esa-sceva/satcom-chunk-collection huggingface dataset:
# Robot Navigation Based on Human Trajectory Prediction and Multiple Travel Modes
## 2 Methods
### Trajectory Detection and Optimization Based on Predicted Pedestrians' Positions
#### 2.2.1 Local Trajectory for Obstacle Avoidance Optimization Based on Predicted Pedestrian Trajectory  
The movements of pedestrians cause the optimal local trajectory of the relevant topology to change, which manifests an increase or decrease in cost and the failure or formation of a feasible trajectory, as shown in Figure 3. The robot searches for the feasible trajectory online and optimizes it. When it detects that there is no feasible trajectory in the current topology or that the optimal trajectory is not optimal, it switches to the optimal trajectory in another topological space.  
There is a problem with the above method. When a pedestrian is at position \(p_{t}\) at time \(t\), to make the robot continue to move with pedestrian avoidance, there are four steps that must conducted, as follows: Pedestrian detection, position extraction, trajectory planning, and control law generation. When the move command is transmitted to the robot at time, \(t+\Delta t\), the pedestrian actually reaches a new position, \(p_{t+\Delta t}\); therefore, the optimal trajectory corresponding to \(p_{t+\Delta t}\) has changed, and the trajectory being executed is not optimal or is even unfeasible. In low-density and medium-density crowds, as the number of pedestrians increases, the gap between the trajectory generated by this delayed response mode may become worse.  
Figure 2: Overview of the socially conscious model.  
To reduce the adverse effect of a delayed response and improve the navigation efficiency of the robot, we incorporate the predicted position of pedestrians at the future time, \(t+t^{\prime}\), the costs of all the feasible trajectories, \(t+t^{\prime}\), are predicted, and the optimized trajectory with the lowest global cost is selected. In this paper, \(t^{\prime}\) takes two values, i.e., 0.5 s and 0.2 s, with an assumed pedestrian velocity of 1.5 m/s, the predicted displacements are 0.75 m and 0.3 m separately. With the 0.75 m predicting displacement, the robot has a large enough distance to the pedestrian in his/her walking direction and leads to remarkable variations in the cost of local optimal trajectories at both the front and back sides of the pedestrian, which will benefit global optimal trajectory planning, and the large distance to the pedestrian also ensures the safety of the planned trajectory. Additionally, considering the possible predicting error caused by a sudden change in the pedestrian's movement, such as the pedestrian slowing down or changing his/her direction, with the 0.3 m predicting displacement, the safety of the planned trajectory can still be ensured.
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## 3 Self-Similarity Analysis of Satellite Network Traffic
### Traffic Model of a LEO Mega-Constellation Network  
In LEO mega-constellation networks, the traffic of sink nodes comes from satellites and ground terminals. Thus, multiple services of multiple nodes converge at the sink nodes of the LEO mega-constellation network. Since traffic transmitted through satellite networks can be described by packet sizes and packet intervals, the source of satellite networks can be regarded as an ON/OFF source that satisfies the Pareto distribution [28].  
In this work, the ON/OFF model is utilized to represent the sources of satellite networks alternating between the sending states and non-sending states. The ON state corresponds to the data sending duration, during which sending nodes generate data at a constant rate independently. The OFF state corresponds to pause duration. It can be considered that the ON and OFF duration are independent and identically distributed. According to the above analysis, a LEO mega-constellation network can be regarded as the  
Figure 2: Architecture of a LEO mega-constellation network integrated with a terrestrial network.  
joint action of the superposition of numerous ON/OFF sources, as shown in Figure 3. Network traffic is transmitted through the satellite network and finally converges at ground stations, i.e., the receivers in Figure 3.  
