esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 3 The Composition of the INS
### INS Components
#### 3.1.10 Global Positioning System (GPS)  
The GPS offers satellite-based positioning, speed, and time information. It consists of three segments, namely space segment, control segment, and user segment [60]. At least 24 GPS satellites are in service, but additional satellites are operational, to enhance GPS performance [61]; since 9 January 2021, 31 GPS satellites have been operational [61]. A GPS receiver is installed on vessels for navigational purposes; this must have the capability to process the data transmitted by the DGFS [60]. The DGPS and DGLONASS are terrestrial systems to enhance position accuracy [62]. Combined GPS and GLONASS receivers may be fitted onboard ships; these should be able to process the data of DGPS and differential GLONASS (DGLONASS) as per the IMO requirements [63]. Other GNSS combinations (e.g., GPS/Baideu) are available in the market as well [64]; such combinations are called Multi-GNSS. The essential advantage of Multi-GNSS is that it improves position accuracy.  
GPS performance can be also improved by a Satellite-based Augmentation System (SBAS). Several countries have their own SBAS. For instance, the European Union (EU) provides the European Geostationary Navigation Overlay Service (EGNOS); the USA operates the Wide Area Augmentation System (WAAS); Japan has the Michibiki Satellite Augmentation System (MSAS), etc. [65].  
Performance standards for a GPS receiver are divided into two types. If the GPS receiver is installed on or after July 1 st 2003, it must comply with Resolution MSC.112(73) [60]. If the GPS receiver was installed before 1 July 2003, it must comply with Resolution A.819(19) [66]. One of the most essential differences between these two resolutions is that Resolution MSC.112(73) specifies that the GPS receiver must generate and output to the digital interface information on the Course Over Ground (COG), Speed Over Ground (SOG), and Universal Time Co-Ordinated (UTC). The IMO also issued performance standards for combined GPS/GLONASS receivers [63,67].
# Classification of Shoulder X-ray Images with Deep Learning Ensemble Models
###### Abstract  
Fractures occur in the shoulder area, which has a wider range of motion than other joints in the body, for various reasons. To diagnose these fractures, data gathered from X-radiation (X-ray), magnetic resonance imaging (MRI), or computed tomography (CT) are used. This study aims to help physicians by classifying shoulder images taken from X-ray devices as fracture/non-fracture with artificial intelligence. For this purpose, the performances of 26 deep learning-based pre-trained models in the detection of shoulder fractures were evaluated on the musculoskeletal radiographs (MURA) dataset, and two ensemble learning models (EL1 and EL2) were developed. The pre-trained models used are ResNet, ResNeXt, DenseNet, VGG, Inception, MobileNet, and their spinal fully connected (Spinal FC) versions. In the EL1 and EL2 models developed using pre-trained models with the best performance, test accuracy was 0.8455, 0.8472, Cohen's kappa was 0.6907, 0.6942 and the area that was related with fracture class under the receiver operating characteristic (ROC) curve (AUC) was 0.8862, 0.8695. As a result of 28 different classifications in total, the highest test accuracy and Cohen's kappa values were obtained in the EL2 model, and the highest AUC value was obtained in the EL1 model.  
biomedical image classification; bone fractures; deep learning; ensemble learning; shoulder; transfer learning X-ray +
Footnote †: journal: Applied Sciences
## 4 Materials and Methods
### Bioinformatics Analysis  
For HMGR phylogenetic tree construction, the amino acid sequences of the identified _HMGR_ family members were aligned via MAFFT v7.310 [63]. Subsequently, the preliminary alignment was trimmed using trimAL v1.2.rev59 (key parameter: \(-\)gt 0.50) [64]. The trimmed alignment was used to construct the phylogenetic tree using IQ-TREE multicore version 2.0.3 and 1000 bootstrap replications [65]. The conserved motif analysis was performed in the MEME program ([[https://meme-suite.org/meme/tools/meme](https://meme-suite.org/meme/tools/meme)]([https://meme-suite.org/meme/tools/meme](https://meme-suite.org/meme/tools/meme)), accessed on 25 April 2023) using full-length amino acid sequences, as the default setting was 10 for the motif