esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Analysis of a Large LNG Tank Considering Different Site Conditions
## 4 Seismic Analysis of a Large Liquefied Natural Gas (LNG) Tank
### Finite Element Model  
This study uses the ABAQUS software [33; 34] to establish a finite element model (FEM) as shown in Figure 5. Because the tank model is extremely large, the associated interactions are very complex. The 'Embedded' option in ABAQUS is used to embed rebar rods within the tank wall, which enables the rebar elements to work in relation to each other. The outer tank and the insulation layer are represented by C3D8R mesh elements [33; 34]. The inner tank is represented by S4R shell elements [33; 34] because its thickness is much lower than its radius of curvature. The aforementioned \(U_{s}-U_{p}\) equation in Section 3 is used to discretize the LNG liquid into particles. The element size is chosen according to the small tank in Section 3 that produces an accurate sloshing frequency calculation. The ratio of liquid element size to container size of the large tank is identical to that of the small tank in Section 3.  
The protective cover encompasses 19T15S steel cables with a tensile strength of 1860 MPa. The top and bottom of the concrete wall consist of 122 vertically anchored steel cables. 220 circumferential steel cables are anchored in semicircles on four vertical supporting columns at 90\({}^{\circ}\)[35].  
Prestress in ABAQUS can be applied using different approaches, such as the initial stress method, the model predictive control (MPC) method, the single rebar element method, and the falling temperature method. The falling temperature method is used in this study to apply prestress by decreasing temperature. The initial temperature of the prestressed rebar is calculated with the following Equation:  
\[\Delta=\frac{N}{\lambda}\times E\times A \tag{20}\]  
where \(N\) is the tensile force in the prestressed rebar, which shall be no more than 75% of the prestressed rebar's standard strength; \(\lambda\) is the thermal expansion coefficient; \(E\) is the elastic modulus; \(A\) is the cross-sectional area.  
The bottom boundary of the model is fixed without considering any interaction between soil and structure. The static condition of the LNG tank is obtained based on its own weight to make sure that the LNG liquid reaches steady state.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 3 Security Protections in IoT Environments
### Defense at the Application Level
#### 3.6.3 Encryption of Data at Rest and in Transit  
Encryption of data at rest and in transit are two important concepts in data security that can be implemented at the application level. Data-at-rest encryption refers to the encryption of data stored on a device or server so as to protect the data from unauthorized access [78]. This type of encryption prevents data breaches even if IoT devices are stolen or data are illegally accessed.  
Data-in-transit encryption refers to the process of encrypting data when the data are being transmitted over a network, or \"in transit\", between two IoT systems [79]. Encryption of data in transit is a fundamental way of protecting sensitive information from interception and eavesdropping attacks in IoT environments.
## 3 Geological Structure and Fault Blocks Onshore and Offshore Samos
### Onshore and Offshore Faults  
Major faults have formed the current morphology of Samos. Recent studies comprising onshore geological and neetectonic mapping [7; 9] and offshore studies based on continuous seismic reflection profiles, partly calibrated with drilling results [6], described 15 major faults dissecting onshore and offshore Samos. Only five of them were characterized as active structures [6; 7; 8; 9; 17] based on their geometric properties, their kinematic characteristics, and their geomorphological and morphotectonic features. These onshore faults are (Figure 5):  
1. The Karlovasi fault is a NE-SW striking and northwestward dipping strike-slip fault in the northwestern part of Samos, which defines its linear coastal morphology. Based on its length reaching 13 km, the maximum expected magnitude is estimated at 6.5 [8] or 6.4 [9].
2. The Vathy fault is an 8-km-long, WNW-ESE striking and northward dipping normal fault defining the linear coastline and the steep seabed morphology of the western coastal part of the Vathy bay. The maximum expected magnitude from the rupture of the Vathy fault is estimated at 6.3 [8; 9].
