esa-sceva/satcom-chunk-collection huggingface dataset:
# Stressing State Analysis of CFST Arch Supports in Deep Roadway Based on NSF Method
## 4 Expanded Experimental Data Through the NSF Method  
### Accuracy Verification of Expanded Experimental Data  
In order to verify the precision of the NSF method, the 17 out of 19 measured strain points on the mid span section of CFST arch support-D are adopted to construct the strain filed, and then the interpolation results at the other two points of the cross section could be obtained. Hence, these values are used to compare with the real measured ones, and the error of the \(i\)th point at the \(j\)th load step between them can be calculated by  
\[\delta_{ij}=\left|\frac{\varepsilon_{ij}^{s}-\varepsilon_{ij}^{e}}{\varepsilon_ {ij}^{e}}\times 100\%\right| \tag{10}\]  
where \(\delta_{ij}\) is the error of the \(i\)th point at the \(j\)th load step between interpolation and experiment, \(\varepsilon_{ij}^{s}\) and \(\varepsilon_{ij}^{e}\) are respectively the interpolating and experimental strains of the \(i\)th point at the \(j\)th load step.  
The average error of the \(i\)th point in the whole loading process can be estimated by  
\[\overline{\delta}_{i}=\frac{1}{N}\sum_{j}^{N}\delta_{ij} \tag{11}\]  
where \(\overline{\delta}_{i}\) is the average error of the \(i\)th point, \(N\) is the total number of load steps. Therefore, the accuracy of the NSF method could be demonstrated through the error values and the comparison curves between interpolating and experimental data.  
The measured points 6 and 8 are used as an example and the experiments and interpolation curves of them are plotted in Figure 5 to observe fitting degrees of the data clearly. The two curves for the same point have very high fitting degrees even overlapping with each other in the whole loading process. The maximum and average errors of the points 6 and 8 are respectively 5.77% and 3.42%, 6.57% and 3.56%. As for strain data of other four supports not listed here, the results also present high fitting degree and relatively small errors, which can meet the requirement of application. As a consequence, the NSF method with sufficient accuracy could be utilized to expand experimental data so as to further investigate the structural working behavior of CFST arch supports.  
Figure 4: Restraint devices: (**a**) the restraint devices of the end; (**b**) the actual lateral devices; (**c**) the structure of the lateral devices.
## 3 Results
### Classification Results and Accuracy Assessment
#### 3.3.2 Sentinel-2A Image Classification Accuracy Assessment
Figure 6: Overlaid cotton root root classification maps from airborne and Sentinel-2A images for Fields 1–8 and the zoomed in rectangle scenes for the areas marked on the maps are shown at the right. (**a**) Sentinel-2A classification map based on the spectral model; (**b**) Sentinel-2A classification map based on the texture model; (**c**) Sentinel-2A classification map based on the spectral-texture model. Figure 7 illustrates the differences in the OA, PA, and UA for the infested category for Fields 1-8 based on the spectral model, texture model and spectral-texture model. From Figure 7a, the spectral model achieved the best OA in the discrimination between healthy and infested cotton for Fields 1-5, which had relatively low infestation, followed by the spectral-texture model and the texture model. The spectral-texture model achieved the best OA for Fields 6-8, which had relatively high infestation, followed by the spectral model and the texture model. The overall accuracies of the texture model and spectral-texture model for Fields 1-7 were higher than 80%. From Figure 7b, the PA for the infested category based on the three models was higher than 80% for Fields 1 and 4-7. In addition, combining the spectral and texture features could enhance the performance of detecting more root rot areas, thus resulting in higher PA in cotton fields with low and medium levels of infestation. The UA for the infested category based on the three models was almost below 80% for all fields (Figure 7c), indicating that higher than 20% of the infested areas on the classification map were actually healthy areas for all fields. Moreover, the commission error of the texture model was the highest for all fields. The UA for the infested category of Fields 6 and 7 with severe infestation was higher than that for the other fields. This result indicates that it is feasible to identify cotton root rot in fields with moderate and severe infestation using different features, especially spectral features, and combinations of spectral and texture features. However, it has certain limitations in identifying cotton root rot with Sentinel-2A images for fields with less than 1% infestation.
