esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Wrinkles on the Dorsal Skin of Nude Mice  
BALB/c nude mice were selected for this study because of their unique characteristics that make them particularly suitable for investigating UVB-induced skin aging. These mice are athymic, resulting in a lack of functional T lymphocytes and an impaired immune response [26]. This immunodeficiency minimizes variability in immune-mediated responses, allowing for a more accurate assessment of the direct effects of UVB exposure on skin aging. Furthermore, BALB/c nude mice possess skin properties that closely resemble those of human skin, including similar epidermal layers and keratinization processes. This similarity enhances the translational relevance of the findings to human conditions. Studies have demonstrated that these mice develop UVB-induced skin damage and aging features that parallel those observed in humans, including alterations in skin elasticity and collagen degradation [25]. Additionally, the consistent and reproducible response of BALB/c nude mice to UVB exposure makes them an effective model for evaluating the impact of potential therapeutic interventions [27]. Thus, the use of BALB/c nude mice provides a valuable and relevant model for studying UVB-induced skin aging and assessing the efficacy of anti-aging treatments.  
[PERSON] (2013) demonstrated that skin aging is characterized by coarse wrinkles, abnormal keratinocyte proliferation, dryness, laxity, and hyperpigmentation. In particular, the degree of wrinkle formation can be used as an indicator of skin damage [28; 29]. Figure 1 shows the appearance of wrinkles on the back of nude mice in each group. These wrinkles increased in number and became drier after 13 weeks of UVB irradiation. At week 13 of administering high doses of LPTWK10 or the spray-dried LPTWK10 supernatant, the dorsal skin of the nude mice was devoid of wrinkles and smoother than that of mice in the UVB-induced group. These results show that high doses of LPTWK10 or the spray-dried LPTWK10 supernatant effectively minimize the formation of wrinkles on the skin.  
Figure 1: Effects of the administration of LPTWK10 or LPTWK10 spray-dried supernatant powders on dorsal wrinkles in UVB-induced nude mice. (**A**) NC (**B**) UVB (**C**) PC (**D**) LPLD (**E**) LPLD (**F**) LPLD (**G**) SPLD (**H**) SPMD (**I**) SPHD. Data are presented as mean \(\pm\) SD (\(n\) = 7 or 8). UVB: ultraviolet B; NC: normal control; PC: positive control; LPLD, LPLD, and LPLD: administration of LPTWK10 at 2 \(\times\) 10\({}^{8}\) CFU/kg bw/day, 2 \(\times\) 10\({}^{9}\) CFU/kg bw/day and 2 \(\times\) 10\({}^{10}\) CFU/kg bw/day, respectively; SPLD, SPMD and SPHD: administration of TWK10 spray-dried supernatant powders at 0.30 g/kg bw/day, 0.60 g/kg bw/day, and 1.20 g/kg bw/day, respectively. Red arrow: wrinkles.
## 4 Hybrid Models
### Healthcare Data  
In the field of medical care and specifically regarding the prediction of COVID-19 cases, the work of [PERSON] et al. [58] moves on three levels: Modeling the overall trend in the number of cases over time, short-term forecasting on the order of ten days in countries with extremely high population density such as India, and determining which algorithm presents the best metrics performance in accurately modeling the linear and non-linear characteristics of the case count time series. Various individual prediction models based on the Prophet, Holt-Winters, LSTM, and ARIMA algorithms were used, as well as the ARIMA-NARNN (Nonlinear Autoregressive Neural Network) hybrid model. The simple ARIMA algorithm performed better than other individual models; however, the hybrid combination of ARIMA and NARNN had the best overall performance, with RMSE values almost 35.3% better than ARIMA.
