esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Shortest Unique Substring Queries
## 3 Position-SUS Queries
### Linear Time Approaches
#### 3.3.2. [PERSON] et al.'s Framework  
[PERSON] et al. [13] improved [PERSON] et al.'s (ICDE 2013) upper bounds for the shortest unique substring problem as well. They consider two types of \(\mathsf{SUS}\) queries. The first one computes a \(\mathsf{SUS}\) for any position in the string \(\mathcal{S}\) in constant time after preprocessing \(\mathcal{S}\) in \(\mathcal{O}(n)\) time. Thus, reporting a \(\mathsf{SUS}\) for all positions in \(\mathcal{S}\) takes \(\mathcal{O}(n)\) time in total. Their next algorithm outputs all the \(\mathsf{SUS}\) covering a query position. Note that [PERSON] et al.'s algorithm reports only one \(\mathsf{SUS}\) for a query position. By preprocessing \(\mathcal{S}\) in \(\mathcal{O}(n)\) time, [PERSON] et al. provided an algorithm which reports all \(\mathsf{SUS}\) containing a query position in \(\mathcal{O}(occ)\) time, where \(occ\) is the number of output.  
Although their algorithm follows a similar technique to [PERSON] et al. [6], their work was independent. Their main idea is finding \(\mathsf{SUS}\)s from \(\mathsf{MUS}\)s using the following lemma.  
**Lemma 3** ([13]).: _Every position-\(\mathsf{SUS}\) contains exactly one \(\mathsf{MUS}\)._  
They use \(\mathsf{lcp}\) array data structure to compute all \(\mathsf{MUS}\)s of \(\mathcal{S}\) in linear time. We summarize their results in the following lemma.  
**Lemma 4**.: _All the \(\mathsf{LSUS}\), \(\mathsf{RSUS}\), and \(\mathsf{MUS}\) can be computed from \(\mathcal{S}\) in \(\mathcal{O}(n)\) time._  
In order to compute \(\mathsf{SUS}\)s from \(\mathsf{MUS}\)s, they define the concept of _meaningful_ and _meaningless_\(\mathsf{MUS}\)s, where meaningful \(\mathsf{MUS}\)s drive \(\mathsf{SUS}\)s corresponding to some positions. Then, they present an algorithm to collect all the \(\mathsf{SUS}\)s from the meaningful \(\mathsf{MUS}\)s in linear time. Theorem 4 summarizes their results.  
**Theorem 4**.: _A string \(S\) of length \(n\) can be preprocessed in \(\mathcal{O}(n)\) time and space so that the shortest unique substring queries can be answered in \(\mathcal{O}(occ)\) time, where \(occ\) is the number of shortest substrings returned. Notably, outputting a single \(\mathsf{SUS}\) can be done in \(\mathcal{O}(1)\) time._
# Dynamic QoS Management for a Flexible 5G/6G Network Core: A Step toward a Higher Programability
## 3 Related Work
### Motivation  
We can conclude that most research focuses on access networks and edge computing. Keeping in mind the heterogeneity of future networks, we point out that it is necessary to consider the potential challenges in the core of future networks in detail. To achieve the fundamental goals of 5G/6G, we need to foster higher system capacity, higher data rate, lower latency, and improve the Quality of Service (QoS). There are studies that deal with the implementation of dynamic QoS in 5G/6G networks. However, they do not deal with the issue of dynamic queue management (i.e., the creation of queues and their modification and removal).  
Dynamic queue management should significantly provide the necessary flexibility in core networks. Relying solely upon predefined queues, none of the works representing the state of the art of existing research has foreseen the possibility of the dynamic creation of QoS queues [50]. Regarding QoS, such an approach is a limiting factor in system flexibility. Our focus is, thus, to implement a solution that can dynamically ensure the Quality of Service (QoS) in SDN as a core of networks.  
