esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 FL Synergy with Other Digital Technologies in the Medical Context  
In this section, to attain multiple goals, we discuss the established integration of FL with other technologies in the context of medical aspects. For example, FL works in a distributed manner and is vulnerable to different kinds of privacy attacks (e.g., parameter disclosure, data/gradient leakage, and update theft). To resist these attacks, differential privacy is increasingly used with FL to safeguard privacy issues [29]. In the DP model, algorithm \(\mathcal{A}\) satisfies \(\epsilon\)-DP if for all subsets \(X\subseteq Range(\mathcal{A})\) and all \(D\) and \(D^{{}^{\prime}},d(D,D^{{}^{\prime}})=1\) (e.g., \(D\) differs from \(D^{{}^{\prime}}\) by just one record):  
\[\frac{Pr(\mathcal{A}(D)\in X)}{Pr(\mathcal{A}(D^{{}^{\prime}})\in X)}\leq exp(\epsilon) \tag{2}\]  
where \(\epsilon\) denotes the privacy loss budget, and its value is usually higher than 0 (i.e., \(\epsilon>0\)). Figure 7: Important applications of FL developed/proposed in the era of COVID-19. To improve utility, (\(\epsilon\), \(\delta\))-DP has also been widely integrated with FL to improve both privacy and utility [30]. There are two main types of DP: local DP and global DP. In local DP, noise is added to each data item, \(x_{i}\), before sharing it with the server, because the server is considered untrustworthy. In contrast, noise is added to \(x_{i}\) at the server in global DP, and the server is considered trustworthy. Apart from DP, FL has been amalgamated with many other technologies to improve its technical deficiencies. In Table 1, we demonstrate the synergy of FL with different technologies in the context of the medical domain. These synergies are imperative to improving FL in different contexts, as well as to controlling the pandemic's impact on the general public. As shown in Table 1, synergies between FL and other technologies benefit each other from many technical aspects. The synergy between FL and other emerging technologies is likely to grow in the coming years. In addition, due to greater abilities in working with images and video data, FL will be a mainstream technology in future data-driven applications. It is worth noting that FL has integration with more than one technology also to effectively meet the security and privacy requirements [31; 32; 33; 34]. In some cases, three different techniques have been integrated with the FL paradigm for strong privacy preservation [31]. In the future, integration with multiple technologies will be imperative in order to accomplish multiple tasks or to overcome FL performance issues.
## Abbreviations  
ITE Intelligent Tactile Edge  
QoS Quality of Service  
QoE Quality of Experience  
URLLC Ultra Reliable and Low Latency Communication  
E2E End-to-End  
ECN Explicit Congestion Notification  
SNR Signal-to-Noise Ratio  
PSNR Peak Signal-to-Noise Ratio  
SSIM Structural Similarity Index Measure  
HBSIM  
Haptic Structural Similarity Index Measure  
MSE Mean Square Error  
MOS Mean Opinion Score
# Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data
## 3 Materials and Methods
### Modeling  
For creating predictive models, we employ three well-known techniques that are commonly used for churn prediction when the problem is cast as a binary classification one: Logistic Regression (LR) with L2 regularization, Support Vector Machines (SVM) with linear kernel, and Random Forests (RF). Given that we use different lengths of observation periods for deriving \(DF\) (i.e., \(DF_{num}\)), which directly leads to different numbers of input features, for each \(DF_{num}\) in our experiment and each classifier, we perform a nested 10-fold cross-validation where we use the inner loop to tune hyperparameters with respect to the chosen evaluation metric (AUC, which is described in the following subsection). To this end, we employ a grid search by using hyperparameter values within ranges reported in Table 2. Prior to modeling, for LR and SVM we standardize features to have a zero-mean and unit-variance (which is not required for RF).
