esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Currently, research on clothing recognition mainly focuses on different types of clothing styles [18; 19; 20], such as upper garments and lower garments or clothing styles with significant differences in contours [21; 22; 23]. Recognizing similar styles with small differences in the same category poses a challenge. Deep learning plays a key role in computer recognition. Common classifiers include recurrent neural networks (RNNs), Convolutional Neural Networks (CNNs), and artificial neural networks (ANNs). Compared to RNNs and ANNs, CNNs have better generalization [24; 25], which provides a reference for this study. She ethnic clothing from different regions has similarities in shape and texture features, but their color features have a certain degree of discriminability. The innovation of this work lies in proposing a recognition method for She ethnic clothing based on FPA-optimized color feature fusion and a CNN. The main contributions are as follows: (1) A method for color feature fusion and dimension reduction was proposed. The extracted color histogram features and color moment features were normalized and then optimized using the Firefly Algorithm (FPA) to obtain optimal weights. To reduce the dimensionality of the input algorithm, the proposed method also employed kernel principal component analysis (PCA) to fuse high-dimensional features, thereby reducing the complexity of the system. (2) When classifying clothing from different regions of the She ethnic group, the use of receptive fields and weight-sharing techniques reduced the number of required weights and the computational burden during network training. Through supervised learning, the information retrieval capability of the classification model was further enhanced, effectively improving the classification accuracy. To do this, we structure the rest of this paper as follows: In Section 2, we briefly summarize the research framework, and then the experimental methods (color feature extraction, fusion, dimensionality reduction, etc.) are elaborated. In Section 3, the color analysis and She ethnic clothing classification and comparison are conducted. In Section 4,we discusses the experimental results, limitations, and future works. Finally, we present some conclusions in Section 5.
# Sensitivity Analysis for Gaussian-Associated Features
## 5 Sensitivity Matrix Associated with a Least-Squares Sphere Fit to Data
### Cap of a Sphere  
We consider here the case \(-\pi\leq\theta\leq\pi\) and \(-\pi/2\leq\beta_{1}\leq\phi\leq\pi/2\). For this case, the nonzero elements of \(H_{\gamma}=\frac{1}{|A|}H_{A}\) are determined by  
\[H_{\gamma}(1,1) =\frac{1}{2(1-\cos\gamma)}(2/3-\cos\gamma-\cos^{3}\gamma/3),\] \[H_{\gamma}(2,2) =\frac{1}{2(1-\cos\gamma)}(2/3-\cos\gamma-\cos^{3}\gamma/3),\] \[H_{\gamma}(3,3) =\frac{1}{1-\cos\gamma}(1-\cos^{3}\gamma)/3,\] \[H_{\gamma}(3,4) =\frac{1}{1-\cos\gamma}(1-\cos 2\gamma)/4,\] \[H_{\gamma}(4,4) =1,\]  
where \(\gamma=\pi/2-\beta_{1}\). Thus, \(\gamma\) is the angle between the lower edge of the spherical cap and the North pole. The nonzero elements of \(V_{\gamma}=H_{\gamma}^{-1}\) are determined by  
\[V_{\gamma}(1,1) =1/H_{\gamma}(1,1),\] \[V_{\gamma}(2,2) =1/H_{\gamma}(2,2),\] \[V_{\gamma}(3,3) =\frac{1}{D_{34}}\] \[V_{\gamma}(3,4) =-\frac{1}{D_{34}}H_{\gamma}(3,4),\] \[V_{\gamma}(4,4) =\frac{1}{D_{34}}H_{\gamma}(3,3),\]  
where  
\[D_{34}=H_{\gamma}(3,3)-H_{\gamma}^{2}(3,4),\]  
the determinant of the bottom right \(2\times 2\) submatrix of \(H_{\gamma}\). If the point-cloud data are associated with variance matrix \(\sigma_{R}^{2}I\), then the variance matrix \(V_{A}\) associated with the fitted sphere parameters \(\mathbf{a}\) is approximated by  
\[V_{A}\approx\frac{\sigma_{R}^{2}}{m}V_{\gamma}.\]  
For \(\gamma\) approaching zero, corresponding to measurements on a cap of a sphere, \(D_{34}\ \approx\gamma^{4}/48\), and  
\[V_{\gamma}\approx\left[\begin{array}{cccc}4/\gamma^{2}&0&0&0\\ 0&4/\gamma^{2}&0&0\\ 0&0&48/\gamma^{4}&-48(1-\gamma^{2}/4)/\gamma^{4}\\ 0&0&-48(1-\gamma^{2}/4)/\gamma^{4}&48(1-\gamma^{2}/2)/\gamma^{4}\end{array} \right]. \tag{18}\]  
The quantities \(s(a_{j})=\sqrt{V_{\gamma}(j,j)}\) for selected values of \(\gamma\) are given in Table 2. The uncertainty associated with the \(x\) and \(y\)-coordinates of the sphere center scale with \(1/\gamma\) while the uncertainties associated with the \(z\)-coordinate and radius scale with \(1/\gamma^{2}\), for small \(\gamma\). The estimate of the \(z\)-coordinate of sphere center is almost perfectly negatively correlated with the estimate of the radius. These results are in line with results associated with an arc of a circle discussed in Section 4. These calculations are also relevant to determining the radius of curvature for other surfaces such as paraboloids and aspherics that have low curvature.
