esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of Image Classification Accuracy with Fourier Ptychography
## 2 Methodology
### Image Classification
The direct benefit of using a 1 by 1 convolution filter is that the network requires fewer parameters and more memory efficient. The subsequent downsampling (e.g., pool10) enables the larger activation map, thus is beneficial for maximizing the classification accuracy. For the apple pathology dataset, a Google inception-based network was used for the classification of the apple diseases shown in Figure 7. Google inception employs the inception layer as the backbone of the network. In total, nine repeated inception modules were used. Within the inception modules, different sizes of filters were used. Among them, 1 by 1, 3 by 3, and 5 by 5 filters were used within the inception module. By doing this, it can save weight parameters of the deep network. Additionally, by dividing the sequential filters into four branches within the inception layer, it further reduced the numbers of parameters, thus enabling a further increase in the computational efficiency. Through training, a fine-tuned approach is utilized. The beginning three layers of the network are frozen and only the last two layers are trained. The data are augmented through reflection along one side of the image, followed by random translation and scaling along both sides of the image. A minimum batch size of 5 was used per batch throughout the training process. Random shuffling of the data was performed to reduce the likelihood of over-fitting. Twenty percent of the data was used for validation of the training accuracy. For achieving good training accuracy, a small initial learning rate of 0.0003 was used. In total, 6 epochs of training was found to be sufficient for achieving a converged training accuracy. Figure 5: Residual convolution neural network architecture for image classification training using the Caltech 101 dataset. Res2s_branch2a represents the second stage and the second branch. Following the training of the MNIST, Fashion-MNIST, CIFAR, Caltech 101, flowers, and apple pathology datasets, the image classification accuracy was obtained for the FPM reconstructed image, lower resolution image, as well as the ground truth image. Furthermore, PSNR and SSIM are also calculated for the FPM reconstructed image, lower resolution image, as well as ground truth image. The calculation of PSNR and SSIM values uses the ground truth image as the reference. Multiple linear regression is also performed for the PSNR, SSIM (independent variables), and image classification accuracy (dependent variables). Figure 3.0 SqueezeNet convolution neural network architecture for image classification training using the flowers dataset. Fire2_squeeze means the second stage squeeze module. Figure 3.1 Google inception convolution neural network architecture for image classification training using the apple pathology dataset. Conv2_3 \(\times\) 3 represents the second stage, 3 \(\times\) 3 convolution filter. variable). A \(p\)-value of 0.05 is used as the significance threshold of the F-Test performed against the goodness of fit of the regression.
# Interactive Part Segmentation Using Edge Images
## 3 Method  
### The Proposed Models  
We propose two interactive segmentation models. We set the DeeplabV3+ [20] with ResNet-101 [21] trained with edge images--proposed model 1. Since the input images are edge images, not color images, only the structure of ResNet-101 is used without using the trained parameters of ResNet-101. In the preliminary experiment, when only the edge image is used as input, the accuracy of DeeplabV3+ with ResNet-101 is higher than that of DeeplabV3+ with ResNet-50. It seems to represent the edge image better as the number of layers and parameters of the CNN model increases.  
The interactive segmentation model is mainly composed of an encoder and decoder. The encoder is in charge of extracting features from the input images. The encoder of the proposed model 1 used ResNet-101, which is the one most commonly used. Among the recently published papers, we used EfficientNetV2 [22], one of the most accurate classification models for the ImageNet [23] dataset, as the encoder of another proposed model--proposed model 2. EfficientNetV2 [22] is one of the models that can most accurately recognize the object classes in ImageNet datasets with a smaller number of parameters and short training time.  
The decoder is the part that restores the image suitable for the feature. In the interactive segmentation model, the decoder predicts the mask of the target object or partly based on the features generated from the encoder. The decoder of proposed model 1 uses the structure of decoder used in DeeplabV3+ [20] as it is. The trained parameters of the decoder from DeeplabV3+ [20] are not used. The proposed model 2 using EfficientNetV2 [22] as an encoder improves the decoder as shown in Figure 1. The proposed model 1 to which DeeplabV3+ [20] is applied is the same except for FineNet in Figure 1. The output value from the decoder of the proposed model 2 to which EfficientNetV2 [22] is applied are concatenated with input data, then the concatenated value is passed through FineNet to obtain the final estimated mask. FineNet consists of two \(3\times 3\) convolutions and the last one is \(1\times 1\) convolution. The final estimated mask of FineNet has the same size as the input image, but the number of channels is one. For specific parameter values for proposed model 1 and proposed model 2, please refer to the Supplementary Materials.
