esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Federated Learning and Blockchain: Brief Review
### Federated Learning  
Federated learning is a new technology that orchestrates connected clients to gain knowledge collaboratively. It was first introduced by Google in 2015 [17] to overcome three main issues: the huge amount of data gathered from many devices is unbalanced, non-independent and identically distributed (non-IID); the communication overhead of distant and massively distributed devices; and the insecure centralized data-storing mechanisms [10].  
In federated learning, the learning burden is shared among connected nodes, usually referred to as clients, to train the ML model locally and upload the learning gradients to a central aggregator that levels all the learning gradients to a shared global model.  
Figure 2 shows the basic topology of the federated learning procedure. It includes the list of clients selected to join the process by training the models locally. The aggregator is a central trusted server that could provide the aggregation results. Many communications can happen between different clients and the aggregator server. The process of FL is started by selecting clients, and next choosing the model. After performing the local training, the results will be sent to the aggregator server where they are aggregated. A global update can be performed next from the server and sent to the different clients. Figure 2 can be described by these five essential steps:  
1. Clients selection: Participants' devices are selected to join the training iterations. This selection could depend on a number of factors, such as device processing capabilities and storage capacity, and is determined by definitive selection protocols [10].
2. Model selection: The primary model is chosen, and its main parameters are determined and shared with clients to start the federated learning [12].
3. Local model training: Clients independently train the model with the local device data storage [7].
4. Local model gradients updates: After each iteration, clients push the training gradients to the aggregator device [10].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Related Work** & **Summary** & **Limitation** \\ \hline [14] & The authors outlined existing solutions that deal with applying FL and Blockchain for security and privacy-preserved methods in the IoV ecosystem. & Focused only on IoV data and did not discuss other types of IoT ecosystems. \\ \hline \multirow{3}{*}{This paper} & Provided a technology summary and reviewed existing integrations of Blockchain, federated learning, and IoT while performing a security and privacy analysis of each reference found in the literature. & - \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._Global model update: The aggregator applies an aggregation technique to level the trained model gradients and propagate the update to the clients to start the next round [7].
## 4 Results and Discussion
### Discussion
If a continuous spectrum then contained redundant channels, we would expect the SBG  
Figure 12: Radiance vs. wavelength (**A**) and albedo vs. wavelength (**B**) for two simulated samples, both with the same relative azimuthal angle of 90\({}^{\circ}\), viewing zenith angle of 22.5\({}^{\circ}\), and solar zenith angle of 45\({}^{\circ}\). The blue line has grain size of 2500 \(\upmu\)m and impurity concentration of \(10^{-8}\), while the red line has grain size of 640 \(\upmu\)m, and impurity concentration of \(10^{-6}\). Both samples have the same VSWIR (380–2500 nm) albedo of 0.231, shown by the dashed black line. algorithm and Max-Min algorithm to perform similarly. However, there appears to be a marked improvement in accuracy when using continuous spectral coverage. While the Max-Min algorithm performed better than the other two multi-band approaches, it shares their discrepancies between predicted values and model data for large snow grain sizes and the same tendency to overestimate small impurity concentrations. Since changes in grain size, impurity, and geometry angles all affect the TOA radiance in unique ways, it is perhaps not surprising that having more spectral channels leads to increased accuracy. Our results point to better neural network training and prediction algorithm performance when continuous spectral coverage is utilized. However, equidistant spectral sampling, such as that proposed/envisioned for SBG, does not necessarily guarantee optimum performance. It seems reasonable to expect that placing more sampling points in spectral regions with steep gradients than in regions where the spectrum is relatively flat could be beneficial.
## 4 Results and Discussion
### Reusability Studies  
The durability of CPW, APWH, APWN, and APWP was tested by reusability experiments (Figure 9). This directly influences the economic viability of the samples. All the sample's adsorption capacity was lower after each cycle than the first use. This is due to the blocking of the active sites on the surface of the adsorbent. However, after four consecutive cycles, all the samples retained more than 60% of their uptake capability.  
Figure 9: Adsorption–desorption studies for removing (**a**) Cr(VI) and (**b**) MB.
