esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Ris-DUGL Methodology
### Feature Extraction Using Deep CNN  
Convolutional Neural Network (CNN) is an Artificial Neural Network (ANN) that uses convolutional operations and has fewer connections. CNNs consists of convolution layer, pooling (sub-sampling) layers, and fully connected layer followed by the output layer, as shown in Figure 8. Each convolutional layer gets random parameters initialized with the filter or neuron values. It may get starting values also from the pre-trained model [44]. There exist various architectural variations in deep convolutional neural networks, enhancing their capabilities. Generally, a max-pooling layer is a part of CNN  
Figure 7: Architecture of deep-sparse auto-encoder (SAE).  
architecture, as it summarizes the outputs of neighboring groups of neurons in the same kernel map.  
In the proposed work, transfer learning is employed to extract the final features using a recent variant of CNN architecture, namely, VGG-16 [31]. Figure 9 shows the architecture of VGG-16, which consists of sixteen layers, including convolutional pooling layers, fully connected layers, followed by the output layer. In the proposed technique, a pre-trained model on ImageNet [45] is used, which consists of one thousand classes of images with one million images per class. It saves us from the difficulty to train VGG-16 from scratch. The fully connected layer is discarded to extract all features directly from the convolutional feature maps' activation before the last layer. This step feeds a three-dimensional matrix (224 \(\times\) 224 \(\times\) 3) and computes a 4096-D vector on each image. An important step is to apply an activation function. We used one of the most powerful differential functions, namely, the rectified linear unit (ReLU), which thresholds the values at zero. The need to do this step because each layer's activation is also a threshold during the training of the network on ImageNet.  
Another successful variant of CNN is Alex-Net [44], which is an eight-layer deep architecture, as shown in Figure 10. It is most widely used to perform feature extraction in computer vision-related applications. In this work, the exploitation and identification of the proposed methodology's learning ability and competence are carried out. The mechanism behind the feature extraction using Alex-Net is the same as mentioned for VGG-16. By using multiple deep learning models, this research highlights the generalization and flexibility of the proposed reverse image search system, i.e., we do not constrain the model. An important concept in the deep learning technique is to adopt already trained models for improving performance-related tasks, usually referred to as transferring knowledge of similar models, known as Transfer Learning (TL).  
Figure 8: Overview of CNN architecture.  
Figure 9: VGG-16 architecture.
## 3 Results
### Performance Data  
The results for speed skating (forward, backward) show that the forwards performed slightly (but not significantly) faster than the defenders in four of the six parameters (Table 2). Regarding backward sprinting, a typical defensive action, defenders showed a markedly higher performance level than forwards. These were also the largest differences over all parameters (10 m: d = 0.57; 30 m: d = 0.74). The agility performance tests (Table 3) displayed the largest differences between forwards and defenders for weave agility with the puck (\(p\) = 0.014; \(\mathrm{\eta_{p}}^{2}\) = 0.043; d = 0.46). Both position groups moved on almost the same performance level for the transition parameters. Shot performance data (Tables 4 and 5) displayed small advantages for the defenders. A total of 79% (11/14) of all maxima were found for defenders. The largest difference (d = 0.41) between positions was calculated for shot accuracy with preload (after finishing the sprints and agility runs); defenders: 4.86 \(\pm\) 0.74 vs. forwards: 4.49 \(\pm\) 1.06, Table 5). