esa-sceva/satcom-chunk-collection huggingface dataset:
# Quadcopters in Smart Agriculture: Applications and Modelling
## 4 Modelling of Quadcopters
### Motion Equations  
Two techniques can be used to derive the motion equations of quadcopters: the Newton-Euler technique or the first principle of approximation. The first technique is based on the Newton-Euler and Euler-Lagrange methods, while the second technique relies on quaternion and superposition approaches. Many methods, such as angular orientation, voltage-based techniques, and force-moment, are based on the Newton-Euler technique, since it is widely used and more preferred and efficient than the first principle of approximation [117; 125]. Therefore, the mathematical motion equations presented in this work are based on the Newton-Euler technique, specifically on the angular orientation approach.
# Achieving Sender Anonymity in Tor against the Global Passive Adversary
## 4 The Proposed Protocol
### Set-Up Phase
#### 4.2.2 Token Generation  
After exchanging the forward keys, at a given time \(t_{0}\), each node \(r_{i}\) generates \(k-1\) empty tokens and sends them to its next. In turn, \(next(r_{i})\) forwards the tokens to its next, and so on. Each token is encrypted by \(r_{i}\) with \(k_{r_{i}}\); then it is sent to \(next(r_{i})\), which decrypts it with \(k_{r_{i}}\), processes the token, re-encrypts it with \(k_{next(r_{i})}\) and forwards it to \(next(next(r_{i}))\).  
The structure of these tokens is the following: \(\langle F,PDH,R,H\rangle\) where \(F\) is a flag denoting whether the token is empty (\(F=0\)) or filled (\(F=1\)), \(PDH\) is a field containing a public Diffie-Hellman parameter (possibly encrypted), \(R\) is a random playing the role of identifier and \(H\) is a hashed value (the exact meaning of these fields will be clear in the following). Observe that \(PDH\), \(R\) and \(H\) are meaningful only if \(F=1\). The tokens are born with \(F=0\). Therefore, at the beginning, there are \(k(k-1)\) empty tokens turning in the ring.  
Starting from a time \(t_{1}>t_{0}\), each node \(r_{i}\) waits a random time \(\delta_{i}\), and then fills the first available empty token, as explained in the following.
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 3 Data Collection II: Stationary Target Reflection
### Signal Model at the Target  
General target behavior can be modeled as a Doppler shift \(e^{2\pi\
u_{n}t}\) with a fast-time varying amplitude \(\sigma_{n}(t)\), where \(\
u_{n}=2\
u f_{n}/c\) is the Doppler shift caused by a target with radial velocity \(v\) and \(\sigma_{n}(t)\) is the fast-time amplitude of the target reflection at fast-time \(t\). The target behavior is incorporated into Equation (1) as  
\[F_{tgt}(t;R_{t},\theta_{t})=\sum_{n=0}^{N-1}\sigma_{n}(t)\,e^{2\pi\
u_{n}t}\,e ^{2\pi\
u_{n}(t-R_{n}/c)}. \tag{7}\]
# A Parallel Open-World Object Detection Framework with Uncertainty Mitigation for Campus Monitoring
## 3 Method
### Parallel OWOD Framework with Uncertainty Mitigation  
Our parallel OWOD framework is based on the state-of-the-art object detection model D-DETR [16]. As illustrated in Figure 2, our framework comprises an objectness-driven discovery module, a known object detector, and a collaborative open-world classifier. The framework utilizes a parallel architecture to decouple the learning processes to address the challenges of uncertainties within OWOD. The objectness-driven discovery module converts all known objects into the same class and guides the model to focus on the object ness derived from the supervision of different objects, thereby alleviating the unknown discovery uncertainty. The objectness-driven discovery module effectively aids in the discovery of more potential objects distinct from the background including unknown objects. The known object detector retains the fundamental approach of D-DETR to ensure precise detection performance for known classes. Detection of known and unknown classes is carried out separately by different sub-modules within the parallel architecture, which ensures that the learning of known and unknown classes does not interfere with each other, effectively alleviating the known discrimination uncertainty.  
During the inference phase, the image is separately processed by both the objectness-driven discovery module and the known object detector. The outputs are then integrated through the collaborative open-world classifier, facilitating cooperative learning for both known and unknown classes. The framework ultimately leads to strong detection performance for both known and unknown classes.
