esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Research Gaps and Prospects  
The current study assumed a fixed transmission ratio (0.5) and neglected manufacturing tolerances (\(\pm\)1% [44]) and belt slip (0.5-2% [45]), potentially introducing cumulative errors in dynamic conditions. To address the limitations of assuming fixed transmission ratios and neglecting belt slip dynamics, future work should integrate multi-source signal fusion, combining cigarette lighter electrical signatures with engine block vibration signals acquired via MEMS accelerometers. This hybrid approach leverages vibration-derived crankshaft speed (immune to belt slip) and electrical harmonics to dynamically compensate for load-induced slip errors through adaptive algorithms (e.g., federated learning [46] or Kalman filters [47]), while edge-computing frameworks (e.g., TinyML [48]) could enable real-time processing on embedded hardware. Additionally, extending the SAE J1939 protocol to standardize alternator harmonic fingerprints and vibration spectrum templates would bridge the OEM-specific diagnostic gaps for self-driving cars and electric vehicles; this will be collaboratively validated in the future through SAE J3084-compliant dynamometer testing (0-6000 rpm), as well as in urban fleet trials.
## 2 Materials and Methods
### Simulation Study
DMAS is conceived from the traditional DAS at which the detected signals are delayed proportional to the distance between the imaging target and the position of the elements. Summation of the delayed signal intensities is then performed, and an image is reconstructed. The equation for DAS, to determine each voxel intensity, is as follows:  
\[y_{DAS}(k)=\sum\limits_{i=1}^{M}x_{i}(k-\Delta_{i}) \tag{1}\]  
where \(y_{DAS}(k)\) is the output of the beamformer, \(k\) is the time index, and \(M\) is the number of elements. \(x_{i}(k)\) and \(\Delta_{i}\) are the detected signals and the corresponding time delay for detector \(i\), respectively. The time delay for each transducer element is equal to the distance between the transducer and the focused imaging pixel (this imaging pixel is specified by time index \(k\)) and is represented as:  
\[\Delta_{i}=d_{i}/c \tag{2}\]  
DMAS consists of time-shifting radio-frequency (RF) signals of each transducer element to align them in phase (delaying procedure), followed by multiplying and summing them. DMAS can be represented as:  
\[\begin{split}& y_{DMAS}(k)=\sum\limits_{i=1}^{M-1}\sum\limits _{j=i+1}^{M}x_{id}(k)x_{jd}(k)=\\ &\underbrace{[x_{1d}(k)x_{2d}(k)+x_{1d}(k)x_{3d}(k)+\ldots+x_{1d  
Figure 1: Simulation results. (**a**) Simulated configuration of 50 equidistant transducers on a 12 cm diameter hemisphere using k-Wave toolbox for imaging a three-leg phantom; (**b**) bottom view of the 3D model of 50 element hemisphere cap using Fusion 360 software; (**c**) image quality assessment in terms of contrast to noise ratio when different image reconstruction algorithms were used for reconstruction of the three-leg phantom in a 50-element PACT; Delay-and-Sum, Delay-and-Sum Coherence Factor, Delay-Multiply-and-Sum, and Universal Back Projection with 5, 15, and 25 MHz cutoff frequencies; (**d**) (i–iv) reconstructed images using 14, 26, 38, and 50 equidistant 12.7 mm transducers; for image reconstruction, DMAS algorithm was used; (**e**) quantitative evaluation of reconstructed images in terms of contrast to noise ratio with square root function fit; (**f**) fluence map of full-field and overhead configurations in the axial (i,ii) and coronal plane (iii,iv), and (v) comparison of fluence homogeneity for full-field and overhead configurations.
## 2 Materials and Methods
### Determination of the Physicochemical Properties of Baked Burgers
#### 2.5.4 Color Parameters  
The color parameters of the burgers were determined using the reflection method in the CIEL*a*b* color space (L*--lightness, a*--red/green coordinate, b*--yellow/blue coordinate). For this purpose, a Konica Minolta Chroma Meter CR-400 (Minolta, Osaka, Japan) was used with the following settings: D65 light source, 2\({}^{\circ}\) observer, and 2 mm aperture size. Each time, before starting a series of measurements, the measuring head was calibrated against the white standard (Y = 84.2, x = 0.3202, y = 0.3373). Measurements were made on the surface and in the cross-section of the burgers (in six repetitions).  
