esa-sceva/satcom-chunk-collection huggingface dataset:
# Edge-Guided Dual-Stream U-Net for Secure Image Steganography
## 3 The Proposed Method
### Encoder Architecture  
The dual-stream U-Net module in our encoder is depicted in Figure 2. The module receives the cover image and the enhanced edge image as input. The features of the shrinking and expanding paths are fused through a specific jump-joining strategy. The original input stream introduces multi-scale median attention (MSMA) in the contraction path, while  
Figure 1: The overall framework of the proposed method.  
the edge enhancement stream introduces the InceptionDMK module combining depth-separable convolution with multi-scale filtering, as detailed in the following structure:  
Dual-stream Input: The edges and contours of an image are critical areas for steganography. To better learn these features, we design a dual-stream U-Net structure with both the cover image and the edge-enhanced image as inputs. The edge-enhanced image emphasizes edge features, assisting the network in learning the image's texture and structural information more effectively.  
Skip Connections: Different skip connection strategies are used to fuse feature maps between the contracting and expanding paths, ensuring that image details and structure are preserved, as shown in Table 1. Specific network structure of dual-stream U-Net. The feature flow \(Group\)\(n(E)\) from the contracting path's enhanced image is fused with the feature flow \(Group\)\((10-n)\) from the expanding path, expressed as \(G_{fusion(5-n)}=fusion(G_{nE}+G_{10-n})\)\((n=1,2,3,4)\). The fused features are then concatenated with the feature flow \(Group\)\(n(C)\) from the contracting path's cover image, expressed as \(G_{concat(5-n)}=concat\Big{(}G_{nc},G_{fusion(5-n)}\Big{)}\).  
Multi-scale median attention (MSMA): To further enhance performance, MSMA is applied after each layer of the contracting path for the original input stream, but not in the enhanced image stream. This decision is based on two considerations: (1) the original image typically contains more noise and redundant information, which MSMA can effectively suppress; and (2) the edge features of the enhanced image are already significantly highlighted, and applying MSMA could risk overfitting.  
InceptionDMK Module: This module combines depthwise separable convolutions with multi-scale feature extraction to effectively capture multi-scale and multi-directional features of the image. It consists of four branches: a bypass branch retaining the original information, a 3 \(\times\) 3 convolutional kernel branch, and 1 \(\times\) 11 and 11 \(\times\) 1 strip convolutional kernel branches for capturing edge information from different directions. The outputs from these branches are concatenated along the channel dimension, preserving tensor shapes and significantly improving edge detail extraction.  
The following sections will provide a detailed explanation of the edge-enhanced image technique, the MSMA mechanism, and the InceptionDWL module.  
Figure 2: General structure of dual-stream U-Net and InceptionDWL.
## 3 Environmental Applications of Organoclays
### Water Pollution

Table 3: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Clay** & **Modifier** & **Methodology** & **Application** & **Efficiency** & **Reference** \\ \hline Montmorillonite (China) & Biochar composites & Cation exchange & Heavy metal Removal & 8.163 mg g\({}^{-1}\) & [98] \\ \hline Montmorillonite (China) & Cetyl pyridinium chloride & Cation exchange & Heavy metal Removal & 95.99\% (47.83\) mg g\({}^{-1}\)) & [99] \\ \hline Montmorillonite (Viet Nam) & Starch & Cation exchange & Heavy metal Removal & Pb(II) 99\% Cd((II) 96.7\% N((II) 52.8\%) & [100] \\ \hline Montmorillonite (Iran) & chlorosulfonic acid & Cation exchange & Trihalomethanes & 87\% & [101] \\ \hline Montmorillonite (Egypt) & Chitosan & Cation exchange & Dye Removal & 276.03 mg g\({}^{-1}\) & [102] \\ \hline Montmorillonite (Iran) & Sodium dodecyl sulfate & Cation exchange & Dye Removal & 98.24\% & [103] \\ \hline