esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Routing Protocols for UWSN
### Protocols Featuring Channel Properties
#### 3.3.1 Directional Flooding-Based Routing (DFR)  
For improving the reliability, [PERSON] et al. proposed Directional Flooding Based Routing (DFR) [57]. Authors suggested to consider larger number of nodes to achieve better reliability. These few more forwarding nodes make the data packets reach to the sink reliably. The forwarding activity is performed per hop. Few researchers have already worked on flooding based routing but [PERSON] et al. have the most prominent research work on DFR [57, 69]. The authors suggested broadcasting a data packet by way of a source node that has its own location information and one other parameter. This parameter contains an angle known as a base_angle which is set to its predefined minimum value according to network density. Upon receiving a packet, the node measures an angle between two vectors-from  
Figure 7: Network architecture in Reliable Energy Efficient Pressure Based Routing (RE-PBR) [56].  
source node to itself and from itself to sink and this angle is called current_angle. When node receives a packet it compares base_angle with current_angle and decide whether to forward a packet. If the node's base_angle is bigger than that of current_angle, it is considered as out of flooding scope and it is discarded. In reverse case, base_angle is adjusted so that it can maintain link quality with the nearby nodes. Two conditions must be satisfied by all the nodes to maintain the link quality, that is, (i) the current_angle of the neighbors must be larger than that of current_angle of the forwarder, and (ii) the distance of the neighboring nodes to a sink must be less than that of the distance of the forwarder to the sink.  
Nodes will not transmit packet until the above conditions are fulfilled. The forwarder node then broadcasts the packet with a source location and a new base angle. Packet transmission in DFR is shown in Figure 8.  
The source S broadcasts a packet with its location and base_angle whose value is set to A_min. When F receives the packet, it compares its current_angle (angle between \(\overrightarrow{FS}\) and \(\overrightarrow{FA}\)) with the base_angle. F adjusts its base_angle with B and C according to the average link quality in case the current_angle is larger than base_angle. It then floods the packet with the adjusted base_angle and its location in it, otherwise it just drops the packet. Simulations have shown that DFR gives a better delivery ratio compared to VBF. Moreover, it offers 73% shorter end-to-end delay and 43% less communication overhead than VBF, even though it has some drawbacks. When the sink finds no node in its range, then a hole is created in the network which may disrupt the network.
## 2 Modeling of Internal Pressure for a Single Factor
### Carbody Deformation  
ABAQUS and STAR-CCM+ have extensive application cases and experience in the fields of rail transportation and aerodynamics. The combination of these two tools provides higher computational efficiency and better convergence for large-scale models and highly nonlinear fluid-structure interactions. References [18; 19] utilized the fluid-structure interaction capabilities of ABAQUS 2020 and STAR-CCM+ 2020 software to conduct coupled simulations of the carbody structure and the internal airflow field. These simulations examined the effects of tunnel pressure wave disturbances on carbody deformation and internal pressure fluctuations under different speed conditions. By fitting the scatter data of the internal-external pressure differential and the carbody volume deformation coefficient, a relationship between carriage volume and the internal-external pressure differential was established. Additionally, the relationship between the carbody deformation coefficient and the internal-external pressure differential is further explored. The relationship between the carbody deformation coefficient \(\theta\) and the internal-external pressure differential is as follows:  
\[\theta=4.423\times 10^{-4}[P_{i}(k)-P_{e}(k)] \tag{12}\]  
Therefore, the volume of the carriage \(V\) can be expressed as:  
\[V=V_{0}\Big{\{}1+4.423\times 10^{-4}[P_{i}(k)-P_{e}(k)]\Big{\}} \tag{13}\]  
where \(V_{0}\) is the initial volume of the carriage. The internal pressure fluctuations caused solely by carbody deformation can be calculated using the ideal gas law:  
\[P_{id}=\frac{m_{i}RT_{i}}{M_{i}V(k)} \tag{14}\]
## 4 Discussion
### Challenges
#### 4.1.6 Costly and Time-Consuming  
One of the detection approaches for insider threats is the supervised learning approach, which trains data to build a classification model. However, most of the introduced detection methods built are based on supervised learning. Thus, the need remains for contextual data entries about users and a training process for supervised learning methods that are specific to insider threat detections. Despite such capabilities, this approach tends to be costly and time-consuming [39; 42; 80; 103].
