esa-sceva/satcom-chunk-collection huggingface dataset:
# Adhesion Performance between Solid Waste and Bitumen Based on Surface Energy Theory
## 4 Experimental Program
### Results  
Each bitumen or aggregate was tested with the same reagent five times, and the contact angles and average values obtained are, respectively, shown in Tables 5 and 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Limestone**} & **Glass** & **Ceramics** \\ \hline \multirow{5}{*}{Ethylene glycol} & 1 & 30.80 & 29.10 & 47.50 \\  & 2 & 31.20 & 31.90 & 45.40 \\  & 3 & 30.50 & 26.40 & 45.50 \\  & 4 & 30.80 & 29.50 & 46.50 \\  & 5 & 30.50 & 29.00 & 45.80 \\  & Average value & 30.76 & 29.18 & 46.14 \\ \hline \multirow{5}{*}{Formamide} & 1 & 38.00 & 25.00 & 31.90 \\  & 2 & 39.20 & 20.50 & 29.30 \\ \cline{1-1}  & 3 & 37.80 & 23.00 & 31.90 \\ \cline{1-1}  & 4 & 38.50 & 22.40 & 29.20 \\ \cline{1-1}  & 5 & 37.60 & 22.80 & 30.50 \\ \cline{1-1}  & Average value & 38.22 & 22.74 & 30.56 \\ \hline \multirow{5}{*}{Distilled water} & 1 & 40.70 & 50.10 & 63.90 \\ \cline{1-1}  & 2 & 42.60 & 56.20 & 50.50 \\ \hline \end{tabular}
\end{table}
Table 6: Aggregate contact angle results (\({}^{\circ}\)).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Reagents**} & \multicolumn{5}{c}{**Surface Energy Parameters/(m)\(\cdot\)m\({}^{-2}\))**} \\ \cline{2-5}  & \(\gamma_{I}\) & \(\gamma_{I}^{LW}\) & \(\gamma_{I}^{AB}\) & \(\gamma_{I}^{+}\) & \(\gamma_{I}^{-}\) \\ \hline Ethylene glycol & 48.30 & 29.30 & 19.00 & 1.92 & 47.00 \\ Formamide & 58.20 & 39.50 & 18.70 & 2.28 & 39.60 \\ Distilled water & 72.80 & 21.80 & 51.00 & 25.50 & 25.50 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Surface energy parameters of liquid reagents.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**70\# Bitumen**} & **SBS-Modified Bitumen** \\ \hline \multirow{5}{*}{Ethylene glycol} & 1 & 83.70 & 80.50 \\  & 2 & 83.50 & 80.10 \\  & 3 & 83.40 & 80.20 \\  & 4 & 83.20 & 81.20 \\  & 5 & 83.70 & 80.60 \\  & Average value & 83.50 & 80.52 \\ \hline \multirow{5}{*}{Formamide} & 1 & 89.70 & 87.90 \\  & 2 & 89.80 & 86.90 \\  & 3 & 89.40 & 87.50 \\  & 4 & 90.30 & 87.50 \\  & 5 & 89.40 & 87.70 \\  & Average value & 89.72 & 87.50 \\ \hline \multirow{5}{*}{Distilled water} & 1 & 94.60 & 93.50 \\  & 2 & 94.50 & 93.70 \\ \cline{1-1}  & 3 & 94.70 & 93.50 \\ \cline{1-1}  & 4 & 94.90 & 93.60 \\ \cline{1-1}  & 5 & 95.00 & 93.20 \\ \cline{1-1}  & Average value & 94.74 & 93.50 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Bitumen contact angle results (\({}^{\circ}\)).
# Effect of Bentonite Addition on the Properties of Fly Ash as a Material for Landfill Sealing Layers
## 3 Results and Discussion
### Cohesive Soil  
Figure 10 shows the dependency of hydraulic conductivity on effective stress at hydraulic gradient \(i\) = 32 of fly ash, fly ash-bentonite mixes and cohesive soil.  
