esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Commercial and Environmental Compatibility
### Commercial Viability  
NS has been used as additive in the concrete and insulation industries for over 30 years. Ultrafine powder (approximately 150 nm in size) silica fume and NS (5-100 nm in size) have been used in concrete to provide rapid strength gain, enhanced strength, better durability, and surface finish. Silica fumes are widely available in the UK and across the globe. Although, it is not often marketed as nanomaterials by manufacturers. These are expensive products and are only modestly adopted in commercial settings. In a smaller scale, NS is infrequently used as an insulator and in the form of silica aerogels. These are readily available on the market, but their broad use is blighted by their typically 6 to 10 times higher costs in comparison with traditional, but less efficient, insulation products [51].  
In fractured rocks and soil, the main practical advantages of NS are high penetration capacity and a chance of controlling the gelling time and hence the penetration depth. For a 5% NS solution, treatment of each cubic meter of sandy soils (of a porosity of 35%) on average costs around US$ 60 [55]. This is relatively lower than the treatment costs incurred using other alternative non-cement-based grouts (that offer better penetrability than cement), including polyurethane, epoxy resin, acrylamide, methacrylate, acrylate, and sodium silica. For example, treatment of one cubic meter of sand with acrylamide, on average, costs around US$ 325. Apart from the higher cost, recent environmental leakage incidents (e.g., in Romeriksporten tunnel, Norway and Hallandsas tunnel, Sweden, reported in [56]) cast doubt on the suitability of these materials for permeation grouting.  
NS is commercially available as low viscosity grout in gel state with a strict use-by-date. Examples are CemSyn manufactured in India, Meyco MP320 manufactured in Sweden and MP320T manufactured in Germany. The main difference between these commercial products is their silica content and particle specific surface area values [57].
## 4 Discussion
### Methods Applications and Uncertainty  
In this study, we provided a pure satellite-based method, which was driven by MODIS GPP and LST products, to estimate and partition Re, and we confirmed it could generate comparable results by validation and improvement from tower- and chamber-based measurements. High temporal-spatial resolution of remote sensing products give the method two potential applications. The direct application is to estimate and partition Re into autotrophic and heterotrophic contributions in similar alpine meadows, which would help in accurate quantification of carbon balance under future climate change [11; 46; 85; 95]. Moreover, many parameter inputs used in bulks of biogeochemical cycle models, such as Q\({}_{10}\)[98], net ecosystem productivity (NEP, GPP-Ra) and net carbon sequestration (1-Re/GPP) [99], can also be simply estimated.  
However, there are some cautions should be taken for further application of this satellite-based Re estimation and partition method. One is that spatial extrapolation for other vegetation types or regions should be based on appropriate validation, such as chamber-based measurements as we have suggested in this study. For example, PCM underestimated GPP at this alpine meadow, but it could be used as an alternative method for GPP estimation, albeit with a bit of overestimation about 10% of GPP_EC, in near alpine swamp meadow [30] and in the ecosystems of northern China [34]. Thus, ground validation is needed for further scale extrapolation.  
The other fact is that the satellite-based Re estimation and partition method is not sensitive to pulse effect of rainfall on Re, resulting in underestimation of Ra/Re compared to the chamber-based Re partition. However, this study confirmed that the satellite-based Ra/Re could explain chamber-based Ra/Re without significant discrepancies. For one reason, this kind of instantaneous underestimation would be weakened after reconciling Re measurements at eight-day composited time resolution. For another reason, instantaneous change of components of Rs due to disturbance of rainfall pulse might gradually tend to re-equilibration with recovery of the soil gas-permeability [87]. Thus, chamber-based Re measurements should effectively avoid the rainfall events for stable and representative measurements [47].  
The remaining uncertainties are likely to originate from the undemanding agreement of temporal-spatial matching between MODIS-based products and ground measurements [33; 100]. Specifically, MODIS products has an regular eight-day time step and 1 km pixel, while chamber-based measurements only reflects the specific Re state in the measurement day and the restricted samples plot we selected, thus the errors of temporal-spatial match is inevitable. However, this mismatch failed to significantly affect the accuracy of GPP and Re estimation, and Re partitioning in this alpine meadow.