Montmorillonite (Iran) & Alum & furance & Dye Removal & 2500 mg g\({}^{-1}\) & [104] \\ \hline \hline \multicolumn{5}{c}{2021} \\ \hline Montmorillonite (Germany) & Cetyl trimethylammonium bromide & Cation exchange & Dye Removal & 769.23 mg g\({}^{-1}\) & [105] \\ \hline \multicolumn{5}{c}{In situ the free radical polymerization} \\ Montmorillonite (Egypt) & with poly(vinylimidazole) & Croton exchange & Dye removal & 99.99\% (909.1\) mg g\({}^{-1}\)) & [106] \\ \hline Montmorillonite (China) & (3-Mercaptopropyl) trlmethodoxysilane & Cation exchange & Heavy metal Removal & 97\% (Pb) & [107] \\ \hline Montmorillonite (India) & Nano-structured hetero-nuclear poly-hydroxox complexes & Cation exchange and Calcination & Arterial drug dicodidine Removal & 96.5\% & [108] \\ \hline Montmorillonite (China) & \multirow{2}{*}{L-methionine} & Cation exchange & Phenol Removal & 647.7 mg g\({}^{-1}\) & [109] \\ \cline{1-1} \cline{4-5} \cline{5-5} \cline
## 3 Results & Discussion
### Toxic Equivalence Factor (TEF)  
TEF was calculated to assess the risk presented by PAHs during the entire sampling periods in the study area. Toxic compounds with the same mechanisms of action can generally be expressed in relation to members of the most toxic group; this is known as the toxic equivalence factor (TEF) [4]. The TEF of PAHs was proposed by [PERSON] and [PERSON] in 1992 and is calculated based on the most toxic of the PAHs, BaP [56]. In this study, following the methods of previous studies, biological PAH levels were corrected for biological activity by multiplying the concentration of each PAH in the air by the TEF (the value expressed as BaP\({}_{\text{TEF}}\)) [57]. The TEFs used in this study are as follows: 1 for BaP and DBA; 0.1 for BaA, BBF, and BkF; and 0.01 for Chry.  
The results of this and previous studies calculated using the TEF are shown in Table 2[58; 59; 60; 61]. The average \(\sum\)BaP\({}_{\text{TEF}}\) concentration was 0.8765 ng/m\({}^{3}\), which was similar to or lower than the target  
Figure 5: Ratios of BC, EC, and OC according to the analysis of PM\({}_{2.5}\).  
Figure 6: Time series concentrations of OC, EC, and BC and the operational period of the asphalt mix plant.  
values of the European Commission Air Quality Standard (1 ng/m\({}^{3}\)) and the French Atmospheric Quality Standards and Criteria (0.9 ng/m\({}^{3}\)). However, the maximum concentration of \(\sum\)BaP\({}_{\text{TEF}}\) was 5.0280 ng/m\({}^{3}\) during the study period, and about 28% of the time, the values exceeded 1 ng/m\({}^{3}\). Seasonal \(\sum\)BaP\({}_{\text{TEF}}\) concentrations were as follows: winter (2.284 ng/m\({}^{3}\)), spring (0.575 ng/ m\({}^{3}\)), autumn (0.550 ng/m\({}^{3}\)), and summer (0.176 ng/m\({}^{3}\)).  
The \(\sum\)BaP\({}_{\text{TEF}}\) concentrations in the study area were compared with those in the Korean capital and the background location. As shown in Table 2, \(\sum\)BaP\({}_{\text{TEF}}\) in the study area was about 6.5 times higher than the background concentration of 0.1317 ng/m\({}^{3}\) and more than three times higher than that in the capital city (0.2863 ng/m\({}^{3}\)). This clearly shows that there are sources of pollutants that emit PAHs in the study area.  
The \(\sum\)BaP\({}_{\text{TEF}}\) concentrations found in the study area were compared to those of major cities (Table 2). \(\sum\)BaP\({}_{\text{TEF}}\) concentrations in the study area were lower than those in Riyadh, Saudi Arabia and Wuhan, China, but higher than those in Minnesota, USA and Taichung, Taiwan. Riyadh, Saudi Arabia is known for its high PAHs due to the presence of heavy traffic as well as refineries and power plants near the city [59]. Wuhan, China is the largest city in central China and is known to produce a large number of airborne pollutants, such as a high concentration of fine dust [58]. \(\sum\)BaP\({}_{\text{TEF}}\) in Minnesota, USA, shown in Table 2, is concentrated around roadsides in urban areas, and \(\sum\)BaP\({}_{\text{TEF}}\) in Taichung consists of PAHs concentrated in areas around coal-fired power plants [60; 61].
