esa-sceva/satcom-chunk-collection huggingface dataset:
# SR-Inpaint: A General Deep Learning Framework for High Resolution Image Inpainting
## 5 Experimental Results and Discussion  
Our proposed method (SR-Inpaint) is evaluated on the DIV2K dataset [46] and 200 2K-images as well as 200 4K-images of people, animals, nature, cities, objects, etc. Results are compared against the state-of-the-art HR image inpainting technology (HiFill by [PERSON] et al. [17], CVPR 2020) both qualitatively and quantitatively. In the experiment, the damaged pixels were 10.612% of the total pixels for the 2K-image test set, and the damaged pixels were 12.799% of the total pixels for the 4K-image test set.
# Autocorrelation Matrix Knowledge Distillation: A Task-Specific Distillation Method for BERT Models
## 4 Experiments
### Experimental Results on GLUE
Figure 3 shows the average performance of different models on the GLUE task. It can be seen intuitively that \(\text{BERT}_{\text{TINY}}\text{-AMKD}\) has significantly improved performance compared to other models. In the MNLI task, \(\text{BERT}_{\text{TINY}}\text{-AMKD}\) achieved accuracy rates of 82.1% on MNLI-m and 81.7% on MNLI-mm, significantly higher than the 76.5% and 76.1% achieved by \(\text{BERT}_{\text{TINY}}\text{-KD}\). Furthermore, it outperformed the 79.9% and 79.3% achieved by \(\text{BERT}_{\text{4}}\text{-PKD}\), as well as the 78.9% and 78.0% achieved by \(\text{DistilBERT}_{\text{4}}\). On the CoLA (Corpus of Linguistic Acceptability) dataset, despite the significant performance gap between all four layer models and the teacher model, \(\text{BERT}_{\text{TINY}}\text{-AMKD}\) still achieved notable improvements. To further analyze the performance of different methods on GLUE tasks, we give a detailed comparison of several methods in Figure 4. AMKD outperforms other models in every task, with a performance almost close to that of the teacher model. \(\text{BERT}_{\text{4}}\text{-PKD}\) and \(\text{DistilBERT}_{\text{4}}\) initialize the student model from specific layers of \(\text{BERT}_{\text{BASE}}\) during training, which requires the student model to match the hidden states dimensions of the teacher model. In contrast, our proposed AMKD method allows for greater flexibility in the configuration of the student model. AMKD not only allows the student model to learn the features of the teacher model, but also captures the complex relationships among these features. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **MNLI-m** & **MNLI-mm** & **QQP** & **QNLI** & **SST-2** & **MRPC** & **RTE** & **Avg** \\  & **(393K)** & **(393K)** & **(364K)** & **(105K)** & **(67K)** & **(3.7K)** & **(2.5K)** & **Avg** \\ \hline \(\text{BERT}_{\text{BASE}}\) (Google) & 84.6 & 83.4 & 89.2 & 90.5 & 93.5 & 88.9 & 66.4 & 85.2 \\ \(\text{BERT}_{\text{BASE}}\) (Teacher) & 84.3 & 83.2 & 91.3 & 91.6 & 93.7 & 89.8 & 73.6 & 86.8 \\ \hline \(\text{BERT}_{\text{TINY}}\text{-FT}\) & 75.4 & 74.9 & 83.5 & 84.8 & 87.6 & 83.2 & 62.6 & 78.9 \\ \(\text{BERT}_{\text{SMALL}}\) & 77.6 & 77.0 & 87.0 & 86.4 & 89.7 & 83.4 & 61.8 & 80.4 \\ \(\text{BERT}_{\text{4}}\text{-PKD}\) & 79.9 & 79.3 & 88.3 & 85.1 & 89.4 & 82.6 & 62.3 & 81.0 \\ \(\text{DistilBERT}_{\text{4}}\) & 78.9 & 78.0 & 87.6 & 85.2 & 91.4 & 82.4 & 54.1 & 79.7 \\ \(\text{TinyBERT}_{\text{4}}\) & 80.5 & 81.0 & 88.5 & 85.7 & 90.7 & 83.3 & 63.5 & 81.9 \\ \(\text{BERT}_{\text{TINY}}\text{-KD}\) & 76.5 & 76.1 & 84.6 & 85.1 & 88.2 & 83.5 & 62.8 & 79.5 \\ \(\text{BERT}_{\text{TINY}}\text{-AMKD}\) & **82.1** & **81.7** & **89.4** & **87.9** & **92.1** & **86.9** & **65.4** & **83.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comprehensive evaluation results of various models on the GLUE benchmark. The best result for each task is shown in bold.
