esa-sceva/satcom-chunk-collection huggingface dataset:
# Pseudo-Spin Polarized One-Way Elastic Wave Eigenstates in One-Dimensional Phononic Superlattices
## 2 Model and Method  
### Model System and Dynamic Equations of Motion  
The binary superlattice system under study is formed by periodically repeating unit cells composed of two segments of different one-dimensional mass-spring harmonic chains (see Figure 1). Each mass is identical and equals 1; the stiffness levels of the springs in segments 1 and 2 equal \(K_{1}\) and \(K_{2}\), respectively; and the spacing between adjacent masses equals \(a\). The lengths of the segments are \(d_{1}=na\) and \(d_{2}=pa\), where \(n\) and \(p\) are integers. Segments 1 and 2 are separated by interfaces labelled I and II. In this model, we assume that the stiffness of the springs (bond strength) in a segment remains the same up to the interfaces. When \(n=1\) and \(p=1\), the system becomes the Su-Schrieffer-Heeger (SSH) model [12]. For large values of \(n\) and \(p\), in the long wavelength limit, the system approaches a continuous superlattice. The displacement of a mass, \(m\in[0,n-1]\), in segment 1 of unit cell \(N\) is defined as \(u_{A,m}^{N}\). Similarly, the displacement of masses in segment 2 of unit cell \(N\) is labelled  
Figure 1: Schematic representation of a one-dimensional discrete superlattice. A periodically repeating unit cell \(N\) is composed of equally spaced identical unit masses coupled through linear springs with stiffnesses of \(K_{1}\) and \(K_{2}\). See text for more details. with \(l\in[0,p-1]\). The masses obey bulk equations of motion in segments 1 and 2 for \(m\in[1,n-2]\) and \(l\in[1,p-2]\), namely:  
\[\frac{d^{2}u_{A,m}^{N}}{dt^{2}}=K_{1}\Big{(}u_{A,m+1}^{N}-u_{A,m}^{N}\Big{)}-K_{1 }\Big{(}u_{A,m}^{N}-u_{A,m-1}^{N}\Big{)} \tag{1a}\] \[\frac{d^{2}u_{B,l}^{N}}{dt^{2}}=K_{2}\Big{(}u_{B,l+1}^{N}-u_{B,l}^{N}\Big{)}-K_{2 }\Big{(}u_{B,l}^{N}-u_{B,l-1}^{N}\Big{)} \tag{1b}\]  
Equation (1a,b) supports plane wave solutions of the form:  
\[u_{A,m}^{N}=\Big{(}A_{+}^{N}e^{ik_{1}ma}+A_{-}^{N}e^{-ik_{1}ma}\Big{)}e^{i \omega t} \tag{2a}\] \[u_{B,l}^{N}=\Big{(}B_{+}^{N}e^{ik_{2}la}+B_{-}^{N}e^{-ik_{2}la}\Big{)}e^{i \omega t} \tag{2b}\]  
The wave number \(k_{j}\) with \(j=1,2\) is related to the angular frequency via the well-known dispersion relation of infinite harmonic chains:  
\[\omega^{2}=4K_{i}\Big{(}\sin k_{j}\frac{a}{2}\Big{)}^{2} \tag{3}\]  
The behavior of the superlattice will, therefore, be dependent on the overlap of the bulk bands (density of states) of the materials forming segments 1 and 2. Since the system is periodic with period \(L=d_{1}+d_{2}\), we consider Bloch wave solutions, i.e., we choose \(A_{\pm}^{N}=e^{i\eta NL}A_{\pm}\) and \(B_{\pm}^{N}=e^{i\eta NL}B_{\pm}\), where \(q\) is the wave number. We remove the upper script \(N\) on \(A_{\pm}\) and \(B_{\pm}\) when the periodicity is explicitly accounted for in the plane wave term \(e^{i\eta NL}\).
