esa-sceva/satcom-chunk-collection huggingface dataset:
# Bitumen and Bitumen Modification: A Review on Latest Advances
## 3 Bitumen Chemical Modifiers
### Reactive Polymer Modifiers  
Reactive polymers, also known as reactive ethylene terpolymers (RET) because of their ethylene-based structure, are polymer-based compounds grafted with reactive groups derived from, glycidyl methacrylate (GMA), acrylic acid, etc. The use of these types of polymer has been introduced to increase the compatibility between polyolefin (e.g., PE, PP, etc.) and bitumen by creating covalent linkages. Polymer grafting is a good solution and often simple to realize because it involves a single-step melt process. However, a possible drawback is the uncontrolled and undesired crosslinking of unsaturated polymers because of the radical mechanism of the grafting. Therefore, saturated polymers, such as styrene-ethylene-butylene-styrene (SEBS), PE, PP, etc., are more suitable for this process.  
Owing to acrylic functionalization, the use of reactive polymers showed some advantages, like increased polarity and reactivity. However, higher costs compared to the other types of polymers and gelation problems due to the rather high number of reactive groups on a single macromolecule are the mainly-reported disadvantages that have been observed. To overcame these drawbacks, the amount of RET that could be used is around 2 to 2.5% and, in many cases, is even lower than 1% by weight [143]. Owing to these problems RETs are not ideal as bitumen modifiers, because the overall effect on the performances can be quite poor [143; 190; 191]. Therefore, their use could be mainly for increasing the compatibility between bitumen and other polyolefins, as shown by [PERSON] et al [144]. In this respect, using differential scanning calorimetry (DSC), they demonstrated an improvement in the bitumen/polymer interactions relative to unfunctionalized PP. Recently, two poly(ethylenebutylacrylate-glycidylmethacrylate), known as Elvaloy(rAM and Elvaloy(rAM)4170 (produced by DuPont, Wilmington, DE, USA), were blended with neat bitumen in order to study the blend storage stability [192]. After high-temperature storage a complete homogeneity of all the samples was observed. This indicates the use of RET basically guarantees the storage stability, although under severe conditions of temperature and storage time another study has shown the instability of the blend [193]. Low-density polyethylene (LDPE) was used to compare the storage stability properties of blends prepared by using LDPE before and after grafting with GMA [148]. As can be observed from Figure 8 the use of GMA-grafted LDPE shows a higher stabilizing effect than LDPE.  
Finally, a particular mention goes to silicon-based polymers like polyoxyalkylene with reactive silicon groups which form siloxane bonds by the silanol condensation reaction [194].
## 1 Introduction
The main findings of those few studies are as follows: (1) experimental study of drilling fluid used to extract natural gas hydrate has focused on drilling in a marine settings; (2) less attention has been given to drilling for natural gas hydrate in permafrost regions and the related evaluation of the rheological properties of low-temperature drilling fluid; (3) the study of the formation of NGH has concentrated mainly on the inhibitory action; and (4) fewer experimental studies have been conducted related to the appropriate drilling fluid that can be used when drilling for natural gas hydrate in permafrost regions, as well as the rheological properties with temperature response characteristics of this type of drilling fluid. Therefore, theoretical, experimental, and microscopic analyses were used to study the rheological properties with temperature response of three different types of SFPDF: Semen Lepidii natural vegetable gum (KL), polyacrylamide (PAM), and xanthan gum (XC). Since KL for the vegetable gum, PAM for the synthetic polymer, and XC for the biopolymer are commonly used at present, using a six-speed rotatory viscometer, the curves of shear stress with shear rates under different temperature conditions were measured and drawn. In addition, a rheological model related to SFPDF was analyzed. Additionally, the laws for changes in rheological parameters in response to temperature were determined. Moreover, the rheological characteristics of this fluid and a temperature response state equation were established. In this way, the viscosity and temperature response characteristics of fluids used in the drilling of permafrost regions for NGH were studied. Finally, the mechanism underlying the rheological properties with temperature response mechanisms were analyzed using the infrared spectrum and a scanning electron microscope.
