esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we have presented an improved approach to modelling satellite SIF by taking the influence of illumination on the SIF\({}_{\text{yield}}\) into account. The cosine of sun zenith angle at noon (cos (SZA\({}_{0}\))) was used to represent the illumination conditions, and together with the reflectance, NDVI, and air temperature, was used to generate a spatially-continuous TanSat SIF product with a resolution of 0.05\({}^{\circ}\) for the period 2017-2019. The results showed that cos (SZA\({}_{0}\)) is an important factor affecting the SIF yield. It was also shown that, of the variables in the RF model, cos (SZA\({}_{0}\)) was the most important, and that the model accuracy could be significantly improved by including cos (SZA\({}_{0}\)) as an explanatory variable. The RF model also modelled the TanSat SIF well (R\({}^{2}\) = 0.74 for 2017, 0.74 for 2018, and 0.81 for 2019). The global, continuous TanSat SIF data were highly consistent with TROPOMI SIF, with an R\({}^{2}\) of 0.73. Furthermore, the continuous TanSat product was able to capture the seasonal characteristics of the evergreen needleleaf forest site at Niwot Ridge, where the SIF is low in winter. Therefore, the globally continuous TanSat SIF product contains more physiological SIF information and can serve as a reliable global SIF dataset for monitoring plant photosynthetic activity. And the global spatially continuous TanSat SIF product was available at the zenodo website ([[https://zenodo.org/record/3884309](https://zenodo.org/record/3884309)]([https://zenodo.org/record/3884309](https://zenodo.org/record/3884309))).  
Conceptualization, Y.M. and L.L.; methodology, Y.M., R.C., S.D. and L.L.; software, Y.M., X.L.; validation, Y.M. and L.L.; formal analysis, Y.M., R.C., S.D., and X.L.; investigation, Y.M., R.C. and S.D.; resources, S.D.; data curation, S.D.; writing--original draft preparation, Y.M. and L.L.; writing--review and editing, L.L.; visualization, Y.M.; supervision, L.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2017 YFA0603001, the Key Research Program of the Chinese Academy of Sciences (ZDRW-ZS-2019-1), the National Natural Science Foundation of China, grant number 41825002, and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA15017800).  
The authors gratefully acknowledge the MODIS products obtained from the Land Products Land Processes Distributed Active Archive Center (LP DAAC), and the GLDAS products obtained from the obtained from NASA Goddard Earth Sciences Data and Information Services Center (GES DISC).  
The authors declare no conflict of interest.
# Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland
###### Abstract
Furthermore, by exploring crime patterns across different demographic factors, including age, gender, and race, utilizing comprehensive crime data collected within the state, this study aims to identify and inform interventions that address disparities in crime rates and victimization, thereby fostering more equitable and effective public safety measures [8]. The societal impact of applying machine learning to crime analysis in Maryland is profound. By harnessing the predictive power of these models, law enforcement agencies can anticipate crime hotspots, understand demographic factors influencing crime, and implement proactive interventions [9]. This approach not only aids in identifying underlying socio-economic factors contributing to crime but also assists policymakers in designing more targeted and effective crime prevention programs, fostering safer communities across the state. The findings of this study are intended to inform law enforcement and community stakeholders, aiming in the crafting of more effective interventions that address the specific needs of these demographics. By highlighting areas with pronounced crime rates and identifying trends over time, we provide a foundation for future research and policymaking that is responsive to the changing landscape of urban crime. Through this investigation, we seek to contribute to the broader discourse on public safety, equity, and justice, ultimately aiming to foster a safer and more inclusive community for all residents [10; 11; 12]. To guide readers through the rest of the paper, the remaining sections are structured as follows. First, we review the relevant literature on crime analysis and machine learning, establishing the context and identifying gaps in the literature addressed by our work. Next, we detail the methodology, including the data sources, preprocessing steps, and configuration of the machine learning models employed. Subsequently, we present the results of our data analysis, highlighting key findings on crime trends and model performance across Maryland. This is followed by a discussion of the implications of these findings, with a focus on differences between urban and rural crime dynamics. Finally, we conclude the paper by summarizing its contributions and outlining the study's limitations, as well as identifying directions for future research.
