esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Bird Nest Deployment
### Hardware Setup  
We have one Linux server machine running in the James Reserve server room. The server communicates with four Stargates, which have been placed around James Reserve, and together constitute the upper tier of the network. The server and the Stargates are connected via Ethernet or 802.11. Each Stargate is connected to a Mica2 mote, which all have 8.5 dBi omnidirectional antennas. These Mica2s are used to communicate with nearby Cyclops nodes, which comprise the low power, lower tier of the network. This network topology is detailed in Figure 10. The nestboxes are generally sparse, being spread 50 to 100 m apart in areas of dense trees and foliage. Newer 2.4-GHz radios tend to perform poorly in these environments, since they do not penetrate foliage well. This is the reason why we used Mica2s with 433 MHz radios instead as our wireless communication hardware in the mote-tier. This drastically limits available bandwidth, but propagates farther through the foliage. While our system does support multihop routing (and we did log some temporary multihop paths), nestbox placement was determined from a previous, single-hop deployment, which, when combined with the large Stargate antennas, eliminated most multihop route formations [3].  
Figure 8: Sun/shade change caused image transfers.  
Figure 9: Darkness without IR flash generates continuous false positives.  
Each sensor node contains a nestbox, power infrastructure and the embedded hardware necessary for imaging, environmental sensing and communication. Wooden nestboxes are all custom-made and contain a removable shelf in the top of the box, which holds the Mica2 mode for communication, a Cyclops camera for imaging and an MDA300 board for environmental sensing (internal temperature, internal humidity, external temperature, external humidity and voltage sensors are connected to the  
Figure 11: The outside of a typical nestbox. Notice the solar panel mounted at a distance for optimal sunlight, the nestbox mounted on a pole to avoid predation and the shelf at the top holding the hardware.  
Figure 10: Map of the location and topology of our deployment at James Reserve.  
MDA300 board). Figure 11 shows the outside of a nestbox. The shelf has a clear plastic bottom through which the camera takes images, while protecting the hardware from dirt and disturbance by nesting birds. The camera was facing downward toward the bottom of the box. The power infrastructure is a medium-sized 12-V sealed lead acid battery continually charged by a solar panel. For most nodes, the solar panel provides enough power for unlimited node uptime (on a few nodes, the solar panel did not receive enough solar radiation throughout the day, which resulted in failed nodes due to the loss of power.). Finally, while most nodes had small 433-MHz dongle antennas mounted on the top, a few nodes with extremely poor connectivity required 9 dBi directional antennas pointed at the nearest Stargate.
## 5 Performance Analysis
### Performance Application on Transportation  
The results of the mixed approach proposed in [21], the discrete choice model [22], the land-use transportation model [23], the integrated approach [24], the conceptual model [25], and the IUTS [26] are discussed with traffic prediction dataset collected from Kaggle. The URL of the dataset is given as [69]. In total, 500 sensor nodes are deployed to conduct the simulation.  
Figure 9 illustrates the Energy Consumption (\(EC\)), as defined in Equation (3), for six different existing methods analyzed with varying numbers of traffic data points ranging from 100 to 1000. For 800 traffic data points, the \(EC\) recorded for these methods was 49, 58 J, 43 J, 62 J, 68 J, and 72 J, respectively. The data show that the land-use transportation model demonstrates the lowest \(EC\) compared to the other methods. This efficiency can be attributed to its centralized network type and mesh network topology, which effectively addresses \(EC\) assessment challenges. Additionally, the comprehensive framework of the land-use transportation Mmdel evaluates the impact of city policies on \(EC\) by integrating decisions related to actor location and travel behavior. As a result, the land-use transportation model reduces \(EC\) by 17%, 32%, 38%, 44%, and 47% compared to the other approaches.  
The overall performance results for \(EC\) across five existing methods are depicted in Figure 10. The analysis reveals that the land-use transportation model achieved the lowest \(EC\) among the evaluated methods. Specifically, the \(EC\) values for the existing methods were measured at 43 J, 52 J, 36 J, 57 J, 63 J, and 66 J, respectively. These findings highlight the effectiveness of the land-use transportation model in optimizing energy efficiency compared to the other methods.
