esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
## Data and Methods
### Description of the Workflow  
The workflow consists of four modules: (1) database creation and image preparation; (2) glacier surface displacement calculation; (3) data calibration and geo-database creation; and (4) post-processing with data filtering and averaging to obtain glacier surface velocity maps. All codes are written entirely in open-source programming languages: Python 3.7 and Fortran. The processing chain is designed for massively parallel processing with SQL (Structured Query Language).
## 3 Results and Discussion
### Environmental Sustainability  
An additional goal of this research is to reduce the environmental impact of mining operations by lowering carbon emissions and the reliance on fossil fuels. Transitioning from conventional grid power (primarily generated from coal and gas) to solar energy significantly decreased the site's carbon footprint. Figure 10 provides an overview of the project's carbon emissions. The key results are as follows:  
* The project achieved a 67% reduction in carbon emissions, primarily due to the shift from fossil fuel-based electricity to solar power.
* Although diesel-powered, the backup generator, was used sparingly, further minimizing greenhouse gas emissions.
* By utilizing RE for the majority of its power needs, the mining site positioned itself as a leader in environmental responsibility within industry.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Optimization Method** & **Cost (USDS)** & **Computation Time (s)** & **Cost Reduction (\%)** & **Accuracy (\%)** \\ \hline LSTM-Classical & 2769 & 1.153 & 57.03 & 99.247 \\ \hline TF & 2733 & 1.164 & 56.21 & 99.015 \\ \hline RL & 2681 & 1.169 & 55.67 & 98.832 \\ \hline GA & 2643 & 1.172 & 55.28 & 98.541 \\ \hline TS & 2591 & 1.181 & 54.71 & 98.043 \\ \hline PSO & 2583 & 1.972 & 54.21 & 97.682 \\ \hline GWO & 2547 & 2.014 & 53.89 & 97.429 \\ \hline ACO & 2522 & 2.029 & 53.31 & 97.156 \\ \hline BA & 2499 & 2.048 & 52.95 & 95.025 \\ \hline \hline \end{tabular}  
* TF = Transformer, RL = Reinforcement learning, GA = Genetic Algorithm, TS = Tabu Search, PSO = Particle Swarm Optimization, GWO = Grey Wolf Optimizer, ACO = Ant colony optimization, BA = Bat Algorithm.  
\end{table}
Table 3: Optimal cost savings.  
Figure 9: Projected cost savings of the system (**a**) Without optimization (**b**) With optimization.
# Power System Voltage Correction Scheme Based on Adaptive Horizon Model Predictive Control
## 3 Adaptive Horizon Parameters
### Voltage Regulation Ability Evaluation
Similarly, when \(S_{k(i,j)}\cdot(V_{\rm r}-\hat{V}^{*}_{(i)}(t_{n}+t_{\rm pn}))<0\), the optimization tends to reduce the \(j\)-th control in order to reduce (when \(S_{k(i,j)}>0\) and \(V_{\rm r}<\hat{V}^{*}_{(i)}(t_{n}+t_{\rm pn})\)) or raise (when \(S_{k(i,j)}<0\) and \(V_{\rm r}>\hat{V}^{*}_{(i)}(t_{n}+t_{\rm pn})\)) the voltage magnitude of bus \(i\) towards its desired value. The maximum voltage variation value of bus \(i\): \[\Delta V_{k(i,j)\max}(t_{n}+t_{\rm pn})=\left\{ \begin{array}{ll}S_{k(i,j)}(t_ {n}+t_{\rm pn})(-\Delta u_{(j)\max})&if\;\;\;u_{k(j)}-\Delta u_{(j)\max}\geq u_ {(j)\min}\\ S_{k(i,j)}(t_{n}+t_{\rm pn})(u_{(j)\min}-u_{k(j)})&if\;\;\;u_{k(j)}-\Delta u_{( j)\max}\leq u_{(j)\min}\end{array} \right.\] (14) where \(u_{(j)\min}\) is the \(j\)-th element in vector \(u_{\min}\). The maximum voltage variation of the \(i\)-th evaluation target considering all candidate control actions applied at sampling point \(k\) can be expressed as: \[\Delta V_{k(i)\max}(t_{n}+t_{\rm pn})=\sum_{j=1}^{N_{j}}\Delta V_{k(i,j)\max} (t_{n}+t_{\rm pn})\] (15)
4. Considering multi control steps applied at the first \(m\) sampling points, The maximum voltage regulation ability at time instant \(t_{n}+t_{\rm pn}\) caused by the control sequence (\(\Delta u_{1}\),\(\Delta u_{2}\),..., \(\Delta u_{m}\)) is evaluated as follows: \[V_{\rm a(i)m}=\hat{V}^{*}_{(i)}(t_{n}+t_{\rm pn})+\sum_{k=1}^{m}\Delta V_{k(i )\max}(t_{n}+t_{\rm pn})\] (16) where \(V_{\rm a(i)m}\) is the evaluated voltage regulation ability of the \(i\)-th evaluation target when the length of control horizon is \(mt_{\rm s}\).
