esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Localised Information Retrieval with Query Resolution
### Localised Data Query Distribution with the Sink Proxy  
Localised Data Query Distribution (LDQD) consists of two steps: Query unicast, and Query geocast. The unicast distribution is used to deliver query messages from a sink node to the sink proxy. The sink node calculates a source route to a sink proxy based on the nodes' location. There are a number of potential approaches to building a source route. For example, the sink node first selects the node that is nearest the sink proxy among one-hop neighbours of the sink node. The selected node is included in the source route. In the same way, the sink node continues to calculate the next node in the source route, by selecting the node nearest to the sink proxy among the neighbours of the previous selected node in the source route. This process continues until a source route is found to the sink proxy. Similar approaches were elaborated in [35].  
After the query message is delivered to the sink proxy in the query area, it is geographically broadcast to all sensor nodes inside the query area. The sink proxy forwards the query message to its one-hop neighbours. As illustrated in algorithm1, upon receiving the query message, each sensor node checks whether it should relay the packet to its neighbours by the following rules: a) Has not received this packet before; b) In the query area. If both a) and b) are satisfied, a sensor node will relay the packet to its neighbouring nodes. Otherwise, the sensor node just discards the packet.
# Topology and Polarization of Optical Vortex Fields from Atomic Phased Arrays
## 5 Numerical Results and Discussion
The plots at different propagation distances \(z\) coincide, validating the analytic result of Equations (41) and (42) that the polarization parameters near the vortex center are independent of observation distance \(z\) in the continuous limit. Figure 5: Polarization parameter (**a**) \(p_{z}\) and (**b**) \(p_{zz}\) as a function of radial position \(\rho_{x}\) for varied phase parameters \(l=1,2,3\) (\(m_{z}=-1\), \(z=z_{R}\), and \(N=12\)). The plots are obtained by substituting the expressions (26) into Equation (30). The plots behave differently at the origin for different phase parameter \(l\), validating the qualitative discussion at the end of Section 4. Figure 6: Polarization parameter (**a**) \(p_{z}\) and (**b**) \(p_{zz}\) vs. radial position \(\rho\) for different numbers of emitting atoms \(N=3,6,12\) (\(l=2\), \(m_{z}=-1\), and \(z=z_{R}\)). Note different behavior for \(N=3\) (see the text for details). The plots are obtained by substituting the expressions (26) into Equation (30). They show the transverse distributions of \(p_{z}\) and \(p_{zz}\). This shows the least number of sources required to generate vortex field with phase singularities and validates the discussion at the end of Section 5. For sparse arrays, \(N=3,6\), polarization singularities form a lattice, as shown in Figure 7 for \(p_{z}\). The condition for generating a vortex field is met. The peaks shown in (c) and (d) are exactly the sign of vortex fields generated. Since they are all vortex fields, the properties of the polarizations near the vortex center are the same no matter how many sources we use. Therefore, (c) and (d) in Figure 7 are similar. The distributions of the peaks of \(p_{z}\) and \(p_{zz}\) are closely related to the lattice-like distribution of the intensities, while the spatial separation between the singularities increases with the propagation distance \(z\). However, the spatial extent of an individual singularity in the vortex center remains propagation-independent and \(N\)-independent (once the requirement for the least number \(N\) is met), as seen in the bottom plots in Figure 7. The polarization \(p_{zz}\) shows similar behavior.
# List-Based Threshold Accepting Algorithm with Improved Neighbor Operator for 0-1 Knapsack Problem
## 1 Introduction  
The list-based threshold accepting (LBTA) algorithm [1] falls under the category of threshold accepting (TA) algorithms [2]. In a manner analogous to simulated annealing (SA), LBTA accepts suboptimal solutions to prevent being trapped in local optima. Instead of setting to a fixed temperature, LBTA utilizes an adaptive threshold list that is updated iteratively based on the search space topology. This allows LBTA to achieve better results with fewer tuning parameters compared with SA and other local search methods. Owing to its flexibility, LBTA is effectively utilized in solving both combinatorial and continuous optimization problems, such as the vehicle routing problem (VRP) and its variants [3], the job shop scheduling problem (JSSP) [4], the zero-wait scheduling problem [5], and the traveling salesman problem (TSP) [6].  
