esa-sceva/satcom-chunk-collection huggingface dataset:
# Ride Comfort Prediction on Urban Road Using Discrete
###### Abstract  
The prediction of ride comfort holds significant potential for enhancing the driving experience of both human drivers and autonomous vehicles, as it is closely correlated with pavement roughness. However, in urban road scenarios, the presence of shorter road segments and local irregularities introduces added complexity to ride comfort prediction. To better capture and characterize the irregularities and short road sections' unevenness, we adopt the discrete roughness index (DRI) instead of the commonly used international roughness index (IRI) for assessing road profile unevenness, which is more suitable for urban roads. Ride comfort prediction is developed through numerical simulations using an eight-degree-of-freedom full-car model. The maximum transient vibration value (MTVV) is adopted to assess ride comfort. Through comparing the correlations between the MTVV and pavement roughness indices, it is indicated that the fitting degree of MTVV-DRI outperforms that of MTVV-IRI on short sections. Then, a set of speed-related DRI thresholds to estimate ride comfort distribution on a given road section is proposed, with considerations of vehicle speed, time period, and wheel paths. A hyperbolic-tangent-based speed control strategy is also proposed to avoid abrupt speed and acceleration changes during deceleration. This prediction method can assist drivers or autonomous vehicles in generating driving control strategies and maintaining a high level of ride comfort.  
pavement roughness; ride comfort; discrete roughness index; urban road; maximum transient vibration value +
Footnote †: journal: Applied  
Article  
Ride Comfort Prediction on Urban Road Using  
Pavement Roughness Index
## 3 Methods
### Defenses for Comparison  
For comparison to existing anomaly detection techniques, we implemented a detector solely using message timing (defense-static-timing-only), a variant additionally using a simple frequency analysis step, an LSTM-based detector CANet [33] and a custom CNN binary classifier with an identical network architecture to our proposed RL approach. The frequency analysis in the message timing variant allows a limited number of repetitions (up to 2) of the same message type-value pair occurring in the correct time window since the last allowed message (defense-static-timing-frequency). To choose appropriate timing windows, we analyzed the message time deltas for each message frequency (e.g., 20 ms, 50 ms) occurring within the collected training data (see Figure 4). The chosen timing windows for the static timing-based defenses are as follows: \(\pm\)5% for 0x01A, 0x02F, and all 100 ms messages; \(\pm\)10% for 0x058, 0x06D, 0x083, 0x098, and all 50 ms messages. Note that the static timing defense requires exact knowledge of the expected timing for each message type; this must be hard-coded for each individual system and cannot detect masquerading attacks whatsoever.  
Figure 4: Violin plots of the time deltas for all Chassis ECU emanated messages. Message types are in hexadecimal.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Selection Option** & **Option Probability** \\ \hline No injections & 5\% \\ \hline Single message type & 22\% \\ injection & \\ \hline Add message type to prior message injection list & 73\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Message injection selection probabilities during training.  
CANet [33] is an LSTM- and autoencoder-based anomaly detection algorithm for CAN bus data. The model is trained on a dataset with no injections, and messages with reconstruction errors exceeding a threshold are deemed anomalous. Each message is fed to a separate LSTM encoder corresponding to its message type. The latent output from each LSTM is then concatenated for a temporal representation vector of the message history. This vector is then fed to a shared three-layer MLP network with exponential linear unit activation functions, which outputs the full message space. The mean squared error reconstruction loss is weighted by the message type frequency in the dataset to counteract bias. During evaluation with injections, a threshold loss value of 0.0001 was chosen for anomaly detection.  
To ablate our use of RL in place of the usual supervised learning techniques for this problem, we implemented a binary classification model with identical CNN architecture and relevant hyperparameters. Specifically, this network consists of three 2D convolution layers with rectified linear unit activation functions, followed by a single fully connected layer with one output node and sigmoid activation to map into (0, 1) output logits. The CNN classifier is trained with binary cross-entropy loss between the predicted and true message labels. By changing as little as possible from our approach to this classifier, we hope to highlight the benefits of RL.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using Off-Policy
#### 3.2.10 Navigation II  
In [78], the authors introduce a unique framework for robot navigation in unknown environments, integrating DRL-based perception with model-based control to create a reliable and effective navigation system. This framework is called the \"_Dynamic Window Approach Long Short-Term Memory (LSTM) module and is integrated within the soft actor-critic (SAC) network (DWA-LSAC)_\" [78]. It allows the robot to learn from its experience, gradually improving its navigation capabilities over time.  
