esa-sceva/satcom-chunk-collection huggingface dataset:
# STDecoder-CD: How to Decode the Hierarchical Transformer in Change Detection Tasks
## 1 Introduction
The CNN architecture is good at capturing such local structures by using local receptive fields, shared weights, and spatial subsampling [21] and thus achieves some degree of shift, scale, and distortion invariance, as well as certain performance benchmarks [24]. (2) The hierarchical structure of CNN kernels learns visual patterns that take into account the local spatial context at varying levels of complexity, from simple low-level edges and textures to higher-order semantic patterns. Thus, models that interleave or combine CNN and a transformer in a \"hybrid\" way have generated successful results in a variety of computer vision tasks, such as image classification, object detection, semantic segmentation, change detection, etc. (see [25; 26]). There are two ways to achieve this combination in the literature: one is through the algorithmic combination of CNN and a transformer, which comes in the variant form of classic ViT (vision transformer) models [27], such as TransCNN [24], ConViT [28], CvT [21], etc. ; the other one is through the architectural combination, in which the transformer module acts either as a backbone [20] or as a decoder [19]. In this paper, we focus on the latter, and a new hybrid model, termed STDecoder-CD, is proposed, in which \"ST\" represents a Siamese transformer backbone, and \"Decoder\" means an elaborately designed decoder module. We make the following contributions:  
1. Transformer networks have strong context shaping ability, but very few works have been conducted on transformers for CD. Thus, we introduce TransCNN to the CD task to capture long-range contextual information within the spatial/temporal scope, which is beneficial for identifying relevant changes in multitemporal images. 2. Using Siamese TransCNN as the backbone, three types of decoding strategies were investigated to provide practical guidance for change detection. 3. Extensive experiments on three CD datasets validated the effectiveness and efficiency of the proposed algorithms, i.e., STDecoder-CD and its variant networks. Given the above, this study aimed to (1) assess the performance of the TransCNN-based CD algorithm and (2) investigate the effect of different decoding strategies in predicting the change patterns. To this end, the remainder of the article is organized as follows:Section 2 describes the experimental materials and methodology. The comparative experimental results and ablation studies are reported in Section 3. Section 4 provides a further discussion of the obtained results. Finally, conclusions are stated in Section 5.
## 2 Materials and Methods
### Data Collection  
Thirty lateral cephalograms were randomly selected from a pool of adult (age 18 or older) orthodontic patients treated in the Indiana University School of Dentistry (IUSD). Inclusion criteria included patients with a class I skeletal relationship (defined as ANB between 0.5 to 5.0\({}^{\circ}\)) and an angle class I dental relationship. Patients were excluded if they had craniofacial anomalies, missing or unerupted permanent incisors, missing or unerupted permanent molars, or poor quality initial cephalometric radiographs. The cephalometric radiographs were saved as JPEG files at a resolution of 200 dpi.  
A calibration session led by an orthodontist with 25 years of experience was conducted to clarify the cephalometric landmark identification process and to define each of the landmarks. Four examiners, including an orthodontist with two years of experience, a second-year orthodontics resident, a fourth-year dental student, and a general dentist with one year of experience, each separately and manually identified 31 cephalometric landmarks on the 30 radiographs (Figure 1). The same examiners retraced all radiographs after a period of one week in a randomized order. After completion of the manual tracings, the examiners traced the same cephalometric radiographs with the aid of a deep-learning AI (AI/human augmentation method). For these tracings, AI first located all of the landmarks, and the examiners later adjusted points as needed. The AI/human augmentation method tracings were completed again after one week in a randomized order. Additionally, AIalone identified each of the landmarks without any adjustments and repeated the process one week later.  
The gold standard for landmark positions was identified with the collaboration of an orthodontist and dental radiologist, each with 25 years of experience within their respective fields. Any disagreements resulted in the point being located at the midpoint both vertically and horizontally between the two points.
