esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on VVC Intra-Frame Bit Allocation Scheme Based on Significance Detection
## 2 Related Work  
### Video Coding Standard  
A wide variety of video applications have given rise to multiple video coding methods from the very beginning. To ensure interoperability of encoded streams and standardization of decoding across a broad spectrum, international standards for video coding typically embody the latest advancements in video coding technology from the same generation [5]. Figure 1 shows the development of each family of video coding standards.  
The H.261 [6] coding standard is predominantly designed for low-bit rate and high real-time video transmission. Being the inaugural iteration of video coding standards, it holds significant guiding importance within the domain of video coding. In 1995, the International Telecommunications Union-Telecommunications Standardization Sector (ITU-T) and the International Organization for Standardization (ISO)/International Electrotechnical Commission (IEC) organizations collaborated for the first time to develop the  
Figure 1: Development process of video coding standards in various series.  
H.262/MPEG-2 [7] standard, which improves the computational power and complexity of video coding algorithms compared to the H.261 standard. The H.263 [8] standard was developed for the transmission of low-bit-rate video communication, which provides more efficient video coding than the H.261 coding standard and better single-frame images in the video. Advanced Video Coding (AVC) [9] greatly improves video compression performance while having the Bit Error Rate (BER) resistance to transmit the information over the network. HEVC [10] follows the basic coding framework of AVC, with updated substitutions of techniques in the basic modules. Compared to the AVC coding standard, the HEVC standard reduces the BER and increases the correctness of real-time transmission. The VVC standard [11] is similar to previous international video coding standards in that it still employs a hybrid coding framework, i.e., multiple methods are used for coding at the same time, but more advanced coding techniques are used in key modules [12]. Under the same Peak Signal to Noise Ratio (PSNR) conditions, the performance of the VVC standard saves an average of 50% bitrate compared to HEVC, while the decoding complexity is no more than twice that of the HEVC standard [13]. So far, the VVC test model (VTM) has been released up to version 22.2.
# Modulation Characteristics of High-Speed Transistor Lasers
## 3 Modulation Characteristics of TL
### Optimization of Each Layer in the Base Region  
As shown in Figure 4b, the base region thickness (W\({}_{\text{B}}\)) is mainly composed of QW thickness (W\({}_{\text{QW}}\)), barrier width (L\({}_{\text{b}}\)), and SCH layer thicknesses (W\({}_{\text{SCH1}}\), W\({}_{\text{SCH2}}\)). The modulation speed of TL is highly dependent on the base region because both the recombination of carriers and the generation of light occur in the base region. The carrier transit time in the SCH layer, the recombination time within the QW, and the transit time between QWs affect both the photon generation and TL modulation rates. These parameters are closely related to the width of each layer structure in the base region. Therefore, the adjustment of the width of each structure in the base region to improve the modulation rate is feasible [47]. W\({}_{\text{QW}}\) affects the ability of QWs to trap and confine carriers. By changing W\({}_{\text{QW}}\), the carrier recombination lifetime can be regulated and the relaxation oscillation can be adjusted. L\({}_{\text{b}}\) determines the main mode of carrier transport between wells and the ratio of diffusion to tunneling. The SCH layer acts as a composition-changing layer to increase the additional potential energy and accelerate the transport of carriers, which can further improve the modulation bandwidth.
## 2 Problem Definition
### MP-Based Improvement Search
The overall pseudocode of our hybrid metaheuristic, denoted by HQ-ACO, is presented in Algorithm 2. The initialization requires the definition of the quantum values \(\alpha_{ds\ell}\), \(\beta_{ds\ell}\), which are employed to initialize the pheromone values \(\phi_{d,s,\ell}\) associated with activating a lot \(\ell\) in subdistrict \(s\) of district \(d\). Then an outer loop with a time-limit arrest condition is executed. At every execution of the loop, \(M\) ants iteratively build \(M\) complete Parking Lot States, thus defining \(M\) feasible solutions to the optimization problem. If a solution is better than the best found, an update is conducted. At the end of each construction phase of the \(M\) ants, the pheromone trails are updated. Once the time limit of the outer loop is reached, the quantum-ACO construction phase ends and the best solution found is used as a basis to define the neighborhood used in RINS. ```
1:Let \(x^{\text{best}}\) denote the best solution found and set its corresponding value to \(v(x^{\text{best}}):=-\infty\)
2:Calculate the values \(\alpha_{\delta\ell}\), \(\beta_{\delta\ell}\) by Formulas (7) and (8)
3:Initialize the pheromone values \(\phi_{\delta s,\ell}\)
4:while a global time limit is not reached do
5:for\(m:=1\) to \(M\)do
6: Build a complete Parking Lot State (PLS) and let \(x^{\text{PLS}}\) be the corresponding solution
7:if\(v(x^{\text{PLS}})\geq v(x^{\text{best}})\)then
8: Update the best solution found by setting \(x^{\text{best}}:=x^{\text{PLS}}\) and \(v(x^{\text{best}})=:v(x^{\text{PLS}})\)
9:endif
10:endfor
11: Update the pheromone values by Formula (9)
12:endwhile
13: Run the exact neighborhood search solving problem BLP-ESPLR(\(x^{\text{best}}\)) for improving \(x^{\text{best}}\)
```  
**Algorithm 2** Hybrid Quantum-based ACO (HQ-ACO)
# An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets
## 2 GNSS Satellite Constellation Determination
### Optimization Algorithms  
In the current study, five optimization algorithms (ABC, ACO, GA, PSA, SA) were implemented to find the optimal GNSS satellite subset by using the parameter CNR-WGDOP as a cost function, of which the minimum value would indicate the corresponding optimal GNSS satellite subset. In contrast to the TM, optimization algorithms can identify an optimal solution (i.e., the optimal GNSS satellite subset) without checking the quality of all possible solutions, requiring potentially less time. However, the accuracy of the optimal solution of optimization algorithms is subject to their functioning principles, settings, and parameters, which are explained briefly below.
