esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Climatology (2002-2022) of Longitudinal Structures in the Lower Thermosphere
For this purpose, the authors used SABER temperature data from 2002-2006 and the nitric oxide density measured from the StudentNitric Oxide Explorer (SNOE) satellite [87] for the period of 11 March 1998-30 September 2000, i.e., for a full 2.5 years. The authors found that in the altitude range of \(\sim\)105-110 km where the NO has maximum, the WN4-like signature between March and October is produced by DE3, while the WN3-like signature in December and January is introduced by DE2 and SE1. This study revealed the large influence of nonmigrating tides on the NO variability in the lower thermosphere regardless of the fact that the SABER/TIMED and SNOE observations represent different time intervals; hence, the NO density is used as a tracer of the nonmigrating tidal dynamics. In a similar way, [PERSON] et al., 2014, demonstrated that the mesospheric ozone derived from the SABER/TIMED is a tracer of the nonmigrating tides DE3, DW2, and SW3 [31]. The DE3 modulation of the EEJ is also an example of the presence of the WN4 structure registered in the lower thermosphere [45; 46]. The above-mentioned examples of the observed longitudinal structures in the lower thermosphere show quite limited time intervals of observations. Additionally, usually these structures and especially the WN4 one is connected only with the DE3, but this is not always the case. It is known that besides DE3 and DE2 responsible mainly for the WN4 and WN3 structures other nonmigrating tides, and SPWs are involved in their formation. The basic aim of this section is to present evidence for the existence of longitudinal structures in the lower thermosphere based on large statistics. The formation of these structures will include all those nonmigrating tides and SPWs which contribute to the formation of these structures, except the quarterdiurnal ones, which are very weak.
## 1 Introduction
### Performance vs. Trust  
In this study, we conduct a performance comparison of two well-known Byzantine Fault Tolerant (BFT) blockchain architectures, Quorum [4] and Tendermint [5], against the non-BFT Hyperledger Fabric (version 2.2), which utilizes Apache Kafka [6] as the ordering mechanism. Apache Kafka is a centralized, replicated, and distributed database [7] that is employed by Hyperledger Fabric. This version of Hyperledger Fabric is crash fault tolerant (CFT) but not Byzantine fault tolerant (BFT).  
To isolate the impact of Hyperledger Fabric, we also examine the performance of deploying Apache Kafka. The objective is to gauge the performance difference between CFT-based protocols and BFT variants, representing the additional performance cost incurred by BFT's enhanced trust. Moreover, it is reasonable to believe that Hyperledger Fabric, the CFT blockchain variant of Hyperledger Fabric, would outperform its BFT rivals. Our findings, later explored in this paper, elaborate on that outcome.
## 3 Prototype and Experiments  
Figure 13 presents the prototype of the developed robot with the camera display panel and control panel. The robot is mainly composed of three modules with omnidirectional wheels. The wheels can be classified into six driving wheels and four supporting wheels. The front camera and headlamp are mounted on the front module. The rear camera and control cable are attached to the rear module. The parameters of the robot are summarized in Table 3. As shown in the table, several experiments were conducted to evaluate the performance of the prototype, e.g., the maximum velocity, payload capacity, and vertical distance that the robot is capable of climbing without slipping. The robot is also able to pass through a straight pipe in both horizontal and vertical directions.  
Figure 11: A diagram of the camera display panel.  
Figure 12: A diagram of the robot’s control panel.  
Figure 13: (**a**) The prototype of the developed in-pipe inspection robot; (**b**) the camera display panel and the robot’s control panel.  
