esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Description  
The DFC scheme involves pointing a digital camera on an electronic screen to capture the display output [18]. The 2D-DFC scheme involves embedding the data in two dimensions of an image frame [19]. Figure 1 shows a typical block diagram of a 2D-DFC system with pilot signal assistance. As shown in Figure 1, at the transmitter, the modulator maps binary input data bits to data symbols, which are then embedded into the spectral domain of a image. Then, the 2D-inverse discrete Fourier transform (IDFT) was applied, and the resulting image was displayed on the screen. In conventional 2D-DFC, reference image frames (without data embedding) are inserted between neighboring data-embedded frames to minimize the visual artifacts that may be visible to the human eye. However, in the current system model, reference frames were not transmitted.  
At the receiver, the camera captures a sequence of images from the screen, which are then transformed from the spatial domain into the frequency domain using a 2D-discrete Fourier transform (DFT). In the first iteration, the information symbols are decoded based on pilot observations. Once all the information symbols were decoded and presumed to be accurate, some pixels were used as virtual pilots for the second iteration, and the spectral domain image was reconstructed accordingly. The subsequent iterations refine the image pixel estimates using the feedback information symbol estimates.
# Static Compressive Properties of Polypropylene Fiber Foam Concrete with Concave Hexagonal Unit Cell
###### Abstract  
For the purpose of studying the influence of fiber on the negative Poisson's ratio effect of foam concrete, a concave hexagonal unit cell structure of polypropylene fiber foam concrete was proposed. The effects of different fiber volume contents on the structural mechanical parameters, Poisson's ratio, and energy absorption capacity of the unit cells were studied by static compression of concave hexagonal unit cells and cube specimens. The results show that the compressive strength of foam concrete is reduced by adding polypropylene fiber, and the peak stress of concave hexagonal unit cells decreases less rapidly than that of cube specimens. The proper amount of polypropylene fiber can enhance the deformation ability of the unit cells in foam concrete, and the Poisson's ratio of the unit cells in foam concrete with 1.5% fiber content is the lowest. In the process of failure of concave hexagonal unit cells, the failure phenomenon is mainly concentrated on the concave surfaces on both sides, and the cracks are distributed in the form of \"upper left and lower right\" or \"lower left and upper right\". When the content of polypropylene fiber is 0.5%, the total energy absorbed by the concave hexagonal cells in the compression deformation process increases by 12.98%.  
foam concrete; fiber; negative Poisson's ratio; concave hexagon; energy absorption +
Footnote †: journal: Applied Sciences  
Article
## 3 Digital Implementation
### CORDIC-Based Neuron Design  
In order to solve the prior function, the coordinate rotation digital computer (CORDIC) algorithm is used in this paper to complete the conversion between rectangular coordinates and polar coordinates. The CORDIC algorithm can use the addition, shift, and look-up table operations to calculate basic transcendental functions, which are complicated for the hardware computation, including \(\sin\theta\), \(\cos\theta\), \(\sinh\theta\), \(\cosh\theta\), \(\mathrm{e}^{\theta}\), \(\ln\theta\). The major method of the CORDIC algorithm is to rotate series of angles which are related to the specific values for the approximation of the target angle, which is based on multi-iteration operations. It is worth noting that the results can become more accurate as the number of iterations increases. All the computations are based on addition and shift operation of the specific computing results.  
The unified CORDIC algorithm employs a parameter of the plane coordinate system m to integrate the linear rotation, circle rotation, and hyperbolic rotation in a CORDIC function set, the related equation set can be written as follows:  
\[\left\{\begin{array}{l}X_{i+1}=X_{i}-m\xi_{i}Y_{i}\cdot 2^{-i}\\ Y_{i+1}=Y_{i}+\xi_{i}X_{i}\cdot 2^{-i}\\ Z_{i+1}=Z_{i}-\xi_{i}\cdot\theta_{i}\end{array}\right. \tag{6}\]  
In the function set, \(X_{i}\) and \(Y_{i}\) are the result values of the \(i\)th iteration. \(Z_{i}\) is the angle value of the \(i\)th iteration. The number of iterations can be expressed as parameter \(i\), and \(\xi_{i}\) is the judgment operator. If \(\xi_{i}>0\), then rotate counterclockwise, and if \(\xi_{i}<0\), then rotate in the opposite direction, that is, clockwise. This parameter has a specific angle used to represent the \(i\)th rotation, where m refers to the parameter of the coordinate system. The parameter m can be equal to \(-1\), \(0\), \(1\), and different values of m correspond to different nonlinear functions. If \(m=-1\), the function set can be used to represent \(\sinh\)\(\theta\) and \(\cosh\)\(\theta\). If \(m=0\), the function set can be used to calculate multiplication and division. In addition, if \(m=1\), the function set can compute \(\sin\)\(\theta\) and \(\cos\)\(\theta\). Besides, different initial values can result in different computing results. If \(X_{0}=\) A + 1/4, \(Y_{0}=\) A - 1/4, and \(m=-1\), the function set can be used to solve \(\sqrt{A}\). If \(X_{0}=\) A + 1, \(Y_{0}=\) A - 1, and \(m=-1\), the function set can be used to solve 0.5 ln A.  