## 4 Materials and Methods
### SPME/GC-MS Conditions  
For HS-SPME analysis (30 min exposure to a 2 cm DVB/CAR/PDMS fiber (Supelco, Bellefonte, PA, USA)), about 0.5 g of roasted sample was put in to headspace vials and kept in a laboratory water bath at 50 \({}^{\circ}\)C. Next, 0.1 ug of equilibrium mixture of 3-ethyl-2,5-dimethylpyrazine and 2-ethyl-3,5-dimethylpyrazines (Sigma Aldrich, Saint Louis, MO, USA) as an internal standard was added. Calibration function was constructed for 3-ethyl-2,5-dimethylpyrazine and 2-ethyl-3,5-dimethylpyrazines ranging from 0.001 to 1 microgram (in vial suspended and intensively shaken in water before use), with excellent linearity, with an R\({}^{2}\) value 0.993. We observed two signals with equal ratio. Semi-quantification of compounds was based on calculation of the area of unknown signals and comparison with the regression equation for the internal standard.  
Analyte desorption (220 \({}^{\circ}\)C for 3 min) was performed on Shimadzu apparatus (Shimadzu, Kyoto, Japan) equipped with a Zebron ZB-5 MSI (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) column (Phenomenex, Shim-Pol, Warsaw, Poland). Fiber composition was chosen due to previous optimizations [71]. The potential OAV was calculated by dividing the concentration of the compounds in the sample by the sensory thresholds obtained from the literature. The concentration of the compounds was established by a standard calibration curve. The Kovats retention index values were calculated for each according to [PERSON] [72], with a comparison of the obtained data with the values presented in NIST17 (NIST/EPA/NIH Mass Spectral Library) database peaks by comparing their retention characteristics with those of the two closest eluting aliphatic hydrocarbons from the retention index standard, analyzed under identical conditions. Presumptive identification can often be made by comparing the Kovats retention index value with a value previously published in literature references. Identification of the compounds was done by comparison: I: spectrum presented in NIST17 (NIST/EPA/NIH Mass Spectral Library); II: calculated retention index values with database NIST17; III: retention times of unknown compounds with available standards (1, 3, 5, 8, 11, 14, 15, 16, 18, 20, 23, 30, 32, 39, 40, 43, 44, 45, 47, 48).
## 1 Introduction
The performance differences in the receiver under different detection schemes of IM/DD and HD are systematically compared and analyzed. Finally, the Monte Carlo numerical simulation is used to verify the correctness of the above theoretical derivation results and to investigate the influence of different core parameters on the system performance. The major contributions of this study are summarized as follows:  
1. Propose a novel composite channel fading environment that models an RF link experiencing Generalized K distribution and atmospheric path loss fading under the \"decode-and-forward\" mechanism, while the UWOC system undergoes GGD ocean turbulence, zero-axis pointing error loss, and Elamassie underwater path loss. 2. This paper analyzes the performance of a novel and complete double-link composite fading channel model. First, using advanced transcendental Meijer-G functions and Fox-H functions, we derive detailed theoretical closed-form expressions for the average outage probability of a RF-UWOC hybrid two-hop relay transmission system using the Decode-and-Forward (DF) mechanism. We also provide asymptotic expressions under high signal-to-noise ratios and systematically compare the performance differences in the receiver using different detection schemes of IM/DD and HD. Subsequently, we validate the correctness of the above theoretical derivations through Monte Carlo numerical simulations and examine the impact of key parameters such as oceanic fading index and jitter deviation on system performance. Table 1 summarizes key differences between the proposed model and related works.
# Numerically Optimized Fourier Transform-Based Beamforming Accelerated by Neural Networks
## 7 Discussion of the Results
### Beamforming Methodology Limitations  
The optimization methodology that we propose in this work was implemented using interpreted MATLAB scripts. Moreover, in order to have a visual feedback, a real-time indication of the optimization convergence through MATLAB's Integrated Development Environment (IDE) was used. Because of this, the numerical optimization was quite time-consuming, which forced us to consider only a limited subset of possible beamforming scenarios. Porting the developed code to platforms such as C++ can potentially speed up the algorithm by orders of magnitude and shall give us an opportunity to study PMS with larger dimensions, which is reserved for a future work.  
