esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Beetroot Extracts on Alternaria Mycotoxins Production
BEE controlled the higher amounts of TeA when the extract concentration was increased in the isolates Alam1 and NO3. Other authors have reported similar behavior regarding aflatoxinigenic isolates. The decrease or rise in the amount of aflatoxin produced by several _Aspergillus_ or _Alternaria_ spp. depended on the extract concentration combined with environmental conditions [40, 42, 43, 44]. For this reason, the effect of vegetal extracts on mycotoxin biosynthesis must be evaluated to determine the adequate dose for the development of natural antifungals. Both AME and TeA have been considered as phytotoxins that played a crucial role in plant pathogenesis, according to [PERSON] and [PERSON] [3]. It is vital to avoid suboptimal doses that could reduce the fungal activity but increase the mycotoxin accumulation. The data showed that BEE and BME had anti-mycotoxici properties that were not dependent on the TPC. This was in accordance with the results of others [45], who reported that beetroot was among the tested plants which extracts had the best antifungal properties. However, its TPC and total flavonoid content are lower than those of _Hibiscus sabdariffa_. The two mycotoxins produced by the three tested isolates did not have the same sensitivity against the tested treatments. The tolerability of each mycotoxin depended on the fungus isolate that produced it. The data were in accordance with those of [PERSON] [41], who found that Citrinin produced by _Penicillium citrinum_ did not have the same sensibility as the same
# Effects of Nanoparticles on Algae: Adsorption, Distribution, Ecotoxicity and Fate
## 1 Introduction
Because algae cells are sensitive to many poisons and have the advantages of a short growth period, can be easily isolated and cultured, can be directly observed and show the symptoms of poisoning at the cellular level [22], many countries use algae for chemical risk assessment and have established several standardized test methods. Therefore, algae can be used as a model organism to study the biological toxicity of nanoparticles. At present, many studies have been conducted in this area. Most of the research has focused upon the toxic effects of nanoparticles on algae. The results show that nanoparticles had effects on the growth condition, chlorophyll content, protein content and enzyme activity of algae. The toxic effects were related to the morphology, size, chemical composition, concentration, solubility and dispersion of the nanoparticles, which was also dependent on the cell structure and physiological and biochemical characteristics of tested algae [23; 24; 25]. Some contents on the ecotoxicity of NPs have been summarized in published review papers. These reviews mainly summarized the behavior and fate of NPs in the environment, including the interaction of NPs with the organic matter in the environment, as well as the interaction between NPs and organisms, and highlight the adverse effects of NPs on organisms such as algae, plants and fungi [26; 27]. The toxicity of NPs to microalgae and their behavior in the environment were also mentioned in some reviews [23; 28]. Bioassay methods for nanotoxicology of microalgae were discussed and recommendations for further research were provided [29]. In this paper, we focused on the effect of NPs to algae. For this purpose, this review will systematically summarize and discuss the adsorption and distribution of NPs in algal cells, the ecotoxic effects of NPs on algae and affecting factors, as well as their fate in the aquatic food chain.
# A High-Resolution MIR Echelle Grating Spectrometer with a Three-Mirror Anastigmatic System
## 2 Dispersers and Methods
For a more detailed derivation process, refer to [13]:  
Figure 1:2  
Figure 1:3  
Figure 1:4  
Figure 1:5  
