esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, the changes in volatile terpenoids in _Z. bungeanum_ were determined as described. As the fruit develops, volatile terpenoids continue to accumulate until they reach their peak. At this point, any further development results in the content of volatile terpenoids beginning to decline but it remains higher than the levels in the early stages of development (the first period). The composition of volatile terpenoids of the two cultivars in the five periods was different. Hanchengdahongpao has more volatile terpenoids than Fuguhuajiao. Furthermore, \(\alpha\)-pinene is the most abundant terpene in the two cultivars, reaching 24.74% and 20.78% in Hanchengdahongpao and Fuguhuajiao, respectively. A total of 39 VOCs were detected in five periods, which were mainly terpenoids. A total of 35 types of VOCs were detected in Hanchengdahongpao, including 24 volatile terpenoids. Fuguhuajiao has 16 types of VOCs, including 11 volatile terpenoids. Monoterpenes are the main type of terpenoids found. We detected 24 volatile terpenoids in Hanchengdahongpao, 18 of which are monoterpenoids. Among the 16 volatile terpenoids in Fuguhuajiao, there are 9 types of monoterpenoids. The results of real-time quantitative PCR were consistent with physiological indicators as most of the genes were upregulated and the change trend of gene expression was consistent with the change involatile terpenoids. Among them, the key rate-limiting enzyme _ZbDXS_ in the MEP pathway showed the strongest upregulation in gene transcript level. The results of the phylogenetic tree of \(ZbDXS\) show that \(DXS\) gene family can be clustered into three independent branches, with the closest relationship occurring between \(ZbDXS\) and \(DXS\) of _Citrus sinensis_, _CitDXS_.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/1999-4907/10/4/328/s1](http://www.mdpi.com/1999-4907/10/4/328/s1)]([http://www.mdpi.com/1999-4907/10/4/328/s1](http://www.mdpi.com/1999-4907/10/4/328/s1)), Table S1: Relative expression levels of terpenoid metabolic pathway candidate genes in \(Z\). _bungeanum_ in fruit during 5 developmental stages.  
**Author Contributions:** [PERSON] conceived the project. [PERSON] and [PERSON] designed the experiments and performed the experiment. [PERSON] wrote the paper. All authors ([PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]) discussed the results and commented on the manuscript.  
**Funding:** This study was financially supported by the National Key Research and Development Program Project Funding (2018 YFD1000605).  
**Acknowledgments:** We thank [PERSON] for his advice and help for this experiment.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Review and Experimental Evaluation on Split Learning
## 3 Experimental Evaluation
### Experimental Results and Analyses
#### 3.2.3 Comparison with Typical FL Paradigms  
In this section, we compare the performance of FedAvg with that of SL paradigms on the MNIST and CIFAR-10 datasets. The experimental results of the SL paradigms are shown in Figure 7, and those of FedAvg are shown in Figure 10.  
Figure 8: Performance of each client in PSL for different \(\alpha\)s when the number of clients is 3. We use a colored line to indicate the performance of each client.  
Figure 9: Visualization results for “smashed data” of PSL using t-SNE, where points are colored with client and label, respectively. (a) IID; (b) Non-IID (\(\alpha=0\)).  
First, we observe that on both datasets, SplitFed can always converge relatively faster than FedAvg with the same data partition method. For example, on the MNIST dataset, SplitFed is always able to achieve a similar accuracy to that of centralized learning at around 10 epochs, whereas FedAvg does not achieve it except in the IID case. Similar conclusions can be drawn for the CIFAR-10 dataset. The results show that when the value of \(\alpha\) is the same, SplitFed consistently achieves a higher accuracy than FedAvg. This is probably because the server in SplitFed holds most of the network layers, which can be trained on the entire dataset.  
Second, FedAvg outperforms other SL paradigms in the non-IID setting, particularly when both the dataset and model are simple, such as training LeNet-5 with the MNIST dataset. When \(\alpha\) is small, FedAvg shows superior accuracy comparable to most SL paradigms, with SplitFed as the only exception.  
However, when the data and models become more complex, FedAvg learns much more slowly than the SL paradigms. For example, on the CIFAR-10 dataset, most SL paradigms can achieve better accuracy than FedAvg, indicating the difficulties of FedAvg in training more complex models.
