esa-sceva/satcom-chunk-collection huggingface dataset:
# Curcumin Innovative Delivery Forms: Paving the Yellow Brick Road' of Antitumoral Phytotherapy
## 1 Introduction
### The Chemistry of Curcumin  
The isolation of the active phytochemicals in turmeric dates back to 1815, when the first crude extract was obtained and described as \"a matter of yellow color\" [17]. Many of the properties of curcumin could already be observed in this extract: it was insoluble in water, solubilizing upon the addition of alkali to form a reddish-brown solution, and able to react with salts of metals such as lead and tin [17]. The extract was later found to contain a mixture of curcuminoids along with some oils and resins, and only in 1870 was curcumin first purified and isolated in the form of orthorhombic crystals [18]. Its chemical structure was determined in 1910 [19]. Curcumin has the chemical formula \(\mathrm{C_{21}H_{20}O_{6}}\) (Mw of 368.38) and it is also referred to as diferuloylmethane, since its IUPAC denomination is quite  
Figure 1: The three main curcuminoids found in _Curcuma longa_.  
long: (1\(E\),6_E_)-1,7-bis(4-hydroxy-3-methoxyphenyl)-1,6-heptadiene-3,5-dione. Its chemical structure comprises two aromatic ring systems with _o_-methoxy phenol groups connected by a seven-carbon linker consisting of an \(\alpha\),\(\beta\)-unsaturated \(\beta\)-diketone moiety that exhibits keto-enol tautomerism in solution [20]. Due to extended conjugation, the \(\pi\) electron cloud is distributed all along the molecule and thus curcumin is quite hydrophobic, with a log \(p\) value of 3.38 and an extremely low solubility in water (1.34 \(\pm\) 0.02 mg/L) [21]. According to the Biopharmaceutics Classification System (BCS) [22], curcumin is a class IV drug; that is, a compound having low solubility and low permeability. Class IV drugs usually are \"not well absorbed over the intestinal mucosa and a high variability [in the absorption profile] is expected\".  
Curcumin is reasonably stable in water at pH \(<\) 7.0 due to structural stabilization by the conjugated diene; in PBS and at pH \(>\) 8 it may degrade rapidly (10 min) [2]. In fact, curcumin possesses three ionizable protons with pKa values of approximately 8.5 (enolic proton) and 10-10.5 (two phenolic protons) [23; 24].  
Curcumin absorbs light from the near ultraviolet (around 340 nm) to the indigo-blue spectral region (450-460 nm), with absorption peaking at 410-430 nm (violet light) [25]. It presents a fluorescence band between 460 and 560 nm. Furthermore, curcumin is sensitive to ultraviolet radiation and its degradation is accelerated by exposure to sunlight [26; 27]. When irradiated with light above 400 nm, curcumin undergoes a self-sensitized photo-decomposition where singlet oxygen is involved, but when reactive oxygen species are not available, other decomposition mechanisms are triggered. Photodegradation products include vanillin, vanillic acid, 4-vinyl-guaiacol, ferulic aldehyde, and ferulic acid [28].
## 2 Imaging System Design  
### Platform Mechanism  
The platform (Figure 1) is composed of three axes, namely the pitch axis, roll axis, and azimuth axis. The three axes are orthogonal and independently rotate so that the LOS and imaging orientation can be simultaneously adjusted. The pitch mechanism is assembled on the rolling mechanism, and the rolling mechanism is assembled on the azimuth mechanism. The roll axis and pitch axis will rotate accordingly while the azimuth axis rotates; therefore, the azimuth axis conforms to the rule of rigid body rotation. The orientation of the pitch axis will change with the rotation of the roll axis. Rotation dependencies are essential for image attitude calculation, which will be further discussed in Section 3. The rolling mechanism employs a cantilever structure. Its bearing connects the pitch and azimuth mechanism. A back-to-back angular contact bearing is applied to the rolling mechanism, which can support radical torque and increase the bearing weight of the payload. The cantilever structure can increase the payload space of the pitch frame and enlarge the rotating range of the roll axis. The rotation range of the azimuth axis is from \(-180^{\circ}\) to \(+180^{\circ}\), the rotation range of the pitch axis is from \(-45^{\circ}\) to \(+45^{\circ}\), and the rotation range of the roll axis is from \(-85^{\circ}\) to \(+85^{\circ}\). The rolling mechanism of the platform sets up the foundation for large-angle sweep imaging.  