As shown in Equations (10) and (11), the optical path difference function when the light emitted from object point \(A\) is focused on the image point \(A^{\prime}\) is given by:  
Figure 1:6  
\(\rho^{\prime}\) is the curvature radius of the concave grating on the sagittal plane. For the concave grating, \(\rho=\rho^{\prime}\), i.e., only the horizontal plane and the radial plane thin beams are considered, thus, \(y=0;z=0\):  
Figure 1:7  
Figure 1:8  
When the optical path difference is zero, the concave grating focusing conditions are given by:  
Figure 1:9  
\[\frac{d\beta}{d\lambda}=n(\lambda).\frac{m}{dcos\beta cos\gamma} \tag{3}\]0  
\[\frac{d\beta}{d\lambda}=n(\lambda).\frac{m}{dcos\beta cos\gamma} \tag{3}\]1 (**a**) The Wadsworth mounting is based on a concave grating. (**b**) Schematic diagram of the optical path of the concave grating. The system should satisfy the condition of parallel light illuminating the concave grating, Thus, \(s=\infty\) in the Wadsworth imaging system:  
\[\frac{d\beta}{d\lambda}=n(\lambda).\frac{m}{dcos\beta cos\gamma} \tag{3}\]2  
The second and higher order aberration formulas for concave surfaces can be expressed as:  
\[ \begin{array}{c}-\rho\cos\varphi^{\prime}TA_{2}^{\prime}\delta\epsilon=\frac {3A_{1}}{2}z^{2}+\frac{3B_{1}}{2}y^{2}+C_{1}yL_{0}^{\prime}\\ +\frac{3B_{1}}{2}y^{2}+\frac{D_{1}}{2}L_{0}^{\prime}-\rho TA_{y}^{\prime}=B_{2} +C_{2}zL_{0}^{\prime}\\ =B_{2}zy+C_{2}zL_{0}^{\prime}\end{array}  \tag{19}\]  
\[A_{1}=\frac{s^{\prime}}{s}\cos\varphi\sin\varphi\Big{(}\frac{\rho\cos\varphi}{ s}-1\Big{)}+\cos\varphi^{\prime}\sin\varphi^{\prime}\Big{(}\frac{\rho\cos \varphi}{s^{\prime}}-1\Big{)} \tag{20}\]  
\[B_{1}=\frac{s^{\prime}}{s}\sin\varphi\Big{(}\frac{\rho}{s}-\frac{\cos\varphi} {k}\Big{)}+\sin\varphi^{\prime}\Bigg{(}\frac{\rho(1-K)^{2}}{s^{\prime}}- \frac{\cos\varphi}{k}\Bigg{)} \tag{21}\]  
\[B_{2}=\frac{s^{\prime}}{s}\sin\varphi\Big{(}\frac{\rho}{s}-\frac{\cos\varphi} {k}\Big{)}+\sin\varphi^{\prime}\Big{(}\frac{\rho(1-K)}{s^{\prime}}-\frac{ \cos\varphi}{k}\Bigg{)} \tag{22}\]  
\[C_{1}=\frac{\rho\sin\varphi}{s}-\frac{(1-K)\rho\sin\varphi^{\prime}}{s^{ \prime}} \tag{23}\]  
\[C_{2}=\frac{\rho\sin\varphi}{s}-\frac{\rho\sin\varphi^{\prime}}{s^{\prime}} \tag{24}\]  
\[D_{1}=\frac{\rho(\sin\varphi+\sin\varphi^{\prime})}{s^{\prime}}=\frac{k \lambda\rho}{ds^{\prime}} \tag{25}\]  
The Wadsworth system meets the following conditions:  
\[s=\infty,s^{\prime}=\frac{\rho\cos^{2}\varphi^{\prime}}{\cos\varphi+\cos \varphi^{\prime}} \tag{26}\]  
When \(\varphi^{\prime}=0\), all other aberration coefficients are zero except the \(D_{1}\) coefficient.
# A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller
## 4 HMM-R Detection Scheme
### Detection Features and Performance Indices  
#### 4.2.1 Detection Features  
The detection features we use are source IP and destination IP of data traffic, and their Renyi entropy is calculated to analyze their distribution changes.  
The attack sources of L-DDoS attacks are dispersed, attackers use multiple attack sources to cause bad experience of servers or links. Therefore, when the attack occurs, although the attack rate is very small, the distribution of the source IP will also change, especially when the attack speed is high, the distribution will change dramatically, resulting in the increase of the Renyi entropy of source IP.  
In a normal data center network, because of multi service requests, multiple servers cooperate and communicate with each other, thus the transmission of data packets between a server and multiple servers, the distribution of IP is relatively uniform. Most of the attacks are to explore the vulnerable hosts, servers, and links in the network. In order to get a better attack effect, the attacker generally chooses the same target to attack, thus the destination IP distribution is concentrated and its Renyi entropy decreases.  
We can distinguish normal traffic from attack traffic by the Renyi entropy changes of source IP and destination IP.
