esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
As the printed MWCNTs/AC/BNNTs composite film has shown a high electrochemical-performance in terms of specific capacitance, so the possible working electrochemical mechanism of the ternary composite is illustrated in Figure 7 and explained as follows: Firstly, it is a ternary composite material, where each of the present material has participated individually during the electrochemical testing. Secondly, the presence of spherical-shaped AC particles has provided highly conducting channels for the ultra-fast movement of electrons and ions. Thirdly, the presence of optimized BNNT contents have resulted in an MWCNTs/AC/BNNTs system with better dispersion, extra electrolyte uptake and less agglomeration. Therefore, the electrolyte ions have better and improved access to the electroactive materials to respond with highly specific capacitance. Finally, the printed process has helped to obtain maximum areal density of the electroactive MWCNTs/AC/BNNTs material to display its suitable electrochemical-performance. Figure 5: Electrochemical characterizations of printed the MWCNTs/AC/BNNTs ternary composite material with different contents of BNNT material: (**a**) cyclic-voltammetry (CV) response measured at a scan rate of 30 mV s\({}^{-1}\); and (**b**) galvanostatic charging–discharging (GCD) curves measured at a current density of 1 A g\({}^{-1}\). Figure 6: Electrochemical characterizations of the printed MWCNTs/AC/BNNTs ternary composite material with an optimized BNNT content: (**a**) cyclic-voltammetry (CV) response measured at various scan rates; (**b**) galvanostatic charging-discharging (GCD) curves measured at various current densities; (**c**) measured specific capacitance at various current densities; (**d**) electrochemical impedance spectroscopy (EIS) measurements; and (**e**) cycling stability performance of the MWCNTs/AC/BNNTs ternary composite material.
# Multi-Field Models of Fiber Reinforced Concrete for Structural Applications
## 4 Electro-Mechanical Analyses with Applications to Self-Sensing
### Analyses and Results  
For the sake of example, a unit cell containing a fiber dosage of \(V_{f}=0.2\%\) is subjected to uniaxial load in the \(x\)-direction. As before, a fiber aspect ratio of 50 and a conductivity ratio of \(\sigma_{f}/\sigma_{m}=1000\) are assumed. Each load step is followed by calculations of steady-state potential flow, induced by a potential difference of \(\Delta\phi=1\) acting across the unit cell. The corresponding length changes of the matrix and fiber elements, due to the mechanical loading, cause changes in conductivity or resistivity (i.e., the inverse of conductivity).  
Figure 11 shows the change in specimen resistance under sinusoidal mechanical loading, where \(t\) is pseudo-time used to represent the stage of loading; \(\Delta R\) is change in resistance of the composite material within the unit cell; \(R_{0}\) is resistance in the unloaded state; and \(\varepsilon_{max}\) is the maximum strain measured over length \(L\). In typical applications, service loads are considered and their application is transient (e.g., due to vehicle movement over a bridge deck), such that concrete creep and fracture are not primary factors in the resistance measurements. If the change in resistance is based simply on the length changes of individual elements, the composite gauge factor is slightly less than unity. As seen in Figure 11, however, a composite gauge factor of unity is obtained if strain in the loading direction is used to define length change. Whereas \(R_{0}\) for the discrete and semi-discrete approaches differed by a small amount, the normalized quantities plotted in Figure 11 are essentially the same.  
In actual applications, the sensitivity of fiber resistance to strain depends not only on axial length change, but also on the influences of Poisson's ratio and any piezoresistivity of the fiber material. These factors can be roughly considered by assigning a gauge factor \(\eta\) to the fiber material. In other words, the dependence of fiber resistivity on straining was amplified by a factor of \(\eta\) within the simulations, which produces changes in the composite gage factor (Figure 11). As expected for this non-percolated state, however, changes in fiber gauge factor produce only small changes in composite gauge factor. The ratio of elastic moduli was assumed to be \(E_{f}/E_{m}\approx 7\), but the simulated resistance values are not sensitive to this parameter. The axial strains of the fiber are essentially compatible with those of the matrix over the fiber length, except near the fiber ends where shear lag behavior occurs.  