The perception module analyzes the robot's status, LiDAR data, and target location to produce a set of optimized local path points to avoid collisions. These path points are inputted into a planner based on the dynamic window approach (DWA), which creates smooth and feasible paths for safe and efficient navigation. In this work, an ROS facilitated the communication between the different components. The authors used simulation and real-world experiments to show the performance of their DWA-LSAC framework. The results show that DWA-LSAC achieved a higher success rate, shorter navigation time, and smoother trajectories compared to purely learning-based methods such as PPO, DDPG, and SAC. The inclusion of the LSTM module enables the robot to better perceive its surroundings by considering multiple continuous-time environmental information. For more information on the hardware and software used, please see Table 4.  
The above-mentioned work shows the adaptability of their framework by training it in a dynamic environment with mobile obstacles and fusibility. The integration of the DWA module for path planning and the use of LSTM within the SAC network contributed significantly to these improvements. The authors' work offers a notable advancement in robot navigation, specifically tackling the obstacles presented by unstructured environments.
# Faster MDNet for Visual Object Tracking
## 5 Discussion  
In this paper, we show that Faster MDNet can maintain a better balance among tracking accuracy, tracking speed, and model complexity. The results may be attributed to the following facts. The feature extraction part of Faster MDNet is smaller than other current trackers, containing only three convolutional layers. The channel attention module uses a negligible number of parameters to improve tracking accuracy and maintain tracking speed by capturing inter-channel information efficiently. The number of parameters in the adaptive spatial pyramid pooling layer is much smaller than that of the fully connected layers of MDNet. Therefore, using the adaptive spatial pyramid pooling layer can dramatically accelerate the tracking speed.  
However, some limitations of this study still exist. Due to a huge amount of candidate samples requiring feature extraction, Faster MDNet could only run at a speed of 36 FPS, barely reaching real-time tracking. Moreover, to achieve a better balance, our tracker loses tracking accuracy to some extent. Finally, the results cannot prove that Faster MDNet can solve the occlusion problem as effectively as our network.  
In summary, we will focus on accelerating our tracker and improving the tracking accuracy in complicated scenes with occlusions in our future work. We will utilize the idea of dynamic adaptive distribution from domain adaptation techniques. Specifically, we will dynamically reduce the distance between the distribution of the features when the target is occluded and when it is not occluded. Subsequently, the model will be more robust to track the object.  
Figure 5: Visualization comparisons of the proposed method with state-of-the-art trackers on five video sequences: Basketball, Bird2, CarScale, BlurBody, and Dancer. Our tracker can robustly track the target in each video sequence.
# Practical Performance Analysis of Interference in DSS System
## 5 Field Test and Performance Analysis
### Test Results for Buffer Setting  
As can be seen through Section 5.2, only LTE has a more serious downlink impact on NR and therefore requires interference mitigation solutions. In Section 4.2, we propose the solution of buffer setting to solve the interference problem. In this section, we verify the feasibility by field tests.  
We verified the LTE interference to the NR of DSS test cell when one and two circles of base stations were turned off, respectively, and the test results are shown in Figures 18 and 19.  
Figure 17: LTE terminal to NR uplink interference test results.  
As can be seen in Figures 18 and 19, whether it is no load or 50% load, closing one circle of interfering cells can effectively improve the NR throughput of the test cell, with an average of about 30%. Closing two circles of interfering cells can greatly improve the throughput of the test cells, by an average of about 60%, and at test point 6, it can be increased by up to 99%. The closer the distance to the interfering cell is, the more obvious the bit rate improvement effect. The test results are consistent with the theoretical analysis results in Section 4.2.  
It can be learned that setting the buffer between DSS cells can effectively reduce downlink interference from LTE cells to neighbor NR cells and improve user experience. However, the impact of setting the buffer on the six test points varies greatly and is not stable. Therefore, we further test and verify the rate-matching scheme.
