esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Calculation Method
### Establishment of Calculation Method
#### 3.2.1 Calculation When the Sliding Surface Is Horizontal
Moreover, the position of the force point between the bars changes with the inclination of the sliding surface. [PERSON] method assumes the ratio of horizontal and vertical forces between strips is constant, so the calculation is simple. [PERSON] method assumes that the position of the point of force between strips is 1/3 of the sliding surface, so the calculation may be difficult to converge. The thrust force along OB under E\({}_{0}\) is \(E_{0}\text{cos}\Big{(}45^{\circ}-\frac{\phi}{2}\Big{)}\). The thrust perpendicular to OB under E\({}_{0}\) is \(E_{0}\text{sin}\Big{(}45^{\circ}-\frac{\phi}{2}\Big{)}\)  
\[W_{1}=\Big{(}\gamma HL-\frac{\gamma}{2}h_{1}L\Big{)}b\]  
\[L=\frac{h_{1}}{\text{tan}\,\beta}\]  
Therefore,  
\[W_{1}=\gamma b\text{cot}\,\beta\Bigg{(}Hh_{1}-\frac{h_{1}^{2}}{2}\Bigg{)} \tag{3}\]  
The equilibrium equation can be derived from the limit equilibrium condition of the force:  
\[E_{0}\text{cos}\,\beta=E_{1}+W_{1}\text{sin}\,\beta+(W_{1}\text{cos}\,\beta+E _{0}\text{sin}\beta)\,\text{tan}\phi+\frac{ch_{1}}{\text{sin}\,\beta} \tag{4}\]  
(2) h\({}_{2}\) calculation when the sliding body does not slide along the passive earth pressure rupture surface BD. Force analysis was carried out on the overtop slider BDEF (in order to make the design results biased to safety, only the force analysis was carried on the wedge BDEF, without considering the gravity of the wedge EDG). It was assumed that the body weight of the wedge BDEF soil mass was W\({}_{2}\), and the landslide thrust on the BD interface was E\({}_{1}\) (E\({}_{1}\) and E\({}_{1}\) were equal in size and opposite in direction). The thrust force along BD under E\({}_{1}\) is \(E_{0}\text{cos}\,\beta_{1}\). The thrust perpendicular to BD under E\({}_{1}\) is \(E_{0}\text{sin}\,\beta_{1}\)  
\[W_{2}=\frac{\gamma b\text{cot}(\beta_{1}+\beta)}{2}(h_{2}-h_{1})(2H-h_{2}-h_{1}) \tag{5}\]  
Then, according to Equation (9), the relationship between h\({}_{1}\), h\({}_{2}\) and \(\beta_{1}\) can be obtained when the pile section is inclined, namely,  
\[E_{0}=[m-g(\beta_{1})]\left(Hh_{1}-\frac{h_{1}^{2}}{2}\right)+g(\beta_{1})\left( Hh_{2}-\frac{h_{2}^{2}}{2}\right)+\frac{c}{q(\beta_{1})}(h_{2}-h_{1})+\frac{c}{ \sin\beta}h_{1}^{\prime} \tag{11}\]  
In other words, when the pile position is inclined, Equation (11) must be satisfied to ensure that the soil behind the pile does not slide along the OBD surface. The above solution process provides a strict solution to the optimal pile length in the load section of the sunk pile in the failure mode over the top, and the solution process can be completed with the help of MATLAB.
## 4 Promising Neuropeptides Inhibitors for the Development of Effective Cancer Therapy
### Neuropeptides Inhibitors: A Promising Approach also for Other Cancers
#### 4.2.2 Prostate Cancer  
Prostate cancer is one of the most common cancers in men and occurs mainly due to genetic and environmental factors [148]. Various NPs involved in the development and progression of prostate cancer have been identified. Therefore, specific inhibitors against these NPs have been developed. For example, a specific GRPR antagonist 68 Ga-RM2, also known as BAY 86-7548 (68 Ga-DOTA-4-amino-1-carboxymethyl-piper- diene-D-Phe-Gln-Trp-Ala-Val-Gly-His-Sta-Leu-NH\({}_{2}\)), has been developed and frequently used for inhibiting prostate cancer progression [149]. 64 Cu-CB-TE2A-AR-06 (CB-TE2 APEG4-D-Phe-Gln-Trp-Ala-Val-Gly-His-Sta-Leu-NH\({}_{2}\)), another GRPR antagonist, was recently developed and found to be metabolically stable and efficient for cancer treatment and inhibition. Neuropeptide Y and its receptors have also been found to play a prominent role in prostate cancer development and progression. Specific inhibitors targeting NPY, such as BIBP32226, a recently discovered Y1R antagonist, could be used effectively in the treatment of prostate cancer by inhibiting cellular proliferation and growth [150].
