esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Sensory Evaluation  
The sensory evaluation showed that the freshness, color, texture, odor and overall appearance scores of non-impregnated leaves on day 0 which were the freshly bought spinach leaves were 62.7%, 71.7%, 53%, 69.3% and 62.7%, respectively. Based on Table 1, the non-impregnated leaves, SA, GABA and sucrose impregnated leaves were significantly different in the category of texture. The non-impregnated leaves were significantly different from GABA and sucrose impregnated leaves in freshness and overall appearance categories whereas, in the category of color, only GABA impregnated leaves showed significant differences with non-impregnated leaves. However, for the odor parameter none of the impregnated leaves recorded significant difference with non-impregnated leaves. Throughout 7 days of storage time, there was no significant difference in the category of freshness, texture, odor and overall appearance for non-impregnated and all impregnated leaves. The significant difference was observed only in color category between day 0 and day 7. Nevertheless, on the 7 th day of storage time, non-impregnated leaves showed the lowest score for freshness (42%), color (58%), texture (44%) and overall appearance (51%) as compared to the impregnated leaves. Among the impregnated leaves, GABA impregnated leaves showed the highest score for all categories, which are freshness (67%), color (71%), texture (69%), odor (68%) and overall appearance (69%) followed by sucrose and SA impregnated leaves. Figure 3: **(a–c)** Effect of different impregnating solutions on color changes during storage at 4 \({}^{\circ}\)C. Spinach leaves were either non-impregnated (\(\Box\)) or impregnated with 2 mM SA (\(\blacktriangle\)), 5 mM GABA (\(\blacksquare\)) and 0.3 M sucrose (\(\blacklozenge\)) solutions. The leaves were stored for 7 days. Color parameters: (a) L* (from 0 black to 100 white), (b) a* (from \(-\)a* green to \(+\)a* red), (c) b* (from \(-\)b* blue to \(+\)b* yellow). Bars represent \(\pm\) S.E.
## 6 Conclusions  
The analytical and theoretical results were validated in a testbed. Over 200 measurements were taken and analyzed, all of which were consistent with the analytical equations. The measured operating points closely overlapped with the theoretical Bode plots in both magnitude and phase, validating both the analytical model and the effectiveness of the POD-P algorithm in grid-forming inverter-based resources (GFM IBRs). The algorithm operates within a narrow frequency band in the droop region, ensuring that the inherent capabilities of the GFM system are maintained.  
The optimal and most recommended approach for providing additional damping is tuning the grid-forming parameters, as this is significantly simpler. If this approach is not feasible or does not provide sufficient damping, a POD-P algorithm can be employed. However, in such cases, it is essential to validate the preservation of grid-forming properties.  
Conceptualization, M.Z., A.O. and A.S.-R.; Methodology, M.Z. and A.O.; Software, M.Z., A.O. and A.S.-R.; Validation, M.Z. and A.O.; Formal analysis, M.Z., A.O. and A.S.-R.; Investigation, M.Z., A.O. and A.S.-R.; Resources, M.Z., A.O., A.S.-R., M.T.B. and P.C.; Data curation, M.Z., A.O. and A.S.-R.; Writing--original draft, M.Z. and A.O.; Writing--review & editing, M.Z., A.O., A.S.-R., M.T.B. and P.C.; Project administration, M.T.B. and P.C.; Funding acquisition, M.T.B. and P.C. All authors have read and agreed to the published version of the manuscript.  
This work is financially supported by the Basque Government under the Grant IT1647-22 511 (ELEKTRIKER research group).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Beacon Success Rate versus Gateway Density in Sub-GHz Sensor Networks
## 2 System Model  
In this section, the channel access and interference model, along with the path loss model, are presented, all of which are used in our simulations. The IEEE-802.15.4 standard specifies the MAC and PHY layers of Low-Rate Wireless Personal and Wide Area Networks [1]. It provides the basis of various IoT standards that find applications in smart home automation and sensor networks. Hence, the system parameters selected in this study are based on the 802.15.4 specification.
