esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions  
### Spatial Variation of Global Solar Radiation  
The average value obtained by dividing the annual global solar radiation threshold in Table 1 by 365 days was used as the classification threshold of daily global solar radiation, and the classification standards are detailed in Table 2. The annual average daily global solar radiation and the proportion of daily global solar radiation on different grades in the six cities are shown in Figure 2.  
It can be seen from Figure 2 that the ten-year mean daily global solar radiation of Harbin, Shenyang, Beijing, Wuhan, Shanghai, and Guangzhou were 13.64 MJ/m\({}^{2}\), 14.64 MJ/m\({}^{2}\), 12.19 MJ/m\({}^{2}\), 12.86 MJ/m\({}^{2}\), and 12.84 MJ/m\({}^{2}\), respectively. Among them, Shenyang had the highest number of daily global solar radiation at Grade 1--in the past ten years, it reached 1357 days--followed by Beijing with 1348 days, and the fewest number of days in this level was 960 days in Guangzhou. Additionally, Guangzhouhad slightly more days of daily global solar radiation at Grade 2, reaching 766, while the other five cities showed little difference in this grade, ranging from 432 days to 489 days. Furthermore, the number of days with daily global solar radiation at Grade 3 for the six cities in the past ten years was very close, the least is Harbin, 504 days, and the most is Guangzhou, 595 days. Moreover, Wuhan had the most days at Grade 4, with 1565 days of daily global solar radiation at at this grade in the past ten years, followed by Harbin with 1488 days, and Shanghai ranked the third with 1462 days; Guangzhou, Shenyang, and Beijing were relatively close, with 1332 days, 1314 days, and 1283 days, respectively. Grade 4 indicates that the daily global solar radiation is relatively poor, and the number of days in this grade is the most important factor affecting the development and utilization of solar energy resources in a city.
# Evaluation of a Novel Condition Indicator with Comparative Application to Hypoid Gear Failures
## 2 Test Methodology
### Testing Procedure  
The testing procedure of a ring and pinion gear pair consists of several stages, starting with the assessment of the contact pattern at given loads and EPG\(\alpha\) positions, with the simultaneous measurement of the TE and contact pattern. This process essentially \"tunes\" the contact conditions to find the best mesh for load sharing.  
After the first assessments, the relevant testing for crack and damage detection starts and the samples are loaded with a defined load and speed. The initial (unloaded) EPG\(\alpha\) values are set and the rig monitors reaction forces in order to recreate system dynamics by adjusting and maintaining EPG\(\alpha\). After running-in for 1 h, the test specimen contact pattern is re-evaluated. If it is necessary, minor adjustments of EPG\(\alpha\) positions are made to reset the desired contact pattern. After this point, all boundary conditions (oil temperature, torque, speed, position of the gears) are constant during testing until the sample fails.  
To stop testing shortly after the first fracture occurs, a preliminary version of the detailed algorithms was used together with torque monitoring. However, it was not intended to stop testing as the first crack was initiated. The goal of stopping the test was only to prohibit secondary damages because of the failure of the samples.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Sensor Name** & **Type** \\ \hline Accelerometer & PCB M608A11 \\ Torque transducer & HBM T12 HP rated at 5 and 10 kNm \\ Rotation speed & INC-4-225-191001-ABZ2-RFC3-24-AN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the used sensors for CM.  
The same failure did not always occur, because of the variation of the loads, EPGa positions and aleatoric uncertainties in the gear production (e.g., non-metalic inclusions in the material at random locations). As the test was not stopped during the run, the investigation of the failure modes and crack propagation was not possible. Furthermore, the results of metallurgical investigations are not available for all samples. However, the test data recorded allow off-line evaluation of the CM process to evaluate the effectiveness of the CI.
## Appendix A Derivation of the Proposed Cost Terms
### Train Dwell User Cost (\(C_{duiT}\))  
Similar to Appendix A.1, the average cost of dwell time for the train (\(C_{duiT}\)) for each feeder bus route is determined by the demand in the rail station multiplied by the passenger boarding and alighting rate. Therefore, the average dwell cost is derived as follows:  
\[C_{duiT}=\mu_{I}\left(Q_{k}\times t_{dT}\right) \tag{11}\]  
Similar to the user feeder bus dwell time, the time spent on boarding and alighting at each rail station is different. Accordingly, the number of passengers and the dwell cost would be different. Figure 11 shows the actual passenger demand at each rail station in the transit system.  