# Task Offloading Scheme Based on Proximal Policy Optimization Algorithm
## 5 Experiment and Performance Verification  
### Experimental Environment and Parameter Settings  
The simulation experimental platform consisted of a device with a frequency of 2.7 GHz, 16 GB of RAM, and an Intel(R)Core(TM) i5-11400H CPU. The device runs a 64-bit Windows 10 operating system, and the simulation software was Pycharm2024.2.1.  
This experiment included one central cloud server, \(M\) edge servers, and \(N\) mobile terminal devices. Each terminal device had a computation task that needed to be executed. Due to the limited computing power of mobile edge devices, all computation tasks in this chapter could only be offloaded to edge servers or the central cloud for execution. The complete parameter settings in the experiment are shown in Table 2.  
\begin{table}
\begin{tabular}{l l l} \hline
**Step** & **Description** & **Expression** \\ \hline Input & Collect \(\{a_{t},s_{t},r_{t}\}\) tuples & - \\ Output & Update model & - \\ \hline \(i=1,2,\ldots,N_{1}\) & Run policy \(\theta\) for \(T\) times, collect \(\{a_{t},s_{t},r_{t}\}\), & - \\  & estimate advantage \(\hat{A}_{t}=\sum_{t^{\prime}>t}\gamma^{t^{\prime}-t}r_{t^{\prime}}-V_{\theta}( s_{t})\) & - \\ \(j=1,2,\ldots,N_{2}\) & Optimize policy & \(J^{\theta}_{PPO}(\theta)=J^{\theta}(\theta)-\beta KL(\theta,\theta^{\prime})\) \\ \(k=1,2,\ldots,N_{3}\) & Update value function & \(L_{\text{CLIP}}(\phi)=-\sum_{t=1}^{T}\Bigl{(}\sum_{t^{\prime}>t}\gamma^{t^{ \prime}-t}r_{t^{\prime}}-V_{\phi}(s_{t})\Bigr{)}^{2}\) \\ \hline \end{tabular}
\end{table}
Table 1: PPO algorithm steps.  
\begin{table}
\begin{tabular}{l l l} \hline
**Symbol** & **Definition** & **Value Range** \\ \hline \(B^{m}_{t}\) & Bandwidth & 1–20 MHz \\ \(P^{tran,d}_{n}\) & Transmission power of terminal device \(n\) & 0.1–2 W \\ \(P^{tran,e}_{m}\) & Transmission power of edge node \(m\) & 1–5 W \\ \(P^{tran,e}_{c}\) & Transmission power of central cloud \(c\) & 10–50 W \\ \(P^{comp,e}_{m}\) & Computing power of edge node \(m\) & 10–100 W \\ \(P^{comp,c}_{c}\) & Computing power of central cloud & 50–500 W \\ \(N_{0}\) & Noise power spectral density & \(10^{-9}\) W\(/\)Hz \\ \(g\) & Channel transmission gain & \(2\times 10^{-10}\)\(\sim 2\times 10^{-9}\) \\ \(C_{m}\) & Cycles per bit required by edge node \(m\) & 500–2000 cycles/bit \\ \(f_{c}\) & CPU frequency of cloud server & 2–3.5 GHz \\ \(f_{m}\) & CPU frequency of edge node & 1–2.5 GHz \\ \(D_{n}\) & Data size of computation tasks & 1 KB–1 MB \\ \(W_{m}\) & Return path bandwidth & 100–1000 Mbps \\ \hline \end{tabular}
\end{table}
Table 2: Simulation delay parameter descriptions and corresponding value settings.
# Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks
## 6 Case Studies
### Evaluation Results of the Case Studies
#### 6.3.2 Comparison of the Discontinuation-Order Evaluation  
To answer RQ2, we determined how close the discontinuation order predicted by our framework was to the actual discontinuation order. Correctly predicting which ads are likely to discontinue allows ad operators to set operational priorities. This experiment assumed the wear-out case; hence we evaluated the output of the long-term model, as described in Section 6.1.  
