esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Freeway Merging Framework
### Rule-Based Lane-Changing Strategy
#### 3.1.1 Determination of Target Gap for Lane Changing  
The core problem of lane changing is finding a suitable entrance gap in the mainline's adjacent lanes. When a vehicle changes lanes, the lane-changing may cause a rear-end or lateral collision with a vehicle in the target lane if the lane-changing vehicle's speed is too high/low or the target gap estimation is unreasonable. Therefore, the lane-changing vehicle must meet a certain speed, and the target gap must meet the merging requirements so that the vehicle can safely merge into the adjacent lane.  
The method to determine the target gap for lane changing is as follows. First, we look for adjacent front and rear vehicles in the Inner Lane and then calculate the above-mentioned vehicles' headway. The headway \(H\) of the two adjacent vehicles at time t is calculated as Equation (1), where \(S_{A}(t)\) and \(S_{B}(t)\) are the position coordinates of the front and rear vehicles, respectively, at time \(t\); \(v_{B}\) is the speed of the following vehicle; and \(L\) is the length of the vehicle, as shown in Figure 3.  
\[H=\frac{S_{A}(t)-S_{B}(t)-L}{v_{B}}, \tag{1}\]  
After that, it is necessary to judge whether the headway meets the requirements. A vehicle collision is likely to occur if adjacent vehicles' headway in the Inner Lane is not enough. Therefore, before the vehicle in the Outer Lane changes lanes, it is necessary to determine whether the headway calculated in Equation (1) is less than the minimum safe headway for lane changing. In this study, the minimum safe headway \(H_{s}\) was calculated as  
\[H_{s}=\frac{H_{\text{m}}v_{B}+H_{\text{m}}v_{\text{max}}+L}{v_{B}}, \tag{2}\]  
where \(H_{\text{m}}\) is the minimum safe headway between the front and rear two adjacent vehicles in the same lane and \(v_{\text{max}}\) is the maximum speed.  
The vehicle in the Outer Lane can be selected to change lanes if the headway \(H\) of two adjacent vehicles in the Inner Lane satisfies Equation (3).  
\[H\geq H_{s} \tag{3}\]
## 2 Case Study Tunnel  
The case study tunnel selected for this research is the Xiangyangshan Tunnel located along the Qujing-Kunming Expressway in Yunnan Province, Southwest China. The Xiangyangshan tunnel is situated in the middle of the eastern Yunnan platform fold belt. The geological structure is highly intricate, particularly with a well-developed fault structure. The clearance width of a single tunnel on the standard section is 15.53 m, and the maximum excavation area is 167.6 m\({}^{2}\). It is a three-lane extra-large section highway tunnel. The tunnel is designed as a left and right tunnel with a typical shallow buried section and a double tunnel spacing of 23.07 m. The cross-sectional dimensions of the lining structure and the tunnel's portal site under construction are illustrated in Figure 1. The surrounding rock mass of the tunnel is categorized as grades V\({}_{2}\) to III\({}_{1}\), as assessed in accordance with the Chinese industry standard \"Code for Design of Road Tunnel (JTG D70-2004)\" (2020). The tunnel exhibits numerous fault fracture zones, developed joints, and fissures, leading to a rock mass that is relatively fragmented and unstable.  
Based on the geological survey report, three low-resistance fractures have been identified in the surrounding rock of the tunnel, suspected to be structural fracture zones. The shallow rock in the tunnel area exhibits significant weathering, resulting in poor rock integrity. The deep joint fissures range from underdeveloped to moderately developed, with the surface primarily featuring two sets of joint fissures, which are detrimental to the stability of the surrounding rock of the tunnel.  
The basic seismic intensity of the tunnel site area is rated at VIII on China's intensity scale. However, to account for the critical function of the expressway, a design fortification intensity of IX degree (strong earthquake level) was adopted for seismic design and control measures. The peak ground acceleration value for tunnel analysis was conservatively set at 0.4 g. Three types of input earthquake waves were incorporated into the subsequent physical model tests, namely the El-Centro, Taft, and artificial synthetic waves. The former two consist of seismic wave records from renowned historical earthquakes, whereas the latter are artificial seismic waves generated using probability function techniques. These waves represent different seismic characteristics that the tunnel may encounter. The peak value of original records was also adjusted and used in these physical model tests when input.
