esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Objects  
### HTS Racetrack Coil  
The tested coil is one of 16 coils that were made for the production of an experimental electric machine with an annular winding design. The coil has a double pancake structure with a racetrack form made of American Superconductor (AMSC) HTS tape with the magnetic substrate. The parameters of the coil are shown in the following Table 1.  
The coil is impregnated with LOCTITE(r) STYCAST 2850 FT [24] resin to eliminate electromagnetically induced noise and vibrations and strengthen the structure. This resin has proven itself well in the manufacturing of the current leads [25] and transformer winding [26]. According to the article [27], the use of the compound may cause a decrease in the critical current, but in our case [28], the decrease in the critical current is acceptable and is mainly due to the mechanical bending of the tape when laying on a small radius, as well as the changed configuration of the magnetic field in comparison with a short sample. Nevertheless, we are working on the search for new compounds for impregnation, including the development of our own mixture of binder polymer and filler; the results will be published later. The potential (voltage) taps are located approximately in the middle of the straight part of the outmost turn (Figure 4a).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline
2G tape type & AMSC, 5 mm wide \\ Min. critical current \(l_{c}\), at 77 K, A & 100 \\ Insulation & Kapton \\ Coil type & Double pancake \\ Number of turns & 24 \\ Length of linear part, mm & 100 \\ Coil height, mm & 10 \\ Winding height, mm & 7 \\ Total wire length, m & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the coil.  
Figure 4: (**a**) Manufactured racetrack coil prepared for soldering of current leads and potential taps; (**b**) assembled package of annular stator winding (without wiring). 1—HTS coil, 2—iron yoke, 3—brass connectors, 4—copper bridge between coupled coils, and 5—positioning elements between coils.
# New Insights on the Seismic Activity of Ostuni (Apulia Region, Southern Italy) Offshore
## 3 Analysis of the Historical and Instrumental Seismicity  
The area of Central and Southern Apulia is historically characterized by low seismicity [35] and is classified as a low-seismic-hazard area. In Figure 5, the historically documented events that occurred within 35 km from Ostuni and reported by different catalogues are plotted on the map as circles representing the focal volumes, according to Bath and Duda formula [36].  
In this area, the largest earthquake and the only one with a significant damage potential is that which occurred on 26 October 1826, with Mw = 5.22 and macroseismic intensity VI-VII MCS (Mercalli-Cancani-Sieberg), near Grottaglie (see red circle, data from Parametric Catalogue of Italian Earthquakes, CPT115 v. 4.0 [37,38]).  
This event is not reported by the Catalogue of Strong Italian Earthquakes (CFT15 MED) [39,40], which has selected only a subset of the events historically documented; however, it includes four smaller earthquakes with magnitudes between 3.2 and 3.7) (grey circles in Figure 5), all based on \"a single location\" and therefore considered of uncertain reliability.  
References to a set of additional events are found in the PFG catalogue [41], compiled within the Geodynamic Finalized Project. This set consists of 16 events (yellow circles in Figure 5), often with unreliable locations. Only four of them have intensities that could have caused damage (I \(\geq\) VI MCS): one of these events is that of 1826, reported by the CPT15; two others, of magnitude 4.1, that occurred in 1833 were attributed identical locations; and the fourth, which occurred on 26 February 1947, was placed in the offshore Adriatic Sea, but its location, based on few instrumental observations, was very uncertain, and the CPT15 catalogue reports it with a completely different location (in the Tyrrhenian Sea).  
Figure 4: Structural sketch map of the Murge area and the Northern Salento (modified after [32]); the fault striking from WNW (near the town of Matera) to ESE (south of the town of Brindisi) borders the Murge (to the north) from the Salento (to the south); the Monte Giove submarine relief and Rosaria Mare basin (modified after [11,28]) are also indicated; red filled squares represent epicentral relocation of seismic events for which the focal mechanism was determined in the present paper.  
With regard to instrumental seismicity, the map in Figure 5 shows, with blue circles, the earthquakes that occurred around the town of Ostuni between 1981 and 1999 (before the network was digitized; source, Italian Seismicity Catalog--CSI catalogue [42]). Only 17 earthquakes are present in the analyzed area, with M\({}_{\rm max}\) = 3.2.  
