esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Model Evaluation and Measurement Accuracy  
Table 1 demonstrates the measurement accuracy and the validity of the machine learning model.  
Table 1 indicates that the coefficients of determination (\(R^{2}\)) for RF, SVR, KNN, and MLP all exceed 0.8, demonstrating the strong explanatory power of the prediction model for the features extracted from the two-phase flow video using pyramid L-K optical flow. This observation indicates the capability of the model to predict the two-phase flow rate, based on optical flow features. The RMSE reaches 1.83 m\({}^{3}\)/h, while the MAE reaches up to 1.37 m\({}^{3}\)/h. Notably, the GBR model emerges as the optimal choice with an \(R^{2}\) of up to 0.90, demonstrating a high level of goodness of fit and the ability to explain 90% of the data in the test set. Furthermore, the GBR model exhibits lower RMSE (1.32 m\({}^{3}\)/h) and MAE (0.92 m\({}^{3}\)/h) than the other models.  
The predicted values from the best performing model are contrasted with the experimental true values, as depicted in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation Indicators** & **RF** & **SVR** & **KNN** & **MLP** & **GBR** \\ \hline RMSE (m\({}^{3}\)/h) & 1.58 & 1.83 & 1.70 & 1.75 & 1.32 \\ MAE (m\({}^{3}\)/h) & 1.23 & 1.24 & 1.37 & 1.17 & 0.92 \\ \(R^{2}\) & 0.85 & 0.80 & 0.83 & 0.82 & 0.90 \\ MAPE (\%) & 24.6 & 35.1 & 26.6 & 32.7 & 17.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Regression model evaluation table.  
In Figure 9a, the horizontal coordinates represent the true values, while the vertical coordinates represent the predicted values. The proximity of the points to the diagonal line indicates a closer alignment between predicted and true values. The scatter represents the flow rate fitted to the test set. This figure illustrates the model prediction results for the test set, where 78.9% of the training set predictions fall within the 20% error line, with some individual values exhibiting larger fitting errors.  
In Figure 9b, the results of the test set fitting generally align with the real values. It is worth noting that certain values are predicted to be high for low flow rates, while for large-flow values, predictions are lower. This discrepancy may be attributed to the significant variance in flow states within the gas-liquid two-phase flow. The substantial variability between small and large-flow rates results in considerable inconsistency within the dataset. It should be noted that machine learning models require a consistent distribution of training data, and such significant differences may lead to large errors.
# VLC Network Design for High Mobility Users in Urban Tunnels
## 5 Evaluation Methodology
### Experimental Setup
The value of Radiation Pattern is shown in Figure 15.  
Finally, the following parameters were established for the handover process: the disconnection time \(t_{dis}\), recovery time \(t_{recovery}\), association latency \(t_{a}\), and the handover latency \(t_{h}\). They determine the latency of the handover and the outage duration when this process fails. A vehicular node will lose synchronization when it has not received a beacon frame after \(t_{recovery}=40\) ms, then it will be disconnected after \(t_{dis}=300\) ms. This tolerance period allows the vehicle to do the handover process when the network has a gap area between two APs. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline \multicolumn{4}{c}{Phy Layer} \\ \hline Optical Clock & 60 MHz & Headlamp power & 15 watts \\ \hline AP Separation & 8-15 m & Tunnel Lumminary power & 50 watts \\ \hline Vehicel speed & 80 km/h & Receiver area & 1 mm\({}^{2}\) \\ \hline Modulation & OOK & Run-length limited & 8b10b \\ \hline PHY mode & II & Forward Error Correction & none \\ \hline Rx FOV & 60\({}^{\circ}\) & Lens & No \\ \hline Radiation Pattern & See Figure 15 & MMC number of ray & 50,000 \\ \hline Reflection coefficient concrete & 0.17 & Bound per ray & 3 \\ \hline Reflection coefficient asphalt & 0.07 & Channel update & 1 ms \\ \hline \multicolumn{4}{c}{MAC Layer} \\ \hline Frame Header & 207 bits & BO, SO & 5,7 \\ \hline Signaling message size & 500 bits & User Throughput & 450 Kbps \\ \hline aBackoffUnit & 200 & Frame payload & 1000 bits \\ \hline aBaseSuperframeDuration & 60 & Number of backoff max & 5 \\ \hline \multicolumn{4}{c}{Handover} \\ \hline THDL & 19 dB & Recovery time & 40 ms \\ \hline THUL max & 75 dB & Disconnection time & 300 ms \\ \hline THUL min & 50 dB & Association latency & 200 ms \\ \hline Time-to-Trigger & 10 ms & Handover latency & 2 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. Figure 15: On the left side, the headlamp radiation pattern, and on the right the tunnels lamp radiation pattern.