## 8 Simulation Setup and Results
### Attack Pattern and Evaluation Metrics  
To resist against different kinds of internal malicious attacks, the three routing protocols behave as follows, (1) AASR applies an initial complex cryptographic algorithm during initialization. (2) AODV takes no action against these attacks and forwards the packet as it gets. (3) TER can detect the malicious user via the updated trust score and get rid of the attackers. Furthermore, we have considered the zigzag and black-hole attacks as an adversary model.  
Also, packet throughput, latency, and packet loss are evaluation metrics. First, the performance analysis is carried out among our proposed TER, Authenticated Anonymous Secure Routing AASR, and original Ad-hoc On-demand distance vector routing AODV using different vehicle mobility and increasing malicious nodes. In other performance analysis, TER and AODV are tested under black-hole and zigzag attack pattern. The time complexity analysis to validate the efficiency of TW is also carried out by considering two real-world data sets, i.e., (Advogato and PGP). Third, uncertainty analysis is also done that predicts the general behavior of vehicles during network initialization and setup.
## 4 Mobile Localization Algorithms
### Centralized Localization Algorithms
#### 4.1.1 Absolute Positioning Scheme (APS)  
Autonomous Underwater Vehicles (AUVs) provide researchers with new forms of access to ocean. Exploiting AUVs requires AUVs' accurate position information. Thus, in [36], an Absolute Positioning Scheme (APS) is proposed to localize an AUV. As shown in Figure 7, an AUV transmits an interrogation pulse at a fixed rate. The signals received at the surface consist of a direct signal from the AUV and a reply from each of the transponders. The time difference of arrivals along with depth measurements from an onboard pressure sensor are used to localize the AUV.  
APS is proposed to localize one AUV and localization coverage is limited by the acoustic interrogation pulse. Both the AUV and the ship are moving, so the time difference of signal arrivals is not accurate enough. Furthermore, using the GPS-positioned hydrophone aboard the ship to localize AUV incurs high hardware cost and high energy consumption. Therefore, another AUV localization system is proposed in [37], namely Particle Filter [38] based Localization System (PFLS). PFLS is implemented onboard the AUV to localize itself in real-time using ranging information obtained from an UWSN. The AUV is equipped with an acoustic modem allowing it to communicate with the surrounding sensor nodes. Based on the round trip time, the AUV can determine its distances to neighbor nodes. Compared with APS, PFLS are much more efficient.
# Development of Active Microvibration Isolation System for Precision Space Payload
## 2 System Composition  
### Configuration and Composition of Active Vibration Isolation Platform  
A vibration isolation system for a space optical cavity with a voice coil motor was designed as an actuator. Figure 1 shows the structural layout of an active vibration isolation platform (AVIS). The entire system consists of a foundation platform, a load platform, and eight vibration isolation modules. Each module is composed of a coaxially mounted sensor and a voice coil motor to ensure that the measured signal and control force are coaxial and in the same direction. For vertical and horizontal stiffness, the load platform was connected to the base platform using springs. The eight vibration isolation modules are arranged orthogonally in the vertical and horizontal directions, and the layout direction is shown in Figure 1. The acceleration signal of the load platform is picked up by the sensor, and it is sent to the driver through the controller. The control force generated by driving the voice coil motor acts on the load platform. By using a decentralized control approach, each vibration isolation module features its sensor and actuator as the single-input single-output (SISO) [27]. In this way, the control force output by multiple SISO loops is used to achieve vibration suppression of the system in three directions.
## 4 Results and Discussion
### Sensitivity Analysis
#### 4.5.1 The Electricity Production Mix  
For the comparisons made in this paper, the European electricity mix was applied, representing 276 g of CO\({}_{2}\) produced per kWh. As EVs are only exploited to their full environmental potential when its electricity originates from renewable sources, this is where the focus of European policy concerning energy production should be. Thus, we emphasize the agreed commitments towards carbon-neutral economy by 2050. As indicated by [PERSON] et al., the climate change impact of EVs powered by energy from renewable sources could be reduced to approx. 40 g of CO\({}_{2}\)-equivalents per kilometer driven. The other way around; if energy production would shift to fossil fuels such as oil  
Figure 10: The impact on human toxicity at a local level per powertrain technology, considering refinery-to-tank (RTT) instead of well-to-tank emissions.  