In Appendix A.1, the geometric series equation was adopted to develop a more accurate model for distributing the dwell cost of the train stations along the rail line. The algebraic proof of the geometric series used, as well as the detailed derivation of the feeder dwell cost are presented below:  
\[C_{duiT}=\mu_{I}\left[(Q_{1})+(Q_{1}+Q_{2})+\left(Q_{1}+Q_{2}+Q_{3}+....+Q_{j} \right)\right](t_{dT})\,,\quad j=1,2,...,J \tag{12}\]This can be re-written as follows:  
\[C_{duiT}=\mu_{I}\left[\left(J(Q_{1}+(J-1)Q_{2}+(J-2)Q_{3}+.....+(J+1-j)Q_{j}) \right)\right](t_{dT}),\quad j=1,2,...,J \tag{10}\]  
Passenger demand at rail station \(j\) can be defined as:  
\[Q_{j}=\sum_{i=1}^{I}q_{i}\times Y_{ij} \tag{11}\]  
where \(q_{i}\) and \(Y_{ij}\) are, respectively, the demand of the bus stop and a binary variable: a value of one if stop \(I\) is assigned to station \(j\). For the algebraic proof from Equation (10), we have T (\(J\)) written as follows:  
\[T\left(J\right)=J+(J-1)+...+3+2+1 \tag{12}\]  
where the \(j\)-th term is now \(J+1-j\) for j from one to \(J\). Thus, we will have:  
\[T(J)=\sum_{j=1}^{j=J}(J+1-j) \tag{13}\]  
Therefore, the dwell cost can be formulated as follows:  
\[C_{duiT}=\mu_{I}\sum_{j=1}^{J}\left[\left(\sum_{i=1}^{I}q_{i}\times Y_{ij} \right)\times(t_{dT}\times(J-j+1))\right] \tag{14}\]
# Nearshore Benthic Mapping in the Great Lakes:
## 3 Results
### Component Classification Comparison
This speaks to the dominant role of spectral information from the hyperspectral imagery and is consistent with similar approaches ([PERSON] et al., 2020); however, what is less clear, and not evaluated as part of this study, is the impact of dimensionality reduction techniques applied to the hyperspectral imagery (such as MNF or principal component analysis), which has had mixed results in terms of classification accuracy in other studies ([PERSON] et al., 2020). Additionally, out of the top five performing airborne data combinations for the substrate component, two included depth with spectral data inputs, though the inclusion of depth resulted in a slight accuracy decrease. In contrast, depth and bathymetric  
Figure 21: Tier 1 plot of substrate and biotic components classification performance, comparing percent overall accuracy and percent producer accuracy for airborne and satellite data combinations. Note the strong break in overall accuracy between substrate and biotic components and producer accuracy between the larger homogeneous components of the study area. reflectance data inputs were present in the top-performing data combinations for the biotic component, such as AB DE + CA MNF FU + CA MNF WLR and AB RF + AB DE + CA MNF FU + CA MNF WLR. This suggests an important role for the addition of Lidar-based data inputs for the classification of biotic classes, although this is somewhat mixed for other studies [22; 23; 52], and yet [22] acknowledges that the quality of the bathymry data is likely the limiting factor. The CMECS structure used herein allowed for a more thorough review of the influence upon classification accuracy with respect to the component, whereas other studies tend to develop an inclusive classification system in which substrate classes (i.e., rock, sand, etc.) and biotic classes (i.e., seagrass, algae, etc.) are analyzed simultaneously. As a result, inclusive classification systems can make it difficult to understand the influence of data type on classification accuracy.