The 0-1 KP, which is recognized as a classic NP-hard problem in combinatorial optimization, has practical applications in various decision-making domains [7]. The problem involves a collection of \(n\) items, labeled from 1 to \(n\); each item has a weight \(w_{i}\) and a value \(v_{i}\), as well as a maximum capacity \(c\) for the knapsack. The aim is to maximize the sum of the values of the items in the knapsack while making sure that the sum of the weights does not surpass the knapsack's capacity, as follows (see Equation (1)):  
\[\begin{split} maxf(x)=\sum_{i=1}^{n}v_{i}x_{i}\\ s.t.\sum_{i=1}^{n}w_{i}x_{i}\leq c\wedge x_{i}\in\{0,1\}\end{split} \tag{1}\]where \(x_{i}\) represents the number of items \(i\) included into the knapsack, and \(f(x)\) is the target function.  
Aiming to fill the gap in the literature, this paper introduces a binary LBTA designed for tackling the 0-1 KP. The proposed algorithm features a feasible threshold list that effectively narrows the search space, alongside a hybrid greedy repair operator that surpasses traditional density-based operator regarding optimization and convergence. Moreover, a bit-flip mutation operator is employed to generate trial solutions, which provides an elastic local search area and a sufficient perturbation to escape local minimum. Extensive experimental evaluations demonstrate the efficacy and competitiveness of the LBTA algorithm, with comparative results on two large 0-1 KP instances highlighting its advantages.  
The remainder of this paper is structured as follows: Section 2 offers a brief introduction of the basic LBTA algorithm, the 0-1 KP, and algorithms for the 0-1 KP. Section 3 presents the proposed LBTA algorithm, while Section 4 analyzes its behavior. In Section 5, the performance of the LBTA algorithm is compared with other state-of-the-art metaheuristics. Finally, Section 6 summarizes the findings of this study and outlines potential avenues for future research.
## 1 Introduction
The handful of land surface models that have considered the P cycle and C-N-P interactions, such as CASA-CNP [53], JSBACH-CNP [54; 55], CLM-CNP [56], QUINCY [52], and data assimilation framework, GOLUM-CNP [57], show that increases in atmospheric CO\({}_{2}\) limits P in terrestrial ecosystems, especially lowland tropical forest [52; 54; 56; 57; 58]. Recently, the ORCHIDEE-CNP model has been evaluated at a global scale using remote sensing data and provided updated information regarding the need to incorporate P cycle processes to estimate global C stocks under climate change [59]. However, the validation of model is still a big challenge. There are very few models, such as CASA-CNP, JSBACH-CNP, and ORCHIDEE-CNP, which have been validated by comparing the model simulations against the observations across global ecosystems. In addition, the Jena Soli Model (JSM) [60] focused on improving the microbial soil organic carbon (SOM). The importance of more detailed representation of plant P processes is needed to link nutrient availability and ecosystem productivity [61]. Moreover, the above models are rarely studied to confirm the sensitive parameters and adjustment parameters used in the phosphorus cycle. There are also few models that were validated by site-to-site across multiple variables on the global scale. The objectives of the present study were to (1) incorporate the P cycle and its interactions with the C and N cycles into the existing ecosystem model TRIPLEX-GHG [62; 63] in order to develop a new process-based model, called TRIPLEX-CNP; (2) identify the parameters to which TRIPLEX-CNP is the most sensitive to GPP and biomass by sensitivity analysis; and (3) evaluate the performance of TRIPLEX-CNP model for simulating GPP, SOC, and total biomass against global observations.
# A Survey of Secure Time Synchronization
## 2 Time Synchronization
### The Importance of Time Synchronization  
Cyber-physical attacks to network time synchronization can degrade the performance of the network, for example, data disordering, unsynchronized task execution, duty cycling, and malfunctions [22]. Authors have explained that the attacks which can break time synchronization may lead to increased interference in the network, packet collusions, and delays in the communication messages. Thus, to avoid these circumstances, secure time synchronization becomes a key element for wireless sensor networks to provide its services securely. Poor time synchronization can lead to faulty timestamps, false estimates about the location of nodes, packet loss, and increased cost of energy by disturbing the sleep-wake-up schedule [5]. Sensor nodes, however, monitor data that can be leaked and may lead to personal privacy breaches [4], and more cyber-physical attacks will lead to more energy cost for resynchronization [22]. WSNs require that all nodes work efficiently by synchronizing with each other to save energy consumption [2]. The time synchronization scheme can be affected by many factors that happen in a network, such as communication overhead, available bandwidth, accuracy requirements, scalability, and infrastructure requirements. As described above, sensor nodes have limited resources and low power, and therefore, the energy saved in the synchronization process can be used for security purposes [22]. To avoid the circumstances mentioned above, security for sensor networks must be considered during the design period to ensure the operation of safety, sensitive data safety, and people' privacy [4].  
