esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Simulation of Laser Heating Processes on Thin Metal Plates with FFT Using CPU/GPU Hardware
## Methodology
### Scheme 3--Odd-Symmetry 1D FFT  
In this scheme, the original list of Fourier coefficients is also duplicated in size in each direction (\(2M\times 2N\)). The idea is to take advantage from the odd symmetry of the sine function at \(k\pi\) (with \(k\in\mathbb{N}\), see Figure 4). Therefore, the added coefficients are set by mirroring the original \(M\) and \(N\) coefficients (multiplied by \(-1\)) in each direction (rows and columns). The final temperature is obtained from the imaginary (sine) component of the 1D FFT result in each direction. The mathematical proof of this scheme is presented in Appendix C.  
Algorithm 3 presents the method used to retrieve the temperature of Equation (A9) at any given time \(t\) using two nested 1D FFTs. Line 1 initializes the extended matrix of Fourier coefficients with \(M,N\) trailing zeros. Lines 3-5 and Lines 6-8 add the reversed sequences of Fourier coefficients (with negative sign) in each dimension, respectively (see Figure 5). Lines 10 and 14 compute the 1D FFT of the padded arrays for the \(y\) and \(x\) dimensions, respectively. Lines 11 and 15 extract the complex (imaginary component) of the result. Finally, Line 12 removes the mirrored part from solution while Line 13 applies initial and boundary conditions. The complexity of the presented algorithm is \(\mathcal{O}(MN\log(MN))\).  
Figure 4: Odd symmetry of the sine function at \(k\pi\) (\(k=0,1,2,\ldots\)).  
Figure 3: Matrix structure for the zero padding FFT. The blue block contains the original Fourier coefficients \(\theta_{mn}\). The remainder of the matrix is filled with zeros.  
```
0:\(\Theta\in\mathbb{R}^{(M-2)\times(N-2)},\quad u_{\infty}\in\mathbb{R}\)
0:\(U\in\mathbb{R}^{M\times N}\)
1:\(\Theta_{ODD\_SYM}\leftarrow\text{zeros}(2M,2N)\)
2:\(\Theta_{ODD\_SYM}[1:M-1,1:M-1]\leftarrow\Theta\)
3:for\(m=M+1,m<2M-1,m\gets m+1\)do
4:\(\Theta_{ODD\_SYM}[m,:]\leftarrow-\Theta_{ODD\_SYM}[2M-m-1]\)
5:endfor
6:for\(n=N+1,n<2N-1,n\gets n+1\)do
7:\(\Theta_{ODD\_SYM}[n,:]\leftarrow-\Theta_{ODD\_SYM}[2N-n-1]\)
8:endfor
9:for\(n=1,n<N-1,n\gets n+1\)do
10:\(arr\leftarrow\text{fft}(\Theta_{ODD\_SYM}[:,n])\)
11:\(\Theta_{ODD\_SYM}[:,n]\leftarrow\text{imag}(arr)\)
12:endfor
13:for\(m=1,m<M-1,m\gets m+1\)do
14:\(arr\leftarrow\text{fft}(\Theta_{ODD\_SYM}[m,:])\)
15:\(\Theta_{ODD\_SYM}[m,:]\leftarrow\text{imag}(arr)\)
16:endfor
17:\(U\leftarrow\text{zeros}(M,N)\)
18:\(U\leftarrow\Theta_{ODD\_SYM}[0:M-1,0:N-1]\)
19:\(U\gets U+u_{\infty}\)
20:return\(U\).
