esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Autonomous Bridge Inspection: Sensor Mounting Using Aerial Manipulators
## 7 Simulation
### Bridge Sensor Mounting  
Since the concept of this paper was to mount inspection sensors on a bridge, the simulation trials were tailored in the same direction. After spraying the first component, it is necessary to achieve and maintain a stable contact while the second adhesive component on the sensor dries. Since the manipulator is attached above the propellers, the workspace of the manipulator is limited to contact above the UAV or on the plane perpendicular to the ground.  
Naturally, the first set of simulation trials were conducted by holding the desired force on a plane perpendicular to the ground. In this case, the contact force only acts along the \(x\) axis and the response is depicted in Figure 10. The time delay between the planned and executed contact is present due to the impedance filter which slows down the dynamics of the referent trajectory. After the initial contact, there are some oscillations and an overshoot which diminish over time and the desired force reference is achieved.  
The second set of simulation trials included an inclined contact plane. This requires the UAV approach from below the plane and achieving contact perpendicular to the plane. Since the plane is inclined for \(\delta=68^{\circ}\), the planned force referent values have components in both the \(x\) and \(z\) axes, as shown in Figure 11. Similarly to the previous example, the force response has some oscillations around the instance of contact, but it eventually settles and reaches the desired force reference.  
The simulation tests for \(\delta=0^{\circ}\) and \(\delta=68^{\circ}\) were performed \(n=10\) times for each case, as depicted in Figure 12. The left portion of the figure is a dot product between the normal of the blob \(\mathbf{r}_{t}\) and the end-effector orientation vector \(\mathbf{r}_{ee}\). If the value of the dot product \(\mathbf{r}_{t}\cdot\mathbf{r}_{ee}=1\), the two vectors are parallel which results in a successful approach. For  
Figure 11: Force response in the case of the contact plane inclined for \(\delta=68^{\circ}\).  
Figure 10: Force response in case of a contact plane perpendicular to the ground, \(\delta=0^{\circ}\).  
both angles, the dot product is very close to 1 and the orientation error is negligible. On the right, the distance between the center of the target and contact point is shown. The error distance is in both cases less than 0.1 m, which ensures the relatively high precision of sensor mounting, well within margins for the bridge inspection. The accompanying video of simulation tests can be found on our YouTube channel [34].
## 2 Materials and Methods
### Data Preprocessing  
The missing value interpolation and data normalization were performed with a home-developed Python program on an Anaconda Jupyter Notebook (version 6.3.0). The missing value interpolation was performed with a mean interpolation method. Then, data normalization was performed with the 'L2' method, where the intensity of each ion was divided by the square root of the sum of the squares of the intensity of all the ions. Afterwards, pairwise coupling was performed on the 163 ions, and totally 13,203 new features could be obtained. From these features, a two-tail \(t\)-test was performed and new features with \(p<0.05\) were selected (total 3630 features) by Excel software. Principle component  
\begin{table}
\begin{tabular}{c c} \hline sampling time: & 50 ms \\ sampling position: & \(-\)46.0 mm \\ ionization time: & 200 ms \\ heat block temperature: & 30 \({}^{\circ}\)C \\ interface voltage: & 2.3 kV (+)/ \(-\)2.0 kV (\(-\)) \\ DL temperature 1: & 250 \({}^{\circ}\)C \\ mass-scan range: & 100–1000 Da \\ \hline \end{tabular} \({}^{\dagger}\) The desolvation line temperature.  
\end{table}
Table 1: The measurement conditions.  
Figure 1: The experimental and machine learning protocols with the FNAB samples.  
analysis (PCA), PLSRegression (PLS), and T-distributed stochastic neighbor embedding (TSNE) were used to obtain components for the classifier training.
