esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### The New Solution Proposed in This Work  
The negative selection algorithm with spherical detectors presented in the previous section was developed in the context and for the needs of semi-supervised anomaly detection. In this work, we propose how to use and adapt this framework for unsupervised anomaly detection. Therefore, the goal is to develop an approach that can be applied to data that do not necessarily represent only self-data, but also to data that typically contain a small percentage of anomalies, which should be identified without prior knowledge of which points they are.  
The novelty in the proposed approach is the presentation and testing of a new unsupervised anomaly detection algorithm. It is worth emphasizing that previous original algorithms based on the negative selection model were developed for semi-supervised anomaly detection and cannot be used in the unsupervised context. In Section 2, Methodology, several algorithms are presented. To clearly specify the new proposed approach, we indicate the following. The Negative Selection Algorithm Using Spherical Detectors with Constant Radius presented in Section 2.1 is a well-known algorithm in the literature for generating spherical detectors and is used in the context of semi-supervised anomaly detection. The Negative Selection Algorithm Using Spherical Detectors with Variable Radii described in Section 2.2, and the Negative Selection Algorithm Using Spherical Detectors with Variable Radii and Decreasing Radius Strategy from Section 2.3 are enhancements of the first algorithm; they are also known and are included here for completeness and clarity of the overall approach.  
The novelty lies in the algorithm proposed in Section 2.4, Unsupervised Anomaly Detection Using Spherical Detectors and Local Neighborhood Sampling, which utilizes the previous algorithms in its operation but introduces a new mechanism of local neighborhood sampling around the analyzed example to determine the degree of its anomalousness. This allows the use of NSA in unsupervised anomaly detection tasks. A somewhat less significant novelty is the use of an ensemble of detector populations instead of a single population trained by the basic semi-supervised NSA. To the best of our knowledge, this is the first such algorithm for unsupervised anomaly detection based on the negative selection mechanism.  
The aim of this publication is to propose a new unsupervised anomaly detection algorithm, test it on selected problems, and compare it with the most popular algorithms. The conducted analyses have their obvious limitations. It is not feasible to compare the proposed method with all the recent approaches available in the literature, such as dynamically evolving deep-learning methods. However, considering the permissible length of this publication, the presented results allow for positioning this newly proposed algorithm in the context of the most classic algorithms, which is a valuable achievement for a first publication introducing a new computational methodology.
# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
## 1 Introduction
### Ethical Considerations  
The ISIC 2020 dataset consists of de-identified medical images, and the dataset providers ensured that all necessary ethical approvals and patient consents were obtained prior to data distribution. The ISIC organization guarantees that all required ethical guidelines are followed, in compliance with international medical standards. Researchers utilizing this dataset adhere to the ethical guidelines outlined by the ISIC and the relevant medical authorities. This dataset is publicly available for further experimentation, ensuring reproducibility and transparency in research.
