esa-sceva/satcom-chunk-collection huggingface dataset:
# Hourglass 3D CNN for Stereo Disparity Estimation for Mobile Robots
## 4 Experiments
### Evaluation Results and Discussion
In terms of the Noc metric (the proportion of incorrect pixels in non-occluded areas), our proposal improved from 6.10% to 4.93% on the KITTI 2012 test set and from 7.12% to 5.78% on the KITTI 2015 test set compared to AAFS [12]. Moreover, in terms of the All metric (the proportion of incorrect pixels in total), our proposal significantly improved from 6.94% to 5.64% on the KITTI 2012 test set and from 7.54% to 6.14% on the KITTI 2015 test set. We evaluated runtime based on Google Colab (Nvidia Tesla T4), and the results showed that our method is approximately equal to AAFS [12]. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**KITTI 2012**} & \multicolumn{2}{c}{**KITTI 2015**} & \multicolumn{2}{c}{**Params**} & \multicolumn{1}{c}{**Time**} \\  & **Noc** & **All** & **Noc** & **All** & **(M)** & **(ms)** \\ \hline PSMNet [10] & 1.49 & 1.89 & 2.14 & 2.32 & 5.22 & 1260 \\ Fast-ACVNet [29] & - & - & 1.77 & 1.91 & 3.07 & 140 \\ DeepPruner-fast [20] & - & - & 2.35 & 2.59 & 7.47 & 210 \\ GwcNet [18] & 1.32 & 1.70 & 1.92 & 2.11 & 6.52 & 692 \\ DeepPruner-best [20] & - & - & 1.95 & 2.15 & 7.39 & 685 \\ AAFS [12] & 6.10 & 6.94 & 7.12 & 7.54 & 0.023 & 12 \\ \hline Ours & 4.93 & 5.64 & 5.78 & 6.14 & 0.12 & 13 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison on the KITTI 2012 and 2015 test benchmarks. Figure 5: Qualitative comparisons between PSMNet, AAFS, and our proposed method. (**a**) Qualitative comparison of results on the KITTI 2012 test set. From top to bottom: PSMNet, AAFS, and our proposal. From left to right: left image, disparity prediction, and error map. (**b**) Qualitative comparisons of results on the KITTI 2015 test set. From top to bottom: PSMNet, AAFS, and our proposal. From left to right: left image, disparity prediction, and error map.
# Applying the DEMATEL - ANP Fuzzy Comprehensive Model to Evaluate Public Opinion Events
## 2 Construction of a Network Public Opinion Evaluation Index System  
Establishing a reasonable index system is the prerequisite for judging the degree of urgency of public opinion, but the current ones are cumbersome. Some of them lack a consideration of feasibility, making it difficult to extract a certain indicator from a large amount of data and affecting the overall assessment accuracy; some rely on human subjective assignments, which may cause one-sided results; and some rely too much on the complete data of the whole process of a certain public opinion event, making it difficult to be applied when the amount of collected data is insufficient at the early stage of public opinion fermentation.  
This paper takes the laws and changes related to network public opinion transmission fully into account when emergent events occur. By referring to research [9; 10; 11; 12; 13; 14; 15; 16; 17; 18] and consulting with relevant experts who have published papers in the field of public opinion, an index system of 5 first-level indicators and 13 secondary-level indicators was eventually established for emergent events after repeated rectification and selection. All the indicators in the index system are within the acquired capability and meet the construction principles of being important, professional, easy to achieve, quantifiable, and correlated with each other, helping to compose a scientific network public opinion index system. The principles  
Figure 1: The procedure of constructing DEMATEL–ANP fuzzy comprehensive model.  
of constructing the index system are shown in Table 1, and the whole network public opinion evaluation index system for emergent events is shown in Table 2.  