Given a source node \(S_{1}\), if the ON duration of \(S_{1}\) satisfies \(E(\hat{S}_{1})=1/\mu\), the OFF duration of \(S_{1}\) satisfies \(E(\hat{S}_{1})=1/\
u\) and the average speed of the source node is \(\mu\
u/(\mu+\
u)\). It has been demonstrated that the traffic generated by the superposition of a series of ON/OFF sources has the property of self-similarity, where the time intervals of the ON and OFF states obey a heavy-tailed distribution [7]. Considering that the Pareto distribution is the most common heavy-tailed distribution, the probability density function and distribution function of Pareto are, respectively,  
\[f(x;a,b)=\left\{\begin{array}{l}0,x\leq b\\ \frac{b}{x}\left(\frac{b}{x}\right)^{a+1},x>b\end{array}\right. \tag{1}\]  
\[F(x;a,b)=1-\left(\frac{b}{x}\right)^{a} \tag{2}\]  
where \(a\) denotes the shape parameter, which indicates the degree of the heavy tail of the Pareto distribution and \(b\) denotes the minimum cut-off parameter, which indicates the lower bound of \(x\). Since the ON and OFF durations both obey a heavy-tailed distribution, the superimposition of numerous ON/OFF sources will finally result in the self-similarity of the satellite network traffic.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 5 Security Analysis of the Proposed Protocols
### Security Analysis of the PQC Standalone PKE Protocol
#### 5.1.5 Overall Security  
The overall security of this protocol is determined by its reliance on post-quantum cryptographic primitives for both key exchange and data encryption.  
Advantages  
1. Post-Quantum Security: Provides resistance against quantum adversaries due to reliance on PQC algorithms.
2. Simplified Design: Avoids additional complexity introduced by hybrid schemes.  
Limitations  
1. Single Point of Failure: The protocol's security entirely depends on the strength of the PQC algorithms used (both KEM and public-key encryption). If these algorithms are compromised, both key exchange and data confidentiality are at risk.
2. Performance Overhead: PQC standalone encryption/decryption operations are computationally intensive compared to symmetric cryptography like AES-256.  
Formal Statement  
The overall security of this protocol is bounded by the security assumptions of its underlying PQC algorithms. If an adversary breaks the post-quantum hardness assumptions(e.g., Module-LWE problem), they can compromise both key exchange and data confidentiality. Unlike hybrid approaches, breaking one component compromises overall security.
## 3 Results
### Topographic Model
#### 3.1.2 User Friendly Application  
Having obtained several orthorectified images detecting the snorkeler in various locations, a tool was required to extract the coordinates of the snorkeler. To address this need, we developed a user-friendly Python application that eases the target coordinates extraction from images. The user can sequentially recall the orthorectified images providing the appropriate file path, zoom in on the snorkeler, and click on the target on his back. The application then automatically extracts the target coordinates and saves them to a separate text file along with the corresponding frame name (Figure 8).  
Figure 8: The interactive Python application for snorkeler coordinates and frames extraction on the orthophotos.  
Figure 7: UAV image, orthorectified image, and orthorectified image overlaid on the DSM (right). The DSM is color-coded based on elevation, starting from blue at the lowest elevations (zero) and transitioning to red at the highest elevations (as shown in Figure 5).  
Additionally, a Python script was developed to correlate the orthorectified images with the corresponding underwater frames using time synchronization, as detailed in Section 2.1. The computed coordinates are then assigned to their respective underwater frames, ensuring accurate georeferencing. This process guarantees accurate positioning of the snorkeler, enabling the precise integration of terrestrial and underwater photogrammetric data.
# Two-Phase Deep Learning-Based EDoS Detection System
## 5 Experimental Setup
### Detailed Implementation and Test Preparation  
Figure 7 shows our testing topology, which consists of a virtual machine as a victim server, another virtual machine as a normal user that installed the Packet Sender tool [31] for generating normal traffic, two virtual machines with the Bonesi tool installed as attackers, a physical machine on which the installed Whireshark tool and Apache spark framework act as the Collector, Feature Extractor, and two detectors, i.e., an ANN model and an LSTM model. In the victim machine, we installed an Nginx server following the instructions in [32]. The Linux IPTables [33] will be implemented in the victim virtual machine. OpenVswitch is installed in a virtual machine. All of the virtual machines running Ubuntu 20.04 OS in our testbed are created by Oracle Virtualbox version 5.2.42 [34]. The physical machine's configuration is Intel(R) Core(TM) i7-4770 CPU, 3.40 GHz and a total of 48 GB memory, running 64 bit Ubuntu Linux v18.04. For training and implementing the two deep learning models, we use the Keras 2.4.3 library--an open-source software library that provides a Python interface [35]. The models are also training on the physical machine mentioned earlier.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scenario** & **Number of Requests** \\ \hline S-1 & 1000 \\ S-2 & 2000 \\ S-3 & 3000 \\ S-4 & 4000 \\ S-5 & 5000 \\ S-6 & 6000 \\ S-7 & 7000 \\ \hline \hline \end{tabular}
\end{table}
Table 6: EDoS attack simulation with different numbers of requests.
