esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Research on Secure Aggregation for Federated Learning
## 4 Aggregation Techniques for Federated Learning
### Secure Aggregation Algorithms
#### 4.2.2 Secure Aggregation Based on Secure Multi-Party Computation
In [104], to reduce the computational and communication costs of existing secure aggregation protocols and improve robustness to client dropout, a secure aggregation protocol is proposed with a novel multi-secret sharing scheme based on the Fast Fourier Transform. Experimental results show that this protocol achieves significantly lower computational costs than existing schemes while maintaining comparable communication costs. In [105], a scheme combining secure multi-party computation and differential privacy techniques is proposed to prevent local and global models from leaking user data information, protecting the privacy of both the computation process and the results. Experimental results show that the scheme is scalable and robust to client dropouts, and when the number of clients reaches 1000, its efficiency is comparable to that of FedAvg. [PERSON]'s secret sharing, verifiable secret sharing, and additive secret sharing do not theoretically affect the model training process or the numerical representation of gradients. For example, Refs. [92; 97; 100] report modeling accuracies consistent with FedAvg. In terms of security, verifiable secret sharing offers the highest level of protection, as it guards against malicious clients. [PERSON]'s secret sharing provides a moderate level of security, while additive secret sharing offers the most basic protection. For example, Ref. [97] can verify aggregation integrity and protect against server tampering, while Ref. [92] provides drop tolerance for clients. However, compared to [92; 97], Ref. [100] offers only basic protection against server-client collusion. Regarding communication and computational overhead, the order from highest to lowest is as follows: verifiable secret sharing, [PERSON]'s secret sharing, and additive secret sharing. In this paper, secure aggregation schemes based on secure multi-party computation are categorized according to the secret sharing schemes they employ, as summarized in Table 2.
## 4 Methods
### Scalogram Image Feature Extraction  
A typical sharp wave with a length of 70-250 ms contains spectral components ranging between 4-14.3 Hz (theta and alpha range). The clinical sampling rate of 256 Hz is spectrally rich-enough to capture sharps with a sufficient resolution. We have previously shown that Gaussian 2 mother wavelet (or Mexican hat) holds ideal features which desirably match with inherent spectral characteristics of a HI sharp wave; hence is a preferred basis function for the analysis of sharp wave transients [13; 14]. To extract spectral features, this work generates the CWT scalogram images of the EEG sections over a broad scale-range of 1 to 40 using Gaussian 2 mother wavelet at a readable resolution of 303 \(\times\) 404 pixels. Figure 1A-D illustrate examples of the HI sharp wave transients in the clinical data, taken from the raw neonatal EEGs, post-HI insult, whereas examples of the non-sharp events are demonstrated in Figure 1I-L.  
The corresponding 2D wavelet scalograms generated from the HI sharp patterns in Figure 1A-D and the non-sharp events in Figure 1I-L are shown in Figure 1E-H and Figure 1M-P, respectively. The illustrations demonstrate how spectrally-rich feature maps of EEG sections can be optimally extracted in the form of high-resolution scalograms images for deep training of a CNN pattern classifier. The WS-CNN pattern classifier was trained on the normalized/zero-meaned raw HI EEG to generalize the performance of the classifier in distinguishing sharp wave patterns from the background EEG and artifact.  
Figure 1: Examples of post HI micro-scale sharp waves along profoundly suppressed neonatal EEG in 256 Hz clinical recordings (**A–D**) as well as non-sharp events (**I–L**) alongside with their corresponding Wavelet scalograms in (**E–H,M–P**), respectively, using CWT with Gaus2 basis function of scales 1 to 40. The example wavelet scalogram images in e-h and m-p were used for training, validation and testing of the deep WS–CNN classifier.
## 4 A Survey on Cognitive Radio Implementation Research
### SFF SDR Platform from Texas Instruments  
The Small Form Factor (SFF) Software Defined Radio (SDR) development platform provided by Lyrtech in collaboration with Texas Instruments (TI) and Xilinx is a self-contained platform consisting of three separate modules: the digital processing module, the data conversion module and the RF module as shown in Figure 19[109].  
