esa-sceva/satcom-chunk-collection huggingface dataset:
# Hierarchical Reinforcement Learning for Crude Oil Supply Chain Scheduling
## 4 Hierarchical RL Scheduling Scheme
### Lower-Level Agent
#### 4.3.1 LP Formulation  
We define decision variables \(x_{e_{f}}\), as a transportation volume of crude on the road \(e_{f}r\) from transfer station \(v_{f}\) to refinery \(v_{r}\) at time step \(t\) (i.e., \(w^{t}_{e_{f}}\)), and \(x_{r}\) as a processing volume of crude oil in refinery \(v_{r}\) at time step \(t\) (i.e., \(q^{t}_{r}\)), respectively.  
Since the LP problem is independent of time step \(t\), we omit the superscript \(t\) in the LP formulation for simplicity. We rewrite the definition Equations (1)-(4) of the inventory volumes here, and we call \(m^{c}_{f}\), \(m^{c}_{r}\), \(m^{d}_{r}\) and \(m^{g}_{r}\) the auxiliary variables.  
\[m^{c}_{f}\leftarrow m^{c}_{f}+\sum_{e_{f}\in E_{f}}w_{e_{ef}}+\sum_{e_{pf}\in E_{pf}}w_{e _{pf}} \tag{11}\] \[-\sum_{e_{f}\in E_{f}}x_{e_{f}}-m^{c}_{f}\] (12) \[m^{c}_{r}\leftarrow m^{c}_{r}+\sum_{e_{dr}\in E_{r}}w_{e_{ar}}+\sum_{e_{pr}\in E_{ pr}}w_{e_{pr}}+\sum_{e_{f}\in E_{fr}}x^{c}_{e_{f}}\] (13) \[-n^{c}_{r}-x_{r}\] (14) \[m^{d}_{r}\leftarrow m^{d}_{r}+\rho^{d}_{r}x^{c}_{r}-n^{d}_{r}\] (15) \[m^{g}_{r}\leftarrow m^{g}_{r}+\rho^{g}_{r}x^{c}_{r}-n^{g}_{r} \tag{16}\]  
Then, the original LP problem can be formulated as:  
\[\min_{x_{e_{f}},x_{r}}J =\mathcal{H}^{t}=\sum_{e_{f}\in E_{f}}\lambda_{e_{f}r}x_{e_{f}} \tag{17a}\] \[\text{s.t.}\quad m^{c}_{f}=\tilde{m}^{c}_{f},\quad\forall v_{f} \in V_{f}\] (17b) \[m^{c}_{r}=\tilde{m}^{c}_{r},\quad\forall v_{r}\in V_{r}\] (17c) \[m^{d}_{r}=\tilde{m}^{d}_{r},\quad\forall v_{r}\in V_{r}\] (17d) \[m^{g}_{r}=\tilde{m}^{g}_{r},\quad\forall v_{r}\in V_{r}\] (17e) \[x_{r}\leq\tilde{U}_{q_{r}},\quad\forall v_{r}\in V_{r}\] (17f) \[l_{q_{r}}\leq x_{r}\leq u_{q_{r}},\quad\forall v_{r}\in V_{r}\] (17g) \[0\leq x_{e_{f}}\leq u_{e_{fr}},\quad\forall v_{f}\in E_{fr} \tag{17h}\]  
The objective is to minimize the total transportation costs \(\mathcal{H}^{t}\), as shown in Equation (17a) (equivalent to Equation (6)) when meeting conditions of inventory capacity in Equations (17b) and (17e) (split from Equation (7b)), processing capacity in Equation (17f) and (17g) (split from Equations (7e) and (7d)), and transportation capacity in Equation (17h) (split form Equation (7c)). According to the overall architecture, the auxiliary variables \(m^{k}_{i}\) representing end-stage inventory volumes are required not to deviate from the inventory suggestions of the upper agent.  
