esa-sceva/satcom-chunk-collection huggingface dataset:
# Postural Control in Children with Cerebellar Ataxia
## 2 Materials and Methods
### Recordings  
Body kinematics was recorded by means of a six-cameras optoelectronic system (SMART-E, BTS, Milan, Italy) using a full-body marker set [36], which allowed estimating the Centre of Mass (COM) and its trajectory, according to [PERSON] et al. (2014) [18]. A dynamometric force plate (9286 AA, KISTLER, Winterthur, Switzerland) was used to compute the Center of Pressure (CoP) position. Wireless probes (FREEEMG 1000, BTS, Milan, Italy) were employed bilaterally to record the surface electromyographic (EMG) activity of Tibialis Anterior (TA), Soleus (SOL), Rectus Femoris (RF), Biceps Femoris (BF), and Erector Spinae (ES). Electrodes were placed according to the Surface Electromyography for the Non-Invasive Assessment of Muscles (SENIAM) guidelines [37]. Synchronous data acquisition was accomplished by the SMART-E workstation; sampling rate being 60 Hz for optoelectronic cameras, 960 Hz for dynamometric signals, and 1000 Hz for EMGs.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Patient** & **Age** & **Gender** & **Molecular Diagnosis** & **SARA** \\ \hline SlowP\_01 & 9 & M & mutation in a candidate gene & 8 \\ SlowP\_02 & 8 & M & mutation in a candidate gene & 14 \\ SlowP\_03 & 12 & M & EXOSC3: c572G \(>\) A & 15 \\ SlowP\_04 & 13 & F & KCNC3: c1268G \(>\) A & 17 \\ SlowP\_05 & 13 & F & to be evaluated & 13 \\ SlowP\_06 & 16 & F & ADCK3: c547C \(>\) T; c1042C \(>\) T & 13 \\ SlowP\_07 & 17 & M & to be evaluated & 18 \\ NonP\_01 & 10 & M & NPHPI: c1358G \(>\) T; c1438-4C \(>\) T & 12 \\ NonP\_02 & 12 & F & to be evaluated & 15 \\ NonP\_03 & 18 & M & to be evaluated & 14 \\ NonP\_04 & 9 & M & AHII: c1829G \(>\) C; c2671C \(>\) T & 15 \\ NonP\_05 & 12 & F & SUFU: c1217T \(>\) C & 11 \\ NonP\_06 & 9 & M & SUFU: c1217T \(>\) C & 15.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic and clinical characteristics of children with PCA. SlowP: slowly progressive ataxia; NonP: non-progressive ataxia (Joubert syndrome); EXOSC3: exosome component 3; KCNC3: potassium voltage-gated channel subfamily C member 3; ADCK3: aarF domain-containing kinase 3; NPHPI: nephrocystin 1; AHII: Abelson helper integration site 1; SUFU: negative regulator of hedgehog signaling; PCA: pediatric cerebellar ataxia. Details about a molecular diagnosis can be found at [34].
## 4 Conclusions  
This paper investigates the influence of various attributes such as machining time, dimensional accuracy, kerf width, and surface roughness on the selection of the best machine tools (Plasma Arc Machining--PAM, Laser Beam Machining--LBM, Abrasive Water Jet Machining--AWJM). For this purpose, the grey relational analysis is applied. First, grey relational analysis was applied, which was necessary to normalize the raw data into values from 0 to 1. Then, a fuzzy logic system was used, consisting of a fuzzification, a fuzzy inference system (rule base and membership functions), and finally a defuzzification. The analysis of the fuzzy model showed that the laser machining is the most suitable for cutting steel plates with a thickness of 8 and 10 mm, while the plasma machining is recommended for a thickness of 5 mm, from the aspects of machining time, dimensional accuracy, and surface quality. The obtained results are appropriate only in the context of this research. The proposed hybrid multi-criteria method, which includes GRA and FL, has been concretely applied in a manufacturing company. Due to its flexibility, it can be very easily integrated into the Industry 4.0 system. Future research should be based on the extension of the decision model by introducing other cutting methods, other materials and other machining performances.  
Conceptualization, D.L. and D.R.; methodology, D.L., D.R. and R.C.; software D.R., D.L. and M.M.; validation, D.L., D.R., A.Z. and V.T.; writing--original draft preparation, D.L. and D.R.; writing--review and editing, D.R., D.L. and M.M.; visualization, D.R. and V.T.; supervision R.C., A.A. and A.Z.; project administration, D.L., R.C. and A.A. All authors have read and agreed to the published version of the manuscript.
