esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Evaluation
### Section A: Understanding the Data and Metadata  
In the first part, the participant was given a brief introduction to the research and what was expected of them in this study. They were presented with a sample of the anonymised CSV file containing the data analysed (Figure 2). Each entry contains the date and time of an event, the location ID corresponding to a particular space of the house, and the sensor status, with \"1\" representing that the sensor was activated.  
The custom-built tracker was a (Passive Infrared) PIR sensor attached to a Raspberry Pi 4. This tracker device was placed in each room of the house to continuously, anonymously and unobtrusively monitor the transitions between locations by measuring the RSSI strength of the Bluetooth signal emitted by a BLE device worn by the monitored subject. The novel linking of the PIR and RSSI was imposed to avoid false positives due to the proximity of the rooms and fluctuations in RSSI signal measurements. The indoor tracking system and the collection of data for testing and evaluation were approved by the Research Ethics and Integrity Committee of the TU Dublin.  
Then, they were shown a representation of the layout of the house where the monitored subject lived. This Tube-map visualisation of the house is a digitised pseudo map designed to make it easier to understand the possible transitions between rooms, e.g., adjacent rooms. To this end, the rooms are represented by circles of different colours, i.e., every bathroom is coloured pink and every bedroom green, and the possibility to move between two locations (which we define as a transition) is represented by a straight line (which we define as an edge), as shown in Figure 3. In addition, the average distance in metres between two rooms is also shown, calculated as the distance from the centre point of one room to the transitioning area, i.e., door, open wall, lift, or staircase, and from this point to the centre of the adjacent room. Finally, the average time it would take to cover this distance for a 70- to 80-year-old person is also indicated. It was explained to the participants that this information is obtained from the expanded Building Information Model (BIM) based on the original BIMXML model, which is a key enabler for the reusability of the process, regardless of the architectural characteristics of the house.  
Based on this information, the participant was asked to rate, on a scale from 0--Very difficult to 4--Very easy, their ability to understand the possible movements that can be made by a resident based on the floor plan of the house.  
Figure 2: Sample extracted from the CSV dataset.
# Three-Dimensional Modeling of a Robotic Fish Based on Real Carp Locomotion
## Three Dimensional Dynamic Model and Motion Equations of the Robotic Fish
### Modeling of the Up-Down Motion Mechanism  
In nature, up-down motions such as ascending and descending are another important criterion to exhibit high swimming performance. In order to generate ascending and descending motions, a fish changes its center of mass (COM) position and uses the air bladder. Figure 7 gives the descending pattern of the carp during 1.05 s. In this analysis, pitch angle of the carp is measured as 35\({}^{\circ}\). This performance is the high motion ability for a real fish and similar values of this angle are validated by using the derived model.  
In Figure 8a,b, the model of the up-down motion mechanism and flow chart, which represents the working principle of the mechanism, are given, respectively. An up-down mechanism is designed and modeled by using the Lagrange approach to generate and control pitch and roll motions.  
Figure 7: Descending pattern of the carp during 1.05 s.  
This mechanism is placed inside the anterior robotic fish body. While the robotic fish is swimming under water, swimming speed is not zero and the depth position of the robotic fish in the vertical plane can be controlled. Control of the orientation angles are performed by changing COM position. There are two masses \((m_{x},m_{y})\) moving on the shaft. Shafts are connected to the pulley sets with DC motors. By moving the masses along the x and y-axis, the up-down mechanism changes the pitch and roll angles.  
When mass positions change, pitch and roll torques are generated. These mentioned torques are considered in Equations (10) and (11), respectively.  
\[\tau_{x}=g((l_{x}\sin(\phi)\sin(\theta)-h_{x}\sin(\phi)\cos(\theta))m_{x}+(l_{ y}\cos(\phi)-h_{y}\sin(\phi)\cos(\theta))m_{y}) \tag{10}\]  
\[\tau_{y}=g((l_{x}\cos(\theta)-h_{x}\sin(\theta))m_{x}+(h_{y}\sin(\theta))m_{y}) \tag{11}\]  
Figure 8: (**a**) Model of the up-down motion mechanism; (**b**) Flow chart of the up-down motion.  
