Figure 3. An eddy probe was fixed by a magnetic base to measure the tacho impulse trains of the output shaft, and three accelerometers were respectively mounted on the planetary-gearbox housing, as shown in Figure 2a. The data were implemented by an NI USB 9234 card with a sampling rate of 51.2 kHz.  
In this study, a vibration picked up by accelerometer II close to the ring gear was used. Four conditions of the planetary gearbox, that is, normal; planet gear-root crack; sun gear with a tooth-root crack; and planet carrier with a crack, are shown in Figure 2b-d. The tooth-root-crack length of the faulty planet and sun gear was 4 and 3.7 mm, respectively. The crack size of the carrier was 21 mm length, 5 mm depth, and 0.18 mm width.
## 4 Discussion
How well our predictions of properties across the surface of a sample match actual measurements was not explored. In terms of within-ring and pith-to-bark variation, the predictions were consistent with our knowledge of SG, MOE, and MOR variation in conifers; however, further study is required to explore the accuracy of predictions. An instrument such as SilviScan [75] could be used to scan radial strips at a resolution equivalent to or greater than the pixel size for the NIR-HSI utilized in our study. While the approach we used to develop our calibrations (average the spectra and calibrate against a property measured on whole-piece basis, and then use the model to predict at high spatial resolution) has been utilized in other NIR-HSI studies, it is unknown what impact this approach has on the quality of predictions. As the calibration is based on data (lumber SG, MOE, and MOR) that has a range that is narrower than what it was used to predict (SG, MOE, and MOR variation within rings), then we expect that the model would both overestimate and underestimate the extremes. We made no effort to improve the quality of the transverse surface of the samples prior to scanning, and the success of our models based on a reasonably rough surface indicates that the prediction of stiffness variation over the transverse surface of a log is feasible. [PERSON] et al. [44; 45] have demonstrated the prediction of wood properties over a transverse surface (discs); however, they examined dry disks with surfaces that had been carefully prepared prior to scanning. [PERSON] et al. [48] utilized green logs, and while they did not explore spatial variation (it was not an objective of their study), they had the required data. Potentially, spatial wood property data could be utilized in a mill to give an optimal sawing decision for a log aiming to maximize grade recovery, not just volume recovery. If implemented correctly, the value of such an approach could be considerable. Exploration of options for manipulating information provided by NIR-HSI may also improve our ability to estimate lumber properties. For example, latewood and its proportion is an important determinate of SG, MOE, and MOR. If spectra from latewood regions of rings were weighted greater than earlywood when determining average spectra, it may improve our calibrations. Analysis of ring curvature could yield predictions of the location of the pith, which could allow for the estimation of the age of the material, which in turn would be insightful given the increasing trend of mechanical properties with cambial age. Further improvement in predictions could also be obtained by combining measurements; for example, the HSI information could be combined with the acoustic velocity of the log to refine predictions.
# Remote and Proximal Sensing Techniques for Site-Specific Irrigation Management in the Olive Orchard
## 3 Results
### Irrigation and Cluster Effect on Tree Water Status, Yield and Vegetative Growth
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Cluster**} & \multirow{2}{*}{**Irrigation**} & \multicolumn{3}{c}{**Stem Water Potential (MPa)**} \\ \cline{3-5}  & & **DOY 187** & **DOY 219** & **DOY 259** \\ \hline C1 + C2 & Full & \(-1.43\pm 0.07\) & \(-1.49\pm 0.06\) a & \(-1.57\pm 0.06\) a \\  & Deficit & \(-1.39\pm 0.09\) & \(-2.49\pm 0.13\) b & \(-2.65\pm 0.07\) b \\  & Rainfed & \(-1.46\pm 0.07\) & \(-3.65\pm 0.09\) c & \(-3.89\pm 0.11\) c \\ C1 & Full & \(-1.43\pm 0.08\) & \(-1.50\pm 0.09\) a & \(-1.58\pm 0.09\) a \\  & Deficit & \(-1.43\pm 0.10\) & \(-2.55\pm 0.05\) b & \(-2.64\pm 0.11\) b \\  & Rainfed & \(-1.42\pm 0.08\) & \(-3.60\pm 0.05\) c & -3.88 \(\pm 0.08\) c \\ C2 & Full & \(-1.42\pm 0.08\) & \(-1.48\pm 0.03\) a & \(-1.56\pm 0.03\) a \\  & Deficit & \(-1.38\pm 0.12\) & \(-2.45\pm 0.13\) b & \(-2.63\pm 0.00\) b \\  & Rainfed & \(-1.50\pm 0.05\) & \(-3.70\pm 0.10\) c & \(-3.90\pm 0.15\) c \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Stem water potential measured on olive trees grown in different orchard zones identified as Cluster 1 (C1), and Cluster 2 (C2) and subjected to full irrigation, deficit irrigation and rainfed conditions. Values are means \(\pm\) standard deviation of six trees per irrigation treatment and three trees per each cluster-irrigation regime combination. Different letters indicate significant differences within each cluster after ANOVA (\(p<0.05\)). DOY 187: one day before the beginning of the differentiation of the irrigation treatments. Figure 4: Fruit yield and trunk cross sectional area (TCSA) increment in olive trees subjected to different irrigation regimes (Full irrigation, FI; deficit irrigation, DI; rainfed conditions, RF) and located in different orchard zones (Cluster 1, C1; Cluster 2, C2). Histograms represent the average of 18 (FI), 40 (DI), 15 (RF), 33 (C1), 39 (C2), 7 (FI-C1), 11 (FI-C2), 17 (DI-C1), 23 (DI-C2), 9 (RF-C1), 6 (RF-C2) trees per each irrigation treatment and cluster combination. Different letters indicate significant differences of LSD test after ANOVA (\(p<0.05\)).