The main limitations in the presented beamforming results are related to the moderate number of the controllable elements in the PMS and the relatively small size of the PMS. Indeed, the considered PMS only has \(3\times 10=30\) unit cells, which allows for a rather restrained set of beamforming scenarios. There are two reasons to keep the unit cell count low: (1) feasibility of the initial prototype implementation and (2) reduction of the computation time during phase distribution optimizations with MATLAB scripts. However, even with such a limited PMS we have realized a number of single- and dual-beam beamforming scenarios. We have also trained the BFNN and the CTRLNN networks on the generated datasets and confirmed that the proposed beamforming approach is feasible.  
To generate beamforming datasets for the prototype structure with 30 unit cells, 32 approximately equidistant directions have been selected in the frontal PMS hemisphere. In fact, the number of such assumed beam directions as well as the number of PMS unit cells limits the achievable beamforming accuracy. Therefore, by increasing the number of unit cells and the number of predetermined beam directions, more elaborate beamforming scenarios can be realized.
## 3 System Flexibility Digital Representation
### Flexibility Feature Extraction  
Physical System for Data Collection  
The physical system for flexibility evaluation composes several design schemes, which are designed according to the requirements of new products production. In accomplish the PSDS, the detailed design requirements, design documents, design models and other design data constitute a complete physical system, and the information and data used for the flexibility evaluation can be extracted from the physical system.  
Flexibility metrics for system static and dynamic evaluation are proposed. Static evaluation metrics are proposed in consideration of the system's structural attributes, and dynamic evaluation metrics focus more on the ability of the system to respond to environmental changes. According to the proposed static evaluation metrics and combined with the production system structure description metamodel, the basic static feature parameters describing the flexibility of the PSDS are defined, which include product variety \(v_{i}\), machine type \(m_{k}\), process type \(p_{j}\), product family \(\mathit{vty}_{f}\), production throughput \(c_{i}\), and process adjustment time \(t_{h}\). The static characteristic parameters describe the various dimensions characteristics of the flexibility for the PSDS. The physical system consists of lots of design data, such as product design requirements after multiple iterations, customized machine, process design for different products, etc., which constitute the source data for the quantitative extraction of static feature parameters. Environmental adaptability is defined as the dynamic evaluation metric, and the device failure cause for process losing is assumed to be the most occurred internal environment change, which leads to system breakdown. The flexibility dynamic evaluation of PSDS is defined as the system production capacity in the face of possible dynamic changes of device failures. The device failure rate \(mal\), is taken as the characteristic parameter for the flexibility dynamic evaluation, which is a random variable. The data collected for the flexibility dynamic evaluation includes the equipment layout extracted from the design model, the timing data designed by the production volume, and the production process determined by the production demand.
# Prediction of Ground Surface Settlements Induced by EPB Shield Tunneling in Water-Rich Soft Strata
## 4 Prediction of Short-Term Ground Deformation
### Interaction between EPB Shield Machine and Soil  
As suggested by [PERSON] and [PERSON] (2007) [19], the effect of shield driving on the surrounding soils can be divided into two separate parts, ground loss and working loads. Figure 6 gives a brief description of the interaction between the EPB shield machine and soil. The ground loss induced by shield tunneling is caused primarily by the following factors: overexcavation caused by shield conicity, the tail void between shield and lining, and the deformations of the lining. The working loads consist of three parts, namely face support pressure, shield shell friction, and cutter head rotation, as described in Figure 6. In this study, a three-dimensional superposition method is suggested to obtain the short-term ground deformation induced by shield tunneling. In the proposed method, the cavity expansion theory and [PERSON]'s solution are used to calculate the displacements caused by ground loss and working loads, respectively.
# Phtyromediation of Toxic Metals: A Sustainable Green Solution for Clean Environment
## 9 Enhancement Technique of Phyto-Extraction Efficiency
### Common Approaches to Increase Toxic Metal Bioavailability
#### 9.1.1 Chelate-Assisted or Induced Phyto-Extraction  
Chemically induced phyto-extraction has been offered as an alternative to plants' slow growth rate and low biomass, in which large biomass and fast-growing crops are employed to extract vast quantities of toxic metals whose mobility in soil is increased by chelating agents [11; 239; 240]. Plants have been identified as chelating agents for removing and detoxifying harmful metals [20; 241]. Chemically induced phyto-extraction with chelating agents can be accomplished in several ways. For example, increasing the concentration of toxic metals in the soil solution promotes the migration of metal-EDTA complexes towards roots. Second, many subsequent complexes and less negative complexes destroy negatively charged cell components of the plant cell wall or physiological barriers in roots.  
