esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study and Results: An Assessment of Earthquake Hazards in Portugal
### Identification and Data Gathering  
The basis of our analytical structure is rooted in a comprehensive data gathering process. Our assessment is supported by a variety of datasets, with a focus on the following:  
* Earthquake Information: Central to our study, we gathered information on both near-field (EC2) and far-field (EC1) earthquakes [26; 99]. These data provide a detailed look at Portugal's history of seismic events. Additionally, we utilized geospatial data from the Portugal cities border shapefile, obtained from the Global Administrative Areas (GADM) database, to define geographical boundaries that are very important for our analysis;
* Demographic and Socioeconomic Data: We collected detailed demographic and socioeconomic information from the Instituto Nacional de Estatistica (INE), Portugal's National Statistics Institute [100]. These data cover all 308 municipalities in Portugal, offering a comprehensive view of the social and economic landscape;
* HASC Naming Convention: To ensure a precise and clear understanding of Portugal's municipalities and cities, we adopted the Hierarchical Administrative Subdivision Codes (HASC) system [101]. This coding, which combines letters and dots, distinctly identifies each municipality within its city. For example, 'PT.AV.AG' signifies the municipality of Agueda in Aveiro. This methodical naming system aids in accurately overlaying earthquake data, demographic, and socioeconomic information across the 308 municipalities, thereby thoroughly encapsulating the intricacies of each area. Utilizing such a structured naming convention enhanced the precision of our analysis and makes our findings more accessible and understandable to stakeholders.
## 3 Dynamic Analysis and Problem Description during Engine Starting Process
### Problem Description during Engine Starting Process  
Fuel economy can be effectively improved using the switching working modes for a PHEV [36,37]. However, as shown in Figure 7 for the compound PS-PHEV, during the mode switching process, which includes the engine start-up process, the distribution torque command of the two motors can be obtained by the superposition calculation of the motor distribution torque from Equation (14) and the active damping compensation control (ADCC) torques [19,20]. However, in some engine starting conditions, due to the ERT, the elastic characteristics of the transmission system, limitations of battery power, and limitations of motor maximum torque, the torques of the two motors, are limited, which make them unable to effectively compensate or offset the fluctuating torque of the powertrain. These factors will result in obvious transmission jerks and resonance, which will eventually make driving less comfortable and reduce the service life of the vehicle.  
Therefore, in view of the above problem, it is necessary to design a dynamic torque-coordinated control that considers engine starting conditions to further reduce the system shocks and vibrations.
# Neutron and Gamma Pulse Shape Discrimination by Robust Determination of the Decay Shape
## 4 Conclusions  
The robust determination of the decay shape as a new alternative for neutron/gamma discrimination has been proposed. It has been shown that the determination of the pulse shape in the decaying part by a robust estimation method can be used to successfully discriminate neutrons and gamma rays. For a given dataset, the corresponding FOM value improves on the FOM value of the standard charge comparison method by 10%. The determination of the pulse shape with a time window starting a little after the time of maximum amplitude can also provide satisfactory results, though of slightly lower quality, both by least squares and robust estimation. In terms of computing time, the robust estimation algorithm is around one order of magnitude slower than least squares, with computing times of tens of seconds instead of a couple of seconds for the dataset in use with a standard personal computer (Intel Core i5-12400 with 32 GB RAM). Additional research in order to test the proposed method (and the corresponding computing codes provided) in other datasets seems necessary to better assess its effectiveness.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14135532/s1](https://www.mdpi.com/article/10.3390/app14135532/s1)]([https://www.mdpi.com/article/10.3390/app14135532/s1](https://www.mdpi.com/article/10.3390/app14135532/s1)), Computer code S1: Shape_discrimination_1.m, least squares results for all decaying part; Computer code S2: Shape_discrimination_2.m, robust estimation results for all decaying part; Computer code S3: Shape_discrimination_3.m, least squares results for the time window; Computer code S4: Shape_discrimination_4.m, robust estimation results for the time window.  
