esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Diagnostics of Covariate Balance  
We outline the methods used for assessing balance in covariates suggested in [PERSON] et al. [48]. These diagnostics compare whether the distributions of relevant covariates are similar between the treated and untreated subjects [30; 49].  
The common measures of balance include the standardized differences and comparison of the distributions of covariates. The standardized mean difference (SMD) is defined as  
\[\text{SMD}_{1\;\text{\tiny PS}\;0}=\frac{\mid\overline{X_{1}}-\overline{X_{0}} \mid}{\sqrt[2]{(S_{1}^{2}+S_{0}^{2})}/2}\;,\]  
where \(\overline{X_{1}},\;\overline{X_{0}}\) are the sample means of the covariate for the two arms and \(S_{1}^{2}\), \(S_{0}^{2}\) are the corresponding sample variances. The SMD for the dichotomous variable is  
\[\text{SMD}_{1\;\text{\tiny PS}\;0}=\frac{\mid\overline{p_{1}}-\overline{p_{0} }\mid}{\sqrt[2]{(\;\overline{p_{1}}(1-\overline{p_{1}})+\overline{p_{2}}(1- \overline{p_{2}}))/2}}\;,\]  
where \(\overline{p_{1}}\), \(\overline{p_{0}}\) are the sample proportions for the two treatment arms. This formula can be extended for categorical variables where the comparison can be employed at each level of the variable or the Mahalanobis distance can be used, as proposed by [PERSON] [50]. SMD is interpreted as the mean difference in a unit of the standard deviation and does not depend on sample sizes or units of the variable that is measured. Because SMD does not depend on the measurement unit, it allows for comparisons between variables with different units. SMD is often presented with a love plot that graphically displays a covariate balance before and after adjusting. Generally, 0.1 represent reasonable SMD thresholds for balance [48]. Other common balance measures include the Kolmogorov-Smirnov (KS) test statistics and t-statistics [51]. Variance is the second central moment of the mean and should also be compared. A variance ratio of 1 between treatment groups indicates a good balance, and a variance ratio between 0.5 and 2 is generally acceptable [48].  
Moreover, we can look at the interactions, higher order terms, etc. The standard distribution tests can be employed, such as empirical cumulative distribution functions or non-parametric estimates of the density function of each covariate. Simple plotting, such as with side-by-side boxplots, Q-Q plots, and residual plots, is helpful and convenient.  
Prognostic scores, defined as the predicted probability of the outcome in the control, can be used for assessing balance as well [52]. We can first regress the response on covariates only in the control group; then, that fitted model is employed to predict an outcome for all subjects. Simulation study has shown that the prognostic score approach can outperform mean differences and significance tests for assessing balance [52].
## 4 Description of the System
### Infrastructure Components  
Compliance with vehicle arrival/departure schedules, the availability of payment systems that appeal to travelers, and real-time information systems for travelers are features that significantly impact the service quality of highway transport. As a result, transit companies are currently equipped with a series of resources that facilitate real-time control and information for vehicle operations and payment via smart devices (touch-free smart cards and mobile phones). The proposed system takes advantage of the required resources to provide the following functionalities:  
* Vehicle positioning system. The positioning system that is most commonly utilized in public transit vehicles is the global positioning system (GPS). This system provides the geographic coordinates of a vehicle (latitude, longitude, and elevation), its measurement quality, and the moment in which it was acquired in coordinated universal time (UTC). The measurements  
Figure 1: System overview.  
provided by this system may contain errors due to different factors, such as environmental conditions (in the ionosphere and atmosphere), the curvature of the surface of the Earth, shadowed areas that do not enable signals from satellites to be detected, or a random error introduced by the system. A conventional system is assumed to provide measurements with a maximum error of 100 m when the measurement quality is sufficient, which is obtained when the GPS receiver is able to acquire the signal from a minimum of four satellites.