Moreover, based on the results of the basic color coordinates, the total color difference (\(\Delta\)E\({}_{1}\)) between hybrid/plant burgers and the meat burger was calculated according to the following equation [30]:  
\[\Delta\)E\({}_{1}\) = [(L2*-L1*)^{2}+(a2*-a1*)^{2}+(b2*-b1*)^{2}]^{1/2} \tag{2}\]  
where \(\Delta\)E\({}_{1}\)--total color difference in hybrid/plant burger samples compared to the meat burger sample; L1*, a1*, b1*--color parameters determined for the meat burger sample; L2*, a2*, b2*--color parameters determined for hybrid/plant burger samples.  
In order to determine the degree of browning of the surface of burgers after heat treatment, the Browning Index (BI) was calculated using the following equations [31]:  
\[\text{BI}=100\times[(x-0.31)/0.17] \tag{3}\]  
where:  
\[x=(a*+1.75\;L^{*})/(5.645\;L^{*}+a*-3.012\;b*) \tag{4}\]
# The Shear Strength of Granite Weather Soil Under Different Hydraulic Paths
## 5 Conclusions  
In this paper, the shear strength test of two weathered granite soils with different saturations under the influence of different hydraulic paths is carried out. The effect of mineral composition and particle size gradation on shear strength are further discussed, and a shear strength model of unsaturated soil considering capillary and adsorption effect is established. The main conclusions are as follows:  
1. The shear strength of the prepared specimen increases first and then decreases with the increase of saturation. It reaches the peak strength at the optimal saturation \(S_{\rm r}=40\%\), showing an \"arch\" change. The shear strength of the dehydrated specimen increases linearly with the decrease of saturation. The cohesion of weathered granite soil varies significantly with saturation, while the amplitude of the internal friction angle with saturation varies little.
2. There are three different contact forms: direct contact, meniscus contact, and cement contact between soil particles. Different granite weathering degree makes the residual soil and fully weathered soil different in particle size gradation and mineral composition, the contact  
Figure 11: Comparison of experimental and simulated results of shear strength of weathered granite soil: (**a**) Preparation sample of fully weathered soil; (**b**) Dehydrated sample of fully weathered soil; (**c**) Preparation sample of residual soil; and (**d**) Dehydrated sample of residual soil.  
form between the soil particles is jointly determined under the effect of the hydraulic path, which ultimately affects the strength characteristics of the soil under different saturations.
3. A strength model which can consider both the capillarity and adsorptive effect is established, which can thoroughly explain the action mechanism and influence law of capillarity and adsorptive on soil strength, and well explain and simulate the evolution law of shear strength of granite weathering soils with different saturation under different hydraulic paths. According to the deficiencies of the existing strength model, this model provides a clear theoretical basis for the strength research of special soils such as weathered granite soil, and it is easy to generalize and apply to other simple soil types in unsaturated soils. The prediction and verification of other soils will be improved in the next step.  
[PERSON] and [PERSON] designed and performed the experiments, analyzed the data, and wrote the paper. [PERSON], [PERSON], and [PERSON] cooperated with the data analysis and manuscript elaboration. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Fundamental Research Funds for the Central Universities (2020 CZ002, 2020 JM048), the National Natural Science Foundation of China (51722802, 51678041, and U1834206), the Natural Science Foundation of Beijing Municipality (8202038), and Beijing Nova Program (Z181100006218005).  
The authors declare no conflict of interest.