# A Study of Cislunar-Based Small Satellite Constellations with Sustainable Autonomy
## 5 Visual Simulations and Analysis (Constellation Scenarios)
### Earth-Based Constellation Simulation
#### 5.1.1 Autonomous Multi-Parametric Analysis (Earth's Side)  
The parametric analysis needs to be generated autonomously while the small satellite constellation is delivering its daily operations attaining the required mission objectives and adding value towards the Cislunar sustainability. The major impact of each parameter was closely observed, studied, and plotted for their propagation cycles in the given operating environment. A systematic alignment to the proposed method and the Cislunar orientation was keenly taken into consideration to extract the importance of each of these parameters and their value in real-time operations. Though explanations for each of the plots are detailed in the discussions section, the significance of every parameter assisting the sustained form of propagation with autonomy is a view of assurance in each frame. Figure 6 shows this analysis visually obtained from the proposed constellation.  
Figure 6: _Cont_.
## 3 Materials: The Data
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Country**} & \multirow{2}{*}{**Time**} & \multicolumn{6}{c}{**Values**} \\ \cline{3-6}  & & **Mean** & **Volatility** & **Variance** & **95 th Percentile** & **Variance/Mean** \\ \hline \multirow{4}{*}{Germany} & 9 & 574.1 & 101.0 & 10,195.5 & 715.2 & 17.8 \\  & 10 & 604.0 & 66.7 & 4452.9 & 695.0 & 7.4 \\  & 11 & 588.2 & 64.1 & 4109.0 & 681.0 & 7.0 \\  & 12 & 562.6 & 76.3 & 5814.4 & 662.0 & 10.3 \\  & 9 & 196.1 & 32.0 & 1026.5 & 240.0 & 5.2 \\  & 10 & 198.4 & 18.7 & 350.7 & 225.2 & 1.8 \\  & 11 & 191.8 & 25.3 & 640.7 & 227.0 & 3.3 \\  & 12 & 197.4 & 30.3 & 917.1 & 241.0 & 4.6 \\  & 9 & 560.7 & 111.4 & 12,417.2 & 714.0 & 22.1 \\  & 10 & 576.8 & 90.0 & 8108.2 & 706.0 & 14.1 \\  & 11 & 579.5 & 92.7 & 8593.2 & 715.0 & 14.8 \\  & 12 & 585.5 & 111.3 & 12,377.3 & 739.2 & 21.1 \\  & 9 & 206.5 & 32.7 & 1071.2 & 252.0 & 5.2 \\  & 10 & 216.3 & 21.5 & 464.3 & 246.0 & 2.1 \\  & 11 & 224.9 & 33.1 & 1098.4 & 270.2 & 4.9 \\  & 12 & 217.4 & 22.8 & 518.5 & 250.0 & 2.4 \\  & 9 & 197.6 & 41.6 & 1729.5 & 258.0 & 8.8 \\  & 10 & 216.4 & 37.5 & 1404.5 & 272.0 & 6.5 \\  & 11 & 212.6 & 29.8 & 889.4 & 258.0 & 4.2 \\  & 12 & 208.8 & 35.0 & 1223.1 & 259.0 & 5.9 \\  & 9 & 12.2 & 4.5 & 20.7 & 20.0 & 1.7 \\ Luxembourg & 10 & 12.2 & 4.6 & 20.7 & 20.0 & 1.7 \\  & 11 & 11.6 & 4.4 & 19.7 & 19.0 & 1.7 \\  & 12 & 13.9 & 5.1 & 26.2 & 22.0 & 1.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Historic mean, variance and risk for FABEC countries between 9 and 12 h.  
