esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Study Area  
We used a dataset composed of two years of extreme climate behavior: heavy rainfall in 2009 and extreme drought in 2012. We chose 2009 and 2012 based on [18], which characterizes the climatic variability of the region. The study indicates that the annual rainfall totals in the region is typical of a tropical-arid climate, ranging between 400 mm and 800 mm. Considering a climatological perspective with 30 years of observation, 2009 was the rainiest year, while 2012 was the driest. To calibrate the SITE model, we used a set of two years (2014 and 2015) of EC data. The EC system was installed in a fragment of the Caatinga biome (Figure 1), located in the Serido Ecological Station (ESEC-Serido), where the exchanges of energy fluxes and CO\({}_{2}\) were systematically and uninterruptedly monitored for two consecutive years (2014-2015) [10].  
The monitoring was performed using a flux tower equipped with an EC system, in addition to instruments for measuring soil heat flux, shortwave, and longwave radiative components. The tower belongs to the INSA (National Institute of the Semiarid) and is part of the NOWCDCB network (National Observatory of Water and Carbon Dynamics in the Caatinga Biome). The tower is situated at the ESEC-Serido (6\({}^{\circ}\)34\({}^{\prime}\)2\({}^{\prime\prime}\) S, 37\({}^{\circ}\)15\({}^{\prime}\)05\({}^{\prime\prime}\) W 205 m above sea level) near the municipalities of Serra Negra do Norte and Caico, in the State of Rio Grande do Norte, Brazilian Semiarid (Figure 1).  
The climate in the region is semiarid, and the ESEC-Serido exhibits a distinctive type of caatinga characterized by its dry and sparse nature, with bushes and trees reaching heights of up to 2 m. The soils of the Serido region are formed on a crystalline shield and  
Figure 1: Geographical location of the micrometeorological tower installed in the ESEC-Serido, Brazil.  
have moderate natural fertility, especially in the alluvial lowlands. However, these soils are subjected to an irregular and limited distribution of rainfall. The vegetation develops on a shallow and stony soil, resulting in distinct physiognomies within the boundaries of the conservation unit. These variations are influenced by factors such the type of soil, water availability, and topography [36; 37].
# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 6 Applications and Policy Implications
### Evidence-Based Safety Interventions
#### 6.1.2 Infrastructure Modifications and Design Interventions  
Infrastructure modifications have been subject to rigorous evaluation with varying degrees of success. [PERSON] and [PERSON] [107] demonstrated the effectiveness of smart channel conversions for right-turn lanes, employing time-to-collision metrics and extreme value theory. Their finding of a 34% reduction in severe conflicts, though with limitations regarding merging conflicts, provides valuable guidance for intersection design policies.  
[PERSON] and [PERSON]'s [81] evaluation of toll plaza designs found that hybrid toll plaza result in 44.7% fewer crashes than traditional toll plaza, while all-electronic toll-collection systems achieve 72.6% fewer crashes. For hybrid systems, crash risk in diverge areas is 23% higher than in merge areas. These findings provide clear guidance for toll plaza design as they indicate that all-electronic toll plazas are significantly safer.  
[PERSON] and [PERSON] [85] modeling results found that roads with safety zones of 2 m or less resulted in 50% more run-off-road crashes, while strong curvature increases run-off-road crashes by three times compared with straight roads. Roadside barriers were found to reduce 50% of run-off-road crashes compared with roads with small safety zones. These specific percentages provide quantitative guidance for rural road design standards.  
However, not all infrastructure interventions prove effective. [PERSON] and [PERSON]'s [83] statistical analysis showed that ice warning signs were not a significant factor in reducing accident frequency or severity, indicating that this common safety measure may not provide the expected benefits and resources might be better allocated elsewhere.
## 4 Experimental Results and Discussion  
As we know, a well-designed equalizer is useful for resolving nonlinear issues and has been successfully applied in wireless communications. However, the selection of an error function is an important factor that affects the residual error of the blind equalizer; thus, the performance of the equalizer is different. In our experiment, we introduce some equalizers in our proposed schemes and compare their performance, such as the typical CMA equalizer with taps and the DD-LMS equalizer.