# Applications of Wireless Sensor Networks: An Up-to-Date Survey
## 2 Main WSN Applications
### Flora and Fauna Applications
#### 2.4.1 Greenhouse Monitoring  
An important sector of the agriculture domain involves greenhouses. Within them many crops can be grown to provide sustainable food while climate crops can be harvested all year round if certain conditions are applied within the greenhouse. Therefore, WSNs can be applied in greenhouse monitoring and control to improve their operation. Below are some examples of these applications  
Figure 11: A depiction of a geophone sensor with a wireless antenna deployed in the field [23].  
In [27], a system of this type, called the Agricultural Environment Monitoring System (AEMS), is presented. It is an inexpensive and easy to apply system that can collect and monitor data related to crop growth, inside or outside a greenhouse via WSN sensors and CCTV cameras. The system gathers vital environmental parameters such as temperature, light intensity, humidity, air pressure, rainfall level, pH, and electrical conductivity (EC).  
A relevant system developed for greenhouses which has energy management and indoor climate control capabilities, is introduced in [28]. The system monitors vital greenhouse parameters such as indoor luminance, temperature, and relative humidity, via sensor nodes. The indoor climate control is possible by the utilization of two fuzzy logic controllers, P (Proportional) and PD (Proportional-Derivative) that use the desired indoor climatic set-points. Furthermore, the system utilizes output actuations of heating units, motor-controlled windows and shading curtains, artificial lighting, etc. in order to achieve more precise greenhouse control.
# FANT: Flexible Attention-Shifting Network Telemetry
## 4 Metrics Categorization Strategy
### Basic Idea  
The fundamental idea of the metrics categorizing strategy is based on the attributes of the metrics themselves or the relationships between metrics. Specifically:  
The Attribute of the Metric ItselfContinuous MonitoringIf a metric is continuously monitored by a network application (such as the Rx/Tx Packets of a port, which are consistently required for a network-wide status panel), it should be classified as a basic metric. **Impact on Functionality:** A metric classified as detailed and triggered in the subsequent measurement cycle should not impair the functionality of network applications. For example, in a classic ANT application like a network-wide status panel, if an increase in port packet loss is observed at time \(t\), and further increases in port packet loss and IPv4 packet loss are observed at time \(t+1*\delta t\), the application's functionality should remain virtually unaffected. Otherwise, if any metric does impair the functionality, it must be categorized as a basic measurement.  
The Relationships Between MetricsDependencyIf metric A is unavailable without the value of metric B, we say metric A depends on metric B. For instance, a queue length is only valid if the queue id is valid. If B is a detailed measurement, A can be either a basic or a detailed measurement; if B is a basic measurement, then A must also be a basic measurement. **Inference:** If a change in metric A might cause a change in metric B, but not necessarily vice versa, we say metric A can infer metric B. For example, if the bandwidth utilization of a port increases, the packet loss rate might also increase. In such cases, if metric A and metric B do not have any other relationships, we can categorize A as a basic metric and B as a detailed metric. **Similarity:** If metrics such as A, B, C, etc., can infer each other, we consider them similar, for instance, queue length, queue occupancy rate, and queuing time. If any one of these is in basic measurement, the others can be inferred. In such cases, when not influenced by other relationships, it is advisable to choose the one with the smallest bit width to minimize overhead.