To guarantee that a feasible solution can be searched for in any \(a_{t}\), so that the lower-level agent always passes back a meaningful value \(J\) to the upper-level agent, we relax the constraints and reformulate the LP problem with barrier functions \(B(m)\), which are detailed as described in Section 4.3.2.  
\[\min_{x_{e_{f}},x_{r}}J =\mu_{1}\mathcal{H}^{t}-\mu_{2}B(m^{c}_{f})-\mu_{3}B(m^{c}_{r})-\] \[\mu_{4}B(m^{d}_{r})-\mu_{5}B(m^{
## 2 Theory Background  
### Entropy Weight  
Entropy is a special measure of information and is mainly used to depict the degrees of fuzziness and intuitionism for a given information set [20]. It was first proposed in 1947 [21] and was further developed in 1982. The entropy weight method is based on the difference in data, and the weight of each indicator is obtained using the entropy calculation formula, which is widely used in various fields [22]. hile subjective methods (e.g., Delphi and AHP)are used to determine the subjective weighting of criteria, objective methods like entropy weighting are used to remove artificial instabilities and produce more realistic results [23]. The entropy weight method can reflect the utility value of sample information entropy values without introducing subjective assumptions, producing an indicator weight that is more objective [24]. Subjective weights could be obtained directly from the decision makers' opinions like many other MCDM processes [25]. When the method is applied, firstly, experts are invited to score the actual situation of the project and the initial matrix is obtained, then the initial matrix is normalized, the entropy value of each indicator is solved and the weight of each indicator is calculated.
## 3 Automated Pipe Jacking Friction Resistance Identification and Warning System  
### Resistance Sensor Distribution  
Mechanical analysis was conducted on the soil disturbed during the construction of the jacking pipes, particularly focusing on areas where significant frictional resistance occurs between the jacking pipe and the surrounding soil during the jacking process. This ensures the effectiveness of the resistance sensor installation locations. The disturbance analysis of the soil during pipe jacking is shown in Figures 3 and 4.  
Figure 3: Longitudinal disturbance soil partition and resistance sensor installation location.  
The soil disturbed during pipe jacking construction can be divided into seven distinct disturbance zones, as shown in Figures 3 and 4. These are the compression disturbance zone, shear disturbance zone (front/back), unloading disturbance zone (top/bottom), grouting shear disturbance zone, and consolidation zone. During the jacking process, the frictional resistance between the pipe jacking shell and the surrounding soil generates a force that results in a shear disturbance zone in the soil surrounding the tunnel boring machine. This zone has a relatively smaller range than other zones [25,34,35]. When no grouting is applied, the soil in the grouting shear disturbance zone experiences shear friction resistance between the pipeline and surrounding soil, leading to a movement back and forth along the pipeline axis during the construction process [36]. After grouting, the slurry transforms the dry friction between the pipeline and soil into wet friction, thus reducing the frictional resistance and supporting the surrounding soil, and reducing the soil deformation caused by pipe jacking construction. Taking all of these factors into consideration, the installation positions of the sensors were determined, as shown in the sensor location diagrams in Figures 3 and 4.
# Influence of Atmospheric Flow Structure on Optical Turbulence Characteristics
## 2 Used Methods
### Method to Estimate the Strength of Small-Scale (Optical) Turbulence
#### Analysis of Structure of Atmospheric Flows in the Lower Layers
Subfigure (**c**) shows the energy spectra of spatial wind speed fluctuations calculated without filtering (blue lines) and with low-pass filtering (orange lines). Analyzing the changes in the Fried parameter, we can note that its minimum values were observed during intensive meso-scale turbulence both in the entire considered spectral range and in the range from 500 m to 12 km. Decrease in wind speed meso-scale fluctuations was especially pronounced in the morning and at night. Accordingly, the parameters \(E_{n}\) and \(\int_{f_{1}}^{f_{2}}E(f)df/Int\) were about 0.3-0.4 and 0.3-0.5, respectively. In the daytime, these parameters increased by 2.5-3.3 and 2.0-3.4 times, respectively. The daytime spectrum of atmospheric meso-scale turbulence was close to the classical spectrum of strong turbulence. In the night and morning, the spectrum had a steeper slope on small scales. We associate this spectral behaviour with a suppression of atmospheric turbulence on scales of 2-2.5 km in the surface layer, under conditions of stable thermal stratification. In these spectra, the positions of the peaks show that the characteristic horizontal scales of the meso-scale vortex structure with an anticyclonic direction of airflows, the center of which is formed north of the BAO, vary from 10 to 20 km.
