esa-sceva/satcom-chunk-collection huggingface dataset:
# Landslide Recognition Based on DeepLabv3+ Framework Fusing ResNet101 and ECA Attention Mechanism
## 4 Experiments and Results
### Discussion  
When applying the DeepLabv3+-ResNet101-ECA model to other regions, it is necessary to be aware of the following limitations. Since this model is a deep network model, the training and inference process requires high computational resources. Optimization techniques such as pruning and quantization are required to reduce the computational cost when necessary. Moreover, this model requires high-quality, large-scale landslide datasets, which can be alleviated by transfer learning or data augmentation techniques if applied to areas where data are scarce or difficult to obtain.  
Due to the complexity of DeepLabv3+-Resnet101-ECA, the risk of overfitting is also high, especially when the amount of data is limited. To mitigate this risk, we should use methods such as cross-validation to evaluate the model performance and ensure that we validate our models with a localized test suite to ensure their accuracy and reliability before actual deployment.  
Although the DeepLabv3+-ResNet101-ECA models perform well in specific environments, their performance may suffer in areas with limited resources or widely varying environmental conditions, and users may need to explore optimization techniques such as pruning or quantization to reduce the computational costs.  
In this paper, the research on landslide recognition based on the DeepLabv3+-ResNet101-ECA model is not limited to the inventory of phenomena but deeply involved in the whole monitoring process, realizing the full process of coverage from data collection and processing to information transmission, effectively supporting the user's decision-making process. How to embed the model into the early warning system to provide users with visual event information remains to be further studied.  
Figure 12: Comparison of the prediction masks of DeepLabv3+-ResNet101-ECA and DeepLabv3+-ResNet101-ECA-Trans-Learning for Luding landslide data. The red marked part is the important feature area of the landslide image. The part marked in red in Figure 12 is an important feature area of the landslide image, and it is also the area that needs to be correctly predicted by the model. The model will focus on extracting and learning the input image RGB and DEM, so as to identify the landslide efficiently and accurately.
## 3 Model and Parameter Identification
### Multi-Model and Multi-Algorithm Combination  
Based on the above analysis, a multi-model and multi-algorithm combination (MM-MA) method, considering SOC distribution, was developed to improve the global accuracy of the ECM. As shown in Figure 7, the whole SOC area was divided into low SOC area and high SOC area, and different ECMs and optimization algorithms were used in different SOC areas. In the SOC estimation, different ECMs and corresponding model parameters were selected.  
Figure 8 shows the identification results of the MM-MA method, which indicated that this method had obvious advantages compared with 2 RC, especially in the low SOC area. Moreover, the accuracy of off-line identification was higher than that of on-line identification. It was shown that the MM-MA method could effectively improve the accuracy of the model, and could lay the foundation for accurate SOC estimation.
## 2 Shear Lag and Effective Flange Width of a Composite Beam  
Our research found that the shear strains in the concrete flange of a composite beam do not allow for the cross-sections to remain plane, leading to a non-uniform normal stress distribution along the slab width. This phenomenon was named the shear lag effect [14; 15]. Thus, [PERSON]'s theory cannot be used for studying the composite beam. Instead, by replacing the acting flange width with an appropriately reduced, uniformly stressed effective flange width, shear lag effects can be accounted for. Therefore, the elementary beam bending theory can be used. Nowadays, to consider the shear lag effect and avoid complex evaluations, utilizing the effective width of the RC slab or composite slab with profiled steel sheeting to evaluate the combined effect of the slab is a standard method in scientific research or practical project design. Using effective flange width of a concrete slab to do global structural analysis is mainly concentrated in the three engineering fields of civil engineering, reinforced concrete structural buildings [16] and reinforced concrete bridges [17], steel bridge and composite steel bridge [18], and steel structural building [19] and composite steel structural building [20].  
