esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Existing Publisher-Subscriber Approach  
#### 2.1.1 Centralized Architecture  
The quality of IoT data is significantly influenced by the centralized ownership of data brokers [15]. Centralized nature poses inherent risks, as unauthorized access could compromise data integrity [16]. Tampering incidents pose a significant threat [17], as any alterations to the collected data can result in the dissemination of poor-quality data to the subscribers [18]. A decentralized architecture would provide enhanced security, making it more challenging to tamper with the data before it is sent to the subscriber [19].  
The centralized architecture of the data broker preceding data transmission to the IoT platform is susceptible to various vulnerabilities, including data tampering, which can compromise data quality [20]. Past studies have sought to enhance the security of this storage by integrating it with blockchain networks [21; 22; 23; 24; 25; 26; 27; 28]. The smart broker is the best alternative solution to this, as it already receives all the data in real time and can make opinions about the data relative to other data in real time without worrying about storing any information for a long period of time.
## 4 The Proposed Algorithm
### Markov Decision Process Modeling
#### 4.1.1 State Space  
The state space in the multi-UAV MEC system includes UAV states, mobile device states, and channel states. Specifically, the UAV state is represented by its battery energy, position, and current CPU frequency. The ground environment state is characterized by the positions of all mobile devices, the remaining task volume, the randomly generated task volume of each mobile device, and the channel obstruction status between mobile devices and UAVs. Thus, the state of the UAV MEC system at time slot \(t\) is defined as  
\[s_{t}=(s_{1},\ldots,s_{u},\ldots,s_{M},s_{env}), \tag{34}\]  
where \(s_{1},\ldots,s_{u},\ldots,s_{M}\) represent the states of all UAVs, and \(s_{env}\) denotes the task volume, mobile device positions, and channel obstruction status of the environment. Specifically, the UAV state and environment state at time slot \(t\) are expressed as  
\[s_{u}=(e_{u}(t),z_{u}(t),f_{u}(t)), \tag{35}\]  
\[s_{env}=(Z^{md}(t),D_{remain}(t),D^{md}(t),B^{md}(t)), \tag{36}\]  
where \(e_{u}(t)\) is the remaining energy of UAV \(u\), \(z_{u}(t)\) is the position of UAV \(u\), \(f_{u}^{nav}(t)\) is the CPU frequency of UAV \(u\), \(Z_{md}(t)=z_{1}^{md}(t),\ldots,z_{N}^{md}(t)\) represents the positions of mobile devices \(1\) to \(N\), \(D_{remain}(t)\) is the remaining task volume of the system, \(D_{md}(t)=D_{1}(t),\ldots,D_{N}(t)\) denotes the task volumes generated by mobile devices \(1\) to \(N\), and \(B_{md}(t)=b_{1,u}(t),\ldots,b_{l,u}(t)\) indicates the channel obstruction status between UAV \(u\) and mobile devices \(1\) to \(l\). At the initial time slot (\(t=1\)), \(e_{u}(t)=E_{max}\), and \(D_{remain}(t)=D_{max}\). At the final time slot (t=T), \(D_{remain}(t)=0\).
## 3 Results and Discussion
### Numerical Simulation Results  
To ensure that the FPZ can be simulated by cohesive elements and reduce grid dependency, the length of the cohesive elements should be smaller than the FPZ. The numerical specimen was refined near the pre-existing crack, and the size of the cohesive elements was set to 0.1 mm. Additionally, the grid was randomized to prevent the crack propagation direction from being influenced by the position of the cohesive elements. Fixing the boundary at the bottom of the specimen during modeling prevents horizontal or vertical displacement during compression loading. The sides of the numerical specimen were free surfaces. Free boundary conditions were applied to allow for the sides to expand freely without introducing additional constraints. The established numerical simulation model is shown in Figure 9.  
Figure 8: Variation of energy density and UCS with crack dip angles.  
Figure 9: Numerical simulation model.  
In the simulation, displacement was applied to represent the loading conditions of the experiment. This was achieved by applying a displacement of 0.5 mm at the top of the specimen. It should be noted that in laboratory tests, the loading rate is controlled at 0.05 mm/min to ensure that the specimen is always under quasi-static loading conditions. In numerical simulation, the impact of dynamic loads caused by loading rate on the test results should also be minimized as much as possible. Therefore, this study set the loading time step to \(5\times 10^{-6}\). The rationality of the numerical model was ensured by comparing the stress-strain curves obtained from numerical results with those obtained from experiments. The comparison results are shown in Figure 10.
# Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems
## 3 Diversity Balancing for Two-Stage Collaborative Filtering
### Rating Prediction and Top-N Re-Ranking
#### 3.3.2 Top-N Re-Ranking  
The conventional recommendation list uses rating values to rank items. Given the difficulty of identifying the potential interests of users, its objective is simply to increase recommendation accuracy. Thus, a diversity factor is introduced to adjust the proportion of items with high diversity. The predicted ratings of the top \(M\) items in the candidate list are assumed to exceed the average ratings of users. Equation (9) compares the similarities between the [(1-\(\beta\))\(N\),\(M\)] items and the top \(\beta\)\(N\) items. The item with the lowest similarity is then selected for use as a replacement item.  
\[min\sum_{i\in[0,\beta\)\(N\),\(j\in[(1-\beta)\)\(N\),\(M\)]}\left(\frac{R_{i}-R_{j}}{P_{i}-P_{j}}\right) \tag{9}\]  
where \(R_{i}\) and \(R_{j}\) are the rating values that user \(u\) gives for items \(i\) and \(j\), respectively, and where \(P_{i}\) and \(P_{j}\) are the popularity of items \(i\) and \(j\), respectively.  
For a clear understanding of the proposed DBTS algorithm, the detailed steps of the method are given in Algorithm 1.  
```
Input: rating matrix \(R\), social trust matrix \(T\), active user \(u\), trust threshold \(\theta\), interest threshold \(\gamma\), ranking strategy threshold \(\beta\). Output: the top-N recommendation list. Begin //Stage I: calculate user similarities. Step 1 Calculate similarities between users using (1). Step 2 Divided users into low similarities set and high similarities set using \(\theta\). //Stage II:calculate the integrated weights. Step 3 Use K-means to cluster items. Step 4 Calculate interest weights between active user and other users using (6). Step 5 Integrate interest weights and similarities using (7). //Rating prediction and ranking. Step 6 Sort users based on their weights in descending order. Step 7 Choose the top \(n\) users as active user neighbors. Step 8 Predict unknown ratings for the active user. Step 9 Select \(n\) items with highest predicted ratings. Step 10 Calculate replacement items using (9). Step 11 Put items with the lowest ratings into recommendation list. Step 12 Generate the top-N recommendation list. End
```  
**Algorithm 1** Diversity Balancing for Two-Stage collaborative filtering (DBTS)
## 1 Introduction  
Vegetation can be affected by climate change through the energy and mass exchange between vegetation and atmosphere [1, 2, 3]. Carbon flux is a mass exchange that is related to global warming [4, 5]. Accurate estimation of carbon flux plays an important role in carbon cycle study. Generally, two types of methods can be used to estimate carbon flux: observing and modeling. Observing instrument (such as Eddy covariance) can measure the continuous exchange of carbon flux at a specific site with an area of several square kilometers. However, model can efficiently estimate carbon flux at regional scale.  
Remote sensing is an important source of data for simulating carbon flux. In recent years, many remote sensing based carbon flux models were developed, such as Glo-PEM [6], C-Fix [7, 8], VPM [9], MODIS GPP/NPP algorithm [10], and DCFM [11]. All of these models use air temperature as an input. At many observation stations, the air temperature is observed above the canopy, and is different from the canopy temperature [12, 13], which actually controls photosynthesis and respiration of vegetation. During the growing season, the land surface temperature (LST) within a high vegetation coverage area is dominated by the canopy temperature. With the development of remote sensing, more and more thermal infrared data is available to estimate LST. MODIS provide thermal infrared image twice every day with spatial resolution of 1 km. Landsat series data provide thermal infrared image every 16 days with spatial resolution of 100 m. These datasets can be used to retrieve continuous LST, which can force carbon flux model. Thus, LST could be used as inputs in remote sensing based carbon flux estimation.  
The Heihe River Basin, which is located in Northwest China, is the second largest inland river basin in the country [14]. In the midstream of the Heihe River Basin, yearly rainfall is less than 100 mm. Most of the area is Gobi Desert, which has little carbon flux. The largest vegetated area in the midstream of the Heihe River Basin is irrigated cropland. Another well-vegetated area is wetland because of abundant water supply from ground water and river water. Irrigated croplands and wetlands are the two ecosystems with the highest production in the midstream of the Heihe River Basin. Carbon fluxes of these two ecosystems contribute the majority of carbon flux in the midstream of the Heihe River Basin. Thus the accurate estimating of carbon fluxes of these two ecosystems is very important to evaluate the regional scale carbon flux of the midstream of the Heihe River Basin.  
