esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, we addressed the limitations of existing models for road extraction in remote sensing imagery by introducing MixerNet-SAGA, a groundbreaking deep learning architecture. Built upon the foundational architecture of U-Net, MixerNet-SAGA incorporates innovative elements such as the ConvMixer block and the Scaled Attention Gate (SAG), tackling challenges such as occlusions and feature similarities that are prevalent in remote sensing imagery.  
Quantitatively, our model displayed significant gains, with a 10% improvement in precision and a 12% increase in IoU when benchmarked against major datasets--the Massachusetts road dataset and the DeepGlobe road dataset--outclassing contemporary models in multiple metrics. Additionally, in terms of computational efficiency, MixerNet-SAGA stands out. With only 50.08 million parameters and requiring a mere 1.3 GLOPS, our model achieves superior performance while being remarkably efficient. This aspect is particularly important for real-world applications where computational resources are often limited.  
Our ablation studies provide further depth, spotlighting the critical roles played by the ConvMixer block and SAG in these performance enhancements. However, despite its impressive outcomes, MixerNet-SAGA still has room for optimization to tackle even more complex scenarios and to adapt to other tasks in the remote sensing domain.  
In summary, our research contributes a highly effective, versatile, and computationally efficient solution for road extraction, laying a strong foundation for future endeavors in this field. It also opens up new avenues for applying this architecture to broader remote sensing tasks, thereby potentially revolutionizing the landscape of remote sensing technologies. Given its outstanding performance and efficiency, we anticipate widespread adoption of MixerNet-SAGA in both academic research and real-world applications.  
Methodology, W.W. and C.R.; Validation, W.W.; Writing--original draft, A.Y.; Writing--review & editing, X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 42064003).  
Not applicable.  
The authors declare no conflict of interest.
# Energy-Efficient Hybrid Routing Protocol for IoT Communication Systems in 5G and Beyond
## 1 Introduction  
Recent advances in the field of wireless communications, electronics, and embedding system technologies have enabled the proliferation of large-scale wireless sensor networks (WSNs), with low-power, low-cost, multi-functional miniaturized wireless sensor nodes to communicate with each other using a radio frequency (RF) [1]. Such small sensor nodes, usually interconnected via an RF network and having a cooperative feature, are capable of sensing and controlling the intended environment, processing the collected data, and transmitting it back to a central station (CS). In particular, the convergence of WSNs and Internet of Things (IoTs) enables a ubiquitous interaction of things that have enormous applications both in private and public domains such as healthcare, industrial control systems, smart agriculture, smart cities, and environmental monitoring.  
Fifth-generation (5G) mobile technology has become the standard that can handle the proliferation of IoT devices while maintaining the required quality of services. Furthermore, potential enablers of 5G such as Multiple-Input Multiple-Output (MIMO) [2] have the capability to improve performance, reduce energy consumption in transmission, and become more desirable for IoT devices. However, the wireless sensor nodes have resource limitations [1, 3] such as memory, computation, and energy-constrained batteries. Thishas been one of the major problems in WSNs and related applications. Therefore, energy efficiency has been the prime goal when designing and deploying WSNs [4; 5].  
Wireless sensor nodes can be organized hierarchically into groups of clusters, to minimize energy consumption and provide a uniform distribution of load over the network [3]. Each cluster consists of Cluster Head that gathers data from the same cluster's sensor nodes and transfers this data to a central station. Several clustering techniques are introduced in the WSNs, including the Low-Energy Adaptive Clustering Hierarchy (LEACH) [6], Power Efficient Gathering in Sensor Information Systems (PEGASIS) [7], and Energy-Efficient Unequal Clustering (EEUC) [8]. However, the unsolved issues of accelerated energy depletion, hotspot problem [9; 10] in the previous clustering protocols for WSN should be addressed.
