esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effect of Growth Conditions on the Composition of Common VOCs of \(K\). parriflora Tissues  
As mentioned above, a total of 40 compounds were found to be common between the two tissues and growth conditions. Aside from the qualitative assessment, environment-based changes in the relative abundance of VOCs were evaluated among those compounds. The metabolite compositions in tissues from the field condition relative to the growth room condition (control) were recorded based on log\({}_{2}\)-transformed fold-change values of normalized responses of each annotated compound, and the extent of any significant difference was assessed with a \(p\) value\(<\) 0.05 (Supplementary Table S3).  
In the case of the rhizomes, the relative abundance of almost all volatiles were significantly higher in the field conditions compared to the growth room, except for three compounds: 2 hexenal, octanal, and hexadecenal. A similar trend was observed in leaf volatiles. Only neophytadiene and borneol showed no significant differences with the change of growth environment. These results suggest that in both tissues, the field condition contributed a significant change in the VOC profile of the plant in terms of quality and relative quantity. This is consistent with the PCA results that the Field_ Rhizome and Field_Leaf groups were clearly separated from the growth room samples.  
Figure 5: Scatter plot generated from log\({}_{2}\)-transformed values of normalized peak intensities of annotated compounds identified from the rhizomes and leaves of \(K\). _parriflora_ grown in growth room (**a**) and field (**b**) conditions. The \(X\)-axis represents the log\({}_{2}\)FC values and the \(Y\)-axis represents the \(-\)log10 of the \(p\) value. The red dots of the positive side explain the significantly increased volatiles of the leaves, and negative side represents the significantly increased volatiles of the rhizome (\(p\) value \(<\) 0.05). Grey dots represent no significant difference in relative abundance.
# Parameter Identification of Structures with Different Connections Using Static Responses
## 5 Parameter Identification for a Frame with Semi-Rigid Connections
### Parameter Identification for a Frame Sample with Joint Damage  
A plane frame structure with a one-story and one-bay configuration is illustrated in Figure 8. This structure is used to demonstrate the joint damage identification method for the steel frame structure. The modulus of elasticity is 206 GPa. All members have the same cross-sectional geometric properties. In \"as-built\" condition, the frame has cross-sectional areas \(A\) = 2.25 \(\times\) 10\({}^{-2}\) m\({}^{2}\) and moment of inertia of \(I\) = 4.21875 \(\times\) 10\({}^{-5}\) m\({}^{4}\), and the nodal rotational stiffness of all joints is infinite. Let us assume that damages existing in joints 2 and 3 cause these two joints to transform from rigid connections to semi-rigid connections. The \"as-is\" nodal rotational stiffnesses of joints 2 and 3 (in the member 2) are 8 \(\times\) 10\({}^{3}\) and 1.2 \(\times\) 10\({}^{4}\) kN\(\cdot\)m/rad, respectively, which are unknown and need to be determined. Next, let us use applied forces of 100 and \(-\)100 kN along degrees of freedom 1 and 5, respectively, and measured displacements along degrees of freedom 1, 2, 4, 5, 7, and 8, respectively.  
The member stiffness matrix \(\mathbf{k^{\prime}}\) of the frame member with semi-rigid connections at the ends is obtained from Equation (8), and the damage to joints 2 and 3 can be identified using Equation (4). Simplex method [33] is easy to implement and has been extensively applied to solve optimization problems. The starting point of the nodal rotational stiffness variable \(R\) was zero. Figure 10 shows the variation in \(R_{2}\) and \(R_{3}\) with the number of iterations. Figure 11 shows the variation of objective function with the number of iterations. After 212 iterations, the final optimal values of \(R_{2}\) and \(R_{3}\) are 8.000 \(\times\) 10\({}^{3}\) and 1.200 \(\times\) 10\({}^{4}\) kN\(\cdot\)m/rad, respectively. The optimal values are consistent with the \"as-is\" values, and the results are all convergence.
# Load-Balanced Dynamic SFC Migration Based on Resource Demand Prediction
## 5 Performance Evaluation  
### Simulation Setup  
In order to evaluate the effectiveness of the above algorithm, we conducted simulations using the SFC simulation platform SFCSim [16] using the Python environment and tested the program on a cellular network topology.  