Figure 9: An overview of the well-to-wheel impact on human toxicity per powertrain.  
or coal, the impact per kilometer would significantly exceed the impact of conventional cars. This is shown in Figure 11, for which the 40 kWh EV's impact is distinguished per energy production source (based on [42]). A drastic shift to renewable sources would thus mean the well-to-tank emissions for EVs could be marginalized, which will also have a substantial impact on the other three discussed midpoint indicators.  
In the current absence of large-scale power storage facilities for renewable sources such as wind and solar power, EVs might prove to be a solution as well. Slow-charging facilities could, therefore, be maximally provided with green power, although this is far less likely for fast-charging facilities unless ultracapacitors are applied. In the case of fast-chargers, energy is needed immediately at high power rates, which is why the share of renewable energy is likely to be on the low side.
## 6 Simulation and Experiment
### Experiment  
The method proposed in this paper is validated on the Sailfish AUV--a kind of torpedo underwater vehicle independently developed at the Underwater Vehicle Lab of the Ocean University of China, as shown in Figure 15. Its displacement is 260 Kg. It measures 3.60 m in length, 0.324 m in diameter. A detailed set of experiments is carried out in the Yellow Sea of China to prove the performance of this algorithm, as shown in Figure 16. After the appropriate motion control parameters adjustment, 3 DOFAPLOS and the conventional LOS experiment are tested, which is used to confirm the performance improvement relative to LOS. The experimental data are analyzed by graphs and tables as follows. The predetermined range of look-ahead distance of _APLOS_ uses 8-15 m, and the look-ahead distance of _LOS_ uses 15 m.
# Multi-Connectivity-Based Adaptive Fractional Packet Duplication in Cellular Networks
## 6 Conclusions and Future Work  
Radio resources are very limited and need to be efficiently used to meet the reliability and low-latency requirements in 5G. Multi-connectivity adds spatial diversity but also helps with beam forming and massive-MIMO in the case of mmWave connections. Our proposed adaptive fractional packet duplication scheme allows for flexibility in the network to turn ON and OFF the PD.  
Our multiple schemes using the SINR or fade threshold were the most effective as they required small changes in real network algorithms. Since a complete PD is wasteful over the entire transmission time, all our simulation results clearly show when and where PD is effective and help to clearly understand the channel conditions. A network operator can, thus, decide on PD depending on the resource availability and application requirements. Future work can include more than two connections and can also include the WiFi6 standards to improve data rates and help with cellular network offloading.  
Conceptualization and methodology, C.B. and R.A.P.; software and validation, C.B. and R.A.P.; formal analysis, C.B. and R.A.P.; investigation, C.B. and R.A.P.; resources, C.B. and R.A.P.; data curation, C.B.; writing--original draft preparation, R.A.P.; writing--review and editing, C.B. and R.A.P.; visualization and supervision, C.B. and R.A.P.; project administration, C.B. and R.A.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
## 5 Evaluation
### Impact of Proposed Algorithm on RA KPIs
#### 5.4.4 Algorithm Overhead Analysis  
Generally, the potential drawback of learning algorithms is the resulting overhead from implementation when the algorithm makes an inference. We discuss the overhead of the proposed DQN algorithm in terms of the storage(space) and running time. Our deep reinforcement learning algorithm, as applied to the aforementioned environment, has a possible advantage in terms of memory overhead over other algorithms, such as Q learning. This is because it does not store the increasing number of state and action pairs for training. It rather approximates that function, and we use replay memory for storing experiences as Algorithm 1 illustrates. Then, a min-batch size of the memory is used for experience replay. Therefore, space complexity is minimal.  