It can be observed that sandy silty clay achieved the smallest values of the hydraulic conductivity, from \(1.3\times 10^{-9}\) m/s to \(1.8\times 10^{-11}\) m/s. The value obtained at load 400 kPa was lower by more than two orders of magnitude than for FA+15%B. In the case of sasiCI, an increase of load from 50 to 100 kPa, and from 100 to 200 kPa, caused a decrease of \(k\) value by one order of magnitude. At the 50 kPa load, sasiCI had a hydraulic conductivity value slightly higher than FA+15%B (\(1.2\times 10^{-9}\) m/s). Hydraulic conductivity of sasiCI at 100 kPa load had the value of \(2.2\times 10^{-10}\) m/s--lower by one order of magnitude than FA+15%B (\(1.1\times 10^{-9}\) m/s), at 200 kPa \(k\) = \(5.7\times 10^{-11}\) m/s according to Figure 10 for sasiCI and \(k\) = \(1.0\times 10^{-9}\) m/s for FA+15%B, and at 400 kPa for sasiCI \(k\) = \(1.8\times 10^{-11}\) m/s, which is lower by more than two orders of magnitude than \(k\) = \(1.0\times 10^{-9}\) m/s for FA+15%B. Tested cohesive soil was characterized by finer grain size than fly ash-bentonite mixes (FA+15%B) and greater specific surface area. The specific surface area of FA+15%B has a value of 67.79 m\({}^{2}\)/g, while sandy silty clay is 102.58 m\({}^{2}\)/g (Table 2). According to [PERSON] et al. [51] in the case of soils which have specific surface area greater than 1 m\({}^{2}\)/g, a significant role is played by the physico-chemical phenomena in the  
Figure 10: Hydraulic conductivity of sandy silty clay, fly ash and fly ash-bentonite mixes in the relation to effective stress at hydraulic gradient \(i\) = 32.  
Figure 9: Hydraulic conductivity for bentonite content of materials tested at hydraulic gradient \(i\) = 32.  
soil. When the \(S_{\rm s}>1\) m\({}^{2}\)/g, the surface-related forces have an impact on, among others, changes in internal hydraulic and electrical conduction as well as chemical diffusion processes within the soil. The results obtained--the specific surface area (Table 2) and the hydraulic conductivity (Figure 10) of tested materials--confirm this statement. With the change of specific surface area, the plasticity index changes [52]. The influence of the specific surface area on hydraulic conductivity of cohesive soil has been described in References [53; 54; 55; 56], where it can be seen that the higher the amount of clayey particle, the lower the hydraulic conductivity. The same trend can be observed in tested material--the higher the amount of bentonite, the lower the hydraulic conductivity of the fly ash, which is related to the increasing value of the specific surface area (Table 2).
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 5 Simulation Results
### Multi-Ray Bouncing Analysis  
To investigate the impact of the ray tracing (RT), including multiple bouncing rays in the 60 GHz band, UC1 and UC2 are simulated again in comparison to the single ray tracing. Results reveal that even though RT is a detailed and precise simulation approach, there is only a little incremental change in the simulation metrics with a high simulation time cost, as indicated below.  
For the rural scenario (UC1), only 1.5% of possible links use a reflecting ray. All of the used links are direct LoS beams. This means that none of the reflected links has sufficiently lower path loss to be selected as a serving link. Due to this, the user coverage and the number of ENs are similar.  
For the urban scenario (UC2), around 3% of possible links are NLoS, and nearly 2% of the used links are NLoS. This is due to the street canyon characteristic of the downtown urban environment. The results have a small increment in the user coverage from 96.6% to 98.0% (1.4% increment). However, the number of used ENs increments by more than 5%.  
In addition, the improvement depends on the EN locations. For FA, no difference was found. However, for LP, the user coverage is slightly higher (98.3%) compared to FL (98.0%), mostly due to the optimisation of the number of ENs used.  
