esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation Results
### Effects of \(\alpha\) on the Performance of the Proposed Routing Protocol  
In this subsection, the effects of \(\alpha\) on the performance of the HLPRP are discussed. Recall that when calculating packet delivery predictability, we can adjust the effect on packet delivery predictability of the predicted information for the near future and the distant future by using tunable parameter \(\alpha\). Figure 3 shows the network performance for various values of \(\alpha\) and \(TTL\). The \(PDR\) is shown in Figure 2(a). In general, the \(PDR\) is higher with a longer \(TTL\). For the same value of \(TTL\), the \(PDR\) does not change much. For example, with \(TTL=4\) h, it reduces slightly when \(\alpha\) increases from 0.1 to 0.3, and then it slightly increases from 0.7506 to 0.7521 when \(\alpha\) increases from 0.6 to 0.7. Figure 2(b) displays the \(PDL\). The \(PDL\) increases when the \(TTL\) increases. For low values of \(\alpha\), latency is high. Then, it decreases when \(\alpha\) increases. For example, with the \(TTL=4\) h, the \(PDL\) is 3269 s when \(\alpha=0.1\) and 3225 s when \(\alpha=0.7\). The \(DC\) is presented in Figure 2(c). The \(DC\) is larger with a longer \(TTL\). The value of \(\alpha\) does not affect the \(DC\) much. It is similar for the various values of \(\alpha\). For example, the \(DC\) is 37.4 when the \(TTL=1\) h for all different values of \(\alpha\).  
From the obtained results in Figure 3, the value of \(\alpha\) was set to 0.7 in the proposed routing protocol.
### Our Contributions  
This paper focuses on federated settings where the dataset is partitioned amongst collaborating institutions that wish to perform statistical tests securely. To that end, we present communication efficient methods for computing the test statistics that are more efficient in terms of communication and runtime compared to an equivalent SMPC-based method. An overview of the system architecture is shown in Figure 1.  
As a prerequisite, we first present a new method for the secure division fully using MHE. We find that even for a relatively moderate batch of divisions, our MHE-based method is far more efficient in terms of communication and outperforms equivalent SMPC-based methods in terms of runtime.  
We then use this secure division method to compute the [PERSON]'s \(t\)-test and \(\chi^{2}\)-test statistics on real world datasets and find that our method remains highly accurate while requiring far less communication than the SMPC method. This communication efficiency is leveraged to result in a significant speedup in runtime for our MHE-based method.  
Lastly, we design and implement a novel protocol to perform privacy-preserving table lookup from a secret shared index. This is used to calculate the \(p\)-value of the Welch's \(t\)-test for which both the statistic and degree of freedom contain private information, and polynomial approximations are hard to craft. This is a similar yet different problem from that of private information retrieval (PIR) [15] where the table is held by the server and the index is held by the client. In order to compute the secret shared index from the encrypted statistic and degree of freedom, we design and implement another protocol that switches between the MHE and SMPC representations--specifically one that preserves the packing and \"real\" number encoding of the Cheon-Kim-King-Song variant of MHE [16].
# Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time
## 4 Results
### Performance Assessment
#### 4.2.2 Monthly Series Analysis  
We selected four boxes with the same size of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) to examine the performance of seven methods in reproducing monthly time series. The central longitude and latitude of the four boxes are: A, 109.3\({}^{\circ}\)E, 22.3\({}^{\circ}\)N; B, 120.3\({}^{\circ}\)E, 29.3\({}^{\circ}\)N; C, 112.5\({}^{\circ}\)E, 33.3\({}^{\circ}\)N; D, 95.7\({}^{\circ}\)E, 34.2\({}^{\circ}\)N, as shown in Figure 1a. These four boxes represent different magnitudes of daily mean precipitation intensity: 7, 5, 3, and 1 mm/day for A, B, C, and D respectively. Figure 5 shows the monthly precipitation time series from CMPA, GPM, and the seven correction methods. It is evident that GPM does not capture the seasonal cycle well, with an overestimation during a portion of the rainy season at all four boxes. This overestimation is reduced in most of the corrected products. For box A, GPM substantially overestimates precipitation in May and June, which contributes to a larger RMSE value (145.44 mm/month). All the correction methods decrease precipitation in May and July, which leads to better estimates against the CMPA precipitation. The SMP-Linear correction is the best with an RMSE value of 24.96 mm/month. For box B, the correction methods also decrease the RMSE values by alleviating overestimation occurring in June with GPM. The GPM-Linear method is the best based on the smaller RMSE (22.55 mm/month). For box C, all the correction methods work well except for GPM-Nonlinear, but the decreases in RMSE from GPM to correction methods are small with the lowest RMSE value of 19.01 mm/month by SMP-Nonlinear. For box D, GPM-Linear, GPM-CDF, and SMP-CDF have the large RMSE value against CMPA, indicating that these methods are not suitable for applying to west of China. However, combining SMP with linear or nonlinear fitting method shows an effective way to reduce RMSE, as SMP-Linear and SMP-Nonlinear have lower RMSE value with 15.94 mm/month and 15.68 mm/month. For all boxes with a variety of precipitation magnitude, most of the correction methods help in reducing the RMSE for most cases. Although the best correction method in each grid may be different, the SMP-Linear and SMP-Nonlinear correction show their stability and efficiency, which is consistent with the national map of RMSE on the annual scale (Figure 3).  
Figure 5: Time series of monthly rainfall in May to September of 2017 obtained from AMPA, GFM, and the seven corrected products (SMP, GPM-Linear, GFM-Nonlinear, GPM-CDE, SMP-Linear, SMP-Nonlinear, and SMP-CDF) for the four boxes A, B, C and D shown in Figure 1. RMSE: root-mean-square error in mm/month.
## 3 Data and Methods  
The following methodological section consists of five parts, shown in the workflow chart in Figure 2. In the first part (i), the DInSAR displacement time series is generated from Sentinel-1 data using snappy and pyrate. In the second part (ii), LOS displacement from ascending and descending orbits is decomposed into horizontal East-West (E-W) and vertical Up-Down (U-D) displacement. Consecutively, the elevation change and direction of movement are derived. In the third step (iii), reference data from orthophotos and UAV imagery are generated to assess the accuracy of the decomposed DInSAR displacement rates. In the fourth step (iv), the seasonality of the displacement is calculated. In the final step (v), a detailed correlation analysis of the spatiotemporal dynamics is carried out to identify drivers for movement rates and direction as well as intensity and timing of seasonal acceleration. An overview of all used software can be found in Appendix B (Table A1).
# Research on Students' Action Behavior Recognition Method Based on Classroom Time-Series Images
## 4 Experimental Results and Analysis
### Experimental Results on the Public Dataset  
In the experiments on the AVA dataset, the model was trained using the pretraining parameters of the Kinetics-700 dataset for weight initialization. The input to the network is sixty-four frames, \(\alpha=8\),\(\tau=16\), i.e., thirty-two frames for the fast pathway and four frames for the slow pathway. To reduce the number of parameters, the short edges of the video frames are cropped to 256, two GPUs are used for training, and the SGD optimization algorithm is used with a batch size of four and an initial learning rate of 0.00025, which is adjusted in the first 2k iterations using linear warm-up. In this paper, we refer to the  
Figure 8: Example of student action behavior in the classroom.  
literature [31] and evaluate the performance of the model using frame-level mAP with an IoU threshold of 0.5.  
In this paper, the MsTA module and MsCSA module are fused to the AIA network; the classification loss function is replaced with the EFL function, which is compared with the SlowFast [13], X3D [14], ACAR [17], AIA [11], MRSN [18], DOAD [15], and HIT networks [19]; and the experimental results are shown in Table 1.  
ACAR and AIA models are results reproduced in the environment provided by the authors and the rest are reported results from the paper. In the experiments on the CUF101-24 dataset, the input to the network is 32 frames, \(\alpha=4\), \(\tau=4\); trained using a single GPU with a batch size of eight; and the other settings are the same as the experiments on the AVA dataset. The MsTA module and MsCSA module are fused to the AIA network and compared with the ACT [40], STEP [41], AIA [11], ACAR [17], MRSN [18], DOAD [15], and HIT networks [41]. The experimental results are shown in Table 2.  