Conceptualization, E.M.; methodology, E.M. and S.B.; software, S.B.; validation, E.M. and S.B.; formal analysis, E.M. and S.B.; investigation, E.M. and S.B.; resources, S.B.; data curation, S.B. and E.M.; writing--original draft preparation, S.B. and E.M.; writing--review and editing, S.B. and E.M.; visualization, S.B.; supervision, E.M.; project administration, E.M. and S.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used for the study are openly available in Zenodo at [[https://zenodo.org/records/7754573](https://zenodo.org/records/7754573)]([https://zenodo.org/records/7754573](https://zenodo.org/records/7754573)) (accessed on 20 June 2024).  
The authors would like to express their gratitude for the preparation and release of the above-mentioned openly accessible dataset with more than 10,000 neutron gamma radiation pulse signals.  
The authors declare no conflicts of interest.
# Carbon Nanotube-Based Composite Filaments for 3D Printing of Structural and Conductive Elements
## 2 Materials and Methods  
### Materials Characterizations  
The materials used for the fabrication of composites filaments consist of multiwall carbon nanotubes NC7000 purchased from Nanocyl SA., Belgium, and acrylonitrile butadiene styrene HF-0660I obtained from Starex. As a solvent for ABS during the process, acetone purchased from Linegal Chemicals, Poland, was used. NC7000 CNTs are characterized by an average diameter of 20 nm, an average length of 1-5 \(\upmu\)m, and a specific surface area of 250-300 m\({}^{2}\)/g. Figure 1 shows a scanning electron microscopy (SEM) image of CNT.  
ABS polymer was selected as the matrix material because of its widespread use in 3D printing due to its good mechanical properties, suitable processing temperature, and suitable rheology for FDM technology. CNT was selected as the functional phase because of its unique electrical and mechanical properties that allow it to develop electrically conductive composites with improved mechanical properties as an alternative to other non-carbon conducting phases, such as metal powders, that usually decrease the mechanical properties of the composite [32; 33; 34].
## 2 Beam Dynamics Design
### Choice of Beam Dynamics Parameters
#### 2.2.3 Inter-Vane Voltage  
In the subsequent stage of the design procedure, determining the optimal inter-vane voltage \(V\) becomes important to achieve a high accelerating gradient \(E_{acc}\) while ensuring that the peak surface electric field \(E_{sp}\) remains within acceptable limits. Both \(E_{acc}\) and \(E_{sp}\) are proportional to \(V\). In RFQ cavities, the power consumption per unit length \(P/L\) is expressed as [29]:  
\[\frac{P}{L}=\frac{E_{acc}^{2}}{\frac{R}{Q_{0}}Q_{0}}, \tag{5}\]  
where \(\frac{R}{Q_{0}}\) is the geometric shunt impedance in \(\Omega/\)m; it only depends on the geometry of the structure. Given that the quality factor \(Q_{0}\) of SC cavities is typically 5 orders of magnitude higher than that of NC cavities, approximately in the range of \(10^{9}\)-\(10^{10}\), relatively large inter-vane voltages can be applied to SC cavities and achieve remarkably low power consumption.  
We have investigated the \(E_{sp}\) achieved in the vertical tests of international SC half-wave resonators (HWRs). Due to advancements in processing and post-treatment technology, experimental \(E_{sp}\) values have increased a lot. For instance, in the SC HWR study at the Institute of High Energy Physics (IHEP) in 2013 and the Rare Isotope Science Project (RISP) study at the Korea Institute of Basic Science in 2016, the achieved \(E_{sp}\) values when field emission occurs were approximately 66.2 MV/m and 72 MV/m, respectively [30; 31]. In the SC HWR experiment at Argonne National Laboratory in 2017, \(E_{sp}\) reached 134 MV/m [32]. Based on the experimental data, in order to ensure the stable operation of our SC RFQ, we opted for a scheme with an inter-vane voltage of 240 kV, yielding an \(E_{sp}\) of approximately 40 MV/m.
