esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Coordinate Transformation Model and Calibration System
### Sensor Prototype and Calibration System  
In this study, a MEMS 3D electric field sensor prototype was developed, with its external casing and internal structure illustrated in Figure 4. The sensor features sensing chips as its core components, integrated with a meteorological data acquisition module, a wireless communication module, a data processing unit, and a battery-powered system. This configuration enables real-time electric field measurements in complex environments. The sensor housing is made of foamed plastic material to enhance thermal insulation. Its surface is covered with a layer of aluminum foil, which is electrically connected to the sensor's ground to prevent charge accumulation. The housing measures 25 cm in length with an outer diameter of 5.6 cm. The surface-sensing electrodes are made of gold material, with both length and width measuring 3.5 cm. The sensing electrodes adopt a dual-orthogonal configuration, employing a differential detection method to enhance measurement accuracy and improve resistance to interference in 3D electric field detection. Additionally, a wireless communication module is utilized to transmit measurement data, eliminating potential electric field distortions caused by traditional wired connections and ensuring accuracy and stability during the calibration process. The sensing chip of the MEMS electric field sensor is designed and fabricated based on the SOI-MEMS process [23]. The physical prototype of the MEMS electric field sensor is shown in Figure 5.  
To standardize the calibration procedure and ensure the accuracy and completeness of data acquisition in the decoupling calibration process, a 3D electric field decoupling calibration system was constructed, as schematically illustrated in Figure 6.  
Figure 4: Sensor housing and internal structure.  
Figure 5: Physical prototype of the MEMS electric field sensor.  
This system primarily consists of calibration plates, a pair of high-voltage DC power supplies, a data receiver, and a calibration fixture. The calibration plates are composed of parallel positive and negative electrodes powered by the high-voltage DC sources, generating a uniform electric field between them. The spacing between the plates is set to 20 cm, and the DC power supply can generate a high voltage ranging from 0 to 20 kV, ensuring a stable and controllable calibration environment. The sensor's wireless communication module emits a signal at a frequency of 400 MHz, which is received by the data receiver. The received data are then transmitted to the host computer via the RS232 protocol, ensuring real-time monitoring while preventing external wiring from interfering with the standard electric field. Furthermore, a calibration fixture was designed to allow the multi-angle rotation of the sensor. The fixture consists of two fixed shafts and a central rotating ring. The sensor is mounted on the rotating ring and can rotate freely along the fixed shafts, enabling electric field calibration and measurement at various orientations.
## 7 Qualitative Evaluation and Open Discussion
### SSFXG Challenges and Open Discussion  
#### 7.4.1 Threat Model and Security Analysis  
The proposed SSFXG framework introduces several security enhancements to protect user identity and privacy in a decentralized wireless communication environment. However, like any system, it must address potential threats across various attack vectors. One key threat category is identity theft and impersonation attacks, where malicious actors attempt to gain unauthorized access to a subscriber's identity credentials. To counter this, the SSFXG framework leverages secure distributed SCs for storing identity credentials, which are protected with strong cryptographic mechanisms. These SCs ensure that subscriber data remain isolated and tamper-proof, preventing unauthorized modifications or duplication. Additionally, the use of blockchain technology fortifies the system against data-tampering by maintaining an immutable and transparent record of transactions. In the event of attempted identity forgery, the framework's reliance on cryptographic verification ensures that only valid credentials can be authenticated by network providers.  
Another major threat vector is man-in-the-middle (MITM) attacks and eavesdropping, especially during identity verification and communication sessions. The decentralized architecture of SSFXG inherently reduces exposure to centralized points of failure, mitigating the risk of large-scale data breaches. Moreover, during the identity verification process, the system utilizes anonymized credentials and cascaded VPN-like tunnels to safeguard user privacy. This ensures that sensitive identity details are never exposed, even to network providers, and eliminates traceability. Furthermore, the use of consortium blockchain in SSFXG ensures the integrity of the interactions between network participants, such as providers and brokers, by securing transactions with cryptographic signatures and distributed validation. By decentralizing operations and anonymizing interactions, the SSFXG framework minimizes opportunities for adversaries to intercept or compromise sensitive data, offering a robust defense against these threats.