Graphically, the three-dimensionally changing normal stress at a distance \(x\), \(\sigma_{x}=\sigma_{x}(y,z)\) is represented by its maximum value, shown as \(\sigma_{x}\), max in Figure 3. The force effects on the flange are required to be the same for both cases, as shown in Figure 3. The relationship is expressed as Equation (1), where \(b_{\rm e}\) is the one-side effective flange width of the composite beam, \(2b\) is the beam spacing or slab span, \(t\) is the thickness of the slab, and \(\sigma_{x}\) is the normal stress in the \(x\)-direction. As illustrated in Equation (1) and Figure 3, \(b_{\rm e}\) is a function of \(x\) and the stress distribution changes with loading conditions. Because the variations (the geometry parameters, loading conditions, etc.) strongly affect the effective flange width, design specifications seek to incorporate simple rules that can deliver appropriate values of effective flange width and apply a different philosophy of simplification. Thus, the other compromise between accuracy and simplicity can be seen in the following sections with compared results.  
\[b_{\rm e}=\frac{\int_{-t/2}^{t/2}\int_{0}^{b}\sigma_{x}(y,z)\,\mathrm{d}y \mathrm{d}z}{t\cdot\sigma_{x,\max}(y,z)} \tag{1}\]
# Modeling and Reasoning of Contexts in Smart Spaces Based on Stochastic Analysis of Sensor Data +
## 4 Principles of Context Reasoning
### Overall Approach
#### 4.1.1 Modeling  
The initial step for reasoning contexts is to establish a context graph \(\mathcal{C}_{G}\) with a given set of state spaces \(\hat{\mathcal{S}}\) which is formed from training datasets. \(\mathcal{C}_{G}\) is built by applying the statistical analysis method of sensor datasets [4]. The statistical analysis method runs in three main phases. First, the dataset is analyzed by a conditional probabilistic table (CPT) based on Bayesian networks and estimates the number of contexts, that is, \(K\). Then, the dataset is clustered into \(K\) contexts by applying \(K\)-means clustering. Thirdly, conditions for entering a context are discovered by utilizing principal components analysis (PCA). PCA is a way of finding important components and reducing the dimensions of a given dataset by projecting it into the lower dimension [39; 40]. The use of PCA helps to find the most important sensors for a given context and optimizes both the context modeling and reasoning, which will be explained in detail in the reasoning step.  
Once \(\mathcal{C}_{G}\) is established, the current state space \(\mathcal{S}_{c}\) and the present context \(\mathcal{C}_{\rho}\) are initialized. \(\mathcal{S}_{c}\) is simply configured by the current status of sensors. Initialization of \(\mathcal{C}_{\rho}\) is performed by the Algorithm 2. The purpose of the algorithm is to find an initial present context \(\mathcal{C}_{\
u}\) which has the highest similarity with \(\mathcal{S}_{c}\).  
```
INPUT: current state space \(\mathcal{S}_{c}\) context graph \(\mathcal{C}_{G}\) OUTPUT: the highest similarity scored contexts \(\mathcal{C}_{\
u}\)
1. Next context \(\mathcal{C}_{\
u}\leftarrow\)NULL
2. for each context \(\mathcal{C}_{\tau}\) in \(\mathcal{C}_{G}\)
3. if similarity \((\mathcal{S}_{c},\mathcal{C}_{\tau})>\) similarity \((\mathcal{S}_{c},\mathcal{C}_{\
u})\)
4. then \(\mathcal{C}_{\
u}\leftarrow\)\(\mathcal{C}_{\tau}\) //replace \(\mathcal{C}_{\tau}\) as a next context
5. end if
6. end for
```  
**Algorithm 2**Initializing Context
# VIIRS Day/Night Band--Correcting Striping and Nonuniformity over a Very Large Dynamic Range
## 3 Results
### Evaluating Causes of Striping and Nonuniformities
#### 3.2.4 Detector Nonlinearity  
Characterization of radiometric response was very limited for the S-NPP DNB, but on JPSS-1 much more extensive characterization was done. Analysis showed that there were large nonlinearities in many of the detectors and that these nonlinearities varied among the detectors and among the aggregation modes [37]. These nonlinearities are significant only in the lowest 1% of the dynamic range for each stage, but unfortunately, this is where the cross-calibration occurs. We see this in Figure 9, showing gain as a function of radiance. Note the logarithmic scale for plotting radiance on the x-axis.  
