esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design of the Wind Tunnel Experiments  
### Design and Manufacturing of the DCFJ Rotor Airfoil Model  
The airfoil used for baseline comparison is the OA312 rotor airfoil, which is a classical helicopter rotor airfoil with high lift-to-drag ratio and relative thickness of 12%. Based on the OA312 airfoil, the CFJ and DCFJ rotor airfoils, named CFJ312 and DCFJ312, respectively, were designed. The schematic diagrams of CFJ and DCFJ principles are illustrated in Figures 1 and 2, respectively. The injection is located near the high negative pressure region of the leading edge, thus it is easier for the jet to be injected into the main flow. Moreover, the suction is located near the high-pressure region of the trailing edge, so the air is easier to be drawn in the duct. The air is driven by the ducted fans installed in the beam. In this case, the locations of the injection and suction are beneficial to energy consumption of the ducted fans. When the injection slot is an open slot, the airfoil is CFJ airfoil. When the tabs are arranged with a certain distribution to discretize the injection, the airfoil is the DCFJ312 airfoil.  
According to the experimental results in reference [23], the DCFJ312 and CFJ312 airfoils are designed using the parameters tabulated in Table 1. The chord and spanwise lengths are 0.8 m and 1.6 m, respectively. The injection and suction slot heights are 0.63%  
Figure 1: Schematic diagrams of CFJ principle. (**a**) Front view; (**b**) isometric view.  
Figure 2: Schematic diagrams of DCFJ principle. (**a**) Front view; (**b**) isometric view.  
(5 mm) and 1.25% (10 mm) of the chord length, respectively. The injection and suction are located at 5% and 80% of the chord, respectively.  
The DCFJ312 airfoil model is shown in Figure 3. In order to facilitate the installation of ducted fans inside the airfoil, the upper surface of the DCFJ312 airfoil model can be separated from the main model and connected to the beam with countersunk screws. In order to reduce the interference between ducted fans, four ribs are used to divide the inside of the DCFJ312 airfoil into five independent channels. Four ducted fans are installed in each channel, as shown in Figure 4. In addition, two hundred tabs of the DCFJ312 airfoil model are independently manufactured, as shown in Figure 5.
# On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments
## 3 Computational Concept  
The whole computational concept can be divided into two steps: (i) prediction of the radio channel conditions between service centres and customer locations using selected propagation model; and (ii) employment of the developed models to find optimal locations to deploy service centres.  
An important note here is that, before the actual deployment of the proposed models, it is appropriate to consider whether the network infrastructure can be optimized by changing the parameters of gNodeB nodes only. With this in mind, we recommend testing methods aimed at optimizing the gNodeB parameters of the network before deploying these models [47]. If it is not possible to meet the requirements of end-users by simply changing the parameters of gNodeB nodes, it is suitable to apply the proposed computational model.
## 3 Security Attacks in Smart Healthcare Systems
### Malware Attacks  
Smart healthcare systems are susceptible to malware attacks. Particularly, electronic healthcare record systems are at a high risk of being exploited by malware. This is because these systems have up-to-date information about their patients (e.g., patient medical data, patient health insurance, patient billing information), collected by the smart healthcare system for the purpose of providing patient care. The attackers use modern tools, tactics, and procedures to deploy different malware variants in the healthcare infrastructure to compromise the electronic healthcare record systems. Formally, trojans are the widely used malware in the healthcare industry, which deliberately target the infrastructure and propagate their impact via the internet. Trojans are remotely controlled by attackers who can perform undesired operations on the victim's computers, such as modifying or deleting the patient's data from the electronic healthcare record system. Furthermore, ransomware is the type of malware that makes the system and its files inaccessible to authentic users until the ransom is paid.  
