esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiple Mobile Sinks for Quality of Service Improvement in Large-Scale Wireless Sensor Networks
## 3 Methodology
### Cluster-Based Routing Protocols  
Many recent articles have treated the impact of sink mobility in the WSN with multi-hop routing mechanisms [15; 16]. In such a type of routing, the nodes closest to the sink dissipate their energies rapidly since they retransmit the collected data from distinct sensors to the sink, which divides the network, isolates the sink, and creates energy holes.  
The use of mobile sinks has considerably alleviated these concerns in terms of reliability, throughput, and consumed energy. However, the data delivery delay was still modest due to the accumulated delay for each hop. Furthermore, this routing technique is not suitable for larger networks since the required number of hops is influenced by the number of deployed nodes. In such case, other than the number of hops, the delivery delay increases, and interferences between packets also increases, which rapidly and significantly degrades the throughput [15; 16].  
The cluster-based routing protocols have proven good energy conservation results and low data delivery latency [11; 12; 13; 14]. For these reasons, in this study, we investigate the effect of using multiple mobile sinks with a cluster routing paradigm in large-scale WSNs. We will introduce the technique of such a routing protocol with the LEACH [14], P-LEACH [51], and EA-CRP [52]. The Table 1 provides a brief comparison between the key features of the three routing protocols to be studied.
# Fluorescent Imprinted Nanoparticles for Sensing of Chlorogenic Acid in Coffee Extracts
###### Abstract  
Green coffee beans are particularly rich in chlorogenic acids (CGAs), and their identification and quantification are usually performed by HPLC, coupled with mass spectrometry (LC-MS). Although there are a few examples of molecularly imprinted polymers (MIPs) for chlorogenic acid (5-CQA) recognition present in the literature, none of them are based on optical fluorescence, which is very interesting given its great sensitivity. In the present manuscript, fluorescent polymeric imprinted nanoparticles were synthetized following the non-covalent approach using hydrogenated 5-O-caffeoylquinic acid (H-5-CQA) as the template. The capability of the polymer to bind 5-CQA was evaluated by HPLC and fluorescence. A real sample of coffee extract was also analyzed to verify the selectivity of the polymer. Polymer fMIP01, containing 4-vinylpyridine and a napthalimide derivative as monomers, showed a good response to the fluorescence quenching in the range 39 uM-80 mM. In the real sample, fMIP01 was able to selectively bind 5-CQA, while caffeine was not recognized. To demonstrate this, there is a promising system that can be exploited in the design of an optical sensor for 5-CQA detection. Polymer fMIP01 was immobilized by physical entrapment on a functionalized glass surface, showing a quenching of fluorescence with an increase of the CGA concentration between 156 uM and 40 mM.  
polyphenols; chlorogenic acids; molecularly imprinted polymers; fluorescence; sensor 222 29874.
# Feasibility Analysis of Adopting the Hydrogen Hydrostatic Thrust Bearing
## 4 Application Parameters
### Orifice Diameter  
Static parameters with different orifice diameters are presented in Figure 8. Other parameters remained unchanged, as shown in Table 3. Increasing the orifice diameter around the designed orifice value could expand the load capacity. Additionally, the peak value of static stiffness decreased with increases in the orifice diameter. The film clearance corresponding to the peak static stiffness gradually increased with increases in the orifice diameter.
# Rod-Airfoil Interaction Noise Reduction Using Gradient Distributed Porous Leading Edges
## 1 Introduction
[19] to numerically study the aeroacoustics and aerodynamics of airfoils equipped with porous leading edges. They found that the porous leading edges reduce interaction noise by dampening surface pressure fluctuations, but may result in lift reduction and drag increase. Although porous materials have been used to reduce interaction noise, they have dominantly been applied to verify the feasibility and effectiveness of the concept, and the porous parameters have not been optimized [17]. The porous materials used in the current bio-inspired noise reduction concept generally have homogenous pore properties, such as porosity and pores per inch (ppi). The effect of nonhomogenous porous materials with gradient-distributed properties [20; 21] on the rod-airfoil interaction noise reduction is not well-understood. These two aspects motivated our experimental study, in which we aimed to advance the field by using piecewise gradient distributed porous leading edges to reduce rod-airfoil interaction noise. The detailed acoustic measurements in an open jet wind tunnel indicated that the proposed porous leading edges reduce the peak SPL of the rod-airfoil configuration, and can significantly reduce the medium- to high-frequency noise above the peak frequency. Moreover, parametric tests on the porous properties (coverage, position, and arrangement order) provided the relevant design criteria for the gradient distributed treatments, i.e., larger porous coverage and reasonable arrangement order.
