esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Detection Models
They show that they can use the same text generation model, or a variation of the GROVER model, to identify documents generated by itself. The verifier was constructed by passing the last hidden state of a text string to a linear layer that predicts if the text was written by a human or a GROVER model. [PERSON] et al. [17] analyzed texts written by seven different GPT models versus human-written text in the context of argumentative essays. To detect these from one another, they trained SVM-based classifiers based on several different linguistic features and a RoBERTa-based classifier. Their best-performing SVM model, which was trained using function words, achieved an accuracy of 94.33%, whereas their RoBERTa-based classifier gave them an accuracy of \(\approx\)99% across all seven GPT models. They further used ChatGPT to distinguish between all of the different texts using zero-shot and few-shot prompting. This resulted in an average accuracy of \(\approx\)50%. [PERSON] et al. [45] developed a model to distinguish text generated by ChatGPT and humans within an academic writing context. The authors trained a language model on a large selection of academic science writing and then prompted the model to generate such texts. They then trained a classifier to distinguish between the two versions of texts using a set of 20 features grouped by word choice, syntax, and grammar. They report a classification accuracy of 99.4%. The authors state that their approach or model can be used to improve the accuracy of plagiarism detection systems, identify misinformation, and improve the quality of academic writing of students and researchers. Other detection models include perplexity-based models. A perplexity-based model indicates the model's uncertainty in predicting the next token. A lower score indicates that the tokens are predictable and therefore more likely to be generated by AI, while a higher score indicates human-generated text [46]. [PERSON] et al. [46] developed both a perplexity-based model and a transformer-based model to detect AI-written restaurant reviews. The perplexity-based model gave them an accuracy of 84%. On the other hand, their transformer-based model, which was a fine-tuned DistilBERT classifier, gave them an accuracy of 98% in detecting AI-generated reviews. Other examples of detectors that are based on perplexity measures include GPTZero, developed by [PERSON].
# Real-Time Mitigation of the Mobility Effect for IEEE 802.15.4g SUN MR-OFDM
## 2 Analysis of the Doppler Shift  
In this paper, we consider smart meter applications of the MR-OFDM transceivers that are located in harsh or radioactive areas where energy is very important for communication devices. To measure and collect environmental parameters in such areas, we cannot use conventional technologies; instead, we need to use LPWA technology. There are a number of reasons for this. First, in extremely harsh environments, we cannot usually change the battery or use electricity networks to provide energy for sensors. In such situations, LPWA technology is advantageous. Second, when the sensors are located in a large field, we need technologies that can transmit data long distances. Third, we use unmanned aerial vehicles (UAV) that fly over these regions to collect data daily. When a large amount of data is required in one day, we need a high-bit-rate digital communication link that consumes low power. Then, we focus on MR-OFDM out of the three SUN PHYs. This scenario is represented in Figure 1.  
The high speed of the receiver mobility leads to a Doppler shift in the system. The Doppler shift causes the CFO to affect the system performance. A Doppler shift occurs when there is a movement between the receiver and the transmitter:  
\[f_{d}(t)=f_{c}.\frac{v(t)}{c}\cos\alpha(t)=f_{\max}\cos\alpha(t) \tag{1}\]  
Here, \(f_{c}\) is the center carrier frequency; \(v(t)\) is the velocity of the receiver; \(c\) is the speed of light; and \(\alpha\) is the angle of arrival, which is the angle between the arriving wave and the direction of motion, as show in Figure 1.  
When the receiver moves with the constant velocity \(v\), the changes in the angle of arrival are given as follows [8]:  
\[\cos\alpha(t)=\frac{D_{s}/2-v(t).t}{\sqrt{d^{2}+\left(D_{s}/2-v(t).t\right)^{ 2}+\Delta h^{2}}}\text{ with }0\leq t\leq D_{s}/v(t) \tag{2}\]  
\[\cos\alpha(t)=\frac{-1.5D_{s}+v(t).t}{\sqrt{d^{2}+\left(-1.5D_{s}+v(t).t\right) ^{2}+\Delta h^{2}}}\text{ with }D_{s}/v(t)<t\leq 2D_{s}/v(t) \tag{3}\]  
\[\text{ With }\cos\alpha(t)=\cos\alpha(t\text{ mod }(2D_{s}/v(t))),\ t>2D_{s}/v(t) \tag{4}\]  
Here, \(D_{s}\) is the distance between two of the transmitters; \(d\) is the minimum distance between the transmitter and the moving direction (as in Figure 1); and \(\Delta h\) is the height difference between the receiver and the transmitter. According to [9], the CFO \(\varepsilon\) is the ratio of the offset frequency to the subcarrier spacing \(\Delta f\). As mentioned above, the CFO causes a frequency shift at each subcarrier of the receiver. This is presented as follows:  
\[\varepsilon=\frac{f_{offset}}{\Delta f} \tag{5}\]  
At the velocity of 75 m/s, the maximum Doppler value is 600 Hz because the maximum CFO value is 0.0576, and \(\Delta f\) is 10,417 Hz. This shows that the FFO causes ICI, which causes a non-orthogonality between the subcarriers. This reduces the system performance. In this paper, we propose a novel scheme that can estimate and compensate for the Doppler effect in real time by using the CP part, which was added to the OFDM symbol to combat ISI.
