esa-sceva/satcom-chunk-collection huggingface dataset:
# Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer
## 5 Experimental Results
### UIAI-Lab Dataset  
In the second scenario, the SVM + FOR-ACO, SVM + FOR-SPIO, and SVM + FOR-PIO are used to test multi-type fault identification in two different conditions: crack-variant and load-variant conditions.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Techniques** & \multicolumn{2}{c}{**SVM + FOR-ACO**} & \multicolumn{2}{c}{**SVM + FOR-SPIO**} & \multicolumn{2}{c}{**SVM + FOR-PIO**} \\ \hline Crack Sizes (inch) & 0.007 & 0.014 & 0.021 & 0.007 & 0.014 & 0.021 & 0.007 & 0.014 & 0.021 \\ NRM & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% & 90\% & 90\% & 90\% \\ BLF & 95\% & 97\% & 100\% & 91\% & 90\% & 91\% & 79\% & 79\% & 80\% \\ ORF & 96\% & 97\% & 100\% & 90\% & 90\% & 94\% & 76\% & 78\% & 80\% \\ IRF & 94\% & 97\% & 100\% & 90\% & 91\% & 91\% & 75\% & 77\% & 79\% \\ ACA & 96.3\% & 97.8\% & 100\% & 92.8\% & 92.8\% & 94\% & 80\% & 81\% & 82.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: The Accuracy of Single-Type Fault Identification When the Torque Load is 3-hp.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Techniques** & \multicolumn{2}{c}{**SVM + FOR-ACO**} & \multicolumn{2}{c}{**SVM + FOR-SPIO**} & \multicolumn{2}{c}{**SVM + FOR-PIO**} \\ \hline Crack Sizes (inch) & 0.007 & 0.014 & 0.021 & 0.007 & 0.014 & 0.021 & 0.007 & 0.014 & 0.021 \\ NRM & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% & 91\% & 91\% & 88\% \\ BLF & 96\% & 97\% & 97\% & 91\% & 90\% & 91\% & 80\% & 81\% & 83\% \\ ORF & 87\% & 93\% & 94\% & 87\% & 90\% & 90\% & 73\% & 75\% & 79\% \\ IRF & 94\% & 97\% & 97\% & 90\% & 92\% & 92\% & 80\% & 84\% & 84\% \\ ACA & 97.8\% & 96.8\% & 97\% & 92\% & 93\% & 93.3\% & 81\% & 82.8\% & 83.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: The Accuracy of Single-Type Fault Identification to Test the Load-Variant Dataset.  
Based on Tables 10-13, the SVM + FOR-ACO (proposed scheme) outperforms the SVM + FOR-SPIO and SVM + FOR-PIO for fault identification in bearing. Moreover, the average accuracy of fault classification for the SVM + FOR-ACO is about 97.8%, while the SVM + SOR-SPIO and SVM + FOR-PIO show accuracy values of 95.5% and 79.7%, respectively. Besides, the SVM + FOR-ACO outperforms the SVM + FOR-SPIO, yielding, on average, the accuracy of identification improved by about 2.3% and 2.2% for 3 and 6 mm crack sizes, respectively. Furthermore, the SVM + FOR-ACO outperforms the SVM + FOR-PIO, yielding, on average the accuracy of identification improved by about 18.4% and 17.7% for 3 and 6 mm crack sizes, respectively.
# Fingerprinting Indoor Positioning Based on Improved Sequential Deep Learning
###### Abstract  
Accurate indoor positioning is essential for many applications. However, current methods often fall short in complex environments due to signal fluctuations. We propose a new indoor positioning approach, that is, improved sequential deep learning (ISDL), to address this issue. First, we apply sequential classification algorithms to progressively narrow the search space, reducing potential location regions into smaller neighborhoods. Next, we combine a deep neural network (DNN) with Weighted K-Nearest Neighbors (WKNN) to refine the final location prediction. Then, we validate our method using the publicly available _U1 IndoorLoc_ dataset, demonstrating superior accuracy compared to existing methods. Specifically, we achieved 95% floor prediction accuracy and reduced the average positioning error to just 7.82 m. By combining sequential classification and the DNN-WKNN hybrid model, we achieve better localization in complex indoor environments. This system offers practical improvements for real-time location-based services and other applications requiring precise indoor positioning.  
