esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 6 Scheme Analysis
### Security Analysis
#### 6.1.3 Patient Privacy and Security
In summary, with patient key security assured, it can be considered that patients are anonymous in the blockchain network of this scheme, and their personal privacy will not be compromised due to data mining or other attack methods. However, this study assumes that patient keys remain undisclosed in discussing security issues. The improper storage of patient keys could pose significant privacy risks. Therefore, in future work, further research should focus on enhancing key management and addressing data update issues resulting from key changes.
# Physico-Chemical Characteristics of _Rosa canina_ L. Seeds and Determining Their Potential Use
###### Abstract  
_Rosa canina_ is well-known plant, and its fruits have been used for centuries. The seeds have been less utilized and remain untested. The objective of this study was to examine the physico-chemical properties of rosehip seeds and to identify potential applications based on their properties. The physico-chemical properties of the seeds were determined, including swelling index (2.51), color in the CIE L * a * b * space (b * = 26.2), as well as carbohydrate (79.2 g/100 g), soluble dietary fiber (71.6 g/100 g), protein (7.67 g/100 g), fat (6.23 g/100 g), and ash (1.96 g/100 g) content. Among the amino acids, glutamic acid dominated (1.58 mg/100 g), while among the fatty acids, linoleic acid (53.19%), linoleic acid (20.24%), and oleic acid (17.63%) were the most dominant. In addition, rosehip seeds contained large amounts of calcium (3851 mg/kg), potassium (2732 mg/kg), and phosphorus (991 mg/kg), as well as vitamin C (1783 \(\upmu\)g/g). It was found that the content of other valuable phytochemicals, such as phenolic compounds (2633 \(\upmu\)g/g) and carotenoids (3.13 \(\upmu\)g/g) influenced the antioxidant properties of the tested raw material. This activity examined by the ferric-reducing antioxidant power (FRAP) and DPPH radical scavenging activity methods was assessed at a level of 191 and 229 \(\upmu\)mol TE/g, respectively. The results indicate the potential use of rosehip seed, a by-product of the food processing industry, as a new high-value ingredient for health products, such as nutraceuticalals, pharmaceuticals, and cosmeceuticals.  
Rosa canina; seeds; physico-chemical characteristics; antioxidant activity; waste by-products +
Footnote †: journal: Applied sciences  
[PERSON]  
1 Department of Dietetics, The University College of Applied Sciences in Chelm, Pocztowa 54, 22-100 Chelm, Poland; [EMAIL_ADDRESS] Garden of Cosmetics Plants and Raw Materials, Research and Science Innovation Centre, Tarasowa 4/96, 20-819 Lublin, Poland; [EMAIL_ADDRESS]
## 4 Results and Discussion
### Removal of Chemical Oxygen Demand  
A reduction in chemical oxygen demand (COD) was observed during the anaerobic digestion process. On day 3, the highest COD level was recorded for the CD:FW (2:1) ratio, measuring 8001 \(\pm\) 776.40 mg/L, while the blank control exhibited the lowest COD at 4181 \(\pm\) 1132.78 mg/L. Among the co-digestion ratios, WH:FW (1:2) showed the highest COD at 7470 \(\pm\) 212.13 mg/L, followed closely by WH:FW (2:1) at 7429 \(\pm\) 154.14 mg/L and WH:FW (1:1) at 6433.5 \(\pm\) 48.79 mg/L (Figure 4).  
The highest COD reduction percentages were achieved in the blank sample, registering a 94.48% reduction, suggesting inherent processes or factors contributing to COD reduction in the absence of specific treatment components. Following closely were the mono-digestions involving CD with WH, notably the CD:WH (1:1) and CD:WH (2:1) combinations, which demonstrated robust COD reduction percentages of 91.68% and 90.99%, respectively. Moreover, combinations of CD with FW in mono-digestion also exhibited notable efficacy, ranging from 86.45% to 90.98%. However, slightly lower COD reduction percentages were observed in the combinations involving equal proportions of CD and FW (1:1). Figure 4 shows the removal of COD during the AD experiments.  
