esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous Manipulator of a Mobile Robot Based on a Vision System
## 5 Conclusions
The transformation matrices between successive coordinate systems of the manipulator parts are presented below. \[M_{1}^{0}=Rot_{z}\theta_{1}\cdot Trans_{z}d_{1}\cdot Trans_{x}a_{1}\cdot Rot_ {x}-\pi/2= \begin{bmatrix}\cos\theta_{1}&0&-\sin\theta_{1}&a_{1}\cos\theta_{1} \\ \sin\theta_{1}&0&\cos\theta_{1}&a_{1}\sin\theta_{1}\\ 0&-1&0&d_{1}\\ 0&0&0&1\end{bmatrix}  \tag{12}\]\[M_{3}^{2}=Rot_{z}\theta_{3}\text{-}Trans_{z}d_{3}\text{-}Trans_{x}a_{3}\text{-}Rot _{x}-\pi/2= \begin{bmatrix}\sin\theta_{3}&0&\cos\theta_{3}&\text{a}_{3}\sin \theta_{3}\\ -\cos\theta_{3}&0&\sin\theta_{3}&-a_{3}\cos\theta_{3}\\ 0&-1&0&d_{3}\\ 0&0&0&1\end{bmatrix}  \tag{10}\]  
\[M_{4}^{3}=Rot_{z}\theta_{4}\text{-}Trans_{z}d_{4}\text{-}Trans_{x}a_{4}\text{ -}Rot_{x}\pi/2= \begin{bmatrix}\cos\theta_{4}&0&\sin\theta_{4}&\text{a}_{4}\cos \theta_{4}\\ \sin\theta_{4}&0&-\cos\theta_{4}&\text{a}_{4}\sin\theta_{4}\\ 0&1&0&d_{4}\\ 0&0&0&1\end{bmatrix}  \tag{11}\]  
\[M_{5}^{4}=Rot_{z}\theta_{5}\text{-}Trans_{z}d_{5}\text{-}Trans_{x}a_{5}\text{ -}Rot_{x}-\pi/2= \begin{bmatrix}\cos\theta_{5}&0&-\sin\theta_{5}&\text{a}_{5} \cos\theta_{5}\\ \sin\theta_{5}&0&\cos\theta_{5}&\text{a}_{5}\sin\theta_{5}\\ 0&-1&0&d_{5}\\ 0&0&0&1\end{bmatrix}  \tag{12}\]  
Multiplying these matrices, we obtain a transformation matrix that defines the position and orientation of the U\({}_{6}\) gripper system in relation to the U\({}_{0}\) basis system. \[M_{6}^{0}=M_{1}^{0}\text{-}M_{2}^{1}\text{-}M_{3}^{2}\text{-}M_{4}^{3}\text{-} M_{5}^{4}\text{-}M_{6}^{5}= \begin{bmatrix}n_{x}&s_{x}&a_{x}&p_{x}\\ n_{y}&s_{y}&a_{y}&p_{y}\\ n_{z}&s_{z}&a_{z}&p_{z}\\ 0&0&0&1\end{bmatrix}  \tag{13}\]  
Equation (13) describes the kinematic model of the presented manipulator.
# Numerical Analysis of Human Cancer Therapy Using Microwave Ablation
## 2 Description and Formulation of the Problem
### Numerical Method and Model Validation  
The set of equations describing the electromagnetic wave propagation and the heat transfer can be solved by the finite element method (FEM) with the Galerkin approach [35]. To find the numerical solution of these coupled equations, a computer code was developed. The physical domain was composed of three subdomains, namely, the liver, the tumor, and the antenna. The axial symmetry 2D global domain was divided into triangular elements using Lagrange quadratic shape functions. The sensitive domain was refined with a better mesh quality.  
Figure 5 displays a flow chart describing the simulation scheme. The coupling of the equations lies from the fact that the volumetric source term in the heat equation depends on the amplitude of the electric field, and, inversely, the relative permittivity and the electrical conductivity of the medium depend on the temperature.  
This is a two-way coupling. The approach which was adopted to solve the equations is given below.  
1. Specify the geometrical, thermal, and physical parameters as well as the input microwave power, the frequency, and the voltage of the excitation signal.
