esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Evaluation of an IoT-Based Platform for Maritime Transport Services
## 5 Experimental Evaluation  
### Testbed  
An end-to-end testbed has been implemented to verify the interoperability of all the sub-systems composing the 5 GT System and assess their performance. All the components of the on-board chain (i.e., CTDs, Smart cell, Smart mIoT gateway and 5 ST) were installed in the same laboratory to reproduce the physical proximity between the devices as in the cargo ship environment, while the RF channel between the 5 ST and 5 SG was not implemented by the satellite link, but through a local loop link. More specifically, the Smart cell, the Smart mIoT Gateway and the 5 ST were directly connected as planned for the cargo-ship environment. The Smart cell antenna and the CTDs were placed inside an anechoic chamber, where the CTDs were positioned next to each other on a desk. The Smart mIoT Gateway provided the commands to turn-on and/or turn off the Smart cell, in order to simulate the near sea to open sea transition (and vice versa). During the tests, some CTDs communicated directly with the Smart Cell (through the NB-IoT interface), while some other CTDs communicated indirectly with the Smart Cell (through the mesh network). Finally, the attenuation affecting the signal transmitted by the Smart Cell was gradually increased to simulate the radio propagation issues raised by the cargo-ship environment. Figure 2 shows the communication schemes that implement the E2E testbed. The blocks colored in orange correspond to 5 GT System sub-modules, while the blocks colored in blue belong to the telecommunication commercial network.
# Fatigue Damage Evaluation of Aviation Aluminum Alloy Based on Strain Monitoring
## 2 Crystal Plasticity Modeling and Numerical Implementation
### Definition of FIPs  
To investigate the impact of microstructure on fatigue crack formation, this study introduces a computable fatigue indicator factor (FIP) as a measure of the cyclic driving force for fatigue crack initiation and early crack extension within the grain [43; 44; 45; 46]. In this paper, two FIPs are utilized as quasi-measures of fatigue crack initiation. The cumulative plastic slip (\(P\)) [47] criterion suggests that plastic slip in the microstructure can accumulate, and crack initiation occurs when a critical slip is reached. P is defined as a function of the plastic velocity gradient \(L^{p}\):  
\[P=\int_{0}^{t}\dot{P}dt \tag{14}\]  
\[\dot{P}=\left(\frac{2}{3}L^{p}:L^{p}\right)^{1/2} \tag{15}\]where \(L^{p}\) is the plastic deformation gradient tensor mentioned in Equation (2), \(\dot{P}\) is effective plastic slip rate, and double dot product notation \":\" stands for the reduction and merging operation of the \(L^{p}\) tensor.  
The definition of the cumulative plastic slip critical value is primarily based on experimental data. It involves establishing a baseline strain amplitude and determining the critical value of cumulative plastic slip for the material by multiplying the fatigue crack initiation life obtained from fatigue experiments at this strain amplitude by the cumulative plasticity value of a single week. The stress-strain behavior stabilizes quickly under the appropriate load, allowing for the determination of cumulative slip for each cycle and prediction of the critical slip value. This critical value is then used to predict the fatigue crack initiation life at other strain amplitudes. Mathematically, the critical value is the product of the number of cycles of crack initiation, n, and the plastic slip, \(p_{cyc}\), per cycle, as shown below:  
\[p_{cpst}=N_{i}\times p_{cyc} \tag{16}\]  
The critical value of accumulated plastic slip, \(p_{cpst}\), is a material characteristic that represents the cumulative plasticity associated with the initiation of small fatigue cracks. It is an essential parameter that remains unaffected by loading conditions at a specific temperature for a given material [47]. [PERSON], [PERSON] [48], along with other researchers, used the strain energy dissipated by the crystal during each fatigue loading cycle to estimate the fatigue crack nucleation life. The formula for calculating strain energy dissipation is as follows:  
\[W=\sum_{\alpha}\int_{0}^{t}\tau^{\alpha}\dot{\gamma}^{\alpha}dt \tag{17}\]  
where \(\tau^{\alpha}\) is the shear stress in the \(\alpha\) slip regime and \(\dot{\gamma}^{\alpha}\) is the shear strain rate in the \(\alpha\) slip regime. Analogous to the critical value for cumulative plastic slip, the critical value for strain energy dissipation can be defined as follows:  
\[W_{cpst}=N_{i}\times W_{cyc} \tag{18}\]
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 2 Related Works
### Onboard Device-Based Techniques  
In [21], the authors proposed a method to detect GPS spoofing attacks in both single- and multiple-UAV formations. The proposed scheme involves reporting the self-position of a node N and at least three of its neighboring nodes to a ground control station (GCS) at random intervals. The GCS then verifies the relative positions between node N and its neighbors by comparing their values with the positions obtained from the GPS. Any disparity indicates the presence of a spoofing attack. In the case of a UAV operating in isolation, the system fuses position information provided by the inertial measurement unit (IMU) with GPS data to identify spoofed signals. However, the study assumed the constant presence of the GCS and a stable communication link with UAV nodes, which is challenging to maintain in real-world scenarios.  