* Communications system. To permit data communications with vehicles, highway public transit companies utilize mobile communication systems, such as General Packet Radio Service (GPRS) or UMTS (3G or 4G) for long-distance communication, Institute of Electrical and Electronics Engineers (IEEE) 802.20 (iBurst) for urban areas, and IEEE 802.11 (WI-FI) for communications at stations. Recently, short-distance communication technologies, such as IEEE 802.15.1 (Bluetooth), IEEE 802.15.4 (ZigBee), or radio-frequency identification (RFID) have been utilized to provide personalized services to people with special needs in different contexts of mobility, such as public transit.
* On-board sensors. To improve vehicle security, vehicles are equipped with open-door sensors. These sensors are used to prevent a vehicle from moving when passengers are entering or exiting the vehicle. In addition, open-door sensors are utilized to activate payment terminals located at the entry and exit points of vehicles. Open-door sensors are connected to the vehicle infrastructure via an electrical connection panel, where the signals from detectors at the end of the carriage assembly of each door are located; from this panel, they are connected to the digital inputs of the payment terminals. When a door is closed, its sensor is in a closed state (normally closed is coded as a logical 0), whereas this state changes to an open state (logical 1) at the moment the door begins to open.
## 3 Results
### Power Smoothing Evaluation Using the Ramp Rate (V2 GSmooth) Filter Under Varied SoC Conditions
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**[\%]SoC\({}_{SC}\)** & **Variance** & **Variance with** & **Variance** & **PV Energy** & **Delivered** & **Delivered** & **Delivered** \\
**Initial** & **Without** & **LI-ION BESS** & **LI-ION BESS** & **Reduction [\%]** & **Without LI-ION** & **PV Energy** & **Utilized** \\  & & & & & **BESS [kWh]** & **with LI-ION** & **Life Cycles** \\ \hline \(SoC_{SC}>80\) & 15.81 & 13.23 & 16.32 & 8.49 & 8.36 & 2.70 \\ \(SoC_{SC}=50\) & 15.74 & 13.03 & 17.22 & 8.47 & 8.43 & 2.87 \\ \(SoC_{SC}<20\) & 15.64 & 13.01 & 17.82 & 8.46 & 8.49 & 2.91 \\ \hline \end{tabular}
\end{table} 
Table 2: Results of PV power output with and without RR (V2 GSmooth) filter for power smoothing. Figure 7: Response of LI-ION BESS to RR (V2 GSmooth) filter under its three \(SoC_{LI}\), conditions: Case 1 with \(SoC_{LI}\) around 90%. Case 2 with \(SoC_{LI}\) around 50%, and Case 3 with \(SoC_{LI}\) lower than 20%. * **Case 3 (SoC < 20%)**: Despite the low SoC, the filter continues to reduce variability in PV power generation, with the variance decreasing from 15.64 to 13.01, representing a 17.82% reduction. In this case, the energy delivered to the grid without the LI-ION BESS is 8.46 kWh, and with the LI-ION BESS, it slightly increases to 8.49 kWh, reflecting a small improvement in compensated energy. This outcome highlights the storage system's ability to enhance energy delivery, even at low charge levels.
## 3 Results and Discussion  
To find the optimum design parameters, a parametric study was conducted while yielding a realizable structure. The time-domain solver in CST MWS was used for the design and parametric tolerance analysis. Simulations and measurements were performed in the frequency band of 1-7 GHz.
# Long-Tail Instance Segmentation Based on Memory Bank and Confidence Calibration
## 3 Proposed Method
### Dct  
In the instance segmentation field, the quality of the mask is a crucial metric, as is the case with the instance segmentation problem for long-tail datasets. To enhance the mask quality, binary grid mask representation is widely used in instance segmentation. When the resolution of the binary mask representation is increased from 28 \(\times\) 28 to 128 \(\times\) 128, the mask quality is improved; however, the excessive resolution still leads to a significant increase in computational complexity, which is not conducive to the training of the model. Therefore, the method proposed in this paper uses a DCT mask to improve the mask generation quality and enhance the mask quality while reducing the computational complexity. This process is shown in Figure 5.  