# Detection of Quasi-Static Trapped Human Being Using Mono-Static UWB Life-Detection Radar
## 1 Introduction
In this article, we aim to propose an effective detection strategy dealing with general and practical cases when there exist quasi-static trapped human beings. A novel Golay complementary coded UWB radar prototype system was designed to verify the proposed detection strategy. In the strategy, two different operating modes, including stationary operating mode and scanning operating mode, are adopted. The stationary operating mode is suitable for detecting the quasi-static trapped human beings with non-periodic but non-weak respiration, using one radar to achieve single observation points. The scanning operating mode is for the quasi-periodic but weak respiration, using several radars to achieve multiple observation points. In the stationary operating mode, a novel detection method based on the cross-correlation in slow time domain is proposed, which is suitable for the non-periodic but non-weak micro-motion life detection scene. The cross-correlation operations on the slow-time observation data of a single radar are performed in this proposed method, which can enhance and extract components with similar echo characteristics in the measured area, achieving the accurate detection of the non-periodic but non-weak micro-motion targets. In the scanning operating mode, the novel deployed UWB radar system for rescuing trapped persons can take advantage of the distributed radar configuration, and the SNR of vital sign is improved compared to that of the traditional case, achieving the accurate detection of the quasi-periodic but weak micro-motion targets. \begin{table}
\begin{tabular}{c c c c} \hline
**Radar Name** & **Developer** & **Max Distance (m)** & **Characteristics** \\ \hline Xaver & Camero (Israel) & 8 & pulse radar, portable \\ LifeLocator TRx & GSSI * (USA) & 12 & pulse radar, earlier used \\ CE400 & NovaSky (China) & 20 & FMCW radar, MIMO, bulky \\ Jia [24] & UESTC * (China) & 5 & SFCW * radar, dual-station \\ “RF-Capture” [22] & MIT * (USA) & 3 & FMCW radar, MIMO, 5.46\(\sim\)7.24 GHz \\ Xia [16] & IECAS* (China) & 12.5 & Pseudo-random radar, portable \\ \hline \end{tabular}  
* * MSSI: Geophysical Survey System Inc., UESTC: University of Electronic Science and Technology of China, MIT: Massachusetts Institute of Technology, IECAS: Institute of Electrics, Chinese Academy of Sciences, SFCW: Stepped-Frequency Continuous Wave.  
\end{table} 
Table 1: The maximum detection range of typical radars around the world. In summary, this article presents a flexible detection strategy including stationary and scanning operating mode, to detect two typical vital signs effectively, which are quite common in the actual rescue environments. Based on the developed UWB radar system with wireless networking, the authors make an interesting attempt in the actual rescue scene and introduce two typical vital signs in different scenarios, indicating that the UWB radar system has better scene adaptability.
# An Innovative and Cost-Advantage CAD Solution for Cubitus Varus Surgical Planning in Children
## 3 Results and Discussion
Mainly, free open-source software (Invesalius, made by Centro de Tecnologia da Informacao Renato Archer (CTI), Brazil; MeshLab, made by Visual Computing Lab of CNR-ISTI, Italy; Meshmixer, made by Autodesk, Inc. 111 McInnis Parkway San Rafael, CA 94903. USA) were used. Table 4 shows a comparison between our procedure and the more common ones present in the literature in terms of procedure used, software, material used for printing, sterilization process and printer used. It should be noted that most of the procedures use commercial software (such as Mimics which is the most widely used software in the biomechanical field) or closed source software. Furthermore, the sterilization method used for prototypes is often not mentioned or specified. The estimated cost for a specialized operator is 20 EUR/h. The design phase of the CAD surgical guide requires 20-30 h. of work depending on the complexity of the case treated, while the printing of the guide obtained takes about 3 h. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Production** & **Informatic Procedure** & **Material** & **Sterilization** & **3D Printing Technology** \\ \hline THIS & & & & & \\ PROCEDURE & In-house & & & & \\  & & & & & \\ \hline [PERSON] et al. & 2011 [31] & In-house & Materialise Mimics, Imageware & Acrylate Resin & Sterilization is not mentioned & SLA \\ \hline [PERSON] et al. & 2013 [32] & Nakashima & Bone Simulator & & & \\
2013 [32] & Medical & (ORTHREE), Magics & Resin & & \\ \hline [PERSON] et al. & 2019 [36] & In-house & Materialise Mimics & Nylon & \
\begin{tabular}{c} The sterilization method \\ used is not specified \\ \end{tabular} & SLS \\ \hline [PERSON] et al. & 2020 [33] & In-house & Materialise Mimics 17.0 & PLA &
\begin{tabular}{c} Sterilization is \\ not mentioned \\ \end{tabular} & FDM \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Procedure comparison. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Printing Technique** & **Materials** & **Materials Cost (€/kg)** & **Printing Cost (€)** \\ \hline  & ABS & 32.50 & \\ \cline{2-3}  & PLA & 37.30 & \\ \cline{2-3} FDM & HTPLA & 68.00 & from 150 to 800 \\ \cline{2-3}  & PETG & 45.30 & \\ \hline SLS & Nylon (PA12) & 85.70 & from 7000 to 145,000 \\ \hline SLA & Medical-grade resin & 429.99 & from 250 to 1000 \\ \hline SLA/Polijet & Resin & 58.99 & 13,500 + 41,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cost comparison. Ultimately, with all above considerations made, the cost of the surgical guide made is around 485 EUR.
