esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Blasting Efficiency Using Crack-Inducing Holes and Pre-Splitting Method in Blasting
## 2 Full-Scale Experimental Blasting of the Combined Blasting Method  
### Experimental Site and Rock Mass Conditions  
The experimental area is located at the Samsung-Dongtan railway tunnel site in Suseodong, Seoul, South Korea. The Samsung-Dongtan railway tunnel is a metropolitan express railway connecting Gangnam-gu, Seoul, and Hwaseong-si. Figure 1 shows the satellite image and location map of the blasting experiment area.  
Figure 1: Satellite image and route map of the experimental blasting area.  
The geological condition of the experimental blasting face can be divided into Pre-cambrian gneiss intruded by Mesozoic igneous rocks, both unconformably overlain by Quaternary alluvial deposits. The stratigraphy, based on the ground surface, consists of soil (weathered soil), weathered rock, soft rock, moderately hard rock, and hard rock in sequence.  
The RMR classification value of 41, obtained from face mapping at the experimental blasting site, indicates that the rock mass is in a fair condition. Figure 2 shows the geological map and the face condition at the experimental blasting location.
# FIQ: A Fastener Inspection and Quantization Method Based on Mask FRCN
## 3 Semantic Segmentation Algorithm Based on Track Fasteners--Mask FRCN  
In this section, we present Mask FRCN [21], an image segmentation algorithm specifically tailored for track fastening scenes. The architecture of this algorithm can be divided into three stages. The first stage involves feature extraction, primarily governed by the backbone. The second stage is the feature fusion stage, dominated by Feature Pyramid Networks (FPNs). The third stage comprises three prediction header networks, including two detection networks for object detection and a semantic segmentation header network for object segmentation.  
Initially, ResNet-101 [22; 23]was chosen as the feature extraction network, and a Residual Attention Module was constructed. To achieve improved segmentation results and ensure that more profound image features are extracted at the initial stage of network training, a CBAM attention mechanism [24] was added to the tail of the residual module at each stage. Next, to address the issue of semantic segmentation algorithms having numerous computational parameters and slow inference speeds, Channel-Wise Knowledge Distillation (CWD) [25] was employed in KD to perform knowledge distillation on the FPN backbone. CWD distilled the knowledge from ResNet-101 into ResNet-18, reducing the model size by eliminating redundant parameters while enhancing a portion of the accuracy.  
Lastly, to refine the network segmentation algorithm's head prediction network, this paper introduces an edge-refinement module based on the image-segmentation algorithm, FRCN, which aims to improve the accuracy of normal fastener segmentation.
# Hybrid Wavelet-CNN Fault Diagnosis Method for Ships' Power Systems
## 5 Results
### Hybrid Discrete Wavelet-CNN Model  
The hybrid discrete wavelet-CNN model recognizes if the operation of IM is healthy or faulty on the basis of the analyzed DWT current of Phase A. It was also trained to recognize two types of fault (A-B and A-G short circuits); thus, it is considered a 3-class classifier. It is crucial that our trained hybrid model does not have problems with overfitting. Overfitting means that the model is excellent at recognizing faults in a training set, but is not capable of recognizing them in cases outside of the distribution of the training set. Therefore, the time series were processed from various simulations. The hybrid discrete wavelet-CNN method was implemented in Python.
# Brake Light Detection Algorithm for Predictive Braking
## 5 Discussion  
### Algorithm Performance  
As seen in the results, the developed algorithm performs well in detecting lit brake lights when tested with the images from the dataset as well as with test video material.  
Figure 8: Range test with preprocessing at 25 m and 150 m, warning triangle indicating detected brake lights.  
Figure 9: Example of the algorithm output. Upper row shows successful detections, and lower row unsuccessful detections. Respective confidence values in the collage from left to right: (**a**) 0.98 and 0.9, (**b**) 0.9, (**c**) 0.95, (**d**) 0.97, (**e**) 0.53, (**f**) 0.7, (**g**) 0.59, and (**h**) 0.86.  
