esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Scenarios
### Protocol Stack  
The IP brings interoperability through established end-to-end protocols and frameworks, whereas non-IP data transport has a lower overhead and potentially leads to a higher capacity and energy efficiency. SCHC provides the best of both worlds by enabling an efficient use of the IP stack on constrained networks and devices. The proposed protocol stack providing interoperability and security for the IoT is shown in Figure 3. On the left-hand side lies the protocol stack without SCHC. On the right-hand side, the use of SCHC allows the support of IPv6 to be introduced, whose header is compressed as well as the DLMS, UDP and DTLS. Note that the headers of message exchange protocols such as DLMS are compressed prior to encryption when DTLS is used.  
Figure 3: IoT protocol stack without SCHC vs. with SCHC.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Without SCHC** & **With SCHC** \\ \hline Library & MbedTLS & MbedTLS \\ \hline Configuration & not customized & customized \\ \hline Connection ID & not used & enabled \\ \hline Hello Cookie & used & removed \\ \hline Compression & not applied & DLMS, UDP, IP, DTLS (2 layers) \\ \hline Encryption & DLMS & DLMS, UDP and IPv6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of DTLS implementations with and without SCHC.  
In NB-IoT, multiple paths can be used to transmit data, leading to different protocol stacks (see Section 4.4 for more details). Consequently, data can be sent over the IP or NIDD. In both cases (SCHC and without SCHC), DLMS/COESM is used in the application layer. The application data are sent directly over NIDD when SCHC is not used. Using SCHC with NIDD enables IPv6 and a UDP to be introduced, for which the headers are compressed by SCHC. When sent over the IP path, the application data are sent via the UDP over IPv4 when SCHC is not used. In the case of using SCHC over an IP path, IPv6 and a UDP are introduced, for which their headers are compressed and sent over IPv4. The following sections further illustrate the possible paths of data over NB-IoT and the structure of the packet for the different scenarios considered in this paper.
# A New Passive Islanding Detection Solution Based on Accumulated Phase Angle Drift
## 4 Application of Composite PAD Solution
### Rate of Change of Frequency  
For system operation events, such as load or generation switching, which cause the system frequency fluctuation, normally the rate of the change of frequency normally is less than 0.5 Hz/s with relatively large system inertia. If the ratio of the inverter-based DG is relatively higher in the whole power system, the maximum transient ROCOF value will increase. The basic principle is that the setting for \((df/dt)\_set\) is slightly larger than the maximum value of ROCOF due to the planed system operations.  
In the UK, G59 [23] recommends the ROCOF settings for loss of main protection is 1.0 Hz/s with 500 ms time delay for the new installed DG units. The ROCOF relay with these recommended settings should be stable for all the manifestations of expected ROCOF and its accompanying voltage vector shift events that are expected on the power system. G59 also recommends the minimum setting for ROCOF is 0.5 Hz/s.  
The default setting for \((df/dt)\_set\) is suggested as 0.5 Hz/s. The stable setting for \((df/dt)\_set\) can be 1.0 Hz/s. Even if the transient maximum ROCOF value is larger than the setting \((df/dt)\_set\), the islanding condition will not be claimed by the PAD solution before the accumulated phase angle shift achieves the threshold \(\sum Ang\_set\).
## 2 Theoretical Background
### Spatial Fuzzy C-Means  
The traditional FCM clustering does not take into account spatial relationship among neighboring pixels, making it sensitive to noise and other imaging artifacts [37; 38]. Breast lesions generally tend to grow in an isotropic way, preserving a pseudo-spherical appearance [39]. Relying on those features, it is expected that neighbouring pixels in a digital image are highly correlated and that the probability that they belong to the same cluster is great. Therefore, the use of the sFCM, taking advantage of spatial relationship of neighbouring pixels, can help image segmentation [40; 41]. The spatial function used by sFCM is defined in Equation (11):  
\[h_{ij}=\sum\limits_{k\in\mathcal{N}(x_{j})}{u_{ik}}, \tag{11}\]  
where \(\mathcal{N}(x_{j})\) represents a square neighborhood (in the spatial domain) around the pixel \(x_{j}\). The term \(h_{ij}\) represents the probability that the pixel \(x_{j}\) belongs to \(i\)-th cluster: as a result, the spatial function of a pixel for a cluster is large if the majority of its neighbourhood belongs to the same cluster. The contribution of the spatial function modifies the classic FCM membership function according to Equation (12):  
\[u_{ij}^{\prime}=\frac{u_{ij}^{p}h_{ij}^{q}}{\sum\limits_{k=1}^{C}{u_{kj}^{p}h _{kj}^{q}}}, \tag{12}\]  
where \(p\) and \(q\) control the relative importance of both functions. In a homogeneous region, the spatial functions simply emphasizes the original membership and the clustering remains unchanged. On the other hand, for a noisy pixel this formula reduces the weighting of a noisy cluster by the labels of its neighboring pixels: as a result, misclassified pixels from noisy regions can easily be corrected. The sFCM clustering process involves two steps at each iteration: the former, which is the same as that in standard FCM, allows for calculating the membership function in the features domain, while the latter maps the membership information of each pixel into the spatial domain and allows to calculate the spatial functions for each pixel of the image. At this point, the FCM iteration proceeds with the new membership that is incorporated with the spatial function and stops when the maximum difference between cluster centers at two successive iterations is lower than a certain threshold. After the convergence, a defuzzification scheme (i.e., maximum membership) is applied.