In the research model in this paper, the Izhikevich model is mainly used in the neuron model, and the electrical synapse model is used to build the synapses. The CORDIC algorithm is used to realize the functions of multiplication, division, and exponential operations. In terms of the multiplication operation using the CORDIC algorithm, the parameters are set as \(m=0\), \(X_{0}=\) A, \(Y_{0}=\) B, and \(Z_{0}=0\), and the function of \(Z=\) A \(\times\) B can be calculated as:  
\[\left\{\begin{array}{l}X_{i+1}=X_{
# Simple HPLC-PDA Analysis to Determine Illegal Synthetic Dyes in Herbal Medicines
## 2 Materials and Methods
### HPLC-PDA Analysis Conditions  
The employed HPLC instrument consisted of a Waters e2695 separation module and PDA detector (Waters 2998) running Empower3 (Waters, Milford, MA, USA). A YMC-Pack ODS-A column (250 \(\times\) 4.6 mm, 5 \(\upmu\)m, Tokyo, Japan) was used for the HPLC analysis. The HPLC system was coupled with a PDA detector. The detection wavelengths for Tartrazine, Auramine O, and Metanil yellow were set to 428 nm, whereas those for Orange II, Amaranth, New Coccine, Sunset yellow, Azorubine, Erythrosine B, and Acid red 73 were set to 500 nm. The mobile phase comprised acetonitrile (solvent A) and 50 mM ammonium acetate in distilled water (solvent B). A linear gradient was employed as follows: 0 min, 5% solvent A; 15 min, 45% solvent A; 40-45 min, 45% solvent A; 46 min, 5% solvent A. The flow rate was 1.0 mL/min, and the injection volume was 10 \(\upmu\)L.
# A Review of Research on Secure Aggregation for Federated Learning
## 2 Federated Learning
### Applications
#### 2.3.1 Mobile Device  
Federated learning is applied to smartphones to predict human behavioral trajectories while preserving user privacy. Google built federated learning to train language models among Android mobile users [22] to further improve keyboard prediction of keystrokes, which improves keyboard prediction accuracy and protects privacy compared to the previous one. The use of federated learning to train word- and character-level recursive neural network models for predicting keystrokes and emoticons [23] enables server-based data collection and training in a commercial environment.  
Mobile devices also include devices in IoT environments, and federated learning has also been applied to IoT. In [24], federated learning is combined with smart home IoT to build a federated multi-task learning framework that learns customized, context-aware policies from multiple smart homes while preserving privacy.  
Applying federated learning to wireless communications, such as edge computing and 5G networks, can solve the energy, bandwidth, latency, and data privacy problems in wireless communications. In [25], federated learning is applied to wireless networks and edge mobile device computing, where a distributed stochastic gradient descent scheme over a shared noisy wireless channel enables faster model convergence, higher accuracy, and more efficient utilization of limited channel bandwidth resources. In [26], federated learning is applied to heterogeneous mobile edge devices to address three key challenges: communication efficiency, statistical heterogeneity, and system heterogeneity.