Note that the variance depends on the time of the flight (from 1-24 h), with 10 h being the time when traffic is heaviest. However, a look at the 95 th percentile shows that the highest risk occurs at 9 h, given that there are more than 715 flights in 5% of cases. Table 1 also shows that there are big differences between volatility and the variance/mean ratio depending on the hour. Note that neither variance nor volatility values take into consideration whether the values on the database are high or low, but the ratio does take this into consideration. This is why the use of the ratio is recommended. However, from the point of view of airspace saturation, more attention must be paid to volatility during peak traffic hours, i.e., when the 95 th percentile shows high values, because in such situations the risk of saturation is much higher. Table 2 presents the mean air traffic figures per hour and per day of the week in Germany. It illustrates that time and day of the week patterns may exist, such as substantial increases in flights between 6 h and 19:00 h. Big differences may arise between different days of the week. Table 3 shows some data indicators for all the countries in the FABEC area for times from 9:00 to 12:00. These are the hours with the greatest air traffic for all these countries except Belgium, where the peak is at 16 h. A clear overdispersion can be noted.
###### Abstract  
End-to-end systems have been introduced to address the issue of independent signal processing module design in traditional communication systems, which prevents achieving global system optimization. However, research on indoor end-to-end Visible Light Communication (VLC) systems remains limited, especially regarding the channel modeling of high-speed, high-capacity Direct Current-biased Optical Orthogonal Frequency Division Multiplexing (DCO-OFDM) systems. This paper proposes three channel modeling methods for end-to-end DCO-OFDM VLC systems. The accuracy of the proposed methods is demonstrated through R-Square model fitting performance and data distribution analysis. The effectiveness of the proposed channel modeling methods is further validated by comparing the bit error rate (BER) performance of traditional receivers and existing deep learning (DL)-based receivers. The results show that the proposed methods can effectively mitigate both linear and nonlinear distortions. By employing these channel modeling methods, communication systems can reduce the spectral occupancy of pilot signals, thereby significantly lowering the complexity of traditional channel estimation methods. Thus, these methods are suitable for use in end-to-end VLC communication systems.  
end to end; DCO-OFDM; visible light communication; channel model; deep learning +
Footnote †: journal: _Photonics_
# AgriSecure: A Fog Computing-Based Security Framework for Agriculture 4.0 via Blockchain
## 4 Security Threats in Modern Agriculture
### Data and Device Security Issues and Threats in Agriculture 4.0  
As was previously mentioned, harsh environmental factors that can seriously harm electromechanical equipment, such as extreme heat, moisture, moisture, shocks, and other anomalies, have a direct impact on the agricultural sector [70]. The majority of these sensors are prone to failure, making it feasible for them to produce inaccurate data and instructions that could cause a manufacturing disaster [71]. Additionally, sensors and network equipment are predominance reachable. This poses a serious risk to farmlands since anybody with bad intentions can access them and corrupt or harm them to cause them to malfunction [72].  
Another potential security concern is the online transfer of the information gathered by IoT sensors and other agricultural machines. Major security concern in the agriculture industry is security, privacy, and control [73], as farmers may incur a significant financial and personal loss in the event of a data breach. Ref. [74] Divides the threats to confidentiality into four distinct categories:  
* Intentional data theft through smart platforms and applications that do not adhere to security and privacy standards;
* Internal information thefts from a stakeholder in the supply chain intended to harm an agri-business or a farmer;
* Unethical data sales intended to reduce profits for farmers or to harm them; and other threats.
* Unsupervised foreign access to private and sensitive information using drones, sensors, and cameras with the intention of utilizing them to harm farmers or jeopardize public safety [64; 75].  
Every application of modern technology, IoT in agriculture, carries the aforementioned dangers, weaknesses, and threats. A more thorough and complex explanation of the risks and their sources in aspects of cyberattacks in the agro sector is provided in the following paragraph, which is primarily focused on illegal behavior and exploited vulnerabilities in ICT systems.