## 3 Results and Discussion
### Artificial Neural Networks  
Due to the influence of the number of neurons, the type of functions, and learning algorithms on the output of the models, different networks were designed and evaluated. Table 2 shows the comparison of different structures in determining the \(MR\) of parboiled hulls.  
Figure 7: Effect of air temperature on \(MR\) in different IR powers during parboiled hulls drying.  
Table 2 displays the best selected ANN models with their topology, activation functions, and various algorithms used to anticipate the \(MR\) of parboiled hulls in an IR-HA drying. Among all the topologies depicted in Table 2, the powerful one for estimating the \(MR\) of parboiled hulls with topology 3-8-8-1 and activation function TANSIG-TANSIG-TANSIG belong to the LM algorithm. The error values of \(MSE\), \(MAE\), and \(R^{2}\) of the network were 0.0059, 0.4234, and 0.9991, respectively.  
In a study, [PERSON] et al. [56] reported that a model contained an input layer with four neurons (radiation intensity, slice thickness, distance between slices and infrared lamps), two hidden layers (5 and 7 neurons) and one neuron in the output layer (\(MR\)) with TANSIG transfer function and the LM training algorithm was the best model for predicting kiwi slice in an infrared dryer. [PERSON] and [PERSON] [57] predicted the moisture ratio of the onion slice in the microwave drying with the highest \(R^{2}\) = 0.997 and the lowest MSE = 0.000228 values (Best structure: three inputs including microwave power, samples thickness and drying time, the multi-layer feed-forward network with a topology of 3-18-1, Levenberg-Marquardt training algorithm and transfer function of LOG- PUR). Various studies have been performed on the drying \(MR\) of other crops such as potato [41], apricot [27], and stevia [28] using ANN.
# Multi-Source Satellite Imagery and Machine Learning
## 5 Experiments and Results
### Data from OR Spectrometric Parameters (DORSPs)  
Table 6 presents the performance evaluation of three machine learning algorithms (Gradient Boosting, XGBoost, and Random Forest) applied to data from OR spectrometric parameters. The results indicate that:  
Gradient Boosting shows the best performance with an accuracy of 30.03%, an F-score of 27.32%, and an MCC of 12.16%, despite a higher execution time (11.12 ms).  
Figure 10 illustrates the ROC curve and the confusion matrix of the model that achieved the highest accuracy.
# Microseismic Signal Denoising and Separation Based on Fully Convolutional Encoder-Decoder Network
## 2 Methods and Network Training
### Methods
Firstly, the noisy signal is transformed into time-frequency domain using STFT. The real and imaginary parts of time-frequency coefficients of the noisy signal are input into the well-trained network. The network produces two masks for estimating both the time-frequency coefficients of microseismic signal and noise as outputs. The output masks are applied to the real and imaginary parts of time-frequency coefficients of noisy signal for producing the estimated time-frequency coefficients of the microseismic signal \(\overline{MS(t,f)}\), and noise \(\overline{N(t,f)}\), respectively. Finally, the estimated microseismic signal and noise are obtained through inverse transforming \(\overline{MS(t,f)}\) and noise \(\overline{N(t,f)}\) back into the time domain. Figure 1: The neural network structure of the proposed convolutional neural network (CNN)-denoiser. Inputs are the real and imaginary parts of time–frequency coefficients of the noisy signal. The gray rectangles represent the neural network layers. Arrows represent different operations applied between two adjacent layers. Batch normalization and concatenate are used to improve convergence during training. The dimension of each layer presented above indicates “frequency bins \(\times\) time points \(\times\) filters”. Outputs are two masks for the estimation of the time–frequency coefficients of the microseismic signal and noise. One of the advantages of the presented method is that instead of manually defining different features and thresholds to improve the SNR, it can automatically learn the richer features from the semi-synthetic noisy signals to obtain the estimated signals and noise in the time-frequency domain. Thus, deep learning has great potential to provide more efficient and accurate performance on signal denoising, which makes it possible to apply in other challenging tasks such as signal detection and onset time picking.
