esa-sceva/satcom-chunk-collection huggingface dataset:
# FPGA Implementation of ECT Digital System for Imaging Conductive Materials
## 5 System Implementation
### Matrix-Vector Multiplication Scheme  
The crucial computation model of the image reconstruction algorithms Equations (2) and (3) consists of Matrix-Vector Multiplication, MVM; while the core unit of the MVM entails inner-product. A large MVM requires an efficient design and implementation of the FPGA hardware architecture to achieve the real-time performance requirements. The MVM is implemented by multiplying rows of matrix \(G\) by the corresponding column elements of vector \(C\). The operations of the MVM are:  
1. Reading row elements of matrix \(G\) and column elements of vector \(C\),
2. Storing these elements in internal buffers,
3. Multiplying row and column elements,
4. Adding the multiplier output and storing the results in the output buffers.  
Each stage of the MVM is built using a shared segmented parallel inner-product architecture to attain the trade-off between the high performance and the limited resources of the FPGA. The parallel implementation of the MVM realizes higher performance within a single computation cycle than serial implementation, but requires huge FPGA resources; \(MN\) multiplier and \(M(N-1)\) adders. Therefore, implementing the MVM using a shared parallel inner-product architecture can fulfill the performance/resource-usage trade-off.  
Building a parallel inner-product architecture by applying parallel multiplication operations of the inner-product procedure will enhance the performance against the cost of increasing the required resources. Realization of the inner-product of two \(N\) vectors in a single computation cycle requires \(N\) multiplier and \((N-1)\) adders. The parallel multiplication of all pairs of elements of the inner-product input vectors then adding them to produce the final result reduces the combinational path delay.  
Segmentation of the inner-product to multiple segments reduces the combinational paths delay for large vectors and lower the number of resources. Every computational cycle, only one segment from both input vectors enters the segmented inner-product unit. The combinational path delay consists of multiple clock-cycles via a delay-counter which enables writing the partial inner-product to a memory buffer at the end of the computation cycle.  
In the model-based design at the system-level, the segment length is an input to the design flow which greatly minimizes the development time and effort and relieves remodeling the system in each design cycle. The first LBP matrix-vector multiplication stage of the iLBP algorithm is organized as to feed each row vector of \(S^{T}\) matrix as well as the \(C\) vector to the segmented inner-product unit.
# Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants
## 3 Design Pipeline
### Deep Learning Model Selection  
A convolutional neural network (CNN) is a class of deep learning that uses a linear operation called convolution in at least one of its layers. Equation (4) represents a convolution operation, where \(x\) is the input and \(w\) represents the kernel, which stores parameters for the model. The output \(s\) is called the feature map of the convolution layer.  
\[s(t)=\int x(a)w(t-a)da \tag{4}\]  
In a CNN, the first layer is a convolution layer that accepts a tensor as an input with dimensions based on the size of the data [31]. The second layer, or the first hidden layer, is formed by applying a kernel or filter that is a smaller matrix of weights over a receptive field, which is a small subspace of the inputs. Kernels apply an inner product on the receptive field, effectively compressing the size of the input space [12]. As the kernel strides across the input space, the first hidden layer is computed based on the weights of the filter. As a result, the first hidden layer is a feature map formed from the kernel applied on the input space. While the dimension of the kernel may be much smaller in size compared to the initial inputs of the convolution layer, the kernel must have the same depth of the input space. The inputs and convolution layers are often followed by rounds of activation, normalization, and pooling layers [12]. The precise number and combination of these layers are specific to the problem at hand.  
For the proposed respiratory classification problem, our CNN model consists of one convolution layer, which is activated by a rectified linear unit (ReLU). The ReLU activation is a suitable choice for non-linear transformation without the problem of vanishing gradient. The filter size is set to 64 and the kernel size to 1. This layer is followed by a one dimensional Max Pooling layer with a pool size and stride length of 1, each. The next layer is a Flattening layer followed by a Dropout layer. The Dropout layer randomly sets input neurons to 0 with a rate of 0.01 at each step during training time. This is done to prevent overfitting. The dropout layer is followed by two fully connected hidden layers. The first hidden layer consists of 200 neurons with ReLU activation, and the second hidden layer contains 100 neurons with a ReLU activation function and a Softmax function for the output layer. Softmax is a choice for the output layer, for output to be interpreted as normalized probabilities. Overall, the proposed CNN model uses one-dimensional convolutions and therefore, this model is referred to as _1 DCNN_. Figure 5 shows the proposed 1 DCNN architecture along with the dimension of each layer.  
