esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Rationale of Deep Learning Implementation
### RNN with Gate Recurrent Unit (GRU)  
Similar to an LSTM cell, a GRU uses gating units that modulate the flow of information inside the unit. It has only two gate structures, the _reset gate_ and the _update gate_. The forget and input gates from the LSTM, are combined into a single update gate in the GRU cell that decides which information to forget and which to add.  
At time \(t\), let \(X_{t}=[x_{t}^{1},x_{t}^{2},\dots,x_{t}^{N}]\) be the input data and let \(H_{t}=[H_{t}^{1},H_{t}^{2},\dots,H_{t}^{K}]\) be the hidden units of the memory state of the GRU network (Figure 3). When the input vector \(X_{t}\) is provided to the cell, it is divided into three branches: (i) one towards the reset gate, (ii) another towards the update gate, and (iii) a final one towards the outputs. The reset gate \(r_{t}=\sigma(W_{r}X_{t}+U_{r}H_{t-1})\) is similar to the forget gate in LSTM. When \(r_{t}\) is close to 0, it allows the previously computed state to be forgotten. The update gate \(z_{t}=\sigma(W_{x}X_{t}+U_{x}H_{t-1})\) decides how much the unit updates its activation \(H_{t}=(1-z_{t})H_{t-1}+z_{t}\hat{H}_{t}\), which is the linear interpolation between previous activation \(H_{t-1}\) and candidate activation \(\hat{H}_{t}\). The procedure of taking a linear sum between the existing state and the newly computed state is similar to the LSTM unit [50].  
Figure 2: Architecture of LSTM [49].
## 2 Numerical Model and Calculation Scheme
### Governing Equation  
In the fluid domain, the continuity, momentum, and energy equations are expressed in Equations (1)-(3). The x- and y-axes are set as shown in Figure 3.  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot(\rho\textbf{U}\textbf{I})=0 \tag{1}\]  
\[\frac{\partial(\rho\textbf{U})}{\partial t}+\
abla\cdot(\rho\textbf{U}\textbf {U}\textbf{U})=-\
abla p+\
abla\cdot\tau+\textbf{S}_{M} \tag{2}\]  
\[\frac{\partial(\rho e)}{\partial t}+\
abla\cdot(\rho\textbf{U}e)=\
abla\cdot \left(\lambda_{f}\
abla T\right)-\
abla\cdot(p\textbf{U})+S_{E} \tag{3}\]  
Endothermic pyrolysis occurs when hydrocarbon fuels absorb specific amounts of heat. The mass, substance, and quantity of each species involved in these reactions depend on conservation laws. The component conservation equations are expressed as Equations (4) and (5).  
\[\frac{\partial(\rho Y_{i})}{\partial t}+\
abla\cdot(\rho Y_{i}u)=-\
abla\cdot( \rho Y_{i}u_{d,i})+S_{i} \tag{4}\]  
\[S_{i}=\omega_{i}\cdot M_{\omega,i} \tag{5}\]  
Figure 4: Sectional structure diagram of cooling channel.  
Figure 3: Model of the cooling channel developed from the real structure.  
The chemical reaction rate \(\omega_{i}\) of the species \(i\) can be obtained by the Arrhenius formula, as shown in Equation (6).  
\[\omega_{i}=A_{i}T\text{exp}(-E_{i}/(R_{u}T)) \tag{6}\]  
To ensure accuracy and computational efficiency, this study adopts the 18 species 24 steps model proposed by [PERSON] et al. [22] to describe the molecular reaction kinetics of n-decane. The pressure implicit with splitting of operators algorithm was utilized to solve the velocity-pressure coupling in the momentum equation. In the solid domain, the heat transferred to the wall is converted into an internal heat source, and the energy equation is expressed as Equation (7).  
\[\rho_{w}c_{pw}\frac{\partial T}{\partial t}=\
abla\cdot(\lambda_{w}\
abla T)+ \dot{\phi} \tag{7}\]  
By establishing a coupling condition at the interface between the solid domain and the fluid domain, the following equations can be derived as follows in Equations (8) and (9).  
