esa-sceva/satcom-chunk-collection huggingface dataset:
# Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
## 6 Discussion
### Class-Imbalance Learning Is Necessary When Building Software Defect Prediction Models  
The experimental results show that all class-imbalance learning methods are helpful to improve defect prediction performance in terms of \(F_{1}\), AGF, MCC, and AUC, which is consistent with the results of previous studies [40; 44; 45; 46; 64]. We also notice that, among all class-imbalance learning methods, CIB always makes the largest improvement over no class-imbalance learning method (i.e., None) in terms of \(F_{1}\), MCC, or AUC). Moreover, although RUS is the simplest class-imbalance learning method, it makes the smallest improvement over None in terms of \(F_{1}\) and MCC as compared with other class-imbalance learning methods. Therefore, users, who try to select a class-imbalance learning method to address the class-imbalance problem, should consider both the easy implementation and its actual performance. To sum up, CIB is a more promising alternative for addressing class-imbalance problem.
# Combination Analysis of Future Polar-Type Gravity Mission and GRACE Follow-On
## 5 Conclusions  
In this paper, we investigate the orbit configurations for Future Polar-type Gravity missions (FPG) with emphasis on orbit inclination selection via full-scale simulations based on both standalone and joint GFO constellations. Four monthly models in January 2006 under FPG configurations with varying inclinations (87, 89, 91, and 93 degrees) show similar performances except that FPG-87 and FPG-93 suffer from relatively serious deficiencies in the zonal SHC estimation. To improve the GFO-only monthly models, the information from different FPGAs are respectively added to them, leading to different GFO & FPG combined models. Among combined models, the retrograde-orbit FPGAs (FPG-91 and FPG-93) generally outperform the prograde ones (FPG-89 and FPG-87). The advantages are revealed not only in spectral domain by smaller degree geoid height error (DGHE) and cumulative geoid height error (CGHE), but also in the spatial domain by obvious fewer north-south stripes, especially at low and medium latitudes. More comprehensive investigation is carried out for FPG-89 and FPG-91 by analyzing one-year time series of GFO-KBR joint monthly models, which further highlights the superiority of FPG-91 over FPG-89. Detailed regional analysis shows that improvements mainly reside over those low- and medium-latitude regions. The mechanism behind is explained by more isotropic GFO & FPG observation system that integrates two different orbit types (prograde and retrograde). Based on our study, the FPG configuration with 91-degree inclination is therefore worthy to be recommended for the further orbit design of FPG.  
Y.S. conceived and designed the experiments; Y.N. and Q.C. performed the experiments; Y.N. and Q.C. analyzed the data; Y.N. wrote the paper; Y.S. and Q.C. revised the manuscript.  
This research was funded by the Natural Science Foundation of China (41731069) and Alexander von Humboldt Foundation.  
We are grateful to [PERSON] for the GRACE Matlab Toolbox (GRAMAT) [62] used in some figures' plotting of the article. The first author would like to thank [PERSON] from Shandong University of Science and Technology and [PERSON] from Tongji University for their valuable suggestions on preparing the article. Two anonymous reviewers and the academic editor are sincerely appreciated for improving the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Experiments