2. Give an arbitrary temperature.
3. Solve [PERSON]'s equations.
4. Deduce \(Q_{ext}\).
5. Solve the bio-heat equation and deduce a new value of the temperature.
6. Determine the values of \(\varepsilon_{r}(T)\) and \(\sigma(T)\).
7. Repeat steps 3-6 until convergence.
8. Deduce the field \(T(t,r,z)\) and other useful quantities.  
In order to check the accuracy of our model, the simulation results obtained for the bio-heat model proposed in this work were validated with experimental data obtained by [PERSON] et al. [36]. Figure 6 exhibits the validation related to the temperature distribution in the tissue liver at two positions during the MWA process. These positions are defined by a distance of 4.5 mm or 9.5 mm away from the  
Figure 5: Flow chart describing the simulation scheme.  
antenna. In this case, the microwave was operating at 2.45 GHz with 75 W input power and the initial temperature of the liver tissue was assumed to be 8 \({}^{\circ}\)C.  
The results show that the temperature has the same trend as the experimental curve. The difference is very small. This confirms the validation of the present model.
# Performance Optimization of Object Tracking Algorithms in OpenCV on GPUs
## 3 Analysis of OpenCV Object Tracking Algorithms
### Object Detection Algorithm
#### 3.1.3 Profiling Results  
The GPU profiler offers a variety of features for analyzing OpenCL kernels, including an occupancy calculator and hardware performance counter. Table 3 shows the profiling results of the runHaarClassifier kernel using the GPU profiler. As shown in the table, the number of VGPRs and SGPRs, the LDS memory, and the global work size do not change regardless of the image resolution. The two rows #_of VGPR-limited waves_ and #_of LDS-limited waves_ show the number of maximum wavefronts when each resource becomes the limiting factor. In particular, only 12 out of 40 wavefronts could be activated owing to  
Figure 6: Experiment results of the Haar object detection algorithm by resolution. (**a**) Frame processing time. (**b**) Frame processing time breakdown.  
being limited by the VGPR, which is shown in the row # of _VGPR-limited waves_. On the contrary, the LDS memory usage allows 17 active wavefronts, which is larger than that by the VGPR usage. Consequently, the VGPR usage of the kernel is the primary limiting factor to the wavefront activation, and the kernel occupancy is eventually 30% (12 over 40). In other words, reducing the VGPR usage can help increase the occupancy, thereby improving the performance of the kernel.  
The GPU profiler can also utilize the hardware performance counter to collect the hardware information used by the kernel. Table 3 also shows the collected information during the execution of the runHaarClassifier kernel. In the table, the most directly impacting factors on the kernel performance are the _Busy_ values (_VALUBusy_, _SALUBusy_, and _MemUnitBusy_); each value represents the utilization of the corresponding resources (i.e., vector ALU, scalar ALU, and GPU memory, respectively). Hence, high utilization indicates the kernel is being executed while effectively utilizing the hardware resources. However, the runHaarClassifier kernel shows low ALU utilizations, less than 40% for VALUBusy and 10% for SALUBusy. Furthermore, the MemUnitBusy is only about 50%. This is because the number of wavefronts is very low compared to the total available on the computing device. Although with 8 CUs, a total of 320 wavefronts are available (40 \(\times\) 8 CUs), the actual number of active wavefronts is only 96 (12 wavefronts per CU) in Table 3.  
To sum up, the resource utilization of the computing device deteriorates because excessive VGPR usage lowers the occupancy and the number of active wavefronts. Therefore, managing VGPR usage can improve the throughput of the object detection algorithm. Section 4.1 presents improvement techniques to resolve these issues.
## 5 Simulation and Validation  
To validate the two proposed models, an industrial standard vehicle dynamics simulation software, CARSIM(r) was used to carry out simulations [16; 19; 20]. A standard SUV model was chosen from CARSIM(r) vehicle models as shown in Figure 8. Simulation and validation results of vehicle roll behavior were carried out under steering wheel (SW) input \(10^{\circ}\) condition as an example case to facilitate better analysis of vehicle roll behavior.  
Based on the above analysis, half-car and full-car models proposed in Section 4 were simulated in MATLAB to study performance of vehicle roll behavior with or without road excitation (two representatives). It is assumed that the tire does not loose ground contact. Here, the J-turn maneuver [15] was utilized to illustrate roll behavior for steering wheel at \(10^{\circ}\) at a velocity of 80 km/h with presence of road excitation. It was concluded from the results vehicle roll behavior mainly depends on roll angle, roll rate, and yaw rate. It should be noted that roll angle is variable under varying steering wheel input, and roll angle can be seen as a reference validation for vehicle roll behavior.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 6 Practical Challenges in Underwater SLAM
### Energy Efficiency
Overall, ensuring robust underwater SLAM performance requires tackling high power consumption at both algorithmic and system levels. As missions expand in duration and scope, energy-aware methods will be crucial to sustaining effective mapping and navigation without frequent manual intervention.
