esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Theoretical Procedures
#### 2.2.4 MD Simulation and Stability  
At this stage of the research, the goal is to analyze the stability of the complexes formed between the synthesized molecules and BSA protein, comparing these results with the reference complex of Aspirin bound to BSA. This evaluation will further allow for an assessment of the synthesized molecules' potential to inhibit BSA protein activity and their anti-inflammatory properties. To achieve precise and reliable stability measurements, a molecular dynamics simulation was conducted over 300 ns.  
The optimal synthesized molecules from the docking studies, alongside Aspirin as a reference, were further examined through molecular dynamics simulations employing Gromacs-2023 and the CHARMM36 force field to assess their interactions and stability [39]. Moreover, SwissParam was utilized to generate detailed molecular parameters for the synthesized molecule and the reference ligand, ensuring accurate input for further simulation processes [40]. The simulations were conducted for 300 ns in dodecahedral boxes using the TIP3P water model on Ubuntu (v 24.04), with the systems neutralized. Subsequently, the solvated systems underwent energy minimization via the steepest descent method until the maximum force fell below 10.0 kJ/mol. NVT equilibration was performed for 500 ps at 300 K using a V-rescale thermostat. Following this, NPT equilibration was conducted for 100 ps employing a Berenson pressure coupling method with a coupling constant of 2.0 ps [41; 42]. Finally, to comprehensively assess the stability and binding affinity of each system, we carried out a series of calculations, including the root mean square deviation (RMSD), to monitor the structural variations over time, the root mean square fluctuation (RMSF) to evaluate the mobility of individual residues and the radius of gyration (Rg) to analyze the compactness of the protein structure. Additionally, we measured the solvent-accessible surface area (SASA) to determine the extent of exposure to solvent molecules. The free energy landscape (FEL) was constructed to visualize the energy profiles associated with different configurations, while the dynamic cross-correlation matrix (DCCM) was used to investigate correlated movements within the protein. Finally, we employed the MM-PBSA method to estimate the binding free energy of the complexes, providing further insights into their interactions.
## 3 Trajectory Data
### Data Assumption  
As mentioned in Section 3.1, this study assumes that PTDP data are the location data that constitute the trajectories. The set of IDs linked to the mobile devices (where each ID is linked to exactly one mobile device) is denoted by \(K\), and the set of location data corresponding to ID \(k\in K\) is denoted by \(L_{k}\). Note that all the IDs are reset on a daily basis. If the maximum length of the location data in a day is denoted by \(\mathcal{T}\), the location data size for a day is \(|L_{k}|\leq\mathcal{T}\), i.e.,  
\[L_{k}=\{\mathbf{x}_{k,t}\mid t\in\mathbb{N}_{\leq\mathcal{T}}\},|L_{k}|\leq \mathcal{T},\]  
where \(\mathbb{N}_{\leq\mathcal{T}}\) is a natural number that is less than or equal to \(\mathcal{T}\). Note that \(\mathbf{x}_{k,t}\) is a point of ID \(k\) on the geographic coordinate system at time \(t\), and \(\mathbf{x}_{k,t}=(x_{k,t}^{\text{lon}},x_{k,t}^{\text{lat}})^{\top}\), \(x_{k,t}^{\text{lon}}\in[\text{180}^{\circ}\) W-180\({}^{\circ}\) E], \(x_{k,t}^{\text{lat}}\in[\text{90}^{\circ}\) S-90\({}^{\circ}\) N].  
Although the acquisition timing of location data \(\mathbf{x}_{k,t}\) is at a unit time \(t_{\text{unit}}\) interval, if a mobile device with ID \(k\) does not cause any event, location data are not acquired. Hence, for almost all sets \(L_{k}\) for \(\mathcal{T}\), a large portion of the location data acquired by mobile devices are missing, i.e., \(|L_{k}|\ll\mathcal{T}\).
