esa-sceva/satcom-chunk-collection huggingface dataset:
# A Framework for Real-Time 3D Freeform Manipulation of Facial Video
## 2 Facial Manipulation Framework
### Registration
Here, the corresponding landmarks on the model \(\mathbf{X}\left(\boldsymbol{\alpha}\right)\) are determined by the mean face model and the linear combination of the shape basis and coefficients:  
\[\mathbf{X}\left(\boldsymbol{\alpha}\right)\subset\mathbf{M}_{s},\ \mathbf{M}_{s}=\mathbf{\tilde{M}}+\mathbf{M}_{sb}\cdot\boldsymbol{\alpha}. \tag{3}\]  
The expression coefficients \(\boldsymbol{\beta}\) are then computed using the estimated shape \(\mathbf{M}_{s}\) in a similar way. Finally, head pose and coefficients are iteratively updated and refined. As shown in Figure 3, however, the facial outline landmarks are not consistent for changes of the head pose; thus, the proposed framework searches directly for the corresponding landmarks on the model rather than using the predefined mapping between corresponding 2D and 3D landmarks. In [21], the vertices of the 3D face model, which belong to the front face from the current viewpoint and overlap others, are selected and projected on the video frame. Projected vertices within a certain distance of facial landmarks detected in the frame are defined as the corresponding landmarks on the model. In this approach, multiple landmarks are searched, especially when the viewpoint is oblique, which incurs a high computational cost in the aforementioned energy minimization. In Figure 3b, the correspondences of the 2D landmarks in Figure 3a are plotted on the 3D model; the number of correspondences marked with red dots is much larger than the number of 2D ones. To tackle this problem, the proposed framework refines selected corresponding landmarks on the basis of several constraints. When multiple landmarks are found, the three closest vertices are pre-selected first. The intersection point between the backprojection ray of the landmark in the frame and the triangle mesh formed by the pre-selected three vertices is then computed and finally determined as the correspondence. As shown in Figure 3c, only one correspondence is taken for each landmark as a result of the correspondence searching method in the proposed framework. Figure 3: Results of face model fitting: (**a**) face landmarks detected from the video frame; (**b**) corresponding landmarks on the model searched by [PERSON] et al. (2011); and (**c**) corresponding landmarks on the model searched by the proposed framework. Green dots indicate facial landmarks, and red dots highlight facial outline landmarks.
## 5 Case Studies
### DBL in a Course of Fluid Mechanics
The teacher explained how to collect the data from the experimental facility and how to use of the measurement instruments. 2. The students collected the experimental data by measuring water height and velocity at intervals of 0.5 cm in a rectangular channel that was 3 m long and 24 cm wide. The channel bottom could be changed during the lesson to make it either horizontal or with a constant slope in some sections. The flow rate could also be varied by adjusting the opening or closing of a valve that controlled the flow through the channel. In this way, the students generated their own dataset and registered the data in an Excel spreadsheet. 3. Under the guidance of the teacher, the students selected the data in the dataset that reflected the flow behaviour in terms of steady flow (constant discharge), uniform-gradually varied-rapidly varied flow (water depth longitudinal variation), subcritical-supercritical flow (calculating the dimensionless Froude parameter using water depth and velocity), and the presence of hydraulic jumps (detecting large variations in water depth in a small longitudinal displacement). After that, students could extract results from the datasets according to the learning objectives and present them. In case they could not extract any result, it was necessary to go back to work with the data, guided by the teacher. The lesson ended with the teacher guiding the discussion to achieve the proposed objectives. During asynchronous learning (1 h approximately), the students relied on the materials provided by the teacher (books, lecture notes, references) to support their analysis, and complete and improve their work. After the lesson, by conducting surveys on the methodology for learning these basic concepts in free surface flow and the concepts learned by the students themselves, the teacher sought feedback from the students. Based on the results obtained, the teacher considered possible improvements to be introduced in this data-driven teaching process, as well as extending this methodology to other laboratory practice sessions.