## 2 Statistical Methodology
### Coherence of a Variable Set  
To define a notion of distance between two variables \(X^{i}\) and \(X^{j}\), which are not directly connected in the graph, we use the notion of capacity (see [21; 22]) of a path \(P\) between the corresponding nodes \(N_{i}\) and \(N_{j}\) in \(G(N,E)\). A path \(P\) of a graph \(G\) from \(X^{i}\) to \(X^{j}\) of length \(\Lambda\) is a list of indices \(\{d_{1},\ldots,d_{\Lambda}\}\subset\{1,\ldots,p\}\) such that \(X^{i}=X^{d_{1}}\), \(X^{j}=X^{d_{\Lambda}}\), and \(w_{d_{i},d_{i+1}}\) is known and is not equal to \(0\), for all \(l=1,\ldots,\Lambda-1\). The capacity \(cap(P)\) of path \(P\) is then the minimal weight of its edges, i.e.,  
\[cap(P)=\min_{l=1,\ldots,\Lambda-1}w_{d_{l},d_{l+1}} \tag{1}\]  
We also denote by \(\mathbf{P}_{i,j}\) the set of all possible paths connecting \(X^{i}\) to \(X^{j}\). The coherence \(c(X^{i},X^{j})\) between \(X^{i}\) and \(X^{j}\) is then defined by considering the path \(P\) having the maximum capacity among the paths of \(\mathbf{P}_{i,j}\)[22], i.e.,  
\[c(X^{i},X^{j}) =\max_{P\in\mathbf{P}_{i,j}}cap(P)\] \[=\max_{P\in\mathbf{P}_{i,j}}\min_{l=1,\ldots,\Lambda-1}w_{d_{l}, d_{l+1}} \tag{2}\]  
If the weight \(w_{i,j}\) is known, it is interesting to remark that the coherence \(c(X^{i},X^{j})\) is not necessarily equal to its value. For instance, both \(X^{i}\) and \(X^{j}\) may be very similar to a third variable \(X^{k}\), but not similar to each other. From a computational point of view, the similarity in \(w_{i,j}\) may also be unknown if the edge \(e_{i,j}\) is not stored in a sparsified version of the complete graph. Since \(n\ll p\), pertinent relations may finally be lost in \(w_{i,j}\) but recovered in \(c(X^{i},X^{j})\) thanks to other relations that would be captured. We believe that these points are particularly important to define coherent variable sets in the complex data case.  
We now denote by \(S\) a connected subset of the variable set \(X\). The coherence \(\mathfrak{c}(S)\) of this variable subset is the minimal coherence between the variables it contains:  
\[\mathfrak{c}(S)=\min_{(X^{i},X^{j})\in S^{2}}c(X^{i},X^{j}) \tag{3}\]  
If all the variables of \(S\) have a coherent observed behavior, then \(\mathfrak{c}(S)\) is high. The use of this notion on synthetic data is illustrated in Appendix A. The coherence of a subset measures the strength of the variables it contains. The more coherent \(S\), the more sense it makesto consider that its variables share common features measured by the chosen similarity. Decomposing the graph into maximal groups sharing a strong similarity, i.e., finding all the groups of variables with a large enough coherence is the core of the following subsections on CORE-clusters selection.
# Key Technologies of Photonic Artificial Intelligence Chip Structure and Algorithm
## 2 Hardware Structures for Linear Operation
### MRR  
In the CNN [34], MRR completes matrix operation by the weight of the micro-ring. The multiplexed wavelengths enter the MRR array. The amplitude of each wavelength is multiplied by its corresponding micro-ring weight, and then, output. The multiplication is realized by adjusting the resonant peaks of the micro-rings to their respective laser wavelengths [31]. Figure 5 shows the convolution operation with input feature mapping. This greatly facilitates the calculation.
# 4-Class MI-EEG Signal Generation and Recognition with CVAE-GAN
## 3 Methods
### Datasets and Conversion Based on STFT  
We evaluated our approach on a private EEG dataset collected by ourselves and additionally a public dataset (Competition IV Data sets 2a). The data were separated into 70% training set and 30% test set repeatedly for cross-validation. The data detail is shown in Table 1.  