Figure 4: Post-processing calibration application.  
The DCT method reconstructs the mask by encoding the binary mask \(M_{gt}\) into a compressed vector and then decoding the compressed vector. For the binary truth mask \(M_{gt}\in R^{H\times W}\) (\(H\) and \(W\) represent the height and width of the mask, respectively), we resize it into a bilinear interpolation of \(M_{K\times K}\in R^{K\times K}\), where \(K\times K\) is the mask size. We set the value of K to 128 when the DCT is applied in Mask R-CNN. As shown in Figure 5, four convolutional layers are used to extract the features of the mask (the convolutional layers are set up in the same way as they are in Mask R-CNN), and three fully connected layers regress the DCT mask vectors. The output dimension of the first two fully connected layers is 1024, and the output dimension of the last layer is the dimension of the DCT mask vector.
## 3 Problem Statement
### Proposed Method
\(E_{residual,i}=E_{0}\cdot\text{sum}(P_{\text{consumed}}[i][j]]\) for j in range (1, t))
6. calculate distance \(D_{I,BS}\) for node \(i\) to BS
7. if \(D_{I,BS}<d_{\text{threshold}}\):
8. \(P_{\text{Transmit}}=P_{0}\;^{*}(D_{I,BS}/d_{0})\;^{*}\gamma\)
9. end if
10. end for
11. while GAN has not converged:
12. generate synthetic clusters using \(G\) (\(z\), \(\theta\_g\))
13. classify clusters with \(D\) (\(x\), \(\theta_{d}\)) to distinguish real from synthetic data
14. adjust \(G\) and \(D\) using min-max objective until discriminator cannot distinguish real from generated clusters
15. end while
16. for each cluster \(k\):
17. for each node \(i\) in cluster \(k\):
18. calculate \(S_{CH[i]}=\alpha\;^{*}(E_{residual,i}(i)/E_{max})-\beta\;^{*}(d_{I,BS}/d_{max}) -\gamma\;^{*}\) (distance (i, \(C_{k}\))/\(d_{avg}\))
19. end for
20. choose node with max \(S_{CH}\) as CH
21. end for
22. for each node \(i\):
23. broadcast \"_Hello_\" message to discover neighbors
24. for each neighbor \(j\) of \(i\):
25. \(d_{ij}\) = calculate Euclidean distance between node \(i\) and node \(j\)
26. \(\tau_{ij}(0)=1/d_{ij}+\epsilon/\)/Initial pheromone setting
27. end for
28. end for
29. for each node i:
30. for each destination d and neighbor j:
31. calculate \(P_{ijd}=\left(\tau_{ijd}\right)^{\alpha}\left(D_{ijd}\right)^{\delta}\left( \eta_{ijd}\right)^{\delta}\left(BP_{ijd}\right)^{\delta}\left(E_{ijd}\right) ^{\text{k}}\) /sum of weights over all neighbors
32. end for
33. end for
34. for each transmission on link \((i,j)\):
35. if transmission is successful:
36. \(\Delta\tau_{ij}=Q/L_{ij}\)
37. else:
38. \(\Delta\tau_{ij}=0\)
```  
**Algorithm 1**WSN Cluster-Based Routing with ACO algorithm
## 3 Results
### Optimisation Problem Formulation
#### 3.1.2 Constraints  
The optimisation is subject to the following constraints:  
1. Unique Assignment: Each event must be assigned exactly once (Equation (2)): \[\sum_{i\in T}\sum_{r\in R}x_{e,t,r}=1\quad\forall e\in E\] (2)
2. Resource Conflict Avoidance: A resource cannot be assigned to more than one event at the same time (Equation (3)): \[\sum_{e\in E}x_{e,t,r}\leq 1\quad\forall t\in T,\forall r\in R\] (3)
3. Feasibility of Assignment: An event cannot be assigned to an infeasible slot (Equation (4)): \[x_{e,t,r}=0\quad\text{if isFeasible}(e,t,r)=\text{False}\] (4)
## 3 Infrastructure Inspection and Monitoring Carried Out
### Special Inspection  
The identification of the previous damage during the visual inspection gives rise to research on the structure to determine the extent of the damage and its real effect on the structural safety index. The research was based on the following process.  