## 2 Materials and Methods  
### Phantom  
The radiomic phantom used for this study was described in detail in a previous article [8]. Briefly, it consists of a pelvis-shaped container filled with an aqueous solution of MnCl\({}_{2}\) to reproduce the transverse relaxation time T\({}_{2}\) of the abdominal muscle tissue. The phantom includes four cylindrical inserts prepared with a mixture of agar gel and polystyrene spheres to simulate the T\({}_{2}\) average signal and the texture of female pelvic tumors. The inserts differ in the spheres' diameter, which varies from 1 mm to 8 mm, to obtain a range of different textures. One insert is prepared with 1 mm diameter spheres only (_small_ spheres), two inserts have 3-4 mm diameter spheres (_medium_ spheres), and the other one contains both small, medium, and 7-8 mm diameter spheres (the latter referred to as _large_ spheres). The phantom was imaged with an axial T\({}_{2}\)-weighted sequence on three MRI scanners, exploiting the usual setup for pelvic diagnostic imaging in clinics. The phantom was imaged at IEO European Institute of Oncology IRCCS, Milan, Italy, using a 1.5T GE Healthcare Optima MR450w scanner, and at Champalimaud Clinical Centre, Champalimaud Foundation, Lisboa, Portugal, using a 1.5T and a 3T Philips Healthcare Ingenia scanner. On each scanner, two acquisitions were performed in a test-retest setting, meaning that the phantom was positioned, scanned a first time, and scanned again after fifteen minutes without repositioning the phantom. These images, along with other acquisitions, have been previously exploited for a multicentric study on the repeatability and reproducibility of MRI radiomic features [9]. The imaging parameters are listed in Table 1.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Strontium-Doped Zinc Oxide Films  
The strontium (Sr)-doped ZnO films were deposited on the substrates under various concentrations of strontium [163]. SEM and XRD studies showed the flower-like morphology and crystal orientation transformation occurred (from the (002) to the (101) plane) with doping concentrations. During the experiment, the band gap increased (3.02 to 3.2 eV) and enhanced the ammonia gas sensing behavior with the increasing strontium concentration.
# Shared eHMI: Bridging Human-Machine Understanding in Autonomous Wheelchair Navigation
## 2 Related Works
### Pedestrians' Concerns or Opinions about the Uncertainty of Autonomous Vehicles  
The majority of previous research has focused on user acceptance of automated vehicles (AVs). [PERSON] et al. examined the receptiveness of pedestrians to AVs, partially filling the gap in research on pedestrian acceptance of AVs [45]. Pedestrian behavior is challenging to predict due to its dynamic nature, lack of training, and a tendency to disobey rules in certain situations [13]. Pedestrians may feel unsafe interacting with autonomous personal mobility vehicles (APMVs) when uncertain about their driving intentions. A lack of clarity regarding the APMV's intentions can lead to hesitation or even feelings of danger among pedestrians [46]. The understanding of driving intentions by pedestrians is by means of trust in human-machine interactions [47]. AVs are capable of reliably detecting pedestrians, but it is challenging for both AVs and pedestrians to predict each other's intentions.  
[PERSON] et al. examined the effects of using explicit hand gestures and receptive eHMI in the interaction between pedestrians and AVs. The eHMI was proved to facilitate participants in predicting the AV's behaviors [48]. In exploring interactions between pedestrians and autonomous wheelchairs, both [PERSON] et al. and [PERSON] et al. identified that explicitly conveying the wheelchair's movement intentions, through methods such as ground-projected light paths or red projected arrows, notably enhanced the smoothness of interactions and augmented pedestrians cooperation [49; 50]. This underscores the significance of transparently communicating driving intentions in alleviating pedestrians concerns regarding the uncertainty of autonomous vehicles. However, interactions and scenarios between autonomous wheelchairs and pedestrians, such as yielding behaviors of the wheelchair, have not been extensively studied. Moreover, the systematic exploration of eHMI used in autonomous wheelchair is needed, along with research into pedestrian acceptance based on these systems.