Throughout the experiments, subjects were scheduled in front of a computer screen and instructed to perform motor imagery tasks including executing the movement of the right or left hand. Each trial took 8 s with same inter-trial resting period's length. The fact is that the energy in mu band (8-13 Hz) observed in the motor cortex of the brain decreases, called event-related de-synchronization (ERD), meanwhile the energy increase caused in the beta band (17-30 Hz) is called event-related synchronization (ERS) after executing the MI task. MI tasks were found to cause ERD and ERS, respectively, on the right and left sides of the motor cortex affecting EEG signals at C4 and C3 electrodes. Cz is mainly affected by both tongue and feet MI tasks. Consequently, the datasets we used in this work included recordings from three electrodes (C3, Cz, and C4) of left/right handle MI task and short time Fourier transform (STFT) was applied to converting the 2 s long sequential EEG trial to temporal-spectrum power image. A set of time-frequency power image was generated by a short time window sliding along the time axis of the sequential EEG signal. The STFT can be defined as:  
\[STFT(\tau,\omega)=\int_{-\infty}^{\infty}s(t)h(t-\tau)e^{-j\omega t}dt \tag{1}\]  
where \(h(t)\) is a window with a limited number of nonzero points and \(\tau\) is the window position on the temporal axis. In the case of the 250 Hz signal, this corresponded to 500 samples. The window size of STFT was set to 64 and its number of points to overlap between segments was set to 50. STFT was computed for 32 windows over all samples leading to are 257 \(\times\) 32 as the size of the spectrum images, where 257 presented the number of sample frequencies and 32 meant the number of segment times. Ultimately, mu and beta frequency bands of the spectrum images were extracted. The final input sizes of the normalized image for mu and beta band were empirically chosen as 32 \(\times\) 32.
## 2 Model Formulation and Analysis
### Mathematical Modeling
\begin{array}{l}\pi r_{\rm c}^{2}(\sigma_{\rm c}+{\rm d}\sigma_{\rm c }-\sigma_{\rm c})=2\pi r_{\rm c}\tau_{\rm c}\tau_{\rm c}dz\\ \gamma_{\rm rz}=\phi\frac{dw}{dr}\\ \tau_{\rm rz}={\rm G}_{\rm s}\gamma_{\rm rz}\\ G_{\rm s}=\frac{E_{\rm s}}{2(1+v)}\end{array} \right. \tag{17}\]  
where \(r_{\rm c}\) is the radius of column inclusion, \(E_{\rm s}\) and \(G_{\rm s}\) are [PERSON]'s modulus, and the shear modulus of the surrounding soil. Equation (17) can be re-written as the following equation:  
\[\frac{d\sigma_{c}}{dz}=\frac{2G_{s}\dot{u}\phi}{r_{c}} \tag{18}\]  
Substitute the boundary condition \(\sigma_{c}^{0}=\sigma_{i}\),  
\[\sigma_{c}=\sigma_{i}+\frac{2 aG_{s}\,h}{r_{c}\,\eta}\,[C_{2}\,I_{1}\,(a\times r _{c})-C_{1}K_{1}\,(a\times r_{c})]\bigg{[}coth(\eta)-\frac{cosh(\eta(1-\frac{z }{h}))}{sinh(\eta)}\bigg{]} \tag{19}\]  
From Equation (19), the settlement of columnar inclusion can be obtained. \[ \begin{cases}S_{c}=\frac{\sigma_{i}h}{M_{c}}+\frac{2 aG_{s}h^{2}}{r_{c}\eta M_{ c}}(C_{2}I_{1}(a\times r_{c})-C_{1}K_{1}(a\times r_{c}))(\cot(\eta)-\frac{1}{ \eta})\\ M_{c}=\frac{(1-\
u)E_{c}}{(1+\
u)(1-2\
u)}\end{cases}  \tag{20}\]  
It is assumed that there will be no slip happening between the column inclusion and the surrounding soil. Since the unit cell is symmetric, the following boundary conditions can be substituted into Equation (16):  
\[ \begin{cases}\frac{S_{c}=w(r_{c})}{dr}\Big{|}_{r=\frac{d}{2}}=a\times C_{2} \times I_{1}(a\times\frac{d_{c}}{2})-a\times C_{1}\times K_{1}(a\times\frac{d _{c}}{2})=0\end{cases}  \tag{21}\]  
The coefficients \(C_{1}\) and \(C_{2}\) can be obtained from Equation (21) (the detail can be seen in Appendix A) and the deformation of the surrounding soil can be derived.
