esa-sceva/satcom-chunk-collection huggingface dataset:
# Tightly-Coupled Data Compression for Efficient Face Alignment
## 2 Basics of SDM  
This section briefly introduces the main workflow of SDM. Please refer to [6] for details.  
We assume that the face feature points are represented by \(N\) 2D landmarks \(\mathbf{s}=[x_{1},y_{1},\ldots,x_{N},\)\(y_{N}]^{T}\). Usually, \(N=68\). Figure 2 shows the definition of the 68 face landmarks. Given a face image \(\mathbf{I}\) and the initial 2D landmarks \(\mathbf{s}_{0}\) estimated from the detected face region, our aim is to find a series of regressors:  
\[\mathbf{R}=\mathbf{r}_{1}\cdot\cdot\cdot\mathbf{r}_{D}\]  
where \(\mathbf{r}_{d}=\{\mathbf{A}_{d},\)\(\mathbf{b}_{d}\}(d=1\cdot\cdot\cdot D)\), \(\mathbf{A}_{d}\) is the projection matrix that can also be called the descent direction and \(\mathbf{b}_{d}\) is the bias term. In SDM, \(D\) is usually chosen between 4 and 6, and the estimated 2D landmarks at \(d\)th step are calculated according to the following equation:  
\[\mathbf{s}_{d}=\mathbf{s}_{d-1}+\mathbf{A}_{d}\cdot\mathbf{f(I,s}_{d-1})+ \mathbf{b}_{d} \tag{1}\]  
**f(I, s\({}_{d-1}\))** are the shape related features that can be SIFT (Scale-Invariant Feature Transform)features [35] or HoG(Histograms of oriented Gradients) features [36] for better performance [37]. These features can calculated at the landmarks \(\mathbf{s}_{d-1}\) in image \(\mathbf{I}\). The final estimated facial landmarks are \(\mathbf{s}_{D}\).  
Given \(M\) training images, SDM aims to minimize a series of the following equations and get \(\mathbf{r}_{d}\) sequentially:\[\underset{\mathbf{A}_{d},\mathbf{b}_{d}}{\operatorname{argmin}}\sum_{i=1}^{M} \left\|\Delta\mathbf{s}_{d-1}^{i}-\mathbf{A}_{d}\cdot\mathbf{f}\left(\mathbf{I} _{i},\mathbf{s}_{d-1}^{i}\right)-\mathbf{b}_{d}\right\|^{2} \tag{2}\]  
where \(\Delta\mathbf{s}_{d}^{i}\) is the shape residual of the \(i\)th training image at the \(d\)th regression step:  
\[\Delta\mathbf{s}_{d}^{i}=\mathbf{s}_{*}^{i}-\mathbf{s}_{d}^{i} \tag{3}\]  
\(\mathbf{s}_{*}^{i}\) are the ground truth landmarks locations of the \(i\)th training image. Equation (2) is a standard linear least squares problem and can solved in closed-form.  
The following Figure 3 shows the flow chart of the training algorithm of SDM.  
Figure 3: The flow chart of the training algorithm of SDM.  
Figure 2: Definition of the 68 facial landmarks. (Image courtesy of [38]).
# Detecting Axial Ratio of Microwave Field with High Resolution Using NV Centers in Diamond
## 3 Experiments and Results
### Wide-Field MW Magnetic Field Imaging with Ensemble NV Centers
Taking pictures of the same region while scanning the MW frequency applied to the strip line, the ODMRsignal of the region can be extracted. To verify the wide-field MW magnetic field sensing  
Figure 5: **(a)** Schematic diagram of wide-field fluorescence microscopy. The green light was focused at the back focal are of the object to excite NV centers with collimated light. The red fluorescence emitted by the NV centers was collected by the same object. The dichroic mirror was used to separate the laser and fluorescence. At last, the fluorescence was further filtered and focused on the EMCCD. **(b)** Wide-field fluorescence imaging of the diamond sample without the MW field fed in. The dark strip indicates the position of the metal MW strip line. method, ODMR signals of Spot A were extracted with the MW magnetic fields of different source powers P transmitted to the MW strip line (Figure 6a). Obviously, the magnetic resonance signal contrast increased with the power of the MW field applied, as the red square in Figure 6b indicates [18]. Thus, simply by tuning the NV electron spin transition frequency in resonance with the MW field, the qualitative MW magnetic field strength of specific polarization and spread direction [19] can be determined from the NV fluorescence signal contrast. Besides, the blue dot in Figure 6b shows that the full width at half maximum (FWHM) of the NV magnetic resonance signal decreased with reduced MW source power till a threshold value. The minimum FWHM of NV ODMR spectra reflects the NV electron spin decoherence property [18]. Moreover, the NV axial directions in the diamond were the same as the four tetrahedral diamond axes [25]. The MW magnetic field spread along each NV axis can be detected. Thus, the axial ratio of an MW field spread along an arbitrary direction can be extracted. Further applying the Rabi measurement sequence in the wide-field microscopy system, precise detection of MW magnetic field strength and the axial ratio is attainable [26; 27].