## 3 The Proposed Fuzzy Relevance-Based Cluster Head Selection Algorithm
### Cluster Head Selection  
Efficient selection of the cluster head (CH) has a big influence on the cluster structure. This paper proposes the use of FRD to select the CH that is different from existing mechanisms such as CBRP [9], WACA [3], SCAM [1], and SCA (Secured Clustering Algorithm) [13]. The selection of the cluster head is complex and inaccurate in CBRP [9] based on Lower ID, MOBIC [25] based on mobility, and SCA (Secured Clustering Algorithm) [13] based on trust value. Existing mechanisms select the clusterhead using only one of the following parameters: ID, mobility, and trust value. The proposed method, however, uses parameters jointly to select the cluster head, and the cluster head is selected by FRD and determined by the available power, signal strength, and distance between the nodes, which is presented as follows.
# A Virtual Reality Soldier Simulator with Body Area Networks for Team Training
## 6 Experimental Results
### Error Analysis  
In the first set of simulations, we explored the characteristic of the error quaternion \(\mathbf{q}_{\text{err}}\). With reference to the analysis in Section 4.2, the rotation errors are assumed to be negligible in the roll angle and heading angle measurements, and the measurement error in the pitch angle is considered to be \(\Delta\theta\). With angle information (e.g., the heading angle \(60^{\lx@math@degree}\), the roll angle \(30^{\lx@math@degree}\), the pitch angle \(15^{\lx@math@degree}\)) and \(\Delta\theta\), Figure 17 presents the behavior of the error quaternion \(\mathbf{q}_{\text{err}}\) when varying the measurement error of the pitch angle. Note that, given \(\Delta\theta\) ranging from \(0^{\lx@math@degree}\) to \(0.2^{\lx@math@degree}\), the vector parts of \(\mathbf{q}_{\text{err}}\) (i.e., \(q_{x}^{(err)}\), \(q_{y}^{(err)}\), \(q_{z}^{(err)}\)) are approximately linear with respect to the \(\Delta\theta\), which can provide a sensible way of describing the error behaviors of rotation X, rotation Y, and rotation Z. According to Equation (27), given the quaternion error \(\mathrm{q}_{\text{err}}\) and the sensor frame \(\mathrm{u}_{\text{s}}\), the perturbation of the earth frame \(\mathrm{u}_{\text{e}}\) can be described. As shown in Figure 17, when the measurement error in the pitch angle is small, the small vector components of \(\mathrm{q}\ \rightarrow_{\text{err}}\) lead to a small perturbation of the earth frame \(\mathrm{u}_{\text{e}}\). In contrast, as the measurement error in the pitch angle increases, the perturbation in the Y-axis increases, which results in a larger error component in the Y-axis (e.g., with \(\Delta\theta=0.1\), \(q_{x}^{(err)}=0.006\), \(q_{y}^{(err)}=0.05\), \(q_{z}^{(err)}=0.0004\)).  
Figure 17: The scalar part and the vector part of the quaternion error for a small rotation of measurement error of pitch angle.
