esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Practical Applications and Flexibility  
This methodology is primarily designed for robot manufacturers who already have a range of parts and modules. It enables them to utilize their existing inventory while  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Angle [Deg]** & **Fgx1 [N]** & **Fgy1 [N]** & **Fpx [N]** & **Fpy [N]** \\ \hline
22.5 & 5759 & \(-\)12,008 & \(-\)4941 & 11,929 \\
45 & 12,920 & \(-\)12,181 & \(-\)12,102 & 12,102 \\
67.5 & 19,084 & \(-\)7646 & \(-\)18,266 & 7566 \\
90 & 20,389 & \(-\)80 & \(-\)19,571 & 0 \\
112.5 & 15,961 & 6273 & \(-\)15,144 & \(-\)6273 \\
135 & 11,973 & 11,156 & \(-\)11,156 & \(-\)11,156 \\
157.5 & 5701 & 11,788 & \(-\)4883 & \(-\)11,788 \\
180 & 818 & 9763 & 0 & \(-\)9763 \\
202.5 & \(-\)5759 & 11,929 & 4941 & \(-\)11,929 \\
225 & \(-\)12,920 & 12,102 & 12,102 & \(-\)12,102 \\
247.5 & \(-\)19,084 & 7566 & 18,266 & \(-\)7566 \\
270 & \(-\)20,389 & 0 & 19,571 & 0 \\
292.5 & \(-\)15,96 customizing the final product to better meet customer needs. By integrating topological optimization, the method emphasizes reducing one of the most critical operational parameters--manipulator mass--without compromising key performance indicators.  
It is essential to highlight that the proposed method serves as a tool for manipulator designers. Constraints and objectives must be tailored to each specific project. In some instances, minimizing mass may be the primary objective; in others, mass may merely be a constraint while different performance metrics (e.g., reach, lifting capacity) are optimized. Designers must also determine whether to account for dynamic loads resulting from rapid movements or impacts. In the provided examples, key indicators included the ability to lift heavy objects at maximum reach and near the robot base, as well as the overall reach of the manipulator--parameters that are inherently influenced by mass.  
Models derived from topological optimization are incorporated into the solution library. Subsequent iterations of the procedure can then assess whether the mass reductions significantly impact the optimization of the manipulator's fundamental parameters, potentially enhancing performance further. The results obtained in this process also serve future projects.  
Although the presented application example focused on a specific manipulator joint, the approach employed can be applied to any component of the manipulator's kinematic chain.
## 1 Introduction
### China's Gaofen Satellite Constellation  
China's High-Resolution Earth Observation System (CHEOS) was approved in 2010 by the Chinese government. It consists of a space-based observation system, a near-space observation system, an aviation observation system, a ground system and an application system. Seven civil satellites have been successfully launched so far, which are named after Gaofen (GF). The Gaofen satellite constellation combines the advantages of high temporal resolution and high spatial resolution. Various sensors are deployed on the Gaofen series satellites.  
The GF-1 satellite carries two panchromatic multispectral cameras (PMS, 2 m for PAN and 8 m for MS) and four wide-field-of-view (WFV) cameras with a resolution of 16 m. The GF-1 satellite is used mainly in public security, disaster prevention and urban land surveys. The sensor resolution on the GF-2 satellite is higher than that of the GF-1 satellite, with a resolution of 1 m for PAN and 4 m for MS. The GF-2 satellite can be used in high-precision land-use surveys, public security, reconnaissance and other application fields. The GF-3 is equipped with a C-band multipolarization synthetic aperture radar (SAR), 12 imaging modes and a spatial resolution of up to 1 m. The satellite payload can work under any weather conditions, day or night, because the electromagnetic waves (EMWs) radiated from the SAR sensor can penetrate clouds, surface vegetation, loose sand and even snow. The GF-3 satellite is widely used in marine protection, early-warning disaster-risk prevention and forecasting, water resource management and weather forecasting. The payloads and mission objectives of the Gaofen series satellites are shown in Table 1[13].
## 2 Circuit Design  
Current practice to measure the cardiac activity is the measurement of potentials arising on the surface of the human body. Hence, the ECG is registered as a potential difference (\(\Phi_{1}\)-\(\Phi_{2}\)) using two active measurement electrodes. In contrast, the newly designed circuit is based on the assumption that current reflects the cardiac activity equally to the surface potential [19] and therefore intends to amplify current rather than voltage.  