Tables 1 and 2 show the results of comparing this paper's improved AIA network with other models on the AVA dataset and the UCF101-24 dataset, respectively: (1) The experimental results of the two datasets show that the improved AIA network in this paper outperforms most of the models and has good generalizability. (2) On the AVA dataset, the mAP values are lower than those of the MRSN model and the HIT model; however, the MRSN model requires pretraining of a base network to extract the features of the video clips as memory features and the HIT model requires an additional human pose estimation network to extract the keypoints of the human body. (3) On the UCF101-24 dataset, the mAP values are lower than those of the ACAR model and the HIT model but the ACAR model models actor-context interactions, which may generate background noise.
## 5 Proposed Hybrid V2X Technology Selection Algorithm
### Selection Criteria  
The following selection criteria are considered in the proposed intelligent hybrid V2X technology selection decision making algorithm.  
1. One-way end-to-end CAM latency: Elapsed time from the instant a data packet is generated at the source application to the instant it is received by the destination application, calculated using the timestamps i.e., CAM creation time and CAM reception time. This in further divided into three parameters, one for each of the three considered technologies i.e., DSRC, PC5 and 5G Uu, and denoted as \(L_{D}\), \(L_{P}\) and \(L_{Uu}\), respectively. Each of them represents an average value based on all CAM receptions from a specific technology during the time sliding window.
2. Inter-vehicular distance: Distance between V2X nodes, calculated using the longitude and latitude information from the received CAM and the current longitude and latitude of the V2X node where intelligent decision is to be made. This is further divided into three parameters, one for each of the three considered technologies and denoted as \(D_{D}\), \(D_{P}\) and \(D_{Uu}\) for DSRC, C-V2X PC5 and 5G Uu, respectively. Each of them represents an average value based on all CAM receptions from a specific technology during the time sliding window.
3. CAM density: Number of CAMs received via DSRC, C-V2X PC5 and 5G Uu during the time sliding window, denoted as \(Dense_{D}\), \(Dense_{P}\) and \(Dense_{Uu}\), respectively.
4. C-ITS message type: CAM only or CAM with DENM. This is a crucial parameter as efficient resource allocation and technology selection is needed to avoid compromising the DENM transmission performance.
5. Speed: Speed (5) in km/h of the V2X node where the intelligent decision is to be made. Speed is compared with a speed threshold, \(St_{thr}\), to decide if the V2X node is moving fast, with an aim to preempt possibly rapidly changing traffic dynamics.  
Criteria 1-3 are based on the received CAMs, whereas, criteria 4 and 5 are relevant to the V2X node on which the intelligent V2X technology selection decision is to be made.
## 3 Signal Processing Methods
### Wavelet Filter  
Wavelet transform has been used in many applications as an efficient filter method [15,16]. In wavelet transform, a mother wavelet (\(\psi(x)\)) with scale (\(a>0\)) and shift (\(b\)) parameters were used instead of the Gaussian window in the Gabor transform, as expressed in Equation (9); where the \({}^{*}\) denotes the complex conjugate. By changing the scale \(a\), the multi-resolution of the signal could be obtained. The noise signal could be efficiently removed by thresholding the scale coefficients, \(W\).  