Here, \(g\) is the acceleration of the gravity, \((l_{x},l_{y})\) are the distance from the origin and \((h_{x},h_{y})\) are the heights from the axis.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 3 Issues Faced by RPL during Mobility
### Lack of Identification of Mobile IoT Nodes  
In IoT, a node can either be stationary or mobile, so it is pertinent to identify both of these types. Mobile nodes result in a time-varying network topology that is most likely to cause invalid routes and link breakage in DAGs. However, traditional RPL has no mechanism to differentiate between stationary and non-stationary nodes [51]. This differentiation is important as it enables RPL to optimize routing in the existence of mobile nodes [24; 38; 52].
## 5 Adhesion Improvement Assessing
### Microcomposites Interphase Characterization  
After the fragmentation test, the morphology of cryogenically obtained fracture surfaces of microcomposites was analyzed by SEM. Figure 16 shows the appearance of both untreated and treated samples with different concentrations of hydroxy-\(\alpha\)-olefin, as observed at 6,000\(\times\) magnifications. For the sample PP/F0/PP, the neat fiber pullout and the absence of polymer attached to the fiber surface clearly indicate a very limited adhesion between PP and the fiber (Figure 16a). On the other hand, the fracture surfaces of microcomposites made with _in situ_ polymerized fibers show features indicating a better fiber-matrix adhesion. In fact, as documented in Figures 16b-e, PP partially adheres to the fiber surface. Figure 17, at higher magnification, also shows that PP is adhered to the the fiber surface of F1.0 and F1.5 samples. The increment in adhesion is also demonstrated by the radial fracture cracks emanating from the fiber interface (Figures 16d, e and 17b). This latter feature indicates that the PP-glass interface strength is more stable than the PP-PP entanglements and the fracture occurs in the PP region around the fibers, not at the interface.  
Details of the fiber-matrix \"interphase\" for the two best conditions (1% and 1.5%) are shown in Figure 17. In these it is clear that the fiber-matrix bond is retained after the fracture (micro-void). Much of the polymer bonding remains and is evenly distributed on the fiber surface. Major approaches in other areas of the fiber surface after fracture (Figure 18) confirm the earlier observations. The polymer is evenly distributed on the fiber surface and is seen as a bump smaller than 200 nanometers. This is consistent with the polymer attaching to the surface and then splitting at room temperature and returning to its \"curled up\" structure.  
These observations support the hypothesis that the fracture in the fiber microcomposites copolymers preferably occurs in the polymer mass surrounding the fiber, with the entanglement breaking off rather than splitting between the glass and the polymer, confirming that the technical compatibility proposal greatly improves adhesion.  
Figure 16: SEM microphotographs (6,000x) from cryogenic fracture surface of different microcomposites: (**a**) PP/F0/PP; (**b**) PP/F0.5%/PP; (**c**) PP/F1%/PP; (**d**) PP/F1.5%/PP; (**e**) PP/F2%/PP [51].
## 2 Photo-Grafting Reactions
### Monolithic Columns with Bonded Ligands
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Monolith** & **Reaction** & **Graft energy** & **Time** & **Reference** \\ \hline