## 4 Discussion  
From the above calculations for trim and stability, we found the following interesting conclusion: while both wash-out and forward-sweep can help seagulls achieve longitudinal trim, each has advantages and disadvantages.  
The wash-out method achieves lower trim weight but has the characteristics of longitudinal stability. The trim frequency achieved by forward-sweep is high, so the power consumption characteristics are also relatively poor.  
The forward-sweep method can achieve a larger trim weight, but because the pitching moment decreases with the angle of attack, it has an unstable longitudinal characteristic, and has a lower power consumption due to lower trim frequency.  
By using the wash-out and forward-sweep methods at the same time, it can help seagulls achieve trim at different angles of attack and different weights, and both have longitudinal stability. If a larger take-off weight, lower power consumption, and stronger longitudinal stability are required, both the wash-out and the forward-sweep methods should be considered at the same time.  
Through the analysis of the dynamic stability characteristics, it is found that the trim states obtained at certain flapping frequencies are all dynamically stable and will not change significantly with a change in the angle of attack. This shows that the cruising flight of the seagull is very likely to be dynamically stable, which increases wind resistance stability in-flight, reduces the energy of active control, and increases the environmental survivability of the seagull.  
Although the induced drag caused by the three-dimensional effect of flapping wings considered in this paper is calculated, it is only an estimation method, and aerodynamic force can be more accurately modeled by a three-dimensional CFD method in the future. In addition, the flexibility of the wings will also affect the calculation of thrust and drag; subsequent research can consider more accurate modeling of the aerodynamic characteristics of flexible wings through the fluid-structure interaction method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**AoA/Deg** & **Characteristics** & **Phugoid Mode** & **Short-Term Mode** \\ \hline \multirow{3}{*}{3} & \(\lambda=\eta\pm i\omega\) & \(-0.0151\pm 1.48\)i & \(-4.16\pm 22.2\)i \\ \cline{2-4}  & Period/s & 4.25 & 0.283 \\ \cline{2-4}  & \(t_{halve}/s\) & 45.9 & 0.167 \\ \hline \multirow{3}{*}{6} & \(\lambda_{1,2}=\eta\pm i\omega\) & \(-0.0235\pm 1.46\)i & \(-3.01\pm 29.0\)i \\ \cline{2-4}  & Period/s & 4.3 & 0.217 \\ \cline{2-4}  & \(t_{halve}/s\) & 29.49 & 0.230 \\ \hline \multirow{3}{*}{9} & \(\lambda_{1,2}=\eta\pm i\omega\) & \(-0.0724\pm 1.38\)i & \(-4.00\pm 37.9\)i \\ \cline{2-4}  & Period/s & 4.55 & 0.166 \\ \cline{1-1} \cline{2-4}  & \(t_{halve}/s\) & 9.57 & 0.173 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Characteristics of longitudinal stability at three angles of attack.
## 2 Materials and Methods
### Image Acquisition and Procession  
The dataset construction process in this study was divided into four steps: image acquisition, sliding window cropping, data annotation, and data augmentation, as depicted in Figure 1. Using the image acquisition system depicted in Figure 2, full-frame surface images of the particleboard were acquired at a resolution of 8192 pixels \(\times\) 4096 pixels.  
In order to preserve small target details and meet the input requirements of mainstream artificial neural networks (ANNs), this paper employed a sliding window cropping approach to process the full-frame particleboard surface images [23]. A sliding window of 1024 pixels \(\times\) 1024 pixels extracted sub-images from the original image, starting from the top-left corner of the image. To prevent detection omissions, the step size of the window movement was set to 768 pixels.  
Figure 1: The dataset construction process.  
The image dataset established in this study contained 1900 images, which were categorized and annotated into 7 types of defects, including spot-like defects, shavings, oil pollution, edge breakage, chalk marks, scratches, and cracks, as displayed in Figure 3. The annotations followed the YOLO standard format.
# DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images
## 2 Proposed Method
### DySample Module  
Traditional interpolation upsampling (such as bilinear upsampling) is prone to generate jagged edges or blurring when restoring the boundaries of water bodies, especially those of severely damaged irregular water systems (such as curved rivers). Therefore, we introduce a DySample sampling layer [31] to improve the ability to restore water body contours.  
