esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Applicability of Waste Glass Wool and Waste Mineral Wool as Fiber Reinforcement
## 4 Results and Discussion
### Compressive Strength of Waste Fiber-Reinforced Concrete  
Figures 11 and 12 show the results of measuring compressive strength for 28 days and 90 days according to the type and addition rate of waste fiber wool. As shown in Figure 11, the results of compressive strength at 28 days for concrete to which waste fiber wool was added decreased compared to the reference concrete. Reference concrete showed compressive strength of 38.4 MPa at 28 days, and concrete to which waste glass wool and waste mineral wool were added showed compressive strength of 36.6, 36.4, and 34.9 MPa (waste glass wool) and 32.0, 33.5, and 34.7 MPa (waste mineral wool), respectively, depending on the addition rate. The compressive strength of the waste glass wool decreased as the addition rate increased, but the compressive strength of the waste mineral wool fiber also increased as the addition rate increased. However, in the case of waste mineral wool fibers, the maximum compressive strength was 34.7 MPa (2.0%), while for the specimen with added glass fiber wool, the lowest compressive strength was found to have a compressive strength similar to that of waste mineral wool at 34.9 MPa  
Figure 10: Air content test results.  
Figure 9: Slump test results.  
(2.0%), despite the decrease in compressive strength as the addition rate increased. This could be confirmed more clearly in terms of strength reduction rate, and the maximum compressive strength reduction rate of the waste glass wool-added specimen was 10%, but the maximum compressive strength reduction rate of the waste mineral wool specimen was about 17%. Figure 12 is a graph showing the results of measuring compressive strength for 90 days under the same conditions, and reference concrete showed compressive strength of 43.7 MPa at 90 days, and in specimens added with waste glass wool and waste mineral wool fibers, concrete showed compressive strength of 43.0, 41.8, and 41.4 MPa (waste glass wool) and 38.6, 38.5, and 40.7 (waste mineral wool), respectively. The compressive strength of 90 days also showed a similar tendency to 28 days, but the reduction rate of compressive strength was found to decrease significantly. The maximum reduction rate of compressive strength of waste glass wool was found to be about 5% at the addition rate of 2%, and for waste mineral fiber wool was confirmed to be 12% at addition rate of 0.5%. The reason why the compressive strength decreased as the addition rate of waste fiber wool increased is judged to be the result of the high absorption property of fibers. In general, when fibers are mixed into concrete, the mixed water is absorbed by fibers due to the high absorption, which is the characteristic of the fiber itself, and as a result, a number of interfaces with poor adhesion between cement paste and fibers are formed, which adversely affected the compression load.
## 2 Advances in Multiple Access Techniques for 6G Mobile Wireless Networks
### Comparison of RSMA Technology with Existing Multiple Access Technologies
Additionally, if the common message contains only a specific user's portion and the corresponding user has no private message, then RSMA becomes NOMA. Since NOMA fully decodes the message of other users, the order of SIC implementation is crucial. An improper SIC order can significantly degrade performance, sometimes even falling below that of SDMA. Moreover, while NOMA has no flexibility in message construction, RSMA constructs a common message that includes parts of each user's message, simplifying the SIC order to just two steps. Consequently, RSMA is more resilient than NOMA. Further, RSMA has scalability with an increasing number of users. When the number of users grows, both SDMA and NOMA require more accurate CSI and involve more complexity in the process of interference management. On the other hand, RSMA can dynamically adjust the ratio of common and private messages, leading to less performance degradation with an increasing number of users and offering excellent scalability.
