esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Characterization of Fennel Extracts
#### 2.5.3 In Vitro Antioxidant Capacity by ABTS Radical Scavenging Assay  
The ABTS** scavenging capacity assay was determined as described by [PERSON] et al. [18]. The method is based on the decolorization of the ABTS (2,2-Azino-bis-(3-ethylbenzothazoline)-6-sulphonic acid) radical cation to determine the antioxidant potential of the samples. The ABTS radical cation solution was prepared in advance by reacting aqueous ABTS solution (7 mM) with potassium persulfate (2.45 mM). In the analysis, diluted ABTS + solution with an absorbance of 0.70 \(\pm\) 0.02 at 734 nm was used. The reaction system was composed of 800 \(\mu\)L of diluted ethanolic extract in ethanol (1:5 _v/v_) and 4000 \(\mu\)L of ABTS + solution, followed by incubation for 6 min at room temperature. The absorbance values were measured by a UV-VIS spectrophotometer 800 XI (FEMTO, Sao Paulo, Brazil) at 734 nm. The free radical scavenging activity was expressed as a percentage of the absorbance of the ABTS + control, obtained from the following equation: \(\%\;\text{Activity}=[(\text{A}_{\text{ABTS}}\bullet^{\star\star}-\text{A}_{ \text{sample}})/\text{A}_{\text{ABTS}}\bullet^{\star\star}]\times 100\), where A\({}_{\text{ABTS}}\bullet^{\star\star}\) is the absorbance value of the ABTS control and A sample is the absorbance value of the test solution. A calibration curve was plotted from the absorbance reduction and concentration of the Trolox (5-50 \(\mu\)g/mL, R\({}^{2}\) = 0.998). The results were expressed as microgram of Trolox equivalents per gram of fennel (\(\mu\)g TE/g).
## 5 Analysis of the Scheme
### Analysis of Security on the Blockchain  
This section will introduce the common blockchain attack models and how this scheme can resist these typical attacks.  
Conspiracy attacks: In the distributed key management system, the attacker collusion attack would require M nodes to collect the shares of the master key. This means the attacker must pay a high cost for collecting all the pieces together. Furthermore, each user attribute secret key will generate a uniformly and independently distributed random vector \(\hat{\mathbf{i}}\leftarrow\mathcal{X}_{1}\) when generating in our scheme, and the random vector of any user secret key is different; therefore, the user attribute secret key is hidden by information theory from anyone, so that the conspiracy attack cannot be realized.  
Middleman attacks: middleman attack means that the attacker sets up independent exercises at both ends of the correspondence, exchanges the received data, and monitors or tampers the information. In this scheme, all the correspondences among the nodes are conducted in the form of transactions. The transaction is signed by the initiator using the blockchain secret key, and the returned secret data is encrypted by the other party's public key, so the middleman cannot pass the verification by tampering with the address or forging the signature. Therefore, this scheme can greatly prevent middleman attacks.  
Link attacks: link attacks mean the adversary searches for the user's private data by linking multiple transactions with the same address. In our scheme, the related authorization process of user attributes can be determined by the user, and the user can choose to disclose his identity attributes or hide the identity information to protect privacy. In addition, the relevant data is the ciphertext on the blockchain, and the security is guaranteed by the algorithm security, so the attacker cannot get the relevant information of the user, thus resisting the link attacks.
