esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Types of Attacks in FL
### Data Attack  
Data attacks in FL aim to corrupt the training data on client devices to influence the learning process adversely. These attacks can manifest in several forms:  
**Data Poisoning:** Malicious clients inject false or misleading data into their local datasets [17]. This can skew the model's training process, leading to a degraded or biased global model [57]. For example, in an energy forecasting network, an attacker can inject false data into the forecasting system [58].  
**Label Flipping:** A specific type of data poisoning where the labels of the training data are intentionally flipped [59; 60]. For instance, in an image classification task, images of cats could be labeled as dogs.  
**Backdoor Attacks:** The attacker modifies the training data to introduce a backdoor into the model [61]. For example, specific triggers in the input data can cause the model to output a particular incorrect result.  
The impact of data attacks in FL begins with local data corruption at the client level, where the attacker corrupts the local dataset. These corrupted data then influence the local model update during the model training phase. Subsequently, the poisoned local model is sent to the central server for aggregation. At this stage, the central server aggregates updates from all clients, including the poisoned ones, resulting in a compromised global model. Finally, this compromised global model is distributed back to all the clients, leading to degraded performance or backdoor exploitation across the entire system.
# A Review of AlGaN-Based Deep-Ultraviolet Light-Emitting Diodes on Sapphire
## 5 Reflective p- and n-Electrodes
### Interdigital Reflective n-Contact  
The part of the EL traveling backwards to the device layers, which is reflected from the sapphire-air boundary, is absorbed in the p-GaN layer of DUV-LEDs, resulting in the waste of the EL. To reduce the waste EL on this mechanism, an n-reflective contact was investigated [40]. The sheet resistance of the n-contact on Al\({}_{x}\)Ga\({}_{1-x}\)N (x \(>\) 50%) was recorded to be around \(10^{-4}\)\(\Omega\)cm\({}^{2}\)[122] using vanadium. The carrier concentration of the n-AlGaN layer and the electron mobility were  
Figure 6: **(a)** Meshed p-GaN contact pattern is shown by narrow white lines besides the black area, which preserves the reflective area (black). Beside the hatched zone, a closed curve zone (white) indicates the Al-based n-contact area with annealing treatment. **(b)** Result of the reliability test. The reduction in the lifetime was suppressed. The reliability test was carried out at as low as 20 mA to avoid the further reduction in the lifetime. (**(a,b)** are permitted to be reused from Jpn. J. Appl. Phys. [40]. © Japan Society Applied Physics).  
reported [123]. However, because the n-AlGaN cladding layer and the n-contact are highly resistive compared with those of n-GaN, an interdigital n-electrode is usually applied [13; 26] to supply the current uniformly to the junction area, as shown in Figure 6. An increased length of the mesa sidewall enables a narrow n-contact area adjacent the junction area. The reflectivity in DUV at n-contact is lost by the annealing treatment to lower the contact resistivity. This annealing process is currently unavoidable to minimize the increase in V\({}_{\text{f}}\). The part of the n-electrode adjacent to the junction area was annealed to form a low-resistivity contact, and then the Al was post-deposited on the rest of the contact area of n-AlGaN. As a result, an EQE was improved by 1.2-fold [40] with a small increase in V\({}_{\text{f}}\). Since the area of an n-reflective electrode has a trade-off relationship with the junction area, the practical use of n-reflective electrodes should be limited, because the current density in the junction area must be reduced by increasing the junction area to obtain a longer lifetime. Therefore, a n-reflective contact is not applied to our device layers.