# Modeling User Acceptance of In-Vehicle Applications for Safer Road Environment
## 5 Data Analysis and Results
### Correlation Analysis  
The Pearson product-moment correlation coefficient \(r\) was calculated between the variables. The r value is interpreted as 0.0-0.1 (negligible), 0.10-0.39 (weak), 0.40-0.69 (moderate), 0.70-0.89 (strong) and 0.90-1.00 (very strong). Based on Table 10, in general, positive relationships exist among the TAM variables. Strong relationships exist between the PU and T and also the SC and SI with r values more than 0.7. In addition, strong  
Figure 3: Factor loadings.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**No. of Indicator**} & \multicolumn{3}{c}{**AVE**} & \multicolumn{3}{c}{**AVE/Indicator**} \\ \hline PU & 5 & 0.7295 & 0.8541 \\ EU & 4 & 0.5473 & 0.7398 \\ AT & 5 & 0.7809 & 0.8837 \\ T & 5 & 0.7778 & 0.8818 \\ SC & 5 & 0.8808 & 0.9388 \\ SI & 5 & 0.5859 & 0.7655 \\ PC & 6 & 0.5537 & 0.7441 \\ BI & 4 & 0.6717 & 0.8195 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Convergent and discriminant validity.  
relationships also exist between the AT and BI as well as the SI and T. However, the EU and BI are the only variables with weak relationships compared to the other variables with moderate relationships.  
Furthermore, the relationship among the personal characteristic (PC) variables was also assessed and illustrated in Figure 4. There is no correlation (negligible) among most of the variables. However, weak relationships exist between a few variables. This shows that although the response of one variable may change the other correlated variables, the relationship is not strong and can be ignored.
# Application of Cinnamon Essential Oil in Active Food Packaging: A Review
## 4 _Cinnamomum_ Genus  
Cinnamon is a prized aromatic spice obtained from the inner bark of several species within the genus _Cinnamomum_. This genus (family Lauraceae) includes 250 species of trees and shrubs distributed throughout Southeast Asia, China, and Australia. Asia is the largest producer globally [21]. In 2022, its production was around 222,523 tons, with China, Vietnam, and Indonesia accounting for 40.62%, 23.92%, and 23.22% of the total production, respectively (Figure 1) [22].  
Species of the genus _Cinnamomum_ are evergreen, reaching heights of between 10 and 15 m in some cases. The bark is widely used as a spice. Its leaves are ovate and 7-18 cm long. The flowers are arranged in panicles and have a greenish color. The fruit is a purple berry containing a single seed [23]. It is found in tropical rainforests, where it grows at different altitudes [24].  
This plant has been exploited as a spice for centuries and is currently widely used by industries for the extraction of EOs for use in cosmetics and food [25]. The worldwide demand for cinnamon has also increased over the last few years due to its potential antioxidant, anti-inflammatory, and antitumor applications [26]. In the food industry, cinnamon is used to increase the nutritional value of food and to improve the sweet sensation of the food matrix. Moreover, bioactive compounds obtained from cinnamon have also been shown to have many crucial functions in increasing the shelf life of food due to their antimicrobial and antioxidant properties [27].  
In the food industry, the most commonly used species are _Cinnamomum zeylanicum_ (previously known as _Cinnamomum verum_), native to Sri Lanka, and _Cinnamomum cassia_ (Chinese cinnamon), although others such as _Cinnamomum burnannii_ (Indonesian cinnamon), _Cinnamomu loureiori_ (Vietnamese cinnamon), and _Cinnamomum tamala_ (Indian cinnamon) also have some economic importance [27].
# Modeling and Analyzing Preemption-Based Service Prioritization in 5G Networks Slicing Framework
## 4 Numerical Analysis
To conclude, the numerical analysis was conducted given simultaneously increased uniform offered loads at the NSIs, while the average service times and service requirements were individual. The given Queueing System (QS) model in Section 3 facilitates the evaluation or estimation of proposed PP-scheme efficiency with respect to some baseline. As one key result, the 2 nd NSI, instantiated to service requests with \"medium\" requirements, significantly gains in performance when using the proposed PP-scheme, compared to the classical RR-scheme. Indeed, the arriving 4K 360-degree VR Panoramic Video requests at the 2 nd NSI use more often the guaranteed network capacities of other neighboring NSIs. The highlighted result is illustrated in the figures in this section.
