esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study aimed to analyze image biomarkers of the parotid glands in ultrasonography images with reduced shape effects, providing a reference for the radiomic diagnosis of parotid gland lesions. **Methods:** Ultrasound (US) and sialography images of the parotid glands, acquired from September 2019 to March 2024, were reviewed along with their clinical information. Parotid glands diagnosed as within the normal range were included. Overall, 91 US images depicting the largest portion of the parotid glands were selected for radiomic feature extraction. Regions of interest were drawn twice on 50 images using different shapes to assess the intraclass correlation coefficient (ICC). Feature dimensions were statistically reduced by selecting features with an ICC \(>\) 0.8 and applying four statistical algorithms. The selected features were used to distinguish age and sex using the four classification models. Classification performance was evaluated using the area under the receiver operating characteristic curve (AUC), recall, and precision. **Results:** The combinations of the information gain ratio algorithm or stochastic gradient descent and the naive Bayes model showed the highest AUC for both age and sex classification (AUC = 1.000). The features contributing to these classifications included the first-order and gray-level co-occurrence matrix (high-order) features, particularly discretized intensity skewness and kurtosis, intensity skewness, and GLCM angular second moment. These features also contributed to achieving one of the highest recall (0.889) and precision (0.926) values. **Conclusions:** The two features were the most significant factors in discriminating radiomic variations related to age and sex in US images with reduced shape effects. These radiomic findings should be assessed when diagnosing parotid gland pathology versus normal using US images and radiomics in a heterogeneous population.  
parotid gland; radiomics; individual biological variation; ultrasonography; machine learning Article
# Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix
## 5 Experiment and Analysis
### Experimental Setup
The formula is as follows:  
\[\text{L1 Loss}=\frac{1}{n}\sum_{i=1}^{n}{|y_{i}-\hat{y}_{l}|}. \tag{14}\]  
where \(n\) represents the number of trajectory points, \(y_{i}\) represents the true value of the \(i\)-th sample, and \(y_{i}\) represents the predicted value of the \(i\)-th sample. In the trajectory prediction model, MSE and L1 Loss are used to measure the prediction ability of the model, where MSE is suitable for smoother prediction results, and L1 Loss is suitable for more attention to outliers or more robust prediction results.
# A Comparative Study on the Schedulability of the EDZL Scheduling Algorithm on Multiprocessors
## 2 Background
### \(Edf^{(k)}\) Schedulability Test  
EDF\({}^{(k)}\) gives the highest priority to the jobs of \((k-1)\) largest-utilization tasks. And the jobs of the remaining tasks are given a priority according to EDF policy. This algorithm is simple but effective. We can show that EDF\({}^{(k)}\) dominates EDF-US\([m/(2m-1)]\) and fpEDF. EDF\({}^{(k)}\) can generates EDF-US\([m/(2m-1)]\) schedule or fpEDF schedule by controlling \(k\). For a given task set, EDF\({}^{(k)}\) can generate the same schedule as EDF-US\([m/(2m-1)]\) by setting \(k\) such that \(u_{k-1}>m/(2m-1)\) and \(u_{k}\leq m/(2m-1)\). Similarly, EDF\({}^{(k)}\) can generate the same schedule as fpEDF by setting \(k\) such that \(u_{k-1}>1/2\) and \(k\leq m-1\)Therefore, any task set schedulable by EDF-US[\(m/(2m-1)\)] or fpEDF is also schedulable by EDF\({}^{(k)}\). Lemmas 1 and 2 follow.  
Lemma 1: \(EDF^{(k)}\) _dominates EDF-US[\(m/(2m-1)\)]._  
Lemma 2: \(EDF^{(k)}\) _dominates fpEDF._  
In reference [21], the authors proposed a scheme that calculates a minimum number of required processors to successfully schedule a given task set (Theorem 3). Based on this, we can give a schedulability test for EDF\({}^{(k)}\), as shown in Corollary 1.  
