esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Influence of Sensor Intrusion on Vocal Fold Contact Pressure Accuracy
### Contact Pressure Transducer Specifications  
A Millar Mikro-Cath(tm) Pressure Catheter (Millar Inc., Houston, TX), designed to directly measure arterial pressure, was adapted for use as an _in vivo_ contact pressure sensor. The transducer utilizes a piezoresistive sensing element to measure pressure over a dynamic range of -6.7 kPa to 40 kPa, and has a flat frequency response from DC to 10 kHz, with an accuracy of \(\pm\)1% of full-scale output (FSO) over the range of -6.7 kPa to 6.7 kPa. The size of the intraglottal pressure transducer is shown in Figure 2. The transducer is cylindrical in shape and has a diameter of 1.17 mm, and is 4.80 mm long. The sensing element that acquires the pressure is 1 mm \(\times\) 1 mm in size, and is recessed from the upper surface of the cylindrical sensor by 0.37 mm (see Figure 2). The sensor is powered by a two-channel signal conditioning unit (PCU-2000) that provides calibration and offset control. A 120 cm long electrical cable connects the transducer to the signal conditioner.  
Despite the small physical size of the intraglottal pressure transducer, it is expected to over-estimate impact pressure measurements because during collision, physical intrusion of the sensor causes local surface curvature of the contacting tissue. The stiffness of the contacting surface is also expected to influence the results, with stiffer surfaces resulting in decreased contact forces due to the tissue deforming less and not reaching the surface of the recessed sensing element.
## 1 Introduction
\begin{split}&\text{ }x^{(r+1)}=G_{(3k+2)^{th}MBJ}(x^{(r)})=\mu_{k}(x^{(r)}),\\ &\mu_{j}(x^{(r)})=\mu_{j-1}(x^{(r)})-H_{2}(x^{(r)})[\Phi^{\prime }(x^{(r)})]^{-1}\Phi(\mu_{j-1}(x^{(r)})),\quad H_{2}(x^{(r)})=2I-\tau(x^{(r)})+ \frac{3}{2}(\tau(x^{(r)})-I)^{2},\\ &\mu_{0}(x^{(r)})=G_{5^{th}MBJ}(x^{(r)}),\quad j=1,2,...,k,\,k \geq 1.\end{split}  \tag{6}\]  
To derive the order of convergence for the iterative methods, higher-order derivatives are used although such derivatives are not present in the formulas. The solutions of the equation \(\Phi(x)=0\) are rarely found in closed form and hence most of the methods for solving such equations are usually iterative. Convergence analysis of these iterative methods is an important part in establishing the order of any iterative method. As the convergence domain is narrow, one needs additional hypotheses to enlarge it. Furthermore, knowledge of initial approximations requires the convergence radius. Therefore, the applicability of the methods depends upon the assumptions on the higher-order derivatives of the function. Many research papers which are concerned with the local and semi-local convergence of iterative solutions are available in [10; 11; 12; 13]. One of the most attractive features of each numerical algorithm is how the procedure deals with large-scale systems of nonlinear equations. Recently, [PERSON] et al. [14; 15] proposed conjugate gradient algorithms with global convergence under suitable conditions that possess some good properties for solving unconstrained optimization problems. The primary goal of this manuscript is to propose higher-order iterative techniques which consumes less computational cost for very large nonlinear systems. Motivated by the different methods available in literature, a new efficient two-step method is proposed with convergence order five for solving systems of nonlinear equations. This new method consists of two functional evaluations,two Frechet derivative evaluations and two inverse evaluations per iteration. The order of this method can be increased by three units whenever an additional step is included. This idea is generalized for obtaining new multi-step methods of order \(3k+5\), \(k\geq 1\), increasing the convergence order by three units for every step. Also, this method requires only one new function evaluation for every new step. The remaining part of this manuscript is organized as follows. Section 2 presents two new algorithms, one with convergence order five and the other is its multi-step version with order \(3k+5\), \(k\geq 1\). In Section 3, the analysis of convergence of the new algorithms are presented. Computational efficiencies of the proposed algorithms are calculated based on the computational cost and a comparison with other methods in terms of ratio is reported in Section 4. Numerical results for some test problems and their comparison with few available equivalent methods are given in Section 5. Two application problems known as 1-D and 2-D Bratu problems have been solved using the presented methods in Section 6. Section 7 includes a short conclusion.
