esa-sceva/satcom-chunk-collection huggingface dataset:
# Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics
## 2 Literature Review  
Understanding customer demand can help a company estimate and manage production capacity, research and development, advertising, and investments [14]. Correctly matching the demand with the right supply can allow businesses to gain customer satisfaction and competitive advantage. [PERSON] et al. [15] presented a demand and supply matching recommendation for parking spaces, and driver requests in sharing economy using the classification scheme. Products and services also depend on the resources and production capabilities available to the business. A firm needs to know how a new product or service would fit customer demand and company supply [16]. [PERSON] and [PERSON] [17] reported that customer demand influences facility design and equipment in service sectors. To ensure adequate service levels, services that experience large, fast, and unpredictable changes in the market tend to have enough capacity to serve the higher levels of expected demand. Therefore, demand and capacity are generally required simultaneously for decisions. To develop new services efficiently, firms must be able to appropriately identify and understand demand, including customer needs and behavior [18].  
The business model should be designed collectively to implement the plan. Business models help decide the structure of components, the relationship between the elements, and the dynamics to plan systematically. The Business Model Canvas developed by [PERSON] and [PERSON] [19] defines how companies generate revenue and make a profit through the overall structures of process, customers, suppliers, channels, resources, and capabilities. The critical goal of service businesses is to understand customers and their behavior, necessitating identification of key consumer groups [20], while prioritizing the customer to allocate attention and resources to designated customer groups [13].  
Researchers have tried to adapt DA tools for the service industry using association rules, clustering techniques, and decision trees to solve for several objectives. [PERSON] and [PERSON] [21] conducted three DA techniques to discover various patterns of online behaviors and to predict outcomes. [PERSON] [22] proposed a three-step DA for improved customer satisfaction. [PERSON] et al. [23] used association rules and clustering techniques for mining customer knowledge among online customers. [PERSON] and [PERSON] [24] created association rules and cluster analysis to identify patterns in tourism and to suggest related activities. The literature review in the present study focuses on DA tools that could be used to identify demand for the RS: (1) to identify potential customers, (2) to identify potential products, (3) to predict the suggested matches of shippers with consignees, and (4) to simulate the estimated revenue. Five analysis techniques are reviewed: behavior segmentation, association rules, clustering techniques, decision trees, and Monte Carlo simulation. Each of these is discussed further below.
# Effect of Ultrasonic Treatment on the Quality of Pumpkin Juice Fermented by Yeast
## 3 Results
### Influence of Ultrasonic Treatment on the Flavonoid Content in Pumpkin Juice  
The influence of ultrasonic treatment on the flavonoid content in pumpkin juice is shown in Figure 2M. The results indicate that ultrasonic waves have a certain impact on the flavonoid content in pumpkin juice fermented by yeast. [PERSON] [30] optimized the conditions for the ultrasonic-assisted extraction of flavonoids from the leaves of Ixeris dentata var. Mongolia and determined that the optimal ultrasonic extraction power for obtaining the highest total flavonoid content was 411.43 W, suggesting that the extraction of flavonoids is related to ultrasonic power. As the ultrasonic power increases, the flavonoid content in pumpkin juice shows a trend of increasing first and then decreasing. When the ultrasonic power is 100 W, the flavonoid content in the pumpkin juice reaches its peak, at 1.27 mg/mL (or another appropriate unit). This finding aligns with the research of [PERSON] et al. [31], who found that ultrasonic treatment could increase the flavonoid content in juice samples via ultrasonic bath treatment. The mechanism behind this trend can be explained as follows: At lower ultrasonic power levels, the increasing ultrasonic power enhances the cavitation effect in the extraction system. The generated cavitation effect and oscillatory action correspondingly strengthen, increasing the degree of cell wall damage. This facilitates the dissolution of flavonoids inside the cells, thereby increasing the flavonoid yield. However, as the ultrasonic power continues to rise, both ultrasonic radiation and the thermal effect caused by ultrasonic treatment can induce changes in the structure of flavonoids. Excessively high ultrasonic power may damage the internal structure of flavonoids, leading to flavonoid loss and a subsequent decrease in the flavonoid yield. Notably, the flavonoid content in the pumpkin juice treated with 100 W ultrasonic waves is 112% of that in the untreated pumpkin juice, confirming the positive effect of moderate ultrasonic power on flavonoid extraction.