## 4 Model Parameters and Simulation Scheme
### Determination of Basic Parameters
#### 4.1.2 Formation Velocity and Density Information of Geological Model  
Through histogram analysis of the acoustic and density logging curves of well D1 and D2 in the study area, the model filling speed and density information of rock salt and mudstone can be determined, as shown in Figures 8 and 9. The velocity and density of the brine layer in the upper part of the salt cavity can be substituted with theoretical values: a laboratory-measured brine velocity of 1553 m/s and a brine density of 1.17 g/cm\({}^{3}\). The sediment layer at the bottom of the salt cavern consists of insoluble residues and brine within the residue voids, with a velocity value falling between that of brine and mudstone. The density of this layer can be calculated using Gardener's formula. Table 1 presents the velocity and density information for the entire salt cavity model.  
Figure 8: Histogram analysis of lithological velocity. The blue dashed arrow refers to the preferred velocity value of mud rock lithology. The red dashed arrow refers to the preferred velocity value of salt rock lithology.  
Figure 7: Seismic profiles integrating depth migration data with pure wave seismic data. The light-yellow zone refers to salt rock strata in He-1 Member of the Hetaoyuan Formation.  
Figure 9: Histogram analysis of lithological density. The blue dashed arrow refers to the preferred density value of mud rock lithology. The red dashed arrow refers to the preferred density value of salt rock lithology.
# A Review of High-Sensitivity Tracking Techniques for Satellite Navigation Signals
## 2 Methods
### Conventional Approach
#### Direct Position Estimation
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Advantages** & **Representative References** \\ \hline Maximum-Likelihood Estimation (MLE)-Based DPE & Foundational DPE framework; derives & \\ \hline \multirow{2}{*}{Bayesian DPE} & Extends DPE to Bayesian framework; enables prior information fusion. & [PERSON] (2008) [50], [PERSON] (2010) [52] \\ \hline UKF-Enhanced DPE & Applies unscented Kalman Filter to optimize DPE likelihood estimation. & [PERSON] (2015) [55] \\ \hline Multi-Receiver DPE & Fuses signals from multiple receivers to jointly estimate PVT solution. & [PERSON]. (2019) [57] \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of Direct Position Estimation (DPE) methods.
# GPSMAP 66 sr: Assessment of Its GNSS Observations and Centimeter-Accurate Positioning
## 4 Precise Positioning Results
### Dual-Frequency GPS/Galileo Precise Point Positioning
Such an antenna setup should be good enough for positioning accuracies on the level of 10 cm. We collected data in several sessions of up to 16 h, more than 60 h altogether. The power supply was provided by the internal battery. The PPP results were compared with centimeter-accurate ITRF 2014 coordinates determined by short baselines to a local reference station. The datasets were divided into sessions of various lengths from 30 min (\(n\) = 126) to 8 h (\(n\) = 8). Some sessions had to be discarded since the number of tracked GPS/Galileo satellites with dual-frequency observations amounted on average to less than five. The percentages of discarded sessions reached up to 20% for the shorter session lengths. No session had to be discarded from the set of the 8 h sessions. The RMS values of coordinate deviations of the remaining sessions are presented in Figure 16. The overall position accuracy is now considerably lower than that in our first trial at the roof-top site. Again, the north component is determined more accurately than the east and height components. After just 30 min, the achieved accuracy is on the level of several 10 cm. A horizontal accuracy on the level of 10 cm (RMS) is obtained after 4 h, with even longer observation durations the results keep improving. Figure 15: Receiver setup at the adverse site with the help of a trekking pole. We conclude that, with such a simple setup and with many hours of observations (virtually overnight), the GPSMAP 66 sr is capable of providing ITRF coordinates on the level of 10 cm even at an unfavorable GNSS site.
## 3 Results and Discussion  
### Characteristics and Behaviour of the Tested Surfactants  
A low surface tension favours micelle formation and subsequent solubilisation of the contaminant. The critical micelle concentration (CMC) indicates a minimum concentration of the surfactant at which the contaminant removal would become efficient. The biosurfactants A and H reduced the surface tension most strongly, to low values of 37 mN/m and below (Figure 1).  
