esa-sceva/satcom-chunk-collection huggingface dataset:
# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 3 Interference Alignment in Cognitive Radio: First Paradigm
### Techniques For IA in CR Networks
#### 3.2.3 Symbol Extensions IA  
The idea of using symbol extensions was introduced in refs. [20; 21] for the two user MIMO \(X\) channel where all nodes equipped with \(M>1\) antennas achieved enlarged DoF by using linear beamforming across multiple channel uses [89]. Single-antenna networks are known to have unlimited dimensions as the channel symbol could be extended as long as possible. This provides drastic convenience for the design of IA as well as showing enhanced sum rates for both PUs and SUs [90; 91]. However, the typical setup of wireless networks is the MIMO scenario that is based on setting the signal space over limited dimensions. Even though research endeavours based on symbol extensions such as refs. [92; 93] show higher performance gains, the extent to which interference can be aligned over a limited number of dimensions still remains an open problem [23; 80].  
The work on finite symbol extensions have been extended to cellular systems in ref. [93], which makes use of a 2D space time spreading code system model containing linear coding design. The achievable sum DoF is formulated by firstly minimizing the dimension of the interference subspace as a rank minimization problem and then subsequently solving the problem by using a grouping algorithm (GA), which aligns interfering message streams into a low-rank inter-cell interference subspace as a group with a packing ratio as extensive as possible. Alignment rules for obtaining a feasible solution to the rank minimization problem are meticulously formulated to achieve sum DoF that is significantly better than prevailing results [94] and attains the upper bounds obtained in refs. [95; 96], thus taking full advantage of the limited number of dimensions provided by space and time.  
The work done in ref. [97] differs from the refs. [92; 93] as it considers not only multiple SUs in the CR network, but multiple PUs as well that are cooperatively utilising IA. As is usually the case, the SUs are trying to gain access to the licensed spectrum, without degrading performance of the PU network. The unique condition that avoids degrading the sum rate of the PUs is a minimal-impact threshold for the quantity of SU-Tx antennas, that is set in such a way that those SU-Txs with greater or equal antennas than this threshold can utilize the licensed spectrum. When the SUs satisfy the zero-impact threshold, the optimum and suboptimum SU precoders are calculated, such that these precoders will not degrade the sum rate of the PU's. A specific successive IA precoding is also proposed and presented to be optimum for several network setups determined by the number of PUs, SUs and antennas at each node. Thus this piece of work makes a case for enough antennas at the SUs as well as providing new analysis for the case of inadequate antennas at the SUs.
## 5 Conclusions  
1. Multi-junction cells: highest efficiency but lower energy yield.
2. Super-multi-junction cell: compensation of spectrum-mismatching loss by sharing photons generated by radiation recombination due to surplus current of spectrum mismatching.
3. Annual performance: the model considering spectrum mismatching was validated and applied to super-multi-junction design.  
Figure 14: Possibility of the future high-efficiency solar cell technology based on the implication from the super-multi-junction solar cell.  
* [iv] Super-multi-junction solar cell performance: robust to spectrum change. Its annual average efficiency levels off at 50% with realistic spectrum fluctuation.
* [v] Future multi-junction solar cells: may not be needed to tune the bandgap for matching the standard solar spectrum, or for relying upon artificial technologies such as ELO, wafer-bonding, mechanical-stacking, and reverse-growth, but merely to be used for upright and lattice-matching growth technologies.  
Conceptualization, K.A.; methodology, K.A.; software, K.A., H.S., and H.T.; validation, K.A., H.S., H.T., and Y.O.; investigation, Y.O. and M.Y.; data curation, H.S., H.T., and Y.O.; writing--original draft preparation, K.A.; writing--review and editing, K.A., and Y.O.; visualization, K.A., and Y.O.; supervision, Y.O.; project administration, K.N. and M.Y.; funding acquisition, K.N. and M.Y.  