An analysis of the variation in the reduction in COD during overall (both co-digestion and mono-digestion) digestion is presented in Supplementary Table S4. Analysis showed reduction in COD from various substrates showed a significant result (\(p<0.001\)), which implied a graduation reduction in COD while graduation increase in biogas production.  
Figure 3: Modified Gompertz model (**a**): Co-digestion of WH and FW; (**b**): Mono-digestion of FW with CD; (**c**): Mono-digestion of WH with CD.  
In a study conducted by [PERSON] [83], anaerobic co-digestion experiments were carried out utilizing fish by-products over 30 days at a temperature of 35 \({}^{\circ}\)C. The investigation assesses the reduction in COD in samples with fishery by-products broth. The results indicated COD reductions of 45.32%, 51.26%, and 46.66% for the respective mixtures of 70% sludge and 30% by-products broth, 50% sludge and 50% by-products broth, and 30% sludge and 70% by-products broth. The highest reduction was observed when the experiments were performed using 50% sludge and 50% by-product broth.
# Surface Defect Detection Model for Aero-Engine Components Based on Improved YOLOv5
## 4 Conclusions  
Aiming at the problems of low efficiency and poor accuracy in surface defect detection in aero-engine components, we proposed the YOLOv5s-KEB model based on the YOLOv5 algorithm for surface defect detection in aero-engine components. First, the model uses a k-means clustering algorithm to optimize the size of the preset anchors and increase the matching degree between anchors and real samples. After that, the ECA-Net attention mechanism is added to the end of the backbone network to enhance the ability of feature expression. Finally, BiFPN modules are used instead of the PANet structure in the neck network to improve the feature fusion ability of the model. The experimental results show that, with our self-made dataset, the mAP for the YOLOv5s-KEB model reached 98.3% and the inference time was 2.8 ms. Compared to the original YOLOv5s model, the mAP was increased by 1.0% and the inference time reduced by 10.3%, but the detection speed slightly dropped. Compared with the Faster R-CNN, YOLOv3, YOLOv4 and YOLOv4-tiny algorithms, our model has obvious advantages in terms of detection accuracy and model size. In further work, the dataset will be expanded to add micro-defect samples and develop the ability to identify micro-defects. We will also consider how to improve the detection speed of the model and apply it to mobile devices.  
Conceptualization, C.W.; methodology, H.J.; software, X.L.; validation, X.L. and Z.L.; formal analysis, X.L.; investigation, H.J. and Z.L.; resources, H.J.; data curation, X.L.; writing--original draft preparation, X.L.; writing--review and editing, C.W.; visualization, X.L. and Z.L.; supervision, C.W.; project administration, C.W. and H.J.; funding acquisition, C.W. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**AP**} & \multirow{2}{*}{**mAP\%**} & \multirow{2}{*}{**FPS**} & \multirow{2}{*}{**Weight**} \\ \cline{2-2} \cline{5-7}  & **Crack** & & & & & & \\ \hline Faster R-CNN & 75.4\% & 78.1\% & 58.7\% & 83.1\% & 73.8\% & 14.29 & 109 MB \\ YOLOv3 & 82.1\% & 89.8\% & 85.1\% & 76.1\% & 83.3\% & 22.63 & 235 MB \\ YOLOv4 & 90.0\% & 93.2\% & 86.6\% & 79.0\% & 87.2\% & 18.55 & 244 MB \\ YOLOv4-tiny & 64.3\% & 48.8\% & 45.5\% & 41.7\% & 50.1\% & 85.08 & 22.4 MB \\ YOLOv5s-KEB & 96.9\% & 99.5\% & 99.2\% & 97.6\% & 98.3\% & 46.50 & 16.5 MB \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of detection performance for different algorithms.  
