esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Performance Evaluation
### Performance Metrics  
Packet delivery rate: The ratio of the number of DATA packets delivered successfully to the sink node \(N_{sink\_rec}\) to the number of the DATA packets transmitted by the source nodes \(N_{source\_send}\) which is given by Equation (18) [17].  
\[PDR=\frac{\sum\limits_{i=1}^{N_{\text{simu}}}\frac{N_{\text{sink\_rec}}}{N_{ source\_send}}}{N_{\text{simu}}} \tag{18}\]  
where \(N_{simu}\) is the number of simulation experiments.  
Total energy consumption: The average of the total energy consumed by all nodes in the network in their operating states for \(N_{simu}\) simulation experiments, which is given in Equation (19) [17].  
\[TEC=\frac{\sum\limits_{i=1}^{N_{\text{simu}}}\left(E_{\text{send}}\ +E_{\text{rece}}\ +E_{\text{idle}}\ +E_{\text{sleep}}\ \right)}{N_{\text{simu}}} \tag{19}\]  
where \(E_{send}\) is the energy consumed by all nodes in the network in the sending state, \(E_{rec}\) is the energy consumed by all nodes in the network in the network in the receiving state, \(E_{idle}\) is the energy consumed by all nodes in the network in the idle state, and \(E_{sleep}\) is the energy consumed by all nodes in the network in the sleeping state.
# Combating Fake News with Cryptography in Quantum Era with Post-Quantum Verifiable Image Proofs
## 1 Introduction
Our key original contributions are as follows:  
* We design and implement a post-quantum secure hash pipeline that combines a lattice-based matrix hash and a modified Poseidon hash adapted for zk-SNARK circuits and quantum resilience. * We introduce two operational modes for proving image provenance--one for resource-constrained devices (e.g., cameras) and another for computationally powerful environments (e.g., cloud-based AI systems). * We develop a zk-SNARK proof system based on HyperPlonk and FRI for high-resolution images, supporting image operations, such as cropping, resizing, blurring, and grayscale with formal guarantees. * We present a new proof construction architecture that supports the real-time verification of 30 MP images--a scale not previously achieved in prior zk-image provenance work. Compared to earlier systems, such as VerITAS [5] and C2 PA [4], which lack post-quantum security and full support for real-world image edits, our work is the first to provide quantum-era security, real-time performance, and zero-knowledge verifiability across practical applications. In this paper, we adopt the definition of fake news as \"fabricated information that mimics news media content in form but not in organizational process or intent\" and disinformation as \"false information deliberately spread to deceive people\". These phenomena pose serious risks to public discourse, especially when amplified by social media platforms. Our goal is to create a cryptographic system that supports verifiable image provenance, thereby reducing the spread and impact of visual misinformation.
Numerical analyses provide relatively high accuracy, but it is difficult to obtain the necessary parameters through field observations, and as such, the observational data required to verify the results of such analyses are insufficient [2]. Thus, while field observations and numerical analyses are suitable for developing and testing methods to predict the behavior of debris flows, flume experiments are more suitable for conducting research on the behavior of debris flows under controlled conditions to develop related prediction equations [8]. Indeed, flume experiments are the only available method for identifying the flow and deposition characteristics of debris flows through repeatable experiments with reproducible results [25]. For these reasons, flume experiments have been mainly used in the past to identify the behaviors of debris flows. In this study, flume experiments were therefore performed to analyze debris flow characteristics and mobility ratios according to channel slope, volumetric concentration of sediment, and presence of a berm. The flow velocity, flow depth, Froude number, and mobility ratio of the debris flows were then calculated based on the experimental observations. In addition, flow resistance coefficients were calculated by substituting the observed flow velocity and flow depth into the debris flow velocity estimation equations.
# Performance Evaluation of Reinforced Recycled Aggregate Concrete Columns under Cyclic Loadings
## 2 Numerical Modelling  
### Description of the Modeling Method  
A fiber beam model based primarily on the section discretization into fibers was used in this study to predict the hysteretic response of RRC columns subject to lateral cyclic loadings. The SeismoStruct software [45] was employed as the platform to implement the model. A cantilever-type column (i.e., the column's lower end is fixed while the top end is free) was modeled in order to simulate the boundary conditions commonly adopted in column cyclic tests (see Figure 1).  
