esa-sceva/satcom-chunk-collection huggingface dataset:
# An Implementation of the HDBSCAN* Clustering Algorithm
## 2 Background and Literature Review
### Hdbscan
#### 2.2.1 Other Methods  
There are other clustering algorithms which can be used to perform clustering when the number of clusters are unknown. Perhaps the most similar to DBSCAN and HDBSCAN is OPTICS, which stands for ordering points to identify clustering structure [9]. OPTICS can be used for cluster analysis, although the original algorithm does not produce an explicit clustering of the data. Instead, it produces an ordered representation of the data density-based clustering structure. It overcomes the limitation of DBSCAN by identifyingcluster structures of varying density, but is not as comprehensive as HDBSCAN* to identify clusters while excluding outliers.  
Hierarchical clustering methods are another form of clustering analysis which can work without the need to specify the number of clusters. These methods construct clusters by recursively partitioning the data points in either a top-down or bottom-up approach [10]. Divisive clustering starts with a single cluster containing all points and is divided into sub-clusters based on some criteria, which are successively divided into further sub-clusters. There are different strategies to determine when the process should be terminated. Agglomerative hierarchical clustering starts with each data point in its own cluster and merges clusters sharing some criteria, and continues to successively merge clusters. Again, there are different strategies which can be employed to terminate the process. Hierarchical clustering methods can provide high interpretability since the results can be presented in a dendrogram, and the choices for cluster splits or merges are well defined. These methods do not provide any mechanism for identifying outliers. They also tend to have high asymptotic complexity and large memory requirements.  
In the previous section, it was mentioned that partitioning algorithms, such as K-Means require the number of clusters, k, to be defined as input to the algorithm. In some cases, the value for k is unknown. There are techniques which can be used to determine the optimal number of clusters from a dataset, such as the frequently mentioned elbow method. The elbow method is somewhat subjective, and, therefore, unreliable. There are also more analytical techniques that leverage silhouette analysis to estimate the optimal number of clusters from a dataset. One such example is an algorithm called k-SCC [11]. Combining the k-SCC algorithm with K-Means effectively achieves a natural discovery of clusters from a dataset. However, this combination is not able to distinguish outlier points from the clusters as can be done using HDBSCAN*.
# Co-Operatively Increasing Smoothing and Mapping Based on Switching Function
## 4 Simulations
### Experiment 2  
IMU-camera data anomalies were introduced. We conducted a simulation experiment in the case of IMU-camera data anomalies to verify the positioning effect of the method proposed in this paper when dealing with IMU-camera data anomalies. The paper set the drone motion time to 60 s and let the drone perform random motions in space. We added a normal level of noise to the IMU-camera data and added outliers at some moments, such  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** & **Description** \\ \hline \(w\)x & \(w_{v}^{k},w_{l}c_{i}^{k},w_{h}^{k}_{ij}\) & switching function \\ \(\Phi_{\gamma}\) & 0.2 (m)\({}^{2}\) & UWB measurement covariance \\ \(\Sigma_{UIWB}\) & 1 (m)\({}^{2}\) & VIO system covariance \\ \(\Sigma_{cam}\) & 0.1 (m)\({}^{2}\) & camera covariance \\ \(\Sigma_{lamuark}\) & 0.2 (m)\({}^{2}\)/10\({}^{\circ}\) & landmark-bearing covariance \\ \(\Sigma_{limu}\) & 0.3 (m)\({}^{2}\)/5.73\({}^{\circ}\) & IMU covariance \\ \(\Sigma_{UWB}^{prior}\) & 0.2 (m)\({}^{2}\) & UWB measurement prior value \\ \(\Sigma_{VIO}^{prior}\) & 1 (m)\({}^{2}\)/5.73\({}^{\circ}\) & VIO system prior value \\ \hline \end{tabular}
\end{table}
Table 1: The parameters used in the proposed algorithm are numerical values for experiments.  
