esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Advantages of Forest Classification Using HJ-1 Images and Object-Based Method  
The HJ-1-based forest mapping in this study has obvious advantages, including high spatial resolution and high temporal resolution (2-days). The HJ-1-based forest map in Figure 7 provides detailed fragmentation of the forest in most plains and hilly areas, which is ascribed to the fine resolution of HJ-1 imagery. Many previous studies about forest mapping associated with times series phenology are often supported by high-temporal-resolution sensors images, like AVHRR and MODIS, and moderate-to-high sensors images, like SPOT and Landsat. However, these data have either too low spatial resolution or too low temporal resolution to work at a regional scale. High temporal resolution makes HJ-1 imagery feasible to derive NDVI, NDWI and EVI time series data and to highlight significant seasonal differences among various forest types, which is very useful for separating areas dominated by evergreen tree species from forest dominated by deciduous tree species.  
Although forest versus non-forest maps can be extracted automatically or semi-automatically from images over some mountainous terrains, visual interpretation of satellite imagery is still the primary means to do this in the very rugged areas ([PERSON] et al., 2018). It is a very tedious and time-consuming process and the accuracy of results largely depends on the experience of professionals. Hence, the visual interpretation is difficult to be repeated over a large area. To map forest cover on a large scale with Landsat-like images, it is necessary to utilize automated classification routines as much as possible to save human resources [67]. In this study, a simple object-based decision tree algorithm was developed to carry out a rapid regional forest mapping. We found that the combination of object-based image analysis and decision tree classification was an effective method of classifying forest across a large area and is accurate based on comparison to ground truth data and global products. Utilizing a decision tree provides a transparent, repeatable, and easily interpreted process to facilitate regular updates and assessments of forest area and their spatial distribution [33]. Forest subcategory classification requires multi-temporal data to incorporate greater spectral variability within each class. Multi-resolution segmentation within the context of object-based classification has been proved to be useful for this more complex discrimination of forest. This method was straightforward and easy to extend to other large-scale regions. The rules created in this study only utilized a few variables, but through further testing and implementation this process may become increasingly automated and accurate.
## 2 Related Work
For this purpose, the IOTSim platform equips the native CloudSim architecture with a storage layer and, then, provides the software primitives for the simulation and storage-induced delays. However, the core processing engine supported by the current IOTSim release is MapReduce-compliant, so that it implements batch processing models for the support of delay-tolerant big data applications. iFogSim [13] is another toolbox whose implementation is an extension of CloudSim. It aims to simulate IoT-based Fog platforms by providing: (i) suitable software Java-based primitives for modeling the energy-delay performances of sensors, actuators, Fog, and Cloud nodes; (ii) two service models, e.g., the Sense-Process-Actuate and the Stream processing services; and (iii) two heuristic task allocation policies, namely the All-Cloud and Edge -ward task placement policies. However, in the current release of iFogSim, we have that: (i) all the supported devices' profiling facilities and task allocation policies are inherently static; (ii) the network delays are assumed fixed; and (iii) support for the dynamic allocation and/or tracking of network bandwidths and computing frequencies is not provided. Very recently, a third research line focused on the development of the software toolkit EdgeCloudSim [15], for the simulation and performance evaluation of general multi-tiered Cloud-Fog computing platforms. The main feature of EdgeCloudSim is the provisioning of a software environment for the setting and dynamic simulation of the profiles of WLAN/WAN networks, wireless network traffic, device mobility, Fog nodes, and Cloud nodes. For this purpose, EdgeCloudSim: (i) provides a customizable edge orchestration module for the performance evaluation of different task placement policies under different time scales; (ii) implements a customizable load generator that allows simulating the workload of the served mobile devices as streams of independent tasks with Poisson-distributed arrival times; and (iii) relies on the CloudSim support for the creation/management/migration/shut-down of VMs. Hence, like the proposed VirtFogSim, EdgeCloudSim aims at simulating the performance of multi-tiered networked Fog-Cloud platforms for the support of time-critical tasks. However, _unlike_ VirtFogSim, EdgeCloudSim: (i) does not support the utilization of application DAGs and, then, does not allow the characterization of the inter-task dependency; (ii) being VM-oriented, does not support the characterization of container-based virtualization; (iii) does not provide software primitives for the tracking of the allocated network/computing resources; and (iv) being mainly focused on the delay-performance of the simulated platform, does not provide support for the modeling of the power/energy profiles of the Mobile, Fog, and Cloud nodes.
