esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Simulation Scenario Design
#### 2.2.1 Sign Design  
The IV of directional signs significantly impacts driver information processing in interchange sections. The traditional method to calculate information quantity, proposed by [PERSON], involves assessing the number and types of bytes in the information [36]. However, due to the differences in character units and word formation between English and Chinese, this approach may not be entirely suitable for determining the IV of Chinese signs. Some researchers opt to use Chinese character blocks, such as road names, as the calculation standard for sign information load, while others base their calculations on the content conveyed by the sign [37; 38]. This study combines these methods, considering the types of information transmitted by guide signs (including location, direction, etc.) as  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Province** & **Position** & \begin{tabular}{c} **Limit Speed** \\ **(km/h)** \\ \end{tabular} & \begin{tabular}{c} **Tunnel Length** \\ **(m)** \\ \end{tabular} & \begin{tabular}{c} **Connection** \\ **Distance (m)** \\ \end{tabular} & \
\begin{tabular}{c} **Traffic Volume** \\ **(pcu/h)** \\ \end{tabular} &
\begin{tabular}{c} **Information Volume** \\ **of Signs (units)** \\ \end{tabular} \\ \hline Jiangsu & S73-G228 & 70 & 949 & 507 & 420–1120 & 8 \\ Jiangsu & S73-G310 & 70 & 3105 & 89 & 400–1020 & 6 \\ Fujian & S81-S1531 & 80 & 1210 & 104 & 520–1090 & 10 \\ Fujian & S81-S1531 & 80 & 1210 & 464 & 570–1130 & 9 \\ Fujian & G104-S1531 & 80 & 5102 & 234 & 360–720 & 7 \\ Yunnan & G78-G85 & 100 & 1108 & 112 & 540–1240 & 11 \\ Yunnan & G56-S33 & 80 & 1426 & 298 & 480–1207 & 13 \\ Yunnan & S22-G5611 & 100 & 3100 & 310 & 386–870 & 6 \\ Shaanxi & S30-G65 & 80 & 2789 & 306 & 400–860 & 11 \\ Shaanxi & S65-G30 & 80 & 1300 & 667 & 405–934 & 12 \\ Shaanxi & G5-S21 & 80 & 7300 & 791 & 420–875 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Field survey of tunnel–interchange connecting sections in China.  
Figure 2: Roadway design and sign setting under the simulated scenario. The signs displayed feature Chinese characters for specific geographical locations (Zhashui, Ankang, Qujiang, etc.) pertinent to the study’s context in China and presented in their native script for accuracy.  
individual information units and establishing specific rules for calculating information on highway guide signs.  
Previous research indicates that when information units exceed seven, it can impact driver cognition [30]. Thus, this study employs guide signs with 5, 7, 9, 11, and 13 information units in the simulation. The layout, font size, and positioning of these signs comply with G5 expressway standards in Shaanxi Province. To prevent driving adaptation, exit names are altered across different simulation scenarios. The detailed sign design is shown in Figure 3.
# AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities
## 6 Limitations and Future Work  
There are limitations in the current study that can be addressed through further research. First, while focusing on the Big Ten allowed us to identify meaningful patterns in AI guidance for HEIs, we recognize that the findings should not be generalized without considering cultural and socioeconomic differences among HEIs across the world. Specifically, HEIs in non-English-speaking countries and those in the Global South may face different challenges at the organizational level, and insights from the Big Ten may not be applicable in their scenarios. Future studies may seek to compare AI governance in universities with different cultural and societal backgrounds. Second, even though most AI guidelines are straightforward and clearly written, readers may still have different perceptions that misalign with the guidelines' intention. Investigating the perception of AI guidelines among different university members is important for assessing the effectiveness of AI guidelines in HEIs' AI governance. Another key limitation is the methodology of the case study, which mainly relies on qualitative analysis. Quantitative data, such as surveys or statistical analyses, could provide additional support for the thematic conclusions. Future research could incorporate stakeholder perceptions of AI governance, gathered through surveys or interviews, to quantify the themes identified in this study and offer a broader perspective on the issues at hand. Moreover, while this research focuses on AI governance strategies and their general characteristics, it does investigate specific, practical examples of how these guidelines have been implemented or their tangible effects on stakeholders. A follow-up study could explore real-world case studies within these universities to demonstrate the impact of AI governance in practice, adding further depth to the discussion. Student perspectives are also essential to understanding how AI guidelines are experienced by the primary users in educational settings. Future research could incorporate student feedback through interviews or surveys, examining challenges such as ethical dilemmas or technical complexities encountered during the implementation AI guidelines.
