esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Surrogate-Based Optimization (SBO)
### Orthogonal Array  
The initial sample points are generated by the OA method to construct the initial KM. Valuable information can be obtained by analyzing a small number of factor (parameter) combinations, and the expression of the orthogonal array is \(L_{a}(b^{c})\). Among them, \(L\) represents the Latin square; \(a\) is the number of test runs, and \(b\) and \(c\) are the level and number of factors, respectively. This study mainly explores three parameters, namely \(\phi\), \(Re\), and the normalized nozzle-to-plate distance (\(H/d\)), and each parameter has four levels. Hence, \(L_{16}\big{(}4^{3}\big{)}\) orthogonal array is used, as shown in Table 2. In addition, the objective function needs to be defined first before the initial KM can be constructed. Nusselt number (\(Nu\)) is a dimensionless parameter commonly used in heat transfer analysis. It is defined as the ratio of convective heat transfer to conductive heat transfer at the solid-fluid interface [49], as shown in Equation (12):  
\[Nu=\frac{hd}{k}=\frac{qd}{k(T_{w}-T_{ad})} \tag{12}\]  
where \(h\) is the local convective heat transfer coefficient, \(k\) is the thermal conductivity of the fluid, \(T_{w}\) is the wall temperature, and \(T_{ad}\) is the adiabatic flame temperature. For a  
Figure 7: The process of surrogate–based optimization.  
simplified problem of axisymmetric, the average Nusselt number (\(\overline{Nu}\)) of the impingement plate is calculated as follows:  
\[\overline{Nu}=\frac{1}{A}\int_{A}Nu\ dA=\frac{2}{R^{2}}\int_{0}^{R}Nu(r)r\ dr \tag{13}\]  
\(\overline{Nu}\) is mainly used to evaluate a heating or cooling system's global convection heat transfer performance [50,51] to analyze and optimize the system further. Therefore, it is very appropriate to use \(\overline{Nu}\) as the objective function of this study.
# Farm Monitoring System with Drones and Optical Camera Communication
## 3 Proposed Scheme
### System Model
#### 3.3.4. Transmission Time  
Here we formulate the data transmission time from a sensor node. The transmission rate of an OCC link is determined by the modulation number and symbol rate. Optical spatial modulation and color-shift keying (CSK) are employed to increase the data rate. Multiple light sources are employed in optical spatial modulation. CSK exploits the design of three-color luminaires of LED. Optical signals are modulated by modifying the light intensity to generate predefined constellation symbols [35]. The range of the symbol rate is constrained by the frame rate of the receiver camera and the image processing speed.  
The transmission rate is formulated as  
\[R_{i}=S_{i}D\log_{2}N_{i}, \tag{8}\]  
where \(R_{i}\) is the data rate, \(S_{i}\) is the optical spatial multiplicity, \(D\) is the symbol rate, and \(N_{i}\) is the number of constellation symbols. The maximum transmission time is computed as  
\[T_{i}=\frac{M_{i}}{R_{i}}, \tag{9}\]  
Figure 4: Ground coverage of drone-mounted camera (a: top length, b: bottom length, c: height of trapezoid).  
where we define the maximum data size as \(M_{i}\). This transmission time has a direct impact on the trajectory of the drone, as it must remain within a sufficient time range to complete the data reception.
# Feature Importances: A Tool to Explain Radio Propagation and Reduce Model Complexity
## 2 Propagation Mechanisms According to the Transmitter's Height  
For a given built-up environment, the arising propagation mechanisms are dictated from the transmitter's height. More specifically, for the case of placing the transmitter well below the rooftop level, the received field (for NLOS conditions) is characterized by multiple reflected rays from building walls, in addition to diffractions from perpendicular building corners [10,11]. By comparison, when the transmitter is placed well above the rooftops, electromagnetic waves propagate above them until they are diffracted down to the receiver [11,12]. For the intermediate case, when the transmitter is placed at a height near building rooftops, a mixture of propagation mechanisms takes place. That is, neither propagation inside streets nor over-rooftop diffraction can be neglected [13]. This situation is difficult to simulate because many propagation mechanisms have to be taken into account.  
From a machine learning model's perspective, simulation of the above-mentioned mechanisms proceeds with feature engineering [14]: that is, the inputs of the model should describe those parameters of the built-up environment that have the greatest influence on the dominant propagation mechanisms. For the case of placing the transmitter well above the rooftop height, the inputs should describe the mechanism of over-rooftop diffraction [15]; this is why the position of the tallest building along the Line of Sight path near the receiver is expected to determine the level of the received power. However, when the transmitter height is reduced, the contribution of over rooftop diffraction also decreases, steadily giving rise to the mechanism of multiple reflections and the creation of an urban street canyon. A feature that could be coupled with this mechanism is that which depicts the number of buildings that obstruct the LOS path, thus producing the aforementioned reflections.
