esa-sceva/satcom-chunk-collection huggingface dataset:
# Antisense-Mediated Down-Regulation of Factor V-Short Splicing in a Liver Cell Line Model
###### Abstract
In turn, FV-short stabilises TFPI\(\alpha\) in the circulation [22; 24] and potently enhances TFPI\(\alpha\)-mediated inhibition of FXa in the presence of protein S [25], which binds to both TFPI\(\alpha\) and FV-short and strengthens their interaction [26]. FV-short was originally identified in a family with FV-short over-expression due to a \(F5\) gene mutation that enhances FV-short splicing, resulting in 10-fold increased TFPI\(\alpha\) levels and a bleeding tendency (East Texas bleeding disorder) [22; 27]. More recently, two other \(F5\) gene mutations (\(F5\)-Amsterdam [28] and \(F5\)-Atlanta [29]) up-regulating FV-short splicing and plasma TFPI\(\alpha\) levels have been reported in unrelated patients with similar histories of trauma-related bleeding. As a possible therapeutic approach to these haemorrhagic disorders, we have explored the feasibility of decreasing FV-short expression by antisense-mediated \(F5\) splicing modulation. To this end, we designed specific antisense oligonucleotides targeting FV-short splicing and tested them _in vitro_ in a liver cell line (HepG2) that naturally expresses full-length FV and FV-short.
# Dynamic Malware Analysis Based on API Sequence Semantic Fusion
## 4 Experiment
### Dataset  
In our experiment, we adopt the dataset from the Alibaba Cloud Security Malware Detection Competition [37], where the benign samples are collected from regular software stores and the malicious samples are collected from the Internet and virus databases. The dataset includes 12,390 samples from more than 70 million API calls after data filtering and cleaning. The malicious samples include infectious viruses, Trojans, mining programs, ransomware, and others, and the labels have been verified by the Virus Total platform. It should be noted that the official only provides samples in the form of API instruction sequences, which have been desensitized to ensure that these data will not be used by attackers with malicious motives.  
The category and number of samples are shown in Table 4. There are 7 types of samples, and the normal category accounts for the largest proportion, accounting for 40% of all. Worm viruses accounted for the least, at only 0.8%. There is a problem of data imbalance in each category of samples, which is of great significance for testing the classification effect of the model.  
Figure 5: Schematic diagram of the calculation process of the attention mechanism.  
Shown as Table 5, the table of the dataset includes the API sequences of all samples. Each record includes 5 fields, \(file\_id\), label, API, tid, and index. All records of one sample have the same file_id. The label field takes different values to represent benign samples or different malware families. One malware or benign sample occupies thousands of records in the table. The field API means the textual name of the API function. The 'tid' field represents the thread that called the API function. The 'Index' field represents the sequence number of the API call in the same thread. The APIs inside the thread are arranged according to the index number, and there is a sequential relationship. If there is multithreading concurrency, API functions will not be sorted by thread sequence number. According to statistics, the API call sequence in the dataset contains a total of 295 different API names.
## 3 Energy-Efficiency Solutions and Technologies
These can be summarized in the following categories: research works on the enhanced management of dynamic power [42, 43]; research works on AI-driven optimization methods [44]; and research works on the integration of renewable energy sources [36, 37]. All these efforts combined will contribute to the enhancement of the energy efficiency of 5G networks and thus facilitate our movement towards 6G sustainable technology. [PERSON] et al., 2024 [45], proposed an innovative design of distributed photovoltaic 5G base station micro-grid structures. Through an energy management strategy, they managed to enhance the energy efficiency of 5G base stations by integrating a maximum power point tracking (MPPT) algorithm for improved information passage and better energy flows. However, the cooling of PV cells is still required and should be integrated into thermal management systems in order to increase the overall efficiency of the cells and, at the same time, ensure control of the temperatures of the different electronic devices and thier components. [PERSON] et al., 2023 [46], shed light on energy consumption in 5G networks; this remains a multifaceted challenge due to the continuous growth in demand. Researchers like [PERSON] et al., 2019 [47] and [PERSON] et al., 2023 [48] have been exploring innovative solutions in attempts to improve energy efficiency. Further research and development efforts are required to better assist in the evolution of 5G networks towards 6G technology. [PERSON] et al., 2022, developed a joint optimization algorithm to better control base station states and the association of users in wireless caching networks [49]. [PERSON] et al., 2022 [50], developed a reinforcement learning technique that is applied for energy optimization in 5G heterogeneous networks employing multi-sleeping controls. Energy Consumption: Future Directions and ChallengesIn terms of futures directions, it is obvious that research works on the energy consumption of 5G networks are still rare and disperse. This points to the urgent requirement that we develop future research by combining efforts from interdisciplinary sciences. A good direction for this is to couple artificial intelligence with renewable energy sources in pursuit of the goal of reducing the energy consumption of 5G and 6G technologies for a sustainable future.
