esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### Traffic Load Calculation  
To reduce energy overheads, the traffic load for nodes with a trust value that is higher than the value of the trust threshold is identified. Further, to detect network congestion, '\(CongTh_{Min}\)' and '\(CongTh_{Max}\)' thresholds that fall within the range of buffer length in the queue are set. Depending on the fixed threshold values, network congestion can be classified into three types, as follows:  
* Less : If the queue length falls below the threshold \(congTh_{\min}\), the network is considered as having congestion.
* Medium: If the queue length is between \(congTh_{\min}\) and \(congTh_{\max}\), the network is considered as having medium congestion.
* High: If the queue length is higher than the threshold \(congTh_{\max}\), then the congestion level is considered as being high.  
Let \(T_{s}^{k}\) represent the buffer length of the '\(k^{th}\)' node, represented as  
\[CI_{k}=f(T_{s}^{k}),CI_{k^{\prime}}=1-CI_{k} \tag{9}\]  
where \(T_{s}^{k}=CI_{k}\).  
If \(T_{s}^{k}=CongTh_{Min}\), the quality of congestion will be lower, where \(C\) refers to a lower quantity.  
If \(T_{s}^{k}=CongTh_{Min}\leq T_{s}^{k}\leq CongTh_{Max}\),  
\[CI_{k}=1-\mathcal{C}\cdot\frac{T_{s}^{k}-CongTh_{Min}}{CongTh_{Max}-CongTh_{ Min}}+\mathcal{C}\]  
If \(T_{s}(k)>congTh_{\max}\), the congestion is high, and it becomes 1.
## 5 Discussion  
The results indicate that the evaluation of the integrity of a cracked structure can be done by the combination of the XFEM method and the volumetric method. The data obtained from these two methods contributed to the construction of the FAD diagram. As the sensitivity of the stress distribution along the defect requires the use of the XFEM method to eliminate all types of errors due to the use of other techniques mentioned above, the stability of a cracked structure can be examined using the famous FAD diagram.  
By comparing these results to those presented in [41], and as we have taken the same dimensions of defects, we found that the curve of the stress distribution corresponding to the model CFEM method and the curve of the stress distribution corresponding to the model XFEM method are almost identical. They have a relative error of 0.72%. We chose the XFEM model to improve the quality of the computation results and to precisely eliminate the singularity effect at the crack point.  
The fracture parameter used in this research project is the stress intensity factor; the difference between this research and other researches is in the method of extraction of this parameter. That is why we have combined the XFEM method and the volumetric method. It should be noted that the method used (volumetric method) to extract the stress intensity factor is generally exploited for notch processing. However, we have assumed that the crack behaves as a notch. We have obtained 10.164 MPa \(\sqrt{m}\). Among the reasons for applying this idea is that the study of crack propagation in Abacus software does not allow us to directly extract this parameter, except that the crack remains stable, and this is not the case here. The FIC is one of the parameters used to construct the famous FAD diagram. Normally, the point (0.0, 1.0) limits this diagram on the \(Y\)-axis and (1.0, 0.0) on the \(X\)-axis. Figure 7 shows an offset on the \(X\)-axis, but it does not influence the quality of the results. It may be due to the effect of plasticity at the crack tip, but this is not the aim of this research; we will study this effect in future work. Finally, the stability of the cracked structure depends on the value of the safety factor, which is 4.23. This value shows that the structure can function correctly with this type of defect and this type of loading.  
Therefore, it can be concluded that the strategy provided in this paper has been successfully validated on cracked pipelines. The results obtained by the use of FAD based on XFEM permit the dealing of localised cracks in welds in future works, as well as the possibility to replace the stress intensity factor by nonlinear mechanical parameters including the integral J or crack-type opening displacement CTOD. The effect of plasticity is not studied in this paper, although the results obtained encourage us to go further to treat the effect of plasticity at the crack tip as the XFEM method has not yet justified an accuracy in this field. It is necessary to develop a user subroutine to define an element code to integrate it with XFEM.
