esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In this study, we propose a multi-source information fusion approach for misinformation detection, which integrates global structural information with local semantic features of messages, leveraging both message content and propagation path features to improve detection accuracy and robustness. The discrete and fragmented nature of post texts is transformed into a propagation graph structure, while the BERT [14] (Bidirectional Encoder Representations from Transformers) model is employed to extract deep semantic features from both source texts and propagated texts. As an augmented node attribute within the dissemination graph framework, the bidirectional graph convolutional neural network is utilized to capture the topology of the event diffusion network during the analysis of information propagation patterns. Finally, the resulting propagation graph, enriched with reply semantic information, is combined with the semantic features of the source declaration and processed by the classifier. Based on the interplay between dissemination architecture and content, the global forwarding relationships are incorporated into the false information detection process. The key contributions of our study are as follows:* We leverage the structural information of information dissemination to propose an effective disinformation detection method, termed ICP-BGCN. During the propagation of false information, interactive data, such as user comments and retweets, are embedded and represented using a bidirectional graph convolutional neural network. This network extracts global coupling features from both the nodes within the message propagation graph and the relationships among them, enabling feature extraction of disinformation dissemination data based on graph-based information representation. * The proposed method is validated using two real-world datasets, Twitter15 and Twitter16. Experimental results show that the proposed model outperforms existing baseline methods in detecting false information. The rationality and effectiveness of the proposed ICP-BGCN method are further confirmed through ablation experiments. * We investigate the characteristics of propagation paths for both disinformation and non-disinformation, as well as the differences in their dissemination dynamics, through visualization and statistical analysis of various topological metrics of the message propagation graphs, including degree distribution, diameter, and average path length of network nodes.
## 3 Topology Adaptive Differential Protection  
The adaptive current differential protection developed in the paper can automatically change its configuration settings if a change of the topology of the 10 kV distribution networks is detected. For example, if a new T-line is added to a feeder, the two-terminal feeder becomes a three-terminal line, as shown in Figure 2(b). Before the topology change, the DTU1 communicates with DTU2 for its current differential protection function, as shown in Figure 2(a). After the topology change from a two-terminal circuit to a three-terminal circuit, DTU1 not only needs to communicate with DTU2, but also needs to communicate with DTU3 for its current differential protection, this requires change the configuration settings of current differential protection in the DTUs. For traditional differential protection, the settings are normally changed manually, but it is time-consuming to do it manually.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Test Number** & **Wireless Timing Synchronization Errors (\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{ }}}}}}}}}}}}}}}\) \\ \hline
1 & Mean 2.466 \(\upmu\)s and standard deviation 10.093 ns for 100 samples \\ \hline
2 & Mean 2.533 \(\upmu\)s and standard deviation 22.549 ns for 100 samples \\ \hline
3 & Mean 2.507 \(\upmu\)s and standard deviation 3.036 ns for 100 samples \\ \hline \hline \end{tabular}
\end{table}
Table 1: IEEE 1588 timing test results.  
Figure 3: _Cont._  
Another scenario of changing the topology of the primary circuit could be removing or adding substations. For example, if RMU2 shown in Figure 1 is removed, DTU1 then needs to communicate with DTU3 for its current differential protection instead of DTU2. This requires changing of the configuration settings of current differential protection to reflect the change of the topology of the primary circuit. Traditionally, the settings are changed manually. In this paper, an adaptive function of the automatic changing of the setting is developed. After the topology change, the DMS knows the topology change from the information obtained by it, and then it automatically sends the new configuration settings of the differential protection to the affected DTU. Therefore, manually changing the settings of the current differential protection is not required, and this saves outage time for system operations.