Hence, the objectives of this study were to (1) analyze the carbon flux characteristics of irrigated croplands and wetlands in midstream of the Heihe River Basin; (2) develop a remote sensing based carbon flux algorithm, RS-CFLUX, to simulate the carbon flux quantity and dynamics at the two ecosystems.
# Micro-Satellite Systems Design, Integration, and Flight
## 4 Attitude Control System (ACS) Development
### Attitude Control
#### 4.3.5 Tested Software on Teensy Flight Computer  
All the hardware components in the ACS system were checked by verification testing. The microcontroller was the first component to be tested. It is a Teensy 3.5 microcontroller, and it is used for Alpha. The CubeSat's flight software is run by the Teensy 3.5 controller. The testing of the microcontroller's hardware was to make sure it met requirement 5.0 in Table 5.  
The ACS controller algorithm was added as a library [13] to the flight software of the Teensy and then tested. The script was run using the plant model in the code of the Teensy. Controller runtime, computational speed, and accuracy were measured and plotted. These plots were compared with the MATLAB(r)/Simulink simulations. No major differences were seen. Different timesteps were tested. A refresh rate of 0.01 s was chosen for using the Teensy. This timestep is a safe estimate because the Teensy was also running the plant simulation, which it will not do when it is deployed. A test was performed where the Teensy would keep running until the plant had steady values. The test was repeated three times consecutively. The Teensy was verified to be able to run the ACS control algorithm.
# Bi-Objective Optimization for Joint Time-Invariant Allocation of Berths and Quay Cranes
## 1 Introduction
In recent years, the application of metaheuristic algorithms in solving complex optimization problems has gained a lot of attention because they can find the near-optimal solution in polynomial time, whereas traditional methods require exponential time. The multi-objective cuckoo search (MOCS) algorithm is an emerging metaheuristic algorithm inspired by the breeding behavior of cuckoos. Due to its unique optimization mechanism and fewer variable parameters, the MOCS algorithm has a powerful global search capability and is widely used in various engineering problems. Examples include the berth allocation problem [6], image classification problem [7], and path planning problem [8]. This paper investigates the application of an improved multi-objective cuckoo search (IMOCS) algorithm in BACASP, where the optimization objectives are the terminal's operating cost and customer satisfaction, which are crucial for improving the terminal's operational efficiency and competitiveness. Numerical experiments show improvements of 7.5% and 7.7% in the proposed IMOCS algorithm with respect to the MOCS algorithm on the two objectives, respectively. From the manager's point of view, after considering customer satisfaction, the level of customer dissatisfaction is significantly reduced by 16% with an increase of 5.7% in the average terminal cost. The remainder of this paper is organized as follows. Section 2 provides a review of studies on BAP, QCAP, and BACASP. Section 3 presents and models the proposed BACASP. Section 4 describes the flow of the IMOCS algorithm. Section 5 discusses the numerical experiments and evaluates the performance of the IMOCS algorithm. Finally, Section 6 concludes this paper and offers suggestions for future research.
###### Abstract  
With global warming, permafrost is undergoing degradation, which may cause thawing subsidence, collapse, and emission of greenhouse gases preserved in previously frozen permafrost, change the local hydrology and ecology system, and threaten infrastructure and indigenous communities. The Qinghai-Tibet Plateau (QTP) is the world's largest permafrost region in the middle and low latitudes. Permafrost status monitoring in the QTP is of great significance to global change and local economic development. In this study, we used 66 scenes of ALOS data (2007-2009), 73 scenes of ALOS-2 data (2015-2020) and 284 scenes of Sentinel-1 data (2017-2021) to evaluate the spatial and temporal permafrost deformation over the 83,000 km\({}^{2}\) in the northern QTP, passing through the Tutoutouche, Beilhu, Wudaoaling and Xidatan regions. We use the SBAS-InSAR method and present a coherence weighted least squares estimator without any hypothetical model to calculate long-term deformation velocity (LTDV) and maximum seasonal deformation (MSD) without any prior knowledge. Analysis of the ALOS results shows that the LTDV ranged from \(-20\) to \(+20\) mm\(/\)year during 2007-2009. For the ALOS-2 and Sentinel-1 results, the LTDV ranged from \(-30\) to \(30\) mm\(/\)year during 2015-2021. Further study shows that the expansion areas of permafrost subsidence are concentrated on braided stream plains and thermokarst lakes. In these areas, due to glacial erosion, surface runoff and river alluvium, the contents of water and ground ice are sufficient, which could accelerate permafrost subsidence. In addition, by analyzing LTDV and MSD for the different periods, we found that the L-band ALOS-2 is more sensitive to the thermal collapse of permafrost than the C-band sensor and the detected collapse areas (LTDV \(<-10\) mm\(/\)year) are consistent with the GF-1/2 thermal collapse dataset. This research indicates that the InSAR technique could be crucial for monitoring the evolution of permafrost and freeze-thaw disasters.  