\[q_{w,in}=q_{f} \tag{8}\]  
\[T_{w,in}=T_{f} \tag{9}\]  
To accurately capture the turbulent flow near the adjacent wall, the SST \(k\)-\(\omega\) turbulence model [23] was chosen. This model considers the Reynolds time-averaged equation and [PERSON]'s hypothesis [24]. The expressions for the SST \(k\)-\(\omega\) turbulence model are written in Equations (10) and (11).  
\[\frac{\partial\rho k}{\partial t}+\
abla\cdot(\rho k\mathbf{U})=\
abla\cdot\left[ (\mu_{l}+A_{k}\mu_{l})\
abla k\right]+P_{k}-\beta^{*}\rho k\omega \tag{10}\]  
\[\frac{\partial\rho\omega}{\partial t}+\
abla\cdot(\rho\omega\mathbf{U})=\
abla \cdot\left[(\mu_{l}+A_{\omega}\mu_{l})\
abla\omega\right]+\alpha\frac{\omega} {k}P_{k}-\beta\rho\omega^{2}+C_{D} \tag{11}\]
# Properties and Model of Pore-Scale Methane Displacing Water in Hydrate-Bearing Sediments
###### Abstract  
The flow characteristics of methane and water in sedimentary layers are important factors that affect the beneficial exploitation of marine hydrates. To study the influencing factors of methane drive-off water processes in porous media, we constructed nonhomogeneous geometric models using MATLAB 2020a random distribution functions. We developed a mathematical model of gas-water two-phase flow based on the Navier-Stokes equation. The gas-driven water processes in porous media were described using the level-set method and solved through the finite element method. We investigated the effects of the nonhomogeneous structure of pore media, wettability, and repulsion rate on gas-driven water channeling. The nonhomogeneity of the pore medium is the most critical factor influencing the flow. The size of the throat within the hydrophilic environment determines the level of difficulty of gas-driven water flow. In regions with a high concentration of narrow passages, the formation of extensive air-locked areas is more likely, leading to a decrease in the efficiency of the flow channel. In the gas-water drive process, water saturation changes over time according to a negative exponential function relationship. The more hydrophilic the pore medium, the more difficult the gas-phase drive becomes, and this correlation is particularly noticeable at higher drive rates. The significant pressure differentials caused by the high drive-off velocities lead to quicker methane breakthroughs. Instantaneous flow rates at narrow throats can be up to two orders of magnitude higher than average. Additionally, there is a susceptibility to vortex flow in the area where the throat connects to the orifice. The results of this study can enhance our understanding of gas-water two-phase flow in porous media and help commercialize the exploitation of clean energy in the deep ocean.  
hydrate bearing sediments; pore-scale; gas driving water; effective flow channel; level-set method +
and Engineering
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 4 Proposed Fast CU Size Selection Algorithm  
The proposed method utilises SVM models at two levels in the HEVC coding architecture. The SVM models at level 1(L-1) are generated and utilised before the encoding of the current CU depth level (See Figure 5). The SVM model at level 2 (L-2) is generated and used after the evaluation of the CU for the PU modes at the current CU depth level. The following section describes the data collection, feature selection, and model parameter computations carried out in the proposed algorithm on both these levels.  
Figure 4: An illustration of two SVM hyperplanes that are created using Weighted SVMs using noisy sample data. The hyperplane S1 corresponds to a case where a larger weight is applied to “split” samples. Similar, hyperplane S2 represent a case where larger weights are applied to “non-split” samples of the training set.
# Deep Learning-Based Bird's Nest Detection on Transmission Lines Using UAV Imagery
## 3 Automatic Detection Framework-Related Work  
### Annotation Boxes Clustering  
Faster RCNN uses nine default size anchor boxes for object detection. In this work, the statistical analysis of the size of annotation boxes reveals that the ground truth bounding box of the bird's nest dataset used in this paper are relatively small. In order to enhance the efficiency of bird's nest detection with different aspect ratios, we perform k-means clustering on bird's nest annotation boxes to find the sizes of the anchor boxes that are more suitable for the dataset. This is then used to design the anchors. Consequently, the convergence time of the model decreases, and the accuracy of bounding box coordinates is improved. When new images are added as input, the clustering method will automatically adjust the anchors according to the updated clustering result of the annotation boxes. This can adapt better to the characteristics of the dataset.  
