esa-sceva/satcom-chunk-collection huggingface dataset:
# Flexible Antennas: A Review
## 11 Conclusions  
The field of flexible antennas is fascinating and interdisciplinary involving electrical engineering, materials science, and mechanical engineering. Flexible antennas are one of the critical components in the realization of flexible electronic devices. The flexible antenna is ideal for current and futuristic wireless communication and sensing applications primarily due to its lightweight, reduced form factor, low-cost fabrication, and ability to fit non-planar surfaces. The choice of materials for antenna fabrication is based on application preferences such as environment, seamless integration with rigid and non-rigid devices, cost, and mass manufacturing aspects of the fabrication process. Highly conductive materials such as Ag nanoparticles inks, Cu tape or clad, conductive polymers, PDMS embedded conductive fiber, and graphene-based materials have been typically used to implement the conductive patterns in the antenna.  
Kapton polyimide, PET, PEN, PANI, liquid crystal polymer, electro-textile, and paper have been preferred as flexible substrates. Applications of flexible antennas in the different frequency band below and above 12 GHz indicates the versatility of flexible antennas. Different miniaturization techniques have been discussed, along with challenges and limitations. Flexible antennas for biomedical applications with implantable and ingestible functionality shows the promising nature of the electromagnetic devices in health care. Bending, stretching, and proximity to the human body's impact on flexible antennas performance has been discussed. Corrective measures such as increasing the bandwidth or symmetry of the designed antenna can help account for deviations caused by deformation and other spurious factors.  
Finally, challenges for designing and realizing a flexible antenna has been discussed considering material challenges for the substrate and the conducting material. Flexible antennas for the future wireless system as a part of IoT, BAN, and biomedical devices have been reviewed with the citation of recent literature. The latest research on flexible antennas with emphasis on power sustainability via energy harvesting is discussed. Despite the limitations of flexible antennas, these non-rigid devices can be engineered to meet the futuristic demand for a compact wireless solution to fit the surface of any curvature.  
This research received no external funding.  
The author [PERSON] was supported by the undergraduate summer research program facilitated by the School of Engineering and Computer Science at Washington State University Vancouver.  
The authors declare no conflict of interest.
# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 5 A Case Study of UAV NTN Airborne Network in Mountainous Area  
Unmanned aerial vehicles are ideal platforms for carrying network equipment, such as cellular base stations, to provide communication network coverage to ground users. They serve as flying base station platforms in an NTN use case. Cellular networks utilizing UAVs as airborne base stations have shown great potential to expand the service area for users as needed.. For multiple UAV base stations, it is worth noting that an effective and efficient handover scheme is crucial for ensuring the quality of service (QoS).  
In rural or mountainous areas, wireless communication coverage is not always accessible due to cost-inefficiency or the impossibility of building the terrestrial infrastructure required. Due to environmental factors such as inclement weather, rugged terrain and diverse vegetation, communication coverage is unstable. The unreliable information transmission network coverage result in service interruptions, signifcantly impacting the daily lives of the residents living in those areas and hindering tasks such as disaster management.  
To tackle this issue, utilizaing UAV airborne NTNs is a feasible approach for providing wireless communication services in this particular scenario. The UAV planning of UAV in NTN communication service and data transmission optimization architecture is a crucial issue that necessitates the use of technologies such as network coding, multi-path transmission, and real-time task offloading to greatly enhance the real-time operation and reliability of communication networks [196; 197; 198].
## 2 Experimental Section
### General Procedure for PEIT (and PET) Synthesis  
Polymer samples were synthesized via a two-step, melt polycondensation reaction (esterification and transesterification, see Figure 1) in a 7.5-L stainless-steel batch reactor equipped with a heating system, a mechanical stirrer with torque measurement, a distillation column, a vacuum line, and a nitrogen-gas inlet.  
