esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
From August 2013 to April 2020, 23 patients affected by a primary bone tumour of the pelvis or appendicular skeleton were treated at our Institute with 3D printing custom made prosthesis and PSI technique.  
All patients underwent biopsy and histological characterization and complete staging before surgery. Patients affected by malignant tumours received multimodality treatment including surgery, radiotherapy and chemotherapy. Surgery was addressed for limb salvage and to achieve \"wide\" surgical margins.  
Tumour area and surrounding anatomical structures were studied with a CT scan with 1 mm slices to achieve a representative 3D model, while MRI images were needed for an adequate evaluation of tumour extent within the bone (both medullary and cortically) and in surrounding soft tissues. Patient-specific instruments (PSIs) and implants were designed according to the planned resection strategy.  
The fabrication process both for PSIs either for custom implant is in charge of a company which has a specific expertise and industrial workflow for additive manufacturing. Commercial softwares as well as Mimics(Materialise, Leuven, Belgium) or Invesalius(freeware) are mostly used for segmentation. PSIs can be molded in nylon or printed in titanium alloy according to the clinical needs and to the know-how of selected companies.  
PSIs had bone specific contact surfaces to fit into unique position on the bony structure of the patient based on the shape of the bone acquired by CT scan. PSIs were equipped with flat surfaces and holes to be pinned temporarily on the bone. The final PSI is printed in Nylon or titanium and made sterile for operation.  
The virtual custom prosthesis was created defining the better method of fixation according to the part to be reconstructed mirroring the opposite part.  
The Ti-6 Al-4V implants were fabricated through the deposition of few micron layers of titanium powder melted by electron beams technology (i.e., Electron Beam Melting technique). Implant porosity is widely variable to increase performance and macro-porosity of the body to lighten the implant till micro-porosity at the bone surface contact. These surfaces have pores with an average size of 0.7 millimetres, allowing the host bone to grow directly inside the implant spaces to achieve a stable biological fixation. The prosthesis was provided of tools to allow fixation to the host bone as well as plates or rods, while screws position and lengths were previously planned as well. For articular reconstruction, a polished manufacture or a surface layer (polycaprolactone, PCL or poly-lactic acid, PLA) can be integrated in porous titanium structure.  
All analysis was completed using the Statistical Package for Social Science (IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp.).  
All subjects gave their informed consent for inclusion before they participated in the study.  
The study was conducted under Ethical Approval of our Institute (approval code 0024015/2014) and in accordance with the Declaration of Helsinki, all data were retrieved from medical and radiological records of our hospital.
## 4 Proposed Sensing Results Separation
### Sensing Results Separation without Duplication of Sensing Results
#### 4.2.2 Construction of ICI Replica and Process of Interference Cancellation  
In Equation (5), the \(v\
eq n_{k}\) spectrum components, \(Y(v\
eq n_{k})\), are ICI. Note that the CSI, \(\gamma_{k}\), is constant regardless of subcarrier number, \(v\). Therefore, the CSI estimated in main lobe is used for constructing ICI replica. After the CFO and the CSI are estimated using Equations (9) and (10), respectively, the ICI replica is constructed using Equation (5). FC can substitute the constructed ICI replica from the detected frequency spectrum given by Equation (3) and thus it can remove the ICI components.  
After ICI cancellation, the spectrum with maximal power is selected from the detected frequency spectrum again. As a result, the main lobe of the subsequent subcarrier can be detected. After that, the estimation of CSI and CFO, the construction of ICI replica, and the cancellation of ICI are performed repeatedly, until the subcarriers sent by all the sensor nodes are detected. In the proposed technique, for detection of the main lobe of the subcarrier, ICI is removed. Therefore, the masking of ICI to the other subcarrier and the false detection of subcarrier are avoidable. Note that in PhyC-SN the number of detected subcarriers is equal to the number of sensor nodes as far as the duplication of sensing results does not occur.