## 3 Results
### Modeled LAI  
The modeled LAI obtained through the inversion of the SCOPE model showed a clear seasonal pattern, where the value of LAI started to increase with each measurement until it reached its peak in July. After that, it tends to decrease towards its starting value for both sites. The modeled LAI of CR vegetation is steadily increasing from the end of February till its peak in July, while the modeled LAI of CL vegetation increased moderately till the middle of May and then more steeply till the peak in July. There were no statistically significant differences among treatments for any day of measurement, except modeled LAI on 16 June for CR vegetation (Supplementary Table S1), when LAI of W is significantly higher than LAI of C (\(p\)-value = 0.0258).  
The differences between the two vegetation types (CL and CR) were observed to be significant only around the peak of the season (i.e., summer; Supplementary Table S2).  
The correlation of measured LAI with modeled LAI and discussed VIs was observed to be weak (Figure 5). In contrast, the modeled LAI was well correlated with all examined green biomass-related VIs (NDVI, EVI, NIRv; Figure 5).  
Figure 3: The changes in measured and modeled Leaf Area Index (LAI) along with Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and near-infrared reflectance of vegetation (NIRv) for CL climate manipulation site at a different time in the year of 2021. C is for the control plot, W is for warming plots, and WP represents the warming and reduced precipitation plot.  
Figure 4: The changes in measured and modeled Leaf Area Index (LAI) along with Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and near-infrared reflectance of vegetation (NIRv) for CR climate manipulation site at a different time in the year of 2021. C is for the control plot, W is for warming plots, and WP represents the warming and reduced precipitation plot.
## 5 Experiment Results and Discussion
### Scenario 1: Loading Rate at 0.1 MPa/s
#### 5.1.1 Results on X-Direction Strain Dynamics
It is then deduced that the strain value of failure is between 0.0044 and 0.011. It can be seen from Figure 8 that the strain value at point B before t = 137 s generally increases linearly. The strain value is 0.0036 at t = 137 s when strain begins to show nonlinear change and  
Figure 6: Strain–time profile along the x-direction (loading rate = 0.1 MPa/s). Figure 7: X-direction strain–time relationship at point A (loading rate = 0.1 MPa/s). eventually reaches its strain limit. At t = 140 s, the strain value suddenly increases to 0.0177. Within this period, strain value change is dramatic and instantaneous. The strain value of failure is then expected to be between 0.0036 and 0.0177. As is shown in Figure 9, the strain value before t = 140 s changes very slightly and almost linearly for point C. At t = 140 s, it is 0.0045, while at t = 140.5 s, it is 0.018. During this period of time, similar to those at points A and B, the strain value changes significantly. The strain value of failure is expected to be between 0.0045 and 0.018. From the above, points A, B, and C fail one after another during the loading process. Through the detailed study on the failure processes at each point, the range of the strain values at failure related to points A, B, and C can be revealed. It can be concluded that the strain value of failure for sandstone is approximately between 0.004 and 0.011 when the loading speed lies at 0.1 MPa/s.
## 3 Results and Discussion
### Emulsion Activity and Emulsion Stability  
The emulsion activity is the potential of a substance to form emulsions and it is useful in the formulation of products like mayonnaise or in the preparation of foods where the prevention of phase separation is required, i.e., in the preparation of beverages having both an oil and water phase. As seen in Figure 5, the % emulsion activity of all the hydrolysates was between 56% \(\pm\) 0% and 65% \(\pm\) 1.4%. Similar emulsion activity ranges have been reported by other authors working on marine-based protein hydrolysates [35]. Proteins in the hydrolysate are the chief component responsible for the emulsion activity seen. However the thermal stability of these emulsions was noted to be very poor and there was complete phase separation on heating the emulsions. The results show that the mussel hydrolysates have appreciable emulsion activity, which can be explored for cold temperature applications like cold soups and blended smoothies.  
