esa-sceva/satcom-chunk-collection huggingface dataset:
# KGGCN: Knowledge-Guided Graph Convolutional Networks for Distantly Supervised Relation Extraction
###### Abstract
Thus, encoding all words equally in the sentence without any distinction will confuse the feature extractor and degrade the performance of the relation extraction model. (2) _How to make full use of the informative sentences in a bag and integrate them to generate bag-level features for predicting the given relations_. The traditional multi-instance learning model only selects one sentence which has the maximum probability to be a valid candidate for representing the sentence bag [11]. This strategy does not make full use of the supervision information of the sentences in the bag, which may exacerbate the inadequate issue of training data. Calculating the average of all sentences in the bag to obtain the bag-level features is an improved method, but this method will introduce noise due to the existence of false positive instances. Moreover, previous methods for relation extraction only based on the individual semantic features in the textual sentences of the entity mentions. In fact, the human prior knowledge from external lexical resource is critical for reducing the reliance on training data, and it can improve the relation extraction performance. To address the first challenge, a knowledge-guided graph convolutional network (KG-GCN) based on the word-level attention mechanism is proposed to encode the sentences, which attends to relation indicators that are useful in predicting relations. This is motivated by the fact that each word in a sentence has different importance for relation inference [12]. Specifically, the relation indicators are human prior knowledge obtained from the external lexical resource, which represent the key words and cue phrases of relations in sentences. The sentence encoder utilizes the knowledge attention to calculate the attention weight of each individual word and capture the informative linguistic clues of relations. In this way, the model increases the weights of critical words and cue phrases, while reduces the weights of trivial words. As a result, the critical words and cue phrases will contribute more to sentence encoding, which can form a purified representation for sentences and generate more informative sentence-level features. To address the second challenge, we propose a knowledge-guided sentence-level attention model to select multiple valid sentences. For the distantly supervised relation extraction, the entities and relations are derived from existing knowledge graph. Thus, the knowledge about these entities and relations can be used as supervisory information to guide the selection of valid sentences. In previous methods, the relations just act as relation labels to specify the class of sentences in the training stage. The structural and semantic information between the entity pair and the relation is completely ignored, which can actually be used as additional knowledge for relation extraction. To this end, our work explores the structural and semantic information of triples from knowledge graph to guide the selection of valid sentences.
# A Comprehensive Survey of Recommender Systems Based on Deep Learning
## 3 Overview of the Recommender Systems
### Cross-Domain Recommendation
#### 3.3.3 Multi-Target CDR  
Multitarget domain CDR is a recommendation to users in either domain for items in both domains. However, its objectives are more intricate, aiming to provide a comprehensive solution to the data sparsity problem. The fundamental concept behind multitarget CDR is to harness additional auxiliary information from multiple domains to enhance recommendation performance. Theoretically, if one can identify and effectively utilize a sufficient number of relevant domains, the longstanding data sparsity issue can be substantially mitigated. Nonetheless, beyond the challenges that are inherent in single- and dual-target-domain CDR scenarios, negative transfer becomes inevitable in practical multitarget-domain CDR contexts. Specifically, with the addition of domains, the recommendation performance of the constructed model might deteriorate, especially in scenarios where the domains are sparse.  
The multitarget domain task presents a particularly challenging recommendation scenario, and as of now, no perfect solution tailored to this objective is available. In [128], the authors employed a shared heterogeneous graph to generate richer user and item feature embeddings across domains. The MDCDR [41] utilizes auxiliary data from source domains to improve recommendation quality across multiple domains. However, none of these approaches effectively addresses the negative transfer issue.  
As the complexity of multiplatform and multidevice environments intensifies, the demand and significance of cross-domain recommendations are anticipated to escalate further. Advanced models, such as deep learning and graph neural networks, may offer more robust support for cross-domain recommendation tasks. These developments have introduced new challenges such as feature mapping and negative transfer. These emerging research trends inspire us to delve into the challenges within CDR and provide an overview of the ongoing research advancements.
# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 3 The Modulation Recognition Method Based on Deep Learning
### Application of the Recurrent Neural Network in Modulation Recognition  
#### 3.2.1 Recurrent Neural Networks  
Recurrent Neural Networks (RNNs) are different from typical multi-layer networks with feedforward connections. RNNs are extended by the concept of recursive connections to feedback information to the previous layer (or the same layer). Its structure is shown in Figure 12. The input of the RNN is the data of different moments. Firstly, the information will be input from the moment of the signal. The input gate decides whether the information of this moment is input into the memory neuron. The output gate decides whether the information of this moment is output. The forgetting gate decides whether the information of this moment is forgotten. If it is not forgotten, it can be transmitted to the next moment, and the process is cycled until the whole input signal is processed. According to the above characteristics, it is known that RNN is very good at processing sequence signals, such as time series, text sequences, and audio data [58]. The communication signal is a signal that changes with time, so some scholars began to use RNN to process the communication signal [59]. Therefore, this section presents the application of RNN in modulation recognition.
# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 3 Analysis of ATM Big Data Sharing
### ATM System Concepts
#### 3.1.1 Functions and Objectives  
ATM is an integrated management system leveraging Communication, Navigation, and Surveillance (CNS) technologies and information networks. Through \"air-space-ground\" coordination (e.g., satellite links, ground radar, onboard systems), it enables real-time data sharing and dynamic decision-making, with core objectives of ensuring safe, efficient, and orderly aircraft operations.  
Per ICAO's Global ATM Operational Concept [22], ATM comprises seven key components, shown in Figure 1.  
These components form ATM's foundational framework, enabling functions like traffic control, flight information dissemination, conflict resolution, and flow optimization to address complex airspace environments and diverse flight demands. The system's complexity and adaptability stem from its wide-area, multi-source heterogeneous data fusion and high real-time requirements [4].
# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
## 2 Materials and Methods
### Model Construction and Evaluation Methods
#### 2.4.1 Build the Model  
The rural visual landscape serves as a setting for the daily recreation of villagers, with spatial stimuli predominantly evoking positive or tranquil emotional responses. We observed that the samples labeled \"\(-2\)\" and \"\(-1\)\" in the valence and arousal datasets were significantly smaller than other samples, which resulted in poor recognition of negative emotions in the training model. To achieve a balanced class distribution, we employed the Synthetic Minority Over-sampling Technique (SMOTE) [72], utilizing k-nearest neighbor interpolation (k = 5, determined via grid search) to augment the number of samples in each  
Figure 8: Signal feature extraction diagram. The bold is the 36 indicators after feature extraction, and the italic is the deleted feature indicators.  
minority class to match those of the majority class. Additionally, we implemented Bayesian optimization for hyperparameter tuning. Hyperparameter optimization plays a pivotal role in enhancing the performance of machine learning models by intelligently searching the parameter space to maximize the model's generalization capacity. In this study, the HyperOpt Python 3.9 (Python Software Foundation, Wilmington, DE, USA) library [73, 74] was used for hyperparameter tuning within the classifier element pool. Compared to traditional grid search and random search methods, this optimization strategy is particularly well suited for the multi-level classification system (binary, ternary, five-element classification) developed in this research. By defining the objective function as the weighted accuracy index of the test set (independently verified by villages), SMOTE effectively mitigated the potential risk of data leakage post-cross-validation [75]. Consequently, the classification robustness of the model was significantly enhanced in a few categories.  
In classifier selection, we observed that single classifiers such as Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Artificial Neural Network (ANN), and Random Forest (RF) are commonly employed [76, 77, 78, 79]. However, ensemble learning has been shown to yield superior predictive performance by aggregating the predictions from multiple models. Consequently, we utilized two single classifiers and two ensemble classifiers for model training. The single classifiers were LR-GD and DT, while the ensemble classifiers consisted of RF (Python 3.9) and XGBoost (Python 3.9).