As is shown in Table 2, the index system has a two-tier hierarchy. The indicators at the second level are the individual elements of the overarching indicators set at the first level. The first level consists of five degrees referring to five dimensions of public opinion events: degree of heat, degree of quantity, degree of strength, degree of focus, and degree of variation. The degree of heat is measured by the volume of media issuance and the length of time netizens are concerned about the issue. The degree of quantity represents the degree of participation of netizens in the discussion of public opinion topics, which is mainly reflected in the aspects of the number of clicks, shares, and comments. The degree of strength characterizes the diffusion trend of public opinion in the transmission process, which is explained by the number of opinion leaders and the network area distribution. The degree of focus concerns the emotion and attitude of netizens, which can be counted as different opinion holding rates. The degree of variation reflects the dynamic change in the transmission process explained as the click growth rate, the share growth rate, and the comment growth rate.
# Intelligent Reception of Frequency Hopping Signals Based on CVDP
## 3 Algorithm Simulation and Performance Analysis  
### Data Set Generation and Hyperparameter Setting  
The set of frequency hopping frequencies is used for training the network, the frequency set is in (10 MHz, 20 MHz) with 25 KHz interval, the number of frequencies sets is 400, and the hopping frequency is selected randomly when generating samples, and the hopping speed is 2000 hop/s. The training data set contains two kinds of hopping signals' time-frequency spectrograms. The one kind is the hopping signals under Gaussian white noise background with SNR (Signal-Noise Ratio) ranging from \(-\)10 dB to 10 dB at 2 dB intervals, generating a total of 13,200 sets of signals. The other kind is the frequency hopping signals under four different interferences, which are multi-tone interference, single-band interference, multi-band interference, and swept frequency interference with SIR (Signal-Interference Ratio) ranging from \(-\)10 dB to 10 dB at 2 dB intervals, and 1200 sets of hopping signals are generated in each SIR, resulting in a total of 52,800 sets of signals. A signal is processed by STFT with a window length of 1024 points and is corresponding to a hopping frequency. A training dataset containing 66,000 samples of time-frequency spectrograms is obtained.  
The test data set contains the hopping signals under different interference and Rayleigh fading channels. The interference of the above four interfering signals superimposed is called mixed interference signals. SIR ranges from \(-\)16 dB to 2 dB at 2 dB intervals, and 500 sets of hopping signals under each of five kinds of interferences are generated in each SIR, resulting in a total of 25,000 sets of signals. Frequency hopping signals under frequency flat Rayleigh fading channels and frequency selective Rayleigh fading channels are generated with SNR ranging from \(-\)16 dB to 2 dB at 2 dB intervals, and the number of signal sets per SNR is 500. So the total sample size of time-frequency spectrograms of the test data set is 35,000.  
MATLAB software was used to generate the training and test datasets. The experimental environment and configuration are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Environment** & **Environment Configuration** \\ \hline Operating System & Win 10 \\ CPU & Intel Core i5-12400F \\ GPU & NVIDIA RTX3060 \\ Memory & 16 GB \\ Programming Languages & Python 3.9 \\ Deep Learning Framework & PyTorch 1.12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental environment and configuration.  
To ensure the network training effect while speeding up the training speed, let the initial learning rate be 0.001 and the total of rounds be \(\lambda=6\). After each round of learning, the learning rate decreases by 0.1\({}^{\lambda}\). [PERSON] is chosen for the optimizer and the training batch size is 64.
# The Role of Elasticity in the Vortex Formation in Polymeric Flow around a Sharp Bend
## 4 Discussion  
It has been shown during this research paper that higher polymer concentrations and higher inertia are both predicted to lead to larger vortices in a sharp bend geometry. Higher polymer concentrations lead to elastic vortices located upstream of the bend. Conversely, higher Reynolds numbers lead to inertial vortices located downstream of the bend.  
It was also found that after vortices are formed their size is predicted to be primarily determined by the properties of the polymer solution and the Reynolds number. In the case of elastic vortices, the vortex size is set by both the Weissenberg number and the viscosity ratio. In the case of inertial vortices, the vortex size is primarily set by the Reynolds number with the Weissenberg number playing a secondary role.  
What is more interesting is the fact that predictions indicate vortices can be suppressed by adjusting the properties of the polymer solution, elasticity and viscosity ratio, for a given solution volumetric flow rate. The  
As the present work summarizes, our current primary interest was to evaluate the role that elasticity plays in the formation of lip vortices in the flow around a sharp corner. It is clear that modifying the underlying properties of the polymer solution predicts that the fluid dynamics can be significantly altered. Vortices, elastic and inertial, could both be suppressed or promoted by adjusting the polymer solution properties for a given volumetric flow situation.