The importance of time synchronization is mostly ignored during the current protocols; thus, a new secured protocol is required to keep data safe and to provide safe services in sensor networks, and it needs to compete with all the challenges and fulfil all the requirements of secure time synchronization.
## 4 Simulation and Test Verification
### Simulated Test
#### 4.1.3 Verification of Anti-Jamming Performance  
The anti-jamming performance comprehensively considers anti-jamming loss and interference suppression capability, using the quality of the signal after anti-jamming as the evaluation target. Therefore, the CNR loss was used as the measure of the anti-jamming performance to verify the anti-jamming performances of different algorithms, defined as \(Loss=CNR-CNR_{out}\), where \(CNR\) is the CNR of the input signal, and \(CNR_{out}\) is the CNR of the output signal from the anti-jamming module. In the simulation, the frequency domain anti-jamming theoretical loss was used as a reference. If the interference is completely suppressed, the CNR loss introduced by frequency domain anti-jamming can be calculated as follows: [44]  
\[Loss\cong\frac{\left(\int\limits_{-\beta/2}^{\beta/2}H_{f}(f)G_{S}(f)df \right)^{2}}{\int\limits_{-\beta/2}^{\beta/2}G_{h}(f)G_{S}(f)df} \tag{10}\]  
where \(\beta\) is the bandwidth of the receiver's front-end filter, \(G_{S}(f)\) is the normalized signal power spectral density, and the power spectral density of the frequency domain anti-jamming filter is \(G_{h}(f)=\left|H_{j}(f)\right|^{2}\). The frequency domain response of the anti-jamming filter is  
\[H_{f}(f)=\left\{\begin{matrix}0,\left|f-f_{j}\right|\leq B_{j}/2\\ 1,else\end{matrix}\right. \tag{11}\]  
where \(f_{j}\) is the jamming signal center frequency, and \(B_{j}\) is the jamming signal bandwidth.  
A simulation analysis of the anti-jamming performances of different algorithms was performed, and the specific experimental parameter settings were as shown in Table 3.  
Figure 8: The interference detection ratio (IDR). (**a**) Scene One; (**b**) Scene Two; (**c**) Scene Three.  
Figure 9 shows the simulation results for different scenarios, with the horizontal axis representing the jamming signal bandwidth and the vertical axis representing the CNR loss.  
The simulation results indicate that the CNR loss of the SCCI algorithm after interference suppression is within 2 dB of the theoretical value, while the SCCME algorithm consistently exhibits a significantly higher CNR loss.
## 4 Proposed Architecture and Implementation  
Our implementation proposes a secure communication system for autonomous and connected vehicles using blockchain technology. The system addresses the key challenges of data integrity, authentication, and privacy in vehicle-to-everything (V2X) communication. The architecture combines the blockchain with smart contracts and consensus mechanisms to ensure a secure, decentralized, and reliable data exchange between vehicles (V2V) and between vehicles and infrastructure (V2I).
## 3 Materials and Methods
### Causal Modeling  
Causal modeling aims to identify and quantify causal relationships between variables, going beyond mere correlations. Unlike traditional predictive approaches, causal modeling allows answering \"what if...?\" questions by estimating the effect of interventions in a system. Bayesian networks, the Neyman-Rubin framework (potential outcome models), and instrumental variable methods [19,20] are some of the most common ways to deal with biases and confounding in observational data. These approaches are fundamental in fields where understanding the causal impact of decisions or treatments is crucial [21,22].  
To build the causal inference model, we used the PyMC v5.7.2 library, which enables the definition of probabilistic models and Bayesian inference. The structural causal model was designed based on human knowledge, where relationships between variables were manually defined. The model was shown in the form of a causal graph, which demonstrates how important game variables like mineral amount, gas amount, strategy, and attack time are connected. The causal structure was iteratively refined by testing dependencies through statistical validation and expert feedback. Figure 2 illustrates the resulting causal graph, depicting the directed dependencies between variables.  
