esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Compared with rain field retrieval, besides the size difference between rainwater drops and cloud water drops, another major difference is the altitude of the drops in the atmosphere. Raindrops usually exist from the ground up, while the cloud bottom is mostly at least several hundred meters above ground [9]. Consequently, the blind zone near the ground, due to receivers' minimum elevation angle requirement [6], will have less impact on the cloud LWC retrieval than on rainwater. To test the feasibility of the cloud LWC measurement proposal, we utilize a synthetic cloud field generated by the Weather Research and Forecasting (WRF) model [10; 11] version 3.9.1.1. The cloud field is from an idealized simulation of a mesoscale convective system (MCS) initialized with a central U.S. MCS inflow sounding [12]. It is expected to include realistic cloud microphysics and cloud dynamics [13]. The model horizontal and vertical grid spacing is 250 m. Combining a LEO satellite trajectory with a satellite-to-ground signal model, we simulate the SNR estimation at the ground receivers and then use a tomographic algorithm to retrieve the attenuation field induced by the cloud LWC. It is assumed that only the SNR estimation is available at the receivers and that parameters including the transmitted signal power, the system noise power, and the attenuation due to other factors are unknown. The sky noise generated by cloud radiation is modelled and simulated, too. Because of the nonlinear relation between the sky noise and the path-integrated attenuation, a piecewise linear function is proposed to post-process the retrieved attenuation field in order to counterbalance the attenuation effects of the sky noise. Different basis functions for the attenuation field are introduced and simulated, with their effects on cloud field retrieval analyzed. Simulation results show that the proposed approach has great potential for cloud attenuation field retrieval. Even with a limited number of receivers with restricted locations, the partially retrieved field is still satisfactory. The rest of this paper is organized as follows. A detailed description of the system model can be found in Section 2. The proposed attenuation field retrieval method is presented in Section 3. Numerical results in regard to the synthetic cloud field and the tomographic retrieval under different conditions are given in Section 4. Finally, Section 5 concludes this paper.
## 6 Case Study
### Evaluation of the Single Transportation Mode Networks
Due to the particularity of water transportation, the relevant indicator values for cities with no water transportation modes passing through were set to 0. Meanwhile, Harbin is an inland river transportation city with a relatively small freight volume, mainly connecting some ports in Russia and not directly connected with other Chinese domestic node cities. Therefore, the relevant indicator value for Harbin was also set to 0. Therefore, there were 27 node cities in the water transportation network. As shown in Table 16, the rankings of the 27 candidate consolidation centers were determined based on the indicator values. It can be seen from the ranking that there were no node cities in Northwest China and Northeast China due to the lack of water transportation. Correspondingly, most of the selected node cities are mainly located in the coastal areas and some selected cities are located in the Yangtze River basin. The node cities in coastal areas were relatively high in the overall ranking of the water transportation network.
# Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring
## 2 Materials and Methods  
We implemented GNSS-IR water-level detection system on a printed circuit board (PCB), Figure 2 and deployed on Swarm network by help of our software. The PCB includes from right to left: a Swarm M138 LEO Modem, a Raspberry Pi Pico, and pads for four GNSS modules for GNSS-IR water-level measuring (together with GNSS antennas). The proposed scheduling algorithm was implemented on a dual-core ARM Cortex M processor of the Raspberry Pi Pico, where each processor core executes one process of the code. The board is sending one 128-bit message per hour, to fit within a single Swarm data plan for USD 60/year.  
Figure 3 presents the schematic for the final prototype PCB design produced in this project. The left-hand side displays spaces for four GPS receivers and four GPS antenna connectors, which are the project-specific sensing components for GNSS-R water level sensing. The remaining two-thirds of the schematic are generalizeable to other projects that use the Swarm M138 LEO modem, including an mPCI-e connector, decoupling and feed-through capacitors, and headers for the microcontroller.