Keywords: indoor positioning; sequential deep learning; deep neural network; weighted K-nearest neighbor +
Footnote †: journal:
# Image Segmentation for Human Skin Detection
## 1 Introduction
This process induces a loss in the resulting qualities, mainly in applications based on images with discrete textures or small skin regions. However, image compression methods--such as the discrete cosine transform (DCT)--can eliminate the coefficients with relatively small amplitudes, which are a minimum deformation in the image. These processes still incur a loss of image information, mainly related to texture [15]. Therefore, texture analysis is a non-viable method for forensic applications [16]. Therefore, this work brings a new approach to human skin segmentation in digital images. The contributions of this work are as follows:  
Figure 1: Example of the results of an image submitted to the compression process during transfer by a message transfer application: (**a**) original transferred image and (**b**) received image result with compression. 1. A segmentation model with the ability to classify and quantize the pixels of human skin present in an image according to the regions of the human body;
2. The proposed model presents the possibility of segmenting and quantizing the pixels of human skin according to the instantiation of the object of interest in the image (i.e., of people);
3. The generation of a new dataset compiled through the collection of datasets present in the literature, thus generating a heterogeneous dataset;
4. The proposed model allows image analysis, especially in forensic applications, to focus on regions of interest to the human body, reducing the possibility of false positives in its examination. The new dataset is due to existing challenges in the skin pixel segmentation technique of considering lighting conditions, ethnic differences, image quality, capture equipment, and relevant exposure of regions with the presence of skin. In this work, we propose a solution for skin and non-skin pixel detection in digital images, even with compromised images by events related to capturing and storage. In the following sections, we review the background of the computational vision algorithm (Section 2). In Section 3, we describe the skin detection process. We present and discuss our results in Section 4. Lastly, we conclude this work (Section 5).
# Vapor Pressure of Ionic Liquids with a Common Tetrabutylammonium Cation and Three Different Anions
## 2 Materials and Methods  
### Materials  
Tetrabutylammonium trifluoromethanesulfonate (TBA-TFO, CAS No.: 35895-70-6) and tetrabutylammonium bis(trifluoromethylammoniumally)imide (TBA-NTF2, CAS No.: 210230-40-3) were purchased from Sigma Aldrich (Darmstadt, Germany), while tetrabutylammonium bromide (TBA-Br, CAS No.: 1643-19-2) was obtained from TCI (Zwijndrecht, Belgium). All samples had a purity \(\geq\) 99% (see Table 1). The geometry and chemical composition of the ions making up the materials used in this study or those ones used for comparison are reported in Figure 1. It must be noted that in a large portion of the literature, the TFSI or TFSA acronyms for the bis(trifluoromethylomenthanesulfonyl)imide are adopted, especially in the field of electrochemistry [32; 33; 34]. These specific ionic compounds have some interesting properties concerning their safety. Indeed, TBABr has been reported to be environmentally friendly, operationally simple, non-corrosive, and easily recyclable [35].  
Figure 1: Schematic representation of the cations and anions composing the ILs cited in the main text. Dark grey, light grey, purple, light red, yellow, green, and dark red spheres represent carbon, hydrogen, nitrogen, oxygen, sulfur, fluorine, and bromine atoms, respectively.
## 4 A Test Case: Alkane Chains
### The Case of Platinum-Graphene Hybrid Junctions  
In the previous subsection, we have considered hybrid Au-graphene electrodes for two different anchoring groups in alkane-based molecular junctions and we have observed an important reduction in the attenuation factor. Here, we will observe the effect of a change in the metallic electrode by substituting the gold electrode with a platinum one. As a matter of comparison, we consider again the alkanedithiol molecular junction, following the same experimental and theoretical procedure [47]. Similarly to the Au-graphene alkanedithiol molecular junction, the conductance presents an exponential decay, still within the framework of the non-resonant tunneling. The corresponding attenuation factor is slightly lower, around 0.3 per C atom, which is attributed mainly to the difference in work function between gold and platinum. It has to be noticed that this system is again very well modeled through the Simmons approach, because of the strong coupling to the electrode and the reduced electronic potential barrier.  