### Third Experiment: Textual Semantic Similarity in Representation Space
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **MR** & **CR** & **SUBJ** & **MPQA** & **SST-2** & **TREC** & **MRPC** & **Avg.** \\ \hline \multicolumn{8}{c}{**Unsupervised models**} \\ \hline Glove embeddings (avg.) \({}^{\dagger}\) & 77.25 & 78.30 & 91.17 & 87.85 & 80.18 & 83.00 & 72.87 & 81.52 \\ Skip-thought \({}^{\blackdiamond}\) & 76.50 & 80.10 & 93.60 & 87.10 & 82.00 & 92.20 & 73.00 & 83.50 \\ \hline Avg. BERT embedding \({}^{\ddagger}\) & 78.66 & 86.25 & 94.37 & 88.66 & 84.40 & 92.80 & 69.54 & 84.94 \\ BERT-[CLS] embedding \({}^{\ddagger}\) & 78.68 & 84.85 & 94.21 & 88.23 & 84.13 & 91.40 & 71.13 & 84.66 \\ IS-BERT\({}_{\blacksquare}\) \({}^{\ddagger}\) & 81.09 & 87.18 & 94.96 & 88.75 & 85.96 & 88.64 & 74.24 & 85.83 \\ \(\text{CT-BERT}_{\text{sup}}\) \({}^{\ddagger}\) & 79.84 & 84.00 & 94.10 & 88.06 & 82.43 & 89.20 & 73.80 & 84.49 \\ SimCSE\({}_{\text{sup}}\)-BERT\({}_{\text{base}}\) & 81.18 & 86.46 & 94.45 & 88.88 & 85.50 & 89.80 & 74.43 & 85.51 \\ SimCSE\({}_{\text{sup}}\)-BERT\({}_{\text{base}}\)-MLM & 82.92 & 87.23 & **95.71** & 88.73 & 86.81 & 87.01 & 78.07 & 86.64 \\ \hline \multicolumn{8}{c}{**Supervised models**} \\ \hline InferSent-Glove \({}^{\ddagger}\) & 81.57 & 86.54 & 92.50 & **90.38** & 84.18 & 88.20 & 75.77 & 85.59 \\ Universal Sentence Encoder \({}^{\ddagger}\) & 80.09 & 85.19 & 93.98 & 86.70 & 86.38 & **93.20** & 70.14 & 85.10 \\ \hline \hline \multicolumn{8}{l}{\(\text{BERT}_{\text{base}}\)\({}^{\ddagger}\)} & 83.64 & 89.43 & 94.39 & 89.86 & **88.96** & 89.60 & 76.00 & 87.41 \\ \(\text{SG-BERT}_{\text{sup}}\)\({}^{\ddagger}\) & 82.47 & 87.42 & 95.40 & 88.92 & 86.20 & 91.60 & 74.21 & 86.60 \\ SimCSE\({}_{\text{sup}}\)-BERT\({}_{\text{base}}\) \({}^{\ddagger}\) & 82.69 & 89.25 & 84.81 & 89.59 & 87.31 & 88.40 & 73.51 & 86.51 \\ SimCSE\({}_{\text{sup}}\)-BERT\({}_{\text{base}}\)-MLM & 82.68 & 88.88 & 94.52 & 89.82 & 88.41 & 87.60 & 76.12 & 86.86 \\ SupMPN-BERT\({}_{\text{base}}\) & 82.93 & 89.26 & 94.76 & 90.21 & 86.99 & 88.20 & **76.35** & _86.96_ \\ \hline SupMPN-BERT\({}_{\text{large}}\) & **84.06** & **90.25** & 94.59 & 90.26 & 88.58 & 91.00 & 75.48 & **87.75** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results on the transfer-learning tasks. We report prediction accuracy. The best result in each column is in bold. \(\dagger\): [52], \(\blacktriangle\): [56], \(\lozenge\): [34], \(\diamond\): [34], \(\diamond\): [57], and all the other results from [37]. Figure 2: Semantic textual similarity using sentence embeddings from **(a)** BERT, **(b)** SimCSEsup, and **(c)** SupMPN, starting from BERT-base checkpoint, on four topics, weather, sport, smartphone, and food. We used cosine similarity for scoring.
# Deep-Learning Based Algorithm for Detecting Targets in Infrared Images
## 4 CMF-3 DLSTM  
The infrared image target detection model CMF Net based on multiscale feature fusion proposed in Section 3 adopts two multiscale feature extraction mechanisms and feature fusion methods for the final output feature graph of the backbone network. It adapts to the multiscale characteristics of the target. It can be regarded as the feature fusion of spatial dimension, which is of great help to improve the performance of infrared image target detection. CMF Net can achieve better target detection performance when the background environment is the relatively simple spacing between targets. However, the problem with  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Description** & **Value** \\ \hline The Anchor scale & 32, 64, 128 \\ MiniBatch Quantity & 256 \\ PRN foreground–background ratio & 1:1 \\ IOU threshold used by NMS for RPN training & 0.3 \\ IOU thresholds used by NMS for RPN prediction & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: RPN training parameters list.  
CMF Net is that it is easy to misjudge when multiple targets are close together, overlapping, and semantically confusing, as shown in Figure 4.  