# Hub-and-Spoke Network Design Considering Congestion and Flow-Based Cost Function
###### Abstract  
This paper presents a model for a \"hub-and-spoke network design considering congestion and flow-based cost function\". The number of hubs and spokes is unknown, and the objective is to minimize the cost (including the transportation cost, lost demand, and facility setup cost). In the post-pandemic era, it is expected to have government-imposed restrictions on the congestion of airports, as a measure of health and safety. Unlike the current literature which considers a monetary penalty for congestion, we consider congestion as an externally imposed factor, which should be modeled as a constraint. We take a gravity-based modeling approach to obtain the desirability of a facility and calculate the demand matrix of the network. To solve the model, a Benders decomposition approach is proposed. Without the Benders decomposition approach, only instances with up to ten nodes were solved within a reasonable time, but with the Benders decomposition approach, instances with up to forty nodes were solved. A heuristic algorithm is developed to have a mechanism for dealing with larger instances. A set of experiments are conducted using data from the Turkish Network dataset to study various aspects of the proposed formulation and different parameters' effects on the performance of the model.  
transportation; hub-and-spoke network design; congestion; flow-based cost function; COVID-19 +
Footnote †: journal: Applied  
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Footnote 1: email: [EMAIL_ADDRESS]  
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Footnote 2: email: [EMAIL_ADDRESS]
# Stripe-Based Futurism-like Image--A New Type of Art and Its Application to Cover Communication
## 3 Data Embedding and Extraction via Art Images
### Idea of the Proposed Data Hiding Method
#### 3.1.1 Adjustment of Message Bit Data before Embedding  
As mentioned previously, to achieve covert communication, the stripes in each image region are colored to embed message data. In detail, instead of coloring the stripes in a fixed alternative order, the stripe colors controlled by the embedded message bits. It should be mentioned that in some extreme cases of the above-mentioned data hiding method, the stripes in a region or in a large part of the region may all be filled with an identical color (with the average region color, or the color white only) due to the embedding of a sequence of identical bit values (all 0s or all 1s), resulting in an art image that might be seen as strange by people. In order to avoid this situation, the secret message sequence is adjusted in advance by transforming every two bits into three before the message bits are embedded into the art image. As a result, the stego-image will not become the extreme case mentioned. The transformation is accomplished by the use of a table, as shown in Table 2, called _2-to-3 mapping table_ subsequently.  
For example, if a segment of a message to be hidden appears to be a sequence of eight 0s, then originally the corresponding stripe coloring result will be eight consecutive stripes all filled an identical color, namely, the color white, which integrally appears to be a larger white stripe and will possibly arouse the observer's notice. Now, according to Table 2, the eight 0s will be mapped to be 101101101101 (i.e., every two 0s are mapped to be 101) which has segments of no more than two 1s with 0 inserted in between. Therefore, no large stripe of a single color will then appear.
## 7 Simulation Results and Discussion
### Analysis of Proposed Scheme Results against Existing State of the Art
#### 7.2.3 PDR  
The PDR of all schemes is monotonically increasing as depicted in Figure 10. However, the proposed work supersedes all the existing compared schemes because of the incorporation of sonobuoys mobility.  
Figure 8: Fraction of void nodes plots.  
Figure 9: Depth adjustment plots.  
Although all the three proposed schemes have opted void node recovery mechanism, cost associated with each scheme is different. At the beginning, GDGOR-IA performs same as GEDAR but the interference avoidance mechanism reduces the chance of data loss resulting in high PDR.  