## 6 Conclusions  
This study investigated the seismic performance of masonry walls retrofitted with STPU through shaking table tests and finite element analysis. A simplified micro-model was adopted to simulate the structural response under out-of-plane seismic loading. The main conclusions are as follows:  
1. In the shaking table tests, the non-retrofitted specimens (MWN) failed at 0.4-0.5 g, whereas the STPU-retrofitted specimens (MWP2) showed improved ductility and resisted up to 0.7-0.8 g. The hybrid specimens with both GFRP and STPU (MWPF5) exhibited the highest resistance, withstanding seismic loads up to 0.9-1.0 g. Crack patterns were concentrated near the lower fixed bracket in all specimens, confirming the effectiveness of the boundary constraint design.  
2. The finite element model successfully predicted the PGA values at failure for each specimen type, showing strong agreement with the experimental results. The location and mode of failure observed in the simulation also corresponded well with the experimental crack patterns.  
3. The constitutive models applied--Drucker-Prager for masonry, Arruda-Boyce for STPU, and the lamina model for GFRP--reflected the mechanical properties of each material and enabled realistic simulation of structural behavior under dynamic loading.  
4. A parametric study varying STPU thickness revealed that a 2 mm layer provided the most efficient strengthening effect, comparable to thicker applications but with advantages in material cost and constructability.  
5. While the simplified micro-model showed reliable results, further improvements can be achieved by adopting detailed micro-modeling to capture localized cracking and post-failure behavior more accurately. Moreover, applying probabilistic analysis methods can help assess the uncertainty in material properties and seismic demands, leading to a more robust evaluation of seismic performance in real-world applications.  
Conceptualization, J.-H.J.K.; methodology, J.-H.J.K.; validation, T.-H.L.; investigation, T.-H.L., J.-W.K., and S.L.; writing--original draft preparation, T.-H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Technology Innovation Program (RS-2024-00408368, Development of Carbon fiber application technology for life care and utilization technology to replace steel strand wire for construction usage) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this research are available on request from the first author due to privacy and confidentiality agreements with the collaborating institution.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 1 Introduction
Secondly, the use of VR requires a headset device, but AR can be used with a smartphone. Moreover, VR can only enhance fictional reality, but AR can improve real and virtual worlds. Virtual simulators have an edge over physical simulators as they allow the operator to go back in time and make amendments to the procedure that has been done. These virtual practice sessions improve the efficiency of the operating personnel and make theoutcome predictable [5]. According to the degree of immersion experienced by the user, virtual reality has three broad classifications: immersive virtual reality, which consists of a head-mounted display system, non-immersive virtual reality, which is based on a computer-aided (CAD) network, and semi-immersive virtual reality, based on a cave automatic virtual environment [1; 6]. In immersive virtual reality, the operator is immersed in a virtual environment where they can interact with recorded 3D images/objects using a wearable device/headset and a pen-shaped manipulator. Eye movements and leap motion of the hands are detectable through this device [4]. However, in non-immersive virtual reality, the operator interacts with 3D simulations on a desktop computer displayed on a flat-screen 2D monitor using a mouse instead of a wearable device and manipulates the virtual images without being a part of a virtual scenario [4]. In regard to the sub-specialties of dentistry, so far, augmented reality and virtual reality have been mostly applied in the fields of dental implantology and orthognathic surgery. In augmented reality scenarios, the visual information gathered by registering anatomical structures and overlaying them on actual operative sites provides better guidance for surgical and other dental procedures. In some AR systems, 2D-projected computer graphics (CG) do not provide the viewer with the perception of depth at the surgical site, thus reducing the safety and accuracy of the procedure. Hence 3D images generated either via stereoscopy [3], integral imaging (or integral photography) and holographic display [7] are projected onto the site of the procedure, which enables the user to spatially visualize and perceive the depth of tissues from all locations, which enhances safety and accuracy. Furthermore, the contemporary Microsoft Hololens waveguide AR system, significantly advances the optical design of current AR display systems and may also be applied to a broad range of optical systems, including high-precision imaging, sensing, and advanced photonic devices [6]. The purpose of this review was to provide an update on the contemporary knowledge, to report on the ongoing progress of AR and VR in various fields of dental medicine and education, and to identify further research needs that will achieve their clinical translation.