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{1}{c}{**Defenders (\(n\) = 44)**} & **Forwards (\(n\) = 94)** & **Total (\(n\) = 138)** \\ \hline \multirow{4}{*}{10 m sprint without puck [s]} & P10 & 1.76 & 1.73 & 1.74 \\  & P25 & 1.82 & 1.79 & 1.80 \\ \cline{2-4}  & **MEDIAN** & 1.95 & 1.87 & 1.89 \\ \cline{2-4}  & P75 & 1.99 & 1.95 & 1.97 \\  & P90 & 2.05 & 2.02 & 2.03 \\ \hline \multirow{4}{*}{30 m sprint without puck [s]} & P10 & 4.24 & 4.19 & 4.20 \\  & P25 & 4.32 & 4.30 & 4.31 \\ \cline{2-4}  & **MEDIAN** & 4.49 & **4.41** & 4.44 \\ \cline{2-4}  & P75 & 4.57 & 4.56 & 4.56 \\  & P90 & 4.71 & 4.64 & 4.64 \\ \hline \multirow{4}{*}{10 m sprint with puck [s]} & P10 & 1.71 & 1.73 & 1.73 \\  & P25 & 1.79 & 1.77 & 1.78 \\ \cline{2-4}  & **MEDIAN** & 1.91 & **1.88** & 1.88 \\ \cline{2-4}  & P75 & 2.00 & 1.98 & 1.99 \\ \cline{2-4}  & P90 & 2.08 & 2.05 & 2.06 \\ \hline \multirow{4}{*}{30 m sprint with puck [s]} & P10 & 4.31 & 4.27 & 4.29 \\  & P25 & 4.47 & 4.37 & 4.40 \\ \cline{2-4}  & **MEDIAN** & 4.58 & **4.52** & 4.55 \\ \cline{2-4}  & P75 & 4.69 & 4.66 & 4.66 \\ \cline{2-4}  & P90 & 4.82 & 4.78 & 4.80 \\ \hline \multirow{4}{*}{10 m backward sprint without puck [s]} & P10 & 2.17 & 2.18 & 2.18 \\  & P25 & 2.23 & 2.28 & 2.24 \\ \cline{2-4}  & **MEDIAN** & **2.29** & 2.37 & 2.33 \\ \cline{2-4}  & P75 & 2.37 & 2.47 & 2.44 \\ \cline{2-4}  & P90 & 2.47 & 2.58 & 2.53 \\ \hline \multirow{4}{*}{30 m backward sprint without puck [s]} & P10 & 5.12 & 5.19 & 5.18 \\ \cline{2-4}  & P25 & 5.23 & 5.36 & 5.29 \\ \cline{2-4}  & **MEDIAN** & **5.35** & **5.54** & **5.48** \\ \cline{1-1} \cline{2-4}  & P75 & 5.49 & 5.76 & 5.66 \\ \cline{1-1} \cline{2-4}  & P90 & 5.74 & 5.95 & 5.85 \\ \hline \hline \end{tabular} P = percentile.  
\end{table} 
Table 2: Speed skating percentile by playing position. Performance peaks are highlighted in bold based on the 50 th percentile (P50 = MEDIAN).
# Uncertainty Propagation through a Point Model for Steady-State Two-Phase Pipe Flow
## 2 Materials and Methods
### Flow Model
#### 2.1.5 Interpolation by Reynolds Number  
Several dimensionless numbers \(g\) in the model are computed as \(g_{\mathrm{I}}\) for laminar cases and as \(g_{\mathrm{t}}\) for turbulent cases. By interpolation, we ensure continuity in \(g(\mathrm{Re}_{p})\), also in the transition from laminar to turbulent. Let the laminar region be \(\mathrm{Re}_{p}<a\), the transitional region be \(a<\mathrm{Re}_{p}<b\) and the turbulent region be \(b<\mathrm{Re}_{p}\). A natural interpolation is  
\[g(\mathrm{Re}_{p}) =wg_{\mathrm{I}}+(1-w)g_{\mathrm{t}} \tag{16a}\] \[w =\frac{b-\mathrm{Re}_{p}}{b-a}. \tag{16b}\]  
We chose \(a=1700\) and \(b=4000\) for the friction factors in Equation (11) except for \(f_{\mathrm{s}}\). For \(f_{\mathrm{s}}\) and the slopes in Equation (5), we use \(b=3000\) and \(a\) such that \(f_{\mathrm{I}}(\mathrm{Re}=a)=f_{\mathrm{t}}(\mathrm{Re}=a)\).  
However, using weights \(w\) will not produce a smooth function \(g\). In fact, the derivative of \(g\) with respect to \(\mathrm{Re}\) is discontinuous at \(a\) and \(b\). This far, we have outlined the model given by [PERSON] et al. (2012),  
Figure 2: Flowchart of the point model. First holdup is computed, then pressure drop.  
but we suggest replacing the weights by \(w^{\prime}=\sin^{2}(\pi w/2)\). These weights provide continuity in the derivative of \(g\). The change is demonstrated in Figure 3. The histograms in the left panel show the distribution of holdup estimates obtained by perturbation of a certain set of inputs with a Reynolds number close to 3000. Blue gives the holdup estimates using the original model with weights \(w\), while orange gives the estimates obtained using the new weights \(w^{\prime}\). The right panel shows \(C_{0}(\mathrm{Re})\) in the transition from laminar to turbulent Reynolds numbers. We prefer the modified model because the distribution with small changes in input is more straightened out. However, the distribution of pressure drop is nearly unchanged.