Initially, the PDR is very high of RE-PBR because of the consideration of link quality during the selection of forwarder node. The inclusion of link quality metric, enables reliable delivery of data packets at the destination as illustrated in Figure 10. The increase in PDR is gradual with the increase in node number because of consistent rotation of forwarder node, which avoids dramatic death of node. However, when node density reaches 350, the proposed schemes GDGOR-SM and GRMC-SM outperform RE-PBR because mobile sinks collect data directly from sensor nodes.  
PDR in EnOR is very much high as compared to AUV-CH and even from proposed scheme, GDGOR-IA because of its ability to assign priorities to each node which ensures imbalance energy dissipation throughout the network operational time. However, the major reason of not beating all schemes is the absence of mobile sonobuoys due to which only data is delivered via multi-hopping. If void node occurs, then no mechanism is defined to recover data packet which results in data loss. While AUV-CH performs not well because of its ability to gather data from every node which takes time and gathers less data as compared to the proposed schemes.
# Reversible Image Processing for Color Images with Flexible Control
## 3 Proposed Method
### Image Processing
#### 3.1.1 Sharpening and Smoothing  
As shown in Figure 3a, the _Max_ values are divided into target and reference regions in a checkered pattern. We define each _Max_ value in the target and reference regions as target or reference points, respectively. Our method carries out the sharpening or smoothing function for each target point by using four reference points. Figure 3b shows the relationship among the target and adjacent points. We give details on the procedure below:  
1. Divide the _Max_ values into the target and reference regions.
2. Apply Equation (5) for sharpening or Equation (6) for smoothing to each target point: \[\begin{split} Max^{\prime}(i,j)&=round[\;2\times Max (i,j)-\frac{1}{5}\times\{Max(i-2,j+1)\\ &\quad+Max(i-1,j-2)+Max(i,j)\\ &\quad+Max(i+2,j-1)+Max(i+1,j+2)\}\;],\end{split}\] (5) \[\begin{split} Max^{\prime}(i,j)&=round[\;\frac{1}{5 }\times\{Max(i-2,j+1)+Max(i-1,j-2)\\ &\quad+Max(i,j)+Max(i+2,j-1)+Max(i+1,j+2)\}\;].\end{split}\] (6) Given an image with a size of \(M\times N\), \(Max(i,j)\) and \(Max^{\prime}(i,j)\) denote the original and output target points, respectively. \(Max(i-2,j+1)\), \(Max(i-1,j-2)\), \(Max(i+2,j-1)\), and \(Max(i+1,j+2)\) represent the four reference points, where \(3\leq i\leq M-2\) and \(3\leq j\leq N-2\).
3. Obtain \(Min^{\prime}\) with the following equation on the basis of Equation (3): \[Min^{\prime}=Max^{\prime}-S_{cone}.\] (7) This step prevents saturation distortion.  
The proposed method rounds the fractional parts of _Max_. Another type of error due to underflows (UFs) might be caused for \(Min^{\prime}\) by Equation (7). Such errors prevent the raw image from being recovered. Therefore, our method preserves the errors as recovery information. We will elaborate on this in Section 3.1.3.  
Depending on the user's intention, the above procedure can be repeated by exchanging the target and reference regions. The proposed method can iterate a series of the above steps until the amount of the recovery information exceeds the capacity of the embedding  
Figure 2: Block diagrams of proposed method. (**a**) Image processing. (**b**) Raw image recovery.  
process. We define the number of processing times for sharpening and smoothing as \(I_{VSH}\) and \(I_{VSM}\), respectively. These parameters enable us to control the degree of effect of these functions.
## 3 Methodology  
The ET downscaling method mainly consists of three components (Figure 3), including (1) derivation of eleven Landsat 8 data-based indicators that are associated with vegetation growth, surface temperature and moisture, and upscaling of the indicators so as to be consistent with the MODIS 8-day ET product; (2) development of machine learning models using samples with Landsat 8-derived indicators as predictor variables and MODIS 8-day ET as a response variable; and (3) generation of ET with 30 m resolution based on the developed models, and accuracy assessment through comparison between the downscaled ET, MODIS and _in situ_ ET measurements.