# Review of Control Technologies for Quiet Operations of Advanced Air-Mobility
## 4 Active Noise Control (ANC) Technology
### Active Tonal Noise Control of Multi-Rotor Air Mobility Vehicles at Approach  
ANC of rotors has been investigated recently by [54], [PERSON] et al. (2019). [50], and [49], among others. They argued that it is the in-plane fundamental thickness noise that needs to be reduced as it becomes the significant source during approach. This is particularly true with the low-frequency high amplitude noise. They proposed placing a point actuator on the blades to produce some loading noise to cancel the in-plane thickness noise. Actuators such as a flap, winglets and tip-blowing have been investigated. The above-mentioned work is, however, limited to the case of a single rotor, and a single actuator. Single point actuators make the loading actuation per area too excessive. Extension to AAM multi-rotors with distributed actuators is needed and is the subject of [PERSON]'s [55] work.  
An Active Noise Control (ANC) technology is developed by [PERSON] & Mankbadi [55] to reduce the in-plane thickness noise associated with multi-rotor Advanced Air Mobility Vehicles (AAM). They considered two in-line rotors and showed that the FWH-determined actuation signal can produce perfect cancellation at a point target. However, the practical need is to achieve noise reduction over an azimuthal zone, not just a single point. Furthermore, for practical applications the single point actuator is replaced by distributed micro actuators system (Figure 12). Note that \(F_{s}\) is the span-wise force, \(F_{c}\) is the chord-wise force, and \(L_{r}\) is the resultant loading vector in the target observer direction. To achieve this zonal noise reduction, an optimization technique is developed to determine the required actuation signal produced by the on-blade distribution of embedded actuators on the two rotors.  
Figure 12: Sketch showing the directivity of the forces and the loads in relation to the observer as well as the dual piezo speaker configuration [55].
# Applying Selective Laser Melting to Join Al and Fe: An Investigation of Dissimilar Materials
## 3 Results and Discussion  
### Deformation in the Selective Laser Melting (SLM)  
Due to the layer-by-layer principle of the SLM printing process, the printed section becomes increasingly thicker, and products are formed progressively. Therefore, to obtain a cubic thickness of 10 mm, the process must complete the riskiest period first, before a 10 \(\times\) 10 mm plate of metal is formed.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Wavelength & 1075 nm \\ Maximum output power & 200 W \\ Beam quality & \textless{}1.1 \\ Beam spot & 70 \(\upmu\)m \\ Building size & \(150\times 150\times 250\) mm \\ Maximum scanning speed & 7 m/s \\ \hline \end{tabular}
\end{table}
Table 1: Technical parameters of the selective laser melting (SLM) MetaSys 150.  
Figure 3: (**a**) SEM image of pure aluminum powder particles, (**b**) powder particle size distribution, and (**c**) laser scanning strategy.  
Residual stress is always present in products printed by SLM [9; 10], as presented in Figure 4. Because new layers are printed on other solidified layers, flake behavior is the result of an accumulative process. After printing a layer shown in Figure 4a, the solidified layers increase, creating a sheet indicated in Figure 4b. In the first several layers, the thickness of the printed layers was too thin; therefore, there was no steep temperature gradient with thickness. During heating, thermal compressive stresses develop in the printed layers, which results in a large amount of thermo-elastic strain, possibly producing local thermo-elasto-plastic buckling of the material. When the laser beam of the printer passes, the buckle is generated along the direction of laser scanning [13].  
