esa-sceva/satcom-chunk-collection huggingface dataset:
# Exact Method for Generating Strategy-Solvable Sudoku Clues
## 4 Our Method
### Overview  
The key of our method is to encode a given SSC instance to an equivalent constraint satisfaction problem (CSP) instance and then to solve it using a generic CSP solver. For the CSP encoding, we introduce variables for representing states of grid and other auxiliary variables. Using such variables, we represent constraints for states, state transitions, particular strategies, and so on.  
Any variable assignment that satisfies all such constraints substantially corresponds to a sequence of states \(q_{0},\ldots,q_{k}\) with \(q_{0}\) strategy-solvable, as characterized in Definition 1. Here, the initial state \(q_{0}\) represents an assignment of digits to clue cells and the whole sequence represents a history of evolving grids and applied strategies, which eventually reaches a Sudoku solution. Hence, by applying a CSP solver to the encoded constraints, we eventually obtain strategy-solvable Sudoku clues, if they exist; otherwise, we eventually recognize the strategy-unsolvability of the clue cells, that is, no assignment of digits to the clue cells is strategy-solvable.  
The details of our method are explained as follows. At first, we present constraints for a state transition framework, which is almost independent of particular strategies. As stated above, we introduce variables for representing states of grid and auxiliary variables. We then represent various constraints using such variables. After that, we shift to particular strategies. We present constraints for number placements and constraints for number removals. We finally provide the whole picture of our method as well as some remarks.
# Short-Term Passenger Flow Prediction of Urban Rail Transit Based on a Combined Deep Learning Model
###### Abstract  
It is difficult for a single model to simultaneously capture the nonlinear, correlation, and periodicity of data series in the passenger flow prediction of urban rail transit (URT). To better predict the short-term passenger flow of URT, based on the long short-term memory network (LSTM) model, a deep learning model prediction method combining the time convolution network (TCN) and the long short-term memory network (LSTM) based on machine learning is proposed. The model couples the external factors such as date attributes, weather conditions, and air quality, to improve the overall prediction performance and solve the difficulty of accurate prediction due to the large fluctuation and randomness of short-term passenger flow in rail transit. Using the swiping data and related weather information of some stations of Chongqing Rail Transit Line 3, the TCN-LSTM model is verified by an example, and the prediction results of the single LSTM model are given for comparison. The results show that the TCN-LSTM model can better predict the passenger flow characteristics of different stations at different times. Compared with the single LSTM model, the TCN-LSTM model has better prediction accuracy and data generalization ability.  
short-term passenger flow of urban rail transit; passenger flow prediction; deep learning; long short-term memory network; temporal convolutional network +
Footnote †: journal: selected
# Local Back-Propagation for Forward-Forward Networks: Independent Unsupervised Layer-Wise Training
## 2 Related Works
### Federated Learning and Memory Efficiency  
Federated learning on edge devices introduces challenges related to memory limitations and hardware heterogeneity. Traditional BP-based training requires the full model to reside in memory throughout both forward and backward passes, which increases computational burden and limits scalability in low-resource environments.  
Ha et al. [27] have proposed layer-wise training strategies to address this issue. A layer-wise processor selection method for on-device learning allows each layer to be processed independently using either CPU or GPU depending on system latency. Their results show improved memory usage and up to 28% reduction in training latency.  
Inspired by this direction, the proposed LBP method performs unsupervised training in a strictly sequential, layer-wise manner. At each stage, only one layer is loaded into memory, trained using local objectives, and then released before proceeding to the next.  
Unlike BP, this approach requires no global gradient synchronization or simultaneous layer loading. As a result, LBP enables deep network training in memory-constrained environments, such as mobile or federated systems, with improved scalability and lower memory usage.
## 2 Materials and Methods
### Reflectance Measurements and Vegetation Indices
#### 2.2.3 Sensor Response Validation  
A dual-channel field spectrometer (Unispec-DC, PP-Systems, Amesbury, MA, USA) was used to validate the spectral response of the SRS-PRI sensors independently. Additionally, diurnal spectrometer measurements were collected in concert with cross-calibration data to confirm the effect and accuracy of mid-day vs. diurnal cross-calibration efforts. The Unispec-DC is a high precision spectrometer able to provide simultaneous measurements of radiance and irradiance, has a spectral range of 310-1100 nm, and a 3.3 nm FWHM. The upwelling/radiance was collected using a 2 m fiber optic cable (600 \(\upmu\)m HCS LOH, SMA-Custom Ferrule-100 mm, Uni-684, PP Systems) with a 9 mm FOV restrictor providing a modified FOV of 18\({}^{\circ}\). For the downwelling/irradiance, a similar 2 m fiber optic cable was used (600 \(\upmu\)m HCS LOH, SMA-Custom Ferrule-25 mm, Uni-686, PP Systems) with the addition of a cosine head (UNI435, PP Systems) allowing near 180\({}^{\circ}\) FOV.  