Theorem 3 (Theorem 8 in [21]): _On \(m\) identical processors, task set \(\tau\) will be correctly scheduled by EDF\({}^{(k)}\) where_  
\[m=(k-1)+\left\lceil\frac{U(\tau^{(k+1)})}{1-u_{k}}\right\rceil \tag{7}\]  
Corollary 1: _On \(m\) identical processors, task set \(\tau\) will be correctly scheduled by EDF\({}^{(k)}\) if there exists \(k=1,2,\ldots,m\) such that_  
\[m\geq(k-1)+\left\lceil\frac{U(\tau^{(k+1)})}{1-u_{k}}\right\rceil \tag{8}\]
## 4 A Real Case Study
### The Number of Needed Operators
The machine utilization rates (excluding setup times) are as follows:  
\[\mu_{1}=89.11\%,\;\mu_{2}=88.05\%,\;\mu_{3}=88.77\%,\;\mu_{4}=86.14\%,\;\mu_ {4}=52.41\%.\]  
The operators' workload is:  
\[B_{1}=12\times 0.0288\times 100=34.56\%,\;B_{2}=5\times 0.0288\times 100=14.40\%.\]  
Figure 6 illustrates the allocation of machines 1-5 to operators using a donut chart. The red segments represent setup times, the yellow segments indicate idle times, and the blue/gray segments denote production times. It is clearly visible that the setup times for machine 5 overlap with those of machines 1-4, reinforcing the conclusion that a second operator is required. This visual evidence complements the results of the mathematical programming model and enhances the transparency of the operator assignment decision. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Operator 1**} & \multicolumn{6}{c}{**Operator 2**} \\ \hline \(i\) & \(j\) & \(a_{ij}\) & \(b_{ij}\) & \(b_{ij}+T\psi_{j}\) & \(i\) & \(j\) & \(a_{ij}\) & \(b_{ij}\) & \(b_{ij}+T\psi_{j}\) \\ \hline \multirow{2}{*}{1} & 5 & 0.0000 & 0.0288 & 0.4878 & \multirow{2}{*}{5} & 14 & 0.0000 & 0.0288 & 0.2337 \\ \cline{2-3} \cline{5-8}  & 8 & 0.4878 & 0.5166 & 0.9487 & & 3 & 0.2337 & 0.2625 & 0.3605 \\ \hline \multirow{3}{*}{2} & 1 & 0.0288 & 0.0576 & 0.4465 & \multirow{2}{*}{5} & 12 & 0.3605 & 0.3893 & 0.4747 \\ \cline{2-3} \cline{5-8}  & 9 & 0.4465 & 0.4753 & 0.8351 & & 16 & 0.4747 & 0.5035 & 0.5756 \\ \cline{2-3} \cline{5-8}  & 13 & 0.8351 & 0.8639 & 0.9957 & & 10 & 0.5756 & 0.6044 & 0.6681 \\ \hline \multirow{3}{*}{3} & 2 & 0.1520 & 0.1808 & 0.5262 & \multirow{3}{*}{5} & & & & \\ \cline{2-3} \cline{5-8}  & 4 & 0.5262 & 0.5550 & 0.8897 & & & & \\ \cline{2-3} \cline{5-8}  & 11 & 0.8897 & 0.9185 & 1.1261 & & & & \\ \hline \multirow{3}{*}{4} & 7 & 0.0861 & 0.1149 & 0.3765 & & & & \\ \cline{2-3} \cline{5-8}  & 6 & 0.3765 & 0.4053 & 0.6559 & & & & \\ \cline{2-3} \cline{5-8}  & 15 & 0.6559 & 0.6847 & 0.9188 & & & & \\ \cline{2-3} \cline{5-8}  & 17 & 0.9188 & 0.9476 & 1.0627 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The production plan. Figure 5: Gantt chart of the production plan of a representative cycle for the first operator. **Legend**: **White—**Production time; **Red—**Setup time; **Yellow—**Idle time.
# Grid-Based Vessel Deviation from Route Identification with Unsupervised Learning
## 2 Related Work
### Grid-Based Systems  
[PERSON] et al. [12] proposed an algorithm based on the frequency of activity of each vessel along the Miami Harbour. The model utilized a neural network which constructed a multidimensional Gaussian model for each found category of vessel behaviours. The network can both infer and learn on the fly. [PERSON], [PERSON], and [PERSON] [13] provided a study on grid size optimization, its limitations, and differences between high-seas navigation, during which vessel speed is higher, and ports. The anomaly detection was tested with an approach based on what the authors call a charge score, which indicates the number of vessel visits reported in a given location. Locations with high charge, meaning multiple vessel visits, correspond to normal trajectory locations, whereas places with very low reported vessel signals correspond to abnormal trajectory locations. [PERSON] et al. [14] applied a bounding box approach, in order to derive a gradient image for navigational fields, represented as a matrix. The vessel trajectory prediction is derived from the calculated matrix, which takes into consideration the vessel trip, preferred routes, and other scenarios, such as the presence of vessels or ports nearby. [PERSON] et al. [15] discretized the vessel location by placing a square grid over the area of interest and predicted the future position of a vessel according to its current speed and direction. This information could then be used to determine if a vessel is deviating from its normal course. [PERSON] et al. [16] incorporated contextual information into a Kallman filter to determine attracting and repellent regions of interest. Vessels with behaviour that is not compliant with the region they are currently inserted in are flagged as suspicious. [PERSON] et al. [17] proposed an approach based on a neural network combined with an a contrario detector. The AIS data is transformed in order to be fed to neural networks as a \"four-hot\" vector, which is the name used by authors to refer to latitude, longitude, speed, and course information transformed into binned one-hot arrays. The bin size of each variable are hyperparameters of the network, which have been tuned to 0.01\({}^{\circ}\) for longitude and latitude, 1 knot for speed and 5\({}^{\circ}\) for the course. To this data, a variational recurrent neural network (VRNN) is applied, to model the distribution underlying in the data. To this neural network, an a contrario detector is applied, which figures out the threshold from which a vessel movement is considered to be abnormal when compared to the modelled data, depending on the vessel characteristics.