# Cross-View Outdoor Localization in Augmented Reality by Fusing Map and Satellite Data
## 4 Results
### Ablation Studies
#### 4.4.1 Importance of Ground-Level Data for Alignment  
If the model simply made a guess based on just the top-level image, it would invalidate the purpose of the model. To validate the use of the ground-level images, in Table 3, we compare the results of the images with and without the correct ground-level image. We show that for all but one category, the original ground-level image indeed shows better results. On the other hand, the results where the ground-level images are switched are still competitive and do not show large degradations. This shows that, while the model does seem to use the ground-level image to some extent, a large part of the good results are due to a good prior understanding of the possible places the image could have been taken. A reason for this could be the predictable nature of the dataset.
## 5 Scaling Laws and Optimisation
### Non-Power Law Scalings and \(f_{ren}\) for Different Optimisation Levels of LHD  
For the LHD device, the ratio between the scaling laws obtained with SR via GP and the ISS04 is indicated with \(R_{LHD/ISS04}\) and reads as follows:  
\[R_{LHD/ISS04}\approx 0.5907\cdot a^{0.25}\cdot R^{0.33}\cdot p^{0.01}\cdot n_{e} ^{-0.08}\cdot B^{-0.17}\cdot\hat{\frac{t}{3}}^{-0.09}\cdot\left(1+e^{-(\frac{ \left(\frac{\delta}{2}\right)-0.19}{0.017})}{}^{2}\right) \tag{5}\]  
In the top of Figure 3, the trend of the energy confinement time is plotted versus the ratio \(R_{LHD/ISS04}\). From these plots, it is easy to see how the scaling of Equation (2), obtained with SR via GP, can reproduce the effects of the various optimization levels on the 0D quantities equally well, if not better, than the renormalization factor \(f_{ren}\). The  
Figure 2: The trends of the equations obtained with SR via GP compared to the ISS04. For the ISS04, two scalings are shown: one with a renormalization factor equal to 1 and one (indicated by \(<\)\(f_{ren}\)\(>\)) with the renormalization factor averaged over the entries in the DB.  
ratio \(R_{LHD/ISS04}\) depends basically on major and minor radius, with a small residual effect on the magnetic field. The part of the ratio depending on the minor and major radii, \(R_{LHD/ISS04}\)_(a,R)_, is plotted vs. the major radius in Figure 4, while the other main trends are illustrated graphically in Figures 11-13. For an easier visual comparison with \(f_{ren}\), the values of this ratio have been averaged for each level of optimisation.  
In the light of the evidence reported in Figures 3 and 4 and Figures 11-13, it is possible to explain how the models, derived with SR via GP, can reproduce quite well the LHD entries at different optimization levels, on the basis of only 0D quantities and without any renormalisation factor. Thanks to its high exploratory capability, symbolic regression can capture the signature left in the 0D quantities by the different optimisation levels. Indeed, the part of the scalings including the geometrical quantities, \(R_{LHD/ISS04}\)_(a,R)_, fits quite well \(f_{ren}\) and basically takes into account the effects of the changes in the configurations implemented to optimise confinement. Moreover, the good quality of the fits, testified by the high values of the indicator \(R^{2}\), suggest that practically all the effects of the optimisation efforts in LHD can be ascribed to the modifications in the 0D quantities.  
Figure 3: (**Top**) comparison of the trend of the energy confinement time with \(R_{LHD/ISS04}\) and \(f_{ren}\) for LHD. (**Bottom**) comparison of the trend of the energy confinement time with \(R_{W\text{-}AS/ISS04}\)-and \(f_{ren}\) for W7-AS (for \(t_{2/3}=0.3\)).
## 3 Stress Measurements and Data Updating Based on Grey Theory  
### On-Site Stress Measurements for Huangsha Harbor Bridge under Construction  
Before the concrete pouring of the 0-blocks above the piers, the vibrating wire stress transducers are attached to longitudinal reinforcing bars arranged on the top and the bottom of the cross-section of fixed ends of cantilevers for stress measurements (see Figure 3). As shown in Figure 2, four transducers are arranged on the corners of one cross-section. Shortly after important construction steps, such as the formwork erection, the concrete pouring, and the pre-stress tension for each block, the stresses are measured where transducers are arranged.  