To evaluate the robot's capability to pass through a curved pipe, two experiments were conducted in an 8-inch (203 mm) diameter pipe, as shown in Figure 14a,b: a curved up elbow (horizontal to vertical) and a curved down elbow (vertical to horizontal). The results demonstrated that the robot can stably move in the upward and downward directions for both curved up and curved down conditions. However, irregular welds or uneven weld beads may occasionally cause the wheels to slip. Furthermore, the robot can move on the ceiling of the pipe, as shown in Figure 14c, in order to avoid any liquid or slag in the pipe. Figure 15 shows the real-world visual inspection from when the robot was used in a horizontal oil drainpipe at a real site.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Material & Aluminum, nylon, and PLA \\ Dimension & 110 mm \(\times\) 99 mm \(\times\) 450 mm \\ Weight & 3 kg \\ Magnet & Neodymium block 40 \(\times\) 20 \(\times\) 10 mm N35 \\ Wheel diameter & 10 cm \\ Power & 220 V 50 Hz \\ Top speed & 30 cm/s \\ Working space & In-steel pipe, from 5-inch (127 mm) diameter to \\  & flat plate \\ Payload & 59 N \\ Vertical distance & 10 m \\ Control cable length & 50 m \\ Motor & Maxon EC16 30-watt brushless DC motor \\ Motor gear & GP16A ratio 104:1 \\ Robot gear & Spur gear ratio 4:1 \\ Analog camera & FOV 160\({}^{\circ}\) 1000 TVL \\ Camera display & 7 inches \\ Sensors & 3 humidity sensors and 4 temperature sensors \\ \hline \hline \end{tabular}
\end{table}
Table 3: The specifications of the prototype of the developed robot.  
Figure 14: The experimental setup to evaluate the robot’s capability to pass (**a**) through a curved up elbow, (**b**) through a curved down elbow, and (**c**) by attaching to the ceiling.
## 2 Literature Review
### Theoretical Background
#### 2.2.1 Technological Context  
Considering the technological context means to analyze innovative technology adoption from a technological perspective. This is not only related to the newly adopted technology but also to the enterprise's current system, and their internal and external operations [24; 28]. [PERSON] et al. proved that compatibility, comparative advantage, and complexity are significant determinants [4], results which are in line with the results found by [PERSON] in the study of small enterprises adopting enterprise information systems [29]. [PERSON] et al., in the study of SMEs' adoption of big data analytics, found that observability, aside from compatibility and relative advantage, has significant effects [30]. [PERSON] et al. also suggested that innovative technology integration depends on the compatibility with the information systems already possessed by an enterprise and the ability to communicate with their business partners [31]. [PERSON] then proved that complexity has a negative impact on the implementation of mobile marketing applications in SMEs [32]. Furthermore, [PERSON] et al. indicated that in the technological context, the trialability significantly affects SMEs' adoption of cloud computing [22]. Given the findings in this section, the above five factors were included in the consideration of technological context.
# A Channel Allocation Mechanism for Cellular Networks
## 2 Related Work
### Distributed DCA Mechanism  
Due to economic growth and the demand of the quality of service in PCS, DCA mechanisms have been studied in recent years. Several simulation results and analyses have shown that the centralized DCA mechanism can provide near optimum channel allocation, but it will cause the immense management costs in the central control. Therefore, distributed DCA mechanism is proposed. The difference between the centralized DCA mechanism and the distributed DCA mechanism is that the distributed DCA mechanism doesn't have the central pool for channel control. The number of available channels and signal strength measurements are considered and used by the proposed distributed DCA mechanism [10].
## 3 Experiments and Results
### Classification Performance at Group Level  
The interpretation of EEG signals is still a challenge due to large inter-subject variability [19; 20; 21]. To address this challenge, we conducted group-level experiments to further evaluate the classification performance of our 1 DCNN-BiRAU model in binary and four-category classification. We selected five subjects and ten subjects for the group-level training, and the experimental results are presented in Figure 5.  
The results show that promising classification performance can be consistently obtained for group-level experiments. The proposed model in this paper can effectively mitigate overfitting while also achieving consistent and stable results. It achieves accuracy of 91.21% (five subjects) and 84.57% (ten subjects) in the binary classification task, and accuracy of 92.18% (five subjects) in the four-category classification task. The findings demonstrate that our proposed model is capable of maintaining good classification performance when applied to a group of subjects. These results also suggest that our model effectively captures inter-subject differences and generalizes well across multiple subjects.  
Figure 4: Individual-level performance (accuracy) in four-category classification.  
Figure 3: Individual-level performance (accuracy) in binary classification.
