esa-sceva/satcom-chunk-collection huggingface dataset:
# A Social Life Cycle Assessment Framework for Additive Manufacturing Products
## 4 Exploratory Case Study  
According to [36], the case study approach is most suited for research areas that are either new or for which the existing theory is incomplete. According to [37], case studies can be used for different research purposes, such as exploratory, theory-building, and theory testing. The case study developed for the present study is exploratory, since it aims to validate the initial set of indicators proposed in the developed methodology. The research purpose is to determine if these indicators really capture/reflect the social impacts of AM technology. The case study was conducted in a company based in Bristol, England, which specializes in manufacturing compact heat exchangers using AM technology. The company was founded in 2012 by two employees. In the last few years, the company has been continuously growing and currently has around 50 employees, most of whom are expert engineers qualified with Ph.D. or Masters level degrees. The AM technique used by this company is Selective Laser Melting (SLM). The company currently has eight 3D printers. Its main costumers are in the automotive sector; however, due to the unique characteristics of its products, the company recently entered other markets, such as aerospace, defense, energy generation, and motorsports. In this case study, both primary and secondary data were used. The secondary data were collected through the company's website and provided detailed and reliable information about the company's structure, as well as its main products and processes. For the collection of the primary data, a structured interview questionnaire was developed considering the initial set of proposed indicators. The goal of the questionnaire was to capture how AM technology affects each of the indicators, more specifically whether AM increases, decreases, or does not have any impact on them. The responses were provided according to the perception of the respondent--who was, at the time, an engineer working in the area of quality and inspection--on the effects of AM technology on the social performance of the organization. The indicators that were considered to have no impact were excluded. The results of the questionnaire are presented in Table 3. The answers (\"Increase\", \"Decrease\", \"No Impact\") are given, according to the respondent's perception of the effects of AM technology on the social performance of its organization. For some indicators, the respondent was not able to identify the impact of AM technology so, in these cases, the phrase \"Does not know\" is displayed in the response column. Supporting evidence for the answers are also given. Based on the responses, from the initially proposed 26 indicators, 21 were selected, and 5 were regarded as \"No impact\", which were excluded. It was also possible to determine the type of impact (i.e., positive or negative).
## 2 The Basic Theory
### Squeeze Film Damper  
The oil film force of SFD can be calculated according to the theory of hydrodynamic lubrication. The instantaneous pressure distribution can be computed from the incompressible Reynolds equation [14]:  
\[\frac{1}{{R_{\mathrm{s}}}^{2}}\frac{\partial}{\partial\theta}\left({h^{3} \frac{\partial p}{\partial\theta}}\right)+{h^{3}\frac{\partial^{2}p}{ \partial z^{2}}}=-12\mu_{\mathrm{s}}\Omega\frac{\partial h}{\partial\theta}+ 12\mu\frac{\partial h}{\partial t}. \tag{13}\]  
where, \(p\) is the pressure distribution of oil film, \(R_{\mathrm{s}}\) is the radius of the damper, \(\mu_{\mathrm{s}}\) is the oil viscosity, \(h\) is the oil film thickness, \(z\) is the axial displacement in the z direction, \(\Omega\) is precession angular velocity of the axis, and \(\theta\) is the angular displacement from the maximum oil thickness position.  
The oil film bearing forces can be obtained by using short bearing theory and the Sommerfeld transform, expressed as:  
\[\left\{\begin{array}{l}F_{\mathrm{sx}}=-\frac{\mu_{\mathrm{s}}R_{\mathrm{s}} L^{3}}{c^{2}\sqrt{x^{2}+y^{2}}}[x(\dot{\varepsilon}I_{1}+\varepsilon\Omega I_{2})-y( \dot{\varepsilon}I_{2}+\varepsilon\Omega I_{3})]\\ F_{\mathrm{sy}}=-\frac{\mu_{\mathrm{s}}R_{\mathrm{s}}L^{3}}{c^{2}\sqrt{x^{2}+ y^{2}}}[y(\dot{\varepsilon}I_{1}+\varepsilon\Omega I_{2})+x(\dot{ \varepsilon}I_{2}+\varepsilon\Omega I_{3})]\end{array}\right.. \tag{14}\]  
and:  
\[I_{1}=\int_{\theta_{1}}^{\theta_{1}+\pi}\frac{\cos^{2}\theta}{(1+\varepsilon \cos\theta)^{3}}d\theta,\;I_{2}=\int_{\theta_{1}}^{\theta_{1}+\pi}\frac{\sin \theta\cos\theta}{(1+\varepsilon\cos\theta)^{3}}d\theta,I_{3}=\int_{\theta_{ 1}}^{\theta_{1}+\pi}\frac{\sin^{2}\theta}{(1+\varepsilon\cos\theta)^{3}}d\theta\]  
where \(\varepsilon=\frac{\varepsilon_{B}}{c}\) is the journal eccentricity ratio, \(e_{B}\) is the eccentricity of the journal and \(\dot{\varepsilon}\) is the first derivative of journal eccentricity ratios with respect to time.
