esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
The relationships between remote sensing-based indicators, HFRS risk and rodent population density were examined by [PERSON]'s correlation. We applied a cross-correlation analysis, which detects if there are time-lag relationships. Given the time lag in the rodent population response to environmental conditions, we kept the remote sensing-based indicators series still and shifted the rodent population density series backward by one month each time (up to six months). To detect the lag effect of rodent population density on HFRS risk, we shifted the incidence of HFRS backward and kept rodent population density still. A cross-correlation analysis was used to assess the associations, and was calculated as:  
\[\rho_{\text{xy}}\left(t\right)=\gamma_{\text{xy}}\left(t\right)/\left(\sigma_ {x}\sigma_{y}\right)\text{t}=0,\,\pm 1,\,\pm 2 \tag{1}\]  
where \(\rho\) (\(t\)) is the cross-correlation coefficient at time lag \(t\); \(\sigma\) is the standard deviation for rodent population density, incidence of HFRS and remote sensing-based indicators; and \(\gamma\) is the covariance function.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MODIS Land Product** & **Indicator** & **Spatial Resolution** & **Temporal Resolution** & **Temporal Period** \\ \hline MOD13A2 & NDVI & 1-km & 16-day & 2005-2012 \\ MOD15A2 & LAI & 1-km & 8-day & 2005-2012 \\ MOD15A2 & FPAR & 1-km & 8-day & 2005-2012 \\ MOD17A2 & GPP & 1-km & 8-day & 2005-2012 \\ MOD17A2 & PsnNet & 1-km & 8-day & 2005-2012 \\ MOD11A2 & LST & 1-km & 8-day & 2005-2012 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Satellite data used in this study.
# Wind Load Characteristics of Wind Barriers Induced by High-Speed Trains Based on Field Measurements
## 2 Field Measurements  
### Profile of the Test Site  
Field measurements were conducted on the Nanning-Guangzhou high-speed railway, which is a double-line electric railway at National grade I, with a designed speed of 250 km/h. The train used in this test was the CRH380A EMU (electric multiple unit), and each train was composed of 8 coaches with a total length of 203 m. The width and height of the CRH380A EMU are 3.38 m and 3.70 m, respectively. Generally, the wind speed on the bridge is usually stronger than the wind speed on the ground. Therefore, a wind barrier is usually installed on the bridge to ensure the running safety of the high-speed train. In the present study, the wind barrier installed on the Xijiang Bridge was monitored through field measurements. A schematic drawing of the Xijiang Bridge is illustrated in Figure 1. It is a half-through steel box arched bridge with a main span of 450 m and a rise-to-span ratio of 1:4. The distance between the bridge floor and the apex of the arch is 74 m, and the navigable designed water level is 50 m. The interval between adjacent hanger rods of the main span is 12 m. In order to minimize the interference of arch ribs and other attached structures on the measurement results, the wind barrier was only installed on one side of the bridge's mid-span, as shown in Figure 2.  
Figure 1: Schematic drawing of the Xijiang Bridge (unit: mm).  
Figure 2: Photograph of the field test site.
## 5 Conclusions  
This paper proposes a WOA-Attention-BILSTM model for ship trajectory prediction in order to enhance the intelligent perception capabilities of ships. The model employs a bidirectional structure and an attention mechanism, which enables it to capture important information within the trajectory sequence and better retain contextual information in ship trajectories. Additionally, the use of the WOA algorithm optimizes the parameter design capabilities and generalization of the model, thereby avoiding the problem of model convergence caused by artificial parameter design. Furthermore, this paper includes the ship's COG and SOG in the feature vector, which facilitates the extraction of multi-modal features and improves the accuracy of the model's prediction. To validate the effectiveness of the proposed model, the paper processed a public AIS dataset using methods such as deduplication, filtering, resampling, and missing value completion and conducted extensive comparative experiments based on this dataset. The experimental results demonstrate that the proposed model has higher accuracy compared to other models, strong parameter optimization capabilities, and robust generalization ability. Thus, this method can provide support for the ship's intelligent decision-making system and ensure safe navigation. It should be noted that the features used in this study are based on a single-step neural network model. In the future, consideration should be given to multi-step predictions to reduce the decrease in accuracy caused by recursive predictions and further improve the accuracy of trajectory prediction.  