# Investigations on the Complex Band Diagram of Flexural Wave through the Fluid-Loaded Phononic Plate
## 3 Numerical Examples and Discussions
### Vibration Transmittance of Finite Structures  
Vibration transmittance of finite structures has drawn particular interest as plate structures are usually applied with finite extension in practice. A finite plate made of \(1\times 10\) unit cells is considered to evaluate the attenuation magnitude of flexural waves. We adopt the finite element method to calculate the transverse vibration transmittance of such a finite structure. As illustrated in Figure 4, the excitation is designed as a line harmonic force excitation as it is more likely to produce waves propagating along the \(\Gamma\)X direction. The notations of 'Point A' and 'Point B' denote the locations where the transverse vibration responses are interested. The transmittance \(T\) is given by the following equation [41]:  
\[T=20\log\biggl{(}\biggl{|}\frac{w_{A}}{w_{B}}\biggr{|}\biggr{)} \tag{26}\]  
where \(w\) represents the transverse displacement of the finite plate.  
The external fluid is assumed infinite and interacted with the finite plate. The Rayleigh integral on the plate surface is coupled with the derived finite element formulation to model fluid-structure interaction of the baffled plate subject to heavy fluid loading [42]. Then, the displacement of the finite plate with heavy fluid loading can be obtained. Figure 5a shows the values of \(T\) as a function of frequency. Due to the existence of the directional stop bands, two frequency ranges with significant transmittance loss are observed. They are depicted by grey rectangles in the plot. The fist gap \(g_{1}\) appears as a sharp notch and the second one \(g_{2}\) appears as a smooth valley, whose bandwidth is in accordance with those of the local resonance and the Bragg directional band gaps observed from Figure 3a. One can also find that the maximum absolute value of the transmittance dip inside the gap \(g_{1}\) is much larger than that in the gap \(g_{2}\), and this can be explained by the attenuation constant that is presented subsequently. Figure 5b shows the vibration transmittance of the same finite plate without fluid loading. The transmittance gaps are seen at higher frequencies. The two plots show different values of the maximum transmission drop inside the gaps, and this is further explored in the following subsections.
# GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments
## 2 GNSS-Assisted Visual Dynamic Localization Method Framework
### Initialization  
Initialization is the process of obtaining a position estimation from the first three frames of an image, and it includes feature extraction and matching, relative position-solving with epipolar geometry constraints and a homography matrix, and, finally, triangulated feature points.  
As Figure 2 shows, \(I_{1}\), \(I_{2}\), and \(I_{3}\) were three frames of an image with GNSS positioning data, and their corresponding camera photocenter positions were \(C_{1}\), \(C_{2}\), and \(C_{3}\), while the GNSS coordinates were \(X_{C_{1}}^{n}\), \(X_{C_{2}}^{n}\), and \(X_{C_{3}}^{n}\). Here, the camera coordinate system \(O_{C_{1}}X_{C_{1}}Y_{C_{1}}Z_{C_{1}}\) at \(C_{1}\) was assumed to be the world coordinate system \(O_{W}X_{W}Y_{W}Z_{W}\).
# Adversarial Attacks on Medical Segmentation Model via Transformation of Feature Statistics
## 3 Experiments
### Case Study: Visualization of Adversarial Samples and Predictions  
We display both adversarial samples and prediction by each method with regard to diverse hyper-parameters which is randomly sampled. In Figure 10, the original CT image, the ground truth and the prediction by the target segmentation model are depicted. Moreover, we show adversarial samples generated by our method in Figure 11 and corresponding predictions in Figure 12.  