Finally, simulation time was the most affected metric. FA lasted 7.1 h per simulation, while LP 16.7 h and FL nearly 37 h. This is an increment of more than 50 times the simulation time without the multi-ray reflection. In conclusion, the single ray analysis is sufficient to account for the needed infrastructure and the requested coverage, since the reflected rays had little impact.
# A High-Resolution SAR Focusing Experiment Based on GF-3 Staring Data
## 1 Introduction
But in spaceborne ST mode, the VRP is just within the imaged scene and the approach in [20] could not compensate for the cubic phase error thoroughly. In this experiment, the range history of the reference target in the middle of imaged scene is calculated and the derivation between the actual and perfect hyperbolic range history can be obtained. We subtly compensate the derivation in azimuth-time domain. Then, the cubic phase error of every pixel in azimuth is calculated and compensated in range-Doppler domain. Gaofen-3 (GF-3), a remote sensing satellite of China's Gaofen project, which is the first C-band multi-polarization spaceborne SAR in China, was launched in 2016. GF-3 can work in 12 different modes, and some of its parameters such as incidence angle, nominal resolution, nominal swath, and polarization can be found in Table 1 in [21]. This satellite not only plays an important role in the fields of ocean surveillance, disaster reduction, and water conservancy, but also provides high-quality data for scientific experiments, which greatly promotes the development of SAR. In normal SL mode, GF-3 sets its antenna beam on a VRP below the ground while it flies in space, which can result in a trade-off between resolution and coverage in azimuth, and achieves the nominal resolution of 1 m with a swath of 10 km in both range and azimuth. In order to further explore GF-3's potential in improving azimuth resolution, ST imaging experiments were performed after the satellite had been in operational application. In this mode, the antenna can always point to the same scene and get the highest theoretical resolution of about 42 cm by setting its steering angle range within \(\pm\)1.9\({}^{\circ}\). However, ST mode is not a regular imaging mode as shown in Table 1 in [21], and the original data processor cannot give qualified products. So there are still many difficulties that must be resolved before obtaining a well-focused image [22]. On the one hand, the stop-and-go approximation in imaging algorithms would be a bad influence on the quality of the image. This problem could be solved by introducing two items respectively in range-Doppler domain and two-dimensional frequency domain [23; 24]. On other hand, GF-3 uses an active phased array antenna, but the steering angle range and power gain are modulated by array elements. This problem is very common in SL/ST and terrain observation with progressive scan (TOPS) [25] modes and can be solved by compensating the gain-loss ratio in the raw data level [26]. This paper introduces a GF-3 ST focusing experiment that aims to address problems in higher-resolution SAR images and make preparations for China's next generation of spaceborne SAR. In Section 2, the data acquisition and preprocessing are illustrated briefly, and then, data processing including model error compensation, the imaging algorithm, stop-and-go approximation, and antenna pattern modulation is explained in detail. Experimental results are presented in Section 3, followed by discussion and conclusions in Section 4.
## 3 Preliminaries
### System Model  
We describe WSNs where \(N\) sensor nodes are randomly deployed in a square area. The system model is represented by a connected graph \(G(V,E)\), where the vertex set \(V\) denotes the nodes in the networks, and the edge set \(E\) denotes the wireless links between the different nodes. Node \(i\) can communicate with node \(j\) if they are involved in the communication range. We assume that the single hop distance \(d_{ij}\) between node \(i\) and node \(j\) can be represented as a Euclidean distance. At a sampling instant, each sensor node \(i\) takes a measurement \(x_{i}\); the goal of the data gathering in WSNs is to collect sufficient information to reconstruct the \(N\)-dimensional signal \(X=[x_{1},\ldots,x_{N}]^{T}\). In this paper, the energy consumption model is similar to that in [29], namely, Equation (5) and Equation (6). When the distance between transmission node \(i\) and receive node \(j\) is greater than \(d_{0}\), the multi-path fading model is utilized. When the distance is less than \(d_{0}\), the free-space model is adopted.  
