esa-sceva/satcom-chunk-collection huggingface dataset:
# Spinal Cord Segmentation in Ultrasound Medical Imagery
## 4 Experimental Results
### The Used Datasets and the Evaluation Metrics
#### 4.1.2 The Evaluation Metrics  
To measure the efficiency of the semantic segmentation model, five metrics are used--Intersection over Union (IoU), Accuracy, Recall, Sensitivity, and Dice coefficient. The most important metric that allows to judge the global efficiency of the model is the IoU. IoU is calculated for every class independently before concluding the average IoU for all the classes. Giving two sets of data \(A\) and \(B\), IoU is computed using the expression below:  
\[IoU(A,B)=\frac{size(A\cap B)}{size(A\cup B)}. \tag{4}\]  
Going deeper, the IoU is computed using four measures: \(TP\) (True Positives), \(TN\) (True Negatives), \(FP\) (False Positives), and \(FN\) (False Negatives). If we consider a semantic class \(C\), \(TP\) is the total of pixels of class \(C\) that was classified successfully as \(C\) by the algorithm. \(TN\) is the number of pixels that don't belong the class \(C\) and the algorithm did not associate them to \(C\). \(FP\) corresponds to the number of pixels that don't belong to \(C\) and the algorithm associated them falsely to the class \(C\). \(FN\) is the count of pixels that belong to the class \(C\) and the algorithm was not able to classify them as \(C\). Hence, IoU can be expressed explicitly as:  
\[IoU=\frac{TP}{TP+FN+FP}. \tag{5}\]  
Similarly, the other metrics are computed using the following expressions:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{6}\]  
\[Precision=\frac{TP}{TP+FP} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Patient IDs** & **Patient IDs** & **Number of Images** & **Number of Images** \\  & **in the** & **in the** & **in the** & **in the** \\  & **Train Set** & **Test Set** & **Train Set** & **Train Set** \\ \hline Dataset-B1 & 2...10 & 1 & 50 & 13 \\ Dataset-B2 & 1,3...10 & 2 & 60 & 3 \\ Dataset-B3 & 1,2,4...10 & 3 & 60 & 3 \\ Dataset-B4 & 1...3,5...10 & 4 & 57 & 6 \\ Dataset-B5 & 1...4,6...10 & 5 & 61 & 2 \\ Dataset-B6 & 1...5,7...10 & 6 & 49 & 14 \\ Dataset-B7 & 1...6,8...10 & 7 & 59 & 4 \\ Dataset-B8 & 1...7,9,10 & 8 & 58 & 5 \\ Dataset-B9 & 1...8,10 & 9 & 57 & 6 \\ Dataset-B10 & 1...9 & 10 & 56 & 7 \\ Dataset-B (cross validation) & 1...10 & 1...10 & 63 & 63 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Composition of Dataset-B (cross-validation).  
\[Sensitivity=Recall=\frac{TP}{TP+FN} \tag{8}\] \[Dice\;Coeff=F1\;Score=\frac{2*TP}{2*TP+FP+FN}. \tag{9}\]
# Research on Feature Identification and Trajectory Planning of Pavement Cracks
## 3 Intelligent pavement Crack Detection and Repair Algorithm  
### Algorithmic Framework  
In this paper, an intelligent pavement crack detection and repair algorithm is proposed. The algorithm consists of four parts to realize pavement crack feature identification and trajectory planning, and its framework is shown in Figure 1. First, the algorithm calls the system camera to capture the scene images with a fixed size, and performs a preliminary binarization operation on the images collected by the camera one by one at the system terminal to obtain the binarized crack image. For the binarized scene image, the algorithm adopts the threshold discrimination method, and compares the preset threshold with the global variance of the pixel matrix, and then divides the corresponding original crack image into a high-density noise wide crack type and a low-density noise narrow crack type. The algorithm proposes targeted filtering policies for the two types of graphs, respectively. For the high-density noise wide crack type image, the algorithm extracts the original image again, smoothes the texture features of the original image based on the median filtering method, and uses the Otsu threshold algorithm to eliminate the dense noise group and capture the main crack feature. For the low-density noise narrow crack type image, the algorithm obtains the binarized crack image based on the Otsu thresholding algorithm and uses the eight-neighborhood maximum connected component extraction algorithm to achieve accurate extraction of fine crack features. In the Hough space transformation stage, the algorithm performs Canny edge detection and Hough lines fitting on the feature-extracted image and collects all the endpoints of the fitted feature lines. Finally, according to the complexity of the crack shape, the algorithm uses the K-Means clustering algorithm to divide the fitting feature endpoint set into several clusters, and connects each cluster center in the output stage to form a crack repair trajectory.