## 6 Conclusions  
Aiming at problems such as low computational efficiency and poor adaptability in the target reachable zone prediction of a boosting gliding vehicle, the database method and six-point method are proposed. Firstly, to increase time efficiency, the six-point method, which can determine whether a new goal point is reachable, is first presented. Secondly, the database method for vehicle reachable zone prediction which can provide the potential for online replanning is suggested depending on the six-point method. Finally, the results suggest that the database method achieves very high time efficiency with less loss of accuracy than the six-point method. With an accuracy error of less than 1%, it can forecast \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(t_{p}\)**/(s)** & **TIM/(s)** & **DBM/(s)** \\ \hline \multirow{3}{*}{Case 1} & 175 & 10.2241 & 0.0117 \\  & 475 & 7.6253 & 0.0117 \\  & 775 & 5.1281 & 0.0117 \\ \hline \multirow{3}{*}{Case 2} & 175 & 10.3393 & 0.0129 \\  & 475 & 7.6492 & 0.0129 \\  & 775 & 5.2271 & 0.0127 \\ \hline \multirow{3}{*}{Case 3} & 175 & 10.3478 & 0.0100 \\  & 475 & 7.5949 & 0.0100 \\  & 775 & 5.2143 & 0.0100 \\ \hline \multirow{3}{*}{Case 4} & 175 & 9.7778 & 0.0112 \\  & 475 & 7.2189 & 0.0112 \\ \cline{1-1}  & 775 & 4.8675 & 0.0112 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Time consumed between TIM and DBM. Figure 8: The result of case 1: (**a**) is \(t_{p}=175\) s; (**b**) is \(t_{p}=475\) s; (**c**) is \(t_{p}=775\) s; (**d**) is the boundary point trajectory. the vehicle's reachable zone within 0.02 s, which is a significant improvement over the integral method, providing a fast guarantee of safe re-entry control for hypersonic vehicles. However, the accuracy declines with an increased number of standard bank reversals, which is a relevant component for additional study in this method. Conceptualization, Q.H. and Z.D. ; methodology, Q.H. ; software, Q.H. ; validation, Q.H., Z.D., and L.L. ; formal analysis, Q.H. ; writing--original draft preparation, Q.H. ; writing--review and editing, Z.D. and L.L. ; supervision, L.L. ; project administration, L.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 61973326. Not applicable. All data generated or analyzed during this study are included in this published article. The authors declare no conflict of interest. The following are the theoretical model equations for the D-V profile method:  
Once the reference D-V profile is determined, the standard lift-to-drag ratio of the vehicle along that designed profile can be found, denoted as \(\left(L\cos{v}/D\right)_{0}\).
# Fast-M Adversarial Training Algorithm for Deep Neural Networks
## 6 Experiments
### Verification of Fast-M on CIFAR10  
#### 6.2.1 Number of Iteration Stage Selection  
The key to Fast-M adversarial training lies in dividing the adversarial training process into several different stages. This is because different numbers of stages result in different maximum iteration numbers for model training, which affects the training effect. Moreover, having too many stages significantly increases the time required for adversarial training. On the other hand, having too few stages may result in a poor or even no training effect. Therefore, it is necessary to select a suitable number of training stages \(M\).  
This paper experiments with five different cases by setting \(M=2\), \(M=3\), \(M=4\), \(M=5\), and \(M=6\), while keeping the training step size \(\alpha=8/255\). Each case is subjected to 10 independent rounds of adversarial training with a gap of 5 rounds between 5 and 50 rounds, and the training time is recorded. Then, the models trained under each case are evaluated using PGD-20 adversarial attacks, and the results are recorded. The specific experimental results are shown in Figure 2.  
From Figure 2b, it can be observed that with the increase in training rounds, the difference in training time between different values of \(M\) gradually increases, which is consistent with the theoretical data in Table 1. Moreover, from Figure 2a, the model's accuracy gradually improves with increasing \(M\). When \(M=4\), the accuracy of the adversarial training model for PGD-20 reaches a large and stable value. As \(M\) continues to increase, the accuracy only slightly increases, while the training time increases significantly. Therefore, on the CIFAR10 dataset, selecting \(M\) to be 4 or 5 is appropriate.  
Figure 1: Comparison of Fast-3 adversarial training and original fast adversarial training on the MNIST dataset.: (**a**) accuracy for the original clean sample; (**b**) accuracy for a PGD-20 attack; (**c**) accuracy for a PGD-50 attack; (**d**) training time.
###### Abstract
Additionally, the latest advancements in STSC applications, such as solar-powered seawater desalination, wastewater treatment, atmospheric water harvesting, hybrid solar thermal photovoltaic systems, and solar thermoelectric generators, are also examined. Hence, the present survey research provides impactful insights, critical discussions, and future trends for improved STSCs, as well as offering further inspiration for future sustainable applications of STSCs. Figure 3 shows a flowchart describing the overall organization of the literature review in this paper.