This section proposes CMF-3 DLSTM, an infrared image target detection model based on spatio-temporal feature fusion and attention mechanism. This model first inherits the multiscale feature fusion strategy of CMF Net to achieve feature fusion in the spatial dimension. Then, the model is based on 3 DLSTM, which extracts contextual information along with the positive and negative directions of each dimension from the length, width, and height dimensions of the 3D feature map. Meanwhile, the model uses an attention mechanism to assign weights to the contextual features extracted in various dimensions and directions. CMF-3 DLSTM effectively improves target detection performance in complex situations such as multiple targets approaching each other, overlapping each other, and semantic confusion.
# Human Clustering Based on Graph Embedding and Space Functions of Trajectory Stay Points on Campus
## 2 Methodology
### Human Network Construction  
Most individuals are students, faculty members, and staff. Typical activities on campus include lecturing, self-studying, meetings, dining, sleeping, and various events. Individuals staying in spaces with the same function and similar temporal patterns can be converted to a network of individuals. The human network is constructed based on the stay-point data, with nodes representing individuals and edges reflecting their relationships. An edge is established between two individuals if they are in spaces with an identical space function and the time that the two individuals stay in such spaces exceeds a specified duration. On a university campus, two hours typically represents the duration of a class lecture for students and lecturers. Four hours represents a typical working time that research staff and graduates spend in their labs, and 24 h represents the time spent on campus by campus inhabitants. The specific duration spent in a space of a specific function is a key indicator reflecting the intention of activities, corresponding to stay points of trajectories. Consequently, three human networks are constructed with three time durations. The pseudocode used to built  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute** & **Attribute Content** \\ \hline IndividualID & Unique Individual Identifier \\ PositionID & Unique Stay-Location Identifier \\ RoomName & Room Name of Stay Point \\ UpTime & User Online Time \\ DownTime & User Offline Time \\ Duration & Stay Duration \\ APClassification & Space Function \\ Building & Building Name \\ \hline \hline \end{tabular}
\end{table}
Table 1: Stay-point attributes.  
Figure 2: Top-10 most visited space functions.  
a human network for a specific time length is shown in Algorithm 1 below, in which the specific hour is the corresponding time duration.  
```
1:Create an empty Network G
2:for each individual and its corresponding data group do
3: Compute AP classification statistics APClassification
4: Compute total duration for the individual Duration
5: Add node to Network G with APClassification and Duration
6:endfor
7:for each pair of distinct individuals do
8: Find AP classifications common_conditions
9:if common conditions are met then
10: Compute duration for both individuals
11:if duration for both individuals > specific hours then
12:Add or update an edge in Network G between the two individuals with weight as the common duration
13:endif
14:endif
15:endfor
16:Output the number of nodes and edges in the final network G
17:Extract connection information for each node and create connection_information
18:Compute connection counts for each node and merge connection_information
19:Return network G and connection_information
```  
**Algorithm 1** Get_Network
## 1 Introduction
The S-JTSK/05 is defined with the geodetic coordinates on the GRS80 ellipsoid of the European Terrestrial Reference System 1989 (ETRS89). For the planar coordinates, the S-JTSK/05 uses the modified Krovak projection, which is defined as the Krovak projection with the addition of biquadratic re-transformation coefficients, using the procedure, e.g., in [22], and re-transformation coefficients, e.g., in [23; 24]. The coordinates in the S-JTSK/05 are close to those in the S-JTSK (within 20 cm on average); thus, they are identical to the S-JTSK under the resolution of the cartographic works [25]. In order to obtain the S-JTSK coordinates, differences defined in the territory of the Czech Republic in a regular grid with a step of 2 km must be added to the S-JTSK/05 coordinates. The latest version of the table of differences has the designation 1710 [26], and it was published in 2017. The scheme of transformation from the ETRS89, ETRF2000 geodetic coordinates B, L, H to the planar coordinates Y, X in the S-JTSK is shown in Figure 1.  
