esa-sceva/satcom-chunk-collection huggingface dataset:
# Metal Carbides for Biomass Valorization
## 4 Conclusions and Perspective  
Transition metal carbides have been shown to be good candidates to substitute noble metal-based catalysts for biomass transformation. Their metal-acid bifunctionality makes them effective catalysts for the hydrogenolysis, hydrogenation and isomerization of biomass-based feedstock to fuels and chemicals. Metal carbides exhibit similar catalytic performance to those of Pt-group metals, but present a lower cost. For example, W\({}_{2}\)C has attracted much interest, because it exhibits high catalytic performance in a variety of reactions, such as hydrogenation and hydrodeoxygenation of cellulose, hemicellulose and lignin derived molecules. Another example is W\({}_{2}\)C/AC, which is highly active and selective in the cleavage of aryl C-O bonds in lignin, breaking the \(\beta\)-O-4 bond as well as the \(\alpha\)-O-4 and \(\beta\)-\(\beta\) linkages, which are the more resistant ones. Moreover, the C-O bond cleavage aptitude, important in the HDO of lignin, was kept constant for up to 5 cycles.  
A drawback of these catalytic systems is deactivation, which has been often observed in the liquid phase reaction, due to coke deposition and leaching, but also due to the over-reduction of the carbide to the metallic form. It has been shown that one strategy to enhance the metal carbides' stability is to deposit them on the external surface of a support, such as on activated carbon or carbon nanotubes. However, a strong interaction between the metal carbides and the support is never observed, and the deactivation due to leaching of the active phase is still an issue that needs to be solved. A possible solution to overcome the low durability of these systems is the incorporation of finely dispersed carbide nanoparticles into, rather than onto, the porous supports, such as CNTs or mesoporous carbon. This incorporation not only allows a much better control of the particle size during the synthesis, but also provides a further stabilization of these carbides during the reaction. Moreover, several attempts in promoting theses carbides with Ni or Co. are reported, but more efforts should be dedicated to improving the activity over a longer time and the selectivity to the desired products of reaction. Trusting that the properties of transition metal carbides can soon be improved, it can be confidently stated that these environmentally green catalysts represent an efficient step forward in the challenge of the cost-efficient production of aromatic chemicals from biomass.  
[PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Fitting of the Formula for the Average Oil Volume Percent Inside the Bearing  
The average oil volume percent inside the bearing is the basis for judging the lubrication state of the bearing and is also the foundation for analyzing the fluid viscosity friction of the bearing. In order to predict the oil distribution inside the under-race lubrication ball bearing at different operating conditions, a multiple linear regression method is used to fit and establish an estimation method for the average oil volume percent in the bearing ring cavity. Through the above analysis, due to the insignificant impact of oil density changes on the average oil volume percent, only the effects of three sensitive parameters,  
Figure 23: Radial oil volume fraction at different oil densities.  
Figure 24: Oil volume fraction near the zone of inner ring and outer rings at different oil densities.  
Figure 25: The ratio of oil volume fraction between the zone of the outer and inner ring at different oil densities.  
namely bearing rotating speed, oil flow rate, and oil viscosity, were considered. The fitted expression is as follows:  
\[X_{oil}=1593\frac{Q^{0.949}\mu^{0.117}}{N^{0.875}} \tag{14}\]  
where \(X_{oil}\) is the average oil volume percent inside the bearing; \(Q\) is the oil flow rate, expressed in L/min; \(\mu\) is the oil viscosity, expressed in Pa-s; \(N\) is the bearing rotating speed, expressed in rpm.  
As shown in Figure 26, this is a comparison of the calculated results obtained using the formulas mentioned in this paper with those in references [PERSON]'s [12] and [PERSON]'s [13]. It can be seen from the figure that the results obtained using [PERSON]'s [12] formula are higher than those obtained using the other two formulas. This is because [PERSON]'s [12] formula provides an estimation expression for the average oil volume percent inside jet lubrication ball bearings, while [PERSON]'s [13] formula is more suitable for under-race lubrication bearings. In this case, the expression provided in this paper is considered reliable as it is in close agreement with [PERSON]'s [13] formula.