In the first step, the reactor was charged with 2656 g (16 moles) of terephthalatic acid and 19.2 moles of diol (comprising isosorbide and ethylene glycol). Other additives such as anti-diethylene glycol, antioxidants, and catalysts were also fed into the paste. To exclude residual oxygen from the isosorbide crystals as much as possible, four vacuum-nitrogen cycles were carried out between 60 and 80 \({}^{\circ}\)C. The reaction mixture was then heated up to 260 \({}^{\circ}\)C under a nitrogen pressure of 5.0 bar (270 \({}^{\circ}\)C and  
Figure 1: General scheme of poly(ethylene-co-isosorbide terephthalate (PEIT) synthesis from terephthalatic acid, isosorbide, and ethylene glycol.  
6.6 bar for PET) and stirred at a constant rate (150 rpm). The esterification rate was estimated from the quantity of distillate collected. In the second step, the pressure was reduced in 90 min to 0.7 mbar, and the temperature was gradually increased to 270 \({}^{\circ}\)C (285 \({}^{\circ}\)C for PET). Low-pressure conditions were maintained for 90 min, and the viscosity variation of the polymer was measured from the torque applied on the stirrer. A polymer strand was then withdrawn from the bottom drain valve of the reactor and quenched in a water bath. PEIT pellets (15 mg) were obtained after granulation. Using such a procedure avoids contact of the warm polymer with oxygen, thus reducing coloration and thermo-oxidative degradation.  
For comparison purposes, PIT was synthetized using the same procedure.
###### Abstract  
SS 316L, a low-carbon 316 Stainless Steel, has been used to manufacture swivel mechanisms for paraplegic patients, but its weight is relatively high compared to a few materials in its range of properties. Aluminum alloy 6061 and Titanium alloy (Ti6 Al4V) offer lightweight and incredible strength-to-weight ratio, hence their use for medical, aerospace, and automotive applications. This study, therefore, seeks a replacement for SS 316L. A 3D model of a swivel mechanism was developed to compare the performance of the swivel mechanism made with SS 316L, AA 6061, and Ti6 Al4V. The kinematic analysis of the mechanism based on a range of weights: 1 kN, 1.1 kN, 1.2 kN, 1.3 kN, 1.4 kN, and 1.5 kN was carried out to generate the inputs for the simulation. The 3D model was made with SolidWorks, and the results of the kinematic analysis were used to define the simulation parameters for the mechanism. Two scenarios generated depicted the full collapse of the mechanism and the full extension. The results showed that AA 6061 and Ti6 Al4V outperformed SS 316L with higher yield strength and factor of safety. Therefore, swivel plates made with AA 6061 and Ti6 Al4V have higher yield strength than those made with SS 316L, adding to the advantage that they have a higher strength-to-weight ratio. From this analysis and known knowledge of the cost of these materials, the optimal replacement considering cost with yield strength is AA 6061. However, Ti6 Al4V is a better alternative for the strength-to-weight ratio for SS 316L.  
SS 316L; AA 6061; Ti6 Al4V; swivel mechanism; mechanical properties +
Footnote †: journal: applied sciences
## Literature Review  
### Definition of the Problem  
The scheduling of the continuous casting-hot rolling process in steel plants has always been a focus of attention for many scholars. The continuous casting process is the process of casting high-temperature molten steel into continuous casting slabs on the continuous casting machine, which is the main process connecting upstream steelmaking and downstream hot rolling in the steel plant system. The hot rolling process, as one of the most important processes in steel manufacturing, uses slabs as raw materials to process hot-rolled products. The reheating furnace is used to heat the slab from the slab warehouse or continuous casting process to the required temperature for rolling before the hot-rolling process. The scheduling of the slabs, an important intermediate equipment, of the reheating furnace is not only related to the heating quality of the slabs and the energy-saving effect but also has a significant impact on the production rhythm of hot rolling. The traditional scheduling of the continuous casting and hot rolling process mostly considers the production scheduling of continuous casting and hot rolling machines while neglecting the important factor of the reheating furnace and failing to achieve collaborative operation of the three. Not only is it impossible to achieve energy savings, but it is also difficult to match the scheduling results obtained to production reality.
