esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Dimensional Gaussian Splatting Integrating 2D and 3D Gaussians for Surface Reconstruction
## 2 Related Work  
### Novel View Synthesis with 3D Gaussians  
Recently, 3D Gaussian Splatting (3 DGS) [9] has demonstrated high-quality rendering at significantly higher speeds than prior implicit methods. By utilizing explicit and differentiable Gaussian representations, 3 DGS accelerates both training and rendering without employing multi-layer perceptrons (MLPs). Additionally, each Gaussian employs spherical harmonics to efficiently render colors from all viewing directions. The real-time rendering capability of 3 DGS has motivated extensive research on both performance optimization, such as accelerating training and rendering [12; 13; 14], and applications in various domains. In particular, 3 DGS has been employed in simultaneous localization and mapping (SLAM) [15; 16; 17], interactive applications [18; 19], and large-scale scene reconstruction [20; 21]. Additionally, the integration of diffusion models has recently been explored to enhance 3D reconstruction performance across diverse environments [22; 23]. Recent studies have further extended 3 DGS to surface reconstruction tasks, aiming to enhance geometric accuracy and streamline mesh generation, thereby broadening its applicability in fields such as robotics [24], computer graphics [25], and augmented reality [26].
# Structural Topology Design Optimization Using the Binary Bat Algorithm
## 5 Numerical Examples  
Four benchmark structural topology optimization problems were employed to examine the search capability of the bit-array representation with the BBA and the proposed filtering algorithm and merit function (Equation (8)).  
The structures were analyzed using the finite element (direct stiffness) method, and the algorithms were coded using MATLAB. The BBA algorithmic parameters were set as follows: population size is 25, the maximum number of iterations (_maxIter_) \(10,000\), loudness (\(A\)) is 0.75, and pulse rate (\(r\)) is 0.01. These parameters are selected depending on the problem. Thus, in this study, they are set based on the experience gained after some testing runs. The minimum and maximum pulse frequencies are 0 and 2, respectively.  
Because the BBA is stochastic in nature, five independent optimization runs were performed for each example and the best topology is reported here. It is interesting to note that each independent run produced not only a different compliance but also different configuration because of the random search nature of metaheuristic algorithms.  
In all problems, the objective was to find the minimum compliance subjected to a maximum volume constraint of 0.5 (i.e., \(Vol_{solid}/Vol\leq 50\%\)), Young's modulus is 1 unit force/unit area, and Poisson's ratio is 0.3. For design examples 1, 2, 4, and 5, the design domain is rectangular of 2 unit lengths in the \(x\) direction and 1 unit length in the \(y\) direction. For the finite element analysis, the domain is divided into \(100\times 50\) quadrilateral square plane stress elements. That is, the total number of design variables is 5000 because each element is a design variable with 0 or 1 values. For design example 3, the design domain is rectangular of 3 unit lengths in the \(x\) direction and 1 unit length in the \(y\) direction. For the finite element analysis, the domain is divided into \(150\times 50\) quadrilateral square plane stress elements (7500 elements). This mesh size is chosen because the SIMP method becomes less mesh-dependent when the mesh is fine. Although a finer mesh produces a slightly better design, it is more computationally expensive. The numerical results from the present bit-array representation with the BBA are compared with those from the SIMP method using [PERSON]'s 99 line topology optimization code written in MATLAB [27]. In the power law approach, the penalization power and the filter size are set to 3 and 1.2, respectively.
## 3 Remote Sensors at the SAS  
In this section we describe the main sensors located at the SAS, including a geomagnetic sensor, a vertical incidence ionosonde and a oblique incidence ionosonde. The geomagnetic sensor and the vertical incidence ionosonde are commercial solutions from third parties independent of this project. The oblique incidence ionosonde, which is used to sound the ionospheric channel between Antarctica and Spain, was developed by LA SALLE within the framework of this project.
