esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Distribution Method for Observation Scheduling in the AEOS Constellation
### Consensus and Target Removal Phase  
Algorithm 4 is executed independently on each satellite, which can lead to a target being allocated to more than one satellite or the significance value of a target being different on different satellites, resulting in conflicts. Thus, the second phase is required to ensure that each target \(g\) is scheduled at most once and a consensus significance value for each target is reached among all satellites. This phase includes two steps: consensus and task removal. **Consensus**: By (16), the significance value of target \(g\) is dependent on the target order of the sequence that \(g\) is assigned, thus the strong synergies exist between targets. According to the basic idea of PI algorithm, the significance list is broadcasted among satellites through a series of local ISL-based communication, and the significance value of every target is gradually increased. If we just aim to find the biggest target significance value duringcommunication, the significance of some targets may converge to a nonexisting value (in the sense of not being assigned to any satellite). To avoid such a problem, we use the heuristic rules called winning bids proposed in the work [33] to reach the consensus of the significance list. This method is discussed in detail as follows. By using ISLs, AEOS \(s\) sends the message lists \(Q_{s}\), \(Z_{s}\) and \(u_{s}\) to its neighbor \(c\) with \(H[s,c]=1\) and receives the corresponding lists \(Q_{c}\), \(Z_{c}\), and \(u_{c}\). Then, \(s\) updates the stored messages \(Z_{sg}\) and \(Q_{sg}\) for each target \(g\) according to the rules given in Table 2. There are three actions in Table 2 and the default is the _Leave_ action. * Update: \(Z_{sg}=Z_{cg}\), \(Q_{sg}=Q_{ci}\);
* Leave: no change made on \(Z_{sg}\) and \(Q_{sg}\);
* Reset: \(Z_{sg}\rightarrow\infty\), \(Q_{sg}\to 0\). The first two columns of Table 2 record target \(g\)'s observers that are believed by the sender \(c\) and receiver \(s\), respectively. The third column outlines the action taken on \(Q_{sg}\) and \(Z_{sg}\) with respect to receiver \(s\). Note that once a piece of message is passed, timestamp \(u_{i}\) is updated according to (19) to obtain the latest time information. **Target removal:** Only the local message lists \(Q_{s}\) and \(Z_{s}\) are updated during the above consensus procedure, but the target sequence \(\mathbf{a}_{s}\) is not affected. Thus, it is possible that \(Z_{sg}\
eq s\) for some \(s\in S\) and \(g\in\mathbf{a}_{s}\) when the consensus is completed. That means that \(s\) believes that it does not observe a target which is however included in its sequence \(\mathbf{a}_{s}\) Then, the second step, namely, target removal, is needed to remove these problematic targets \(De=\{g\in\mathbf{a}_{s}\ |\ \ Z_{sg}\
eq s\}\) from \(\mathbf{a}_{s}\). The removal criterion is given as follows:
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 3 Results
### Association Rule Mining Results
#### 3.2.4 Key Driving Behavior Pattern Recognition
In summary, the FP-Growth algorithm is used to mine association rules from the composite transaction data of driving behavior and traffic conflict and to screen strong association rules through methods such as enhancement degree and confidence tests [42; 43], which can effectively identify key driving behavior patterns that cause traffic conflict and realize the prevention and control of traffic accidents from the source of driving behavior. These high-risk driving behavior patterns and the types and severity of conflicts induced by them can be used to guide drivers to improve their safety awareness and correct their driving habits and provide a basis for traffic safety publicity and education. At the same time, this information can also guide the traffic management department to reduce the risk rate of accidents and improve the road environment.
## 3 Results and Discussion  
### Phase Diagram of Xylan and PEG  
Water-in-water emulsions can be obtained by mixing two aqueous solutions of hydrophilic polymers. A biphasic system only is obtained when a certain polymer concentration is reached. These emulsions recently have been granted important attention due to the great potential applications in food and cosmetics preparations. In this sense, the achievement of a monophasic dispersion is mandatory [19]. The water-in-water emulsification process allows obtaining microparticles with narrow size distribution loaded with an active compound by avoiding the use of organic solvents and surfactant agents [11]. [PERSON] and [PERSON], still in the 1990s, showed that the average size of the particles prepared using the water-in-water emulsion method depends on the volume ratio of the discontinuous/continuous phase, the viscosity of the solutions, and the molecular weight of the polymers [20].  