Permanent magnet, direct-current torque motors are used to drive the shaft of each axis directly, which eliminate transmission clearance and guarantee rotation accuracy. The direct drive mechanism improves structural stiffness and increases the closed-loop bandwidth of the servo system accordingly. A gyro is mounted on each axis to provide the angular rate in inertial space. The POS is mounted on the pitch frame, which can provide the position, velocity, pitch angle, roll angle, and azimuth angle in inertial space. The area camera is loaded on the bearing base of the pitch frame so that the inertial attitude of the area camera can be measured directly.
# A High-Resolution, Wide-Swath SAR Imaging System Based on Tandem SAR Satellites
## 3 Signal Model and Imaging Algorithm
### Minimum-Energy-Based Imaging Algorithm
#### 3.2.3 Observation Vector Extraction  
According to the definition in Equation (5), the sensing matrix can be constructed by  
\[\mathbf{A}=\left[\mathbf{A}_{1},\mathbf{A}_{2},\cdots,\mathbf{A}_{k},\cdots, \mathbf{A}_{K}\right] \tag{11}\]  
with  
\[\mathbf{A}_{k}=W_{a}(\eta-\eta_{c,m})\cdot\exp\biggl{\{}-j\frac{4\pi}{\lambda} R(\eta,k)\biggr{\}}\]
## 2 Principle of the Conveyance System
### Principle of the Digital Actuator Array  
The micro conveyor is formed with an array of EDAs: a Digital Actuator Array (DAA). Adjacent EDAs share a pair of FPMs to decrease the DAA size (Figure 2). The conveyed object is placed on a glass ray that contacts the top side of the MPMs. The DAA combines individual actions of each EDA to obtain a cumulative displacement of the conveyed object based on a \"stick-slip\" principle. The strategy to convey an object on top of the DAA using this collaborative actuation is presented in Figure 3. The conveyed object is placed on top of the MPMs (a). The object is considered to be non-ferromagnetic to avoid magnetic interactions with the actuators. The EDAs are simultaneously switched to displace the object (b). During this step, the friction force between the MPMs and the conveyed object accelerates the object until the MPMs reach their discrete position (stick phase). Once the MPMs stop, the dynamic friction due to the relative movement between the moving object and the static MPMs acts as a brake to the object until it stops, reaching a \"displacement step\". The second phase \"slip\" resets the configuration for a new displacement step: each MPM is individually switched back to return to its initial position ((c) to (d)). The friction force between a single MPM and the object is lower than the opposition force of the other MPMs. During steps (c) and (d), the MPM slides relatively to the object which, in theory, does not generate motion. In practice, a small \"return displacement\" was observed due to non-uniform distribution of the object weight on the MPM and friction inhomogeneity. This influence will be studied in the experimental section. When all the MPMs are back to  
Figure 1: Elementary Digital Actuator (EDA).  
the initial position, a new step of displacement of the object can be done (a). The current used to return the MPMs to the initial position is noted as return current (I\({}_{\rm r}\)) in Figure 3. The DAA can be extended in a modular way by adding EDAs to the array.
# An Application to Predict Range of Electric Two-Wheeler Using Machine Learning Techniques
## 3 Materials and Methods
### Model Deployment  
In order to deploy our machine learning model for predicting the range of two-wheelers, we have assigned the selected model to run in the cloud. This approach allows us to take advantage of the scalability and reliability of cloud computing resources and ensures that our model is always available for use by the application's users. Before deploying the model in the cloud, we performed extensive evaluation and testing to ensure that it meets our performance and accuracy requirements. Once the model was deemed ready for deployment, we uploaded it to our cloud infrastructure and configured it to receive user data from the application.  