The baseband processing part is designed around the TMS320 DM6446 System on Chip (SoC) [110] from TI and Virtex-4 SX35 FPGA from Xilinx. The DM6446 SOC is equipped with a DSP core and a ARM9 general-purpose processor(GPP) core on a single chip, it also comes with a complete set of peripherals necessary for SDR development, including serial, USB and Ethernet ports, as well as DDR2 memory and NAND flash memory [109]. The data conversion module is equipped with a 125 MSPS, 14-bit dual channel ADC and a 500 MSPS 16-bit dual channel interpolating DAC provided by TI. The RF module is configured to have either 5 or 20 MHz bandwidth with working frequencies of 200-930 MHz for the transmitter and 30-928 MHz for the receiver, other higher band products with working  
Figure 18: WARP design flows.  
frequency from 1.6-2.3 GHz and Wi-Fi band, Wi-Max band are also optional [109]. The platform also uses TI's MSP430 ultra low-power MCU and power management technology [111].  
Lytrech selected the real-time operating system INTEGRITY from Green Hills Software as the underlying software foundation of the SFF SDR development platforms and integrated various components such as System Generator for DSP from Xilinx, as well as MATLAB, Simulink, and Real-Time Workshop from The MathWorks. These components provide the board's development package with a module-based design ability. Model-based design supports IP reuse by being able to include legacy code among the other blocks. In the Simulink environment, this is done by using S-functions for the DSP, and black boxes for the FPGA. On the other hand, a developer can integrate his or her model-based algorithms to the low-level coded design of the rest of the team with the use of Embedded Coder, another tool from the Mathworks tailored for embedded processors [109, 111]. The software design flow is shown as Figure 20.  
The platform also integrates Software Communications Architecture (SCA) that specifies interactions between hardware and software elements and Common Object Request Broker Architecture (CORBA) communications middleware standard for the SFF SCA Development Platform. This makes the development process much easier [111].  
Figure 19: SFF SDR platform and the functional block diagram.  
Figure 20: Using SFF SDR platform in module-based design flow.
## 2 Materials and Methods
### vePOD Faster R-CNN: Vehicle-Edge Cooperative Object Detection for Privacy Preservation  
A region proposal network (RPN) iterates through a feature map and generates nine anchor boxes in three different aspect ratios and scales for each anchor point. Then, the bounding box regressor layer and classifier layer determine the shifted location of each anchor box and whether it is in the foreground or background with a probability score. The non-maximum suppression (NMS) groups anchor boxes based on an intersection over union (IOU) threshold. NMS selects the one with the highest probability score from each group. The 2000 filtered anchor boxes, together with their probability scores, are saved as region proposals. Those selected region proposals are projected to the feature map based on their coordinates. The projected regions on the feature maps are defined as the regions of interest (ROIs). ROIs are detected by the RPN based on the values in the feature map. The generated ROIs go through ROI pooling and a prediction network to detect objects.  
The RPN and NMS need the whole image to find bounding boxes and refine their coordinates. As a result, image-level secret sharing causes the RPN to generate incorrect bounding boxes, as each edge server only possesses a secret share of the original image. Therefore, we adopt feature-level secret sharing in the design of vePOD Faster R-CNN.  
Figure 2 depicts the overall structure of vePOD Faster R-CNN and its key components. Both the vehicle and edge servers are involved in executing the object-detection network. On a vehicle, an input image goes through a backbone CNN and RPN; then, the generated feature maps are partitioned and encrypted. After receiving a feature-level secret share from the vehicle, an edge server runs an ROI pooling and a detection network on the secret share along with the region proposals. In order to produce correct detection results while processing encrypted secret shares, vePOD enhances the ROI pooling and detection network by introducing secure functions.  
The detection network consists of two fully-connected (FC) layers. FC performs linear transformations on the input feature maps. Since a linear transformation does not change the additive relationship (Section 2.2) between secret shares, it does not affect the functional correctness when combining results from all secret shares. As a result, the secure function for the detection network is the same as the original detection network in Faster R-CNN.  
In contrast, the ROI pooling performs non-linear transformations on input feature maps, which can change the additive relationship among the secret shares. The design of its secure function needs to address this problem.