Here, a trojan acts as a carrier that carries ransomware as a payload, and once installed in the healthcare system, it encrypts the patient's information. It uses asymmetric cryptography to generate public-private key pairs that encrypt and decrypt all files associated with the system. Here, the attackers only publicize the private key only when the victim pays the ransom. Such malware compromises the entire healthcare organization in terms of finance and reputation. For instance, the Karakurt ransomware group extended its cyberattacks and impacted thousands of lives. First, it performs scanning and reconnaissance to gather information about its targets; then, it attempts to acquire sensitive patient information. Once the information is acquired, it encrypts the files until the ransom is paid [67]. Further, North Korean-based Maui ransomware is targeting US-based healthcare sectors, especially electronic health records, imaging, and diagnostic services [68]. It is designed to remotely control and encrypt a particular file on an infected machine; it uses advanced encryption standard (AES) 128-bit encryption standard with unique public-private key pairs, here the key pairs are further encrypted using the Rivest Shamir Adleman (RSA) algorithm. Moreover, the authors of [69] explored different types of ransomware attacks and their possible countermeasures in smart healthcare systems. They discussed the significant benefits of modern technologies, such as AI, blockchain, software-defined networks, and IoT, safeguarding the smart healthcare ecosystem against malicious attacks.
# In-Situ Seawater Gamma Spectrometry with LaBr\({}_{3}\) Detector at a Nuclear Power Plant Outlet
## 3 Results
### Self-Stabilizing Spectrum Correction  
Due to factors such as changes in ambient temperature, the measured energy spectrum is affected by spectral drift, i.e., changes in the channel positions of \(\gamma\)-rays of the same energy on the spectrum. In order to solve the problem of energy spectrum drift, a quadratic polynomial relationship between the energy and the channel position of the standard and actually measured spectrum is obtained using the characteristic peaks of the detector crystal naturally occurring radioactivity at 36 keV, 1472 keV, and 2635 keV, which enables the self-stabilizing spectrum correction, shown in Figure 4.  
Figure 3: Comparison of normalized detection efficiency measured values and Monte Carlo simulated values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Radionuclide** & \(\gamma\)**-ray Energy (keV)** & **Emission Probability (\%)** & **FWHM (\%)** \\ \hline \({}^{60}\)Co & 1173.23 & 99.85 & 1.93 \\  & 1332.49 & 99.98 & 1.82 \\ \hline \({}^{137}\)Cs & 661.66 & 85.1 & 2.60 \\ \hline \({}^{241}\)Am & 59.54 & 35.9 & 10.22 \\ \hline \multirow{4}{*}{\({}^{152}\)Eu} & 121.78 & 28.53 & 6.71 \\  & 344.28 & 26.59 & 3.53 \\ \cline{1-1}  & 964.06 & 14.51 & 2.01 \\ \cline{1-1}  & 1112.08 & 13.67 & 1.99 \\ \cline{1-1}  & 1408.01 & 20.87 & 1.49 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Full width at half maximum (FWHM) of four radionuclides.
# Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review
## 3 Results
### Main Landslide Events  
The Himalayan region has been affected by several landslides, and a few of the most researched and damaging landslides are mentioned here. Most studies have been carried out in the Uttarakhand region. Some major landslide events in the Uttarakhand region occurred in the Okhimath area in Mandakin Valley, which was significantly damaged by the August 1998 landslide event, triggered by heavy rainfall. In total, 466 landslides were triggered, leading to 103 deaths and damaging 47 villages. Thereafter, cloudburst on July 2001 in a part of Mandakin valley led to more than 200 landslides causing 27 fatalities and affecting almost 4000 people [14]. The region also suffered from a heavy rainfall of more than 200 mm during 13-14 September 2012, which led to the death of 51 people and caused 473 landslides [15]. June 2013 witnessed another heavy rainfall of 350 mm within a period of five days in the Chamoli and Rudraprayag districts of Uttarakhand [16]. Other major landslides in the Uttarakhand region were the 2009 landslide in Pittorgarh region with a loss of 43 people.  
Another major landslide affected region is Himachal Pradesh, of which the area of Katropi has often been studied [17; 18]. The area has a history of recurring landslides since 1977 and recently it suffered from major landslide in August 2017 claiming 46 lives [17]. The Pawari landslide zone has also been an active landslide zone located in the southeastern part of the state, which expanded 7% during 2005-2014 [19]. The Luggarbhati landslide in 1995 and Dharla landslide event in 2007 claimed 65 and 62 lives, respectively. The Jammu and Kashmir region has suffered from major cloudbursts such as the Leh Nalla cloudburst (2005, 2006), Leh cloudburst (2010), and Baggar cloudburst (2011) [20].  
