esa-sceva/satcom-chunk-collection huggingface dataset:
# UCMAC: A Cooperative MAC Protocol for Underwater Wireless Sensor Networks
## 5 Performance Evaluation
### Simulation Results
At the same time, elimination of packet collisions also helps UCMAC to achieve the high single-hop PDR (Figure 10c). This means that cooperation is highly effective in the delivery of DATAs even when the traffic is heavy, and UCMAC can be reliable under any circumstances. In CD-MACA, more DATAs end up being discarded due to collisions, which results in the lower single-hop PDR in spite of cooperation. Figure 9: System throughput versus \(N_{\text{coop}}^{\text{max}}\) (\(\lambda=0.002\)). Obviously, non-cooperative MACA-U has the lowest single-hop PDR as the destinations rely solely on their own sources. In terms of energy consumption, our analysis also shows that UCMAC is most efficient (Figure 10d). Figure 10: _Cont._  
Unlike CD-MACA where all neighbors commonly included in both source and destination coverages serve as cooperators without considering whether they have better channel-to-destination than sources, UCMAC selects only beneficial neighbors in terms of channel quality as cooperators and accordingly saves energy by using lower transmission power and fewer retransmissions. Furthermore, since no packet collisions occur between retransmitted DATAs as aforementioned, energy consumption can be significantly reduced. The reason CD-MACA is more energy-intensive than MACA-U is because all cooperators wastefully participate in every retransmission in CD-MACA. The decrease in energy efficiency of UCMAC in the range of low \(\lambda\) largely stems from the extra overhead caused by cooperator RTC responses and destination NACK transmissions, which are more noticeable in lower traffic loads. As \(\lambda\) grows, energy efficiency in each scheme converges to its own steady state. Figure 11 verifies that the waiting times provided in the manuscript guarantee the highest network performances in UCMAC. In the figure, each result of the performance metrics is normalized to result of the waiting times (orange-colored bar) obtained through the Equations (2)-(10) that could potentially yield the best network performances. Adding extra time of 10-30% to the calculated waiting times degrades the performances of the network only by a small margin. That is largely due to the waste of the channel resources. On the other hand, with a 10% reduction of the waiting times (blue-colored bar), the network's performance is significantly degraded. That means the values we got from the equations provide sufficient, but not excessive time for the desired packets to arrive. As a result, the maximum cooperative diversity gains require a careful decision of the waiting times, which compensates for the effects of the space-time uncertainty problem. Figure 11: Performance variation with different lengths of the waiting times (\(N_{\text{coop}}^{\text{max}}=2\), DATA size = 1 kbits, \(\lambda=0.01\) ). Figure 10: Performance comparison with comparing schemes (\(N_{\text{coop}}^{\text{max}}=2\), DATA size = 1 kbits): (**a**) System throughput; (**b**) Latency; (**c**) Single-hop PDR; (**d**) Energy efficiency.
## 3 Dynamic Forecasting Model Based on VMD-SLSTM Network
### Vmd-Slstm Forecasting Model  
The landslide displacement-time curve is a non-stationary time series. If a forecasting model is applied directly to the landslide displacement, it is very difficult to learn the intrinsic law of displacement change. A better idea is to decompose the original displacement into several stationary subsequences that are more suitable for modeling.  
Figure 3 shows the landslide displacement forecasting flow chart based on the VMD-SLSTM network. The landslide displacement is decomposed into \(K\) components by the VMD algorithm. On the other hand, the SLSTM network is used to forecast each component. Hence, the forecast values of landslide displacement are obtained by reconstructing the forecast values of all components.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 1 The History of Optimization  
The beginning of the science of optimization techniques, also known by the names _Operations Research_ (OR) and _Mathematical Programming_, dates back to the early years of the World War II. At that time, it was extremely necessary to allocate scarce resources to the different military operations and to the activities for each operation in an efficient manner. For that purpose, the British and then the U.S. military management asked a large number of scientists to apply a scientific approaches for that purpose. In other words, they were asked to do _research_ on military _operations_[23]. These teams of scientists were the first OR teams. By developing effective methods of using the the newly invented radar at that time, these teams had an important role in winning the Air Battle of Britain. Through their research on how to better manage convoy and antisubmarine operations. Similar efforts played a major contribution in winning the Battle of the North Atlantic.  
When the war ended, the success of OR in the military field boosted interest in applying it other fields too. As industries started to boom after the war, the problems caused by the increasing complexity and specialization in organizations were arising. These seemed basically the same problems as the ones that used to be considered by the military but in the context of a different field. By the early 1950s, researchers had introduced the use of OR to a variety of organizations in business, industry, and government. The rapid spread of OR soon followed.  
