esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Flow Field Performance of a Small Circulating Water Channel Based on Porous Grid Plate
###### Abstract  
Low-cost and high-efficiency circulating water channels are widely used in the hydrodynamic tests of an underwater device. The current research mainly focuses on obtaining a better velocity uniformity of the test section by optimizing the curve function of the boundary in the contraction section. While, for small underwater device, their hydrodynamic characteristics are sensitive to turbulence. Thus, the circulating water channel, which can obtain the required turbulence characteristics, is urgently needed. A small circulating water channel, which can reduce the turbulence intensity based on a porous grid plate and can be used to test the hydrodynamic characteristics of a small underwater device, is designed. The relationships between porosities and resistance coefficients of a porous grid plate are established. The effects of the honeycomb (porosity and thickness); screen (porosity, number of layers, and spacing); and pumping flow rate on the turbulent characteristics of the test section are studied. The relationships between the parameters and the turbulent characteristics of the test section are established, and the methods to achieve the required flow characteristics of the test section are proposed. Experiments are carried out, and the validity of the obtained results is verified. In this work, the turbulence intensity of the fluid field in the test section can be restrained to 0.0491, which is enough to meet the turbulence requirements for the hydrodynamic test of a small underwater device. This work can provide references for the construction of a hydrodynamic test platform for small underwater devices.  
circulating water channel; turbulence intensity; porous grid plate; velocity inhomogeneity Article
## 4 CLAP Correlation and Information Sharing Services
### Correlation and Cross-Correlation Process  
Previous to the correlation of the collected events, there is a phase of Pre-processing and Policy Filtering, in which the platform verifies if the user has specified some conditions to filter the incoming events before they arrive to the correlation engine (e.g., source/destination IP, port, time/date range, type of event, or the SIEM agent where the event is collected). These filtering conditions help in reducing the volume of events arriving to the correlation engine (and consequently improving its performance), as well as on dividing the processing in different correlation processes (e.g., to have separated correlation by each client organization in the case of multi-tenant deployment). In addition, this pre-processing is important to determine the type of data included in each field of the normalized incoming event (e.g., string, float, integer, double, etc.) since it is relevant for providing a high-performance correlation.  
Cross-layer correlation includes security events from multiple layers (e.g., application, transport, network layers). They are generated from different sensors, using different formats and generally referring to different situations with a common parameter (e.g., IP address, protocol, port number, etc.). The Correlation Engine is the core of the XL-SIEM suite and uses Event Processing Language ([[http://esper.espertech.com/release-5.4.0/esper-reference/html/event_patterns.html](http://esper.espertech.com/release-5.4.0/esper-reference/html/event_patterns.html)]([http://esper.espertech.com/release-5.4.0/esper-reference/html/event_patterns.html](http://esper.espertech.com/release-5.4.0/esper-reference/html/event_patterns.html)), accessed on 5 June 2021) (EPL), which allows a flexible and complex definition of the correlation rules. It is a SQL-like language that includes for example the detection of patterns, the definition of data windows or the aggregation and filtering of incoming events into more complex events.  
Since the architecture of the monitoring engine is designed to have its processes running in a distributed way in an Apache Storm cluster, it is possible to define different correlation processes called \"correlation bolts\". It is then possible to have a different set of rules (different security directives) and a different data schema (different type of incoming events) for each correlation bolt working in separate hosts.  
The proposed Cross-Layer Analytic Platform follows a four-step correlation process: (i) define the security policy; (ii) Define correlation bolts; (iii) define statements; and (iv) define directives.
## 5 Results
### Effects of Pre-Processing
#### 5.2.2 Classification Level Effects of Filtering and Scaling  
As a final evaluation step we did an analysis regarding the impact of filtering and scaling on the classification performance. To this end we need to consider not only how the pre-processing impacts the comparison between the classification within simulated sensor data, but also how it impacts the performance of the absolute recognition rates of the real sensor data based models. It makes little sense to apply pre-processing methods that make the results of the simulated signals based model equal to that of a real signals based one but at the cost of making both much worse. In Figure 15 we thus consider the effect of 8 Hz filtering and scaling in different combinations on the recognition for different placements (wrist and ankle) of the sensor for the real and the simulated data. The training was done on all the data designated for training and the testing on all testing data as described in Section 4.2. We can see that the recognition rates on the real data show little sensitivity to the filtering and scaling. The performance on the simulated data is also fairly invariant for the wrist sensor placement while for the ankle placement frequency filtering does indeed make a significant difference. However, it must also be noted that for the ankle placement the recognition rate with the simulated data is very poor to start with (around 40%).  