## 3 Results
### The Fungal Data Set
\begin{table}
\begin{tabular}{c c c c} \hline
**Genus** & **Fungal Name** & **References** & **Environment** \\ \hline _Lactarius_ & _Lactarius_ sp. & [66] & C-NHE \\ \hline _Lecanicillium_ & _Lecanicillium pallidae_ & [38,59] & C-HE, NC-HE \\ \cline{2-3}  & _Lecanicillium_ sp. & [68] & C-HE \\ \hline _Leptobacillus_ & _Leptobacillus_ & [37] & NC-HE \\ \hline _Leptosphaeria_ & _Leptosphaeria_ sp. & [65] & O-SPE \\ \hline _Leptosphaerulina_ & _Leptosphaerulina_ sp. & [64] & NC-HE \\ \hline _Macrophomina_ & _Macrophomina phaseolina_ & [28,31,32] & NC-HE \\ \hline _Malbranchea_ & _Malbranchea_ sp. & [46] & C-NHE \\ \hline _Mammaria_ & _Mammaria echinobutyrooides_ & [83] & C-HE \\ \hline _Mennoniella_ & _Mennoniella_ sp. & [31] & C-NHE \\ \hline \multirow{4}{*}{_Metapochonia_} & _Metapochonia bulbillosa_ & [47] & C-HE, NC-HE \\  & (syn. _Verticillium bublbillosum_) & [52] & NC-HE, C-NHE \\ \cline{2-3}  & _Metapochonia suchlasporia_ & [52] & NC-HE, C-NHE \\  & (syn. _Verticillium suchlasporium_) & [60] & NC-HE \\ \hline _Meyerozyma_ & _Meyerozyma quilliermondii_ & [60] & NC-HE \\ \hline \multirow{4}{*}{_Microascus_} & _Microscusbreviaulis_ & [21,52,68] & C-HE, NC-HE, C-NHE \\  & (syn. _Scopuliaropsis breveiaulis_) & [21,52,68] & C-HE, NC-HE, C-NHE \\ \cline{2-3}  & _Microscus chartarum_ & [62] & NC-HE, C-NHE \\  & (syn. _Scopulariopsis chartarum_) & [62] & NC-HE, C-NHE \\ \cline{2-3}  & _Microscus cirrosus_ & [67] & SC-NHE \\ \cline{2-3}  & _Microscus_ sp. & [68] & C-HE \\ \hline _Microdochium_ & _Microdochium lycopodium_ & [38] & C-HE \\ \hline \multirow{4}{*}{_Monilinia_} & _Monilinia_ sp. & [82] & C-NHE \\  & (syn. _Monilia_ sp.) & [82] & C-NHE \\ \cline{2-3}  & _Monocillium_ & [23] & C-HE \\ \hline \multirow{4}{*}{_Monodictys_} & _Monodictys castaneae_ & [21] & C-NHE \\  & (syn. _Stenphyllum macrosporoideum_) & [21] & C-NHE \\ \cline{2-3}  & _Monodictys_ sp. & [31,58] & C-HE, C-NHE \\ \hline \multirow{4}{*}{_Mortierella alpine_} & _Mortierella alpine_ & [47] & NC-HE \\ \cline{2-3}  & _Mortierella ambigua_ & [38] & C-HE \\ \cline{2-3}  & _Mortierella parcispora_ & [31] & C-NHE \\ \cline{2-3}  & (syn. _M. gracilis_) & [31] & C-HE \\ \cline{2-3}  & _Mortierella_ sp. & [47,58] & C-HE, NC-HE \\ \hline \multirow{4}{*}{_Mucor_} & _Mucor plumbus_ & [21] & C-NHE \\  & (syn. _M. spinosus_) & [28,32,47,60] & NC-HE \\ \cline{2-3}  & _Mucor racemosus_ & [28,32,47,60] & NC-HE \\ \cline{2-3}  & (syn. _M. globosus_) & [28,32,32] & NC-HE \\ \cline{2-3}  & _Mucor silvaticus_ & [28,32,3] & NC-HE \\ \cline{2-3}  & _Mucor_ sp. & [23,46,58,64,82,93] & C-HE, NC-HE, C-NHE \\ \hline \multirow{2}{*}{_Myxotrichum_} & _Myxotrichum stipittatum_ & [46] & C-NHE \\ \cline{2-3}  & _Myxotrichum_ sp. & [46] & C-NHE \\ \hline _Nectria_ & _Nectria_ sp. & [15] & C-NHE \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# Effect of Shot Peening on the Evolution of Scale on T91 Steel Exposed to Steam
## 3 Results
### Characteristics of the Oxidation Scale  
Figure 4 shows cross-sectional EBSD micrographs of untreated and shot peened specimens after steam exposure at 650 \({}^{\circ}\)C for 200 h. The phase identification revealed that the oxides are Fe\({}_{2}\)O\({}_{3}\) in the top layer, followed by Fe\({}_{3}\)O\({}_{4}\) layer and then a Fe-Cr-O spinel layer, which agrees with the oxidation scale structure reported by [PERSON] et al. [5].  