## 3 Discussion
Compared to previous studies [25; 26; 28; 57; 58; 59], our study revealed several novel TF families, ARR-B, ARF, MIKC-MADS, HD-ZIP, G2-like, and CPP, were associated with _TPSs_ expression and volatile terpenoid formation. In the future, more experimental evidence is required to confirm the role of these candidate transcription factors. Regarding to _TPS_ genes identified based on WGCNA, six belong to TPS-a family, five belong to TPS-b and one belongs to TPS-g (Cm107390). TPS-a members were associated with sesquiterpene formation, TPS-b contribute to monoterpene biosynthesis, TPS-g is associated with monoterpene and sesquiterpene formation. Therefore, these identified TPS may have important roles in productionof volatile terpenoids in finger citron. The _TPS_ gene Cm310520 sequence is too short to include in the phylogenetic tree construction. Among these thirteen _TPS_ genes, four (Cm105420, Cm028750, Cm074160, Cm107390) members had low sequence identity (\(<\)50%) to _Citrus TPS_ genes with known functions. Relatively, high sequence identities (up to 97.76%) with characterized _Citrus TPS_ genes were observed for eight TPSs of finger citron. As the final enzymatic step of MVA and MEP pathways, TPSs are responsible for the direct synthesis of terpenoids. However, in most case expression level of these _TPS_ genes do not have a linear correlation with their product contents. The uncertainty arises for two main reasons. Firstly, a considerable number of TPSs are multi-product enzymes which produce several volatiles from a single substrate. Secondly, the replication and evolution of the TPS family has produced functioning isozymes express differently in time and space [4; 20; 60]. We have previously characterized a sesquiterpene synthase, _CmTPS1_, which has substrate selectivity, catalyzing the formation of 15 sesquiterpenes from FPP, with the main product bicyclogermacrene, followed by aromadenrene and elixene [24]. On the other hand, genes from separate clades have also evolved towards a similar function [8; 12]. Therefore, more experiments are necessary to test functions of these 13-candidate finger citron TPS, such as overexpression in _E.coli_ for enzymatic analysis in vitro and stable transformation for function analysis in vivo.
# Prediction of Beck Depression Inventory Score in EEG: Application of Deep-Asymmetry Method
## 2 Materials and Methods
### Data Preprocessing  
We selected the channel based on the international 10-20 system [36] among the EEG channels configured in the dataset to apply the deep asymmetry methodology [29] devised by us. Frontal (Fp1, Fp2, F7, F3, Fz, F4, F8), central (C3, Cz, C4), parietal (P7, P3, Pz, P4, P8), occipital (O1, O2), and temporal (I7, T8) channels, a total of 19, were selected.  
The EEG data were normalized using the min-max normalization method [37] so that the amplitude scale between each channel could be similarly matched. Min-max normalization is the most common method for normalizing data, and for all channels, the minimum value is 0 and the maximum value is 0.  
In addition, we used an independent signal analysis (ICA) method to reduce noise and artifacts in the EEG data and extract features. This method is considered effective for the cleansing and feature extraction of EEG signals [38]. We preprocessed the EEG using FastICA [39].  
We used data segmentation to increase the number of samples in the dataset. A small number of samples in the dataset creates problems while training the model. Therefore, we used a method of dividing a sample of data into meaningful segments. This is a potential approach to address data quantitative limitations [12] and has been adopted in existing EEG studies [22; 27]. We divided each dataset into small epochs (5120 samples) with a window size of 10 s (no overlap). Each epoch was assigned the same value.
