esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones
## 1 Introduction
However, even in the current mature phase of the mission, the need to apply a very stringent data quality control (QC) to the processed CYGNSS winds limits the mission's ability to monitor storms in a reliable manner. The CYGNSS spatial sampling configuration, with multiple thin tracks with spatial/temporal gaps in between, makes it hard for the users to easily extract a snapshot of information about a storm at a specific time. Additionally, some forecast systems assimilate easy-to-track storm parameters, such as the storm intensity and radii of gale/storm/hurricane-force winds, rather than track-wise wind retrievals [24; 25]. These storm parameters can be more easily determined from gridded storm-centric maps. With this objective in mind, the CYGNSS science team recently developed a preliminary storm-centric L3 product by aggregating the CYGNSS SDR V3.0 data in a \(\pm 6\) h window in a \(7.2^{\circ}\times 7.2^{\circ}\) box around the storm center [26]. We assess this storm-centric product by observing several case storms in the Atlantic and Eastern Pacific, comparing it with the wind fields seen by the radiometers' TC-winds, and assessing its ability to provide estimates of the storm parameters. The manuscript begins with a description of CYGNSS, the radiometers, and other wind datasets, along with the methodology utilized in these analyses (Section 2). Then, a preliminary assessment of the available public L2 datasets is described for all wind regimes, with an intercomparison of the main features in each dataset, including bias maps, globally averaged statistics, and spatial sampling of the quality-controlled tracks (Section 3). We then focus on the assessment of the CYGNSS L2 retrievals at high winds in the global tropical belt (Section 4), and in tropical cyclones for a selection of 86 storm scenes during the period 2017-2020 (Section 5), where we display statistics and several case studies for individual TCs. We then examine the storm-centric L3 CYGNSS wind product and its ability to provide a reliable representation of the surface wind fields within a TC (also in Section 5). We conclude the manuscript with an example of a different application for which CYGNSS winds can provide critical information missed by other satellite sensors: the assimilation of CYGNSS winds in rain at low-to-moderate wind regimes into CCMP, a global satellite-based 6-hourly wind analysis product (Section 6). The manuscript ends with a discussion about the results, and suggestions for future improvements (Section 7).
## 4 FTC Structure and Controller Design for NT-DRAS  
### FTC Structure for All Fault Process  
Since the proposed NT-DRAS may be subject to all faults including gradual types and sudden types, the whole FTC structure for all fault processes is given in Figure 4. In the structure, there are PFTC controllers, AFTC controllers and controller switching mechanisms for NT-DRAS. The switching law is designed based on the output result of the FDD scheme and a performance evaluation mechanism is first designed to provide quantitative results for FDD scheme. The index of the following performance evaluation criterion in Euclid norm form is given as:  
\[e_{\rm perf}(t)=\left\|r(t)-y(t)\right\|_{2} \tag{18}\]  
where \(r(t)\) is the reference signal, while \(y(t)\) is the NT-DRAS output. In order to measure the conservatism of the NT-DRAS under different fault conditions, also when used as FTC strategies, the mean and maximum value of \(e_{\rm perf}(t)\), \(t\in[t_{1},t_{2}]\), are also defined in the following forms and used as the overall performance measures.  
\[\left\{\begin{array}{l}\overline{e}_{\rm perf}(t)=\frac{1}{t_{2}-t_{1}}\int_ {t_{1}}^{t_{2}}e_{\rm perf}(t)dt\\ \epsilon_{\rm perf-max}=\max\left\{e_{\rm perf}(t),t\in[t_{1},t_{2}]\right\} \end{array}\right. \tag{19}\]  
A preset tracking performance value \(\delta\) is also given as a threshold, which is used to determine whether to switch the PFTC controller. When \(e_{\rm perf}(t)\leq\delta\), it means the PFTC controller is still effective for the gradual fault conditions, once \(e_{\rm perf}(t)>\delta\), AFTC controller should be enabled to maintain the NT-DRAS performance under severe fault conditions. This paper is dedicated to the PFTC controller design and the AFTC strategy design for severe faults is listed as future research work.