## 3 Methods
### The Data Collection and Analysis Methods
#### 3.4.1 Observational and Performance Data  
During the experiment, each task was video-recorded for each participant (with consent prior to the simulation) from an angle that captured the participants' hand gestures in interacting with the TV-screen-based interface and the XR-based spatial user interface. Also, the monitoring screen that controlled and monitored the interfaces (especially the first-person view via the Meta Quest 3) of the TV and XR platforms in each instance was screen-recorded to check the overall usability of the hand gestures as well. The entire recordings per participant were saved as anonymous documents and were later reviewed to conduct in-depth observations of usability. The objective measure was the \"gesture execution time (GET)\" for each gesture type. Via Adobe Premiere Pro (ver. 25.2.3, Adobe Inc., San Jose, CA, USA), the time intervals that represented the GET for each individual gesture type were observed, and the interval within a recorded video clip was marked using a marker tool (Figure 6) to visually identify the intervals where specific gestures were performed in the video clip.  
The measurement method for the duration of the performed gestures was organized as follows. In the TV and XR environments, the total duration of each ring gesture was measured under the default conditions of a 30 fps timecode. The measurement began the moment that a user raised their hand or wrist to initiate a small movement and ended when the final home gesture was completed, triggering a screen transition. Even if gestures were repeated for confirmation or there were long intervals due to conversation, these durations were included in the total time, as higher engagement (hedonic quality) implies focus on the gesture. Additionally, if no corresponding video was available, XR measurements were taken using the monitoring screen recordings.  
Figure 6: A screenshot of marking specific gesture segments using the marker tools in Adobe Premiere Pro (ver. 25.2.3).
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 6 Implementation
### Testbed #1: University Facilities
In our particular case, the first 8-bit number identified the network prefix shared by all devices, and the second one the host part, whose value for GWs was 0 and for STAs was selected from 1 to 255. All tests began with a _network association primary beacon_ in which all nodes tried to associate to the network. From then on, the GW emitted a new (_network association_ or _data_) _primary beacon_ every \(T_{p}=3\) min. _Data primary beacons_ could ask STAs for a new _application_ or _statistics packet_. In all our tests, _application_ and _statistics packets_ generated by STAs contained, respectively, \(L_{\rm{ap}}=10\) bytes and \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Operational State** & **Current** \\ \hline
**Microprocessor** & Processing (CPU) & \(l_{\rm{CPU}}=13\) mA \\ ARM Cortex-M3 & Low power mode (LPM) & \(l_{\rm{LPM}}=0.4\)\(\mu\)A \\ \hline
**Radio Module** & Receiving (RX) & \(I_{\rm{RX}}=19\) mA \\ TI CC1200 & Transmitting (TX) & \(I_{\rm{TX}}=39\)–61 mA \\  & Sleeping (SL) & \(I_{\rm{SL}}=0.12\)\(\mu\)A \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Current values of ZolertiaTM RE-Mote operational states (from [47]). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Error Config.** & **Data Error** & **ACK Error** \\ \hline \(E_{0/0}\) & 0\% & 0\% \\ \(E_{10/5}\) & 10\% & 5\% \\ \(E_{20/10}\) & 20\% & 10\% \\ \(E_{30/15}\) & 30\% & 15\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Definition of error configurations in the proposed testbed. \(L_{\text{sp}}=20\) bytes of net information (Implementation of IEEE 802.15.4g in Contiki OS increases the minimum length of any transmitted packet up to 43 bytes after including headers and, if necessary, applying padding. ).
## 4 Simulation Setup and Experimental Results
### Average Energy Consumption  
Secure data aggregation should focus on improving QoS, so for the network to consume minimum energy for enhanced data communication is paramount. After completing the experiments on the network, we determined the energy salvation process by monitoring and data-forwarding packets to the data aggregation and sink nodes. We generated three scenarios involving malicious and non-malicious nodes. Based on the simulation results, we perceived that the energy consumption upsurges when event-monitoring rounds increase. Figure 7 depicts the result for the non-malicious scenario; 2.6 joules of energy are consumed during the 36 event-monitoring rounds using our proposed SDAAA approach, whereas other contending approaches such as SD, EEHA, HAS, IIF, and RHC consumed 3-3.62 joules.  
