esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of Volatile Compounds
Of particular interest was the substantially lower content of volatile compounds in C-C.O, indicative of the pronounced impact of the refining process on the volatile compound composition. Therefore, these findings suggested the potential utility of volatile compounds in distinguishing between different camellia oil varieties [31]. In addition, DAG-enriched camellia oils, specifically C-C.O-DAG (80%) and C-C.O-DAG (40%), exhibited significantly distinct volatile compound profiles compared to their raw oils (C-C.O). Notably, alcohols and hydrocarbons were more abundant in C-C.O-DAG (40% and 80%), whereas ketones and aromatics were present in lower amounts. These oils, derived from C-C.O through enzymatic hydrolysis and molecular distillation, possess a unique flavor profile attributed to the processing-induced alterations in the volatile compound composition. Enzymatic hydrolysis with lipase catalyzes the breakdown of triacylglycerolglycerol (TAG) into diacylglycerolcerol (DAG), monoacylglycerol  
Figure 6: Classification of volatile compounds in different types of camellia oils. C.O, _C. oleifera_; C.S, _C. semiserrata_; C.G, _C. gauchowensis_; C-C.O, commercial _C. oleifera_; C-C.O-DAG (40%), commercial _C. oleifera_ diacylglycerolglycerol oil (40%); and C-C-O-DAG (80%), commercial _C. oleifera_ diacylglycerolglycerol oil (80%). acids (FFAs) [36], resulting in the release of volatile compounds, including alcohols and hydrocarbons. Molecular distillation, conducted at high temperatures, further modifies the volatile profile by removing low-boiling-point components and inducing oxidative reactions [55]. For instance, the removal of some ketones and aromatics during this process resulted in their lower levels in DAG-enriched camellia oils. Additionally, a previous study has linked the presence of aldehydes, ketones, and heterocyclic compounds to the oxidation of unsaturated fatty acids, while alcohols are recognized as intermediate products of the Strecker degradation pathways [56]. Furthermore, high levels of unsaturated fatty acids (UFAs) across all the types of camellia oils were observed, as described in Table 2. Consequently, the distinctive flavor of DAG-enriched camellia oils can be attributed to the processing methods employed, highlighting the role of processing in shaping volatile compound profiles and flavor characteristics.
## 3 Results
### Olive Trees' Trunk Humidity and Thermal Pattern Variations  
The microclimate under the olive trees canopy in the study area during the infrared images acquisition displayed a mean ambient temperature of 19.4 \(\pm\) 0.9 \({}^{\circ}\)C, a mean relative humidity of 55.6 \(\pm\) 4.0%, and a mean solar intensity of 9.3 \(\pm\) 0.3 W/m\({}^{2}\). These climatic parameters played a crucial role in the accurate calibration of the infrared images. Olive tree trunks had a mean temperature of 17.7 \(\pm\) 1.3 \({}^{\circ}\)C, with a minimum temperature of 14.6 \(\pm\) 1.5 \({}^{\circ}\)C and a maximum temperature of 20.4 \(\pm\) 1.5 \({}^{\circ}\)C. Throughout the IRT procedure, we gathered a total of 88 infrared images, encompassing two images per tree trunk. Trees' humidity patterns, as described by the trunk humidity indices, exhibited a mean interquartile range for Side A (H\({}_{\rm IQR}\) Side A) of 3.33 \(\pm\) 1.30% and an interquartile range of 1.45 \(\pm\) 0.69% for the opposite side of the trunk (H\({}_{\rm IQR}\) Side B). The H\({}_{\rm OPR}\) Side A displayed a mean value of 15.63 \(\pm\) 6.29%, while the H\({}_{\rm OPR}\) Side B recorded a value of 6.34 \(\pm\) 2.72%. The H\({}_{\rm IQR}\) difference and the H\({}_{\rm OPR}\) difference displayed 1.87 \(\pm\) 1.09% and 9.29 \(\pm\) 6.65% values, respectively. Shifting our focus to tree thermal patterns, represented by the trunk thermal indices, the mean value for T\({}_{\rm IQR}\) Side A was 0.81 \(\pm\) 0.40 \({}^{\circ}\)C, while for T\({}_{\rm IQR}\) Side B it stood at 0.44 \(\pm\) 0.19 \({}^{\circ}\)C. Furthermore, the outer percentile ranges for both sides, T\({}_{\rm OPR}\) Side A and T\({}_{\rm OPR}\) Side B, had mean values of 3.12 \(\pm\) 1.62 \({}^{\circ}\)C and 1.28 \(\pm\) 0.75 \({}^{\circ}\)C, respectively. The T\({}_{\rm IQR}\) difference had a mean value of 0.38 \(\pm\) 0.32 \({}^{\circ}\)C and the T\({}_{\rm OPR}\) difference had an average of 1.84 \(\pm\) 1.12 \({}^{\circ}\)C.  