## 4 Results and Discussion
### Quantitative Evaluation of Overpressure in Different Genesis Types  
The current pressure results from various geological factors throughout geological history [44; 45]. Thus, in analyzing the mechanisms of overpressure formation, it is essential to quantitatively assess the contributions of different geological processes to abnormal pressure. The overpressure in the Qiongzhusi Formation arises from disequilibrium compaction, fluid expansion, and pressure transmission. The Sichuan Basin, influenced by the Himalayan Movement, underwent significant uplift, which led to a decrease in geothermal temperature and large-scale gas release, ultimately resulting in a reduction in the pressure coefficient of the Qiongzhusi Formation [46; 47]. Consequently, this study quantitively analyzed the contributions of disequilibrium compaction, pressure transmission, uplift erosion unloading effect, and geothermal decline to the current pressure.
# Application of Multi-System Combination Precise Point Positioning in Landslide Monitoring
## 6 Discussion  
(1) In the early stage of the experiment, we conducted a single-system, dual-system and system combined static PPP calculating experiment to determine the benchmark system to carry on the static simulation dynamic experiment with a better applied combination in the measured dynamic data. It has important practical significance and application value for the analysis of current positioning performance of the combined system dynamic PPP.  
(2) Early experiments on the measured dynamic data decoding showed that in the measured dynamic data quality that the IGS stations provide, the static data quality is poor. This is because the measured dynamic data observation environment is more complex, satellites change frequently, cycles slip and gross error is more often caused. In the future, with the BDS and Galileo precision products, precision ascension and receiver-end and PCV PCO model elaboration, the combined system dynamic PPP positioning accuracy and convergence time will improve further.  
(3) For the combined system presented in this paper, in the PPP calculating monitoring data we only used the rapid ephemeris products offered by GFZ. In future research, this can be combined with the CNES, BKG and ESA real-time precision products offered by institutions to determine the accuracy of the evaluation of landslides. Furthermore, it can also be combined with the relevant data such as the comprehensive meteorological, hydrological, geological evaluation of the stability of landslides and deep discussion on the mechanism of the formation of landslides.
## 3 Materials and Methods
### Evaluation Metrics  
A statistical or machine learning model's performance and efficacy are assessed using evaluation metrics, which are numerical measurements. These measurements make it easier to compare various models or algorithms and offer information on how effectively the model is working.  
With \(N\) representing the number of predicted classes, the confusion matrix is a \(N\times N\) matrix. Since \(N=2\) for the pertinent problem, we obtain a \(2\times 2\) matrix (Table 5). For machine learning classification issues, where the output may include two or more classes, it acts as a performance metric. The confusion matrix is incredibly helpful for measuring recall, specificity, accuracy, and AUC-ROC curves since it includes four distinct combinations of predicted and actual values.  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multirow{2}{*}{**True Label**} & \multicolumn{2}{c}{**Predicted Label**} \\ \cline{2-3}  & **Positive** & **Negative** \\ \hline Positive & True positive (TP) & False negative (FN) \\ \hline Negative & False positive (FP) & True negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion Matrix.  
* **Accuracy (ACC):** It is calculated as the ratio of all accurate samples to all correctly anticipated samples. When the dataset is unbalanced, which is a prevalent problem in real-world credit datasets, ACC can produce deceptive findings, making it an unreliable metric for measuring the overall predictive effectiveness of models. \[ACC=\frac{TP+TN}{TP+TN+FP+FN}\] (16)
* **Precision (PRE):** In a scenario that is positively predicted, it is the metric that signifies success. \[PRE=\frac{TP}{TP+FP}\] (17)
* **Recall (REC):** It is the parameter that shows how successfully positive situations are predicted. \[REC=\frac{TP}{TP+FN}\] (18)
* **F1 Score:** It is the harmonic mean of precision and sensitivity values for a classification problem. An F1 score of 1 indicates that both precision and sensitivity are good, meaning that the model correctly identifies all positive cases without misclassifying any negative cases as positive. \[F1=2*\frac{PRE*REC}{PRE+REC}\] (19)
* **F2 Score:** It is the weighted harmonic mean of precision and sensitivity. Unlike the F1 score, which gives equal weight to precision and sensitivity, the F2 score gives more weight to sensitivity than precision. In cases where false negatives are considered to be worse than false positives, sensitivity should be given more weight [63]. \[F2=5\bigg{(}\frac{(PRE)(REC)}{((4)(PRE))+REC}\bigg{)}\] (20)
* **The area under the curve (AUC):** A method for arranging, choosing, and visualizing classifiers according to their performance is a receiver operating characteristics (ROC) chart. A two-dimensional depiction of classifier performance is the ROC curve. Reducing ROC performance to a single scalar value that represents predicted performance is another effective way to compare classifiers. The area under the ROC curve is calculated to determine the AUC. The AUC value will always fall between 0 and 1 because it is a fraction of the unit square's area [64].