# Automatic Fabric Defect Detection Method Using PRAN-Net
## 2 PRAN-Net Fabric Defect Detection Method Based on Faster R-CNN
### Priori Anchor Generation
#### 2.2.2 Shape Prediction  
As shown in Figure 3, the second step is to predict the shape of the priori anchors \(a_{priori}\). The \(IoU_{cam}\) is the intersection over union (IoU) of the candidate anchor and the matched GT'. We try to make the IoU larger by adjusting the width and height of the candidate anchor. First the shape prediction network \(N_{s}\), which includes a \(1\times 1\) convolutional layer and an element-wise transform layer is used to predict the width \(w^{\prime}_{0}\) and height \(h^{\prime}_{0}\) as follows:  
\[w^{\prime}_{0}=w_{0}\cdot e^{l_{w}} \tag{2}\]  
\[h^{\prime}_{0}=h_{0}\cdot e^{l_{h}} \tag{3}\]  
where \(t_{w}\) and \(t_{h}\) are the scale parameters obtained by the above \(1\times 1\) convolution layer. Adjust \(w^{\prime}_{0}\) and \(h^{\prime}_{0}\) until the adjusted candidate anchor demands the following condition:  
\[IoU(a_{priori},gt^{\prime})=\max(IoU_{cam}(a_{cam},gt^{\prime})) \tag{4}\]  
The last width \(w^{\prime}_{0}\) and height \(h^{\prime}_{0}\) is the shape of the priori anchor. Different from thousands of fixed anchors for each feature map, only sparse prior anchors are generated by PRAN-Net. The priori anchor shape loss \(L_{shape}\) is as follows:  
\[L_{shape}=smoothL_{1}(1-\min(\frac{w^{\prime}_{g}}{w^{\prime}_{0}},\frac{w^{ \prime}_{0}}{w^{\prime}_{g}}))+smoothL_{1}(1-\min(\frac{h^{\prime}_{g}}{h^{ \prime}_{0}},\frac{h^{\prime}_{0}}{h^{\prime}_{g}})) \tag{5}\]
# Privately Generated Key Pairs for Post Quantum Cryptography in a Distributed Network
## 4 Experimental Validation
### Biometry as a Multi-Factor with [PERSON]
Secondly, because of the distance-preserving property of the mapping, applying the matrix to two different n-dimensional images created by the same biometric input will result in the output of two close k-dimensional images. Applying the matrix to a vector created by different biometric inputs will result in a very different output. We will now provide details of how a lookup table can be created. Suppose we want to create an M by M table, with M as a large integer (128, 256, 4096,...). Begin with a vector v created from biometric measurements that have been mapped to the n-dimensional space. Randomly generate M seeds \(\text{S}_{1},\ldots,\text{S}_{\text{M}}\). Map these seeds \(\text{S}_{\text{i}}\) to M and distinguish k by n close-to-orthogonal matrices \(\text{C}_{\text{i}}\). Call these \"challenges\". Compute M distinct responses \(\text{R}_{1}=\text{C}_{1}.\text{v}\) These are k-dimensional vectors. This collection (\(\text{C}_{\text{i}}\), \(\text{R}_{\text{i}}\)), i = 1,..., M is the lookup table. Now, choose a random subset of half of the integers from 1 to M: \(\text{j}_{1},\text{j}_{2},\ldots,\text{j}_{\text{M}/2}\). This random subset can be used to map a biometric input to a unique and deterministic sequence of M bits as follows. We assume that the user of the lookup table has knowledge of the subset of landmarks to use, which measurements to take, and how to map the vector of measurements to the n-dimensional space. Receive a photo from the user (along with proof of liveness, etc....)  
Take measurements and create an n-dimensional vector  
For each k between 1 and \(\frac{\text{M}}{2}\), pick the challenge matrix \(\text{C}_{\text{i}\text{k}}\) and compute \(\text{R}^{\prime}_{\text{i}\text{k}}=\text{C}_{\text{j}\text{k}}.\text{v}\)  
Compare \(\text{R}^{\prime}_{\text{i}\text{k}}\) with \(\text{R}_{\text{i}\text{k}}\) from the lookup table. If the distance between vectors is below a threshold, then place a 1 in position \(\text{J}_{\text{k}}\) and continue. If the distance is above the threshold output: INVALID USER. When all k responses have been compared and matched the vector with 1's in the positions \(\text{J}_{\text{k}}\), the validation as the secret key corresponding to the user is complete. If this is performed in a signature verification setting, so knowledge of a secret key corresponding to a given public key is being proven (rather than the key itself being revealed), then the subset would be randomly generated by the user, signed by the public key, and then given to the CA by the user, along with a photo, which would regenerate the same subset that was signed.
