esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Calculation and Results
### Ray Tracing and Result Analysis  
After verification of the correctness of the optical machine modeling, the optical machine system is applied to ray tracing. The more ray tracing is set, the more accurate the simulation results, and the simulation workload will be increased. Simulated light rays are incident at different off-axis angles, tracing the incident light rays in different directions from 0 to 80\({}^{\circ}\), with 5\({}^{\circ}\) as a step; a total of 15 incident directions are used for corresponding ray tracing, and the corresponding point source transmittance (_PST_) of off-axis angles are calculated.  
_PST_ is one of the important indicators to verify the suppression ability of stray light in the optical system. The _PST_ is the ratio of the irradiance \(E(\theta)\) generated when a point light source with an incident angle of \(\theta\) is imaged by the optical system, and the irradiance \(E_{0}(\theta)\) generated when the point light source irradiates the image system perpendicular to the optical system; the value of _PST_ are given by Equation (9):  
\[PST=\frac{E(\theta)}{E_{0}(\theta)} \tag{9}\]  
The value of _PST_ represents the suppression ability of stray light in the optical system and is not related to the stray light radiation intensity outside the optomechanical system. The smaller the _PST_ value, the stronger the ability of the optomechanical system to suppress stray light. After setting the _PST_ threshold, use Trace pro software to simulate the irradiance value of each incident angle outside the tracking field of view and bring it into Equation (9) together with the irradiance of the on-axis point light source after passing through the optomechanical system. The suppression ability of stray light in the optical system can be calculated.  
The light from the point light source at infinity can be regarded as 0\({}^{\circ}\) field of view light. A surface light source is placed in front of the optical system, and the light emission direction is perpendicular to the light source surface. The incident light fills the entire entrance pupil, the total light flux of the light source is 1 W, and the number of light traces is set to 1 million. As the off-axis angle increases, the energy threshold gradually reduced from 10\({}^{-4}\) to 10\({}^{-8}\).  
Ray tracing is performed on 15 rays with different incident directions between 0\({}^{\circ}\) and 80\({}^{\circ}\), and the _PST_ value curve analysis chart at 0\({}^{\circ}\) azimuth is shown in Figure 10.  
Figure 10: _PST_ curve of the optical system.  
As shown in Figure 10, with the incident angle increases, the \(PST\) value of the aerial camera optical system gradually decreases, and the stray light radiation entering the optical system also decreases. When the incident angle of the light source is greater than 0.5\({}^{\circ}\), the \(PST\) value drops rapidly. When the incident angle is 30\({}^{\circ}\), the \(PST\) value of the system is in the order of 10\({}^{-8}\). The aspherical aerial camera optical system's ability to suppress stray light meets the design requirements.
# Smart Cities in Russia: Current Situation and Insights for Future Development
## 4 Smart City Components
### Smart Services  
Cities provide various services for their citizens. Such services can include cleaning of the streets, police, healthcare, education, housing, and communal services. These services are essential for city life. This section focuses on describing some of the mentioned services in a smart city.  
1. **Smart healthcare:** Many approaches exist to increase the operational convenience of hospitals. For example, in the article [27], the authors proposed an IoT-aware smart hospital system with the purpose of automatically monitoring and tracking personnel, patients, and biomedical devices inside hospitals. These systems are meant to reduce costs and increase the quality of service. Connecting hospitals to the smart communication system gives another vector of development for the smart city. However, healthcare is not only about the hospitals. Technology can improve many aspects of modern city healthcare. For example, personalized recommendations on food, exercises, and even vacation trips could improve health. Furthermore, remote control of key characteristics such as pulse, temperature, and unexpected falls could help to reduce the risk of injuries and serious consequences for health.
2. **Smart education:** According to the article [28], the focus of smart education is seamless learning aimed at promoting learners' intelligence. This concept facilitates students' problem-solving ability in smart environments. The use of video conferencing, progress tracking apps,and individual recommendations based on AI can improve education quality. However, making all these services work together is a challenge.