Figure 3: Actual view of an arranged vibrating wire stress transducer.  
Figure 2: Cross-section sketch of Huangsha Harbor Bridge (unit: cm).  
As concrete is a heterogeneous compound material [16], its stress measurement using the vibrating wire stress transducers is a difficult measuring practice, and the measured data usually deviate from the true values due to many adverse effects. To minimize the different adverse effects, the construction monitoring personnel of Huangsha Harbor Bridge employ transducers for long-term stability and easy installation; request cooperation from the construction workers to protect the transducers and the signal wires; test the performance of the transducers regularly for their effectiveness; choose the right time for the first reading to minimize the stress effects due to initial setting of the concrete; reduce the duration from the time of the concrete pouring to the time of the measurement beginning to reduce the effects of the concrete shrinkage and creep. Although the above-mentioned countermeasures are undertaken, it should be admitted that the measured results might still be inaccurate. An adverse influence for the accuracy of the measured data is the temperature effects, which are difficult to eliminate. More precisely, when the temperature of the whole girder is not uniform, the measured stresses inevitably contain the temperature gradient-induced stresses. In the following subsection, a new practice adopted by the authors on the location to compensate for deviation in the measured stress caused by the temperature gradient is introduced.
## 5 Decentralized Decision-Making
### Decentralized Decision-Making Mechanisms
#### 5.3.3 Smart Contracts and DApps
Guaranteeing the accurate and timely implementation of all contractual obligations, smart contracts have the capacity to automate the buying, selling, and renting processes of properties. Blockchain-based property record storage helps parties to quickly confirm ownership and transaction history, therefore reducing fraud and building trust. This technology might maximize processes like mortgage approvals and property inspections, thus improving the reliability and speed of real estate transactions [71]. Furthermore, the use of smart contracts and blockchain technology may significantly enhance the healthcare industry, namely in the areas of patient data management and administrative process optimization. Smart contracts have the capability to automate the process of exchanging patient records among healthcare providers. This ensures that the transmission of sensitive information is performed safely and only with the agreement of the patient. This can enhance the synchronization of healthcare delivery and alleviate administrative encumbrances. Furthermore, blockchain technology can enable the creation of transparent and unchangeable records of clinical trials, therefore improving the reliability of medical research. Smart contracts in the pharmaceutical supply chain may guarantee the genuineness of pharmaceuticals by monitoring their entire route from the manufacturer to the patient, thereby minimizing the likelihood of counterfeit drugs [72]. In addition, smart contracts facilitate the functioning of decentralized autonomous organizations (DAOs), which are intricate multi-party agreements that may run independently on the blockchain. DAOs are autonomous organizations that utilize smart contracts to formalize regulations and decision-making procedures, allowing stakeholders to engage in transparent and democratic governance of the company. While their advantages are several, smart contracts come with several possible disadvantages, such as possible security risks, scalability restrictions, and legal complexity. Smart contract codes with security flaws can be used for exploitation, therefore resulting in either money losses or confidence breaches--both of which are likely results. Particularly for transactions that are sophisticated or very high in volume, the processing cost of smart contract execution on the blockchain imposes limits on scalability. The main causes of the legal problems that have emerged are the lack of established legal precedents and legislative systems for managing smart contracts. This raises issues concerning their validity and obligation in the framework of court conflicts [73].