# Cross-Feature Transfer Learning for Efficient Tensor Program Generation
###### Abstract  
Tuning tensor program generation involves navigating a vast search space to find optimal program transformations and measurements for a program on the target hardware. The complexity of this process is further amplified by the exponential combinations of transformations, especially in heterogeneous environments. This research addresses these challenges by introducing a novel approach that learns the joint neural network and hardware features space, facilitating knowledge transfer to new, unseen target hardware. A comprehensive analysis is conducted on the existing state-of-the-art dataset, TenSet, including a thorough examination of test split strategies and the proposal of methodologies for dataset pruning. Leveraging an attention-inspired technique, we tailor the tuning of tensor programs to embed both neural network and hardware-specific features. Notably, our approach substantially reduces the dataset size by up to 53% compared to the baseline without compromising Pairwise Comparison Accuracy (PCA). Furthermore, our proposed methodology demonstrates competitive or improved mean inference times with only 25-40% of the baseline tuning time across various networks and target hardware. The attention-based tuner can effectively utilize schedules learned from previous hardware program measurements to optimize tensor program tuning on previously unseen hardware, achieving a top-5 accuracy exceeding 90%. This research introduces a significant advancement in autotuning tensor program generation, addressing the complexities associated with heterogeneous environments and showcasing promising results regarding efficiency and accuracy.  
autotuning; deep learning compilers; heterogeneous transfer learning; tensor program generation +
Footnote †: journal: _Applied Sciences_
## 2 Measuring Method and Measuring System
### Sensor  
An integrated optical electric-field sensor is developed based on the Pockels effect [17; 18; 19]. In this optical sensor, an optical waveguide is fabricated on a LiNbO\({}_{3}\) substrate by titanium diffusion. The refractive index of the optical waveguide would be changed under an external electric field (\(E\)). Thus, while a polarized light passes through the optical waveguide, there would be a phase difference between its vertical and horizontal components. The modulated polarized light was transmitted to a common path interferometer via a polarization maintaining optical fiber, while the phase difference caused by external electric-field is demodulated into light intensity. Eventually, the light intensity is transferred into an electrical signal (\(U_{\text{out}}\)) by a laser receiver. The relationship between \(E\) and \(U_{\text{out}}\) is given as [20; 21]:  
\[U_{out}=A\cdot\left[1+b\cdot\cos\left(\phi_{0}+\frac{E}{E_{\pi}}\pi\right)\right] \tag{6}\]  
where \(A\) represents the photoelectric conversion coefficient and the transmission loss. \(b\) denotes the extinction ratio. \(\phi_{0}\) can be controlled near \(\pi\)/2 through the production of the sensor. In the case that the external electric field is much smaller than the half-wave electric field (\(E_{\pi}\)), by Taylor's expansion, (6) can be rewritten as:  
\[U_{out}=A\cdot\left[1+b\cdot\frac{E}{E_{\pi}}\pi\right]. \tag{7}\]  
Thus, the output voltage of the optical electric-field measuring system is linear to the external electric field.  
Figure 2 shows the schematic diagram of the developed optical sensor which has the dimensions of 18 mm by 18 mm by 48 mm and a weight of dozens of grams. The sensors, as well as the input and output fibres, are made of dielectric materials which could guarantee the safety under high-voltage conditions. Moreover, the sensor indicates a good directivity as it is sensitive to the electric-field component perpendicular to the front of the sensor. On the field measurement, the sensor is usually facing the measured conductor. Thus, instead of (4), (5) is normally used to compute the transient voltage.
## 4 Discussion
### Applicability of the Improved Algorithm  
Although the proposed CS-DINEOF was exclusively developed for the reconstruction of chl-a concentration in the Bohai Sea region, due to the intuitive nature of the concentration-stratified approach, it can be customized or adapted to any coastal area and open-sea applications.  
