esa-sceva/satcom-chunk-collection huggingface dataset:
# DNN-Based FES Control for Gait Rehabilitation of Hemiplegic Patients
## 3 Experiments
### FES Control System  
A dual-channel FES unit (Roscoe Medical, USA) was used to stimulate the soleus and TA (Figure 7a). A microcontroller unit (Arduino, Italy) was used to control the FES device using Bluetooth wireless communication. The FES signal with a frequency of 40 Hz and pulse width of 200 us was applied to human subjects. The maximum amplitudes of the FES were determined by the main threshold of each subject.  
Modularized wireless EMG sensors (Delsys Inc., Boston, MA, USA) were used to measure muscle activity in the soleus and TA of the subjects. The bipolar surface electrodes were positioned on the skin surface of the bellies of the muscles along with the muscle fiber directions. The skin surface, where the electrodes were placed, was shaved and scrubbed. The EMG sensors were then fastened using elastic bandages to avoid noises caused by electrode movements.  
A pair of customized shoes with four FSR sensors (Ohmite Manufacturing Co., Rolling Meadows, IL, USA) was used to detect foot-ground contact at the heel and toe in contact. The FSR sensors were attached to the soles of the heel and the first metatarsal head of the shoes. To measure the joint angles at the ankles, goniometers were taped to the shoes. The  
Figure 6: FSM control algorithm of the gait rehabilitation system. (\(t_{L}\): heel contact time of left foot, \(t_{R}\): heel contact time of right foot, \(TDH\): time difference of heel contact, \(\Delta_{t}\): elapsed time of the system, \(f(t)\): envelope function of the soleus, \(g(t)\): envelope function of the TA).  
signals from the sensors were transmitted to the microcontroller unit using XBee modules (Digi International, Minnetonka, MN, USA) attached to the lateral sides of the shoes. The total weight of the shoe and the wireless communication module was about 120 g, which is light enough to barely affect normal walking.  
The EMG and goniometer data were sampled using a data acquisition device (National Instruments, Austin, TX, USA) at a frequency of 1 kHz, while the FSR data were sampled at 250 Hz.
# 2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation
## 1 Introduction
Furthermore, [PERSON] used a convolution neural network (CNN) to perform a 3D model for 2D X-ray image registration to estimate the position relationship between surgical instruments and human tissues in real time. However, using deep learning to achieve registration requires many pairs of medical images for training, and the clinical data do not have such an amount of data [35]. This paper mainly studies the 2D/3D medical image registration method based on iterative regression, mostly the rigid registration algorithm in 2D/3D medical image registration. Many scholars have researched and improved the normalized cross-correlation (NCC) method. [PERSON] et al., proposed an improved normalized cross-correlation algorithm for SAR image registration [36]. [PERSON] et al., proposed a robust stereo matching method with adaptive normalized cross-correlation [37]. In this study, on the basis of the original normalized cross-correlation, the correlation coefficient of the Laplacian operator is added to the traditional normalized cross-correlation algorithm. Cosine similarity of linear combination Laplacian of Gaussian operators is proposed. The Gaussian Laplacian operator increases the edge information of the image and the internal details such as isolated points and lines. Based on the improvement of the Sobel operator, the angle between the gradient vectors is introduced, and the sum of the weight function of the angle between the gradient vectors and the normalized cross-correlation are multiplied to increase the sensitivity of the rotation transformation, thereby improving the accuracy and increasing the convergence area. Aiming at the computational efficiency problem caused by the normalized cross-correlation algorithm, a multiresolution algorithm is adopted. In this method, image registration can be achieved by using a single image through projection and back-projection. Compared with traditional 3D reconstruction methods, we use fewer CT images, save time and cost, and allow patients to receive lower radiation doses.
# Quantification and Analysis of Carrier-to-Interference Ratio in High-Throughput Satellite Systems
## 4 Influencing Factors and Selection Strategies of the CIR  
### Influencing Factors  
According to the proposed quantification method, it can be summarized that the influencing factors of the CIR include but are not limited to the following aspects:  
1. System coverage: such as the service area, beam number, overlapping level of these beam areas, etc.  
It is evident that the system coverage can affect the distance between the interfering beams and the target beam, hence yielding a quite different CIR. We can easily judge that the one with a larger distance corresponds to better performance in the CIR.
2. Frequency plan.  
The frequency plan can affect the number of interfering beams, the distance between the interfering beams and the target beam, and the overlapping frequency band between them. In Section 5, we can further conclude the significant effect of the frequency plan on the system CIR through simulation.