\begin{table}
\begin{tabular}{c c c} \hline
**Method** & **Mean Average Error (m)** & **Positioning Success Rate (\%)** \\ \hline IMU–camera only & 0.87 & 80 \\ UKF & 0.55 & 93.3 \\ LS & 0.52 & 86.7 \\ Multi-iSAM [19] & 0.44 & 93.3 \\ CI-SAM (this paper) & 0.41 & 100.0 \\ \hline \end{tabular}
\end{table}
Table 2: Results of Experiment 1.  
as sudden increases or decreases. An evaluation of the localization results of the various methods is shown in Table 3.  
It can be seen that the method in this paper was still able to maintain a high localization accuracy and precision in the presence of anomalies in the IMU-camera data. This indicates that the method in this paper is effective and robust in terms of dealing with IMU+ camera data anomalies.
# Multilevel Bipolar Electroforming-Free Resistive Switching Memory Based on Silicon Oxynitride
## 4 Conclusions  
A silicon oxynitride (SiO\({}_{\mathrm{x}}\)N\({}_{\mathrm{y}}\))-based nonvolatile resistive switching memory device exhibiting electroforming-free, multilevel bipolar switching characteristics was successfully fabricated by employing a p\({}^{+}\)-Si substrate and Au/Ni as the bottom and top electrodes, respectively. The device exhibits multilevel resistive switching from HRS to IRS, IRS to LRS, HRS to LRS, and vice versa. The switching mechanism was explained based on the I-V characteristics of the devices. The resistive switching between HRS and IRS is attributed to the electronic charge trapping and detrapping from the defects present in the bulk SiO\({}_{\mathrm{x}}\)N\({}_{\mathrm{y}}\) layer. The resistive switching between the IRS and LRS is attributed to the formation and rupture of filaments at the Ni/SiO\({}_{\mathrm{x}}\)N\({}_{\mathrm{y}}\) interface region. Both types of switching can be controlled by the magnitude of the applied voltage and the compliance current. The variation of the write current influences the switching performance in the filamentary mode of the interface region, and the variation of the thickness affects the switching performance in the bulk SiO\({}_{\mathrm{x}}\)N\({}_{\mathrm{y}}\) layer.  
Figure 10: Retention property of the Au/Ni/SiO\({}_{\mathrm{x}}\)N\({}_{\mathrm{y}}\)/p\({}^{+}\)-Si memory device.  
Figure 9: Pulse endurance property of the Au/Ni/SiO\({}_{\mathrm{x}}\)N\({}_{\mathrm{y}}\)/p\({}^{+}\)-Si memory device.  
**Author Contributions:** Each co-author contributed equally. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Creative Materials Discovery Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2017M3 DIA1040828) and Samsung Electronics.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 7 New Challenge: Multimodality
### Interaction with Human-Driven Vehicles  
In mixed traffic environments [86], AVs must predict the behavior of human drivers, who do not communicate via V2V. Multimodal systems might misinterpret human actions if satellite or sensor data does not align with unpredictable human behavior--e.g., a sudden lane change not signaled to infrastructure.
## 2 VMD-ESN Model
### Variational Mode Decomposition  
The VMD process seeks to minimize the total bandwidth of all modes while maintaining perfect signal reconstruction. Essentially, VMD formulates the signal decomposition as a constrained variational optimization problem, which can be mathematically expressed as follows [32]:  
\[\left\{\begin{array}{l}\underset{\{u_{k}\},\{\omega_{k}\}}{\min}\left\{ \underset{\{\Sigma\}}{\sum}\left\|\partial_{t}\right|\left[\left(\delta(t)+ \frac{j}{\pi t}\right)u_{k}(t)\right]\mathrm{e}^{-\mathrm{j}\omega_{k}t}\right\| _{2}^{2}\right\},\\ \text{s.t.}\qquad\qquad\underset{k}{\sum}u_{k}(t)=f(t),\end{array}\right. \tag{1}\]  
where \(\omega_{k}\) represents the center frequency of the \(k\)th mode. The constrained variational formulation presented in Equation (1) is resolved through the incorporation of two parameters: a penalty factor \(C\) and a Lagrangian multiplication operator \(\theta(t)\). This transformation  
Figure 1: Illustration of the VMD-ESN model.  