However, there are limitations to the CS-DINEOF approach. In terms of data quality control, all sub-datasets are required to meet specific SMR limitations. This introduces stricter data-checking rules, resulting in a reduction in the overall data volume, particularly in the number of valid images. In fact, the refined dataset, obtained by the CS-DINEOF quality control procedure, represents the highest-quality data in the original dataset. Stratified reconstruction applied to this refined dataset achieves high-precision and high-efficiency reconstruction, but with relatively diminished temporal continuity. If enhanced temporal continuity is required for better monitoring the continuous changes in practical applications, it is feasible to implement the ordinary DINEOF on other data in the dataset that do not meet the SMR limitation for sub-datasets but have an acceptable OMR. By incorporating the DINEOF reconstruction results into the high-quality CS-DINEOF results, it can further enhance the overall spatiotemporal continuity and practical application value of the final gap-free datasets.
# Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR
## 3 Methods
### Sparse Occupancy Grid  
The measurements taken by the sensor are continuously integrated into a sparse octree using the implementation of the octomap framework [42]. In this data structure, space is discretised in voxels, each one reflecting the probability of containing an obstacle. Occupancy information is provided in the form of three states: (1) Unknown, (2) free, and (3) occupied. The Octomap implementation has a small memory footprint since each node only stores three variables: The maximum occupancy likelihood, a local identifier, and a pointer to the array of children nodes that is only initialised if the voxel contains known space. It is a sparse world representation because when voxels with the same state are together, they merge into a larger voxel. The variable resolution is the primary characteristic that allows the system to scale to large scenarios. Larger voxels allow significant amounts of space to be analysed at the same time while using smaller voxels to represent intricate details.
# Effect of Gradation Segregation on Mechanical Properties of an Asphalt Mixture
## 4 Conclusions  
In this study, 2D DEM models were developed for asphalt mixtures with different gradation segregation levels. Based on these models, the influences of gradation segregation on the structural characteristics and mechanical properties of asphalt mixture skeleton were studied. The main conclusions can be drawn from the above:  
Figure 13: Contribution rate to load resistance for aggregates with different particle sizes.  
1. The aggregates skeleton of the asphalt mixture is mainly composed of coarse aggregates which are in contact with each other and whose particle sizes are greater than a certain value, while the fine aggregates mainly play the role of filling. The gradation segregation affects the constitution of the aggregate skeleton.
2. With the gradation from coarse to fine, the VMA gradually decreases, the number of contact points between aggregates increases sharply, and the average contact force and the maximum contact force on the contact points gradually decrease.
3. With the gradation from coarse to fine, the ability of the aggregate skeleton to resist a load gradually increases, and the segregation of coarse aggregates has a more significant effect on load resistance; the VSTP of the mixture skeleton gradually decreases, and the influence of segregation of coarse aggregate on it is larger; the LSDP of the mixture skeleton gradually weakens, and there is no obvious correlation between the LSDP and the degree of segregation when the segregation of coarse aggregate occurs, but when the segregation of fine aggregate occurs, the LSDP increases with the degree of segregation intensifying.
4. The VSTP of the mixture skeleton gradually decreases with the increase of model height, and there is no obvious correlation between the LSDP and the model height.
5. The contribution rate to resist the compressive load of the skeletal structure composed of aggregates whose particle size is above 2.36 mm is more than 70%. With the gradation from coarse to fine, the contribution rate of coarser aggregates decreases and that of finer aggregates increases. The contribution of each group of aggregate tends to be balanced.  
Conceptualization, W.W. and Z.T.; methodology, Z.Z. ([PERSON]); software, W.W.; validation, W.W., Z.T. and Y.L.; formal analysis, Z.T.; investigation, W.W.; resources, W.W.; data curation, Z.T.; writing--original draft preparation, Z.T. and Z.Z. ([PERSON]); writing--review and editing, [PERSON] ([PERSON]); visualization, W.W.; supervision, W.W.; project administration, W.W.; funding acquisition, Y.L.\"  
This research was funded by the National Natural Science Foundation of China, grant number 51008131 and 51038004.  
The authors declare no conflict of interest.
