esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT-Enabled Smart Cities: A Review of Concepts, Frameworks and Key Technologies
## 3 Review of IoT-Enabled Smart City Components and Solutions
### Smart Healthcare
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Smart City Domains**} & \multicolumn{2}{c}{**Services, Applications**} & \multicolumn{2}{c}{**IoT and Sensing**} & \multicolumn{2}{c}{**Real-World Cases**} \\  & \multicolumn{2}{c}{**and Features**} & \multicolumn{2}{c}{**Technologies Involved**} & & \\ \hline \multirow{6}{*}{Smart Governance} & - & e-government & \multirow{6}{*}{-} & Web- and mobile-based & - & Barcelona [46; 47] \\  & - & Citizens’ participation & applications for G2C, G2B and & - & Mario de Janeiro [3; 49] \\  & - & Collaborative and shared & G2G [41; 42] & - & Singapore [50] \\  & & decision-making policies & & & Toronto [51; 52] \\  & & & & - & Songdo [53; 54] \\ \hline \multirow{6}{*}{Smart Living and Infrastructure} & \multirow{6}{*}{-} & \multirow{6}{*}{Smart buildings} & - & BIM models [57] & \multirow{6}{*}{-} \\  & & - & Indoor/outdoor sensors and & & \\ \cline{1-1}  & - & Smart homes & & WSN technologies [59; 62] & \multirow{6}{*}{-} \\ \cline{1-1}  & - & Smart homes & - & Web and mobile apps, & & \\ \cline{1-1}  & - & Smart tourism & & location-aware services, virtual & - & Los Angeles [68] \\ \cline{1-1}  & - & Smart education & & and augmented reality and social & - & Dubai [69] \\ \cline{1-1}  & & & & media [64] & \multirow{6}{*}{-} \\ \cline{1-1}  & & & & E-learning systems and & \\ \cline{1-1}  & & & & decentralized education [66] & \\ \hline \multirow{6}{*}{Smart Mobility and Transportation} & \multirow{6}{*}{-} & \multirow{6}{*}{Traffic management} & - & City sensors and actuators, & \multirow{6}{*}{-} \\  & & & personal devices & & \\ \cline{1-1}  & & - & IoT and intelligent & - & Berlin [79] \\ \cline{1-1}  & - & Traffic management & transportation & - & Florence [3] \\ \cline{1-1}  & - & Dynamic routing & systems (ITSs) [71] & - & Atlanta [3] \\ \cline{1-1}  & - & Smart parking & - & Vehicle-to-vehicle & - & London [80] \\ \cline{1-1}  & - & Vehicle sharing & communication (V2V), & - & Santander [81] \\ \cline{1-1}  & - & Sustainable mobility & & vehicle-to-infrastructure & - & Bilbao [66] \\ \cline{1-1}  & & & communication (V2I) & - & Seoul [82] \\ \cline{1-1}  & & & - & VANES [73] & \\ \cline{1-1}  & & & - & On-board diagnostic (OBD) & \\ \cline{1-1}  & & & & tools [78] & \\ \hline \multirow{6}{*}{Smart Economy} & - & e-business & \multirow{6}{*}{-} & \multirow{6}{*}{-} & \multirow{6}{*}{Shenzen [86]} \\ \cline{1-1}  & - & e-commerce & & & \\ \cline{1-1}  & - & Peer-to-peer marketplaces & & recommendation systems [85] & \\ \cline{1-1}  & - & Peer-to-peer labor services & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of smart city domains: services and features, IoT technologies employed and real-world cases.
## 5 Conclusions  
The geographic routing protocol with its simple structure, low energy consumption as well as high scalability has attracted a lot of attention from researchers in recent years; however, its blindness in data transmission has been a major challenge for network reliability. This paper fundamentally solves this problem by link detection and repair algorithm, where each node can judge the validity of its own communication status. In addition, the existing routing protocols always use all the nodes to work, and lack of literature systematically combining routing protocols with coverage control techniques, which is the core work conducted in this paper, in which the proposed node collaborative scheduling algorithm breaks through the limitations of the traditional disk coverage model so that the whole network has a greater improvement in network lifetime, packet delivery rate, network coverage, and delay so that such a comprehensive and robust routing scheme will make a good contribution to unmaintained and unmanaged application scenarios. We hope to continue deepening the research in this field, using scientific and systematic solutions to solve more important problems in WSN.  