# Current Status of Optical Systems for Measuring Lycopene Content in Fruits: Review
## 4 Current Systems for Estimating Lycopene
### High-Performance Liquid Chromatography (HPLC)  
In the food sector, one of the main techniques that allows identification of the internal composition of food is high-performance liquid chromatography (HPLC). This lycopene measurement technique requires its extraction from the food. This is a process used to obtain an oil that contains the carotenoid. Conventionally, it is done with the use of solvents, heat, and agitation to separate the compounds from the sample. The HPLC technique employs a non-polar stationary phase and a mobile phase that acts as a carrier for the sample can be composed of various solvents. The components of the solution migrate according to the non-covalent interactions of the compounds with the column. These chemical interactions determine the separation of the contents in the sample [50]. Figure 5 shows the basic elements that integrate HPLC. These are the solvents of the mobile phase, the pumping system, the chromatograph, the injection system, the column for liquids, the detectors, waste, the control, and processing. The limit of detection (LOD) and limit of quantitation (LOQ) are important characteristics used to evaluate the efficiency of HPLC. [PERSON] et al. [51] reported that this method can determine concentrations of lycopene LOD = 0.6 \(\upmu\)g. For the case of LOQ = 0.11, this parameter is related to the lowest concentrations with acceptable repeatability and accuracy.  
Figure 6 shows the optical system used by HPLC to identify different study molecules. It has seven elements, which are a UV-lamp (1), lamp mirror (2), flow cell (3), fold mirror (4), programmable or fixed slit (5), grating (6), and diode array detector (DAD) (7). Its operation uses the UV-lamp light, which is focused by the lamp mirror, towards the inlet of the flow cell. Subsequently, the light is guided by the optofluidic waveguides to the opposite end of the flow cell. This is focused on the fold mirror using the holographic grating. Subsequently, the light is scattered over the diode array detector (DAD) in order to have simultaneous access to all the information related to the wavelengths [37].
# Smart Devices Security Enhancement via Power Supply Monitoring +
###### Abstract  
The continuous growth of the number of Internet of Things (IoT) devices and their inclusion to public and private infrastructures has introduced new applciations to the market and our day-to-day life. At the same time, these devices create a potential threat to personal and public security. This may be easily understood either due to the sensitivity of the collected data, or by our dependability to the devices' operation. Considering that most IoT devices are of low cost and are used for various tasks, such as monitoring people or controlling indoor environmental conditions, the security factor should be enhanced. This paper presents the exploitation of side-channel attack technique for protecting low-cost smart devices in an intuitive way. The work aims to extend the dataset provided to an Intrusion Detection Systems (IDS) in order to achieve a higher accuracy in anomaly detection. Thus, along with typical data provided to an IDS, such as network traffic, transmitted packets, CPU usage, etc., it is proposed to include information regarding the device's physical state and behaviour such as its power consumption, the supply current, the emitted heat, etc. Awareness of the typical operation of a smart device in terms of operation and functionality may prove valuable, since any deviation may warn of an operational or functional anomaly. In this paper, the deviation (either increase or decrease) of the supply current is exploited for this reason. This work aimed to affect the intrusion detection process of IoT and proposes for consideration new inputs of interest with a collateral interest of study. In parallel, malfunction of the device is also detected, extending this work's application to issues of reliability and maintainability. The results present 100% attack detection and this is the first time that a low-cost security solution suitable for every type of target devices is presented.  
a Article Smart Devices Security Enhancement via Power Supply Monitoring +
Footnote †: 2020, 12, 48; doi:10.3390/612030048
## 5 Conclusions  
In this paper, Landsat satellite and HJ-1 B satellite sequence images were fused using a temporal and spatial feature-based multi-source time series remote sensing image fusion method. The fused time series was then used to monitor the long-term growth trend of the forest land in Fuling district of Chongqing, China during the 1999-2013 period based on a combination of the Mann-Kendall trend detection and Theil Sen Slope analysis methods. The monitoring results reflect the spatial-temporal evolutionary characteristics of forest growth conditions. A majority of forest land has continued to grow during the study period. A small number of forest areas in the southeast of the study area showed a decreasing trend. Necessary attention and policy should be given to these decreasing areas by the relevant departments. The confusion matrix shows that the dynamic monitoring of the fused time series performs well with high overall, producer's, and user's accuracies.  