Figure 14: Loss for all windows using different training strategies for acceleration (**left**) and gyroscope norm (**right**).  
Figure 15: Comparison of classification performance for models trained with real (**upper**) or simulated data (**down**) under different preprocessing techniques using the acceleration norm. Results are shown for each different sensor placement separately.
## 4 Results and Discussion
### Results and Discussions of Attribute Bidirectional Performance
#### 4.3.2 Comparative Analysis of Attributes' Bidirectional Performance  
To better analyze the antecedents of both high and low customer ratings, the percentages of negative sentiment values and positive sentiment values concerning six attributes in terms of hotel star ratings are respectively calculated, shown in Figures 5 and 6. Results show that room, cleanliness and location account for about 75% of the sum of attribute negative sentiment values, meanwhile these three attributes also account for about 75% of the sum of attribute positive sentiment values for hotels of each star rating. Room, cleanliness and location are core attributes of hotels, in line with some prior research [35; 94; 105; 106]. This finding also implies the main contributors to high customer ratings and causes of low customer ratings are the same for hotels of each star rating, similar to the studies of [PERSON] et al. [94] and [PERSON] et al. [107]. Value, service and sleep quality have less impact on both high and low customer ratings, contrary to some previous research. For instance, the study of [PERSON] et al. [105] implied that intangible service has the greatest impact on customer satisfaction. The results also imply that the percentage of positive/negative sentiment values concerning location, value and service fluctuate with hotel star rating. Thus, the effect of good/poor performance concerning location, value, service on high/low customer ratings varies across hotel star ratings. For location, its good/poor performance contributes less to customer satisfaction/dissatisfaction in four-star hotels than in other star hotels. For value, the percentages of both positive and negative sentiment values gradually drop with the increase in hotel star ratings above three-star hotels. That is, for value, poor performance in
# Machine Learning-Based Detection of Cognitive Impairment from Eye-Tracking in Smooth Pursuit Tasks
## 1 Introduction  
Mild cognitive impairment (MCI) refers to a decline in cognitive abilities that is larger than expected for an individual's age and level of education, yet not severe enough to significantly disrupt daily functional activities [1]. Originally, the term was introduced to describe the intermediate stage between normal ageing and Alzheimer's disease (AD) [2], but it is now recognised as a potential precursor to various forms of dementia [1]. In clinical cohorts, annual conversion rates from MCI to AD are between 10 and 15 percent [3]. AD is the most widespread neurodegenerative disease and its prevalence is increasing. As new drugs for the treatment of AD have recently been approved worldwide [4], there is a need for an early diagnosis of cognitive impairment, which can potentially slow the progression of the disease and thus improve patients' quality of life.  
Smooth pursuit eye movements (SPEM) are slow controlled eye movements that allow the eyes to closely follow a moving object. There is growing evidence that smooth pursuit eye movements are closely linked to cognitive processes [5]. SPEM have been linked to attention, working memory, executive function, and decision making [5]. SPEM have been functionally linked to the frontal eye filed, the dorsolateral prefrontal cortex, the basal ganglia, and the cerebellum [6]. SPEM deficits have been found in different neurological disorders, including Parkinson's disease [7, 8] and mild cognitive impairment [9]. Differentimpairments of SPEM have been described in MCI so far, including reduced pursuit gain, increased saccadic intrusions, impaired predictive pursuit, and delayed initiation [9]. Similar findings have been described in AD as well [10]. However, the applicability of SPEM to differentiate between MCI and normal healthy subjects has not been specifically studied so far. Therefore, the main aim of this study was to assess the efficacy of horizontal and vertical SPEM with different velocities to differentiate between patients with MCI from healthy subjects by the use of machine learning algorithms.  