## 5 Performance Evaluations
### Simulation Environment  
We comparatively evaluated the performances of our proposed method with SEED protocol [19] and we considered it as a baseline for our evaluation. For simulations, we used Castalia simulator that was based on OMNET++ [38]. Random topology was used with 100 heterogeneous nodes randomly deployed in a 100 \(\times\) 100 m area. It was assumed that all nodes were heterogeneous in terms of residual energy, storage (e.g., buffer capacity) and computational capability. The child nodes were resource-constrained, whereas the CHs were resourceful nodes in terms of energy source, storage capacity, and computational capability. The CH nodes required high resources in order to perform their operation efficiently. The optimal percentage of CHs was 5% [16; 39], We deployed all nodes (resource-constrained and resourceful) in the ratio of 70% and 30%, where 70% of them were resource-constrained nodes (child nodes) and 30% were resourceful nodes (which could become CH). The rationale of choosing the aforementioned scale was that the child nodes sensed a large environment and transferred the sensed data towards their respective CH node(s). Therefore, the proportion of child nodes was high as compared to CH nodes. The initial energy of high resources nodes varied from 6-10 J and the per bit energy consumption was 0.5 uJ. The reason is, usually the work load on CHs (i.e., aggregation operations, cluster management and collective data transmission) was an order of magnitude larger as compare to work-load on child nodes which only perform sensing operations. Furthermore, the maximum storage capacity of CH nodes was \(2^{24}\) bits. Since the proposed scheme considered the packet size of 4000 bits, the maximum number of packets that a node could accommodate in its memory was approximately 4100 packets. All the nodes were location aware (e.g., nodes are equipped with global positioning system (GPS)). The simulation time was 2000 s. Each experiment was conducted five times, and the results were acquired from the average values of measurements. The main simulation parameters are summarized in Table 1.
###### Abstract  
Pervasive healthcare services have undergone a great evolution in recent years. The technological development of communication networks, including the Internet, sensor networks, and M2M (Machine-to-Machine) have given rise to new architectures, applications, and standards related to addressing almost all current e-health challenges. Among the standards, the importance of OpenEHR has been recognized, since it enables the separation of medical semantics from data representation of electronic health records. However, it does not meet the requirements related to interoperability of e-health devices in M2M networks, or in the Internet of Things (IoT) scenarios. Moreover, the lack of interoperability hampers the application of new data-processing techniques, such as data mining and online analytical processing, due to the heterogeneity of the data and the sources. This article proposes an Internet of Medical Things (IoMT) platform for pervasive healthcare that ensures interoperability, quality of the detection process, and scalability in an M2M-based architecture, and provides functionalities for the processing of high volumes of data, knowledge extraction, and common healthcare services. The platform uses the semantics described in OpenEHR for both data quality evaluation and standardization of healthcare data stored by the association of IoMT devices and observations defined in OpenEHR. Moreover, it enables the application of big data techniques and online analytic processing (OLAP) through Hadoop Map/Reduce and content-sharing through fast healthcare interoperability resource (FHIR) application programming interfaces (APIs).  
e-Health; pervasive healthcare; OpenEHR; M2M; IoMT; semantic interoperability; data warehouse; data mining; FHIR
# The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 2: Uncertainty and Validation
## 2 Data
### Ancillary Data  
#### 2.2.1 European Center for Medium Range Weather Forecasting  
European Center for Medium Range Weather Forecasting's (ECMWF) 6 h analyses are used as atmospheric data input to forward model the IASI observed brightness temperature calculations. The ECMWF data has 0.25\({}^{\circ}\) (-18 km) horizontal resolution and 91 vertical levels with 0.1 hPa pressure at the model top.
# Detecting Reinforced Concrete Rebars Using Ground Penetrating Radars
## 4 Signal Processing
### Determination of the Coordinates at the Peak of the Hyperbola  
The locus of the positive peak reflections from the rebars extracted from a radargram are not smooth curves, and their peak coordinates (\(t_{p}\) and \(x_{p}\)) are not provided. The curves can be approximated by a biquadratic equation to determine the coordinates as follows:  
\[t_{i}\approx Ax^{4}+Bx^{3}+Cx^{2}+Dx+E \tag{8}\]  
The reason for choosing biquadratic Equation (8) is its close fit to the theoretical hyperbola (Equation (7)) with extremely high precision, as demonstrated in Figure 7. The \(x_{p}\) coordinate represents the horizontal position of the rebar, whereas \(t_{p}\) corresponds to its depth. The peak coordinates (\(t_{p}\) and \(x_{p}\)) can then be identified by solving the first derivative of Equation (8).  
\[\frac{dt_{i}}{dx}=0 \tag{9}\]  
Developing a computer code with a fully graphical interface using Delphi, the authors addressed the limitations of the current commercial GPR device's post-processing software.  