permafrost; InSAR; Qinghai-Tibet Plateau; ALOS; ALOS-2; Sentinel-1; thermal melting collapse +
Footnote †: journal: remote sensing
## 2 Materials and Methods
### Data Collection
After this series of processes, a dataset containing 37,135 valid data points was constructed. The data for the influencing factors were obtained from authoritative platforms such as the National Bureau of Statistics of China and the National Earth System Science Data Center--National Science and Technology Infrastructure Platform. A total of 13 indicators (listed in Table 1), including digital economy level, technological innovation capacity, regional development vitality, residents' digital literacy, and policy environment, were included to showcase a more comprehensive potential causal relationship from different perspectives. Additionally, to eliminate population differences between regions and dimensional differences between indicators, standardization and classification processing of the research data were conducted using min-max normalization and natural breaks methods.
## 3 Methodology and Data Sources
### Identification of Straw Open Burning Sources  
#### 3.4.1 Preprocessing Wildfire Data  
The satellite wildfire data were extracted via the spatial distribution of farmland to screen the fire spots caused by SOB. That is, the fire spots located on farmland were identified as SOB sources, and other types of biomass burning and abnormal thermal emissions, such as forest fires and industry, were excluded.  
Near-real-time wildfire data were obtained from a Himawari-8 Advanced Himawari Imager (AHI) fire product [43]. The AHI has a high temporal resolution of 10 min and a spatial resolution of 2 km. The wildfire data include the geographical location (latitude and longitude), acquisition time, hotspot intensity, and hotspot area of the fire spots, which were used to detect the spatiotemporal distribution and burning area of wildfires in different geographical regions. Wildfire data with confidence levels \(>85\%\) were used. It should be noted that to avoid repetitive statistics of hourly fire occurrence, we only counted once the Himawari-8 fires were in the same location and over continuous time within one hour. The distribution of the farmland of Jilin was identified using the MODIS Land Cover Product (MCD12Q1) at a spatial resolution of 500 m [44].
# Retrieval of Cloud Ice Water Path from FY-3F MWTS and MWHS
## 2 Satellite Data and ERA5 Data
### ERA5 Data  
ERA5 is the fifth-generation reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) [22]. Building upon ERA-Interim, it provides higher temporal and spatial resolutions. ERA5 contains a wealth of climatic data, including cloud microphysical variables such as cloud water and ice. However, it does not directly assimilate cloud-specific variables like IWP or cloud fraction. Instead, these properties are derived computationally through a numerical weather prediction model, constrained by assimilated data such as temperature, humidity, and wind speed, which determine the spatial and temporal distribution of clouds. Notably, the IWP data in ERA5 exclusively represents cloud ice and does not account for snow, defined as aggregated ice crystals. Comparisons with satellite observations show that the spatial distribution of IWP in ERA5 closely aligns with satellite-retrieved data [23; 24]. The ERA5 dataset has a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) and a temporal resolution improved from 6 h to 1 h, offering comprehensive spatial and temporal coverage. Furthermore, ERA5 utilizes the Integrated Forecasting System (IFS) \"Cy41r2\" developed by ECMWF, which features a prognostic microphysical scheme with distinct variables for cloud liquid, cloud ice, rain, and snow. This advancement enables ERA5 to represent atmospheric ice more accurately compared to its predecessor, ERA-Interim [23]. This study uses FY-3F observational data to examine IWP retrieval algorithms and analyzes the results in conjunction with ERA5 data.