Flexibility and scalability should go hand in hand [51], and we believe that network slicing combined with dynamic queue management could address the diverse service requirements. In that sense, we intend to apply an appropriate solution to provide a better slice organization within the existing multi-slice environment. Our idea of slice organization is to deploy a group of similar services as a single slice. Such a slice can consist of different providers' services belonging to the same service group.  
This approach would significantly benefit the network operator and the users since it would eliminate several limiting factors. Namely, the operator expands and enriches its services by allowing different providers to offer the same service group. In this way, users also benefit from the opportunity to choose a provider. The deployment of such a solution within the 5G/6G network implicitly encourages competition and puts user experience firmly in the foreground.
## 1 Introduction
These research findings have, to some extent, revealed the mechanisms of narrow coal pillar failure in goaf areas during roadway excavation, proposing methods to determine coal pillar width and corresponding control measures. However, there is currently limited research on the influence of water in goaf areas on the control of surrounding rocks of narrow coal pillars in goaf-side roadway. Therefore, this study focuses on the Jianxin Coal Mine 4301 tailgate, with the impact of water accumulation in goaf areas on the stability of coal pillars under mining activity as its core. This study established a reasonable width for coal pillars in the 4301 panel. Additionally, this study innovatively proposed support techniques for controlling surrounding rock in different zones of the roadway, and successfully applied these techniques in practice. The importance and novelty of this research lie in its contributions to both science and industry. Our study provides valuable insights into the complex interactions between coal pillar width, stress distributions, plastic zones, and dissipated energy in coal mine roadways adjacent to goafs with water accumulation. This understanding is crucial for designing stable and safe mine roadways, addressing a critical challenge in the mining industry. Furthermore, the results obtained from this study are highly relevant to the mining sector. They offer practical guidance on determining optimal coal pillar widths and establishing effective support systems for roadways in challenging geological conditions. The techniques employed in this research, including FLAC\({}^{\text{3D}}\) modeling and the Analytic Hierarchy Process (AHP) for zoning control, while not entirely novel, are applied in a unique and innovative manner to coal mine roadways.
# Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction
## 2 Proposed Methods
### Spectral Purity Analysis  
Spatial clustering obtains a set of homogeneous partitions, but the existence of mixed pixels in each partition cannot be avoided, which reduces the accuracy of representative endmembers. It is necessary to perform spectral purity analysis to choose pure pixels and remove the mixed pixels.  
In the HSI, if all pixels are projected onto the same vector, endmembers will be projected to the endpoints of the vector, that is, endmembers will obtain maximum or minimum projection values. Given that each partition is considered to mainly contain one feature, pixels with higher projection values are more likely to be pure pixels. Based on that, PCA is performed on each partition, and a set of principal components decomposed by PCA is obtained denoted by \(\mathbf{U}=\{\mathbf{u}_{1}\}_{i=1}^{\mathbf{b}}\). The first principal component \(\mathbf{u}_{1}\) is chosen as the projection vector, as it captures most of the information of the hyperspectral data. Then all pixels in each partition are projected on it as follows:  
\[\mathbf{x}=\mathbf{u}_{1}^{\mathrm{T}}\mathbf{S}, \tag{7}\]  
where \(\mathbf{S}=(\mathbf{s}_{1},\mathbf{s}_{2},\ldots,\mathbf{s}_{\mathrm{N}})\in \mathbb{R}^{\mathrm{b}\times\mathrm{N}}\) is a partition containing N pixels, and \(\mathbf{x}=(\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x}_{\mathrm{N}})\)\(\in\mathbb{R}^{1\times\mathrm{N}}\) is a set of projection values of this partition.  
To find pixels with high spectral purity, sort \(\mathbf{x}\) in descending order and obtain new vector \(\mathbf{\hat{x}}\). Choose pixels in order according to certain proportion \(\mathrm{Pt}\) from \(\mathbf{\hat{x}}\), and take the average of the chosen pixels as a representative endmember for each partition, which alleviates the local-scope spectral variability and random noise.
