esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Composition of the Resins  
The resin was extracted using dimethyl ether (DME) in a solid liquid extractor. DME is a promising green solvent applicable for the extraction of organic molecules from biomaterials; it has a low boiling point (\(-23\)\({}^{\circ}\)C), a medium polarity, and a partial miscibility with water and is a good alternative to conventional solvents because it is safe and environmentally friendly [27]. Using this technique of extraction, we obtained satisfactory yields in resin: 17.7% in the Kompolti cultivar and 19.1% in the Tisza cultivar (Table 1). Table 2 shows the results of the analysis of the resin extracted from the infloresences of the two _Cannabis_ varieties under consideration. From these data, it is possible to observe that we have a high identification rate: over 95% of the constituents in the resin were identified for both varieties. In particular, 97.74% of the constituents present in Tisza and 95.97% of the constituents present in Kompolti were identified. CBD dominates; it is the main component of the resin in both varieties, constituting 84.21% \(w/w\) in Tisza and 86.54% \(w/w\) in Kompolti. There is also a modest quantity of THC; compared to CBD, the THC levels are significantly lower, with delta-9-THC ranging from 0.56% to 0.89% and delta-8-THC ranging from 1.68% to 2.45%. In both varieties, non-psychotropic cannabigerol, normally a minor constituent of _Cannabis_, is present in modest concentrations (0.75% in Tisza and 1.16% in Kompolti). During plant growth, most of the cannabigerol is converted into other cannabinoids, mainly tetrahydrocannabinol (THC) or cannabidiol (CBD), leaving approximately 1% cannabigerol in the plant. There are no other compounds present in high concentrations except for the two terpen beta-myrcene and alpha-pinene, whose total concentration does not exceed 3% (see Table 3).
## 3 Results and Discussion  
The potential application of avocado biomass for the adsorption of contaminants, including toxic metals, has been previously described [43; 72; 73; 74; 75; 76]. [PERSON] and [PERSON] [43] published a review considering several pretreatments of raw avocado fiber (including sodium hydroxide and phosphoric acid, among others) but did not mention that ethylene glycol or ethanol pretreatments were used. However, efficiency testing of avocado fibers for contaminant removal is widespread. [PERSON] [90] compared avocado with two other materials (sour orange and walnut) and found that the avocado leaf-based fiber adsorbed the most Pb. The activated carbon obtained from avocado was more efficient than that obtained from the other fibers. [PERSON] and [PERSON] [91] evaluated the adsorption of a mixture of metals on avocado-based activated carbon. Copper (Cu), Cd, and Pb were adsorbed in the highest quantities (up to 700 mg for Cu). For Cd, the efficiency was the highest, reaching 99.5%. [PERSON] et al. [73] also mentioned the high performance of modified avocado biomass for Pb and Cr retention in water samples. In addition, [PERSON] et al. [92] managed to remove hexavalent chromium using modified avocado seeds.  
These studies were performed using a batch method (simple agitation and maceration) and showed promising results indicating that avocado seed and peel biomass can effectively retain and immobilize metal ions such as Cd\({}^{+2}\), Cu\({}^{+2}\), Cr\({}^{+6}\), Ni\({}^{+2}\), Pb\({}^{+2}\), and Zn\({}^{+2}\). Nevertheless, studies regarding fixed-bed columns as filters made of avocado peel and seed waste have not been found. This is particularly important considering that, in developing countries, high costs often limit the use of conventional treatment methods on a large scale. Therefore, the application of the technical-economic feasibility of the remediation process based on the adsorbent properties of locally available materials at a small scale is the first step to the recurring challenge of removing toxic metals from aqueous solutions, with possible future applications in wastewater decontamination.
# Two-Path Spatial-Temporal Feature Fusion and View Embedding for Gait Recognition
## 3 Proposed Method
### View Embedding Module
#### 3.3.2 HPP with View Embedding  
In this section, horizontal pyramid pooling (HPP) [4] is utilized on the multi-scale spatial-temporal feature maps and then the weights under the best view are connected with these multi-scale spatial-temporal features by matrix multiplication to acquire the final feature.  
Figure 6: The detailed structure of view prediction.  
