esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Inertial Force Attenuation Structure and Semi-Active Control of Regenerative Suspension
## 4 The Improved Configuration of Energy Regenerative Suspension  
### Improved Two-Degree-of-Freedom Energy-Regenerative Suspension Mathematical Model  
The improved energy-regenerative suspension is shown in Figure 5. The improved suspension configuration adds a high-frequency vibration reduction structure (HVRS) between the regenerative shock absorber and the wheel to reduce the impact of equivalent inertial mass. In Figure 5, \(m_{h}\) represents the mass of the HVRS, \(k_{h}\) represents the stiffness coefficient of the HVRS, \(c_{h}\) represents the damping coefficient of the HVRS, and \(z_{h}\) represents the displacement of the HVRS. The ratio of the HVRS damping coefficient to the wheel damping coefficient is \(\chi=\text{ch}/\text{cu}\); the ratio of HVRS stiffness to suspension stiffness is \(\delta=k_{h}/k_{s}\); the ratio of HVRS mass to unsprung mass is \(\zeta=m_{h}/m_{u}\). According to Figure 5, when the deformation \(s\) of the HVRS is less than its stroke \(L_{h}\), the HVRS takes effect. The dynamic equation of a suspension with an HVRS with two degrees of freedom is established as Equation (10). When the deformation \(s\) of the HVRS is greater than or equal to its stroke \(L_{h}\), the HVRS is not effective. Assuming no impact occurs when the HVRS is stretched or compressed to the end of its stroke, the new suspension configuration has the same characteristics as the traditional suspension configuration, and its suspension motion is shown in Equation (4). \[\left\{ \begin{array}{l}m_{s}\dot{z}_{s}=k_{s}(z_{u}-z_{s})+c_{s}(z_{h}-\dot {z}_{s})+m_{r}(\ddot{z}_{h}-\ddot{z}_{s})\\ m_{h}\dot{z}_{h}=k_{h}(z_{u}-z_{h})+c_{h}(\dot{z}_{u}-\dot{z}_{h})-m_{r}(\ddot {z}_{h}-\ddot{z}_{s})-c_{s}(\dot{z}_{h}-\dot{z}_{s})\\ m_{u}\ddot{z}_{u}=k_{s}(z_{s}-z_{u})+k_{u}(z_{r}-z_{u})+k_{h}(z_{h}-z_{u})+c_{ u}(\dot{z}_{r}-\dot{z}_{u})+c_{h}(\dot{z}_{h}-\dot{z}_{u})\end{array} \right. \tag{10}\]  
By performing the Fourier transform on Equation (10), the amplitude-frequency characteristics of the sprung mass acceleration \(\ddot{z}_{s}\), relative dynamic load \(F_{d}/G_{j}\), suspension dynamic deflection \(s_{d}\), shock absorber velocity \(v_{sh}\), and HVRS shock absorber velocity \(v_{hu}\), equivalent inertial mass acceleration \(a_{hu}\) relative to the road excitation velocity \(\dot{z}_{r}\) can be obtained, as shown in Equations (11)-(16). \[|H_{h}(\dot{\text{u}}\omega)|_{\ddot{z}_{s}\sim\dot{z}_{r}}=\left|\frac{\ddot {z}_{s}}{\dot{z}_{r}}\right|=\omega\left|\frac{X_{z_{sh}}}{X_{z_{ph}}}\right| \tag{11}\]  
Figure 5: Energy-regenerative suspension configuration with HVRS.
## 3 Explanatory Application of the Method  
As an example, the method described in the previous sections is applied to a database relevant to the urban area of Naples (Italy). A COSMO-SkyMed dataset [53] of MT-InSAR displacement timeseries (persistent scatterers [54]) relevant to 411,582 target geographical points, recorded from December 2008 to August 2014, was used as a work set. The dataset is a matrix of as many rows as the target geographical points, and 319 columns, collecting ID label, latitude, and longitude of each geographical point, as well as a sequence of 316 displacement samples. The raw data are processed to calculate the vertical component of the displacement by projecting the line of sight (LOS) [55]. Figure 4 shows the QGIS map of the whole set of sampling points. Some instances of displacement time sequences are shown in Figure 5 together with the satellite image highlighting the geographical points where the displacements were remotely recorded.  
Figure 4: QGIS map of the considered Naples dataset.  