## 5 Conclusions  
In the present study, the capability of DAS to detect and localize transient events on pipes was investigated. It was found, that in conjunction with a fitting sensor configuration of the optical fiber, mechanical impacts can be identified and localized along the pipe. Depending on the favored application, a fully distributed configuration, represented by continuous helical wrapping of the sensor fiber around the pipe or a quasi-distributed configuration consisting of short local sections of densely wound fiber, may be favorable. The localization accuracy and precision in general depends on the chosen fiber configuration, experimental settings (i.e., pulse length) and the procedure of data evaluation. For the parameters tested here, values down to 20 cm were achieved. In summary, the methods presented in this study may be understood as a toolbox for practical implementation of DAS on pipes.  
In a second set of experiments the technique was applied for spatially continuous tracking of pressure waves in pipelines following gas explosions. It was demonstrated that the employment of DAS with a fully distributed sensor configuration not only allows for observation of propagation with a constant velocity but also of accelerated motion. In contrast to conventional testing of gas explosions with local pressure sensors, the proposed technique may provide a means to identify the point of deflagration-to-detonation transition without gap and without a priori knowledge of its location.  
The approach is therefore of interest not only in condition monitoring of pipes but also in investigations of the behavior of gas explosions in closed pipe systems.  
Conceptualization, M.-T.H. and A.K.H.; Methodology, M.-T.H. and A.K.H.; Software, M.-T.H. and S.C.; Validation, M.-T.H., S.C., J.P., T.H., and D.A.; Investigation, M.-T.H., S.C., D.A., and S.S.; Resources, M.-T.H., S.C., J.P., T.H., D.A., S.S.; Data Curation, M.-T.H. and S.C.; Writing--Original Draft Preparation, M.-T.H.; Writing--Review and Editing, M.-T.H., J.P., T.H., and D.A.; Visualization, M.-T.H.; Supervision, A.K.H.; Project Administration, A.K.H.  
This research received no external funding.  
This work is part of the project AGIFAMOR within the focus area \"Infrastructure\" at BAM. The authors would like to thank all partners of the project for fruitful discussions and exchange of ideas.  
The authors declare no conflict of interest.
# Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization
## 3 Method
### Reconstruction Network
#### 3.2.1 Residual Attention Module  
The Residual Attention Modules are integrated after the concatenation of features to enhance feature representation and capture relevant information. These modules leverage residual connections and attention mechanisms to selectively emphasize notable features and suppress irrelevant ones. By focusing on informative regions and enhancing feature discrimination, the residual attention modules improve the network's ability to generate high-quality reconstructions. In addition, the residual connections address the issue of vanishing gradients. By propagating gradients more effectively through the network, the residual connections enable faster convergence and improve the accuracy of the model. The specific structure of the residual attention module is shown in Figure 3. It comprises global pooling, convolutional, and activation layers. In both pathways, a \(1\times 1\) convolutional layer is employed to adjust the number of feature channels.  
Figure 2: The overall architecture of the proposed reconstruction network.
## 3 Applications of WSNs
### Examples of Application Requirements  
Due to the wide variety of possible applications of WSNs, system requirements could change significantly. For instance, in case of environmental monitoring applications, the following requirements are typically dominant: _energy efficiency_, nodes are battery powered or have a limited power supply; _low data rate_, typically the amount of data to be sensed is limited; _one-way communication_, nodes act only as sensors and hence the data flow is from nodes to sink(s); _wireless backbone_, usually in environmental monitoring no wired connections are available to connect sink(s) to the fixed network.  
