esa-sceva/satcom-chunk-collection huggingface dataset:
# AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features
## 3 Proposed Method
### Depth-Sensitive Feature Enhancement
#### 3.2.2 Extracting Foundational Depth-Sensitive Features  
In the Auxiliary Depth Feature (ADF) module, foundational depth-sensitive features are generated using an auxiliary depth estimation task modeled as a sequential classification problem. Given the input feature map \(F\in R^{\text{C}\times H\times W}\) from the Backbone, we  
Figure 4: Backbone network architecture and multi-scale feature extraction.  
apply two convolutional layers to predict the probability distribution of discretized depth bins \(D_{b}\in R^{D\times H\times W}\), where \(D_{b}\) denotes the number of depth categories. The predicted probability represents the confidence of each pixel belonging to a specific depth bin:  
\[P_{i,j,d}=\frac{exp\Big{(}z_{i,j,d}\Big{)}}{\sum_{k=1}^{D_{b}}exp\Big{(}z_{i,j,k }\Big{)}}. \tag{4}\]  
where \(z_{i,j,d}\) is the raw score for pixel \((i,j)\) in bin \(n\).  
To discretize continuous depth values into bins, we utilize Linear-Increasing Discretization (LID), which refines depth granularity for closer objects while allocating broader intervals for farther ones. \(D\) represents the total number of discretized depth bins, and \(i\) denotes the index of the corresponding depth bin. The depth binning strategy is defined as follows.  
The discretization can be formulated as follows:  
\[B_{i}=\left\{\begin{array}{ll}\frac{(i+1)^{2}}{D},&if\ i<\sqrt{D},\\ \frac{(i+1)}{D^{*}},&otherwise.\end{array}\right. \tag{5}\]  
By applying depth-wise separable convolutions, the intermediate feature map \(X\in R^{C\times H\times W}\) efficiently captures depth-sensitive features with reduced computational overhead. These features, further enhanced through attention mechanisms, provide a robust foundation for downstream 3D object detection tasks.
## 3 Hydrogen Production Technologies
### Background Concepts
#### 3.1.2 Oil and Naphtha Reforming  
Also known as catalytic reforming, this is a complex chemical process used to convert petroleum refinery naphthas (distilled from crude oil) into high-octane liquid refor  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Storage Technology** & **Compression** & **Liquefaction** & **Chemical** & **Ref.** \\ \hline
**Energy Density (LHV)** & & & & \\ Gravimetric (MJ/kg) & 120 & 120 & 18.60 & [27,34] \\ Volumetric (MJ/L) & 4.50 & 8.49 & 12.70 & [27,34] \\ \hline
**Transfer Mechanism** & & & & \\ Discharge method & Pressure release & Evaporation & & \\  & & & (T = 400 \({}^{\circ}\)C) & \\ Extraction energy & - & 0.91 & 30.60 & [27,41] \\ (kJ/mol-H\({}_{2}\)) & & & & \\ \hline
**Miscellaneous** & & & & \\  & Ambient temperature & & & \\  & operation; low cost; & Ambient pressure & & \\ Advantages & rapid (dis-)charge & & operation; lighter & temperature and & \\  & cycles at wide range & & cheaper storage tanks & pressure operation; \\  & of temperatures; & & & non-reactive \\  & simple operation & & & \\  & Very high pressures & High energy & & \\  & needed; large and heavy & consumption; very low & & \\ Disadvantages & storage tanks required; & temperatures needed; & & \\  & gas volatility; premature & severe insulation & & \\  & degradation of tanks & requirements & & \\ \hline \end{tabular}
\end{table}
Table 1: Cont.  
mates, which are stocks for gasoline [45]. The process converts linear hydrocarbons into branched alkanes and cyclic naphthenes, which are then partially dehydrogenated to produce high-octane aromatic hydrocarbons--and also significant amounts of hydrogen gas, as a byproduct.