An overview of our sensor-system, including the housing, can be seen in Figure 1; the general circuit is illustrated in Figure 2. An ECG signal is registered by a monopolar electrode placed somewhere on the body where electrical currents are induced by the changes in the heart's electrical field. The obtained signal is processed through a transimpedance amplifier and subsequently filtered using a highpass-lowpass configuration. Two filter configurations are implemented and can be selected via a switch. While one filter path provides a wide bandwidth-filtering of 0.5 Hz-1 kHz, the second was implemented with a narrow bandwidth of 7 Hz-24 Hz. This was done in order to have the possibility to measure clinically relevant frequencies (wide) and to also allow a precise and very robust detection of the R-peaks (narrow) [22]. The filtered signal is then amplified using a differential amplifier and converted into a digital signal using an analog to digital (A/D) converter.  
Figure 1: Developed underwater ECG sensor. (**a**) Housing of the sensor. (**b**) Board of the sensor together with the used accumulator.  
Figure 2: Block diagram of the developed circuit for current-based measurements of the cardiac activity. The circuit was implemented for two bandwidths. In the upper path, the wider range of the bandwidth (0.5 Hz–1 kHz) is illustrated. In the lower path, the small range of the bandwidth (7 Hz–24 Hz) is shown.
## 3 Test Scheme and Result Analysis
According to the stress diffusion effect in the composite rock stratum and the structural plane position and anchorage length designed by the model, the prestress diffusion effect of end anchorage is obviously better than that of lengthening anchorage. From the perspective of stress diffusion effect in composite rock layers, the prestress diffusion effect of end anchorage is significantly better than that of lengthening anchorage. When anchoring at the end, a compressive stress distribution zone is formed near the structural plane, which can effectively suppress the phenomenon of delamination and collapse in weakly bonded structural planes. However, when the anchorage is lengthened, the stress of the surrounding rock at the position of the structural plane is manifested as tensile stress, which is not conducive to the stability of the structural plane from the perspective of prestressing alone.
## 6 Numerical Simulations
### Vibration Accelerations of the Components
(**a**,**d**,**g**,**j**) the carrier accelerations in \(x\)-, \(y\)-, \(z\)-, and \(u\)-directions, represented as \(\mathrm{a_{cv}}\), \(\mathrm{a_{cy}}\), \(\mathrm{a_{cz}}\), and \(\mathrm{a_{cu}}\), respectively; (**b**,**e**,**h**,**k**) the sun accelerations in \(x\)-, \(y\)-, \(z\)-, and \(u\)-directions, represented as \(\mathrm{a_{sv}}\), \(\mathrm{a_{sy}}\), \(\mathrm{a_{sz}}\), and \(\mathrm{a_{su}}\), respectively; (**c**,**f**,**i**,**l**) the planet accelerations in \(x\)-, \(y\)-, \(z\)-, and \(u\)-directions, represented as \(\mathrm{a_{p1x}}\), \(\mathrm{a_{p1y}}\), \(\mathrm{a_{p1z}}\), and \(\mathrm{a_{p1u}}\), respectively. amplitudes in the \(u\)-direction (i.e., torsional direction) of each component. From Figure 11g,h,j and Figure 12g,h,j, it can be observed that for the model with the planet-gear eccentric error excitation, the vibration accelerations in the \(z\)-direction of the herringbone sun, herringbone planet, and carrier are small but nonzero; while for the model in the absence of error excitations, the \(z\)-direction vibration accelerations of the herringbone sun, planet, and carrier disappear, similar to the spur planetary gears. This means that manufacturing error excitations are the vibration source of the axial direction and directly impact the axial vibration of each member in the HPGT system. Through comparing the results obtained from the model in the presence of the planet eccentricity shown in Figure 11 and those in the absence of the error shown in Figure 12, it is seen that manufacturing error excitations such as the planet eccentricity \(E_{p1}\) enhance the vibration acceleration responses in each DOF direction of each component in HPGT system, and the vibrations in the axial direction of HPGT are smaller and even vanishes similar to PGT with spur teeth when the planet-gear eccentricity is sufficiently small. In short, manufacturing error excitations such as the planet eccentricity increase the vibration acceleration responses of the herringbone planetary gears.