## 5 Experiments
### Interpretability and Case Study  
To address RQ 3, we extracted the attention vector from the attention layer and used it to calculate feature importance. \(\alpha(s^{i})\) denotes the attention vector of the instance \(i\).  
\[\alpha=softmax(\mathbf{W}_{t_{attn}}\mathbf{Z}+\mathbf{b}_{t_{attn}}),\alpha\in\mathbb{R}^{ 1\times D}. \tag{16}\]  
We aggregated attention vectors on the instance level [43] as  
\[IMP=\frac{1}{S}\sum_{i}^{S}\alpha(s^{i}), \tag{17}\]  
in which \(IMP\in\mathbb{R}^{1\times D}\) denotes feature importance.  
We evaluated the value of each feature in the construction of an attention network by summing their relevant feature importances. For example, \(IMP_{i:i+n-1}\) denotes the \(n\) feature importance related to the tweet post, and the feature importance of the post is formulated as \(\sum_{i=i}^{i+n-1}IMP_{k}\). From Figure 17, we can see the importance of each feature set. It is clear that in both real news and fake news datasets, the two most significant feature sets are network and user timeline. Tweet posts and user profiles are less influential, while the trend is the least impactful.  
Moreover, we divided the cascades arranged by size into five sequential intervals on the \(p\)-quantile (\(p=[0,0.87,0.95,0.997,0.999,1]\)). In Figure 18, the x-axis tick indicates the size of a cascade on a scale of 0 to 4. We wanted to know which feature sets would become more important as the cascades size increased.  
Figure 17: Feature importance for each component on different veracity.  
Figure 18: Attention weight for each component of different veracities.  
In two datasets of different veracities, we found that the same pattern appeared in the changes in the importance of **networks, tweet posts, user timeline (history)**, and **trends**, whereas the **user profile** took an opposite route.  
Generally, the **network** importance increases then decreases for both datasets because the more significant the post, the less important the dissemination structure of early hours. This implies that the initial structural features become less important as cascades grow, which is consistent with previous work [14].  
**Trends** (public concern) tend to be more important on large-scale than on small-scale cascades. However, both **user timelines** (history) and **tweet posts** decrease as scale increases. The visualization of the chart corresponds with common sense; as the cascade size increases, more attention is paid to the trend rather than the post itself and the user timeline.  
In the real dataset, the importance of **user profiles** steadily rises, but in fake datasets, it is just the reverse. We believe that even if the information posted by ordinary people is accurate, very few people will retweet it. As a result, when the cascade size increases, the user's data also becomes more valued.
# Measuring the Centrality of DNS Infrastructure in the Wild
## 7 Conclusions  
In this paper, we perform a comprehensive and systematic measurement to explore the centrality of DNS infrastructure from both the client-side and server-side. We propose a novel and lightweight resolver pool measurement approach, which is capable of discovering the implicit resolver pool structure in a fast and low-cost manner, after demonstrating the limitation of the _CNAME_ chain-based resolver pool discovery approach. Our measurement results reveal several previously unexplored findings on the centrality of DNS, including the fact that more than 90% of FDNSes forward their queries to 5% of indirect resolvers, and that 48.5% of domain names across 1138 gTLDs are resolved by 0.45% of all name servers, which are operated by only 10 DNS providers. Our findings indicate that DNS infrastructure is much more centralized than previously believed. Furthermore, we investigate the client-side and server-side DNS infrastructure from multi-dimensional perspectives, which helps researchers understand the current DNS ecosystem. As DNS is a core infrastructure of the internet, the trend towards centralization trend requires ongoing attention. We leave Long-term tracking and measurement as future work to reveal the evolution of centralization in the DNS system. Additionally, it is also worth examining how the implementation of new DNS features, such as DoH (DNS over HTTP) and DoT (DNS over TLS), affects the centralization of DNS.  
Conceptualization, C.X. and H.S.; methodology, C.X. and Y.Z.; software, C.X., B.G. and Y.Z.; validation, C.X., Y.Z. and F.S.; formal analysis, H.S.; investigation, Y.L., P.X. and F.S.; resources, H.S., F.S. and Y.L.; data curation, Y.Z., B.G. and F.S.; writing--original draft preparation, C.X. and Y.Z.; writing--review and editing, C.X., H.S., P.X. and Y.L.; supervision, H.S.; project administration, H.S. All authors read and agreed to the published version of the manuscript.  
