esa-sceva/satcom-chunk-collection huggingface dataset:
# Which, When, and How: Hierarchical Clustering with Human-Machine Cooperation
## 2 Algorithm Framework
### Our Approach  
We want to improve the accuracy of the agglomerative hierarchical clustering by HMCs, where the untrained machine acts as the dominant task solver. The machine can get limited help, in terms of question operations, from the trained human. The question operation has the format of comparing two data points (i.e., two images), since it serves as a basic unit that takes minimal human computations. For example, comparisons among three data points can be regarded as three sets of comparisons between two data points. The number of question operations is limited, since human computations are costly. We study the machine's strategy on handling the question operations, in terms of \"which\", \"when\", and \"how\".  
Algorithm 2 shows our framework, in which lines 4, 5, and 6 represent the machine's strategy on handling the questions. Line 4 corresponds to the \"when\" problem (When should the machine ask the question?); Line 5 shows the \"which\" problem (Which pair of data points should the machine ask?); Line 6 is the \"how\" problem (How does the machine adjust the current clustering result?). For simplicity, two assumptions are made as follows: (1) the machine is untrained, and thus it may make the wrong decisions during the clustering process. Meanwhile, the machine can ask the human to judge whether two data points are in the same cluster or not. We assume that the human is trained, and thus his/her judgement is always correct; and (2) we assume that the human answers a question instantaneously, i.e., there is no extra waiting time for answers.  
```
Data: Distances between pairs of data points; The number of available questions. Result: The clustering result
1 Treat each data point as a singleton cluster;
2 whilethe stop criterion is not metdo
3 Merge the closest two clusters;
4 ifthe machine decides to ask a questionthen
5 Select two data points to ask the human;
6 Adjust the clustering based on the answer;
7 Number of available questions reduces by one;
8
9 end if
10
11 end for return the remaining clusters.
```  
**Algorithm 2**Our Framework.  
Our approach is tolerant of data noise, compared to traditional agglomerative hierarchical clustering [11]. This is because HMC is likely to fix partial problems caused by the data noise. Even if data points are incorrectly clustered, the human could help the machine to fix its error. However, the objective of our approach is not the clustering robustness under noise, meaning that the data noise problem still remains in some extreme cases. One of our future directions could be using humans to mitigate the data noise problem for clusterings.
# Using Data Mining in Educational Administration: A Case Study on Improving School Attendance
## 5 Results and Discussion
### Evaluating the Impacts of Initiatives I1 and I2
#### 5.2.1 I1: Frequent Rewards for Full Attendance  
Draws were held every month during the Spring and Summer terms of 2018/19, except for April, for all pupils that had full attendance during the month. The April draw was omitted given that April had fewer than 10 school days in that month.  
From Table 5, it is evident that the shorter, more meaningful rewards for full attendance have contributed to a significant improvement in overall attendance for the Spring and Summer terms in 2018/19 with the attendance for every session being considerably higher than the attendance in the previous three years. This result was consistent with the findings in [1]  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Session (J\({}_{1}\))** & **2015/16** & **2016/17** & **2017/18** & **2018/19** \\ \hline Mon-AM & 94.1\% & 93.9\% & 94.3\% & 95.8\% \\ Tues-AM & 94.2\% & 95.2\% & 95.0\% & 96.2\% \\ Wed-AM & 95.1\% & 95.5\% & 95.4\% & 96.2\% \\ Thur-AM & 94.9\% & 95.5\% & 95.1\% & 96.3\% \\ Fri-AM & 94.4\% & 95.0\% & 94.2\% & 95.6\% \\ \hline Mon-PM & 94.7\% & 94.6\% & 94.8\% & 96.5\% \\ Tues-PM & 94.9\% & 95.8\% & 95.5\% & 96.8\% \\ Wed-PM & 95.6\% & 95.8\% & 95.8\% & 96.6\% \\ Thur-PM & 95.6\% & 95.8\% & 95.6\% & 96.8\% \\ Fri-PM & 94.9\% & 95.1\% & 94.1\% & 96.0\% \\ \hline Average AM & 94.6\% & 95.1\% & 94.8\% & 96.0\% \\ Average PM & 95.2\% & 95.4\% & 95.2\% & 96.5\% \\ Overall (\(O\)) & 94.9\% & 95.2\% & 95.0\% & 96.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average attendance for Spring and Summer terms: 2015/16, 2016/17, 2017/18, and 2018/19.