## 1 Introduction
To combine the advantages of the above two rotation methods and minimize their weaknesses, we propose an improved rotational modulation scheme based on multi-position stop and continuous rotation for tri-axis RINS and analyze the error characteristics of the improved rotational modulation scheme. The improved rotation modulation scheme employs the continuous forward-reverse-reverse-forward rotation of the middle and outer gimbals to strengthen the modulation effect on temperature error terms and the coupling errors of rotation angular velocity. It also takes advantage of the symmetrical positions of the inner gimbal to eliminate zero-bias errors along the rotation axis. Simulations and experiments prove that the improved rotational modulation scheme can effectively improve the navigation accuracy of tri-axis RINS. The rest of the manuscript is as follows: Section 2 introduces the structure, coordinate system, and symbol definition of tri-axis RINS and the error propagation equation. The improved rotational modulation scheme is described, and its error characteristics are analyzed in Section 3. Section 4 discusses the simulation and experimental results, and Section 5 provides the conclusions.
## 2 Materials and Methods
### Fabrication of PSCs  
For PSC fabrication, a 2.5 \(\times\) 2.5 cm\({}^{2}\) indium tin oxide (ITO, AMG-Tech, Atlanta, GA, USA) glass was pretreated with ultraviolet (UV)-ozone for 30 min (Figure 1). The NiO was spin-casted (at 5000 rpm for 30 s) on clean ITO substrates as an HTL and then annealed at 350 \({}^{\circ}\)C for 30 min in the air. When the PTAA/NiO layer was employed, a PTAA solution was prepared by dissolving 1 mg PTAA in 1 mL toluene. The PTAA solutions were spin-coated on the NiO substrates at 3000 rpm for 30 s and heated at 100 \({}^{\circ}\)C for 10 min in a glove box. The perovskite solution of Cs\({}_{0.175}\)FA\({}_{0.750}\)MA\({}_{0.075}\)Pb(\({}_{0.880}\)Pb\({}_{0.120}\))\({}_{3}\) was prepared by mixing 1.45M CsI, FAI, PbI\({}_{2}\), PbBr\({}_{2}\), and MABr in a 4:1 ratio solution of DMF and DMSO. Next, the perovskite solution (200 \(\mu\)L) was spin-coated onto the HTL/ITO/glass substrate at 500 rpm for 5 s and 4000 rpm for 45 s [40]. At 30 s before the end of spin-coating, chlorobenzene or anisole (0.4 mL) was dripped onto the substrate, and the sample was subsequently heated at 100 \({}^{\circ}\)C for 30 min in a glove box under N\({}_{2}\) atmosphere. Then, the electron transport material C\({}_{60}\) of 35 nm and SnO\({}_{2}\) of 3 nm were deposited by using a thermal evaporator at a high vacuum of 8 \(\times\) 10\({}^{-6}\) Torr. Finally, an Ag electrode of 120 nm was deposited by using a thermal evaporator. The effective device area, which was defined by a shadow mask, was 1.12 cm\({}^{2}\).
# Formation Control of Dual Auto Guided Vehicles Based on Compensation Method in 5G Networks
## 3 Formation Control System Design
### AGV Kinematic Model and Posture Estimation  
Section 3.1 describes the total delay in control systems. The following work uses total delay and AGV motion model to estimate the posture of leader AGV.  
Figure 8 shows the kinematics model of two-wheel differentially driven AGV. \(\textbf{P}\) (\(X\), \(Y\), \(\Theta\)) and \(\textbf{p}\) (\(x\), \(y\), \(\theta\)) represent the posture of AGV in global \(XOY\) coordinates and co-moving _xoy_ coordinates. The origin of _xoy_ is at the center of the two differential wheels and the \(x\)-axis points to the forward direction of the AGV. The transfer matrix from global frame to co-moving frame is  
\[\textbf{T}=\left[\begin{array}{ccc}\cos\Theta&\sin\Theta&0\\ -\sin\Theta&\cos\Theta&0\\ 0&0&1\end{array}\right] \tag{9}\]  
Figure 7: Control system structure of follower AGV.  