## Appendix C Parameter Tuning
### Descriptive statistics of the overall performance for study case 2: Cam-linkage mechanism.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**CHT** & **Algorithm** & **Mean** & **std** & **Median** & **Minimum** & **Maximum** \\ \hline SR & DER1B & 0.04564 & 0.01582 & 0.04094 & 0.0148 & 0.09932 \\ SR & DER1E & 7.262 & 13.89 & 0.04702 & 0.02393 & 44.38 \\ SR & DER1B & 0.5216 & 1.512 & 0.04276 & 0.01174 & 7.343 \\ SR & DER1E & 0.7596 & 1.715 & 0.1027 & 0.02227 & 6.513 \\ SR & CR & 87.61 & 91.56 & 60.56 & 0.03306 & 301.4 \\ SR & DERCB & 0.4961 & 0.1162 & 0.5248 & 0.235 & 0.6868 \\ SR & DERC1B & 51.32 & 184.1 & 0.1216 & 0.03939 & 1004 \\ SR & DERC1E & 0.02671 & 0.01325 & 0.02941 & 0.003726 & 0.04865 \\ SR & GA & 0.2966 & 0.02964 & 0.2934 & 0.256 & 0.3475 \\ SR & PSO & 0.1187 & 0.09226 & 0.07567 & 0.05316 & 0.4158 \\ cC & DER1B & 0.02942 & 0.007475 & 0.03029 & 0.005715 & 0.03749 \\ eC & DER1E & 0.01729 & 0.01095 & 0.01764 & 0.002085 & 0.03321 \\ eC & DER1B & 0.04082 & 0.01317 & 0.04182 & 0.01743 & 0.05896 \\ eC & DEB1E & 0.0286 & 0.01351 & 0.02925 & 0.004001 & 0.05625 \\ eC & CR & 0.03108 & 0.006818 & 0.03157 & 0.01884 & 0.0462 \\ eC & DECB & 0.03389 & 0.01216 & 0.03374 & 0.01447 & 0.06199 \\ eC & DECK1B & 0.03121 & 0.007929 & 0.03368 & 0.01265 & 0.04719 \\ eC & DECR1E & 0.0315 & 0.006558 & 0.03454 & 0.01178 & 0.03626 \\ eC & GA & 0.03387 & 0.00113 & 0.03524 & 0.00771 & 0.04701 \\ eC & PSO & 0.03464 & 0.009985 & 0.03127 & 0.01969 & 0.05899 \\ PF & DER1B & 0.02611 & 0.005129 & 0.02606 & 0.01551 & 0.03761 \\ PF & DER1E & 0.01265 & 0.040983 & 0.01281 & 0.003099 & 0.02219 \\ PF & DER1B & 0.02822 & 0.0111 & 0.02901 & 0.008141 & 0.06079 \\ PF & DEB1E & 0.03803 & 0.01028 & 0.03771 & 0.01184 & 0.05553 \\ PF & CR & 0.02607 & 0.01001 & 0.0293 & 0.005715 & 0.03949 \\ PF & DECKB & 0.01989 & 0.01117 & 0.01766 & 0.004001 & 0.03941 \\ PF & DECR1B & 0.03768 & **0.001529** & 0.03785 & 0.0353 & 0.04146 \\ PF & DECR1E & 0.01371 & 0.00562 & 0.01297 & 0.005481 & 0.025 \\ PF & GA & 0.2107 & 0.02938 & 0.2136 & 0.1417 & 0.2513 \\ PF & PSO & 0.03619 & 0.01384 & 0.03593 & 0.01575 & 0.06243 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Descriptive statistics of the overall performance for study case 2: Cam-linkage mechanism.
## 2 Full DC Microgrid System Modeling
### Battery Storage System  
The BS system can supply and absorb power to keep the microgrid power balance. Due to low cost and a high recycling rate, a lead-acid battery is most commonly used in a small power microgrid. To avoid over-charging and over-discharging, it is necessary to limit its state of charge represented as \(soc_{BS}\). The \(soc_{BS}\) is calculated according to Equation (6), where \(C_{REF}\) is the BS reference capacity, \(v_{BS}\) is the BS voltage, and \(soc_{BS}\) is limited between \(SOC_{BS\_MIN}\) and \(SOC_{BS\_MAX}\) as in Equation (7). The BS power noted as \(p_{BS}\) represents BS charging when \(p_{BS}\) is positive and BS discharging when \(p_{BS}\) is negative. The BS charging and discharging powers are limited by \(P_{BS\_MAX}\) and \(-P_{BS\_MAX}\) respectively, as in Equation (8), to protect the BS from operating at a large current state.  
