esa-sceva/satcom-chunk-collection huggingface dataset:
## Abbreviations  
M-estimators Maximum-likelihood estimators  
LMedS Least Median of Squares  
L-estimators Linear combination of order statistics  
R-estimators Estimates based on rank transformations  
MSE Mean square error  
DNN Deep neural networks  
DL Deep Learning  
BP Backpropagation  
RMSE Root Mean square error  
MAPE Mean absolute percentage of error  
LTS Least Trimmed Squares  
LMLS Least mean log square  
QoS Quality of service  
ITS Intelligent Transportation Systems  
AI Artificial Intelligence  
IoT Internet of Things  
ML Machine Learning  
ANN Artificial Neural Networks  
MFNNs Multilayer feedforward neural networks  
V2X Vehicle to Everything  
WANET Wireless ad hoc network  
MANET Mobile ad hoc network  
VANETs Flying ad hoc networks  
LMLS The least mean log squares  
Traincgf Conjugate gradient backpropagation with Fletcher-Reeves updates  
SLA Service level agreement  
NSP Network service provider
# RoseCliff Algorithm: Making Passwords Dynamic
## 2 Encryption
### Hybrid Encryption  
Hybrid encryption combines the strengths of symmetric and asymmetric encryption, thus striking a balance between security and efficiency. In this method, two parties establish a secure communication channel for key exchange, typically using asymmetric encryption. They exchange a shared secret key, which then facilitates a switch to efficient symmetric encryption for data encryption and decryption. Symmetric encryption algorithms, such as AES, are used for the actual data protection, and the shared secret key remains confidential. This approach ensures secure and efficient data transmission and storage, thereby overcoming the limitations of pure symmetric or asymmetric encryption methods [19]. Hybrid encryption is crucial in modern cryptography for several reasons. Researchers have employed hybrid models fusing encryption algorithms or hashing with honey encryption to create a two-layer protection mechanism. This approach optimizes security and efficiency by harnessing the strengths of both encryption paradigms and scales effectively for varying data sizes. Additionally, it ensures perfect forward secrecy, thus bolstering security with new symmetric keys generated for each session. Hybrid encryption promotes compatibility across systems and devices, thus offering resilience against emerging threats, including those from quantum computing. In summary, it is a versatile cryptographic technique playing a crucial role in efficiently securing data across a broad range of applications [28; 29; 30].
## 2 Materials and Methods
### Process Design
#### 2.2.1 Fabrication Method
The two conductive fabric patches tightly adhered to the back of the sensing area can be equated to switches K\({}_{1}\), K\({}_{2}\) due to their very small surface resistance (\(\leq\)0.05 \(\Omega\)). The structural design of the fabric's sensing area is shown in Figure 3. These two sensing areas are essential for touch interaction on the fabric. When the user presses the sensing area on the fabric's surface, it causes the conductive fabric patches on the backside to come into contact with the conductive yarn in the back layer, resulting in a significant change in the resistance value of the conductive region (Figure 3c). In conjunction with Figure 2d, the user pressing the sensing area is equivalent to closing the switch K\({}_{1}\) or K\({}_{2}\), causing a portion of the conductive yarn's resistance to be shorted, which ultimately affects the resistance value of the conductive region. In this way, users do not directly touch the conductive yarn, effectively avoiding interference from human body resistance and grounding conditions on the electrical signal changes during the interaction process. To prevent the conductive fabric patches from making contact with the conductive yarn before interaction, an insulated flexible support material can be added between the face and back layers (Figure 3b). Figure 1: Looming drafting of the interchanging double weave (1 and 2 represent the face warp and weft and I and II represent the back warp and weft, marked boxes in the weave diagram represent warp floating-point and blank boxes represent weft floating-point): (**a**) Pre-interchange; (**b**) post-interchange.