# Time to First Fix Robustness of Global Navigation Satellite Systems: Comparison Study
## 3 Results
### Signal Power  
The power of the GNSS signals facilitates the tracking of the satellites and, consequently, the reduction in the TTFF. Therefore, the TTFF is more robust if the power of its signals is high enough to be detected and used by the receiver. Apart from the original signal power of the satellite and the constellation geometry [32], which may vary between GNSSs, several environmental factors can degrade the SNR detected at the receiver. These include atmospheric effects such as ionospheric scintillation [33], physical obstructions and multipaths caused by natural or human-made objects [34], and unintentional or intentional radio-frequency interference.  
In this study, the environmental factors are identical for all GNSSs, as the same recorded spectrum is applied for each of them. Therefore, the differences in SNR between  
Figure 4: Tracked satellites versus attenuation for the different GNSSs.  
Figure 3: Satellites tracked along the 4 days of the test for the different GNSSs without attenuation.  
them are only due to their signals and constellation characteristics. In order to assess this, the average SNR of the different GNSSs as a function of attenuation is shown in Figure 5. These SNR values are obtained as the mean of the SNR levels of all the tracked satellites, provided by the GSV NMEA message, at the time of the first fix or at the end of the test run (10 min) if no fix is achieved. It must be noted that the set of tracked satellites may vary for different attenuation values, preventing a simple linear relationship between SNR and attenuation.  
The results show that, for low attenuation levels, BeiDou exhibits the highest SNR values. However, from 8 dB onwards, GPS is the one that provides the best results. In addition, Galileo outperforms GLONASS for an attenuation higher than 2 dB and matches the behaviour of BeiDou after 10 dB. GLONASS is also able to keep an acceptable value of SNR for the highest attenuation level of 20 dB.  
For further assessment, these signal power results should be analysed together with the tracked satellite data presented above. For instance, in some cases, better PVT solutions, i.e., both lower TTFF and positioning error, may be achieved by using more satellites with smaller but acceptable SNR values, rather than by employing fewer satellites with higher SNR levels.
## 3 Methodology
### Proposed Work Flow Model  
Firstly, let us examine a scenario in which the model can be applied to enhance the network's overall quality of service (QoS). Consider a company that uses VoIP calls for customer service and grievance handling. In such a scenario, voice traffic from customers has the highest priority and needs to go across the network with minimum latency and maximum throughput. Other forms of traffic, such as data traffic and video traffic, from sources like Facebook and YouTube on their personal devices, should be given less priority without affecting the organization's performance. The proposed model may work well in this situation since it classifies traffic first and then simply balances the load according to the various types of traffic.  
To achieve excellent performance and quality of service (QoS) in modern SDN environments, efficient flow management of network traffic and load balancing across multiple controllers are essential. As network traffic continues to expand rapidly due to diverse applications and varying traffic patterns, real-time precise traffic management and load control across many controllers play a significant role in improving the QoS [53].  
Additionally, to manage dynamic traffic patterns and guarantee optimal resource use, traditional load balancing solutions might not be adequate. In order to handle any controller failures or overload scenarios, load balancing must be conducted intelligently and adaptively in order to classify network traffic in real-time and perform load balancing based on the priorities and characteristics of the traffic [54].  
After the steps of traffic classification, the packets are forwarded to the respective controllers assigned for each traffic type, and then the data is sent to the destination address based on the priority and queuing policies defined in the Ryu controller and OpenFlow switches.  
The goal of this research work is to create a traffic-driven controller-load-balancing solution for multi-controller SDN systems. To achieve precise traffic classification and dynamic load distribution, ML techniques will be utilized.  
This study has been broadly classified into two sections, namely traffic classification and load balancing.
