esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The secondary parameters were not assessed in all seven studies, with BOP reported in five studies and CAL gain in six studies. NSAID intake did not appear to have an impact on BOP, as the differences at the follow-up timepoints compared to baseline were comparable between the NSAID and control groups in the five studies. The same was true for the CAL gain, which showed no difference between patients that received NSAID adjuvant therapy and those that did not. The quality of the initially identified 12 studies was assessed using the Oxford quality scoring system. Three of the seven studies reviewed, [PERSON] et al. (2019), [PERSON] et al. (2019), and [PERSON] et al. (2019), received the highest scores, not addressing randomization, blinding, and drop-out rate. Two of these studies, those authored by [PERSON] et al. (2019) and [PERSON] et al. (2019), observed an impact of adjuvant NSAID use on PD reduction and CAL gain, while the study by [PERSON] et al. (2019) saw a larger improvement of these parameters in the control group. The other four studies scored comparatively low, with scores ranging from zero to three. Nonetheless, the Oxford quality scoring system is only one of such systems and does not consider factors, such as the clear formulation of a research question and the risk of bias. The present systematic review has several limitations that may explain the results and should be addressed. First of all, seven studies with relatively few patients could be identified that met all the inclusion criteria, and therefore the numerical data for a statistical comparison was limited. The low number of suitable studies also meant that in order to quantitatively assess differences between NSAID and non-NSAID patients, the results of all seven studies had to be pooled for this comparison independent of the timepoint at which the assessment took place. It is therefore difficult to directly compare the results, as there were only two studies available for each timepoint and parameter. Furthermore, the type, dose, and duration of administration of the NSAID may have an impact on the outcome, yet the patients in the seven studies took different NSAIDs (Etoricoxib, Meloxicam, Flurbiprofen, Tenoxicam, Naproxen, Nimesulide, Celexocib) at different dosages (7.5-325 mg). It therefore remains unclear whether the impact of NSAID administration may have been more pronounced if the same duration of the follow-up timepoint and the same NSAID with the same dosage would have been compared.
## Appendix A  
In the PML-V2 model, GPP is calculated based on photosynthesis (\(A_{gs}\)), constrained by the surface vapor pressure deficit (\(D\)).  
\[GPP=A_{gs}f_{D}\] (A1)  
\[A_{gs}=\frac{P_{1}C_{a}}{k(P_{2}+P_{4})}\left\{kLAI+ln\frac{P_{2}+P_{3}+P_{4} }{P_{2}+P_{3}\exp(kLAI)+P_{4}}\right\}\] (A2)  
\[P_{1}=A_{m}\beta I_{0}\eta,\ \ P_{2}=A_{m}\beta I_{0},\ \ P_{3}=A_{m}\eta C_{a},\ \ P_{4}=\beta I_{0}\eta C_{a}\] (A3)  
\[A_{m}\cong 0.5\times V_{m}\] (A4)  
\[V_{m}=\frac{V_{m,25}\epsilon xp[a(T-25)]}{1+\exp[b(T-41)]}\] (A5)  
\[f_{D}=\left\{\begin{array}{lr}1&D<D_{min}\\ \frac{(D_{max}-D)}{D_{max}-D_{min}}&D_{min}<D<D_{max}\\ 0&D>D_{max}\end{array}\right.\] (A6)  
where \(f_{D}\) is the \(D\) constraint function, \(D_{min}\) is the threshold below which there is no vapor pressure constraint, \(D_{max}\) is the threshold above which there is no assimilation, \(D_{0}\) is the reference vapor pressure deficit at stomatal conductance reduction, \(I_{0}\) is the photosynthetically active radiation estimated from downwelling shortwave radiation, \(Ca\) is the atmospheric CO\({}_{2}\) concentration, \(V_{m}\) is the maximum catalytic capacity of Rubisco per unit leaf area at 25 \({}^{\circ}\)C, \(\beta\) is the initial photochemical efficiency, \(\eta\) is the initial value of the slope of CO\({}_{2}\) response curve, \(m\) is the stomatal conductance coefficient, \(P_{1}\) represents the term calculated using the maximum photosynthetic capacity (\(A_{m}\)), \(\beta\), \(I_{0}\), and the \(\eta\). \(P_{2}\) includes the same components except for the efficiency factor, reflecting the direct effect of light on photosynthesis. \(P_{3}\) defines the interaction between the maximum photosyntheticcapacity and \(C_{a}\) along with efficiency. \(P_{4}\) represents the combined effect of light intensity, CO\({}_{2}\), and efficiency.
