esa-sceva/satcom-chunk-collection huggingface dataset:
# Airborne and Dermal Collection Methods of Gunshot Residue for Toxicity Studies
## 2 Materials and Methods
### Collection
#### 2.2.3 Hand Swabs and De-Leading Wipes  
In addition to the airborne samples, dermal samples were collected from the shooter using hand swabs, individually packaged in 70% isopropyl alcohol (Med Pride(r), Hackensack, NJ, USA). The hand swab towelettes were wiped evenly over the front and back of both hands for approximately one minute before and after firearm discharge. Commercially available de-leading wipes that were advertised to remove heavy metals such as Pb, Ni, Cd, As, Ag, Hg, Cr, and Zn from hands or other surfaces (D*Wipe(r) Tows by ESCA Tech Inc., Milwaukee, WI, USA) were also used with identical procedures to the hand swabs. After each shooting scenario, a hand swab was used followed by a de-leading wipe and finally another hand swab. This design allowed for the collection of blanks and samples after shooting and after the use of a de-leading wipe. The de-leading wipes were collected and \(\frac{1}{4}\) of the wipe was cut with ceramic scissors and stored at 4 \({}^{\circ}\)C until subsequent analysis. Blank hand swabs and de-leading wipes were taken on the shooter's hands for blank correction.
# Tradeoffs in Key Rotation Strategies for Industrial Internet of Things Devices and Firmware
## 1 Introduction
### Threats to Firmware Signing Keys  
The cryptographic material used to sign a firmware segment constitutes the weakest link because if that material is lost, then the secure boot process is compromised. Unfortunately, there are many ways that the private keys used in firmware signing are compromised [13]. Methods to compromise private keys include activities such as the following:  
* Attacker gains physical access to the key storage medium;
* Attacker deceives an authorized individual with access to the signing key;
* Insider sells or loses the key;
* Attacker reverse engineers the key due to weak encryption;
* Attacker uses malware to access key storage;
* Attacker intercepts the key during a signing operation;
* Other means such as flaws in the algorithm.  
As an example, [PERSON] estimates that about one in five private Bitcoin keys have been lost [14]. This is confounded by the fact that most key management systems have weaknesses [15], IIOT devices have long lifetimes [16], and many IIOT devices are designed and produced by small, start-up developers. This motivates the need for key rotation capability, despite the low energy, small resources, and small capacity of many IIOT. Most standards provide layers of controls to protect the devices and their systems. For example, [PERSON] and Bignale [17] provided a summary of the International Electrotechnical Commission (IEC) 62443 family of standards for securing industrial automation and control systems. While IEC 62443 standards [17] cover hundreds of security controls across 14 publications summing to over 800 pages, the [PERSON] and Bignalet [17] paper includes specifically one security concept that has been difficult to implement--key rotation (specially rotation of firmware signing keys for IIOT). IEC 62443 Part 4-2 is the standard that lists security controls especially for industrial automation and controls devices, but is not explicit about requirements for key rotation--instead, it focuses on the need for verifying the integrity of the firmware, allowing for firmware updates, methods for dealing with incidents such as lost or stolen keys, etc.
## 2 Literature Review
### Fuzzy-Failure Mode and Effects Analysis (Fuzzy-FMEA)  
Failure Mode and Effects Analysis (FMEA) is a proactive risk assessment approach that identifies, prioritizes, and takes action on known or potential system failure modes before they occur. It is widely utilized in a variety of industries to ensure security and dependability. In recent years, academics at home and abroad have integrated traditional FMEA into the Product Service System (PSS). [PERSON] et al. (2023) employed the risk priority number of FMEA to completely assess and identify five key failure modes in the design and production process of automobile rear axle housing, therefore improving product and service quality [30]. [PERSON] et al. (2020) offered a two-stage Failure Mode and Effects Analysis (FMEA) approach to optimize crucial components and systems in offshore wind turbines [31]. The proposed method was given along with its application in assessing the risk of an intelligent household appliance servicing system [32]. [PERSON] (2024) proposed a structural design decision model that combines FMEA and TRIZ to create product designs that meet user demands [33].  