# An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevine
## 5 Conclusions  
The present study focuses on an IoT approach to disease detection in grapevine plants in field conditions by implementing deep learning (DL) algorithms, trained under the transfer learning (TL) technique and using commercially available data sets.  
Firstly, this study demonstrates the effective use of TL in adapting pretrained DL models to the context of grapevine disease detection. This approach strikes a balance between model efficiency and computational requirements, enabling the deployment of lightweight models on edge devices like the Raspberry Pi while maintaining high accuracy.  
Additionally, the Mobile-UNet was successfully implemented for real-time segmentation of plants from their background, with a high intersection over union (IoU) accuracy and fast inference times, which play a crucial role in the generation of the final model's decision.  
Among the DL models considered, MobileNet has been identified as the optimal algorithm for disease detection in this study. It has showcased superior performance while necessitating lower power and memory resources. As a result, MobileNet enables real-time disease detection with the designated frame rate of the RGB camera, contributing to efficient and timely results.  
This study goes beyond traditional disease detection by integrating the DL models into an IoT framework that can be further connected to robotic platforms, using ROS, for automated, real-time monitoring and response systems in agriculture.  
Emphasizing the use of open-source software and commercially available datasets, this research addresses the practical constraints of agricultural technology deployment, making it accessible and replicable.  
In conclusion, this research not only highlights the feasibility of employing IoT devices with modest power and memory requirements for the real-time detection of grapevine diseases under field conditions, it also introduces novel methodologies and integrations that have the potential to significantly advance disease monitoring and management in the grapevine industry and beyond.  
Conceptualization, A.M., K.D. and G.T.; methodology, A.M., K.D. and G.T.; software, A.M. and K.D.; validation, A.M., K.D., R.B. and G.T.; formal analysis, A.M., R.B., K.D. and G.T.; writing--original draft preparation, A.M., G.T. and K.D.; writing--review and editing, A.M., G.T., K.D., X.E.P., D.K., R.B. and D.B.; visualization, A.M., G.T. and K.D.; supervision, D.K. and D.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All the data are confidential under the regulations of ATLAS H2020 project.  
This work was supported by the HORIZON 2020 Project \"ATLAS: Agricultural Interoperability and Analysis System\" (project code: 857125) financed by the European Union under the call H2020-DT-2018-2020.  
The authors declare no conflict of interest.
# Optical OFDM Error Floor Estimation by Means of OTDR Enhanced by Front-End Optical Preamplifier
## 3 Test Results  
### Test System  
We used the HP 8147A OTDR, designed for fiber characterization by network operators for installation and field maintenance of their optical transmission systems [9].  
The OTDR in Figure 7 was used with the 1310/1550 nm plug-in optical interface module, and it was connected in the test configuration alternately with the BER tester (BERT) according to Figure 8, where it can be seen that the OC port 2 is isolated from the adjacent OC port 3, as these are externally interconnected by the OA.  
Figure 7: OTDR under test.  
We used a standalone Ethernet tester to detect and count erroneous frames of 1518 Bytes, each containing at least a single bit error. In this work, we consider the frame error rate as BLER, and adopted the operation with maximal possible throughput while performing the out-of-service end-to-end Ethernet measurements according to Rec. RFC 2544 [1], with the SW loopback at the far-end.  
As network operators prefer in-service practical test methods, which include fiber testing with the OTDR, as well as the end-to-end transmission performance testing with the BERT, we used both of these connected to a Gbit/s \"dark\" fiber, to not interrupt active fibers carrying live traffic by neither OTDR nor BER testing (The end-to-end connection encompasses both fiber and microwave radio-relay sections [22]).  