The k-means clustering method [21] comprises 3 steps. First initializing the number of categories and cluster centers. Second, calculating the distance between each bounding box and all cluster centers and assigning the bounding box to the nearest cluster. The mean value of every cluster is updated after each assignment. Finally, repeating the two aforementioned steps until the mean position of each cluster becomes stagnant.  
Figure 3: Multi-scale anchors.  
In this work, we use 9 clusters for k-means clustering. The width and height of the ground truth bounding boxes are used as input features. The size distribution of bounding boxes in the bird's nest dataset after k-means clustering is presented in Figure 4. After clustering, the length and width of nine anchors are obtained. These are used as the basis for designing the anchors of the model in this paper.
## 2 Literature Review
### AI in Service Delivery  
Service delivery represents the area where AI adoption is most visible. Governments increasingly deploy AI-driven solutions, such as chatbots and virtual assistants on public websites, to enhance citizen interaction. These AI systems handle routine inquiries, guide users through services, and even process simple requests, greatly improving operational efficiency and user satisfaction [23]. Studies show that conversational AI can save between 40 and 50% of staff hours in administrative and analyst roles, with potential savings rising to 80% as AI systems learn and improve [23]. For example, virtual agents now assist citizens in filling out forms online, reporting issues, and accessing services around the clock.  
Furthermore, AI in service delivery enhances personalization and targeting by analyzing user data to tailor services to individual needs. In healthcare and education, AI provides personalized recommendations, such as suggesting health interventions or training programs. A study in India showed that AI in citizen services can increase satisfaction by proactively addressing needs and streamlining procedures [24]. In transportation and urban services, AI optimizes public transit routes, manages traffic with smart signals, and controls utilities like energy grids to improve service reliability, creating more responsive and data-driven service delivery systems.  
Despite these benefits, citizen acceptance remains crucial. A 2022 study found that citizens preferred AI solutions for routine services, viewing them as faster and more reliable [17]. However, for sensitive services involving personal counseling or high-stakes decisions, human officials were strongly preferred. Concerns about errors, lack of empathy, and privacy issues significantly influenced citizen trust. These findings suggest governments must carefully select where to deploy AI and communicate transparently to build public confidence.
## 2 Description of the HDRSF
### HDRSF Production Process  
The shaft furnace is pivotal equipment for producing hydrogen-based DRI [34]. The HDRSF model is shown in Figure 1. Generally, an HDRSF can be divided into three sections from top to bottom: the preheating section, the reduction section, and the cooling section. During normal operation, iron ore enters the shaft furnace from the top and descends due to gravity. The reduction reaction takes place in the reduction section, where the iron ore is exposed to endothermic heat from the preheating section, which raises it to the necessary temperature for reduction. Because there is no obvious boundary between the preheating section and the reduction section, they are collectively referred to as the reduction section for the convenience of modeling and calculation.  
Due to economic considerations, it is generally recommended to use the mixture of high-temperature H\({}_{2}\) and CO as the reducing gas in HDRSF processing. The heat brought in by the reducing gas is the main heat source of the shaft furnace. The high-temperature mixed reducing gases of H\({}_{2}\) and CO flow upward after entering from the middle of the shaft furnace, where there is reduction reaction with the iron ore inside to generate high-temperature DRI. The H\({}_{2}\)O and CO\({}_{2}\) gases generated by the reduction reaction and the unreacted surplus H\({}_{2}\) and CO gases are discharged from the top of the shaft furnace. To safeguard the shell of the vertical furnace in the cooling section and prevent the re-oxidation of high-temperature DRI after discharge, it is essential to cool the DRI using gases such as N\({}_{2}\) or H\({}_{2}\). The cooling gas enters from the lower part of the cooling section and is discharged from the upper part. After the DRI product is cooled to the tap temperature, it is discharged at the bottom of the shaft furnace.