The overall performance of the algorithm on the utilized challenging and extensive dataset is close to those of previous daytime brake detection studies [18; 19]. However, a direct comparison to previous algorithms is not productive due to usage of different datasets. The dataset utilized here features vehicles at considerable distances of up to 150 m, thus differentiating the contributions of this paper from previous studies which focused on vehicles at shorter distances. The lack of an open dataset for benchmarking brake light detection algorithms has been acknowledged in the literature [19], and the dataset used here will be published to allow for accurate future benchmarking.  
Due to the aim of the use case to improve ride comfort, the algorithm was designed to include a bias towards false negatives. In practice, the algorithm did not detect all the braking events. Nevertheless, the algorithm hardly produced any false positives, indicating that it would be extremely rare for predictive braking to be unnecessarily engaged. This behavior was here considered beneficial, because the brake light detection algorithm was aimed at complementing the radar readings in the decision-making process. False negatives from the camera are eventually corrected by the radar, only leading to an occasional slightly more steep braking operation. Still, the reliable true positives complement the braking decision making, resulting in more frequent comfortable decelerations.
## 3 Results
### Actuation System
#### 3.2.2 Designing Rack and Pinion  
In the process of gear design, the maximum traveling length for each handle (the rack) and available space for the rack and pinion are important parameters. These parameters serve as input information for calculating the number of teeth and gear ratio. To ensure a precise fit between the pinion and rack teeth, the pinion was designed first, and then the rack was developed accordingly. The calculations and design of the rack and pinion for the gripper were performed based on parameters like module, pitch, tooth profile, backlash, clearance, etc. [21]. NYLON Carbon 12 was chosen as the material for the prototype due to its favorable characteristics, such as lightweight, high strength, and rapid reproducibility of parts.  
Prior to manufacturing a prototype of the rack and pinion system, a CAD model was created to visualize and analyze the components. A simple support beam analysis was conducted to assess the performance of this gear system. In the design, a spur gear was chosen with a diametral pitch of 25.4 cm (10 in) and a pressure angle of 20 degrees. Similarly, the rack was designed with the same diametral pitch and pressure angle. The motion analysis was performed within the CAD software, and the results were validated with printed parts with a 3D printer. The final design of the rack and pinion system can be observed in Figure 6.
## 2 Materials and Methods
### Quality Assessment  
As shown in Table 1, data quality assessment was rated using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Cohort Studies [30]. The tool included an evaluation of various domains that may impact the level of certainty, such as limitations in study design and execution, inconsistencies or heterogeneity, reliability of methods employed for the measurements of outcomes, imprecision, reverse causality, and publication bias. Additionally, it also considers domains that can enhance the level of certainty, such as a large magnitude effect, opposing plausible residual bias or confounding, and the appropriateness of statistical analysis. The discrepancies were addressed through a productive discussion involving a third author.
