esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Security Analysis
### Achieving Security Properties
#### 8.4.1 Preserving End-to-End Data Flow Integrity  
The objective of our approach is to preserve end-to-end data flow integrity in the sense that when this property cannot be preserved (i.e., an attacker has violated the integrity of the exchanged flow), publishers and subscribers can acknowledge this.  
As formally demonstrated in [9], this property cannot be satisfied when at least one of the attacks identified in Section 8.3 is performed. Thus the goal of an attacker is to perform one of the identified attacks without being detected.  
Suppose an attacker is able to tamper with the flow of messages exchanged during a temporal round on a given topic by performing one of the attacks identified in Section 8.3. Then, this modified flow is received by the subscribers. However, once a subscriber builds the hashchain for that topic, this hashchain differs from the one computed publisher-side. Then the subscriber has to reconstruct the path from the hashchain to the root of the MHT sent by the broker to it. In order for the attack to be undetected, no event should be emitted by the smart contract. Therefore, the attacker should be able to forge a path from the modified hashchain to the root actually computed publisher-side and published on the blockchain.  
However, due to the **hash function properties** assumption, an attacker cannot be able to do so. Indeed the **hash function properties** assumption ensures that the adversary cannot alter the messages in the hashchain or MHT without detection. Moreover, it cannot construct a valid path from an arbitrary hashchain to the actual MHT root.  
This ensures that a violation of the end-to-end data flow integrity property cannot go undetected.
## 3 Results and Discussion
### The Volatile Profile of Shihtake mushrooms under Different Hot Air Drying Conditions by CG-MS
Sulfur compounds and C8 compounds have been reported to be the main volatile compounds in shitake mushrooms, and sulfur compounds contributed the characteristic flavor of dried shitake mushrooms and C8 compounds related to the fresh mushroom odor [37; 38; 39]. There were seven sulfur compounds, including carbon disulfide, dimethyl disulfide, thiovanic acid, dimethyl trisulfide, 1,2,4-trithiolane, 1,2,4,5-tetrathtiane, and 1,2,4,6-tetrathtiapane, which were identified in the three samples by GC-MS analysis. The total concentration of sulfur compounds in sample A was 24.63 ug/g, significantly lower than those in sample B (38.18 ug/g) and sample C (33.63 ug/g) (_p_ < 0.05). Considering that sample A was at the higher drying rate than sample B and C, this result might indicate that higher drying rate led to lower content of sulfur compounds. Sulfur-containing compounds were mainly formed by sulfur-containing amino acids through Maillard reaction [7; 15; 40]. Different drying rate caused varied severities of reactions, which led to the dissimilarities in the contents of sulfur compounds among samples. There were only three C8 compounds, including 1-octene-3-ol, 3-octanone and phenylethyl alcohol, which were identified in the three samples by GC-MS analysis. It was due to the fact that the drying process caused the oxidation and decomposition of C8 compounds [16; 27]. The total content of the C8 compounds in the sample B (11.40 ug/g) was significantly higher than those in the sample A (4.52 ug/g) and sample C (3.84 ug/g) (_p_ < 0.05), especially 1-octene-3-ol and 3-octanone, which were reported to contribute to mushroom-like odor [38]. The lesser extent of dryness exerted on sample B retained higher content of C8 compounds, compared with the sample A and C. The results were consistent with those obtained by GC-IMS. It revealed that the drying degree had a certain effect on the C8 compounds, and the lower the drying degree was, the less degradation C8 compounds was subjected to and the more mushroom-like odor was retained.
# Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi
## 4 Discussion
### Effect of Different Segments and Epochs  
There are four types of segments: the first kind of segment has 8 samples, the second segment has 16 samples, the third segment has 32 samples, and the last segment has 64 samples. In this experiment, different epochs were tested: 20 epochs, 200 epochs, 1000 epochs, 2000 epochs, and 3000 epochs. In addition, the layer size in this experiment was 10. The results are shown in Figure 9.  