Third, greater mobility of complexes has more metal translocation capability from roots to shoots than free ions [242]. EDTA (ethylene diamine tetra acetic acid) has been utilized as a chelating agent to improve the phyto-extraction process since the 1990s. Even though EDTA can increase toxic metal accumulation by a factor of a hundred in comparison to EDTA-trace element complexes [243; 244], both are extremely hazardous to plants and the soil microbial population [245; 246]. Although EDTA has poor biodegradability, it also promotes toxic metal leaching, resulting in groundwater pollution [247]. In this way, microorganism-produced ethylene diamine disuccinate (EDDS) is a naturally occurring boosting material that is particularly successful at increasing toxic metal intake while reducing the risk of water pollution [248]. In contrast to EDTA, which is less bioavailable to Pb and Cd, EDDS improves the solubilizing and mobilization of Cu, Ni, and Zn [249; 250]. Trace element-EDDS complexes penetrate the roots and are subsequently delivered directly to the shoots [251]. EDDS, on the other hand, is harmful to some plants but not to soil microbes. Furthermore, nitrilotriacetic acid (NTA) is a biodegradable chelating agent with no phytotoxic effects that have been employed to improve phyto-extraction proficiency [252; 253; 254; 255].
# The Size Effect of Shear Bands in Dense Sands--A Discrete Element Analysis
## 3 The Observation of the Size Effect
### Size Effect in the Strain-Stress Curve  
The strain-stress relationships at different isotropic confining stresses are plotted in Figure 5. It is observed that there are clear size effects after the peak strength point.  
* The peak strengths of different-sized specimens were very close. As long as the deviatoric stress was less than the peak value, the stress-strain curves for different-sized specimens were very close. This indicates that the size effect was not obvious at the initial stage of the deformation.
* A clear difference in the strain-stress curve was observed after the peak strength. Specifically, as the specimen size increased, the residual strength decreased. Such a size effect diminished as isotropic confining stress increased, and was evident in both biaxial compressive and biaxial tensile tests.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Specimens**} & \(\mathbf{H}\) & \(\mathbf{W}\) & \multirow{2}{*}{**W\(/\overline{R}\)**} & \multirow{2}{*}{**Particle Number**} \\  & **(mm)** & **(mm)** & & \\ \hline S & 40 & 20 & 171 & 16,397 \\ M & 60 & 30 & 257 & 36,316 \\ L & 80 & 40 & 342 & 64,073 \\ XL & 100 & 50 & 482 & 99,577 \\ \hline \hline \end{tabular} Note: \(H\) is specimen height, \(W\) is specimen width, and \(\overline{R}\) is the average radius of particles.  
\end{table}
Table 2: The specimens in different sizes.  
Figure 5: _Cont._  
Based on the above finding, it is believed that, as the axial strain gradually increases, a transition from an internally stable state to an unstable state occurs at the peak strength point. Because strain concentrates within the narrow shear band, the deformation of the specimen became non-uniform. This might explain why different-sized specimens led to different stress-strain curves.
## 2 Materials and Methods
### System Evaluation  
In order to assess the performance of the system, researchers have defined two important time parameters: Seizure Prediction Horizon (SPH) and Seizure Onset Period (SOP). SOP refers to the time period during which a seizure is predicted to occur, while SPH refers to the period from when the prediction alarm is issued to the start of SOP (Figure 9).  
Based on this test, the algorithm issues warnings in the form of false alarms or true alarms. This prediction range is set before the onset of seizures, ranging from a few minutes to a few hours. When a disease outbreak occurs outside the SPH range but within the SOP range, it is considered a correct prediction [40]. Any other state is considered an incorrect prediction. The SPH setting should take into account the time needed for clinical intervention and protective measures while also considering the patient's anxiety levels. In this study, the SPH was set at 5 min and the SOP was set at 30 min, based on relevant research.