converts the originally constrained optimization problem into an unconstrained equivalent problem:  
\[\begin{array}{ll}\mathrm{L}(\{u_{k}\},\{\omega_{k}\},\theta)=&C\sum\limits_{k} \left\|\partial_{t}\left[\left(\delta(t)+\frac{j}{\pi t}\right)u_{k}(t)\right] \mathrm{e}^{-j\omega_{k}t}\right\|_{2}^{2}\\ &+\left\|f(t)-\sum\limits_{k}u_{k}\right\|_{2}^{2}+\left\langle\theta(t),f(t)- \sum\limits_{k}u_{k}(t)\right\rangle,\end{array} \tag{2}\]  
where \(\left\|f(t)-\sum\limits_{k}u_{k}(t)\right\|_{2}^{2}\) is the second penalty term, and \(\langle\cdot\rangle\) represents the inner product operation.  
The resolution of the unconstrained variational Equation (2) is achieved through the alternating direction method of multipliers, where the optimization parameters \(u_{k}(t)\), \(\omega_{k}\) and \(\theta(t)\) are alternately updated as follows:  
\[\hat{u}_{k}^{n+1}(\omega)=\frac{\hat{f}(\omega)-\sum\limits_{i\
eq k}\hat{u}_ {i}(\omega)+\left(\hat{\theta}(\omega)/2\right)}{1+2C(\omega-\omega_{k})^{2}}, \tag{3}\]  
\[\omega_{k}^{n+1}=\frac{\int_{0}^{\infty}\omega|\hat{u}_{k}(\omega)|^{2}d \omega}{\int_{0}^{\infty}|\hat{u}_{k}(\omega)|^{2}d\omega}, \tag{4}\]  
\[\hat{\theta}^{n+1}(\omega)=\hat{\theta}^{n}(\omega)+\tau\left[\hat{f}(\omega) -\sum\limits_{k}\hat{u}_{k}^{n+1}(\omega)\right]. \tag{5}\]  
Performing an inverse Fourier transform on \(\hat{u}_{k}(\omega)\), the real part is the solution \(\{u_{k}(t)\}(k=1,2,\ldots,K)\). By employing an iterative optimization framework to resolve the constrained variational formulation, VMD effectively separates the input signal into distinct quasi-orthogonal modes. During the procedure, VMD performs adaptive frequency-domain adjustments to each component, enabling reasonable spectral segmentation, which finally produces \(K\) narrowband modes.