Let the radius of two driving wheels be \(r\) and the distance be \(l\), and the angular velocity of the left and right wheels could be represented by \(\omega_{l}\,\omega_{r}\), then [30]  
\[\dot{\mathbf{P}}=\left[\begin{array}{c}\dot{X}\\ \dot{Y}\\ \dot{\Theta}\end{array}\right]=\left[\begin{array}{cc}\frac{r}{2}\cos\Theta &\frac{r}{2}\cos\Theta\\ \frac{r}{2}\sin\Theta&\frac{r}{2}\sin\Theta\\ -\frac{r}{7}&\frac{r}{7}\end{array}\right]\left[\begin{array}{c}\omega_{l} \\ \omega_{r}\end{array}\right]=\left[\begin{array}{cc}\cos\Theta&0\\ \sin\Theta&0\\ 0&1\end{array}\right]\left[\begin{array}{c}v\\ \omega\end{array}\right]=J\mathbf{q} \tag{10}\]  
where \(v\) is the AGV linear velocity, \(\omega\) is the AGV rotational velocity, \(\mathbf{q}\) is the AGV velocity vector in the co-moving coordinate, and \(\mathbf{J}\) is a Jacobian matrix.  
Assuming the angular velocities of the two wheels do not change in the time \(\Delta t\) and the posture \(\mathbf{P}(k)=[X(k)\,Y(k)\,\Theta(k)]^{T}\), then there is  
\[\mathbf{P}(k+\Delta t)=\left[\begin{array}{c}X(k)+v(k)\Delta t\cos\left(\Theta( k)+\frac{\Delta t}{2}\omega(k)\right)\\ Y(k)+v(k)\Delta t\sin\left(\Theta(k)+\frac{\Delta t}{2}\omega(k)\right)\\ \Theta(k)+\omega(k)\Delta t\end{array}\right] \tag{11}\]  
Therefore, the leader [PERSON] sends its posture \(\mathbf{P}\) and velocity \(\mathbf{q}\) to the follower [PERSON]. Follower [PERSON] could use the \(\mathbf{P}\), \(\mathbf{q}\) and \(\tau_{sum}\) to estimate the posture of leader [PERSON] in current time.
## 5 Experimental Results
### Results Using the Dataset with Pathologies
#### 5.1.7. Results for XGBoost with Evolutionary Feature Selection  
The results obtained by applying the evolutionary FS to XGBoost are presented below.  
**GA with _Stud_ selection for XGBoost (XGB-S):** As shown in Table 2, an average accuracy rate of 0.7216 and a maximum of 0.7831 was achieved using 20 variables. Similar to other cases, variables such as sex, age, edu, and those related to alcoholic beverages appear. Nine of the selected variables belong to the group of pathologies. Among them, we find several types of cancer, apnea, diabetes, and metabolic syndrome.  
**GA with tournament selection for XGBoost (XGB-T):** In this case, using a total of 19 variables, a mean accuracy ratio of 0.7029 was obtained, and a maximum of 0.7559 was reached, as seen in Table 2. Among the most common variables, we found sex, edu, and apnea, but age did not appear. In addition, five variables related to smoking and up to nine related to pathologies were selected.