# Negative Poisson's Ratio Lattice Structure with Chiral and Re-Entrant Properties
###### Abstract  
Materials exhibiting negative Poisson's ratio (NPR) effects possess several advantageous properties, including high specific strength, specific stiffness, and impact resistance. As a result, they hold significant importance in various industries such as aerospace, medicine, transportation, sports, and other related fields. In the realm of natural materials, the NPR effect is infrequent. Consequently, the creation of an NPR structure necessitates a manual design. In this paper, we present an architected lattice system inspired by ancient Chinese window grills. The proposed system exhibits a unique combination of chiral and re-entrant properties. Experiments and numerical simulations were conducted in order to quantify the variation in NPR and elucidate the deformation mechanism of window grill structures. We observed a significant NPR effect in our designed structure, which was found to be dependent on the geometric parameters of the structure. The present study provides a foundation for the development of a new category of partially auxetic lattice metamaterials and the expansion of their design possibilities.  
mechanical metamaterial; negative Poisson's ratio; finite element analysis; tensile test +
Footnote †: journal: Applied Sciences
## 2 CIDAE: Continuous Imputation Denoising Autoencoder
### Part 1: Learning from the Original Data  
In the first part, there are many existing models [10; 11; 12; 13; 14] to choose from for learning the original data. In this paper, we chose the traditional autoencoder (AE) [20].  
For each user \(u_{i}\), some items in the original data are of interest to \(u_{i}\), while the remaining items are the missing parts of the original data. The goal is to predict the items that are of interest to \(u_{i}\) from the missing parts of the original data.  
In this part, labels of the items that the \(u_{i}\) is interested in are set to 1, and labels of the remaining items are set to 0. Similar to [16; 21], labels for all items constitute the initial value of the user vector \(\mathbf{u_{i}}\) (\(u_{ij}=0\) or 1).  
According to the AE, we use \(\mathbf{u_{i}}\) as the input and get the reconstructed \(\mathbf{u_{i}}\) by encoding and decoding. The specific process is as follows:  
**Encoding:** Map \(\mathbf{u_{i}}\in\mathbb{R}^{n}\) to the \(d\)-dimensional hidden layer by encoding function \(f\).  
\[\mathbf{y}=f(\mathbf{u_{i}})=\sigma(\mathbf{W}\mathbf{u_{i}}+\mathbf{b}) \tag{1}\]  
where \(\mathbf{W}\in\mathbb{R}^{d\times n},\mathbf{b}\in\mathbb{R}^{d}\), \(\mathbf{y}\in\mathbb{R}^{d},n\) is the total number of items and \(\sigma\) is a non-linear mapping function, e.g., a sigmoid function.  
**Decoding:** Map \(\mathbf{y}\in\mathbb{R}^{d}\) to the \(n\)-dimensional space by decoding function \(g\) for reconstructing \(\mathbf{u_{i}}\). Let \(\widetilde{\mathbf{u}}_{i}\) denote the reconstructed vector of \(\mathbf{u_{i}}\).  
\[\widetilde{\mathbf{u}}_{i}=g(\mathbf{y})=\sigma\big{(}\mathbf{W^{\prime}}\mathbf{y}+\mathbf{b^{ \prime}}\big{)} \tag{2}\]  
Figure 1: The structure of CIDAE.  
where \(\mathbf{W}^{\prime}\in\mathbb{R}^{n\times d},\mathbf{b}^{\prime}\in\mathbb{R}^{n}\), \(\widetilde{\mathbf{u}}_{i}\in\mathbb{R}^{n}\). We hope that \(\widetilde{\mathbf{u}}_{i}\) and \(\mathbf{u}_{i}\) are as similar as possible, and the minimum objective function of AE is as follows:  
\[\underset{\mathbf{W},\mathbf{W}^{\prime},\mathbf{b},\mathbf{b}^{\prime}}{argmin}\sum_{i=1}^{m} \mathcal{L}(\mathbf{u}_{i},\ \widetilde{\mathbf{u}}_{i})+\frac{\lambda}{2}\left(||\mathbf{W}||_{F}^{2}+||\mathbf{W}^{ \prime}||_{F}^{2}+||\mathbf{b}||_{F}^{2}+||\mathbf{b}^{\prime}||_{F}^{2}\right) \tag{3}\]  
\[\mathcal{L}(\mathbf{u}_{i},\ \widetilde{\mathbf{u}}_{i})=-\frac{1}{n}\sum_{j=1}^{n}u_{ ij}log\widetilde{u}_{ij}+\big{(}1-u_{ij}\big{)}log(1-\widetilde{u}_{ij}) \tag{4}\]  
where \(m\) is the total number of users and \(\mathcal{L}\) is the loss function. Furthermore, we choose the cross entropy as the loss function. \(\lambda\) is a regularization parameter to prevent overfitting. Just like the traditional autoencoder, we optimize the objective function by using the gradient descent method.
# Fuzzy Decision Algorithm for Health Impact Assessment in a 5G Environment
## 6 Results and Discussion
### Results--Fuzzy Model with the Mamdani Inference Method (Type 1)  
The hands-on decision-making methodology was achieved using FLS-T1 and the _if-then_ rules. According to the frequency ranges (Figures 1 and 6) and the fuzzy measurement algorithm (Figure 7), a complex Mamdani fuzzy model is presented (Table 1, Figures 9 and 10).  