# A Multiclassification Model for Skin Diseases Using Dermatoscopy Images with Inception-v2
## 4 Results and Discussion  
The model for this experiment was implemented on the Windows 10 operating system, which was equipped with 64 GB of RAM, an NVIDIA RTX 4090 graphics card, a 24 GB GPU, a 2 TB solid-state drive, the PyTorch 1.7 framework, and the Python 3.7 programming language. Two hundred training iterations, 16 batch samples, and a uniform input image resolution of 224 \(\times\) 224 were employed to facilitate the training of the model. The input image resolution was standardised to 224 \(\times\) 224 to facilitate the training of the model. During the training process, this paper saved the model with the highest accuracy and the smallest loss function and selected the model with the highest test accuracy as the optimal model according to comparison. In this paper, the pre-trained dataset is then trained and tested on the Resnet-50, Densenet, and Inception-v2 network models, respectively. The results of the experiments  
Figure 8: Example of hair removal. on the three network models for seven skin diseases using the NLLLoss loss function are presented in Table 2. The results obtained for the same three models using focal loss are presented in Table 3.  
From Table 2, it can be seen that NNLLoss, as a traditional commonly used classification loss function, is not balanced in the case of the original dataset in terms of the classification effect, and its performance indicators are not as good as those for the focal \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Class** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{8}{*}{Resnet-50} & MEL & 0.7577 & 0.8283 & 0.8122 & 0.8168 \\  & NVs & 0.8133 & 0.6760 & 0.7568 & 0.7230 \\  & BKL & 0.7650 & 0.8123 & 0.7434 & 0.7721 \\  & AK & 0.7877 & 0.8865 & 0.8821 & 0.8822 \\  & BCC & 0.8124 & 0.7256 & 0.7977 & 0.7531 \\  & VASCs & 0.8365 & 0.8006 & 0.9113 & 0.8612 \\  & DF & 0.8428 & 0.6577 & 0.8345 & 0.7455 \\ \hline \multirow{8}{*}{Densenet} & MEL & 0.8275 & 0.8503 & 0.8290 & 0.8345 \\  & NVs & 0.8014 & 0.7566 & 0.7803 & 0.7753 \\  & BKL & 0.8169 & 0.8177 & 0.8554 & 0.8441 \\  & AK & 0.8024 & 0.9059 & 0.8624 & 0.8824 \\  & BCC & 0.8299 & 0.7134 & 0.7805 & 0.7306 \\  & VASCs & 0.8254 & 0.8178 & 0.7942 & 0.8081 \\  & DF & 0.8999 & 0.8502 & 0.8177 & 0.8355 \\ \hline \multirow{8}{*}{Inception-v2} & MEL & 0.8105 & 0.8802 & 0.8053 & 0.8237 \\  & NVs & 0.8521 & 0.9011 & 0.7825 & 0.8460 \\ \cline{1-1}  & BKL & 0.7346 & 0.8152 & 0.8821 & 0.8513 \\ \cline{1-1}  & AK & 0.8829 & 0.9326 & 0.8765 & 0.9003 \\ \cline{1-1}  & BCC & 0.8199 & 0.7144 & 0.7538 & 0.7325 \\ \cline{1-1}  & VASCs & 0.8214 & 0.8527 & 0.8110 & 0.8211 \\ \cline{1-1}  & DF & 0.8702 & 0.8860 & 0.8723 & 0.8724 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification results obtained using the NNLLoss loss function.
## 2 Related Works
### Optimization Methods  
Optimization methods are further subdivided into exact, heuristic and search-based algorithms [4]. In exact algorithms, the route planning problem is formulated as an optimization problem, and integer linear programming [33] and Answer-Set Programming [34; 35] have been used as solution methods. These methods are applicable in small problem instances to obtain exact solutions [33; 35] or suboptimal solutions [36; 37; 38].  