3. **Smart security:** Security in a smart city is mainly associated with the network of video surveillance cameras. The use of such systems together with modern AI technologies can predict and prevent crimes [29], and detect criminals in a crowd. Furthermore, such systems can manage access to restricted territories. Security systems process a lot of personal data. Additionally, storing such data securely should be a prime concern for software vendors. Furthermore, automatic processing of personal data raises ethical questions. Should we track a person's movements? Can we record the meetings of people? As a society, we should answer these and other questions which smart security systems raise.
# Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous Driving
## 2 Related Work
### Path Planning  
This section describes the optimal controller using modeling (Section 3.1), the ODG algorithm (Section 3.2), and MPC (Section 2.2). The cost function consists of ODG, the tracking of the target path, and input control. The cost function is set to the weight value of \(\alpha\), and the weight matrices of \(\beta\), \(\gamma\), and \(\zeta\) are set for ODG, the tracking of the target path, and input control, respectively, as in (33). Optimal control is computed using QP for the cost function:  
\[\begin{split}&\min J=\alpha\,\min\sum_{h=1}^{N_{p}}\left[ODG_{ Total}(p_{\gamma}(h))\right]+\left\|p_{\gamma}(h)-p_{\gamma_{ref}}(h)\right\|_{ \beta}^{2}\\ +&\left\|p_{\chi}(h)-v_{\chi_{ref}}(h)\right\|_{ \gamma}^{2}+\|u(h)\|_{\zeta}^{2},\end{split} \tag{33}\]  
where \(p_{\gamma_{ref}}\) indicates the position of the lane selection when the vehicle is driving. As the vehicle speeds up, the risk associated with the vehicle increases. Therefore, increasing the vehicle speed in high-risk situations creates additional risks. We adjust the speed as shown in (34) to reduce the driving speed as the danger increases. In conclusion, the average value of the ODG is compared with the risk of the initial set of obstacles. If the average value of the ODG is close to zero, the vehicle drives at the default speed, and if the average value  
Figure 12: Vehicle obstacle-dependent Gaussian (ODG) with model prediction control longitudinal position.  
Figure 13: Total obstacle-dependent Gaussian (ODG) with model prediction control longitudinal position.  
increases, the vehicle decelerates. If it is equal to the \(\omega\), the target speed is zero, and the vehicle stops.  
\[v_{ref}=v_{init}\left(1-\frac{\sum\limits_{h=1}^{N_{p}}ODG_{Total}(p_{\gamma}(h) )}{N_{p}\times\omega}\right). \tag{34}\]  
We must consider the speed, acceleration, lane, and so on for the vehicle. However, the advantage of MPC is that the vehicle can be controlled by multiple constraints for complex vehicle states. The vehicle should not violate the maximum and minimum speed requirements of road regulations. Therefore, as shown in (35), various vehicle restrictions may be applied, such as maximum acceleration, maximum speed, or minimum acceleration, minimum speed, when decelerating:  
\[u_{\min}\leq u_{k}\leq u_{\max},\ \ and\ \ \Delta u_{\min}\leq\Delta u_{k}\leq \Delta u_{\max}, \tag{35}\]  
respectively, depending on the actual driving situation.
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 4 Solution Architecture
### Azure Resource Provision
#### 4.2.3 PostgreSQL Flexible Server  
PostgreSQL Flexible Server is a managed database solution chosen for its high availability, automated backups, and fine-grained network control. The database module provisions an Azure Database for the PostgreSQL Flexible Server, designed to support secure, scalable, and environment-specific configurations. Parameterization of administrative credentials, storage capacity, and performance SKUs ensures that development environments can utilize cost-effective resources while production systems can be scaled for high availability and performance. The module also supports VNet integration connectivity, ensuring that database traffic remains on Azure's internal network backbone for improved security and compliance. The Bicep template provides the following:Private endpoint connectivity to the VNet.