To decide on a discontinuation order using our framework, we utilized the predicted discontinuation date and the hazard probability. To predict the wear-out case, our framework used only the first 10 days of data from serving. In each time interval, we sorted ad creatives based on the probability, and we concatenated the ordered ad creatives in each time interval.  
To compare the evaluation results, we introduced two practical rule-based indicators, which are similar to practical operations. One was to sort by sales, namely, _sales order_. The business is seriously affected if an ad with high sales is discontinued. The other was to sort by the CPA ratio, namely, the _CPA ratio order_, because ads with a high CPA ratio are easy to discontinue. These indicators are considered to be correlated with human decision-making. (Ad operators usually monitor these indicators daily).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Short-Term** & **Single-Task** & **Multi-Task** & **Human Performance** \\ \hline CPA ratio & 1.13 \(\pm\) 0.92 & **1.12 \(\pm\) 0.78** & 1.18 \(\pm\) 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the CPA ratios for predicting cut-out in the practical evaluation experiment. The CPA ratio is calculated as Actual CPA/Acceptable CPA. The closer the CPA ratio is to 1, the more profitable it is for the advertiser and the media. Human performance is calculated on the day the ad operator discontinues the ad creative.  
Figure 4 shows the NDCG of the prediction of our framework and the actual discontinuation orders for each continuous serving day. This experiment targeted ad creatives that have been served for more than 10 days. Each evaluation was conducted every 10 days for ad creatives within continuous serving days. For example, when the \(x\)-axis value was 20, the \(y\)-axis showed the NDCG value with ad creatives with 20 or more days of continuous serving. This evaluation determined how accurately we could predict future discontinuation.
# Prediction of Air Quality Combining Wavelet Transform, DCCA Correlation Analysis and LSTM Model
## 3 Results
### DCCA Coefficients on Each Scale and Their Relationship
#### 3.2.3 AQI Correlation with Pressure  
The red line in Figure 3 represents the relationship change curve between the AQI and the barometric pressure. The majority of inter-relationships between the AQI and the barometric pressure were positive, demonstrating a positive correlation between the AQI and the barometric pressure, and the A-P coefficient was zero around \(n=700\).
# A Multi-Segment Expanded Anchor for Landslide Emergency Management
## 3 Field Tests
### Analysis of Test Results
The bearing blades were not in contact with the hole wall after gradually expanding, thus the prestress was almost 0. At stage A1B1, friction occurred between the bearing blades and the hole wall, and the tensile force increased to 138 kN. At stage B1C1, the bearing blades slid along the hole wall, and the tensile force dropped instantly to 60.60 kN when it was greater than 138 kN. At stage C1D1, the tensile force increased from 131.8 kN to 172.75 kN, the steel strands had an elastic deformation displacement of 14 mm, and the load-bearing body 2-1 no longer slide as its upper end pressed against the lock at the underream. The curve OA2B2 represents the tension process of the load-bearing body 2-2. At stage OA2, the bearing blades gradually expanded. At stage A2B2, friction occurred between the bearing blades and the hole wall, the tensile force of the load-bearing body increased rapidly to 170 kN, and the total tensile force of the MSEAwas 342.75 kN, meeting the design requirements. Figure 5a-f show that the lock-off load of various MSEAs after the initial tension was greater than the design value of their total bearing capacity without grouting (300 kN), meeting the design requirements. Moreover, the No. 2 load-bearing bodies in MSEAs Nos. 4-6 did not utilize the bearing capacity of the lock at the underream. Figure 5g-i show the failure test results of MSEAs Nos. 4-6 without grouting. As shown in Figure 5g, load-bearing body 4-2 failed when the oil pressure reached 30 MPa, with a load of approximately 226 kN; the load-bearing body 4-1 failed when the oil pressure reached 47.5 MPa, with an ultimate load of approximately 480 kN. As shown in Figure 5h, load-bearing body 5-2 failed first at a load of approximately 329 kN, and then load-bearing body 5-1 failed at an ultimate load of approximately 470 kN. As shown in Figure 5i, load-bearing body 6-2 failed first at a load of approximately 227 kN, and then load-bearing body 6-1 failed at an ultimate load of approximately 419 kN. These results suggest that, load-bearing body No. 2, which did not utilize the platform to bear pressure, always failed prior to load-bearing body No. 1 in all the MSEAs. Figure 5: Tension curves of the MSEAs. (**a–f**): The tension processes of MSEAs Nos. 1–6, respectively; (**g–i**): failure test results of MSEAs Nos. 4–6 without grouting, respectively.