\[E_{Ti}(L,d)=\left\{\begin{array}{ll}E_{dec}\times L+E_{amp}\times L\times d ^{4},&d\geq d_{0}\\ E_{dec}\times L+E_{fs}\times L\times d^{2},&d<d_{0}\end{array}\right. \tag{5}\]  
\[E_{Rj}(L)=E_{ele}\times L, \tag{6}\]  
where \(E_{Ti}(L,d)\) and \(E_{Rj}(L)\) describe the energy consumption of transporting and receiving the \(L\) bit data packet. \(E_{elec}\) denotes the power expended to run the transmitter or receiver circuitry of the sensor node. \(E_{amp}\) and \(E_{fs}\) represent energy consumption for a multi-path fading amplifier and free-space amplifier, respectively.
# Development of Post Processing for Wave Propagation Problem: Response Filtering Method
## 4 Numerical Examples
At least, we can find the shape of the wave propagation by the numerical method in Figure 15. The numerical solution and the results of applying the RFM to the numerical solution (\(\Delta t=4.0\times 10^{-7}\) s) are shown in Figure 16. As in the previous examples, spurious oscillation of the numerical solution is observed. This time, the effect of the RFM with different filtering length, \(FL\) (not \(FR\)), is describe in Figure 16; Figure 17 to investigate feature of the RFM with respect to \(FL\). In the frequency domain, the increase in the frequency components can be predicted by Theorem 1. For example, the noticeable frequency component of numerical (FEM) solution is distributed from \(0\) to \(4\times 10^{5}\) Hz, and we can expect an additional frequency component by RFM (\(FL=2\)) as \(8\times 10^{5}\) to \(12\times 10^{5}\) Hz by Theorem 1: \(\widetilde{f}=\frac{f_{\star}}{FL}\cdot m\pm f=\frac{25\times 10^{5}}{2} \cdot 1\pm 4\times 10^{5}\). Without the RFM, the frequency components in the low-frequency region are dominant, whereas high-frequency components are generated with the RFM. This example shows that the RFM can work well even if the geometry of a structure is complex. Figure 14: Example 2: problem geometry, boundary conditions, and force condition (\(L_{1}=~{}21.5\) in, \(L_{2}=~{}7.5\) in, \(L_{3}=4.5\) in, \(W_{1}=1.5\) in, \(W_{2}=W_{3}=1\) in, thickness \(=1\) in). Figure 15: Shape of wave propagation viewed through the von [PERSON] stress and deformation for Example 2 (Ansys 19.0 stress contour package). The illustration of wave propagation is shown at (**a**) \(t=1.85\times 10^{-5}\) and (**b**) \(t=1.4\times 10^{-4}\) s.  
Figure 16: Changes before and after filtering with different filtering lengths: (**a**) without filtering, (**b**) with \(FL=2\), (**c**) with \(FL=4\), and (**d**) with \(FL=8\). Figure 17: Changes in the numerical solution after filtering with different filtering lengths in the frequency domain (the dashed and solid lines represent the numerical solution without RFM and filtered numerical solution, respectively): (**a**) direct numerical solution, filtered solution (**b**) with \(FL=2\), (**c**) with \(FL=4\), and (**d**) with \(FL=8\).