Conceptualization, Y.Y.; Methodology, H.J. and J.A.; Software, H.J.; Validation, Y.Y.; Formal analysis, Y.Y.; Investigation, H.J.; Resources, R.F.; Data curation, H.J.; Writing-original draft, H.J. and Y.Y.; Writing-review & editing, H.J. and Y.Y.; Supervision, H.J.; Project administration, H.J.; Funding acquisition, H.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Central Guidance on Local Science and Technology Development Fund of Guangdong Province, China (Grant No. 2021 Szvup017).  
Not applicable.  
The data presented in this study are available upon request.  
The authors declare no conflict of interest.
## 4 Validation of FE Model  
The original intention of the saw-cuts in CRCP's is to induce cracks from the desired locations (saw-cut tips) which leads to the development of an improved cracking pattern (regular spaced cracks) [12; 22]. The reference FE model, with the same configurations of the saw-cuts used in the E313 test section as discussed in Section 3.1, is developed to simulate the crack induction in CRCP. The maximum tensile stress occurs over the saw-cut tips in comparison to the rest of the concrete slab at the age of 81 h (3.37 days). The development of tensile stress against the tensile strength over the saw-cut tips along the length of pavement illustrated in Figure 6 indicates the crack induction from the saw-cut tips. Figures 7 and 8 demonstrate the crack strains (mapped cracking) represented by \"Eknn\", at the age of 91 h (3.7 days) and 144 h (6 days), respectively. It can clearly be observed in Figures 7 and 8 that cracks initiate exactly from the saw-cut tips and propagate along the width of pavement as observed during field observations on the E313 test section which is discussed above in Section 2. Moreover, the development of maximum stress in the 3 rd longitudinal steel bar just ahead of saw-cut tips is demonstrated in Figure 9. The steel bar experiences the maximum stress exactly below the transverse crack induction. It indicates that the methodology and assumptions considered in the development of the reference FE model are appropriate for simulating the crack induction in CR  
Figure 8: Crack propagation along the width of concrete slab.  
Figure 6: Development of maximum tensile stress against tensile strength over the saw-cut tips along the length of pavement.  
Figure 7: Crack initiation from the saw-cuts.
# Roboethics in Remote Human Interactions and Rehabilitative Therapeutics
## 5 iManus for Remote Rehabilitation and Learning
### Common Good: Benefits of iManus  
Technological developments using the internet, virtual reality, 3D modeling, and high-level sensors are being applied to health care interventions beyond what was previously thought possible. Even with sophisticated technology, in-person supervision is required, and the devices are not portable, and therefore are not compatible with remote health care.  
iManus is useful to support stroke rehabilitation programs remotely while social distancing measures are in place, during the pandemic, and once the pandemic response has ended. Health care settings such as rehabilitation clinics require program standardization and objective evaluation. iManus can give patients unbiased quantitative feedback in real-time. As therapists can upload rehabilitation programs to the server, patient programs can be designed to evidence-based guidelines and be individualized for specific patient needs. This provides rehabilitation clinics and health care researchers with a unique opportunity to standardize programs and assess the simulation session, with patients being the only experimental variable. Tactile Robotics is now carrying out studies for a comprehensive evaluation and comparison of rehabilitation programs in iManus-enabled versus standard clinical settings.  