## 1 Introduction
The synthesis equations of each mechanism for nine precision points are derived as a system of 56 quadratic polynomial equations with 58 unknowns, respectively. By assuming two unknowns as free choices, the system can be solved. However, since the system size is very large, it is necessary to decrease its size so as to reduce the calculation time for pursuing an efficient dimensional synthesis. To this end, some of the unknowns are eliminated from the synthesis equations to simplify the system to eight equations in eight unknowns. Then, in order to apply the multi-homogeneous Bezout theorem [35; 36], four auxiliary equations that render the system a set of homogeneous polynomials are defined, and the unknowns are divided into appropriate two-homogeneous groups. Through this process, the synthesis equations for Watt II and Stephenson III six-bar slider-crank linkages are formulated into eight quartic polynomials and four auxiliary quadratic polynomials in 12 unknowns, and the multi-homogeneous Bezout number of the system that represents the maximum number of solutions is 286,720. The synthesis equations for the two mechanisms are solved by using the [PERSON] software program [37] that is based on the homotopy continuation method. The computation results provided 25,630 and 36,061 nonsingular solutions, respectively. Among the nonsingular solutions, mechanisms that allow full rotation of the input crank and satisfy nine precision points on a single stroke of the slider are finally selected as feasible solutions. As a result, 37 Watt II and 31 Stephenson III six-bar slider-crank linkages without kinematic defects are determined. The paper is arranged as follows. In Section 2, the dimensional synthesis equations for Watt II and Stephenson III six-bar slider-crank function generators are derived and a process for simplifying the synthesis equations is presented. Section 3 describes the procedure for calculating the multi-homogeneous Bezout number by applying the multi-homogeneous Bezout theorem. Section 4 then explains how to select feasible solutions that enable prescribed precision points to lie on a single stroke as well as the input link to rotate continuously. Section 5 presents examples of applying the dimensional synthesis process proposed in this paper. Finally, Section 6 concludes with a summary and discussion of the research.
## 3 Results and Discussion
### Characterisation  
#### 3.2.1 TGA  
The decomposition patterns of UiO-66 and UiO-66-NH\({}_{2}\) were determined, with UiO-66 showing a higher decomposition temperature at 480 \({}^{\circ}\)C compared to UiO-66-NH\({}_{2}\)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Weight Ratio** & **UiO-66-LC\%** & **UiO-66-NH\({}_{2}\)-LC\%** \\
**MOF:MTX [mg:mg]** & **UiO-66-LC\%** & **UiO-66-NH\({}_{2}\)-LC\%** \\ \hline
1:1 & 40.7 \(\pm\) 1.4 & 8 \(\pm\) 1.3 \\
2:1 & 29.6 \(\pm\) 1.0 & 8 \(\pm\) 1.7 \\
4:1 & 15.8 \(\pm\) 0.3 & 21.41 \(\pm\) 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: MOF loading capacity (LC%). Values reported are the average \(\pm\) SD of 3 independent repeats of the experiment.  
which exhibited a lower decomposition temperature at 360 \({}^{\circ}\)C (Figure 3A,B), as previously reported in the literature [46; 52]. Significant weight loss occurred [26; 46; 53] due to decomposition of the ligands [54]. The weight loss of MTX-loaded MOFs was higher than that of their pristine counterparts, and this is possibly due to a higher proportion of organic compounds in the loaded MOFs leading to their decomposition and lower residual mass. A TGA analysis of UiO-66-NH\({}_{2}\)-MTX showed a similar trend. Quantification of MTX from TGA was not possible due to no sharp change in weight upon decomposition of the drug.
# Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa
## Materials and Methods  
### Study Area and Field Data Collection  
We focused on 14 counties (i.e., Bungoma, Busia, HomaBay, Kakamega, Kericho, Kisumu, Kisii Migori, Nandi, Nyamira, Siaya, Trans Nzoia, Uasin Gishu, and Vihiga) in the western Kenya region (Figure 1) where One Acre Fund (1 AF), an agricultural microfinance, distribution, and training organization, is active. This region is dominated by smallholder agriculture, with about 25% of fields smaller than 0.5 acre in size and more than half less than 1 acre [9]. Maize is the primary staple crop in this region, yet is often intercropped with other crops such as bean, banana, cassava, and sugar cane. In this study, a field with only maize planted is referred to as \"purestand field\" and a field with a mixture of maize and other crops is referred to as \"intercropped field\". The sowing window for maize is typically March to April, following the onset of the main rainfall season. Two sets of yield data were collected during the 2016 growing season. The first dataset consists of household survey information for 766 maize fields in Bungoma and Kakamega counties (Figure 1; hereafter, B-K sub-region). Farmers were randomly selected by 1 AF and visited twice, once before harvest to solicit information on management practices (such as sowing and harvest dates) and to outline individual plots, and once after harvest to solicit information on final harvest details. Plot boundaries were measured by Garmin GPSMAP64 devices with a reported 3-m accuracy. The second dataset is nearly 4000 crop cuts and their corresponding planting dates across the 14 counties in western Kenya (Figure 1; hereafter, 1 AF region). For each field, two 5 \(\times\) 8 m\({}^{2}\) plots were randomly selected, and the average yield of each of the two plots was reported. The locations of the crop cuts were not accurately georeferenced, and, therefore, we aggregated the data to the level of District (subunit of County) and Division (subunit of Districts). The median number of crop cuts was 52 for districts and 13 for divisions.  
