esa-sceva/satcom-chunk-collection huggingface dataset:
# A Normalization Strategy for Weakly Supervised 3D Hand Pose Estimation
## 2 Related works
### Techniques for Addressing Limited Annotated 3D Pose Training Data  
Self-supervised learning has been used in various methods to address the challenge of annotating 3D pose data, achieving notable results [9; 17; 30; 31; 32]. For instance, a self-supervised module [17] is proposed that utilizes 2D spatial relationships and 3D geometric knowledge to overcome domain gaps in hand pose estimation and reduce the need for abundant 3D pose labeled data. In [PERSON] et al. [33], depth images are utilized for 3D pose estimation, which uses an image-to-image translation technology for pre-training and a dual-branch network for pixel-wise estimation in a decoupled way. Additionally, S2 HAND [32] was proposed to jointly estimate pose, shape, texture, and camera viewpoint. It utilizes 2D detected keypoints to obtain geometric cues from the input image and learns an accurate hand reconstruction model through the consistency between 2D and 3D representations.  
Weakly supervised learning is another widely used approach to enhance the generalization capability and reduce the reliance on expensive annotations in 3D pose estimation tasks [12; 14; 15; 34; 35; 36; 37]. For example, depth images obtained from commodity RGB-D cameras are utilized during training [35], thus reducing the necessity for 3D annotations. A method [15] is proposed to extract weak 3D information directly from 2D images without 3D pose supervision, which uses 2D pose annotations and perspective prior knowledge to generate relative depth labels, and a weakly-supervised pre-training strategy based on a 2D pose dataset to distinguish the depth relationship between two points in an image. In [PERSON] et al. [12], a set of losses is developed to constrain the prediction to lie within the range of biomechanically feasible 3D hand configurations and improve the accuracy in such a weakly supervised manner. Our work draws inspiration from their approach to extract biomechanical features. In addition to those weakly supervised techniques, a Multi-View Video-Based 3D Hand dataset, MuViHand [38], has been proposed. It uses synthetic images and provides a valuable resource for improving the generalization ability of pose estimation models.  
In addition to weakly supervised and self-learning techniques, many studies [10; 11; 12; 13; 39] have utilized multi-datasets to train models jointly. These studies focus on improving performance by exploring unconstrained monocular 3D hand pose estimation [10], developing novel compressed latent distribution representations [11; 39], or leveraging prior hand biomechanical knowledge [12]. Meanwhile, they utilize multi-dataset joint training to enhance the performance of the network. Although it is a straightforward approach, it has demonstrated efficacy in improving pose estimation performance.
## Appendix A Description of Relaxation in Time and Frequency
### Truncation Error in Small-\(\omega\) Expansion  
In this appendix, an upper bound for the truncation error of the small-\(\omega\) expansion (14) is derived, improving upon a weaker and unproven estimate in [2]. We consider the cosine transform, and write the Taylor expansion with Lagrange remainder as  
\[Q_{\beta}(\omega)=\sum_{k=0}^{n-1}Q_{\beta}^{(k)}(0)\frac{\omega^{k}}{k!}+Q_{ \beta}^{(n)}(\xi)\frac{\omega^{n}}{n!} \tag{85}\]  
with \(0\leq\xi\leq\omega\). Going back to the definitions (5) and (4), we have  
\[\left|Q_{\beta}^{(n)}(\xi)\right| = \left|\text{Re }F_{\beta}^{(n)}(\xi)\right|\] \[\leq \left|F_{\beta}^{(n)}(\xi)\right|\] \[= \left|\frac{\text{d}^{n}}{\text{d}\xi^{n}}\int_{0}^{\infty}\, \text{d}t\,\text{e}^{i\xi t}\text{e}^{-t^{\beta}}\right|\] \[= \left|\int_{0}^{\infty}\,\text{d}t\,(it)^{n}\text{e}^{i\xi t} \text{e}^{-t^{\beta}}\right|\] \[\leq \int_{0}^{\infty}\,\text{d}t\,\left|(it)^{n}\text{e}^{i\xi t} \text{e}^{-t^{\beta}}\right|\] \[= \int_{0}^{\infty}\,\text{d}t\,t^{n}\text{e}^{-t^{\beta}}\] \[= \left|F_{\beta}^{(n)}(0)\right|\]  