# A Magnetic-Balanced Inductive Link for the Simultaneous Uplink Data and Power Telemetry
## 4 Optimizations of the Coil Position Parameters
### Optimization of \(d_{13}\) for Minimizing \(k_{13}\)  
When the distance (\(d_{13}\)) between the external power coil and the external data coil changes, the magnetic flux through the external data coil could be zero at a specific point because of the balanced inside and outside magnetic flux. Thus, the coupling coefficient (\(k_{13}\)) between the external power coil and the external data coil could be minimized to approach zero at an optimal \(d_{13}\). Figure 5 shows the calculated \(k_{13}\) versus different \(d_{13}\) based on Equation (A17). As seen, there is an optimal \(d_{13}=26.2\) mm where \(k_{13}\) equals zero. It is known that the direction of the magnetic field, which comes from the inside of the external power coil, is opposite the direction of the magnetic field that comes from the outside of the external power coil. When \(d_{13}=26.2\) mm, the magnetic field in the data coil is balanced; the flux from the coil inside magnetic field is equal to the flux from the coil outside magnetic field. Thus, the total flux inside the data coil tends to be zero on this occasion, and the coupling between the coils approaches zero.
## 3 All-Fiber Lasers Mode-Locked with m-SWCNT Saturable Absorber
### Erbium-Doped Fiber Laser  
The all-fiber erbium-doped laser cavity was directly pumped with a 980 nm semiconducting laser diode. Due to the relatively low modulation depth of the m-SWCNT at \(\sim\)1560 nm, mode-locked operation was supported only in a narrow pump power range of 10-12 mW. A maximum average output power of 0.3 mW was observed. The generated optical spectrum centered at 1559 nm was 4.35 nm wide (Figure 4a). The autocorrelation trace presented in Figure 4b was fitted with a sech\({}^{2}\) curve, yielding a 617 fs pulse duration. The time bandwidth product of the emitted pulses was 0.33, indicating slight chirping of the solitons.
# Filter Pruning via Attention Consistency on Feature Maps
## 4 Experiment and Analysis  
### Experimental Settings  
**Evaluation metrics and Datasets:** To evaluate the performance of FPAC, the widely-used CNN compressed evaluation metrics are adopted, i.e., required Floating Points Operations (FLOPs). The number of parameters is compared to the state-of-art pruning methods for the image classification task. FPAC is compared with the currently popular methods for image classification tasks. The benchmark datasets used in the experiment are CIFAR-10 [38] and ImageNet [39]. On the CIFAR-10 dataset, the top-1 accuracy and pruning ratio (PR) of the different methods are presented. On the ImageNet dataset, the top-5 accuracy is also presented.  
**Configurations.** All of the experiments are implemented in the PyTorch framework with the Stochastic Gradient Descent algorithm (SGD) optimization strategy. Data argumentation strategies are the same as in the PyTorch official examples. On CIFAR-10, the networks are fine-tuned with a momentum of 0.9 for 150 epochs with a batch size of 256. The learning rate starts from 0.1 and decreases by a factor of 10 every 50 epochs. On ImageNet, the weight decay is set to 1e-4 and 90 epochs are given for fine-tuning. The learning rate is set as 0.1 and divided by 10 every 30 epochs. Referring to the implementation of HRank, pruning ratios are set at each layer to determine the number of filters pruned.
# Video over DSL with LDGM Codes for Interactive Applications
## 2 Channel Codes
### LDPC Codes
The order of encoding complexity then becomes \(n+g^{2}\), where \(g\) is a small constant or scales as a small fraction of \(n\)[20]. The algorithm's software complexity does increase as a result, whatever the theoretical computational complexity. Therefore, LDGM is an alternative way to create a sparse generator matrix, as now discussed.
