esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
Overall, participants have positive perceptions of AI in higher education [3,12], as all mean values are above 3.5. However, the variability in responses (as reflected in the standard deviation) suggests that not everyone agrees on AI's role, especially in teaching and learning. Besides, the highest mean score (4.23) for ethical and social implications shows that issues like fairness, bias, and data privacy are important factors in AI adoption. This finding highlights the need for responsible AI policies that ensure equity and transparency in AI-driven education. To provide a comprehensive interpretation, the results should be discussed in relation to previous studies and the study's working hypotheses. Additionally, the broader implications of these findings warrant further exploration, including potential directions for future research.  
Regarding AI's future role, the relatively high mean score (4.10) suggests that most respondents expect AI to become increasingly integrated into education, aligning with [PERSON]'s [18] findings. However, the presence of lower minimum values indicates that not all participants share this optimism, with some expressing concern about the challenges AI may introduce. This variation in perspectives suggests that while AI's expansion in education is widely anticipated, skepticism remains regarding its implications.  
Since gender differences are not significant [1,3], AI-related policies and educational strategies can be designed without major gender-based distinctions. Meanwhile, age appears to have a weak but notable relationship with AI perceptions. Younger individuals generally exhibit more positive attitudes toward AI, yet they also express significantly greater concerns regarding its ethical and social implications. This generational difference may reflect varying levels of awareness or differing perspectives on AI's social impact. Despite this, age does not strongly influence overall AI perceptions, although younger individuals 'critical stance of AI's ethical dimensions are worth further investigation.  
Experience with AI emerges as a key factor of positive attitudes toward its role in higher education. Participants with more AI experience are more likely to recognize its value in learning and its future importance. Additionally, younger individuals tend to have greater AI exposure, which may contribute to their overall more favorable perceptions. Interestingly, while frequent AI users tend to have more positive attitudes toward AI in education and its future significance, those with greater AI experience report using it less frequently. This suggest that expertise may lead to more strategic or efficient AI usage rather than habitual reliance.  
These findings contribute to the broader discourse of AI adoption in education, emphasizing the importance of ethical considerations, experience-driven perceptions, and demographic influences. Future research should further explore these dimensions, particularly the nuanced relationship between AI familiarity, critical awareness, and usage patterns, to inform more effective AI integration strategies in higher education.
# Variational Multiscale Nonparametric Regression: Algorithms and Implementation
## 4 Numerical Study  
In this section, we compare the practical performance of the Chambolle-Pock, ADMM and semismooth Newton algorithms in computing MIND and demonstrate the empirical performance of MIND with respect to various choices of dictionaries. We refer to, e.g., [16; 28; 29; 31; 32; 36; 38] for further numerical examinations of MIND. We select in particular Sobolev \(H^{1}\) and TV seminorms as the regularisation functional, with the former differentiable but the latter non-differentiable and the 50%-quantile of \(\|\sigma e\|_{\text{MS}}\) in (5) as \(q_{n}\) for MIND. Concerning measure of image quality, we consider the peak signal-to-noise ratio (PSNR [66]), the structural similarity index measure (SSIM [67]) and the visual information fidelity (VIF [68]) criteria. The implementation in MATLAB for the Chambolle-Pock algorithm, together with code that reproduces all the following numerical examples, is available at [[https://github.com/housenli/MIND](https://github.com/housenli/MIND)]([https://github.com/housenli/MIND](https://github.com/housenli/MIND)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Dependence on** & **Theor. Convergence** & **Practical** \\  & **Initialisation** & **Speed** & **Performance** \\ \hline Chambolle–Pock & no & linear & Good for smooth \\  & & & and nonsmooth \(R\) \\ \hline ADMM & no & linear & Too slow \\ \hline Semismooth Newton & yes & superlinear & Good for smooth \(R\) \\  & & & Unstable otherwise \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the comparison of the Chambolle–Pock, ADMM and semismooth Newton algorithms.