# Dual-Band MIMO Antenna with Enhanced Isolation for 5G NR Application
## 2 Antenna Design and Analysis  
### Antenna Geometry  
The configuration and prototype of the designed dual-band antenna are shown in Figure 1. The radiator of the antenna element consists of an L-shaped strip and a defective circular radiator composed of a semicircle with step-shaped strips. The radiator is fed by a 50 \(\Omega\) micro-strip feedline. Two identical antenna elements are printed on a 1.59 mm thick FR4 substrate with relative permittivity of 4.4 and loss tangent of 0.02. A cambered ground plane is printed on the bottom of the substrate. High isolation is achieved by etching a C-shaped slot, a rectangular slot, and a rectangular slit. Through simulation and optimization, the detailed parameters are shown in Table 1.  
Figure 1: _Cont._
## 4 Parametric Analysis on Creep Deformation of PMMA Frustum Observation Windows
### Simulation Model Validation by Experiment  
Under high pressure, the space between the inner window and the window seat of the metal spherical shell compresses due to an increase in displacement. At greater depths, the failure deformation of the observation window may result in an intolerable pressure level for humans. Consequently, the observation window is a crucial component in the design of the pressure hull. Therefore, a full-scale model test was carried out on the PMMA observation window, as shown in Figure 8. The type of strain gauge used is 120-3 AA, block 120.3 \(\pm\) 0.1 \(\Omega\), substrate size 6.6 \(\times\) 3.2 mm, wire grid size 3.0 \(\times\) 2.3 mm, and the manufacturer is CHENG TEC. The strain measurement instrument model is UCAM-60B-AC, the power supply specification is 85 to 264 VAC, 50/60 Hz, and the manufacturer is KYOWA.  
The pressure-holding experiment for the observation window involves loading the pressure to 115 MPa and maintaining the load for 5 h, with a loading speed not exceeding 4.5 MPa/min. Each load increment is 5 MPa, followed by a 2 min wait before the next loading cycle. The indoor temperature is 30 \({}^{\circ}\)C. The improved time-hardening model is implemented in UMAT and subsequently calculated using ABAQUS, demonstrating good agreement with the experimental results, as depicted in Figure 9. It shows that the improved time-hardening model is well-suited for the creep analysis of the PMMA observation window.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mesh Size (mm)** & **Element Number** & **Displacement (mm)** & **Relative Error** \\ \hline
10 & 198 & 7.1121 & 0.0197\% \\
12 & 144 & 7.1108 & 0.0014\% \\
14 & 109 & 7.1111 & 0.0056\% \\
15 & 90 & 7.1107 & - \\
16 & 75 & 7.1108 & 0.0014\% \\
18 & 55 & 7.1109 & 0.0028\% \\
20 & 46 & 7.1110 & 0.0042\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The finite element simulation results corresponding to different meshes of the window seat.  
Figure 7: Mesh convergence analysis of the window seat.
## 4 Sub-Optimal Data Gathering
### Calculation of the Sub-Optimized Value of \(\beta\)  
We simulate our protocol for different values of \(\beta\) at a time equal to \(2.5\times 10^{4}\) s. Figure 7b shows the variation of \(\beta\) from 0-1 with a margin of 0.01. Figure 7b depicts that with the increasing value of \(\beta\), the number of dead nodes decreases. The reason is that when the value of \(\beta\) is zero, the trajectory of the AUV is circular, and hence, the distance between the end node and the AUV increases. This leads to increased energy consumption on transmission(s), reception(s) and processing of aggregated data. However, when the value of \(\beta\) is equal to one, the multiple transmissions and receptions decrease due to the reduced number of hops. Therefore, the maximum nodes are alive at \(\beta=1\), as shown in Figure 7b, compared to the previous values. Moreover, we simulate the network throughput of the AEDG for different values of \(\beta\), as shown in Figure 7a. As we have discussed above, the maximum number of nodes are alive for a longer duration at \(\beta=1\). Therefore, the network throughput is also maximized at \(\beta=1\).  