# Damage Location Diagnosis of Frame Structure Based on a Novel Convolutional Neural Network
## 5 Conclusions  
In this paper, in order to solve the problem of damage location diagnosis of the frame structure under a strong noise environment, firstly a wavelet multi-scale decomposition was used to filter the low- frequency noise in the original vibration signal, and CEEMDAN was used to decompose and restructure the signal, which highlighted the differences between the vibration signals with small differences. Then, WDCNN was used to extract the digital features of the reconstructed signal, and the two-layer LSTM was used to extract the correlation information between the reconstructed signals. Taking the four-story steel structure frame of Columbia University as the research object, the fault diagnosis method proposed in this paper is used to carry out experimental research under strong noise conditions. The following conclusions can be drawn.  
1. The fault diagnosis method for strong noise proposed in this paper can effectively filter the low frequency noise and highlight the feature information in the denoised signals.
2. The WDCNN + LSTM network model proposed in this paper can effectively extract the feature information in signals and the correlation feature information between signals, so it has a higher diagnostic accuracy.
3. Compared with other methods, the damage location diagnosis method proposed in this paper for the building frame structure has strong anti-noise ability and accuracy, and the accuracy can still reach 99.97% when the signal-to-noise ratio is -4 dB. Therefore, the method proposed in this paper has strong advantages in anti-noise and can be used for fault diagnosis of frame structures under a strong noise environment.  
The conclusions of this paper were obtained when Gaussian white noises were added to the original data, but in the actual environment, the noise is complex. Therefore, in the next research, we will study the damage location diagnosis of the frame structure in  
Figure 10: TSNE clustering results.  
different noise environments. In addition, only five damage locations of frame structures were diagnosed in this paper, and there are at least nine damage situations in the open-source data provided by Columbia University. Therefore, the types of damage locations will be expanded in the next research.  
H.X.: Formal analysis, Investigation, Methodology, Writing--original draft and Writing--review and editing. C.C.: Funding acquisition, Methodology, Project administration, Formal analysis, and supervision. Y.C.: Formal analysis, Software, Validation and Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Nature Science Foundation of Hebei Province grant no. E2020402060, and Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province (Hebei University of Engineering) under Grant 202204 and 202206.  
**Data Availability Statement:** Not applicable.  
The authors declare no conflict of interest.
# A Predictive Analytics Infrastructure to Support a Trustworthy Early Warning System
## 4 The Predictive Analytics Infrastructure and the Early Warning System
### The Early Warning System
#### 4.2.2 Next Activity At-Risk Simulation  
The PGAR model provides a binary prediction about the likelihood of failing a course on the basis of the already assessed activities of the course. However, it lacks usefulness to impact a learner because they might already be at-risk when the prediction is computed.  
The EWS uses the PGAR to provide early and personalized information, feedback, and recommendations concerning the next assessment activity. We define the Next Activity At-Risk (NAAR) simulation as the process for detecting the minimum grade the learner must obtain in the next activity to be out of risk of failing. This simulation is performed by using the submodel associated with the activity we want to analyze. The submodel uses the previous activities' grades and simulates all possible grades for the last activity. The simulation will identify the grade that changes the prediction from failing to pass the course.  
**Example 2**.: _Let us take the submodel \(Pr_{AA1}\)(Fail?) of Example 1. For the minimum grade to be known, six simulations are performed on the basis of the grades the learner can obtain in AA1 and the learner's profile. Each simulation will predict the likelihood of failing on the basis of the assigned grade. An example is shown next on the basis of the first assessment activity of a hypothetical course._  
\(Pr_{AA1}\)(Fail?) = (Profile, N) \(\rightarrow\) \(Fail?\) = Yes \(Pr_{AA1}\)(Fail?) = (Profile, D) \(\rightarrow\) \(Fail?\) = Yes \(Pr_{AA1}\)(Fail?) = (Profile, C\(-\)) \(\rightarrow\) \(Fail?\) = Yes \(Pr_{AA1}\)(Fail?) = (Profile, C\(+\)) \(\rightarrow\) \(Fail?\) = Yes \(Pr_{AA1}\)(Fail?) = (Profile, B) \(\rightarrow\) \(Fail?\) = No \(Pr_{AA1}\)(Fail?) = (Profile, A) \(\rightarrow\) \(Fail?\) = No  
Figure 6: Procedure to obtain each submodel of the PGAR model.  
