esa-sceva/satcom-chunk-collection huggingface dataset:
# Screening of Sourdough Starter Strains and Improvements in the Quality of Whole Wheat Steamed Bread
## 2 Results and Discussion  
### Acidity and Microbial Counts of Cts  
As shown in Table 1, the pH values of CTS from different regions were between 3.61 and 5.19. The TTA value showed an opposite trend, differing from 6.40 mL to 16.32 mL. Acidity is an important index to measure the maturity of sourdough, and the sourdough maturity is decided by the organic acids produced by LAB metabolism. Different degrees of acidification can affect the formation components of sourdough structure, such as starch, gluten, and arabinoxylan [15]. According to previous studies, the sourdough acidity is usually between 4.0 and 4.5 [16]. The differences might result from changes in processing technology, such as different starter, flour type, and fermentation conditions [17].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Source**} & \multirow{2}{*}{**pH**} & \multirow{2}{*}{**TTA (mL)**} & \multicolumn{2}{c}{**Microbial Counts (Log cfu/g)**} \\ \cline{5-6}  & & & & & **Yeast** & **LAB** \\ \hline CTS 1 & Henan & 4.56 \(\pm\) 0.02 c & 9.15 \(\pm\) 0.26 c & 6.73 \(\pm\) 0.06 d & 4.96 \(\pm\) 0.13 a \\ CTS 2 & Henan & 5.19 \(\pm\) 0.02 a & 6.40 \(\pm\) 0.35 a & 3.64 \(\pm\) 0.12 a & 6.02 \(\pm\) 0.08 b \\ CTS 3 & Anhui & 3.87 \(\pm\) 0.04 d & 15.27 \(\pm\) 0.37 d & 5.21 \(\pm\) 0.09 b & 7.58 \(\pm\) 0.15 c \\ CTS 4 & Anhui & 3.61 \(\pm\) 0.06 e & 16.32 \(\pm\) 0.31 e & 6.20 \(\pm\) 0.25 c & 9.57 \(\pm\) 0.21 e \\ CTS 5 & Shandong & 5.07 \(\pm\) 0.05 b & 7.28 \(\pm\) 0.60 b & 6.18 \(\pm\) 0.17 c & 8.29 \(\pm\) 0.11 d \\ \hline \hline \end{tabular}  
* Means in the same column with different superscript lowercase letters of the same modulus indicate significant difference (\(p<0.05\)).  
\end{table}
Table 1: Acidity and microbial counts of CTS.  
According to the microbial counts, the yeast had the highest density (6.73 Log cfu/g) in CTS1, while the LAB had the highest density (9.57 Log cfu/g) in CTS4, among others. There were some differences in LAB numbers in different CTS, with a maximum difference of three to four orders of magnitude. The content and proportion of yeast and LAB were different in the samples. The difference in the counts of microbial levels reflected the quality difference of sourdough, which may be determined by many factors, such as the flour type, production technology, added ingredients, fermentation environment, storage time and conditions, etc. [18].
# Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation
## 7 Conclusions  
This study developed a Fuzzy-Based Control System for a Solar-Powered Bulk Service Queueing Model with Vacation, applying a Mamdani-type fuzzy inference system to adjust service parameters based on real-time input conditions such as arrival rate, queue length, and solar energy availability. The proposed approach was implemented using a structured methodology involving fuzzification, \(\alpha\)-cut analysis, and Parametric Nonlinear Programming (P-NLP), allowing for the dynamic control of service behavior under uncertainty. The numerical results demonstrated how different fuzzy scenarios influenced queue performance and cost outcomes, with fuzzy-based decisions offering more flexibility compared to fixed-rule systems. While the findings suggest the potential for improved resource allocation and operational responsiveness, further validation through comparative benchmarks and real-time implementation is necessary to confirm the model's full effectiveness in practical applications.  
Future work will focus on incorporating adaptive membership functions to enhance responsiveness to dynamic environments and improve control precision. The integration of hybrid optimization techniques (e.g., fuzzy-neural or fuzzy-genetic models) could further refine the decision-making process. Real-time implementation using IoT and sensor feedback will support practical deployment in smart grid systems. Additionally, comparative validation across varying load and weather conditions will strengthen the model's robustness. While this study employs a fuzzy inference system for adaptive control, future extensions may incorporate advanced optimization algorithms or neural network-based learning to automatically fine-tune membership functions and rule sets. Furthermore, Explainable AI (XAI) methods can be explored to improve the interpretability and transparency of the decision-making framework.  