The coefficients \(Q_{\beta}^{(k)}(0)\) in Equation (85) are zero for odd \(k\). Therefore we are free to choose \(n\) even. In this case, the upper bound derived in Equation (86) equals \(|Q_{\beta}^{(n)}(0)|\) so that we have  
\[\left|Q_{\beta}^{(n)}(\xi)\frac{\omega^{n}}{n!}\right|\leq\left|Q_{\beta}^{(n) }(0)\frac{\omega^{n}}{n!}\right| \tag{87}\]  
In conclusion, the truncation error is not larger than the first neglected term. This proof can be easily transcribed for the sine transform \(V_{\beta}\), and for the primitive of \(Q_{\beta}\),  
\[P_{\beta}(\omega)=\sum_{k=0}^{n-1}Q_{\beta}^{(k)}(0)\frac{\omega^{k+1}}{(k+1)!}+Q_{\beta}^{(n)}(\xi)\frac{\omega^{n+1}}{(n+1)!} \tag{88}\]
## 2 Related Work
### Contention-Based MAC Protocols  
The authors in [15] proposed a contention-based MAC protocol that enhances the system performance of an underwater acoustic sensor network. The proposed MAC protocol employs a widely popular machine-learning technique using only Q-learning. The proposed protocol allows a sensor node that intelligently selects back-off slots and schedules data transmission in such a way that it minimizes collision without taking care of the vulnerabilities of the MAC. However, the authors in [15] admitted that the sensor nodes are not required to exchange scheduling information. This presupposes that the proposed protocol may include low complexity and high overload.  
In another development, the authors in [16] estimated that contention-based underwater MAC protocols could utilize total underwater channel bandwidth. However, the protocols do not provide any means of secure data channels in communication. In addition, two classifications of contention-based MAC protocols are handshaking and random-access MAC. Handshaking-based MAC protocols utilize sender and receiver nodes, and they can exchange control packets, before enabling data transmission, for channel reservation and collision avoidance. Thus, in [16], there is no secure processing of data transmission used in the network, which may lead to trustworthiness concerns.  
Random access-based MAC protocols, however, utilize probabilistic approximation in collision avoidance. The reason is that there is no prior coordination in the sender data transmission. This presupposes that trustworthiness provision is a design concern in the network.  
Therefore, handshaking-based MAC protocols are dominant over random access-based protocols. This assumption can be possible since the protocols incur collision avoidance and insecure channel data processing, which leads to energy inefficiency, and introduces latency/delay in their network.  
The authors in [17] proposed a handshake-based MAC protocol design analysis with delay variation in underwater acoustic networks. The proposed MAC protocol aims to identify an essential characteristic of two-scale delay variation based on field test results. Based on this, we performed a simulation to study the impact of delay variations in the MAC of the network. The results suggested a slot length adaptation scheme for handshake and slotting-based MAC protocols. This protocol, also known as slotted-FAMA, has an adaptive slot length estimated to achieve more throughput.  
The authors in [18] proposed a protocol based on slotted FAMA. This protocol used only adaptive slot duration to avoid packet collision, using a handshaking technique, without providing any secure method.  
The authors in [19] proposed a protocol based on cooperative authentication in underwater acoustic sensor network MAC protocol. This proposed protocol includes a new algorithm for message authentication in UWSN. However, the protocol behavior mimicked the channel without associated legitimate data transmission.
## 3 Results  
### Mineral and Elemental Composition of the Samples  
The occurrence of microbial processes in the samples requires biophilic elements, especially sulfur, phosphorus, carbon, and nitrogen.  