Figure 5: Distribution of landslide studies across the states in Indian Himalayan region.  
In the northeast part of the region, most reported landslides have been in the Darjeeling area of West Bengal and Sikkim. The Darjeeling region has a history of several landslides with the first major recorded occurrence in 1899, which led to the death of 72 people. Thereafter, landslides happened in 1950 (127 people died) and 1968 (667 people died) [21]. The region has also suffered significant landslides in the late 20 th and 21 st century (1991, 1993, 2004, 2005, 2006, 2009, and 2015) [22]. Similarly, the Sikkim region faced numerous landslides such as the Lanta Khola landslide [23], south Sikkim area [24]. The prominent unstable sections in the northeast region of the Indian Himalaya are National Highway (NH)-40 [25] and NH-44 [26] in Meghalaya. In Mizoram, NH-44A has suffered several landslides, in 2011, 2013 and 2017 [27].
## 4 Experiments and Results
### The Power Consumption in Sensing Part (\(P_{sense}\))
#### 4.1.2 Least Squares Analysis  
We select the least squares analysis to analyze each module's current consumption. The least squares analysis is used to find the best-fitting solution to a system of linear equations when an exact solution may not exist. It minimizes the sum of the squares of the residuals, which are the differences between the observed and predicted values. Here is the general form of the least squares analysis:  
\[\mathbf{Ax}=\mathbf{b} \tag{5}\]  
where A is an \(m\times n\) matrix of coffecient, \(\mathbf{x}\) is an \(n\times 1\) vector of unknowns, and b is \(m\times 1\) vector of observed values. The least squares solution is to find \(x\) that minimizes the residual sum of squares:  
\[\min_{\mathbf{x}}\|\mathbf{Ax}-\mathbf{b}\|^{2} \tag{6}\]  
and the solution to this optimization problem is given by:  
\[\mathbf{x}=(\mathbf{A}^{T}\mathbf{A})^{-1}\mathbf{A}^{T}\mathbf{b} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Round** & **TC1** & **TC2** & **TC3** & **TC4** & **TC5** & **TC6** \\ \hline
1 & 75.43 & 80.33 & 898.93 & 879.04 & 1471.53 & 1413.87 \\
2 & 79.95 & 78.52 & 910.33 & 812.14 & 1481.72 & 1407.71 \\
3 & 66.39 & 74.89 & 908.52 & 877.32 & 1484.03 & 1408.25 \\
4 & 76.20 & 75.01 & 905.77 & 821.46 & 1474.93 & 1408.65 \\
5 & 72.31 & 77.68 & 912.07 & 840.21 & 1456.05 & 1408.88 \\
6 & 72.19 & 78.36 & 903.02 & 930.00 & 1487.73 & 1406.49 \\
7 & 88.75 & 77.84 & 906.62 & 871.78 & 1452.40 & 1405.08 \\
8 & 82.40 & 80.54 & 902.56 & 860.95 & 1483.15 & 1404.99 \\
9 & 75.37 & 79.52 & 905.18 & 863.53 & 1475.94 & 1409.27 \\
10 & 83.53 & 79.37 & 906.36 & 877.05 & 1443.95 & 1408.14 \\ \hline Mean & 77.25 & 78.21 & 905.93 & 863.34 & 1471.14 & 1408.13 \\ s.d. & 6.50 & 1.96 & 3.85 & 33.43 & 15.10 & 2.51 \\ \hline \end{tabular}
\end{table}
Table 3: The overall current consumption (mA/10 rounds) of the experiment I.  
Figure 6: Current consumption of all test cases.  
Based on the least squares analysis, the estimated current consumption for each module in the system is as follows: \(I_{node}\) consumes approximately 51.819 mA, \(I_{pol}\) consumes 43.350 mA, \(I_{deep}\) consumes 8.469 mA, \(I_{sense}\) consumes 806.913 mA, and \(I_{send}\) consumes 554.996 mA. These values provide a detailed breakdown of the individual module contributions to the overall consumption observed across test cases TC1 to TC6, highlighting the significant consumption levels of \(I_{sence}\) and \(I_{send}\) compared to the other modules.