# A Review of Supply Chain Resilience: A Network Modeling Perspective
## 2 Overview of Supply Chain Resilience Development
### Fundamental Concept of Resilience
#### 2.1.2 The Relationship Among Resilience, Flexibility, Robustness, Vulnerability, and Reliability
Flexibility emphasizes a system's adaptability, allowing it to adjust to meet new demands or environments without necessarily maintaining its original functionality. Highly flexible systems can cope with dynamic environments but may not consistently demonstrate strong robustness. Resilience highlights a system's capacity to adapt and recover after experiencing damage. A system may fail to function during disruptions (indicating low robustness) but can restore normal operations through rapid repair or adaptive adjustments (indicating high resilience) [22; 23]. For instance, a system can be robust (capable of operating under external disturbances) but vulnerable (susceptible to specific types of attacks). Likewise, a highly flexible system is better equipped to adapt to future changes, while a resilient system can recover quickly from disruptions. Furthermore, flexibility generally pertains to operational-level management, whereas resilience and robustness are more relevant to strategic considerations [24]. These system characteristics overlap yet have distinct focal points. A simple analogy can help illustrate these system characteristics. Consider a tree as a system: when strong winds blow, a tree with high flexibility bends with the wind and survives. A resilient tree also bends but returns to its upright position once the wind subsides. In contrast, a robust tree remains upright during the windstorm without significant impact [25]. In summary, vulnerability represents a system's weaknesses, robustness denotes its ability to resist disturbances, flexibility refers to its capacity to adapt to changes, and resilience describes its ability to adapt and recover. These characteristics can coexist, but the design and requirements of different systems may emphasize one trait over others. A conceptual comparison of these properties is shown in Figure 2.
# Gait Phase Estimation Based on User-Walker Interaction Force
## 2 Method for Asymmetric Gait Phase Estimation
### Adaptive Rules for Transient Parameters
#### 2.4.1 Switching Mechanism  
Robotic walkers are designed for users who require assistance or rehabilitation to enhance their physical abilities. Many people suffer from neuromuscular impairments or muscle atrophies, which lead to asymmetric gait patterns [21]. Thus, consecutive steps can notably vary in timing, hindering gait phase estimation using one AO.  
In the 1990s, various control approaches with model switching were introduced to improve the performance of adaptive control under large and rapid changes in the system dynamics [25; 26]. In particular, [PERSON] [27] proposed switching between different controllers for a plantar biped walker to achieve high control stability under different cadences. Similarly, we adopt a simple switching mechanism for AOs to track rapid changes in the walking frequency. Specifically, two AOs with the same structure work in parallel, but only one is active at any instant. As the states of \(r\), \(\varphi\), \(\alpha\) are shared between the AOs, their values are used by the active AO. On the other hand, there are two independent frequencies, \(\omega_{1}\) and \(\omega_{2}\), for the two AOs corresponding to the two legs.  
During estimation, the value of phase \(\varphi(t)\) monotonically increases. A modulo operation is applied to the gait phase estimated by the AOs:  
\[C_{step}=mod(\varphi(t),2\pi) \tag{8}\]  
Switching occurs when \(C_{step}\) reaches \(2\pi\), and the gait characteristics of two consecutive steps are estimated by two different AOs. Thus, a sudden change in frequency caused by an asymmetric gait pattern between legs is considered by the proposed switching mechanism.  
For some users, there is a substantial difference between the two AOs in amplitude. However, the amplitudes of both AOs vary substantially during walking. The improvement in estimation accuracy from using two independent amplitudes for AOs is limited. Furthermore, two AOs switch when the reconstructed signal \(y\) is at its peak; therefore, the switch in amplitude will lead to a sudden change in the waveform of the reconstructed signal in a discontinuous manner. It is a perturbation to the AOs, which will eventually degrade the accuracy of phase estimation. We can choose a large \(\delta\) which can scale the converge rate of \(\alpha\), so that the AO can achieve a higher estimation accuracy when the amplitude of the input signal changes rapidly.