# A Packet Content-Oriented Remote Code Execution Attack Payload Detection Model
## 2 Preliminaries  
### Remote Code Execution  
Compared with compiled programming languages, interpreted programming languages offer excellent flexibility and are widely used to develop various web applications. As a major feature of the interpreted programming language, dynamic code execution can treat the user's input as a code and execute it, which is helpful to solve many complicated problems. However, if the user's input is not effectively checked and sanitized, an RCE vulnerability may be caused.  
RCE is a type of ACE (Arbitrary Code Execution). Attackers can exploit an RCE vulnerability to execute arbitrary code on a remote host through LAN (Local Area Network), WAN (Wide Area Network), or Internet. This allows them to perform malicious actions such as stealing sensitive data, modifying important information, and gaining control of the system. Figure 1 shows the RCE attack process for web applications. First, the attacker looks for controllable input points on the web page. After identifying a controllable input point, the attacker constructs a malicious payload and uploads it to the victim server. The victim server then executes the malicious payload or requests malicious content preconstructed by the attacker from a host specified by the attacker.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 4 Proposed SLAM Classification Based on UWSNs
### New Classification Based on UWSNs
#### 4.2.1 Standalone SLAM Systems  
Standalone SLAM systems operate independently without relying on external communication networks. These systems are suitable for environments where communication is limited or unavailable, relying solely on onboard sensors and processing capabilities. However, since real-time data transmission is often infeasible in underwater scenarios, standalone systems must store large volumes of sensor data locally, requiring high-capacity storage solutions. Post-mission, the collected data can be processed in a deferred manner, either when the vehicle resurfaces or through opportunistic communication with ground stations or drones once it establishes a connection. While standalone systems are robust in isolated settings, their performance is constrained by the limitations of individual sensors, the lack of external data inputs, and the challenges of managing extensive onboard data storage [1].
## 7 Simulation Results
### Time Delay Effects  
WAMS have inherent communication delays and as a result the effect of delay is considered here. For consistence of results only case 2 was chosen. For the purpose of studies and just to show the effect of delay, in our simulations we varied the time delay to implement the load-shedding. Four scenarios are compared as shown in Figure 11. The greater the time delay the lower the minimum frequency. For large delays, the system will lose synchronism. Mathematically, the objective is met since the error is finally zero but before we reach that point the system would have long lost stability.
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 4 Q-Learning-Based Position-Aware Routing Protocols in FANETs  
According to our discussion in Section 1.1, the routing protocols in FANETs are categorized as topology-based and position-based protocols. In this section, we extensively review the QL-based position-aware routing protocols in FANETs, which is a new research trend. The QL-based routing protocols incorporated with position-based forwarding can be considered as a special subcategory of position-based forwarding, where RL is exploited to make the routing decision. As a result, we do not give any further classification for QL-based position-aware routing protocols as they fall into a similar category. The QL-based position-aware routing protocols along with their optimization objective, advantages, and limitations are comprehensively reviewed according to the order of the published year.
# A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
## 3 Background
### System Model  
As shown in Figure 2, the proposed scheme has four entities: the LBSP, the fog node, the user, and the blockchain, which describe the following scenario: due to the storage and computing advantages of fog computing, the LBSP outsources its private data to the fog nodes. However, to guarantee data security, the outsourced data need to be encrypted and the storage structures of outsourced data also need to be constructed to support the secure keyword search over the encrypted outsourced data. When a user requests a certain type of POI information in a specified query region, \(s/he\) can send a POI query request to the third party (i.e., the fog node) based on some parameters obtained from the LBSP. Similar to [9], the third party (i.e., the fog node) is considered to be a dishonest entity that could maliciously delete the stored outsourced data or tamper with the user's query result; the secure verification objects should be constructed and contained in the outsourced data. When a query ends, the query result along with the corresponding verification objects are returned to the user. Finally, the user can implement the correctness and completenessverification based on the received verification objects, taking a step towards consistency verification under the support of the blockchain.