## 2 The Mechanism of EMA and Flame Retardant
### EMA
Interfacial polarization is especially critical in composite materials because the interfaces between MOFs and other components generate a significant amount of interfacial charge [47,48]. The reciprocating movement of these charges in the electric field causes enormous energy dissipation. It is worth mentioning that the migration of free carriers (such as electrons and holes) under an alternating electric field generates displacement currents and conduction currents, causing conductivity loss, and its loss efficiency is positively correlated with the electrical conductivity of the material [49]. Furthermore, the porosity of MOFs provides various reflection and scattering channels for microwave propagation, increasing absorption path length and energy dissipation. Magnetic loss is also an important mechanism for EMA, especially when magnetic metals (such as Fe, Co, Ni) or their oxides are integrated with MOFs [50,51]. An applied magnetic field causes the magnetic moments of the magnetic components to precess, dissipating microwave energy through natural resonance and exchange resonance during this process. Moreover, the movement of magnetic domain walls and hysteresis loss are also significant energy dissipation pathways. At the same time, the incorporation of magnetic components can match the impedance characteristics of the material with the wave impedance of free space, minimizing the surface reflection of microwaves and allowing more energy to enter the material and be absorbed [52,53]. MOF-based composites exhibit varying electromagnetic responses to electromagnetic waves of different frequencies (1-20 GHz). In the low-frequency range (1-2 GHz), due to the longer wavelength of microwaves, achieving high EMA performance is challenging and requires thicker or more complex material designs. Magnetic MOFs dominate energy dissipation through magnetic resonance effects, while moderate conductivity (such as MOF-derived carbon) aids in dielectric loss and suppresses surface reflection [54]. In this range, material thickness and the content of magnetic components are key design parameters. In the mid-frequency range (2-8 GHz), the synergy between dielectric loss and magnetic loss becomes the core. The hierarchical porosity and heterogeneous interfaces of MOFs enhance interfacial polarization, while the resonance frequencies of magnetic components cover this frequency band. Optimization of the conductive network (e.g., CNT/MOF composites) can further improve broadband EMA performance [55]. In the high-frequency range (8-20 GHz), dielectric loss (especially interfacial polarization and relaxation effects) dominates. The high specific surface area and nanoscale channels of MOFs provide abundant polarization sites, while the incorporation of conductive fillers (e.g., graphene, MXene) enhances high-frequency EMA through conductive loss and multiple scattering [56].
## 2 Materials and Methods
### Data Assessment  
All patients received the same perioperative management. Full weight-bearing with an early rehabilitation was performed for all patients. Clinical and radiological follow-up was conducted at 2 months and then 12 months post-operatively. The radiological assessment included weight-bearing anteroposterior and lateral views, axial view of the patellae at 30\({}^{\circ}\) of flexion, and standing long-leg radiographs. We measured the Hip Knee Ankle (HKA) angle, the mechanical femoral and tibial axis, and the tibial slope. The complications and revisions were recorded at the last follow-up. All measurements were performed by the same independent and blinded surgeon observer using the scale provided by the radiological software (Centricity Universal Viewer, version 6.0 SP10.2, GE Healthcare, 540 West Northwest Highway, Barrington, IL 60010, USA). Our primary endpoint was the tibial mechanical angle (TMA), representing the alignment of the tibial component in the frontal plane. Secondary endpoints were the tibial slope (sagittal tibial alignment) and the overall mechanical axis of the lower limb (HKA angle). We also measured the femoral mechanical angle (FMA) as an indicator because it influences the HKA but does not correlate with the tibial cutting technique being tested. We also reported the proportion of \"outliers\" for each angular value measured. Data were classified as \"outliers\" if they were beyond 3\({}^{\circ}\) of the targets (HKA: 180\({}^{\circ}\), TMA: 90\({}^{\circ}\), tibial slope: 90\({}^{\circ}\), FMA: 90\({}^{\circ}\)).
# TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing
## 5 Security Analysis
### Trust and Transparency  
The endorsement policy in Hyperledger Fabric is defined by channel administrators and determines which peers must approve a transaction before it proceeds to the ordering service. Compliance with the policy ensures successful transaction submissions. Utilizing an endorsement policy that involves multiple peers from different organizations enhances transparency and trust, preventing single points of failure by setting a minimum number or percentage of endorsing peers.  
