esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Evaluation Metrics  
In this study, root mean square error (RMSE) was used to evaluate the performance of the assimilation. Each output (CO\({}_{2}\) concentration, surface layer soil moisture, NEP, and GPP) of the model employed RMSE with its corresponding observation (SIO CO\({}_{2}\) concentration, ESA-CCI soil moisture, Jena CarboScope NEP, and FLUXCOME GPP).  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(O(i)-M(i))^{2}} \tag{2}\]  
where \(N\) is the total number of observations, \(O\) is the observed value, and \(M\) is the simulated value.  
Another correlation coefficient of determination (\(R^{2}\)) was employed to assess the representativeness of model outputs to inter-annual variability and the GPP response to drought.  
\[R^{2}=1-\frac{\sum_{i=1}^{N}(O(i)-M(i))^{2}/m}{\sum_{i=1}^{N}(O(i)-\overline{M} )^{2}/m} \tag{3}\]  
Figure 1 shows the framework of methodology in this study. The calculation of these evaluation metrics and other unspecified data analysis tasks such as pre-processing and post-processing of data were all performed in python.
## 4 Materials and Methods
### Impact Channels Resulting from the Operation of Facilities Generating Renewable Energies  
The value-added effects from the operation of facilities generating renewable energies can be broken down into three impact channels (Figure 2):  
* The operation of the facilities results in expansionary effects on aggregate demand and thus also on the value added of companies that satisfy this demand either directly or indirectly (as suppliers of intermediate goods along the value chains). Firstly, demand for maintenance work and replacement parts arises from the operation of facilities generating renewables. In the case of electricity produced from biomass, additional demand is generated by the need for fuels. At the same time, the operation of such facilities generates income, which in turn boosts private consumption and stimulates aggregate demand. This income consists of the wage and salary payments of operators and from the feed-in tariff, less operating and financing costs.
* The financing of the feed-in tariff via the _EEG_ levy leads to contractionary effects on aggregate demand due to its direct and indirect effects on purchasing power and thus also household demand. On the one hand, households lose purchasing power directly through the payments they make, while, on the other hand, they also indirectly bear the costs of the levy on companies. The cost-push effect associated with the increased production costs is determined with the aid of an I-O price model. The reduction in purchasing power has negative effects on private consumption, which in turn results in negative effects on value added. This effect is examined using the I-O quantity model.
* The preferred feed-in of electricity from renewable energy sources leads to a contractionary-acting substitution effect, as electricity from conventional sources is displaced. The analysis works on the assumption that for every kWh of electricity generated from renewable energy sources, one kWh of electricity from conventional power plants (i.e., conventional energy technologies, CET) is displaced. This is associated with reduced demand for maintenance, replacement parts, and above all fossil fuels, which in turn leads to a reduction in aggregate demand and thus also value added. At the same time, this also results in a reduced import of fossil fuels.
## 3 Results
### Data Balancing: Application of Enhanced Sampling Methods
#### 3.3.1 Over-Sampling Results  
After applying the SMOTE method to the original dataset, the number of samples for each label was balanced to 10,666 (Figure 6). To examine the impact of the data balancing results on the consistency of sample distribution, principal component analysis (PCA) was used to visualize the main features before and after balancing processing, and frequency density curves of the two principal components were plotted (Figure 7b,e). It can be observed that SMOTE increased the sample capacity of the dataset while essentially maintaining the original distribution shape of the samples under the principal component attributes.  
Figure 6: The number of samples before and after data balancing processing.  
Figure 7: Principal component frequency densities.  
After applying the ADASYN over-sampling algorithm to the dataset, the minimum number of samples for each label is 10,411 (label FS), and the maximum number of samples is 10,733 (label PS). Through ADASYN over-sampling, the sample counts for each class are close to the original majority class (label M). From the frequency distribution of the principal components (Figure 7c,f), it can be observed that ADASYN can maintain the consistency of the distribution between the over-sampled dataset and the original dataset.