The heuristic algorithm can find a solution to route planning using a series of primitive operations that specify the AGV's actions in various situations. The optimality of the solution obtained by this approach is not guaranteed. However, it can find a feasible solution in a reasonable time in an environment with many AGVs [39; 40].  
Popular search-based methods are the conflict-based search (CBS) [14] and A* approaches. One of the difficulties is that the required computation time may increase exponentially with the number of AGVs. CBS is a method that first finds the shortest route for all AGVs ignoring collisions, then adds constraints to avoid collisions. In recent years, many improvement methods have been proposed for CBS, such as ICBS [15] and CBSH [16; 17]. Algorithms have also been proposed that use machine learning to learn optimal policies [41] or quasi-optimal policies [42] for search. The same search-based approach has also been proposed for A*-based algorithms [11; 12; 13].
# MIMO Self-Heterodyne OFDM Using Band Selection Technique
## 2 The FSTBC MIMO Self-Het OFDM System
### Bs-Fstbc Mimo Self-Het OFDM System  
When the BS proposed technique is applied to the FSTBC MIMO self-het OFDM proposed system, the information symbols are sent according to the better sub-channel gains in upper band or lower band of the four transmitting antennas. To choose the highest sub-channel gain, \(\mathrm{H_{max1}}\) and \(\mathrm{H_{max2}}\) are calculated as:  
\[\mathrm{H_{max1}}=\left(\sum_{\mathrm{a=1}}^{2}\lvert\mathrm{H_{1a}}\rvert\,^{ 2}\right)+\left(\sum_{\mathrm{a=1}}^{2}\lvert\mathrm{H_{2a}}\rvert\,^{2} \right), \tag{20}\]  
\[\mathrm{H_{max2}}=\left(\sum_{\mathrm{a=3}}^{4}\lvert\mathrm{H_{1a}}\rvert\,^{ 2}\right)+\left(\sum_{\mathrm{a=3}}^{4}\lvert\mathrm{H_{2a}}\rvert\,^{2} \right), \tag{21}\]  
where \(\mathrm{H_{max1}}\) represent the sum of channel gains from 1 st and 2 nd transmitting antennas to the 1 st and 2 nd receiving antennas, while \(\mathrm{H_{max2}}\) is the sum of channel gains from 3 rd and 4 th transmitting antennas to the 1 st and 2 nd receiving antennas and a is \(a\)th transmitting antenna.  
Again, let \(i\) be the number of the \(\mathrm{H_{max1}}\) multipath component and \(\mathrm{q}\) is the number of the \(\mathrm{H_{max2}}\) multipath component, then, there are three scenarios for performing the BS which are represented as follows:  
1. If the value of \(i\) and the value of \(q\) are in the upper sub-band, the data will be sent on the upper sub-band in the 1 st and 2 nd transmitting antennas while the 3 rd and 4 th transmitting antennas will use the lower sub-band as shown in Figure 4.
2. If the value of \(i\) and the value of \(q\) are in the lower sub-band, the data will be sent at the lower sub-band on the 1 st and 2 nd transmitting antennas and the 3 rd and 4 th transmitting antennas will use the upper sub-band as shown in Figure 5.
3. If the value of \(i\) and the value of \(q\) are in different bands, the data will be sent on each pair of transmitting antennas through the band where the maximum values of \(\mathrm{H_{max1}}\) and \(\mathrm{H_{max2}}\) occure.  
A pilot bit = 1 is added to indicate the band which the data symbol will be transferred on. At the receiver, if the bit = 1, the data symbols are sent on the upper sub-band for the 1 st pair of transmitting antennas and on the lower sub-band for the 2 nd pair of transmitting antennas and if the bit \(\
eq\) 1, the data symbols are sent on the lower sub-band for the 1 st pair of transmitting antennas and on the upper sub-band for the 2 nd pair transmitting antennas.  
Figure 4: FSTBC MIMO self-het OFDM transmitter when the data is transmitted in the upper band for 1 st and 2 nd transmitting antennas and in the lower band for 3 rd and 4 th transmitting antennas.