# Investigation of Debonding Effect in Internal Replacement Pipe System Under Lateral Loading
## 2 Finite Element Analysis
### Debonding Mechanism Using Lap Shear Strength  
#### 2.3.1 Debonding Modelling and Validation  
This section presents and validates the FEA modeling technique for the adhesive layer using lap shear strength as the primary metric. The numerical results are compared and verified against experimental data obtained from a flat cast iron sample. The adhesive  
Figure 10: Equivalent stress results on the IRP of the 12.7 mm (0.5 inch) discontinuous IRP system at the system at 69.1 kN (15.5 kip) (magnified 25 times).  
Figure 9: Pull-off stress results on the adhesive layer of the 12.7 mm (0.5 inch) discontinuous IRP system at a maximum load of 69.1 kN (15.5 kip) (magnified 18 times).  
used in this study is a generic epoxy, which demonstrates a lap shear strength of 4.95 MPa (0.72 ksi) until adhesive failure occurs.  
Figure 11 illustrates the geometry, constraints, and loading conditions employed in the FEA modeling, which adhere to the ASTM D5868 standard test method for lap shear adhesion in fiber-reinforced plastic (FRP) bonding [24]. The cast iron (CI) flat sample is fixed completely along a 50.8 mm (2 inch) section from the edge. The adhesive is placed between the CI flat sample and the IRP flat sample (presumably referring to the fiber-reinforced plastic). In the FEA model, the contact conditions between the adhesive and the flat samples were assumed to be fully bonded. The IRP sample is assumed to have the same Young's modulus of 2.59 GPa (375.07 ksi) and a Poisson's ratio of 0.463 as the adhesive. A force of 4.5 kN (1000 lbf) is applied longitudinally and outwardly along a 50.8 mm (2 inch) section from the edge of the IRP flat sample.  
For the analysis, surface elements with dimensions of 2 mm \(\times\) 2 mm (0.08 inch \(\times\) 0.08 inch) were used, with three elements employed in the thickness direction, as depicted in Figure 12. A total of 4563 elements are employed and recorded using the standard ANSYS SOLID 186 elements.  
Based on the experimental results, the adhesive layer tends to fail when the applied load exceeds approximately 2.2 kN (500 lbf). This finding indicates that the shear stress in the adhesive layer surpasses 4.95 MPa (0.72 ksi) once the load exceeds 2.2 kN (500 lbf).  
Figure 11: FEA modeling and constraints for the lap-shear test in accordance with ASTM D5868.  
Figure 12: Meshing configuration of the lap-shear testing.  
As shown in Figures 13 and 14, the adhesive material experiences a complete loss of strength, with most of the adhesive elements being removed due to excessive shear stress immediately after being subjected to a force of 2.2 kN (500 lbf). The numerical results align closely with the experimental findings, providing strong validation for using the lap-shear strength criterion for debonding in the numerical simulation of the IRP system.  
Figure 14: The force-displacement relationship of the load head during the adhesive validation test, as determined through numerical simulations.  
Figure 13: The debonding progression of the adhesive layer with load increment.
# Smart Footwear Insole for Recognition of Foot Pronation and Supination Using Neural Networks
## 3 Neural Network Architecture  
As explained before, the system allows the user to store data from the instrumented insole and train the neural network with it. The neural network architecture used for the training process is a custom implementation based on the Fast Artificial Neural Network library (FANN) [23], which is implemented in C language. This library has been adapted to be used in C# inside MS Visual Studio.  
We use a Feed-Forward Neural Network with a fully-connected Multilayer Perceptron architecture (MLP) [24], composed by an input layer, a hidden layer and an output layer. The training mechanism is ruled by back-propagation algorithm [25] and the classification result is based on a Winner-Take-All model [26]. These layers are described below:  
* Input layer: the neural network needs an input for each force sensor placed in the instrumented insole. So, the input layer has seven neurons (see Figure 9).