**Funding:** This research was funded by National Natural Science Foundation of China, grant number 92060202.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Spatial Fuzzy Co-Location Pattern Mining Method Based on Interval Type-2 Fuzzy Sets
## 1 Introduction
However, these functions have a common feature: there are many parameters that determine the confidence and the width of the FOU, and the coupling between these parameters is very high, which makes it very difficult to select the appropriate parameters, for example, a type-2 fuzzy membership function based on an extended \(\pi\) function [38]. However, this is not the case for type-2 fuzzy membership functions based on elliptic curves [39; 40] as there is only one parameter. Therefore, we generate an interval type-2 fuzzy membership function based on an elliptic curve. We have noticed that spatial data are often accompanied by attribute information expressed by specific values, such as the content of heavy metals in a certain geographical location [4]. People are usually not sensitive to a specific value of the attribute information. However, when the attribute value changes fundamentally--that is, when it changes from a certain range to a new range--people will notice this and attach importance to the result. In this paper, in order to solve the large deviation caused by the method of mining fuzzy co-location patterns based on type-1 fuzzy sets [4], a method of mining fuzzy co-location patterns based on interval type-2 fuzzy sets and cliques from spatial datasets with attribute information is proposed. The purpose of this paper is first to construct a special kind of interval type-2 fuzzy membership function--a granular type-2 fuzzy membership function based on interval type-2 fuzzy sets, which is used to improve the original type-1 fuzzy membership function. This reduces the deviation caused by the subjective evaluation of experts as far as possible, that is, to reduce the deviation caused by the uncertainty of a type-1 fuzzy membership function. Furthermore, we would like to find the influence of the uncertainty of fuzzy membership functions on the prevalence degree of fuzzy co-location patterns (FCPs) and measure the prevalence degree.
## 5 Conclusions  
Accurate prediction of bus arrival times is an important issue in the field of smart transportation, and it is also a challenging problem. Most of the existing solutions are based on static information, such as the number of stations, the spacing of adjacent stations, and the number of intersections on each road segment. These static data collection costs are low (which can be obtained directly from Google Maps if the information is correct). These methods have guiding significance for the planning of bus routes, but their prediction accuracy has not yet met the more sensitive traveling time requirements such as estimated waiting time and real-time bus scheduling.  
In this paper, we improved the prediction accuracy by inputting dynamic factors. In addition to the methods in the experiment, this method of improving the prediction accuracy can theoretically be applied to other methods. The experimental results show that the ability to learn long-term dependencies is the main reason for RNN to gain an advantage in the prediction task. From the perspective of extracting the dominant factors and improving the learning ability for long-term dependency, the attention mechanism is crucial for prediction.  
In the future, we aim to develop this approach further by incorporating more heterogeneous dynamic factors, such as weather, road congestion, traffic signal status, etc. Moreover, considering the interactions between multiple bus lines, the influence of the connection relationship between the stations in the public transportation network on bus arrival times is an interesting research direction.  
Conceptualization, X.Z., P.D.; Data curation, X.Z., P.S.; Formal analysis, X.Z.; Investigation, P.D.; Methodology, X.Z.; Project administration, J.X.; Resources, P.D., Jianping; Software, X.Z.; Supervision, J.X.; Validation, X.Z.; Visualization, X.Z.; Writing--original draft, X.Z.; Writing--review & editing, P.D., J.X. and P.S.  
This research received no external funding.  
This work was supported by China Computer Program for Education and Scientific Research (NGII20161001), CERENT Innovation Project (NGII20170101), Shandong Province Science and Technology Projects (2017 CXGC0202-2). And thanks [PERSON] provides the raw data for this paper.  
The authors declare no conflict of interest.
# Nickel and Cobalt Price Volatility Forecasting Using a Self-Attention-Based Transformer Model
## 4 Empirical Results  
The results are compiled based on two parts. In the first, the results of the accuracy measures of TFTs are considered with respect to other deep learning models such as BiLSTM, GRU, and the No change forecasting model. In the second, the application of the Temporal Fusion Transformer model (TFT) is considered based upon 6 months and 12 months of historical data.  
The total dataset was divided into training and test sets, with approximate proportions of 97% and 3%, respectively. The TFT model was estimated with the last 365 observations of the training set, which corresponds to a one-year history. A No change forecast model was also developed, with an assumption that the volatilities would remain constant for future periods. Additionally, the daily return values of WTI crude oil prices were added as an external variable. The given model accuracy was compared with other comparative deep learning models, namely, BiLSTM and GRU, given that the TFT model was estimated with the same number of observations as the training set, which corresponds to about a preceding year of history.  