After achieving a certain thickness, because of heating, a steep thermal gradient in the manufactured layers leads to differences in thermal expansion throughout the thickness. At the initial stage, the top surface is heated because it faces the laser shown in Figure 4c. Rapid thermal expansion of the top solid layers in contrast to the bottom solid layers, as well as restriction by the surrounding material, creates a concave downward shape. At a certain temperature, the shape and degree of bending produce maximum elastic strain. Any additional thermal expansion is converted into plastic compression. After the laser moves away, cooling ensues, and shrinkage occurs shown in Figure 4d.
# An Adaptive Neuro-Fuzzy Propagation Model for LoRaWAN
## 1 Introduction  
In recent years, the exponentially increasing number of wireless devices has made the maintenance and efficient planning of wireless networks crucial. Therefore, it is essential to gain a better understanding of propagation and be able to readily predict it in various scenarios. The area of radio propagation has been studied over many decades, during which the physics of the electromagnetic wave and its propagation has been unchanged. Hence, the same free space path loss (FSPL) formula holds to this date. However, telecommunication devices, network requirements and the propagation environment have undergone many developments. Perhaps, adapting to these rapid changes and lack of efficiency and generalization in current models has been the main research incentive in this field. It is clear that establishing an adaptive, efficient and accurate modeling of modern wireless technologies, that is, LoRaWAN, is of great importance. The main contributions of this paper are:  
1. A novel method of propagation modeling is proposed by using an adaptive-network-based fuzzy inference system (ANFIS). This knowledge-based system enables an adaptive model that is capable of incorporating new modeling parameters, depending on the wireless technology, propagation environment and an expert's knowledge.  
A robust method of determining the network size is used to avoid overfitting.
3. Results from the models are critically analyzed with real-world measurements using a relatively new wireless technology.  
This novel implementation has several advantages, however, for the purpose of comparison, firstly, a brief review of current models would be required. The rest of the paper is organized as follows: Section 2 provides a critical review of the conventional propagation models. Section 3 explains the implementation details and advantages of the proposed model. In Section 4, the results of the practical and comparative analysis are demonstrated, and Section 5 provides the findings of this research along with final conclusions.
# An LSTM-Based Deep Learning Approach for Classifying Malicious Traffic at the Packet Level
## 4 Evaluation Results
### Discussion  
There is a trade-off between the word size and the classification performance, i.e., accuracy and detection time. Reducing the word size can potentially increase the detection time, and training time as well, due to more words involved, longer sentence created and dimension extended. For accuracy, the performance can vary and depend on the considered attack type. Apparently, we can't scale the size too small. In our evaluation, we set the word size to two bytes as aforementioned. However, there is promising research to propose an algorithm to auto-calculate this value properly on the deployed environment, even that for specific attacks.  
In addition, the approach to consider the classification at the packet level can open the door to accelerating the detection since we can schedule the packets for parallel processing. However, the explicit shortcoming is to increase the training time and resource usage (e.g., memory) due to a large number of parameters and data size putting to the training model. Balancing the factor of acceptable training time but still gaining the best classification performance is a non-trivial task and a potential research direction.  
Finally, the DL-based classification approach is highly susceptible to the data poisoning attack due to its dependence on the training data. Thus far, we have found few attack models targeting the evasion of the deep learning-based malicious classification systems, including ours. However,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Mirai-RGU**} \\ \hline \multirow{2}{*}{**Dataset**} & **Accuracy** & **Precision** & **Recall** & **F-Score** & **FPR** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline Mirai-CCU & 97.22 & 96.25 & 98.73 & 97.5 & 0.36 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Performance evaluation results of the Mirai-CCU dataset validation on the Mirai-RGU training model.  
this can be soon changed when the popularity of deep learning will attract more attackers to exploit its vulnerabilities for hacking or monetization. Generating/preventing adversary models against deep learning is thus one of the most interesting and promising security research topics.