Conceptualization, R.K. and S.P.N.; methodology, R.K. and S.P.N.; software, R.K. and S.P.N.; validation, R.K., S.P.N. and S.V.; formal analysis, R.K.,S.P.N. and S.V.; investigation, R.K. and S.P.N.; resources, R.K., S.P.N. and S.V.; data curation, R.K. and S.P.N.; writing--original draft preparation, R.K. and S.P.N.; writing--review and editing, S.V.; visualization, S.P.N. and S.V.; supervision, S.P.N. and S.V.; project administration, S.P.N.; funding acquisition, S.V. All authors have read and agreed to the published version of the manuscript. The APC was funded by TRANSILVANIA University of Brasov, HBS 3264/2025.  
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 no conflicts of interest.
# Research on Monocular-Vision-Based Finger-Joint-Angle-Measurement System
###### Abstract  
The quantitative measurement of finger-joint range of motion plays an important role in assessing the level of hand disability and intervening in the treatment of patients. An industrial monocular-vision-based knuckle-joint-activity-measurement system is proposed with short measurement time and the simultaneous measurement of multiple joints. In terms of hardware, the system can adjust the light-irradiation angle and the light-irradiation intensity of the marker by actively adjusting the height of the light source to enhance the difference between the marker and the background and reduce the difficulty of segmenting the target marker and the background. In terms of algorithms, a combination of multiple-vision algorithms is used to compare the image-threshold segmentation and Hough outer- and inner linear detection as the knuckle-activity-range detection method of the system. To verify the accuracy of the visual-detection method, nine healthy volunteers were recruited for experimental validation, and the experimental results showed that the average angular deviation in the flexion/extension of the knuckle was 0.43\({}^{\circ}\) at the minimum and 0.59\({}^{\circ}\) at the maximum, and the average angular deviation in the adduction/abduction of the knuckle was 0.30\({}^{\circ}\) at the minimum and 0.81\({}^{\circ}\) at the maximum, which were all less than 1\({}^{\circ}\). In the multi-angle velocimetry experiment, the time taken by the system was much less than that taken by the conventional method.  
monocular vision; human joint angle measurement; visual detection method; hand disability +
Footnote †: journal: Sensors
## 2 Materials and Methods  
### Sampling and Experimental Designs  
Water samples for incubation were collected from the surface water (0.5 m depth) at three stations along the channel of the freshwater zone (Figure 1A) in May, August, and November 2009, and in January 2010 based on the seasons. In addition, water samples were collected from one or two stations during March 2010 (Sts. A and C), and February 2011 (St. A). Water properties, including in situ water temperature (\({}^{\circ}\)C), turbidity (NTUs), and pH, were measured to examine the initial conditions before incubation using a YSI Model 6600 multiparameter probe. Water samples (15 mL) for dissolved inorganic nutrient analysis were filtered through Whatman(r) 25 mm GF/F glass microfiber filters (0.7 \(\upmu\)m). Subsamples for ammonium (NH\({}_{4}\)+), nitrite + nitrate (NO\({}_{2}\)\({}^{-}\) + NO\({}_{3}\)\({}^{-}\)), orthophosphate (PO\({}_{4}\)\({}^{3-}\)), and dissolved silica (DSi) analyses were frozen (\(<\)\(-\)20 \({}^{\circ}\)C) for storage. The ratios of dissolved inorganic N (DIN) to P (DIP) and DSi were analyzed to estimate the potential nutrient limitation, where DIN and DIP represent the sum of NH\({}_{4}\)+ and NO\({}_{2}\)\({}^{-}\) + NO\({}_{3}\)\({}^{-}\) and soluble reactive phosphate (SRP; PO\({}_{4}\)\({}^{3-}\)), respectively.  
