esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Muscle Activity and Gait Ability Measurements  
Lower limb muscle activity on the paretic side was measured using surface electromyography (sEMG) (freeEMG1000, BTS Bioengineering, Milan, Italy) targeting five muscles (rectus femoris, biceps femoris, tibialis anterior, gastrocnemius, and gluteus medius). Dissposable wireless bipolar Ag/AgCl surface electrodes (with an interelectrode distance of 20 mm) were placed on each muscle's belly following the sEMG for non-invasive assessment of muscle guidelines [25]. The skin was prepared by shaving hair (if needed), lightly abrading it, and cleaning it with alcohol to reduce impedance before electrode placement. The EMG signals were recorded at a sampling rate of 1000 Hz. A band-pass filter (20-500 Hz) was applied in real time to attenuate movement artifacts and high-frequency noise. Collected EMG data were analyzed using the BTS EMG Analyzer software(version 2.9.37.0). The raw EMG signals were first band-pass-filtered (20-500 Hz) and then full-wave-rectified. We processed the rectified signals to calculate root-mean-square envelopes with a moving window of 50 ms, which provided a smoothed measure of muscle activation amplitude. For each muscle, the EMG values were normalized to a reference voluntary contraction (RVC) and expressed as a percentage of the RVC (%RVC). Normalization to maximal voluntary isometric contraction is often recommended for comparing muscle activity across individuals; nevertheless, the population with stroke may struggle to generate consistent maximal isometric contractions, owing to motor impairment, fatigue, and concerns over safety [26]. Thus, to obtain the %RVC of each muscle during the stance phase, the EMG signal recorded from the same muscle during standing without movement was used as a reference value. The lower limb muscle activity was measured three times, and the average value was used for the final statistical analysis.  
Figure 1: Study flowchart.  
Gait ability (paretic and non-paretic side stride length, stance and swing phases, and single-limb support time) was measured using a three-axis accelerometer (G-Walk; BTS Bioengineering, Milan, Italy). The three-axis accelerometer, which enclosed four inertial platforms and a Global Positioning System (GPS), was attached to the first sacral vertebral level of the participants using a special Velcro strap. The sampling frequency was set to 100 Hz, and data were acquired using G-Studio (BTS Bioengineering, Milano, Italy). The three-axis accelerometer sent data to a G-Studio connected via Bluetooth during the gait ability measurement. Gait parameters recorded during the test were displayed automatically. In addition, the gait asymmetry index was calculated using recorded gait parameters. A gait asymmetry index of 0% indicates perfect symmetry. Based on a previous study, the gait asymmetry index was calculated using the following formula [27].  
Gait asymmetry index \[=\frac{\text{Non\ paretic\ side\ parameter}-\text{Paretic\ side\ parameter}}{\text{Non\ paretic\ side\ parameter}+\text{Paretic\ side\ parameter}}\times 100\]
## 4 Results and Discussion
### Comparison between Numerical and Experimental Results  
The validation of the current work is carried out by comparing the results obtained through numerical method with the experimental results. One-dimensional transient heat transfer modeling was undertaken to study the cooling-down profile of the PCM in a copper tumbler from the initial ambient temperature to the phase change temperature of the PCM. As the cool air flows over the tumbler, the heat is absorbed through convection while the temperature inside the PCM drops due to the conduction phenomenon, considering the homogeneous properties of the PCM. The governing equation for heat transfer affecting the change in the PCM temperature is given by Equation (1).  
\[\alpha\frac{\partial^{2}T}{\partial x^{2}}+\frac{h}{\rho_{air}C_{p~{}air}}(T_{ air}-T_{pcm})=\frac{\partial T}{\partial t}, \tag{1}\]  
where \(\alpha=\frac{k_{p}}{\rho_{p}~{}c_{p}}\) is the thermal diffusivity of PCM, and \(k\), \(\rho\), and \(c_{p}\) are the thermal conductivity, density, and specific heat of the PCM. Also, \(h\), \(T\), and \(t\) are the conductive heat transfer coefficient, temperature, and time, respectively. After discretizing Equation (1)  
Figure 13: PCM sample test (placed in front of evaporator). (**a**) Copper container. (**b**) Stainless steel container.  