# ReFuse: Generating Imperviousness Maps from Multi-Spectral Sentinel-2 Satellite Imagery
## 5 Results
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Network** & **Encoder** & **Bands** & **Accuracy** & **IoU** \\ \hline FCN-8s & - & R, G, B & 89.54\% & 69.76\% \\ FCN-8s & - & R, G, B, NIR & 88.25\% & 69.55\% \\ FCN-8s & - & All 13 bands & 84.80\% & 60.35\% \\ U-Net & VGG16 & R, G, B & 87.45\% & 70.03\% \\ U-Net & ResNet-34 & R, G, B & 90.13\% & 70.54\% \\ U-Net & ResNet-50 & R, G, B & 92.39\% & 73.50\% \\ U-Net & ResNet-50 & R, G, B, NIR & 92.07\% & 71.37\% \\ U-Net & ResNet-50 & All 13 bands & 89.37\% & 70.32\% \\ U-Net & ResNet-101 & R, G, B & 90.39\% & 70.57\% \\ U-Net & EfficientNetB7 & R, G, B & 94.48\% & 74.61\% \\ DeepLabv3+ & ResNet-50 & R, G, B & 92.19\% & 71.35\% \\ DeepLabv3+ & ResNet-50 & R, G, B, NIR & 91.32\% & 71.29\% \\ DeepLabv3+ & ResNet-50 & All 13 bands & 88.25\% & 68.50\% \\ \hline
**ReFuse** & **ResNet-50** & **(R, G, B) + (B7, B8, B11)** & **95.72\%** & **75.85\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results obtained by the proposed approach (last row) and the considered competitors for the imperviousness map extraction. For the proposed approach (ReFuse), the use of brackets under the “bands” column highlights the ability of the proposed approach to use different bands type, without the need for resizing. spatial resolution of 10 m is too low to capture such levels of detail. Indeed, even observing Sentinel-2 images (Figure 12b-f) by the naked eye, we can exhibit the same difficulty in discriminating between impervious and non-impervious pixels. It is worth noting, finally, the presence of mislabelled data shown in Figure 12c. Despite the presence of trees and gardens, everything is labelled as impervious. Even more interesting here is the result obtained. Despite errors in the ground truth, the network correctly detects the green infrastructure within the area, i.e., trees, grass and parks, showing an ability to generalize the result that is sometimes superior to that of the training data. This result is of fundamental interest in this work because, as stated in Section 1, one of the objectives was to address the difficulties of current solutions in extracting imperviousness maps with a high level of detail within the urban context. Figure 11: Example of an imperviousness mask obtained using ReFuse. Top left is the considered path (only RGB bands have been reported for visualization reasons). Top right is the corresponding ISPRA Ground Truth. Bottom left is the imperviousness mask obtained by the proposed ReFuse architecture. Bottom right is the ReFuse segmentation errors. (**a**) RGB patch. (**b**) Ground truth. (**c**) ReFuse. (**d**) Error mask: FP (blue), FN (red).
# The MADAG Strategy for Fault Location Techniques
## 1 Introduction
### Research Purpose  
The key research issues in this paper are as follows:  
1. _How many test cases does MADGA need to achieve the same level of effectiveness of fault localization?_
2. _Is it still efficient to apply the SBFL technique with the whole test cases?_  
In this paper, we propose a novel test case prioritization strategy, Minimal Aggregate of the Diversity of All Groups (MADAG), to select test cases from an existing test suite. Our strategy aims at minimizing the number of test cases to save the developer's effort in locating the fault. The selected test cases can be applied to SBFL techniques to locate the fault, while keeping the fault localization effectiveness as when whole test cases are run. In addition, the MADAG strategy does not obtain the test results in advance (besides the initial failed test case). We use the information about the execution trace of each test case, further computing the diversity of traces and selecting test cases. Moreover, in order to investigate the effectiveness of the MADAG approach, we performed an experimental study on 7 Siemens programs and 5 UNIX programs that are particularly widely used benchmarks for fault localization [1; 2; 14; 15]. The experimental results showed that the MADAG strategy selected a substantially smaller subset of test cases from the test suite and keep approximate fault localization effectiveness.  
The main contribution of this paper is twofold: (1) We propose a MADAG strategy to select the least number of test cases using SBLF (e.g., Jaccard, Tarantula, and Ochiai) to locate the fault, such that even if the number of test cases is reduced, the difference in the fault localization effectiveness will be not significant with checking more than 20% codes, compared with whole test cases. (2) We present that applying whole tests from the test suite is not likely to obtain the best effectiveness of fault localization in comparison with the selected test cases by the MADAG strategy.  
The rest of this paper is organized as follows: Section 2 revisits test case prioritization techniques and fault localization techniques to be used in the experiment. In Section 3, the MADAG strategy is proposed in detail. Section 4 describes the empirical study, followed by its results. Section 5 reviews related work. Section 6 concludes our paper.
# Data Analysis of the TK-1G Sounding Rocket Installed with a Satellite Navigation System
## 5 Conclusions  
The following conclusions can be drawn from the analysis of the experiment of the TK-1G sounding rocket flight test. Firstly, the rocket sonde installed with the satellite navigation system has a high data acquisition rate (>95%) during the whole process, which can obtain the comparatively complete trajectory data and meteorological data. Secondly, by comparing the measurements gained by the rocket sonde with those collected by several other means, we found they largely bear good agreements, which indicates that the TK-1G sounding rocket system and the data processing methods are viable and reliable. Thus, the flight test proves that the TK-1G sounding rocket system can accurately measure the atmospheric elements in the height range of 20-60 km, which is of significance for making up for the shortcomings of the meteorological detection of near space.  
