esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiple PUE Attack Detection in Cooperative Mobile Cognitive Radio Networks
## 7 Conclusions  
Using the energy detection and authentication processes provides a framework for distinguishing between legitimate PUs and malicious PUE attacks. The energy detection mechanism identifies potential PU/PUE signals by comparing the received signal's energy against a predefined threshold. This is followed by an authentication process that helps to verify whether the detected signal is from a legitimate PU or an impersonating PUE. The combined use of these methods allows for a high probability of accurate PUE detection, as demonstrated by experimental results.  
The implemented DNN for PUE detection increases the system's ability to classify and detect multiple PUE attacks. The DNN was trained with features derived from both energy detection and authentication results. Experimental results showed that the DNN achieved a detection accuracy of 90% at an SNR of -9 dB. The DNN's capability to process and analyze large datasets improves the system's adaptability to multiple and moving network users and attackers.  
The proposed methodology was tested in a controlled SDR-based environment, validating its performance in real-world settings. The experiments involved single and multiple PUE attacks, with the DNN achieving a detection probability of over 90%, even in challenging conditions. The results demonstrated that the detection system can effectively identify and manage multiple simultaneous PUE attacks, maintaining high performance across different SNR conditions.  
The design, experiments, and environment proposed in this paper were conceived by E.C.M.; SDR implementation and test, E.C.M., G.C.P. and A.A.M.; results' analysis, discussion, and editing of this paper, E.C.M., G.C.P. and A.A.M. All authors participated in proofreading, and read and approved the final manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Universidad Santo Tomas, Proyecto FODEIN 2023.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
We thank Universidad Santo Tomas for funding this project.  
The authors declare no conflicts of interest.
## 3 Results
### Fabrication and Measurement
Instead of using PSO as an external optimizer of a full-wave simulator, IE3D is used to generate data that relates the antenna bandwidth to the physical dimensions of the patch antennas to be optimized. Analytical functions are synthesized using curve fitting and PSO is used to find the maximum of these functions, thus determining the antenna dimensions required for wider bandwidths. Ref. [30] is distinct in their treatment of particle boundary conditions, which they claim to reduce computation time. The bandwidth improvement achieved by such an approach is 13.5% and 13.8% for [47], respectively, compared to that achieved by DH-BPSO. [PERSON] et al. [48] utilize the original binary PSO algorithm [14] to maximize the voltage standing wave ratio (VSWR) of a pixelized planar monopole antenna in the frequency range 3.1 to 10.6 GHz. The antenna VSWR is simulated via the Discrete Green's function instead of a full-wave simulator such as HFSS or IE3D. Their optimized antenna achieves a 27.4% increase in bandwidth relative to a reference staircase monopole antenna with bandwidth 6.2 GHz. [PERSON] et al. [49] use the continuous-domain real PSO to determine the dimensions of a Printed Log Periodic Dipole Array (PLPDA) antenna for maximum bandwidth. They enhance the initial 5 GHz  
Figure 10: Comparison of (**a**) the simulated and measured peak gain and (**b**) the measured efficiencies of the base and DH-BPSO-optimized IFAs. bandwidth to 7.4 GHz, giving a measured increase of 48%. Compared to existing literature, DH-BPSO achieved a significantly higher bandwidth increase.
## 5 Discussion
### Correlation between InSAR Deformation Results and Multiple Factors
#### 5.1.3 Slope Factor  
Figure 12 illustrates the slope map of the study area computed using NASADEM HGT v001. The dashed blue line represents the pre-disaster lake contour outlined using Landsat 8 images acquired on 25 June 2023, while the solid red line delineates the extent of the landslide. Analysis of deformation results derived from the ascending images reveals a  
Figure 11: The correlation between the time-series deformation of point P1 derived from the descending images of Sentinel-1 before the disaster and the variation in the area of South Lhonak Lake from January 2021 to July 2023.  
Figure 10: Landsat optical images covering the South Lhonak Lake region from January 2021 to October 2023. The red dashed line delineates the extent of the landslide and floating debris, based on GF-2 image.  
significant acceleration at point P1 from May to July 2021, surpassing the velocities observed at other selected points. Notably, P1 exhibits the highest deformation rate among the four selected points (P1-P4). Furthermore, the slope map depicted in Figure 12 highlights that within the landslide area, the region housing point P1 exhibits the steepest slope.  
From Figure 6, it is evident that the accumulated deformation in the vicinity of P1 and P2 exceeds that of P3 and P4, possibly due to the presence of a gully near the area of P1 and P2. Previous research has indicated that river erosion plays a pivotal role in triggering landslides [42; 43], leading us to speculate that water flow erosion contributes to the substantial deformation observed in this area.