# Pointer Meter Recognition Method Based on Yolov7 and Hough Transform
## 4 Experiment
### Analysis of Experimental Results
#### 4.4.3 OCR Evaluation Index  
PaddleOCR calculates three OCR end-to-end related indicators, namely precision (precision rate), recall (recall rate), and Hmean (F1 value). Precision refers to the ratio of the number of correct characters in the OCR recognition result to the total number of characters. The recall rate refers to the ratio of the number of correct characters in the OCR recognition result to the total actual number of characters. The Hmean is the harmonic mean of precision and recall. Their calculation formulas are in Formulas (14)-(16) [41]:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}, \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **MioU** & **Acc** & **Kappa** & **Dice** & **Class IoU** & **Class Precision** & **Class Recall** \\ \hline ann & 0.8084 & 0.9967 & 0.7639 & 0.8819 & [0.9967, 0.6202] & [0.9984, 0.7609] & [0.9983, 0.7703] \\ pspnet & 0.807 & 0.9966 & 0.7618 & 0.8809 & [0.9966, 0.6175] & [0.9985, 0.7431] & [0.9981, 0.7851] \\ deplabv3 & 0.8029 & 0.9966 & 0.7555 & 0.8777 & [0.9966, 0.6093] & [0.9984, 0.7475] & [0.9982, 0.7672] \\ deplabv3+ & 0.8773 & 0.9981 & 0.8604 & 0.9302 & [0.998, 0.7565] & [0.9991, 0.8577] & [0.999, 0.865] \\ danet & 0.8059 & 0.9965 & 0.7601 & 0.88 & [0.9965, 0.6153] & [0.9985, 0.7337] & [0.998, 0.7921] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of different models for image segmentation on the pointer dataset.  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}, \tag{15}\]  
\[\text{Hmean}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}}, \tag{16}\]  
Among them, TP represents the number of characters correctly recognized by OCR, and FP represents the number of characters incorrectly recognized by OCR [42].  
In this experiment, the accuracy rate of PGNet is 98.04%, the recall rate is 81.97%, and the Hmean rate is 89.29%. The reason why the Hmean value is not high may be that the pointer will cover the scale value in some cases, resulting in the inability to accurately identify the scale, which is a common problem. As shown in Figure 10, the pointer covers the \"0\" scale, making it impossible to accurately identify the \"0\" scale value. However, under normal circumstances, the pointer will not reach the maximum value of the meter, which has little effect on the operation of taking the maximum scale value.
# Special Issue on Microgrids/Nanogrids Implementation, Planning, and Operation
## 2 Special Issue Contributions
In this context, a smart algorithm has been developed; namely the quasi-oppositional Bonobo optimizer for the optimal economic design of a stand-alone microgrid in Aswan, Egypt. The proposed study achieved results that highlighted the efficiency of the adopted optimization algorithm for different hybrid system scenarios. Furthermore, a sensitivity analysis has shown that the PV system sizing is critical as it significantly impacts the microgrid's overall performance. [PERSON] et al. in [8], carried out a study to evaluate the potential of long-term forecasting of energy production and demand in microgrids. In this context, in terms of the scale of a research building, both energy demand and production on a long-term scale have been proposed to be predicted using R software in combination with microgrid data records. The proposed study identified supporting evidence supporting the potential to model both solar power generation and the microgrid (building in this study) energy consumption simultaneously, based on the parameter of solar irradiance and in combination with known time parameters affecting building occupancy. [PERSON] et al. in [9], proposed a comprehensive overview of state-of-the-art trends in hierarchical microgrid control. This review-based study has shown the variability of coordinated control schemes in terms of objectives and applications. This review highlighted decentralization and centralization technique characteristics, along with non-communication versus communication approaches. Primary and secondary levels were described by showing different modes of operation as a hierarchical control strategy. Furthermore, a comparative study highlighted the pro and cons of controllers, including the inner loop, power sharing, voltage and frequency restoration, and grid synchronization schemes.
