esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method Implementation
### Dataset
#### 3.1.2 Introduction to Available Evaluation Datasets  
* **UFPR-ADMR Dataset.** This dataset has two versions: UFPR-ADMR-v1 and UFPR-ADMR-v2. UFPR-ADMR-v1 includes 2000 m dial images captured in the field by Parana Energy Company (Copel) employees, with each image in JPG format and a resolution of 320 \(\times\) 640 or 640 \(\times\) 320 pixels, depending on camera orientation. UFPR-ADMR-v2 expands the dataset to 5000 images. Both versions are split into training (1200/2000 images), validation (400/1000 images), and testing (400/1000 images) sets. Each image includes annotations for the counter's angular coordinates, dial position, and reading.
* **UFPR-AMR Dataset.** This dataset contains 2000 JPG images captured inside Parana Energy Company (Copel)'s warehouse, with resolutions between \(2340\times 4160\) and 3120 \(\times\) 4160 pixels. Like UFPR-ADMR, it is divided into training (800 images), validation (400 images), and testing (800 images) sets. Each image is annotated with details such as the camera used, counter position (x, y, w, h), reading, and position of each digit. All counters in the dataset feature 5-digit numbers, leading to a total of 10,000 manually annotated digits.  
Figure 7: Examples of dataset images. (**a**) Pointer meter dataset examples; (**b**) digital meter dataset examples.
## 2 Literature Review and Hypotheses Development
### Brand Trust  
Previous studies have defined brand trust as the degree of credibility consumers place in a particular brand [18; 46]. A brand carries implicit meanings, including its image, consistency, differentiation, and value [14; 15]. Consumers who trust a brand tend to have more favorable appraisals of its products. Furthermore, brand trust increases the likelihood of purchase, as consumers are influenced by the vendor's market reputation [17; 18]. Thus, brand trust is likely to enhance both individual evaluations and decision making. To be specific, [PERSON] et al. [46] argued that consumers consider brand trust when evaluating food products. [PERSON] et al. [15] also suggested that brand trust is highly valued in the evaluation of organic products. [PERSON] and [PERSON] [14] found that brand trust played a significant role in fostering positive appraisals of agricultural products. Previous studies documented that health perception influences brand trust in the case of the food itself [47; 48]. However, in the context of health-related products, the exploration of heuristics, such as the tendency to assume a product is good for health simply because of trust in the brand, has been somewhat underexplored. Additionally, [PERSON] et al. [18] studied green product consumers and found that brand trust positively influences purchase intention. Similarly, [PERSON] et al. [17] revealed a positive association between brand trust and purchase intention in the luxury goods market. [PERSON] et al. [49] examined food delivery application users and documented the positive effect of brand trust on purchase intention. Based on this body of literature, the following hypotheses are proposed:  
**Hypothesis 3a.**_Brand trust positively affects the healthiness of propolis products._  
**Hypothesis 3b.**_Brand trust positively affects the intention to purchase propolis products._
## 5 Discussion  
The paper presents a method of real-time monitoring of stresses induced in glass during ion exchange processes. The presented results refer to the BK7 glass and K\({}^{+}\)\(\leftrightarrow\)Na\({}^{+}\) ion exchange. The proposed method is non-destructive and is based on the knowledge of the elasto-optic constants of the glass. It is also necessary to know the temperature dependence of the kinetics of the ion exchange in glass. Additionally, the knowledge of the function describing the time relaxation of stresses at the glass surface is required.  
In all numerical calculations simulating the shaping of the \(\sigma(x)\) function, it was assumed that the stress relaxation time \(\tau\) (Equations (10) and (11)) is isothermal. The justification is the actual temperature of the processes for which the simulations were used according to (11). In all cases, they do not differ by more than 3 \({}^{\circ}\)C from the temperature of 400 \({}^{\circ}\)C. A large temperature span of processes (over 30 \({}^{\circ}\)C) occurs only in the case of determining the temperature dependencies of diffusion coefficients (see data in Table 1).  