From Figure 9, the best performance was observed for 32 sample segments and 64 sample segments. Among them, the best results were seen at 3000 epochs. For 32 sample segments, the result was 96.8% and, for 64 sample segments, it was 98.5%. Hence, the segments of 32 and 64 samples at 3000 epochs were tested in the next experiment.  
Figure 8: Comparison of testing time of raw data.  
Figure 9: Accuracy of different samples in each segment.
## 3 Dataset Analysis and Description
### Statistical Figures on Seriousness and Non-Seriousness
#### 3.2.4 Application of the PERSONA Technique for Visualization of Key NLP Features  
In our analysis, we applied the PERSONA technique, which is a systematic statistical method to visualize the relationship between NLP features and the seriousness level of adverse events reported in the FAERS dataset. As depicted in Figure 6, we conducted a statistical analysis to determine the correlation of over 5400 NLP features against the seriousness of events. Utilizing the PERSONA technique, we were able to prioritize and select the top 50 features that exhibited the strongest correlation, either positive or negative, with the seriousness of adverse events. This visualization is pivotal as it clearly demonstrates which features have the most substantial relationship with event seriousness, thus providing valuable insights into predictive factors of adverse drug reactions. By highlighting the 50 most correlated features, the PERSONA technique significantly contributed to our understanding and interpretation of the data, emphasizing those attributes that were most critical for our predictive model's focus on enhancing drug safety monitoring.  
Figure 5: Number of active substances in the drug.
# Compensation of Frequency Drift in Frequency-Sweep Polarization-Modulation Ranging System
## 1 Introduction  
Absolute distance measurement (ADM) technologies are of significant application in scientific and engineering fields such as large equipment manufacturing, spacecraft fabrication, and general-purpose coordinate measurements [1; 2; 3]. Various methods of ADM have been developed over the past decades, such as phase shift-based ranging [4; 5; 6], frequency scanning interferometry-based ranging [7; 8; 9; 10], multi-wavelength interference-based ranging [11; 12; 13; 14], and femtosecond optical comb-based ranging [15; 10; 16; 17]. In recent years, the latter three technologies have achieved great accuracy in laboratory, but the systems are complex and their performance needs to be enhanced in an industrial setting. The phase shift ranging method is a common technology in industry, in which the phase differences between continuous emitted and reference waves are compared. However, in phase shift ranging systems, the performance of phase discrimination is influenced by parasitic parameters in the circuit, and as a result ranging precision is limited.  
The frequency-sweep polarization-modulation (FSPM) method is another phase ranging method in which modulation frequency is scanned and searched when emitted and returned waves are in phase, thus avoiding the environmental disturbance as well as shortage of phase discrimination [18; 19; 20].  
It was proposed by Kern & Co. Ltd. and first applied in the commercial distance meter ME5000 [21; 22; 23]. Due to its simple structure and high resolution, the FSPM ranging technique has the potential for small size and high performance. However, in the FSPM ranging system, the frequency is vulnerable to phase delay variation due to the sensitivity of crystals (e.g., LiNbO\({}_{3}\)) in the electro-optic modulator (EOM) to temperature disturbance. The heat dissipation of electronic components and laser irradiation will accelerate the temperature variation [24], thus leading to distance measurement errors.  
In order to investigate this problem, a theoretical model was developed for the FSPM ranging systems. Based on the model, a correction approach for diminishing the thermally-induced errors in the system was proposed. A reciprocating sweeping method and multi-frequency interval method were respectively proposed to improve the measurement accuracy of frequency and the corresponding wavelength multiple. The validity of the method was proved by theoretical analysis and experiments. The article is organized as follows: In Section 2, the principle of the FSPM-based system is introduced, measurement errors due to phase delay are discussed, and the compensation methods are proposed. In Section 3, experiments are conducted to prove the effectiveness of the methods. Finally, a brief conclusion is given in Section 4.
## 4 Evolution towards Greater Complexity
As search and rescue systems involve diverse mobile entities (e.g., robots and smartphones), cross-system integration control, intelligence learning for mobile entities in an OppSense environment, and more complex distributed computing in OppNets become challenging.