## 4 MPPT Control Design  
The MPPT is an algorithm used to obtain the maximum produced power from a source of energy (PV, PEMFC). In this work, in order to keep the system running at an efficient power point, the MPPT based on the backstepping algorithm is used to drive the boost converter, which is placed between the stack cell and the load (Figure 6). The central problem addressed by the MPPT is that the PEMFC efficiency depends on the amount of supplied hydrogen, the cell temperature, and the load variations. In order to keep this efficiency at the highest value, the system must be optimized to obtain the current closest to the current at which the PEMFC characteristic gives the maximum available power. Hence, the aim of the MPPT is to find the MPP and force the PEMFC to operate at this point. Thus, it helps to overcome the difficulties of choosing the most efficient current under the influence of the inputs and the load variations. Therefore, the MPPT can be considered as the fundamental phase for obtaining good performance in a PEMFC. The MPPT control algorithm is usually based on changing the converter duty cycle \(d\) to compel the PEMFCs to function at its MPP.  
In this section, the proposed MPPT method is designed based on two steps. The first is the determination of the current reference estimator (\(I_{mpp}\)), which corresponds to the current of the MPP. For the controller, \(I_{mpp}\) represents the reference current (\(I_{ref}\)), which the operating current must achieve. The second step is the development of the backstepping technique, which commands the duty cycle of the boost converter through the pulse width modulation (PDM).
## 4 Characterization of Real-Life Signals
### Feature Space Design  
Simple and reliable features are essential for automatic technology recognition on constrained devices. We limit the selection of features to narrowband RSSI. This means features from the wideband spectrogram are excluded, due to its requirement for both the RF front end capability and the baseband processing power. In the subsequent sections, we first discuss features extracted from  
Figure 7: The normalized histograms of RSSI for Wi-Fi, LTE and DVB-T signals obtained by USRPB200 mini with the sample rate of 1 MHz.  
the RSSI distribution (i.e., histogram) to distinguish Wi-Fi, DVB-T and LTE; then, supplementary features obtained from time domain RSSI series are introduced for categorization among multiple non-streaming technologies.
## Results and Discussion
### Effect of Inclination Angle
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Case** & **Inclination Angle, \(\alpha\) (Degree)** & **Volume Flow Rate (m3/s)** & **Cross-Sectional Area (m2)** & **Contact Area (m2)** \\ \hline Square-i2 & 25.82 & 6.30 \(\times\) 10\({}^{-10}\) & 4.84 \(\times\) 10\({}^{-8}\) & 5.28 \(\times\) 10\({}^{-6}\) \\ Square-i4 & 13.89 & 6.29 \(\times\) 10\({}^{-10}\) & 4.64 \(\times\) 10\({}^{-8}\) & 5.18 \(\times\) 10\({}^{-6}\) \\ Base case & 0 & 6.28 \(\times\) 10\({}^{-10}\) & 4.84 \(\times\) 10\({}^{-8}\) & 5.12 \(\times\) 10\({}^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Volume flow rate and contact area of slanted microchannels with square shape. Figure 17: The velocity streamlines for vertical and slanted microchannel cases: (**a**) circular 0\({}^{\circ}\), (**b**) circular 13.89\({}^{\circ}\), (**c**) circular 25.82\({}^{\circ}\), (**d**) square 0\({}^{\circ}\), (**e**) square 13.89\({}^{\circ}\), (**f**) square 25.82\({}^{\circ}\). the increase in the contact area is the major reason why the volume flow rate increases with the increasing inclination angle of microchannels.
## 6 Performance Evaluation
### Simulation Setup  
We use the epidemic routing protocol to compare the performance of the _ASMSS_ with the random working mechanism and the periodical working mechanism. In the random working mechanism, nodes in the network randomly choose the corresponding slots to be listening or sleeping according to the value of \(D\) for duty cycle operation. In the periodical working mechanism, nodes in the network choose to be listening or sleeping in the beginning time of the experiment randomly, and perform this chosen mechanism periodically. In epidemic routing protocol, packets are simply flooded to the nodes in the network. We conduct the simulations with the _Infocom6_ dataset which is used for node activity driving and can be downloaded from CRAWDAD [30], and last updated in August 2016. In _Infocom6_ trace, users carry the portable devices equipped with bluetooth to record the contacts with other devices. The detailed information about the _Infocom6_ is listed in Table 1. In the simulation, we set  
Figure 5: The Complementary Cumulative Distribution Function (CCDF) of inter-contact time in the _Infocom6_.  
the \(T\) to be 5 min and \(T_{slot}\) to be 30 s. Other related parameters in the simulation are listed in Table 2. Then we evaluate the performance in terms of the following indicators.