## 3 Data and Methods
### Unsupervised Machine Learning Models
#### 3.3.2 Spectral Clustering  
Spectral clustering is another unsupervised machine learning model, which clusters through the characteristic vector of the Laplacian matrix of sample data. Spectral clustering maps data from a high-dimensional space to low-dimensional space, and then, uses other clustering algorithms to cluster in a low-dimensional space. Compared with k-means, spectral clustering uses a dimension reduction algorithm, which is more suitable for high-dimensional data processing and more effective for sparse data processing. Spectral clustering outputs clusters \(A_{1}\), \(A_{2}\),..., \(A_{n}\) by inputting \(n\) sample points \(X=\{x_{1},x_{2},\;\ldots,x_{n}\}\) and the number of clusters k. In this model, the kernel function parameter and cluster number are the influential parameters. The specific steps are below:  
1. Calculate the similarity matrix \(W\) of \(n*n\), which includes the minimum proximity method, k-proximity method, and full-connection method. The full-connection method used in this study is as described: \[s_{ij}=s(x_{i},x_{j})=\sum\limits_{i=1,j=1}^{n}\exp\frac{-\left\|x_{i}-x_{j} \right\|^{2}}{2\sigma}\] where \(s_{ij}=\) the similarity matrix and \(\sigma=\) kernel function parameter, which controls the neighborhood width of the sample point.
2. Calculation matrix \(D\): \[d_{i}=\sum\
olimits_{j=1}^{n}\omega_{ij}\]where \(D\) is the \(n*n\) diagonal matrix formed with \(d_{i}\).
3. Calculate the Laplacian matrix \(L=D-W\).
4. Calculate the characteristic value of \(D\) and sort it from small to large, then take the first \(k\) characteristic values and calculate their feature vector \(u_{1},u_{2},\ \ldots,u_{n}\).
5. Form the matrix \(U=\{u_{1},u_{2},\ \ldots,u_{n}\},U\in R^{n+k}\).
6. Let \(y_{i}\in R_{k}\) be the vector of the line \(i\) of \(U\), \(i=1,2,\ \ldots,n\).
7. Cluster the datasets \(Y=\{y_{1},y_{2},\ \ldots,y_{n}\}\) into clusters \(C_{1},C_{2},\ \ldots,C_{k}\).
8. Output clusters \(A_{1},A_{2},\ \ldots,A_{k}\), among which \(A_{i}=\{j|y_{i}\in C_{i}\}\).
## 2 Methodology
### Theoretical Calculation Methodology
#### 2.3.2 Anti-Sliding Resistance Calculation Method  
Sliding arises from insufficient horizontal bearing capacity under external loading, where asymmetric soil confinement--caused by seabed inclination or proximity to pre-existing footprints--further diminishes anti-sliding resistance. Both failure mechanisms involve large-deformation soil behavior, including shear localization, strain softening, and stress redistribution due to soil flow. The total anti-sliding resistance \(R_{H}\) of the spudcan embedded in the seabed is mainly composed of the base sliding resistance \(R_{B}\) and the lateral soil resistance \(R_{E}\) from the embedded sidewall area:  
\[R_{H}=R_{B}+R_{E} \tag{5}\]  
where \(R_{E}\) is governed by the differential between passive \(R_{p}\) and active \(R_{a}\) earth pressures:  
\[R_{E}=R_{p}-R_{a}=\sum_{i}P_{p,i}\cdot\Delta A_{i}-\sum_{i}P_{a,i}\cdot\Delta A _{i}\] (5a) where \[P_{p}\] is the passive earth pressure, \[P_{a}\] denotes the active earth pressure (neglected when negative), and \[\Delta A_{i}\] represents the incremental area of the spudcan-soil interface.  
For clay soil, the formulas for earth pressures are as follows:  
\[P_{p}=\gamma^{\prime}\cdot z+2s_{u}(z) \tag{6}\]  
\[P_{a}=\gamma^{\prime}\cdot z-2s_{u}(z) \tag{7}\]  
Base resistance:  
\[R_{B}=s_{u0}\cdot A_{0} \tag{8}\]  
where \(\gamma^{\prime}\) represents the buoyant unit weight of soil; \(s_{u}(z)\) denotes the undrained shear strength at depth \(z\); \(A_{0}\) is the spudcan base contact area; and \(s_{u0}\) is the undrained shear strength at the contact interface. In the above formula, when \(P_{a}\) is negative, it is not considered.  
