esa-sceva/satcom-chunk-collection huggingface dataset:
# The Design of a Long-Distance Signal Transmission System in a Bistatic RCS Testing System
## 2 System Principle and Simulation
### System Simulation
For convenience in calculations, it was set to \(\mu=0.5\). A comparison between the system's output signal \(S_{r}^{\prime}(t)\) and input signal \(S_{r}(t)\) is shown in Figure 9:  
Figure 8: Comparison of the modulating and modulated signals with m = 1. Figure 7: Comparison of the modulating signal and the modulated signal. In the figure, it can be observed that the demodulated signal has an added DC component and a higher amplitude than the modulating signal, while the frequency remains almost identical to the input signal. Due to the group delay \(\tau_{0}\) being only 2420 ns, the phase difference \(\omega_{m}\cdot\tau_{0}\) in \(\Delta\varphi\) caused by the group delay is negligible, resulting in a phase difference of \(\pi\) between the two signals. Performing a discrete Fourier transform with 18,850 points on the demodulated signal yields the results shown in Figure 10.  
Upon comparing the DFT results of the demodulated and modulating signals, it becomes evident that the proportion of the second harmonic component in the demodulated signal \(S_{r}{}^{\prime}(t)\) is much smaller than that of the first harmonic and the DC component. This also confirms that neglecting the higher-order components of the demodulated signal, as mentioned in Equation (14), is a reasonable approximation. The simulation results corroborate the theoretical derivations, with both confirming that the system is fundamentally feasible. These findings provided a solid foundation for the subsequent practical measurements. Moving forward, real-world testing will further validate the system's feasibility and help identify any potential issues that may not have been captured in the theoretical analysis.
###### Abstract  
The availability of in situ snow water equivalent (SWE), snowmelt and run-off measurements is still very limited especially in remote areas as the density of operational stations and field observations is often scarce and usually costly, labour-intense and/or risky. With remote sensing products, spatially distributed information on snow is potentially available, but often lacks the required spatial or temporal requirements for hydrological applications. For the assurance of a high spatial and temporal resolution, however, it is often necessary to combine several methods like Earth Observation (EO), modelling and in situ approaches. Such a combination was targeted within the business applications demonstration project SnowSense (2015-2018), co-funded by the European Space Agency (ESA), where we designed, developed and demonstrated an operational snow hydrological service. During the run-time of the project, the entire service was demonstrated for the island of Newfoundland, Canada. The SnowSense service, developed during the demonstration project, is based on three pillars, including (i) newly developed in situ snow monitoring stations based on signals of the Global Navigation Satellite System (GNSS); (ii) EO snow cover products on the snow cover extent and on information whether the snow is dry or wet; and (iii) an integrated physically based hydrological model. The key element of the service is the novel GNSS based in situ sensor, using two static low-cost antennas with one being mounted on the ground and the other one above the snow cover. This sensor setup enables retrieving the snow parameters SWE and liquid water content (LWC) in the snowpack in parallel, using GNSS carrier phase measurements and signal strength information. With the combined approach of the SnowSense service, it is possible to provide spatially distributed SWE to assess run-off and to provide relevant information for hydropower plant management in a high spatial and temporal resolution. This is particularly needed for so far non, or only sparsely equipped catchments in remote areas. We present the results and validation of (i) the GNSS in situ sensor setup for SWE and LWC measurements at the well-equipped study site Foret Montmorely near Quebec, Canada and (ii) the entire combined in situ, EO and modelling SnowSense service resulting in assimilated SWE maps and run-off information for two different large catchments in Newfoundland, Canada.  
s 2019 9 44 10.3390/geosciences9010044 www.mdpi.com/journal/geosciences
# Multi-Objective Ship Route Optimisation Using Estimation of Distribution Algorithm
## 4 Proposed Solution
### The Solution (Design) Space Representation  
Discretisation of the original problem domain may be seen as a two-phase process [50]. In the first phase, we construct a multi-stage graph (\(G_{2}\)) that creates the \"_route space_\" (i.e., \(c_{k}\)-space/spatial-dimension of \((\mathbf{u}_{k})\), see Remark 2). In most instances, this graph is regular with equidistant nodes grouped in rows and columns: \(n_{c}\) columns and \((n_{r}-2)\) regular rows, plus two special (single-node) rows--one with point \(A\) and the other with point \(B\) (see Figure 2).  