## 4 Performance Evaluation and Discussion
### Performance of Shortened RA Code  
Table 3 shows the parameters of the shortened RA code used in this evaluation. As the RA code used for the shortened RA code, a code with 600 bits of unencoded bit length \(k\), 1200 bits of codeword length \(n\), and 3 of column weight \(q\) was designed. The interleaver after the repeater is a random interleaver, and the row weight \(r\) is uniquely determined by the random interleaver. The shortened information length \(m\) using distance information is varied from 0 to 500. The LogMAP algorithm was used as a decoding algorithm, and the maximum number of repetitions was set to 100.  
Figure 6: Evaluated simulation model with one sender, one receiver and one cyberterrorist. The sender and the receiver can use up to three RATs for communication.  
Figures 7 and 8 show the BER of the designed shortened RA code under the AWGN channel. Figure 7 shows the BER characteristics of the receiver for which the shortened information is known. The BER characteristics are improved as the shortened information length is increased. When the shortened information length is 300, it corresponds to the coding rate \(\mathrm{R}=(600-300)/(1200-300)=1/3\). Theoretically, as compared with the coding rate \(\mathrm{R}=1/2\) before shortening, \(10\log_{10}(3/2)=1.76\) (dB) characteristic improvement can be expected, but almost the same improvement has been obtained. Figure 8 shows the BER characteristic of the cybererrorist for which the shortened information is unknown. Here, it is assumed that the cybererrorist knows about shortened code other than the shortened information, e.g., the parity check matrix. The BER characteristics degrade as the length of the shortened information is increased. When the shortened information length is 100 bits, the required SNR difference between the receiver and the cybererrorist at BER = \(10^{-4}\) is 0.9 dB, while when the shortened information length is 400 bits, it is expanded to 6.2 dB. From this, it was confirmed that providing the receiver with a high correction capability and the cybererrorist with a low correction capability can be achieved by using the designed shortened RA code.
# Thermoelectric Generator-Powered Long-Range Wireless Real-Time Steam Leak Detection in Steam Traps
## 5 Main Implementation and Results
### Data Analysis  
The primary results focus on the discharge process of a steam trap. By utilizing the measured inlet and outlet temperatures (as shown in Figure 17), along with ultrasound measurements, it is possible to differentiate between the normal operational state of the steam trap and potential failures, such as valve leakage (open) or valve blockage (closed). In Figure 17, we have emphasized the region where the steam trap is leaking steam in brown. It is thanks to ultrasound that the leakage was detected. The duration was approximately 40 min per cycle.
## 1 Introduction  
About 850,000 new head and neck squamous cell carcinoma (HNSCC) cases are diagnosed each year worldwide [1]. HNSCC patients are primarily managed with definitive chemoradiation or surgical resection of the primary tumor and lymph nodes, followed by adjuvant radiation, with or without platinum-based chemotherapy. Despite the utilization of these multi-disciplinary treatment modalities, \(\sim\)50% of these patients develop recurrent/metastatic (R/M) disease within 3 years of initial diagnosis [2]. In R/M HNSCC patients without surgical or re-irradiation options, immunotherapy, as a single agent or in combination with platinum-based chemotherapy, is the preferred alternative.  
Immunotherapies, particulary checkpoint inhibitors, have shown dramatic clinical responses in numerous solid malignancies including HNSCC [3; 4; 5; 6; 7; 8]. In 2016, anti-PD-1 immunotherapies, nivolumab and pembrolizumab, were approved in the second-line setting for platinum-refractory, R/M HNSCC patients. More recently, in 2019, pembrolizumab was approved as a first-line treatment option in unresectable, R/M HNSCC patients. PD-L1 immunohistochemistry has demonstrated some value in identifying responders to anti-PD-1 treatment; in fact, single-agent pembrolizumab is indicated as a first-line, single-agent therapy for R/M HNSCC patients whose tumors express PD-L1 with a combined positive score (CPS) \(\geq\) 1 [7; 8].  