Figure 4 depicts the physical network, which consists of 42 links and 19 physical nodes. The server's CPU and memory resources are set to roughly \((250,\,300)\) MIPS and \((600,\,1000)\) GB, respectively, uniformly divided [17]. The link delay is uniformly distributed in units of \((1,\,4)\) ms, and we have set the link bandwidth capacity to \(500\) Mbps. The node overload threshold is \(0.7\).  
We consider eight types of VNFs. Each SFC chooses a subset of VNFs at random, and the length of each SFC is randomly selected from two to five. The entrance and exit are chosen at random from \(V^{5}\). The bandwidth factor ratios of the various VNF variants are chosen from \(0.5\), \(1\), and \(1.5\).  
The CPU coefficient \(coeff_{c}^{f}\) and memory coefficient \(coeff_{m}^{f}\) of different types of VNFs are uniformly distributed in \((0.1,\,0.2)\). The maximum delay of the SFC is \(30\) ms. We set the network to have \(20\) paths in a \(24\)-hour period using the Clearwater VNF Dataset's traffic dataset [18].
## 3 Methods  
### Calculation of the Optimum Temperature for Photosynthesis  
We calculated the optimum temperature for photosynthesis at the site scale using in situ GPP and the global scale using satellite SIF data, respectively. Referring to [PERSON] et al. [32], we first corresponded the long time series in situ GPP (or satellite SIF pixel values) to temperature to get the gray scatter plot in Figure 1. Then, these points were divided into multiple temperature bins at 1 \({}^{\circ}\)C intervals (as shown in Figure 1, the blue part was a temperature bin), and the 90% quantile of the in situ GPP (or satellite SIF pixel  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Model Type** & \
\begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} &
\begin{tabular}{c} **Temporal** \\ **Resolution** \\ \end{tabular} & **References** \\ \hline MOD17 GPP & & 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) & 8 days & [PERSON] et al. [59] \\ rEC-LUE GPP & & 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) & 8 days & [PERSON] et al. [26] \\ VPM GPP & & 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) & 8 days & [PERSON] et al. [60] \\ MuSyQ GPP & & 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) & 8 days & [PERSON] et al. [19] \\ GOSIF GPP & Data-driven & 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) & 8 days & [PERSON] and [PERSON] [61] \\ FLUXCOM GPP & model & 0.083\({}^{\circ}\)\(\times\) 0.083\({}^{\circ}\) & 8 days & [PERSON] et al. [62] \\ LPI-GUESS GPP & Process-based & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) & monthly & [PERSON] et al. [63] \\ SDGVM GPP & model & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) & monthly & [PERSON] et al. [64] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Multiple GPP products selected for comparison in this study.  
value) in each bin was extracted as the response value at that temperature (as shown in Figure 1, the red point in the blue bin was the response value to this temperature). Finally, the response values in all the temperature bins were connected together to obtain the temperature response curve, and the highest value of the curve was set as the optimum temperature (Figure 1). The optimum temperature calculated by the in situ GPP was used to verify the global optimum temperature based on satellite SIF data.
# How Automated Machine Learning Can Improve Business
## 3 Results and Discussion
### Impact of AutoML on Business Operations
#### 3.4.9. Equipment Maintenance  
AutoML profoundly impacts equipment maintenance by revolutionizing how businesses approach predictive maintenance and asset management. In industries where machinery and equipment play a critical role, such as manufacturing, energy, and transportation, unplanned downtime due to equipment failure can result in significant financial losses and operational disruptions [82]. Traditional maintenance approaches, such as scheduled or reactive maintenance, are often inefficient and costly, as they either involve unnecessary maintenance activities or address issues only after a failure has occurred. AutoML addresses these challenges by enabling predictive maintenance, which leverages machine learning models to predict equipment failures before they happen [83]. With AutoML, businesses can build models that analyze vast amounts of sensor data, historical maintenance records, and environmental factors to identify patterns and anomalies indicative of potential equipment failures [84]. These models can detect early warning signs that might be missed by human operators, such as subtle changes in vibration, temperature, or pressure, allowing for timely interventions that prevent breakdowns. Automating the process of monitoring and analyzing equipment performance reduces the reliance on manual inspections and routine maintenance schedules, leading to more efficient use of resources.  
