esa-sceva/satcom-chunk-collection huggingface dataset:
# Personalized Federated Multi-Task Learning over Wireless Fading Channels
###### Abstract  
Multi-task learning (MTL) is a paradigm to learn multiple tasks simultaneously by utilizing a shared network, in which a distinct header network is further tailored for fine-tuning for each distinct task. Personalized federated learning (PFL) can be achieved through MTL in the context of federated learning (FL) where tasks are distributed across clients, referred to as personalized federated MTL (PF-MTL). Statistical heterogeneity caused by differences in the task complexities across clients and the non-identically independently distributed (non-i.i.d.) characteristics of local datasets degrades the system performance. To overcome this degradation, we propose _FedGradNorm_, a distributed dynamic weighting algorithm that balances learning speeds across tasks by normalizing the corresponding gradient norms in PF-MTL. We prove an exponential convergence rate for _FedGradNorm_. Further, we propose _HOTA-FedGradNorm_ by utilizing over-the-air aggregation (OTA) with _FedGradNorm_ in a hierarchical FL (HFL) setting. _HOTA-FedGradNorm_ is designed to have efficient communication between the parameter server (PS) and clients in the power- and bandwidth-limited regime. We conduct experiments with both _FedGradNorm_ and _HOTA-FedGradNorm_ using MT facial landmark (MTFL) and wireless communication system (RadCompynamic) datasets. The results indicate that both frameworks are capable of achieving a faster training performance compared to equal-weighting strategies. In addition, _FedGradNorm_ and _HOTA-FedGradNorm_ compensate for imbalanced datasets across clients and adverse channel effects.  
multi-task learning; dynamic weighting; federated learning; personalized federated learning; hierarchical federated learning; over-the-air aggregation  
11 October 2022  
Accepted: 3 November 2022  
Published: 9 November 2022
## 2 Materials and Methods
### In Situ Hybridization (ISH) of Matched Patient FFPE Pancreatic Cancer Tissues  
The matched pancreaticoduodenectomy tissue specimens were collected at the Erasmus Medical Center for diagnostic evaluation. Residual FFPE material was used for biomarker analysis. Four-um thick tissue sections on extra adhesive glass slides (Leica, Biosystems) were processed in the Discovery Ultra instrument (Ventana, Roche). The following automated Discovery Universal protocol was used: tissues were preheated at 70 \({}^{\circ}\)C for 4 min then deparaffinized at 70 \({}^{\circ}\)C for 12 min. Pretreatment was performed with CC1 for 16 min (cat. no. 950-224, Ventana). One drop of DISC inhibitor (cat. no. 760-4840, Ventana) was applied and incubated for 12 min. The 3' and 5'-DIG labeled miRCURY LNA miRNA Detection probes ( hsa-miR-125b-5p cat. no. YD00611756-BCG; hsa-miR-99a-5p cat. no. YD00619276-BCG; positive control hsa-U6 cat. no. YD00699002-BCG and negative control Scramble-miR cat. no. YD00699004-BCG, from Qiagen) were diluted in formamide-free MiRCURY LNA miRNA ISH Buffer (Qiagen cat. no. 339450) to a final 20 nM concentration, applied to the slides and incubated for 8 min. Denaturation was established at 90 \({}^{\circ}\)C for 8 min, followed by hybridization for 1 h (at 55 \({}^{\circ}\)C for miR-125b-5p; 53 \({}^{\circ}\)C for miR-34a-5p; 52 \({}^{\circ}\)C for miR-99a-5p; 54 \({}^{\circ}\)C for U6 and at 57 \({}^{\circ}\)C for the Scramble-miR). Slides were washed twice with SCC (DISCOVERY Ribowash 1\(\times\) cat. no. 760-105, Ventana) and heated to 55 \({}^{\circ}\)C for 8 min. Slides were washed and heated again to 55 \({}^{\circ}\)C for 8 min. One drop of anti-DIG HRP enzyme conjugate (cat. no. 760-4822, Ventana) was applied and incubated for 16 min. Discovery amplification was performed using one drop of DISC AMP TSA BF and one drop of DISC AMP H2O2 BF (cat. no. 760-226, Ventana) for 32 min of incubation. One drop of DISC anti-BF HRP (cat no.760-4828, Ventana) was incubated 16 min, followed by one drop of DISC Ag C silver (cat. no. 760-227, Ventana) incubation for 16 min. The tissues were counterstaineded with Hematoxylin II (cat. no. 790-2208, Ventana) for 8 min, followed by incubation with Bluing Reagent Post Counterstain (cat. no. 760-2037, Ventana) incubation for 4 min. Adjacent tissue sections were stained with hematoxylin and eosin (H&E). The slides were scanned using the Nanozoomer 2.0-HT slide imager (Hamamatsu).