## 4 Results
### Human Attention Maps
#### 4.3.2. Cross-Correlation  
The ANOVA revealed a main effect of human-human comparison, \(F\)(1.6,90.7) = 227.835, \(p\) < 0.001, \(\eta\)p\({}^{2}\) = 0.800, a main effect of image type, \(F\)(2,57) = 7.632, \(p\) = 0.001, \(\eta\)p\({}^{2}\) = 0.211 but no interaction, \(F\)(4,114) = 0.760, \(p\) = 0.554, \(\eta\)p\({}^{2}\) = 0.026 (see Figure 5A, grey bars). The main effect of human-human comparison indicated that all comparisons between human tasks yielded different correlations, all \(p\)s < 0.001. While the two eye movement tasks had the highest overlap (0.73), the correlation between drawing and gaze-pointing (0.45) also was higher than that between drawing and spontaneous gaze (0.39). The main effect of image type indicated that correlations were lower for objects (0.43) than indoor scenes (0.61), \(p\) = 0.001, while landscapes (0.54) yielded correlations that were not significantly different from either of the two other image types, both \(p\)s > 0.084. The absence of an interaction indicated that a similar pattern of human-human comparisons was found for all image types, with the two eye movement tasks correlating higher with each other than with drawing, all \(p\)s < 0.001. Moreover, drawing showed slightly higher correlations with gaze-pointing than with spontaneous gaze for both objects and landscapes, both \(p\)s < 0.035, but not for indoor scenes, \(p\) = 0.431.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 7 Future Work  
Our future work will include experiments with real hardware drones instead of simulations. In addition, we intend to incorporate the following areas into our research:  
* Here, we have considered only perfect circular-shaped coverage for drones; we intend to engage in a detailed exploration of other shapes as well.
* The present work concentrates on the algorithm used to fill the area without voids using stationary drones. We intend to study the effects of achieving the same using moving drones. In addition, we may explore different technologies available for establishing and maintaining communication between the drones and base stations.
* A detailed comparison between the region-filling algorithm and other network coverage management methods should be explored.  
Finally, we intend to optimize the region-filling algorithm by finding the best specific terrain locations for the base stations.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/computers11090139/s1](https://www.mdpi.com/article/10.3390/computers11090139/s1)]([https://www.mdpi.com/article/10.3390/computers11090139/s1](https://www.mdpi.com/article/10.3390/computers11090139/s1)), Video S1: 3D simulation of drone deployment in Pettimudi, created in MatLab, Video S2: Simulation of drone deployment in Pettimudi, created in OmNet++.  
**Author Contributions:** Conceptualization, K.M.B.S.K.; methodology, B.V.V., P.S.K. and K.M.B.S.K.; software, B.V.V., P.S.K., R.S.J. and J.T.; validation, B.V.V., P.S.K., R.S.J. and J.T.; formal analysis, B.V.V. and P.S.K.; investigation, R.S.J. and J.T.; resources, K.M.B.S.K.; data curation, R.S.J.; writing--original draft preparation, B.V.V., P.S.K., R.S.J. and J.T.; writing--review and editing, K.M.B.S.K.; visualization, J.T.; supervision, K.M.B.S.K.; project administration, K.M.B.S.K.; funding acquisition, K.M.B.S.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by TEQIP Phase II of the Rajiv Gandhi Instituite of Technology, Kottayam, India.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank TEQIP Phase II of the Rajiv Gandhi Institute of Technology, Kottayam, India, for providing financial assistance for this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Conclusive Model-Fit Current-Voltage Characteristic Curves with Kink Effects
## 2 Preparation of as Measured Data and Fitting
### Fitting \(I_{\text{DS}}\)-\(V_{\text{DS}}\) and \(I_{\text{DS}}\)-\(V_{\text{GS}}\)  
For MOSFET devices, the commonly conventional formula is derived as follows by starting with \(\text{J}_{\text{DS}}\), the current density:  