All elements required for cell growth were present in samples B-4, B-5, and K-1 (Table 2), with the highest levels of carbon and nitrogen found in the bentonite clay B-5 and kaolin clay K-1. The sample B5 had also the highest content of phosphorus and sulfur. In the samples B-1 and B-2, concentrations of nitrogen and phosphorus were below the detection limit. Iron was present in all samples, with its highest content in sample B-1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Sample** & **K** & **Na** & **Ca** & **Mg** & **Fe** & **S** & **C\({}^{*}\)** & **N** & **P** & **C/N** \\ \hline B-1 & 0.06 & 1.40 & 1.50 & 2.87 & 14.0 & 0.05 & 0.19 & 0 & 0 & 0 \\ B-2 & 0.05 & 1.07 & 1.45 & 3.33 & 3.2 & 0.05 & 0.14 & 0 & 0 & 0 \\ B-3 & 1.08 & 0.1 & 1.68 & 3.42 & 3.4 & +0.02 & 0.01 & 0 & 0.05 & 0 \\ B-4 & 0.71 & 0.91 & 2.05 & 2.73 & 2.4 & 0.04 & 0.34 & 0.01 & 0.1 & 34.2 \\ B-5 & 0.78 & 0.57 & 3.93 & 1.73 & 4.2 & 0.08 & 0.86 & 0.03 & 0.2 & 25.2 \\ K-1 & 1.01 & 0.68 & 1.54 & 2.87 & 3.4 & +0.02 & 0.41 & 0.03 & 0.1 & 13.7 \\ K-2 & 1.3 & 0.03 & 0.30 & 0.53 & 1.6 & +0.02 & 0.11 & 0 & 0 & 0 \\ \hline \end{tabular} \({}^{*}\)Organic carbon, measured by TOC analysis with HCl pretreatment.  
\end{table}
Table 2: Elements content in clay samples, mass %.  
In all clay minerals, Fe, K, and organic sediments containing C, N, S, and P were present as impurities. Sample B-5 had the highest content of organic matter; in sample K-1 its content was considerable compared to the other samples. The presence of potassium in the samples was due to mica and potassium feldspars. The X-ray amorphous phase in sample B-1 was probably associated with high content of iron oxides as mineral forms (e.g., ferrihydrite), which may explain the high iron content in this sample. Iron and potassium are known to play the most important role in microbial clay transformation, since microbial iron reduction initiates the processes resulting in disruption of the crystalline lattice of clay minerals, while potassium is among the most important biophilic elements. Thus, several groups may be identified based on the elemental and mineral composition of the samples: those with high content of organic matter (B-5, K-1), high content of iron (B-1, B-5), and high content of potassium (B-3, K-1, K-2); these samples showed high potential risks of intensification of microbial processes.
# The Quanta Image Sensor: Every Photon Counts
## 3 Imaging Characteristics
### Multi-Bit QIS  
To further improve flux capacity, the multi-bit QIS was proposed. In the multi-bit QIS, the readout result can result in \(2^{n}\) states, where \(n\) is the readout bit depth (a single-bit QIS is, in essence, a special case of a multi-bit QIS with \(n=1\)). For example, if \(n=2\) then 4 possible states can be considered, (1) no photoelectron, \(\mathbb{P}[0]=e^{-H}\) as before, now coded logically as \"00\"; (2) one photoelectron, \(\mathbb{P}[1]=He^{-H}\) now coded logically as \"01\"; (3) two photoelectrons, \(\mathbb{P}[2]=H^{2}e^{-H}/2\) coded logically as \"10\"; and (4) 3 or more photoelectrons, \(\mathbb{P}\left[k>2\right]=1-e^{-H}-He^{-H}-H^{2}e^{-H}/2\), coded logically as \"11\". During readout of the signal from the jet, each of these states must be discriminated, such as by using a 2b  
Figure 3: (**a**) Single-bit QIS bit density as a function of quanta exposure, in \(D\)-log(\(H\)) format. Sparse and over exposure regions are labelled [9]; (**b**) \(D\)-log(\(H\)) curve for photographic plates as reported by [PERSON] in 1890 [37].  