# Three-Dimensional Pulp Volume Analysis in Lip and Palate Cleft Population
###### Abstract  
Aim: Cleft lip and palate (CLP) patients have a greater predisposition to tooth malformation, which could affect pulp volume. The aim of this study is to evaluate the dental pulp volume of central incisors in 3D images between individuals with and without CLP. Materials and Methods: This retrospective case-control study is single-centered and was recruited between January 2016 and October 2022. Ninety-four patients who were followed in the Institute of Orthodontics were evaluated and divided into two groups: a control group of patients without CLP and a test group of patients with CLP. The 3D data were imported by 3D image semi-automatic segmenting software named ITK-SNAP to calculate tooth pulp volume. Results: The dental pulp volume for both groups, control and CLP, did not show statistically significant differences. In the cleft group, when comparing the pulp volume between the cleft side and the non-cleft side, the cleft side showed a smaller volume. Regarding age and sex, no statistically significant differences were observed. Conclusions: Although there are no differences in mean pulp volume between patients with and without CLP, there is a pulp volume reduction in the teeth on the cleft side when compared to the unaffected contralateral side.  
cleft palate; cone beam computed tomography; lip cleft; pulp canal; secondary dentin deposition; 3D images +
Footnote †: journal: Medical Medical Informatics
## 4 Results and Comparative Analysis  
This section describes the security audit findings from the three main layers of the PHGL-COV platform: the server-side stack, the mobile mediator, and the wearable end-point. The audit discovered a range of vulnerabilities including associated outdated system components or unpatched CVEs on containerized services, overprivileged mobile application permissions, and unencrypted Bluetooth communications signals sent to wearable devices.  
Instead of analyzing each layer independently, this evaluation explores how the responses of an actor, threat, or adversary can cross architectural boundaries and lead to chains of compound attacks that degrade the confidentiality of the data, disrupt authentication mechanisms and ultimately affect the integrity of the system. By correlating vulnerabilities found across multiple layers, we assess their individual severity, and more importantly, evaluate the compounded impact if exploited sequentially, an often overlooked consideration in mHealth security investigations.  
The following subsections provide a layer-by-layer description of the findings and a matrix of these findings in terms of the risk, which estimates the exploitability and likelihood of each threat that was identified. This layer-based analysis demonstrates how decisions made in one layer (for example, wearable communications) can lead to vulnerabilities in another layer (for example, backend APIs) and illustrates the need for an integrated security solution for all operational layers in mHealth deployments [49; 50; 41].  
Figure 2: BLE communication flow: Galaxy Watch 7 to Samsung Galaxy A55 with annotated attack surfaces.
## 2 Materials and Methods  
### Samples' Preparation  
Specific test samples produced with the laser sintering technique used for customized implantable titanium meshes were obtained from two different manufacturers, BoneEasy (Arada, Ovar, Portugal) and BTK (Vicenza, Italy), and came with two different shapes: surfaces without perforations and with calibrated perforations 1.2 mm in diameter.  
The samples were characterized by the following dimensions: 40 mm length, 10 mm width, and 0.5 mm thickness, to conduct the test according to the protocols of ISO5832-3:2014, ASTM F136:2013, ASTM F2934:2014, ASTM F3001:2014, and ASTM B348:2013.  
The chemical composition declared by the manufacturer was TiAl6V4 with the following percentages of the different elements: Al 5.5-6.5% and V 3.5-4.5%.  
All the tests were conducted on:  
* Three BoneEasy specimens (BE) without perforations,
* Three Biotek specimens (BTK) without perforations,
* Three BoneEasy specimens (BE) with perforations, and
* Three Biotek specimens (BTK) with perforations.