## Materials and Methods  
### Materials  
EDOT, PEDOT:PSS (3 wt.% in H\({}_{2}\)O, conductive grade), iron (III) tosylate hexahydrate (Fe(Tos)\({}_{3}\)), iron(III) trifluoromethane sulfonate (Fe(Trif)\({}_{3}\)), and chemicals used for synthesis: Pluronic(r) F-127, imidazole, dimethoxythiophene, p-toluene sulfonic acid, diethyl malonate, hexyl bromide, sodium hydride, lithium-aluminium hydride, and anhydrous toluene and tetrahydrofuran solvents were purchased from Sigma-Aldrich. From Alfa-Aesar, 2,2-dibutylpropane-1,3-diol was purchased. Ethanol, methanol, acetonitrile, n-metilpyrrolidone (NMP) and dichloromethane (DCM) used as solvents were of reagent grade. Cellulose acetate esters filter membranes (A) were obtained from HAWP Millipore(r) (0.45 \(\upmu\)m); filter paper of grade 40 (8 \(\upmu\)m) was provided from Whatman(r) (W); cellulose microfibers from kraft pulp were kindly provided from Stora Enso Innovation Centre, Sweden.
# Bistatic Landmine and IED Detection Combining Vehicle and Drone Mounted GPR Sensors
## 2 Methodology
### Inversion  
To compare the results for the different configurations, the simulated field is represented in the time-domain (B-scan) and post-processed with a SAR algorithm. SAR reflectivity at point \(\mathbf{r^{\prime}}\) of the investigation domain is given by Equation (3), where \(R_{r}^{t}\) is the path length between the \(t\)-th transmitter (located at \(\mathbf{r}_{t}\)), the point where the reflectivity is calculated \(\mathbf{r^{\prime}}\) and the \(r\)-th receiver \(\mathbf{r}_{r}^{t}\).  
\[\rho(\mathbf{r^{\prime}})=\sum_{n=1}^{N}\sum_{t=1}^{T}\sum_{r=1}^{R}E(f_{n},\mathbf{r} _{t},\mathbf{r}_{r}^{t})\exp(+j\beta_{0}R_{r}^{t}) \tag{3}\]  
Assuming free-space propagation, \(R_{r}^{t}\) would be equal to \(\|\mathbf{r}_{t}-\mathbf{r^{\prime}}\|+\|\mathbf{r}_{r}^{t}-\mathbf{r^{\prime}}\|\). This assumption provides good results when the incidence angle is close to normal incidence and the permittivity and conductivity of the soil are low. When these conditions are fulfilled, it is possible to detect the object in the SAR image at approximately \(\sqrt{\epsilon_{rs}}d\) depth (being \(d\) the true depth of the buried target). To obtain better results and to detect the object at its real depth, the constitutive parameters of the soil must be taken into account. The common approach consists of calculating the refraction point at the air-soil interface (for each point \(\mathbf{r^{\prime}}\) in the investigation domain, and each combination of transmitter and receiver positions). This requires solving a fourth-order equation derived for Snell's Law. However, instead of calculating the refraction point, \(R_{r}^{t}\) is modified so as to consider the permittivity of the soil [16; 17]. Thus, \(R_{r}^{t}\) is given by Equation (4), where \(n_{s}=\sqrt{\epsilon_{rs}-1}-\sqrt{\epsilon_{rs}}\) and the other parameters are defined according to the scheme shown in Figure 4.  