## 5 Discussion
### Paleosalinity  
Paleosalinity is the most direct manifestation of changes in paleoclimate and pale-oenvironment, influencing the types and abundance of organisms that thrive in different salinities, which in turn affects the formation of source rocks. Sr (Strontium) and Ba (Bar  
Figure 10: Relationship between V/(V + Ni) and Cu/Zn.  
ium) are two similar alkaline earth elements among trace elements, but they have different solubility products. Barium precipitates as barite (BaSO\({}_{4}\)), while strontium tends to precipitate under higher salinity conditions. Therefore, the Sr/Ba ratio recorded in sediments can reflect changes in salinity and has a strong positive relationship with paleosalinity. Based on previous studies [29], an Sr/Ba ratio greater than 1 indicates a marine environment, a ratio between 0.5 and 1 indicates a brackish environment, and a ratio less than 0.5 indicates a freshwater to slightly brackish environment. In the study area, the Sr/Ba ratios of the mudstone samples range from 0.01 to 0.16, with an average value of 0.056, preliminarily indicating a freshwater-to-slightly-brackish environment.  
The migration capabilities of Ca (Calcium) and Fe (Iron) elements also differ in waters of varying salinities, and the Ca/(Ca + Fe) ratio can be used to judge the salinity of the water body. When the ratio is less than 0.4, it indicates a slightly brackish environment; a ratio between 0.4 and 0.6 indicates a brackish environment; and a ratio greater than 0.6 indicates a marine environment. The Ca/(Ca + Fe) ratio of the mudstone samples in the study area ranges from 0.1 to 0.44, with an average value of 0.23. A total of 93% of the samples have values less than 0.4. Combining the relationship between the Sr/Ba ratio and the Ca/(Ca + Fe) ratio (Figure 11), the overall paleosalinity of the water body in Ying IV of the Xuijaweizi area is comprehensively analyzed to be a freshwater-to-slightly-brackish environment.
## 2 Materials and Methods
### Knowledge Distillation  
Larger models can often be used to achieve higher accuracy in the same dataset, and in many computer vision tasks, including target detection and semantic segmentation, larger models can often give better performance. However, larger models are often accompanied by high hardware resource requirements and the disadvantage of long model inference times, which pose many obstacles to practical applications. Therefore, improving the accuracy of models while keeping them lightweight has become a key research direction and is the basis for their application on the ground.  
Knowledge distillation is one of the common methods to reduce model size [28]. Unlike pruning and quantized model compression methods, knowledge distillation involves constructing a small, lightweight model and using a larger model with better performance to guide this small model so that the output of the small model is close to that of the large model, with a view to achieving better performance and accuracy. It aims to improve the accuracy of the small model without changing the computational efficiency.  
The aim of this study is to ensure that the third molar detection and classification model is lightweight while improving the accuracy of the third molar detection classification model. Therefore, this paper chose YOLOv5x, which has the largest number of parameters and the highest detection accuracy among the four versions of YOLOv5, as the teacher network for knowledge distillation, and YOLOv5s, which has the smallest number of parameters, as the student network for knowledge distillation, so that the detection accuracy of YOLOv5s can be improved while ensuring the lightweight.
## 3 Methodology
### Preliminaries
#### 3.1.5. Linear Regression and Convolutional Neural Networks (CNNs)  
In our ship status data compression process, linear regression and CNN models are used. Linear regression is a fundamental statistical method used to model the relationship between two or more variables. Its simplicity and interpretability make it a widely applied technique in various fields, including economics, social sciences, health sciences, and machine learning.  
At its core, linear regression aims to establish a linear relationship between a dependent variable (the outcome of interest) and one or more independent variables (predictors or explanatory variables). The model assumes that the relationship between these variables can be approximated by a straight line.  
Mathematically, the linear regression model can be represented as:  
\[\hat{y}=\theta_{0}+\theta_{1}x_{1}+\theta_{2}x_{2}+\ldots+\theta_{n}x_{n} \tag{2}\]  
The predictive values \(\hat{y}\) are calculated as the weighted sum of input features, with \(n\) representing the number of features, \(x_{n}\) representing the \(i\)th feature value, and \(\theta_{j}\) representing the \(j\)th model parameter (particularly, \(\theta_{j}\) is the intercept, indicating the value of y when all independent variables are zero) [26]. However, it should be noted that these linear regression models assume a linear relationship by default [27].  