# Circular Regression Applied to GNSS-R Phase Altimetry
###### Abstract  
This article is dedicated to the design of a linear-circular regression technique and to its application to ground-based GNSS-Reflectometry (GNSS-R) altimetry. The altimetric estimation is based on the observation of the phase delay between a GNSS signal sensed directly and after a reflection off of the Earth's surface. This delay evolves linearly with the sine of the emitting satellite elevation, with a slope proportional to the height between the reflecting surface and the receiving antenna. However, GNSS-R phase delay observations are angular and affected by a noise assumed to follow the von Mises distribution. In order to estimate the phase delay slope, a linear-circular regression estimator is thus defined in the maximum likelihood sense. The proposed estimator is able to fuse phase observations obtained from several satellite signals. Moreover, unlike the usual unwrapping approach, the proposed estimator allows the sea-surface height to be estimated from datasets with large data gaps. The proposed regression technique and altimeter performances are studied theoretically, with further assessment on both synthetic and real data.  
GNSS-reflectometry; altimetry; circular statistics; GNSS signal processing Article
# Passive Microwave Imagers, Their Applications, and Benefits: A Review
## 2 PMWI Applications and Impact Areas
### Applications and Benefits of PMWIs
#### 2.1.3 Fire Severity and Carbon Emission  
Tb observations from AMSR-E and AMSR-E/2 sensors at 6.925, 10.65, and 18.7 GHz aboard NASA's Aqua satellite provide valuable information due to their capability to estimate soil moisture and Vegetation Optical Depth (VOD) [17; 18]. Additionally, the 36.5 GHz frequency aids in effective temperature estimation and is a valuable tool in assessing fire severity and carbon emissions estimations [17; 18]. Additional sensors such as SMMR (6.6 GHz, 10.7 GHz, 18 GHz, and 37 GHz), SSM/1 (19.3 GHz and 36.5 GHz), and TMI (10.7 GHz, 19.4 GHz, and 37 GHz) play a vital role in constructing a comprehensive global soil moisture dataset [67]. Another dataset, called GlobScar, is derived from observations made by the Along Track Scanning Radiometer (ATSR-2) instrument aboard the European Space Agency (ESA) European Remote Sensing Satellite-2 (ERS-2) satellite. GlobScar is instrumental in detecting biomass and assessing burnt areas globally [68]. Moreover, the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), produced by NASA's Global Modeling and Assimilation Office (GMAO), incorporates PMWI data, including instruments like SSM/I, SSMIS, and WindSat, for estimating surface wind speed [4]. This information is critical for managing and mitigating the effects of wildfires, contributing to better environmental conservation and safety practices.
## 3 Results
### Analysis of NEP Change Trend  
In order to describe the spatial distribution characteristics of the _NEP_ change trend in the Yangtze River Delta region over the past 20 years, the unitary linear regression analysis  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vegetation Cover Type** & **Mean _NEP_ (g\(\cdot\)C\(\cdot\)m\({}^{-2}\)\(\cdot\)a\({}^{-1}\))** & **Carbon Source/Sink** & **Standard Deviation** \\ \hline Forestlands & 378.37 & Carbon Sink & 31.51 \\ Grasslands & 304.55 & Carbon Sink & 25.96 \\ Wetlands & 185.80 & Carbon Sink & 23.79 \\ Croplands & 192.17 & Carbon Sink & 34.77 \\ Built-up Lands & \(\backslash\) & Carbon Source & \(\backslash\) \\ Unused lands & \(\backslash\) & Carbon Source & \(\backslash\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average _NEP_ values of different land cover types.  
Figure 8: Interannual changes of the _NEP_ of different land cover types in the Yangtze River Delta region from 2000 to 2019.  
method was used to analyze the _NEP_ change trend of each pixel from 2000 to 2019, and the annual mean _NEP_ variation slope was calculated pixel by pixel (Figure 9).  