As our BER floor model requires high SNR and thus large DR, both earlier considered as achievable by longer transmitted pulses (at the price of being less realistically considerable as impulses), we selected 1 us as a good compromise for the fiber under test.
# A Review on Styrene Substitutes in Thermosets and Their Composites
## 4 Properties of Styrene-Free Thermosets and Their Composites  
### Processibility of Thermosets  
Processability should be primarily considered for the development of new resins in composites preparation. Viscosity of resin is one of the most important flow parameters to determine the end application of thermosetting resin. Viscosity not only affects the production cost of resin, but also is related to the properties and application of composites. Thus, in the process of developing resins with new RDs, the following factors that affect the viscosity of resin should be concerned. (1) Molecular weight of RDs: the increase of molecular weight can cause a sharp increase of apparent viscosity. Viscosity is directly related to the average molecular weight of resin. (2) Intermolecular force: generally, if there is an internal force such as hydrogen bond, the mobility of resin will be restricted and thus the viscosity increased [80]. For instance, the replacement of styrene with triglyceride in UPE significantly increased the viscosity of the resin system, which dramatically increased the difficulty of fabricating bamboo fibers reinforced composites [81].  
Curing temperature of resins is closely related to the production cost of composites because of the required energy consumption. The maximum curing temperature of a resin system is determined by the reactivity of C=C bonds in the comonomers and the types of initiators. For the resins with  
Figure 11: Synthesis of divinyl rosin and trivinyl rosin from rosin acid [61].  
different RDs, the curing temperature is affected by the reactivity of RDs and their reactivity ratio with main component. For example, the reactivity ratios (\(r\)) of VL-UPE (\(r_{\text{VL}}=0.01\) and \(r_{\text{UPE}}=0.81\)) system are lower than 1 [60], which attests that both monomers are more likely to copolymerize rather than homopolymerize with a tendency toward alternation. To demonstrate the influence of RDs on the curing temperatures of resins, the AESO resin systems with different RDs initiated by _tert_-butyl perbenzoate (TBPB) were investigated. It is reported that NVP-AESO resin has higher curing temperature than styrene-AESO resin because of the high reactivity of NVP [30; 82]. Similarly, isocyanatoethyl methacrylate (IEM) containing methacrylate groups has a much higher reactivity than styrene, which makes the maximum curing temperature of IEM-AESO resin significantly lower than that of styrene-AESO resin [83].
# Statistical Analysis and Modeling for Optical Networks
## 2 Literature Review on Statistical Analysis and Modeling for Networks
### Statistics and Network Characterization
This involves analyzing how statistical analyses reveal hidden patterns, correlations, and critical dependencies within optical networks that might not be apparent through deterministic approaches. For example, insights into the \"small-world\" or \"scale-free\" properties of optical networks derived from nodal degree distributions, or the impact of link length variability on overall network resilience, will be highlighted. Finally, a crucial aspect of this methodology is the explicit analysis of the relation between the statistical models and the performance of the optical networks. This segment will bridge the gap between theoretical statistical constructs and their practical implications for network behavior. It explores how accurately estimated parameters from distributions like GEV and Johnson S\({}_{\text{B}}\) translate into quantifiable improvements in network design or how they facilitate the identification of critical network bottlenecks and vulnerabilities, thereby guiding effective mitigation strategies. The aim is to provide a holistic understanding of how statistical rigor directly contributes to enhancing the efficiency, reliability, and scalability of optical communication infrastructures. The findings from the literature review and statistical tool analysis were synthesized to demonstrate the overall importance of statistical analysis and modeling in optical network design and optimization. The study discussed how leveraging statistical insights can lead to the development of more efficient, reliable, and scalable optical communication systems.
## 4 Computational Simulations and Experimental Results
### Hardware Experimental Testing
#### 4.3.2 NLOS Communication BER Testing  
For the evaluation of NLOS scattering communication capabilities in UV communication, outdoor communication tests were conducted. During these tests, the transmitter and receiver were positioned on opposite sides of various obstructing elements such as trees and buildings. Due to the obstruction caused by these elements, communication between the transmitter and receiver relied on scattering. The linear distance between the two ends was 65 m, with obstacles reaching a maximum height of 4 m. Both the transmitter and receiver were set at a 20-degree offset angle. Figure 10 illustrates the physical setup of the experimental equipment.  
In Figure 10, \"TX\" represents the transmitter location, and \"RX\" represents the receiver location. \"PC\" stands for the computer used for data transmission, \"UV system\" represents the prototype of the system, \"DC-power supply\" is the voltage source powering the prototype, and \"Portable power station\" is the outdoor power source supplying power to various electrical devices.  
Figure 9: Different stages of signal states: (**a**) carrier signals output from FPGA; (**b**) the modulated carrier signal from the transmitter; (**c**) photodetected signals from the receiver.  