# Influence of Electrohydrodynamics on the Drying Characteristics and Volatile Components of Ginger
## 3 Results and Discussion  
### Effect of Different Voltage Levels on the Water Content of Ginger Slices  
As shown in Figure 2a, the control group showed the slowest decrease in moisture content, while the decrease in moisture content in the experimental group began to increase with increasing voltage. We also found that the water content versus time curves almost overlapped at 25 kV and 30 kV, indicating that a maximum voltage exists for ginger drying under the current conditions and that further increase in drying voltage will not accelerate the drying process. [PERSON] et al. (2021) found that electrohydrodynamic (EHD) drying is mainly driven by ionized air on the tip of a needle, which creates an ionic wind on the fiber surface. The ionic wind diffuses the water molecules on the surface of ginger, causing these water molecules to accelerate away from the surface. However, with the prolongation of drying time, the water content in ginger decreases, the decrease of water content becomes slower, and the drying rate gradually slows down. Figure 2c shows the average drying time and rate under different drying methods and voltages. Compared with the control group, the drying time of the experimental group outside 15 kV was reduced by half, and the average drying time was also reduced with increasing voltage. Electrohydrodynamic (EHD) drying significantly reduced the drying time (\(p<0.05\)) and increased the drying rate compared to the control. This implies that the electrohydrodynamic (EHD) drying process can also be used to dry ginger material.
# Research on Microstructure and Mechanical Properties of Laser Welding of BR1500 HS Joints
## 3 Results and Discussion
### Fracture Morphology  
All tensile samples fracture at the base material before quenching and fracture at the joint after quenching. The fracture morphology is very similar. Therefore, the fracture morphology of samples No. 4\({}^{\#}\) and No. 5\({}^{\#}\) are taken as an example to analyze. The fracture morphologies of the base material of samples No. 4\({}^{\#}\) and No. 5\({}^{\#}\) before quenching are shown in Figures 6a and 7a and the fracture morphologies of the joint of samples No. 4\({}^{\#}\) and No. 5\({}^{\#}\) after quenching are shown in Figures 6b and 7b. As shown in Figures 6a and 7a, the fracture surface of the base material is plastic macroscopically, and the surface is not flat. It consists of fiber area, extension area, and sheer lip. The fracture is very close to the \"quasi-cleavage\" of the \"cleavage\" morphology. The dimples of the base material are small and dense, with few secondary microcracks distributed on the fracture surface. The microstructure of the joint consists of martensite with high strength and brittleness after quenching. Therefore, as can be seen from Figures 6b and 7b, after quenching, there are the cleavage facets, few shallow dimples, and dissociation river patterns in the fracture morphology. Moreover, the fracture morphology is smooth, signifying that a small plastic deformation occurred before the fracture and the fracture surface is brittle macroscopically.  
Figure 5: The tensile strength of all the samples at different heat inputs after quenching.  
Figure 6: Tensile fracture morphology of samples No. 4\({}^{\#}\): (**a**) base materials (BM); (**b**) joint.
## 1 Introduction
In this paper, we investigate the performance of using SOA as a booster and pre-amplifier for power budget improvement in 100 Gb/s/\(\lambda\) PAM-4 TDM-PON at O-band. In order to mitigate the SOA-induced pattern effect and bandwidth limitation-induced ISI, DSPs are required in both OLT and ONU. However, for cost consideration, most of the complex computations are configured on the OLT side, while only a simple feed-forward equalizer (FFE) is needed for ONU. As a result, an IEEE PR-30 [22] power budget requirement is achieved for both directions, and the dynamic range is as high as 18 dB.
## 2 State of the Art of DNA Microarray Image Compression
### Preprocessing
#### 2.1.1 Denoising  
Microarray images contain noise, which is sometimes considerable. This is due to imperfections in conducting the experiment and also in the image digitalization process. Being able to reduce noise can be useful to extract more accurate genetic information and to obtain better compression results. However, denoising-based compression is not lossless because denoising can be considered an irreversible transform that is applied before storing the image. There has been much research on denoising, but most of it is focused on the microarray analysis process and not on compression.  