In the second phase, each node of \(G_{2}\) (apart from the one corresponding to point \(A\)) is replicated \(n_{v}\) times to add the \(n_{v}\)-dimension to the solution space (of control inputs), which can be seen as an addition of the \"third dimension\" to \(G_{2}\). This new \"_3D-graph_\" (\(G_{3}\)) has the following traits [50]:  
* Has \(n_{v}[n_{c}(n_{r}-2)+1]+1\) nodes;
* Has \(n_{c}n_{v}[(n_{r}-3)n_{c}n_{v}+n_{v}+1]\) edges;
* Represents \(n_{v}(n_{c}n_{v})^{n_{r}-2}\) different control input sequences. (trajectories).  
**Remark 3**: _The piecewise-linear approximation of the ship route simplifies the problem significantly [50]. Instead of one complex, continuous two-dimensional problem, we have a series of simple one-dimensional subproblems--each corresponding to a single route segment only._
# Grammar Correction for Multiple Errors in Chinese Based on Prompt Templates
## 3 Methodology
### Error-Detection Layer
#### 3.1.3 CRF Layer  
The Conditional Random Field (CRF) network is a discriminative model used for sequence labeling tasks, primarily aimed at assigning labels to sequential data. In this study, because BiLSTM does not consider label information during the prediction process, the CRF network is employed to compensate for this deficiency, serving as a constraint to improve the accuracy of predicted labels. The objective of the CRF network is to learn a conditional probability \(P(Y|X)\), given the input \(X\) = (\(x1\), \(x2\),..., \(xn\)), which represents the probability of an output label sequence \(Y\) = (\(y1\), \(y2\),..., \(yn\)).  
In the CRF network, given the input \(X\), the label sequence scores for every possible output \(Y\) are typically computed based on two components: transition scores and emission scores. Transition scores consider the transitions between adjacent labels in the label sequence, while emission scores consider the matching degree between labels and corresponding input features at each position. The computation formula is shown in Equation (8):  
\[Score(X,\ Y)=\sum_{i=1}^{n}L_{i,y_{i}}+\sum_{i=1}^{n}k_{y_{i-1},y_{i}} \tag{8}\]  
where \(L_{i,y_{i}}\) represents the emission score, indicating the matching degree between the given input at position \(i\) and the label \(y_{i}\). \(k_{y_{i-1},y_{i}}\), which represents the transition score from label \(y_{i-1}\) to label \(y_{i}\).  
Next, the scores of the label sequences are exponentiated, and the exponential sum of all possible label sequences is normalized to obtain the probabilities of the label sequences. The computation formula is shown in Equation (9):  
\[p(Y|X)=\frac{e^{Score(X,Y)}}{\sum\limits_{\widetilde{Y}\in Y_{X}}e^{Score(X, \widetilde{Y})}} \tag{9}\]  
Finally, the Viterbi algorithm is used to obtain the label sequence \(\overline{Y}\) with the highest probability. The computation formula is shown in Equation (10):  
\[\overline{Y}=\text{argmax}Score(X,\widetilde{Y}) \tag{10}\]In addition, when using the CRF network, it is necessary to add label constraints to avoid generating illegal sequences. In this study, the BIOES annotation scheme is employed to label four types of errors: redundancy (R), missing (M), disorder (W), and wrong word (S). Specifically, the errors are represented by the following prefixes: the prefix \"B-\" indicates the beginning of an error, \"I-\" is for the middle part, \"E-\" represents the end, \"S-\" is used for a single character error, and \"O-\" is for other situations. For example, the labels for redundancy (R) errors can include \"B-R\", \"I-R\", \"E-R\", and so on.
# TTH-Net: Two-Stage Transformer-CNN Hybrid Network for Leaf Vein Segmentation
## 3 The Proposed Method
### Loss Function  
The total loss of the TTH-Net includes two parts, one part being the features from multiple stage outputs in the ViT of the first stage. Via the MLP module and upsampling  
Figure 2: Illustration of cross-stage semantic enhancement module.  
operations, these auxiliary losses guide the Transformer in recognizing coarse-grained features. The other part of the loss is the difference between the output of the second stage and the labels. By reweighting the two parts of the losses, TTH-Net can accurately segment leaf veins. The loss is calculated as follows:  
\[Loss(x,y)=\lambda\cdot DL(x,y)+(1-\lambda)\cdot CE(x,y) \tag{6}\]  
where CE \((\cdot)\) is the cross-entropy loss function [34], DL\((\cdot)\) represents the dice loss function [35], and the hyperparameter \(\lambda\) controls the fusion coefficient of the loss functions.
