esa-sceva/satcom-chunk-collection huggingface dataset:
# Tunnel-Free Distributed Mobility Management (DMM) Support Protocol for Future Mobile Networks
## 2 Proposed Tunnel-Free DMM Support Protocol
### Overview
The newly configured address is registered by the MN while the previous address is still valid. The address information is extracted by BMA2 upon receiving the NBU message, where the movement of the MN to BMA2 is updated with the help of this extracted information. The BMA2 then sends the NBAck message containing the newly configured address of MN to BMA1, indicating the complete configuration of the new address. In the architecture, at the access network level, the BMAs are distributed. The address is assigned to the MN based on a network prefix provided by the BMA. Using the BU message, the MN then registers the assigned address with the BMA. When the MN on the move changes its attachment point to another BMA, the MN then configures a new address based on the network prefix provided at the new access network; however, it retains the previous address. Upon registration by a BU message, the MN informs the current BMA about the previous address along with the configuration of the new address. As the current BMA attains the previous address data from the BU message, it sends an NBU message to the previous BMA, which updates the mobility and routing status of the MN. The IP handover is confirmed by an NBAck. Figure 1 shows this scenario. As discussed above, the proposed approach depends on distributed BMAs at the access network level, preventing a network bottleneck and avoiding a single point of failure. The proposed approach gives network scalability a fair chance, and it improves the performance of end-to-end packet transmission by removing the tunneling tradition and making use of the MBB. Figure 1: _Cont._
# A Framework for User Traffic Prediction and Resource Allocation in 5G Networks
## 1 Introduction  
Cellular Telecommunication Networks have been a huge part of public and private communications in the last few decades. The current standard for these networks is the Fifth-Generation (5G) networks, which deal with constantly changing patterns in user traffic as well as the different requirements of each user and application connected to the network. The huge amount of data in extremely dense networks causes congestion [1]. This creates a need to optimize resource allocation in such networks to ensure Quality of Service (QoS). Resource allocation in 5G networks is a field with a significant research interest, while the capability to predict user traffic in such networks can assist any system of the former.  
Machine and deep learning (ML/DL) have been proven to be useful for optimizing resource allocation and user traffic in 5G networks, along with tasks such as energy efficiency, network accuracy and latency [2, 3]. However, this requires the training of such algorithms through the use of large accurate datasets gathered from currently active 5 Gnetworks. The advantage of ML, when provided with quality large-scale data, is that it can provide fast and high-quality results with minimal loss of effectiveness.  
This paper presents a novel approach for predicting user traffic and using these predictions to aid with the allocation of the users to base stations in a 5G environment. Thus, the resource allocation of the network can be performed in a proactive way that can be adapted in real-time to changing network conditions. The main strength of our framework lies in the ability to approximate long-term trends in time-series data, an open research question in the 5G communication field. Furthermore, the ML models comprising our framework offer a good balance between performance and usage of computational resources.
## 2 The TPB-IC Specim for Mixed-Mode I/III Fracture Study  
### The TPB-IC Specim for the Mixed-Mode I/III Fracture Model  
The loading configuration and geometric structure of the TPB-IC specimen for a mixed-mode I/II/III fracture are shown in Figure 1. The TPB-IC specimen is a rectangular beam of length (L), thickness (B) and height (W). It contains a straight edge crack of length (a) in the direction of the through thickness. a/W is defined as the crack height ratio of the specimen. The specimen is supported in a three-point bending fixture with a span (S). On the top of the specimen, there is a uniformly distributed load (P) applied along the thickness direction. The crack forms an angle \(\alpha\) with the loading line on the bottom surface of the specimen, which is defined as the horizontal deflection angle (Figure 1c). The crack on the front surface of the specimen forms an angle \(\beta\) with the vertical direction, which is defined as the vertical deflection angle (Figure 1b). When \(\alpha\) and \(\beta\) are both zero, the loading conditions and the geometry are symmetrical, and the specimen fails through a pure mode I fracture. However, for other angles \(\alpha\) and \(\beta\), the loading conditions and the geometry are asymmetric with respect to the crack surface, and mixed-mode fracture occurs in the specimen. The crack deflection angles \(\alpha\) and \(\beta\) are the principal influencing parameters in mixed-mode fracture. The contribution of mode I, mode II and mode III SIFs in mixed fractures depends on \(\alpha\) and \(\beta\).  