Moreover, we can deduce from this result, combined with the results obtained on Au-graphene junctions, that the reduction of the attenuation factor is caused by the symmetry breaking of the graphene introduction in the system. This is not related to the nature of metallic electrode, but rather to the difference of interaction strength at each electrode interface. Indeed, the metallic electrode is coupled covalently to the molecule through the anchoring group, while the graphene electrode is coupled in a much weaker manner through van der Waals interaction. This is particularly true in the case of the thiol anchoring group, which deprotonates at the gold interface, leading to a -S radical (thiolate) that is very reactive with the gold electrode, whereas it remains in the thiol form -SH at the graphene interface, leading to van der Waals contact. Consequently, this is the interaction symmetry breaking at each molecular end, covalent/van der Waals, which leads to the important decrease in the attenuation factor and the increase in the molecular conductance. In this respect, one can anticipate that a molecule weakly coupled to two graphene electrodes through van der Waals interaction would probably present a similar attenuation factor as the one obtained for symmetric Au-Au junctions. As a remark, a similar junction has been studied using a graphitic tip for the I(s) measurements and the usual graphene electrode. The resulting attenuation factor was found to be very similar to the one of the Au-graphene junction, around 0.4 per C atom. This was due again to a coupling difference, since the graphitic tip was very reactive in this situation and coupled covalently to the molecule, whereas the graphene counter electrode coupled weakly to the molecule [48].
## 4 Cost Function to Be Minimized
### Capital and Operating Costs for the Cooling Stations
In Equation (26), \(\Delta P_{f}\) and \(\Delta P_{an}\) are the pressure drops in the nitrogen flowing in the former and in the annular gap, respectively. It is worth noting that the termination stations have to handle a single pressure drop, as they must restore the pressure in a single inlet flow. Then, \(\eta_{cry}\) is the efficiency of the cooling system, and \(\eta_{p}\) is the efficiency of the circulation pumps. Finally, \(\psi\) is the annual effective discount rate for the operating costs, which varies based on the country of cable installation. In this approach, the impact of price inflation has been neglected. It should be kept in mind that, as detailed in Section 3.4, the cable design is optimized by minimizing Equation (14) with the simplifying hypothesis of having a constant \(P_{max}\) over all the yearly timespans (\(h_{y}\)) and consequently assuming a constant \(Q_{c_{k}}\) and \(Q_{p_{k}}\). Upon completing the optimization process, \(C_{cool_{op}}\) is recalculated, summing the operating costs computed with Equation (25) for the single time intervals during a year where the delivered cable power and the corresponding \(Q_{c_{k}}\) and \(Q_{p_{k}}\) values vary. This approach operates under the assumption that it is reasonable to adjust the power output of the cooling and pumping stations, originally sized for a maximum rated power, based on the instantaneous power requirements.
## 4 Self-Sensing Geosynthetics
### Intrinsically Conductive-Polymer-Based Geosynthetics
Poly 3,4-ethylenedioxythiophene (PEDOT) is an intrinsically conductive polymer that has a wide range of applications [180]. [PERSON] et al. [181] developed a stretchable sensor made of PEDOT dispersed in a thermoplastic polyurethane (TPU) host matrix and compared the performances of FeCl\({}_{3}\) and fluoroalkylsilane (FTS) dopants. Although increasing the dopant concentration diminished the polymer elasticity, the plasticity mitigated the FTS-induced polymer stiffening. Therefore, FTS was eventually chosen to fabricate very stretchable (above 300%) and durable (more than 100 cycles) strain sensors exhibiting a proper GF (approximately 10 at 100% strain). [PERSON] et al. [182] used poly (2-acrylamido-2-methyl-1-propane sulfonic acid (PAAMPSA),ytic acid (PA), and PANI to develop a strain sensor laminate wherein PA serves as a cyclic protonic acid, PANI is an inherently conductive polymer, and PAAMPSA is an ionic polyelectrolyte. In addition, PA reportedly forms cationic complexes. Because of their suitable characteristics, PAAPMSA and PA were employed as a PANI dopant and cross-linker, respectively. The strain sensor components were fabricated using a soft, flexible membrane and reportedly exhibited extremely high stretchability, bending, and longitudinal strain sensitivity. Moreover, the strain sensor reportedly exhibited repeated self-healing owing to electrostatic interactions and considerable hydrogen bonding in the polymer complex, which also provided the exceptional stretchability. By constructing reduced (GO)- and  
Figure 21: Schematic illustrating charge transport mechanism and structure of intrinsically conductive polymers. PA-based nano-rod arrays on fabric substrates, [PERSON] et al. [183] developed a textile-based flexible stress sensor exhibiting a linear response over a wide stress range (0.0005-40 kPa) and pressures as low as 0.5 Pa. Similarly, nano-rods consecutively assembled on the textile offered a GF of \(-78\) when utilised as strain sensors. In addition, the sensitive components exhibited remarkable cycling durability, with 1000 and 11,000 cycles reported for the strain and pressure sensors, respectively.
