esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The purpose of this work is to create an efficient optimization framework for demand-responsive feeder transit services to assign vehicles to cover all pickup locations to transport passengers to a rail station. The proposed methodology features passengers placing a personalized travel order involving the subway schedule chosen by passengers and windows of service time, etc. Moreover, synchronous transfer between the shuttle and feeder bus is fully accounted for in the problem. A mixed-integer linear programming model is formulated to minimize the total travel time for all passengers, which consists of ride-time for vehicles from the pickup locations to the rail station and wait-time for passengers taking the subway beforehand. Different from conventional methods, the proposed model benefits from using a real distribution of passenger demand aggregated from cellular data and travel time or the distance matrix obtained from an open GIS tool. A distributed genetic algorithm is further designed to obtain meta-optimal solutions in a reasonable amount of time. When applied to design a feeder bus system in Nanjing City, China, case study results reveal that the total travel time of the proposed model was reduced by 2.46% compared to the traditional model. Sensitivity analyses were also further performed to investigate the impact of the number of vehicles on the output. Finally, the difference in results of Cplex, standard GA, and the proposed algorithm were compared to prove the validity of the algorithm.  
D RDC transit system; synchronous transfer of rail transit; personalized travel; heuristic algorithm +
Footnote †: journal:  
_Future Internet_**2018**, _10_, 61; doi:10.3390/610070061
# Time-Varying Reliability Evaluation of Concrete Based on Carbonation Depth
## 9 Conclusions  
1. On the basis of the statistics (13,198) carried out from existing buildings, the concrete failure time was obtained by employing time-varying reliability.
2. Compared with the SM method, we recommend the TM method for its higher accuracy, in which skewness, the third dimensionless central moment, is involved.
3. Tested by MC simulation, the prediction model of time-varying reliability based on the TM method was accurate, and the errors were within 4%.
4. Different from accelerated experiments, the carbonation depths analyzed in this paper were obtained considering the effects of the natural environment. Therefore, the environmental factors were considered, leading to more persuasive results.
5. In this study, the relation between carbonation depth and compressive strength was investigated, indicating that carbonation-induced corrosion might cause the decline of the compressive strength of concrete.
6. In light of the probability density distribution of carbonation depth, exponential distribution, compared with gamma distribution, normal distribution, and lognormal distribution, is more appropriate to describe the distribution of carbonation depth.  
Conceptualization, Y.Z. and J.W.; methodology, J.W.; software, W.Z.; validation, J.W. and W.Z.; formal analysis, W.Z.; investigation, W.Z.; resources, X.Z.; data curation, J.W. and Y.Z.; writing--original draft preparation, W.Z.; writing--review and editing, W.Z.; visualization, W.Z.; supervision, J.W.; project administration, Y.Z. and X.Z.; funding acquisition, Y.Z. and X.Z.  
This research work was supported by the Natural Science Foundation of China (Grant No. 51878413).  
The authors declare no conflict of interest.  
The data used to support the findings of this study are available from the corresponding author upon request.
## 4 Discussion
### Fishing Vessel Operational Status Recognition  
Accurately determining the operational state of fishing vessels holds paramount importance in fishing spatial information mining. Typically, the sailing speed of fishing vessels is employed to ascertain their operational state, a method widely used in the literature [24; 25]. However, the definition of speed thresholds often relies on expert knowledge and statistical modeling [11; 14; 15]. While this approach is efficient, especially for vast fishing vessel trajectories, it has limitations, as the operational status is influenced by various factors. The statistical findings in this study revealed a notable number of low-speed trajectory points during the daytime. Importantly, light purse seines generally do not operate during daylight hours. Failing to differentiate between daytime and nighttime information can lead to an overestimation of fishing effort and fishing pressure in the fishery, potentially misguiding fisheries research and management. To address this issue, this study utilized the Solar R package to calculate solar radiance, defining trajectory points with a solar radiance of 0 as nighttime trajectory information. Further analysis of the bimodal distribution of fishing vessel speeds shows that daytime speed distribution is slightly higher than at night. This is expected, as clearer visibility during the day allows for more flexible and efficient operations. By examining speed distributions across the entire day, nighttime, and daytime, we found that the first peak speed across all the time periods is primarily below 1.5 km. This observation enables us to clearly define 1.5 km as the upper threshold for fishing vessel operational speed. The bimodal distribution phenomenon and the differences in speed distribution across different times of day provide important insights for identifying fishing activities during various periods. This information serves as a valuable reference for optimizing fishery management and resource conservation strategies, enhancing the scientific monitoring and management of fishing activities. Additionally, the exploration of deep learning for fishery operation state recognition has shown promising results. Future endeavors should involve the integration of deep learning methods with field survey data to enhance the accuracy of fishing vessel operation state recognition models. This approach holds the potential for improving the precision and reliability of identifying fishing vessel operational states in practical applications.