## 2 Materials and Methods  
### The Division of Human Gait Cycle  
A complete gait cycle of normal people during level walking is shown in Figure 1. It usually begins with the heel striking the ground and ends with the heel striking the ground again. When the heel strikes the ground, the knee angle is approximately 4\({}^{\circ}\) from the time the heel strikes the ground until the knee stops flexing. This is called initial standing or stance flexion. The middle standing is from the toe of the side leg off the ground to the heel landing. The end of standing is from the maximum knee flexion to the full extension of the knee. The power source of the knee during stance extension is the hip or the opposite lower limb push or forward body impulse. When the knee is standing and straightened, it is flexed again to prepare for the swing phase. From flexing again until the toe is off the ground, this is called pre-swing. The pre-swing phase of one leg usually begins after the opposite leg hits the ground with its heel. The period when both legs touch the ground and support the body is called the bipedal support phase.  
The main purpose of the prosthetic knee is to restore the motor function of the amputated knee by mimicking the behavior of the biological knee. Figure 1 shows the knee angle and torque curve of a normal person during a gait cycle. In this figure, the stride cycle begins and ends when the heel strikes the ground. As shown in the figure, toe off the ground occurs approximately 62% of the stride period. The initial flexion and extension of  
Figure 1: A complete gait cycle. The relationship of knee angle and torque.  
the knee joint keeps the center of gravity of the body relatively vertical and horizontal. The net power during the swing phase is basically negative, but the positive power output of the prosthesis is needed during the stance extension phase of the natural gait. The positive power output occurs in about 14% to 30% of the gait cycle, whereas the active torque cannot be provided by mechanical passive or variable damping prosthetic knee.  
The key design objectives of the semi-active prosthetic knee are as follows:  
1. The structure should have the ability to simulate the biological knee motion.
2. The active torque and passive damping should be provided alternately.
3. The weight, size, and energy consumption should be reduced compared to powered prostheses.
## 2 Field Test
### Monitoring Results
#### 2.2.3 Pore Water Pressure  
Figure 12 shows the evolution of excess PWP measured by pore water pressure piezometers. The excess PWP was measured at five predetermined depths (viz., 1, 1.5, 2, 3, and 4 m) at the measurement point. As can be seen from the figure, the excess PWP at all depths increased with the increased number of passes. RDC had a significant effect on the PWP of the soil in the upper part of the embankment. The impact resulted in excess PWP of about 15 kPa, 12 kPa, and 7 kPa at depths of 1.0 m, 1.5 m, and 2.0 m, respectively. Little excess PWP was generated in the soil at a depth of 3 m. This was essentially the same as the depth of impact influence obtained through horizontal displacement observations. The excess PWP at 1 m depth is plotted versus the number of passes, as shown in Figure 13. Excess PWP was generated mainly within the first 20 passes. As the number of passes increased, the increments of PWP decreased. The PWP increase tends to level off toward a limit value. This trend was similar to the monitoring results for settlement and horizontal displacement. When the test embankment was compacted, the soft soil was compressed, and the additional stresses resulting from the impact caused the PWP to rise. After compaction, the excess PWP gradually dissipated. To maintain the original equilibrium, the effective stress increased. The soil structure of the test embankment improved.  
A combination of monitoring data reveals that RDC can cause soil deformation (ground settlement and extrusion of the sides), and elevated PWP. Settlement is closely related to other factors observed, hence methods for predicting settlement using site monitoring data can be established.  
Figure 11: The variation of the average horizontal displacement at the surface with the number of passes.  
Figure 12: The effect of RDC on excess PWP at different depths.