The paper is organised as follows. First, we present the materials and methods, including the enrolment criteria, assessment procedures, the description of our cohort, and the methods used for the data analysis. Next, in Section 3, we present the results of the statistical and machine learning analyses, followed by the related work in Section 4. The discussion of our results is presented in Section 5, and the conclusion is in Section 6.
# Slotted ALOHA on LoRaWAN-Design, Analysis, and Deployment
## 2 Related Works
[26], which confirmed that increasing the number of gateways can improve the global performance, but cannot eliminate fast saturation. Indeed, generating packets with Poisson law and uniform distribution of the payload lengths between one and 51 bytes, as simulated in Ref. [5], the maximum expected channel capacity usage is 18%, which is similar to the Pure-ALOHA (P-ALOHA) throughput, with a link load of 0.48. This may be an issue because several packets could be lost; indeed, at the maximum channel load, around 60% of the packets transmitted are dropped because of collisions. To verify the correct reception of each packet an acknowledgment (ACK) method could be applied, but it requires two successive transmissions in order to be successful, the payload in uplink and ACK in downlink, increasing the collision probability with other messages. The S-ALOHA protocol is well known in local wireless communications [34] since the 70's. In S-ALOHA the channel time is divided into slots (Figure 1), which have fixed length \(T\) and are basically composed of two parts: transmission time (\(T_{\rm r}\)) and the tolerance interval (\(T_{\rm b}\)), as presented in Figure 1a. Every end node must transmit a packet only at the beginning of a slot. If two or more end nodes transmit their packet during the same period, a collision occurs; otherwise, no collision is generated, and the data are properly sent (Figure 1b). If no terminals access in one slot, it will be free, and no device will start the transmission in the middle of the time-slot. The maximum theoretical S-ALOHA channel throughput is 37%. Figure 1: (a) Slot width definition in S-ALOHA, (b) example of free, collided and successful slot.
## 6 Conclusions  
Link asymmetry poses a major challenge for many wireless sensor network based applications that require high reliability as well as stringent QoS parameters. While asynchronous MAC protocol provides significant improvement in energy efficiency, it very poorly deals with the link asymmetry. In this paper, a novel cooperative communication based asynchronous medium access control protocol (COASYM-MAC) is proposed for minimizing the adverse effects of asymmetric links and fulfilling sufficient QoS requirements. It forms a tree architecture among the nodes that allows a node to choose an alternative path to the sink in case of link asymmetry. Such tree formation significantly decreases the hidden terminal collisions. It also proposes a wakeup scheduling algorithm that uses the level number, duty cycle, and metrics value (residual energy, channel state information and distance) for the dynamic selection of wakeup time for each node. Finally, an efficient relay node selection algorithm is proposed that takes into consideration both cases: when the link is fully symmetric and when there is an asymmetric link. OMNeT++ based simulation was carried out to compare the performance of COASYM-MAC protocol with the state-of-the-art ASYM-MAC protocol. Results show that the  
Figure 15: Control Packet Overhead (**a**) for 27 nodes (**b**) for 54 nodes (**c**) for 90 nodes.  
proposed protocol outperformed the existing ASYM-MAC protocol in terms of energy efficiency, throughput, delay, and control packet overhead.  
[PERSON] conceptualized the protocol; [PERSON] and [PERSON] implemented the protocol; [PERSON] performed the simulation; [PERSON], [PERSON] and [PERSON] wrote the paper; [PERSON] and [PERSON] analyzed the simulation results; and [PERSON] supervised this research work.  
This research received no external funding.  
The authors would like to express their sincere gratitude to the anonymous reviewers for their very useful and valuable comments that helped significantly in improving the quality of this paper.  
The authors declare no conflict of interest.
# W-Band Modular Antenna/Detector Array for the Electron Cyclotron Emission Imaging System in KSTAR
## 4 Fabrication and Measurement
### Proposed Modular Antenna/Detector Array for KSTAR Operation  
In Figure 12, an array box configuration deployed with the proposed modular antenna/detector array is presented. The proposed modular antenna/detector array has been adopted in the KSTAR ECE imaging receiver since the 2021 operation with the above configuration. The beams of the ECE signal (as RF) and the LO signal are radiated perpendicularly and pass through one of two windows, respectively. Each of the two signals are divided and combined by a beam splitter, and the combined RF-LO signal beams in two paths are delivered to the two antenna/detector arrays, respectively.  