Note that all the aforementioned MPP algorithms work with a converter, and do not employ any mechanism to detect whether the converter is in continuous conduction mode (CCM) or discontinuous condition mode (DCM). When a dc-dc boost converter is installed in a PV system, the design of the converter's components, operating points on the I-V curve, MPPT algorithm, and control aspects are set up in accordance with the continuous conduction mode of the converter. In this regard, the novelty of the present research work is as follows:  
* We present an extensive investigation of a converter in DCM under conditions of varied weather and design of the components, which has not been previously well studied in the literature. The findings of this investigation indicate that there are several conditions during which converter enters DCM mode because of nonlinear characteristics of the PV source. * This paper proposes a new \(P\&O\) method, in which a unique feature is introduced to restrict the converter's movement into DCM. Mode identification of CCM and DCM is executed through a unique formula, and no new sensors are required to execute this process. * Compared to previous \(P\&O\) algorithms, the proposed method results in the converter not becoming caught in DCM mode, and as such is better able to utilize PV-generated power.
# Prediction System for Overhead Cranes Based on Digital Twin Technology
## 3 Development of the Digital Twin System for Overhead Cranes  
### Framework Design  
Unity3D was originally proposed as a 3D game development tool. With the development of the industrial IoT, it has been widely used in multiple digital twin simulation fields. It can not only render 3D simulation models in real time but also display received sensor data or cloud platform data synchronously. With this tool, staff can grasp actual usage status information for external IoT equipment on time and can use multiple platforms such as AR/VR/MR for human-computer interaction to realize remote and safe control of machinery and equipment [21].  
Timely information exchange and synchronization between IoT equipment and software programs should be supported as perfectly as possible. TCP is a traditional communication protocol, and with this protocol, IoT equipment reads relevant sensor data through PLC and uploads it to the cloud server in JSON format. The front end of the Unity3D digital twin performs real-time data acquisition and processing through Socket or HTTP protocols and displays the real operating status of the IoT equipment. Similarly, peripherals can be reversely controlled through the simulation software; instructions are sent from the program to the cloud server and then forwarded to the PLC of the IoT equipment, thereby issuing device physical-operation instructions [22].  
The design of the digital twin software is shown in Figure 1. Accuracy, real time, and effectiveness of data transmission are critical to ensuring the complete digital visualization process. For further design, the visualized data is highly dependent on data and information collected by sensors. In terms of software development, advanced, high-efficiency algorithms are employed to conduct global perception and operation monitoring of data. Furthermore, historically accumulated data is integrated for calculation and presentation. Moreover, IoT equipment-failure risk-assessment predictions can be made effectively via adding physical characteristics and behavioral logic calculations to the model [23].
# FFG-YOLO: Improved YOLOv8 for Target Detection of Lightweight Unmanned Aerial Vehicles
## 1 Introduction
These limitations hinder real-time small-target detection in UAV photography, demanding significant computational resources and a careful balance of model parameters, complexity, speed, and accuracy. Therefore, there is another lightweight and accurate solution for UAVs that enhances small-target detection in complex, highly occluded backgrounds. Therefore, we developed a lightweight small-target detection method for UAVs based on YOLOv8 (FFG-YOLO). In this study, FFG-YOLO integrates three modules: a feature enhancement block (FEB) for multi-scale feature extraction without increasing parameters, a feature concat block (FCB) to minimize semantic bias in multi-scale feature fusion, and a global context awareness block (GCAB) to fuse global context and improve attention. The modules are used to distinguish small targets from complex backgrounds. We employed the Wasserstein distance loss (WDLoss) function, derived from optimal transmission theory, for accurate small-target positioning and precise measurement of similarity between predicted and ground-truth bounding boxes, enhancing pixel-level accuracy with blurred or confusing backgrounds. Experiment results demonstrated that FFG-YOLO outperformed algorithms, including YOLOv8, YOLOv10, YOLOv11, and real-time detection transformer (RT-DETR) in detecting small targets in aerial photos on variable scales and overlapped objects, while maintaining its lightweight characteristics appropriate for UAV photography.