\[soc_{BS}(t+\Delta t)=soc_{BS}(t)+\frac{100\%}{3600C_{REF}v_{BS}}\int_{t}^{t+ \Delta t}p_{BS}(t)dt \tag{6}\]  
\[SOC_{BS\_MIN}\leq soc_{BS}(t)\leq SOC_{BS\_MAX} \tag{7}\]  
\[-P_{BS\_MAX}\leq p_{BS}(t)\leq P_{BS\_MAX} \tag{8}\]
## 4 Arsenic Removal Processes  
A major approach for reducing arsenic poisoning is by treatment of arsenic-contaminated water and the new and emerging treatment technologies have received considerable evaluation and have also been recently reviewed [47; 78; 79; 80]. The major categories of treatment methods that have been used for this purpose are described below [81]:  
1. Oxidation and filtration process: This usually involves the oxidation of arsenic with inorganic iron and manganese oxides, followed by removal of the residues by filtration [82; 83]. Due to the uncharged nature of As(III) complexes, an oxidation step is often employed to oxidize As(III) complexes to As(V) complexes [84].
2. Biological oxidation: Microorganisms are used in this case to oxidize As(III) to As(V), followed by As(V) removal with iron and manganese dioxide [85; 86].
3. Co-precipitation: This involves the addition of a suitable oxidizing agent to oxidize As(III) to As(V), followed by coagulation, sedimentation and filtration [87; 88].  
1. [(iv)] Adsorption: This approach involves the adsorption of arsenic with activated alumina, activated carbon, hydrated iron oxide, iron based adsorbents and zero valent iron [89; 90; 91].
2. Ion exchange: This involves the use of relevant exchange resins (cation or anion) for separation of arsenic [92; 93].
3. Membrane technology: This includes the use of processes such as electrodialysis, nanofiltration and reverse osmosis for removal of arsenic [94; 95; 96; 97; 98].  
The following sub-sections provide more detailed discussion of some of these treatment methods.
# Prediction of Machine Inactivation Status Using Statistical Feature Extraction and Machine Learning
## 3 The Proposed Method
### Features Extraction Using Statistical Analysis
#### 3.2.1 Reliability Features
Additionally, the lognormal distribution complements  
Figure 3: Result of the fitting curve of Weibull distribution. the Weibull distribution well for modeling the reliability of a machine. The formula of the lognormal distribution is written as follows:  
\[f(t^{\prime})=\frac{1}{\sigma^{\prime}\sqrt{2\pi}}e^{-\frac{1}{2}(\frac{r^{ \prime}-\mu^{\prime}}{\sigma^{\prime}})^{2}}, \tag{5}\]  
In Equation (5), \(t^{\prime}\) is \(ln(t)\), where \(t\) is TBNA calculated in Equation (1), \(\mu^{\prime}\) the logarithm of MTBN calculated in Equation (2), and \(\sigma^{\prime}\) the standard deviation of the natural logarithms of TBNA. Figure 4 shows the result of lognormal distribution probability. In Figure 4, the \(x\)-axis indicated the elapsed time (in minutes) from which the machine was active, and the \(y\)-axis indicated the probability of the non-active state of the machine obtained using the lognormal distribution in Equation (5). From the figure, we can observe that a machine enters active and non-active states over time. Here, if the value on the \(y\)-axis was 1, the machine was activated, and if the value was close to 0.0, the probability of non-activation increased. In other words, we can see that lognormal distribution is used to describe the probability of a specific event occurring. Similar to the Weibull and lognormal distributions, the exponential distribution was typically used in reliability engineering and exhibited a simple distribution [26]. The exponential distribution was utilized to model the behavior of units that have a constant failure rate. The primary probability density function for the exponential distribution is expressed as follows. \[f(t)=\lambda e^{-\lambda t}=\frac{1}{m}e^{-\frac{1}{m}t},\ t\geq 0,\ \lambda>0,\ m>0, \tag{6}\]  
In Equation (6), \(\lambda=1/m\) is the constant rate in the non-active state per unit of measurement, \(m\) the mean TBNA, and \(t\) the TBNA during the machine operation. Figure 5 illustrates the result of the exponential distribution. In Figure 5, the \(x\)-axis indicated the elapsed time (in minutes) from which the machine was active, and the \(y\)-axis indicated the probability of a non-active state of the machine obtained using exponential distribution in Equation (6). Similar to Figure 4, Figure 5 demonstrated that a machine entered active and non-active states over time. Unlike lognormal distributions, the exponential distribution model the time elapsed between events and represented the amount of time until the machine enters into a non-active state. Figure 4: Result of lognormal distribution. In feature extraction using reliability analysis, we extracted 4 features, including TBNA, Weibull probability, lognormal probability, and exponential probability. These 4 features will be used later to model the prediction of the non-active state of machines. Table 2 shows the 4 elements extracted from the reliability analysis.