Moreover, we simulate our proposed protocol for different values of \(\beta\), as shown in Figure 6. We simulate different trajectories of the AUV by varying the value of \(\beta\), as shown in Figure 6. We use Equation (C2) to calculate the different elliptical paths of the AUV. The value of \(\beta\) plays a significant role to increase the stability period, as well as the throughput of the network. In our case, the value of \(\beta\) is varying from 0-1, as shown in Table 1. When the value of \(\beta\) is equal to zero, the trajectory of the AUV is circular. In the circular trajectory, the distance between end nodes and the AUV increases, and hence, the number of transmission(s) and reception(s) also increases. This leads to increased energy consumption in transmitting, receiving and processing the sensed data. As we increase the value of \(\beta\) from zero, the distance between end nodes and the AUV decreases, which leads to the decrease of the number of hops. A reduced number of hops results in low transmissions and receptions, which lead to a decrease in the energy consumption. Therefore, the maximum number of nodes are alive for a longer duration by increasing the value of \(\beta\), as shown in Figure 7a,b. Simulation results with varying values of \(\beta\) depict that the majority of the nodes are alive for a longer duration when the value of \(\beta\) is equal to one. Hence, the majority of nodes are alive for a longer duration, which increases the network throughput, as shown in Figure 7b.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\beta\) & **b** & **a = b + b\(\beta\)** \\ \hline
0 & 50 m & 50 m \\
0.2 & 50 m & 60 m \\
0.4 & 50 m & 70 m \\
0.6 & 50 m & 80 m \\
0.8 & 50 m & 90 m \\
1 & 50 m & 100 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different values of \(\beta\).
# Architectural Simulation of Hybrid Energy Harvesting: A Design Experiment in Lanzarote Island
###### Abstract  
This study conducts research on an architectural design based on energy harvesting technology. The research subject is a pergola-style structure to be built in a square in Arrecife, the Spanish territory of Lanzarote Island. The architectural design based on the energy harvesting technology developed in this research utilizes solar energy. To install a solar panel on the roof of the pergola, the optimal tilt angle from January to December was derived by using a function that considered the latitude and solar declination value of the study site, and the amount of renewable energy generation was calculated. The architectural design based on energy harvesting also utilizes wind power. To transform wind power into renewable energy, piezoelectric materials that trigger renewable energy with the micro-vibrations generated by wind power are applied to the architectural design. The amount of energy generation was calculated considering the wind power and wind direction in the location where the pergola should be built; in addition, this calculation used information from prior studies on piezoelectric materials. This article is significant, as it has developed an architectural design where hybrid energy harvesting technology that utilizes two types of natural energy (solar and wind) is applied to a building facade.  
hybrid energy harvesting; energy-independent architecture; renewable energy; solar panel; piezoelectric material; architectural design +
Footnote †: journal: Applied Sciences
# Fast-RRT: A RRT-Based Optimal Path Finding Method
## 3 Method
### Improved RRT
#### 3.2.1 Fast Sampling  
The basic RRT algorithm randomly samples a state in the entire state space and guides the tree to expand to it. When a new node is expanded by the RRT algorithm, the RRT calculates the distance \(d\) between the node and the target point. If \(d\) is less than the set threshold \(r\), the algorithm then finds the target point until the end of searching; otherwise, the search continues. Therefore, every time a new node is expanded, the RRT algorithm detects the area with the node as the center and the distance from the center less than r. We defined this area as the explored area. The RRT algorithm can finally realize the exploration of all areas through continuous sampling and growth.  
However, this sampling strategy may produce many invalid expansions and result in a large variance in the search time. An example of invalid expansion is shown in Figure 2. The blue areas in the figure indicate the explored area of three nodes. \(x_{rand}\) is the state sampled by the RRT algorithm, \(x_{new}\) is the state generated by steering operation, and then \(\{x_{near},x_{new}\}\) is added to the edge set. However, this expansion is invalid because the red exploration area of the newly generated node completely falls within the original exploration area and fails to expand the newly explored area.  