Figure 5: Our proposed 1 DCNN architecture.
# Three-Dimensional Dead-Reckoning Based on Lie Theory for Overcoming Approximation Errors
###### Abstract  
This paper proposes a dead-reckoning (DR) method for vehicles using Lie theory. This approach treats the pose (position and attitude) and velocity of the vehicle as elements of the Lie group \(SE_{2}(3)\) and follows the computations based on Lie theory. Previously employed DR methods, which have been widely used, suffer from cumulative errors over time due to inaccuracies in the calculated changes from velocity during the motion of the vehicle or small errors in modeling assumptions. Consequently, this results in significant discrepancies between the estimated and actual positions over time. However, by treating the pose and velocity of the vehicle as elements of the Lie group, the proposed method allows for accurate solutions without the errors introduced by linearization. The incremental updates for pose and velocity in the DR computation are represented in the Lie algebra. Experimental results confirm that the proposed method improves the accuracy of DR. In particular, as the motion prediction time interval of the vehicle increases, the proposed method demonstrates a more pronounced improvement in positional accuracy.  
Keywords:dead-reckoning; Lie theory; exponential function; non-linear space; extended three-dimensional special Euclidean group (\(SE_{2}(3)\)) +
Footnote †: journal: Applied
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 4 Experimental Results
### Sensitivity Analysis  
To evaluate the stability of the proposed method under different parameter configurations, we conducted a sensitivity analysis by individually varying key parameters while keeping others fixed. This helps validate the robustness of the design choices and facilitates reproducibility for future implementations.  
Table 7 shows the results of modifying three parameters: the semantic masking weight \(\gamma\) in Equation (2), the quantization scaling factor \(Q\), and the block selection threshold \(\tau\). Average BER and PSNR are reported across all CIF videos. The analysis confirms that the method tolerates small deviations in these parameters without major performance degradation. The best trade-offs between BER and visual quality are obtained near the default values \(\gamma\) = 0.3, \(Q\) = 0.75, and \(\tau\) = 30, which were thus retained in all experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Attack**} & \multicolumn{4}{c}{**BER (\%)/SSIM**} \\ \cline{2-5}  & **CIF** & **4 CIF** & **HD** & **Overall** \\ \hline Rotation \(\pm 5^{\circ}\) & 3.9/0.87 & 4.5/0.85 & 4.8/0.83 & 4.4/0.85 \\ \hline Rescaling to 90\% & 2.5/0.90 & 3.2/0.88 & 3.5/0.86 & 3.1/0.88 \\ \hline Cropping 10\% & 1.9/0.92 & 2.1/0.91 & 2.4/0.89 & 2.1/0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Watermark robustness against geometric attacks.
# Surface Plasmon Resonance-Based Fiber Optic Sensors Utilizing Molecular Imprinting
## 6 Fiber Optic SPR Sensor Based on MITs
### Experimental Instrumentation  
The typical schematic diagram of the experimental setup used for the characterization of the sensing probe is shown in Figure 8.  
Both ends of the fabricated probe are cleaved using a sharp stainless steel blade for the launching of maximum light into the fiber. The probe is fixed in a flow cell with the facilities of inserting and removal of the sample solutions. This is to ensure the interaction of sample solution with the MIP layer/sensing medium. The setup is then mounted on a three-dimensional translational stage to ensure that the maximum light could be guided through the fiber. The light from a polychromatic source is launched through one end of the fiber. This may be referred to as the input end. The light interacts with the sensing medium and changes its characteristics (due to the analyte interaction in MIP layer) which are recorded as the transmitted response at the other end of the fiber using spectrometer which is interfaced with a computer/laptop. In the case of metal nanostructures, usually absorbance is recorded instead of transmittance because of absorption of light by metal nanoparticles. The wavelength corresponding to the resonance condition is called peak absorbance wavelength. In the following section, various sensors reported in the literature for the detection of analytes having applications in environmental monitoring, food/water safety, agricultural production, drug/vitamins detection, etc. are discussed.