# Rapid Restoration Techniques for Software-Defined Networks
## 4 Path Recovery Framework and Algorithms
### Community Detection
#### 4.4.2 CD-Based Fault Handling  
Based on the global view of the underlying topology that is constructed from the topology parser, the community detection-based fault handling module is responsible for determining the demand paths that carry the flows across the network. Invocation of the route finder occurs in two situations: (1) when a new packet arrives on the network and the calculation of a new path is required; (2) when a failure occurs and it is necessary to recalculate the path for an existing path. Two algorithms are developed to obtain the shortest path based on [PERSON]'s algorithm to meet the needs of these two use-cases. In our approach, the community-based algorithm tackles failures based on the affected community, for example, \(c_{j}\in C\), instead of recalculating paths based on the the whole graph \(G\) (cf. lines 2-3 of Algorithm 2). Currently, Algorithm 2 takes advantage of the community based approach when a failure occurs within a community and it falls back on the Global recovery approach when the failure occurs on links between communities, as a future development the framework will be extended to include the inter-link failure among the set of communities. The worst case computational complexity of [PERSON]'s algorithm bounds the computational complexity of Algorithms 1 and 2. Using a Fibonnaci heap the worst case computational complexity for the entire graph \(G\) is \(O(|E|+|V|\log(|V|))\). An appealing property of our approach is that when community detection is performed the node and edge-sets within the communities are much smaller than the node and edge sets for the entire graph. The worst-case computational complexity in the case of a failure is much lower in Algorithm 2 as [PERSON]'s algorithm is only run on the sub-graph contained within a community. In comparison, when Algorithm 1 is used in the case of a failure, the entire graph, \(G\), is considered in the [PERSON] update step. This is because Algorithm 2 performs [PERSON]'s algorithm based on the affected community in order to learn an alternative path rather than on whole the graph, as in Algorithm 1. The relationship between the computational complexity saving for the worst-case scenario for the entire graph and a community \(c_{i}\) is expressed as:  
\[O(|E|+|V|\log(|V|))>O(|E_{i}|+|V_{i}|\log(|V_{i}|)):|V|>|V_{i}|\wedge|E|>|E_{i}| \tag{16}\]  
As the number of communities detected in the graph increases, the left-hand-side of this inequality dominates. On the other hand, if too many communities are detected the range of alternative paths available to Algorithm 2, to determine an alternative path is restricted. In the worst it may not be possible to determine a better path within the community.
# A Robust Sharding-Enabled Blockchain with Efficient Hashgraph Mechanism for MANETs
## 3 Preliminary  
Hashgraph [16] is a promising consensus mechanism based on DAG, realizing asynchronous byzantine fault tolerance. The decentralization feature of DAG enables hashgraph to work in a leaderless, asynchronous and parallel manner. Additionally, hashgraph achieves consensus on the order of transactions through processes: events generation, gossip about gossip, votes counting and ordering events.  
Some concepts and operations of hashgraph are defined:  
EventAn event is a data block, whose structure is denoted as (1). _timestamp_ is the generation time of the event, \(hashId\) is the hash value of the event, and \(txList\) is the transactions to be updated. \(H1\) and \(H2\) are the hash values of the node's previous event and the received event, respectively.  
\[<timestamp,hashId,txList,H1,H2> \tag{1}\]  
Gossip about gossipThe event is sent to another node along with received gossip information and its own previous event.  
Absolute MajorityMore than \(2/3\) of all nodes in the network.  
Strongly SeeingEvent \(y\) strongly sees event \(x\) if the paths of \(y\) seeing \(x\) cover the absolute majority. For example, from the blue lines in Figure 2, event \(b1\) is strongly visible to event \(c3\), because \(c3\) finds events \(a2\), \(b2\), \(c2\) and \(d2\) seeing \(b1\).  