# Broadband Spectral Domain Interferometry for Optical Characterization of Nematic Liquid Crystals
## 5 Conclusions  
In this article, spectral domain interferometry is used for LC mixture optical characterization, relying on broad spectral bandwidth and a low-coherence Michelson interferometer. This method is innovative for optical characterization of thermotropic nematics and it provides single-shot acquisition of the chromatic dispersion of both indices. The relevance and accuracy of the measured chromatic dispersion are first validated for a well-known mixture (\(E7\)). Then, a nearly unknown mixture (\(MLC2132\) by Merck) is characterized for the first time and the coefficients for the Extended Cauchy equation are proposed. The thermotropicity of the LC mixtures is also studied increasing the inner temperature up to the isotropic phase transition. Once again, measurements are first validated for a well-known mixture (\(E7\)) and subsequently, the fitting coefficients for the 4 _parameter parabolic_ equations for \(MLC2132\) are determined. To conclude, \(MLC2132\) can be preferred to \(E7\) when a larger phase shift (or GVD shift) is required. \(MLC2132\) can also be selected when the environment has some temperature fluctuations due to the higher nematic-to-isotropic phase transition temperature. Conversely, E7 can be preferred when less dispersive materials or high temperature sensitivity are necessary.  
Conceptualization, A.J.; Data curation, V.M.d.P. and A.J.; Formal analysis, V.M.d.P. and A.J.; Funding acquisition, A.J.; Investigation, V.M.d.P.; Methodology, V.M.d.P.; Project administration, A.J.; Resources, A.J.; Supervision, A.J.; Validation, V.M.d.P. and A.J.; Visualization, V.M.d.P. and A.J.; Writing--original draft, V.M.d.P. and A.J.; Writing--review & editing, V.M.d.P. and A.J. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the ANR (Agence Nationale de la Recherche) under the project Labcom SOFTLITE (ANR 15-LCV1-0002-01) and under the project Investissements d'Avenir UCA-JEDI with the reference number ANR-15-IDEX-0001 (THERMOPTIX). It has received funding from the European Union's HORIZON 2020 research and innovation program under the [PERSON] grant agreement No. 641272. The publication reflects only the author's view. The Research agency of the European Union is not responsible for any use that may be made of the information it contains.  
The authors have no competing interest to declare.
## 2 Methodology
### Data Analysis and Processing
#### 2.1.4 Evaluation Indicators  
Building heat load prediction belongs to regression prediction, and regression prediction cannot be measured by the accuracy rate. The effect of model prediction is generally evaluated by the error assessment. In this paper, the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are used as the error evaluation indexes to quantitatively analyze the prediction effect of the model. The smaller the value of both error indicators, the better the prediction effect is. RMSE is sensitive to outliers and can test the effect of outliers, while MAE is closer to reality. Through the situation of these two error values, the effect of the model prediction can be measured. The specific formula is as follows:  
\[RMSE=\sqrt{\frac{1}{n}\frac{\sum\limits_{i=1}^{n}(y-y^{\prime})^{2}}} \tag{3}\]  
\[MAE=\frac{1}{n}\sum\limits_{i=1}^{n}\left|y-y^{\prime}\right| \tag{4}\]  
In the above equation, \(n\) denotes the number of predicted samples, \(y\) denotes the real value, and \(y^{\prime}\) denotes the predicted value.