# Research on the Scope of Spectral Width Parameter of Frequency Domain Methods in Random Fatigue
## 1 Introduction
[PERSON] [21; 22] proposed a new method to predict fatigue life in frequency domain, and pointed out that in the wide-band random process, the narrow-band distribution method and its improved method will produce some conservative results, and the T-B method and Dirlik method are superior to other frequency-domain methods. When the load frequency range of the structure changes, the frequency range of the structural response will also change, resulting in the change of the spectrum width parameter of the response spectrum. At this time, for random fatigue damage and life calculation, the choice of the life prediction model is particularly important for the adaptability of different spectral width parameters. In the calculation of random fatigue life, the most commonly used method is the frequency domain method. At present, most frequency-domain fatigue life prediction models only specify whether they are applicable to narrowband or wideband, and do not indicate the specific application range of the spectrum width parameter. Different frequency domain methods have more stringent applicability to spectral width parameters, rather than universality. Improper model selection will lead to large errors. Different frequency domain methods have more stringent applicability to spectral width parameters, rather than universality. Improper model selection will lead to large errors. At present, there is no clear boundary between the narrowband and wideband. Thus far, the range of narrowband and wideband spectral width parameters has not been clearly defined [23]. Therefore, it is more difficult to choose the appropriate fatigue life prediction model. At this time, it is more important to determine the application range of different spectrum width parameters for different frequency-domain methods. This article mainly studies the applicability of several common frequency-domain methods to different spectral width parameters.
# Development of a Capsule-Type Inspection Robot Customized for Ondol Pipelines
## 1 Introduction  
Pipeline inspection is crucial in building maintenance. Pipe conditions require intensive management due to their impact on the comfort of the living environment, energy consumption, and maintenance costs [1]. Visual inspection of pipes is difficult due to issues such as cracks, blockages, and aging, making pre-emptive detection challenging. Furthermore, repair costs can be substantial once problems arise [2; 3]. Therefore, a strategic approach to pipeline maintenance is essential.  
In Korea, the inspection of onodol heating system pipes has emerged as a critical issue. Ondol, a representative heating method in Korea, circulates hot water through pipes embedded in the floor slab to heat the floor and, subsequently, the indoor space with radiant heat [4]. This method offers greater comfort and sustained indoor warmth compared to typical heating systems that blow hot air or provide direct heat. However, as pipes are embedded in finished floors and cement mortar, inspection and maintenance pose challenges [5]. During pipe installation, minor holes can occur due to pipe folding, nails, or improper construction. Careless handling by workers during cement mortar topping can also lead to damage or blockage [6]. These defects result in time and cost losses for the project. Finding the exact location of the defect is very difficult, and if it is not found, it will lead to greater losses during use. Improper functioning of onodol heating can reduce the quality of life and increase costs, making the maintenance of heating pipes crucial. Leaks can damage finishing materials, contributing to defect disputes in residential buildings [7].  
Despite the importance of inspecting onodol pipes, existing methods rely heavily on manpower and have limitations in reliability and efficiency. Methods such as pressuredifferential testing, acoustic detection, thermal imaging, and wired endoscopic cameras depend on specialized skills and the expertise of operators, which challenges maintaining comprehensive inspection records and data acquisition [6; 8]. Consequently, developing more reliable and efficient inspection technologies is essential for the effective maintenance of onodl heating systems.  
Therefore, this study aims to develop a robot capable of inspecting the conditions of onodl pipeline systems and validate its performance. Initially, we analyzed the characteristics of the onodl pipe to derive system requirements and proceed with the design. Subsequently, we developed the prototype of a compact capsule-shaped inspection robot and conducted laboratory tests. Through these tests, we verified that the system proposed in this study ensures a performance suitable for field applications by assessing curvature maneuverability and reliability of defect detection according to the onodl characteristics. This study will be able to realistically contribute towards the advancement of pipeline inspection technology and the smart maintenance of onodl heating systems by implementing and validating a customized system for inspecting onodl pipes.
# Practical Inner Product Encryption with Constant Private Key +
## 6 Conclusions  
In this work, an efficient IPE scheme in which the size of the private keys and the number of pairings for decryption are constant is introduced; moreover, this scheme isoselective IND-CPA secure under the modified decisional Diffie-Hellman assumption. Comparison and experimental results are also provided to illustrate that the size and computing cost of this scheme are small. In future works, we aim to improve the efficiency by reducing the ciphertext length and provide a security proof for stronger security concerns under standard assumptions. Because the proposed scheme is based on bilinear pairing, it cannot resist quantum attacks, unlike lattice-based IPE schemes. In future work, we will explore how to construct an efficient and practical quantum-resistant IPE scheme.  