## 5 Results and Discussion
### RQ3: What Are the Parameters That Are Optimized to Enhance the Performance of LoRaWAN Networks?
#### 5.4.2 Energy Efficiency and Optimization  
Energy consumption remains a critical issue in LoRaWAN networks, especially because of the typical deployment of battery-operated devices over extensive areas. Studies [38; 50] highlighted the gap in creating more efficient energy management strategies. The study by [52] discussed gaps in LoRaWAN performance and EE optimization. The author suggested addressing the issues caused by LoRaWAN's Aloha-like nature, which affects the network's reliability under high-traffic and large-scale deployments. These could involve refining existing models to more accurately predict energy consumption based on a variety of operational conditions or developing new technologies that minimize energy usage without compromising the network's efficiency. The author's [50] work, using Markov Decision Processes for dynamic adaptation, points to the potential of using sophisticated modeling to enhance energy predictions and device lifespan estimations.  
Figure 8: Optimized Parameters in studies.
# A Compute and Wait in PoW (CW-PoW) Consensus Algorithm for Preserving Energy Consumption
## 5 Protocol Demonstration  
The four conditions of a consensus are: accord, integrity, validity and termination. In fact, we see that there is a fifth condition that must be satisfied in every consensus to be perfect. This condition is equality of opportunity (no domination). In the following, We explain how our protocol verifies these five conditions.  
1. [label=0., ref=1]
2. Accord: In the final round, in the event that there is only one winner, all the nodes will agree on him. In the case where there is more than one winner, for each winner, we will calculate the standard deviation of his solutions found during all the rounds and we will choose the minimum of these standard deviations. Mathematically, this value is unique. In case there are two branches of the blockchain (fork problem), this problem is solved in basic PoW by the golden rule, where the nodes will choose the longest chain. Finally, the decided value is the same for all the correct nodes and in all cases.
3. Integrity: Once the \(P_{x}\) process obtains a valid nonce \(Hash\) (\(Block_{i}+Nonce_{1}\)) \(<\)\(Target\), the process broadcasts it over the network with a specific date (timestamp). Of course, this nonce is concerned with a specific block (\(B_{i+1}\)) and Nonce (\(N_{1}\)), i.e., \(P_{x}(B_{i+1}\), \(N_{1}\)). After having obtained a second nonce \(N_{2}\), the process will broadcast it (\(P_{x}(B_{i+1}\), \(N_{2})\)), and we notice here that there are two nonces \(N_{1}\) and \(N_{2}\) for the same block \(B_{i+1}\). In this case, there are several actions we can take, including to choose the smallest nonce. Eventually, each process will participate with at most one value, so we have fulfilled the condition of integrity.
4. Validity: For each solution received, the node will check its validity. The miner at the end is a network node, having a unique public address and resolving the PoW in all rounds.
5. Termination: Nodes express their acceptance of the block by working on creating the next block in the chain, using the hash of the accepted block as the previous hash [6]. PoW resolution is estimated at 10 min, and these 10 min are more than enough for the propagation in the network, and the candidate block will have been added to the blockchain. In the proposed technique, the difficulty is minimized and the number of rounds is increased. To ensure the termination in a finite time, we just need to balance between the two factors difficulty and rounds. So, if the PoW has a termination, the proposed protocol also has a termination.