Figure 1: The loading configuration and geometry of the TPB-IC specimen. (**a**) Panorama of specimen loading. (**b**) Front view of specimen loading. (**c**) Bottom view of specimen loading.
## 4 The Principle of Safe Storage Technology of Spatiotemporal Big Data Blockchain
### Data Upload and Download  
The secure storage process for spatiotemporal big data is depicted in Figure 8, which comprises two primary processes, namely, the upload and download processes. Figure 8a shows the upload of spatiotemporal big data. To begin with, the spatiotemporal big data are first encoded, as exemplified by #000, #001, #010, #011, #100, #101, #101, and #111, which represent diverse spatiotemporal data file information. Data security storage is approached by taking into account the identification of data stealth. In the case where the spatiotemporal big data entail private data, such as military, industrial, medical, and other information, they are first encrypted and subsequently stored in blocks. The data undergo direct scrambling into blocks, with the scrambled data being encoded as follows: #001, #011, #010, #000, #110, #101, #111, and #100. Block information Id-Data are then added, and the block information is stored in the blockchain, while the corresponding data are stored in IPFS. This methodology reduces the data cost and running time of the blockchain, while simultaneously recording block information in the blockchain network ledger. On the other hand, nonprivate data require no encryption and are uploaded directly to IPFS. Every data access and storage is recorded in the blockchain, ensuring data security. To download data, user requesters are required to authenticate and authorize their access by checking the Id-block file associated with the requested data in the blockchain. Once authorization has been granted, the manager utilizes the data retrieved from the blockchain to retrieve the requested data information. As illustrated in Figure 8b, space-time big data download a process. Following privacy assessment, the manager proceeds to reconstruct the data into their original form through defragmentation and decryption. File scrambling data encoding is reduced from #001, #001, #010, #000, #110, #101, #111, and #100 to #001, #001, #010, #101, #101, and #111, ultimately obtaining the correct information. Nonprivate data are reconstructed and restored directly.  
The Id Data record the order of the ENCRYPT PART file, which is sent to IPFS. Figure 7 has been labeled SN, ON.  
Figure 8: Spatiotemporal big data upload and download process.  
Upload data: First, select a file in the spatiotemporal big data file library and divide it into parts (#000-111). The second step is to encrypt the sensitive information of the judgment data. ENCRYPT the PART and record it as ENCRYPT PAR (#000-#111). In the third step, self-ordering, ENCRY PART (#001-#100), the previous order ENCRYPT PART (#000-#111) is stored in Id-Data.  
Download data: First, restore the ENCRYPT PART (#001-#100) to the original order according to the order of the Id-Data ENCRYPT PART (#000-#111) in the blockchain. In the second step, the ENCRYPT PART (#000-#111) of this data is decrypted. The PART data are then combined into a file.
# A Low-Brightness Image Enhancement Algorithm Based on Multi-Scale Fusion
## 4 Experiment and Analysis
### Objective Assessment of Image Quality
#### 4.2.2 Visual Information Fidelity  
Another important metric used to assess image quality is visual information fidelity (VIF). The VIF assesses the information fidelity of the image by comparing the degree of information agreement in two HVS (human visual system) results. A higher VIF value indicates better visual information. Table 2 presents the VIF values and shows that our method does not achieve the highest VIF values, but it is higher than the other four methods. Therefore, our method still produces images with good visual information.