3. The Marathokampos fault is an 11-km-long, E-W striking and southward dipping normal fault with a dextral component at the southeastern part of Samos. It constitutes the southern boundary of Kerketeas Mt and defines the current linear morphology of the southwestern coast of Marathokampos bay. The maximum earthquake magnitude expected from this fault has been estimated at 6.4 [9].
4. The Pythagoreio fault is a WNW-ESE striking and southward dipping normal fault at the central-south and southeastern part of Samos, separating the northern hilly area from the southern flat lowlands. The maximum earthquake magnitude of this fault from the rupture of the fault in its entirety is estimated at 6.7 [17].
5. As regards offshore structures, a major offshore fault has been detected north of the island based on the linear coastline and the extremely steep seabed morphology north and northwest of Samos [3; 6; 10]. It is a 25-km-long WNW-ENE striking and NNW-ward dipping normal fault, separating Samos to the south (footwall) and the offshore northern Samos basin (hanging-wall) with a depth of more than 1000 m. The maximum earthquake magnitude expected from this fault has been estimated at 6.8 [8; 9] or 6.9 [17].
# Improving Passband Characteristics in Chebyshev Sharpened Comb Decimation Filters
## 2 Compensators: Applications and Design
### Design
#### 2.2.1 Narrowband Design  
In this design, the passband frequency \(\omega_{p}<\pi/4M\). The magnitude characteristic is chosen as in [13],  
\[C_{1}\Big{(}e^{jM\omega}\Big{)}=1+C_{11}sin^{2}\bigg{(}\frac{\omega M}{2} \bigg{)}, \tag{3}\]  
where \(C_{11}\) is the parameter of the design.  
The system function of this filter is given as [13],  
\[C_{1}(z)=2^{-2}\Big{[}\Big{(}-1+2z^{-1}-z^{-2}\Big{)}C_{11}+2^{2}z^{-1}\Big{]}. \tag{4}\]  
The compensator requires three adders and one multiplier. In a multiplierless design, the number of required adders equals:  
\[N_{1}=3+N_{11}, \tag{5}\]  
where \(N_{11}\) is the number of adders in the signed-power-of-two (SPT) form of \(C_{11}\).
# A Model for Working Environment Monitoring in Smart Manufacturing
## 4 Experiment  
For the purpose of validation of the proposed model and for measuring its efficiency and usability, the testing platform was developed. The testing platform was used in the  
Figure 5: Part of the training set: (**a**) class none, (**b**) class both, (**c**) class top-left, (**d**) class bottom-right.  
experiment with two goals. The first goal was to prove that Arduino/Genuino Uno and similar platforms could be used for development of the prototypes of the sensor nodes, and for testing smart industrial systems. The second goal was to estimate the usability and functionality of the MQTT protocol in industrial system environments.  
The testing platform was designed as follows:  
1. Sensor nodes were based on Arduino/Genuino UNO Rev3 clones with integrated ESP8266 module. A total of 10 sensor nodes were developed and used in the scenarios. The sensor nodes are presented in Figure 6, and had the following configuration: * Wemos D1 R2: 5 pcs. (four with MQ-135 and one with MQ-2 sensor), * Espduino: 2 pcs. (one with MQ-135 and one with MQ-2 sensor), and * NodeMCU 12E v1.0: 3 pcs. (all three with MQ-135 sensor).
2. A laptop was used as the system hosting computer, with the following components: CPU Intel(R) Core(TM) i3-4005U CPU @ 1.70 GHz, 4 GB RAM, DDR3 450 GD SATA HDD, internal WLAN adapter (optionally with TP-Link 722N external USB WLAN adapter), and
3. Wireless access point/router  
Software components of the system were:  
1. Windows 10, Mosquito broker, Python script for subscription (irrelevant for the testing), screen display, and writing to SQLite database (irrelevant for the testing), installed on the laptop described above.