###### Abstract  
In complex urban road environments, vehicles inevitably experience frequent or sustained interruptions of the Global Navigation Satellite System (GNSS) signal when passing through over-passes, near tall buildings, and through tunnels. This results in the reduced accuracy and robustness of the GNSS/Inertial Navigation System (INS) integrated navigation systems. To improve the performance of GNSS and INS integrated navigation systems in complex environments, particularly during GNSS outages, we propose a convolutional neural network-gated recurrent unit (CNN-GRU)-assisted factor graph hybrid navigation method. This method effectively combines the spatial feature extraction capability of CNN, the temporal dynamic processing capability of GRU, and the data fusion strength of a factor graph, thereby better addressing the impact of GNSS outages on GNSS/INS integrated navigation. When GNSS signals are strong, the factor graph algorithm integrates GNSS/INS navigation information and trains the CNN-GRU assisted prediction model using INS velocity, acceleration, angular velocity, and GNSS position increment data. During GNSS outages, the trained CNN-GRU assisted prediction model forecasts pseudo GNSS observations, which are then integrated with INS calculations to achieve integrated navigation. To validate the performance and effectiveness of the proposed method, we conducted real road tests in environments with frequent and sustained GNSS interruptions. Experimental results demonstrate that the proposed method provides higher accuracy and continuous navigation outcomes in environments with frequent and sustained GNSS interruptions, compared to traditional GNSS/INS factor graph integrated navigation methods and long short-term memory (LSTM)-assisted GNSS/INS factor graph navigation methods.  
CNN-GRU; factor graph; GNSS outage; integrated navigation +
Footnote †: journal: Applied  
Article  
A Method for Assisting GNSS/INS Integrated Navigation System during GNSS Outage Based on CNN-GRU and Factor Graph  
[PERSON]  
1 School of Automation, Beijing Information Science & Technology University, Beijing 100192, China; [EMAIL_ADDRESS] (H.Z.); [EMAIL_ADDRESS] (W.C.)  
2024
# Two-Dimensional Permutation Vectors' (PV) Code for Optical Code Division Multiple Access Systems
## 5 Proposed 2D-PV W-T-Based OCDMA-PON
Since the number of \(OCU1_{RN_{i}}\) used throughout the network to support the given number of users was dependent of the value of \(Y^{ht}\) code sequences, it was of prime importance to keep the number of codes as few as possible to address the power constraints of the ring-based topology. The ONT module for the proposed 2D architecture employed a time-based decoding arrangement, which was designed in accordance with the temporal encoder. It split the incoming signal into two equal portions while using a \(1:2\ OS_{TD}\). Each leg of the \(OS_{TD}\) was connected to a \(TU\) that was calculated as \((S-1-j)\), where \(j\) is the chip position in the \(Y_{0}^{T}\) code sequence for user \(U_{0,0}\). Outputs from the TUs were combined with the help of a \(1:2\ OC_{TD}\) and forwarded for spectral decoding. The spectral decoder essentially contained a balanced decoding arrangement commonly referred to as complementary subtraction detection schemes (CSD). CSD for the proposed architecture consisted of a 1:2 \(OS_{ONT}\) that split the time decoded-signal into two equal portions. One portion was fed to a filter arrangement in the upper leg of the CSD decoder, whereas another portion was fed to another filter arrangement at the lower leg of the decoder, as shown in Figure 6. Filters in the upper leg of the CSD decoder contained a combination of fiber Bragg-grating (FBG) filters that were centered in accordance with the \(X_{1}\) code spectral signature. The lower leg of the CSD decoder employed a combination of FBG filters configured to complement to the 1s in the \(X_{1}\) code sequence. In other words, the lower leg of the CSD decoder for user \(U_{0,0}\) extracted the spectrum that was in complement with the \(X_{1}\) code sequence. Then, resulting signals from both legs of the spectral decoder related to respective PIN photodiodes in order to convert the signal from optical to electrical domain for necessary processing. The output from the PIN photodiodes was further connected to the subtractor arrangement that subtracted the output from both legs of the CSD decoder to recover the intended spectrum with maximum power units for the intended subscribers and 0 power units for interfering users.