# A Flexible FPGA-Based Stochastic Decoder for 5G LDPC Codes
## 4 Proposed Architecture
### Routing Network
#### 4.3.5 Updater  
Using the values from the ROM1-location, this component transfers these \(W_{v}=30\) blocks of \(\eta_{v}=56\) bits back into the SCND at the proper location. Moreover, the ROM3-weight is utilized in this case to guarantee that only the most recent \(W_{v}\) values are written, removing any potential interference from non-\({}^{\prime}\)-\(1\)' entries or \"don't care\" data. As D-flip-flops are being used to implement the SCND, each layer can be written right away.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
**AAGS** & Average attitude/specular gate \\
**AARI** & Russian Arctic and Antarctic Research Institute \\
**AGC** & Automatic gain control \\
**AIS** & the global ship automatic identification system \\
**ALT** & Altimeter \\
**ASCAT** & Advanced Scatterometer \\
**CNSA** & the China National Space Administration \\
**COCTS** & Chinese Ocean Color and Temperature Scanner \\
**CZ 4** & Chang Zheng 4 (CZ meaning \"Long March\"), The Long March 4 is a Chinese three-stage, \\  & liquid-propellant orbital launch vehicles. \\
**CZI** & Coastal Zone Imager \\
**DCS** & the marine buoy measurement data collection system \\
**DTU** & the Technical University of Denmark \\
**ENVISAT** & European Space Agency Environmental Satellite \\
**ERS-1/2** & European Remote Sensing satellite-1/2 \\
**GDR** & Geophysical Data Records \\
**GEOS-3** & Geodynamics Experimental Ocean Satellite 3 \\
**HY-2A/B** & Haiyang \(-\) 2 A/B, the Chinese Marine Dynamic Environment Satellites, Haiyang meaning \\  & \"Marine\" \\
**IGDR** & Interim Geophysical Data Records \\
**KNN** & K-nearest-neighbor \\
**LIBSVM** & Lib-support vector machine \\
**MCT** & the model compatible tracker of altimeter \\
**NSOAS** & the Chinese National Satellite Ocean Application Service \\
**OCOG** & Offset Center of Gravity \\
**OSI-SAF** & the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean \\  & and Sea Ice Satellite Application Facility (OSI-SAF). \\
**PP** & Pulse peaking \\
**SAR** & Synthetic aperture radar \\
**SGDR** & Sensor Geophysical Data Records \\
**SMLE** & Suboptimal Maximum Likelihood Estimation methods \\
**SSMIS** & Special Sensor Microwave Imager/Sounder \\ \end{tabular}
## 3 Multi-Dimensional Fusion Feature-Enhanced Prediction Method
### Data Augmentation of Multi-Dimensional Feature Engineering
#### 3.1.5 Lag Feature Design  
In order to prevent data leakage and ensure that only historical data are used for calculation and integration in the process of feature construction, this framework adopts lag feature design. Through the lag processing of each feature, the model can only be predicted based on the past information in the training process, so as to avoid misleading the model training due to the early intervention of future information, thus ensuring the timing integrity and reliability of the prediction.  
For example, for a certain characteristic \(X\) (such as closing price, trading volume, technical indicators and volatility indicators, etc.), the lag characteristic \(X_{lag\_i}\) of the \(lag\_i}\) period (\(i\) is a positive integer) is calculated, and the calculation formula is as follows:\[X_{lag,i}(t)=X(t-i). \tag{16}\]  
That is, the value of the feature at time \(t-i\) is taken as the lag eigenvalue at time \(t\). By setting different lag orders (\(lag\_i\), \(i\) is the lag order), each feature is lagged. These lag characteristics can offer the evolving trends of past prices and relevant data to the model, aiding it in more effectively grasping the temporal dependencies within price sequences and enhancing the precision of forecasts.