In recent years, it has become popular to use neural networks to simulate the learning process of the human brain to construct models and provide automated solutions within the field of Artificial Intelligence (AI). Neural networks consist of interconnected neurons with numerous parameters, and they exhibit layers connecting input and output [28]. Convolutional Neural Networks (CNNs) [29] represent a class of deep neural networks specifically designed to process visual data. They have revolutionized various fields, ranging from computer vision to natural language processing, due to their capability to automatically extract intricate features from input data. Originally inspired by the visual processing mechanism of the human brain, CNNs have demonstrated remarkable success in tasks such as image classification, object detection, and segmentation. The essence of CNNs lies in their architecture, which comprises convolutional layers, pooling layers, and fully connected layers. Convolutional layers apply learnable filters to input images, extracting features hierarchically by convolving across the image. Pooling layers subsequently downsample these features, reducing computational complexity while retaining essential information. Fully connected layers, situated at the end of the network, utilize these learned features for classification or regression tasks. If the two-dimensional convolution in its convolutional layer is changed to a one-dimensional convolution, it can also be used for sequence data. In our ship data compression scenario, we applied CNNs in the predictive coding step and achieved relatively higher prediction accuracy than other deep neural networks, such as RNNs (Recurrent Neural Networks).
# Biomechanical Performance of the Cemented Hip Stem with Different Surface Finish
## 2 Experimental Work
### Interface Strengths
[16] also reported that the interfacial shear strengths were 0.53 and 9.85 MPa for polished (Ra = 0.03 \(\upmu\)m) and grit-blasted (Ra = 4.65 \(\upmu\)m) surfaces. Recent investigations by [PERSON] et al. [11; 13] showed that steel rods with polished or blasted surface roughness exhibited different shearing strengths: 2.95 MPa in the case of polished rods (Ra = 0.56 \(\upmu\)m), 4.36 MPa for glass bead-blasted rods (Ra = 1.33 \(\upmu\)m), and 16.42 MPa for grit-blasted rods (Ra = 5.21 \(\upmu\)m). [PERSON] et al. [14] found that the mixed-mode interface strengths of the cement-implant specimens were positively related to the surface roughness generated by a grit-blasting method. For varying surface roughness (Ra = 0.89-2.76 \(\upmu\)m), the interface strengths under pure shear and tensile loadings were measured as 1.95-9.90 MPa and 0.58-6.67 MPa, respectively. Overall, these findings indicate that tensile and shear bonding strengths vary greatly, and that there is considerable discrepancy among measurements. A well-known phenomena that has been revealed in the present study and previous studies [7; 12; 13] is that metal samples treated by the sand-blasting method showed superior bonding ability with cement against shearing force when compared to smooth metals. Meanwhile, we found that the polished surface produced a higher interfacial bonding strength than the sand-blasted surface when resisting tensile loading. In addition, reviewing the published works [11; 13; 14], we can find that a variety of cement of different type and viscosity were used for the preparation of stem-cement interface specimens, which also contributed to a great variation in the measured interface strengths, apart from the variability induced by metal samples with different surface morphology. According to studies [12], a bonding surface with higher roughness may allow more cement to flow into the cavities between surface asperities, thereby generating a better bonding layer to resist shear force. A less roughened surface processed by bead or grit-blasted method would produce a lower bonding strength under shearing and tensile loading because of weaker adhesion with the cement [12; 13; 14; 29]. However, more gaps were also found to form at the interface between the cement and the roughened stem, which indicated that the cement was not well integrated with the rougher surface [29]. This effect might bring to the measurement of [PERSON] et al. [30] that the matte stem surfaces generated lower interfacial shear strength than the polished with stem preheating. Conversely, a polished surface with less surface asperities might create uniform and strong adhesive layers between the cement and metal, thereby enhancing resistance against tensile forces [26].
# Non-Cooperative Spacecraft Pose Measurement with Binocular Camera and TOF Camera Collaboration
## 3 Detailed Description of the Proposed Method
### Binocular Initial Pose Acquisition  
The main purpose of this part is to approximately gain the motion state of the target spacecraft and provide a more accurate relative initial value for the subsequent point cloud pose estimation.