2. Wireshark Network Analyzer tool installed on the same laptop.  
The deployment of the testing platform components and their connectivity are shown in Figure 7. At the PHY layer all nodes and server were connected to the Wi-Fi wireless access point/router (4) using IEEE 802.11n technology. Laptop (1) had Mosquito MQTT broker (2), data store (3), and Wireshark network analyzing tool installed. It also had Python script installed for displaying sensor data from the queue, and for storing same data in the SQLite database. The Python script and data store are not relevant for this research. The sensor nodes were presented with: five Wemos D1 R2 (5), three NodeMCUs (6), and two Espduino (7) Arduino clones. All sensor nodes used same firmware developed with the Arduino IDE software, and had one gas sensor, as described in the beginning of Section 4.  
Figure 6: Three prototype sensor nodes: two Wemos D1 R2 (left and central), and one NodeMCU (on the right).  
This is the general purpose platform, designed to be deployed in industrial, laboratory, and classroom environments as a test-bed for the development and testing of prototype software applications for design and teaching purposes. The platform was tested in laboratory conditions, in a small sized room (about 10 square meters) with office furniture, and a high level of interference from other wireless devices not part of the platform.
## 3 Proposed Solution
This ensures that the array does not radiate energy in the opposite direction. These constraint vectors are imposed, and the optimization problem is formulated as a constraint convex optimization problem, which is efficiently solved using gradient descent numerical optimization technique. Mathematically the technique is described as,  
\[ \begin{split}&\text{{minimize}}\;w_{m,n}\sum_{\theta,\varphi}\left|F_{ def}w_{cor}-F_{pl}w_{pl}\right|^{2}\\ &\text{{subject}}\;to\left|F_{def}w_{cor}-b\right|\leq\beta\\ & F_{c}=\left[F_{def}(\theta_{1},\varphi_{1}),F_{def}(\theta_{1}, \varphi_{2})\cdots F_{def}(\theta_{m},\varphi_{n})\right]^{T}\\ & b=\left[E_{pl}(\theta_{1},\varphi_{1}),E_{pl}(\theta_{1}, \varphi_{2})\cdots E_{pl}(\theta_{m},\varphi_{n})\right]^{T}\\ &\qquad\qquad\left(F_{def}\right)^{\dagger}=F_{def}^{H}\left(F_ {def}\;F_{def}^{H}\right)^{-1}\end{split}  \tag{6}\]  
The term \(w_{cor}\) constitute the corrected weight excitation, and is given by  
\[w_{cor}=K\cdot w_{pl} \tag{7}\]  
\[K=F_{pl}\left(F_{def}\right)^{\dagger}=F_{pl}F_{def}^{H}\left(F_{def}\;F_{def}^{ H}\right)^{-1} \tag{8}\]  
where \(F_{c}\in\mathbb{C}^{p\times(m\times n)}\) and \(b\in\mathbb{C}^{q\times(m\times n)}\) are the Matrices containing the \(p\) and \(q\) constraint vectors at \((m\kappa n)th\) position of \(M\times N\) conformal array in deformed array and planar array, respectively. \(F_{pl}\in\mathbb{C}^{m\times n}\) is the matrix that includes the electric field pattern of each radiating patch of the planar array that has the initial weights \(w_{i}=w_{pl}\). \(\dagger\) indicates the pseudo inverse and \(\beta\) is the convergence factor of the search space ranges from \(0\leq\beta\leq 1\). \(F_{def}(\theta,\;\varphi)\) represent the individual array element pattern in deformed structure and \(E_{pl}(\theta,\;\varphi)\) is the electric field pattern of planar array.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 2 Background and Related Research
### Mission Threats  
Threats to the uninterrupted delivery of energy to critical loads on a military base are called a variety of terms such as initiating events, mission threats or threats, disruptions, etc. While there are well-understood and well-quantified initiating events available for external threats (events that occur outside a facility) and internal threats (events that occur inside a facility) [33], many threats of specific interest to the military are unique to military installations and not included in existing resources. For instance, a nation-state adversary conducting a coordinated cyber-attack and physical attack against a variety of electrical infrastructure both internal and external to a facility is generally not considered in most existing initiating event handbooks. In the Methodology section of this paper we propose a minimum list of potential initiating events of interest to the military.