Incidence of human papillomavirus-associated (HPV+) HNSCC, which predominantly originates in the orophnx, has increased dramatically in recent decades and this trend is expected to continue for the foreseeable future [9]. HPV+ orophnx squamous cell carcinoma (OPSCC) is diagnosed using p16 immunohistochemistry, and are biologically and clinically distinct from HPV- OPSCC [10]. Patients with p16+ OPSCC generally have superior treatment responses and outcomes to p16-OPSCC patients [11; 12; 13; 14]. The landmark phase III trial, Checkmate 141, suggests that nivolumab may be more active in the p16+ than in the p16- R/M setting; median OS was 9.1 months for p16+ OPSCC patients and 7.5 months for p16- OPSCC patients [7]. The final analysis of another randomized phase III study, KEYNOTE-048, showed that single-agent pembrolizumab is equally superior, with an identical hazard ratio of 0.81, to cetuximab + platinum-based chemotherapy in p16+ and p16- OPSCC [8]. However, neither of these studies were designedor powered to investigate the effects of p16 status on treatment response with these PD-1 inhibitors. Therefore, the impact of p16 status on anti-PD-1 immunotherapies in the OPSCC population remains unresolved.
## 2 Wavelet Transform Algorithm Transmittance
### Relationship between Transmittance and Matrix Form of Algorithm  
Based on Equation (1), the transmittance in the \(\mathbb{Z}\) domain can be determined for the selected row of the transformation matrix in the form  
\[H_{i}(z)=a_{i,0}+a_{i,1}z^{-1}+\ldots+a_{i,N-1}z^{-N+1}=\sum_{k=0}^{N-1}a_{i,k }z^{-k}, \tag{2}\]  
and after substituting \(z=\mathrm{e}^{\mathrm{i}\omega T_{s}}\), where \(T_{s}=\frac{1}{f_{s}}\) is the sampling period corresponding to the sampling frequency \(f_{s}\), the dependence describing the transmittance of the selected \(i\)-th row in the pulsation domain denoted as \(G_{i}(\mathrm{j}\omega)=H_{i}(\mathrm{e}^{\mathrm{i}\omega T_{s}})\) is obtained. Based on the determined transmittance, the amplification \(K_{i}(\omega)\) and phase shift \(\varphi_{i}(\omega)\) for the selected harmonic of the signal can be described in the following way:  
\[K_{i}(\omega)=\left|G_{i}(\mathrm{j}\omega)\right|=\sqrt{\left( \Re\Big{(}G_{i}(\mathrm{j}\omega)\Big{)}\right)^{2}+\left(\Im\Big{(}G_{i}( \mathrm{j}\omega)\Big{)}\right)^{2}},\] \[\varphi_{i}(\omega)=\arctan\left(\frac{\Im\Big{(}G_{i}(\mathrm{ j}\omega)\Big{)}}{\Re\Big{(}G_{i}(\mathrm{j}\omega)\Big{)}}\right).\]  
The transmittance of the object being discussed will impact the error signal components in a manner that alters their amplitude and phase.
## 3 Results and Discussion
### ANN Model Results
#### 3.2.3 Streamlined Interface for ANN Model Predictions  
The introduction of a user-friendly interface complements this approach by offering a simplified, one-click prediction process. This user-friendly interface ensures that users, regardless of their technical proficiency, can effortlessly harness the predictive capabilities of the ANN model. For example, to utilize the ANN Optimal Dry Unit Weight Model, one can open the 'ANN Gama model.ipynb' Python file using the Jupyter Notebook app (see Supplementary Materials). With a simple press of Shift + Enter, the interface swiftly materializes, providing a user-friendly experience as shown in Figure 24. This same process seamlessly applies to the ANN Optimal Applied Stress model, ensuring a consistent and accessible user interaction.  
Figure 24: User-friendly interface.  
Figure 23: Comparing the actual and predicted values for the training and test datasets in the ANN model for (**A**) \(\sigma_{min}\) and (**B**) \(\gamma_{max}\).
# Spatial Deformation Calculation and Parameter Analysis of Pile-Anchor Retaining Structure
## 2 Spatial Deformation and Soil Pressure Distribution of Retaining Structure
### Distribution of Soil Pressure behind the Structure  
The soil pressure model has a significant impact on the deformation analysis of foundation pits. Numerous monitoring data indicate that the displacement of retaining piles directly affects the distribution and magnitude of soil pressure. Previous studies suggest that the soil pressure on the back wall of the foundation pit retaining structure can be approximated as Rankine active soil pressure [22; 23], as shown in Figure 3. The calculation formulas for active soil pressure \(P_{a}\) and passive soil pressure \(P_{b}\) are:  
\[P_{a}=(q+\gamma z)k_{a}-2c\sqrt{k_{a}} \tag{5}\]  
\[P_{b}=m(z-h)\cdot u(x,z) \tag{6}\]  
In the given formula, the variables represent the following parameters:  
\(c\): Cohesive force of the soil mass (kPa).  