An application of the proposed method to the dataset of Naples is described below. Section 3.1 illustrates the preprocessing of data and the training phase. Sections 3.2 and 3.3 provide instances of the monitoring done through Strategy 1 (rare behavior) and Strategy 2 (noticeable event), respectively (see Section 2.2.1).  
To test the ability of the method to detect the early stage of critical events on the basis of similarities among timeseries, the monitoring procedure was applied with reference to past instances of the considered database.
## 4 Model Parameters and Simulation Scheme
### Battted Well Salt Cavity Model  
Given the intricate nature of actual salt cavities, this study simplified the real shape of the old cavern to facilitate a more straightforward comparative analysis, as illustrated in Figure 10. The cavity model primarily comprises three components: symmetrical brine cavities on either side, residue on both sides, and connecting channels. This study mainly investigates the response laws of various salt cavity morphologies on three-dimensional seismic profile, considering five key factors: cavity spacing, cavity diameter, cavity height, sediment height, and connecting channel height. To this end, five sets of salt cavity theoretical models were designed, each encompassing 4-6 different values. Due to the extensive number of model permutations and combinations, a specific value was selected for each factor in each set of models based on statistical results and insights from previous data (Table 2). A single-variable analysis method was used to conduct parameter sensitivity forward simulation analysis. The design scheme for this analysis is presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Media Type** & **Velocity (m/s)** & **Density (g/cm\({}^{3}\))** \\ \hline Mud rock & 3200 & 2.38 \\ Salt rock & 4300 & 2.10 \\ Brine & 1553 & 1.17 \\ Sediment & 2500 & 2.20 \\ \hline \end{tabular}
\end{table}
Table 1: The velocity and density information of salt cavern model.  
Figure 10: Schematic diagram of directional butted well salt cavern model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Salt** & **Well Name** & **Spacing** **(m)** & **Maximum** & **Cavern** & **Sediment** & **Connection** \\
**Mine** & **Well Name** & **Diameter** & **Height** & **Height** & **Channel Height** \\ \hline \multirow{3}{*}{Yexian} & S-3/S-4 & 304.12 & 97/107 & 216/169 & 136/97 & 12 (prediction) \\  & S-5/S-6 & 238.15 & 109/85 & 184/210 & 104/114 & 12 (prediction) \\  & S-7/S-8 & 340.07 & 91/91 & 216/179 & 142/120 & 12 (prediction) \\ \hline \multirow{2}{*}{Zhangxin} & ZX2/X2X2 & 275 & 90/72 & 294/167 & 237/125 & 10 (prediction) \\  & ZX3/XZX3 & 356 & 42/56 & 428/189 & 349/131 & 10 (prediction) \\ \hline Chuzhou & A24/A25 & 330 & 77/107 & 200/225 & 173/180 & 9 (actual) \\ \hline \multicolumn{2}{c}{average} & 307 & 85 & 223 & 159 & 11 \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of old cavities with directional butted well.
# Static Magnetic Fields Effects on Polysaccharides Production by Different Microsalgae Strains
## 1 Introduction
Some studies demonstrated that SMF can influence the electron transportation [21; 27], protein and enzyme conformation and activity [28] and membrane permeability [29] and then the metabolism in its globality. According to [PERSON] [30], intensities higher than 1 T result in the inhibition of biochemical processes. On the other hand, a weak SMF may also increase the efficiency of the biological decomposition of organic compounds [31] as in the study of [PERSON] et al. [32] that using 17 mT, stimulated enzyme activity and increased the rate of organic matter biodegradation by the bacterial community. Other authors have shown that SMF is able to improve the microalgal growth and biomolecules production, such as proteins, lipids and pigments [17; 33; 34]. Furthermore, previous studies with _Spirulina_ under SMF demonstrated an increase in carbohydrate content [35; 36], but the studies about SMF action on EPS production are very scarce. [PERSON] et al. [37] treated _Chlorella vulgaris_ with 20 to 200 mT and observed a decrease of soluble EPS content while increasing MF strength. Based on the above, this study aimed to evaluate different conditions of 25-30 mT SMF in _Spirulina_ and _Chlorella_ in indoor and outdoor conditions, in order to confirm the SMF influence on polysaccharide production, evaluate which polysaccharide fraction is modulated by SMF and highlight a possible modification of EPS composition.