Validation experiments consisted of the sampling the diurnal response of a \(\sim\)1 m tall _Populus tremuloides_ seedling stand, simultaneously, with the field spectrometer and SRS sensors, throughout a 5-day period. The spectrometer's radiance fore-optic and SRS-PRI field-of-view pair were placed at matching height (0.37 m) above the canopy, producing a target footprint of about 1.0 m\({}^{2}\). The SRS-PRI sensors log every minute continuously throughout the diurnal cycle; while the spectrometer measurements were collected every minute for 10 min on the hour throughout the diurnal cycle.  
Spectrometer reflectance data was corrected using a 99% reflective white standard panel (Spectralon, Labsphere Inc.). Corrected reflectance was expressed as:  
\[\rho_{corrected}=\frac{\text{r}_{\text{target}}}{\text{i}_{\text{sky}}}\ \times\frac{\text{i}_{\text{sky}}}{\text{r}_{\text{standard}}} \tag{6}\]  
where \(\rho_{\text{corrected}}\) represents corrected reflectance. The first term (r\({}_{\text{target}}\)/i\({}_{\text{sky}}\)) represents the raw reflectance, expressed as a ratio of the upwelling radiance to the downwelling irradiance over the target. The second term (i\({}_{\text{sky}}\)/r\({}_{\text{standard}}\)) represents the cross-calibration value, calculated as a ratio of the downwelling irradiance to the radiance of the standard panel [44]. Corrected reflectance at 531 nm and 570 nm was used to derive a scaled photochemical reflectance index (sPRI). Corrected sPRI measurements from SRS-PRI sensors were compared to the spectrometer derived sPRI to validate the accuracy of these automated sensors.
# Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm
## 4 Trajectory Optimization
### Time Allocation  
The time allocation in polynomial optimization has a direct impact on the generated trajectories. In this paper, the total time \(T\) is calculated based on the preset velocity and the total length \(L\) of the initial trajectory generated according to the AP-JPS algorithm. In general, the allocation time of each trajectory is evenly distributed in proportion to the path length, but in order to weaken the degree of influence of the trajectory length on the allocation time and make the robot travel faster in the longer path and relatively slower in the trajectory with more inflection points to increase the safety of robot motion, the time allocation strategy used in this paper is shown in Equation (26).  
\[T_{i}=\frac{(L_{i}/L)^{\gamma}}{\sum\limits_{i=1}^{m}\left(L_{i}/L\right)^{ \gamma}}T \tag{26}\]  
where \(T_{i}\) denotes the time allocated to the \(i\)th trajectory, \(\gamma\in(0,1)\) denotes the time allocation coefficient, and \(L_{i}\) is the length of the \(i\)th trajectory. Once the time allocation is determined, the velocity, acceleration, jerk and other parameters of the trajectory can be obtained by calculation, and if the parameters do not satisfy the constraints, the time allocated to the segment is adjusted in a fixed proportion until the constraints are satisfied.
## 1 Introduction
Dock repair requires significant economic and time cost, while underwater cleaning can save 60% of dock repair cost [8]. In addition, it is estimated that a single underwater cleaning can improve fuel efficiency by 6%. For ships with an annual fuel consumption of more than 10 million tons, the annual energy-saving benefit of underwater cleaning by robots can reach 200 million CNY [35]. In terms of global shipping, underwater cleaning by robots can save 15 billion USD per year and reduce one billion tons of greenhouse gas emissions from fleets [8]. In order to improve the prediction accuracy and overcome these problems that the difficulty in obtaining fouling samples, testing all working conditions, showing obvious damage at low pressures, and defining target material constitutive models, this paper uses refractory bricks to simulate marine fouling as erosion target, and erosion depth as the output index of the prediction model. Erosion tests are carried out as the test scheme designed by the orthogonal table. Through statistical analysis of range and variance, the jet pressure, impact time and jet angle are three main factors and are used as the input variables of the prediction model. Based on the experimental data, an artificial virtual data generation method is used to increase the input data for the prediction model. Then, an improved optimization algorithm MAGA is proposed. After verifying the optimization effect of MAGA with four stand test functions, the initial thresholds and weights of the BP neural network are optimized by MAGA. Thereby, the erosion effect of the submerged low-pressure water jet under different working conditions is predicted. Finally, the optimized erosion prediction method can be applied to the formulation and control of the hull underwater cleaning scheme, which is an important part of the ship hull underwater cleaning robot project. This paper is organized as follows: Section 2 shows a test method based on orthogonal experimental design to obtain the data needed in the study, and uses a hybrid analysis based on range and variance to determine main factors affecting erosion results, so that the main factors are used as input variables  
Figure 1: Marine fouling adhering to the ship hull underwater. of the prediction model. Section 3 presents an improved optimization algorithm and validates the improved optimization effect of this algorithm with the certain set parameters. Section 4 describes the prediction model established by the input variables in Section 2, and uses the optimization algorithm in Section 3 to optimize the initial thresholds and weights of the network model. Section 5 discusses the prediction accuracy and reliability of prediction models, proving that the optimized erosion prediction method is feasible. Finally, Section 6 shows the results and applications of this paper with recommendations for future research.