**Author Contributions:** Conceptualization, Y.-F.T. and Z.-Y.L.; Methodology, Y.-F.T. and Z.-Y.L.; Investigation, Z.-Y.L.; Writing--Original Draft Preparation, Z.-Y.L.; Writing--Review and Editing, Y.-F.T. and R.T.; Supervision, R.T.; Project Administration, R.T.; Funding Acquisition, Y.-F.T. and R.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Ministry of Science and Technology, Taiwan (ROC), under Project Numbers MOST 108-2218-E-004-001-, MOST 108-2218-E-004-002-MY2, MOST 109-2218-E-011-007-, and by Taiwan Information Security Center at National Sun Yat-sen University (TWISC@NSYSU).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Specification** \\ \hline OS & Ubuntu 18.04 LTS \\ \hline CPU & Intel i7-4790 3.6 GHz \\ \hline RAM & 8 gb \\ \hline Language & Python 3.6 \\ \hline Library & Charm-Crypto v0.50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Environment of the implementation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & **Encryption Time** & **Decryption Time** & **Private Key Length** & **Ciphertext Length** \\  & **(ms)** & **(ms)** & **(kb)** & **(kb)** \\ \hline
[16] & 100 & 100 & 31.7 & 0.937 \\
[29] & 170 & 140 & 0.955 & 17.5 \\
[15] & 260 & 140 & 1.59 & 25.9 \\ \hline Ours & 20 & 10 & 0.37 & 31.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Implementation results.
# Efficient Cluster Tree Topology Operation and Routing for IEEE 802.15.4-Based Smart Grid Networks
## 4 Proposed Scheme
### Proposed Collision Avoidance Scheme  
For the multi-channel algorithm described in Section 4.3, there is no means to supplement the interference in the channel of the parent node. Therefore, this section proposes a structure that can simultaneously access the parent node using the main and auxiliary channels to transmit data, even when a problem occurs with the main channel. Figure 18 shows an example of an SD collision using a secondary channel.  
In Figure 18, in the proposed technique, when a new node receives a beacon frame from CH A and CH B, CH D can use one of the two radio channels as a primary channel and the other as a secondary channel. The proposed technique uses one channel as the main channel to transmit data, and the other can transmit data from the auxiliary channel when a problem occurs in the main channel or when urgent data are transmitted. There are no problems with regard to which channel was used as the main channel in the proposed technique. However, in the case shown in Figure 18, if CH D operates in SD 3 of channel 1, it collides with the SD section of CH B when the auxiliary channel is used. Accordingly, CH D cannot use channel 2 as an auxiliary channel. Therefore, when determining the SD number of the child cluster head, the SD selection algorithm is used (see Figure 19).  
In Figure 19, when CH D receives a beacon frame from CH A and CH B, the main channel is set to one, and the SD section selects the SD number that does not overlap with the SD of CH A or CH B. In this case, one can communicate with one's child nodes and parent node CH A through the main channel, and with CH B through a secondary channel if it is infeasible to communicate through the primary channel during the operation. As shown in Figure 18, CH D can perform this interference prevention algorithm by selecting an SD number of five, which does not overlap with CH A or CH B.  
Figure 18: Example of SD conflict when using a secondary channel.
## 1 Introduction
To capture spatio-temporal correlations and produce prediction series, we develop STGPT-2 with an STB and TGPT-2. The former is leveraged to mine spatio-temporal correlations. The latter is a variant of GPT-2, which is employed to generate future wind speed. To the best of our knowledge, our work devises a brand-new hybrid architecture that combines the spatio-temporal model and GPT-2 for wind speed prediction in the turbine network. * To optimize STGPT-2's training, a GAN is introduced to integrate unsupervised training and supervised training in a complementary fashion. During supervised training, STGPT-2 attempts to minimize the MSE loss, converging predicted values to true values. During unsupervised training, STGPT-2 minimizes the adversarial loss, intending to learn the true data's distribution. * The extensive experiments are conducted using one real-world wind speed dataset. The experimental results demonstrate that STGPT2 UGAN is superior to advanced benchmarks in terms of prediction accuracy. The remainder of this paper is organized as follows. Section 2 reviews the existing works on wind speed prediction and LLMs on time sequence analysis. Section 3 introduces the preliminaries. In Section 4, the detailed design of STGPT2 UGAN is illustrated. Section 5 describes the experimental results and analysis on one real-world wind speed dataset. In the end, Section 6 concludes this paper.
