esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification of Terahertz Link Modulation in Atmospheric Weather Conditions
###### Abstract
In this work, we report an attempt to bridge this research gap, with the investigation of a modulation identification method in a terahertz wireless link propagating under rain, fog, haze and wind weather conditions. This work aims to research the feasibility and effect of modulation identification in terahertz wireless communication, which is conducive to alleviating the burden of terahertz signal processing under severe weather conditions. Similar work has never been conducted before, but we think such comprehensive research is really necessary for the outdoor applications of terahertz wireless communication techniques. The method proposed in this work and the relevant conclusions obtained provide valuable references for the future construction of terahertz base stations. This method can be extended to other application scenarios, such as indoor or space-ground terahertz transmission.
## 2 Initiation of the Project
### Project Timeline  
In order to deliver the satellite on time for launcher integration, it is crucial to define a detailed work plan, and control the time spent on every phase of the mission. As an example, Figure 1 summarizes the timeline for the complete SE01 mission, and Figure 2 shows the most important milestones that were completed during the mission.  
A preliminary design should be completed without full knowledge of all parameters, since external factors have an influence on the project. For example, the frequency allocation process, which often involves a lengthy formal procedure, may affect the communications subsystem. This is the reason it is important to work on the design of the satellite, the test campaign, the frequency allocation and the ground station in parallel right from the beginning of the project.  
The inspection of components can start as soon as they arrive at the laboratory. Once the structural components are inspected, the integration of the satellite can begin without the need to inspect all components. The satellite may need additional adjustments to meet unexpected requirements or deviations from the design.  
It is possible to start programming the satellite at the beginning of the project. However, some software developing practices, such as Test-driven Development (TDD), recommend testing the software while it is being developed [30]. Therefore, in practice, software should be directly programmed on the target computer.  
Preparations for the test campaign consist of defining the tests, designing fixtures, and finding laboratory facilities that can perform the tests according to the specifications. The pure test campaign can be done in two weeks. However, functional tests are performed every time a new version of the software is developed, and thus may take a longer time.  
Figure 1: SE01’s project timeline.  
Figure 2: SE01’s project milestones.  
Setting up a ground station for the mission may take as much time as for making a satellite ready for launch. Furthermore, having one ground station does not guarantee reliable and consistent access to the satellite once it is in orbit. Opportunities for communication in LEO are scarce, with up to five contacts per day of less than 12 min. The viable approach to maximizing communication time with the satellite is to increase the number of ground stations used for the mission by creating a network of ground stations. Finding interested institutions and coordinating them is often a long-term initiative, which may take more time (and effort) than one CubeSat mission.
## 4 Conclusions  
Forecasting the unit cost of every product type in a factory is an important task. After the unit cost of every product type in a factory is accurately forecasted, several managerial goals (including pricing, cost down projecting, capacity planning, ordering decision support, and guiding subsequent operations) can be simultaneously achieved. However, it is not easy to deal with uncertainty in the unit cost. This paper presents some fuzzy collaborative forecasting models based on a few well-known fuzzy linear regression methods to predict the unit cost of a product. An example is used to illustrate the applicability of the proposed methodology. According to the experimental results,  
1. The effectiveness of the unit cost forecasting was greatly improved through the collaboration of the experts, especially when using FCF(CL2(\(o_{1}\), \(d_{1}\), \(m_{1}\)), CL2(\(o_{2}\), \(d_{2}\), \(m_{2}\))).
2. With respect to the quality of collaboration on the forecasting precision, only one performance measure is proposed and the proposed performance measure can effectively compare the differences among the models.
3. With respect to the forecasting accuracy on the forecasting accuracy among the performance measures, the one that considers MAPE can effectively compare the differences among the models.  
The contribution of this study includes the following:  
1. Six fuzzy collaborative forecasting models for the unit cost forecasting are investigated. From this, the most effective one can be identified.
