esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Neural Network Interpretability Through Deep Prior-Guided Expected Gradients
## 2 Related Work
### Expected Gradients  
Expected gradients builds upon integrated gradients by addressing the issue of reference point selection, which can introduce bias in feature attribution. EG averages gradients computed from multiple reference points sampled from a distribution, providing a more robust evaluation of feature importance [22]. The EG for feature \(i\) is formally defined in Equation (5).  
\[\text{ExpectedGradients}_{i}(x):=\int_{x^{\prime}}\text{ IntegratedGradients}_{i}(x,x^{\prime})p_{D}(x^{\prime})dx^{\prime} \tag{5}\]  
which can be expanded to reveal Equation (6).  
\[\text{ExpectedGradients}_{i}(x)=\int_{x^{\prime}}\Bigl{(}\bigl{(}x_{i}-x^{ \prime}_{i}\bigr{)}\times\int_{\alpha=0}^{1}\frac{\delta f(x^{\prime}+\alpha( x-x^{\prime}))}{\delta x_{i}}d\alpha\Bigr{)}p_{D}(x^{\prime})dx^{\prime} \tag{6}\]  
An equivalent stochastic representation is given by Equation (7).  
\[\text{ExpectedGradients}_{i}(x)=\mathop{\mathbb{E}}_{x^{\prime}\sim D,\alpha \sim U(0,1)}\Bigl{[}\bigl{(}x_{i}-x^{\prime}_{i}\bigr{)}\times\frac{\delta f( x^{\prime}+\alpha\times(x-x^{\prime}))}{\delta x_{i}}\Bigr{]} \tag{7}\]  
This equation highlights the process of calculating the gradients of the model output \(f\) along the path between the reference point \(x^{\prime}\) and the input \(x\). Each gradient value is scaled by the difference between the input feature and the reference feature \(\bigl{(}x_{i}-x^{\prime}_{i}\bigr{)}\), reflecting the contribution of that feature to the prediction of the model. By sampling reference points \(x^{\prime}\) and the integration variable \(\alpha\), the integral is approximated as an expectation, leading to an efficient calculation of the expected gradients.  
Expected gradients enhances model training by optimizing feature attributions for smoothness and sparsity, improving interpretability while reducing overfitting [22]. By sampling multiple baselines, EG mitigates bias and embeds attribution directly into training objectives, balancing performance and transparency. Its applications span image classification, NLP, and high-stakes domains like medical diagnosis and autonomous systems.
## 2 Design Concept  
Figure 1a presents the spacecraft's in-orbit launch design for in-orbit vibration isolation. The base frame is fixedly connected within the in-orbit launch system. When the launch outlet is closed, the spacecraft \(m\) is supported by four springs. Two vertical springs, \(k_{1}\) and \(k_{3}\), provide negative stiffness, and, together with the other two transverse springs, \(k_{2}\) and \(k_{4}\), form quasi-zero stiffness characteristics. Therefore, these four springs constitute a quasi-zero stiffness (QZS) vibration isolation system to isolate the vibration \(\dot{y}\) generated by the base framework.  
Figure 1b shows the spacecraft's in-orbit launch design for in-orbit launch. When the launch outlet is open, the spacecraft \(m\) is supported by the two pre-stretched vertical springs \(k_{1}\) and \(k_{3}\) and the pre-compressed transverse spring \(k_{2}\). The vertical springs \(k_{1}\) and \(k_{3}\) produce negative stiffness to enhance launch speed. As shown in Figure 2, first, in the initial state (State 1), the spacecraft \(m\) is prepared for release and then disconnects from these three springs when it moves to the position of State 2. Finally, the spacecraft \(m\) leaves the base frame (the in-orbit launch system) with an initial velocity \(v_{0}\).  
Figure 1: Schematic of the spacecraft in-orbit launch design: (**a**) vibration isolation and (**b**) in-orbit launch.
