esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive and Blind Audio Watermarking Algorithm Based on Chaotic Encryption in Hybrid Domain
## 3 Detailed Implementation Steps  
The embedding and extracting principle of this proposed audio watermarking technology are described in Section 2. The framework diagram of this algorithm is shown in Figure 3.  
Two sets of data packets are obtained after the original audio media are carried out by DWT and DCT, and then a 1 bit encrypted watermark is hidden into the audio medium according to Formulas (14) and (15). When it is necessary to extract the hidden watermark in the audio media, two sets of data packets are obtained by executing DWT and DCT in the audio media, and the watermarks can be extracted by comparing the average magnitudes of this two packets according to Formula (23). The following steps are given for the implementation of this algorithm, including embedding watermark and extracting watermark.  
Figure 2: Performance comparison at different embedding depth of five groups: (a) the signal-to-noise ratio (SNR) comparison; (b) the BER comparison.
# A Virtual Reality Soldier Simulator with Body Area Networks for Team Training
## 2 System Description
### Sensor Modeling  
This subsection the sensors are modeled in order to formulate the orientation estimation problem. The measurement models for the gyroscope, accelerometer, and magnetometer are briefly discussed in the following subsections [27].  
Figure 4: Deployment of sensor nodes and the sink node.  
Figure 3: Top view of sensor nodes (**Left**); the wearable sensor node (**Right**).  
_(1) Gyroscope:_ The measured gyroscope signal \({}^{s}\omega_{t}^{m}\) can be represented in the sensor frame \(s\) at time \(t\) using  
\[{}^{s}\omega_{t}^{m}={}^{s}\omega_{t}+b_{\omega,\ t}+e_{\omega,\ t} \tag{1}\]  
where \({}^{s}\omega_{t}\) is the true angular velocity, \(b_{\omega,\ t}\) is the bias caused by low-frequency offset fluctuations, and the measurement error \(e_{\omega,\ t}\) is assumed to be zero-mean white noise. As shown in Figure 5, the raw data of the measured gyroscope signals in the x, y and z directions includes true angular velocity, bias and error.  
_(2) Accelerometer:_ Similar to the gyroscope signal, the measured accelerometer signal \({}^{s}a_{t}^{m}\) can be represented using  
\[{}^{s}a_{t}^{m}\approx{}^{s}a_{b,t}+b_{a,\ t}+e_{a,\ t} \tag{2}\]  
where \({}^{s}a_{b,\ t}\) is the linear acceleration of the body after gravity compensation, \(b_{a,\ t}\) is the bias caused by low-frequency offset fluctuations, and the measurement error \(e_{a,\ t}\) is assumed to be zero-mean white noise. As shown in Figure 6, the raw data of the measured accelerometer signals in the x, y and z directions includes true linear acceleration, offset and error.  
Figure 5: Mean and variance of the gyroscope in the x, y and z directions.  
Figure 6: Mean and variance of the accelerometer in the x, y and z directions.  
_(3) Magnetometer:_ For the magnetometer, the measured signal \({}^{s}m^{m}_{t}\) is often modeled as the sum of the earth's magnetic field \({}^{s}m_{t}\), magnetic offset \(b_{m,\,t}\), and noise \(e_{m,\,t}\), which yields  
\[{}^{s}m^{m}_{t}\approx{}^{s}m_{t}+b_{m,\,t}+e_{m,\,t} \tag{3}\]  
As shown in Figure 7, the raw data of the measured magnetometer signals in the x, y and z directions includes the earth's magnetic field, offset and error.
# Autonomous Load Regulation Based Energy Balanced Routing in Reshargeable Wireless Sensor Networks
## 6 Performance Evaluation
### Analysis of Impact of Load Regulation Accuracy on Network Lifetime  
In order to determine the parameter values to be used in the simulation, we ran the simulator for different values of \(\gamma\) starting from zero to one with a step of 0.1 and \(P=0.3W\). Without loss of generality, we conducted 10 simulations and selected the average of the experimental results for analysis, and then estimated the network lifetime. The results are shown in Figure 3.  
The network lifetime first increases and then decreases along with increasing of \(\gamma\) and the maximum is reached when \(\gamma=0.4\) in all cases. This is because the intensity of balancing the load becomes weaker as \(\gamma\) increases in [0.4, 1] and the load deviation of nodes is larger; therefore, the nodes with high loads are prone to premature death. In addition, the load difference of nodes is inevitable, so  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Value** \\ \hline Node number & 100–250 \\ Packet size & 2000 bits/packet \\ \(R\) & 3 m \\ \(D\) & 30 m \\ \(E_{0}\) & 0.5 J \\ \(T\) & 600 s \\ \(S\) & 10 m/s \\ \(P\) & 0–0.5 W \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 3: Network lifetime with different values of \(\gamma\) for different node numbers.  
the algorithm should permit a small difference among loads. If the value of \(\gamma\) is too small, some nodes may contract their relay radius excessively in order to bring their load below the mean. Many nodes cannot find an available forward neighbor. They can only determine the next hop according to the residual energy--these communication links are not optimal, so this would reduce the network lifetime. Thus, the network lifetime reaches its maximum when \(\gamma=0.4\).