number. The number of introns and exons was analyzed using the Gene Structure Display Server 2.0 ([[http://gsds.gao-lab.org/](http://gsds.gao-lab.org/)]([http://gsds.gao-lab.org/](http://gsds.gao-lab.org/)), accessed on 25 April 2023). The cis-acting elements of the promoter region (2000 bp sequence upstream of the gene) of the _LerHMGR_ genes were analyzed using the PlantCARE program ([[http://bioinformatics.psb.ugent.be/webtools/plantcare/html/](http://bioinformatics.psb.ugent.be/webtools/plantcare/html/)]([http://bioinformatics.psb.ugent.be/webtools/plantcare/html/](http://bioinformatics.psb.ugent.be/webtools/plantcare/html/)), accessed on 25 April 2023). The physicochemical properties, including theoretical Mw, theoretical pI, aliphatic index, and grand average of hydropathicity of HMGRs, were analyzed by Ex-PaSy ([[http://web.expasy.org/protparam/](http://web.expasy.org/protparam/)]([http://web.expasy.org/protparam/](http://web.expasy.org/protparam/)), accessed on 25 April 2023). The prediction of transmembrane helices in HMGR was performed using the TMHMM Server v.2.0 ([[https://services.healthtech.dtu.dk/services/TMHMM-2.0/](https://services.healthtech.dtu.dk/services/TMHMM-2.0/)]([https://services.healthtech.dtu.dk/services/TMHMM-2.0/](https://services.healthtech.dtu.dk/services/TMHMM-2.0/)), accessed on 25 April 2023). Gene synteny and duplication types of _HMGRs_ were analyzed using the 'MCScanX' and 'duplicate_gene_classifier' programs implemented in the MC-ScanX package [66]. The Ks of gene pairs were calculated with IBtools software (V1.098696) using the Simple Ka/Ks calculator [67]. For the Ks frequency distribution, MCScanX [66] was used to identify synthetic blocks within the genome of _L. erythrorhizon_ and extract information on synteny gene pairs. Then, the wgd package ([[https://github.com/arzwa/wgd](https://github.com/arzwa/wgd)]([https://github.com/arzwa/wgd](https://github.com/arzwa/wgd)), accessed on 25 April 2023) was used to calculate the Ks values between synteny gene pairs, and ggplot2 ([[https://github.com/tidyverse/ggplot2](https://github.com/tidyverse/ggplot2)]([https://github.com/tidyverse/ggplot2](https://github.com/tidyverse/ggplot2)), accessed on 25 April 2023) was used to plot histograms and fit curves (Ks values less than 0.05 and greater than 5 were filtered out).
# Research on Lane Detection Based on Global Search of Dynamic Region of Interest (DROI)
## 4 Lane Detection and Tracking
### Special Point Analysis of Lane Line Detection
#### 4.2.2 Lane line estimation based on another lane line  
Some information from the raw image will be lost due to gray processing and the aerial view transformation. During image preprocessing, incomplete and worn lane lines can also be eliminated when some noise is removed. Thus, the lane lines will not be detected. In order to avoid this, the completed lane line can be utilized to estimate another lane line position. Concrete images are shown in Figure 10.
# Dynamic Analysis for a Reciprocating Compressor System with Clearance Fault
## 2 Model of the reciprocating Compressor
### Kinematic Mechanism Model  
The kinematic mechanism model consists of the crankshaft, linkage, crosshead, piston assembly, and cylinder. The reciprocating compressor, a typical piece of volumetric machinery, utilizes the piston moving in a reciprocating linear motion driven by the crankshaft to compress and deliver the medium gas. Figure 2 shows the schematic diagram of the compressor. As shown in Figure 2, the crankshaft rotates with a constant speed, driven by a motor. Although the crankshaft rotates the entire circumference and is driven by the connecting rod, the crosshead slider always reciprocates linearly. The crosshead and pistons are connected by a piston rod and fastened by a thread, which is considered part of the same rigid body, and therefore the piston has the same motion with the crosshead.  
According to the above analysis, the motion of reciprocating compressor can be described by the displacement of the piston as shown in Equation (1):  
\[x=S_{0}+R\cdot(1-\cos\theta)+L\cdot(1-\sqrt{1-\lambda^{2}\sin^{2}\theta}) \tag{1}\]  
where \(S_{0}\) is the clearance volume, \(x\) denotes the displacement of the mechanism, \(R\) represents the crank length, \(\theta\) denotes the angle of rotation for the crankshaft, \(L\) represents the linkage length, and \(\lambda\) denotes the ratio of the crankshaft length to the linkage length.  