# Example usage calculate_urban_resilience('input_file_name')
#### 5.4.3 Vulnerable Regions  
The dataset reveals a detailed picture of vulnerability across various regions and municipalities in Portugal, factoring in several key indicators. These vulnerability assessments consider several factors: (i) Municipal Revenue per Inhabitant: A higher revenue per inhabitant usually means more resources for infrastructure and services, reducing vulnerability. Conversely, lower revenue indicates limited financial capabilities, increasing susceptibility to disaster impacts; (ii) Upper Secondary Education Rate: Municipalities with higher education rates typically have populations that are more aware of and prepared for seismic risks, reducing vulnerability. Lower education rates suggest a less informed populace, increasing the risk during seismic events; (iii) Ageing Ratio: A higher proportion of elderly residents often means increased vulnerability due to the mobility and health challenges inherent in disasters; (iv) Housing Density (House stock per 1000 population): Lower housing density, implying overcrowding, can heighten vulnerabilities in disasters due to resource limitations and emergency response capacities; (v) Population Density: Densely populated areas are more vulnerable in seismic events due to their potential for higher casualties and evacuation complexities.  
This comprehensive analysis (Figure 6) enables stakeholders to pinpoint areas needing focused attention or intervention, optimizing resource allocation to effectively address vulnerabilities across Portugal. In Aveiro, vulnerability scores range from 0.634 in Murtosa to 0.678 in Anadia, illustrating diverse risk levels. The Azores show a wider variation, with Corvo at a lower vulnerability of 0.558, and Praia da Vitoria at a high of 0.811. In Beja, vulnerability scores vary from 0.504 in Barrancos to 0.635 in Beja, indicating differing degrees of susceptibility. Esposende in Braga shows a notable high vulnerability of 0.767, while Terras de Bouro is relatively lower at 0.628.  
Figure 5: Map depicting Portugal’s 308 municipalities and their exposure areas.  
Figure 6: Map depicting Portugal’s 308 municipalities and their vulnerability.
# Static Wind Reliability Analysis of Long-Span Highway Cable-Stayed Bridge in Service
## 3 Functional Function and Parameter Distribution for Aerostatic Instability Analysis  
### Functional Function of Static Wind Instability  
If the whole structure or a part of the structure exceeds a specific condition, the structure won't meet a specified functional requirement in the design or operation stage. This specific condition is called the limit state of the structure which is at the dividing point of structure reliability and unreliability. The calculation of structural reliability is to  
Figure 3: Calculation flow chart of checkpoint method for reliability analysis.  
judge whether the structure reaches the limit state. For reliability analysis of bridges under static wind load, the limit state function \(Z\) is described by the difference between the actual critical aerostatic instability wind speed \(U_{cr}\) and the design wind speed \(U_{m}\) of the bridge, it is written as:  
\[Z=U_{cr}-U_{m} \tag{11}\]  
Through Equation (11), it can be found that the bridge structure will suffer from wind-induced static instability damage when the actual critical instability wind speed of the bridge is less than the design wind speed. In Equation (11), the design wind speed \(U_{m}\) can be calculated by the gust coefficient multiplied by the 10 min average wind speed at the bridge site, namely:  
\[U_{m}=G_{v}U_{b} \tag{12}\]  
where \(U_{b}\) stands for the 10 min average wind speed at the height of the main girder and \(G_{v}\) stands for the gust coefficient reflecting the influence of wind speed fluctuation, they are essentially random variables, which both need to be described by the probability distribution and mathematical statistics methods.  
\(U_{cr}\) stands for the actual critical aerostatic instability wind speed of the bridge, and it should be corrected by a factor \(C_{w}\) because of the randomness of the natural wind, structural parameters, and calculation model of the bridge structure, namely:  
\[U_{cr}=C_{w}U_{s} \tag{13}\]  
where \(U_{s}\) is the calculated critical aerostatic instability wind speed determined by finite element analysis, and \(C_{w}\) is the correction factor for static wind instability critical wind speed which is also a random variable and should also be represented by the probability distribution and mathematical statistics methods similar to \(G_{v}\).  
Finally, the functional function for the aerostatic reliability analysis of the bridge structure is further written as:  
\[Z=C_{w}U_{s}-G_{v}U_{b} \tag{14}\]
# Study on Cold Resistance Performance of Composite Subgrade Structure in Seasonal Frozen Regions
## 3 Results and Discussion
### Analysis of Temperature Rise Effect  
In order to more intuitively incarnate the influence of the cold resistance layer on temperature distribution inside the subgrade, the average values of temperature of the subgrade with and without XPS plates at the same depth were collected during the monitoring period, and the results are listed in Table 7.  
