esa-sceva/satcom-chunk-collection huggingface dataset:
# Spot Market Cloud Orchestration Using Task-Based Redundancy and Dynamic Costing
## 6 Discussion  
The results of the aggregate experiments given in Tables 1 and 2 demonstrate that our proposed algorithm achieved a lower total cost (the sum of the running and failure costs) across the scenarios tested.  
In the first experiment, where the cost of failure was varied, the total cost was reduced by between 37% and 46%, while in the second experiment, where the spot price was varied, the total cost was reduced by between 68% and 72%.  
We propose that this improvement is a reflection of the market-following behavior of our proposed algorithm, which consistently maintains the level of redundancy towards an optimum level with respect to the spot price and cost of catastrophic failure.  
By the definitions of the utility functions in (9) and (10), we propose that the specific level of redundancy selected by the algorithm is influenced by the relationship between the cost of catastrophic failure and the spot price.  
Additionally, assuming the spot market price to be well-known, the performance of our proposed algorithm relative to the control is influenced by the level of accuracy with which it can predict the cost of catastrophic failure. In practice, an incorrect estimation of this parameter may result in a non-optimal outcome, i.e., either excessive or insufficient redundancy in the system.  
Another factor we considered was the latency in detecting a failure. As shown in Table 2, a latency of up to 10 samples was introduced to each experiment configuration (the specific latency being probabilistically determined by the time of failure of a container within this range). We made two observations with regard to this parameter.  
Firstly, our algorithm accounted for the length of time between orchestrator runs when computing the expected cost of failure. Thus, we observed a modest increase in the running cost and a decrease in the failure cost, possibly the result of a higher level of redundancy used to compensate for the increased likelihood of a failure occurring given the longer time period.  
Secondly, the magnitude of the improvement in total cost was greater when a latency was applied. Table 2 shows that even though the control algorithm had a much lower failure cost, its running cost was much greater than the running cost of our proposed algorithm, thereby accounting for the difference in total cost.  
This is perhaps the strongest argument for the utility of our proposed algorithm. If the cost of a failure can be suitably quantified, our proposed algorithm can achieve a lower total cost in comparison with an algorithm based on a static reliability requirement through its market-following behavior.
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 4 Performance Evaluation
### Simulations
#### 4.3.5 Performance Evaluation for the Large Scale Network Scenario  
The previous experiments and simulations confirmed that GCS-routing outperforms other routing protocols in practical situations. We performed an additional simulation for a large network scenario, which would have been difficult to conduct using real drones and devices. Figure 18 shows a simplified view of the scenario. Two end drones conducted 1 Mbps UDP data communication through at least a 26-hop drone network. Distance between consecutive drones on the data communication path was 50 m, so source and destination were about 1300 m apart. To reflect battery limitations of real drones, drones between the end drones were sequentially replaced during the simulation, and 52 drones in total participated in the simulation. As the simulation proceeded, packets were transmitted through different paths as the drones kept moving and were replaced, and at least 24 handovers occurred in simulation period. We conducted this simulation 10 times for each routing protocol.  
Table 12 shows that GCS-routing provides superior throughput and network stability than other protocols. Throughputs using OLSR and DSDV were relatively very low due to drone mobility, large number of hops, and frequent handovers. GCS-routing provided stable communication in the large network scenario similar to the previous evaluation results. Thus, GCS-routing redistributed routing tables properly and is also appropriate for large drone networks.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Period** & **A** & **B** & **C** \\ \hline GCS-routing & 9265.4 & 7354.8 & 7239.7 \\ DSDV & 7860.2 & 6257.3 & 6450.0 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Simulated throughput (Kbps) for each time period for the replacement scenario with TCP traffic.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Period** & **A** & **B** & **C** \\ \hline GCS-routing & 2036.0 & 1621.0 & 1568.2 \\ DSDV & 2491.3 & 1899.2 & 2405.5 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Simulated throughput standard deviation for the Large Scale Network Scenario with TCP traffic.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Period** & **A** & **B** & **C** \\ \hline GCS-routing & 0.1 & 8.7 & 5.1 \\ DSDV & 20.6 & 75.1 & 134.2 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Simulated network disconnected time (s) for each time period for the replacement scenario with TCP traffic.