We can observe that learners with the profile \"Profile\" have a high probability of failing on grades below B. In other words, learners who get a grade from N to C+ in the first activity tend to fail the course. However, it is possible to pass the course with these grades but less frequently. Note that the simulation will consider previous activity grades on further activities. Thus, personalization increases while increasing the available data about learners.
# Probing Ferroic States in Oxide Thin Films Using Optical Second Harmonic Generation
###### Abstract  
Forthcoming low-energy consumption oxide electronics rely on the deterministic control of ferroelectric and multiferroic domain states at the nanoscale. In this review, we address the recent progress in the field of investigation of ferroic order in thin films and heterostructures, with a focus on non-invasive optical second harmonic generation (SHG). For more than 50 years, SHG has served as an established technique for probing ferroic order in bulk materials. Here, we will survey the specific new aspects introduced to SHG investigation of ferroelectrics and multiferroics by working with thin film structures. We show how SHG can probe complex ferroic domain patterns non-invasively and even if the lateral domain size is below the optical resolution limit or buried beneath an otherwise impenetrable cap layer. We emphasize the potential of SHG to distinguish contributions from individual (multi-) ferroic films or interfaces buried in a device or multilayer architecture. Special attention is given to monitoring switching events in buried ferroic domain- and domain-wall distributions by SHG, thus opening new avenues towards the determination of the domain dynamics. Another aspect studied by SHG is the role of strain. We will finally show that by integrating SHG into the ongoing thin film deposition process, we can monitor the emergence of ferroic order and properties in situ, while they emerge during growth. Our review closes with an outlook, emphasizing the present underrepresentation of ferroic switching dynamics in the study of ferroic oxide heterostructures.  
multiferroic; ferroelectric; SHG; domain engineering; thin films; BiFeO\({}_{3}\); PZT; domain walls +
Footnote †: journal: Applied Physics Letters B
## 3 Interference Coordination Scheme Analysis
### Optimization Strategy for User Fairness
\(\mathbf{C}_{3}\) denotes the overall transmit power of each subchannel below a maximum power threshold and \(\mathbf{C}_{4}\) guarantees that the transmit power of a weak user is greater than that of a strong use on the same subchannel of the same BS. On the other hand, in order to evaluate the user fairness in NOMA HetNets, [PERSON]'s fairness index (JFI) is applied as a measurement of network fairness. JFI is one of the commonly used fairness metrics for wireless networks [15] and can be expressed as:  
\[F(\mathbf{\xi},\mathbf{\eta})=\frac{\left(\sum\limits_{u=1}^{N_{\text{U}}}C_{u}\right) ^{2}}{N_{\text{U}}\sum\limits_{u=1}^{N_{\text{U}}}(C_{u})^{2}}, \tag{12}\]  
where the total number of users of the networks is denoted by \(N_{\text{U}}\) and the throughput of the user \(u\) is denoted by \(C_{u}\). The larger the value of \(F\) is, the smaller the difference between the performance of user throughput is. While the performance of user throughput is consistent, the value of \(F\) will be 1. Energy efficiency [11] is also one of the metrics for evaluating network performance, the expression of EE is given as:  
\[EE(\xi,\eta)=\frac{\sum\limits_{u=1}^{N_{\rm{U}}}C_{u}}{\sum\limits_{m=1}^{N_{ \rm{M}}}\left(\xi_{m}\,P_{\rm{M}}^{\rm{max}}\right)+P_{\rm{M}}^{\rm{sta}}+\sum \limits_{b=2}^{N_{\rm{B}}}\left(P_{\rm{P}}^{\rm{max}}+P_{\rm{P}}^{\rm{sta}} \right)}, \tag{13}\]  
where \(P_{\rm{M}}^{\rm{sta}}\) and \(P_{\rm{P}}^{\rm{sta}}\) is the static power consumed by the hardware circuit. Because the MBS is silent during ABS, the power consumption of MBS in the macrocell \(m\) can be expressed as \(\xi_{m}P_{\rm{M}}^{\rm{max}}\).
# A Systematic Review of the Role of BIM in Building Sustainability Assessment Methods
## 2 Building Sustainability Assessment Methods  
### Breeam  
The Building Research Establishment Environmental Assessment Method (BREEAM) was created in 1990 by the Building Research Establishment (BRE) in the United Kingdom. BREEAM was launched as a credit award system for new office buildings but quickly developed systems for other buildings, such as homes, supermarkets or industrial buildings. BREEAM credits are divided over ten categories: Energy, Health and Wellbeing, Innovation, Land Use, Materials, Management, Pollution, Transport, Waste and Water [29]. Each category is subdivided into a set of assessment issues, each with its own aim and benchmarks. Every benchmark needs to be determined by a BREEAM expert assessor before credits can be assigned to the project. Once the assessment is entirely performed, the final score is determined by the sum of the weighted category scores [30]. BREEAM encompasses both mandatory and optional credits and allows to \"trade\" compulsory credits in different categories, while always setting minimum standards in essential areas.  