## 3 Materials and Methods
### Experimental Setup
#### 3.6.4 Hardware and Software Platform Configuration and Parameter Settings  
In terms of hardware configuration, the NVIDIA Tesla V100 GPU (NVIDIA, Santa Clara, CA, USA) was selected, featuring 5120 CUDA cores and 640 Tensor cores, providing high floating-point operation performance and tensor computation capabilities. This setup enables efficient training on large-scale datasets and complex models. Additionally, the server is equipped with an Intel Xeon Platinum CPU (Intel, Clara, CA, USA) to support GPU training tasks. The system also includes 128 GB of RAM, ensuring sufficient memory resources when processing large-scale data to avoid memory bottlenecks affecting the training process. Regarding the software environment, Ubuntu 20.04 was chosen as the operating system with Python 3.8 as the programming language. PyTorch 1.8 and TensorFlow 2.4 were primarily used as deep learning frameworks. These frameworks are widely adopted and validated in academia and industry, offering efficient tensor computation and automatic differentiation functions, as well as supporting distributed training and large-scale parallel computation to meet experimental requirements.  
For training strategies, hyperparameters were set and adjusted to optimize model performance and training efficiency. The initial learning rate was set at 0.001, which is a value that has proven suitable for many deep learning tasks. A learning rate decay strategy was employed to dynamically adjust the learning rate based on validation set performance during training to avoid overfitting and local minima issues. The batch size was set at 32, which is a commonly used value in deep learning tasks that balances computational resources and training speed. Smaller batch sizes can enhance the model's generalization ability but may slow down training speed; larger batch sizes can improve computational efficiency but may lead to overfitting. Therefore, a middle value was chosen to balance both aspects. The number of training epochs was set at 100, ensuring sufficient training time for the model to learn patterns and features in the data. Additionally, cross-validation was employed to further improve the model's generalization ability and stability. Specifically, the dataset was divided into training and validation sets, and model performance was evaluated using the validation set after each training epoch to guide hyperparameter adjustments and model optimization. Five-fold cross-validation was chosen to achieve a good balance between training and evaluation.  
Through these settings, the user-centered data privacy protection framework's performance can be comprehensively and systematically evaluated, ensuring effectiveness and stability across different application scenarios. In future work, hardware configurations and training strategies will be continuously optimized to further enhance model performance and privacy protection capabilities, providing robust technical support for user data privacy protection.
# An Integrated Photorefractive Analog Matrix-Vector Multiplier for Machine Learning +
## 1 Introduction
The optimal hardware implementation of a crossbar array is a topic of scientific investigation. In this study, a photonic photorefractive crossbar array is discussed, and the status of its development is outlined. What makes this design particularly interesting is the support for ANN training. Figure 2: A schematic representation of the data flow in a 3-layer ANN (Figure 1a) [9]. The colored squares denote matrix operations and are candidates for acceleration by a crossbar array. Additional layers can be included trivially, and the contribution of a single layer is highlighted by the blue striped boxes. In the Materials and Methods section, the concept is discussed. The photorefractive effect is exploited to induce refractive index gratings that diffract light between photonic waveguides. The synaptic interconnect layer is thereby realized in hardware. We explain how ANN training is enabled without intermediate memory access to the synaptic weight values. In the results section, experimental data is presented based on prototype hardware. Previously, free-space grating writing, beam shaping, and input stages have been demonstrated [4]. The fabrication of integrated photonic circuits based on photorefractive GaAs thin-films and the characterization thereof is new.
## 1 Introduction
As shown in previous studies, plasma-assisted catalysis has recently gained attention for its ability to convert methane and carbon dioxide--major components of biogas--into valuable chemicals at low temperatures. Unlike traditional thermal processes that rely on high heat (typically above 773 K), non-thermal plasma (NTP) uses energetic electrons and reactive species to activate these otherwise stable molecules under near-ambient conditions [14; 16]. This capability has made NTP-cataly systems an exciting area of research, as the reactive species generated in the plasma can interact with catalyst surfaces to drive selective chemical transformations, including the formation of C-C and C-O bonds. Among the catalysts studied, nickel supported on gamma-alumina (Ni/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\)) has shown strong potential. The addition of iron not only improves the dispersion and electronic properties of nickel but also enhances resistance to coke formation--one of the key challenges in plasma catalysis [16]. The investigation herein focused on the evaluation of catalysts composed of nickel and iron deposited onto gamma-alumina (\(\gamma\)-Al\({}_{2}\)O\({}_{3}\)), specifically examining their efficacy in enhancing the synthesis of oxygen-containing compounds during plasma-assisted dry reforming of biogas. This process was carried out under ambient temperature and atmospheric pressure conditions. These catalytic materials, recognized for their robust performance and specificity in both biogas transformation and the generation of oxygenates from synthesis gas, underwent meticulous characterization. The objective was to elucidate their inherent characteristics and to determine their impact on the plasma-mediated catalytic conversion of biogas via dry reforming.