# A New Integrated Multi-Criteria Decision-Making Model for Resilient Supplier Selection
## 4 Case Study
### Weights Calculation  
In this phase, five experts' opinions on the criteria were collected, and the grey relational grades (GRG) which are the importance levels were determined using GRA as  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** \\ \hline R1 & 4 & 4 & 5 & 4 & 5 & 5 & 5 & 4 \\ R2 & 4 & 4 & 4 & 3 & 2 & 4 & 4 \\ R3 & 3 & 4 & 4 & 4 & 3 & 4 & 3 \\ R4 & 2 & 3 & 5 & 3 & 5 & 3 & 5 \\ R5 & 1 & 2 & 5 & 5 & 4 & 4 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experts’ opinions on criteria for GRA (Phase 1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**C** & **Criteria** & **Definition** \\ \hline C1 & Quality & Ability to meet or exceed customers’ expectations. \\  & & [3,6,11,12,14,16,18,19,21,38] \\ C2 & Lead Time & Time taken from releasing an order to receiving the materials. \\  & & [3,11,12,14,16,19,21] \\ C3 & Cost & Total SC cost contributed by a supplier in operating a SC. \\  & & [6,10,11,12,14,16,18,19,20] \\ C4 & Flexibility & Readiness to react to different supply chain turbulences. \\  & & [3,6,9,11,11,12,13,14,16,18,19,21] \\ C5 & Visibility & Transparency in sharing relevant business information. \\  & & [3,6,9,11,13,18,38] \\ C6 & Responsiveness & Ability to respond to customer demands in a minimal time. \\  & & [6,10,13,15,16,19,21] \\ C7 & Financial Stability & Ability to have positive and growing cash flow. \\  & & & [9,14,16,21,38] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Resilience criteria.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Best to** & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** \\ \hline C3 & 9 & 8 & 1 & 7 & 4 & 5 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experts’ opinions on comparison of best criterion over others for BWM (Phase 2).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **S1** & **S2** & **S3** & **S4** & **S5** & **S6** & **S7** & **S8** & **S9** & **S10** \\ \hline C1 & 3.8 & 3.8 & 4.2 & 3.8 & 3.8 & 4.0 & 3.8 & 3.4 & 3.8 & 4.2 \\ C2 & 2.8 & 2.4 & 2.6 & 4  
shown in Table 10. Following this, the weights of each criterion were computed using BWM as illustrated in Table 11. The result consistency was then determined by calculating the consistency ratio as shown in Table 12. To ensure the consistency, the consistency ratio should be close to 0.
## 5 Examples
### Environment Spectrum Compilation with Altitude Features  
According to the fuzzy clustering results, the service environment spectrum was compiled using the altitude data of 153 flight profiles from aircraft. The results are shown in Figure 9. The time spent on flight training is not positively correlated with the FCDF. The time proportion of each cluster, namely, the sum of the flight time for all profiles in each cluster divided by the total flight time of the 153 profiles, is sorted in descending order at 2, 4, 3, and 1. Though the largest time proportion occurs in cluster 2, the FCDF is 2.78 in this cluster. This means that, although the FCDF is not the highest, the mid- to low-altitude flights take more time for flight training.
## 2 Architectural Principles
### Horizontal M2M
It is seen that more real-time messaging, capabilities to handle not always on mobile devices and capabilities for more dynamic topologies are needed. Therefore, XMPP technology has been selected to enable real-time M2M messaging, presence management and dynamic topologies. To enable the interoperation of ETSI M2M SCL with XMPP, development of required interworking proxy is under work. The challenge related to the constrained embedded M2M devices can be solved with the aid of M2M gateway. Such a gateway can take care of mapping of protocols to be more applicable for embedded capillary networks and devices, and enable interoperability between various proprietary networks. For example, M2M gateway (may also be called as a border router) can translate HTTP to CoAP, IPv6 to 6 LowPan, XMPP to Bluetooth Smart and 6 LowPan to Bluetooth Smart messages. Traditionally, creation of trust can be established in hop by hop manner between M2M asset devices and M2M applications. This kind of model can be challenging in M2M systems, because of M2M information content may be business critical and it may contain high privacy requirements. Therefore, it is here proposed that creation of trust and M2M data distribution can be separated from each other, and divided to distinct stakeholders, if it is required by the case. Mechanisms and a new model for credential management have been provided in this work to enable end to end security for M2M applications with a separate trust provider and authentication server.
## 4 Discussion
### The Role of Urbanization in Vegetation Change  
Expanded analysis revealed a strong correlation (R\({}^{2}\) = 0.6872) between nighttime light intensity and NDVI, indicating urbanization as a major driver of greening. Statistical results are supported by scatter plots illustrating this relationship. Additionally, a threshold effect was observed: beyond a certain urbanization level, NDVI gains stabilized or declined, highlighting the limits of greening in highly urbanized areas. Strategic urban planning integrating green spaces is critical for sustainable vegetation growth.  