* Hidden layer: the number of neurons in the input layer is not standard and depends on several aspects such as the number of input neurons, the number of output neurons and the linearity of the input data. [PERSON] and [PERSON] [27] made a study about the different methods to estimate the number of neurons in the hidden layer and propose a new method. According to them, the number of neurons is estimated by Equation (1). In our case, we will use five neurons in the hidden layer (see Figure 9). \[\#Neurons_{hidden\_layer}=\frac{4*N_{i}^{2}+3}{N_{i}^{2}-8}\quad,\qquad N_{i}: \#Neurons_{input\_layer}\] (1)
* Output layer: as detailed before, the aim of this system is to distinguish between the types of footprints, so a user can be classified as pronator, supinator or neutral. Then, the output layer has one class (output neuron) for each classification state (see Figure 9).  
Figure 8: User Application 2: (**left**) Classification, (**right**) Results.  
During the training process, the library applies a back-propagation algorithm to obtain the connections' weights with the given dataset in order to minimize the classification error. Three parameters are taken into account during this process: number of epochs, learning rate and momentum. These parameters can be modified by the user in the application and are described by [PERSON] and [PERSON] [21].
# Multiband and Lossless Compression of Hyperspectral Images
## 4 Experimental Results  
We performed experiments on two datasets of AVIRIS hyperspectral images: the _1997 AVIRIS Dataset_ (Section 4.1) and the _CCSDS Dataset_ (Section 4.2).  
In our experiments we considered also the PAQ8 algorithm (described in [25]) for the coding of the prediction error. PAQ8 is a state-of-the-art lossless compression algorithm that belongs to the PAQ family of compression algorithm. It is important to note that the PAQ8 family is strictly related to the well-established _Prediction by Partial Matching scheme_ (_PPM_) [26]. In general, the PAQ8 algorithm achieve a high degree of compression performances, but the PAQ8 scheme has significant computational complexity. Therefore, such scheme is not fully adequate to be used for on board applications.  
The experiments are performed by using a non-optimized _Java-based proof-of-concept_ of our approach, which employs few minutes on a medium-end laptop (equipped with an Intel Core i5 4200 M processor and 8 GB of RAM).