## 4 Comparison Study
### Support Vector Machine (SVM)  
SVM is a machine learning tool proposed by [PERSON] in 1996 [43] that has been used for 20 years to solve several problems, including lithology and electrofacies classification [29; 43; 44]. In SVM, the algorithm finds a hyper-plane in an M-dimensional space where M is the number of features that distinctly classifies the data values. Hyper-planes are actually the boundaries of decision that make the classifications the data points. Data points falling on any side of the hyperplane are considered to be in separate classes [43]. Data points that are closer to the hyperplane can influence the orientation and position of the hyper-plane, and they are called support vectors. To examine how SVM performs, the same datasets that we used for RST were used. For training and testing the datasets, the same training and testing ratio (70:30) was selected. We implemented nonlinear type-1 SVM and the following settings were selected:  
1. Kernel = radial basis function kernel
2. C value = 1
3. Cache size = 200
4. Gamma =auto deprecated  
Where C is the regularization tradeoff parameter for the soft margin cost function that controls the effect of each individual support vector. The gamma parameter is used as a similarity measure between two points. A small gamma value defines a Gaussian function with a large variance, where two points can be considered similar although they are away from each other. On the contrary, a large gamma value creates a Gaussian function with small variance, where two points in close distance are considered similar.  
In Figure 5, along the y-axis, the prediction scores for training and cross-validation are illustrated with respect to the change in gamma values in x-axis. In Table 7, the training and the cross validation scores are shown (best result achieved when \(\gamma=10^{-1}\)).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **RST** & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**Deep Learning**} & \multicolumn{2}{c}{**RFC**} \\ \hline  & Accuracy & Accuracy & PD (\%) & Accuracy & PD (\%) & Accuracy & PD (\%) \\
**Training** & 0.9875 & 0.9833 & \(-\)0.0043 & 0.9806 & \(-\)0.0070 & 0.9868 & \(-\)0.0007 \\
**Validation** & 0.9808 & 0.9798 & \(-\)0.0010 & 0.9794 & \(-\)0.0014 & 0.9794 & \(-\)0.0014 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Training and testing accuracies for RST and other methods and their percent difference with RST.
# A Deep Learning Model for Data-Driven Discovery of Functional Connectivity
## 2 Materials and Methods
### Materials
#### 2.1.2 Preprocessing  
The fMRI data were preprocessed using statistical parametric mapping (SPM12, [[http://www.fil.ion.ucl.ac.uk/spm/](http://www.fil.ion.ucl.ac.uk/spm/)]([http://www.fil.ion.ucl.ac.uk/spm/](http://www.fil.ion.ucl.ac.uk/spm/)), accessed on 22 February 2021) under the MATLAB 2019 environment. A rigid body motion correction was performed to correct subject head motion, followed by the slice-timing correction to account for timing difference in slice acquisition. The fMRI data were subsequently warped into the standard Montreal Neurological Institute (MNI) space while using an echo planar imaging (EPI) template and they were slightly resampled to \(3\times 3\times 3\) mm\({}^{3}\) isotropic voxels. The resampled fMRI images were then smoothed using a Gaussian kernel with a full width at half maximum (FWHM) = 6 mm. After the smoothing, the functional images were temporally filtered by a finite impulse response (FIR) bandpass filter (0.01 Hz-0.15 Hz). Subsequently, for each voxel, six rigid body head motion parameters, white matter (WM) signals and cerebrospinal fluid (CSF) signals, were regressed out using linear regression.  
We selected subjects for further analysis [15] if the subjects have head motion \(\leq\)3\({}^{\circ}\) and \(\leq\)3 mm, and with functional data providing near full brain successful normalization [16]. This resulted in a total of 311 subjects with 151 healthy controls and 160 subjects with schizophrenia. Each subject is represented by \(\mathbf{X}\in\mathbb{R}^{\times\times\text{y}\times\text{z}\times\text{t}}\), where \(\text{x},\text{y},\text{z}\) represent the number of voxels in each dimension and t is the number of time points that are 160. In order to reduce the affect of noise, we zscore the time sequence of each voxel independently. Thus, time series of every voxel is replaced by the z-score of the time series. This does not have any effect on the data dimensions.  