# Insights into Native Fermentation Process of Apples (_Malus domestica_) in Low Sodium Conditions
## 3 Results
### Physical and Chemical Analysis
#### 3.1.2 Dry Matter  
The dry matter content has an important impact on the textural properties of fermented products [25]. The highest dry matter content (Figure 1) was recorded for fresh apple sample (14.94 \(\pm\) 0.25 g/100 g product). During fermentation, the dry matter content decreased for all samples until day 28. The highest decrease of this parameter was found for the samples fermented in the NaCl brine solution. The dry matter content decreased by 26.17%, reaching 10.62 \(\pm\) 0.19 g/100 g product. This reduction in dry matter content could be due to lactic acid bacteria using the carbohydrates, minerals and other components necessary for their development during the fermentation period. After 28 days of fermentation, the dry matter for all samples started to increase. The reason for this increase could be the difference in osmotic pressure that exists between the saline solution and the fermented plant material, respectively, due to the mass transfer that occurs by diffusion. At the end of the storage period, after 35 days, the dry matter varied between 13.00 \(\pm\) 0.45 g/100 g  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **7 Days** & **14 Days** & **21 Days** & **28 Days** & **35 Days** \\ \hline
**NaCl** & 0 \(\pm\) 0.01 A & \(-\)0.33 \(\pm\) 0.02 B & \(-\)0.97 \(\pm\) 0.01 C & \(-\)1.26 \(\pm\) 0.02 D & 0.94 \(\pm\) 0.02 E \\ \hline
**KCl** & 0 \(\pm\) 0.01 A & \(-\)0.24 \(\pm\) 0.01 B & \(-\)1.25 \(\pm\) 0.02 B & \(-\)1.01 \(\pm\) 0.02 C & \(-\)0.67 \(\pm\) 0.02 D \\ \hline
**MgCl\({}_{2}\)** & 0 \(\pm\) 0.02 A & \(-\)0.32 \(\pm\) 0.01 B & \(-\)1.25 \(\pm\) 0.01 C & \(-\)2.10 \(\pm\) 0.01 D & \(-\)0.97 \(\pm\) 0.01 E \\ \hline \end{tabular} The values on the same line marked with different letters are different in a statistically significant manner (\(p\) < 0.05).  
\end{table}
Table 1: Mass differences between fresh and pickled apple samples (%).  
product (for the samples fermented in KCl brine solution) and 13.67 \(\pm\) 0.29 g/100 g product (for the samples fermented in MgCl\({}_{2}\) brine solution).
# Research on Synthetic Aperture Radar Processing for the Spaceborne Sliding Spotlight Mode
## 7 Conclusions  
In this paper, the key techniques in spaceborne sliding spotlight SAR are analyzed, including the imaging geometric parameters, the method of equivalent velocity acquisition and ambiguity elimination. The mechanism of the CBP algorithm and PFA algorithm for SAR imaging are studied, and the process of the fast application in spaceborne sliding spotlight SAR is analyzed in detail.  
The large amount of computation is the main problem that restricts the application of CBP. In order to reduce the computation, a fast implementation method based on Quadtree sub-images is studied in this paper. The azimuth filtering and down sampling based on sub-image interception and FFT method for fast filtering is studied to avoid interpolation process, and through the method of Quadtree recursive decomposition, the processing efficiency of CBP can be greatly improved and the image quality can be ensured.  
PFA is also a classical algorithm for spotlight SAR. In order to expand the scope of PFA effective scenes for the sliding spotlight mode, based on the idea of sub-images, a modified PFA algorithm suitable for a large scene is proposed. As long as the sub-images are small enough, it is within the effective range of traditional PFA imaging scene restrictions. Finally, after geometric distortion correction, all sub-images are stitched to get the full image without defocus.  
Based on the proposed algorithm, the sliding spotlight SAR imaging can be realized quickly according to the process, and the algorithms are validated by simulation and measured data to lay the foundation for future spaceborne applications.  
[PERSON] wrote the manuscript, [PERSON] and [PERSON] implemented the method and analyzed the experimental data; [PERSON] revised the manuscript. All authors read and approved the final version of the manuscript.  
The authors declare no conflict of interest.