The element type \"inelastic displacement-based frame element\" provided in SeismoStruct was chosen, which was capable of accounting for both material and geometric nonlinearities accurately. It assumed that the concrete and steel bars were rigidly connected, since [PERSON] et al. [46] have clearly concluded that the bond-slip effect on the flexure-controlled RC columns was generally insignificant. Only flexural bending failure was considered in the proposed model, because such a failure is typical for slender or well-reinforced RC columns [47; 48; 49; 50; 51]. Coping with column shear failure was, thus, out of the scope of this study.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 9 Joint Routing, Power and Bandwidth Allocation in FDMA WSNs
### Solution Method  
The augmented Lagrangian for the problem's global consensus reformulation is obtained with respect to the consensus constraints. An \(L_{2}\) norm penalty term is added to regularize the non-differentiable optimization function so that convergence is possible due to the non-differentiable nature in the objective function \(g_{i}\left(p_{i},w_{i},\hat{f}_{i}\right)\). The ADMM method consists of a sequence of optimization phases over the primal variables followed by a gradient method that updates the dual variables.  
In each node, phase 1 minimizes the augmented Lagrangian over the node local variables (\(p_{i},w_{i}\) and \(\hat{f}\)). The second phase minimizes over the global flow variable (\(f_{i}\)) for each node \(i\). Then, the dual variable corresponding to each link for the node is updated with the constant step size \(\rho\). Phase 1 problem is a quadratic convex optimization problem in the local resource variables and the local flow variables. Interior-point methods were used by the authors to solve it. The phase 2 problem was manipulated algebraically to give the simple form:  
\[f_{l}^{k}=\left\{\begin{array}{c}\frac{1}{2}\left(\hat{f}_{tran(l)}^{k}+\hat{f} _{rec(l)}^{k}\right),for\ all\ but\ the\ sink\ node\\ \hat{f}_{tran(l)}^{k},for\ the\ sink\ node\end{array}\right. \tag{23}\]  
where \(k\) is the iteration index, \(tran\) (\(l\)) and \(rec\) (\(l\)) are the transmitting and receiving nodes on the link \(l\) respectively. Thus, the global flow variables are obtained at each iteration \(k\) by averaging out the corresponding updated local variables.  
The only information that needs to be shared among the nodes are the local flow variables. These have to be broadcasted by each node to its neighbors. The communications overhead therefore depends on the network density, rather than the number of nodes.
# Calibration Method of Orthogonally Splitting Imaging Pose Sensor Based on General Imaging Model
## Materials and Methods
### Calibration
#### 2.2.3 Data Acquisition Method  
The data set is composed of N points' image coordinates \(x_{i}\) and N points' world coordinates \(w_{i}\), \(\{x_{i}\to w_{i}\}i=1,\cdots,N\). The image coordinate of each point on the target can be measured by orthogonally splitting the imaging pose sensor and its numerical unit is pixel. The corresponding world coordinate can be acquired based on the distribution of points on the target. As shown in Figure 4, the horizontal distance between every two points is 60 mm, and the vertical distance between every two points is 60 mm. The point lying on the upper left corner of the target is set as the origin of the world coordinate system, then the x and y values of the world coordinate can be calculated. When setting the pose sensors in the initial position, the z value of the world coordinate is 0. On the motorized stage the pose sensor is driven to move away from the target for a distance of 20 mm, and the z value of the world coordinate is 20 mm. In this way the three-dimensional world coordinate can be obtained.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Apparatus** & **Parameters** \\ \hline LED & The power is 1 W, the working current is 350 mA, and the working voltage is 3 \(\sim\)3.8 V \\ \hline Line array CCD & Resolution is 12,288 pixels, pixel size is 5 \(\upmu\)m, mechanics is 76 \(\times\) 76 \(\times\) 56 mm\({}^{3}\) \\ \hline \multirow{3}{*}{Motorized stage} & Ball screw drive mode, the guide rail adopts linear bearing, the resolution under the \\  & 8 subdivision is 2.5 \(\upmu\)m, the maximum speed is 40 mm/s, and the repeated positioning \\ \cline{1-1}  & accuracy is less than 5 \(\upmu\)m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Apparatus parameters.  
Figure 4: Experimental apparatus.
## Case Study
### Discussion on Practical Implementations
#### 5.3.2 The Computation Time  
The solution time of the PSO determines whether it can be used for online decision-making. In each iteration of PSO, the process of evaluating and updating the particles according to the power grid simulation results takes a very short time, and almost all the time is occupied by the electromechanical transient simulation. This work selects a computer (the CPU is 2.90 GHz AMD Ryzen 7 4800H 8-Core 16-Thread processor with 16 GB of RAM) for electromechanical transient simulation, and the simulation duration is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Device Type**} & **Absolute Time** & **Improved Time** & \multirow{2}{*}{**State Margin**} & **Dynamic** & **Absolute Time** & **Dynamic** \\  & **Margin** & **Margin** & & **Priority Value** & **Margin Priority** & **Priority** \\ \hline EV & 79.66 & 0.0067 & 0 & 0.0067 & 3 & 1 \\ EV & 81.7797 & 0.0067 & 0.0422 & 0.0489 & 2 & 4 \\ EV & 103.0145 & 0.0067 & 0.3194 & 0.3261 & 1 & 14 \\ EV &  
12 s. Each sub-process performs one electromechanical transient simulation. The average time consumption of the simulation is shown in Table 6.  
