esa-sceva/satcom-chunk-collection huggingface dataset:
# Quality of Service and Congestion Control in Software-Defined Networking Using Policy-Based Routing
## 4 Proposed Methodology
### Proposed Policy-Based Routing
#### 4.1.2 Minimum Delay Path Calculation
The model assumes well-provisioned networks with consistent delays, such as modern high-bandwidth systems. In highly dynamic networks (e.g., wireless or mobile systems), additional factors like link reliability or adaptive algorithms may be needed to enhance decision making. * Heavy Traffic: If the network is heavily loaded (high \(\beta_{i}\)), the queuing delay becomes significant, and the choice of \(N_{\text{next}}\) is crucial to avoid bottlenecks. * Light Traffic: In lightly loaded networks (low \(\beta_{i}\)), all links might have similar delays, so the choice of the next hop could depend on other factors like load balancing or path reliability. This model assumes that minimizing the immediate delay is the best strategy. However, in some scenarios, a slight increase in delay on one link could be justified by the  
Figure 2: Optimal next hop selection. avoidance of future congestion or better overall network efficiency. However, for simplicity, the focus here is on immediate delay minimization. To summarize, the mathematical model for choosing the next hop from \(N_{1}\) is  
\[N_{\text{next}}= \begin{cases}N_{2}&\text{if }\frac{\beta_{1}}{\alpha_{1}}\leq \min\left(\frac{\beta_{2}}{\alpha_{2}},\frac{\beta_{3}}{\alpha_{3}},\ldots, \frac{\beta_{n}}{\alpha_{n}}\right)\\ N_{3}&\text{if }\frac{\beta_{2}}{\alpha_{2}}\leq\min\left(\frac{\beta_{1}}{\alpha_{1}}, \frac{\beta_{3}}{\alpha_{3}},\ldots,\frac{\beta_{n}}{\alpha_{n}}\right)\\ \vdots&\vdots\\ N_{n}&\text{if }\frac{\beta_{n}}{\alpha_{n}}\leq\min\left(\frac{\beta_{1}}{\alpha_{1}}, \frac{\beta_{2}}{\alpha_{2}},\ldots,\frac{\beta_{n-1}}{\alpha_{n-1}}\right) \end{cases}  \tag{8}\]  
This generalized model effectively determines the optimal next hop from \(N_{1}\) by focusing on minimizing the queuing delay, which is the most variable and critical component of network delay in many scenarios. By using the ratio \(\frac{\beta_{i}}{\alpha_{i}}\), the model simplifies the decision-making process, allowing for efficient routing decisions that enhance network performance, particularly in congested environments. In practice, this model can be expanded or adapted to include other network conditions, such as link reliability, error rates, or adaptive load balancing strategies, depending on the specific requirements of the network.
## 3 Results and Discussions
### Surface Quality Measurements
#### 3.1.2 Surface Quality Resulting from Laser Cutting of Wood  
The processing with the laser has a different interaction with the wood surface than the CNC. The laser seems to affect more the earlywood areas, which appear burned and black in Figure 12, bottom.  
Another interesting phenomenon was a surface waviness having a similar pattern with the annual growth areas and increasing in magnitude with the grain angle with respect to the cutting direction. The measured profiles appear like indentations, with highest peaks in the latewood and valleys in the earlywood. The laser seems to have more pronouncedly carbonized earlywood; especially, at the annual ring limit creating gaps below the processed surface (Figure 13). This observation was also made by researchers [8] when examining spruce engraved by the laser. However, the anatomical structure is still identifiable in earlywood areas, in contrast to the latewood, areas of higher density, where the degradation process did not cause a total carbonization, but a melting stage of the wood material appearing under the microscope like a honey topping, as previously noticed by other researchers [8]. The melted material has penetrated the cell lumens and solidified so that no anatomy structure is identifiable in latewood areas and the result was a glossy surface. These areas can be identified in the roughness profiles by a less detailed region, irregularities seem smoothed in comparison with earlywood (Figure 14).  
Figure 11: The variation of the roughness parameter Pa: (**a**) for various cutting angles, with the feed speed; (**b**) for various feed speeds with the cutting angles.  
Figure 12: Larch processed by laser at \(90^{\circ}\) angle as relate to the grain (face-top image and cut edge —bottom image).  