Figure 4: Changes in lipid content (%) for mussel by-products meat (M) and Protamex® hydrolysates (H). All 9 samples from the three sampling sites, Mulroy bay (MB), Killary harbor (KH) and Ardgroom (A), collected from November 2018 to October 2019, were used for analysis. The lipid content was quantified using AOAC Method 2008.06 with an Oracle Rapid NMR Fat Analyser. Analysis was performed in triplicate (n = 3).
## 2 Related Work  
### Mobile Edge Computing (MEC)  
MEC was originally developed as an offloading mechanism to provide more computing resources at the edge of the network. As shown in [2] there has been a lot of work done in MEC. Two recent projects are EDGELESS [3] and CODECO [4]. EDGELESS is an ongoing EU project that is developing a secure edge-cloud platform that can dynamically adjust itself to ensure a high Quality of Service (QoS) for applications. Although the IEE shares some of these goals, there are significant differences. Unlike EDGELESS, the IEE will manage and run services at the edge of the network by default to support real-time applications with very tight requirements for low latency and high bandwidth; thus, in the IEE there will be no central Cloud platform. The CODECO project is enhancing the container orchestration platform, Kubernetes, with a cognitive edge-cloud management framework that provides support for real-time industrial applications. While CODECO is looking at a software solution to provide real-time support for Kubernetes, the IEE is committed to providing end-to-end low latency using smart networks, caching, and prefetching algorithms as well as low-latency data access and processing via fast hardware, AI and Unikernels.
## 3 Results and Discussion
### Volatiles Analysis
#### Changes of Volatiles in Kiwifruit Pulp Beverage after HHP
On the contrary, HT treatments resulted in significant loss in acids. This observation agreed with work of [PERSON] et al. [53] who found that thermal treatment decreased content of volatiles. Collectively, 400-500 MPa for 5-15 min resulted in highest levels of main volatiles and volatile groups, suggesting that HHP ranged from 400 to 500 MPa seem to be suitable for preserving the original aroma and enhancing characteristic aroma in kiwifruit pulp beverage.
## 4 Functional Model of the Framework
### Technologies Used at Each Level
#### 4.5.4 Fourth Level--Applications and Support Services  
They have an indirect role in solving the congestion problem. The services provided include:  
* A Smart Parking App to reserve parking in advance to avoid the search for stopping places, which otherwise results in congestion.
* The use of public transportation for school students or employees, instead of private cars, to avoid congestion at peak times. In addition, modifying the attendance times of workplaces and schools can contribute to reduce congestion.
## 4 Network Model
### MS Mobility Model (\(m=1\))
The above analysis has implicitly neglected the packet transmission delay during each sensor-MS encounters. However, if the message length is not negligible, the message has to be split into several segments and deliver to multiple sinks. _Message delivery delay_ can be mainly attributed to the packet transmission time. In case of packet segmentations, the split packets are assumed to be sent to different sinks and reassembled. Assume the sensor will alternate between two states, active and sleep, whose durations will be exponentially distributed with a mean of 1/\(\lambda\). Thus the message arrival is a Poisson process with arrival rate \(\lambda\). For constant message length of L, constant channel bandwidth w, the number of time slots required to transmit a message is T = L\(\cdot w\). Then with a service probability p = mrr2, the service time of the message is a random variable with Pascal distribution (Lemma 1 of [7]). That is, the probability that the message can be transmitted within no more than x time slots, is:  
\[\rm{F_{x}(x)=\sum_{i=0}^{x-T}\binom{T+i-1}{T-1}\,p^{T}(1-p)^{i}} \tag{23}\]  
Figure 5: Computing the distribution of sensor-MS meeting delay. Such a Pascal distribution with mean value of \(\frac{T}{p}=\frac{L}{\pi m\omega r^{2}}\). Under an average Poisson arrival rate \(\lambda\) and a Pascal service time with \(\mu=\frac{p}{T}=\frac{\pi m\omega r^{2}}{L}\), data generation and transmission can be modeled as an M/G/1 queue. Then the average message delivery delay can be expressed as follows:  
\[D_{2}=\frac{1}{\lambda}\ [\rho+\frac{\rho^{2}+\lambda^{2}\rho^{2}}{2(1-\rho)}] \tag{24}\]  
where \(\rho=\frac{\lambda}{\mu}\), for simplicity, we neglect the impact of arrival rate and set \(\lambda=1\), thus:  
\[D_{2}=\frac{1}{\mu-1}=\frac{1}{\frac{\pi m\omega r^{2}}{L}-1} \tag{25}\]  
This result shows that, by decreasing message length L, or increasing transmission range \(r\) and number of mobile sinks m, the message delivery delay can be reduced. However, for simplicity the value of m was assigned to be \"1\" for the forthcoming discussions.
# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
## 3 Materials and Methods
### Identification and Tracking  
A comprehensive pipeline including training, validation, and testing stages of identification and monitoring models was designed and implemented. This approach allowed achieving 100% accuracy in the detection and tracking of key mining equipment, evidencing a significant improvement in performance metrics, such as loss function reduction and response optimization in real scenarios. At this stage, the following hypotheses are proposed:  
**Hypothesis 1.** (_Accuracy of mining equipment classification_).  
* **H10.**_The trained neural network does not achieve an average accuracy higher than 95% in the classification of mining equipment (pickup truck, excavator, operator, drill, scoop) in both the validation and test sets.
* **H11.**_The trained neural network achieves an average accuracy greater than 95% in classifying mining equipment (pickup, excavator, operator, drill, scoop) in both the validation and test sets._  
**Hypothesis 2.** (_Accuracy by specific class_).  
* **H20.**_There are no specific classes (such as excavator or drill) that reach 100% accuracy in the validation and test stages._
* **H21.**_Specific classes (such as excavator or drill) reach 100% accuracy in the validation and testing stages._  
**Hypothesis 3.** (_Evolution of precision_).  
* **H30.**_The evolution of precision (accuracy) and recall metrics shows no significant stabilization patterns between majority and minority classes across epochs._
* **H31.**_The evolution of precision and recall metrics shows significant stabilization patterns towards the later epochs, being more consistent in the majority classes than in the minority classes._  
**Hypothesis 4.** (_Convergence between metrics_).  
* **H40.**_There is no significant trend of convergence between metrics across training epochs._
* **H41.**_There is a significant trend of convergence between metrics across training epochs._
# Similarity-Based Decision Support for Improving Agricultural Practices and Plant Growth
## 2 Materials and Methods
### Example: Distance Calculation Between Two Farmers  
To clarify how distance influences similarity analysis in the similarity-based method, we illustrate the step-by-step computation using two farmer profiles from the dataset. Farmer 1 and Farmer 2 have the attribute values shown in Table 2.  
Step 1: Categorical Distance. The categorical attributes in this context are Soil Type, Fertiliser Type, Climate, Pest Management, Plant Time, Crop Harvested, Harvest Colour, Seed Supplier, and Season. Out of these nine attributes, Farmer 1 and Farmer 2 match only on Climate and differ on the remaining eight. We apply the simple matching distance, where a value of 0 indicates a match and 1 a mismatch:  
\begin{table}
\begin{tabular}{c c c} \hline
**Attribute** & **Farmer 1** & **Farmer 2** \\ \hline Soil Type & Loamy & Clay \\ Fertiliser Type & Compost & Chemical \\ Climate & Tropical & Tropical \\ Humidity Level (\%) & 83 & 76 \\ Pest Management & Integrated Pest & Chemical Control \\  & Management & Organic Pesticides \\ Plant Time & Winter & Early Spring \\ Crop Harvested & Corn & Wheat \\ Water Temperature (\({}^{\circ}\)C) & 21 & 20 \\ Harvest Colour & Medium-Rare & Medium \\ Seed Supplier & GreenFields & AgriSeeds Co. \\ Season & Spring & Autumn \\ Distance to Retailer (km) & 131 & 133 \\ Harvest Yield (\%) & 50 & 45 \\ \hline \end{tabular}
\end{table}
Table 2: Attribute comparison between Farmer 1 and Farmer 2.  