\[R_{r}^{t}=2d\sqrt{\epsilon_{rs}-1}+\frac{d_{t}(d_{t}-dn_{s}\cos(2\phi_{t}))}{ d_{t}+dn_{s}\sin^{2}(2\phi_{t})}+\frac{d_{r}(d_{r}-dn_{s}\cos(2\phi_{r}))}{d_{r}+dn_{ s}\sin^{2}(2\phi_{r})} \tag{4}\]
## 5 Experimental Frequency Mixing Performance
However, it is difficult to show the eye diagram for every mixed signal because of the large number, forty, of eye diagrams for co- and counter-directions at 4/\(s\) + \(f_{m}\) related to the fourth harmonic of the sampling signal. In this case, we select and display an eye diagram for the QPSK up converted signal at 4/\(s\) + \(f_{10}\) at BR = 100 Gbit/s for co- and counter-directions. Hence, we only show two constellations and eye diagrams, side by side as shown in Figure 17. For the co-direction system, the constellation and eye diagrams were better compared to the counter-direction. The eye opening corresponds to the opening in the middle, which measures the effect of noise on the signal. In (b) and (c), there is less noise, so the opening is narrower than in (e) and (f). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**EVM (\%)**} \\ \cline{2-7}
**4/\(s\) + \(f_{m}\)** & \multicolumn{3}{c}{**Co-Direction**} & \multicolumn{3}{c}{**Counter-Direction**} \\ \cline{2-7}  & **Real** & **Stimulated** & **Difference** & **Real** & **Stimulated** & **Difference** \\ \hline Channel One 4/\(f_{s}+f_{1}\) & 15 & 12.5 & 2.5 & 17 & 14.5 & 2.5 \\ Channel Two 4/\(f_{s}+f_{2}\) & 15.3 & 12.7 & 2.6 & 17.5 & 14.7 & 2.8 \\ Channel Three 4/\(f_{s}+f_{3}\) & 15.5 & 12.9 & 2.6 & 17.6 & 14.9 & 2.7 \\ Channel Four 4/\(f_{s}+f_{4}\) & 15.7 & 13.1 & 2.6 & 17.8 & 15.1 & 2.7 \\ Channel Five 4/\(f_{s}+f_{5}\) & 16 & 13.3 & 2.7 & 18 & 15.3 & 2.7 \\ Channel Six 4/\(f_{s}+f_{6}\) & 16.4 & 13.5 & 2.9 & 18.3 & 15.5 & 2.8 \\ Channel Seven 4/\(f_{s}+f_{7}\) & 16.9 & 13.8 & 3.1 & 19 & 15.8 & 3.2 \\ Channel Eight 4/\(f_{s}+f_{8}\) & 17.5 & 14.2 & 3.3 & 19.6 & 16.2 & 3.4 \\ Channel Nine 4/\(f_{s}+f_{9}\) & 18.2 & 14.8 & 3.4 & 20.3 & 16.8 & 3.5 \\ Channel Ten 4/\(f_{s}+f_{10}\) & 19 & 15.5 & 3.5 & 21 & 17.5 & 3.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Real and stimulated EVMs of the ten mixed signals in co- and counter-direction schemas at 4/\(s\) + \(f_{m}\) at the bit rate of 100 Gbit/s. Figure 17: Demodulation of the QPSK up-converted signal at 4/\(s\) + \(f_{10}\) at BR = 100 Gbit/s. For co-direction (**a**–**c**), constellation diagram (**a**), I-Eye diagram (**b**), and Q-Eye diagram (**c**). For counter-direction (**d**–**f**), constellation diagram (**d**), I-Eye diagram (**e**), and Q-Eye diagram (**f**).