```  
**Algorithm 3** Retrieve temperature using 1D FFTs by applying odd symmetry to the original coefficients
# Frequency-Domain and Spatial-Domain MLMVN-Based Convolutional Neural Networks
## 5 Simulation Results and Discussion
### Custom Normalization for CNNMVN and Experiments
The \"winner\" neuron is determined by the closeness of its weighted sum to the desired output of that neuron. Another observation is that the network continues long-term learning without overfitting and maintains stable recognition accuracy with the custom normalization approach (Figures 6 and 7). It is also evident that CNNMVN with default normalization learns much more slowly and is unable to achieve the same accuracy as the model with custom normalization. The experiments have also shown that both pooling approaches (\"max\" and \"average\") lower the classification accuracy. This is quite obvious due to the loss of information. Also, it is still an open question how to perform pooling efficiently for complex-valued data and define the most valuable data. We also checked the recommendation provided in [53], to use max pooling over magnitudes of the respective complex numbers. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Topology**} & \multirow{2}{*}{**Pool Type**} & \multicolumn{2}{c}{**Type of Error Normalization**} & \multicolumn{2}{c}{**Epoch When Maximum Accuracy Reached**} & \multirow{2}{*}{**Accuracy**} \\ \cline{1-1}  & & & & \\ \hline
16C5-h128-o10 & None & Custom & 18 & 97.2 \\
16C5-h256-o10 & None & Custom & 19 & 97.18 \\
32C5-h128-o10 & None & Custom & 19 & 97.42 \\
32C5-h256-o10 & None & Custom & 19 & 97.46 \\ \hline
32C5-P2-h256-o10 & Max. pool & Custom & 10 & 96.36 \\
32C5-P2-h256-o10 & Average pool & Custom & 19 & 94.63 \\ \hline
32C5-h256-o10 & None & Default & 20 & 75.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification results for CNNMVN for MNIST dataset. Figure 6: CNNMVN accuracy for MNIST dataset with different normalization. Figure 7: CNNMVN accuracy for Fashion MNIST dataset with different normalization. However, while it worked better for the complex-valued hyperbolic tangent activation function in [53], it did not show improvements in conjunction with the MVN activation function (4) in our experiments. Thus, pooling in the complex domain still remains an open problem and should be a subject for future work.
###### Abstract  
In this paper, a novel combined heat and power (CHP) system is proposed in which the waste heat from a supercritical CO2 recompression Brayton cycle (sCO2) is recovered by a LiBr-H2O absorption heat pump (AHP). Thermodynamic and exergoeconomic models are established on the basis of the mass, energy, and cost balance equations. The proposed sCO2/LiBr-H2O AHP system is examined and compared with a stand-alone sCO2 system, a sCO2/DH system (sCO2/direct heating system), and a sCO2/ammonia-water AHP system from the viewpoints of energy, exergy, and exergoeconomics. Parametric studies are performed to reveal the influences of decision variables on the performances of these systems, and the particle swarm optimization (PSO) algorithm is utilized to optimize the system performances. Results show that the sCO2/LiBr-H2O AHP system can obtain an improvement of 13.39% in exergy efficiency and a reduction of 8.66% in total product unit cost compared with the stand-alone sCO2 system. In addition, the sCO2/LiBr-H2O AHP system performs better than sCO2/DH system and sCO2/ammonia-water AHP system do, indicating that the LiBr-H2O AHP is a preferable bottoming cycle for heat production. The detailed parametric analysis, optimization, and comparison results may provide some references in the design and operation of sCO2/AHP system to save energy consumption and provide considerable economic benefits.  
supercritical CO2 cycle; absorption heat pump; LiBr-H2O solution; parametric study; optimization
## 4 Results and Discussion
### Leakage Prediction
#### 4.1.3 Influence of the Hole Shape on Leakage  
The effects of different hole shapes such as circular, triangular, rhombic, square, and rectangular on the leakage are investigated. The dimensional drawing of the designed GMB holes are shown in Figure 6. To avoid the influence of the different hole areas on the leakage, hole areas of all shapes are assumed to be 1960 mm\({}^{2}\). The cut-off wall hydraulic coefficient, head loss and GMB thickness are assumed to be 1 \(\times\) 10\({}^{-9}\) m/s, 1 m and 0.0015 m, respectively [31]. It can be calculated that the leakage in the circular hole is 5.3 \(\times\) 10\({}^{-11}\) m\({}^{3}\)/s while leakages for other hole shapes are in the range of 1.28 \(\times\) 10\({}^{-10}\) m\({}^{3}\)/s-1.32 \(\times\) 10\({}^{-10}\) m\({}^{3}\)/s. This is mainly because the shape factor for the circular hole is 1.15-1.3 times lower than that for other shapes of holes with the same area [25]. It leads to the appearance of a lower leakage in the circular hole. Similar conclusions have also reported by [PERSON] et al. [37]. They found that compared with other hole shapes, the stress distribution around the circular hole is more uniform. This can help minimize tearing or failure of the GMB caused by the stress concentration at any single point.  
Figure 5: Leakages for different hydraulic conductivities and head losses with different GMB thicknesses.  
Figure 6: Dimensional drawing of different GMB hole shapes.
## 3 Hardware Architecture
### Fixed Beacons  
The beacons are anchored in the underwater environment and emit the EM signals. The signal-generating unit of each beacon is a custom-made circuit board with a RadiometrixTM USX2 multi-channel half duplex UHF transceiver operating in the 433-MHz band. The circuit board is shown in Figure 9.  
