esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Mission Description and Simulation Results
These operating levels involve an increase in flight time and a corresponding increase in the propellant mass required to complete the assigned orbital transfer. Figure 9: Optimal CubeSat three-dimensional transfer trajectory (right side) and its ecliptic projection (left side). The scale of the \(z\) axis in the graph on the right is exaggerated to better visualize the three-dimensionality of the mission scenario. Black line \(\rightarrow\) CubeSat trajectory; blue line \(\rightarrow\) Earth’s orbit; red line \(\rightarrow\) asteroid’s orbit; star \(\rightarrow\) perihelion point; blue dot \(\rightarrow\) starting point; red square \(\rightarrow\) arrival point; orange circle \(\rightarrow\) the Sun. (**a**) Case of a single engine unit; (**b**) case of two engine units. For example, when \(P_{\oplus}=100\,\mathrm{W}\) and the CubeSat has a single engine unit, the optimal control laws are reported in Figure 12, which shows also the temporal variation of the Sun-spacecraft distance \(r\) and the corresponding electric power given by the solar arrays. From Figure 12, we observe that, the engine uses three operating levels during the transfer, while the available electric power drops below \(95\,\mathrm{W}\) in the second half of the flight. Recall, indeed, that the payload and the other subsystems require an electric power equal to \(25\,\mathrm{W}\), so that when the available power is \(95\,\mathrm{W}\) the engine unit can use only a value of \(95\,\mathrm{W}-25\,\mathrm{W}=70\,\mathrm{W}\), which is not sufficient to activate the operating level \(\mathrm{Id}=5\); see also the last row of Table 1.  
Figure 10: Optimal control laws as a function of the number of engine units. Blue dot \(\rightarrow\) start; red square \(\rightarrow\) arrival. (**a**) Case of a single engine unit; (**b**) case of two engine units. Figure 11: Optimal flight time and propellant mass consumption as a function of the electric power at a distance of 1 AU from the Sun. (**a**) Case of a single engine unit; (**b**) case of two engine units.
## 8 Conclusions Remarks  
In this study, the applicability of the TIRF based on the linear superposition method for the ultimate shear and bending strengths of an LNG CCS under the sloshing impact loads was investigated. Because the TIRF can only be used in a linear system, the results of applying the TIRF to a simplified model of the LNG CCS box with the linear boundary condition were compared with the results obtained by performing a direct analysis on the nonlinear model. Based on the results of this study, the following conclusions are drawn.  
* In Model 1 with the steel boxes, the dynamic shear and bending responses using the TIRF and DIR, including the linear and nonlinear boundary conditions, were almost identical to each other in terms of peak pressure and impulse values;
* In Model 2 with the LNG cargo containment boxes, the dynamic shear and bending responses using the TIRF and DIR, including the linear and nonlinear boundary conditions, also matched comparatively well in terms of peak pressure and impulse values. However, the differences in the peak pressure and impulse in Model 2, using the TIRF and DIR, were found to be relatively higher than those in Model 1;
* In Model 3, in which the hull structure was additionally included as an onboard condition, the differences in the dynamic shear and bending responses using the TIRF and DIR were found to be relatively large compared to those of Model 1 and Model 2 owing to an additional nonlinear boundary effect originating from hull deflections. However, the overall results showed good agreement with each other. The maximum difference in the peak shear response and bending response was 6.1% and 20.9%, respectively;
* The design of partial material safety factors for calculating the design dynamic shear and bending capacities of an LNG cargo containment system are proposed based on the statistical analysis results from current series FE assessments;
* Finally, the ultimate shear and bending capacities were calculated using the method proposed in this study and the results were compared with those obtained through direct nonlinear FE simulations. It showed conservative results against direct nonlinear FE simulations;
* In this study, a simple design method based on 5h3 TIRF is presented to design the LNG cargo containment system for use in the early design stage. However, the current study was limited to the GTT NO96-type LNG cargo containment system and further study of the GTT MARK III-type system is required.  