The ECMWF data used in this paper were provided by the European Centre for Medium-Range Weather, and the SABER data were provided by the TIMED/SABER team. We acknowledge the contributions to this work from the European Centre for Medium-Range Weather and the TIMED/SABER team. The study was partly supported by the National Natural Science Foundation of China (Grant No. 41375028) and the National Natural Science Foundation of Jiangsu, China (Grant No. BK20151446).  
[PERSON] conceived and designed the experiments; [PERSON], [PERSON], and [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON], and [PERSON] analyzed the data; [PERSON] wrote the paper.  
The authors declare no conflicts of interest.
## 5 Experiment  
### Data Sets  
In this work, we conducted experiments and evaluated the related methods using real-world databases of KG, commonly used in previous works: WordNet [24] and Freebase [25]. WordNet is an extensive lexical database of English. Nouns, verbs, adjectives, and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept, and with interlinked sysnets employing conceptual-semantic and lexical relations. Freebase is a large collaborative knowledge base consisting of data compiled mainly by its community members. It is an online collection of structured data harvested from many sources, including individual wiki contributions. The most representative dataset in WorldNet is WN18, and FB15k in Freebase.  
In those datasets, WN18 contains 18 relations and 40,943 entities, whereas FB15k contains 1345 relations and 14,951 entities. However, both of them suffer from test leakage through inverse relations: a large number of test triples can be obtained simply by inverting triples in the training set. Therefore, we introduced FB15k-237, a subset of FB15k, in which reversible relations were removed. Similarly, WN18 was corrected by WN18 RR. Therefore, we selected WN18 RR and FB15k-237 as datasets in our experiments.
## 4 Description of the Developed Method
### Linear Kalman Filter  
With the results obtained in the previous section, the number of harmonic components composing the input signal is determined. The number of components is used to determine the number of variables necessary to implement the equations of the LKF (5-17). The obtained voltage and frequency values of each component are used to provide initial values for the LKF variables. Once the LKF is implemented, during the first 200 ms of its execution, the values of each of the spectral components are considered invalid, as it is assumed that the LKF is still in its transient stage. After the initial instants, the error in the estimation of the input signal is compared with the maximum allowable error (Figure 8).  
Figure 8: Structure of the developed method.  
Figure 7: Spectral components grouping process.
## 2 Relate Work
### Related Work in Sound Event Detection Systems  
In recent years, in the field of acoustic communication, the research on speech recognition such as emotion recognition, emotion analysis, and sound event detection has been greatly expanded and enriched. Among them, audio event classification is a hot problem in current audio research, and its application scenarios are very wide. At the same time, it has important applications in video retrieval [16], security monitoring [17], medical and health care, automatic driving, etc. This has high research value. However, there are some difficult issues that were not well addressed in that study, such as the diversity and randomness of audio events. In the field of the sound event classification, most of the initial researchers used the energy on the Mel band or the MFCC as the input value [18], and then combined with the Gaussian mixture model (GMM) or a support vector machine (SVM). Further, more speech feature filter banks or time-frequency descriptors have been developed to classify speech events in combination with the CNN, RNN, and convolutional recurrent neural networks (CRNNs) and have demonstrated advanced performance in DCASE challenges [19]. However, these models discard the temporal order of frame-level features in their construction, leading to unsatisfactory final results. Different scholars have adopted different solutions to this type of time series information loss problem; for example, [PERSON] used End-to-End neural networks to solve this type of problem [20], [PERSON] proposed an attention model and explained this model in a novel probabilistic perspective [21]. In addition, he proposed a convolutional neural network converter (CNN-Transformer) for audio tagging and SED and showed that the performance of the CNN-Transformer is similar to that of the CRNN [22].  
The starting point of this study is the design of an audio event classification model based on the characteristics of these audios, combined with the current popular deep learning methods, and the experimental validation of the proposed theoretical conjectures.