## 4 Evaluation Methodology
### Simulation Environment  
The simulation environment we leverage to benchmark our design models the robot as a rigid body--i.e., it is characterized by its Cartesian coordinates and its orientation \((x_{r},y_{r},\theta_{r})\) and it moves in a bi-dimensional space characterized by landmarks. The landmarks are modeled as points in space and are defined by their Cartesian coordinates and a label \((x_{l},y_{l},label)\). The robot is considered equipped with a generic range sensor and an inertial sensor. The sensors are characterized by a measurement noise modeled as zero-mean Gaussian white noise.  
As for the inertial sensors, linear and rotational speed \((v,\omega)\) are generated with the following:  
\[v_{meas}=v_{true}+\mathcal{N}(0,\sigma_{v}^{2})\] \[\omega_{meas}=\omega_{true}+\mathcal{N}(0,\sigma_{\omega}^{2})\]  
and the range and bearing signals \((\rho,\beta)\) coming from the range sensors are computed as  
\[\hat{\rho} =\sqrt{(x_{l}-x_{r})^{2}+(y_{l}-y_{r})^{2}}+\mathcal{N}(0,\sigma_ {\rho}^{2})\] \[\hat{\beta} =\arctan\left(\frac{y_{l}-y_{r}}{x_{l}-x_{r}}\right)-\theta_{r}+ \mathcal{N}(0,\sigma_{\omega}^{2})\]  
The simulation environment runs on CPU and is schematized in Figure 4.
## 2 Methodology  
### System Approach  
The framework of the system approach used in this study is illustrated in Figure 1, the physical flow of energy is the key part of the analysis as involving with various stages, energy types and energy technologies of energy use in Malaysia. To observe the energy use of Malaysia, we mapped the whole physical flow of energy onto a Sankey diagram with details of the production, import and export, transformation and final consumption of various energy, as introduced in Section 2.2.  
Referring to the literatures mentioned in Section 1 and the methodology used in a previous study by the authors [17], we concluded that the development of Malaysia's energy flows is mainly influenced by four key factors as follows:1. _Energy demand_: In the end-use sector, energy is consumed to provide energy services, like passenger transport, cooling comfort and illumination. The variation of energy demand will influence energy supply. Currently, the manufacturing sector, transportation sector and domestic sector (residential, commercial and agriculture) are the major energy consumers in Malaysia.
2. _Energy resource availability_: The energy supply structure of a region is dependent on its energy resource availability, including domestic production and imports. The lack of energy resource availability will require restricting energy demand. Though Malaysia has abundant energy resources, to ensure more reliable energy supply and strategy purpose, some energy resources are imported.
3. _Technology choice_: Adoption of different technologies will not only influence the energy supply structure, but also the environmental emissions. The major uncertainty of technology choices in Malaysia is the future development of the electricity generation technologies, which will mainly be decided by the availability and the cost of energy resources. Besides, biodiesel may be an important alternative energy for vehicle fuels in Malaysia.
4. _Policy adjustment_: Policymakers can implement policies and enact acts to regulate or intervene in energy supply and energy consumption for strategic purposes. The main aim of the Malaysian energy policy is to ensure energy security. The previous policy adjustments have successfully optimized Malaysia's energy structure. Along with the climate change issue, the development of renewable energy and nuclear power are hot topics.