Ref. [22] proposed a visual odometry-based method to detect sophisticated GPS spoofing attacks. Cameras were used as aiding sensors to capture images of the UAV's sub-trajectory, which were then compared with the absolute path of the UAV obtained fromthe GPS. The difference between the two trajectories was calculated using three different dissimilarity measures, namely the Euclidean distance, angular distance, and taxicab distance. This method proved effective against such attacks. However, it had several limitations, particularly in low-light conditions at night or in areas with challenging visual features, such as regions covered by water or snow.  
Ref. [23] presented a method to detect GPS spoofing attacks targeting single- or multiple-UAV nodes. In this technique, each UAV in the formation reports its GPS location data to a GCS. If two adjacent nodes report identical locations, this indicates a spoofing attack as spoofed signals typically affect multiple nodes simultaneously. In cases where only a single UAV is targeted, the approach compared the distance between the target UAV and its neighbors obtained from the GPS coordinates with the distance measured using impulse radio ultra-wideband (IR-UWB) or other ranging technologies. Although this method eliminated the need for additional onboard equipment that may drain the UAV's battery, it still relies on the availability of the GPS and neighboring UAV nodes.
# Comparative Analysis of Producer Mobility Management Approaches in Named Data Networking
## 2 Related Work
### Rendezvous Point
In the proposed scheme, the current router and the new router interchange messages before layer 2 handoff. The new router carries the required information of the mobile producer to the RV to carry location update activity for achieving fast registration.
# Radiation-Induced Structural Changes of _Miscanthus_ Biomass
## 3 Results and Discussion
### Influence of Irradiation Treatment on Structural Properties of Miscanthus Biomass
#### 3.3.6 SEM Analysis  
The morphology of _Miscanthus_ biomass after irradiation treatment was investigated by SEM, and the images are presented in Figure 6. The morphology of untreated _Miscanthus_ (0 kGy) showed a smoothly compacted and ordered surface. After irradiation treatment, the surface morphology of  
Figure 4: FTIR spectra of _Miscanthus_ irradiated with different absorbed doses.  
Figure 5: 1H NMR (**a**) and 13C NMR (**b**) profiles of _Miscanthus_ irradiated with different doses.  
samples displayed many small fragments, and the structure was rough and irregular. The degree of structural damage grew stronger with the increase in absorbed doses up to 1200 kGy. The phenomenon shows that irradiation treatment can effectively destroy the tight structure of the _Miscantthus_ biomass, which was confirmed by many researchers in literature [18; 28; 29]. The morphology change of cellulose by irradiation treatment will improve the accessibility of cellulase to biomass, leading to high enzymatic digestibility [14; 30].