# Compensation Observer-Based Adaptive Output Feedback Control for Multi-Agent Systems
## 2 Establishing the Problem Model  
### Preliminaries  
According to the principles of the graph theory presented in [10; 11; 12], the relationship among followers is represented here by a directed topological graph \(G=\{U,T,M\}\), where \(U=\{1,\cdots,N\}\) denotes the set of nodes, \(T=U\times U\) defines the set of edges, and \(M=\left\{a_{ij}\right\}\in R^{i}\) is the adjacency matrix. If node \(j\) can send data to node \(i\), then \(a_{ij}=1\) ; otherwise, \(a_{ij}=0\). To facilitate system analysis, we employ the following lemmas and inequality theore  
**Lemma 1** ([5]).: _The following formula is valid as long as there exist normal numbers \(\Delta\), \(\sigma_{1}\), and \(\sigma_{2}\) and a symmetrical positive definite matrix \(P\), where \(E\) is the identity matrix. \(D=\text{diag}[0\ \ 1\ \ \cdots\ \ n_{i}{-}1]:\)_  
\[\sigma_{1}E\leq DP+PD+2\Delta P\leq\sigma_{2}E. \tag{1}\]  
**Lemma 2** ([17]).: _There is a positive definite matrix \(P\) that meets the following requirements if matrix \(A_{i}-q_{i}c_{i}^{T}\) is a Hurwitz matrix:_  
\[(A_{i}-q_{i}c_{i}^{T})^{T}P+P(A_{i}-q_{i}c_{i}^{T})=-E. \tag{2}\]  
**Lemma 3** ([9]).: _The following inequality is met for any \(d>0\) and \(x\in R^{i}\) (where \(R^{i}\) represents the set of real numbers):_  
\[0\leq|x|-\frac{x^{2}}{\sqrt{x^{2}+d^{2}}}<d. \tag{3}\]  
**Theorem 1**.: [PERSON]'s inequality:  
\[\left\{\begin{array}{l}\Theta_{i}\Gamma e_{ij}\leq\frac{\varrho}{2}\theta \parallel\ \Gamma\parallel^{2}e_{ij}{}^{2}+\frac{1}{2\varrho}\\ e_{ij}\varepsilon_{2j}\leq\rho l_{i}^{1+2\Delta}e_{ij}{}^{2}+\frac{1}{4\varrho }l_{i}\varepsilon_{2j}\end{array}\right. \tag{4}\]  
\[\left\{\begin{array}{l}\frac{\partial a_{i-1}}{\partial y_{i}}e_{2j}\leq \rho l_{i}^{1+2\Delta}\left(\frac{\partial a_{i-1}}{\partial y_{i}}\right)^{ 2}e_{ij}{}^{2}+\frac{1}{4\varrho}l_{i}^{1}\varepsilon_{2j}^{2}\\ \frac{\partial a_{i-1}}{\partial y_{i}}\Theta_{i}Te_{ij}\leq\frac{\varrho}{2} \left(\frac{\partial a_{i-1}}{\partial y_{i}}\right)^{2}\theta\parallel\Gamma \parallel^{2}e_{ij}{}^{2}+\frac{1}{2\varrho}\end{array}\right.. \tag{5}\]  
The parameters that are already in place are known positive design parameters. The other parameters will be determined later.
## 4 Experiments and Results
### Results and Analysis  
#### 4.4.1 Experimental Results of DFPN-YOLO  
DFPN-YOLO achieved a high performance when it was tested on the RSOD datasets and DIOR datasets. The result of each categories as shown in Figure 10.  
The above figures show the results of the DFPN-YOLO on the DIOR datasets and the RSOD datasets, including the average precision of the different categories and the mAP of the total categories. Our DFPN-YOLO model had a better detection performance on the RSOD datasets, but the slightly lower performance on overpass images was difficult to improve due to a fewer number of training samples. On the DIOR datasets, our model had 13 classes with AP values greater than 0.7. Some of the test results are shown in Figure 11.  
However, we found that there are some categories with low detection performance on the DIOR datasets, such as vehicles, bridges, and stadiums. According to our analysis of the test set results, our DFPN-YOLO model had a large number of false positives for small dense objects, such as ships and vehicles, as shown in Figure 12.  
The reason for the high number of false positives is that our model detects some small objects that are not labeled but do exist, such as vehicles and ships. Since we add a detection layer for small objects, our model detects some real objects with lower confidence, which have an impact in the calculation of mAP despite their lower confidence, resulting in a lower final accuracy. Figure 12b shows that although there are many small vehicles, no vehicles are marked in the labeled figure. However, our model detects some of the  
Figure 10: Results of each categories on the RSOD datasets (**a**) and the DIOR datasets (**b**).  
small cars. Furthermore, there were a small number of training samples for objects such as stadiums, increasing the difficulty of training the model for these objects.
## 2 HSI: Basic Concepts
### HSI: Device Components  
#### 2.1.1 Light Source  
The light source has to be as homogeneous as possible without damaging the sample. The choice of the type of light source (e.g., halogen lamps, light-emitting diodes (LEDs), lasers) depends on the optical range which the latter is able to cover. Halogen lamps operate in ultraviolet (UV), visible (Vis), and infrared (IR) spectral regions, covering a wide spectral range (340-2500 nm) [10]. A filament produces an incandescent emission, which generates a broadband illumination with a smooth continuous spectrum. However, this kind of light source has a few disadvantages, such as a short duration and high heating that can damage  
Figure 1: Literature review of the last 20 years using the keywords “hyperspectral imaging”, “fruit and vegetables”, “quality”, “fresh-cut”, and “postharvest”: documents published and indexed on Scopus and Web of Science (**a**) and documents included in this review (**b**).  
the sample or cause a shift in the spectral peaks, and they can also have an unstable output due to voltage changes and sensitivity to vibrations [4].  