Because the gain in the cross-calibration range is not the same as the gain over the other 99% of the dynamic range, gain ratio error occurs and it varies with detector, causing striping. So even if the MGS and HGS response were perfectly linear for all detectors in aggregation mode 21, this gain ratio error would appear in those stages over their entire dynamic range.  
The MGS/LGS gain errors resulting from the nonlinearity in cross-calibration range were computed from the radiometric response test data for JPSS-1, and are shown for all the aggregation modes in Figure 10. The individual detector numbers are shown with different colors and symbols. The spread in the detector errors is a proxy for the striping that would result. There is also an apparent overall bias for all detectors that increases to about 7% at nadir.  
Relative gain errors in the S-NPP MGS are shown in Figure 11. These were computed by the destriping algorithm that is described in Histogram Matching Method Destriping Algorithm Description. Comparing Figures 10 and 11 the biggest difference is the bias in Figure 10. However, the destriping algorithm can only determine relative error, not bias, so this is to be expected. Otherwise, they are qualitatively similar. The overall magnitude of the spread in detectors is about the same, and the spread tends to increase in the aggregation modes near the ends of the scan. This is the cause of most of the striping seen in Figure 4. Another similarity is the two out-of-family detectors that form the symmetric \"horns\" seen on both sides of nadir. These \"horns\" are in aggregation mode 3 and they cause some of the striping seen in Figures 2 and 5.  
Figure 10: JPSS-1 Day-Night Band (DNB) MGS/LGS gain ratio error, computed from radiometric response tests.  
Figure 9: LGS nonlinearity for JPSS-1 in aggregation mode 21 showing gain as a function of radiance.
# QoE Modeling on Split Features with Distributed Deep Learning
### Distributed Learning in QoE
Our contributions differentiate from prior-art as follows:  
* _Low-level NumPy based framework for split-learning:_ Our _vFL_ implementation is based on Split Neural Network (SplitNN) [12], but unlike PySyft[16], it does not operate on existing frameworks with high level abstraction such as TensorFlow or PyTorch. Hence, the most important difference in our framework is that we have full control including the communication protocol and the algorithms. This enables low level algorithmic development to optimize for computation, privacy, network footprint, training time, and energy-efficiency while sustaining good and robust model development in diverse scenarios especially in heterogeneous data settings. Our framework is not  
Figure 3: Topologies of Distributed learning approaches are illustrated. S0 and S1 denote two different sets of machine learning input features. Figure 2: The scope of the article is illustrated in bold on the state-of-the-art distributed learning structure [8]. limited to a simulation on a single node and can easily be deployed on a Kubernetes cluster. * _A comparison of different techniques for training datasets with different feature spaces:_ In this article, we present _Distributed Ensemble Learning_ (_DEL_), and _Vertical Federated Learning_ (_vFL_) to address the aforementioned challenge. These techniques are appealing for different domains including the telecommunication segment where datasets are typically decentralized. The _DEL_ approach is machine learning model-agnostic, while the _vFL_ approach is based on neural networks. Furthermore, we recommend a few techniques to reduce network footprint, training time, and energy consumption of the model training process. * _Jointly trainable QoE modeling with split features:_ While we are demonstrating our _vFL_ solution in this article, to the best of our knowledge, this work is the first of its kind, that demonstrates a QoE model training in a distributed deep learning setting with split features. This article is structured as follows: In Section 2, we first present related work on _hFL_, _DEL_, and _vFL_. Section 3 presents the dataset and the feature description, that is then followed by a motivation of a QoE use case related to attribute splitting in Section 4. The contribution of each attribute to the model prediction is studied in detail, and compared via a use case where content-based features such as Temporal Complexity index (_TI_) and Spatial Complexity index (_SI_) cannot be shared. Section 5 presents the _vFL_ technique together with a baseline _DEL_ technique. Results including the model performance evaluation and comparison of different experiment scenarios from the two approaches are given in Section 6. The article is concluded in Section 7 along with a brief discussion on limitations and directions for future work.