Significantly different are the requirements of a typical industrial application where wireless nodes are used for cable replacement: _reliability_, communication must be robust to failure and  
Figure 2: Event detection application.  
interference; _security_, communication must be robust to intentional attacks; _inter-operability_, standards are required; _high data rate_, the process to be monitored usually carries a large amount of data; _two-way communication_, in industrial applications nodes typically act also as actuators and hence the communication between sink(s) and nodes must be guaranteed; _wired backbone_, sinks can be connected directly to the fixed network using wired connections.  
Even if requirements are strongly application dependent, one of the most important issues in the design of WSNs, especially in such scenarios where power supply availability is limited, is _energy efficiency_. High energy efficiency means long network lifetime and limited network deployment and maintenance costs.  
Energy efficiency can be achieved at different levels starting from the technology level (e.g., by adopting low consumption hardware components), physical layer, MAC, routing protocols up to the application level.  
For example, at physical and MAC layers, nodes could operate with low duty cycle by spending most of their time in sleeping mode to save energy. This poses new problems such as that nodes may not wake up at the same time, due to the drifts of their local clocks, thus making the communication impossible. Suitable network synchronization schemes are mandatory in this case [8; 25].
## 1 Introduction
Several other studies applied latent class clustering for incident pattern recognition, and yielded useful information regarding clusters/subgroups of such incidents and injuries [17; 18; 19; 20; 21; 22; 23]. Although the latent class clustering approach has been popular in crash severity analysis, its application in occupational safety and health analysis has been limited. [PERSON] et al. [24] performed a study on 2,445 employees with diabetes to segment them into separated clusters based on potential risk factors for work disability in future. Another study used patients' data and applied the latent class clustering method to identify factors associated with the risk of low back pain [15]. Despite ongoing improvement in coordinated prevention measures, improved technology, training, and higher education of the workforce [25], agriculturally-related industries are among the most hazardous work environments [26]. Understanding injury patterns and the underlying mechanisms of incidents with respect to a specific industry may produce effective insights for boosting policymaking, training, and incident/injury intervention and prevention efforts [27]. The aim of this study is to identify distinguished and meaningful subclasses of occupational injuries with inflated costs in agribusiness industries incidents based on workers' compensation claims data. The inflated costs are indicative of the severity of the injury; the higher the claim-incurred amount in dollars, the more severe the injury. The novelty of this study is that it introduces a novel application of latent class clustering in the segmentation of high severity occupational incidents in agribusiness industries. The results will contribute to informed decision-making that could help either prevent the occurrence or reduce the frequency of severe injuries with inflated costs in agribusiness industries.
## 4 Experiments
### Parameter Sensitivity and Convergence  
In this section, we conducted parameter sensitivity analysis on the proposed method. We analyze the sensitivity of this method to the regularization parameters \(\alpha\), \(\gamma\) and \(\theta\) in the loss function Equation (10). Figure 6 shows the impact of these regularization parameters on the Example-F1 indicator of our proposed method on the Scene dataset. In Figure 6a, we determine \(\alpha=1\) and change \(\gamma\) and \(\theta\) within a similar range \(\left[10^{-2},10^{4}\right]\). When \(\theta=1\), the value of \(\gamma\) does not affect the experimental effect. In Figure 6b, we determine that \(\gamma=1\). When \(\alpha=10\) and \(\theta=1\), the experimental results are good. In Figure 6c, we determine \(\theta=1\). When \(\alpha=1\) and \(\gamma=10\), the results are better. In Figure 6d-f, When \(\alpha=0.1\), \(\gamma=10\) and \(\theta=1\), The Micro-F1 indicator yields better results. In Figure 6g-i, When \(\alpha=100\), \(\gamma=100\) and \(\theta=100\), The Hamming-Loss indicator yields better results. From the figure, it can be seen that when we keep the values of these three parameters within a specific range, the final performance of the method on Example-F1, Micro-F1 and Hamming-Loss is not sensitive to the selection of parameters. It can be seen from Figure 6 that the proposed method is very sensitive to hyperparameters. In addition, the performance is best at some intermediate values of \(\alpha\), \(\gamma\) and \(\theta\).  