# Angle-Only Cooperative Orbit Determination Considering Attitude Uncertainty
## 2 Attitude and Orbit Determination Model
### Observability Matrix  
In this paper, the observability matrix (OM) is taken as a metric to evaluate the feasibility of the two-spacecraft system. With measurements collected \(k\) times sequentially, denoted as \(\{\mathbf{y}_{0},\mathbf{y}_{1},\cdots,\mathbf{y}_{k-1}\}\) from time epoch \(t_{0}\) to \(t_{k-1}\), the OM is represented as:  
\[\mathbf{N}=\left[\begin{array}{c}\tilde{\mathbf{H}}_{0}\\ \vdots\\ \tilde{\mathbf{H}}_{k-1}\end{array}\right] \tag{20}\]  
\[\tilde{\mathbf{H}}_{k}=\mathbf{H}_{k}\mathbf{\Phi}(t_{k},t_{0}) \tag{21}\]  
where \(\mathbf{\Phi}(t_{k},t_{0})\) is the state transformation matrix (STM) from \(t_{0}\) to \(t_{k}\) and  
\[\mathbf{H}_{k}=\left.\frac{\partial\mathbf{y}}{\partial\mathbf{X}}\right|_{t=t_{k}} \tag{22}\]  
The differential equation of the STM is as follows:  
\[\dot{\mathbf{\Phi}}(t_{k},t_{0})=\left.\frac{\partial\mathbf{F}(\mathbf{X})}{\partial\mathbf{X }}\right|_{t=t_{k}}\mathbf{\Phi}(t_{k},t_{0}) \tag{23}\]and is initialized by:  
\[\mathbf{\Phi}(t_{0},t_{0})=\mathbf{I}_{n_{x},n} \tag{24}\]  
where \(n\) is the dimension of the state vector to be estimated and \(\mathbf{I}_{n\times n}\) is an \(n\)-dimensional unit matrix. Note that the differential term \(\partial F(\mathbf{X})/\partial\mathbf{X}\) is also known as the Jacobi matrix of state model (14).  
An OM with a full rank (i.e., \(\operatorname{rank}(\mathbf{N})=n\)) indicating that the two-spacecraft system is observable using the given measurements [1]. Moreover, the observability degree of the two-spacecraft system can be described by the condition number (CN) of OM, represented by \(\operatorname{cond}(\mathbf{N})=\left\lVert\mathbf{N}\right\rVert\cdot\left \lVert\mathbf{N}^{-1}\right\rVert\). The smaller the CN, the better the observability [24].
# Time Classification Algorithm Based on Windowed-Color Histogram Matching
## 2 Related Works  
Some researches for time classification of the single image have been conducted relying on computer vision algorithms. We categorized into three types: traditional image processing techniques, deep learning-based models, sky detection-based algorithms.
# Laser R-Test for Angular Positioning Calibration and Compensation of the Five-Axis Machine Tools
## 2 System Uncertainty Analysis and Calibration  
### Analysis  
The rotary table of the five-axis machine tool is described by the blue circle shown in Figure 3. The rotary table rotates a \(\varphi\) angle of 30 degrees, point A is the initial position, and point B is the current position. In order to estimate the uncertainty of the proposed system, the \(\varphi\) angle is derived in a mathematical equation by applying the Cosine theorem shown in Equation (1). The possible uncertainty factor is shown in Equation (2), and partial differentiation of the possible uncertainty factor can be shown in Equation (3).  
\[\varphi_{\rm i}=\cos^{-1}\left(\frac{\left(\rm{h_{i}}+\delta\rm{h_{i}}\right) ^{2}+\left(\rm{c_{i}}+\delta\rm{c_{i}}\right)^{2}-\left(\rm{g_{i}}+\delta\rm{ g_{i}}\right)^{2}}{2\rm{h_{i}}\rm{c_{i}}}\right)=\sec\left(\frac{\left(\rm{h_{i}}+ \delta\rm{h_{i}}\right)^{2}+\left(\rm{c_{i}}+\delta\rm{c_{i}}\right)^{2}-\left( \rm{g_{i}}+\delta\rm{g_{i}}\right)^{2}}{2\rm{h_{i}}\rm{c_{i}}}\right) \tag{1}\]  
\[\varphi_{\rm i}=\rm{f}(\rm{g_{i}},\rm{h_{i}},\rm{c_{i}}) \tag{2}\]  
Figure 2: The ISO definition of rotary axis error schematic [20; 21].  
The uncertainty of this system is shown in Equation (7), and applying the above parameters yields the uncertainties for \(\varphi\) = 30 degrees and \(\varphi\) = 10 degrees, respectively.  
\[\sigma=\pm\sqrt{-5^{(-5^{2})}+2.97^{2}+2.97^{2}}=\pm 4.2\text{ for }\varphi=30\text{ (degrees)} \tag{8}\]  
\[\sigma=\pm\sqrt{-1.67^{(-1.67^{2})}+0.99^{2}+0.99^{2}}=\pm 1.31\text{ for }\varphi=10\text{ (degrees)} \tag{9}\]  
That means the prediction uncertainty of the system could be re-written as follows:  
\(\varphi\) = 30\({}^{\circ}\)\(\pm\) 4.2 arcsec; or \(\varphi\) = 10\({}^{\circ}\)\(\pm\) 1.31 arcsec.