## 4 Case Study  
### Data Preparation  
When using the historical inspection data of highway loop bridge in Jinan, Shandong Province, China as a case study, as shown in Figure 3, there were a total of 8 interchanges and 222 bridges along the route. In order to establish a degradation model of the bridge elements, 40 prestressed reinforced concrete continuous box girder bridges in the route were selected, including 285 box girder elements. The technical condition score data of the box girder members in the periodic inspection reports of 40 bridges from 2008 to 2019 were extracted for pre-processing, and the panel data formed were used as the input measured data of the degradation model. The bridges undergo regular inspections and maintenance every three years. In order to verify the validity of the project-level decision method for bridges, a continuous beam bridge on the route was selected as the case study bridge. The main girder of the continuous beam bridge was a prestressed reinforced concrete box girder, the span of which was \(25+28+2\times 25+20+4\times 25+22.7+35+58+35+4\times 30+4\times 30\) m. Consisting of 21 elements, the total length was 627.06 m, and the total width was 12.75 m, as shown in Figure 4. The purpose of this case was to find the optimal disease maintenance strategy for the pre-stressed reinforced concrete box girder of the bridge.  
The state distribution vector \(\text{D}_{6}=\left(\begin{smallmatrix}1&\frac{1}{21}&\frac{1}{21}&\frac{4}{21}& \frac{12}{21}&\frac{3}{21}\end{smallmatrix}\right)\) for the sixth year of the main girder elements of the bridge was known. By establishing an SMDP model and utilizing a dynamic programming algorithm, the optimal maintenance strategy for the box girder elements of the case study bridge for the next 3 years will be calculated and determined.  
Figure 4: Selected bridge inspection photos. (**a**) Side view and (**b**) bottom view.  
Figure 3: The route bridges’ distribution of a highway loop.
## 3 Results and Discussion
### Method Comparison with a Commercial ELISA Kit in Clinical Samples  
The method developed was compared for the detection of GA in clinical samples using a commercially available ELISA kit. Three samples from non-DM population (samples 1-3) and two DM samples (sample 4,5) were tested with both aptasensor and ELISA assay. At the 95 % confident interval, the calculated values of student's \(t\)-test (\(p\) = 0.05) and \(F\)-test were less than their tabulated values, implying that there was no noticeable difference between the performance of the two compared methods (Table 4). Therefore, the developed electrochemical aptasensor showed a satisfactory accuracy for GA detection in clinical utility.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**GA (mg mL\({}^{-1}\))**} \\ \hline  & **Mean** & **Std. Deviation** & **Std. Error** & **Min** & **Max** \\ \hline Diabetic (\(n\) = 10) & 16.17 & 9.36 & 2.96 & 8.90 & 37.50 \\ \hline Non-diabetic (\(n\) = 20) & 5.86 & 2.47 & 0.55 & 2.10 & 11.40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Descriptive statistics table showing different concentrations of GA in diabetic and non-diabetic sera.  
Figure 7: Statistical comparison of GA concentration (mg mL\({}^{-1}\)) in DM and non-DM groups. The box plot shows statistically significant different GA levels between non-DM and DM groups (*** \(p\) < 0.001).
# AI-Driven Innovations in Software Engineering: A Review of Current Practices and Future Directions
## 2 Literature Review
Responsible AI development requires the establishment of ethical guidelines, legal frameworks, and security protocols to ensure that AI technologies are used safely and ethically in software development [37]. Furthermore, educational initiatives must evolve to prepare future professionals to navigate the complexities of AI-integrated SE, fostering a workforce that is both technically proficient and ethically aware [38]. The evolving relationship between AI and SE necessitates significant adjustments in educational curricula. Incorporating AI literacy into SE education ensures that upcoming professionals are equipped to leverage AI technologies effectively. AI virtual assistants and recommender systems can enhance learning experiences by providing personalized support and leveraging collective knowledge [38]. By integrating AI-focused modules and practical applications into SE programs, educational institutions can prepare students to meet the demands of an AI-enhanced software development landscape [23]. Below is the summary of the systematic analysis of findings:  
* Automated Code Generation: Tools like GitHub Copilot and AlphaCode have demonstrated significant improvements in developer productivity and accuracy. However, concerns remain regarding the reliability and ethical implications of AI-generated code. * Defect Prediction: Machine learning algorithms trained on historical data enable the early identification of potential bugs, reducing testing time and enhancing software reliability. * Ethical and Legal Challenges: Bias in AI models and intellectual property concerns require robust frameworks and continuous oversight to ensure responsible adoption. Future research should aim to explore the full potential of AI in software engineering, focusing on developing strategies to mitigate associated risks and integrating AI into existing development practices effectively. Key areas for future investigation include the following:  
* Enhancing the capabilities of AI-driven tools to address current limitations in software modeling tasks. * Developing robust ethical guidelines and legal frameworks to govern the use of AI in SE. * Creating secure AI systems that mitigate vulnerabilities and ensure data privacy. * Improving reproducibility and replicability in ML applications within SE to advance research and practical implementations. * Exploring the impact of AI integration on developer roles, team dynamics, and job requirements. This literature review highlights the current advancements and challenges in the integration of AI and SE, emphasizing the need for continued exploration and innovation to fully harness the potential of AI in enhancing software development practices.