**Poly(BuMA-co-EDMA)** & Single and sequential two step grafting of monomers. & 500 W & 60 * min & [7] \\ Poly(BuMA-co-EDMA) & Sequential grafting of PEGMA and VAL. & 500 W & 60 * min & [8] \\ \hline
**Poly(BuMA-co-EDMA)** & Single step grafting of gradients of AMPS. & 500 W & 1 to 10 min & [10] \\ Poly(BuMA-co-EDMA) & Single step grafting of gradients of AMPS. & 3 J/cm\({}^{2}\) & N/A & [11] \\ \hline
**Poly(BuMA-co-EDMA)** & Sequential grafting of PEGMA, VAL, and SPM. & 1 J/cm\({}^{2}\) each step & N/A & [13] \\ Poly(BuMA-co-EDMA) & Single step grafting of AMPS. & 0.25 to 7 J/cm\({}^{2}\) & N/A & [29] \\ \hline
**Poly(BuMA-co-EDMA)** & Single step grafting of AMPS and butyl acrylate. & 500 W & 0.5 to 1.5 & [36] \\ Poly(BuMA-co-EDMA) & Single step grafting of AMPS and VAL. & 500 W & 30 min & [37] \\ \hline
**Poly(BuMA-co-EDMA)** & Single step grafting of META and butyl acrylate. & 1.4 mW/cm\({}^{2}\) & 3 min & [67] \\ Poly(BuMA-co-EDMA) & Single step grafting of VAL for dual function monolith. & 15 mW/cm\({}^{2}\) & 3 min & [68] \\ \hline
**Poly(GMA-co-EDMA)** & Single step grafting of META. & 0.25 J/cm\({}^{2}\) & N/A & [69] \\ \hline Poly(BuMA-co-EDMA) & Single step grafting of AMPS and VAL. & N/A & 60, 30 min & [70] \\  & & & respectively & [71] \\ \hline
**Poly(GMA-co-EDMA)** & Sequential grafting of PEGMA and VAL. & 12 mW/cm\({}^{2}\) & 4 * 1 to 6 & \\  & & & min (VAL) & [71] \\ \hline
**Poly(BuMA-co-EDMA)** & & & & \\
**and** & Sequential grafting of PEGMA and VAL. & N/A & 4 *, 2 min & [72] \\
**Poly(GMA-co-EDMA)** & & & & \\ \hline
**Poly(BuMA-co-EDMA)** & Sequential grafting of PEGMA and VAL. & 3 * J/cm\({}^{2}\) & N/A & [74] \\ \hline
**Poly(BuMA-co-EDMA)** & Single step grafting of VAL. & 3 J/cm\({}^{2}\) & N/A & [75] \\ \hline
**Poly(BuMA-co-EDMA)** & Single step grafting of spiropyran monomer using VIS & & & \\
**Poly(BuMA-co-EDMA)** & LED at 660 nm. & 0.5 cd & 2 min & [77] \\ \hline
**Poly(LMA-co-EDMA)** & Multi step grafting of VAL. & 1 * J/cm\({}^{2}\) & N/A & [78] \\ \hline
**Poly(LMA-co-EDMA)** & Multi step grafting of GMA. & 1 * J/cm\({}^{2}\) & N/A & [79] \\ \hline \end{tabular}  
* Each step _i.e._, initiator immobilization and subsequent grafting reaction.  
\end{table} 
Table 1: Reactions used in the production of photo-grafted stationary phases.
## 3 Material and Methods
### Machine Learning Model Descriptions
#### 3.3.5 LightGBM  
Light Gradient Boosting Machine (LightGBM) is a machine learning algorithm built on the gradient boosting framework. LightGBM is known for offering high efficiency and speed, low memory usage, and high accuracy rates on large datasets. Unlike traditional gradient boosting methods, instead of randomly selecting samples in the dataset, LightGBM processes the data using innovative techniques such as gradient-based one-sided sampling (GOSS) and exclusive feature bundling (EFB). These strategies significantly reduced the computational load and memory usage, making LightGBM widely used in energy forecasting systems [25].
# Advanced Assistive Maintenance Based on Augmented Reality and 5G Networking
## 6 Prototyping and Testing
### Testing Environment  
Obstacle detection tests were conducted with four human testers inside a Vodafone Base Transceiver Station (BST) to simulate real-case maintenance tasks.  
Each tester was located inside the BTS wearing the prototype AR helmet. HoloLens was connected wirelessly to the CPE and two computers were connected to the same network: a terminal for the remote assistant in the control room; the MEC, hosting the developed algorithms for obstacle detection. Two applications were running on HoloLens: a video call app, allowing the communication between the on-field tester and the remote assistant; the obstacle visualization app, which receives 3D coordinates from the MEC and projects holograms onto the AR viewer in correspondence of the spatial locations of the detected objects. Both on-board and MEC applications were developed in Unity.  
Thermal sensing integration tests were conducted during the _Milano Digital Week 2019_--held in March 2019, in Milan, Italy--on the occasion of the first presentation of the prototype. Tests were carried out by utilizing an incandescent lamp that was being detected by the thermal camera and whose coordinates were correctly estimated by the algorithm described in Section 5.1 and displayed by a 3D holographic marker on the AR viewer. The choice of which points to reproject from the thermal camera plane for 3D triangulation was done according to a temperature threshold of 60 \({}^{\circ}\)C.