# Learning-Based Traffic Scheduling in Non-Stationary Multipath 5G Non-Terrestrial Networks
## 4 Discussion
### Learning Performance  
Figure 5 shows the total discounted rewards obtained by the agent in a set of contexts represented by different colors. As explained above, in each context, we had different pairs of satellite elevation angles and, consequently, different channel models characterized by different channel state transition probabilities. As a result, in each context, the agent had to retrain to track the change in the channel model, as shown by the different discounted rewards that the agent obtained in each context. In the figure, the smooth semi-plateaus within the colored stripes show the steady states the model reached within the context. In contexts 2, 3 and 4, the rewards were lower in relation to the other contexts because the satellites were at low elevation angles, forcing the agent to struggle when compensating for the loss due to low LOS probabilities and high loss probabilities. Note that the reward was proportional to the lost traffic in each episode. On the other hand, in contexts 5 and 6, the satellite elevation angles were relatively high. Thus, the channel conditions were favorable, and the agent could easily reach convergence with high rewards. These results show that our AC agent can dynamically detect the change in a satellite's elevation angle that triggers the change in LOS probability and schedule traffic appropriately to overcome the loss. The relative motions of each satellite are shown in Figure 3, which similar to Figure 5 shows the sequence of contexts, the relative elevation angles and two abrupt handoff events. Figure 5 also shows the average duration it took the agent to retrain the NN model after a change in elevation angle, juxtaposed with the relative context duration. As mentioned earlier, this is of the utmost importance to optimally utilize the satellite visibility time. It was found that the RL agent required on average 2000 iterations to update the NN parameters and reach a local steady state, which is equivalent to 0.1\(\times\) the satellite visibility time at a given elevation angle, guaranteeing efficient use of satellite visibility.
## 2 Materials and Methods
### Machine Learning Modelling  
The impact of the national protest on the concentrations of NO2, SO2, CO and O3 was quantified through a machine learning (ML) approach. A model was trained to predict business-as-usual concentrations of the pollutants from hourly measurements of meteorological and temporal variables. Due to the nonlinear relationships between meteorology and air quality, we selected a nonparametric algorithm called gradient boosting machine (GBM). It is a state-of-the-art decision tree-based ensemble method ([PERSON], 2001). As explained in our previous study on the strike effect on the concentration of fine particulate matter ([PERSON], 2019), we applied a meteorology-normalized technique ([PERSON] et al., 2019) to get a prediction that considers the real climatic conditions during the unusual anthropogenic event.  
The meteorology was analysed through seven features: solar radiation, temperature, pressure, relative humidity, precipitation, wind direction and wind speed. The temporal attributes were: hour, weekday, year day, and trend (date index). The dataset was split into a training set (1 January 2015-1 April 2019) and a testing set (2 April 2019-1 October 2019). Therefore, the performance of the models was tested six months prior to the protests. The accuracy of the models was measured through the coefficient of determination (R\({}^{2}\)) and the root mean squared error (RMSE) (see Equation (1)). In the latter, \"_prediction_\" values are provided by the ML model outputs for the 12 days before the strike, while \"_observation_\" are observational data during the same period. This RMSE for the ML model will be compared to the RMSE for the inter-period method for each pollutant, in order to estimate the performance of the modeling approach in relation to the baseline (i.e., inter-period accuracy).  