# Resonant Coupling Effects in a Double-Layer THz Bandpass Filter
## 2 Theory and Experiments
\(Z_{L1}\), \(Z_{inp1}\), and \(Z_{L2}\) in the ECM can be expressed as  
\[Z_{L1}=\frac{Z_{0}\times Z_{FSS}}{Z_{0}+Z_{FSS}} \tag{5}\]  
\[Z_{inp1}=Z_{0}\frac{Z_{L1}+jZ_{0}tan(\beta_{0}\times d)}{Z_{0}+jZ_{L1}tan( \beta_{0}\times d)} \tag{6}\]  
\[Z_{L2}=\frac{Z_{inp1}\times Z_{FSS}}{Z_{inp1}+Z_{FSS}} \tag{7}\]  
where \(\beta_{0}=\omega/c\), \(\omega\) is the angular frequency and \(c\) is the speed of light in a vacuum. \(Z_{L2}\) obtained from Equation (7) is the load impedance of ECM. The reflection coefficient \(\Gamma\) and the transmittance \(T\) of the ECM can be calculated as follows:  
\[\Gamma=\frac{Z_{L2}-Z_{0}}{Z_{L2}+Z_{0}} \tag{8}\]  
\[T=1-\Gamma^{2} \tag{9}\]  
Single-layer FSSs with geometrical parameters \(l=280\)\(\upmu\)m, \(w=65\)\(\upmu\)m, \(t=10\)\(\upmu\)m, and \(p=350\)\(\upmu\)m were fabricated on \(10\)\(\upmu\)m Al foils using a femtosecond laser micromachining system [13; 31]. The femtosecond laser has a wavelength of \(800\) nm, a pulse width of \(45\) fs, and a repetition rate of \(1\) KHz. The Al foil was placed on a computer-controlled two-dimensional translation platform with a moving speed of \(500\)\(\upmu\)m/s and an acceleration of \(500\)\(\upmu\)m/s\({}^{2}\), and the laser power used was \(10\) mW. The calculated focal spot size is \(1.44\)\(\upmu\)m in diameter, and the power density (or irradiance) is about \(0.48\) MW/cm\({}^{2}\). We stacked two single-layer FSSs to form a double-layer structure. To precisely assemble the two FSS layers, we first put one layer clinging to the other layer (spacing \(d=0\)). We aligned the cross aperture arrays using visible light projection. Then, we moved one filter mounted on a one-dimensional translation stage with respect to the other fixed filter to control the spacing between the two layers. The sample was measured using a home-made time-domain THz spectroscopy system, which employs a Ti: Sapphire femtosecond amplifier and ZnTe crystals for both THz generation and detection. The system has a usable spectral range from \(0.2\)-\(2.5\) THz and a power spectrum dynamic range \(>\)\(50\) dB. Time-domain signals were acquired over a time window of about \(25\) ps, corresponding to a frequency resolution of \(40\) GHz (\(\Delta f=1/T_{\mathrm{window}}\)).
## 3 Experiments and Results
### Study of the Applied Classifiers
#### 3.1.2 Discriminant Analysis  
Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) are two classic methods of classification for multi-class systems having closed form solutions, which can be easily computed [37]. In this work, LDA is applied. According to [37], LDA can be mathematically formulated from probabilistic models, which characterize the class conditional distribution of a sample data \(P(X|y=k)\) for each class \(k\). Using [PERSON] theorem,  
\[\begin{split} P(y=k|X)=\frac{P(X|y=k)P(y=k)}{P(X)}\\ =\frac{P(X|y=k)P(y=k)}{\sum_{n}P(X|y=n)P(y=n)}\end{split} \tag{8}\]  
here class \(k\) is selected, which maximizes this conditional probability. According to [37], for a more specific application of LDA, \(P(X|y)\) is modeled as a multivariate Gaussian distribution with density, which can be expressed as:  
\[\begin{split} P(X|y=k)=\frac{1}{(2\pi)^{d/2}|\sum_{k}|^{1/2}}& \exp(-\frac{1}{2}(X-\mu_{k})^{T}\\ (\sum_{k})^{-1}(X-\mu_{k}))\end{split} \tag{9}\]  
here \(d\) is the number of features and \(\mu_{k}\) denotes the class means. For LDA, the Gaussians for each class are assumed to share the same covariance matrix: \(\sum_{k}=\sum\)[37].