## 3 Proposed Method
### Failure Probability of the Matrix of Test Vectors
Testers cannot determine whether the first group of nets have a short-circuit fault with the second (as shown by the red dotted line), so the confusion occurred. Based on the analysis in Section 3.1 of this paper, the fault model considered in this paper is 2-order, 3-order short-circuit fault model. At the same time, it is stipulated that the test vectors generated in this paper are all carried out under the optimal compactness index, that is, the number of columns(m) of MTV is an upward rounding of \(log_{2}(n+2)\). Under this premise, we can divide the phenomenon of misjudgment and confusion in MTV into the following kinds of events:  
* Misjudgment caused by 2-order short-circuit: the result of wired-AND of 2 STVs is the same as another STV. Figure 4: An example of misjudgment. Figure 5: An example of confusion. * Misjudgment caused by 3-order short-circuit: the result of wired-AND of 3 STVs is the same as another STV. * Confusion caused by 2-order short-circuit: the result of wired-AND of 2 STVs is the same as the result of wired-AND of other 2 STVs. Definition: The failure probability of \(MTV\) (\(P_{MTV}\)) is the probability of the above three events in a set of \(STVs\). Let S be the set of \(STVs\). Let power set \(A\in 2^{S}\), and \(|A|=2\). Let power set \(B\in 2^{S}\), and \(|B|=3\). Let power set \(C\in 2^{A}\), and \(|C|=2\), it can be calculated by the following equation. \[P_{MTV}=1-\prod_{[STV_{11},STV_{2}]\in A}(1-k_{i1i2}\cdot p_{i1i2}).\prod_{[STV _{11},STV_{2},STV_{3}]\in B}(1-k_{i1i2i3}\cdot p_{i1i2i3})\] \[\cdot\prod_{[(STV_{11},STV_{2}),(STV_{3},STV_{4})]\in C}(1-k_{i1i2i3 i4}\cdot p_{i1i2}\cdot p_{i3i4}) \tag{13}\]  
\(p_{i1i2}\) is the probability of 2-order short-circuiting fault between networks \(i1\) and \(i2\). \(p_{i1i2i3}\) is the probability of 3-order short-circuit fault between networks \(i1\), \(i2\), and \(i3\). When the 2-order short-circuit between \(i1\) and \(i2\) causes misjudgment, \(k_{i1i2}\) is 1, otherwise it is 0. When the 3-order short-circuit between \(i1\), \(i2\), \(i3\) causes misjudgment, \(k_{i1i2i3}\) is 1, otherwise it is 0. When the 2-order short-circuit between \(i1\) and \(i2\) and 2-order short-circuit between \(i3\) and \(i4\) causes confusion, \(k_{i1i2i34}\) is 1, otherwise it is 0. Since different networks have different short-circuit probabilities, and each network corresponds to an \(STV\) in \(MTV\), and if each \(STV\) in \(MTV\) is treated as a unit of a sequence, the whole \(MTV\) is treated as a sequence, then the generation of \(MTV\) problem with minimum \(P_{MTV}\) will transform into the optimal cost sequence search problem.