2. More performance measures on the quality of collaboration have been proposed.
# Palm Date Leaf Clipping: A New Method to Reduce PAPR in OFDM Systems
## 2 OFDM Systems and PAPR  
The principle of the OFDM system is presented in Figure 1. On the transmitter side, the input data are mapped by one of the modulation (Mod) schemes: Quadrature Amplitude Modulation (QAM), or Phase-Shift Keying (PSK). Then, the signal is converted from Serial to Parallel (S/P), and after that, an inverse of Fourier Transformation is applied using the Inverse Fast Fourier Transformation algorithm (IFFT). The obtained signal is converted from Parallel to Serial (P/S) before being transferred to the channel. On the receiver side, the received signal is converted from Serial to Parallel (S/P) and demodulated by Fast Fourier Transformation (FFT). Then, the signal is converted from Parallel to Serial (P/S) and demodulated (Demo) to obtain data output (data Output).  
An OFDM signal \(x(t)\) is defined by the following equation:  
\[x(t)=\frac{1}{\sqrt{N}}\sum_{k=0}^{N-1}X_{k}*e^{(\frac{2\pi kt}{T_{s}})}\text{ with }0\leq t\leq T_{s} \tag{1}\]  
where \(T_{s}\) is the period of an OFDM symbol, \(N\) is the number of subcarriers, and \(X_{k}\) is the \(k\)th subcarrier of the same symbol. The baseband OFDM signal is sampled by applying the Nyquist rate (t = \(T_{S}\)/\(N\)). Therefore, the discrete OFDM signal in the time-domain can be expressed as  
\[x(n)=\frac{1}{\sqrt{N}}\sum_{k=0}^{N-1}X_{k}*e^{(\frac{2\pi k}{N})}\text{ \ }with\text{ \ }0\leq\text{ }n\leq N-1. \tag{2}\]  
The PAPR of the time domain OFDM symbol is defined as the ratio of maximal instantaneous power to the average power as  
\[PAPR=\frac{Max\left(\left|x(n)\right|^{2}\right)}{E\left(\left|x(n)\right|^{2 }\right)},\text{ \ \ }0\leq\text{ }n\text{ \ }\leq N-1, \tag{3}\]  
where \(E\{.\}\) represents the expectation value.  
The PAPR performance is portrayed using a Complementary Cumulative Distribution Function (CCDF), which is the probability that PAPR exceeds a threshold value. The CCDF is defined by the following formula:  
\[CCDF(PAPR_{0})=\Pr[PAPR\geq\ PAPR_{0}], \tag{4}\]  
where \(PAPR_{0}\) is a threshold value and \(N\) is the number of subcarriers.
# Joint Design of Transmit Waveforms for Object Tracking in Coexisting Multimodal Sensing Systems
## 4 Waveform-Dependent Performance Optimization Methods  
Our main objective was the design of a transmit waveform that is common for coexisting radar and MU communications systems under some performance metric constraints. As multiple studies have demonstrated over the last decades, each system considered separately encounters numerous performance tradeoffs when designing their transmit waveform. Considering the pulse-Doppler radar in Section 3.2, its transmit waveform can be selected, for example, to maximize the radar's range and range-rate resolution in order to reduce the MSE of the estimated position and velocity of a target when moving in a noisy environment. The transmit waveform for a single user in a wireless communications system must be selected to maximize the gross bit rate while reducing the BER performance. When multiple users are transmitting, each user's transmit waveform must also be designed to reduce interference between each other. As a result, performance tradeoffs must be considered between desirable gross bit rate, BER and MAI. While the aforementioned performance metrics were system-specific, other metrics, such as one system acting as interference to the other system, affect both systems.  
It is unrealistic to expect that one common transmit waveform can optimize system-specific and common performance metrics for both systems. As a preprocessing step, however, we started our waveform design by making use of some established results. In particular, it has been well-established that radar transmit waveforms with quadratic time-varying phase function improve range resolution in radars [39]. We have also recently demonstrated that these waveforms improve radar MSE performance under low signal-to-interference-plus-noise ratio (SINR), and in particular, in the presence of high communications interference [17; 21]. As a result, we first concentrated on a performance metric that is specific only to MU communications systems: We applied waveforms with quadratic time-varying phase function to reduce MAI. We can then extend the CoWS design to optimize common system performance metrics.