GBM builds an ensemble of weak and shallow consecutive trees with each tree learning and improving the previous one. The algorithm sequentially adds new models to the ensemble. At each iteration, a new weak base-learner model is trained from the error of the entire learned ensemble. A model is considered weak if its error rate is just slightly better than random guessing. In this boosting method, a weak model is built at each iterative sequence, in order to improve the remaining errors. The generalization of the algorithm is a stage-wise additive model of \(n\) individual regression trees as shown in Equation (2).  
\[f(x)=\sum_{n=1}^{N}f^{n}(x) \tag{2}\]  
For each pollutant, the concentration change was computed from the mean difference between ML-based business-as-usual and the actual value measured during or after the strike (e.g., % change = (BAU \(-\) observed)/(BAU) \(\times\) 100%). Then, from those, the average values and standard deviations for each pollutant were calculated for the whole of Quito. The GBM models were implemented in R, using the H2O package. The parameters of the library were tuned for stopping the training (i.e., increment in the number of trees) if ten successive trees did not reduce the prediction error.
## 5 Performance Results of RACD Strategy
### Performance Results
#### 5.2.3 Impact of Tree Depth on Performance  
Figures 7 and 8 show the effect of tree depth on the performance of the sharing and resource allocation strategies by calculating the hit ratio and hop reduction ratio for each strategy. For the sharing strategy, we observe a slight increase in the results for both classes of data in terms of hit and hop reduction ratio as depth increases. We also observe a large difference in the results for the two classes, with a 50% difference in hits, and a difference ranging from 60% to 35% in hop reduction results between OD-Sharing and UD-Sharing. The increase in the results obtained is due to the number of nodes that increase with length, allowing more cache space to be allocated. This allows more data to be cached, including low-consumption data, such as university-owned data (UD-Sharing), which results in a higher hit ratio and less hop ratio.  
Regarding the resource allocation strategy, the results differ depending on the \(k\) factor. When \(k=0.1\), we notice a very slight increase in the hit and hop reduction results with the increase in depth D. We also notice a big difference between the results obtained for the two classes of data, being that this difference grows up to \(85\%\) between the UD-RA and the OD-RA, where OD-RA achieves the lowest percentage. Indeed, the use of a coefficient \(k=0.1\) leads to a very high priority for the data that belong to the university compared to the other data. With the use of \(k=0.9\), we see the opposite of the results given with the use of \(k=0.1\). This is because the coefficients between the two classes of data become closer, and thus the priority becomes dependent on the amount of consumed data for both classes. Since the amount of consumed data that are not affiliated with the university is much larger than the others, OD-RA gained a significant advantage within the given space, leading to the obtained results. At \(k=0.4\), there is an improvement in the results of both classes as the depth D increases. The difference between the two classes of data is between \(20\%\) and \(60\%\) for the hit ratio, and between \(40\%\) and \(25\%\) for the hop reduction ratio, with a preference for the UD-RA. The convergence of the results obtained for the two classes is due to the fact that the space given to them is similar, with a bias towards the space reserved for the university data.  
Through this study and the results obtained, we can say that the proposed strategy offers the possibility to control the data in an excellent way, even at the levels where there is a big difference between the consumed data classes. Indeed, by using this strategy, we are able to determine the desired priority level, which in turn can allocate space for the desired data resources.
# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 3 Results
### Bias-free MRI Scan:
#### 3.1.8 Motion Correction  
MRI acquisition is a time consuming process, and keeping the head still in the scanner for the whole duration is challenging for patients. Subject motion in MRI scanning can introduce blurring and artifacts in the resultant images that severely deteriorate the image quality. Motion correction is the one crucial application during the preprocessing phase of the diagnosis [136; 137] CGAN generates images devoid of artifacts from images distorted by motion. Combining a deep CNNs (DCNN) generator with a classifier discriminator introduces sharpness in the output of the DCNN [138]. Different acquisition protocols of motion-free and motion-corrupted MR data give alignment deformity. Cycle-MedGAN is free from dependency on co-registered datasets since unpaired images are used for training without prior co-registration or alignment. It employs the cycle-style and the cycle-perceptual losses functions to supervise the generator network and the self-attention architecture with convolutional layers and several residual blocks to improve the long-range spatial dependency for the motion correction task [139]. The supervised adversarial mode of correcting the motion artifacts raises the alignment imperfections that result from two distinct acquisition processes; one is motion-free, and the other is motion-corrupted. On the contrary, a co-registered dataset does not affect the unsupervised mode. A framework consisting of cycle-MedGAN [140; 141; 142] utilizes any paired datasets during training and a smaller subset of paired data for validation with its self-attention characteristic and a new loss function.