## 3 Results
The use of tested EXP adjuvants resulted in CA reduction to 58.7\({}^{\circ}\) (EXP 2), and 65.8-69.1\({}^{\circ}\) with EXP 1, and EXP 2, compared to water with EXP, 67.5, 66.4, and 69.1\({}^{\circ}\), respectively. Standard adjuvants AtB, and S contributed to a further reduction in the CA of water and spray liquid, 51.9-51.1\({}^{\circ}\) and 62.4-52.4\({}^{\circ}\), accordingly. The laboratory results indicated that ST and CA during the first 15 s after treatment were slightly reduced for ST, and had a greater decrease for CA. Both the ST and CA of spray liquid containing FEM 1 (full rate) and FEM 2 (reduced rate) were similar but higher than treatments with adjuvants (Figures 1 and 2). The adjuvants added to FEM 2 spray liquid reduced ST, the most with S, next EXP 1, AtB, EXP 3, and EXP 2, and CA with S, AtB, EXP 3, EXP 1, and EXP 2. The final ST of the spray liquid containing the adjuvants was in the range of 25-38 mN m\({}^{-1}\) and CA between 30 and 50 degrees, compared to 40-45 mN m\({}^{-1}\) and about 72-74 degrees, respectively, for FEM 1 and FEM 2. The highest weed mass in sugar beet was observed in 2017 and 2018, and the lowest in 2019. The weed mass on the untreated check at that time was 4.3, 4.1, and 3.0 kg m\({}^{-2}\), respectively. The weed community consisted of _Chenopodium album_ L., _Echinocla crus-galli_ (L.) Pal. Beauv., _Geranium pusillum_ L., _Polygonum aviculare_ L., _Viola arrensis_ Murr., _Anchusa arrensis_ L., _Laminum purpureum_ L., _Thlaspi arrense_ L., _Veronica heterifolia_ L., _Galinsoga pariflora_ Cav., _Brassica napus_ L., _Sonchus arrensis_ L., _Elymus repens_ (L.) [PERSON], _Galium aparine_ L., _Papaver rhoas_ L., _Stellaria media_ Vill., and _Avena fatua_ L. The results showed that reducing the dose of FEM always led to a statistically significant decrease in its efficacy and that the inclusion of any experimental adjuvant in the spray liquid restored efficacy to a level higher than at the reduced or even full dose. The FEM mixture with the addition of test adjuvants performed similarly to that with the addition of standard adjuvant AtB, and was slightly better than with S (Figure 3). Correlation coefficients between the physical and chemical properties of spray liquid, and the efficacy indicate negative and statistically significant relationships. The results in Table 5 show a negative, moderate correlation in 2017 and 2019, and weak, not significant relationships between ST and herbicide efficacy in 2018. In the case of CA, the relationship between this parameter and weed control efficacy was proven in each year of the study, with a moderate correlation. Statistical analysis showed no correlation between liquid pH and weed control efficiency. Figure 2: Impact of adjuvants (EXP 1, EXP 2, EXP 3, AtB, and S) on the contact angle (CA) of droplets of spray liquid including phenmedipham, ethofumesate, and metamitron (FEM). FEM 1—200 + 200 + 700 g ha\({}^{-1}\); FEM 2—140 + 140 + 700 g ha\({}^{-1}\); EXP 1, EXP 2, EXP 3, AtB at 1.5 L ha\({}^{-1}\), S—0.1% \(v\)\(v^{-1}\).
## 2 Materials and Methods
### Experimental Setup  
Two PAP systems were assembled parallel with the dimensions of 0.3 m \(\times\) 0.3 m \(\times\) 0.7 m (length \(\times\) width \(\times\) height) in perspex containers (Figure 2). Each perspex container was packed with 10 cm porous asphalt concrete (PAC) with a void ratio of 24.2%, and 30 cm crushed gravel with a void ratio of 37.6%. Below the gravel, in one PAP system a normal size limestone bedding layer of 1-2 mm was set with a height of 20 mm and in the second PAP system, a zeolite bedding layer was placed with the same dimensions. Both PAP systems have the same volume in each layer to make them identical in dimensions. To separate the surface layer and the choker course a permeable non-woven geotextile membrane was set at the upper level [20].  
The outlet of the zeolite-based PAP system was joined to a horizontal filter column (50 mm \(\times\) 600 mm \(\times\) 50 mm) packed with modified zeolite powder porous microspheres (MZP-PM) to enhance nitrogen and phosphorus removal. The horizontal filter column was packed with 1.2 kg material into a 600-mm long polyvinyl chloride (PVC) column. To drain water from the column's outlet, a peristaltic pump was connected.  
Figure 1: Preparation of modified zeolite powder porous microsphere.  