## 2 Materials and Methods
### Materials Characterization  
The nanoparticle structure was assessed by transmission electron microscopy (TEM). The drop of nanoparticle suspension was deposited on a copper grid coated with perforated carbon foil. The suspension deposited on the grid was dried prior to TEM analyses. The analyses were performed with a transmission electron microscope (Jeol JEM 2100, Jeol, Akishima, Japan) operating at 200 kV. The magnetic properties of the nanoparticles were measured at room temperature by vibrational sample magnetometry (VSM) (Lake Shore 7307 VSM, Lake Shore Cryotronics, Westerville, OH, USA). The SPION crystalline structure was analyzed by X-ray diffraction (XRD, Bruker D8 Advance, Bruker, Billerica, MA, USA, Cu K\(\alpha\) radiation). The zeta potential measurements, as a function of the suspensions'pH (at a volume of 15 mL), were carried out at a final nanoparticle concentration of 0.1 mg/mL in an aqueous solution containing KCl (final concentration of 10 mM). Zeta potential measurements were performed on Zeta PALS, Brookhaven Instruments Corporation, Holtsville, NY, USA.  
The iron concentrations in the samples were determined by elemental analysis using inductively coupled plasma atomic emission spectrometry (ICP-AES) (iCAP6200 duo, Thermo Fisher Scientific, Waltham, MA, USA). Samples were dispersed in an HNO\({}_{3}\) and HCl solution (5 mL), the acid solution was evaporated, and 5 mL of a 1% HCl solution was added for the analysis. The final range of concentration was adjusted to match 10-100 ppb in iron.
## 4 Simulation and Analysis
### Power Spectrum Performance  
Before the BER performance comparison, we first analyze the power spectrum density (PSD) for the three promising multi-\(h\) CPM schemes to see their characteristics. The PSD is calculated by the method provided in [8], and the results are shown in Figure 4. We see that the lower modulation index CPM has substantial savings in spectral occupancy. However, it may bring lower minimum square distance, leading to worse BER performance [37]. It is verified in the following test. Note that the three CPM schemes have ignorable difference in implementation complexity using the same reduced-complexity techniques. It seems that the CPM with \(h=\{\frac{5}{16},\frac{6}{16}\}\) has a better tradeoff between the spectral efficiency and BER performance than the other two schemes.
## 2 Materials and Methods
### Downscaling SMAP TB
#### 2.2.3 Calculating \(Tb_{f}\)  
An illustration of the steps we used to calculate the 3 km SMAP/CYGNSS TBs (\(TB_{F}\)) associated with a single 9 km SMAP enhanced TB (\(TB_{C}\)) is provided in Figure 3b. First, we calculated \(\Gamma_{C}\) by finding the median of all CYGNSS reflectivity values with specular points that fell within the appropriate 33 km box within \(\pm\) half the time between successive SMAP observations. Second, we identified which of these CYGNSS reflectivity values had specular points that fell within the central 9 km grid cell and assigned these \(\Gamma_{F}\) to their appropriate 3 km grid cells. On the rare occasion that 2 or more CYGNSS observations fell within the  
Figure 4: Examples of 45-day SMAP emissivity and CYGNSS reflectivity data pairs and their corresponding linear regressions in (**a**) India, (**b**) the United States, (**c**) China, (**d**) Australia, (**e**) Brazil (Amazon Rainforest), and (**f**) Mauritania (Sahara Desert). Geographic coordinates, landcover class, \(\beta\) value (slope), and correlation value (R) are provided for each.  
same 3 km grid cell within \(\pm\) half the time between successive SMAP observations, they were averaged. Finally, using Equation (3), we calculated \(TB_{F}\) for each \(\Gamma_{F}\). Regardless of whether \(\Gamma_{F}\) occurs on the same day as \(TB_{C}\), the output \(TB_{F}\) are for the same day as \(TB_{C}\).  
Figure 5 includes regional maps of all the parameters in the SMAP/CYGNSS TB algorithm (Equation (3)).
## 2 Literature Review
### Living Labs as a Methodology  
Living labs, known as laboratories in everyday life, are a user-cocreation approach that integrates research and innovation processes in real life settings [22]. Living labs serve as a user-centered innovation platform in which actual users participate as a key actor for innovation activities [23]. In this regard, living labs can be understood as an innovation methodology for identifying and applying the needs and opinions of actual users in an innovation project, such as product development and technology commercialization [24].  