# ML-AKA: An Authentication Protocol for Non-Standalone 5G-Based C-IoT Networks
## 5 D2D Authentication Process for Cellular IoT Network  
In the studied cellular IoT network, the authentication methods authenticate the legitimacy of any subscriber through the mutual authentication process, which allows communicating parties to conform to the required standards. The LTE system architecture uses IP-based mobility control technology that includes the access system and the core servers. The access system is E-UTRAN and the core network is EPC [22; 23]. When it comes to access network security, various factors that are taken into consideration are listed below:  
* UE/DE and the core network should be mutually authenticated.
* UE, MME, abd eNodeB should preserve ciphering, integrity and replay protection.
* To prevent identity theft, the access network should use a temporary identity instead of the permanent identity of the user.  
The above-mentioned factors are crucial for secure D2D communication in a C-IoT network. The first step in the key agreement protocol is to generate a certain key and perform a mutual authentication process through this key. Different standard authentication and key generation protocols used for LTE networks are re-evaluated in the following subsection [24; 25].
## 3 Experimental Results
### Bending Moment of Piles  
#### 3.4.1 Calculation of Bending Moment  
The bending moment \(M\) of specimens in the elastic range can be calculated from the measured strains by assuming that the section remained elastic, and can be calculated by the following formula:  
\[M=\frac{EI(\varepsilon_{t}-\varepsilon_{c})}{D} \tag{1}\]  
Figure 14: Cracking and crushing displacements of the concrete specimens. (**a**) Cracking displacement. (**b**) Crushing displacement.  
Figure 12: Strain distribution of RP-4 specimen. (**a**) Compressive and tensile strain distributions. (**b**) Tensile strain versus loading history. (**c**) Compressive strain versus loading history.  
Figure 13: Strain distribution of HP-5 specimen. (**a**) Compressive and tensile strain distributions. (**b**) Tensile strain versus loading history. (**c**) Compressive strain versus loading history.  
where \(E\) is the elastic modulus of concrete; \(I\) is the moment of inertia for the section; \(\varepsilon_{t}\) and \(\varepsilon_{c}\) are the measured tensile and compressive strains at the outmost edges of the section, and \(D\) is the diameter of pile. All specimens remained elastic under small-displacement loadings. The measured tensile and compressive strains were used to calculate the bending moments. Figure 15a-d show the bending moment distributions at the 2, 5, 8 and 10 mm loading steps for all specimens.  
As Figure 15 shows, the maximum moment of the CP-1 to CP-5 and PC-6 to PC-7 specimens was at the depth of 0.7 m and for the RP-8 and HP-9 specimens was at the depth of 1.05 m and 0.7 m, respectively. For the CP-1 to CP-5, PC-6 to PC-7 and RP-8 specimens, the location of the plastic hinge or concrete cracking and crushing was at around the maximum bending moment location.  
It can be observed from Figure 16 that with the increment of reinforcement ratio and buried pile depth, the maximum bending moment of the CP-1 to CP-5 specimens also increased. However, with the increase of the prestressing level, the maximum bending moment of the CP-2 and PC-6 to PC-7 specimens decreased. The maximum bending moment of the PHC specimens was almost the same, showing that the variation of the prestressing level has an insignificant effect on the bending moment in the PHC specimens. Moreover, the rectangular RP-8 specimen showed relatively a higher bending moment capacity (53.8% higher) than the circular CP-2 specimen owing to its larger inertia moment.  
Figure 15: Bending moment of specimens. (**a**) At 2 mm loading step. (**b**) At 5 mm loading step. (**c**) At 8 mm loading step. (**d**) At 10 mm loading step.