# A Systematic Analysis of Real-World Energy Blockchain Initiatives
## 2 Background
### Potential Benefits  
The most obvious benefit of real-world energy blockchains is the democratisation of energy transactions, whereby blockchain technology may be used to remove intermediaries from the energy value chain and enable communities to execute transactions independently. These independent two-way markets should provide market participants (e.g., customers) with sufficient transaction transparency, security and reliability, which can be satisfied using the blockchain's immutable and decentralised architecture [10; 12; 14; 15]. Additionally, energy blockchains can reduce transaction costs and settlement times [10], substitute intermediaries with consensus models [10], and increase profitability and minimise costs for participants using sophisticated market intelligence (e.g., machine learning) [12].
## 4 Classification of Energy Efficient MAC Protocols for UWSNs
### Depth-Based Layering MAC (DL MAC)  
In UWSNs, depth-based layering MAC (DL-MAC) provides energy-saving and collision-free characteristics. DL-MAC addresses near-far effects as well as hidden/exposed terminal concerns in underwater networks, in addition to spatial temporal uncertainty [89]. The suggested innovative protocol employs layering and a distributed clustering algorithm to efficiently communicate data packet exchanges, while reducing the likelihood of data collisions inside a network, hence improving the network's overall energy efficiency [90; 91]. Table 4 depicts the summery of TDMA-based energy-efficient MAC protocols for UWSNs.
# Arbitrary Sampling Fourier Transform and Its Applications in Magnetic Field Forward Modeling
###### Abstract  
Numerical simulation and inversion imaging are essential in geophysics exploration. Fourier transform plays a vital role in geophysical numerical simulation and inversion imaging, especially in solving partial differential equations. This paper proposes an arbitrary sampling Fourier transform algorithm (AS-FT) based on quadratic interpolation of shape function. Its core idea is to discretize the Fourier transform integral into the sum of finite element integrals. The quadratic shape function represents the function change in each element, and then all element integrals are calculated and accumulated. In this way, the semi-analytical solution of the Fourier oscillation operator in each integral interval can be obtained, and the Fourier transform coefficient can be calculated in advance, so the algorithm has high calculation accuracy and efficiency. Based on the one-dimensional (1D) transform, the two-dimensional (2D) transform is realized by integrating the 1D Fourier transform twice, and the three-dimensional (3D) transform is realized by integrating the 1D Fourier transform three times. The algorithm can sample flexibly according to the distribution of integrated values. The correctness and efficiency of the algorithm are verified by Fourier transform pairs. The AS-FT algorithm is applied to the numerical simulation of magnetic anomalies. The accuracy and efficiency are compared with the standard Fast Fourier transform (standard-FFT) and Gauss Fast Fourier transform (Gauss-FFT). It shows that the AS-FT algorithm has no edge effects and has a higher computational speed. The AS-FT algorithm has good adaptability to continuous medium, weak magnetic catastrophe medium, and strong magnetic catastrophe medium. It can achieve the same as or even higher accuracy than Gauss-FFT through fewer sampling points. The AS-FT algorithm provides a new means for partial differential equation solution in geophysics. It successfully solves the boundary problems, which makes it an efficient and high-precision Fourier transform approach with promising applications. Therefore, the AS-FT algorithm has excellent advantages in solving partial differential equations, providing a new means for solving geophysical forward and inverse problems.  