The relationship between NDVI and nighttime light in our study does not follow a simple linear trend; instead, it presents a curved or two-step linear trend, suggesting the presence of a potential threshold effect. This pattern indicates that as nighttime light intensity--a proxy for urbanization--increases, vegetation (indicated by NDVI) initially shows a positive correlation with urban expansion, likely due to enhanced greening efforts, such as urban landscaping and the establishment of green spaces. However, beyond a certain threshold of urbanization intensity, the growth in NDVI levels begins to stabilize or even diminish.  
This trend could reflect a stage of urban development where further greening efforts become secondary to the demands for construction and infrastructure expansion, which reduce available space for vegetation. In highly urbanized areas, vegetation cover may reach an upper limit, after which additional nighttime light intensity primarily indicates dense urban infrastructure rather than increased vegetation. This finding aligns with observations in other regions where early urban growth is associated with urban greening, but excessive urbanization reduces the relative importance of green spaces [62].  
These insights reveal that there may be a limit to urban greening under intense urbanization pressures. This threshold effect underscores the importance of strategic urban planning that prioritizes sustainable green space integration to maintain ecological balance in rapidly urbanizing areas. Future studies could explore this phenomenon further by examining detailed land-use data to better understand how different stages of urbanization impact vegetation dynamics.
### Application of RAM in Mechanized Tunneling Projects  
Investigating the delays of several mechanized tunneling projects, [15; 16; 17] concluded that more than 60% of the whole project delays are associated with TBM system delays. In addition, [18] suggested a general method for calculating system utilization by considering delays in mechanized tunneling projects. Citation [19] provided an alternative muck removal system for the third line of the Tabriz urban railway by studying delays during the construction of the first line. By defining a block definition diagram (BDD), [20; 21] used a hybrid discrete-event simulation approach to predict the total duration of an Earth pressure balance machine (EPBM) project by considering all project delays. A few studies [22] considered RAM application in TBM projects. These researchers have all studied the effects of delays, but the impact of TBM failures and TBM's RAM have not explicitly been investigated.  
By dividing the EPBM into mechanical, hydraulic, pneumatic, water, and electrical subsystems and running a RAM analysis, [22] evaluated the EPBM's condition using the network modeling (block diagram) and calculated the availability of the EPBM to be about 48%. The belt conveyor lines of the Tabriz urban railway line 2 were also analyzed using the same method (network modeling) by [23], and RAM values were calculated. Instead of using network modeling, for the first time [4] used the Markov chain to analyze the RAM values of EPBM for an irrigation tunnel project in India. By dividing the EPBM into four subsystems, he formed the transition diagram of the system and calculated its RAM values with related equations.  
In this paper, the availability value for sections of the Isfahan metro Line 1 construction is calculated using network and Markov methods to determine the most appropriate method. Reliability and maintainability diagrams are then analyzed using the best de termined method. Finally, RAM values of Isfahan metro Line 2 construction using the refurbished EPBM of Line 1 are analyzed and compared to the Line 1 values.
# A Resource Oriented Route Selection Framework Using Contextual Information Based on Fuzzy Logic
###### Abstract  
Internet of Things (IoT) is expected to have a significant impact on city's service provisioning and make a smart city more accessible and pragmatic since the deployment of heterogeneous smart devices in each infrastructure of cities is increasing. So far, the IPv6 routing protocol for low power and lossy networks (RPL) is considered to fit on IoT infrastructure for achieving the expected network requirements. While RPL meets the IoT network requirements quite well, there are some issues that need to be addressed, such as adaptability to network dynamics. This issue significantly limits the use of RPL in many smart city application scenarios, such as emergency alerts with high traffic flows. As part of a smart city vision, IoT applications are becoming more diverse, which requires context-awareness in routing protocols to support the behavior of the network. To address this issue, we design an objective function that performs the route selection based on fuzzy logic techniques while using contextual information from the application. For this, we present a new context-oriented objective function (COOF) that comprises both nodes as well as link metrics. Further, we suggest two new routing metrics, known as queue fluctuation index (QFI) and residual energy index (REI), which consider the status of queue utilization and remaining energy, respectively. The metrics used are designed to respond to the dynamic needs of the network. The proposed approach has been examined and evaluated in different scenarios when compared to other similar approach and default RPL objective functions. Simulation experiments are conducted in Cooja network simulator for Contiki OS. The evaluation results show that COOF can cope with network dynamics and IoT-based smart city application requirements.  
low power and lossy network; internet of things; 6 LoWPAN; RPL; routing metrics; objective function +
Footnote †: journal: Accepted: 10 September 2019, Published: 12 September 2019