esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Porous Carbons Characterization
#### 3.1.2 Elemental Analysis and Ash Content  
Table 3 presents the elemental analysis and ash content of ANS biomass [29], ANS-derived biochar, ACK, and CC carbons, as well as the pH\({}_{\text{PZC}}\) of carbons.  
The ANS composition was published before [29], highlighting the very low ash content in this biomass. Thus, the resulting biochar and ACK porous carbon from ANS also presented a low ash content. As expected, the amount of elemental carbon increased after the ANS pyrolysis from 47.8 to 74.6 wt%, while the oxygen content decreased significantly from 45.2 wt% to 21.4 wt%. After the chemical activation of the biochar with KOH, the carbon content of the resulting carbon ACK decreased slightly, but the oxygen content increased from 21.4 wt% to 26.9 wt%, suggesting that the KOH treatment increased the amount of oxygen functional groups a little bit. Nevertheless, ACK carbon presented a neutral pH\({}_{\text{PZC}}\); thus, the oxygen surface functional groups must be nonacidic. On the other hand, CC carbon showed a higher amount of C and ashes but lower O content compared to ACK carbon. As previously demonstrated [29], this commercial carbon presented a basic nature.
Large, turbocharged, piston-driven systems (over 100 kW) are used on UAS meant for high-altitude weather data collection typically. These turbochargers tend to be relatively heavy and multi-spool, and they are also optimized for performing at much higher altitudes (18,000 m) than commercially available microturbines [13, 14]. Figure 1: The 7.3 kW turboelectric system integrated into a UAS. This gap in the market creates a niche whereby a smaller-scale UAS could use a turboelectric system when operating at specific altitude regimes. Compared to batteries alone, a turboelectric system provides the option to utilize the improved energy density of hydrocarbon fuels over battery-only power sources while maintaining the flexibility and convenience of electrified propulsion. Higher-altitude missions are also in colder ambient conditions than corresponding lower-altitude missions. These low and potentially extreme operating temperatures are a significant concern for battery-based systems. There is not a significant body of research focusing on the actual fabrication and implementation of turboelectric systems known to the authors. Most research to date related to turboelectric systems has been performed in theory or has been simulated. Notably, work has begun by NASA at the Glenn research center on ground test hardware for a megawatt-scale turboelectric system [15]. NASA has also created a 200 kW ground test device that has successfully operated and distributed power [16]. Overall, there is a lack of research on the actual construction of such a system. In particular, there has been little focus on the more practical real-life considerations of design, especially for the UAS scale, where there is limited research being conducted. Far-reaching survey papers on the subject offer various piston-driven hybrid aircraft but contain no mention of turbine-based hybrid systems [17].
## 2 Materials and Methods  
### Data  
The earthquake data used in this research was obtained from the BMKG network of broadband stations in the form of waveform data from three-component seismograms (BHZ, BHN, and BHE) from 1 January 2010 to 31 December 2017. A total of 32 stations were used, covering the area 4.8\({}^{\circ}\) S-4.25\({}^{\circ}\) N and 120.5\({}^{\circ}\) E-134.5\({}^{\circ}\) E (Figures 1 and 2).  
Considering the distribution and locations of the regional stations, earthquakes with a magnitude \(\geq\) 4 were chosen to facilitate the identification of P- and S-wave arrival times. A total of 1647 earthquake events have been recorded in the study area. Picking was performed manually to obtain P- and S-wave arrival times using SeisGram2K version 7.0 software [32]. P- and S-wave arrival times were determined in pairs at each station; the arrival times are clearly identified and recorded by at least four stations (Figure 3). The arrival times of a total of \(\sim\)17,628 P-waves and \(\sim\)17,628 S-waves were identified. The highest number of P- and S- phases were recorded in the southern part of the earthquake epicenter, while a smaller number of phases were recorded in the eastern part of Seram Island. The arrival times distribution for each station are shown in Figure 2, and its histogram in Figure S2.  