Figure 4: EBSD results of the oxidation scale of T91 test: **(a)** IQ map, **(b)** IPF with orientation map, **(c)** phase map of untreated specimens after 200 h steam exposure, **(d)** IQ map, **(e)** IPF with orientation map, and **(f)** phase map of SP-treated specimens after 200 h steam exposure.  
Figure 5 shows cross-sectional SEM micrographs of the untreated (a1-a4) and SP-treated (b1-b4) specimens after steam exposure at 650 \({}^{\circ}\)C for 100, 600, 800, and 1000 h. After steam exposure, the thickness of the scale formed on the untreated specimens increased rapidly with oxidation time. In contrast, the oxidation scale formed on the shot peened specimens was relatively stable and grew only slowly with increasing oxidation durations. In addition, numerous defects, such as voids and micro-cracks, formed between the Fe\({}_{2}\)O\({}_{3}\) and the Fe\({}_{3}\)O\({}_{4}\) layer in the untreated specimens, as shown in Figure 5(a1-a4). With a further increase of exposure time to 800 h, exfoliation of the outer layer and breakage of the inner layer were observed in the untreated specimens as shown in Figure 5(a3). However, the shot peened specimens maintained a compact scale, with fewer defects during the entire steam exposure, Figure 5(b1-b4). The diffusion of iron beyond these defects is greatly retarded and thought to be the main reason for the separation of the outer layer. The process of the exfoliation of the outer layer progress is as [PERSON] proposed [13].
## 5 Numerical Experiments
### Performance Analysis of Algorithm  
In this section, we analyze the performance of the improved round heuristic and Gurobi on different datasets. The proposed heuristic algorithm is coded in Python 3.6, and the Gurobi version is 9.1.2. All numerical experiments were completed on a Windows 10 personal computer with Intel(R) Core (TM) i7-6700 CPU 3.40 and 16.0 GB of RAM. As  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **List of Stops** & **ID** & **List of Stops** \\ \hline
1 & s17-s8-s6-s4-s1 & 40 & s1-s6-s10-s11-s17 \\
2 & s17-s11-s1 & 41 & s1-s8-s11-s14-s17 \\
3 & s17-s10-s8-s6-s4-s1 & 42 & s1-s8-s12-s17 \\
4 & s17-s11-s1 & 43 & s1-s8-s10-s12-s13-s17 \\
5 & s17-s11-s10-s8-s5-s1 & 44 & s1-s4-s6-s8-s11-s17 \\
6 & s17-s12-s10-s6-s1 & 45 & s1-s6-s8-s15-s17 \\
7 & s17-s12-s11-s10-s8-s1 & 46 & s1-s6-s10-s11-s14-s17 \\
8 & s17-s13-s11-s8-s4-s1 & 47 & s1-s8-s10-s12-s13-s14-s17 \\
9 & s17-s11-s6-s1 & 48 & s1-s8-s11-s13-s17 \\
10 & s17-s12-s10-s8-s1 & 49 & s1-s6-s8-s12-s17 \\
11 & s17-s12-s10-s8-s1 & 50 & s1-s4-s17 \\
12 & s17-s11-s3-s1 & 51 & s1-s6-s17 \\
13 & s17-s8-s7-s1 & 52 & s1-s8-s17 \\..  
shown in Table 3, we randomly generated five datasets with a different number of OD pairs. What is more, we set the parameter 'TimeLimit' of Gurobi to 30 min.  
