esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of a Centrifugal Compressor Using the Design of Experiment Technique
## 3 Design of Experiment  
### Design Space  
To investigate the effect of each factor on the objective function, the full factorial design technique can be used. A \(2^{k}\) full factorial design space is built by \(k\) parameters each at two levels results in \(2^{k}\) design points. This type of design is helpful in early stages of design to recognize the most effective parameters in the results.  
In the case with \(A\), \(B\), \(C\) and \(D\) as the factors, each at two levels, \(2^{4}\) combinations are generated. Four of them are the main effects shown by \(A\), \(B\), \(C\) and \(D\) estimate the main effects of the related factors on the the result. In six of them, the two factor interactions considered are shown by \(AB\), \(AC\), \(AD\), \(BC\), \(BD\) and \(DC\). The three parameters interactions are \(ABC\), \(ABD\), \(ACD\) and \(BCD\). The four factor interaction for this case is \(ABCD\). The main effect of \(A\), is the average of the results, in which the combinations with \(A\) at the high level are positive ones and the combinations with \(A\) at the low level are negative ones. For each parameter two levels are shown by \(+\) or \(-\) respectively. If the average response at the combinations in which \(X\) at the high level is noted by \(\overline{y}_{x^{+}}\) and the average response with \(X\) at the low level is noted by \(\overline{y}_{x^{-}}\), The effect of \(X\) combination on the objective function [5] will be,  
\[E_{X}=\overline{y}_{x^{+}}-\overline{y}_{x^{-}} \tag{3}\]  
To recognize the significant factors, a normal or half-normal probability plot analysis is the simplest tool. Normal probability plots show that whether sample data conform a normal distribution or not [5].  
Figure 1: Bézier curve and the effect of control point position change.  
For constructing a normal plot, the effects are first ranked from smallest to largest, \(E_{1}<E_{2}<...<E_{n}\), Then the ordered \(E_{i}\) are plotted versus:  
\[\frac{100(i-0.5)}{n} \tag{4}\]  
Then a straight line interpolated between the 25 th and 75 th percentile points. The factors that have negligible effect on the objective function have normally distributed \(E\)s and they fall along a straight line, whereas most effective factors are far from the straight line and hence normal distribution [5].
# Study on the Influence of Rock Shape on Rolling Distance
## 6 Conclusions and Prospects  
### Conclusions  
1. The shape of rolling stones is a critical factor that impacts rolling distance, and it is associated with the number of edges in the stone's two-dimensional cross-section perpendicular to the rolling surface. We can quantify the level of difficulty in stone rolling using the _RF_ coefficient introduced in this study.
2. Deviations or alterations in the rolling axis while a stone is rolling will influence the rolling distance. The degree of this impact is determined by the magnitude of  
Figure 34: Rolling behavior of test block F on composite ground.  
Figure 33: Rolling behavior of test block B on composite ground.  
variation in the deviation angle, with greater variations leading to increased energy consumption.
3. When stones roll on composite ground with varying hardness on each side, the existence of \"steps\" created by differences in hardness can result in collisions or deviations at the boundary line. The degree of this impact relies on the size ratio between the stone and the \"step\". A greater ratio leads to a reduced impact, while a smaller ratio has the opposite effect.
4. Based on experimental data, the influence mechanism of shape on rolling stone distance was analyzed. A parameter quantifying the impact of stone shape on rolling distance was proposed and introduced into kinematic formulas. This led to rolling distance calculation formulas for stones of different shapes on different types of ground. Validation using experimental data showed that the errors between calculated and experimental values were small. It was also noted that energy consumption resulting from stone deviation and changes in the rolling axis during rolling reduced the rolling distance.
5. By using the proposed formulas and considering the deviation of stones with different shapes, the influence range of stones with different shapes rolling at different speeds can be plotted. Model tests were conducted for validation. However, the reduction in the actual terrain and geomorphology in natural rockfall disasters in model tests is limited, and only some factors that affect the rolling distance of rock blocks can be considered. It is impossible to completely replicate the natural characteristics of mountainous terrain. Therefore, after on-site testing verification, these findings can serve as a reference for the assessment of rolling stone danger zones and protection design.