The waviness phenomenon has increased with the cutting angle with respect to the grain, as shown by the parameter Wt, the total height of the waviness profile (Figure 15a). The surface roughness measured by Rk and Ra increased with the cutting angle, marking two peaks at 15\({}^{\circ}\) and, more obvious, for 60\({}^{\circ}\). The worse surface roughness seems to have occurred for a cutting angle of 60\({}^{\circ}\) (Figures 15b and 16a).  
Figure 14: Surface measurement of larch processed by laser at a 90\({}^{\circ}\) angle with respect to the grain: (a) primary profile (profile having roughness and waviness together); (b) roughness profile (after the waviness was subtracted).  
Figure 15: The variation of the roughness parameters Wt and Rk for various cutting angles, with the feed speed: (a) Wt; (b) Rk.  
When the combined effect of surface roughness and waviness was examined by the parameter Pa (Figure 16b), the best surface quality was obtained for a direction of cutting along the grain, which decreased with the cutting angle. In addition, Pa shows a sudden decrease in quality at 15\({}^{\circ}\) with respect to the grain. As in the case of CNC cutting, when processing with the laser, the surface quality seems particularly affected by the cutting angles of 15\({}^{\circ}\) and 60\({}^{\circ}\).
## 2 3-Phase AC/DC PWM Converter Used in DC Distribution System  
### 3-Phase AC/DC PWM Converter [12; 13; 14]  
The 3-phase AC/DC PWM converter that is used for DC distribution is an AC/DC step-up converter that outputs a boost DC while using an appropriately designed AC input side reactor. Using this 3-phase AC/DC PWM converter, the grid side AC current can be maintained at a waveform that is close to a sinusoidal waveform with a relatively low total harmonic distortion (THD), and the power factor can be freely controlled. In addition, a bi-directional energy flow is possible by using a power semiconductor switch capable with bidirectional power flow, such as IGBT instead of a diode, so it can operate as both a DC/AC inverter and an AC/DC inverter.  
Figure 2 shows the equivalent circuit of the 3-phase AC/DC PWM converter that is shown in Figure 1. The AC input voltage of each phase satisfies Equation (1), and the relationship between the input current \(i_{abc}\) and the voltage across the AC input reactor \(V_{Labc}\) is expressed, as shown in Equation (2).  
\[e_{abc}=v_{Rabc}+v_{Labc}+v_{Cabc} \tag{1}\]  
\[v_{Labc}=j\omega L_{abc}i_{abc} \tag{2}\]  
As can be seen from Equation (2), increasing the \(V_{Labc}\) increases the AC input side current, \(i_{abc}\). Conversely, if the value of \(V_{Labc}\) becomes negative, the direction of \(i_{abc}\) is reversed and a regenerating operation is thus performed, the power of which flows from the DC load to the grid side. If the switching loss and harmonic loss of the 3-phase AC/DC PWM converter are ignored, the input power and the DC output power have the relationship that is shown in Equation (3).  
\[V_{C}\cdot C\frac{dV_{C}}{dt}=v_{Ca}i_{a}+v_{Cb}i_{b}+v_{Cc}i_{c}=e_{a}i_{a}+e_ {b}i_{b}+e_{c}i_{c} \tag{3}\]  
Figure 3 shows a single-phase equivalent circuit of a 3-phase AC/DC PWM converter for analyzing the grid voltage and the current in a phasor diagram. When the converter performs a  
Figure 2: Equivalent circuit of 3-phase AC/DC PWM converter.  
Figure 3: Equivalent circuit of a 3-phase AC/DC PWM converter.  
- regenerative operation, the power factor angle of the AC power source in the steady state can be varied from leading to lagging, as shown in the phasor diagram of Figure 4 by controlling the AC side of the 3-phase AC/DC PWM converter. Figure 4 shows that the AC voltage is the smallest when the lagging operation is conducted. If the DC voltage is smaller than the maximum value of the AC input voltage, the constant DC output voltage can be controlled by lagging operation.
## 3 Results
### Influence of Meteorology on Phenometrics and the RPI  
Table 3 shows the partial correlation coefficients between the phenometrics and the meteorological factors in the four study sites. \(L_{season}\), \(L_{vcg}\), and \(L_{rep}\) were all significantly (\(p<0.001\)) correlated (negatively) with the daily mean air temperature during the corresponding period. For both of the NDVI- and phase-space-based phenometrics, \(L_{season}\) and \(L_{vcg}\) showed moderate correlations (\(-0.55<r<-0.45\)) with the \(T_{season}\) and \(T_{vcg}\), respectively, while \(L_{rep}\) showed weak correlations (\(-0.34<r<-0.33\)) with the \(T_{rep}\). Similarly, \(L_{season}\), \(L_{vcg}\), and \(L_{rep}\) were significantly (\(p<0.001\)) correlated (negatively) with the daily mean solar radiation during the corresponding period. For both of the NDVI- and phase-space-based phenometrics, \(L_{season}\) and \(L_{vcg}\) showed moderate correlations (\(-0.55<r<-0.40\)) with the \(R_{season}\) and \(R_{vcg}\), respectively, while \(L_{rep}\) showed weak correlations (\(-0.31<r<-0.29\)) with the \(R_{rep}\).  
