esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Lossless Image Compression with Contextual Memory
## 3 PAQ8 PX Algorithm for Lossless Image Compression in Detail
### Adaptive Probability Maps  
Adaptive probability maps (APM), sometimes referred to as secondary symbol estimation, have a probability and a context value as inputs, and a probability as output. The context value serves as an index in a set of transfer functions. Once selected, a set of interpolation points are available. In the initial state, they should map the input probability to the same value. The input probability is quantized between two points of the set; the output value is the linear interpolation of the value of the points weighted by the distance from them. In the update phase, the two end values are updated so that the output probability is closer to the value of the predicted bit.  
There are variations of the APM. One of them, which is used in PAQ8, has a stretched probability as an input with the benefit of having more interpolation points towards the zero and one probability,where compression benefits from the fine-tuning. Other compression programs use APM with two quantized predictions as inputs with a 2D interpolation plane.  
It is not necessary to use only a single APM since they can be connected in a network. PAQ8 PX uses different architectures based on the type of stream detected. For 8 bpp grayscale images, three APMs are used. Two of them take the output of the context mixing phase as an input and have functions as contexts, including the current known byte bits, the number of mispredictions in the past, and whether the prediction falls in a neighborhood plane or not. The output of the first APM is again refined and the final prediction is a fixed weight linear combination of the three probabilities.
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 4 Machine Learning Classifiers  
We encountered in this study several challenges related to the dataset, including class imbalance, non-stationary features, varying numerical scales, and the multiclass nature of the problem. Unlike distance-based models such as k-Nearest Neighbor (k-NN), Support Vector Machines (SVMs), and neural networks, which are highly sensitive to feature scales and require standardization or normalization, decision tree-based models are able to natively handle features with varying ranges without rescaling. In addition, normalizing GPS signal data can sometimes disrupt the intrinsic relationships between features. For instance, C/N\({}_{0}\), which reflects signal strength, directly affects the accuracy of pseudorange measurements.  
To handle class imbalance, Ref. [35] applied mode imputation by replacing missing values with the most frequent occurrence. However, this method risks over-representing certain values and introducing bias. Therefore, tree-based machine learning algorithms offer  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Mission** & **Modality** & **Occurrence** \\ \hline Mission 1 & 0.020000000186264 & 64613 \\  & 0.0199999999604187 & 30414 \\ \hline Mission 2 & 0.0199999999895226 & 42488 \\  & 0.020000000186264 & 23905 \\ \hline Mission 3 & 0.020000000186264 & 191888 \\  & 0.0199999999604187 & 90368 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Distribution of DELTA_RX for the three missions.  
Figure 2: Dataset shapes before and after conversion.  
built-in mechanisms to highlight informative features through feature importance measures and incorporate class weighting to address class imbalance. This strategy preserves the original feature scales and their relationships while maintaining the integrity of the data.  
In our study, to counteract the threat of GPS spoofing attacks, we investigate tree-based models, namely Decision Tree, Random Forest, and XGBoost, implemented using the commands from sklearn.tree import DecisionTreeClassifier, from sklearn.ensemble import RandomForestClassifier, and from xgboost import XGBClassifier, of the Python library Scikit-learn [38].
## 2 Research Background
### Underwater Localization Survey
Moreover, it is not always feasible to deploy anchor nodes at the sea floor, especially for deep ocean environments [14]. On the other hand, existing 3D underwater localization schemes require non-coplanar anchor nodes to uniquely localize a sensor, which implies that at least one anchor with position information needs to be underwater. The drawback of latest deployment is either infeasible or cost-prohibitive [15].
# Experimental Investigation on Effective Distances of Acoustic Emission in Concrete Structures
## 4 Discussion
In the monitoring system, the above result implies that if the installation distance between the AE sensors exceeds the effective distance, the collected AE signal cannot provide reliable damage information of the structure. That is, the installation distance of the sensor should be appropriately considered based on the effective distance in the monitoring system. The result of the experimental data is specifically based on the representative example and, as such, it should be applied according to the specific cases considering environments, individual parameters of the AE event, and materials of the structure. Therefore, the suggested concept of effective distance is useful in determining the sensor spacing and array in a field monitoring system. For greater improvement in AE monitoring system applicability, further studies will be needed to develop filtering techniques of the AE signal noise. Furthermore, it is necessary to investigate the method for evaluating structure damage based on AE parameters.
