esa-sceva/satcom-chunk-collection huggingface dataset:
# No-Reference Image Quality Assessment Based on Image Multi-Scale Contour Prediction
## 3 Approach
### Multi-Task Model  
Humans have various intuitive perceptions of different types of distortions. The types of distortions affect humans' evaluation of image quality to a certain extent. Therefore, from the point of view of IQA, the detection of the distortion type is also of certain significance. At the same time, additional feature information of the distortion type is added to more strongly constrain the model and reduce the risk of overfitting.  
The multi-task learning model adopts the hard parameter sharing method and adopts the basic structure of the MSIQA model proposed in 3.1. Task one is IQA, and task two is a classification of image distortion types. The overall framework of the model is shown in Figure 5.  
The image convolution operation can only obtain the relationship between local channels, and the network should learn important feature information from different feature channels. Referring to the idea proposed by [PERSON] et al. [37], a feature channel weight module is added to the model, and the framework is shown in Figure 6.  
First compress the input from size \(C\times W\times H\) to \(C\times 1\times H\):  
\[Fs(x)=\frac{1}{W}\sum_{i=1}^{W}x(i) \tag{8}\]  
Figure 5: The overall framework of the model.  
where \(x\) is the input and \(W\) is the width of the input. After size compression, through two convolutional layers, the input channel weight is finally obtained, and then dot-multiply with input.  
The loss weight of the two tasks adopts a dynamic weighting method. The loss function is defined as:  
\[LOSS=\sum_{i=1}^{2}\omega_{i}(t)*L_{i} \tag{9}\]  
where \(\omega\) is the weight of loss defined as:  
\[\omega_{i}(t)=\frac{2\exp(r_{i}(t)/T)}{\sum_{i}\exp(r_{i}(t)/T)},r_{i}(t)= \frac{L_{i}(t)}{L_{i}(t-1)} \tag{10}\]  
where \(L_{i}(t)\) is the loss of task \(i\) in step \(t\), \(T\) is a constant.
## 3 Smart Contract Design
### User Access Control and User Management
```
0: address owners
0: mapping(bytes32 \(\Rightarrow\) mapping(address \(\Rightarrow\) bool) roles
0: bytes32 A \(\leftarrow\) keccak256(\"Admin\")
0: bytes32 W \(\leftarrow\) keccak256(\"Write\")
0: bytes32 R \(\leftarrow\) keccak256(\"Read\")
1:procedureonlyrole(role)
2: require(roles[role][msg.sender], \"not authorized\")
3:endprocedure
4:proceduregrantadmin(acct)ONlyrole(A)
5: roles[A][acct] \(\leftarrow\) true
6: roles[W][acct] \(\leftarrow\) true
7: roles[R][acct] \(\leftarrow\) true
8:endprocedure
9:proceduregrantwriter(acct)ONlyrole(A)
10: roles[W][acct] \(\leftarrow\) true
11: roles[R][acct] \(\leftarrow\) true
12:endprocedure
13:proceduregrantreader(acct)ONlyrole(A)
14: roles[R][acct] \(\leftarrow\) true
15:endprocedure
16:proceduredeleteadmin(acct)ONlyrole(A)
17:if is owner of contract then
18: roles[A][acct] \(\leftarrow\) false
19: roles[W][acct] \(\leftarrow\) false
20: roles[R][acct] \(\leftarrow\) false
21:endif
22:endprocedure
23:proceduredeletewriter(acct)ONlyrole(A)
24: roles[W][acct] \(\leftarrow\) false
25: roles[R][acct] \(\leftarrow\) false
26:endprocedure
27:proceduredeleteadre(acct)ONlyrole(A)
28: roles[R][acct] \(\leftarrow\) false
29:endprocedure
```  
**Algorithm 1** Algorithm for user access control. Figure 6 illustrates the four roles defined in our smart contract and related operations. The Owner is the account that deploys the smart contract, which has the highest privilege. The Owner assumes the Admin role automatically. Both the Owner and the Admin may grant other accounts the Admin, Writer, or Reader roles. However, only the Owner may delete an Admin account. In the sensing data logging application scenario (more about this scenario in Section 4.1), the sensing data processing component would function as the Owner role. This component would also take the Admin and Writer roles because it is in charge of uploading sensing data to the datastore. If a data visualization component is present in the system, then this component would need to be granted a Reader role so that it may query the datastore. In the secure genome analysis application scenario (more about this scenario in Section 4.2), each institution that enters the competition would function in the Owner and the Admin role because the institution would develop its own smart contract for data insertion and query as specified by the competition organizer. In a more realistic scenario where researchers could upload their genome experiment observations to the datastore for data sharing, such researchers would need to acquire the Writer role. Other researchers who are interested in downloading the genome experiment observations would need to acquire the Reader role. The institution that makes the datastore available to the researchers would function as the Owner and Admin.