Determination of the concrete mechanical characteristics (modulus of elasticity and strength): analyzing its composition and making an electron microscopy observation, providing pictures like the ones shown in Figure 9. In both cases, the results confirmed the existence of an aggregate-alkali reaction in all the extracted cores.  
In the case of the Colmenar Bridge, all the cores extracted showed, to a greater or lesser extent, fractured aggregates partially altered by chemical degradation processes, with the presence of pores and aggregate-time interfaces clogged with whitish deposits. Likewise, a 54% average loss of elastic modulus was determined in the cores extracted.  
Determination of the chloride content and other processes of a chemical attack on the concrete, observing a very high chloride content in some of the cores extracted (1.57% referred to concrete compared with 0.2% admissible according to the Spanish Structural Concrete Standard EHE-08).  
In this sense, the analysis of chloride penetration is particularly important in the case of post-stressed concrete slabs because it can cause the corrosion of the post-tensioning strands, especially when these have injection deficiencies and other execution defects, as will be seen later on. The lack of an adequate waterproof treatment of the deck is also a determining factor in these cases.  
In addition to the venting pipes check carried out in the initial phase and visual examination of some areas of prestressing elements, an inspection of the post-tensioning strands was carried out by using a borescope to evaluate the slurry and post-tensioning strand's condition.  
The main damage detected in this inspection was some bad connections between the venting pipes and the sheaths (lack of drill holes in the sheath), incomplete filling of the sheath to a greater or lesser degree depending on the areas, leaving unprotected strands both in high points and adjacent sections, corroded and even broken strands due to a stress-corrosion process, slurry exudation and others. Figure 10 shows some examples of the previously mentioned damages. It was also observed that the layout elevations in high points were different from the design elevations, with a vertical displacement of the tendon axis with respect to the theoretical elevation up to 220 mm.  
Figure 9: Appearance of the gels detected by microscopy.  
Defects that revealed malpractice in the execution of the prestressing, such as the presence of venting pipes simply supported on the sheath without any connection hole, were also observed, as shown in Figure 11.
# Fractal Cardioid Slot Antenna for Super Wideband Applications
## 3 Experimental Results and Discussion
Range (GHz)** & **BW:1** & **BW \%** & **Electrical Dimensions 1 ** & **BDR** \\ \hline
[23] & 1.4–18.8 & 13.0:1 & 172\% & 0.17 \(\lambda\)\(\times\) 0.37 \(\lambda\) & 2762.7 \\
[25] & 3.4–37.4 & 11  
Based on the measurement results shown in Figure 13, a pretty good match of the simulated and measured radiation patterns can be noted. All measurements of the antenna radiation patterns are performed in free space, since the anechoic chamber is not available. Some discrepancies between measured and simulated results can be explained by this fact. It can be observed that the proposed antenna exhibits almost omnidirectional radiation patterns up to 5.8 GHz, which is wider than one octave (3.2:1). It is not possible to increase radiation pattern's bandwidth without decreasing the impedance bandwidth. The design of this antenna was devoted to obtaining the maximum antenna impedance bandwidth because its primary purpose was energy harvesting. The one method to improve antenna radiation patterns at higher frequencies is to reduce antenna dimensions, but the impedance bandwidth will be reduced at low frequencies.
