esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultra-Wideband Bandpass Filters Using Tapered Resonators
## 1 Introduction  
The Federal Communications Commission (FCC) had authorized the unlicensed use of the ultra-wideband (UWB) frequency spectrum (range of 3.1-10.6 GHz) for short-range and high-speed wireless communication in 2002 [1]. This raised the demand for the design of UWB microwave components. The development of new UWB bandpass filters (BPF) has increased using different methods and configurations [2, 3, 4, 5]. The design of the UWB BPFs focused on compact size, good performance, and low cost. Several configurations of UWB filters are proposed in the literature. Currently, multiple mode resonator (MMR) [6, 7, 8] and short-circuited stubs [9, 10, 11, 12] are the most common techniques that are widely used in the design of UWB bandpass filters. Several other techniques may also be used in designing UWB filters such as hybrid microstrip/coplanar-waveguide (CPW) [13, 14] and cascaded bandpass and band-stop filters [15]. In addition, several approaches are used in the designing of UWB filters with single and dual notch bands for Wi-Fi and Wi-Max applications [16, 17, 18].  
In [6], an UWB filter using MMR is proposed to enable covering the entire UWB bandwidth with 2 dB insertion loss and 10 dB return loss. The filter proposed in [7] is designed using three open-ended stubs in parallel to a stepped-impedance resonator. This results in an UWB BPF with an insertion loss lower than 0.8 dB and a return loss higher than 14.3 dB. A UWB filter using a dual mode resonator had been proposed in [8] with a return loss of 15 dB from 5.7 to 10.7 GHz.  
UWB BPFs using five short-circuited stub resonators separated by connecting lines are proposed in different configurations for a 110% fractional bandwidth [9, 10]. The authors of [11] presented an UWB BPF, using five short-circuited quarter-wavelength resonators, with a better than 7.5 dB return loss and an insertion loss less than 1.27 dB over thebandwidth from 2.7 GHz to 9.83 GHz. Some additional configurations for UWB BPF can be found in [2,4,5].  
In this work, new UWB bandpass filters are proposed. The proposed filters consist of four short-circuited tapered transmission line stubs separated by identical uniform transmission lines. The integration of tapered transmission lines (TTLs) results in element length reduction, and in a wider operational bandwidth that is controlled by the resonator taper ratio. Exponential and linear tapered transmission lines are theoretically analyzed, formulated, and used to design UWB filters. Moreover, UWB LC lumped-element models of the exponential and linear tapered short-circuited stubs are derived and validated.
# DAWE: A Double Attention-Based Word Embedding Model with Sememe Structure Information
## 2 Notation and Definition  
The symbolic conventions that are used below are given here: W, S and X represent word set, sense set and sememe set, respectively. For each word \(w\in W\), there are multiple senses \(s_{i}^{(w)}\in S^{(w)}\), and \(S^{(w)}\) represents the sense set corresponding to the word \(w\); for each sense \(s_{i}^{(w)}\), corresponding to several different sememes \(x_{j}^{(s_{i})}\in X_{i}^{(w)}\), \(X_{i}^{(w)}\) represents the sememe set of the ith sense corresponding to the word \(w\) and \(C(w)\) represents the context word set corresponding to the word \(w\). We use the bold form \(w/s/x\in\mathbb{R}^{D}\) corresponding to \(w/s/x\) to represent the vectors of word/sense/sememe, where D is the dimension of those vectors. **Definition 1**. : _Word Embedding._  
Figure 2: Interpretation of weight changes of sense and sememe. Sense means the meaning that exists within the word and does not change with the context, but its semantic contribution to the word is different in different contexts. For example, the word “Apple” has three different “senses”: “Apple Brand”, “Apple (Fruit)” and “Apple Tree”. In the context “I am going to the \(\sim\) store now.”, the semantic of the word “apple” tends to the sense “Apple Brand”. The sememe’s contribution to the sense should be different. As shown in Figure 3, for the text corpus C, word embedding maps each word \(\mathrm{w}\in\mathrm{W}\) to a continuous low-dimensional space \(\mathbb{R}^{\mathrm{D}}\), while ensuring that the final embeddings (vectors) can represent the semantic relevance between words in the original text corpus C.  
**Definition 2**. : _Encoding Words with Sememes._  
It is a process of using sememes as a semantic supplement to encode words. In the process, word embedding can be simplified as the encoding of words from sememes to words, which means word vectors can be obtained by encoding corresponding sememes of words:  
\[\mathrm{w}=\mathrm{f}_{\mathrm{X}\rightarrow\mathrm{w}}\big{(}\mathrm{X}^{( \mathrm{w})},\mathrm{\Theta}_{\mathrm{X}\rightarrow\mathrm{w}}\big{)}, \tag{1}\]  
where \(\mathrm{\Theta}_{\mathrm{X}\rightarrow\mathrm{w}}\) represents the parameters when encoding sememe set \(\mathrm{X}^{(\mathrm{w})}\) to its corresponding word \(\mathrm{w}\).