## 2 Materials and Methods
### Training Data Set  
In order to directly identify the blink artifacts from the original detected MEG data, we need to train the ANN with the calibrated MEG signal first. In this work, the GoogLeNet is utilized to recognize the spatial distribution characteristics of the blink artifacts, which are highlighted by drawing 2D images. These 2D images are our training data.  
Since each sampling data point is recorded by 274 sensors located at different locations on the head surface, it comprises a matrix with dimensions of 274 \(\times\) 1. Each sampling data point can be used to draw a 3D image of the signal strength at different locations on the head surface, as is shown in Figure 3a. One second of MEG data can be used to draw 600 images. However, 3D images are difficult for the GoogLeNet to recognize. The locations of the sensors distributed on a 3D surface can be topographically mapped onto a 2D plane. The signal amplitude measured with each sensor is subsequently color-coded as 2D images, as is shown in Figure 3b. These 2D images can be the input of the GoogLeNet. According to the calibrated MEG signal, a total of 66,780 2D images can be obtained with 8 data sets for training and testing of the ANN. The 1 st, 3 rd, 5 th, and 7 th data sets are employed to draw the training and validation 2D images. All data sets are employed to draw the testing 2D images.  
The 1 st data set contains 20 blink artifacts, each of which lasts for approximately 0.4 s. In order to obtain the spatial information of the blink artifacts and expand the training data set, we capture a 0.1 s signal segment of the center of the blink artifacts and label these data as blink data, which means that we have a length of 0.1 (seconds) \(\times\) 20 (blinks) \(\times\) 600 (sampling rate) blink data. Since it is recorded with 274 sensors, the training data has the form of a matrix with dimensions of 274 \(\times\) 1200. For signal segments with no artifacts, we randomly select 2000 sampled data for training and label them as non-blink data. Thus, we get a data matrix with dimensions of 274 \(\times\) 3200, which can be used to draw 3200 2D images to train the ANN. Four 2D images of blink data and four 2D images of non-blink data are randomly selected from 3200 2D images, as is shown in Figure 4. Similarly, the 3 rd, 5 th, and 7 th data sets include 71, 65, and 211 blink artifacts, respectively. We can get 2820, 3900, and 12,660 blink 2D images from these 3 data sets, respectively. At the same time, 3000, 4000, and 12,000 non-blink 2D images can be randomly selected from these data sets, respectively. These 2D images are then employed to train the ANN. The training data set is shown in Table 2.  
Figure 3: The 3D view display of a sampling signal in which the signal amplitude is color-coded (**a**) and is plotted on a 2-dimensional plane (**b**).
# A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation
## 2 Related Work
### Attention Mechanism  
Attention mechanisms have seen significant advancements in both design and application within computer vision. Among these, attention mechanisms that operate along the channel and spatial dimensions have proven particularly effective. Channel-wise attention adaptively reweights each feature channel, diminishing less informative responses while strengthening those most critical for the task. In contrast, spatial-wise attention emphasizes salient regions across spatial dimensions by learning position-dependent weights, thereby improving the model's capacity to capture spatial contextual details. This allows the model to focus on semantically important areas while reducing the impact of irrelevant background, thereby enhancing recognition accuracy. To effectively integrate these attention strategies into practical models, several notable architectures have been proposed. SENet [28] adaptively recalibrates channel-wise feature responses, significantly enhancing the network's representational capacity. When integrated into ResNet-50 [29], it improves Top-1 accuracy by approximately 1.3% with only a marginal increase in computational cost. Building on the principles of channel and spatial attention, CBAM [30] introduces a sequential attention module that captures intricate inter-channel and inter-spatial relationships with negligible additional computational cost. Compared to the SE module, CBAM achieves a 0.5% improvement on the COCO dataset. BAM [31] applies channel and spatial attention in parallel, placing greater emphasis on deep semantic features. On the ImageNet classification task, it outperforms SE by approximately 0.22 percentage points. ECA-Net [32] replaces fully connected layers with one-dimensional convolutions to eliminate dimensionality reduction, preserving efficient feature modeling with fewer parameters.  