\[\begin{array}{l}\frac{I_{\text{DS}}}{W\text{D}}=I_{\text{DS}}=ne\mu\frac{dV}{ dl};\\ \int\limits_{0}^{L}I_{\text{DS}}dl=\int\limits_{0}^{V_{\text{DS}}}\mu W(neD)dV; \\ I_{\text{DS}}=\frac{\mu W}{L}\int\limits_{0}^{V_{\text{DS}}}(neD)dV\end{array}\]  
where \(W\) and \(D\) denote the channel width and channel depth after strong inversion, n means the density of the carriers, and \(e\) = 1.6 \(\times\) 10\({}^{-19}\) coulomb signifies the charge of the carriers. Also, \(neD\) = \(Q^{(1)}\) = \(C^{(1)}\Delta V\) = \(C^{(1)}(V_{GS}-V_{th}-V)\) is treated as the carrier charge per unit area charging in the unit capacitance \(C^{(1)}\), and \(\mu\) (the mobility of carriers) is treated as a constant at a fixed \(V_{\text{GS}}\) for the integration over the channel voltage from 0 V to \(V_{\text{DS}}\). In addition, the term, \((1+\lambda V_{DS})\), is deliberately added in the triode region as compared to the conventional one when both the short-channel effect and charging sharing model are taken into account to address the channel length, \(L\) = \(L_{o}(1+\Delta L/L_{o})\). The modified formulas are then proposed in Equation (1):  
\[\begin{array}{l}I_{\text{DS}}(\text{t}oride)=\frac{\mu W\text{C}^{(1)}}{L_{o }}[(V_{\text{GS}}-V_{th})V_{\text{DS}}-\frac{V_{\text{DS}}^{2}}{2}](1+\lambda V _{\text{DS}})\\ -\alpha\exp[-\beta(V_{\text{DS}}-\chi)^{2}]\end{array} \tag{1}\]  
and  
\[\begin{array}{l}I_{\text{DS}}(\text{s}aturation)=\frac{\mu W\text{C}^{(1)}}{L_{o }}[\frac{(V_{\text{GS}}-V_{th})^{2}}{2}](1+\lambda V_{\text{DS}})\\ -\alpha\exp[-\beta(V_{\text{DS}}-\chi)^{2}]\end{array} \tag{2}\]  
Somehow, without or with the added term, \((1+\lambda V_{\text{DS}})\), in the triode region in Equation (1), the discontinuity may appear or disappear as the triode region goes on to the saturation region, as shown in Figure 1. The coefficients (\(\alpha\) and \(\beta\)) in Equations (1) and (2) are referencing parameters for the kink effects. \(\alpha\) (A) is mainly the maximum subtracted or added current, and \(\beta\) (\(1/V^{2}\)) is from \(-10\) to \(-20\), depending on how wide the kink is. \(\chi\) is always found to be located around \(\chi\) = \(V_{\text{GS}}-V_{th}\).
# A 11 mW 2.4 GHz 0.18 \(\upmu\)m CMOS Transceivers for Wireless Sensor Networks
## 3 Circuit Implementation
### Mixer and Analogue Baseband Circuits  
Since the low noise amplifier is single-ended, a single balanced active mixer is used to save power consumption. Shown in Figure 6, the topology in this design is quite conventional and the major task here is the proper sizing of these devices to achieve low power consumption subject to acceptable gain and linearity (the noise figure of the active mixer is not an issue compared with a passive one). The gain of the mixer is linearly proportional to the trans-conductance of the transistors MN2 and MN3 and the reload resistance of the mixer. To save power consumption, it is desirable to use a larger resistance, such as 6 k\(\Omega\) in this work. Using the biasing current of 0.6 mA, it is possible to achieve over 300 mV output swing and over 15 dB gain at 2.4 GHz to 2.5 MHz (IF) gain. For the linearity, it is mainly decided by the biasing condition of the MOS transistor.  
With regard to the low data-rate in this work, the IF frequency is set to 2.5 MHz (reconfigurable). The signal is amplified with an IF amplifier with programmable gain control. The design is a conventional MOS switch-based amplifier [13]. By controlling the biasing condition and switching of different loads. The amplifier has a tuning range of 30 dB in gain.  
Figure 5: Simulation results of the LNA (**a**) S parameters and noise figure; and (**b**) IP3.  