###### Abstract  
In the aerospace sector, structural and non-structural composite components are usually subjected to a wide range of environmental conditions. Among all, moisture can seriously damage these materials' performance, reducing their mechanical, thermal, electrical, and physical properties as well as their service time. Lightweight protective barrier coatings capable of reducing the diffusion of gases and/or liquids in a material can improve the material's resistance in humid environments. In this work, nanolamellar nanocomposites characterized by a high in-plane orientation of nanoplatelets have been employed as protective coatings for Kevlar sandwich panels, reproducing the construction of a nacelle engine. The effectiveness of the protection against water uptake of nanocomposites reinforced with graphite nanoplatelets (GNPs) at high filler contents (70, 80 and 90 wt%) has been investigated using moisture uptake and Ground-Air-Ground (GAG) tests in an environmental chamber. GNP coatings effectively work as barrier by generating highly tortuous paths for molecule diffusion. Results showed a dependence of the absorption on the coating composition and inner structure. Films #70 wt% GNPs showed the best protection against moisture uptake by delaying the phenomenon and reducing the absorption by \(-80\%\) after 3 days and \(-35\%\) after 41 days.  
graphite nanoplatelets; moisture uptake; barrier properties +
Footnote †: journal: Applied Sciences
## 4 Case Study
### Design of Retractable Joints  
To reduce reinforcement stress and control joint opening, a segmented lining system with retractable joints was developed, as illustrated in Figure 14. The lining is composed of reinforced concrete segments connected by specially designed mechanical joints. These joints include bolts, steel sleeves, washers, and internal springs, which together allow for controlled elongation under pressure. The core of the retractable joint is a two-headed bolt with a narrowed central section. One end connects to rebar via internal threads; the other fits into a steel sleeve joined by a spring. As the bolt elongates under internal pressure, the spring compresses and absorbs deformation. A mechanical stopper limits the movement, ensuring that the maximum elongation does not exceed \(\Delta L\), as shown in Figure 15.  
Figure 13: Maximum crack width of the lining with varying preset seams.  
These joints are prefabricated and assembled by threading them into the steel reinforcement before concrete casting. One segment embeds the bolt; the adjacent one houses the sleeve, with a gap between segments. When the internal pressure increases, the seams open, activating the joint system to release strain. This prevents excessive tensile stress in the reinforcement and limits concrete cracking. Although increasing the number of joints reduces stress and joint opening, it also weakens structural continuity. The top segment is especially prone to damage. The retractable joint system addresses this trade-off by balancing flexibility and structural stability.  
The rebar joints are represented using nonlinear spring elements. The axial load-displacement relationship follows a three-stage curve. During tension, the joint first behaves like a soft spring with stiffness \(k_{\mathrm{s}}\). This continues until the deformation reaches a threshold value \(\Delta L\) corresponding to the full compression of the internal spring. Beyond this point, both the sleeve and rebar begin to deform. In this stage, the stiffness increases and matches the axial stiffness of the steel bar, denoted as \(K\). In compression, if the rebar remains elastic, the joint responds linearly with stiffness equal to \(K\). Notably, the spring stiffness \(k_{\mathrm{s}}\) is much smaller than the steel bar's axial stiffness \(K\), making the system highly deformable in the initial stage.
## 1 Introduction
### IBCA for Monitoring of Caco-2 Cells Membrane Integrity and Viability in Real-Time
At the bottom of the E-plate 16, there is a biosensor-free window (Figure 1B) that facilitates microscopic visualization of cells and correlates electronic signals with morphological changes in cells observed under a microscope. According to the manufacturer, the magnitude of impedance depends on the number, shape, size, and degree of attachment of the cells. A small disruption to the cellular proteins or membranes, mainly due to the addition of a test compound, can lead to detachment of cells from the plate bottom or cause injury or death to the cells that can be immediately observed as a decrease or loss of impedance in real-time [15]. Although the xCELigence real-time device was already used to study the integrity