# Low-Cost Handheld Spectrometry for Detecting Flavescence Doree in Vineyards
## 5 Conclusions  
This study evaluated the field applicability of low-cost portable VIS-NIR and NIR spectrometers for detecting FD symptoms in a cv. A Garganega vineyard was cultivated by using all bands and reduced features in the range of 350-840 and 900-1700 nm. Five field surveys were carried out to acquire the spectral data at different growing stages to identify the best suitable time to identify FD-associated signals in reflectance data. Five different classifiers were used to identify the healthy and diseased groups by using all bands in addition to the extracted features by ensemble and GA methods. The results of the study suggest that spectral wavelengths have discriminatory power to differentiate the healthy from the infected vines. The critical bands are found in the spectral range of 374, 392, 395, 416, 479, 534, 622, 654, 683, 692, 713, 756, and 798 nm, and were associated with symptomatic as well as asymptomatic samples. Moreover, such common wavelengths could differentiate healthy and phytoplankton-infected leaves throughout the different phenological stages of grapevines across the growing seasons. The GA-based features classification accuracies were significantly higher than those obtained with all bands. Overall, the proposed low-cost optical sensors in the VIS-NIR domain were found suitable under field conditions for non-destructive photoplasma detection in grapevines, at an early stage when the disease symptoms are not visible on the leaves. Future studies will be needed to collect more spectral data from photoplasma-infected and healthy leaf samples of other grapevine varieties in order to consolidate the results reported in the present study and extend their validity. Moreover, additional studies on different biotic and abiotic stressors using the same approach would be of utmost importance to address the crucial issue of remote sensing specificity in plant disease diagnosis.  
Conceptualization, R.G., I.I., F.M., A.P., A.Z. and H.A.I.; data curation, H.A.I., I.I., A.P. and A.Z.; methodology, H.A.I., A.Z., I.I., F.M., A.P., D.D., A.B. and R.G.; formal analysis, A.Z., H.A.I.; writing--original draft preparation, H.A.I. and A.Z.; writing--review and editing, H.A.I., A.Z., A.P., F.M.; supervision, R.G., F.M. and A.P. All authors have read and agreed to the published version of the manuscript.  
This work is part of a project funded by the Autonomous Province of Trento under the provincial law 13/12/99 number 6 art. 5 to Metacortex S.r.l for research and development.  
Not applicable.  
The authors would like to thank the Autonomous Province of Trento for the valuable opportunity, [PERSON] (AGREA, Verona), and [PERSON] (Fondazione Edmund Mach, San Michele All'adige TN) for the collaboration, and for the knowledge sharing.  
The authors declare no conflict of interest.
## 3 Results  
### Environmental Factors and GPP Annual Variation Characteristics  
#### 3.1.1 Intra-Annual Variation Characteristics of the Daily Average of Environmental Factors  
In 2020, the environmental factors (PAR, Tsoil, Tair, and VPD) of each underlying surface showed significant seasonal fluctuations (Figure 4). The attribute values of environmental factors were significantly higher in spring and summer and significantly lower in autumn and winter. Among these values, the annual average of PAR was approximately 375.79 \(\upmu\)mol/m\({}^{2}\)/s in grassland areas, 376.84 \(\upmu\)mol/m\({}^{2}\)/s in desert vegetation areas, and 341.75 \(\upmu\)mol/m\({}^{2}\)/s in farmland areas. The non-growing season's PAR values were lower than those in other seasons and the highest values were observed in desert vegetation areas. The daily average maximum of Tsoil was approximately 24.61 \({}^{\circ}\)C (June) in grassland areas, 34.36 \({}^{\circ}\)C (July) in farmland areas, and 38.62 \({}^{\circ}\)C (July) in desert vegetation areas. Tsoil peaked in June and July, and the highest daily average values were in desert vegetation areas. The variation characteristics of Tair were similar to those of Tsoil (first increasing and then decreasing). The maximum values of Tair occurred at the same time as those of Tsoil. The highest daily average values of Tair occurred in desert vegetation areas (approximately 32.78 \({}^{\circ}\)C, July), followed by farmland areas (approximately 29.70 \({}^{\circ}\)C, June), and grassland areas (approximately 18.72 \({}^{\circ}\)C, July). The daily average of VPD fluctuated significantly, reaching its maximum value in July. The daily average of VPD in desert vegetation areas ranged between 0.08 and 1.38 kPa, in grassland areas it ranged between 0.04 and 1.21 kPa, and in farmland areas it ranged between 0.05 and 1.60 kPa. Desert vegetation areas had the highest annual average of VPD values among all underlying surfaces.  
The SWC did not show apparent seasonal variation characteristics across the different underlying surfaces. It had overall irregular variations and generally low values (Figure 4). The largest fluctuations occurred in spring due to snowmelt, and then the fluctuations  
Figure 4: Intra-annual variation characteristics of the daily average values of five environmental factors (PAR, VPD, SWC, Tair, and Tsoil) at each station.  
reduced due to rising temperatures and sparse precipitation. Overall, the variation characteristics of SWC varied depending on the underlying surface, with the order being farmland areas > grassland areas > desert vegetation areas.
