esa-sceva/satcom-chunk-collection huggingface dataset:
# Voltage Tunable Spoof Surface Plasmon Polariton Waveguide Loaded with Ferroelectric Resonators
## 4 Conclusions  
In summary, a LSSP resonator with a BCZT compound ferroelectric substrate is designed and employed for the tunability of the SSPP waveguide. The results show that high dielectric tunability can be achieved in the BCZT laminated structure, which is beneficial for the deep tunability of the notch in the plasmonic waveguide. When a pair of mirror-symmetrical LSSP resonators are coupled to the SSPP WG, the central frequency of the notch can be dynamically adjusted in real-time by about 8.8% due to the high dielectric tunability of the BCZT substrate. In addition, the notch generated by the plasmonic coupling enables the excitation of triple Fano resonances, which stem from a unidirectional EM coupling between the LSSP resonator and the SSPP waveguide. The proposal of this study achieves the real-time dynamic adjustment of waveguide performance, which is a significant innovation in the field of waveguide design. Meanwhile, the BCZT layer, as a ferroelectric material, exhibits excellent voltage response characteristics. By applying a voltage to it, the dielectric constant can be significantly changed, thereby enabling the real-time control of the SSPP waveguide performance. This enhancement of voltage tunability is a major highlight of this study.  
The method provides a new idea for a compact physical size, minimal insertion loss, large tunability, and real-time flexible regulation in notch filter scenarios. The proposed novel waveguide structure provides new ideas and methods for the development of waveguide technology. By adjusting the waveguide performance in real-time, it can meet the needs of different application scenarios and promote the application of waveguide technology in fields such as communication, sensing, and imaging. Also, since the proposed LSSP is a planar-textured disk, it is more flexible in engineering a Fano line shape, which can be of practical interest for a variety of applications including enhanced biosensing, on-chip communication, and optical information processing.  
Conceptualization, M.H. and C.W.; methodology, J.S. and S.L. ([PERSON]); validation, J.S. and C.Z.; formal analysis, S.L. ([PERSON]) and C.Z.; investigation, C.Z. and C.W.; data curation, S.L. ([PERSON]) and S.L. ([PERSON]); writing--original draft preparation, J.S.; writing--review and editing, M.H. and C.W.; supervision, M.H.; project administration, C.W.; funding acquisition, M.H. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (No.62261008), the High Level Innovative Talent Project of Science and Technology Department of Guizhou Province (GCC2023086), and the Fundamental Research Funds for the Central Universities (WUT: 2019 III029).  
Not applicable.  
The data generated by the process can be found in the text, and no additional data need to be added.
## 2 Materials and Methods
### Sampling and Exposure  
Prismatic paste specimens measuring 40 \(\times\) 40 \(\times\) 160 mm\({}^{3}\) were prepared from each of the four mixtures. The mixing was inspired by the guidelines of the French standard NF EN 196-1 [37]. Binders and water were initially blended at a low rotational speed of 100 revolutions per minute (rpm) for a duration of 60 s. Subsequently, the mixing device was switched to a high-speed setting of 300 rpm for 30 s. Following this, the mixer was deactivated, and a scraping process was executed for 90 s. The entire mixture was then re-mixed at the high-speed setting for an additional 60 s. The resulting paste was uniformly poured into molds in a single layer. It is worth noting that the only method of inducing vibrations during this process involved the application of four external shocks on the mold using a hammer. After a curing period of 24 h, the samples were removed from their molds and manually ground at room temperature (-22 \({}^{\circ}\)C) until the particle sizes were reduced to less than 2.5 mm, as shown in Figure 1a. This step aimed to increase the surface area for an increased interaction between the cementitious materials and the sulfate and chloride ions present in the aqueous solutions.  