On the other hand, the microencapsulation into xylan-based microparticles of low molecular weight molecules, such as 5-ASA, has been a pharmaceutical technology challenge due to the restricted microparticles' physicochemical stability and the use of harmful solvents to produce them [10]. In this regard, water-in-water emulsion systems can solve all drawbacks to produce such xylan-based microparticles. The phase diagram is the more common approach used to develop water-in-water emulsions [11]. Using such an approach, it is not only possible to define the region of the phase separation, but also to control the size of the internal phase domains, which will be further used as a template to produce the microparticles.  
In this regard, the phase diagram prepared with PEG and xylan solutions was obtained and the results are available in Figure 2. The obtained data revealed the potential templates for the preparation of XBM. The region that shows the different and stable combinations between PEG and xylan are identified by the points above the tie-line (solid line, Figure 2). Located at this region, the emulsion constituted by PEG and xylan, at the concentration of 32%(w/v) and 4%(w/v), respectively, was further used due to the clear formation of droplets at the micro-scale range (data not shown).
# A Case Study of Floating Offshore Super-Long Steel Pipeline Combing with Field Monitoring
## 3 Monitoring Instruments and Monitoring Programs
### Monitoring Programs
#### 3.2.2 Field Monitoring Arrangement  
As shown in Figure 7 that Considering that there may be a problem of stress concentration near the bending point, strain monitors were performed near the bending section of the pipeline Sections 1 and 2 with a larger bending angle. At the same time, select a strain monitoring section near the middle of the straight pipeline section and the inclined pipeline section. Because the inclined pipeline section is longer in pipeline Section 2, two strain monitoring sections are selected here, one at the midpoint of the entire inclined pipeline, and one between the two pontoons with a larger distance. Three measuring points were arranged in each section. In the case that the water flow was perpendicular to the straight pipeline section, taking Section 1 of pipeline 1 as an example, the measuring point 1-1 belongs to the facing water, 1-2 was the top, and 1-3 was the backwater. As the bending Angle of pipeline Section 2 was only 1\({}^{\circ}\), and the cable had been uncoiled to meet the construction needs before floating, the pipeline section had been flipped by 90\({}^{\circ}\). Taking Section 4 as an example, the measuring point 4-1 was the top, 4-2 was the backwater, and 4-3 was at the bottom. In general, the number of measurement points had little influence on the modal parameters of natural frequency. Four modal monitoring parts were arranged in Section 2, and two measuring points were arranged on each monitoring part. Taking Section 1 as an example, measurement points 1-1 were the top and 1-2 were the backwater.  
During the process of pipeline floating, the pilot boat moved synchronously with the pipeline to obtain monitoring data. The test boat of pipeline Section 1 was equipped with DH3819, which was used to receive 9 strain signals in 3 sections of the inclined pipeline section. DH3819 and DH5902N were arranged on the test boat of pipeline Section 2 to receive 12 strain signals in 4 sections of the inclined section and 8 vibration acceleration signals in 4 sections of the straight section.
# Object-Oriented Remote Sensing Image Change Detection Based on Color Co-Occurrence Matrix
## 1 Introduction
The proposed approach abandons the traditional method of describing the texture features in multi-channel images, which involves separately extracting texture features from single-band gray images and then concatenating them to describe multi-channel texture features. The CCM is innovatively used to establish inter-channel correlation and directly extract color texture features from multi-channel images for change detection. 2. The cumulative backward search strategy (CBSS) is proposed to eliminate the impact of instability in feature sorting under the one-time training on feature selection, which is beneficial for finding more representative and effective feature subsets. In this study, Shenzhen and Dapeng are selected as the research areas, and Sentinel-2 and Skysat images constitute the research data. Considering that forest protection and bareland supervision are the focus of attention in Shenzhen, the forest-bareland change type is selected as an example to verify the effectiveness of the proposed method. The remainder of this manuscript proceeds as follows: Section 2 introduces each aspect of the proposed algorithm, Section 3 provides the experimental results and discussion, and Section 4 presents the conclusion and prospect.