## 1 Introduction
The results show that the output power of the Invelox system at 40 m is 87.5% higher than at 10 m, but the noise level is also 39.3% higher. In addition, to further increase the output power of the Invelox system, they added a funnel to the existing system. This increases the output power of the whole machine by 44%. While the Invelox system has many advantages, it also has some drawbacks. For example, if a portion of the air entering the funnel escapes from the leeward side, efficiency of this system is reduced. One of the most important parameters of the Invelox wind turbine system is Speed Ratio (SR), which is the ratio of the average wind speed in the Venturi tube to the speed of the incoming flow. [PERSON] et al. [23] proposed the improvement measures of installing baffles. The results show that at ambient wind speeds of 3-12 m/s, the installation of a leak-proof baffle can increase the average  
Figure 1: Schematic diagram of the Invelox wind turbine system. wind speed in Venturi tubes by about 25%. Although the Invelox system is claimed to accept wind from all directions, it is only effective in the range of \(-90^{\circ}\)-\(90^{\circ}\). In most of the existing studies on the aerodynamic characteristics of the Invelox wind turbine system, the ambient wind speed is considered uniformly. In fact, the wind turbine system as a power generation device must be placed in the atmosphere in order to have practical significance. The wind in the atmospheric environment is not uniformly distributed but exponentially or logarithmically distributed, which is the atmospheric boundary layer (ABL) wind profile. [PERSON] et al. [22] considered the ABL wind profile in their study. However, they did not consider the effect of the angle between the ambient wind and the axis of the Venturi on the velocity distribution of the system. In this paper, a straight-through layout with a windshield was proposed to improve the efficiency of Invelox. The aerodynamic characteristics and velocity ratios of the original Invelox wind energy collection device is studied in detail under different incoming wind directions. Numerical simulations of the flow in the improved Invelox with a straight-through layout were then carried out. Numerical results are compared with those in the original configuration. Finally, the differences of flow field characteristics between the atmospheric boundary layer wind profile and the uniform flow wind are compared.
## 2 Proposed Method
### Global Face Model Learning  
First, we provide the input HR face images with misalignment and apply the online dictionary to create the dictionary H for the HR face images. Figure 3 demonstrates the global face model learning. To construct a dictionary to obtain face image \"sparsity prior,\" face images are preferentially represented by the basis vectors of the same misalignment variation so that the super-resolved HR face images are constrained in a subspace of certain misalignment variation.  
HR face images are always highly dimensional (the dimension is \(127\times 158\) for an HR image and \(50\times 62\) for an LR image in this paper), and the training set needs to be large enough for training the redundant dictionary. However, in practical applications, the computational complexity of the sparse problem is very high with an extensive dictionary. Generally speaking, the computational cost is proportional to the size of the dictionary H, i.e., a size of \(\frac{1}{4}\) nm, where n is the dimension of the HR face image, and m is the number of basis vectors in the dictionary. Since the solutions are usually an iterative method dealing with a large dictionary matrix, the storage requirements and computational cost are significant. In our case, if all images with different misalignment variations were selected to compose a dictionary, it would be difficult to solve the super-resolution problem. Thus, we considered dictionary learning to compress a dictionary to reduce the operational burden. Batch-based methods have problems dealing with an extensive training matrix effectively. This paper employed an online dictionary learning method to process the images, one at a time, with low memory consumption and lower computational costs. The number of basis vectors in the dictionary was 1024. The following steps were required to perform online dictionary learning.  
