esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Analysis
### CarSim Model
#### 4.2.1 CCRs  
In the CCRs test condition, the front vehicle remains motionless while the rear vehicle runs at 80 km/h, with a starting distance of 60 m between the two vehicles above. Figure 10a-e depict the responses of the inter-vehicle distance, the trailing vehicle's speed, the relative speed, the deceleration, and the TTC. The results indicate that the vehicle equipped with a seventh-degree polynomial automatic braking system effectively prevents rear-end collisions, with a gap of approximately 2.1 m from the leading vehicle. During braking, the vehicle's deceleration changes smoothly, without the abrupt shifts typical of the TTC control algorithm, ensuring good comfort. However, due to the consideration of the suspension system's characteristics, the vehicle's pitch motion during braking causes fluctuations in the vertical tire loads, which in turn lead to variations in the wheel braking force, resulting in some oscillation in the longitudinal deceleration. Throughout the braking process, the minimum TTC recorded is 0.94 s, occurring at \(t=2.9\) s.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Scenario** & **The Speed of the Front Vehicle (km/h)** & **The Speed of the Following Vehicle (km/h)** \\ \hline CCRs & City and Inter-Urban & 0 & 10–80 \\ CCRm & Inter-Urban & 20 & 30–80 \\ CCRb & Inter-Urban & 50 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CCR test scenario [41].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & **Unit** \\ \hline Sprung mass & \(m\) & 1370 & kg \\ Wheelbase & \(L\) & 2.866 & m \\ Height of sprung mass center of gravity (C.G.) & \(h_{\text{g}}\) & 0.52 & m \\ Roll inertia & \(I_{xx}\) & 671.3 & kg\(\cdot\)m\({}^{2}\) \\ Pitch inertia & \(I_{yy}\) & 1972.8 & kg\(\cdot\)m\({}^{2}\) \\ Yaw inertia & \(I_{zz}\) & 2315.3 & kg\(\cdot\)m\({}^{2}\) \\ Distance from C.G. to front axle & \(L_{1}\) & 1.11 & m \\ Distance from C.G. to rear axle & \(L_{2}\) & 1.756 & m \\ Front suspension stiffness & \(K_{sf}\) & 153,000 & N/m \\ Rear suspension stiffness & \(K_{sr}\) & 82,000 & N/m \\ Tire type & - & 215/55 R17 & - \\ Type of brake system & - & Hydraulic & - \\ Road friction & \(\mu\) & 0.9 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: The following vehicle’s main parameters.  
Figure 9: AEB simulation test scenario.
## 3 Improved YOLOv8n Algorithm
### MPDIoU Loss Function  
The YOLOv8n model uses CIoU [25] in the target detection task as a regression loss function, and, compared to the traditional IoU [26], CIoU calculates the loss by combining the overlap area of the predicted and ground-truth boxes, the distance from the center point, and the aspect ratio. When using this loss function, \(v=0\) in the CIoU results in a loss of validity when the predicted bounding box agrees with the true box aspect ratio. To solve this problem, [PERSON] et al. [27] proposed a bounding box similarity comparison metric based on the minimum point distance--MPDIoU. MPDIoU directly minimizes the upper-left and lower-right point distances between the predicted bounding box and the ground-truth bounding box, which more intuitively reflects the positional relationship between the bounding boxes. The specific calculation formula is as follows:  
\[MPDIoU=1-IoU+\frac{d_{1}^{2}}{h^{2}+w^{2}}+\frac{d_{2}^{2}}{h^{2}+w^{2}} \tag{1}\]  
\[d_{1}^{2}=\left(x_{1}^{p}-x_{1}^{gt}\right)^{2}+\left(y_{1}^{p}-y_{1}^{gt} \right)^{2} \tag{2}\]  
\[d_{2}^{2}=\left(x_{2}^{p}-x_{2}^{gt}\right)^{2}+\left(y_{2}^{p}-y_{2}^{gt} \right)^{2} \tag{3}\]  
where \(d_{1}^{2}\) and \(d_{2}^{2}\) are the Euclidean distance between the diagonal of the prediction box and the real box, \(w\) and \(h\) are the width and height of the bounding box, \(\left(x_{1}^{gt},y_{1}^{gt}\right)\) and \(\left(x_{2}^{gt},y_{2}^{gt}\right)\) are the upper-left and lower-right coordinates of the real box, and \(\left(x_{1}^{p},y_{1}^{p}\right)\) and \(\left(x_{2}^{p},y_{2}^{p}\right)\) are the upper-left and lower-right coordinates of the prediction box, respectively.  