## 3 Results and Discussion  
### FT-IR Spectroscopy  
At first, the composition of the studied impression materials was studied with FT-IR and TGA. FT-IR allowed for qualitative identification of the materials' components, while the TGA allowed for quantitative analysis of the organic (alginate) fraction within the formulations (Figure 4). As the alginate salts are a common ingredient present in all the formulations, both commercial and those prepared within this study, sodium alginate and potassium alginate were used as spectral standards for the sake of comparison and to identify the absorption bands associated with the functional groups of the alginate component (Figure 4A). From the compositions prepared within this study, only X3 was shown, as their spectra were virtually identical; it was compared with the commercial samples (Figure 4B). The FT-IR spectra for pure potassium and sodium alignates exhibited characteristic signals in the range of 3400-3200 cm\({}^{-1}\), which originated from the stretching vibrations of hydroxyl groups (OH) and carboxyl groups (-COOH) present in alginate molecules. The bands at 2933 cm\({}^{-1}\) can be attributed to stretching vibrations of C-H groups occurring in polysaccharides. In the range of 1428-1400 cm\({}^{-1}\), there are bands originating from C-OH deformation vibrations and symmetric O-C-O stretching vibrations. The signals located at 1025 cm\({}^{-1}\) correspond to the stretching vibrations of the C-O carboxyl group. The presence of vibrations of the carbonyl group (-C=O) confirms the signal at 1593 cm\({}^{-1}\). The signals at 1123 cm\({}^{-1}\) and 947 cm\({}^{-1}\) are assigned to the C-O stretching vibrations occurring in the pyranosyl ring. Commercial alginate samples contain other substances, such as calcium sulphates, elite, phosphates, oxides, and colouring and flavouring substances. FT-IR-ATR spectra show characteristic signals coming from silica, which is the main component of elite. The intense band at 1068 cm\({}^{-1}\) corresponds to asymmetric Si-O-Si vibrations, while 790 cm\({}^{-1}\) corresponds to symmetric stretching. Signals at 1620-1615 cm\({}^{-1}\) confirm the presence of vibrations of the carbonyl group (-C=O), while those at 1415-1405 cm\({}^{-1}\) correspond to C-OH deformation vibrations and symmetric O-C-O stretching vibrations. The range of 3400-3200 cm\({}^{-1}\) is assigned to the stretching vibrations of hydroxyl groups (OH). The Tropical IQ sample exhibits additional signals at wavelengths of 1416 cm\({}^{-1}\) and 874 cm\({}^{-1}\), which originate from the vibrations of inorganic carbonates [37]. The presence of these carbonates has been confirmed by TGA analysis, as described in Section 3.2.
## 4 Evaluation
### Performance Analysis for Various Workload Sizes  
We now consider the first experiment to analyze the performance of CSS for various sizes of workloads. Among the NPB benchmark workloads, the MG workload with the highest ratio of IO operations was run, and the execution time was measured. In the experiment, the non-_groups_ environment to which _groups_ were not applied and _groups_ environment in which CSS was internally executed were compared as baseline. The MG workload was subdivided into several classes based on size. Among all classes, A, B, C, and D were executed, and the execution time was compared. In order to generate some resource contention, two containers processing the MG workload were run simultaneously. Figure 6 shows the results of these experiments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(\lambda\) & 30 \\ \hline \(\mu\) & 2 \\ \hline Resource Management Interval & 5 \\ \hline Cleaning Interval & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 4: CSS parameters used in the experiment.  
As a result of the experiment, CSS in class A and class B took more execution time than in the non-_groups_ environment, and class C showed a similar execution time. However, class D showed a 3% performance improvement. Through these results, we observed that CSS only achieves performance improvement in an environment where a certain level of workload size is executed.