By contrast, the RPI showed generally weak or nonsignificant correlations with the meteorological variables (Table 4). The NDVI-based RPI showed stronger correlations (\(-0.30<r<-0.08\)) with the daily mean air temperature and the daily mean solar radiation compared to the phase-space-based one (\(-0.26<r<-0.01\)). The correlations between the RPI and meteorological factors during the vegetative phase were relatively stronger (\(-0.26\) and \(-0.21\)).
# A Personalized Recommendation Algorithm Based on the User's Implicit Feedback in E-Commerce
## 5 The Recommendation Algorithm Based on the User's Implicit Feedback
### Experimental Results and Analysis
#### 6.3.3 Experiment 3: Comparison with Other Methods  
Because our method was based entirely on the user's implicit feedback, we called it BUIF. To verify the effectiveness of BUIF, the precision, recall, and F1 were compared with the following algorithms while using the same dataset.  
**BUCF**: traditional CF based on users. BUCF calculates the user's similarity by the number of common items that are purchased by the users.  
**BICF**[3]: traditional CF based on items. BICF calculates the item's similarity by the number of common users that purchased the same item.  
**FPP**[6]: builds a rating matrix solely based on the purchase data of the users. FPP integrates CF-based recommendations and SPA-based recommendations.  
**BUPCB**: proposed in this paper, it calculates the user's similarity by the Pearson correlation coefficient with the user's purchase behavior and comparison behavior.  
**CBOW-CF**: trains item-sequences with the CBOW model to obtain the item vectors, and then, combines the traditional CF to generate the algorithm CBOW-CF.  
**BUIF**: Our methods described above.  
From Figure 7a-c, it can be seen that the precision, recall, and F1 of BUCF and BICF are worse, which is because they regard all behaviors as one type and select the nearest neighbors based on the whole user set and item set. The FPP solely considers the user's purchase data and disregards the information included in clicking, adding, and collecting, which causes the recommendation quality to be reduced. The BUIF method performed better than the others in terms of the precision and recall because BUIF not only considers the user's purchase behavior but also comparison behavior that improved the accuracy of the user's similarity, and BUIF calculates the item's similarity by an item vector that considers the item-sequence information. In addition, the overall accuracy and quality of recommendation were improved effectively through reordering the item's candidate items.  
Figure 7: (**a**) Describes the precision compared with other methods; (**b**) describes the recall compared with other methods; (**c**) describes the F1 compared with other methods.  
There is also an example of a real JD user represented by user_id (290054). We used the above methods to recommend items (number of recommended is five) to the user (290054) and the results are shown in Table 6.  
In Table 6, the items are also represented by sku_id. Additionally, the red sku_id means purchased items by the user (290054) in the test data. From Table 5, it can be seen that the recommendation results of BUIF were purchased more than other methods, which means our method will obtain a high degree of user satisfaction.
## 2 Irrigation System Modeling
### Field Capacity and Permanent Wilt Point  
The amount of water retained in well-drained soil is the field capacity (\(Cc\)) and the permanent wilting point (\(Pmp\)). When the crop is unable to extract water, it will have reached the permanent wilting point [28]. To estimate soil moisture sheets for root depth, Equations (2) and (3) should be taken into account [27].  
\[L_{Cc}=Cc\cdot D_{a}\cdot Z_{r} \tag{2}\]  
\[L_{Pmp}=Pmp\cdot D_{a}\cdot Z_{r} \tag{3}\]  
where \(L_{Cc}\) is the stored sheet at field capacity in mm, \(Cc\) is the moisture content at field capacity (%), \(L_{PMP}\) is the stored sheet at permanent wilting point in mm, \(Pmp\) is the moisture content at permanent wilting point (%) and \(Z_{r}\) is the depth of the root zone.  