## 5 Efficiency  
In this section, we first analyze and compare the efficiency of the proposed scheme with the previous aggregation schemes, that is [PERSON] _et al._'s scheme (GJ) [3], [PERSON] _et al._'s scheme (LLL) [5,6], and [PERSON] _et al._'s scheme (LLYLLS) [21] from a theoretical perspective. We also discuss its efficiency when implemented with specific parameters.
# Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks
## 3 Materials and Methods
### Statistical Analysis
The BLE-LoRa Hybrid network exhibits a similar behavior to the BLE network, with both datasets not following a normal distribution before and after mitigation. [PERSON]'s test determines whether the variability (variance) in retransmissions remained consistent before and after mitigation. A high \(p\)-value suggests that the variance is homogeneous, while a low \(p\)-value implies significant differences in variance. In the BLE network, the \(p\)-value of 0.54121 suggests that the retransmission variability remained stable after mitigation. Similarly, the LoRa network's \(p\)-value of 0.19191 indicates that its retransmission variance also remained relatively unchanged. However, the BLE-LoRa Hybrid network had a \(p\)-value of 0.00000, signifying a drastic change in variance, meaning the mitigation strategy significantly altered the retransmission patterns. The statistical significance test examines whether the difference in retransmissions before and after mitigation is statistically significant. A low \(p\)-value indicates that the difference is meaningful and unlikely due to random variation. The BLE network results show a \(p\)-value of 0.00000, confirming a highly significant difference in retransmissions after mitigation. The LoRa network also experienced a statistically significant change, with a \(p\)-value of 0.00205, suggesting that retransmissions were successfully reduced. The BLE-LoRa Hybrid network had a \(p\)-value of 0.00040, which is also very low, indicating a substantial shift in retransmission behavior after mitigation. The results indicate that the mitigation strategy was effective across all networks, reducing retransmissions significantly. The BLE network maintained stable variance, suggesting that while retransmissions were reduced, the pattern of retransmission values remained similar. In the LoRa network, the shift from normal to non-normal distribution after mitigation suggests a more irregular impact on retransmission values. The BLE-LoRa Hybrid network was the most impacted, showing significant changes in both retransmission counts and variance, indicating a major structural shift in network behavior after mitigation. These findings reinforce the effectiveness of the mitigation strategy, with the BLE-LoRa Hybrid network experiencing the most substantial transformation.
# Nozzle with a Feedback Channel for Agricultural Drones
## 3 Experiment Methods
### Tested Nozzles  
The spraying performance of the proposed nozzle was compared with that of two other nozzles. The first was a fan-shaped commercial nozzle (TEEJET XR11002, Spray Systems), which is currently employed in commercial agricultural drones. The other was a nozzle without a feedback channel to confirm the feedback channel effect on the designed nozzle. Except for the feedback channel, the other physical dimensions were the same as those of the proposed nozzle. The flow rate for the nozzle test was determined at 500 mL/min, which was similar to the condition in real-world applications.