# Determining the Optimal Configuration of the Multi-Ring Tree for Bluetooth Multi-Hop Networks
## 1 Introduction
In [24], a dual-ring tree (DRT) protocol generates a dual-ring subnet in one dense area, which is connected to multiple tree subnets around the other sparsely covered areas. To consider the emerging probability of the multi-hop situation in the dense area, a distributed formation algorithm is presented to cope with the multi-hop instances, while a localized formation algorithm is proposed to cope with the pure single-hop instance. As a result, the DRT topology outperforms conventional BlueHRT in terms of network reliability and routing efficiency for Bluetooth networks. The routing protocol design is another notable leading research issue for Bluetooth multi-hop scatternets [25]. Thus far, many well-known routing protocols have been presented, including proactive [15; 18], reactive [26], and hybrid methods [27] for Bluetooth networks. To achieve the route discovery with the least overhead, a self-routing protocol is presented for both tree [28] and layer-ring topologies of the single-hop scenarios [29]. Currently, the multi-hop self-routing protocol remains as a research challenge. To extend the DRT architecture into a generalized \(n\)-ring network solution, a multi-ring tree with a multi-hop self-routing protocol is proposed. In order to determine the optimal configuration of one dense area and many sparse areas, an optimal multi-ring tree formation algorithm is presented herein. During the leader election phase (LEP), a leader root is elected and then connects to its neighboring nodes as a root in order to build the multi-ring subnet, thus improving the connectivity of the core network in the dense area. Each root in the multi-ring subnet then starts to build its corresponding tree-shaped subnet around other sparse areas. Finally, a multi-ring tree topology is constructed. The multi-ring design aims to determine the optimal configuration for various sizes of network topology. To achieve this goal, a min-path algorithm is introduced to determine the optimal number of rings \(R\) and the best number of nodes in each ring \(P\) for various numbers of nodes \(N\). With a constraint criterion between \(R\) and \(P\), the resulting combination of \(R\) and \(P\) achieves the minimum hop length locally and constructs a more symmetrical multi-ring subnet. To efficiently deliver packets for both multi-ring and tree subnets, a self-routing protocol is an energy-efficient solution to achieve the least route discovery overhead for a multi-hop scenario. The remainder of this paper is arranged as follows. Section 2 proposes the detailed operation of the multi-ring tree formation algorithm. Section 3 presents a self-routing protocol with a self-healing scheme to efficiently maintain the topology and deliver packets over the multi-ring tree network. Section 4 demonstrates the optimal configuration and routing performances via computer simulations. Finally, conclusions are drawn in Section 5.
# Study of Deformation and Fault Mechanisms in Bedding-Locked Rock Slope
## 5 Identification Criterion of Instability State of Locked-Type Slope  
This study examines the identification criterion for the fault disaster evolution state of the inclined locking rock slope under load by observing its deformation and fault phenomenon at different levels of load strength, referring to previous research results. The rock mass loosened and deformed at the top and back of the slope model under the action of different levels of loads. During the test, a tension crack formed from the top to the bottom of the slope, and another formed at the foot of the slope. Eventually, the crack in the slope merged with the tension crack at the foot of the slope, resulting in the fault of the slope model. The deformation and fault of the slope model is here analyzed in detail, where we reveal the three stages which occurred in the evolution of slope deformation, as follows:  
1. Initial stage of deformation and faulting in the model, characterized by the stretching and opening of the rock mass at the top and back of the slope: as the load strength increases, the deformation and fault area of the slope expands, resulting in thin and downward tension cracks.
2. Stage of development, expansion, and interlayer dislocation of slope fractures: as the load strength gradually increases, tension cracks appear in the slope, causing lateral displacement. The slope contains numerous tension cracks that may expand and extend from the top to the foot of the slope. This can cause an increase in interlayer displacement and lead to delamination and fragmentation of the model surface.
3. Fault stage, with the formation of through cracks and large-scale slope instability: as the load strength increases, cracks within the slope expand, resulting in a strong interlayer dislocation. This causes a sharp increase in the displacement of the DH1 measuring point and a rapid rise in signal strength and energy of the S1 measuring point, defining a complete through plane from the top to the foot of the slope model, at the locking section of the slope foot. This triggers the instability and faulting of the slope model.