Figure 2: Arrival times distribution of P-and S-wave for all stations. Colored inverted triangles denote the number of phases per station.  
Figure 3: Example of the 22 November 2017 event waveform from a three-component seismogram (BHZ, BHN, and BHE) which was picked using SeisGram2K version 7.0 software [32]. The blue, green, and red colors from each station represent the vertical component (BHZ), the north–south component (BHN), and the east–west component (BHE). From top to bottom, the recorded seismograms are shown at the KMSI, TMSI, GTOI, SMSI, SANI, TNTI, LBMI, and TOLI2 stations. The vertical P and S lines show the picked arrival times for P- and S-waves, while Ts-Tp is the time difference between the arrival times of the S- and P-waves.
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 3 Implementation
### Cost of Hardware  
The total capital cost (see Table 3) is USD $504, which excludes installation and assembly costs. Solar panels and PWM solar controllers were ordered directly from the factory via an e-commerce platform. Operational costs are dependent on the use cases--whether users more targeted by SMS (see Table 4) or by email (see Table 5). The monthly plan of the Inmarsat BGAN network can accommodate 20 MB of data, while SMS sent by satellite terminals is charged. Each SMS sent by the prepaid SIM card will consume 0.5 units, and the prepaid SIM card will expire every three months.
## 3 System Design and Hardware Prototype  
This section provides an overview of SafeStride, a novel prototype designed as an add-on device for assistive walkers, as seen in Figure 1. SafeStride offers comprehensive fall detection, step counting, and activity tracking capabilities. The hardware design includes selecting components and their functionalities and the integration process.  
An important feature of SafeStride is its onboard machine learning capability, enabling the device to run machine learning algorithms directly in real time. Using data from the inertial measurement unit (IMU) sensor, SafeStride analyzes movement patterns to detect falls, steps, and other motions. As an IoT application, SafeStride can establish wireless connections with other devices, such as smartphones and web servers, enabling real-time alerts and intelligent monitoring. Moreover, healthcare professionals can access the data log to monitor a patient's recovery progress and provide personalized care.
# Design of Tracking, Telemetry, Command (TT&C) and Data Transmission Integrated Signal in TDD Mode
## 3 The Receiving Method of the Integrated Signal in the TDD Mode  
In this section, we will elaborate on the acquisition method used to obtain the TT&C results. However, considering that the tracking process of TT&C and the demodulation of 16 APSK have no difference from the traditional method, and will not be discussed here. Finally, the motion compensation method in the TDD mode is shown.
# Acoustic Parametric Signal Generation for Underwater Communication
## 1 Introduction
### Approach  
Firstly, this paper proposes a theoretical study in order to optimize the acoustic parameters of an underwater communication system in order to be able to evaluate its performance in terms of sound pressure level and signal-to-noise ratio according to the power and geometry of the acoustic source [7].  
Secondly, a study of measured signals is carried out by a plane emitter transducer, determining the relevant characteristics, such as amplitude and directivity of secondary beams with respect to the primary ones. In order to do this, an analysis of the measurements is performed by cross-correlating the emitted signal with the received one and thus obtaining the primary beam [8; 9]. To obtain the secondary beam or parametric signal, the received signal is filtered at low frequencies and correlated with the second derivative of the envelope squared of the emitted signal.  
Thirdly, the influence of noise in the received signal is studied. The secondary beam is analyzed to determine how the directivity and a proposed Relative Amplitude is affected by noise. All these studies are also interpreted regarding acoustic communication performance in terms of a rate of reconstruction of bits.
