esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Solution Verification  
To verify the semi-analytical solutions, a numerical model is established with the finite element method. In the numerical simulation, the computation domain is the confined aquifer, whose top and bottom boundaries are considered as non-flow boundaries. In the horizontal direction, the lateral boundary away from the excavation is the constant-head boundary. The boundary on the left side corresponds to the position of the cut-off wall of the excavation. Throughout its entire depth, the upper portion represents the location of the wall, serving as an impermeable boundary, while below the wall constitutes a constant flux boundary.  
Table 1 shows the input data of the verification tests in this study. The boundary conditions and mesh of the hydrogeological numerical model can be seen in Figure 2. In numerical simulations, a quadrilateral grid is employed with grid refinement near the cut-off wall to capture potential significant variations in flow direction and to obtain accurate results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Parameters** & **Value** & \begin{tabular}{c} **Dimensionless** \\ **Parameters** \\ \end{tabular} & **Value** \\ \hline Strip excavation pit width (\(2a\)) & 100 m & \begin{tabular}{c} Dimensionless thickness \\ of confined aquifer (\(M\)) \\ \end{tabular} & 2.5 \\ Discharge per unit width (\(Q\)) & 2 m\({}^{2}\)/day & \begin{tabular}{c} Dimensionless boundary \\ distance (\(R_{\rm D}\)) \\ \end{tabular} & 10 \\ \hline Boundary distance (\(R\)) & 500 m & \
\begin{tabular}{c} Dimensionless boundary \\ distance (\(R_{\rm D}\)) \\ \end{tabular} & 10 \\ Depth of the cut-off wall in the confined aquifer (\(d\)) & 4 m &
\begin{tabular}{c} Penetrating depth ratio of \\ the cut-off wall (\(n_{1}\)) \\ \end{tabular} & 0.2 \\ \hline Vertical hydraulic conductivities (\(k_{\rm v}\)) & 1.5 m/day & Anisotropic (\(k\)) & 1.67 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input data for verification.  
Figure 3 illustrates the comparison between the results from the presented solution and the numerical simulation at different times according to the above parameters. The symbol box indicates the results from the numerical solution while the solid line expresses the results from the semi-analytical solution. It can be observed that the results obtained from the semi-analytical solution have an excellent agreement with the numerical results. Therefore, the semi-analytical solution for the model of the strip excavation pit with the cut-off wall is reasonable.
###### Abstract  
Femtosecond pulsed laser irradiation was performed to investigate the feasibility and fundamental characteristics of embedding silver nanoparticles onto zirconia ceramic surfaces. By irradiating laser, nanopores were fabricated on the surface of the yttria-stabilized zirconia (YSZ) substrate, and silver nanoparticles were infiltrated and immobilized into the pores using a commercial nano-silver dispersion solution. Numerous nanopores embedded with silver nanoparticles were successfully obtained on the YSZ surface while keeping the grains' shapes unchanged by controlling laser parameters. Optimizing laser fluence and scanning speed near the ablation threshold made it possible to remove only the excess dispersant that remained on the surface while keeping silver in the pores and without causing machining of the surface of the YSZ substrate. In addition, about 60% embedding in the nanopores was achieved. It was found that the shorter pulse width was suitable to avoid evaporating both dispersant and silver. Cross-sectional observation revealed that the silver nanoparticles were agglomerated to form clumps and were embedded without a gap at the bottom of the pores at a depth of about 600 nm. After laser irradiation, no significant laser-induced phase change was observed in the YSZ substrate, indicating that there was no in-process thermal damage to the bulk. These findings demonstrated the possibility of adding a metal nanoparticle to the zirconia surface by using only a laser process without damaging the properties of the base material during the process. New applications of zirconia, such as the generation of functional surfaces with antibacterial properties, are expected.  
nanopore; yttria-stabilized zirconia; femtosecond pulsed laser; silver; phase transformation; ceramic material +
Footnote †: journal: Applied sciences
## 3 Hybrid Network Implementation Using LoRaWAN and LoRaMESH
### LoRaWAN  
LoRaWAN networks are being represented in a star topology where gateways relay messages between end devices and a central network server [123], as also shown in the Figure 10. The gateways are connected to the network server via standard IP communication while the end devices use LoRa or FSK communication of a single frequency for one or many gateways [124]. All communications are usually bi-directional, although uplink communication from an end device to the network server is expected to be the predominant traffic.  