## 4 Conclusions  
It is challenging to handle occluded, textureless and discontinuous regions in stereo matching. In order to obtain better disparity maps in large textureless regions, this paper proposed a local stereo matching method using efficient combined matching cost measurement and adaptive shape guided filter. A matching cost computation method that combines enhanced gradient-based matching cost with improved census transform-based matching cost, which is more robust against exposure variations and illumination changes as well as textureless areas, is proposed. Besides, cross-based adaptive shape support region is constructed for each pixel using adaptively calculated color similarity threshold and an adaptive shape guided filter based on this cross-shaped support region is implemented to aggregate matching cost and thus improve the accuracy of disparity estimation for large low texture regions. Experiments were conducted using Middlebury benchmark dataset to validate the proposed methods. The experimental results indicate that our proposed stereo matching method can produce more accurate disparity maps for large low texture regions. The average percentage of bad pixels of our proposed method is about 9.40%, which is much lower compared with the original guided filter-based method.  
Conceptualization, H.L. and R.W.; methodology, H.L. and R.W.; software, R.W.; validation, H.L., R.W. and X.Z.; formal analysis, X.Z.; writing--original draft preparation, H.L. and R.W.; writing--review and editing, H.L., R.W., Y.X. and X.Z.; supervision, Y.X.; project administration, Y.X.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China, grant number 41962018. The authors would like to thank the anonymous reviewers for their valuable comments which helped to improve this paper. The authors declare no conflicts of interest.
## 3 Orbital Repeat Time of BDS Satellites  
Multipath is related to the geometric relationship among the receiver, reflection source, and satellite positions in a stable environment. It is necessary to consider the operating period of the BDS satellite to calculate the ORT using the ASF method. Moreover, the heterogeneous constellation of BDS has three orbit types, namely GEO, IGSO, and MEO, and the operating periods of various satellites need to be considered separately. Among the three types of orbits, the GEO and IGSO ORTs are approximately a sidereal day, and the MEO ORTs are approximately seven sidereal days. Therefore, it is necessary to calculate the time shift of the ORT for each satellite. As the orbit radius and the angular velocity correction are given by the broadcast ephemeris, the angular velocity of the satellite at that moment can be calculated as follows:  
\[\omega=\sqrt{GM}/a^{3/2}+\Delta\omega \tag{5}\]  
where \(\omega\) and \(\Delta\omega\) are the angular velocity of the satellite and the correction parameter of the angular velocity, respectively; \(\sqrt{GM}\) is the square root of the product of the gravitational constant and the mass of the earth, and its value is 1.996498 \(\times\) 10\({}^{7}\); and \(a\) is the square of the semimajor axis of the satellite orbit ellipse. Then, the operating period of the satellite can be obtained based on the angular velocity of the satellite. The time shifts of the GEO/IGSO and MEO satellite operating periods are:  
\[\Delta T_{\rm GEO/IGSO}=86,400-2\pi/\omega \tag{6}\]  
\[\Delta T_{\rm MEO}=7\times 86,400-2\pi/\omega \tag{7}\]  
This experiment was carried out based on the broadcast ephemeris from 3 July 2019, to 21 June 2020 (355 days) broadcast by the IGS Data Center of Wuhan University  
Figure 1: The framework of the ASF and SD-MHM construction and multipath mitigation.  
Figure 2 shows that the calculated time shift at a different time of each day fluctuates significantly, caused by tidal gravity. For IGSO, there is a significant time leap of approximately 50 s in the time shift of each satellite about every six months on average. Figure 3 delineates the time shifts of the MEO ORTs of BDS-2/BDS-3. Among them, the fluctuation of the MEO time shift of BDS-2 is complicated and has a certain trend. The fluctuation of the MEO time shift of BDS-3 is more regular, and it can maintain stable fluctuations around 1700 s.  
The BDS satellites of different orbit types have different time shifts. The time shifts of the same orbit type satellites are also different. Moreover, with the satellite operation time, there is a significant change for the time shift. Therefore, it is necessary to calculate the time shift of each satellite ORT while using the ASF in the observation-domain. Obviously, this will limit the application of ASF in real-time.  
Figure 3: The time shifts of BDS-2/BDS-3 MEO ORTs.  
Figure 2: The time shifts of GEO and IGSO ORTs.