We could observe from Table 1 that the volatilities of Cobalt and Nickel future prices presented similar mean values and standard deviations of around 1.3 and 0.4, respectively. The minimum and maximum volatilities reached by Cobalt returns were 0.54 and 2.41, respectively, while for Nickel returns the minimum and maximum volatilities were 0.75 and 3.10, respectively. On the other hand, WTI crude oil prices averaged around USD 65 per barrel during the study period, reaching a minimum price of around USD 19 per barrel and a maximum price of approximately USD 123 per barrel after adjusting for the outliers. All the variables presented a positive asymmetry, and distributions were more flattened than for a normal distribution (platykurtic) since the Kurtosis coefficient was less than 3 for all the variables; this was confirmed by the Jarque Bera test, with a \(p\)-value very close to zero with respect to three variables, indicating the rejection of the null hypothesis that establishes the distributional normality of the variables. This also suggested that the  
Figure 1: Shows the daily returns of (**a**) Nickel, (**b**) Cobalt and (**c**) WTI Crude Oil for the Period under study.  
right branch of the curve was more elongated than the left one, indicating the presence of very high volatility values and WTI prices that could possibly act as outliers.
# Evaluation of 5G Positioning Performance Based on UTDoA, AoA and Base-Station Selective Exclusion
###### Abstract  
Accurate and reliable positioning solution is an important requirement for many applications, for instance, emergency services and vehicular-related use cases. Positioning using cellular signals has emerged as a promising solution in Global Navigation Satellite System (GNSS) challenging environments, such as deep urban canyons. However, harsh working conditions of urban scenarios, such as with dense multipath and Non-Line of sight (NLoS), remain as one of the key factors causing the detriment of the positioning estimation accuracy. This paper demonstrates that the use of joint Uplink Time Difference of Arrival (UTDoA) and Angle of Arrival (AoA) gives a significant improvement in the position accuracy thanks to the use of antenna arrays. The new advances of this technology enable more accurate user locations by exploiting angular domains of propagation channel in combination with time measurements. Moreover, it is shown that a better localization is achieved by combining the joined UTDoA and AoA with a base-station selective exclusion method that is able to detect and eliminate measurements affected by NLoS. The proposed approach has been tested through simulations based on a deep urban deployment map, which comes with an experimental data file of the user's position. A sounding reference signal of 5G new radio operating in the centimeter-wave band is used. The obtained results add value to the use of advance antennas in 5G positioning. In addition, they contribute towards the fulfillment of high-accuracy positioning requirements in challenging environments when using cellular networks.  
antenna arrays; positioning; 5G cellular networks; new radio; NLoS; integrity; cmWave; hybridization; time of arrival; angle of arrival +
Footnote †: journal: Sensors  
0000-0001-5893-4000-0000][PERSON]. [PERSON]  
1 IEE-CERES, Universitat Autonoma de Barcelona (UAB), 08193 Bellaterra, Barcelona, Spain; [EMAIL_ADDRESS] {J.A.L.-S.}; [EMAIL_ADDRESS] {G.S.-G.}
# Dam Deformation Monitoring Model Based on Deep Learning and Split Conformal Quantile Prediction
## 3 Case Study  
### Overview of the Arch Dam Project  
The proposed interval prediction model is validated with historical monitoring data from an arch dam located in south-central China. The construction of the arch dam began at the end of 1958, and the first phase was completed by the end of 1992. The major engineered task of this arch dam is flood control, but it also delivers water supply, power generation, and other associated functions. The dam has a height of 157 m, a base width of 35 m, a crest width of 7 m, a central crest arc length of 438 m, and an installed capacity of 500,000 kilowatts. Figure 5 shows the downstream area of the dam and the surrounding environment ([[https://www.dongjianghu.com/](https://www.dongjianghu.com/)]([https://www.dongjianghu.com/](https://www.dongjianghu.com/)), accessed on 11 November 2024).  
