esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis  
### Co-Registration and Performance Evaluation  
Taking the airborne LiDAR point cloud of 01.16 as the reference, the closest iterative point (ICP) algorithm in the CloudCompare software was used to register automatically the five-stage dense matching point cloud. Specifically, the transformed parameters calculated by the ICP algorithm on the point cloud subset of the stable region were applied to the entire point cloud. Three stable regions were selected near the control points as identified in the observation of the surface movement, as depicted in Figure 2(a). These regions had less vegetation and almost no subsidence. Figure 2(b) represents the number of overlapping images used to calculate each pixel; also, the data obtained in the stable region would not be affected by insufficient image overlap.  
The Multiscale Model to Model Cloud Comparison (M3C2) algorithm of the CloudCompare software was used to calculate the distance between the multi-temporal point clouds in the stable region after co-registration to evaluate the quality of co-registration [22]. The standard deviation of the distance was used as an indicator of the measurement precision, and the mean was regarded as a measure of accuracy of the point cloud. The results are presented in Table 3. The microtopography of the stable surface would change due to weathering or vegetation, therefore, it was difficult for the results for co-registration to reach the theoretical value of 0 m.  
As shown in Figure 4, the calculated M3C2 histogram reveals that the distribution is Gaussian in shape but with a certain degree of positive skewness, the red bars represent the number of point clouds in each interval and the vertical black lines represent where the mean is located. Furthermore, the mean values for the data of 07.20 and 07.31 are relatively large, and the presence of dense vegetation has a certain impact on the results at these time periods. The standard deviation for the distance was between 0.13 and 0.19, indicating that the repeatability of the UAV multi-temporal photogrammetry data was good.  
Figure 3: Selection of the stable region: (**a**) stable zone for collaborative registration; and (**b**) number of overlapping images computed for each pixel of the DOM.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Mean (m)**} & \multicolumn{2}{c}{**Standard**} & \multirow{2}{*}{**Duration (day)**} & \multirow{2}{*}{**Platform**} \\  & & & & \\ \hline
01.16/06.14 & 0.24 & 0.13 & 581 & LiDAR/UAV \\
01.16/07.20 & 0.34 & 0.15 & 545 & LiDAR/UAV \\
01.16/09.07 & 0.30 & 0.19 & 496 & LiDAR/UAV \\
01.16/11.15 & 0.32 & 0.14 & 427 & LiDAR/UAV \\
01.16/07.31 & 0.35 & 0.15 & 169 & LiDAR/UAV \\ \hline \hline \end{tabular}
\end{table}
Table 3: The mean and standard deviation of the M3C2 distance in the stable region.
## 2 Background
### Augmented Reality  
Augmented reality is defined as adding digital information generated by the computer to the real world to enhance and enrich it while viewing a real-time physical real-world environment [3]. The method of combining real-time with digital information helps the user to interact with both worlds at the same time [29]. Three main characteristics of the AR system can be defined: (1) real and virtual combining, (2) real-time interactivity, and (3) three-dimensional (3D) registration. Registration means aligning virtual objects seamlessly into 3D space in the real world, which affects the whole process if it is not accurate [3].  
Augmented reality (AR) incorporates virtual components into the real environment. This can be effective for exposure therapy with specific phobias. AR exposure therapy (ARET) is a sort of exposure treatment that allows clients to interact with a virtual representation of an object or scenario in the real world in order to lessen fear or avoidance. In contrast to VR exposure treatment (VRET), the client is not immersed in a whole virtual environment using a head-mounted display (HMD), but rather a virtual object (e.g., a spider) is added to the current surroundings. Clients can utilize HMDs in ARET, but the technology also allows them to use projection displays or commonplace portable devices like smartphones and tablets. AR can use markers (stylized real items on which the software overlays a virtual element) or a markerless technique, in which the content is arranged relative to the environment, GPS position, and compass of the device, to successfully see an object in the real world. The absence of markers in AR could lead to a greater sensation of presence and anxiety of the virtual aspect.  
Augmented reality exposure therapy has been used to treat specific phobias of small animals (spiders and cockroaches) in multiple studies which prove its capability of exposure therapy treatment. VRET and ARET approaches have been proved to improve most patients' phobia levels. VRET is used for multiple types of anxiety disorder in comparison to ARET. ARET provides a better experience since it adds digital information to the real world instead of generating a whole new virtual one. Moreover, realism can be achieved better by ARET than VRET.