## 5 Results and Discussion
### Simulation Scenarios  
Various parameters of the cloud movement model affect the dynamic heliostat field scene, including shape, size, speed, and sampling period. However, the number of heliostats shadowed by clouds can describe their impact. Similar cloud shadowing effects can result from different parameter combinations. In order to avoid duplicate calculations and better illustrate the impact of the cloud movement model on the heliostat field scene, an equivalent velocities parameter has been proposed. This parameter equates the effects of various cloud parameters and provides an intuitive description of different dynamic heliostat field scenes.  
To illustrate the dynamic cloud cover changes in the heliostat field, we sampled the cloud pathways above it along the mid-line of the Y-axis based on the cloud movement model. Due to varying parameter combinations, the number of shadowed heliostats differs. Thus, we used different cloud equivalent velocities to depict these changes, as shown in  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline
**Azimuth (/deg)** & \(az\in[0,30,60,\dots,360]\) \\
**Elevation (/deg)** & \(ele\in[0,20,40,\dots,90]\) \\
**Radius of cloud (/m)** & \(R\in[10,20,\dots,200]\) \\
**Velocity of cloud (/m)** & \(v_{do}\in[1,2,\dots,20]\) \\
**Sampling period (/s)** & \(sap_{per}\in[1,2,\dots,50]\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the cloud movement model.  
Figure 7: Cloud layer movement path.  
Figure 8, where black circles indicate the heliostat field and red circles represent shadowed heliostats. To enhance the accuracy in depicting heliostat field scenarios under various cloud equivalent velocities, we calculated and charted the number of heliostats covered by the moving cloud per unit sampling period for each of the cloud equivalent velocities, which is shown in Figure 9.  
The performance of Modified PSO in various heliostat field scenarios is depicted in Figure 10. Our tests across different cloud equivalent velocity heliostat fields reveal that  
Figure 8: Different heliostat fields based on cloud equivalent velocities from 1.0 to 10.0, which is related to the parameters of the cloud movement model listed in Table 2.  
the performance of a heliostat for aiming strategy real-time optimization deteriorates when the cloud equivalent velocity exceeds 4.0. This decline is primarily due to the classical PSO algorithm's population mechanism, which, as the number of covered heliostats per unit sampling period increases, leads to higher computation demands. The heliostat struggles to complete optimization within the iterative constraint period, indicating significant potential for its further optimization.
# Industrial Dust Explosions. A Brief Review
## 2 Dust Explosion Prevention and Mitigation
### Mitigation of Dust Explosions Initiated in Spite of Preventive Measures
#### 2.3.6 Automatic Explosion Suppression of Dust Explosions  
The basic working principle of systems for automatic explosion suppression is the very fast automatic injection of an extinguishing agent into the part of the dust cloud where the dust flame starts to develop. The method works well for most organic dusts. However, it is not clear to what extent the method is also applicable to very fast-burning dust clouds. Automatic explosion suppression systems are comparatively expensive both to purchase and run.  
By combining explosion venting and automatic explosion suppression, the maximum explosion pressure inside the process equipment can, in some cases, be reduced to a level significantly lower than the level that can be obtained by explosion venting only.  
[PERSON] and [PERSON] [20] conducted dust explosion suppression experiments in large vessels of volumes up to 250 m\({}^{3}\) and they were able to demonstrate that successful suppression of explosions in clouds of organic dusts is possible in such large volumes.  
Traditionally, halogenated hydrocarbons (halons) were used as flame- extinguishing agents (suppressants) in automatic dust explosion suppression systems. However, even before the environmental problems that were caused by these chemicals became a major issue. [PERSON] [9] found that powder suppressants, such as NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\), were, in general, much more effective for suppressing dust explosions than halons.