## 3 Results and Discussion
### Method Validation
#### 3.2.8 Cell Viability Results
However, this ameliorating effect of metformin was not noticed at 10 mM concentration, and the cells looked as stressed as HG-only-treated cells. When metformin was co-added into HG treated cells, viability was improved to 87.25% (\(\pm\)6.08) in HG + M2.5, and to 102% (\(\pm\)1.41) in HG + M5. However, cell viability dropped to 44.5% (\(\pm\)4.44) in HG + M10 (Figure 6C). After 5 days of exposure of HEI-OC1 cells to all treatments, only cells exposed to metformin 5 mM (M5) looked viable (more attached and confluent) than M2.5- and M10-treated cells, where cell viability of M5-treated cells was 77.5% (\(\pm\)4.12). The cell viability of M2.5 and M10 were 48.5% (\(\pm\)3.42) and 67.5% (\(\pm\)3.42), respectively. Cells exposed to 25 mM glucose (HG-treated cells) looked very stressed and had a cell viability of only 13.5 % (\(\pm\)5.45). Metformin seemed to partially mitigate the harmful effect of HG at the concentration of 5 mM, where the cell viability of (HG + M5)-treated cells was 67.5% (\(\pm\)3.42). This beneficial effect of metformin was not noticed in other concentrations, where the cell viability of (HG + M2.5) and (HG + M10) did not exceed 35.5% (\(\pm\)2.52) and 10% (\(\pm\)4.32), respectively (Figure 6D). Activating AMPK and inhibiting complex I of the mitochondria by metformin was shown to play a crucial role in protecting the auditory function from insults to the organ of Corti [69]. Improved mitochondrial function, mitochondrial biogenesis and proliferation are evidenced effects of metformin [70; 71]. The antioxidant characteristics of metformin and its role in controlling the oxidative surge (uncontrolled production of free radicals) are also well reported [4; 72]. Prevention of AGE accumulation and suppression of the AGE/RAGE pathway are also reported effects with metformin therapy [73; 74]. Inhibitory action to caspase 3 activation and poly-ADP-ribose polymerase levels was reported as a protective effect of metformin from the toxicity caused by cisplatin to HEI-OC1 cell lines [48], while activation of the autophagy process by AMPK/FOXO3a was reported as another protective pathway of metformin against cisplatin toxicity by others [47]. [PERSON] et al. reported that the inhibited expression of caspase 3 and the cleavage of poly-ADP ribose polymerase as a protective pathway of metformin from gentamicin toxicity to HEI-OC1 cell lines [43]. Our results reveal the novel protective effect of metformin to HEI-OC1 cell lines from hyperglycaemic conditions, and further studies are needed to elucidate the exact biochemical interventions that metformin exerts in HEI-OC1 cell lines under simulated diabetes conditions and the exact pathway(s) involved in this protective effect.
# A Comparative Analysis of Big Data Frameworks:
## 4 Big Data Processing Frameworks
### Samza
#### 4.4.3 Scheduling  
For task scheduling and resource negotiation, Samza relies on pluggable cluster managers like YARN and Mesos.
###### Abstract  
In this study, the multi-criteria decision-making (MCDM) technique is used in collaboration with K-medoids clustering to establish a novel algorithm for extending the lifetime of wireless sensor networks (WSNs) in the presence of uncertainty. One of the most important problems in WSNs is the energy consumption. Furthermore, extending the network lifetime in WSNs is highly dependent on selecting the appropriate cluster heads (CHs), and this can be a challenging task for the decision makers. In addition, parameters associated with WSNs are completely unexpected due to uncertainty. Therefore, after proposing K-medoids clustering and a MCDM technique, we have developed a novel algorithm for extending the lifetime of WSNs. As criteria, we have taken into account four important aspects of the proposed WSN: the distance from sink, average distance of cluster nodes, reliability of cluster and residual energy. To represent uncertain parameters in this work, we have considered triangular fuzzy numbers (TFNs). Finally, an experiment involving a WSN under uncertainty was investigated, and the findings have been graphically displayed. In this research, it has been observed that the proposed strategy with the novel algorithm exhibits 42% greater network lifetime as compared with a hybrid energy efficient distributed (HEED) algorithm and 11% and 18% with respect to optimal clustering artificial bee colony (OCABC) and particle swarm optimization (PSO) applied to a clustering optimization problem. We have also conducted statistical hypotheses for the purpose of confirming the presented outcomes.  