## 3 Results and Discussion  
### Effects of Lithology  
The intricate CO\({}_{2}\)-brine-rock matrix interaction in different lithological carbonate reservoirs, particularly limestone and dolostone reservoirs, showcases markedly different physicochemical responses under equivalent operational conditions. Figure 2a,b demonstrates these disparities, focusing on the respective variations in pore volume augmentation and pressure escalation within the limestone and dolostone reservoirs during CO\({}_{2}\) injection at a fixed rate of 0.1 mol/min. With an underlying assumption that the limestone is exclusively composed of calcite and the dolostone solely of dolomite, the depicted outcomes reveal a more pronounced pore volume enlargement observed in the limestone reservoir in comparison with that in the dolostone reservoir. Specifically, the introduction of 4200 mol of CO\({}_{2}\) into the limestone reservoir catalyzed an 8.5% increment in pore volume. However, this value is reduced to a paltry 0.16% in the dolostone reservoir, one-fifth of the limestone value. This pronounced disparity can be attributed to the inherent reactivity characteristics of the reservoir materials, where calcite is characterized by a significantly higher propensity to react with carbonate brine compared to dolomite. This is further evidenced by the superior reaction rate between calcite and carbonate brine. Moreover, the reactivity variance precipitates a more rapid pressure accumulation in the limestone reservoir in comparison to that in the dolomite reservoir, as delineated in Figure 2b. This observation is primarily driven by two key factors. Firstly, the rapid interaction between the limestone's rock matrix and carbonate brine facilitates an accelerated CO\({}_{2}\) consumption, which consequently results in a smaller amount of CO\({}_{2}\) in the pore space in the limestone reservoir in comparison to that in the dolostone reservoir. As a result, the reservoir pressure increases at a slower rate in the limestone reservoir than its dolostone counterpart. Second, the above-mentioned faster increase in the pore volume within the limestone reservoir, relative to the dolostone reservoir, plays a crucial role. According to the real gas law expressed as \(P=ZnRT/V\), a larger pore volume can lead to a lower reservoir pressure. There, an expedited volumetric expansion effectively slows down the rate of reservoir pressure build-up in the limestone reservoir.  
These findings underscore the paramount importance of mineralogical composition in the practice of CO\({}_{2}\) storage and sequestration in carbonate reservoirs. For example, dolostone reservoirs commonly have superior initial injectivity due to their more developed natural features compared to limestone reservoirs. However, the limestone reservoirs may hold greater potential for CO2 storage and less operational difficulties in CO2 injection in the long run because of their higher reactivity with carbonate brine [31].
# Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization
## 3 Method
### Reconstruction Network
#### 3.2.2 Selective Kernel Feature Fusion (SKFF)  
Our decoder generates four feature maps with different resolutions, and we employed the SKFF [31] module for feature fusion. The SKFF allows for the selection of different convolutional kernels at different spatial positions to facilitate the fusion of features from different scales, enabling the integration of multiscale reconstruction features. This approach avoids directly connecting each feature map and instead aggregates weighted features, addressing the issues of a large number of parameters and higher computational complexity in the M-net.
## 4 Feasibility of Monitoring-Sensor Technology
The numerical simulations are also very useful in selecting the proper range of motion and tension sensors. Sophisticated and integrated research is needed for the optimal design of highly functional monitoring system. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Environmental Condition**} & \multicolumn{4}{c}{**Surge Motion of the Location Buoy**} & \multicolumn{2}{c}{**Rope Tension**} & \multicolumn{2}{c}{**Mooring Tension \#1**} \\ \hline \multirow{2}{*}{\(H_{s}\) **(m)**} & \multirow{2}{*}{\(T_{p}\) **(s)**} & \multirow{2}{*}{\(V_{c}\) **(m/s)**} & **Average** & \multirow{2}{*}{**STD (m)**} & **Maximum** & **STD** & **Maximum** & **STD** \\  & & & **(m)** & & **(kN)** & **(kN)** & **(kN)** & **(kN)** \\ \hline \multirow{4}{*}{1.0} & \multirow{4}{*}{5.25} & 0.25 & 56.788 & 1.119 & 1.092 & 0.089 & 4.217 & 0.387 \\  & & 0.50  
Figure 11: Time histories of rope tension in different wave conditions under identical current velocity (\(V_{c}\) = 1.0 m/s). Figure 10: Time histories of rope tension at different current velocities under identical wave excitations (\(H_{s}\) = 1.0 m, \(T_{p}\) = 5.25 s). Figure 12: Time histories of mooring tension at different current velocities under identical wave excitations (\(H_{s}\) = 1.0 m, \(T_{p}\) = 5.25 s). Figure 13: Time histories of mooring tension in different wave conditions under identical current velocity (\(V_{c}\) = 1.0 m/s).
# Development of a Novel Deployable Solar Panel and Mechanism for 6U CubeSat of STEP Cube Lab-II
## 5 Experimental Validation
### Launch Vibration Environment Test
The dynamic displacement of the VMLSA is reduced by a factor of 4.3 relative to a typical PCB solar panel owing to superior damping resulting from shear deformation of viscoelastic adhesive tapes. Figure 16: The results of VMLSA’s corresponding axis random vibration tests in the \(x\)-, \(y\)-, and \(z\)-axis excitations.