However, there are some drawbacks to using the traditional FMEA analysis method in practice, such as oversimplification due to the same weight of failure risk elements, difficulty accurately evaluating the three failure risk factors, and difficulty reflecting the fuzzy uncertainty evaluation method. Experts at home and abroad proposed the fuzzy-Failure Mode and Effects Analysis (fuzzy-FMEA) approach, which uses fuzzy logic to improve the risk assessment process [34]. [PERSON] and [PERSON] (2023) employed a rule-based fuzzy failure mode and impact analysis approach to assess possible hazards that might arise during propagation operations [35]. [PERSON] (2021) used fuzzy-Failure Mode and Effects Analysis to continuously monitor and enhance the structural components of hydropower facilities [36]. To deal with experts' doubts and vague ideas, [PERSON] (2022) suggested a combined system using IF-FMEA and IF-TOPSIS to sort the listed failure causes in order of how similar they are to the ideal solution [37]. [PERSON] et al. (2023) used image fuzzy set theory to improve the three basic processes, making expert assessment more efficient and accurate [38].  
Although the fuzzy-FMEA method efficiently resolves ambiguity, it has several limitations when dealing with varied cultural emotional experiences, such as ignoring user cognitive biases resulting from various cultural backgrounds and failing to classify user demands on multiple levels. Some studies tried to combine fuzzy-FMEA with other methods to solve issues [39], which gave us insights. We should integrate the index system with fuzzy-FMEA, using a staged approach to addressing various concerns.
## 3 Results
### Economical Implications
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Coal Mines**} & \multicolumn{2}{c}{**Coal Ash Yield**} & \multicolumn{2}{c}{**Volatile Matter**} & \multicolumn{2}{c}{**Inorganic Sulfur**} \\  & \multicolumn{2}{c}{**(Dry Basis, Unit \%)**} & \multicolumn{2}{c}{**(Dry and Ash-Free Basis, Unit \%)**} & \multicolumn{2}{c}{**(Dry Basis, Unit \%)**} \\ \cline{2-7}  & **Range** & **Average** & **Range** & **Average** & **Range** & **Average** \\ \hline \multicolumn{7}{c}{Jurassic coals} \\ \hline Maikuben & 1.9–4.1 & 2.7 & 44.2–52.4 & 46.6 & 0.4–1.4 & 0.9 \\ \hline Shubarkol & 1.1–12.8 & 8.8 & 33.8–45.6 & 40.9 & 0.02–3.1 & 1.5 \\ \hline \multicolumn{7}{c}{Carboniferous coals} \\ \hline Saradyr & 17.0–40.6 & 26.9 & 8.8–28.6 & 21.5 & 0.06–0.6 & 0.2 \\ \hline Lenin & 3.7–47.6 & 14.8 & 8.9–24.6 & 21.8 & 0.01–2.3 & 0.4 \\ \hline Bogatyr & 2.7–42.9 & 31.9 & 13.7–29.0 & 20.8 & 0.01–0.8 & 0.1 \\ \hline \multicolumn{7}{c}{OCV: range 20–35} & \multicolumn{2}{c}{OCV: 25 \(\pm\) 1.75} & IS range 1.42–5.3 \\ \hline \end{tabular} Note: optimal combustion values (OCVs); IS (impact sulfur).  
\end{table} 
Table 5: Comparison of ash yield, volatile matter, and inorganic sulfur content in coal samples from five mines with optimal combustion values [60] and impact sulfur level threshold [56]. tion and potential economic benefits in targeting enriched or slightly enriched elements for extraction. Additional rare-earth elements, including La, Ce, and Nd, were identified through EDS analysis, indicating a promising economic presence of valuable elements as well. Yb was detected in coal ash samples using XRF, while La, Ce, and Nd were identified in unchanged coal matrices using SEM-EDS analysis, which allows for detailed elemental analysis of the coal structure without alteration. This distinction highlights the different analytical capabilities of XRF and SEM-EDS in characterizing rare-earth elements in coal and its by-products.
## 5 Experimental Results  
To evaluate the effectiveness of the proposed real-time collision avoidance system, a series of indoor flight tests were conducted using the custom-built quadcopter described in Section 3. The experiments aimed to verify the drone's ability to detect nearby obstacles, generate repulsive forces, and dynamically modify control signals to avoid collisions during teleoperated flight.