## 3 Results
### Soil Moisture Estimation
Of course, in this case, the difference between the three points in Figure 10 is mainly driven by crop coefficients (Figure 9), as the soil properties were uniform across the grid and no irrigation applications were assumed. For example, during October 2017, a high precipitation event over a long time period occurred, which resulted in a higher relative soil water content variability between the three points, as the different crop coefficients caused the AET to respond differently to the increased wetness for the three points. However, in November 2017 a high precipitation event during a short time frame occurred. This, coupled with the similar crop coefficient in November and December 2017, caused the variability in the relative water content to decrease substantially. Finally, Figure 11 shows a Time-Depth Graph for the three selected study locations. The figures clearly show the variability of soil water content due to precipitation. While the first layer (5 cm) reacted very fast to rainfall events and drying events, the bottom layer (200 cm) had much lower variability, with the relative soil water content continuously decreasing throughout the simulated period. Considering that the simulation started at field capacity, this decrease shows that the soil has depleted due to the deficit between precipitation and evapotranspiration. Ideally, these results would be validated using observed soil moisture observations in the field, but these are currently not available. Figure 8: MOHID Land porous media output of relative water moisture at 25 cm soil depth (from the terrain’s elevation) for the last time period of each month from July 2017 to June 2018 for Site **b**. Figure 10: Relative soil water content (-) time series from 1 July 2017 to 30 June 2018, and MOHID. Land accumulated precipitation (mm) (rainfall) during the same period for locations 1, 2, and 3 (Figure 9). Figure 9: **(Top)** Map of the position of three selected study points overlaying the satellite detected crop coefficient for the month of October 2017 in site **b**. (**Bottom**) Sentinel 2 data-derived (Equation (1)) crop coefficient time series from July 2017 to June 2018 for the three selected study points at site **b**. Figure 11: _Cont_.
## 4 Limitations and Future Work  
Although MFA-UperNet demonstrates excellent performance in mangrove remote sensing image semantic segmentation tasks, there are still some limitations and areas for improvement. First, the current experimental validation is primarily based on the MSSDBG dataset from specific geographical regions, and the model's generalization performance across different geographical locations, seasons, and growth states of mangroves needs further validation. Second, mangrove remote sensing images are easily affected by environmental factors such as weather conditions, tidal changes, and atmospheric conditions, and the potential impact of these factors on model performance has not been fully explored. Additionally, there is room for improvement in the model's recognition effectiveness for sparsely distributed or small-area mangrove regions. In practical applications, the model's computational complexity and hardware requirements may also become limiting factors. Future work will focus on expanding the geographical and temporal coverage of the dataset, enhancing the model's adaptability to environmental changes, improving small target recognition performance, and exploring unsupervised methods based on multispectral remote sensing images and drone imagery to further enhance the model's performance in practical applications such as mangrove change detection.
## 4 Implementation Based on CAD Optimization Tools  
As presented in the previous sections, the PKM structure was designed and created in Catia V5 using design tools from the part, assembly and DMU Kinematics modules. The simulation process was also accomplished with the help of the CAD software Catia V5, because of the multiple algorithms offered in the module of product engineering optimizer.  
In the optimizer module, we selected the following parameters with the aim of maximizing the workspace volume. The optimization parameter was determined with the smart volume function, which needs a single attribute of the modelled volume. The free parameters were as follows: the inf. range equal to 30\({}^{\circ}\), the sup. range equal to 60\({}^{\circ}\) and the incrementation value was set to 1 degree for a more optimal result. The maximum number of updates was set to 450 and a safety parameter for the maximum number of updates was set to be equal to 50. The optimization was performed on a computer with the following hardware characteristics: CPU 3.30 GHz, memory 16 gigabytes [17].  
The optimization was based on the simulated annealing algorithm as it is very stable and offers accurate results even in complex problems, as presented in the work of multiple researchers with a raw form or even a derived version [21]. The working principle is similar with the one used in metallurgy; more precisely, the process involves increasing the main parameter to the highest limit, then slowly decreasing it until it reaches the initial value [22; 23; 24; 25; 26; 27]. The main parameter used for the optimization was the inclination \(\varphi_{1}\), which is known to be equal to \(\varphi_{3}\). Based on the selected algorithm, it was possible to obtain an extensive range, of which the most important values are presented in Table 2.  