1. Assume the training set, such as the first dictionary and number of iterations.
2. Reset the past information.  
Figure 2: Gaussian mixture formation.  
3. Find the sparse coding using LARS (least angle regression coefficient). \[\delta_{t}=\min_{\delta\in\mathbb{R}^{2}}\frac{1}{2}\|\chi_{t}-Di_{t-1}\delta\|_{ 2}^{2}+\delta\ ||\delta||_{1}\] (4) Here \(\delta\) is dictionary coefficient \(\delta\) is Lagrange's multiplier. \(\chi\) is an Original data. \(Di\) is the created dictionary. Suffix 2 is a normalization factor.
4. Compute the learned dictionary, \[Di_{t}=\min_{Di\in\mathbb{C}}\frac{1}{t}\sum_{i=1}^{t}(\frac{1}{2}\|\chi_{i}- Di\ \delta_{i}\|_{2}^{2}+\ \delta\ ||\delta_{i}||_{1})\] (5) Here \(i\) is loop iteration.
5. Get the updated dictionary.  
Figure 3: Global face model learning.
# A Next-Generation Core Network Architecture for Mobile Networks
## 3 A Novel Architecture
### Enhanced eNodeB  
The key idea behind the eeNodeB is to modify the interface between eNodeB and core network to better take advantage of the new core network architecture. In particular, Figure 7a,b show the protocol stacks for the eeNodeB referring to both user and control planes, respectively. As one can see, in the user plane, the eeNodeB behaves essentially as an MPLS router while, in the control plane, it can \"talk\" directly to the P2P core network. In particular, in the latter case, it interacts with the P2P core network (i.e., queries the DHT) in order to identify the correct SN for a given UE and to route the traffic accordingly. Moreover, it handles redirects for traffic flows and notifies an SN of specific events (e.g., the UE has moved).  
It is important to note that the LTE _Uu_ interface between UE and eeNodeB has not been modified and is identical to that between UE and legacy eNodeB. This makes the new eeNodeB backwards-compatible with existing UEs.  
Figure 6: Node functionalities. (**a**) SN; (**b**) VCN.
## 5 Experiment and Results Description
### Dataset Preprocessing  
The JSRT image data set ([[http://db.jsrt.or.jp/eng-01.php](http://db.jsrt.or.jp/eng-01.php)]([http://db.jsrt.or.jp/eng-01.php](http://db.jsrt.or.jp/eng-01.php)), accessed on 1 September 2018.) was developed and published by the Japanese Society of Radiological Technology, and has a high reputation and influence in the field of medical imaging. It contains 247 normal chest X-ray images and 93 diseased chest X-ray images, which are clinical images digitized using structural non-uniformity technology. Each image has a corresponding mask that annotates the position and contour of the lungs and heart, as well as ground truths and contours of the affected area. According to the relevant data, the data are from 14 hospitals around the world, and the results are confirmed by imaging doctors. The JSRT database is widely used for algorithm evaluation and performance comparison in the field of medical image processing. The information contained in the JSRT database is provided anonymously and strictly complies with relevant laws and regulations to protect the privacy and security of participants. The image is 2048 \(\times\) 2048 pixels, as shown in Figure 6 above, where each image is associated with standard segmentation.  
To meet the needs of network training, verify that the rotated and displaced images were segmented by the network and compare the results with those of other networks, the data were preprocessed. The specific preprocessing steps were performed as follows. First, the original CXR data were converted into image data. If the original X-ray image is used for training, the size of the network convolution kernel is too small, and substantial computations are needed, increasing the training difficulty. However, if the size of the network convolution kernel is too large, the image features are easily lost, and it is difficult to develop an effective segmentation model with a small dataset. Therefore, for the experimental data in this paper, the original and segmented images were uniformly reduced to 128 \(\times\) 128 for training. The segmentation result is classified by binarization. The value of the cardiac area is 1, and the value of the background area is 0. Since we also evaluated the network's ability to perform target segmentation at different positions and angles, the targets in the test set were shifted by 10 and 20 pixels and rotated by 10 and 20 degrees. Finally, the dataset was divided into two parts: one part was used for networktraining, and the other part was used for network testing. Furthermore, the training set was randomly divided into training and validation sets according to the ratio 7:3.