## 5 Conclusions  
In this paper, a decomposition approach of the streamed AE signals is developed in order to distinguish the crack related characteristics (i.e., burst signals) and the background noise characteristics. As the time-dependent waveform recording is based on the theory that the crack related AE signal is embedded into the streamed AE signal, it is important to identify the proper time window for extracting AE features. Using the NAVAIR gearbox simulator as representative noise signal and the laboratory scale UIC fatigue test of the planar spline section as representative crack signal, then the minimum window size for extracting the crack information is determined as 1 ms. The decomposition approach of the streamed AE signals is demonstrated on the identification of burst signals that are embedded into the streamed signals obtained from the spline testing of the gearbox simulator at the NAVAIR test rig. While the AE method cannot detect the crack size, it can indicate the presence of unusual behavior in highly noisy environments when the time-dependent waveform streaming approach is implemented.  
This material is based upon work supported by the U.S. Naval Air Systems Command (NAVAIR) under Contract No. N68335-13-C-0417 entitled \"Hybrid State-Detection System for Gearbox Components\" awarded to the Metis Design Corporation. The authors would like to acknowledge the Research Open Access Publishing (ROAAP) Fund of the University of Illinois at Chicago for financial support towards the open access publishing fee for this article. The authors also would like to thank [PERSON] and [PERSON] for their inputs on the design of the test specimen. C-SAM equipment was acquired by DoD Equipment Contract No. W911 NF-16-1-0500. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NAVAIR.  
[PERSON] and [PERSON] conducted the laboratory experiments and analyzed the data recorded in laboratory and NAVAIR facility. [PERSON] provided inputs in the analysis of waveform streaming data, and embedding crack signal into the streamed data. [PERSON] and [PERSON] performed the NAVAIR gearbox experiments at the NAVAIR facility and provided feedback on fatigue experiments and data processing.  
The authors declare no conflict of interest.
# A Data-Driven State Estimation Based on Sample Migration for Low-Observable Distribution Networks
## 4 Case Study
### Test System Introduction  
According to the distribution network topology modeling method introduced in Section 3.2, all 26 node distribution network topologies in a certain region are clustered in this paper. Firstly, adjacency matrices are constructed for different distribution network topologies. Secondly, singular value sequences of each adjacency matrix can be calculated. Finally, based on the K-means clustering algorithm, these singular value sequences can be clustered to obtain typical topology sets for different distribution networks. The singular value sequence calculation results of the 26  
Figure 5a illustrates the topology of a distribution network with high observability, referred to as the source domain, while Figure 5b depicts the topology of a distribution network with low observability, referred to as the target domain. As can be seen from the diagram, the topologies of the two distribution networks are largely similar, but there are significant differences in the number of topological connections. Additionally, the target domain topology has a branch that is disconnected compared to the source domain. Therefore, during the sample migration stage, for the disconnected branch power flow measurements in the target domain, this paper supplements them with zeros to maintain consistency in feature dimensions during model training.  
Figure 4: Elbow curve of 26-node distribution network.  
Figure 5: Topology diagram of two actual distribution networks: (**a**) high-observable distribution networks; (**b**) low-observable distribution networks.  
Figure 3: Topology singular value sequence of 26-node distribution networks in a certain region.  
The proposed SE algorithm has higher accuracy compared to WLS and WLAV in Figure 5 and Table 1. Compared to the WLS algorithm, the average absolute errors of node voltage magnitudes and node voltage phase angles are reduced by 58.07% and 55.60%, and the maximum absolute errors are reduced by 47.56% and 49.65%. Compared to the WLAV algorithm, the average absolute errors are reduced by 45.68% and 41.45%, and the maximum absolute errors are reduced by 32.70% and 36.93%.  