# In-Home Evaluation of the Neo Care Artificial Intelligence Sound-Based Fall Detection System
## 1 Introduction
Another emerging solution utilizes smart speakers trained to recognize sounds indicative of emergencies, such as a third or glass breaking. However, exploring the use of smart speakers equipped with AI models for audio-based fall detection could offer a more convenient and unobtrusive approach to monitoring fall risk, particularly in home settings. The Neo Care Monitoring System, hereinafter referred to as \"Neo\", is a compact device that employs AI and assistive technology to identify household sounds that may signify emergencies, including falls (Figure 1). The Neo operates similarly to popular smart speakers, like Google Home. Its main purpose is to listen for sounds that could signal potential issues, such as cries or the sound of breaking glass. It records 10 s audio clips of various events for analysis and can engage in voice interactions to assist occupants during emergencies. Upon detecting a concerning sound, Neo activates an AI-driven aged care assistant that converses with the user to assess the situation through a series of questions. If a crisis is confirmed, or there is no response, Neo attempts to contact a designated caregiver or relative's mobile phone. The core algorithm driving Neo's functionality is a machine learning model trained on a vast dataset of audio samples representing various household sounds and events, including potential emergency situations like falls (Figure 2). This algorithm continuously analyzes the incoming audio stream from Neo's microphone, identifying patterns that may signify a concerning event, which then triggers further analysis and response workflows. Additionally, the algorithm leverages natural language processing capabilities to engage in contextual voice interactions with users, interpreting responses to accurately assess emergencies and determine appropriate actions. This system offers a potentially more cost-effective solution compared to other environmental monitoringsystems, especially those requiring a multitude of smart devices and sensors. However, this innovation solution requires rigorous evaluation before it can be widely adopted. The primary objective of this study was to assess the accuracy and responsiveness of the Neo in real-world emergency scenarios, focusing on falls. Specifically, we aimed to answer the following research questions:  
Figure 1: Photos of the Neo Care Monitoring System’s hardware. Figure 2: Overview of the Neo Care Monitoring System. 1. What is the technical accuracy of the Neo in detecting events of concern, measured in terms of sensitivity and specificity? 2. How effectively is the Neo system's post-detection response also measured in terms of sensitivity and specificity? Additionally, we sought to understand users' experiences and perceptions of the Neo system, focusing on its perceived effectiveness, ease of use, and areas for improvement. The study also sought to understand the factors that influence future user adoption, including their willingness to recommend the system, and to incur associated costs.
# Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges
## 4 Smart Mobility Services and Applications
### Outdoor Navigation Technologies  
Outdoor navigation represents an important component of one of the services that can be delivered to citizens under smart mobility. In this section, we mention some of the techniques that are being used to achieve that and serve as the basis to deliver applications and services to citizens in order to ease one of the most common tasks nowadays, namely when we are in unknown places or also when we are talking about people with permanent or temporary disabilities such as visually impaired people, autistic people or people in wheelchairs for whom navigation represents a major issue. One of the main and first technologies for outdoor navigation is the Global Positioning System. It is at the base of the guidance systems that emerged in cars, and its main limitation is its limited accuracy. GPS technology was first made available with a deliberate error--called Selected Availability (SA)--that could cause an error of 100 m for civilian usage, which highly limited applications that need to have an accurate positioning [102]. A GPS receiver is based on signals received from several non-geo-stationary satellites. The fact that the number of satellites, as well as their position, is variable directly affects the accuracy [103; 104; 105; 106]. Even after the SA limitation was over in 2000, the accuracy achieved with only the use of GPS is limited for solutions such as high-precision agriculture or pedestrian navigation in locations with tall buildings [107]. To solve these limitations, alternatives have emerged, such as those based on differential corrections and which support methods such as RTK or DGPS, which still have the signal degraded or have interruptions depending on the range and quality of
# A Flower-Shaped Miniaturized UWB-MIMO Antenna with High Isolation
###### Abstract  
An ultra-wideband (UWB) multiple-input, multiple-output (MIMO) antenna with a reasonably compact size of \(30\times 18\times 1.6\) mm\({}^{3}\) is presented in this paper. The proposed antenna contains two radiating components, each of which is made up of three elliptically shaped patches situated 60 degrees apart, and resembles the shape of a flower. Moreover, the proposed antenna design incorporates a T-like ground branch that functions as a decoupling structure, and is composed of two modified inverted-L branches and an I-shaped stub, offering an isolation of more than 20 dB over the whole operation band (4.3-15.63 GHz). Furthermore, the proposed antenna system was fabricated and tested, and the envelope correlation coefficient (ECC), diversity gain (DG), and total active reflection coefficient (TARC), as well as the radiation characteristics and MIMO performance, were analyzed. The proposed UWB-MIMO antenna may be a suitable candidate for diverse UWB applications, based on the simulated and measured results of this study.  