## 2 Materials and Methods  
In this work, we focus on silicon-based semiconductors. However, there are always back-end interconnected structures that contain metals with high Z materials, such as copper and refractory metals. As the spallation cross-sections of neutrons and protons are correlated to Z, the high LET secondary particles are mostly generated in the back-end structures after neutron and proton radiation, and these particles can hit the semiconductor region producing SEEs.  
Another common silicon-based semiconductor material is silicon-germanium (SiGe). SiGe is an upcoming advanced silicon-based IC technology as SiGe technology effectively merges the desirable attributes of conventional silicon-based CMOS manufacturing (high integration levels, at high yield and low cost) with the extreme levels of transistor performance attainable in classical III-V heterojunction bipolar transistors (HBTs) through bandgap engineering. This renders SiGe HBTs with several key merits with respect to operation across a wide variety of so-called \"extreme environments\", potentially with little or no process modification, ultimately providing compelling advantages at the circuit and system level, and across a wide class of envisioned commercial and defense applications. Thus, both silicon and SiGe materials are studied in this work.  
Since back-end interconnected structures are the major sources of high LET secondary particles that can induce SEEs, we need to confirm the hypothesis that the spectra of secondary particles generated from the back-end structures by protons are similar to those from the fast neutrons in the LANSCE, as shown in Figure 1 [8]. We performed this hypothesis testing using Geant4 Monte Carlo simulations [9].  
Geant4 is a well benchmarked general-purpose Monte Carlo code for both macroscopic and microelectronic scales. Intel has used Geant4 to build their \"Intel Radiation Tool\" for radiation effect simulation [10]. [PERSON] et al. also established a Monte Carlo approach for estimating SEEs using Geant4 with TCAD [11].
# Worldwide Connectivity for the Internet of Things Through LoRaWAN
## 4 How an End Device Joins a LoRaWAN(tm) Network
### Flow Chart of the Join Procedure in Case of Roaming
Obviously, any join server will be designed not only with the security constraint in mind but also, for example, with the availability constraint, leading most likely to a federated/distributed architecture. Figure 4: Flow chart of operations for joining a visiting network. JS and AS denote the join server and the application server, respectively.
# Adversarial Attack and Defense on Deep Neural Network-Based Voice Processing Systems: An Overview
## 2 Background
### Attack
#### 2.1.3 Metrics  
To evaluate the quality of adversarial examples, we use some standard metrics for efficiency and inconspicuousness.  
Word Error Rate (WER): The WER calculation is based on a measurement called the \"Levenshtein distance\" [20]. The Levenshtein distance is a measurement of the differences between two \"strings\". We compute WER with the Levenshtein distance \(\ell\):  
\[WER=100\cdot\frac{\ell}{N}=100\cdot\frac{S+D+I}{N} \tag{8}\]  
where the sum overall substituted words is \(S\), inserted words \(I\), and deleted words \(D\). According to Equation (8), lower WER often indicates that the ASR software is more accurate in recognizing speech. A higher WER, then, often indicates lower ASR accuracy, while in the attack situation we except the high WER, which demonstrated a better attack rate. Therefore, this is an intuitive and effective indicator for evaluating attack performance. Segmental Signal-to-noise ratio (SNReseg): The WER can only measure the success of an adversarial example in fooling an ASR system. Considering the ASV system, we find the perturbation as a common factor. In addition, for adversarial examples, the smaller the disturbance, the better the attack performance. Specifically, we use the Segmental Signal  
Figure 2: The masking effect of a pure tone with a 500 Hz loudness of 80 dB in the presence of other pure tones, and this picture is cited from [8].  
to-Noise Ratio (SNRseg) [21] to measure the added perturbations. Given the original audio signal \(x(t)\) and the adversarial perturbations \(\delta(t)\) defined over the example index \(t\), the SNRseg can be computed via  
\[SNRseg(dB)=\frac{10}{K}\sum_{K=0}^{K-1}\log_{10}\frac{\sum_{t=Tk}^{Tk+T-1}x^{2}( t)}{\sum_{t=Tk}^{Tk+T-1}\delta^{2}(t)} \tag{9}\]  
It can be obtained from Equation (9) that, the smaller the SNR value, the smaller the added perturbation.  