## 3 Results and Discussion
### Technical Parameters
#### 3.6.4 Thermal Resistance  
The measured thermal resistance, steam resistance and steam permeability of PETE fabric and PETE-Cu\({}^{(0)}\) composites are listed in Table 7.  
The obtained results of thermal resistance exhibited a 1.75-2.25 fold increase of PETE-Cu\({}^{(0)}\) composites in comparison with starting PETE fabrics, namely from 0.004 m\({}^{2}\)K/W for PETE to 0.007 m\({}^{2}\)K/W for PETE-Cu\({}^{(0)}\)-1 (the one-side metaled sample) and 0.009 m\({}^{2}\)K/W for PETE-Cu\({}^{(0)}\)-2 (the sample coated on both sides), respectively.  
The steam resistance [m\({}^{2}\)Pa/W] of PETE-Cu\({}^{(0)}\) composites exhibited ca 2%-17% increase in comparison with starting PETE fabrics, namely 2% increase (from 2.770 for PETE to 2.82 for PETE-Cu\({}^{(0)}\)-1) and a 17% increase for PETE-Cu\({}^{(0)}\)-2 (from 2.770 for PETE to 3.24 for PETE-Cu\({}^{(0)}\)-2), respectively.  
The steam permeability [g/m\({}^{2}\) Pah] of PETE-Cu\({}^{(0)}\) composites exhibited ca 2%-15% decrease in comparison with starting PETE fabrics, namely a 2% decrease (from 0.537 for PETE to 0.528 for PETE-Cu\({}^{(0)}\)-1) for PETE-Cu\({}^{(0)}\)-1 and a 15% decrease for PETE-Cu\({}^{(0)}\)-2 (from 0.537 for PETE to 0.459 for PETE-Cu\({}^{(0)}\)-2), respectively.  
Recapitulating, the metallization of PETE fabrics increased the thermal resistance and steam resistance of obtained composites PETE-Cu\({}^{(0)}\) in comparison with the starting PETE fabrics in degrees dependent on the copper content. The opposite trend was observed for steam permeability, with a decrease of permeability increasing with the copper content.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**PETE**} & \multicolumn{2}{c}{**PETE-Cu\({}^{(0)}\)**} \\ \cline{3-4}  & & **PETE-Cu\({}^{(0)}\)-1** & **PETE-Cu\({}^{(0)}\)-2** \\ \hline Average thickness [mm] & 1 kPa & 0.66 \(\pm\) 0.02 & 0.67 \(\pm\) 0.02 & 0.67 \(\pm\) 0.02 \\ \hline \end{tabular}
\end{table}
Table 6: Thickness of the unmodified PETE fabrics and PETE-Cu\({}^{(0)}\) composites, in accordance with the EN ISO 5084:1999 [117].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**PET**} & \multicolumn{2}{c}{**PETE-Cu\({}^{(0)}\)**} \\ \cline{3-4}  & & **PETE-Cu\({}^{(0)}\)-1** & **PETE-Cu\({}^{(0)}\)-2** \\ \hline Thermal resistance [m\({}^{2}\)K/W] & 0.004 & 0.007 & 0.009 \\ Steam resistance [m\({}^{2}\)Pa/W] & 2.770 & 2.820 & 3.240 \\ Steam permeability [g/m\({}^{2}\)Pah] & 0.537 & 0.528 & 0.459 \\ \hline \end{tabular} The results have been measured in triplicate and presented as a mean value, estimated to three decimal points.  
\end{table}
Table 7: Thermal resistance, steam resistance and steam permeability of the unmodified PETE fabrics and PETE-Cu\({}^{(0)}\) composites, according to the EN ISO 11092:2014-11 [118].
## 5 Test Results and Discussion  
To illustrate the test results, sample RCBE_10 is examined in detail. Figure 5a shows the test results for all four consolidation stages. As expected, \(G\) increases with increasing effective stress. However, under anisotropic stress conditions, a marginal increase in \(G_{0}\) can be observed for the same \(p^{\prime}\). This tendency can be observed in the BE and RC tests. The increase in \(G_{0}\) may be the result of anisotropic stress conditions or a slight decrease in \(e\) due to anisotropic loading stage (consolidation and creep).  