This research was supported by the National Key Research and Development Program of China (Grant No. 2021 YFB3100500).  
Not applicable.  
We thank the anonymous reviewers for their helpful suggestions.  
The authors declare no conflict of interest.
# Analysis of Selected IT Tools for Environmental Product Assessment
## 3 Materials and Methods
### Review of Selected IT Tools for Environmental Product Assessment
#### 3.1.4 GaBi (Sphera)  
GaBi is a software also known as Sphera, dedicated to the analysis of the product life cycle [48]. Like other tools, it allows modeling the life cycle from the extraction of raw materials to disposal. It is distinguished by a rich database containing a wide range of materials, processes, and environmental indicators. The final results of the calculations are presented in the form of graphs, and the generated reports allow for the identification and optimization of areas requiring improvement. Examples of environmental assessment methods available in this tool are Eco-Indicator 99 and ReCiPe.
## 2 Description of a Simultaneous Service Process
### Example 2  
The second way called \(CSFP\) (count server for phase) in [30], assumes keeping track of the number of servers providing service at various phases. Following this way, we obtain that when the number of customers receiving service is equal to \(n\), then the auxiliary process \(\eta_{t}\) is defined as \(\eta_{t}=(x_{t}^{(1)},x_{t}^{(2)},\ldots,x_{t}^{(M)})\) where the component \(x_{t}^{(m)}\) is the number of servers at the \(m\)th phase of service, \(m=\overline{1,M}\). Expressions for the matrices \(H_{n}^{0}\), \(H_{n}^{-}\), and \(H_{n}^{+}\), are derived in [35]. In formula (1) in [35], the \(H_{n}^{0}\) corresponds to the matrix \(A_{n},\ n=\overline{0,N}\), \(H_{n}^{-}\) corresponds to the matrix \(L_{N-n},\ n=\overline{1,N}\), and \(H_{n}^{+}\) corresponds to the matrix \(U_{N-n},\ n=\overline{0,N-1}\). The non-diagonal entries of the matrix \(A_{n}\) define transition rates of the process \(\eta_{t}\) which do not cause service completion on any of the \(n\) busy servers. The diagonal entries of the matrix \(A_{n}\) define exit rates of the process \(\eta_{t}\) from its states when \(n\) customers are processed. The matrix \(L_{N-n}\) defines the rates of transitions leading to service completion in one of \(n\) busy servers. The matrix \(U_{N-n}\) defines transition probabilities of the process \(\eta_{t}\) when \(n\) servers are busy and a new service starts. Recursive formulas for computation of the matrices \(A_{n},\ L_{N-n},\ U_{N-n}\) given in [35] are simplified in [36].  
description of simultaneous service in the retrial queue of \(MAP/PH/N\) type as given in example 2 was used in [37].  
Expressions for matrices \(H_{n}^{0}\), \(H_{n}^{-}\), and \(H_{n}^{+}\) are more transparent and easily derived when the \(TPFS\) way is used. However, when \(M\) is small, the size of these matrices is essentially smaller if the \(CSFP\) way is used; for concrete formulas and numbers, see, e.g., [30]. Mention that if the service time has an exponential distribution, then the matrices \(H_{n}^{0}\), \(H_{n}^{-}\), and \(H_{n}^{+}\) become scalars equal to \(-n\mu\), \(n\mu\), and \(1\), respectively.
## 4 Conclusions  
According to the influential factors and the evaluation methods, recent studies of GSP have been investigated over the last twenty years in this review article. It is concluded that:  
* Microstructure can be analyzed by microscopic observation method in the study of influential factors. However, the result is generally subjective, because image capturing and judging mainly rely on personal experience. Thus, associating tests should be introduced to acquire results in qualitative and quantitative ways;
* Effects of raw materials such as asphalt content and fine aggregate on GSP are not clear enough, due to assumptions in GSP design. More conditions and parameters need to be revised and developed for the evaluation of GSP;
* Individual admixture cannot completely meet the requirement for the low-temperature performance of GSP, while the combination of admixtures cannot be designed reasonably. More effective modifiers should be developed to improve the cracking resistance of GSP;
* Some other characteristics of GSP including interface strength, skid resistance, and other properties are seldom studied, which should be explored to assess the performance of GSP;
* Models used in GSP simulation are based on assumptions of traditional AC. Therefore, these computations and predictions cannot be accurate, as they ignore the difference in behaviors and parameters between GSP and AC.