## 5 Performance Evaluation
### Experimental Results
#### 5.3.2 The Influence of the Data Size  
Subsequently, we delve into the performance of the three algorithms under varying task data sizes. We change the size of the input data from \(224\times 224\) to \(504\times 504\) with a step length of 56. When the size of the data is large, it is equivalent to simulating the performance of our algorithm under high task loads. As the data size increases, the time and power consumption required for servers to complete tasks escalate, leading to a higher likelihood and duration of task queuing, which in turn elevates the average latency and energy consumption across all tasks. This investigation is conducted using 10 edge servers, each equipped with 8 to 15 cores. As depicted in Figure 6, the experimental results reveal a consistent trend in the performance metrics of all three algorithms: as the task data size grows, the average latency, average energy consumption, and the composite indicator all increase. However, our algorithm consistently outperforms the two baseline algorithms. As shown in the strip distribution figure, the superiority of our algorithm is consistent. This superiority can be attributed to our greedy algorithm, which dynamically determines the optimal queuing sequence and assigns the most suitable server for each task upon the arrival of multiple tasks. Such adaptability enables our algorithm to alleviate latency and energy consumption issues, ultimately enhancing overall performance. Furthermore, Figure 6 compares the performance of our method against that of fixed partition granularity. It is evident that our method surpasses the fixed partition granularity approach in terms of latency, energy consumption, and the composite indicator. This underscores the advantage of dynamically selecting the optimal partition granularity for each task. The specific experimental data on latency and energy consumption as the data size increases are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Size of Data**} & \multicolumn{5}{c}{**Delay/ms**} & \multicolumn{5}{c}{**Energy Consumption/J**} \\ \cline{2-9}  & **Greedy** & **Genetic** & **Random** & **Fixed** & **Greedy** & **Genetic** & **Random** & **Fixed** \\ \hline
224 * 224 & 0.20 & 6.72 & 11.61 & 259,839 & 0.00191 & 0.0207 & 0.0509 & 0.0364 \\ \hline
280 * 280 & 0.32 & 6.99 & 11.81 & 494,168 & 0.00297 & 0
# On the Lagged Diffusivity Method for the Solution of Nonlinear Finite Difference Systems
## 5 Numerical Experiments
### Boundary Layer  
Lastly, for completeness, let us consider a stationary case with non-constant \(\bar{\sigma}\) which presents a boundary layer solution.  
Let us consider the problem given by  
\[-\sigma_{1}\frac{\partial^{2}u}{\partial x^{2}}-\sigma_{2}\frac{\partial^{2}u }{\partial y^{2}}+v\
abla u=0 \tag{19}\]  
and  
\[\sigma_{1}=u^{2}+1\qquad\sigma_{2}=u^{2}\qquad v_{1}=\lambda u^{2}+\lambda \qquad v_{2}=\lambda u^{2}. \tag{20}\]  
with \(\lambda<0\) real parameter and boundary condition \(u=e^{\lambda x}+e^{\lambda y}\) on \(\partial\Omega\). The solution of this test problem is evidently  
\[u^{*}=e^{\lambda x}+e^{\lambda y}, \tag{21}\]  
which presents a boundary layer as the absolute value of \(\lambda\) increases. Since we do not have any nonlinear reaction term, at each iteration of the LDM we just have to solve a linear system, which we do by the BiCGstab(\(l\)) algorithm.  
The results as \(|\lambda|\) increases are reported in Table 7 and the computed solutions are in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(n^{*}\) & \(t_{0}\) & \(t_{f}\) & \(res_{0}\) & \(res\) & \(err_{h}\) & \(err_{2}\)**rel.** & \(\max u\) & \(\min u\) & \(\max u^{*}\) & \(\min u^{*}\) \\ \hline \(u_{2}^{*}\) & 0.981 & 0.99193 & \(3.67\times 10^{8}\) & \(7.42\times 10^{-7}\) & \(6.02\times 10^{-4}\) & \(2.87\times 10^{-4}\) & 62.539 & 0.999 & 62.539 & 0.999 \\ \(u_{3}^{*}\) & 0.965 & 0.97141 & \(2.75\times 10^{8}\) & \(7.48\times 10^{-5}\) & \(2.07\times 10^{-3}\) & \(2.41\times 10^{-4}\) & 61.276 & 0.980 & 61.276 & 0.980 \\ \(u_{4}^{*}\) & 0.783 & 0.79343 & \(6.87\times 10^{8}\) & \(1.05\times 10^{-6}\) & \(4.33\times 10^{-2}\) & \(5.89\times 10^{-3}\) & 60.203 & 0.984 & 60.203 & 0.984 \\ \(u_{5}^{*}\) & 0.795 & 0.802771  & \(1.13\times 10^{9}\) & \(2.15\times 10^{-7}\) & \(1.38\times 10^{-2}\) & \(9.34\times 10^{-3}\) & 61.238 & 0.502 & 61.238 & 0.502 \\ \hline \hline \end{tabular}  
* \({}^{1}\) Again, we slightly exceed the blow-up time; this is again due to the discretization.  