Figure 8: Smith chart of the underwater antenna.  
Figure 7: Photo (top) and schematic (bottom) of an underwater sleeve-dipole antenna for EM localization.  
The beacons consist of the circuitry, which is housed in a sealed cylindrical polymer tube with the antenna being exposed to water. The UHF transceivers are powered by 12-V LiPo-batteries. In order to spatially fix the beacon configuration, they are arranged as an array on a frame. This allows placing the localization systems at almost any desired position in the work space. Figure 10 shows a submerged array of four beacons and a detailed photo of a single beacon.
## 2 Experiment Equipment  
### RT Navigation System  
The real time (RT) navigation system of inertial and GPS navigation systems from Oxford Technical Solutions are instruments for making precision measurements of motion in real time [21]. Figure 1 is the RT3102 navigation system.  
To obtain high-precision measurements, the RT uses mathematical algorithms developed for use in fighter aircraft navigation systems. An inertial sensor block with three accelerometers and three gyros (angular rate sensors) is used to compute all of the outputs. A WGS 84 modeled strapdown navigator algorithm compensates for earth curvature, rotation and Coriolis accelerations, while measurements from high-grade kinematic GPS receivers update the position and velocity navigated by the inertial sensors. Figure 2 shows the schematic of the RT internal components. where PPS is Pulses Per Second, DSP is Digital Signal Processor, EMC is External Memory Controller.  
This innovative approach gives the RT several distinct advantages over systems that use GPS alone. The RT has a high (100 Hz or 250 Hz) update rate and a wide bandwidth. The outputs are available with very low, 3.5 ms latency. All outputs remain available continuously during GPS blackouts, when, for example, the vehicle drives under a bridge. The RT recognizes jumps in the GPS position and ignores them. The position and velocity measurements that the GPS makes  
Figure 1: RT3102 navigation system.  
high-frequency noise. The RT makes many measurements that the GPS cannot make, for example acceleration, angular rate, heading, pitch and roll. The RT takes inputs from a wheel speed sensor in order to improve the drift rate when no GPS is available.  
The outputs of the system are derived directly from the strapdown navigator. The role of the strapdown navigator is to convert the measurements from the accelerometers and angular rate sensors to position. Velocity and orientation are also tracked and output by the strapdown navigator. Figure 3 shows a basic overview of the strapdown navigator in the RT3201 navigation system. Much of the detail has been left out, and only the key elements are shown here.
## 5 Conclusions  
A detailed analysis of image quality and activity recovery led to the selection of two reconstructions as the most suitable for \({}^{166}\)Ho SPECT imaging: 5 iterations with 15 subsets and 10 iterations with 8 subsets. It was found that applying full MC collimator modeling significantly improved both contrast and activity recovery coefficients in these reconstructions. However, the extensive computation time required for these imagespresents a major limitation for their practical use in clinical settings. Another important conclusion relates to the CF and how its value changes with different reconstruction parameters. It was observed that applying full MC collimator modeling resulted in lower CF values, which, in turn, explains the higher ARCs obtained for these reconstructions.  
Conceptualization, R.A. and J.S.; methodology, R.A. and V.A.; validation, J.S.; formal analysis, R.A.; investigation, R.A., V.A. and J.S.; writing--original draft, R.A.; writing--review and editing, R.A. and J.S.; supervision, J.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\({}^{166}\)Ho-RE Radioembolization with holmium-166 microspheres \({}^{90}\)Y Yttrium-90 ARC Activity Recovery Coefficient CF Calibration Factor CNR Contrast-to-noise ratio CV Coefficient of variation EANM European Association of Nuclear Medicine FWHM Full Width at Half Maximum MC Monte Carlo MELP Medium Energy Low Penetration OSEM Ordered Subset Expectation Maximization PVE Partial Volume Effect Q\({}_{\rm H}\) Contrast Recovery Coefficient SIRT Selective Internal Radiation Therapy SPECT/CT Single-Photon Emission Computed Tomography/Computed Tomography VOI Volume Of Interest
# Augmenting GPS with Geolocated Fiducials to Improve Accuracy for Mobile Robot Applications
## 5 Results and Discussion
### Results: Experiment 1  
The drift in the results for the latitude, longitude and altitude is shown in Figure 8. This drift may initially seem very large, but it is taken over a period of over 44 h and is within the standard specified operational accuracy for traditional GPS systems.  