# Electro-Optical Imaging Technology Based on Microlens Array and Fiber Interferometer
## 3 Theory and Algorithm
This type of algorithm has extremely high measurement accuracy, anti-interference, and high calculation speed [16; 17; 18]. In this study, the visibility and phase of interference fringes are calculated by a four-step phase shift formula. The result can be expressed as [17; 18]:  
\[\left\{ \begin{array}{c}I_{1}=I_{0}(1+|\mu|\cos(\varphi))\\ I_{2}=I_{0}(1+|\mu|\cos(\varphi+\pi/2))\\ I_{3}=I_{0}(1+|\mu|\cos(\varphi+\pi))\\ I_{4}=I_{0}(1+|\mu|\cos(\varphi+3\pi/2))\end{array} \right. \tag{4}\]  
\[\left\{ \begin{array}{c}\varphi=\tan^{-1}\left(\frac{I_{4}-I_{2}}{I_{1}-I_{3} }\right)\\ |\mu|=\frac{I_{4}-I_{2}}{2\sin\varphi}\end{array} \right. \tag{5}\]  
We adopt the parameters in Table 1 to simulate a point source imaging process rectangular microlens array, and the results are shown in Figure 3. For the numerical simulation, we used a square-arranged microlens array and a centrally symmetric arrangement. Figure 3a presents the light intensity distribution of the target source. Figure 3b shows the detected spatial frequency distribution. Figure 3c shows the image obtained by an inverse Fourier transform. The results validate the imaging principle and simulation model.
## 3 Research Framework of Internet of Emergency Services
### Real-Time Data Collection and Analysis
#### 3.3.4 Augmented Reality/Virtual Reality (AR/VR)
* Legal and Ethical Issues: The use of real-time data for emergency management and response raises legal and ethical issues related to privacy, confidentiality, and data ownership. Emergency responders need to ensure that they are following applicable laws and regulations and ethical standards when collecting, analyzing, and using real-time data. In summary, real-time data collection and analysis have the potential to significantly improve emergency management and response. However, there are still several challenges and limitations associated with these processes that need to be addressed to ensure their effective and responsible use.
# Internet Video Delivery Improved by Super-Resolution with GAN
## 4 Video Super-Resolution with GAN  
This research assesses the video super-resolution technique to address the delivery of high-quality video content. More specifically, in the context of a cloud-based video service, a super-resolution model is utilized to lower the cost of conventional methods that address end-to-end network congestion. The SR model is a generative adversarial network, i.e., a deep-learning model for image up-scaling that super-resolves sharper and more realistic images [19; 22]. We named the proposed model improved Video Super-Resolution with GAN (VSRGAN+) and present it in the following subsections.
## 4 Experiment, Results and Discussion
### Forensics Analysis: Chrome Application as a Case
#### Limitations of FORC  
This section discusses the constraints and potential challenges associated with using FORC for mobile forensic data acquisition and SQLite evidence extraction. Key limitations include:  
* Device and Platform Dependencies: FORC's effectiveness may vary depending on the specific mobile device and operating system versions. It is essential to acknowledge that different devices and platforms may have unique SQLite database structures and security measures, potentially affecting evidence extraction.
* Encrypted Data: While FORC is designed to streamline evidence extraction, it may encounter difficulties when dealing with strongly encrypted data. Advanced encryption methods can pose challenges in retrieving certain types of data from mobile devices.
* Performance Variability: The performance of FORC in extracting SQLite evidence may depend on factors such as the size and complexity of databases. Extremely large or highly fragmented databases could impact the tool's efficiency.
* Privacy and Legal Considerations: When using FORC, investigators must adhere to legal and privacy regulations governing digital evidence acquisition. The tool's usage should be consistent with applicable laws and regulations, and proper authorization should be obtained.
* Automation Limitations: While FORC offers automation benefits, it may not fully replace the expertise of forensic analysts. Human oversight and interpretation remain crucial to ensuring the accuracy and relevance of extracted evidence.
# A Review on Design Considerations and Connection Techniques in Modular Composite Construction
## 3 Area of Research
### Connection Techniques in Precast Systems
#### 3.3.3 Hybrid Connections  
Incorporating steel sections, whether they are complemented by bolts or not, to connect precast elements provides superior seismic performance in strength, stiffness, and ductility. This approach is particularly advantageous for high-rise constructions. Hybrid connections demonstrate superior performance by integrating shear studs, steel plates, and inclined bars to dissipate forces efficiently. Failures commonly arise from yielding steel sections or bolts, buckling under compressive forces, or cracking within the surrounding concrete [60]. As shown in Figure 11, the hybrid connection system employs steel couplers, gusset plates, and primary steel plates for the interconnection of precast elements. The figure further delineates the incorporation of inclined steel bars, shear studs, and backing plates, which collectively improve load transfer efficiency, ductility, and seismic resilience. This exemplifies the intricacy and durability of hybrid connections in precast construction by illustrating the coordinated function of various steel components in effectively distributing loads and mitigating localised failures under conditions of elevated stress. By offering enhanced strength, stiffness, and ductility, hybrid connections could benefit modular construction in high-rise applications, where superior load transfer and seismic resilience are critical.  