## 3 Results and Discussion
### SEM and EDS  
Figure 3 presents SEM micrographs of the surface morphology of the upper layer of the heterojunction before and after the ionic exchange with selenium. Figure 3a,b shows a flake-like morphology, corresponding to plumbonacrite film at 50 and 5 \(\upmu\)m scale, respectively. Meanwhile, Figure 3c,d exhibits the change in morphology after transformation to PbSe, where the \"flakes\" of the precursor film disappear, showing a composite surface consisting of a flat background plus a roughness with different shapes. As can be observed in Figure 3d, there are cubic crystalline shapes with sizes around 1-2 \(\upmu\)m, agreeing with the XRD results for PbSe.  
Figure 2: XRD diffractograms of (**a**) CdS thin films, and (**b**) CdS/PbSe.  
A cross-sectional view of CdS/Pb\({}_{5}\)(CO\({}_{3}\))\({}_{3}\)O(OH)\({}_{2}\) was used to estimate its thickness, which was found to be about 3.5 \(\upmu\)m (Figure 4a). This value is similar to that obtained when plumbonacrite is deposited on coming glass substrates, as we have previously reported [26]. Figure 4b shows a perspective view of EDS elementary mapping of the CdS/PbSe heterojunction, with only a qualitative assessment of the present elements and their distributions. It evidences the presence of CdS thin film as the first layer of the heterojunction, followed by a second layer formed from PbSe in agreement with XRD results. On the other hand, the elemental mapping analysis of EDS exhibits a high concentration of sulfur in the upper layer, which does not correspond to the chemical composition of the PbSe film. Nonetheless, this result is due to the superposition of the K\(\upalpha\) band of sulfur with the M\(\upalpha\) band of lead, whose values are 2.309 and 2.342 keV, respectively.  
Figure 3: The surface morphology of the upper layer of the heterojunction with: (**a**) and (**b**) plumbonacrite film, (**c**) and (**d**) PbSe film.
## 2 Literature Survey
### Problem Statement  
Channel assessment is needed in the MIMO systems to improve energy as well as spectral efficiency. The MIMO system leads to different challenges, such as an increment in voltage causing the change in the digital output, complete loss of the amplitude information of the received signal, and hardware complexity resulting in high-resolution ADC on the receiver side. Therefore, deep learning techniques have been developed in MIMO channel estimation using cascaded multi-scale networks. Estimation of channel condition is essential for many reasons. The accurate estimation and prediction help to improve the performance, such as improved video streaming, reduced energy consumption, and better scheduling. Many different approaches have been introduced over the past two decades. Following that, in our research work, intelligent approaches were introduced for channel estimation. The challenges and features of various existing channel estimation models in MIMO models are given in Table 1. The Blind channel estimation technique reduces the overhead loss and BER in the channel and decreases the delivery delay. However, the energy consumption is a little high and the life duration is less when compared to other networks. DCNNs have productively decreased the packet overhead and network flexibility, and during the transmission of data in wireless networks, dead nodes are easily identified. The dependent outcomes and the computation cost are high. Large-scale antennas give high production and stable network lifetime; however, it reduces the quality of the network, and the BER is high. GAN provides the long-range communications possible in this wireless network and generates artificial data that are very similar to real data. Still, the energy consumption is very problematic, and the accomplishment cost is increased. BP's dependability path and data communication time in the network are low. However, it does not contain a fault tolerance mechanism, and amplitude information is completely lost in the network. RNN and CNN have a reduced transmission distance to improve communication in a wireless network and Packet information dropping is reduced using an RNN network. However, long sequences of data are difficult to access, and data traffic is high in this network. FCNN avoids multiple data and reduces sensor-optimized energy usage, and the size of memory is sufficient. However, data security is one of the challenging tasks. TCNN and DNN produce high power, reduce the collision in the network, and increase the service quality; however, overfitting problems and network mobility are high. The above-mentioned challenges are reduced by implementing a new channel estimation model using serial cascaded deep learning.