# Grape By-Products in Sustainable Cosmetics: Nanoencapsulation and Market Trends
## 5 Grapes and Their By-Products for Cosmetic and Skincare Applications
### Identified Skincare Biological Activities
#### 5.1.1. Grape Seeds: Extract, Oil, Powder, and Paste  
Several studies report important biological effects coming from grape seeds. For instance, grape seed extract is reported to increase cell viability, to protect against UVA damage, and to exert antioxidant activity [26; 35; 36; 37; 39]. Additionally, it is reported to have anti-tyrosine, anti-ageing, anti-acne, and anti-inflammatory activities [41; 43; 46]. Grape seed extract is also reported to have anticaries, antidandruff, antifungal, antibacterial, and antioxidant properties, and to have light stabilizing and sunscreen applications. In addition, grape seed powder is reported to function as an abrasive and exfoliant by-product [47; 48]. In the case of grape seed oil, it has been associated with antioxidant and antimicrobial activities. The oil was extracted using n-hexane as solvent for a solid-liquid extraction process with the following conditions: 5:1 (\(v/w\)) solvent-sample ratio, for 105 min at  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Grapevine Variety** & **Grape By-Product** & **Type of Study** & **Biological Effect** & **Reference Source** \\ \hline  & Seed extract & In vitro & Antioxidant activity, sun & \\  & Seed extract & In vitro & protection factor (SPF) & [37] \\  & & & booster & \\ \hline _Touriga Nacional,_ & & Pomace/seed & & \\ _Touriga Francesca,_ & & extract and oil & In vitro & Antioxidant and & \\ _Tinta-roriz_ & & extract and oil & & antimicrobial effects & [29] \\ \hline _Vitis vinifera_ & & & & \\ \hline _Temgranillo_ & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \multirow{2}{*}{Red grape} & Seeds and stalks & In vitro, keratinocyte and & & \\  & extract & fibroblast cell cultures & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \multirow{2}{*}{_Vitis vinifera_ L.} & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \multirow{2}{*}{_Vitis vinifera_ L.} & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \multirow{2}{*}{_Vitis vinifera_ L.} & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \multirow{2}{*}{_Vitis vinifera_ L.} & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \multirow{2}{*}{_Vitis vinifera_ L.} & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \multirow{2}{*}{_Vitis vinifera_ L.} & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \multirow{2}{*}{_Vitis vinifera_ L.} & & & & \\  & & & & \\  & & & & \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._70 \({}^{\circ}\)C. The solvent was then evaporated with a rotary evaporator at a bath temperature of 35 \({}^{\circ}\)C [29]. Moreover, grape seed paste was described as exhibiting anti-tyrosine, -elastase, and -collagenase activities [42].
# Using IoT for Cistern and Water Tank Level Monitoring
## 3 Monitoring of Water Architecture
### Water Tank and Cistern System Description  
We cannot control the water that enters a cistern. However, we can monitor and control the water that leaves the cistern and rises into the tank.  
For this reason, we propose designing an experimental system or physical scale model to reproduce two water tanks (cistern and water tank) and an Internet interface that publishes the states or levels measured by these sensors placed inside each water tank.  
Figure 7 includes the complete elements that integrate the proposal, including cistern, water tank, flow meter, water quality sensor, and others.  
We are looking at the project from three points:  
1. Measure and publish the cistern and water tank's water level on the Internet; with these data, we can promptly know if we should save water to the maximum or use the washing machine and other services.
2. Measure and publish the flow or pressure of the pipe that fills the cistern on the Internet and calculate the cistern's filling time.
3. Measure and publish the water quality supplied by the piped water on the Internet. With these data, chlorination of the water is possible.  
The ultrasonic sensor on top of the cistern measures the height between the sensor and the water. The water level is calculated from this; the smaller the distance between the sensor and the water, the greater the quantity or volume of water contained in the cistern.  
Inside the cistern is a sensor that measures the water quality and a submersible water pump, which raises the tank's water level when the water level sensor sends the message that the water level is below 50%. A sensor (flow meter) measures the water pressure at the tank's inlet. This sensor measures the force the water brings to the tank from the municipal water. The higher the flow rate, the more water is expected to fill the tank quickly.  
Another ultrasonic sensor is used for the water tank. This sensor is placed on top of the tank and measures the distance between it and the water. The smaller the distance between the sensor and the water, the greater the amount or volume of water in the tank.  
Figure 5: (a) WeMos D1 mini ESP8266 WiFi. (b) NodeMCU v2 ESP8266 WiFi.  
Figure 6: (a) Raspberry Pi 4 Model-B 8 GB RAM. (b) Relay module. 5V DC SRD-5 VDC-SL-C.