3. Antenna radiation pattern.  
The antenna radiation pattern reflects different gain functions in Equations (15) and (17), thus having a significant effect on the system CIR. It is worth noting that the gain function can be obtained through numeric processing of the antenna radiation pattern. Figure 8 shows an example of the antenna radiation pattern.  
1. User terminal locations.  
The user terminal locations can affect the deviation angle in Equations (15) and (17). In detail, as mentioned before, the CIR of the uplink is not related to the position of the target user but rather only to the positions of interfering users. The CIR of the downlink for the target user is not related to the positions of the interfering users within their respective beams but rather only to the position of the target user.
2. Antenna imperfection, which influences the cross-polarization interference.
3. Nonlinearity of amplifiers, which generates nonlinear interference. For instance, spectrum regeneration causes interference among adjacent beams, etc.
## Results
### Classification of Oils by Unsupervised PCA  
From the whole spectra (3600-700 cm\({}^{-1}\)) and for the fingerprint region (1800-700 cm\({}^{-1}\)), the PCA scores for all oil classifications are shown in Figure 3. Using Unscrambler X10.4 software, the first few PCs described most of the observed variance, showing distinct segregation and clustering between the eight groups of oil. Firstly, for the fingerprint region, an explained variance of 76% (PC1) and 20% (PC2) covered the total variance of 96% (Figure 3). For the whole domain (3600-700 cm\({}^{-1}\)) the first 2 PCs had an explained variance of 71% (PC1) and 21% (PC2) covering a total variance of 92% and no significant overlapping of samples (data not shown)  
Figure 3: PCA scores for the oils’ classification using Unscrambler software. Classification based on the peak region 1800–700 cm\({}^{-1}\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Wavenumber**} & \multirow{2}{*}{**Region no.**} & **Mean Peak** & \multirow{2}{*}{**RMS**} & **ANOVA** & \multirow{2}{*}{**Log 10(\(p\))**} & **FDR** & **Random Forest** \\  & & & **intensity** & & \(p\)**-Value** & & **MDA** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 3: The main FT-MIR absorption wavenumbers and regions identified in ATR-FTIR spectra: mean peak intensity values, RMS, ANOVA \(p\)-value, log 10(\(p\)), FDR (false discovery rate) and prediction values according to Random forest mean discriminatory accuracy (MDA) values of all samples.  
Generally, a good discrimination between oil groups was observed and were very similar in both plots. Meanwhile it was possible to identify the individual oils which showed different locations in the plot, against the authentic, genuine samples. Specifically, the position of the SBO2 sample comparing with the authentic sample SBO1, WO1 location comparative to the WO4, LSO1 comparative to LSO3, HO2 comparative to HO4, PO1 comparative to PO4. Meanwhile, for the olive oils, two groups were separated: EVOO1 and 3, the extra virgin ones versus EVOO2 and 4, the virgin labeled oils (in this case no authentic oils could be provided). Only for SFO and SO oils was the grouping compact and close to the authentic samples (SF4 and SO1, respectively).
## 3 Results and Discussion  
### Hyperspectral Imaging  
#### 3.1.1 Spectral Data Interpretation  
Original spectra were collected from the selected ROI of each sample from 397 nm to 1004 nm. Therefore, 30 spectra from each group were obtained and plotted for the individual classes shown in Figure 3a-c. The spectral pattern was similar for all three classes. Figure 3d shows the mean spectra for each class, where AK exhibited the highest reflectance in comparison to the other groups from 680 nm to 1000 nm. However, USDA prime had a higher reflectance than USDA choice in the same region. The spectral reflectance is highly correlated with the fat and lean content of the beef. Generally, fat reflects the light more than lean content because of its color and chemical content [3].
# The Impact of Optogenetics on Regenerative Medicine
## 4 Conclusions and Future Insights  
Optogenetics has been used in several fields of medicine, and one of the most promising is undoubtedly regenerative medicine. [PERSON] et al. [66] have performed experiments using the optogenetic technique to selectively excite cells. Precisely, induced pluripotent stem cell-derived neural progenitor cells (iPS-NPCs) of mice models were combined with specific proteins and activated by photo-stimulation: in vitro stimulation, increased post-synaptic density, and promoted neurite growth; in vivo stimulation, activated cells residing into the ischemic cortex of the mouse, creating novel pathways with a better functional recovery. In conclusion, non-invasive optogenetic treatments may provide a possible new regenerative cell therapy [67].  