## 2 Materials and Methods  
### The Study Site and Field Data  
The study is conducted over an agricultural area in Temiskaming Shores, Ontario, Canada (Figure 1). It resides in the Clay Belt at the border between northeastern Ontario and northwestern Quebec. Despite of the long snowy winter and unpredictable rainfalls in a relatively short growing season, the fertile clay soil, climate warming, transportation services and inexpensive lands, have promoted the expansion of agriculture in the region [41]. Major crops include oats, barley, soybean, mixed grain, corn, canola, and hay. Field data were collected from both landowner interviews and planter/harvester recording in the 2019 growing season, which include crop types, and 67 fields for seeding dates and 77 fields for harvest dates (Table 1). Spatial distribution of the fields is illustrated in Figure 1. In addition to the crop fields, three forest areas were also identified for comparing their SAR signatures with that of the annual-crop fields. Seasonal meteorological data were also obtained from the Earlton Climate Station maintained by Environment and Climate Change Canada (ID = 6072230). Figure 2 shows daily precipitation (mm), daily mean air temperature (\({}^{\circ}\)C), and snow depth on the ground (cm).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Crop** & **Oats** & **Barley** & **Canola** & **Soybean** & **Spring Wheat** & **Peas** & **Total** \\ \hline Seeding & 19 & 9 & 3 & 9 & 20 & 7 & 67 \\ Harvest & 23 & 12 & 5 & 8 & 22 & 7 & 77 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of fields observed for 2019 growing season (n = 84).  
Figure 1: Study area in Temiskaming Shores, northern Ontario, overlaid with observed crop fields and forest. Background is a Landsat image acquired on August 24, 2019.  
Figure 2: Meteorological condition of the study area in 2019, obtained from the Earlton Climate Station (ID 6072230), Ontario, Canada: (**a**) daily precipitation; (**b**) mean daily temperature and snow depth
# A Method for Building a Mixed-Reality Digital Twin of a Roadheader Monitoring System
## 2 Architecture of Digital Twin Monitoring System
### Overall Architecture of Digital Twin Monitoring System
#### 2.1.4 User Interaction System  
The user interaction system is the user interaction part of the digital twin framework and the medium through which the digital twin monitoring system of the roadheader establishes a relationship with the user. The user interface refers to the collection of the hardware or software aspects that the user can see (or hear or perceive in other ways), and the program mechanisms that the user uses to input their operations and data. The user is directly connected to it and interacts with it to carry out activities. User interface design is a comprehensive design of the way and process by which the user establishes an interactive behavior with the system. The parameters of the roadheader are sent to the control end through the industrial network, and a digital twin virtual entity of the roadheader is constructed on the control end device. The digital twin virtual entity is driven according to the received parameters of the roadheader cutting process to realize the synchronous actions of the virtual model and the physical entity, thereby achieving the construction of the roadheader monitoring control system and the cutting visualization assistance system.
# Dynamic Gesture Recognition System with Gesture Spotting Based on Self-Organizing Maps
## 3 Gesture Recognition System
### Gesture Spotting  
An important function required for the dynamic gesture recognition system is the gesture spotting which detects when gesture ends so that a meaningful gesture is segmented from the sequence of hand motions. The gesture spotting is implemented in the SOM-Hebb classifier by using \(V_{2}(c)\) that is the vector distance of the winner neuron's weight vector in the SOM-2 to the input vector. The SOM-Hebb classifier performs the recognition for every input frame and generates its recognition results \(\hat{C}_{h}\). However most of the \(\hat{C}_{i}\) are not correct because the contents of the shift registers are not complete vector sequence for the current input gesture until the last gesture frame is input. The recognition result \(C_{h}\) is outputted only when the spotting module detects the end of gesture.  
The end of gesture is detected by observing the transition of the vector distance \(V_{2}(c)\). Figure 3C shows the transitions in the shift registers, which is development process of the posture sequence vector, \(\overrightarrow{X}\). Each gesture consisted of 10 frames in this example, therefore the shift register is filled with appropriate vector's elements at 10 th frame (\(f=9\)) and posture sequence vector \(\overrightarrow{X}\) is completed as shown in Figure 3C. The completed vector \(\overrightarrow{X}\) matches with one of the weight vectors in SOM-2, which decreases the vector distance \(V_{2}(c)\) remarkably as shown in Figure 3D. After that, the distance increases because the next gesture vector elements are loaded into the register. Therefore, the end of gesture can be detected by searching a dip in the transition of the vector distance \(V_{2}(c)\). However, the actual distance transition is not as smooth as that plotted in Figure 3C. The transition in the actual input fluctuates, which makes it difficult to find the dip. In order to solve the problem, a moving average of the vector distance is employed. The moving average \(\overline{V_{c}(f)}\) is computed as;  
\[\overline{V_{c}(f)}=\frac{1}{L}\sum_{l=0}^{L-1}V_{c}(f-l), \tag{17}\]  
where \(V_{c}(f)\) is the \(V_{2}(c)\) at video frame \(f\), and \(L\) is the number of samples to be averaged.