LED is a cheap alternative radiation source. This semiconductor light source can produce both narrowband light (at various wavelengths in the ultraviolet, visible, and infrared spectral regions) and high-intensity broadband light. Compared to halogen lamps, LEDs are low-cost components which are compact and durable with low energy consumption, low heat generation, and fast response [4].  
While halogen lamps and LEDs are applied in reflectance and transmittance HSI devices, lasers are a directional, narrowband, monochromatic light source widely used in fluorescence and Raman applications, as well as LEDs [2].
## 3 Results and Discussion
### Far-Field Distance  
Similar to the Section 3.1, combined with the six NACA0012 models, two turbulence models and two flux types, the following simulation configurations are obtained: SST + blunt (ROE/AUSM), SA + blunt (ROE/AUSM), SST + NACA4 (ROE/AUSM), SA + NACA4 (ROE/AUSM), SST + 132 points (ROE/AUSM), SA + 132 points (ROE/AUSM), SST + 200 points (ROE/AUSM), SA + 200 points (ROE/AUSM), SST + 264 points (ROE/AUSM), SA + 264 points (ROE/AUSM), SST + 400 points (ROE/AUSM), and SA + 400 points (ROE/AUSM). For each far-field distance, applying the parameters described in Table 8, twenty-four numerical simulations are performed, based on the results of which the related error ratios are calculated and the optimal performance is confirmed.
## 3 Results
### Implementation Details
To objectively assess the accuracy of reconstructed speech signals, we analyze SDR, PESQ, and STOI at various SNR levels, which measure how well the system preserves both the structure of the signal and its perceptual quality under different channel conditions. The following figures illustrate the performance evaluation in AWGN, Rayleigh, and Rician (K = 5) channels, using SDR, STOI, and PESQ metrics. Figure 3 shows the SDR comparison, indicating that the proposed system is better equipped to handle multipath fading, leading to improved signal clarity and lower distortion in Rayleigh and Rician channels. Figure 4 illustrates the STOI results, showing that the proposed system achieves slightly higher intelligibility scores in fading environments compared to the traditional system. However, under AWGN, the conventional method remains better. Figure 5 presents the PESQ scores, demonstrating that while traditional methods perform well under AWGN, the proposed system provides better perceptual quality under severe channel impairments. Next, we visually compare the performance of the proposed and conventional models through spectrograms for a sample speech signal. Figure 6 illustrates the spectrograms of the original speech signal, while Figure 7 and 8 present the spectogram of reconstructed speech signals under Rician and Rayleigh fading channels for both the proposed and conventional models. These visualizations demonstrate the effectiveness and robustness of our model in preserving spectral features within fading environments, outperforming conventional methods by maintaining critical spectral content. Figure 4: STOI value versus SNR for the conventional communication system summarized in Table 2 and semantic—aware proposed system over AWGN, Rician, and Rayleigh channels. Figure 5: PESQ score versus SNR for the conventional communication system summarized in Table 2 and semantic—aware proposed system over AWGN, Rician, and Rayleigh channels. Figure 6: Reference original clean speech signal spectrogram. Figure 7: Conventional versus proposed reconstructed speech spectrograms under the Rician (K = 5) channel at SNR = 10 dB. The upper and lower spectrograms correspond to the conventional and proposed models, respectively.
# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 6 Security Analysis and Formal Verification
### Security Analysis  
The advantages of the blockchain-based multi-layer aviation big data security sharing scheme are demonstrated as follows:  
1. A blockchain-based data-sharing architecture eliminates security and privacy leakage risks associated with centralized trust mechanisms.
2. The optimized block deployment solution enhances both the security and efficiency of ATM data sharing in resource-constrained scenarios (e.g., airborne nodes) while achieving deep integration between blockchain and ATM operational workflows.
3. The publish-subscribe data security sharing scheme implements fine-grained access control and flexible, diversified data management, strengthening both security and convenience of data sharing in ATM environments.
4. The evaluable federated learning (FL) model sharing achieves dual privacy protection for both raw data and shared models, promotes high-quality data sharing, and prevents poisoning attacks through weighted aggregation of trained models, thereby ensuring secure data sharing.