# Enhancing MusicGen with Prompt Tuning
## 2 Related Work
### Text-to-Music Generation Models  
Text-to-music generation systems [5, 6, 7] have evolved rapidly as larger datasets and expressive neural architectures have become available. Early rule-based or sequence-to-sequence methods could not capture music's rich temporal and harmonic structures. Transformer-based models, such as MusicGen [1], alleviate many of these limitations. In MusicGen, textual conditioning is obtained by encoding the user prompt with a frozen transformer text encoder (adapted from a large language model). The resulting embeddings provide a fixed conditioning signal to a large autoregressive transformer decoder that operates over the discrete latent representations produced by a 32 kHz EnCodec audio tokenizer. Music is generated by predicting the sequence of multi-stream (codebook) EnCodec tokens autoregressively; these tokens are subsequently passed through the corresponding EnCodec decoder to reconstruct up to minute-long stereo waveforms that reflect the prompt-specified musical attributes (e.g., genre, mood, tempo).  
Building on this baseline, subsequent studies have explored complementary directions (see Table 1). Mustango [6] shifts the focus to symbolic control: by emitting track-separated MIDI events from textual prompts, it enables precise editing, instrumentation changes, and rapid region-wise regeneration--capabilities that are difficult to realize in waveform models. AudioLDM [5] pioneers the use of CLAP-guided latent diffusion for text-to-audio; its iterative denoising markedly improves timbral richness and reduces codec artefacts, demonstrating the value of diffusion processes for perceptual quality. Extending this paradigm, MusicLDM [7] introduces a beat-synchronous mix-up augmentation that enlarges the rhythmic diversity of training data and thereby enhances the novelty and beat coherence of the generated music without increasing model size.  
These contributions delineate a design space in which high-fidelity waveform synthesis, symbolic editability, and perceptual richness via diffusion occupy different operating points. Despite these advances, all models continue to show reduced stylistic authenticity and weaker prompt alignment in under-represented genres (see Table 2), motivating the parameter-efficient adaptation strategy proposed in this work.
# Congestion-Aware Geocast Routing in Vehicular Delay-Tolerant Networks
## 6 Conclusions and Future Work  
We proposed the new V-GRADIENT geocast routing protocol for VDTNs that efficiently disseminates messages within an ROI centered in the source node. The V-GRADIENT monitors node density, buffer occupation, and group penetration rate. Messages are disseminated within a buffer zone with sizes proportional to the size of the ROI and a function of node density and buffer occupation. The V-GRADIENT also uses a spraying mechanism that spreads a limited number of message copies to nodes that are not directly interested in the messages, and helps to relay messages towards their destinations in sparse networks and/or when the geocast groups have low penetration rates. The V-GRADIENT uses both the messages' initial TTL values and an estimation of the penetration rates of distinct geocast groups in order to lay out a flooding region comprised within the ROI.  
After a comprehensive analysis of the simulation results was obtained, one can conclude that the V-GRADIENT is not only able to adjust its operation mode dynamically, according to the network density conditions, but can also cope with a wide variety of geocast application requirements. Simulation results show that the V-GRADIENT protocol outperforms the remaining geocast strategies in terms of the overall delivery ratio, which is the most relevant performance metric. The V-GRADIENT protocol achieved the second-lowest delivery latency outcome, surpassed only by the GeoEpidemic constrained, which has a very low delivery ratio as it only delivers messages quickly, near the ROI center. The protocol overhead performance of the V-GRADIENT is a compromise when compared to the GeoS&W and GeoDirectDelivery protocols, particularly for geocast groups with low penetration rates and/or under high-density conditions.  
In our future work, we intend to test the V-GRADIENT in other scenarios, implementing message prioritization schemes and evaluating scenarios with misbehaving nodes that do not cooperate with message relaying.  
Conceptualization, P.R.P. and H.N.; software, H.N.; analysis and writing, N.M., P.R.P. and H.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Portuguese national funds through FCT, Fundacao para a Ciencia e a Tecnologia, under project UIDB/50021/2020, by Portuguese national funds through FITEC - Programa Interface, with reference CIT \"INOV - INESC Inovacao - Financiamento Base\" and by LASIGE Research Unit, ref. UIDB/00408/2020.  
The authors declare no conflict of interest.