# Variation in Crop Yield Depending on the Tractor Tire Contact Area and Clay Loam Soil Moisture
###### Abstract  
As the demand for productivity increases, machines are becoming heavier, and large coverage implements cause the slippage of tractor driving wheels, which initiate soil compaction and displacement. Sources indicate that compaction has an adverse impact on crop growth and grain yield. However, heavy machinery under draft is not always to blame for yield losses. The aim of the six-year research was to establish the relationship between grain yield and the slippage of tractors on heavy clay loam soil under two different moisture conditions. The spring cereals were grown as peas, spring barley, and spring wheat. Each treatment plot was compressed from track-to-track separately, altering the tire contact area on each treatment plot. Different tire contact areas were achieved by adjusting tire pressure and adding double wheels, while simulating a constant draft. Wheeled plots with the smallest tire contact area produced 9% less crop yield, whereas wheeling with the largest tire contact area showed an 8% increase in crop yield compared to the control plot without compaction in the dependency of soil moisture. Wheeling is like rollers breaking down clay loam soil clods and slightly compacting the toposoil, helping to retain moisture, leading to increased yields.  
Keywords:tire contact area; soil compaction; soil displacement; slippage +
Footnote †: journal: Applied sciences
## 2 The Geometrically Exact Beam Model
### Beam Statics and Its Linearization
\right] \tag{15}\]  
where \(\tau\) is the shear coefficient. The notation \(A\) in Equation (15) denotes the area of the cross-section and four constant section properties are calculated from the following formulas:  
\[I_{22}=\int_{A}\xi_{2}^{2}\mathrm{d}A,\qquad I_{33}=\int_{A}\xi_{3}^{2} \mathrm{d}A,\qquad I_{32}=\int_{A}\xi_{2}\xi_{3}\mathrm{d}A,\qquad I_{0}=I_{22 }+I_{33} \tag{16}\]  
The stress measures can be given by:  
\[\mathbf{\tilde{S}}=\mathbf{DE}=\left[ \begin{array}{cc}\mathbf{N}^{\mathrm{T }}&\mathbf{M}^{\mathrm{T}}\end{array} \right]^{\mathrm{T}} \tag{17}\]  
in which the stress resultant \(\mathbf{N}=\left[ \begin{array}{cc}\mathbf{N}_{1},\ \mathbf{N}_{2},\ \mathbf{N}_{3} \end{array} \right]^{\mathrm{T}}\) represents one normal force and two shear forces in local frame; the stress couple \(\mathbf{M}=\left[ \begin{array}{cc}\mathbf{M}_{1},\ \mathbf{M}_{2},\ \mathbf{M}_{3} \end{array} \right]^{\mathrm{T}}\) includes one torsional moment and two bending moments in local frame. According to Equation (9) the variation of the strain measures can be written as:  
\[\mathbf{\tilde{\delta E}}=\mathbf{\delta\varepsilon}=\left[ \begin{array}{ cc}\mathbf{\delta\gamma}\\ \mathbf{\delta\kappa}\end{array} \right]=\mathbf{B}\mathbf{\delta h} \tag{18}\]  
with \(\mathbf{\delta h}=\left[ \begin{array}{cc}\mathbf{\delta x}^{\mathrm{T}},\ \mathbf{\delta\Theta}^{\mathrm{T}}\end{array} \right]^{\mathrm{T}}\) and the strain operator:  
\[\mathbf{B}=\left[ \begin{array}{cc}\mathbf{R}^{\mathrm{T}}\frac{\partial}{ \partial\xi_{1}}&\widetilde{\mathbf{R}^{\mathrm{T}}\mathbf{x}^{\prime}}\\ 0&\frac{\partial}{\partial\xi_{1}}+\widetilde{\mathbf{\kappa}}\end{array} \right] \tag{19}\]  
case of small deformation, the geometric stiffness is unnecessary in general. However, numerical examples show that the application of geometric stiffness can improve the convergence of Newton iterations for cases of both small deformation and finite deformation.
## 4 Evaluation Experiment: Shadow and Caustics
### Main Experiment
#### 4.2.1 Experimental Environment  
The virtual transparent objects and floor patterns used in the experiment are shown in Figure 3. A glass sphere and diamond were used as the transparent objects, and wood grain and stone tile patterns were prepared as floor patterns. Three virtual transparent object representations were used: (a) no phenomenon, (b) caustics, and (c) shadow + caustics. Because they had a complex and realistic pattern, stone tiles were chosen as one of the patterns to assess the realism of representing the transparent objects within a real-like environment. As in the preliminary experiment, the real floors were rendered with the reflection reduced as much as possible. The methods used to render virtual transparent objects and generating shadows and caustics are shown in Section 3.  