UWB; MIMO antenna; decoupling; diversity performance +
Footnote †: journal: Electronics  
Article
## 2 Radio Communication Technologies
Its company partners with local service providers to build a global IoT network;
* Random Phase Multiple Access (**RPMA**), a communication system offered by Ingenu;
* **Weightless, a proposed proprietary wireless technology standard. ** Its idea is to exchange data using unoccupied channels for TV transmission providing great security levels [4]. Figure 2: Classification of licensed (3 GPP) and non-licensed (non-3 GPP) LPWAN technologies. The emerging LPWAN choices such as RPMA and Weightless were not considered in this work due to the interferences from WiFi and Bluetooth that may occur and the limited hardware availability [7; 8].
# A Novel Physics-Guided Neural Network for Predicting Fatigue Life of Materials
## 2 Materials and Methods
### The Structure of the Proposed Method  
#### 2.2.1 Introduction of Artificial Neural Network  
An artificial neural network is a mathematical model similar to the structure of brain synaptic connections to process information. Similar to brain cells, an ANN consists of a large number of processing units called neurons. The most widely used ANN is back the propagation neural network (BPNN) [6]. The BPNN is a multi-layer, supervised,feedforward network using back propagation rules. The BPNN can learn and store a large number of input-output mode mapping relationships without revealing the mathematical equations describing this mapping relationship in advance. Its learning rule is to use the steepest descent method to continuously adjust the weight and bias of network neurons through back propagation, so as to minimize the error of the network. Its main feature is that the signal is forward propagation, and the error is back propagation.
## 4 Power Flow Diagram of Herringbone Gear Planetary Drive System  
### System Power Flow  
The two-stage herringbone planetary transmission system is illustrated in Figure 8. A schematic representation of the structure is depicted in Figure 8a and the corresponding power flow diagram is shown in Figure 8b. The power flow diagram uses the following conventions: components are represented by a solid circle, bearing contacts are represented by a hollow circle, and teeth contacts are shown as a hollow triangle. In the first diagram, 1 represents the frame, 2 denotes the first-stage sun gear, 3 represents the first-stage transmission planet carrier, 4 is the first-stage ring gear, 4\" represents the secondary sun gear, 5 is the secondary planetary gear, 6 represents the secondary ring gear, and power is output via the secondary ring gear. Hollow circles P\({}_{R1}\), P\({}_{R2}\), and P\({}_{R3}\) represent bearing contact power losses; hollow triangles P\({}_{G2-3}\), P\({}_{G3-4}\), P\({}_{G4^{-}5}\), and P\({}_{G5-6}\) represent gear meshing power losses; and subscript 2-3 indicates that gear 2 and gear 3 are meshed.
## 3 Results and Discussion  
The TIS-II GFP Imager was transported to the HMP RS in the 2006 summer field season for the primary purposes of defining the operational parameters of including the TIS-II GFP Imager as part of the sensor suite in the ACMG. Before installation in the ACMG the imager was bench tested at HMP with a laptop computer for passing control and data, operating via the local one (1) gigabit-per second switched fiber network. During the bench testing phase, the imager was enabled for remote internet access and data exchange control originating with the Experiments Monitoring Area at Kennedy Space Center (KSC). These communications were managed by the high-speed network infrastructure of the HMP, designed to emulate planetary exploration networks, and demonstrated the processing independence of the TIS-II GFP Imager, while also ground-testing the fundamental communications protocols for sending commands and receiving data from the imager, regardless of distance. Commands to take images, store images, process images and download images from KSC proved effective. This test established that the native high-speed HMP network infrastructure, satellite communications, and protocols were sufficiently robust to handle the command and data flow, and that the size and structure of the imager commands and image data files were appropriate for stable deployment within the HMP system.
## 6 Performance Evaluation
### Detection Performance Evaluation of Generated Data  
CNN was used to verify the usability of the data generated using the generative model. Training was conducted by constructing generated images and noise images. The test for verification consisted of images not used in the generative model, generated images, and noise images. The CNN model for learning the generated image used the same model as Figure 11. This is the same model that tested the detection performance of the image size.  