The main results summarized in Table 2 are extracted in ascending order of angle value \(\varphi_{1}\) to expose the maximum and minimum volume limits as a relation with the tilt angle. The simulation for the PKM structure starts from the initial value of 30\({}^{\circ}\) and almost immediately reaches the upper limit of 60\({}^{\circ}\), which follows a period of descent, until it stabilizes by itself [22; 23; 24; 25; 26; 27]. The results have been post-processed to present them as clearly as possible and to draw attention to the maximum value obtained for the workspace volume. As is shown in Figure 10, the volume obtained through the module of product engineering optimizer had the maximum value of 427,839,567 mm\({}^{3}\), when the tilt angle \(\varphi_{1}\) reached the value of 47.1531 deg.  
Figure 9: Representation of the volume distribution based on the values of tilt angles \(\varphi_{1}\) and \(\varphi_{2}\).
## 2 Materials and Methods
### Mathematical Model
#### 2.2.3 Model Implementation  
Starting from equations from (3) to (11), a Matlab script [30], able to solve the set of ODEs both for adsorption and desorption process, has been implemented. In particular, for each \(i\)-\(th\) node, a state vector \(\mathbf{u}=\left(T_{f}\,\ T_{s}\,\ T_{r},\ T_{r}\,\ T_{v}\,\ M_{vi}\,\ M_{vac}\,\ x\right)\) has been defined, whose components are the unknown variables to be calculated for each time instant. Therefore, the set of ODEs has been rewritten in the following matrix form: \(\mathbf{M}(t,\mathbf{u})\cdot\dot{\mathbf{u}}=\mathbf{f}(t,\mathbf{u})\), where \(\mathbf{M}(t,\mathbf{u})\) is the mass matrix and \(\mathbf{f}(t,\mathbf{u})\) is a vector containing the terms of differential equations not dependent from the derivative. The solution of the ODEs systems depends on the initial state vector \(\mathbf{u}_{0}\) and inlet parameters (in general, time-dependent functions), which are represented by: the HTF temperature at the inlet of the \(i\)-\(th\) node, the HTF flow rate, the pool temperature and the water vapour pressure. By means of an iterative process, extended to the N nodes into which the reactor is divided, the parameters entering the node (\(i+1\)) are updated with those leaving the previous node.  
The model allows to determine the trends of the different variables along the longitudinal axis of the finned heat-exchanger, the heat exchanged between thermal energy storage and HTF, the thermal energy stored or released and the water uptake during the sorption/desorption process.  
The above-mentioned set of ODEs apply only to the \"all-in-one\" reactor configuration, where the vapour is confined in the vacuum case that encloses the zeolite bed. However, the current configuration of the reactor allows also to investigate the desorption process of a typical thermochemical storage, where the vapour condensation is obtained in an external chamber (\"phase changer\" configuration). This can be achieved by keeping the water pool empty and extracting the water vapour through the vacuum pump. This operating condition involves a simplification of the mathematical model applied to the desorption process. Hence, Equation (8), related to the energy balance of the surrounding water vapor, becomes unessential and the term \(\left(UA\right)_{r}\) in Equation (6) is replaced by the convective heat transfer between the internal walls of the zeolite bed and the steam current produced by the vacuum pump. Since the condensing vapour flow is equivalent to the vapour flow exiting from the zeolite bed, the mass balance Equation (10) also becomes redundant. Therefore, the new ODEs system, describing the desorption process without pool, reduces to 5 equations with a state vector \(\mathbf{u}=\left(T_{f}\,\ T_{s}\,\ T_{r}\,\ M_{vi}\,\ x\right)\). The solution of this adjusted set of ODEs allows to investigate the effects of the vacuum pump for controlling the pressure inside the reaction chamber.