* VNet integration for traffic isolation.
* Tier and compute size parameters, allowing environments to use cost-optimized or production-grade configurations.
* Admin credentials stored securely in Azure Key Vault, with deployment-time parameterization.  
This approach ensures that production data remains secure while supporting lighter-weight dev/QA configurations. The complete infrastructure definition for secure PostgreSQL provisioning, including private network integration, is included in Appendix C.
## 2 sRNA as Orchestrators of Bacterial Pathogenesis
### Adherence to the Host
#### 2.1.4 STnc640  
STnc640 is an sRNA that plays a key role in adhesion and virulence regulation in _Salmonella_ Enteritidis strain 50336. Sequence analysis revealed that it shares 97% homology to its counterpart in _Salmonella_ Typhimurium [83]. Using bioinformatics and RT-qPCR, [PERSON] et al. demonstrated that STnc640 represses _fimA_, the major structural gene encoding type 1 fimbiae, via an Hfq-mediated posttranscriptional regulatory mechanism [60]. The deletion of _STnc640_ enhanced bacterial adhesion to Caco-2 cells and increased virulence in a chicken infection model, suggesting that STnc640 acts as a negative regulator of adhesion-related genes. Future studies, including overexpression experiments, are necessary to confirm its precise role in regulating fimbrial expression and virulence attenuation in _Salmonella_ Enteritidis.
# Seeing the Unseen: Real-Time Micro-Expression Recognition with Action Units and GPT-Based Reasoning
## 2 Related Work
### Traditional Computational Approaches  
Initial efforts in automatic micro-expression recognition relied on handcrafted features and classical image processing techniques. Methods such as optical flow were employed to track pixel-level movements between video frames [8]. Feature descriptors like LBP-TOP (Local Binary Patterns from Three Orthogonal Planes), HOG, and their temporal extensions were subsequently developed to capture the dynamic texture of facial regions [9; 10; 11]. While these approaches were foundational, they often lacked robustness across subjects and environmental conditions, limiting their practical applicability.  
Recent advancements in micro-expression recognition highlight the importance of robust spotting techniques, with [12] proposing the MSOF method based on bi-directional optical flow to enhance motion analysis in complex scenarios, and [13] introducing a head pose segmentation approach to improve spotting accuracy under uncontrolled conditions.
###### Abstract  
Reliability is a key consideration when microgrid technology is implemented in military applications. Droop control provides a simple option without requiring communication between microgrid components, increasing the control system reliability. However, traditional droop control does not allow the microgrid to utilize much of the power available from a solar resource. This paper applies an optimal multidimensional droop control strategy for a solar resource connected in a microgrid at a military patrol base. Simulation and hardware-in-the-loop experiments of a sample microgrid show that much more power from the solar resource can be utilized, while maintaining the system's bus voltage around a nominal value, and still avoiding the need for communication between the various components.  
solar; microgrid; droop control; patrol base 2  
on a communication link between the microgrid components--this eliminates the electrical system communications as a single source of failure that would be especially vulnerable in a military setting.  
Previous work has demonstrated the use of multidimensional [11] optimal [12] droop control with wind resources, in a residential microgrid setting. This paper expands upon previous work, applying multidimensional optimal droop control with solar resources. A load model for a typical patrol base is presented, and used in simulation of a military microgrid example to demonstrate the proposed controller.