According to the historical record of earthquakes felt in Ostuni, reported by the Database Macrosismico Italiano (DBMI15) [43], only one event caused slight damage (VII MCS), due to the strong Salento earthquake that occurred on 20 February 1743. The other 11 earthquakes were only perceived in Ostuni, with a maximum of V MCS on the occasion of the disastrous Irpinia earthquake of 23 November 1980.
# Text-Guided Refinement for Referring Image Segmentation
## 4 Experiment
### Implementation Details  
The proposed framework was implemented using the public TensorFlow toolbox and trained on an Nvidia GTX 1080 GPU for 200,000 iterations. Consistent with prior works [2; 12], we adopted DeepLab-ResNet101 [19] pretrained on the PASCAL-VOC dataset [44] as the CNN backbone for extracting visual features. Specifically, the outputs from Res3, Res4, and Res5 layers were utilized for multi-level feature fusion and were subsequently fed into the proposed decoder. For pre-processing, input images and their corresponding ground truth segmentation masks were resized to \(320\times 320\). The feature channel dimensions were unified across the framework and set as \(C_{m}=C_{r}=C_{h}=C_{y}=500\). Model training employed the binary cross-entropy loss averaged over all pixels, with optimization performed using the Adam optimizer. The initial learning rate was set to \(2.5\times 10^{-4}\), and weight decay was applied at \(5\times 10^{-4}\). Notably, the CNN backbone parameters were frozen during training to focus the optimization process on the proposed decoder and multi-modal fusion components.
# Evaluating the High Frequency Behavior of the Modified Grounding Scheme in Wind Farms
## 4 Simulation Results and Discussion
### Single Wind Turbine  
First, validation of the PMGS was investigated during indirect/direct strikes on a single wind turbine without any other protective devices. Next, the impact of other high-frequency parameters such as FD cable, electrode lengths, and SAs on the amounts of overvoltage was taken into account to achieve a complete model for further study. Finally, the comprehensive single wind turbine was used for an investigation of a direct strike.  
The circuit diagram for the simulated single wind turbine with the PMGS is shown in Figure 2.  
Figure 1: Proposed Modified Grounding Scheme (PMGS).
# Atom Probe Tomography for Catalysis Applications: A Review
## 3 Case Studies
### Catalysts as Supported Nanoparticles  
Many applications require the use of nanoparticles of catalysts dispersed on a support. The role of the support can be purely for the stabilization of the catalysts, i.e., to avoid the coalescence of nanoparticles and ensure a high specific surface area [135; 136], but can also enhance the reactivity by charge transfer [137] or participate in the catalytic process [138; 139; 140; 141; 142]. This section focuses on the study of metallic particles embedded in a metal-oxide matrix, and on the study of C-supported catalysts.
## 3 System Design
### High Sensitivity GNSS Receiver Architecture
By utilizing this external memory, the acquisition hardware accelerator can efficiently store received signals temporarily and compute the CI, bypassing the PS. The PL memory is also accessible from the embedded processor, allowing the processor to read the results of the CI performed by the hardware accelerator. The PS is also connected to another DDR4 memory component supporting the embedded OS and used for program execution in the embedded processor. The memory component connected to the processing system is not illustrated in Figure 3.  
To implement the high-sensitivity capabilities, the following modifications were applied to the GNSS-SDR software receiver: a new acquisition block was created in GNSS-SDR. This block processes the results computed by the new high-sensitivity acquisition hardware accelerator and conducts part of the acquisition algorithm in the embedded processor. GNSS-SDR was also modified to utilize assistance data for performing Doppler prediction, narrowing down the Doppler search space during acquisition. Moreover, GNSS-SDR was updated for tracking severely attenuated signals. Finally, GNSS-SDR was extended to use assistance data for obtaining navigation solutions in situations where the navigation message of weak GNSS signals cannot be reliably demodulated due to the presence of noise. With all these enhancements, the proposed HS-GNSS receiver produces PVT solutions in the presence of weak signals and in real-time. These features are elaborated further in the subsections below. Figure 4 shows a picture of the HS-GNSS Receiver prototype. The receiver has been fully constructed with Commercial Off-the-Shelf (COTS) components. Incorporating the ZCU102 and AD-FMCOMMS5-EBZ development boards results in a sizable design. The ZCU102 board measures 23.749 cm \(\times\) 24.384 cm, and the analog front-end measures 14 cm \(\times\) 9 cm. Despite these dimensions, the receiver remains portable. For instance, it can be placed in backpacks to facilitate testing in soft indoor environments. The ZCU102 includes several components that are unnecessary for the implementation of the HS-GNSS receiver. Therefore, the size of the receiver could be potentially reduced by using a custom-designed Printed Circuit Board (PCB).