## 1 Introduction
### Implementation as Medical Form for Clinical Care or Research  
Instead of focusing only on producing semantic structures addressing the needs defined by existing health informatics specifications, our approach aims to also produce semantic structures able to be widely implemented for use in systems. There are multiple open source initiatives for EHR systems and research platforms that provide mechanisms to define implementable forms. They are able to import and process the defined forms from an external file that specifies how to display multiple items on a screen, including presentation and user interaction functionalities. This research study has identified the following systems as relevant initiatives able to test the implementation of semantic structures for data collection or analytics tasks:  
* Diraya is an open source EHR system developed by the Andalusian Health Service able to be shared with any public administrations in all the primary care centers of the region. This system has been deployed to provide care for 8 million people through the full network of 29 hospitals and 1500 Primary care centers distributed throughout the region [15; 16].
* RedCap and OpenClinica are the most widespread open source Clinical Trial Management Systems (CMTS). OpenClinica is an open source software that has been used in 3 million clinical trials in 100 different countries [17]. REDCap is an open source software that has been used by 2800 institutions in 120 countries in 500,000 projects [18]. These systems allow the documentation of patients' clinical information in accordance with FDA regulations.
* TranSmart and i2b2 research platforms are patient cohort analysis systems designed to analyze data obtained through conventional care while ensuring compliance with patient privacy requirements. This system supports the anonymized analysis of specific patient cohorts. As a consequence, it facilitates the generation of new research hypotheses based on the analysis of the patient population treated at their centers [19; 20].
* The CroniCare system is a mobile health platform that is focused on establishing mechanisms for an agile and customized development of interventions based on the use of mobile technologies. The system allows users to configure the information, interactions, questionnaires and alarms that each patient will access through the mobile app in order to improve the capacity for self-management, empowerment and control of chronic and multimorbidity patients [21; 22].
# Assessment of Batteries' Contribution for Optimal Self-Sufficiency in Large Building Complexes
###### Abstract  
The EU has set ambitious targets to combat climate change. Incorporating renewable energy technologies to reduce greenhouse gas emissions is a critical aspect of achieving the European Union's (EU) 2030 climate goals. Similarly to all member countries of the EU, Greece shares the same climate goals. In order to achieve these goals, ensuring a consistent supply and the effective use of clean energy is pursued, as it has a significant impact on the sustainable development and growth of the country. As the Greek tourism sector is one of the most energy-consuming of the national economy and a major contributor to the country's GDP, opportunities are presented for innovation and investment in sustainable practices. Such investments must focus on buildings and facilities, where the energy consumption is concentrated. One of the most popular holiday destinations in Greece is the island of Crete. Visitation patterns are seasonal, which means during the summer months, Crete is exceptionally popular and more demanding energy-wise. One of the highest energy-demanding types of tourism-based businesses is the hospitality industry. Energy demands in hotels are driven by factors such as heating, cooling, lighting, and hot water. Thus, such activities require thermal and electrical energy to function. Electrical energy is one of the most essential forms of energy for hotels, as it powers a wide range of critical systems and services throughout the establishment. Therefore, the hotels are highly susceptible to fluctuations in energy prices which can significantly impact the operational costs of hotels. This paper presents an analysis of the annual consumption for the year of 2022 of five hotels located in Crete. An algorithm is also implemented which strives to minimize the capital expenditure (CAPEX), while ensuring a sufficient percentage of self-sufficiency.  