In 2006, [PERSON] _et al_. published their approach [7] based on the translation invariant (TI) transform. They argue that wavelet based denoising techniques that are not translation invariant suffer from an adverse pseudo-Gibbs phenomenon at discontinuities. This is especially important in microarray images since the edge of each spot can be considered a discontinuity. In their work, the original image is shifted horizontally, vertically and diagonally, and each of these shifted images plus the original one are denoised separately. The resulting images are then shifted back and combined to obtain the denoised image. [PERSON] _et al_. proposed three different approaches for combining the deshifted images: TI-hard, TI-median and TI-median2. The TI-hard method consists of outputting the average value of each pixel in the four deshifted images. The TI-median technique outputs the median instead of the average. The TI-median2 technique works by applying the TI-median technique twice: first, the TI-median is applied on the original image and an auxiliary image is produced; then the TI-median technique is applied again on the auxiliary image to obtain the denoised output image.  
Many more authors have covered microarray image denoising for genetic information extraction, but only some of the most recent and relevant are referenced here. In 2005, [PERSON] proposed a method [8] based on fuzzy logic and local statistics for noise removal. Also in 2005, [PERSON] _et al_. discussed the peer group concept [9] as a means to remove impulsive noise. In 2007, [PERSON] and [PERSON] presented a simple method for denoising microarray images [10] based on comparing the edge features of the red and green channels. Recently, in 2010, [PERSON] _et al_. have designed multiwavelet transformations [11] to denoise images.
## 4 Meteorological Analysis of the 28 December 2020 Sea Storm Event
### Meteorological Analysis at Regional Scale  
The satellite image and sea level pressure map (Climate Forecast System-CFS Reanalysis) relative to 28 December 2020-12:00 UTC (Figure 5c,f) show that the center of the cyclone was located over northern France, and it was characterized by a sea level pressure equal to 972 hPa and a 500 hPa geopotential height equal to 5060 m. The mesoscale scenario triggering the analyzed meteo-marine event can be associated with the rapid barometric fall over the central Mediterranean, induced by the direct advection of cold arctic air masses. The dynamics of cold air affecting the northern side of the Alps led to the formation of a secondary low pressure over the Ligurian Sea (Genova Low), with a minimum value of 986 hPa at 12:00 UTC. Over the Tyrrhenian Sea, we can observe very close isobars associated with strong SW currents in the low troposphere, active since the early morning of 28 December 2020. Satellite images relative to 12:00 UTC show the cold front extending from Gibraltar to the Balearic Islands up to Corsica and northern Italy (Figure 5f).  
The synoptic wind force is directly related with the horizontal barometric gradient that reached its extreme values, while the maximum gusts occurred on the coastal strip of Naples. Barometric gradient (G) is the physical quantity equal to the ratio between the difference in pressure (\(\Delta\)P) between two points, A and B, of the Earth's surface (at the same elevation above sea level or considering pressure in reference to the sea level):  
\[\text{G}=\Delta\text{P}\cdot\text{D}^{-1}, \tag{2}\]  
where: \(\Delta\text{P}=\text{P}_{\text{A}}-\text{P}_{\text{B}}\); D = distance between A and B (expressed in km).  
Extracting the barometric data from the map in Figure 6b, we have a barometric difference \(\Delta\)P between the extreme northern part of Campania (992 hPa) and Capo Palinuro (1002 hPa) of about 1000 Pa at a distance D of 190 km, resulting in an average barometric gradient G of 5.26 Pa\(\cdot\text{km}^{-1}\). This extreme value is very often associated to storm winds. At 18:00 UTC, the cold front was going to cross the Campania region and was preceded by most intense SW storm gusts. The cyclonic system was very complex, in advanced stage of development with occluded fronts over France (Figure 7).
## 3. Key Supporting Technologies for the WSN
### Collision Avoidance Mechanism  
According to the ZigBee protocol, WSNs that work at 2.4 GHz frequency have 16 communication channels. In order to reduce the channel interference among WSNs, orthogonal channels are assigned to the WSNs within the interference range. That means the channel of any WSN in the integrated monitoring system is orthogonal to the channels of neighboring WSNs within the interference range.  