In all cases, both the humidity and thermal indices exhibited clear differentiation between the two sides of the tree trunk (Figure 5). T-tests performed to assess these differences between the trunk sides revealed statistically significant variations for (a) H\({}_{\rm IQR}\) [t (86) = 8.403, \(p\) < 0.001; Figure 5a], (b) H\({}_{\rm OPR}\) [t (86) = 8.982, \(p\) < 0.001; Figure 5b], (c) T\({}_{\rm IQR}\) [t (86) = 5.512, \(p\) < 0.001; Figure 5c], and (d) T\({}_{\rm OPR}\) [t (86) = 6.811, \(p\) < 0.001; Figure 5d].  
Figure 4: Box plots showing the productivity values of the 44 centennial olive trees during the four harvest years. Horizontal lines: medians; boxes: interquartile ranges (25–75%); whiskers: data ranges.
## 4 Ground Track Repeat Period of GPS, GLONASS, and BDS Satellites
### Results Analysis of Ground Track Repetition Period
However, the blue line means the trajectory of the B20 satellite on DOY:196, which is exactly same as DOY:203. Thus, based on the above analysis, to investigate the influence of floods on the BDS MEO satellite, the data set collected seven days prior to the flood should be used. Figure 6: Skyplot of the GLONASS R14 satellite on DOY:193 (denoted by blue line), DOY:200 (denoted by brown line), and DOY:201 (denoted by pink line), 2021. The vertical means the elevation angle, and the circle represents the azimuth angle. Figure 7: Skyplot of the BDS MEO B20 satellite on DOY:196 (denoted by blue line), DOY:202 (denoted by brown line), and DOY:203 (denoted by pink line), 2021. The vertical means the elevation angle, and the circle represents the azimuth angle.
## 4 Conclusions and Further Work  
Based on three cars and the specific simulated geometric conditions, the impact of windbreak transition (WT) on the train aerodynamic performance and dynamic responses under crosswinds were studied in the current work. The following conclusions were drawn:  
1. The impact of the WT was the largest on the head car, and the continuous impact time of the WT was approximately \(\Delta t^{*}=47\), \(\Delta t^{*}=50\), and \(\Delta t^{*}=50\) for the head car, middle car, and tail car, respectively. The sudden wind loads induced by the WT resulted from the position of the right-angle structure and an insufficient height region.
2. The action mechanisms of the WT were the airflow rushed into the railway from the right-angle windbreak region and the slope region, with the airflow directly impacting the train. In particular, two main vortices generated by the WT, \(V10\) and \(V11\), played a key role.
3. The WT region resulted in sudden and continuous dynamic impacts on the train, and the dynamic responses when the train recovered to the original state were also drastically. Under current running conditions, the dynamic overturning coefficients reached to 0.53, the lateral displacement was 60 mm, and the rolling angle was \(2.5^{\circ}\).  
In the current work, the entire process of aerodynamic and dynamic responses was studied in detail when the train ran through the WT region. Due to the complex terrain along the railway, different structural shapes of WT could be studied in further work. Furthermore, mitigation measures should be investigated to reduce the impact of the WT on the operational safety of trains. In addition, with respect to aerodynamical torques arising on the surface of high-speed trains when they move through the surrounding air medium, including those stemming from both laminar and turbulent regimes under the influence of wind acting on a train in a sideway direction [44; 45], investigation of the stability motion of high-speed trains will be conducted in future work. Furthermore, the economic parameters of building structural shapes of WT used in actual engineering should be investigated in future works.  