In the first step, the geodetic coordinates B, L, H (latitude, longitude, and height) in the ETRF2000 reference frame on the GRS80 ellipsoid are converted to the rectangular Cartesian coordinates X, Y, Z. A seven-parametric similarity Helmert transformation with three translations, three rotations, and one scale change is then applied. In this step, the 3D rectangular coordinates are transformed from the ETRF2000 reference frame to the S-JTSK/05 reference frame. Subsequently, the rectangular Cartesian coordinates are converted into the geodetic coordinates B, L, H on the Bessel ellipsoid. Using a modified Krovak projection including the re-transformation, the planar coordinates Y, X are obtained. The ellipsoidal heights H are converted to heights h in the Bpv reference height system using the undulation of the quasi-geoid over the ellipsoid--model CR2005-v1005; see [26]. The S-JTSK system is unsuitable for higher precision work due to local deformations, e.g., for the delineation of linear railway structures [13], especially modern high-speed lines. In such cases, it is necessary to focus on the influence of the S-JTSK deformations on the positioning accuracy--in particular, local changes in the length scale and non-conformities (i.e., the dependence of the length scale change on the azimuth). In this paper, we present an evaluation of the changes in the S-JTSK length scale when considering the deformations of the system. The method employed is outlined in the following section, and the results obtained are summarized and evaluated in the final section.
# Signal Consensus in TSP of the Same Grid in Road Network
## 2 Prior Knowledge and Consensus Algorithm  
### Prior Knowledge  
\(\mathbf{G}=\{\mathbf{V},\mathbf{E}\}\) represents the topology graph and it is composed of set of nodes \(\mathbf{V}=\{1,2,3,\ldots,n\}\) and set of edges \(\mathbf{E}\in\mathbf{V}^{2}\). There is an edge between node \(i\) and node \(j\) if they have information interaction. The nodes are disordered in the undirected graph that is \((i,j)\in\mathbf{V}\Leftrightarrow(j,i)\in\mathbf{V}\).The edges have direction in the directed graph.  
Suppose G is a directed graph, then: if every vertex is reachable from every other vertex in G then G is a strongly connected graph; the number of head ends adjacent to a vertex is called the in-degree of the vertex and the number of tail ends adjacent to a vertex is its out-degree.  
The adjacency matrix A is used to represent the relationship between the nodes, the values in A as follows:  
\[a_{ij}=\left\{\begin{array}{l}1,(v_{i},v_{j})\in E;\\ 0,otherwise.\end{array}\right. \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Strategy** & **Main Methods** \\ \hline \multirow{4}{*}{Passive priority strategy} & Adjustment of cycle length \\  & Transit movement repetition in the cycle \\  & Green time bias towards transit movement \\  & Phasing design bias towards transit movement \\  & Linking for transit progression \\ \hline \multirow{4}{*}{Active priority strategy} & Phase extension \\  & Early phase activation \\  & Special transit phase \\  & Phase suppression \\ \hline \multirow{4}{*}{Real-time strategy} & Delay optimizing \\  & Intersection control \\ \cline{1-1}  & Network control \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main strategies and methods of Transit signal priority (TSP).  
There is an edge pointing from node \(i\) to node \(j\) when \(a_{ij}=1\), the degree matrix D represents the value of in-degree of each node in the graph, and the values in D as follows:  
\[d_{ii}=\left\{\begin{array}{l}0;i\
eq j\\ \sum\limits_{j\
eq i}a_{ij}.\end{array}\right. \tag{2}\]  
The Laplacian matrix \(\mathbf{L}=\mathbf{D}-\mathbf{A}\) represents the relationship between the nodes and the edges in the graph.
## 3 Results and Discussion
### General Observations and Failure Modes
#### 3.1.2 NEWRCF Frame  
The overall failure process of the NEWRCF was identical to NEWRC1; during the initial drift ratio of 0.25%, no apparent cracks were observed. The first flexural cracks were observed during the second cycle at a 0.5% drift ratio level, and the first diagonal crack was observed in the first cycle with a 1% drift ratio. In general, the first cracks were observed at the bases of the column on both sides, followed by the column and beam connections. The longitudinal rebar on both columns (piers) yielded around a drift ratio of 1.5-2%. NEWRCF achieved its peak load at a 3% drift ratio, following which the cracks intensified at the base of both the piers, thus leading to the cover concrete spalling. The longitudinal rebar buckling was observed around a 6% drift ratio, which eventually led to shear strength degradation, and the experiment ended at a drift ratio of 7%. Overall, the failure mode was flexural dominated. Figure 7 shows the detailed images of the failure modes and damage concentrated around the top and bottom of the piers' connection.  