## 3 Methodology
### Receiver
#### 3.2.3 Quantum State Measurement  
Here, quantum state measurement was carried out from \((\mu^{\prime})\) using Heterodyne Detection for the verification of the secret key and extraction of the raw data from the signal received. Heterodyne Detection is an optical and quantum measurement technique used to extract information from a signal by mixing it with a reference signal. This process is used in the conversion of a signal's frequency into a more easily measurable form, typically through a beat frequency to detect electronic devices. First, \((\mu^{\prime})\) is mixed with a strong reference signal, which is shown as  
\[\hat{\gamma}(\bar{t})=u^{\prime}(\bar{t})\cdot\cos(\omega_{I}(\bar{t})) \tag{35}\]  
Here, \(\hat{\gamma}(\bar{t})\) signifies the output of the mixed-signal and \(\cos(\omega_{I}(\bar{t}))\) signifies the reference signal at a time \(\bar{t}\). Then, the frequency shifting is carried out in \(\hat{\gamma}(\bar{t})\) and is given as follows:  
\[f^{\prime\prime}(\bar{t})=\hat{\gamma}(t)\cdot e^{\varphi\triangle\Psi t} \tag{36}\]  
Here, \(f^{\prime\prime}(\bar{t})\) signifies the frequency shifted signal of \(\hat{\gamma}(t)\), and \(\triangle\)\(\Psi\) signifies the applied frequency shift. Next, the beat frequency is calculated from \(f^{\prime\prime}(\bar{t})\) which provides the phase and amplitude information of the quantum state.  
\[V^{\prime}(\bar{t})=\left[r_{e}(f^{\prime\prime}(\bar{t}))\right] \tag{37}\]  
\[\widetilde{V}(\bar{t})=\left[i_{m}(f^{\prime\prime}(\bar{t}))\right] \tag{38}\]  
Here, \(V^{\prime}(\bar{t})\) and \(\widetilde{V}(\bar{t})\) are the amplitude and phase quadrature, respectively, and \(r_{e}\) and \(i_{m}\) signify the real and imaginary parts of \(f^{\prime\prime}(\bar{t})\), respectively. Therefore, the quantum state helps the verification of the secret key based on \(V^{\prime}(\bar{t})\) and \(\widetilde{V}(\bar{t})\). The communication between the transmitter and receiver was established when the secret key was verified and the desired raw data denoted as \(Z_{raw}\) were collected.
## 3 Results and Discussion
### Post-Treatment Using CW
#### 3.2.2 Effects of Vegetation  
Although CWs are distinguished by the macrophytes they contain, the exact contribution of plants and their effect on pollutant removal is still under discussion [65; 67; 68; 76; 77; 78]. It is well-known that vegetation provides favorable conditions for microbial growth in the root zones of CWs, and simultaneously transfers oxygen via its roots to within the CW body [66]. To examine the effect of vegetation, a one-way ANOVA was performed. Results showed that the vegetation did not significantly affect d-COD removal (\(p\) > 0.05).
## 2 Materials and Methods
### System Design
#### 2.2.5 PDA  
The feeding system was regulated as a whole by the CAN, but the ESFs also required a convenient, one-to-one management method. As a type of GUI software with excellent performance and high visibility, Android application technology was used to implement the key functions related to activity, service, broadcast, content provider, and other components. The application running on the PDA (equipped with a laser scanner and Android 8.0 operating system) was developed to manage an individual ESF.  
The application includes \"ScanReceiver\", \"ConnectService\", communication protocol, function page, and so on. \"ScanReceiver\" extended \"BroadcastReceiver\", received the scanning broadcast from PDA, and parsed out the valid content. \"ConnectService\" extended service, connected to WLAN according to SSID, and established a one-to-one socket pipeline. The communication protocol was employed to safeguard against malicious attacks through message parsing and validation. The function pages served as the gateway to ESF management. Figure 5 illustrates the workflow of using the PDA to manage the ESF. The steps involved for breeders to manage the ESF using the PDA were as follows:  
1. The PDA scanned the QR code attached to the ESF and transferred the byte content to the APP via broadcast.
2. The content was parsed to extract the Service Set Identifier (SSID), representing the unique WLAN generated by an ESF.
3. Based on the SSID, the app established a socket pipeline with the ESF.
4. Breeders could manage the ESF through function pages such as Entry, Exit, Breeding, Backfat Adjustment, Diagnosis and Test, and Information Query pages.  
Figure 4: (**a**) Operating status page and (**b**) feeding scheme page.  
Figure 3: (**a**) Delivery mechanism and (**b**) electronic circuit.  
5. Connectivity between the app and ESF had to be maintained. If the ESF did not receive a heartbeat from the app every 3 s, it would interrupt the connection to prevent data inconsistency.  