Subsequently, 15 g of powder from each specimen was completely immersed in 120 g of tap water (EXP0), sodium chloride solution (30 g/L) (EXP1), sodium sulfate solution (15 g/L) (EXP2), and sodium sulfate (15 g/L)-sodium chloride (30 g/L) solution (EXP3), yielding a solution to solid ratio of 8 (Figure 1b). This immersion allowed for the exposure of cementitious materials to the respective solutions. This facilitated the investigation of the chemical effects of chloride and sulfate ions on the specimens. EXP0 served as the reference exposure, EXP1 was used to assess the effect of chloride alone, EXP2 was employed to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **CEMI** & **Blast Furnace Slag** & **Metakaolin** & **Fly Ash** \\ \hline Mix1 (Reference) & 100\% & - & - & - \\ Mix2 & 55\% & 45\% & - & - \\ Mix3 & 55\% & 35\% & 10\% & - \\ Mix4 & 55\% & 20\% & 10\% & 15\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Composition of the mixes.  
Figure 1: (**a**) The powders obtained from the 4 mixes after grinding the specimens and (**b**) the exposure immersion set up.  
evaluate the individual effect of sulfate, and EXP3 was utilized to examine the combined effects of chloride and sulfate, representing the central focus of this study.
# Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition
## 2 Literature Review
### K-Nearest Neighbor  
K-Nearest Neighbor (K-NN) is a simple yet fundamental non-parametric supervised classification model requiring little data distribution knowledge [9]. It is known as a lazy learner because there are no parameters for KNN [10]. The general steps to how the K-NN model works written by [11] goes as follows: Based on the input data, KNN finds the K most similar instances to an unseen observation by calculating the distance between them. Then, the algorithm picks the k points based on the training set closest to the new observations made and calls it set A. The last step is assigning the new observations to the most probability class. This is determined by searching for the match that closely looks like the new example among the learned training observations. Because KNN offers such simplicity, it is a commonly used classification algorithm, but when the dataset and its features are large, the model can lose much of its efficiency [12].  
Because KNN is a classifier that employs the scikit-learn library in Python, the optimal hyperparameters for the model can be determined using GridSearchCV or RandomizedSearchCV methods. These methods explore all possible parameter combinations and samples from parameter distributions and can leverage cross-validation schemes and score functions provided by scikit-learn [13].  
In [14], they created an ISL recognition system where the input image was first categorized as either a single-handed or two-handed sign language gesture using a HOG-SVM classifier. Then, they used feature extraction using SIFT and HOG, and finally, they used KNN to classify the images. They used a dataset containing 520 images to train their model and 260 images to test their model, where 60 of those images were from 6 single-handed sign language gestures and 200 images were divided into 20 double-handed sign language gestures. They achieved an accuracy of 98.33% on their single-handed data and 89.5% on their double-handed data.  
In [11], they created a KNN ASL sign language recognition model where they used logarithmic plot transformation and histogram equalization for image reprocessing, then canny edges and L*a*b color space for segmentation. They used HOG for feature extraction on a dataset containing 26 alphabet signs, with 14 samples for training and eight for testing. Their KNN model achieved 94.23% accuracy on their test set.
# Static and Dynamic Response of Micropiles Used for Reinforcing Slopes
## 2 Model Test
### Loading Scheme and Measuring Scheme
#### 2.3.2 Measuring Scheme  
The data collected in the static model test and the shaking table test mainly include the force on the micropiles, the bending moment of the micropiles, the deformation of the landslide mass, and the distribution of the earth pressure on the front and the back of the micropiles. The force of the micropiles includes both the axial force and landslide thrust on the micropiles. In this paper, piles #2, #4, #6, #9, #11, #13 (see Figure 7) were selected as test piles. 8 pairs of BX120-5 AA resistance strain gauges were attached on the outside of the test piles. During the test, the strain gauges generated strain (it was prescribed that the back side of the pile is tensioned as positive). According to material mechanics [25], the bending moment and axial force at the corresponding measuring point can be obtained by Equations (1) and (2), respectively.  