## 2 Materials and Methods  
### Raw Image Acquisition  
The image data of damage symptoms for _Brevipalpus_ spp. were collected from the grapevine Cabernet Sauvignon between June 2023 and September 2024 at Zhihui Yuanshi Winery in Yinchuan City, Ningxia province of China (106.5\({}^{\circ}\) E, 39.1\({}^{\circ}\) N), using an iPhone 14. The data collection took place during the grapevine growing season, from June to September each year. In the early growth stages (June to July), the damage was generally light. In the later stages (August to September), the damage symptoms were more obvious. Image acquisition was more frequent in the later period so to capture these more noticeable symptoms. A total of 1144 images in JPG format were captured and saved.  
The iPhone 14 has a fixed focal length and limited zoom capabilities, limiting the ability to capture small harmful features. Its automatic exposure and white balance settings may lead to inconsistencies in image quality, resulting in some images being skewed in detail and consistency. Nonetheless, the iPhone 14's portability and real-time processing capabilities still offer advantages, especially in terms of greater ease and efficiency in the actual data collection process.
# A Review of SATis5: Perspectives on Commercial and Defense 5G SATCOM Integration
## 4 Overview of Recent and Existing Commercial SATis5 Testbeds and Projects
### Integration of UAV and Satellites with 5G Network--mMTC-NB-IoT Use Case  
Recently, [21] has addressed the IoT environment as UAV integrated with satellite and 5G terrestrial networks. This reference has addressed the joint integration of UAVs and satellite technologies for future 5G networks. The IoT environment includes both short and long-range IoT operations. The short-range IoT includes Bluetooth, Zigbee, and WiFi. The long-range IoT includes LoRaWAN SigFox and Ingenu. The UAV technology can have many applications in practice, including but not limited to homeland security, border surveillance, and goods transportation. When UAV technology is integrated with IoT technology, the integrated technology can be applied to support defense operations,  
Figure 12: 5G Edge Slicing Technology Enabler in 5G VPN: (a) 5G Edge Slicing Deployment, and (b) 5G End-to-End Slicing Solution for 5G VPN Edge Slicing [20].  
disaster management, crowd surveillance, real-time road traffic monitoring, earthquake, and environmental disasters monitoring, meteorology management, etc. Reference [21] provides a solution for the integration of the combined mMTC-NB-IoT-and-UAV with satellites and 5G terrestrial networks. Figure 13 shows a top-level architecture for integrating IoT-UAV with satellite and 5G terrestrial networks. The key features associated with this proposed SATis5 architecture are:  
* Satellite Layer: Handover mechanisms for both satellites and UAVs are required to manage the connectivity during the handover events--the handover mechanism is required to implement on both satellite and satellite ground stations.
* UAV: Routing protocols are required onboard UAVs for distributing data to IoT devices. In addition, UAV's onboard storage is required to address the loss of connectivity between UAVs or between satellites and satellite gateways caused by outage events.
# Vehicle Routing Problem with Drones Considering Time Windows and Dynamic Demand
## 5 Computational Experiments
### Algorithm Performance Test
#### 5.1.2 Comparison of the Results of Different Strategies  
To analyze the impact of fusing the sine-cosine operator and the simulated annealing mechanism on algorithm improvement, the datasets were tested under different customer scales and algorithm combinations. In each example, each algorithm combination is run 20 times; the obtained objective function values are given in Table 4. CHOA denotes the objective function value obtained from the traditional chimp optimization algorithm, while CHOA+CS represents the solution obtained after integrating the sine-cosine operator into CHOA's attacker position change. Similarly, CHOA+SA denotes the solution obtained after incorporating the simulated annealing mechanism into the CHOA objective function value. Finally, \(GAP^{Y}\) indicates the difference between SACHOA and the optimal solution obtained following the introduction of each strategy, \(GAP^{Y}=(SACHOA-Y)/Y\times 100\%\), where \(Y\) represents CHOA/(CHOA+CS)/(CHOA+SA).  