# Energy-Based Prediction of the Displacement of DCFP Bearings
## 4 Numerical Simulation of the Experiments
### Numerical Results Using a Constant Friction Coefficient  
In order to find the constant friction coefficient that was suitable for the most amount of time during most seismic situations, a nominal friction coefficient, \(\mu_{n}\), was defined. The value of \(\mu_{n}\) was 0.043 for both sizes of the sliders based on experimental results, and it represented the nominal friction coefficient under seismic load conditions. This value was determined by taking the average of the friction coefficient of the DCFPB at zero displacement points in the second cycle (points c and d in Figure 7) of the input sine wave with a constant pressure of 60 N/mm\({}^{2}\) and a maximum velocity of 400 mm/s (T01 of the dependency test in Table 1).  
The accuracy of using \(\mu_{n}\) under strong excitation and weak excitation is shown in Figure 8. The results showed that \(\mu_{n}\) was suitable for strong excitations but could not be used under weak excitations. In the other tests in Table 2, which can all be considered to be strong excitations, the simulations using \(\mu_{n}\) all showed that the force-deflection relations of the DCFP bearings had no large differences [19]. Therefore, the constant friction coefficient, \(\mu_{n}\), could be applied for the rough behavior estimation of DCFP bearings under seismic loads. For example, it could be applied for the response force simulation when the response displacement of the bearing is known.  
In the numerical simulation of DCFP bearings, pressure, velocity and temperature dependencies all have influence on the behavior of the bearings. However, for DCFP bearings under stable pressure, the temperature variation caused by friction heating will dominate the influence. This influence is more obvious when the temperature is relatively low (e.g., 20-100 \({}^{\circ}\)C), meaning that it is under weak excitations, as shown in Figures 6b and 8b. However, when the temperature is higher, the influence will ease up, as shown in Figures 6a and 8a, and this is the reason why the constant friction coefficient can be applied for rough estimation under strong excitations such as seismic loads.  
Figure 6: Accuracy of the precise and simplified models under (**a**) strong excitation (seismic) and (**b**) weak excitation (wind-like).  
Figure 7: Experimental data used to obtain \(\mu_{n}\).
The main pozzolanic reaction from calcined clay can be summarised in Equation (1), and the presence of limestone and gypsum extends this reaction to Equations (2) and (3). \[A.2S\left(from\;calcined\;clay\right)+CH\left(from\;OPC\;hydration\right)+H_{2}O \;\to CSH+CASH \tag{1}\]  
\[A^{-}(from\;MK\;or\;clinker)+CC^{-}+3 CH\to C_{3}A^{-}CC^{-}H_{11} \tag{2}\]  
\[3C_{4}AS^{-}H_{12}+2 CC^{-}+H_{18}\to C_{6}AS_{3}^{-}H_{32}+2C_{4}AC^{-}H_{11} \tag{3}\]  
where \(A^{-}\) = aluminates, \(A\) = alumina (\(Al_{2}O_{3}\)), \(C\) = quickline (CaO), \(C^{-}\)= \(CO_{2}\), \(CH\) = portlandite [\(Ca(OH)_{2}\)], \(H\) = water (\(H_{2}O\)), \(S\) = silica (\(SiO_{2}\)), and \(S^{-}\) = sulphate (\(SO_{3}\)). Calcium aluminium silica hydrate (CASH), tetringite (Aft phase-\(C_{6}AS_{3}^{-}H_{32}\)), and mono-sulfo-aluminate (AFm phase, \(C_{4}AS^{-}H_{12}\)) phases are formed when metakaolin (\(Al_{2}O_{3}.2 SiO_{2}\)) reacts as a pozzolanic material with calcium hydroxide (CH), water (H), and sulphate (\(CaSO_{4}\)) [4,6,14]. Calcite (CaCO\({}_{3}\) from limestone) reacts with tricalcium aluminate/aluminium calcium oxide (\(C_{3}\)A/\(Al_{2}\)Ca\({}_{3}\)O\({}_{6}\)) from clinker to form hemi- and mono-carbo-aluminate phases (\(C_{3}A^{-}CC^{-}H_{11}\) and \(C_{4}AC^{-}H_{11}\), respectively). Aluminates from metakaolin also react with calcite in LC\({}^{3}\), enhancing carpo-aluminate phase formation [6] and inhibiting the further formation of AFm [1]. The contents of these phases in LC\({}^{3}\) composites indicate the extent of pozzolanic reactions [15] and were investigated in this study by X-ray diffraction (XRD) analysis. The pozzolanic activity and filling effect of calcined clay and limestone can efficiently reduce porosity and improve cement-based materials' microstructure densification and mechanical characteristics [13,16]. In this study, we investigated the performance of three South African clay samples for the production of LC\({}^{3}\). The kaolinite content of each clay sample was determined since this is significant to the suitability of the clay samples for LC\({}^{3}\). The limestone, PC, and three clay samples were characterised to understand their properties' influence on the LC\({}^{3}\) binder and mortar. The hydrate phase contents of the mortar were determined to examine the extent of pozzolanic activity and to understand the microstructure and strength properties obtained from the mortar samples. The study's findings provide information to the South African cement industry that could encourage embracing LC\({}^{3}\) as a viable, sustainable alternative cement. It also adds to the pool of knowledge on clays from other geological origins, as there has been increased study on the efficacy of clays having various geologic characteristics [7,13].