## 2 Related Works
The proposed algorithm protects model geometry (vertex coordinates) and topology (polygon structure) but not texture properties and normals. Some different techniques add watermark according to volume moment [29]. The first step is the conversion of mesh vertex coordinates from the Cartesian system to the cylindrical system. This step is called mesh normalization. After the normalization of the mesh, the mesh is divided into patches and its \(h\) and \(\theta\) domains are discretized in the cylindrical system. For patches selected to participate in the watermark-embedding step, it is necessary to insert some auxiliary vertices that are removed after the watermark-embedding procedure is performed. Patch modification is performed iteratively. Deformation of the patches is performed by a smooth mask so as to be invisible to the average observer. There are three approaches to copyright protections of digital content, namely cryptography, digital rights management (DRM), and digital watermarking and fingerprinting [64]. The authors created three scenarios for the illegal distribution of 3D content: digital sharing environment for 3D objects without 3D printing, sharing the printed model obtained from the digital model, and sharing the original printed model by the author. They also showed two approaches to copyright protections for 3D content, which are digital domain techniques and additional hardware and material addition. Digital Domain techniques are non-blind watermarking, blind watermarking, and watermark inside a printed 3D object. On the other hand, additional hardware and material involves the use of radio-frequency identification (RFID) tags, then using spectral signatures produced from chemical taggants, as well as 3D printing anti-counterfeiting technology based on quantum dot detection [65; 66]. This method is based on quantum dots that are added randomly to the object itself, thus creating a unique physically unclonable fingerprint known only to the manufacturer. An original embedding algorithm for the secure transmission of 3D polygonal mesh through a sensor network was presented in [30]. This method is based on \"prongs\" selecting, i.e., on selecting prominent feature vertices as common descriptors of 3D surfaces. The authors used protrusive feature vertices because these regions contain the most information about the surface shape. The method is robust against the cropping attack, noise, smoothing, and mesh simplification. The geometrical data of the automatically restituted 3D model of the Chateau de Versailles are protected by using optimized adaptive sparse quantization index modulation (QIM) for embedding some data bits into a 3D-generated model [67]. An efficient selective encryption method for 3D object binary formats was also created [68]. Geometrical distortions are introduced to partially or fully protect the 3D content and the authors partially encrypt the floating representation of coordinates of vertices using a secret key. This method encrypts selected bits of the 3D model geometry to visually protect it and this procedure happens without increasing the file size.
# Digital Holographic Positioning Sensor for a Small Deployable Space Telescope
## 3 The Positioning Sensor Design
Then the optical path from the source can be computed as  
\[T_{1}=\sqrt{(x_{M}-x_{L})^{2}+(y_{M}-y_{L})^{2}+(z_{M}-z_{L})^{2}}, \tag{6}\]  
\[T_{2}=\sqrt{(x_{D}-x_{M})^{2}+(y_{D}-y_{M})^{2}+(z_{D}-z_{M})^{2}}, \tag{7}\]  
\[T=T_{1}+T_{2}. \tag{8}\]  
So the electric field vector from this source at the given point of the detector is  
\[E_{i}=\frac{1}{T}exp\Big{(}-i\cdot\pi\cdot T/\lambda\Big{)}, \tag{9}\]  
Figure 5: Operation of the digital holographic positioning sensor: (**A**) illumination of the telescope focal plane by the auxiliary laser sources; (**B**) sample interferogram recorded with pixel binning. where \(\lambda\) is the laser wavelength (633 nm in our example). The intensity of interference pattern is computed as  
\[\vec{R}_{i}\times\vec{N}=-\vec{R}_{r}\times\vec{N}, \tag{2}\]0  
Using Equations (1)-(10) we can produce the simulated interferograms for the nominal optical design and for that with the optical element misplacements. In each case, we can compare the two interferograms and propose simple metrics correlated with the displacement to be measured. In order to simplify the computations, hereafter we consider pixel binning with an 8-pixel pitch, so all the testing interferograms represent matrices with 64 \(\times\) 64 elements. We should note that the sensor with such an element size acts as an additional spatial frequencies filter. In this case, we detect only long-periodic modulations of the fringes, but not the exact fringes. The latter ones have periods depending on the wavelength and angle of incidence, which is equal to \(a=\lambda/(\sqrt{2}\cdot sin(\Theta))=0.633/(\sqrt{2}\cdot sin(15.1^{\circ}))=1.7\)\(\upmu\)m in our case [18]. However, the frequencies of interest remain stable with respect to the sampling. In order to demonstrate it, we compare the interferogram simulations for 128 \(\upmu\)m (Figure 6A) and 64 \(\upmu\)m (Figure 6B) pixel pitch values. As one can see from the figure, the increase of sampling by a factor of 2 does not change the long-periodic modulations. For clarity, we compare the Fourier spectra of these patterns--the case of 128 \(\upmu\)m pitch is shown in Figure 6C and that for 64 \(\upmu\)m in Figure 6D, respectively. One can see that the spectra are re-scaled, but all the features remain identical. \[\vec{R}_{i}\times\vec{N}=-\vec{R}_{r}\times\vec{N}, \tag{2}\]1 Sampling effect on the detected interference pattern: (**A**) interferogram detected with 128 \(\upmu\)m element pitch; (**B**) interferogram detected with 64 \(\upmu\)m element pitch; (**C**) Fourier spectrum for 128 \(\upmu\)m element pitch; (**D**) Fourier spectrum for 64 \(\upmu\)m element pitch. Thus, the approach based on the Fourier spectra analysis described before should remain valid regardless of the detector undersampling effect. Moreover, we assume that since we use a modulated pattern, our technique should not be sensitive to \(2\pi\) ambiguity, which is typical for some classical interferometric methods.