As a lightweight dynamic upsampling module, DySample realizes efficient upsampling by dynamically generating sampling offsets to learn sampling positions. DySample uses the PixelShuffle operation to expand the H and W dimensions of the feature map while maintaining the C dimension, thereby increasing the spatial resolution of the feature map. This enables the model to capture broader global contextual information without complex convolution operations, helping to understand the relationship between different regions, thereby improving the accuracy of semantic segmentation. The ability to integrate the global context and local details renders DySample upsampling highly suitable for water body segmentation in remote sensing imagery, effectively preserving water body edge details and enhancing the completeness of small tributaries and boundary contours.
# Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data
## 3 AF Arrhythmia Detection Methods
### Summary of RR-Based Automated AF Detection Research Studies  
By means of the state-of-the-art literature described, we presented a selection of works that have kept studies on algorithms and features suited for automated atrial fibrillation detection at the forefront of research. Originally, the QRS detection and the simple RR interbeat interval estimation supporting the computation of tachogram (RR series) and HRV features gave rise to a wide set of statistical, spectral and nonlinear approaches applied to such variables. This field, however, has recently experienced a significant surge prompted by the advances seen in health monitoring device development initiatives and the opportunities brought by a set of more mature IoT and Cloud infrastructures giving way to remote processing paradigms traditionally deemed as computationally demanding. Although the different reported performance metrics make comparisons between studies difficult, the following Table 2 (univariate) and Table 3 (multivariate) summarize the choice of studies conducted after year 2000 with the aim of providing some insight on the main features used, algorithm approaches followed, datasets utilized, best performance and observed limitations. It is worth noting that the generation of induced variables using the time derivatives of RR interval time series [73; 74] coupled to machine learning is a well-established approach paving the way for a new research lead of automatic arrhythmia detection. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
# Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
###### Abstract  
Traditional dehazing approaches that rely on prior knowledge exhibit limited efficacy when confronted with the intricacies of real-world hazy environments. While learning-based dehazing techniques necessitate large-scale datasets for effective model training, the acquisition of these datasets is time-consuming and laborious, and the resulting models may encounter a domain shift when processing real-world hazy images. To overcome the limitations of prior-based and learning-based dehazing methods, we propose a self-supervised remote sensing (RS) image-dehazing network based on zero-shot learning, where the self-supervised process avoids dense dataset requirements and the learning-based structures refine the artifacts in extracted image priors caused by complex real-world environments. The proposed method has three stages. The first stage involves pre-processing the input hazy image by utilizing a prior-based dehazing module; in this study, we employed the widely recognized dark channel prior (\(DCP\)) to obtain atmospheric light, a transmission map, and the preliminary dehazed image. In the second stage, we devised two convolutional neural networks, known as RefineNets, dedicated to enhancing the transmission map and the initial dehazed image. In the final stage, we generated a hazy image using the atmospheric light, the refined transmission map, and the refined dehazed image by following the haze imaging model. The meticulously crafted loss function encourages cycle-consistency between the regenerated hazy image and the input hazy image, thereby facilitating a self-supervised dehazing model. During the inference phase, the model undergoes training in a zero-shot manner to yield the haze-free image. These thorough experiments validate the substantial improvement of our method over the prior-based dehazing module and the zero-shot training efficiency. Furthermore, assessments conducted on both uniform and non-uniform RS hazy images demonstrate the superiority of our proposed dehazing technique.  
remote sensing image dehazing; zero-shot learning; dark channel prior 2023
## 4 Methodology
### Digital Image Processing
#### 4.2.2 Decorrelation Stretch (DS)  
Decorrelation stretch (DS) is a type of color stretch used to solve the problem of overly concentrated image tone changes or decreased image saturation. It reduces the correlation between spectra and is widely used as a means of spectral enhancement [23; 70; 71]. For example, [PERSON] used the 7-5-4 band decorrelation stretch with TM data to identify ultrabasic rocks when mapping the Oman ophiolite [22].