## 5 Discussion
### Computational Complexity  
To determine the computational complexity of the SCF-based beamformers, let \(M\) denote the number of elements in the aperture, and \(N\) denote the number of the tilted angles as mentioned in Section 2. For each sample of the RF image, the computational complexity of the beamformers is shown in Table 4. The number of floating operations in uSCF is between sSCF and aSCF.  
As reported in [18], the sign bit factor and aperture data can be calculated separately in real time. Moreover, the GPU platforms can also be used to execute the beamforming for plane wave imaging and factor computation because of the parallelism of the calculation. Thus, the uSCF and aSCF could get the same frame rate. Since real-time visualization is not our primary goal, the RF signals were calculated offline on CPU in our study.
## 2 Materials and Methods
### Data Processing  
The initial step in data processing involved using a custom Matlab (MathWorks(r)) script to filter the joint angle kinematic data with a 2 nd order Butterworth low-pass filter with a 6 Hz cutoff frequency. Due to the fact that heel strike did not occur during a significant number of steps (i.e., toe-walking), the sagittal plane angle waveforms for each joint were partitioned into individual strides using the sample that reflected peak knee flexion [23]. Stride times were then determined based on the number of samples per stride when accounting for sample rate. The filtered waveforms were temporally normalized so that each stride and joint was represented by 100 samples. The mean waveforms for each joint and participant were then calculated.  
The data for the neurotypical participants was obtained from a publicly available data set [24]. The authors stated that \"This normative data can be used for comparison of pathological gait, thereby improving the interpretation of pathological gait and finally contributing to better clinical decision making (p. 3).\" Participants ranged in age from 3 to 17 years old, with multiple participants in each age group. A preferred walking speed was identified prior to data collection. Data was collected in three conditions, in which participants were instructed to walk at their (1) preferred speed, (2) a fast speed (30% greater than preferred), and a slow speed (30% less than preferred). This data set was collected and processed using procedures very similar to those described above, including being collected with the Vicon(r) camera-based motion analysis system, motorized treadmill walking, and data processing using Matlab(r) R2023b. The raw data was filtered and the joint angle data separated into individual strides and then temporally normalized to 100 samples. For each participant, the average bilateral joint angles for the hip, knee, and ankle were reported. For this study, the data collected at the slow speed was used for comparison as the stride times matched those of our SRD participants. To develop the neurotypical data set, joint angles from participants of the same age as the SRD participants were randomly selected from the published data. For example, for each nine-year-old in the SRD data set, the data from a nine-year-old in the neurotypical data set was randomly selected to contribute to the neurotypical data set. This process resulted in age-matched data sets. Each joint time-series waveform for each participant (neurotypical and SRD) was demeaned by subtracting each individual waveform's mean value from each sample in the  
Figure 1: The data collection set up.  
waveform. This served to reduce the between-participant variability of the waveforms. All variables described below were computed using custom Matlab (MathWorks(r)) scripts.
## 3 Applications  
In order to verify the feasibility of the deduction, an example calculation of the theory and a prototype test were carried out on the same PCCP with broken wires. The specimen was an embedded prestressed concrete cylinder pipe (ECP) and the calculation process used is illustrated in Section 3.2. The center spacing of steel strands, calculated through the deduction, was then applied to the same pipe in a prototype test (Section 3.3).
# Experimental Study on Deformation Monitoring of Bored Pile Based on BOTDR
## 1 Introduction  
The bored pile is formed at the project site using a pile hole in the soil by means of mechanical drilling, placing a steel cage and pouring concrete therein. It uses the bottom of the pile to cross the soil layer, the upper load from the top of pile is carried by the soil or rock body. In recent years, due to the advantages of relatively simple and mature construction, high bearing capacity, small deformation, stable performance and relatively low cost, bored piles have been widely used in the fields of transportation, high-rise buildings, water conservancy and hydropower [1].  
In order to study the deformation characteristics of bored piles, it is necessary to monitor the strain of bored piles.  