Depending on the application area, larger fiber-matrix conductivity ratios and larger fiber aspect ratios need to be considered. The simulation of conductivity enhancements associated with fiber-to-fiber contacts (or near contacts) is also necessary for comprehensive analyses of fiber composite designs.  
Figure 11: Change in resistance of the numerical specimen under sinusoidal mechanical loading.
# DWPIs: Dynamic-Weight Parallel Instance and Skeleton Network for Railway Centerline Detection
## 4 Experiments and Analysis
### Main Result  
Our network achieved 93.98% mAP for instance segmentation, 51.9% F-score for skeleton detection and 60.32% \(W_{mean}\) for the whole task. The evaluation metrics are shown in Table 1 and some classic examples for detection masks are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Network** & **AP** & **AP\({}_{\mathbf{50}}\)** & **AP\({}_{\mathbf{75}}\)** & **AP\({}_{\mathbf{S}}\)** & **AP\({}_{\mathbf{M}}\)** & **AP\({}_{\mathbf{L}}\)** & **mAP** & **F-Score** & **Wmean** \\ \hline Ours & 93.6 & 98.3 & 96.3 & - & 86.5 & 95.2 & 93.98 & 51.9 & 60.32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation metrics results of our network.  
Figure 5: Examples of our dataset. (**a**): Original images. (**b**): Instance labels. (**c**): Skeleton labels.  
The model performs well for images with a single railway or multiple railways, as well as straight railways or curved railways, as indicated by Figure 6(A1,A2). The UAVs need to fly at different heights to carry out the tasks, so the model must be suitable for various scales  
Figure 6: Examples of results of our network. (**A1,A2**) Single railway, multiple railways, straight railways and curved railways. (**B1–B3**) Different flying heights of UAVs. (**C1–C3**) Obscured railway. (**D1–D4**) Different lighting conditions.  
of railways in the images. As shown in Figure 6(B1-B3), the model can always accurately identify the railways and skeletons when the flying height changes from low to high. In certain environments, the railway may be shaded by trees or other objects, which increases the difficulty of the task. As shown in Figure 6(C1,C2), the railways and skeletons are fully detected even though the railway is partly shaded by sparse trees. However, in Figure 6(C3), the detected results are truncated by a tree because it is so dense that it completely obscures the railway. In order to operate during different times of the day and in various weather conditions, the UAVs must be able to detect the skeleton of a railway under different lighting conditions. As demonstrated in Figure 6(D1-D4), the results of instance segmentation and skeleton detection are always right, whether the light is strong or dim.  
Compared to the original SOLOv2 with ResNet-50 backbone, our changes to the backbone and attention module result in a 3.58% increase in mAP for instance segmentation. Compared to the original AdaLSN with a fixed architecture and Inception-v3 backbone, our novel network achieves a 2.2% improvement in skeleton detection.
## 3 Results
### Operating Pressure  
The influence of operating pressure on the breakdown voltage and voltage dispersion of the corona-stabilized switch is depicted in Figure 10. The higher the pressure of the switch, the less variation in breakdown voltage at different frequencies. This suggests that the insulation recovery degree of the switch changes less over time within a range of 500 us to 1 s, indicating that most of the insulation capacity may have been recovered within 500\(\mu\)s. The difference in breakdown voltage between switches with lower operating pressure at high- and low-repetition frequencies was significant, indicating that the insulation recovery time of the switch is longer and the recovery rate is slower.  
The effect of pressure on the stability of repetitive breakdowns in a corona-stabilized switch can be summarized as follows: (1) high pressure can cause an increase in voltage dispersion and reduce the stability of the switch; (2) as the operating frequency decreases, there will be a slight improvement in the stability of repetitive breakdowns for the corona-stabilized switch with high pressure.