# Two-Stage Single-Channel Speech Enhancement with Multi-Frame Filtering
## 3 Method
### Two-Stage Training Strategy  
A two-stage training strategy is employed to train the system. To be specific, in the first stage, only DNN\({}_{1}\) is trained. The input is the noisy spectrum \(\mathbf{Y}\), while DNN\({}_{1}\) estimates the spectrum of the clean speech. In the second stage, the well-trained DNN\({}_{1}\) is fixed, and its output \(\mathbf{\hat{X}}^{(1)}\) is first fed into the Filter Module to calculate a multi-frame filter \(\mathbf{h}\). \(\mathbf{h}\) is then applied to \(\mathbf{Y}\) to get the enhanced spectrum \(\mathbf{\hat{X}}^{(F)}\). Finally, \(\mathbf{Y}\), \(\mathbf{\hat{X}}^{(1)}\), and \(\mathbf{\hat{X}}^{(F)}\) are concatenated and fed into DNN\({}_{2}\). DNN\({}_{2}\) then outputs the final estimated spectrum of the clean speech. In a nutshell, the training stage can be formulated as:  
Stage 1:  
\[\mathbf{\hat{X}}^{(1)}=\text{DNN}_{1}(\mathbf{Y}) \tag{1}\]  
Stage 2:  
\[\mathbf{h}=\text{FilterModule}(\mathbf{\hat{X}}^{(1)}) \tag{2}\]  
\[\mathbf{\hat{X}}^{(F)}=\mathbf{h}^{H}\mathbf{Y} \tag{3}\]  
\[\mathbf{\hat{X}}^{(2)}=\text{DNN}_{2}(\text{Cat}(\mathbf{Y},\mathbf{\hat{X}}^{ (1)},\mathbf{\hat{X}}^{(F)})) \tag{4}\]
## 2 Communication Middleware Technology Trend for Smart Factory
### The Need for MTConnect  
As mentioned above, communication middleware/protocols, such as DDS, OPC, and OPC-UA, are being developed for mutual data exchange between heterogeneous devices. However, for interactions with heterogeneous devices and systems, a higher level of interoperability is required with a simple data exchange/conversion.  
Recently, the industrial network environment has been evolving from a closed structure to a service-oriented structure from the point of view of information technology. Data distributed in a closed way inside the factory are open to the outside for company-wide visibility. In other words, the need for interoperability is increasing. MTConnect is an industrial communication protocol standard based on communication/analysis using 'agent/API' and so on. MTConnect is a standard based on XML message format and RESTful interface [11].
## 3 Methods
### Waveform Stacking and Return Energy Profile Generation  
When the upper vegetation is dense, more energy from a pulse is intercepted by the vegetation, so the small-footprint waveform LiDAR always acquires faint ground return. However, sufficient ground return is important for estimating the vegetation _LAI_ using the method based on the Beer-Lambert law [38]. To enhance the ground returns, the plot size was chosen as the aggregation cell size according to [PERSON] et al. [38], and all small-footprint waveforms within one cell were stacked into a large pseudo-waveform. Using this method, the ground returns could be easily acquired from the pseudo-waveform. After waveform stacking, the return energy profile was generated.
## 2 Materials and Method
### Simulation
#### 2.1.4 Collimator  
The 2 mm-thick collimator was placed 8.5 cm below the source and was made of stainless steel. It limited the beam of emitted photons to a rectangular field covering the detector surface corresponding to the image size of 18 cm \(\times\) 24 cm with a 5 mm margin on each side.
# Collaborative Analysis of Learners' Emotional States Based on Cross-Modal Higher-Order Reasoning
## 2 Related Works
### Cross-Modal Feature Fusion
#### 2.1.2 Late fusion  
Late fusion is a method where each modality is processed independently before fusion. In late fusion, each modality undergoes separate deep processing, for example, using convolutional neural networks (CNNs) for image processing and recurrent neural networks (RNNs) for text processing, and their output features are then fused together.  
The principle of late fusion lies in fully exploring the feature information within each modality and modeling it through independent processing. Through processing each modality separately, late fusion can better preserve the specific information of each modality, thereby improving the model's expressive power and generalization performance. Additionally, late fusion can also flexibly perform inter-modal information interaction, thus better capturing the correlation and semantic association between modalities.  