## 4 Conclusions  
The freezing effect of the FSPR adopted in the construction of Gongbei tunnel was analyzed in this paper based on the field temperature data. The results showed that FSPR maintained satisfactory water resistance performance during both the positive freezing period and excavation period. The following conclusions about freezing characteristics of FSPR can be drawn:  
For the positive freezing period,  
1. The development of frozen wall met the design requirements in less than 80 days of freezing considering the most unfavorable situation.
2. The distribution of frozen soil along the entire tunnel is non-uniform. Some of the bottom section of the frozen wall is more than 3 m, which may cause frost heave and thaw weakening problems.
3. Extreme weather like hurricanes only has a limited effect on the top of the frozen soil wall. The safety of the construction work was under control. For the excavation period,  
1. The strict control of over-excavation was helpful in reducing the thermal disturbances during excavation. There was an 8 \({}^{\circ}\)C difference of the largest temperature rise between the trial excavation and the official excavation. The \"thermal attack\" from the air at the excavation face on the frozen soil was much fiercer than before when the near frozen soil was dug out.
2. The frozen soil at the excavation side was visibly influenced by the replenishment of heat due to excavation. The thickness reduction of the frozen soil due to excavation thermal disturbances was less than 20 cm, whereas the frozen soil at the outside of the excavation face was much less influenced.  
Conceptualization, X.H.; methodology, X.H.; validation, Y.W. and X.L.; formal analysis, Y.W.; writing--original draft preparation, Y.W. and X.L.; writing--review and editing, Y.W.  
This research was funded by the National Natural Science Foundation of China, grant number 51478340.  
The authors would like to thank [PERSON], [PERSON], [PERSON], and [PERSON] for the field data measurements.  
The authors declare no conflict of interest.
# Cyber-Security Threats and Side-Channel Attacks for Digital Agriculture
## 5 Research Challenges and Future Directions
### Intrusion Detection and Prevention System  
Traditionally, intrusion detection and prevention systems (IDS/IPS) are developed for large data networks. However, the requirements of digital agriculture are different and include low-rate sensor data, sparse observation and attenuation, unattended deployment, and remote control. Therefore, new intrusion detection/prevention algorithms should be developed for digital agriculture. Currently, there is no IDS/IPS dataset available for DigAg applications. Existing datasets are either traditional IoT-smart home datasets [55] or computer networks [56]. The availability of an open-source agriculture-based dataset would fuel the research and development of such algorithms and systems. AI algorithms can be handy in the development of IDS/IPS systems. Further, using AI at edge computing and blockchain would be useful to mitigate some of the existing attacks. Considerable work is needed to deploy edge-based IDS systems for digital agriculture.
# Minimizing the In-Cloud Bandwidth for On-Demand Reactive and Proactive Streaming Applications
## 3 Optimizing Cloud-Based Streaming Internal Bandwidth: Case of an Unpopular Video
### SASST Principle
#### 3.1.2 SASST Variant  
The original version of SST does not consider FTS sharing between the latest streaming nodes. In this paper, we consider the sharing of FTS between them to further minimize the consumed bandwidth. Figure 3 represents the FTS handling with SST and SASST at a high workload where at least an SN request comes per slot. It can be seen that FTSs are shorter in the SASST case illustrated in Figure 2(b) than in the SST case shown by Figure 2(a). Let's take the example of the 8 th SN node. As it arrives during the 7 th time slot, using SST, it will be assigned a 7 slot FTS duration to serve the 7 missed slots. However, with SASST, it will only be assigned a 4 slot FTS duration (8 th SN FTS), which saves 3 time slots compared to SST. This is because the 8 th SN node obtains video slots 2, 4 and 6 through the 7 th SN FTS and obtains video slots 1, 3, 5 and 7 through the 8 th SN FTS. Hence, any late SASST SN node will share the FTS opened for the previous SN to further reduce the consumed bandwidth with regard to SST. Recall that SN requests that are received within the same time slots share the same FTSs.  
