esa-sceva/satcom-chunk-collection huggingface dataset:
# Transformer-Based Distillation Hash Learning for Image Retrieval
## 4 Experiment and Analysis
### Analysis of the Lifting Effect of Transformer and Distillation  
In this section, we provide an experimental study on using the Transformer model and fast student model for retrieval. To analyze the effectiveness of the Transformer model and fast student model, we report the experimental results of our method with different modules on MIRFlickr-25K and NUS-WIDE datasets. Our main findings are summarized below.  
**Transformer teacher model is better than the student model in retrieval accuracy.** Table 1 compares the various variants of our approach. We observe that the attention-based Transformer model outperforms the linear projection-based fast student model. This suggests that the self-attentive mechanism in Transformer has the potential to improve the ability to discriminate features and thus improve retrieval quality.  
**The student model has significant advantages over the teacher model in terms of retrieval efficiency.** As we can see from Table 1, although the Transformer-based teacher model achieves high accuracy, it is too slow to generate hash codes. On the other hand, the fast student model achieves nearly 10 times the computational speed of the teacher model. This is because Transformer is computationally intensive. There exists a large amount of computation in the self-attention process.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Train Data** & **Length** & **MAP** & **Time** \\ \hline \multirow{3}{*}{Teacher} & \multirow{3}{*}{MIRF Flickr-25K} & 24 & 0.7582 & 2.11 s \\  & & 48 & 0.7435 & 2.18 s \\ \cline{1-1} \cline{3-5} \multirow{3}{*}{Student} & \multirow{3}{*}{MIRF Flickr-25K} & 24 & 0.7112 & 0.19 s \\  & & 48 & 0.7088 & 0.21 s \\ \hline \multirow{3}{*}{Teacher} & \multirow{3}{*}{NUS-WIDE} & 24 & 0.6932 & 2.11 s \\  & & 48 & 0.6882 & 2.18 s \\ \cline{1-1} \cline{3-5} \multirow{3}{*}{Student} & \multirow{3}{*}{NUS-WIDE} & 24 & 0.6473 & 0.19 s \\  & & 48 & 0.6482 & 0.21 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Transformer-based teacher model and fast student model comparison. Length is the size of hash codes. Time indicates the time for the model to generate the hash codes on the query image.  
**Distillation improves the student model.** In Table 2, we compare two methods on NUS-WIDE and MIRFlickr-25K datasets, one with no teacher guidance and directly using the loss functions Equations (6) and (7) to train the student model, and the other with teacher guidance for distillation training denoted as Transformer. As seen in Table 2, the distillation method improves the performance of the student model by more than 10% on MAP when trained on NUS-WIDE, which significantly reduces the gap between the slow and fast models. On the other hand, the improvement when trained on MIRFlickr-25K is not as significant as on NUS-WIDE, probably due to the more minor training data of MIRFlickr-25K. In addition, the teacher model has limited ability to generalize the knowledge learned on the small training data, and thus has a lower upper limit when distillation learning.
# Performance of Granulated Ti-Dopped Manganese Oxide Oxygen Carriers in Chemical Looping Processes
## 3 Results
### Batch Fluidized Bed Experiments
#### 3.3.1 Experiments with N\({}_{2}\)-Air Cycles  
Three oxygen release-oxidation tests were performed at both 850 \({}^{\circ}\)C and 950 \({}^{\circ}\)C with the MnTi_20 oxygen carrier. The most significant fact is that the MnTi_20 was capable of reoxidizing in air even at 950 \({}^{\circ}\)C, although the oxidation rate was low. Figure 9 shows the oxygen concentration profiles obtained in the third cycle at the two temperatures mentioned. During reduction, the oxygen concentration decreased as the oxygen carrier was more reduced, to values close to the oxygen concentration expected at equilibrium at the reactor temperature. This type of behavior in the oxygen evolution was previously observed in oxygen carriers based on mixed oxides [33].  
It is interesting to note the behavior of the oxygen carrier at 850 \({}^{\circ}\)C. The oxygen concentration initially experiences a minimum, then goes through a plateau, and then slowly decreases. This initial behavior could indicate the existence of what can be called an \"induction period\" in the oxygen release at this temperature. This phenomenon will be more deeply considered in future research works. At 950 \({}^{\circ}\)C, the oxygen release rate was faster, and the maximum in the oxygen release rate was not observed. After 15 min of reduction experiment, a final value of 1.1% O\({}_{2}\) was reached at 950 \({}^{\circ}\)C.