of the Caco-2 membrane, the described protocol included 8 days of post confluence culture in a medium containing 10% FBS [16]. However, the exact time of incubation time before confluence was not given by the authors. Thus, the proposed incubation time could be insufficient for full Caco-2 cell differentiation, as at least 17-18 days of cell growth is recommended. Moreover, the exact number of cells seeded on E-plate 16 was not reported in the article, and the type of E-plate used was not given, either [16]. The applied xCELigence system (Roche Applied Science), known as the Real-Time Cell Electronic Sensing System (RT-CES, ACEA Biosciences), is also not currently offered for sale by Agilent Technologies. Thus, in the present work, we decided to optimize the xCELigence RTCA S16 system, enabling real-time monitoring of Caco-2 cell proliferation, viability, morphology, and membrane integrity on E-plates 16 so that appropriate experiments could be carried out on fully differentiated Caco-2 cells. The optimization procedure included determination of the appropriate number of cells for seeding, selecting the suitable concentration of FBS as well as the time of media changes. The differentiated Caco-2 cell cultures on E-plate 16 were treated next with either 100 ng/mL of TNF-\(\alpha\) or 100 ug/mL of 3,4-dihydroxy-L-phenylalanine (L-DOPA) in order to verify the possibility of real-time testing the membrane integrity or viability of Caco-2 cells by the xCELigence RTCA S16 system. Figure 1: Schematic representation of current flow in the RTCA system. Cells are grown on working electrodes, and the AC current flows under the working electrode (**A**). The Caco-2 cells on E-plate 16 after 9 days of incubation (**B**). The formation of a monolayer was observed by Leica DMi8 microscopy (10\(\times\)). The cells were seeded in the concentration of 2000 cells/well and incubated in 20% FBS-containing media for the first 6 days and for the next 8 days in 1% FBS. At the bottom of the E-plate 16, there are both electrodes and a biosensor-free window that facilitates microscopic visualization of cells and correlates electronic signals with morphological changes in cells, which can be observed under a microscope. The electrode diameter is approximately 90 μm and is marked on the Figure 1B.
# A History of Channel Coding in Aeronautical Mobile Telemetry and Deep-Space Telemetry
## 3 Deep-Space Telemetry
### Mariner
This was not achievable with the worst-case link conditions using the (32,6) Reed-Muller code  
Figure 7: A block diagram of the _Mariner_ 6 spacecraft. alone. For this reason, a concatenated coding scheme illustrated in Figure 8 was used [30]. The outer code was a short (6,4) \"generalized Hamming code\" defined over GF(2\({}^{6}\))--each 6-bit word was treated as a code element--with minimum distance \(d_{\text{min}}=3\) that was capable of correcting \(t=1\) element error. The inner code was the (32,6) Reed-Muller code described above. The idea was that when the (32,6) Reed-Muller code made an error, the erroneous decoder output was the wrong 6-bit word. If only one Reed-Muller decoder error occurred in six consecutive words, then the outer code saw this as a single error and could correct it. The field generator polynomial used for the (6,4) code was [30],  
\[F(X)=X^{6}+X+1 \tag{4}\]  
over GF(2). The generator matrix was as follows:  
\[G= \begin{bmatrix}1&1&1&0&0&0\\ 1&\alpha&0&1&0&0\\ 1&\alpha^{2}&0&0&1&0\\ 1&\alpha^{3}&0&0&0&1\end{bmatrix}  \tag{5}\]  
where \(\alpha\) is a primitive element in GF(2\({}^{6}\)). Decoding was accomplished using syndrome decoding. The equivalent bit-level encoder and decoder are described in [30]. _Mariner_ 9 was the first to use concatenated coding for space communications. Unlike later programs, such as _Voyager_ 1 and _Voyager_ 2, which used a convolutional code as the inner code, _Mariner_ 9 used a block code as the inner code. What is common is the use of soft-decision decoding for the inner code.
# Performance Modeling Framework for IoT-over-Satellite Using Shared Radio Spectrum
## 4 SINR Model Approximation and Optimal Parameter of IoT-over-Satellite  
As mentioned in the previous section, a comprehensive uplink SINR model can help to estimate the quality and performance of the IoT-over-Satellite channel and thus to provide the PER and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Spreading Factor** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline SINR (dB) & \(-5\) & \(-8\) & \(-10\) & \(-14\) & \(-16\) & \(-18\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The lowest SINR values limit in LoRa chirp spread spectrum (CSS) modulation with six spreading factors and 125 KHz bandwidth.  