To address the above issue, we propose a sampling algorithm called Fast-Sampling. By refusing to sample in the explored area, Fast-Sampling can avoid a large number of invalid growths. Figure 3 indicates the principle of Fast-Sampling, where a new vertex \(x_{new}\) is added to the tree, its surrounding area \(x_{around}=\Big{\{}x\in X\Big{|}\|x-x_{goal}\|<r\Big{\}}\) is set as the explored area. As shown in Figure 3, the sampled state \(x_{rand}\) is located in the exploredarea, the corresponding guided expansion is then invalid. The Fast-Sampling samples another random state \(x_{rand2}\), which guides the random tree \(T\) expand to an unexplored area. Compared with RRT, Fast-Sampling speeds up the exploration speed and reduces memory usage. Algorithm 5 presents the detail of Fast-Sampling.
# Model Updating Using Frequency Response Functions Based on Sherman-Morrison Formula
## 4 Case Study  
The simulation examples of the truss and the solar wing and the experiment of aluminum solar panel frame are used to verify the feasibility of the model updating method using FRFs based on Sherman-Morrison formula in this section.
## 3 RTS/CTS Scheme for IEEE 802.15.6  
The IEEE 802.15.6 standard organizes the nodes in a star topology network, where a single node (called the hub) controls operation and resource allocation in the network. This standard supports beacon and non-beacon enabled modes. In the latter, the hub may operate in (1) superframe-based allocations, where the hub utilizes a managed access phase defined in the standard for bilink allocation intervals, and (2) superframe-free based allocations, where the hub does not use superframe structures and adapts unscheduled type 2 allocations to send short and a fixed number of data packets. In the beacon-enabled mode, the hub employs multiple access protocols, such as slotted-ALOHA, CSMA/CA, and polling according to the traffic requirements. In this mode, the communication is bounded by superframe boundaries comprising many access phases such as random, exclusive, managed, and contention access phases. The most common access procedure used in the random-access phase is the IEEE 802.15.6 CSMA/CA protocol, because it supports eight priority levels according to the traffic classification. However, as mentioned above, this protocol encounters poor performance in the presence of hidden node problems.
## 1 Introduction
[PERSON] and [PERSON] [24; 25] established a nonlinear dynamic model based on the Hertz contact theory, with the purpose of investigating the effects of axial preload, spindle eccentricity, inner/outer waviness amplitudes on the dynamic response in both time domain and frequency domain. [PERSON] [26], discussed the mathematical description and evaluation method on spindle radial error motion, the axial thermal displacement, radial error motion and modal characteristic of spindle were verified by the designed experiment. Some researchers [27; 28; 29; 30] investigated the vibration response induced by the nonlinear stiffness, surface waviness and distribute defects of the bearing. It is helpful to the study of running accuracy of the spindle; however, it is insufficient to investigate the modeling method and formative mechanism of the dynamic rotational error motion. From the literature reviews, it is worth noting that most research studies have been focused on how to measure the rotational accuracy of the SRB system and how to improve the measuring accuracy and the vibrations response induced by the nonlinear stiffness and surface waviness of the bearing. However, few papers are published associating these error motion sources with the spindle rotational accuracy, especially for dynamic rotational accuracy. The modeling and formative mechanism of the dynamic rotational error motion are not well understood. Motivated by this, this paper mainly focuses on the modeling method and the formative mechanism of spindle rotational error motion. The structure of this paper is arranged as follows: Section 2 introduces a dynamic model of the spindle-rolling bearing (SRB) system considering the bearing nonlinearity to analyze the effecting mechanism of the error factors. Based on the model of Section 2, the influence of the error factors is analyzed quantitatively by utilizing the numerical simulation in Section 3. In Section 4, an experimental setup is designed to verify the proposed model. A series of measuring experiments for the dynamic rotational error corresponding to different influenced factors are performed. The conclusions and prospects of this study are given in Section 5.
