esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Aerospace Engineering Applications
### Other Applications to Aerospace
fabricated four different configurations of the self-deploying solar sails, which are different for folding ways and surface reduction, and deployed configurations were analyzed by using a laser scanner to measure the planarity degree and the critical areas [122]. [PERSON] et al. also dealt with the feasibility and reliability for the use of SMA actuators as a mechanical actuator for a solar sail self-deployment system [123]. For the activation of the SMA actuator, visible lamps were used to act out the solar radiation in space, and temperature characteristics over time were investigated for various sail configurations and environments. The folding configuration self-deployed solar sail is presented in Figure 10. The results of various applications using shape memory alloys performed in the aerospace field are summarized in Table 5.  
Figure 9: Folding action demonstration of artificial foldable hind wing [115] Reproduced with permission from Elsevier under STM Permissions Guidelines. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Controller** & **Application** \\ \hline
[113,114] & passive control (thermo-mechanical modeling) & active jet engine (VGC) \\ \hline
[115] & passive control & MAV (flapping wing) \\ \hline
[116] & passive control (pre-compressive displacement modeling) & compressed mesh washer \\ \hline
[117] & passive control & micro-vibration isolation \\  & & spaceborne cryocooler \\ \hline
[118] & passive control & releasing system \\ \hline
[120] & passive control & self-deployed solar sail \\ \hline
[121] & passive control & self-deployed solar sail \\ \hline
[122] & passive control & self-deployed solar sail \\ \hline
[123] & passive control & self-deployed solar sail \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Control methods for other aerospace engineering applications.
## 5 The One-Dimensional Analytical Model
### The Standard One-Dimensional Model
It should be noted that this pressure is averaged (area-weighted) based on the \(x\)-direction of the reaction force and \(x\)-direction of the converging wall area. Also, note that \(\dot{m}_{p}\) and \(\dot{m}_{s}\) (Equation (12)) are equal to \(\dot{m}_{1}\) and \(\dot{m}_{2}\) (Figure 13), respectively. If it is assumed that the average static pressure on the wall of the converging mixing section is equal to the static pressure at station 2, then the reaction force in the \(x\)-direction, \(R_{x}\), can be written as \(R_{x}=P_{2}(A_{1}+A_{2}-A_{3})\). This is illustrated in Figure 11:3 It should be noted that \(P_{2}\) has also been assumed previously to be equal to the stagnation pressure of the secondary flow inlet. Recall assumption no. 4 where \(v_{2}\) is assumed to be negligible and, thus, \(P_{2}\) is the stagnation pressure at station 2. Manipulating Equation (12) in conjunction with Equations (5), (6), (13), and the assumption of \(P_{CV,in,avg}\) equals \(P_{2}\), the following equation, Equation (14), will be found. Figure 11:4  
After that, Equation (15) is obtained by applying the mass conservation across station 3 along with the ideal gas equation of state. Figure 11:5  
If the previous equations, i.e., Equations (5) to (8), are considered in conjunction with Equations (14) and (15), the number of unknowns will be eight while the number of equations will be six. The unknowns are \(ER\), \(\dot{m}_{p}\), \(\dot{m}_{s}\), \(\dot{m}_{4}\) (or \(\dot{m}_{3}\)), \(T_{4}\), \(v_{3}\), \(T_{3}\), and \(P_{3}\). Note that \(v_{1}\) and \(P_{1}\) are not included because they have been obtained before through Equations (9) to (11). Therefore, to solve these eight unknowns, two more equations are needed and one of them can be obtained from control volume D (Figure 12). The other  
Figure 11:6 Forces and momentum flow on control volume C.  
equation is found based on the definition of Mach number and the assumption of knowing the value of \(M_{3}\) (assumption no. 8). Applying energy balance on control volume D together with assumptions no. 2 and 4, Equation (16) is obtained. Figure 11:7  
Using the definition of Mach number at station 3, we will get Equation (17). Figure 11:8  
Thus, now, the system of equations is closed with eight equations (Equations (5) to (8) and Equations (14) to (17)) and eight unknowns (\(ER\), \(\hat{m}_{p}\), \(\hat{m}_{s}\), \(\hat{m}_{4}\) (or \(\hat{m}_{3}\)), \(T_{4}\), \(v_{3}\), \(T_{3}\), and \(P_{3}\)). Control volume E, i.e., the diffuser (Figure 12), will be used to get the last equation that will solve for pressure at the diffuser outlet (i.e., the ejector outlet), \(P_{4}\). Applying energy balance on this control volume along with assumptions no. 2, 4, and 7, we will obtain Equation (18). Figure 11:9  
Hence, by using Equation (18), we can get the value of \(P_{4}\) since all other parameters have been obtained previously. Recalling assumption no. 7, the diffuser isentropic efficiency, \(\eta_{D}\), is known and assumed to have a value of 0.8.