## 2 Materials and Methods
### Parameter Selection
#### 2.2.1 UAV NATO Classification  
The aim of the NATO UAV classification system (Table 2) is to provide a certain way of describing different UAVs based on a wide range of characteristics, such as their size, range, endurance and operational capabilities. The classification intends to facilitate interoperability and equipment interchangeability among NATO member-states by providing  
Figure 1: Methodology flowchart.  
a common ground about UAVs, and it consists of three main parts: operational category, performance capabilities and design approach.  
In the present study, the categorization is primarily made in terms of GTOW. Especially when it comes to Class III, the authors also employ the parameters of service ceiling (i.e., to set the \"MALE\" and \"HALE\" categories apart) and mission type (i.e., for the UAVs that belong in the \"Strike\" category), provided that the GTOW is above 600 kg [23].  
Using a standardized classification system facilitates a systematic universal approach to categorizing UAVs based on engineering/design criteria and market/operational constraints. By observing the numbers referring to weight and ceiling, it is evident that fixed-wing UAVs vary considerably in terms of size. This observation supports the claim made in the introduction section (Section 1), i.e., that by ignoring the classification system and by employing design trends extracted for all UAVs alike, a design engineer can draw confusing, or simply inaccurate, conclusions and guidelines for the design space that refers to their study (in a similar way that employing trends for double-aisle jets can confuse a general aviation aircraft designer).
# Stance Detection in the Context of Fake News--A New Approach +
## 4 The Proposed Method and FNC Analysis
### Pipeline of the Proposed Method
#### 4.2.2 Second Stage: Pre-Trained Embeddings-Based Neural Network Model, Multi-Label Classification  
In the second stage of the experiment, we utilized Neural Network (NN) models in combination with pre-trained word embeddings from the GloVe dataset (specifically the \"glove.6B.50d.txt\" file). This file contains word vectors with a dimensionality of 50, trained on a corpus of 6 billion tokens from Wikipedia and Gigaword 5.  
Figure 5: Training data by stance.  
The architecture of the NN model consists of three layers. The first two layers use the Rectified Linear Unit (ReLU) as the activation function, which is a widely used activation function in neural networks due to its ability to introduce non-linearity while avoiding the vanishing gradient problem. The ReLU function outputs the input directly if it is positive; otherwise, it outputs zero. The final layer uses the Softmax activation function, which is common in classification tasks as it converts the output into a probability distribution over the predicted classes.  
We trained the model over 120 epochs, meaning the entire training dataset was passed through the model 120 times to update the network's weights. A relatively small batch size of five was used, meaning the model's weights were updated after every five samples. This small batch size is beneficial for making more frequent updates to the model parameters, potentially leading to more fine-grained adjustments to the learning process.
## 2 Underwater Sound Source Excitation and Detection Principle  
### Underwater Sound Source Excitation Model  
The underwater speakers cause vibrations on the water surface through excitation, and the vibrations formed on the water surface are the WSAW. Figure 1 illustrates the experimental system diagram of the FMCW radar detecting the WSAW, where a signal generator sends a specific frequency signal to the underwater sound source that radiates sound pressure to the water surface. Due to the characteristic impedance mismatch of the two media, the WSAW will be formed on the water surface, with its frequency consistent with the vibration of the underwater source. We use an FMCW radar to detect the WSAW and analyze the underwater sound source information from the detected signal to realize the underwater information transmission to the water.  
In Figure 1, it is assumed that the underwater sound source radiates sound waves in the vertical direction, and the distance from the sound source to the water surface is much greater than the wave number \(k=2\pi/\lambda_{\mathrm{w}}\). Therefore, the propagation rule of underwater sound source radiation can be approximated by the far-field plane wave. Combined with the water wave theory and the perturbation expansion and solution of the governing equation of water surface wave, a three-dimensional mathematical model of WSAW can be obtained:  
\[y=\frac{2p_{\mathrm{i}}}{\omega\rho c_{\mathrm{w}}}\mathrm{e}^{-\alpha\sqrt{x^ {2}+z^{2}}}\cos\left(k\sqrt{x^{2}+z^{2}}-\omega t\right) \tag{1}\]  
where \(p_{\mathrm{i}}\) denotes the sound pressure level, \(\omega=2\pi f_{\mathrm{w}}\) is the angular frequency of the underwater sound source, \(f_{\mathrm{w}}\) is the frequency of the underwater sound source, \(\lambda_{\mathrm{w}}\) is the wavelength of the WSAW, \(\rho\) is the density of water, and \(c_{\mathrm{w}}\) is the acoustic velocity in water, \(x\) and \(z\) are the axes of the two-dimensional water surface and \(\alpha\) is the attenuation coefficient of the WSAW, which is affected by the viscous force in the water media and is determined  
Figure 1: Schematic diagram of the experimental system.  