Z.-W.C.: writing--original draft preparation; E.-Z.R.: data curation and investigation; T.-H.L.: writing--review and editing, supervision, and funding acquisition. Y.-Q.N.: writing--review and editing; X.-S.H.: visualization; Y.-T.X.: validation; W.-H.L.: formal analysis; Z.-J.G.: software and validation; L.Z.: writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China (Grant No. 2020 YFA0710903), the National Natural Science Foundation of China (Grant No. U1334205), the Open Project of Key Laboratory of Traffic Safety on Track of Ministry of Education, Central South University (Grant No. 502401002), and The Hong Kong Polytechnic University's Postdoc Matching Fund Scheme (Grant No. 1-W16W).  
Not applicable.  
The authors declare that they have no conflict of interest.
## 3 FEM (Finite Element Method) Simulation for Smaller PZT Patches
All four models were meshed with element quality over 0.9 to achieve the best outcome before obtaining the simulation results. Figure 7 shows the impedance signatures acquired from the four FEM models in the frequency range 20-200 kHz. With the 0.1 mm square PZT patch (Figure 6a), there is no resonance at all. This suggests that using such a small PZT will possibly result in a failure when detecting damage unless resonance can be found over the 200 kHz frequency range. Observing the 1 mm square PZT patch (Figure 6b), it has the highest value compared to the rest of the signatures. Larger than the signatures with 5 mm square PZTs (Figure 6c,d). This shows that larger PZT size does not always result in bigger amplitude. The two impedance signatures for the last two FEM models have completely different frequency ranges. The thinner bottom metal layer FEM model resulted in multiple resonance range with five resonance peaks below 150 kHz, whereas the last FEM model with the thicker metal layer had two resonance peaks (very small one at 90 kHz and another one near 200 kHz). Although these simulation results were not verified with experimental results due to the fact that it is virtually impossible to conduct experiments with PZT sizes of 0.1 mm \(\times\) 0.1 mm \(\times\) 0.01 mm, the fact remains that PZT sizes smaller than 10 mm \(\times\) 10 mm can be used to detect damage as long as resonance is found. Furthermore, changing the bottom metal plate can also create resonance to overcome the problem of resonance-free signatures, which may cause the EMI technique to fail at detecting damage.
###### Abstract  
This research investigated the effect of fly ash content on the compressive strength development of ultra-high-performance concrete (UHPC) at different curing conditions, i.e., the standard curing condition and the heat curing. A total of 20 mixtures were prepared to cast specimens to measure the compressive strength at different ages from 3 days to 180 days. Additionally, 300 specimens were prepared to estimate the appropriate heat curing period at the early ages in terms of enhancing the 28-day compressive strength of UHPC with high content of fly ash (FA). From the regression analysis using test data, empirical equations were formulated to assess the compressive strength development of UHPC considering the FA content and maturity function. Test results revealed that the preference of the addition of FA for enhancing the compressive strength of UHPC requires the early heat curing procedure which can be recommended as at least 2 days under 90 \({}^{\circ}\)C. Moreover, the compressive strength of UHPC with FA under heat curing mostly reached its 28-day strength within 3 days. The proposed models based on the _fib_ 2010 model can be a useful tool to reliably assess the compressive strength development of UHPC with high-volume fly ash (HVFA) (up to 70% fly ash content) under a heat curing condition that possesses a different performance from that of normal- and high-strength concrete. When 50% of the cement content was replaced by FA, the embodied CO\({}_{2}\) emission for UHPC mixture reduced up to approximately 50%, which is comparable to the CO\({}_{2}\) emission calculated from the conventional normal-strength concrete.  