The water samples were filtered through a 70 \(\upmu\)m mesh to remove herbivores and were transported to the Mokpo National Maritime University, Mokpo, S. Korea. Incubation water (2 L) was placed in a transparent polycarbonate ester container for the mesocosm experiments. Two sets (replicates) of the control group containing natural water with no HA addition (control) and the HA treatment group containing HA (Sigma-Aldrich(r), St. Louis, MO, USA) were incubated in the incubation pools made of transparent acrylic sheets (Figure 1B) for 9-20 days depending on the season. Incubation experiments were conducted at various concentrations (2.5, 5, and 10 ppm) of HA in March 2010. HA (5 ppm) obtained from the International Humic Substance Society (IHSS) and HS from Sigma-Aldrich(r) were used for the incubation experiment in February 2011. Minimum volumes (\(<\)10 mL) of subsamples were collected from the containers to investigate the changes in phytoplankton community structure and nutrient levels over the course of incubation.
## 3 Results and Discussion  
### Overall Evaluations during the Typhoon Season  
To evaluate the influence of the entire typhoon season, a non-typhoon season, i.e., DOY 99-150 was defined with the same interval of time as the typhoon season, starting from the available date of the CYGNSS data. Figure 1 shows the change of precipitation (GPM-derived, b1-b3) and SR (CYGNSS-derived, c1-c3) over southeast China before and after the 2017 typhoon season. The accumulated SRs of the non-typhoon and typhoon seasons were gridded into a 10 km resolution. There was no significant correlation when directly comparing the values of precipitation and the SR (Figure 1). However, note that the differences of the SRs (typhoon minus non-typhoon, see Figure 1c3) were positive over the northeast and south of this region (see blue boxes), while they were negative over the middle of the region, which is the Yangtze River basin (see black box). This phenomenon is consistent with the differences in precipitation shown in Figure 1b3. Additionally, the terrain of the middle coast is relatively high (see Figure 1a1), which could prevent the wind and rain from passing through to inland areas. For some paddy fields within the Yangtze River basin (Figure 1a2), higher SRs were observed during the non-typhoon season than during the typhoon season. This is likely due to the high water coverage during the rice transplanting period and the vegetation attenuation of the GPS L band microwave signal during the growing period [17].
## 2 Methodology
### Complex Measures  
Dynamical measures, such as Lyapunov exponents, were developed for equation-governed systems. [PERSON] [32] proposed an algorithm to reconstruct a time series in one dimension to N dimensions using phase space reconstruction. The delay vectors \(x_{n}\) are obtained as \(x_{n}=(x_{n},x_{n-\tau},x_{n-2\tau},\cdots,x_{n-(d-1)\tau})\), where \(\tau\) and \(d\) are the time delay and embedding dimensions, respectively. In this study, the methods of mutual information and false nearest neighbors were used to determine the optimal time delay [33] and embedding dimensions [34], respectively. Mutual information is defined as  
\[I(\tau)=-\sum_{h=1}^{j}\sum_{k=1}^{j}P_{h,k}(\tau)\ln\frac{P_{h,k}(\tau)}{P_{h }P_{k}} \tag{5}\]  
where \(P_{x}\) represents the probability of finding a value in the \(x\) interval and \(P_{h,k}(\tau)\) is the joint probability. The optimal time delay is taken as the first minimum value of \(I(\tau)\).  
Consider two points in the phase space, \(P(i)\) and \(P(j)\). The embedding dimension is the normalized distance \(R_{i}\) between the \((m+1)th\) embedding coordinate of the point \(P(i)\) in the m-dimensional embedding space and its nearest neighbour \(P(j)\) using the expression  
\[R_{i}=\frac{|x_{i+m\tau}-x_{j+m\tau}|}{\|P(i)-P(j)\|} \tag{6}\]  
The optimal embedding dimension is chosen as the embedding dimension for which the false nearest neigbhor is zero.  
In this study, a dynamic (Lyapunov exponent) and metric (correlation dimension) approach were used to evaluate chaos in the time series. The Lyapunov exponent algorithm proposed by [35] was used because of its robustness to noise and efficiency over a small sample size.  