The model was put together in this way: we used Equation (1) to find coefficients that showed how the predictor variables affected each variable of interest, such as marine count and military unit count.  
\[\beta\sim\mathcal{N}(0,1) \tag{1}\]  
These coefficients were modeled as normal distributions with a mean of 0 and a standard deviation of 1. The causal relationships were expressed as linear combinations of the predictor variables, and the observations were modeled as normal distributions centered around these linear combinations. The results are shown in Table 3. We used Markov Chain Monte Carlo (MCMC) sampling [23] and obtained 92% alignment with the data [24] with two chains and 500 tuning iterations.  
The integration between the causal model and the neural network was achieved by using the causal model's predictions as additional features in the neural network. This approach allowed combining the structural knowledge of the causal model, which captures causal relationships between game variables, with the nonlinear learning capacity of the neural network. The causal predictions were concatenated with the original game features to form an expanded input vector, which was then used as input for the neural network. This hybrid approach has been shown to improve both accuracy and interpretability in dynamic decision-making tasks [4,10,25].  
Figure 1: Comparison of the correlation matrices between real and synthetic data. The similarity in correlations confirms the consistency of the generated data.
## 2 Materials and Methods
### Numerical Model
#### 2.3.2 Model Configuration  
A high-quality numerical grid was realized with ANSYS MESH. The mesh of the DSTSC is composed of 18 million tetrahedral elements and was the result of a mesh independency study carried out in the Ref. [18]. The mesh independency revealed to us the importance of the mesh quality to correctly predict parameters such as the temperature and velocity gradients.  
According to the literature, the RNG k-\(\epsilon\) turbulence model is the most appropriate and reasonable when considering accuracy and computing time for numerical simulations regarding Transpired Solar Collectors [13; 19; 20]. Thus, we set the RNG k-\(\epsilon\) turbulence model combined with the Enhanced Wall Treatment option including thermal effects and pressure gradients.  
The solar radiation was integrated through the surface-to-surface model after showing good convergence and accuracy in previous studies [21]. The surface-to-surface model can be computationally expensive for geometries with numerous surfaces, and was so for our case, considering that the studied geometry contained 100 holes. Thus, we created surface clusters to reduce the computation time.  
The inlet and outlet surfaces presented in Figure 3 were set as the pressure inlet and mass-flow outlet. Considering that the geometry represents a slice of the real-scale DSTSC, lateral walls were set with symmetry boundary conditions to reduce the size of our computational model.
## 2 Related Work and Background
### Related Work
#### 2.2.4 Dynamic Watermarking Methods  
[PERSON] et al. [51] introduced DeepSigns, the first scheme that embeds a sequence of bits as a watermark into the probability density function (PDF) of the activation maps of multiple layers in a model, contrary to previous schemes that work with model parameters. This is accomplished through a fine-tuning process that relies on a trigger dataset (a secretly selected subset of input samples) and the use of specialized regularizers to balance model accuracy and watermark detection. The fundamental idea behind DeepSigns is to cluster the activation maps of the model training set and embed the watermark within a secret subset of the clustered classes. To insert the message, the mean of each of these classes is computed on the trigger dataset and modified through fine-tuning such that its projection in a secret space defined by a matrix matches the watermark. The extraction procedure only requires knowledge of the trigger dataset, calculating the statistical mean of each class's activation maps, and their projection in the secret space to extract the watermark. Although DeepSigns achieves good model accuracy after watermarking, it suffers from poor insertion capacity and significant vulnerabilities to certain attacks, such as FTA, PA, and OWA, as the watermark size increases.