The results of the determination of the \(\sigma(x)\) function were compared in this paper with the results of measurements made using the optical method. The basis of such proceedings is the knowledge of the elasto-optic constants of a given glass. The measurement verification required performing long (with a duration of over 24 h) diffusion processes. It resulted from the necessity to determine the refractive index profile using the waveguide method (multimode waveguides), which allows to determine the values of effective refractive indexes with an uncertainty of \(\Delta N\)\(\sim\)10\({}^{-4}\). This translates into the uncertainties \(\Delta\sigma\) of several dozens of MPa. Using the waveguide method based on the mode characteristic equation, it is not possible to determine refractive index profiles for single-mode waveguides. Fidelity to the reconstruction of the refractive index profile using this procedure increases with the number of modes conducted through the waveguide [21]. For this reason, the verification of stresses arising as a result of short diffusion processes is not presented here. This is however not an obstacle to numerical simulation of such processes according to (11).  
Figure 9: A comparison of simulation results of the \(\sigma(x)\) function and the measurements (\(\lambda\) = 677 nm) of (**a**) diffusion processes, (**b**) heating processes.  
The method described in this paper has been used to simulate the one-dimensional stresses and is based on simulating the refractive index profile of a planar structure. It is also possible to simulate (in the real time of the technological process) the two-dimensional refractive index profiles \(n(x,y)\) and, therefore, to simulate a two-dimensional form of stress function \(\sigma(x,y)\).
## 4 Methodology
### TDRS Orbit Determination  
The FDF plays a crucial role in TDRS constellation navigation. The FDF's Navigation Operations (NavOps) team is prime for performing precise orbit determinations, predicting future orbits, and assisting with planning and executing any necessary maneuvers to ensure the satellites remain in their desired configurations.  
To meet the operational requirements of the TDRS system, the FDF must maintain a high level of accuracy in its orbit determinations and predictions. Specifically, the current operational accuracy requirements for predicting TDRS orbits over a period of one and a half days are 75 m in position and 5.5 mm/s in velocity. These stringent requirements are driven by the need to deliver precise TDRS ephemerides to Terra (EOS AM-1), which relies on the TDRS system for its operations [18].  
This study employs the same approach for orbit determination as the operational GTDS solution utilized for TDRS. The force budget for the TDRS satellites encompasses both gravitational and non-gravitational forces. Since the TDRS satellites are positioned in a geosynchronous orbit, the force model used in this study excludes atmospheric drag forces. At this altitude, drag forces are negligible. However, solar radiation pressures are significant and were considered by modeling a spherical area-model. Furthermore, the force model also includes the 8x8 JCM-2 Earth gravity field model to account for the non-uniformity of the Earth's gravitational field due to the Earth's shape and density distribution.  
In this analysis, the BRTS measurements were excluded, and only the TT&C measurements were utilized to estimate the initial state of the TDRS satellites. The TT&C measurements were adjusted for the effects of ionospheric (using Bent model [19]) and tropospheric refraction (using Marini model [20]), as well as polar motion using Earth orientation data by the International Earth Rotation and Reference Systems Service (IERS) [21]. Along with the initial state vectors, the solar radiation pressure force coefficient and the bias in the TT&C measurements were also estimated. Chapters 7 and 8 of [PERSON] et al., 1989 [10] provide comprehensive mathematical insights into the GTDS estimation process for these parameters. By removing the BRTS measurements, a focused investigation into the role of TT&C measurements in estimating the initial state of the TDRS satellites was achieved.
# A New Framework for Visual Classification of Multi-Channel Malware Based on Transfer Learning
## 5 Conclusions and Prospects  
This paper presented a new method of malware detection. First, three different methods were used to generate grayscale images, and an interpolation algorithm was used to deal with the problem of image scale. The generated grayscale images were fused into multi-channel images. Then, we pre-trained the parameters of the model on the ImageNet dataset and combined this with the ResNet34 convolutional neural network. Finally, the improved model was used to classify the images. This method does not require feature engineering and reverse analysis and can directly extract features for training. The model uses the data enhancement method to solve the overfitting problem caused by sample imbalance.  