## 3 Genetic Approaches
### Crispr  
The advent of Crispr/cas9 technology has brought hope to the treatment of FSHD, which can accurately edit the error genes. [PERSON] et al. [67] first reported the improvement of pathogenic gene expression in FSHD using CRISPR (Table 1). The study overcame the technical barriers of infecting primary myoblasts using a centrifugation-based continuous infection technique, successfully delivering CRISPR/Cas9 to target the D4Z4 repeat sequence in FSHD, and altered chromatin and DUX4-fl expression, demonstrating the efficacy of CRISPR technology in correcting the epigenetic dysregulation in FSHD. They used the same method to suppress DUX4 activators [68] BAZ1A, BRD2, KDM4C, and SMARCA5, confirming that targeting DUX4 activators reduced DUX4 expression in FSHD cardiomyocytes (Table 1). They also treated FLExDUX4 mice using AAV delivery of CRISPR/Cas9 combined with local muscle injection [73]. The results showed a reduction in DUX4 mRNA and decreased expression of DUX4 target genes following treatment. It is important to note that this method does not directly inhibit the DUX4 gene, but rather improves downstream expression by suppressing the epigenetic factors of DUX4. Crispr has been used to correct the FSHD2-related SMCHD1 mutation [69] (Table 1; Figure 3), which is a missense variant of intron 34, and an extra-frame 53-bp pseudoexon has been introduced into the final transcript. Crispr/Cas9 with guide RNA (gRNA) targeting the flanking intron sequence of the pseudoexon restored the reading frame of SMCHD1 and increased the expression of wild-type SMCHD1 in myotubes of primary and immortalized patients, resulting in the decrease of DUX4 mRNA expression. Crispr can also be used for targeted regulation of gene expression. In a myotube study of myoblast differentiation in primary patients treated, 45% DUX4 knock-down was achieved [67], and an increasing trend of chromatin inhibition of DUX4 at the contraction site was observed. Crispr/Cas9 was also used in genome-wide knockout screening to identify genes that can inhibit DUX4 cytotoxicity [74]. A recent study [70] developed AAV6-CRISPR-Cas13 to silence DUX4 mRNA, showing that DUX4 mRNA was reduced by over 50% in FSHD mice after delivery and improved histopathological outcomes. The study also considered the host immune response, finding a significant immune response in wild-type mice, including immune cell infiltration and proinflammatory factors. In summary, previous studies have clearly demonstrated that CRISPR can target and reduce DUX4 expression in FSHD muscle, but it is essential to minimize the immune response.  
Some new therapies have been developed vigorously in recent years, but only in the clinical trial stage, so there is still a long way to go before clinical application. We analyze the advantages and disadvantages of various treatment schemes in clinical application and discuss the problems that must be solved when they are applied in clinic.
# An Improved Particle-Swarm-Optimization Algorithm for a Prediction Model of Steel Slab Temperature
## 2 The Prediction Model of the Slab Temperature
### The Prediction Model and Optimization Problem  
The most important quality criterion of reheating furnace is the average outlet temperature of the steel slab. There are many factors affecting the slab's discharging temperature. By analyzing the field data and some related research ([22,23]), we can confirm some key factors: the initial temperature of the billet, \(T_{0}\); the furnace nozzle temperature, \(T_{j}\); the mean temperature of each zone, \(T_{p}\); the reheating time of the billet in the furnace, \(\theta\); the material of the slab, \(\alpha\); the thickness of the slab, \(d\). Thus, the inputs of the prediction model are \(X=[X_{1},X_{2},X_{3}]^{T}\), which consist of the slab's physical parameters, \(X_{1}=[\alpha,d,\theta,T_{0}]^{T}\); the vector of the furnace nozzle temperatures, \(X_{2}=[TN_{1},TN_{2},\cdots,TN_{m}]^{T}\); and the vector of furnace zone temperatures, \(X_{3}=[TP_{1},TP_{2},\cdots,TP_{6}]^{T}\). Notice, \(m\) is the total number of burners in the reheating furnace. The outputs are the predicted temperature \(Y=TE\), which is the vector of the predicted temperatures of the slabs when discharged out from the furnace. Finally, the formula of predicting model is constructed as:  
\[Y=f(X,W)=w_{0}d+w_{1}\alpha+w_{2}T_{0}^{1}+w_{3}T_{0}^{2}+\sum_{i=1}^{m}TN_{i}w _{(i+3)}TN_{i}+\sum_{j=1}^{6}w_{(j+m+3)}cos\big{(}TP_{j}\theta\big{)}+\sum_{j= 1}^{6}w_{(j+m+9)}sin\big{(}TP_{j}\theta\big{)} \tag{1}\]  
where \(W=[w_{0},w_{1},\cdots,w_{m+15}]\) represents the vector of unknown weight parameters, which will be determined by the proposed XPSO algorithm. The formula \(f\) is composed of a polynomial and a Fourier function, which was designed by the author based on experience. To make the analytical response \(Y\) equal to the measured value \(Y^{*}\), an optimization problem should be established with the objective of minimizing the error between the theoretically calculated values and the measured data. To alleviate overfitting and improve generalization performance, the strategy of regularization is employed for the optimization problem. Finally, the formula of the optimization problem is shown as follows.  
\[\begin{array}{rl}Minimize&J=\|Y-Y^{*}\|^{2}+\lambda_{1}\|W\|+\lambda_{2}\|W \|^{2}\\ &st.&Y=f(X,W)\end{array} \tag{2}\]  
where \(Y^{*}\) represents the measured temperature of the slab, and \(\lambda_{1},\lambda_{2}\) are the regularization parameters.  