## 3 Result Analysis
### Energy Consumption
As shown in Figure 10, local damage increases the energy consumed by the test beam, which causes a reduction in the overall energy consumption. With the U\(-\)shaped hoop in place, the overall energy dissipation ratio is somewhat lower than for the B1 and B3 test beams, showing that the test beams' overall damage is currently the same. The U\(-\)shaped hoop's placement, however, causes the stiffness of the beam body to increase further and the local damage to worsen, which reduces the total energy dissipation rate. The remaining energy of each test piece during the impact process ranged from 22.48% to 59.35%, mainly consisting of the energy associated with crack development, concrete crushing energy, friction energy between steel bars and concrete, steel plate and beam peeling energy, vibration energy between the beam body and steel plate, and slip energy. The remaining energy proportion of the beams with the bonded steel plate was smaller than that of the beam without reinforcement. This was due to an increase in the overall energy dissipation capacity of the reinforced beam, a decrease in the overall damage, and a reduced risk of beam fracture development. The concrete cover peeled off simultaneously as a result of the steel plate and the tendency of concrete to peel. Therefore, it is believed that the predominant sources of residual energy are the energy associated with the creation of cracks, the energy required to crush concrete, and the energy associated with the peeling of the steel plate and beam.
## 3 Model Design
### Working Time
#### 3.1.1 Degree of Intimacy  
The degree of intimacy between nodes can be roughly divided into two types: one is a direct communication node pair, and the other is an indirect communication node pair. The number of encounters of nodes \(i\) and \(j\) of direct communication at time \(t\) is expressed as \(noc_{ij}(t)=\sum\limits_{i=1}^{n}1\). The number of times that nodes \(i\) and \(k\) of indirect communication meet at time \(t\) is expressed as \(noc_{ik}(t)\). The number of times \(k\) and \(j\) meet at time \(t\) is expressed as \(noc_{jk}(t)\). The above situation is equivalent to \(i\) and \(j\) exchanging message. Through the above analysis, the intimacy function of nodes \(i\) and \(j\) at time \(t\) can be computed by the following formula, where \(\mu\) is a constant integer and the threshold of the number of node encounters:  
\[DI(t)=\beta\times\frac{\min(\mu,noc_{ij}(t))}{\mu}+(1-\beta)\times\frac{\min( \mu,\sum\limits_{k\in N_{ij(t)}}\min\left(noc_{ik}(t),noc_{jk}(t)\right))}{\mu} \tag{1}\]  
The first part of the above formula indicates the direct encounter strength of the node. If the direct encounter times between nodes \(i\) and \(j\) are larger than \(\mu\), then the strength of the two nodes is equal to one, which proves that the relationship between the two nodes is more intimate. The second part of the above formula represents the indirect encounter strength of the node. The more common friends they have, the closer they will be. \(N_{ij(t)}\) is defined as a collection of encountering nodes. \(\beta\) is the weight proven by a large number of experiments, indicating the impact of direct encounter strength and indirect encounter strength on the calculation of the degree of intimacy.
## 3 Experiment and Turbulent Wind Generation
### Turbulent Wind Flow Characteristics in the Wind Tunnel  
The DDTG may be subjected to turbulent winds in different surrounding terrain conditions. The turbulent features appear to have a direct influence on the buffeting force on the bridge girder. Therefore, the turbulence effect on the bridge girder should be taken into account.  
Usually, the turbulent flow in the wind tunnel tests was generated by spires, cubes, or a combination of them. In Bj-1, the segment model should be attached to the wind tunnel sidewalls at their mid-height (see Figure 2), and the wind field produced by the cubes had little effect on the aerodynamic force on the segment model due to their location and distance to the wind test region. Therefore, the turbulent flows in this test were only produced by four spires (1.5 m in height; see Figure 4).  
Figure 4: Experimental setups of turbulent flows (m): (**a**) spires in the wind tunnel; (**b**) set of spires (unit: m).  
Figure 3: Cross section of the segment model for the service and construction stages: (**a**) for the service stage; (**b**) for the construction stage.  