# Density Functional Theory Study on the Adsorption of Co(II) in Aqueous Solution by Graphene Oxide
## 5 Experimental Verification of Adsorption Properties of GO
### Results and Discussions
#### (2) Temperature  
Under the conditions of pH = 6, \(c_{0}\) = 10 mg/L and t = 3 h, the influence of temperature on the adsorption of Co\({}^{2+}\) by GO is shown in Figure 11. When the temperature ranges from 30 \({}^{\circ}\)C to 60 \({}^{\circ}\)C, the adsorption capacity of GO for Co\({}^{2+}\) reaches the highest value at 50 \({}^{\circ}\)C, which is 11.23 mg/g, indicating the uncertainty of the effect of temperature on the adsorption effect.  
Figure 8: The infrared spectrum of GO model calculated by Gaussian software.  
Figure 9: Infrared absorption spectra of GO.  
(3) Interfering Ions  
The effects of the co-existence of Na\({}^{+}\), K\({}^{+}\), Ca\({}^{2+}\) and Mg\({}^{2+}\) on the adsorption of Co\({}^{2+}\) by GO were investigated at 50 \({}^{\circ}\)C. During the adsorption process, pH = 6, t = 3 h and the initial concentrations of Co\({}^{2+}\)\(c_{0}\) = 10 mg/L; the results are shown in Figure 12. It can be seen that Na\({}^{+}\), K\({}^{+}\), Ca\({}^{2+}\) and Mg\({}^{2+}\) contained in the solution have an inhibitory effect on the adsorption of Co\({}^{2+}\) on GO. With the increase in the concentration of interfering ions in the solution, the equilibrium adsorption capacity of GO for Co\({}^{2+}\) decreases rapidly at first and then gradually slows down until it approaches 0. The above experimental phenomenon further shows that some functional groups on GO can also bind to the above co-existing ions, which will have a certain effect on the adsorption of Co(II).  
Figure 11: Effect of temperature value on Co\({}^{2+}\) adsorption by GO.  
Figure 10: Effect of pH value on Co\({}^{2+}\) adsorption by GO.
## 2 Methodology  
This section provides an introduction to the RTK measurement model, including the signal state check and observation consistency check. Afterward, the INS dynamic model is introduced utilizing smartphones' INS sensors (the InvenSense ICM-20690 IMU model for the Xiaomi Mi 8 (Xiaomi, Beijing, China) and ST-Microelectronics LSM6 DSO IMU model for the Galaxy S21 Ultra (Samsung, Suwon-si, Republic of Korea). Then, we use a robust Kalman filter to integrate the INS dynamic model and RTK measurement model.
## 3 Materials and Methods
The low-cost ADXL335 accelerometer sensor [57] is compatible with ESP32 and can be used for fall detection and system activation (by motion detection). ESP32 is designed for mobile, wearable electronics and IoT applications. It has all the most recent features of low-power chips, including fine-grained clock gating, multiple power modes and dynamic power scaling. For example, in a low-power IoT sensor hub application scenario, the ESP32 is enabled periodically and only when a specified condition is detected. The low load cycle is used to minimize the amount of power the chip consumes. The output of the power amplifier is also adjustable, thus contributing to an optimized trade-off between communication range, data rate and energy consumption [58]. To control vital signs, we can connect the body temperature sensor [59], body humidity [60] and pulse rate [61] to the ESP32 microcontroller. The equipment is installed in a device suitable for each person (bracelet, waistcoat, etc. ), in order to make it safe, concealed and comfortable, eliminating user interaction in most operations. Communication is carried out automatically through the communication of the main module with the LoRaWAN gateway, sending user monitoring data to the TTN at pre-defined intervals, which are stored in a database with real-time analysis by the entities and authorized in a network scheme similar to that shown in Figure 1. In this context, it is important to note some definitions [53]:  
* End Device, Node, Mote: an object with an embedded low-power communication device. * Gateway: devices that form the bridge between other devices and The Things Network. These devices use low-power networks like LoRaWAN to connect to the gateway, while the gateway uses high bandwidth networks like Wi-Fi_33, ethernet or cellular connections to connect to The Things Network. * Network Server: servers that route messages from end devices to the right application, and back. * Application: a piece of software running on a server.