Nowadays, the BRE has different BREEAM Standards available for Communities, Infrastructures, New Construction, In-use and Refurbishment and Fit-out, and it is recognised in more than 60 countries.  
BRE has already started research about the capabilities of BIM. Currently, they have available different BIM-related services, such as certification, consultants, training and some research projects [31]. BRE has also released a BREEAM API to explore and integrate its rating data on thousands of certified building assessments across 50 countries, available for different tools, websites or software [16].
# Model-Based Real-Time Motion Tracking Using Dynamical Inverse Kinematics
## 4 Experiments
### Models  
The motion tracking is performed by using two different human models defined as in Section 2.2. Both the models are composed by 23 _physical links_ representing segments of the human body. Each physical link is attached to the next one through a certain number of rotational joint connected through _dummy links_, i.e., links with dimension zero, in order to model human joints with multiple DoFs. In one human model (_Human66_) all the physical links are connected through spherical joints (3 rotational joints), i.e., a total of 66 DoFs and 67 links. The second model (_Human48_) is based on the modeling of the human musculoskeletal system as described in clinical studies [44; 45; 46], it has a reduced number of joint, i.e., 48 DoFs, and takes into account human joint limits.  
Additionally, we consider experiments with a model of the _iCub_ humanoid robot [47]. The motivation behind this is to highlight the performance in achieving motion tracking, and motion retargeting from the human to a humanoid. The iCub model is composed of 15 physical links connected through 34 rotational joints. Model joint limits are defined according to the real robot mechanical constraints, including linear system of constraints involving multiple set of joints due to coupled joints mechanics.  
The human [[https://github.com/robotology/human-gazebo](https://github.com/robotology/human-gazebo)]([https://github.com/robotology/human-gazebo](https://github.com/robotology/human-gazebo)) and humanoid [[https://github.com/robotology/icub-models](https://github.com/robotology/icub-models)]([https://github.com/robotology/icub-models](https://github.com/robotology/icub-models)) models are open-source resources. The definition of link frames for the human model and the robot model is highlighted in Figure 4. Considering that all the models have rotational joints only, the inverse kinematics problem is defined using rotational and angular velocity targets for each physical link, i.e., \(n_{o}=23\) for the human model and \(n_{o}=15\) for the humanoid model. Additionally, as both the models are floating base, a position and linear velocity target is used for the base frame, i.e., \(n_{p}=1\).  
Figure 4: Model of the human (**left**) and iCub (**right**) in _T-pose_, with the corresponding links frame definition.
# Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar
## 2 Materials and Methods
### Auxiliary Data  
To objectively perform an accuracy assessment, we used 934 randomly selected forest and non-forest samples, which were collected via visual interpretation of high-resolution images on GE for four years (i.e., 2010, 2015, 2017, and 2020). Based on the overlay of the consistency distribution maps of the eight datasets, 747 sample points were obtained by random sampling at different consistency levels at 0.01%, the remaining samples were from the Global validation sample set [31]. The spatial distribution of the validation samples is shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Time** & **Satellite** & **Spatial Resolution** & **Classification Scheme** \\ \hline GLC-FCS & 2015 & Landsat TM/ETM+/OLI & 30 m & \
\begin{tabular}{c} Global surface \\ fine classification \\ \end{tabular} \\  & 2020 & Landsat/Sentinel-1 SAR & 30 m &
\begin{tabular}{c} Global surface \\ fine classification \\ \end{tabular} \\ \hline GlobeLand30 & 2020 & Landsat TM5/ETM+/OLI/ & 30 m & 10 classes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
To analyze the driving factors for spatial consistency from the aspects of terrain and climate, we chose (1) the Digital Elevation Model (DEM) data with 30 m resolution from Shuttle Radar Topography Mission (SRTM) [32; 33] and calculated the slope data based on it; (2) KOPPEN-Geiger climate classification [34] scheme includes six climate zones in Myanmar (Figure 2): Tropical Monsoon Climate (Am), Tropical Savanna Climate (Aw), Humid Subtropical Climate (Cwa), Subtropical Highland Climate (Cwb), Sub-boreal Monsoon Climate (Dwc) and Boreal Tundra Climate (ET).  