# Power Control Method for Energy Efficient Buffer-Aided Relay Systems
## 5 Proposed Power Control Method for Energy Efficient RN
\[P_{r}(d_{rd},P_{c})=-8.462+27.89d_{rd}+0.5436P_{c}-21.9d_{rd}^{2}+0.5985d_{rd}P_{ c}-0.008475P_{c}^{2} \tag{10}\]  
and it is shown in Figure 7. Similarly, for the optimal points, \(P_{r}^{o}\)'s of RN without a buffer in Figure 6 can be modeled as a surface. The quadratic polynomial surface of \(P_{r}^{o}\) of RN without a buffer can be modeled as  
\[P_{r}(d_{rd},P_{c})=-8.12+25.1d_{rd}+0.5353P_{c}-15.29d_{rd}^{2}+0.2674d_{rd}P_{ c}-0.003055P_{c}^{2}. \tag{11}\]  
Figure 6: Optimal power \(P_{r}^{o}\) of RN with and without a buffer over \(d_{rd}\) and \(P_{c}\), where \(P_{r}^{o}\) is obtained from the results in Figure 5.  
Figure 7: Surface fitted from \(P_{r}^{o}\) with a buffer in Figure 6 by using a polynomial surface fitting method. Using Equations (10) and (11), the RN can determine its transmit power \(P_{r}\) if it knows \(P_{c}\) and \(d_{rd}\). Here, it is worth noting that \(P_{c}\) is local information, which can be readily measured at the RN, and that the distance \(d_{rd}\) can also be measured at the RN based on the received signal strength from DN. Therefore, the proposed RN power control method can be easily implementable at the RN. For the applications of discrete-level power control, the quantized output of the quadratic polynomial surfaces in Equations (10) and (11) can be used. In this study, we leave the measurement or prediction method for \(P_{c}\) and \(d_{rd}\) as future work. In the next section, the proposed RN power control methods using Equations (10) and (11) are verified by evaluating the EE performance.
## 1 Introduction
The relationship between the error in acceleration time history calculated from displacement time history via the central difference method and the number of truncated modes is then investigated. Consequently, a calculation method for soil layer acceleration response under seismic excitations using the modal superposition method is developed. This approach has the following advantages for analyzing the acceleration response of site under seismic excitation, as follows: (1) It determines the mode number for the acceleration response of the soil sites under seismic excitations; (2) There is only the displacement response and no acceleration response to be calculated in the iterative calculation process, which would improve calculation efficiency of the equivalent linearization calculation. A homogeneous site and a scarp topography site are analyzed to verify the computational accuracy of the proposed method.
## 5 Results
### Seismic Event Results  
To begin the analysis of seismic records, a compilation of all recorded events throughout the study was made, given that the amount of individual data is limited. With these unified data, the same analysis approach used for ambient vibrations was continued, focusing on the same frequency range previously identified. This procedure allows a coherent comparison between the dynamic modes associated with seismic events and those obtained in the ambient vibration analysis, thus enriching the understanding of the structural behavior under different conditions.  
Based on this approach, the analysis of seismic records now considers all identified frequencies per analysis, focusing only on those with a participation factor greater than 10%. In Figure 15, the graph illustrating these identified frequencies by model order can be observed.  
Distinct frequency bands are observed, particularly around 2.4 Hz, 2.5 Hz, and 2.7 Hz, where higher participation factors are noted. This concentration at specific frequencies indicates that these ranges have a notable influence on the structural dynamics.  
To analyze the concentration of data by order in the seismic records, boxplots were constructed using the 2.5 Hz threshold as a reference, despite the limited amount of data. This analysis seeks to provide insights into the dispersion and variability patterns for each order, maintaining consistency with the results from the ambient vibration analysis. Figure 16 presents the chart, where frequencies above 2.5 Hz are shown at the top and frequencies below 2.5 Hz at the bottom.  
Figure 16: Boxplots of orders grouped by a frequency threshold of 2.5 Hz for seismic events. (**a**) Orders with frequencies greater than 2.5 Hz. (**b**) Orders with frequencies less than 2.5 Hz.  
Figure 15: Identified frequencies in the X direction per order for data filtered at 2.5 Hz for seismic events (only frequencies with >10% participation are shown).  
For frequencies above 2.5 Hz, a greater level of data dispersion is evident. The mean values across different orders in this range are higher than those observed in the ambient vibration records, potentially due to the smaller dataset available for the seismic analysis compared to the ambient vibration analysis. This increased dispersion may indicate that the structure exhibits greater variability in its dynamic response within this frequency range.  
In contrast, frequencies below 2.5 Hz display more uniform and stable dispersion, suggesting that the modes in this range are more consistent and less affected by dynamic interactions. Within this range, the frequency of 2.4 Hz emerges as a notable and stable value in the structure. This frequency holds particular relevance, as it is consistently identified in both seismic records and ambient vibration analyses, reinforcing its importance in the structure's dynamic behavior.