## 5 Results and Discussion
### Static Bending
\[\text{epoxy}:E_{\text{M}}=3.0\,\text{GPa},\,\
u_{\text{M}}=0.34,\, \rho_{\text{M}}=1.2\,\text{g}/\text{cm}^{3}\] \[\text{GPLs}:E_{\text{G}}=1.01\,\text{TPa},\,\
u_{\text{G}}=0.186,\, \rho_{\text{G}}=1.06\,\text{g}/\text{cm}^{3}\]  
The geometric dimensions of GPLs are: \(l_{\text{G}}=2.5\) um, \(w_{\text{G}}=1.5\) um, \(h_{\text{G}}=1.5\) nm. If not specified, the default is taken as \(f_{\text{G}}=1.0\%\) and \(N_{L}=10\). Figures 3 and 4 investigated the effects of the total number of layers \(N_{L}\) and the GPLs weight fraction \(f_{\text{G}}\) on the percentage deflection ratio \(w_{c}/w_{\text{M}}\) of the FG-GRC square plates under four different distribution patterns of GPLs using the plate theories IRPT and RPT, respectively. \(w_{c}\) is the center deflection of FG-GRC plates and \(w_{\text{M}}\) is the center deflection of plates without GPLs reinforcement (matrix material plates). It can be clearly seen that when the GPLs are asymmetrically distributed (FG-A), the difference between the results obtained from the two plate theories (RPT and IRPT) is very obvious. When the GPLs are symmetrically distributed within the FG-GRC plates (UD, FG-O, and FG-X), the difference between the results calculated by the two plate theories is very small. This is due to the asymmetric distribution of GPLs in the transverse thickness direction, resulting in a more significant transverse anisotropy in FG-A-type FG-GRC plates. Figure 4: Effect of GPLs weight fraction \(f_{\text{G}}\) on the deflection ratio \(w_{c}/w_{\text{M}}\) of FG-GRC plates under RPT and IRPT. Figure 3: Effect of the total number of layers \(N_{L}\) on the deflection ratio \(w_{c}/w_{\text{M}}\) of FG-GRC plates under RPT and IRPT. That is to say, the more significant transverse anisotropy of the FG-A-type FG-GRC plates compared to the symmetrically distributed modes of GPLs leads to a significant difference in its deflection ratio in the calculated results of RPT and IRPT, and this difference is evident in both thick plates with \(a/h=4\) and moderately thick plates with \(a/h=10\). It can also be seen from Figures 3 and 4 that the difference in deflection ratio \(w_{c}/w_{\text{M}}\) decreases with decreasing \(a/h\) values for different distribution modes, and the effect of thickness stretching tends to increase as the total number of layers \(N_{L}\) and the weight fraction \(f_{\text{G}}\) of GPLs increase. However, as the total number of layers \(N_{L}\) and the weight fraction \(f_{\text{G}}\) of GPLs increase, the difference due to the effect of thickness stretching tends to stabilize.
## 5 Results and Discussion
### Experimental Validation of Quantitative Reliability Calculations
#### 5.2.2 Quantitative Calculation of Reliability of Sea to Land Missions  
In the GO diagram model for the reliability of the ship electronic information system for the sea-to-land missions, operators 1 and 2 represent the RF integration equipment and the shipboard aircraft, respectively, as the input units of the system. Operators 13 and 14 are two-state units for the land-to-land proximity weapon and land-to-land long-range weapon, respectively. Operator 32 is a linear combinatorial generator with a combinatorial linear probability of \([0.4,0.6]\), so that the output of operator 32 can be introduced as follows.  
\[A_{R32}(1)=0.4A_{R12}(1)P_{C13}(1)+0.6A_{R12}(1)P_{C14}(1) \tag{39}\]  
Figure 11: Quantitative calculation results of the sea-to-air mission based on GO modeling.  
Substituting the equation for the cumulative probability of states of \(A_{R12}(1)\), the equation for the success rate of the sea-to-land mission \(A_{R32}(1)\) can be derived as shown below.  
\[\begin{array}{l}A_{R32}(1)=0.4A_{R12}(1)P_{C13}(1)+0.6A_{R12}(1)P_{C14}(1)\\ =[0.4P_{C13}(1)+0.6P_{C14}(1)][\left[P_{S1}(1)+P_{S2}(1)-P_{S1}(1)P_{S2}(1) \right]^{2}A_{R22}{}^{2}(1)A_{R25}{}^{2}(1)\\ A_{R28}{}^{2}(1)A_{R31}(1)P_{C4}(1)^{2}P_{C5}(1)P_{C6}(1)P_{C7}(1)P_{C8}(1)P_{C9 }(1)P_{C10}(1)P_{C11}(1)P_{C12}(1)]\end{array} \tag{40}\]  
The probability of success of the sea-to-land mission is 0.8879 by substituting the unit reliability data in Table 6 and taking the probability of co-causal failure \(C_{1}=C_{2}=C_{3}=C_{4}=\lambda\beta=0.00075\). The results of the quantitative calculation of the sea to land mission are shown in Figure 12.  