Table 6 shows that due to the limitation of cache capacity, a large number of parallel sub-processes increases the time for each iteration of PSO. In order to be applied to online decision-making, two improvement measures are given in this work. The first method is that in each iteration of PSO, the electromechanical transient simulation is carried out by multiple computers in parallel. For example, if the population size is set to 40 and the maximum iteration number is 20, the solving time of PSO can be less than 4 min when using four computers in parallel, and if the population size is set to 60 and the maximum iteration number is 30, the solving time of PSO can be less than 5 min when using eight computers in parallel. Both the above two computation times meet the requirements of online decision-making. The second method is to utilize a server/personal computer with a higher clock frequency processor. High processor frequency can speed up the electromechanical transient simulation, and combining it with the first method can further shorten the solution time of PSO.
# Asynchronous Chirp Slope Keying for Underwater Acoustic Communication
## 2 Materials and Methods
### Demodulation
#### 2.4.2 Pulse Compression by Fast Hilbert Cross-Correlation  
If time and magnitude of a received chirp are of interest, the calculation of the analytic signal after pulse compression through a matched filter is convenient. Hence, the next signal processing step is to convolve (operator ) the received signal with the matched filter for both chirp slope signs  
\[y_{\mathrm{mff}} =y_{\mathrm{ddc}}\,\,\,\,\,y_{\mathrm{rbb}\uparrow},\] (23) \[y_{\mathrm{mff}} =y_{\mathrm{ddc}}\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\, \,This increases the memory allocation to \([N_{\text{rx}},N_{\text{lo}},2]\) samples, as the downsampled sequences are compressed by both, up and down chirps. In case more different chirps are used, this increases the added dimension accordingly. The compressed pulse's envelope is then calculated as the analytic signal through the norm of the signal and its Hilbert transform  
\[y_{\text{fhx}}=\sqrt{y_{\text{mf}}^{2}+\mathcal{H}\{y_{\text{mf}}\}^{2}}. \tag{24}\]  
The calculations, both, matched filtering and envelope extraction, are performed in the frequency domain for convenience. After the Fourier transformation of the raw signal, we perform a bin-wise multiplication against the complex conjugated reference signals to obtain the compressed signals for both up and down chirps.
# A Usable Encryption Solution for File-Based Geospatial Data within a Database File System
## 3 Proposed Security Solution for Spatial Files  
Our approach to protecting valuable spatial files in enterprise GIS environments comprises multiple components. Figure 1 shows the proposed storage security solution in the form of a spatial end-user interaction model in an enterprise GIS environment. Spatial users, using any protocol server, are first authenticated against their ECMSDK accounts. After a successful login, each file is decrypted. Access to spatial folders and files is controlled with the DBFS _access control mechanism_: that is, the ECMSDK _Access Control Model_ (ACM). Upon user log out, files are encrypted for protection. User credentials can be protected in transit using SSL encryption, which can be integrated with ECMSDK [50].
## 5 Results and Analysis
### Deceleration-Acceleration Mode at Variable Torque  
In this section the model is simulated to run at different values of torque (variable torque) for a real-life application such as transport or during variable loads on the electric grid. The flywheel is initially charged by a positive torque (90 N\(\cdot\)m) for 50 s and then the torque is reduced to 70 N\(\cdot\)m where the flywheel speed is reduced and the charging rate is slowed down as shown in Figure 10. At \(t=80\) s the torque is further reduced to 50 N\(\cdot\)m and the flywheel charges slower than before with reduced three-phase currents. It can be seen from Figures 10 and 11 that SCIM-FESS operates stably under different load conditions and the three-phase currents and speed of the flywheel comply with the torque variations during acceleration and deceleration modes.  
Figure 12 shows DC-link voltage at a reference value of 600 V during acceleration and deceleration at different torque values. It can be seen that voltage remains stable at a reference value of 600 V and varies \(\pm 2\) V from the reference value as shown in a magnified view in Figure 12. Better regulation and stability of DC voltage under different load conditions (torques values) exhibits the better performance of the GSC controller as discussed in Section 4.1.2.  