# Developing Quantum Trusted Platform Module (QTPM) to Advance IoT Security
###### Abstract  
Randomness is integral to computer security, influencing fields such as cryptography and machine learning. In the context of cybersecurity, particularly for the Internet of Things (IoT), high levels of randomness are essential to secure cryptographic protocols. Quantum computing introduces significant risks to traditional encryption methods. To address these challenges, we propose investigating a quantum-safe solution for IoT-trusted computing. Specifically, we implement the first lightweight, practical integration of a quantum random number generator (QRNG) with a software-based trusted platform module (TPM) to create a deployable quantum trusted platform module (QTPM) prototype for IoT systems to improve cryptographic capabilities. The proposed quantum entropy as a service (QEAaS) framework further extends quantum entropy access to legacy and resource-constrained devices. Through the evaluation, we compare the performance of QRNG with traditional Pseudo-random Number Generators (PRNGs), demonstrating the effectiveness of the quantum TPM. Our paper highlights the transformative potential of integrating quantum technology to bolster IoT security.  
Keywords:IoT security; quantum random number generator (QRNG); trusted platform module (TPM); quantum entropy as a service (QEAaS) +
Footnote †: journal:  
_Article_
# Sim-Real Mapping of an Image-Based Robot Arm Controller Using Deep Reinforcement Learning
## 2 Materials and Methods
### Real and Virtual Workspace  
The virtual space was constructed using the 3D dynamics engine MujoCo to simulate the motion control of the actual arm. The setup of this study is shown in Figure 2a,b for the virtual and actual space, respectively. In the design of the virtual scene, the color tones, lighting positions, shadows, and reflectance were adjusted to minimize differences from the real space environment.  
Picking tasks play a very important role in the operation of a robot arm, and it is expected that RL can be used to optimize the operation of picking [19]. In this study, the target is to control a pick-and-place task of a 4-axis robot arm using image inputs. As such, a target (cylinder) is placed on the desk in front of the robot arm, and images are acquired from the x, y, and z directions using three cameras: Figure 2c-e. The robot arm was installed so that the joint state of the robot arm could be seen from camera 1, and the gripper and cylinder could be seen from camera 2 and 3. Each camera location is highlighted in Figure 2 for both the virtual and actual machine.  
A cylinder with a height of 50 mm, a diameter of 25 mm, and a mass of 8 g was used as the grasping object as shown in Figure 3. Previous studies implemented 7-DoF with a cube target, thereby making gripper rotation necessary [15]. Contrary to this, the cylinder can be grasped from any angle, hence a 3-rotational axis was not required in the current study. As such, the focus of the study shifted towards efficient learning and not gripping. Gripping operation is handled programmatically and will be described later in this section. A successful grasp is defined as when the robot arm grasps the cylinder and lifts it 100 mm. If the robot arm does not grasp the cylinder or drops the cylinder from the desk, the grasp is considered to be unsuccessful.  
Figure 2: The proposed virtual and real workspace and corresponding camera outputs. (**a**) Virtual simulation scene. (**b**) Actual robot setup. (**c**) Camera 1 output. (**d**) Camera 2 output. (**e**) Camera 3 output.  
Previous studies have shown that the task success rate varies depending on the initial position of the robot arm [15]. The task success rate is low when the robot arm is trained at extreme distances from the target position, and the learning process is time-consuming. Therefore, in this study, the initial angles of the robot arm are set as follows, Motor 1 is set to 0\({}^{\circ}\), Motor 2 to 20\({}^{\circ}\), Motor 3 to 90\({}^{\circ}\), and Motor 4 to 45\({}^{\circ}\). The gripper is assumed to be open at 60\({}^{\circ}\).  
The placement points of the target were initialized in either of the three positions, left, right, and center as shown in Figure 4. That is, the target was placed \(\pm\)30 mm in the \(x\)-axis direction from the center (Figure 4). The reason for this is that the higher the number of placement points, the wider the search space becomes, which increases robustness in learning.
# Retrieving Freeze/Thaw Cycles Using Sentinel-1 Data in Eastern Nunavik (Quebec, Canada)
## Materials and Methods
### SAR Sentinel-1  
The Sentinel-1 (S1) mission is designed as a two-satellite (S1A and S1B) constellation. Since the launch of Sentinel-1A in April 2014, the Sentinel-1 mission continues to provide an all-weather, day-and-night supply of imagery of the Earth's surface from a dual-polarization C-band synthetic aperture radar (SAR) instrument. The S1 data show a large advantage by providing free and open-access SAR data at high spatial and temporal resolutions with good stability.  