These values were in the range of those of the synthetic surfactants. For example, Triton X-100 revealed a minimum surface tension of 32 mN/m (Figure 1). This suggests that the effectiveness in contaminant clean-up of biosurfactants A and H potentially might be comparable with that of the synthetic surfactants. Surface tension for the biosurfactant G was, at about 65 mN/m, quite high compared to the surface tension for corresponding concentrations of the other surfactants. Accordingly, G was excluded from further tests. The CMC values for Triton X-100 (103 mg/L) and H (108 mg/L) were comparable. This finding was significant as Triton X-100 is known to be an effective surfactant for hydrocarbon remediation.  
Figure 1: Surface tension (mN/m) as a function of surfactant concentrations (mg/L).  
In the oil-spreading test, biosurfactant H gave a clear zone with a diameter of 7.6 cm, the largest among the biosurfactants tested and comparable to the 8 cm observed for Triton X-100 (Table 3). This suggests that H may be equally effective for oil removal as the synthetic detergent Triton X-100. C did not form a clear zone under the tested conditions.  
The emulsification index, \(E_{24}\) was determined for crude oil, besides kerosene, because the test soil had a contamination profile similar to C10-C40. With crude oil, the \(E_{24}\) values were comparable between the biosurfactants, with the highest value, at 18%, recorded for A. Triton X-100, in contrast, gave a quite low value of 5.9% (Table 4).  
For kerosene, the highest \(E_{24}\) (22%) was found for Triton X-100 whereas the biosurfactants A and H revealed considerably lower values of about 4.4%. This illustrates that the emulsification index markedly depends on the oil type. The emulsifying property of a surfactant is reflected in its foaming effect. A low surface tension favours foam formation, but does not necessarily guarantee a good response to other important features like solubilisation. There are other governing factors for the assessment of the surfactant interactions with the contaminant, such as molecular weight, micellar weight, shape of the micelles, etc.
# Two-Phase Deep Learning-Based EDoS Detection System
## 7 Discussion  
Based on the comprehensive results given above, we summarize some of the outstanding points demonstrating the effectiveness of the two-phase deep learning-based EDoS detection system in detecting EDoS attacks conducted on our practical testbed:  
* Our proposed model achieves a high detection rate, accuracy, and F1-score, and a low error rate, which clearly shows that it outperforms other state-of-the-art existing solutions. More specially, the proposed two-phase deep learning-based EDoS detection system achieves 98.163%, 98.139%, 98.163%, 4.333% of accuracy, detection rate, f1 score, and false alarm rate, respectively. These values are much better than SVM (86220%, 85.392%, 78.859%, 13.779%) and neural network-based (78.887%, 77.901%,  
Figure 14: CPU usage of the victim server under control of the proposed model.  
78.859%, 21.113%) models in [13]. Compared with R-EDoS systems, which use GRU (another variant of RNN models), the proposed model also overcomes in terms of accuracy, detection rate, f1-score, and false alarm rate.
* Our model can defense against three common types of EDoS attack, i.e., HTTP flooding, TCP-SYN flooding, and ICMP flooding attacks.
* Our scheme can detect and mitigate each abnormal flow in network traffic within an extremely short period of time, i.e., 0.54 s. This result highly outperforms other existing approaches, especially R-EDoS [35], which uses another variant of RNN and achieves pretty high accuracy in EDoS detection. The quick detection time makes the response time of the entire system much lower than other existing solutions despite the high amount of network.
* Using our proposed EDoS detection model, low CPU (32% to 36.3%) and memory (2.8% to 3.5%) resources are consumed.
* Using our defense system, a cloud user can avoid being forced to pay for the unexpected cost coming from the EDoS attacks. In other words, our two-phase deep learning-based EDoS defense system is the most suitable approach to protect cloud systems from various EDoS attacks and brings a better service quality for the protected cloud services.  
Although our proposed system is very efficient in handling EDoS attacks, it does have a limitation. The accuracy of the period detector, i.e., ANN model is not too high (from 95.896% to 96.439%). Some recent advanced algorithms in image processing proposed in [36,37] could be considered to apply for period detection. These algorithms are both CNN-based models and improved to reduce computation and enhance detection speed.