using the explicit method into five nodes along the length of the rectangular cross-section of the tumbler, the node temperatures are calculated as follows:  
\[T_{i,j+1}=T_{i,j}+\frac{\Delta t}{\Delta x^{2}}\left[T_{i+1,j}-2T_{i,j}+T_{i-1,j }\right]+\frac{h}{\rho_{air}C_{p~{}air}}(T_{air}-T_{pcm}), \tag{2}\]  
where \(\Delta x\) and \(\Delta t\) are the nodal distance and time step considered during the simulation [24]. The numerical modeling was undertaken using computational code developed in Python (version 3.7). Figure 14 shows the numerical and experimental values of the temperature at the center of the test container with variation in time, and it can be concluded that the numerical data are consistent with the experimental values obtained during the PCM sample testing.
## 5 Field Tests and Discussion on Results
### Field Test of NB-PLC-Based SACs on Conventional and Inverter Based Air Conditioners  
The field tests on both conventional- and inverter-based air conditioners by sending NB-PLC control signals for DSM are performed. The ratings of both types of air conditioners considered for field tests are 1.5 tons. The main difference between both types of air conditioners is; unlike conventional air conditioners, inverter-based air conditioners are operated by a variable frequency drive, which keeps on regulating the compressor's motor speed, making it an energy-efficient machine.
## 3 Results
### Temporal and Spatial Variation Characteristics of ET in the YRB
#### 3.1.2 Variation Characteristics of ET during the Year  
As shown in Figure 4, the monthly average ET values in the study area for the past 20 years ranged from 12.79 to 94.63 mm and presented a single peak periodic variation trend. ET showed an increase followed by a decrease from January to December, i.e., it fluctuated up from January, reached a maximum in July, and fluctuated down from August to November, while the value of ET from December to February was generally low, with a monthly average value of only about 15 mm.  
It could be found that the variation of ET in the YRB was significantly different in different months (Figure 5), and there was a trend of significant or extremely significant increase in January to April, June, and December in a large area. ET decreased significantly or extremely significantly in July and November and was mainly distributed in the middle and lower reaches of the YRB. However, the change in ET was not obvious in most regions. The interannual variation trend of ET showed an increasing trend in most regions of the headwaters of the YRB. However, the trend of monthly ET variation in the headwaters of the YRB mostly showed an increase or non-significant change. In April, June, July, and September, ET in the headwaters of the YRB showed an increasing trend in large areas, and, only in December, ET in a very few areas showed a decreasing trend. At the annual scale, the ET changes in most of the middle reaches of the YRB were not obvious, and there were also a few regions with decreasing ET changes, and they were mainly distributed in sub\(-\)basin C. However, at the monthly scale, the ET in the middle reaches of the YRB decreased intensively in July, September, and November and increased intensively in January, February, and December. At the annual scale, ET change in the lower reaches of the YRB mainly showed a non-significant trend, and there were a few areas in sub-basin  
Figure 4: Box plot of monthly ET in the YRB from 2001 to 2020.  
J where the ET showed an increasing trend. There were very few areas with decreasing ET change, and they were sporadically distributed in sub\(-\)basin L and the Yangtze River Delta. The study showed that the ET in the lower reaches of the YRB decreased intensively in July and increased intensively in in March, April, and August. Compared with the study of interannual ET trends in the YRB, it can be found that the study of monthly ET variation shows the variation of ET in more detail.
# Carbon-Isovalent Dopant Pairs in Silicon: A Density Functional Theory Study
## 5 Conclusions  
Here, we considered the structure and formation of carbon-isovalent defects in Si, which is scientifically and technologically important. We employed DFT calculations to provide detailed information on the structure of carbon-isovalent substitutional pairs, where the carbon atom resides at a substitutional or an interstitial site in the Si lattice. The main conclusion is that the larger the dopant, the more stable the defect pair. Additionally, for all the cases considered, the \(\rm C_{s}D_{s}\) defects are more bound than the \(\rm C_{i}D_{s}\) defects. Here the criterion is binding energies, and we did not consider in detail the formation of these defects. Using DFT calculations, we characterized the geometry (bond lengths and angles) of all the defect pairs considered, aiming to provide information to future experimental and theoretical studies. It is anticipated that the present study can be the basis for future experimental and theoretical work on this system and, in particular, the kinetics to form the extended C-related defects. In essence, the present work can serve as a paradigm that can be used in a transferable manner to investigate carbon-dopant and related defects in semiconducting systems.  