by the relationship between the wave energy loss per unit time and unit volume and the wave energy [30]. According to the relationship of energy conservation:  
\[\alpha=\frac{4\mu k^{2}\sqrt{kg+\sigma^{\prime}k^{3}/\rho}}{(g+3\sigma^{\prime} k^{2}/\rho)} \tag{2}\]  
where \(\mu\) is the viscosity coefficient of the water medium, \(\sigma^{\prime}=72.75\times 10^{-3}\) N/m is the water surface tension coefficient at 25 \({}^{\circ}\)C, and \(g=9.8066\) m/s\({}^{2}\) is the acceleration of gravity.  
From Equation (1), the frequency of the underwater sound source is consistent with the frequency of the WSAW, i.e., the underwater transmission information demodulation can be achieved by estimating the WSAW frequency and can be approximated with a sine signal.
# Energy-Efficient Multi-User Routing in a Software-Defined Multi-Hop Wireless Network
## 3 System Model
### Delay Model  
The time delay of links generally consists of propagation delay, transmission delay, and queuing delay. Propagation delay is determined by link length, namely the physical distance between two points. The capacity of the bottleneck link on the path has a significant impact on the transmission delay. The queuing delay is related to the load of the network and the burst of information flow. In this paper, the link delay only considers the propagation delay and the transmission delay, where the transmission delay is calculated according to Formula (2). The propagation delay is defined as follows:  
\[t_{i,j}=\frac{d_{i,j}}{v}\quad i,j=1,2,3\ldots N \tag{4}\]  
where \(v\) denotes the propagation rate of packets on the channel; to simplify the analysis, we assume that the value between any two devices is a constant. Therefore, the delay model can be expressed as follows:  
\[T_{i,j}^{total}=\frac{I_{i,j}}{S_{i,j}^{i}}+\frac{I_{i,j}}{S_{i,j}^{j}}+\frac{ d_{i,j}}{v}\quad i,j=1,2,3\ldots N \tag{5}\]  
Here, assuming that the size of transmitted packets is \(I=500\) KB, we can obtain the network topology with 25 nodes as shown in Figure 2. Moreover, energy consumption and delay between two points are recorded on each link.  
Figure 2: The network of the simulation.
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 3 Analysis of Resource Management in the MLSCS
### Primary Challenges in Resource Management
#### 3.2.1 Non-Uniform Distribution of SBSs  
Due to the inherent global configuration of the Walker constellation, the amount of satellite resources that users can see at different latitudes is different. In the multi-satellite coverage simulation shown in Figure 1, the colors represent the number of satellites covering the areas. For example, the green areas are covered by one satellite, while the red areas are covered by six satellites. As shown in this figure, in a typical mega-LEO satellite constellation system, and especially in the polar orbit constellation, users in low-latitude areas can see fewer satellites than in high-latitude areas, especially in the polar areas. With the exception of the orbital seam region, most of the mid- and-low latitude areas are single- or double-covered. With increasing latitude, the phenomenon of multiple coverage becomes more and more serious. The north/south pole areas can evenly be covered by 20 satellites at the same time.  
The global distribution of SBSs is highly unbalanced, with mid- and low-latitude areas experiencing limited satellite resources, particularly in service hotspots. In contrast, high-latitude areas experience a concentration of satellite resources, leading to potential intra-system interference due to overlapping coverage and electromagnetic spectrum usage. To mitigate this issue, strategies such as turning off satellite payloads over the polar regions, interference avoidance, and interference elimination techniques should be considered.  
To address the unbalanced distribution of SBSs, which leads to insufficient resources in low-latitude areas and intra-system interference in high-latitude areas, it is necessary to adopt elastic and flexible resource scheduling methods. These methods can help optimize satellite resource allocation and mitigate interference, ensuring more efficient and reliable satellite communication services.