# Sustainable Plant-Based Diets and Food Allergies: A Scoping Review Inspired by EAT-Lancet
###### Abstract  
**Background**: The escalating prevalence of food allergies, alongside the global call for environmentally sustainable dietary transitions, has drawn attention to plant-based dietary models--particularly those inspired by the EAT-Lancet Commission. These frameworks not only reduce reliance on animal-sourced foods, benefiting planetary health, but may also play a role in modulating immune tolerance and allergic responses. **Methods**: This scoping review followed PRISMA guidelines and included 53 peer-reviewed studies published between 2000 and 2024, retrieved from PubMed, Scopus, and Google Scholar. Eligible articles were classified into two thematic domains: prevention of food allergy onset (\(n=31\)) and modulation of allergic symptoms in sensitized individuals (\(n=22\)). Included studies comprised randomized controlled trials (\(n=6\)), observational studies (\(n=17\)), systematic reviews and meta-analyses (\(n=11\)), and narrative/scoping reviews (\(n=19\)). **Results**: Sustainable plant-based diets were consistently associated with a lower incidence of allergic sensitization and reduced symptom severity. These effects were partly due to the exclusion of common allergens (e.g., dairy, eggs, and shellfish) but more importantly due to immunomodulatory mechanisms. Fermentable fibers can enhance short-chain fatty acid (SCFA)-producing bacteria (e.g., _Faecalibacterium prausnitzii_), elevating butyrate and acetate levels, which interact with G-protein-coupled receptors 43 and 109A (GPR43 and GPR109A) to induce regulatory T cells (Tregs) and reinforce epithelial integrity via tight junction proteins such as occludin and claudin-1. Polyphenols (e.g., quercetin and luteolin) can inhibit Th2-driven inflammation by stabilizing mast cells and downregulating IL-4 and II-1. **Conclusions**: Following sustainable dietary guidelines such as those proposed by the EAT-Lancet Commission may confer dual benefits: promoting environmental health and reducing the burden of allergic diseases. By emphasizing plant-based patterns rich in fiber and polyphenols, these diets support microbiota-mediated immune education, mucosal barrier function, and immunological tolerance. When properly supervised, they represent a promising tool for allergy prevention and symptom management. Larger randomized trials and long-term population studies are needed to confirm and operationalize these findings in clinical and public health contexts.  
**Keywords:** sustainable diets; plant-based nutrition; EAT-Lancet Commission; food allergy prevention; allergic symptom modulation; gut microbiota; short-chain fatty acids (SCFAs); polyphenols; immune tolerance; dietary fibers
# Automated Clinical Impression Generation for Medical Signal Data Searches
## 3 Experiments
### Retrieval Performance  
The retrieval performance of the methods described in Section 3.2 was compared. The impression given by every signal using each implemented method was generated. After that, they were indexed and stored into Elasticsearch using a standard tokenizer [28] and n-gram from 1 to 4. A total of 1700 queries were searched in the test set. The relevance score was calculated by measuring the similarity between query and the documents stored in Elasticsearch using default settings such as BM25 [13]. Although commercial search engines typically display 10 documents per page (e.g., Google and Bing), the comparisons in this study provide NDCG scores for 5 to 30 documents for more detailed comparisons,  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{Input: a segment of the signal} \\ \hline Conv1d (C, 64, sfreq/ / 2, sfreq/ 16) & Conv1d (C, 64, sfreq/4, sfreq/ 2) \\ BatchNorm1d (64) & BatchNorm1d (64) \\ ReLU & ReLU \\ \hline MaxPool1d (8, 8) & MaxPool1d (4, 4) \\ Dropout & Dropout \\ \hline Conv1d (64, 128, 8, 1) & Conv1d (64, 128, 6, 1) \\ BatchNorm1d (128) & BatchNorm1d (128) \\ ReLU & ReLU \\ \hline Conv1d (128, 128, 8, 1) & Conv1d (128, 128, 6, 1) \\ BatchNorm1d (128) & BatchNorm1d (128) \\ ReLU & ReLU \\ \hline MaxPool1d (4, 4) & MaxPool1d (2, 2) \\ \hline \multicolumn{2}{c}{Output: concatenation along the last axis} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed structure and hyper-parameters of DeepSleepNet Encoder.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Parameter Name**} & **Value** \\ \hline Embedding dimension & 512 \\ The number of heads & 8 \\ The number of encoder layers & 1 \\ The number of decoder layers & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyper-parameters of the transformer.  
in increments of five, as depicted in Figure 4. Since NDCG is calculated based on the number of search results, a higher score is achieved with more search results.  