# RMCSat: An F10.7 Solar Flux Index CubeSat Mission
## 3 RMCSat Mission
### Performance Requirements and Constraints
#### 3.1.3 Deorbiting Requirements  
The mission design life of RMCSat is one year, all components must continue to operate throughout this timeframe. While it is likely the mission will extend beyond this design life, international guidelines require satellites to deorbit from LEO within 25 years [26]. The results of an analysis conducted for three common CubeSat launch  
Figure 3: The effect of main beam gain on the measured F10.7 value, for F10.7 = 100 sfu. (**a**) The full main beam response. (**b**) A close-up of the central beam region. If the Sun passes through the main beam off-boresight, the measured F10.7 is lower than the actual value, as shown by the blue curve. The red line indicates the point at which the measured solar flux density is 1% less than the actual value, corresponding to an antenna gain reduction of 0.043 dBi.  
altitudes and various drag coefficients (\(c_{D}\)) shown in Table 3 indicate an altitude of 550 km or lower is necessary for atmospheric drag forces alone to deorbit the satellite.  
Consequently, a design altitude of 550 km is chosen to allow for drag forces to deorbit the satellite and to minimize thermal emission signals from Earth. A lower altitude is feasible if a launch altitude of 550 km is not possible, however, the impact of thermal noise environment and radio noise interference on the antenna would require re-calculation.
# Developing a Serious Video Game to Engage the Upper Limb Post-Stroke Rehabilitation
## 2 Materials and Methods
### Software  
At the software level, multiple technologies were employed. Unity 3D [18] was utilized to develop the application's interface and underlying logic. Unity allows the creation of 3D game scenarios with ease, utilizing a highly user-friendly interface that simplifies all forms of testing and debugging in an efficient, rapid, and visual manner. The programming language selected for this project is C Sharp (C#) due to its readability and ease of implementation [22]. To acquire avatars for the application, the Mixamo [23] website was selected, as it offers pre-existing human models that are highly compatible with Unity 3D. To enable the functionality of Kinect V2 on a Windows-based computer, the Kinect V2 Software Development Kit (SDK) (KinectSDK-v2\(0\)1409) was downloaded and installed from the Microsoft website. For other operating systems, such as Ubuntu or macOS, supplementary tools are required to achieve compatibility. Kinect V2 was selected for this work due to its seamless integration with the Unity 3D development platform. At the database level, SQLite was chosen for this initial version since data storage will be managed locally, as there is no need for communication with external servers to fully enjoy the game. SQLite uses a file-based data storage system; it is atomic, consistent, isolated, and durable (ACID) even in the event of system crashes and power failures. Additionally, it is lightweight, straightforward to use, does not require external dependencies, and supports cross-platform environments, such as Android, iOS, Unix, macOS, Solaris, and Windows [24]. It was utilized to store all necessary and relevant information for the game.
# FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings +
## Appendix A Real-World Example: PubMed Dataset  
To help illustrate the aforementioned problems in the real-world, we processed the publicly available PubMed dataset courtesy of the U.S. National Library of Medicine, that comprises more than 31 million citations for biomedical literature from various sources, with metadata ranging from author names to their Orcid IDs. An Orcid ID is a unique author identifier used to distinguish researchers. It is useful information to uncover ambiguous author names (an author name that has multiple Orcid IDs) or author name duplicates (distinct names that correspond to the same Orcid ID). From a quick analysis of the 17 millions author data present in the dataset, there exist 154,247 duplicate author names (different names that refer to the same author), and 62,506 ambiguous author names (different authors with the same name). A further visualization in Figure 11 shows the frequency histogram of ambiguous author names with multiple Orcid IDs which rangesfrom 2 up to more than 200 per name. The most common cause for this problem is that some distinct authors share the same name, which is very common for names for authors of Asian background, as the romanization of their name is often ambiguous. The most ambiguous names in the dataset are '[PERSON]' and '[PERSON]', both with more than 200 different authors associated with each of them.  
As for duplicate author names, shown in Figure 10, the main cause is multiple name variations for the same author, for example, the Orcid ID 0000000240600292 belongs to 4 variations of the name '[PERSON]' ([PERSON], [PERSON], and [PERSON]) that refer to the same author in the dataset. The second cause is erroneously parsed metadata provided by the PubMed baseline dataset, where the coauthors share the same Orcid ID as one of the other authors.  
Both of those problems are the result of both the processing and parsing tasks, which are very common for such data type.