# Compact Multilayer Yagi-Uda Based Antenna for IoT/5G Sensors
## 2 Materials and Methods
### Planar Yagi-Uda Design  
The design of the antenna starts with the radiating element, by creating the two arms of the dipole (with global length L\({}_{\mathrm{dip}}\) and width W\({}_{\mathrm{dip}}\)) over a substrate layer, which are separated by a small gap, which should be carefully selected as it affects the input impedance of the dipole. Then, three strip directors (with size L\({}_{\mathrm{dir}}\)\(\times\) W\({}_{\mathrm{dir}}\)) were introduced after the dipole, separated by a distance DirSpa, which was adjusted in the simulation process to improve the radiation performance of the antenna. The number of directors was a trade-off between the global size of the antenna and its directivity.  
Then, the feeding part, composed of an impedance matching network, a microstrip balun, and two coplanar printed lines, were connected to the dipole. The coplanar lines allow one to connect each arm of the dipole to each output of the balun, and also to separate the microstrip structure from the dipole of a distance Lcps. This is important because the microstrip section also acts as a reflector element of the Yagi [22]. The balun creates the balanced feeding, by splitting the input signal in two outputs with half-power and half-wavelength phase delay, and finally, a double microstrip transformer matches the input impedance of the antenna to a 50 \(\Omega\) microstrip line.  
The layout of the designed planar Yagi antenna is presented in Figure 2a. The planar Yagi-Uda antenna was simulated and its dimensions were optimized. After several simulations, the optimal values for the design parameters were accomplished and are summarized in Table 1. As function of the wavelength, it is found that the optimal value for the dipole's length is equivalent to 0.415 \(\lambda\), which is reasonably close to theoretical approaches for printed Yagi antennas. The manufactured prototype is shown in Figure 2b.  
Figure 2: Planar Yagi-Uda antenna: (**a**) layout and parameters; (**b**) photograph of the prototype.
## 2 Related Works
Furthermore, the authors explored how the proposed standard bit manipulation extension in RISC-V can be effectively utilized for the efficient implementation of AES-GCM (Galois/Counter Mode). The GIFT family of block ciphers was utilized in various NIST candidates but required optimization techniques such as bit-slicing and fix-slicing for optimal performance. The researchers of [21] developed assembly implementations for GIFT-64 and GIFT-128 using the RV32I ISA, evaluated their performance in the HiFive1 development board, and achieved clock cycle reductions of 88.69% (GIFT-64) and 95.05% (GIFT-128) using fix-slicing with the key pre-computation technique. ASCON, a recently standardized lightweight cryptographic algorithm by NIST, has been implemented by [PERSON] in the base RV32I processor. The proposed work [22] implemented non-standard RISC-V instructions, and an end-to-end test environment was formed by extending the GNU Compiler Collection and Spike RISC-V ISA Simulator. Another work, [23], proposed a new and efficient validation platform by deploying a cryptographic SoC as a virtual prototype using a hybrid hardware and software design strategy. Compared to RTL simulation, the custom virtual prototype demonstrated significant performance advantages, performing approximately 10-450 times faster while maintaining a simulation error of only about 4%. Among the numerous related research projects and works, we have identified a smaller sub-set that is more connected to and comparable to our work, thereby distinguishing it from those that may have more distant or peripheral relevance. First, the work [24; 25] presented standalone implementations of the PRINCE block cipher in an FPGA, aiming for low resource usage. For PRESENT-80, the work [26] integrated the crypto co-processor for both AES and PRESENT-80 in an FPGA-based SoC platform with an emphasis on energy efficiency and performance. The implementation of ChaCha [27] by [PERSON] et al. enhanced efficiency by interleaving independent tasks and minimizing data dependencies. Concerning the SHA-3 hash functions, since NIST's announcement of SHA-3, there has been a surge in research and numerous hardware implementations dedicated to this algorithm, including [28; 29]. The current state-of-the-art, well-optimized ASIC implementation [30] in 7 nm TSMC also falls into this category. Evaluations between these works and our results will be discussed in Section 7. Similarly to our work, two earlier works [31; 32] designed and incorporated algorithm acceleration processing into the SoC, but in two distinct ways: once by optimizing hardware instructions and once by optimizing software instructions.