lifetime extension; K-medoids; wireless sensor networks; cluster head selection; TOPSIS; multi-criteria decision making; residual energy +
Footnote †: journal: Medical Imaging
# Nonlinear Robust Adaptive Control of Universal Manipulators Based on Desired Trajectory
## 2 Dynamic Model and Preliminary
### Dynamic Model  
For a universal six-axis manipulator, the dynamic model can be expressed as:  
\[M(q)\ddot{q}+C\big{(}q,\dot{q}\big{)}\dot{q}+G(q)+F\big{(}\dot{q}\big{)}=u \tag{1}\]  
where \(M(q)\in R^{6\times 6}\), \(C\big{(}q,\dot{q}\big{)}\in R^{6\times 6}\), \(G(q)\in R^{6}\), and \(F\big{(}\dot{q}\big{)}\in R^{6}\), respectively, are the inertia matrix, the Coriolis and centrifugal matrix, the gravity vector, and joint friction vector; \(q,\dot{q},\ddot{q}\in R^{6}\) represent the joint positions, velocities, and accelerations; and \(u\in R^{6}\) is the control output in the form of torque. The joint friction term is taken by the most widely used Coulomb-viscous friction model:  
\[F\big{(}\dot{q}\big{)}=F_{c}sign\big{(}\dot{q}\big{)}+F_{v}\dot{q} \tag{2}\]  
where \(F_{c}=diag\{c_{1},c_{2},c_{3},c_{4},c_{5},c_{6}\}\), \(F_{v}=diag\{v_{1},v_{2},v_{3},v_{4},v_{5},v_{6}\}\), and the diagonal elements are constants.
# Modeling the Transient Response of Thermal Circuits
## 4 Analytical Modeling of a Complete Thermal System
### Steady State Model  
The thermal circuit used to model the complete system is depicted in Figure 6b. The thermal resistances \(R_{1}\) and \(R_{3}\) represent the resistance of the heat flow to the air, and the thermal resistance \(R_{2}\) represents the resistance of the heat flow within the heat transfer medium. Using the [PERSON]'s circuit laws applied to thermal circuits, one can write the following equations:  
\[Q =q_{1}+q_{2}, \tag{56}\] \[R_{1}q_{1} =(R_{2}+R_{3})q_{2}. \tag{57}\]  
Figure 6: Complete thermal system model: (**a**) system scheme; (**b**) steady state thermal circuit; (**c**) transient thermal circuit.  
Solving this system of equations, one can obtain the heat currents \(q_{1}\) and \(q_{2}\):  
\[q_{1} =\frac{R_{1}}{R_{1}+R_{2}+R_{3}}Q, \tag{58}\] \[q_{2} =\frac{R_{2}+R_{3}}{R_{1}+R_{2}+R_{3}}Q. \tag{59}\]  
Defining the ground temperature as being \(T_{amb}\), and since the temperatures \(T_{cold}\) and \(T_{hot}\) can be obtained by \(q_{2}R_{3}+T_{amb}\) and \(q_{1}R_{1}+T_{amb}\), respectively, then one arrives at  
\[T_{cold} =\frac{R_{1}R_{3}}{R_{1}+R_{2}+R_{3}}Q+T_{amb}, \tag{60}\] \[T_{hot} =R_{1}\frac{R_{2}+R_{3}}{R_{1}+R_{2}+R_{3}}Q+T_{amb}. \tag{61}\]  
Hence,  
\[\Delta T=T_{hot}-T_{cold}=\frac{R_{1}R_{2}}{R_{1}+R_{2}+R_{3}}Q. \tag{62}\]  
Since \(R_{2}=\frac{L}{KA}\), \(R_{1}=\frac{1}{h_{1}Af}\) and \(R_{3}=\frac{1}{h_{3}A}\), where \(A\) is the sectional area, \(f\) is the fraction of area in contact with the ambient, \(L\) the thickness of the heat transfer medium, \(k\) the heat transfer medium effective thermal conductivity and \(h\) the heat transfer coefficients,  
\[\Delta T=\frac{Lh_{3}}{A(kh_{3}+Lh_{1}fh_{3}+h_{1}/k)}Q. \tag{63}\]