## 4 Discussion
Automated processing of medical data in dental practices by AI can detect much more than is currently expected. From automated cephalometric radiography to growth and ageing predictions, age determination, or even future forensic applications [47; 48]. In fact, nowadays dental nurses' workflow is changing profoundly. Therefore, future research is needed to combine AI technology with the use of recently introduced low noise instruments [49], experimental computerized devices [50], and new preventive strategies [51] to increase workplace security and allow a better experience for patients. The authors of this article believe that personal care by nurses and dental assistants will become more valuable as various AI chatbots become widely available and human attention becomes more precious and expensive. The focus of human nurses and dental assistants will be on more complex tasks that will not be stereotypical and will require high efficiency in terms of empathy and complex practical thinking, followed by actions that engage the physical body. This paper suggests that the implementation of AI, particularly AI-powered language models and teledentistry, is changing the role of dental assistants and nurses in orthodontic practices. The study presents evidence of the benefits and drawbacks of automated assessments of orthodontic treatment progress and the impact of AI on personalized care. The research concludes that AI has the potential to improve dental practice by providing more precise and personalized treatment and creating new roles and responsibilities for trained medical professionals. However, the implementation of AI in dentistry raises new ethical and legal issues for dental practices [10; 11; 45; 52; 53; 54]. In the context of other evidence, this paper adds to the growing body of research that suggests that AI has the potential to transform orthodontic healthcare, not only in the fields of intelligence-based remote monitoring of clear aligner therapy [55], crowding categorization from radiographs [56], AI-automated cephalometrics [57], or CBCT AI segmentation [54]. The use of AI in dentistry is still in its early stages, but this study provides evidence that AI can improve orthodontic treatment workflows and create new roles for dental assistants and nurses. The implications for future research are significant, as this workflow can also be applied to non-aligner therapies. In non-aligner therapies, the DM does not evaluate the tracking of aligners but all other previously mentioned features as well as the debonding of the brackets, their damage, or releasing the orthodontic wire. Further research is needed to explore the potential benefits and drawbacks of AI in dentistry, particularly in other areas of dental practice beyond orthodontics. In addition, research is needed to address the ethical and legal issues raised by the implementation of AI in dentistry. These include issues related to privacy, patient consent, and the regulation of AI in healthcare. In summary, this paper highlights the potential of AI to transform healthcare and emphasizes the need for further research to fully understand its implications.
## 4 Non-Coaxial Image Motion Compensation Method
### Image Motion Compensation Vector Model
The pitch angle \(\theta\) and the compensation encoding angle \(\kappa\) can be obtained through algebraic convergence iteration. However, this method requires significant computational resources, making it unsuitable for real-time onboard processing.