As shown in Table 3, several tests indicate that the computation time of Gurobi increases nonlinearly with the number of OD pairs. When there are more than 200 OD pairs, the optimal solution cannot be found within a given time limit. Due to the mature large-scale linear solution technology, the improved round heuristic algorithm can find an acceptable solution in a relatively short time, and the worse gap with Gurobi is 9.45%. With the increase of the OD pairs, the gap gradually decreases. Experiments show that our improved algorithm is more suitable for solving real-scale cases.
# Adversarial Hierarchical-Aware Edge Attention Learning Method for Network Intrusion Detection
## 4 Methodology
### Edge Attention Mechanism Embedding
This formulation explicitly incorporates edge features \(\mathbf{e}_{uv}\) during neighborhood aggregation, preserving protocol-specific semantics critical for network intrusion detection. Finally, \(K\) layers of message-passing edge embeddings are generated by concatenating the features of their incident nodes. \[\mathbf{z}_{uv}\leftarrow\text{CONCAT}(\mathbf{z}_{u}^{(k)},\mathbf{z}_{v}^{(k)})\]  
This ensures that each edge embedding captures the collaborative representation of its source and destination nodes, enriched by the edge attention mechanism. The architecture achieves three key advantages: (1) Edge feature preservation through \(\mathbf{e}uv\) integration prevents information loss common in conventional GNNs. (2) Adaptive attention weights \(\mathbf{z}_{uv}\) suppress benign flow patterns while amplifying suspicious connections. (3) Layer propagation captures multi-grained attack, with shallow layers detecting anomalies and deeper layers identifying attack types.
## 2 Materials and Methods
### Record Selection and Dataset Balancing  
The generated dataset is extremely large, containing more than 72 million records. As it is a real-time dataset, i.e., it has been collected over a realistic IoT network, it is a highly imbalanced dataset that mostly contains attack categories. Hence, we have selected about three million records, including an equal number of attack and normal categories. This huge amount of data has been used to train our model accurately.  
Handling Class Imbalance  
The BoT-IoT dataset, while highly comprehensive, presents a significant challenge in the form of severe class imbalance. This imbalance arises due to the overwhelming representation of certain attack categories and subcategories compared to others, which can lead to biased model training and poor generalization for underrepresented classes. To address this, we adopted a hybrid sampling approach to achieve class balance. Our hybrid sampling strategy combines both oversampling and undersampling techniques to effectively mitigate the imbalance without overfitting or discarding valuable data. Random oversampling  
Figure 1: A representation of used methodology.  
was applied to DDoS_HTTP and DoS_HTTP to amplify their representation in the dataset. Additionally, we utilized the Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic samples of Keylogging and Data Theft classes, whose representation in data was very low, ensuring that the new data points are realistic and maintain the underlying data distribution. Simultaneously, random undersampling was employed for the majority classes to reduce their dominance while retaining the most representative samples. We carefully selected representative samples from the majority classes using random undersampling to preserve the diversity of the dataset and minimize information loss. By combining these two methods, our hybrid sampling approach achieves a balanced distribution across all classes, as shown in Figure 2.
## 4 Joint Extraction on Tunnel Faces Based on Image Segmentation Neural Network Models
### Comparison of Tunnel Face Joint Recognition Effect and Acquisition of Joint Morphology Parameters
#### 4.4.2 Comparison of Recognition Effects  
Table 2 evaluates the segmentation effectiveness of three segmentation methods--traditional image processing, U-Net convolutional neural network, and Mask R-CNN convolutional neural network--using the metrics described in Section 4.4.1.  