The characteristics of the flows simulated in the wind tunnel were measured using a Cobra Probe (Turbulent Flow Instrumentation, Victoria, Australia) in front of the test model, at the same height as the segment model, which could resolve three components of the mean and fluctuating velocities and had a sampling frequency of up to 2,000 Hz. In this test, the sampling frequency of the probe was set to 1024 Hz, and the sampling time was 180 s. To examine the uniformity of the turbulent flow field, the wind data were repeatedly measured at the front position of the test model. In the wind tunnel, the turbulent flow behind the spires has a horizontally isotropic character [26], while the longitudinal and vertical wind components can be described by the Kaimal spectrum (Equation (8)) [27] and Panofsky spectrum (Equation (9)) [28], respectively. Figure 5 shows a comparison between the longitudinal and vertical power spectral densities (PSDs) measured in turbulent flow fields and the target PSDs. The two curves in Figure 5a,b are mainly in good agreement. Therefore, the frequency range of the wind simulated in the wind tunnel can meet the requirements of the wind tunnel test.  
\[S_{u}(n)=\mu_{*}^{2}\frac{200f}{n(1+50f)^{5/3}}\ (\text{Kaimal spectrum}) \tag{8}\]  
\[S_{w}(n)=\mu_{*}^{2}\frac{6f}{n(1+4f)^{1/2}}\ (\text{Panofsky spectrum}) \tag{9}\]  
where \(f=nz/U\), \(n\) is the wind frequency; \(\mu_{*}\) is the friction velocity; and \(u\) and \(w\) indicate the longitudinal direction and vertical direction, respectively.
## 4 Evaluation Results and Discussion
### Performance Analysis
#### 4.2.4 Analysis of the Second Scenario of \(Vnr_{2}\)
Notwithstanding this, from the data depicted in Table 13, it becomes clear that the QUAKE metric identifies the diverse demands an XR-enabled application has. Thus, it primarily suggests longer paths than \(BEF\), like \(BCDGF\) and \(BCD HGF\). These selections exhibit balanced measurements for \(PJ\) and \(L\), and simultaneously abundant BW and CPU resources, by having significantly high \(RNR^{norm}\) and \(RCR^{norm}\) values. By expanding the scenario boundaries to all calculated paths, like in the case of \(VNR_{1}\), Tables 14 and 15 reveal that the QUAKE-based resource evaluation strategy continues to yield overall better candidate path performance-wise. Particularly, more than half of the two-hop and three-hop paths, prominent choices of the SP methodology, are unacceptable mapping candidates, due to inadequate \(RNR^{norm}\) and \(RCR^{norm}\) measurements, as Table 14 highlights. The same inefficiency is exhibited by the delay-based procedure. Table 15 shows that over 50% of the paths with the second most desirable set of \(L\) values and prominent choices of the delay-based strategy would lead to significantly inferior embeddings (matching the fourth and fifth QUAKE value groups), while almost a third of the ones that hold the best delay-oriented profiles and pose as top suggestions of the \(L\)-oriented evaluation mechanism fall into the third and fourth groups of QUAKE values. To recapitulate the findings of the second experimental scenario, the QUAKE framework evaluates and suggests the most suitable networking resources for XR-based industrial activities. By providing a four-dimensional description of all paths, it constantly offers \(VNR_{2}\) choices with balanced \(PJ\) and \(L\) values and, at the same time, robust \(RNR^{norm}\) and \(RCR^{norm}\) measurements. In contrast, the SP- and delay-based strategies fail to identify the complex needs of an immersive application such as \(VNR_{2}\) and promote overall unsuitable candidate paths for its embedding stage.