## 4 Databases and Evaluation Parameters
### Evaluation Parameters
#### 4.2.3 Segmentation Measuring Parameters  
The McCann Retinex algorithm validation is based on the retinal blood vessel segmentation and the abnormal region segmentation of the brain MRI. We used retinal imaging databases and brain image databases, and our main objective was to test the extraction capacity of the retinal vessels and the brain tumor region detection. We used four parameters, namely sensitivity, specificity, accuracy, and area under curve (AUC). These parameters are represented mathematically below.  
\[\mathrm{Sensitivity(Sen)}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}}. \tag{4}\]  
\[\mathrm{Specificity(Sp)}=\frac{\mathrm{TN}}{\mathrm{TN}+\mathrm{FP}}. \tag{5}\]  
\[\mathrm{Accuracy(AC)}=\frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{TP}+\mathrm{TN}+ \mathrm{FP}+\mathrm{FN}}. \tag{6}\]  
\[\mathrm{AUC}=\frac{\mathrm{Sensitivity}+\mathrm{Specificity}}{2}. \tag{7}\]  
where TP, TN, FP, and FN represent True Positive (TP) pixels, True Negative (TN) pixels, False Positive (FP) pixels, and False Negative (FN) pixels, respectively. True Positive Pixels, True Negative Pixels, False Positive Pixels, and False Negative Pixels are validated measurements used to verify the quality of a segmented image. They are used in the scenario where we want to compare the segment image with the ground truth image, taking the ground truth image as the basis for comparison. The foreground is \"white or vessels or MR brain image tumor region\" pixels, and the background is \"black or non-vessels or MR brain image normal region\" pixels in the ground truth. TP stands for correctly segmented pix  
Figure 3: Contrast Determination of Retinal Fundus Images and MRI Brain Images. The left hand image shows the retinal fundus image contrast calculation, and the right hand image represents the MR brain image contrast calculation.  
els in the foreground, FP stands for falsely detected pixels in the foreground, TN stands for correctly segmented pixels in the background, and FN stands for falsely detected pixels in the background.
# Weakly Supervised Conditional Random Fields Model for Semantic Segmentation with Image Patches
## 2 Related Work
### Superpixel Based Image Segmentation  
As a newly proposed splitting and merging method, superpixel based image segmentation has achieved great progress in recent years. As first proposed by [PERSON] et al. [16], superpixel is defined as a sub-region composed of adjacent pixels with similar texture, color and other visual characteristics. Superpixel based image segmentation is the process of clustering pixels into superpixels, and relevant algorithms can be roughly divided into graph-based and gradient descent-based methods. Graph-based methods mainly include: normalized cut (NC) [17], superpixel lattices (SL) [18], and Felzenszwalb and Huttenlocher (FH) algorithm [19]. Typical gradient descent-based methods are watershed [20], Meanshift [21] and the Simple Linear Iterative Cluster (SLIC) algorithm [22] et al.  
Simple linear iterative clustering was first proposed in [22]. [PERSON] et.al [23] proposed an image segmentation algorithm based on SLIC superpixel, and region merging based on 5-D spectral clustering and boundary-focused region clustering. [PERSON] et.al [24] proposed a novel image segmentation method based on interactive region merging, but users should roughly mark the location and region of the target and background. [PERSON] et.al [25] proposed an algorithm to add the color covariance matrix to the features of superpixel to improve the accuracy of image segmentation.  
Compared with pixels, the advantages of superpixels are reflected in two aspects: the calculation is simple, which is helpful to reduce the size of processing objects and the computational complexity of subsequent processing. The number of superpixels can be controlled by adjusting the parameter \(K\), however \(K\) needs to be set manually. If \(K\) is too large, the advantage of superpixel segmentation will be lost and the unnecessary computational complexity of image segmentation will be increased. If \(K\) is too small, the accuracy of image segmentation results will be reduced. Therefore, it is more challenging to manually set the appropriate number of superpixels.  
Image patches are merged into larger image regions based on weakly supervised information. Each image patch has only one semantic category, but a target region can be composed of multiple image patches. Compared with superpixel, the proposed method has more advantages in using image patches as the basic unit of weakly supervised ISS. There are two main advantages: the number of image patches is much less than the number of superpixels, which can greatly reduce the complexity of the algorithm. Image patches have more neat object boundaries, which can improve the accuracy of semantic label inference.