# Editorial for Special Issue: Underwater Acoustics, Communications, and Information Processing
## 2 Underwater Channel Characterization
In contrast with the orthogonal matching pursuit (OMP) algorithm searching in both the time domain and the Doppler domain, the proposed WT-based algorithm only searches in the time domain by using the Doppler invariant characteristic of hyperbolic frequency modulation signal. Extensive simulations showed that the WT-based algorithm performs slightly better than the conventional OMP algorithm, especially in the small signal-to-noise ratio, while using less computational complexity.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 4 Proposed SLAM Classification Based on UWSNs
### Traditional SLAM Classifications
#### 4.1.2 Computational Approaches  
From a computational perspective, SLAM methods can be divided into filter-based, optimization-based, and learning-based techniques. Filter-based methods, such as EKF and particle filters (PF), estimate the system's state by sequentially updating probabilities,  
Figure 7: Underwater SLAM classifications based on sensor modalities, computational approaches, environmental adaptability, and collaboration level.  
making them suitable for real-time applications [14]. Optimization-based methods, such as graph-based SLAM, refine pose estimates by minimizing localization errors throughout the trajectory, providing more accurate and consistent mapping results [22]. Learning-based methods incorporate machine learning (ML) and DL algorithms to model complex, nonlinear relationships in the data, potentially improving robustness and adaptability in challenging environments [46].
# Prediction of Flight Delays at _Beijing_ Capital International Airport Based on Ensemble Methods
## 5 Results and Discussion
### Departure Pressure  
As shown in Figure 15, the marginal effect of departure pressure on delay tended to increase when the number of actual flights was greater than 25. The departure pressure defined in this paper can also reflect the congestion level of the airport to some extent.
## 3 Proposed Permutation to Binary Conversion Methods
### Conversion Method-1  
The first binary conversion method is called the matrix method (M1) since the permutation is arranged in a matrix format. If we consider \(N\) numbers of distinct objects, then it generates a matrix of \(N\times N\). In this matrix, the rows are indexed by the natural order of the objects and the columns are indexed by the order of the objects in the permutation. Then the cell that corresponds to a particular object is marked by 1. Then certain number of cells to the left and right of that particular cell are also marked by 1. The number of cells to the left or right is termed as half-width (HW), which plays a significant role during conversion. If the half-width is too wide, then the outcome binary representation will not distinguish two permutaion with more or less order. However, if this width is considerably narrow, changing a bit in the sequence may generate nonidentical matrixes in such a way that similar permeations are considered very dissimilar. Hence, HW is an important parameter in method-1 conversion. Figure 1 illustrates an example of binary conversion method-1 considering 20 distinct objects. According to method-1, 20 \(\times\) 20 matrix is created and put 1s accordingly. Here, an HW of 3 (3 positions from the left and right) is examined. In this figure, the second row from the bottom represents the permutation of 20 objects, and the bottom row represents the location of these objects in the permeation. For instance, at location 10 is object 19. From this object, we move 19 rows upward, and the cell at row 19, column 10, is marked as 1. Then nearby cells, three steps to the left and three steps to the right, are also marked where the three is the value of HW. The complete binary vector representation of the permutation sequence is achieved by concatenating the matrix's rows formed in Figure 1.
# Locality-Sensitive Hashing for Information Retrieval System on Multiple GPGPU Devices
Hierarchical LSH can be used by a hierarchical computer system to increase the productivity of a hardware structure or a distributed storage space [5]. We can apply LSH to solve the approximate nearest neighbor problem by calculating the hash value of the query and then find the corresponding bucket by using the family of hash functions. After that, the bucket will indicate the data/items that have high similarity with the input query [4]. The hash table in LSH is a mapping table that indexes the hash value (key) to the list of data/items in the database. Instead of using a dynamic hash table, using a static hash table can increase the search speed [4]. However, when the requirements are changed from a static data set to a dynamic data set, we have to use a different LSH hash table structure to adapt to constantly updated data sets. In [6], we introduced the Dynamic Locality-Sensitive Hashing (DLSH) algorithm, which can handle constantly updated data sets. However, DLSH uses a more complex hashing structure and that requires more memory usage. The browsing process of DLSH for each bucket requires a large overhead in computational complexity due to it needing to read the additional information. The memory size of a single GPGPU device is limited [7]; thus, it is practically impossible to store the entirety of a big data set on a single GPGPU device. Using multiple GPGPUs is recommended for handling big data with multiple data clusters; each data cluster can be made the necessary size to be stored on a GPGPU device. When the data set has been separately stored on different GPGPU devices, it is necessary to propose an appropriate search algorithm for multiple sub-data sets of the LSH system, as the system may obtain different results on different sub-data sets. The main contributions of this paper are increasing the performance of DLSH and reducing the overhead of the search process by using the sequence shuffling approach in a multiple-GPGPU system. The shuffle stage search is mainly introduced to eliminate duplicate search processes on different nodes/cores for the same query array. With its advantages regarding dynamic data sets, it is demonstrated that DLSH is a suitable LSH algorithm for similarity searches in real-world databases.