The measurement redundancy in distribution networks affects the estimation results. Table 2 presents the estimation results for low-observable distribution networks at different levels of measurement redundancy.  
As shown in Table 2, when the measurement redundancy decreases, the precision of the approach will be affected. The reason is that the method is model-based sample migration, which to some extent relies on the redundancy of instantaneous measurements. However, in the SE of low-observable distribution networks, the proposed method is significantly better than traditional algorithms.
## 5 Performance Evaluation  
In this section, the performance of the LRSV estimator using the proposed \(\kappa\) estimator is evaluated. The performance using \(\kappa\) obtained by other four different methods are also evaluated, including conventional \(\kappa\) estimator [9], the measured ground truth \(\kappa\) calculated with measured DRR and \(T_{60}\) (fullband and subband) according to Equation (8), and the scanning-optimal \(\kappa\) obtained by scanning method which scans \(\kappa\) successively from 0.05 to 1.5 at intervals of 0.01. Besides, the quality of the dereverberated speech using proposed method is also evaluated and compared to a recent method using recursive MSPP [10].
# Method for Calculating of Lightning Impact Level on Overhead Transmission Line
## 2 Materials and Methods  
### Math Function  
We first present the mathematical function used for the estimation of lightning impacts intensity on all OHTL supports. Under the intensity of the lightning's effect on the support, we will take the value equal to one if it directly hit the support and decreasing to zero as the lightning approaches the boundary of the power line buffer. In this case, the lightning radius is equal to the buffer radius. The input data for this function are: first, the geographic coordinates of lightning in the studied area during the specified time interval; second, the geographic coordinates of all towers of the studied power line; finally, the buffer size for this line. Logically, the larger the buffer, the more lightning it will contain, and the greater the impact will be accounted for by the power line.  
Let \(D\) be a matrix of distances between all flashes and all power line towers. For general \(n\)-dimensional Euclidean distance, we can exploit Equation (1) [13]:  
\[D^{2}(pl,\,st)=(pl-st)^{T}(pl-st)=pl^{T}pl+st^{T}st-2 pl^{T}st, \tag{1}\]  
where \(pl\) and \(st\) are the support and the lightning vectors, respectively, and \(pl^{T}\) is the transpose operation. The resulting matrix \(D\) contains the number of rows corresponding to the number of flashes and the number of columns corresponding to the number of towers.  
The next step is to calculate the matrix \(E\). This is the difference between the buffer size \(b\) and the distance matrix \(D\):  
\[E=b-D. \tag{2}\]  
Next, all matrix \(E\) elements less than zero are taken as having no effect on the transmission line tower. Let us equate these values to zero:  
\[E[E<0]=0. \tag{3}\]  
The final step is to obtain the lightning intensity vector \(e\) by summing the rows of the matrix \(E\). Let us take the impact of lightning as one. Hence, we divide the rows of matrix \(E\) by the buffer size \(b\). The length of this vector will be equal to the number of transmission line towers:  
\[e=\sum_{i=1}^{t}\left(\frac{E(i_{i})}{b}\right), \tag{4}\]  
where \(t\) is the number of power line towers. To obtain the annual average value, the vector \(e\) should be divided by the number of thunderstorm observations years.