# Recent Advances in Efficient Dynamic Graph Processing
## 4 Algorithms
### Centrality  
Centrality measures the importance of various vertices in a graph. It can be used in the applications such as identifying the most influential persons in a social network, key infrastructure nodes in the internet, and super-spreaders of disease. Since the \"importance\" of a vertex can be characterized from different perspectives, many different definitions of centrality are investigated in the literature. Typical definitions of centrality include _betweenness centrality_, _closeness centrality_, and _eccentricity_.
## 4 Results and Discussion
### Scenario II: Unbalanced and Sinusoidal Source Voltage
Hence, from all the results presented in this category, it can be confirmed that the enhanced-ADALINE algorithm is able to correctly manage operation of SAPF under Scenario II. Figure 14: Simulation result obtained under Scenario II for Load A, showing the overall dc-link voltage \(V_{dc}\), and individual voltage of each split capacitor \(V_{dc1}\) and \(V_{dc2}\), demonstrated by SAPF while applying the enhanced-ADALINE algorithm. Figure 13: Simulation results obtained under Scenario II for Load A, showing source voltage \(v_{Sahc}\), load current \(i_{Labc}\), injection current \(i_{injabc}\), source current \(i_{Sahc}\), and neutral current before and after mitigation, demonstrated by SAPF while applying the (**a**) enhanced-ADALINE and (**b**) STF-dq0 algorithms. Figure 16: Simulation result obtained under Scenario II for Load B, showing the overall dc-link voltage \(V_{dc}\), and individual voltage of each split capacitor \(V_{dc1}\) and \(V_{dc2}\), demonstrated by SAPF while applying the enhanced-ADALINE algorithm. Figure 15: Simulation results obtained under Scenario II for Load B, showing source voltage \(v_{\text{S}alc}\), load current \(i_{\text{L}alc}\), injection current \(i_{\text{in}jabc}\), source current \(i_{\text{S}alc}\), and neutral current before and after mitigation, demonstrated by SAPF while applying the (**a**) enhanced-ADALINE and (**b**) STF-dq0 algorithms.
# Multi-Target Detection Algorithm for Fusion Images Based on an Attention Mechanism
## 7 Conclusions  
We investigate multi-target detection based on fused infrared and visible-light images, aiming to enhance the accuracy, robustness, and all-weather applicability of vision-based surveillance systems. To adapt to the specific characteristics of the target detection dataset, three key improvements are proposed to the YOLO-based detection framework: (1) a small-target detection layer to better preserve positional information and improve the recognition of small objects; (2) the integration of the Global Attention Mechanism (GAM) to enhance the model's ability to focus on critical features; and (3) the replacement of CIoU with WIoU in the loss function to improve robustness against low-quality data and promote better model convergence.  
To support this work, we constructed a custom dataset of over 4000 fused infrared-visible image pairs, using both web-crawled content and style-transfer-generated pseudo-infrared images.  
Experimental results demonstrate that our improved model outperforms single-modality baselines in terms of both detection accuracy and robustness. Ablation studies confirm the individual contribution of each component, and the model maintains reliable performance in challenging conditions such as nighttime, fog, and urban occlusion.  
This approach shows significant promise for real-world applications, particularly in domains where reliability under adverse conditions is critical, such as autonomous driving, urban surveillance, disaster response, and search-and-rescue.  
In future work, we plan to explore transformer-based fusion architectures to enhance long-range feature dependency modeling, and to evaluate the generalization ability of our method on public benchmarks such as the FLIR or KAIST datasets. We also aim to extend our model to video-based detection and real-time streaming scenarios using embedded hardware such as Jetson Xavier, enabling practical deployment in field robotics and mobile surveillance systems.  
**Author Contributions:** Conceptualization, H.F. and Z.Q.; methodology, Z.Q.; software, Z.Q. and H.F.; validation, H.F., Z.Q. and Y.G.; formal analysis, H.F.; investigation, H.F.; resources, H.F.; data curation, H.R.; writing--original draft preparation, Z.Q. and H.F.; writing--review and editing, Y.G. and H.R.; visualization, Z.Q.; supervision, Z.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquires can be directed to the corresponding author.  
**Acknowledgments:** We thank the members of our lab for their technical support and insightful suggestions.  
**Conflicts of Interest:** The authors declare no conflicts of interest.