###### Abstract  
As the number of resident space objects (RSOs) orbiting Earth increases, the risk of collision increases, and mitigating this risk requires the detection, identification, characterization, and tracking of as many RSOs as possible in view at any given time, an area of research referred to as Space Situational Awareness (SSA). In order to develop algorithms for RSO detection and characterization, starfield images containing RSOs are needed. Such images can be obtained from star trackers, which have traditionally been used for attitude determination. Despite their low resolution, star tracker images have the potential to be useful for SSA. Using star trackers in this dual-purpose manner offers the benefit of leveraging existing star tracker technology already in orbit, eliminating the need for new and costly equipment to be launched into space. In August 2022, we launched a CubeSat-class payload, Resident Space Object Near-space Astrometric Research (RSONAR), on a stratospheric balloon. The primary objective of the payload was to demonstrate a dual-purpose star tracker for imaging and analyzing RSOs from a space-like environment, aiding in the field of SSA. Building on the experience and lessons learned from the 2022 campaign, we developed a next-generation dual-purpose camera in a 4U-inspired CubeSat platform, named RSONAR II. This payload was successfully launched in August 2023. With the RSONAR II payload, we developed a real-time, multi-purpose imaging system with two main cameras of varying cost that can adjust imaging parameters in real-time to evaluate the effectiveness of each configuration for RSO imaging. We also performed onboard RSO detection and attitude determination to verify the performance of our algorithms. Additionally, we implemented a downlink capability to verify payload performance during flight. To add a wider variety of images for testing our algorithms, we altered the resolution of one of the cameras throughout the mission. In this paper, we demonstrate a dual-purpose star tracker system for future SSA missions and compare two different sensor options for RSO imaging.  
Space Situational Awareness (SSA); resident space objects (RSOs); commercial off-the-shelf (COTS); stratospheric balloon; star tracker  
23 November 202333 January 20241 February 202 and further develop capabilities to identify both foreground (satellites) and background objects (debris).  
Ground-based observations of RSOs are constrained by weather conditions, atmospheric interference, limited access time, and visibility to RSOs. In contrast, space-based RSO observations, where imaging is conducted by satellites, enable multi-site simultaneous observations and improved imaging conditions. Hence, recognizing the need for space-based observation, the United States Space Force is already planning a satellite constellation, named \"Space-Based Space Surveillance\" (SBSS), to assist ground infrastructure [4].
## 3 Multi-Scope State Area (MSSA)
### State Programming  
The ability of a switch to expose packet processing logic and allow the control plane to systematically, quickly, and completely reconfigure it is referred to as programmability. Unlike traditional fixed-function network devices, which allow only a limited number of forwarding strategies to be changed (for example, adding static IP routes or changing ACLs), the programmable switch offers a forwarding table that matches any header field and atomic actions for packet processing [43]. In the data plane, state programming is based on such ability. Moreover, the programmable switch can save the state, update the state, and change the packet processing decision based on the local state [23].  
MSSA does not require the addition of new types of tables or actions, instead relying on the programmable switch's existing functions (match-action tables, arithmetic and logic operations, comparison jumps instructions, etc. [36]) to support state programming. We use the method proposed in the article [45] for supporting multiple types of data processing on the data plane, using {type, offset, length} to represent packet fields and different types of states. The data type is indicated by the type. We used five data types in this paper: packet, packet state, flow state, table state, and global state; offset indicates the offset of the data relative to the starting position. For example, the offset of the packet field is relative to the offset of the header's starting position, and the offset of the state is relative to the offset of the state area in which it is located. Moreover, length indicates the length of the data. By reusing the match-action table and instructions in the switch, state storage, update, and state-based packet processing can be realized.
## 1 Introduction
We experimentally demonstrate that DSP-free LDPC-coded SS-based 4D modulation scheme significantly outperforms the corresponding LDPC-coded 16-QAM counterpart in both back-to-back configuration and after 40 km transmission. This invited paper is organized as follows. In Section 2 we describe the proposed SS-based LDPC-coded 4D modulation scheme. Experimental setup and experimental results are provided in Section 3. The last section summarizes the paper.
## 2 Methods  
### Airborne TEM System and Waveform of Current Source  
The airborne TEM system with a central-loop configuration is shown in Figure 1. A current source with a certain waveform is loaded in the transmitter loop and generates the primary field. When the current pulse is cut off, the eddy current in the anomaly produces a secondary field, which can cause an induced voltage response in the receiver loop [36; 37; 38]. In contrast to land applications, both the primary and secondary fields penetrate the sea layer. As seawater has a higher conductivity than soil and rock, it exhibits strong absorption in the EM field. According to the masking effect, the lower the frequency is, the deeper the EM wave penetrates. Therefore, the current waveform should have more power in the low-frequency range.  