# Green Production and Biotechnological Applications of Cell Wall Lytic Enzymes
## 4 Production of CWLEs in Microbial and Plant Expression Systems
### Heterologous Expression of CWLEs in Plants
martiima_ is strongly reduced in the temperature range of plant growth (green box) and increases the chance of preserving the productivity of the transgenic plant. AnPGII: endo-1_A_-\(\alpha\)-polgalacturonase II from _A. niger_ (98); Cel6B: endo-1_A_-\(\beta\)-glucanase from _T. fusca_, (100); En-Cel E1: endo-1_A_-\(\beta\)-cellulase from _A. cellulolytica_, (102); and CBHI: cellobiohydrolase I from _T. reseei_, (104). reaction mixture from the contamination of mesophilic and thermophilic microbes [123]. Importantly, high temperatures inactivate plant CWLE-proteinaceous inhibitors, which are localized in the cell wall as a defense mechanism [38; 124; 125], thus preventing them from interfering with enzymatic degradation. Moreover, the robust protein structure, a common feature of many HCWLEs, allows their use even in the presence of anionic surfactants, extreme pH conditions, and harsh chemicals [126]. In turn, industrial practices can exploit these severe conditions in order to deconstruct lignocellulosic, favoring downstream enzymatic reactions [127]. Another attractive strategy resides in the use of microalgae as a platform for the expression of CWLEs. Microalgae are promising expression hosts since they are characterized by a relatively fast growth cycle, and their cultivation is less expensive compared to that of other microorganisms (e.g., bacteria and yeasts) [128]. Moreover, differently from higher plants, microalgae do not require arable lands for their cultivation, thus avoiding the loss of areas that may be employed in the agri-food sector. Contrary to plant cells, some species of unicellular green algae, such as _Chlamydomonas reinhardtii_, possess a cell wall mainly constituted by proteins [129]; thus, the lack of polysaccharides in their cell wall circumvents the deleterious effects of expressing CWLEs in plants. Although _C. reinhardtii_ secretes two different endo-\(\beta\)-1,_A_-glucanases upon cellulose perception [130], these endogenous enzymes are not sufficient alone to efficiently degrade hydrolysis-recalcitrant substrates such as lignocellulosic. Thus, an interesting perspective is to express a set of secreted CWLEs (including pectinases, hemicellases, and cellulases) in a unique algal culture, similar to yeast systems that have yielded strains able to grow on cellulose [81]. Alternatively, the expression of CWLEs can be carried out in the microalgal chloroplast in order to maximize the yield of recombinant proteins by circumventing gene-silencing events that affect nuclear expression [131].
# Quadcopters in Smart Agriculture: Applications and Modelling
## 4 Modelling of Quadcopters
### Movements  
Quadcopters can reach specific locations and modify their height and attitude based on four basic movements in the cross configuration, shown in Figure 10:  
* Thrust: this movement occurs when all the rotor speeds are decreased or increased by the same value, causing the quadcopter to raise or lower its altitude;
* Roll: This movement occurs when increasing the speed of rotor 1 and decreasing the speed of rotor 3 or vice versa, while keeping rotors 2 and 4 at the same speeds. This creates a torque around the \(x\)-axis with respect to the B-frame, as shown in Figure 11, causing the quadcopter to roll;
* Pitch: This movement occurs when increasing the speed of rotor 4 and decreasing the speed of rotor 2 or vice versa, while keeping rotors 1 and 3 at the same speeds. This generates a torque around the \(y\)-axis with respect to the B-frame, as shown in Figure 11, causing the quadcopter to pitch;
* Yaw: This movement occurs by increasing the speed of rotors 2 and 4 and decreasing the speed of rotors 1 and 3 or vice versa. This creates a torque around the \(z\)-axis with respect to the B-frame, as shown in Figure 11, causing the quadcopter to yaw.
# The Effect of Ageing on Chemical and Mechanical Properties of Asphalt Mortar
## 2 Materials and Methods
### Experimental Methods  
As an efficient, convenient, and inexpensive tool, the FTIR is widely used to determine the chemical characteristics of bituminous materials. In the infrared spectrogram of bitumen, the absorbance bands corresponding to carbonyls and sulfoxides show a significant increase with ageing [22; 23; 24]. The chemical characterization in this study was performed using the Spectrum 100 FT-IR spectrometer from PerkinElmer. A single-beam configuration was used. The sample was scanned 20 times with a fixed instrument resolution of 4 cm\({}^{-1}\). The wavenumbers were set to vary from 600 to 4000 cm\({}^{-1}\). At least three repetition tests were performed for each ageing condition.  
Figure 2: Asphalt mortar sample: (**a**) geometry of sample; (**b**) silicone mold for sample preparation; (**c**) measuring system for cylinder sample; (**d**) silicone mold for sample storage.  
DSR is commonly used to investigate the rheological properties of bituminous materials [25; 26; 27]. In this study, the linear viscoelastic (LVE) strain range of the bitumen and asphalt mortar samples were initially determined using amplitude sweep tests. Within the LVE strain range, frequency sweep tests were performed at four different temperatures (0, 10, 20, and 30 \({}^{\circ}\)C). During the tests, the frequency varied in a logarithmic manner from 50 Hz to 0.01 Hz. Uniaxial tensile tests were performed at 0 \({}^{\circ}\)C with 0.2% (0.024 mm/s) tensile rate. The load and deformation in the testing process were automatically collected with a frequency of two measurements per second.