The multi-scale spatial-temporal feature maps after HPP can be denoted as \(F_{HPP,j}\), \(i=1,2,3,\cdots n\), where \(n\) means the number of strips into split in HPP, \(F_{HPP,j}\in R^{D}\). Supposing the predicted view of \(F_{ST}\) is \(\theta\), the embed procedure can be realized by:  
\[F_{final,i}=W_{i}F_{HPP,i} \tag{16}\]  
\[F_{final}=\left[F_{final,1},F_{final,2},\cdots,F_{final,n}\right] \tag{17}\]  
where \(i=1,2,3,\cdots n\), \(W_{i}\in Z_{\theta}\), \(F_{final,i}\in R^{D}\), \(F_{final}\in R^{n\times D}\).
## 5 Experiment and Analysis
### Experimental Environment and Parameters Setting
#### 5.1.1 Experimental Data  
The data details used in the DSKG construction process are illustrated in Table 2.  
An example of data annotation is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c|c c} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Entities & 1203 & Relations & 1193 \\ Event Feature & 212 & Describe & 94 \\ Casting Event & 425 & Lead to & 445 \\ Quality Defect Solution & 154 & Feature is & 231 \\ Defect Characteristics & 81 & Take action & 164 \\ Quality Defect Name & 103 & Cause & 259 \\ Cause of Quality Defect & 223 & Total & 2396 \\ \hline \end{tabular}
\end{table}
Table 2: Data details in DSKG.  
Figure 6: Example of data labeling.  
1.2 Hyperparameter Setting of DSKG Construction Method for Quality Engineering of Continuous Casting  
The hyperparameter settings for the SAPRM and the knowledge alignment model are presented in Table 3. All the hyperparameters were tuned empirically within reasonable ranges via grid search based on validation performance.
# Privacy and Security in Digital Health Contact-Tracing: A Narrative Review
## 3 Results
### Social Influence and Attitudes  
#### 3.7.1 Social Influence  
Social influence refers to how people's behaviours are affected by the actions of others. In digital contact-tracing, the adoption decision was seen to be influenced by the people around them. In a study conducted in the United Kingdom across three waves, it was found that individuals who believed that people in their social circles (family, friends, work) supported the use of the contact-tracing app were more willing to adopt it [58]. A study conducted in India found that social influence is a significant positive factor in app adoption, and often seek their approval before considering adopting contact-tracing applications [114]. In a study conducted in France, a significant positive indirect effect of subjective norms on the intention to use was reported through the mediation of perceived usefulness [129]. In the United States, it was discovered that the endorsement of an injunctive norm and a descriptive norm positively impacted both the intention to adopt and actual behaviour [109]. A Singaporean study revealed that both injunctive norms and descriptive norms were significant positive determinants influencing the adoption of contact-tracing apps [95]. The social pressure and recommendations from influential individuals or groups encouraged users to adopt the contact-tracing app adoption in Malaysia [131]. In Belgium, social influence had a positive impact on intention to adopt contact-tracing apps [53].  
In contrast, in a Belgian study, social environment and societal factors did not encourage some people to download the contact-tracing app [70]. In line with this, social risk, social concern, and social interaction were not identified as significant determinants of behavioural intention in Saudi Arabia [38].
# The Efficiency of Calcium Oxide on Microbial Self-Healing Activity in Alkali-Activated Slag (AAS)
###### Abstract  
Alkali-activated slag (AAS) materials are one of the most promising sustainable construction composites. These novel materials are highly characterized by their improved mechanical and durability properties. Nevertheless, the high shrinkage rate hinders their full-scale applications. The low Ca/Si ratio, complex hydration process, and fine pore microstructure are the main causes of the reported shrinkage behavior. This study introduces _Bacillus subtilis_ culture for healing the cracking behavior. The enzymatic action leads to precipitating calcium carbonate crystals that fill AAS cracks and pores. Incorporating calcium oxide has been recommended in multiple studies. The main purpose of adding calcium oxide is to enhance the engineering properties of AAS and provide more calcium ions for the biochemical reactions induced by the added bacteria. However, inconsistent findings about the influence of calcium oxide have been reported. This research provides further insights into the effect of calcium oxide (CaO) on the performance of microbial self-healing efficiency in AAS composite. The results highlight that incorporating calcium oxide as 7% of the binder partial replacement has an impact on the engineering properties of bio-AAS materials. The study recommends correlating the percentage of free calcium ions within the AAS mixture with the microbial activity.  
alkali-activated slag; self-healing; microbial self-healing; bacteria; _Bacillus subtilis_; calcium oxide
# Damage and Failure in a Statistical Crack Model
## 2 Statistical Crack Models
### Crack Growth  
An isolated crack will grow when the applied stress at \"infinity\" exceeds a critical value. The critical value may be modified by the collective effect of many other cracks which enhance the stress felt at the crack. The critical value for isolated crack growth may also be reduced by the local effect of nearest neighbors. In all these situations, I will refer to this stage as it refers to an individual crack as growth in Griffith mode.  