## 4 Simulation Test and Result Analysis
### Data-Balancing Tests
In order to gauge the effectiveness of data-balancing experiments in augmenting the accuracy of FDIA detection, a sequence of comparative experiments was conducted, encompassing both the dataset subjected to the balancing procedure and the unbalanced dataset for FDIA detection. The accuracy curve depicting training iterations is illustrated in Figure 7, serving as an intuitive portrayal of the accuracy trend across epochs. Furthermore, Table 2 presents a comprehensive array of metric results, facilitating an exhaustive evaluation of model performance. Through the orchestration of these comparative experiments, the impact of data-balancing interventions on FDIA detection accuracy was thoroughly explored. \begin{table}
\begin{tabular}{c c c c c} \hline
**Arithmetic** & \(\lambda_{\text{Accuracy}}\) & \(\lambda_{\text{Precision}}\) & \(\lambda_{\text{Recall}}\) & \(\lambda_{\text{F1}}\) \\ \hline GAN-GRU & & & & \\ +Transformer & 0.9832 & 0.9837 & 0.9815 & 0.9824 \\ Transformer & 0.8436 & 0.8527 & 0.8395 & 0.8412 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of FDIA detection experiment results before and after GAN-GRU data-balancing processing. Figure 6: Loss value variation in data during the GRU-GAN training process. Figure 7: Comparison of FDIA detection experiment accuracy indices before and after GAN-GRU data-balancing processing. Upon a meticulous examination of the experimental outcomes, a conspicuous enhancement in FDIA detection accuracy is discerned subsequent to the implementation of data-balancing experiments. This discernible enhancement underscores the pivotal role played by data-balancing experiments in ameliorating the predicament of data imbalance intrinsic to FDIA detection. By augmenting the representation of false data injection attack samples, the model's ability to discern and assimilate the distinguishing attributes of genuine and spurious data is significantly enhanced, thereby culminating in an elevation of the accuracy in FDIA detection. This empirical endeavor attests to the effectiveness of the data-balancing approach based on GAN-GRU within the realm of FDIA detection.
## 5 Conclusions  
A lightweight and functional design has been introduced and potentials offered by a combination of TO techniques and AM technologies have been described. Current TO issues and challenges have been summarized and the connection between design approaches and tool capabilities for actual applications has been highlighted. Thus, since the need for assessment approaches to select proper design tools is relevant, a method for this purpose is presented. It is based on KPI matrices connected with the tasks to implement TO. It relies on clarity and impartiality principles for the sake of objectivity and it is built with the idea of being expandable and customizable to guarantee flexibility and a wide range of applications. In particular, KPIs can be selected and different weights can be assigned to meet different designs and project needs. An automotive ICE piston is the case study for a redesign based on TO, which is concurrently set up through four different pieces of commercial software at the state of art. The assessment approach is therefore applied and the four benchmarked TSs are compared to select the platform to perform the final product development. The approach can be used to evaluate either CAD  
Figure 9: Comparison of Phase-focused Summary Charts from the investigated TSs.  
platforms or different arrays of standalone tools, and can be used to monitor software development over time, since it has universal applicability. Therefore, the method can sustain the key design tool selection phase for DfAM, as well as the CAX platform selection for industrial settings embracing AM technologies. Further developments can concern the approach extension to subsequent phases of a DfAM workflow, such as product simulation, build preparation or process simulation, etc. Considering both product optimization and process optimization, it could be possible to support a holistic approach for the design and manufacturing system.  
Conceptualisation, E.D., F.P. and F.L.; methodology, E.D., F.P.; validation E.D. and F.P.; writing--original draft preparation, E.D.; writing--review and editing, E.D., F.P.; supervision, F.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors gratefully acknowledge [PERSON] and [PERSON] for their valuable support.  
The authors declare no conflict of interest.
## 3 Validation of CCPD Model  
### 3D Reconstruction of Real Blood Vessels  
Considering the approximate parallelism of the blood vessel with the observation plane in the DSC model, the blood vessel axis was regarded on a horizontal plane. The dorsal blood vessels of male Sprague-Dawley rats weighing 100-120 g were used as a target to be irradiated. To make the observation chamber, the dorsal skin was lifted and fixed between two symmetrical titanium frames containing a central annular window (\(\O=15\) mm). The entire epidermal, dermal, and subcutaneous connective tissues of one side of the skin in the central annular window were carefully removed to expose the vascular structure (Figure 3). The fascia on the exposed site was carefully removed to improve the imaging. The rats were placed on a specially engineered microscope translator stage containing three pins that interlock with holes in the implanted optical chamber so as to minimize motional disturbances by the breathing of the anesthetized rats during imaging. Proper targeted blood vessel was oriented by mounting the xyz-translator stage. Then, the skin images were captured with a CCD camera (DP71, Olympus Corp., Tokyo, Japan) connected to the microscope (\(10\times\) objective, NA = 0.30, BX51, Olympus Corp.) at a resolution of \(1360\times 1024\). The images were simply processed and displayed by cellSens standard software. All of the procedures involving the animal experiments were approved by the Institutional Animal Care and Use Committees of Xi'an Jiaotong University.  