# Analytical Model for the Structural Behavior of Pipelines During Lowering-In
## 1 Introduction
Catenary method is ordinary techniques to analyze pipelines and is used in pipeline modeling for offshore installations. However, this methodology is insufficient for analyzing the structural behavior of the dominant pipeline to the bending in the lowering phase. In the model developed by [PERSON] and [PERSON] [20], a combination of a rigid seabed or BOEF for the seabed and 'cable or cable and elastic beam' for a pipeline is considered in the modeling of the construction process and the model is applied for two-dimensional plane behavior. The applied semi-infinite boundary used for the seabed is not suitable for analyzing the initial step of J-lay and is not able to achieve the double curvature of the pipeline present during lowering-in. These previous analytical models use the semi-infinite boundary condition in the BOEF, which cannot effectively deal with finite contact and lift-off problems for the pipeline. In this study, a new analytical model of a pipeline applicable to various construction sequences were developed, especially for lowering-in. By adopting existing methodologies in the new model, the pipeline is partitioned with several elements considering geometric deformation and boundary conditions. Two parameters, BOEF and a Euler-Bernoulli beam with axial force, are used for the new analytical model to consider both the pipeline-soil interaction and the sectional forces of a pipeline during lowering-in. More details are presented in the following sections and the validity of the new model was evaluated through comparison with finite element (FE) analysis. Finally, three examples representing various construction steps during lowering-in are presented at the end of this paper. Figure 1: Analytical model for the pipeline proposed by [PERSON] [16] (as cited from [PERSON] et al. [17]).
## 3 Results and Discussion  
### Total -SH and Carbonyl Content  
Figure 1a presents total -SH content of tilapia processed by various SV conditions. At 50 \({}^{\circ}\)C of SV cooking, the total -SH content significantly decreased when processed with a longer time (\(p<0.05\)), whereas there was no significant difference between samples treated at 60 \({}^{\circ}\)C (\(p>0.05\)). Among all samples, the lowest total -SH content was obtained in the control (\(p<0.05\)), accounting for 10.46 nmol/mg protein. The decrease in the total -SH content might be associated with the unfolding or changing of MPs during heating, initiating the oxidation of hidden/surface SH groups [20]. The results were consistent with the carbonyl content, as depicted in Figure 1b. It was found that the control had the highest carbonyl content (5.68 nmol/mg protein), whereas various SV-cooked tilapia were found to be 1.47-3.34 nmol/mg protein. Generally, -SH and carbonyl contents are two vital indexes regarding the degree of protein oxidation. The formation of carbonyl groups has been noticed as one of the most outstanding modifications in oxidized proteins. The negative effect on meat quality governed during thermal processing might be caused by increasing reactive oxygen species (ROS) production and may have led to the higher conformational changes and denaturation/oxidation of the protein [21], which may promote a significant decrease in nutritional value in terms of availability of essential amino acids, i.e., lysine, and eating quality in terms of promoting some undesirable texture or flavor. In fact, oxidative  
or proteolytic reactions play a key role in the releasing of volatile compounds, which have a direct impact on the flavor and aroma of the meat [22]. From this experiment, cooked tilapia using the SV technique can lower the denaturation/oxidation of protein; thus, some nutritional value or characteristics of SV products may be improved and be better than traditional cooking.  
Figure 1: Total -SH content (**a**), carbonyl content (**b**), FFA content (**c**), PV (**d**) and TBARS value (**e**) of SV-cooked tilapia processed by various SV conditions. Different lowercase letters indicate significant differences (_p_ < 0.05).
## 4 Results
### Identification and Description of Error Modes  
HTA and SHERPA techniques were applied to each task under evaluation, yielding a total of 59 identified error modes. The installation of brackets accounted for the highest number of error modes with 34 (58%), followed by the installation of cushioned loop clamps with 19 error modes (32%), and the installation of cap covers with six error modes (10%). We found errors in four of the five categories proposed by the SHERPA taxonomy: action, retrieval, checking and selection. The absence of communication errors could be explained by the limited amount of teamwork in the context under study where the necessary information is usually supplied to the worker electronically by the MES. For the sake of simplification and the need to avoid repeated tables, only an extract of the SHERPA output table associated with critical task No.1 (install brackets) is presented in Table 2. Similar tables were obtained as a result of SHERPA analysis of the rest of the critical tasks object of study. The consolidated results of the error modes obtained during the SHERPA analysis are presented in Table 3. Estimated cycle times are also presented at the end of Table 3 for comparison purposes. A more detailed description of each error mode is presented subsequently. Figure 8: HTA diagram for the breakdown of the critical task No.1 (install brackets).
# Sentiment Analysis of Twitter Data
## 5 Different Levels of Analysis
### Aspect-Level Sentiment Analysis  
In contrast to the previous two levels of analysis, a fine-grained analysis is conducted in aspect-level sentiment analysis. It typically comprises three steps: identification, categorization, and aggregation. Here, not only the overall sentiment of an item, but also the sentiments of all its components are examined. The stage of identification identifies the target pairs in the provided content that are relevant to the sentiment, and classification classifies their sentiments based on the predetermined sentiment values. Aggregation is the process of integrating the sentiment values of all components for a comprehensive perspective [40].