## 3 The Multi-Agent Framework
### Framework Design
#### 3.4.2 Selecting a Cooperative Neighbour for Relaying
As argued in [39], stochastic policies can work better than deterministic policies in partially observable environments (such as packet routing in WSNs, where each sensor has only partial information about the entire WSN). In this algorithm, the policy search is based on the difference between the benefit of an action and the current average benefit. Thus, this algorithm encourages each sensor to select the cooperative neighbours, which are associated with high benefits, for packet relaying. Since the benefit of selecting each cooperative neighbour is dynamic, a sensor does not always select relaying neighbours from a small part of its cooperative neighbours. Therefore, the relaying role can be automatically rotated among the sensor's cooperative neighbours, which can then prolong the lifetime of each cooperative neighbour. ```
\(\backslash\) according to \(v_{i}\)'s view \(\backslash\) 1\(v_{i}\) initialises Q-values and \(\mathbf{\pi}\);
2 for each cooperative neighbour of \(v_{i}\), i.e., \(v_{j}\in N^{\prime}_{i}\)do
3\(Q(v_{j})\leftarrow(1-\alpha_{2})Q(v_{j})+\alpha_{2}\cdot\mathcal{B}(v_{j})\);
4 endfor
5\(\bar{\mathcal{B}}\leftarrow\frac{1}{|N^{\prime}_{i}|}\sum_{v_{j}\in N^{\prime}_ {i}}\mathcal{B}(v_{j})\);
6 foreach cooperative neighbour of \(v_{i}\), i.e., \(v_{j}\in N^{\prime}_{i}\)do
7\(\pi(v_{j})\leftarrow\pi(v_{j})+\tau(Q(v_{j})-\bar{\mathcal{B}})\);
8 endfor
9\(\mathbf{\pi}\)\(\leftarrow Normalise(\mathbf{\pi})\);
10\(v_{i}\) selects a cooperative neighbour based on \(\mathbf{\pi}\);
```  
**Algorithm 2**Choice of a cooperative neighbour for relaying  
In this framework, each sensor node in the network has to have a global unique ID to distinguish different nodes in the network (see Figure 1). Although some addressing schemes, e.g., [40, 41], can be used to handle this problem, it is still sometimes difficult for each node to have a global unique ID, especially in a large WSN. This is a drawback of this framework, and overcoming this drawback will be the subject of one of our future studies.
## 1 Introduction
### Concept  
Figure 1 shows the basic concept for stacking by recording in a dual-wavelength sensitive multi-layer structure. Holographic recording of lenses is achieved through the interference of two beams that mimic the input and output beams of the lens device. The interference pattern created is recorded in the volume of the photosensitive material as a periodic structure in which the spatial frequency and slant at a particular point are defined by the two wave-vectors normal to the interfering wave fronts at that point. This structure diffracts incoming light of the same wavelength and incident angle to re-create the original beams. In this case, a dual layer is prepared in which each layer responds optimally to a limited range of wavelengths in blue or green. This means that two sequential recordings in blue and green create a stack of two lenses, without the need for repositioning or adjustment of the layer. Due to the high selectivity of volume diffractive lenses, each lens reconstructs only the appropriate wavelength range and the output beams are identical for both wavelengths. This approach has potential in reducing chromatic dispersion in HOE lenses.  
Figure 1: Schematic diagram for (**a**) recording two lens patterns, using two different wavelengths in a dual-sensitive layer to create stacked lenses. (**b**) Replay process showing that the stacked lenses reproduce the same collimated beam for both input wavelengths.
## 4 Coordinated Control Example and Simulation
### Coordinated Control Example
#### 4.1.1 Example Data Generation
For the two-way coordinated control of arterial roads, the two relative phase differences of adjacent intersections have a certain constraint relationship, the expression is: \(O^{down}+O^{up}=C\). This paper sets the relative phase difference in the downward direction of the arterial road \(\sigma^{down}\) as 30 s, and the relative phase difference in the upward direction \(\sigma^{up}\) is signal cycle length minus 30 s.  
Based on the above data generation principles, 178 sets of arterial road coordination control examples were generated.
## 3 Development of Thermal Condition Monitoring System
### Concept Development  
The LM35 temperature detector senses the thermal conditions from the respective assigned DERs points and converts into an electrical signal per the state conditions [98; 99]. The Arduino ATMEGA328P microcontroller SIM900 with GSM/GPRS/XBee on the distribution field processes the signal that transmits to the GSM/Xbee (receiver) at the DMS-DMU. The DMS-DMU is the data management unit of the distribution management system, where data is monitored and archived from the field measured thermal conditions. The GSM/XBee module/unit attached to the PC incorporates displaying information. Figure 2 is the block diagram of the concept design system.