esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
The authors also present models combining word embeddings and linguistic features for three ML classifiers: Random Forest (RF), a decision tree classifier, Sequential Minimal Optimization (SMO), and a Support Vector Machine achieving the best accuracy of 85.17%. In a deeper research, [PERSON] et al. [37] explored whether social media can be used as an indicator of sexual violence in the U.S., by tracking misogynistic tweets. Using the FBI Uniform Crime Reports provides rape statistics in the U.S. at the state level and a 10% sample of the Twitter stream produced during 2012. The authors manually compiled a list of 90 terms that are commonly used as misogynistic insults. With such filters, they obtained georeferenced tweets that contain misogynistic language and location (either latitude/longitude or a free-form location string) and mapped them to states, using state boundary data from the U.S. Census Bureau. The final dataset contains roughly 170 million georeferenced tweets, of which 1.2 million contain misogynistic language. As a result, the authors found a significant association between tweets that contain misogynistic language and rape crime statistics for each state in the U.S. A similar project is presented in [38], where the author delves into the relationship between the rate of misogynistic tweets and the rate of femicides in Mexico. Data consisted of femicide reports from the Executive Secretariat of the National Public Security System of Mexico (SESNSP) and about twelve million georeferenced tweets in 2017-2018. Some regions were found to have particularly high rates of both misogyny and femicides. Furthermore, the Spearman correlation coefficient between both variables is 0.2515 with a significance level of 0.16; in other words, there is an interdependence, although very low, between both indicators, but the risk of concluding that there is a correlation, when, in reality, there is not, is only 16%.
# FPGA Implementation of a Real-Time Edge Detection System Based on an Improved Canny Algorithm
## 4 Conclusions  
In this paper, an improved Canny edge detection algorithm is proposed and implemented on an FPGA. In this new Canny edge detector, an approximate method is used to reduce the complexity of computing the gradient magnitude and orientation, facilitate hardware implementation, and reduce resource usage. In addition, [PERSON]'s algorithm is used to adaptively determine the image threshold. Compared with the fixed threshold scheme, this results in better edge detection performance under the condition of a variable detection environment. Due to the high complexity of [PERSON]'s algorithm, it is difficult to implement it directly on FPGA. Therefore, the logarithmic operation unit is introduced to reduce the complexity of [PERSON]'s algorithm, which further simplifies the hardware architecture and reduces resource usage. The result shows that the proposed algorithm's implementation requires only 1.231 ms to detect the edges of the 512 \(\times\) 512 image when clocked at 50 MHz. In addition, the proposed hardware architecture uses the fewest logical resources and is more suitable for hardware platforms with limited resources. In the future, this system can be a candidate for real-time edge detection applications.  
Conceptualization, L.G. and S.W.; Methodology, S.W.; Software, S.W.; Validation, S.W.; Formal Analysis, SW.; Investigation, S.W.; Resources, S.W.; Data Curation, S.W.; Writing-Original Draft Preparation, S.W.; Writing-Review & Editing, L.G. and S.W.; Visualization, L.G. and S.W.; Supervision, L.G.; Project Administration, L.G.; Funding Acquisition, L.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Independent Research fund of The State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines (No. SKLMRDPC19 KF09) and the Key Research and Development Plan Anhui Province (202004a05020080).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **GTX 80 [15]** & **GT 80 [17]** & **GT 200 [17]** & **Fermi [17]** & **Proposed** \\ \hline Image size & 512 \(\times\) 512 & 321 \(\times\) 481 & 321 \(\times\) 481 & 321 \(\times\) 481 & 512 \(\times\) 512 \\ Freq (MHz) & 768 & 1500 & 1300 & 1150 & 50 \\ Time (ms) & 3.4 & 5.47 & 2.95 & 2.3 & 1.231 \\ Norm. time (ms) & 52.224 & 278.62 & 130.22 & 89.82 & 1.231 \\ \hline \end{tabular}
\end{table}
Table 3: Run time comparison of GPGPU and proposed FPGA implementation.