We generated 2% malicious nodes in Figure 8. We observed that nodes consume more energy in adversarial node scenarios. Our proposed SDAAA approach consumes 2.91 joules over 36 event-monitoring rounds compared to other competing approaches like SD, EEHA, HAS, IIF, and RHC, which consumed 3.5-3.94 joules in a similar scenario. In Figure 9, the generated scenario comprises 5% malicious nodes, and the results show that as the number of malicious nodes increases, additional energy is consumed. The increased number of malicious nodes significantly affects QoS and reduces the network lifetime. Our proposed SDAAA approach required 3.32 joules, while other contending approaches like SD, EEHA, HAS, IIF, and RHC consumed 4.39-4.52% over 36 event-monitoring rounds. Our proposed SDAAA approach consumed less energy overall. The reason behind the minimal energy consumption in our proposed SDAAA approach is the incorporation of an energy-efficient model that helps cause less energy consumption in the network.  
Figure 6: Showing average throughput of our proposed SDA and other contending approaches: EEHA, SD, HAS, IIF, and RHC in the presence of malicious nodes.  
Figure 7: Energy consumption of proposed SDAAA and other contending approaches: EEHA, SD, HAS, IIF, and RHC during different event-monitoring nodes.
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 4 Integration of Various RS Technologies
### Integration of the Vision-Based Technologies Together and with Other RTLS
#### 4.3.2 Photogrammetry Integration with RTS  
It is a time-dependent dynamic modeling technique in which the assets are identified by using digital imaging capabilities, while RTS units automatically provide the geo-referenced localization information for photo-based 3D modeling. In fact, each RTS unit automatically locks on a reflective glass prism which has been installed on the equipment or target object and tracks its location. Saying that, the cameras and RTS units should be placed on site with a good distance and angle of view from each other in order to enhance the quality of photogrammetry modeling and to reduce the residual error due to human factors. Besides, all cameras timer and RTS units should be time-stamped and synchronized together and being initialized in a predefined object coordinate system [7].  
This integrated system makes it possible to have a dynamic visualization and tracking system for a moving object on site. In a case study [7], the sling lengths of a moving rigging system engineered for lifting heavy modules in industrial construction was tracked to ensure quality and safety. The absolute location error was estimated to be in the order of five centimeters for the measurement of the moving sling with a length of about 20 m.  
Some newer versions of the total stations have spatial imaging capabilities with longer ranges. Moreover, by improving the machine control application, in future more integrated and synchronized actions will be done by these systems [24]. This integration enhances the 3D modeling of the moving objects by providing more accurate and reliable scaling information for the image processing-based model. However, the high cost of the RTS units and their limited capability to tack multiple moving objects are challenges for expanding their application in tracking a large number of moving objects [7].
# UX in AR-Supported Industrial Human-Robot Collaborative Tasks: A Systematic Review
While there are some attempts to design user-centered AR interfaces for different applications [24; 25; 26; 27; 28], for robotics applications, AR interfaces are usually developed by technology experts and not by UX designers. As a result, interfaces are technology-driven and not user-driven and they usually appear not fully centered on the users' perspective [10]. Only recently, a limited number of papers focused on the need to apply structured HCD methods to the design of HRI, focusing on the understanding and satisfaction of human needs. For instance, a UX-oriented methodology has been recently defined to investigate the human-robot dialogue and map the interaction with robots in performing shared tasks, eliciting the requirements for a valuable HRI design [10]. Similarly, another study has considered the role and relevance of UX in HRI and defined the actual trends concerning the inclusion of UX related to socially interactive robots [29]. Another work also proposes an innovative user-centered design tool to design AR platforms for maintenance operations [30]. Despite this, they did not specifically focus on the design of AR interfaces to support human-robot collaborative tasks, where a limited attention is paid to user perception, ergonomics, and usability issues. Contrarily, the nature of AR and the role that such applications can assume in the context of O4.0 requires great attention to human aspects. Interdisciplinary research is also advisable to achieve high-quality HRI. In this context, the paper provides two main contributions:  
1. A systematic review on AR-supported applications for human-robot collaborative tasks in industry, focusing on human aspects. As a result, the reader can understand whether and how UX approaches are currently adopted in the design of AR-supported collaborative solutions, as well as the main benefits and challenges of the application of UX methods in this field;
2. A UX-driven framework to design user-centric AR interfaces for industrial HRI, discussing also the main potential future developments, after having revealed the lack of such structured framework in literature.