# Big Data Perspective and Challenges in Next Generation Networks
## 2 Major Milestones of Next Generation Networks
### Millimeter Wave  
Challenges for mmWave  
The propagation of mmWave is challenging due to _path loss, blockage, atmospheric impacts, high directional beams, hand shaking, linking with previous cellular networks and transceiver design_.  
**Pathloss:** High path loss is one of the drawback of mmWave. The required antenna size decreases when frequency increases and, thus, the free space path loss between transmit and receive antenna increases. The free space path loss (FSPL) can be expressed as,  
\[FSPL=\left(\frac{4\pi d}{\lambda}\right)^{2}=\left(\frac{4\pi df}{c}\right)^{2} \tag{2}\]  
where \(d\) corresponds to the distance between the transmitter and receiver antennas.  
**Blockage:** As mmWave greatly resembles a directional beams so they have strong sensitivity to blockages. mmWaves cannot pass through large obstacles such as buildings or thick walls. mmWaves are thus desirable only for short range or line-of-sight communication. As the distance between transmitter and receiver increases for line-of-sight propagation, the path loss gets closer to the free space value of 20 dB/decade [27; 29; 29].  
**Atmospheric Impacts:** For mmWave communication, the atmospheric effects and rain absorption are challenges of key importance. mmWaves are absorbed by the gases present in the atmosphere. mmWaves will also be affected by rain as the strength will be reduced by absorption of rain. Therefore, mmWaves will be suitable only for short ranges, about a kilometer [30].  
**Hand Shaking:** The mmWaves are directional and narrow beam (such as flashlight). For such narrow beams, it is challenging to establish a link between users and BSs, so hand shaking is not easy in mmWave based communications, as it may require a great deal of scan of angular positions between user and BS to find a suitable link.  
**Linking with Previous Cellular Networks:** mmWave will be used in 5G network for data transmission in small cells BS. On the other hand, microwave or previous 4G standard can be used for macro cells BS. This phenomenon of having two different spectral ranges will require a strong and reliable connection between the two for fast data transmission [31].  
**Transceiver Design:** For mmWave, the design of an analog to digital or digital to analog converter is challenging. To deal with such high frequency waves, there should be large antenna arrays and high sensitivity receiver, making the design unfeasible. These issues may also lead to increase in cost of the overall system.
# ReFuse: Generating Imperviousness Maps from Multi-Spectral Sentinel-2 Satellite Imagery
## 3 Materials and Methods
### Multi-Spectral Bands Fusion Network
At first, we modified the down-sampling block of a ResNet-50, changing the stride of the first convolutional layer from 2 to 1. The idea behind this is that a stride of 2 with a kernel size 1 \(\times\) 1 ignores three quarters of the input feature map. To leave unchanged the spatial size, we switched the size of the strides in the first two convolutions, so that the second convolutional layer will have a stride of 2. Second, for the  
Figure 6: The proposed _ReFuse_ encoder–decoder architecture: the encoder consists of two parallel ResNet-50 networks where every residual block output from the second branch is fused into the main one by a feature map summation. Feature maps from the encoder are then connected with the decoding part by feature map concatenation, such as in the U-Net cross-connections. same reasons, we replaced a 2-stride convolution with an average pooling layer followed by a 1 stride convolution, keeping intact the output dimensions: adding a 2 \(\times\) 2 average pooling layer with a stride of 2 before the convolution 1 \(\times\) 1 with a stride of 1 the model will not overlook any information. Finally, we changed the first convolutional layer of the ResNet (i.e., a 7 \(\times\) 7 convolution) with three 3x3 convolutions, since the replacement will make the model easier to train [36]. Finally, to leverage transfer learning, we adopted a three-step training approach: (i) the encoder weights are initialized from a ResNet50 pre-trained on ImageNet; (ii) the training is started with all the encoder layers frozen (with the aim of training only the decoder ones); (iii) the network is trained again by considering all the layers trainable. This procedure helps the network to converge while giving the decoder enough information to learn how to produce helpful segmentation masks properly.
# Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning
### Camera Calibration  
Camera calibration in GCP-controlled UAV blocks is discussed by several papers [10; 37; 45]. While pre-calibration is recommended in principle, the stability of camera parameters is questionable: [PERSON] et al. [45] found less accurate results using laboratory calibration parameters, while [PERSON] et al. [10] found on-the-job self-calibration just as accurate. Self-calibration is likely to be thestandard choice for practical reasons. An accurate and dense GCP network should be complemented by oblique imaging with rotary wings [10; 27] and cross strips at different elevations [37] with fixed wings, in order to limit projective coupling between interior and exterior orientation parameters and DSM deformations.  
On the other hand, as getting rid of GCP is the rationale for using AAT, optimal conditions for self-calibration will be hardly met in such cases, as only cross strips and oblique imaging would help. Pre-flight (on-site) calibration, if feasible, is probably the best option. However, in practice, measuring some GCPs is necessary, e.g. in order to get the same accuracy level of GCP-controlled blocks, or more importantly, to prevent biases from false ambiguity fixing or camera calibration errors. Notice that, even in blocks flown by UAV with direct georeferencing capability, tie point extraction and the use of at least one GCP are exploited [46]. Whenever high accuracy and a good confidence level on the outcome are sought, it is therefore just common sense to provide control and check information on the ground.
## 3 Proposed ASVMR Protocol
### Recovery Mechanism  
In situations where a node is unable to find neighboring nodes located closer to the sink node, a void node emerges [32]. During data transmission, selecting a void node as the next hop will result in the data packet loss, which depletes energy and diminishes the data transmission efficiency. To address this issue, the proposed routing method avoids selecting void nodes to proactively trigger the recovery mode when encountering a routing void.  
First, the number of neighboring nodes is taken as a dimension in the feature space for training. The likelihood of selecting a node as the next hop increases if it has a higher number of neighboring nodes. However, the approach does not completely eliminate the occurrence of routing voids, and there is still a possibility that certain void nodes may be selected. Thus, a recovery mode is incorporated that enables void nodes to locate a suitable next hop for forwarding the data downward, bypassing the void area effectively.  
In the recovery mode, the candidate forwarding set for node \(n_{i}\) is composed of the nodes below and inside its pipeline, denoted as \(\mathcal{S}_{\text{below},i}(t)\), as illustrated in Figure 5. Then, the next hop is determined by the value of \(V_{t}\). Upon successfully transmitting the data packet to a non-void node, the void node concludes the recovery mode and resumes its role in routing data packets towards the water surface. During this operational state, the node maintains a record of the previous hop node's ID to avoid any occurrence of routing loops.
# FTIR Characterization of Asphalt SARA Fractions in Response to Rubber Modification
## 4 Conclusions  
This study explored the chemical interactions between asphalt SARA fractions and rubber particles through FTIR-based investigation, accompanied by SARA fractionation and thermal and rheological analyses to better understand the effect of these chemical interactions. How rubber swelling, partial dissolution, and exchange of components with asphalt SARA fractions influenced the performance of A-RBs was emphasized. The conclusions from this study are as follows:  
1. Asphalt-Rubber Interactions Influenced the Distribution of SARA Fractions: In A-RB 190-3000, saturates and aromatics decreased by 9.38%, while asphaltenes and resins increased by 12.28%, indicating that rubber absorbed light fractions and promoted swelling.
2. Rubber Released Its Components Partially in SARA Fractions: Rubber partially dissolved and released C=C aromatics at 1560 and 1571 cm\({}^{-1}\) into aromatics and saturates. Aliphatic C-H peaks in saturates were intensified, highlighting the migration of rubber aliphatics. Rubber PB and PS components emerged in asphaltenes at 971 and 735 cm\({}^{-1}\), respectively, for A-RB 190-3000.  
Figure 10: Rubber compositional components in (**a**) original; (**b**) 190–600-extracted; (**c**) 190–3000-extracted; and (**d**) 220–3000-extracted rubbers.  