# Study on Permeability Stress-Sensitivity in
## 3 Numerical Simulation of Tight Sandstone Gas Reservoirs
### Governing and Constitutive Equations
#### 3.1.1 Mathematical Model of Seepage Field  
The basic differential equation of the gas-water two-phase seepage based on the mass conservation law and Darcy's law is as follows [29]:  
\[\left\{\begin{array}{l}\
abla\Big{[}\rho_{\rm g}\frac{k_{\rm rg}}{\mu_{\rm g} B_{\rm g}}\big{(}\
abla p_{\rm g}-\rho_{\rm g}g\
abla H\big{)}\Big{]}\pm q_{\rm mg }+q_{\rm g}=\frac{\partial}{\partial t}\Big{(}\frac{\rho_{\rm g}\phi S_{\rm g} }{B_{\rm g}}\Big{)}\\ \
abla\Big{[}\rho_{\rm w}\frac{k_{\rm rg}}{\mu_{\rm w}B_{\rm w}}\big{(}\
abla p _{\rm w}-\rho_{\rm w}g\
abla H\big{)}\Big{]}\pm q_{\rm mw}+q_{\rm w}=\frac{ \partial}{\partial t}\Big{(}\frac{\rho_{\rm w}\phi S_{\rm w}}{B_{\rm w}}\Big{)} \end{array}\right. \tag{2}\]  
where \(k\) is absolute permeability, \(k_{\rm rg}\) and \(k_{\rm rw}\) are relative permeability of gas and water, respectively, \(\mu_{\rm g}\) and \(\mu_{\rm w}\) are viscosity of gas and water, respectively, \(p_{o}\) and \(p_{\rm w}\) are the pressure of gas and water, respectively, \(\rho_{\rm g}\) and \(\rho_{\rm w}\) are density of gas and water under standard conditions, respectively, \(S_{\rm g}\) and \(S_{\rm w}\) are saturation of gas and water, respectively, \(S_{\rm g}+S_{\rm w}=1,H\) is elevation, \(\phi\) is porosity, \(q_{\rm g}\) and \(q_{\rm w}\) are seepage of gas and water, respectively, \(B_{\rm g}\) and \(B_{\rm w}\) are the bulk coefficients of gas and water, respectively, \(q_{\rm mg}\) and \(q_{\rm mw}\) are matrix-fracture transfer terms of gas and water, respectively. When \(q_{\rm mg}\), \(q_{\rm mw}\) are preceded by minus sign, Equation (2) is the matrix seepage differential equation, when \(q_{\rm mg}\), \(q_{\rm mw}\) are preceded by plus sign, Equation (2) is the fracture seepage differential equation.
## 5 Case Studies
### Reflections on Material Selection and Planning Challenges in Asset Conservation
#### 5.3.5 Case Study 2: A Missed Opportunity for Modernisation  
The framework application in Case Study 2 yielded less positive results, with the project facing significant setbacks due to incorrect assumptions and overconfidence in prior planning decisions. The unverified expectation that an aluminium alternative would be permitted--based on its approval for a similar project at Preston Station--was a critical misjudgement. This oversight stemmed from a failure to properly define the benefits of the proposed material change and an inability to present clear, substantiated data from the Preston project to the planning officers. As a result, the project faced underestimated costs and an overly optimistic delivery forecast.  
Additionally, delayed engagement with town planners led to major modifications to the original proposal, as the LPA only approved a like-for-like material substitution. The selection of engineered modified timber aligned with the preservation requirements but significantly increased both implementation costs and future maintenance demands. These maintenance obligations introduce additional financial and operational burdens, particularly due to the challenges of working over an active railway network. The consequence of these constraints is that the gables will likely deteriorate more rapidly than anticipated, ultimately reducing the asset's lifecycle efficiency.  
While some minor positives can be drawn from introducing modified timber and a high standard of craftsmanship, the overall financial and logistical impact has been detrimental. The high costs and project delays have strained the budget, diverting resources from other necessary maintenance projects. This, in turn, increases the risk of accelerated deterioration in other assets, highlighting a missed opportunity for modernisation.