In BCM, the critical stress is prescribed by a generalized Griffith criterion [1]. Griffith theory [13] predicts that the critical stress depends inversely on the square root of the crack radius so that bigger cracks are weaker and begin to grow sooner under increasing loading. The long range (collective) effects of all the other cracks in the cell are included indirectly in BCM through self-consistent field corrections to the effective moduli, described below. The effect of local interactions with nearest neighbors is not included in BCM. Note that the generalized [PERSON] criterion predicts that cracks may grow in tension, in shear and possibly even in compression. For the case of only normal-tension \(\sigma_{yy}\), a crack may grow if its radius is greater than the critical radius  
\[c_{crit}\equiv Gr/\sigma_{yy}^{2}\,. \tag{1}\]  
Here, the Griffith constant  
\[Gr=\frac{\pi TE}{2(1-v^{2})} \tag{2}\]  
where \(E\) is [PERSON]'s modulus, \(v\) is [PERSON]'s ratio, and \(T\) is the surface tension, i.e., the energy required to create new surface area.  
[PERSON]'s criterion is based on a thermodynamic argument that cracks become unstable to growth when the strain energy released by growth exceeds the energy required to build new crack surface. The excess of energy over the critical balance goes into the kinetic energy of the crack and determines the velocity with which the crack grows. [PERSON] [3] derived a model for crack propagation velocity by adding the kinetic energy to the [PERSON] energy balance. [PERSON] [14] derived estimates for the asymptotic velocity, though the issue of a maximum achievable propagation velocity is still not settled [18]. BCM uses [PERSON]'s law with the Rayleigh wave speed \(v\) as its asymptotic velocity:  
\[\frac{dc}{dt}=v\sqrt{1-\frac{c_{crit}}{c}} \tag{3}\]  
Here,  
\[v\equiv\left[\frac{0.87+1.12v}{1+v}\right]\sqrt{\frac{2G}{\rho}}, \tag{4}\]  
where \(G\) is the shear modulus, \(v\) is Poisson's ratio and \(\rho\) is the material density.  
Cracks may also grow by coalescence, in which two individual cracks interact and merge into one larger crack. Coalescence was not considered in BFRACT nor in BCM, but will be part of the model described in this paper. I will refer to cracks that grow by coalescence as being in avalanche mode, for reasons that will become more apparent when the crack interaction model is described.
## 2 Design and Development of 3D Curvature Sensor of FBG  
### Curvature Sensing Principle of FBG  
Due to the cross-sensitivity of temperature and strain of FBG, the relationship between the Bragg wavelength change \(\Delta\lambda_{B}\) and the axial surface line strain \(\varepsilon\) of the measured position of the FBG sensor can be expressed as follows [28]:  
\[\Delta\lambda_{B}=\lambda_{B}(1-P_{\varepsilon})\varepsilon+\lambda_{B}( \alpha_{\Lambda}+\alpha_{n})\Delta T \tag{1}\]  
where \(\lambda_{B}\) is the initial wavelength of the FBG; \(P_{\varepsilon}\) is the effective elastic optical coefficient of optical fiber, and the value is about 0.22; \(\alpha_{\Lambda}\) and \(\alpha_{n}\) are, respectively, the thermal expansion coefficient and the thermal light coefficient of optical fiber; \(\Delta T\) is the temperature variation.  