Figure 6 displays some of the PDA screens, each with specific responsibilities:  
1. The \"ENTER STALL\" page facilitated the association between a sow and an ESF.
2. The \"EXIT STALL\" page allowed for the disassociation between a sow and an ESF.
3. The \"SOW BREEDING\" page enabled inputting mating date to adjust the feeding scheme accordingly.
4. The \"BACKFAT ADJUSTMENT\" page allowed input of the backfat thickness to notify the ESF to update the feeding scheme.
5. The \"FAULT DIAGNOSIS\" page provided a means to query fault information related to the ESF.
6. The \"QUERY INFORMATION\" page provided functionality to inquire about the sow's status.  
Figure 5: Workflow of PDA management of ESF.  
Figure 6: (**a**) Home page, (**b**) “ENTER STALL\" page, and (**c**) “BACKFAT ADJUSTMENT” page.
# Learning-Assisted Rain Attenuation Prediction Models
## 4 Insights of LARA Models
### LARA Model with One Factor
**Disadvantages:** It is not mentioned whether the training data is from the terrestrial or the earth-space link. **Future improvement:** As a result, the LS-SVM can forecast rain attenuation with improved efficiency, compromising accuracy. However, the accuracy needs to be increased. [PERSON] et al. [11] developed a rainfall forecast model based on a back-propagation neural network (BPNN) with four years of data collected. The model's predicted results matched with two years of real rain rate from 2017 to 2018. The BPNN network consists of input, hidden, and output layers. The synaptic weights are updated by the backpropagation with the condition that:  
\[\frac{dE}{dO_{a}}\ =\ -(O_{t}-O_{a}), \tag{5}\]  
where \(O_{t}\) is the desired output (target), \(O_{a}\) is the actual output, and \(E\) is the error. The minimum error results in:  
\[\Delta w_{i}\ =\ \eta\frac{dE}{dO_{i}}\bigg{|}_{i=\ 1,2,3,\ldots,J}, \tag{6}\]  
where \(\Delta w_{i}\) is the weight change on the \(i\) th input, \(\eta\) is the learning rate, \(O_{i}\) is the output contributed by the \(i\) th input, and \(i\) is the input. This results in a BPNN network that consists of a single input, a single output layer, three hidden layers, and consists of three nodes. Further, they calculated the directional synaptic weight matrices in the forward and backward direction as well as synaptic weights. **Advantages:** As per the validation result, the model can successfully predict the attenuation. **Disadvantages:** The model needs low sample timings of 1 min or less. Further, they created five attenuation classes, and they did not justify these five attenuation class boundaries' marking. **Future improvement:** Find the BPNN models' structure and weight matrices for relatively short sample timings of about 10 s.
# Priority Queues with Fractional Service for Tiered Delay QoS
## 3 Fractional Service Buffers
In Figure 5a, Packet \(Y\) of Class-0 arrives and is assigned to Buffer-0. In this case, the threshold of Class-0 has not been reached (_i.e._, \(Q_{0}<K_{0}\)) and thus \(Y\) can be admitted to Buffer-0. However, the combined number of packets from Class-0 and Class-1, \(Q_{1}\) will exceed the Class-1 threshold (_i.e._, \(Q_{1}>K_{1}\)) as a result. As shown in Figure 5b one of the Class-1 packets, Packet 18, has to be overflowed to the _head_ of Buffer-2 and counted as a delay violation. This is similar to \"push-out on threshold\" policies in buffer management [16]. Certainly, the FSB has to limit the frequency of overflow for each class, denoted \(\delta_{i}\) for Class-_i_, to maintain a meaningful delay QoS for that class. In this latter case, the successful insertion of \(Y\) grants both Class-1 and Class-2 service credits. Because \(m=1\), Class-1 and Class-2 receive credits of 1 and 1/2 respectively. As a result, Class-1 has sufficient credit to have its leading packet promoted to Buffer-0 while Class-2 does not. Since promoting a packet from Buffer-1 to Buffer-0 does not change the value of \(Q_{1}\), the promotion can take place even though \(Q_{1}\) is at its threshold level \(Q_{1}=K_{1}\). Figure 5c displays the renumbered packets in the buffers after the arrival, the overflow, and the promotion. In particular, Packet \(Y\) is now Packet 05, Packet 06 is the promoted Packet 11, and Packet 21 is the overflowed Packet 18 in Figure 5b. We note that each buffer in FSB can be implemented with a link-list and a promotion is achieved by simply moving the tail pointer of the higher-priority buffer by one (packet) to accommodate for the packet promoted from the lower-priority buffer as shown in Figure 5c. In this case, pointer to the tail of Class-0 is moved, while pointer to the tail of Class-1 is not because Class-2 has not earned sufficient credits. With this pointer management scheme, packets do not have to be physically moved once they are placed in the buffer memory. Figure 2: (**a**) Packets are assigned to different buffers in accordance with their delay classification; (**b**) Packet movements between buffers as directly by the FSA. Figure 4: First scenario of a packet overflow. \(K_{0}=6\), \(K_{1}=12\), \(K_{2}=18\). Figure 5: Second scenario of a packet overflow. \(K_{0}=6\), \(K_{1}=12\), \(K_{2}=18\). Figure 3: Logical concatenation of the buffers.