## 4 Conclusions
The GNSS data are available from SONEL ([[ftp://ftp.sonel.org/gps/data](ftp://ftp.sonel.org/gps/data)]([ftp://ftp.sonel.org/gps/data](ftp://ftp.sonel.org/gps/data))), CORS ([[https://geodesy.noaa.gov/corsdata](https://geodesy.noaa.gov/corsdata)]([https://geodesy.noaa.gov/corsdata](https://geodesy.noaa.gov/corsdata))), EUREF ([[ftp://gnss.bev.gvat/pub/obs/](ftp://gnss.bev.gvat/pub/obs/)]([ftp://gnss.bev.gvat/pub/obs/](ftp://gnss.bev.gvat/pub/obs/))), Natural Resources Canada (webapp.geod.nrcan.gc.ca), SOPAC ([[ftp://garnerucs.edu](ftp://garnerucs.edu)]([ftp://garnerucs.edu](ftp://garnerucs.edu))), TrigNET ([[ftp://ftp.trignet.co.za](ftp://ftp.trignet.co.za)]([ftp://ftp.trignet.co.za](ftp://ftp.trignet.co.za))), RBMC Brazil ([[https://geoftp.ibge.gov.br/informacoes_sobre_posicionamento_geodesico/rbmc/](https://geoftp.ibge.gov.br/informacoes_sobre_posicionamento_geodesico/rbmc/)]([https://geoftp.ibge.gov.br/informacoes_sobre_posicionamento_geodesico/rbmc/](https://geoftp.ibge.gov.br/informacoes_sobre_posicionamento_geodesico/rbmc/))), and RAMSAC ([[https://www.ign.gob.ar/NuestrasActividades/Geodesia/Ramsac/DescargaRinex](https://www.ign.gob.ar/NuestrasActividades/Geodesia/Ramsac/DescargaRinex)]([https://www.ign.gob.ar/NuestrasActividades/Geodesia/Ramsac/DescargaRinex](https://www.ign.gob.ar/NuestrasActividades/Geodesia/Ramsac/DescargaRinex))). The IGS ROTI polar maps are available at IGS CDDIS ([[https://cddis.nasa.gov/archive/gps/products/ionex/](https://cddis.nasa.gov/archive/gps/products/ionex/)]([https://cddis.nasa.gov/archive/gps/products/ionex/](https://cddis.nasa.gov/archive/gps/products/ionex/))). The access date is 4 July 2024. **Acknowledgments:** The authors acknowledge NASA ([PERSON], [PERSON], and [PERSON]) and the Lockheed Martin Corporation ([PERSON], [PERSON]) for providing the GOES GPS observations and support during the NASA ROSES project. **Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Results and Discussion
### Method 2: The Fuzzy Boundary Approach
This can be seen in Figure 15a, where the fault F6 is injected between t = 2.5 s to t = 3.0 s, the'monitor_res' variable is 'Intermittent,' and there are no further diagnostic results. Similarly, in Figure 15b, multiple faults are injected for 0.5 s duration, (F5 between t = 2.5 s to 3.0 s, and F8 between t = 1.5 s to t = 2.0 s) and the end result is shown as 'Intermittent'. Thus, the hybrid combination of the ANFIS monitor with causal reasoning is capable of detecting and diagnosing both steady and unsteady state fault scenarios in the EPS, and it is also tunable as required. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Region** & **Tests** & **F1** & **F2** & **F3** & **F4** & **F5** & **F6** & **F7** & **F8** & **F9** & **F10** \\ \hline \multirow{2}{*}{5} & All DCValves\_V = V \_Nominal & - & - & - & - & - & - & - & - & - & - & - \\  & Any DCValve\_V \textless{V} \_Nominal & X & X & X & X & - & - & - & - & - & - \\ \hline \multirow{2}{*}{4} & Any DCValve\_V \textless{V} \_Nominal & X & X & X & X & - & - & - & - & - & - \\ \cline{2-10}  & Any ACLamp\_V \textless{V} \_Nominal & - & - & - & - & - & - & - & X & X & - \\ \hline \multirow{3}{*}{3} & Any ACLamp\_V \textless{V} \_Nominal & - & - & - & - & - & - & - & - & X & - \\ \cline{2-10}  & All DCValves\_V \textless{V} \_Nominal & - & - & - & - & - & - & X & - & - & - \\ \hline \multirow{3}{*}{1} & All DCValves\_V \textless{V} \_V \_Nominal & - & - & - & - & X & - & - & - & - & - \\ \cline{2-10}  & All DCValves\_V \textless{V} \_Nominal & - & - & - & - & X & - & - & - & - & - \\ \hline \multirow{3}{*}{1} & AC\_Motor\_Speed \textless{Speed} \_Nominal \&\&\&\& & & & & & & & & & \\  & AC\_Motor\_Torque \_> & - & - & - & - & - & - & - & - & - & - & X \\ \cline{2-10}  & AC\_Motor\_Torque\_min & & & & & & & & & & & & \\ \cline{2-10}  & AC\_Motor\_Speed \textless{Speed} \_Nominal \&\&\&\& & & & & & & & & \\ \cline{2-10}  & AC\_Motor\_Torque \_min & & & & & & & & & & & & \\ \hline \multirow{3}{*}{0} & AC\_Motor\_Speed \textless{Speed} \_Nominal \&\&\& & & & & & & & & & \\ \cline{2-10}  & AC\_Motor\_Torque \_> & - & - & - & - & - & - & - & - & - & - & X \\ \cline{2-10}  & AC\_Motor\_Torque\_min & & & & & & & & & & & & \\ \cline{2-10}  & AC\_Motor\_Speed \textless{Speed} \_Nominal \&\&\& & & & & & & & & & \\ \cline{2-10}  & AC\_Motor\_Torque\_min & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Diagnostic matrix for faults vs. tests. Figure 14: (**a**) Corresponding reaction in apparent power for DC TRU_CBtrip (F5) fault injection. (**b**) Method 2 results with the ANFIS monitor and causal reasoning. Figure 13: (**a**) Corresponding reaction in apparent power for ACMotor_Fault Injection (F6). (**b**) Method 2 results with the ANFIS monitor and causal reasoning.
## 3 Results and Discussion
### Model and Parameters
#### 3.2.2 Parameter Calibration  
In this study, a linear parallel bond model was used to characterize the macroscopic mechanical properties of the Xiyu conglomerate. The parallel-bond component acts in parallel with the linear component and establishes an elastic interaction between the pieces. The existence of a parallel bond does not preclude the possibility of slipping. Parallel bonds can transmit both force and moment between the pieces [19; 42; 43]. This model can provide two different interfaces of distinct mechanical behavior by simulating a finite-sized piece of cement-like material deposited between the two contacting pieces [44; 45]. Due to the above-mentioned properties, this model can be applied to simulate the Xiyu conglomerate.  
The discrete element numerical simulation of particles requires the input of micro-parameters that characterize the material properties. A common method for determining the micro-parameters for the discrete element simulation is based on the macro-mechanical parameters of the material and using numerical tests to calibrate the micro-parameters of the material [46].  
Several parameters were calibrated in this study: the effective modulus (\(E^{*}\)) and parallel bond effective modulus (\(\overline{E}^{*}\)) are the effective moduli for both the frictional interface and bonded interface, respectively, and they are roughly equal. The k ratio (\(K^{*}\)) and parallel bond k ratio (\(\overline{K}^{*}\)) are the ratios between the normal and shear stiffness for each interface, respectively. The parallel bond tensile strength (\(\overline{\sigma}_{c}\)) and parallel bond cohesion \(\overline{c}\) are the micro-mechanical parameters that allow the bonded surface to withstand tensile and shear forces, respectively. The friction angle (\(\overline{\varphi}\)) is analogous to the internal friction angle in the macro-mechanical parameters. The friction coefficient (\(\mu\)) is the ratio between normal force and friction force. The micro-parameters of the Xiyu conglomerate are presented in Table 2, where the corresponding mechanical parameter ratio of the matrix to the gravel is 0.45 and the gravel-matrix interface to the matrix is 0.2.