Conceptualization, Y.I.P.; methodology, J.-H.K.; validation, Y.I.P.; formal analysis, S.H.L.; investigation, S.H.L.; writing--original draft preparation, J.-H.K.; writing--review and editing, Y.I.P.; visualization, S.H.L.; supervision, Y.I.P.; project administration, Y.I.P.; funding acquisition, J.-H.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Dong-A University research fund.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Surface Defect Detection Methods for Industrial Products: A Review
## 4 The Key Problems
### Small Target Detection Problem  
Small target detection problem is also one of the difficulties in the field of surface defect detection of industrial products. Small target refers to a target with a small size in the image. There are two definitions of \"small\". One is small in absolute size, which is usually considered as a small target with a size lower than 32*32 pixels. The other is small in relative size, that is, the target size is lower than a certain proportion of the original image size, such as 0.1, that is, the target is considered small target. Literature [95] integrated the features of different layers through rainbow concatenation (pooling and deconvolution), increasing the number of feature maps in different layers while increasing the feature map relationship between different layers, which solves the problem of small goals to a certain extent. Literature [96] proposed a Cascade R-CNN multi-stage targetdetection framework composed of multiple detectors with different IOU thresholds, in the whole framework, the proposal adjusted by the previous stage is used as the input for the next stage training. This method is used for small targets, the detection results of small targets were significantly improved by this method. At present, there are some skills to solve the small target detection problems, summarized as the following:  
1. Feature fusion: fusion of deep semantic information into shallow feature maps, using deep features to enrich semantic information while using the characteristic of shallow features that to be suitable for detecting small targets;
2. Data Augmentation: increase the type and number of samples in the small target in the training set;
3. Image Pyramid + Multi-scale Sliding Window: set different input sizes for images, select a scale randomly from them during training, scale the input image to this scale, and send it to the network;
4. Reduce the network downsampling rate: by reducing the downsampling rate to reduce the loss of the object on the feature map, a common method is to directly remove the pooling layer and use the hole convolution at the same time;
5. Reasonable anchor design: the main methods include: border clustering, that is, clustering a set of suitable anchors on the labels of the training set; statistical experiments, that is, putting the anchor and the center point of the label together with using only the width and height information to carry out matching experiment to find a group of anchors with the most consistent aspect to height ratio distribution; set smaller and denser anchors and matching strategies, such as not setting too strict IoU threshold for small objects;
6. Appropriate training method: use high-resolution images for pre-training while magnifying the input image and then fine-tune on the small-resolution image;
7. Use GAN to magnify the small objects and then detect them;
8. Use Context information: establish a connection between the target and its Context.
###### Abstract  
The main aim of the new agricultural scheme, Environmental Land Management, in England is to reward landowners based on their provision of 'public goods' while achieving the goals of the 25 Year Environment Plan and commitment to net zero emission by 2050. Earth Observation (EO) satellites appear to offer an unprecedented opportunity in the process of monitoring, reporting, and verification (MRV) of this scheme. In this study, we worked with ecologists to determine the habitat-species relationships for five wildlife species in the Surrey Hills 'Area of Outstanding Natural Beauty' (AONB), and this information was used to examine the extent to which EO satellite imagery, particularly very high resolution (VHR) imagery, could be used for habitat assessment, via visual interpretation and automated methods. We show that EO satellite products at 10 m resolution and other geospatial datasets enabled the identification and location of broadly suitable habitat for these species and the use of VHR imagery (at 1-4 m spatial resolution) allowed valuable insights for remote assessment of habitat qualities and quantity. Hence, at a fine scale, we obtained additional habitats such as scrub, hedges, field margins, woodland and tree characteristics, and agricultural practices that offer an effective source of information for sustainable land management. The opportunities and limitations of this study are discussed, and we conclude that there is considerable scope for it to offer valuable information for land management decision-making and as support and evidence for MRV for incentive schemes.  
ver high resolution satellite data; earth observation; habitat suitability; habitat assessment; Environmental Land Management  
1 (July) 2021  
14 August 2021  
1 (July) 2021
## 7 Open Research Challenges in UWSNs
### Efficient Multiple Access  
Communication is a vital element in UWSNs and enables collaboration among sensor nodes and interactions between the node and sink to perform data transmission/exchange, location and configuration processes.  