Throughout the experiments to ensure a constant quality of water, synthetic runoff stormwater was prepared by mixing de-chlorinated tap water with sediments collected from a local stormwater wetland with different concentrations of nitrogen and phosphorus (NH4\({}^{*}\)-N and TP). The concentrations of NH4\({}^{*}\)-N were 5.2 \(\pm\) 0.2 mg/L and TP were 1.2 \(\pm\) 0.1 mg/L, the same as reported by the urban stormwater runoff [21,22]. A simulated event of rainfall with total precipitation (82.31 mm), an average intensity (0.63 mm/min), rainfall duration (120 min), and 5 years return period were designed through the Rainstorm Intensity Formula of Nanjing. While the renowned Chicago method [23] was adopted to design the temporal distribution of the event, the rainfall event with 120 min of duration, step time of 5 min, 5 years of return period and \(\gamma\) = 0.40 were set as in the experiment. Samples were collected from the initial flushing effluent to evaluate the preliminary effects of the PAP approach on the removal of NH4\({}^{*}\)-N and TP, and the sample was collected every 15 min from the time of water discharge in the study.
# Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
## 2 Introduction
Literature [12] describes the different types of defects and compares mainstream and deep learning methods for defect detection. Various defect detection techniques are discussed in literature [20], including ultrasonic inspection, machine vision, and deep learning. Literature [21] focuses on the use of AI-enhanced metrology, computer vision, and quality assessment in the Zero Defect Manufacturing (ZDM) process. The study also highlights the use of IoT/IIoT technology as a means of supporting these tools and implementing AI algorithms for data processing and sharing. Literature [22] discusses deep learning methods for detecting surface defects, then discusses three critical issues related to small samples and real-time defects detection. In [23; 24], the authors analyze and compare the benefits and drawbacks of the above methods. There are also defect detection surveys in several application domains, including fabric defects [25], corrosion detection [26], pavement defects [27], metal defect detection [28], and industrial applications [29]. The investigation shows that, in the field of surface defect detection of industrial products, there is currently a limited literature review on machine learning methods, and while some papers summarize the challenges and problems, the mentioned solutions are not systematic. The first section of this paper addresses the above issues by summarizing the research status on the detection of surface defects on industrial products using deep learning algorithms and then discusses the issues in the process of industrial surface defect detection, such as unbalanced sample identification problems, small sample problems, and real-time problems. This paper is organized as follows. Section 3 provides an overview of deep learning methods for surface defect detection in industrial products from three perspectives, along with a common dataset for surface defect detection. In Section 4, we summarize the recent research status of deep learning methods for X-ray image defect detection. A discussion of the main problems and their solutions is provided in Section 5. In Section 6, a brief description of future research directions is provided and Section 7 concludes the paper with a conclusion.
## 3 Proposed MPPT Control Method
### Proposed MPPT Control Method  
Figure 6 shows the circuit diagram using the proposed MPPT control method. The peak value \(i_{LP}\) yields by using the peak hold circuit of \(i_{L}\). \(V_{T}\) and \(I_{T}\) can be estimated by Equations (36) and (37). The impedance matching condition is to match \(v_{T}\) to the voltage of \(R_{M}\). The reference value of the boost chopper is given by  
\[V_{ref}^{*}=I_{T}R_{M} \tag{41}\]The PI controller uses to track the reference and reduce the error, which is obtained by subtracting \(V_{T}\) from \(V^{*}_{ref}\) close to zero. The duty ratio \(D_{1}\) of the MODE 1 is calculated by the output value \(v_{C}\) of the PI controller. The duty ratio \(D_{2}\) of the MODE 2 is calculated by the discontinuous period of \(i_{L}\) and \(D_{1}\).  
Thus the MPPT control method in this paper can be estimated the output of the TM with only current sensor by analyzing the boost chopper using state space averaging method. The internal resistance of the TM is constant at the maximum power point whether the temperature difference or not. Hence, the voltage of the maximum power point can be decided without calculation of the power.
## 2 Solar-Hydrogen Storage System  
### Solar-Hydrogen Technologies and Storage System  
The solar-hydrogen storage system is an innovative technique for capturing and storing renewable energy [16]. This section presents a comprehensive review of the technology, focusing on the synergy between solar power and hydrogen as an energy carrier. This technology solves the intermittent nature of solar power and promotes efficient energy storage by turning solar energy into electricity and then using electrolysis to make hydrogen [17].  