Figure 9: Thermal effects are dominant in the sub-millimeter range (FLS-T1).  
Exposure time is one of the most important parameters, but we did not take it into account for frequencies higher than 30 GHz because there are no guidelines for adverse non-thermal effects at high RF frequencies. The results of future long-term research can be included in the presented model.  
Fuzzy sets have also been applied to environmental science and policy. We consider the following four forms of fuzzy numbers (4)\(-\)(7):  
1. Gaussian curve membership function: \[f(x;\sigma,c)=e^{\frac{-(x-c)^{2}}{2\sigma^{2}}}\] (4)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input and Output** & \begin{tabular}{c} **Group/Class** \\ _Membership Function (MF)_ \\ \end{tabular} & **Fuzzy Intervals:** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Frequency Band \\ (input) \\ [GHz] \\ \end{tabular} } & \begin{tabular}{c} Sub-6 \\ _Generalized bell-shaped MF_ \\ 25-40 \\ _Generalized bell-shaped MF_ \\ 40-100 \\ _S-shaped MF_ \\ \end{tabular} & [2.5; 3; 4.5] \\ \hline \multirow{4}{*}{\begin{tabular}{c} Biological impact \\ (input) \\ [\%] \\ \end{tabular} } & \begin{tabular}{c} Small \\ _Z-shaped MF_ \\ Medium \\ _Gaussian curve MF_ \\ Big \\ _S-shaped MF_ \\ \end{tabular} & [10; 50] \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Health risk \\ (output) \\ [\%] \\ \end{tabular} } &
\begin{tabular}{c} No \\ _Z-shaped MF_ \\ Low \\ _Gaussian curve MF_ \\ Medium \\ _Gaussian curve MF_ \\ High \\ _S-shaped MF_ \\ \end{tabular} & [20; 40] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Membership functions.  
Figure 10: Non-thermal effects are dominant in the millimeter range (FLS-T1).  
Generalized bell-shaped membership function: \[f(x;a,b,c)=\frac{1}{1+\left|\frac{x-c}{a}\right|^{2.b}}\] (5)
3. Z-shaped membership function: \[f(x;a,b)=\begin{cases}1,&x\leq a\\ 1-2\cdot\left(\frac{x-a}{b-a}\right)^{2},&a\leq x\leq\frac{a+b}{2}\\ 2\cdot\left(\frac{x-b}{b-a}\right)^{2},&\frac{a+b}{2}\leq x\leq b\\ 0,&x\geq b\end{cases}\] (6)
4. S-shaped membership function: \[f(x;a,b)=\begin{cases}0,&x\leq a\\ 2\cdot\left(\frac{x-a}{b-a}\right)^{2},&a\leq x\leq\frac{a+b}{2}\\ 1-2\cdot\left(\frac{x-b}{b-a}\right)^{2},&\frac{a+b}{2}\leq x\leq b\\ 1,&x\geq b\end{cases}\] (7)
# Static Magnetic Fields Effects on Polysaccharides Production by Different Microsalgae Strains
## 3 Results
### Polysaccharides in Chlorella Microalgae Biomass
Starch accumulation acts as a fast stress response [56] and mechanisms of starch accumulation by microalgae as well as the degree of starch polymerization and cellular location vary with species and algae division stage [12]. _C. fusca_ was found to accumulate more starch with SMF action, with 23% increase for continuous application indoor and 13% increase for 1 h d\({}^{-1}\) in outdoor condition (Table 1). The main strategy to accumulate starch in microcalga cultures is generally nitrogen starvation [7], but other operating conditions can also lead to this overproduction. [PERSON], et al. [57] have shown that _C. vulgaris_ and _C. variabilis_ biomass contained higher starch amount when the culture was enriched with CO\({}_{2}\). However, this appears to be strain dependent as no modification of starch accumulation was noticed for _C. sorokiniana_ and _C. minutilisima_. [PERSON], et al. [37] studied the influence of SMF on EPS content of _Chlorella vulgaris_. These authors revealed that the higher the SMF strength, the lower soluble EPS content. In the present study _C. fusca_ was cultivated under SMF that is supposed to act as a stress for the cells, modifying metabolism of microorganisms, affecting cell growth and molecules synthesis, explaining the greater starch content observed when applying SMF. Combining SMF with other stress (for example nitrogen starvation) could then further enhance starch accumulation, that could be of interest as feedstock for bioethanol production. This approach at a larger scale could improve the biomass production and, consequently, increase the biofuel production being feasible outside the laboratory. Additionally, as the total carbohydrate content was not found significantly increased, it can be supposed that the content in polysaccharides other than starch as thus decreased. As polysaccharides encountered in _Chlorella_ biomass are only starch and cell wall polysaccharides, we can suppose that the content of the latter has decreased. This could also be of great interest for further biomass processing, as it can decrease the cell-wall rigidity and facilitate the downstream processes of cellular lysis and starch extraction.