The model's performance is presented in Table 6, using evaluation indices such as accuracy, precision, recall, and f1-score. The training results demonstrate high performance, achieving an accuracy of approximately 0.98, a precision of 0.94, a recall of 0.96, and an f1-score value of 0.95 when training was conducted solely with generated images. However, the detection performance using the test set appears to be relatively lower. This can be  
Figure 18: Loss value of generative model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Count** & **FID** & **Cross-Correlation** \\ \hline
1 & 1.6798 & 0.9845 \\
2 & 8.2314 & 0.7722 \\
3 & 0.7558 & 0.9513 \\
4 & 11.1742 & 0.8512 \\ Average & 5.4603 & 0.8898 \\ \hline \hline \end{tabular}
\end{table}
Table 5: FID and cross-correlation for each count.  
attributed to the fact that the model was well-trained to distinguish the characteristics of the images during the training stage. Moreover, the inclusion of both malware and normal images in the practice may lead to slightly lower performance for detecting normal images. Nevertheless, the overall training results indicate promising performance, showcasing the model's ability to accurately classify and distinguish between different image types.  
Table 7 presents the results of the comparison with other models. It has been observed that the accuracy of our model surpasses that of previous studies. While precision, recall, and f1-score were only available in certain studies, our model still exhibited superior performance compared to the previous approaches.  
The proposed model offers several distinct advantages over simulation in a digital twin environment. First, it demonstrates the ability to effectively detect new malware that did not exist before and combat new threats. Second, the detection model is trained without separate pre-work for the detection model. Unlike previous studies, our model simplifies the overall workflow by eliminating the need for feature extraction via specific filters or APIs. Third, it simplifies and streamlines the process by eliminating the requirement to directly run the model in a separate sandbox.  
Leveraging these advantages, our proposed model represents a significant advance in malware detection, providing a more streamlined and robust approach compared to existing methods in the field. Simulation results of the digital twin environment demonstrate the robustness and effectiveness of the proposed model in protecting against evolving threats.
## 3 Model Assumption and Toss-and-Catch Algorithm
### GPS Positioning of OIS  
The OIS's positioning information is obtained at set intervals using GPS. \(t_{n}\) (\(n=0\), 1, 2, 3,... ) is defined as the time series of the location data as obtained by the OIS's GPS. When the OIS is performing a kernel selection, the only located-related information that it obtains is that relating the SBSs. As we lack information regarding the OIS's current position, the OIS's GPS hardware is therefore used to obtain its latitude and longitude coordinates at \(t_{n}\), which are then converted for use in the kernel coordinate system; the OIS's relative positional relationship (with the nearby SBSs ahead of it) is subsequently obtained to determine if a handover is necessary at \(t_{n+1}\), as well as to plan ahead and identify SBS options for the handover. In Figure 9, the train is in Cell B at \(t_{0}\), and it is in the process of determining its current position and whether a handover is necessary at \(t_{1}\) based on this position; at the same time, it is also attempting to identify suitable SBS options for the handover at its estimated location (Cell D or Cell F) at \(t_{1}\).  
Figure 8: Identification of auxiliary small cell base stations (ASBSs) and other small cell base stations (OSBSs).  
Figure 9: Determination of small cell base station (SBS) for handover by the OIS.  
With \(t_{n}\) being the current time point, the kernel coordinates of the OIS at \(t_{n}\) were defined as \((T_{x}^{t_{n}},T_{y}^{t_{n}})\), and \(v^{t_{n}}\) was defined as the OIS's velocity (in the kernel coordinate system) at \(t_{n}\). The velocity components for directions \(x\) and \(y\) were \(v_{x}^{t_{n}}\) and \(v_{y}^{t_{n}}\), respectively, and expressed as:  
\[v_{x}^{t_{n}} =v^{t_{n}}\cos\tan^{-1}m, \tag{8}\] \[v_{y}^{t_{n}} =v^{t_{n}}\sin\tan^{-1}m. \tag{9}\]  
Assuming a minimal value for \(t_{n+1}-t_{n}\), the coordinates \((T_{x}^{t_{n+1}},T_{y}^{t_{n+1}})\) of OIS at \(t_{n+1}\) were expressed as:  
\[T_{x}^{t_{n+1}} =T_{x}^{t_{n}}+v_{x}^{t_{n}}(t_{n+1}-t_{n}), \tag{10}\] \[T_{y}^{t_{n+1}} =T_{y}^{t_{n}}+v_{y}^{t_{n}}(t_{n+1}-t_{n}). \tag{11}\]  
The acquisition of the OIS's current and predicted positions at \(t_{n}\) and \(t_{n+1}\) facilitates subsequent calculations performed by the score-based selection and overload support mechanisms and during the handover stage.