## 2 Literature Review
(2021) developed a set of 15 AR applications for teaching Geography to first-year university students. The study included 95 students divided into two groups: control and experiment. The research revealed that AR reduces students' cognitive load levels while increasing their achievements and motivation, concluding that AR technology is beneficial also for higher education. However, according to [PERSON] et al. (2020), the efficiency of using AR in Geography needs to be studied further. They conducted an experiment including 64 students of the 10 th grade, divided into the control and the experimental group. During the experiment, the experimental group used an AR Geography application, and the control group continued with traditional textbook education. The findings revealed that there was an increase in the motivation and interaction of students using the AR technology, but there were no major variations in the learning outcomes, implying that the research should be expanded. Summarizing, incorporating AR technology into education can be advantageous in enhancing students' experience as well as improving motivation and knowledge. In addition, the use of new learning styles enriches the teaching and learning process in the domain of Geography, since abstract concepts and phenomena can now be visualized and perceived by students. Despite the benefits, AR in the specific domain of Geography has to be further examined to investigate whether it is beneficial or not.
# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 4 Random Errors
Lastly, the doppler centroid correlation (\(\gamma_{DC}\)) is influenced by the displacement of the Doppler centroids between two images [28; 32; 36; 38]. Coherence can be calculated as a product of all the aforementioned components, as shown in Equation (22) [30; 32]. A critical range of coherence between 0.15 and 0.2 was proposed to judge if useful phase information could be retrieved from an interferogram [39; 132]. When the coherence is above the critical range, phase information can be constructed, and the fringe pattern will become more recognizable and readable as the coherence increases. When the coherence lies within the range, it is possible to recover phase information but with higher risks of failure. It is likely not possible to retrieve phase information if the coherence value is below the range [39; 132]. \[\gamma=\gamma_{temp}\times\gamma_{B}\times\gamma_{volume}\times\gamma_{SNR} \times\gamma_{processing}\times\gamma_{DC} \tag{22}\]  
Decorrelation, or loss of coherence, is more often used to discuss the physical reason why correlation decreases. It is defined as \(\delta_{X}=1-\gamma_{X}\), where \(X\) is the subscripts of each coherence component, i.e., _temp_, \(B\), _volume_, _SNR_, _processing_, and _DC_[38]. An increased decorrelation value refers to an increased interferometric phase noise variance. The decorrelation sources are seen as the cause of random errors in the signal. The following paragraphs will elaborate on each decorrelation factor in more detail.
# Performance Analysis of Dual-Hop DF Multi-Relay FSO System with Adaptive Modulation
## 2 Description of the System  
The considered cooperative system consists of a source (S), a destination (D), and \(L\) DF relays, \(R_{l}\), \(l\in\{1,\cdots,L\}\) without a direct link between the source and destination. The source is equipped with \(L\) apertures and has the ability to control the transmission from them, and the relay decodes and retransmits the received signal to the destination. It is assumed that the channel gains of all hops follow \(\mathcal{F}\) distribution; then, the PDF of \(\mathcal{F}\) turbulence with pointing error impairment of individual hop can be expressed as [37]  
\[f_{l_{j},l}(I)=\frac{\xi_{j,l}^{2}}{\Gamma(a_{j,l})\,\Gamma(b_{j,l})}G_{2,2}^{ 2,1}\Bigg{[}\frac{a_{j,l}}{\left(b_{j,l}-1\right)A_{o_{j,l}}}\,I\,\Big{|}1-b_{ j,l},1+\xi_  
For SIM, the instantaneous SNR, \(\gamma_{j,l}\), of individual hop is given by \(\gamma_{j,l}=(\hat{\gamma}_{j,l}/A_{0j,l}^{2}h_{j,l}^{2})I_{j,l}^{2}\), where \(\hat{\gamma}_{j,l}\) is the average SNR (ASNR) and \(h_{j,l}=\xi_{j,l}^{2}/(\xi_{j,l}^{2}+1)\). Using the variable transformation, the PDF of \(\gamma_{j,l}\) can be written as  
\[f_{\gamma_{j,l}}(\gamma)=\frac{\xi_{j,l}^{2}}{2\gamma\,\Gamma(a_{j,l})\,\Gamma (b_{j,l})}\,G_{2,2}^{2,1}\Bigg{[}\frac{a_{j,l}h_{j,l}}{\left(b_{j,l}-1\right)} \left(\frac{\gamma}{\hat{\gamma}_{j,l}}\right)^{\frac{1}{2}}\Bigg{|}1-b_{j,l},1+\xi_{j,l}^{2}\Bigg{]}, \tag{2}\]  