Conceptualization, M.W. and Y.W.; methodology, M.W. and Z.Z.; software, M.W. and Z.Z.; validation, M.W., Z.Z. and S.X.; writing--original draft preparation, Z.Z.; writing--review and editing, M.W., Y.W. and S.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China under Grant 61971215 and in part by Scientific Research Fund of Hunan Provincial Education Department under Grant 21A0276.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 4 Numerical Simulation of Roof Cutting Parameters
### Numerical Simulation of Cutting Height
#### 4.3.2 Load-Bearing Characteristics of Roof Cutting 'Key Structure'  
The fixed end of the short cantilever beam structure formed by the roof cutting is the solid coal pillar. The cantilever end is separated from the roof of the gob under the action of the roof cutting of the dense drilling. The structure protects the roof of the roadway from the influence of the roof rotation and subsidence of the gob. The short cantilever beam and the roof constant-resistance anchor cable together form the 'key structure' for roof control. As shown in Figure 8(a), when the roof cutting height is less than the roof control height of the constant-resistance anchor cable, the constant-resistance anchor cable can anchor the 'key structure' and the overlying strata, but when the roof cutting height is greater than the roof control height of the constant-resistance anchor cable, as shown in Figure 8(b,c), the roof strata exceeding the roof control height are deformed under the action of roof cutting. At this time, the anchor cable not only bears the weight of the 'key structure' but also bears the load applied by the deformation of the overlying roof.  
Figure 8: The stress distribution law of roadway surrounding rock under different roof cutting.  
Figure 9: The bearing characteristics of ‘key structure’ with different roof cutting heights.  
Comparing the stress concentration characteristics of coal pillars under different roof cutting heights, as shown in Figure 10, when the roof cutting height is 8 m, the peak value of the lateral stress of the coal pillar is 3.3 MPa, the influence range of the lateral stress is 8.2 m and the average load-increase coefficient is 1.82; when the roof cutting height is 9 m, the peak value of the lateral stress of the coal pillar is 3.8 MPa, the influence range of the lateral stress is 9.3 m and the average load-increase coefficient is 2.18. When the roof cutting height is 10 m, the peak value of the lateral stress of the coal pillar is 4.4 MPa, the influence range of the lateral stress is 9.6 m and the average load-increase coefficient is 2.73. It can be seen that when the roof cutting height exceeds the bearing range of the 'key structure' of the roof control, the stress concentration will occur on the side of the solid coal pillar (the fixed end of the short cantilever beam), and the stress influence range and the load-increase coefficient are positively correlated with the increase of the roof cutting height. Based on the above analysis, 8 m is selected as the optimal roof cutting height.
## 3 Materials and Methods
### Data Processing and Analysis
Further, for analyzing the effect of droughts on specific land use/cover types, average EVI anomalies and the area with deviations exceeding two standard deviations were calculated for cropland, grassland, and forest. The investigated cropland consisted of irrigated and non-irrigated arable land and rice fields in accordance to the CORINE land cover classes 211, 212, and 213. Irrigated crops and rice are part of the included pre-defined crop types. However, they are almost completely absent in Germany, and effects of irrigation can be neglected. To analyze the drought within forests, broad-leaved, coniferous, and mixed forests, corresponding to the classes 311, 312, and 313, were combined. Grassland consisted of the classes 231 and 321, which are pastures and natural grasslands. Only MODIS pixels that were covered at least by 75% of the specific land cover type, according to the higher spatial resolution CLC 2012, were taken into account. When considering specific land cover types, it is possible that some counties include only small amounts of that land cover type, and displaying the county mean would not be representative. Thus, for the analysis of spatial patterns of EVI deviances in cropland and grassland, counties with a valid pixel count of less than 10 are excluded and shown as no data in the maps.