In order to better evaluate the performance of the method proposed in this paper, the existing ResNet series of networks was extensively evaluated, and the grayscale image and color graphics were also compared and analyzed. The experimental results showed that the malware detection method proposed in this paper achieved 99.99% classification accuracy and a 99.35% F-score. The designed model has strong generalization and excellent classification ability, effectively improving malware classification accuracy.  
In future work, we will classify and detect malware in reality and study a more diversified and larger collection of malware family samples. To ensure that the data results are more convincing and to avoid random errors, the next experiment will use the cross-validation method to evaluate the average results quantitatively. When processing the size of grayscale images, it is easy to cause the loss of information. Later, we hope to input images of any size into the model.  
Conceptualization, S.Y. and D.Z.; methodology, Z.Z.; software, Z.Z.; validation, S.Y.; formal analysis, S.Y.; writing--original draft preparation, Z.Z.; writing--review and editing, D.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Project of China (2020 AAA0107700), the National Natural Science Foundation of China (62172244), the Taishon Scholars Program (tsqn202211210), the Shandong Provincial Natural Science Foundation (ZR2020 YQ06, ZR2021 MF132), and the Young innovation team of colleges and universities in Shandong province (2021 KJ001).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Method** & **Accuracy** &
\begin{tabular}{c} **F-** \\ **Score** \\ \end{tabular} \\ \hline [PERSON] et al. [44] & Denoising autoencoders + dilated residual networks & 0.9894 & 0.9813 \\ [PERSON] et al. [45] & Multi-label fuzzy clustering & 0.9756 & 0.8921 \\ [PERSON] et al. [46] & Opcode + bytecode & 0.9905 & 0.9852 \\ [PERSON] et al. [47] & CNN + transfer learning + early stopping & 0.9319 & \textbackslash{} \\ This paper & Proposed method & 0.9999 & 0.9935 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with other relevant literature when using the Microsoft dataset.
## 3 Materials and Methods
### Calculation of Angles and Scales
#### 3.4.2 Calculation of Scale  
The range for template picture zoom is defined by the target's real growth. The maximum distance from the central point of the template picture to any point on the edge is represented by the variable \(D\). The displacement of points after a scale transformation is \(\Delta s_{e}\). The offset of the edge point must not exceed half a pixel \(\Delta s_{e}\leq 0.5\) as this would result in a mismatch issue. If we allow \(\Delta s_{e}=0.5\), the scale change step can be derived using Equation (8).  
\[\Delta sD=\Delta s_{e}+D\Rightarrow\Delta s=\frac{\Delta s_{e}}{D}+1 \tag{8}\]
# A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
## 2 Principles of Ensemble Learning Algorithms
### Stacked Generalization  
Stacked generalization, also known as stacking, was proposed by [PERSON] in 1992 [45]. It is a heterogeneous learning technique that combines diverse base learners by training a model, unlike the homogeneous bagging and boosting methods which directly aggregate the outputs of several learners to obtain the final prediction. If properly designed, stacking can take full advantage of different base learners and should perform better than an individual base learner, whether using majority voting or weighted averaging [20; 46; 47; 48].  
Generally, stacking consists of several base learners (level 0) and a meta learner (level 1), in which the outputs of base learners serve as inputs of the meta learner. Both the precision and variety of base learners affect the performance of a stacking algorithm [49; 50].  
Diversity is a measure of the dependence or complementariness among learners [51]. That base learners have high diversity implies that they are skilled in different ways, and thus stacking them could lead to improved results. There are many measures of diversity, such as Q-statistics in classification, and the variance of ensemble predictions around their weighted mean in regression [52]; no one measure has been shown to be the best [51; 53; 54]. In addition to the diversity and accuracy of base learners, the number of base learners also affects the performance of stacking. More base learners are not always associated with more accurate predictions, but they do require additional memory usage and computing time [55]. Some studies have suggested that three to four base learners stacked together are a suitable choice [56], and some action should be taken to ensure that an optimal subset of base learners is used in stacking.  