The error over the 44 h test can be quantified using the 2 DRMS error which is a way of expressing 2D GPS accuracy. 2 DRMS is computed as double the square root of the average of the squared errors. For the 44 h test 2 DRMS = 5.457 m  
Figure 6: Error simulation comparisons show the FAGPS data has the offset of the centre of the cluster removed. (**a**) Error simulation for non-FAGPS data; (**b**) Error simulation for FAGPS data.  
Figure 7: Simulation shows the FAGPS starts with a significantly lower error which over time becomes similar to the non-FAGPS scheme.
###### Abstract
The technical contribution of this study lies in its comparative analysis of preprocessing methods and training efficiencies, providing valuable guidance for developing more effective and efficient DR detection models. Improved early detection can lead to timely interventions and better patient outcomes. Furthermore, reducing training time makes AI-driven diagnostics more accessible in real-world healthcare settings, particularly where computational resources are limited [16]. Given the increasing need for precise and efficient diagnostic tools, our objective is to develop a compact CNN model capable of accurately classifying DR into its five stages using these advanced techniques. By enhancing both accuracy and computational efficiency, we aim to create a model practical for deployment in resource-constrained environments. Numerous approaches have been proposed to effectively diagnose and classify DR levels using convolutional neural networks (CNNs). This article provides an exhaustive literature review of these techniques, emphasizing the paramount role of the CNN method for diagnostic purposes. From the examined studies, a considerable proportion of data for DR diagnosis was sourced from EyePACS [6; 7; 8; 10; 12; 14; 16; 18; 20]. This dataset includes a diverse range of retinal images categorized by different DR severity levels, offering a strong foundation for model training. It also presents opportunities to enhance image filtering techniques and improve data comparison, which could optimize diagnostic accuracy. Notably, most of the diagnoses aimed to identify early symptoms or stages of DR, such as No DR, Mild DR, and Moderate DR. The focus on early-stage DR is critical given that all diabetic patients necessitate DR screening, yet the majority do not present with DR [4; 5; 6; 7; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. Hence, tools that facilitate early-stage DR identification may significantly enhance diagnostic efficiency. Patients diagnosed with DR can then be referred to specialists for subsequent treatment. Table 1 illustrates the diversity of research studies, data sources, the research aims, CNN techniques, and their respective accuracy in diagnosing DR. Several literature studies have demonstrated the high accuracy of different deep learning models, including VGG, ResNet, and Inception V3, in image processing for DR diagnosis.
# Privacy and Security in Federated Learning: A Survey
## 3 Security in Federated Learning
### Common Attacks in FL
#### 3.2.2 Generative Adversarial Network (GAN) Based Attacks  
Poisoning attacks have evolved and new methodologies for creating poisoned models have been proposed. One of them is called PoisonGAN [27]. This attack uses a generative adversarial network (GAN) to generate realistic datasets that are controlled at will by the attacker. A GAN is optimized on a given client side, relying on model updates with the aim of manipulating the parameters of the global model, as shown in Figure 3. Attacks using GANs have several advantages. Indeed the attacker does not need to have a dataset before making the attack [6]. Such kind of attack is actually facilitated by the federated learning infrastructure since the attacker has access to the local model, which would be more difficult with centralized learning.  
Finally, such GAN-based attacks are the most difficult to detect since the generated data are realistic. Its application can yield disastrous consequences for the model's accuracy by introducing strong and controlled bias.
## 4 Experimental Setup
### Development Dataset and Cross-Validation Procedure  
A development dataset was collected and used to carry out cross-validation experiments, first to tune wav2 letter++ hyperparameters (using half of the dataset) and then to measure WER performance (using the other half), considering 20 random partitions and reporting the average WER. The development dataset comprises a set of segments extracted from the five BP sessions held in February, 2022 (thus not overlapping with the training set). Segments were extracted in the same way as the training segments, meaning that they did not correspond to complete sentences but to pieces of one or two sentences. These segments were manually audited (the audio listened to and the transcripts fixed) only at sections where the recognized sequence of words did not match the text in the minutes. These sections were located automatically, and involved any number of substitutions, deletions and/or insertions. The transcript resulting after auditing could be either the recognized sequence of words, the text provided in the minutes or a different sequence of words not matching any of them. Finally, each segment was automatically classified as containing only Spanish, only Basque or being bilingual (probably with a code switching event). This allowed us to disaggregate ASR performance by language. Details about this dataset are shown in Table 3.  