# Cycling Biomechanics and Its Relationship to Performance
## 5 Electromyography
Bi-articular muscles such as the _rectus femoris_, _biceps femoris_, _semi-tendinos_ and _gastrocnemius_ muscles (_cf._ Figure 4) create opposite torques at two different joints. For example, the _biceps femoris_ muscle, which is activated with the knee extensors (_the vastus_ muscle) around the top dead center, creates a flexion torque at the knee and an extension torque at the hip. This results in a torque at the intermediate limb (i.e., the thigh in this example) that depends on the difference between the moment arms of the muscles at the two joints [69], and this difference may actually favor lower limb extension. Therefore, when considering bi-articular muscles, the presence of antagonist co-activation is not necessarily a sign of poor coordination in cycling. The EMG, as well as torque measurements, gives an indirect measure of the muscular forces. However, it has the advantage of being related solely to the muscle that is recorded. This particularity allows us to obtain information from agonist and antagonist muscle activity, for example, which is not possible using joint torque measurements. The EMG signal also appears to bear a relation to metabolic power [64]. However, compared with metabolic power, the EMG signals provide higher temporal resolution than the estimates obtained through gas exchange. For these reasons, the EMG-based cost functions, computed as the sum or weighted sum of the EMG signals of several muscles, have been used to assess the cost of cycling. EMG-based cost functions have been used, for example, to identify the optimal cadence at a given power output [70; 71] or the optimal power output at which the standing position should be adopted [44]. By dividing the power-output by the cost-function a measure similar in essence to mechanical efficiency can also be attained [46; 71]. Therefore, cost-function measures can be performed using either an EMG or joint torque estimation [44], and they may be particularly useful for optimizing a cyclist's technique. To the best of our knowledge no other studies have tested whether these cost functions could be used as feedback to improve the cycling technique. In conclusion, the EMG is a simple and relatively precise measurement of cyclist technique. It provides a way to estimate the amount of energy spent and amount of muscle stress. However, even if there is evidence of changes in muscle coordination with expertise, defining an optimal level of muscle coordination is not straightforward, especially when considering the activity of bi-articular muscles.
# Synthesis of Hydrogel-Based Microgels and Nanogels Toward Therapeutic and Biomedical Applications
## 2 Hydrogel Materials
### Synthetic Hydrogels
#### 2.1.5 Polyurethane-Based Hydrogels  
Polyurethane(PU)-based hydrogels are represented by their exceptional elasticity and mechanical durability [60,61]. Polyurethanes are copolymers consisting of alternating soft and hard chain segments, such as polyols and isocyanates. Soft segments increase flexibility and elasticity of the resulting gels, whereas the hard segments contribute to mechanical strength and structural integrity [54; 60; 61; 62]. This unique characteristic enables PU-based hydrogels to be tailored to exhibit high tensile strength and excellent water retention at the same time. The hydrophilicity of PU hydrogels can be determined by the abundance of hydrophilic functional groups, commonly known as functionalities capable of forming hydrogen bonds such as hydroxyl, carboxyl, and amino groups, in its polymeric backbone. High hydrophilicity promotes facile swelling in aqueous solutions just like other hydrogels, but PU hydrogel offers an efficient strategy to finely tune its property though adjusting the ratio of hard-to-soft segments, molecular weight, and crosslinking density [5; 61; 62; 63; 64; 65]. The main synthesis strategies of PU hydrogels are chemical crosslinking and physical crosslinking. Chemical crosslinking utilizes the spontaneous reaction between diisocynates and polyols, which results in the formation of a covalently linked network of urethane bonds [61]. Physical crosslinking mechanisms rely on intermolecular interactions, such as van der Waals force or hydrogen bonding, to create a network of reversible and dynamic bonds. Advanced polymerization techniques such as photopolymerization or solvent-free copolymerization have been adapted into the synthesis of PU hydrogels as well. PU hydrogels are well known as a core element of wound healing patches and soft tissue applications, owing to their biocompatibility, flexibility, and ability to absorb wound exudates while maintaining the physical environment to stimulate therapeutic efficacy or native healing process (Table 1) [40; 66; 67]. PU hydrogels are also widely utilized in drug delivery systems by taking advantage of their controllable permeability and degradability, which makes PU a suitable material for sustained drug release, especially at the proximity of the circulatory system or even within the blood vessels. A unique aspect of PU hydrogels is their self-healing properties, which are imperative for the material specialized for durable and responsive devices. Despite the advantages, limited biodegradability and potential cytotoxicity caused by residual isocyanate moieties are major drawbacks for the implantable or injectable form of PU-based hydrogels [40; 61; 62; 63; 46].