The COVID-19 pandemic was an important factor in changing the way we use traditional in-person healthcare models, and technology could help health care evolve to reach people who are unable to attend face-to-face visits with their providers. iManus allows patients to perform their prescribed exercises in their own time, which allows them to monitor and analyze their own progress, leading to faster recovery and better long-term outcomes. Since iManus is portable, it allows access to care for patients with brain injury around the globe, who may not otherwise be able to travel to a state-of-the-art rehabilitation clinic. Standardized programs could provide excellent clinical rehabilitation for patients living or working in remote places, far from quality health care facilities. To preserve the principle of equitable access to technology, Tactile Robotics has a marketing plan that can be adapted for interested facilities worldwide. Both right-hand and left-hand iManus smart-gloves are available, enabling treatment of impairments on either side. This parallels in-person therapeutic encounters and is inclusive of all patients in need of hand rehabilitative care. The outcomes from this rehabilitative approach are not only patient-oriented but also benefit society as whole, by reducing the family and community costs of care, and promoting the return to work and participation in daily activities.
# Railway Bridge Condition Monitoring Using Numerically Calculated Responses from Batches of Trains
## 3 Inverse Newmark-\(\beta\) Method to Calculate AP
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Property** & **Symbol** & **Unit** & **Value** \\ \hline Carriage body mass & \(m_{v}\) & kg & \(32.4\times 10^{3}\) \\ Carriage body moment of inertia & \(l_{v}\) & kg m\({}^{2}\) & \(1.99\times 10^{6}\) \\ Speed & \(v\) & m/s & \(33\) \\ Bogie mass & \(m_{b}\) & kg & \(2615\) \\ Bogie moment of inertia & \(l_{b}\) & kg m\({}^{2}\) & \(1476\) \\ Wheelset mass & \(m_{w}\) & kg & \(1813\) \\ Primary suspension stiffness & \(k_{p}\) & N/m & \(2.4\times 10^{6}\) \\ Secondary suspension stiffness & \(k_{s}\) & N/m & \(0.86\times 10^{6}\) \\ Primary suspension damping & \(c_{p}\) & N s/m & \(7\times 10^{3}\) \\ Secondary suspension damping & \(c_{s}\) & N s/m & \(16\times 10^{3}\) \\ Distance between bogie centre of gravity and wheelsets & \(L_{b}\) & m & \(1.28\) \\ Distance between main body centre of gravity and bogies & \(L_{o}\) & m & \(9.5\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Vehicle properties used to calculate the AP. Figure 4: ‘True’ AP and calculated AP using the Inverse Newmark-\(\beta\) equations.
# A Novel Adaptive Image Data Hiding and Encryption Scheme Using Constructive Image Abstraction
## 2 Our Proposed Methods  
We describe our scheme in this section. Figure 1 illustrates the flowchart of our algorithm and the output images at each step. Our adaptive data hiding scheme utilizes image abstraction to embed secret messages, thereby constructing an abstracted stego image. The encryption algorithm utilizes a two-dimensional logistic tent modular map and a random permutation scheme to perform bit-level permutation and byte-level diffusion. In Figure 1a, the original input image \(I\) undergoes the abstraction process to produce an abstraction result and a quantization result. An image composition process combines edges produced from difference of Gaussian (DoG) filter [9] to produce an intermediate abstracted image, \(I_{A}\). Moving on to Figure 1b, we utilize \(I_{A}\) as an input for our adaptive data embedding process to construct the abstracted stego image \(I_{S}\). Next, to ensure the security of the image content and the concealed secret messages, we encrypted \(I_{S}\) using our proposed image encryption algorithm, thereby producing the encrypted image \(I_{E}\). At the recipient's end, as shown in Figure 1c, stego images are decrypted before extracting secret messages. These processes are detailed as below.
# A Study on a Scenario-Based Security Incident Prediction System for Cybersecurity
## 7 Proposal for Security Incident Simulation Method
### Legacy Integration and Automated Security Audits  
To integrate legacy systems and automate security audits, the security incident scenarios should be matched with the tactics provided by the MITRE ATT&CK framework. Scenario-based penetration tests would then be conducted to determine the success rates of various types of security incidents. This process will help identify vulnerable areas and  
Figure 16: Security incident scenario creation procedure.  