## 4 Simulation and Analysis
(**b**) Time course of \(S_{2}\) and \(S_{3}\), the upper and lower horizontal dotted lines indicate the bounds of \(\Omega_{S2}\) and \(\Omega_{S3}\). Figure 2: Simulation of the CSTR network model (1)–(4), first case. (**a**) Time course of \(S_{1}\); the upper and lower horizontal dotted lines indicate the bounds of \(\Omega_{S1}\). (**b**) Time course of \(S_{2}\) and \(S_{3}\), the upper and lower horizontal dotted lines indicate the bounds of \(\Omega_{S2}\), and \(\Omega_{S3}\). Also, the three simulation cases illustrate the results stated in Theorems 1 and 2: (i) the invariant nature of \(\Omega_{s1}\), stated in Theorem 1 and discussed in Remark 2: one can see that once \(S_{1}\) enters \(\Omega_{s1}\), it remains inside; (ii) the equivalence of the convergence regions \(\Omega_{s2}\) and \(\Omega_{s3}\) defined in Theorem 2: one can see that \(S_{2}\) and \(S_{3}\) converge to the same regions; (iii) the definitions of \(\Omega_{s1}\) and \(\Omega_{s2}\), which indicate that \(\Omega_{s1}\) and \(\Omega_{s2}\) can be quite different, as \(\Omega_{s2}\) depends on the reaction rate \(r_{x2}\): one can see this remarkable difference in the regions to which \(S_{1}\) and \(S_{2}\) converge; (iv) the invariant nature of \(\Omega_{s}\), stated in Theorem 2 and discussed in Remark 6: one can see that once the three state variables are inside the convergence region \(\Omega_{s}\), that is, \(S_{1}\in\Omega_{s1}\), \(S_{2}\in\Omega_{s2}\), \(S_{3}\in\Omega_{s3}\), simultaneously, they remain inside afterwards.
# The Impact of Eclipsing GNSS Satellites on the Precise Point Positioning
## Materials and Methods
### The Attitude Model for GNSS Satellites
* The condition for ON- > YS: (1) \(|\beta|>4^{\circ}\) and \(|\varphi_{n}|\leq 5^{\circ}\); or (2) \(|\beta|>4^{\circ}\) and \(5^{\circ}<|\varphi_{n}|<20^{\circ}\) when \(\varphi_{n}\dot{\varphi}_{n}>0\). When the satellite enters into the noon maneuver or shadow crossing regime, the actual yaw angle can be modeled according to different attitude models ([PERSON] et al., 2014; [PERSON] et al., 2014; [PERSON] et al., 2015; [PERSON] et al., 2015). In this contribution, the process of the acceleration of hardware yaw angle during the noon maneuver and the shadow crossing is ignored. The details and the results of the modeled yaw angles in this contribution are shown in Section 3.2. When the satellites enter into the eclipsing regimes, if the nominal yaw angle is used instead of the actual one, the induced-errors of the wind-up effect and antenna PCO will deteriorate the positioning accuracy, especially when \(\beta\) is small. These errors, especially the antenna PCO, can also be reflected in the phase observation residuals.
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Benefits
#### 8.1.4 Improved Security  
Using network data, KDN can learn, using either machine learning or any other heuristic model-based method, the behavior of normal network traffic and traffic with anomalies. When an anomaly is detected, it indicates a security threat. For instance, if there is an unusual amount of traffic coming from a user, such suspicious users can be isolated or reported to network administrators to make decisions. Additionally, if KDN uses machine learning to identify a malware attack, it may instantly isolate the affected device to stop it from spreading. To train machine learning models to accurately identify unknown assaults, they can be synthesized from data from existing attacks using semi-supervised learning techniques such as variational autoencoders, generative adversarial networks, etc. This allows the detection of unknown attacks by KDN systems that otherwise may have gone undetected in SDN systems. Furthermore, using the prospect of knowledge plane, KDN can be used to detect different types of attacks, such as DDoS attacks, TCP-SYN attacks, ICMP flood attacks, malware attacks, spoofing attacks, injection attacks, etc. allowing the detection of a vast variety of attacks, where appropriate actions can be taken based on the type of attack detected, thus improving the overall security of the network. Therefore, KDN's machine learning algorithms allow detection and response to such threats more effectively than traditional network security measures.
## 3 Experiments and Results
### Datasets  
We apply two different datasets in our experiments and present the experimental results over them. One of the datasets applied for experiments is the publicly available OCT dataset provided by the joint efforts from Duke University, Harvard University, and University of Michigan [25]. The Duke OCT dataset consists of retinal SD-OCT images from 45 subjects (15 AMD, 15 DME, and 15 NOR). The number of OCT B-scans in each subject varies from 36 to 97.  