The procedure of 3D reconstruction of real blood vessels is shown in Figure 4. After a snapshot of the DSC model was taken (Figure 4a), the binary image of the main blood vessels was transformed (Figure 4b), and the skeleton and contour lines of the blood vessels were extracted from the single 2D image (Figure 4c). After a smooth and loft operation (Figure 4d), the 3D blood vessel model was generated on the basis of the skeleton line and the contour [27] (Figure 4e). Only one image instead of multislices was used for reconstruction; thus, complex operations, such as identifying and assembling  
Figure 3: Dorsal skin chamber model.  
blood vessel regions between slices, were avoided, and the reconstruction steps were greatly simplified. The generated 3D geometric model was imported into GAMBIT and discretized by tetrahedral meshes.
# Analysis of Key Issues on GNSS-R Soil Moisture Retrieval Based on Different Antenna Patterns
## 2 Theoretical Analysis of Key Issues on Soil Moisture Retrieval
### Specular Reflection Point of GNSS Signals
Then, based on the height difference (\(\Delta h\)) between the Earth's true surface and the WGS84 ellipsoid surface, the changes in latitude and longitude can be obtained. These changes are then added to the latitude and longitude of the WGS84 ellipsoid to obtain the latitude and longitude of the specular reflection point on the Earth's true surface. The overall procedure is shown in Figure 3.
# Docking Control of an Autonomous Underwater Vehicle Using Reinforcement Learning
## 1 Introduction
Here, two separate RL algorithms were applied to the docking control of an AUV onto a fixed platform for the first time. The deep Q network uses a continuous state space and a discrete action space, while the deep deterministic policy gradient presents continuous state and action spaces. The performance of the two schemes was assessed against PID and parametric optimal control. In the following section, the dynamic model of an AUV limited to motions in surge, heave and pitch is developed. This greatly decreases the computational effort, whilst not reducing the generality of the developed method. Afterwards, the strategies are described for the docking of the AUV onto a fixed platform using optimal control theory and deep RL. Results are then presented for the case study of the REMUS-100 AUV. These are followed by a discussion and comparison of the two procedures.
# Nano- and Micropatterning on Optical Fibers by Bottom-Up Approach: The Importance of Being Ordered
## 3 Bottom-Up Fabrication of Periodic Structures on OFs  
This section provides a concise description of the main bottom-up fabrication approaches to produce highly ordered nanostructures and their application attempts on the OF. For the most part, bottom-up nanofabrication relies on self-assembly processes. Nature-inspired processes have shown to be an extremely useful and versatile tool for building nanostructures, hierarchically ordered structures and sophisticated micro- and nanopatterns, and therefore most of them have already been widely applied to OFs [4].  