It can be seen from Figure 9 that the change rate of the _NEP_ value in the Yangtze River Delta region from 2000 to 2019 was not consistent (with increase and decrease); however, increase was the dominant trend of the entire region, and the proportion of pixels with \(\theta_{slope}>0\) reached 69.0 %, that is, the carbon sink capacity increased and the carbon source capacity decreased. However, there were certain spatial differences in the entire region. The proportions of pixels in provinces and municipalities with \(\theta_{slope}<0\) in this region were 14.50% (Zhejiang Province), 9.10% (Anhui Province), 6.65% (Jiangsu Province), and 0.79% (Shanghai). In terms of the changes of pixels with \(\theta_{slope}<0\) in each province and municipality, the pixels with \(\theta_{slope}<0\) in Shanghai account for the largest proportion (62.7%), followed by Zhejiang Province (48.9%), Jiangsu Province (24.64%), and Anhui Province (21.59%). This data indirectly showed that the Yangtze River Delta region did not stop the ecological construction at the same time of stable urban development and expansion in the past 20 years.
## 4 Discussion  
This study presents an overarching view of the changes in regrowth potentials from raw water to the tap. Specifically, we addressed the question of how surface water treatment affects bacterial regrowth potential, how much of the regrowth potential is contained in drinking water after distribution, and how heating drinking water in a DWI affects regrowth factors. Regrowth factors increased as a consequence of water treatment. In the examples of river water treatment, and especially in the case of canal water treatment, the maximal cell numbers in finished waters even exceeded the ones in the corresponding raw waters. When defining biological stability on the basis of regrowth factors (and, thus, the possibility to produce new biomass), the stability of the water decreased. Regrowth factors herein refer to the ratio between the regrowth potential ICC\({}_{\text{day 7}}\) and the actual bacterial concentration ICC\({}_{\text{day 0}}\) and, therefore, to the net increase, whereas the ICC\({}_{\text{day 7}}\) values represent the upper limit of intact cell numbers (i.e., maximal growth potential). It is important to understand that the regrowth potentials do not need to be met in practice, for example, if drinking water does not stagnate (meaning it is consumed rapidly) or if it is not exposed to elevated temperatures that accelerate the regrowth kinetics. In cases of drinking water with a disinfect residual, regrowth is suppressed if the residual does not decay below a critical threshold. Still, ideally, the risk of possible regrowth should be avoided, as it can possibly result in the emergence of specific groups of unwanted bacteria related to esthetic, technical or hygienic problems. Especially in DWIs, regrowth is typically not a controlled process and is, therefore, a threat to water safety [29, 30].  
Independent of the absolute regrowth potential, water treatment reversed the ratios of biologically fixed and dissolved nutrients. Eventually, drinking water with predominantly dissolved nutrients is distributed. This applies both to drinking water with and without residual disinfect, as the final disinfection is typically not followed by biological stabilization. This biological instability of drinking water contrasts with environmental waters, where the availability of easily accessible dissolved nutrients should be limited under stable environmental conditions.
## 5 Conclusions  
We applied an improved SPH method to simulate the Ti-6 AL-4V cutting process and proposed a Surface Particle Method (SPM) to evaluate the variance trend of surface roughness with different cutting parameters. Then we employed the Taguchi method to investigate the effects of cutting parameters on the variance trend of surface roughness. The investigation applied the \(S\)/\(N\) ratio and variance analysis to analyze the effect of different levels of cutting parameters. We found that the rake angle and feed have an obvious effect on surface roughness, while the variance of cutting speed had little influence on the surface roughness. Comparing our results with the experiments, we concluded that although the surface roughness calculated based on the simulated surface particles is different from the measured one in the experiment, they had the same variation trend. This means we can use this SPH model to investigate the effects of cutting parameters on surface roughness effectively. Our work proved there are merits to using the improved SPH cutting model to optimize cutting parameters. These merits are listed as:  
1. The improved SPH method avoids the misconvergence caused by mesh distortion of FEM and increases the accuracy of the simulation. Meanwhile, the TANH constitutive law improves the accuracy of the description for the dynamic properties of the material under a high stress rate compared with JC constitutive law.
2. This paper proposed a Surface Particle Method (SPM) to evaluate the variance trend of surface roughness with different cutting parameters. Although there are differences between the surface roughness calculated by SPM and the measured surface roughness from the experiment, they have the same variation trend when the cutting parameters are changed. Thus, the surface roughness based on SPM is valid as an alternative for the measured variance trend of surface roughness with different cutting parameters for investigating and optimizing cutting parameters.