# Yolo Based Defects Detection Algorithm for EL in PV Modules with Focal and Efficient IoU Loss
###### Abstract  
Considering the defect detection issues in electroluminescence (EL) of photovoltaic (PV) cell systems, lots of factors result in performance degradation, including defect diversity, data imbalance, scale difference, etc. Focal-EIoU loss, an effective defect detection solution for EL, is proposed based on the improved YOLOv5. Firstly, by analyzing the detection background and scale characteristics of EL defects, a binary classification is carried out in the system. Subsequently, a cascade detection network based on YOLOv5 is designed to further extract features from the binary-classified defects. The defect localization and classification are achieved in this way. To address the problem of imbalanced defect samples, a loss function is designed based on EIoU and Focal-F1 Loss. Experimental results are illustrated to show the effectiveness. Compared with the existing CNN-based deep learning approaches, the proposed focal loss calculation-based method can effectively improve the performance of handling sample imbalance. Moreover, in the detection of 12 types of defects, the Yolov5 algorithms can always obtain higher MAP (mean average precision) even with different parameter levels (Yolov5m: 0.791 vs. 0.857, Yolov5l: 0.798 vs. 0.862, Yolov5x: 0.802 vs. 0.867, Yolov5s: 0.793 vs. 0.865).  
EL defects in photovoltaic components; YOLOv5; serial network; Focal-EIoU loss; EIoU
# Design of Liquid-Air Hybrid Cooling Garment and Its Effect on Local Thermal Comfort
## 6 Discussion  
### Effect of Liquid-Air Hybrid Cooling on Cooling Performance  
The liquid-air cooling garments provided effective convection and conduction of heat dissipation compared with the no-cooling state, resulting in a significant decrease in local skin temperature, mean skin temperature, mean torso skin temperature, thermal sensation, and an increase in thermal comfort, while having little effect on core temperature. The liquid-air cooling garments showed significant differences in skin temperature in five local regions of the torso, including the neck, chest, belly, shoulder, and back, compared with the CON, and its cooling effect was mainly for the torso. In the local skin temperature comparison of YAL with AAL and SAL, it was found that the back skin temperature of YAL was lower than that of AAL and SAL among the three cooling garments, which indicates that the design form of its garment has the best cooling effect on the back. This is because when wearing the YAL, the back of the body was cooled by the conduction of the liquid cooling tube and also by the air-cooling cycle of the fan across the torso. With AAL and SAL, the back was only cooled by air. The skin temperature of the back area showed that when the local area was cooled by a combination of liquid and air cooling, the cooling effect was better than air cooling only. The experimental results of [PERSON] [39] also showed that among the four parts of the torso, back cooling was the most effective. For the mean torso skin temperature, it was found that the mean torso skin temperature of the aluminum tube (AAL) was always lower than that of the inner yarn (YAL) during the experiments, which indicated that the cooling of the torso with the aluminum tube (AAL) design was better than that of the inner yarn group (YAL). For the mean skin temperature, the inner yarn group (YAL) was mostly lower than that of the aluminum tube (AAL). It indicates that the two garments do not have the same cooling effect on the mean skin temperature and the mean torso skin temperature, and the YAL has a better overall cooling effect for the whole body, while the AAL has a better local cooling effect for the torso.  