To estimate the largest Lyapunov exponent, the time series is embedded in the phase space as an \(m\times n\) matrix consisting of the elements \(\mathbf{X}_{i}=[x_{i},x_{i+I},\ldots,x_{i+(m-1)I}]\), where \(J\) and \(m\) are the time delay and embedding dimension, respectively. The closest neigbhor \(x_{j}\) to the point \(x_{i}\) is determined using the Euclidean distance. For a chaotic system, the divergence (div) (\(d_{i}(k)\)) between \(x_{i+k}\) and \(X_{j+k}\) will follow the power law  
\[d_{i}(k)=c\rho^{ik} \tag{7}\]where \(\lambda\) is the Lyapunov exponent. An estimate of the system complexity can be determined from the correlation dimension. It is determined using [36]'s method. Here, the correlation function \(C(r)\) is defined as  
\[C(r)=\lim_{N\rightarrow\infty}\frac{2}{N(N-1)}\sum_{i,j=1}^{N}H(r-|x_{i}-x_{j}|) \tag{8}\]  
where \(H()\), \(r\), \(N\), and \(|\cdot|\) are the Heaviside function, radius, number of points, and distance between the two vectors, respectively. The correlation dimension (\(D_{2}\)) can be estimated from the scaling relationship between \(C(r)\) and \(r\) as  
\[C(r)\approx\alpha r^{D_{2}} \tag{9}\]  
where \(\alpha\) is a constant. A finite fractional value of the correlation dimension is indicative of a chaotic system.
# Stronger Cumulative than Lagged Effects of Drought on Vegetation in Central Asia
## 4 Discussion
### Vegetation GPP Response to Drought
By having analyzed the lagged and cumulative effects of drought on vegetation, this study clarifies the importance of considering the cumulative effect when discussing the effects of drought on vegetation GPP. In general, the adverse impacts of drought events on vegetation development can persist for some time [26]; the lagged effect only considers that the current state of vegetation is affected by an earlier drought at a specific point in time, neglecting the continuous dynamic conditions that affect vegetation development after the occurrence of a drought event [19; 36]. Previous studies have also shown that vegetation growth status is closely related to the cumulative effect of climatic factors [106; 107; 108]; this is consistent with the findings on the effects of drought on vegetation growth on the Mongolian Plateau, and comparing the contribution of the cumulative and lagged effects of drought to vegetation Normalized Difference Vegetation Index (NDVI) after 1998, it was found that the main driver of NDVI has shifted from a lagged effect to a cumulative effect [74]. Typically, vegetation in arid zones responds to drought events for a longer period of time, as evidenced by the response to the cumulative effect of drought; the average cumulative number of months of drought in the present study reached 9 months. The ability of vegetation to tolerate prolonged periods of drought is attributed to the development of drought tolerance through physical mechanisms that reduce water runoff and enhance the root system's ability to take up water [109; 110]. Sensitivity to drought varies among vegetation types [111; 112]. The cumulative effect of drought on scrub was stronger in this research because most of the scrub species in Central Asia are desert shrubs, and the main moisture for vegetation growth in this region originates from snowfall in winter. The snowmelt in spring affects the growth conditions of shrub for a year; thus, this type of shrub is more sensitive to the cumulative effect of drought. In summary,future discussions on the impacts of drought on vegetation should focus on the cumulative effect of drought to reveal the interactions between climate change and vegetation more comprehensively.
# Effect of Non-Homogeneous Soil Characteristics on Substation Grounding-Grid Performances: A Review
## 1 Introduction  
Generally, the earth is made of multiple compositions with distinct soil properties. The electric constants of soil, such as permittivity (\(\varepsilon\)) and resistivity (\(\rho\)) depend on the type of soil such as sand, limestone, clay, or gravel [1, 2, 3, 4, 5, 6, 7]. The effect of various electric constants of soil on the grounding performances has been tested through many types of research. For example, authors in [8, 9, 10, 11, 12, 13, 14] explained the fundamental knowledge on soil resistivities and their effects on the performances of grounding systems; the effects of grounding system design in two-layer soil are well presented in [15, 16, 17, 18, 19, 20, 21, 22]; the grounding system responses in multilayer soil with different resistivities are also explained in [18, 23, 24, 25, 26], and algorithms developed to evaluate apparent soil resistivity [27, 28, 29, 30, 31, 32, 33, 34]. These values typically differ on the surface of the soil from layer to layer, which may affect the effectiveness of the touch and step potentials and grounding resistance.  