Communication between end devices and gateways is spread over different frequency channels and data rates. Data rate selection is a compromise between communication interval and message duration, communications with different data rates do not interfere with each other. LoRa data ranges from 0.3 kbps to 50 kbps. Thus, it is possible to configure the LoRaWAN module considering the Bandwidth (BW) ranging from 125, 250 or 500 KHz, Data Rate (DR) ranging from 0 to 4 and Spreading Factor (SF) ranging from 7 to 12.
# Sparsity-Inducing Super-Resolution Passive Radar Imaging with Illuminators of Opportunity
A compressed sensing (CS) based algorithm was proposed in [18] to reconstruct passive ISAR images, but it is effective only when the multiple non-adjacent DVB-T channels are spectrally separated. In this paper, we propose a super-resolution passive ISAR imaging framework based on CS for the case with two illuminators and a small rotation angle. In this framework, the passive radar imaging is recast as a reconstruction problem of sparse signal with unknown amplitude and frequency. Next, the log-sum penalty function-based CS algorithm is employed to recover the amplitude and frequency parameters of strong scatterers. Simulated results demonstrate the superiority of the proposed method over other existing super-resolution algorithms such as ESPRIT and RELAX under the condition of low SNR and limited measurements. The remaining sections are organized as follows. In Section 2, the parametric passive radar imaging model is established. Section 3 presents the CS-based algorithm for super-resolution passive radar image formation. In Section 4, we compare the performance of the proposed algorithm and other super-resolution algorithms with simulated data. Finally, conclusions are drawn in Section 5.
## 2 Background and Methods
### Carbon Emission Accounting Boundaries and Methodologies  
#### 2.2.1 System Boundary Definition  
The accounting boundary for this study was defined as the intra-plant processes of WWTPs, encompassing influent reception, pretreatment, biological treatment, advanced treatment, disinfection, and sludge dewatering. Excluded from the boundary were sludge disposal operations and emissions occurring after wastewater discharge into receiving waters. Based on organic carbon origins, CO\({}_{2}\) emissions within WWTPs were classified into two categories: biogenic and fossil-derived [19]. The majority of CO\({}_{2}\) emissions originated from biogenic carbon present in wastewater, with only a minor fraction attributable to fossil fuel combustion. Consistent with IPCC guidelines, biogenic CO\({}_{2}\)--traceable to organic matter produced through recent plant photosynthesis--was excluded from the accounting framework, as it represents a short-term carbon cycle between atmospheric and biogenic pools. Conversely, fossil-derived CO\({}_{2}\) emissions were fully incorporated into the assessment.  
Electricity-related carbon emissions included auxiliary power consumption for both production and administrative activities. While this component does not directly contribute to treatment processes, it was included in the accounting due to two practical considerations: (1) lack of universal sub-metering for office electricity across all surveyed plants and (2) its  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Design Scale/** (10\({}^{4}\) m\({}^{3}\)-d\({}^{-1}\))** & **Quantity** & **Number Fraction** & **Total Design Scale/** (10\({}^{4}\) m\({}^{3}\)-d\({}^{-1}\))** & **Proportion of Total Design Scale** & **Total Processing Scale/** (10\({}^{4}\) m\({}^{3}\)-d\({}^{-1}\))** & **Proportion of Total Processing Scale** \\ \hline
0–1 & 11 & 11.2\% & 9.7 & 2.2\% & 6.9 & 1.9\% \\
1–5 & 62 & 63.3\% & 193.9 & 43.3\% & 147.5 & 41.5\% \\
5–10 & 19 & 19.4\% & 146 & 32.6\% & 123.1 & 34.7\% \\
10–20 & 6 & 6.1\% & 98.1 & 21.9\% & 77.5 & 21.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Scale distribution of WWTPs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Process Type** & **Quantity** & **Number Fraction** & **Total Design Scale/** (10\({}^{4}\) m\({}^{3}\)-d\({}^{-1}\))** & **Proportion of Total Design Scale** & **Total Processing Scale/** (10\({}^{4}\) m\({}^{3}\)-d\({}^{-1}\))** & **Proportion of Total Processing Scale** \\ \hline AAO & 68 & 69.4\% & 323 & 72.1\% & 255.5 & 72.0\% \\ AO & 13 & 13.3\% & 37.5 & 8.4\% & 29 & 8.2\% \\ SBR & 4 & 4.1\% & 17 & 3.8\% & 14.2 & 4.0\% \\ MBR & 2 & 2.0\% & 15.5 & 3.5\% & 12.1 & 3.4\% \\ OD & 9 & 9.2\% & 52 & 11.6\% & 42.3 & 11.9\% \\ UCT & 2 & 2.0\% & 2.7 & 0.6\% & 1.8 & 0.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Process distribution of WWTPs.  
relatively minor contribution (0.7-1.6% of total electricity use) to the overall carbon footprint of WWTP operations [20]. This pragmatic inclusion ensures comprehensive system-level analysis while maintaining methodological consistency.