# W-IFL: An Improved Maximum Power Point Control Model to Promote Renewable-Powered Vehicles
## 3 The W-IFL Methodology
### Model Construction  
As existing tracking methods, in general, require frequently and artificially adjusting monitoring parameters offline and increasing sensors to ensure the controlling accuracy online, which may inevitably increase MPP oscillation, resulting in high cost and complexity, the tracking model can be set up as a neural-fuzzy (NF) structure to optimize accuracy and efficiency, where the predictor trains the controller's output, and the prediction from predictor is used as controller's input.  
However, since current ANN and FL methods have the subjective definition of their initializations, leading to the lack of systematicity and interpretability of the controlling process, e.g., the widely used BP network [40] or classic FL controller [34] use fixed transfer or membership functions. These methods, in general, have good accuracy but loss flexibility, and they may not meet the actual tracking requirements.  
Therefore, the proposed W-IFL MPP Control Model, namely W-IFL MPPT Model in Figure 1A, can be used to better support effective and efficient tracking. For example, the wavelet function eases the calculation of orthogonal basis and weight independently, thus avoiding blindness when designing a NN structure. Moreover, the discrete training data collected from PV cells, as shown in Table 1, also indicate the feasibility of W-IFL to process non-timing signals.  
The model structure, as described in Figure 1B, illustrates that the inputs to the wavelet predictor are temperature, time, and irradiance, which output the voltage at predicted MPP to the fuzzy controller. Therefore, the fuzzy controller also requires the actual voltage and current for fuzzing and the variables, namely ke, kec, and kdu, for controlling. In addition, feedback from the controller to the predictor transfers the offset voltage, thereby supporting online training, prediction, and control.
# Aerodynamic Characteristics of a Square Cylinder with Vertical-Axis Wind Turbines at Corners
## 2 Numerical Simulation Configurations
### Computational Configuration and Mesh Arrangement  
The 2D computational domain, 92 \(W\) long and 25 \(W\) wide, is illustrated in Figure 1a in which the width (\(W\)) of the cross section is 0.1 m and the cylinder centroid is defined as the origin of the coordinate. The lateral boundary is 12.5 \(W\) away from the cylinder centroid in terms of the previous studies [22]. In order to allow flow redevelopment behind the wake region, the distances between the cylinder centroid and the inlet and the outlet boundary are 30.5 \(W\) and 61.5 \(W\), respectively.  
As for the boundary conditions, a smooth flow condition with a wind speed of 9 m/s is used at the inlet (\(u=9m/s\), \(\partial p/\partial n=0\)) and the Reynolds number is \(6.0\times 10^{4}\), which is consistent with that in [22]. At the outlet boundary, a zero static pressure is adopted (\(p=p_{0}=1.013\times 10^{5}\), \(\partial u/\partial n=0\)). Two lateral boundaries are defined as a symmetry boundary condition (\(v=0\), \(\partial u/\partial n=0\)), which is used to model zero-shear slip walls in viscous flows. An empty condition is enforced on the top and bottom surfaces of the computational domain, which is an ordinary method to solve the 2D problem in OpenFOAM. Two vertical-axis wind turbines are installed at two leading corners of the square cylinder and the nonslip wall boundary condition (\(ui=0\), \(\partial p/\partial n=0\)) is applied to surfaces of the cylinder and the wind turbines, where \(n\) refers to the normal direction, \(u\) and \(v\) represent the velocity components in x and y directions, respectively.
## 3 Results and Discussion
### Dynamic Shear Rheometer  
#### 3.4.1. Putting Parameter and Complex Shear Modulus  
Figures 6 and 7 show the obtained results of the DSR experiment. The figures display the rutting parameter (G*/sin(\(\delta\))) and complex shear modulus (G*), respectively. To understand the behavior of each modifier, the optimum content for each modifier was selected (e.g., TPS 3%, PP 3.5%, and CRM 4%). These concentrations represented the ideal percentages to improve the rutting resistance property as well as the medium- and high-temperature properties of the modified bituminous binders. Figure 6 shows that the G*/sin(\(\delta\)) decreased when the temperature increased. Furthermore, the values obtained from the CRM-modified binder was higher than those of the PP- and TPS-modified binders. The increase in G*/sin(\(\delta\)) increased the exact grade temperature of the asphalt binder. Thus, the PG-temperature grade of the modified asphalt roe significantly when CRM content increased, ranging from PG58-xx to PG88-xx for the binders with modifier concentrations ranged from 2% to 4%. The PG grade achieved by the PP-modified binder ranged from PG80-xx at 4% PP. Yet the PG grade obtained by the TPS-modified binder was not significant and ranged from PG73-xx for all concentrations.  