## 4 Discussion
### Regions of Study  
The geographic region of study started to assume an important role in rs+pheno research, mainly during the last ten years. In 1999-2008, boreal and tropical forests were the main ecoregions analyzed; the only countries largely considered were North America (in particular, USA) and Asia, with the former associated with the phenology/climate topic and the latter with the agriculture/classification one. On the contrary, in 2009-2018, the different topics identified involved different study regions, according to their specific characteristics and requirements. For instance, the pheno-climatic cluster included regions like Cerrado, Tropic, Savanna, North America, and Tibetan Plateau, which are broad, highly natural, and homogeneous areas where climate change effects can be clearly identified and monitored [109; 110; 111; 80]. In the agricultural cluster, Thailandia, Northern China, and Germany were the most occurent study regions, maybe due to the high food demand/production, water management, and the farming practices typical of these regions [114; 115; 116]. Finally, the classification cluster included regions like Greece and Japan, where issues related to crop mapping and anthropogenic impacts (e.g., fire, urbanization, invasive species, etc.), also characterizing the cluster, represents a major concern [103; 117; 118].  
Such pieces of evidence highlight the general tendency that rs+pheno studies are moving from global to regional/local scales. The main research questions are not only dealing with large scale dynamics but also with small scale observations, being driven by site-specific characteristics and requirements. While the former seemed to be mainly linked to forestry and climate-based studies(by using platforms like MODIS and GIMMS), the latter was mostly regarding agriculture and classification topics (by using satellites like Rapideye, SPOT, Sentinel, Worldview, and Landsat). In this perspective, the recent advent of very high spatial resolution satellites (like S2) and new missions (like PRISMA--Hyperspectral Precursor and Application Mission, VIIRS--Visible Infrared Imaging Radiometer Suite, and ECOSTRESS onboard the International Space Station) represents a major support tool, able to guarantee, and, at the same time, to boost multi-scale rs+pheno studies.
# SambotII: A New Self-Assembly Modular Robot Platform Based on Sambot
## 1 Introduction
### The Present Work  
In the present work, a new SMR SambottII is developed based on Sambott [15], a previously-built SMR (Figure 2a), In SambottII (Figure 2b), the original IR-based docking guidance method is replaced by a vision-based method. A laser-camera unit and a LED-camera unit are applied to determine the distance and angle between the two docking surfaces, respectively. Besides, a group of color-changeable LEDs are taken as a novel target feature. With the help of these units and the new target feature, the autonomous docking can be achieved with higher efficiency and accuracy.  
Figure 1: The docking processes of existing SMR systems. (**a**) The docking process of M-TRAN and three white LEDs in its side face [12], reproduced with permission from [PERSON] et al. [12]; (**b**) Special configuration of M-TRAN used for error tolerance [12], reproduced with permission from [PERSON] et al. [12]; (**c**) The docking process of CKBot [11], reproduced with permission from [PERSON] et al. [11]; and, (**d**) The docking process of UBot [13], reproduced with permission from [PERSON] et al. [13].  
An Intel x86 dual-core CPU is applied to improve the computing ability for image processing, information extraction, and other tasks with large computing consumption in the future. Besides, a five-layer hierarchical software architecture is proposed for better programming performances and it is a universal platform for our future research.  
Compared with existing SMRs utilizing the vision-based method, the SambotII has three main advantages: (1) The autonomous docking process is more independent, because the whole procedure, including image process and information extraction, is controlled by the SambotII system itself. (2) Apart from distance and orientation measurement, the target feature can be used to identify different modules simultaneously. (3) The docking process is more accurate and efficient, because it costs less than a minute and no extra sensors or procedures are needed to eliminate the docking error.  
In the remaining parts, a brief description is given at first for the mechanical structure, electronic system, and software architecture. Then, a detailed introduction is made on the principle of the laser-camera unit, LED-camera unit and docking strategy. Finally, docking experiments are preformed to verify the new docking process.
## 3 Mathematical Model
### Model Of Uncertainty  
In the aforementioned optimization model, the output data of PV&WPP are based on their forecasted values. However, in practice, it is challenging to obtain accurate forecast values, as prediction errors are inevitable. We can determine that:  
\[P_{W,t}^{E}=P_{W,t}^{E}+\xi_{t} \tag{32}\]  
where \(P_{W,t}^{E}\) and \(P_{W,t}^{E}\) denote the practical and predicted output of PV&WPP, respectively. \(\xi_{t}\) represents the prediction error at time t, and it is the main source of uncertainty in the zero-carbon building system.  