## 4 Discussion
More complex upscaling approaches or those utilizing very high-resolution sensors (e.g., LIDAR) to bridge the scaling gap may yield better results [102; 105; 106]. In particular, there is an exciting potential for unmanned aerial vehicles to provide critical upscaling information from field to satellite [21]. When using EO LAI products in the absence of reliable validation data, we recommend that the provided uncertainties are treated cautiously. Remote-sensing LAI products require improved algorithms with particular attention given to generating robust uncertainty estimates. New cloud and aerosol detection techniques based on time series and spatial analysis may help to improve the uncertainty products and LAI estimated for evergreen forests [107]. In addition, the new remote-sensing systems such as Global Ecosystem Dynamics Investigation (GEDI) [108], National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) and Synthetic aperture radar (SAR) provide promising ways to solve cloud coverage issues [109]. Given the importance of data uncertainties (e.g., integration within terrestrial carbon modeling frameworks [92; 93; 94]), there is an urgent need for robust uncertainty characterization, based on links to in situ observations. The findings in this study are constrained to southern China. Future studies could explore the LAI and error products for other regions, or focus on the seasons where products show the largest uncertainties [19].
## 3 Rice Phytoalexin Biosynthesis
### Biosynthesis of Rice Diterpenol Phytoaleoxins
#### 3.1.1 Biosynthesis of Momilactones  
Although several enzymes involved in momilactone biosynthesis have been identified, the order of known steps and the reconstitution of multiple characterized enzymes in vivo remains elusive [127; 128]. To date, the diterpene synthases OsCPS4 and OsKS4 responsible for the tricyclic momilactone scaffold formation (Figure 6) have been characterized [129; 130; 131; 132]. These enzymes are encoded by genes clustered with genes encoding cytochrome P450 enzymes (CYPs), _CYP99A2_ and _CYP99A3_, and a short-chain dehydrogenase reductase (SDR), _OsMAS_, in the biosynthetic gene cluster on rice chromosome 4 (c4 BGC) [125] (Figure 7). This BGC has also been found in the genomes of other _Oryza_ species with AA-genome, namely _O. rufipogon_ and _O. punctata_, as well as in the distantly related momilactone-producing species _Echinoclla crus-galli_ and _Caloluprum plumiforme_[113; 133; 45] (Table 1). CYP99A3 and/or CYP99A3 together with two non-clustered CYPs, CYP701A8, and CYP76M8 (coded by genes located on chromosome 6 and 2, respectively), oxidize the diterpene scaffold _syn_-primaradiene at different positions [134; 135] (Figure 8). The order in which these enzymes act to form a skeleton oxidized at different positions has not yet been fully understood [134; 135; 124]. In vitro tests showed that SDR encoded by _OsMAS_ genes is capable of oxidizing 3\(\beta\)-hydroxy-9\(\beta\)-pimara-7,15-dien-19,6\(\beta\)-olide to form momilactone A [120].  
Figure 7: Schematic diagram of loci corresponding to genes involved in diterpene biosynthesis in rice. The biosynthetic gene clusters on chromosome 2 (c2 BGC) and chromosome 4 (c4 BGC) are located between LOC_Os02g35970 and LOC_Os02g36300 and between LOC_Os04g09800 and LOC_Os04g10240, respectively (adapted from [119; 44; 119; 126]).  
More details on the biosynthesis and biogenesis of momilactones and related rice diterpenoids can be found in two excellent reviews recently published by [PERSON] et al. [20] and [PERSON] et al. [12].
## 5 Experimental Results and Simulations
### Evaluation of Classification Algorithms
By tuning the parameters using the hyper-parameter optimization method instead of manually selecting these parameters during the learning process enables the selected model to explore different sets of combinations of hyper-parameter values. This process will give us a robust tuning method for internal parameters based on minimizing the model classification error. Inspecting the results in Table 6 and Figure 5, it can be inferred that the best classifiers are KNN* and ensemble DT*. In this regard, only KNN* and ensemble DT* are adopted in the rest of the experiment.