# A Method for Processing Static Analysis Alarms Based on Deep Learning
## 7 Discussion  
In this study, we presented a novel approach based on DL for processing static analysis alarms. Our results demonstrated promising performance in identifying false alarms and actionable alarms. However, several points should be discussed in interpreting the results.  
Firstly, the effectiveness of our approach was primarily evaluated on a specific dataset limited to the Java programming language. Although the dataset covers a wide range of Java code, the generalizability of our model to other programming languages, such as C, C++, Python, JavaScript, and others, remains uncertain. The context and syntax of the code in different programming languages can vary significantly, which may impact the performance of our model when applied to code written in these languages. Further research is required to explore the transferability of our approach across different programming languages. Moreover, the domain of software applications also represents a potential limitation. Our study primarily focuses on alarms present in Java programming code, but the nature of alarms can differ depending on the software domain. For instance, alarms in AI applications, games, or scientific software may exhibit unique characteristics and require specific detection techniques. Therefore, the applicability of our approach to various software domains should be investigated in future studies to assess its effectiveness and adaptability. Regarding the scope of alarms covered by our training data, it is essential to acknowledge that our model's performance is contingent on the types of alarms it has been trained on. Although we trained the model using a diverse set of project samples, it is unlikely to cover all possible warning scenarios. Thus, there is a possibility of encountering alarm scenarios that our model may not effectively detect. Ongoing efforts should focus on expanding the training dataset to encompass a broader range of warning types.
# Real-Time Kinematics-Based Self-Collision Avoidance Algorithm for Dual-Arm Robots
## 3 Self-Collision Avoidance Strategy
### Repulsive Velocity at the End-Effector  
As illustrated in Figure 6, a bounding sphere is centered at point of the left arm and at point \(b\) of the right arm of the robot. These control points both have repulsive velocity \(v_{r,k}(k=l\text{ or }r)\) when \(\beta_{ab}=\beta_{lrmin}=\min(\beta_{lr})\leq 1\), which implies that these spherical bounding volumes will collide. The repulsive velocity acts on the centers of the spheres, as discussed in Section 2.  
The repulsive velocity needs to be controlled at the joint velocity level. To determine the repulsive velocity of the control point, only the joints prior to this control point should be given a repulsive joint velocity. In the case shown in Figure 6, the repulsive joint velocity is generated on joints 1-5 of the left arm, and on joints 1-7 of the right arm.  
Figure 6: Two redundant arms work in 3D space.  
Defining part of the repulsive joint velocity of the \(k\) arm from joint 1 to joint \(i\) (\(i\leq j\)) as \(\dot{q}_{rep,k}=\left[\dot{q}_{1k}\cdot\dot{a}_{ik}\right]^{T}\in R^{i}\) (\(k=l\) or \(r\)) of the arm, the repulsive joint velocity of this part of the arm can be calculated as follows:  
\[\dot{q}_{rep,k}=\left.\int_{r\
u,k}^{\#}\left[\begin{array}{c}v_{r\
u,k}\\ \omega_{r\
u,k}\end{array}\right],\ (k=l\text{or}\ r),\right. \tag{12}\]  
where \(J_{r,k}=\left[J_{r,\
u,r}\ J_{r,\omega,\omega}\right]^{T}\in R^{6\times i}\) are Jacobian matrices for the control points, as illustrated in Figure 2a in arm \(k\) (\(k=l\) or \(r\)). Here, only the positions of the two control points are controlled, so we set \(\omega_{r\
u,k}=\left[0\ 0\ 0\right]^{T}\in R^{3}\) in arm \(k\) (\(k=l\) or \(r\)).  