Figure 1: Schematic diagram of a heating furnace’s structure.
###### Abstract  
This study aimed to evaluate the biomechanics of single-leg drop landing in individuals with functional ankle instability (FAI) during cognitive tasks, contrasting these findings with those of healthy controls to provide insights for evidence-based rehabilitation strategies. Fifteen FAI participants, identified using clinical tools, were age- and activity-matched with controls. They performed drop landings with and without a cognitive task, and the data were analyzed using a 2 \(\times\) 2 mixed ANOVA. At the initial ground contact (IC), the FAI group's affected side showed a significantly smaller plantarflexion angle than the control group (\(p\) = 0.008). With cognitive tasks, this angle increased in the FAI group (\(p\) = 0.005). The FAI group also had larger knee flexion at contact (\(p\) = 0.002) and greater knee valgus at peak vertical ground reaction force (vGRF) (\(p\) = 0.027). They exhibited a higher peak vGRF, shorter time to peak vGRF (T-vGRF), and higher loading rate (LR) (all \(p\) < 0.05). No differences were found in other variables (\(p\) > 0.05). This study shows that FAI individuals make specific biomechanical adjustments under cognitive tasks, notably increased plantarflexion at IC, suggesting reactive compensations. Despite similar motor control to controls, this may reflect long-term adaptations rather than equal proficiency.  
functional ankle instability; cognitive load; drop landings; sports injuries; biomechanics 2024 14 10297 10.3390/appl42210297
# LaRoAwan CubeSat with an Adaptive Data Rate: An Experimental Analysis of Path Loss Link Margin
## 4 Methodology  
In this study, two approaches have been established to investigate the end nodes LoRa signal performance. The first method is the theoretical analysis of the orbit geometry for LEO satellites with respect to the ground observer. The second method is an experiment simulating the path loss using the 0\(-\)150 dB attenuation levels with a step of 30 dB. The purpose is to prove that the LoRaWAN with ADR capability enables direct connectivity from the end nodes to the satellite.
## 1 Introduction
An open-delta-shaped negative permittivity (\(\epsilon\)-negative) MM based on a square ring resonator [21] indicated three resonance peaks at 4.32 GHz, 7.55 GHz and 9.76 GHz that covered the C- and X-bands. Meanwhile, in 2015, [PERSON] et al. offered a tunable metamaterial comprising an SRR [22] used for sensing, filtering and cloaking. A triple-band metamaterial with a pie-shaped resonator centred with SSR (8 \(\times\) 8 mm\({}^{2}\) in dimension) covered the S-, C- and X-bands [23]. In a recent study, a polarization-dependent, circular-shaped, triple-band metamaterial was designed using a Rogers RT6002 substrate base MM (8 \(\times\) 8 mm\({}^{2}\) in dimension) resonated at 3.924 GHz, 7.254 GHz and 14.832 GHz [24]. To enhance the antenna gain for satellite communication, a Minkowski-fractal-shaped metamaterial was discussed in another study [25]. The triple-band metamaterial designed with two circular rings and one rectangular SRR exhibited a two-resonance frequency of 2.56 GHz and 5.6 GHz, which was used for Wi-MAX and WLAN networking [26]. Additionally, [PERSON] et al. [27] in 2020 proposed a penta band based on complementary OSRR, which combined five resonance peaks that were suitable for Wi-MAX, WLAN, Satellite band and X-band applications. Meanwhile, [PERSON] et al. [28] proposed a new two-element, ultra-wideband (UWB) MIMO antenna to yield the miniaturisation and performance using two antennas and ensure proper operation of the multiple-input-multiple-output system. This study offered a compact and innovative metamaterial muster whose unit cell comprised two SRRs with double inverse-epsilon-formed metal strips. This design established three resonance frequencies covering the S-, C- and X-bands (2-4 GHz, 4-8 GHz and 8-12 GHz) in the microwave spectra, in addition to an indication of the epsilon-negativeand negative refractive indices (n) at the same frequencies. Moreover, for the consistency of its performance, the numerical results were verified by using different validation processes. The results attained from the HFSS and from the equivalent circuits designed by ADS were used to compare with the CST simulated results. Different important parameters, including permittivity (\(\varepsilon\)), permeability (\(\mu\)) and refractive index (n), along with S\({}_{11}\) and S\({}_{21}\), were analysed to characterise the metamaterial. To compare the performance, the EMR-based table was discussed in the results segment. The doubleinverse-epsilon-shaped SRR-based metamaterial designed in this study was very much compact because a << \(\lambda\)/4 [for 2.38 GHz \(\lambda\)/4 = 31.51 mm, whereas size a = 10 mm]. This design produces three resonance peaks at 2.38 GHz, 4.55 GHz and 9.42 GHz with \(-\)29.40 dB, \(-\)39.65 dB and \(-\)43.35 dB, respectively. The EMR indicated the goodness of the unit cell material at 12.61.