In case of performance failure due to a single or multiple channel(s), the specific channel module(s) can be easily removed and replaced simply by opening the side metal plate thanks to the modular structure of the proposed array. The specific channel(s) can be individually tuned or improved for performance enhancement. Moreover, when changes in the plasma operation conditions require adjustment of the ECE antenna/detector performance, the proposed antenna/detector modules (including the mini-lens, antenna, and antenna balun) can be easily and independently adjusted or improved due to the modular array setup. Therefore, the proposed modular array can improve the quality of the ECE imaging system by allowing efficient channel maintenance and performance enhancement, thus minimizing the interrupted time for maintenance, and enabling specific system tuning under a variety of tokamak operation conditions.
# A Reliable Handoff Mechanism for Mobile Industrial Wireless Sensor Networks
###### Abstract  
With the prevalence of low-power wireless devices in industrial applications, concerns about timeliness and reliability are bound to continue despite the best efforts of researchers to design Industrial Wireless Sensor Networks (IWSNs) to improve the performance of monitoring and control systems. As mobile devices have a major role to play in industrial production, IWSNs should support mobility. However, research on mobile IWSNs and practical tests have been limited due to the complicated resource scheduling and rescheduling compared with traditional wireless sensor networks. This paper proposes an effective mechanism to guarantee the performance of handoff, including a mobility-aware scheme, temporary connection and quick registration. The main contribution of this paper is that the proposed mechanism is implemented not only in our testbed but in a real industrial environment. The results indicate that our mechanism not only improves the accuracy of handoff triggering, but also solves the problem of ping-pong effect during handoff. Compared with the WirelessHART standard and the RSSI-based approach, our mechanism facilitates real-time communication while being more reliable, which can help end-to-end packet delivery remain an average of 98.5% in the scenario of mobile IWSNs.  
Keywords:Industrial Wireless Sensor Networks (IWSNs); handoff triggering; reliable communication; mobility aware +
Footnote †: journal: Sensors
## 4 Estimation of Time in the Linear Approximation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Toxin 1** & \multicolumn{2}{c}{**Toxin 2**} & \multicolumn{2}{c}{**Toxin 4**} \\ \multicolumn{2}{c}{_Staphylococcus aureus_} & \multicolumn{2}{c}{**Toxin Shock**} & \multicolumn{2}{c}{**Staphylococcus aureus**} & \multicolumn{2}{c}{_E. coli_ **Shiga Toxin 2**} \\ \multicolumn{2}{c}{**Alpha-Hemolysin**} & \multicolumn{2}{c}{**Syndrome Toxin-1**} & \multicolumn{2}{c}{**Enterotoxin Type A**} & \multicolumn{2}{c}{**(One Subunit A and Five Subunits B)**} \\ \hline \(\kappa=\Big{[}\sqrt{\frac{\varepsilon RT}{2z^{2}C_{M}F^{2}}}\Big{]}^{-1}\) & 56,324 & 58,122 & 28,575 & 185,741 \\ \(\left(\mathrm{m}^{-1}\right)\) & & & & \\ \hline \(L\left(\mathrm{m}\right)\) & & \(\leq\)\(10^{-2}\) m & & \\ \hline \(\lambda_{S}\) & \(2.71\times 10^{-9}\) & \(2.43\times 10^{-9}\) & \(2.52\times 10^{-9}\) & \(3.49\times 10^{-9}\) \\ \hline \(\varphi_{l}^{max}(0)=\Big{(}\frac{RT}{2}\Big{)}\) (V) & 0.011 & 0.011 & 0.022 & 0.003 \\ \hline \(\frac{t_{m}}{L}\left(\frac{s}{\mathrm{mm}}\right)\) & 547 & 470 & 1000 & 210 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Calculation of toxin movement time \(\frac{t_{m}}{L}\) (completion time per unit width). \(\varphi_{l}^{max}(0)\): the maximum potential applied to one side of the duct for which the linear approximation holds, \(L\): width of the duct. The time constant equation takes the form  
\[\tau\cong\frac{L}{2D\kappa(\lambda_{s}\kappa+1)} \tag{26}\]  
As in all exponential quantities, the phenomenon is theoretically completed in infinite time, but in practice it has reached 99% of its final value in a time of \(5\tau\). However, in time less than \(5\tau\), e.g., \(3\tau\), there is also significant reduction. It should be mentioned that:  
1. The time constant in the linear regime as well as the completion time are proportional to the width of the duct. Therefore, it is useful to calculate the completion time per unit width (s/mm) \[\frac{t_{tot}}{L}=\frac{5\tau}{L}=5\cdot[2D\kappa(\lambda_{s}\kappa+1)]^{-1}\] (27) and the results are represented in Table 4.