This research was funded by the New Energy and Industrial Technology Development Organization (NEDO) under the Ministry of Economy, Trade, and Industry (METI), Japan.  
NEDO in Japan has partially supported this work.  
The authors declare that there is no conflict of interest.
## 3 Test Result and Analysis
### Deformation Analysis  
Figure 16 shows the effect of \(e_{0}\) and \(\rho_{\mathrm{s}}\) on the load-axial displacement curves. While the overall trends of these curves are similar, there are notable differences. The axial deformation of the specimens consists of two components: the axial compression deformation of the specimens themselves and the axial displacement due to bending. The latter is the dominant factor. The curves show an almost linear relationship in the early and middle stages of loading. However, the curves start to smooth out in the later stages, especially near the peak load. The curves become progressively smoother as \(e_{0}\) increases (or \(\rho_{\mathrm{s}}\) decreases), indicating an increase in ductility.  
Figure 17 illustrate the effects of \(e_{0}\) and \(\rho_{\mathrm{s}}\) on the load-midspan lateral displacement curves. The overall trends of these curves are consistent. Cracks on the tension side of the column appear earlier in the early stage of loading, as \(e_{0}\) increases (or \(\rho_{\mathrm{s}}\) decreases). This leads to a significant reduction in the lateral stiffness. Consequently, there is a marked increase in the midspan lateral deflection. The lateral displacement curves of the column become steeper as \(e_{0}\) increases (or \(\rho_{\mathrm{s}}\) decreases) during the mid-to-late stages of loading.  
Figure 16: \(N/f_{\mathrm{c}}bh_{0}\)—axial compressive displacement curves.  
Figure 15: \(N/f_{\mathrm{c}}bh\)—crack width curves.  
Figure 18 presents the lateral displacement curves of SCGC2, SCGC5, and SCGC6 under various loading stages. It can be observed from Figure 18 that the lateral displacement curves exhibit a half-sine wave distribution form from the initial loading of the SCGC until the failure stage. The shape conforms to the function of a standard sine curve, expressed as \(f_{1}=f\text{sin}(\pi x/l)\), where \(f_{1}\) is the lateral deflection, in mm;\(f\) is the maximum midspan lateral deflection under each load stage, in mm; and \(l\) is the height of the column, in mm.  
Figure 17: \(N/f_{\text{c}}bh_{0}\)—midspan lateral displacement curve.  
Figure 18: Deflection distribution in the direction of sample height.
###### Abstract  
This paper investigates the potential of a novel vibration-based thermal health monitoring method for continuous and on-board damage detection in fiber reinforced polymer sandwich structures, as typically used in aerospace applications. This novel structural health monitoring method uses the same principles, which are used for vibration-based thermography in combination with the concept of the local defect resonance, as a well known non-destructive testing method (NDT). The use of heavy whiskers for applying strong excitation and infrared cameras for observing thermal responses are key hindrances for the application of vibration-based thermography in real-life structures. However, the present study circumvents these limitations by using piezoelectric wafer active sensors as excitation source, which can be permanently bonded on mechanical structures. Additionally, infrared cameras are replaced by surface temperature sensors for observing the thermal responses due to vibrations and damage. This makes continuous and on-board thermal health monitoring possible. The new method is experimentally validated in laboratory experiments by a sandwich structure with face layer debonding as damage scenario. The debonding is realized by introduction of an insert during the manufacturing process of the specimen. The surface temperature sensor results successfully show the temperature increase in the area of the debonding caused by a sinusoidal excitation of the sandwich structure with the PWAS at the first resonance frequency of the damage. This is validated by conventional infrared thermography. These findings demonstrate the potential of the proposed novel thermal health monitoring method for detecting, localizing and estimating sizes of face layer debonding in sandwich structures.  
vibration-based thermography; fiber reinforced polymer; thermal health monitoring; sandwich structure; face layer debonding; aerospace structures; structural health monitoring; non-destructive testing +
Footnote †: journal: Applied Sciences
# Region Pixel Voting Network (RPVNet) for 6D Pose Estimation from Monocular Image
## 2 Materials and Methods
### Proposed Method  
According to Section 2.1.2, if the corresponding 2D projections of object's keypoints can be found in the image, then the pose can be inferred through PnP algorithm. Traditional methods extract handcrafted features to detect keypoints in the image. However, they are not robust to complex environment.  