## 2 Overview of Retrieval-Augmented Generation
### Retrieval
#### 2.1.2 Retrieval Sources  
Retrieval sources in RAG systems are diverse; they encompass structured, unstructured, and specialized data repositories for optimizing their generation process.  
Structured databases, like relational databases and knowledge graphs (e.g., SQL-based systems and Wikidata [29]) provide organized and logically grouped information. Complex queries are presented to well-structured relational data that are systematically determined to provide detailed functional reports. In contrast, unstructured text corpora are large collections of text, including academic papers (e.g., PubMed [30]), books, and news articles. They offer rich, diverse content across various domains. These corpora are essential for retrieving detailed, domain-specific information that can augment the depth and breadth of LLM responses. Additionally, domain-specific repositories--MEDLINE [31] for medical research and LexisNexis for legal documents--are tailored to different disciplines and provide crucial specialized knowledge to generate accurate, contextually appropriate responses in specialized domains. Furthermore, web content--including real-time data obtained via web scraping and search engines--give RAG systems access to the most current information available. This is very important for generating responses that require up-to-date knowledge or instantaneous context. Finally, pre-trained knowledge bases--including general-purpose repositories like Wikipedia [32] and datasets like Common Crawl [33]--offer broad, general knowledge applicable across a wide range of tasks. Specifically, RAG systems frequently use Wikipedia for its extensive factual, structured information, which is available in various versions that range from billion-token to trillion-token levels [34].  
Collectively, these retrieval sources equip RAG systems with relevant, current, and context-specific information. This in turn enables LLMs to generate accurate, reliable, and informed responses across diverse applications.
# Promising Cr-Doped ZnO Nanorods for Photocatalytic Degradation Facing Pollution
## 4 Photocatalytic Properties
### Change of Reaction Rate in the Presence and Absence of a Quenching Agent  
To clarify the major active species in the photodegradation process, we used the trapping experiments to investigate the change in the photodegradation rate. For this purpose, 10% of ethanol and benzoquinone were added separately into the reaction system to quench the OH\(\bullet\) radicals and O\({}_{2}^{\bullet-}\) superoxides, respectively. As shown in Figure 8, the addition of ethanol induced a slight reduction in the photodegradation (up to 20%), indicating that the photoexcited holes of the photocatalyst and the subsequent OH\(\bullet\) radicals are minor factors. However, a relatively large reduction in the dye photodegradation (up to 46%) in the presence of BQ agent demonstrated superoxide quenching and established that the O\({}_{2}^{\bullet-}\) superoxide is the main active species for the dye photodegradation in the presence of the ZnO-Cr\({}_{1}\) photocatalyst.  
The various entities composing the mixture undergo various reactions under UV-visible irradiation (Figure 9). These reactions can be summarized as follows:  
\[ZnO+h\
u\to ZnO\big{(}e^{-}+h^{+}\big{)} \tag{1}\]  
\[e^{-}+O_{2}\to O_{2}^{\bullet-} \tag{2}\]  
\[h^{+}+H_{2}O\to OH\bullet+H^{+} \tag{3}\]  
\[O_{2}^{\bullet-}/OH\bullet+\ Methyl\ Orange\to CO_{2}+H_{2}O \tag{4}\]  
Figure 8: Change in the reaction rate in the presence and absence of a quenching agent.  
Under visible light irradiation, it is anticipated that such levels appearing among those detected by PL could be the origin of the observed improvement in the photocatalytic activity of modified ZnO nanorods. Actually, the interstitial oxygen site O\({}_{1}\) is the dominant defect observed at 2.28 eV, and it could act as a deep donor with an energy level located at 2.28 eV, above the top of the valence band. Therefore, the energy position of O\({}_{1}\) is optimal for the generation of holes in the valence band under visible irradiation (less than 2.95 eV). The created holes can systematically help the photodegradation of RhB through the following reaction:  
\[\mathrm{O}_{1}^{+}+\mathrm{e}^{-}\stackrel{{\mathrm{hv}<295 eV}}{{ \rightarrow}}\mathrm{O}_{1}+\mathrm{h}^{+} \tag{5}\]  
\[\mathrm{H}_{2}\mathrm{O}+\mathrm{h}^{+}\rightarrow\mathrm{OH}^{\bullet}+ \mathrm{H}^{+} \tag{6}\]  
It is worth noting that a direct interaction between MO dye and light could transform MO to MO* excited. Thus, MO* excited can transfer electrons to the conduction band (or a surface state) of ZnO nanorods. Finally, two pathways are feasible: the injected electrons react with O\({}_{2}\) molecules adsorbed on the ZnO surface to yield O\({}_{2}^{\bullet-}\) radical anion, while h\({}^{+}\) in VB moves to the backside of the ZnO nanorods' surface and reacts with either H\({}_{2}\)O or OH\({}^{-}\) to produce an active species such as OH radicals; or mineralization of MO takes place through the reaction of the active species, which could be considered a very efficient way to degrade MO.