# Continuous Rotor Dynamics of Multi-Disc and Multi-Span
## 3 Numerical Simulations and Discussion
### Calculating Speed  
Figure 32, which represents the time consumption of calculating unbalance response of each computational example by CRDAM, FEM, and Ricatti, is obtained. From Figure 32, the calculating speed of FEM is the slowest among the three methods. When calculating rotor g1.1, g2.2, g1.2, g1.3, h1.1, h2.2, h3.3, h1.2 and h1.3, the calculating speed of CRDAM is faster than that of Ricatti. When calculating rotor g3.3, g4.4, g5.5, g1.4, g1.5, h4.4, h5.5, h1.4 and h1.5, the calculating speed of CRDAM is slower than that of Ricatti. It means that when the rotor becomes complicated, the calculating speed of CRDAM is slower than of Ricatti. For example, calculating the unbalance response of rotor g5.5 and h5.5 by CRDAM needs 93.53 s and 231.8 s, respectively. However, it cost 34.91 s and 57.82 s using Ricatti. Moreover, the calculation time is very long for g5.5 and h5.5, which means complex rotors may take more calculation time than simple rotors when using CRDAM. The average time consumption of CRDAM, FEM and Ricatti is 44.64 s, 154.84 s and 30.45 s, respectively. Moreover, the time consumption of CRDAM increases with the complexity of the calculation instance according to Figure 32.  
In addition, by dividing the shaft into more elements, more calculating time are needed for FEM and Ricatti, but the time consumption of CRDAM remains unchanged theoretically. Hence, the calculating speed of CRDAM can be the fastest.
## 4 Overview of Pseudo-Dynamic Testing and Result Analysis
### Overview of Existing Seismic Test Methods
In the numerical analysis stage, based on the amount of response to a specific deformation, input seismic acceleration, and the amount of response in the current step measured from specimens during the loading test, the corresponding motion equation is calculated using numerical integration, and the response deformation in the next step is estimated. In the loading test stage, the estimated response deformation is then applied to specimens using loading devices, such as an actuator. In the process, the corresponding displacement hysteresis is measured. The procedures described above are conducted in a repetitive manner. In short, response deformation caused by earthquake-like loading is applied to specimens, and then their seismic response is calculated through numerical analysis to determine the seismic response of the target structure. Pseudo-dynamic testing is very similar to existing quasi-static testing, except that the displacement that is applied to the structure is numerically determined during the test. While most seismic response prediction based on numerical dynamic analysis requires that an assumption about hysteretic characteristics be made, pseudo-dynamic testing allows such information to be directly measured from specimens so that it can implement a seismic response that is very similar to the actual one. Figure 9: Illustration of the pseudo-dynamic testing system developed in the present study.
## 5 Conclusions  
In this paper, a multi-step hybrid prediction model is established, aiming to accurately capture the variation trend in the short-term deformation of concrete dams, which is simplified into the CSSKEE model. The model is applied to the displacement prediction of a dam in China, and the experimental results show that it can effectively predict the dam deformation variation trend so as to reflect the resulting structural behavior induced by the deformation variation, thus better ensuring the safety of dam operation. The main conclusions of this work are summarized below:  
1. The CEEMDAN and SE methods can effectively distinguish the noise term and characteristic term of the measured deformation sequence, which can remove the redundancy of the prototype dam displacement sequence effectively and efficiently.
2. The SSA algorithm can better solve the local-trapped problem by applying the KHM method to clustering problems, which achieves better clustering results of the sequence and improves the accuracy of the prediction model.
3. The CSSKEE method proposed in this paper achieves the estimation of dam deformation trends by performing multi-step predictions on dam deformation. Compared to traditional single-step prediction methods, this approach elevates the prediction of a single measurement value to the prediction of deformation trends using multiple measurement values. As a result, it can better reflect future changes in the safety status of the dam, effectively enriching and expanding the theory and methods of dam safety monitoring.  
[PERSON] and [PERSON] conceived and designed the experiments and performed the modeling. [PERSON] and [PERSON] analyzed the data and reviewed and edited the paper. [PERSON] provided relevant technical support. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Performance comparison of different number of clusters after denoising on the test set.  