Through the analysis of the core temperature, the maximum temperature difference of the core temperature was only 0.1 \({}^{\circ}\)C during the whole experiment, which was not significant. The reason for this is that the subjects sat still and did office work during the experiment without strenuous exercise; thus, the effect on the core temperature was not significant. This was also detected in the studies of [PERSON] et al. [40]  
Figure 16: Thermal comfort votes of the torso in the different clothing conditions. _\"\"\"_ means significant difference at the \(p<0.01\) level.  
and [PERSON] et al. [41], in which the authors found that exercise intensity had a great effect on the core and skin temperatures.
## 2 Mobile Social Networking
### Service Designs for Context-Aware Mobile Social Networking  
We highlight some of the choices that context-aware mobile social networking service's designers make below.  
**Network architecture type**: We find three architectural categories for CAMSN services: client-server, peer-to-peer, and hybrid. _Client-server_ CAMSN services are centralized: their end-user platforms provide content by utilizing communications with fixed and authoritative application servers. _Peer-to-peer_ CAMSN services are decentralized: they seek to avoid the usage of fixed servers or other authoritative entities. Peer-to-peer CAMSN services therefore need to spontaneously provide (1) localization or proximity functionalities, (2) network setup, and (3) application level functionalities. _Hybrid architecture_ CAMSN services are a mixture of client-server and peer-to-peer architectures. In subsection 2.2, we describe in more details the network architecture types used by CAMSN services, and also provide examples of services using each.  
**Source of context and user information**: A majority of CAMSN services derive application layer contextuality from either location information or proximity information. _Location information_ refers to the knowledge of physical locations for all users (e.g. GPS coordinates) of a service, allowing the service to correlate and match user locations. On the other hand, _proximity information_ for a user refers to knowledge of nearby users as determined via short range communications (e.g. Bluetooth). In subsection 2.3, we describe in more detail the methods with which CAMSN services can derive location or proximity information, and provide examples of services using each.  
We find a further distinction among CAMSN services regarding where their user profile information is taken from. Most services require end-users to create a new, specific profile for usage on the service; these are _non-augmenting_ services. _Augmenting_ CAMSN services, however, allow users to augment existing, traditional social networking profiles (e.g. Facebook) with externally provided contextual information. An example of an augmenting CAMSN is WhoZThat [5]. The data flow in WhoZThat is bidirectional - information flows from end-user platforms to the WhozThat service (the IDs of proximate peers) and likewise flows in the opposite direction (the profile data of proximate peers). An augmenting CAMSN need not have this bidirectional data flow; CenceMe is one such example [6]. Client devices in this service push context information to their appropriate traditional social networking profiles. However, these same client devices do not receive context information from the service, they cannot answer the question \"what devices are near me?\".
# Breast Cancer Image Classification Using Phase Features and Deep Ensemble Models
## 2 Literature Review
### Convolutional Neural Network-Based Methods  
Convolutional neural networks have demonstrated significant potential in breast cancer classification due to their ability to automatically learn hierarchical image features, achieve robust performance, and reduce the need for handcrafted feature extraction. Table 2 summarizes recent CNN-based methods applied to breast cancer classification. A CAD system based on the You Only Look Once (YOLO) framework was proposed for ROI extraction, using feedforward CNN, ResNet50, and InceptionResNet-V2 architectures for classification [16]. This system, trained on augmented DDSM and INbreast datasets with a 70/20/10 split, reported accuracies of 97.50% and 95.32%, with corresponding AUCs of 0.9750 and 0.9501, respectively. A CNN model with three convolutional layers, four max-pooling layers, and three fully connected layers was proposed for binary classification [32]. Using ROIs extracted through Otsu's binarization and contrast-limited adaptive histogram equalization (CLAHE), the model was evaluated on DDSM, MIAS, and INbreast datasets, reporting accuracies of 91.20%, 95.30%, and 96.52%, respectively. A recent study compared classification performance with and without data augmentation, considering AlexNet, DenseNet, and ShuffleNet models [33]. Performance evaluation on the INbreast dataset showed that DenseNet achieved an accuracy of 99.72% in binary classification, while ShuffleNet reached 97.84% accuracy in multi-density classification, using an 80/20 train-test split. A model