# Bridge Model Updating Based on Wavelet Neural Network and Wind-Driven Optimization
## 5 Finite Element Model Updating of Ningbo Bund Bridge
### Introduction of Ningbo Bund Bridge  
The Ningbo Bund Bridge is a cable-stayed bridge made of steel with a single tower and four cable planes, as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Respenses**} & **Network** & **Updated** & **Actual** & **Difference I** & **Difference II** \\  & **Value** & **Value** & **Value** & **(\%)** & **(\%)** \\ \hline \multirow{6}{*}{Natural frequency \(\mathrm{Hz}\)} & 1 st order & 13.415 & 13.274 & 13.295 & 0.90 & \(-\)0.16 \\  & 2 nd order & 17.174 & 16.996 & 17.040 & 0.78 & \(-\)0.26 \\ \cline{1-1}  & 3 rd order & 24.973 & 24.714 & 24.788 & 0.75 & \(-\)0.30 \\ \cline{1-1}  & 4 th order & 52.996 & 52.416 & 52.429 & 1.08 & \(-\)0.02 \\ \cline{1-1}  & 5 th order & 59.955 & 59.377 & 59.568 & 0.65 & \(-\)0.32 \\ \cline{1-1}  & 6 th order & 72.214 & 71.453 & 71.722 & 0.69 & \(-\)0.37 \\ \cline{1-1}  & C1 & \(-\)19.263 & \(-\)19.361 & \(-\)19.243 & 0.10 & 0.61 \\ \cline{1-1} Displacement & C2 & 11.367 & 11.468 & 11.589 & \(-\)1.91 & \(-\)1.04 \\ /mm & C3 & \(-\)20.082 & \(-\)20.305 & \(-\)20.926 & \(-\)4.03 & \(-\)2.97 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of structural responses.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Optimal Position of Air Particles** & **Corrected Value** \\ \hline \(E_{1}\)/\(\times 10^{4}\) MPa & \(-\)0.444 & 2.889 \\ \(E_{2}\)/\(\times 10^{4}\) MPa & \(-\)0.576 & 2.782 \\ \(E_{3}\)/\(\times 10^{4}\) MPa & \(-\)0.674 & 2.702 \\ \(\rho\)/\(\times 10^{3}\) kg/m\({}^{3}\) & 0.470 & 2.849 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The optimization results of WDO.  
The bridge's total length is 337 m, and the span arrangement is (\(225+82+30\)) m from west to east. The main girder of the bridge adopts a separate steel box girder, which is connected by the cross-beam. The main girder adopts a varying cross-section along the longitudinal direction. The maximum beam height is 2.4 m, designed as a two-way six-lane bridge, and the standard width of the one-way is 21.4 m.  
There are 64 stay cables in the bridge, including 46 main-span stay cables and 18 side-span stay cables. The stay cables are anchored on the head part of the front tower column and the inner and outer webs on both sides of the girders. The stay cables are anchored to the tower and inner and outer webs of the steel girder. The bridge tower adopts a triangular inclined tower structure composed of front tower columns, rear-inclined rods, horizontal rods, and other parts. The elevation of the bridge is shown in Figure 8.
## 2 Materials and Methods
### Leaf Segmentation
#### 2.3.2 Octree  
Use of the \(k\)-means algorithm helps in careful segmentation of the PCD clusters, but running the program across all PCDs has limitations of memory and time consumption. For these reasons, the leaf PCDs were spatially discretized into subspaces based on an octree structure [31] to reduce computational complexity and memory usage before running them through the \(k\)-means. The octree structure is an effective approach for assigning point clouds to uniform subspaces. On the basis of an octree structure, the leaf PCDs were split into eight subspaces in every iteration until the number of points was less than the specified number (Figure 3b). The number of points in each octree space unit was limited to 1500 in both the sampled species to maintain high computational efficiency for each batch processing of \(k\)-means in every octree unit space. Besides, an estimation of the number of leaves in an octree unit is helpful in deciding the initial \(k\)-value and reducing unnecessary computation in the \(k\)-means algorithm. This number was initially unknown, but the points of a single leaf were counted to approximately estimate the total number of leaves that might be included in an octree unit space. In our case, the average number of points in a single leaf was between 500 and 1500. Therefore, one octree unit space (Oi) was expected to contain 1 to 3 leaves.