MPDIoU comprehensively considers both the position and size offsets between the bounding boxes. It simplifies the similarity comparison between the predicted box and the ground-truth box in the bounding box regression process, and it effectively converges in cases where the predicted and ground-truth boxes overlap or do not overlap. Compared with CIoU, MPDIoU not only measures the similarity between the predicted and real frames more accurately but also effectively prevents detection frame distortion, especially when the target is partially or completely occluded. The finer frame matching mechanism improves the accuracy in occlusion situations, reducing the detection frame distortion caused by fruit overlap or occlusion and lowering the risk of missed detection. Therefore, the use of MPDIoU in this study can effectively solve the detection frame distortion caused by fruit overlap and the issue of missed target detection.
## 4 Results and Discussion
### Boundary Conditions and Simulation Settings  
#### 4.2.1 Pressure Drop in Horizontal Pipe  
Figure 3 presents a comparison between the predicted pressure drop in horizontal pipe by using the three frictional stress models and the corresponding experimental data for  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\mathbf{\alpha_{s,min}}\) & \(\mathbf{\alpha_{s,max}}\) & \(\mathbf{\epsilon_{ss}}\) & \(\mathbf{\phi_{i}}\) & \(\mathbf{\epsilon_{sw}}\) & \(\mu_{w}\) & \(\mathbf{\beta_{\theta}}\) & \(\mathbf{a}\) \\ \hline
0.4 & 0.63 & 0.7 & 32.0\({}^{\circ}\) & 0.5 & 0.5 & 0.3 & \(1.8\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mathematical model parameter setting.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mesh Specifications**} & \multirow{2}{*}{**Inlet Grid Number**} & \multirow{2}{*}{\begin{tabular}{c} **Axial Grid** \\ **Size (mm)** \\ \end{tabular} } & \multirow{2}{*}{**Total Grid Number**} & \multirow{2}{*}{\
\begin{tabular}{c} **Simulated** \\ **Pressure Drop (kPa)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Experimental** \\ **Pressure Drop (kPa)** \\ \end{tabular} } \\ \hline Mesh A & 180 & 2 & 216,000 & 3.77 & \\ Mesh B & 288 & 1.5 & 460,800 & 3.91 & \\ Mesh C & 420 & 1.25 & 806,400 & 4.06 & \\ Mesh D & 576 & 1 & 1,382,400 & 4.09 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Predicted pressure drop in horizontal pipe with different mesh specifications.  
Figure 2: Grid division of horizontal pipe: (**a**) Inlet cross-section grids; (**b**) Pipe wall surface grids.  
different supplementary gases. By comparing the simulation results with the experimental data in the figure, it can be observed that the predictions of the pressure drop in horizontal pipe by frictional stress models are highly accurate, with a relative error ranging from \(-4.91\%\) to \(+7.60\%\). Additionally, as the supplementary gas increases, both the simulation results and the experimental data exhibit the same trend of the pressure drop in horizontal pipe initially decreasing and then increasing. Hence, it can be concluded that the three frictional stress models have provided accurate predictions of both the pressure drop in horizontal pipe and its variation with the increase of supplementary gas.
# Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics
## 2 Literature Review
### Association Rule  
The association rule, an important data mining technique is widely used to extract patterns of interest [45]. It is mainly used to determine relationships between features that occur synchronously in databases and is expressed as X ==> Y. The left part of the rule is the antecedent, and the right is the consequent. Sets X and Y are disjointed, and the same item cannot be found in both the antecedent and consequent. A rule is judged on the basis of its support and confidence levels [46]. Association rules surpass minimum support (Min sup) and minimum confidence (Min conf) thresholds defined by a user [47]. Support determines the frequency of the transaction that satisfies X and Y within the set of all transactions. If a rule has low support, it might be happening simply by chance. Conversely, confidence is the conditional probability that Y will occur, given that X has occurred. Thus, the higher the confidence, the more likely it is for Y to be present in transactions that contain X. [PERSON] et al. [48]proposed an association rule to identify associations between a customer profile and the product items purchased, to establish a method of mining changes in customer behavior. [PERSON] et al. [49] applied an association rule to extract frequent combinations of stores to characterize shoppers' behaviors. [PERSON] and [PERSON] [24] created an association rule to identify patterns of tourism activities in Thai orchards and to suggest related activities.  
The Apriori algorithm provides a basic association rule, reading data once for every iteration [50], while [PERSON] et al. [51] proposed an Frequent Pattern Growth (FP-Growth) algorithm to increase the efficiency of the data mining process by scanning the file twice. Their approach is efficiently search frequent itemsets with minimum accesses to the original database. Moreover, it reduces the problem of excessively large amount of candidate itemsets. Many studies have compared the two [52; 53; 54] and specific adaptive methods have been proposed [55; 56; 57] to improve efficiency. This study uses FP-Growth as a tool for identifying product recommendations.
## 5 Results and Discussion
### Packets Loss Ratio (PLR)  
The MRLAM routing scheme maintains a PLR less than 30% for all scenarios of node speed which is a significant performance improvement for efficient data transmitted in MANETs environment. These results attribute the effectiveness of the proposed scheme to selection of optimal route synchronized with Q-learning process for multiple parameters during the route establishment process. The Q-learning algorithm provides higher rewards to the node which has better link quality with a neighbor node, which reduces the chances of frequent link failure and increases the successful delivery probability of packets. The MRLAM scheme exploits EAX that decreases the number of intermediate nodes in the selection of optimal route, the impact of which reduces the number of dropped packets during data transmission. In addition, EAX decreases the number of trials for data retransmission and reduces link failure probability resulting from the dynamic nature of networks, thereby keeping a lower PLR than other multipath schemes. In establishing a reliable route, the MRLAM scheme avoids the nodes which change their positions frequently and quickly exhaust their battery power. For high-speed node scenarios, it can be seen from Figure 6 that MRLAM scheme achieves better performance compared to both routing schemes, and the improvement percentages are approximately 30.76% and 24.59% compared to MP-OLSR and MP-OLSRv2, respectively.  
Figure 5: End-to-end delay with different speed of nodes.
# Airborne GNSS Reflectometry for Water Body Detection
## 4 Segmentation of the GNSS Signal
### Change Localization  
In this work, the dynamic of the change is not known. We propose an off-line Maximum Likelihood Localization Estimate (MLLE) for change point localization. We show in Figure 7 the architecture of the proposed change point estimate.  
According to the signal model of Figure 4 and assuming that the noise is additive, white-centered, and Gaussian, we derive a maximum likelihood estimate of \(t\), the starting instant of the transition and \(\Delta t\), the duration of the transition. The estimates are processed with the GNSS signal amplitudes observations in the working window \((\hat{x}_{\hat{n}^{e}_{l,i-1}},\dots,\hat{x}_{\hat{n}^{e}_{l,i+1}})\). The GNSS amplitude observations \(\hat{x}_{k}\) are obtained from the in-phase observations \(y_{k}\)[44]. \(\hat{n}^{e}_{l,i}\) is the localization of the \(ith\) change for satellite \(l\) and \(\hat{t}^{n}_{l,i}\) is the \(ith\) detected change instant provided by the CUSUM detector for satellite \(l\). In the rest of the section, we will note \(\hat{n}^{e}_{l,i}\) as \(n^{e}_{i}\) and \(\hat{t}^{n}_{l,i}\) as \(t^{a}_{i}\) to simplify notations. \(N\) is the number of samples in the working window defined between \(n^{e}_{i-1}\), the previous localized change by MLLE and \(\hat{t}^{n}_{i+1}\), the next detected change by CUSUM.  