The 9251 segments of this dataset are organized chronologically, in the same order as they were produced in the original BP sessions. To define each partition, first an index \(k\) is chosen randomly between 0 and 9251, so that the half starting at \(k\) (from \(k\) to \((k+4625-1)\) mod 9251) is assigned to the tuning set, while the half ending at \(k-1\) (from \((k+4625)\) mod 9251 to \((k-1)\) mod 9251) is assigned to the test set. This guarantees temporal coherence within both subsets, which will possibly contain different speakers and different topics, making the partition more realistic.  
For each partition, we compute WER performance on both the tuning and test sets, obtaining one global and three per-language WER figures, for the Basque, Spanish and bilingual subsets of segments. In this way, we end up with eight WER results, by computing averages for the 20 partitions considered in cross-validation experiments. Besides the averages, standard deviations and 95% confidence intervals for the averages (using normal distributions) are also computed.
# Exploring a Moderate Fallow Scale of Cultivated Land in China from the Perspective of Food Security
## 2 Land Use Change and the Reserved and Lost GPP in China
### Reversible Cultivated Land Use Change
#### 2.1.3 Reserve of GPP Due to Agricultural Structure Adjustment  
The food consumption structure of residents has changed greatly with the development of the social economy, especially increased demands for meat, vegetables, and fruits, which has adjusted the agricultural structure [31]. According to the National Bureau of Statistics of China, the proportion of the sown area of grain crops declined between 1990 and 2017 (Figure 1), from 76.48% in 1990 to 66.13% in 2017, a decline of 9%.  
In contrast, the cultivation area of cash crops such as vegetables and fruits continued to increase (Figure 2). The cultivation areas of orchards and vegetables increased by 931 \(\times\) 10\({}^{4}\)\(\text{hm}^{2}\) and 1702.42 \(\times\) 10\({}^{4}\)\(\text{hm}^{2}\), respectively, which were 2.5 times and 3.2 times the initial stage, respectively. It is worth mentioning that the adjustment of agricultural structure is reversible, especially the transformation between cultivated land and garden land [32]. Therefore, the conversion of cultivated land to garden land can be regarded as reserved GPP, but the land has not yet been fully excavated. In addition, when the ploughing layer of cultivated land is converted to fishponds, the land is not destroyed, and this part of cultivated land can still be a backup source of emergency cultivated land.  
Studies have shown that the productivity of cultivated land after fishpond reclamation did not decrease significantly but moderately increased [32]. Therefore, it is necessary to regard cultivated land that is converted to a water surface as a kind of grain reserve when evaluating the GPP of cultivated land in China.
## 3 Results and Discussion
### Physicochemical Characterization of Compost and Vermicompost
In this study, a broad band of 3400 cm\({}^{-1}\) was observed, corresponding to -NH inter and intramolecular bonds and -OH elongations of carboxylic, phenolic, or alcoholic nature and whose intensity significantly decreased in the compost and the vermicompost. At approximately 2930 cm\({}^{-1}\), C-H stretching vibrations appear, characteristic of saturated carbons pertaining to aliphatic chains, which progressively decrease when the maturation of the material progresses. At 1620, 1509, and 1416 cm\({}^{-1}\), C=C stretching vibrations contribute characteristics of unsaturated carbons of aromatic structures and C=O vibrations of conjugated quinones and ketones. The broad band appearing at 1094 cm\({}^{-1}\) corresponds to C-O vibrations of esters, significantly present as the process evolves and, in this case, more intense in vermicompost. With regard to the band appearing at 1035 cm\({}^{-1}\), it corresponds to C-O vibrations associated with structures of polysaccharide remnants, whose intensity significantly decreases in the spectra of the product, probably due to the degradation of the carbohydrates as the process evolves. The bands in the areas between 450 and 800 cm\({}^{-1}\) correspond to C-H vibrations of substituted aromatic rings, which appear at the final stages of the processes and are related to the humic transformation of the material. Evidently, the FT-IR spectra shows an increase in the intensity of the bands related to the aromatic groups in the products compared to the control, especially in the case of vermicompost. Interpretations of the IR spectra are based on work found in the literature [47; 48; 49; 50; 51; 52] and are consistent with the analysis that compared pH and C/N ratio in compost (6.8, 12.4) and vermicompost (7.8, 10.9), whose values indicated greater maturity in the material that underwent vermicomposting. Figure 3: Fourier transform infrared (FT-IR) spectra to initial matter, compost and vermicompost.