## 4 Overview of the Proactive Transmission of Replicated Frames (PTRF) Mechanism  
As we have already mentioned, PTRF is a time redundancy mechanism based on the proactive replication of frames. This technique aims at increasing the probability of at least one replica reaching the destination, i.e., increasing the probability that all message editions of a stream reach their destination. If more than one replica reaches the destination successfully, the receiver must detect and eliminate the surplus replicas to deliver only one frame to the application.  
Even though there are other time redundancy techniques, proactive replication is deterministic in the time and bandwidth consumption and it allows scheduling time-triggered traffic using the techniques that are already in place in TSN systems. Furthermore, it can be easily integrated with other fault-tolerance mechanisms, as we showed in [12]. Please see [11] for a detailed discussion of all the advantages of proactive replication.  
In this section, we describe the fault types and failure modes assumed in the design of the PTRF mechanism and we describe the most relevant aspects of PTRF for this work. For a thorough description of the mechanism the reader can refer to [11].
# Burst Channel Error Reduction Based on Interleaving for Efficient High-Speed Wireless Communication
## 3 Proposed Interleavers  
This section presents the proposed interleavers, including the two-dimensional prime interleaver and the newly proposed FRF interleaver.
## 5 Discussion
### Other Potential Factors Producing Ground Subsidence  
Other factors besides post-liquefaction consolidation could cause ground subsidence, such as seasonal changes in aquifers and natural ground subsidence. We ruled out any impact of changes in the groundwater table on the observed ground sinking by looking at the depth of groundwater levels from multiple wells near the study area, as depicted in Figure S3a (data available from: [[https://www.arpae.it/it/temi-ambientali/acqua/dat-acque/acque-sotterrane](https://www.arpae.it/it/temi-ambientali/acqua/dat-acque/acque-sotterrane)]([https://www.arpae.it/it/temi-ambientali/acqua/dat-acque/acque-sotterrane](https://www.arpae.it/it/temi-ambientali/acqua/dat-acque/acque-sotterrane)), accessed on 3 June 2024). Even if the sampling rate was not perfect, we did not see clear signs of prolonged groundwater depletion in the first 200 m depth from 2010 to 2015 (Figure S3b). Therefore, we can infer that the observed ground subsidence was not linked to changes in the groundwater table.  
Regarding natural ground subsidence, InSAR displacement time series data from the ENVISAT satellite constellation, covering the period 2003-2010 and available from the Italian Ministry of the Environment, Earth and Sea (Earth and Sea (2016), show the potential ground subsidence in the area before the earthquake sequence (Figure S4). The area experienced linear displacement rates of approximately \(-\)2 mm per year along ascending (Figure S4a) and descending (Figure S4b) orbits, with maximum values reaching \(-\)3--4 mm per year at a few locations. Vertical and east-west horizontal displacement rates (Figure S4c and d, respectively) obtained by combining the ascending and descending orbits (Earth and Sea, 2016) showed similar amplitudes and did not reveal any significant pattern over the investigated area. Therefore, these displacement rates are compatible with the natural consolidation subsidence, as in [PERSON] and [PERSON] (2017), and are considered negligible in the context of the observed ground subsidence during the 2012 earthquake sequence.