SASST could be easily implemented over IP multicast using Real Time Protocol (RTP) or multicast-HTTP over QUIC. The SN needs to know the currently opened CS and FTS through a session description or announcing mechanism. Session Description Protocol (SDP) or DASH like manifest files could be used in this context. Once done, it starts downloading the video slots through multicast channels as described in the previous paragraph. The storage node could push the video slots using RTP or HTTP/3 packets.
## 6 Result Discussion
### Result Accomplished
#### 6.3.1 Identification Accuracy  
This parameter highlights the actual score of correctly identifying the proportion of misbehaved nodes after implementing the proposed scheme. Mathematically, the computation of accuracy was carried out using Equation (7):  
\[Accuracy=\frac{I_{n}}{T}\times 100 \tag{7}\]  
As shown in Equation (7), the computation of _Accuracy_ was achieved considering positively identified misbehaved nodes, \(I_{n}\), with respect to total nodes, \(T\). The outcome of this analysis is shown in Figure 6, which shows that the proposed system offers significantly higher accuracy in contrast to the existing game model.  
The justification behind this outcome is that though adopting the Nash equilibrium model offers better detection of the payoff, the identified payoff does not remain the same during the attack, especially if the attackers employ a dynamic strategy. This problem is somewhat addressed using Bayesian Nash Equilibrium, where a better convergence rate is obtained due to better consistency in its belief-system formulation. However, the formulated belief and its respective consistency do not cope with the dynamic strategies adopted by the misbehaving nodes over time, which is one downfall of this approach. Nonetheless, this is one of the best approaches if the attacker's behaviour is known, assuming it will not change until the allocation of payoff. A similar problem also exists in the Stackelberg game model.  
On the other hand, the proposed scheme, by adopting the concept of distributed TRM, allows a faster progression towards the payoff computation upon finding any deviation in computed \(GT\) or \(QM\). Hence, the proposed scheme's accuracy in identifying misbehaved nodes in large-scale deployment is relatively higher. Therefore, this scheme offers a reliable outcome when the mobile IoT nodes are deployed in an ad hoc manner in a large-scale deployment area.
# Optimization of High-Performance Computing Job Scheduling Based on Offline Reinforcement Learning
## 5 Conclusions  
Our study aims to optimize HPC job scheduling through an offline reinforcement learning model trained on historical data. This approach avoids the resource inefficiencies seen in trial-and-error learning stages typical of online reinforcement learning and aims to improve scheduling performance on average job wait time and bounded slowdown.  
Experimental results of training on different historical datasets confirm the robustness of our model, showing that the hybrid dataset (with both expert-guided and trial trajectories) leads to improved performance compared to models trained on random, replay, or expert-only datasets. Through experiments on HPC job trace datasets (CTC-SP2, HPC2N, Lublin-256, and SDSC-BLUE), we demonstrated that an offline RL-based scheduler can outperform traditional heuristic and online RL methods in HPC environments. The model's performance metrics highlight the effectiveness of our offline reinforcement learning scheduler across diverse workload scenarios, showcasing its generalizability and reliability. These findings validate the potential of offline reinforcement learning as a practical and efficient tool for optimizing resource allocation in HPC environments, reducing job waiting times, and improving system utilization.
# On the Needs and Requirements Arising from Connected and Automated Driving
## 3 5 GCAR Use Cases
### Remote Driving for Automated Parking  
Automated parking is an exemplified use case of remote driving. Its goal is to remotely drive a vehicle from the \"last mile\" near a parking to the parking spot without a human driver inside the car. More specifically, the remote cloud server provides the vehicle with the appropriate trajectory and maneuver instructions for efficient and safe parking. To do so, the vehicle and the remote cloud server should be mutually authenticated for sharing video and sensor data. Moreover, the vehicle should have enough perception capabilities and allow access to its actuators. In addition, the vehicle should be able to communicate with the cloud server. Last but not least, the parking area needs to be equipped with sensors and/or cameras that facilitate the remote cloud server decision for optimized trajectories. During remote driving, the vehicle first collects relevant data, including sensor information, vehicle status, and video streaming images, and then continuously transmits them to the cloud server. Based on the received information from both vehicle and parking facilities, the remote cloud server calculates the driving commands (including steering, speed, and acceleration) and sends them to the vehicle. Subsequently, the vehicle executes the driving commands accordingly. An example is illustrated in Figure 5.  