Furthermore, AutoML models continuously learn and improve as they are exposed to new data, making them increasingly accurate over time. This adaptability ensures that maintenance strategies remain effective even as equipment ages or operating conditions change [25; 85]. The ability to predict and prevent equipment failures minimizes downtime and maintenance costs and extends the lifespan of assets, providing long-term financial benefits. Additionally, AutoML can help businesses optimize their spare parts inventory and maintenance staffing levels, further reducing costs and improving operational efficiency [86]. Therefore, AutoML transforms equipment maintenance from reactive to proactive, enabling businesses to maintain high levels of productivity, reduce costs, and ensure the reliability of their critical assets.
## 2 Materials and Methods
### Performance and Parameter Evaluation  
In this paper, we used Accuracy, Precision, Recall, F1-score, and Cohen's Kappa to evaluate the performance of our proposed method.  
Accuracy, which is the total of correctly classified samples (True Positive + True Negative) divided by the sum of properly and incorrectly classified samples (True Positive + True Negative + False Positive + False Negative), is one of the most often used metrics in multi-class classification.  
Precision reflects how far we can rely on our model when it identifies an individual as Positive and it is calculated by dividing the ratio of True Positive samples by the sum of positively predicted samples (True Positive + False Positive).  
Recall indicates our model's capability to detect all Positive samples in the dataset and it is computed by dividing the ratio of True Positive samples by the sum of True Positives and False Negatives.  
F1-score combines Recall and Precision into a single metric that incorporates both features. It merges both metrics using the harmonic mean notion as shown in Equation (17).  
\[\text{F1}-\text{score}=\left(\frac{2}{\text{precision}^{-1}+\text{recall}^{-1} }\right)=2\times\left(\frac{\text{precision}\times\text{recall}}{\text{ precision}+\text{recall}}\right) \tag{17}\]  
[PERSON]'s Kappa is a metric based on the agreement principle that attempts to account for assigning the same item in each class at random. It can function on any number of classes into which the dataset is classified. It can be expressed mathematically as shown in Equation (18), where \(p_{e}\) denotes the expected agreement and \(p_{o}\) stands for the observed agreement.  
\[\text{Cohen}^{\prime}\text{s Kappa}=\frac{p_{o}-p_{e}}{1-p_{e}}=1-\frac{1-p_{o} }{1-p_{e}} \tag{18}\]
# Fault Detection Method for Wind Turbine Generators Based on Attention-Based Modeling
###### Abstract  
Aiming at the problem that existing wind turbine gearbox fault prediction models often find it difficult to distinguish the importance of different data frames and are easily interfered with by non-important and irrelevant signals, thus causing a reduction in fault diagnosis accuracy, a wind turbine gearbox fault prediction model based on the attention-weighted long short-term memory network (AW-LSTM) is proposed. Specifically, the gearbox vibration signal is decomposed by empirical modal decomposition (EMD), to contain seven different frequency components and one residual component. The decomposed signal is passed through a four-layer LSTM network, to extract the fault features. The attention mechanism is introduced, to reweight the hidden states, in order to strengthen the attention to the important features. The proposed method captures the intrinsic long-term temporal correlation of timing gearbox signals through a long short-term memory network, and resorts to recursive attentional weighting, to efficiently distinguish the contribution of different frames and to exclude the influence of irrelevant or interfering data on the model. The results show that the proposed AW-LSTM wind turbine gearbox fault prediction model has an inference time of 36 s on two publicly available wind turbine fault detection datasets, with a root mean square error of 1.384, an average absolute error of 0.983, and an average absolute percentage error of 9.638, and that the AW-LSTM prediction model is able to efficiently extract the characteristics of wind turbine gearbox faults, with a shorter inference time and better fault prediction.  