## 3 Test Data Analysis
### Parametric Impact
#### 3.2.4 Thermal Influence  
Process thermal evaluation distinctly motivated for commercial applications was considered when utilizing Table 1. The impact of temperature on BG dye adsorption onto HNT are presented in Figure 4d. The test readings relevant to thermodynamic parameters--\(\Delta\)G\({}^{\circ}\), \(\Delta\)H\({}^{\circ}\), and \(\Delta\)S\({}^{\circ}\) reveal the reaction features. Indicatively, the negative \(\Delta\)H\({}^{\circ}\) (enthalpy change) for selected temperatures specify exothermic interaction. Negative \(\Delta\)G\({}^{\circ}\) (free energy change) affirm the suddenness and suitability of the adsorption interaction. The magnitude of \(\Delta\)G\({}^{\circ}\) projects fasts and extensive adsorption at low thermal conditions. Further, it was inferred that the negative values of \(\Delta\)S\({}^{\circ}\) (entropy) suggest minimum changes in the internal structure of HNT. A similar observation has been reported elsewhere [32].
# Non-Parallel Articulatory-to-Acoustic Conversion Using Multiview-Based Time Warping
## 4 Experimental Setup
### Dataset  
The proposed alignment algorithm was evaluated on an A2A task involving the synthesis of speech from articulatory data captured using PMA. PMA is a technique for capturing the movements of the vocal apparatus during speech. The technique is based on sensing the changes in the magnetic field generated by the movement of small magnets attached to the speech articulators (lips and tongue, in our case) as the speaker'mouths' words. In the current set-up, a total of six magnets were employed: four on the lips with dimensions of 1 mm (diameter) \(\times\) 5 mm (height), one on the tongue tip (2 mm \(\times\) 4 mm), and one on the middle of the tongue (5 mm \(\times\) 1 mm). The three spatial components \((x,y,z)\) of the magnetic field generated by the magnets were then acquired by four triaxial magnetic sensors mounted on a rigid frame. Only three of the sensors were used for capturing articulatory movements, while the remaining sensor was used to compensate the effects of the earth's magnetic field on the captured articulatory data. More information about this technique can be found in [12,22].  
Parallel data was recorded by four nonimpaired British subjects (2 males and 2 females) while reading aloud a subset of the CMU Arctic corpus [32]. Two alignment conditionswere evaluated: (i) intrasubject alignment, where PMA and speech signals recorded by the same subject in different sessions are aligned, and (ii) crosssubject alignment, where PMA signals recorded by a given subject are aligned with speech recorded by a different subject (with a possibly different gender as well). We also attempted to align PMA signals recorded from a female larynxectomy patient with a set of speech recordings made by her before losing the voice. The details of the dataset used for our experiments is summarized in Table 1.  
Feature Extraction  
The PMA and speech signals were parameterized as a series of feature vectors computed every 5 ms from 25 ms analysis windows. The speech signals were first downsampled from 48 kHz to 16 kHz and then converted to sequences of 28-dimension vectors using the WORLD vocoder [33]: 25 mel-generalised cepstral coefficients (MGCCs), 1 band periodicity (BAP) value, 1 continuous \(F_{0}\) value in logarithmic scale, and 1 U/V decision. \(\log F_{0}\) values in unvoiced frames were linearly interpolated from adjacent voiced frames. The 9-channel, background-canceled PMA signals were first oversampled from 100 Hz to 200 Hz to match the 5 ms frame rate. Then, segmented features were computed from the raw PMA signals by applying principal component analysis (PCA) over contextual windows with 11 frames. That is, for each frame of 25 ms, the preceding and succeeding 5 frames, along with the current one, were concatenated and PCA was applied for dimensionality reduction by retaining the 99% of the variance. Finally, the PMA and speech features were normalized to have zero mean and unit variance.