This section examines the unique advantages afforded by solar-hydrogen technology, highlighting the limits of typical energy storage technologies. These benefits include increased storage capacity, scalability, and the ability to store energy for long periods of time. Furthermore, the system's ability to generate a clean energy carrier (hydrogen) makes it an appealing approach to lowering carbon emissions.  
Solar-hydrogen technologies cover a wide range of approaches for harnessing solar energy and converting it to hydrogen for storage. Typically, numerous critical components are involved in the integration of various technologies (Table 2) [18; 19; 20].
## 4 Application of UWSN  
UWSN is slowly being integrated into the mainframe of applications mainly due to the introduction of sophisticated hardware and software. They are mainly used in monitoring and surveillance systems of the deep seas, monitoring different aquatic environments, resource exploration, and exploitation using sensors over an area that can communicate with each other to forward the information to the base station over water. The applications of UWSN can be categorized into the following categories.
# Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search
## 3 Problem Formulation
### Measurement Model  
Each sensor node in UWSNs is equipped with a wireless acoustic sensor. The measurement model for the acoustic sensor contains a base frequency measurement model (narrow-band processing), an acoustic spectrum pattern model (narrow-band processing), and an acoustic sound pressure measurement model (wide-band processing) [28; 29]. In this paper, sensors measure distance to the target via transmitting acoustic pulses and calculate time of arrival (TOA) from the pulse to echoes. The measurement model of the sensor \(s_{th}\) at time \(k\) is given by  
\[Z_{k+1}^{s}=h_{k+1}^{s}(\mathbf{x}_{k+1},x^{s},y^{s},z^{s})+v_{k+1}^{s}, \tag{4}\]  
\[h_{k+1}^{s}(\cdot)=\sqrt{(\mathbf{x}_{k+1}^{1}-x^{s})^{2}+(\mathbf{x}_{k+1}^{ 3}-y^{s})^{2}+(h-z^{s})^{2}}, \tag{5}\]  
where \(\mathbf{x}_{k+1}^{1}\) and \(\mathbf{x}_{k+1}^{3}\) are the first and third elements of target state vector \(\mathbf{x}_{k+1}\), which represents the position of target at time \(k+1\); \((x^{s},y^{s},z^{s})\) is the location of sensor \(s\); \(h_{k+1}^{s}(\mathbf{x}_{k+1},x_{k+1}^{s},y_{k+1}^{s},z_{k+1}^{s})\) is the measurement function; \(v_{k+1}^{s}\) is the measurement noise, which is assumed to be independent across time steps and across sensors, and follows a Gaussian distribution with parameters \(\mathcal{N}(0,R)\).
# Feasibility of a 3D Surgical Guide Technique for
## 3 Results  
The average time for the participants to complete ST extraction was \(213.65\pm 167.45\) sec with the conventional technique and \(114.76\pm 42.87\) sec with the surgical guide technique (Table 1). Significantly  
Figure 3: Digital measurements of the area of the window opening and 3D volume of the material removed. (**A**) Digital scans of models obtained after supernumerary tooth extraction and of the window opening were reproduced. The area of the window opening was calculated using 3D analysis software. Note that the brown circle indicates the extent of the window opening. (**B**) 3D volume of the material removed was measured by a polyvinylsiloxane (PVS) impression of the inner surface of the model.  
less time was required for impacted ST extraction with the surgical guide technique (\(p\) = 0.028). Table 2 shows the results of the area of the window opening and the volume of the material removed for ST extraction. The required area of the window opening was 48.10 \(\pm\) 9.44 mm\({}^{2}\) with the conventional technique and 45.90 \(\pm\) 8.17 mm\({}^{2}\) with the surgical guide technique with no significant differences (\(p>0.05\)). The required volume of the material removed was 121.65 \(\pm\) 43.69 mm\({}^{3}\) using the conventional technique and 99.12 \(\pm\) 17.88 mm\({}^{3}\) using the surgical guide technique. Significantly less material removal was required for impacted ST extraction with the surgical guide technique (\(p\) = 0.031).