# Multi-Constraint and Multi-Policy Path Hopping Active Defense Method Based on SDN
## 4 Design of Multi-Constraint Multi-Strategy Hopping Algorithm
### Path Hopping Execution Process  
As shown in the SDN network topology in Figure 2, H1 and H2 are communication hosts. During the communication process, path hopping is executed, which requires the controller to update the flow table on the switch. This process will cause the problem of inconsistent flow table updates, resulting in data loss. Therefore, this paper adopts the flow table pre-distribution strategy and the flow table update strategy of \"reverse addition, sequential deletion\". When updating the flow table, the flow table entries are first delivered to the last switch. When deleting, start deleting from the first switch, and the priority of the newly added flow table entries should be higher than the old ones. Assuming that H1 and H2 are communicating, the steps of their hopping execution are as follows:  
1. H1 initiates communication and sends the packet to the switch OF-S1. At this time, there is no corresponding flow table entry on the switch, and the communication information will be encapsulated in the packet_in packet and sent to the controller.  
Figure 2: Process of SDADM path hopping.  
After the controller receives the packet_in packet, the controller calculates all communication paths between H1 and H2 according to the SDADM algorithm, selects a path based on the weight, assumed to be (OF-S1, OF-S2, OF-S6, OF-S8), and adopts the reverse addition method to issue the forward and reverse flow tables, that is, the flow table is issued forward from the switch OF-S8.
3. When the hopping time \(T\) is reached, the controller has re-selected the hopping path according to the SDADM algorithm, assumed to be (OF-S1, OF-S5, OF-S7, OF-S8). The controller issues the flow table in reverse order to the nodes on the path, and the match priority of the flow table is higher than the priority of the path (OF-S1, OF-S2, OF-S6, OF-S8). During the process of issuing the flow table in reverse order, when the flow table is not installed in the intersection node with the previous path (such as OF-S1), the communication data are transmitted according to the original path. When the flow table is issued to OF-S1, the data will be transmitted according to the new path.  
When generating the flow table, the survival time hard_timeout and idle_timeout of the flow table will be set. When the time of the flow table exceeds hard_timeout, the flow table entry will be forcibly deleted, generally set to 2 RTT (round-trip time), to ensure that the data on the original communication path can be transmitted normally during path hopping, reducing the packet loss rate. When the flow table entry is not used within the idle_timeout time, it will be assumed that communication between the hosts has stopped, and the controller will end the path hopping and delete the flow table entry, usually set to a round-trip cycle. These two settings ensure normal communication while also reducing the consumption of node capacity and preventing overflow of flow table entries.
# Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation
## 2 Related Work
### Resource Allocation Optimization Problem  
Optimization problems for the power-bandwidth pair versus distortion address resource constraint challenges in 3D video pseudo-analog transmission. For example, the objective of minimum distortion optimization is to minimize the overall distortion for a given resource usage:  
\[\begin{split}\min& D\\ \text{s.t.}& R<R_{available}\end{split} \tag{4}\]  
where \(D\) is the distortion and \(R_{available}\) represents the available power and bandwidth resources. The target distortion optimization minimizes the resource usage for a given distortion:  
\[\begin{split}\min& R\\ \text{s.t.}& D<D_{exp}\end{split} \tag{5}\]  
where \(D_{exp}\) is the expected distortion. It must be noted that as pseudo-analog systems transform video data into independent chunks, which means each chunk contributes independent distortion to the entire video quality, the distortion of a chunk caused by a transmission error does not affect any other chunk. Hence, discarding chunks to fit the bandwidth does not prevent each receiver from obtaining a video quality proportional to its channel conditions, and due to the heavy data traffic involved in 3D video transmission, it is crucial to optimally select chunks for transmission and reuse the saved power efficiently.  
In summary, the problem becomes: (a) the optimal allocation of the total power between texture videos and depth maps and (b) the joint optimal resource (power and bandwidth) allocation for the entire signal sequence to obtain satisfactory video quality.