The horizontal deformation of the dam is measured using the pendulum method. In this study, the horizontal displacement (i.e., L5H291R) measured at an elevation of 291 m by pendulum L5, located on the crown cantilever of Dam Block 15, is selected as the dataset for modeling [37]. The position of the pendulum is shown in Figure 6. The selected dataset covers a total of 341 deformation observations of the arch dam from June 2000 to May 2014 (see Figure 7). These data contain 274 training samples and 67 test samples. The monitoring curves of the dam's environmental variables, including upstream reservoir water level and temperature, are shown in Figures 8 and
# Intelligent Interference Management in UAV-Based HetNets +
###### Abstract  
Unmanned aerial vehicles (UAVs) can play a key role in meeting certain demands of cellular networks. UAVs can be used not only as user equipment (UE) in cellular networks but also as mobile base stations (BSs) wherein they can either augment conventional BSs by adapting their position to serve the changing traffic and connectivity demands or temporarily replace BSs that are damaged due to natural disasters. The flexibility of UAVs allows them to provide coverage to UEs in hot-spots, at cell-edges, in coverage holes, or regions with scarce cellular infrastructure. In this work, we study how UAV locations and other cellular parameters may be optimized in such scenarios to maximize the spectral efficiency (SE) of the network. We compare the performance of machine learning (ML) techniques with conventional optimization approaches. We found that, on an average, a double deep Q learning approach can achieve 93.46% of the optimal median SE and 95.83% of the optimal mean SE. A simple greedy approach, which tunes the parameters of each BS and UAV independently, performed very well in all the cases that we tested. These computationally efficient approaches can be utilized to enhance the network performance in existing cellular networks.  
artificial intelligence; double deep Q learning; FeICIC; HetNets; LTE-advanced; UAV +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Optimisation of Shrinkage and Strength on Thick Plate Part Using Recycled LDPE Materials
## 3 Results and Discussion  
### Results of Simulation Studies  
Through the FFD, the contribution of each input parameter on \(x\) and \(y\) directions of shrinkage are shown in Table 5. It can be observed that the most significant processing parameter affecting the shrinkage in both directions was the packing pressure. This is due to the fact that in order to compensate the part volume reduction due to cooling, the right packing pressure allows enough molten plastic to be injected into the remaining mould cavity, hence reducing the shrinkage on the moulded part. Meanwhile, the least significant processing parameter on shrinkage in the \(x\) and \(y\) directions as shown in the table was the cooling time.  
Through the ANOVA in CCD, the mathematical model was then developed to examine the relationship between processing parameter and responses. The mathematical model was formed in second-order polynomials equations, as shown in Equations (5) and (6) and was then used to conduct the optimisation of PSO. PSO generate processing parameters  
Figure 5: Point measurement in the \(x\) direction.  
Figure 6: Point measurement in the \(y\)-direction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**\% Contribution of Shrinkage**} \\
**Parameters** & \(x\) **Direction** & \(y\) **Direction** \\ \hline A. Mould Temperature & 14.60 & 18.71 \\ B. Melt Temperature & 14.60 & 16.65 \\ C. Packing pressure & 47.31 & 36.47 \\ D. Cooling Time & 0.58 & 1.50 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Contribution factors in the \(x\) and \(y\) directions affecting the shrinkage.  
values and the polynomials equations formed in CCD were used to calculate the shrinkage values. The results from RSM and PSO methods are shown in Table 6. As illustrated in the table, the part moulded using the recommended setting of processing parameters, RSM and PSO showed better shrinkage percentages. The shrinkage in the \(x\) direction of RSM and PSO improved 8.71% as compared to the part moulded using the recommended setting. Meanwhile, shrinkage in the \(y\) direction for RSM and PSO improved 8.64% and 9.73% respectively as compared to the part moulded using the recommended setting.  
\[\begin{split} x=5.78555-0.053559\text{A}-0.015490\text{B}-0.12845 \text{C}-0.03688\text{D}+0.00025\text{AB}+0.0003931\text{AC}+0.00035\text{AD}\\ +0.0003931\text{BC}+0.001104\text{CD}\end{split} \tag{5}\]  
\[\begin{split} y=-3.6694-0.041347\text{A}+0.069879\text{B}+0.027560 \text{C}+0.015196\text{D}+0.000213\text{AB}+0.000369\text{AD}+0.000295\text{BC}\\ -0.00021\text{BD}-0.00023\text{B}^{2}-0.00298\text{C}^{2}\end{split} \tag{6}\]