Figure 5: The packet error rate (PER) versus signal-to-interference-plus-noise ratio (SINR) with different Spreading Factor (SF) in LoRa modulation.  
availability of the satellite channel link. Therefore, we derived the SINR model based on the simulation and channel models explained in Section 3. The linear mathematical expression is shown in the following:  
\[\overline{\gamma}\left(\theta,t\right)=\frac{P_{T}\times G_{T}\times G_{\text{R} }(\theta,t)}{L_{\text{Sat}}\times\rho\times\overline{N}_{\text{active}}}\, \tag{16}\]  
where \(\overline{\gamma}\) at a measurement point highly depends on the average IoT devices population \(\overline{N}_{\text{active}}\) in the area, and due to the reason that IoTs population of remote and rural areas tend to be constant as shown in Figure 6, we assume that the average interference per user (\(\overline{P_{I}}\)) in the remote or rural area can be simplified into a constant \(\rho\), and \(G_{\text{R}}\) is a function based on the coordinates of the satellite cube-sat as shown in (3). The coordinates are varying over the period of time.
## 3 Proposed One Dimensional Atrous Convolution Nerual Network
### Atrous Convolution Layer  
In CNN, the convolution layer is a part for feature extraction. In hyperspectral image classification using 1D-CNN, it is used to extract spectral information features. As mentioned earlier, the convolution layer in the existing 1D-CNN features only adjacent spectral information. This can be confirmed from Equation (2). On the other hand, the Atrous Convolution Layer can extract features from distant spectral information according to the size of 1 as shown in Equation (3). Figure 5 represents 1D-atruos convolution with a kernel size of 3 and a rate size of 3.  
\[y[n]=x[n]*h[n]=\sum_{\infty}^{\infty}x[k]h[n-k] \tag{2}\]  
\[y[n]=(x*R\cdot h)[n]=\sum_{\infty}^{\infty}x[k]h[n-R\cdot k]. \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layers** & **Kernel Size** & **Feature Maps** & **Dilated Rate** & **Stride** & **Padding** \\ \hline
**Conv1** & 1 & 1 & 1 & 1 & 1 \\ \hline
**Conv2\_1** & 1 & 32 & 1 & 1 & 0 \\ \hline
**Conv2\_2** & 3 & 32 & 6 & 1 & 6 \\ \hline
**Conv2\_3** & 3 & 32 & 12 & 1 & 12 \\ \hline
**Conv2\_4** & 3 & 32 & 18 & 1 & 18 \\ \hline
**Conv3** & 1 & 64 & 1 & 1 & same \\ \hline
**Conv4** & 3 & 32 & 1 & 1 & same \\ \hline
**Conv5** & 3 & 32 & 1 & 1 & same \\ \hline
**FC1** & \multicolumn{4}{c}{Input: feature size, Output: feature size/2} \\ \hline
**FC2** & \multicolumn{4}{c}{Output: 128} \\ \hline
**FC3** & \multicolumn{4}{c}{Output: number of classes} \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of ODPA-CNN.  
Figure 4: Proposed CNN Model, ODPA-CNN: AtConv is 1D-Atrous Convolution. Conv is normal 1D-Convolution. Table 1 has detailed parameters of each convolution. In this model, AtConv is processed in parallel to extract features for bands separated by various distances.
# RRT*-Fuzzy Dynamic Window Approach (RRT*-FDWA) for Collision-Free Path Planning
## 4 Dynamic Path Planning Algorithm Based on RRT*-FDWA  
### RRT*-FDWA Algorithm Flow  
The RRT*-FDWA path planning algorithm uses both the RRT* and FDWA algorithms. When the global optimal path is completed according to the RRT* algorithm, a path replanning judgment is performed. When a moving obstacle is encountered, the FDWA algorithm is used for local path planning; after successfully avoiding the obstacle, the RRT* algorithm plans a new global path for the mobile robot to continue driving. The RRT*-FDWA process algorithm is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\theta_{d}\) & \(O_{d}\) & PS & PM & PB \\ \hline
0 & PB & PB & PS & 0 \\ PS & PB & PB & PS & 0 \\ PM & PB & PM & PM & 0 \\ PB & PB & PM & PM & PS \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(\beta\) rule list.  
Figure 3: Change in heading angle.  
The algorithm flows as follows:  
1. Create a map of the known environment and set target points.
2. The RRT* algorithm plans a collision-free global optimal path based on the already established map environment and its own sensors, such as lidar.
3. Determine whether the robot can reach the target point according to the end condition of the algorithm; if so, end the algorithm; if not, continue the execution.