## 3 Results and Discussion
### Volatile Composition and Sensory Analyses  
Table 2 shows the main differences in the volatile profile among all studied samples. Up to 69 compounds belonging to 9 chemical families were detected in the juices: alcohols, aldehydes, ketones, acids, furans, esters, benzene derivatives, terpene derivatives, and lactones. All these groups, but lactones, had been reported in commercial pomegranate juices by [PERSON] _et al._[27]. Sample 981 was the juice with the highest concentration of volatile compounds (Table 2); esters, benzene derivatives, and terpenes predominated in this sample. Esters are significant aromatic compounds for fruits, synthesized only by intact cells during the \(\beta\)-oxidation of fatty acids or from amino acid metabolism [28], but have been reported previously in pomegranate fresh juices [11, 27, 29], but in much lower contents, especially in the headspace of the juices. Due to the high concentration of these compounds and benzene derivatives, it could be assumed that were used as flavorings to increase the overall aroma of the juice. Pomegranate juice has low concentration of volatile compounds, which leads to low intensities in odor and aroma [11]. Sample 981 was the only concentrate, so during its production there was a concentration stage in which the volatile compounds would be lost and replaced, or collected and after that added back to the juice.
## 5 Review of Considered Study
### Medical Treatment Phobia
#### 5.4.2 Dental Phobia
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Paper** & **Phobia Type** & \
\begin{tabular}{c} **Treatment Condition and** \\ **Sample Size** \\ \end{tabular} & **Age Range** & **Solution** & **Session Number** &
\begin{tabular}{c} **Session** \\ **Length** \\ \end{tabular} \\ \hline
[24] & Snake Phobia & VRET (\(n=9\)) & 19–32 &
\begin{tabular}{c} User exposed to 2 \\ different levels from forest \\ to cave with snakes \\ \end{tabular} & 2 levels & \\ \hline
[32] & Height Phobia & VRET (\(n=10\)) & 21–25 &
\begin{tabular}{c} Users exposed to 3 \\ different stages (river, city, \\ and mountain) \\ \end{tabular} & 3 stages & \\ \hline
[33] & Shark Phobia & VRET (\(n=1\)) & 30 &
\begin{tabular}{c} Three stages navigated by \\ the user, pool then lake \\ finally the sea, the user \\ can walk and swim freely \\ \end{tabular} & 3 stages & \\ \hline
[34] & Height Phobia & VRET (\(n=20\)) & 20–21 &
\begin{tabular}{c} Two levels used one for \\ the mountain and the city \\ other is the city \\ \end{tabular} & 2 levels to 8 sessions & 25 min \\ \hline
[35] & Cockroach & AR Mobile app (\(n=13\)) & 20–23 &
\begin{tabular}{c} Four different scenarios \\ displayed to the patients \\ include single static \\ cockroach, single dynamic \\ cockroach, multiple static \\ cockroaches, and multiple \\ dynamic cockroaches \\ \end{tabular} & 3 levels & 60 min \\ \hline
[36] & Spider Phobia & AR Mobile app (\(n=16\)) & 18–20 &
\begin{tabular}{c} Four stages determined \\ based on the patient fear \\ level \\ \end{tabular} & 3 levels & 60 min \\ \hline
[14] & Dental Phobia & VRET (\(n=2\)) & 24 and 56 &
\begin{tabular}{c} The patient was exposed \\ to five scenarios including \\ seating with no dental \\ tools, mouth mirror, and \\ oral cavity, injection, \\ using the muted drill, and \\ using a drill with sound \\ \end{tabular} & 2 sessions & 45 min \\ \hline
[37] & Height Phobia & \
\begin{tabular}{c} 1: e-VRET (\(n=6\)) \\ 2: p-VRET (\(n=6\)) \\ \end{tabular} & 18–65 &
\begin{tabular}{c} Two virtual worlds \\ subway station and \\ 24-storey tower block \\ \end{tabular} & 6 sessions & \\ \hline
[19] & Dental Phobia & \
\begin{tabular}{c} 1: VRET (\(n=15\)) \\ 2: Psychoeducation with \\ information pamblet (\(n=15\)) \\ \end{tabular} & 18–50 &
\begin{tabular}{c} Five scenarios contain \\ seating with no dental \\ tools, mouth mirror, and \\ oral cavity, injection, \\ using the muted drill, and \\ using a drill with sound \\ \end{tabular} & 1 session & 40 min \\ \hline
[39] & Spider Phobia & \
\begin{tabular}{c} 1: VRET (\(n=50\)) \\ 2: IVET (\(n=50\)) \\ \end{tabular} & \(\geq\)18 &
\begin{tabular}{c} VRET contains 8 levels of \\ increasingly realistic \\ spider \\ IVET with 3 different size \\ spiders \\ \end{tabular} & 1 session & 180 min \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Existing augmented reality and virtual reality exposure therapy for specific phobia treatment.