The \(\Delta u\) induced by RC vibrations reduces the effective stresses at which the test is performed. Similarly to liquefaction studies, \(R_{u}\) is expressed as [60]:  
\[R_{u}=\frac{\Delta u}{\sigma^{\prime}_{3i}} \tag{10}\]  
where \(\sigma^{\prime}_{3i}\) is the initial horizontal effective stress (after consolidation stage). Based on the HSS model, the corrected value of \(G_{corr}\) was calculated assuming \(c^{\prime}=0\) kPa:  
\[G_{corr}=G\cdot\left(\frac{c^{\prime}\cdot cosg^{\prime}-\sigma^{\prime}_{3i} \cdot sing^{\prime}}{c^{\prime}\cdot cosg^{\prime}-\sigma^{\prime}_{3}\cdot sing ^{\prime}}\right)^{m}=G\cdot\left(\frac{\sigma^{\prime}_{3i}}{\sigma^{\prime }_{3}}\right)^{m} \tag{11}\]  
where \(\sigma^{\prime}_{3}\) is the horizontal effective stress during test, \(\varphi^{\prime}\) is the friction angle, and \(m\) is the parameter determined from the results of the tests performed, which is approximately 0.7. As shown in Figure 5b, \(\Delta u\) leads to a negligible degradation of \(G\) in the strain range up to 7.0 \(\times\) 10\({}^{-4}\). This correction agrees well with [PERSON] and [PERSON] [61], who proposed a low threshold for strength reduction due to \(\Delta u\) for salts and clays with a \(PI\) between 14 and 30%, where \(\gamma\) is between 2.4 \(\times\) 10\({}^{-4}\) and 6.0 \(\times\) 10\({}^{-4}\). Only at larger strains is some degradation of \(G\) due to \(\Delta u\) observed.  
Figure 5: (**a**) The test results of the RC and BE tests of sample RC_10 for all consolidation stages. (**b**) Correction of \(G\) due to \(\Delta u\).
## 5 Unbalanced PSI-CA Protocol Based on Cuckoo Filter  
Although [PERSON]'s PSI-CA protocol [35], constructed based on the DH key-exchange mechanism, provides an effective way to compute the intersection's cardinality of two datasets, especially under the premise of protecting participants' data privacy, this paper observes that its efficiency is significantly impacted when dataset sizes are extremely unbalanced. In particular, as shown in Table 1, the runtime increases significantly as the size of the larger dataset increases, reflecting the performance limitations of [PERSON]'s PSI protocol when dealing with unbalanced datasets.  
In order to overcome these limitations, the first protocol proposed in this paper adopts a different technical strategy, which effectively reduces the computational burden under unbalanced conditions by introducing Cuckoo filter. This not only optimizes the data processing process, but also improves the overall operational efficiency. In the new protocol, the increase in run time is not as dramatic as that in the Cristofaro PSI-CA protocol [35], based on DH key exchange; even with unbalanced dataset sizes, this allows for more efficient and balanced data processing. This improvement is particularly important for datasets of different sizes frequently encountered in practical applications.  
Therefore, based on the above introduction, as shown in Figure 2, this paper first proposes a PSI-CA protocol based on the discrete logarithm problem difficulty and the correctness (high false positive rate) of Cuckoo filter. This protocol is divided into two phases; the specific details are as follows.
## 3 Approximation by Removing the Number of Paths with Smaller Coefficients
### Approximation of Gram Matrix
#### 3.1.1 Applications in Data Science  
We also find the kernel matrix \(G\) for the example real-world datasets [36] used in machine learning algorithms: Iris flower data, images of handwritten digits data, vectorized form of 20 newsgroups data, and labeled faces in the wild [37] data, which are loaded through Python scikit-learn package [38]. To make the size of \(G\) a power of 2, we choose the first 128 samples for the iris dataset and 1024 samples for the other datasets. As seen in Figure 4, the norm of the approximation error is 0.161 for the iris, 0.421 for the newsgroup, 0.197 for the digits, and 0.21 for the faces. Here, note that the norm gives us the accumulated error, and the mean squared error is much smaller: e.g., for the faces, it is around \(0.42\times 10^{-7}\).  