# A Data-Driven State Estimation Based on Sample Migration for Low-Observable Distribution Networks  
[PERSON]  
1 State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, China; [EMAIL_ADDRESS] (C.W. ); [EMAIL_ADDRESS] (L.W. ); [EMAIL_ADDRESS] (Y.  
scholars have proposed robust state estimation algorithms based on non-quadratic estimation criteria to enhance the robustness of the estimation results [9]. Reference [10] introduces a resilient state estimation algorithm that utilizes the weighted least absolute value (WLAV) method, while Reference [11] presents a maximum exponential absolute value estimator that can identify bad data. Although these algorithms offer good robustness, the introduction of non-quadratic estimation criteria increases model complexity and reduces computational efficiency. The model-driven SE algorithms discussed above are relatively mature. However, in practical distribution networks, issues such as low observability, uneven data quality, and flexible, variable topology structures [12] can affect the accuracy and reliability of these algorithms. With the rapid progress of artificial intelligence and deep learning in recent years, data-driven SE methods have gained increasing attention from researchers worldwide [13]. Pseudo-measurement modeling based on data-driven methods is attracting the research interest of many scholars [14; 15; 16; 17]. References [15; 16] established pseudo-measurement models, based on artificial neural networks, which enhanced measurement redundancy. Reference [17] utilized a support vector machine to establish a pseudo-measurement model which takes the uncertainty of distributed generations into consideration. Moreover, reference [18] used neural networks to initialize Gauss-Newton method, which improves the accuracy and efficiency of SE. Reference [19] introduced a deep neural network-based (DNN-based) power system state estimation model that can quickly compute the system state online. Reference [20] presented a data-driven resilient state estimation approach that enhances the overall estimation performance. Reference [21] presented a physics-guided deep learning state estimation method that leverages deep learning to capture temporal correlations between variables. While these methods demonstrate high estimation accuracy when observability is high, there are significant differences in the measurement configurations of real-world distribution networks. Traditional algorithms, such as WLS-based, WLAV-based and DNN-based state estimators, are primarily suited to distribution networks with high observability and do not apply to those with low observability. This presents a significant challenge for state estimation in low-observable distribution networks. Reference [22] utilized a transfer learning (TL) technique to achieve online dynamic security assessment of power systems, using a pre-trained source domain model to evaluate unknown fault sets. This offers a new approach to solving the aforementioned challenges. TL serves as a bridge between the source domain and the target domain by utilizing the abundant sample data from the source to enhance the target domain.
## 5 Detection of the Robot Mounting Release from the Base Using Machine Learning Algorithms
### Testing Chosen Machine Learning Models for Unffastening Detection  
Models that were introduced in the Section 4.5 were trained on data from each of the sensors. To avoid overfitting and to examine the accuracy of each model, a cross-validation method was chosen.  
The classification results after training are shown in Tables 7 and 8. The accuracy of all models based on sensor 1 data is significantly lower than that of sensor 2 and sensor 3, for which the accuracy is comparable and above 90%. This fact can be explained by the configuration of the workstation and the robot's behaviour: all the tasks that the robot performs during the working cycle take place in front of the robot's base, and thus vibrations caused by the releasing of the robot's fastening are more visible on sensors 2 and 3, which are located at the rear part of the mounting plate (see Figure 3).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Boosted Trees** & **Bagged Trees** & \
\begin{tabular}{c} **Subspace** \\ **Discriminant** \\ \end{tabular} & **Subspace KNN** &
\begin{tabular}{c} **RUSBoosted** \\ **Trees** \\ \end{tabular} \\ \hline Sensor 1 & 87.58\% & 87.91\% & 87.47\% & 85.82\% & 88.92\% \\ Sensor 2 & 92.64\% & 96.92\% & 93.30\% & 96.26\% & 96.81\% \\ Sensor 3 & 94.25\% & 96.59\% & 92.97\% & 95.82\% & 95.15\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ensemble model accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Narrow Neural** & **Medium Neural** & **Wide Neural** & **Bilayered** & **Trilayered** \\  & **Network** & **Network** & **Neural Network** & **Neural Network** \\ \hline Sensor 1 & 90.09\% & 91.55\% & 90.88\% & 89.89\% & 89.01\% \\ Sensor 2 & 97.36\% & 96.81\% & 97.47\% & 96.48\% & 96.92\% \\ Sensor 3 & 97.25\% & 97.69\% & 97.25\% & 98.13\% & 96.92\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Neural network model accuracy.  
Figure 9: Examples of placement of BCM-type sensors for correct vibration measurement [44].  