## 2 Materials and Methods  
### Participants  
A priori power analysis was conducted using G*Power (Version 3.1.9.7) to determine the required sample size for a mixed-design analysis of variance (ANOVA) involving both between-subject (gender: male, female) and within-subject factors (sitting strategy and chair type). Assuming a medium-to-large effect size (f = 0.45), an \(\alpha\) level of 0.05, power (1 \(-\) \(\beta\)) of 0.80, two gender groups, and nine repeated measurements (3 strategies \(\times\) 3 chairs), the analysis indicated that a minimum of 30 participants (15 per group) was required to detect statistically significant effects [35]. Our final sample met this criterion, consisting of 30 healthy young adults (15 men and 15 women), thereby ensuring adequate statistical power for detecting differences in postural variability--specifically in HI, TA, and KA--across different sitting strategies and chair types.  
Participants were recruited from Ming Chi University of Technology and were screened to ensure they had no musculoskeletal disorders or injuries within the past year, particularly low back pain [2]. Exclusion criteria included any history of spinal surgery, current spinal discomfort or pain, recent lower limb or trunk trauma, and any neurological conditions affecting posture or balance. Prior to participation, all individuals received a comprehensive explanation of the study's aims and procedures. The experiment complied with the ethical standards set forth in the 2013 Declaration of Helsinki and was approved by the Ethics Committee of National Taiwan University. Written informed consent was obtained from all participants, who were also compensated for their time. To minimize confounding effects on postural performance, participants were asked to refrain from strenuous physical activity, alcohol consumption, and sleep deprivation for at least 24 h before testing [36]. The male participants (_n_ = 15) had a mean (\(\pm\)SD) age of 24.0 \(\pm\) 4.6 years, height of 174.5 \(\pm\) 6.8 cm, and weight of 74.8 \(\pm\) 13.8 kg. Female participants (_n_ = 15) had a mean age of 21.0 \(\pm\) 2.4 years, height of 158.7 \(\pm\) 5.1 cm, and weight of 52.3 \(\pm\) 10.2 kg. These values were compared to normative Taiwanese anthropometric data [25] to confirm sample representativeness.
## 2 Optimized Distributed Raman Amplification Design over Single Fiber Span
### Distributed Raman Amplification with Backward Pumping Only
In Figure 5b, using the non-optimized pump power configuration (600 mW second-order pump power), the peak Kerr product power reduction was improved to \(\sim\)30 dB, but once the pump power was optimized to maximize the symmetry (330 mW second-order pump power), the Kerr product reduction was increased to 39 dB. The random-fiber-laser-based scheme gave the lowest symmetry, and therefore the reduction in Kerr product power with OPC was limited to \(\sim\)20 dB, as illustrated in Figure 5c. Figure 3: (a) Raman-fiber-laser-based amplification with second-order pumping; (b) simulated signal power profiles. Figure 2: (a) Dual-order backward pumping; (b) signal power profiles along the fiber. An inline coherent transmission experiment was conducted by replacing the two CW lasers shown in Figure 4 with a 256 Gb/s DP-16 QAM (32 GBaud) signal (centered at 194.8 THz). After the transmission, the signal or its conjugate (centered at 194.65 THz) was amplified with an EDFA as the receiver amplifier before being detected by a polarization-diverse coherent receiver (100 GSa/s, analog bandwidth of 33 GHz). Commercial digital signal processing (DSP) software was used to process the captured data from the scope with Q\({}^{2}\) factors calculated from the bit-error-rate of 500,000 bits. As shown in Figure 6, \(\sim\)5 dB improvement in the nonlinear threshold and \(\sim\)7 dB increases in launch power for a fixed Q\({}^{2}\) in the nonlinear region are achieved when using the nearly perfect (\(\sim\)97%) signal power symmetry provided by dual-order BW-pumping DRA. The optimum Q\({}^{2}\) factor was reduced because of the short transmission distance (100 km) and the additional ASE noise added by the inclusion of the OPC. However, using the optimized OPC in a long-haul transmission system has been proven to significantly improve the system performance, since the transceiver and OPC noise are negligible compared with the accumulated link noise which is the majority of the linear noise [23]. Figure 4: Schematic diagram of nonlinear product measurement using dual-order DRA in mid-link OPC systems. Figure 5: Theoretical predication of nonlinear Kerr product power as a function of frequency separation between the two CW lasers over 50 km SSMF. (**a**) First-order Raman amplification. (**b**) Dual-order Raman amplification with optimized pump power and non-optimized pump power). (**c**) Random Raman-fiber-laser-based amplification.