\end{table}
Table 6: Error and data of the lagged diffusivity procedure for the last computable solution for the problems in Figure 4 for \(\Delta t=10^{-5}\), \(N=250\).  
Figure 7: Solution of the boundary layer test problem for \(N=250\); inner solver: BiCGstab(4).  
We notice that the number of iterations of the linear solver decreases as \(|\lambda|\) increases. The global errors, instead, increase, but they never get larger than \(10^{-3}\), confirming the good approximation of the solution. In particular, Figure 7c, correctly reproduces the profile of a boundary layer solution.
# Multimodal Dynamic Journey-Planning +
## 3 The Multimodal Dynamic Timetable Model
### Update Algorithm  
We shall now present our update algorithm, based on [10] and named MDTM-U, for updating the timetable when a delay occurs, that is, for updating the corresponding MDTM graph.  
Given a timetable \(\mathcal{T}\), we assume that a delay \(\delta\) occurs first on a connection \(c_{0}\) of \(\mathcal{T}\), and it is propagated to the (affected) connections \(c_{0},c_{1},...,c_{k}\), which are performed by the same vehicle. Also, let \(d_{0},d_{1},...,d_{k}\) be the departure nodes corresponding to the affected connections. If \(\mathcal{T}\) is represented as a MDTM graph \(G\), then the MDTM update algorithm computes the MDTM graph \(G^{\prime}\), corresponding to the disposition timetable \(\mathcal{T}^{\prime}\), as follows.  
* Edge weight increase: Starting with \(c_{0}=(h,S_{d},S_{a},t_{d},t_{a})\), the weight of arc \((d_{t_{d}},\sigma_{S_{a}})\) is increased by \(\delta\)* Node reordering: For each of the other connections \(c_{i}\), \(i=1,..,k\), its associated departure node \(d_{i}\) has its departure time \(t_{d}(d_{i})\) increased by \(\delta\). Due to that increase, the arrival time ordering of the departure nodes on the affected stops may be invalidated. Hence, along with the new arrival times, the departure node \(d_{i}\) might need to be moved to its correct position within its group, that is, before a departure node with arrival time greater than \(t_{a}(d_{i})\).
# New Landslide Disaster Monitoring System: Case Study of Pingding Village
## 2 Material and Methods
### Data Collection  
This section shows the organization of the research methods used. Figure 4 provides a flowchart of the research methods adopted to create a disaster monitoring and management system. First, for simulating the landslide disaster monitoring system, we visited the site to carry out surveys. Second, based on the results of the survey, we decided on and installed the appropriate equipment for the study site. Third, the thresholds for the sensors were defined. Fourth, based on the results of the analysis, we identified the accuracy of the measurements and validated the threshold value. Finally, a landslide monitoring displaying system in real time was constructed.  
Figure 4: Research methodology flowchart.  
Figure 3: Collapse areas of Pingding River upstream, Linbeiken, and Pingding across different periods of time [28]. Note: The yellow parts refer to the collapse on June 2002 (caused by Jiji Earthquake and Typhoon Toraji); the black parts are the collapses on 9 September 2004, while the parts in red are the collapses on 11 January 2005. The arrows indicate the sliding directions of each collapse.
## 3 Proposed Method
### Step of Feature Selection and Network Design  
The optimization results of the evolutionary neural network include the feature selection and the structure of the fully connected neural network. Using the above datasets as a measure for the calculation of fitness in the evaluation process of evolutionary neural networks, the feature selection and network design method is implemented as follows.  