# A Systematic Review of Cybersecurity Risks in Higher Education
## 10 Limitations and Future Work
### Critique of Incident Statistics  
The amount of white papers and technical reports depicting information security threats in higher education was also scarce. However, these white papers and technical reports have weak sources as they are usually referencing news articles and other media regarding the events.  
We also found the number of reported events in several of the white papers and technical reports highly questionable, considering for example, the 2019 Verizon data breach report where \(n=99\) for HE [63] or the Hackmageddon numbers with \(n=74\) for 2018, and \(n=174\) for 2019. The data provided in Ncube and Garrison [2] is 11 years old and only contains 290 incidents collected over 5 years from multiple universities. Comparing these numbers to the \(n=550\) for one year of data collected at one university [9], approximately 6100 from JANET [8], or the \(n=965\) from the UNIT report [6] confronts us with a two-fold problem: Firstly, it is highly likely that there is incomplete and biased data combined with under-reporting in the previously mentioned commercial reports. Where the latter may be caused by local detection capacity which is a major component in incident management. Herein also lies the problem of the reports provided by the commercial security companies:The data is highly biased towards the security company's tasks, privileges, and detection capacities within the client's networks. Another limitation is that these capacities are often of a technical kind, such as intrusion detection systems and centralized anti-malware solutions. This technical bias is evident in the statistics from Hackmageddon, Table 10, and Verizon, Table 9, both of which primarily reporting only about attacks detectable by such means. The second part of the problem with the incident statistics is that there is no unified approach to neither defining what a security incident is nor how to classify it. Our findings clearly document the ambiguity in the reporting of security incidents. Consider the incident classifications documented in Tables 7, 9 and 10, Figure 2. While the vocabulary is similar, the reporting is different from source to sources. This problem leads to a level of incomparably of the reported data and is also discussed extensively by [PERSON] [9]. HE needs to agree on an incident classification to allow for data comparison and aggregation.  
Furthermore, there is are interesting differences in the sector CERT/SOC data and the data published directly from the Norwegian University, but the data is to scarce to draw any conclusions. Both HE sector and University CERTs and SOCs should be encouraged to publish more statistics directly to the scientific community.
# Addressing the Non-Stationarity and Complexity of Time Series Data for Long-Term Forecasts
## 3 Experiments
### Implementation Details  
We trained our model using L2 loss and the Adam [58] optimizer. Hyperparameters such as the initial learning rate, number of ConvMixer layers, and batch sizes were determined via grid search on the held-out validation datasets. Early stopping was deployed to prevent overfitting. The code was implemented in PyTorch (version 1.11, [[https://pytorch.org](https://pytorch.org)]([https://pytorch.org](https://pytorch.org)), accessed on 18 April 2022), and the experiments were performed on a single NVIDIA TITAN RTX 24 GB GPU. For ensuring fair comparisons with other baselines, we set the look-back window lengths (LWLs) similar to that of Autotformer [20].  
To gain an insight on the performance of our model, we compared our model's performance with eight other baseline methods: SCINet [21], Autotformer [20], Informer [4], Reformer [59], LogTrans [15], LSTNet [57], N-BEATS [60], DeepAR [32], and ARIMA [7].