## 3 System and Security Model
### System Model
#### 3.1.2 Proxy  
The proxy is equipped with a database that stores the encrypted data and the re-encryption keys for different data users generated by the different data owners. It performs the data re-encryption with corresponding re-encryption keys on the delegation path. The proxy is considered as honest but curious.
## 4 Experiments
### Correlation Analysis before and after Driving Clips  
In the previous part of the experiment, we randomly selected 20 driving clips for learning. For each clip, this experiment wants to explore whether there is some correlation between the clips before and after the clip. In other words, if so, we can use different driving clips continuously (rather than just relying on a single driving segment) to determine whether the driver is driving fatigued. The testing method of this experiment is to use the ANM system that has been trained for a long time in the previous experiment and conduct individual tests on each of the first one to four driving clips and the last one to four driving clips used in the training period.  
This experiment randomly selected 6 of the 20 learned systems from the first experiment. The correlation between a specific driving clip's first and last four driving clips is tested for each learned system. If their loss values are not much different from each other (compared with the loss value that the system has not learned; please refer to Table 1), it means that there is some correlation between adjacent driving clips. In other words, driver behavior changes step by step rather than in leaps and bounds. The results (Figure 15) show that driver behavior is highly similar when the gap between two driving clips is relatively tiny. As the gap gradually increases, so does the difference in driver behavior. The important thing is that there is some U-shaped relationship between them. This relationship represents two meanings. The first is that it again shows that the ANM system has a gradual transformation capability when the system's performance function will slowly change due to changes in the input data. The second is in driving fatigue detection; we can verify whether the driver is fatigued through a series of driving behaviors (clips).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cluster** & **Run** & \
\begin{tabular}{c} **Loss at** \\ **Cycle 1** \\ \end{tabular} &
\begin{tabular}{c} **Loss at** \\ **Termination** \\ \end{tabular} & **Improvement Rate** \\ \hline
6 & 36 & 2692.3 & 774.4 & 71.2\% \\ \hline
7 & 42 & 2844.9 & 722.6 & 74.6\% \\ \hline
8 & 48 & 4427.9 & 706.0 & 84.1\% \\ \hline
9 & 54 & 4836.1 & 580.6 & 88.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cluster** & **Run** & \
\begin{tabular}{c} **Loss at** \\ **Cycle 1** \\ \end{tabular} &
\begin{tabular}{c} **Loss at** \\ **Termination** \\ \end{tabular} & **Improvement Rate** \\ \hline
1 & 6 & 2768.5 & 709.2 & 74.4\% \\ \hline
2 & 12 & 2659.0 & 729.5 & 72.6\% \\ \hline
3 & 18 & 2570.7 & 743.3 & 71.1\% \\ \hline
4 & 24 & 3157.3 & 665.0 & 78.9\% \\ \hline
5 & 30 & 2628.6 & 688.1 & 73.8\% \\ \hline
6 & 36 & 2770.8 & 769.7 & 72.2\% \\ \hline
7 & 42 & 3034.5 & 756.8 & 75.1\% \\ \hline
8 & 48 & 3885.2 & 724.8 & 81.3\% \\ \hline
9 & 54 & 2726.7 & 657.3 & 75.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Participant C’s performance during the first cycle and termination time while driving on highways in a heavy traffic environment.
###### Abstract  
More accurate self-forecasting not only provides a better-integrated solution for electricity grids but also reduces the cost of operation of the entire power system. To predict solar photovoltaic (PV) power generation (SPVG) for a specific hour, this paper proposes the combination of a two-step neural network bi directional long short-term memory (BD-LSTM) model with an artificial neural network (ANN) model using exponential moving average (EMA) preprocessing. In this study, four types of historical input data are used: hourly PV generation for one week (168 h) ahead, hourly horizontal radiation, hourly ambient temperature, and hourly device (surface) temperature, downloaded from the Korea Open Data Portal. The first strategy is employed using the LSTM prediction model, which forecasts the SPVG of the desired time through the data from the previous week, which is preprocessed to smooth the dynamic SPVG using the EMA approach. The SPVG was predicted using the LSTM model according to the trend of the previous time-series data. However, slight errors still occur because the weather condition of the time is not reflected at the desired time. Therefore, we proposed a second strategy of an ANN model for more accurate estimation to compensate for this slight error using the four inputs predicted by the LSTM model. As a result, the LSTM prediction model with the ANN estimation model using EMA preprocessing exhibited higher accuracy in performance than other options for SPVG.  