# Molecular Mechanisms of Biofilm Formation in Helicobacter pylori
## 4 Mechanism of Biofilm Formation  
### Initiation of Adherence  
#### 4.1.1 Role of Adhesion Protein  
The biofilm formation process relies heavily on adherence, necessitating a stable foundation surface. In particular, an adhesion protein is key in attaching to the gastric epithelial surfaces and promoting biofilm formation. The _alpB_ gene is responsible for initiating the adherence of _H. pylori_, and when this gene is knocked out, there is a reduction in biofilm formation [25]. It is also reported that _cagE_ is involved in biofilm formation [26],and it is located on cag pathogenicity islands, supporting cagA translocation to gastric cells [27]. _cagE H. pylori_ adhesion to the AGS cell is less than the wild type, leading to lower biofilm formation [28]. Strains lacking other adhesion proteins, such as OipA and SabA, produce less biofilm.
# Cloud Security Using Fine-Grained Efficient Information Flow Tracking
## 1 Introduction
### Research Problem  
Cloud computing technologies used to supplement IT systems pose real security threats to consumers and providers. Securing data both at the local premises of the consumer and within the cloud services is one of the difficulties that the research community has yet to fully address [8]. Many methods, techniques, and technologies have been proposed to handle these threats. These approaches include: (i) Same Origin Policy (SOP) [9], (ii) Content Security Policy [10], (iii) Cross-Origin Resource Sharing (CORS) [11], and (iv) Sandboxing [12]. These approaches try to mitigate the effect of untrustworthy parties using the same cloud or web service by means of an all-or-nothing approach. The techniques restrict user access to the same service by controlling how access is granted. However, once permission is granted, users have all the privileges over the shared service (i.e., there are no fine-grained controls over user actions). Indisputably, this has created an issue of trust among Cloud Service Consumers (CSC) and has caused some to retreat from cloud usage, while those planning to adopt a cloud service strategy are reluctant.  
The conventional security measures embedded within cloud infrastructures, such as authentication, access control, and data privacy mechanisms, are foundational components. However, despite their presence, they often fall short of meeting the diverse and evolving security needs of cloud consumers [13]. These needs encompass various aspects, including stringent data protection requirements, comprehensive access management, and assurance of regulatory compliance. Specifically, conventional security measures struggle to adequately address concerns related to controlling how user information is processed to and from the cloud environment and within. Maintaining trust and ensuring the security of user data in the cloud is exigent. In this context, effective and efficient IFT emerges as a promising approach to address these challenges.
## 5 Reactive Protection Concept for Industrial Automation Systems
### Components of the Reactive Protection System
#### 5.2.1 Communication Module  
The _Communication Module_ provides the communication capabilities of the presented system. It receives security events from third-party components. These events are forwarded to the _Active Response Module_ for analysis. Provided this analysis results in the execution of an action that requires providing information to third-party components in order to disrupt or counteract an intrusion, this module also delivers the information required by such components. An example of this is the following. The _Active Response Module_ receives information of a Denial of Service (DoS) attack being carried out. After this information has been analyzed, it has been selected to add a new rule to a Firewall located on another device. Hence, the _Active Response Module_ provides the information required to add such rule to the _Communication Module_, which later forwards it to the corresponding device.  
This module is also capable of receiving Reactive Protection System-specific configuration information, which allows for remotely configuring it. This configuration information is comprised of security and operational policies, as well as information required to tune the reactive actions. This provides high configurability and interoperability as defined by _R1_ and _R4_ in Section 3.
## 2 Algorithms
### Attribute-Based Encryption  
When it comes to ABE, it has an access structure and Linear Secret Sharing Schemes (LSSS) [16]. The access structure stipulates who the eligible users are, and what the corresponding attributes should be. LSSS is an expressive monotone access structure used to generate the matrix of access structure for mathematical calculation. Because we designed it for resource-constrained devices with MQTT, we made use of the characteristics with topic names like in [6]. Figure 1 shows the access structure. We used MQTT topic names such as User1, WiFiDevice, Led, Temp, and Humidity.  