## 3 Results and Analysis  
### Results of the Heating Performance Test  
Table 5 summarizes the maximum heating performance test results according to the parameters of each specimen, and Figure 5 shows them in comparison. As seen in Table 5 and Figure 5, the results of measurement for 3600 s after supplying three different applied voltages (DC 10 V, 20 V, 30 V) for different electrode spacings (40 mm, 120 mm) showed that, in the case of the PM specimen, the maximum heating temperature was 20.1 \({}^{\circ}\)C, 20.2 \({}^{\circ}\)C and 20.3 \({}^{\circ}\)C, respectively, showing very low heating performance without apparent dependence on applied voltages or electrode spacings. Even in the case of the ECM specimens mixed with SF under the same conditions, the maximum heating temperature was slightly improved to 20.2-20.6 \({}^{\circ}\)C, 20.3-21.2 \({}^{\circ}\)C and 20.4-21.8 \({}^{\circ}\)C, respectively, and we could not find any distinct differences in heating performance due to the difference in electrode spacing. The heating values of the ECM specimens mixed with SF and PM specimens were at most 0.3 \({}^{\circ}\)C and 1.8 \({}^{\circ}\)C, respectively, showing very low heating performance and almost no heating effect. Therefore, because electrically conductive properties were not improved in this way, it was judged that snow and ice accumulated on the surface of the pavement could not be removed. However, the maximum heating temperature of the ECM specimens mixed with CF witnessed significant improvements in the heating performance compared to the PM specimen and the ECM specimens mixed with SF. At three different applied voltages (DC 10 V, 20 V, 30 V) and different electrode spacings (40 mm, 120 mm), the maximum heating temperatures increased greatly to 20.6-34.8 \({}^{\circ}\)C, 24.9-73.7 \({}^{\circ}\)C and 30.1-145.1 \({}^{\circ}\)C, respectively. As the CF contents and applied voltages increased, the maximum heating temperature tended to increase. In particular, the highest heating temperature occurred in the ECM-CF125 specimen mixed with CF, with applied voltage of 30 V and electrode spacing of 40 mm, reaching a maximum of 145.1 \({}^{\circ}\)C. The reason for such better heating performance is that the CF in the specimen contact each  
other to form more electrically conductive networks through which electricity can flow easily. This is known from previous research results also stating the enhanced heating performance effect [38]. It is expected that this generated heat can be effectively used to melt snow and ice accumulated on the surface of the pavements.
# Defect Detection for Wear Debris Based on Few-Shot Contrastive Learning
## 1 Introduction  
Wear debris comprises the friction particles found in lubricating oil as a result of friction on equipment, and it carries much information about the running state of a machine.The shape, amount, and size of wear debris can reflect and reveal the degree and mechanism of wear of a machine. Wear debris techniques can be used to monitor the status and long-term trends of the wear of parts of steel production equipment, and they play an important role in preventive measures, which are helpful for avoiding faults in steel production equipment.  
Convolutional neural networks (CNNs) have made great progress in general object detection. Deep learning requires a large amount of annotated training data. Few-shot learning aims to train a model with a strong generalization ability with just a small amount of data. Existing few-shot learning methods can mainly be grouped into the two categories based on the model architecture: single-branch models [1; 2; 3] and two-branch models [4; 5; 6; 7; 8; 9; 10]. Single-branch models attempt to exploit transfer learning to achieve quick adaptation to novel domains. Two-branch models mimic a Siamese network [11] in order to handle the query and support branches, and the distance between the two branches is naturally learned through logistic regression. The key in a two-branch model is to make good use of the guidance information of the support branch.  
Unlike object detection in a natural scene, the defect detection with wear debris has the following difficulties: (1) The probability of the occurrence of serious defects in wear debris is low, which makes it difficult to collect enough defect samples. So, the defect detection with wear debris is a typical few-shot object detection task. (2) The features of wear debris defects have a smaller category variance, and it is difficult for commonly used object detection models to learn their distinguishing features. (3) Recognizing objects atvastly different scales is a fundamental challenge in computer vision, and wear debris defects have excessive variations in scale. The shape and size of wear debris defects are unpredictable, which brings great difficulties for models that are used to classify and locate them. So, it is necessary to fuse multi-scale features.  
We propose an improved two-branch model that combines contrastive learning and multi-scale feature fusion for few-shot learning. We improve the two-branch model from the following perspectives: (1) Aligned contrastive loss is designed to reduce the variance of object proposals embedding from the same category while pushing instances of different categories away from each other, and the misalignment problem is handled. (2) Multi-scale fusion is proposed in order to adaptively fuse features from different resolutions in the query branch. Comprehensive experiments are conducted to assess each method fairly. The extensive experimental results show the effectiveness and robustness of the proposed model.