# Dynamic Response Analysis of Overpass Ramp Based on Grey System Theory Model
## 3 Analysis of Experimental Results
### The Influence of Speed on the Impact Coefficient  
The impact coefficient is the vertical dynamic amplification factor generated when a vehicle passes over a bridge structure. It is a factor that accounts for the increase in vertical dynamic effects on the bridge structure due to moving vehicles, and it is used to calculate the total vertical vehicle load effect on the bridge structure. The impact coefficient can be obtained through an analysis and a calculation of bridge dynamic load testing. In dynamic load testing, a test vehicle is driven at a constant speed over the bridge to excite the bridge structure being tested. Dynamic change signals, such as dynamic deflection and dynamic strain, generated on the bridge structure are detected using elements such as velocity sensors, strain sensors, and electromechanical dial indicators placed on the bridge. Based on the measured dynamic strain and dynamic deflection curves obtained from field testing, the impact coefficient of dynamic loads on the bridge structure can be analyzed and calculated [25]. The calculation of the impact coefficient is shown in Equation (1) [26].  
\[\mu=\frac{f_{dmax}}{f_{jmax}} \tag{1}\]  
where \(f_{dmax}\) is the maximum dynamic deflection value or dynamic strain value at the measurement point under dynamic loading, while \(f_{jmax}\) is the maximum deflection value or strain value at the measurement point under the corresponding static loading. The peak value of the centerline trace of the waveform amplitude was obtained or derived through low-pass filtering.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Speed (km/h)**} & **Maximum Dynamic** & **Maximum Dynamic** & **Vibration** \\  & **Strain (\(\mu\)x)** & **Deflection (mm)** & **Acceleration (mm/s2)** \\ \hline
20 & 6.4 & 0.825 & 3.017 \\
30 & 7.2 & 1.047 & 3.249 \\
40 & 8.5 & 1.126 & 3.288 \\
50 & 9.0 & 1.344 & 3.326 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dynamic response at different speeds.  
The vehicle's speed was varied, with a fixed curvature radius of 270 m and a vehicle weight of 15 tons. Under excitation from vehicle speeds of 20 km/h, 30 km/h, 40 km/h, and 50 km/h, dynamic strain data were collected from the bottom of the monitored cross-section of the bridge structure. Using the formula above, the impact coefficient was calculated. The impact coefficients under excitation from different vehicle speeds are shown in Figure 8.  
An analysis of the test results for the impact coefficient under excitation from different vehicle speeds reveals that the higher the vehicle speed, the larger the impact coefficient. The measured impact coefficient values above met the specifications for the inspection and assessment of a highway bridge carrying capacity, indicating that the bridge deck has a good smoothness and that the impact effect of vehicles on the structure is relatively small.
# Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents
## 4 HybridSumm: A Hybrid Summarization Model for Maritime Judgment Documents
### Nezha-UniLM-CC: An Abstractive Summarization Model
#### 4.2.2. UniLM Generative Model  
UniLM, proposed by Microsoft Research Asia, is a pre-trained Transformer model based on the BERT architecture [69]. By integrating three pre-training paradigms--unidirectional, bidirectional, and Seq2 Seq language modeling--UniLM achieves excellent performance in both natural language understanding and generation tasks. Its core innovation lies in a dynamic attention masking mechanism that enables multi-task compatibility. Unidirectional modeling predicts masked tokens using left-to-right or right-to-left contexts to capture local dependencies within text sequences. Bidirectional modeling leverages both the left and right contexts to predict masked tokens, capturing global semantic features. Seq2 Seq modeling employs an encoder-decoder framework to learn complex mappings between input and output sequences. This multi-paradigm joint pre-training strategy allows UniLM to dynamically generate diverse context-aware representations, laying the foundation for downstream NLG tasks.  