Figure 6: Theoretical hyperbolas for rebars.  
Employing the abovementioned principles and equations, the program can automatically detect and determine the depth and size of rebars, as well as the electromagnetic wave velocity in concrete slabs. It attracts all hyperbolic reflections represented by positive peaks from radargrams and fits them best with biquadratic equations. An iterative method was employed to solve Equation (7) and determined the unknown values to fit the biquadratic equation accurately.
# Trajectory Tracking Control Strategy of 20-Ton Heavy-Duty AGV Considering Load Transfer
## 4 Design of Trajectory Tracking Control Strategy Considering Load Transfer
### Lateral Load Transfer IMPC Control Strategy  
The conventional PID controller should have limited external interference ability, complex parameter adjustment, and experience dependence when performing trajectory tracking control, and it is difficult to deal with multivariate and constraint problems. The MPC controller can effectively solve the above problems. By continuously establishing the prediction model and designing the objective function, the optimal input control variables of each step are obtained, which act on the controlled object. It has the advantages of strong robustness and convenient consideration of the constraints of the controlled system. When the conventional MPC controller is used for trajectory tracking control, the established prediction model only predicts the pose error information of AGV, and the designed objective function only aims at the minimum tracking error and control increment. In this paper, the conventional MPC controller is improved. When designing the prediction model, the evaluation variables of load transfer are considered to predict the output, and the objective function with the minimum load transfer, tracking error, and control increment is designed.
###### Abstract  
The design of new oxygen- and proton-conducting materials is of paramount importance for their possible utilization in solid oxide fuel cells. In the present work, La\({}_{2}\)(Hf\({}_{2-x}\)La\({}_{x}\)O\({}_{7-x/2}\) (\(x=0,0.1\)) ceramics were prepared using ball milling of oxide mixtures (La\({}_{2}\)O\({}_{3}\) and HfO\({}_{2}\)) followed by high-temperature annealing at 1600 \({}^{\circ}\)C for 10 h in air. La\({}_{2}\)Hf\({}_{2}\)O\({}_{7}\) ceramics exhibit an ordered pyrochlore-type structure, whereas La\({}_{2}\)(Hf\({}_{1.9}\)La\({}_{0.1}\))O\({}_{6.95}\) has a defect pyrochlore structure type with oxygen vacancies at the 48f positions. The oxygen ion and proton conductivity of La\({}_{2}\)(Hf\({}_{1.9}\)La\({}_{0.1}\))O\({}_{6.95}\) \"stuffed\" pyrochlore ceramics was investigated by electrochemical impedance spectroscopy (two-probe AC) and four-probe DC measurements in a dry and a wet atmosphere (air and nitrogen). The use of two distinct conductivity measurement techniques ensured, for the first time, the collection of reliable data on the proton conductivity of the La\({}_{2}\)(Hf\({}_{1.9}\)La\({}_{0.1}\))O\({}_{6.95}\) \"stuffed\" haftrate pyrochlore. La\({}_{2}\)Hf\({}_{2}\)O\({}_{7}\) was found to be a dielectric in the range 400-900 \({}^{\circ}\)C, whereas the La\({}_{2}\)(Hf\({}_{1.9}\)La\({}_{0.1}\))O\({}_{6.95}\) \"stuffed\" pyrochlore had both oxygen ion and proton conductivities in this temperature range. The proton conductivity level was found to be equal to -8 \(\times\) 10\({}^{-5}\) S/cm at 700 \({}^{\circ}\)C. Clearly, the proton conductivity of the La\({}_{2}\)(Hf\({}_{1.9}\)La\({}_{0.1}\))O\({}_{6.95}\) \"stuffed\" haftrate pyrochlore is mainly due to the hydration of oxygen vacancies at 48f positions.  