For example, the effect on grounding resistance is more significant when there are large differences between the soil layer resistivity. The nature and structure of soil available such as sand, gravel, etc., determines the soil resistivity. Although some fundamental information and equations on two-layer soil structure are available--most standards and regulations specify grounding grid designs based on homogenous soil conditions.  
Homogeneous soil conditions are used as an input to the measurement of safety threshold values in most substation grounding system safety assessments and design protocols. Authors in [35; 36; 37] examined the performance of grounding systems under homogenous soil conditions. However, due to the difficulty of the numerical computations required, multiple soil layers are usually ignored when designing a grounding system. The substation grounding design will be inaccurate if non-homogeneous soil conditions are not taken into account during the grounding designing phase [25]. Thus, as a result, this study offers a comprehensive review of the effects of non-homogeneous soil structure (two and three-layered horizontal soil and two-layer vertical soil), with various characteristics on grounding system performance.
# A Complete Key Management Scheme for LoRaWAN v1.1 +
## 2 Related Work
The proposed scheme delegates the management of the root keys to a master device (a device with high computing power and more resilient to attacks). Thus, the end devices are no longer under security threat for exposing the root keys. However, this scheme heavily relies on the assumption that the master device and end-devices must be closed to each other. Hence, this scheme might not be applicable if the master device is not able to allocate close to the end devices. [PERSON] et al. [12] are proposed a secure architecture for key management based on smart contracts and permissioned blockchain to enhance security and availability in LoRaWAN networks. The blockchain allows multiple peers (similar roles to the join server) instead of one join server to ensure the availability of the handling of the OTAA procedure and storing copies of all encryption keys. Also, all OTAA attempts would be kept by all peers allows clear auditing. No further changes are needed to the framework due to all changes are related to the join-server. However, due to all changes are done on the join server-side, it overcomes the problem related to the failure of the join server, but the issues associated with other parts of the network might still exist. For example, the malicious attacker still has the chance to decipher the application data if the attacker can crack the session key from the result of infrequent key updates. Hence, inefficient session key generations and infrequent key updates are still posed security challenges. Furthermore, we reported a fast session key generation algorithm to handle the session key generation part of the key management in [6]. However, this paper presents further detailed security analysis with a complete key management scheme discussed below. Also, a higher efficient session key generation algorithm is developed since then. In this paper, the Modified Rabbit v1 refers to the key generation algorithm in [6]. Our enhanced session key generation is 14.8% faster than the algorithm in [6]. A detailed discussion of the work is conducted in Section 6 of this paper. In contrast to the works just described, our work covered the gaps that some of the abovementioned schemes did not cover. A detailed discussion of our works and other key management schemes can be found in Section 7.
## 2 Materials and Methods
### Experiment Design  
#### 2.3.1 Study Area and Periods  
As shown in Figure 1, western Europe was chosen as the study area because: (1) western Europe is a highly industrialized region in which anthropogenic CO emissions account for the majority of surface-CO fluxes, and biogenic CO fluxes from the ecosystem in the winter season will not change much in a short period of several years, as shown in Table 3; (2) western Europe has a well-developed and organized statistical infrastructure that enables it to have quite accurate emission inventories with uncertainty of less than 5% [42]; (3) all western European countries are Annex I countries [97], and the annual national emissions inventories they submitted to the UNFCCC can be used to evaluate the CO flux inversion results, and (4) there are relatively abundant ground-based CO observations available for evaluation.  
Four consecutive winter seasons (i.e., December-February) from 2017 to 2020 were selected as the study periods because: in Europe, in the wintertime, the CO concentrations and anthropogenic CO emissions are at the highest level [38,43] and ecosystem activities are at the lowest levels, which means biogenic CO emissions and biogenic atmospheric sources of CO, such as CH\({}_{4}\) and NMVOC, are at their lowest levels in a year. Furthermore, wildfire and biomass-burning CO emissions are also at their lowest levels during this period of a year [44,45]. Therefore, according to the prior CO fluxes shown in Table 3, it was assumed that the anthropogenic CO emissions accounted for the super majority of the winter CO fluxes in western Europe. Based on this assumption, emission inventories submitted to the UNFCCC [46] by western European countries were used to evaluate the accuracy of winter CO fluxes inverted by the inversion system.