## 4 An IoT-Based Real-Time Location Monitoring System by Using BLE  
Mining is one of the most speculative businesses around the globe. Most of the mines all over the world are lagging in different safety measures causing many casualties and deaths. The basic causes of death in underground mines are gas accidents, rock falling, ventilator accidents, fire, explosions, etc. Considering the safety issues of the employees/workers inside the mines, real-time location tracing of those employees becomes a major concern. Effective underground communication is necessary to collect more information about the mines or workers. However, there are various constraints while collecting the real-time data inside the mines such as restricted transmitting power, large attenuation of the transmitted signal from the rock wall and low penetration of the electromagnetic signal. In this regard, it is always beneficial to take the potential advantages of low-power and short-range communication technologies such as, RFID, Zigbee, Bluetooth, Bluetooth low energy (BLE), etc.  
In this section, a scenario for a Bluetooth low energy (BLE) beacon-based real-time location monitoring of employees/workers by using the IoT is presented. A BLE beacon and microcontroller are used to design this asset-tracking product and have been implemented in the IoT here by connecting this device to the cloud.
## 4 Simulation Results
### Potential Attack Analysis  
Usually, if the encryption can resist the chosen-plaintext attack, it can resist other common attacks. Based on the proposed method, the illegal user uses the encryption to encrypt arbitrary images to obtain fake private keys, which would be used to decrypt the original image. At the same time, the encryption key such as R, r, \(\lambda\), and the 2D kernel is known by the illegal user. Figure 10a-d shows the decrypted image, which doesn't have any information about the original image. It shows that the proposed method can resist the chosen-plaintext attack.  
Figure 8: The decrypted results of the encrypted image with different strength noise: (**a**) \(k\) = 0.05, (**b**) \(k\) = 0.07, (**c**) \(k\) = 0.10.  
Figure 9: The decrypted results of the encrypted image with different pixels occlusion: (**a**) \(4\times 4\), (**b**) \(6\times 6\), (**c**) \(8\times 8\).  
Phase retrieval attack is usually used to test the security of the encryption method. Figure 11 shows the relation between the CC value of the combined image and iteration numbers. It shows that the CC value quickly converges to around 0 with the increase of iteration numbers, which indicates that the proposed method can resist phase retrieval attack effectively.  
An encryption system with good encryption effect should be able to resist differential attack. Generally, the number of pixel change rate (NPCR) and unified average changing intensity (UACI) are used to evaluate the ability to resist the differential attack, which are defined as follows:  
\[NPCR\ =\ \frac{1}{M\times N}\sum_{i\ =\ 1}^{M}\sum_{j\ =\ 1}^{N}D(i,j)\times 100\%, \tag{23}\]  
\[D(i,j)\ =\ \left\{\begin{array}{c}1,E(i,j)\
eq E^{\prime}(i,j)\\ 0,E(i,j)\ =\ E^{\prime}(i,j)\end{array}\right., \tag{24}\]  
\[UACI\ =\ \frac{1}{M\times N}\sum_{i\ =\ 1}^{M}\sum_{j\ =\ 1}^{N}\frac{\left|E(i,j)-E^{ \prime}(i,j)\right|}{255}\times 100\%, \tag{25}\]  
where \(E\) and \(E^{\prime}\) are two ciphertexts, generated from two original images that have only one different pixel, and \(M\times N\) is the size of image. To obtain NPCR and UACI, we choose one of the images to change a random pixel, and encrypt them with the same key. The results of NPCR and UACI are shown in Table 3. It shows that the proposed encryption can resist differential attack very well.  
Figure 11: CC values change along with the iteration number.  
Figure 10: Decryption results of chosen-plaintext attack without key (**a**) R, (**b**) r, (**c**) \(\lambda\), (**d**) 2D kernel.