We collected weather data for various sites through aWhere (2017), a globally available, cloud-based resource database of forecasts, current, and historic observations. For each reported location, weather variables were interpolated using aWhere's in-house 3D curvilinear algorithm that assimilates information from multiple nearby stations. Data resolution for eastern Africa is around 0.1\({}^{\circ}\), depending on the distribution density of weather stations. During the 2016 growing season, weather conditions were warmer and drier in the west part of the study area, and cooler and wetter towards the northeast and southeast (Figure 1).
## 3 Results
### Analyses Using X-ray Microtomography  
#### 3.4.1 Density  
The mean densities of the tablets' inner structure are presented in Table 3.  
Overall, a significant difference was observed in the density of the inner structure among all the analyzed tablets. The post hoc analysis revealed significant differences in the following pairs of tablets: unexpired vs. expired (\(p<0.001\)), unexpired vs. unexpired dried in a vacuum oven (\(p<0.004\)), expired vs. unexpired dried in a vacuum oven (\(p<0.001\)), and expired vs. expired dried in a vacuum oven (\(p<0.001\)). No differences were observed when expired tablets dried in a vacuum oven were compared with both unexpired tablets (\(p=1.000\)) and unexpired tablets dried in a vacuum oven (\(p=0.071\)). The highest mean density was observed in the case of unexpired tablets dried in a vacuum oven (1.273 g/cm\({}^{3}\)), while the lowest density was found in the expired tablets (1.261 g/cm\({}^{3}\)). Thus, unexpired tablets showed better homogeneity than expired tablets.  
Figure 4: Mean values of total reflectance for unexpired and expired effervescent tablets containing magnesium and vitamin B6 in seven measured wavelength ranges (\(n=24\) of each tablet’s type). The standard deviation bars are presented in the chart. The highest SD value was observed in the range of 335–380 nm (0.083 for unexpired tablets and 0.118 for expired tablets). The remaining SDs had a value in the third decimal place, thus they may not be visible on the chart.
# A Multi-Agent Deep Reinforcement Learning System for Government Interoperability
## 3 Methodology
### Ethical Considerations and Security Measures in Multi-Agent DRL  
The proposed multi-agent deep reinforcement learning (DRL) framework is designed with strong ethical considerations and security measures to ensure the safe handling of sensitive governmental data. To protect data integrity and confidentiality, advanced encryption techniques such as AES-256 for data at rest and TLS (transport layer security) for data in transit are implemented. These encryption standards prevent unauthorized access and data breaches while ensuring that only authorized parties can decrypt and use the data. Additionally, cryptographic hashing techniques like SHA-256 verify data integrity, ensuring that stored information remains unaltered.  
To further enhance security, role-based access control (RBAC) is employed to regulate access based on predefined user roles. This ensures that individuals only have access to the data and functionalities relevant to their responsibilities, reducing the risk of misuse. The principle of the least privilege (PoLP) is enforced, granting the minimum necessary access to users, while multi-factor authentication (MFA) adds an additional security layer, requiring multiple forms of verification before access is granted. These measures collectively safeguard against unauthorized access and data leaks.  
Continuous monitoring and real-time anomaly detection are integrated into the framework to identify and mitigate potential threats. The system uses machine learning-powered anomaly detection to recognize unusual access patterns, unauthorized modifications, and suspicious behaviors. Security mechanisms such as log analysis, intrusion detection systems (IDS), and behavior-based monitoring help detect cyber threats before they escalate. Additionally, automated alerts and mitigation protocols ensure that security incidents are addressed promptly. Regular penetration testing and security audits are conducted to assess vulnerabilities and reinforce the framework's defenses, making it more resilient against emerging threats.  
To ensure compliance with international data protection regulations, the framework adheres to standards such as the General Data Protection Regulation (GDPR) and ISO/IEC 27001. Compliance measures include data anonymization, transparent audit logs, and consent management systems, ensuring that sensitive information is managed ethically and legally. Policies governing data retention and secure disposal prevent unauthorized long-term storage, further strengthening data protection.  
By integrating encryption techniques, access control mechanisms, continuous monitoring, and regulatory compliance, the multi-agent DRL framework upholds ethical standards and security best practices. These measures not only safeguard sensitive governmental data but also promote transparency, accountability, and trust in AI-driven governance systems.