These results indicate that in many cases, we can approximate \(G\) as a single term in the following form: \(\alpha Q_{1}\otimes\cdots\otimes Q_{n}\) or as a sum of a few of these terms. As shown in Sections 4.1 and 4.2, these reduced forms can be used to perform classical and quantum computations more efficiently.  
Figure 3: Histogram of the coefficients for \(X\) produced randomly with different distributions. \(n\) represents the number of qubits required to represent \(vec(X)\): i.e., the dimension of \(vec(X)\) is \(2^{n}\).
# Design of a Single-Stage Transformerless Buck-Boost Inverter for Electric Vehicle Chargers
## 4. Mathematic Model  
Let \(T_{s}\) denote the switching period, \(T_{on}\) represent the switching-on time of \(S_{2}\) and \(S_{3}\) or \(S_{1}\) and \(S_{4}\) in each switching cycle, \(T_{off}\) stand for the switching-on time of \(S_{5}\) or \(S_{6}\) in each switching cycle, and \(d_{o}=T_{on}/T_{s}\) designate the duty cycle. The state equation description of the proposed buck-boost inverter can be derived from Figure 2 as follows:  
\[\begin{bmatrix}\frac{d{i_{L}}}{dt}\\ \frac{d{v_{\rm{C}}}}{dt}\end{bmatrix}=\begin{bmatrix}0&0\\ 0&\frac{-1}{R_{o}C_{f}}\end{bmatrix}\begin{bmatrix}i_{L}\\ v_{\rm{C}}\end{bmatrix}+\begin{bmatrix}\frac{1}{L_{f}}\\ 0\end{bmatrix}v_{dc}, \tag{1}\]  
\[\begin{bmatrix}\frac{d{i_{L}}}{dt}\\ \frac{d{v_{\rm{C}}}}{dt}\end{bmatrix}=\begin{bmatrix}0&\frac{-1}{L_{f}}\\ \frac{1}{C_{f}}&\frac{-1}{R_{o}C_{f}}\end{bmatrix}\begin{bmatrix}i_{L}\\ v_{\rm{C}}\end{bmatrix}+\begin{bmatrix}0\\ 0\end{bmatrix}v_{dc} \tag{2}\]  
where (1) and (2) represent the equations for switching-on and switching-off, respectively. The state space average model of the proposed inverter can be further derived through adding (1) and (2) with their respective weighted coefficients of \(d_{\rm{o}}\) and \(1\)-\(d_{o}\) as follows:  
\[\begin{bmatrix}\frac{d{i_{L}}}{dt}\\ \frac{d{v_{\rm{C}}}}{dt}\end{bmatrix}=\begin{bmatrix}0&\frac{-(1-d_{o})}{L_{f}} \\ \frac{1-d_{o}}{C_{f}}&\frac{-1}{R_{o}C_{f}}\end{bmatrix}\begin{bmatrix}i_{L}\\ v_{\rm{C}}\end{bmatrix}+\begin{bmatrix}\frac{d_{o}}{L_{f}}\\ 0\end{bmatrix}v_{dc}. \tag{3}\]  
Based on (3), the transfer functions from the dc-link \(v_{dc}\) to the inductor current \(i_{L}\) and capacitor voltage \(v_{c}\) can be derived as:  
\[\frac{i_{L}(s)}{v_{dc}(s)}=\frac{d_{o}(C_{f}R_{o}s+1)}{C_{f}L_{f}R_{o}s^{2}+L_ {f}s+R_{o}(1-d_{o})^{2}}, \tag{4}\]  
\[\frac{v_{\rm{C}}(s)}{v_{dc}(s)}=\frac{R_{o}d_{o}(1-d_{o})}{C_{f}L_{f}R_{o}s^{2} +L_{f}s+R_{o}(1-d_{o})^{2}}. \tag{5}\]  
In steady-state, (4) and (5) can be reorganized as:  
\[\frac{I_{L}}{V_{dc}}=\frac{d_{o}}{R_{o}(1-d_{o})^{2}}, \tag{6}\]  
\[\frac{V_{\rm{C}}}{V_{dc}}=\frac{d_{o}}{1-d_{o}}. \tag{7}\]  
It should be noted that (1)-(7) are only applicable to the case of positive output voltages (see Figure 2). In the case of negative output voltages, as shown in Figure 3, the \(v_{dc}\) in (1)-(7) should be replaced by \(-v_{dc}\). This nonlinear behavior will bring in additional concerns to controller design. In order to address this problem, the absolute value of duty cycle \(do\) is  
Figure 3. Switching patterns for the negative half-wave of \(v_{c}\): (a)Inductor charged, (b)Inductor discharged.  