# A Novel DFA on AES: Based on Two-Byte Fault Model with Discontiguous Rows
## 1 Introduction
In 2012, [PERSON] [22] showed AES can be cracked with the same multi-byte fault proposed by [PERSON] (M1 and M2), and successfully recovered AES-128, AES-192, and AES-256 keys. In 2017, [PERSON] et al. [26] carried out a fault model with unknown and random multi-byte fault to crack AES-128, using on average only 2.17 PCFCs to recover the last round-key. In 2019, [PERSON] et al. [13] presented a fault model by exploiting a two-byte fault model with discontiguous rows to crack AES-128, which requires two PCFCs without exhaustive search. [PERSON] et al. first proposed the two-byte fault with Dcor, which fault was induced at the key schedule. We were inspired by the two-byte fault with Dcor at the key schedule; in this article, a two-byte fault model with Dcor at the state is proposed to crack AES and successfully crack AES-128, AES-192, and AES-256. The method has the following advantages compared to previous works:  
* A Dcor fault model that has been developed calls for fewer PCFCs than the existing model. Using the M1 model, for instance, [22] shows that although breaking AES-192 requires two PCFCs and \(2^{8}\) exhaustive searches, cracking AES-256 requires three PCFCs and \(2^{32}\) exhaustive searches. The proposed Dcor fault model, on the other hand, requires fewer PCFCs and thorough searches to recover AES-192. More specifically, recovering AES-192 only requires two PCFCs. The quantity of PCFCs and thorough searches needed for AES-256 relies on the use of different PCFCs. AES-256 can be cracked with \(2^{32}\) (or \(2^{16}\)) exhaustive searches and two (or three) PCFCs, respectively. * The fault model with faults induced at Dcor of the state. The multi-byte fault is condensed into the Dcor fault, which is present in the first column of the state, as opposed to the earlier one [21; 22]. The faults are dispersed throughout each state's column in M0, M1, M2, and M3, which are more intricate. AES-128 is cracked in [13], and the authors presented a two-byte model with Dcor in the key schedule. However, the Dcor model proposed in this article deals with all variants of AES. * The location of the fault induced is invariant. The location of the fault injection remains the same throughout the study, the conditions of fault induction (e.g., voltage, frequency) don't need to be altered. The rest of this article is organized as follows: In Section 2, AES is introduced. In Section 3, the Dcor fault model is proposed and attacks AES. Experimental results and discussion in Section 4. Finally, Section 5 concludes this paper.
###### Abstract  
To evaluate the effects of hyperglycemia and insulin treatment on the proportion of serotonin-immunoreactive (5-HT-IR) hyperenteric neurons, samples were taken from the duodenum, ileum, and colon of diabetic, insulin-treated diabetic, and control rats 10 weeks after the onset of streptozotocin-induced hyperglycemia. Myenteric whole-mount preparations were immunostained with anti-5-HT and pan-neuronal anti-HuCD markers. In controls, the 5-HT-IR myenteric neurons represent a small proportion (\(\sim\)2.5%) of the total neuronal number in the investigated gut segments. The proportion of 5-HT-IR myenteric neurons was significantly higher in the duodenum (\(p<0.01\)) and colon (\(p<0.0001\)) of diabetic rats compared to the controls but exhibited a slight increase in the ileum. Immediate insulin treatment resulted in a significantly lower proportion of myenteric 5-HT-IR neurons in each segment (duodenum \(p<0.0001\); ileum \(p<0.01\); and colon \(p<0.0001\)) compared to the untreated diabetics. Our study demonstrates that the proportion of 5-HT-IR myenteric neurons was enhanced in type 1 diabetes in a region-specific manner. Immediate insulin treatment prevents a higher hyperglycemia-induced amount of 5-HT-IR neurons and restores it to the control level in each investigated gut segment. Despite the low proportion of 5-HT-IR myenteric neurons, hyperglycemia-related changes of these neurons may play a crucial role in gastrointestinal symptoms in type 1 diabetes.  
diabetic enteropathy; gut segment specificity; myenteric neurons; serotonin; type 1 diabetes; enteric nervous system +
Footnote †: journal: Applied Sciences
# Networked Unmanned Aerial Vehicles for Surveillance and Monitoring: A Survey
## 2 General Target Surveillance
### Research Gap and Future Directions  
Different from the widely studied target tracking for surveillance, which requires a target to be within the field of view of a UAV, a few recent publications have investigated covert surveillance for security purposes [36; 37], which aims at avoiding being noticed by the target. The authors of [38] studied the problem of navigating a UAV to carry out covert video surveillance on a single mobile target. It proposed a novel metric to characterize the disguising performance of a UAV with respect to a target. Based on this metric, the authors developed an online trajectory planning method with a balanced consideration of the energy efficiency, covertness, and manoeuvrability of the surveillance UAV. The authors in [38] first designed a new metric to quantify the covertness of the UAV. Specifically, the UAV disguises its intention by changing the relative UAV-target angle and distance as drastically and frequently as possible. Then, they formulated a multi-objective trajectory planning problem to maximize the disguising performance and to minimize the trajectory length of the UAV and presented a forward dynamic programming method to solve the problem. A similar study can be seen in [39], in which the authors proposed a bioinspired bearing-only navigation law for a video-surveillance UAV to covertly monitor a moving target. In particular, the proposed method is based on sliding mode and is inspired by motion camouflage stealth behaviour observed in some attacking animals. It can navigate the UAV to monitor a moving target while concealing its motion with respect to the target's visual system. Moreover, the proposed navigation law is based on bearing-only measurements, i.e., directions from the UAV's current position to the moving ground target. It does not require any information on the targets' velocity and the distance to the target.  