The results of the on-site tests, as presented in Table 1, revealed that out of a total of 131,072 transmitted data bits, 98 bits were found to be erroneous, resulting in a BER of 7.52 \(\times\) 10\({}^{-4}\). The variation in the bit error rate was observed by altering the receiving angle while maintaining the transmission angle fixed at 20 degrees. This variation is illustrated in Figure 11, where the receiving angle ranged from 10 degrees to 30 degrees. The simulated data were simultaneously incorporated into Figure 11 to compare the simulated and actual experimental results.  
Within the range of receiver angles from 10 degrees to 20 degrees, the BER exhibits a linear trend. However, beyond a receiver angle of 20 degrees, the BER shows a significant  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Transmission Range & 65 m \\ Height of Obstacle & 3–4 m \\ Scattering angle & \(\approx\)20\({}^{\circ}\) \\ UV Light Wavelength & 254 nm \\ System Rate & 5 kbps \\ Number of Error bits & 98 \\ Total Number of bits & 131,072 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters and results of outdoor NLOS field test.  
Figure 11: The relationship between BER and \(\theta_{2}\).  
Figure 10: The NLOS test site and the transmitter-side special platform.  
increase. It can be observed that the experimental and simulation results exhibit the same trend of variation, with the simulation BER generally outperforming the experimental BER. The simulated BER is relatively close to the experimental results at lower angles, but as the angle increases, the disparity gradually widens.
## 2 Design of the Wind Tunnel Experiments
### Fundamental Parameters of the DCFJ Rotor Airfoil  
Two fundamental parameters of DCFJ technology are the obstruction factor (OF) [19] and relative unit length (_RUL_). As illustrated in Figure 9, the combination of one tab and one injection slot is defined as one blocking unit of the DCFJ injection, and the DCFJ injection contains a lot of blocking units. The OF is defined as the ratio of each tab length to the blocking unit length. The _RUL_ is defined as the ratio of the blocking unit length to the airfoil chord length. Figure 9, the black regions denote the tabs, and the blank regions denote the injection slots.  
The fundamental parameters are defined as:  
\[l=a+b \tag{1}\]  
\[OF=a/l \tag{2}\]  
\[RUL=l/c \tag{3}\]  
where \(a\) indicates the tab length, \(b\) implies the injection slot length, \(l\) denotes the blocking unit length and \(c\) is the airfoil chord length.
## Abbreviations  
The following abbreviations are used in this manuscript:  
IoT Internet of Things D2D Device-to-Device EE Energy Efficiency HTC Human-Type Communication MTC Machine-Type Communication QoE Quality of Experience eMBB enhanced Mobile Broadband mMTC massive Machine Type Communication URLLC Ultra-Reliable Low Latency Communications V2X Vehicle-to-Everything CMS Conventional Multicast Scheme MCS Modulation and Coding Scheme SFN Single Frequency Network NR New Radio SCS Subcarrier Spacing OMA Orthogonal Multiple Access OFDM Orthogonal Frequency Division Multiplexing TTI Transmission Time Interval CQI Channel Quality Indicator RB Resource Block KPI Key Performance Indicator BLER BLock Error Rate TDD Time Division Duplexing ADR Aggregate Data Rate NEE Network Energy Efficiency
# Gaze-Dependent Image Re-Ranking Technique for Enhancing Content-Based Image Retrieval
## 3 Gaze-Dependent Image Re-Ranking
### Initial Retrieval  
The proposed method takes the query image \(I_{\text{q}}\) as the input for the conventional CBIR model and ranks candidate images \(I_{n}\) (\(n\) = 1,..., \(N\); \(N\) represents the total number of candidate images) for the first step. As the proposed method necessitates the use of textual information to process captioning information during the re-ranking part, we expect that the CBIR model can handle both image and text elements naturally. Therefore, the proposed method focuses on cross-modal retrieval methods to perform the initial retrieval. Specifically, we introduce the CLIP model [23], which has been widely employed in contemporary image retrieval research and has exhibited significant retrieval precision.  
The CLIP model employs a transformer-based encoder to create a shared latent space that facilitates the comparison of feature vectors obtained from both the query image and the candidate images. Furthermore, the initial results can be obtained by calculating their similarity \(s_{\text{q},n}\) as shown in the following equation.  
\[s_{\text{q},n}=\frac{\mathbf{v}_{\text{q}}\cdot\mathbf{v}_{n}}{|\mathbf{v}_{\text{q}}||\bm {v}_{n}|}, \tag{1}\]  
where \(\mathbf{v}_{q}\) and \(\mathbf{v}_{n}\) represent the feature vectors of the query image and candidate images, respectively. The operator \".\" represents the dot product that takes two vectors and returns a scalar value. The dot product of two vectors is defined as the product of their magnitudes and the cosine of the angle between them.