Within a WSN, all WSN nodes are working over the same channel. If they transmit simultaneously, collisions will happen, and the interference will lead to the failure of data transmission [17]. Collisions happen on both the layers of a WSN: collisions among the routing nodes in the backbone network, and collisions among the sensor nodes with the same parent routing node in the perception network.  
To deal with the collisions among routing nodes in the backbone network, a kind of unslotted carrier sense multiple access with collision avoidance (CSMA/CA) algorithm is employed. According to the ZigBee protocol, the unslotted CSMA/CA algorithm means that the WSN node waits for a random time period, namely the avoidance time, before it sends data. After the avoidance time, the  
\begin{table}
\begin{tabular}{c c c} \hline
**Item** & **Size (Byte)** & **Description** \\ \hline _hops_ & 1 & number of routing nodes from itself to the sink node \\ _LQI_ & 1 & LQI of the last communication \\ _flag_ & 1 & distinguish among parent route, minimum route and backup route \\ _next hop address_ & 2 & network address of the next hop to the sink node \\ \hline \end{tabular}
\end{table}
Table 1: Routing table.  
node starts to listen to the channel. If the channel is idle, it sends immediately; on the contrary, if the channel is busy, it waits and then tries to access to channel again.  
In the WSN, a routing node normally has more than one child sensor node. When these sensor nodes send data to the routing node simultaneously, collisions will happen. For this reason, the time is divided into slots and allocated to each sensor node according to the order in which it joins the routing node. The order is computed through Equation (1):  
\[n=A_{n}-A_{parent}-C_{skip}(d)*R_{m} \tag{1}\]  
where, \(n\) is the order in its parent node, \(A_{n}\) is the network address of the sensor node assigned by its parent routing node, \(A_{parent}\) is the address of its parent routing node, \(C_{skip}(d)\) is the size of the address sub-block being distributed by each parent with depth of \(d\) to its children routing node, and \(R_{m}\) is the maximum number of routing nodes a parent may have as children.  
By using this timeslot allocation mechanism, collisions among sensor nodes under the same parent routing node are effectively avoided. It also helps the routing node in gathering environmental information and managing the children sensor nodes.
## 3 Data and Scenarios  
### Dataset  
The vehicle trajectory data in highway work zones (for convenience, the dataset is denoted as the wzone dataset from this point on) were extracted from UAV videos with a total length of five hours. In the wzone dataset, the average height of the UAV and the average length of the highway segments covered in the videos were 291 m and 468 m, respectively. At ten work zone sites, more than 26,300 complete trajectories of vehicles, including cars and trucks, were extracted in both directions of the road.
###### Abstract  
In the aging population, imbalance leading to falls is of critical concern; thus, it is imperative to determine and quantify neuromuscular changes because of rehabilitative balance training. (1) Background: Previous studies that have examined changes in balance due to rehabilitative training placed a focus on clinical measures (i.e., behavioral, kinetic, and kinematic outputs); however, irregularities due to abnormalities of underlying neural origin were unrevealed by the aforementioned measures. (2) Methods: Examining muscle activity was used to determine strategies pre- and post-six weeks of balance training in twenty-three healthy older adults (69.5 \(\pm\) 5.7 years old) and five survivors of stroke (66.4 \(\pm\) 9.48 years old). Surface electromyographic (sEMG) signals were recorded from eight of the lower limb muscles while participants performed forward walking (FW), forward tandem walking (FT-W), backward walking (BW), and backward tandem walking (BT-W) tasks. The sEMG data were then conditioned and muscle synergies were extracted using non-negative matrix factorization (NNMF). (3) Results: It was observed that muscle synergies and activation patterns changed for pre- versus post-balance training in older adults (i.e., healthy individuals and those who had suffered from stroke). (4) Conclusions: From our findings, it was indicative that muscle activation and muscle synergies could be used to quantify and inform rehabilitative balance training in older adults.  
Keywords:gait analysis; balance; electromyography (EMG); elderly; stroke; muscle synergy +
Footnote †: journal: Applied sciences