Table 7 shows that the average temperature at the center of the XPS plates increased by about 3.0 \({}^{\circ}\)C. The temperature of the subgrade with XPS plates across the full range of depth was higher than  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Depth (cm).** & **10** & **20** & **30** & **40** & **65** & **75** & **85** & **95** \\ \hline
**With XPS** & 2.9 & 1.2 & 0.5 & \(-\)0.1 & \(-\)0.9 & \(-\)0.6 & 0.6 & 0.7 \\
**Without XPS** & 0.7 & \(-\)1.9 & \(-\)2.6 & \(-\)3.3 & \(-\)3.9 & \(-\)3.5 & \(-\)1.5 & \(-\)1.2 \\
**Temperature** & \multirow{2}{*}{2.2} & \multirow{2}{*}{3.1} & \multirow{2}{*}{3.1} & \multirow{2}{*}{3.2} & \multirow{2}{*}{3.0} & \multirow{2}{*}{2.9} & \multirow{2}{*}{2.1} & \multirow{2}{*}{1.9} \\
**difference** & & & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 7: Average temperature at different depths during the monitoring period (\({}^{\circ}\)C).  
Figure 10: Depth of the positive temperature area at the bottom center of the XPS plates.  
that of the subgrade without XPS plates. The extent of the increase in temperature took the XPS plates as the center, attenuating upward and downward. For road structures in seasonal frozen regions, this can not only reduce the frost damage of subgrade, but it can also improve the service condition of the bearing layer structure above the XPS plates, which is beneficial to improving road structure durability.
## 1 Introduction  
Variations in the physical properties of the subsoil, such as electrical resistivity, caused by the occurrence of seismic activity have been studied in recent decades. Authors such as [PERSON] et al. [1,2] established a non-linear relationship between the magnitude of the earthquakes and the amplitude-duration of the recorded anomaly in the apparent resistivity \(\rho_{a}\). Additionally, various electromagnetic (EM) anomalies have been reported, both in the ionosphere and subsurface, for large-magnitude seismic events [3,4]. These mentioned studies use the maximum entropy method (MEM) and spectral density to analyze their signals.  
This study seeks to estimate the variation, both in time and in space (depth), of the \(\rho_{a}\) of the subsoil using seismic events of magnitude \(>\) 4.5 in Colombia, using the magnetotelluric (MT) method. In the MT method, deepened and applied to geophysical exploration by [PERSON] [5], relationships are obtained that allow the electrical resistivity and its distribution in the subsoil to be inferred from the relationship between the fluctuations in the orthogonal components of the natural electric and magnetic fields. These equations were coupled in an algorithm developed in Python to obtain \(\rho_{a}\) variations as a function of time from the time series of the registered electric and magnetic fields. The method allows us to reach depths from tens of meters to hundreds of kilometers [6], considering that this depends on the used time window. By choosing different time windows, we can achieve a depth ranging between \(\sim\)100 m and \(\sim\)100 km. It is possible to reach greater depths, for which it would be sufficient to vary the time window used.
## 4 Basic Data Striping Schemes in Cloud-Based Systems  
The major concerns of moving towards Cloud-based storage have been the security and availability of data when accessed. Several researchers have opinioned that a multi-cloud storage platform might improve security and resilience [PERSON] and [PERSON] (1960); [PERSON] (1960); [PERSON] (1960). Based on this, to ensure effective data splitting and security of stored data, a secret sharing algorithm such as Shamir secret sharing has been proposed for multi-cloud storage [PERSON] and [PERSON] (1960); [PERSON] (1960); [PERSON] (1960); [PERSON] (1960); [PERSON] (1960); [PERSON] (1960). Previous methods split the data into pieces and then reconstruct when needed, while replicating data at different storage areas using encryption to safeguard them. This type of methodology can be costly (in terms of network bandwidth and storage facilities), and not quite safe in scenarios involving poor key management.  
[PERSON] (2008) states that a basic secret sharing scheme which lacks redundancy is given by two algorithms: Sharing (_Share_); and Recovery (_Rec_). Share takes a message \(M\) and split it into \(n\) pieces. Since \(M\) is secret, _Share_ must introduce randomness (that is, Share is probabilistic); _Rec_ is a deterministic algorithm which recreates the message from some or all of the shares:* **The Sharing Algorithm:**  
Share(\(M\)) \(\rightarrow\) (S\({}_{1}\), S\({}_{2}\),.... S\({}_{n}\), \(pub\)). The secrets S\({}_{1}\), S\({}_{2}\),.... S\({}_{n}\) are distributed securely among servers 1 through to \(n\), and pub is a public share.