## 2 Materials and Methods
### Preliminary Tests  
#### 2.3.1 Ordinary Concrete Specimens  
In this preliminary test, the designed operating system was verified by monitoring the behavior of concrete under stable temperature conditions, with humidity being monitored but not actively controlled due to minimal climate variation. For this purpose, humidity and temperature sensors were implanted into two cylindrical specimens of conventional concrete, made with ordinary Portland cement. These specimens had a diameter of 15 cm and a height of 30 cm, in accordance with the specifications of the UNE-EN 12390-1:2021 standard [41]. Furthermore, the specimens were manufactured following the proceduresdescribed in UNE-EN 12390-2:2019 [42]. During the test period, the specimens were placed in a stable environment with regulated climate control. The laboratory conditions were maintained at a constant temperature of 22 \(\pm\) 1 \({}^{\circ}\)C and relative humidity of 55 \(\pm\) 5%, under standard climate-controlled conditions. The mix design included CEM 1 42.5 R Portland cement, a water-to-cement ratio of 0.50, natural sand (0-4 mm), and gravel (4-12 mm), targeting a compressive strength of 30 MPa.  
Three temperature sensors and three humidity sensors were used in each specimen. The temperature sensors have been placed at different depths in each specimen to observe data variations based on depth. Due to the fact that the humidity sensors consist of two parts--a probe that can be embedded in the concrete and a control module that cannot--the sensors have been placed near the surface of both specimens, ensuring the probe is embedded in the concrete while the control module remains accessible to the air. The sensor locations are indicated in Figure 2.  
Figure 1: Components of the data collection system.  
Figure 2: Placement of sensors in the cylindrical test specimens. Units in millimeters.  
For specimen A, three pairs of sensors were placed equidistant from the edge of the specimen, following a uniform distribution across its entire surface. All sensors were located near the surface of the specimen.  
For specimen B, three pairs of sensors were inserted equidistantly along the edge of the specimen, following a similar uniform distribution as specimen A. However, unlike specimen A, in specimen B, the three temperature sensors were placed at a greater depth, reaching the core of the specimen at a depth of approximately 15 cm.  
The nomenclature of the sensors shown in Figure 2 uses the following designations in sequential order:  
* T: Temperature sensor;
* H: Humidity sensor;
* S: Surface;
* D: Depth;
* A: Specim A;
* B: Specim B.  
The use of multiple sensors is carried out to ensure the accuracy of the data collected in the same area and to be able to calculate an average between them.
# Possibility of Bridge Inspection through Drive-By Vehicles
## 4 Drive-By Inspection through 3D Virtual Simulations
### Bridge Frequencies Detection through Vehicle Responses  
When vehicle speed decreases to 1 m/s, the FFT results of the vehicle front body accelerations can be used to identify frequencies of the truck and the bridge.  
Using the vehicle vertical acceleration data collected from the front tire, bridge frequency values can be identified, shown in Figure 15 and Table 5.  
Figure 14: The mid-span displacement time history of the first span when the truck passes at 32.5 m/s.  
Figure 15: Identified frequencies using the front tire acceleration time history.  
Table 5 shows that most bridge frequencies can be identified through the FFT results of the vehicle's vertical acceleration.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 3 Mobile Ad Hoc Networks
### Routing in MANETs  
Similar to traditional routing protocols, MANET routing aims to establish and maintain optimal paths between source and destination nodes. This process prioritizes minimizing delay and packet loss. However, MANET routing protocols face unique challenges compared to traditional networks due to several factors [13; 14]:  
* Decentralized network: Unlike centralized networks, there is no single entity managing routing in a MANET. Each node acts independently.
* Limited resources: Nodes in a MANET often have limited battery power and processing capabilities. Routing protocols need to be efficient to minimize resource consumption.
* Control overhead: Routing protocols generate control packets to discover and maintain routes. Excessive control overhead can lead to network congestion.
* High node mobility: As nodes move, the network topology constantly changes. Routing protocols need to adapt quickly to these changes and incorporate new network discovery mechanisms to locate nodes entering or leaving the network.  
There are three main categories of topology-based routing protocols classified based on their approach to acquiring routing information for packet forwarding: proactive, reactive, and hybrid. A selection of example protocols can be seen in Figure 2[15; 16].  
Figure 1: Ad hoc infrastructure comparison.