## 5 Result and Discussion
### Velocity Gradient  
Vorticity is obtained by the curl of the velocity vector. In order to study which component of velocity has a higher contribution in each aforementioned vorticity type, it is useful to study the velocities' gradient behavior in the different diameters of the rotor in the streamwise direction. The gradient of the velocity components are calculated by Equation (9); \(V_{i}\) is representative of radial velocity, circumferential velocity, and axial velocity:  
\[\
abla V_{i}=dx.\frac{\partial V_{i}}{dx}+dy.\frac{\partial V_{i}}{dy}+dz.\frac {\partial V_{i}}{dz} \tag{9}\]  
In Figure 11, the meridional view of the velocity component gradients in the streamwise direction in equal and partial cases is indicated. The meridional view shows the contour averaged in the circumferential direction.  
In equal admission, all of the velocities' components change more severely than partial admission. Circumferential velocity has the highest variation among all of them in the streamwise direction in the different diameters of the rotor. It increases sharply at the leading edge, and after that, it starts to diminish at about the mid-length of the blade. Axial velocity has the lowest gradient in comparison with the other velocity components, and in equal admission, its variation is similar to the partial admission except for two small regions in the shroud tip at the leading edge and trailing edge. The radial velocity increases at the leading and trailing edge of the blade in the streamwise direction.  
The Tr. vortex is located at the trailing edge, which caused a sharp radial velocity increase, but a reduction in the axial and circumferential components of the velocity. In the LR vortex, circumferential velocity increases sharply, but radial velocity decreases strongly, and axial velocity is not affected significantly. In the tip vortex, the circumferential velocity increases hugely, the axial velocity drops significantly, and the radial velocity increases slightly.  
Figure 11: Meridional view of velocity component gradients in the streamwise direction in equal and partial cases.
# Incremental Algorithm for Association Rule Mining under Dynamic Threshold
## 3 Literature Review
### Apriori Algorithm  
The Apriori algorithm is an icon algorithm in the ARM field [2; 13]. Basically, this algorithm is employed to collect the frequent itemset based on pre-defined minimal support threshold. All parts (subsets) of the frequent itemset are frequent, whereby the itemset is considered frequent by the algorithm only when all subsets are frequent; otherwise the itemset is considered infrequent. The frequent pattern discovery process is described as an iterative process [28]. At the start of the iteration process, the algorithm counts the 1-itemsets. The algorithm seeks for the itemsets within the 1-itemsets to generate candidate 2-itemsets. The algorithm retrieves the database to count the support of 2-itemsets. Subsequently, the algorithm moves on to 3-itemsets, 4-itemsets, and K-itemsets. The algorithm derives a k-itemset candidate itemset from a k-1-itemset frequent itemset. Since the Apriori algorithm adheres to the downward closure lemma, all the candidate itemset subsets should be frequent. Generation of candidate sets continues until it is impossible to generate a new candidate itemset. Another essential algorithm for ARM is FP-growth [29]. The FP-growth determines both growth pattern and data structure based on FP-tree, which varies from the approach of Apriori that applies itemset data structure [29; 30]. One of the many strategies on pattern discovery refers to the search for frequent patterns without specific support threshold, as employed in ARMGA [31] and G3 PARM [32]. This strategy addresses one of the long-time standoff issues, which is support threshold parameter. However, this strategy is time consuming as it enters a specific threshold for every itemset to generate the rule, as well as to calculate the confidence for the rule and the support for the itemset. The next sub-section presents a review for incremental approaches.