## 2 Calculation Model and Method
### Thermal-Hydraulics Model
#### 2.3.2 Heat Transfer to Coolant  
The heat transfer from cladding to coolant in the \(i\)th node along the reactor axis direction can be described by the basic mass and energy conservation equations as:  
\[\rho_{c}^{i}V_{c}^{i}C_{c}^{i}\frac{dT_{c}^{i}}{dt}=h_{cl,c}^{i}A_{cl,c}^{i} \bigg{(}\left.T_{cl}^{i}\right|_{r=r_{cl}}-T_{c}^{i}\bigg{)}-G_{c}^{i}C_{c}^{i} \Big{(}T_{c,out}^{i}-T_{c,in}^{i}\Big{)} \tag{9}\]  
where \(\rho_{c}^{i}\), \(V_{c}^{i}\), \(C_{c}^{i}\) and \(T_{c}^{i}\) denote the density, volume, specific heat and temperature of coolant in the axial node \(i\), respectively, \(h_{cl,c}^{i}\) denotes the heat transfer coefficient between cladding and coolant, \(A_{cl,c}^{i}\) is the heat transfer area between cladding and coolant, \(G_{c}^{i}\) denote the mass flow rate of coolant, and \(T_{c,in}^{i}\) and \(T_{c,out}^{i}\) denote coolant inlet and outlet temperatures, respectively.  
In the design of fast spectrum reactors, heavy liquid metals such as lead or LBE are usually adopted as the coolant materials for high heat transfer coefficient and large heat capacity. The fuel assembly in the core is usually composed of triangular rod bundles, and thus the Ushakov correlation is used to analyze the heat transfer correlation between the heavy liquid metal coolant and the cladding [32]. The heat transfer coefficient between cladding and coolant is calculated by:  
\[h_{cl,c}=\frac{k_{c}}{D_{c}}N_{u} \tag{10}\]  
where \(k_{c}\) is the heat conductivity of the coolant, \(D_{c}\) is the hydraulic equivalent diameter; the Nusselt number \(N_{u}\) in the rod bundles is as follows:  
\[N_{u}=7.55\frac{p}{d}-20\left(\frac{p}{d}\right)^{-13}+\frac{3.67}{90(p/d)^{2} }Pe^{(0.56+0.19p/d)} \tag{11}\]  
where \(p/d\) is the ratio of the pitch of fuel pins to its diameter, \(Pe\) is the Peclet number. It is valid for \(1.2<p/d<2.0\), and \(1<Pe<4000\).  
The package of thermophysical properties of materials was developed to insert into the thermal hydraulic model based on the experimental data and empirical correlation [33; 34]. The correlations of the thermophysical properties of coolant, cladding, and fuel are presented in Table 1.  
Figure 5: Node division in a fuel element.
## 5 Key Findings and Future Research  
### Key Findings  
1. Students' online learning activity sequences can be used to effectively predict students' learning performance. Compared with students' demographic features and assessment scores, a small part of the online learning activity sequences can be used to predict students' performance at the beginning, rather than waiting for all learning activities to be completed. The prediction result based on online learning activity sequence has better stability, and the prediction accuracy is proportional to the length of the online learning activity sequence.