1. Limited Bandwidth  
According to [130], factors such as noise, multiple paths, path loss, and Doppler spread can affect UWSN communications and lead to a limitation of channel bandwidth. Although many studies addressed communication based on simulation analysis and real experiments, there are many more challenges and concerns that need to be resolved in future, particularly MAC protocol in the data link layer. The objective of the medium access control protocol (MAC) is to coordinate all nodes in the UWSN network to access shared channels. It is also to ensure that the data sent is valid and that it reaches the destination efficiently.  
2. Delay Variance  
Delay variance is an element that causes inaccurate estimates of round-trip time (RTT) values and presents difficulty in determining the waiting time in the MAC protocol. However, ref. [131] found that most of the studies on MAC protocols did not consider variance delay in their solutions.  
3. Propagation Delay  
Commonly, the acoustic signal does not propagate well underwater and is five times lower than electromagnetic waves in the air. The propagation delay is the critical challenge, particularly in the MAC protocol solution. The waiting time for MAC or retransmission time-out (RTO) has a direct impact on throughput. In [131], the authors found that the existing fixed RTO is inefficient.  
Figure 9: Open Research Challenges in UWSN.  
Furthermore, due to the long propagation delay in UWSNs, a handshaking process, which enables channel sharing among all nodes, results in high costs compared to the terrestrial sensor network. It will eventually lead to handshaking overheads that cause low bandwidth.
# Agile Stage-Gate Approach for Design, Integration, and Testing of a 1U CubeSat
## 1 Introduction
The present work highlights the idea of combining different approaches oriented toward hardware development. This paper describes a methodology for implementing an Agile Stage-Gate approach in the product development of a nanosatellite. Additionally, given the emphasis on the hybrid development of a physical product, we explain the full process from initiation to completion of the project. Stakeholders, team members, and customers are identified along with the main objectives of the project. The key milestones (stages or phases) are also highlighted. Each gate is described, and we explore how activities within each gate are distributed in an Agile development. This work focuses on the development of a new product, a 1U CubeSat with no earlier starting point. To this end, we conducted a study including conceptualization, design, integration, physical testing, and launching that integrated Agile iterations to achieve successful verification and validation of a product.
## 3 Results and Discussion
### Fiber Composition Analysis  
The fiber composition of kenaf-retted fibers from the non-sterilized retting treatments is shown in Figure 6. It was revealed that there was a significant increase in the cellulosic fiber compositions after retting compared with non-retted raw kenaf fiber (57.84%). The NCGs were eliminated at a differential rate, which affects the overall fiber compositions after treatment. The highest gum removal and elevated cellulose compositions (84.65%) were observed in the APPB-treated fibers combined with seawater treatments. However, the common phenomena of the retted fiber composition analysis showed that these retting treatments did not fully remove any of the major cell wall constituents.  
The major chemical component of kenaf fibers that was significantly reduced due to retting was pectin, which plays a major role in achieving optimum degumming. Among the inoculated treatments, APPB1 with seawater yielded fibers showed maximum cellulose with the least gum's compositions. This improvement in the cellulose composition of the retted kenaf fibers is indeed the result of the removal of non-cellulosic gums, including lignin, hemicellulose, and pectin. The variations in the balanced composition of NCGS and cellulose fibers were attributed to the lack of optimum degumming [62].  