## Materials and Methods
### Auto-Regressive and Moving Average Model (ARMA)
#### 2.3.1 Model Ordering  
The ARMA model is the system's memory of its past self-state and noise of entering the system. Determining the order of the model and the value of the unknown parameter according to a set of observation data is the model order. Firstly, through the correlation analysis, the autocorrelation function (ACF) and partial autocorrelation function (PACF) of the sample are calculated, then the order of the model is preliminarily judged by the trailing nature or censored nature of the ACF and PACF, as shown in Table 1.  
It can be seen that the PACF of AR(\(p\)) is censored at point \(p\), while the ACF of MA(\(q\)) is censored at point \(q\). The \(p\) can also be determined by observing the oscillation period of the PACF. In this way, the order of AR(\(p\)) or MA(\(q\)) can be preliminary determined while the model is being identified.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Model Type** & **AR(\(p\))** & **MA(\(q\))** & **ARMA(\(p\), \(q\))** \\ \hline ACF & trailing & censored & trailing \\ PACF & censored & trailing & trailing \\ \hline \end{tabular}
\end{table}
Table 1: Sequence characteristics table of ARMA \((p,q)\) model. ARMA: Auto-Regressive Moving Average; ACF: autocorrelation function; PACF: partial autocorrelation function; AR: Auto-regressive; MA: Moving Average.  
The Akaike Information Criterion (AIC) [41] is used as the ARMA model ordering criterion. The AIC function is:  
\[\begin{array}{l}\text{AIC}(p,q)=\min_{0\leq p,q\leq L}(p,q)\\ =\min_{0\leq p,q\leq L}\left\{\ln\hat{\sigma}^{2}+\frac{2(p+q)}{N}\right\}\end{array}. \tag{16}\]  
Among them, \(\hat{\sigma}^{2}\) is the estimation of the variance of the noise term, \(N\) is the known observation data sample size, and \(L\) is the highest order given in advance. The use of the AIC criterion to determine the order refers to the points \(p\) and \(q\) that seek to minimize the statistic AIC \((p,q)\) within a certain range of \(p\), \(q\), which is used as an estimate of \((p,q)\)[42]. Theoretically, the larger the \(L\), the higher the prediction accuracy. However, considering the calculation time, and the increase in \(L\), the prediction accuracy is not significantly improved. In general, the value is \(N/10\), \(\ln N\), or \(\sqrt{N}\).  
From Equation (16), when the model parameter \(N\) is gradually increased, the fitting error is improved significantly and the AIC \((p,q)\) value will show a downward trend. As the model order increases, the fitting residual improves a little. The AIC value also increases gradually; when the AIC \((p,q)\) obtains the minimum, the corresponding \(p\), \(q\) is the ideal order of the model.
# Enhancing Reliability Analysis with Multisource Data:
## 2 Proposed Method to Mitigate Adverse Selection in Bridge Monitoring and MRR
### Establishing a Fair Representation of Bridge Capacities from the NBI Data  
This study hypothesizes that the NBI operating load rating (LR), as per the definition given in the NBI guidelines [12], provides a fair representation of bridge capacity. To test this hypothesis and establish a relationship between bridge capacities and operating LR, a multivariable linear regression analysis is performed between NBI attributes including condition ratings (CR) and operating LR. The significance of the regression analysis is to observe the effect of changes in operating LR on the changes in bridge CR along with other NBI attributes (e.g., age, CR, structure length, no of lanes). The process is summarized in Figure 2, on the bottom left part of a flowchart.  