# Scene Adaptive Segmentation for Crowd Counting in Population Heterogeneous Distribution
## 5 Conclusions  
This research designs a novel end-to-end SASNet that can generate high-level density maps for crowd counting, which is an easily trained method. We adopt spatial pyramid pooling and full convolution to obtain a scene adaptive segmentation ratio, and receive two images of different densities. By training different views in the stage of crowd counting, the number of people is estimated from the mentioned two parts of the image, respectively. We conducted three mainstream crowd datasets compared with the SOTA performance. In the next research, we could exploit the division approach of scene density levels and extend our method to other counting tasks.  
Conceptualization, H.G.; methodology, H.G.; software, H.G.; validation, H.G., M.D. and W.Z.; formal analysis, W.Z.; investigation, W.Z; resources, M.D.; data curation, H.G; writing--original draft preparation, H.G; writing--review and editing, W.Z.; visualization, H.G.; supervision, D.Z.; project administration, D.Z.; funding acquisition, M.D. All authors have read and agreed to the published version of the manuscript.  
Figure 4: The histogram of average counts estimated by different approaches on six density levels from ShanghaiTech Part_A dataset.  
Figure 3: Visual comparisons of different methods on ShanghaiTech Part_A. (**a**): the sample images. (**b**): the ground truth. (**c**–**e**) correspond to the estimated density maps from MCNN, CAT-CNN, and the proposed SASNet, respectively.  
**Funding:** This research is supported by the National Key R&D Program of China (2018*******02), and the Major Public Welfare Project of Henan Province (201300311200).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Proposed Model Architecture
#### 2.3.4 Similarity Calculation and Loss Function  
The model learns to differentiate between matching and non-matching job-candidate pairs through a triplet loss function. Specifically, the GradualTripletLoss is applied. The core idea of triplet loss is to minimize the distance between an anchor embedding (\(A\), e.g., job description) and a positive embedding (\(P\), matching resume) while maximizing the distance between the anchor and a negative embedding (\(N\), non-matching resume).  
The loss for a triplet \((A,P,N)\) is defined as  
\[L(A,P,N)=\max\bigl{(}d(A,P)-d(A,N)+\alpha,0\bigr{)} \tag{1}\]  
where \(d(x,y)\) represents the distance between embeddings \(x\) and \(y\), while \(\alpha\) is the margin hyperparameter. In our implementation, the embeddings \((A,P,N)\) were L2-normalized before passing them to the loss function. The distance \(d\) is implicitly the Euclidean distance, which, for normalized vectors, relates directly to cosine similarity. The objective pushes the distance between the anchor and negative \(d(A,N)\) to be larger than the distance between anchor and positive \(d(A,P)\) by at least the margin \(\alpha\).  
The GradualTripletLoss variant modifies the margin based on the label strength of the positive sample (encoded numerically, e.g., 1 for 'positive', 2 for'soft_positive'), potentially applying a smaller effective margin for weaker positive matches. This is controlled by the margin (\(\alpha=0.4\)) and a scaler parameter (scaler = 4.0):  
\[L_{\text{gradual}}(A,P,N,label_{P})=\max\Bigl{(}d(A,P)-d(A,N)+\alpha\times\frac {5.0-label_{P}}{scaler},\,0\Bigr{)} \tag{2}\]  
The model was trained by minimizing the mean of this loss across the batches. The final similarity score between a job and a candidate for ranking purposes is typically calculated using the cosine similarity between their respective output embeddings after training.
# Panchromatic and Hyperspectral Image Fusion Using Ratio Residual Attention Networks
## 4 Results
### Ablation Experiment  
To validate the effectiveness of the key modules proposed in this work, we conducted an ablation study to assess the individual and combined contributions of the ratio transformation module and the residual attention module to the overall fusion performance.  
Tables 1 and 2 present the ablation study results of the proposed method on the EO-1 and Chikusei datasets, respectively. Overall, the complete proposed method significantly outperforms the baseline model, demonstrating the positive contributions of each module to fusion performance.  