# Demand Forecasting Model for Airline Flights Based on Historical Passenger Flow Data
## 2 Materials and Methods  
The principal challenge inherent in medium-term demand forecasting is the necessity to consider a multitude of intricate and interrelated variables, including country-level economic fluctuations [14; 15], changes in consumer preferences [16], airport transport accessibility, and the competitive environment. Concurrently, demand estimation models must be capable of integrating various data types, including historical demand data, economic indicators, airfare data, socio-economic indicators (income and population), and other external factors [17].  
In order to ensure the functionality of a demand model, it is essential to consider fluctuations in time and seasonal trends, the flexibility and adaptability of the models themselves, and the capacity to adjust forecasts in response to incoming data regarding changes in the external environment. The highlighted functional requirements ensure the development of efficient and adaptive flight demand forecasting models and should be taken into account in the development of the model proposed in the study.
## 3 Methodology and Experimental Set-Up
### SIDS and Ruleset Configurations Included in the Study  
We have selected three different SIDS able to detect web attacks: _InspectorLog_[18] (a tool developed by the authors that applies rules in Snort format to HTTP traces), _ModSecurity_[38] (a widely adopted Web Application Filter (WAF) that uses OWASP rules, and Nemesida (the free version of the commercial WAF) [45] that uses its own repository of signatures.  
_ModSecurity_ offers four predefined configurations termed Pananoia Level (PL) that range from 1 to 4. Each of these configurations selects a different subset of the full ruleset. In this study, we will use PL1 (the least sensitive), PL2 (medium sensitivity) and PL4 (the most sensitive). In the case of _InspectorLog_, we first created a baseline ruleset from the original Talos ruleset by filtering rules not related to web attacks. Then, we defined the following configurations or subsets of rules: Default (activate only recommended rules, less sensitive), All but DELETED (activate all rules but those marked as DELETED, medium sensitivity), All (activate the full ruleset, most sensitive). Nemesida WAF in its free trial version does not offer user-defined configurations so we used the default one (according to their website, the commercial versions include extra features that reduce FP from 3% (Free version) to 0.01% and increase the detection accuracy by 30%). Table 1 summarizes the main characteristics of the SIDS used in this study.
# Dispersive FSO Performance Estimation with Gaussian Pulses and Laplace Modeled Time Jitter
## 5 Conclusions  
In this study, the availability and the reliability of FSO links was studied by assuming the propagation of longitudinal optical Gaussian pulses as information carriers in the dispersive atmosphere under the joint influence of chromatic dispersion and the Laplace-distributed TJ. Considering these effects, new closed-form mathematical expressions were derived for the accurate estimation of the outage probability and the average BER of each  
Figure 3: Average BER as a function of the average SNR with various original pulsewidths, TJ strength parameters, and opposite chirp values for an FSO link of (**a**) \(z=5\) km and (**b**) \(z=10\) km.  
Figure 2: Outage probability as a function of the normalized average SNR with various original pulsewidths, TJ strength parameters, and opposite chirp values for an FSO link of (**a**) \(z=5\) km and (**b**) \(z=10\) km.  
specific FSO link. Using the derived mathematical expressions and typical parameter values, the corresponding indicative outcomes were plotted and the influence of each of the abovementioned effects was investigated. Furthermore, it should be mentioned that many other propagation effects, such as atmospheric turbulence, could be studied jointly to achieve more accurate outcomes.  
Conceptualization, P.J.G., D.O. and H.E.N.; methodology, P.J.G. and H.E.N.; software, P.J.G., E.K. and H.E.N.; validation, P.J.G., D.O., D.K., A.D.T. and H.E.N.; formal analysis, P.J.G., D.K. and H.E.N.; investigation, P.J.G., D.O., A.K. and H.E.N.; resources, P.J.G. and H.E.N.; writing--original draft preparation, P.J.G. and H.E.N.; writing--review and editing, P.J.G., A.D.T., E.K., A.K. and H.E.N.; supervision, P.J.G., A.D.T., A.K. and H.E.N.; project administration, A.D.T. and H.E.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 2 Study Area and Data  
### Study Area  
Yuecheng Reservoir is located in Ci County, Handan City, Hebei Province, China. It is a national super-large reservoir that serves as an urban water supply; it integrates flood control, irrigation, power generation, and other functions. It is an important water source for Handan City, Hebei Province, and Anyang City, Henan Province, with a storage capacity of nearly 1.3 billion m\({}^{3}\). The control catchment area of the Yuecheng Reservoir (i.e., the study area) is 18,072 km\({}^{2}\) and is located at 112\({}^{\circ}\)26\({}^{\prime}\)-114\({}^{\circ}\)14\({}^{\prime}\)E and 35\({}^{\circ}\)52\({}^{\prime}\)-37\({}^{\circ}\)36\({}^{\prime}\)N. Although Yuecheng Reservoir is in Hebei Province, most of the catchment area of the Yuecheng Reservoir is in Shanxi Province, as shown in Figure 1. The Yuecheng Reservoir catchment is located in the southeast of the Loess Plateau with complex terrain and high average altitude which is above 1000 m.  