Under the condition of constant temperature [27]:  
\[\Delta\lambda_{B}/\lambda_{B}=(1-P_{\varepsilon})\times\varepsilon \tag{2}\]  
The 3D curvature sensor of FBG selected in this study is a circular section. Under the condition of pure bending, the relationship between the axial strain and the curvature of the elastic beam with a circular section is as follows:  
\[\varepsilon=r/\rho=r\cdot K \tag{3}\]  
where \(r\) is the distance from the fixed surface of the FBG to the neutral surface; \(\rho\) is the radius of curvature of the measuring point; \(K\) is the curvature corresponding to this point. Given \(r\) and \(K\) in Equation (3), the strain \(\varepsilon\) of the FBG can be obtained. According to Equation (2), the strain \(\varepsilon\) is directly proportional to the change of the central wavelength \(\Delta\lambda_{B}\) of the FBG, so the curvature \(K\) is directly proportional to \(\Delta\lambda_{B}\). Therefore, according to Equations (2) and (3), the relationship between \(K\) and \(\Delta\lambda_{B}\) can be obtained:  
\[K=\frac{\Delta\lambda_{B}}{(1-P_{\varepsilon})\lambda_{B}r}=\frac{\Delta \lambda_{B}}{M} \tag{4}\]  
where \(M\) is defined as the curvature sensitivity coefficient of the FBG, which is a constant value and proportional to \(r\). According to Equation (4), it can be known that  
\[\Delta\lambda_{B}=\frac{M}{\rho} \tag{5}\]  
Thus, the relation between the change of Bragg wavelength and the radius of curvature of the measuring point can be obtained.
## 2 Materials and Methods
### Methodology
#### 2.2.1 Gully Inventory Map  
Accurately predicting and modeling gully erosion susceptibility requires a high-quality gully erosion map is essential, which thus must be carefully prepared. We obtained an inventory map with 242 gully locations from the Administration of Natural Resources of Markazi Province. The gullies were mapped from aerial photographs and satellite images and were confirmed in the field. Typically, gullies in the study area have concave and vertical heads, indicating that they are active. Longitudinal  
Figure 3: Flowchart of the study.  
Figure 2: Examples of gully erosion in the study area.  
profiles are typically straight to convex, but gully widths differ greatly. Gullies on agricultural land commonly have V-shaped cross-sections, whereas those on rangeland more commonly are U-shaped.  
Depending on map scale, a gully may be considered a point or a polygon. Most authors who have studied gully erosion consider the heads of gullies to be gully locations [76; 99; 100], because gully heads are the sources of much of the sediment carried by the gully channels and delivered to the fluvial system below [101; 102]. However, some researchers have used grid cells to create gully polygons to prepare gully erosion susceptibility maps [92; 103; 104], whereas others have converted gully polygons to points using 'feature to point' tool in ArcGIS software [105]. However, an active gully is a dynamic landform, and its head moves landward over time as erosion proceeds. A gully consists of three parts: its head, the main channel, and its end point. For long gullies, we used these three points to define their locations. For short gullies, we considered only the head location point. For this study, we randomly selected 242 non-gully locations in the study area. We randomly chose 70% (169) of the mapped gullies to construct the model for gully erosion; the remaining 30% (73) were used to evaluate the predictive performance of model (Figure 3).
## 3 Results
The first graph in Figure 10 represents the displacement (green color) of the active joints corelated with the motion of the tip of the instrument from Figure 10, the second  
Figure 10: Time history diagram of the active joints. Figure 9: Time history diagram of the coordinates at the tip of the instrument. graph represents the velocity (blue colour) of the active joints, and the last graph represents the acceleration (red color) of the active joints. Figures 9 and 10 show the time history diagrams for both the end-effector and active joints. These diagrams display smooth trajectories, consistent velocities, and controlled accelerations, which indicate that the system can follow the desired trajectory accurately and with minimal jitter. The acceleration profiles and joint speed data suggest that the system responds smoothly to control inputs without excessive overshoot or oscillation. This dynamic consistency is crucial for maintaining operational precision during surgical maneuvers, where any abrupt motion could compromise safety and accuracy. While the simulation studies strongly support the asserted advantages, we acknowledge that further experimental validation using phantom models is necessary to fully verify the operational precision and stability in real surgical environments. In order to confirm the validity of the optimization process several comparisons regarding the robot before are analyzed. Table 1 presents the results of the comparison of the robot before and after the optimization. Reachability and workspace were analyzed for the 6951 points recorded in the first step of the algorithm. With the initial configuration of the RCM [100, 0, 300], 5241 points out of 6951 yielded real values for the active joint of the robot, while for the optimized RCM [119.003337, \(-\)146.610801, 269.07376], the inverse geometric model returned real solution for 6906 points, revealing a 23.96% increase in reachability. Using the same values for the RCM, the workspace of the robot (handle of the instrument) was analyzed, the analysis revealed a 30.54% increase in the workspace of the robot. The second comparison refers to the optimization of the kinematic links. The value before optimization of the links was [380, 295, 80], and after optimization [215, 250, 50], the comparison revealed a 10.89 increase in reachability and 1.35 increase in the workspace of the robot. The genetic algorithm combined with fuzzy logic successfully determined optimal robotic configurations based on critical performance metrics, such as robot workspace, surgical instrument insertion depth, and the position of the instrument outside the patient by determining the optimal position of the RCM and the optimal length of some structural links. Simulation tests demonstrated robust system performance, confirming that the optimized parameters resulted in superior workspace, better reachability, and thus, better manoeuvrability and reliability. Additionally, virtual simulations validated the practical feasibility of integrating the optimized parameters into a functional parallel robotic system within an operating room setting.