\[\frac{EI(\varepsilon_{1}-\varepsilon_{2})}{y} \tag{1}\]  
where M is the bending moment of the pile body (N\(\cdot\)m), \(EI\) is the bending stiffness of the pile body (N\(\cdot\)m\({}^{2}\)), \(y\) is the distance from the strain measuring point to the neutral axis (m), and \(\varepsilon_{1}\), \(\varepsilon_{2}\) are the strains at the measuring points at the front and the back of the pile, respectively.  
\[\frac{EA(\varepsilon_{1}+\varepsilon_{2})}{2} \tag{2}\]  
where N is the axial force of the pile body (N), EA is the compressive stiffness of the pile (N), and \(\varepsilon_{1}\), \(\varepsilon_{2}\) are the strains at the measuring points at the front and the back of the pile, respectively. In addition, the miniature earth pressure cells were arranged in a symmetrical position along the test column to measure the distribution of landslide thrust on the pile. The layout of the test components is shown in Figure 8.  
Figure 6: Schematic view of loading (**a**) static loading; (**b**) shaking table loading.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Input Wave** & **Acceleration Peak** & **Duration** \\ \hline
1 & 0.1 g & \\
2 & 0.2 g & \\
3 & 0.3 g & \\
4 & EL-Centro & 0.4 g & 13.5 s \\
5 & 0.6 g & \\
6 & 0.8 g & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The test loading sequence.
## 3 Underwater Channel Characterization
### EM Propagation in Freshwater  
Freshwater is considered as a low-loss medium due to its low conductivity. The amount of turbidity is negligible. This enables EM waves to propagate in freshwater [24].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Water** & **Conductivity Values** \\ \hline Freshwater & \(0\leq\delta<\)1 \\ Riverwater & \(1\leq\delta<\)2 \\ Seawater & \(\delta\geq 2\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Water conductivity.
## 4 Discussion
### Security  
The mobility of edge nodes and the dynamics of network topology bring new challenges to the security and confidentiality of vehicle networks. Cooperative perception in the IoV environment mainly solves the information interaction between edge nodes and autonomous vehicles. However, edge nodes, including vehicles, RSUs, and edge servers, are deployed in a distributed manner, resulting in low single-point protection capabilities and attacks by malicious users. When a malicious node sends false environmental information to other vehicles, it sends a large number of computing tasks to the RSU to occupy computing resources and network bandwidth, and the autonomous vehicle that receives the computing tasks will not execute them. The reliability of network services will be destroyed, and the safety of autonomous vehicles will be greatly threatened. Therefore, the realization of autonomous driving cooperative perception in the IoV environment not only needs to consider the trust issue between nodes but also needs to pay attention to the security of the system network. For example, [PERSON] et al. [118] used elliptic curve cryptography and other security strategies to verify the authenticity of the IoV user access to ensure the security of network communication between vehicles. However, because the IoV is a network without a central node and has no corresponding architecture, coupled with the limited computing resources of the vehicle itself, it is difficult to apply mature traditional network defense solutions to the IoV [119]. The study found that the application of blockchain technology [120; 121] can effectively solve the security and privacy protection issues of cooperation between autonomous vehicles and edge nodes under decentralized conditions. Therefore, in the future, it can take advantage of its decentralization, anonymity, and the non-tampering of data to establish a corresponding trust mechanism between edge nodes and autonomous vehicles to solve the trust problem of node data and to reduce network interference.