\[W(a,b;V_{RMS}(z),\psi(z))=\int_{-\infty}^{+\infty}V_{RMS}(z)\frac{1}{a}\psi^{ \prime}\Bigg{(}\frac{z-b}{a}\Bigg{)}dz \tag{9}\]  
In the multivariate signal filtering, the threshold values could be automatically determined by incorporating with principal component analysis (PCA). The basic idea is to decompose each univariate input signal into detail and approximate components at different resolution (level); then, perform PCA on the concatenated detail and approximate components of the same level to remove noise. The denoised signal is obtained by inverting the wavelet transform using the processed detail and approximate components. The wavelet filter for multivariate signals based on PCA scheme is as follows [15,16]:* _Input setting_: mother wavelet, WT max level \(L\), remained a number of principal components for each level \(P_{i}\) (\(1\leq i\leq L\)) and reconstructed signal (step 5);
* _Step 1_: Perform WT at \(L\) levels for each input signal (\(V_{RMS}(z)\)) to obtain detail components \(\mathbf{D_{i}}\) (\(1\leq i\leq L\)) and approximation component \(\mathbf{\Lambda_{L}}\) at the max level \(L\);
* _Step 2_: Concatenate each detail component \(\mathbf{D_{i}}\) in the same level of all the input signal to build a 2D detail matrix \(\mathbf{D}\). Then, perform PCA on the detail matrix \(\mathbf{D}\) to remove the noise signal by retaining only a few numbers of principal components \(P_{i}\);
* _Step 3_: Similar to step 2, concatenate the approximation component \(\mathbf{A}_{L}\) of all the input signals to build the 2D approximation matrix \(\mathbf{A}\). Then, perform PCA on the matrix \(\mathbf{A}\) to remove the noise signal;
* _Step 4_: Reconstruct the signal \(\hat{V}_{RMS}\) from the processed approximation and detail components by inverting the wavelet transform;
* _Step 5_: Perform PCA again on the reconstructed signal \(\hat{V}_{RMS}\) to remove the noise if remained.  
The key in the wavelet filter is choosing a suitable number of remained principal components for each scaled signal according to the retained variance of the signal. For instance, the principal components are kept if the eigenvalues were exceeding the mean of all the eigenvalues (Kaiser's rule) or exceeding 0.05 times of the sum of all the eigenvalues (heuristic rule) as using in the MATLAB Wavelet Toolbox 5.2. However, the principal components could be dominated by a strong signal that leads to discarding low-intensity signals, which may be the corrosion signal.
## 4 Proposed Trajectory Optimization Approach
### SMDP  
An MDP is often used to simplify the modeling of scenarios in reinforcement learning. It assumes that the state transition of the environment has Markov properties; that is, the probability of transitioning to the next state is only related to the previous state. The agent continuously observes the environment with Markov characteristics and makes habitual decisions in an MDP. An MDP with \(M\) agents consists of a quadruple \(\langle\mathcal{S},\mathcal{O},\mathcal{A},\mathcal{R}\rangle\), where \(\mathcal{S}\) represents the state space of the environment, which is used to describe the state of each entity. In addition, \(\mathcal{O}\) represents the observable state space of each agent, \(\mathcal{A}\) represents the action space of each agent, and \(\mathcal{R}\) represents the reward function. Common MDP is mainly used to model the traditional decision-making process. However, the time between decision-making stages is not constant in the actual scene. Thus, it can be hard for the traditional MDP to model such problems. The SMDP, as an extension of the MDP, is an option-based decision-making process that can model this kind of control problem with variable decision-making times.  
An SMDP is mainly composed of a quintuple \(\langle\mathcal{S},\mathcal{A},\mathcal{O},\mathcal{P},\mathcal{R}\rangle\), where \(\mathcal{O}\) represents the set of options, and other elements in the tuple have the same meaning as in the MDP. The pion can be represented by a triplet \(\langle I_{o},\pi_{o},\beta_{o}\rangle\), where \(I_{o}\in\mathcal{S}\) is the initial state set of the option, \(\pi_{o}\) represents the strategy adopted within the option, and \(\beta_{o}\) represents the termination condition of the option. In other words, the option \(\langle I_{o},\pi_{o},\beta_{o}\rangle\) is available only when the current state \(s_{t}\in I_{o}\). If the option is chosen, the agent has to choose the next behavior according to the strategy \(\pi\), and \(\beta_{o}\) is responsible for judging whether it reaches the termination state. If so, the agent chooses the next option to enter. The MDP can be converted into an SMDP by defining the set \(\mathcal{O}\) of options on the basis of MDP. The original MDP is more like a special case of the SMDP, in which each action in the MDP is an option that only lasts for a moment.  
The upper-level strategy in the SMDP \(\mu:\mathcal{S}\times\mathcal{O}\rightarrow[0,1]\) applies to the reinforcement learning of the selection of an option; that is, to choose an option from \(\mathcal{O}\) according to the current state, and then proceed to the next round of selection after the option reaches the terminate state. As for the strategy \(\pi\) in each option, it can include some pre-defined rules, or a mapping function from a state to an action space learned through a reinforcement learning algorithm.