buildings' energy consumption; self-sufficiency; energy storage; photovoltaics; hotels +
Footnote †: journal: Applied System
# Fine-Grained Boundary Conditions in Field-Based Routing
## 5 Advanced Fine-Grained Boundary Conditions
### Advanced Fine-Grained Boundary Conditions
#### 5.2.1 Outer Boundary Conditions
Fundamentally, the boundary conditions for real outer boundary nodes are equivalent to Robin boundary conditions, whereas the boundary conditions can be interpreted as the Dirichlet boundary conditions from the perspective of the virtual nodes. More precisely, the real outer boundary nodes calculate potential values by placing the virtual nodes in necessary cases as described in Figure 2, following the same principles as general mesh nodes. The boundary conditions for the real outer boundary nodes can be mathematically expressed as shown in (2). \[\phi(b)=A+Bg(\phi(v)),\text{on}\;b,v\in\partial\Omega \tag{2}\]  
where \(\phi(b)\) indicates the potential value of outer boundary node \(b\) while \(\phi(v)\) represents that of virtual node \(v\). \(A\) and \(B\) are simplified constant terms. Additionally, \(g(\phi(v))\) represents a function that indicates a change in potential value. It behaves like a derivative function with respect to \(\phi(b)\) for given \(\phi(v)\). This change is influenced not only by a fixed potential value by the proposed outer boundary conditions but also by the proximity to gateways. Actually, (2) is conceptually equivalent to Robin boundary conditions in Table 1.
# Improving RF Fingerprinting Methods by Means of D2D Communication Protocol
###### Abstract  
Radio Frequency (RF) fingerprinting is widely applied for indoor positioning due to the existing Wi-Fi infrastructure present in most indoor spaces (home, work, leisure, among others) and the widespread usage of smartphones everywhere. It corresponds to a simple idea, the signal signature in a location tends to be stable over the time. Therefore, with the signals received from multiple APs, a unique fingerprint can be created. However, the Wi-Fi signal is affected by many factors which degrade the positioning error range to around a few meters. This paper introduces a collaborative method based on device-to-device (D2D) communication to improve the positioning accuracy using only fingerprinting and the direct communication to nearby devices. The results presented in this paper show that the positioning error can be reduced around 44% by considering D2D communication in the operational stage without adding new infrastructure for fingerprinting or complex resource-consuming filters. Moreover, the presence of very large errors is significantly reduced when the collaborative positioning based on D2D is available.  
RF Fingerprinting; D2D; 5G; indoor positioning +
Footnote †: journal: Electronics
## 4 Conclusions  
In this study, we demonstrated the characteristics of rice-bran-derived biochar at different pyrolysis temperatures (400, 500, 600, and 700 \({}^{\circ}\)C) and ball-milling times (30, 60, 120, 180, and 240 min). The effects of the pyrolysis temperature, ball-milling time, adsorbent amount, and contact reaction time on the RhB removal performance were also investigated. The pyrolysis temperature and ball-milling time had a significant impact on the physicochemical and structural properties of the biochar. The FTIR analysis revealed that all of the biochar samples contained similar functional groups (C-O, C=C, and C-H). With increased pyrolysis temperature, the biochar exhibited a highly ordered aromatic carbon structure. In addition, as the pyrolysis temperature was increased, the RhB removal efficiency increased as well.  
When compared to pristine biochar, ball-milled biochar demonstrated improved physicochemical properties, and its RhB removal efficiency was remarkable compared to that of unmilled biochar. Among the samples, the 180-min ball-milled RB600 biochar sample exhibited the highest RhB removal efficiency due to the tiny particle size and high surface area obtained through ball milling and high pyrolysis temperature. These results reveal that the ball-milling technique has a high potential for efficiency improvement and is an environmentally friendly and cost-effective technique for a wide range of environmental applications. In the future, we will further investigate the physicochemical properties of biochar by extending the pyrolysis temperature and ball milling time to enhance the generalizability of this study's findings. In addition, the adsorption kinetics and isotherms of heavy metals and dyes to rice-bran-derived biochar will be studied in detail.  