# Development and Field Testing of a Wireless Data Relay System for Amphibious Drones
## 1 Introduction
Despite these intensive efforts for aerial operations, the lack of experimental evidence regarding the networking capability of multiple amphibious drones on sites may contribute to the hesitancy in adopting large-scale aquatic environmental monitoring with drones. To overcome the limited availability of wireless communication tools and knowledge on water, we developed a wireless data relay system for multiple amphibious drones in a reproducible way using commercially available products and an affordable off-the-shelf 3D printer. The system was validated through field tests of data relaying and established basic theories, particularly emphasizing extension of the communicable distance through easily reproducible routines for environmental scientists and engineers. Figure 1: _Cont._
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 2 Materials and Methods
### Image Processing
#### 2.4.2 UAV Image Co-Registration for Time-Series Analysis  
The UAV orthomosaics were not co-registered. In order to define geometries marking areas common to all the images in the collection (for example geometries marking areas of land and channels), geo-location errors due to GPS inaccuracy need to be corrected. Thus, images were co-registered using the GEE registration algorithm. The images from each channel site were co-referenced to its corresponding 12 July image using the normalized difference vegetation index (NDVI) band.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Channel** & **Length** & **Width** & **Area** & **Treatment** & **2 July** & **12 July** & **21 July** & **1 August** & **9 August** & **23 August** \\ \hline U & 500 m & 5 m & 50,000 m\({}^{2}\) & Untreated & 14:00 & 13:20 & 13:20 & 13:40 & 13:20 & 13:50 \\ C1 & 1200 m & 4 m & 120,000 m\({}^{2}\) & Chemical & 13:40 & 13:00 & 13:00 & 13:20 & 12:50 & 13:30 \\ C2 & 1200 m & 7 m & 120,000 m\({}^{2}\) &
# Integrated Evaluation of Reliability and Power Consumption of Wireless Sensor Networks
## 7 Related Work
### Reliability  
Existing works on assessing the reliability of WSNs have adopted RBD [34], DRBD [35], Markov Chain [36; 37], HOL [38], minimal path set [39].  
[PERSON] [34] proposed the use of RBD for evaluating the reliability of WSNs. The reliability of the whole WSN is defined regarding the reliability of nodes and communication links. In turn, the reliability of the node is calculated considering the applications executing on the node, operating system, middleware, hardware, radio and. The reliability of the links is influenced by the routing algorithms used in WSNs.  
[PERSON] [35] proposed the use of DRDB (Dynamic Reliability Block Diagram) and Petri to evaluate the reliability of WSNs. A WSN is viewed as a system made of nodes and whose reliability is defined regarding the reliability of individual nodes. Besides, due to sleep/wake-up standby policies and possible interference of the wireless communication, the WSN is viewed as a dynamic system.  
[PERSON] [36] used Markov models to evaluate the reliability of WSNs. These models assume that a WSN node has a redundant replacement node that can replace the faulty one without delay (hot-standby).  
[PERSON] [37] addressed reliability of WSNs in a particular scenario having clustered sensor networks under epidemic-malware propagation. The authors adopt a strategic game to predict malware's infection behaviour. Next, they integrate the behaviour into the transition probability upon infection of a WSN node. From this point, it is possible to determine how to relate the intent of malware infection to the CTMC's (Continuous-Time Markov Chain) randomness. Finally, [PERSON] proposes a novel measure to reflect the reliability of a WSN node.  
[PERSON] [38] adopted HOL (High-Order Logic) theorem prover to analyse RBD-based models of WSN transport protocols. In practice, [PERSON] uses HOL to formalise series, parallel and parallel-series RBDs, which are then used to analyse the reliability of the end-to-end (e2e) data transport mechanism, and the Event to Sink Reliable Transport (ESRT) and Reliable Multi-Segment Transport (RMST) data transport protocols.  
[PERSON] [39] used minimal path sets to model the reliability of WSNs. The path set is defined as the minimal set of functioning WSN nodes that ensure a certain threshold of events detected by the sensor network. The proposed models are then evaluated using a discrete-event simulation environment.  
[PERSON] [40] presented models to evaluate the reliability of WSN transmissions.The proposed models are mission-oriented and based on transmission paths. The models are defined mathematically and evaluated by a dynamic framework also proposed by the authors.  