However, late fusion also presents some challenges, with the most significant being increased computational costs and model complexity. Since each modality requires independently designed and trained specialized models, this may result in higher computational and storage overheads. Therefore, in practice, it is necessary to carefully design the model structure to balance performance and complexity and fully utilize existing computational resources. The schematic diagram of late fusion is illustrated in Figure 2.  
Figure 1: Early fusion diagram.  
Furthermore, to optimize late fusion, researchers are also exploring more efficient feature fusion techniques, such as learning-based fusion layers or adaptive fusion mechanisms. These methods may further improve the efficiency and accuracy of models. Such advancements ensure that late fusion maintains broad application prospects when dealing with complex multimodal data.
## 7 Conclusions
### Contributions  
Overall, this research paper has proposed and evaluated the \"Call Me Maybe\" security framework for VoIP networks. The design of the framework was based on advancements of ideas presented in recent literature, with the goal of providing an adaptable protection mechanism against DoS attacks targeting VoIP networks. The design consisted of three main features: integration of an alternative network in which the VoIP devices operate using TCP, a method of detecting the presence of a DoS attack on the network, and a method of dynamically alternating between the two protocols. To test the efficacy of the proposed framework, Cisco Packet Tracer was used to implement a network that utilised the \"Call Me Maybe\" security framework. The Cisco Packet Tracer design used in this work is available in the Supplementary Materials. The response time and packet loss rates were tested when using the standard VoIP protocol (UDP), in comparison to the proposed design using VoIP over TCP. Examining the results using the independent t-Test, it was concluded that the proposed design (VoIP over TCP) improves the response times to a statistically significant degree when a network is experiencing a (D)DoS attack from between two and eight devices. The results also showed the response time for the TCP phones began to level off when around six DoS devices were conducting an attack. As the solution was only tested to a maximum of eight devices, further research must be done to evaluate the efficacy of the design at greater scales of DoS attack and to see if this trend continues. The detection times were also analysed, showing that as the strength of DoS attack increased, so did the time it took to detect the attack.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/network2040032/s1](https://www.mdpi.com/article/10.3390/network2040032/s1)]([https://www.mdpi.com/article/10.3390/network2040032/s1](https://www.mdpi.com/article/10.3390/network2040032/s1)), Packet Tracer file used in the experiments.  
Conceptualization, J.K. and T.V.; methodology, J.K. and T.V.; software, J.K.; validation, J.K.; formal analysis, J.K.; investigation, J.K.; resources, J.K. and T.V.; data curation, J.K.; writing--original draft preparation, J.K.; writing--review and editing, J.K. and T.V.; supervision, T.V.; project administration, T.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data available on request.  
The authors declare no conflict of interest.
# Achieving Sender Anonymity in Tor against the Global Passive Adversary
## 4 The Proposed Protocol
### Communication Phase
#### 4.3.1 Structure of the Token  
As in the set-up phase, in the communication phase, a token-based mechanism is enabled. We assume that a given number of tokens move through the ring encrypted, hop-by-hop, from one node to the next, with the forward key exchanged in the set-up phase.  
Figure 1: Set-up phase.  
These tokens are managed (generated and disposed) by some nodes of the ring according to the network requirements (throughput, bandwidth, etc.). The specifications of these requirements are discussed in Section 8.  
The structure of a communication-phase token is the following: \(\langle F,HID,CI,DA,P\rangle\). In Figure 2, an expanded description of this structure is reported.  
As the communication phase is the core of Ring2 Tor, we describe in detail how the token is organized. Its size is 539 bytes, of which 41 are reserved for the header, and 498 for the payload. The size of the payload is set to the same value as the size of the payload of the relay Tor cells.  
First, we describe the meaning of the field \(F\). It is composed of two bits (even though we reserve 1 byte for this field), with following possible meanings: 00 means _empty_ token; 01 means token _reserved_ for a given communication identifier; and 10 means that it is _used_ for a message. A token in the state 01 (reserved) or 10 (used) is said to be _filled_.  
During the description of the protocol, which we provide next, the meanings of the remaining fields are clarified.