It is worth noting that the target model achieves a Dice score of 0.425400, as depicted in Figure 10. However, when our method is applied with \(\alpha_{\mu}\) and \(\alpha_{\varphi}\) values of 1.2 and 0.9, respectively, as shown in Figures 11 and 12, the Dice score increases to 0.519836. This  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Organ** & **Pre-Augmentation** & **Post-Augmentation** & **Difference** \\ \hline Background (BG) & 0.9856 & 0.9861 & +0.0004 \\ Splean (SP) & 0.6454 & 0.6599 & +0.0145 \\ Right Kidney (RK) & 0.5710 & 0.5708 & \(-\)0.0003 \\ Left Kidney (LK) & 0.5618 & 0.5796 & +0.0177 \\ Gallbladder (BG) & 0.1803 & 0.1626 & \(-\)0.0177 \\ Esophagus (ES) & 0.0000 & 0.0640 & +0.0640 \\ Liver (LV) & 0.9371 & 0.9379 & +0.0008 \\ Stomach (ST) & 0.4870 & 0.4881 & +0.0011 \\ Aorta (AO) & 0.8744 & 0.8901 & +0.0157 \\ Inferior vena cava (IV) & 0.5351 & 0.6405 & +0.1054 \\ Portal and Splenic Vein (PVSV) & 0.2536 & 0.2467 & \(-\)0.0069 \\ Pancreas (PA) & 0.3018 & 0.3003 & \(-\)0.0015 \\ Right Adrenal Gland (RA) & 0.0000 & 0.1103 & +0.1103 \\ Left Adrenal Gland (LA) & 0.0000 & 0.1098 & +0.1098 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dice score changes with data augmentation.  
Figure 10: Ground truth and target model’s prediction, presented with the corresponding Dice score of 0.4254.  
improvement suggests that our method effectively reduces false positives in the segmentation model. Thus, our approach can serve the dual purpose of launching attacks on a segmentation model and enhancing its prediction performance.  
Since \(\alpha_{\mu}\) and \(\alpha_{\sigma}\) serve as transformation factors for the mean and standard deviation of the feature vectors, respectively, their values significantly influence the appearance of the generated images. The results, as illustrated in the figures, indicate a trend where extreme values for the mean transformation factor (around 0.3 and 1.5) coupled with a relatively small standard deviation transformation factor tend to effectively compromise the model, as evidenced by a decrease in the Dice score. Furthermore, the generation of realistic images is often achieved when \(\alpha_{\mu}\) and \(\alpha_{\sigma}\) maintain a similar ratio. This observation suggests a strategy for optimizing the search for suitable pairs of transformation factor values by maintaining a consistent ratio between them, potentially accelerating the optimization process.
## 2 Materials and Methods
### Data Processing  
Kinematics data were low pass filtered by a zero lag fourth order Butterworth filter with an 8 Hz cutoff frequency. Pedaling cycles were separated using the right knee angle, and kinematic data of 10 continuous pedaling cycles were selected for further analysis. The maximum knee angle was assigned as top dead center (TDC) of the pedaling cycle, and each pedaling cycle was defined as the TDC to the next TDC. Each pedaling cycle time series was time normalized to 101 data points and expressed as a percent of the pedaling phase such that the propulsive phase and recovery phase were identified during 0 to 50% and 51-100% of the pedaling cycle, respectively.
# Deposition of 3 YSZ-TiC PVD Coatings with High-Power Impulse Magnetron Sputtering (HiPTMS)
## 4 Discussion
The high flux of metal ions increased the sputtering due to the self-sputtering, and more neutral target atoms can be ionized, which also can participate in the self-sputtering and contribute to the high current measurements. The main experiments of the parameter study support the results from the preliminary experiments with regard to the influence of the working gas flow. Furthermore, the table current decreased with the increasing frequency because of the decreasing cathode peak power. Increasing the frequency reduced the off-time of the cathode and the charging time of the capacitor bank for the next cathode pulse. The lower peak power was also the cause of the decreasing current with the increasing pulse width. The authors plan to investigate the speculated formation of a third working gas in a future study to classify the ratio of self-sputtering and working gas formation of the results. With a frequency of 400 Hz, the process could not be stabilized. The higher the on-time of the cathode, the lower the measured maximum table current was. With lower on-times, more arcs occurred, and the process was less stable. The on-time of the bias had no significant impact on the table current. Nevertheless, the ideal offset of the bias pulse depended on the bias on-time. This offset value lay in the range of 30 and 50 \(\upmu\)s, with 30 \(\upmu\)s being mostly for a low