## 3 Results and Discussion
### Comparison of Spatio-Temporal Variation in GPP  
Figure 11 examines the spatio-temporal variation in annual GPP produced by the different satellite data-driven models. Each point on the scatter diagrams represents a mean annual value for a particular site and year where only the independent sites (i.e., not used in training of either \(\mathrm{NIR}_{V}\) or FluxSat-7) are shown and all datasets are at 0.0833\({}^{\circ}\) resolution. Only complete or nearly complete years (i.e., a few missing eight-day segments) are shown, and the same sampling was done for all datasets (i.e., if there was a missing data point for one dataset, it was eliminated for all datasets). The metrics were best for FluxSat-7, but the model differences were not statistically distinguishable in this case.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{4}{c}{**Muscle Fibre Type**} \\ \cline{2-4}  & **IIB** & **IIA** & **I** \\ \hline \multicolumn{4}{c}{Muscle fibre type percentages \%} \\ Colour parameter L & 0.114 (0.651) & \(-\)0.062 (0.067) & \(-\)0.102 (0.581) \\ Colour parameter a\({}^{*}\) & \(-\)0.222 (0.098) & **0.350 (0.0301)** & 0.087 (0.089) \\ Colour parameter b\({}^{*}\) & 0.020 (0.89) & 0.086 (0.541) & \(-\)0.038 (0.556) \\ Infuramuscular fat (\%) & \(-\)0.244 (0.072) & \(-\)0.199 (0.087) & 0.186 (0.406) \\ Protein content (\%) & 0.010 (0.308) & 0.214 (0.922) & \(-\)0.175 (0.178) \\ pH\({}_{45}\) & 0.039 (0.409) & \(-\)0.246 (0.0881) & 0.094 (0.519) \\ \hline \multicolumn{4}{c}{Muscle fibre type size (\(\Delta\) μm)} \\ Colour parameter L\({}^{*}\) & 0.034 (0.081) & 0. The meat of the Zlotnicka Spotted breed of pigs was characterised by good quality, with an appropriate dark colour and an optimal IMF content level. A similar conclusion was drawn by [PERSON] et al. [82]. Hybrid (ZS \(\times\) ZS) \(\times\) D pigs were characterised by the highest IMF content in the _longissimus thoracis_ muscle resulting in the lowest cutting force of roasted loin and the lowest thermal drip (Table 6). Table 7 presents quality traits of raw smoked loin derived from the _longissimus_ muscle of Zlotnicka Spotted breed pigs and hybrids. The best tenderness was recorded in the raw smoked loin derived from the _longissimus_ muscle of Zlotnicka Spotted breed pigs (20.99 N), while tenderness of the same loin from the _longissimus_ muscle of ZS \(\times\) D and ZS \(\times\) PLW hybrids was worse (from 36.08 to 43. 72 N). This could have been caused by the highest proportions of fibres in the _longissimus thoracis_ muscle of pigs of Zlotnicka Spotted breed. [PERSON] et al. [17] demonstrated that fibres were the main location of the IMF deposition. Therefore, it is possible that the higher percentage proportions of muscle fibres observed in the muscles of the Zlotnicka Spotted breed could have effectively improved loin tenderness. Moreover, [PERSON] et al. [16] found that the increased surface area of white muscle fibres correlated positively with tenderness and negatively with juiciness. On the other hand, the surface area of muscle fibres correlates positively with juiciness and negatively with tenderness. The surface increase of muscle fibres may be associated with the daily body weight gains [25; 59]. Studies in pigs showed that the frequency of fast glycolytic fibres (white fibres--type IIB) was negatively correlated with toughness [83]. Other studies suggest that differences between the same muscles may only be evident in specific breeds of any one species. It was shown that the sheer force of the _longissimus_ muscle differs significantly in Duroc but not in Berkshire or Large White pigs [54]. The relationship between some quality traits (e.g., textural parameters) and muscle fibre characteristics remain unclear [84].
# Rockfall Modelling in Forested Areas: The Role of Digital Terrain Model Grid Cell Size
## 2 Materials and Methods
### Used Datasets
#### 2.3.2 Surface Roughness Parameters and Soil Types  
Surface roughness parameters (rg) represent rocks lying on the slope that form obstacles for the falling rocks [21]. The parameters define the surface roughness, which is expressed as the size of the material covering the slope's surface in the downward direction of the slope. Rg70, rg20, and rg10 correspond to 70%, 20%, and 10% of the cases during a rebound on the slope, respectively, and they represent values from 0 to 100 meters (0 represents a smooth surface).  