Available water (\(AD\)) or useful water is the amount of usable soil water for the plant and is between the field capacity limits (\(Cc\)) and the permanent wilting point (\(Pmp\)), located at different depths of the irrigated soil. To determine the available water sheet see Equation (4) [28].  
\[L_{AD}=(Cc-Pmp)\cdot D_{a}\cdot Z_{r} \tag{4}\]  
where \(L_{AD}\) is the available water lamina in mm and \(D_{a}\) is the bulk density of the soil.  
The plant absorbs water from the soil without effort when the water is at field capacity and its yield is maximum, so it should not be expected that the available water is close to the permanent wilting point, for which the threshold \(p\) is taken into account, which depends on the type of crop and allows the water to be readily available water (\(RAW\)) for the crop, ensuring optimum plant performance [28]. If the water content in the soil is below the permanent wilting point, the crop will begin to suffer water stress [29]. The readily available water table (\(L_{AFD}\)) is obtained with Equation (5).  
\[L_{AFD}=(Cc-Pmp)\cdot D_{a}\cdot Z_{r}\cdot p \tag{5}\]  
where \(L_{AFD}\) is the readily available water sheet in mm and \(p\) is the irrigation threshold.  
Figure 1: Water balance [29].  
Regarding the precipitation (\(P\)) produced in a day, if the rainfall is less than 0.2 mm it is assumed that it will evaporate in its entirety. For an optimal irrigation scheduling, the precipitation that may occur should be considered, as well as the possible lamina contributed by such precipitation [28; 29].  
Deep percolation (\(PP\)) appears if in irrigation the water applied to crops is greater than required, according to the field capacity of the soil, and this water moves beyond the roots, causing water loss by deep percolation [27].
## 3 Results and Discussion
### Emissions Caused by Use of Different Gasoline-Combustion Technologies  
Two types of combustion technologies--MPI and GDI--exist for use in gasoline vehicles. GDI-based vehicles benefit from enhanced fuel efficiency and reduced CO\({}_{2}\) generation compared to  
Figure 3: (**a**) Rates of CO\({}_{2}\) emission of gases constituting different fuels. Mass fractions all gases except CO\({}_{2}\) in (**b**) gasoline, (**c**) liquefied petroleum gas (LPG), (**d**) diesel particulate filter (DPF)-equipped diesel, and (**e**) non-DPF-equipped diesel. All values correspond to total averages calculated during NIER chassis-dynamometer tests  
their MPI-based counterparts. However, GDI-based vehicles emit higher amounts of PM compared to those incorporating MPI owing to their inherent incomplete combustion and droplet wall wetting of cylinder surfaces [17]. In 2014, the Korean government imposed regulations on PM emissions from gasoline with GDI engine- and diesel-powered vehicles (previously, only diesel-powered vehicles were regulated). For gasoline-powered vehicles equipped with GDI engines, the specified PM-emission limit was set to 0.004 g km\({}^{-1}\)[1]. This change in emission regulations led to a change in the composition of exhaust gases emitted from the latest gasoline-powered vehicles. Figure 4 depicts observed emission trends from gasoline-powered vehicles equipped with different combustion systems--MPI, GDI 1 (before PM regulations), and GDI 2 (after PM regulations). As observed and previously discussed, total pollutant emissions were observed to decrease corresponding to an increase in average vehicle speed. Additionally, emission rates of gases other than CO\({}_{2}\) demonstrated a decrease with increasing vehicle speed up to a certain point; this was followed by a subsequent increase thereafter. CO accounted for the majority of non-CO\({}_{2}\) emissions. Vehicles incorporating MPI and GDI 2 technologies demonstrated emissions with NH\({}_{3}\) mass fractions in the range of 2-28% of all non-CO\({}_{2}\) pollutants across all chassis-dynamometer tests. In contrast, GDI-1-equipped vehicles demonstrated very low NH\({}_{3}\) emissions (mass fraction of approximately 0.3% of all non-CO\({}_{2}\) emissions). Given that NH\({}_{3}\) is produced via reactions involving CO, H\({}_{2}\)O, and NO inside TWCs [18] its emission rate depends on the vehicle's combustion-system type, after-treatment device technology, driving patterns, and vehicle manufacturer strategy towards emission control.