# A Refined Shape Sensing Method for Skin Antenna Structure Based on Inverse Finite Element Method
###### Abstract  
An important issue in the existing inverse finite element method (iFEM) is that reconstruction accuracy cannot satisfy the analytical demand for the flexible structure. To address this issue, this paper presents a multi-nodes iFEM that reconstructs the displacement of structure based on surface measurement strains in real time. Meanwhile, in light of the response characteristics of iFEM, an innovative interpolation method is adapted to regenerate the full field deformation again. The proposed method substantially expands the size of inverse elements, which reduces the numbers of sensors and improves the reconstruction accuracy. The effectiveness of the method to predict displacement is verified by a flexible antenna panel subjected typical boundary conditions.  
reconstruction accuracy; multi-node iFEM; interpolation method; flexible antenna panel +
Footnote †: journal: Applied Sciences
## 3 Methods
### Multiscale Prediction Model
#### 3.2.2 Multiple Linear Regression  
When there are two or more independent variables in regression analysis, and the independent variable and the dependent variable have a linear correlation, the resulting analysis is called multiple linear regression (MLR). Compared to conventional neural networks, MLR offers high prediction accuracy, easy parameter adjustment, and fast operation speed when dealing with low frequency components that are highly periodic and have a flat trend [38]. The expression and expansion of the MLR matrix are:  
\[Y=X\times\beta+\mu \tag{7}\]  
\[\begin{bmatrix}y_{1}\\ y_{2}\\ \vdots\\ y_{n}\end{bmatrix}=\begin{bmatrix}1&x_{11}&\cdots&x_{1n}\\ 1&x_{21}&\cdots&x_{2n}\\ 1&\vdots&\ddots&\vdots\\ 1&x_{n1}&\cdots&x_{nn}\end{bmatrix}\times\begin{bmatrix}\beta_{0}\\ \beta_{1}\\ \vdots\\ \beta_{n}\end{bmatrix}+\begin{bmatrix}h_{1}\\ h_{2}\\ \vdots\\ h_{n}\end{bmatrix} \tag{8}\]  
where \(Y\) is the dependent variable, \(X\) is the independent variable, \(\beta\) is the regression coefficient, and \(\mu\) is the random variable. The regression function can be obtained by solving the parameters using the least squares method, and its formula is:  
\[\hat{\beta}=\left(X^{T}X\right)^{-1}X^{T}Y \tag{9}\]
# Reducing Videoconferencing Fatigue through Facial Emotion Recognition
## 5 Discussion  
In this work, we have experimentally verified our hypothesis that certain combinations of emotions conveyed by the presenter, or shown by the audience, are indicators of higher perceived presentation quality. Based on recent advances in FER in deep learning based methods, particularly CNN, we compared the emotions of participants in a seminar taught over a semester through their facial expression captured in videoconferencing. As the regression coefficients in Table 4 show, the larger the deviations of happiness and fear among the audience, the higher the is presentation scored. In other words, if the spectators  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Coefficient** & **Standard Error** & **T-Statistics** & \(p\)**-Value** \\ \hline Intercept & \(-\)0.076 & 0.071 & \(-\)1.063 & 0.295 \\ deviation (happy) & 7.866 & 1.411 & 5.574 & 2.8 \(\times\) 10\({}^{-6}\) \\ deviation (fear) & 12.026 & 6.372 & 1.888 & 0.067 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary statistics of the OLS regression with the audience’s deviation in happiness and the audience’s deviation in fear as independent variables and the presentation score as the dependent variable (N = 41).  
Figure 6: **(a)** Illustration of the correlation between presentation score and the audience’s deviation in happiness. We can see that presentations which caused wide fluctuation in happiness among the spectators achieved higher scores (**b,c**) than presentations which did not cause fluctuation in happiness among the spectators (**d,e**).  
experience a broad range of emotions with wide fluctuations in happiness and fear, they scored the presentation the highest. The same insight can also be gained from Table 3, which correlates the presentation score with the different emotions. We find that neutral faces, probably indicating the boredom of the audience, are negatively correlated with the score. The highest positive correlation with the presentation score is, again, found with the deviation of happy, neutral, and fear, which confirms the regression result. Surprisingly, the appearance of fear on the faces of the audience correlates positively with the perceived quality of the presentation. Simply providing constant happiness is also positively associated with the average score of all presentations and this is shown by the positive correlation of ratio_audience (happy) with the score. Presenters achieve the most engaged audience and obtain the highest score when they smile--showing a happy face, illustrated by the positive correlation between the happiness in the face of the speaker and the score of its presentation. Happy presenters will also reduce neutral faces among the audience, as illustrated in Table 2. Having fewer neutral faces is associated with a higher presentation score.