# Thermoelastic Investigation of Carbon-Fiber-Reinforced Composites Using a Drop-Weight Impact Test
###### Abstract  
Composite materials are becoming more popular in technological applications due to the significant weight savings and strength offered by these materials compared to metallic materials. In many of these practical situations, the structures suffer from drop-impact loads. Materials and structures significantly change their behavior when submitted to impact loading conditions compared to quasi-static loading. The present work is devoted to investigating the thermal process in carbon-fiber-reinforced polymers (CFRP) subjected to a drop test. A novel drop-weight impact test experiment is performed to evaluate parameters specific to 3D composite materials. A strain gauge rosette and infrared thermography are employed to record the kinematic and thermal fields on the composites' surfaces. This technique is nondestructive and offers an extensive full-field investigation of a material's response. The combination of strain and infrared thermography data allows a comprehensive analysis of thermoplastic effects in CFRP when subjected to impacts. The experimental results are validated using numerical analysis by developing a MATLAB(r) code to analyze whether the coupled heat and wave equation phenomenon exists in a two-dimensional polar coordinate system by discretizing through a forward-time central-space (FTCS) finite-difference method (FDM). The results show the coupling has no significant impact as the waves generated due to impact disappears in 0.015 s. In contrast, heat diffusion happens for over a one-second period. This study demonstrates that the heat equation alone governs the CFRP heat flow process, and the thermoelastic effect is negligible for the specific drop-weight impact load.  
CFRP; drop test; strain; infrared thermography; NDT; thermoelastic effects; impacts; numerical analysis; FDM +
Footnote †: journal: Applied Sciences
# Additive Manufacturing in Underwater Applications
## 4 Technologies
### 3D Printing Technologies  
#### 4.1.1 Technologies Based on Solid Filaments  
This group is the most widely used technique for 3D printing. Among the particular technologies in this category are fused deposition modeling (FDM), laminated object manufacturing (LOM), and wire and arc additive manufacturing (WAAM). In the case of underwater applications, some investigations have been performed with FDM technology. They prove the possibility of its use underwater for polymer printing [36]. However, these trials have no scientific nature; they only show the potential of this solution for further research. FDM and other technologies based on paste extrusion have also been investigated for artificial reef manufacturing and the creation of marine infrastructure in situ [35].  
Another trial was performed on the development of systems for cementitious materials. This technology was primarily connected with a robot arm to print concrete materials underwater, layer by layer. This makes it possible to transform design drawings directly into real infrastructure elements [21; 26; 27]. Currently, the devices dedicated to the underwater research of cementitious materials are most often located in the air and only a nozzle works underwater (Figure 2).  
This technical solution applied to the device means that the main works connected with improved 3D printing for underwater applications focus on nozzle construction [30]. The presented solution (Figure 2) is built upon conventional technology for the 3D printing of concrete materials, which is based on paste extrusion by a nozzle. In the first step, suitable ingredients are premixed and then pumped through the hose to the hopper. From the hopper, the material is delivered to the nozzle, which is located in an underwater environment (water tank). After leaving the nozzle, the material comes into direct contact with the aqueous environment, where the layers are joined to obtain the appropriate shape of the printed element. The material is cured in the water [29].
## 3 Results
### Effect on Specific Energy  
#### 3.4.1 Effect of Penetration Depth on Specific Energy  
The cutting depth directly affects the cutting performance and efficiency of excavation machines. Regardless of the type of cutting tool, the specific energy decreases exponentially or as a power function as the penetration depth increases [10; 19; 30; 32; 39; 40]. [PERSON] et al. [14] stated that the specific energy in unelieved mode cutting does not change effectively when the penetration depth is greater than 9-10 mm. [PERSON] et al. [19] reported that the power value is not related to the type of rock in the power function relationship between the specific energy and penetration depth.  
Figure 17 shows the relationships between the specific energy and penetration depth obtained from cutting using the pre-cutting machine cutting tool. At the same penetration depth, the greater the rock strength, the greater the specific energy. In addition, in all the rock specimens, the specific energy and penetration depth formed a power-function relationship. These results show that the effect of penetration depth on the specific energy of the pre-cutting machine is the same for other excavation machines. The details of the specific energy according to the penetration depth are listed in Table 8.