The experimental environment is shown in Figure 5. The HoloLens was again used as the OST-HMD in this experiment. Twenty-three participants, all in their twenties, were involved (21 men, two women). All participants had been involved in the preliminary experiment. The lighting conditions were the same as those of a typical office room. While observing, the participants were allowed to move their head freely within the natural range of motion in a standing posture.  
In each experiment, the participant donned the HoloLens; they were given an answer sheet and made to stand approximately 1500 mm from the virtual objects. To inform the participants of the location of each virtual object, a white dummy cube appeared at the same location at which the sphere/diamond would appear. After confirming that the participant were able to perceive the dummy correctly, two pairs of the three representations in (a), (b), and (c) in Figure 3. The stimuli were presented to each participant in a different order to account for the order effect. The following procedure was used to compare each pair of combinations. The first object was displayed for 3.0 s and then removed, 2.0 s, the second object was displayed for 3.0 s. The objects were displayed for 3.0 s to allow the participants' eyes to adjust to the shadow inducer and enabled them to produce a judgment intuitively without deliberation.  
After observing each pair, the participant answered the question \"which object looked like a real dense glass sphere/diamond placed on a flat surface?\" To indicate which item appeared more convincing, the participant answered either \"the former\" or \"the latter.\" From the start of the observation to the answering of each set of questions, each participant performed a total of six sets of observations covering all object/background combinations. Each observation was followed by a 30 s interval that included time to answer the question. After the experiment, we asked the participants for their comments.  
Figure 5: Experimental environment.
# A SiCN Thin Film Thermistor Based on DVB Modified Polymer-Derived Ceramics
###### Abstract  
Carbon-rich SiCN ceramics were prepared by divinylbenzene (DVB)-modified polysilazane (PSN2), and a high-conductivity SiCN thin film sensor suitable for medium-low temperature sensing was fabricated. The modified liquid precursors were patterned by direct ink writing to produce SiCN resistive grids with line widths of several hundreds of micrometers and thicknesses of several micrometers. The introduction of DVB not only increases the critical thickness of SiCN ceramics several times, but also significantly improves the conductivity of SiCN, making it meet the conductivity requirements of sensing applications in the mid-low temperature range. The electrical conductivity and microstructure of DVB-modified SiCN ceramics were studied in detail. In the temperature range of 30-400 \({}^{\circ}\)C, the temperature resistance performance of DVB modified SiCN resistance grid was measured. The SiCN ceramics with low DVB content not only have excellent electrical conductivity, but also have good oxidation resistance.  
polymer-derived ceramics; thermistor; SiCN; conductivity +
Footnote †: journal:  
strategy is to fill the PDC with conductive particles that exceed the penetration threshold [6; 7]. This strategy not only improves the electrical conductivity of PDCs film but also improves their critical thickness. However, the dispersibility and stability of solid-phase particles in the precursor solution are difficult to guarantee. Therefore, liquid-phase modified PDCs would be preferred. Divinylbenzene (DVB), as a widely used precursor of carbon sources, has been used to improve the electrical conductivity of PDCs [19; 20; 21]. For example, the conductivity of DVB-modified PDC-SiCN is improved by 5 orders of magnitude [11]. However, currently, DVB-modified PDC devices are usually millimeter-scale and difficult to integrate with structural components, which significantly decreased the sensitivity and response time of the sensor [11]. The advantages of PDCs in additive manufacturing and controlled molding have not been well utilized.  
In this study, the electrical conductivity of PDC-SiCN was increased by adding a DVB carbon source precursor to a commercially available polysilazane (PSN). An in-situ integrated thin-film temperature sensor was successfully fabricated via DIW platform based on the Weissenberg effect and tested from room temperature (30 \({}^{\circ}\)C) to 400 \({}^{\circ}\)C, expanding the application of PDC sensors in the mid-low temperature range.