Then based on the designed AADL transformation GO model, the unit reliability data in Table 6 is substituted into the GO model, and the quantitative calculation results of the sea-to-land mission can be obtained by running in the GO method platform. The system output is 32-1, and the calculated value of mission reliability is 0.8779.
# Study on the Population Balance Dynamics Simulation of Grinding under Impact Crushing
## 4 Conclusions  
The following conclusions were deduced from this study:  
(1) According to the JK drop weight test method, based on the impact test data, the break distribution functions of quartz and pyrrhotite were solved using Matlab programming and Origin function-fitting mathematical methods. Based on batch-grinding test data, the selection functions of quartz and pyrrhotite under different conditions were obtained using the zero-order yield method, and the relationship between the selection functions of different mineral samples and grinding time was established.  
(2) Based on the population balance dynamics simulation of grinding, using the break distribution function and selection function of mineral samples, the particle size distribution results of grinding products of quartz and pyrrhotite under impact action were obtained, and the impact grinding characteristics of these two minerals were obtained.  
Figure 10: The variation in product yield with time for pyrrhotite with the feed particle size of \(-1.7+1.18\) mm: (**a**) +0.425 mm particle size, (**b**) \(-0.425\) mm particle size.  
(3) The selection function of quartz and pyrrhotite decreases with the prolongation of the grinding time, and the selection function of a coarse particle size is significantly different from that of a fine particle size. At the same time, the selection function decreases as the feed particle size decreases. The smaller the feed particle size, the lower the probability of it being broken.  
(4) The \(E_{cs}\) of quartz and pyrrhotite in different feed particle sizes in the mill is different, and under the same feed particle size conditions, the \(E_{cs}\) of pyrrhotite is lower than that of quartz. This means that the degree of mineral fragmentation is closely related to the impact energy, feed particle size, and mineral properties.  
Conceptualization, J.Y. and S.M.; Data curation, X.Y. and H.L.; Formal analysis, Z.L. and P.Z.; Funding acquisition, S.M.; Investigation, H.L. and X.Y.; Methodology, J.Y. and S.M.; Project administration, J.Y. and X.Y.; Validation, H.L. and X.Y.; Writing--Original draft, H.L. and J.Y.; Writing--Review and editing, J.Y. and S.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 52274258, No. 51874105).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the privacy of the experimental data.  
The authors declare no conflicts of interest.
# Measuring Cranivertebral Angle Reference Values in Adults Using Kinovea Software
## 4 Discussion  
The purposes of this study were to identify craniovertebral angle reference values in healthy adults and describe the effect of age and sex on head position measured by craniovertebral angle in the same population. The study sample of 122 healthy adults demonstrated a mean craniovertebral angle of 48.76\({}^{\circ}\) (6.77), which is similar to values reported in previous meta-analyses [4; 6].  
Linear regression analysis in the present study indicated a lower craniovertebral angle in men compared to women with a difference of 2.46\({}^{\circ}\) (95% CI \(-\)4.52, \(-\)0.4 \(p\) < 0.05). In contrast, no significant differences were found when the comparison was made between sex by each age strata, except for the 36-45 and 46-55 age groups. This is likely due to the small size of each subgroup when considering both age and sex. Differences between groups and linear regression coefficients, although significant, were small in size. Also,  
Figure 4: Distribution of CVA by age and sex.  
Figure 5: Distribution of CVA stratified by age.  
a significant reduction in the cranivertebral angle of 1.6\({}^{\circ}\) per decade of age was shown (95% CI 2.27, 0.93 \(p<\) 0.001). Despite its small size, it has an important effect across all age groups, reducing the cranivertebral angle by about 10 degrees. No prior studies directly exploring the relationship between cranivertebral angle and age or sex were identified for comparison. However, our findings align with the meta-analysis by [PERSON] et al. (2019), which differentiated between adolescent and adult participants and raised the possibility of age-related differences in the cranivertebral angle as a confounding factor in the study of the relationship between forward head posture and neck pain [4].  