The ML model in the application works specifically for each user, based on their individual driving habits, road conditions, and vehicle performance history. The work process of range prediction in our application is illustrated in Figure 5. The application first recognizes the UE_ID of the specific user who is trying to access the range prediction service. This unique ID is created by combining the user and two-wheeler information during the user registration process, which was discussed earlier in the paper. Once the UE_ID is recognized, the application checks whether the model for that specific user's two-wheeler is already available in the cloud or not. If the model is found, the application checks whether it needs to be updated or not. If the model needs to be updated, the application collects the latest historical data and uses it to train the model. Once the model is updated or a new model is generated, the application uses it to predict the remaining range of the user's two-wheeler. The prediction result is then sent back to the application for display, allowing the user to plan their journey accordingly.  
Figure 5: Flowchart of the work process of range prediction in navigation application.
## 2 Analysis of Autonomous Control Algorithm for Mining Electric locomotives
### Theoretical Analysis
#### 2.2.2 Improved \(\varepsilon\)-Greedy  
In reference [3], in order to balance the relationship between exploration and exploitation, the traditional \(\varepsilon\)-greedy algorithm was improved by changing the value of \(\varepsilon\) in Formula (6) to the value of \(\varepsilon\) in Formula (7).  
\[\varepsilon_{1}=(\varepsilon_{\text{initial}}-\varepsilon_{\text{final}}) \cdot(1-episode/max\_episodes) \tag{6}\]  
\[\varepsilon_{2}=\{\begin{array}{l}(\varepsilon_{\text{initial}}-\varepsilon_ {\text{final}})\cdot(0.5+\sqrt{0.25-(episode/max\_episodes)^{2}}),\ (episode/max\_episodes)\in[0,0.5]\\ (\varepsilon_{\text{initial}}-\varepsilon_{\text{final}})\cdot(0.5-\sqrt{0.2 5-(1-episode/max\_episodes)^{2}}),\ (episode/max\_episodes)\in(0.5,1]\end{array} \tag{7}\]  
For the improved \(\varepsilon\)-greedy, the selection of actions follows the following rules:  
\[a_{t}=\left\{\begin{array}{l}\underset{a}{\text{argmax}}Q(s_{t+1},a),for\quad probability \quad 1-\varepsilon_{t}\\ random\quad from\quad\mathcal{A},for\quad probability\quad\varepsilon_{t} \end{array}\right. \tag{8}\]  
We can obtain the probability distribution of whether an action is the optimal action:  
\[\pi(a|s)=\left\{\begin{array}{l}1-\varepsilon+\frac{\varepsilon}{|\mathcal{ A}(s)|},\ if\ a=\underset{a}{\text{argmax}}Q(s,a)\\ \frac{\varepsilon}{|\mathcal{A}(s)|},\ if\ a\
eq\underset{a}{\text{argmax}}Q(s,a) \end{array}\right. \tag{9}\]  
The specific steps to prove the convergence of improved \(\varepsilon\)-greedy strategy are detailed in Appendix B.  
In summary, RL and improved \(\varepsilon\)-greedy strategy applied to autonomous control of mining electric locomotives is convergent. The autonomous control method for mining electric locomotives based on the improved \(\varepsilon\)-greedy strategy is feasible.
###### Abstract  
(1) Background: Plants act as the natural sink for a variety of toxins in the environment, including _ veterinary antibiotics_ (VAs). The objective of this study was to evaluate the uptake and fate of _sulfadimethoxine_ (SDZ), _oxytetracycline_ (OTC), and _streptomycin_ (STR) in lettuce (_Lactuca sativa_ L.), carrot (_Daucus carota_), and pepper (_Capsicum annum_) grown in VAs amended soil. (2) Methods: 0, 50, and 100 mg kg\({}^{-1}\) VA laced manure was applied in a sandy clay loam soil. (3) Results: 30-d (lettuce) and 60-d (carrot and pepper) greenhouse experiment showed that SDZ and OTC were taken up by all three plants, with concentrations in plant tissue ranging from 0.1 to 1.2 mg kg\({}^{-1}\) dry weight. The concentration of VAs in plant tissues increased with a corresponding increase of antibiotics in manure. The highest plant tissue concentrations were found in carrot and lettuce, followed by pepper. An increase in NADPH P450 reductase and glutathione-s-transferase enzyme activity with increasing SDZ and OTC concentration was evident, signifying the induction of the detoxification process. The activity of plant detoxification enzymes under STR treatment was found not to be significantly different from control. (4) Conclusions: These results raise potential human health concerns of consuming low levels of antibiotics from produce grown on manure-amended soils. The result indicates that SDZ, OTC, and STR antibiotics posed high, medium, and low acute ecological risks in lettuce, carrot, and pepper plants when grown in sandy clay loam soil.  