As shown in Table 2, the Dice similarity coefficient, Precision, and Recall of traditional image processing are 60.59%, 57.31%, and 70.03%, respectively. For the U-Net network, the Dice similarity coefficient, Precision, and Recall are 75.36%, 70.85%, and 85.58%, respectively. For the Mask R-CNN network, these values are 87.48%, 89.74%, and 84.73%, respectively.  
Among the three metrics, the Dice similarity coefficient most accurately reflects the true effect of target segmentation. Although the Recall value of U-Net is slightly higher than that of Mask R-CNN, its Precision is significantly lower, indicating that the U-Net network's segmentation results are rougher and contain more non-target information points. Comprehensive comparison and analysis show that the Mask R-CNN network has the best segmentation effect on the face joints of the tunnel.
###### Abstract  
Temperature information has a certain significance in thermal energy systems, especially in gas combustion systems. Generally, measurements and numerical calculations are used to acquire temperature information, but both of these approaches have their limitations. Constrained by cost and conditions, measurement methods are difficult to use to reconstruct the temperature field. Numerical methods are able to estimate the temperature field; however, the calculation process in numerical methods is very complex, so these methods cannot be used in real time. For the purpose of solving these problems, a two-dimensional temperature field reconstruction method based on the proper orthogonal decomposition (POD) algorithm is proposed in this study. In the proposed method, the temperature field reconstruction task is transformed into an optimization problem. Theoretical analysis and simulations show that the proposed method is feasible. Gas combustion experiments were also performed to validate this method. Results indicate that the proposed method can yield a reliable reconstruction solution and can be applied to real-time applications.  
Keywords:temperature field reconstruction; proper orthogonal decomposition (POD); dimension reduction +
Footnote †: journal: Applied Physics Letters
# A Decision Framework for Choosing Telecommunication Technologies in Limited-Resource Settings
## 5 Networks in the Design Phase  
Next, three case studies in the design stage are evaluated and a solution based on the proposed model recommended. The three case studies include a network for education in Papua New Guinea (PNG), a network to be operated by Kepler University in a Rwandan refugee camp called Kiziba, and an extension of a network linking together health posts in the Altiplano of Bolivia, Camacho Net.
# A Three-Port Power Electronic Transformer Based on Magnetic Integration
## 6 Economic Benefits Analysis
### Magnetic Volume Comparison
Then the total volume of the three integrated transformers can be calculated by  
\[V_{\rm 3\times intTr}=3\times K_{\rm vol}\left(\frac{2u_{\rm mx\_rms}i_{\rm P \_rms}+P_{\rm LVDC}/3}{K_{\rm f}K_{\rm W}J_{\rm rms}B_{\rm max}f_{\rm m}} \times 10^{4}\right)^{0.75}({\rm cm}^{3}) \tag{16}\]  
where \(u_{\rm mx\_rms}\) and \(i_{\rm P\_rms}\) is the rms value of each primary split wingding, \(u_{\rm mx\_rms}\) is equal to the amplitude of HF voltage \(U_{\rm m}\) = 2.4 kV. \(i_{\rm P\_rms}\) can be calculated by \(i_{\rm P\_rms}=[(^{1}/_{3}i_{\rm MVDC})^{2}+(^{1}_{3}i_{\rm x\_rms})^{2}+(^{ 1}_{\rm mx\_rms})^{2}]^{\frac{1}{2}}\), where \({}^{1}/_{3}i_{\rm MVDC}\), \({}^{\frac{1}{2}}i_{\rm x\_rms}\) and \(i_{\rm mx\_rms}\) are the rms value of DC, LF and HF components of arm current, respectively. Note that, \({}^{1}/_{3}i_{\rm MVDC}\) = 56 A, \({}^{\frac{1}{2}}i_{\rm x\_rms}\) = 101 A and \(i_{\rm mx\_rms}\) = 70 A can be obtained according to three-port power condition. It can be found from (15) that, the total transformer volume of MMC + distributed DABs PET is positively related to the number of transformers \(6N\). In this study case with \(n\) = 30, which is meeting medium voltage applications, it can be found that transformers volume of the proposed PET (1.02 m\({}^{3}\)) is much smaller than that of MMC + distributed DABs PET (1.46 m\({}^{3}\)). Figure 12: The processed power of each winding of integrated transformer. Furthermore, the proposed PET saved six arm inductors which are needed in MMC + distributed DABs PET. According to (14), these six arm inductors volume can be calculated as  