## 3 Comparative Parameters and Dimensional Analysis
### Relative Change in Gas Volume Within the Chamber  
Considering the uneven distribution of gas parameters within the cavity in the three-dimensional simulation, this study identifies the change in gas mass \(m\) within the cavity as a key comparative parameter for distinguishing different stages of the response process and quantifying the influence of inertial forces within the cavity. The ranges of \(m\) corresponding to the various stages are presented in Tables 3 and 4, where \(m_{s}\) represents the total mass of gas exiting the cavity when the response process reaches a steady state; \(m_{Crest}\) denotes the peak value of the total mass of gas exiting the cavity; and \(m_{Trough}\) indicates the trough value of the total mass of gas exiting the cavity. The relevant expressions for \(m\) are provided in Equations (3) and (4).  
\[m=\int qdt \tag{3}\]  
\[q=C_{d}P_{1}A\sqrt{\frac{2\gamma}{(\gamma-1)RT_{1}}\left[\left(\frac{P_{B}}{P _{1}}\right)^{\frac{2}{\gamma}}-\left(\frac{P_{B}}{P_{1}}\right)^{\frac{2+1}{ \gamma}}\right]} \tag{4}\]  
\(P_{1}\)--Chamber pressure; \(P_{B}\)--External pressure; \(A\)--Inlet and outlet area of the chamber; \(\gamma\)--Specific heat ratio of the gas; \(R\)--Gas constant; \(T_{1}\)--Inlet temperature at the orifice; \(C_{d}\)--Flow coefficient.  
Based on the above equations, this study will conduct research under the condition that \(P_{1}\), \(P_{B}\), and \(T_{1}\) remain constant, comparing the distribution of \(Ma\) within the flow paths at moments \(m=m_{s}\), \(m=m_{Crest}\), and \(m=m_{Trough}\), as well as the values of \(m\). To facilitate subsequent comparative analysis, the parameters required for the study will be non-dimensionalized. The change in gas mass-related parameters within the cavity will be non-dimensionalized using the initial gas mass \(m_{1}\) within the cavity, with the expressions for each parameter presented in the following equations:  
\[m_{1}=M\frac{P_{1}V_{Vessel}}{RT_{1}} \tag{5}\]  
\[\overline{m}=\frac{m}{m_{1}} \tag{6}\]  
\[\overline{m}_{s}=\frac{m_{s}}{m_{1}} \tag{7}\]  
\[\overline{m}_{Crest}=\frac{m_{Crest}}{m_{1}} \tag{8}\]  
\[\overline{m}_{Trough}=\frac{m_{Trough}}{m_{1}} \tag{9}\]  
\(V_{Vessel}\)--Flow path volume; \(M\)--Molar mass of the gas; \(\overline{m}\)--Relative mass of gas exiting the chamber; \(\overline{m}_{Crest}\)--First peak of the oscillation of the relative mass of gas exitingthe chamber; \(\overline{m}_{Trough}\)--First trough of the oscillation of the relative mass of gas exiting the chamber; \(\overline{m}_{s}\)--Steady value of the relative mass of gas exiting the chamber.
## 5 Discussion
Both the corresponding cumulative gas desorption and desorption rates from broken coal are 4.6 times larger than those from intact coal within the same short time period, and the gas desorption rate from broken coal approximately increases by 21.5 times compared with the gas release rate from coal before being broken at the initial time. At an adsorption equilibrium pressure of 1 MPa, the free gas is 0.3161 cm\({}^{3}\)/g, and the adsorption gas content is 13.3367 cm\({}^{3}\)/g. Within 30 s, when the coal mass is crushed to 7.5 mm, the released free gas volume is 0.2845 cm\({}^{3}\)/g, and the released adsorption gas volume is 1.0585 cm\({}^{3}\)/g. Further crushing the coal to 0.075 mm, the released adsorption gas volume is 3.7445 cm\({}^{3}\)/g. Therefore, when an outburst occurs, coal will be broken by the combined ground stress and drag force caused by flowing gas through coal. Broken coal would release a large amount of gas in a short time to further aggravate or sustain the outburst, which might explain how absorbed gas within coal takes part in the coal and gas outburst. The accuracy of the desorption model for broken coal depends on the diffusion model and particle size distribution of the coal particles. In these diffusion models, coal particles are typically assumed to be spherical. The effective diffusion coefficient decreases as diffusion time increases. At the same time, in a short time period like 30 s, the effective diffusion coefficient changes little, and the outburst is short-lived. Therefore, when estimating the amount of desorbed gas during an outburst, it is assumed that the effective diffusion coefficient remains constant. Theoretical analysis provides an explanation for the correlation between smaller broken coal sizes and faster gas release rates, shedding light on the mechanism behind the sudden and massive gas emissions during coal and gas outbursts.