4. Determine whether there is an environment or moving obstacle in the established map; if the lidar determines that the moving obstacle is dangerous, jump to the FDWA algorithm for local path planning to avoid the obstacle.  
Figure 4: RRT*-FDWA flow chart.  
* Perform a new global path plan, determine the optimal path, and continue driving along the global path. Judge whether it is possible to reach the target point: if 'No', jump to the Step 3 loop; if 'Yes', end the algorithm. Figure 5 represents the global path planning, encountering moving obstacles in the path replanning process.
# Molecular Science of Lubricant Additives +
## 2 Individual Lubricant Additives and Their Working Mechanism
### Rheo-Improvers (Rheological Properties Improvers)
#### 2.2.2.2. Pour Point Depressant (PPD)  
Lubricants may freeze if while machine pauses at low temperatures. This causes lubrication failure and leads to serious damages to machine elements. A pre-heating system can prevent the problem, but it consumes considerable time and energy. PPDs are beneficial for machineries under cold climates. PPDs are typically branched hydrocarbons [68]. Each branched alkyl group can interact with oil molecules, thereby preventing the crystallization of the oil molecules together. Because of the branching structure, the PPD molecule can keep free volume around the molecule that this makes the partial structure mobile (Figure 11).  
Both VM and PPD are macro-molecules (polymers) compared to the molecular mass of base oil molecule. However, the functions of VM and PPD are different. The main function of VM is to suppress mobility at higher temperature thereby increasing the viscosity.  
On the other hand, branched structure is beneficial for PPDs because the molecule has to be responsible for mobility at lower temperatures. The functions of both additives involve a physical effect and the changes are reversible by temperature change unless the molecule decomposes.
# Spatial Image-Based Walkability Evaluation Using Regression Model
## 2 Proposed Method
### Data Acquisition
#### 2.1.2 Crowdsourcing  
Walkability evaluation is conducted by having crowdsourcing participants [27; 28] select the image of the environment they consider more suitable for walking from two randomly presented road images. The pairwise image comparison employs the comparison set generation algorithm proposed by [PERSON] et al. [29], and a total of 193,600 pairwise comparison datasets are constructed using 21,168 images out of 42,360 road images. The image pairwise comparison dataset is represented as Equation (1).  
\[\mathrm{P}\ =\ \left\{\mathrm{i}_{\mathrm{x}},\mathrm{j}_{\mathrm{x}},\mathrm{s}_{ \mathrm{x}}\right\}_{\mathrm{x=1}}^{\mathrm{N}},\ \mathrm{i},\mathrm{j}\in\left\{1,\ldots,\mathrm{N}\right\},\ \mathrm{s}\in\left\{+1,-1\right\}, \tag{1}\]  
where \(\mathrm{i}_{\mathrm{x}}\) and \(\mathrm{j}_{\mathrm{x}}\) denote the images and \(\mathrm{N}\) denotes the total number of images. Additionally, \(\mathrm{s}=+1\) indicates that \(\mathrm{i}_{\mathrm{x}}\) was selected, whereas \(\mathrm{s}=-1\) indicates that \(\mathrm{j}_{\mathrm{x}}\) was selected.  
The TrueSkill algorithm is used to calculate the walkability scores (mean, standard deviation) of road images used in the walkability evaluation. TrueSkill obtains stable scores by reducing noise in the binary predictions for the pairwise image comparisons [30]. The algorithm calculates the scores \(\mathrm{N}\left(\mathrm{\mu}_{\mathrm{i}},\mathrm{\sigma}_{\mathrm{i}}^{2}\right)\) and \(\mathrm{N}\left(\mathrm{\mu}_{\mathrm{j}},\mathrm{\sigma}_{\mathrm{j}}^{2}\right)\) for images i and j, respectively, and normalizes their values to between 0 and 100. These scores are updated each time an evaluation is conducted and are used to determine the final ranking. Table 1 presents the image information dataset, which includes the number of times an image was compared with other images in the walkability evaluation, the number of wins in the comparison, and the image score calculated through the TrueSkill algorithm. Table 2 presents the pairwise image score dataset containing the image scores before and after the comparison and the results of the comparisons.  
Figure 2: Road views of Jeonju-si, Republic of Korea (**a**) left, (**b**) front, (**c**) right, (**d**) back.