## 2 Related Works
### Vehicle Routing Problem and Variants  
The VRP is defined as the optimal set of routes for a fleet of vehicles and agents that travel to perform delivery to a given customer. This is referred to as a combinational integer optimization problem [11]. The VRP is a generalization of the traveling salesman problem; as it is NP-Hard, polynomial-time algorithms are unlikely to exist unless \(P=NP\). Despite the computational complexity of these problems, novel heuristic methods are able to obtain near-optimal solutions within seconds for a few specific datasets.  
There are several VRP variants [12; 13; 14] as follows:  
* Multi-depot vehicle routing problem (MDVRP): In this variant, multiple vehicles leave from different depots and return to their original depots at the end of their assigned jobs.
* Capacitated vehicle routing problem (CVRP): This variant is a particular case of the MDVRP in which the number of depots is one. Each vehicle has a limited carrying capacity.
* Vehicle routing problem with pickup and delivery (VRPPD): Several items/jobs must be moved/processed from certain pickup locations to other delivery locations. The objective of the VRPPD is to determine a set of optimal routes for the vehicle to visit the pickup and delivery locations.
* Open vehicle routing problem (OVRP): In this variant, the vehicle does not have to return to the depot.  
Given that methods with exact solutions such as vehicle flow formulations, commodity flow formulations, and set partitioning problems [15] generally fail to solve real-world problems within feasible computation time frames, the emphasis of VRP solution methods has shifted to heuristic approaches. However, there is no guarantee of quality solutions.  
Heuristic algorithms for VRP can be categorized into constructive and improvement methods. Constructive methods are typically quick, greedy approaches. Improvement methods are more sophisticated and typically require a VRP solution as the input [16]. Well-known constructive methods for VRP include the nearest neighbor heuristic algorithm, sweep algorithm [17], and the cluster-first path-second method [18]. Heuristic improvement methods utilize a strategy called metaheuristics, iteratively moving from one solution to a new solution via modifications. The metaheuristics for the VRP include simulated annealing [19], tabu search [20], genetic algorithms [21], memetic algorithms [22], and neural networks [23].
## 4 Experimental Results and Analysis
### Evaluation Indicators  
Model usage precision, recall, mean of average precision (mAP), model size, parameter count, and FLOPs are used as evaluation indicators for the overall performance of the algorithm. The calculation expression for precision, recall, and mAP is as follows:  
\[P=\frac{TP}{(TP+FP)} \tag{10}\]  
\[R=\frac{TP}{(TP+FN)} \tag{11}\]  
\[AP=\int_{0}^{1}P(R)dR \tag{12}\]  
where _TP_ denotes the count of actual positive samples, _FP_ denotes the count of incorrectly identified positive samples, and _FN_ denotes the count of correctly identified negative samples. mAP is typically obtained by calculating the Precision-Recall curve (PR curve) for each class and then averaging the Area Under the Curve (AUC) of all classes to derive the mAP.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Layer** & **Input Size** & **Stride** & **Repeat** \\ \hline \multirow{5}{*}{Stage 2} & Image & \(224\times 224\times 3\) & - & - \\  & Conv & \(112\times 112\times 24\) & 2 & 1 \\  & MaxPool & \(56\times 56\times 24\) & 2 & 1 \\  & Shuffle-b & \(28\times 28\times 48\) & 2 & 1 \\  & Shuffle-a & \(28\times 28\times 176\) & 1 & 3 \\  & Shuffle-b & \(14\times 14\times 96\) & 2 & 1 \\  & Shuffle-a & \(14\times 14\times 352\) & 1 & 9 \\  & Shuffle-b & \(7\times 7\times 192\) & 2 & 1 \\  & Shuffle-a & \(7\times 7\times 704\) & 1 & 3 \\  & Conv & \(7\times 7\times 1024\) & 1 & 1 \\  & GlobalPool & \(1\times 1\times 1024\) & - & - \\  & FC & k & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Refactored ShuffleNetv2 structure.