###### Abstract  
When the carrier of the Satcom-on-the-Move (SOTM) antenna is in a high maneuvering state of motion, the accuracy of the array antenna beam pointing towards the satellite may decrease, which may cause a decline in communication effectiveness for SOTM or even cause the antenna to deviate from the target satellite and lead to communication interruption. This paper proposes a multi-beam searching and capturing method for SOTM array antennas, which divides the array antenna into multiple parts to form multiple independent and controllable sub-beams. When the SOTM carrier is in a high maneuvering state of motion, multiple sub-beams are obtained by dividing the array antenna, thus obtaining multiple points in the radiation pattern. We designed an optimal pointing analyzer, which calculates the SOTM array antenna beam direction based on whether the SOTM antenna is pointing towards the satellite and utilizes different approaches for processing. Experimental verification was conducted for the proposed scheme. The experimental results show that if the antenna beam does not lose the satellite, the proposed scheme can obtain better communication quality compared to conventional schemes. If the SOTM antenna beam loses the satellite and causes communication interruption, the proposed scheme in this paper can capture the satellite faster than conventional schemes. The experimental results demonstrate the effectiveness of the proposed scheme.  
satellite communication; array antenna; multi-beam; SOTM; high maneuvering
# A MAP Overhead Aware Two-Dimensional OFDMA Burst Construction Algorithm
## 2 Background and Related Works
### Related Works  
Because burst allocation is non-deterministic polynomial time (NP) problem [7], there are some algorithms proposed to maximize throughput. Positioning bursts can be viewed as a variation of a bin or strip packing problem [3; 7; 8; 9; 10; 11; 12; 13; 14].  
Reference [7] gives a fixed MAP space and then allocates the bursts by minimizing the variance of sub-blocks on leftover space and by maximizing leftover area flexibility for the following bursts. In Reference [8], the author proposed algorithms which try to maximize throughput by minimizing internal fragmentation and by minimizing the leftover space. These methods place downlink burst from right to left and bottom to top, and then they find the best suitable place in unusual space. However, fully utilizing the bandwidth resource does not mean higher throughput, since they all do not consider the problem of subchannel diversity. Therefore, the algorithms proposed in References [9; 10] are to reduce the number of bursts by grouping bursts with the same modulation coding scheme (MCS) but for different connections, so the MAP size is minimal. The adopted MCS of each connection is given in advance. This implies that each connection will adopt one MCS in different subchannels. Reference [9] even suggests that there is only a slightly difference in the carrier-to-noise ratio (CNR) between different subchannels for connections.  
References [3; 12; 13; 14] treated the burst construction as a job assignment problem and modified the Hungarian Algorithm (HA) [11]. That is, they transform the burst construction problem, i.e., \(r\) bursts with \(r\) subchannels, to minimum the matching problem with \(r\) workers and \(r\) jobs. The element indicates the cost if the activity is assigned to the related person. The Hungarian Algorithm finds the best matches between workers and jobs.  
Figure 3: Map element structure.  
However, the above methods do not consider the design that the burst can split into different bursts placing in the subchannel with good channel qualities. They also do not consider the burst indexing overhead problem.