The connection methodologies described in this section, initially designed for precast systems, establish a basis for investigating their relevance to modular construction. The experimental findings and attributes of these connections, analysed in recent studies presented in the subsequent section, provide essential information for evaluating their appropriateness in modular systems.  
Figure 10: Grounded sleeve coupler connection.
# Special Equipment Safety Supervision System Architecture Based on Blockchain Technology
## 2 Requirement Analysis
### Role Analysis
#### 2.1.4 Supervisory Agencies  
Supervisory agency means the national, or any state, county, or municipal agency, or department having the statutory authority to supervise the safety condition or inspection operation of a special equipment. The supervisory agency is usually the superior organization of the primary supervisors. It is the most important work for all supervision agencies to maintain the efficient operation of a safety supervision system of special equipment. Even more noteworthy is that the supervisory agency has the power to trace the responsibility for an accident, unlike the primary supervisors. It must be mentioned that the level of supervisory agency corresponds to the level of accidents for which it can trace responsibility in China [41]. Serious accidents are handled by state agencies, while ordinary accidents are handled by municipal agencies.
# Harmonic Detection Method Based on Parameter Optimization VMD-IWT Combined Noise Reduction
## 2 VMD Parameter Optimization and Wavelet Noise Reduction
### Optimize VMD Based on RIME
#### 2.2.3 Hard Rime Piercing  
This process simulates the growth of rime particles under challenging conditions and is primarily employed in the optimization stage to enhance the interchangeability of algorithm particles, thereby improving algorithm convergence and the ability to escape local optima. The location update formula for this process is as follows:  
\[R_{ij}^{new}=R_{best,j},r_{3}\textless F^{normr} \tag{9}\]  
where \(normr\) is the normalized value of the current individual fitness value, representing the probability of selecting the \(i^{\text{th}}\) frost-ice individual. \(r_{3}\) is a random number within the range \([-1,1]\).
## 2 Application Scenarios and Key Concepts/Enablers for RF Hardware in JCRS  
In this section, the needs for communication-assisted radars and radar-assisted communications scenarios are further illustrated, along with the implications for RF hardware requirements. Different use cases exist in automobile, mobile, Industry 4.0, virtual/augmented reality (VR/AR), robotics, IoT, and other evolving applications. However, not every application requires a completely new RF hardware. Some applications may require a modified front-end, some an updated antenna structure, and some may require completely co-designed RF hardware. Some applications may even work with existing hardware and will require only an update in software to enable the sensing capability. To summarize, there will be different levels of integration. The authors of [7] envisioned the levels of integration into four different types of systems--isolation, coexistence, cooperation, and co-design. In this case, isolation means no interaction between these two systems, while co-design is the most integrated and tightly coupled system. While co-design is the most desired approach to obtain the most out of hardware reuse [8], the immediate use cases may greatly benefit from coexistence and cooperation approaches. The challenge of standardization in the radar domain and the privacy aspects of sensing in multiple use cases may also limit usage. However, the use of passive radars to aid in communication can be implemented possibly with limited efforts [9]. At the same time, the sharing of information with a cooperation approach can be done using a system tied at higher layers (e.g., software). In general, coexistent systems may work in different bands, whereas cooperation may share the spectrum, but with separate hardware. The co-designed approach aims to share both spectrum and RF hardware. The extensive usage of hardware that can address both of the systems' requirements can lead to ubiquitous sensing along with ubiquitous communications, thus making radars cheaply available and RaaS a reality in consumer applications.
# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 3 Background
### Dataset  
Despite the availability of several datasets for autonomous vehicle research, such as the Waymo Open [34], nuScenes [35], and ApolloScape [36], which focus on specific tasks like motion planning, object detection, and tracking, a broader perspective is necessary to comprehensively address the challenges in autonomous vehicle research.  
We propose the use of a taxi dataset as a warm start to comprehensively address the challenges in autonomous vehicle research. Leveraging real-world datasets can aid in predicting travel time, evaluating autonomous vehicle performance, and identifying transportation system patterns. This approach can bridge the gap between simulated and real-world scenarios, providing a better understanding of the challenges and limitations of autonomous vehicles in complex urban environments.  