## 2 Model Description
### Mesh Independency Study  
In order to make sure the results obtained independent of the mesh refinement for the model developed, three meshes with distinct levels of refinement were created with \(8.5\times 10^{4}\), \(1.54\times 10^{5}\), and \(2.86\times 10^{5}\) elements, respectively. The mesh containing about \(1.54\times 10^{5}\) elements results in a 2% deviation in terms of pressure drop and outlet temperature, compared to the mesh with around \(2.86\times 10^{5}\) elements. The deviation in results is found to be about 9% between the coarsest mesh (containing \(8.5\times 10^{4}\) elements) and the finest one (\(2.86\times 10^{5}\) elements). Thus, the intermediate mesh consisting of around \(1.54\times 10^{5}\) elements was chosen for all the further numerical investigations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Property** & \(\mathbf{c_{p,s}}\) & \(\mathbf{\rho_{s}}\) & \(\mathbf{k_{s}}\) & \(\mathbf{d_{p}}\) \\ \hline Value & 1000 (J/kg\(\cdot\)K) & 3000 (kg/m\({}^{3}\)) & 2.68 (W/m\(\cdot\)K) & 0.8, 1.0, and 1.2 (m) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermal energy storage material (rock) thermophysical properties.
# Feature Normalization Reweighting Regression Network for Sugar Content Measurement of Grapes
## 1 Introduction
### Contribution  
In this study, we divided the grape dataset into multiple categories based on the imbalanced distribution of the grape's sugar content. A deep convolutional neural network, transfer learning method, and transformer model are adopted to extract the image features of the grapes from the input data. We then present a computer vision system for non-destructive testing of table grapes. The system was upgraded by optimizing the loss function, which improved the accuracy of the test dataset. The major contributions are as follows:  
1. A feature normalization reweighting regression network (FNRR-Net) is proposed for imbalanced distribution grape datasets, which has a high degree of confidence in predicting the sugar content of grapes.
2. We group and label the datasets in different sugar content intervals and propose a balanced loss function under the visual transformer model to accommodate the imbalanced grape datasets.
3. The non-destructive measurement of a grape's sugar content is efficient, economical, and convenient.
# Mitigating DDoS Attacks in LEO Satellite Networks Through Bottleneck Minimize Routing
## 1 Introduction
### Motivation  
Since bottleneck paths exist in the network, once attackers identify and continuously probe these bottleneck links, they can repeatedly locate them with low-rate probing traffic, and then launch concentrated floods at critical moments, seriously damaging network availability; once an attacker identifies and continuously probes these bottleneck links, they can repeatedly locate them with low-rate probing traffic and then unleash a concentrated flood at critical moments, severely impairing network availability.  
Current multipath routing implementations predominantly rely on random or load balancing to enhance path unpredictability [23], yet fundamentally fail to mitigate bottleneck exploitation risks. Even emerging intelligent solutions, such as deep learning-based anomaly detection [15] or blockchain-secured routing [24], show limited practicality in resource-constrained LSN environments due to their high computational overhead. At the control layer, several studies have focused on fractional-order multi-agent systems [25], incorporating adaptive mechanisms to counteract time-varying attack gains induced by deception attacks [26]. This comprehensive survey [27] shows that intrusion detection system (IDS) methods have been introduced in spatial networks, but their computational overhead may introduce non-trivial detection delays under bursty or high-volume traffic and may also cause detection performance degradation under high-volume traffic.  
Prior work [28] has shown that under malicious intervention Border Gateway Protocol (BGP) prefix hijacking can concentrate originally dispersed routes onto a single bottleneck link. Further amplifying the bottleneck effect [29] demonstrated, in their LSN study, that by leveraging publicly available TLE orbital data and simple throughput probes, attackers can accurately identify and continuously overwhelm the current busiest ground-satellite link, achieving up to a 3.4\(\times\) reduction in global user traffic. This implies that unless defenses incorporate an assessment of high-risk bottlenecks and avoid them, attackers can repeatedly probe and reuse the same hotspot links, continuously evading mitigation.  