# Research Progress and Future Prospects of Key Technologies for Dryland Transplanters
## 1 Introduction
Tests revealed that the seedling extraction success rate exceeded 90%, but the substrate integrity rate decreased with increasing rotational speed, accompanied by an increase in seedling damage. Owing to the rapid advancement in seedling picking mechanisms, modern transplanting machines have evolved from semi-automatic models requiring manual seedling picking to fully automatic systems featuring integrated automatic seedling picking. This evolution has effectively addressed challenges such as labor shortages, high labor intensity, low efficiency, and elevated operational costs inherent in manual and semi-automatic transplanting. Seedling picking mechanisms have thus become a core research focus in vegetable transplanting technology [37; 38]. In summary, transplanting is an indispensable component of modern agriculture. It can effectively increase crop yields and land resource utilization, while mechanized transplanting can significantly reduce labor demand in transplanting operations. For transplanting machinery, semi-automatic transplanters requiring manual seedling feeding and fully automatic transplanters capable of autonomous seedling extraction have been developed. Additionally, numerous researchers have conducted extensive studies on their key components (seedling picking mechanisms). However, there remains a lack of systematic synthesis in the field of dryland transplanting machinery. Therefore, this paper reviews the research progress of dryland mechanized transplanting technologies and equipment. It summarizes the research progress of mechanized transplanting technologies and equipment for arid regions. It outlines the research status of typical mechanized transplanting equipment and key technologies, focusing on the working principles, structural characteristics, and design methods of various transplanting mechanisms. The article further analyzes the challenges in mechanized transplanting in drylands, provides suggestions and references for the research of mechanized transplanting technologies and equipment, and forecasts the future development trends of mechanized transplanting technologies based on these analyses. This work aims to offer guidance and references for the research and development of dryland transplanting technologies and equipment.
## 2 Related Work
### Graph-Based and Hypergraph-Based Methods  
Graph-based and hypergraph-based methods have gained increasing attention in location prediction due to their ability to model high-order and non-Euclidean interactions among users and locations. [PERSON] et al. [20] construct multi-view graphs--including global location prediction feature and distance graphs, as well as user-specific historical graphs--to jointly capture long-term preferences and spatial constraints through the fusion of GCN and Transformer architectures. Similarly, Graph-Flashback [21] builds a spatial-temporal knowledge graph (STKG) to encode weighted location prediction transition patterns, employing GCNs and personalized similarity functions to refine sequential representations and enhance user-specific modeling. To further enrich relational semantics, HKGNN [22] leverages hyper-relational knowledge graphs by integrating user-location prediction-time mobility patterns, social ties, and location prediction attributes into a unified hypergraph structure. It combines a hypergraph neural network with attention-based encoders and decoders to capture complex structural and contextual dependencies, yielding robust performance under data sparsity.  
Hypergraph-based models extend beyond conventional pairwise graphs by modeling multi-entity interactions as hyperedges. DHCL [23] constructed hypergraphs with three different perspectives to decouple and represent user preferences. MSTHN [24] constructs an enhanced spatial-temporal hypergraph that captures both local sequential dependencies and global collaborative signals, applying a dual-level aggregation strategy to adaptively represent dynamic user behaviors. STHGCN [25] also adopts a hypergraph approach, aggregating multi-hop spatiotemporal trajectory information to better model complex check-in patterns. While these methods demonstrate strong capabilities in modeling structural relationships, most do not explicitly disentangle the diverse behavioral factors,such as collaborative co-visitation, sequential mobility, and geographic proximity, into independent representations, potentially limiting generalization. In contrast, our proposed framework addresses this gap through multi-perspective hypergraph construction and contrastive disentanglement.  
While traditional collaborative filtering and matrix factorization methods offer foundational insights into user-location preferences, and RNN and attention-based models advance sequential and contextual modeling, they often fall short in capturing complex, high-order, and multi-relational dynamics. Graph and hypergraph-based approaches have further pushed the boundaries by modeling structured interactions and high-order associations, yet most existing models still entangle heterogeneous behavioral signals into unified latent spaces, hindering adaptability. Motivated by these limitations, our work proposes a novel framework that explicitly disentangles user preferences from collaborative, sequential, and geographical perspectives using multi-perspective hypergraphs and contrastive learning, thereby enabling more robust and personalized location prediction.