esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Designer Trust in Artificial Intelligence-Generated Content: TAM/TPB Model Study
## 2 Literature Review
### Theoretical Framework  
#### 2.2.1 Technology Acceptance Model  
In 1989, scholar [PERSON] introduced the Technology Acceptance Model (TAM) to explain and predict public acceptance of new technologies. This theory posits that users' intention to use a technology is decisive for actual usage behavior and that attitudes towards the technology, along with perceptions of its usefulness and ease of use, jointly determine this intention. Specifically, attitude refers to the user's subjective positive or negative feelings towards using the new technology [11]. Perceived usefulness is the belief that the technology can enhance work efficiency and quality, while perceived ease of use is the belief that the technology has low learning costs. Using the framework provided by TAMs, researchers can easily understand users' attitudes towards new technology and predict their usage behavior. This theory has been widely applied across various high-tech fields, including the internet, smart manufacturing, and AIGC collaboration [21].  
The original framework of the Technology Acceptance Model (TAM) and its extended models are frequently used to understand users' acceptance of new technologies. They measure perceived usefulness, perceived ease of use, and usage intention and predict usage behavior. In the context of the artificial intelligence era, TAM's applicability is even broader. For instance, scholar [PERSON] used TAMs to understand users' acceptance of AR wearable devices, finding that perceived enjoyment and information richness are key factors influencing behavior [22]. Scholar [PERSON] applied TAMs to explore users' acceptance of autonomous vehicles, identifying social influence and initial trust as critical factors affecting technology use [23]. Scholar [PERSON] employed TAMs to investigate customers' purchase intentions for mobile application technologies, concluding that social factors and peer influence are crucial determinants of purchasing behavior [24].  
Overall, a TAM proves to be universally applicable across various domains. Additionally, extended TAMs have received broad recognition. For example, scholar [PERSON] combined TAMs with the Theory of Reasoned Action (TRA) to form the TRAM framework, exploring the role of technological readiness in individuals' intentions to use health products and fitness applications [25]. Scholar [PERSON] integrated a TAM with the Task-Technology Fit (TTF) model to create a new theoretical framework, examining students' satisfaction with learning social media [26]. Furthermore, scholar [PERSON] merged a TAM with the UTAUT2 model to measure users' attitudes towards mobile banking applications and the factors affecting behavioral change. These studies confirm that a TAM and its extended models are effective in understanding users' acceptance of various technologies [22]..
## 3 Results and Discussion
### Uses of Produced VFAs  
The produced VFAs are a valuable resource that can be utilized though several different processes. This includes the production of electricity through MFC, biodiesel, and methane through anaerobic digestion. The distribution and amount of VFAs produced per examined pH is expected to affect the optimum pH for each process. It should be noted that this is not a full technoeconomic analysis of each process. The operational cost can vary greatly per process and can lead to very different results.
# Spacing Allocation Method for Vehicular Platoon: A Cooperative Game Theory Approach
## 2 Literature Review
### Game Theory Application in Transportation Research  
In the field of transportation research, non-cooperative games are widely used to describe the interaction of the drivers [16]. However, it is keenly aware that the game model constructed is different from the actual situation due to the unpredicted human driver behaviors, which creates complexity and randomness in traffic flow models. With the development of autonomous driving technology in recent years, it seems feasible to consider the local motion controller of autonomous vehicles as a rational participant, and the game theory will play an important role in the analysis of the transportation behavior, especially the cooperative control of the vehicles which can be considered as agents [17]. In the control of automated vehicles, scholars have explored the application of non-cooperative game theory concepts to behavioral decision-making methods. For example, game theory is used to solve the security problem with the analysis of the information flow topology in the vehicle platoons, and a Nash equilibrium is adapted to select the appropriate security improvement strategy by [PERSON] et al. [18]; [PERSON] et al. combined optimal control with game theory to calculate the optimal spacing required to ensure safety, given the conflict between safety and fuel consumption in the platoon [19]; and a congestion game is proposed to explore the motivation to form a platoon by [PERSON] [20]. While these studies have concentrated on game theory in cooperative platoon control, others have concerned the conflict between vehicles while lane-changing. For instance, [PERSON] et al. proposed a novel game-theory-based model, using interactions with surrounding drivers based on turn signals and lateral movement to simulate the lane-changing behavior of human drivers [21]. Similarly, [PERSON] developed a game-theory-based algorithm for autonomous vehicles at uncontrolled intersections, with simulations demonstrating that the algorithm could reduce travel time and delay efficiently [22]. [PERSON] proposed a coordinated multi-vehicle strategy for mandatory lane changes that improved the average speed of the vehicles and driving stability in simulations [23], while [PERSON] developed a repeat-game-theoretical decision-making model with an updated payoff function for human drivers [24]. In addition to the non-cooperative games, cooperative games also have potential value in the study of vehicle platoons.  