# Structural Modifications in Epitaxial Graphene on SiC Following 10 keV Nitrogen Ion Implantation
## 3 Results
### Afm
It was reported previously that sputtered C atoms undergo agglomeration into small clusters due to their high structural fluidity during the process of ion irradiation [31; 32]. Direct and indirect defects cause agglomeration of sputtered atoms which presumably resulted in the formation of nanostructures that are observed in this study on ion-implanted EG/SiC in Figure 1c,d. The Monte Carlo simulation of defect generation upon 0.1 to 1000 keV energy range in ML graphene supported by SiO\({}_{2}\) substrate showed that C Frenkel pairs are primary defect than Si Frenkel pairs [6]. Defect generation in 0.25-50 keV ion-implanted SiC using molecular dynamics simulation reported that most defects are carbon vacancies and carbon interstitials with small fraction of antisite defects (atoms of different type exchanged position) [29]. It was proposed that the substrate plays a major role in enhancing the sputtering yield [6] when C sputtering yield was presented to be higher in graphene supported by SiO\({}_{2}\) substrate than in suspended graphene. Sputtering following ion implantation breaks graphitic carbon bonds and causes graphene fragmentation as observed in Figure 1b. AFM results showed that ion implantation provides a single step process for nano-patterning of EG/SiC in controlled manner which can be further used for developing optoelectronic, electronic and magnetronic devices of high performance. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Ion Type** & **Ion Energy** & **Electronic Energy** & **Nuclear Energy** & **Projected Range** & **Longitudinal** & **Lateral** & **Straggling** & **S\({}_{\text{n}}\)/S\({}_{\text{e}}\)** \\  & **(keV)** & **Loss (S\({}_{\text{e}}\))** & **Loss (S\({}_{\text{n}}\))** & **(nm)** & **(nm)** & **(nm)** & **(nm)** & **(nm)** \\ \hline N\({}^{+}\) & 10 & 0.1013 & 0.3104 & 10.4 & 3.0 & 1.9 & 3 \\ \hline \end{tabular}
\end{table} 
Table 1: Electronic and nuclear energy loss, longitudinal straggling and lateral straggling calculated from stopping and range of ions in matter (SRIM) simulations.
## 2 Results
### Selected Volatile Compound Quantification  
Since aldehydes and alcohols were the most abundant volatiles in watermelon rind, these volatiles were selected for quantification to investigate the aroma-active volatiles in the rind with the odor activity values (OAVs). A total of 27 compounds were quantified, which accounted for approximately 80% of the total peak area for each sample. These compounds included 17 aldehydes, nine alcohols, and one ketone, or they also included six six-carbon and 14 nine-carbon compounds (Table 2). Results indicated nine-carbon aldehydes and alcohols were the most dominant volatiles in flesh, and aldehydes were most dominant in watermelon rind. The concentration of these 27 compounds in watermelon  
Figure 4: Heat map of 61 selected volatiles (peak area, filtration interquartile range threshold = 0.01) in watermelon rind, flesh, and rind- flesh blends. V: volatiles, which could be found in Table S1.  
rind (100% rind sample) ranged from 0.7-2349 \(\mathrm{\SIUnitSymbolMicro g}\)/kg fresh weight, while in watermelon flesh (0% rind sample) it ranged from 0-4255 \(\mathrm{\SIUnitSymbolMicro g}\)/kg fresh weight.  
The major aroma characteristics for the 27 quantified volatiles were fresh, green, cucumber-like, and fruits with fatty nuances (Table 2). According to their corresponding concentrations and thresholds, volatile compounds potentially contributing aromas to watermelon rind included five unsaturated nine-carbon alcohols (Z-3-nonen-1-ol; _Z_-6-nonen-1-ol; \(E\),_Z_-3,6-nonadien-1-ol; \(Z\),_Z_-3,6-nonadien-1-ol; and \(E\),_Z_-2,6-Nonadien-1-ol) and 16 aldehydes. The 16 aldehydes consisted of three six-carbon aldehydes (hexanal, Z-3-hexanal, and _E_-2-hexenal), all eight nine-carbon aldehydes (nonanal; _E_-6-nonenal; _Z_-6-nonenal; _Z_-6-nonenal; _Z_-2-nonenal; _E_-2-nonenal; \(E\),_E_-2,6-nonadienal; and \(E\),_Z_-2,6-nonadienal), and six other compounds (acetaldehyde, propanal, 2-methylbutanal, 3-methylbutanal, pentanal, and _E_-2-ocatel).