This hypothesis test will support the main assumption that changes in bridge load carrying capacity resulting from condition changes and deterioration are fairly represented by changes in the NBI's operating LR. Machine learning methods, such as eXtreme Gradient Boosting (XGBoost) and CatBoost, are explored to search for a meaningful relationship. The main predictors include age, length, number of lanes, CR for deck, superstructure, and substructure, and the target variable is the bridge operating LR, which is hypothesized  
Figure 2: Flow chart showing the WIM data driven reliability assessment.  
to represent bridge capacities. Machine learning model parameters such as tree depth and learning rate are tuned using a training data set (70% of the total dataset), and the performance is evaluated by multiple error estimation methods including the Root Mean Squared Error (RMSE), R-squared (R2) value, and K-fold cross validation [24; 25].
# A Novel Approach for a Inverse Kinematics Solution of a Redundant Manipulator
## 3 Inverse Kinematic Model and Computing Algorithm
### Obstacle Avoidance Task  
During the obstacle avoidance task, the control system investigates the relation between manipulator links and obstacles in their environment. The aim of this secondary task is to prevent the collision between any part of the manipulator and potential obstacles, regardless of whether the shape of the obstacle is regular or irregular. Every obstacle of irregular shape can be geometrically modeled as a cylinder, with the obstacle being situated in the center of the cylinder; the diameter of the cylinder determines the distance of influence of this obstacle.  
The coordinate of an obstacle in the end-effector task space is \(\mathrm{s_{0}}\). The projection of the line from the i-th joint of the manipulator link to the center of a particular cylinder on the i-th link is [33]:  
\[\mathrm{p_{i}}=\mathrm{e_{i}^{T}}(\mathrm{s_{0}}-\mathrm{s_{i}}) \tag{12}\]  
The coordinate of the potential link point which could collide with the obstacle is:  
\[\mathrm{s_{ci}}=\mathrm{s_{i}}+\mathrm{p_{i}}\mathrm{e_{i}} \tag{13}\]  
The distance between the potential point of collision on the link and the center of the cylinder is expressed as:  
\[\mathrm{d_{ci}}=\parallel\mathrm{s_{ci}}-\mathrm{s_{0}}\parallel \tag{14}\]The unit vector of the potential point of collision to the center of the obstacle is:  
\[\mathrm{u_{i}}=\frac{\mathrm{s_{ai}}-\mathrm{s_{0}}}{\mathrm{d_{ci}}} \tag{15}\]  
Analogous to the joint limit avoidance task, the Jacobian matrix also has to be determined for the obstacle avoidance task. The i-th row of the Jacobian matrix can be written as:  
\[\mathrm{J_{ci}}=-\mathrm{u_{i}^{T}J_{s_{ci}}} \tag{16}\]  
The matrix \(\mathrm{J_{s_{ci}}}\) is:  
\[\mathrm{J_{s_{ci}}}=\frac{\partial\mathrm{s_{ci}}}{\partial\mathrm{q}} \tag{17}\]  
The Jacobian matrix \(\mathrm{J_{c}}\) consists of submatrices \(\mathrm{J_{ci}}\). The dimension of the Jacobian matrix is \(\mathrm{J_{c}}\in\mathrm{R^{c\times c}}\), where \(c\) represents the number of manipulator links which could collide with the obstacles.
# SMT Assembly Inspection Using Dual-Stream Convolutional Networks and Two Solder Regions
## 8 Conclusions  
In this paper, we proposed a CNN model that combines two solder regions as inputs. We extracted solder regions based on vertical and horizontal projections and then constructed a CNN model. Through defect classification experiments, we confirmed that this late-merge dual-stream CNN has a higher performance and lower weight than can be obtained by conventional methods. The proposed method improved the F1-score by 5.3% compared to single-stream CNN and reduced the weight by about 4000 KB. In addition, the inference time of the proposed method was slightly faster, about 0.3 ms, compared to a single-stream CNN. However, the proposed method requires an accurate solder region extraction. In addition, due to the loss of low-level features, the classification performance of defects was inferior to that of a single-stream CNN for some defects such as missing defects.  
Notable advantages and disadvantages of our paper can be summarized as follows.  
1. The proposed method achieves a higher accuracy and F1-score than single-stream CNN based on a full chip component image.