## 2 Methods  
The YOLOv8 model, proposed by the Ultralytics team, consists of four main parts: Input, Backbone, Neck, and Head. (1) Input utilizes Mosaic data augmentation technology, which can provide the model with more training samples, enhancing the model's performance and generalization capability. (2) Backbone adopts the Darknet53 structure, composed of 53 convolutional layers, including Conv modules, C2f (Faster Implementation of CSP Bottleneck with 2 convolutions) modules, and Spatial Pyramid Pooling Fast (SPPF) modules. (3) Neck utilizes a combination of Path Aggregation Network (PAN) and Feature Pyramid Network (FPN) [37], which can merge feature maps of different sizes, aiding in improving the model's ability to detect targets of various scales. (4) Head adopts a decoupled head structure, separating classification from detection. Additionally, YOLOv8 transitions from an anchor-based to an anchor-free approach, reducing design complexity and enhancing model performance.  
To meet the specific requirements for detecting typical components of satellites in space environments, this paper proposes LTSCD-YOLO, an improvement based on YOLOv8. The improved network structure is shown in Figure 2. (1) Input: It receives the original satellite images and processes them into a uniform size of \(640\times 640\times 3\) (height \(\times\) width \(\times\) channels) for input into the network. (2) Backbone: Based on YOLOv8, the main network of the Backbone part is replaced with the lightweight network EfficientNet-B0. Table 1 displays the network of EfficientNet-B0. (3) Neck: Uses a cross-scale fusion network structure to merge feature maps of different scales, enhancing the model's adaptability to scale changes and its ability to detect small-scale objects. In addition, the C2f module in the Neck part is replaced with F-C2f (Faster-C2f) to further achieve model lightweighting. (4) Head: Uses RepHead based on RepConv [38] to enhance the model's feature extraction capabilities. Finally, Focal-EIoU [39] is adopted as the loss function of the model to improve detection accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stage** & **Resolution** & **Channels** & **Layers** \\ \(i\) & **Operator** & \(H_{i}\times W_{i}\) & \(C_{i}\) & \(L_{i}\) \\ \hline
1 & Conv\(3\times 3\) & \(640\times 640\) & 32 & 1 \\
2 & MBConv\(1\), k\(3\times 3\) & \(320\times 320\) & 16 & 1 \\
3 & MBConv\(6\), k\(3\times 3\) & \(320\times 320\) & 24 & 2 \\
4 & MBConv\(6\), k\(5\times 5\) & \(160\times 160\) & 40 & 2 \\
5 & MBConv\(6\), k\(3\times 3\) & \(80\times 80\) & 80 & 3 \\
6 & MBConv\(6\), k\(5\times 5\) & \(40\times 40\) & 112 & 3 \\
7 & MBConv\(6\), k\(5\times 5\) & \(40\times 40\) & 192 & 4 \\
8 & MBConv\(6\), k\(3\times 3\) & \(20\times 20\) & 320 & 1 \\
9 & Conv\(1\times 1\) \& Pooling \& FC & \(20\times 20\) & 1280 & 1 \\ \hline \hline \end{tabular} Each row describes a stage \(i\) with \(L_{i}\) layers, with input resolution \(H_{i}\times W_{i}\) and output channels \(C_{i}\).  
\end{table}
Table 1: EfficientNet-B0 baseline network.