## 1 Introduction
### Three Principal Methods for Rotor System  
Many researchers have successfully used various methods for determining the critical speeds, modes, and unbalance responses of the rotor-bearing systems. Three principal methods are reviewed. They are the transfer matrix method (TMM), the finite element method (FEM), and the generalized polynomial expansion method (GPEM). These three approaches have been successfully applied to rotordynamics.
# Hybrid CMP Slurry Supply System Using Ionization and Atomization
###### Abstract  
Chemical mechanical planarization (CMP) is frequently used in semiconductor manufacturing to polish the surfaces of multiple layers in a wafer. The CMP uses a slurry that aids in fabricating a smooth surface by removing the excess materials. However, excessive use of slurry affects the environment and is expensive. Therefore, we propose a hybrid slurry supply system that combines ionization and atomization to reduce slurry consumption and improve the polishing quality. The proposed hybrid system atomizes the ionized slurry using electrolysis and a spray slurry nozzle. We compared the material removal rate (MRR) and polishing uniformity based on the slurry supply systems used in Cu and SiO\({}_{2}\) non-patterned wafers. Additionally, the step height reduction and dishing were compared in the Cu-patterned wafers. The experimental analysis using the hybrid system confirmed a 23% and 25% improvement in the MRR and uniformity, respectively, in comparison with the conventional slurry supply system. This improvement can be attributed to the chemical activation and uniform supply of the ionized and atomized slurries, respectively. Moreover, a significant reduction was observed in dishing and pitch-size dependence. Furthermore, the proposed system prevents heat accumulation between the CMP processes, serving as a cooling system.  
CMP; chemical mechanical planarization; hybrid; slurry supply system; ionization; atomization; material removal rate (MRR); uniformity +
Footnote †: journal: Applied Sciences
# EEG-Based Measurement for Detecting Distraction in Coal Mine Workers
## 2 Experiment
### Procedure  
The Sustained Attention to Response Task (SART) is a widely adopted experimental paradigm in psychology and neuroscience, aimed at assessing individuals' sustained attention and their ability to detect unexpected events [30]. In SART, participants are asked to perform a repetitive response task, where the \"target\" stimuli require a response, while other \"non-target\" stimuli do not. This requires special attention and a response, allowing the assessment of participants' sustained attention and their ability to inhibit responses to non-target stimuli. In our study, we integrated a hazard recognition task into the SART framework, where hazardous images served as target stimuli and safe images as non-target stimuli, mirroring miners' daily hazard identification challenges. Both hazardous and safe images were evenly distributed with a 1:1 ratio across all tests.  
Each trial began with a red \"+\" fixation point displayed for 1000 ms, followed by a randomly presented safe or hazardous image (Figure 3). Subjects were required to quickly assess potential hazards within 6000 ms of the image's appearance. Failure to respond within this timeframe was noted as an error in hazard recognition. After each response or timeout, the image disappeared, and the next trial started. To prevent learning biases, the sequence of testing in quiet and noisy environments was randomized across participants. Each test consisted of 30 trials, resulting in a total of 60 trials per participant.  
Between test sessions, participants rated their attention levels using a five-question scale, such as \"I can maintain focus during tasks,\" scored from 1 (strongly disagree) to 5 (strongly agree), with a maximum total score of 25. The experimental setup involved E-Prime software (Psychology Software Tools, Inc., Pittsburgh, PA, USA) and EEG equipment to record responses and brain activity simultaneously. Although self-reported attention scores provided valuable insights into participants' perceived attention levels, they may have been influenced by biases, such as social desirability or recall limitations. Therefore, combining self-reported attention scores, task performance, and EEG data helps to reduce subjective biases and provides a more comprehensive and objective reflection of participants' attention states and corresponding brain activity.