Figure 10: (**a**) Variable torque values; (**b**) Three phase currents at variable toque values; (**c**) Magnified view of change in torque command; (**d**) Magnified view of currents at \(t=50\) s.
## 3 Results and Discussion
### Artificial Neural Network Prediction Analysis
#### 3.2.3 Key Performance Indices for ANN Study  
The performance prediction capability of the trained ANN network is assessed using the following parameters:  
Correlation Coefficient (R): This number indicates the fitting of predicted data with the actual output data and it is given by Equation (12). Better performance of the ANN model is reflected in a value of R close to unity.  
\[\mathrm{R}=\frac{\sum\limits_{i=1}^{n}\big{(}Y_{P,i}-\overline{Y}_{P}\big{)} \big{(}Y_{A,i}-\overline{Y}_{A}\big{)}}{\sqrt{\sum\limits_{i=1}^{n}\big{(}Y_{ P,i}-\overline{Y}_{P}\big{)}^{2}\sum\limits_{i=1}^{n}\big{(}Y_{A,i}-\overline{Y}_{A} \big{)}^{2}}} \tag{12}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Range** \\ \hline \multicolumn{2}{c}{_Input parameters_} \\ \hline Volume coefficient & 0.004–0.051 \\ \hline Ellipsoidal Ratio & 0–1.3 \\ \hline \multicolumn{2}{c}{_Output parameters_} \\ \hline Head Coefficient (Ellipsoidal) & 0.4440–0.4898 \\ \hline Relative Theoretical Efficiency (Ellipsoidal) in percentage & 52.79–71.65 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Range of input and output parameters.  
Figure 22: ANN model of present work.  
Root Mean Square Error (RMSE): This is a measure of the standard deviation of actual results of CFD analysis from the ANN-predicted results and is given by Equation (13). It describes the spread of predicted results around the regression line. The accuracy of the predicted result is better if the value of RMSE is lower.  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(Y_{A,i}-Y_{P,j})^{2}} \tag{13}\]
## 4 Results and Discussion  
### Data Preprocessing  
After applying data augmentation methods to images and their masks, the results are shown in Figure 5. It presents an image and its mask with augmentations. The top row shows the original image and mask, the middle row shows a rotated image and mask, and the bottom row shows an image and mask after a horizontal flip.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Pretrained & Yes (ImageNet) \\ Optimizer & Adam \\ Activation & SoftMax \\ Learning rate & 0.0001 \\ Proposed backbone & ResNet34 \\ Batch sizes & 8, 16, 32, 64, 128 \\ Epochs & 30, 40, 50 \\ Alternative backbones & ResNet50, VGG19, DenseNet121, EfficientNetB0 \\ \hline \end{tabular}
\end{table}
Table 7: Summary of the parameters used in the segmentation model.  
Figure 5: The results of an image and its mask with augmentations.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Optimizer & Adam \\ Activation & Sigmoid \\ Learning rate & 0.0001 \\ Loss function & Binary cross-entropy \\ Proposed model & EfficientNetB0 \\ Alternative models & DenseNet121, VGG19, ResNet50 \\ \hline \end{tabular}
\end{table}
Table 8: Summary of the parameters used in the classification model.  
This study utilized Gaussian and median filters to improve image quality, with the Gaussian filter showing superior PSNR performance for grayscale and RGB images. This study underscores the effectiveness of the Gaussian filter in image denoising tasks.
# Optimization of Coverage and Capacity Using Smart Antennae
## 3 Methodology
### Capacity Model  
The calculation of network capacity is based on the Shannon Capacity [16] Formula (2), where B represents the system bandwidth, received power denotes the power received by \(u_{k}\), and noise power represents the power of other interference noise  
\[\text{Shannon capacity}=B\cdot log_{2}\bigg{(}1+\frac{\text{\it received }\text{\it power}}{\text{\it noise power}}\bigg{)} \tag{2}\]  
In the system model in this study, the capacity for each \(u_{k}\) is given by Formula (3), as follows:  
\[B_{k}\cdot log_{2}\Bigg{(}1+\frac{p_{i}-\bigg{[}PL(d_{1}\ )+10\cdot log_{10} \bigg{(}\frac{d_{k},\ \bar{S_{i}}}{d_{1}}\bigg{)}^{\gamma}\bigg{]}}{\sum_{h\
eq i} \bigg{\{}p_{h}-\bigg{[}PL(d_{1}\ )+10\cdot log_{10}\bigg{(}\frac{d_{k},\ \bar{S_{i}}}{d_{1}}\bigg{)}^{\gamma}\bigg{]}\bigg{\}}+\sigma\bigg{)}} \tag{3}\]  
where \(p_{i}\) is the transmission power of \(S_{i}\), \(B_{k}\) is the bandwidth allocated to \(u_{k}\), \(PL(d_{1}\ )\) represents the signal attenuation value when \(u_{k}\) is 1 m away from \(S_{i}\), and \(PL\big{(}d_{k},\ \bar{S_{i}}\big{)}=PL(d_{1}\ )+10\cdot log_{10}\bigg{(}\frac{d_{k},\ \bar{S_{i}}}{d_{1}}\bigg{)}^{\gamma}\) represents the signal attenuation value when \(u_{k}\) is \(d_{k},S_{i}\) meters away from \(S_{i}\). \(p_{h}\) represents the transmission power of \(S_{h}\) when \(h\
eq i\), and \(\sigma\) is the Gaussian noise, which considers the interference caused by base stations other than \(S_{i}\) to \(u_{k}\).  