## 4 Understanding Centralised Identity Systems
### OAuth2.0: The Open Authorization Standard
#### 4.8.2 Process of Requests Flow in OAuth  
The process flow in OAuth involves several steps (see Figure 6):  
1. **Request Initiation**: The user uses the client app to call the \"/authorize\" endpoint on the Authorization Server to initiate a request to access the protected resources [42; 44]. The client app constructs the URL, typically including parameters such as response_type=code client_id=CLIENT_ID redirect_uri=REDIRECT_URI scope=read write state=UNIQUE_STATE
2. **Authorization Request**: After the request reaches the Authorization Server's authorization endpoint, it logs the request and grants permission accordingly [42].
3. **Authorization Code**: Once approved by the resource owner, an authorization code is generated and returned to the client app along with a unique state [42; 43].
4. **Code Exchange**: The client app sends a POST request to the Authorization Server's token endpoint, exchanging the authorization code for an access token [42].
5. **Token Usage**: The client app accesses the protected resources using the access token.
6. **Resource Access**: Upon receiving the request with the token, the resource server validates the token using a public key and checks parameters such as validity, scope, and expiration [43].
7. **Refresh Token**: As the access token has limited validity, the client app can request a refresh token from the Authorization Server if needed [42; 43].
## 4 Explosive Capability Assessments
### Countermovement Jump Test  
The countermovement jump (CMJ) test assesses explosive power in a VJ, with athletes standing up straight, then bending their knees and quickly extending them to leave the ground and rise up as high as possible. The athletes are usually instructed to place their hands on their hips during the jump to minimize upper limb momentum. Players perform up to three jumps in total, with about two minutes of rest between jumps. Jumps can be performed using one or both legs and a transmitting and receiving bar is used that enables the accurate measurement of flight and contact times during jumps [2; 82].  
The CMJ test plays a vital role in assessing explosive power, a critical attribute in ball games, including basketball. By measuring the height achieved during a vertical jump, coaches and trainers gain valuable insights into the athletes' ability to generate force and power with their lower body. This information allows for the evaluation of performance, identification of areas for improvement, and the design of targeted training programs to enhance explosive power [31; 36; 98].  
Performing the CMJ test with one or both legs enables the assessment and comparison of bilateral and unilateral explosive power, providing valuable data on athletes' leg strength and coordination. This assessment can assist coaches in identifying potential asymmetriesor imbalances that may impact performance or increase the risk of injuries. By addressing these imbalances with targeted training, athletes can improve their overall explosiveness and reduce the likelihood of injury [1; 2; 82; 98].  
Moreover, the use of transmitting and receiving bars in the CMJ test ensures accurate measurement of flight and contact times, offering detailed information on athletes' jumping technique and efficiency [2; 99]. These data can be analyzed by coaches and trainers to refine jumping mechanics, optimize power production, and minimize energy loss during explosive movements. The insights gained from the CMJ test can guide the development of individualized training programs, targeting specific areas for improvement and enhancing overall athletic performance on the basketball court [1; 5; 98].  
In summary, the countermovement jump (CMJ) test is a crucial assessment tool for evaluating athletes' explosive power in ball games, with specific relevance to basketball. By measuring the height achieved during a vertical jump, coaches and trainers can assess performance, identify areas for improvement, and design training programs to enhance explosive power [2]. The test allows for the assessment of bilateral and unilateral explosive power, aiding in the detection of imbalances [1; 2]. The use of transmitting and receiving bars ensures accurate measurement, enabling the analysis and refinement of the jumping technique. Ultimately, the CMJ test plays a significant role in optimizing athletic performance and enhancing explosiveness in the game of basketball [2; 18; 22].