fault prediction; gearbox; wind turbine; vibration signal +
Footnote †: journal: Applied  
1
## 3 Results and Discussion
### Characterization of the Mineral by XRD
#### 3.3.2 Characterization of the Mineral Composition in Output Streams
The concentration of celestine is shown using a grey scale and the increasing celestine concentration is shown as a darker colour. Without desliming, the highest percentage of celestine is obtained from the output stream with the larger particle size (UH) for the dense medium C40 and FM (Figure 8). Figure 7: Bar graphs showing the distribution of mineral phases in each output stream for each medium used in the experiments. The highest celestine concentration is obtained in the UH stream with C40 (Figure S7) when desliming mineral is the starting material. [PERSON] & [PERSON] (1994) demonstrated that the separation efficiency of coarse particles (>2.0 mm) in a dense medium is determined by the stability of the medium (an increase in medium density results in better separation efficiency), whereas when the medium particles are finer (<0.5 mm), the rheological properties play a more important role. In this sense, opposite trends can be shown between separation efficiency and medium density. At high medium densities (>1.5 s.g.), the effect of medium rheology becomes dominant. In this study, the best results were obtained with C40 (82-90% < 45 um), which has a viscosity similar to FM and CG, but is more stable. Figure 9 shows the celestine mass recovered in each stream in the different experiments. The highest amount of celestine is recovered in the UH stream. This result is more evident for CG, F, and FM without desliming and in experiments using the FM_D medium. For the DMS process, the density of the mineral in heavy media should be at least twice the required medium density. For this reason, it would be better to use ferrosilicon (with a density of more than twice 3.00 kg/L) than magnetite [44]. However, these results coincide with the current study if the mineral is deslimed. At high medium densities (>1.50 kg/L), the effect of viscosity becomes dominant; it is beneficial in this density range to use coarse mediums to improve separation efficiency [45]. Figure 8: Celestine concentration in the output streams starting from the run-of-mine mineral (without desliming; sample 70).
# LBFT: An Asynchronous Committee-Based Blockchain Storage Strategy on Zero Trust Model
## 5 Performance Analysis  
### Communication Complexity  
When we construct LBFT, we focus on improving the decoding phase to significantly reduce the communicated bits by introducing the concept of a committee. In the consensus phase, we just partition the blocks before encoding to decrease the communication complexity slightly. Hence, in this section, we emphatically analyze the communication complexity in the decoding phase. As illustrated in Figures 2 and 3, we can see that in each decoding round, there is:  
1. \(k\)_one_\(-\)_to_\(-\)_\(c\) round: leader \(\xrightarrow{decode}\) followers, generating a total of \(O(k\cdot c)\)\(l\)-bit decode messages (where \(l\) is the length of decode message);
2. \(k\)_c_\(-\)_to_\(-\)_one_ round: followers \(\xrightarrow{f_{k}(i)}\) leader, incurring \(O(k\cdot c\cdot\frac{T}{c})\) communicated bits altogether (where \(T\) is the average size of an original block);
3. a \(k\)_to_\(-\)_\(k\) round: committees \(\xrightarrow{B_{i}(h)}\) committees, transmitting \(O(k^{2})\) recovered blocks of size \(T\);
4. a \(one\)_to_\(-\)_\(k\) round: committees \(\xrightarrow{0/1}\) ABA instance, where the leader of each committee (i.e., \(k\) committees altogether) inputs one bit to the common ABA instance;
5. a \(k\)_to_\(-\)_one_ round: ABA instance \(\xrightarrow{0/1}\) committees, where the common ABA instance broadcasts a one-bit result to every committee.  
Hence, the communicated bits of the decoding process in LBFT can be deduced as follows:\[O(k\cdot c\cdot l)+O(k\cdot c\cdot\frac{T}{c})+O(k^{2}\cdot T)+O(k)+O(k)=O(k\cdot c \cdot l+k^{2}\cdot T),\]  
Compared to BFT-Store, apparently, the \(O(n^{2})\) term has been eliminated in communication complexity in LBFT.
## 6 Continuous Transmission of Packet Traffic
### Use of TOA  
Signals can be split in time deterministically (Time of Arrival--TOA). Users who access resources depending on their turn avoid interference by avoiding time-overlapping signals [64]. While TDMA may be more flexible, a user synchronization is required to guarantee that each user can access a separate time slot. Many systems and protocols are built on this type of underlying time-division structure, but it also calls for some synchronization and lookout times to account for abnormalities in handling the transmission delays.