# Developmental Screening System for Patient Vibration Signals with Knee Disorder
## 3 Results and Discussion
### Ligament after Recovery Assessment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Test Point of Lateral Collateral Ligament (TP\({}_{\text{LCL}}\))**} \\ \cline{3-8}
**Bands** & & **4K–2K** & **2K–1K** & **1K–500** & **500–250** & **250–125** & **125–62.5** & **62.5–31.25** \\ \hline Control & & & & & & & \\ group & & 8.2 \(\pm\) 6.9 & 8.8 \(\pm\) 8.2 & 9.6 \(\pm\) 12.3 & 13.8 \(\pm\) 18.7 & 6.3 \(\pm\) 5.9 & 3.7 \(\pm\) 3 & 3.4 \(\pm\) 2.5 \\ Experimental & \multicolumn{1}{l}{Ref. Surgery} & 4.8 \(\pm\) 5.2 & 5.9 \(\pm\) 5.6 & 6.7 \(\pm\) 6.7 & 8.4 \(\pm\) 8.5 & 13.1 \(\pm\) 11.3 & 6.7 \(\pm\) 6.1 & 7.8 \(\pm\) 16.1 \\ group & \multicolumn{1}{l}{Diff. with C.G. 1  } & \(-\)41.21\% & \(-\)33.69\% & \(-\)29.35\% & \(-\)39.41\% & 107.87\% & 78.90\% & 131.25\% \\  & \multicolumn{1}{l}{Aff. Surg. 1m  } & 6.3 \(\pm\) 4.4 & 8.5 \(\pm\) 5.5 & 10.3 \(\pm\) 5.5 & 15.4 \(\pm\) 9.6 & 21.8 \(\pm\) 14.1 & 9 \(\pm\) 8 & 6.1 \(\pm\) 7.4 \\  & \multicolumn{1}{l}{Diff. with C.G. & \(-\)24.01\% & \(-\)3.56\% & 8.01\% & 11.22\% & 247.68\% & 139.69\% & 82.65\% \\  & \multicolumn{1}{l}{Aff. Surg. 2m} & 6.7 \(\pm\) 4.2 & 9.9 \(\pm\) 6.3 & 13.8 \(\pm\) 8.5 & 20.7 \(\pm\) 13.8 & 36.4 \(\pm\) 33.7 & 11 \(\pm\) 8.3 & 4.6 \(\pm\) 3.8 \\  & \multicolumn{1}{l}{Diff. BEF. Sur. 3  } & 38.86\% & 68.58\% & 103.97\% & 147.39\% & 178.41\% & 65.04\% & \(-\)41.24\% \\  & \multicolumn{1}{l}{Aff. Surg. 3m} & 8.7 \(\pm\) 2.8 & 12.6 \(\pm\) 3.7 & 16 \(\pm\) 5.8 & 23.3 \(\pm\) 7.7 & 32.9 \(\pm\) 11 & 11.8 \(\pm\) 4.3 & 5.7 \(\pm\) 3 \\  & \multicolumn{1}{l}{Diff. BEF. Sur. } & 79.74\% & 115.40\% & 137.17\% & 178.67\% & 152.11\% & 76.36\% & \(-\)26.75\% \\  & \multicolumn{1}{l}{Aff. Surg. 6m} & \(-\)35.30\% & \(-\)8.60\% & \(-\)15.29\% & 3.97\% & 227.26\% & 107.02\% & \(-\)10.69\% \\  & \multicolumn{1}{l}{Diff. BEF. Sur. } & 5.4 \(\pm\) 3 & 7.9 \(\pm\) 4.5 & 8.7 \(\pm\) 5.5 & 15.3 \(\pm\) 10.1 & 22.2 \(\pm\) 13.7 & 9.4 \(\pm\) 6.8 & 3.7 \(\pm\) 5.8 \\  & \multicolumn{1}{l}{Aff. Surg. 12m} & \(-\)34.87\% & \(-\)10.66\% & \(-\)8.77\% & 10.76\% & 253.98\% & 151.18\% & 10.67\% \\  & \multicolumn{1}{l}{Diff. BEF. Sur. } & 8.2 \(\pm\) 6.9 & 8.8 \(\pm\) 8.2 & 9.6 \(\pm\) 12.3 & 13.8 \(\pm\) 18.7 & 6.3 \(\pm\) 5.9 & 3.7 \(\pm\) 3 & 3.4 \(\pm\) 2.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Object average data of lateral collateral ligament between the control and experimental groups. Figure 4: Average differences in signal bands at the ACL sensing point of postoperative knees in the experimental group. Figure 3: Average differences in signal bands at the MCL sensing point of postoperative knees in the experimental group.