# State Merging and Splitting Strategies for Finite State Machines Implemented in FPGA
## 2 Materials and Methods  
### Idea of the Method  
The idea of the approach is to execute sequential operations of splitting or merging the states if it is possible. In this way, new equivalent FSMs are built, whose further implementation can give various outcomes in terms of area, speed, and power consumption.  
In this article, three different strategies for combining and splitting states are shown:  
* Merge-then-split strategy (MS), where state merging is performed first and, when it is impossible, state splitting is performed.
* Split-then-merge strategy (SM), where state splitting is performed first and, when it is impossible, state splitting is performed.
* Combined strategy (COMB), where the decision as to whether to perform state merging or splitting is made at each subsequent finite automaton transformation operation.  
There can be also different optimization criteria considered in the proposed strategies: power consumption, speed, area, and balanced optimizations. To calculate the evaluation parameters and then implement the FSM, a state assignment using the selected method should be performed. To determine which state to split or which pair of states to choose, trial merging and trial splitting operations are executed.  
The implementation cost is not a critical boundary because contemporary FPGA devices have a large number of logical elements. For this reason, the area parameter is not considered in this work, but it was investigated in earlier works [34].
# An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks
## 4 The Proposed Affinity Propagation-Based Self-Adaptive (APSA) Algorithm  
In this section, a detailed illustration of the affinity propagation-based self-adaptive (APSA) algorithm will be given. Initial phase, set-up phase and communication phase are contained in APSA. During the initial phase, sensors obtains the necessary information from their neighbors for network forming. After all the preparations are finished, set-up phase will start. In set-up phase, the network topology is determined by AP and the modified K-medoids. Then the network enters into the communication phase and data transmission is conducted in this phase.
## 6 Conclusions  
In view of the practical difficulties of the classic EMD and ARMA methods in practical applications, such as mode mixing, and determination of real IMF and the model order, as well as ARMA requiring the time series to be a series of stationary random processes with a zero mean value, this paper proposed a hybrid method to improve the structural modal parameter identification, by means of combining IEMD and ARMA. IEMD was used to preprocess the signal, and a cluster analysis was used to determine the real IMFs. The free vibration response was obtained using the NExT method and used as the input signal for the ARMA method. The modal test of a seven-story steel frame showed that IMFs, which were used as an input signal for ARMA and were obtained by preprocessed of IEMD, which could resolve the mode mixing problem and make the determination of ARMA order easier. Thus, the structure frequency and modal shape were identified successfully, which proved the effectiveness of the proposed method and further expands the application range of ARMA. However, the application of this proposed method will be limited in the case of some dense modal vibration signals, when FFT cannot correctly divide the frequency band. In addition, recognition of time-varying signals was not considered in this paper, and continuous research work will be devoted to this aspect in the future.  
All authors took part in the discussion of the work described in this paper. [PERSON] wrote the paper, [PERSON]-[PERSON] revised and finalized the paper. All authors have read and agreed to the published version of the manuscript.  
Figure 17: The first six vibration modes identified using the IEMD/ARMA method.  
**Funding:** The work was supported by National Thirteenth Five-Year Key Research Projects of China (Grant No. 2020.YFD1100403), Scientific Research Fund of Institute of Engineering Mechanics, China Earthquake Administration (Grant No. 2020 EEEVL0402) and Fujian Science and Technology Planning Project (Grant No. 2017Y0412).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Versatile Video Coding-Post Processing Feature Fusion:
## 3 Proposed Method  
Figure 1 depicts the post-processing pipeline, where a CNN-based network is introduced as a refinement step after frame decoding, aiming to boost the reconstruction quality of the output video. This advanced technique utilizes artificial intelligence to optimize the decoded video, offering a visually superior experience through the refinement of the reconstructed video. The original video sequence is first compressed by the VVC encoder, which uses advanced methods such as intra-prediction, inter-prediction, transform coding, and entropy coding to reduce the data size of the raw video. The compressed video is then represented as a bitstream, which is fed into the VVC decoder. The decoder reverses the compression process, reconstructing the video from the bitstream. However, this reconstructed video may still contain imperfections and artifacts due to the lossy compression. To address this, the reconstructed video is then fed into a deep neural network (DNN)-based post-processing module. This AI-powered module analyzes the video, detects imperfections, and applies advanced algorithms to refine the video, reducing or eliminating these imperfections. By leveraging the power of deep learning, the post-processing module significantly improves the video quality. The final output video, refined and enhanced by the DNN module, is then displayed on the screen, providing a high-quality and visually stunning video experience. By integrating a deep learning-based post-processing approach into the VVC coding workflow, the reconstructed video is substantially improved, resulting in a better viewing experience for the end-user.