## 3 Artificial Neural Network (ANN)
### Regression Tree  
The regression tree algorithm constructs a decision tree from a provided training set \(S\). After constructing a tree in the learning phase, it predicts the output \(\text{F}(x^{q})\) of current input vector \(x^{q}\) by searching the tree. Starting from the root node, it selects a child node depending on the value of the feature specified at each node. The leaf node shows an estimated value of the function, i.e., \(\text{F}(x^{q})\). We built the regression tree as follows:  
**Step 1**. Initially, the mean of the output value for set \(S\) is computed as:  
\[m=\frac{1}{n}\sum_{y^{j}\in S}y^{j} \tag{4}\]  
Then the variance is computed as:  
\[\text{Var}=\frac{1}{|S|}\sum_{y^{i}\in S}\left(y^{i}-m\right)^{2} \tag{5}\]  
Set S is made the root node of the regression tree.  
**Step 2**. For each feature \(x_{i},i=1,\ldots,d\), search all possible binary splits \(\left\{S_{1}^{i},S_{2}^{i}\right\}\) from set \(S\) of the parent node.  
Binary split using the ith feature is the partition of \(S\), as follows;  
\[\text{S}=S_{1}^{i}\cup S_{2}^{i},\ S_{1}^{i}=\left\{\left(x^{i},y^{i}\right) \Big{|}l_{1}<x_{i}^{j}\leq u_{1}\right\},\ S_{2}^{i}=\left\{\left(x^{i},y^{i} \right)\Big{|}l_{2}<x_{i}^{j}\leq u_{2}\right\},\ \text{and}\ u_{1}=l_{2} \tag{6}\]and compute the mean \(m_{1}^{i}\), \(m_{2}^{i}\) for two split sets \(S_{1}^{i}\), \(S_{2}^{i}\), respectively.  
\[m_{k}^{i}=\frac{1}{n_{k}^{i}}\sum_{y^{i}\in S_{k}^{i}}y^{j},\;k=1,2 \tag{7}\]  
where \(n_{k}^{i}\) is the number of data in set \(S_{k}^{i}\).  
The variance for split is computed as:  
\[\text{Var2}=\frac{1}{\left|S_{1}^{i}\right|}\sum_{y^{i}\in S_{1}^{i}}\left(y^{ i}-m_{1}^{i}\right)^{2}+\frac{1}{\left|S_{2}^{i}\right|}\sum_{y^{i}\in S_{2}^{i}} \left(y^{j}-m_{2}^{i}\right)^{2} \tag{8}\]  
Select a binary split that has minimum variance from all possible binary splits using every feature. Compute the decrease of variance from the parent node.  
If the decrease in variance is less than the predefined threshold \(\alpha\), then stop. Otherwise, accept that split, and create two child nodes, where each node corresponds to \(S_{1}^{i}\), \(S_{2}^{i}\), respectively.  
**Step 3**. For each child node, go to step 2.
###### Abstract
These electrons, possessing relativistic energies, freely pass through targets up to several tens of microns thick. Escaping from the back side, they are decelerated by an emerging charge separation field and initiate ionization of the rear surface of the target and the expansion of the resulting plasma. As a result, a plasma sheath is formed, with electrons heated to relativistic temperatures and a laminar flow of cold ions [22]. Continuing escaping electron bunches form a current on the axis of the sheath and generate an azimuthal magnetic field [23]. At low laser pulse intensities and durations, this field does not significantly alter the expansion dynamics, although it can lead to the observed changes in the spectrum of the expanding ions. Recently, however, it was discovered that for pulses of several hundred femtoseconds duration and intensity above \(10^{20}\) W/cm\({}^{2}\), the magnetic field is sufficient to magnetize the electrons and thereby appreciably slow down the expansion of the plasma [24; 25]. In this work, we studied in more detail the properties of the resulting plasma and found that conditions for a cold magnetized relativistic plasma are realized in it, allowing using this mechanism for observing relativistic magnetic reconnection under laboratory conditions.