## 3 Survey Results
### Software Level Tasks  
User and kernel level tasks and services are described in Table 14. The task count and objectives are an estimate of the authors of this deployment survey, developed based on information available from research articles. Networking, time synchronization and remote reprogramming protocols are considered kernel services, if not stated otherwise. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Nr** & **Codename** & \
\begin{tabular}{c} **Kernel service** \\ **count** \\ \end{tabular} & **Kernel services** &
\begin{tabular}{c} **App-level** \\ **task count** \\ \end{tabular} & **App-level tasks** \\ \hline
1 & Habitats & 0 & & 1 & sensing + caching to flash + data transfer \\
2 & Minefield &? & & 11 & \\
3 & Battlefield & 2 & MAC, routing & 2 + 4 & Entity tracking, status, middleware (time sync, group management, sentry service, dynamic configuration) \\
4 & Line in the sand &? &? &? &? \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Deployments: software level tasks. Figure 9: **Operating systems used in analyzed deployments—Sixty percent of deployments use the _de-facto_ standard: TinyOS. Seventeen percent use self-made or customized OSs. **  
Most of deployments use not more than two kernel services (55%) (Figure 10). For some deployments, up to five kernel services are used. The maximum service count must be taken into account when designing a task scheduler--if static service maps are used, they must contain enough entries to support all kernel services. In the application layer, often, just one task is used, which is typically sense and send (33% of cases) (Figure 11). Up to six tasks are used in more complex applications. _Design rule 23_: The OS task scheduler should support up to five kernel services and up to six user level tasks. An alternative configuration might be useful, providing a single user task to simplify the
# Doped Epoxy Resins as an Alternative to Luminescent Optical Sensors
## 3 Results and Discussion
### Thermal and Spectroscopic Analysis
It can be clearly stated that the incorporation or suspension of photosensitive substances into the structure of the resulting polymer did not affect their photoluminescence properties. There is a significant difference between the pure matrix and doped compositions. Moreover, higher glow intensity is observed in the case of compositions based on EB600 resin. It is also worth noting that this intensity changes with the change in the NVP content.
# A New Method of Geological Modeling for the Hydrocarbon Secondary Migration Research
## 2 Characterization of 'Unit Element' for Reservoir Modeling
### Meandering Fluvial Environment
Through this research, 25 individual meander shapes were identified, and classified into the following sub-groups within 4 parent groups: open asymmetric, open symmetric, open angular, and bulbous (Figure 2B). Among these, open symmetric shapes are the most commonly observed, while angular and bulbous shapes occur less frequently. Each meander shape exhibits different patterns of scroll bars, and the resulting preserved shape represents the path and evolution of the fluvial point bar, along with its growth characteristics. This study incorporates the variability in the meander shape and scroll bar pattern to expand the database of point bar unit elements, which can be used in subsequent object-based reservoir modeling.
## 3 V2X System Architecture When Integrating Blockchain Technology and MEC
### System Model
#### 3.4.1 Communication Model  
This subsection starts with an introduction to the model of communication, where the system environment is composed of \(\mathbb{B}\) and \(\mathbb{A}\) sets of RSUs and vehicles, respectively. In addition, each vehicle has a computation task to be accomplished, which can be identified using a tuple \(\{a_{i},c_{i}\}\). More specifically, \(a_{i}\) is used to denote the size of transferred data (i.e., code and parameters) for the computation task and \(c_{i}\) is used to denote the CPU cycle number demanded to complete the computation task. Guided by the works [34; 35], the \(a_{i}\) and \(c_{i}\)' values can be obtained through task execution profiling.  
Regarding the intuition shown in [36], in this study, we chose to disregard the consumption overhead in terms of time and energy for transmitting the result back to the vehicles, since the output data are few, relative to the input data.  
Note that, in the case of multiple vehicle transmissions in the same cell, the orthogonal frequency-division multiplexing method was utilized to mitigate the intracellular interference [36; 37]. In addition, according to [PERSON]'s law [38], the uplink data rate for communication between the vehicle \(i\) and the connected RSU \(j\) is given by  
\[r_{i,j}=B_{ij}log_{2}(1+\frac{p_{i}s_{0}^{2}}{\omega B_{i,j}}) \tag{2}\]  
where \(B_{i,j}\) indicates the uplink channel bandwidth, \(p_{i}\) indicates the vehicle \(i\)'s transmission power and \(\omega\) and \(g_{0}\) indicate the noise power density and the corresponding channel gain between the vehicle and the connected RSU.