## 7 Discussion
### Geomorphological Map from PBC, OBC, Geology Map Comparison  
For seismic microzoning, this study aimed to find the best representative for V\({}_{\text{S30}}\). V\({}_{\text{S30}}\) varies based on the soil's properties. According to current practice, geological units are the base map, which is a limitation for the PBC and OBC geomorphological maps. There are two important points of  
Figure 9: V\({}_{\text{S30}}\) map based on the reclassified geomorphological map with NEHRP soil characteristic standards.  
discussion in this subsection: consideration of the PBC and OBC geomorphological map suitability and the ability of this map to become the proxy for V\({}_{\text{S30}}\) between geological and geomorphological units.  
In general, the PBC and OBC methods of classification in photogrammetry have been debated. Past studies have made many comparisons. [PERSON] et al. (2016) and [PERSON] et al. (2010) reported that OBC is suitable for high-resolution image classifications, while PBC is better for landcover classifications ([PERSON], 2011; [PERSON], 2011). As stated in previous sections, this study followed the geomorphological classification method proposed by [PERSON] and [PERSON] (2007) with an adjusted AOI and input resolution. The PBC geomorphological map was selected to obtain the V\({}_{\text{S30}}\) representing the seismic microzoning because it can define the V\({}_{\text{S30}}\) values more precisely (see Figures 6 and 7). Figure 10 presents the zoomed-in geological map, the OBC geomorphological map, and the PBC geomorphological map. This figure shows that the boundaries are somewhat agreed (Figure 10) based on visual verification, and that the representational ability results are convincing (Section 6.2). The unit boundary from the OBC geomorphological map agrees better with the geological unit. However, in lowlands, the PBC geomorphological map class units of 11 and 15 (see Figure 10(iii)), which are shown in navy and dark-navy colors, could not be separated from the geological map and OBC geomorphological map (see Figure 10(i,ii)). From V\({}_{\text{S30}}\), units 11 and 15 are highly different (see Figure 7). The output suggested that the PBC map results in higher details than the OBC map, especially in lowland areas, where seismic microzoning has a greater focus.  
Hence, the locations of the V\({}_{\text{S30}}\) stations are available in six geological units, i.e., fluvial deposit (flood plain [Qff]), terrace and colluvium [Qt], alluvium (Qa), rhyolite [PTrv], semiconsolidate [Tmm], and granite [Trgr] ([PERSON], 2011). From Figure 10, the eroded plain of weak rock (major class eroded rock plain) and fluvial plain (in yellow tone) of the geomorphological map is under the fluvial deposit (i.e., Qff, Qt, and Qa) unit of the geology map, which covers NEHRP classes C and D. Therefore, the geology units have limitations in distinguishing the subsoil characteristics based on NEHRP standards.
# NMR-MPar: A Fault-Tolerance Approach for Multi-Core and Many-Core Processors
## 6 Materials and Methods
### Target Device
#### 6.1.1 Programming Multi-Purpose Processing Array Issues  
As was mentioned, the MPPA is a co-processor of the MPPA Developer platform used in the present work. The latter is a development platform based on an Intel core I7 CPU operating at 3.6 GHz and running a Linux CentOS 7 x86 64, Eclipse 4.3 (aka Kepler) and MPPA ACCESSCORE v1.4. MPPA ACCESSCORE is a set of libraries given by the manufacturer to manage the MPPA-256. It includes three programming models targeting different application needs and development constraints: the POSIX, the Kalray OpenCL and the Lowlevel programming models [43].  
The MPPA many-core is available within the Developer as an accelerator of the X86 Host CPU. In order to program the MPPA Developer, the application is commonly divided into a HOST part and an MPPA part. However, it is also possible to run applications only on the MPPA. The HOST part can use full Linux capabilities available on the CPU host. For the MPPA part, each Compute Cluster or IO cluster can run an executable file. Therefore, the many-core processor can simultaneously run as many executable codes as there are clusters. The execution of a multi-binary file on the hardware or on the platform simulator can be accomplished either by Joint Test Action Group (JTAG) or the PCIe expansion bus [40; 43].  
The main challenges the programmers face when adapting parallel applications to this device are the following: (1) the use of NoC primitives for communication; (2) the cache coherence must be guaranteed by the programmer; and (3) the limited memory inside the cluster (2 MB for OS, code and data).