Formal analysis, S.-R.G.C., E.N.S. and A.C.; Investigation, C.A.L. The manuscript was written and edited by all authors. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData is contained within the article.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c} \hline Defect & Binding Energy/eV & Comments \\ \hline \(\rm C_{i}(Si_{I})\) & \(-2.34\) & Ref. [19] \\ \(\rm C_{i}O_{i}\) & \(-1.6\) & Ref. [19] \\ \(\rm C_{i}C_{s}\) & \(-1.68\) & Ref. [19] \\ \(\rm C_{s}Ge_{s}\) & \(-0.10\) & present study \\ \(\rm C_{s}Sn_{s}\) & \(-0.42\) & present study \\ \(\rm C_{s}Pb_{s}\) & \(-0.56\) & present study \\ \(\rm C_{i}Ge_{s}\) & \(-0.03\) & present study \\ \(\rm C_{i}Sn_{s}\) & \(-0.30\) & present study \\ \(\rm C_{i}Pb_{s}\) & \(-0.32\) & present study \\ \hline \end{tabular}
\end{table}
Table 1: The binding energies of carbon-related defect pairs in Si.
## Article
###### Abstract  
In this study, three slots containing an anode chamber, a cathode chamber, and a middle pole chamber were designed by applying the Hittorf method, and a two-way coupling model of the flow field and electric field was established using the COMSOL system. The electric field distribution in the constructed model was simulated, and the model reliability, boundary conditions, and related parameters were verified. A three-chamber tank was utilized to investigate the migration numbers change rule and migration mechanism of Y(III) ions in the AlF\({}_{3}\)-(Li,Na)F system. The migration number of Y(III) ions in the AlF\({}_{3}\)-(Li,Na)F-Y\({}_{2}\)O\({}_{3}\) molten salt linearly increased from 0.70 to 0.80 with an increase in temperature from 900 to 1000 \({}^{\circ}\)C. When the (Li,Na)F/AlF\({}_{3}\) molar ratio was between 2.0 and 2.5, the migration number of Y(III) ions was relatively constant, and its average value was approximately 0.75. Meanwhile, at (Li,Na)F/AlF\({}_{3}\) molar ratios higher than 2.5, the migration number of Y(III) ions linearly decreased from 0.75 to 0.45. Finally, in the current density range of 1.0-2.0 A/cm\({}^{2}\), the migration number of Y(III) ions increased almost linearly from 0.65 to 0.85.  
molten salt; Y(III) ion; Hittorf method; migration number; three-chamber tank
# Bit Error Rate Closed-Form Expressions for LoRa Systems under Nakagami and Rice Fading Channels
###### Abstract  
We derive exact closed-form expressions for Long Range (LoRa) bit error probability and diversity order for channels subject to Nakagami-_m_, Rayleigh and Rician fading. Analytical expressions are compared with numerical results, showing the accuracy of our proposed exact expressions. In the limiting case of the Nakagami and Rice parameters, our bit error probability expressions specialize into the non-fading case.  