The other dataset is obtained from clinics in Beijing, using CIRRUS TM (Heidelberg Engineering Inc., Heidelberg, Germany) SD-OCT device. The dataset consists of 1680 OCT B-scan images (560 AMD, 560 DME and 560 NOR).
## 4 Physical and Mechanical Parameters
### P-Wave Velocity  
Figure 2c shows the effect of high temperature on the P-wave velocity. As the heating temperature rises, the P-wave velocity decreases linearly. Below 600 \({}^{\circ}\)C, there are minor changes in P-wave velocity. However, beyond 600 \({}^{\circ}\)C, there is a substantial decrease, up to 92% compared to untreated samples, indicating the generation of numerous new thermally induced cracks and significant damage to the granite samples. According to many studies, the relationship between P-wave velocity and temperature (T) can be divided into two segments: a mild decline up to 600 \({}^{\circ}\)C and a pronounced decline following treatment temperatures exceeding 600 \({}^{\circ}\)C. However, it is difficult to observe these regions in Figure 2c.
###### Abstract  
The Internet of Things (IoT) is a paradigm that has rapidly evolved in recent years. The ability to connect many devices is driving the development of new types of applications that allow the remote monitoring of a wide variety of devices. One of the great challenges that has been identified in this field is the monitoring of sensors scattered in wide areas or located in environments with poor or even no communications coverage. To deal with this problem, different approaches based on cellular or satellite communications have been considered. However, these alternatives are complex and very expensive. To overcome these drawbacks, we designed a system based on unmanned aerial vehicles and LoRa technology that enables data transfer from sensors to a central system. Furthermore, machine learning techniques were applied to process and classify the data retrieved from the sensors. Finally, a Java-based application was developed, providing services such as data storage, processing, and visualization. To verify the correct operation of the proposed system, manual and autonomous flight tests were carried out, verifying the correct transmission of the data from the sensors to the central system.  
umanned aerial vehicles; sensors; data processing; LoRa communication Article
## 5 Results and Discussion
### Energy Consumption  
Comparison of node energy consumption during operation time of the network is shown in Figure 7. The result shows that MRLAM performs better than the other routing schemes in terms of energy consumption during path establishment and in exchanging routing topological information as it selects intermediate nodes based on low EAX value for probe messages. Moreover, it has already been established that MRLAM selects the path with less chances of link failure, resulting in lower energy consumption as it forwards data packets toward destination node. If effect, the Q-learning technique provides high rewards value to the nodes which have lower energy consumption, thereby enhancing energy utilization during data transmission. However, as the node speed increases, the energy consumption of nodes increases. Energy consumption increases from 56.67 mAh to 57.26 mAh for MP-OLSR, from 56.43 mAh to 56.926 mAh for MP-OLSRv2, and from 56.15 mAh to 56.72 mAh for MRLAM approximately, when the node speed increases from 10 m/s to 60 m/s. Overall, it can be observed that MRLAM has less energy consumption rating than MP-OLSR and MP-OLSRv2 scheme because the source node forwards traffic flow towards intermediate nodes which have the highest level of energy as opposed to its counterpart routing schemes.  
Figure 6: Packet loss ratio with different speed of nodes.
# Weed Detection in Rice Fields Using Remote Sensing Technique: A Review
## 5 Weed Detection Using Remote Sensing Technique
### Feature Extraction and Selection  
Following the spectral calibration, feature extraction can be extracted or computed for different image processing purposes using various approaches (Table 3). This process will be helpful for the classification and identification of weeds in paddy fields. Feature extraction techniques are beneficial, especially in shape and pattern recognition. As features define the behavior of an image, they show its place in terms of storage taken, classification efficiency, and, obviously, in time consumption [60]. Therefore, optimizing the feature subset is required before feeding it into the machine learning (ML) and deep learning (DL) algorithms for improving the classification process and making it cost and time-efficient [61].