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Year of** \\ **Research** \\ \end{tabular} & \begin{tabular}{c} **Main** \\ **Researchers** \\ \end{tabular} & \begin{tabular}{c} **Space Scale** \\ **Resolution** \\ \end{tabular} & \begin{tabular}{c} **Methodology** \\ **(Sept)** \\ \end{tabular} & \
\begin{tabular}{c} **Evaluation** \\ **Type** \\ \end{tabular} &
\begin{tabular}{c} **Main Findings** \\ \end{tabular} \\ \hline
2013 & [PERSON] [27] & \begin{tabular}{c} National \\ (China) \\ \end{tabular} & \(1\) km \(\times\) 1 km & \
\begin{tabular}{c} Artificial neural \\ network model \\ \end{tabular} & Static &
\begin{tabular}{c} Identified high-risk areas \\ across China \\ \end{tabular} \\ \hline
2017 & [PERSON] [28] & Global & \(1\) km \(\times\) 1 km & \
\begin{tabular}{c} A heuristic \\ fuzzy approach \\ \end{tabular} & Static &
\begin{tabular}{c} Developed a global landslide \\ susceptibility map \\ \end{tabular} \\ \hline
2018 & [PERSON] [29] & \begin{tabular}{c} National \\ (China) \\ \end{tabular} & \(1\) km \(\times\) 1 km & \
\begin{tabular}{c} Logistic \\ regression model \\ \end{tabular} & Static &
\begin{tabular}{c} Evaluation of landslide \\ hazards, vulnerabilities, and risks in China \\ \end{tabular} \\ \hline
2021 & [PERSON] [30] & \begin{tabular}{c} National \\ (China) \\ \end{tabular} & \(1\) km \(\times\) 1 km & \
\begin{tabular}{c} GAM (generalized \\ additive model) and \\ GAM (generalized \\ additive mixed model) \\ \end{tabular} & Static &
\begin{tabular}{c} Assessing the vulnerability to \\ landslides triggered by \\ rainfall in China \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical overview of landslide susceptibility studies conducted nationally and globally.
## 3 Materials and Methods
### 5G Network  
This approach and the testing of smart grid protocols over 5G was implemented using a local, in-lab 5G testbed setup located at the Advanced Telecommunication Engineering Laboratory (TEL) at the University of Nebraska-Lincoln. The testbed architecture was designed and developed to accurately represent regular private 5G network deployments, incorporating a 5G Core Network (CN), gNodeB (gNB), and User Equipment (UE). OAI was utilized as the software foundation for establishing and operating this network, allowing for software control of configurations and hardware flexibility in the Radio Access Network (RAN) operation.  
OAI provides full support for operating the UE, gNB, and CN, all of which are provided as an open-source solution. Furthermore, all components and features are compliant with the 3 GPP standard. The provided RAN currently complies with Release 15 [30], and the CN complies with Release 16 [31], with development continuing for this project. The CN's Virtualized Network Functions (VNFs) are implemented as individual Docker images hosted on top of a Ubuntu 22.04 LTS Linux server with the updated low-latency kernel installed. The gNB is implemented using software radio principles using GNURadio v3.10.7.0, with the signal processing aspects performed by a bare-metal (non-Dockerized)implementation on a server with operating system and kernel specifications identical to those of the CN. The radio frontend utilized is an Ettus Research B210 Universal Software Radio Peripheral (USRP) [32]. This USRP is configured by the OAI software v1.5.1 to operate on band n78 in Time Division Duplex (TDD) mode at a center frequency of 3.6192 GHz and a 40 MHz wide channel. The network architecture of the 5G network is shown in Figure 2. Communication between the USRP and its host machine is facilitated through USB 3.0 to allow for high-bandwidth communications. The operations of the USRP are controlled via the interface between the OAI software and Ettus' UHD (USRP Hardware Drivers) v4.4.0.  
In our testbed, the UE functionality is provided by a Raspberry Pi 4 with Ubuntu 22.04 LTS installed, along with a Quectel RM520N-GL 5G HAT Modem, as shown in Figure 3. Quectel provides proprietary firmware and Linux drivers in order to seamlessly integrate the radio modem with the desktop environment as a cellular interface. Like the USRP, the modem is connected to the Raspberry Pi via USB 3.0. As the UE is the only component  
Figure 1: Testbed network architecture [26].  
Figure 2: Testbed component architecture.  
facilitating communication between the 5G network and the DUT, the UE runs additional software to forward any applicable OT packets from the test controller. In the case of the GOOSE protocol, encapsulated packets are extracted and forwarded onto the switch for the DUT. With Modbus and DNP3, we incorporate the UE as the DUT for the packets since the UE is not required to extract and forward any payload.