Keywords:SOFCs; haftrates; solid oxide electrolytes; ionic conductivity +
Footnote †: journal: Applied Physics Letters
# Energy-Efficient BWP Configuration for Multi-Slice Users
## 6 Performance Evaluation
### The Price of Anarchy  
To adequately compare the centralized and distributed approaches, we have recourse to the well-known Price of Anarchy (PoA), which is computed as follows:  
\[\text{PoA}=\frac{GlobalCost_{optimal}}{GlobalCost_{NE}} \tag{15}\]  
Therefore, it is the ratio of the sum of all users' costs obtained with the optimal centralized approach and the sum of all users' costs at NE for the distributed approach. The PoA is between 0 and 1, and the higher its value, the closer the performances of the distributed approach to the optimal centralized one.  
Figure 9 displays the PoA as a function of the number of users. As can be seen, the PoA is \(\approx\)1 regardless of the number of users. This explains why the distributed approach attains very close performances to the centralized one.  
Figure 8: Total throughput CDF for 30 users.  
Figure 9: Price of Anarchy as a function of the number of users.  
As for the convergence time of each approach, it is represented in Figure 10 for the distributed approach with the best response algorithm and the centralized approach with CPLEX solver for the ILP problem.  
The complexity of Algorithm 1 is \(O(N_{users}\times N_{strategies}\times N_{iterations}=2)\) where \(N_{users}\) is the number of users, \(N_{strategies}\) is the number of strategies that can be selected which is equal to 2 (\(MN\) and \(SN\) strategies), and \(N_{iterations}\) is the number of iterations until convergence is reached, which is also equal to 2 iterations. In fact, the algorithm duration is \(\approx\)0.01 s regardless of the number of users, which is very swift to meet the stringent delay requirements. Nonetheless, the centralized solution with the ILP problem takes a slightly higher computation time of \(\approx\)10 s when \(N_{users}\leq 20\) but starts to increase exponentially when \(N_{users}>20\). In the latter case, the convergence time is too long, which will result in failing to respect the tolerated latency. However, the learning of the optimal choice of strategy may be learned offline prior to the arrival of users in order to make fast decisions. Also, it is important to note that for the centralized approach, the ILP problem solver CPLEX remains much faster than the exhaustive search algorithm where the convergence time is \(\approx\)300 s for \(N_{users}=20\).  
We conclude that the distributed approach is able to achieve almost optimal results with a much lesser computational time compared to the optimal centralized approach where the convergence time becomes significantly important with an increasing number of users.
## 5 Reactor Systems and Process Parameters
### Photocatalytic Reactor Designs
The study provided insights into the degradation pathways and highlighted the effectiveness of the falling film reactor design in treating pharmaceutical contaminants in water. Emerging technologies such as fluidized bed and membrane photocatalytic reactors integrate advanced features like enhanced catalyst suspension, improved mixing, and combined separation and reaction zones. [PERSON] et al. (2024) explored a gas-liquid-solid mini-fluidized bed reactor for wastewater treatment, demonstrating its potential as a high-efficiency photocatalytic system [140]. The reactor exhibited improved degradation rates of pollutants, indicating its suitability for industrial applications. Another study performed by [PERSON] et al. (2023) investigated the photocatalytic degradation of acetaldehyde, a common indoor air pollutant, using a fluidized bed reactor [141]. In this setup, nano titanium dioxide (TiO\({}_{2}\)) was employed as photocatalyst. The reactor design facilitated the effective contact between the gaseous pollutant and the photocatalyst particles, enhancing the degradation efficiency. The study also conducted kinetic analyses to understand the degradation process, proposing an optimal kinetic model based on reactor performance. This research underscores the potential of fluidized bed reactors in addressing volatile organic compounds (VOCs) in air purification applications. This example highlights the practical application of fluidized bed photocatalytic reactors in air purification, demonstrating their effectiveness in degrading harmful indoor pollutants like