# Computational Study of Inertial Flows in Helical Microchannels
## 4 Results--Effects of the Simulation Parameters on the Cell's Trajectory
### Modification of the Helical Microchannel Geometry. Tube Radius  
The trajectories of the smaller cell in a channel with a modified tube radius are shown in Figure 11. The tube radius was difficult to increase from the same reason as the modification of the helix pitch. The space between two adjacent loops was already quite small in the reference case; therefore, we did not had much space to increase the tube parameter. From this reason, we tested only smaller values of the tube radius in comparison with the reference case. The tests were made only for the smaller cell. We can observe that the lower stabilised position vanished for both examined values of tube radius, and the cell converged to an upper stabilised position.  
Figure 10: The length of the cell trajectory with modification of the curvature radius over 10,000 \(\upmu\)s of simulation. Negative values on the x-axis are for the inner wall of the cross-section, and positive values are for the outer wall of the cross-section. Left, the cell with a radius of 3.91 \(\upmu\)m. Right, the cell with a radius of 2 \(\upmu\)m.  
Figure 9: The trajectory of cells with modification of the curvature radius. Cross markers denote the extrapolated stabilized positions. Negative values on the x-axis are for the inner wall of the cross-section, and positive values are for the outer wall of the cross-section. (**a**) The cell with a radius of 3.91 \(\upmu\)m. (**b**) The cell with a radius of 2 \(\upmu\)m.  
With using (10) and (11), the lift force over the Dean force ratio change with \(\sim\)\(D_{h}\)\({}^{-3}\) for the shear-gradient-induced lift force and with \(\sim\)\(D_{h}\)\({}^{-6}\) for the wall-induced lift force. With increasing the tube radius, the total lift force becomes less important in comparison with Dean force. The trajectory of the cell is, thus, driven mainly by the Dean force for channels with a large tube radius. This is also what we can observe in our case--the cells are dragged by Dean vortices in the case of the larger tube, and there are two stabilised positions defined by positions of the Dean vortices.  
The increasing of tube radius yields to adding another stabilised position (in the case that there was only one), or to increasing the cross-section area linked to the lower stabilised position (in the case of two stabilised positions). Therefore, the increasing of the tube radius enables differentiation between the cells according to their size.
# Adaptive Method for Packet Loss Types in IoT: An Naive Bayes Distinguisher
###### Abstract  
With the rapid development of IoT (Internet of Things), massive data is delivered through trillions of interconnected smart devices. The heterogeneous networks trigger frequently the congestion and influence indirectly the application of IoT. The traditional TCP will highly possible to be reformed supporting the IoT. In this paper, we find the different characteristics of packet loss in hybrid wireless and wired channels, and develop a novel congestion control called NB-TCP (Naive Bayesian) in IoT. NB-TCP constructs a Naive Bayesian distinguisher model, which can capture the packet loss state and effectively classify the packet loss types from the wireless or the wired. More importantly, it cannot cause too much load on the network, but has fast classification speed, high accuracy and stability. Simulation results using NS2 show that NB-TCP achieves up to 0.95 classification accuracy and achieves good throughput, fairness and friendliness in the hybrid network.  
Internet of Things; wired/wireless hybrid network; TCP; naive bayesian model +
Footnote †: journal: Electronics Electronics