## 4 Intraoral Molar Distalization Methods
### Miniscrew-Assisted Frog Appliance
#### 4.2.2. Buccal Miniscrew-Assisted Distalization Applications  
Distalization methods using miniscrews placed in the buccal interradicular area (Figure 10) result in less soft tissue irritation and greater patient comfort compared with applications in the palatal region [55]. However, placing miniscrews between the tooth roots introduces the risk of root contact during distalization in the sagittal direction, posing a potential disadvantage of the buccal position [56]. To minimize this risk, it has been suggested that miniscrews be placed at a 30\({}^{\circ}\)-40\({}^{\circ}\) angle to the long axis of the tooth [50]. Additionally, due to this concern, molar distalization with buccally applied miniscrews is recommended for Class II molar relationships requiring less distalization, typically up to 3 mm [57].  
[PERSON] et al. [58] studied to determine the effects of linear force vector(s) from interradicular miniscrews on the distalization pattern of the maxillary arch in adult Class II patients. Either single or dual miniscrews were inserted in the posterior interradicular  
Figure 10. Buccal miniscrew-assisted distalization applications.  
Figure 9. Modified Keles Slider.  
area to deliver a distalizing force to the main archive. The displacement patterns of the maxillary incisors and molars were measured and compared. As a result, significant distalization in the molars and incisors was shown in both groups. Significantly greater distalization and intrusion of the first molar and intrusive displacement of the incisor, together with significant reduction in the mandibular plane, were noted in the dual screw group, in contrast with the rotation of the occlusal plane in the single screw group [58].
## 4 Results
### Point Extractability Evaluation  
To identify the quantitative improvement in bathymetry performance achieved using the PGD method, decomposed components were registered to coordinated points and subsequently compared with a point cloud generated utilizing LBASSD software. Because the bathymetry performance depends on seafloor observation, bottom points were extracted by manually classifying each point cloud generated by PGD and LBASSD. Figure 9 depicts the process of generating and classifying points from different types of waveforms decomposed by PGD.  
The number of points and point densities were compared for the entire point cloud and point clouds classified as bottom (Table 4). In both test sites, the point cloud extracted through PGD exhibited significantly improved performance compared to the point cloud generated by the LBASSD software. In test site 1, the total number of points (979,283) and point density (2568 pts/m\({}^{2}\)) generated through PGD were 127% higher than those from the LBASSD results (number of points: 430,376; point density: 1.128 pts/m\({}^{2}\)). Because the PGD decomposed the water column part into separate components, the total number of points considerably increased. Regarding the bottom points, which are more significant than the total number of points, the number of points (76,336) and point density (0.200 pts/m\({}^{2}\)) increased by 18%. In test site 2, the total number of points (545,020) and point density (2.533 pts/m\({}^{2}\)) of the PGD results increased by 32% compared to those from the LBASSD results (number of points: 412,760, point density: 1.918 pts/m\({}^{2}\)). For the bottom point cloud, the number of points (71,945) and point density (0.334 pts/m\({}^{2}\)) increased by 14%. The visual comparison shown in Figures 10 and 11 confirms that the PGD significantly extracted the bottom points not captured by LBASSD. At test site 1, the bottom points missed by LBASSD in relatively deep areas could be extracted by PGD. This may be because the PGD effectively decomposed weak return components. On the other hand, the extractability of the bottom points in the shallow area (depth \(<\) 2 m) at test site 2 was improved by PGD.  
Figure 8: Comparison of waveform decomposition results between CGD and PGD techniques: (**a–d**) CGD results; (**e–h**) PGD results.  
This demonstrates that the PGD effectively decomposed the shallow bottom component that overlapped closely with the water surface component, as shown in Figure 8f.