It achieves a slightly better classification performance than CBAM while maintaining lower complexity. SFA [33] further improves upon these designs by capturing long-range spatial dependencies through strip pooling and enhancing computational efficiency via grouped channel attention. Compared to other mechanisms, SFA achieves higher accuracy, while requiring fewer parameters and less computational resources.
# Numerical Simulation and Analysis of Fish-Like Robots Swarm
## 2 Computational Methods  
### Geometrical Model and Kinematic Model  
The present study of fish-like robot swarms was based on two-dimensional simulations. For convenience, the fish-like robot is abbreviated as fish. In our work, we used a cross-section of the three-dimensional fish body that is in the work of [PERSON] and [PERSON] [37] as the two-dimensional simulation model. The half outline of the two-dimensional fish body was described by \(w(s)\), which is defined as the analytical functions of the arc length \(s\) (measured from the nose) along the midline of the body. The analytical description of \(w(s)\) is divided into three regions:  
\[w(s)=\left\{\begin{array}{ll}\sqrt{2w_{h}s-s^{2}}&0\leq s<s_{h}\\ w_{h}-(w_{h}-w_{t})(\frac{s-s_{h}}{s_{t}-s_{h}})^{2}&s_{h}\leq s<s_{t},\\ w_{t}(L-s)/(L-s_{t})&s_{t}\leq s\leq L,\end{array}\right. \tag{1}\]  
where \(L\) is the body length, \(w_{h}=s_{h}=0.04L\), \(w_{t}=0.01L\) and \(s_{t}=0.95L\). Figure 1a shows the half outline of the straight body.  
Generally, the kinematics for the anguilliform swimmers can be considered as a backward travelling wave, with the amplitude increasing almost linearly from the head to the tail of the fish [38]. Here we adopted the equation specified by [PERSON] [39] to describe the deforming motion of the fish body. The lateral undulation of the midline \(y(s,t)\) is given as follows:  
\[y(s,t)=0.125\frac{s/L+0.03125}{1.03125}\sin(2\pi(\frac{s}{L}-\frac{t}{T})), \tag{2}\]  
where \(T\) is the undulation period. Controlled by this equation, the wavelength of the body undulations was \(L\), and the maximum lateral excursion of the tail was \(0.125L\).
# A Gas-Kinetic BGK Scheme for Natural Convection in a Rotating Annulus
###### Abstract  
In this paper, a gas-kinetic Bhatnagar-Gross-Krook (BGK) scheme is developed for simulating natural convection in a rotating annulus, which arises in many scientific and engineering fields. Different from most existing methods for the solution of the incompressible Navier-Stokes (N-S) equations with the Boussinesq approximation, compressible full N-S equations with allowable density variation are concerned. An appropriate BGK model is constructed for the macroscopic equations defined in a rotating frame of reference. In particular, in order to account for the source (non-inertial) effects in the BGK model, a microscopic source term is introduced into the modified Boltzmann equation. By using the finite volume method and assuming the flow is smooth, the time-dependent distribution function is simply obtained, from which the fluxes at the cell interface can be evaluated. For validation, a closed rotating annulus with differentially heated cylindrical walls is studied. A conventional N-S solver with the preconditioner is used for comparison. The numerical results show that the present method can accurately predict the variation of the Nusselt number with the Rayleigh number, but no preconditioning is required due to its delicate dissipation property. The computed instantaneous streamlines and temperature contours are also investigated, and it is verified that the Rayleigh-Benard convection in a rotating annulus is very similar to that in a classical stationary horizontal enclosure.  
gas-kinetic scheme; BGK model; source term; natural convection; rotating annulus; Rayleigh-Benard convection +
Footnote †: journal: Applied Physics Letters