There were two key aspects that led to the development of optimization methods [23]:  
* A large progress was made early in improving the techniques of OR. After the war, many of the researchers who had participated on OR teams or who had heard about this work were motivated to continue research in that direction, which lead to advancements in the state of the art. A leading example is the simplex method for solving linear programming problems, developed by [PERSON] in 1947.
* Computer revolution which lead to the development of electronic computers. This is because a large amount of computations is usually required to deal most effectively with the complex problems typically considered by OR. Doing this by hand would be impossible, besides the fact that in most of the practical problems, deriving a closed form expression for the solution is not possible neither.
# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
## 3 New Energy-Aware Routing Protocols  
In this section, we first describe the general WSN framework that underpins our wildlife monitoring system. We then propose three energy-aware strategies for DTN routing protocols, namely Threshold, RRE and ORRE strategies. These approaches extend common DTN protocols to consider energy and they aim to achieve effective utilization of residual energy. These new techniques use the standard, well-known routing algorithms such as PROPHET, Epidemic, and Spray and Wait, and then add an energy threshold below with packets that are forwarded only to gateways. Specifically, we focus on a family of applications that track mobile objects with regular daily motion patterns that rely on periodic replenishing of energy resources.
# Influence of Deep Foundation Pit Excavation on Adjacent Pipelines: A Case Study in Nanjing, China
## 4 Numerical Simulations
### Single-Factor Numerical Simulation Analysis
#### 4.4.2 Distance between Pipeline and Pit  
Based on the results of numerical simulation trials, it was determined that the impact of foundation pit excavation on surface settlement extended to about 40 m outside the pit. Considering the pipeline in this project was located 10 m from the pit, the distance between the pipeline and the pit was varied from 0.2 to 1.6 times the depth of pit excavation. Six levels were chosen for numerical simulation: 5, 10, 15, 20, 30, and 40 m.  
Figure 9, which was derived from numerical simulation results, compares the vertical and horizontal displacements of the pipeline at different distances from the pit. From Figure 9a, it can be observed that when the distance between the pipeline and the pit exceeds 30 m, the magnitude of the vertical displacement of the pipeline remains relatively unchanged with increasing horizontal distance from the \(y\)-axis. This indicates minor uneven vertical displacement in this range, having minimal impact on pipeline deformation. However, when the distance is less than 30 m, a significant abrupt change in vertical displacement occurs near the end of the pit, leading to substantial uneven vertical displacement, potentially causing tearing or rupture of the pipeline. Figure 9b reveals that the pattern of change in the pipeline's horizontal displacement is broadly consistent with its vertical displacement. Significant uneven displacement, impacting the pipeline's integrity, can be observed when the distance between the pipeline and pit is less than 30 m. Therefore, when the distance between the pipeline and the pit is greater than 1.2 times the depth of the excavation, the uneven displacement of the pipeline is reduced, and the impact of pit excavation on the integrity of the pipeline is less significant.  
Figure 8: Comparison of pipeline displacements for different pit excavation depths. (**a**) Vertical displacement; (**b**) horizontal displacement.  
Figure 9: Comparison of pipeline displacements for different pipeline-to-pit spacing. (**a**) Vertical displacement; (**b**) horizontal displacement.
## 5 Comparative Analysis of Incremental Launching Monitoring Results
### Guide Girder Displacement Monitoring  
The theoretical deflection of the front end of the guide girder under five working conditions is given by the finite element simulation outcomes. The displacement at the leading edge of the guide girder is related to whether the guide girder can reach the piers smoothly. Therefore, the theoretical deflection is contrasted with the actual monitoring outcomes. Table 3 gives the calculated values and monitoring values of the deflection of the guide girder under five working conditions. In the second working condition, the guide girder experiences the highest theoretical deflection, and the cantilever of the guide girder is 35.5 m; 35.5 m/200 = 177.5 mm. According to Table 3, the deflection monitoring value and calculation value of the guide girder under working condition 2 are less than 177.5 mm. Table 3 indicates that the deflection values of the guide girder are within the control range, and the deflection of the guide girder meets the safety design requirements.
## 2 Concepts and Applications of TMPR
### Transformation Method from Two 2D X-Ray Pixel Images to 3D Real-World Coordinates (TMPR)
The closest point on \(\mathbf{I}_{\textit{proj}}\) to \(\mathbf{p}_{\textit{corr}}\) (foot of the perpendicular line) can be determined to \(\mathbf{p}_{\textit{opt}}\) by applying the soft epipolar line constraint [19]. The \(Z_{\textit{init}}\) corresponding to \(\mathbf{p}_{\textit{opt}}\) can be calculated by (9) and (10). \(Z_{\textit{init}}\) is later used as the initial value for the optimization process of the \(\alpha\)-expansion method described in Section 2.5. Finally, we can calculate \(\mathbf{P}_{\textit{init}}\) in the 3D coordinates of the C-arm utilizing the calculated \(Z_{\textit{init}}\) and \(\mathbf{p}_{\textit{ref}}\) in (4). In conclusion, we can determine the 3D coordinates of a point in the coordinate system of the C-arm from two 2D X-ray images captured from different views.