# A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning
## 2 Materials and Methods
### Model Evaluation and Data Production  
The modelling performance was evaluated by the year-to-year and station-to-station validations. In the year-to-year validation, the data from 1999 to 2017 in each station were used as a training dataset, and to evaluate the accuracy of the models on a temporal scale, the generated carbon fluxes by three models were compared with the observed carbon flux towers data in 2018. In the station-to-station validation, randomly selected data from 90% of the stations from 1999 to 2018 were applied for training, and the data from the rest stations were used to evaluate accuracy of the three models on a spatial scale. This process was repeated 10 times in order to make use of every station to evaluate the models. Finally, we computed the averaged results of the above 10 evaluations.  
In this study, the coefficient of determination (R\({}^{2}\)), root mean square error (RMSE), and bias were used to evaluate modelling performance as:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}}{\sum_{i=1}^{n}(y_{i}-y)^{ 2}}, \tag{1}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}}{n}}, \tag{2}\]  
\[bias=\hat{y_{i}}-y_{i} \tag{3}\]  
where \(y_{i}\) and \(\hat{y_{i}}\) are the observed and predicted values, respectively, and \(y\) is the averaged value of observations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Search Range** & **Experiment** \\ \hline RF No. of trees & 100–900 & 500 \\ \hline RF minimum samples at a leaf node & 1–10 & 5 \\ \hline ANN No. of dense layers & 1–5 & 3 \\ \hline ANN No. of neurons & 10–90 & 90 \\ \hline CNN No. of dense layers & 1–5 & 3 \\ \hline CNN No. of neurons & 10–90 & 70 \\ \hline ANN and CNN learning rate & 0.1, 0.01, 0.001 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The simulation setting.  
Due to the evaluation of three models (see Sections 3.1 and 3.2), the CNN model was chosen to produce the global dataset (GCFD). GCFD is provided with two resolutions, i.e., 30 arc second (\(\sim\)1 km) and 0.1 degree (\(\sim\)9 km) 3 times per month, covering January 1999 to June 2020, which is in accordance with the CGLS version 2 dataset used as predictors. The three carbon fluxes were set to zero for the LAI values less than 0.1.
## 5 3D Simulations
The path for the manipulator is calculated in 117 ms, which is surprisingly short for a 3D environment. The manipulator reaches the goal in 14,623 ms. A simulation of the example is given without auxiliary elements in Video S1 and with auxiliary elements in Video S2. The number of the beams affects navigation time. When \(d_{p}\) value is taken as 8 pixels, the number of the beams becomes 13,128. The potential field is calculated in 13,280 ms with 600 iterations. After having the path in the middle, calculations of the start and end coordinates of the beams are completed in 32,500 ms, the path for the manipulator is calculated in 281 ms and navigation of the manipulator takes 30,251 ms.  
As mentioned before, if the manipulator navigates successfully through such obstacles as in the first example, it is easy for the manipulator to navigate in the workspaces cluttered with many small and/or large obstacles. In order to show the proposed algorithm is a generalized algorithm, motion of the manipulator in a different environment which has small-sized obstacles is simulated. The workspace of the second example is shown in Figure 20a. As seen from Figure 20b,c, the manipulator navigates through the obstacles and reaches the goal without any collision. Motion of the manipulator is given in Figure 20d without showing the obstacles. In addition, a simulation of the example is given without auxiliary elements in Video S3 and with auxiliary elements in Video S4 for the \(d_{p}\) value of 16 pixels. Potential field is calculated with 650 iterations in the second example. For two different value of the distance between two successive points on the path (\(d_{p}\)), number of the beams (\(n_{B}\)), calculation time of the potential field (\(t_{PFC}\)), calculation time of the beam couples (\(t_{BC}\)), calculation time of the manipulator path (\(t_{MPC}\)) and navigation time of the manipulator from the start to the goal point (\(t_{N}\)) are given for the both examples in Table 1.  