# Machine Learning Techniques for Soil Characterization Using Cone Penetration Test Data
###### Abstract
Many ML algorithms, such as gradient boosting, random forest, support vector machine (SVM) artificial neural network (ANN), and decision trees (DT), have been used in various geotechnical applications, including soil classification [27; 28; 29; 30; 31; 32; 33], \(V_{s}\) prediction [23; 24; 25; 26; 34], liquefaction analysis [35; 36; 37; 38; 39; 40], stability analysis [41; 42; 43; 44; 45], and settlement prediction [46; 47; 48]. The application of ML algorithms in geotechnical engineering has shown promising results in terms of efficiency and accuracy. For example, [PERSON] et al. [25] successfully employed an ANN model to characterize soil stratigraphy and predict \(V_{s}\). This study demonstrates how ML approaches can be used to improve soil characterization and prediction of important geotechnical parameters. [PERSON] et al. [24] and [PERSON] et al. [49] have also used ML algorithms, including RF and XGBoost, to predict \(V_{s}\). Their findings confirm that ML models can achieve high accuracy and performance in predicting \(V_{s}\). Previous research has also shown that SVM performs well in predicting \(V_{s}\)[50; 51]. These studies collectively demonstrate the potential of ML algorithms in improving the accuracy of \(V_{s}\) prediction in geotechnical engineering applications. The aim of this study is to improve the prediction of \(V_{s}\) using various ML algorithms with minimal input features. Four ML algorithms, namely RF, SVM, DT, and eXtreme gradient boosting (XGBoost), are employed to predict \(V_{s}\) from CPT data. The study also aims to minimize the need for expensive and time-consuming fields or laboratory measurements. The development of ML models can lead to higher accuracy and performance in predicting \(V_{s}\). The improvement in the accuracy of \(V_{s}\) prediction has significant implications for site response assessment and seismic risk reduction. By utilizing ML to predict \(V_{S}\), this study has the potential to enhance existing knowledge and inspire future research in the field of ML applications for soil characterization. The rest of this document is organized as follows: Section 2 discusses dataset preprocessing and visualization, Methodology and performance metrics are described in Section 3, Section 4 describes the ML models, and Section 5 presents the results. Finally, Section 6 outlines the main results of the study and concludes by suggesting future research.
## 3 LDAF Background and Overview
### LDAF Architecture  
#### 3.1.1 Messages  
A LDAF message is comprised of a header and payload. As can be seen in Figure 2, the header has two fields, namely Type and SequenceNumber. With default settings each field is 1 B long, but can be reconfigured if more message types and longer sequences are required.  
A message sequence is required to associate the responses with the appropriate request. However, it could be applied in the future to increase the reliability of message transfer at the application layer (reordering message sequences, detecting message loss, ARQ). In push messages from the server to the client the SequenceNumber is omitted because such a message is not a response to a specific request.  
The message type determines which schema must be applied to decode the binary message content. Both the message Type and the SequenceNumber are emitted to the available Services, so that the corresponding Service decodes and processes the message.  
There can be an arbitrary number of message types required by one Service. This would make static type assignments inefficient; we therefore applied a dynamic assignment. The message type is indicated by the value at the corresponding location in the Type parameter. The Type parameter is interpreted by a service offset, which points to the first message type for each particular service.  
Figure 1: Overall architecture of low-bandwidth distributed application framework (LDAF): LDAF Server, LDAF Clients, and external application programming interfaces (APIs).  
Figure 2: LDAF message format.  
The following service offset points to the location after the last message type of the previous service. The offsets of all services applied in a connection are specified in the service definition.  
We thus minimized the data required for the appropriate type identification. For each message we thus introduced 2 B of additional overhead and only 1 B for push messages. The remainder is the payload. With 1 B for the Type field we can organize, e.g., 1 Service with up to 256 different messages types or two Services, one with 20 and the other with up to 236 types. For the Ethereum--based smart grid meter, for example, four types of messages are required: (i) a periodic block notification, (ii) an event notification, (iii) a request, and (iv) a response for transaction verification.  
LDAF messages are thus encoded in two substages. The first is the efficient binary serialization of the message content (e.g., protocol buffers [42] and js-binary), and the second is building the message header, which consists of SequenceNumber and Type, as depicted in Figure 2 and explained previously. If required, other serialization methods can be included in the framework.