# Fuzzy-Based Quality Adaptation Algorithm for Improving QoE from MPEG-DASH Video
## 6 Simulation Results
### Summary  
Table 9 shows the average performance of the adaptation algorithms from all the experiments, and demonstrates that the proposed algorithm achieved the highest video rate and the lowest bandwidth inefficiency; this guaranteed the highest user experience among the state-of-the-art algorithms. Furthermore, the proposed algorithm avoided unnecessary playback interruptions during all the experiments. However, it experienced slightly more video rate switches. The reason is that that the proposed algorithm simultaneously focuses on efficiently utilizing bandwidth while mitigating playback interruptions. In order to achieve this, the proposed algorithm quickly reacts to changes in throughput. That results in slightly more video rate switches, but this approach helps the algorithm in downloading high-quality video segments while avoid playback interruptions. DBT and FDASH waited for the buffer volume to increase above (or decrease below) predefined thresholds. That minimized the switching ratio, but compromised video quality. Moreover, the FDASH algorithm estimates the buffer level for the next 60 s. In an environment where the bandwidth is unstable and the buffer size is small, this approach leads to a high frequency of rebuffering events. The throughput-based algorithms such as QLSA and DASH-Google do not have information on the playback buffers; therefore, they did not risk conservative reactions to changes in bandwidth. This resulted in more video rate switches.  
The results also indicate that the proposed algorithm guaranteed high QoE irrespective of buffer size, segment duration, and video sequence. However, the performance of the other state-of-the-art algorithms varied from one setting to the other. The reason is that these algorithms employ fixed control strategies for all settings.  
In the following summary, consecutive numbers represent the results compared to FDASH, DBT, QLSA and Google-Dash, in that order.  
The proposed algorithm  
1. increased the average video rate by 5.7%, 18.2%, 6.3%, and 5.2%;
2. increased bandwidth efficiency by 30%, 45%, and 31%, whereas it achieved the same efficiency as the Google-Dash algorithm;
3. outperformed other schemes in terms of QoE by 30%, 12.8%, 12.7%, and 16%.
## 2 Methodologies
### Lstm  
LSTM has superior performance in extracting the long-term dependencies of historical and future information for sequence data [51]. Figure 4 presents the structure of LSTM, in which there are three gates, including input gate \(i_{t}\), forget gate \(f_{t}\) and output gate \(o_{t}\). The unique gates system is introduced in the LSTM model to control the information flow. Firstly, the input gate \(i_{t}\) is used to decide how much the memory cell state will be updated by the block input. Secondly, what information from the previous cell state should be forgotten is decided by the forget gate \(f_{t}\). Finally, the output gate \(o_{t}\) controls which part of the current memory cell state should be output.  
The formulas of the control parameters can be written as follows:  
\[\sigma(x)=\frac{1}{1+e^{-x}} \tag{11}\]  
\[f_{t}=\sigma\Big{(}W_{f}\times[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{12}\]  
\[i_{t}=\sigma(W_{i}\times[h_{t-1},x_{t}]+b_{i}) \tag{13}\]  
\[\stackrel{{\sim}}{{c}}=\tanh(W_{c}\times[h_{t-1},x_{t}]+b_{c}) \tag{14}\]  
Figure 4: Structure of LSTM.  
Figure 3: Structure of the residual block.  
\[\tanh(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{15}\]  
\[c_{t}=f_{t}c_{t-1}+i_{t}\overset{\sim}{c} \tag{16}\]  
\[o_{t}=\sigma(W_{o}\times[h_{t-1},x_{t}]+b_{o}) \tag{17}\]  
\[h_{t}=o_{t}\times\tanh(c_{t}) \tag{18}\]  
where \(x\) is the input variable, \(x_{t}\) and \(h_{t}\) are the input and output at time step \(t\), respectively, \(W\) and \(b\) are weight matrices and bias matrices, respectively, \(\sigma(\cdot)\) and \(\tanh(\cdot)\) are two activation functions for the update of cell state and the selective output, respectively, and \(c_{t}\) and \(\overset{\sim}{c}\) denote the current values and new candidate values for the cell state, respectively.