# Monitoring of Evolving Laser Induced Periodic Surface Structures
## 4 Results
### Influence of Geometrical Properties of the Surface Structure  
#### 4.1.1 Size of Structured Area  
We first tested if the fringes appear in the simulation allover the beam profile even if only the central part of the sample is structured. This is important since in the experiment fringes are observed in the Airy rings although the structure is created only in the central beam part. The results of comparative simulations are shown in Figure 6. To emphasize the structure-induced changes to the far-field we plot the difference between the far field of the light backreflected from the structured and unstructured surface. In (a) we show the structure-induced difference, if the structure is applied all over the sample while in (b) the structure is present only in the central 30 \(\upmu\)m diameter spot. As already seen in Figure 5, the entirely structured sample shows a reduced reflectivity and the appearance of vertical fringes. If only the central part is structured (b), the reflectivity in the region of the Airy rings is nearly unchanged but in addition to the vertical fringe pattern also a circular fringe pattern appears which originates from diffraction from the circularly shaped structured area. The distance between the circular fringes is significantly larger than the period of the vertical fringe pattern. From these simulations we conclude that the fringes observed in the Airy rings are caused by the structure in the central part of the beam.
## 3 Results
### Evaluation Results  
Table 4 gives the average AUC, sensitivity, specificity, and accuracy of various prediction models. The average AUC values across the ML techniques and resampling methods are displayed as a heatmap in Figure 2. In general, the AUC values of NB (0.602-0.661) and LR models (0.539-0.659) were higher than those of the other ML techniques, whereas MLP models (0.545-0.563) had the lowest AUC values. Among the data resampling methods, the undersampling method improved the performance of prediction for most of the ML techniques. On the contrary, the SMOTE method resulted in lower performance for all the ML techniques. Figure 3 shows the average AUC values ordered from the highest to the lowest. NB models with class weighting, undersampling, imbalanced, and oversampling, and LR with class weighting ranked the top five. The AUC values between the top five models were not significantly different according to paired \(t\)-tests.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Selection Times (out of 30)** \\ \hline Age & 30 \\ Prior ED visits within one year & 30 \\ Pre-stroke mRS & 30 \\ NIHSS & 30 \\ BMI group & 30 \\ GCS group & 30 \\ Use of nasogastric tube & 30 \\ Failed dysphagia screening test & 29 \\ CAD & 29 \\ Cancer & 29 \\ Heart failure & 28 \\ Atrial fibrillation & 27 \\ Recent infection & 25 \\ Prior hospitalization within one year & 23 \\ eCCr stage & 22 \\ Use of Foley catheter & 22 \\ Hematocrit & 18 \\ ESRD & 18 \\ Hemoglobin & 18 \\ Diabetes mellitus & 7 \\ ED arrival mode & 4 \\ Occupation & 2 \\ Careed by attendant & 2 \\ Education & 1 \\ Use of birth control pill & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Features selected among 30 training sets. BMI = body mass index. CAD = coronary artery disease. eCCr = estimated creatinine clearance rate. ED = emergency department. ESRD = end-stage renal disease. GCS = Glasgow Coma Scale. mRS = modified Rankin Scale. NIHSS = National Institutes of Health Stroke Scale.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **AUC** & **Sensitivity** & **Specificity** & **Accuracy** \\ \hline Imbalanced & & & & \\ C4.5 & 0.583 & 0.097 & 0.960 & 0.808 \\ CART & 0.568 & 0.071 & 0.965 & 0.808 \\ \(k\)NN & 0.585 & 0.044 & 0.980 & 0.815 \\ LR & 0.649 & 0.075 & 0.983 & 0.823 \\ MLP & 0.547 & 0.188 & 0.884 & 0.762 \\ NB & 0.660 & 0.328 & 0.868 & 0.773 \\ RF & 0.607 & 0.070 & 0.956 & 0.800 \\ SVM & 0.500 & 0.000 & 1.000 & 0.824 \\ Undersampling & & & & \\ C4.5 & 0.600 & 0.531 & 0.642 & 0.623 \\ CART & 0.599 & 0.526 & 0.657 & 0.634 \\ \(k\)NN & 0.611 & 0.434 & 0.740 & 0.686 \\ LR & 0.653 & 0.553 & 0.680 & 0.657 \\ MLP & 0.563 & 0.494 & 0.621 & 0.599 \\ NB & 0.660 & 0.503 & 0.742 & 0.700 \\ RF & 0.599 & 0.578 & 0.575 & 0.575 \\ SVM & 0.614 & 0.465 & 0.763 & 0.711 \\ Oversampling & & & & \\ C4.5 & 0.581 & 0.472 & 0.656 & 0.624 \\ CART & 0.588 & 0.516 & 0.643 & 0.620 \\ \(k\)NN & 0.587 & 0.482 & 0.646 & 0.617 \\ LR & 0.652 & 0.550 & 0.688 & 0.663 \\ MLP