# Detecting Phishing URLs Based on a Deep Learning Approach to Prevent Cyber-Attacks
## 5 Performance Evaluation and Comparison with Existing Approaches
In [49] proposed a CNN to be used for detection with random forest models to determine feature significance at various levels; they attained 98.68% accuracy, but the results and model could have been improved [47]. In 2023, [51] proposed combining a multi-head self-attention mechanism with a CNN and generative adversarial network model to create a URL detector, and the results for UCI phishing domains showed that the model attained an accuracy of 97.83% [49]. Extending these findings, our proposed 1D CNN model has shown 99.7% accuracy; moreover, it has a lightweight architecture and a complete end-to-end web-based system that can distinguish phishing from legitimate websites.
## 3 Results and Discussions
### Taste Characteristics
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Inoculation Amounts** & **Hardness** & **Springiness** & **Chewiness** \\
**(cfu/100 g)** & **(g)** & & **(g)** \\ \hline Control & 250.16 \(\pm\) 31.54 \({}^{\text{a}}\) & 1.00 \(\pm\) 0.01 \({}^{\text{a}}\) & 163.4 \(\pm\) 20.3 \({}^{\text{a}}\) \\ \(10^{6}\) & 203.02 \(\pm\) 26.84 \({}^{\text{bcd}}\) & 0.71 \(\pm\) 0.06 \({}^{\text{ef}}\) & 45.53 \(\pm\) 18.11 \({}^{\text{ef}}\) \\ \(10^{7}\) & 171.64 \(\pm\) 16.33 \({}^{\text{de}}\) & 0.80 \(\pm\) 0.19 \({}^{\text{ef}}\) & 42.86 \(\pm\) 9.95 \({}^{\text{ef}}\) \\ \(10^{8}\) & 176.85 \(\pm\) 21.91 \({}^{\text{cde}}\) & 0.88 \(\pm\) 0.09 \({}^{\text{de}}\) & 54.19 \(\pm\) 9.04 \({}^{\text{de}}\) \\ \(10^{9}\) & 186.78\(\pm\)20.6 \({}^{\text{cd}}\) & 0.75 \(\pm\) 0.10 \({}^{\text{f}}\) & 31.66 \(\pm\) 9.43 \({}^{\text{f}}\) \\ _P. acidilactici_ & & & \\ \(10^{6}\) & 178.21 \(\pm\) 3.90 \({}^{\text{cd}}\) & 0.99 \(\pm\) 0.01 \({}^{\text{ab}}\) & 111.4 \(\pm\) 2.8 \({}^{\text{bc}}\) \\ \(10^{7}\) & 171.81 \(\pm\) 32.46 \({}^{\text{de}}\) & 0.97 \(\pm\) 0.02 \({}^{\text{b}}\) & 61.39 \(\pm\) 2.65 \({}^{\text{de}}\) \\ \(10^{8}\) & 174.32 \(\pm\) 26.46 \({}^{\text{cde}}\) & 0.79 \(\pm\) 0.09 \({}^{\text{d}}\) & 67.31 \(\pm\) 5.12 \({}^{\text{d}}\) \\ \(10^{9}\) & 162.28 \(\pm\) 16.33 \({}^{\text{de}}\) & 0.84 \(\pm\) 0.09 \({}^{\text{ef}}\) & 44.31 \(\pm\) 6.26 \({}^{\text{ef}}\) \\ _P. pentosacus_ & & & \\ \(10^{6}\) & 246.96 \(\pm\) 26.27 \({}^{\text{ab}}\) & 0.87 \(\pm\) 0.06 \({}^{\text{cd}}\) & 125.1 \(\pm\) 3.6 \({}^{\text{b}}\) \\ \(10^{7}\) & 247.29 \(\pm\) 35.79 \({}^{\text{ab}}\) & 0.84 \(\pm\) 0.03 \({}^{\text{d}}\) & 94.07 \(\pm\) 4.92 \({}^{\text{c}}\) \\ \(10^{8}\) & 220.27 \(\pm\) 11.41 \({}^{\text{abc}}\) & 0.91 \(\pm\) 0.03 \({}^{\text{c}}\) & 98.97 \(\pm\) 19.33 \({}^{\text{c}}\) \\ \(10^{9}\) & 130.35 \(\pm\) 31.86 \({}^{\text{e}}\) & 0.94 \(\pm\) 0.06 \({}^{\text{bc}}\) & 109.6 \(\pm\) 18.2 \({}^{\text{bc}}\) \\ \hline \hline \end{tabular}  
* Notes: Different letters denote significant differences in the same column (_p_ \(<\) 0.05).  
\end{table} 
Table 1: Textural properties of Zhayu samples prepared by spontaneous fermentation (Control) and different inoculation amounts of _L. plantarum_, _P. acidilactici_, and _P. pentosacus_.