\[C_{d} =1\ (\text{Soil})+1\ (\text{Fertiliser})+0\ (\text{Climate})+1\ (\text{Pest})\] \[\quad+1\ (\text{Plant Time})+1\ (\text{Crop})+1\ (\text{Harvest Colour}) \tag{5}\] \[\quad+1\ (\text{Supplier})+1\ (\text{Season})=8\]  
Step 2: Numerical Distance. The numerical attributes considered are Humidity Level, Water Temperature, Distance to Retailer, and Harvest Yield. These are compared using the Euclidean distance formula:  
\[(83-76)^{2} =49\quad(\text{Humidity Level})\] \[(21-20)^{2} =1\quad(\text{Water Temperature})\] \[(131-133)^{2} =4\quad(\text{Distance to Retailer})\] \[(50-45)^{2} =25\quad(\text{Harvest Yield})\] \[d(A,B) =\sqrt{49+1+4+25}=\sqrt{79}\approx 8.89\]  
Step 3: Total Distance. The overall distance between the two farmers, used for neighbour ranking, combines both categorical and numerical components:  
\[T_{d}=C_{d}+d(A,B)=8+8.89=16.89 \tag{6}\]  
Interpretation and Influence on Analysis. This total distance reflects a moderate similarity score. While numerical environmental parameters such as humidity, water temperature, and harvest yield are relatively close, the significant number of categorical mismatches highlights diverging agricultural practices. In the context of the similarity-based method, such a distance affects the recommendation strength: Farmer 2 may provide useful input on environmental conditions and operational metrics, but their advice on crop-specific strategies, planting periods, or pest control may be less reliable for Farmer 1. By quantifying both exact and approximate similarity, the system ensures more context-aware and informed decision-making for farmers.
## 4 Quantum Key Storage and Provision  
The key generation rate in the current QKD system is still low. Consequently, quantum keys are extremely valuable network resources in QKDON. Generally, a key storage device embedded in each QKD node is used to store quantum keys.  
Constructing a quantum key pool (QKP) can improve the quantum key provisioning capability [47]. We virtualize quantum keys generated between two QKD nodes and store them in a cache device, namely, QKP. Two QKD nodes directly connected via a fiber link can construct QKPs, such as QKP\({}_{12}\) and QKP\({}_{23}\) in Figure 3. Node-1 and Node-3 cannot distribute keys directly, so the end-to-end QKP\({}_{13}\) is constructed using the relaying method mentioned in Section 3. QKP stores the generated keys continuously. Therefore, QKP supplies the accumulated keys in a timely manner when the encryption service is in high demand.  
Figure 2: Schematic diagram of the relay principle.  
The key-sharing process between non-adjacent nodes requires quantum keys of each relay node. It is necessary to select the QKD path reasonably and manage the limited quantum key resources. QKP monitors the remaining number of quantum keys and the key generation rate in real time. The SDN controller manages QKPs through real-time information and schedules key resources to meet the encryption requirements [47]. The QKD links between QKD nodes in Figure 3 include QCh and PCh, which are used for the QKD process. Optical nodes are connected through optical communication links (OC links, i.e., data channels) to transmit service data. Based on WDM technology, the above three channels can be multiplexed into the same optical fiber [25].