D.-Y.K.: Methodology, Conceptualization, Data curation, Formal analysis, Visualization. G.-B.J.: Conceptualization, Supervision, Writing--review & editing, Funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the 2021 Research Fund of Chosun University.  
Not applicable.  
All authors declare that they have no conflict of interest.
# Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland
## 2 Study Area and Data  
### Study Area  
The Mongolian Plateau is located in north-central Asia, with a latitude and longitude range of 87.43\({}^{\circ}\)E-126.04\({}^{\circ}\)E, 37.22\({}^{\circ}\)N-53.20\({}^{\circ}\)N. Administratively, it comprises Mongolia and the Inner Mongolia Autonomous Region of China, with a total area of about 2.7 million square kilometers (Figure 1).  
The Mongolian Plateau has a temperate continental climate, and the main climatic features are windy, dry, and cold. The region is mountainous in the northwest, with vast deserts in the southeast and large hills in the center and east. The terrain slopes gradually from west to east, with an average altitude of about 1600 m. Precipitation gradually decreases from northeast to southwest, and the average annual rainfall is about 200 mm. The lowest temperature in the region can reach \(-45\)\({}^{\circ}\)C in winter, which is one of the coldest on the Asian continent; the highest temperature in summer can reach 30-35 \({}^{\circ}\)C.  
The Mongolian Plateau is located in an arid and semi-arid area, and its ecosystem is fragile. Grassland is the most important land cover type in the region, accounting for 70% of the area [45; 46]. It mainly includes forested grassland, savanna grassland, and typical grassland [47; 48].  
In the model verification phase of this study, we selected three sample points for each type of grassland. Their spatial distribution is shown in Figure 1. Among them, a-c is the sample points of forested grassland; d-f is the sample points of savanna grassland; g-i is the sample points of typical grassland, where i is the Xilingol Temperate Typical Grassland Flux Station.
## 2 Materials and Methods
### Methods  
#### 2.2.1 Experimental Design  
To differentiate the effects of the meteorological elements and LAI/FPAR on the GPP estimates, we designed the following four experimental groups to recalculate GPP under different scenarios. (1) Following the standard MOD17A2 products completely, NCEP-DOE Reanalysis II meteorology data and MOD15A2 LAI/FPAR products were used to estimate GPP (MOD_NCEP GPP); (2) Site meteorology data and MOD15A2 LAI/FPAR products were used to estimate GPP (MOD_Tower GPP); (3) NCEP-DOE meteorology data and GLASS LAI/FPAR products were used to estimate GPP (NCEP_GLASS GPP); (4) Site meteorology data and GLASS LAI/FPAR products were used to estimate GPP (Tower_GLASS GPP). Finally, all the GPP estimates were directly compared with the EC flux derived GPP (Flux_Tower GPP) to assess their accuracies and uncertainties.
# A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans
## 4 Results
### Classification Performance Evaluation  
In the evaluation phase, we use specific metrics to see how our algorithm worked with the given images. The validation metrics that were used are accuracy, precision, recall, F1 score, sensitivity and specificity.  
A predicted sample can be classified in one of the four states in binary classification problems: true positive (TP), true negative (TN), false positive (FP), and false negative (FN). True positive means that the label of the image is benign, and it is classified correctly. False positive means that the label of the image is not benign, and it is classified as benign. True negative means that the label of the image is not benign, and it is classified as not benign. Similarly, false negative means that the label of the image is benign, and it is classified as malignant.  