Compared with non-cooperative games, cooperative games pay more attention to the method of the profit's allocation [25; 26]. After enough trainees, the result of the non-cooperative game will lead to the result of the cooperative game. We can reasonably speculate that the vehicles driving on the road are fully trained and rational participants. Therefore, the greatest economic benefits which will be assigned to all vehicles in platoon can be achieved through cooperation and competition.
# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 2 Two-Dimensional Human Pose Estimation
### Two-Dimensional Single-Person Pose Estimation  
For single-person scenarios, two-dimensional human pose estimation (2D HPE) commonly adopts either regression-based or heatmap-based approaches. Regression-based approaches formulate keypoint localization as an end-to-end regression task that maps image features directly to absolute coordinates or relative offsets of body joints. In contrast, heatmap-based methods cast keypoint detection as a probabilistic density estimation task, in  
Figure 1: Taxonomy of two-dimensional human pose estimation methods.  
which each joint is represented by a heatmap peaking at its most probable location. Figure 2 illustrates the workflows of (a) regression-based and (b) heatmap-based approaches.  
Each approach has distinct advantages and limitations, making them suitable for different application scenarios. Regression-based methods predict continuous joint coordinates directly, avoiding quantization errors and offering high computational efficiency, making them particularly well-suited for real-time applications on mobile or embedded systems. Regression-based methods have gained attention for their lightweight design and end-to-end architecture. However, these methods often lack the capacity to model spatial structural priors, making them susceptible to keypoint drift or loss in the presence of occlusion or complex poses. Moreover, the lack of pixel-level supervision generally leads to lower localization accuracy compared to heatmap-based approaches.  
In contrast, heatmap-based methods localize joints by generating pixel-level probability maps, which more effectively capture spatial dependencies and exhibit greater robustness to occlusion. However, these methods require high-resolution heatmaps, which significantly increase computational costs. Moreover, the discretization of joint locations on heatmaps can introduce quantization errors. Although heatmap-based methods achieve high spatial precision, their performance deteriorates significantly under partial or complete occlusion. This limitation stems from their strong dependence on local image textures and limited capacity to model global contextual information, which is especially problematic in crowded multi-person scenes. Despite these drawbacks, heatmap-based approaches remain the most widely adopted strategy in 2D human pose estimation. Representative methods from both paradigms will be discussed in the subsequent sections.  
Figure 2: Methods for 2D single-person pose estimation: (a) regression-based approach and (b) heatmap-based approach.