## 2 Theoretical Background
### Seismic Performance Assessment
#### 2.2.3 Seismic Hazard Analysis  
The IDA-based fragility curves for different performance levels need to combine with the site seismic hazard analysis to determine risk assessment for any structural system [30,31]. Probabilistic seismic hazard analysis (PSHA) is a well-known basic theory to determine probabilistically the earthquake shaking for a seismically induced site. Presented in [PERSON] [32], [PERSON] [33] and [PERSON] and Palasri [34], accounting for all the aleatory uncertainties, which will cover the surrounding i earthquake sources from the generation point and all moment magnitudes, PSHA expresses the exceedance rate [34] for a given IM as follows:  
\[\lambda\ (IM>x)=\sum_{i=1}^{N_{output}}\lambda_{i}\ (M_{i}>m_{min})\sum_{j=1}^{N_{M} }\sum_{k=1}^{N_{R}}P(IM>x|m_{j}r_{k})\ f_{M_{i}}\big{(}m_{j}\big{)}f_{R_{i}}( r_{k}) \tag{8}\]  
where \(\lambda(M_{i}>m_{min})\) is the probability of earthquakes happening which is greater than \(m_{min}\), and \(\lambda(IM>x)\) is the rate of \(IM>x\). \(f_{M_{i}}\big{(}m_{j}\big{)}\)_and_\(f_{R_{i}}(r_{k})\) are the probability density functions for the earthquake size and the source to site distance, respectively. \(P(IM>x|m_{j}r_{k})\) is the probability of \(IM>x\) for an earthquake of size m occurred at source-to-site distance \(r\). \(\lambda\) is the inverse of the return period \(T_{R}\), from where the structural yearly failure probability of any specific earthquake level can be determined. The PDF of a taken seismic source magnitude-frequency follows the Gutenberg-Richter relationship [35] as expressed by the following:  
\[\lambda_{m}=10^{\alpha-bm}=\exp(\alpha-\beta m) \tag{9}\]  
where the annual rate of exceedance of m is denoted by \(\lambda_{m}\), a and b values are known as the Gutenberg-Richter recurrence parameters, \(\beta=2.303b\) and \(\alpha=2.303a\).  
All process in the hazard analysis has been carried out by the own developed software \"PSHRisk-tool\" (available in [[https://www.kim2](https://www.kim2) kie.com/3_ach/PSHRisk-tool/PSHRisk_]([https://www.kim2](https://www.kim2) kie.com/3_ach/PSHRisk-tool/PSHRisk_)tool.php, accessed on 25 October 2020) [36] has been used, where can be found the detailed about the theory and application.
# Empirical Remarks on the Translational Equivariance of Convolutional Layers
## 3 Fusion Strategy for Frequency Image as Complementary Information  
According to the translational property of the Fourier transform known as the shift theorem [21], two images have the same magnitude when they differ by translation, i.e., the magnitude has a translational invariant property. Figure 2 shows spatial images and their corresponding frequency images (the log-scaled magnitude) with different translations where all the frequency images are the same, while the spatial images are shifted by 0 to 5 pixels, respectively. Here, we can easily get the frequency images by FFT [22].  
The frequency images are translational-invariant, so they are not affected by pixel shifts in spatial images. Therefore, we can regard that frequency images are appropriate for translational invariance. However, we should note that the discrimination power of frequency images is not strong because local information is lost in frequency images. The phase spectrum represents local information, but this information is initially excluded from frequency images. In Figure 3, we can notice that all the frequency images share the bright center area. This property of frequency images can reduce discrimination power.  
Therefore, we propose to use both spatial images and frequency images as complementary information to CNNs for translation invariance. To utilize both images, we propose one early-fusion strategy and two late-fusion strategies. Figure 4 illustrates these strategies. The early-fusion strategy feeds CNNs the concatenation of spatial and frequency images. The late-fusion strategy creates a two-stream model with convolutional networks of the same structure, and then fuses the features of each stream followed by fully-connected layers. For the late-fusion strategies, we simply design a fusion layer for addition and concatenation respectively.  