The inconsistency in kenaf fiber quality is the primary concern associated with the variations in its chemical compositions. This unsteadiness was primarily attributed to the lack of optimum degumming [62]. Moreover, under and over retting yielded inconsistent fiber compositions. Extended retting periods decompose the basic cellulosic fibers and under retting remained the gums unblemished. This is due to non-selective naturally occurring microflora, as some produce specific enzymes and the rest act as non-producers. This microbial mixture releases intermediate-level enzymes, which eventually delayed the process. The extended retting produced fiber rotting and degraded the basic cellulose components [14; 15]. In contrast, inadequate degumming affects the overall chemical composition and affects the quality and end-uses [16; 17].  
Figure 6: Compositions of the kenaf bast fibers retrieved from augmented retting in comparison with unretted raw kenaf.
# Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory
## 2 Proposed Method
### Improved Adaptive Region Correction
#### 2.4.2 High-Light Area Enhancement  
In cases where the brightness of the block area falls within the medium-to-high light range, it indicates that the pixel gray levels are more widely spread throughout the histogram. As illustrated in Figure 4, the highlight area exhibit a relatively uniform distribution of the gray histogram, and the enhancement method in Section 2.4.1 cannot achieve a satisfactory enhancement effect. To address this situation, this section proposes a combination of the Weber-Fechner law, which leverages the logarithmic linear relationship between subjective luminance and target image luminance and introduces a coefficient K into the calculation of \(V_{out}\). By fully considering the pixel distribution in the enhanced area, a suitable correction function is proposed, which can both enhance the highlighted areas and retains detail. The formula is expressed as follows:  
\[V_{out}=\mathrm{clg}(V_{in})+c_{0} \tag{16}\]  
where \(c\) and \(c_{0}\) are both constants. To reduce the amount of calculation in Formula (16) and improve the adaptability of the enhancement algorithm, Formula (17) is used to fit the curve characteristics of Formula (16):  
\[V_{out}=\frac{V_{in}(255+K)}{V_{in}+K} \tag{17}\]\[K=\frac{1}{W}\sum_{i=1}^{W}I_{s} \tag{18}\]  
The value 255 is the gray level of the image, and \(K\) is the adaptive adjustment parameter. Substituting \(K\) into the final transformation function obtains:  
\[V_{out}=\frac{V_{in}(255+\frac{1}{W}\sum_{i=1}^{W}I_{s})}{V_{in}+\frac{1}{W} \sum_{i=1}^{W}I_{s}} \tag{19}\]  
where \(I_{s}\) is the pixel value in the area and \(W\) is the total number of pixels in the area.
# An Insight to the Outage Performance of Multi-Hop Mixed RF/FSO/IWOC System
###### Abstract
In [22], the author put forth a dual-hop mixed RF/UWOC system based on UAVs and generated innovative expressions for OP and ABER of the system. The cooperative AF relay-based system is proposed and the outage analysis of the same is presented by the author in [23]. In [24], the authors have derived the closed-form expressions for the OP and ABER for both AF and DF relay-based mixed RF/UWOC systems. In [25], the asymptotic outage analysis and the secrecy outage performance analysis is carried out for a dual-hop RF/UWOC system. The authors in [26] have considered a DF relayed three-hop RF/FSO/UWOC system and evaluated the outage and error performance of the system. The RF, FSO, and UWOC links are modeled by Nakagami-m distribution, Double Generalized Gamma (DGG) distributed fading, and Rayleigh distributed pointing errors and Exponential Generalized Gamma (EGG) distribution, respectively. However, the mathematical modeling of the ABER of the proposed system is not tractable, and thus not easy to understand. Motivated by the above advantages of the mixed communication systems (RF systems and optical systems), we propose in this paper a DF relay-based three-hop RF/FSO/UWOC system.
# Advances on Water Quality Detection by UV-Vis Spectroscopy
## 2 Basic Principles
### Basic Principle of UV-Vis Spectrometry  
UV-Vis spectroscopy is based on the fact that pollutant molecules in the water can absorb UV-Vis light of a specific wavelength. Light of specific wavelength is absorbed by electron movement from the ground state to an excited state, which reduces the amount of transmitted light. According to the Lambert-Beer law, the absorption spectrum and the concentration of the substance have a strong correlation, which is the principle of UV-Vis spectroscopy being used to detect water pollutants [13].  