bias on-time and 50 \(\upmu\)s for a high bias on-time. The low bias on-times decreased the time, and ions were actively forced to move to the substrate table by the bias pulse. High bias on-times decreased the maximum table current and increased contamination of the coating with non-ionized atoms. However, as shown in Table 4, the highest maximum table currents could be reached with a 40-60 \(\upmu\)s bias on-time and a 20-30 \(\upmu\)s bias offset. Most arcs, as well as the highest process instability, were found especially for the low bias on-times between 20 and 30 \(\upmu\)s, in addition to the low (0-30 \(\upmu\)s) or high (60-80 \(\upmu\)s) bias offsets. Further experiments should show the influence of the maximum table current on the mechanical properties of the coating, and the assumption of the formation of carbon-oxide gas out of the target material needs to be confirmed. The measured mechanical properties of the coating observed were higher than in other studies. [PERSON] et al. deposited pure yttrium-stabilized zirconia with electron beam physical vapor deposition (EB-PVD) and found a measured hardness up to almost 22 GPa [20]. Thermal spray coatings of YSZ are often spongy, owing to their use as a thermal barrier coating, with a low hardness up to 800 HV and a Young's modulus no greater than 95 GPa [21; 22]. Due to the good process capability and promising mechanical properties, further experiments could reduce the TiC addition in the target material, because the electrical conductivity of oxide-based EDM ceramics rises with a volume fraction of 20 TiC from a few thousand S/m to over 20 kS/m with 28 vol.-% TiC, as reported for ZTA [6].
## 2 Methodology
### Neural Network Models  
#### 2.2.1 LSTM  
Long Short-Term Memory (LSTM) networks are a specialized type of Recurrent Neural Network (RNN) designed to address the vanishing and exploding gradient problems that often occur when handling long sequential data. With its memory cell and gating mechanisms, the LSTM is effective in capturing long-term dependencies, making it particularly suitable for tasks involving sequential data. For battery state prediction, LSTMs have proven to be powerful tools, enabling accurate estimation of battery SOC and SOH. In this work, LSTM is employed to predict the battery state data, serving as a baseline model for comparison with other models. As shown in Figure 3, the LSTM unit is composed of several crucial components: input gate, forget gate, and output gate. These gates regulate the information flow into, flow through, and flow out of the memory cell.
## 2 Thrust, Torque, and Power Estimation Model for Modified EPB TBM in Mixed-Face Ground
### Torque Model  
Torque is essential in the design and manufacturing of TBMs. Nowadays, some projects refer to the empirical formula to estimate the torque of TBMs. When a lesser torque factor is employed, the torque may be insufficient, and it is difficult to get out of trouble when tunneling in mixed-face strata. A larger torque factor can raise not only the cost of the TBM drive device but also the hassle of transportation and installation. When the modified EPB TBM is tunneling in the RSI mixed strata, the cutterhead rotates under motor driving. The cutterhead generates frictional torque \(T_{1}\) and \(T_{2}\) against the ground on its front and sides. Subsequently, the slag enters the work chamber through an opening in the panel and generates frictional torque \(T_{3}\) against the back of the cutterhead. The cutters on the cutterhead generate cutting torques \(T_{4}\). There are several more torque components, such as the clipping torque \(T_{5}\) at the cutter opening, the agitating torque \(T_{6}\) from the stirring bar mounted on the back of the cutterhead, the rotating torque \(T_{7}\) of the main bearing, and the torque \(T_{8}\) of the cutterhead closure. The EPB TBM torque mainly consists of eight components, which can be expressed as the following [7]:  
\[T=T_{1}+T_{2}+T_{3}+T_{4}+T_{5}+T_{6}+T_{7}+T_{8} \tag{32}\]  
where \(T\) is the overall torque of the cutterhead. In fact, the components that have the most significant influence on the cutterhead torque are \(T_{1}\), \(T_{2}\), \(T_{3}\), and \(T_{4}\), which account for about 90% of the total torque. The remaining parts have a minimal degree of influence on the torque and can be neglected in the analysis. In this paper, we simplify the torque into four parts:  
\[T=k_{EPB}(T_{1}+T_{2}+T_{3}+T_{4}) \tag{33}\]  
where \(k_{EPB}\) is the torque amplification factor, which is taken as 1.1. Later, \(T_{1}\), \(T_{2}\), \(T_{3}\), and \(T_{4}\) will be introduced in detail.