# Adaptive Model Output Following Control for a Networked Thermostat +
## 5 Simulation and Experimental Results
### Experimental Results  
In experiments, the feedback gain \(k_{e}\) is set to 0.1, and the reference model does not change. The reference input varies as follows:  
\[u_{m}(t)=\left\{\begin{array}{ll}4,\ 0\ \mathrm{s}<t<1520\ \mathrm{s}\\ 4+2\sin\left(\frac{\pi t-1520\pi}{1140}\right),\ 1520\ \mathrm{s}\leq t<3800\ \mathrm{s}\\ 3,\ 3800\ \mathrm{s}\leq t<6650\ \mathrm{s}\end{array}\right.. \tag{61}\]  
Figure 12 shows the parameter estimation results. Let \(a_{1}=1\); the other two parameters are shown in Figure 13. They certify that the NCS model is time-varying. When the reference input does not change, the estimated parameters change slowly. While \(u_{m}(t)\) changes as the sine function, the parameter estimation process can not converge.  
Figure 11: Parameter estimation of the proposed method for time-varying NCSs.  
Figure 10: Simulation results of the proposed method for time-varying NCSs.  
The tracking performance is shown in Figure 14. In the beginning, parameter estimation does not converge, and the maximum abstract error is 1.88 \({}^{\circ}\)C. From 300 to 1520 s, the system output becomes steady, and the steady-state error is 0.2 \({}^{\circ}\)C. When the reference input changes as the sine function from 1520 to 3800 s, the maximum tracking error is 0.8 \({}^{\circ}\)C. In the last period, the steady-state error is 0.16 \({}^{\circ}\)C. The experimental results show that the proposed methods can ensure the tracking performance for time-varying NCSs.  
When the reference input changes as follows:  
\[u_{m}(t)=\left\{\begin{array}{ll}5,\ 0\ \mathrm{s}<t<1140\ \mathrm{s}\\ 4+2\sin(\frac{\pi t-1140\pi}{1140}),\ 1140\ \mathrm{s}\leq t<3420\ \mathrm{s}\\ 2,\ 3420\ \mathrm{s}\leq t<4408\ \mathrm{s}\end{array}\right., \tag{62}\]  
the tracking performance is shown in Figure 15. Compared with the previous experiment results, the system performance is similar to that. The maximum abstract error is 3.08 \({}^{\circ}\)C in the beginning, while the steady-state error is 0.23 \({}^{\circ}\)C from 400 to 1140 s. When the reference  
Figure 12: Experimental parameter estimation of the proposed method.  
Figure 13: Parameter estimation when \(a_{1}\) becomes 1.  
input varies from 1140 to 3420 s, the maximum tracking error is 0.83 \({}^{\circ}\)C. In the last period, the steady-state error is 0.08 \({}^{\circ}\)C.
## 3 Thermal Buckling Analysis and Optimization
### Optimization Procedure  
The flowchart of the optimization procedure of the ITPS is shown in Figure 8. The procedures are presented in the following:  
First of all, a corrugated sandwich-structure ITPS was selected as a reference configuration. By simplifying the geometry of the initial ITPS, a 2D model was built for thermo-mechanical coupling analysis and a 3D model was built for thermal buckling  
Figure 8: Flowchart of the optimization procedure of ITPS.  
analysis. Temperature distribution versus time was obtained. These nodal temperature data were processed to determine the maximum TFS and BFS temperature. In the following topology optimization process, temperature boundary conditions were determined referring to these two temperatures.  
Secondly, 2D topology optimization was conducted to obtain the ideal configuration of the ITPS core section. In order to achieve the most reasonable design in this process, extreme service condition, including the maximum service temperature and the maximum aerodynamic load during flight, was employed. In topology optimization, the objective function was calculated as follows: (1) Q was calculated under thermal analysis; (2) W was obtained from the thermo-mechanical analysis; and (3) objective function value was calculated according to Equation (14). The globally convergent method of moving asymptotes (GCMMA) was employed to establish an optimization solution. The optimization is considered converged when the maximum difference of material density between two iterations is less than 0.01. The optimal 2D core configuration can be obtained in this part.  