# Default Detection Rate-Dependent Software Reliability Model with Imperfect Debugging
## 5 Numerical Example
### Performance Verification
#### 5.3.1 Performance Analysis of the FDR-Related Imperfect Debugging Model
It can also be seen that the five models of type II imperfect debugging have the same curve shape, but the distance between the curves is larger than that of type I ID. The reason is that the phenomenon of new faults is considered in the modeling of the type II ID model. A set of differential equations is used to model and describe the model, and the final solution is obtained. The cumulative fault detection function \(m(t)\) is quite different from each other. To further distinguish the performance differences among the five models in the type II imperfect debugging, Table 3 gives the fitting metrics of the five models. As Table 3 shows, model IDII_5 is either optimal or suboptimal in each performance index and has the best overall performance. Model IDII_5 has the same modeling process as the other four similar models, but the difference lies only in the use of \(b5(t)\), which proves that \(b5(t)\) has better performance than the other four FDR functions. In summary, through the validation of two kinds of imperfect debugging models on failure data sets, the experimental results show that the model with \(b5(t)\) participation has the best performance, which is superior to other models of the same kind, and both kinds of imperfect debugging models adopt their own unified models, so it can be concluded that \(b5(t)\) is more accurate in describing the number of fault detections. Thus far, the optimal models of two kinds of imperfect debugging models were obtained, namely, IDI_5 (the performance of IDI_2 is very close to that of IDI_5 because they have the same modeling process and the same FDR function with the same results) and IDII_5. These two optimal models are all \(b5(t)\)-related models. Figure 3: IDII Model Fitting Curve. (**a**) IDII Model Fitting Curve on DS1; (**b**) IDII Model Fitting Curve on DS2; (**c**) IDII Model Fitting Curve on DS3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **DS** & **MSE** & **R-Square** & _Variation_ & **RMS-PE** & **BMMRE** \\ \hline IDII\_1 & & 8.117125 & 0.739869 & 3.414002 & 3.55666 & 0.232516 \\ IDII\_2 & & 1.654242 & 0.955286 & 1.329353 & 1.332958 & 0.095028 \\ IDII\_3 & DS
## 2 Methods
### Experimental Procedure
#### 2.2.1 Experimental Equipment  
1. Motion Capture System: An eight-camera Vicon MX13 infrared motion capture system (Vicon, Oxford, UK) was used to collect kinematic data at a sampling frequency of 200 Hz. Sixteen reflective markers were placed on anatomical landmarks of the lower limbs following the Vicon lower-limb model.
2. Force Platform: A Kistler 3D force platform (Kistler, Winterthur, Switzerland) with an external signal amplifier was used to measure ground reaction forces (GRF) at 1000 Hz.
3. Surface Electromyography (sEMG): Muscle activity of the rectus femoris (RF), biceps femoris (BF), tibialis anterior (TA), and gastrocnemius (GAS) was recorded using a multi-channel surface EMG system (Biovision, Wiesbaden, Germany) at a sampling frequency of 1024 Hz. Disposable Ag/AgCl electrodes were placed parallel to muscle fibers with a 2-3 cm inter-electrode distance.
4. Treadmill: A Zebris FDM series treadmill (Zebris Medical GmbH, Isny im Allgau, Germany) was used for the fatigue protocol, equipped with integrated sensors to measure gait parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristic** & **Value** \\ \hline Number & 30 \\ Age (years) & 24.8 \(\pm\) 0.6 \\ Height (cm) & 173.2 \(\pm\) 3.3 \\ Weight (kg) & 71.7 \(\pm\) 4.9 \\ BMI (kg/m\({}^{2}\)) & 23.24 \(\pm\) 3.22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of participants.  
Heart Rate Monitor: A Xiaomi sports watch (Wuhan, China) was used to monitor participants' heart rates during the fatigue intervention.