Figure 4: High Definition Local Map use case.
# An Improved NSGA-II-Based Method for Cutting Trajectory Planning of Boom-Type Roadheader
## 2 Methodology
### Design of Cutting Space Under Kinematic Constraints
#### 2.2.3 Mathematical Modeling of Target Section  
The primary focus of research is on rectangular roadway tunneling, as it constitutes the main type in coal mine tunneling. As illustrated in Figure 4, the rectangular roadway cross-section model is composed of a three-dimensional space enclosed by multiple planes \(\pi_{1}\sim\pi_{6}\). By precisely controlling the movement trajectory of the cutting head within this space, the target roadway can be accurately excavated.  
The roadway width \(W_{h}\), height \(H_{h}\), and specified cutting depth \(C_{h}\) were set. The tunneling process is regarded as forming a rectangular prismatic space through multiple cycles of cutting. Let \(R_{h}\) represent the distance from the current cross-section to the starting point of the roadway. Ideally, \(R_{h}\) equals the product of the number of cuts \(n\) and the cutting depth \(C_{h}\), where \(n\) denotes the cutting sequence starting from the origin. Hence, the coordinates of any point within the cross-section space can be denoted as \(p_{d}=(x_{d},y_{d},z_{d})\), where \(0<x_{d}<C_{h}\), \(-0.5W_{h}<y_{d}<0.5W_{h}\).  
Figure 4: Schematic diagram of cross-sectional model.  
Figure 3: Projection model of cutterhead.  
Based on the kinematic model of the boom-type roadheader, the roadway coordinate system differs from the cross-section coordinate system only in terms of an offset \(R_{h}\)x along the \(x\)-axis, with the other coordinate axes being consistent. Accordingly, the transformation matrix \(\overset{h}{d}T\) is expressed as:  
\[\overset{h}{d}T=\begin{bmatrix}1&0&0&R_{h}\\ 0&1&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix} \tag{7}\]  
In the roadway coordinate system, the coordinates of any point \(p_{d}\) in the cross-section coordinate system are \(P_{d}=\overset{h}{d}T\cdot p_{d}\). For non-full-section excavation, a minimum of two cuts are required to complete the excavation of a single cross-section. In this paper, the cross-section is divided into left and right parts based on the roadway centerline, and Left-Section and Right-Section models are constructed accordingly as \(P_{d\_left}=(x_{d},y_{left},z_{d})\), \(P_{d\_right}=(x_{d},y_{right},z_{d})\). Specifically, \(P_{d\_left}=(x_{d},y_{left},z_{d})\) represents the left cross-section model, while \(P_{d\_right}=(x_{d},y_{right},z_{d})\) represents the right cross-section model.
# An Optimization Routing Algorithm for Green Communication in Underground Mines
## 3 GCUM and the Energy Consumption Model
### Nodes in the GCUM  
In this paper, the different mobile nodes are reflected in the GCUM. These three types of nodes are anchor nodes (ANs), ordinary nodes (ONs), and multifunction nodes (MNs).  
ANs have wired power supplies fixed on the tunnel walls in underground mines. The sensors on these nodes are used to collect environmental information, such as temperature, humidity, and gas concentration data. If the power wires of these nodes are damaged in accidents, their power supplies switch to batteries. However, to achieve green communication, the energy consumption of anchor nodes must be considered, even for wired power supplies. ONs are deployed on the miners with localization and emergency calling functions and are powered by batteries. Under normal working conditions, ONs only receive, send, or forward packages with localization information. In the case of an emergency, a miner could press the call button to send a distress package to upper-level systems. MNs include all the functions of ONs and have many additional functions, including environmental status monitoring and camera, audio, and video functionality. Therefore, upper-level systems can monitor the real-time status of underground mining in a variety of ways, such as via photographs, audio or videos. Furthermore, if the initial energy of ONs is limited, video and audio data are not forwarded by ONs to reduce energy consumption. Video and audio data are uploaded to upper-level systems by ANs only.