Keywords:LPWAN; Internet of Things; MIMO; Nakagami; Rayleigh; Rice +
Footnote †: journal: Sensors
## 3 System Model
### Model Building
\begin{array}{cc}\left(\begin{array}{cc}0&s_{2}\tilde{ p}_{1}(\tilde{p}_{2}+p_{2}\frac{K+1}{\alpha})&0\\ 0&s_{1}\tilde{p}_{1}(\tilde{p}_{2}+p_{2}\frac{K+1}{\alpha})&0\end{array}\right)_ {2\times 3}&m=H+1\\ \\ \left(\begin{array}{cc}s_{2}\tilde{p}_{1}(\tilde{p}_{2}+p_{2}\frac{K+1}{ \alpha})&0\\ s_{1}\tilde{p}_{1}(\tilde{p}_{2}+p_{2}\frac{K+1}{\alpha})&0\end{array}\right)_ {2\times 2}&m\geq H+2\\ \\ \end{array} \right. \tag{14}\]  
When \(m=n=K+1\), \(\mathbf{W}_{m,n}\) is expressed as follows. \[\mathbf{W}_{m,n}=\left( \begin{array}{cc}s_{2}\tilde{p}_{1}p_{2}(1-\frac{k+1}{ \alpha})+\tilde{s}_{2}\tilde{p}_{1}&p_{1}\\ s_{1}\tilde{p}_{1}p_{2}(1-\frac{k+1}{\alpha})&s_{1}p_{1}+\tilde{s}_{1}\end{array}  \right)_{2\times 2} \tag{15}\]  
All submatrices have been discussed, and the transition probability matrix \(\mathbf{W}\) can be further obtained. We define \(\pi_{x,y,z}\) as the steady\(-\)state probability distribution of the 3 DMC, and \(\pi_{x,y,z}\) is expressed as follows. \[\pi_{x,y,z}=\lim_{t\to\infty}\mathrm{P}\{R_{t}=x;S_{t}=y;T_{t}=z:0\leq x\leq K +1,0\leq y\leq 1,0\leq z\leq 1\} \tag{16}\]We can further define the steady\(-\)state vector \(\mathbf{\Pi}\) as follows. \[\mathbf{\Pi}{=}\left\{ \begin{array}{ll}(\pi_{000},\pi_{1,0,1},\pi_{1,1,1},\ldots, \pi_{K+1,0,1},\pi_{K+1,1,1})&H=0\\ \\ (\pi_{000},\pi_{1,0,0},\pi_{1,0,1},\pi_{1,1,1},\ldots,\pi_{H,0,0},\pi_{H,0,1}, \pi_{H,1,1},\\ \pi_{H+1,0,1},\pi_{H+1,1,1},\ldots,\pi_{K+1,0,1},\pi_{K+1,1,1})&1\leq H\leq K \end{array} \right. \tag{17}\]  
According to the structure of the matrix \(\mathbf{W}\), the 3 DMC is irreducible, aperiodic, and normally recurrent. Based on the equilibrium equation and normalization condition [28], we can obtain the following equations. \[\left\{ \begin{array}{ll}\mathbf{\Pi}\mathbf{W}=\mathbf{\Pi}\\ \mathbf{\Pi}\mathbf{e}=1\end{array} \right. \tag{18}\]  
where \(\mathbf{e}\) is a column vector with all 1 s. A numerical calculation method [29] for calculating the equations above is provided in Table 2.
# Estimating Position from Millisecond Samples of GPS Signals (the \"FastFix\" Algorithm)
## 6 Conclusions  
The algorithm described here allows ultra-low-power, reasonably accurate global position determination using very short sequences of GPS data. The computational effort required to measure position is a function of both the initial uncertainty in position and the local receiver clock offset. The results in stationary testing show a median error of 27.7 m which is worse than the median error of traditional GPS single-frequency receivers, but similar to existing snapshot algorithms [21]. I speculate that this is due to the lack of a code-tracking filter that can refine the pseudorange estimates during the relatively long period that a traditional GPS device spends receiving navigation information. Despite this limitation, there are several interesting avenues to explore for improving the robustness and accuracy which were discussed in Section 4.4. The performance of the algorithm in practical moderate-precision applications such as wildlife tracking is sufficient as demonstrated in Section 5, and the power consumption was extremely low, which enabling long-term solar-powered, or primary-cell tracking of wildlife.  
Another avenue for further work would be to use Bayesian inference techniques to estimate a posterior distribution of receiver positions. This would also enable uncertainty quantification and, consequently, a systematic exploration of the lower limit on the amount of stored data necessary to recover position to a given accuracy. An additional advantage of a Bayesian approach would be avoiding the decision to reject or accept a satellite as 'visible' from its correlation strength \(X_{max}/\sigma_{X}\).  
This research was partially funded by the Ministry of Business, Innovation and Employment (MoBIE; formerly FoRST). Grant Number: UOOX0904.  
I would like to thank [PERSON] and [PERSON] who assisted with the development of the FastFix wildlife tag electronics. [PERSON] also developed the packaging and attachment system for the FastFix tags, and [PERSON], who attached and recovered the tags to the albatross.  
The author declares no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript. The author allowed a provisional patent on this technology [WO2011046455A1] to lapse, and the algorithm is therefore in the public domain.