## 4 Discussion
These values are comparable to our study, where measured concentrations ranged from 11.49 to 56.27 ng/mL at fortification levels of 10 and 50 ng/mL, with %RSD values consistently below 7.07%, demonstrating high repeatability and accuracy. For isotonitazene, Krotulski et al. reported postmortemblood concentrations between 0.4 and 9.5 ng/mL, with an average of 2.2 ng/mL Our study similarly found lower recovery rates for isotonitazene (70.4-75.6%), likely due to matrix effects, with mean concentrations of 7.55 ng/mL at 10 ng/mL fortification and 35.18 ng/mL at 50 ng/mL fortification. This aligns with Rice et al. (2021), who noted matrix-related recovery challenges in urine samples [21]. Despite these limitations, our method demonstrated high sensitivity, with LOD (0.192 ng/mL) and LOQ (0.584 ng/mL) values enabling the reliable detection of trace levels of isotonitazene. Our study's reproducibility results (%RSDwR) for flualprazol and isotonitazene align with the existing literature, highlighting analytical challenges. Rice et al. reported flualprazol concentrations in postmortem blood between 1.1 and 35.1 ng/mL [21]. Ha et al. found levels ranging from 2.0 to 620 ng/mL, with a median of 8.2 ng/mL [22]. Similarly, our study showed flualprazol concentrations of 11.78 ng/mL and 11.48 ng/mL at 10 ng/mL fortification and 55.45 ng/mL and 56.94 ng/mL at 50 ng/mL fortification, with %RSDwR values of 1.88-6.63%, indicating good reproducibility. For isotonitazene, Krotulski et al. reported postmortem blood concentrations between 0.4 and 9.5 ng/mL, with an average of 2.2 ng/mL, emphasizing its typically low concentrations [19]. Our study found mean concentrations of 7.48 ng/mL and 7.44 ng/mL at 10 ng/mL fortification, and 36.19 ng/mL and 36.78 ng/mL at 50 ng/mL fortification, with %RSDwR values of 1.81-5.54%. However, lower recoveries (72.4-74.8%) suggest potential matrix effects or stability issues. This aligns with Pardi et al., who noted similar challenges in urine analysis, reinforcing the need for highly sensitive and reproducible analytical methods [14].
Revolutionizing Free-Space Optics: A Survey of Enabling Technologies, Challenges, Trends, and Prospects of Beyond 5G Free-Space Optical (FSO) Communication Systems  
Isiaka A. Alimi  
1 Instituto de Telecomunicacoes, University of Aveiro, 3810-193 Aveiro, Portugal; paulo.monteiro@ua.pt  
2 Department of Electronics, Telecommunications and Informatics, University of Aveiro, 3810-193 Aveiro, Portugal  
234  
Paulo P. Monteiro  
1 Instituto de Telecomunicacoes, University of Aveiro, 3810-193 Aveiro, Portugal  
2
## 7 Results and Discussion
### Comparative Analysis of Client Selection Strategies
#### 7.3.3 Battery Level over 300 Rounds  
As shown in the battery consumption plots for MNIST Figure 26, EMNIST Figure 27, Fashion-MNIST Figure 28, and CIFAR-10 Figure 29, all methods begin with the same initial battery level and exhibit a significant drop during the early communication rounds. This behavior corresponds to the training phase where clients are actively participating in the FL process. After a certain point (which varies per dataset), the battery level decreases more slowly. This shift is due to the fact that although the server stops selecting certain clients for training, those clients continue performing peripheral tasks (e.g., monitoring, communication, or local computation), which still consume energy but at a reduced rate. Among the methods, \"Battery\" and \"RBPS\" consistently show better energy preservation, while \"Accuracy\" and \"Random\" lead to faster battery depletion. The overall trend confirms the trade-off between participation intensity and energy efficiency across different datasets.  
Figure 23: Loss over 300 rounds (EMNIST).  
Figure 24: Loss over 300 rounds (Fashion-MNIST).  
Figure 25: Loss over 300 rounds (CIFAR-10).  
Figure 28: Battery level over 300 rounds (Fashion-MNIST).  
Figure 26: Battery level over 300 rounds (MNIST).  
Figure 27: Battery level over 300 rounds (EMNIST).  
Figure 29: Battery level over 300 rounds (CIFAR-10).
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 2 Background
### Changing Addressing Semantics Whilst Maintaining Interworking  
Our solution is based on a radical new approach to addressing. The Identifier Locator Network Protocol (ILNP) [7; 16] is an evolution of IPv6. It introduces improved functionality at the network layer through cleaner semantics of the IPv6 address by splitting the address into a Locator (L64) and a Node ID (NID), respectively, as shown in Figure 1. The L64 functions as a routing prefix, with the same syntax and semantics as for the IPv6 routing prefix, allowing ILNP packets to be viewed as IPv6 packets by routers and switches, and to allow deployment of ILNP without modification to existing routing and forwarding infrastructure. A NID has the same syntax as the IPv6 IID, but different semantics: it labels a _node_, rather than a single _interface_ on that node. Unlike IPv6, only the NID values are used in transport-layer state--the L64 bits are excluded [7; 16]. The L64 and NID together form an Identifier-Locator Vector (I-LV) which is used in place of the address bits in an IPv6 packet. This separation of identity and location facilitates mobility and multihoming, and also improves privacy properties, including perturbing flow correlation attacks. The implementation of our mechanism is publicly available [17].