# An Overview of Cube-Satellite Propulsion Technologies and Trends
## 11 Miscellaneous Propulsion Technologies and Design Methodologies
### Design Methodologies  
The propulsion systems CGP, LP, SRP, resistojets and a subset of PPTs use nozzles, while the remaining do not. The performance characteristics, namely specific impulse and exit velocity, for these propulsion systems using nozzles are functions of nozzle geometry (expansion ratio). Hence, it is essential to have a nozzle design that optimizes the performance [8]. Computational Fluid Dynamics (CFD) based analyses can be used for the purpose of nozzle design optimization, except for rarefied effects that can not be accurately analyzed, especially near the nozzle exit region. In these special circumstances, Direct Simulation Monte Carlo (DSMC) method may be used; this method was used for micro-propulsion resistojet systems to investigate numerical optimization of nozzle geometries in low Reynolds number micro-nozzles. This procedure was found to be advantageous because it accounts for viscosity and rarefied effects inside the low Reynolds number micro-nozzle [140].  
Thrust degradation due to background effects during laboratory testing is an important factor for characterizing a spacecraft propulsion system on the ground. The background pressure experienced by a propulsion system in the laboratory is orders of magnitude greater than those experienced while in space. Micro-propulsion systems are more susceptible to background effects where small changes in thrust generated can lead to significant errors. The ratio of measured-to-on-orbit thrust can also vary depending on the facility geometry, pumping configuration, thrust stand geometry, experimental setup, characteristics of the thruster and its corresponding exhaust plume. A study has shown that the the error in thrust range is less than 2% as long as the background pressure is less than \(1.33\times 10^{-3}\) Pa (\(10^{-5}\) torr) and thrust measurements are below 500 N [141].
# Prototype of Augmented Reality Technology for Orthodontic Bracket Positioning: An In Vivo Study
## 2 Materials and Methods
### Method for Applying the AR-Assisted Bracket Navigation System
#### 2.3.1 FACC Detection Module  
The FACC detection module performed morphological analysis to roughly segment the contour of the entire tooth. To accurately extract the detailed tooth contour, the GrabCut algorithm was adopted to facilitate interactive foreground extraction and fix the contour of each tooth. Specifically, the algorithm, targeting regions of interest in each tooth extracted from the morphological analysis, implemented foreground image segmentation using source and sink nodes, foreground-background separation, graph cuts, and energy function. During image segmentation, pixels were considered nodes, and these nodes comprised two types, namely source nodes and sink nodes. Source nodes represented the foreground image of regions of interest, whereas sink nodes indicated unwanted background image regions. This technique can create a great pixel difference on the edge of the target image, allowing the system to distinguish regional foregrounds from backgrounds, isolate the tooth contour, and achieve tooth segmentation [14]. After extracting the contour of each tooth using real-time imaging, we extracted the bounding box of the closed contour formulated by each tooth to detect the anatomical FACC. For example, when segmenting the maxillary teeth, the tooth zenith can be identified along the upper side of the bounding box, whereas the bottom side denotes the incisor line. By connecting the zenith and incisor line, the FACC can be detected. The FACC, as defined by [PERSON] (1979), serves as a reference line in straight-wire appliance design. We adopted the FACC because it is highly rediscoverable and reliable as a reference line, susceptible to the surroundings, and unlikely to change in the entire lifetime; moreover, theaxis does not require X-ray imaging in clinical practice, and can be seen by the naked eye, thereby allowing direct observation during treatments [15].