based on the multi-scale attention-guided network (MSANet) was proposed to improve feature extraction [34]. This model was evaluated on the DDSM and INbreast datasets, reporting AUC values of 0.942 and 0.9285, respectively. A transfer learning-based model combining ECA-Net50 and ResNet50, and employing focal loss (FL) to address class imbalance, was proposed for binary classification using the INbreast dataset. This model achieved an accuracy of 92.9% and an AUC of 0.960 [17]. Another method incorporates phase information from mammograms using the Riesz transform and evaluates multiple CNN models, including VGG16, InceptionV2, and ResNet50 [35]. The highest reported accuracy of 82.2% was achieved using the ResNet50 model on the combined MIAS, DDSM, INbreast, and CSAW-M datasets. A CAD system based on AlexNet, VGG-16, and VGG-19 was proposed and trained on the combined datasets of MIAS, DDSM, and INbreast. The system achieves an overall accuracy of 92.27% on the combined datasets and accuracies of 95.95%, 96.53%, and 96.53% on the MIAS, INbreast, and DDSM datasets, respectively, using an 80/20 train-test split [18].
## 6 Modeling for SAR Simulation
The higher this frequency, the faster it attenuates in space and the less it penetrates the human head. The highest values of absorption are observed on the surface of the human head. The developed model calculated only the local values of the \(SAR\) parameter. The maximum local \(SAR\) value is always higher than the maximum mean \(SAR\) value. The amount of energy absorbed by the human head affects the temperature to which the tissues of the head are heated. The study of the processes of temperature distribution in the human head and on its surface is possible with the help of the created computer model using the COMSOL Multiphysics software. The frequency of the signal of the mobile device was selected to be 900 MHz. The transformation of the absorbed energy into heat was conducted with a biothermal equation. The change in temperature is a function of the physiological properties of biological tissues and blood circulation in the human body [21]. The thermal effects on and in the human head are shown in Figure 4.  
Due to the created computer model, it is possible to study the processes of penetration of the electromagnetic field into the human head and the effects caused by this, thus, thoroughly simulating different situations and different characteristics of the head model and mobile phone characteristics. The visualization capabilities of the COMSOL Multiphysics software are impressive and allow a detailed view of the simulation results. Figure 3: \(SAR\) visualization at different levels in the human head.
## 4 Implementation
### Emergency Response Based on Digital Twin and Knowledge Graph
Based on the risk evaluation, an emergency response plan is initiated, involving coordination among multiple groups--including monitoring units, equipment teams, and external coordination bodies--for tasks such as backfilling, utility shutdowns, obstacle detection, and pipeline monitoring. The digital twin platform supports this process by enabling real-time data access and visualization at each decision point, thereby enhancing the efficiency and clarity of emergency management operations. This coordinated approach allows for the definition of interdepartmental responsibilities, enabling each team to operate within its designated domain. By leveraging ontology-based reasoning, the system determines the most likely risk sources and automatically delegates relevant tasks to appropriate units. In doing so, the framework facilitates responsive, decentralized, and intelligent emergency management, ensuring that mitigation strategies are both timely and data-informed. This methodology not only improves operational efficiency in high-risk scenarios but also exemplifies how the digital twin--knowledge graph integration can support smart and proactive infrastructure resilience. The proposed framework enables real-time monitoring and early warning across a range of tunnel construction risk factors, including not only TBM operational parameters  
Figure 10: Emergency plan in the digital twin system. (e.g., thrust, pitch, torque) but also external impacts such as ground and building settlement. Through the digital twin platform, all stakeholders--including site supervisors, control room personnel, and project decision-makers--can access a unified, real-time visualization of both internal machine behavior and surrounding structural responses. Alerts are automatically triggered based on data anomalies and risk thresholds, significantly reducing the response time compared with traditional manual inspection and reporting processes. Field feedback from a recent deployment indicates the system achieves high early warning accuracy, particularly in identifying subtle changes in settlement trends that may precede larger structural risks. Although a detailed quantitative comparison with traditional systems is planned for future work, these initial results demonstrate the system's strong potential in improving risk visibility and operational responsiveness.