2. The weight of the model is less than for single-stream CNN.
3. The proposed method has a faster inference speed than single-stream CNN.  
Disadvantages:  
1. The proposed method requires accurate solder region detection.
2. The classification accuracy of single-stream CNN is higher for some defects.  
In the future, it would be useful to study both partial and full images, as defect classification performance can be increased by using features extracted from these images. In addition, it is likely possible to reduce the inference time of the proposed method by using only the feature region in which the defect appears as the input of the CNN.  
Figure 13: Failure case in defect classification using a dual-input CNN.  
**Funding:** This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2020-0-01462) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation)  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Efficient Superframe Structures with Adaptive Duty Cycle \((Ess_{ADC})\)
### Link Utilization  
It has been observed that a significant amount of bandwidth is wasted during CFP in the standard. Higher the slot size more will be the wastage. Though ESS reduces this wastage by reducing the CFP slot size to half however, it is not optimal with adaptive data traffic in each BI. If \(D_{i}\) data is required to be transmitted by node \(i\), then the time required to send this data \(t_{d}\) to PAN coordinator is estimated as:  
\[t_{d}=\frac{D_{i}}{C} \tag{16}\]  
Here \(C\) is the data rate through which node communicates. If \(K_{i}\) is the number of CFP slots required to send \(D_{i}\) data, then it is computed as:  
\[K_{i}=\frac{D_{i}}{N_{bps}} \tag{17}\]  
here, \(N_{bps}\) is the number of maximum bits, that can transmitted during each CFP slot and is calculated as:  
\[N_{bps}=15\times 2^{\text{SO}+3} \tag{18}\]  
Larger the slot size more will be its capacity in transmitting data in a CFP. \(N_{bps}\) is fixed in ESS due to its unchanged SO throughput, however in \(ESS_{ADS}\), it varies due to its adjustment according to adaptive data traffic. If a node \(i\) requires \(K_{i}\) slots in transmitting its data \(D_{i}\) to PAN-coordinator, then link utilization \((U_{i})\) for node \(i\) is calculated as:  
\[U_{i}=\frac{t_{d}}{K_{i}\times t_{s}} \tag{19}\]  
here, \(t_{s}\) is the time in seconds of each CFP slot and it is computed as  
\[t_{s}=15\times e^{-6}\times 2^{\text{SO}+5} \tag{20}\]  
If \(p\) nodes are successfully allocated CFP slots, then the link utilization \((U_{CFP})\), for \(p\) number of nodes is computed as:  
\[U_{CFP}=\sum_{i=1}^{p}\frac{t_{d}}{K_{i}\times t_{s}} \tag{21}\]  
However, link utilization of same node \(i\) for the IEEE 802.15.4 standard \((U_{o_{i}})\) is calculated as:  
\[U_{o_{i}}=\frac{t_{d}}{K_{o}\times t_{o}} \tag{22}\]here \(k_{o}\) is number of CFP slots required to send data during CFP in current standard and \(t_{o}\) is the time of each CFP slot in seconds and it is calculated as:  
\[t_{o}=15\times e^{-6}\times 2^{SO+4} \tag{23}\]  
If \(q\) nodes have been successfully assigned CFP slots, then link utilization \(U_{CFP_{o}}\) during a specific BI is calculated as:  
\[U_{CFP_{o}}=\sum_{i=1}^{q}\frac{t_{i}}{K_{o}\times t_{o}} \tag{24}\]
Advances in computer technology have led to the development of new interfaces in areas where traditional devices are often inadequate. Visualization technologies in such environments require more intuitive forms of interaction and are therefore looking for new UIs that support such interactions. Another important aspect of educational software systems is accessibility. Digital accessibility refers to the extent to which a computer program, website, or device is acceptable and suitable for use by people of all groups, including people with disabilities and older people, leading to a higher degree of their inclusion in society. Despite the rapid development of visualization technology, research into the accessibility and integration of these technologies still lags the needs of the market, and solutions need to be found that can overcome the barriers that these technologies present to users. The goals of this research were to investigate and define the basic accessibility parameters by evaluating 3D user interactions in educational software and to formulate recommendations for improving the accessibility of existing and future educational soft ware solutions. The accessibility guidelines defined in this study are specifically tailored to the needs of 3D interaction. They enable the creation of UI that not only fulfills the basic accessibility requirements but also improves the user experience. Based on previous research findings and the theoretical framework, three hypotheses were formulated and then tested against the results of the study. The sections of this paper are organized as follows. After the introduction, the second section of this paper describes previous research in the field of 3D user interaction and accessibility of educational software systems with a focus on natural interactions. The third section describes the research conducted, focusing on the research instrument used in the form of an educational application and the metrics used to define the accessibility parameters. In this section, research hypotheses were formulated. The quantitative and qualitative research results are described in detail in the fourth section, while the fifth section summarizes the findings, highlights the limitations of the research conducted, presents the testing of the previously formulated hypotheses, and gives recommendations for adapting the representation of 3D objects and 3D interactions in educational software systems. Conclusions and plans for future research are presented in the sixth section.