Determination of rg subareas was mapped directly on the field at each rockfall site (Figure 2). At RS1, we have determined three different rg surface slope type areas (Figure 2a): the first area represents the scree terrain, which is built from material from previous rockfall events; the second area is the area with dwarf pines and other coniferous tree species that are overgrowing rocky terrain, and the third area is the area of continuous forest area overgrowing steep terrain represented by surface rockiness, rock deposits, and laying logs. Soil type was at all areas set as type 4 (latus slope with \(\mathcal{O}>\) -10 cm, or compact soil with large rock fragments). At the RS2 (Figure 2b), four different rg subareas were determined: the first area represents scree material; the second area represents the terrain within the mixed forest that is characterized by surface rockiness, rockfall deposits, laying logs, and stumps; the third area is a meadow within the spruce forest and open-planed meadow; and the forth area is the river torrent. Correspondingly, two types of soil types were set; rg area 1, 2, and 4 were set as type 4 (latus slope with \(\mathcal{O}>\approx\)10 cm, or compact soil with large rock fragments), while rg area 2 was set as type 1 (fine soil material with depth \(\geq\) 100 cm). Based on the guidelines of the model, we defined the main rg values for each rg area at both rockfall sites (see Table 1); however, in order to calibrate the model,ranges of rg values were used in the calibration process for each rg area separately (see Section 2.4). The number of simulations was set to 1000, as recommended by the authors of Rockyfor3D.  
Figure 2: Delineation into roughness parameters (rg) surface slope type areas into rg subareas at (**a**) rockfall site 1 (RS1) where there are three rg subareas, and (**b**) rockfall site 2 (RS2) where there are four types of rg subareas. Percentage (%) value on individual polygons indicates the percentage of conifers is that rg area. If one area has different percentage of conifers within, the polygon is additionally divided with the dashed line.
# Estimation of Stride Length, Foot Clearance, and Foot Progression Angle Using UWB Sensors
## 5 Conclusions  
This study estimated the three gait parameters SL, FC, and FPA through gait experiments using shoes installed with four UWB sensors on the right and left feet. The accuracy of the results was similar to that of the studies that applied to the IMU.  
The UWB sensor used in this study contains measurement error occurrence factors that originate from compensating data or the setting of the coordinate system for identifying the UWB sensor location. However, it enables direct determination of the SL. Furthermore, although it displays a measurement error similar to that in previous studies using IMU sensors, the drift that occurred with the IMU sensors was absent. Therefore, the applicability of the UWB sensor for gait characteristic analysis was considered high.  
In future research, based on the results of this study, the gait phase detection method using an insole device could be applied to develop a more accurate foot trajectory tracking technology and conduct gait disorder diagnosis on actual patients.  
Conceptualization, J.S.P. and C.H.K.; methodology, J.S.P. and C.H.K.; software, J.S.P.; validation, J.S.P. and C.H.K.; formal analysis, J.S.P. and C.H.K.; investigation, J.S.P. and C.H.K.; data curation, J.S.P. and B.L.; writing--original draft preparation, J.S.P.; writing--review and editing, S.P. and C.H.K.; supervision, C.H.K.; project administration, C.H.K.; funding acquisition, C.H.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of the Korea Institute of Science and Technology (approval no. KIST-HR-001 in 2022).  
Informed Consent Statement: Informed consent was obtained from all the participants involved in the study.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available owing to ethical concerns (because they were obtained in a clinical trial).  
This research was funded by the Korea Institute of Science and Technology (KIST) Institutional Program (project nos. 2E31642 and 2E32341).  
The authors declare no conflict of interest.