Clearly, covert surveillance by UAVs is relatively less investigated in the literature. The existing publications have only considered a single UAV. Moreover, experimental validation is lacking. Therefore, promising research directions include the investigation of multiple UAVs on covert surveillance and experiments to confirm the effectiveness.
## 4 Discussion  
The investigated material solution was developed under a commission from an industry partner. Certain constraints were stipulated in the development of the presented mixtures regarding their cost-effectiveness, which were reflected in low contents of cement, fibrous reinforcement, and utilization of fly ash as a binder.  
The specific factor in the presented study was the utilization of a mixture of polypropylene and alkali-resistant glass fibers, which has not been thoroughly investigated or presented in the literature. Additionally, the investigated mixtures contained significantly smaller amounts of fiber reinforcement than those found in the works of other researchers. The typically investigated amounts of fiber reinforcement in this type of setting range from 0.10 to 1% by mass, while the presented work used 0.05 and 0.10% of fibrous reinforcement. It should also be noted that, in most cases, other researchers investigated the use of fiber reinforcement in fine soils rather than in high-quality and controlled aggregate mixtures.  
The incorporation of the fiber mixture in the hydraulically bound mixtures yielded results that were comparable to or more desirable than other similar solutions utilizing one type of fiber--polypropylene or glass. The optimum performance in both uniaxial compression and indirect tensile tests was obtained with a 0.05% fiber content, which was significantly less than the average content of fibrous reinforcement found in the literature. The obtained increases in the uniaxial compressive strength and indirect tensile strength of the tested mixtures due to the introduction of the fiber reinforcement amounted to as much as 142% and 300%, respectively.  
The utilization of alternative binders, such as fly ash, may alter the rate at which the required strength of the hydraulically bound material is attained. For fly ash mixtures, the ultimate compressive strength is typically evaluated after 42 days of curing, as opposed to 28 days in cement-bound mixtures. The obtained results have not only shown that the fiber reinforcement increased the compressive strength of the fly ash mixtures, but also that the largest magnitude of this increase was seen after the first 14 days of curing. Therefore, the fiber reinforcement significantly contributed to the early strength of these mixtures and can be considered an additional advantage of such formulations.
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 5 Results and Discussion
### Performance Comparison
#### 5.4.3 Throughput Scalability with Network Size  
Throughput performance has been assessed as the number of nodes scaled from 10 to 100, with comparisons against [PERSON] et al. [33], [PERSON] et al. [39], and [PERSON] et al. [40]. The results are shown in Figure 7.  
At small scales, i.e., 10-30 nodes, all models perform similarly, with throughput ranging from 1.6 to 3.5 Mbps. The proposed framework maintains parity here, achieving 3.4 Mbps at 30 nodes. However, as the network grows beyond 40 nodes, our framework demonstrates a clear throughput advantage, highlighting its scalability.  
At 80 nodes, our system achieves 11.8 Mbps, which is:  
* +2.5 Mbps (26.6%) higher than [PERSON] et al. [40]
* +4.9 Mbps (71%) higher than [PERSON] et al. [33]
* +2.9 Mbps (32.5%) higher than [PERSON] et al. [39]  
At full scale, i.e., 100 nodes, the proposed method reaches 15 Mbps, outperforming [PERSON] et al. [40] by +2.5 Mbps, and exceeding [PERSON] et al. [33] and [PERSON] et al. [39] by nearly +7 Mbps, representing a 93% and 50% improvement, respectively.  
This throughput gain is a direct result of the trust-guided routing and ledger-verifiable trust validation, which reduces misrouting and packet retransmission due to malicious nodes. The absence of centralized decision-making also allows the network to avoid bottlenecks and maintain high data flow as it scales.
# Application and Evaluation of the Gaofen-3 Satellite on a Terrain Survey with InSAR Technology
## 3 Interferometric Data Processing
### Phase Filtering
Our method not only filtered out the phase noise, but also preserved the original phase details and fringe edges more completely. The multi-scale phase-filtering method in the wavelet domain was applied to the flattened interferogram (Figure 3b) of Gaofen-3. As shown in Figure 9, the average coherence value of the filtered interferogram is increased to 0.834.