Despite these demonstrated risks, no existing LSN DDoS mitigation framework systematically incorporates bottleneck risk profiling into routing weight optimization models. This oversight perpetuates the exposure of high-frequency attack surfaces, underscoring the urgent need for risk routing paradigms.
# A Random Subcarrier-Selection Method Based on Index Modulation for Secure Transmission
## 4 Simulation Results
### The Simulation Results of Computational Complexity  
To visualize the computational complexity of the two schemes, we simulated the number of addition, multiplication, and modulo operations and selected \(N_{s}=512/1024\) and \(N_{T}=64\). When \(\delta_{l}\) firstly reached 95% of the maximum random degree, the number of loops \(N=23/22\). The concrete computational complexity is depicted in Figure 7. Hence, we could see that the number of additions, multiplications, and modulo operations in RSCS-IM were all less than that of PSS Plus RP. The complexity of RSCS-IM was lower. Comparing Figure 7a with Figure 7b, when \(N_{s}=1024\), the complexity of PSS Plus RP was basically unchanged since the number of loops was basically independent of \(N_{s}\), while the complexity increased slightly, when employing RSCS-IM, in addition and multiplication  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline \(f_{c}\) & 2.404 GHz & Reference frequency. \\ \(B\) & 20 MHz & Signal bandwidth. \\ \(N_{s}\) & 512/1024 & The number of total subcarriers. \\ \(N_{T}\) & 64 & The number of antenna array elements. \\ \(E_{b}/N_{0}\) & 9 dB & Average bit energy to noise power ratio. \\ \((\delta_{0},R_{0})\) & (\(30^{\circ}\),100 m) & The desired position. \\ \(\varphi_{0}\) & 0 & The constant of Equation (13). \\ \hline \hline \end{tabular}
\end{table}
Table 3: The system parameters in our simulation.  
operations. However, the complexity of RSCS-IM was still substantially lower than that of PSS Plus RP when \(N_{s}\) was not particularly large.
## 5 LBV of Key Hydrocarbon Components in Aviation Fuel  
Hydrocarbons, as the primary constituents of aviation fuel, play a crucial role in determining combustion characteristics. By employing the stagnation flame method, [PERSON] and [PERSON] corrected significant measurement errors in alkanes caused by flame stretch, thus obtaining more accurate LBV data [67]. Their findings revealed that, excluding methane,n-alkanes have nearly identical LBVs. Since [PERSON] et al.'s work on the impact of flame stretch on LBV, accounting for flame stretch effects has become standard practice in LBV measurements. [PERSON] et al. compiled extensive LBV data for low-carbon alkanes, documenting whether numerical corrections for flame stretch were applied. However, aviation fuels predominantly comprise hydrocarbons with a carbon number distribution centered around C12, typically following a normal distribution from C8 to C16 [11]. Consequently, LBV data for low-carbon alkanes alone are insufficient for studying aviation fuel components. Comprehensive research and a summary of LBV characteristics for n-alkanes, iso-alkanes, cycloalkanes, and aromatic hydrocarbons in aviation fuels are necessary. Such research will enhance the understanding of how various components influence the LBV of aviation fuels and improve the combustion performance of both traditional aviation fuels and sustainable aviation fuels through targeted component blending.
# Principle and Implementation of Stokes Vector Polarization Imaging Technology
## 3 Implementation of Stokes Vector Polarization Imaging Technology
### Non-Simultaneous Imaging  
In the 1970s, non-simultaneous polarization imaging, which works by rotating polarizing and wave plates, emerged. In the early days, photographic film was used to record images, which were carried on u-2 high-altitude reconnaissance planes to spy on former Soviet missile sites. Non-simultaneous polarization imaging can obtain images of different polarization states of the same scene at different times. The non-simultaneous polarization imaging device is simple in structure and low in cost, but different polarization images are acquired at different times. According to [PERSON] theory, the measurement of four Stokes parameters of a target must be carried out on the same target under the same imaging and illumination conditions so as to truly reflect the polarization characteristics of the measured target. At present, non-simultaneous polarization imaging is mainly realized in two ways: rotary polarization imaging and electrically controlled polarization imaging.