In practice, the CUSUM detector detects a change after its actual position, as shown in Figure 8a. In this case, the working window is not optimal, but nearly optimal with a difference of very small number of samples at the end of the window. This number of samples represents the difference between the detected \(t^{n}_{i+1}\) and localized \(n^{e}_{i+1}\) change point position at the instant \(i+1\).  
To estimate the localized change instant \(n^{e}_{i}\), we define the likelihood function as:  
\[f(\hat{X}_{
## 2 Principle
### RPO-Based Bit-Wise Optimization  
Since the training of the autoencoder is at the symbol-wise level, the shaping scheme generated by the autoencoder will no longer conform to Gray coding. Although it can guarantee the lowest SER in transmission, it cannot guarantee the lowest BER. Thus, the pairwise optimization algorithm [PERSON] (2014) is applied to optimize the shaping scheme generated by the autoencoder at the bit-wise level. We use a two-dimensional I/Q constellation with M equally likely symbols \(\{s_{1},\ldots,s_{M}\}\) to encode \(\log_{2}M\) bits, whose bit mapping is represented by \(\beta_{i},i\in(i,\ldots,M)\). The Hamming distance \(h\big{(}\beta_{i},\beta_{j}\big{)}\) is defined as the number of bits that differ between \(s_{i}\) and \(s_{j}\). The upper bound of the analytical expression of the symbol error rate (SER) can be expressed as \(Q(\|s_{i},s_{i}\|/\sqrt{4N_{0}/8})\), where \(Q(x)\) represents the Gaussian \(Q\) equation, \(\|\cdot\|\) represents the norm operation of the vector, and \(N_{0}\) is the variance of the two-dimensional Gaussian noise. When converting the symbol error rate (SER) into the bit error rate (BER), the result obtained by using the Gaussian \(Q\) equation is the weight of the Hamming distance between symbols. Ultimately, the objective function of the revised pairwise optimization (RPO) algorithm for two-dimensional constellation can be expressed as  
\[Minimize:\frac{1}{M}\!\!\sum_{i=1}^{M}\!\!\sum_{j=1,j\
eq i}^{M}h(\beta_{i}, \beta_{j})\cdot Q(\frac{\|s_{i}-s_{j}\|}{\sqrt{4N_{0}/N}}) \tag{4}\]  
Driven by the objective function, by fixing all constellation points and continuously adjusting the bit mapping between any two symbols, we can achieve the bit-wise optimization of the constellation generated by the autoencoder and finally obtain the jointly optimized shaping scheme.  
To evaluate the performance of the shaping scheme and demonstrate the effectiveness of the autoencoder-based geometric shaping model (AEGSM) framework, taking the eight-order symbols as an example, we deploy the autoencoder-based 8 QAM (AE-8 QAM) scheme generated by the AEGSM in the simulation system and the real system, respectively, and compare it with the Norm-8 QAM scheme and the PO-8 QAM scheme, which is generated entirely by the PO algorithm in detail.
## 3 VRIS Image Steganography Model
### Hybrid Loss Function  
#### 3.4.1 Mean Square Error Loss  
Mean Squared Error (MSE) is a commonly used regression loss function to measure the difference between predicted and true values. In the image steganography task, our goal is to achieve that the encrypted image formed after embedding a secret image into a cover image is as similar as possible to the cover image in terms of visual effect, so as to avoid arousing suspicion. In order to quantify this difference, we use the mean square error as a loss function to compute the reconstruction loss between the secret image and the cover images.  
\[MSE=\frac{1}{T}\sum_{i=1}^{T}(y_{i}-\hat{y_{i}})^{2} \tag{1}\]  
where \(y_{i}\) represents the true value of the ith sample, \(\hat{y_{i}}\) represents the predicted value of the predicted value of the ith sample, and T is the number of samples, because the MSE is obtained by calculating the mean of the squares of the difference between the predicted value and the true value; it is so-called because it is more sensitive to large errors.