# An Optimized Vector Tracking Architecture for Pseudo-Random Pulsing CDMA Signals
## 3 Problem Description
It is supposed that the time-varying property of \(T_{k}\) is ignored in the tracking loop design. In that case, the frequency error cannot be accurately estimated, resulting in jitter degradation of tracking performance, which is also described in Reference [25]. Similarly, the code phase discriminator output is also time-varying as the code phase error is driven by the same, albeit scaled, dynamics as the carrier phase over short time intervals [39]. The variation of code phase discriminator output is  
\[ \begin{split}\
abla\overline{\Delta\tau_{k}}&= \beta\Delta f_{k}T_{k-1}\\ \beta&=\frac{f_{code}}{f_{c}}\end{split} , \tag{12}\]  
where \(f_{code}\) indicates the frequency of spreading code. According to Equations (9), (10), and (12), the error parameter is related to the time intervals \(T_{k-1}\) of adjacent effective timeslots. These parameters can be accurately estimated by designing a scalar tracking loop of variable update period, as researched in Reference [24; 25; 27]. In the vector tracking structure, the input observations of the navigation filter need to be sampled simultaneously. However, the estimation time of the errors in each channel is inconsistent and time-varying in PLPS. Figure 4 depicts this problem. The dotted arrows represent the navigation filter sampling time, while the solid arrows indicate the error estimation time in each channel. Unfortunately, there exists a time difference between them. Therefore, the input observations of the navigation filter cannot reflect the errors during this time (gray shaded part). Even worse, this time difference is pseudo-random, causing jitter degradation of the navigation filter when using conventional VTL loops to track PRPS. Hence, it is necessary to design the VTL according to the characteristics of the PRPS. Figure 3: The comparision between \(\
abla\overline{\Delta\phi_{k}}\) in tracking continuous CDMA signal and PRPS. (**a**) \(\
abla\overline{\Delta\phi_{k}}\) of tracking continuous CDMA signal; (**b**) \(\
abla\overline{\Delta\phi_{k}}\) of tracking PRPS.
### Snow Mass Mission Trade-Offs  
To make global snow mass estimation even more complicated, a future snow mission will face a trade-off between sensor design, spatial resolution, and revisit frequency. For example, a different orbital configuration (largely as a function of inclination angle and satellite altitude) changes the nadir track, which directly influences which portions of the globe are, or are not, observed. Similarly, a wider swath width for a given sensor increases the revisit frequency but likely results in larger errors along the swath periphery due to slant range geometry effects or significant reductions in backscatter, e.g., associated with changes in forward scattering characteristics as the sensor looks increasingly off-nadir [41]. An increase in satellite altitude or changes in orbital parameters could impact the spatial resolution, as well as the frequency with which the globe is viewed. However, both the fine spatial resolution and short revisit interval are of interest in SWE estimation considering the strong spatiotemporal dynamics of the snow [42; 43]. Additional concerns about this trade-off include non-uniform distribution of snow cover and diversity of snow types or snow features. The majority of snow occurrence is distributed in the high-latitude or high-altitude regions. Consideration of the different suitable remote sensing techniques to capture the different types of snow, such as tundra, taiga, or ephemeral snow, adds even more complexity to the task of global snow mass estimation.  
Given the difficulties listed above, along with the general lack of uptake of PMW estimates of SWE into hydro-meteorology and hydro-climatology applications [35], the research presented here aims to study the efficacy of different orbital configurations and sensor characteristics on snow mass detection from the perspective of maximizing the global snow coverage to be viewed. The goal of this exercise is to facilitate the mission planning process and enhance the future potential of snow remote sensing, e.g., PMW, LiDAR, and SAR.  
The first challenge is to link and combine the prediction ability of different satellite orbits and estimation of snow dynamic extent. The sensor's viewing extent is estimated under various orbital configurations coupled with a snow cover climatology as a function of different snow classes. The goal, hence, is not to quantify global snow mass (which is to be pursued in a follow-up study) but, rather, to explore the different options to quantify snow mass, as well as how best to maximize global coverage (in space and time) en route to estimating global snow mass.  
This paper is structured as follows. In Section 2, we introduce the methods to simulate the sensor's viewing extent given a particular orbital configuration and proposed metrics to analyze the coverage in space and time. We then apply this method to orbital configurations for five different sensors, as well as four different constellations, in Section 3, and then evaluate their effects on snow observability. Sections 4 and 5 provide a discussion and conclusion on the findings.