For all models used, except Boosted Trees and Subspace Discriminant at a given stage, the main research hypothesis is assumed to be met using data from sensors 2 and 3 (classification accuracy is above 95%). Therefore, we excluded Boosted Trees and Subspace Discriminant models from subsequent analysis. Next, since sensors 2 and 3 differ only in their axis orientation, and the prediction results after validation on the training data are largely the same, we decided to focus only on sensor 2 for the following analysis and testing using new data.
# How Will Rwandan Land Use/Land Cover Change under High Population Pressure and Changing Climate?
## 3 Results  
### Dynamics of Land Use/Land Cover Change from 1990 to 2015  
Figure 5 indicates that the area of forest, grassland, cropland, wetland, water, urban land, and unused land in Rwanda accounted for 29.64%, 18.13%, 41.51%, 3.77%, 6.06%, 0.82%, and 0.08% of the total area, respectively. Among them, forest, grassland, and cropland are the main LULC types; together, they account for 89.28% of the study area (Appendix A Table A1). As shown in Figure 5, the areas of forest, grassland, and cropland changed significantly from 1990 to 2015 and of the urban land area showed an increasing trend, whereas the area of other land use types showed no obvious change trend. From 1990 to 2015, the forest area gradually decreased with time, the relationship between cropland and forest was a declining relationship, and the grassland area first decreased and then increased. In 1990, the forest area in Rwanda was 10,894.89 km\({}^{2}\), which was much higher than the cropland area in the same period (6241.79 km\({}^{2}\)). However, in 2000, the cropland area (11,262.8 km\({}^{2}\)) was higher than the forest area (7471.05 km\({}^{2}\)) (Table 2). From 1990 to 2000, the cropland area increased by 5021.01 km\({}^{2}\), accounting for 19.80% of the national area; conversion from forest accounted for 3525.38 km\({}^{2}\) of this area, while conversion from grassland accounted for 1387.17 km\({}^{2}\) of this area. The ratio of area converted from forest to  
Figure 4: Schematic diagram showing the coupling of the Markov chain and patch-generating land use simulation model.  
that converted from grassland was 0.72:0.28. Between 2000 and 2010, 2415.23 km\({}^{2}\) of forest area was converted to cropland, whereas 2632.37 km\({}^{2}\) of cropland area was converted to forest during the same period. The area of cropland converted to grassland was 744.76 km\({}^{2}\), and the area of grassland converted to cropland was 921.74 km\({}^{2}\). The area of grassland converted into forest was 335.90 km\({}^{2}\). From 2010 to 2015, the forest area was reduced to 4322.12 km\({}^{2}\), resulting in the cropland area reaching 13,446.27 km\({}^{2}\) in 2015 and the grassland area increasing to 4816.66 km\({}^{2}\). The area converted from grassland to forest was 335.90 km\({}^{2}\).  
From 2010 to 2015, the forest area decreased to 4322.12 km\({}^{2}\), whereas the cropland area reached 13,446.27 km\({}^{2}\) in 2015, and the grassland area increased to 4816.66 km\({}^{2}\). Compared with 2010, the cropland area increased by 2280.07 km\({}^{2}\), the forest area reduced by 3066.17 km\({}^{2}\), and the grassland area increased by 819.45 km\({}^{2}\). The net area converted from forest to cropland was 2497.92 km\({}^{2}\), and the net area converted from forest to grassland was 512.67 km\({}^{2}\), at a ratio of 0.83:017.
## 2 Materials and Experimental Characterizations
### Thermal Characterization Method
#### 2.7.3 Thermal Diffusivity Measurement of Composites and Crushed Fibers  
For thermal diffusivity measurements, the sample is placed in the second box which is equipped with a halogen lamp of 1000 W. The thermal diffusivity of the samples and the fibers were determined by the flash method. The measuring principle consists in applying a pulse with a short duration on the bottom face of the specimen and recording the temperature evolution on the opposite face (not irradiated face) as a function of time. The thermal diffusivity was identified from experimental thermogram and theoretical model in literature [30]. As an example the thermograms of crushed fibers packed bed (with inter particle porosity equal to 70.05%) and reference mortar (without fibers) samples are shown in Figure 6. It can be observed that the evolution of the temperature until to reach up the maximum value inside the packed bed of the crushed fibers is very slow compared to that of the reference mortar. These results tend to show that the crushed fibers are more resistant to heat propagation than the reference mortar.  
Figure 6: Experimental thermogram of opposite faces of crushed fibers and reference mortar sample.