# Speeding up Statistical Tolerance Analysis to Real Time
## 5 Methods: Detailing the Approach to Speeding Up Tolerance Simulations
### Implementation of the Monte Carlo Simulation  
The sample generation and simulation are the most runtime-intensive steps of the tolerance optimization process, as a sufficient number of samples must first be generated and subsequently processed in the simulation step in order to obtain a trustworthy estimate for each tolerance configuration. Accelerating either of these steps would therefore provide the largest reduction of runtime of the overall process. Therefore the two steps'sample generation' and'simulation' have been implemented using the hardware architectures of the CPU and GPU of present-day desktop computers. To investigate the influence of different memory storage layouts and floating-point number configurations on the runtime, different implementations have been included in this study as well as an implementation in MATLAB (see Tables 3 and 4 for an overview of the CPU and GPU implementations).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Backend** & **Storage Layout** & **Float Format** \\ \hline NumPy Float32 C-Order & Python+NumPy & Row-major & Single-precision * \\ NumPy Float64 C-Order & Python+NumPy & Row-major & Double-precision \\ NumPy Float32 F-Order & Python+NumPy & Column-major & Single-precision * \\ NumPy Float64 F-Order & Python+NumPy & Column-major & Double-precision \\ MATLAB Float32 & MATLAB & Column-major & Single-precision \\ MATLAB Float64 & MATLAB & Column-major & Double-precision \\ \hline \hline \end{tabular}  
* Since only double precision was available for the sample generation process in NumPy v.18.4, the double precision sample was cast to a single precision float after creation.  
\end{table}
Table 3: Implementations of statistical tolerance analysis using a CPU.  
Figure 4: Virtual reproduction of the manufacturing, assembly and inspection of each part by statistical tolerance analysis using Monte Carlo simulation.  
The generation of samples and the simulation is accomplished either on the CPU or with the use of the graphics unit. In more detail, the NumPy implementations use _np.random.normal_ to generate a normal distribution pseudorandom sample and the _np.random.uniform_ function for a uniform distribution. The simulation step was realized with NumPy arrays for fast vectorized array operations. The CuPy implementations use the same code as the NumPy implementations, but with CuPy as a drop-in replacement for NumPy to perform the sample generations and simulations on the GPU. For the MATLAB implementations, mlfg6331_64, _RandStream_ is used to generate the samples using the CPU, but mrg32k3a, _parallel.gpu.RandStream_ for the GPU implementation. The simulation is implemented using MATLAB matrix and vector operations. The Python program used is available on GitHub [32], accessed on 28 April 2021 (see page 17).
## 3 Experimental Analysis
### Feature Extraction and Classification Using 1-D CNN  
For the feature extraction and classification, a 6-layer 1-D CNN was used. The network architecture contained three 1-D convolutional layers, with each layer followed by a max-pooling layer, 2 dense layers, and one final classification layer. The network architecture was 32-64-32-800-256-10. All the layers consisted of kernel filters of the size \(9\times 1\), a stride of 1, and max-pooling layers of size \(4\times 1\). The network architecture is shown in Figure 6 and the information regarding the number of parameters in each layer is shown in Table 3. The total number of parameters used in the network was 276,552. Regarding other parameters used in the network, a rectified linear unit (ReLU) was used as an activation unit for all the convolution layers, and for the classification, SoftMax was used. The GlorotNormal initializer, which is also called the Xavier normal initializer, was used for initializing the variables. This initializer draws samples from a truncated normal distribution centered on zero with standard deviation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{\
\begin{tabular}{c} **Motor Speed** \\ **(rpm)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Load** \\ **(hp)** \\ \end{tabular} } & \multicolumn{6}{c}{**Fault Condition**} \\ \cline{3-11}  & & & \multicolumn{3}{c}{**Ball Fault**} & \multicolumn{3}{c}{**IR Fault**} & \multicolumn{3}{c}{**OR Fault**} & \multicolumn{3}{c}{**Normal**} \\ \hline A & 1772 & 1 & B007 & B014 & B021 & IR007 & IR014 & IR021 & OR007@6 & OR014@6 & OR021@6 & None \\ \hline B & 1750 & 2 & B007 & B014 & B021 & IR007 & IR014 & IR021 & OR007@6 & OR014@6 & OR021@6 & None \\ \hline C & 1730 & 3 & B007 & B014 & B021 & IR007 & IR014 & IR021 & OR007@6 & OR014@6 & OR021@6 & None \\ \hline \hline \end{tabular}
\end{table}
Table 1: Other necessary details of the dataset used.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Train Set** & **Test Set** & **Validation Set** \\ \hline
48k\_DE\_Load1 (A) & 1499 & 714 & 167 \\ \hline
48k\_DE\_Load2 (B) & 1908 & 909 & 213 \\ \hline
48k\_DE\_Load3 (C) & 1908 & 909 & 213 \\ \hline D (A/B/C) & 5317 & 2532 & 591 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Length of train, test, and validation set used in the research.  
Figure 5: Data pre-processing for 1-D CNN.  
Figure 6: Architecture of proposed 1-D CNN model.