## 2 Preliminaries
### Lightning Strike Phenomena  
Lightning is a natural phenomenon that originates randomly from thunderstorms and clouds [18; 20; 21]. In this section, we describe the mathematical model behind lightning strike development. According to [18], a lightning strike develops through several distinct phases. The first phase is called a _stepped leader_ phase, where the head of the stroke is initiated from its origin (cloud base). This head makes a series of random jumps downward from its initialisation point toward the destination point (_priori_) on the earth's surface. The behaviour of the stepped leader creates a path between the cloud base and the ground. The average speed of this phase is approximately 0.001 \(\times\) 300 m/us, including pauses that the stepped leader makes between stochastic jumps.  
As the stepped leader touches the surface, the _streamer phase_ is initiated. This phase helps transfer charge between the earth and the cloud using the same path created by the stepped leader. The average speed of the streamer is higher than that of the stepped leader (0.01 \(\times\) 300 m/us), as it uses a previously created path with large currents with magnitudes exceeding hundreds of kilo-amperes (kA). A pause of a few milliseconds occurs upon completing the streamer phase. The end of this phase initiates the _dart leader_ phase.  
The dart leader starts from the cloud base and moves down to the ground. The dart leader is faster than the stepped leader since it uses a channel that is previously ionised. As soon as the dart leader touches priori on earth, a return stroke is initiated but with a much lower current. This procedure of dart leaders and return strokes continues for several iterations, resulting in a lightning flash composed of consecutive strokes [20; 22].  
As depicted in Figure 2[18], the stepped leader head is initiated at height \(h_{0}\) and randomly makes \(j\) jumps towards the earth's surface. The streamer is immediately initiated back to the cloud. As time \(t\) passes, several dart leaders occur with less surge current \(i\). Once the current level reaches the minimum, the stroke stops.  
The probability density function that represents the lightning strike is computed by Equation (1):  
\[P(I)=0.5\cdot erfc(u_{0}) \tag{1}\]  
where \(erfc(u_{0})\) is calculated in Equations (2) and (3) as follows:  
\[erfc(u_{0})=1-\frac{2}{\sqrt{\pi}}\sum_{n=0}^{\infty}\frac{(-1)^{n}\cdot u_{0} ^{2n+1}}{n!(2n+1)} \tag{2}\]  
\[u_{0}=\frac{log(I)-\log(I_{\mu})}{\sqrt{2}\cdot l_{\
u}} \tag{3}\]  
Figure 1: Stream cipher encryption operation.  
According to [23], the mean value (\(I_{\mu}\)) and standard deviation (\(I_{\sigma}\)) of the current peak of the lightning can have the specific values shown in Table 1. The values are defined under the assumption that strokes can have a positive or negative polarity.
## 5 Experimentation, Results, and Analysis
### Human Evaluation
#### 5.4.9 Ablation Study  
To dissect the individual contributions of the various components in the DCAT model, we conduct an ablation study. The study involves the systematic removal of one component at a time from the full model while measuring the impact on performance metrics like BLEU, METEOR, CES, and CA. The results are shown in Table 18.  
Our ablation study reveals several key insights:  
* **Context Embedding Layer:** Removing the Context Embedding Layer resulted in a noticeable drop in all metrics across both datasets, confirming its importance in capturing high-level contextual cues.
* **Dynamic Spatial Attention:** The performance drops, but not as significantly as when the Context Embedding Layer is removed. This suggests that while spatial attention is important, it is not as critical as the context embedding.
* **Dynamic Temporal Attention:** Similar to the Spatial Attention, the Temporal Attention also proves to be valuable but not as critical as the Context Embedding Layer.  
Overall, each component in DCAT plays a crucial role in enhancing the model's performance, thereby justifying their inclusion in the architecture.