## 3 Results and Discussion
### Effect of Different Microwave Drying Times on the Major Aruna-Active Compounds in Injube
The concentrations of the three ketones 4-hydroxy-2,5-dimethyl-3(2H)furanone, 4-cyclopentene-1,3-dione, and acetoin(2,3-butanedione) decreased, starting with sample B5. This may be because the intermediate ketones continue to react with amino acids to produce melanigiioids with the intensification of the Maillard reaction. However, the pattern of change in ketones requires further study and discussion. Two alcohols (1-octen-3-ol, 2-furanmethanol) and three acids (acetic acid, butanoic acid, hexanoic acid) were identified as aroma-active compounds of jujube, which were found to decrease after microwave treatments, except for 2-furanmethanol. 2-furanmethanol (burnt and bitter flavor) was the typical product of the Maillard reaction [25], and it was one of the typical flavor substances of dried jujube. With a critical contribution to the flavor of jujube slice samples, the typical C8 alcohol, 1-octen-3-ol, with a mushroom note (also known as \"mushroom alcohol\"), was detected. Additionally, its concentration decreased with increasing drying time, which may contribute to reducing the floral attribute in jujube slice samples. Acids were identified as the key aroma contributor in juibbe samples [36; 39]. According to the sensory results, the sour taste was one of the important  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Compound Name**} & \multirow{2}{*}{**CAS**} & \multicolumn{8}{c}{**OAV A**} & \multicolumn{8}{c}{**DF B**} & \multirow{2}{*}{**Odor**} \\ \cline{3-14}  & & & **B1** & & & **B2** & & & **B3** & & & **B4** & & **B5** & & **B6** & & **B7** \\ \hline aldehydes & & & & & & & & & & & & & & & & & & & & \\
2 & Hexanal & 66-25-1 & 4.3 & 3.7 & 2.9 & 2.6 & 2.2 & \(<\)1 & \(<\)1 & 1 & 0\({}^{\text{\text{\  
flavor characteristics of the original jujube slices. Notably, the sour characteristic gradually decreased with the increase in drying time. This change in sensory characteristics was consistent with the trend of acid content. Furthermore, the acid decrease was probably due to the formation of esters and volatilization loss during the drying process [25].
# Hyperparameter Optimization of Ensemble Models for Spam Email Detection
###### Abstract  
Unsolicited emails, popularly referred to as spam, have remained one of the biggest threats to cybersecurity globally. More than half of the emails sent in 2021 were spam, resulting in huge financial losses. The tenacity and perpetual presence of the adversary, the spammer, has necessitated the need for improved efforts at filtering spam. This study, therefore, developed baseline models of random forest and extreme gradient boost (XGBoost) ensemble algorithms for the detection and classification of spam emails using the Enroni dataset. The developed ensemble models were then optimized using the grid-search cross-validation technique to search the hyperparameter space for optimal hyperparameter values. The performance of the baseline (un-tuned) and the tuned models of both algorithms were evaluated and compared. The impact of hyperparameter tuning on both models was also examined. The findings of the experimental study revealed that the hyperparameter tuning improved the performance of both models when compared with the baseline models. The tuned RF and XGBoost models achieved an accuracy of 97.78% and 98.09%, a sensitivity of 98.44% and 98.84%, and an F1 score of 97.85% and 98.16%, respectively. The XGBoost model outperformed the random forest model. The developed XGBoost model is effective and efficient for spam email detection.  