## 3 Results and Discussion
### Salmonella Populations during Storage
reported the growth potential of _Salmonella_ in nine ready-to-eat vegetables [44], which kept unchanged or declined in most vegetables when stored at 7 \({}^{\circ}\)C, while increasing significantly at 15 \({}^{\circ}\)C. In our study, the trends of _Salmonella_ populations in the wax gourd of the control samples were similar to those in mango, papaya, and dragon fruit when in storage at 25 \({}^{\circ}\)C. However, it increased slowly and reached a stationary phase when stored at 10 \({}^{\circ}\)C, which is different from the results in mango and papaya stored at 12 \({}^{\circ}\)C. When stored at 4 \({}^{\circ}\)C, the fate of _Salmonella_ populations in wax gourd was more similar to that in dragon and papaya. The differences in the fate of _Salmonella_ population may be due to variations in the nature of the vegetables, including differences in pH, texture, and antimicrobial properties. _Salmonella_ population levels in the treated group were significantly lower than those in the control group during storage at different temperatures. This difference between the groups could be attributed to the combined treatment of TP and TS. TP is a non-volatile bacteriostatic agent derived from tea, which can remain in the vegetable and continuously inhibit _Salmonella_ growth. The TS was found to have better bactericidal ability than the use of sonication and thermo treatment alone. When used with a chemical sanitizer such as TP, TS can improve sanitization efficiency. Therefore, the combination of TP and TS demonstrated a good sterilization effect in fresh-cut wax gourd. This rapid assay had the potential to provide a pasteurization method for fresh-cut vegetable processing, further strengthening food safety assurance. It is widely acknowledged that fresh-cut products require proper refrigeration and strict time and temperature control for preservation. In the food industry and commercial sales, 4 \({}^{\circ}\)C and 10 \({}^{\circ}\)C are commonly employed for low-temperature storage. Our research findings indicated that low-temperature storage, following TP and TS treatment, could significantly inhibit the growth of _Salmonella_ and even reduce the total CFU. The inhibitory effect was more pronounced at 4 \({}^{\circ}\)C as compared to 10 \({}^{\circ}\)C. However, storing fresh-cut products at room temperature (25 \({}^{\circ}\)C) was unsuitable for preservation, as our results confirmed that samples stored at 25 \({}^{\circ}\)C experienced texture loss and spoilage within a short period of time even with TP and TS treatment, resulting in the complete loss of commercial value. Furthermore, the oral infection dose of _Salmonella_, which could cause serious intestinal diseases, is known to be at a level of about 10\({}^{3}\) CFU [45]. However, many countries and regions worldwide have stringent regulations for the absence of _Salmonella_ in food to safeguard public health. Therefore, it would be worthwhile to investigate the population changes of _Salmonella_ at a low start concentration during the preservation process of wax gourd in future studies.
## 4 Results and Discussion  
The proposed approach has been tested on two different distribution systems; the first one is a 12-bus system with the total active and reactive peak load of 435 kW and 405 kvar respectively, and the total real power loss of 20.7 kW. The single line diagram of this test system is shown in Figure 7. The line and load data for this test system is available in [46]. The second test system, as depicted in Figure 8, contains 33 buses and 32 branches; it is a radial system with the total peak load of 3715 kW and 2300 kvar, and the total real power loss of 211 kW. The line and load data for the 33-bus test system is available in [47]. The third test system which is shown in Figure 9 is 69-bus radial system, it has the total active and reactive peak load of 3802.19 kW and 2694.60 kvar respectively, and the total real power loss of 225 kW. The detailed data for this system appear in [48].  
Figure 6: Flowchart to determine the optimal penetration level, and power factors for desired DG locations.  
In order to evaluate the power losses reduction (PLR) in these two test systems, the following formula is used:  
\[PLR=\ \frac{P_{Loss,\ noDG}-\ P_{Loss,With\ DG}}{P_{Loss,\ noDG}}\ \times 100\% \tag{24}\]  
where: \(P_{Loss,\ noDG}\) is the total real power losses in the network without DGs (in kW), and \(P_{Loss,With\ DG}\) is the total real power losses in the network in the presence of DGs (in kW).