The traditional strain sensors such as strain gauges were placed at different sections of the pile in order to monitor the internal force and deformation of the bored pile. However, with the development of research, the traditional strain sensors are more and more difficult to meet the monitoring needs. During the construction of bored piles, there are some problems such as neck shrinkage, mud-sludge and hole wall collapse, which affects the accurate acquisition of pile strain information. At the same time, due to the characteristics of the traditional point sensor, it is difficult to obtain comprehensive andaccurate pile strain information [2]. The traditional sensors are prone to rust damage, insufficient data representation, severe electromagnetic interference, and are easily destroyed in complex geotechnical environments. It is difficult to realize real-time multi-point automatic monitoring. Therefore, a higher requirement is put forward for the development of monitoring technology [3].  
The distributed fiber optic sensing technology is a new type of sensing and monitoring technology that can be used to monitor the strain of pile foundation in real time and efficiently. The optical fiber used is small in size, light in weight, and good in flexibility, so it is easy to carry, install, and not easy to damage [4]. In addition, the optical fiber is developed from quartz glass and has the characteristics of flame resistance, explosion protection and electromagnetic interference resistance; thus, it is more vulnerable to electromagnetic interference and damage than the traditional strain gauge sensor. The advantages of optical fiber are superior, and it is more suitable for pile strain monitoring in complex environments [5]. The Brillouin optical time domain reflectometer (BOTDR) as one of the distributed optical fiber sensing technologies is used in the bored pile monitoring field experiment, the strain of pile is obtained, and the different way of processing the noise and smooth implementation of data are used to make the strain information more reasonable and reliable.
## 3 Results and Discussion
### The MAS Model to Identify the Intensity of Connectivity  
Before proceeding to the diachronic simulation, it is necessary to verify whether the MAS reconstructs the water paths and thus the connectivity between the plots in the basin.  
Figure 8: Evolution of the landscape structure in the Glape basin.  
To perform this validation, the model is tested by reconstructing the flow paths of an actual episode that has been documented.
## 2 Diagnosis
Last but not least, DXA provides a measurement of areal BMD (g/cm\({}^{2}\)) rather than volumetric density (g/cm\({}^{3}\)), which can give an underestimation of BMD in children with small stature s and an overestimation of BMD in children with tall stature [21]. There are other methods, like digital X-ray radiogrammetry of the hand, peripheral quantitative computed tomography (both standard (pQCT) and high resolution (HR-pQCT)), and quantitative ultrasound, that may overcome some of these limitations. However, these are currently not in routine clinical use. A recent systematic review, including a meta-analysis, reviewed the literature on the relationship of digital X-ray radiogrammetry, pQCT, and quantitative ultrasound with DXA [22]. According to this study, digital X-ray radiogrammetry had the strongest positive relationship with DXA (correlation coefficient of 0.71). Digital X-ray radiogrammetry uses web-based software (for example BoneXpert), which can assess both skeletal maturation (bone age) and bone strength, expressed as the bone health index (BHI). This BHI is a measurement of cortical thickness and cortical mineralization, which results in a representation of bone quality. The BHI reference values are specific for gender and bone age. Digital X-ray radiogrammetry is less stressful for pediatric patients than DXA and is easy to obtain. Often performing an X-ray of the hand does not involve additional exposure to ionizing radiation since hand radiographs for the assessment of bone age are regularly obtained in at-risk children [20]. In a prospective study of 101 pediatric patients with a high probability of low bone strength, digital X-ray radiogrammetry was compared with DXA [18] in children with the following profile: mean age was 11.7 years, 38 were non-ambulatory, and 52 had a neurological disorder. The mean BMD z-score was \(-\)1.3 for the group and for the non-ambulatory children \(-\)2.2. Digital X-ray radiogrammetry had a sensitivity of 67% and a specificity of 83% for BMDz-score \(\leq-2.0\). Additionally, If the BMD z-score was \(<-2.0\), digital X-ray radiogrammetry z-scores (being also \(<-2.0\)) demonstrated a percentage of 92.4% agreement. (HR-) pQCT is used to measure cortical and trabecular volumetric bone mineral density separately and microarchitectural bone morphology. Movement artifacts are a real limitation [23]. The use of (HR-) pQCT is limited due to the lack of standardized pediatric reference data for young children, and only a few (HR-) pQTC scanners are available for patient care. The correlation coefficient of (HR-) pQCT with DXA is only 0.57 [21]. Quantitative ultrasound has the same correlation coefficient. Practical information about who and how to screen for osteoporosis is given in the Section 4 further in this manuscript.