arbitrary sample; Fourier transform; numerical simulation of magnetic anomalies; shape-function interpolation +
Footnote †: journal: Applied Sciences
# Effect of Processing on Volatile Organic Compounds Formation of Meat--Review
## 4 Thermal Processing
### Smoking
On the other hand, the use of maguey leaves (_Agave salmiana_) when cooking lamb produces volatile compounds from the group of aldehydes, terpen, and benzene [71]. An interesting correlation was observed when analyzing volatiles in commercially smoked chickens. Smoking with sawdust from fruit trees increases the phenolic content, while sugar lowers the amount of furans, which contribute to the sweet caramel aroma [72]. The addition of sugar, tea, and rice significantly influences the profile of aromatic compounds responsible for the smoked, bitter, and caramel aromas. The species and breed of animals are also factors that determine the formation of specific volatile compounds in smoked products [73]. This issue was investigated by [PERSON] et al. [74], who analyzed volatile compounds formed in smoked bacon obtained from two different crosses of pigs (white pig and black pig). The results of their work proved that smoked black pig products had fewer volatile compounds than those of the white pig. Among them, the authors detected aldehydes, ketones, alcohols, esters, hydrocarbons, acids, fourteen phenols, aromatic hydrocarbons, nitrogen-containing compounds, and furans. These differences are mainly due to the basic composition of meat, including the amount of fat, and differences in lipid and protein degradation in both non-enzymatic and enzymatic reactions. Even the element of the carcass will play a role in shaping the volatile compound profile. [PERSON] et al. [75] analyzed the aromatic content of chicken rings and skin after the sugar-smoking process. The higher content of substances reacting with thiobarbituric acid and the higher ratio of polyunsaturated fatty acids to saturated fatty acids caused that the skin was characterized by a higher content of volatile compounds than the breast. The volatile fingerprint results revealed that the heterocycles were a distinctive flavor developed during the smoking process of sugar. In contrast, hexanal, nonanal, furural, 5-methyl-2-furancarboxaldehyde, and 2-acetyl-5-methylfuran were the main volatile substances in chicken skin. This is due to the lipid oxidation and caramelization reactions. Pre-treatment, such as pickling or salting before smoking, has a significant impact on the aromatic compounds produced. Among these pre-treatments is the partial replacement of NaCl by chloride salts during the treatment of Cecina deer. Such action significantly reduces the amount of acids and furans in the final product profile [76].
## Acknowledgments  
The author would like to thank the four anonymous reviewers for their very constructive remarks.  
\begin{table}
\begin{tabular}{c c c} \hline
**Section** & **Method (General)** & **Advantages** & **Achieved** \\
3.1 & Interferometric & Enables better differentiation of slightly damaged and & _ca._ 45\%–49\% \\  & coherence & undamaged areas. & _ca._ 45\%–49\% \\ \hline
3.2 & SAR intensity & More sensitive to larger changes (stronger damages) on the ground. & \\  & correlation & Provides still useful information at spatial and temporal baselines, & _ca._ 41\%–55\% \\  & & which are too large for useful coherence application. & \\
3.3 & Combination of & Strong increase of the damage assessment accuracy compared to & \\  & coherence and & the application of only interferometric coherence or intensity & _ca._ 52\%–60\% \\  & (and SAR backscatter) & correlation, respectively. & \\ \hline
3.4 & Use of additional data & Additional increase of the damage assessment accuracy (compared & \\  & (e.g., optical imagery & presentation of the damage level at meaningful distribution (e.g., & _ca._ 77\%–88\% \\  & and GIS data) & city parcel boundaries, building blocks, _etc._) enables the generation & \\  & & of damage maps more suited to the user. & \\ \hline \end{tabular}
\end{table}
Table 5: Advantages of the damage assessment approaches reviewed in Sections 3.1–3.4.
## 3 Review of Energy Efficiency in WSNs
### Lifetime Metric and Energy Efficiency in WSN
The energy hole issue in sensor nodes has been identified in the situation where the nodes are located proximately to the sink [62]. The failure of nodes is caused by undistributed network loads [63]. Based on forwarding and routing selection data, the entire network employs data forwarding and routing selection techniques. Therefore, it is important to maximize the efficiency of the energy sources. In order to achieve this, energy use solutions are introduced through algorithms that help close the power consumption gap of moving sensors. These algorithms also use unbiased energy, and by constructing cooperative clusters among themselves, these mitigation models help to reduce network breakdown and stability. These models strike a compromise between load provision and energy efficiency. Finding of the review. Various alternative methods for creating hierarchical clustering protocols based on the application's needs have been discussed in the literature. Important aspects such as energy management and total network life should be considered in the protocols. The majority of reviews in WNSs routing protocols focused on alternative methods to hierarchical clustering. Besides that, constraints in building clusters and CH selection are also discussed. The benefits and drawbacks of each protocol were presented. The hierarchical grouping was categorized as grid-based and cluster-based which will be discussed in the next section. Energy-efficient composite event detection is intended to improve energy efficiency while handling massive amounts of data [64; 65]. Until recently, the majority of current techniques had been applied to composite event identification [66; 67], consuming a significant amount of energy. The effectiveness of WSNs can be measured using a variety of criteria [68]. Connectivity, SN deployment type, latency, network area lifetime, scalability, packet delivery ratio, reliability, energy conservation, and energy utilization cost are some of the parameters that could be configured [66].