## 5 Flexural Capacity and the Load-Displacement Relationship
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Specimens**} & **Loading of First Crack** & **Specimen’s** & **Crack Widths at the** & \multirow{2}{*}{**Mode of Failure**} \\  & **Occurrence (kN)** & **Failure Load (kN)** & **Peak Load (mm)** & \\ \hline
0 SF-0 RCA-WTD & 20.0 & 70.0 & 12.0 & Shear \\
0 SF-0 RCA-2 DTD & 20.2 & 80.1 & 12.0 & Shear \\
0 SF-0 RCA-1 DTD & 20.6 & 90.5 & 5.0 & Flexural \\
0 SF-50 RCA-WTD & 20.1 & 70.2 & 7.0 & Shear \\
0 SF-50 RCA-2 DTD & 20.2 & 90.1 & 6.5 & Shear \\
0 SF-50 RCA-1 DTD & 20.6 & 90.7 & 2.0 & Flexural \\
0 SF-100 RCA-WTD & 20.1 & 80.0 & 11.0 & Shear \\
0 SF-100 RCA-2 DTD & 30.4 & 90.0 & 4.0 & Flexural \\
0 SF-100 RCA-1 DTD & 20.8 & 90.5 & 3.0 & Flexural \\
1 SF-0 RCA-WTD & 20.2 & 80.1 & 8.0 & Shear \\
1 SF-0 RCA-2 DTD & 20.8 & 100.0 & 9.0 & Flexural \\
1 SF-0 RCA-1 DTD & 30.2 & 100.6 & 3.5 & Flexural \\
1 SF-50 RCA-WTD & 10.1 & 80.2 & 5.0 & Shear \\
1 SF-50 RCA-2 DTD & 20.0 & 90.6 & 3.0 & Flexural \\
1 SF-50 RCA-1 DTD & 20.6 & 95.4 & 2.5 & Flexural \\
1 SF-100 RCA-WTD & 20.0 & 80.1 & 9.0 & Shear \\
1 SF-100 RCA-2 DTD & 20.6 & 100.2 & 3.5 & Flexural \\
1 SF-100 RCA-1 DTD & 40.4 & 100.9 & 3.2 & Flexural \\
2 SF-0 RCA-WTD & 20.2 & 80.0 & 3.6 & Shear \\
2 SF-0 RCA-2 DTD & 40.4 & 90.0 & 3.4 & Shear \\
2 SF-0 RCA-1 DTD & 40.8 & 100.5 & 3.5 & Flexural \\
2 SF-50 RCA-WTD & 20.0 & 90.1 & 4.0 & Shear \\
2 SF-50 RCA-2 DTD & 20.1 & 90.7 & 3.5 & Flexural \\
2 SF-50 RCA-1 DTD & 20.6 & 100.3 & 3.5 & Flexural \\
2 SF-100 RCA-WTD & 30.0 & 90.1 & 5.0 & Shear \\
2 SF-100 RCA-2 DTD & 40.2 & 100.5 & 3.5 & Shear \\
2 SF-100 RCA-1 DTD & 50.5 & 110.2 & 2.5 & Flexural \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cracking and load corresponding to crack occurrences. Therefore, the characteristics of these specimens are demonstrated in Table 6. Moreover, in order to provide an accurate analysis, the results were compared with those of the literature and are presented in Figure 21[59; 60]. The crack width increased under loading after increasing the shear rebars' spacing. Furthermore, using RCA resulted in lower crack widths and a greater flexural moment resistance when cracks occurred. Using SFs led to lower flexural crack widths and the flexural strength was also significantly increased after cracks appeared (Figure 21b,e). As seen in Figures 20c and 21f, the flexural crack widths were increased by increasing the tensile rebars' diameter in the RCARC beams. Furthermore, Figure 21a-d shows that the effect of longitudinal tensile rebars was more than the effect of the shear rebars on the crack widths. Therefore, providing enough shear rebars limited the crack propagation and the crack widths decreased. Furthermore, the crack at the peak load increased due to increasing the RCA contents. Additionally, cracks developed less by adding SFs due to the bridging role of the fibres. However, in specimens with no SFs, the crack propagation was considerably lower than that with SFs. Therefore, it could be observed that the cracks were more irregular in the SF-reinforced beams relative to those of the control specimens (with no SFs or RCA).
## 2 Data and Methods
### Data Source  
The research data are obtained through the Google Earth Engine (GEE) platform [[https://eartheengine.google.com/](https://eartheengine.google.com/)]([https://eartheengine.google.com/](https://eartheengine.google.com/)), accessed on 6 September 2022), which can provide satellite data products on a global scale. The GEE's powerful computing power is used to solve research questions on spatiotemporal large scales [38; 39; 40]. The research data mainly include NPP ([[https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD17A3](https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD17A3) HGF/]([https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD17A3](https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD17A3) HGF/), accessed on 6 September 2022), meteorological data (temperature, solar radiation precipitation, [[https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_HOURLY/](https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_HOURLY/)]([https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_HOURLY/](https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_HOURLY/)) (accessed on 21 October 2022), [[https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_DAILY#bands/](https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_DAILY#bands/)]([https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_DAILY#bands/](https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_DAILY#bands/)) (accessed on 30 October 2022)). We acquire NPP data through the MYD17A3 HGF V6 product, with a spatial resolution of 500 m and a temporal resolution of 8 days. The maximum value compos  
Figure 1: The Yangtze River Basin.  