# Characteristics and Processing of Hydrogen-Treated Copper Powders for EB-PBF Additive Manufacturing
## 2 Materials and Methods
### Feedback Characterization  
Each of the three powder conditions were characterized before and after hydrogen treatment, for size distribution by laser diffraction (Microtrac S3500, Microtrac Inc., Montgomeryville, PA, USA), and density by helium pycnometry (ASTM B923-16, Quantachrome MicroUltrapyc 1200e, Quantachrome Instruments, Boynton Beach, Florida, USA). The oxygen and hydrogen content of all the powders were quantified by inert gas fusion analysis (LECO OHN836, LECO, St. Joseph, MI, USA). The reported values herein were acquired immediately prior to the experiments. The powder morphology was characterized by a JOEL 6010 LA scanning electron microscope (JEOL USA, Inc., Peabody, MA, USA). An additional powder characterization was performed on individual powder particles using focused ion beam (FIB) milling for the cross-sectional analysis by secondary electron and gallium ion contrast on a FEI Quanta 3D Field Emission Gun (FEG) (Thermo Fisher Scientific, Hillsboro, Oregon, USA).  
Figure 1: Hydrogen brazing furnace at SLAC National Accelerator Laboratory Technology Innovation Directorate (TID) (left), and stacked-stainless steel fixture (right) for treating multiple trays of copper powder in 2-mm thick layers.  
Scanning transmission electron microscopy (STEM) analysis and energy dispersive spectroscopy (EDS) were performed on a FEI Talos (Thermo Fisher Scientific, Hillsboro, Oregon, USA), to assess the chemical composition, size of surface, and internal oxide species.  
A thermogravimetric analysis (TGA) was performed using a TA SDT 650 Simultaneous Thermal Analyzer (TA Instruments, New Castle, DE, USA) under flowing nitrogen, while the samples were heated at 5 \({}^{\circ}\)C/s to 600 \({}^{\circ}\)C in an alumina sample holder in order to identify the temperature at which the H\({}_{2}\)O can escape from the treated copper powders.
## 3 Biotechnological Preparation of Cereals Enriched with PUFA via Fungal Solid State Fermentation
### Microorganisms for Cereal Utilization  
One of the key factors for a successful SSF process is the selection of appropriate microorganisms that can grow on various cereal substrates and, simultaneously, synthesize a range of compounds in large enough quantities. Therefore, suitable microorganisms should meet the following criteria [29]: (a) sufficient coverage of the cereal substrate surface and penetration into the cereal particles for nutrient utilization, (b) production of necessary enzymes (e.g., amylases, proteases, lipases) for the hydrolysis of polymeric compounds in the substrate, (c) adequate growth at reduced water activity, (d) active biosynthetic machinery for the formation of desired compounds, (e) they must not produce any toxins, and (f) the ability to decrease the amount of anti-nutrient compounds in the substrate. To fulfil these criteria, screening of microorganisms has led to the selection of lower filamentous fungi, especially those belonging to Zygomycetes as the best candidates for SSF processes (Table 1). Amongst them, _Thammidium elegans_, _Cunningham echimulata_, _Cunningham elegans_, and _Mortierella_ (_Umbelopsis_) _isabellina_ have been used as producers of gamma-linolenic acid (GLA) and _Mortierella alpina_ as a producer of dihomo-gamma linolenic acid (DGLA), AA, and EPA [14].  
Moreover, several _Mucor_ strains [25; 30] and _Umbelopsis isabellina_[31; 32] have been described to form both GLA and \(\beta\)-carotene. A variety of cereals and cereal products (e.g., rice bran, wheat bran, oat flakes, peeled barley) [33] and legumes [34] have been tested as useful substrates to achieve fermented products (FP) with a high PUFA content. In all cases, the amount of PUFA in the fermented mass was consistently lower when cereals were used as a sole substrate and/or without any pre-treatment. Therefore, microbial transformation of cereal substrates and legumes into a desired fermented mass requires optimization of the cultivation process.
## 4 Discussion
* Power dependency: in the event of a power failure or battery problems, wireless modules may stop working, which can be especially critical in terms of safety. * Privacy and data security: user data related to smart home modules may be exposed to threats related to cybercrime and privacy violations.