# Anomaly Detection through Grouping of SMD Machine Sounds Using Hierarchical Clustering
## 4 Experiment
### Dataset
#### 4.1.1 Data Preprocessing  
Before training the model, we transformed the data into spectrograms \(\mathbf{s}\), representing the data in the time-frequency domain. \(\mathbf{s}\in R^{T\times D}\), where \(T\) is the length of the spectrogram and \(D\) is the number of frequency bins. As shown in the Time column in Table 1, the lengths of the data vary, and the length \(T\) of the converted spectrogram \(s\) also varies. We use temporal adaptive average pooling (TAAP) [9], which is an effective data preprocessing method that can reduce the variable length \(T\) to a target length \(L\) using an adaptive kernel. TAAP is calculated as follows:  
\[k=\left\lceil\frac{T}{L}\right\rceil, \tag{26}\]  
\[\overline{s}_{j}=\frac{1}{k}\sum_{i=j\times k}^{(j+1)\times k-1}s_{i}, \tag{27}\]  
where the \(\lceil\ \rceil\) symbol refers to the ceiling function, \(j=0,1,\cdots,L-1\). \(T\) is a multiple of \(L\); otherwise, zero-padding is used for spectrogram \(s\). By using TAAP, data corruption can be minimized, and since it can be reduced to a fixed length, the calculation speed is increased because the data are not truncated or padded when configuring the batch, and the model can be trained reliably. After TAAP, we normalize the sequence \(\overline{s}\) by min-max normalization.
## 5 Validation of RPAL SCAOF in Simulations
### Adaptation and Improvement of Simulator, Protocol Stack and Application  
The COOJA cross-level simulator platform is adopted in order to evaluate the above proposals. Thanks to the TI MSP430x MCU emulator models, it could help install a full version of the uIPv6 protocol stack with RPAL model and perform evaluation experiments [72]. Therefore, this simulation can provide cross-level debugging for IWoTCore boards [71], and parts of the MiLive WMSN platform [73].  
Moreover, the IEEE 802.15.4 PHY layer is the foundation of the IoT network stack recommended by the ROLL WG [74]. It will be the only available choice before the standards of low-power Wi-Fi, Bluetooth and Power Line Communication (PLC) are ready. ROLL WG had a clear statement about IEEE 802.15.4 MAC layer, especially its cluster topology and beacon-enabled mode, which are not well suited for LLNs because these two mechanisms are extremely power consuming due to network management and resynchronization procedures. From the perspective of low-power networks, radio transceivers must be switched off as long as possible to save energy, so the MAC layer solution in Contiki has been adopted and validated by RIME stack [75] and IoT compatible uIPv6 stack.  
To prove the RPAL routing protocol can be adapted to agricultural applications, a compatible application protocol is required to equip A-LLNs with WoT technologies. More importantly, the affiliation of CoAP, RPL, IPv6 and 6 LoWPAN can be evaluated in the constrained environment of A-LLNs. CoAP is a RESTful application layer protocol which is designed for low-power embedded networks and considered energy, memory and processing constraints of wireless embedded devices [76]. As precision agriculture applications have multiple services, this evaluation work will also present the performance results when a specific application is considered as a WoT use case [77] from the perspective of network protocols rather than building an agricultural web model. We choose the CoAP implementation in Contiki OS which can be applied rapidly [78].  
To sum up, the accessions of 6 LoWPAN and CoAP enable better evaluation of the RPAL model since an incomplete netstack without low level layers (_i.e._, IEEE 802.15.4 PHY/MAC and RDC protocol) or high level layers (_i.e._, 6 LoWPAN, IPv6 and CoAP) will not be available to support the full functions of RPAL. Thus, a complete netstack should be adopted (Figure 4). Moreover, due to this flexible netstack, the RPAL model can cooperate well with other emerging protocols and models which are the driving forces pushing traditional WSN technologies into the era of IoT in the agricultural domain.