\[V_{6\times\mathrm{Larm}}=6\times K_{\mathrm{vol}}\left(\frac{L_{\mathrm{arm}} \left(\frac{1}{3}i_{\mathrm{MVDC}}+\frac{1}{2}I_{\mathrm{MVAC}}\right)^{2}}{K_{ \mathrm{W}}I_{\mathrm{rms}}B_{\mathrm{max}}}\times 10^{4}\right)^{0.75}\left( \mathrm{cm}^{3}\right) \tag{17}\]  
where \(\frac{1}{2}I_{\mathrm{MVAC}}\) = 143 A is peak value of LF current, which can be obtained according to MVAC port power condition. Then, total arm inductors volume can be calculated as 1.94 m\({}^{3}\). Finally, six arm inductors and 6\(N\) transformers in MMC + distributed DABs PET have a total magnetic volume of 3.40 m\({}^{3}\). Since these transformers and arm inductors are simplified into three integrated transformers in the proposed PET, it has a lowest magnetic volume (1.02 m\({}^{3}\)). The magnetic volume of the other two existing typical PETs (CHB + common HF bus PET and MMC + ISOP DABs PET) can be obtained by the same way discussed above, and the results are also shown in Table 2. It can be found that the proposed PET saves more than 60% of magnetic volume compared with the existing typical PET.
## 3 Proposed System Model  
In this paper, the author proposes a novel architecture which consists of three main components. The first one is a DIY-based toolbox aimed at assisting the overall mission design. The DIY approach is used because MC-IoT applications have very clear requirements and have to be evaluated before deployment on actual physical devices. The DIY approach, in this regard, is the preferred approach because once the requirements are clear, the design is straightforward, even by the general public. The proposed design is shown in Figure 1. The mission-planning toolbox has a registry of mission-critical devices. It creates tasks based on the profile of these devices. The tasks are  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**SoA** & **Goal** & **PoC** & **MC-IoT Support** & **DIV Support** & **Open Source** & **Target Audience** & **General Purpose** \\ \hline
[23] & Mobility in MC-IoT applications & No & Yes & No & Partial & Users & No \\ \hline
[24] & Optimal Data Dissemination in MC-IoT networks & No & Yes & No & No & Users & No \\ \hline
[26] & General-purpose platform for low-powered, reliable, and guaranteed real-time data dissemination and analysis & Yes & Yes & No & No & Users & Yes \\ \hline
[27] & Designs a protocol for MC-IoT applications to model deterministic latency and improved throughput & No & Yes & No & Yes & Application Designer & No \\ \hline
[25] & Proposes uses of edge computing to optimize the latencies in communication & Yes & Yes & No & No & Application Designer & No \\ \hline
[14; 19] & Proposed a Model-Driven approach for the design of MC-IoT applications & No & Yes & No & No & Application Designer & Yes \\ \hline
[20] & Remote Monitoring and Configuration in Smart Space & Yes & No & Yes & Yes & General Public & Yes \\ \hline
[34] & Provide SAM template to setup business cases & Yes & No & Yes & Yes & General Public & Yes \\ \hline
[35] & IoT Application Service Composition & Yes & No & Yes & Yes & General Public & Yes \\ \hline
[36] & Wiring data of web-enabled IoT devices & Yes & No & Partial & Yes & General Public & Partial Public \\ \hline
[37] & Web-based interface for building IoT mashups & Yes & No & Yes & Yes & General Public & Yes \\ \hline Proposed System & Enabling the involvement of stakeholders in the design of MC-IoT applications & Yes & Yes & Yes & Yes & General Public & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the state-of-the-art (SoA) methods with reference to the proposed architecture.  
autonomously mapped onto the device virtual representations based on an approach which will be described in the subsequent section. The configuration is stored in a cloud-based web server. The web server is where the end users can interact and monitor their applications. The system makes use of modularization and has three different sub-modules. In the next subsections, each sub-module is illustrated.