The NYC green taxi dataset obtained from the NYC TLC [37] was chosen for this study due to its convenience, relevance, and size. The dataset spans from August 2013 to December 2015 and consists of approximately 33 million taxi trips. Each trip entry includes the pickup and drop-off GPS coordinates, timestamps, number of passengers, fare amount, tip amount, payment type, and other relevant attributes. It is important to note that starting from the second half of 2016, the TLC grouped the original GPS coordinates into designated Taxi Zones due to privacy concerns. To ensure the proper evaluation and analysis, the dataset was split into three subsets as follows: a training set comprising 80% of the data, a validation set containing 10%, and a testing set consisting of the remaining 10%.  
Figure 2: Layered architecture of the Embedded Higher-order Agent (E-HoA).
## 3 Results
### Comparative Experiments
#### 3.2.2 Green Space Segmentation Accuracy
However, after applying WRFM for haze removal, these issues are significantly alleviated, and the segmentation accuracy is largely restored to the level achieved under normal weather conditions. Figure 12 presents a detailed comparison, illustrating the segmentation results of images under adverse weather conditions with and without the WRFM module. From the left-side images, it can be observed that after WRFM processing, the vegetation areas are correctly segmented, whereas the original image fails to achieve the segmentation target. From the right-side images, it is evident that only a small portion of the vegetation area is segmented in the unprocessed image, with the majority of the vegetation regions remaining unrecognized by the model. In contrast, the processed image successfully segments all vegetation areas, with clear boundaries between trees distinctly visible. Figure 13 provides a detailed comparison, showcasing the segmentation results with and without the application of SCP-Net. The differences within the black boxes prominently highlight the effectiveness of SCP-Net. In the image without SCP-Net processing, certain vegetation areas are missed during segmentation. However, after correction, the previously unsegmented regions are accurately segmented, successfully improving the overall segmentation accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **OA** & **Recall** & **Precision** & **mIoU** \\ \hline K-means & 0.4982 & 0.4906 & 0.3768 & 0.2708 \\ FCN & 0.5796 & 0.6625 & 0.5521 & 0.4309 \\ PointNet++ & 0.5595 & 0.6301 & 0.5257 & 0.4017 \\ SAM & 0.6986 & 0.8135 & 0.7394 & 0.6322 \\ UGSAM & **0.8615** & **0.9345** & **0.9027** & **0.8490** \\ \hline \hline \end{tabular} The best results are highlighted in bold.  
\end{table} 
Table 6: Experimental results comparing UGSAM with conventional methods in adverse condition. The checkpoint of the SAM is ViT-H.  
Figure 11: Visualization of the impact of the WRPM on segmentation accuracy. **(a)** The street view image captured under harsh weather conditions. **(b)** The segmentation result obtained without any preprocessing. **(c)** The segmentation result after applying the WRPM. **(d)** The ground truth for comparison. Figure 12: Visualization of segmentation details for both the original image and the image processed with the WRPM. The arrows indicate a magnified view of the details within the red box in the original images.
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 4 Conclusions and Future Scope  
Feature extraction is one of the most crucial steps in HSI classification. It is essential to capture comprehensive spatial and spectral information for accurate feature extraction. For image reconstruction, the conventional 2D-SSA algorithm usually extracts spatial features directly by applying the embedding window to the entire image. However, HSI scenes frequently encompass a broader area and contain several items. As a result, spatial information pertaining to local objects must be recovered. To solve this problem, in the proposed method, a superpixel-based SSA technique was presented, which can capture the object specific spatio-spectral information accurately.  
In this work, the original HSI was first divided into various semantic sub-regions by the superpixel segmentation algorithm. Next, each segment was reconstructed individually by applying 2D-SSA. The generated reconstructed HSI was then classified using the SVM classifier, and the final classification map was produced. Local characteristics may be collected effectively in the suggested method since 2D-SSA is applied at the superpixel level. However, two parameters must be adjusted: the amount of superpixels and the embedding window size. Future developments will aim at finding the optimal criteria to determine the parameters of the procedure and to investigate relationships between the characteristics of the HSI and quality of the results.  
Figure 16: (**a**) Original scene at band 667 nm (**b**) Reconstructed scene by 2D-SSA (**c**) Reconstructed scene by SP-SSA (**d**) Difference image for the 2D-SSA reconstructed scene (**e**) Difference image for the SP-SSA reconstructed scene.  
**Author Contributions:** Conceptualization, P.K.B. and R.P.; methodology, S.S.; software, R.P. and S.S.; validation, S.S., R.P. and P.K.B.; formal analysis, S.S.; investigation, S.S. and R.P.; resources, F.D. and P.K.B.; data curation, S.S.; writing--original draft preparation, S.S.; writing--review and editing, F.D.; visualization, S.S. and F.D.; supervision, F.D.; project administration, F.D. and P.K.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets have been utilized for conducting the experiments. Details are provided in the text of the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.