## 2 Related Works and Our Contributions
### Related Works
Similarly, [PERSON] and [PERSON] [13] used their Att-ResBiGRU model with fixed sensor placements, showing reliable generalization with low variance across multiple fixed positions in datasets such as PAMAP2 and REALWORLD16. Summary of Limitations: While fixed sensor positions improve recognition accuracy in controlled environments, they reduce adaptability in real-world, variable settings. Our study extends this approach by investigating HAR using realistic smartphone placements, such as the chest and leg pockets, which are more applicable to real-world usage scenarios. To address these constraints, position-independent HAR methodologies have been explored. For instance, [PERSON] et al. [24] applied a 1D-CNN-LSTM model to manage intra- and inter-position sensor variability, achieving recognition rates between 68.66% and 73.64% across various device placements. [PERSON] and [PERSON] [13] further applied position-independent HAR using the Att-ResBiGRU model, achieving high F1 scores across multiple datasets, demonstrating robustness across diverse positions, albeit primarily in wearable devices. Our Improvements: Our work builds upon these studies by testing HAR in flexible smartphone positions, aiming to expand position-independent HAR applications in transportation by utilizing multiple sensors to increase adaptability and recognition accuracy in varied scenarios. Integrating both machine learning and deep learning methods has shown strong results in HAR. [PERSON] and [PERSON] [14] highlighted deep learning's strength in handling complex HAR tasks, although traditional models are often favored for their lower computational requirements. They also noted the importance of temporal modeling for activity prediction, suggesting that techniques like LSTM can leverage these temporal dependencies for better activity forecasting. Expanding Model Comparisons: Our research includes an expanded comparison of both traditional and deep learning techniques, now incorporating a wider range of models to meet the diverse demands of ITS applications. These models include Random Forest (RF) [25; 26], a widely-used ensemble model that balances accuracy with low computational demand, and XGBoost [27], known for its scalability and enhanced performance with large datasets. For sequential and complex patterns, we utilize LSTM and Temporal Convolutional Networks (TCN) [28], as well as Gated Recurrent Units (GRU) and Transformer models [29], both recognized for their efficiency in capturing dependencies in time-series data. This expanded approach enables us to capture a broader range of user behaviors in transportation scenarios, further advancing real-time HAR adaptability. Addressing Model Limitations: Our research compares both machine learning and deep learning techniques for HAR, including DT, KNN, RF, SVC, XGBoost, GRU, LSTM, TCN, and Transformer, aiming to balance computational demands with improved accuracy in dynamic, real-world conditions typical of ITS.
## 2 Materials and Methods
### Data Processing
#### 2.3.1 Comparative Analysis and Scoring  
Peak heights detected within a particular \(m/z\) region were systematically compared between each stage and the control group for a variable range of \(m/z\) windows from 5 to 500 \(m/z\). Statistical significance between stages and control peaks was tested using the non-parametric, two-tailed Wilcoxon-Mann-Whitney test. Median signal intensities (MIs) were computed for each \(m/z\) region and were compared with the control if the overlap between these groups was lower than 15% as previously described [18]. The enrichment of peaks within a particular \(m/z\) region was also computed for each stage in comparison to the control group as previously described [18; 20]. Peaks exclusively present in cancer stages and not in the control were also searched based on the computed enrichment values. Based on the above-mentioned comparative metrics, we developed a scoring-based mathematical model for ovarian cancer stage classification (OCS) with the following mathematical equation:  
\[\text{OCS}=\sum_{\text{i=1}}^{\text{n}}r\text{DIMi}\times\text{Ei}\ +\sum_{\text{j=1}}^{\text{m}}\text{Ej}\]  
In this mathematical model \(r\text{DIMi}\) is the relative deviation between the median mass spectral signal intensity (peak height) characteristic of a particular stage on a particular \(m/z\) region i; \(Ei\) is the peak enrichment on a particular \(m/z\) region i; \(n\) is the total number of mass spectral intensity differences characteristic of a particular stage; \(Ej\) is the enrichment of a peak exclusively present in a particular stage; and \(m\) is the total number of characteristic peaks exclusively found in a stage.