As shown in Table 5, the Snort rules were tailored to detect specific attack patterns, including packet content and payload characteristics. For instance, the rule alert ip 10.0.0.33 5001 -> any any (msg: \"ESE Attack 17\"; content: \"POST/fileUpload\"; nocase; sid:100004;) was used to detect unauthorized file uploads, while another rule targeted DLL tampering by identifying the changeFile command. These customized rules demonstrated the flexibility and precision of the detection system in addressing varied attack scenarios.To evaluate the approach further, the simulation results were compared against traditional methods used in cyber ranges. The proposed system showcased improved detection rates and adaptability to complex attack patterns, particularly in scenarios involving lateral movement and data exfiltration. While these tests were conducted in a controlled environment, future plans include testing in live operational settings to validate scalability and real-time response capabilities, ensuring practical applicability and enhanced security resilience [42].  
5.1 Custom Attack Simulation 1--Transmitting an Excel File Containing Customer Information from an Internal Agent to an External Destination  
- Detection Rule for the Packet: alert ip 10.0.0.33 5001 -> any any (msg: \"ESE Attack 17\"; content: \"POST/fileUpload\"; nocase; sid:100004;)  
Snort is executed as shown in Figure 20 below.  
After executing the customer information leakage attack from the attack scenario list, check the Snort logs shown in Figures 21-23 below. It shows the security incident scenario.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Command** & **Description** \\ \hline alert & Rule action (Alert, Log, Pass, Drop, Reject, etc.) \\ \hline tcp & Protocol designation (Tcp, Udp, Icmp, Ip) \\ \hline any & Source IP (10.0.0.33) \\ \hline any & Source PORT (5001) \\ \hline -> & Direction (->, \(<\)): Indicates one-way and two-way directions, no reverse direction like “\(<\)-” \\ \hline msg & The message that will be alerted when the packet is detected \\ \hline content & String to check in the packet payload(If checking binary values, expressed as “(00 01 02)” format) \\ \hline nocase & Case-insensitive detection \\ \hline Sid & Detection rule ID \\ \hline \hline \end{tabular}
\end{table}
Table 5: Snort command.  
Figure 20: Run Snort.  
In the log, you can see that the S1I005_private.xlsx file was uploaded to 10.0.0.44:8091 using the HTTP POST method.  
Figure 21: Attack scenario list(1).  
Figure 22: Snort log(1).
## 3 Stationary Localization Algorithms
### Centralized Localization Algorithms
#### 3.1.3 Sensor Arrays-Based Localization Approach (SLA)  
In [15], Maximum-Likelihood Source Localization approach (MLSL) is proposed. The UWSN in [15] consists of sensor arrays. Each sensor array is equipped with an array of sensor nodes that are attached to the sensor array via wired connections. Each target waiting to be localized periodically emits a narrow-band acoustic signal. For each sensor array, using the negative log-likelihood function, sensor nodes which have received the signal can obtain the target locations and signal amplitudes. The maximum likelihood estimate of the target location is obtained based on the global likelihood function, which is the sum of the local likelihood function. MLSL approach does not need distance measurement and time synchronization. Computation overhead of sensor nodes and targets are low, while communication overhead and energy consumption are high as all the local likelihood functions are forwarded to a fusion center. Moreover, the local wired and global wireless network architecture is not feasible for large scale UWSNs.  
Another localization method using sensor arrays has been proposed in [16] named Energy-based Source Localization Method (ESLM). ESLM uses the acoustic signal energy attenuation model to estimate distance. The method reduces energy consumption of accurate time synchronization among nodes. However, when the distance between two nodes is too far (greater than 8 meters), localization result based on the acoustic signal energy attenuation model is more sensitive to noise and it is not reliable.
## 3 Performance Evaluation of RAN Network Slicing
### Relationship between RAN Network Slicing and Numerology  
The bands in the system were configured with different numerology types and the effect of the numerology change on the throughput and latency was investigated. Tests were carried out for three different types of numerology: 1, 2 and 3. The bandwidth and number configuration used during the tests are shown in Table 4.  