1. Calculate the 120 features of the training and testing data and form them into feature vectors;
2. Design the parameters of the evolutionary neural network, where the parameters include population size, crossover probability, variation probability, and the number of evolutionary generations, and randomly generate a primitive population;
3. For the genetic information in the population, generate the feature vector corresponding to each individual with the neural network, and calculate the fitness of each individual. In the process of fitness calculation, the neural network is trained with 1500 epochs using the training set mentioned above, and the classification score of the trained network for the test set is the score of the classification performance in the fitness calculation of this network;
4. Generate offspring populations through election, crossover, and mutation of parent populations;
5. Determine whether the requirement of evolutionary generations is satisfied. If so, the individual with the highest adaptation degree is output; if not, repeat step 3.  
After the process of evolution, an individual with optimal adaptation is obtained, and is used in a fully connected neural network with one seismic signal feature, one sound signal feature, and two hidden layers. The selected features are  
\[kurtosis=\frac{\frac{1}{n}\sum_{i=1}^{n}(x_{i}-\overline{x})^{4}}{\left[\frac {1}{n}\sum_{i=1}^{n}(x_{i}-\overline{x})^{2}\right]^{2}}-3, \tag{6}\]  
\[Vibration\ energy=10\log_{10}(\frac{1}{n}\sum_{i=1}^{n}x_{i}^{2}), \tag{7}\]  
where \(x_{i}\) is the \(i-\) th data point in \(x\) and \(n\) is the length of the signal \(x\). The optimized network has only two hidden layers, where the number of nodes in the first layer is 12 and the number of nodes in the second layer is 8.  
The network uses cross-entropy loss as the objective function. Hence, the classifier has loss function  
\[L(\Theta)=-\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{C}t_{ij}\times\log\bigl{(}p \bigl{(}y_{j}|x_{i},\Theta\bigr{)}\bigr{)}, \tag{8}\]  
where \(N\) is the number of samples, \(C\) is the number of target types, \(t_{ij}\) is the probability that the \(i\)-th sample belongs to category \(j\), and \(\Theta\) denotes the set of all parameters.  
Figure 5: Crossover and variation.
## 10 Soft Decision Cognitive Radio and Hybrid Overlay/Underlay Cognitive Radio Waveform Design
### SD-SMSE Underlay Waveform  
Unlike overlay-CR waveforms that only operate in unused spectrum bands, underlay-CR waveform operates in _underused_ spectrum regions. An underlay-CR waveform spreads its signal over a wide bandwidth to minimize interference to existing primary users and to achieve the required processing gain to improve its own performance. Underlay-CR approaches have been generally associated with UWB technology. By definition, a signal is defined as UWB if it occupies a bandwidth that is greater than 500 MHz. Therefore, not all underlay-CR waveforms can be classified as UWB per this definition. For example, a low data rate underlay waveform used as a control channel might only require a few mega hertz of bandwidth. In the SD-SMSE context, UWB is a special implementation of an underlay-CR waveform. An UWB transmission uses underlay waveform which operates across all spectral components while minimizing interference to primary users by limiting its transmission power spectral density. Hence, its allowable transmission power spectral density is dictated by the primary user (among all those present) that is most sensitive to interference. In this case, all frequency components are treated as _underused_ components. Hence, by setting  
\[\mathbf{u}=[0,0,\ldots,0] \tag{101}\]\[\mathbf{b}=[K,K,\ldots,K],0<K<1 \tag{102}\]  
The first summation in (97) can be eliminated which results in a CR underlay waveform corresponding to an UWB transmission:  
\[s_{k}[n]=\frac{1}{N_{f}}Re\left\{\sum_{m=0}^{N_{f}-1}Kd_{m,k}w_{m}e^{j(2\pi f_{m} t_{n}+\theta_{d_{m,k}}+\theta_{c_{m}}+\theta_{w_{m}}+\theta_{om,k})}\right\} \tag{103}\]  
where \(K\) is a constant obtained by taking the minimum value of the weighted power spectral density shown in Figure 50. Note that **b** was assumed to constant for simplicity purpose, in general each _underused_ spectral components can have different spectral weights capable of employing adaptive baseband modulations.