used instead of its actual value. Furthermore, when the reference duty cycle crosses zero from a positive value to a negative value, the driving pulses of \(S_{2}\) and \(S_{3}\) are shifted to \(S_{1}\) and \(S_{4}\), and vice versa. Through this approach, the sign of the output voltage \(v_{c}\) can always be regulated to follow the sign of its reference, and (1)-(7) are applicable to \(\left\lvert\,v_{c}\,\right\rvert.\)
## 5 Experimental Results
### Experimental Results Analysis
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{3}{c}{**Total Transfer Time (min)**} & \multicolumn{3}{c}{**Time Reduction Ratio**} \\
**Experimental Scenario** & \multicolumn{3}{c}{**Multi-Objective Optimization**} & \multicolumn{3}{c}{**FIFO**} & \multicolumn{3}{c}{**A* Algorithm**} & **[PERSON]’s** \\  & **A* Algorithm** & **Algorithm** & & **Algorithm** \\ \hline (a) Fixed edible oil weight & 124.65 & 132.86 & 220.67 & 43.51\% & 39.79\% \\ (b) Variable edible oil weight & 419.19 & 419.19 & 757.34 & 44.64\% & 44.64\% \\ (c) Fixed edible oil weight & 124.65 & 127.65 & 240.33 & 48.13\% & 46.88\% \\ constraints & & & & & \\ (d) Variable edible oil weight & 423.76 & 423.76 & 777 & 45.46\% & 45.46\% \\ constraints & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental scenario improvement summary—Time function value. other on the Pareto front, leading A* and [PERSON]'s algorithm to make different scheduling choices. This ultimately caused differences in oil transportation times and cost functions. Overall, when observing the results of both methods, the scheduling outcomes based on A* were slightly superior to those based on [PERSON]'s algorithm. In summary, the proposed multi-objective optimization scheduling method demonstrates a significant advantage in handling real-world variations in edible oil production orders. Compared to the conventional FIFO approach, this method more effectively balances multiple performance indicators, such as transportation time and resource utilization, achieving optimized task allocation. By dynamically adjusting task execution order, the method minimizes idle time and mitigates resource contention, substantially enhancing overall transportation efficiency and system performance. The result is a notable reduction in the total task makespan, particularly beneficial in edible oil pipeline operations, where task durations and pipeline availability vary considerably. Consequently, the proposed Edible Oil Transportation System Framework exhibits strong adaptability to changing production requirements. It not only significantly shortens task durations but improves resource allocation efficiency, enabling the concurrent execution of multiple tasks. The system maintains high operational efficiency even under complex demands and proves its practical applicability and flexibility through its rapid responsiveness to unexpected events. The experimental results indicate that pipeline resources are allocated and released during task execution. Consequently, when pipeline congestion occurs, the scheduling of subsequent tasks is temporarily suspended. Task scheduling and execution resume once sufficient pipeline resources become available, ensuring that all tasks can be completed. In the cost function defined by Equation (6), users incorporate weights to represent emergency states based on pipeline conditions. Situations such as pipeline congestion and faults are also accounted for by allowing users to adjust these weights, thereby maintaining flexibility in task execution.