For each study site, an individual S1 timeseries was built from images acquired from both the Sentinel-1A (S1A) and the Sentinel-1B (S1B) satellite constellations with the time limitation from the first available acquisition to August 2020. All images were level-1 ground range detected (GRD) products in ascending mode. The entirety of the database was acquired in the interferometric wide swath mode (IW) in both VV and VH polarization. In practical processing, the polarizations VV and VH were tested separately.  
The S1 timeseries were all collected and created on the Google Earth Engine (GEE) platform, which is a planetary-scale platform for Earth science data and analysis powered  
Figure 1: GlobPermafrost zonation, permafrost extent after [PERSON] et al. (1997) and difference between borehole and modelled MAGT for Canada and Alaska, yellow frame: the Quebec–Labrador Peninsula [37].  
by Google's cloud infrastructure [46; 47; 48]. Every S1 scene on the GEE was preprocessed with the Sentinel-1 Toolbox using the following steps: thermal noise removal, radiometric calibration, and terrain correction using SRTM 30 or ASTER DEM for areas greater than 60\({}^{\circ}\) latitude, where SRTM is not available. The final terrain-corrected values were converted to decibels via log scaling (10 \(\times\) log10(\(x\))). In addition to the default preprocessing in GEE, several spatial filters were used to improve the data quality and evaluated in later chapters. The used spatial filters mainly included the basic moving window filter, in which the mean of the values of all pixels in the specified range was directly used as the value of the center pixel, and the gamma maximum a posteriori (MAP) speckle filter, which combines geometric and statistical properties to produce the values of the pixel and the average of neighboring pixels using moving windows.
## 1 Introduction
### Related Works and Issues
Compared with the PSRS scheme, the EPUSBRF significantly improves the security of the source location privacy, and avoids the generation of the invalidate paths without increasing the energy consumption. In recent years, the improvements for the source node location privacy protection of WSNs are mainly based on the idea of phantom routing. In Reference [19], [PERSON] et al. proposed a trace time-constrained routing algorithm for preserving source-location in WSNs. Its main idea is that the routing path from the SoN to the SN is generated and distributed dynamically and randomly, and the lasting time of a routing path is shorter than the time for the adversary to trace to the PN. Hence, it is more difficult for the adversary to trace to the SoN, thus increasing the protection strength. In Reference [16], [PERSON] et al. proposed a source location privacy preservation Routing Protocol Based on Multi-Path (RPBMP), which performs random routing based on multi-path selection and multiple PNs jump. This scheme greatly increases the number of routing path between the SoN and the SN, extends the safety period. The notion of the safety period is proposed in Reference [6] as a performance indicator of source location privacy protection in WSNs, and has been widely used in subsequent researches. In Reference [6], the safety period is measured by the number of packets the SoN has sent before it is caught by the attacker. To solve the problem of failure path in phantom routing protocols, in Reference [20], [PERSON] et al. proposed an improved routing algorithm for WSN source node location privacy protection based on the minimum path routing. The improved routing algorithm has higher safety period and does better in protecting source location privacy in WSNs. In Reference [21], [PERSON] et al. proposed a virtual ring-based routing protocol of source-location privacy protection, which avoids the generation of failure path and extends routing path to the annular region where SoN resided with the random virtual ring. This scheme greatly increases the diversity and randomness of the routing paths, and makes it harder for an attacker to deduce the SoN according to the routing path. In Reference [22], [PERSON] et. al. proposed a location privacy protection strategy called PRABNS (Phantom Routing Based on Area and Brother Neighbor Selecting). The PNs selected by this strategy maintain a certain angle and distance, and the adjacent data packets have a certain angle space through the selection of partial region. This strategy increases the diversity for the path of the SoN to SN by selecting sibling nodes. However, the PNs determined by this strategy are still evenly distributed around the SoN, and the shielding nodes cannot guarantee the robustness of the network and the packet loss rate increases.