# Profit-Efficient Elastic Allocation of Cloud Resources Using Two-Stage Adaptive Workload Prediction
## 2 Related Work
### Queueing Theory in Cloud Computing  
Queueing theory [25] is a useful tool for analyzing task processing in the cloud center context, which has been used to analyze the waiting time, task dropping, resource utilization, queueing length, and other aspects. [PERSON] et al. [26] used a queueing model to evaluate the expected request completion time, rejection probability, and system overhead rate of VM scheduling and request handling process. [PERSON] et al. [27] leveraged a queuing-based model to study VM migration dynamics, offering insights into resource optimization, task processing, and performance metrics. In [28], the authors highlighted the crucial role of the queuing theory in cloud computing through integrating the M/M/c/K queuing model with the improved dynamic Johnson sequencing algorithm (DJS) to optimize task scheduling and resource allocation. [PERSON] and [PERSON] [29] employed a multi-queuing model to analyze and optimize edge server load percentages in mobile cloud computing, minimizing the task response time while ensuring fairness in offloading delays. In [30], the authors modeled fog devices as M/M/1 queues and cloud systems as M/M/C queues. To address the fog-cloud workload distribution problem, the NSGA-II algorithm was employed, with the objective of minimizing both energy consumption and system delays.  
These previous studies demonstrated that queueing theory can be used to model a cloud service and examine the performance of task processing in cloud computing. Importantly, the queueing model can also be used to optimize resource management. In practice, due to the constraints of load monitoring and the minimum resource rental time interval (at least one hour), real- or fine-time granularity methods cannot be used to analyze task processing in the cloud directly. Hence, we used stochastic queuing theory to model task processing in the context of cloud computing in one-hour intervals, in order to optimize the management of computing resources. However, mathematical analysis is required to study how to transmit the predicted value to suitable cloud resources using the stochastic queueing model. Thus, one of the key contributions of this study is presenting a detailed mathematical model of the process.
# Topology Optimization and Testing of Connecting Rod Based on Static and Dynamic Analyses
## 2 Materials and Methodology
### Design and Material Selection
#### 2.1.1 Material Selection  
The chosen connecting rod was made from 36 MnVS4 and is used in the second generation of fracture-split material. This is the latest technology used while manufacturing connecting rods nowadays; in this method, the connecting rod is manufactured as a single component and then it is split into two halves at the crank end [16]. Based on the selected material, the mass of the initial connecting rod was 654 g. The major composition of this low-alloy steel is manganese and vanadium which improve its durability to withstand tensile, compressive, and buckling loads. Other materials like carbon, silicon, sulfur, phosphorus, and chromium can also be found in small quantities [17]. The properties of 36 MnVS4 steel are shown in Table 1.
# Two-Dimensional Topology Optimization of Headtube in Electric Scooter Considering Multiple Loads
## 5 Discussion
First, while the optimized headtube design demonstrates a more uniform strain energy distribution, certain regions may still experience localized stress concentrations owing to geometric transitions, material redistribution, and external loading conditions. One critical area is the steering column-headtube junction, which is subject to torsional loads from steering and lateral impacts. In this region, sudden material changes can cause localized stress amplification. Second, another potential stress concentration area is the lower fork connection point, where the braking forces and front-wheel impact loads are directly transferred to the headtube. This region experiences high compressive and shear stresses, which can lead to premature fatigue failure if not correctly designed. Although topology optimization design has been successfully performed under multiple loading conditions, some limitations and additional work are required to apply this methodology to real-world problems. First, in the modeling stage, the design of the electric scooter was simplified, and detailed structures were excluded. Consequently, while the optimization results may differ from the actual model, the design simplification significantly reduced the analysis time. Second, topology optimization was performed by assuming plane stress and two dimensions. However, owing to its symmetrical structure, an electric scooter is subjected to maximum stress in the longitudinal or sagittal plane. Nevertheless, previous studies have demonstrated the effectiveness of sagittal plane-based assessment methods in healthcare applications [41; 42]. A major challenge in this study was demonstrating the feasibility of the proposed design using topology optimization considering multiple loads. Even though several researchers have used larger FE models than the model in this study, their objective was to determine a lightweight design. Conversely, this study performed topology optimization to improve the driving safety of electric scooters. Other important aspects are the large array size required for optimization and the additional computational cost incurred owing to the management of the arrays in the optimization algorithm. A 3D analysis with an equivalent resolution will require preparing and managing thousands of design variables. Therefore, the efficiency of topology optimization should be improved in future work.