# [PERSON] Glacier Status Analysis Based on Historical Data and Moving Detection Using RPAS
## 6 Data Processing
### RPAS Data Processing
#### 6.1.1 Creating of Point Clouds  
The data could only be processed in a laboratory at the university and not directly during the expedition. The calculation was performed in Pix4D and later in Metashape software; both software work with parameters from GNSS and IMU devices on board the eBee drone. Metashape software provides a more detailed point cloud, which is why these outputs were used in the next processing.  
A computing of the glacier shift measured from two points clouds is non-trivial, because of the nonlinear movement of the glacier. The glacier moves slowly at the edges and fastest in the middle. The difference or the discrepancies between two points clouds is usually calculated in CloudCompare software.  
By the computing in Metashape software, 74,416 tie points were found in the first set of images for a sparse point cloud. In the second set of images, the number of tie points was 92,693. After the depth map was created, 221,341,562 points for a dense point cloud were created for the flight and 231,635,556 for the second flight. Both point clouds were exported without modifying their original coordinate systems. It means that the second point cloud was not transformed into the coordinate system of the first point cloud.  
A point cloud, orthophoto, and DSM was made from each photo flight and exported to the ArcGIS software (Figures 10 and 11).  
Figure 9: The Heinkel seaplane with open cockpit for three persons (pilot, radio-operator, and photographer in the back (photo: the Arctic Institute, [[https://arktiskinsitiut.dk](https://arktiskinsitiut.dk)]([https://arktiskinsitiut.dk](https://arktiskinsitiut.dk))). The taking of the photos was carried out from a height of up to 4500 m at a temperature of \(-40\) degrees Celsius frequently. Nowadays, these conditions are hardly imaginable.  
Figure 10: Flight no.1 and flight no.2 (orthophoto in near infrared range). On the second orthophoto there are shadows, which make the picture more plastic.  
The absolute position error of point clouds and orthophotos is given by the onboard GNSS device, which was in this case a common GNSS working with code mode with a precision of 3-5 m. For this reason it was not possible to use originally geocoded data for computing of the glacier movement, which was expected in m only. However, the internal accuracy is much higher, and in this case reaches 20 cm according to the pixel size. The precision of points in the point cloud derived from the photographs depends on filtering  
Figure 11: Flight no.1 and no.2—digital surface model (DSM).  
procedures. Points computed from only two photos were deleted, such as points with errors more than three pixels on images.
# Challenges and Future Trends in the Treatment of Psoriasis [PERSON] and [PERSON]
## 4 Future Aspects in the Treatment of Psoriasis
### Biologic Agents and Small Molecule Inhibitors under Development
#### 4.1.3 IL-36 Inhibitors  
IL-36, a member of the IL-1 family, binds to its receptor and activates the NF-\(\kappa\)B and MAPKs pathways via the MyD88/IRAK complex [57]. A severe form of generalized pustular psoriasis (GPP) has been associated with loss-of-function mutations in the IL-36 Ra gene [58], as has plaque psoriasis. Recently, a small molecule inhibitor of IL-36 called A-552 has been shown to effectively inhibit IL-36\(\gamma\) and the production of other cytokines induced by IL-36\(\gamma\) in both human and mouse cells [59]. Furthermore, a monoclonal antibody targeting the IL-36 receptor, spesolimab, has demonstrated efficacy in a Phase I study, and Phase II and III studies evaluating its use in GPP are currently underway [60]. Therefore, anti-IL-36 agents hold significant potential in the treatment of psoriasis, and further research is needed to evaluate their efficacy and safety.  
Additionally, recent studies have suggested the potential therapeutic role of IL-37 and IL-38, which are MAST cell cytokines, in the treatment of psoriasis [61].