## 3 Manifold Dimension Transformation of Raman Spectra of Oil-Paper Insulation
### Isometric Mapping Feature-Extraction Method
In order to preserve the distances between Raman spectral samples as much as possible after feature extraction, we ensure that they remain consistent with the original high-dimensional distance, that is:  
\[\left\|\mathbf{x}_{i}-\mathbf{x}_{j}\right\|^{2}=\left\|\mathbf{z}_{i}-\mathbf{z}_{j}\right\|^ {2}. \tag{9}\]  
Let \(\mathbf{B}=\mathbf{Z}^{T}\mathbf{Z}\in\mathbf{R}^{n\times n}\), where \(\mathbf{B}\) is the inner product matrix of the entire Raman spectral samples \(\mathbf{Z}\) after feature extraction, that is, \(b_{ij}=\mathbf{z}_{i}^{T}\mathbf{z}_{j}\) and \(d_{ij}=\left\|\mathbf{z}_{i}-\mathbf{z}_{j}\right\|^{2}\), then:  
\[d_{ij}=b_{ii}+b_{jj}-2b_{ij}. \tag{10}\]  
Let the Raman spectral samples \(\mathbf{Z}\) after feature extraction be centered, that is, \(\sum\limits_{i=1}^{n}\mathbf{z}_{i}=0\), then:  
\[b_{1j}+b_{2j}\ldots+b_{nj}=0. \tag{11}\]  
In the same way, \(\sum\limits_{j=1}^{n}b_{ij}=0\), then:  
\[\sum\limits_{i=1}^{n}d^{2}_{ij}=tr(\mathbf{B})+nb_{jj}, \tag{12}\]  
and the same is true for the \(j\)-th components: \(\sum\limits_{j=1}^{n}d^{2}_{ij}=tr(\mathbf{B})+nb_{ij}\), that is:  
\[\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}d^{2}_{ij}=2m\times tr(\mathbf{B}). \tag{13}\]  
Then, the inner product matrix \(\mathbf{B}\) of the Raman spectroscopy matrix \(\mathbf{Z}\) after feature selection is:  
\[b_{ij}=\frac{1}{2}[\sum\limits_{n=1}^{n}d^{2}_{ij}+\frac{1}{n}\sum\limits_{j =1}^{n}d^{2}_{ij}-\frac{1}{n^{2}}\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}d^ {2}_{ij}-d^{2}_{ij}]. \tag{14}\]  
The eigenvalue decomposition on the inner product matrix \(\mathbf{B}\) is then performed as \(\mathbf{B}=\mathbf{V}\mathbf{\Lambda}\mathbf{V}^{T}\), where \(\mathbf{V}\) represents the eigenvector matrix and \(\mathbf{\Lambda}=diag(\lambda_{1},\lambda_{2},\ldots,\lambda_{d})\) which is composed of the eigenvalues of \(\mathbf{B}\). Taking the first \(d^{\prime}\) constitute from \(\mathbf{\Lambda}^{\prime}=diag(\lambda_{1},\lambda_{2},\ldots,\lambda_{d^{\prime}})\), it can be found that:  
\[\mathbf{Z}=(\mathbf{\Lambda}^{\prime})^{1/2}/\mathbf{V}^{T}\in\mathbf{R}^{d^{\prime}\times n}. \tag{15}\]  
In this way, based on manifold dimensionality reduction, the Raman spectrum samples are reduced from \(m\) to \(d^{\prime}\) dimensions. Moreover, the distance between a particular Raman spectrum sample and its non-neighboring samples can only be obtained through its neighboring samples. This amplifies the distances between samples of different aging states in the low-dimensional space, thereby enhancing the differences between Raman spectrum samples of different aging states. This feature-extraction method provides a simpler and more distinguishable data source for subsequent discrimination models.
## 2 The Ring Vector Sensor Receiver and Its Signals  
A vector sensor has been found to be useful for multichannel equalization in a communication receiver [12]. Among various designs [18], we consider a ring with four segments, as shown in Figure 1, due to its relative ease of implementation. In response to the transmitted signal \(s[i]\), with \(i\) being the time index, it measures the two signals given in Equation (1) that are the acoustic particle velocities, i.e., the vector components of the field in the \(x\)-\(y\) horizontal plane  
\[\big{[}r_{x}[i]\ r_{y}[i]\ r_{p}[i]\big{]}^{T}=\big{[}h_{x}[i]\ h_{y}[i]\ h_{p}[i] \big{]}^{T}\oplus s[i]+\big{[}n_{x}[i]\ n_{y}[i]\ \mathrm{n}_{p}[i]\big{]}^{T}, \tag{1}\]  
where \({}^{T}\) is the transpose. The signal \(r_{p}[i]\) in Equation (1) is the acoustic pressure, representing the scalar component of the field, and is measured using a scalar sensor encapsulated in the receiver. The symbol \(\oplus\) in Equation (1) is the convolution; \(h_{x}[i]\), \(h_{y}[i]\) and \(h_{p}[i]\) represent the \(x\) and \(y\) acoustic particle velocity and the acoustic pressure channel impulse responses, respectively; and \(n_{x}[i]\), \(n_{y}[i]\) and \(n_{p}[i]\) represent the \(x\) and \(y\) acoustic particle velocity and the acoustic pressure noise components, respectively. Note that since the acoustic particle velocity in a particular direction is the spatial gradient of the acoustic pressure in that direction [19], we have the following relations for the \(x\) and \(y\) particle velocity components  
\[r_{k}[i]=\partial r_{p}[i]/\partial k,\ h_{k}[i]=\partial h_{p}[i]/\partial k,\ n_{k}[i]=\partial n_{p}[i]/\partial k,\ k=x,y. \tag{2}\]  
In the next section, Section 3, we consider a multichannel signal acquisition method that can be used to study the signal acquisition performance of the ring vector sensor receiver. A simpler method that is less complex to implement is also considered and studied in Section 3. Comparison and analysis using experimentally measured data are presented in Section 4.
