esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Aroma Dilution Analysis
These four aroma events correspond to retention times of compounds simultaneously identified by mass spectrometer: (1) ethyl octanoate; (2) ethyl decanoate; (3) 1-pentanol; and (4) ethyl isobutyrate. Ethyl butyrate was also present but not discussed because OD = 1 for this event. The aroma event at 13.0 min also had an OD = 1 and was not detected by the mass spectrometer. It is possible that not enough mass of analyte was directed to the mass spectrometer at retention time 13.0 min, via the open-split interface to sniff port, and therefore did not generate a chemical signal. It is likely that this compound has a very low ODT, and the human nose was a better detector for this compound. Figure 6: Aroma dilution analysis plot of wine made from Frontenac grapes harvested at 22\({}^{\circ}\) Brix. The compounds most impactful in the total aroma of this sample occurred at: (1) 13.3 min, assigned odor dilution number 16; and (2) 18.0 min, assigned odor dilution number 16, while simultaneously identified using a mass spectrometer as: (1) ethyl hexanoate; and (2) ethyl octanoate. Figure 7: Aroma dilution analysis plot of wine made from Frontenac grapes harvested at 24\({}^{\circ}\) Brix. The compounds most impactful in the total aroma of this sample occurred at: (1) 18.0 min, assigned odor dilution number 8; (2) 22.3 min, assigned odor dilution number 8; (3) 8.6 min, assigned odor dilution number 4; and (4) 6.3 min, assigned odor dilution number 4, while simultaneously identified using a mass spectrometer as: (1) ethyl octanoate; (2) ethyl decanoate; (3) 1-pentanol; and (4) ethyl isobutyrate. Five compounds are identified as key aromas in Marquette and Frontenac wine samples from ADA. These compounds are ethyl butyrate, ethyl decanoate, ethyl hexanoate, ethyl isobutyrate, and ethyl hexanoate. Concentrations of the five compounds in wine samples are calculated by external calibration. Quantitation range and coefficients of determination (R\({}^{2}\)) from the linear model are given in Table 3. Ethyl butyrate and ethyl decanoate concentrations are lower than the quantitation limit for this method. Calculated concentrations of ethyl hexanoate, ethyl isobutyrate, and ethyl octanoate in Frontenac and Marquette wines produced from berries harvested at 22\({}^{\circ}\) and 24\({}^{\circ}\) Brix are summarized in Figure 8. Generally, these compounds have decreased concentrations in the headspace of wine samples as sugar content of the grapes at harvest increased. Marquette wines had higher concentrations of ethyl hexanoate (9.5 and 1.3 ppm), ethyl isobutyrate (0.7 and 0.5 ppm), and ethyl octanoate (41.9 and 17.9 ppm). Analyte concentrations in Frontenac wines were 1.3 ppm (not detected in wine produced from 24\({}^{\circ}\) Brix) of ethyl hexanoate, 0.5 ppm of ethyl isobutyrate, and 21.5 and 15.6 ppm of ethyl octanoate.
# Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT
## 3 Logistics Warehouse Object Detection
### Object-Detection Test
#### 3.3.2 Second Training Detection Model  
The second dataset used for training is a set of 790 photos taken from the same logistics warehouses (i.e., Company S's logistics warehouses). The 790 images consisted of 100 images of specific goods (parcels with adhesive tape), 100 images of workers and 590 images of spatial interference between workers and the goods. The test results of this object detection are shown in Figure 12.  
As can be seen from Figure 12, the detection of obscured targets was significantly improved, and [EMAIL_ADDRESS] reached 0.976. At the same time, the model was still able to detect targets with a relatively small size. Compared with the first training, the second training was improved dramatically, and the trained model detected the targets individually even when the goods blocked each other.  
As for the proposed tracking task, a continuous-detection bounding box for the target, as stated above, was not enough; the tracking model needed to maintain the stable invariance of the tracking IDs. In the tracking task, each target was given a unique ID, and keeping the ID constant when the target reappearing was a vital metric for tracking performance reference [25]. To achieve tracking task, we combined the detection model with DeepSORT to preserve the continuity of target IDs.