# A Trusted Lightweight Communication Strategy for Flying Named Data Networking
## 6 Challenges in FNDN Security
### Trust Management  
Different from traditional TCP/IP-based trust management for FANET where trust is divided into entity-based, data-based, and hybrid trust model, FNDN by nature, only supports data-based trust model. Thus, efficient data trust management schemes are essential in FNDN. It is worth noting that entity trust cannot be completely ignored in FNDN because the data trust must be linked to the publishers/producers; however, due to mobility and intermittent connections, managing trust level for individual nodes will incur huge overhead. Therefore, an efficient alternative must be developed. Furthermore, trust lifetime is another aspect that needs further investigation. Depending on the type of application, context-aware trust management scheme could perform better in FNDN where the trust parameters could be changed based on the context of the application. To this end, both delay-sensitive and delay-tolerant applications must be taken into account while working with trust.
## 3 Research Model and Hypotheses
### Hypotheses
#### 3.2.4 Student-Student Interaction (SST)  
Student-student interaction, also known as peer interaction, refers to the engagement, interaction, and collaboration among students, with or without being facilitated by educators. Student-student interaction in online learning courses allows enrolled students to exchange ideas, insights, and perspectives related to their coursework. It facilitates group work, discussions, and collaborative assistance with assignments. According to [PERSON] et al. [41], implementing peer interaction in online learning enhances the sense of belonging and social involvement between students. Moreover, they argued that students in online courses characterized by high levels of interaction tend to be more satisfied compared to those in less interactive courses. This highlights the importance of peer interaction features in online learning environments for enhancing students' satisfaction; the hypothesis was developed as follows:  
**H4:**: _Student-student interaction positively influences student satisfaction_.
# Review of Acoustic Sources Alternatives to a Dodecahedron Speaker
## 2 Acoustic Source Alternatives to a Dodecahedron Speaker
### Shotshell Primer  
Shotshell primers contain a small amount of explosive, which is ignited inside of a tube by compressing its back section, crushing the explosive mixture. The tube directs the hot gas away from the holder region, the effective sound source being at the end of the tube where the gas expands out thus creating an impulsive sound. Research by [PERSON] et al. [119] has investigated the properties and design of a shotshell primer as an impulsive source. The source has also been used for measuring the effects of moisture content on soil impedance [120] and for the measurement of sound barrier attenuation [59].  
Considering omnidirectionality, according to a polar graph presented in the research by [PERSON] et al. [119], the source appears to not fulfill the recommendations of ISO 3382-1. It is found experimentally that the sound field is conically symmetric around the axis formed by the tube.  
Concerning repeatability, due to differences in the primers, small variations were measured in the peak levels typically by \(\pm 1\) dB. Hence the source appears to have acceptable repeatability.  
Regarding sound pressure levels, changes in the level tube can produce peak levels from 140 to 150 dB approximately. Increasing the tube length from 0.5 m to 2 m caused the peak level to drop by 10 dB. A number of different brands of primers have been tested with the peak level characteristics vary slightly, depending on the amount of explosive.  
Relating to the frequency response, a normalized frequency spectra presented by the authors contains energies at frequencies between 100 Hz and 10 kHz, with the maximum intensity around 1 kHz. However, there is a steep roll-off in the low- and the high-frequency range.  
In regard to measurements of acoustic parameters, the accuracy of measurements with a shotshell primer as an acoustic source has not been compared with measurements with a source that fulfills the ISO 3382-1 standard. However, as mentioned before the source have been used for measuring the effects of moisture content on soil impedance [120] and for the measurement of sound barrier attenuation [59]. For the measurements of sound barrier attenuation, the experimental results were compared with predictions from a variety of approximate and exact diffraction theories which allowed the attenuation to be determined with an accuracy of 1 dB. For the measurement of real and imaginary components of soil impedance, good agreement was obtained between measured and calculated values.  
In conclusion, the shotshell primer as a sound source does not meet the ISO 3382-1 sound source requirements since it cannot be considered an omnidirectional source and also does not have sufficient sound power in the low-frequency range.