For sand, the difference between the active earth pressure and passive earth pressure is  
\[P_{p}-P_{a}=\big{(}k_{p}-k_{a}\big{)}\cdot\gamma^{\prime}\cdot z \tag{9}\]  
Figure 10: Schematic diagram of spudcan penetration resistance analysis: (**a**) homogeneous soil; (**b**) layered soils.  
where \(k_{p}\) and \(k_{a}\) are the coefficients of passive and active earth pressure, respectively, calculated using the following formulas:  
\[k_{p}=\frac{(1+sin\varphi^{\prime})}{(1-sin\varphi^{\prime})} \tag{9a}\]  
\[k_{a}=1/k_{p} \tag{9b}\]  
The calculation formula for the base resistance \(R_{B}\) is  
\[R_{B}=\alpha\cdot tan\varphi^{\prime}\cdot F_{v} \tag{10}\]  
where \(\varphi^{\prime}\) is the effective internal friction angle, and \(\alpha\) is the friction reduction coefficient, with a typical range of 0.6-0.8.
# Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors
## 2 Related Work
### PDR-Related Method  
PDR methods can provide continuous trajectory yet they are prone to be influenced by accumulative errors [24]. Related research aims to improve the estimation of step detection, stride length, and heading orientation [24; 25]. Commonly, PDR methods need a starting location (initial state), and its errors in orientation would accumulate along with walking. Accurate headings are quite important for PDR. To compensate direction errors, PDR is incorporated with other positioning techniques. [PERSON] et al. presented tests about the combination of PDR, WiFi fingerprinting, and magnetic matching (MM) [7]. The RMS of position error can be reduced to 3.8 m with the group of PDR/WiFi, and the RMS regarding PDR/WiFi/MM can be reduced to 3.2 m.  
Map matching methods are applied with PDR to better estimate locations [26; 27]. [PERSON] et al. (2017) alleviated the accumulated error of PDR by correcting locations with a navigation network [26]. With the help of particle filter on the network, the positioning accuracy of PDR can be raised up to 1.23 m. In addition, PDR, human activity recognition (HAR), and indoor landmarks are included to better estimate user trajectories [28]. Although these rigid constraints can improve the PDR trajectory, they may not be suitable for real-time positioning without a priori knowledge. To compensate the headings of smartphone, we design a method to subdivide pedestrian trajectories into linear motions in terms of turns, and update Kalman filtering for each trajectory segment.
## 3 Methodology
### Dataset Pre-Processing  
This experiment was performed on 113 subjects taken from OASIS-3: Longitudinal Multimodal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and AD archive [46]. The MRI images are available in NiTI format. A total of 247 and 410 MRI scans were used for cognitively normal and mild cognitive impairment patients respectively. T1-weighted structural sequences were processed on the axial plane of brain MRIs. The Clinical Dementia Rating (CDR) Scale was used to determine the dataset's dementia status; a score of 0 on the scale indicates cognitively normal, while a score of more than 0 but less than 2 indicates mild impairment. Each image in the dataset is completely characterised, and complete details are given.  
The first task with this dataset was converting the image files from NifTI to PNG. Performing this conversion resulted in 255 slices of the axial view of the brain for each subject, as shown in Figure 1. Multiple axial scans of the brain of each patient were extracted, particularly the images containing the hippocampus embedded deep into the temporal lobe and some areas which demonstrate a significant shrinkage in some parts of the brain, were fed as an input to the neural network. Multiple slices of MRIs were extracted from each subject for accurate prediction of the disease. The middle slices contain more tissue than the border slices. Since tissues are a biomarker for dementia, central slices aid the system in precisely classifying disorders that resemble AD. For the categorisation of AD, the slices along the border are less helpful and more deceptive since they contain more of the bones and skulls. The central slices are therefore utilised for training. This section goes into great detail on the dataset preparation.  
To make our predictions more robust, data augmentation technique was applied to our dataset. Since it is a classification model, the visual transformation mainly focused on flipping random images horizontally; rotating them by a certain degree and min-max normalisation; and rescaling inputs to the range of [0, 1], computed as follows:  
\[y=\frac{(x-\min(x))}{(\max(x)-\min(x))}, \tag{2}\]  
where \(\min(x)\) and \(\max(x)\) are the minimum and maximum intensity values, respectively, for the entire dataset, and \(y_{i}\) represents the normalised intensity value against location \(x_{i}\) (where \(i\in[1,n]\)). This helps our dataset to resolve the class imbalance issue, creating variations and generalizations for our model.