* **The Recovery Algorithm:**  
(S\({}^{\prime}\), S\({}^{\prime}\),.... S\({}^{\prime}\), \(pub\)) \(=M\). The correctness property of the algorithm defines that, for any message \(M\), Rec(Share(\(M\))) \(=M\)  
where \(\rightarrow\) depicts randomness.  
Secret sharing schemes are important in cryptography and are a key building block for many secure protocols, such as for: Threshold Cryptography; Access Control; Attribute-Based encryption; Byzantine agreement; generalised oblivious transfer; and general protocol for Multi-Party computation [14]. For general algorithms, the following thresholds are useful:  
* \(t_{r}\)**--the privacy threshold.** This describes the maximum number of servers that cannot determine the secret, even when they combine their shares together.
* \(t_{r}\)**--the fault-tolerance threshold.** With this, supposing that every server is honest, this is the minimum number of servers you need to recover the secret (if the other servers are absent).
* \(t_{r}\)**--the robustness threshold.** This is arguably the most important, as this is the minimum number of correct shares you need to recover the secret if all other servers are compromised.
* \(t_{s}\)**--the soundness threshold.** This determines the minimum number of correct shares such that it is not possible to recover the wrong secret.
* \(t_{r}\)**--the information rate threshold.** This describes the amount of information that the server needs to keep on a secret and determines the efficiency and idealness of a secret sharing scheme.
# An Overview on Denial-of-Service Attacks in Control Systems: Attack Models and Security Analyses
## 4 Probabilistic Approaches to Models of DoS Attacks and Non-Malicious Transmission Failures
### SINR-Based Probabilistic Jamming Models
In [37], we considered a more restricted version of Assumption 7 to study the joint effects of jamming interference and disturbance in networked control systems. The following result shows that, under Assumption 7, the failure indicator process \(l(\cdot)\) defined in Equation (23) belongs to the class \(\Lambda_{\rho}\) for certain values of \(\rho\). **Theorem 8** ([26; 36]). : _Let \(\hat{\rho}\colon[0,\infty)\to[0,1]\) be a continuous, nondecreasing, and concave function such that \(\hat{\rho}(v)\geq p(v)\) for \(v\in[0,\infty)\). Suppose that the transmission failure indicator \(l(\cdot)\) is given by Equation (23), where the jamming interference power process \(\{v(i)\in[0,\infty)\}_{i\in\mathbb{N}_{0}}\) satisfies Assumption (7) with \(\overline{\
u}_{\mathfrak{j}}\geq 0\). Then, \(l\in\Lambda_{\rho}\) for \(\rho\in(\bar{\rho}(\overline{\
u}_{\mathfrak{j}}),1]\)._  
Theorem 8 indicates that the probabilistic characterization through the class \(\Lambda_{\rho}\) can also be utilized for wireless channels with SINR-based jamming models. Notice that the term \(\hat{p}(\overline{\
u}_{\mathfrak{j}})\) provides the lower bound of the range of \(\rho\) for which \(l\in\Lambda_{\rho}\) holds. Here, \(\hat{p}\) is a concave function that upper-bounds the transmission failure probability function \(p\) (see Figure 6 for an example), and \(\overline{\
u}_{\mathfrak{j}}\) is the upper bound of average jamming interference power. Theorem 8 is proved in [26] for the setup where the processes \(\{r(i)\in[0,1]\}_{i\in\mathbb{N}_{0}}\) and \(\{v(i)\in[0,\infty)\}_{i\in\mathbb{N}_{0}}\) are mutually independent. In the networked control problem discussed in Section 2.1.1, this assumption restricts the strategies of the attacker so that they are independent of the state and the control input information. Our recent work [36] shows that Theorem 8 also holds when \(\{r(i)\in[0,1]\}_{i\in\mathbb{N}_{0}}\) and \(\{v(i)\in[0,\infty)\}_{i\in\mathbb{N}_{0}}\) may depend on each other. The dependent case allows state-dependent attack strategies. In [36], a state-dependent attack strategy that maximizes the expected state norm in a rolling-horizon fashion is considered.