Figure 13:0 Tight maneuvering in the second example. (**a**) General view of the workspace and start of the motion. (**b**) Tight maneuvering. (**c**) Reaching the goal. (**d**) The whole part and auxiliary elements (Obstacles are not shown).
# Analysis of Dynamic Complexity of the Cyber Security Ecosystem of Colombia
## 3 Cyber Security Ecosystem of Colombia  
### Legislation and Political Guidelines of Cyber Security in Colombia  
In the last few years, Colombia has become conscious of the importance of legislating and regulating in the area of information security. This is why the government has created a series of laws, notices, and resolutions that significantly strengthen the State's capabilities to face threats that are generated in cyberspace, such as: Law 527 of 1999--electronic commerce; Law 599 of 2000--illicitviolation of communications; Notice 052 of 2007--minimum security and quality requirements in the management of information through distribution channels for clients and users of the Financial Superintendence of Colombia; Law 1273 of 2009--the protection of information and data; Law 1341 of 2009--in which concepts and principles regarding information societies and the organization of Information and Communication Technologies are defined; The resolution of the Commission of Regulation of Communications 2258 of 2009--security of network services and network providers of telecommunications Statutory; Law 1581 of 2012 for the protection of personal data.  
Additionally, in 2011 the CONPES 3701 document defined the guidelines of policies regarding cyber security and cyber defence in Colombia which seek to improve the capabilities of the government to face cybernetic threats [9].  
The general objective of CONPES 3701 document is to \"strengthen the capabilities of the State to confront the threats that act against its security and defence in cybernetic realms, creating the adequate conditions to provide security in cyberspace\" [9].  
CONPES 3701 document expired in 2015. Because of this a new version of this document is being developed, which sets out, amongst other things, the strengthening of entities to support cyber security, the generation of a Cyber security ecosystem that defines the diverse entities that are considered key factors for the development of cyber security in Colombia, the development of a national strategy of Cyber security and cyber defence to close the gaps that were identified in the CONPES 3701 document. This new version was approved in April of 2016.
## 2 Earthquake Catalogs
### Known PGA Data  
The known PGA data were acquired and recorded to the strong motion database through the strong motion station, and the main known strong motion databases include the Next-Generation Attenuation (NGA) database [29], the Center for Engineering Strong Motion Data (CESMD) database [30], and the COSMOS (Consortium of Organizations for Strong-Motion Observation Systems) Virtual Data Center database [31]. Considering the data volume, data time range, and demand parameters, the final data set utilized was the CESMD, jointly established by the United States Geological Survey (USGS) and the California Geological Survey (CGS), and the strong motion data and strong motion station are shown in Figure 2.  
The Initial screening of the obtained data was performed. Since the boxed range was controlled by the latitude and longitude, the selected area in Figure 2 is larger than the California area, and the redundant data were removed by combining the actual California range in the data. With the increase in the number of strong seismograph arrangements, more strong motion data were measured for the same strong earthquake. The increase in the amount of data can improve the accuracy of the prediction results, but the propagation of the ground motion is greatly affected by the existence of the earth's soil, and the more distant the measured locations are, the higher the loss in the confidence rate; therefore, only the data closest to the seismic source were selected to be used in this study. The measured PGAs were provided in the form of two horizontal components and one vertical component, and the largest PGA in the same earthquake was used due to the disparity between the ground motion components, which has not been considered in this paper.  
The GMPE generally calculates the PGA using the magnitude and distance (epicenter distance or hypocentral distance), but in fact, the epicenter distance is the distance between the ground location of the seismic source and the seismic station, and the hypocentral distance is the distance between the seismic source and the seismic station, so the calculated PGA is affected by the focal depth. To obtain more accurate PGA calculation results, the magnitude, epicentral distance, focal depth, and PGA were obtained in this paper. In total, 107 known PGA data points were obtained through combining the above selection principles. The relationship between the magnitude and the epicenter distance of these data is shown in Figure 3.  
Figure 2: California strong motion database map.