Common waveforms in TEM systems include square, trapezoidal, half-sine, and triangular waves, all of which have been studied deeply and applied widely in land TEM systems [39]. As stated above, the airborne TEM system for marine investigations is heavily influenced by the masking effect; thus, a lower frequency with more power is necessary for deep detection. According to the frequency spectra of these waves, it can be observed that the trapezoidal and square waves have similar distributions in lower frequencies, and they have more power than the others. However, it is difficult to yield an absolute square wave in practical systems [31; 33; 38]. Therefore, the trapezoidal wave was selected as the source waveform in our investigation. The trapezoidal wave can be expressed in the Laplace domain as  
\[f_{\rm s}=\frac{1}{t_{d}s^{2}}-\frac{1}{t_{d}s^{2}}e^{-st_{d}}-\frac{1}{t_{d}s ^{2}}e^{-s(t_{d}+t_{s})}+\frac{1}{t_{d}s^{2}}e^{-s(2t_{d}+t_{s})}, \tag{1}\]where \(t_{s}\) is the duration time of the stationary period; and \(t_{d}\) is the falling edge, which is equal to the rising edge.
# Compliant Detachment of Wall-Climbing Robot Unaffected by Adhesion State
## 4 Experiments and Results
### Detachment Force Trace during Climbing  
The detachment force trace can be used to study detachment behavior based on the profiles of the tangential force and the normal force during detachment. Besides, the magnitude of the peeling angle \(\theta\) can be analyzed, and the variation trend of the adhesive force during peeling can be obtained.  
A platform equipped with force sensors, shown in Figure 5a, was used to collect the detachment force trace. Each acrylic plate is a three-dimensional force sensor (precision: \(+/-0.05\) N, frequency: 200 Hz). Each limb was attached to its own force sensors to prevent contamination by other limbs. The slope angle of the test platform was set to 80\({}^{\circ}\). The robot climbed the platform under TTC and OIAC, respectively, and the tangential force and normal force during detachment were recorded. The measured single--limb detachment force trace is shown in Figure 6.  
For both TTC and OIAC, in the peeling acceleration stage (\(S(b)\)-\(S(t)\)), the peeling speed was low; thus, the peel strength was small (see Equation (1)). The peeling angle and speed in this phase increased gradually. In peeling stage (\(S(t)\)-\(S(e)\)), the peeling speed was at a constant value and the peeling angle increased until complete adhesion failure. The adhesion force then rapidly returned to zero.  
The force trace values under the two control methods are quite different, especially the normal force on \(S(e)\), which has the greatest influence on robot stability. Based on a statistical analysis of the results of many experiments, under trajectory tracking control, the mean value of normal force on \(S(e)\) was \(-0.76\) N and the corresponding peeling angle \(\theta\) was 102.97\({}^{\circ}\); under OIAC, these values were \(-0.09\) N and \(174.29^{\circ}\), respectively. These results indicate that the wall-climbing robot with the proposed method exhibits compliant and stable detachment behavior.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{L}\) & \(\mathbf{H}\) & \(\mathbf{L^{\prime}}\) & \(a_{mid}\) & \(\mathbf{k_{mid}}\) \\ \hline
70 mm & 60 mm & 10 mm & 0.45 & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters for adhesion foot trajectory.  
Figure 5: (**a**) Force-time data of one motion period measured by force platform (\(F_{T}\) represents tangential force, \(F_{N}\) represents normal force). (**b**) IBSS–8 with trajectory tracking control (TTC) and online impedance adaptive control (OIAC), respectively, climbed the platform and the attitude angle was recorded.
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 2 Materials and Methods
### Methodological Framework
#### 2.2.1 Principle of the FP-Growth Algorithm
An illustration of this construction process is demonstrated in Figure 4.  