With the availability of more and more remote sensing image data with different resolutions from different sources in the future, remote sensing image analysis based on multi-source time series will become a long-term research topic and is beneficial to many application areas such as environmental monitoring, disaster risk assessment, agricultural prediction and more. Therefore, it is of great significance to study this research topic both for theoretical and practical purposes.  
[PERSON] conducted the overall analysis, led the writing of the manuscript and contributed to the implementation of the framework. [PERSON] provided technical support in remote sensing data analysis, developed the data analysis. [PERSON] led the algorithm development for the research. [PERSON] helped perform the analysis with constructive discussions.  
This research was funded by State Grid Scientific Project 2016 (No. GCB17201600036) 'Research on data processing theory and methods of the auxiliary lines selection based on satellite remote sensing image' and Project for Follow-up Work in Three Gorges (2017 HXNL-01).  
Thanks to the Yangtze River Water Resources Commission and Chongqing Fuling District Immigration Bureau for providing basic data on the study area. Thanks to [PERSON] for helping to improve the translation of this article. Thanks to all reviewers for their valuable advices.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Positive** & **Unchanged** & **Negative** & **Total** & **User’s** \\  & **Change** & & **Change** & **Accuracy (\%)** \\ \hline
**Positive change** & 614 & 32 & 23 & 669 & 91.78 \\
**Unchanged** & 24 & 116 & 41 & 181 & 64.09 \\
**Negative change** & 17 & 8 & 256 & 281 & 91.10 \\
**Total** & 655 & 156 & 320 & 1131 & - \\
**Producer’s** & 93.74 & 74.36 & 80.00 & Overall & 87.18 \\
**Accuracy (\%)** & & & & Accuracy (\%) & 87.18 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Trend detection accuracy.
## 3 Results and Discussion  
### Phase Behavior of AAE(6 EO4 PO)  
In all the model oils and all the temperature systems, we observed Winsor Type III, which appeared in a wide range of salt concentrations. Winsor Type III seemed mostly cloudy in the middle phase. Figure 1 shows some results of the salinity scans of the n-decane systems at 25 \({}^{\circ}\)C and 40 \({}^{\circ}\)C. At reference temperature (25 \(\pm\) 2 \({}^{\circ}\)C), Winsor Type III was observed at 1-18% wt/v of NaCl for the n-decane system. When the temperature increased to 40 \(\pm\) 2 \({}^{\circ}\)C, Winsor Type III was observed at 6-18% wt/v of NaCl. The different temperatures and NaCl concentrations affected the zeta potential of the micelles that resulted in the different salt concentrations forming Winsor Type III [57; 58; 59].  
The IFT of each sample was measured to identify the optimal salt concentration for each temperature and oil. It was found that the optimal salinity tended to decrease when the temperature increased, as shown in Figure 2. At high temperatures, the interactions between the head of the nonionic surfactant and the water molecules are lower because of weaker hydrogen bonds [1; 56]. This resulted in fewer interactions with the head of the surfactants. If this was not the case, the head-head interactions of the surfactants would be more repulsive forces. Therefore, the optimal salt concentration was reduced to obtain the optimal condition. The effect of the model oils is presented in Figure 2. The optimal salinities increased with increases in EACN, which is in line with the study by [PERSON] et al. (2016) [56]. Because higher EACNs exhibited stronger interactions between the oil molecules, therefore, stronger interactions of the surfactant heads were required to solubilize the oils [27; 60; 61]. The addition of salt reduced the repulsive force of the surfactants, which resulted in stronger head-head interactions of the surfactants. Thus, the six-molecule ethylene oxide in the AAE(6 EO4 PO) structure played an important role in achieving optimal salinity.  
Figure 1: Salinity scans of 1% wt/v AAE(6 EO4 PO) in the n-decane systems at temperature (**a**) 25 \({}^{\circ}\)C and (**b**) 40 \({}^{\circ}\)C.  