# TOAR: Toward Resisting AS-Level Adversary Correlation Attacks Optimal Anonymous Routing
## 2 Background and Motivation
Specifically, an adversary may observe: (1) the data flow from the client to the entry relay and the data flow from the exit relay to the server; (2) the data flow from the client to the entry relay and the TCP acknowledgment traffic from the server to the exit relay; (3) the TCP acknowledgment traffic from the entry relay to the client and the data flow from the exit relay to the server; and (4) the TCP acknowledgment traffic from the entry relay to the client and the TCP acknowledgment traffic from the server to the exit relay. The adversary can examine the TCP headers in the observed traffic to retrieve the TCP sequence number and acknowledgment number fields and analyze the correlation between these fields over time. Thus, the asymmetry of Internet routing allows four segments of the routing paths between the two communication endpoints in Tor to be observed, increasing the chances of an AS-level adversary monitoring the traffic, which may effectively compromise Tor's anonymity. Figure 1: Scenarios of traffic correlation attacks by a single AS-level adversary. Traditional AS-level routing generally uses the Border Gateway Protocol (BGP), which has a crucial role in inter-domain connectivity. With BGP, each AS can configure and perform its own policy to select routes, then announce the selected routes to its neighbor AS. Although BGP provides maximum network autonomy, it lacks mechanisms to prevent natural churn and routing attacks such as BGP hijacking and interception. Moreover, BGP does not offer programmable control, making it challenging to provide flexible end-to-end routing. To address the issue of anonymity degradation due to correlation attacks by AS attackers in the Tor network, striving for an inter-domain routing protocol that balances performance and security--specifically, the efficiency of low-latency anonymous communication and the probability of paths being compromised by attackers--is a substantial breakthrough. This consideration motivated the construction of TOAR as an underlay optimal routing scheme. This paper studies the problem of finding optimal routes with maximum disjointness and policy satisfaction at the network layer to support Tor application services, specifically addressing the situation where the same adversary appears at both ends of the Tor circuit.
## 4 Experiments
### Evaluation Metrics  
Leveraging prior research [10; 42], this study employs two well-established evaluation metrics: Precision@K (P@K) and Mean Reciprocal Rank@K (MRR). The P@K metric measures whether the test item appears in the recommendation list. The MRR@K focuses on the ranking quality, assigning a greater score to relevant items positioned higher in the list. The calculation of these metrics is in Equations (13) and (14), respectively.  
\[P@K=\frac{n_{hit}}{N} \tag{13}\]  
\[M@K=\frac{1}{N}\sum_{v_{target}\in S_{\mathit{Stat}}}\frac{1}{Rank(v_{target})} \tag{14}\]  
where, \(v_{target}\) represents the target item, while \(Rank(v_{target})\) denotes the ordinal position of the target item within the list of recommended items. In this paper, according to the previous studies followed, we consider Top-K (K = 20) values. All experimental results are presented as % deletion.
## 4 Discussion  
Before the research, it was assumed that similar MPE values for all the measurement systems used should give similar values of the results. As experience has shown, it is completely different.  
As shown by the prepared analysis, the results for three different systems are divergent. Differences in the measurement results always occur and it is a natural phenomenon accompanied by the aforementioned variability. We are not always able to monitor this variability, as it largely depends on the resolution of the measurement system. Before beginning the research, such differences in measurements for the tested detail were expected,  
Figure 21: Boxplot chart showing all three measurement systems (own study).  