# Protograph LDPC Code Design for Asynchronous Random Access +
## 3 Code Design for the Asynchronous Random Access Channel  
### Protograph LDPC Codes  
We propose protograph-based binary LDPC code designs tailored to the interference that occurs in an asynchronous RA scenario. The parity-check matrix of these codes is derived from a relatively small matrix, called a base matrix \(\mathbf{B}\), which represents the code constraints. Alternatively, we may describe the base matrix as a bipartite graph, termed protograph, with \(n_{b}\) variable nodes (VNs) and \(m_{b}\) check nodes (CNs). Non-zero entries \(b_{i,j}\) in \(\mathbf{B}\) represent connections between VNs of type \(j\) and CNs of type \(i\). In order to improve the code performance, we may decide to puncture some of the VN types in the base matrix whose number is denoted by \(p_{b}\). The number of not punctured variable nodes is denoted by \((n_{b}-p_{b})\). The parity check-matrix of a protograph-based \((n,k)\) LDPC code is obtained by expansion or lifting of the base matrix. This is done by copying the protograph \(n/(n_{b}-p_{b})\) times and interconnecting the copies among each other following certain rules (see [38] for details). In this work, we consider standard Gray-labelled quadrature amplitude shift keying (QPSK) modulation where the binary labels of a symbol consist of two bits. Thus, a packet with \(n_{s}\) symbols is protected by an LDPC code with codeword length \(n=2n_{s}\) bits.
## 3 Results
### Evaluation Results with Cyst Scatterers  
Figure 6a shows the results of evaluating the robustness against positional errors in the imaging of cyst scatterers. The CNR was highest with the linear array when the positional error was within a range of \(\pm 3\) mm. However, the CNR of the linear array was lower than that of the optimized array when the positional error was outside the range of \(\pm 3\) mm. The CNR of the matrix array and the optimized array showed little variation regardless of the angular error, and under all conditions, the optimized array exhibited a higher CNR. Figure 6b shows the results of evaluating robustness against the angular errors. The CNR with the linear array was higher than the other two arrays when the angular error was within the range of \(\pm 20^{\circ}\). However, at an angular error of \(\pm 30^{\circ}\), the optimized array exhibited the highest CNR.  
Figure 7 shows the examples of the cyst scatterer US images obtained with the linear, matrix, and optimized arrays under four different conditions: two types of position errors (0 mm and 3 mm) and two types of angule errors (0\({}^{\circ}\) and 20\({}^{\circ}\)). US images obtained under all error conditions where both positional and angular errors are present are illustrated in Figures S4-S6. In the images of the linear array with no positional or angular errors, all cysts are most clearly visualized with the average CNR of \(-0.01\) dB. With positional and angular errors, however, the boundaries of the cysts are unclear. Particularly, with the 3 mm positional error and 20\({}^{\circ}\) angular error, the two cysts on the left side are completely invisible and the average CNR decreased to \(-7.98\) dB. In the images of the matrix array, the cysts are mostly invisible mainly due to the grating lobe artifacts, and even with no positional or angular errors, the CNR was \(-12.33\) dB. In the images of the optimized array, all four cysts are visible with little image quality variation regardless of the positional and angular errors, and the average CNR was around \(-6.26\) dB in all cases.  
Figure 6: Evaluation of the robustness of the element arrays against positional and angular errors in the imaging of cyst scatterers. (**a,b**) The average CNR of the four cysts in US images obtained under various positional and angular errors, respectively.
## 2 Fundamentals
### IM-OFDM Module
Subsequently, the baseband discrete signal undergoes digital-to-analog conversion (DAC) and is upconverted to radio frequency (RF) band before transmission. The passband analog signal can be expressed as  
\[\mathbf{x}_{\text{rf}}=\Re\Big{\{}\mathbf{x}_{\text{t}}e^{j(2\pi f_{c}t+\phi_{c})} \Big{\}}, \tag{4}\]  
where \(f_{c}\) and \(\phi_{c}\) are the carrier frequency and initial phase of transmitted signal, respectively. Then, the signal is sent via the Gaussian fading channel.