## 3 Simulation Setup  
The simulation setup and the DSP flow is shown in Figure 4. Figure 4 presents the DSP scheme of the joint LUT and GS-based pre-compensation. Figure 4 shows the DSP scheme of the joint LUT and GS-based post-compensation. At the transmitter, a pseudo-random binary sequence (PRBS) with a length of \(2^{16}\) was generated and used for PAM-4 mapping. The symbol was subsequently processed through the error table. When using the GS-based pre-compensation algorithm, it was resampled to the desired sampling rate and underwent the GS-based pre-compensation algorithm. In the case of using the GS-based post-compensation algorithm, the pre-distortion operation was employed at the transmitter. Then, the electro absorption modulator (EAM) was emulated by the VPI model to investigate the impact of the modulator nonlinearity. The polynomial expressed  
Figure 3: Scheme diagram of GS-based post-compensation algorithm.  
Figure 2: Power spectrum of signal under CD (blue line), under GS (green line), and under CD and GS (orange line).  
as \(|T(V)|^{2}=T_{0}+T_{1}V+T_{2}V^{2}+T_{3}V^{3}+\ldots\) was used to represent the nonlinear transfer function. Figure 4c shows the transfer curve of the modulator. After modulation by the EAM, the signal was transmitted in 40 km SSMF. The variable optical attenuator (VOA) was used to adjust the received optical power (ROP). Then, the PAM-4 signal was normalized and resampled to 2 sps for FFE. At the receiver, the normalized PAM-4 signal was firstly processed by GS-based post-compensation in the application of Figure 4b. Finally, the PAM-4 signal was de-mapped, and we calculated the bit error rate (BER) with 65,503 symbols.
# Parameterized Algorithms in Bioinformatics: An Overview
## 5 Phylogenetics
### Preliminaries  
An evolutionary (or phylogenetic) network \(N\) is a graph whose degree-one nodes \(\mathcal{L}(T)\) (\"leaves\") are labeled (by \"taxa\"). A _rooted_ phylogenetic network \(N\) is a rooted acyclic directed graph whose non-root nodes either have in-degree one or out-degree one and whose out-degree-zero nodes \(\mathcal{L}(T)\) (\"leaves\") are labeled (by \"taxa\"). A (rooted) phylogenetic tree is a (rooted) phylogenetic network whose underlying undirected graph is acyclic. In the context of this section, we drop the prefix \"phylogenetic\" for brevity and sometimes refer to networks as \"phylogenies\". Some works consider trees in which each leaf-label may occur more than once. These objects are called _multi-labeled_ trees (or MUL trees). For a set \(\mathcal{T}\) of networks, we abbreviate \(\bigcup_{T\in\mathcal{T}}\mathcal{L}(T)=:\mathcal{L}(\mathcal{T})\). An important parameter of networks is their _level_, referring to the largest number of reticulations in any biconnected component of the underlying undirected graph (see [141]).The _restriction_ of \(T\) to \(L\), denoted by \(T|_{L}\), is the result of removing all leaves not in \(L\) from \(T\) and repeatedly removing unlabeled leaves and suppressing (that is, contracting any one of its incident edges/arcs) degree-two nodes. A network \(N\)_displays_ a network \(T\) if \(T\) is a topological minor of \(N\), respecting leaf-labels, that is, \(N\) contains a subdivision of \(T\) as a subgraph. Herein, a directed edge can only be subdivided in accordance to its direction, that is, the subdivision of an arc \(uv\) creates a new node \(w\) and replaces \(uv\) by the arcs \(uw\) and \(wv\). This notion can be generalized to the case that \(N\) is the disjoint union of some networks (see Section 5.2.3). For non-binary networks, there are two different notions of display: the \"hard\"-version is defined analogously to the binary case, while we say that a network \(N\) \"soft\"-displays a network \(T\) if any binary resolution of \(N\) displays any binary resolution of \(T\), where a _binary resolution_ of a network \(N\) is any binary network that can be turned into \(N\) by contracting edges/arcs. \"Soft\" and \"hard\" versions of display are derived from the concept of \"soft\" and \"hard\" polytomies, meaning high degree nodes that represent either a lack of knowledge of the correct evolutionary history leading to the children taxa (\"soft\") or a large fan-out of species due to high evolutionary pressure (\"hard\").  
Note that many kernelization results in phylogenetics bound only on the number of labels in a reduced instance. If the input contains many trees or an intricate network, kernelization results should more fittingly be described as \"partial kernel\" (see [142]). We thus usually refer to such results as \"kernel with... taxa\".