## 4 Simulation and Discussion  
In order to illustrate the effectiveness, accuracy, robustness, and generality of the inverse kinematics approach proposed in this article, five simulations of six robots--a three-link planar arm (3R), a selective compliance assembly robot arm (SCARA), a Cartesian manipulator (3P), Universal Robot's UR5, a Stanford Arm, and Barrett Technology's WAM7R robot manipulator--are conducted using the NICCD method, the results of which are compared with the NR method using the Jacobian matrix. The NR method is described in section 6.2.2 of [25]. The PoE parameters of the six robots with the current configurations relative to the base frame are shown in Tables 1 and 2. The last simulation verifies the improvement of ICCD compared with CCD method and the evolution process of NICCD method. All simulations are performed on a desktop computer (Core i5 3.40 GHz, 16 GB RAM, MATLAB 2015b software program).  
```
1: Initialization: Set \(t=1\), \(\text{flag}_{1}=1\) and determine the scale factor \(\lambda\) (see Equation (75))
2:for each \(k\in[1,\max_{k}]\)do
3: Calculate \(\bigtriangledown f(\boldsymbol{\Theta}^{(k)})\) and \(H(\boldsymbol{\Theta}^{(k)})\) (see Equations (11), (12), and (33)\(\sim\)(39))
4:if\(H(\boldsymbol{\Theta}^{(k)})\) is positive definite matrix and \(\text{flag}_{1}=1\)then
5: Set \(\text{flag}_{2}=1\)
6: Determine \(\boldsymbol{\Theta}^{(k+1)}\) by [PERSON]'s method (see Equation (10))
7:else
8: Set \(\text{flag}_{2}=2\)
9: Determine \(\boldsymbol{\Theta}^{(k+1)}_{N}\) by [PERSON]'s method (see Equation (10))
10: Determine \(\boldsymbol{\Theta}^{(k+1)}_{C}\) by ICCD method (see Algorithm 1 )
11: Calculate \(f_{N}=f(\boldsymbol{\Theta}^{(k+1)}_{N})\) and \(f_{C}=f(\boldsymbol{\Theta}^{(k+1)}_{C})\) (see Equation (28))
12:if\(f_{C}<f_{N}\)then
13: Set \(\boldsymbol{\Theta}^{(k+1)}=\boldsymbol{\Theta}^{(k+1)}_{C}\)
14:else
15:\(\boldsymbol{\Theta}^{(k+1)}=\boldsymbol{\Theta}^{(k+1)}_{N}\)
16:endif
17:endif
18:if\(\left\|f(\boldsymbol{\Theta}^{(k+1)})\right\|>\varepsilon_{1}\)then
19:if\(\left\|\boldsymbol{\Theta}^{(k+1)}-\boldsymbol{\Theta}^{(k)}\right\|\leqslant \varepsilon_{2}\)then
20:if\(\text{flag}_{2}\
eq 2\)then
21: Set \(t=1,\text{flag}_{1}=0\)
22:else
23:if\(\text{RErr}(\boldsymbol{\Theta}^{(k+1)})<\text{PErr}(\boldsymbol{\Theta}^{(k+1)})\)then
24: Set \(t=\frac{t}{2}\)
25:else
26: Set \(t=2t\)
27:endif
28: Set \(\lambda=t\lambda\)
29: Determine \(\boldsymbol{\Theta}^{(k+1)}\) by ICCD method (see Algorithm 1 )
30: Set \(\lambda=\frac{\lambda}{t}\)
31:endif
32:else
33: Set \(\text{flag}_{1}=1\)
34:endif
35:else
36: Set \(\boldsymbol{\Theta}^{*}=\boldsymbol{\Theta}^{(k+1)}\)