The early-fusion strategy learns features by feeding two heterogeneous sets of information together as the input. Meanwhile, the late-fusion strategy learns features by feeding each heterogeneous input image separately.  
Figure 3: Digit images in the first row and their corresponding frequency images in the second row where they share the bright center area.  
Figure 2: Spatial images and their corresponding frequency images. (**a**) spatial images of a zero-pixel shift to 5-pixel shifts from the center, (**b**) their corresponding frequency images having the same appearance.
###### Abstract  
Bone characteristics, the implant macrogeometry, and the drilling technique are considered the main important factors to obtain a good implant primary stability (PS). Indeed, although it is known that implant placement in poor bone sites increases the possibility of implant failure, several surgical procedures have been proposed to improve PS, such as site under-preparation. Hence, this in vitro study aimed to evaluate the insertion torque (IT), removal torque (RT), and resonance frequency analysis (RFA) of conical implants (3.3 and 4 \(\times\) 13 mm) placed in under-prepared sites on 10 and 20 pounds per cubic foot (PCF) density polyurethane sheets (simulating a D3 and D2 bone, respectively) with and without a cortical sheet of 30 PCF in density (corresponding to a D1 bone). After using ANOVA or Kolmogorov-Smirnov test to elaborate data, the resulting IT and RT values were directly proportional to the polyurethane block densities (e.g., the lowest and highest IT values were 8.36 \(\pm\) 0.52 Ncm in the 10 PCF density sheet and 46.21 \(\pm\) 0.79 Ncm in the 20 PCF density sheet + cortical for 4 \(\times\) 13 mm implants) and increased with the increasing amount of site under-preparation (the highest results for both implants were found with a 2.2 mm under-preparation, showing a significantly higher IT with a \(p\)\(<\) 0.05 compared with others, especially in the highest-density sheets). Both implants inserted in the 20 PCF density block + cortical with all under-preparation protocols exhibited significantly higher RFA values (\(p\)\(<\) 0.05-0.0001) compared with the corresponding ones in the 10 PCF block. Moreover, 3.3 \(\times\) 13 mm implants showed the same results comparing the 20 PCF block and the 10 PCF block + cortical. In conclusion, in this in vitro study using low-density polyurethane blocks, the under-preparation of the implant insertion sites was shown to be effective in increasing implants' PS.  
Keywords:dental implants; implant primary stability; implant rehabilitation; insertion torque; removal torque; polyurethane; poor bone quality; site under-preparation +
Footnote †: journal:
# Multi-Backup Beams for Instantaneous Link Recovery in mmWave Communications
## 5 Performance Evaluation  
The proposed recovery scheme is now evaluated using key metrics, in particular, the spectral efficiency, received signal profile, and recovery times. Consider Table 1 for the overall system settings.
## 8 Conclusions  
In this paper, we have presented a suite for secure underwater acoustic communication. The security suite comprises a routing protocol and a set of cryptographic primitives (cipher, digest, and re-keying), and has been designed to protect the confidentiality and the integrity while taking into account the unique characteristic and constraints of the underwater networking environment. Experiments and simulations have shown that the security suite introduces limited and sometimes negligible overhead, and thus it is perfectly adequate for the underwater acoustic communication. In particular, the suite achieves the following results.  
* The cryptographic suite provides efficiency by limiting the effect of ciphertext expansion. The resulting overhead is indistinguishable from the performance fluctuations due to the changing sea conditions.
* The Discovery Phase of the secure routing protocol introduces an additional round, and thus a consequent delay, with respect to the insecure protocol. However it causes a negligible additional communication overhead (smaller than 6%). The overhead of the Discovery Phase is paid at system bootstrap and it is amortized over the whole system lifetime.
* The Reconfiguration Phase of the routing protocol does not introduce any additional overhead with respect to the insecure routing protocol. This is a very important feature because network reconfiguration due to node mobility, joining and leaving the application is a normal operational mode of the system.
* The design of the secure routing protocol meets the well-known [PERSON]'s recommendations for computer systems design [39], while reconfiguration must be very efficient, it is sufficient that discovery is able to make progress provided it remains practically sustainable.  
To the best of our knowledge, this is the first work that provides a complete, practical, and efficient solution to integrity and confidentiality for UASNs. In this respect, we hope that our work will have a significant impact in this field.