## 3 Results and Discussion
### Effect of SeNPs on Blood Creatinine Level  
The results of CREA are shown in Figure 2. The level of CREA is used as an indicator of renal function [41]. The blood CREA level increases after Cd administration by 17%, but the treatment with SeNPs in both forms (purified and LSeNPs) significantly decreased the level of this indicator except for the group were the lowest dose of SeNPs was used  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Target** & **Sense** & **Antisense** \\ \hline NF-kB 65 & 5\({}^{\prime}\)CTTGCAACAGCACAGACC3\({}^{\prime}\) & 5\({}^{\prime}\)GAGAGTCCATGTCCGCAAT3\({}^{\prime}\) \\ TNF-\(\alpha\) & 5\({}^{\prime}\)CGTGACCACCGTACG3\({}^{\prime}\) & 5\({}^{\prime}\)TTGAGATCCGTCCGGTTG3\({}^{\prime}\) \\ IL-6 & 5\({}^{\prime}\)AAAGATTTGTGCAATGTGCAATTCT3\({}^{\prime}\) & 5\({}^{\prime}\)AAGTGCATCATGTGTGTTCATACA3\({}^{\prime}\) \\ GAPDH & 5\({}^{\prime}\)CGACTTTCAACAGCAACTCCACTTTCC3\({}^{\prime}\) & 5\({}^{\prime}\)TGGGGTCCAGGTTTTTTACTCCTT3\({}^{\prime}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primer sequences for RT-PCR.  
(0.1 mg/kg). The lowest CREA levels was recorded for the 0.2 mg/kg of LSeNPs group, demonstrated the good efficiency of LSeNPs to annihilate the Cd-induced renal toxic effect.  
No significant differences were observed in CREA level when the male Sprague-Dawley rats were administrated gavage SeNPs at levels between 0.2 and 8.0 mg/kg body weight, demonstrating that SeNPs were not toxic at the concentrations tested [42]. In other study, the CREA levels were higher in male albino rats group that receive Cd compared with the control group, instead not significantly changes were recorded in Se treated group alone or Cd + Se treated group compared with control group [23].
## 2 Methods
### Image Processing  
The final goal of post-processing of recorded MCA imagery is the conversion of measured digital numbers (DN) into georeferenced at-surface reflectance images. This multistage procedure consists of three major components: (i) radiometric image correction; (ii) transformation of sensor coordinates into a geographic coordinate system and image alignment; and (iii) absolute radiometric calibration. The radiometric image correction includes noise reduction, correction of sensor-based illumination fall-off (vignetting) and lens distortion. The transformation of sensor coordinates includes the fusion of recorded GPS measurements with collected images, band-wise automated aerial triangulation (AAT), the minimizing of remaining geometric distortions and the alignment of the 12 single bands to one multispectral image using ground control points (GCPs). Radiometric calibration is the conversion of measured DNs into at-surface reflectance. For image correction we followed a practical approach proposed by [PERSON] and [PERSON] [6], developed for a single image  
Figure 4: Screenshot of the MAVCDesk software. (**Left**) Primary flight display (not active) showing UAV status information; (**Right**) Visualization of the flight path across a map of the CarboZALF experimental area.  
captured by a Mini-MCA 6. They used dark offset imagery to create average noise corrections and images of a homogeneous illuminated near-Lambertian white surface to create a per-pixel flat-field correction factor. The procedure incorporates correction techniques proposed by [PERSON] _et al._[27].
## 6 Conclusions  
1. A method for calculating the instantaneous pose on the flank face has been proposed. A calculation model of the instantaneous wear volume of the tool flank was established. The distribution and variation of the instantaneous wear volume of the cutter tooth's flank face under milling vibration were revealed. The results show that the instantaneous wear depth and instantaneous wear volume on the flank face show unsteady variations with the contact angle and the cutting cycle, and there are obvious differences between different cutter teeth.
2. The instantaneous pose and instantaneous friction velocity and the instantaneous friction stress and instantaneous friction energy consumption model of the micro-element on the cutter tooth were established, and the distribution of instantaneous friction energy consumption on the flank face was revealed. The correlation analysis results showed that the correlation between the cumulative friction energy consumption of the tool flank and the experimental results of the wear depth was greater than 0.860. This model can be used to identify and reveal the friction and wear characteristics. The response property of the friction and wear of the flank face to change the structure and position was revealed.
3. An identification method for the instantaneous friction and wear energy density of the flank face has been proposed. The method revealed the difference in the dynamic relationship between the friction energy and wear on the flank of each cutter tooth of the milling cutter. The comparative analysis results of the process schemes showed that the method could be used to quantitatively characterize the diversity of the dynamic distribution of friction and wear on the flank face, and the milling process scheme can be evaluated accordingly.  