## 4 Results and Discussion
### Effects of Gas Chamber Volumes on Gas Chamber Pressure and PDR  
Figure 6 shows the time dependence of gas chamber pressure and the PDR of different gas chamber volumes (365.59 mL, 690.72 mL, and 1015.85 mL). The equivalent diameters of different defect sizes were evaluated to be 0.352 mm, 0.329 mm, and 0.341 mm, respectively. Taking 365.59 mL gas chamber volume as an example, the measured pressure decreased from 79.54 kPa to 1.23 kPa after 43.3 s, and the measured PDR also decreased from 4.22 kPa/s to 0 kPa after 43.5 s. The pressure can be described as a quadratic function of time and may be presented by the following relationship:  
\[P=72.6909-2.6925t+0.0240t^{2},\ R^{2}=0.9827 \tag{18}\]  
Furthermore, along with the increasing gas chamber volumes, the required time for the measured pressure to decrease to zero became increasingly longer: 43.3 s, 79.5 s, and 112.3 s, respectively. The absolute values of the quadratic term ratio in the parabolic equation became increasingly smaller: 0.024, 0.0098 and 0.0057, respectively. Similarly, the required time for the measured PDR to decrease to zero also became increasingly longer: 43.5 s, 79.5 s, and 112.5 s, respectively. The absolute values of the slope in linear function also got smaller: 0.0619, 0.0189, and 0.0118, respectively. The PDR values in the initial stages got increasingly smaller: 4.22 kPa/s, 2.20 kPa/s, and 1.5  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(P_{g}\) (kPa) & 40 & 60 & 80 & 100 & 120 \\ \hline \multirow{2}{*}{\(r\)} & Pressure & 0.982 & 1.000 & 0.999 & 1.000 & 0.998 \\  & PDR & 0.982 & 0.993 & 0.981 & 0.993 & 0.988 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correlation between measured data and predicted values with different applied pressures.  
Figure 6: The effect of gas chamber volume on the pressure and PDR. (**a**) \(V_{g}\) = 365.59 mL; (**b**) \(V_{g}\) = 690.72 mL; and (**c**) \(V_{g}\) = 1015.85 mL. Reaction conditions: defect size = 0.35 mm, water depth = 30 mm, and initial applied pressure = 80 kPa.  
be because the mass changed higher due to the increasing gas chamber volumes, resulting in longer required time and smaller initial PDR value.  
Similarly, for each gas chamber volume, the simulated curves had the same trend as that of the measured pressure and the measured PDR. A great agreement was observed between the experimental values and the numerical data. The correlation between the two curves is demonstrated in Table 3. The high values of correlation coefficient (more than 0.980) indicated a good modification of the calculation model.