and the CDF of \(\mathcal{F}\) turbulence with pointing errors can be obtained using Equation (2.24.2/2) in [38]. With some operations, it can be expressed as  
\[F_{\gamma_{j,l}}(\gamma)=\frac{\xi_{j,l}^{2}}{\Gamma(a_{j,l})\,\Gamma(b_{j,l}) }\,G_{3,3}^{2,2}\Bigg{[}\frac{a_{j,l}h_{j,l}}{\left(b_{j,l}-1\right)}\left( \frac{\gamma}{\hat{\gamma}_{j,l}}\right)^{\frac{1}{2}}\Bigg{|}1,1-b_{j,l},1+ \xi_{j,l}^{2}\Bigg{]}. \tag{3}\]  
The CDF can also be obtained using Equation (2.24.2/3) in [38], and represented as follows:  
\[F_{\gamma_{j,l}}(\gamma) =1-\overline{F}_{\gamma_{j,l}}(\gamma)\] \[=1-\frac{\xi_{j,l}^{2}}{\Gamma(a_{j,l})\,\Gamma(b_{j,l})}\,G_{3,3 }^{3,1}\Bigg{[}\frac{a_{j,l}h_{j,l}}{\left(b_{j,l}-1\right)}\left(\frac{\gamma }{\hat{\gamma}_{j,l}}\right)^{\frac{1}{2}}\Bigg{|}1-b_{j,l},1+\xi_{j,l}^{2} \Bigg{|}, \tag{4}\]  
where \(\overline{F}_{\gamma_{j,l}}(\cdot)\) is the complementary CDF of \(\gamma_{j,l}\).
## 4 Scheduling Policy
### Implementation of a Round-Based Mechanism  
Once a job is submitted to a GPU cluster, its position in the queue is based on its arrival time. Our scheduling policy periodically assigns resources to jobs within the queue, comprising both unfinished jobs from the prior round (i.e., the job is necessarily interrupted and then resumed) and newly submitted jobs.  
In every scheduling round, the scheduler sequentially allocates resources to jobs in the queue. It selects the GPU architecture and other allocation variables from a varied set of options for each specific job. Subsequently, resources are assigned to the next job in the queue until no resources remain available. In subsequent rounds, the allocation of jobs can change if a better or more optimal option is available.  
Given the results from the model-similarity-based scheduling policy, the scheduler allocates resources to a specific job, aiming to maximise the throughput of each job based on current cluster availability. The overall scheduling is described in Algorithm 2.  
However, training a DL model with a synchronous approach in preemptive scheduling can incur overhead when the training is interrupted [26; 27]. This overhead includes saving and loading the checkpoint. Saving the checkpoint involves storing the training results from the start of training until the interruption. Loading the checkpoint entails retrieving the previous training results from the checkpoint and resuming training from that point.  
The overhead varies depending on several factors, including communication overhead and the characteristics of the DL model. Concerning communication overhead, the overhead occurs when the saved checkpoint is loaded across compute nodes to resume training. On the other hand, overheads associated with the DL model involve factors such as the size of the neural network.  
Previous works have highlighted the significance of reducing these overheads to ensure the efficient implementation of a round-based mechanism. The work in [9] investigates the overhead of saving and loading checkpoints on distributed training. It concludes that this process typically takes approximately a few seconds each time and remains stable even as the number of GPUs used in training increases or exceeds the compute node. Additionally, the work in [8] demonstrates that overhead can be reduced by retaining the allocation from the previous round and allocating it to a job in the current round whenever possible. To implement our scheduling policy with a round-based mechanism, we use the framework provided by the work in [8].
## 3 Proposed MFA-AOA Algorithm
### Routing Using the Aquila Algorithm
#### 3.5.2 Fitness Function for Routing  
The fitness function uses three factors to determine the best path from \(CH\) to \(BS\). The first constraint determines the distance between \(CH\) and \(SN\), the second determines the distance between \(SN\)s and \(BS\), and the third determines the residual energy of \(SN\)s. Below are the derivations of these three parameters.  
(a) Distance between \(CH\) and Sensor nodes:  
\(P_{SCHSN}\) stands for a function that calculates the distance between the \(CH\) and its chosen SNs at random. Equation (33) contains the derivation of this function.  
\[P_{SCHSN}=\sum_{i=1}^{x}dist(CH_{i},SN(CH_{i}) \tag{33}\]where \(SN(CH_{i})\) represents sensor node of \(i\)th \(CH\).  