ites (MVCs) technique derives annual maximum NPP data. Precipitation data are from CHIRPS Daily Version 2.0 products, with a temporal resolution of daily and a spatial resolution of 0.05\({}^{\circ}\), capturing annual total precipitation. Temperature and solar radiation data are from ERA5 Land Hourly data, with a spatial resolution of 0.1\({}^{\circ}\). We also calculate annual mean temperature values and annual total solar radiation. The digital elevation model (DEM) is derived from the SRTM digital elevation image product [[https://strm.csi.cgiar.org/strmdata/](https://strm.csi.cgiar.org/strmdata/)]([https://strm.csi.cgiar.org/strmdata/](https://strm.csi.cgiar.org/strmdata/)) (accessed on 6 September 2022) with a spatial resolution of 30 m.
## 4 Results
### Results of the Deterministic Approach  
The deterministic optimization was performed based on a fixed set of techno-economic parameters (Table 2 in Section 3). This approach provides a baseline scenario without considering uncertainty in the key input parameters. The results in Table 3 represent the optimal pit design under these deterministic conditions, showing the ore and waste tonnage, financial indicators, and overall feasibility of the project. Figure 6 shows the optimal pit shell obtained by the deterministic approach.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Key Metric** & **Value** \\ \hline Ore tonnage & 142,338,255 t \\ Waste tonnage & 281,561,745 t \\ NPV @10\% discount rate & USD 130,751,000 \\ IRR & 17\% \\ Payback period & 3.84 years \\ \hline \hline \end{tabular}
\end{table}
Table 3: The numerical results of the deterministic optimization.  
In this case, the open-pit optimization produces 142.3 million tons of ore and 281.6 million tons of waste material, yielding a net present value (NPV) of USD 130.8 million at a 10% discount rate. The internal rate of return (IRR) is estimated to be 17%, and the investment payback period is 3.84 years.  
While this approach offers a foundation for cost-effectiveness analysis, it does not take into account the variability of key factors.
# Gamma-ray Bursts: 50 Years and Counting!
## 9 The Very High Energy Era (2019-Ongoing)
The observed emission, associated with the afterglow, has been explained as an emission from the inverse Compton scattering of synchrotron photons from high-energy electrons. The announcement of this unprecedented event triggered a renewed push in the theoretical research to understand VHE GRB emission. Moreover, the same year, 5 months later, the observation of the TeV afterglow of GRB180720B by the High Energy Stereoscopic System (H.E.S.S.) telescopes was announced [188]. Located in Namibia, H.E.S.S. is a system of five IACTs that are sensitive in the energy range of 0.03 to 100 TeV. It is currently the only VHE photon observatory in the Southern Hemisphere. The emission was also interpreted in this case as synchrotron photons accelerated by inverse Compton scattering with electrons (synchrotron self-Compton radiation, SSC). That same year, H.E.S.S. observed another TeV GRB, GRB190829A [189], bringing the total of such signals ever observed to three. The following year, the observations of GRB201015A [190], with a signal at the \(>\)3\(\sigma\) level, and GRB201216C [191; 192], with a \(>\)5\(\sigma\) level, were announced by the MAGIC collaboration. After an in-depth analysis of the data taken for GRB160821B, an extremely close short GRB with a redshift of \(z=0.162\), MAGIC unveiled \(\sim 3\sigma\) evidence pointing toward a gamma-ray signal exceeding approximately 0.5 TeV. The observations lasted approximately 4 h after the initial burst [193]. The presence of this signal poses a challenge to the most straightforward interpretation provided by one-zone models of the synchrotron self-Compton emission originating from the external direct shock. These models face difficulties in adequately explaining the reconstructed TeV flux associated with the observed gamma-ray signal. On 9 October 2022, the last (to date) GRB with a TeV emission was observed. The Large High Altitude Air Shower Observatory (LHAASO) [194, 195], the largest High Altitude Water Cherenkov Experiment in the world, located in China, reported the detection of the early onset of the afterglow of GRB221009A, with 64,000 photons (above \(\sim\)0.2 TeV) detected within the first 3000 s [196]. GRB221009A broke every record and remains to this day the Brightest (GRB) Of All Time (B.O.A.T.). This era can be summarized as follows:  
* Ground-based observations are needed to observe photons at TeV. There are two types of instruments, IACTs and EASs, with different performance and merits. * Despite some claims (AIROBICC and Milagrito), no detection of GRB at TeV energies occurred until GRB190114C. * To date, only five GRBs have been observed to have TeV emission with significance \(>5\sigma\), and two more have been observed with significance \(>3\sigma\). Despite enormous efforts, the detection of GRB emissions at TeV remains extremely difficult.