The Yuecheng Reservoir catchment area is dominated by agricultural production. The main crop types include maize, wheat, millet, soybean, and sorghum; maize is the main crop in summer and wheat is the main crop in winter. The multiple cropping index in the  
Figure 1: Location of the study area (Yuecheng Reservoir catchment). The red star in the southeast of the left side is Yuecheng reservoir. Yuecheng Reservoir is located in Hebei Province but most of its catchment area (the study area) is located in Shanxi Province. Land-cover classification of the study area obtained from Global Land Cover (GLC) 2017 shows that cultivated land covers a vast area and is an important type of land use.  
study area usually fluctuates between 0 and 2; the crop rotation mode is mostly winter wheat/summer maize. The terrain of the study area is complex, and the central part is penetrated by the main vein of Taihang Mountain from north to south. The soil type of the study area is mainly loam, with a small amount of sand. The study area is divided into east and west areas, each with their own typical characteristics. The eastern region comprises mostly rocky mountainous areas with high mountains and deep valleys. With the warm and humid air flow from the southeast, heavy rains are easily produced under the action of surface lifting. Additionally, the underlying surface has good production and confluence conditions, which leads to large floods in this area; thus, NSP also flows into the major rivers during periods of flooding, causing water pollution. The western region is surrounded by mountains, the central part is a basin, and the southern part contains limestone areas with developed fissures and serious leakage. Other marginal areas in the western region are rock mountainous areas with steep terrain. These areas experience extensive water and soil loss, and they are the main sources of sediment runoff in the Yuecheng Reservoir catchment area [51]. The complex cultivated land situation, coupled with the influences of the terrain and climate, has created NSP that has considerably influenced the water quality of the Yuecheng Reservoir.
###### Abstract
is well established for many conventional inorganic anti-corrosion pigments, unwanted side effects may arise when new inhibitors, particularly organic ones, are introduced in the coating formulations. These unwanted interactions may induce changes in the polymer matrix, weakening its barrier properties. Moreover, it is of paramount importance to avoid changes in the chemical nature of the inhibitor (e.g., due to side reactions with species present in the coating formulation) and to control early inhibitor de-activation or leaching from the coating. To minimize these effects, one possible route is the storage of the corrosion inhibitors in inorganic carriers with good compatibility with the coating formulation. These carriers are expected to sense certain stimuli in the coating or steel coating interface and use them to control the inhibitor delivery [22]. Molecules serving as organic inhibitors usually contain groups with nitrogen, oxygen, or sulfur, and therefore \(\pi\) electrons that play a key role in the adsorption of the inhibitors onto the metal surface where they can form protective layers that restrict the flow of aggressive species. The effectiveness of corrosion inhibition depends on several factors such as the condition of the metal surface, nature of the aggressive electrolyte, inhibitor concentration, and chemical nature of the inhibitor [13; 23]. In the present work, a new anticorrosive system for carbon steel is proposed. The protective system consists of a polyolefin-based coating loaded with pH-sensitive hydroxyapatite (HAP) particles serving as carriers for tannic acid. The use of pH-sensitive HAP particles has a twofold role: on the one hand they dissolve slowly at a slightly acidic pH, releasing the tannic acid from the loaded carrier, and, on the other hand, they provide a source of soluble phosphate anions that contribute to forming protective phosphate-rich iron compounds. The dissolution is enabled by the local pH acidification that occurs at anodic sites due to hydrolysis of the iron cations [24]. Another important advantage is the low price of HAP particles that are also simple to prepare, non-toxic, and easily loaded with different organic corrosion inhibitors as reported elsewhere [25]. Tannic acid is a well-known corrosion inhibitor that can be derived from bio-renewable resources. This inhibitor has demonstrated interesting anti-corrosion properties when used as an additive in different coatings such as polyurethane [26; 27]. Thus, in this work, the novel approach consists of the modification of HAP with tannic acid (an eco-friendly corrosion inhibitor) and its incorporation into a polyolefin formulation to enhance the corrosion protection performance. The anti-corrosive performance of coated steel samples with and without artificial defects was studied in NaCl electrolytes using electrochemical impedance spectroscopy (EIS). The results demonstrate that the presence of HAP particles loaded with tannic acid in the coating delays the propagation of corrosion and, in the longer term, can provide effective corrosion protection.