## 2 System Model
### Fnm  
The current OCS operates at few milliwatt power, bit rate up to 5 Gbps and bandwidth around 5 GHz. Such type of systems are considered to be linear systems [38]. However, data rate greater than 10 Gbps, bandwidth above 10 GHz and high transmitting power leads towards the introduction of NLEs in the fiber [39]. For WDM systems, the NLEs become more severe [40]. The main effect out of these NLEs is FWM. FWM is the inter-modulation phenomenon, whereby interactions among three wavelengths produce the fourth wavelength. In a WDM system, it induces phase shift within a channel and new frequencies are generated as illustrated in Figure 2. The \(\theta_{k}\), \(\theta_{l}\) and \(\theta_{m}\) are the original transmitted wavelengths while others are unwanted generated wavelengths due to FWM. The FWM effect increases while channel spacing decreases in LHOCS. Thus, the impact of FWM becomes more significant for closely spaced channels' based system even at low data rates. To eliminate the effect of FWM in LHOCS, we investigate IDSP receiver management and polarization mode approach in this paper.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Magnitude** \\ \hline Line width & 10 MHz \\ Initial phase & 0 deg \\ Launch power & \(-6\)–\(6\) dBm \\ output power & \(-27\) to \(-16\) dBm \\ Spacing among transmitted multichannel & 50 to 200 GHz \\ Transmission path & 500 km \\ Phase modulation dispersion & \(0.6\) ps/km\({}^{2}\) \\ Multichannel range & 32 \\ Noise bandwidth & 640 GHz \\ Nonlinear dispersion & \(-3\) ps\({}^{3}\)/km \\ EDFA amplifier & Gain controller of 20 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.  
Figure 1: Main setup of LHOCS for 32 channels, 100 Gbps speed and 500 km transmission range.
## 3 Simulations
### Case Study 2: Faults of Multiple Sensors  
In this case study, the sensors of ACE and STEREO-A for the solar wind speed are faulted at 48 h from the beginning of the time development. The both sensors fail simultaneously in the test data. Figure 12 shows the time development for the test case, however, this case involves the faults of the two sensors. The time development for all sensors is the same up to 48 h from the start of the test sequence. The two sensors of ACE and STEREO-A fail at 48 h from the beginning of the time development. Figure 13 shows network snapshots for the test case involving the faults of the two sensors shown in Figure 9. The sensors of ACE and STEREO-A are identified as abnormal by the other sensors associated in Figure 13b. After detection of the sensors faults, all plots of the credibility oscillates between 0.0 and 0.5.  
For the sensor of STEREO-A, the oscillation period of the credibility is longer than that one of ACE. After oscillation is stable, the plots of the credibility for the sensors of ACE and STEREO-A keep to zero until about 80 h from the beginning of the time development. For ACE, the credibility of the sensor recovered to 1.0 at 78 h. After this recovery, For STEREO-A, the credibility of the sensors also recovered to 1.0 at 82 h. This delay is caused by the oscillation period of the credibility when the sensor faults happen.  
The faulted sensor of ACE is replaced with the virtual sensor at 80 h shown in Figure 13b, and after 24 h both sensors are identified as abnormal. However, the sensor of STEREO-A is still the real sensor because the virtual sensor is created after it is identified as abnormal in 24 h. In Figure 12 the period of the oscillation for the credibility is longer than the value of ACE. At 82 h, the credibility of ACE is changed to 1.0 by replacing with the virtual sensor.  
After two virtual sensors are created, in Figure 12, the plots of the time development is similar to the Case Study 1 in Section 3.2. The credibility of the high-energy electron flux is not successful though the flux level attains to the alert level in the observed data.  
Figure 12: Time development of the credibility of each sensor for the test case shown in Figure 9. The satellite names are labeled in horizontal axes. The plots in the first row and the left one in the second row show the time development of the credibility for the solar wind speed. The right plot in the second row (in the third row) show the time development of the high-energy electron flux (high-energy electron flux 24 h ahead).
## 4 Simulation Verification and Physical Experiment
### Method Validity Verification
#### 4.2.2 Experimental Results of Same-Phase Double-Switch Fault  
Figure 7 presents the diagnostic results of simultaneous OC faults occurring in same-phase \(T_{b1}\) and \(T_{b3}\). Upon the occurrence of faults, the positive current in phase b decays to zero. The detection variable \(R_{b}\) immediately exceeds the threshold \(T_{b}\), achieving effective detection of the phase-b fault. Meanwhile, \(W_{b}<w_{b}\) suggests that the fault is preliminarily located in the positive-level switches. \(\eta_{b}\) simultaneously exhibits significant fluctuation, exceeding the classification threshold \(\delta_{2}\). The result shows that \(T_{b1}\) and \(T_{b3}\) both exhibit OC faults at the same time.  
Figure 6: Simulation results of the \(T_{a2}\) OC fault. (**a**) Three-phase currents. (**b**) Fault detection. (**c**) Initial location. (**d**) Precise location.  
Figure 7: Simulation results of the \(T_{b1}\) and \(T_{b3}\) OC fault. (**a**) Three-phase currents. (**b**) Fault detection. (**c**) Initial location. (**d**) Precise location.