Deep learning methods have been proven to be effective in keypoints prediction. The proposed method takes a two-stage pipeline: the front-end network uses local features to predict direction maps, from which the keypoints are recovered through RANSAC voting. Then, the pose is computed through PnP algorithm.
## 5 Robustness Results and Analysis
### Varying the Node Density  
The second group of scenarios is related to the type and quantity of nodes. The \"50 nodes\" and \"106 nodes\" scenario uses the simulation parameters presented in Table 2, except for the reduced number of nodes. The \"All cars\" scenario also uses the parameters of Table 2, except that there are 200 cars and 0 pedestrians.  
When the node density is modified, it is not easy to predict the results. While it is plausible to assume that 50 nodes will have fewer contacts than 106 or 206 nodes and thus fewer opportunities to exchange messages, the initial location and further movement of the nodes are random. Therefore, it is possible to end up with nodes moving very close to each other in the simulation, increasing the contact opportunities. Furthermore, with fewer nodes, fewer messages need to be exchanged before all malicious nodes are identified. Results depend immensely on nodes' behavior and on the simulation scenario in general. For the scenario with all cars, it is not easy to predict results as well. Having more cars, and therefore more vehicles moving at a higher speed, increases the encounter possibility and can decrease the time available for message exchange.  
The results for the Prophet are presented in Figure 11 for 20% of malicious nodes. Again, the 95% confidence intervals are small, showing good confidence in the results. The detection and false-positive ratios behave as expected, reaching high detection levels and a 0% false-positive ratio. In addition, overall, a better detection ratio and faster convergence of the false-positive ratio to zero are related to a larger number of nodes, resulting in more contacts and, therefore, more message exchanges, thus having more and faster information. For the detection ratio, not a big difference is noted. This means that BiRep does not seem to be greatly affected by the profile or number of nodes.  
Evaluating the routing protocol metrics, the results are shown in Figure 12. The results show a big increase in the delivery ratio and overhead ratio for all scenarios. The latency does not change in such a significant matter, but it does not get worse either.  
Figure 10: Comparison of delivery ratio for good nodes, overhead ratio for good nodes, and average latency for good nodes for Prophet to test the robustness of scenarios with different message generation and transmission settings.  
Overall, BiRep performs very well in all scenarios. The delivery ratio can be, in some cases, even ten times larger than if no reputation is used. The latency is not significantly affected by the node density, speed, or message generation rate when reputation is used. The overhead ratio also presents significant gains as it can be reduced ten times by using the reputation mechanism.
## 2 Materials and Method
### Devices
#### 2.1.2 Infrared Thermal Camera  
The monitoring of the surface temperature was performed with an uncooled microbolometer thermal camera from Xenics (Gobi-384, Figure 2) at a frame rate of 1 image every 10 s. Among its main features, it is worth mentioning: a 384 \(\times\) 288 focal plane array (FPA) sensor equipped with a 10 mm lens, a field of view of 51\({}^{\circ}\)(H) \(\times\) 40\({}^{\circ}\)(V), an operating spectral range between 8 and 14 um and a 16-bits radiometric resolution. For optimum results, this camera should be used in the ambient temperature range between 0 to 50 \({}^{\circ}\)C.  
The camera is installed on top of the borehole heat exchanger, at a distance of 1.4 m from the surface of the sand. The geometry of the temperature sensors used and the container are shown in Figure 3, and more details are given in Reference [33].  
Figure 1: Experimental borehole heat exchanger included for the thermal analysis performed in this research.  