# Effect of Micro-Silica Addition into Electric Arc Furnace Steel Slag Eco-Efficient Concrete
## 3 Results and Discussion
### Hardened Properties
#### 3.2.2 Flexural Strength  
Figure 13 shows the flexural strength changes with respect to the replacement proportion of EAFS aggregates. The results indicate that the replacement of EAFS fine aggregates, although it increases the flexural strength, does not have a significant effect. However, with replacement of more than 75% of EAFS coarse aggregates with natural coarse aggregate, a sudden increase in the flexural strength of the samples is obtained. Although in the seven-day strength, the results of flexural strength of all samples are close to each other, it can be clearly seen that, with the addition of 75% of EAFS coarse aggregates, after ten days,  
Figure 11: Detail of specimens with different percentages of coarse and fine EAFS aggregates.  
the process of obtaining flexural strength increases with a high slope. Considering that the mixing design is the same in all samples and the process of obtaining flexural strength in samples with EAFS fine-aggregates is continuous and slow, it can be concluded that this type of performance is not related to the type of cement used or other items, and it all has to do with EAFS coarse aggregates. This may be due to the fact that EAFS coarse aggregates will have more contacts with each other than EAFS fine aggregates due to greater porosity and rougher surfaces, and as a result, they will increase strength.  
Figure 12: Compressive strength of concrete with different percentages of micro-silica.  
Figure 13: Flexural strength of concrete containing different percentages of EAFS aggregate.  
The results in Figure 14 show that the addition of micro-silica into control concrete does not affect the flexural strength of the sample, and the strength of these samples is equal to control concrete. Therefore, adding 6% of micro-silica to the sample has no special effect on the flexural strength of the sample. This may be due to the fact that micro-silica grains are much smaller than cement. In this case, due to high porosity in EAFS aggregates, micro-silica is forced to fill the aggregate cavities. The greatest effect is related to adding 8% of micro-silica to the sample, when the porosity of the aggregates is saturated with water and filled with micro-silica, and micro-silica plays its role as a pozzolanic adhesive. After increasing the micro-silica to 10% wt. of cement, the flexural strength decreases sharply, which means that by keeping the water to cement ratio and the amount of concrete SP constant, there will not be enough water for hydration.
## 4 Discussion
### Experiment Result
#### 4.2.1 Ablation Experiments
This indicates that the transformer encoder model in the relation classification task can not only improve the baseline model alone, but also have a significant improvement effect on the performance of the baseline model when combined with POS features. The results of the above ablation experiments also show that the lexical features and the transformer encoder in the relation classification proposed in this paper are important for entity-relation extraction tasks, which can greatly improve the comprehensive performance of the span-based entity-relation joint extraction model. Figure 5 shows the confusion matrix of the final model tested on the dataset CoNLL04, with the baseline results, including entity classification as well as relation classification. It can be seen that our proposed ER-LAC model, model 6 in the ablation experiments, has better classification results than the base model for both subtasks, which illustrates the clear advantage of our proposed method in the entity-relation extraction task. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Experiment**} & \multicolumn{3}{c}{**NER (\%)**} & \multicolumn{3}{c}{**RE (\%)**} \\  & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline SpERT & 85.78 & 86.84 & 86.25 & 74.75 & 71.52 & 72.87 \\ SpERT+E(\(\mathbf{e}^{\mathrm{t}}\)) & 88.47 & 85.98 & 87.06 (\(\pm\)0.12) & 73.38 & **75.22** & 73.86 (\(\pm\)0.14) \\ SpERT+E(\(\mathbf{e}^{\mathrm{t}}_{\mathrm{t}}\)) & 87.06 & 86.23 & 86.56 (\(\pm\)0.28) & 76.33 & 70.89 & 73.23 (\(\pm\)0.50) \\ SpERT+E(\(\mathbf{e}^{\mathrm{t}}_{\mathrm{t}}+\mathbf{e}^{\mathrm{t}}_{ \mathrm{t}}\)) & 87.34 & 87.17 & 87.20 (\(\pm\)0.14) & 75.70 & 72.98 & 74.18 (\(\pm\)0.11) \\ SpERT+R(\(\mathbf{e}^{\mathrm{t}}_{\mathrm{t}}\)) +E(\(\mathbf{e}^{\mathrm{t}}_{\mathrm{t}}+\mathbf{e}^{\mathrm{t}}_{ \mathrm{t}}\)) & **89.33** & 86.00 & 87.45 (\(\pm\)0.27) & 75.46 & 74.56 & 74.87 (\(\pm\)0.10) \\ SpERT+T & 88.60 & 86.23 & 87.28 (\(\pm\)0.56) & 74.88 & 73.46 & 74.16 (\(\pm\)1.03) \\ SpERT+R(\(\mathbf{e}^{\mathrm{t}}_{\mathrm{t}}\)) +E(\(\mathbf{e}^{\mathrm{t}}_{\mathrm{t}}+\mathbf{e}^{\mathrm{t}}_{ \mathrm{t}}\)) +T & 87.81 & **88.80** & **88.29 (\(\pm\)0.22)** & **75.72** & 74.91 & **75.13 (\(\pm\)0.33)** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of ablation experiments on the dataset CoNLL04. The SpERT model was used as the baseline model. The macro precision, recall and F1-scores were calculated for named entity recognition (NER) and relation extraction (RE) subtasks. The bolded portion indicates the best result for each column.