The Boolean formula of it is defined as  
\[\text{User1}\ \wedge\ \text{WiFiDevice}\ \wedge\ (\text{Led}\ \vee\ \text{Temp}\ \vee\ \text{Humidity}). \tag{3}\]  
As long as the access structure is constructed by the data owner, it can be transformed to an LSSS matrix according to the method. Our matrix was generated as below:  
\[N=\left[\begin{array}{ccc}0&-1&0\\ 0&0&-1\\ 1&1&1\\ 1&1&1\\ 1&1&1\end{array}\right] \tag{4}\]  
Each row of \(N\) is associated with the attributes User1, WiFiDevice, Led, Temp, and Humidity, respectively. Given an attribute set \(S\), the LSSS is said to be satisfied by \(S\) only if the rows of the \(N\) labeled by the attributes in \(S\) include the vector (1, 0, 0) in their span.  
The research presented in [11] is different from the other ABE algorithm, as its LSSS matrix has no polynomials, which can reduce some computation. Second, replacing the bilinear map with simple scalar multiplication by ECC also reduces some computation.  
PF-CP-ABE generally consists of four parts: Set up, Key generation, Encryption, and Decryption [13]. In Set up, the authority selects its main public keys and private key, and everyone should agree on the same elliptic curve. Key generation is employed to generate a key of every attribute for each user and store them in a corresponding user list. In the Encryption part, the plain text is ciphered by an access structure and corresponding attributes. Then, in the Decryption part, the data user should ask the authority to verify the attributes they possess, and requires a result to correctly calculate the secret number and then map the ciphertext back to the plain text. The algorithm will be described in detail in Section 3.
## 4 CLAP Correlation and Information Sharing Services
### Calculation of Alarm Severity  
Each detected event poses a particular risk to the infrastructure. We measure quantitatively the risk severity in a scale from 0 to 10, where 0 refers to the lowest severity and 10 refers to the highest severity. The proposed Cross-layer Data Analytic platform computes a severity score of each correlated alarm based on Equation (1).  
\[Severity=\frac{Priority\times Reliability\times Assel_{Value}}{100} \tag{1}\]  
The priority parameter represents the importance of the attack if it is realized. It ranges from zero to ten (as the minimum and maximum priority values respectively). A priority value is assigned when the type of event is defined in the monitoring engine for a specific sensor or specific security directives or rules, but it can be modified using a policy to prioritize events in specific situations or conditions.  
The reliability parameter indicates the probability for an attack to be successfully executed. It ranges from zero to ten (as the minimum and maximum reliability values respectively). Like priority, this value is assigned based on expert knowledge for a specific  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Name** & **EPL Statement** \\ \hline Failed\_SSH\_login & pattern [every-distinct (a.src\_ip, 60 s) a = FailedSSHLogin- \\  & Event \(\longrightarrow\) [6] b = FailedSSHLoginEvent ((b.src\_ip = a.src\_ip) and (b.dst\_ip = a.dst\_ip))] \\ \hline Jammer Detector & pattern [every a = jammer\_detector (a.userdata2 = “Attack Running”)] \\ \hline Abnormal observation & pattern [every-distinct (a.src\_ip, 60 s) a = abnormal\_obs] \\ on DST\_IP & \\ \hline Unauthorized PLC & pattern [every-distinct (a.userdata1,a.plugin\_sid, 60 s) a = Unauthorized\_write\_PLC] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Examples of EPL Directives.  
security directive or rule. This value is generally low if the event is detected by only one data source, and it increases if the same event is detected by multiple data sources and become a correlated alarm. The higher the number of data sources, the higher the reliability of the information and thus, the higher the severity level.  
The asset value parameter represents a value associated with the importance of the host being attacked in the infrastructure monitored. It ranges from zero to ten (as the minimum and maximum importance asset values respectively).  
The computed severity indicates the potential impact associated with a correlated alarm. It is defined to help security analysts in prioritizing their mitigation actions. Correlated cross-layer events imply higher severity than correlated singer-layer events, as the former provides a wider vision of the incident with a higher reliability that the latter.