**Funding:** This research is supported by the National Natural Science Foundation of China (Grant No. 52109118 and U22A20585), the Major Project of Chinese Ministry of Water Resources (Grant No. SKS-2022151), the Young Scientist Program of Fujian Province Natural Science Foundation (Grant No. 2020)05108), and the Fuzhou University Testing Fund of Precious Apparatus (Grant No. 2023T031).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Some or all data, models, or code that support the findings of this paper are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experimental Results
### Performance Evaluation: Latency and Jitter for In-Vehicle Traffic
Figure 5: Queue lengths for different priority output port queues at a TSN switch during the in-vehicle traffic simulation, showing stable, periodic behavior and minimal queuing. of the delay attributable to propagation, processing, and queuing was consistently less than 1 \(\upmu\)s for all TT streams. This resulted in a maximum reduction in total end-to-end latency of up to 59% for certain streams compared to SP scheduling. Furthermore, the maximum observed deviation in end-to-end latency (jitter) for streams scheduled by our algorithm was controlled at the nanosecond level, highlighting a significant improvement in transmission predictability. A 100% scheduling success rate was achieved for all TT streams in this scenario, even with their cumulative bandwidth occupancy exceeding 81%. This demonstrated level of deterministic performance not only fulfills the stringent safety requirements of automotive applications but is also directly applicable to other demanding Future Internet domains, such as factory floor automation and remote tele-robotics.
# Measuring the Centrality of DNS Infrastructure in the Wild
## 2 Related Work
### Internet Centrality  
The Internet community has expressed growing concerns about the centrality of critical Internet infrastructure services, such as DNS and CDN. Researchers have performed a lot of research to understand the situation of the centrality issue and its potential impact. The well-known consolidation trend of the Internet has attracted much interest [23].  
As far as we know, we are the first to quantitatively explore the centrality of DNS in terms of its backing infrastructure from both the client side and the server side. [PERSON] et al. [4] measured Internet centralization by analyzing DNS traffic from five leading cloud providers. [PERSON] [2; 24] measured DNS resolver centrality from the market share perspective by embedding domain resolution scripts in Google Ads to gain insights into most mid-to-large ISPs. [PERSON] et al. [3] performed a timely analysis of the evolution of consolidation in the public DNS market. They discussed potential threats that Internet centrality might pose in [25; 26]. [PERSON] et al. [27] investigated the degree of shared infrastructure in web servers and authoritative DNS using DMOZ data and DNS zone files from._net_ and._com_ TLDs. In this paper, we introduce more DNS zone files providing a comprehensive perspective. [PERSON] et al. [28] measured the concentration and shared infrastructure by analyzing authoritative name servers of all domains of the Alexa Top 1 Million. [PERSON] et al. [10] covered infrastructure centralization of authoritative name servers from three TLD zone files. [PERSON] et al. [29] researched the dependency issues caused by Internet consolidation. [PERSON] et al. [30] analyzed the prevalence and impact of direct and indirect third-party dependencies in DNS, CDN, and CA, and discussed the concentration of third-party services. In this paper, we focus on the centrality of infrastructure, which provides the foundational support for the Internet and has a broader and more lasting impact.
# Fault Inference of Electronic Equipment Based on Multi-State Fuzzy Bayesian Network
## 5 Conclusions  
In the inference of fault diagnosis of electronic equipment, because there are many test parameters between modules and presence of overlapping fuzzy intervals in fault states, this study carries out fault inference based on multi-state fuzzy BN. Moreover, the FRM is built so as to guide test program set to rapidly locate faults. Because there is a combination explosion in the multi-state fuzzy system, this inference method is suitable for the circuit modules or circuit systems. The innovations of this research can mainly be stated as:  
1. In view of how to determine fault state through multiple test indicators of test modules, the normal membership function of a single parameter is proposed; Experts assign the weights of test indicators for normalization of membership degree. In order to assist test personnel in understanding and programming, five fault states are divided according to test indicators.
2. In inference based on BN, due to limited samples of electronic modules, according to statistical values of the samples and experts' experience and tables of prior probability and conditional probability, fuzzy inference was performed by using triangular membership function.
3. Based on multi-state fuzzy BN, the bidirectional inference of faults could be conducted to speculate fault state of leaf nodes for fault verification and calculate posterior probability of fault of root nodes, so as to find fault source and rapidly locate faults.  
Conceptualization, L.W. and D.Z.; experiment design, H.Z. and H.T.; experiment operation, C.Z. and X.W.; data analysis, L.W.; writing--original draft preparation, L.W.; writing--review and editing, X.W. and H.Z.  