According to G*/sin(\(\delta\)) values, the CRM-modified asphalt binders have a higher resistance to permanent deformations (rutting) than their PP and TPS counterparts. Thus, CRM is the most appropriate additive for enhancing medium and high-temperature performance properties (i.e., mechanical performance).  
It can be noted that the addition of CRM, PP, and TPS increased the G*. With respect to the base asphalt, the increase in the complex shear modulus (G*) in each modified binder indicated the degree of change in the structures and compositions of bitumen, which allowed for the modifiers to influence the mechanical properties of the modified bitumen. The G* values decreased with the temperature increased as illustrated in Figure 7. The results demonstrated that the G* value increased when TPS concentration increased by 3% and then decreased. Meanwhile, the G* increased when PP concentration increased by 3.5% and then reduced. Therefore, the 3% and 3.5% represent the ideal percentages of the TPS and PP modifiers to enhance the medium- and high-temperature performance. The G* value increased with CRM concentration and increased for all CRM contents from 2 to 4%. Note that Figures 6 and 7 are plotted from these ideal percentages.
# Biopurification of Oligosaccharides by Immobilized _Kluyveromyces Lactis_
## 3 Results
### Impact of Pre-Activation of Immobilized Cells  
With the immobilization of concentrated _K. lactis_, lactose could be removed rapidly in water without any lag period. To design a continuous process for further utilization, the reusability of immobilized cells was evaluated. After the first-round of biopurification, immobilized cells were stored at 4 \({}^{\circ}\)C for 48 h then reused for the next round of biopurification. One group of cells was used after pre-activation in YNB media with 20 g/L of lactose for 4 h, and another group was used without any pre-activation.  
The immobilized cells with or without pre-activation were monitored to observe the lactose consumption and ethanol production (Figure 2). Repeated use of immobilized cells without pre-activation showed remarkable decreases in lactose consumption and ethanol production. Compared to the first-round process, 48 h of storage decreased the lactose removal activity 5.6 fold. Meanwhile, after the pre-activation of immobilized cells by 4 h of incubation restored the activity 2.5-fold higher than that of without pre-activation. It is concluded that at least 4 h of pre-activation is necessary for the recovery of lactose consumption efficiency for the repeated use of immobilized _K. lactis_.  
Figure 2: The impact of pre-activation of immobilized _K. lactis_. Lactose consumption (**A**) and ethanol production (**B**) differences of immobilized _K. lactis_ cells with or without pre-activation. Filled circle, immobilized cells with pre-activation; open circle, immobilized cells without pre-activation.
# 3D Point Cloud Completion Method Based on Building Contour Constraint Diffusion Probability Model
## 4 Experimental Section
### Ablation Experiment
latent code \(M\) exhibits some minor point cloud omissions at the edges of the completion area compared to real buildings. However, the completed point cloud deviates less overall. Our model combines both contour-constrained shape latent codes \(C\) and \(M\). In the completion area, the point cloud at the roof's edge is nearly identical to the real building point cloud. This indicates that the building contour constraints effectively suppress the generation of point clouds outside the building's geometric space, significantly improving the geometric accuracy of point cloud completion and reducing the generation of non-realistic point clouds.
# Multi-Scale Twin Networks for Coastal Zone Change Detection in Remote Sensing Imagery
## 5 Conclusions  
In this work, we propose AMMNet, a multi-scale coastal zone change detection method that leverages advanced attention mechanisms to address the challenges of feature extraction and class imbalance in coastal zone change detection tasks. The model integrates the high-frequency attention module (HFAM), spatio-temporal difference attention module (SDAM), and foreground attention module (FAM) to enhance feature extraction and contextual integration.  
The model first extracts multi-scale features using a ResNet backbone and optimizes them via the HFAM. The fused feature maps are then processed by the SDAM, capturing both global and fine-grained local change information. The FAM further refines these features, allowing the model to focus on change regions, ultimately generating the change map through a thresholding process.  
We used remote sensing data from Gaofen and resource series satellites covering Tianjin, Liaoning, Shandong, and Hebei provinces to create the BCZ_CD dataset for training and testing. The experimental results show that AMMNet outperforms the traditional methods across all six key evaluation metrics--the IoU, precision (Pr), recall (Rc), F1 score, overall accuracy (OA), and Kappa. Specifically, AMMNet achieved outstanding results in the IoU (90.263%) and Kappa (94.735%), which demonstrate its superior performance and consistency in coastal zone change detection.  