## 6 Simulation and Results Discussion
### Results and Discussion
#### 6.2.4 Throughput vs. Delay  
It measures the success rate of packet delivery during the communication with less data loss due to the reduced delay. The throughput will increase with the number of mobile nodes at the range of bits per second, and the delay is measured in milliseconds. Our proposed model, QoSDSBS, provides a decline in packet loss, a reduced delay, and an increase in throughput, as shown in Table 6. Figure 9 shows the throughput and delay with the securities-enabled DSB and other existing techniques. The above experimental results show that the proposed QoSDSBS model provides the desired level with increased QoS parameters compared to EE-OHRA, EA-DRP, and NETAR.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Throughput (Mbps)**} & \multicolumn{4}{c}{**Delay (ms)**} \\ \cline{2-5}  & **EE-OHRA** & **EA-DRP** & **NETAR** & **QoSDSBS** \\ \hline
5 & 17.0178 & 15.789 & 12.842 & 8.518 \\
25 & 19.8112 & 17.459 & 13.741 & 11.314 \\
45 & 26.4712 & 23.541 & 20.845 & 15.8256 \\
65 & 29.4712 & 27.8423 & 25.842 & 23.0362 \\
85 & 38.0023 & 35.7121 & 31.8745 & 29.9489 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of throughput vs. delay.  
Figure 8: Average path lifetime vs. routing control overload.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Lifetime (ms)**} & \multicolumn{4}{c}{**Routing Overhead (10\({}^{4}\) Packets)**} \\ \cline{2-5}  & **EE-OHRA** & **EA-DRP** & **NETAR** & **QoSDSBS** \\ \hline
2 & 3.9178 & 3.5879 & 2.8451 & 2.4456 \\
3 & 3.1128 & 2.8421 & 2.507 & 2.207 \\
4 & 2.7423 & 2.6124 & 2.387 & 2.014 \\
5 & 2.4712 & 2.3156 & 2.145 & 1.8098 \\
6 & 1.9023 & 1.8612 & 1.7451 & 1.5256 \\
8 & 1.437 & 1.372 & 1.278 & 1.0162 \\
12 & 1.254 & 1.197 & 1.1345 & 0.9489 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of average path lifetime vs. routing control overload.
## 4 Experimental Results
### Ablation Experiment  
An ablation study was conducted on the multi-label legal text recognition dataset (CAIL2021) to examine the contribution of different model components. The independent effects of key components were evaluated by removing the multi-layer loss function (Layer), which converts multi-level labels into a single-layer structure, and the batch normalization (BN) layer [36], which ensures stable distribution of prototype parameters. The results are presented in Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Model** & **Dataset** & **Micro-F1** & **Macro-F1** \\ \hline Retrieval & WOS & 81.38 & 73.82 \\  & AAPD & - & - \\ \hline HARNN & WOS & 81.50 & 69.69 \\  & AAPD & 79.58 & 48.83 \\ \hline HiMatch & WOS & 86.20 & 80.53 \\  & AAPD & 80.74 & 57.16 \\ \hline HiAGM & WOS & 85.82 & 80.28 \\  & AAPD & 80.33 & 56.72 \\ \hline HGCLR & WOS & 87.11 & 81.20 \\  & AAPD & - & - \\ \hline MLCL-KNN & WOS & 87.37 & 81.88 \\  & AAPD & - & - \\ \hline HiSR & WOS & 87.52 & 82.04 \\  & AAPD & - & - \\ \hline HALB & WOS & 87.45 & 82.04 \\  & AAPD & - & - \\ \hline DPT & WOS & 87.25 & 81.51 \\  & AAPD & - & - \\ \hline MLCL & WOS & 87.35 & 77.82 \\  & AAPD & 81.75 & 58.75 \\ \hline Ours & WOS & 88.24 & 80.24 \\  & AAPD & 81.21 & 57.65 \\ \hline \end{tabular}
\end{table}
Table 5: Experimental results of multi-label recognition dataset.  