In the simulation environment, when the throughput was tested with numerology type 1 at 100 MHz bandwidth, the average throughput was 49.2 Mbps, when tested with numerology type 2 it was 50.3 Mbps and when tested with numerology type 3 the average throughput was 51.1 Mbps. In theory, it is known that the slot becomes narrower as the value of the numerology type increases. Accordingly, the data rate per unit of time increases. Figure 7 shows the effect of changing the numerology on the throughput according to the simulation results obtained. In this case, it has been shown that for the implementation of RAN network slicing, methods must be developed that use numerology that is specific to the network slice.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Bandwidth** & **Numerology** & **Throughput [Mbps]** \\ \hline
100 & 1 & 49.2 \\
100 & 2 & 50.3 \\
100 & 3 & 51.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Numerology Configuration.  
Figure 6: Impact of number of users on throughput and latency.  
Figure 7: Impact of numerology change on throughput.
# Performance Impact of Optimization Methods on MySQL Document-Based and Relational Databases
## 5 Performance Tests--Results and Analysis
### Select Operation
#### 5.3.1 Simple Select Operation  
To exemplify a simple select operation, for example, a business by its _id_, in the case of relational MySQL, we used a select statement; in the case of document-based MySQL, we used the predefined _getOne()_ method which takes the _id_ of the document as a parameter. These operations are presented in Table 8.  
In the case of this operation, as shown in Figure 8, the proposed optimization methods do not have a significant impact with the increase in the number of elements because the selection operation is performed after a primary key that is actually indexed automatically.  
Figure 7: Performance time (ms) for the update multiple elements and multiple fields operation.  
\begin{table}
\begin{tabular}{c} \hline \hline \multicolumn{1}{c}{**Relational MySQL: Simple Select Operation**} \\ \hline \hline \multicolumn{1}{c}{//repository} \\ @Query(value = “select * from business where id = businessId“, nativeQuery = true) \\ Business getById(@Param“(businessId”) long businessId); \\ \multicolumn{1}{c}{//service} \\ Business business = businessRepository.getById(1234); \\ \hline \multicolumn{1}{c}{**Document-based MySQL: Simple select operation**} \\ \hline DbDoc result = collection.getOne(”000060807055000000000002713”); \\ \hline \hline \end{tabular}
\end{table}
Table 8: Simple select operation.  
However, there is a better performance in the case of document-based MySQL compared to relational MySQL.
## 3 Calculation Results and Discussion
### Influence of Prefabricated Component  
The structural configurations of the PC model are shown in Table 3. The main difference between the PC and benchmark models was that the prefabricated component of the PC model was the prefabricated flange. The other parameters were the same. The stiffness of the prefabricated filler was significantly greater than that of the prefabricated flange because the prefabricated filler consisted of the laminated plate and core, while the preformed flange consisted of only the laminated plate. Figure 19 shows the damage of the PC model and the difference between the two models.  
Figure 18: Comparison of the benchmark and PUD models on each layer: (**a**) Comparison of the results of layer 1 in each model; (**b**) Comparison of the results of layer 2 in each model; (**c**) Comparison of the results of layer 3 in each model; (**d**) Comparison of the results of layer 4 in each model; (**e**) Comparison of the results of layer 5 in each model.  
A comparison of the results of each layer with those of the benchmark model is shown in Figure 20. When the adhesive flange with smaller stiffness was used for bonding, the values of damage of the peak and the valley were higher and lower, respectively, than those of the reference model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Structural Configuration** & **Detail** \\ \hline Position of UD & 20 mm \\ Presence of outer over-lamination & Yes \\ Prefabricated component & Prefabricated flange \\ Chamfer & Straight and perpendicular to the bonding surface \\ \hline \hline \end{tabular}
\end{table}
Table 3: Structural configurations of PC model.  
Figure 20: _Cont._  
Figure 19: Damage of the PC model and difference between the two models: (**a**) Prefabricated filler in the benchmark model; (**b**) Prefabricated flange in the PC model.  
The analysis results of the PC model showed that the stiffness of the prefabricated component had an obvious influence on the damage distribution of the adhesive. In blade design and maintenance, engineers always hope that the maximum damage of the adhesive will be as small as possible. Currently, it is better to use prefabricated components with larger stiffness as much as possible under allowable conditions.