## 2 Preliminary
### Proxy Re-Encryption  
In 1998, [PERSON] et al. [33] proposed atomic proxy cryptography for the first time, in which a semi-trusted proxy computes a function that converts ciphertexts for [PERSON] into ciphertexts for [PERSON] without seeing the underlying plaintext. In Elliptic Curve Based Proxy Re-Encryption, the authors combined elliptic curve, bilinear mapping, and proxy re-encryption and proposed the Elliptic Curve based proxy re-encryption. In their scheme, with setting up a large prime number and G, which is a point on elliptic curve \(E\) of order \(n\), the proxy is entrusted with delegation key \(bG/a\) to change ciphertext from [PERSON] to [PERSON] via computing \((raGbG/a,rG^{2}+P_{m})\), where \(P_{m}\) is a point on the elliptic curve that embeds the message \(m\) in the elliptic curve equation \(f\) (i.e., \(P_{m}=f(m)\)).  
Then we can calculate the message m by finding inverse as \(f^{-1}(P_{m})\). The proxy re-encryption is a natural application to secure the file system. The following scenarios are the Elliptic Curve based proxy re-encryption mechanism.  
1. System parameter establishment Let \(E\) be an elliptic curve over a limited field \(F_{q}\), where \(q\) is a large prime number, and \(G\) is a point on the elliptic curve \(E\) of order \(n\). Let \({Z_{n}}^{*}\) be a multiplicative group. Let the elliptic curve equation \(f\) denote the message embedding function, which maps the message \(m\) to a point \(P_{m}\) on \(E\).
2. Key generation [PERSON] randomly selects a positive integer \(a\in{Z_{n}}^{*}\) as his/her private key and calculates \(aG\) as the public key. [PERSON] randomly selects a positive integer \(b\in{Z_{n}}^{*}\) as the private key and calculates \(bG\) as [PERSON]'s public key.
3. [PERSON] encrypts the plaintext \(m\): 1. \(P_{m}\) is the embedding message, which is calculated by \(f(m)\): \(P_{m}=f(m)\); 2. generate an arbitrary number \(r\in{Z_{n}}^{*}\) and output the ciphertext \((C_{1},C_{2})=(raG,rG^{2}+P_{m})\); 3. send the ciphertext \((C_{1},C_{2})\) to the proxy.
4. Generation of the re-encryption key: 1. [PERSON] wants to authorize the information to [PERSON] such that [PERSON] can decrypt the ciphertext; [PERSON] sends the proxy key \(\pi_{A\to B}=bG/a\) to the proxy. 2. The semi-honest agent proxy re-encrypts the ciphertext \((C_{1},C_{2})\) into \(({C_{1}}^{\prime},{C_{2}}^{\prime})\) and sends it to [PERSON].
5. Re-encryption process: 1. For the ciphertext \((C_{1},C_{2})=(raG,rG^{2}+P_{m})\), the proxy uses the re-encryption key to re-encrypt \((C_{1},C_{2})\) into \(({C_{1}}^{\prime},{C_{2}}^{\prime})\). 2. \(({C_{1}}^{\prime},{C_{2}}^{\prime})\) =(raG\pi_{A\to B},rG^{2}+P_{m})\) =(raGbG/a,rG^{2}+P_{m}) =(rbG^{2},rG^{2}+P_{m}) 3. The proxy sends the converted ciphertext \(({C_{1}}^{\prime},{C_{2}}^{\prime})=(rbG^{2},rG^{2}+P_{m})\) to [PERSON].
6. [PERSON] decrypts the ciphertext: 1. [PERSON] can decrypt the embedding message \(P_{m}\) with key \(b\):\(P_{m}={C_{2}}^{\prime}-b^{-1}{C_{1}}^{\prime}\); 2. then apply the inverse of the function \(f\) to get the original message \(m\) from \(P_{m}\): \(m=f^{-1}(P_{m})\).
## 3 Proposed Identification Methods
### Particle Swarm Optimization
#### 3.3.1 Linearly Decreasing Inertia Weight  
The inertia weight plays a crucial role in guiding the exploration and exploitation processes in the PSO algorithm. One method for dynamically adjusting the inertia weight is the linearly decreasing time-varying inertia weight [52]. This method iteratively adjusts the inertia weight using Equation (11) as follows:  
\[w_{k}=\frac{w_{max}-w_{min}}{k_{max}}(k_{max}-k)+w_{max} \tag{11}\]  
Here, the suggested values for \(w_{max}\) and \(w_{min}\) are 0.9 and 0.4, respectively. Empirical studies have shown that this method can produce good results when the inertia weight is decreased from a high initial value (e.g., 0.9) to a lower value (e.g., 0.4) as the optimization progresses [52]. However, it is important to note that selecting an unsuitable value for the inertia weight can impede convergence or result in sub-optimal solutions.