As outlined in Table 6, the proposed method achieves the best performance. When removing the \"Layer\" component, the recall rate and F1-score of the model decreased, indicating that this mechanism effectively captures the hierarchical structural relationships between labels. The layer-wise approach leverages inter-label dependencies to partially rectify erroneous predictions, thereby significantly enhancing recall capability and improving the adaptability of the model to the complexity of multi-label tasks. Similarly, ablation of the BN layer resulted in notable performance degradation, suggesting that BN enhances model stability and optimizes the balance between recall and the F1-score by normalizing parameter distributions and mitigating training fluctuation. The complete model achieved optimal F1-score performance, which collectively verifies the rationality of our architectural design and the effectiveness of the proposed technical solutions.
# Smart Bandwidth Assignation in an Underlay Cellular Network for Internet of Vehicles
## 5 Proposed Solution Tests and Validation
### Performance Metrics and Baseline Methods  
Proposed solution will be evaluated using three metrics, which will provide a set of results that allow us to analyze the performance of the solution:  
* Throughput, defined as the average number of bits per second transmitted through users' links. In other words, the sum of the data rates that are delivered by all users considered in the tested scenarios.
* Spectral Efficiency, defined as the average number of bytes transmitted per RB. Spectral Efficiency is a key metric as the radio spectrum is a limited resource. As it was introduced in the problem definition section, the main aim of our proposal is to define an underlay RRM capable of increasing the spectral efficiency of the cellular networks for giving access to a higher number of users with the same resources.
* Energy Efficiency, defined as the average number of bytes transmitted per Watt consumed. This metric evaluates how the RRM methodology takes benefit from the transmission power used. Previous metrics are evaluated from two different points of view:  
1. Results obtained from V2V communication links to analyze the communication performance offered to V-UEs by the RRM methodology.
2. Results obtained from all the communication links to evaluate the communication performance offered to all the users of the scenario by the proposed RRM methodology.  
As it has been exposed in Section 1, the design of an efficient underlaying RRM for co-existing C-UEs and V-UEs users with minimal impact on cellular communications is a key problem. Therefore, we evaluate the previous metrics depending on C-UEs' type of traffic, interpreted as the variation of transmitted C-UEs' packet size. This variable allows to analyze how the V-UEs' links performance depends on C-UEs' traffic pattern.  
To carry out the analysis, the scenarios presented in Table 2 have been defined, where it has been taken into consideration the relationship between the number of C-UEs and V-UEs in the scenario, with the total number of users being constant and equal to 50 users. This analysis enables us to evaluate in which situations the proposed RRM method performs better, in the ones where predominate the number of C-UEs or, on the contrary, where the number of V-UEs is predominant.  
To obtain a realistic analysis, we compare our RRM proposal with the following already tested and checked methods:  
1. Underlay RRM methodology which maximizes the sum rate of both C-UE and V-UE using the possibility of sharing both users the same RB for the transmission.
2. Overlay RRM methodology which maximize the sum rate of users using one RB for each transmission with the maximum transmission power.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario** & **C-UEs** & **V-UEs** \\ \hline
1 & 5 & 45 \\
2 & 10 & 40 \\
3 & 15 & 35 \\
4 & 20 & 30 \\
5 & 25 & 25 \\
6 & 30 & 20 \\
7 & 35 & 15 \\
8 & 40 & 10 \\
9 & 45 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Definition of Scenarios.
## 3 Materials and Methods
### Features Extraction Stage  
The foundation of the study is comprised of a collection of statistical information that is relatively comprehensive. The mean, variance, skewness, kurtosis, mode, median, minimum, maximum, summation, and range are the terms used to describe these characteristics [31]. A statistical features tool in Matlab 2022 was used to extract the statistical attributes from the raw dataset.  
The dimension of the employed dataset's feature matrix was (60 \(\times\) 40), where (20 control + 20 benign + 20 malignant) = 60 observations, and the (10 features \(\times\) 4 biomarkers (P, T, C, and HCG)) = 40 attributes.