## 2 Background
### Attacks in V2X Scenarios  
#### 2.4.1.1. _D_enial of Service (DoS) Attack  
A denial of service (DoS) attack in vehicle-to-everything (V2X) scenarios refers to an attack in which malicious entities intentionally flood the network with excessive or unauthorized requests to disrupt or completely stop communication between vehicles and infrastructure. V2X communication, which includes vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-pedestrian (V2P) interactions, relies on seamless data transmission to ensure safety, traffic efficiency, and real-time decision-making. In a DoS attack, attackers aim to overwhelm the network or specific components of the system, rendering them inoperable and compromising the reliability of critical vehicular systems. DoS attacks in vehicular networks can take several forms:  
* Packet flooding-the attacker floods the network with a large number of unnecessary or malicious packets, overloading communication channels and preventing legitimate vehicles from sending or receiving critical data, such as collision warnings or traffic updates.
* Radio jamming in V2V communication-the attacker disrupts direct communication between vehicles by emitting high-power signals that jam the radio frequencies used  
Figure 1: Fog computing architecture with three layers: cloud layer (**top**) for centralized storage and large-scale processing; fog layer (**center**) for decentralized computation closer to data sources; and edge layer (**bottom**) for end devices like vehicles generating and collecting data.  
for vehicle-to-vehicle interactions. This prevents vehicles from exchanging safety-critical information, potentially leading to accidents or traffic congestion.
* Radio jamming in V2I communication-the attacker targets the communication between vehicles and infrastructure, such as roadside units (RSUs), by jamming the radio frequencies used for vehicle-to-infrastructure interactions. This disruption can prevent vehicles from receiving vital data, such as traffic light signals, road conditions, or navigation assistance, affecting both safety and traffic flow.  
These types of DoS attacks are illustrated in Figure 2, which shows examples of packet flooding, V2V radio jamming, and V2I radio jamming in a vehicular communication system. The figure demonstrates how these attacks can severely disrupt real-time data exchange, underscoring the importance of robust security measures to mitigate such threats.
## 3 The Important Role of Optic Flow in Postural Control
### The Functional Role of the Foveal and Peripheral Visual Field
#### 3.1.2 Second Perspective: Central and Peripheral Vision have the Same Functional Role  
[PERSON] et al. carried out an interesting experiment in 1994 [47]. The authors performed occlusions of some parts of the visual field by attaching blinds to a helmet worn by the participants. When the authors stimulated peripheral and central visual fields to the same extent, the postural sway was smaller with central vision. However, if the peripheral field of stimulation was corrected by the cortical magnification factor of the retina in the primary visual cortex such that there was a correspondence between retinal and cortical activity, no difference in the stabilizing effect was found between peripheral and central vision. Thus, according to the results of this experiment, there was no functional specialization of peripheral and central vision in postural control. These results suggested that visual stabilization of quiet stance was a function of the field size and cortical representation of the retina.  
1.3 Third Perspective: Central and Peripheral Vision Have Functional Differences and Complementary Roles  
The study by [PERSON] and [PERSON] in 1985 examined the importance of the stimulation area in the central visual field, by using radial expansion dot patterns with different visual angles [48]. The results suggested that in addition to the specific information processing system that was required for peripheral vision, there was a higher-level system that used information from complex stimuli in the central visual field. [PERSON] in 1985 examined the importance of the central and peripheral visual fields in the control of stance, as a function of the geometrical structure of the optic flow field [12]. [PERSON] performed the experiments in a moving room to measure the magnitude of the compensatory sway in response to movements of the room. When radial optic flow was presented in the peripheral retina, no compensatory sway was found, while some body oscillation was found when lamellar flow was presented in the central field. This result suggested that the optic flow structure interacted with the stimulated retinal area in the control of stance. In 1988, [PERSON] et al. investigated how the optic flow input affected postural readjustments [15]. The authors found that the amplitude of the postural responses did not depend on the magnitude of the optic flow components and that the peripheral visual field did not have an exclusive role in the control of postural sway. [PERSON] et al. [49]showed that both peripheral and central vision contributed equally to children's postural stability at ages 6 and 10. However, at age 8, central vision was more efficient in stabilizing posture. The authors stated that the contribution of central and peripheral visual fields to postural control depended on age and was as a function of the plane of body oscillations.