# Performance Evaluation of Routing Protocols for Underwater Wireless Sensor Networks
## 3 Performance Evaluation and Simulation Results
### Results
#### 3.2.4 Number of Alive Nodes  
Figure 5 represents the total number of active nodes across all examined routing protocols. Based on the simulation findings, the RACE-SM has more live nodes than the other routing strategies. The primary reason for this is to reduce energy consumption. Furthermore, selecting the optimal forwarder node for data transmission is impacted since it will consume the least energy and transport the data at the lowest cost. Consequently, the number of active sensor nodes will be significant. The EH-UWSN is the other routing protocol with the maximum number of live nodes because it uses the maximum of two forwarder nodes and charges the sensor node batteries from surrounding objects; therefore, the nodes are alive for a maximum time.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rounds** & **RACE-SM** & **EH-UWSN** & **MulSi-Co** & **EERBCR** & **MulSi** & **GCORP** & **EEDG** \\ \hline
0 & 105 & 90 & 87 & 85 & 80 & 76 & 76 \\
1000 & 100 & 86 & 84 & 80 & 72 & 72 & 70 \\
2000 & 100 & 82 & 80 & 70 & 66 & 68 & 68 \\
3000 & 95 & 78 & 77 & 75 & 66 & 68 & 68 \\
4000 & 98 & 75 & 72 & 70 & 66 & 65 & 65 \\
5000 & 92 & 72 & 66 & 65 & 62 & 60 & 59 \\
6000 & 88 & 70 & 60 & 60 & 57 & 55 & 53 \\
7000 & 86 & 67 & 55 & 55 & 55 & 55 & 52 \\
8000 & 84 & 63 & 56 & 50 & 50 & 50 & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Addressing PDR.  
Figure 4: Shows Packet Delivery Ratio.  
The number of live nodes in RACE-SM at around 5000 is 100, EH-UWSN has 65, MulSi-Co has 40, and others have approximately 20 active nodes. After 5000 rounds, all routing schemes' active nodes drain energy quickly and die much quicker than the RACE-SM. RACE-SM uses two methods to transmit the data between the source and destination: direct transfer when the sink node is in the communication range and the cooperative method, which means transferring data through multiple hops, which also helps to increase the network reliability. Table 5 addresses the number of alive nodes of all evaluated routing schemes.
## 3 Data
### Data Size  
According to the data format, 1000 segments are packed into multiple compressed files (tars) with a size of 25 GB each. In our experiments, 32 training tars are used as the training set and eight validation tars are used as the testing set. The total number of videos extracted from the segments is 45,000.
## 2 Dataset and Experiments
### Experimental Design
#### 2.1.2 Dataset 2: BEIJING-HYUNDAI AUTO Enterprise  
This experiment is also based on the Sentinel-1 dataset. As shown in Figure 3, the change of the number of vehicles is detected in the parking lot of BEIJING-HYUNDAI AUTO Enterprise, which is indicated by a red box. The sequence of time-series images spans from 9 July 2019 to 25 September 2020 and contains a total of 38 images.  
Figure 1: Comparison of optical image and SAR image of the nearby parking lot. (**a**) Optical image from Google Earth. (**b**) SAR image acquired on 15 September 2022 by Sentinel-1A satellite.  
Figure 2: The collection of truth data (21 October 2022). (**a**) Field situation. (**b**) Truth data modeling.  
The Logarithm Background Subtraction method and the pairwise comparison method were separately employed to detect the vehicle count changes in this parking lot, resulting in vehicle count change curves. A comparative analysis was performed to compare the experimental results of the two methods.
## 6 Discussion  
### Interpretation of Findings  
The application of the Data-Driven Personalized Learning Model (DDPLM) in Edmodo and Duolingo offers compelling proof of the transformative potential of integrating big data analytics in language learning environments. The case studies from which these findings are derived reveal some critical elements of how personalized learning systems work to improve educational results.  
This research emphasizes the improved potential of individualized systems to adjust the learning content and tempo to the needs of the learners [48]. This adjustment results in more productive learning, as is apparent in the diminished time learners took to understand  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Platform** & **Improvement in Engagement** & **Increase in Retention Rates** \\ \hline Edmodo & 20\% & 15\% \\ Duolingo & 25\% & 20\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative Analysis of Case Studies on Edmodo and Duolingo.  
a language while using the DDPLM-improved platforms. This type of efficiency makes life easier for those who want to learn, so students do not become bored or inactive.  
The results highlight that real-time feedback is crucial in the learning process. The immediate corrections and personalized feedback offered by DDPLM have enabled learners to correct mistakes and develop a refined understanding of language elements in no time. This feature of the model supports high learner engagement and deepens understanding of the material.  
Users of the improved Duolingo platform had higher retention rates, pointing to the importance of personalization in learner persistence. The model keeps learners engaged by continually adapting challenges to their ability and providing motivational feedback to prevent language study drop-offs.  