ultra-high-performance concrete (UHPC); high-volume fly ash (HVFA); compressive strength; curing conditions; prediction model +
Footnote †: journal: Applied Sciences
###### Abstract  
In this paper, an improved cubature Kalman filtering (CKF) is proposed using the Sigmoid function to address the problems of positioning accuracy degradation and large deviations in ultra-wideband (UWB) indoor positioning in non-line-of-sight environments. The improved CKF is based on the squared range difference (SRD) model of the time difference of arrival (TDOA) algorithm. The inaccurate impact of model estimation under non-Gaussian noise is reduced by updating the measurement noise matrix in real time. The covariance matrix is estimated using singular value decomposition (SVD) to solve the problem of degraded state estimation performance. The filtering effect of the improved CKF algorithm is evaluated by referring to the checkpoints in the dynamic trajectory. The experimental results show that the proposed algorithm effectively mitigates the impact of UWB ranging outliers in the occluded experimental environment, which makes the dynamic positioning trajectory smoother, better fitted, and more stable. The algorithm improves the positioning accuracy by up to 39.29% compared with the SRD model used alone.  
ultra-wideband; squared range difference model; cubature Kalman filtering; Sigmoid function adaptive; singular value decomposition
# Bingo: A Semi-Centralized Password Storage System
## 5 System Architecture
### Cryptography  
Bingo utilizes a number of state-of-the-art cryptographic functions to ensure that passwords are safely stored and that users cannot be tracked by examining individual password shares.  
1. Murmurhash: Murmurhash (and its variant Murmurhash3) is a non-cryptographic hash function which can be seeded with a unique value to prevent offline rainbow table attacks so long as the seed remains a secret. We use a seeded Murmurhash3 implementation in Bingo to securely hash identifiers for data objects such that an attacker is unable to easily crack the hash. Furthermore, using Murmurhash3 provides the benefit of a consistent output for a given input (i.e., the basic property of hash functions), which aids in the repeated lookups of a plain text identifier. We define the Murmurhash3 function used in Bingo as M.
2. Bcrypt: Bcrypt is a secure password-hashing function with no proven attacks to date. The algorithm uses a defined number of salt rounds to increase entropy and prevent rainbow table attacks. A large number of websites which follow best security practices for hashing users' passwords prior to storage utilize Bcrypt as their hash function. We define the Bcrypt function used in Bingo as B.
## 5 Experimental Results
### Simulation Studies
#### 5.3.3 Test Case 3: A Reduced Power Grid of Central China  
We employ a real power system in Central China to demonstrate our islanding algorithm. This power system contains 1713 buses. We reduce this system to the one with 11 generator buses, 55 load buses and 75 lines, which is shown in Figure 20. In a certain operation mode, the total active power output of the generators is 21,136 MW, and the total active power consumption of loads reaches 20,990.4 MW. All time-domain simulations for this power system are also performed using PSD-BPA.  
We assume that at time \(t=0\) s, a three phase-to-ground fault occurs near bus CTJWat Line 36 and is cleared after 0.3 s. The power angle, voltage and frequency curves of generators are shown in Figure 21a-c. It can be seen from Figure 21a that within a short time after the fault is cleared, the generators are divided into two groups: {CTB, CMEG, CLD, CLTS, CPBG, CXJB, YLHSQ} and {ESXYS, ESXYE, EPQ}. As observed from Figure 21b,c, if the system is not split, the voltage magnitudes of the majority of the generators are beyond the range of stable operation, which lies in [0.95 p.u., 1.05 p.u.]. The frequencies of the generators increase considerably, and if no control actions are taken in time, the system may quickly collapse. In this case, the system needs to be split at 3 s. The islanding solution found by our algorithm is shown in Figure 22. Table 6 summarizes the best islanding solution and post-separation control measures for this test case. This solution was found by our algorithm using approximately 30 ms, and the islanding was conducted at 2.06 s. After islanding, 11 generators are assigned to specific islands, as shown in Figure 22.  
Figure 19: Comparisons with and without the islanding on IEEE the 162-bus system.  
Figure 20: The reduced power system of Central China.  
Figure 21: _Cont._  
Figure 22d-f show the dynamic response of the generators after islanding. We can also see that two islands are successfully created with frequencies between 0.9975 and 1.0120 p.u. The voltage magnitudes shown in Figure 22e lie within [0.95 p.u., 1.05 p.u.], which indicates that this system is stable. These results indicate that our algorithm plays an important role in effectively preventing the blackout.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Minimum imbalance Power** & **Typical Lines** & **Generation Reduction** & **Load Shedding** \\ \hline
27 MW & 58 (YW-XLG)-75 (Y2B-2 ES) & CTB (9 MW), CMG (7 MW), CITS (11 MW) & EXG (15 MW), ELQ (3 MW), ELQ (9 MW) \\ \hline \hline \end{tabular}
\end{table}
Table 6: The best splitting solution of the reduced power system of central China.  