A two-layer stacking model is often adopted in practical applications, although stacking models with more than two layers are sometimes used to further improve results [57]. A two-layer stacking model is constructed as follows (Figure 1). A k-fold cross-validation is initially used to create k verification datasets on all the base learners. The cross-validated predictions are then used as new training data for the meta learner in the second layer; the number of base learners in the first layer is equal to the number of predictors in the second layer, and the test data are created by adding the averages of the k-fold predictions of the base learners [47; 58]. In some classification studies, class probabilities were used instead of predicted classes as input attributes for the meta learner, which provided an effective way of combining base model confidences [20]. The inclusion of engineered original features in stacking has been shown to give better performance [58; 59]. There have also been studies that used weighted stacking to improve results [60].
## 5 Experiments and Results
### Dataset Description  
The dataset used for the experiments in this paper is the Barcelona Bike Sharing dataset, which contains information about stations' location and their status at a specific point in time. More specifically, the dataset consists of two separate tables. The first table stores information about the ID and geographical location, expressed in terms of longitude and latitude, of each station of the bike sharing system. The second table contains the following columns: station ID, timestamp, used slots, and free slots. The latter can be interpreted as a table containing the logged information for each station at regular time intervals of two minutes: at a specific point in time and for a certain station, the number of used slots (i.e., bikes available for new trips) and the number of free slots (i.e., slots available to park a rented bike) is reported. A sample of the content of the two tables is shown in Tables 3 and 4 respectively. Station information is available from 15 May 2008 to 30 September 2008. The dataset was split into train and test according to the following criterion: the last 7 days of each month were assigned to the test dataset, the remaining days constitute the train dataset and are used to extract association rules. The test set is used to evaluate the quality of the proposed methodology on unseen data.
# Deep Learning-Based Bird's Nest Detection on Transmission Lines Using UAV Imagery
## 4 Bird's Nest Detection Network Framework
### Region of Interest (ROI) Mining Method  
In training phase of the two-stage detection network, the number of negative samples may reach tens or even hundreds of times that of the positive samples. Most negative sample features correspond to the background in the receptive field of the input image. This is easy to classify, i.e., the classification loss value is small. Similarly, the positive set also contains many samples whose features are easy to classify. Therefore, during the training process, the decrease in the value of the classification loss function may be due to the correct classification of a large number of easy-to-classify samples. This means that the easy-to-classify samples dominate the decline in the value of the loss function, resulting in the final detector not effectively identifying the small bird's nest in complex scenes where the shapes are not obvious.  
In response to the aforementioned problems, we propose the ROI mining method. Figure 5 presents the flow chart of ROI mining. The proposed method improves the proportion of small objects and difficult-to-classify objects in the classification loss and regression loss by screening and integrating all the ROI regions obtained through the RPN network. This method increases the proportion of small objects and difficult-to-classify objects in the total samples from 1:120 to 3:120. Thereby, a classifier is obtained that is more suitable for the efficient detection of difficult-to-classify nest samples with insignificant features.  
Figure 5: Region of interest (ROI) mining flowchart.  
The detailed process of ROI Mining method is as follows:  
Step 1. Before the training process starts, calculate the area of all the labeled boxes in the bird's nest dataset and sort the annotation boxes descending order. Afterwards, compute the median S using the areas.  
Step 2. During the training process, obtain all the candidate ROIs that are to be used as an input to Fast RCNN classification loss and regression loss calculation. Calculate the corresponding total loss value for each ROI, and sort them according to the loss value from high to low.  
Step 3. Select N numbers of ROIs to calculate loss. Place N/2 ROIs with the total loss values in the top of the set, and the remaining N/2 ROIs, whose loss value is high and area is smaller than S are selected.  
Step 4. Use the replaced N ROIs as input for fast RCNN classification loss and regression loss calculation.  
For the aforementioned process, we select N = 128 and apply this method to the bird's nest detection in this paper. This process makes small objects and difficult-to-classify samples dominate the loss function. Therefore, the class imbalance in the training process of the detector is mitigated, thereby improving the detection accuracy.