# Packet-Forwarding Algorithm in DTN Based on the Pheromone of Destination Node
## 3 The Model of Pheromone and Community  
The initiative community is the basis of the packet-forwarding algorithm proposed in this study. We must design a model for the pheromone of the core node and determine how to spread it to neighbors before running the packet-forwarding algorithm in the initiative community. Accordingly, we first propose the model for the core node pheromone, introduce the model for pheromone spreading and, finally, design the initiative community based on the pheromone of the core node.
# Optimization of the 3D Point Cloud Registration Algorithm Based on FPFH Features
## 4 Experimental Evaluation
### Experimental Analysis of Fine Registration  
In order to verify the accuracy of the precise registration algorithm proposed in this paper, we compared the three existing algorithms with the optimized algorithms and chose RMSE as the registration accuracy. The results are shown in Figure 12. Taking Stanford  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Registration Method** & **Iterations** & **RMSE/m** & **Iterations** & **RMSE/m** \\ \hline \(ICP\) & & \(9.4754\times 10^{-6}\) & & \(8.8821\times 10^{-6}\) \\ \(ICP-Kd-tree\) & 10 & \(7.4715\times 10^{-6}\) & 10 & \(7.419\times 10^{-6}\) \\ \(PL-ICP\) & (Bun000) & \(6.5929\times 10^{-6}\) & (Free-form surface) & \(6.94\times 10^{-6}\) \\ The algorithm in this paper & & \(5.3832\times 10^{-6}\) & & \(5.941\times 10^{-6}\) \\ \hline \(ICP\) & & \(5.0498\times 10^{-6}\) & & \(3.6151\times 10^{-6}\) \\ \(ICP-Kd-tree\) & 25 & \(4.624\times 10^{-6}\) & 25 & \(3.6087\times 10^{-6}\) \\ \(PL-ICP\) & (Bun000) & \(4.5434\times 10^{-6}\) & (Free-form surface) & \(3.5248\times 10^{-6}\) \\ The algorithm in this paper & & \(4.4834\times 10^{-6}\) & & \(3.4832\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analysis of rough registration results.  
Figure 11: Free-form surface information collection system and its cloud images.  
bunny and free-form surface as examples, it can be seen that compared with the classical ICP algorithm, the convergence speed of the proposed algorithm is faster, and the error of the proposed algorithm is reduced by 40.16% and 36.27% compared with the original ICP algorithm when the same iteration is performed for 20 times. In addition, when the RMSE value of the algorithm is 3.2789 \(\times\) 10\({}^{-6}\) m, the iteration times of this algorithm are reduced by 42.9% and 37.14% compared with the original ICP algorithm, which solves the problem of the slow convergence speed of the original ICP algorithm.
## 7 Numerical and Results
### Impact of Beamwidth \(\Phi\)  
As shown in Figure 5a,b, when the beamwidth \(\Phi\) decreases from \(60^{\circ}\) to \(40^{\circ}\), \(P_{out:EE}\) decreases both for FN and NN because of the extended maximum hop distance \(R\) by the antenna gain, which gives smaller end-to-end hop count and higher transmit energy. In addition, in Figures 6a,b, and 7a,b,  
Figure 6: \(\eta\) versus \(\beta\) with \(\lambda=0.015\), \(\Phi=\{40^{\circ},60^{\circ}\}\), and \(R=\{50\) m, \(33.3\) m}.  
with the smaller beamwidth \(\Phi=40^{\circ}\), \(\eta\) and Y of the two routing schemes become more distinct compared to the wider beamwidth \(\Phi=60^{\circ}\) because of the greater \(R=50\) m compared \(R=33.3\) m.
# Software-Defined NB-IoT Uplink Framework--The Design, Implementation and Use Cases
## 5 Uplink Implementation
### OFDM Generation  
The last part of the NB-IoT uplink is the OFDM generation module. It should be noted that, taking into account the former transform precoding module (Section 5.5), the SC-FDMA signal is generated instead of the typical OFDM. Nevertheless, the previously presented modular approach of analyzing each procedure separately justifies such notation. The next subsections refer to the three main OFDM generation functions, i.e., the phase rotation, the inverse fast Fourier transform (IFFT), and the cyclic prefix (CP) attachment.