# Research on Obstacle Avoidance Replanning and Trajectory Tracking Control Driverless Ferry Vehicles
###### Abstract  
This study aimed to solve the problem that is the frequent switching between the acceleration and braking modes of the driverless ferry vehicle, affecting the comfort and stability of speed control. The driverless ferry vehicle encounters unknown obstacles on the road that affect the normal planning and tracking control of the ferry vehicle and finally lead to the problem that the driverless ferry vehicle cannot drive normally. First of all, in the longitudinal control, the fuzzy PID control algorithm was utilized to produce the fuzzy PID acceleration controller by taking into account the difference between the actual and expected speeds and choosing the triangular membership function. According to the relationship between the brake oil pressure and brake torque, the brake controller was designed. The acceleration/braking switching module with acceleration tolerance zone was added to the longitudinal controller, and the acceleration/braking mode-switching controller was designed. Secondly, in the lateral control, the tire cornering stiffness was analyzed, an MPC controller with a planning module was designed, and a lateral motion controller with an obstacle avoidance replanning function was proposed. Finally, according to the prediction time domain of different planning modules corresponding to different speeds, a coordinated control strategy of horizontal and longitudinal motion was proposed by using a real-time speed adjustment planning module to predict the time domain. Through the joint simulation analysis of MATLAB and CarSim, the results show that the driving stability of the ferry vehicle was significantly improved, and the longitudinal speed error of the ferry vehicle was reduced by 43.59%. The ferry's avoidance of obstacles and tracking of reference trajectories were significantly improved, so that the tracking error can be reduced by 61.11%.  
driverless; obstacle avoidance; motion control; coordinated control +
Footnote †: journal: Applied Sciences
The contribution of the proposed LM-RRDH scheme can be briefly explained as follows:  
1. The proposed LM-RRDH transforms the host image into two planes: MSB and LSB, and makes use of the image of the MSB plane for embedding the secret information so that the required robustness of the hidden data can be provided. 2. The image of the MSB plane is divided into sub-blocks, and pixel groups are formed to collect similar-intensity pixels in the same group. Therefore, highly correlated groups are formed to embed secret information. 3. Further, the dynamic selection of a pairwise embedding strategy based on the number of pixels in the group can help in achieving better quality while maintaining the embedding capacity. 4. As a result, the proposed LM-RRDH can provide superior quality while providing comparable embedding capacity. The rest of the paper is organized as follows. Section 2 presents a literature review. The proposed local moment driven robust reversible data hiding scheme is introduced in Section 3. Section 4 presents the experimental results and analysis. Finally, it is concluded in Section 5.
###### Abstract  
A microwave photonic converter based on microwave pre-upconversion is proposed and experimentally demonstrated. Only a single Mach-Zehnder modulator (MZM) is used in the converter system so that the complexity and bandwidth limiting of the link can be reduced. The transmitted and received signals before entering the MZM are firstly upconverted to high frequency (HF) by a microwave upconverter. The HF and local oscillator (LO) signals are combined to drive the MZM. Carrier-suppressed double-sideband (CS-DSB) modulation is introduced to the MZM for effective spectrum utilization. Then, the target signals can be obtained by photoelectric conversion and beating. Experimental results confirm that the mixing spurs including harmonics and intermodulation as well as original signals are all out of system frequency band from 0.8-18 GHz, and the in-band spurious suppression of at least 40 dBc is achieved. In addition, the spurious-free dynamic range (SFDR) reaches 86.23 dB-HZ\({}^{2/3}\) for upconversion and 80.95 dB-HZ\({}^{2/3}\) for downconversion. The proposed microwave photonic converter provides a wideband and high-purity alternative for the applications of radars and signal processing.  
electrical pre-upconversion; in-band spurious suppression; microwave photonics 2022, 9, 388. [[https://doi.org/10.3390/photonics9060388](https://doi.org/10.3390/photonics9060388)]([https://doi.org/10.3390/photonics9060388](https://doi.org/10.3390/photonics9060388))
## 4 Conclusions  
The P(_bi_sMEP)/BC-based nanocomposite systems studied here are a good example of proton-exchange BC-based nanocomposite membranes prepared through a simple and single-step methodology. The incorporation of a non-cross-linked polyelectrolyte onto the three-dimensional BC network originated membranes with thermal stability up to 195-205 \({}^{\circ}\)C, good mechanical performance (Young's modulus > 2 GPa), water-uptake ability (79-155%) and ion exchange capacity ([H\({}^{+}\)] = 1.1-3.0 mmol g\({}^{-1}\)). Additionally, the mono-acidic phosphate moieties were responsible for the maximum protonic conductivity of ca. 0.03 S cm\({}^{-1}\) at 80 \({}^{\circ}\)C and 98% RH for the membrane containing 50 wt % of each component, and the temperature dependence of the conductivity followed an Arrhenius-type behavior typical of proton structural diffusion. The overall positive results of this study show that these polyelectrolyte membranes can be efficient proton-exchange membranes for application as proton separator materials in fuel cells or other devices such as sensors and actuators.  