## 3 Problem Formulation
### Objective Function  
In addressing this type of problem, the voyage or flight time is typically a crucial metric for decision-making. In our study, we select the total time taken by the last UAV to complete its monitoring tasks and return to the agricultural command center. This duration represents the overall time spent for the entire task allocation scheme after all pest monitoring tasks have been completed. The optimal task allocation scheme is defined as the one that minimizes this total time. Thus, the execution time for UAV \(U_{i}\) in a given allocation scheme is represented as  
\[T_{i}=T_{tot\_dis}+T_{tot\_task}\;\;i\in\{1,2,\ldots,n\} \tag{4}\]  
\[T_{tot\_dis}=T_{0}^{M_{1}^{i}}+\sum_{M_{1}^{i}\in Seq^{i}}T_{M_{k}^{i}}^{M_{1 +1}^{i}}+T_{M_{N_{i}}^{i}}^{0} \tag{5}\]  
\[T_{tot\_task}=\sum_{k=1}^{N_{i}}t_{k}^{i} \tag{6}\]  
where \(T_{tot\_dis}\) in the equation denotes the time taken by UAV \(U_{i}\) to execute the voyage it has passed through in the sequence of agricultural tasks to which it has been assigned, and \(T_{tot\_task}\) denotes the sum of the execution lengths required by UAV \(U_{i}\) to execute the individual tasks in the sequence of agricultural tasks to which it has been assigned. \(T_{0}^{M_{1}^{i}}\) in Equation (5) denotes the time required for UAV \(U_{i}\) to fly from the start point to the first task point in the assigned sequence, \(\sum_{M_{1}^{i}\in Seq^{i}}T_{M_{k}^{i}}^{M_{k+1}^{i}}\) denotes the time required for UAV \(U_{i}\) to fly from the kth task point to the k + 1 st task in the assigned sequence, and \(T_{M_{N_{i}}^{i}}^{0}\) denotes the time required for the UAV \(U_{i}\) to fly from the last task point in the allocation sequence to the start point.  
In summary, the longest execution time of a UAV sequence in a given distribution scheme is expressed as  
\[T_{max}=max(T_{i})\ \ \ U_{i}\in U \tag{7}\]  
Then, the optimization objective can be expressed as  
\[min(T_{max}) \tag{8}\]
## 6 Application of the Optimization Techniques: Routing Protocols
### Multipath Routing Protocols
#### 6.4.2 Maximum Lifetime Routing in Wireless Sensor Networks  
This algorithm combines the energy consumption optimization with the use of multiple routes [26]. In this algorithm an active route (also called the primary route) is monitored to control its residual energy. Meanwhile other routes can be discovered. If the residual energy of the active route does not exceed the energy of an alternative route, the corresponding secondary route is then used.
## 2 Materials and Methods
### Determination of Antioxidant Activity  
The antioxidant activity of pomace and sorbet extracts was assessed by the ABTS\({}^{\bullet+}\)-scavenging method, based on a previously described protocol [43], and which has also been applied in elderberry matrices [16; 44]. ABTS\({}^{\bullet+}\) was generated by dissolving ABTS with a 2.45 mM aqueous solution of potassium persulfate to a final concentration of 7 mM ABTS. To ensure complete conversion, the mixture was allowed to stand in the dark at room temperature for 16 h. It was then transferred to polypropylene tubes and stored at \(-18\)\({}^{\circ}\)C until required. On the day of analysis, the ABTS\({}^{\bullet+}\) solution was further diluted with ethanol to adjust the absorbance to 0.700 at 734 nm. For the assays, the extracts were previously diluted 1:5 in methanol, and then 100 \(\upmu\)L of each diluted extract was combined with 2 mL of the prepared ABTS\({}^{\bullet+}\) solution in test tubes. The mixtures were homogenized using a vortex and left in the dark at room temperature for 15 min. Absorbance readings were taken in triplicate at 734 nm using 1 cm polystyrene cuvettes and a UV-visible spectrophotometer (Shimadzu UV-1280, Izasa Scientific, Barcelona, Spain). A blank was also prepared by replacing the 100 \(\upmu\)L of extract with methanol. The percentage inhibition of ABTS\({}^{\bullet+}\) was calculated using the following equation:  
\[\%\text{ ABTS}^{\bullet+}\text{ inhibition}=\frac{A_{\text{(blank solution)}}-A_{\text{(sample)}}}{A_{\text{(blank solution)}}}\times 100 \tag{5}\]  
The antioxidant activity of pomace or sorbet extracts was expressed in mmol Trolox equivalents per gram (mmol TE g\({}^{-1}\)) of dried pomace or per kilogram (mmol TE kg\({}^{-1}\)) of sorbet, based on a Trolox calibration curve.