On the other hand, building occupants are sometimes willing to sacrifice comfort in exchange for lower energy costs in practice, especially during periods of high energy prices. To fully utilize occupants' tolerance toward comfort indicators, the constraint in Equation (31) can be relaxed using a constraint violation probability denoted as \(\rho\), transforming it into a chance-constrained form.  
\[\mathbb{P}\Big{(}P_{CHP,t}^{H}+P_{P2G,t}^{H}\geq P_{load,t}^{H}-\Delta P^{H} \Big{)}\geq 1-\rho \tag{33}\]  
\[\mathbb{P}\Big{(}P_{CHP,t}^{H}+P_{P2G,t}^{H}\leq P_{load,t}^{H}+\Delta P^{H} \Big{)}\geq 1-\rho \tag{34}\]  
In summary, the mathematical model of the proposed problem can be formulated as follows:  
Objective function: Equation (21)  
Constraints: Equations (1)-(20), Equations (22)-(34)  
Based on the characteristic of each device in the system and the form of the objective function and the constraints, we can identify the independent decision variables in the model and denote their set as follows: **X**:  
\[\textbf{X}=[P_{CHP,t}^{H},P_{CHP,t}^{E},G_{GT,t},P_{buy,t}^{E},G_{buy,t},P_{cur,t}^{E},P_{cls,t}^{E},P_{dis,t}^{E}] \tag{35}\]
# A Low-Complexity Volterra Filtered-Error LMS Algorithm with a Kronecker Product Decomposition
## 3 Proposed Algorithm
### Dvmfxlms
A sparse modeling method of the nonlinear secondary path using a second-order Volterra filter can be used here to reduce the computational complexity [39]. To improve the stability of the proposed algorithm, a normalized method is considered, and a regularization parameter \(\lambda\) is also introduced, which is similar to [38]. The updating equation of each sub-filter is finally given by  
\[\mathbf{w}_{p,q}(n+1)=\mathbf{w}_{p,q}(n)-\widetilde{\mu}_{p}\big{(}\overline{\mathbf{u} }_{p,q}(n)\ast\mathbf{s}(n)\big{)}e(n), \tag{19}\]  
where  
\[\widetilde{\mu}_{p}=\frac{\mu_{p}}{||\overline{y}_{p,q}(n)||^{2}+\lambda_{p}}. \tag{20}\]
# Modeling and Calculation of Limit Magnitude Detection of Orbital Optoelectric Tracking System
###### Abstract  
In order to evaluate the tracking capability of optoelectric tracking for an orbital target, the limit magnitude detection performance calculation model and its calculation method are studied. Combining the optical signal characteristics of the tracked orbital target, the background, and the CCD noise, the framework of the limit magnitude calculation model of the system for dynamic target detection is constructed. The relationships between the limit magnitude and the signal-to-noise ratio threshold of the optical signal characteristics, the exposure time of the CCD camera, and the dark current of the CCD imaging are studied and analyzed while considering the sunlight illumination condition, so that the calculation function and its change curve are given. The limit magnitude detection capability of the system is verified by the simulated experiment and the synchronized tracking test, and the detection distance maximum error of the model calculation is 3.6 m. The results show that under certain illumination conditions, when the exposure time of the CCD camera is longer and the SNR threshold is lower, the limit magnitude detection performance of the system is better, and the tracking performance of the system is more stable.  
optoelectric tracking; limit magnitude; detection performance; signal-to-noise ratio +
Footnote †: journal: Applied sciences  
Article
# Cryptographic Encryption and Optimization for Internet of Things Based Medical Image Security
## 3 Proposed Method
### Key Optimization Using Bloom Breeding Optimization
#### 3.5.3 Fitness Evaluation  
A crucial consideration in the bloom breeding optimization algorithm is the choice of fitness. This medical picture security procedure takes into account each image's PSNR with the best possible solution, and it communicates:  
\[Fitness_{F_{i}}=Max(PSNR) \tag{16}\]