Figure 2: Optimal salt concentrations (S\({}^{\intercal}\)) of 1% wt/v AAE(6 EO4 PO).  
Regarding the minimum interfacial tensions (IFT*) in optimal conditions, the results could not reach the level of ultralow IFT (<0.01 mN/m) [17; 18]. However, the IFT* tended to decrease when the EACN value of the oil increased. From the research by [PERSON] et al. (1989) [62], it is known that each nonionic surfactant is suitable for a different EACN. The IFT* trend steadily declined, and then increased again when the hydrophobicity of the oil increased [62]. Therefore, the IFT* results of our study are likely within the first part of this rend.
# Cooperation Based Proactive Caching in Multi-Tier Cellular Networks
## 3 Problem Formulation  
Given this model, our prime goal is to develop a proficient caching approach to improve the cache hit ratio and the user satisfaction ratio based on the user content requests and prediction of those contents that will be requested in future. To achieve this goal, we formulate an average cache hit ratio and user satisfaction ratio optimization problem whose objective is to optimally cache the popular contents on BS of each tier in a such way that user find the intended data instantly and,hence, increasing the cache hit ratio and user satisfaction ration in each tier of the cache enabled multi-tier network.  
\[\underset{X_{t},Y}{max}\ (\alpha+\eta(y)) \tag{4}\]  
Subject to constraints:  
\[X_{mt,f}\in\{0,1\},\ \forall_{mt}\,\ \text{t},\ \text{f} \tag{5}\]  
\[Y_{r}\in\{0,1\},\ \forall_{r} \tag{6}\]  
\[\sum\
olimits_{f\in\mathcal{F}}\text{L}(\text{f})X_{mt,f}\leq S_{mt},\ \forall_{mt,f\in F} \tag{7}\]  
The first term of the objective function accounts for the average cache hit ratio of the multitier cache enabled network. The second term of Equation (4) shows the average satisfaction ratio. In other words, the system is trying to optimize the usage of the limited cache of the BS in such a way that the user can easily find its requested content from its associated or remote BS, thereby, increasing the downloading speed and decreasing the burden on the backhaul. The caching capacity required for each BS is indicated by the system constraint (7).  
This means that the cache entity capacity is bounded by corresponding capacity limit. Constraint (5) and Constraint (6) show that \(X_{mt,f}\) and \(y_{r}\) are binary variables, respectively. Therefore, the problem (1) is a constrained integer programming problem and is generally NP-hard which is very challenging task to solve. Also, storing the most popular data in the cache can increase the number of users which are recipient of the cached data. So caching the data that favours the users in limited cache is a challenging job. It means that such an intelligent system is required to predict the popularity of the data and cache them properly. To achieve this, machine learning plays a pivotal role. Within this frame work, based on past requests, we will first use K means clustering for clustering the BSs that receive similar requests and for predicting the future data, collaborative filtering is used. This will enhance the cache hit ratio. Similarly, for maximizing user satisfaction ratio, two types methods are employed which are based on mutual cooperation among the tier and will be discussed in Section 6.
# A Gap Waveguide-Based Compact Rectangular Waveguide to a Packaged Microstrip Inline Transition
## 6 Experimental Validation
Figure 12: Comparison between predicted response (black solid line), simulated transition taking into account the fabricated value (green dashed-point line) with 150 \(\upmu\)m of misalignment and measured (red dashed line) back-to-back configuration of the second transition model prototype (Figure 1b): (**left**) insertion loss and (**right**) return loss. It can be observed that the insertion losses were higher than the simulated ones. In the predicted performance of the transition the mean insertion loss was 0.35 dB while in the measured results it increased until 3.97 dB. The return loss also showed degradation; in this case the mean value was 10.48 dB instead of 19.75 dB. In this case the degradation can be associated with the losses ascribed to the substrate, whose value may be different to the nominal one since the loss tangent tends to increase with frequency. A possibility to reduce the insertion losses could be the use of a substrate with a lower loss tangent, e.g., a cyclic olefin copolymer (COC)-based substrate. This type of substrate has been successfully used in submillimeter wave applications [35; 36]. However, for this demonstrative work a standard substrate was selected. Furthermore, another degradation factor could be the manufacturing and assembly errors. In particular, alignment between the microstrip and ridge waveguides has been found to have large impact on the transition response, as is shown in Figure 11.0 Misalignments larger than 100 \(\upmu\)m increase the return loss more than 2 dB. Additional losses can also be ascribed to a high roughness of the aluminium block, which directly increases the losses in the waveguide sections. Summarizing, we can observe that the behavior of the return loss is quite similar to the predicted evaluation of the prototype once the fabrication tolerances have been taken into account for both cases.