# ChaMTeC: CHAnnel Mixing and Temporal Convolution Network for Time-Series Anomaly Detection
###### Abstract  
Time-series anomaly detection is a critical task in various domains, including industrial control systems, where the early detection of unusual patterns can prevent system failures and ensure operational reliability. This paper introduces ChaMTeC (CHAnnel Mixing and TEmporal Convolution Network), a novel deep learning framework designed for time-series anomaly detection. ChaMTeC integrates an inverted embedding strategy, multi-layer temporal encoding, and a Mean Squared Error (MSE)-based feedback mechanism with dynamic thresholding to enhance anomaly detection performance. The framework is particularly tailored for industrial environments, where anomalies are rare and often subtle, making detection challenging. We evaluate ChaMTeC on six publicly available datasets and a newly introduced dataset, WaterLog, which is specifically designed to reflect real-world industrial control system scenarios with reduced anomaly rates. The experimental results demonstrate that ChaMTeC outperforms state-of-the-art models, achieving superior performance in terms of F1-CPA (Coverage-based Point-Adjusted F1) scores. The WaterLog dataset, which has been made publicly available, provides a more realistic benchmark for evaluating anomaly detection systems in industrial settings, addressing the limitations of existing datasets that often contain frequent and densely packed anomalies. Our findings highlight the effectiveness of combining channel-mixing techniques with temporal convolutional networks and dynamic thresholding for detecting anomalies in complex industrial environments. The proposed framework offers a robust solution for real-time anomaly detection, contributing to the reliability and sustainability of critical infrastructure systems.  
Keywords:time series; anomaly detection; deep learning +
Footnote †: journal: Applied sciences
# An Optical Encoder Chip with Area Compensation
## 3 Theoretical Analysis of Light Intensity and Compensation Design  
### Analysis of LED Luminous Characteristics  
In the ideal case, a single LED light source can be approximately regarded as a Lambertian light source, and the expression of its light intensity distribution is as follows:  
\[I(\theta)=I_{0}\cos^{m}\theta \tag{1}\]  
where \(\theta\) is the luminous angle, and \(I_{0}\) is the light intensity of the LED along the normal direction of the light source surface (\(\theta=0^{\circ}\)). The value of \(m\) is related to the half-light intensity angle \(\theta_{1/2}\) of the LED, and their relationship is as follows:  
\[m=\frac{-\ln 2}{\ln(\cos\theta_{1/2})} \tag{2}\]  
where \(\theta_{1/2}\) is defined as the angle of luminescence when the luminous intensity is reduced to half of the light intensity in the normal direction. The light source used in this study is a GaAs-based LED chip, whose luminous band is 845-855 nm, and its half-intensity angle \(\theta_{1/2}\) is 62.4\({}^{\circ}\). According to Equation (2), we can calculate \(m=0.9009\).  
When the light source vertically irradiates the receiving surface, the illumination degree of the receiving surface can be expressed by the irradiance \(E\). The relationship between irradiance \(E\), luminous intensity \(I\), and receiving surface distance \(r\) is as follows:  
\[E=\frac{I}{r^{2}} \tag{3}\]  
Figure 2: Reading relationship between incremental signal and M-code signal.  
This is the inverse square law of distance. However, when the receiving plane is not perpendicular to the direction of light irradiation, as shown in Figure 3, the inverse square law is modified as shown in Equation (4):  
\[E=\frac{I\cos\beta}{r^{2}} \tag{4}\]  
where \(I\) is the luminous intensity in the direction of the receiving surface. \(\beta\) is the angle between the light irradiation direction and the normal line of the receiving surface, and \(r\) is the distance between the light source and the receiving surface.  
Substituting Equation (1) into Equation (4), the irradiance \(E(\theta)\) received by the receiving surface at the distance from the light source \(r\) can be obtained as follows:  
\[E(\theta)=\frac{I(\theta)\cos\beta}{r^{2}}=\frac{I_{0}\cos^{m}\theta\cos\beta} {r^{2}} \tag{5}\]  
where \(I_{0}\) is the light intensity of the LED along the normal direction of the light source surface. \(\theta\) is the angle between the light irradiation direction and the normal direction of the light source surface. \(\beta\) is the angle between the light irradiation direction and the normal line of the receiving surface. The value of \(m\) is 0.9009.