Figure 2: Uncooled infrared thermal camera, Gobi-384 (**a**), and the LWIR (long-wave infrared) spectral response (**b**).
## 2 Architecture  
CENet uses a hierarchical architecture, as shown in Figure 1. The gray circles denote slave nodes and the green circles denote master nodes. It has one base station (BS) which connects a computer through UART. We use Mica2 node as master and Dot node as slave, as shown in Figure 2. The node parameters are shown in Table 1. Slave nodes are placed inside cabinets and master nodes are typically installed outside of cabinets. Slave nodes are responsible for sensing data and transmitting packets to their master nodes. Each slave node can select only a master node as its parent. Master nodes form a routing tree and are responsible for forwarding data to the BS. They are not responsible for sensing the environment. In other words, slave nodes are the sensing front tier and master nodes are the communication infrastructure. The communication distances of slave nodes and master nodes are 0-3 meters and 0-15 meters, respectively. This short distance has the advantage of reducing communication interference because nodes are densely deployed.  
The communication stack of CENet is as follows. A modified B-MAC [6] is used as MAC protocol. Mode 4 of B-MAC is used for master node scheduling, while our proposed scheduling based on B-MAC is used for slave node scheduling. The proposed data-aided routing protocol is used for both types of nodes. And the application layer contains two operations: data collection and event alarm.  
Figure 1: CENet topology.  
Figure 2: Mica2 node and Dot node.
## 3 Materials and Methods
### Structural Studies  
#### 3.1.1 Linear Analysis  
Lineament analysis was performed on both the Digital Elevation Model (DEM, SRTM-1 Arc-Second Global) downloaded from [57] and the Digital Surface Model (DSM, ALOS World 3D--30 m) downloaded from [58], with a resolution of approximately 30 m. The density, length, and orientation of the lineaments were analysed. The low resolution of the DEM and DSM images in the low-relief Niah Cave area covered by intense vegetation may  
Figure 2: Summary of the workflow for the fracture network analysis of karstified carbonate build-up.  
have caused an underestimation of the structural investigations. As such, the lineament analysis between the DEM and DSM was compared with the aid of satellite imagery and topographic maps for regional feature identification to enhance the visualisation of the lineaments while lineament extraction was performed manually.  
Lineaments are natural, rectilinear surface features that can be identified as faults, discontinuities, or weakness zones. The shader was generated using the software Global Mapper from the DEM and DSM images, which were then visually checked for lineament delineation. To avoid misinterpretation, several illustrative azimuths of the shader maps were compared, and the lineaments were utilised to calculate the fracture density (number of fractures per unit area), P20, and intensity (total length of fractures per unit area), P21.
###### Abstract  
Research on electronic healthcare (eHealth) systems has increased dramatically in recent years. eHealth represents a significant example of the application of the Internet of Things (IoT), characterized by its cost effectiveness, increased reliability, and minimal human effort in nursing assistance. The remote monitoring of patients through a wearable sensing network has outstanding potential in current healthcare systems. Such a network can continuously monitor the vital health conditions (such as heart rate variability, blood pressure, glucose level, and oxygen saturation) of patients with chronic diseases. Low-power radio-frequency (RF) technologies, especially Bluetooth low energy (BLE), play significant roles in modern healthcare. However, most of the RF spectrum is licensed and regulated, and the effect of RF on human health is of major concern. Moreover, the signal-to-noise-plus-interference ratio in high distance can be decreased to a considerable extent, possibly leading to the increase in bit-error rate. Optical camera communication (OCC), which uses a camera to receive data from a light-emitting diode (LED), can be utilized in eHealth to mitigate the limitations of RF. However, OCC also has several limitations, such as high signal-blockage probability. Therefore, in this study, a hybrid OCC/BLE system is proposed to ensure efficient, remote, and real-time transmission of a patient's electrocardiogram (ECG) signal to a monitor. First, a patch circuit integrating an LED array and BLE transmitter chip is proposed. The patch collects the ECG data according to the health condition of the patient to minimize power consumption. Second, a network selection algorithm is developed for a new network access request generated in the patch circuit. Third, fuzzy logic is employed to select an appropriate camera for data reception. Fourth, a handover mechanism is suggested to ensure efficient network allocation considering the patient's mobility. Finally, simulations are conducted to demonstrate the performance and reliability of the proposed system.  