# A Hybrid DSCNN-GRU Based Surrogate Model for Transient Groundwater Flow Prediction
###### Abstract
[20] proved that the convolutional neural network gated recurrent unit (CNN-GRU) model can extract hidden features of the coupling relationship between groundwater depth and time series, allowing further prediction of groundwater depth at different time series. This study proposes a new architecture to address the computational and representational limitations of CNNs. The method combines a depth separable convolutional neural network (DSCNN) with the GRU framework to construct an efficient surrogate model specifically for predicting groundwater flow. The hybrid model leverages CNN's feature extraction capabilities and uses GRUs to capture long-term temporal dependencies in time series data. The integration of DSCNN is recognized for its lower computational load and reduced parameter requirements, which provides a computationally efficient surrogate without sacrificing representation capabilities [21]. Additionally, this study conducted experiments on the groundwater conditions in the Penola region of southeastern Australia. Using only a CNN model to predict groundwater head fields resulted in lower accuracy compared to using a CNN combined with long short-term memory networks (LSTMs). Replacing CNN and LSTM with DSCNN and GRU further achieves better results. Compared to a single CNN model, the DSCNN-GRU model is more efficient in predicting groundwater head fields over continuous time series in this region. The hybrid DSCNN-GRU model is designed to offer a balanced solution, leveraging the strengths of CNNs and RNNs in feature representation and temporal sequence learning, while addressing the associated high computational demands, providing an efficient approach for groundwater flow prediction. The subsequent sections of this study are organized as follows: Section 2 presents an overview of the hydrogeological conditions in the study area and details the groundwater model layout for three cases. Section 3 briefs the standard CNN and LSTM models, followed by a description of the proposed DSCNN-GRU model. Section 4 outlines the predictive results of varied surrogate models and compares their performance through numerical evaluations. Finally, the main conclusions are summarized in Section 5.
## 5 Key Enablers
### Software-Defined Controllers for Edge Cloud
#### 5.3.5 Hierarchical Edge Cloud SDN Controller  
The hierarchical edge cloud SDN (HECSDN) controller is a multilayer approach to improve scalability and reduce computation delays of edge cloud networks. According to the traffic load, the HECSDN controller system allocates computational tasks sharing the edge and the cloud resources [70; 71]. The system comprises two main components, the edge segment and the cloud segment, as shown in Figure 12. The edge segment includes two types of controllers: first-tier central controller and second-tier local controllers. The cloud segment includes a cloud controller.  
The central controller in the edge segment has a global view of traffic status in the edge network. It is responsible for distributing flow signals among the local controllers and the cloud controller. Local controllers compute the routes for incoming flow signal packets and set flow tables for all the relevant switches. They also provide traffic updates to the central controller to make sure that it maintains the real-time traffic status of the edge network.  
The cloud controller in the cloud segment offers substantial computation capacity, supporting load balancing and delay reduction to satisfy QoS requirements. With the help of the cloud controller, the system can handle large amounts of traffic and greatly decrease the flow level waiting time in the control plane. The central controller continuously monitors the status of both the cloud controller and all local controllers. If input arrival alterations, controller overloads, or the application's QoS requirements are not met, controllers request to the central controller for load redistribution. Based on the traffic status, the central controller optimizes the load arrangement and ensures the QoS requirements of the application [70; 71].
# Internet Traffic Prediction with Distributed Multi-Agent Learning
## 2 Related Work
### Deep Learning-Based Prediction Models  
As a subset of machine learning models, deep learning models are becoming the mainstream solutions for network traffic prediction problems in various scenarios due to their superior performance. Deep learning-based prediction models can be roughly divided into different categories based on the neuron connection method in neural networks, i.e., recurrent neural networks, convolutional neural networks and graph neural networks.  
Recurrent neural networks (RNNs) have been proposed for handling long sequence data, e.g., natural languages and time series, by adding the recurrent connection and memory mechanism. LSTM and GRU are two representative RNN variants and solve the problem of vanishing gradients faced by the original RNN structure. They have been adopted in network traffic problems with both univariate and multivariate time series forecasting problem formulations [7; 27; 28].  
When considering network traffic prediction problems in a regular grid, convolutional neural networks (CNNs) can be used by taking traffic data in different grids as image pixel values [29; 30; 31]. The attention mechanism [32] can be further incorporated into CNNs for a better prediction performance [33]. For example, the attention mechanism is combined with ConvLSTM for capturing long-term spatial-temporal dependency for cellular traffic prediction and helps to achieve accurate prediction under hourly and daily time scales [34]. Based on the Transformer network, a spatial-temporal downsampling neural network model is further proposed for citywide mobile traffic prediction [35].  
More recently, graph neural networks (GNNs) have been proposed as the new frontier of artificial intelligence research, and the input data are formulated as graphs [36]. GNNs have been successfully applied in communication networks, with a wide range of applications including network traffic prediction [37; 38; 39]. To further improve the performance of the GNN-based prediction model in large-scale traffic prediction, transfer learning is introduced for knowledge reuse and computation reduction in cellular traffic prediction [40].  
While there are various network traffic prediction models in the literature, most of them are considered in a centralized approach. Thus, the main research motivation of this paper is to fill in the research gap that the distributed multi-agent learning approach has not been considered in the Internet traffic prediction problem before, in the cooperative way more specifically. Two main research questions arise in this paper, i.e., would the interaction among different agents converge in the long run, and could the distributed multi-distributed learning approach outperform the traditional centralized learning approach? From our research, both questions are answered in the affirmative.