analog-to-digital converter (ADC). The multi-bit QIS has a \(FWC\) given by \(2^{n}-1\) and the flux capacity \(\phi_{wn}\) is increased to:  
\[\phi_{wn}=jf_{r}\left(2^{n}-1\right)/8\overline{7} \tag{4}\]  
The Hurter-Driffield response is modified by multi-bit QIS readout resulting in higher saturation signal, less non-linearity and less overexposure latitude [9; 14]. The expected number of electrons read out from a multi-bit QIS jot is given by \(<\)\(k\)\(>\) where:  
\[<k>=\sum_{k=0}^{FWC}k\cdot\mathbb{P}\left[k\right]+\sum_{k=FWC+1}^{\infty}FWC \cdot\mathbb{P}\left[k\right]\ =\ FWC\left[1-\sum_{k=0}^{FWC}\left(1-\frac{k}{FWC}\right)\cdot\mathbb{P} \left[k\right]\right] \tag{5}\]  
The variance in the number of electrons is:  
\[\sigma^{2}=<k^{2}>-\ <k>^{2} \tag{6}\]  
where \(<k^{2}>\) is given by:  
\[<k^{2}>=\sum_{k=0}^{FWC}k^{2}\cdot\mathbb{P}\left[k\right]+\sum_{k=FWC+1}^{ \infty}FWC^{2}\cdot\mathbb{P}\left[k\right]\ =\ FWC^{2}\left[1-\sum_{k=0}^{FWC}\left(1-\frac{k^{2}}{FWC^{2}}\right)\cdot \mathbb{P}\left[k\right]\right] \tag{7}\]  
The multi-bit signal summed over an ensemble of \(M\) jots is \(M<k>\), and the noise (standard deviation) is \(\sqrt{M\sigma^{2}}\).  
Consider an ensemble of 4096 jots. For a single-bit QIS, the maximum signal obtained by adding together the logical readout signal from each jot (0 or 1) is 4096. For the 2-bit QIS, the maximum signal is increased by three-fold (\(2^{n}-1\)) to 12,288 and the noise characteristic rolls off more steeply. The summed signal of an ensemble of 4096 multi-bit jobs is shown in Figure 4. The predicted signal and noise vs. exposure relationship for a single-bit QIS was first experimentally verified by [PERSON] et al. [29].
# Role of Forster Resonance Energy Transfer in Graphene-Based Nanomaterials for Sensing
## 3 Application of Graphene-Based Nanomaterials in Sensing
### \(H_{2}O_{2}\) Sensor  
Hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) is one of the reactive oxygen species (ROS) that plays many roles in biomedical processes, and its disorder causes central nervous system (CNS) and cancer diseases [61]. Therefore, [PERSON] et al. [62] fabricated a core-shell Ag@AuNP as a receptor, which was decorated with a red emissive GQDs donor via \(\pi\)-\(\pi\) stacking between the single-strand DNA modified Ag@AuNPs and GQDs (Figure 9). The absorption spectrum of Ag@AuNPs overlapped with the donor emission spectrum, which produced a strong FRET process, leading to fluorescence quenching. To evaluate the selectivity  
Figure 7: Proposed sensing mechanism of (**G5**) in the presence of Zr\({}^{4+}\) via FRET process. Reprinted from [58] with permission from the American Chemical Society.  
of the probe **G7**, it was treated with various interfering compounds. Upon increasing the concentration of H2O2 with **G7**, fluorescence was enhanced to 560 nm. For further confirmation, 1.0 mM of ROS scavengers was added to **G7**, the fluorescence recovery was predominantly successful; this confirmed that **G7** could detect H2O2, followed by a Fenton-like-reaction. The detection limit was observed to be 0.49 \(\upmu\)M. Moreover, probe **G7** was analysed against H2O2 detection in living cells.  