Many research institutes, local governments, and organizations are also using living labs for various purposes [25]. A widely spreading form is that living labs are used for the purpose of resolving or minimizing social problems or issues, such as disaster safety, security, and environmental preservation. In these cases, living labs are an integrated model of science, society and field, in which a set of technologies as a potential solution is applied directly to the real-life environment where problems exist. This suggests that living labs are utilized as a way of research and solution development (R&SD) [26].  
Living labs are also evolving in other different forms as a new methodology. Of those, the use of living labs, as a testbed or demonstration project, is of particular interest in this study [27; 28; 29; 30]. For example, in the Bukchon area in Seoul, IoT-based venture companies are testing their technologies to demonstrate, such as a parking sharing service through vehicle number recognition and IoT sensors, beacon and gateway-based fire and environment monitoring service, and AR/VR-based tourist information service. The smart safety living lab at Dongtan area in Korea is now in operation to demonstrate the performance and safety of new products prior to product launch. The living lab is built with several physical spaces, similar to actual home, hotels, nursing homes, and kindergartens, as well as virtual experimental spaces with VR equipment. To monitor user behavior closely, user activity measuring devices, such as observation cameras and eye trackers, are also equipped. Moreover, environmental information monitoring systems will be added by the end of 2020 for various types of demonstration projects [31].  
As this literature review shows, there are many examples of how to utilize living labs. In particular, this study intends to extend the model of living labs as a testbed or demonstration project into the context of verifying the test methods and certification criteria for NCPs. To this end, this study proposes a conceptual framework that can be used to analyze the real-use environments of NCPs and draw design implications for living labs in the verification process. The next section explains it in detail.
## 4 The Proposed Collaborative Weighted Clustering Protocol
### System Optimal Model and Problem Formulation  
To calculate the power dissipation in the WSNs, the first-order radio model used in [52] has been applied. In this model, to transmit/receive a \(k\)-bit message over a distance \(d\), the energy consumption is calculated by the following equations:  
\[\left\{\begin{array}{l}E_{Tx}=E_{elec}\times k+\varepsilon_{amp}\times k \times d^{2}\\ E_{Rx}=E_{elec}\times k\end{array}\right. \tag{11}\]  
where \(E_{elec}\) = 50 nJ/bit is a radio dissipate to run the transmitter or receiver circuitry, and \(\varepsilon_{amp}\) = 100 pJ/bit/m\({}^{2}\) is the transmission coefficient for achieving an acceptable \(E_{b}/N_{0}\). Based on the first-order radio model above, the following assumptions are made to simplify the analysis.  
Firstly, most sensor nodes cannot adjust their transmission power continuously and normally have only a few discrete power levels, which enable data transmissions with different distances [53]. Thus, \(d\) is also discredited in this research. This is realized by dividing the whole of application scenario into \(n\times m\) grids. Each sensor node is located at the grid that is the nearest one to its real position.  
Secondly, it is assumed that the radio channel is symmetric. More specifically, the energy required to transmit a message from node \(A\) to node \(B\) equals the energy required to send the same message back Thirdly, it should also be noted that receiving a message is also an energy-cost operation. The protocols should thus try to minimize not only the transmission distance but also the number of transmission and receiving operations for each message [7]. Since there are always data redundancy in the system and data aggregation is usually necessary.
###### Abstract  
Researchers conduct experiments to discover factors influencing the experimental subjects, so the experimental design is essential. The response surface methodology (RSM) is a special experimental design used to evaluate factors significantly affecting a process and determine the optimal conditions for different factors. The relationship between response values and influencing factors is mainly established using regression analysis techniques. These equations are then used to generate contour and surface response plots to provide researchers with further insights. The impact of regression techniques on response surface methodology (RSM) model building has not been studied in detail. This study uses complete regression techniques to analyze sixteen datasets from the literature on semiconductor manufacturing, steel materials, and nanomaterials. Whether each variable significantly affected the response value was assessed using backward elimination and a \(t\)-test. The complete regression techniques used in this study included considering the significant influencing variables of the model, testing for normality and constant variance, using predictive performance criteria, and examining influential data points. The results of this study revealed some problems with model building in RSM studies in the literature from three engineering fields, including the direct use of complete equations without statistical testing, deletion of variables with \(p\)-values above a preset value without further examination, existence of non-normality and non-constant variance conditions of the dataset without testing, and presence of some influential data points without examination. Researchers should strengthen training in regression techniques to enhance the RSM model-building process.  