Optogenetics has been used to achieve insights into tissue regeneration and organ development: this novel technique also offers great opportunities for developing new tools to build artificial organs, via understanding and mimicking physiological processes. Combining optogenetics and existing technologies (e.g., 3D biological printing, functional materials, and gene-editing techniques) holds great potential for regenerative medicine and can provide substantial appealing for researchers across many fields. Future challenges will address the potential uses of this technique on stem cells [66; 67; 68; 69] combined with innovative biomaterials or bioactive matrices [70; 71]: the next breakthrough will be related to use of optogenetics in the treatment of the most severe clinical conditions, such as syndromic conditions and cancer [72; 73; 74; 75; 76; 77; 78]. Moreover, the use of optogenetics will improve \"smart\" diagnostic applications, based on the current knowledge on wearable devices, smart materials, and tissue engineering [79; 80]: the next research will be used to develop new tools for forthcoming applications combining genetics and molecular biology. In conclusion, optogenetics is a promising technique that will provide several opportunities in several biomedical fields.  
Data curation, M.T., F.G., and G.S.; writing--original draft preparation, M.T., G.S., M.S., C.R., and L.F. all authors contributed to the conceptualization and the methodology of this article. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Vineyard Description and Berry Sampling  
This study was conducted with 4-year-old own-rooted red wine grapevines 'Cabernet Gernischt' (_Vitis vinifera_ L.), which were harvested in the 2016 vintage from Yuquanying (YQY) Farm of Ningxia Autonomous Region, China (38\({}^{\circ}\)14\({}^{\prime}\)19\({}^{\prime\prime}\) N, 106\({}^{\circ}\)2\({}^{\prime}\)0\({}^{\prime\prime}\) E). Vines were oriented in the east-west direction with drip irrigation (2.8 L/h, irrigation time determined by the water consumption). The vines were spaced at 3.0 \(\times\) 1.0 m, the soil was Aeolian sandy soil, the fertility was medium, and pH was 8.2 (Basic physical and chemical properties of Aeolian soil were presented in Table S1).  
This field experiment was designed with a single-factor randomized complete block with three replicates of 6 rows and 100 vines planted in each row (the middle 4 rows were sampling areas, and the two sides were buffers). Four different training systems adapted to the constraints of Ningxia were compared: the Ningxia traditional vertical trunk was used as control (CK); the gobelet (T1); single guyot (T2); slant trunk with vertical shoot positioning (STVSP) (T3), the detailed technical operation was presented in Figure 1 and Table S2. Pest management, irrigation, and fertilization were conducted according to the standard agronomic practices of the region.  
Figure 1: The Ningxia traditional vertical trunk used as control (CK); the gobelet (T1); single guyot (T2); slant trunk with vertical shoot positioning (STVSP) (T3). (**a**,**b**) indicated the positive and side-design sketch, (**c**) indicated the phenotype in winter.  
For each training system, 300 berries as 3 biological replicates were randomly collected from more than 60 vines on both sides of the canopy from pre-veraison to maturity (50, 65, 70, 90, and 120 DAA), frozen in liquid nitrogen immediately, and stored at \(-80\)\({}^{\circ}\)C until analyses.
# A Scheduling Scheme for Improving the Performance and Security of MU-MIMO Systems
## 4 Scheduling Schemes
### Suboptimal Schemes from Previous Works
#### 4.1.1 Frobenius Norm-Based Scheduling Algorithm  
This algorithm was initially proposed in [20] and is analyzed in detail in [47]. Based on the latest work, in which it is assumed that each user terminal has \(A_{u}\) antennas, and considering that the BS has \(A\) receiving antennas, the algorithm first selects the user with the maximum channel energy, that is,  
\[u_{0}=\operatorname*{arg\,max}_{k}\lVert\mathbf{H}_{k}\rVert_{F}^{2}, \tag{13}\]  
for \(k\in\{1,2,\ldots,U_{t}\}\), where \(U_{t}\) is the total number of users competing to transmit, such that \(U_{t}\geq U\) and \(\mathbf{H}_{k}\) is an \(A\times A_{u}\) matrix containing the channel gains of the \(k\)-th user (To keep concordance with the proposed system model, \(A_{u}=1\) is employed). Then, the algorithmselects the next user that provides the maximum sum of the equivalent channel energy together with the selected users. This procedure can be represented as [47]  
\[u_{i}=\operatorname*{arg\,max}_{k\in\Psi}\sum_{\ell\in\{k\}\cup Y}\Bigl{\|} \mathbf{H}_{\ell}^{\text{eq}}\Bigr{\|}_{F}^{2}, \tag{14}\]  
where Y and \(\Psi\) represent the selected and non-selected user groups, respectively. Further, \(\mathbf{H}_{\ell}^{\text{eq}}\) is an equivalent channel matrix generated from the product of the original channel matrix and a precoding matrix [47] (Equation (6)). Finally, \(u_{i}\) denotes the user selected during the \(i\)-th interaction. The algorithm ends when \(U\) users are selected.  