## 5 Discussion
### Effect of Sentinel-1 Temporal Resolution on ROD Extraction  
Snowmelt is a rapidly changing process. This variation is evident not only between different dates but also within a single day, as snowmelt conditions can significantly differ due to changes in temperature or sunlight. Therefore, monitoring snowmelt requires high temporal resolution. However, due to the high-resolution imaging characteristics of SAR satellites and the limitation of their relatively small number, achieving high-frequency monitoring over large areas is currently a challenge. The Sentinel-1 data used in this study include data from both the A and B satellites, as well as ascending and descending orbit data. The orbits and times of Sentinel-1A and Sentinel-1B crossings are shown in Table 2.  
To enhance the temporal resolution, we utilize all available data while disregarding differences in acquisition times. Part of the discrepancy between the ROD extracted from SAR data and that from the SWE can be attributed to the insufficient temporal resolution of SAR data. Combining multiple sources of SAR data, such as Radarsat-2, GaoFen-3, etc., is an effective way to improve the temporal resolution. The upcoming launch of Sentinel-1C will also help compensate for data gaps resulting from the damage to Sentinel-1B.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 3 Security Protections in IoT Environments
### Defense at the Device Layer  
#### 3.3.1 Secure Hardware Components  
The selection of secure hardware components is critical to the overall security of IoT devices [47]. Microcontrollers are the core components of embedded systems, and microcontrollers specifically designed to enhance security can be selected to improve the security of a device. Similarly, hardware security modules [48] (_HSMs_) are widely used in scenarios in which IoT devices are protected by securely managing, processing, and the storing of cryptographic keys and digital certificates. HSMs are widely used in venues (e.g., such as financial services, data protection, and enterprise security) where security is highly desired. HSMs offer robust protection for encryption keys by utilizing dedicated secure cryptographic processors. These processors can be housed within plug-in cards (SAM/SIM cards) or external devices connected directly to computers or web servers. The key discrepancy of HSMs lies in their tamper-resistant design: this often includes a physically secure enclosure that automatically deletes stored keys upon unauthorized physical access, preventing key disclosure. In essence, HSMs offer a secure environment for the entire lifecycle of an encryption key, safeguarding IoT systems from various threats.
# Agricultural Residues of Lignocellulosic Materials in Cement Composites
###### Abstract  
Lignocellulosic material residues in cement composites are a favourable option for new fibre cement formulations in building materials, because they combine good mechanical properties with low density. This study aimed to evaluate the chemical, physical, anatomical, and mechanical properties of five cement panels reinforced with the following lignocellulosic materials: eucalyptus, sugarcane bagasse, coconut shell, coffee husk, and banana pseudostem. Lignocellulosic cement panels were produced with each lignocellulosic material residue, and three replicates of each type of lignocellulosic material were examined (15 panels in total). The lignin, extractives, ash, and holocellulose were examined. After 28 days of composite curing, the following physical properties of the panels were evaluated: density, porosity, water absorption after immersion for 2 and 24 h, and thickness swelling after immersion for 2 and 24 h. Mechanical tests (compression strength, internal bonding, modulus of rupture, and modulus of elasticity) were performed before and after the accelerated ageing test with a universal testing machine. Scanning electron microscopy and supervised image classification were performed to investigate the morphologies of the different materials and the filler/matrix interfaces. Eucalyptus and sugarcane panels had the best results in terms of the evaluated properties and thus, could potentially be used as non-structural walls. However, banana pseudostem, coconut shell, and coffee husk panels had the worst results and therefore, under these conditions, should not be used in building.  
lignocellulosic panels; chemical properties; physical-mechanical properties; residue materials +
Footnote †: journal: Applied Soft. 2020, 100, 8019; doi:10.3390/app10228019