# Sparse Representation and SVM Diagnosis Method for Inter-Turn Short-Circuit Fault in PMSM
## 2 Sparse Representation  
The principle of sparse representation theory is to represent the original signal as a sparse linear combination of the dictionary atoms in an overcomplete dictionary matrix [11]. Therefore, it can extract the basic information from the signal, and avoid the interference of small noise in the signal, such as additional vibration caused by rotor imbalance or distortion of the measurement process. The main idea of sparse representation theory is to concentrate the energy of feature information into a few elements [17]. Therefore, in this paper, sparse representation is used to extract fault features. For an input signal \(x=[x_{1},x_{1},\cdots,x_{n}]^{T}\) of length \(n\):  
\[x=Da \tag{1}\]  
\[x_{i}=\sum_{i=0}^{m}\alpha_{i}d_{i} \tag{2}\]  
where \(D=[d_{1},d_{2},\cdots,d_{m}]\) is the dictionary matrix, the column vector \(d_{i}\) is a dictionary atom and \(||d_{i}=1||\). The dictionary is an overcomplete dictionary when \(m>n\). \(\alpha=[\alpha_{1},\alpha_{2},\cdots,\alpha_{m}]^{T}\) is the solution to the sparse representation of the original signal, and \(\alpha_{i}\) is the sparse representation coefficient. Use the \(l_{0}\) norm to indicate the sparse degree of the sparse coefficients:\[||\alpha||_{0}=\underset{p\to 0}{lim}\sum_{i=1}^{n}|\alpha_{i}|^{p} \tag{3}\]  
Obviously, the \(l_{0}\) norm is actually the number of non-zero elements in \(\alpha\).  
The commonly used optimization algorithm based on \(l_{0}\) norm is matching pursuit (MP) [18]. Firstly, it chooses an atom that matches the input signal best from the dictionary matrix and calculates the residual. Then it continues to select the atom that matches the residual signal best, and iterates over and over like this until it meets the requirement. However, in MP, the residual signal is only orthogonal to the dictionary atom selected in this iteration, and does not necessarily have orthogonality with all dictionary atoms, which will increase the number of iterations, and the result may not be the optimal solution. In order to solve this problem, the orthogonal matching pursuit (OMP) algorithm was proposed. The workflow of OMP is shown in Table 1:  
As for the choice of dictionary matrix, discrete cosine transform (DCT) is suitable for stationary signals and has higher computational efficiency than Fourier transform, so it is used to construct a dictionary matrix. The effective information of the result by DCT is mostly concentrated in the low frequency, which can remove the redundant information of the signal while retaining the basic information.  
In this paper, the sparse coefficients of the current signal and the vibration signal are obtained by sparse representation respectively, and the first and second largest value in the respective sparse coefficients \(\alpha\) are selected to be constructed into four-dimensional feature vectors, which will be input into the SVM.
## 7 Practical Implications and Future Development of the Method  
In exploring NFB, we discovered several knowledge gaps crucial for the future development of this therapy. An accurate and precise understanding of its mechanisms of action remains limited. Questions remain regarding optimal protocols, including target frequencies, therapy duration, feedback form and the most effective neurofeedback methods, complicating cross-study comparisons. The need for an individualized approach is emphasized, though clear guidelines for personalized protocols are still lacking. Long-term effectiveness and combining neurofeedback with other therapeutic approaches require further exploration. Machine learning algorithms could enhance personalized EEG NFB protocols by identifying optimal frequencies for specific symptoms. Integrating virtual reality (VR) or augmented reality (AR) technologies could offer immersive, real-life training scenarios, making neurofeedback therapies more accessible and engaging. For successful clinical integration, proper training for healthcare professionals will be essential.