Keywords:response surface methodology; regression analysis; backward elimination; semiconductor manufacturing; steel materials; nanomaterials _Article_
# An Emergency Message Routing Protocol for Improved Congestion Management in Hybrid RF/VLC VANETs
## 5 Simulation and Performance Analysis
The scenario is event-driven (accident at t = 75 s) and so results focus only on the time period after that event. This demonstrates the algorithm's ability to promptly detect accidents or bottlenecks and route vehicles onto alternate congestion-free routes before queuing kicks in. As a result, fewer vehicles accumulate in crowded areas at any given time compared to the situation without mitigation actions. Figure 8: Number of Congested Vehicles after an Accident. Figure 7: Transmission Delay including Broadcasting plus Rebroadcasting.
# A Novel Cross-Layering Power Control Mechanism for AODV +
###### Abstract  
Wireless networks are technologies with a growing interest in the area of telecommunications, such as the case of MANETs. Despite its advantages, MANETs present several challenges in the transmission of information due to the limited bandwidth, high error rate, energy consumption restriction, and variable topologies. The transmission power can significantly influence some of the aforementioned issues. This paper proposes Density Power Control Mechanism (DPCM), which employs a cross-layering approach between AODV (Ad-hoc On-Demand Distance Vector) routing protocol and the physical layer to adapt power transmission. DPCM aims to reduce collisions, maintain or improve the performance of AODV as well as to save power in the nodes. Our results indicate that our proposal can improve the performance of the network and save power at the same time. Moreover, it is especially useful for low- and medium-density scenarios.  
A 2020, 42, 40; doi:10.3390/ecsa-6-06561
## 6 Results and Discussion  
In this study, solar panels were used to produce renewable energy in the architectural sector. The Signal Box auf dem Wolf, located in Basel, Switzerland, was selected as the target building to produce maximum renewable energy, and solar panels were installed as an architectural design. The optimal angle of the solar panel was derived to maximize the monthly solar energy generation from January to December 2022. The movement of the solar panels was planned according to the optimal monthly tilt angle. This kinetic photovoltaic facade system and the energy generation movement simulation according to the optimal tilt angle were implemented through a parametric design. For the algorithm constituting the parametric design, Dynamo, a Revit software plug-in, was used to control the variables of the solar panel and the optimal tilt angle every month. To measure the optimal amount of renewable energy generated from solar panels, Insight, a Revit software plug-in, was used to control the variables of the site's natural environment every month.  
Thus, optimal energy was generated based on the energy generation simulation. The energy generation in July (47,407 kWh-month\({}^{-1}\)) was the highest, and that in January (1267 kWh-month\({}^{-1}\)) was the lowest. The total energy generation through the kinetic photovoltaic facade system for the year 2022 was 304,566 kWh-year\({}^{-1}\). The total floor area of the study building was 935 m\({}^{-2}\) and the annual solar energy generation per unit area (m\({}^{-2}\)) was 325.7 kWh.  
Figure 6: Energy generation simulation of solar panels by optimal tilt angle (24\({}^{\circ}\)) in July 2022 (**upper image**) and optimal tilt angle (67\({}^{\circ}\)) in December 2022 (**lower image**).  
The average energy consumption of general office buildings ranges from 100 to 1000 kWh per unit area [55]. Based on these data, the 325.7 kWh of solar energy generation derived from this study accounted for 33% of the average energy consumed by office buildings. Thus, the kinetic photovoltaic facade system had a significant effect on the increase in energy independence in the construction field. Additionally, the solar panel developed in this study, which can be moved according to the monthly optimal tilt angle, can be used as a new architectural facade design for the kinetic facade concept to produce optimal solar energy. In particular, the Dynamo algorithm can be used to propose various building designs by simulating solar panels of various colors and textures that are currently developed for building elevation.