## 4 Experimental Verification  
In order to validate the proposed identification method of the nonlinear, rotor-bearing system, a rotor test rig is taken for verification, where the eddy current displacement sensor is arranged to measure the response, and the added bolt is expected to reinforce unbalanced force, as shown in Figure 12a. The test rig is connected with the Labview test system, as shown in Figure 12b.  
Concerned with safeness, the test rig, which has a critical speed of 229 rad/s, is operated under low speed case. Define the multi-harmonic \(\sin(\omega t),\ \omega\in[84,89]\) rad/s as the system input excitation, and the output of the system is considered as the horizontal vibration response.  
The NARX model of the rotor test rig is established with the test data of 84, 85, 86, 87, and 88 rad/s. Based on FROLS algorithm, a 3 order NARX model is identified as follow (Table 3):  
Figure 11: Output comparison in time and frequency domain at an over critical speed.  
Figure 12: Test set up.  
The input excitation \(\omega\) = 89 rad/s is used to verify the identified NARX model in both the time and frequency domains by using the MPO method, and the results are shown in Figure 13. At the same time, the RMSE is calculated as 8.2272 \(\times\) 10\({}^{-6}\) m.  
As Figure 13 implies, the NARX model shows satisfying accuracy. It indicates that the NARX model of the rotor test rig can predict the system output and reflect the system dynamic characteristics. Therefore, the NARX model provides a theoretical basis for analysis, design, and fault diagnosis of the nonlinear, rotor-bearing system.
## 2 Materials and Methods
### UML Diagram Classification
It should be highlighted that dropout helps prevent overfitting by randomly omitting units and their connections during training, effectively averaging the predictions of a variety of \"thinned\" networks without slowing down inference [33]. Finally, the logarithm of the softmax function [34] was applied to obtain the classification probabilities. During the training phases, the number of epochs was set to 10 because additional iterations did not yield significant improvements. Moreover, the batch size was set to 60 elements, the networks were trained using stochastic gradient descent (SGD) [35], the utilized optimizer was the adaptive method Adam [36] with a learning rate of 0.001, and the preferred loss function was cross-entropy [37], which combines the softmax function with negative log-likelihood. The models were evaluated using the accuracy and the F1-score. It is important to highlight that these metrics were averaged over the values obtained from the five iterations of cross-validation. Apart from the F1-score of each classifier, the F1-score corresponding to each UML diagram class is shown.
## 3 Implementation and Results
### Envisat  
The TLE data recovered for the case study in this work cover a period of 3 years, i.e., from 2003 to 2005. The adjacent time between the TLEs averages approximately 24 h, i.e., one TLE was chosen per day to study the orbital parameters. Envisat has an average mean motion of 14 revolutions per day, with an average semimajor axis of 7152 km and an inclination of 98.5\({}^{\circ}\). The green vertical line in Figure 3 illustrates the known maneuver history. Envisat demonstrates a study for evaluating the performance of the algorithm, in which all green lines represent a large amplitude of out-of-plane, orbit control maneuvers with a magnitude in m/s [47]. A total of nine maneuvers performed over a period of 3 years had an average change in velocity magnitude of 1-1.5 m/s. Similar to the case study of TOPEX (see Supplement Section 2 for TOPEX case study), a baseline case was initially demonstrated and manipulated by changing the two parameters to draw an analogy between the sensitivity of the maneuver detection algorithm and the two parameters.
# An Efficient Network Resource Management in SDN for Cloud Services
## 6 Conclusions  
In this paper, we presented an efficient network resource allocation method in SDN to efficiently support cloud services. We dynamically calculated the priority of services over time using a ranking algorithm, based upon which we differentially allocated the network paths. Such a differential path allocation improves the bandwidth utilization of the network paths. To prove its efficiency, we simulated the proposed network resource management system on an SDN and evaluated its performance. The performance evaluation results showed that the proposed method improved the network utilization in comparison with the OSPF method.  
The network model and workloads used in this study may not accurately represent actual service models and workloads. Therefore, in future research, we will consider the configurations of actual network models under various situations. We will also study the effect of the dynamic link cost change, and additionally evaluate the performance on network workloads gathered from the Internet. Finally, we have not yet found a similar mechanism where the goal is the same as ours. We will evaluate its performance and compare it with our mechanism if we find it in the future.  
**Author Contributions:** Conceptualization, M.J. and B.-D.L.; methodology, N.K.; software, Y.J.; validation, Y.J.; writing--original draft preparation, M.J. and N.K.; writing--review and editing, N.K.; supervision, B.-D.L.; project administration, B.-D.L.; funding acquisition, N.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2020R1A6A1A03040583 and NRF-2017R1D1A1B04027874).  
**Acknowledgments:** M.J. and N.K. equally contributed to this work.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 13: Total network utilization results.