## 3 Results and Discussion
### Comparison of Hardness Profiles by Process  
Table 4 lists the hardness values of the RZ, PRZ, and TZ in the friction stir weldments and those from the laser weldments. In the case of the RZ of the DP590, DP780 steel laser welds, the hardness is higher than that of the friction stir welds because the cooling rate of laser welding is higher than that of FSW. On the other hand, in the case of high-strength steels, such as DP980, TRIP1180, MS1300, and HPF1500, the hardness values resulting from the FSW and laser welding processes are similar because of their high hardenability. Comparing the hardness of the PRZ and TZ in the case of FSW and laser welding, we find that softening hardly occurred in either process for DP590 and DP780. In the case of the DP980, TRIP1180, MS1300, and HPF1500 steels, the PRZ and TZ softened in both cases. However, the degree of softening for laser welding with a higher cooling rate was lower than that for FSW with a lower cooling rate.
# A Comprehensive Experimental Emulation For OTFS Waveform RF-Impairments
## 3 Channel Effects Furthermore, RF-Impairments
### Carrier Frequency Offset  
In wireless communication, the propagating electromagnetic wave interacts with many obstacles called scatters; consequently, it is scattered, reflected, or refracted along with the propagation path. Therefore, for \(L_{tap}\) different paths, \(L_{tap}\) waves propagate with different delays and attenuation factors (i.e., multipath components). If the transmitter, receiver, or scatters are moving, these multipath components will be scaled in time equivalently, causing a frequency shift (narrow-band) or frequency spreading (wide-band). Thus, CFO occurs due to either Doppler effects or carrier frequency mismatching between the TX and RX oscillators [47]. Therefore, we will discuss both CFO effects in our experiment.
# Securing Infrastructure-as-a-Service Public Clouds Using Security Onion
## 2 Background
### Cloud Computing
#### 2.2.5. Effects of Attacks on Cloud Resources  
Attacks on Cloud platforms may have one or more implications that further affect the delivery of Cloud services. This section discusses and analyses these implications.  
* **Account or Service Hijacking:** This type of attack can happen as a result of different aspects for example, social engineering of weak passwords/credentials. When an attacker gains access to these credentials they can carry out a variety of malicious acts, such as gaining access to sensitive data, manipulation of data and even redirecting transactions [19].
* **Malicious Manipulation of Customer Data:** The communication between the Cloud and the users is usually via the Internet through web browsers. This attack is usually carried out through the manipulation of data that are sent from the application component to the servers. Examples of these attacks are cross-site scripting (XSS), SQL injection (SQLi), direct object references, etc.
* **Denial of Service:** A malicious user may target a Cloud platform by occupying all possible resources [16]. Thereby making the system unable to satisfy other genuine users due to resources being unavailable.
* **Malicious VM Creation:** Here an attacker who creates an authentic account can create a VM image containing malicious codes, such as worms, viruses and Trojan horses, and store it in the CSP's repository [20].
* **Insecure VM Migration:** During the live migration of VMs logs of a VM's execution state that is being upheld due to a rollback implementation. These logs can become accessible and thiscan give room for attackers to access data illegally, transfer a VM to an untrusted host and cause disruption or DoS as a result of creating and migrating several VMs at the same time [21, 22].
* **Sniffing/Spoofing of Virtual Networks:** An attacker's VM can listen to network or even use ARP spoofing to re-address packets to and from other VMs [23].