However, self-assembly often produces randomly arranged or poorly ordered structures, especially on large scale. The OF is an unconventional substrate for nanopatterning. According to the specific position where the patterned element is introduced (on the tip, inside a portion of the core, around the lateral surface) the pattern formation is limited by several aspects such as the curved topography, the high aspect ratio, the presence of tiny cavities, etc. Definitively, the creation of reproducible ordered patterns on OF is still  
Figure 1: (**a**) Assortment of optical and SEM micrographs of photonic crystal fiber structures including proper defects in the periodic structure. Reproduced from Ref. [11] with permission from the 2003 American Association for the Advancement of Science. (**b**) The pentagonal Penrose tiling, which paved the way for the discovery of quasicrystals, is a self-similar pattern with 5-fold symmetry, long-range order and no translational periodicity. Reproduced from [14]. (**c**) SEM micrograph of an aperiodically-ordered photonic quasicrystal slab operating at near-infrared wavelengths (i) and measured reflectance response (ii), from which a Fano-type resonant lineshape is clearly visible, in good agreement with numerical simulations. Reproduced from Ref. [30]. (**d**) SEM images and colorimetric fingerprints of 2D aperiodic gratings: (i) Penrose lattice (d = 400 nm), and (ii) Gaussian prime lattice (d = 300 nm). Reproduced from Ref. [37]. (**e**) AFM images of periodic nanocavity array (i) and random pyramidal texture (ii), used to evaluate the light-trapping efficiency of protonic nanostructures in hydrogenated amorphous silicon thin-film solar cells (scale bars 1 \(\upmu\)m), with the corresponding drawing of the cell on the bottom. Reproduced from Ref. [41] with permission from the 2012 American Chemical Society.  
a major challenge. To what extent such disorder (the number of unwanted defects as far as the deviations from the desired pattern) affects the overall device functionality is a key aspect settling the boundary between a significant technology shortcoming and specific characteristics of a technological approach.  
In this section, we briefly report the methods used so far to create self-assembled ordered structures on the OF, whilst in the following, we will focus on methods to systematically assess the degree of order. At the end of this section, the main self-assembly methods applied to OFs are recalled and summarized in Table 1.
# Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks
## 2 Related Work
### Cross-Technology Interference Identification  
As the frequency spectrum becomes increasingly congested, managing heterogeneous wireless networks and addressing spectrum scarcity have become critical challenges. This has led to research on signal detection and identification methods, such as distinguishing various wireless technologies that support IoT or recognizing modulation schemes [21]. Early research focused primarily on recognition methods that can extract signal features, such as statistical properties [22]. However, due to their high computational complexity and limited accuracy, machine learning (ML)- and deep learning-based recognition techniques have gained significant attention [23]. These approaches leverage the fact that signals generated by devices using heterogeneous wireless technologies exhibit distinct characteristics across various dimensions of the spectrum, such as time, frequency, and power, to achieve accurate signal identification [19].  
Recent efforts have aimed to expand the number of identifiable signal types, as handling only one or two types is no longer sufficient given the increasing diversity of heterogeneous devices. In [24], Wi-Fi, LTE-license-assisted access (LTE-LAA), and 5G new radio-unlicensed (NR-U) coexistence signal types were detected and distinguished from received signals without explicit decoding, using in-phase and quadrature (I/Q) samples. The researchers employed a convolutional neural network (CNN) model combined with a long short-term memory (LSTM) network and enhanced classification accuracy by applying the STFT to the I/Q sequences. In another study [25], the YOLO (\"You Only Look Once\") CNN model was used to transform signal identification into an object detection problem on spectrograms, achieving over 99% accuracy in identifying Wi-Fi, ZigBee, and LoRa signals. [PERSON] et al. [26] proposed a multi-task open-set recognition network, inspired by CountGAN, to classify unknown signal types without prior training. [PERSON] et al. [20] utilized a multi-task learning technique to simultaneously extract multi-dimensional spectrum usage characteristics, such as signal type, modulation scheme, occupied channels, and power levels, achieving superior performance compared to traditional single-task models.  
These studies collectively demonstrate that signals can be effectively identified by exploiting their unique spectrum usage characteristics. However, it is crucial to consider which features to utilize for model training and how to structure the model's input, depending on the specific objectives. Moreover, since the devices requiring identification are often resource-constrained communication devices, computational complexity and memory usage must be carefully considered during model design to ensure feasibility in target devices and systems [27].