# Satellite Video Moving Vehicle Detection and Tracking Based on Spatiotemporal Characteristics
## 3 Methods
### Constraint Construction Based on the Spatiotemporal Domain
The goodness of the discriminant condition directly determines the effectiveness and stability of the constraint construction. We combined the topological relationship between the two and the growth rate of the  
Figure 3: Flowchart of initial road mask and rough motion heat map fusion. Figure 2: Flow chart depicting the automatic acquisition technique for the approximate motion heat maps. motion heat map and set the discriminant condition as follows: 1 The motion heat map connectivity domain intersects the road mask. 2 The historical aspect ratio and length growth rate of the motion heat map simultaneously satisfy the corresponding thresholds. One of these two conditions must be satisfied. Condition 1 effectively raises the priority of motion targets around the road and retains their cumulative results, while suppressing them for motion targets separate from the road area. The length growth rate under condition 2 can effectively identify the connected domain formed by noise and moving vehicles. The historical aspect ratio represents the aspect ratio of the connected domain during the motion accumulation process, which eliminates the effect of a surge in the length of the connected domain caused by large noise in one frame during the motion accumulation process. As shown in Figure 4, connected domains \(B\) and \(C\) both intersect the road mask and satisfy condition 1. The length growth rate and historical aspect ratio of connected domain \(A\) meet the threshold requirements and qualify for condition 2. In contrast, connected domain D does not meet either condition, and it is selected for elimination. The specific discriminant condition is described by Equation (3):  
\[I_{d}UI_{i}^{\prime}>0\text{ or }\overset{\sim i}{A}_{r}>T_{A}\text{ and }R(l_{i})>T_{R}\text{ }(d=1,2,\cdot\cdot\cdot,m;\ i=1,2,\cdot\cdot\cdot,n) \tag{3}\]  
where \(I_{d}\) denotes the \(d\)-th connected domain in the road mask, \(I_{i}^{\prime}\) denotes the \(i\)-th connected domain in the motion heat map, \(\overset{\sim i}{A}_{r}\) denotes the historical aspect ratio of the \(i\)-th connected domain in the motion heat map, \(R\) denotes the formula for calculating the growth rate of the length of the connected domain, \(T_{A}\) denotes the connected domain aspect ratio threshold, and \(T_{R}\) denotes the connected domain length growth rate threshold; \(m\) is the number of connected domains in the road mask, and \(n\) is the number of connected domains in the motion heat map.
## 5 Discussion
### Significance of Results  
Given that pulse oximeters are ubiquitous in hospitals and healthcare environments, with portable oximeters rising in popularity during the COVID-19 pandemic, it would be easy to deploy this approach within the established systems. This makes the model an attractive solution to continuous blood pressure measurement, especially in the context of low and middle income countries (LMICs), where healthcare settings are often resource-constrained. Blood pressure determination, in this way, would allow for a combined pulse oximetry and blood pressure measurement system, leading to increased patient comfort and faster care, thus freeing up healthcare workers. Adapting the system to operate on wearable health devices could also allow comfortable at-home monitoring of hypertensive patients.
###### Abstract  
This paper deals with the synthesis and studies of new polymer microspheres properties based on ethylene glycol dimethylacrylate (EGDMA), styrene (St), and various quantities of commercial kraft lignin (L). In the first stage of the investigations, the conditions of the synthesis process were optimized by selecting a proper amount of poly (vinyl alcohol), which was a suspension stabilizer. Next, based on EGDMA + St + L, new polymers were synthesized by the suspension polymerization method. The chemical structure of the materials was confirmed by means of the Attenuated Total Reflectance--Fourier Transform Infrared (ATR-FTIR) analysis. The evaluation of the synthesized materials includes susceptibility to swelling in solvents of different character (polar and nonpolar), porous structure of microspheres, and their thermal resistance. Morphology has been specified by the scanning electron microscope and automated particle size, as well as shape analyzer. The obtained pictures confirmed the spherical shape of the materials. The microspheres porosity was characterized using the low-temperature nitrogen adsorption. To increase the porosity (partially blocked by the large lignin molecule), the microspheres (EGDMA + St + 4L copolymer) were impregnated with the aqueous solution of the activating substance (sulphuric acid, nitric acid, phosphorous acid, and silver nitrate) and then carbonized at 400 \({}^{\circ}\)C. After the carbonization process, the increase in the specific surface area was observed. The microspheres were porous with a specific surface area up to 300 m\({}^{2}\)/g. The materials had a desirable feature for their potential use in chromatography, which was confirmed by the results of GC analysis with the acetylsalicylic acid. These materials are an interesting alternative in the field of more environmentally friendly, ecological, and biodegradable polymeric sorbents in comparison to the commonly applied styrene-divinylbenzene (St-DVB) copolymers.  
**Keywords:** lignin; EGDMA; polymer microspheres; sorbent; carbonization +
Footnote †: journal: materials