# GF-1 Satellite Imagery Data Service and Application Based on Open Data Cube
## 2 Methods
### Data Preprocessing Based on the FLASH and RPC Methods
#### 2.1.1 Radiometric Correction Based on the FLASH Method  
Radiometric correction includes radiometric calibration and atmospheric correction. The purpose of radiometric calibration is to apply absolute radiometric calibration coefficients to the multispectral and panchromatic remote sensing images and convert their DN (digital number) values into irradiance to give irradiance images. Then, the irradiance of the images can be converted to reflectance through atmospheric correction. Electromagnetic waves undergo absorption, reradiation, reflection, and spectrum redistribution during their transmission from the radiation source to the sensor, which necessitates an inversion of the true reflectivity of the surface to estimate the interference by atmospheric effects [39]. The FLASH atmospheric correction method was adopted in this paper, and Equation (1) [36] was applied to calculate the radiance of the single-pixel spectra received by the sensor. Only the visible light waves were considered, and the thermal infrared band was neglected. After obtaining the atmospheric correction parameters, the average surface reflectance was calculated by applying Equation (1) to each band pixel, and the average spatial reflectance was estimated.  
\[L^{*}=\frac{A\rho}{1-\rho_{e}S}+\frac{B\rho_{e}}{1-\rho_{e}S}+{L_{a}}^{*}, \tag{1}\]  
where \(L^{*}\) is the radiance of one pixel, \(\rho_{e}\) is the average radiance of one pixel and its surrounding area, \(S\) is the atmospheric spherical reflectance, \({L_{a}}^{*}\) is the atmospheric backscattered  
Figure 1: The Chinese GF-1 spatial and temporal satellite imagery data management framework.  
radiance, and \(A\) and \(B\) are coefficients determined by atmospheric and geometric conditions, independent of the ground surface.
## 4 On-Board Unit Implementation
### Hardware Interfaces with Vehicle's Sensors and Actuators  
There are two possible ways of interfacing our experimental OBU with the in-vehicle sensors and actuators network. One approach is to use the standard OBD interface that is available in all types of vehicles. The OBD interface allows for reading several physical parameters of the vehicle (e.g., speed, motor number of revolutions, etc.), and it is a valuable source of information for the basic diagnosis in mechanical and electronic maintenance.  
The second approach is to interface directly with the CAN bus of the vehicle, where all sensors, actuators, and Electronic Control Units (ECU) of the Advanced Driver-Assistance System (ADAS) are connected. However, this approach is not possible with commercial vehicles due to safety and security aspects. In this work, we have integrated our experimental OBU into a prototype vehicle provided by PSA. The prototype vehicle is a DS7 car, which includes a CAN gateway that was developed by PSA. The CAN gateway acts as the bridge that filters and forwards CAN frames between the CAN bus and the OBU. The OBU is equipped with a CAN to USB adapter. The CAN gateway periodically transmits CAN frames every 50 ms that contain sensors' readings data, such as the speed of the vehicle (km/h) and status information of brakes, emergency brake lights, hazard detection, etc.  
Figure 5 depicts an overview of the vehicle's embedded system. The communication between the vehicle's CAN gateway and the OBU is realised through a PCAN-USB interface. The vehicle is equipped with on-board sensors, like radars, cameras, or accelerometers, which are capable of sensing the environment of the vehicle. The vehicle's CAN gateway receives data from sensors and interprets such data as hazard events or only road perception before transmitting them to the OBU. Moreover, the OBU is responsible for receiving V2X messages from the network infrastructure and backend services, and send them to the vehicle core system. An HMI in the car dashboard enables the vehicle to display messages from sensors, as well as events that were received from the backend services. If the reported event leads to a risk of collision, the vehicle core system triggers embedded actuators, adapting its longitudinal behaviour accordingly.