Unfolded by [PERSON], we can obtain the following:  
\[\sqrt{1-\lambda^{2}\sin^{2}\theta}=1-\frac{1}{2}\lambda^{2}\sin^{2}\theta- \frac{1}{8}\lambda^{4}\sin^{4}\theta+([-\lambda^{2}\sin^{2}\theta]^{3})\approx 1 -\frac{1}{2}\lambda^{2}\sin^{2}\theta=1-\frac{1}{4}\lambda^{2}(1-\cos 2\theta) \tag{2}\]  
Therefore, Equation (1) can be substituted with Equation (3):  
\[x\approx S_{0}+R\cdot(1-\cos\theta)+\frac{\lambda}{4}R\cdot(1-\cos 2\theta) \tag{3}\]  
The velocity and acceleration of the whole motion mechanism can be expressed by using the derivation of the displacement with respect to time as shown in Equation (4):  
\[\left\{\begin{array}{l}\dot{x}=R\cdot\omega\cdot(sin\theta+\frac{\lambda}{2} \cdot\sin 2\theta)\\ \ddot{x}=R\cdot\omega^{2}\cdot(cos\theta+\lambda\cdot\cos 2\theta)\end{array}\right. \tag{4}\]  
Figure 1: Model structure of the reciprocating compressor.  
Figure 2: Diagrammatic drawing of the reciprocating compressor.
# A Cross-Layer Optimization QoS Scheme in Wireless Multimedia Sensor Networks
## 2 Network Model and Design of Queues
### Data Queue at The Network Layer  
The sessions in the wireless multimedia sensor networks have different end-to-end QoS requirements. As traditional Diffserv provides a lack of granularity for QoS guaranteed services and cannot adaptively distribute transmission priorities according to the end-to-end QoS requirements and the real-time end-to-end situation of sessions, this paper utilized a Differentiated Queueing Services (DQS) [18] scheme to maintain data queues at the network layers of nodes.  
According to the DQS scheme, each node maintains one data queue, which contains all data packets at the network layer. For a packet transmitted in the wireless sensor network, \(D\) denotes the maximum allowable end-to-end delay of the packet. Obviously, the maximum allowable end-to-end delay of a real-time application packet is higher than that of a non-real-time application packet. \(d\) denotes the actual delay that the packet experiences on the path. \(T\) represents the remaining lifetime of the packet. Thus, the following equality is set up:  
\[T=D-d\;. \tag{8}\]  
When the packet arrives at a node, if \(T\) of the packet is a positive number, the packet is admitted to the data queue, and its queueing sequence in the queue is decided according to the \(T\) of the packet. The smaller the \(T\) of the packet is, the closer to the head of the queue the packet is arranged. This implies that packets will receive differentiated services according to their end-to-end delay requirements.  
The queue backlog at the network layer of node \(n\) in time slot \(t\) is denoted by \(Q_{n}(t)\), which is updated as:  
\[Q_{n}(t+1)=\max[Q_{n}(t)-\sum_{i\in O(n)}\mu_{m}(t),0]+\sum_{j\in(n)}\mu_{m}(t )+\sum_{m\in M}1_{(n-n_{m})}\cdot r_{m}(t)\,, \tag{9}\]  
where \(\mu_{m}(t)\) is the amount of packets forwarded from node \(n\) to node \(i\) in time slot \(t\). \(\mu_{m}(t)\) is constrained by \(\mu_{m}(t)\in[0,\mu_{max}]\), where \(\mu_{max}\) denotes the maximum amount of packets that can be transmitted between two nodes in a time slot. \(1_{(n-n_{m})}\) is an indicator function which is denoted as \(1\) if \(n=s_{m}\) and \(0\) otherwise. Similar to (6), the following inequality can be derived:  
\[0\leq\mu_{m}(t)\leq C_{m}(t)\cdot t_{min}\;. \tag{10}\]  
\(C_{m}(t)\) denotes the transmission capacity of the link (\(n\),\(i\)) in time slot \(t\).
# Displays Based on Dynamic Phase-Only Holography
## 5 System Application
Since the separation of the spots is \((\Delta\)f/B\(\Delta\)u, \(\Delta\)f/C\(\Delta\)v), the maximum resolution in the replay field in an ideal case will be \((\text{N}_{x}/1.4\), \(\text{N}_{y}/1.4)\). The ideal case assumes a perfect Gaussian beam, no speckle noise, and a perfect optical system. Figure 7: Pixel layout of LCOS. In addition to improving the image quality, the size of the hologram has been reduced, which facilitates the speed of computation. Defining the spot separation is important for reducing the overlap between the side lobes of the spots, which produce unnecessary speckle noise. It will also define the maximum resolution of the display, which is the image size divided by the spot separation (N\({}_{x}\)/1.4, N\({}_{y}\)/1.4). The actual resolution is determined principally by the performance of the LCOS device. As discussed previously, we use thin NLC layers in order to guarantee a phase modulation amplitude between neighboring pixels of the device, which matches the intended pixels by the algorithm used to compute the hologram. Therefore, pixels at the extremity of the image field benefit from the full modulation capability of the backplane.