Distance between _SNs_ and _BS_:  
A function called \(P_{sSNBS}\) is used to calculate the separation between the _SNs_ and _BS_ that were chosen at random. It is energy-efficient to send data from \(CH\) to _BS_ over _SNs_ if the distance between those two devices is kept to a minimum. This function's derivation is provided in Equation (34).  
\[P_{sSNBS}=\sum_{i=1}^{x}dist(SN(CH_{i}),BS) \tag{34}\]  
Residual energy of _SNs_:  
The function \(RE_{SN}\) is used to determine the residual energy of the subsequent _SNs_. If _SNs_ have enough remaining energy, it would be advantageous to be selected as _SN_ by a _CH_. Using Equation (35), the residual energy of _SNs_ is computed as:  
\[RE_{SN}=\sum_{i=1}^{x}RE_{SN}(CH_{i}) \tag{35}\]  
As the overall fitness function \(AO_{routing}\) is a minimization problem, the calculation of \(P_{RESN}\), which reflects a minimization of \(RE_{SN}\), is done using Equation (36).  
\[P_{RESN}=\frac{1}{RE_{SN}} \tag{36}\]  
The concluding fitness function combining the parameter functions described in Equations (33)-(35) is regarded as a minimization problem and is determined using Equation (37), which is also a minimization problem.  
\[\text{Minimize }AO_{routing}=\frac{1}{2}[P_{sCHSN}+P_{sSNBS}+P_{RESN}] \tag{37}\]
# An Overview of Quantum Machine Learning Research in China
## 5 Challenges and Prospects
### Prospects
#### 5.2.3 Application Popularity  
At present, most of the datasets used in QML research are classical ones on which the supremacy of quantum computing cannot be really achieved, as indicated by [PERSON] et al. in [119]. Therefore, for the current era in which quantum datasets are still very scarce [120; 121], most quantum algorithms can only serve as alternatives to mainstream classical algorithms. If we enter the quantum information era in the future, the various detection and sensing techniques might directly acquire, store, and transmit mass data in quantum form. At that time, quantum algorithms will have great potential to replace classical ones and own much broader application popularity.
## 4 System Analysis
### B. Data collection cost and effective sink-proxies for the data query.  
In the many-to-one model of S2 data collection, the energy cost can be approximately given by the sum cost of the replied data unicast from each sensor node to the sink. Note \(\alpha\) is the ratio of sensor nodes that will reply a query message, and \(\alpha*N\) is the node number of sensor nodes corresponding to the query of S3. Let \(\overline{R}_{0}\) be the average route length from each corresponding sensor node to the sink. Thus, the energy cost of S2 data collection, denoted by \(E_{C2}\), can be written as:  
\[E_{C2}=\alpha*N*E_{0}*\overline{R}_{0} \tag{6}\]  
In S3 information retrieval, meanwhile, the data collection cost is the sum cost of local data collection and delivery of the aggregated data. Given \(\overline{R}_{a}\)as the average route length from each corresponding sensor node to the sink proxy, let \(\beta\) be the ratio of aggregated data size to the un-aggregated data size. Thus, the data collection cost in S3 information retrieval \(E_{C3}\) can be denoted by:  
\[E_{C3}=\alpha*N*E_{0}*\overline{R}_{a}+\beta*(\alpha*N)*R_{u}*E_{0} \tag{7}\]  
Consequently, the ratio of data-collection cost of S3 to S2 is computed as:  
\[R_{C}=\frac{E_{C3}}{E_{C2}}=\frac{\alpha*N*E_{0}*\overline{R}_{a}+\beta*( \alpha*N)*R_{u}*E_{0}}{\alpha*N*E_{0}*\overline{R}_{0}}=\frac{\overline{R}_{a }+\beta*R_{u}}{\overline{R}_{0}} \tag{8}\]  
In data collection operations, adopting an appropriate sink proxy consumes no larger energy than the conventional S2 approach. That is:  
\[R_{C}\leq 1 \tag{9}\]  
Figure 4: Selection of effective sink proxy for data query.  
when \(R_{C}\leq 1\) ( \(R_{0}>\overline{R}_{a}+\beta*R_{u}\) ), there is energy saving in S3 data collection compared with S2 data collection.  
This result indicates that the smaller \(\beta\) is, the smaller the relative cost of S3 becomes. The use of a sink proxy enables maximum aggregation of small sensor data and the smallest \(\beta\), since all data are collected at the sink proxy and are aggregated before being transmitting to the sink.  