3. This paper applied the Taguchi method to test the influence of cutting speed and feed and rake angle on the surface roughness of a Ti-6 AL-4V workpiece. The Taguchi method considerably reduces the number of experimental groups under different cutting parameters and provides a solid theoretical foundation for optimizing cutting parameters.
4. This work proves that the effects of cutting parameters on surface roughness can be investigated through simulation instead of real experiments for cutting Ti-6 AL-4V. This avoids the laborious experimental process and substantially reduces work time and cost.  
**Author Contributions:** All authors contributed to the work. [PERSON] established the SPH cutting model and performed the data analysis; [PERSON] guided the research work and designed the experiment. [PERSON] and [PERSON] edited/proof-read the paper.  
**Acknowledgments:** This research work is supported by the National Natural Science Foundation of China (No. 51775445, No.51875475). Xi'an science and technology project (201805042 YD20 CG26-(9)).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Conclusions  
In this study, the electromagnetic parameters of limestone and magnetite were measured. Temperature sweep tests, frequency sweep tests, and LAS tests were performed to evaluate the high temperature rheological and fatigue properties of MFAMs. The following conclusions can be drawn:  
1. Magnetic has a better electric field energy storage ability, a higher dielectric loss and magnetic loss, and a better microwave heating efficiency than limestone. And, it has better magnetic properties than limestone at a low frequency, while the opposite is true at a high frequency. The tan\(\delta\) of magnetite changes significantly with frequency, and that of limestone changes slightly.
2. With the increase in temperature, the deformation resistance and anti-rutting performance of the MFAM drops sharply, its elasticity decreases, and its viscosity increases. Its composition of elasticity and viscosity remains relatively steady when the temperature reaches a certain threshold. The longer the microwave heating time, the faster the high temperature rheological properties of the MFAM decreased with rising temperature.
3. Microwave heating reduces the peak shear stress and yield strain of the MFAM. The fatigue life significantly decreases with the increase in strain and microwave heating time.  
Microwave heating technology has good prospects in promoting asphalt pavement self-healing and de-icing. However, microwave heating can also result in asphalt aging. It is suggested to completely evaluate the aging of asphalt pavements under cycling microwave heating and develop a suitable anti-aging agent to reduce the aging degree of asphalt in future research.
## 4 Simulations and Results Analysis
### Legitimacy Probability (\(P_{l}\))
#### 4.5.2 Accuracy Analysis of \(P_{L}(alarm)\)  
To validate the proposed metric \(P_{L}(alarm)\), we simulate a network with \(1000\) nodes, each one of them with \(P_{tp}=0.6\), \(P_{fp}=0.4\) and \(N=10\) neighbors. We use different values for the consensus parameter \(m\) (from one to \(10\) neighbors) and for the base rate \(P_{M}\) (10%, 30%, 50%, 70% and 90%), which results in a total of 50 scenarios. In this simulation, several nodes are randomly chosen to be malicious and perform attacks against the network. In addition, each node of the network is analyzed by its neighbors, which check, according to their individual hit and miss rates, whether they find that a node is malicious or not. In each scenario, \(10,000\) IDS global evaluation processes (global decision-making processes of the IDS based on the individual outcome of each neighbor) are performed. At the end of each simulation scenario, \(P_{L}(alarm)\) is computed by SDSE (according to Equation (19)) and compared to the actual fraction of legitimate nodes for which alarms were improperly generated.  
Figure 7 shows the accuracy of metric \(P_{L}(alarm)\) obtained from the simulations performed. Notice that for all values of \(P_{M}\) and for all values of \(m\), the accuracy remained very high, between 98% and 100%. This is due to the assumption that the individual miss and hit rates used are the same for all nodes of the network. This is, indeed, a reasonable assumption, given that nodes usually use the same rules database (either attack signatures or normal network behavior profiles) to perform attack detection (although they do not necessarily achieve the same results at the same time due to the different view of the network that each node has). At last, we highlight that, since the metric \(P_{L}(alarm)\) also depends on \(P_{M}\), the more accurate the value of \(P_{M}\) (provided by offline analyses given by the IDS), the greater the accuracy of this metric.  