For the maritime judgment document summarization task, we construct a fine-tuned Seq2 Seq architecture based on UniLM. Specifically, the source text and target summary are concatenated into a joint input sequence \(X=w_{1}w_{2}\ldots w_{n}\) (\(w_{i}\) denotes a Chinese character), with dynamic masking applied to the summary portion to simulate the \"detailed-to-concise\" summarization process. The input sequence \(X\) is first converted into character-level embedding vectors via the Nezha model and then fed into an \(L\)-layer Transformer encoder. Each Transformer layer computes contextual weights using a multi-head attention mechanism, as shown in Equation (6). After \(L\) layers of iterative encoding, the model outputs deep semantic representations \(\mathbf{H}^{L}=\left[\mathbf{h}_{1}^{L},\mathbf{h}_{2}^{L},\cdots,\mathbf{h}_ {n}^{L}\right]\), where \(\mathbf{h}_{j}^{L}\) represents the semantic encoding of the \(i\)-th input unit \(w_{i}\). Finally, the decoder generates the summary autoregressively based on \(\mathbf{H}^{L}\).  
\[\mathbf{A}_{l}=\text{softmax}\bigg{(}\frac{\mathbf{Q}\mathbf{K}^{T}}{\sqrt{d _{k}}}+\mathbf{M}\bigg{)}\mathbf{V} \tag{6}\]  
where \(\mathbf{Q}=\mathbf{H}^{l-1}\mathbf{W}_{l}^{Q}\), \(\mathbf{K}=\mathbf{H}^{l-1}\mathbf{W}_{l}^{K}\), and \(\mathbf{V}=\mathbf{H}^{l-1}\mathbf{W}_{l}^{V}\) represent the query, key, and value matrices, respectively. \(\mathbf{H}^{l-1}\) denotes the output of the previous layer, \(\mathbf{W}_{l}^{Q}\), \(\mathbf{W}_{l}^{K}\), and \(\mathbf{W}_{l}^{V}\) are parameter matrices for the \(l\)-th attention head, \(d_{k}\) is the dimension scaling factor, and \(\mathbf{M}\) is the mask matrix controlling attention visibility between token pairs, as defined in Equation (7).  
\[\mathbf{M}_{ij}=\left\{\begin{array}{ll}0,&\text{position $j$ is visible to $i$}\\ -\infty,&\text{position $j$ is masked from $i$}\end{array}\right. \tag{7}\]
# A Method for Identifying the Wear State of Grinding Wheels Based on VMD Denoising and AO-CNN-LSTM
## 2 Grinding Wheel Wear State Recognition Based on VMD Denoising and AO-CNN-LSTM
### VMD Algorithm for Denoising AE Signals
#### 2.2.3 AO-CNN-LSTM Model Design  
In this paper, the parameters of the proposed CNN-LSTM combined recognition model undergo optimization using the AO algorithm. Through iterative updates facilitated by the AO algorithm, the objective is to seek optimal network parameters characterized by high accuracy and stability, thereby achieving effective recognition of the grinding wheel wear state. Consequently, a neural network model named AO-CNN-LSTM is formulated.  
The key principles governing this neural network model are outlined as follows:  
1. Define the parameters of the CNN-LSTM model as the optimization objective, specify the population size, and determine the maximum number of iterations for the AO algorithm. Initiate the iterative calculation process.
2. Assess based on the iterative conditions, and update the positions of the eagles using the corresponding methods. With each increment in the number of iterations, the positions of individual eagles undergo continuous updates until the termination iteration condition is satisfied, at which point the optimal CNN-LSTM model parameters are output.
3. Training the dataset using the optimized AO-CNN-LSTM neural network.
4. During the training process, the weights of the CNN-LSTM are updated iteratively based on the trends in Loss and Accuracy using the principles of gradient descent. This strategy ensures the gradual convergence of the predictive model, prevents overfitting, and yields satisfactory accuracy.
5. After the iterations cease, the performance of AO-CNN-LSTM is assessed using the test dataset to obtain the optimal model.  
Figures 5 and 6 illustrate the flowchart depicting the optimization of neural network parameters using the AO algorithm and the flowchart delineating the AO-CNN-LSTM neural network model, respectively.  
Figure 5: Flowchart of optimizing neural network parameters using AO.
# A Novel Multi-Cell Interference-Aware Cooperative QoS-Based NOMA Group D2D System
## 4 Numerical Results  
This section presents the numerical results in order to validate the accuracy of the analytical outage probability expression derived in Equations (9)-(11) of Section 3 under the proposed system model presented in Figure 1. In particular, the Monte-Carlo method was utilized to obtain simulation results. In addition, Matlab software is used to generate the numerical results as well as to carry out the Monte-Carlo simulations. Further, in all the results, unless otherwise stated, the default power allocation scheme presented in Equation (6) was adopted. In all simulations, the parameters presented in Table 1 were considered, unless otherwise specified.