Subsequently, the 3D thermal buckling analysis model was created. The core of the new ITPS design was inspired by the aforementioned 2D optimal configuration. Two sensible structures were built and analyzed. These innovative designs show better thermal protection and load-bearing capacities; however, they are at risk of buckling. Several geometric parameters were selected as variables, and parametric models were built. From the thermal buckling analysis, the smallest buckling eigenvalues at different re-entry times were obtained. According to Equation (15), the objective function value can be calculated. In order to maximize the smallest eigenvalues, variables were altered until convergence. The optimal 3D ITPS design can be achieved in this part.  
In summary, the core configuration was obtained from topology optimization firstly. Then it was applied to 3D structures and was further optimized to avoid the risk of buckling. By using this optimization strategy, innovative ITPS concepts with better thermal and mechanical performance can be achieved without weight cost.
## 5 Rain Attenuation in Slant Paths to Geostationary Satellites: The Synthetic Storm Technique  
In this section, as an example of the possible applications of the conversion of \(R_{10}(t)\) into \(R(t)\), we use theory to estimate the rain attenuation in slant paths to geostationary satellites by using a very reliable tool available today, namely, the synthetic storm technique (SST), which needs as input 1 min rain rate time series [1]. We first briefly recall the SST theory; then, we apply it to the sites in Table 1 by simulating, at each site, reliable rain attenuation time series in \(35.5^{\circ}\) slant paths to a geostationary satellite with a 20.7 GHz carrier, representing circular polarization of the electromagnetic wave.
## 4 Proposed Algorithm
### Uniqueness Score with Local Search Algorithm (ULA)
#### 4.4.1 Camera Placement with Uniqueness Score  
In the proposed ULA algorithm, the seed solution for local search is generated by placing cameras based on their uniqueness scores. If a camera placement has a lower uniqueness score than other camera placements, that placement is characterized as lacking cost-effectiveness because the grid points of the target that are covered by the camera placement tend to be covered by other cameras that have lower costs. The uniqueness score of target points and of a camera placement are defined as follows:  
\[u_{o}=\frac{n_{s}-\sum_{s}a_{s,o}}{n_{s}} \tag{11}\]  
\[US_{s}=\frac{\sum_{o}(a_{s,o}+\alpha\times u_{o})}{c_{s}} \tag{12}\]  
First, the uniqueness of target points (\(u_{o}\)) in Equation (11) is calculated for all target points. \(s\) and \(o\) represent the camera placement in the visibility matrix and the target grid point, respectively. \(n_{s}\) denotes the total number of camera placements \(s\) in the visibility matrix. In Equation (12), \(a_{s,o}\) indicates whether the camera placement \(s\) can cover the target point \(o\), which is not covered by selected camera placements in the previous steps. If camera placement \(s\) can cover target point \(o\) uncovered by the selected camera placements, \(a_{s,o}=1\); otherwise, \(a_{s,o}=0\).  
The uniqueness score of the camera placement \(s\) is composed of the weighted sum of the uniqueness of target points and \(a_{s,o}\). \(\alpha\) and \(c_{s}\) denote the uniqueness weight and the installation cost of camera placement \(s\), respectively. Camera placements in the visibility matrix are allocated in a non-increasing order of the total uniqueness score (\(US_{s}\)). When a camera placement is selected, the uniqueness scores of the placements of unselected cameras are updated based on the previous results of selection.  
Figure 7: Overall local search algorithm (ULA) framework.
# Intelligent Data-Enabled Task Offloading for Vehicular Fog Computing
## 4 Proposed Technique
### Cooperative Computing Vehicle Selection and Task Offloading  
As the source vehicle offloads part of the task to a fog RSU, the remaining part of the task is offloaded to a cooperative computing vehicle, which is selected using an ANN-based prediction algorithm. The key idea of the second part of the proposed technique is to predict which neighborhood vehicles will have long-term connectivity with the source vehicle and also have low computation available time (which means that the cooperative vehicle's CPU will be available sooner). When the source vehicle \(v_{s}\) has a task to offload, it will request an RSU to nominate the most suitable cooperative computing vehicle for task offloading.  