# Ear Centering for Accurate Synthesis of Near-Field Head-Related Transfer Functions +
## 3 Evaluation with Calculated HRTF Datasets
### Error Metric  
Target and synthesized HRTF datasets were respectively organized as \(\ \mathbf{H}(b_{i},\Omega_{j},f_{\kappa},s_{\ell})\) and \(\hat{\mathbf{H}}(b_{i},\Omega_{j},f_{\kappa},s_{\ell})\). Index \(i=1,2,\cdots,81\) indicates radial distances; index \(j=1,2,\cdots,642\), directions on the sphere with \(\Omega_{j}=(\theta_{j},\phi_{j})\); index \(\kappa=1,2,\cdots,257\), frequency bins; and index \(\ell=1,2\), individual subjects. The synthesis error across angles and subjects is defined as  
\[E(b_{i},f_{\kappa})=\underset{s_{\ell}}{\text{RMS}}\Bigg{\{}\frac{\underset{ \Omega_{j}}{\text{RMS}}\{H-\hat{\mathbf{H}}\}}{\underset{\Omega_{j}}{\text{ RMS}}\{H\}}\Bigg{\}}, \tag{20}\]  
and the above synthesis error across all distances as  
\[E(f_{\kappa})=\underset{b_{i}}{\text{RMS}}\Bigg{\{}\underset{s_{\ell}}{\text {RMS}}\Bigg{\{}\frac{\underset{\Omega_{j}}{\text{RMS}}\{H-\hat{\mathbf{H}}\}} {\underset{\Omega_{j}}{\text{RMS}}\{H\}}\Bigg{\}}\Bigg{\}}, \tag{21}\]  
where RMS stands for root mean square along either directions \(\Omega_{j}\), individual subjects \(s_{\ell}\) or distances \(b_{i}\).
# Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters
## 4 Simulation of S1 Data with Backscattering Models  
In this section, two models are used to simulate radar backscattering over fields characterized by two different types of land use: bare soil and soil covered by cereals. In the case of bare soils, an empirical model was used. This takes the influence of moisture and roughness into account. In the case of surfaces with a vegetation cover, the water cloud model was applied [42]. This is particularly useful for the inversion of radar data over agricultural soils [30].
# Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold
## 3 Results and Discussion  
### Results  
The final result was synthesized with the processed image and transformed into a polar coordinate system, shown in Figure 12. Oil film areas were calculated according to the image resolution and detection range in Table 2.  
Figure 11: Results of the local adaptive threshold with (**a**) initial identification and (**b**) fine identification.  
Figure 12: Oil film classification results, where (**a-d**) are results of Figure 1a–d, respectively.
# Evaluation of Aging Resistance of Graphene Oxide Modified Asphalt
## 2 Materials and Methods
### Materials
#### 2.1.2 Graphene Oxide  
GO was applied to be an anti-aging modifier for the asphalt binders. The GO with 5-10 layers was provided by the Suzhou Heng Ball Graphene Technology Co., Ltd. (Suzhou, China). The purity of GO was higher than 95%, the specific surface area of GO was about 100-300 m\({}^{2}\)/g, and the lamella diameter of the GO was about 10-50 um.
# Reducing the Cost of Implementing Filters in LoRa Devices
## 3 Simulation Results
### FFT and Signal Spectrograms  
As illustrated in Figure 1, LoRa symbol demodulation begins with a process known as dechirping [6]. Each received LoRa chirp waveform is multiplied by the complex conjugate of a basic upchirp having the same \(SF\) and \(BW\). The product is a pure sinusoid whose frequency corresponds to the frequency offset associated with the modulated symbol value, \(m\)[3; 6]. The frequency of the dechirped signal is then found by taking the \(M\)-point FFT and detecting which frequency bin contains the maximum energy. The index of the detected frequency bin is the LoRa symbol value.  
Since decoding the proper symbol value depends on accurate peak detection, it is important to ensure that the peak associated with the symbol value is clearly distinguishable from that of the quantization noise. Figure 4 shows the \(M\)-point FFTs and spectrograms associated with quantized  
Figure 3: Occupied bandwidth measurements of filtered transmit signals with and without quantization when \(SF=10\) and \(BW=125\) kHz.  
and non-quantized LoRa signals. Each tested LoRa signal corresponds to a symbol value of \(m=841\), spreading factor of 10, and bandwidth of 125 kHz.  
Figure 4: Fast Fourier transform (FFT) and spectrograms for filtered modulated chirps with and without quantization when \(SF=10\), \(BW=125\) kHz, and \(m=841\).  
While there is a clear increase in the noise level when the quantization step size increases, the desired FFT peaks, and frequency ramps remain clearly visible for each case. Even with only 2-bit fractional precision, the quantization noise is well over 20 dB below the peak of the desired frequency bin. Furthermore, the spectrograms for the cases where \(B=8\) and \(B=10\) show hardly any noticeable distortion from the non-quantized case. It is clear that even with significant amounts of quantization noise, information symbols can be decoded properly provided there is no severe noise introduced by the channel.