Formula (3) calculates the capacity for one \(u_{k}\). To calculate the overall network system capacity, considering there are K \(u_{k}\) in total, after implementing intelligent adaptive directional antennas, we know that \(u_{k}\) is within the coverage range of the \(j\)-th antenna (\(X_{j}^{i}=1\)) for \(S_{i}\) and can benefit from an additional signal gain represented by \(G_{j}^{i}\). Therefore, we can derive the overall network system capacity shown in Formula (4) based on Formula (3), as follows:  
\[\sum_{k=1}^{K}B_{k}\cdot\{u_{k}\cdot log_{2}\Bigg{(}1+\frac{p_{i}-[PL(d_{1}\ ) +10\cdot log_{10}\bigg{(}\frac{d_{k},\ \bar{S_{i}}}{d_{1}}\bigg{)}^{\gamma}]+\bigg{(}X_{j}^{i} \cdot G_{j}^{i}\bigg{)}}{\sum_{h\
eq i}\{p_{h}-[PL(d_{1}\ )+10\cdot log_{10}\bigg{(}\frac{d_{k},\ \bar{S_{h}}}{d_{1}}\bigg{)}^{\gamma}]\}+\sigma\bigg{)}}\} \tag{4}\]
## 3 Results
### Sequencing Yield and Quality of Metagenomic DNA  
The optimised DNA isolation protocol [19] was applied to all 20 digestate samples (Table 1) using a MinION device (Oxford Nanopore Technologies, Oxford, UK) with a single flow cell for each DNA sample. We initially used the LSK-108 Kit but changed later to the LSK-109 Kit as we observed better sequencing performance. Figure 2 summarises the sequencing results and the quality scores obtained.  
Figure 1: Effect on DNA quality of different steps during isolation. White boxes indicate the chosen approach for the DNA isolation protocol. (**A**) Default manufacturer’s protocol applied to (**left**) sludge and (**right**) controlled growth cultures. (**B**) DNA yield/length after the introduction of a humic acid removal wash (HA-wash) step. Arrows indicate the use of 0.4 mL sludge instead of 0.2 mL. (**C**) Pre-preparation of sludge and effect on DNA yield/length. (supern.: supernatant removed and replaced with water; DNase: sample pretreated with DNase; control: no pre-preparation; both: supernatant replaced with water and incubated with DNase). (**D**) Impact of different bead beating settings on DNA yield/fragment length.  
We observed a slight decrease in N50 and median read length for pre-frozen samples compared with fresh samples. Sequencing runs via LSK-108 Kits were also performed using flow cells stored for 2 months or longer, which is likely the main reason for the decrease in median Q-score. We did not observe a Q-score reduction of pre-frozen samples on the basis of sample age. Total sequencing yield varied between 18.2 and 28.5 Gb for most samples and, across all samples, we achieved an average of 20.5 Gb. Total sequencing yield was below 10 Gb for only 2 of the 20 samples (03-SW and 04-SW). An average of 6.3 Mio reads per sample could be retrieved while two samples (03-SW and 04-SW) had only 2.1 and 2.0 Mio reads.  
The genus-level richness can be approximately reached with a sequencing depth of at least 1 million reads per sample while using centrifuge [24]. Thus, the DNA isolation protocol presented here yields sufficient DNA from an \"amount of reads\" perspective for nanopore sequencing and an adequate number of long reads in all cases tested. For direct comparison between nanopore and Illumina, we generated an average of 23.5 Mio Illumina reads (corresponds to an average of 7 Gb per sample) using the same DNA sample. Illumina had, on average, 3.7 times more reads per sample but 2.9 times less sequencing yield in Gb (7.1 Gb to 20.5 Gb).