# AU-Guided Unsupervised Domain-Adaptive Facial Expression Recognition
## 3 Methodology
### AU-Guided Annotating  
We introduce the AU-Guided Annotation (AGA) module to assign pseudo-labels for target domain images according to AU detection results. Specifically, we elaborately design several assignment schemes as follows.  
**Source-based hard label assignment** (S-hard). Given an image \(X_{s}^{i}\) and its detected AUs \(E_{s}^{i}\) from the source domain, the S-hard scheme utilizes \(E_{s}^{i}\) as a query to search for target domain images that have same AUs with \(E_{s}^{i}\). For a face image \(X_{t}^{j}\) in the target domain, its label can be defined as follows:  
\[y_{X_{t}^{i}}=y_{X_{s}^{i}}\ \ \text{if}\ \ E_{t}^{j}\equiv E_{s}^{i} \tag{1}\]  
where \(y_{X_{t}^{i}}\) denotes the facial expression label of \(X_{t}^{i}\). S-hard assigns all the retrieval images in the target domain with the label of \(X_{s}^{i}\).  
**Target-based hard label assignment** (T-hard). Different from the S-hard scheme, the T-hard scheme uses target domain images as query images. Given an image \(X_{t}^{i}\) and its detected AUs \(E_{t}^{i}\) from the target domain, it first utilizes the \(E_{t}^{i}\) to retrieve all the images in source domain, denoting them as \([X_{s}^{1},\dots,X_{s}^{k}]\). Then, with the ground truths of the source domain images, T-hard assigns the most-frequent label to the target domain image \(X_{t}^{i}\).  
**Target-based soft label assignment** (T-soft). For a query image from the target domain, unlike the hard assignment scheme, the T-soft scheme directly uses the label distribution of retrieval samples to assign each target domain image a soft label vector. It is worth noting that there does not exist a source-based soft label assignment since we do not have the labels of the target domain images.  
**Learning with AGA**. After assigning pseudo-labels for the target domain, we can train the FER models in traditional ways. Suppose \(Y_{s}\in\mathcal{R}^{1\times M}\) represents the labels of \(M\) source domain images, \(Y_{\text{S-hard}}\in\mathcal{R}^{1\times N}\), \(Y_{\text{T-hard}}\in\mathcal{R}^{1\times N}\), and \(Y_{\text{T-soft}}\in\mathcal{R}^{C\times N}\) denote the labels of \(N\) target domain images in S-hard, T-hard, and T-Soft schemes; we use the following loss function by default to train with AGA module:  
\[L_{c}=CE(P_{s},Y_{s})+\beta((CE(P_{t},Y_{\text{S-hard}})+\text{KL}(P_{t},Y_{ \text{T-soft}})), \tag{2}\]  
where CE denotes the cross-entropy loss function, KL is the KL divergence loss function, and \(P_{t}\) and \(P_{s}\) represent the predictions of the target and source domain images. \(\beta\) is the trade-off ratio between the two loss values, calculated by pseudo-labels and ground truths.