Figure 20: Tooth spacing 10 \(\pm\) 0.05 mm—Cg/Cgk indicators for the Multisensory Measuring Machine Werth Scope Check (own study).  
nevertheless, the differences obtained during the research exceeded the expectations of the authors. While the results from the machines can be compared with each other, the results obtained from the measurements with the use of the height gauge are definitely far from the expected. Reference values for the tested characteristics have been determined in an accredited laboratory for two types of measurements: for contact measurement and optical measurement because it is impossible to relate the results of contact measurements to the results of optical measurements. The values obtained with the same sensor should be compared. Any other comparison may raise additional doubts. As shown by the analysis carried out in the Minitab program, the results obtained from the measurements differ from the standard values, as evidenced by the Cgk index. For the comparison of the systems, the values of the Cg/Cgk index were determined, which confirms in the general classification that the weakest system is the height gauge. The height gauge MPE is not worse than the MPE declared by the machine manufacturer. So what should be done in this situation? Which system is the best, which guarantees the lowest uncertainty while ensuring fast measurement of specific characteristics, and what influence does the operator have on the measurement results? An additional question arises, namely, what strategy should be chosen when measuring with a multisensor machine? Should it be a static or dynamic measurement? What are the differences between these measurements? Can the distance between the holes and their diameters be measured optically, and other characteristics by contact, using the multisensor machine, or should the characteristics be divided into several different available measuring systems? Finally, after the experiment, is it possible to compare the measurement with the contact sensor with the measurement obtained with the optical sensor? We will try to answer these and other questions during further research, but at this point the focus was on the evaluation and comparison of the described measurement systems.
# A Feasibility Study for a Hand-Held Acoustic Imaging Camera
## 2 Acoustic Imaging Concepts
### Aliasing  
The spatial sampling pattern of the microphones can result in aliasing artefacts that distort the acoustic image (Figure 2).  
Aliasing occurs when sources at different angular positions generate identical array signals, preventing unique localization. Uniform linear or grid arrays are especially prone due to their periodic sampling structure. Sources separated by multiples of the angular period:  
\[p=\sin^{-1}\left(\frac{\lambda}{d}\right) \tag{2}\]  
where \(d\) is the grid spacing, will be aliased since the path length difference between microphones is identical. The resulting grating lobes complicate acoustic imaging by introducing ghost sources and ambiguity. For instance, if we simulate a periodic displacement in a planar array of 25 \(\times\) 25 cm\({}^{2}\) by putting 32 microphones in a regular grid (Figure 3), analysing the beampattern in the window of frequencies [2, 6.4] kHz, we found grating lobes, more evident at higher frequency (Figure 4). A common solution is breaking periodicity by using randomized or aperiodic array layouts [18,19]. However, this must be balanced with microphone density and area coverage to retain sensitivity. Careful array optimization is required to design alias-free configurations suited for imaging.  
Figure 2: Broadband beamforming issues (1-D): (a) low directivity at low frequencies and (b) aliasing at high frequencies. \(B(f,\phi)\), beampattern; \(f\), function of frequency; \(\phi\), DOA (direction of arrival) [17].
# A Full-Spectrum Registration Method for Zhuhai-1 Satellite Hyperspectral Imagery
## 3 Methods
### Absolute Registration  
#### 3.2.1 Delaunay Triangulation Construction  
The Delaunay triangulation for a set of points in a two-dimensional domain has two important properties. The first is an empty circumcircle property, which means that a circle circumscribing any Delaunay triangle does not contain any other input points in its interior. The second is a maximum-minimum angle property, which means that the Delaunay triangulation maximizes the minimum angle in the plane. The commonly used Delaunay triangulation construction algorithms include the incremental insertion method, triangulation growth method, and the divide and conquer method. In this study, the divide and conquer method was employed to construct the Delaunay triangulation.  
Delaunay triangulation is created using the matching points obtained from relative registration (Figure 6). In this step, triangulation is constructed for the matching points in the reference band. An affine transformation relationship is derived based on each small triangle vertex in the reference band and the corresponding matching points in the sensed band. The equation is given as:  
\[\left\{\begin{array}{l}X=a_{1}x+b_{1}y+c_{1}\\ Y=a_{2}x+b_{2}y+c_{2}\end{array}\right., \tag{15}\]  
Figure 5: Schematic diagram of the adjustment for matching points. (**a**) Reference band. (**b**) Band to be registered.  
where \(a_{1}\), \(b_{1}\), \(c_{1}\), \(a_{2}\), \(b_{2}\), and \(c_{2}\) represent the affine transformation coefficients for the two triangles; \((x,y)\) are the coordinates for the matching point in the reference band; and \((X,Y)\) are the coordinates for the matching points in the image to be registered.