\(\overline{R}_{0}\), \(R_{u}\) and \(\overline{R}_{a}\) can be calculated at the sink node based on the GPSR route discovery protocol that is described in [36], given the location of the sink proxy and resolved nodes corresponding to the query. \(\overline{R}_{0}\) is a parameter that depends on the position of the corresponding sensor when using the shortest path routing. The values of \(R_{u}\) and \(R_{u}\) depend on the location of the sink proxy. As a result, the ratio of data-collection cost of S3 to S2 depends on the aggregation ratio and the position of sink proxy.
## 3 Results and Discussion
### Experiment Setup
#### 3.3.6 Audio Preprocessing  
Most of the studies reviewed did not mention any preprocessing steps for the audio analysis; thus, it is not clear how background noise was processed and/or removed, or if downsampling was applied. Nevertheless, five manuscripts mentioned using a filtering step to remove noise, as well as high frequencies in the audio files. Different filters were used, including a Butterworth, Chebyshev, elliptic, and a Bessel filter [82]. In [68], a 10 th-order Butterworth band-pass filter with edge frequencies set at 50 Hz and 1500 Hz was used to remove background noise (e.g., bird sounds, workers' voices, and car noises). The work in [28], in turn, relied on a seventh-order Butterworth low-pass filter with a cut-off frequency at 4 kHz for feature extraction for infestation detection. In [80], the goal was bee type identification, and a 12 kHz low-pass filter was applied to eliminate only very-high-frequency sounds, such as machinery. All of these studies, however, did not report results before (i.e., with raw signals) preprocessing, thus it is not clear how much advantage resulted from this signal processing step or if removing high frequencies could be detrimental to some other classification tasks not explored in the related papers. Nonetheless, the gains achieved in storage requirements and real-time processing capability could be well worth the downsampling schemes.
## 2 Ospec Performance Verification  
### Short-Term Performance Verification--Prestress-Force Management Status and Ospec  
A stable long-term measurement method to overcome the disadvantages listed above is therefore required to manage the prestress force of a PC structure. It is desirable to avoid attaching the sensor to the outside of the strand for the protection of the sensor from external factors and to use a sensor that is easy to install and to manage the connection line. This study was conducted using a PC strand with an embedded FBG sensor developed by the Korea Institute of Civil Engineering and Building Technology (optical sensor PC strand; Ospec). The Ospec strand was manufactured by producing a core wire embedded in carbon-fiber-reinforced polymer (CFRP) containing an optical fiber with FBG that could perform measurements and replacing the core wire of a normal stand with the produced core wire. The sensor was located inside the core wire and moved together with the strand to provide a high level of accuracy while exhibiting excellent durability, owing to the use of the optical fiber. Because the FBG sensor could be deployed in several places in the core wire, it was possible to monitor the prestress force on each part inside the strand [16]. Figure 1 shows the appearance of the developed Ospec strand and the tensile test results.  
The maximum tensile strength of the Ospec strand was about 2200 MPa, and the yield strength was also higher than that of a normal PC strand. Nevertheless, the modulus of elasticity was about 195 GPa, which was lower than the tensile strength of 200 GPa of a normal PC strand. Although the reliability of the prestress force measurement of the Ospec strand was verified by [PERSON] et al., it was a study that aimed to determine the reliability of short-term prestress force measurement, with no verification of the long-term performance[PERSON] Therefore[PERSON] in this study, strand specimens were produced for the creep and relaxation tests conducted inside a laboratory equipped with a constant temperature and humidity  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bridge** & **Year** & **Patterns** & **Photo of Destruction** \\ \hline Mid bay bridge [4] & 2000 & [PERSON]begin{tabular}{c} Corrosion of \\ prestressed members \\ and anchorages \\ \end{tabular} & \\ \hline Jeongneungcheon & 2016 & \
\begin{tabular}{c} Corrosion and \\ fracture of \\ prestressed members \\ \end{tabular} & \\ \hline Morandi bridge [6] & 2018 &
\begin{tabular}{c} Bridge collapse due \\ to the corrosion of \\ prestressed members \\ and anchorages \\ \end{tabular} & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Examples of prestressed member damage and bridge collapse.  
function that could meet the conditions of constant temperature and humidity. After the long-term performance of the OSPC strand was verified, it was installed in a concrete beam specimen and introduced with the prestress force equivalent to the design prestress force to examine the change in prestress force over a long period of time while being exposed to external environments with varying temperatures and humidities.