# A Two-Stage Optimization Model for Airport Stand Allocation and Ground Support Vehicle Scheduling
## 4 Modeling
### The Stand Allocation Problem Considering Turnaround Resource Constraints
#### 4.2.2 Constraints of the First-Stage Model  
The constraints of the first-stage model add vehicle constraints on top of the basic constraints of the stand allocation.  
\[\sum_{g\in G}x_{ig}=1,\forall i\in F \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Aircraft Types** & **C** & **D** & **E** \\ \hline Average taxi fuel consumption & & & \\ (kg/min) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average taxi fuel consumption for different aircraft types.  
\[\sum_{j\in F}y_{ijg}\leq x_{ig},\forall i\in F,\forall g\in G \tag{5}\]  
\[\sum_{i\in F}y_{ijg}\leq x_{ig},\forall j\in F,\forall g\in G \tag{6}\]  
\[S_{i}^{F}-M\times\left(1-x_{ig}\right)\leq S_{g}^{G},\forall i\in F,\forall g\in G \tag{7}\]  
\[Sd_{i}x_{ig}+\delta^{g}-Sa_{j}x_{ig}+M\times\left(y_{ijg}-1\right)\leq 0, \forall i,j\in F,\forall g\in G \tag{8}\]  
\[Sd_{i}x_{ig}+\delta^{g}-Sa_{j}x_{ig}+M\times y_{ijg}\geq 0,\forall i,j\in F, \forall g\in G \tag{9}\]  
\[\left|Sd_{j}-Sd_{i}\right|\geq\theta^{t}\times z_{ijt},\forall i,j\in F, \forall t\in T \tag{10}\]  
\[Sa_{i}+t_{rng}^{a}+T^{i}+t_{grn}^{d}\leq Sd_{i} \tag{11}\]  
Equation (4) guarantees that each flight has to be assigned to exactly one compatible stand. Equations (5) and (6) are introduced to ensure that exactly one flight is assigned immediately before a given flight \(j\), and only one flight is assigned immediately after a given flight \(i\). Equation (7) describes the size limitation between the stand and aircraft, which means that the size of the stand should be compatible with the size of the aircraft. Equations (8) and (9) guarantee that the interval between the arrival and departure of consecutive flights assigned to the same stand must be greater than or equal to the minimum safety interval for the stand. Equation (10) is the resource constraints; when different flights are assigned to the same stand serviced by a towing tractor, the planned departure time interval between two adjacent departing flights must be greater than the service interval of the towing tractor (including the minimum service time and the transfer time between stands. Equation (11) limits the time for the completion of ground support services for flights.
# Evaluating MPTCP Congestion Control Algorithms: Implications for Streaming in Open Internet
## 3 Results and Discussion
### Result Analysis
#### 3.1.2 Path Delay (SRRT)  
With respect to SRRT (on both paths), wVegas paired with Reno at the path level and OLIA with BBR performed the best, respectively. These combinations seem to offer the lowest latency and jitter, which are crucial for real-time applications. Conversely, the maximum SRRT was observed for BALIA paired with Cubic and BALIA paired with BBR. In addition, the associated occasional latency spikes can negatively impact the stream consistency, leading to a diminished user experience.  
Detailed insights into the SRRT performance can be obtained from Figure 3, where plots (a), (c), and (e) present the charts from one experiment run. Upon a closer examination, small fluctuations in latency are evident. For the first LTE connection, the transfer appears more stable, with insignificant jitter (about \(\pm\)10 ms). In contrast, for the second connection, the latency fluctuations are more pronounced, especially for BALIA and OLIA, with variations reaching up to \(\pm\)30 ms. However, these variations oscillate between two bounds, suggesting that they can be compensated for with appropriate buffer usage. It is also noteworthy that, barring certain network phenomena resulting in peak-like artifacts, all three SPTCP CC algorithms--Reno, Cubic, and BBR--perform similarly well concerning Path Delay.