# A Hybrid SWIM Data Naming Scheme Based on TLC Structure
## 4 Experiment and Result Analysis
### Comparative Analysis
#### 4.4.2 Scalability Analysis of Different Naming Schemes  
Table 6 is a comparison chart of different naming methods in the case of six layers and different data amounts. The number unit is one million, and the query time is milliseconds. When the number of queries is greater than 160,000, the time for hierarchical naming and flat naming rises sharply, so the search work cannot be carried out normally, indicating that the scalability is low. Although the hybrid naming search time based on the TLC structure has increased slightly, it is generally controllable. Through the analysis of this experiment, it can be concluded that the naming and searching method of the mixed method can control the overall length of the resource name and improve the scalability.
## 3 Results
### PRO as a Quality and Economy Instrument  
PRO as a measurement tool paved its way into healthcare as it offers supplementary indicators on quality and economy. The conceptualization of quality in healthcare is linked to a striving towards an economically effective system, based on the premise that more care does not necessarily lead to better outcomes [44]. In other words, the increased focus on economic effectiveness aspires from a perception that an increase in quality of healthcare and a decrease in expenses not only are attainable but also more sustainable [45]. An approach immanent in the so-called _'Triple Aim'_, where improvements in the quality of healthcare and the health of the population are assessed in comparison to economic efficiency [46; 47]. Aims aligned with PRO's functionality, making it a reasonably useful tool concerning the measurement and assurance of the quality of healthcare [44; 48]. Furthermore, PRO-based quality assessments incorporate patients' perspectives regarding health outcomes most important to them [25; 48]; enabling qualitative healthcare improvements in concordance with a patient-centred approach [49], while ensuring more useful parameters when evaluating patients' health and well-being improvements [46]. A configuration of quality and patient value based on economic terms, meaning that the quality of healthcare supposedly mirrors patients' needs, as it adapts to consumers' wishes [50].  
Especially the digitalization of PROs has enabled its integration into value-based healthcare (VBHC) systems, where quality is captured through the comparison of patient outcomes weighted against treatment costs [45, 48]. In practice, VBHC systems integrate aggregated PRO data providing a more structured measurement of patient value. Consequently, the VBHC approach may to some extent replace, or the least complement the traditional volume-based reimbursement system; in other words, quality gains (values) are to a greater extent merged with quantity measures (volume) when assessing the quality of healthcare [32, 47, 48, 51]. As a result, providers are benchmarked and paid according to their performances; hence, a tool complementing market-oriented healthcare systems [46, 50, 52]. A system that might lead to more transparency by improving the accountability of providers [53, 54], and an approach facilitating knowledge sharing by accentuating best practices, potentially improving for instance patient safety [46].  
Another application of PROs having economic implications is as part of an algorithmic triage system. In this context, patients are automatically sorted into predetermined color categories (green, yellow, red), based on their health status; thus, some patients are allowed to skip consultations at the hospital. A system, potentially ensuring reallocation of resources as it allows providers to move resources from green to red patients; hence, a tool providing room and time for those patients most in need [55]. Essentially, an increase in productivity since the patients staying at home and those showing up at the hospital site are handled simultaneously by the digitalized healthcare system.
## 3 Results  
Kappa test results, with a significance set at 0.5, showed good intra- and interexaminer agreement, with values of 0.90 and above for the different groups. From the microCT analyses, two-dimensional images, three-dimensional images and morphometric parameters were obtained. Two-dimensional images were analyzed to obtain slice-by-slice information along the entire length of the root. Those images showed the presence of AHTD in every slice, before and after the irrigating solution activation. The 2D images, corresponding to a slice (coronal, middle or apical) of a representative sample for each group, are shown in Figures 2 and 3, where the AHTD accumulation areas are circled in red. The three-dimensional images of the root canals, obtained with the transparent effect of the dental tissues, were processed in order to highlight the residues of AHTD colored in red to highlight their presence. The 3D images obtained, with transparency effect, are shown in Figures 4 and 5, where the distribution along the entire root canal of AHTD residues can be observed, before and after the treatment corresponding to the group shown. Figures 4 and 5 show the efficacy of the activation methods: the presence of red (more or less intense in chroma) highlights the presence/absence of AHTD. Specimens of EBgroup showed a reduced presence of red (less AHTD) than the EA group specimens. This result was shown by all the observed samples, while the figures display only representative samples for each group. The obtained quantitative morphometric data, reported in Tables 1 and 2, confirm the visual results shown in 3D images. In EA group specimens, the volume of AHTD in the endodontic system before activation is 4.35%, decreasing to 1.87% after irrigant activation with EA. In EB group specimens, the volume of AHTD in the endodontic system before activation is 4.29%, decreasing to 0.40% after irrigant activation, confirming a statistically significant better result than the activation with EA.  