# Spatial Small Target Detection Method Based on Multi-Scale Feature Fusion Pyramid
## 2 Related Work
### Attention Mechanism  
The attention mechanism improves the performance of the model to some extent by assigning different attention weights to different parts of the model inputs and enhancing the learning of key information according to the attention weight distribution model. The existing attention mechanisms mainly include the channel attention mechanism, spatial attention mechanism, hybrid attention mechanism, and self-attention mechanism. The channel attention mechanism improves the expressive ability of the network by constructing the interdependence between channels, such as SENet [26], ECANet [27], and so on. The spatial attention mechanism mainly focuses on different regions of the image and combines the regional information to obtain the image information at different locations such as RAM, STN, etc. The hybrid attention mechanism mixes channel information and spatial information and extracts information features such as CBAM [28], SANet [29], etc. Self-attention mechanisms compute attention on the input sequence itself by mimicking biological visual saliency detection and selective attention to achieve global attention to the input sequence, such as single-head self-attention, multi-head self-attention, and sliding window multi-head self-attention.
## 3 Results and Discussion  
To validate the performance of the proposed method in terms of dynamic deformation, ionospheric disturbance, and the inherent relation between earthquake and ionospheric disturbance, GPS observations from two earthquakes (the Mw7.0 Lushan earthquake on 20 April 2013 and the Mw9.0 Tohoku-Oki earthquake on 11 March 2011) are processed.
###### Abstract  
Underwater remotely operated vehicles (ROVs) are linked to the surface through a tether that is usually controlled by a human operator. The length of the tether being deployed in the water in real time is a critical determinant of the success of the mission, and the problems of entanglement and cable stretching must be anticipated to the greatest possible extent. This paper describes a low-cost and setup-friendly solution for managing the length of a neutrally buoyant tether using a balanced buoy-ballast system implemented on the part of the tether proximal to the ROV. Embedded in the system is a curvature sensor that helps to control the cable feeder on the surface. This represents a useful solution for smoothing tether movements and to damp external disturbances. The results of experiments carried out in water tanks demonstrate the benefits of this solution in allowing the cable to maintain a semi-stretched shape while ensuring that the ROV avoids being pulled by the cable. Possible applications for a surface vehicle linked to an ROV through a tether equipped with this compliant buoy-ballast system include exploration or cartography missions in shallow waters.  
underwater robotics; tethered robot; buoy-ballast system; length control Article
# Reproducibility and Data Storage for Active Learning-Aided Systematic Reviews
## 3 Data in AL-Aided Screening
### Data in the Set-Up Phase  
The set-up phase prepares the data that is necessary to train the first iteration of the machine learning model on the input data (i.e., the result of the pre-processing phase). The user chooses the software (with a specific version). Also, a stopping rule for when to stop labeling records should be decided upon. This can be far from trivial, though, as discussed elsewhere in great detail, e.g., [38].  
Moreover, the four components of the active learning model need to be selected: (1) The type of machine learning model with its hyperparameters. Random seeds should be used and stored to make any probabilistic process in the model reproducible. (2) The dataset containing text is transformed into a format the model can understand using a feature extraction method, and this feature matrix should be stored. Suppose a user switches to a different active learning model during the screening phase consisting of a different feature extraction technique. In that case, multiple matrices need to be stored. (3) Also, a balancing strategy needs to be selected to deal with the sparseness of the relevant records. For an example of a balancing strategy, see Appendix 3 of [PERSON], [PERSON] [39]. (4) Finally, a query strategy needs to be selected, determining which record will be selected by the model to be shown to the annotator.  
Similarly to the stopping rule for labeling, a stopping rule for model training can be selected. While it might seem logical to continue training new models until the end of the screening process, this probably is not necessary. After enough labels have been provided, a new label will not dramatically change the model. Therefore, to continue training new models might be a waste of energy. However, this model training stopping rule is less important than the labeling stopping rule, because the labeled data are the primary output.  
Furthermore, a training set for the model needs to be created by labeling a set of records as relevant and irrelevant based on prior knowledge (e.g., for the relevant set), or screened randomly (e.g., for the irrelevant set). The minimal required prior knowledge for the training data is at least one label '0' and one label '1', but any amount of prior knowledge for the training data can theoretically be used. It depends on the software implementation and what the options are.  
The data produced in the set-up phase will only have to be stored once at the start of the review. Moreover, except for the feature matrix, the data size does not depend on the size of the input dataset. Therefore, the data generated during the set-up phase will not become too large relative to the input dataset.