carrot; lettuce; pepper; veterinary antibiotics; plants; soil Article
# Theoretical and Numerical Simulation Research on Fire of Large-Span Spatial Structures
## 2 Numerical Simulation Model Verification and Fire Source Condition Setting
### Fire Conditions of Structure  
To investigate the effects of natural air inlet opening areas on the temperature field and smoke flow within a large spherical dome structure during a fire, five distinct working conditions were established for numerical simulation, with CASE0 serving as a control without an air inlet. The scenarios incorporating natural air inlets featured total areas of 20 m\({}^{2}\), 30 m\({}^{2}\), 40 m\({}^{2}\), and 50 m\({}^{2}\)[49], symmetrically arranged as depicted in Figure 9, where A denotes the number of air inlets. These inlets were designed to augment the ambient air, with detailed settings provided in Table 5.  
The temperature measurement setup involves the systematic placement of slices along the \(X\), \(Y\), and \(Z\) axes, with corresponding measuring points uniformly distributed throughout the spatial domain. The THCP01-THCP05 measurement points are arranged near the roof and are separated by 5 m in the horizontal direction, as shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Space Net** \\ **Height (m)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Office, School (\(\times\)10\({}^{4}\) m\({}^{3}\)/h)** \\ \end{tabular} } & \
\begin{tabular}{c} **Stores, Exhibition Halls** \\ **(\(\times\)10\({}^{4}\) m\({}^{3}\)/h)** \\ \end{tabular} &
\begin{tabular}{c} **Factory Buildings and Other** \\ **Public Buildings (\(\times\)10\({}^{4}\) m\({}^{3}\)/h)** \\ \end{tabular} & \multirow{2}{*}{**Warehouse (\(\times\)10\({}^{4}\) m\({}^{3}\)/h)**} \\ \cline{2-2} \cline{6-9}  & **No Spray** & **with Spray** & **No Spray** & **with Spray** & **No Spray** & **with Spray** & **No Spray** & **with Spray** \\ \hline
6.0 & 12.2 & 5.2 & 17.6 & 7.8 & 15.0 & 7.0 & 30.1 & 9.3 \\ \hline
7.0 & 13.9 & 6.3 & 19.6 & 9.1 & 16.8 & 8.2 & 32.8 & 10.8 \\ \hline
8.0 & 15.8 & 7.4 & 21.8 & 10.6 & 18.9 & 9.6 & 35.4 & 12.4 \\ \hline
9.0 & 17.8 & 8.7 &
## 4 Experiments
### Case 2: Jeju Island Sagye Port to Mara Island Saledok Port  
This section describes the shipping-route construction using a different dataset. There are fifteen trajectories in the Case 2 dataset. In a similar vein, the proposed method detected two anomalous trajectories, and the detected outliers are represented on the right-side map in Figure 11.  
The shipping routes were generated using the median curve from the functional data analysis in Figure 12a. Further, a density-based technique produced shipping routes by varying the cell sizes. Case 2 of this study changed the cell sizes ranging from 124 m \(\times\) 440 m to 248 m \(\times\) 879 m in Figure 12b-e.  
Additionally, all results were superimposed on the same map in Figure 13. Overall, this case supports the previous view that the selection of appropriate cell size is vital to a density-based method.  
Figure 11: Outlier detection using functional boxplot.  
Figure 12: Shipping routes construction: (**a**) functional data analysis; (**b**) density-based technique (cell size: 248m \(\times\) 879m); (**c**) density-based technique (cell size: 186 m \(\times\) 660 m); (**d**) density-based technique (cell size: 149 m \(\times\) 528 m); (**e**) density-based technique (cell size: 124 m \(\times\) 440 m).