[PERSON] and [PERSON] conceived, designed and supervised the experiments; [PERSON] wrote the paper; [PERSON] performed the experiments and analyzed the data; [PERSON] and [PERSON] carried out the conductivity measurements and analyzed the corresponding data, and also contributed to the tensile data interpretation; [PERSON] contributed to the structural, morphological, thermal and mechanical data interpretation; all authors participated in the critical revision of the paper.  
This work was developed within the scope of the project CICECO-Aveiro Institute of Materials, POCI-01-0145-FEDER-007679 (FCT Ref. UID/CTM/50011/2013), and UniRCell POCI-01-0145-FEDER-016422 (Ref. SAICTPAC/0032/2015), financed by national funds through the FCT/MEC and when appropriate co-financed by FEDER under the PI2020 Partnership Agreement. The Portuguese Foundation for Science and Technology (FCT) is also acknowledged for the post-doctoral grant to [PERSON]. [PERSON] (SFRH/BPD/84168/2012), doctoral grant to [PERSON] (SFRH/BD/89670/2012), and research contracts under Investigador FCT to [PERSON] (IF/01407/2012) and [PERSON] (IF/01174/2013).  
Figure 8: Arrhenius plot of the in-plane conductivity of the nanocomposite membranes P(_bi_sMEP)/BC_1 (**a**) and P(_bi_sMEP)/BC_2 (**b**) at different RH (relative humidity). The straight lines are linear fits to the Arrhenius model.
# LBFT: An Asynchronous Committee-Based Blockchain Storage Strategy on Zero Trust Model
## 6 Experimental Evaluation
### Throughput  
The system throughput is defined as the number of committed transactions per second in this paper, which is influenced by the batch size of the input, as well as the number of nodes in the system [26]. We set the different batch sizes that each node proposes 256, 512, 1024, 2048, 4096, 8192, 16,384, 65,536, and 131,072 transactions to test the performance of LBFT and presents the relationship between throughput and batch size in Figure 6.  
We can see from Figure 6 that the system throughput increases with the growth of the batch size in LBFT, PartitionChain, and BFT-Store when the number of nodes equals \(n=64\) and \(n=104\). The difference is that the increase rate of the throughput against batch size in LBFT and PartitionChain is more significant than in BFT-Store. Notably, the increase in the number of nodes hardly influences the increasing trend of the throughput in LBFT, while it distinctly slows down the increase rate of the throughput in BFT-Store and PartitionChain; the throughput of LBFT even surpasses that of PartitionChain when the number of nodes \(n=104\), indicating that LBFT has the best scalability.  
Figure 5: The storage consumption of LBFT, full-replication, PartitionChain, and BFT-Store against the number of nodes.  
On the other side, we set the number of nodes in the system to be 32, 40, 48, 56, 64, 80, and 100. The comparison of system throughputs in LBFT, PartitionChain, and BFT-Store is presented in Figure 7.  
It can be inferred from Figure 7 that LBFT and PartitionChain have a similar system throughputs, which are both significantly higher than BFT-Store in the experiment. More precisely, LBFT has approximately three times the system throughput of BFT-Store regardless of the number of nodes, mainly because LBFT partitions the block into smaller pieces and frees the nodes from the pre-set time bound for higher computational efficiency. For instance, when the number of nodes is 104, the throughput of LBFT exceeds \(2.12\times 10^{4}\) transactions per second, while the throughput of BFT-Store merely reaches \(0.81\times 10^{4}\) transactions per second. Additionally, it is observed that LBFT gradually obtains better performance than PartitionChain when the number of nodes increases.