Figure 6: Legitimacy probability of a node when the intrusion detection system (IDS) generates a global alarm, \(P_{L}(alarm)\), _vs._ the consensus parameter \(m\), for different values of \(P_{M}\), \(N=10\), \(P_{tp}=0.6\) and \(P_{fp}=0.4\).
# Research Advances and Application Prospect of Low-Temperature Plasma in Tumor Immunotherapy
## 4 Activation of Anti-Tumor Immunity by LTP
### The Impact of LTP on Non-Specific Immune Responses
#### 4.1.1 LTP Promotes Antigen Presentation of DCs  
Dendritic cells have been recognized as the most potent antigen presenting cells, and the initiator and regulator of the body's immune response. [PERSON] et al. [25] treated pancreatic cancer cells with LTP-activated phosphate buffered saline solution four hours and then co-cultured with the treated-cells with DC at a ratio of 1:1. They found that the maturity and antigen presentation of DCs were enhanced, but their viability was not affected. [PERSON] et al. [26] recently reported that colorectal cancer cells treated with an argon-based plasma jet upregulated the expressions of CD80 and CD86 of monocyte-derived DCs (moDCs) in co-culture. [PERSON] et al. [27] demonstrated that PAM-A375 lysate potentiated the maturation of DCs by up-regulating the expressions of CD83 and CD86. Moreover, in co-culture with allogeneic T cells, DCs loaded with PAM-lysates increased the proportion of cytotoxic T cells. [PERSON] et al. [28] found that moDCs treated with Argon plasma expressed high levels of the costtimulatory molecules, including CCR7, CD25, CD40, CD86, CD83 and HLA-DR. Therefore, LTP can be used in vitro to promote the maturation of DCs, increase their number, enhance the functions of mature DCs, and activate immunotoxic T cells, so as to achieve anti-tumor effects.
## 3 The Proposed Architecture
### Resource Broker  
Resource Broker as shown in Figure 2 consists of Scheduler and global queues. Scheduler, on receiving user requests, schedules the requests following their types and priorities. All user requests in a global queue are served by a priority method. The first time a request enters a global queue, it has the lowest priority, _i.e._, priority 0, regardless of what type the request is, _i.e._, each type of user request has its own priorities. Each time when a request of type \(i\) due to some reason is re-enqueued to type-_i_ global queue, \(i\) = 1, 2 or 3, its priority will be increased by one (_i.e._, one step higher). When a request with priority = 3 is re-enqueued, the priority will not change. The purpose of priority change is to avoid request starvation. The chance that a request R is re-enqueued includes that Scheduler cannot find a resource node for R, and the resource nodes allocated to R for some reason, e.g., leaving, or failure, unable to accomplish R's corresponding job.  
When a user would like to submit a request R to Resource Broker, the procedure is as follows (see Figure 3).  
(1) R's request node N sends R to Collector.  
(2) Collector dispatches Mobile Agent to deliver R to Scheduler that will insert R into the corresponding global queue, based on the type specified by the user and approved by the Resource Broker.  
(3) Scheduler periodically, or on receiving a performance-table-newly-updated message from Calculator, delivers Mobile Agent to Front Unit to retrieve the performance table from Data Center.  
(4) Data Center sends the performance table to Front Unit. Front Unit dispatches Mobile Agent to transfer the table to Scheduler.  
(5) Scheduler checks the requests which are now in the global queues and the requests' priorities, and finds the suitable resource nodes for these requests, based on current levels of the resource nodes recorded in the performance table and the rule with which local resource nodes are allocated first, e.g., when the scores of two nodes are the same, the one with smaller ID  
Figure 3: The procedure of the system execution.  
(note that node ID is not shown in Table 1) will be allocated first. If no local resource nodes are available, Scheduler chooses appropriate resource nodes currently under its neighbor MCI Information Managers, or neighbor servers, e.g., resource nodes X, for R. After that, Resource Broker sends Mobile Agent to inform Collector of the nodes.  
(6) Collector delivers the IPs of X to N and notifies X to service N.  
(7) N on receiving the IPs sends the jobs' data to X through Collector.  
(8) When the jobs are finished, X transfers the results to Collector, retrieves the request with the lowest type (_i.e._, type-1 requests go first) and the highest priority from its waiting queue, and executes the corresponding job. The step repeats until the waiting queue is empty. Then, X changes its status to idle.  
(9) Collector on receiving the result forwards the results to N.