\(\gamma\): Density of the soil mass (kN/m3).  
\(q\): Overload on the slope top (Pa).  
\(k_{a}\): Active soil pressure coefficient.  
\(\varphi\): Internal friction angle of the soil mass (\({}^{\circ}\)).  
\(m\): Foundation resistance coefficient.  
\(H\): Excavation depth of the foundation pit (m).  
These parameters play a significant role in determining the horizontal deformation of the waist beam based on the horizontal component of the anchor cable deformation.
## 4 NFV and SFC Frameworks Taxonomy
### Performance Tuning Frameworks
#### 4.2.10. GNF Framework  
The Glasgow Network Functions (GNF) framework was proposed by [PERSON] et al. [19] as an open and lightweight implementation framework for network functions in OpenFlow network environments. GNF, which is a container-based framework, achieves low overhead in terms of performance, high NF reuse, and fast deployment speed, when compared to most NFV deployments. To achieve the routing of traffic in a typical NFV scenario, policies can be implemented by adding entries or by adding a middle-box in the path of the traffic. These two approaches have drawbacks that the GNF framework attempts to eliminate.  
GNF achieves dynamic placement of network functions by simply rerouting the traffic to the server with the requested NF, which allows service providers to utilise the same hosts when handling network and compute functions, thus minimising the overall infrastructural costs. Using the OpenFlow protocol, GNF can match ingress packets to the match-action table before routing packets to the specified destination.  
For ease of network function deployment and management, GNF provides a user interface for global control and view of the network, a manager, GLANF router, and agent. The life cycle of network functions is handled by the GLANF Manager, which makes use of the OpenDaylight SDN controller for performing tasks such as creating, starting, stopping, and deleting primitives (Figure 12). Tasks such as resource allocation are also handled by the manager, which allocates network functions to hosts that have available resources.  
To evaluate and demonstrate the performance of the GNF framework, six network functions were deployed: a wire, which routes packets from its ingress to egress ports, an HTTP filter, traffic control, a load balancer, intrusion detection, and a firewall, which is based on iptables. Figure 12 depicts the GNF framework, with packets sent from VM1 to VM2, which are sent via the network functions on the GLANF system and to target hosts. The performance of GNF was also compared to ClickOS [12], which shows a significant improvement from 3.6 Gb/s to 13.8 Gb/s packet processing speeds, this also holds true as the number of chained containerised vNFs are increased.  
Figure 11: OpenNetVM framework.
## 4 Discussion
### Comparison with Previous Studies  
As mentioned in the introduction, few studies have analyzed combinations of factors to verify the variability of the predictive models, especially those in which XGBoost has been implemented. Most of these studies use the totality of available factors, without factor selection, presenting a very good performance, according to their AUC value. Thus, [PERSON] et al. [25] applied XGBoost in a road corridor in Chamoli (India), obtaining an AUC = 0.91, higher than that achieved in this study. [PERSON] et al. [26] obtained an even higher performance (AUC = 0.96) by hyperparameter tuning. [PERSON] et al. [30], despite not performing hyperparameter tuning, obtained a very good performance of the model they implemented with XGBoost (AUC = 0.94). [PERSON] in his two studies [27; 28] where he implemented LSM with XGBoost obtained similar performances (AUC = 0.88 and AUC = 0.89, respectively, which are slightly higher than those obtained in the present study); however, in the second case he did perform a combination of conditioning factors as in our study. Finally, [PERSON] et al. [29] and [PERSON] et al. [31] obtained an AUC = 0.86, a performance closer to that achieved in the present study; in the first case, no hyperparameter tuning or combination of factors was performed, and in the second, several hyperparameter tunings were performed. The above reflects the scarcity of research applying hyperparameter tests for XGBoost, so new studies should implement these tests with several algorithms to optimize the model performance and obtain better predictive capacity.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 5 Future Roadmap: The Promise of AI
### Chaotic Neural Networks in Image Encryption  
The application of chaotic neural networks has been of interest to the research community in recent years [263, 264]. Different types of neural networks including the Once Forward-Long Short-Term Memory Structure (OF-LSTMS) [265], Hopefield neural networks [266], and cellular neural networks [267] have been used in this area. Moreover different chaotic maps [267] and sequences [265] have been examined in research in this area.  