# Computation of Implicit Representation of Volumetric Shells with Predefined Thickness
## 5 Examples
### Triply Periodic Minimal Surfaces (TPMSs)  
Periodic minimal surfaces have many potential applications in engineering. There are several ways to define a minimal surface [15]. It can be defined as the surface that locally has the smallest area. Another definition is that a minimal surface has a zero mean curvature. In the following examples, we assume that the TPMS is given by the zero contour of the function \(\mathfrak{F}(\vec{x})\). Without loss of generality, we assume the TPMS to be periodic in a cubic box with side length \(L\).  
The offset functions up to fourth order of convergence for a TPMS are obtained following the proposed procedure.  
We compute four different first-order approximations of the offset function according to  
\[\phi_{1}^{(1)}(\vec{x},h) = \vec{\mathfrak{F}}(\vec{x})-h\;|\
abla\vec{\mathfrak{F}}(\vec{x})| \tag{20}\] \[\phi_{2}^{(1)}(\vec{x},h/2) = \phi^{(1)}(\vec{x},h/2)-\frac{h}{2}\;|\
abla\phi^{(1)}(\vec{x},h/2 )|\] (21) \[\phi_{3}^{(1)}(\vec{x},h/3) = \phi_{2}^{(1)}(\vec{x},h/3)-\frac{h}{3}\;|\
abla\phi_{2}^{(1)}( \vec{x},h/3)|\] (22) \[\phi_{4}^{(1)}(\vec{x},h/4) = \phi_{3}^{(1)}(\vec{x},h/4)-\frac{h}{4}\;|\
abla\phi_{3}^{(1)}( \vec{x},h/4)|. \tag{23}\]  
From this, we obtain different orders of approximation using the extended Richardson extrapolation:  
\[\phi^{(1)}(\vec{x},h) = \vec{\mathfrak{F}}(\vec{x})-h\;|\
abla\vec{\mathfrak{F}}(\vec{x})| \tag{24}\] \[\phi^{(2)}(\vec{x},h) = 2\phi_{2}^{(1)}(\vec{x},h/2)-\phi_{1}^{(1)}(\vec{x},h)\] (25) \[\phi^{(3)}(\vec{x},h) = \frac{9}{2}\phi_{3}^{(1)}(\vec{x},h/3)-4\phi_{2}^{(1)}(\vec{x},h/ 2)+\frac{1}{2}\phi^{(1)}(\vec{x},h)\] (26) \[\phi^{(4)}(\vec{x},h) = \frac{32}{3}\phi_{4}^{(1)}(\vec{x},h/4)-\frac{27}{2}\phi_{3}^{(1) }(\vec{x},h/3)+4\phi_{2}^{(1)}(\vec{x},h/2)-\frac{1}{6}\phi_{1}^{(1)}(\vec{x},h). \tag{27}\]  
The derivation of \(\phi^{(n)}(\vec{x},h)\) is easily automated in a computer algebra system such like Mathematica, Maple, or SymPy.  
Mathematica source code for the generation of offset functions at different orders of convergence is presented in the Supplementary Material published alongside this paper.
# Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images
## 2 Materials and Methods  
### Materials  
The vast publicly accessible collection of MRI scans of brain tumors from the Brain Tumor Segmentation Challenge BraTS 2017 and 2021 datasets [31; 32] was used in this study. In our experiments, a set of 40 carefully selected images that had previously been utilized by other researchers participating in the challenge were used to compare our findings to those of other approaches [33; 34; 35].  
Native (T1), post-contrast T1-weighted (T1 Gd), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (T2 FAIR) are the four grayscale contrast modalities used in each MRI scan.  
In order to allow for model generalization, these images were collected using a variety of clinical protocols and scanners from different institutions.  
Each image was manually segmented by a number of professionals using the same annotation procedure, and the comments were approved by knowledgeable neuro-radiologists. Three labels are used to label the photos with various tumor classes (1, 2, and 4). Label-1 detects necrotic and non-enhancing tumor cores (NCR/NET), Label-2 detects peritumoral edema, and Label-4 detects GD-enhancing tumors (ET).
# Channel Measurement and Feasibility Test for Wireless Avionics Intra-Communications
## 5 Simulations with Measured Data  
We simulated the bit error rate (BER) performance using our measurement data when we considered a candidate sensor network scenario for applying wireless networks in the aircraft such as intra-avionics sensor networks. Note that we utilized channel data samples measured in all measurement points during the experiment for simulations.