# Modular Combined DC-DC Autotransformer for Offshore Wind Power Integration with DC Collection
## 4 Control Strategy of the MCAT
### MCAT Control Strategy in Normal Operation  
One typical scenario is that the high-voltage side of the MCAT is connected to the DC main grid which could be equivalent to a constant DC voltage source. The voltage of the low-voltage DC grid is required to be kept stable under control of the connected MCAT. In MCAT, the control of each MMC is independent and has the same inner loop control, while outer loop control strategy is different. The inner loop control has been introduced in [18], and this section focuses on the outer loop control. Different parts of MCAT adopt different outer loop control strategies. For this, the outer loop control strategy of the MCAT is discussed as follows.  
The entire LVM is aimed to keep \(E_{\text{low}}\) stability because of connecting with the low-voltage DC grid directly, so they adopt DC voltage control as shown in Figure 4a. \(U_{\text{dc}}\) and \(Q_{\text{S}}\) are the DC voltage and reactive power at MMC (LVM and \(\text{MMC}_{\text{H},N+1}\) to \(\text{MMC}_{\text{H},2N}\)) valve side, \(U_{\text{dc}}^{*}\) and \(Q_{\text{s}}^{*}\) are the command references of \(U_{\text{dc}}\) and \(Q_{\text{S}}\). \(i_{\text{dmax}}\) and \(i_{\text{qmax}}\) are used to limit \(i_{\text{d}}^{*}\) and \(i_{\text{q}}^{*}\).  
\(u_{\text{diffd}}\) and \(u_{\text{diffd}}\) of MMCs are varied in response to the variation of the transmission power of the MCAT [16], which causes the AC bus voltage \(u_{\text{bus}}\) fluctuations. Therefore, certain MMCs are supposed to not only control \(u_{\text{bus}}\) but also serve as a power balance node [26; 27]. \(\text{MMC}_{\text{H},1}\) to \(\text{MMC}_{\text{H},N}\) (or \(\text{MMC}_{\text{H},N+1}\) to \(\text{MMC}_{\text{H},2N}\)) in the HVM serve as the balance node and adopt passive control as shown in Figure 4b [28; 29; 30]. \(u_{\text{bus}}^{*}\) and \(u_{\text{bus}}^{*}\) are the command references of \(u_{\text{bus}}\) and \(u_{\text{busq}}\). \(U_{\text{vacq}}\) and \(u_{\text{vacq}}\) are the \(dq\) axis components of \(U_{\text{vac}}\).  
Since \(E_{\text{high}}\) and \(E_{\text{low}}\) are controlled by the DC main grid and the LVM, respectively, \(\text{MMC}_{\text{H},N+1}\) to \(\text{MMC}_{\text{H},2N}\) should adopt DC voltage control to keep voltages of passive-control MMCs stable.  
The AC side control strategies of the LVM and \(\text{MMC}_{\text{H},N+1}\) to \(\text{MMC}_{\text{H},2N}\) are aimed at reducing power losses, so they adopt reactive power control with reference value 0 pu as shown in Figure 4a.  
According to the above analysis, the AC and DC side control strategy in the MCAT normal operation is as shown in Table 1.
## 2 Experimental Test
### Test Cases and Set-Up  
Four embedded depths (\(H\)) were set as the test parameters. For each \(H\), two displacement directions were considered, which are push (away from the front wall) and pull (toward the front wall). Therefore, eight test cases were obtained, the detailed information on which is listed in Table 1 and illustrated in Figure 5a. In the table, the first part of the label (\(H0\), \(H21\), \(H31.5\), or \(H63\)) indicates the embedded depth of the FAS specimen calculated from the sand surface to its lower surface and the second part of the label (Push or Pull) indicates the displacement direction. For example, H63-Push represents the test case of a FAS specimen with a 63 cm \(H\) subjected to a push \(u_{\text{t}}\).  
The test set-up consists of a rigid soil container, sand, FAS specimen, connector, and MTS, as illustrated in Figure 5a. The installation steps of the test set-up were as follows: (1) assemble the rigid soil container; (2) fill and compact the sand under the FAS specimen; (3) erect the FAS specimen; (4) fill and compact the sand above the FAS specimen; and (5) connect the FAS specimen, connector, and MTS hydraulic actuator. A horizontal displacement (\(u_{\text{t}}\)) was applied to the proximal end of the FAS specimen by an MTS hydraulic actuator using a displacement-control method with a peak \(u_{\text{t}}\) of \(\pm\)50 mm. The load rate was 1 mm/s [43].  
Figure 6: Particle grading curve of the sand used in this study.