The results show that adding a sine-cosine operator and simulated annealing mechanism can effectively improve the performance of the algorithm. The main reason is that the sine-cosine operator strategy can continue to search for better solutions, thereby expanding the search range of feasible solutions, which is beneficial for obtaining relatively better solutions. The simulated annealing mechanism can accept the inferior solution to avoid falling into the local optimum. In addition, when a single strategy is adopted, causing the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Single**} & \multicolumn{2}{c}{**Multiple**} & & & \\ \cline{3-8}
**Name** & **CHOA** & \multicolumn{2}{c}{**Strategy**} & \multicolumn{2}{c}{**Strategies**} & & & & \\ \cline{3-8}  & & **CHOA+CS** & **CHOA+SA** & **SACHOA** & & & & \\ \hline R101\_25 & 575.7988 & 560.5092 & 543.353 & 527.6039 & \(-8.37\) & \(-5.87\) & \(-2.9\) \\ R101\_100 & 1633.5445 & 1607.8378 & 1713.8326 & 1598.6787 & \(-2.13\) & \(-0.57\) & \(-6.72\) \\ C101\_25 & 390.68 & 390.7074 & 359.4839 & 337.75 & \(-13.54\) & \(-13.55\) & \(-6.05\) \\ C101\_100 & 2571.4379 & 2353.2128 & 2195.8693 & 1382.78 & \(-46.23\) & \(-41.24\) & \(-37.03\) \\ RC101\_25 & 375.8553 & 367.7014 & 350.1739 & 349.6057 & \(-6.98\) & \(-4.92\) & \(-0.16\) \\ RC101\_100 & 1823.1095 & 1794.3128 & 1658.763 & 1619.0319 & \(-11.2\) & \(-9.77\) & \(-2.4\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of different algorithm strategies.  
Figure 7: Comparative iteration of different algorithms for solving RC101_25.  
algorithm to fall into the local optimum is easy, and the optimization effect is worse than that of the SACHOA algorithm.  
Figure 8 displays the convergence diagrams for the calculation of example RC101_25 using SACHOA, CHOA, CHOA+SA, and CHOA+CS algorithms. The results show that the SACHOA algorithm can converge to the relatively optimal solution faster than other algorithm combinations, which further proves the effectiveness of the algorithm.
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 7 UOC Near-Future Network Scenarios and Research Challenges  
The demands of next-generation networks (NGN) attract UOC, either directly or with RF for data communication, to meet the increasing data transportation requirements. NGN must handle a huge amount of data for every Pico second to connect water, ground, and air points for numerous emerging near-future applications. Proposed NGN architecture and today's research challenges associated with its establishment are reviewed in this section.
# Radiometric Identification of Signals by Matched Whitening Transform
## 1 Introduction
Five radar emitter identification fingerprints based on radar signal transients are compared. Traditional techniques include radio frequency (RF), pulse amplitude, pulse width, intentional pulse modulation type, or pulse repetition intervals. In [40], unintentional modulation information on the emitter waveform is used as RF fingerprints, to tie the received signal and its corresponding the emitter. Unintentional Modulation on Pulse (UMoP) is a method that exploits variations due to manufacturing differences of the transmitter hardware, including the power amplifiers UMoP is like a fingerprint of an emitter and can identify transmitters from the same model [41]. Variational Mode Decomposition to radar identification is reported in [42]. The data set consist of 47 emitters. Some of these emitters were productions of the same radar. Results demonstrate that the effective SNR value should be around 47 dB to obtain a correct classification probability larger than 0.9. In this work, the whitening transform is used as the framework for radiometric identification. This is fundamentally different than multiple discriminant analysis or deep learning. Identification is featureless, meaning it operates on raw complex IQ samples. Dimensionality reduction doesn't apply as the IQ data are two dimensional to begin with. Therefore, the costly feature extraction and dimensionality reduction common to most radiometric identification techniques is avoided. As a whitening detector, radiometric identification differs from multiple discrimination analysis in one key metric. The metric is the degree of whiteness of the transformed data whereas the metric in multiple discriminant analysis is maximum likelihood driven by the distance metric. The distance measure, Forstner-Moonen distance plays a key role in establishing the whiteness of the whitened data. This metric is the input to a mode function followed by the Majority Vote Classifier.
## 4 PSI-CA Protocol Constructed Based on DH Key Exchange Mechanism
### Experimental Analysis  
For this protocol, experiments were conducted and the runtime was recorded for various combinations of dataset sizes, as shown in Table 1. Cardinality here refers to the number of elements in the participant's dataset. For example, \(2^{10}\) means that the dataset has \(2^{10}\) elements. The cardinalities mentioned in the following tables all have the same meaning.  