37:break
38:endif
39:endfor
```  
**Algorithm 2** The Newton-improved cyclic coordinate descent (NICCD) method.
# YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11
## 4 Experiments
### Results Analysis
In multilevel maturity classification, the model not only precisely differentiates among the three main stages (green, color transition, and ripe fruit) but also conducts fine-grained recognition of various substages (10-90% color change) during the color transition. The bounding box annotations precisely mirror the size and position of the targets. The entire system showcases remarkable stability. The standard deviation of the confidence levels for cross-scene detection is lower than 0.05. Even when facing partial occlusion, varied shooting angles, and different distances, the system is able to keep a stable output. These visual detection results strongly validate the reliability and practicality of the proposed YOLO-PGC model in real-world application scenarios, providing robust technical support for intelligent greenhouse management systems. This system not only enables real-time monitoring of tomato growth and accurate prediction of optimal harvesting times but also supplies precise location information for automated harvesting systems. These capabilities enable it to excel in intelligent management for large-scale greenhouse cultivation environments. In future research, we will focus on optimizing the performance of our YOLO-PGC model under extreme lighting conditions and explore the feasibility of implementing advanced functions such as multi-object tracking. This work lays a solid foundation for further enhancing the adaptability of the proposed system to complex agricultural scenarios and expanding its application scope in precision agriculture.
# Path Planning of a Mobile Robot Based on the Improved RRT Algorithm
## 3 Methods
### APS-RRT Planner  
In this section, two important improvements to the sampling process of the RRT algorithm are proposed. First, the uniform sampling strategy is replaced by the adjustable probability strategy, denoted as _AP_ (line 3 in Algorithm 3). Second, the planner checks the growth status of the search tree after each expansion. Once the search tree enters the next-level area, the sampling area is reduced using the adjustable sampling area strategy,  
Figure 1: The expansion process of the RRT algorithm. The new state \(x_{new}\) is inserted into the search tree.  
denoted as \(AS\) (line 12-14 in Algorithm 3). The two strategies are explained in Section 3.1.1 and Section 3.1.2, respectively.  
```
Input: \(x_{init}\), \(x_{goal}\) and Map Output: A path \(\eta^{*}\) from \(x_{init}\) to \(x_{goal}\)
1: Map.init
2:\(\eta_{init}\leftarrow\)Planner(\(x_{init}\), \(x_{goal}\), Map)
3: if \(\eta_{init}\) exist then
4:\(\eta^{*}\leftarrow\)Optimizer(\(\eta_{init}\))
5: else
6: return Failure
7: end
```  
**Algorithm 2**_APSD-RRT_  
```
Input: \(x_{init}\), \(x_{goal}\) and Map; Output: A feasible path \(\eta_{init}\) from \(x_{init}\) to \(x_{goal}\)
1: Tree.init()
2: for\(i=1\) to N do
3:\(x_{rand}\gets AP(Map)\)
4:\(x_{near}\gets Near(x_{rand}\), Tree)
5:\(x_{new}\gets S\)eter(\(x_{rand}\), \(x_{new}\), \(\lambda\))
6: if CollisionFree(\(x_{near}\), \(x_{new}\), Map) then
7:\(\textit{Tree.AddNode}(x_{new})\)
8:\(\textit{Tree.AddEdge}(x_{near},x_{new})\)
9: if \(x_{new}\in X_{goal}\) then
10: return \(\eta_{init}\)
11: end
12: if flag!= 2 then
13:\(\textit{Map}\gets AS(x_{new}\), Map)
14: end
15: end
16: if \(\textit{CamnotFindAreaSolution}\) then
17:\(\textit{Tree}\leftarrow\)FailSample(Tree, Map)
18: end
19: end
```  
**Algorithm 3**_Planner_