Figure 8: The plausible sensing mechanism of GQDs-APTES (**G6**) with TNT via the FRET process. Reprinted from [60] with permission from Elsevier.  
Figure 9: Schematic representation of the fabrication of Ag@AuNPs-DNA/GQDs (**G7**) and the H2O2 sensing mechanism in living cells. Reprinted from [62] with permission from Springer.
# Challenges and Implications of Virtual Reality in History Education: A Systematic Review
## 2 Methods
### Information Source and Search Strategy  
A bibliographic search was conducted in July 2024 using the Scopus (Elsevier) database. To develop the search string, unpublished studies were excluded to ensure that all selected references had undergone peer review. The search string used was \"Virtual Reality\" AND \"History\" in titles and abstracts, with no time restrictions, resulting in a broad selection of studies spanning over thirty years. Specifically, this search retrieved 2794 studies. No duplicate records were identified in the database survey output. No automation tools were employed in any stage of the process.  
Although this systematic review followed a rigorous protocol based on the PRISMA guidelines, one acknowledged limitation is the specificity of the search string used in the search equation. This strategy, designed to maximize the relevance of the selected studies, may have excluded valuable research employing alternative terms such as \"immersive learning\", \"heritage education\", or \"virtual reality in pedagogy\". Future reviews could benefit from expanding the search strategy to encompass a broader range of terminology, allowing for the inclusion of a wider diversity of approaches and experiences related to the educational use of Virtual Reality in historical contexts.
## 4 Conclusions  
This study evaluated the effects of the use of BOF slag as coarse aggregate replacement on the cooling behavior of hot mix asphalt mixtures. From the study test results, the following conclusions can be drawn:  
1. The optimum asphalt content of all asphalt mixtures (dense, SMA, and PA) would be decreased in accordance with the increased proportion of BOF slag. BOF slag has a lipophilic property that can absorb asphalt cement, thus reducing the demanded quantity of asphalt.
2. The stability value of asphalt mixture would be increased with the increased BOF slag substitution ratio. Since the coarse aggregate ratio is high in SMA and PA-graded, and BOF slag was substituted for the coarse aggregate, the stability value of asphalt mixture also increased.
3. The VMA value of asphalt mixture would be decreased with the increased BOF slag substitution ratio; the VMA value of PA-graded mixture is lower than those of the dense and SMA-graded ones. BOF slag has porous properties, and asphalt cement can easily permeate into the aggregate and result in the decreased VMA value. Thus, the VMA value can be used to examine whether or not the optimum amount of asphalt has deviated from the others.
4. In order to avoid breakage and degradation of aggregate during the mixing process, BOF slag replacement of 60% was the optimum substitution for the dense mixture. The replacement of 40% of BOF slag was the optimum substitution for SMA and PA mixtures.
5. After cooling the dense mixture for about 100 min, the temperature tended to be moderated, which showed that the cooling and heat dissipation was gradually completed. However, after cooling, the SMA and PA-graded mixtures for 120 min, the temperature tended to be moderated, which showed that SMA and PA-graded mixtures have better temperature preservation effectiveness.
6. The voids distribution of the dense mixture was not uniform, and the thermal energy temperature was inconsistent as well. The void distribution of SMA and PA-graded mixtures were more uniform than that of the dense mixture, maintaining the heat energy with a small heat dissipation rate in the cooling phase.
7. The temperatures increased as the BOF slag content increased at the same cooling time. The SMA and PA mixtures with higher BOF slag held the heat better than those containing less BOF slag. Mixtures containing BOF slag also required a longer time to lose heat and, thus, require more time for the pavement to open to traffic.