## 3 Methods
Using the Infress software, the fractured porosity and intrinsic permeability tensor elements can be calculated for each cubic volume element of the modelled rock body, if the aperture of the fractures is known [40]. Previous studies show that fracture aperture is in a significant linear correlation with the fracture length [35; 47; 48]:  
\[\text{a}=\text{A}\times\text{L} \tag{2}\]  
where a is the aperture, while the aperture coefficient A is characteristic for the rock body and the fracture system and L is the length of the fracture. The initial aperture can be calculated for each fracture with the modelled length using this correlation. Finally, the modelled fractured porosity and permeability values estimated for each cubic cell depend solely on the geometry of the fracture network and the aperture coefficient (A). The hydraulic aperture of the fractures in well BAF-3 can be estimated by comparing the observed and modelled permeabilities. First, porosity and the 3 \(\times\) 3 intrinsic permeability tensor for an arbitrary cubic cell should be computed based on the generated geometry of the fracture network using an aperture coefficient value. Since the hydraulic measurements of the well do not consider the anisotropy of the permeabilities, the average of the diagonal elements of the permeability tensor is used in the subsequent calibration. The observed permeability can be calculated from the measured transmissivity at any depth interval. The aperture coefficient of the modelled fracture system can be calibrated using the observed and the modelled permeability values. The goal of aperture calibration is to change the value of the aperture coefficient so that the two permeability values become equal. To estimate the permeability tensor with the Infress code, a rock volume of d3 was considered, where d is the length of the interval used for well-hydraulic measurements, usually 20 m. In this way, an average aperture coefficient could be calculated for each interval independently so that the effective porosity can also be calculated for all sections afterwards. The hydrodynamic properties of a rock body can be described by the flow zone indicator (FZI). This indicator is based on the covariation of porosity and permeability and determines hydraulic flow units (HFU). By definition, an HFU is a volume of a rock body in which the geological and petrophysical properties controlling fluid flow are uniform internally. FZI values are related to porosity, permeability, capillary pressure, lithology distribution, mineralogy, and sedimentary structures and are also influenced by diagenetic processes [46]. The FZI can be calculated as follows [47]:  
\[\text{FZI}=\frac{\text{RQI}}{\text{NPI}}=\frac{0.0314\sqrt{\frac{\text{k}}{ \Phi}}}{\frac{\Phi}{(1-\Phi)}} \tag{3}\]  
where FZI is the flow zone indicator in \(\upmu\)m, NPI is the normalised porosity index, RQI is the reservoir quality index, k is the permeability in mD and \(\Phi\) is the porosity in a volume fraction.
# Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection
## 2 Literature Review
### Quantum-Enhanced Deep Learning for 6G Networks  
Quantum-enhanced deep learning for 6G networks aims to leverage quantum computing principles to address the challenges of massive data processing and dynamic network conditions. Quantum approaches show potential advantages in handling network fluctuations through their ability to process multiple network states simultaneously, potentially enabling more efficient resource allocation and adaptive routing in highly dynamic 6G environments. Research on quantum-enhanced image processing for 6G networks has demonstrated promising results in areas such as quantum-assisted feature extraction and quantum convolutional neural networks (QCNNs) [51], which have shown improved performance in image classification tasks under simulated network conditions. However, these approaches are still largely theoretical or limited to small-scale experiments, with significant challenges remaining in scaling quantum algorithms to practical 6G network sizes and implementing them on current quantum hardware.