Further validation on the LEVIR_CD public dataset confirms AMMNet's generalizability and adaptability to different coastal environments. The ablation experiments demonstrated that incorporating the HFAM, SDAM, and FAM significantly enhances model performance, with the best results achieved when all three modules were combined, highlighting the synergy of these attention mechanisms.  
In summary, AMMNet combines multi-scale feature extraction with powerful attention mechanisms, leading to substantial improvements in detection accuracy and model generalization. However, the model's computational complexity and large size remain challenges for real-time deployment on general-purpose hardware. Future work will focus on optimizing the network architecture to reduce these limitations and improve scalability, enabling broader deployment in real-world applications.
# Efficient Route Management Method for Mobile Nodes in 6 TiSCH Network
## Abbreviations  
\begin{tabular}{l l} TSCH & Time Slotted Channel Hopping \\
6 TiSCH & IPv6 over the TSCH mode of IEEE 802.15.4e TSCH \\ RPL & Routing Protocol for Low-Power and Lossy Networks \\ DODAG & Destination-Oriented Directed Acyclic Graph \\ DIO & DODAG Information Object \\ DAO & DODAG transmits the destination Advertisement Object \\ DIS & DODAG Information Solicitation \\ UWB & Ultra-WideBand \\ \end{tabular}
# Inorganic Salt Hydrate for Thermal Energy Storage
## 5 Application
### Salt Hydrates for Hot Water Tanks  
Presently, the most widely used heat storage systems for solar thermal energy are hot water tanks, which process an energy storage density of 69.4 kW\(\cdot\)h\(\cdot\)m\({}^{-3}\) (\(\Delta\)T = 60 K) but a finite storage time [PERSON] et al. (2016). Salt hydrates with melting temperature of 50-60 \({}^{\circ}\)C can be incorporated into hot water tanks of solar domestic to reinforce stratification. Salt hydrate PCMs put off the time of reaching thermal equilibrium between top layer with higher temperature and bottom layer with low temperature. An experimental solar stand was constructed by [PERSON] (2016) with two identical thermal solar collectors. The picture of the installation is shown in Figure 3. One of them was inserted by sodium acetate trihydrate. It was found that the inclusion of a PCM module in water tanks for domestic hot-water supply is a very promising technology. It would allow to have hot-water for longer periods of time even without exterior energy supply, or to use smaller tanks for the same purpose. Three types of encapsulated commercial salt hydrates (DC58, PT58 and HD60) are tested inside tanks through continuous temperature measurements with four sensors installing at various heights [75]. The percentage of liquid state of PCMs remaining after tests was obtained by energetic analysis, from which the solidification processes could be clearly knew.
# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 2 Materials and Methods
### Image Acquisition and Plant Height Measurement in Maize Populations  
In this investigation, a DJI Phantom 4 Pro, including the drone, remote controller, and 20-megapixel CMOS camera was used to acquire a multi-view image sequence of a maize population in a field setting at a resolution of 6000 \(\times\) 4000. The UAV employed a tic-tac-toe flight route planning flight to gather the slanted data in four directions and make sure that the overlap area between two adjacent photographs was 70-80% in order to more thoroughly capture the 3D information of the corn (Figure 2). Each red dot in a denotes a photo taken from the UAV aircraft route, while b indicates the angle and location of the image capture. The selection of 70-80% image overlap was based on its proven effectiveness in improving 3D reconstruction accuracy, particularly in dense canopy conditions:  
1. Enhanced feature matching: A high overlap percentage ensures that a sufficient number of shared feature points are detected across adjacent images. This improves the alignment accuracy during the structure from motion (SfM) process, resulting in a more reliable reconstruction of the maize canopy;
2. Improved spatial consistency: Overlap levels of 70-80% reduce gaps or inconsistencies in the point cloud data, particularly in areas with complex structures like overlapping leaves or varying canopy heights. This ensures a more continuous and detailed representation of the field;
3. Minimized reconstruction errors: In cases of lower overlap, key areas may be under-represented, leading to incomplete reconstructions or errors in dense regions. By maintaining 70-80% overlap, the software (Pix4 Dmapper) is better able to handle variations in lighting, perspective, and texture across the field, reducing potential noise in the resulting 3D model;
4. Balancing processing efficiency: While higher overlap percentages (e.g., above 80%) can theoretically improve accuracy, they significantly increase data storage and processing time. The 70-80% overlap strikes a balance between achieving high accuracy and maintaining reasonable computational requirements.  
This overlap configuration is widely adopted in UAV-based 3D reconstruction studies and has been validated in similar agricultural applications. The resulting point cloud generated in this study achieved sufficient density and precision to accurately measure plant height and analyze maize populations.