## 4 Discussion
Importantly, we also find that the same IC appears in more than one triplet. Specifically, CST-SLF is in both triplet 3 (CST-SLF, Uncus-ITG, and SFG-MFG) and triplet 5 (CST-SLF, Precuneus-PCL, STG), and POG-PCG is in both triplet 1 (ATR-ACR, POG-PCG, and thalamus) and triplet 6 (SCR-CST, POG-PCG, and culmen), suggesting dual functions of one brain region in multiple multimodal brain networks. Additionally, exploring the differences of ICs within the six triplets between HCs and SZs might provide information about potential biomarkers of schizophrenia. By using a two-sample \(t\)-test, we find that the ATR-ACR and CST-SLF from dMRI have stronger activation in HCs than in SZs, implying the WM in these regions is less integrated in SZs than in HCs. For sMRI, we find POG-PCG, STG, Uncus-ITG, and MOG-FG are less activated in SZs than in HCs, meaning that SZs have a reduced average GM volume in these areas. Furthermore, we find that SFG-MFG from fMRI has a stronger activation in SZs than HCs. Most of these regions has been reported to be associated with abnormalities and negative symptoms in SZs. SZs have been shown to have significantly lower FA values than HCs in the ATR [82; 84; 85; 86], ACR [87], CST [88], and SLF regions [89]. In addition, the GM volume in the POG [90], PCG [91], STG [92], uncus [93], ITG [94; 95], MOG [96], and FG [97] has been shown to be significantly decreased in SZs compared to in HCs.
## 3 Results
### Divergence  
The Kullback-Leibler Divergence (KLD) of the Spectral Entropy between brain hemispheres is essentially a non-symmetric property. Thus, we have included both direct and reverse divergence between both hemispheres. This is, for each electrode in one hemisphere, KLD is computed with respect to every electrode in the other hemisphere. As can be seen in Figure 4, the signals of both hemispheres in healthy controls and PD differ in a basal amount of information in the resting-state. This divergence seems to be higher in controls compared to PD but it is not statistically significant (\(p\) = 0.15). The divergence  
Figure 3: Distribution of pairwise raw EEG signals’ Correlation Coefficient in the time domain (left panels). Median and standard deviation of the spectral coherence (right panels). Results are shown for the resting-state, right and left finger tapping. Dashed lines in right panels indicate the resting-state baseline, arrows reflect the change produced by motor activation. FT: Finger tapping. *** \(p\) < 0.001.  
changes with motor activation of the brain cortex were significant in controls (right hand \(p\) = 0.01; left hand \(p\) = 0.015) and PD (right hand \(p\) = 0.02; left hand \(p\) = 0.002). Interestingly, there is a decrease in divergence values in PD; meanwhile there is an increase in controls. Contrary to resting-state, when both groups' divergence is compared during motor activation, there are significant differences (right hand activation PD vs. control \(p\) < 0.001; left hand activation PD vs. control \(p\) < 0.001). There are no significant differences in changes between sides (left vs right controls \(p\) = 0.2; left vs. right PD \(p\) = 0.3).