Perceptual evaluation of speech quality (PESQ): PESQ [22] is an objective index of speech quality, and after the PESQ analysis, a score is given ranging from \(-0.5\) to \(4.5\). A higher score means a better speech quality. The PESQ score is calculated from a stabilised ratio of the Bark spectral density degraded to the reference signal in each time-frequency cell.  
PESQ, as a subjective speech quality evaluation method, can directly and genuinely reflect the real situation of speech quality, so it is a practical assessment index on the question of whether the adversarial example is inaudible.
## 5 Results
### Sensitivity to Land Cover Types  
As a last step of the assessment procedure, the reconstruction quality was evaluated with focus on certain land cover classes. The analyzed categories included agriculture, forest and urban areas. The statistical evaluation was examined for all frequency combinations except C-Band Intensity and C-Band Intensity \(+\) Texture, which were skipped according to the findings of the investigation of the full image. Since the statistics of all frequency combinations are similar, only the frequency combination XL-Band Intensity \(+\) Texture is presented here. Urban areas are most biased (Figure 19(a)). The agriculture and forest areas are biased in approximately the same order of magnitude. The mainly negative values for MB indicate the occurrence of artificial spectral innovations. The DV is negative in general (Figure 19(b)). Therefore, the image content of the restored image can be regarded as reduced in comparison with the original. Again, urban areas highlight the weakest statistical performance in terms of the preservation of image content. Figure 19(c) shows the STD-DI. This measurement indicates that agricultural areas are reconstructed well with respect to the original image. The forest and  
The findings of the land cover specific examination can be outlined as follows:  
1. Urban areas are restored with the lowest quality. The forest and agriculture areas are restored with a similar quality, but with certain differences depending on the facet of image restoration that is focused on.
2. The statistics can be regarded as verification of the theoretical anticipations that more homogeneous land cover classes achieve better restorations results than heterogeneous categories.  
Figure 17: Visual comparison of the image restoration result of a small subset. (**a**) CC 10%; (**b**) Original; (**c**) X-I; (**d**) X-I + T; (**e**) C-I; (**f**) C-I + T; (**g**) L-I; (**h**) L-I + T; (**i**) XL-I; (**j**) XL-I + T; (**k**) XCL-I; (**l**) XCL-I + T; (**m**) Original (3 \(\times\) 3 Median); (**n**) XL-I + T (3 \(\times\) 3 Median); (**o**) XCL-I + T (3 \(\times\) 3 Median).  
Figure 18: Comparison of the original image and the reconstructed image (XL-Band Intensity \(+\) Texture. (**a**) Cloud Cover 20%; (**b**) Original; (**c**) Filled by XL-Band Int \(+\) Tex.