The velocity of the joints after/distal to the control point was set to zero. In this way, the repulsive joint velocity \(\dot{q}_{r,k}\) (\(k=l\) or \(r\)) can be obtained as  
\[\dot{q}_{re,k}=\left[\dot{q}_{rep,k}0..0\right]\in R^{j}, \tag{13}\]  
According to the repulsive joint velocity, the end-effector velocity of the arm is generated with an end-effector repulsive velocity \(v_{r\
u,k,end}\) as  
\[v_{r\
u,k,end}=J_{k}\dot{q}_{re,k},\ (k=l\text{or}\ r), \tag{14}\]  
Due to the collision model, which was built using discretized spheres with different radii and detecting the minimum distance between the spheres, the minimum distance points could change instantaneously, causing discontinuous velocities and non-smoothness in the overall behavior of the system. In this way, we chose to project the velocity in the direction of the end-effector velocity, which only changes the magnitude of the end-effector velocity, to alleviate this situation. Meanwhile, this strategy can also generate the repulsive velocity.
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 2 Related Work and Background
### Cluster Head Selection  
Currently, many researchers are focusing on the problem of CH selection and CH role rotation. The CH nodes are special nodes that collect information from their member nodes and transmit it towards the BS. These nodes are in charge of a cluster and also receiving joint-request messages from different root nodes. Obviously, the selected CHs consume more energy as compared to their member nodes. That is why a lot of the previously published work [7; 11; 18; 29] considers the residual energy of nodes as the basic parameter for the selection of CHs. In SEECH [10], the authors allow the member nodes to have the information about the average energy of the network to improve the selection criteria. These additional metrics increase overload on the network. However, the major issue here is that, as the CH task is rotated between nodes, which confirms that nodes having more energy value have the highest chance to become a CH. Therefore, this technique cannot save energy in a good way. Recently, some other parameters are also applied for the selection of CHs, like nodes location, delay, and node connectivity. In [32], a strategy is discussed for CHs selection by considering two parameters, which are node energy and delay. Centralized Energy Efficient Clustering (CEEC) [15] uses a CH selection mechanism in which CHs are selected by the BS on the basis of current location and remaining energy information from each node in the network. In [33], energy variation is taken into account by authors for selecting CHs.
## 2 Materials and Methods
### Characterization Techniques  
The composition of the samples and their structure was analyzed by transmission electron microscopy (TEM; Tecnai F30, Thermo Fisher Scientific, Waltham, MA, USA) using an FEI Tecnai G2 F20 S Twin with an accelerating voltage of 200 kV, and X-ray dispersion spectroscopy (EDX, EDS, EDAX, Mahwah, NJ, USA). The morphology of the samples was investigated by scanning electron microscopy (SEM; VEGA3 TESCAN, Brno, Czech Republic), which was acquired in the 30 kV acceleration voltage. In order to study the thickness of obtained silica flakes, atomic force microscopy (AFM) (Nanoscope V Multimode 8, Bruker AXS, Mannheim, Germany) was employed. The analysis of the sample composition was made using an Invia Renishaw Raman spectroscope, with a spectral range from 2100 to 100 cm\({}^{-1}\), using a laser with a length of 785 nm (Renishaw, New Mills Wotton-under-Edge, UK). Thermogravimetric analysis (TGA; TA Instrument, New Castle, DE, USA) was carried out on 10 mg samples using a DTA-Q600 SDT TA at a heating rate of 10 \({}^{\circ}\)C/min from room temperature to 900 \({}^{\circ}\)C in air. The crystallographic phase identification was performed using an X'Pert Philips PROX-ray diffractometer (XRD; X'Pert PRO Philips diffractometer, Co. Ka radiation, Almelo, Holland). The specific surface area of the samples was measured through the adsorption of the N\({}_{2}\) isotherm using the Micromerities ASAP(r) 2420 (Micromerities ASAP(r), USA) instrument, interpreted with the Brunauer-Emmett-Teller model. IR absorption spectra were acquired on the Nicolet 6700 FT-IR spectrometer (Thermo Fisher Scientific, Waltham, MA, USA).