# Predictive Models for Correlation of Compaction Characteristics of Weakly Cohesive Soils
## 4 Modeling and Simulation of the Compaction Process
### The Overall Prediction of the Compaction Process Efficiency
* Formulating the governing equation:
* Principle applied: Newton's Law. * Kinematic relationships: Velocity and acceleration of the roller drum are derived from the displacement function with respect to time. * Equilibrium equations: For the static system, forces are summed to zero to satisfy equilibrium conditions. * Incorporating the dynamic properties:
* Mass and damping: Considered to study the dynamics of roller-soil interaction. * Stiffness and damping: Included to analyze roller-soil dynamics using stiffness and damping coefficients. * Simplifying the model (if needed):
* Reduced-order models: Not required, as the model already incorporates only second-order terms for acceleration and has average complexity. * Lumped parameters model: To simplify the distributed characteristics of the mechanical system, lumped parameters such as total mass and stiffness were used. * Developing the mathematical model:
* Differential equations: The governing differential equation is used to describe the vertical motion of the roller-soil system. * State-space representation: Used to directly simulate the system's time evolution. * Validating the model:
* Comparison with available data: The model's performance is evaluated by comparing its results with analytical solutions and experimental tests to assess the accuracy of the numerical applications. * Sensitivity analysis: Parameters are adjusted to determine the model's sensitivity to changes and to evaluate the accuracy of its responses. * Analyzing and using the model:Simulating the response: The model is used to predict behavior under various inputs, such as the frequency and amplitude of roller vibrations (for vibratory working regime) or the applied pressure from the drum's weight (for static regime). * Optimizing the parameters: Numerical method and step size are adjusted to achieve high processing performance, ensuring a precision of 0.001. * Application in control or design: The model can be integrated into larger system analyses, particularly for control applications, if needed.
# Key Technologies of Photonic Artificial Intelligence Chip Structure and Algorithm
## 2 Hardware Structures for Linear Operation
### the micro-ring resonator (MRR).  
The implementation scheme based on MRR is similar to that of SOA. MRR works at different wavelength when the MRR's length or the radius are adjusted. The MRR is featured in wavelength selection. The light after different MRRs forms different channels with forward propagation. Then, through the arrayed waveguide grating (AWG) or multimode interference optical coupler (MMI), the light is summat  
Figure 1: MZI array[20]. A universal 8 \(\times\) 4 optical linear multiplier with two unitary multipliers marked red. It consists of MZIs in a grid-like layout and a diagonal layer marked yellow.  
Figure 2: SOA wavelength division multiplexer[22]. The chip integrates 8 weighted addition circuits, which are used for 8 WDM input vectors and provide 8 WDM outputs. SOA is used to assist the weight sum of each layer. Highlight the block in use in gray.  
A matrix operation is completed. The transmittance of MRR can be adjusted by thermal adjustment [23; 24], ESC [25], phase adjustable materials, etc.  
The PFNN based on MRR is illustrated in Figure 3[26]. The input signals are ported into different MRRs, and then, each channel carries a signal with a specific wavelength. After nonlinear modulation, the output matrix is obtained. In this structure, photonic neurons' output signals are fixed to certain wavelengths. With the WDM signal porting in the network, each connection between a pair of neurons is independently configured by an MRR weight, and each channel has a signal monitor. After that, the operation is completed.
## 5 Design of the PV System Dynamics
### The P&O Algorithm  
The perturb and observe algorithm is a hill-climbing optimization technique [2; 26], which perturbs the independent variable and evaluates the changes on the optimized variable. In the PV system of Figure 1, the independent variable is the MPPT reference \(v_{mppt}\), while the optimized variable is the PV power \(p_{pv}=v_{pv}\cdot j_{pv}\), which are the input and output signals of the MPPT block, respectively.  
The P&O algorithm is summarized in Figure 2, which shows the algorithm flowchart. First, the algorithm measures both \(v_{pv}\) and \(i_{pv}\) to calculate the PV power \(p_{pv}\); then, such a \(p_{pv}\) value is compared with the power of the previous perturbation cycle \(p_{pv,old}\). If the actual power \(p_{pv}\) is lower than the previous power \(p_{pv,old}\), the perturbation direction must be changed. This direction tracking is performed by changing the sign of the perturbation \(\Delta v_{pv}\) in the direction that increases the PV power. Finally, the perturbation of the P&O output \(v_{po}\) is executed, and the \(p_{pv,old}\) value is updated with \(p_{pv}\) for the next iteration. This iterative process is performed continuously for intervals of \(T_{a}\) seconds, which is the perturbation period. Therefore, the P&O algorithm has two parameters: the perturbation size \(\Delta v_{pv}\) and the perturbation period \(T_{a}\), which can both be accurately calculated using the procedures provided in [26; 27].  