The inclusion of \"trust\" reflects the understanding that effective threat intelligence sharing relies on establishing trust relationships between participating entities. Trust is a fundamental element in enabling secure and collaborative information exchange, implying the consideration of mechanisms to authenticate and validate the trustworthiness of the involved parties.  
Hyperledger Fabric operates as a permissioned blockchain, wherein permissions are regulated by one or more membership service providers (MSPs) utilizing cryptographic identities. At each stage, transactions are meticulously examined to verify the authenticity of requests. This meticulous verification process serves to restrict unwanted access and fosters a heightened sense of trust in the system.
# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 5 Experiment and Analysis
### Experimental Setup  
We used Python and PyTorch frameworks via NVIDIA RTX3060 on Windows to implement our method. In the experiment, the batch size was set to 8, and the Adam optimization algorithm [53] was utilized with the following parameters: \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\). The initial learning rate was set to 0.0001 and decreased every 50 rounds. Table 1 shows the specifications of the network structure used for experimentation. The input size of the image was \(256\times 256\).
## 2 Multipath Routing Algorithm for Inter-Satellite Links Based on Load Balancing
### Optimal Path Selection for Inter-Satellite Links  
After obtaining multiple paths, if there is only one shortest path, this path is selected for data transmission. If there are multiple shortest paths, the entire path and the path with the largest bandwidth capacity of neighboring nodes are selected first from all the shortest paths, which can avoid using a highly congested path to improve the system transmission throughput and balance the load. Choosing neighbor nodes with less data usage can avoid the situation where a complete path has the least load, but a single node has a very high data usage rate or even reaches the threshold, and the data usage rate of other nodes on the path is extremely low. If it only relies on the variable with the lowest occupancy rate of all data on the path, data congestion is very likely to occur. In order to avoid this situation as much as possible, we consider the neighbor nodes of the source node as the target, and according to experience, the neighbor nodes, as the first hop node for the source node to transmit services to the distant destination node, often carry a relatively important role. Especially when the source node has business transmission requirements to multiple long-distance destination nodes, if there are multiple neighbor nodes, choosing a suitable neighbor node can not only quickly complete the business needs of each destination node, balance the load, improve the throughput of the entire system, but also realize shunting, which can avoid multiple business needs choosing the same neighboring nodes, affecting the transmission requirements of multiple services, the throughput of the entire system and the delay.  
Use s to record the number of transmission connections of inter-satellite network nodes, w is the weight to represent the node bandwidth capacity, the greater the node bandwidth capacity, the greater the corresponding weight. When there is a data service demand, the link data usage value of the node is increased by 1. If the node fault or is congested, the node weight is set to 0, and the node is no longer used for data transmission. Assuming that the total number of nodes in the network is m, the number of shortest paths obtained is n, and a link with the smallest ratio of node connection number to weight is selected from n links as the optimal path. Use c to represent the number of links, w to represent the weight, s to represent the selected node, and sn to represent the unselected node. We use c(s)/w(s) to measure the link state and then the selected node link S must satisfy: c(s)/w(s) \(<\) c(sn)/w(sn).
## 5 Performance Evaluation
### Parameter Tuning of G-TIAG
#### 5.3.3 Number of GRU Hidden Units  
When the number of GRU hidden units is too small, the learning capability of the G-TIAG needs to be improved. Correspondingly, with the increase in hidden units, the G-TIAG faces two problems: overfitting and high time cost. In order to find the trade-offs among learning ability, classification performance, and time cost, we select four commonly used choices of the hidden unit number and explore the changes of precision, recall, and F1-Score in the test data, as shown in Figure 7b.  
We can see that the G-TIAG classification performance improves significantly as the increase in hidden unit number, and all three indicators are around 0.85 to 0.95. Notably, when the number of units is higher than 256, the G-TIAG shows a stable classification performance on the training set. However, the classification performance starts to decrease slightly on the testing set. The above result is that too many GRU units increase the complexity of G-TIAG, facing the overfitting problem and the decrease in the generalization ability. Therefore, we set the number of GRU units as 256.