spam detection; spam emails; random forest; XGBoost; ensemble; hyperparameter Article
## 4 Discussion
### Verifying Carbon Storage Estimation  
The value of _Quercus aquifolioides_ in forest resource inventory in 2016 was used as ground observed data; the _Quercus aquifolioides_ volume in Shangri-La was found to be 4.04% after calculating and summarizing the data of previous studies [42]; the 3-year growth rate was multiplied with the 2016 observed volume accumulation to obtain the total volume and then the volume-biomass conversion model was used to deduce the amount of total biomass of _Quercus aquifolioides_ in 2019. Finally, the measured carbon storage of _Quercus aquifolioides_ in 2019 was found to be 5,879,608.28 t after multiplying the biomass with 0.48. This result was in the same order compared with the value combined with Landsat 9 optical remote sensing and GEDI LiDAR estimation (total carbon storage was about 5,374,137.62 t, with an average of 42.44 t/hm\({}^{2}\)).  
Previous research also estimated carbon storage in Shangri-La based on remote sensing technology. [PERSON] et al. [43] used a standing tree biomass model and carbon measurement parameters. Based on the forest resources inventory and planning data in Shangri-La, the average diameter at breast height, average tree height, and tree number of dominant tree species in each small class of forest were used to estimate the carbon storage of tree forest; the value of _Quercus aquifolioides_ was 6,453,486 t, and the average value was 51.54 t/hm\({}^{2}\). [PERSON] et al. [44] used TM remote sensing as a data resource, combined with climate, temperature, humidity, and topography data. The carbon storage and density distribution of forest plant communities (soil layer, tree layer, shrub layer, litter layer, and herb layer) were estimated via the multivariate regression model, the BP neural network model, and the remote sensing information model. Finally, the carbon storage of _Quercus aquifolioides_ was estimated to be 8,179,695.73 t. There were certain errors in the estimated carbon storage of _Quercus aquifolioides_ calculated via the traditional models and optical remote sensing by [PERSON] and [PERSON] et al. compared with the ground truth value. All their results overestimated _Quercus aquifolioides_ carbon storage. However, our estimated results that integrated GEDI L2B Spaceborne LiDAR and Landsat 9 optimal remote sensing were closer to the measured value, which demonstrated that the combination of multiple data sources not only compensated for the saturation problem of optical remote sensing in high-value and low-value areas but also fully utilized the forest vertical structure information obtained via LiDAR.
# Polycaprolactone Blended Cosmetic Microneedle for Transdermal Hispidin Delivery System
## 3 Results and Discussion  
### Microstructure and Mechanical Strength of MNs  
We fabricated MNs by blending PLA/PCL via the direct casting and spin-coating method (Figure 1a). The MN structure was casted as a pyramid-shaped tetrahedral structure for the transdermal delivery system. Each needle had a length of 200 \(\upmu\)m and width of 200 \(\upmu\)m (Figure 1b). Previously, pyramid shapes with widths and heights in the range of 250 \(\upmu\)m were found to exhibit effective permeability for drug delivery systems. Particularly for transdermal drug delivery (TDD), among various types of MNs, pyramid-shaped MNs can transfer drugs efficiently into the skin [23]. Furthermore, for effective drug delivery into the epidermis, we measured the tensile properties of various PLA/PCL blended structures (Figure 1c). The tensile strength of each blended PLA/PCL sample had values of 43.76 MPa at 100:0 wt.%, 35.42 MPa at 90:10 wt.%, 51.26 MPa at 80:20 wt.%, and 35.29 MPa at 70:30 wt.%. The highest ultimate strength was observed when PLA:PCL was blended at 80:20 wt.% with a Young's modulus of 13.7 GPa. Therefore, effective physical drug delivery properties were expected to be found for the 80:20 wt.% blend of PLA/PCL. On this MN structure, a coated type of transdermal hispidin delivery system was fabricated. For comparison, two different coated systems were developed. For the first system, dissolved hispidin solution was directly coated onto the MNs surface (Figure 1d). For another system, hispidin dissolved in PLA/PCL blended solution was coated onto the MNs surface (Figure 1e). Both samples showed sharp tetragonal MN structures even after a coating of 0.156 \(\upmu\)m\(\sim\)0.372 \(\upmu\)m on each delivery layer.