# Electrochemical Ion Pumping Device for Blue Energy Recovery: Mixing Entropy Battery
## 1 Introduction  
Increasing energy demand and excess pollution, because of the use of fossil fuels, has created the necessity to generate clean energy using available natural resources, thus precluding the use of fuels derived from petroleum. These natural resources include solar, wind, geothermal and biomass being already utilized in various extents. The ocean is another inexhaustible source of renewable energy, highlighting the use of energy from waves, tide, ocean currents, offshore winds, the thermal gradient of the ocean and different salinity concentrations between two solutions, such as seawater and river water [1]. The chemical energy released in the natural mixture between river water flowing into the sea is known as blue energy or salinity gradient energy [2, 3]. This chemical or entropy energy dissipates rapidly when river water enters into the sea and is estimated at 2.2 kJ of free energy per liter of fresh water [1, 4, 5, 6, 7, 8, 9, 10]. The energy produced through the difference in salinity gradients could potentially generate up to 2 TW [5, 6, 11, 12, 13, 14, 15] which is more than 13% [11] of the energy consumed worldwide. The basic principle of a device to recover energy from salinity gradients is the accumulation of charge atthe interface between an electrode and saline water solution as a result of either capacitive phenomena related to the double layer formed at the electrode/solution interface and/or electrochemical reversible reactions at the electrode surface with electroactive species present in saline solutions. The key aspect of this process is the strong dependence between accumulated charge at the interface or the dissociation degree of the electrochemical reactions with the salt concentration in the solution. In order to take advantage of the differing charge densities between two electrodes dipped in fresh and concentrated water solutions, different designs have been proposed. In this review, we summarize the theoretical background of the mixing process, oriented to the mixing entropy battery system, with a focus on existing different designs and operating aspects to achieve a continuous operation.  
The objective of this review is as follows: (a) To present the theoretical background and recent advancements in mixing entropy batteries through an overview of the state of the art and most recent technological advances; and (b) To describe materials for these batteries together with major challenges in the technology and the need for fundamental research prior to its world-wide deployment.
## 2 Procedure of Operation
### Specific Alignment Techniques
The light leaving the fiber input has to be captured with MO1 without moving MO1. This means that only the MMF can be aligned, so that light propagates through MO1 and is captured with CMOS1. In order to find the proper distance between MMF and MO1, the reference beam has to be blocked and an axicon is displayed once more. The back reflecting light from the input fiber facet is now imaged on CMOS1. As introduced in step 3, the MMF has to be moved until the boundary lines of the axicon are at a minimum thickness, as shown in Figure 8b. Now, the input fiber facet is located in the focal plane of MO1. By coupling the reference beam into the actual fiber output once more, a sharp image of the fiber input facet on CMOS1 can be seen, as shown in Figure 8c. Eventually, if the sheer diffraction grating is displayed  
Figure 6: Optical setup to align the microscope objective. This is a sub-setup from the setup shown in Figure 1.  
on the SLM and the back reflected light from the input fiber facet interferes with the reflection from M5, concentric interference rings appear on CMOS1, as shown in Figure 8d. Figure 7: An auxiliary sub-setup helps to image the SLM plane to the input fiber facet plane. At first, the auxiliary path with L4 and M5 needs to be set properly: (**a**) sub-setup from Figure 1; (**b**) sharp image from an axicon reflected by the auxiliary mirror M5 and captured with CMOS1; (**c**) sharp image of the modulator plane reflected from M5 and captured with CMOS1. The SLM displays the sheer diffraction grating; (**d**) after the retro reflector is set, concentric interference rings appear on CMOS1.
# A Computation Offloading Strategy in LEO Constellation Edge Cloud Network
## 5 Numerical Simulation
### Simulation Setup
#### 5.1.3 Communication Parameter  
Communication parameters mainly include the transmission rate, transmission power, etc., which are summarized in Table 4. These parameters are referenced in [11].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Constellation 1** & **Constellation 2** & **Constellation 3** \\ \hline constellation type & near-polar & near-polar & near-polar \\ number of orbits & 6 & 6 & 12 \\ satellites per orbit & 6 & 12 & 12 \\ inclination & 86.4\({}^{\circ}\) & 86.4\({}^{\circ}\) & 86.4\({}^{\circ}\) \\ semimajor axis & 7158.14 km & 7158.14 km & 7158.14 km \\ altitude & 780 km & 780 km & 780 km \\ longtitude boundary & 75\({}^{\circ}\) & 75\({}^{\circ}\) & 75\({}^{\circ}\) \\ CPU frequency (Ghz) per MEC server & \(\mathcal{N}(1,0.2)\) & \(\mathcal{N}(1,0.2)\) & \(\mathcal{N}(1,0.2)\) \\ computation capacity per satellite & \(\mathcal{N}(2,0.2)\) & \(\mathcal{N}(2,0.2)\) & \(\mathcal{N}(2,0.2)\) \\ (Gcycle/s) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Constellation parameters.  
Figure 5: Constellation 1 snapshot.