Pollutants differ by absorption characteristics and yielded spectral curves but most of them absorb light in the UV-Vis region. Therefore, by using the Lambert-Beer law as the theoretical basis, we can effectively detect the concentration of pollutants in water. Various parameters commonly used for water quality detection, such as the spectral absorption range of NO\({}_{3}\)-N and NO\({}_{2}\)-N, are 200-250 nm. Organic matter and turbidity are effectively absorbed within the range of 380-750 nm (Table 1).
# A Local Discrete Feature Histogram for Point Cloud Feature Representation
## 4 Experimental Results
### Datasets  
We selected two datasets for the experiments: the Bologna 3D retrieval (B3R) dataset [25] and the Kinect dataset [13]. Figure 3 displays two example models and scenes from each dataset.  
The B3R dataset is a synthetic dataset primarily used for 3D shape retrieval scenarios. It contains high-quality data, consisting of 6 models and 18 scenes. The Stanford 3D Scanning Repository is the source of the models [26]. The scenes are constructed by performing random rigid transformations of the models and incorporating Gaussian noise at three different scales. To assess the impact of mesh decimation reduction on local descriptors, we generated four additional sets of scenes. These consist of two levels with different mesh decimations (1/4 and 1/8 of the initial mesh decimation) and two levels that combine mesh decimation and Gaussian noise (0.3 mr Gaussian noise with 1/4 mesh decimation and 0.5 mr Gaussian noise with 1/8 mesh decimation).  
The Kinect dataset consists of 6 models and 16 scenes, obtained using the Microsoft Kinect sensor (manufactured by Microsoft Corporation, headquartered in Redmond, WA, USA). It features 2.5D models and scenes with relatively low grid quality, affected by real noise, occlusions, and clutter. This makes the Kinect dataset highly challenging for use in object recognition.
## Cooperation among Wirelessly Connected Static and Mobile Sensor Nodes for Surveillance Applications  
**[PERSON] 1,2,*, [PERSON] 3, [PERSON] 4, [PERSON] 5, [PERSON] 5 and [PERSON] 4**  
1 Department of Information Technology, Federal University of Santa Maria in Frederico Westphalen, CESNORS, Frederico Westphalen 98400-000, Brazil  
2 Electrical Engineering Department, University of Bras Iia, CP 4386, Bras Iia 70910-900, Brazil  
3 Department of Computer Science, Federal University of Lavras, CP 3037, Lavras 37200-000, Brazil; E-Mail: [EMAIL_ADDRESS]  
4 School of Information Science, Computer and Electrical Engineering, Halmstad University, PO Box 823, Halmstad 301 18, Sweden; E-Mails: [EMAIL_ADDRESS] (A.V.);  
[EMAIL_ADDRESS] (T.L.)  
5 Electrical Engineering Department and Institute of Informatics, Federal University of Rio Grande do Sul, CP 15064, Porto Alegre 91501-970, Brazil; E-Mails: [EMAIL_ADDRESS] (F.R.W.); [EMAIL_ADDRESS] (C.E.P.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +55-55-3744-8964 (ext. 8795); Fax: +55-55-3744-8964.  
_Received: 9 August 2013; in revised form: 17 September 2013 / Accepted: 18 September 2013 / Published: 25 September 2013_  
This paper presents a bio-inspired networking strategy to support the cooperation between static sensors on the ground and mobile sensors in the air to perform surveillance missions in large areas. The goal of the proposal is to provide low overhead in the communication among sensor nodes, while allocating the mobile sensors to perform sensing activities requested by the static ones. Simulations have shown that the strategy is efficient in maintaining low overhead and achieving the desired coordination.  
**Keywords:** wireless sensor network coordination; bio-inspired networking; mobile sensor nodes; surveillance systems