## 3 Metaverse Classroom: A Layered Architectural View
### Security and Privacy Layer
#### 3.6.3 Digital Well-Being  
Digital well-being in a Metaverse classroom ensures students engage with virtual learning in a healthy and balanced way [206]. In immersive environments where students may lose track of time, digital well-being tools help manage screen exposure to prevent fatigue and maintain focus [132]. Time trackers and break reminders prompt students to take regular breaks, promoting healthy screen-time habits [92]. For example, a reminder might encourage a five-minute break after 30 min of continuous activity, helping students stay refreshed and focused [124]. These features prevent eye strain and mental fatigue, particularly during longer lessons or group projects. Ergonomic support guidelines promote physical well-being by helping students set up workspaces to reduce stress and improve posture [132]. On-screen tips can suggest ideal seating arrangements, screen height adjustments, and arm positioning, especially for students using VR headsets or tablets [207]. For instance, students might receive a notification to adjust their chairs or take a stretching break after extended VR use. Encouraging proper ergonomics reduces discomfort and long-term strain from digital device use.  
Mental health resources and mindfulness activities are key components of digital well-being in the Metaverse [208]. Guided mindfulness exercises, breathing techniques, and relaxation activities can help students manage stress and improve focus [124]. For example, students might take a one-minute breathing exercise before a challenging test to calm their minds. Additionally, virtual counseling sessions or access to mental health resources offer students a safe space to seek support if they feel overwhelmed [209]. These resources foster a supportive learning environment, prioritizing emotional well-being. Finally, well-being analytics provide teachers and parents with insights into students' digital use patterns, helping identify those who may need extra support [124]. Data on screen time, break frequency, and engagement levels allow educators to offer personalized interventions without infringing on student autonomy [7]. For instance, if a student consistently exceeds recommended screen time or rarely takes breaks, a teacher might suggest setting stricter limits or incorporating wellness activities. This data-driven approach supports healthy digital practices, creating a balanced learning environment [16]. By integrating digital wellness features holistically, the Metaverse classroom promotes a sustainable learning experience that prioritizes students' physical and mental health [131].
# Analysis and Design of Infrared Search and Track System with Afocal Zoom Telescope
## 5 Conclusions  
A homogeneous coordinate transformation method is proposed in this paper to deal with the residual image wandering issue in IRST optics. We establish a dynamic imaging model and present a method to quantize residual image wandering by using the model with different telescope magnification and integral time; this method can be used to guide the development of IRST optics and improve the efficiency and convenience of the design process. Furthermore, a 6\(\times\) zoom IRST optical system is designed and achieves a continuous zoom range of 60 mm to 360 mm and image wandering of up to 0.6-pixel pitch at an integral time of 16 ms. The results consist of the solutions of the developed model, prove its correctness, and show considerable interest for application in the design of similar electrical optical systems.  
Although the numerical simulation and design of optics yield interesting results, it is limited by the optical simulation models, many other aspects of a real IRST system, such as servo control, image processing, and platform jitter are not taken into consideration. The analysis and treatment of these influencing factors will be important work in the building process of a real system. A prototype is under construction, and its performance will be tested and demonstrated in the near future.  
Conceptualization, R.Q.; methodology, R.Q. and H.Z.; software, R.Q. and L.Y.; writing--original draft preparation, R.Q.; writing--review and editing, H.Z. and W.C.; supervision, W.C. All authors have read and agreed to the published version of the manuscript.  
The Youth Innovation Promotion Association of Chinese Academy of Sciences (2023422).  
Data Availability Statement:Data are available from the authors upon request. The data are not publicly available, because the design result has been used as reference for product development.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Tolerances** \\ \hline Radius & \(\pm\)3 fringe \\ Center thickness & \(\pm\)0.01 mm \\ Mounting tolerances & \\ Decentering & \(\pm\)0.01 mm \\ Tilt & \(\pm\)1\({}^{\prime}\) \\ Integrating tolerances & \\ Decentering & \(\pm\)0.02 mm \\ Tilt & \(\pm\)1\({}^{\prime}\) \\ Surface tolerances & \\ Decentering & \(\pm\)0.01 mm \\ Tilt & \(\pm\)1\({}^{\prime}\) \\ Irregularity & \(\pm\)0.3 fringe \\ Refractive index delta & \(\pm\)0.0003 \\ Abbe number delta & \(\pm\)0.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tolerances for the designed IRST optics.