2. The autoencoder based on a deep neural network can extract latent features with lower dimensions from the original high-dimensional online learning activity sequences, which contains the essential information of learners' online learning behavior, so that  
Figure 9: Evaluation of end-to-end prediction model.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Acc** & **Pre** & **Rec** & **F1** \\ \hline Naïve Bayes & 0.47 & 0.14 & 0.26 & 0.17 \\ SVM & 0.58 & 0.41 & 0.39 & 0.35 \\ Logistic Regression & 0.53 & 0.29 & 0.32 & 0.25 \\ Random Forest & 0.66 & 0.56 & 0.48 & 0.46 \\ KNN & 0.38 & 0.34 & 0.32 & 0.27 \\ Our Model & 0.84 & 0.64 & 0.57 & 0.59 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison between our model and baseline model.  
students with the same performance have a closer distance in the new feature space. Using latent features can further improve the accuracy of performance prediction.
3. The classifier based on a deep feedforward neural network can be used to predict students' performance. Although there is no clear standard for the selection of hyperparameters of this network, the experimental results show that the network with more than five hidden layers and a single hidden layer containing up to 512 artificial neurons can achieve a prediction accuracy of more than 70%. The parameters used to train the autoencoder and classifier based on a deep neural network need to be selected through experiments according to the hardware, volume and properties of the training datasets.
4. The training dataset containing students' performance is often imbalanced, which leads to bias in the prediction model for non-major grades. Distance-based and oversampling methods such as KMeansSMOTE can generate a new, balanced training dataset and improve the performance of the prediction model.
## 8 Integration of Core Assets  
Once configured, core assets are integrated to form an end-to-end ML solution. This includes combining datasets, feature pipelines, models, and workflows into a cohesive system. For instance, a satellite anomaly detection system may involve integrating a telemetry preprocessing pipeline with an anomaly detection model and a real-time monitoring dashboard.
## 3 Results
### Parameter Optimization for Enzyme Production by S. maltohilia PRS8
#### 3.5.2. Optimization of the Significant Variable Using a Central Composite Design  
A two-level CCD was used to screen the significant components obtained by the PBD. The model included three elements and their influence on the enzyme activity  
Figure 7: Pareto chart and a three-dimensional response surface plot showing the significant factor with respect to the effect on the enzyme production. (**A**) Pareto chart showing the effect of the t-value by the important factors [J, cutin; D, NaNO\({}_{3}\), and L (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\)] generated by the PBD. (**B**) Design of the 3D response surface plot of cutin and (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\). (**C**) Design of the 3D response surface plot between cutin and NaNO\({}_{3}\). (**D**) Design of the 3D response surface plot between NaNO\({}_{3}\) and (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\).  
in twenty runs: cutin (A), NaNO\({}_{3}\) (B), and (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) (C) (Supplementary Materials, Table S3). The linear terms A, B, and C and the interactive terms AB, AC, and BC were the significant model terms (Supplementary Materials, Table S4). The following second-order polynomial equation obtained using the multiple regression analysis of the experimental data describes the effect of cutin, NaNO\({}_{3}\), and (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) in enzyme production expressed as the activity (U/mg):  
Specific. Activity (U/mg) = 221.45 + 31.51A + 51.23B - 14.30C + 77.22 AB + 51.02 AC - 51.51 BC  
The model was found to be statistically significant, as implied by an F-value of 6.70 (\(p\) < 0.050), In the model, there was a 0.21% chance that an \"F-value\" this large could occur due to the noise. The response surface plot (AB) in (Figure 7C) showed that both factors had a potential influence on the cutinase-like activity, with a maximum regression coefficient of 77.22, followed by AC (51.02) and BC (51.19) (Figure 7B,D).
# High-Power, High-Efficiency GaSb-Based Laser with Compositionally Linearly Graded AlGaAsSb Layer
## 1 Introduction
In this work, we put forward a novel composition-graded AlGaAsSb layer growth method by precisely controlling the group III cell temperature and group V cell valve position. Based on the analysis of the AlGaAsSb preparation features and MBE cell characteristics, we prepared a composition-graded AlGaAsSb layer sample. AFM and HRXRD results indicate that the AlGaAsSb composition modulation difficulty is overcome, and GaSb-lattice-matched, high-quality composition-graded AlGaAsSb layer epitaxy is achieved. Lasers adopting the composition-graded layer exhibit a high output power of 1.8 W, with a maximum power conversion efficiency of 28%. A high internal quantum efficiency of 83% is also maintained even at 40 \({}^{\circ}\)C, and the low internal loss of 4-5 cm\({}^{-1}\) shows little temperature dependence. A bar with the same epitaxial structure realized a record-high 18 W output power. Our research provides more flexibility for GaSb-based laser design.