## 4 Evaluation of the Platform and Discussion
### IoT PQ Sensor and ThingSpeak  
In order to evaluate the IoT PQ sensor itself as well as its integration with the cloud, an experiment including two IoT PQ sensors is described. Both sensors were attached to a fume hood and a microwave to obtain their energy information in real-time. The IoT devices were programmed to get data from their attached sensor (information about current and voltage) and send them to the ThingSpeak platform every 15 s. To be more precise, the IoT PQ device can provide six parameters: RMS voltage (V\({}_{\text{rms}}\)), RMS current (I\({}_{\text{rms}}\)), active power, apparent power, power factor, and room temperature. This information is sent to the ThingSpeak servers using the concept of channel under the publish/subscribe paradigm. Within a channel, a maximum of eight fields can be added by publication. In the case of study, the IoT PQ device publishes the six parameters mentioned above related to the microwave and fume hood under two different topics. There is another topic number that includes aggregated energy information from the whole kitchen. Figure 7 shows the energy information obtained by an IoT PQ device attached to the kitchen. This information can be accessed from everywhere and any device with an Internet connection. Figure 7(a) contains V\({}_{\text{rms}}\), which is 233 V average during the interval measured. Figure 7(b) presents the I\({}_{\text{rms}}\) varying from 0 to 12.8 A depending on the power selected in the microwave. Figure 7(c) shows the power factor, which as expected is close to one when the loads are connected. Accordingly, before 08:34 the power factor is close to zero as there were no loads connected (I\({}_{\text{rms}}\) = 0). Finally, Figure 7(d) is the active power consumed by those two devices.
## 6 Safety Mechanisms Enabled by AI Agents
### AI-Driven Collision Avoidance  
Collision avoidance is among the most mission-critical functionalities in autonomous maritime operations. Maritime traffic is dense, unpredictable, and regulated by international conventions, making reactive, intelligent maneuvering essential. AI agents serve as the active brains behind modern collision avoidance systems, continuously scanning the environment, modeling risk trajectories, and selecting optimal paths in real time (Figure 4).  
These agents rely on a diverse set of sensors--such as radar, LIDAR, AIS, sonar, and optical cameras--to build a multi-layered perception of the vessel's surroundings. Using these data, agents apply machine learning algorithms, particularly reinforcement learning and recurrent neural networks, to track the movements of surrounding ships, identify potential conflicts, and preemptively adjust the course [108; 109].  
For example, in a congested strait with inbound cargo ships and smaller fishing vessels crossing perpendicularly, the agent may detect a likely crossing point conflict 200 m ahead. It forecasts the collision probability, assesses alternative evasive routes using current weather and wave data, and selects the path that minimizes collision risk while preserving fuel efficiency.  
Unlike traditional rule-based collision avoidance systems, these AI agents can learn from past interactions, adapting their behavior to port-specific traffic patterns, vessel classes, and seasonal changes in traffic flow. This learning capacity enhances not only safety but also navigation smoothness and operational efficiency.  
Importantly, the AI agent does not operate in a vacuum. It considers international navigation rules (COLREGs), port-specific routing constraints, and nearby traffic intentions broadcast via AIS. This allows the agent to behave not only safely but also legally and predictably in multi-agent environments, promoting cooperation with human-crewed vessels [110].  
By making thousands of micro-decisions per second, maritime AI agents ensure that autonomous vessels can anticipate, negotiate, and execute safe maneuvers with precision and foresight.
## 3 Results
### Land Use and Land Cover NDVI MK Trends  
The MK trend was computed for the LULC types found in the district, as significant changes in NDVI are often due to a change in land cover [46]. The NDVI MK trend results computed for each LULC class are presented in Table 3. MK results show a non-significant trend as the \(p\)-values are above 0.05. This could be attributed to extreme climatic events, high climatic variability and change, particularly rainfall variability, which contributed to the insignificant increase in NDVI Sen's slope (Table 3). Wet season NDVI shows a downward trend in commercial cultivation, shrub/grassland and subsistence cultivation, residential area and industrial land as Sen's slope is negative.  
Figure 5: Rainfall MK test trend magnitude (Sen’s Slope) (**a**) and trend significance (\(p\)-value) (**b**) between 1990–2019.  
These decreasing trends are indications of a degrading landscape as this was also detected in the 14 most common LULC conversions computed (Table 4). The conversions (Table 4) were computed by classifying wet and dry LULC on Landsat images over five-year intervals and conversions derived using change detection and transition matrix. Shrub/grassland areas show a strong loss of vegetative biomass, and these areas are converted to bare soil, eroded land or residential area. Bare/exposed rock, eroded land, subsistence cultivation, thicket/dense bush and industrial land recorded a slightly increasing trend with positive Sen's Slope.