These mentioned approaches concentrate on the analysis of reliability separately, i.e., they do not consider the impact of the power consumption on the reliability. Additionally, the sensitivity analysis proposed in this paper helps to deal with the multitude of parameters when a WSN configuration needs to be selected.
# Enhanced Upward Translations for Systems with Clusters
## 2 General Considerations on Clustering as Applied to Upward Translations
We characterize each of the clusters quantitatively by introducing its _representative source_. The latter is a specific pointed source with known intensity \(\mathbf{q}_{clp}\) and location \(\mathbf{y}_{clp}\). In cases, when sources depend on the normal at points of their location, the cluster is additionally supplied with known normal \(\mathbf{n}_{clp}\). Specifically, the intensity, location and normal of the representative source of the \(p\)-th cluster are defined as the sums  
\[\mathbf{q}_{clp}=\sum_{j=1}^{M_{clp}}\mathbf{q}_{j},\ \mathbf{y}_{clp}=\frac{1}{\Big{|}\mathbf{q}_{ clp}\Big{|}}\sum_{j=1}^{M_{clp}}\Big{|}\mathbf{q}_{j}\Big{|}\mathbf{y}_{j},\ \mathbf{n}_{clp}=\frac{\sum_{j=1}^{M_{clp}}\Big{|}\mathbf{q}_{j}\Big{|}\mathbf{n}_{j}}{ \Big{|}\sum_{j=1}^{M_{clp}}\Big{|}\mathbf{q}_{j}\Big{|}\mathbf{n}_{j}\Big{|}}\,\ p=1, \ldots,\ N_{cl} \tag{1}\]  
In (1), the summation index \(j\) presumes an internal enumeration of the sources. The first equation, in contrast with the two others, contains a summation of the vectors of intensities rather than their magnitudes. In the case, when the kernel depends on the normal at a source point and the vector \(\sum_{j=1}^{M_{clp}}\Big{|}\mathbf{q}_{j}\Big{|}\mathbf{n}_{j}\) is zero-vector, the sources mutually annihilate; this means that the cluster is of zero intensity. Then it is reasonable to set \(\mathbf{q}_{clp}=\mathbf{0}\). These steps are performed _only once_ before starting iterations by up- and downward runs over the hierarchical tree. Merely _intensities_ of sources, comprising the clusters, are updated at an iteration. The clusters, as collections of the sources, are not changed. For further discussion of essence is the following consequence of the first of Equation (1). With growing distance \(|\mathbf{x}-\mathbf{y}|\) from a cluster, the total field of sources, comprising it, tends toward the field of the representative source. Therefore, the total field from all leaf sources tends toward the sum of fields from representative sources.
# Combining InSAR and GNSS to Track Magma Transport at Bastic Volcano
## 6 Discussion
### Combining InSAR and GNSS for Complementary Spatial and Temporal Information  
#### 6.2.1. InSAR Static Inversion Constrains the Temporal Inversion  
The Piton de la Fournaise permanent GNSS network is one of the densest GNSS networks in operation on a volcano. However, the case study of the May 2016 eruption reveals that using GNSS time series data to track magma may lead to an unreliable source pressure, area and opening when no a priori (here an elliptical source) are assumed about the deformation source. Indeed, displacement associated with this intrusion was only registered by 10 stations and the area where the displacement was the greatest was not monitored. In these conditions, the trade-off between the depth, pressure and size of the source cannot be resolved. InSAR is complementary to GNSS as it provides maps of displacement along the satellite LOS with a high resolution of 5 m. Combining displacement maps along several LOS allows a more complex magma intrusion geometry to be determined,which can then be used as a priori in the inversion of GNSS time series. We demonstrated that, with this a priori, we can better constrain the evolution of pressure, area and opening as shown by the comparison with the inversion of an interferogram covering the first part of the eruption.