## 2 Material and Methods
### Software Design  
Similar to the modular approach of the hardware design, software components were also developed as independent components and integrated using standard interfaces. To enable autonomous operation of the chamber and machine-to-machine (M2M) communication, the following requirements were established and realized:  
* Design and execution of control application or custom experiment regimes on the designed system
* Access to measurement data and system states for performing data analytics, monitoring, and process control
* Interface to specify optimal operation points (set-points) and control parameters computed externally (expert user or decision support system)  
These requirements were translated to a framework, as shown in Figure 7, with a three part solution: (1) open-source firmware core for control unit with necessary drivers, middleware and application interface for reliable operation of the chamber; (2) cloud platform with data, protocol and web sever for data aggregation. data visualization. and interaction with chambers for process control; and (3) code generation framework for control application or experiment regime development. This architecture facilitates the use of the controlled environment for different subjects and executing different experiments. The following sections explain the software design of the IoT Framework and device firmware in detail.  
Figure 6: Growth chamber with the assembled units and their internal view (highlighted in green).
# Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks
## 3 The Proposed Method
### Proposed Hybrid Deep Learning Approach
We designed our hybrid deep learning model in this way because we wanted to take advantage of the strengths of each individual component. The 1D CNN is effective at extracting features from time series data, while the GRU is able to capture long-term dependencies in this data. The DNN is then able to use these features and dependencies to classify the input data as normal or attack traffic. This hybrid approach has the advantage of being able to capture both short-term and long-term patterns in the input data, which is important for accurately detecting DDoS attacks in SDN. To further improve the performance of our model, we also include a MaxPool layer, which uses a max operation to pool the sets of features, resulting in a reduced number of features. This can help to reduce overfitting and improve the generalization performance of the model. Additionally, we incorporate a Leaky layer, which allows for a slight gradient when a unit is inactive. This can help to prevent the model from becoming stuck in a local minimum during training and improve its performance in real-world scenarios. In addition to the benefits described above, our proposed hybrid deep learning model for detecting distributed DDoS attacks in SDN is also effective in detecting low-rate DdoS attacks and defending against DdoS attacks in general. Low-rate DdoS attacks are a type of attack that involve a small number of compromised devices, sending a relatively low volume of traffic to a target network or server. These attacks can be difficult to detect using traditional machine learning algorithms or other deep learning approaches, as they often do not exhibit the same patterns or characteristics as more traditional DDoS attacks. However, our hybrid deep learning model is able to effectively detect low-rate DDoS attacks by taking into account both short-term and long-term patterns in the input data. This is possible due to the combination of a 1D convolutional neural network (CNN), a gated recurrent unit (GRU), and a dense neural network (DNN) in our model, which allows it to extract and analyze both local and global features in the input data. Overall, our hybrid deep learning model is able to effectively detect and defend against DDoS attacks in SDN, providing a robust and reliable solution for improving the security and reliability of these networks. By accurately identifying and mitigating DDoS attacks, our model can help to ensure that SDN networks are able to operate effectively and efficiently, even in the face of increasingly sophisticated and targeted attacks. Figure 4 further illustrates a diagram of our suggested hybrid algorithm architecture, which is composed of the following:  
Figure 5 illustrates a capture from the Google Colab platform, demonstrating the implementation of our proposed model.
## 4 Experimental Results
### Energy Consumption Analysis  
Figure 8 presents the energy consumption measurements of selected LWSCs during the encryption of individual frames within live video feeds. Grain-v1 exhibits the highest energy requirement per frame, followed by Mickey and Trivium. In contrast, Sosemanuk, Salsa, and Grain-128a demonstrate relatively lower energy requirements for frame encryption, with subtle distinctions observed among them.
## 5 Case Study: Practical Implementation of DT for HEMS
### Parametric Standardized 3D House Model  
For the implementation of the HEMS, a residential apartment design was selected as the basis for the DT model. The two-dimensional floor plan of this apartment includes two bedrooms, an open-plan living and kitchen area, a bathroom, and a central hallway connecting all spaces. The layout also features exterior balcony openings, allowing natural light and ventilation (Figure 8). During the 3D modeling phase, key structural elements were incorporated to closely reflect realistic building characteristics. Exterior walls were constructed with a thickness of 30 cm, integrating both masonry and external thermal insulation to enhance energy efficiency. Interior partition walls, primarily for space delineation, were assigned a thickness of 15 cm. Following the structural setup, openings for external windows, balcony doors, and the main entrance were positioned according to the floor plan, facilitating natural light entry and cross-ventilation. Interior doors were strategically placed to support room connectivity and privacy where needed.  