8. The initial temperature of the SMA and PA mixtures with BOF slag was higher than that of the dense mixture. The SMA specimen coarse aggregate particles form a stone-to-stone contact skeleton that transfers thermal energy and increases all of the temperature zones. Asphalt mixtures have higher porosity and contain higher amounts of coarse aggregate, decreasing the thermal conductivity and resulting in a higher temperature.  
This study is endorsed by the year 2015 Special Research Program of the Ministry of Science and Technology (MOST 104-2221-E-366-007), Taiwan.  
The author declares no conflict of interest.
## 2 Materials and Methods
### Instruments  
#### 2.2.1 Optitrack Motion Capture System  
The motion capture system (Optitrack motive, Corvallis, OR, USA) was set up with six _Optitrack Flex 3_ cameras (Optitrack motive, Corvallis, OR, USA), which allow markers to be tracked in 3D. The marker is placed between the L4 and L5 lumbar vertebrae of the subjects, near the center of mass. The marker consists of a reflective sphere capable of reflecting infrared light in the same direction in which it is received. The light is emitted by the 26 light-emitting diodes (LED) surrounding the camera lens (IR 850 nm). All six cameras worked at 100 Hz, with a shutter speed of 20 us, achieving an overall resolution of 0.001 m. The height of the VJ was determined by the difference in the position on the vertical axis of the marker before the execution of the jump, during the resting phase \(H_{2}\), and at the highest point recorded in the flight phase \(H_{1}\) (Figure 1).  
The signal was sent via a USB cable connection to a computer where it was collected and interpreted using the _Motive Tracker 2_ software. This software synchronizes and calibrates the image collection systems, as well as exports the data obtained in the CSV format for subsequent analysis in a spreadsheet to determine the JH through the difference in positions (Figure 1).  
Figure 1: Determination of jump height from the data obtained by the MoCAP system by the difference between the initial height (\(H_{2}\)) and the highest point registered (\(H_{1}\)).
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 5 The Cross-Layer Design Method
### Intelligent Algorithm-Based Methods
#### 5.2.3 Genetic Algorithm-Based Approaches  
The genetic algorithm, proposed by [PERSON] in 1975 [18], is motivated by biological evolution theory. For the process of biological evolution in the natural world, living things can adapt to the changing environment according to natural selection mutation and genetic evolution. Based on this, researchers use biological evolution to simulate the searching and optimization process. In genetic algorithms, the points in the search space are utilized to represent the point, the object functions represent the ability of biological individuals to adapt to the environment, and the process of good solution replacing bad solutions in optimization and searching represents the process of survival of the fittest. The algorithm can select individuals based on a fitness value in search space and use the biological evolution theory to generate a new population which represents the new searching solutions. This process is similar to biological evolution, in which the offspring populations are much better than the previous generation in adapting to their environment. This property can be used in other research areas to find approximate optimum solutions.  
As proposed in the genetic algorithm inspired routing protocol (GAOUP) [84], the genetic algorithm and simulated annealing are used to enhance the network performance. The GAOUP has great advantages in development time; moreover, the systems are robust and relatively insensitive to noise. In [85], to increase the stable period in cluster-based routing protocols, the evolutionary-based clustered routing protocol (ERP) uses a population of individuals that evolve toward maintaining an optimal number of clusters. Binary tournament selection is used in this algorithm; the selection process chooses the candidate individuals from the population in the current generation based on their fitness values. Even though the ERP can prolong the network lifetime and reduce the energy consumption, but stability awareness performance is poor. The protocol introduced in [86] is used to solve the shortest path routing problem. The algorithm uses the genetic algorithm to select the best route from a set of random routes based on the fitness values of each route. In [87], the authors proposed a multi-constrained multicast routing protocol based on a genetic algorithm which determines near-optimal multicast routes on demand. Based on the multi-constrained genetic algorithm approach, the proposed routing protocol can optimize multiple QoS parameters simultaneously.