acetaldehyde. Membrane photocatalytic reactors integrate photocatalysts with membrane filtration, allowing simultaneous reaction and separation processes. They offer reduced fouling potential and enhanced retention of reaction products but may face challenges related to membrane fouling and high fabrication costs. A novel pilot-scale continuous photocatalytic membrane reactor was tested by [PERSON] et al. (2016), for the degradation of recalcitrant micropollutants [142]. The reactor successfully removed various contaminants, showcasing its effectiveness in advanced water treatment processes. A study conducted by [PERSON] et al. (2024) investigated the performance of a solar-driven slurry photocatalytic membrane reactor for the advanced treatment of municipal wastewater [143]. The reactor utilized iron-doped titanium dioxide (Fe-TiO\({}_{2}\)) as photocatalyst and a polysulfone-based membrane for the separation process. Under both simulated and natural solar light irradiation, the system achieved chemical oxygen demand (COD) removal efficiencies ranging from 66% to 95%, over a 7-hour irradiation period. The results demonstrated the enhanced photocatalytic activity afforded by iron doping and established solar-powered slurry as an effective, low-energy, and environmentally friendly alternative for municipal wastewater treatment. Integrating photocatalysis with membrane separation processes and utilizing solar energy can enhance the degradation of organic pollutants in wastewater.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### Trickle timer-Based Solutions
#### 4.1.4 Elastic Trickle Timer Algorithm  
The major issue found in the standard Trickle algorithm is the long 'listen-only' period where nodes only listen and receive messages instead of broadcasting the messages. Authors [59] propose an Elastic Trickle algorithm allowing dynamic selection of 'listen-only' period depending on the number of surrounding nodes. The number of neighboring nodes determination begins with \(I_{min}\) during the start of the first sub-interval where the node sends a message to join a DODAG.  
In the following sub-interval, the authors have proposed the number of neighbors as \(n1\) and the 'listen-only' period as \(p1\). For neighbors \(n2\), the 'listen-only' period is \(p2\) given that \(p1<p2<p3\) and \(n1<n2<n3\) i.e., the 'listen-only' period is shorter for a smaller number of neighbors helping in avoiding collisions. At the start of the main interval, the counter is set to zero. Listen time is utilized by the nodes to check their state. If the message is received by an unknown ID and the same message is swapped by the neighboring nodes, then the node is said to be consistent, otherwise, it is considered inconsistent if it receives a message from an ID less than the one exchanged by neighboring nodes. Node makes a comparison of counter value with \(k\), if the value of the counter is less than \(k\), the message is transmitted by the node, otherwise, the node goes into the waiting state until the end of the current sub-interval and doubling of the new sub-interval. Simulations results show, compared to basic RPL, that this algorithm converges in 76% less time for a network of 80 nodes. The average Packet Delivery Ratio (PDR) of 87% is obtained in a random topology and 85% for grid topology with 80 nodes.  
Table 1 summarizes the working principle, issues addressed, strengths, and weaknesses of solutions under the 'Trickle Timer-based Solutions'.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Working Principle** & **Issues Addressed** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{Co-RPL [43]} & Reduces frequent node failures by improving localization of mobile nodes. & Issues with the Trickle timer. & Compared to basic RPL, Co-RPL has a lower loss rate of 20\%, 50\% less energy consumption, and 2.5 sec avg delay. & The effect of sink mobility on network performance is not discussed for Co-RPL. \\ \hline \multirow{3}{*}{Enhanced Trickle Algorithm [57]} & Supports nodes mobility by dynamically adjusting DIS message broadcasts based on Doppler frequency. & Issues with the Trickle timer. & Helps in lowering DIS message count to 52.7\%, packet loss rate of 0.3\%, and energy consumption of 72.5 mJ. & Solutions to high PLR due to mobile nodes not matching time intervals for selecting parent nodes are not discussed. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of Trickle Timer-based Solutions.