Created RoundA newly created round starts from event \(y\), when \(y\) strongly sees the events generated by the absolute majority. Event \(y\) is named as a witness. For example, in Figure 2, event \(c3\) strongly sees events \(b1\), \(c1\), \(d1\) and \(e1\), so event \(c3\) creates a new round.  
Famous Witness\(x\) is a famous witness if both \(x\) and \(z\) are witnesses, and \(z\) strongly sees more than \(2/3\) of witnesses, each of which also strongly sees \(x\). Famous witnesses are confirmed by the community, which is proved by [16].  
Figure 2: Illustration of strongly seeing in hashgraph.  
Event Order:Famous witnesses are sorted according to their received round number, consensus time and signature XORing. The sequence is consistent among honest nodes. It means that the order cannot be tampered with unless it is approved by the absolute majority.
## 2 System Description  
### CPM Signal with Laurent Decomposition  
An amplitude-normalized CPM signal can be expressed as follows:  
\[s(t)=\exp(j=\phi(t)),\ t>0 \tag{1}\]  
where \(\phi(t)\) denotes the phase containing the transmitted information; it is given by \[\phi(t)=2\pi h\sum_{n=-\infty}^{N-1}x_{n}q(t-nT),\ nT\leq t\leq(n+1)T \tag{2}\]  
where **x** represents the transmission information, \(T\) denotes the symbol period, \(q(t)\) denotes the phase pulse function, and \(h\) represents the modulation index.  
Because CPM is nonlinear, it needs to be linearly decomposed before being subjected to DFT. A decomposed binary CPM signal expression may be simplified to a single pulse amplitude modulation component according to the Laurent decomposition, as described by [PERSON] et al. (2013).  
\[s(t)=\sum_{n}b_{n}c(t-nT) \tag{3}\]  
where \(c(t)\) represents the partial impulse response, and the pseudo symbol \(b_{n}=\exp(j\pi h\sum_{i=0}^{n}x_{i})\) can be expressed in a recursive form \(b_{n}=jb_{n-1}x_{n}\).
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 3 Mobility with UAV Technology
### UAV Handover Scenarios
#### 3.5.2 HO Scenarios with Normal User  
The UAV acts as a mobile user above the ground, and there will be two possible scenarios involving the UAV changing its connection to a different BS. This is also possible for satellite communication systems, as evidenced by the fact that UAVs can switch from using one satellite node to another. These scenarios are illustrated in Figure 8.
## 2 Materials and Methods
### Methods
#### 2.3.2 Mann-Kendall Trend Test  
The Mann-Kendall trend test (sometimes called the M-K test) is used to analyze data collected over time for consistently increasing or decreasing trends (monotonic) in Y values [30]. Therefore, in this study, we use the MK test to detect monotonic trends in the GPP data series. It can better show the changes of GPP in the BTH over the past 20 years.  
The Mann-Kendall test for a time series \(\mathrm{x_{1}}\), \(\mathrm{x_{2}}\),..., \(\mathrm{x_{n}}\) of length n is supposed to compute the indicator function \(\mathrm{sgn}\left(\mathrm{x_{i}}-\mathrm{x_{j}}\right)\) such that:  
\[\mathrm{sgn}\left(\mathrm{x_{i}}-\mathrm{x_{j}}\right)=\left\{\begin{array}[] {ll}1,&\mathrm{x_{i}}-\mathrm{x_{j}}>0\\ 0,&\mathrm{x_{i}}-\mathrm{x_{j}}>0\\ -1,&\mathrm{x_{i}}-\mathrm{x_{j}}>0\end{array}\right. \tag{3}\]  
\[\mathrm{S}=\sum\
olimits_{i=1}^{N-1}\sum\
olimits_{j=i+1}^{N}sgn(x_{i}-x_{j}) \tag{4}\]  
where \(N\) is the length of the dataset and \(\mathrm{x_{i}}\), \(\mathrm{x_{j}}\) denote the data value of times \(i\) and \(j\). A negative value of S indicates a decreasing trend, while a positive value indicates an increasing trend [31]. The statistical significance of the trend is checked using the variance of the MK statistics, while \(N>10\), which is calculated as:  
\[\mathrm{Var}(\mathrm{s})=\frac{n(n-1)(2n+5)-\sum\
olimits_{i=1}^{p}q_{i}(q_{i }-1)(2q_{i}+5)}{18} \tag{5}\]  
where \(p\) is the number of parallel groups in the data and \(q_{i}\) is the number of data points contained in the k-th parallel group. If the binding group is not available, this summation process is eliminated from the equation. The standard Z-value of the test statistic can be estimated by the following formula:  
\[Z=\begin{cases}\frac{\mathrm{S}-1}{\sqrt{\mathrm{Var}(\mathrm{S})}}\mathrm{S }>0\\ 0;\mathrm{S}=0\\ \frac{\mathrm{S}-1}{\sqrt{\mathrm{Var}(\mathrm{S})}}\mathrm{S}<0\end{cases} \tag{6}\]  
If the calculated standard Z value is greater than the critical value of the standard normal distribution at the specified significance level (\(\alpha\)), it was identified as a significant trend and the null hypothesis is rejected.  