## 3 Experiment and Results Analysis
### Experimental Results and Analysis
#### 3.4.3 Audit Entity Identification Effectiveness and Analysis  
The recognition effect of different models and this model on six types of audit entities is illustrated in Figure 6. Based on the analysis of recognition results, this model demonstrates superior performance in recognizing area and amount entities, achieving an accuracy rate exceeding 95%. These two entity types constitute the largest portion of the dataset and are accompanied by explicit unit prompt words such as \"\(\bigtriangleup\)\" (hectares) and \"\(\bigtriangleup\)\" (Ten thousand yuan). Consequently, the model effectively captures characteristic information associated with these entities, enabling accurate identification. However, the recognition performance for time and address entities is relatively poor due to limited sample size which hinders comprehensive learning of relevant characteristics, resulting in deviations during recognition. The recognition effectiveness for the remaining two entity types slightly decreases primarily due to factors like lengthy entity names and fuzzy boundary detection; nevertheless, their accuracy remains above 92%. Notably, this model outperforms both comparison models in terms of recognizing most entities, thereby demonstrating its efficacy in identifying various named entities within government audit domains.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experimental Model** & **Accuracy** & **Recall** & **F1** \\ \hline Baseline models & 95.76 & 92.79 & 94.25 \\ +CW & 97.06 & 95.20 & 96.12 \\ +GHM & 95.67 & 95.07 & 95.36 \\ CW-CBGC & 97.38 & 96.56 & 97.23 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of ablation experiments.  
Figure 6: Comparison of entity recognition results of different models.
# Privacy-Preserving Data Sharing in Telehealth Services
## 3 Method
This includes validating that smart contracts are properly configured to enforce data access rights according to legal mandates, and that the encryption and anonymization techniques employed are sufficient to meet the legal requirements for protecting patient data. Our framework is designed with the flexibility to adapt to a variety of international privacy laws and regulations, including the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in the European Union. By implementing configurable smart contracts and dynamic consent management processes, the framework can enforce specific regulatory requirements automatically, ensuring compliance across different jurisdictions. Further discussion will include detailed strategies for aligning with other regional regulations, showcasing the framework's global applicability and its ability to seamlessly integrate into diverse legal environments. Figure 1: Sequence diagram for the proposed algorithm. The security of patient private keys is paramount, managed through advanced cryptographic storage solutions such as Hardware Security Modules (HSMs) and secure enclave technology. These technologies provide a fortified environment for key storage, ensuring that private keys are not exposed to the operating system or application software. This level of security prevents unauthorized access and the use of the keys, significantly enhancing the overall security posture of our telehealth framework. Finally, the Compliance System serves as an automated enforcement mechanism that ensures all aspects of the data management process align with regulatory requirements. This includes monitoring data retention policies, ensuring data minimization principles are followed, and confirming that patients' rights to access and modify their data are upheld. In cases where regulatory violations are detected, the Compliance System flags these issues, triggering remediation processes such as tightening access controls or conducting additional audits. Overall, this framework presents a robust solution to the complex challenges of telemedicine data management. By integrating blockchain and cryptographic techniques with automated smart contracts and SMPC, the system achieves a high level of security and privacy while also ensuring operational efficiency and regulatory compliance. The interaction between the various components--encryption, secure data access, computation, auditing, and compliance--forms a comprehensive, scalable solution capable of adapting to evolving cybersecurity threats and regulatory landscapes.
# A High-Utility Differentially Private Mechanism for Space Information Networks
## 5 Conclusions  
In this paper, we design a differential privacy mechanism for distributed communication systems and demonstrate the proposed mechanism with the example of space information networks. Comparing the proposed mechanism to Laplace, sampling, and aggregation mechanisms reveals that it is more beneficial for data. Additionally, the technique allows network nodes to exchange statistical data while preserving correct information privacy. 1665 Starlink satellites are used in this paper's investigations on a bilinear element set gathered by CelesTrak. By comparing the suggested mechanism to existing mechanisms, it is discovered that it has superior data usefulness, excellent robustness and stability. Just as differential privacy can be applied to many fields, it can also be used to other distributed communication systems, not only space information networks, for solving privacy data problems.  