Partial Dissolution of Rubber Enhanced Rheological Performance: Optimal interactions (190-3000-8) enhanced the rheological performance (higher \(G^{*}\) and lower \(\delta\)). Higher temperature and lower speed led to lower rheological performance due to excessive depolymerized or incomplete swelling, respectively.
* Thermal Analyses of Rubber Confirmed its Partial Dissolution: Rubber dissolution reached 82.0% under optimal conditions, with OL, NR, SR, and FR dissolution exceeding 77%. At 220 \({}^{\circ}\)C, dissolution exceeded 90%; however, excessive devulcanization and depolymerization diminished performance merits.
# Pas: Privacy-Preserving Authentication Scheme Based on SDN for VANETs
## 4 The Proposed Scheme
### Privacy Analysis  
1. Minimum disclosure. In authentication protocol, all authentication messages sent can only reveal the information required in the authentication process, but cannot expose more information. In PAS, the signed message only contains a pseudonym, timestamp, and challenge value--no additional messages need to be transmitted.
2. Unlinkability. The unlinkability of vehicle identity and messages is vital to prevent vehicles from being tracked by external adversaries. PAS supports mainstream pseudonym exchange protocols, such as mix-zone, silent period, etc. These mechanisms guarantee that each vehicle identity cannot be linked through cooperation with surrounding vehicles. In addition, when the pseudonym is changed, the public key and private key of the vehicle are also changed. Therefore, in authentication and message transmission, adversaries have no way to link the different messages sent before and after the pseudonym change.
3. Distributed resolution authority. As for the privacy of vehicles, a single authority is not allowed to reveal the vehicle identity, obtain the vehicle track, and revoke vehicle at the same time. In PAS, the mapping of the vehicle's real identity and pseudonymis maintained by the TA, the vehicle's public key and expiration list is protected by the SDN RSU controller, and the misbehavior verification and confirmation of illegal vehicles are performed by the SDN controller. It is not possible for any single authority to decide whether a vehicle can join VANETs or revoke a vehicle from VANETs.
4. Perfect forward privacy. Due to active interference by adversaries, vehicles' long term secret keys are likely to be compromised in the future. Perfect forward privacy ensures that even if the secret key is leaked, the encrypted communication in the past will not be recovered. In PAS, given the public key of vehicle \(v\): \(PK_{v}^{i}\), there is no way for adversaries to compute the other public keys of vehicle \(v\). Therefore, even if the secret key of a vehicle is known by the adversary, the adversary cannot obtain the privacy information of the message sent by the vehicle, which equips PAS with perfect forward privacy.
# Energy-Efficient and Real-Time Wearable for Wellbeing-Monitoring IoT System Based on SoC-FPGA
## 2 Background on Biometric Signals Processing
### Ppg  
PPG is an optical technique that detects blood volume changes in a microvascular tissue [6]. PPG uses a light source for emitting light to the tissue and a photodetector for measuring the consequent received light, by transmission or reflection, from which the blood volume variation is estimated. The principle of PPG is as follows. During the cardiac cycle, arteries suffer blood volume reduction when transiting from the systolic phase to the diastolic phase. The PPG sensor detects this change optically, and its photodetector converts the received light energy into an electrical current. A waveform can be acquired and some physiological parameters extracted; for instance, the variability of the time between heartbeats [7].  
A PPG signal comprises two components: a pulsatile (AC), given by cardiac variations in blood volume caused by heartbearts, and a superimposed (DC), variable with some anatomic factors, such as respiration, thermoregulation, vasomotor and sympathetic nervous system activities [6].  
The monitoring and analysis of PPG signal unveils a wide set of clinical applications, namely, the measurement of heart rate, blood pressure, respiratory rate, blood oxygen saturation and several vascular assessments [6].  
PPG is regarded as a non-invasive and low-cost method, and can be integrated in a portable, ready-to-use and convenient device from the user point of view. PPG sensors can be placed on different anatomical positions, but PPG signal has higher quality at the earlobes or fingertips [8].