## 2 Materials and Methods
At this stage of the methodology, a prototype of the user interface (UI) is created to determine the appearance (view) of the software from the user's perspective: - Design of the main view, in which the processes of coding, error detection, and correction will be visualized; - Navigation and accessibility--the structure of the interface should be simple and easy to understand so that users can be orientated quickly and start working; - Interactive elements, such as data entry fields, buttons for starting simulations, data visualizations, and feedback on correct or incorrect decisions; - Color scheme and design elements that make the interface attractive and intuitive, without unnecessary complexity, especially for younger users or beginners in the field. - Selection of a programming language and technologies (e.g., PHP, JavaScript, HTML/CSS, if it is a web-based application), tailored to the needs of the software and technical requirements;
- Implementation of the basic algorithms necessary for working with the Hamming code--calculating the check bits, detecting errors, correcting them, and providing the result;
- Development of user interaction modules, such as visualizing the error-correction process and providing data entry capabilities;
- Integration of the algorithms and logic with the user interface to ensure smooth and functional interaction. (6) Test. Testing is a critical stage that ensures the quality and reliability of the software:
- Functional testing--checking that all the functionalities set out in the requirements work correctly. This includes:
- Checking the algorithms for encoding and decoding, and whether they successfully detect and correct errors;
- User interface tests for interactivity and ease of use;
- Error testing and debugging--identifying and fixing errors in the code that can cause crashes or incorrect operation of the software;
- Performance and security tests--measuring the execution time of basic operations and ensuring the security of user data. (7) Integration. The final stage is the integration of the software tool into a common virtual laboratory:
- Integrating the tool with other educational software in the laboratory, if any, which may include a common user interface or database;
- Creating easy access to the tool for users, for example through a centralized platform or web portal;
- Testing the integration in a real learning environment to ensure that all components work together properly and interact seamlessly;
- Post-integration support and monitoring to ensure that the tool continues to work properly in real-world conditions and is updated as needed. Designing an Interactive Software Model for Solving Problems with the \"Hamming Code--General Method\"
# A Secure Auditable Remote Registry Pattern for IoT Systems
## 4 The Secure Auditable Registry Based On Message-Oriented Middleware (SARMOM) Pattern
### Known attacks  
The threats above are not just theoretical risks but have actually materialized as attacks on several IoT systems. A cyber attack targeting a critical infrastructure can be catastrophic or even fatal. This section mentions a few recent attacks that were related to the \(\mathrm{SARMOM}\) pattern, to help the reader understand the pattern better [43; 44].  
* The Triton attack (2017) was an attack on industrial cyber security systems, in particular, on a Schneider's Triconex safety instrument that was injected with malicious code that could eventually led to the release of toxic gas, which would have been fatal to human life. \(\mathrm{SARMOM}\) would have contributed to preventing this attack by means of the minimization of data exchange between components of the SUD.
* Data leakage from a North American casino (2017). In this case, attackers gained access to a casino system via a high-tech \"smart\" fish tank featuring internet connectivity for remote management (temperature, salinity and automated feedings). Although quickly detected, this attack succeeded at retrieving up to 10 gigabytes of data, including the casino's database of high rollers. \(\mathrm{SARMOM}\) would have contributed to preventing this attack by means of the isolation and centralization of data exchange between components of the SUD.
* The Oldsmar cyberattack on the US water system (2021) targeted the water treatment system of Oldsmar, Florida, gaining access to the control process of the treatment system. The attackers were successful in increasing the sodium hydroxide content in the water systems to toxic levels. However, a human operator was alert and immediately reduced the toxin levels in the water system. \(\mathrm{SARMOM}\) would have prevented this attack by means of the trusted event sourcing and by minimization of data exchange between the components of the SUD. Moreover, monitoring capabilities enabled by \(\mathrm{SARMOM}\) would have made it possible to detect the problem and stop the attackers before they could do harm.
* In the Colonial Pipeline cyberattack (2021), the control system of a pipeline carrying jet fuel and gasoline in the US was attacked by ransomware, and the infrastructure was down for nearly six days before service restarted. Although this attack focused on the billing infrastructure of the company, it brought down the entire oil pipeline as they could not issue customers with appropriate bills, affecting the entire east coast of the US. \(\mathrm{SARMOM}\) would have made this attack much less severe, by means of the independence of the SUD components, which would have allowed it to contain the effects of the attack in the affected subsystem and would have allowed the system to continue in operation, even if producing bills was not possible, because consumption data would have remained safely stored in the registry for later processing.