Mathematical formulations of the six performance metrics are defined as follows:  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{1}\]  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
\[Recall=\frac{TP}{TP+TN} \tag{3}\]  
\[F1-score=\frac{2*(Recall\times Precision)}{Recall+Precision} \tag{4}\]  
Figure 4: The selected CNN architecture for diagnosis of metastatic breast cancer in bones.  
\[Sensitivity =\frac{TP}{TP+FN} \tag{5}\] \[Specificity =\frac{TN}{TN+FP} \tag{6}\]  
Accuracy is the ratio between the correct labeled samples and all samples. Precision points out the ratio of corrected images of positive class to all images that were predicted for the same category. Recall deals with the division of corrected positive images to all corrected images of both categories.  
F1-score is a harmonic mean between recall and precision. Sensitivity is the proportion of images which test positive for the malignancy (bone metastasis) among those which have the metastasis/malignancy. Finally, we have specificity, which is the balance between the corrected negative images and the sum of numerator and wrong labeled positive images.
## 3 Results and Discussion
### Numerical Simulations
#### 3.1.2 Cold Pressing Process  
Figure 7 shows the strain distribution after the first pass of the cold pressing process. A large strain zone is located at the edges of the upper mold and the area between the stiffener and the smooth plate. The contact surfaces of the T-type rib between the upper and lower molds show quite small strain due to the friction. Therefore, the asymmetric geometry and boundary conditions during the cold pressing process both contribute to the heterogeneous strain distribution. The large strain at the area between the stiffener and the smooth plate indicates the distinct residual stress relief effect.  
Figure 6: Residual stress distribution of quenched T-type rib: (**a**) path ab; (**b**) path cd (depicted in Figure 3).  
Figure 7: Strain distribution of the first pass of cold pressing.  
Figure 8 shows the residual stress distribution of one-pass integral cold pressing and five-pass stepwise cold pressing. The residual stress distribution is symmetrical and uniform at the smooth area while it is inhomogeneous at the area with the stiffener. This can be explained by the strain distribution shown in Figure 7. The stepwise cold pressing process induces inhomogeneous residual stress distribution at both the stiffened area and the smooth area of the T-type rib. It can be seen that the stress fluctuation is quite obvious at the overlap region between the adjacent pass. Therefore, less cold pressing pass is beneficial to the residual stress reduction and its distribution uniformity within the capacity of the cold forging machine.  
To analyze the effect of stepwise cold pressing on the residual stress reduction, two paths corresponding to Figure 5 are selected, as shown in Figure 9. Similar to the quenched stress distribution, the cold pressed stress shows symmetrical distribution at the smooth area and asymmetrical distribution at the stiffened area. Compared to the quenched stress, distinct stress reduction is achieved in the cold pressing process. The longitudinal stress decreases from \(-230\) MPa and \(200\) MPa to \(-161\) MPa and \(133\) MPa; while the transverse stress decreases from \(-230\) MPa and \(145\) MPa to \(-162\) MPa and \(90\) MPa at the stiffened area. Different from the variation of residual stress at the stiffened area, there is almost no difference between the longitudinal and the transverse stress at the smooth area. The residual stress decreases from \(-210\) MPa and \(190\) MPa to \(-145\) MPa and \(81\) MPa at the smooth area.  
Figure 8: Residual stress distribution of the integral and five-pass stepwise cold pressing process: (**a**,**c**) is transverse and longitudinal residual stress after integral pressing, respectively; (**b**,**d**) is transverse and longitudinal residual stress after \(5\)th stepwise pressing, respectively.  
Figure 9: Residual stress distribution of cold pressed T-type rib: (**a**) path ab; (**b**) path cd (depicted in Figure 3).
## 3 Potential Security Implications of IoT in 6G Environment
### Network- or Device-Level Vulnerabilities
#### 3.1.3 Privacy Issues in Data Transmission  
IoT devices frequently collect sensitive personal or medical data, and improper encryption or insecure transmission protocols expose this data to breaches [46]. The promise of ultra-reliable low-latency communication (URLLC) and intelligent edge computing in 6G will lead to the increased processing and transmission of sensitive data, raising concerns about maintaining privacy and confidentiality at all stages of data handling [45].