## 4 Experiments
### Candidate MOT Methods  
Since the scale of the pictures varies greatly under different game types, to compare the impact of the tracking algorithms in this task we adopt four typical and state-of-the-art approaches which can automatically adapt to different input sizes.  
**Trackor++**[41]. The challenge of multi-object tracking is to extract the spatial and temporal positions. Trackor++ exploits the bounding box regression of an object detector (Faster-Rcnn) to predict the spatial position of an object in the next frame by using bounding box regression and initialization, thereby converting a detector into a tracker. Moreover, Motion model and Re-identification are also adapted to extend the ability for improving identity preservation across frames.  
**CenterTrack**[42]. This work presented a point-based framework for joint detection and tracking. Each object is represented by a single point at the center of its bounding box. Center points are then tracked according to the heatmaps of two consecutive frames and the offset vector from the current object center. The tracker is end-to-end trainable and differentiable.  
**Fair-Mot**[39] introduced a framework to fairly balance the detection and re-ID tasks. It eliminates the unfair advantage of the detection branch and effectively learns high-quality re-ID features and obtains a good trade-off between detection and re-ID for better MOT results.  
**TransTrack**[45]. Similar to usual transformer-based detectors [44], TransTrack takes an object query as input to provide common object detection results. TransTrack leverages features from previously detected objects to form another \"track query\" to discover associated objects on the following frames. Under this scheme, TransTrack builds up a novel joint-detection-and-tracking paradigm by accomplishing object detection and object association in a single shot.  
In order to ensure the fairness of the experiment, we used the open source code provided by the authors to train and test on the MOT17 training set, and then used a unified evaluation method mentioned above to evaluate the MOT17 training set. After getting the results (see in Table 4) close enough to the data in the author's papers, we used the same training methods to train and test on each subset of NPSPT dataset separately.  
Figure 7: Track counts of each Id in clips. (**a**) Comparison between our dataset and MOTChallenge dataset. (**b**) Comparison between subdatasets of our dataset.
## 5 Data and Results
### Evaluation Criteria  
Recall, precision and F-score (\(F\)) were considered to evaluate the performance of the previously described segmentation algorithms. These are some measures widely used by the computer vision community. The definitions of the previous measures are provided in Equations (12)-(14), respectively.  
\[recall=\frac{TP}{TP+FN} \tag{12}\]  
\[precision=\frac{TP}{TP+FP} \tag{13}\]  
\[F=\frac{2\times precision\times recall}{precision+recall} \tag{14}\]  
where _TP_ (true positives) are the correct detections in the sequence, _FP_ (false positives) are the mistaken detections and _FN_ (false negatives) are the humans that are not detected, but are really present in the scene.  
Precision is the ratio of true positives with respect to the total number of detections, _i.e._, the ratio of detections that really correspond to a human. Moreover, recall is the ratio of a human on the scene to be really detected or not. Lastly, the F-score considers precision and recall, in this way providing an overall vision of the system performance. The F-score is a weighted average; an ideal system will show an F-score of one.
## 3 Machinery
### Tree Grammars  
A signature \(F\) defines a language of terms, \(T(F)\). We will provide the possibility to limit the set of terms under consideration by the use of tree grammars. ( Our dual view on terms as trees is not strictly necessary. Our tree grammars are actually \"term grammars\", describing subsets of \(T(F)\). Our tree alignments, discussed later, could as well be called \"term alignments\", and so on. However, when terms are considered as data structures, as we do it here, the tree terminology is the more established one.) A _tree grammar \(\mathcal{G}\) over a signature \(F\)_ is defined by the tuple \(\mathcal{G}=(N,F,Z,P)\), where  
* \(F\) is the signature,
* \(N\) is a set of non-terminal symbols, disjoint from \(F\), where each element is assigned a sort.
* \(Z\) is the starting non-terminal symbol, with \(Z\in N\), and
* \(P\) is a set of productions of the form \(X\to t\), where \(X\in N\), \(t\in T(F,N)\), and \(X\) and \(t\) have the same sort.  
\(L(\mathcal{G})\) denotes the language of \(\mathcal{G}\). It is the (sub)set of terms of \(T(F)\) that can be derived from \(Z\) using productions of \(P\).