This research was supported by the China Postdoctoral Science Foundation (2017M612399), the Science and Technology Innovation Project of Henan Agricultural University (KJCX2018A09), the National Natural Science Foundation of China (31671581).  
\begin{table}
\begin{tabular}{c c c c c} \hline \(x_{i}\) & \(P(x_{i}\)=0.5|T=0.5) & \(P(x_{i}\)=1|T=0.5) & \(P(x_{i}\)=0.5|T=1) & \(P(x_{i}\)=1|T=1) \\ \hline \(x_{1}\) & 0.714 & 0 & 0.774 & 1 \\ \(x_{2}\) & 0.556 & 0 & 0.826 & 1 \\ \(x_{3}\) & 0.263 & 0 & 0.156 & 1 \\ \hline \end{tabular}
\end{table}
Table 5: Posteriori probability of leaf nodes when fault state is 0.5 and 1.
## 2 Data and Sensor System
### Profiling: Identifying Events and Their Characteristics
The name of the satellite for the sensor is represented at horizontal axis. In this simulation, the credibility of the nodes oscillates between two states, 0 (not credible) and 1 (fully credible). Figure 8: Time development of credibility for the data shown in Figure 4. The eight-sensor model evaluates the test data. The value on a vertical axis represents a credibility corresponding to a sensor name indicated on the vertical axis. The name of the satellite for the sensor is represented at horizontal axis. In the first and second rows, the plots of the columns correspond to the sensors on ACE, STEREO-A, and STEREO-B, respectively. In the third row, the left-most and right-most plots correspond, respectively, to GOES-10 and GOES-10 24 h ahead. Figure 7: Snapshot of nodes in the dynamic relational network (four-sensor model). The nodes evaluated as faulty from all sensors are colored red. The nodes evaluated as faulty from half nodes are also colored red. The plus and minus signs on arcs represent a diagnosis result (plus = normal/minus = abnormal). Rectangles represent regions of satellites equipped with sensors for observation. The plus and minus signs on arcs represent a diagnosis result (plus = normal/minus = abnormal). The solid lines indicate two nodes diagnose each other. The dashed lines indicate unidirectional diagnosis from one node to another one. The symbol \(\mathbf{V}_{I}\) and \(\mathbf{B}_{zI}\) respectively represent solar wind speed and north-south component of interplanetary magnetic field. The symbol \(\mathbf{E}\) and \(\mathbf{E}_{24}\) respectively represent high-energy electron flux and its 24 h ahead. (**a**) Initial state (Step = 0); (**b**) Step = 84.
# Study on Vibration Effects and Optimal Delay Time for Tunnel Cut-Blasting Beneath Existing Railways
## 3 The Mechanism and Effect of Vibration Reduction Through Waveform Interference
### Rock Mass Classification Effects on Blasting Wave Superposition
Consequently, the stress wave superposition effect is relatively significant in Class V rock conditions. Table 9 records the Maximum Surface Stress, first arrival time, and duration of stress waves under different classes of surrounding rock. As can be seen from the table, as rock quality declines from Class III to V, surface arrival times increase progressively (1.9 ms for III, 3.0 ms for IV, 4.0 ms for V) and peak moments delay correspondingly (5.4 ms \(\rightarrow\) 9.1 ms \(\rightarrow\) 10.2 ms) due to altered rock mechanics. This is because the reduced mechanical parameters enhance intra-rock stress wave absorption, attenuating high-frequency components and decreasing propagation velocity. Additionally, maximum surface stress diminishes from 4.0 kPa (III) to 3.1 kPa (IV) and 2.0 kPa (V). This demonstrates that the damping effect across rock classes causes fundamentally distinct blast wave propagation and superposition behaviors, resulting in different superposition effects for the same millisecond delay durations in different rock classes. \begin{table}
\begin{tabular}{c c c c} \hline
**Class** & \begin{tabular}{c} **Maximum Surface** \\ **Stress (kPa)** \\ \end{tabular} & \
\begin{tabular}{c} **First Arrival Time** \\ **(ms)** \\ \end{tabular} &
\begin{tabular}{c} **Duration (ms)** \\ \end{tabular} \\ \hline III & 4.0 & 1.9 & 4.2 \\ IV & 3.1 & 3.0 & 5.4 \\ V & 2.0 & 4.0 & 9.7 \\ \hline \end{tabular}
\end{table} 
Table 9: Result analysis of the different class rock masses.