In spite of that, we need to store samples of experiences. This depends on the selected batch memory size, which can be fixed (the size doesn't grow) because of being overwritten frequently and storing the most recent batch of experiences. Allowing more size helps the agent to sample from more experiences and cancels correlation, which is helpful for the stability of the algorithm. Therefore, the memory overhead is upper bounded by the replay memory size \(M\). Let \(M\) represent the memory replay size, and the number of bits required to store our current state, next state, the action taken is \(B\) bits. Therefore, the RAM memory overhead \(R_{m}\) of the devices is given by the following equation.  
\[R_{m}=M*B. \tag{27}\]  
For inference, Each layer has a matrix of weights. The size reserved for one of the matrices can be used for the next multiplication operation performed at the next layer. Therefore, space is bounded by the size of the biggest weight matrix. It is given by:  
\[\mathcal{O}(nk),\]  
where \(n\) and \(k\) are the dimensions of the rows and columns of the matrix respectively. Time overhead in forward-propagation constitutes matrix multiplication operations. Suppose \(n\) is the number of layers. Matrix multiplication operation performed at each layer is in the \(\mathcal{O}(n^{3})\) time complexity. The overall time overhead is given by:  
\[(n-1)layers*n^{3}=O(n^{4}). \tag{28}\]
## 3 Results
### Surrounding Rock Excavatability Identification Based on Deep Forest
#### 3.2.4 Influence of Input Features on the Model  
It has been shown that the RF-based feature metric can effectively assess the impacts of individual features on the model's performance [44]. Therefore, this section used the Gini coefficient (Equation (23)) based on RF to measure feature importance. A higher score indicates a greater contribution to the model. The importance scores and rankings of the ten features of the model are shown in Figure 29:  
\[Gini(p)= 1-\sum_{l=1}^{l}p_{l}^{2} \tag{23}\]  
where \(l\) is the SRE classification grade, and \(P_{l}\) is the probability of a sample being classified for the \(l\)th class.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Grade** & **Recall** & **Precision** & **F1 Score** \\ \hline L-1 & 0.9209 & 0.9706 & 0.9451 \\ L-2 & 0.9277 & 0.9625 & 0.9448 \\ L-3 & 0.9720 & 0.9665 & 0.9692 \\ L-4 & 0.9543 & 0.9300 & 0.9419 \\ L-5 & 0.9666 & 0.9753 & 0.9709 \\ L-6 & 0.9872 & 0.9659 & 0.9764 \\ \hline Average & 0.9548 & 0.9618 & 0.9581 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Prediction results of surrounding rock excavatability.  
Figure 29: Importance scores of different input features for surrounding rock excavatability prediction.  
Figure 29 shows that features \(K8\) (penetration) and \(K9\) (advance rate) contributed the most to model performance, with importance scores of 0.2619 and 0.2473, respectively, while \(K1\) (total thrust) scored 0.1298, second only to \(K8\) and \(K9\). In contrast, \(K3\) (the top shield cylinder rod chamber pressure) contributed the least to model performance, with a score of only 0.0268. In terms of SRE classification, \(K8\) and \(K1\) were the two intermediate parameters with significant contributions to model performance. The algorithm was able to identify this law, indicating the feasibility of using these SRE classification criteria. From a practical perspective, the value of \(K9\) was artificially manipulated in practice according to the changes in the surrounding rock during the excavation. \(K9\) was more significantly related to SRE and therefore had a relatively significant contribution to the model's performance.
## 5 Experiment Design and Test Results  
To determine and test the empirical stochastic model of GNSS observations, measurements from two reference stations WUT1 and WUT2 located on the roof of Warsaw University of Technology Main Building (approx. location: 52\({}^{\circ}\)13\({}^{\prime}\)15\({}^{\prime\prime}\) N, 21\({}^{\circ}\)00\({}^{\prime}\)37\({}^{\prime\prime}\) E) were used. Seven subsequent daily session data sets (year 2017, DOY: 337-343) from WUT1 were used to derive the ZB combination, while another subsequent seven daily sessions (year 2018, DOY: 168-174) from both stations were used to calculate the SB combination for 4.75 m baseline. The same set of two Septentrio PolaRx5 receivers was used for both tests, and the measurements were carried out with a 1 Hz interval for the 0\({}^{\circ}\) elevation mask. The precise Center for Orbit Determination in Europe (CODE) Multi-GNSS-EXperiment (MGEX) orbits were used for all calculations [92]. Note that the interval between the two periods did not affect the obtained results, as the observed constellation did not change during this time. However, the presented analyses do not include the BeiDou-3 constellation, which started operating in 2018. All calculations and analysis related to stochastic modeling and positioning solution were performed using in-house MATLAB software.