## 2 Frequencies and Mode Shapes of the Double-Beam System
### Mathematical Formulation
#### 2.1.4 Pined-Fixed Boundary Conditions  
The boundary stiffness is  
\[\left\{\begin{array}{c}k_{\
u,1}=k_{\
u,2}=\infty\\ k_{\tau,1}=0\\ k_{\tau,2}=\infty\end{array}\right. \tag{37}\]  
The boundary conditions can be described as  
\[\left\{\begin{array}{c}\varphi_{1}(0)=0\\ \varphi_{1}^{\prime\prime}(0)=0\\ \varphi_{1}(l)=0\end{array}\right.^{\prime},\left\{\begin{array}{c}\varphi_ {2}(0)=0\\ \varphi_{2}^{\prime\prime}(0)=0\\ \varphi_{2}(l)=0\\ \varphi_{2}^{\prime}(l)=0\end{array}\right. \tag{38}\]  
Equation (18) becomes  
\[\mathbf{\Phi}=\left(\begin{array}{cccccccc}1&1&1&1&0&0&0&0\\ 0&0&0&1&1&1&1\\ 1&1&-1&-1&0&0&0&0\\ 0&0&0&0&1&1&-1&-1\\ e^{\tau_{1}l}&e^{-\tau_{1}l}&e^{\mu_{1}l}&e^{-i\tau_{1}l}&0&0&0\\ 0&0&0&e^{\tau_{2}l}&e^{-\tau_{2}l}&e^{i\tau_{2}l}&e^{-i\tau_{2}l}\\ e^{\tau_{1}l}&-e^{-\tau_{1}l}&ie^{i\tau_{1}l}&-ie^{-i\tau_{1}l}&0&0&0\\ 0&0&0&0&e^{\tau_{2}l}&-e^{-\tau_{2}l}&ie^{i\tau_{2}l}&-ie^{-i\tau_{2}l}\end{array}\right) \tag{39}\]  
Let \(\left[\mathbf{\Phi}\right]=0\) results in the same equations as Equations (34) and (35), so the solutions of \(r_{1}l\), \(r_{5}l\) and frequencies of the pinned-fixed double-beam systems are the same as the fixed-pinned double-beam systems. The mode shapes of the secondary beam of the pinned-fixed double beam systems are obtained by solving Equation (16) to be  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mode** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline \(r_{1}l\) & 3.9266 & 7.0686 & 10.2102 & 13.3518 & 16.4934 & 19.6350 \\ \hline \hline \end{tabular}
\end{table}
Table 2: First six solutions of \(r_{1}l\) for fixed-pinned double-beam systems.  
\[\varphi_{2,n}(x)=C(\sin r_{1}^{(n)}x-\frac{\sin r_{1}^{(n)}l}{\sinh r_{1}^{(n)}l} \sinh r_{1}^{(n)}x) \tag{40}\]  
The mode shapes of the primary beam can be worked out by Equation (22).
# Route Planning for Autonomous Driving Based on Traffic Information via Multi-Objective Optimization
## 5 Conclusions  
In this paper, we proposed a general framework of the route planning method based on multi-objective optimization, considering four factors: travel time, travel distance, travel cost and personal preference. The route planning problem for autonomous driving is abstracted as an optimization problem with multiple variable parameters. We evaluate our general framework of route planning on a real-world transportation network with different settings of the parameters and OD pairs. The general framework shows its good performance on the change of the traveler's demands. Our work provides a route planning method that can be adjusted on demand so that pedestrians can get the best route in many aspects according to their own needs and thus makes an important contribution to the development of high-level and reasonable autonomous driving. However, in this paper, only one road was tested and no migration experiments or comparative experiments were conducted. In the future, we should continue to improve the relevant work and introduce the traffic jam data into the model calculation to make the overall framework more convincing.  
Conceptualization, M.-Y.Z. and S.-C.Y.; methodology, M.-Y.Z.; validation, M.-Y.Z. and Y.-G.C.; formal analysis, Y.-Y.C. and J.-Y.L.; investigation, X.-J.F.; data curation, Y.-Y.C. and J.-Y.L.; writing--original draft preparation, M.-Y.Z.; writing--review and editing, S.-C.Y. and Y.-Y.C.; visualization, X.-J.F. and Y.-Y.C.; supervision, Y.-G.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China, grant number 2021 YFF0601100.  
Not applicable.  
All data is available with an open source license at [[https://github.com/bstabler/TransportationNetworks](https://github.com/bstabler/TransportationNetworks)]([https://github.com/bstabler/TransportationNetworks](https://github.com/bstabler/TransportationNetworks)) (accessed on 3 August 2022).  
The authors declare no conflict of interest.  
Figure 3: Optimal routes for six different OD pairs with parameters of (0.3, 0.3, 0.3). (**a**) Optimal route of OD pair (7, 24), (**b**) Optimal route of OD pair (1, 20), (**c**) Optimal route of OD pair (1, 19), (**d**) Optimal route of OD pair (2, 22), (**e**) Optimal route of OD pair (9, 19), (**f**) Optimal route of OD pair (16, 24).