## 4 New Virtualization Solutions for Network Functions
### Nesting Virtualization with Kata Containers  
Kata Containers is a project started in 2017 and managed by the OpenStack Foundation. It wraps containers into dedicated microVMs and provides strong isolation with a flexibility similar to regular containers. The Kata Containers' runtime (kata-runtime) is compatible with both the Open Container Initiative (OCI) runtime specification and the Kubernetes Container Runtime Interface (CRI). Therefore, it works seamlessly with Docker Engine and the Kubernetes kubelet, deploying a KVM VM for each container or pod created, with multiple hypervisors supported. The deployment of VNFs on Kata can take advantage of some features that its microVMs expose, such as multiple interfaces, DPDK, single-root input/output virtualization (SRIOV), and MacVTaP.  
Figure 6 shows how Kata Containers expose Containerd and CRI to support the Kubernetes scheduling. Containerd-shim-kata-v2 acts as a mediator between Containerd and Kata containers. With its introduction, Kubernetes can launch Pod and containers with one shim per Pod instead of 2N+1 shims, with a significant efficiency improvement. Kata-agent, a daemon running within the microVM that is responsible for the spawning containers processes, communicates with contained-shim-kata-v2 by using a gRPC server exposed via VSOCK interface. The gRPC protocol allows the runtime (integrated into shim-kata-v2) to send container management commands to the agent and carry the I/O streams between the containers and the manage engines [36]. In the NFV architecture, OpenStack can be used as a VIM to manage resources, through its Nova service. It can interoperate with Kubernetes, as already demonstrated for Baidu Container Instances in Ref. [39]. OpenStack itself, either using its Zun container service or leveraging a Kubernetes cluster, can take advantage of Kata efficiency and safety. A Kata microVM can be deployed on top of multiple hypervisors, such as QEMU, NEMU or Firecracker, specifically aiming to create and manage microVMs.  
Figure 5: NetBricks defines an NF connecting blocks from five different programming abstraction.
# Drone and Controller Detection and Localization: Trends and Challenges
## 3 UAV Detection Methods  
As mentioned in Section 2, there are many sectors in which UAVs have been explored and adopted, utilizing their practical and advanced features. The continuous development and improvement of UAV's main systems and components, i.e., flight controller, sensors, gyroscopes, cameras, GPS, etc., increased the demand and reliance on UAVs for accomplishing different civilian and military missions. Moreover, they are widely available in the market at a reasonable cost compared to other solutions.  
Research has been dedicated to designing, developing, and implementing systems for detecting malicious UAVs. Techniques of these systems are classified into passive and active. RF-based, acoustic, and vision-based techniques are among the passive technologies, whereas radar-based techniques are defined as active technologies. These technologies vary in operational conditions, covering range, consistency, accuracy, and many other parameters. This section focuses on UAV detection technologies and discusses the general framework and related work.
# Multi-Scale Convolutional Recurrent Neural Network for
## 2 Background and Related Work
### Long Short-Term Memory (LSTM)  
Using an RNN, one can effectively model time series data [39; 40]. However, there is a limitation with RNNs, and long short-term memory (LSTM), developed to compensate for the shortcomings of the RNN, solves the vanishing gradient problem of the RNN [41; 42]. Compared to an RNN, LSTM has an additional gate called the forgetting gate. It is impossible for existing RNNs to learn from the distant past, but LSTM can learn events from the distant past and can handle not only high-frequency signals but also low-frequency signals. This advantage of LSTM is that it shows good performance in processing time series data. The general structure of LSTM is as follows.  
\[i_{t}=\sigma(W_{i}x_{t}+U_{i}h_{t-i}+P_{i}c_{t-1}+b_{i}) \tag{3}\]  
\[f_{t}=\sigma\Big{(}W_{f}x_{t}+U_{f}h_{t-1}+P_{f}c_{t-1}+b_{f}\Big{)} \tag{4}\]  
\[o_{t}=\sigma(W_{o}x_{t}+U_{o}h_{t-1}+P_{o}c_{t}+b_{o}) \tag{5}\]  
\[c_{t}=f_{t}\circ c_{t-1}+i_{t}\circ T(W_{c}x_{t}+U_{c}h_{t-1}+b_{c}) \tag{6}\]  
\[h_{t}=o_{t}\circ T(c_{t}) \tag{7}\]  
where \(x_{t}\) is a vector input to the current time point \(t\), \(W\) is the weight connecting the input to the LSTM cell, \(U\) is the weight connecting the previous memory cell state and the LSTM cell, \(P\) is the diagonal peephole weight matrices, \(T\) represents tanh nonlinearities, and \(b\) is bias.