## 4 Design of a Self-Adaptive Evolutionary Algorithm
### Fitness Function  
Let \(Q=\{1,\dots,a\}\) be a set of individuals in the SAEA population and \(G=\{1,\dots,b\}\) be a set of generations. The fitness value of individual \(q\) in generation \(g\) (\(Fit_{qg}\)) is calculated in the developed SAEA algorithm based on the following relationship:  
\[Fit_{qg}=\boldsymbol{Z}_{qg}+\alpha\Psi^{H}_{qg}+\beta\Psi^{V}_{qg}\ \forall q\in Q,g\in G \tag{14}\]  
Along with the objective function value of the **BSPSR** mathematical model (\(\boldsymbol{Z}_{qg}\)), the fitness function includes the penalty term for violation of the horizontal clearance requirements (\(\alpha\Psi^{H}_{qg}\), where \(\alpha\)--horizontal clearance violation penalty, measured in h/ft.; \(\Psi^{H}_{qg}\)--horizontal clearance violation for individual \(q\) in generation \(g\), measured in ft.) and the penalty term for violation of the vertical clearance requirements (\(\beta\Psi^{V}_{qg}\), where \(\beta\)--vertical clearance violation penalty, measured in h/ft.; \(\Psi^{V}_{qg}\)--vertical clearance violation for individual \(q\) in generation \(g\), measured in ft.). The penalty terms are introduced to adjust the fitness function values for infeasible individuals (that violate the horizontal and/or vertical clearance requirements for vessels), which could be generated as a result of the SAEA operations. The horizontal clearance violation for individual \(q\) in generation \(g\) can be estimated as a sum of horizontal clearance violations of all vessels that are served at the MCT berthing positions as follows:\[\Psi^{H}_{qg}=\sum_{i\in I}\sum_{j\in I}\sum_{k\in K}max\{0;\left[(L^{V}_{i}+D^{H}_ {i})-L^{B}_{j}\right]\boldsymbol{x}_{ijk}\}\ \forall q\in Q,g\in G \tag{15}\]  
The vertical clearance violation for individual \(q\) in generation \(g\) can be estimated as a sum of vertical clearance violations of all vessels that are served at the MCT berthing positions as follows:  
\[\Psi^{V}_{qg}=\sum_{i\in I}\sum_{j\in I}\sum_{k\in K}max\{0;\left[(H^{V}_{i}+D^ {V}_{i})-H^{B}_{j}\right]\boldsymbol{x}_{ijk}\}\ \forall q\in Q,g\in G \tag{16}\]  
Based on Equations (15) and (16), chances of the individuals to survive will increase with decreasing degree of violation of horizontal and/or vertical clearance requirements. It is important to set the appropriate values of \(\alpha\) and \(\beta\) penalty terms to ensure that infeasible individuals will have much lower chances to survive as compared to feasible individuals. The appropriate values of penalty terms \(\alpha\) and \(\beta\) will be established based on the parameter tuning analysis, which is described in Section 6.2 of the manuscript.
[25] conducted a numerical simulation study on atmospheric-boundary-layer wind field characteristics using WRF and CFD technologies, focusing on the complex terrain around the Shenzhen Meteorological Tower. The study compared the simulation results with meteorological gradient tower measurements. The wind speed simulation results showed that the coupled model results were more consistent with the observational data compared to the single WRF method. This demonstrates that the WRF-CFD coupling approach significantly improves numerical simulation accuracy. The mid-scale WRF model provides relatively accurate boundary conditions for small-scale numerical wind tunnels, and the CFD model achieves finer-scale wind field simulations through more detailed grid discretization. The combination of these models is an effective method for improving the accuracy of wind effect simulations for ultra-high-rise buildings. The issues identified are as follows:  
1. In current research on environmental wind in building fires, numerical simulation boundary conditions often use either a single wind speed or theoretical formulas, which leads to a lack of realism in the boundary conditions. Actual atmospheric wind fields vary over time, with significant differences in wind speed at different vertical heights and highly irregular patterns. Single wind speed boundaries and theoretical formulas fail to replicate the actual wind field in numerical simulations. 2. Some studies have used WRF combined with Fluent to numerically simulate atmospheric boundary wind field characteristics. However, there is limited research specifically on numerical simulations of facade fires in high-rise buildings under various wind environments. Given that FDS simulations of facade fire scenarios require fine grid divisions in the vicinity of the building to accurately model the fire scenario, it is necessary to explore a three-layer grid nesting approach involving WRF, Fluent, and FDS to obtain the microenvironment wind field around the building. This microenvironment wind field can then be used to set boundary conditions for FDS fire simulations. This study focuses on a high-rise building in Fuzhou. First, the mesoscale weather prediction numerical simulation software WRF WRF 4.4.2 was used to obtain wind field data within a 2 km horizontal range around the target high-rise building, and data were extracted at four different vertical heights. Next, a downscaling coupling simulation with Fluent was performed, using the wind field data obtained from WRF as boundary conditions for Fluent, resulting in meter-scale wind field data within a 200 m range around the building. The wind field data simulated by Fluent were then assigned in four layers as boundary conditions for FDS to provide more realistic fire simulation boundary conditions. The numerical simulation results were compared with small-scale experimental results using a scaled model to verify their accuracy. Finally, the study analyzed the effects of strong winds on the spread characteristics of facade fires in high-rise buildings through downscaled nested coupling simulations.