Considering the inverted-parabola shape of the P-V curve of a PV panel [2], the P&O algorithm increases or decreases the PV voltage looking for the direction that increases the PV power. Figure 2 illustrates such behavior in the upper P-V curve, which assumes the operation starting from a PV voltage below the optimal operating condition (maximum power point--MPP). The P&O algorithm increases the PV voltage (blue arrows) to reach the maximum power possible (\(p_{mpp}\)), which occurs at the optimal PV voltage \(v_{mpp}\); when the PV voltage increases over \(v_{mpp}\), the PV power decreases, and the \(P\&O\) changes the perturbation direction to reach the MPP (red arrows) again. Finally, the \(P\&O\) locks the PV system in a stable three-point behavior around the MPP \(\{v_{mpp}-\Delta v_{po},v_{mpp},v_{mpp}+\Delta v_{po}\}\), which ensures the maximum power production. Figure 2 also shows the waveform of the \(P\&O\) reference \(v_{po}\), which describes step changes with infinite derivatives. This is a problem since the derivative limits \(SR_{p}\) and \(SR_{n}\), as reported in (24) and (26), must be fulfilled to ensure a stable operation of the PV system; such a problem is addressed in the next subsection.  
The stable three-point behavior is also observed in the \(v_{po}\) waveform, where the persistent voltage pattern is depicted, and the waveform of the PV power shows that the \(P\)&O drives the PV system to operate around the maximum power (\(p_{mpp}\)). Finally, the \(v_{po}\) waveform also illustrates the perturbation period \(T_{a}\), which defines the tracking speed of the algorithm.
## 2 Literature Review
### Research on Virtual Coupling  
To enhance railway freight transportation capacity, [PERSON] et al. [15] introduced the concept of virtual coupling in 1999. [PERSON] et al. [16] highlighted the primary application challenges of virtual coupling, including technical feasibility, safety, and capacity impact. [PERSON] et al. [17], [PERSON] et al. [18], [PERSON] et al. [19], and [PERSON] et al. [20] developed virtual coupling train optimized models, emphasizing the significant capacity improvement potential of virtual coupling technology. [PERSON] et al. [21] and [PERSON] et al. [22] utilized the Petri net model to simulate virtual coupling, demonstrating a fivefold increase in throughput capacity compared to conventional methods. Virtual coupling necessitates close tracking and cooperative operation of trains at tight intervals. [PERSON] et al. [23], [PERSON] et al. [24], [PERSON] et al. [25], [PERSON] et al. [26], and others have extensively investigated enhancing the safety of virtual coupling systems using diverse calculation and analysis methods.  
In exploring railway transportation organization under virtual coupling technology, [PERSON] et al. [27], [PERSON] et al. [28] and [PERSON] et al. [29] investigated the flexible capacity adjustment strategy for urban rail transit, demonstrating its effectiveness in enhancing service levels and reducing operating costs through practical examples. [PERSON] et al. [30] employed virtual coupling technology on the Beijing-Shanghai high-speed railway to minimize empty seats and mileage, demonstrating the adaptability of virtual coupling technology to the uneven distribution of passenger flow. [PERSON] et al. [31] examined the organization modes of express/local trains under virtual coupling and evaluated their impact on reducing per capita travel time. [PERSON] et al. [32] presented a bi-level programming model to optimize the operation plan of virtual coupling in full-length and short-turn routes, adjusting the number of couplings in overlapping sections. [PERSON] et al. [33] introduced blockchain-based traffic conflict predictions and suggested seven virtual coupling strategies for conflict control. [PERSON] et al. [34] proposed a train operation scheme using virtual coupling technology but overlooked passenger flow differences at various stations. [PERSON] et al. [35] presented an optimization model for the number of virtual couplings on passenger lines, showcasing improvements in train utilization. Additionally, [PERSON] et al. [36] introduced more intricate Y-shaped subway lines to enhance the routing scheme and departure times of virtual coupling trains.