Internet of Things (IoT); eHealth; patch; LED; camera; Bluetooth low energy (BLE); hybrid system; network selection; handover; outage probability +
Footnote †: This article is an extended and modified version of a paper presented in “[PERSON], [PERSON]; [PERSON] [PERSON]; [PERSON], [PERSON]; [PERSON] In Proceedings of the International Conference on ICT Convergence (ICTC 2018), Jeju, Korea, 17-19 October 2018.”
# SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach
## 3 The SCADA System Testbed
### The Testbed Framework  
The purpose of our testbed is to emulate real-world industrial systems as closely as possible without replicating an entire plant or assembly system [13]. The utilization of a testbed allows us to carry out real cyber-attacks. Our testbed is dedicated to controlling a water storage tank, which is a part of the process of water treatment and distribution. The components used in our testbed are commonly used in real SCADA environments. Figure 3 shows the SCADA testbed framework for our targeted application and Table 2 shows a brief description of the equipment used to build the testbed.  
Figure 3: The testbed framework.  
As shown in Figure 3, the storage tank has two level sensors: Level Sensor 1 (LS1) and Level Sensor 2 (LS2) that monitor the water level in the tank. When the water reaches the maximum level defined in the system, the LS1 sends a signal to the PLC. The PLC turns off Water Pump 1 used to fill up the tank, opens the valve, and turns on Water Pump 2 to draw the water from the tank. When the water reaches the minimal level defined in the system, LS2 sends a signal to the PLC, which closes the valve, turns off Water Pump 2, and turns on Water Pump 1 to fill up the tank. This process starts over when the water level reaches LS1. The SCADA system gets data from the PLC using the Modbus communication protocol and displays them to the system operator through the HMI interface.  
There are other ICS protocols which could be used instead of Modbus in our testbed. For example, DNP3 is an ICS protocol that provides some security mechanisms [14; 15]. However, in a recent research, [PERSON] et al. [16] reported that they found 17,546 devices connected to the Internet using the Modbus protocol spread all over the world. They did not count the amount of equipment not directly connected to the internet. Although there are other ICS protocols, many industries still use SCADA systems with Modbus protocol because their equipment does not support other protocols. In this case, solutions to detect attacks can be cheaper than other solutions, for example, changing the devices.  
PLC Schneider model M241 CE40 is used in our testbed to control the process of the water storage tank. The logic programming of the PLC is done using the LADDER programming language. The LADDER language is not covered in this paper; however, more information can be found in [17; 18]. The sensors described in Table 2 are connected to the digital inputs of the PLC. The pumps and valves are connected to the output of the PLC.
## 3 System Requirements for BVLOS Flight Operations
### Geo-Fence and Return-to-Launch (RTL) Functions  
Geo-fence and RTL functions should be set and enabled in the UA autopilot system. As shown in Figure 7a, the Geo-fence defines a box area on the map via several GPS waypoints around the planned flying area, plus a minimum and maximum altitude, between which the UA can fly [13]. Within the fence, the UA can fly normally. If the UA flies outside the fenced area, the autopilot will automatically take over and bring the aircraft back to a pre-defined safe waypoint inside the fenced area.  
The RTL function can command the UA to fly back to the launch location regardless of the existing flight plan in the autopilot. It is engaged when the autopilot loses communication with its GCS for more than 10 s. In the BVLOS operation, if the UA flies too far and loses the communication link with the GCS, the autopilot will command the UA to fly back, so that the communication link can be established again within two minutes before the FTS terminates the engine.