Figure 6: The mixer (**a**) topology and (**b**) simulated gain.
## 4 Discovery of physical TFBSs  
One of the first questions related to TFBS identification would be how to detect a conserved motif in a given set of sequences. The problem can be simply stated as follows: given a set of N sequences \(S=\big{\{}s_{i}\big{\}}_{i=1}^{N}\), \(s_{i}=\big{\{}s_{il}\big{\}}_{l=1}^{k}\), \(s_{il}\in\) A, A = \(\big{\{}A,C,G,T\big{\}}\), identify conserved motifs \(p=\big{\{}p_{k}\big{\}}_{k=1}^{K}\), \(p_{k}\in\) A that are overrepresented, i.e. motifs present in \(S\) at a statistical significant rate. The fundamental assumption is that if the sequences are promoters of genes, then conserved motifs can be assumed to be potential binding sites for TFs. There have been a wide range of possible applications for such _in silico_ motif discovery methods. First, they greatly assist experimental studies aiming towards detection of the collection of binding sites for a given TF [32]. ChIP-chip assays, for example, identify genomic regions to which a TF of interest binds. However, locating exact sites where the TF binds might be very difficult due to the limitations of the assays. As a result, once the DNA sequences to which the TF binds have been collected motif discovery algorithms, e.g. consensus [64], Gibbs sampling [65], MEME [66], are then applied to locate the exact binding sites. Secondly, if one identifies a set of genes that can be considered as regulated by some common TF(s), then one can begin to search computationally for conserved motifs in the corresponding promoter to infer regulating TFs. The underlying assumption of such a computation is that the common patterns are the likely functional ones. Furthermore, motif discovery algorithms can also assist in cross-species extrapolation to improve the specificity of finding TFBSs on a gene promoter. Once a set of corresponding promoters of a gene across multiple species have been extracted, motif discovery algorithms are used to detect conserved sub-sequences in this promoter across species in an attempt to identify all potential _cis_-regulatory elements (discussed more details in the next section). Because of the importance of this problem, a variety of algorithms as well as computational tools have been developed for those problems above for the past twenty years (**Table 1**). However, generally speaking the core algorithms can be classified into two categories: combinatorial and probabilistic [26,
###### Abstract  
This study analyzes the influence of the Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) on the Mars Environmental Dynamics Analyzer (MEDA) station located on board the Perseverance rover (Mars 2020). A novel visualization methodology was developed using a hydrodynamic towing tank and 3D-printed models created through additive manufacturing. This experimental approach, not previously applied in this context, proved to be a cost-effective alternative for studying thermal interactions while providing accurate preliminary insights into the behavior of thermal plumes under Martian-like conditions. Key factors such as the extremely low Reynolds number, an increasing temperature of the model, and atmospheric properties similar to those in Mars were incorporated. The findings suggest that the MMRTG's thermal plume may significantly influence MEDA's performance due to the plume's height and its interaction with the surrounding environment.  
hydrodynamic towing tank testing; mars environmental dynamics; extremely low Reynolds numbers; flow visualization; image analysis Article
# Point Cloud Measurement of Rubber Tread Dimension Based on RGB-Depth Camera
## 3 Methodology and Design
### Point Cloud Data Preprocessing
#### 3.6.4 Point Cloud Alignment  
In the problem of aligning 3D point cloud models, the crucial task is to accurately establish the rigid-body transformation matrix between two sets of points, ensuring optimal alignment of their relative positions and orientations. The mainstream alignment process typically involves establishing the topological relationships between point clouds, computing local geometric features, performing feature matching, estimating rigid-body transformations, and transforming the point clouds accordingly.  