## 4 Fatigue Analysis
### Fatigue Life Calculations  
In order to estimate the fatigue life of the stanchion, cumulative fatigue damage and repeats to failure were calculated using the Palmgren-Miner rule [21]. The cumulative damage \(D\) is given by Equation (2). Value \(Ni\) is the fatigue strength obtained from S-N diagrams, whereas \(ni\) indicates the number of cycles for the stress range. Repeats to failure were calculated as \(R=1/D\).  
\[D=\sum_{i=1}^{k}\frac{n_{i}}{N_{i}}; \tag{2}\]  
Since stress cycles obtained as a result of spectrum reduction had non-zero mean stress, the Goodman correction was used when calculating fatigue strength. The modified Goodman relation is given by Equation (3), where \(\sigma_{a}\) and \(\sigma_{m}\) stand for stress amplitude and mean stress, \(\sigma_{u}\) is ultimate tensile strength and \(\sigma_{N\!f}\) indicates equivalent fully reversed stress.  
\[\frac{\sigma_{a}}{\sigma_{N\!f}}+\frac{\sigma_{m}}{\sigma_{u}}=1 \tag{3}\]  
Fatigue resistance S-N curves were accepted for the calculations in accordance with [19]. S-N curves are shown in Figure 15. Fatigue classes referred to the structural details as follows: FAT160 with unwelded parts, for which fatigue resistance was assessed based on nominal stresses, and FAT100  
Figure 14: Rainflow cycle counting histograms of reversed stress cycles at concentration points.  
with two-sided fillet welds and fillet welds at the end of the reinforcement plates on a rectangular hollow section, for which fatigue resistance was assessed based on structural hot-spot stresses.  
The results of fatigue life calculations are presented in Table 1. An operating time was calculated by multiplying the number of repeats to failure by a single cycle time of 900 s. The mileage to failure was estimated under the assumption that the average vehicle speed is 50 km/h.
## 4 Discussion and Conclusions
### Integration Studies at the Sub-Municipality Level as a Tool for Emergency Planning
The former municipality is characterized by a high level of flood hazard and social vulnerability, resulting in higher classes of raster combination (Figure 12). The latter depicts census tracts affected by medium classes of multi-hazards and a high level of social vulnerability, leading to the higher exposure of this territory to hazards. Furthermore, the integration process applied in this work via the use of a cartographic approach conveys a clear spatial distribution of the socio-economic aspects based on the hazard exposure (Section 3.4). This could be a fundamental tool for emergency planning in the hands of civil protection that needs detailed information at the sub-municipality level. These final maps can let local authorities know the detailed risk context of their competence territories, and also allow them to construct plans capable of protecting the most vulnerable populations. Looking at the general distribution of the multi-hazard context (Figure 11), the study area could be divided into three main areas based on the integration processes that highlight the type of vulnerability under different hazard scenarios. The population living along the Ionian coast is the most vulnerable to flood hazard scenarios, marking the first zone. Moving from the coast towards the northern direction, the second area is represented by inhabitants vulnerable to multi-hazards, followed by people vulnerable to landslide hazards. In this respect, different risk management strategies can be implemented at the local level in order to reduce damages and impacts. The application of physical measures such as barriers, dikes, embankments for floods [56], or soil reinforcements and erosion-prevention strategies in the case of landslides [57] can also be supported by citizen participatory activities such as workshops, information campaigns, education courses, and strategic tools for disaster preparedness [58; 59; 60]. Considering Figure 11, these strategies can be implemented on the basis of the integration level between hazard and social vulnerability, considering only citizen science measures in areas characterized by low integration levels and including physical and non-physical strategies in the highest-risk areas. In both cases, the application of cost-based analyses or multicriteria investigations is highly recommended in order to provide monetary estimations and define various alternative strategies in different scenarios [61; 62; 63]. In addition, the present research points out a relevant outlook on the multidisciplinary methodologies that adopt the co-production of knowledge [64; 65] and different techniques belonging to various study fields in order to promote risk reduction strategies. In this context, the use of \"open-data\" that can be accessed and re-used for research studies [66] is essential for accumulating knowledge and collecting information about the current state of our territories affected by any threats.