## 5 Conclusions  
To improve the tracking performance for weak TC-OFDM signals, a novel carrier loop algorithm based on MLE and KF is proposed in this paper. The algorithm derives the MLE discriminator function to replace the existing discriminator in carrier loop. The Levenberg-Marquardt (LM) algorithm is utilized to obtain the MLE cost function consisting of signal amplitude, residual carrier frequency and carrier phase, and the MLE discriminator function is derived from the corresponding MLE cost function. In order to further reduce the residual frequency error, the KF is used to smooth the MLE discriminator function results. Numerical simulation and real data tests are implemented to verify the performance of the algorithm. The test results indicate that the novel carrier loop algorithm can improve the tracking sensitivity of the TC-OFDM receiver by taking full advantage of the characteristics of the carrier loop parameters. Finally, compared with the current carrier loop algorithm, the tracking sensitivity is effectively improved by 2-4 dB and the positioning accuracy is experimentally improved in the real environments.  
[PERSON], [PERSON] and [PERSON] are both the principal investigators; [PERSON] and [PERSON] assisted [PERSON] to conceive and conduct the experiments and analysis; and [PERSON] analyzed the data and wrote the paper.  
This work is financially supported by the National Key Research & Development Program of China (No. 2016 YFB0502003).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 16: The RMSE positioning accuracy error in horizontal direction.  
Figure 15: The base station distribution of the test environment on the campus.
# On the Competitiveness of Oblivious Routing:
## 5 On the Competitiveness of Oblivious Routing  
Thus far, we have defined the PoC and the EVoC in terms of an arbitrary (but bounded) input demand set \(\Theta\). This provides a convenient _absolute_ performance characterization where we can reason about the performance of some static routing \(\mathcal{S}\) quantitatively by evaluating the PoC and the EVoC for a particular network and a particular \(\Theta\). A perhaps even more descriptive qualitative characterization emerges if we think of the PoC and the EVoC as performance metrics _relative_ to optimal routing.  
For this, we only need to observe that, under the choice \(\Theta=T\), the probability of congestion for an optimal routing function \(\mathcal{S}_{\text{opt}}\) is \(\eta(\mathcal{S}_{\text{opt}})=0\) (as \(R(\mathcal{S}_{\text{opt}})=T\) and so \(\operatorname{Vol}(\Theta\setminus R(\mathcal{S}_{\text{opt}}))=0\)), and, for the expected value of congestion, we find \(\mu(\mathcal{S}_{\text{opt}})<1\). Therefore, under the assumption \(\Theta=T\), the PoC essentially tells the _probability that oblivious routing overloads some link, provided that optimal routing could route input traffic matrices without congestion_. This gives a new _competitive_ measure for comparing oblivious routing to optimal routing. A similar relative interpretation can be given for the EVoC as well.  
In the rest of this paper, we use this relative interpretation to study the competitiveness of oblivious routing. Correspondingly, we shall assume that \(\Theta=T\), and we shall use the short-hand notations \(\alpha=\alpha(\mathcal{S}_{\text{OBL}})\), \(R=R(\mathcal{S}_{\text{OBL}})\), \(\eta=\eta(\mathcal{S}_{\text{OBL}})\), and \(\mu=\mu(\mathcal{S}_{\text{OBL}})\). Evaluating (4) under this assumption yields \(\eta=1-\frac{\operatorname{Vol}(R)}{\operatorname{Vol}(T)}\). Hence, \(1-\eta\) in the competitive interpretation describes the quantity of demands routable by oblivious routing compared to the quantity of demands routable at all. Second, by the substitution \(\frac{\operatorname{Vol}(T)}{\operatorname{Vol}(R)}=\frac{1}{\Gamma-\eta}\) in (7) gives:  
\[\frac{K}{K+1}\bigg{(}\frac{1}{1-\eta}\bigg{)}^{1/K}\leq\mu\leq\alpha\ . \tag{8}\]  
Here, \(\mu\leq\alpha\) is trivial from the definition. Note that \(\frac{1}{1-\eta}\) is well-defined, as \(\operatorname{Vol}(R)>0\) and so \(\eta<1\). \(\mu\geq 1-\frac{1}{K+1}\), which means that, under our traffic model, in very large networks, we can expect at least one link to operate at maximum capacity when using oblivious routing. Observe also that, appealingly, (8) relates each of the three fundamental performance metrics focal in this paper: the competitive ratio \(\alpha\), the probability of congestion \(\eta\), and the expected value of congestion \(\mu\).