Figure 6: Crack pattern at corresponding drift ratio: (**a**) BMRC1; (**b**) NEWRC1.  
Figure 7: Crack pattern at damage at the end of the test NEWRCF: (**a**) P1-Top; (**b**) P1-Bot; (**c**) P2-Top; (**d**) P2-Bot.  
Furthermore, after the test ended, the cover concrete was removed, and it was observed that the 90-degree hooks opened outwards in multiple locations, which led to the buckling of the longitudinal rebar. However, the 135-degree seismic hooks did not open and remained intact. Hence, the comparison between NEWRC columns reflects that using HSR for transverse reinforcement does not necessarily prevent longitudinal rebar buckling.
## 3 Results and Discussion
### Landslide Susceptibility Mapping and Comparison of Models
#### 3.8.2. Landslide Count-Based Verification of Susceptibility Classes  
Figure 20 presents the spatial distribution of observed landslides across susceptibility classes predicted by the LR, RF, and XGBoost models. Ensemble models, particularly RF and XGBoost, exhibited high classification accuracy, with 98.0% and 95.7% of landslides located in the combined High and Very High zones, respectively. In contrast, the LR model captured 81.2% of landslides in the same zones, indicating a more conservative prediction.  
When analyzed individually, the Very High class of XGBoost and RF contained 90.6% and 87.9% of total landslides, compared to 59.8% in LR. Moreover, only 0.1% and 0.4% of landslides fell into the Very Low category under the RF and XGBoost models, compared to 3.0% in the LR model. This clear separation supports the superior discriminatory power of ensemble models, which minimize false negatives in critical zones. These results highlight the operational advantage of RF and XGBoost models for accurate landslide forecasting in seismically active terrains and underscore the superior classification capability of ensemble  
Figure 19: Comparison of landslide susceptibility classes across three predictive models. Colored horizontal bars represent the total area (km\({}^{2}\)) in each susceptibility class, while dashed lines with markers denote the corresponding percentage of the study area.  
models, affirming their reliability for operational hazard assessments and early warning applications in seismically active regions.
# Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment
## 1 Introduction
Therefore, it is desirable to perform more accurate positioning by fusing data from various sensors, rather than performing absolute positioning using only a specific signal. In hybrid positioning, user tracking is performed by pre-learning data for deriving the absolute position and applying IMU sensor data to the positioning algorithm in real time. If the learning data are densely collected, it is possible to improve the accuracy by analyzing the signal distribution in advance at a specific location, but there may be cases in which the IMU sensor data cannot be used, because the amount of computation increases rapidly during positioning. In addition, it is difficult to improve accuracy if the different data collection cycles for each sensor are not properly synchronized. To solve this problem, this paper proposes a method incorporating a localization engine that uses an artificial neural network (ANN), which creates a learning model when learning data are given, before classifying the new data. In the proposed method, the ANN is trained by receiving radio signals and IMU sensor values as inputs to the single localization model, consisting of a recurrent neural network (RNN). Unlike existing methods, the measurement is conducted without maintaining the learning data in a database; hence, the measurement calculation time and the cost for maintaining the database are reduced. In many studies, the various variables used for measurement are determined either empirically or artificially, either by the researcher or by analyzing large amounts of input data. In the proposed method, the variables derived from ANN training automatically can have the same effect. An experiment was conducted on an actual offshore plant to verify the reduction in localization time and the improvement in tracking accuracy. The proposed method achieved a 29% improvement on a deck, compared to a previously proposed method [15] that uses the extended Viterbi algorithm. With faster and more accurate location information, these results can be applied to communicate safety information more quickly to the people who need it in shipboard and offshore environments. The remainder of this paper is organized as follows: Section 2 introduces related research on neural network-based positioning methods. Section 3 outlines the data collection method and describes the RNN-based hybrid localization system (RHLS). Section 4 presents and analyzes evaluation results obtained for the proposed system. Finally, Section 5 concludes this paper.