## 3 Signal Model
### MU-Network MIMO Signal Model
}^{N_{CEU}-1}\mathbf{X}^{(u^{\prime})}}_{ \text{multi-user interference}}\right)+\mathbf{W}^{(u)}, \tag{10}\]  
where \(\mathbf{\Lambda}^{(u)}\equiv\left[\mathbf{\Lambda}^{(u,0)^{T}},\mathbf{ \Lambda}^{(u,1)^{T}},\ldots,\mathbf{\Lambda}^{(u,N_{R}-1)^{T}}\right]^{T}\) is the \(\left|\Im^{(u)}\right|N_{R}\times 3\left|\Im^{(u)}\right|N_{T}\) aggregate channel matrix of user \(u\), \(\mathbf{X}^{(u)}\equiv\left[\mathbf{X}^{(0,u)^{T}},\mathbf{X}^{(1,u)^{T}}, \mathbf{X}^{(2u)^{T}}\right]^{T}\) is the \(3\left|\Im^{(u)}\right|N_{t}\times 1\) aggregate signal vector sent for CEU \(u\) from all 3 coordinated BSs, and \(\mathbf{W}^{(u)}\equiv\left[\mathbf{W}^{(u,0)^{T}},\mathbf{W}^{(u,1)^{T}}, \ldots,\mathbf{W}^{(u,N_{R}-1)^{T}}\right]^{T}\) is the aggregate received noise vector. The aggregate signal vector \(\mathbf{X}^{(u)}\) associated with CEU \(u\) results from jointly processing a \(K^{(u)}\times 1\) data vector \(\mathbf{d}^{(u)}\equiv\left[d_{0}^{(u)},d_{1}^{(u)},\ldots,d_{K^{(u)}-1}^{(u) }\right]\) by all cooperative BSs, where \(K^{(u)}\) is the number of data symbols sent to CEU \(u\) in a network MIMO transmission. Specifically, the cooperative BSs jointly design a \(3\left|\Im^{(u)}\right|N_{T}\times K^{(u)}\) preprocessing matrix \(\mathbf{F}^{(u)}\) for the \(K^{(u)}\times 1\) data vector \(\mathbf{d}^{(u)}\), by which the \(3\left|\Im^{(u)}\right|N_{T}\times 1\) signal vector \(\mathbf{X}^{(u)}\) is produced in the sense of mitigating the multi-user interference from other \(N_{CEU}-1\) co-channel CEUs. In summary, the aggregate signal vector \(\mathbf{X}^{(u)}\) is produced according to  
\[\mathbf{X}^{(u)}=\mathbf{F}^{(u)}\mathbf{d}^{(u)},\ u=0,1,\ldots,N_{CEU}-1. \tag{11}\]  
Define \(K\equiv\sum_{u=0}^{N_{CEU}-1}K^{(u)}\), which is the total number of CEU data symbols sent in a network MIMO transmission. Substituting (11) into (10), we have a more concise expression for the aggregate received DFT vector of (10) as follows:  
\[\mathbf{Y}^{(u)}=\mathbf{\Lambda}^{(u)}\mathbf{F}\mathbf{d}+\mathbf{W}^{(u)}, \ u=0,1,\ldots,N_{CEU}-1, \tag{12}\]  
where the \(K\times 1\) aggregate data vector is  
\[\mathbf{d}\equiv\left[\mathbf{d}^{(0)^{T}},\mathbf{d}^{(1)^{T}},\ldots, \mathbf{d}^{(N_{CEU}-1)^{T}}\right]^{T}\in\mathbb{C}^{K\times 1}, \tag{13}\]  
and \(\mathbf{F}\) is the \(3\Big{|}\Im^{(u)}\Big{|}N_{t}\times K\) completely aggregate preprocessing matrix given by  
\[\mathbf{F}\equiv\left[\mathbf{F}^{(0)},\mathbf{F}^{(1)},\ldots,\mathbf{F}^{( N_{CEU}-1)}\right]\in\mathbb{C}^{3|\Im^{(u)}|N_{T}\times K}. \tag{14}\]
# Privacy-Preserving Data Analytics in Internet of Medical Things
## 2 Background
### Data Distribution Technique  
This technique involves the splitting of data over multiple sites. There are two main methods for distributing data. Both strategies--horizontal distribution and vertical distribution [12]--decentralize data processing and storage in an effort to reduce the possibility of privacy breaches. In horizontal distribution, a subset of a dataset's records or rows is stored at each site. Each subset contains the same attributes (columns) but for different individuals or entities. This technique is frequently employed when different sites have records for different sets of individuals. For instance, medical records for various patients may be kept in multiple hospitals. By distributing records across different sites, a site can implement its own privacy policies and controls according to their specific requirements, with less chance of single-point failure. Only a single subset of data is compromised, regardless of whether a site is compromised. Queries of distributed data can be conducted using secure multi-party computation, which protects individual records from being revealed to unauthorized sites. Every site in a vertical distribution holds a portion of the dataset's properties (or columns). Each subset contains different attributes but for the same set of individuals or entities. Multiple websites may need to maintain various kinds of data on the same people, for instance, financial data may be stored on one website and personally identifiable information on another. In the case of a breach, this method reduces the risk of complete data exposure by separately storing sensitive attributes.