2. The values of the potential with which these times are achieved are extremely small (because only in very low potential the linear approximation can be satisfied)
3. Although we cannot predict time for higher potentials (as in our case), it is logical to assume that by increasing the external voltage, and consequently the potential \(\varphi(0)\), the time it takes for the bulk solution to reach acceptable concentration values will be drastically reduced.
# Effects of Precipitation and Fire on Land Surface Phenology in the Brazilian Savannas (Cerrado)
## 2 Methodology
### Acquisition of MOD13Q1 and HLS Products and Auxiliary Data  
Two satellite products were used to retrieve LSP metrics from the NDVI data: the MOD13Q1 product (version 6.1) and the HLS product (version 2.0). The MODIS MOD13Q1 product provides NDVI composite images at 16-day intervals with a spatial resolution of 250 m. The final NDVI value for each pixel represents the highest record within the 16-day observation period. This approach allows the algorithm to discard noisy pixels affected by clouds, residual atmospheric contamination, and large view zenith angles or off-nadir viewing [26].  
The Harmonized Landsat Sentinel-2 (HLS) product provides a series of surface reflectance images from the Operational Land Imager (OLI), on the Landsat 8 and 9 satellites, and the Multi-Spectral Instrument (MSI), on the Sentinel 2A and 2B satellites. The pre-processing and harmonization steps include atmospheric correction; cloud and cloud-shadow masking; spatial co-registration and common gridding; illumination and view angle normalization; and spectral bandpass adjustment [33]. As a result, the product has a temporal resolution ranging from 2 to 3 days and a spatial resolution of 30 m. The HLS product offers individual spectral bands, and thus, the Red and Near Infrared (NIR) bands necessary to calculate the NDVI [34].  
Ancillary data included two main types of observations: a vegetation map published by [PERSON] [36] and rainfall data from the nearest meteorological station to the park, located in the municipality of Chapadao do Ceu (GO). The major vegetation physiognnies mapped by [PERSON] [36] were grouped in our study into three classes: (i) grasslands, comprising savanna grasslands and shrub savannas; (ii) woodlands, including wooded savannas and woodland savannas; and (iii) others, consisting of riparian forests and small patches of seasonal semideciduous forests. We focused our analysis on the two broad savanna classes: grasslands and woodlands.  
The meteorological station used in our study has provided data since 1983, enabling a precise analysis of the local rainfall regime based on average values calculated over a period of at least four decades, as defined by a Climatological Normal. The daily rainfall records are collected by the National Hydrometeorological Network (RHN) and made available through the HidroWeb Portal, which is part of the National System of Water Resources Information (SNIRH). The data spreadsheet includes four possible collection statuses: 0 = Blank; 1 = Real; 2 = Estimated; 3 = Doubtful; and 4 = Accumulated. The data were carefully reviewed, and those marked as \"doubtful\" were replaced with the average rainfall for the same day from other years in the series. The \"accumulated\" values were retained, as they represent the sum of rainfall (in mm) over a sequence of days. In our study, we calculated both the accumulated rainfall for the rainy season of each growing cycle between 2002 and 2023, covering October to May, and the total accumulated annual rainfall per cycle, spanning from October to September.