## 3 Experiments and Analysis
### The Experiment of Common Lightweight Networks Based on Transfer Learning
In summary, from the overall experimental results, compared with other networks in the task of WTB surface damage identification, LACB_ECBAM Net's accuracy, recall, and number of parameters are excellent, but there is still room for improvement in training time.
## 2 Theoretical Background and Previous Studies
### Analysis on the Status of VDI Utilization in Other Industries  
Prior to applying the VDI to the construction sector, an analysis was conducted on how other industries, which have already utilized virtualization technology, are usingVDI and its main characteristics [7; 19; 20]. The analysis targeted enterprises that have a similar BIM collaborative work environment, such as hospitals, research institutes, and public institutions, etc., where data are being extensively exchanged for work through collaboration. Table 1 itemizes 42 key characteristics and changed environmental conditions after the introduction of the VDI system in a total of 21 enterprises, as shown in Figure 1. The items were further classified into 10 common criteria according to the intended use. As a result, when the VDI system was introduced to the cloud-based collaborative environment, maintenance and repair of the computing system, which greatly affects the life cycle cost, showed the highest level of utilization, followed by scalability that satisfied the requirements of a variable computing environment, mobility of computing, and an improvement in security through centralized control [21; 22; 23; 24; 25]. In other industries emphasizing collaborative work, the VDI system was used for maintenance and repair, scalability, mobility, and security, which accounted for 66% of the reasons for using the VDI. In other words, it indicated that other industries have anticipated enhancement in economic feasibility and convenience by applying the VDI to such sectors.
## 3 Results and Discussion
### Limitations  
The current study has several limitations, which may influence the results of the method comparisons: (1) the accuracy of the photogrammetric reference system was found to be 23 mm \(\pm\) 10 mm for well-defined points. Additional uncertainty might be added in the digitalization of the antenna GNSS antenna center; (2) The resection method used for the matching of the reference system with the GNSS measurements had a mean difference of 16 mm. The SD of the difference was \(\pm\)5 mm; (3) The reference system of Step 2 is not independent. It is therefore theoretically possible that method A had a larger difference from the true trajectory than one of the other methods. However, the reference method was chosen as it allowed assessing how the GNSS system performed for the true dynamics of alpine ski racing with its high accelerations and antenna tilt kinematics in the typical surrounding of alpine skiing. The used reference method however was accurate enough to measure and highlight the differences between methods as most of them are either on the level of cm or m. To assess smaller differences between methods, other reference systems should be used, which likely do not allow the assessment of unconstrained competitive alpine skiing.
## 2 MCR Frequency Modulation Method and Laser Processing Platform
### Laser Processing Platform for MCR Frequency Modulation  
The layout of the laser processing platform for MCR frequency modulation is shown in Figure 2. The wafer is fixed on the stage of XYZ carrier assembly, then the wafer is moved to the preset processing position to wait for processing. The cleaning equipment is used to clean and recycle the gasified silver waste. After each piece of equipment of the laser processing platform for MCR frequency modulation reaches the preset position, the laser processing equipment etches the MCR.  
Figure 1: The MCRs before and after laser processing. (**a**) MCRs before laser processing. (**b**) MCRs after laser processing.  
The laser processing platform for MCR frequency modulation is mainly composed of the frequency test probe fixing mechanism and XYZ carrier assembly, as shown in Figure 3. In the design process, the laser processing platform for MCR frequency modulation needs to meet the delivery function in XYZ directions and the accuracy requirements.  
In the laser processing for MCR frequency modulation, it is necessary to detect MCR frequency and modify the laser processing amount. During the processing, the frequency test probe and the MCR circuit accurately cooperate to form a loop, and the current MCR frequency can be obtained through the frequency test equipment. The frequency test probe fixing mechanism is shown in Figure 4. The frequency test probe is fixed on the probe board. The probe board is fixed on the frame through the gantry. The frequency of multiple MCRs can be tested at the same time.  
Figure 4: Frequency test probe fixing mechanism.  
Figure 3: 3D model of the laser processing platform for MCR frequency modulation.  
Figure 2: Layout of the laser processing platform for MCR frequency modulation.