Once the structural layout was completed, materials were applied to the model to reflect a realistic home environment. Wood flooring was implemented throughout the apartment, except in the bathroom, where tiles were used to resist moisture. Additionally, walls in each space were painted with unique colors to visually differentiate rooms, which supports usability and enhances the Digital Twin model's data segmentation for energy management. Bedrooms, the hallway, and the open-plan area all have distinct hues that improve the user's spatial awareness within the Digital Twin interface.  
Figure 7: Time series data from sensors.  
To improve immersion, essential furnishings were incorporated. Bedrooms were furnished with beds, wardrobes, and curtains for shading. In the open-plan living and kitchen area, a complete kitchen setup with a dining table and appliances was modeled, alongside a living area with a sofa, coffee table, and TV. The bathroom was equipped with standard sanitary fixtures, including a washbasin, toilet, and shower. Following the application of all materials and furnishings, the model was prepared for transformation into a fully interactive Digital Twin. This model provides users with an immersive experience, mirroring the apartment's layout and aesthetics while allowing for advanced energy management, environmental monitoring, and data-driven insights within the Digital Twin framework. Figure 9 presents the parametric standardized 3D house mode.  
Figure 8: 2D model for HEMS.
## 2 Background--Study Areas
### Reservoir Characteristics  
The Hugin Formation is a sequence of sandstones that were deposited in an environment characterized by shallow water and tidal activity. This sequence has a significant lateral extent. The reservoir is relatively clean and homogeneous. Quartz, clay, and mica predominate as the primary mineralogy components of the sandstones [22]. Studies of the fluid in the reservoirs have revealed that the oil found in the Volve field originated in the Sleipner Graben region, located in the northwest part of the field, approximately 10 million years ago. The oil then moved into the Volve reservoir structure. In the upper section of the Draupne Formation, type II kerogen was found, confirmed by a high gamma-ray response, which led to the hypothesis that this could be the source rock [22]. The Volve field oil is under-saturated with 27-29\({}^{\circ}\) API oil, with a gas/oil ratio of 111-157 Sm\({}^{3}\)/Sm\({}^{3}\). It  
Figure 2: Chart showing the lithost stratigraphic column of the North Sea [26]. The highlighted sections in red show the reservoirs of interest: the Heimdal, Hugin, and Skagerrak Sands.  
contains between 2% and 6% asphaltenes, as well as sulfur and aromatic components in high concentrations [26].  
Regarding well logs, the Utsira Sand can be distinguished by its top and bottom layers, and the percentage of clean sand that makes up the reservoir unit typically exceeds 70 percent. The quasi-proportion comprises thin mudstones, which have an average thickness of approximately one meter and are expressed as peaks on the gamma-ray and resistivity logs. The 'five-meter mudstone' is a thicker and more laterally persistent bed than the others, and it differentiates the uppermost sand unit from the main reservoir below it. It has been demonstrated that the mudstone layers present within the reservoir sand act as substantial permeability barriers, which significantly influence the amount of CO\({}_{2}\) that migrates through the reservoir [27].
## 2 HSI Procedure
### HSI System  
Hyperspectral imaging represents one of the advanced technologies which can capture the spatial as well as spectral data of any sample in great detail pixel by pixel. The objective lens, imaging spectrograph consisting of collimator lens, diffraction grating, an input slit, a detector like a CCD or CMOS sensor, and a focus lens constitute the HSI system. This configuration enables high-precision spectral and photometric analysis, making HSI superior to traditional digital cameras in detecting and recognizing features across a wide spectral range [16].  
The technique of HSI produces a three-dimensional data structure, popularly called the hypercube. It consists of two spatial dimensions and one spectral wavelength dimension. Many scanning techniques are utilized, such as pixel-by-pixel scanning, line scanning, and full-field scanning. While this provides very accurate results, it is slow and hence less useful for fast applications. On the other hand, linear scanning enables the simultaneous acquisition of spatial and spectral data, making them more suitable for fast and online detection. Full-field scanning involves no motion of either the sample or the detector to acquire images from a fixed scene, thereby increasing the efficiency of the process.  
The system can also operate in reflectance, transmittance, or interactance mode, depending on whether external, internal, or both kinds of parameters are analyzed (Figure 1) [2; 39]. HSI can be carried out on any sample size and shape in several regions of the electromagnetic spectrum, including ultraviolet, visible and near-infrared (Vis-NIR), and short-wave infrared, for each of which there are specific applications [9]. Combining spectroscopy with imaging results in continuous images taken over all the wavelengths of the investigated range, a fact that constitutes an important advantage of the technique for sample characterization in general.