Conceptualization, M.Z. and W.H.; methodology, M.Z. and W.H.; software, W.H.; validation, M.Z.; formal analysis, W.H.; investigation, L.L. and Z.T.; resources, S.Z.; data curation, M.Z.; writing--original draft preparation, W.H. and M.Z.; writing--review and editing, M.Z. and W.H.; visualization, Z.T.; supervision, S.Z.; project administration, S.Z.; funding acquisition, S.Z. and L.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by 1. The General Program of Sichuan Provincial Department of Science and Technology (Grant No. 2022 YFG0207). 2. National Natural Science Foundation of China (Grant No. 62272089). 3. The Open Project of Intelligent Terminal Key Laboratory of Sichuan Province, P.R.China (Grant No. SCITLAB-20006). 4. Intelligent Terminal Key Laboratory of Sichuan Province (Grant No. SCITLAB-20001). 5. The Cooperative Program of Sichuan University and Yibin (2020 CDYB-30).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Input Data Description
The land-use map used in this study was based on the 2012 Maricopa land-use map developed by the Maricopa Association of Governments (MAG) ([[http://geo.azmag.gov/maps/landuse](http://geo.azmag.gov/maps/landuse)]([http://geo.azmag.gov/maps/landuse](http://geo.azmag.gov/maps/landuse))). In total, 11 land-use types were identified in the Phoenix city including commercial area, industrial area, mesic residential area, seric residential area, institutional area, transportation, waters, recreation park, construction transition, agricultural land, and vacant nature, respectively (Figure 1a). To define the land-cover composition of the land-use types, 50 sampling plots (each with 100 m \(\times\) 100 m size) for each of the 11 land-use types were selected randomly. Then, by visual classification based on a 0.25-m remote sensing images from Google-Earth (Spot5, Phoenix, AZ, USA), we estimated the land fraction of seven land-cover types (woodland, lawn, shrub/bush (shrubland), cropland, waters, bare soil, and impervious surface area) in each land-use sampling plot, and calculated the mean land-cover composition in each of the 11 land-use type in the Phoenix city (Table 2; Figure 1b). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**CIMP5 GCM Products**} & \multicolumn{2}{c}{**Mean Air Temperature**} & \multicolumn{2}{c}{**Annual Precipitation**} \\ \cline{2-5}  & **Correlation (R\({}^{2}\))** & **Bias (\({}^{2}\)C)** & **Correlation (R\({}^{2}\))** & **Bias (mm/Year)** \\ \hline EC-EARTH Model & 0.79 & 5.18 & 0.48 & \(-\)230 \\ HadCM3 Model & 0.85 & 4.05 & 0.29 & \(-\)150 \\ CCSM4 & 0.86 & 9.48 & 0.72 & \(-\)234 \\ CarCM4 & 0.84 & 5.27 & 0.47 & \(-\)253 \\ GEO8-5 & 0.82 & 3.5 & 0.16 & \(-\)216 \\ MIRO5 Model & 0.78 & 4.08 & 0.38 & \(-\)169 \\ MPI-ESM-LR & 0.76 & 3.5 & 0.48 & \(-\)253 \\ MPI-ESM-MR & 0.85 & 2.91 & 0.64 & \(-\)81 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluating the CMIP5 GCM products using historical (2000–2005) monthly climate dataset developed based on field observations by [PERSON] et al. [21]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Land-Use Type** & **Area (km\({}^{2}\))** & **Percentage (\%)** & **Description** \\ \hline \multirow{4}{*}{Commercial area} & \multirow{4}{*}{137.54} & \multirow{4}{*}{4.74} & Commercial High-Community Retail/Regional Retail \\  & & & Commercial Low-Amusement/Movie Theatre/Speciality \\ \cline{1-1} \cline{3-4}  & & & Retail/Neighborhood Retail \\ \cline{1-1} \cline{3-4}  & & & Tourist Accommodations-Motel/Hotel/Resort \\ \cline{1-1} \cline{3-4}  & & & Business Park \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of the 11 land-use types in the Phoenix city.