Considering that \(A_{u}=1\), the total number of complex operations (i.e., complex additions and complex multiplications) performed by this algorithm is [20] (Equation (7))  
\[\begin{split}\text{O}_{\text{f}}&=\sum_{\ell=2}^{ \lceil U/A\rceil}\Bigl{\{}\Bigl{[}8(\ell-1)^{3}+18(\ell-1)^{2}+18(\ell-1) \Bigr{]}A^{2}U\\ &\quad+\bigl{[}2(\ell-1)^{2}+4(\ell-1)\bigr{]}AU\Bigr{\}}(U_{t}- \ell+1)+4U_{t}AU.\end{split} \tag{15}\]
## 3 Laboratory Tests  
### freeze-soak-scour Cycle Test  
In this paper, a laboratory freeze-soak-scour cycle test is designed to simulate the actual condition of asphalt pavement during spring-thawing season. Pavement Material Dynamic Water Scouring Tester (2014, Suzhou Zhixi Environmental Test Equipment Co., Ltd., Suzhou, China) and High-Low Temperature Experimental Box (2015, Changchun, China) are applied in the laboratory freeze-soak-scour cycle test, as shown in Figures 4 and 5.  
The Pavement Material Dynamic Water Scouring Tester is self-developed to simulate the dynamic water scouring phenomenon of saturation pavement, as shown in Figure 5. The main parameters of the test system are the pressure of the water gun, the speed of the disk and scouring time. In order to establish the connection between the laboratory scouring test and the actual condition of pavement,  
Figure 4: High–Low Temperature Experimental Box.  
Figure 5: Pavement material dynamic water scouring tester: (**1**) water gun; (**2**) support plate; (**3**, **10**) bearing; (**4**) pulley; (**5**) disk fixed axis; (**6**) belt; (**7**) motor; (**8**) Marshall samples fixed ring; (**9**) disk; (**11**) temperature sensor; (**12**) U shape heating tube; (**13**) pump intake pipe; (**14**) temperature control dial; (**15**) scouring force control dial; (**16**) disk speed control dial; (**17–19**) pump and its attached equipment.  
a piezoresistive dynamic water pressure sensor is embedded in the Hui-wu expressway (in Songyuan, Jilin, China) to collect the dynamic water scour pressure of pavement, thus adjusting the pressure of water gun and the rotating speed of the disk to realize the laboratory scouring test. The details of a freeze-soak-scour cycle test is described below.  
First, the specimens were treated by vacuum saturation in 97.3 kPa for 15 min and submerged in a container containing water, then the container with specimens were placed in the High-Low Temperature Experimental Box at \(-15\)\({}^{\circ}\)C and frozen 12 h.  
Then, the specimens were soaked in water at 15 \({}^{\circ}\)C for 12 h through controlling the High-Low Temperature Experimental Box.  
Finally, the specimens were removed from the container and placed in the Pavement Material Dynamic Water Scouring Tester for dynamic water test. The pressure of the water gun is set to 2.56 Mpa, the speed of the disk is set to 420 rad/min, and the scouring time of one cycle is 4.8 min.  
As described above, a complete freeze-soak-scour cycle is completed. Then, after 5, 10, 15, and 20 freeze-soak-scour cycles, damaged AC and NHISS-AC specimens were collected for performance testing to explore the influence of NHISS in asphalt concrete under freeze-soak-scour cycles. Three single factor tests were designed to explore the attenuation law of properties of NHISS-AC with the soaking cycle, freezing cycle, and scouring cycle acting seperately.