# High-Precision Ranging Method of 5G NR Co-Band PRS in Industrial Internet Scenarios
###### Abstract  
To meet the demand for high-precision positioning in commercial industrial internet scenarios, 3 GPP introduced the Positioning Reference Signal (PRS) in the 5G standard. However, the PRS signal occupies specific time and frequency resources for transmission in 5G systems, limiting the efficiency of communication signal transmission to some extent. In this regard, we propose a 5G NR Co-Band PRS model that allows for the superimposition of PRS signals on communication signals in a low-power manner, without requiring additional communication resources or causing too much interference to the communication signal. Since orthogonal frequency division multiplexing (OFDM) signals are sensitive to synchronization errors, we have developed a three-stage Co-Band PRS-based reception scheme. First, an innovative weighted window coarse synchronization method is proposed to enhance the performance of capturing communication signals at a low signal-to-noise ratio (SNR). Next, the interference cancellation technique is utilized to remove the communication signals, and the synchronization error is corrected through multipath delay estimation. Finally, to further improve the ranging accuracy, we propose an iterative delay-locked loop (DLL) algorithm that can achieve a tracking accuracy of one percent sampling. Simulation and real environment tests confirm that the proposed Co-Band PRS reception scheme can achieve a ranging accuracy of 0.16 m@90%.  
P +
Footnote †: journal: Applied sciences  
positioning accuracy of 0.2 m@90% in Release 17. The standard specification focuses on optimizing positioning performance by mitigating user equipment (UE) Rx/Tx and/or gNB Rx/Tx latency, estimating UL-AoA/DL-AoD, and eliminating multipath/NLOS.  
The implementation of high-accuracy angular measurements relies on millimeter wave massive Multiple-Input Multiple-Output (MIMO) antennas, and [8; 9] showed with numerical examples that using 5G/6G millimeter wave MIMO with sufficiently high temporal and spatial resolution, it is possible to estimate position and orientation information even in NLOS environments when the corresponding reflector or scatterer is illuminated by a narrow beam. In addition, deploying RIS to improve the signal propagation environment can similarly enhance the accuracy and reliability of localization [10; 11]. Despite the potential advantages of geometry-based millimeter wave and RIS-based technologies for localization applications, they are still in the research and development stage, making it difficult to apply them on a large scale in commercial scenarios.
## 4 Experimental Results and Discussion
### Comparison of Results with Other Algorithms  
Table 5 compares the proposed system, using the improved YOLOv5s6-Yarn Hairiness algorithm, with state-of-the-art object detection methods, highlighting the improvements in bold.  
The metrics for the baseline algorithms (YOLOv5s6, YOLOv8, YOLOv7, YOLOv5n, and Fast RCNN) were obtained through direct implementation and evaluation using the same dataset and experimental conditions described in this article. For consistency and fairness in the comparisons:  
* All baseline models were trained and tested on the same dataset with identical augmentation techniques and hardware settings as the proposed model.
* The default implementations of the models, as provided in their official repositories, were used for training and evaluation.  
By following this approach, the performance comparisons are valid and accurately reflect the relative strengths and weaknesses of the proposed model and the original algorithms.  
The [EMAIL_ADDRESS] of the optimized YOLOv5s6-Hairiness framework showed a 5.4% improvement over YOLOv5s6 Default, 7.5% over YOLOv8, 13.9% over YOLOv7, 7.4% over YOLOv5n, and 33.41% over Fast-RCNN. This demonstrates higher accuracy in detecting loop and protruding fibers proving the effectiveness of the implemented improvements.