## 3 Simulation Setup and Results
### Feasibility of the Proposed Bi-OCDMA
The encoder used the user#1 signature code from the modified M-sequence, that is, 10111000, whereas the decoder was implemented with the user#2 signature code from the modified  
Figure 6: Output spectrum before photodetection for user data bits of “1”: (**a**) total signals at the upper branch; (**b**) total signals at the lower branch. Figure 7: Input and output signals from the proposed bi-OCDMA system: (**a**) binary sequence generated from PRBS; (**b**) received bipolar signals. M-sequence, that is, 01110010, demonstrating the mismatch decoder. Figure 8 indicates the output spectra before photodetection when bit \"0\" was sent. Figure 8a shows the measured spectra at the upper branch, \(\lambda_{2}\), \(\lambda_{4}\), \(\lambda_{5}\), \(\lambda_{6}\), measured with light intensities of \(-30\) dBm. Figure 8b displays the measured spectra at the lower branch, \(\lambda_{1}\), \(\lambda_{3}\), \(\lambda_{7}\), \(\lambda_{8}\), and the light intensities were \(-30\) dBm. In Figure 8a,b, four spectral chips appeared at the upper branch and four spectral chips were also measured at the lower branch, and the electrical subtraction between the upper and lower branches resulted in zero signal amplitude, proving the MAI elimination of the bipolar scheme. Figure 9 illustrates the output signals after balanced photodetection in the case of a mismatch decoder when bit \"0\" was sent. No signal was received by the user#2 decoder when the data bit was sent from the user#1 encoder, verifying the absence of the MAI effect on the other user. A small acceptable noise-floor signal was measured, resulting from the imperfect filter of the FBG decoder. The simulation results proved the feasibility of the bipolar scheme from the proposed Bi-OCDMA with a phase modulator and verified the MAI elimination property of the system. Figure 8: Output spectra before photodetection for the mismatch decoder when bit “0” was transmitted: (**a**) total signals at the upper branch; (**b**) total signals at the lower branch. Figure 9: Output signals of the proposed Bi-OCDMA system for mismatch decoder when bit “0” was transmitted.
## 1 Introduction
### Related Works
#### 1.1.4 Sparse-Direct Methods  
Sparse-direct techniques work directly on pixel information but do not use all the pixels, producing sparser maps using fewer computational resources. The main contributions from this classification are the DSO, LDSO, and DSM. Direct Sparse Odometry (DSO) was introduced by [PERSON] et al. [PERSON] et al. (2018) as the first direct-sparse VO technique. The DSO operates directly on image intensities, optimizing the photometric error instead of the geometric reprojection error. It represents the geometry using inverse depth parametrization and jointly optimizes all the model parameters in real time using a sliding keyframe window. The DSO demonstrated superior accuracy and robustness compared to indirect methods by utilizing edges and intensity variations in featureless areas. However, as a pure visual odometry technique, the DSO suffers from drift over long trajectories as it marginalizes old points and keyframes. [PERSON] et al. presented the LDSO [PERSON] et al. (2018), extending the DSO to a more robust VO system by adding loop closure detection and pose graph optimization. The LDSO adapts the DSO's point selection to favor repeatable corner features and computes the ORB descriptors detecting the loop closures using DBoW2. It then estimates the \(Sim(3)\) constraints by minimizing the 2D and 3D errors fusing them with the covisibility graph from DSO's sliding window optimization in a pose graph. While reducing the accumulated drift, the LDSO still lacks a persistent map ignoring the existing information after loop closures. [PERSON] et al. introduced Direct Sparse Mapping (DSM) [PERSON] et al. (2018), the first direct sparse monocular SLAM system with a persistent map enabling point reobservations. The DSM selects active keyframes based on temporal and covisibility constraints using the Local Map Covisibility Window applying a coarse-to-fine optimization scheme and a robust cost function based on the t-distribution to handle challenges in converging when incorporating distant keyframes. The DSM demonstrated increased accuracy in trajectory and mapping on EuRoC compared to the DSO, LDSO, and ORB-SLAM. The ability to reuse existing map points resulted in more consistent maps without duplicates. In brief, the DSO pioneered direct-sparse SLAM and achieved superior odometry compared to the indirect methods. The LDSO extended it to full SLAM by adding loop closure detection and correction to reduce drift, while the DSM took a further step creating the first direct technique with a persistent map, enabling beneficial point reobservations through key innovations in window selection, optimization, and robustification.