## 2 Materials and Methods
### Methodology of the Study
The following metrics are used for the evaluation:  
* Accuracy--a basic measure, used to estimate the percentage of positive estimates out of all estimates:  
\[Accuracy=\frac{TP+TN}{TP+TN+FN+FP}\;, \tag{1}\]  
where \(TP\) is the number of true positives, \(TN\) is the number of true negatives, \(FN\) is the number of false negatives, and \(FP\) is the number of false positives. * Precision--a measure that gives a percentile estimate of the true positive predictions:  
\[Precision=\frac{TP}{TP+FP}\;. \tag{2}\]
* Recall--a measure that gives a percentile estimate of the correctly estimated true positive predictions:  
\[Recall=\frac{TP}{TP+FN}\;. \tag{3}\]
* F1 score--a measure that gives an average estimate of the Precision and Recall measures:  
\[F1=2\times\frac{Precision\times Recall}{Precision+Recall}\;. \tag{4}\]
* Cohen's Kappa--a commonly used measure for assessing classification performance. It evaluates the level of agreement between the classified and reference data and takes values between 0 and 1, where 0 indicates no agreement at all and 1 indicates perfect agreement. Based on the obtained evaluation metrics, the optimal models are selected for further investigation. **Step 7. ** Once the optimal models are identified, they are applied to the prepared orthomosaic with the \"Histogram equalize\" filter applied. This step ends with the creation of a classification map, which divides the ortho into the \"Yellow rust\" and \"Healthy\" classes for further analysis. **Step 8. ** The logical final step of the proposed methodology is to analyze the generated classification maps. Here, the obtained results are closely examined, and conclusions are drawn about the performance of the algorithms, the necessary agrotechnological procedures, etc.
# A Preliminary Study of Model-Generated Speech
## 3 Method
### Three Models
#### 3.2.2. DiceTL+  
DirectTL+ is an online discriminative training model for string transduction problems. It was initially developed for the grapheme-to-phoneme conversion problem by [PERSON] et al. in 2008. It has also been applied to name transliteration tasks. In order to train the DiceTL+ model, a many-to-many (m2m) aligner is required to align the source and target tokens. The m2m aligner, also proposed by [PERSON], is adopted together with DiceTL+.  
The m2m aligner is based on an expectation-maximization (EM) algorithm. The EM algorithm comprises two steps: expectation and maximization. These two steps are performed alternatively until convergence is achieved. The expectation step estimates the maximum likelihood value with the current hidden variables; the maximization step aims to find the parameters that maximize the quantity.  
After obtaining the many-to-many alignment results, the DiceTL+ online discriminative training framework can be applied to train a model to convert the source sequence \(\mathbf{x}\) into the target sequence \(\mathbf{y}\). For each given pair, \((\mathbf{x},\mathbf{y})\), we can define a feature vector, \(\Phi(\mathbf{x},\mathbf{y})\), representing evidence for the sequence \(\mathbf{y}\) found in \(\mathbf{x}\), with \(\alpha\) as a feature weight vector providing a weight for each component of \(\Phi(\mathbf{x},\mathbf{y})\). Algorithm 1 is the algorithm for DiceTL+.  
```
Data \((\mathbf{x}_{1},\mathbf{y}_{1}),(\mathbf{x}_{2},\mathbf{y}_{2}),\ldots,( \mathbf{x}_{m},\mathbf{y}_{m})\)
```  
```
Learned weights \(\psi\) \(\psi:=0\) for\(k\) iterations do \(j=1\ldots m\)do \(\hat{Y}_{j}=\{\psi_{j1},\ldots,\hat{y}_{jn}\}=\underset{y}{\operatorname{ argmax}}[\psi\cdot\Phi(\mathbf{x}_{j},\mathbf{y})]\)  update \(\psi\) according \(\hat{Y}_{j}\) and \(\mathbf{y}_{j}\) endfor endfor return\(\psi\)
```  
**Algorithm 1** DiceTL+ algorithm pseudocode.  
The algorithm consists of the following three main components: a scoring model, represented by a weighted linear combination of features \((\alpha\cdot\Phi(\mathbf{x},\mathbf{y}))\); a search for the highest scoring phoneme sequence for a given input word; and an online update equation to move the model away from incorrect outputs and towards the correct output.  
For all the training data from the aligned pairs, the algorithm performs \(k\) iterations. In each iteration for each input sequence, \(\mathbf{x}_{j}\), the model generates the n-most possible sequence, \(\hat{Y}_{j}\). The parameters of the model are updated by the difference between \(\hat{Y}_{j}\) and the correct answer in each iteration. The updated model is based on the margin-infused relaxed algorithm (MIRA) ([PERSON] and [PERSON], 2003).