To find the optimal cooperative vehicle, we utilize two key metrics. The first metric is known as link connectivity time, which calculates the time for which the two vehicles (moving either in the same direction or opposite directions) will remain connected [36; 37] and is calculated using the following equation:  
\[T_{i,j}^{LLT}=\frac{-\Delta s_{i,j}\Delta d_{i,j}+|\Delta s_{i,j}|R}{(\Delta s _{i,j})^{2}} \tag{5}\]  
where \(\Delta s_{i,j}\) is the relative speed, \(\Delta d_{i,j}\) is the relative distance between the two vehicles and \(R\) is the communication range dictated by channel conditions.  
Figure 2: Flow chart of the proposed technique.  
The second metric used to find the optimal cooperative computing vehicle is the computation available time, which was already explained in Section 3. Each RSU gathers traffic information for all vehicles within its communication range.  
Once a request from source vehicle \(v_{s}\) to find the optimal cooperative computing vehicle is received at the RSU, it selects all \(j\) neighborhood vehicles of \(v_{s}\) that have a \(T_{s,j}^{L\Gamma}\) greater than a selected threshold. This threshold is selected based on the time taken for the task to be computed and received back at \(v_{s}\). The rationale for choosing such a threshold is to make sure that the source vehicle and cooperative computing vehicle remain connected once the task is to be transmitted back to the source vehicle.  
After the list of the \(j\) potential cooperative computing vehicles is finalized, the RSU then sorts them according to their computing available time and selects the cooperative computing vehicle with the lowest computing available time. The task is then offloaded to the selected cooperative computing vehicle.
# Singing Transcription from Polyphonic Music Using Melody Contour Filtering
## 2 Melody Extraction
### Singing Voice Detection  
After the construction and processing of the salience graph, we filtered out spectral peak in each frame to find the singing voice. We applied edge searching [31], which is widely used in image recognition to find potential contours. Two types of characteristic--length and energy--can be used to determine a melody.  
In the reconstructed salience graph, the frequency and peak energy correspond to a MIDI pitch and instantaneous amplitude, respectively. Each frame contains multiple peaks as valid information. Two problems are still present in melody extraction: voice detection and octave error [17]. At this time, the frequency of the accompaniment, the number of singing voices, and many other interference sources exist in the overall graph. In contrast to automatic singing transcription from a monophonic signal [32], the presence of an accompaniment presents a huge challenge. The salience spectrum constructed in the previous section highlights the features and quantifies the parameters more accurately; thus, a solution to this dilemma is presented.  
On the basis of the salience graph, we first we calculate the energy mean \(\mu\) and the standard deviation \(\sigma\) of the peak points in the whole graph.  
\[\sigma=\sqrt{\frac{\sum_{i=1}^{n}(S_{i}-\mu)^{2}}{n}} \tag{10}\]  
We calculate the threshold value of the human voice as \(v=\mu-\theta\sigma\), where \(\theta=0.5\). The maximum energy of peak points in frames \(S^{+}\) less than \(v\) will be recorded as non-voice frames \(S^{-}\). Frame-level energy filtering seems to be the simplest and most efficient implementation in singing voice filtering.  
Similarly, we filter the peak point \(S<\mu S^{+}\) in each frame and take the smaller parameter \(\mu=0.3\). To some extent, this step is beneficial for removing the smaller values and reducing excessively long contour disturbances in edge searching. The interference caused to the new contour by the last sound of the previous one is blocked by the discontinuity point in the frame. This kind of interference is a problem, as the long continuation of the previous contour may overlay the new contour. Using the features of the edge searching algorithm, it is not possible to determine a highly continuous contour profile from global analysis. Thus, the study of the breakpoint is a critical step for our entire experiment. In the following sections, we also propose new algorithms to solve the problem of excessively long contours.