# Enhancing Voice Cloning Quality through Data Selection and Alignment-Based Metrics +
## 3 Audio Datasets
### Data Analysis
#### 3.4.2 SNR  
The next metric to be evaluated for these voice-cloning datasets is the Signal-to-Noise Ratio (SNR). For the _Hi-Fi TTS_ dataset, [PERSON] et al. highlighted the importance of audio quality in terms of the SNR [36]. After estimating the bandwidth of the speech signal, they calculated the SNR by comparing the noise power in both speech and non-speech segments using a Voice Activity Detection (VAD) module. The _clean_ subset was composed by audio files of a minimum SNR value of 40 dB in the 300 Hz to 4 kHz frequency band, while the files with a minimum SNR value of 32 dB fell into the _other_ subset. For speaker _XRey_, however, the audio quality was relatively lower due to the recordings being conducted in the third quarter of the 20 th Century. In our previous work [3], the SNR values were measured using a Waveform Amplitude Distribution Analysis (WADA) estimation algorithm.  
Figure 1: Absolute frequencies of diphones for the speaker _XRey_ (green), the _valid_ subset of the _Tux_ corpus (orange), and the speaker 92 of _Hi-Fi TTS_ (blue). The \(x\) axis is normalised to the maximum number of diphones for each speaker (386, 446, and 1255, respectively).  
In this study, the forced audio alignments generated in the previous step were used to obtain speech and non-speech segments required for SNR calculation, instead of relying on an external VAD module or WADA-based algorithms. Using that information, the SNR values were computed by comparing the power of the speech vs. the non-speech segments, supposing that the speech signal is the sum of the clean speech and the background noise obtained from the non-speech segment. Therefore, the SNR values obtained through this method may differ from those reported by the other works. The results can be seen in Figure 2 and Table 1.  
Based on the data, it can be noted that the SNR values in the HQ corpora were generally high, indicating that these two datasets are suitable for voice-cloning applications with respect to the signal quality. The same cannot be said, however, for speaker _XRev_. Nevertheless, many of the audio files had an SNR value higher than 20 dB, which can be considered as sufficient quality in some speech applications.
## 5 Flow Rate Estimation by Decentralization of Kalman Filters
### Simulations
As presented in Figure 11e, the flow rate could be precisely estimated by the flow-rate estimation using the DKFs. Figure 8:1 Simulation results of flow-rate estimation with error of tilting angle +0 deg at the start of liquid outflow. (**a**) Input command to motor for tilting ladle; (**b**) Angular velocity of tilting ladle; (**c**) Angle of tilting ladle; (**d**) Liquid height on pouring mouth; (**e**) Flow rate of outflow liquid from ladle; (**f**) Weight of outflow liquid from ladle. Figure 8:2 Reference pouring flow rate. The following disturbance simulations were performed. In the practical pouring process, the volume of molten metal in the ladle was uncertain due to temperature changes in the metal. Therefore, an error between the ideal liquid volume, designed in the control system, and the actual liquid volume occurred (see Figure 12). The error between the ideal and actual tilting angles at the start of the liquid outflow increased alongside an increase in the error between the ideal and actual liquid volumes. In the disturbance simulations, the ideal tilting angle \(\theta_{s}\) at the start of the liquid outflow was given as 20 deg, and the error between the ideal and actual tilting angles at the start of the liquid outflow was +3 deg. The simulation results are presented in Figure 13, where the graphs are illustrated similar to Figure 8:3  
Figure 8:4 Tilting angle of ladle at the start of liquid outflow. Figure 8:5 Simulation results of flow rate estimation with error of tilting angle +3 deg at the start of liquid outflow. (**a**) Input command to motor for tilting ladle; (**b**) Angular velocity of tilting ladle; (**c**) Angle of tilting ladle; (**d**) Liquid height on pouring mouth; (**e**) Flow rate of outflow liquid from ladle; (**f**) Weight of outflow liquid from ladle. As presented in Figure 13e, the error between the reference and the simulated flow rates was the result of disturbance. However, the estimated flow rate converged rapidly to the simulated flow rate. In addition, the error between the reference and the simulated outflow weights was increased by the disturbance as shown in Figure 13f. However, the estimated outflow weight tracked precisely to the simulated outflow weight. DKFs can estimate robustly the outflow weight, even if the disturbances are occurred in the pouring motion.
# Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models
## 4 Materials and Methods for Offline Fire Detection
### YOLO Models
#### 4.2.1 YOLOv8  
Ultralytes introduced YOLOv8 in January 2023, expanding its capabilities to encompass various vision tasks like object detection, segmentation, pose estimation, tracking, and classification. This version retained the foundational structure of YOLOv5 while modifying the CSPLayer, now termed the C2f module. The C2f module, integrating cross-stage partial bottlenecks with two convolutions, merges high-level features with contextual information, enhancing detection precision. Employing an anchor-free model with a disentangled head, YOLOv8 processes object detection and location (termed as objectness in YOLOv8), classification, and regression tasks independently. This approach hones each branch's focus on its designated task, subsequently enhancing the model's overall accuracy. The objectness score in the output layer employs the sigmoid function, indicating the likelihood of an object within the bounding box. For class probabilities, the SoftMax function is utilized, representing the object's likelihood of belonging to specific classes. The bounding box loss is calculated using the CIoU and DFL loss functions, while the classification loss utilizes binary cross-entropy. These losses prove particularly beneficial for detecting smaller objects, boosting the overall object detection performance. Furthermore, YOLOv8 introduced a semantic segmentation counterpart called the YOLOv8-Seg model. This model features a CSPDarknet-53 feature extractor as its backbone, replaced by the C2f module instead of the conventional YOLO neck architecture. This module was succeeded by two segmentation heads, responsible for predicting semantic segmentation masks [39]. More details on the YOLOv8 architecture can be found in [39].
## 4 Methodology
### Sample of Interviewees  
Due to the specificity determined for the profile of the professionals, the sample of interviewees for this study was carried out in a non-probabilistic manner, taking into account the experiences and performances in activities of internal controls, risk management, internal or external auditing, and consulting, in institutions in the financial sector. In addition, the interviewees performed these activities and obtained experiences in information technology and cybersecurity processes.  
The choice of institutions in the financial sector was due to the complex environment and the significant dependence of the financial sector on technology, making it the sector with the second-highest average data breach cost, as demonstrated in [7]. It is also susceptible to elaborate cyber attacks.  
In addition, financial institutions' connections with other areas of the economy can generate systemic impacts if attacked, since they make up the critical infrastructure of several countries, especially those with international financial operations. An example is the growing trend of attacks targeting international financial communication networks, such as the Society for Worldwide Interbank Financial Telecommunications (SWIFT), responsible for international transfers between banks, often involving large sums and in solid currencies, such as the dollar or the pound sterling [51].  
Furthermore, to better represent Brazilian financial institutions, the distribution of interviewees was carried out to reflect the size and international activity of the financial institution, according to the prudential regulations of the Central Bank of Brazil (BACEN).  
According to Bacen, in April 2024 [52], although it is decreasing, 68% of the credit collections carried out in 2023 are still concentrated in banks in segment S1, so we decided to maintain a higher percentage of professionals from this segment among the interviewees. Thus, Table 2 shows the number of interviewees and the final distribution between the segments.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Segment** & **Number of Interviewees** & **(\%)** \\ \hline S1 & 8 & 50\% \\ S2 & 2 & 12.5\% \\ S3 & 2 & 12.5\% \\ S4 & 1 & 6.25\% \\ S5 & 1 & 6.25\% \\ External Audit & 2 & 12.5\% \\ \hline Total & 16 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Interviewees by segments of Brazilian financial institutions.  
It is worth noting that the research team interviewed two professionals who work for External Audit companies. These companies are part of what the market calls the 'Big Four', a group of the largest international audit firms. The target market of these companies mainly includes large institutions operating in various sectors of the global economy and regions of the world. Over the years, the 'Big Four' have led the global market for audit services [53].  
The External Auditors interviewed were selected based on their extensive experience and roles in structuring and independently assessing the maturity of Cybersecurity and Information Technology Audit frameworks in S1 segment banks in recent years.