Through the experimental data, an important phenomenon can be observed. Table 1 shows the estimated runtime of the above protocol under different data volume levels. For example: Initially, when the number of elements in Participant One's dataset is \(2^{10}\) and Participant Two's dataset is \(2^{15}\), the runtime of the protocol is 1.8095 s. In this case, there is a noticeable imbalance between the smaller side (Participant One) and the larger side (Participant Two). Now, if we expand the number of elements in Participant One's dataset (originally the side with fewer elements) to \(2^{15}\), while keeping Participant Two's dataset size constant at \(2^{15}\), the runtime increases to 5.2207 s, approximately three times the original. This indicates that although the runtime increases when the datasets are balanced, the increase is limited. However, if we keep Participant One's dataset size at \(2^{10}\) and increase Participant Two's dataset size to \(2^{20}\) (the same scale of change), the runtime dramatically increases to 56.1277 s, approximately 31 times the initial condition. This phenomenon shows that, in unbalanced dataset conditions, increasing the number of elements in the larger dataset significantly affects the efficiency of the protocol.  
These results reveal the importance of dataset balance in maintaining efficiency during the implementation of this protocol. Unbalanced datasets not only lead to extended runtimes but can also cause low resource utilization and delays in processing. However, in practical applications, when two parties want to obtain the private-set intersection's cardinality, their sets are often unequal and have a significant gap. Therefore, the current situation requires the design of a new protocol to eliminate the impact of dataset size imbalance on protocol efficiency.
# Visual and Textual Sentiment Analysis of a Microblog Using Deep Convolutional Neural Networks
## 3 Methods
### Fusion  
Because some messages have no accompanying images, in accord with reality, we record which microblogs include images before training and testing. When fusing the results, we adopt the following strategies: when an image exists in the current microblog message, we perform sentiment analysis of the message by fusing the visual and textual sentiment prediction; otherwise, we employ only the textual sentiment prediction.  
We employ late fusion to analyze the performance of our model (see Figure 2). We use Logistic Regression to perform sentiment prediction of the text and any related images individually and, finally, fuse the probabilistic results using the average strategy and a weight, which is learned from the labeled data.
## 1 Introduction  
Cloud computing is increasingly being adopted by large businesses, as well as small and medium sized businesses, for \"on-demand\" and \"utility computing\", which holds huge promise for the future of service computing [1]. Virtualization is a key enabling technology for cloud computing environments, which makes it possible to run multiple operating systems and multiple applications on the same hardware at the same time, so as to provide services by a virtual unit [2]. Through virtualization technology, not only can overall hardware utilization improve and lower costs for disaster recovery, but it can also achieve automatic monitoring for all hosts. However, it is very difficult to assign a large number of tasks to dynamic resources for distributed computing. There are a variety of factors that may lead to some nodes in the overload state while others remain in the underload state, such as uneven allocation of resources, user needs changing over time, newly joining nodes, and a high likelihood of failure in the overload nodes, _etc._[3; 4; 5].  
Load balancing is the most effective way to solve the above problem in a cloud computing infrastructure, which ensures that services are delivered transparently regardless of the physical implementation and location within the \"cloud\". In recent decades, great progress has been achieved for load balancing, and one of the most promising branches is swarm intelligence algorithms, such as ant colony optimization [6; 7; 8], artificial bee colony [9; 10], particle swarm optimization [11; 12], _etc._ Ant colony optimization, proposed by [PERSON] in 1992 [13], is a class of stochastic optimization algorithms based on the actions of an ant colony. By analyzing the previous work of ACO, we found that the ant colony optimization is suitable for load balancing applications in cloud computing because [14; 15; 16]: (1) the ant colony is able to crawl among different nodes to search for the optimal solution in cloud computing infrastructure; (2) the ACO is a kind of parallel mechanism that can be applied in distributed computing with high performance; and (3) it is a self-organizing algorithm based on the local information to make judgments and actions, that is, the system does not require a global control center.  
The motivation of this paper is to establish a load balancing mechanism which utilizes ACO to balance the tasks among nodes in cloud computing. The targets, including overload or underload nodes, will be quickly identified to operate load balancing by two types of ants with their communications by pheromones. The rest of the paper is structured as follows. Section 2 outlines the related work for load balancing algorithms. The dynamic load balancing strategy is presented for cloud computing in Section 3. Section 4 describes the details of the proposed strategy using the improved ACO. Simulation and results analysis are carried out in Section 5. At the end, a brief summary is given along with the future work.