Figure 3: EB group—slice evaluation of ATHD: (**a**) before (image left) and after (image right) activation at middle third; (**b**) before (image left) and after (image right) activation at apical third.  
Figure 4: EA group—3D evaluation of ATHD before (image **left**) and after (image **right**) irrigant activation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SkyScan 1072—ISS** & **Before Irrigant Activation** & **After Irrigant Activation** \\ \hline Volume of shaped root canal & & \\ (mm\({}^{3}\)) & 8.0717 \(\pm\) 0.0002 & 8.0717 \(\pm\) 0.0002 \\ \hline Volume of AHTD (mm\({}^{3}\)) & 0.3507 \(\pm\) 0.0001 & 0.1506 \(\pm\) 0.0008 \\ \hline \hline \end{tabular}
\end{table}
Table 1: EA group—volume (mm\({}^{3}\)) of AHTD before and after the irrigant activation.
# Model Predictive Control and Its Role in Biomedical Therapeutic Automation: A Brief Review
## 7 Shell Multivariable Optimizing Controller (SMOC):
### Advantages and Limitations of MPC  
The MPCs are now applicable to more dynamic processes instead of being restricted to slower and more stable processes because of advancements in the computation technologies. Aside from the fact that it handles constraints effectively, an MPC is also very easy to modify and customise. An MPC makes it possible to place constraints on the output of controlled processes (control variable) as well as on the control signals that are inputs to the controlled processes (manipulated variables). Input restrictions can also take the form of rate constraints, such as valves and other actuators with a limited range of slew rates. Model predictive control is also extremely adaptable, and it may be applied in practically any situation. An MPC is used to reduce the amount of error between the set-point and the actual trajectory. It also outperforms conventional controllers when the process has many constraints and/or nonlinear constraints. An MPC can also assist with optimization objectives, since the MPC cost function minimization serves as an optimization tool as well. Furthermore, an MPC is well suited for systems with dead time and slow dynamics since it automatically accounts for dead time. The following are some of the reasons why MPCs are being applied widely across various industries :  
* Multi-variable control problems can be naturally handled by MPCs;
* Actuator limitations can be taken into account by MPCs;
* MPCs permit operations nearer to constraints, resulting in higher performance;
* Structural changes can be handled by MPCs;
* MPCs have sufficient capability for online calculations;
* Unstable processes and non-minimal phases can be handled by MPCs;
* MPCs can be easily tuned.  
Despite the fact that there are numerous advantages to using MPCs, there are also a few limitations. Although MPCs can be employed when there are constraints in the system, the control rule is derived in a complex manner, and the difficulty of its derivation increases with the number of constraints in the system. This kind of process control may result  
Figure 4: Generalized MPC tuning methodology.  
in suboptimal optimization, which is undesirable. As a result of the software package's objective to complete the optimization process as quickly as possible, the MPC may be forced to produce erroneous results. The final problem of the dynamic system modeling is its instability, which is caused by its dependence on time. As a result, while utilising MPCs, it may become difficult to maintain/ensure system stability.
# Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images
## 4 Results
### Error Analysis  
The annual night-light data obtained by the three methods were compared with the total night-light values of the officially published 2015 night-light images. The \(RE\)-values were used as a measure of error and were 15.75%, 19.96%, and 14.83% for the annual average, annual cloud-free average, and month-specific substitution methods, respectively (lower values are better).