[PERSON] et al. [249] proposed a novel chaotic neural network called a Multidirectional Associative Memory Neural Network (MAMNN). An image encryption algorithm utilizing the network was also designed to demonstrate its chaotic capabilities. In a different paper by [PERSON] et al. [175], they proposed an image encryption algorithm based on the Hermite chaotic neural network. This algorithm is targeted for use on medical images, and statistical analysis demonstrated strong cryptographic properties and resilience against common attacks.
## 4 Testing Scenarios  
For testing purposes, we utilized Apache JMeter, version 5.6.3, a software tool that enables the creation of test plans to simulate simultaneous requests from multiple users. Each scenario comprises a thread group, where the number of simulated threads can be specified, an HTTP Request that defines the protocol, URL path, request type, and parameters, and a results list that allows for the analysis of each thread's response. The parameters for each thread are assigned using randomly generated variables.  
In this study, we developed two test plans--one for each database--with 50 threads simulated concurrently and seven randomly generated variables, including the starting point, finish point, two points to avoid, and three intermediate points. To maximize the coverage of scenarios, we established five test cases: one involving manual execution multiple times, and four utilizing the Apache JMeter test plans described above, each with different parameter configurations:  
* Starting point, finish point, and three intermediate and two avoided points;
* Starting and finishing points (with no intermediate or avoided points);
* Starting and finishing points, with only one intermediate point;
* Starting and finishing points, with two avoided points.  
It is important to note that the tests were run multiple times before drawing any conclusions. This was carried out to validate the methods used to calculate the performance metrics and to eliminate any possibility of interference that could impact the final results.
# Application of the Segmented Correlation Technology in Seismic Communication with Morse Code
###### Abstract  
Seismic communication might promise to revolutionize the theory of seismic waves. However, one of the greatest challenges to its widespread adoption is the difficulty of signal extraction because the seismic waves in the vibration environments, such as seas, streets, city centers and subways, are very complex. Here, we employ segmented correlation technology with Morse code (SCTMC), which extracts the target signal by cutting the collected data into a series of segments and makes these segments cross-correlate with the decoded signal to process the collected data. To test the effectiveness of the technology, a seismic communication system composed of vibroresis sources and geophones was built in an environment full of other vibration signals. Most notably, it improves the signal-to-noise ratio (SNR), extending the relay distance and suppressing other vibration signals by using technology to deal with seismic data generated by the system.  
signal extraction; correlation; SNR; vibroresis sources; seismic communication +
Footnote †: journal: Applied Sciences
# The Development of a Soft Robot Hand with Pin-Array Structure
## 3 Design of CTSA-II Hand
### Design Idea  
In response to the shortcomings of the CTSA hand proposed in Section 2, this paper proposes an improved scheme using the concept of soft robot. The improved idea is:  
1. Use the bendable pin to take place of the rigid slide tube assembly of the CTSA hand. The bendable pin has rigidity in its axial direction and can withstand the axial force without obvious deformation. In addition, the bendable pin has flexibility in the directions perpendicular to its axis, which means the pin can be bent when subjected to a lateral load and can revert to the initial position after the lateral load is removed. By replacing the rigid sliding tube with a bendable pin, and replacing the swinging with bending, the mechanical structure is greatly simplified, and the assembly difficulty is reduced, making the possibility of mass production higher.
2. Wrap all pins with a membrane. On the one hand, the pins are isolated from the outside world to avoid damage to the sliding mechanism by impurities such as dust outside, and on the other hand, the contact area between the robot hand and the object is increased, improving the reliability of grasping.
3. The fluid drive is used instead of the tendon driving with pneumatic driving. Changing the driving mode avoids the use of the motor and gears, which can reduce the size of the device and greatly simplify the mechanism of the device.  
Using the above ideas, a soft robot hand with the structure of pin array, CTSA-II hand, was designed. The expected design of CTSA-II hand is shown in Figure 5. The CTSA-II hand includes a base, a set of pin assemblies and a membrane. Each pin assembly includes a spring and a bendable pin. The base is provided with several holes, each of which is a chute for each pin assembly. The membrane wraps all pin assemblies to form a closed cavity for pneumatic driving. The negative pressure causes all of the pins to bend and gather toward the center of the CTSA-II hand.