## 3 Results
### Input Data Intercomparison
#### 3.2.5 Soil Moisture (VWC)  
The soil volumetric water content (VWC) of the upper layer (0-10 cm), expressed in \(\mathrm{m^{3}}_{\mathrm{water}}\)\(\mathrm{m^{-3}}_{\mathrm{soil}}\), reveals a surprising behavior (Table 11 and Figure S8). The observation range is quite extended, from 0.10 to 0.40 \(\mathrm{m^{3}}_{\mathrm{water}}\)\(\mathrm{m^{-3}}_{\mathrm{soil}}\), but with very few observations larger than 0.30 \(\mathrm{m^{3}}\) water \(\mathrm{m^{-3}}_{\mathrm{soil}}\). The soil of the site in CO belongs to the category of the silty clay loam. None of the simulations is able to reach values lower than the respective wilting point (which is 0.22 \(\mathrm{m^{3}}_{\mathrm{water}}\)\(\mathrm{m^{-3}}_{\mathrm{soil}}\) for EXP1 and 0.15 \(\mathrm{m^{3}}_{\mathrm{water}}\)\(\mathrm{m^{-3}}_{\mathrm{soil}}\) for EXP2/3, in which soil belongs to the category of loam), except for the simulation 1a. Almost all simulations calculate a volumetric soil moisture whose distributions differ from the observed one: the majority of values are comprised in the range 0.25-0.40 \(\mathrm{m^{3}}_{\mathrm{water}}\)\(\mathrm{m^{-3}}_{\mathrm{soil}}\) for EXP1 and 0.30-0.40 \(\mathrm{m^{3}}_{\mathrm{water}}\)\(\mathrm{m^{-3}}_{\mathrm{soil}}\) for EXP2/3. Furthermore, EXP1a contains values lower that the wilting point for that soil (0.22 \(\mathrm{m^{3}}_{\mathrm{water}}\)\(\mathrm{m^{-3}}_{\mathrm{soil}}\)). As a result, simulated maxima values almost coincide with observed ones, while minima are overestimated, thus producing mean and median values overestimated. Mean and median values of EXP1a, b and EXP3a, b were coincident. Considering mean values, EXP1a, b were closer to the observed ones.
# Development and Testing of an Active Noise Control System for Urban Road Traffic Noise
## 3 Development of the Active Noise Control Prototype System
### Hardware Platform Construction  
The active noise control prototype system is composed of a field-programmable gate array (FPGA) development board, two audio modules, a reference sensor, an error sensor, and an active loudspeaker. The overall design is depicted in Figure 6, with a detailed breakdown of the hardware components presented in Table 1.  
In the operation of the system, the reference sensor is placed close to the noise source to collect the reference signal, and the error sensor is placed in the noise cancellation area to collect the error signal. Each sensor is connected to the audio input interface of the respective audio module, enabling real-time signal transmission. The audio module performs analog-to-digital conversion and filtering amplification before transmitting the signal to the FPGA for algorithm analysis. The resultant secondary source signal, after the audio module's digital-to-analog conversion and filtering amplification, is transmitted to the active loudspeaker. The secondary source sound wave emitted by the active loudspeaker cancels the noise source sound wave in the noise cancellation area, reducing the noise level in the desired area.  
Figure 6: Hardware platform design of active noise control prototype system.
## 6 Results and Discussion  
The result is that the total costs for an insert made by AM and CM are comparable, while the common thinking is that AM products are more expensive to produce [7]. The fact that they are comparable can be related to the cost associated with the cutting tools for the machining step, which represent a big proportion of the final price, and often their impact is either not included or considerably underestimated.  
If we consider that cutting tools are changed for every insert, the calculation will actually show a total cost of 12,304 \(\epsilon\) for AM and 12,868 \(\epsilon\) for CM. In this case, the AM product is cheaper than the CM product, thanks to the production of a near-net shape component. It is, therefore, essential to strictly monitor the wear and the changeover of cutting tools to avoid unexpected costs.  
Another consideration is the amount of time dedicated to labor and machine time for each stream. With a one-laser LPBF machine for AM, the total lead time is longer than CM (167 h against 88 h), but 89% of the AM time is taken by the machines working unsupervised. On the CM flow, only 68% is only machine time. Another consideration is the use of a newer LPBF machine for the AM chain, for example, equipped with four lasers, which would make the printing approximately 3.5 to 4 times faster.  
It is important to consider that the final AM and CM product is not the same. AM allows to produce a mold insert that has double the number of cavities, and thus improves the performance in the injection molding phase. As a rough evaluation, it was known that the CM insert (16 cavities) has a cycle time of 9.1 s, while the AM insert (32 cavities) has a cycle time of 10.1 s. Considering an insert lifetime of 10 million shots, this corresponds to 117 continuous days for the AM insert and 105 days for the CM insert. With a difference 12 days, 160 million more molded elements can be produced with the AM insert. That also means that, during the same production time, e.g., 100 days, the AM and CM tool inserts will have potentially produced close to 27.4 and 15.2 million parts, respectively, with a potential productivity increase of 80.2% during the same production time of the AM inserts compared with the CM inserts.