# Whole-Body Motion Generation for Balancing of Biped Robot
## 2 Walking Pattern Generation
### Capture Point Reference Generation for Constant ZMP  
Given the predefined footstep locations of \(\mathbf{P}_{0}^{ref},\mathbf{P}_{1}^{ref},\cdots,\mathbf{P}_{N}^{ref}\), which are the reference ZMP, and their respective time intervals (step periods) of \(T_{0}\), \(T_{1},\cdots,T_{N}\), the CP reference trajectory for the \(i\)-th step is obtained with the backward calculation method [31].  
For the \(i\)-th step, with CP at the end, denoted by \(\xi_{i,end}^{ref}\), can be obtained with Equation (7):  
\[\xi_{i,end}^{ref}=\mathbf{P}_{i}^{ref}+\Big{(}\xi_{i,ini}^{ref}-\mathbf{P}_{i}^{ref} \Big{)}e^{\omega T_{i}} \tag{8}\]  
Moreover, since the start of the \(i\)-th step is the end of the \((i-1)\)-th step, by Equation (8):  
\[\xi_{i,ini}^{ref}=\mathbf{P}_{i}^{ref}+\Big{(}\xi_{i,end}^{ref}-\mathbf{P}_{i}^{ref} \Big{)}e^{-\omega T_{i}}=\xi_{i-1,end}^{ref} \tag{9}\]  
When the motion stops at the \(N\)-th step, the final CP of the \((N-1)\)-th step is the same as the \(N\)-th ZMP, \(\xi_{N-1,end}^{ref}=\mathbf{P}_{N}^{ref}\). Using Equation (9), the initial and end positions of the CP position for every step are obtained through backward calculations:  
\[\xi_{N-1,end}^{ref} =\mathbf{P}_{N}^{ref} \tag{10}\] \[\xi_{N-1,ini}^{ref} =\mathbf{P}_{N-1}^{ref}+\Big{(}\xi_{N-1,end}^{ref}-\mathbf{P}_{N-1}^{ref} \Big{)}e^{-\omega T_{N-1}}=\xi_{N-2,end}^{ref}\] \[\xi_{N-2,ini}^{ref} =\mathbf{P}_{N-2}^{ref}+\Big{(}\xi_{N-2,end}^{ref}-\mathbf{P}_{N-2}^{ref} \Big{)}e^{-\omega T_{N-2}}=\xi_{N-3,end}^{ref}\] \[\vdots\] \[\xi_{1,ini}^{ref} =\mathbf{P}_{1}^{ref}+\Big{(}\xi_{1,end}^{ref}-\mathbf{P}_{1}^{ref} \Big{)}e^{-\omega T_{N-2}}=\xi_{0,end}^{ref}\] \[\xi_{init,0}^{ref} =\mathbf{P}_{0}^{ref}+\Big{(}\xi_{end,0}^{ref}-\mathbf{P}_{0}^{ref} \Big{)}e^{-\omega T_{0}}\]  
The CP reference as a function of time in the \(i\)-th step can be calculated by substituting the initial conditions for each step from Equation (10) to Equation (7):  
\[\xi(t)_{i}^{ref}=\left[\begin{array}{c}\xi_{x}(t)\\ \xi_{y}(t)\end{array}\right]_{i}^{ref}=\mathbf{P}_{i}^{ref}+\Big{(}\xi_{i,ini}^{ ref}-\mathbf{P}_{i}^{ref}\Big{)}e^{\omega t} \tag{11}\]  
Figure 2: Cascaded form between CP and COM dynamics.
# Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System
## 4 Results  
The experiments in this study analyzed three different aspects of the validation procedure. The first aspect compares single- and multi-wavelength LiDAR systems. Three channels (i.e., green, NIR, and MIR) were used to simulate single-wavelength LiDAR individually, and the results from single- and multi-wavelength LiDARs were used to compare the accuracy of land cover classification. The second aspect checks the suitability of spectral and geometrical features in land cover classification by evaluating different features extracted from multi-wavelength LiDAR system to understand the significance of the features. The third aspect compares the vegetation indices from passive multispectral images and active multi-wavelength LiDAR, including the results of vegetation classification and correlation of vegetation index.  
To quantify the benefits of the multi-wavelength LiDAR system in land cover classification, we used completeness (also called producer accuracy) and correctness (also called user accuracy) for each land cover in different combinations. Completeness measures the percentage of classes occurring in the reference data and errors of omission; by contrast, correctness measures the percentage of extracted classes correctly classified and errors of commission. The confusion/error matrixes for all cases have been placed in the Appendix A.