###### Abstract  
This study aims to alleviate the serious deformation of surrounding rock (SR) in an extremely soft and fragile fully mechanized caving face roadway (ESFFMCFR, the 8# coal seam, Huaibei mining area) under a conventional support. Laboratory tests of roadway SR were conducted. The results show that in this coal seam, the extremely soft and fragile coal body has a high clay mineral content, so it is of low strength and breaks and softens easily. With reference to the mechanical tests on coal and rock mass around the coal seam and the monitoring results of roadway deformation, the roadway deformation is mainly caused by the development of fractures in the roadway SR, the separation of the support body and SR and the loose supporting structure. Considering the engineering environment and deformation characteristics of SR in the EESFFMCFR (the 8# coal seam, Huaibei mining area), this study proposed a synergistic support system of \"lowering, drilling, anchoring, grouping and flatting (LDAGF)\" for the ESFMCFR based on the synergistic mechanism of support and SR under the basic principles of synergetics. Specifically, the synergistic support system of \"LDAGF\" includes the following measures: floor breaking and side lowering, bolt advance support, anchor cable support, advance water injection and grouting and flat-root U-shaped steel shed support. Furthermore, this synergistic support system was applied on the ESFMCFR in the 8# coal seam of Xinhu and Guobei coal mines, Huaibei mining area. The on-site application results reveal that when the synergistic support system is adopted, the maximum subsidence values in the above roadway roofs are 117 mm and 121 mm and the maximum displacement values of the two sides are 66 mm and 74 mm, respectively, which proves an excellent support effect. The synergistic support system, which can effectively control the serious deformation of the SR in ESFMCFRs and ensure long-term stability and safety of the roadways, is suitable for the support of ESFMCFRs and is of great guiding significance for roadways of the same type.  
extremely soft and fragile; fully mechanized caving face roadway; rock mass characteristics; synergistic support system; surrounding rock stability
## 4 Results
### Data Pre-Processing
#### 4.1.2 Latency Error Correction  
The best gaze-target distance was found to be when gaze data points were 3 position updates behind the target data. Considering the sampling frequency of 120 Hz, in terms of latency, this indicates that the recorded eye position and the actual eye position had a delay error of 25 ms (Figure 4).
# A Two-Phase Approach for Predicting Highway Passenger Volume
## 3 Data Source  
In this study, the dataset, including highway passenger volume and impact factors of urban attributes, was obtained from China's urban statistical yearbook. In China, the urban statistical yearbook is regularly published online to evaluate the social and economic levels. The statistical yearbook covers multiple aspects of urban attributes, including society, economy, etc. People can download the statistical yearbook for academic research, providing a novel macroscopic dataset with the prediction of highway passenger volume.  
Considering the possible complex-relevance between impact factors of urban attributes, it is necessary to select appropriate impact factors for the convenience of data processing. The selection principles in this study are summarized as follows: (1) The selected impact factors can well reflect the urban attributes and have a significant impact on intercity passenger volume. (2) The selected impact factors can be quantifiable and comparable. (3) The selected impact factors can be provided by the urban statistical yearbook and easily accessible. It is noteworthy that some non-quantifiable factors can be comparable by converting into different levels. Yet in this study, most non-quantifiable factors have a high correlation with the existing quantifiable factors. Furthermore, subjective judgment and personal preference are often included in the non-quantifiable level division, which inevitably brings errors into the process. Accordingly, this study only focuses on the prediction of highway passenger volume with the quantifiable impact factors.  
Based on the above principles, a total of 69 impact factors of urban attributes were selected from China's urban statistical yearbook. To facilitate data processing, the selected impact factors of urban attributes were divided into five categories, namely, urban economic level, urban population size and structure, per-capita income and consumption, resource and environment, and urban industrial structure. The selected impact factors of urban attributes and their information are summarized in Table 1 in Appendix A.  
As the data in the statistical yearbook is aggregated from the whole district or city, the authors took the administrative district as the basic unit of data collection. As a result, 3444 samples, including the selected 69 impact factors and highway passenger volume, from 280 administrative districts, were collected. The recorded date is from 2003 to 2014, covering 12 years, because there is a unified statistical standard during this period and the statistical data changed smoothly without a sharp increase or decrease. In which. The highway passenger volume was set as the unique dependent variable, and impact factors were set as the alternative independent variables for predicting highway passenger volume.