solar PV generation; SolPV ELA deep neural network; BD-LSTM; ANN; EMA; MAPE; RMSE +
Footnote †: journal: Applied Soft. 2020, 10, 7339; doi:10.3390/app10207339
# Development and Pilot-Test of Blockchain-Based MyHealthData Platform
## 4 Discussion
### Blockchain and Data Integrity  
To realize personalized medicine, the most important topic of future medical care, an open ecosystem that can freely browse and distribute personal medical information should be established. However, since medical data handles sensitive personal information due to its nature, a considerable level of reliability and security is required. The use of the blockchain will greatly contribute to realizing medical innovation, as it is possible to deliver the medical information safely without falsification.  
The blockchain is a distributed data storage technology based on a chain-form link in which small datasets called blocks are created in a peer-to-peer way [35]. No one can arbitrarily modify it, but since anyone can view the result of the change, it can be used as a data-forgery-prevention technology [35]. Medibloc is a blockchain-based open platform for medical information that enables safe integration and management of medical data scattered in various institutions and information produced through various devices, including smartphones [17]. In this study, the MyData service using Medibloc was implemented to increase the data subject's health data management capabilities, streamline the insurance claim process, and facilitate participation in clinical trials. In addition, it can contribute to increasing the sharing and utilization of research data, strengthening the protection of health information, securing the integrity of medical information, and reinforcing accountability [36; 37; 38].
# Real-Time Vehicle Energy Management System Based on Optimized Distribution of Electrical Load Power
## 5 Experimental Results and Analysis
### Experiment with Constant Electrical Load Power  
Under the ECE15, the experiment with constant electrical load power is conducted. When load power is 0.7 kW, the initial co-state with the shooting method is \(\gamma^{\bullet}(t)=-58\). When \(\Delta SoC=0\), the solution is optimal when \(\gamma(t)\) satisfies all of the constrained conditions of PMP, including the constraint of Equation (7) on battery SoC.  
Figure 13 presents the experiment results, which indicates that the energy management strategy of optimal power distribution (OPD) limits the SoC fluctuation of the battery. The final state differs slightly from the initial state and meets Equation (7). When the vehicle speeds up, the intelligent alternator decreases the output voltage, and the battery becomes the primary power supply and results in an effective increase of vehicle dynamic performance; when the vehicle slows down, the intelligent alternator increases output power, and the battery is charged rapidly to recycle and store kinetic energy into the battery.  
Figure 12: ECE15 and NEDC: (**a**) ECE15; and (**b**) NEDC.  
Figure 13: Results under the OPD strategy for the ECE15: (**a**) battery SoC; and (**b**) output voltage of the alternator; (**c**) the power change comparison.  
To verify the OPD strategy, the same experiment is conducted in the vehicle without an energy management system, which is called the open-loop control (OLC) strategy. Figure 14 indicates that the battery is being charged often in the OLC strategy. The battery power being charged is approximately 0.3 kW, while the alternator power is around 1 kW, which is exactly the sum of the electrical load power and the battery charge power.  
A comparison of the output voltages in Figures 13 and 14 show that the OPD strategy causes the alternator voltage to fluctuate fiercely, while the OLC strategy maintains the voltage stability at around 14 V. The alternator power is adjusted by a voltage change to reduce fuel consumption. This condition also improves the utilization efficiency caused by repeatedly charging and discharging the battery. The comparison of fuel consumption of OPD and OLC is presented in Table 1. For 100 km, OPD consumes 0.15 L fuel less than OLC, and the fuel saving rate is approximately 1.9%.