A 2023 study by [PERSON] et al. investigating the relationship between age, sex, and sagittal plane cervical range of motion identified a significant decrease in active range of motion with increasing age, and significantly less upper cervical and global cervical spine extension in males compared with females [41]. When considered in combination with the present study, these results suggest that a relationship may also exist between the cranivertebral angle and one or more sagittal plane cervical active range of motion values.  
The results of the present study may serve as reference values for future research investigating sagittal plane head position in the presence of cervical spine pathology and associated functional limitations and disability. Furthermore, the reference values may aid researchers aiming to investigate whether physiotherapy interventions to address non-structural causes of forward head posture are effective for changing head position and/or reducing neck pain, headaches, or associated disabilities.
# Anomalous Vehicle Recognition in Smart Urban Traffic Monitoring as an Edge Service +
## 6 Experimental Results  
### Experimental Setup  
A prototype of the proposed SurMon system has been implemented. Two DJI Phantom 3 Professional drones are used to monitor the moving vehicles on the road, and two Nexus 9 tablets are connected to the drone controllers to display the real-time surveillance video. In this prototype, one laptop acts as an edge computing node whose configuration is as follows: the processor is 2.3 GHz Intel Core i7, the RAM memory is 16 GB, and the operating system is OS X EI Capitan. The resolution of video frames is \(1280\times 720\). The OpenCV 3.1 and Eigen 3.2.1 are used for the tracking algorithm. The given FOV (field of view) of the camera mounted on the drones is \(94^{\circ}\), and the actual FOV after calibration is \(89.39^{\circ}\) according to the fact that manufacturers would always make the image plane not perfectly circumscribed with the CCD plate but a little larger than that.
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 2 Materials and Methods
### Datasets
#### 2.2.2 Cropland Data Layer  
Cropland data layer (CDL) is a dataset published by USDA annually to provide information about croplands in the connus (CONUS) [58]. CDL was initially available for several states, and it has covered the whole connus since 2008. CDL is available as a single GeoTIFF raster at a solution of 30 m and can be downloaded or used directly in the Google Earth engine (GEE) [59]. In this study, CDL was used to eliminate the pixels that are not corn to reduce interference of other crops. This is because the data for all variables are aggregated at the county level, and each county can contain pixels that are not corn. CDL was used as a mask for our study area so that only the data falling within corn fields are used.
# Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland
## 6 Limitations and Future Research Directions  
While these predictive models demonstrated strong performance, their broader applicability may be limited by regional differences in socioeconomic and crime patterns.  
Factors such as local law enforcement practices, cultural norms, and policy variations could influence the generalizability of the findings. Additionally, their reliance on historical data may restrict the models' adaptability to rapidly changing conditions, such as public health crises or economic disruptions. Building on the findings from this research, future research should focus on:  
* Incorporating real-time data: Integrating real-time data sources, such as social media analytics, IoT-based surveillance, and mobility data will enable more dynamic and responsive crime prediction models.
* Expanding socio-economic variables: Including indicators such as healthcare access, housing affordability, and environmental stressors will improve the model's explanatory power and applicability to diverse contexts.
* Addressing algorithmic bias: Ensuring fairness and equity in predictive models is crucial for developing ethical AI systems that serve all demographic groups effectively.
* Scaling to broader geographies: Applying this framework to other regions with varying socioeconomic conditions can validate its adaptability and scalability for global crime prevention efforts.
* Incorporating spatial statistical models: To address the limitations noted above regarding spatial correlations, future research will explore advanced spatial statistical models such as spatial autoregressive models (SAR), spatial error models (SEM), and geographically weighted regression (GWR). These models will enable us to account for spatial dependencies and improve the accuracy of crime rate predictions across different counties, offering deeper insights into regional crime dynamics.
* Implementing time-series forecasting: Leveraging historical crime data to train models for future time periods will extend the framework's capabilities, allowing for the proactive anticipation of crime trends beyond the present dataset timeframe. This will enable us to forecast future crime rates and better inform long-term planning and intervention strategies.  
This study demonstrates that data-driven methodologies can significantly enhance the understanding and management of crime dynamics. By integrating socioeconomic insights, predictive modeling, and regional clustering, this research provides actionable recommendations for policymakers and practitioners. The approach not only addresses immediate crime prevention needs but also lays the foundation for long-term, equitable, and sustainable strategies that promote safer communities.