# A Study on the Stall Detection of an Axial Compressor through Pressure Analysis
###### Abstract  
In order to research the inherent working laws of compressors nearing stall state, a series of compressor experiments are conducted. With the help of fast Fourier transform, the amplitude-frequency characteristics of pressures at the compressor inlet, outlet and blade tip region outlet are analyzed. Meanwhile, devices imitating inlet distortion were applied in the compressor inlet distortion disturbance. The experimental results indicated that compressor blade tip region pressure showed a better performance than the compressor's inlet and outlet pressures in regards to describing compressor characteristics. What's more, compressor inlet distortion always disturbed the compressor pressure characteristics. Whether with inlet distortion or not, the pressure characteristics of pressure periodicity and amplitude frequency could always be maintained in compressor blade tip pressure. For the sake of compressor real-time stall detection application, a compressor stall detection algorithm is proposed to calculate the compressor pressure correlation coefficient. The algorithm also showed a good monotonicity in describing the relationship between the compressor surge margin and the pressure correlation coefficient.  
Keywords:stall state; inlet distortion; pressure characteristics; detection algorithm +
Footnote †: journal: Applied Sciences
## 6 Distributed LoWMob (DLoWMob) Mobility Support Scheme
### Route Optimization for Intra-PAN Communication in DLoWMob
#### 6.2.2 Handoff Process in the Route Optimization Scheme  
In the route optimized scheme when a MN moves to a different MSP while communicating with another MN, a new path is needed to be established between the destination and source MSP. This section introduces the modified version of the handoff process (which is explained in section 5.2) to enable creation of a new path between source and destination MSP.  
Figure 12: Message format of a data packet from source MN to destination MN via MSPs.  
In Figure 13, when the communication link between the MN2 and SN26 degrades, SN26 sends a _new_node_ message to the next or future SN i.e., SN32. This time _new_node_ message contains MN1's ID and its MSP's short address, i.e., MSP1's address, along with MN2'ID. Then SN32 forwards the _new_node_ message to its MSP, i.e., MSP3. MSP3 then sends an LU to the GW, and _MN_moved_ message to MSP1. The _MN_moved_ message contains the addresses of both of the MSPs, as source and destination, and the MN's addresses. The MSPs, which forward the _MN_moved_ message to the MSP1, record the two MN's IDs and their respective MSPs. The purpose of this message is to create route specific entries on behalf of the two MNs. When the message reaches MSP1, it broadcast the message to its one hop neighbors (MSPs).This is done so that in case if MN1 has also moved to a new MSP, then the new MSP is informed about the destination MN's movement. Moreover, once the _MN_moved_ message reaches MSP1, it immediately starts forwarding the MN1's data packets, if any; to the next MSP from which it received the _MN_moved_ message i.e., MSP2. When MSP2 checks the destination of the packet, it knows that the MN2 is associated with MSP3 and forwards the packet to the next MSP, i.e., MSP3 (which is the destination MSP as well). Meanwhile, until the _MN_moved_ message reaches the MSP1, MSP1 keeps sending the packets to the old path. The packets are delivered to the MN2 through the tunnel created between the current SN and the previous SN.  
Similarly, when the source MN, i.e., MN1, moves while sending the data packets to MN2, its current SN will inform the next SN about its corresponding MN's MSP. Also the current SN will notify the next SN about its own MSP so that the next MSP can forward MN's data packet to the previous MSP since it already has a path towards the destination MN. Meanwhile, the next MSP can send a _MN_moved_ to the destination MN's MSP, in order to establish a path between itself and destination MSP. It should also be noted that the routing table entries of MSPs will expire when the source stops sending data packets to destination MN.  
Figure 13: Handoff process in DLoWMob with Route Optimization scheme.
## 3 Experiments and Analysis
### Experimental Evaluation Indicators  
#### 3.2.1 Fruits Detection Experiment  
Precision (\(P\)), recall (\(R\)), harmonized average of precision and recall rates (\(F1\)), average Detection Precision (\(ADP\)), and detection time for detecting an image are used to evaluate the performance of the trained network mode. The performance of the obtained network model and the calculation formula is shown below. In addition, video detection accuracy (\(VDA\)) was used to represent the ratio of correctly detected targets to all targets by the model, with higher values indicating better model performance.  
\[F1=\frac{2 PR}{P+R} \tag{7}\]  
\[ADP=\int_{0}^{1}P(R)dR \tag{8}\]