# Dynamic Path Planning for Unmanned Surface Vehicles with a Modified Neuronal Genetic Algorithm
## 3 Mutation
### Performance Evaluation  
In this study, we use three parameters to evaluate the performance of the proposed method (the modified neuronal GA) compared to that of the baseline method (the neural network-based GA). These three parameters include convergence speed, travel distance, and travel time. The speed of convergence is determined according to the total number of genomes required by the model to be able to produce. The fewer total genomes needed, the faster a model will produce a convergent solution or path (the USV does not hit an obstacle). In our research, we used 40 genomes for one generation, so the total number of genomes follows Equation (4):  
\[Total\ Genome=40\ *\sum generation+\sum current\ genome. \tag{4}\]  
The distance evaluation parameter is determined by the total distance traveled by the USV to make one full rotation on the track (from one point back to the starting point). The total displacement path is described as follows (Equation (5)) [11]:  
\[D=\sum_{i=0}^{n-1}\sqrt{\left(x_{i+1}-x_{i}\right)^{2}+\left(y_{i+1}-y_{i} \right)^{2}+\left(z_{i+1}-z_{i}\right)^{2}}, \tag{5}\]  
where \(x_{i}\), \(y_{i}\), and \(z_{i}\) denote the X, Y, and Z coordinate axis values of node \(i\). For the travel time parameter, this is determined based on the time required for the USV to complete one full circle from one place back to the same place. Travel time values are obtained from the simulator platform that we use.  
To measure the significance of the performance of the proposed algorithm compared with that of the baseline method, we used the \(t\)-test. This test is commonly employed when we have two sets of data and want to assess whether or not any observed differences between them are statistically significant. Therefore, the \(t\) statistic for a paired \(t\)-test is as follows [32]:  
\[t=\frac{\overline{X_{1}}-\overline{X_{2}}}{s_{d}/\sqrt{n}}, \tag{6}\]  
where \(\overline{X_{1}}-\overline{X_{2}}\) denotes the mean of the differences between paired observations, \(s_{d}\) is the standard deviation of the differences, and \(n\) is the number of pairs (or the sample size). This \(t\) Stat value will be compared with the \(t\) Critical value to determine whether the performance of the proposed method produces a significant increase or not. If \(t\) Stat > \(t\) Critical, or \(p\)-Value < alpha, then we can conclude that the increase in the performance of the proposed method is significant, and vice versa.
# Modeling and Test Results of an Innovative Gyroscope Wave Energy Converter
###### Abstract
Compared with other structures of WEC, this paper creatively develops a compact, high-safety, and non-polluting purely mechanical wave power generation system. It is anchored underwater and can be lowered to the seabed in extreme ocean conditions, thereby avoiding potential damage near the sea surface. The high-speed rotating flywheel is sealed in a float. When activated by external waves, it proceeds to generate electricity due to the gyroscope principle. This design reduces the energy conversion process and is sensitive to external excitations so that a wider range of waves can be captured. The main work of this paper is as follows. Firstly, the principle of gyroscope WEC is introduced in Section 2 and the mathematical model of the precession torque is established by MATLAB in Section 3. Secondly, the physical model and test environment are introduced in detail, including the various components of the gyroscope and the composition of the test platform, in Section 4. In Section 5, the influence of each input parameter on the power is obtained, and the complete expression of the output is obtained through linear regression and prediction. In Section 6, a modified MPPT algorithm is proposed to test the performance of the system, including a variable step size algorithm, reducing the steady-state error and the application of filters. After that, the performance of the proposed algorithm under different sea conditions is tested and compared with the other two variable step size P&O algorithms in Section 7.2. The results show that the proposed algorithm has excellent stability and good rapidity in various sea conditions. Finally, in Section 7.3, the model is verified through experimental testing, finding a reasonable agreement between simulation and experimental results.
## 2 Materials and Methods
### Method Validation
#### 2.5.2 Linearity  
The linearity of an analytical procedure defines the ability to generate data that are directly proportional to the analyte's concentration over a specified range. To evaluate the linearity of the proposed method, three replicate calibration curves with five data points for each fat-soluble vitamin were constructed in a concentration range of 100-300 ng/band for the standard solution of vitamins D\({}_{2}\), D\({}_{3}\), and E and 20-60 ng/band for vitamin K\({}_{1}\) standard. Peak areas versus the respective concentrations of vitamin D\({}_{2}\) and D\({}_{3}\) standards and peak height versus the respective concentrations of vitamin E and K\({}_{1}\) standards were evaluated by linear regression analysis and the coefficient of determination (\(r^{2}\)), slope (m), y-intercept (c), and standard deviation (SD) of the calibration curves calculated. Calculations were done by using Microsoft Excel.