The figure shows that the proposed method in this work achieves the best performance compared to the others. It is speculated that the DeepSleepNet Encoders (DE) capture the signal features effectively. As shown in the graph, the proposed method and the DE-LSTM and DE-LSTM-ATTENTION methods, which include the DE, outperform the CNN-LSTM method without DE, while the attention mechanism in DE-LSTM-ATTENTION performs better than DE-LSTM. The self-attention and cross-attention in the transformer used in the proposed method have a greater impact on indexing the signal.  
Additionally, in order to ensure the validity of the retrieval performance, the proposed method in this work was compared with each baseline using a \(t\)-test. The \(p\)-values obtained from the \(t\)-test for CNN-LSTM, DE-LSTM, and DE-LSTM-ATTENTION are 0.0174, 0.0377, and 0.0116, respectively. Therefore, the proposed method in this paper significantly outperforms the retrieval performance of the baselines.
# Energy-Efficient Multi-User Routing in a Software-Defined Multi-Hop Wireless Network
## 3 System Model
### Network Model  
This paper defines the topology of the data network as the weighted graph. For a given graph \(G=(V,E)\) with two distinct vertices \(s,t\in V\), \(V\) represents the mobile device and \(E\) represents a collection of network links. For a routing algorithm, we call the metrics that need to be optimized (or minimized) the costs. Meanwhile, the metrics that must be kept below the prescribed boundaries are called constraints. On the basis of this, the cost is \(energy(e)\), the constraint is \(delay(e)\leq D\), and these two variables are set as edge weights. Then, we calculate \(M\) paths from \(M\) source points to their respective target nodes, so that the time delay of these \(M\) paths is no higher than their respective delay constraints \(D_{i}\) and the total energy consumption is minimal. In order to solve the practical problem, the data of the network topology are stored in two-dimensional matrices, including the energy consumption matrix \(Weight_{e}\) and the delay matrix \(Weight_{d}\). In the matrix, if there is an edge between two nodes, the corresponding value is denoted as the weight; if there is no edge, set it to \(\infty\). Before routing algorithms are executed, each edge (link) in the network graph is given a cost and a QoS metric. During the design of the routing strategy, we suppose that the information of the network topology remains the same. After the network topology is established, the following weight models are given to calculate the energy consumption and delay between any two points.
## 2 Preliminaries  
### Write-Only ORAM  
ORAM was first proposed by [PERSON] and [PERSON] [29] to protect the privacy of access pattern on the RAM. Later, it was used to the client-server scenario, in which the server is treated as honest but curious and the client is trustful. Traditionally, the access pattern protected by ORAM contains the logical address and the type of operation, read, or write. First, the logical address is translated into a group of physical addresses. Second, no matter the logical operation is read or write, it is always translated into a group of physical reads and physical writes.  
**Definition 1**: **(ORAM Security):** _Let physicaladd&op\((\overrightarrow{y})\) represents the access pattern containing the physical addresses and operations translated by ORAM from access sequence \(\overrightarrow{y}\). ORAM is secure as long as, for any two access sequences with the same length, e.g., \(\overrightarrow{y}_{1}\) and \(\overrightarrow{y}_{2}\), the two access patterns output by ORAM are computationally indistinguishable._  
\[\text{physicaladd}\&op(\overrightarrow{y}_{1})\approx_{c}\text{physicaladd} \&op(\overrightarrow{y}_{2})\]  
_where \(\approx_{c}\) denotes computational indistinguishability (with respect to a computational security parameter \(\kappa\))._  
The write-only ORAM is a relaxed security notion of ORAM. It sets aside the read access pattern and only protects the write access pattern.  
**Definition 2**: **(Write-Only ORAM Security):** _Let physicaladd\((\overrightarrow{y})\) denote the physical addresses translated from access sequence \(\overrightarrow{y}\) by a write-only ORAM. For two access sequences \(\overrightarrow{y}_{1}\) and \(\overrightarrow{y}_{2}\) with the same length, it holds_  
\[\text{physicaladd}(\overrightarrow{y}_{1})\approx_{c}\text{physicaladd}( \overrightarrow{y}_{2})\]