Pattern Mining: As noted earlier, once the FP-Tree is built, the algorithm uses a geometric approach to retrieve patterns. Starting from the least frequent behavior, it proceeds in reverse order to determine which combinations of behaviors are common. For every behavior, a conditional pattern base is extracted, and an FP-Tree is built. The tree is recursively mined for all frequent patterns until no more significant patterns are found. This makes it easier to determine which combinations of driving behaviors are most closely associated with different types of traffic conflicts on mountain roads, thus enabling safety measures to be put in place.
## 3 Results
### HE Classification Based on CNN + SVM Classification Model  
Table 4 lists the AUC (95% CI) of the CNN + SVM classification model versus classification based on automated segmentation volume alone. Similar to the CNN model, the AUC of the CNN + SVM models predicting \(\geq\)3, \(\geq\)9, and \(\geq\)12.5 mL HE in the ATACH-2 cohort and \(\geq\)3 mL HE in the Charite dataset were higher than HE classification using the automated segmentation volumes alone.  
For CNN + SVM models, we also determined the thresholds for 100%, 95%, and 90% sensitivity and specificity in predicting \(\geq\)3, \(\geq\)6, \(\geq\)9, and \(\geq\)12.5 mL HE among the internal test cohort (ATACH-2) and evaluated their performance in the external validation cohort (Charite) (Supplemental Figures S3-S7). The CNN + SVM model reduced the false-negative misclassification rate in Charite external validation (Table 5).  
Although 100% sensitivity and specificity thresholds for the CNN + SVM models maintained their performance in the Charite external validation cohort, they could exclude fewer than 10% of subjects from expert review of automated segmentation (Supplemental Figures S4-S7). On the other hand, 95% and 90% sensitivity thresholds maintained their performance in the external validation cohort with a 1 to 3% false-negative rate in HE classification. For example, applying the 90% sensitivity threshold can exclude 35-90% of test-negative predictions from expert review of automated segmentations, allowing for a 0.7-2.8% false-negative rate. Similar to the CNN model, high-specificity thresholds for the CNN + SVM models were less effective in minimizing false-positive rates than high-sensitivity thresholds were for reducing false-negative rates.
## 1 Introduction
The current research on operating HSR track slab deformation identification is summarized as follows:  
* Deformation detection methods for operating HSRs can rarely meet the following requirements simultaneously: the method should not interrupt the normal operation of trains or directly contact track slabs and should obtain track deformation information in the track occupancy condition and in real-time;
* HSR field monitoring signals contain massive noise and redundant information, and the commonly used EMD methods cannot effectively obtain vibration signal characteristics because of modal confusion, negative frequency, and endpoint effect problem;
* The train grouping, operating speed, load of high-speed trains, and decay process of wheel-track vibration signals have considerable effect on the vibration signals, resulting in complex track-side vibration signals and thus numerous features or attributes of data. This paper mainly addresses the above mentioned research gaps in track slab deformation identification, and the methodologies proposed in this paper are as follows:  
* The track-side real-time monitoring method based on fiber optic sensing technology is capable of obtaining track-side vibration signals in real time without interrupting normal train operation and in track occupancy conditions, compensating for the shortcomings of existing track slab detection methods;
* The LMD method for feature extraction of complex track-side vibration signal overcomes the problems of modal confusion, negative frequency and endpoint effect problem caused by EMD method commonly used in track structure feature extraction;
* The advantages of the random-forest model, such as fewer parameters, high classification accuracy and robustness to noise, enable simple identification of complex track-side vibration signals containing a great deal of attributes. This paper proposes a novel strategy that combines advanced sensing technique based on fiber optic sensing and an intelligent recognition model based on random forest for identifying track slab deformation by using on-site track-side vibration acceleration data. First, an on-site real-time monitoring system for track-side vibration monitoring to facilitate track slab deformation was developed (Section 2). Second, an automatic preprocessing system for data interception, denoising and fixed window segmentation on the raw data was established (Section 3). Then, the feature extraction of time-domain as well as time-frequency-domain of the selected characteristic product functions (PFs) after LMD was proposed (Section 4). Next, the architecture of random-forest model was developed (Section 5). Finally, the model parameters were then optimized and the performance of random-forest model and the comparison decision-tree model were evaluated (Section 6). The study mainly contributes to building an intelligent method that can quickly and accurately detect the deformation of track slabs. The overview of the proposed identification method is shown in Figure 1.