# Research Progress in Methods for the Analysis of the Internal Stability of Landslide Dam Soils
## 4 Criteria Considering Stress Conditions
### Isotropic Pressure State  
Isotropic pressure condition internal erosion refers to the phenomenon where internal erosion occurs in soil when it is subjected to equal stress in all directions. Under the influence of seepage, the magnitude of confining pressure affects the ability of water to pass through the soil pores [58; 59]. For example, when the confining pressure applied to the soil sample is low, an increase in the confining pressure reduces the sample volume, decreases porosity, and lowers the permeability coefficient [60]. Consequently, the critical hydraulic gradient of the soil increases with the increase in the confining pressure [25; 61; 62; 63].  
Researchers have already obtained the relationship curve between the confining pressure and the critical hydraulic gradient through data fitting. For instance, [PERSON] et al. [64] conducted five groups of experiments using an improved triaxial permeameter on discontinuously graded gravelly soil with confining pressures of 0, 0.2, 0.4, 0.6, and 0.8 MPa. A clear linear relationship between confining pressure and the critical hydraulic gradient was found, as shown in Formula (5).  
\[i_{c}=0.82P+0.204 \tag{5}\]  
In the equation, \(P\) represents the confining pressure applied to the soil sample. Although different scholars have fitted various forms of the relationship curve between the confining pressure and critical hydraulic gradient [25; 64], the starting hydraulic gradient of the soil increases with the increase in the confining pressure, as shown in Figure 2.  
However, under a high confining pressure, the stable structures formed between particles are more prone to failure or collapse, making the soil structure unstable. Even a small hydraulic gradient can cause internal erosion in the soil [61]. Under high hydraulic gradient conditions, the loss of fine particles may lead to the collapse of the original force transmission structure [65]. When the soil fails, the fine particles that originally provided lateral support to the coarse particles in the skeleton are lost, resulting in severe deformation of the soil skeleton [25]. For soils with a fine particle content at or above the critical value, the loss of fine particles further damages the force transmission structure, causing more fine particles to become unstable and easily lost [66].  
Therefore, it is necessary to conduct internal stability tests on dam soils under high stress conditions to comprehensively assess the impact of the stress state on the internal stability of the soils.  
Figure 2: Effect of confining pressure on hydraulic gradient that initiates internal erosion [25; 64].
## 6 Conclusions  
The finiteness of inter-satellite resources in intelligent satellite networks leads to an increase in interference in co-channel spatial signal transmissions. In view of the above problems, this paper presents the following solutions: (i) the algorithm uses the same wireless resources to provide transmission services for multiple unicast packets and packets multicast services at the same time. This can improve the transmission efficiency of the system. (ii) Develop the potential of signal space and construct mutually orthogonal signal space alignment directions. According to channel quality selection and power allocation, the minimum European distance of constellation points is increased. This can improve the BER performance of the system. (iii) The constellation point ambiguity problem of high-order QAM modulation is solved by relocating the constellation points of relay superimposed signals. Numerical results have revealed that the proposed scheme develops the potential of signal space and constructs mutually orthogonal signal space alignment directions, which ensures that the minimum European distance of constellation points is increased in comparison to the traditional SSA. This brings an additional BER performance gain of 2 dB. The scheme exploration heterogeneous strategy can effectively improve the sum rate by 23% when SNR is 25 dB. The experimental results have also shown the effectiveness of the proposed scheme in the degrees of freedom. The signal estimation technology in MIMO systems has a vital impact on the overall performance of the system. Next, we will study the joint algorithm of channel estimation and signal detection, which needs to balance low complexity and high parallelism.  
Conceptualization, Y.W.; methodology, Y.W. and X.W.; writing--original draft preparation, Y.W.; writing--review and editing, X.W.; supervision, Y.W.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Key Research and Development Program of China (number 2018 YFB0804103), and in part by Shaanxi Intelligent Social Development Strategy Research Center.  
Not applicable.  
The authors declare no conflict of interest.