On the EO-1 dataset, the baseline model achieves a Q index of 0.8817, an ERGAS of 6.1072, and a CC of 0.9122. The full proposed method improves the Q index to 0.9104, reduces ERGAS to 5.1973, and raises CC to 0.9221, indicating substantial enhancement in spatial structure preservation and spectral fidelity. Incorporating the ratio transformation module (Baseline + Ratio) notably improves spectral-related metrics such as ERGAS and SAM, suggesting its effectiveness in mitigating spectral distortion and enhancing inter-band consistency. Adding the residual attention module (Baseline + RA) further boosts spatial-related metrics like Q and CC, strengthening spatial details and structural coherence. The complete model synergistically combines these modules to achieve balanced optimization across both spatial and spectral domains.  
Similar trends are observed on the Chikusei dataset. The baseline model records a Q of 0.8821, an ERGAS of 4.7278, and a CC of 0.9182, whereas the full method improves these metrics to 0.9028, 4.1904, and 0.9298, respectively. Both the ratio transformation and residual attention modules contribute progressively to performance gains, with the complete method integrating their strengths. Notably, inference time increases slightly on both datasets (from 1.9335 s to 2.1149 s on EO-1, and from 0.6031 s to 0.6534 s on Chikusei), yet remains within a reasonable range, achieving a good balance between accuracy and efficiency.  
In summary, the ablation study convincingly validates the critical roles and complementary effects of the ratio transformation and residual attention modules in enhancing hyperspectral image fusion quality, highlighting the effectiveness and rationality of the proposed architecture.
## 2 Materials and Methods  
The TFT devices were fabricated on the glass substrate with a bottom-gate structure. First, 300-nm-thick Al-Nd alloy layer was deposited as gate electrode by direct-current (DC) magnetron sputtering and chemically patterned by wet etching. Secondly, a 200-nm-thick Nd-doped AlOx layer was covered on the gate electrode by anodic oxidation. The glass substrate containing 300 nm Al:Nd was immersed in a 3.48 wt.% ammonium tartrate solution (volume ratio of glycol to ammonium tartrate is 3:1). Al-Nd was connected with anode and Pt was connected with cathode. A fixed current of 0.1 mA/cm\({}^{-2}\) was applied and the voltage was increased linearly to 100 volts, then voltage was fixed at 100 volts and the current was decreased until it reached less than 1 \(\upmu\)A. After the anodic oxidation, 200 nm in Al:Nd was oxidized into Nd-doped AlOx. Then a 20-nm-thick Zr-AZTO layer was formed by vapor co-sputtering process with 10 cm target-substrate distance in a 5 mTorr mixed atmosphere (Ar:O\({}_{2}\) = 20:1) at room temperature. To fabricate devices with a different Zr content, the ZrO\({}_{2}\) target (\(\upphi\)50.8 *6 mm) was sputtered by 15 W, 25 W, 35 W, and 45 W while the AZTO target (\(\upphi\)50.8 *3 mm) with an Al\({}_{2}\)O\({}_{3}\):ZnO:SnO\({}_{2}\) ratio of 2:23:75 wt.% was sputtered by 100 W and 120 W. After that, the devices were annealed at 350 \({}^{\circ}\)C in air atmosphere. Finally, the 150-nm-thick Al source and drain electrodes patterned by shadow mask were deposited by 80 W DC magnetron sputtering. With the shadow mask, the channel width and Length was controlled at 500 nm and 700 nm. The electrical characteristics of the devices were measured by a semiconductor parameter analyzer in an air atmosphere. The Zr-AZTO films with different Zr content were tested by \(\upmu\)-PCD to reveal the concentration of carrier in the film and the relationship between the carrier concentration and electrical performance of devices.
# Multi-Task Learning for Small Brain Tumor Segmentation from MRI
## 3 Materials and Method
### Baseline Network  
Our model is based on a variant of U-Net called the dilated multifiber network [43] (DMFNet). We used DMFNet because it consumes a small amount of computation cost and memory, suitable for most GPUs while maintaining good performance. The DMFNet is based on the U-Net with the encoder-decoder network architecture. Firstly, a residual unit with two regular convolution layers is called a fiber [43]. The multifiber design consists of multiple separated residual units, or fibers. The multifiber (MF) unit takes advantage of a multiplexer for information routing and the dilated multifiber (DMF) unit with an adaptive weighting scheme for different dilation rates.  