# A Robust Lightweight Network for Pedestrian Detection Based on YOLOv5-x
## 2 Related Work  
_YOLOv5 Algorithm_  
The YOLOv5 framework, developed by Ultralytics LLC, presents an enhanced iteration within the YOLO series. Its structural composition involves a one-stage detection framework comprising four fundamental segments: input, backbone network, neck network, and output. Drawing inspiration from prior YOLO versions and other detection algorithms, YOLOv5 integrates the focus layer into the input phase to facilitate data augmentation. The YOLOv5 authors further refined the spatial pyramid pooling (SPP) [22] technique, evolving it into SPPF, where SPPF demonstrates improved speed while maintaining the same output. Concurrently, the DarkNet53 backbone is harnessed to extract primary image features. Notably, the neck network incorporates a feature fusion framework that includes the feature pyramid network (FPN) and the bottom-up path aggregation network. This strategic incorporation enhances short-circuit connections and cross-layer fusion for multiscale features. The holistic YOLOv5 architecture is visually presented in Figure 1. The YOLOv5 network is composed of four constituent units, as illustrated below:
# SASC: Secure and Authentication-Based Sensor Cloud Architecture for Intelligent Internet of Things
###### Abstract  
Nowadays, the integration of Wireless Sensor Networks (WSN) and the Internet of Things (IoT) provides a great concern for the research community for enabling advanced services. An IoT network may comprise a large number of heterogeneous smart devices for gathering and forwarding huge data. Such diverse networks raise several research questions, such as processing, storage, and management of massive data. Furthermore, IoT devices have restricted constraints and expose to a variety of malicious network attacks. This paper presents a Secure Sensor Cloud Architecture (SASC) for IoT applications to improve network scalability with efficient data processing and security. The proposed architecture comprises two main phases. Firstly, network nodes are grouped using unsupervised machine learning and exploit weighted-based centroid vectors for the development of intelligent systems. Secondly, the proposed architecture makes the use of sensor-cloud infrastructure for boundless storage and consistent service delivery. Furthermore, the sensor-cloud infrastructure is protected against malicious nodes by using a mathematically unbreakable one-time pad (OTP) encryption scheme to provide data security. To evaluate the performance of the proposed architecture, different simulation experiments are conducted using Network Simulator (NS3). It has been observed through experimental results that the proposed architecture outperforms other state-of-the-art approaches in terms of network lifetime, packet drop ratio, energy consumption, and transmission overhead.  
wireless sensor network; Internet of Things; sensor-cloud; unsupervised learning; node's security +
Footnote †: journal: Sensors
# Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning
## 6 Conclusions  
We proposed the HCPL and HAFRNet for the OSR of the interference signals. The HCPL uses a hybrid loss function combining the inner product-based cross-entropy loss, center loss and prototype radius loss for training the parameters of the network. HAFRNet uses the feature reuse structure and HDAM to improve the OSR performance of the interference signals under different openness while greatly reducing the computation and storage complexity. The experiment results show that HCPL can indeed improve the unknown class rejection performance, while HAFRNet can make HCPL more stable and get better OSR performance, and the proposed method has better OSR performance than CG-Encoder under different openness and various JNR and with a lower complexity than CG-Encoder.  
Our work can only distinguish the unknown class signals from the known class signals but cannot further identify each type of unknown class signal. In a future work, we will achieve open-world recognition of the interference signals by combining other techniques such as deep clustering and extend our experiments to air scenarios to evaluate the performances of the proposed method in real-world environments.  
Conceptualization, Z.Z.; methodology, X.C.; software, X.C.; validation, X.C.; formal analysis, Z.Z.; writing--original draft preparation, X.C.; writing--review and editing, X.C., Z.Z. and X.Y. ([PERSON]); supervision, Z.Z. and X.Y. ([PERSON]); project administration, Z.Z. and funding acquisition, Z.Z., S.Z., C.L. and X.Y. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
Figure 11: Visualization of the 2D feature space. (**a**) HCPL feature space and (**b**) GCPL feature space.  
**Funding:** This research was funded by the National Natural Science Foundation of China under Grant No. U19B2016 and Zhejiang Provincial Key Lab of Data Storage and Transmission Technology, Hangzhou Dianzi University.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.