Figure 21: Comparisons with and without the islanding on the reduced power grid of Central China.  
Figure 22: The islanding solution for the reduced power system of Central China.
# Calibration for Sample-And-Hold Mismatches in \(M\)-Channel TIADCs Based on Statistics
###### Abstract  
Time-interleaved analog-to-digital converter (TIADC) is a good option for high sampling rate applications. However, the inevitable sample-and-hold (S/H) mismatches between channels incur undesirable error and then affect the TIADC's dynamic performance. Several calibration methods have been proposed for S/H mismatches which either need training signals or have less extensive applicability for different input signals and different numbers of channels. This paper proposes a statistics-based calibration algorithm for S/H mismatches in \(M\)-channel TIADCs. Initially, the mismatch coefficients are identified by eliminating the statistical differences between channels. Subsequently, the mismatch-induced error is approximated by employing variable multipliers and differentiators in several Richardson iterations. Finally, the error is subtracted from the original output signal to approximate the expected signal. Simulation results illustrate the effectiveness of the proposed method, the selection of key parameters and the advantage to other methods.  
time-interleaved analog-to-digital converter (TIADC); sample-and-hold (S/H) mismatch; modulo \(M\) quasi-stationary; Taylor series; Richardson iteration +
Footnote †: journal: Applied Sciences
## 2 Related Works
destructor_ mite inside a honeybee cell. However, it does not acquire video nor image data by itself. The last two works show that there remains a space for developing Precision Beekeeping solutions that enable identifying possible threats such as _V. destructor_ mites. Some existing solutions for bee monitoring will be extended for the capability to detect _V. destructor_ mites. Such an approach is presented in [45] with a Raspberry Pi platform-based, energy-efficient, and scalable solution that enables tracking bees locally, sending cropped images to the Cloud. It consists of modules for pollen detection, bee position, and bee type recognition, and a classification module. The system described in [46] is also based on the Raspberry Pi platform and will be extended with a module for _V. destructor_ detection using visual monitoring at the entrance and gas sensors devices. Although the system is not an IoT solution yet, the authors aim to extend it to that type of device. Table 1 presents a short comparison between the most important of the above works. The solutions mentioned above do not fully respond to beekeepers' needs. Although state-of-the-art papers about bee monitoring exist, the vast majority of them refer to controlling the conditions in- and outside the beehive. Moreover, the technological stack applied in most of the above does not stand with the cutting edge devices and algorithms. There are no comprehensive solutions that include image analysis based on edge-processing that could be easily scaled for multiple beehives and would not affect bees at the same time. Meanwhile, it is worth noting that scalability and fault tolerance are important issues while developing complex, network-based systems, such as the one presented in this paper. The decentralized solutions that use Peer-to-Peer connections (P2P) instead of the traditional Master-Slave approach feature good time performance with basically no loss of accuracy of data transmission [47]. Moreover, the P2P types of frameworks are less susceptible to network breakdowns due to the dysfunction of one of the peers, which makes the solution more scalable. The lack of such properties among the existing solutions led us to develop the presented approach that responds to the mentioned needs and fills these technical gaps. Our approach satisfies the scalability of performed analyses through TPU acceleration and the scalability of the monitoring process through IoT-dedicated cloud components. Fault tolerance is achieved by a redundancy of the IoT components, which is native to the cloud. Our proposal, presented in this paper, extends the current spectrum of existing works by:  
* Providing a large-scale, Cloud-based infrastructure for monitoring bees and detecting varroosis that can be easily extended for multiple beehives;
* Enabling real-time analysis and detection of _Varroa destructor_ with embedded artificial intelligence (AI) on the TPU-accelerated Edge IoT devices;
* Ensuring that gathering and processing data will not affect the regular activities of bees.