In this paper, the GPP data from 2000 to 2020 are calculated pixel-by-pixel, the trend of 21 data values of each pixel is calculated, its slope is taken as the trend of GPP change, and its significance is calculated.
## 3 The Establishment of an Improved Knothe Time Function Model  
By analyzing the Knothe time function hypothesis and Equation (3), Equation (4) can be obtained:  
\[\left\{\begin{array}{l}\frac{V(t)}{a(t)}=-\frac{1}{c}\\ \frac{a(t)}{a^{\prime}(t)}=\frac{a^{\prime}(t)}{a^{\prime\prime}  
If \(a(t)=0\), Equation (9) can be converted into:  
\[\left\{\begin{array}{l}t_{1}=0\\ t_{2}=\left(\frac{n-1}{C}\right)^{\frac{1}{n}}\\ t_{3}=\infty\end{array}\right. \tag{10}\]  
According to Equations (8)-(10), in the interval \(\left[0,\left(\frac{n-1}{C}\right)^{\frac{1}{n}}\right]\), the dynamic subsidence acceleration \(a(t)\) is always greater than 0, the dynamic surface subsidence velocity gradually increases, and the dynamic surface subsidence gradually increases from 0. In the interval \(\left[\left(\frac{n-1}{C}\right)^{\frac{1}{n}},\infty\right]\), the surface dynamic subsidence acceleration \(a(t)\) is always less than 0, the surface dynamic subsidence velocity \(V(t)\) gradually decreases and finally approaches 0, and the dynamic surface subsidence increase gradually slows and finally approaches \(W_{0}\). According to the function extreme value principle, when \(t=\left(\frac{n-1}{C}\right)^{\frac{1}{n}}\), the maximum dynamic subsidence velocity value \(V(t)_{max}\) is shown in Equation (11):  
\[V(t)_{0}(x,y)\left(\frac{n-1}{C}\right)^{\frac{n-1}{n}}\exp\left(-\frac{n-1}{ n}\right)_{max} \tag{11}\]  
The corresponding surface subsidence is shown in Equation (12):  
\[W(t)=W_{0}\left[1-\exp\left(-\frac{n-1}{n}\right)\right] \tag{12}\]  
The surface subsidence curves of different models order \(n\), represented by the improved Knothe time function model, the subsidence velocity curve, and the subsidence acceleration curve are shown in Figures 2-4, respectively. Figures 2-4 demonstrate that the improved Knothe time function model is consistent with the actual surface subsidence, and can be used to describe the dynamic surface subsidence process.  
Figures 2-4 show that the surface subsidence initial value is not affected by the model order \(n\), while the time when the surface subsidence reaches the final value is affected by the model order \(n\). The greater \(n\) is, the less time it takes the surface to reach the final subsidence value. As the value of \(n\) increases, the maximum subsidence velocity increases, the time of peak and valley acceleration will be shortened, with the absolute peak value and valley acceleration also increasing.  
Figure 2: The influence of model order \(n\) on surface subsidence.
## 5 Link Budget Estimation for IoT Indoor Scenario
A sudden surge and drop in the graph were due to the constructive and destructive effects of multipath signals. TRX1 and TRX3 had a good LOS from Point D to C until mid-C to B. From this point onwards, they experienced NLOS conditions. In both cases, the antenna could deliver 10 Gbps of data under LOS conditions until a 75 m distance if the link margin was 10 dB. This study indicated that due to the circular polarization of the antenna, the variation in link margin estimation for LOS in both cases was negligibly minimal. Under the NLOS scenario, Case II performed relatively well compared to Case I. This is due to the lower magnitude of the minor axis in CP, which gave rise to elliptical polarization. This means that in NLOS scenarios, the antenna with elliptical polarization had a slight impact on the performance. Figure 16: Computed received signals for (**a**) Case I and (**b**) Case II.