The network exploring the 3D multifiber unit and 3D dilated convolution that helps to reduce computing costs while still having good performance for segmentation of brain tumors. The DMFNet use MF and dilated DMF unit as the basic blocks. In the encoder part, six DMF units extract the multiscale features by using the different kernel sizes in the dilated convolution layers. In the decoder part, the extracted features are upsampled and concatenated with the high-resolution feature map from the encoder part. The Rectified Linear Unit (RELU) functions and batch normalization are conductedprior to every convolution operation of DMF and MF units. Channel grouping is applied to separate the convolution channel into many groups of convolutions. The technique significantly reduces the model parameters between the feature maps.
# Energy Harvesting Hybrid Acoustic-Optical Underwater Wireless Sensor Networks Localization
## 2 System Model and Proposed Technique
### Proposed Localization Technique
#### 2.4.4 Complexity Analysis of the Proposed Technique  
In order to find the location of given sensor nodes, the first step is to find all the pairwise distances. Then, a procedure should be followed to minimize the discrepancy between the pairwise distances and their actual Euclidean distances in a low-dimensional space. To find the pairwise distances between non-neighboring nodes, single hop local distances or numerical methods for distance estimation are used [32; 33]. The time complexity to find out all the pairwise distances is \(O(Z^{3})\), where \(Z=m+n\) are the total number of nodes. The time complexity of the proposed multiple observation technique to compute the pairwise distances is on the order of \(O(Z^{3})\). The transformation from the local coordinates to global coordinates using anchor nodes requires \(O(m^{2})+O(Z)\) amount of time. Thus, the total complexity of the proposed technique is  
\[\text{Complexity}=O(Z^{3})+O(m^{2})+O(Z), \tag{36}\]  
which can be regarded as \(O(Z^{3})\) as the overall complexity is dominated by the first term.
## 3 Results and Discussions
### Dynamic Mechanical Analysis  
Asphal binder is a visco-elastic material which behaves as elastic solid at low temperatures while exhibiting fully viscous properties at higher temperatures. In mid-range temperatures, both elastic and viscous properties are shown. Distresses like fatigue and rutting in binder structure depends on the temperature and loading frequency. Dynamic mechanical analysis was done using DHR apparatus and G* and \(\delta\) were obtained. Complex shear modulus (G*) is the ratio of maximum shear stress to maximum shear strain while the phase angle (\(\delta\)) is the delay in response of material to applied stress and resulting strain.
###### Abstract  
In the context of supply chain sustainability, Physical Internet (PI or \(\pi\)) was presented as an innovative concept to create a global sustainable logistics system. One of the main components of the Physical Internet paradigm consists in encapsulating products in modular and standardized PI-containers able to move via PI-nodes (such as PI-hubs) using collaborative routing protocols. This study focuses on optimizing operations occurring in a Rail-Road PI-Hub cross-docking terminal. The problem consists of scheduling outbound trucks at the docks and the routing of PI-containers in the PI-sorter zone of the Rail-Road PI-Hub cross-docking terminal. The first objective is to minimize the energy consumption of the PI-conveyors used to transfer PI-containers from the train to the outbound trucks. The second objective is to minimize the cost of using outbound trucks for different destinations. The problem is formulated as a Multi-Objective Mixed-Integer Programming model (MO-MIP) and solved with CPLEX solver using Lexicographic Goal Programming. Then, two multi-objective hybrid meta-heuristics are proposed to enhance the computational time as CPLEX was time consuming, especially for large size instances: Multi-Objective Variable Neighborhood Search hybridized with Simulated Annealing (MO-VNSSA) and with a Tabu Search (MO-VNSTS). The two meta-heuristics are tested on 32 instances (27 small instances and 5 large instances). CPLEX found the optimal solutions for only 23 instances. Results show that the proposed MO-VNSSA and MO-VNSSTS are able to find optimal and near optimal solutions within a reasonable computational time. The two meta-heuristics found optimal solutions for the first objective in all the instances. For the second objective, MO-VNSSA and MO-VNSTS found optimal solutions for 7 instances. In order to evaluate the results for the second objective, a one way analysis of variance ANOVA was performed.  
Physical Internet; cross-docking; Rail-Road; sustainability; truck scheduling; energy consumption; Multi-Objective Programming; Lexicographic Goal Programming; hybrid meta-heuristics +
Footnote †: journal: Sustainable Energy Consumption