# A Dyna-Q-Based Solution for UAV Networks Against Smart Jamming Attacks
## 3 System Model and Network Controller Design
### Jamming Attack Model  
There are two types of interference sources: one is a low-mobility jamming station and the other is a high-mobility jamming UAV, as illustrated in Figure 1. When the velocity of the jammer is not much different from the velocity of UAVs, continuous jamming can occur. When the UAV swarm is flying at high speed, the impact of the jamming station is negligible. It can be observed from the analysis mentioned above that the relative velocity of the jammers which have a great influence on the UAV network will be low. Therefore, we can assume that the state information of the whole network can converge in the SDN controller in one time slot.  
Let \(\mathbf{H}_{u_{i}u_{j}}^{T(k)}=\left[h_{u_{i}u_{j}}^{T(k)}\right]_{1\leq i\leq N}\) denote the channel power gains between UAV \(u_{i}\) and UAV \(u_{j}\) at time slot \(k\), and let \(\mathbf{H}_{u_{i}l_{j}}^{I(k)}=\left[h_{u_{i}l_{j}}^{I(k)}\right]_{1\leq i\leq N}\) denote the channel power gains between UAV \(u_{i}\) and jammer \(J_{j}\). Let \(d_{0}\) denote a reference distance and \(\rho\) the path loss exponent; the value of path loss \(PL\) at distance \(d\) can thus be modeled by  
\[PL(\text{dB})=\
u(\text{dB})+10\rho\text{lg}\left(\frac{d}{d_{0}}\right)d>d_{0} \tag{1}\]  
where \(\
u\) is a constant indicating the antenna gain and the path loss exponent \(\rho\) characterizes the radio environment, where \(\rho=2\) describes a free-space propagation and \(\rho=4\) describes a two-ray model. Assuming \(r_{i}\sim N(0,1)\) follow the standard normal distribution, we have \(h_{i}^{g(k)}=r_{i}^{(k)}/PL_{g}\), where \(g=T/J\).
# Development and Evaluation of a LiFi-Transceiver Module for TMTC Intra-Satellite Communication +
## 4 Evaluation
### Bit Error Ratio (BER)  
The Bit Error Ratio (BER) is the ratio of erroneous bits to the total number of all bits sent. To obtain this, the received data are compared with the sent data at bit level, and the total number of erroneous bits is counted. With the total number of bits \(n\) and the number of erroneous bits \(w\), the BER can be calculated using the following equation: \(BER=\frac{w}{n}\).
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 3 Antenna Overview
### Biconical Dipole Antenna  
Biconical antennas (also called butterfly or bowtie antennas) are broadband dipole antennas made up of two roughly conical conductive objects. These antennas have characteristics like that of dipole, with a larger bandwidth achieved as a result of the double cone elements structure. The application of biconical antennas are evident in emission testing, immunity testing, and measurement of shielding effectiveness in anechoic chamber [43; 44]. Figure 4 shows a typical biconical antenna.  
Figure 3: The major types of antenna discussed.
# Ferroelectric Materials: A Novel Pathway for Efficient Solar Water Splitting
## 4 Ferroelectric Materials for Photoelectrochemical (PEC) Water-Splitting Devices
### (5) PbTiO\({}_{3}\)
#### (1) CdS  
Cadmium sulfide (CdS) is the most common ferroelectric chalcogenide investigated in PEC water splitting because of its narrow energy bandgap of 2.4 eV and suitable band edge position [157; 158]. Despite these advantages, the CdS-based PEC devices showed a low performance because of inefficient charge transfer and separation [161; 162; 163; 164; 165].  