To partition the data into regions, automated anatomical labeling (AAL) is used [17], which contains 116 brain regions. Taking sum of the voxels inside a region is an easy and common method, but this gives an unfair advantage to bigger regions. For this, we take the weighted average of the voxel intensities inside a region. Weight is the value of a voxel being inside a region, as these values are not binary. Averaging helps to negate the bias towards bigger regions. This results in a dataset \(D=(S_{1},S_{2},S_{3}.....S_{n})\), where \(S_{i}\in\mathbb{R}^{r\times\text{t}}\), \(n=311\), \(r=116\), \(t=160\).
# Chiral Metasurfaces: A Review of the Fundamentals and Research Advances
## 2 Fundamentals of Chiral Metasurfaces  
### Chirality and Chiroptical Response  
Chirality is a phenomenon in which a device cannot overlap with its mirror through simple translations or rotations, and it is identified as an enantiomer [92, 93]. Charity is obvious in various everyday structures and materials [4, 39, 40, 41]. For example, many organic compounds, such as amino acids and DNA molecules, demonstrate chirality, influencing their biological roles. In optics, chiral molecules can rotate the polarization of light, as seen in some sugar solutions, and chiral structures such as spiral seashells display this property in nature. Recent research on artificial meta-atoms, mainly on the optical response of plasmonic meta-atoms, has aided in the development of chiroptical metamaterials. Metasurfaces can provide new platforms for creating chiral materials with adjustable and strong signals by arranging and shaping nanostructures [94, 95, 96].  
The chiroptical response is the optical response of chiral materials caused by chiral meta-atom differences in the refractive index and extinction coefficient for LCP and RCP light, resulting in CD and OA, respectively [4]. OA and CD are both optical aspects associated with chirality, and they are used in various fields such as material science, chemistry, and biochemistry to study and characterize chiral systems. In material science, OA and CD are used to characterize the secondary structure of proteins and study conformational changes, while in chemistry, they are employed for stereochemical analysis of chiral molecules and assessing reaction kinetics. In biochemistry, OA and CD are valuable for evaluating the stability and folding of biomolecules, including proteins, carbohydrates, and nucleic acids. While OA involves the rotation of linearly polarized light, CD involves the absorption difference of circularly polarized light [39, 40, 41]. Optical rotation (OR), the specific manifestation of OA, occurs when a linearly polarized light beam passes through a chiral medium. CD is the difference between the transmittances of the LCP and RCP light [97].
## 3 Results  
### Phase Velocities and Uncertainties  
From the repeated measurements of the slowness and their uncertainty at each location and period (0.3-0.8 s periods), the phase velocity profile of the Rayleigh wave across the Meishan Fault was established (Figure 6). A prominent low-velocity area occurs at 1.1 km to 2.5 km between the 0.3 s and 0.6 s periods. The highest velocities are at the south end of the array and have a greater than 0.6 s period. Overall, the phase velocities increase with the period. There are a few areas with relatively high uncertainties at a distance of approximately 0.4 km between 0.5 s and 0.7 s periods and at approximately 2.2 km for less than 0.4 s periods and greater than 0.7 s periods (Figure 6b). The high uncertainties may be due to the relatively low SN ratios of the observed Rayleigh waves. However, it is not clear that the Rayleigh waves are weak in those areas, and it might be related to the transition between low- and high-velocity materials.  
### Phase Velocities and Uncertainties  
Figure 7a shows the shear wave velocity profile across the Meishan Fault. In the shear wave velocity profile, velocities generally increase with depth. The Vs is approximately 0.3 km/s at the surface and increases to approximately 1.1 km/s at depth. There are no clear lateral variations at the same depth in the velocity profile; however, the distribution of Vs shows a similar pattern to the phase velocity profile (Figure 6a) of the Rayleigh wave. For example, the contour of Vs = 0.6 km/s from 1.1 to 2.5 km along the profile and between 0.1 km and 0.15 km in-depth, and the U-shaped features mimic the Rayleigh wave phase velocities. Figure 7b shows the perturbations of the Vs profile and the perturbations relative to the average shear velocity at each depth. In the perturbations of the Vs profile, a clear transition between low-velocity and high-velocity materials is observed at approximately 2.5 km distance along the profile.  