# Calculation of View Factors for Building Simulations with an Open-Source Raytracing Tool
## 4 View Factors for Complex Shapes and Obstructions  
Accommodation of complex geometry and obstructions is a critical aspect of view factor calculations involving urban geometry. For example, view factor calculations between solar panels and the sky vault require the consideration of occluding obstructions like buildings and other urban shapes. The following sub-sections provide a high-level description of how curved shapes and obstructions can be incorporated into calculations.
# Anomaly Detection of the Brake Operating Unit on Metro Vehicles Using a One-Class LSTM Autoencoder
## 3 Proposed Methods
### Anomaly Detection  
We train our LSTM autoencoder model using BC pressure data from the BOU dataset that are labeled as normal. In the testing phase, both normal and abnormal sequences are fed into the network. We used Mean Absolute Error (MAE) as our reconstruction error instead of other metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The reason is that in both MSE and RMSE, the errors are squared before they are averaged. It can lead to higher weights given to larger errors. As a result, the model tends to be more sensitive to noise which might cause false positives. We compute MAE between the input features and the reconstructed features from the LSTM autoencoder. Since the network learns on a normal dataset, The MAE is high for abnormal data and low for normal data. Hence, we used a fixed threshold for reconstruction error and used it as a decision boundary for detecting anomalies. The threshold for anomaly detection was set to the maximum value of MAE in the training (normal) dataset. The sequence with a reconstruction error greater than the threshold is classified as an anomaly, while the sequence with a reconstruction error less than the threshold is classified as a normal data point.
# Aerial Robotic Solution for Detailed Inspection of Viaducts
## 2 System Description
### Visual Inspection  
The visual inspection UAV (see Figure 3) is known as AERO-CAM. This aircraft is specialized in taking very high quality images of a structure. This UAV is equipped with a stabilized camera which takes the images of desired areas.  
The AERO-CAM platform is built from a DJI Matrice 600 Pro on which the necessary components to operate have been installed. Since the DJI is a commercial platform, the system is easily replicable. The standard configuration of the UAV has been preserved, with both the autopilot and the rotors and blades being those recommended by DJI. The autopilot includes a GPS/GNSS receiver, a 9-axis IMU, a magnetometer, and a barometric altimeter. In addition, it carries a Lightware Laser Altimeter for precision landing. Regarding the camera system, the UAV is equipped with a Gremsy T3V2 gimbal [30] mounted in the slot available above the platform and carrying a Sony Alpha 7 camera [31]. This mounting location allows the gimbal-camera set to have a better available field of view and can even take pictures pointing completely upwards, as opposed to mounting it on the bottom of the UAV like most commercial camera drone systems. This is especially useful when performing inspections under a viaduct as the UAV will be able to take pictures of the bottom part of the deck. Depending on the space available for the flight and the amount of detail to be obtained in each image, the camera can be equipped with different lenses. This camera is managed by a Raspberry Pi Model 3B+ that implements a software developed using using Sony's Camera Remote SDK [32].  
To provide the platform with autonomous capabilities, the UAV mounts an Ouster OS0-128 LIDAR sensor [33] under the avionics with a custom anti-vibration structure. All previously mentioned sensors are connected together with the autopilot to the onboard computer, which is an Intel NUCi7. Finally, an Ubiquiti Rocket M5 is used for ground communications and to connect the Rapsberry Pi and Intel NUC via Wi-Fi.  
Both onboard computers run Ubuntu 18.04 and ROS Melodic and have their clocks synchronized for greater accuracy in capturing images with metadata. The software of the platform is programmed as nodes that communicate with each other. Figure 4 shows the scheme of processes that operate in the system.  
With all this equipment, the AERO-CAM is able to perform completely autonomous visual inspections even in GNSS-denied environments. It is capable of taking off and landing on its own, as well as carrying out the mission created from the 3D map of the viaduct. These missions are composed of many waypoints that have an image associated with them. Each time the UAV reaches a waypoint, it moves the gimbal and captures the corresponding image autonomously.  
Figure 3: AERO-CAM robot for visual inspection UAV.