The interpretation of these results shows the scalability and flexibility of DDPLM. The model's performance across different platforms, such as Edmodo and Duolingo, each with a unique user base and operational environment, shows flexibility and versatility across different educational settings and disciplines.  
Implementing DDPLM into language learning systems has shown significant advancements in learning effective performance, engagement, and retention. The results provide evidence of the success of personal learning approaches and the key to understanding the process of the method's development through big data analytics. The evidence from these case studies creates a good basis for further research and development of personalized learning systems in wider educational settings.
# Development of Multi-Item Air Quality Monitoring System Based on Real-Time Data
## 1 Introduction
Particle counters that measure particulate matter are developed in various ways from low-cost products to high-priced products [18; 19]. Recently, not only particulate matter but also various types of air quality measuring devices such as particulate matter (PM), volatile organic compounds (VOCs), CO2, temperature, and humidity have been released [20; 21; 22; 23; 24; 25; 26; 27]. The low-cost multi-item device can measure PM, temperature, humidity, carbon dioxide, and total organic compounds and provides measured data to consumers through the IOT linkage function [20; 21; 22; 23]. However, low-cost air quality measurements are not used for professional environmental measuring services due to issues with the accuracy and reliability of sensors. On the other hand, professional environmental measurements with high-precision sensors are rarely applied to IOT linkage, so they cannot provide real-time data to users [24; 26]. Gas detectors widely used in the field rarely have IOT functions [26; 27]. Professional environmental measuring equipment with good accuracy has many inconvenient factors such as a small number of items, lack of IOT function, and the inconvenience of measurement, size, and weight. To overcome these disadvantages, this study was developed with the three goals of expanding the measurement items, improving the accuracy of the measurement device, and increasing the convenience through a smartphone application and a server. To expand the measurement items, we developed a technology that can install a sensor capable of measuring 16 items with high precision in a small space. To improve the accuracy of the measuring device, a high-precision sensor was used, automatic and manual calibration of the sensor was enabled, and a constant current was developed to reduce the error of the sensor and achieve the optimal air flow. To increase convenience, a smartphone application was developed, and a server program was developed so that users could control and monitor from their smartphones and computers. The developed device in this study consists of 16 air pollutant items including PM(PM\({}_{10}\), PM\({}_{4.0}\), PM\({}_{2.5}\), PM\({}_{1.0}\)), carbon dioxide (CO\({}_{2}\)), methane (CH\({}_{4}\)), temperature, humidity, volatile organic compounds (VOCs), oxygen (O\({}_{2}\)), hydrogen sulfide (H\({}_{2}\)S), ammonia (NH\({}_{3}\)), sulfur dioxide (SO\({}_{2}\)), carbon monoxide (CO), ozone (O\({}_{3}\)), and nitrogen dioxide (NO\({}_{2}\)) can be measured in real time. The data of 16 measured items are transmitted to the server and the smartphone application through Bluetooth and Wi-Fi. Based on the transmitted data, it was developed to identify the concentration of air pollution. When the air pollution concentration is high, it is developed to warn of a risk to the user using an emergency alert.
# IMU: A Content Replacement Policy for CCN, Based on Immutance Content Selection
## 4 Performance Evaluation
### Link Load
When the alpha equals 0.8, with a cache size of 0.04, IMU is 55.48 bytes/ms, which is 16.41% lower than LFRU (66.37 ms), 19.60% lower than LFU (69.01 bytes/ms), 17.85% lower than LRU (67.53 bytes/ms), and 20.57% lower than FIFO (69.85 bytes/ms). When the alpha equals 0.8, with a cache size is 0.12, IMU is 48.99 bytes/ms, which is 15.25% lower than LFRU (57.81 bytes/ms), 18.57% lower than LFU (60.16 bytes/ms), 19.21% lower than LRU (60.64 bytes/ms), and 22.19% lower than FIFO (62.96 bytes/ms). When the alpha equals 0.8, with cache size is 0.2, IMU is 44.93 bytes/ms, which is 10.40% lower than LFRU (50.15 bytes/ms), 10.68% lower than LFU (50.30 bytes/ms), 11.51% lower than LRU (50.78 bytes/ms), and 15.37% lower than FIFO (53.09 bytes/ms). As the cache size increases, we observed that the link load decreases, as the proposed scheme removes immature content from the cache. Therefore, IMU maintains the data freshness with popularity within the network. However, none of the previous eviction policies have adopted the concept of immaturity for content selection. Table 13 describes the IMU's improvement in percentage (%) of the link load, which used different content placement strategies along with content eviction policies. We observed that IMU outperformed the other content eviction policies against low to high popularity and cache space. It performed better in a fully redundant and low redundancy environments. IMU contains the most popular and mature content in the cache and makes better use of cache space. Moreover, the consumer is mostly satisfied along the routing path when requesting content. Therefore, the link load value is low because the request is not sent to the publisher.