###### Abstract  
Recently, with the development of big data and 5G networks, the number of intelligent mobile devices has increased dramatically, therefore the data that needs to be transmitted and processed in the networks has grown exponentially. It is difficult for the end-to-end communication mechanism proposed by traditional routing algorithms to implement the massive data transmission between mobile devices. Consequently, opportunistic social networks propose that the effective data transmission process could be implemented by selecting appropriate relay nodes. At present, most existing routing algorithms find suitable next-hop nodes by comparing the similarity degree between nodes. However, when evaluating the similarity between two mobile nodes, these routing algorithms either consider the mobility similarity between nodes, or only consider the social similarity between nodes. To improve the data dissemination environment, this paper proposes an effective data transmission strategy (MSSN) utilizing mobile and social similarities in opportunistic social networks. In our proposed strategy, we first calculate the mobile similarity between neighbor nodes and destination, set a mobile similarity threshold, and compute the social similarity between the nodes whose mobile similarity is greater than the threshold. The nodes with high mobile similarity degree to the destination node are the reliable relay nodes. After simulation experiments and comparison with other existing opportunistic social networks algorithms, the results show that the delivery ratio in the proposed algorithm is 0.80 on average, the average end-to-end delay is 23.1% lower than the FCNS algorithm (A fuzzy routing-forwarding algorithm exploiting comprehensive node similarity in opportunistic social networks), and the overhead on average is 14.9% lower than the Effective Information Transmission Based on Socialization Nodes (EIMST) algorithm.  
Keywords: opportunistic social networks; routing algorithm; mobile similarity; social similarity; social attributes; data transmission +
Footnote †: journal: Information
## 4 Conclusions  
In this study, the effects of carbon nanotubes (CNTs) on the hydration and autogenous shrinkage of cementitious materials were investigated. In the experiments, ordinary Portland cement (OPC) and fly ash (FA) were used as binders, and CNT-reinforced cement composites fabricated with different CNT contents were used. The experimental results are summarized as follows:  
1. CNTs accelerate the hydration of cementitious materials. As the CNT content increased, the induction period decreased, thereby increasing the hydration rate. The cumulative heat release of CRC00 measured for 48 h, however, was identical to that of CRC01 and its difference from those of CRC03 and CRC05 decreased, thereby showing that the long-term effect of CNTs on hydration was insignificant.
2. The compressive strength was greatest when the CNT content was 0.1%, but it decreased as the CNT content was further increased. This appears to be due to the agglomerated CNTs inside the cement composites. Therefore, a method for dispersing CNTs is required to use more than 0.1% of CNTs,
3. Unlike previous studies, the autogenous shrinkage of the cement composites decreased as the CNT content increased in this study. This result indicated that the bridge effect of CNTs decreased autogenous shrinkage even though hydration was accelerated by CNTs.
4. The internal relative humidity (IRH) decreased as the CNT content increased. This is because CNTs accelerate hydration and thereby rapidly consume water inside the pores. The difference in IRH depending on the CNT content, however, decreased over time.  
Figure 14: TG analysis results.  
When the correlation between IRH and autogenous shrinkage was analyzed, it was found that the effect of the IRH reduction on autogenous shrinkage was not significant. This is because the reduction in autogenous shrinkage was due to the decrease in bulk strain caused by the bridge effect of the CNTs.
* 6. Autogenous shrinkage decreased because the bulk strain of cementitious materials decreased by the bridge effect of CNTs even though the hydration accelerated by the addition of CNTs.  
Formal analysis, investigation, resources, data curation, writing--original draft preparation, visualization, B.P.; Conceptualization, methodology, validation, supervision, project administration, funding acquisition, Y.C.C.; All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20181110200070). This work was also supported by the Gachon University research fund of 2020. (GCU-202003060001).  
Data Availability StatementData sharing not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results and Discussions
### Aggregate Quality Tests
#### 3.1.2 Indirect Tensile Strength Test  
For the IDT strength test results, Figure 10 shows the comparison of the average IDT strength at the failure of each material. Based on the results, all the materials passed the IDT strength criteria for surface pavement layer of at least 0.80 MPa. The use of modified binder for the three asphalt mixtures provided sufficient cohesive and adhesive bond within the asphalt mixture. Hence, all of the materials are suitable as a surface pavement layer in terms of crack resistance.  
Comparing the three asphalt mixtures, both the colored asphalt pavement, with and without additional pigment, performed better in terms of IDT strength than the typical SMA mixture. It implies that the crack resistance of the colored asphalt pavement is superior to  
Figure 9: Kim test results.  
that of the typical SMA. Moreover, it can be observed that the IDT strength of the colored pavement with additional pigment is lower than that of the colored pavement without additional pigment, which is also the case for the Kim test results. The additional pigment lowers the stiffness of the binder, making it more susceptible to deformation and cracks.
# Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks
## 3 System Model and Problem Description
### Latency and Satisfied Task Model
Further, the transmission rate in the satellite downlink becomes  
\[s_{k,i}^{\bar{t},M}=W_{k}^{M}\text{log}_{2}\left(1+\frac{P_{k,i}^{\bar{t},M} \delta_{k}^{t,M}}{\sum\limits_{m=1,m\
eq k}^{\bar{t}}\sum\limits_{j=1}^{\bar{t }}P_{m,j}^{\bar{t},M}\delta_{m}^{t,M}+\sigma^{2}}\right), \tag{10}\]  
where \(W_{k}^{M}\) represents the link bandwidth allocated to region \(k\), and \(\sum\limits_{m=1,m\
eq k}^{K}\sum\limits_{j=1}^{\bar{t}}P_{m,j}^{\bar{t},M} \delta_{m}^{t,M}+\sigma^{2}\) is the interference power caused by other regions except \(k\) at slot \(t\). Assume that the satellite works in the full-duplex mode when forwarding the data from terminals to \(M\) via satellites, the transmission rate of \(R_{k,i}^{\bar{t}}\) becomes \(s_{k,i}^{\bar{t},M^{\prime}}=\min\{s_{k,i}^{\bar{t},L},s_{k,i}^{\bar{t},M}\}\). Since the channel state varies across slots, the transmission rate also changes, and the judgment on satisfied conditions resembles Algorithm 1, with a slight difference on the data rate calculation. ```
0: unfinished task \(R^{I}_{k,i}(\bar{t}\leq t)\)
0: judgment result \(\eta^{I,t}_{k,i}\)
1: Initialize \(\eta^{I,t}_{k,i}=-1\)
2:while\(\eta^{I,t}_{k,i}=-1\)do
3: Calculate the size of remaining data \(z^{I,t}_{k,i}\)
4:if\(z^{I,t}_{k,i}\leq 0\)then
5: Obtain the total service latency: \(T^{I,\text{tol}}_{k,i}=(c^{I}_{k,i}/C^{I,L}_{k,i})+(t-\bar{t}+1)\cdot\tau+(2d^ {\bar{t},L}_{k}/c)\)
6:if\(T^{I,\text{tol}}_{k,i}\leq t_{\text{max}}\)then
7:\(\eta^{I,t}_{k,i}=1\)
8:else
9:\(\eta^{I,t}_{k,i}=0\)
10:endif
11:else
12: Calculate the traversed latency: \(T^{\bar{t},\text{tra}}_{k,i}=(t-\bar{t}+1)\cdot\tau+(d^{\bar{t},L}_{k}/c)\)
13:if\(T^{I,\text{tra}}_{k,i}\geq t_{\text{max}}\)then
14:\(\eta^{I,t}_{k,i}=0\)
15:else
16:\(\eta^{I,t}_{k,i}=-1\)
17:endif
18:endif
19:endwhile
```  
**Algorithm 1** Judgment on satisfied conditions of task \(R^{I}_{k,i}(\bar{t}\leq t)\) at slot \(t\)
###### Abstract  
Information technology (IT) security risk analysis preventatively helps organizations in identifying their vulnerable systems or internal controls. Some researchers propose expert systems (ES) as the solution for risk analysis automation since risk analysis by human experts is expensive and timely. By design, ES need a knowledge base, which must be up to date and of high quality. Manual creation of databases is also expensive and cannot ensure stable information renewal. These facts make the knowledge base automation process very important. This paper proposes a novel method of converting attack trees to a format usable by expert systems for utilizing the existing attack tree repositories in facilitating information and IT security risk analysis. The method performs attack tree translation into the Java Expert System Shell (JESS) format, by consistently applying ATTop, a software bridging tool that enables automated analysis of attack trees using a model-driven engineering approach, translating attack trees into the eXtensible Markup Language (XML) format, and using the newly developed ATES (attack trees to expert system) program, performing further XML conversion into JESS compatible format. The detailed method description, along with samples of attack tree conversion and results of conversion experiments on a significant number of attack trees, are presented and discussed. The results demonstrate the high method reliability rate and viability of attack trees as a source for the knowledge bases of expert systems used in the IT security risk analysis process.  
Keywords:expert systems; attack trees; risk analysis; information security; JESS; ATTop; Mitre transformation +
Footnote †: journal: Applied Soft. 2020, 10, 8423; doi:10.3390/app10238423