## 2 Methods
### Limiting Procedures
#### 2.2.1 The ScalarWeno Limiter  
To reconstruct a scalar-valued function \(\psi\) on \(E_{i}\), we first borrow the expression of \(\psi\) from \(E_{k}\) to \(E_{i}\)  
\[\psi_{k\to i}(\vec{x})\coloneqq\psi_{k}(\vec{x})-\langle\psi_{k}\rangle_{i}+ \langle\psi_{i}\rangle_{i} \tag{6}\]  
for each \(k\in K_{i}^{+}\). The key idea of WENO limiters is to build a convex combination of these borrowed functions:\[\psi_{i}^{\text{new}}(\vec{x}):=\sum_{k\in K_{i}^{+}}w_{k\to i}\,\psi_{k \to i}(\vec{x}). \tag{7}\]  
The non-negative weight \(w_{k\to i}\) should be determined from the smoothness of \(\psi_{k\to i}\), so we then calculate the smoothness of \(\psi_{k\to i}\) for each \(k\in K_{i}\):  
\[\beta_{k\to i}=\sum_{|\alpha|=1}^{p}\frac{l_{i}^{2|\alpha|}}{|E_{i}|}\int_{E_{ i}}\left(\frac{\partial^{|\alpha|}\psi_{k\to i}}{\partial x_{1}^{\alpha_{1}} \cdots\partial x_{d}^{\alpha_{d}}}\right)^{2},\qquad|\alpha|:=\alpha_{1}+ \cdots+\alpha_{d} \tag{8}\]  
in which \(|E_{i}|:=\int_{E_{i}}1\) is the measure (i.e., \"area\" for \(d=2\), \"volume\" for \(d=3\)) of \(E_{i}\), and \(l_{i}:=\sqrt[4]{|E_{i}|}\) is the approximated length of \(E_{i}\)'s edges. Once we have these \(\beta\)'s, the weight for each \(\psi_{k\to i}\) could be constructed as  
\[w_{k\to i}=\frac{w_{k\to i}^{\beta}}{\sum_{k\in K_{i}^{+}}w_{k\to i}^{\beta}}, \qquad w_{k\to i}^{\beta}:=\frac{w_{k\to i}^{\star}}{(\epsilon_{0}+\beta_{k \to i})^{2}}, \tag{9}\]  
in which  
\[w_{k\to i}^{\star}=\begin{cases}\epsilon_{1}&k\
eq i\\ 1-\sum_{j\in K_{i}}\epsilon_{1}&k=i\end{cases} \tag{10}\]  
are called the ideal weights. The \(\epsilon\)'s in Equations (9) and (10) are artificial parameters and we use \(\epsilon_{0}=10^{-6}\) and \(\epsilon_{1}=10^{-3}\) as suggested by [23; 24]. This limiting procedure for scalar-valued functions is independent from the conservation laws to be solved, so it can be programmed as an independent module, which we would like to name as the ScalarWeno limiter.
# Edge Effect Analysis and Edge Defect Detection of Titanium Alloy Based on Eddy Current Testing
## 3 The Defect Detection Capability Evaluation
### Simulation Model  
The only difference in the simulation model was that a defect was added to the plate. As shown in Figure 7, the scanning path is along the \(Y\) axis, perpendicular to the edge and the long side of the defect. The parameters of the defect are shown in Table 6.  
The whole length of the scanning path was 15 mm. The coil firstly reached the defect, at 7 mm, which caused a small dip in the self-inductance curve (Figure 8). Then, it reached the edge at 10 mm. At the end of the scanning path, the coil was away from the plate, so its self-inductance reached a relatively steady state.  
Two indicators were defined from Figure 8 to represent the detection capability of the coil:  
1. The Distance between Dip Position and the Real Position of edge (\(DIS\))  
Dis reflects the possibility of defect detection affected by edge effect. The higher the \(DIS\) is, the lower the possibility that dip signal caused by defect will overlap with edge signal, and the easier the edge defect being detected.  
2. Dip-Dip Ratio (\(RATIO\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Shape** & **Length (mm)** & **Depth (mm)** & **Width (mm)** & **Distance from the Right Edge (mm)** \\ \hline cuboid & 4 & 2 & 0.5 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Defect information.  
Figure 7: Geometry of the simulation model.  
Firstly, define two variables \(L_{p-v}\) and \(L_{h-v}\), as marked in Figure 8. _RATIO_ is the ratio between \(L_{p-v}\) and \(L_{h-v}\). If _RATIO_ is large enough, the defect can be easily recognized even when the dip signal caused by defect overlaps with edge signal.