# Research on Progress of Blockchain Consensus Algorithm:
## 3 Blockchain Consensus Algorithm
### Byzantine Fault-Tolerant Consensus Algorithm
#### 3.3.4 DPoS Algorithm  
The core idea of the DPoS algorithm, namely the Delegated Proof of Stake Algorithm,is similar to the representative election system. The number of currencies held by each node in the system is the basis for the number of votes allocated to it. Through voting, nodes that they consider to be more credible are elected as the decision makers in the consensus process. According to the number of votes obtained, multiple decision makers are allocated,  
Figure 8: The working principle of the PoS algorithm.  
and decision makers take turns to obtain the accounting rights of the block. Every node in the system may become a decision maker. If the current decision maker violates the blockchain protocol, the decision-making identity will be cancelled. The system will allocate new decision makers to join the team of decision makers. Through the multi-decision makers mechanism, the accounting rights will not be too concentrated on a single node, which can prevent excessive centralization. The working principle of the DPoS algorithm is shown in Figure 9.  
The DPoS algorithm combines the advantages of the PoS and PoW algorithms, respectively. Based on the multi-decision maker mechanism with accounting right in turn, the communication speed between nodes in the DPoS consensus is faster, and the nodes can quickly complete block packaging, broadcasting, and verification, and significantly improve the system transaction throughput. DPoS does not rely on computing resources and accordingly reduces energy consumption. Due to the election of decision makers, the transaction delay of the system is reduced, and the transaction processing speed has been significantly improved. At the same time, it can accommodate a large number of nodes to enter and exit the blockchain system freely, and the change of the node size will not affect the performance of the system, or the impact will be small, and the scalability is strong. The disadvantage of the DPoS algorithm is that it relies too much on voting rules. Poor voting rules will not only reduce the enthusiasm of participating nodes but also are not conducive to the realization of the decentralization of the entire system. Therefore, in 2020, [PERSON] (2020) proposed the VRF consensus algorithm, which is based on the relatively highly centralized DPoS algorithm and adds virtual nodes to increase the uncertainty in the election process. This algorithm not only retains the characteristics of DPoS's high efficiency and low energy consumption but also improves the degree of decentralization.
## 3 Sensor Technologies in Chemical and Elemental Detection
### Data Acquisition Methods in Spectral Imaging: Principles and Instrumentation  
Spectrometry, multispectral imaging, and hyperspectral imaging are distinct yet complementary spectral measurement techniques used for biochemical trait detection. Spectrometry typically involves point measurements, offering high spectral resolution and precision but lacking spatial context. In contrast, multispectral and hyperspectral imaging techniques capture spatially resolved spectral data. Multispectral imaging captures data in selected discrete bands, providing faster acquisition speed, lower cost, and simplicity, but at the expense of reduced spectral resolution. Hyperspectral imaging, on the other hand, acquires contiguous spectral data across many narrow bands, enabling detailed biochemical characterization but resulting in higher costs, increased complexity, and larger data volumes. Table 1 below summarizes the key differences among these techniques, highlighting their acquisition principles, advantages, and limitations to guide practical decision making and methodological selection.
# Research on High-Performance Fourier Transform Algorithms Based on the NPU
## 3 Background
### Architecture of a Typical NPU  
The typical NPU architecture is equipped with specialized hardware units that can directly perform matrix or vector multiplication and addition to accelerate neural network computations [6]. Take the Huawei Atlas200 AI accelerator module as an example; its built-in AI Core adopts the DaVinci architecture, as shown in Figure 1[29].  
The AI Core is the primary hardware for computational power in NPUs, which contains a cube unit, vector unit, and scalar unit. The scalar unit is responsible for various scalar data operations and handles logical tasks for loop control and branch decisions in operators. The cube unit and vector unit are well-suited for computationally intense tasks involving matrix and vector operations. Specifically, each execution of the cube unit completes matrix multiplication on two 16 \(\times\) 16 matrices of the float16 type. Each execution of the vector unit can simultaneously perform operations such as multiplication, addition, or subtraction on two 128-length vectors of the float16 type.