Figure 1: Spatial distribution of forest and non-forest validation samples in Myanmar (2020).
# Topology and Response Surface Optimization of a Bicycle Crank Arm with Multiple Load Cases
## 3 Results and Discussion
### Topology Optimization Result
#### 3.1.4 Effect of Uphill Angle  
The result of each angle of domination for compliance minimization can be seen in Figure 13. It is seen that the uphill angle gives a direct visible impact to the topological hole symmetry. As the uphill angle domination increases, the generated topological holes become more asymmetric particularly at the top region of the crank arm. Proportionally, in terms of stress distribution, it also shows a similar pattern with the result of the holes. The uphill angle increment makes the stress distribution to be distributed unevenly throughout the body caused by the asymmetrical aspects. This, in turn, also creates higher stress compared to the mass constraint variation. This result is linear with the report from [PERSON] et al. [30] that shows the increment of cycling force during the uphill track, as well as the energy consumption in the cyclist body.
# Application of Machine Learning Techniques to
## 4 Results and Discussion
### GCM Selection  
We investigated the capabilities of 21 CMIP GCMs to reconstruct the highly resolved observation data for precipitation and, using Pearson correlation analysis, identified the most correlated GCMs for a dependable climate projection in a climate zone. The GCMs were selected in every climate zone of the study area using the GCM and APHRODITE seasonal precipitation data at the reference station. The reference stations in every climate zone for every season have been shown in Figure 8. These selected GCMs were then used for the projections of changes in the precipitation trends at every grid point throughout the respective zone in the river basins. Figure 9 shows the spatial distribution of the highly correlated GCMs in each climate zone. For the warm-wet season, we selected the BNU-ESM, MIROC5, CESM, IPSL-CM5A-MR and GFDL-ESM2G GCMs. For the cold-dry season, we selected the CCSM4, MIROC-ESM, IPSL-CM5A-LR, MIROC5 and MPI-ESM-LR GCMs. For the cold-wet season, we selected the MRI-CGCM3, MIROC-ESM, NorESMI, CESM, IPSL-CM5A-MR and GFDL-ESM2G GCMs. For the warm-dry season, we selected the IPSL-CM5A-MR, BNU-ESM, GFDL-CM3, bcc-CSM1-1, inmcm4 and GFDL-CM3 GCMs. The description of the aforementioned GCMs can be checked from the Table S1 in supplementary documents.
# Dataset Transformation System for Sign Language Recognition Based on Image Classification Network
## 3 Materials and Methods
### Dataset Transformation System Structure  
The proposed method in this paper was designed with the structure shown in Figure 4. Then, the system was applied to the image classification model and used for sign language recognition.  
Figure 3: Keypoints extracted using OpenPose : (**a**) 25 keypoints extracted from the body; (**b**) 70 keypoints extracted from face; (**c**) 21 keypoints extracted from one hand.  
First, when sign language video is input, keypoints are extracted through the pose estimation process. In order to use a certain level of keypoints in all data, the normalization proceeded as the next step. Normalized data were converted into the proposed image in this paper through image generation. The generated image dataset is used as an input to the image classification model to learn. This allows us to enable motion recognition in a general image classification model.
## 2 System Model
### Baseline Scheduling Algorithm with Sub-Flow Division  
The baseline scheduling algorithm presented in this work is designed to generate a feasible and efficient Gate Control List (GCL) for Time-Sensitive Networking (TSN) in an in-vehicle environment. It systematically processes data stream requirements, incorporating a sub-flow division mechanism to enhance schedulability for high-bandwidth streams, and employs an iterative SMT-based approach to determine optimal constraint boundaries and compute the final GCL. The overall process can be divided into two main phases: (1) stream preprocessing and sub-flow parameter calculation, and (2) iterative constraint boundary determination and GCL generation.
## 3 Methodology
### Latency Evaluation over the University Wi-Fi and a VPN at the Network Edge  
The next test moved the client connection from the University's internal network to a separate Wi-Fi network, which was still located on the University's campus. This test also introduced a VPN connection into its topology to expand the network distance in these tests to the network edge. The client physically remained in our lab, but its network path length greatly increased, as shown in Figure 4.  
This network configuration introduces additional hops within the network path, including the VPN server, which increases security but adds to the overall latency. The results from this network configuration were collected strictly through each script's internal timers, with the obtained results presented in Section 4.1 below.  
Figure 4: Network diagram of Lab Wi-Fi-to-University network connection scenario including VPN.  
Figure 3: Network diagram of wired non-VPN University network connection scenario.