# Lung's Segmentation Using Context-Aware Regressive Conditional GAN
## 3 Materials and Methods
### Conditional GAN for Lesions and Lungs Segmentation  
One of the biggest problems in the segmentation of lungs and lesions is the identification of boundaries. Conventional deep-learning methods depend upon the depreciation of pixel-wise loss. The role of a misclassified pixel is not prominent for the overall loss but can eventually lead to the segmentation of multiple lungs regions as one. Such kinds of challenges could be tackled by distance map regression [29], concave point identification [30], and contour prediction [31]. CRFs (conditional random fields) have been widely employed [32]. CNN-CRF is also used for context-aware training of models [33].  
Notwithstanding the advancement in CNN-CRF networks, this technique is restricted to unary or pairwise CRFs, which embody only lower-order statistics. According to [32], higher-order statistics benefit medical image segmentation. Similarly, adversarial training permits higher-order potential without restricting a specific type of higher-order consistency and has field-of-view instead of pixel-wise.  
The adversarial model can learn a suitable loss function that avoids manually concoted loss functions. This mechanism has also been explained extensively in [34]. These models can identify small changes in a range of higher-order consistencies between ground-truth and predicted segmentation masks. The suggested conditional GAN learns a mapping \(m\) for lung segmentation, in which \(m\) can acclimate lung images to their segmentation masks. To train the proposed model with pair data for segmentation, the CGAN objective function encompasses an adversarial loss function \(F_{l}\) and pixel-wise loss function \(L_{1}\) to reprimand segmentation errors. The loss in conditional GANs is analogous to cycle-GAN, in which the segmentation network \(S_{n}\) and discriminator \(D_{n}\) play a minimax game in minimizing and maximizing the objective, \(min_{S_{n}}max_{D_{n}}F_{l}(S_{n},\ D_{n})\). Explicitly, \(S_{n}\) interprets lung CT scans to realistic masks to reduce cross-entropy loss of \(D_{n}\). The adversarial loss can be construed as structured loss, in which \(S_{n}\) is reprimanded if pixels in the predicted masks are unrealistic. The objective of the proposed model is expressed as in Equation (2).  
\[\begin{array}{rl}F_{l}(S_{n},D_{n})=&\mathbb{E}_{m,\ n\sim p_{data}(m,n)}[ \log\!D_{m}(m,n)]\\ &+\mathbb{E}_{n\sim p_{data}(n)}[\log(1-D_{m}(m,\ S_{n}(n)))]\end{array} \tag{2}\]  
where \(m\) represent masks and \(n\) denotes lung lesion images. For stabilization of training and bringing prediction closer to the ground truth, we have employed the addition loss function \(L_{1}\) as Equation (3).  
\[L_{1}(S_{n})=\mathbb{E}_{n=m,\ n\sim P_{data}(m,\ n)}[\|\ m-S_{n}(n)\ \|_{1}] \tag{3}\]
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 6 Performance Evaluation
### Single SW Traffic Scheduling Experiment Based on NeSTiNg  
Currently, there are two mainstream experimental frameworks in the field of TSN research, namely NeSTiNg [50] and CORE4 INET [51]. NeSTiNg is an open-source project on the GitLab website [52], which was released in 2019 specifically for TSN simulation. Using the NeSTiNg simulation framework, [PERSON] et al. [53; 54; 55; 56] accomplished a series of research works on network calculus-based TSN network latency analysis and optimization. In order to prepare the raw data on the time delay from a single SW for the routing algorithm proposed in this paper, we built a TSN simulation experimental environment based on NeSTiNg and set up the network topology depicted in Figure 10.  
On the experimental network, we deployed the end-to-end latency optimization methods proposed in [53; 54]. For all flows that were transmitted through switchA, we changed the quantity of TT and non-TT flows participating in queuing and performed several sets of comparative experiments to quantify the correspondence between the WCD and queuing congestion, as depicted in Figure 11. Based on these data, we were able to calculate the correspondence between the queuing delay \(q_{ij}^{k}\) and the quantity of flows to be transmitted to \(v_{j}\) when the routes of non-TT flows overlapped, which was previously discussed in Section 5.1.  
Figure 11: WCD changes with the quantity of flows participating in the queue.  
Figure 10: TSN topology of the simulation experiment that was built based on NeSTiNg.