Uniform carbon-coated CdS core-shell nanostructures are potentially useful as a facile and novel approach to overcome this challenge [166]. The enhanced performance is elucidated by the  
Figure 10: (**a**) P–E hysteresis loop for a 400-nm-thick epitaxial BiFeO\({}_{3}\) thin film with \((001)_{\text{pc}}\) BFO at 10 kHz and 50 kHz frequencies and \((110)_{\text{pc}}\), \((111)_{\text{pc}}\) BFO thin film at 10 kHz. (**b**) Polarization switching measurement of the 50-nm thick \((111)_{\text{pc}}\) BFO thin film photoanodes in different polarization states. The inset shows the magnified portion for the onset potential [4]. Copyright 2018, Springer.  
substantially improved electron transfer, which results from the high electrical conductivity of the coated carbon layer on CdS. CdS widely couples with ZnO nanowires and nanorods as an ideal heterostructure in PEC devices that helps to suppress internal charge recombination and enhance solar-to-chemical conversion [167; 168; 169]. Accordingly, the development of ZnO/CdS nanotube arrays outperforms the previous structures with a larger specific surface, which results in a superior ability for charge extraction, especially in the case of nanoparticle plasmon metal addition [170; 171].  
Besides the common nanostructured CdS, a rare report on the versatile hierarchical structure of CdS film was investigated recently [172]. [PERSON] et al. [173] grew screw-like SnO\({}_{2}\) nanosheets on rod-like single-crystalline SnO\({}_{2}\) nanowires deposited with CdS quantum dots in PEC cells that tend to move toward efficient PEC water splitting. This structure is a combination of both the fast charge transport path of 1D nanostructures and the high porosity and light absorption of 2D nanosheets, which will extend the perspective of high-performance PEC cells in the near future.
## 2 Related Works  
### Codon Label Consistency  
The idea of exploiting label consistency information for promoting model performance is widely used in image and video analysis. For example, when identifying human actions in videos, since the actions in adjacent video frames are often consistent, this constraint can be exploited to improve the accuracy of action recognition [26]. Similarly, in multi-label image classification tasks [27], objects of different categories in the image may have label dependencies, and considering this dependency information often improves the classification accuracy. All the aforementioned applications indicate that we can also benefit from considering codon label dependencies in the coding sequence prediction of an mRNA sequence. Based on this idea, our previous work proposed a skipBRNN for CDS prediction, which models codon label consistency by integrating the three most informative neighboring positions. However, it cannot fully model dependencies among multiple codon labels. In this paper, we employ a TCN for CDS prediction for the first time. A TCN is a common approach for modeling time sequences. It can effectively aggregate the local information of time sequences by virtue of temporal convolution. Hence, it is suitable for problems where there exists local label dependency, such as human action recognition [28], named entity recognition [29], etc. In addition, TCNs exhibit the powerful capability of automatic feature learning. Hence, most of the aforementioned applications often directly perform TCNs on their raw data, which saves the efforts of handcrafted features. In contrast, we incorporate codon usage statistics into the TCN, which not only obtains more meaningful results but also requires less data and is easier to train.
## 4 Discussion  
### Superiority of the Proposed Noise Predicting Model  
In this paper, the proposition of a model based on the LSTM neural network for predicting timely environmental noise was established. The model functions efficiently and saves time. The results demonstrated that the prediction ability of the model has high precision. At the same time, the proposed model outperforms the other three existing classic methods (SAE, RW, and SVM). The average running time of the model is several tens of seconds. From the results of Tables 1 and 2, the setting and selection of parameters have little effect on the prediction results, so there is no need to be particularly cautious when selecting parameters. Moreover, a suitable set of parameters can be applied to all data sets of this study, and the parameters have certain universality. The predicted value can reflect the actual environmental noise level around the monitoring point and has a reference management role for environmental planners and government decision-makers.
## 3 Interpretation of Velocity Signals from Impact Echo Method
### Feature Parameters
#### 3.1.2 Number of Peaks, N  
A normally constructed pile without geometric or material defects would typically have only two peak reflections generated from the impact test measurements, that is, one at the pile top due to impulse from the impact and another due to reflection at the pile tip, as shown in Figure 3. The presence of any intermediate peak reflections in between the pile top and bottom would then signify the existence of any geometric or material defects due to changes in the impedance at the defective sections of the pile. In the presence of any geometric or material defect, the reflected wave would then encounter two variations in impedance, corresponding to either an increase or decrease; first was from the change in impedance due to the presence of any defect at the start of the defective section, and the second was from the change in impedance due to the restored normal conditions at the end of the defective section. Thus, the expected number of intermediate reflection peaks due to defects would always be an even number greater than or equal to two.