## 3 Results
### Experimental Setting  
In this study, the MAV is connected to a laptop for control via a wireless LAN, and the platform camera is connected to the laptop via a USB cable. Figure 8 shows the experimental setting used to validate the proposed landing system. Although a laptop was used to operate the object detection algorithm in the present study, in future work, the proposed algorithm can be executed on the MAV on-board computing as this algorithm requires a small amount of computational resources.  
To test the effects of the MAV starting position on the landing results, the MAV started at eight different locations (i.e., L1-L8), as shown in Figure 8a. The distance between the MAV starting position and the platform was set to 8 m; this distance was chosen because it was similar to the GPS navigation error. Initially, the angle between the MAV and the platform was set as 90\({}^{\circ}\) with respect to the orientation of the platform in order to test the search motion (STEP 1); the setting direction of the MAV is represented by arrows in Figure 8a. Ten experiments were conducted at each initial position while recording the time required for autonomous landing and the landing spot. The supplementary videos (Video S1) show the entire landing procedure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Radius MAE (Pixel)** & **Dark Space** & **Bright Space** \\ \hline HSV filter & 34.59 & 21.94 \\ New algorithm & 1.55 & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 1: MAE of the HSV Filter and the newly developed algorithm.  
Figure 7: Detection results under harsh lighting conditions. (**a**) Results in a dark space with directional light; (**b**) Results in a bright space with excessive backlight.
## 4 Data Results
### Reliability and Validity Tests of Measurement Results  
SPSSAU data analysis platform was used to test the reliability and validity of scale data (Table 2): Cronbach \(\alpha\) of reliability of total scale is 0.964 \(>\) 0.8; Kaiser-Meyer-Olkin (KMO) of validity of total scale is 0.946 \(>\) 0.8; Barth spherical value is 29,886.66; and value of degree of freedom (df) is 2211.0, indicating that the scale has high reliability and good effectiveness. Cronbach \(\alpha\) and KMO of each scale are \(>\)0.7, indicating that each scale has good reliability and validity.
## 8 Reliability and Accuracy Analysis
### Analysis  
When emphasizing the importance of relevant measurements in evaluation of forecasting models, [PERSON] et al., 2010 [23] stated that over 80% of the papers reported that their model outperformed the benchmark model. However, most analysed studies do not consider real world constraints like trading costs and slippage. In this study, for the data presented, the residual analysis has been under discussion, as the usual measurements do not necessarily capture the quality of the time series forecasting in an Emergency Department if considering the applied science aspect, with real-world constraints. In particular, when analysing the environment of an ED, it is often more important to have approximated values constantly close, or within a certain threshold of the actuals, rather than keeping a low perceptual average RMSE.  
To understand this, consider a situation where the average RMSE is low but exhibits occasional spikes. In an ED situation a spike corresponds to high unpredicted demand, such as a sudden large influx of patients, and may have dangerous clinical outcomes for patients if resource capacity is significantly exceeded on those occasions. A prediction which is less accurate on average, but which exhibits fewer spikes in RMSE is much preferred as it predicts changing demand more reliably, even if it is not always exact.  
Figure 17: Comparison of Actual and Prophet with residual distribution chart, January 2020–November 2020.  
Figure 18: Comparison of Actual and GRNN with residual distribution chart, January 2020–November 2020.  
For this reason, in the analysis below (Table 2) every simulation contains confidence bands and accuracy metrics which provide a more readable measure for healthcare professionals (following discussions) to compare predictions. Thus \"Very good\" predictions have residuals that are no further than 15% of the actual mean away from the actual counterpart, \"Good\" are within 15-25%, \"Regular\" are within 25-40% of the mean, and any prediction with residual above 40% of the actual mean is considered \"Unreliable\". Accordingly, all predictions that turned out to be within 40% of the actual values are classed as \"Reliable\" and overall \"Reliability\" (i.e., the total percentage of \"Very good\", \"Good\" and \"Regular\" predictions) is used as a summary metric.  
In addition, the average RMSE is also provided (Table 3) for each model and indicator, as the most neutral statistical residual measurement, even though, for the reasons outlined above, it may not the best fit for the ED environment.  
In both cases, green highlighting indicates the model with the best results.