Figure 5: The slowness measurements along the entire array at periods of 0.3 s, 0.5 s, and 0.7 s. The error bars show the uncertainties of the mean slowness.
## 3 Experimental Results
### Performance Evaluation  
#### 3.3.1 Dataset of 400 Videos  
In Table 1, a 400-video dataset was used to train, validate, and test the models. It can be observed in the table that SynthSecureNet outperformed both MobileNetV2 and ResNet50V2. Results in Table 1 were obtained during the testing phase, and the training duration that was observed during the training phase is also included in Table 1.  
The SynthSecureNet architecture achieved an F1 score of 96%, a recall of 96%, a precision of 96%, and an accuracy of 96%, demonstrating a high level of performance for this model in violence detection. On the other hand, ResNetV2 achieved an F1 score of 91%, while MobileNetV2 achieved an F1 score of 89%, which shows that ResNetV2 outperformed MobileNetV2.  
The training duration of the SynthSecureNet model (15 min) took longer than that of ResNet50V2 (9 min) and MobileNetV2 (5 min). When compared with both MobileNetV2 and ResNet50V2, the SynthSecureNet model's high performance came at a trade-off of a small increase in training period.  
Figure 2 shows the confusion matrices obtained after testing. SynthSecureNet made 2253 correct predictions and only 53 wrong predictions, demonstrating its effectiveness in accurately classifying violent and non-violent videos.
## 1 Introduction
Several researchers have indicated that LED's modulation characteristics depend on its carrier recombination lifetime, and, thus, semipolar LEDs with less QCSE may have a faster radiative recombination and help to improve the modulation characteristics for VLC applications [19]. However, compared with LEDs, micro-LEDs (\(\upmu\)-LEDs) have a better frequency response owing to their higher efficiency, low-power consumption, high brightness and especially longer life [27]. Higher brightness for \(\upmu\)-LEDs corresponds to a faster transmission rate with a lower bit error rate (BER) for VLC applications. Throughout this analysis, we will address factors influencing the modulation bandwidth of LEDs for VLC applications, semipolar-based LEDs for VLC applications, micro-LEDs array-based and micro-LED-based underwater wireless communication, and, finally, how to boost the modulation bandwidth of LEDs for VLC applications, including the potential aspects.
## 7 Performance Evaluation of NDA-EVM-AM on an NLOS Scenario
### Evaluation on Adaptation Strategy  
We evaluated the accuracy of modulation order selection by NDA-EVM-AM and DA-SNR-AM in different NLOS scenarios of WSN. We transmitted data with every modulation order over every different \(\eta-\mu\) fading channel. Under the target SER, the highest modulation order (i.e., \(M_{Th}\)) can be obtained in every \(\eta-\mu\) fading channel. Executing NDA-EVM-AM and DA-SNR-AM at the same channel condition, the optimal modulation order for each scheme can be determined (i.e., \(M_{N}\) is for NDA-EVM-AM, \(M_{D}\) is for DA-SNR-AM). Comparing \(M_{Th}\) and \(M_{N}/M_{D}\), the accuracy of modulation order selection of NDA-EVM-AM/DA-SNR-AM is obtained.  
Figure 4a,b shows the results in different \(\eta-\mu\) channels with fixed \(\eta=1\) and increasing \(\mu\), and fixed \(\mu=1\) and increasing \(\eta\). Figure 4 illustrates that the accuracy of modulation order selection for NDA-EVM-AM is higher than that of DA-SNR-AM. This is because SNR is calculated only during the RS, which does not capture the entire packet duration. The relationship of SNR-SER cannot provide an accurate guidance for the modulation order selection. On the other hand, the channel changes more and more rapidly with the decreasing \(\mu\) or increasing \(\eta\). As a symbol-level performance metric, NDA-EVM has a real-time update for modulation order selection, which is different from DA-SNR. Moreover, the adaptation strategy requires a modulation order selection scheme to jump multiple levels in one step under the rapid-changing channel; NDA-EVM-AM executes these jumps more effectively, which can be observed in Section 4.  
Figure 3: The root of mean-square error (RMSE) of channel quality metric estimation over \(\eta-\mu\) fading channel.  