## 3 Earth Surface Modelling Advances and Links with EO Datasets
### Land Surface Properties
#### 3.3.1 Orography  
For every grid point, models have associated values for surface elevation (orography), sub-grid orography statistics, land cover (used as land/sea mask), lake cover and depth, glacier cover, low and high vegetation type, low and high vegetation cover, albedo, LAI, and soil texture. Within the ECMWF system, these fields are derived from different external sources (e.g., albedo and LAI from MODIS, vegetation type and cover from GLCC/AVHRR, water bodies from Globcover).  
Global datasets come in different resolutions, data formats and projections and need to be interpolated, or merged in the case of non-global coverage. In light of the changing climate signal also affecting surface physiography, multi-year climatological data at 1 km resolution for all surface fields is desirable. One-km resolution represents a challenge for current global NWP and Earth system models, but it is relevant to allow direct comparison with present and future Sentinel missions monitoring the Earth system. Figure 17 shows the orography, ocean and lake bathymetry used operationally at ECMWF. The lake depth is based on [203] and the ocean bathymetry is based on ETOPO1 by [204].  
The land-sea mask and orography are based on the following raw data information: ESA's Globcover V2.2 based on Envisat MERIS (300 m resolution) mapping 2005/2006 (ESA, 2010), the Shuttle Radar Topography Mission (SRTM [205] at about 90 m resolution, the Global Land One-kilometer Base Elevation (GLOBE [206], only north of 60N and south of 60S), and specialised DEMs of Greenland [207], Iceland (Icelandic Meteorological Office, 2013) and Antarctica [208], replacing the corresponding data points on the 1 km latitude/longitude grid. The lake mask has been created from the land sea mask and quality controlled by consistency algorithms. All spatial resolutions that are used by the IFS run in fully coupled forecasts [209].
###### Abstract  
Image blurs are a major source of degradation in an imaging system. There are various blur types, such as motion blur and defocus blur, which reduce image quality significantly. Therefore, it is essential to develop methods for recovering approximated latent images from blurry ones to increase the performance of the imaging system. In this paper, an image blur removal technique based on sparse optimization is proposed. Most existing methods use different image priors to estimate the blur kernel but are unable to fully exploit local image information. The proposed method adopts an image prior based on nonzero measurement in the image gradient domain and introduces an analytical solution, which converges quickly without additional searching iterations during the optimization. First, a blur kernel is accurately estimated from a single input image with an alternating scheme and a half-quadratic optimization algorithm. Subsequently, the latent sharp image is revealed by a non-blind deconvolution algorithm with the hyper-Laplacian distribution-based priors. Additionally, we analyze and discuss its solutions for different prior parameters. According to the tests we conducted, our method outperforms similar methods and could be suitable for dealing with image blurs in real-life applications.  
image blur; blur kernel; sparse optimization; image deblurring 2020
## 4 Measurement Setup and Dataset
### Dataset and Preprocessing
#### 4.2.2 Input Data Format  
For model training, an image time series TS, consisting of \(C=2\) channel images \(\mathbf{x}\), each with intensity \(\mathbf{I}\) and depth \(\mathbf{L}\) channel, is utilized as model input:  
\[\text{TS} =\{\mathbf{x}_{k},...,\mathbf{x}_{k-\mathbf{M}}\} \tag{8}\] \[=\{(\mathbf{I}_{k},\mathbf{L}_{k}),...,(\mathbf{I}_{k-\mathbf{M}},\mathbf{L}_{k- \mathbf{M}})\} \tag{9}\]  
Opposed to that, the corresponding SYNOP precipitation class and rate \(R\) are used as target values, for which they were de-serialized and converted to tensors to allow for fast data loading during training. All models are trained on a time series length of \(\text{M}=200\) images, which is equivalent to a window length of:  
\[t_{\text{int,LiD}}=\frac{\text{M}}{f}=8\,\text{s} \tag{10}\]  
Time-series-based inferencing instead of single image processing is absolutely essential for the noise-based precipitation state prediction, since single noise frames are highly ambiguous with respect to weather influence. Consequentially, the input tensor for all investigated architectures has the shape \((\text{B},\text{M},\text{C},\text{H},\text{W})\) with B describing the batch size as relevant training hyperparameter and \(\text{H}\times\text{W}\) as the dimensions of the corresponding 16 bit monochromatic images, as described earlier.