# Signal Enhancement of Helicopter Rotor Aerodynamic Noise Based on Cyclic Wiener Filtering
###### Abstract  
The research on helicopter rotor aerodynamic noise becomes imperative with the wide use of helicopters in civilian fields. In this study, a signal enhancement method based on cyclic Wiener filtering was proposed given the cyclostationarity of rotor aerodynamic noise. The noise was adaptively filtered out by performing a group of frequency shifts on the input signal. According to the characteristics of rotor aerodynamic noise, a detection function was constructed to realize the long-distance detection of helicopters. The flight data of the Robinson R44 helicopter was obtained through the field flight experiment and employed as the research object for analysis. The detection range of the Robinson R44 helicopter after cyclic Wiener filtering was increased from 4.114 km to 17.75 km, verifying the feasibility and effectiveness of the proposed method. The efficacy of the proposed detection method was demonstrated and compared in the far-field flight test measurements of the Robinson R44 helicopter.  
rotor; aerodynamic noise; cyclic Wiener filtering; long-distance detection +
Footnote †: journal: Applied  
0
Footnote 0: 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# HIPPP: Health Information Portal for Patients and Public
## 4 Results and Validation Approach
### Evaluation Pipeline  
The WBHI evaluation pipeline is currently functioning, but not yet optimised, for the specific kind of data sets we are facing. Right now, we are not greatly concerned about the performance, as we are growing the set of labelled data sources, and we are building the initial classification model. Adopting the QUEST standard evaluation criteria has significantly simplified our design decisions so far, and it also ensures compatibility in principle and comparability with the other WBHI data sets manually classified along QUEST.  
We expect the emergence of additional desirable criteria over time, either for performance improvement or for precision and specialisation, and we will in that case extend the current set of algorithms, SIBs and processes accordingly.  
In terms of validation, so far we have evaluated the correctness/plausibility of the AI algorithms individually on the current data sets. For the task of web content extraction, we have built and labelled a data set of 1500 samples that have been labelled and annotated, achieving positive initial results on test sets. With respect to tone detection, we have collected and labelled a similar number of samples and are currently evaluating a variety of models. However, we wish to collect and annotate more data to ensure the models and the evaluation of those models are more robust.  
Going forward, the validation/evaluation activity will proceed along two lines of action:  
* _Empirical evaluation of the pipeline output_ following ML model evaluation best practices. Here, each ML component will be evaluated using a testing regime, which will assess the models with respect to classification accuracy, robustness, interpretability of output and reproducibility of results. All models will be tested on labelled data sets previously unseen by the models (i.e., not used during model training or validation). A range of statistical tests will also be carried out along with in-variance tests, directional expectation tests and minimum functionality tests.
* _Validation of training data collection processes through PPI groups_. Once we conclude the first phase of data collection and base model building, the training data collection process will be validated through PPI groups. The aim is to get feedback on the kind of WBHI channels through which people access information in order to ensure that there is good coverage for these types of channels in the training data. Subsequent PPI group meetings will help validate how the classifications are displayed and ensure maximum comprehensibility of the evaluation for the target audience.  
An example of the question is whether users feel that receiving the compact classification (e.g., reliable/not reliable) is sufficient, or whether they prefer also an explanation of how the evaluation pipeline reached its conclusions. Here, we see a clear trade-off between the simplicity of use and the level of detail of the answer.