Conceptualization, B.J. and Q.N.; methodology, B.J. and P.Z.; software, S.S. and Q.M.; validation, P.Z. and Q.M.; investigation, Q.N; resources, B.J.; data curation, S.S. and Q.M.; writing--original draft preparation, Q.N.; writing--review and editing, Q.N. and B.J.; supervision, B.J. and P.Z.; project administration, B.J.; funding acquisition, B.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of the Heilongjiang Province of China, ZD2020E008, the National Natural Science Foundation of China: 52105440, and the National Natural Science Foundation of China, 51875145.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Accelerated Refraction-Corrected Geometry-Based Phase Aberration Correction  
We extensively described and evaluated the ARC-GB approach in Ref. [34]. In summary, it consists of 4 steps:  
1. Estimation of the wave-speed in the bone layer with the bidirectional head-wave technique or the autofocus technique [32].
2. Intermediate points (IPs) are defined on the outer surface of the silicone rubber (called SIPs), the shortest travel-time from pixels in the superficial soft tissue layer to the array elements through the SIPs is found, the image of the superficial soft tissue layer is reconstructed using the calculated travel-times, and the near-surface of the bone layer is segmented using [PERSON]'s algorithm [35].
3. IPs are defined on the near-surface of the bone layer (called NIPs), the shortest travel-time from pixels in the bone layer to array elements through the NIPs is found, the image of the bone layer is reconstructed using the calculated travel-times, and the far-surface of the bone layer is segmented using [PERSON]'s algorithm.
4. IPs are defined on the far surface of the bone layer (called FIPs), the shortest travel-time from pixels in the brain to array elements through the FIPs is found, and the brain image is reconstructed using the calculated travel-times.  
For reconstruction, a modified delay-and-sum algorithm (as explained in Refs. [31; 32; 33]) was used. The thickness and the wave-speed in the silicone rubber layer can be found either by ultrasound measurements or the datasheet provided by the probe manufacturer. The image reconstruction depth used in steps 2 and 3 can be determined based on prior knowledge of the thickness of the skin and bone layers [36]. The terms \"near surface\" and \"far surface\" are defined with respect to the probe. The results captioned \"Geometry-based paper\" are generated with the ARC-GB reconstruction technique.
## 4 Results and Discussion
### Performance Comparison  
Hidden Markov models (HMMs) [31] have been tested and shown higher disaggregation accuracy in this context, so HMM-family methods are often adopted as benchmarks for algorithm testing and comparison. In literature [32], FHMM was adopted to identify the appliance states and only power consumption considered as signature. We compared the F-score values of each appliance between our approach and the FHMM in literature [32]. The result is in Table 5. The F-score comparison shows that our approach is able to correctly detect the operating states of each appliance with a higher rate than FHMM. Based on the analysis in literature, the number of possible states for each appliance is limited to 2 in FHMM, and with the increase in the number of appliances and states, the calculation time of the FHMM algorithm increases dramatically.  
Yet, average power and the DTW-based method are used for power estimation separately in References [9] and [32]. We implemented these two methods on AMPds. On the other metrics, the comparison of PCE and R2 values with the result of References [9] and [32] is in Tables 6 and 7. Obviously, the average power based method cannot show the power change during the operation of appliances, so its estimation performance in R2 is worse than that of the DTW-based method and our proposed method. The results showed that our proposed method performs better in providing information on the consumption patterns and contribution of each appliance to the total power except when it comes to the heat pump. Since the consumption pattern of the heat pump is affected by season, there was a slight deviation in the estimated value.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Appliance**} & **Average Power Method** & **DTW-Based Method in** \\  & **in Literature [9]** & **Literature [32]** & **Proposed Method** \\ \hline Dish washer & 0.074 & 0.242 & 0.057 \\ Heat pump & 0.453 & 0.1 & 0.521 \\ Clothes dryer & 0.463 & 0.2 & 0.083 \\ Fridge & 0.952 & 0.077 & 0.090 \\ \hline \hline \end{tabular}
\end{table}
Table 6: PCE (%) values compared with the method in References [9] and [32].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Appliance**} & **Period 1** & **Period 2** & **Period 3** \\ \hline Dish washer & 0.052 & 0.055 & 0.074 \\ Heat pump & 0.511 & 0.505 & 0.544 \\ Clothes dryer & 0.070 & 0.203 & 0.211 \\ Fridge & 0.075 & 0.137 & 0.216 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Power contribution error (PCE) (%) for four appliances from three periods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Appliance**} & **FHMM in Literature [32]** & **Proposed Method** \\ \hline Dish washer & 0.87 & 0.995 \\ Heat pump & 0.96 & 0.980 \\ Clothes dryer & 0.72 & 1 \\ Fridge & 0.98 & 0.999 \\ \hline \hline \end{tabular}
\end{table}
Table 5: F-score values compared with the factorial hidden Markov models (FHMM) method.