## 4 Experiments and Performance Analysis
### Performance Evaluation
In experimental simulations, one may find that the iterations of \(s^{j+1}\) and \(u^{j+1}\) often run out of memory in Algorithm 2, and the complexities of \(a^{j+1}\) and \(b^{j+1}\) are relatively high in Algorithm 3. Therefore, complexity reduction needs to be conducted to address these bottlenecks separately for two ADMM-based algorithms. As shown in Table 4, compared with the original closed-form solutions (17a) and (17b), the complexities of the simplified solutions (18) and (19) are reduced by eight OMs for two datasets. We can see that some variables, i.e., (18) and (19), have the same theoretical complexity, but their PRTs vary greatly owing to the sparsity and structure of the matrices. The theoretical complexity of \(a^{j+1}\) is reduced by only one OM between equations (21a) and (22), but the running time of \(a^{j+1}\) is greatly improved owing to structured matrices. However, in spite of the complexity decrease of three OMs, the improvement of \(b^{j+1}\) in running time is almost negligible, given that \(M\) and \(N\) are far less than \(L\). Without the reduction in complexity described above, the proposed JSMV-CNMF algorithm would take at least 3000 s. Furthermore, comparing the time complexity of all methods, the running time is listed in Table 5 for two datasets. One can observe that the original CNMF algorithm has the best running time for each dataset. The proposed JSMV-CNMF takes much more running time than the baseline methods, affected by a number of factors, such as the size of target image, matrix sparsity, convergence accuracy and stopping rules, which still need further improvement. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **CNMF** & **BSR** & **HySure** & **CSTF** & **Proposed** & **Ideal** \\ \hline \multirow{4}{*}{SNR(Ym) = 40 dB SNR(Ym) = 35 dB} & DD & 71.44 & 94.91 & 98.70 & 73.37 & **42.91** & 0 \\  & RSNR(dB) & 25.38 & 23.25 & 22.86 & 26.60 & **29.55** & \(+\infty\) \\  & RMSE & 111.49 & 142.55 & 149.01 & 103.12 & **68.98** & 0 \\  & SAM & 1.76 & 2.06 & 3.23 & 2.44 & **1.65** & 0 \\  & ERGAS & 1.18 & 1.53 & 1.55 & 1.05 & **0.80** & 0 \\  & SSIM & 0.98 & 0.97 & 0.97 & 0.972 & **0.99** & 1 \\ \hline \multirow{4}{*}{SNR(Ym) = 30 dB SNR(Yh) = 30 dB SNR(Yh) = 30 dB SNR(Yh) = 30 dB SNR(Yh) = 30 dB SNR(Yh) = 2.37 & 147.76 & 157.54 & 108.91 & **86.01** & 0 \\  & SSAM & 2.37 & 2.38 & 3.44 & 2.62 & **2.04** & 0 \\  & ERGAS & 1.35 & 1.58 & 1.64 & 1.16 & **0.92** & 0 \\  & SSIM & 0.97 & 0.96 & 0.96 & 0.97 & **0.98** & 1 \\ \hline \multirow{4}{*}{SNR(Ym) = 25 dB SNR(Yh) = 20 dB ERGAS & DD & 150.43 & 112.29 & 128.10 & 131.06 & **91.07** & 0 \\  & RSNR(dB) & 20.18 & 22.24 & 21.31 & 21.64 & **24.66** & \(+\infty\) \\  & RMSE & 202.99 & 159.99 & 178.24 & 171.45 & **121.16** & 0 \\  & SAM & 5.02 & 2.76 & 3.98 & 4.55 & **2.73** & 0 \\  & ERGAS & 2.07 & 1.71 & 1.86 & 1.89 & **1.28** & 0 \\  & SSIM & 0.91 & 0.95 & 0.94 & 0.92 & **0.96** & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of fusion algorithms for Moffett dataset (the boldface numbers denote the best performance).
## 3 Modeling the Relationship between Field AGB and Satellite Data
### Machine Learning Methods
#### 3.2.3 Random Forests  
As a classification and regression tree (CART) technique proposed by [PERSON] (2001), RF combines bagging [89; 90] with random variable selections at each node [91] to iteratively generate a large group of CARTs. The classification output represents a majority vote (classification), or an average (regression) from the whole ensemble, and hence achieves a more robust model than a single classification tree that is produced by a single model run [89; 92; 93]. A number of decision trees in RF choose their best splitting attributes from a random subset of predictors at each internal node without pruning. Based on the bootstrap sampling procedure, RF ensures at the same time the smallest obtainable bias and very low data variance [94]. There are two main important parameters: numFeatures, which means the number of features for splitting the nodes, whose default value is int[In(numbers of predictors) + 1] in WEKA; and numIterations, which means the number of trees to be optimized in the modeling process, depending on specific application objectives [95].