## 3 Stimuli-Responsive Mechanisms: Avenues for 4D (Bio-)printing
### Electrically and Mechanoelectrically Responsive Materials
Specifically, PAMPS [97], PHEMA [98], and a few others represent best known examples of these hydrogels. Different effects concur with electrical stimulation: (i) increases in the osmotic pressure of the polymer due to an influx of counterions and solvent molecules, (ii) control of the charge/adsorption of polyelectrolyte on the charge opposite to porous materials, and (iii) formation and swelling of active redox polyelectrolyte multilayers [65]. Although PT has shown no evidence of cytotoxicity, few studies have been carried out for understanding potential suitability in the biomedical field [94; 95]. Likewise, PSS has exhibited a low or even no degree of cytotoxicity to primary human cervical cells even at high concentrations, and it is a promising candidate for biomedicine applications [96]. PLLA/poly(3,4-ethylenedioxythiophene) (PLLA/PEDOT) has been used for electrically conductive 3D scaffold for TE application [99]. Some reports used poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) as an electrical stimulus polymer for smart micro-electromechanical systems (MEMS) interface or neural TE (Figure 5) [100; 101; 102]. For example, an immunofluorescence study was carried out using specific antibodies against Tuj1. Neural stem cells on crosslinked PEDOT:PSS substrates with and without electrical stimulation, differentiated into neurons and into astrocytes confirmed by Tuj1 and GFAP \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material(s)** & **Mechanism(s)** & **Application(s)** & **Ref.** \\ \hline Polythiophene (PT) & Electricity & \begin{tabular}{c} Drug delivery, biosensing, \\ implant devices \\ \end{tabular} & [94; 95] \\ \hline Sulfonated-polystyrene (PSS) & Electricity & \begin{tabular}{c} Active ingredients in \\ marketed vaginal \\ contraceptive \\ \end{tabular} & [96] \\ \hline Poly(2-(acrylamide)-2-methyl)propanesulfonic & Electricity & \begin{tabular}{c} Actuators or artificial \\ muscles \\ \end{tabular} & [97] \\ \hline Poly(2-hydroxyethyl methacrylate) & Electricity & Drug delivery & [98] \\ \hline Poly(L-lactic & \
\begin{tabular}{c} Electrically conductive 3D \\ scaffold for tissue \\ engineering \\ \end{tabular} & [99] \\ \hline Poly(3,4-ethylenedioxythiophene) & Electricity &
\begin{tabular}{c} Smart Micro \\ Electro-Mechanical Systems \\ (MEMS) interface \\ applications, neural tissue \\ engineering \\ \end{tabular} & [100; 101; 102] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of the experiments on 4D (bio-)printing and other manufacturing approaches using electricity as a predefined stimulus. immunoreactivity, respectively (Figure 5). Results show that electrical stimulation significantly increases the percentage of Tuj1-positive cells [101].
# SCM-IoT: An Approach for Internet of Things Services Integration and Coordination
## 4 Case Study
### Platform Dependent Computational Model
#### 4.3.1 Definition of the Notebook Data Model  
The edition notebook contains home automation information for each room as defined above. The book records the known information on the status of each room obtained from the last update reported by the sensors and from the decisions taken to put the actuators into operation or not. However, the book has to be placed alongside other notebooks, such as sensors and actuators notebooks, thus forming a collection and defining the appropriate context for treating all relevant information for the IoT application. As illustrated in Listing 7, the element \"house\" is the name of the book collection and establishes the context in which the room book is located.  
The notes found in these sub-elements contain the minimum essential information on the installation location of home automation devices. Consequently, the location determines the room where the sensors detect environmental information or how the actuators modify the local ambient conditions. A note from a sensor that essentially includes the identifier and reading fields. The reading field contains the information that originates in the environment, corresponding to the most recently obtained by the sensor. Listing 8 presents an excerpt from the notebook for the sensors.  
The sensors were grouped by type into sensors for presence, temperature, smoke, and toxic gas under corresponding XML elements of the same name. The other types of sensors have a similar note structure except for the temperature sensor, whose values reported in the reading field take the values indicated above ('LOW', 'NORMAL', 'HIGH', 'VERY HIGH'). The notebook for actuators has a similar structure for sensors, grouping under the XML element actuators, grouped according to their type into heaters, fans (air conditioners), and alarms. The essential information that each note in the actuator book contains is its identifier and its operating status. An extract from the notebook for the actuators is presented in Listing 9.  
Unlike the sensors, the status field is writable because it indicates the order given to the actuator to change its operation immediately, assuming that it is on and in good operating conditions.
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 6 Open Issues
### Channel Modeling and Measurement  
Because of the long propagation distances and mobility of satellite and aerial networks and the mobility of terrestrial networks for mobile nodes, propagation channel modeling and channel measurement in HSTNs is a critical and challenging issue that requires careful attention due to its importance and the many different environment-related challenges that affect wireless propagation channels in HSTNs.