We compare the switching thresholds for the two different adaptation strategies for the NLOS scenarios of WSN. In Figure 5, both SER-NDA-EVM and SER-DA-SNR (which can be translated into the EVM using the formula \(EVM=\sqrt{1/SNR}\)[31]) are presented under the \(\eta-\mu\) channel with parameter values of \(\eta=1\) and \(\mu=0.5\) (i.e., Rayleigh fading channel). It can be seen that, for the target SER, the switching threshold based on the NDA-EVM is easier to achieve than that of DA-SNR at the same modulation order. For example, the 16 QAM switching threshold in terms of the NDA-EVM is \(-19\) dB, whereas, for the DA-SNR, it is \(-22\) dB, which implies that NDA-EVM-AM is easier to adjust to 16 QAM. In other words, NDA-EVM-AM can be used to transmit 16 QAM signals under the target SER, whereas DA-SNR-AM must remain at the next lower modulation order even under the same channel conditions. This effect illustrates that NDA-EVM-AM has a higher average spectral efficiency than that of DA-SNR-AM in NLOS scenarios.  
Figure 4: (**a**) The accuracy of modulation order selection for \(\eta-\mu\) channels with fixed \(\eta=1\) and increasing \(\mu\). (**b**) The accuracy of modulation order selection for \(\eta-\mu\) channels with fixed \(\mu=1\) and increasing \(\eta\).
# Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
## 2 Data-Driven Traffic Estimation and Prediction
### Census Data and Survey Data  
Census data and survey data may also contain traffic information and other information useful for traffic problems, such as locations of residence and workplace. These data are usually collected by governments periodically. For example, in the Commuting Flows ([[https://www.census.gov/topics/employment/commuting/guidance/flows.html](https://www.census.gov/topics/employment/commuting/guidance/flows.html)]([https://www.census.gov/topics/employment/commuting/guidance/flows.html](https://www.census.gov/topics/employment/commuting/guidance/flows.html))) by the US Census Bureau, the primary workplace location from the respondents are collected. In the Migration Data ([[https://www.irs.gov/statistics/soi-tax-stats-migration-data](https://www.irs.gov/statistics/soi-tax-stats-migration-data)]([https://www.irs.gov/statistics/soi-tax-stats-migration-data](https://www.irs.gov/statistics/soi-tax-stats-migration-data))) by the Internal Revenue Service, both inflows and outflows of migration patterns are available for Filing Years 1991 through 2018 with the IRS. More survey data can be found in Table 1.  
The pros of census data and survey data are their large sample size and large coverage, which is usually the entire country. The cons are also obvious. The flows are aggregated at the municipality or county level, which is coarse. There is only limited traffic information. It is also expensive and time consuming to collect this type of data as trip surveys. Census data and survey data are not widely used for traffic estimation and prediction tasks with only a few exceptions [20].
# A Comprehensive Review of Open Caisson Modeling Technology: Current Practices and Future Prospects
## 5 Challenges and Future Directions
### Future Directions
#### 5.2.6 Numerical and Physical Model Coupling (n-p Coupling)  
Numerical-physical coupling (n-p coupling) tests represent a promising avenue for combining the strengths of numerical simulations and physical experiments. By integrating the accuracy of numerical models with the practicality of physical tests, this approach can bridge the gap between theoretical predictions and practical observations, leading to more robust geotechnical designs. Composite modeling has several advantages, including the capability to simulate complex problems which neither physical nor numerical modeling can replicate alone. Additionally, it also increased model accuracy without increasing or decreasing costs and without compromising accuracy. Moreover, it contributed to reductions in uncertainty at the same cost, signifying that reducing uncertainty is inherently linked to enhancing model quality.  
Hybrid testing employed the substructure approach used by [PERSON] et al. [215] by integrating the physical model with a numerical model that enables more comprehensive analysis of complex systems. The physical test examined essential elements of one domain, whereas the numerical simulation accurately simulated the influence of an adjacent domain. By combining these approaches, physical modeling capabilities were enhanced because the combined findings explained the behavior of the entire domain with better precision than ordinary physical modeling with simplified boundary conditions. Real-time coupling between the physical and numerical domains was an important feature of hybrid testing as it ensures continuous data flow and allows the response from one domain to drive the other. This real-time connection was especially important for time-dependent problems because it ensured integrity in boundary conditions, such as forces or displacements, between the two domains. The application of coupling within geotechnical centrifuge modeling has emerged recently; for example, [PERSON] et al. [216] employed displacement-controlled boundary conditions in a geotechnical centrifuge model that were derived based on numerical analyses. In previous studies, hydraulic boundaries which were kept constant or within the domain of the model box can be defined on the base of N-P coupling analyses to replicate the original ground water conditions in future research.