In this study, point cloud alignment is achieved using the ICP algorithm. The iterative process minimizes the Euclidean distance between point clouds \(P\) and \(Q\) until a predetermined threshold is met. The Euclidean distance formula and the target error function are detailed in Equation (5):  
\[\left\{\begin{array}{l}D=\sqrt{\left(x_{q}-x_{p}\right)^{2}+\left(y_{q}-y_{p }\right)^{2}+\left(z_{q}-z_{p}\right)^{2}}\\ F(H_{R},H_{T})=\sum\limits_{i=1}^{N_{P}}\left\|Q_{i}-\left(H_{R}P_{i}-H_{T} \right)\right\|^{2}\end{array}\right. \tag{5}\]  
where \(Q_{i}\) is within the point cloud \(Q\), the distance \(P\) is \(P_{i}\) on the point cloud, the Euclidean distance \(D\) is the shortest point, and \(N_{P}\) is the number of points corresponding to the two point clouds, and iterating the distance to the smallest point yields the final rigid-body transformation translations and rotations matrices \(H_{T}\) and \(H_{R}\).  
The ICP exact alignment process is as follows:  
1. In the initial iterative point cloud set, the point with the smallest Euclidean distance value in the \(P\) and \(Q\) point clouds is taken as the corresponding point, and the corresponding point set is formed;
2. Calculate the translation and rotation matrices \(H_{T}\) and \(H_{R}\) from the corresponding point set to find the current objective loss function \(F_{i}\);
3. With the rigid-body transformation matrix obtained in the second step, perform a rigid-body transformation on the target point cloud, and follow the idea of the first step to update the corresponding point set according to the Euclidean distance between the new point cloud;
4. Repeat the second step when the value of the target error function is larger than the set threshold and stop the iteration when the opposite or the number of iterations reaches the upper limit.  
At the end of the alignment, the Root-Mean-Square Error (RMSE) between the two-point clouds is calculated and used to evaluate the point cloud alignment effect, as shown in Equation (6); the smaller the value of \(V_{RMSE}\), the better the alignment effect.  
Figure 11: Filter effect diagram: (**a**) before filtering; (**b**) after filtering.  
\[V_{RMSE}=\sqrt{\frac{1}{N_{P}}\sum_{i=1}^{N_{P}}(Q_{i}-P_{i})^{2}} \tag{6}\]
## 2 Materials and Methods
### Measurement
#### 3.2.1 LSCM Test  
Laser confocal microscopy experiments were conducted on SBS-modified asphalt samples with different pre-cut particle sizes to observe their microscopic morphology. The test results are presented in Figure 10.  
In the pre-cut SBS-modified asphalt with a particle size of 18-10 mesh, the morphology of the SBS modifier was similar to that in the conventional-process asphalt. Clear SBS particles were observed, but the maximum particle size was smaller than that of the conventional-process asphalt. In the 18-10 mesh SBS-modified asphalt, the SBS particles mostly exhibited a granular distribution with varying sizes, and some smaller particles showed a tendency toward filamentous distribution. In the pre-cut SBS-modified asphalt with a particle size of 30-18 mesh, the SBS particles mostly exhibited a filamentous distribution, with a few appearing as small granular shapes. In the case of the 100-30 mesh SBS-modified asphalt, the SBS morphology included partially filamentous distribution, but with shorter lengths; some particles were in the form of small granules, and there were even extremely small particles that visually resembled a hazy mist.  
Figure 9: BBR test results of pre-cut SBS-modified asphalt mixed for different amounts of time. (**a**) Stiffness modulus, (**b**) m-value.  
According to the morphological transformation process of SBS described in Section 3.1, the conventional process produces SBS-modified asphalt with the fewest cycles of morphological changes, which is why larger SBS particles were still present. The 18-10 mesh SBS underwent a slightly higher number of morphological change cycles, resulting in noticeable SBS particles. The 30-18 mesh and 100-30 mesh SBS underwent even more cycles. This suggests that when preparing modified asphalt through the mixing process, smaller pre-cut SBS particle sizes undergo more cycles of morphological changes. The higher number of cycles for pre-cut SBS-modified asphalt indicates increased contact area with the asphalt, higher heat absorption, and an enhanced blending effect with the asphalt. Based on the microscopic morphology of the SBS-modified asphalt, it can be inferred that pre-cut SBS effectively replaced the high-shear mixing process.