###### Abstract
used the image and spectral characteristics of FY 3 MERSI data from 1 to 5 bands, analyzed the performance characteristics of vegetation, towns, soil, and water on MERSI images, and performed various water recognition models. A water body index expansion model suitable for MERSI data has been put forward by them. Due to the limitations of obtaining the permission of satellite data acquisition, there are relatively few studies on water extraction methods using Fengyun satellite data [21]. This paper primarily uses FY-3/MERSI images to extract surface water from Poyang Lake. The classification of land surface waters is mainly completed by using the single-band threshold, water index, decision tree classification, supervised classification, SNDPSO and SMDPSO+LFE algorithms. After comparing the coefficients of OA, Kappa, f1, and miou of the various models and methods, the best method for the classification of land surface waters is selected. Moreover, the stability and sensitivity analyses are completed using this method. Selecting an accurate, sensitive, and stable method is essential for water body information extraction in the Poyang Lake region. However, seven potential water extraction methods are known, but it is not clear which one is the most acceptable. The following study will determine the most appropriate method for water extraction in Poyang Lake. The research area and data section will explain the selection of the water body information of the Poyang Lake, and the spectroscopic characteristics of FY-3 data the Landsat 8 operational land imager (OLI) data. The source of these data will be explained at the same time. The methodology part includes the pre-processing of FY-3 and Landsat 8 OLI images, and the algorithms of 5 traditional methods, SMDPSO method, and SMDPSO+LFE method. Methods of validation and accuracy evaluation will also be provided. Then, the results section will give the accuracy comparison of the 7 methods and the sensitivity and stability test results of the most accurate method. The advantages and disadvantages of the 7 methods will be discussed in the discussion section. The conclusion of this research will be provided at the end.
## 3 System Model and Security Goals
### Security Goals
#### 3.3.2 Anonymity of Identity  
The adversary searches for loopholes or weak points in the systems. An intruder wants to access confidential data such as user identity, password, secret keys, etc., to perform MITM attacks and impersonate attacks. So, the identity of the remote users and devices must be kept anonymous while messages are exchanged. If an adversary attempts to access the network or data by using malicious attacks, then the system should deny such attempts.
## 3 Target Discrimination  
Although some sea surface background interference can be suppressed after the ROI extraction stage, there are still some false alarms, such as coastal buildings and thick clouds, due to the complexity of the sea surface. Therefore, it is necessary for us to use the feature extraction and machine learning technique to confirm real ships. In this section, we introduce the description operator MLBP combined with SVM training to eliminate false alarms and confirm real targets.
# Enhanced Proton Acceleration from Laser Interaction with a Tailored Nanowire Target
## 1 Introduction
The oblique incidence also creates multi-reflections of the laser pulse in the tailored structure, meaning that the reflectivity of the laser pulse can be suppressed and more energies are coupled to hot electrons. Here, a series of two-dimensional (2D) particle-in-cell (PIC) simulations were carried out to compare the CNT and the PNT. The simulation results verified that the CNT can significantly enhance the cut-off energies of accelerated protons as well as the energy conversion efficiency. Furthermore, the influence of the laser intensity and the parameters of the corrugated surface on the proton acceleration were examined and are discussed below. The simulation results demonstrated that the proposed target design works well and is superior to the PNT at various parameters, thus providing a robust and simple scheme to obtain protons of much higher energies without requiring a higher laser intensity. Figure 1: Schematics of the PNT (**a**) and the CNT (**b**). Both targets consist of solid nanowires and substrate of \(Au^{50+}\) (red part) and a thin hydrogen foil (blue part). The corrugated surface profile is marked by a green-dashed line.