# Task Scheduling Mechanism Based on Reinforcement Learning in Cloud Computing
## 5 Experimental Results
### UQRL Comparison with Q-Learning and PSO  
PSO is a typical heuristic algorithm that is widely applied in various domains, including task scheduling scenarios. In the first stage, we utilized the task allocation method from QMTSF; in the second stage, described here, we employed both UQRL algorithm, Q-Learning algorithm, and PSO algorithm for scheduling to compare their performance.  
From Figure 5, it can be observed that when the number of tasks is small, the makespan of PSO is slightly better than UQRL and Q-Learning, with a negligible difference. However, the makespan of UQRL and Q-Learning becomes significantly lower than PSO as the number of tasks increases. The makespan achieved with UQRL is about 20% lower than with PSO. This is because PSO requires multiple iterations to converge during each scheduling process, which incurs additional iteration time. On the other hand, UQRL and Q-Learning can quickly make scheduling decisions after Q-table training is completed. Another approach is to train the algorithms offline and perform task scheduling online, which improves the initial execution efficiency of the algorithms. Compared to Q-Learning, UQRL can realize time savings of approximately 7% thanks to the faster convergence of its UCB algorithm in finding the optimal solution as compared to the greedy algorithm. In this case, PSO, UQRL, and Q-Learning all show a gradual increase in makespan without  
Figure 4: Average processing time comparison: different task allocation policies employed in Stage I.  
Figure 3: Makespan comparison: different task allocation policies employed in Stage I.  
the significant degradation in performance observed in Figure 3 with random scheduling. This is because dynamic task allocation of QMTSF was already employed in the first stage to achieve optimal matching of tasks to servers. Thus, the performance comparison in this stage focuses on the scheduling of tasks to VMs within the servers.  
From Figure 6, it can be observed that UQRL, Q-Learning, and PSO all show relatively stable average task processing times without significant fluctuations as the number of tasks increases. Compared to the PSO algorithm, the UQRL algorithm can reduce the average task processing time by around 15%. This is because as the task number increases, the time consumed by the multiple iterations needed for convergence of the PSO algorithm is averaged among tasks, resulting in a diminishing impact. On the other hand, after the convergence of the Q-table the UQRL algorithm does not incur significant delays in the process of scheduling tasks to specific VMs. The average task processing time of the UQRL algorithm is not significantly different from the Q-Learning algorithm, as both algorithms adopt the same scheduling selection method following convergence of the Q-table.  
Figure 5: Makespan comparison: different task scheduling policies employed in Stage II.  
Figure 6: Average processing time comparison: different task scheduling policies employed in Stage II.
## 3 Methodology
### Frequency Ratio Methods
#### 3.2.4 Weighting Factors  
As demonstrated in Equation (1), LSI is calculated for each i pixel, by means of the weight of each model variable. The factors or criteria used are slope gradient, lithology and land cover. The weighting methodology used by ELSUS was initially a point of comparison (Table 6). However, the resolution of the data used to calculate slopes in ELSUS is different from that in the digital elevation model produced by the Spanish National Geographic Institute. For all the above, a higher weighting must be appliedhere, and therefore the ELSUS values are not utilized in this research. LSM weights (Table 6) were produced for this study with the analytic hierarchy process, and slope was strengthened at the expense of lithology--the factor that presented the lowest resolution (1:50.000) and followed by land use (1:10.000). Subsequently, the weights were adjusted by an expert panel and applied according to Equation (1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Class** & **GFR** & **EFR** & **CFRcst** & **CFRvlc** & **CFRalc** \\ \hline
1 & 0–10 & 0–2 & 0–4 & 0–6 & 0–4 \\
2 & 10–20 & 2–4 & 4–10 & 6–12 & 4–8 \\
3 & 20–30 & 4–8 & 10–18 & 12–20 & 8–14 \\
4 & 30–40 & 8–12 & 18–26 & 20–26 & 18–20 \\
5 & 40–50 & 12–18 & 26–32 & 26–32 & 20–26 \\
6 & 50–60 & 18–25 & 32–40 & 32–40 & 26–32 \\
7 & 60–70 & 25–38 & 40–50 & 40–50 & 32–44 \\
8 & 70–80 & 38–90 & 50–74 & 50–74 & 44–78 \\ \hline \end{tabular}
\end{table}
Table 5: Intervals defined for slope according to methodologies and provinces described in the text. Values in sexagesimal degrees.  
Figure 2: Example of RFR for Castellon province.  
Previous research has shown that slope gradient is a significant factor in determining general landslide susceptibility at a continental scale [20; 33; 34]. Having a range of values for the same calculation method allows for sensitivity analysis by comparing the differences in the results obtained.