Figure 5: Mean Example-F1, Micro-F1 and Hamming loss of different methods corrupted by varying levels of random “Gaussian” noise on (**a**–**c**) Image, (**d**–**f**) Scene, (**g**–**i**) Yeast, (**j**–**l**) Rcv1s1, (**m**–**o**) Core116k4 datasets.  
From Equations (38)-(40), the KKT conditions can be obtained: \(E-X+VU^{T}=0\), \(Z-U0\), \(H-S=0\). This can be used to determine the following convergence conditions: \(\big{|}\big{|}E-X+VU^{T}\big{|}\big{|}_{\infty}<\varepsilon\), \(\big{|}|Z-U||_{\infty}<\varepsilon\), \(\big{|}|H-S|\big{|}_{\infty}<\varepsilon\). As such, the convergence curves of Figure 7 were drawn on the Scene and Yeast datasets. The observation results in Figure 7 indicate that our method converges quickly within finite iterations.
# An In-Depth Assessment of the New BDS-3 B1C and B2a Signals
## 2 Structure of BDS-3 New Signals  
BDS-1, also known as the COMPASS system, has a constellation composed of three GEO satellites [11]. The frequency used by BDS-1 is the radio determination satellite service (RDSS) frequency band. The uplink signal is in the L band (1610-1626.5 MHz), while the downlink signal is in the S-band (2483.5-2500 MHz). Moreover, RDSS has been extended on the GEO satellites of BDS-2 and BDS-3. The satellites of BDS-2 broadcast B1I (1561.098 MHz), B2I (1207.14 MHz) and B3I (1268.52 MHz) open service signals [12], the signal modulation of which is mainly based on quadrature phase-shift keying (QPSK). The B1I and B3I signals of BDS-2 are inherited to BDS-3, and new public service signals of B1C (1575.42 MHz), B2a (1176.45 MHz) and B2b (1207.14 MHz) are adopted. Furthermore, the Bs (frequency) signal was tested on an experimental satellite of BDS-3e [13]. The BDS signals in L-band are shown in Figure 1.  
The characteristics of the BDS-3 signal are described in detail, and a comparison with other GNSS signals is shown in Table 1. The modulation method of B1I and B3I signals in BDS-3 satellites is strictly consistent with that of BDS-2 satellites, and it is compatible with the open service of BDS-2. The BIC signal is the main open service signal of BDS-3. It provides public services to single/dual/multi-frequency users in the world. The center frequency of  
Figure 1: Beidou System (BDS) signals in the L band.  
the B1C signal is 1575.42 MHz, which coincides with the frequencies of GPS L1C, Galileo E1 OS, QZSS L1, and NAVIC L1. It uses the combined modulation method of the BOC (1,1) and the QMBOC (6,1,4/33) to maintain high compatibility and interoperability with other GNSSs [14; 15]. The B2a and B2b signals are the second and third public service signals of BDS-3, respectively, which can be applied to dual/multi-frequency receivers in the fields of life safety and high-precision position. In addition, specific PPP service information of BDS-3 is broadcast on B2b. The B2a signal is consistent with the center frequency of the GPS L5, Galileo E5a, QZSS L5, and NAVIC L5 signals, and the BPSK (10) modulation method is used for their data component and pilot component. In addition, the receiver can also obtain the B2a+B2b signal with integrated better performance by integrated B2a and B2b in ACE-BOC mode [16].  
The characteristics of the new signals of the BDS-3 satellites are analyzed and evaluated in depth. B1I and B3I of BDS-3 are often used for comparison with BDS-2 satellites co-frequency signals, while B1C, B2a, B2b of BDS-3 are used for comparison with other GNSS co-frequency signals.