## 3 Results and Discussion
### Effect of Basic Soil Properties on WDPT and MED Values
From 38 to 46 % of WDPT and MED variances remained unexplained using basic soil properties as predictors. It is worth noting that logarithmic or quadratic forms used in the regression equations are the statistical approximations, which fitted the data well, but do not represent a physically based relation between soil moisture and WR. Logarithmic or quadratic functions mimic the soil-water characteristic curve, which describes the soil water potential as nonlinear function of soil water content.
## 6 Discussion  
The strength of the proposed algorithm lies in its ability to retrieve horizontal distributions of AOD and SSA with an estimation of vertical location of aerosol layer, as shown in Figure 7. This retrieval will be valuable for gas concentrations from GEMS measurements with its contribution to the improved accuracy of AMF calculations. Because the GEMS is the only payload onboard a geostationary satellite covering Asia with hyperspectral UV measurements, the stand-alone operation of the developed algorithm is useful to assure stability. The validation results, however, indicate that the correlation with AERONET products was not improved relative to that of the OMAERUV algorithm. An approach to improve the performance of the algorithm was revealed by a reference test. Results from the reference test can be summarized as follows. (1) The OE algorithm products are most robust for AOD, followed by SSA and ALH. The large uncertainty in ALH retrieval may be caused by insufficient degrees of freedom (DOF), although the O\({}_{4}\) absorption band (477 nm) is used in this study. (2) Uncertainty in AAE caused by the misclassification of aerosols or aerosol model assumptions is the most important issue in the developed algorithm. Results from the reference test of aerosol misclassification ultimately reflect the effects of uncertainty in AAE, as explained above. Therefore, optimization of AAE for each aerosol type in Asia should be the subject of future work. Recently, an intensive field campaign, the Korea U.S.-Air Quality (KORUS-AQ) campaign, was carried out over South Korea from May to June 2016. During and after the KORUS-AQ campaign, [PERSON] et al. (2016) measured aerosol absorptivity using UV- and visible-MFRSF at Seoul and retrieved spectral SSA variation from 305 to 870 nm. The quantification of the aerosol absorption properties at UV wavelengths may improve assumptions in the aerosol model and thus improve the accuracy of the algorithm. In addition, Figure 13 suggests that errors in particle size increase the error in ALH significantly, unlike what is seen for AOD and SSA retrieval. This is an important issue for the algorithm because the low sensitivity of UV radiance to particle size complicates the distinction of HAF and dust particles. Therefore, updating the GEMS AOP retrieval algorithm with improved aerosol model assumptions and an advanced method for aerosol type selection will be the focus of future work.
## 3 Mathematical Modeling Formulation
### Mathematical Formulation of Technical Objectives
#### 3.4.3 Short-Circuit Level Index  
The short-circuit level plays a pivotal role in the network's technical dynamics, being significantly influenced by variations in generation, consumption, and network configuration. This critical parameter directly impacts protection settings and network planning, thereby influencing the overall safety and efficiency of the network. When strategically deploying BSSs, it is imperative to take into account this often overlooked factor. Failure to consider the short-circuit level can lead to potential damages due to inadequate protection system performance during BSS charge-discharge cycles and diverse generation sources. Therefore, a comprehensive energy management strategy must incorporate defining and addressing the short-circuit level to ensure network stability amidst fluctuations in generation capacity and network layout. This research paper aims to provide a clear definition of the short-circuit level within the context of energy management strategies [26].  
\[\text{Obj}_{5}=\text{SCLI}=\left(\sum_{k=1}^{nb}\left(1-\frac{\left(V_{k}^{*} \right)}{\left(Z_{kk}+Z_{f}\right)}\middle/\frac{V_{k}}{\left(Z_{kk}+Z_{f}\right) }\right)\right) \tag{25}\]  
In the equations provided above, \(V_{k}\) and \(V_{k}^{*}\) denote the voltage at a bus in the network before and after any modifications to the network. \(Z_{kk}\) and \(Z_{f}\) refer to the impedance of the bus itself and the impedance of the fault that occurred at that particular bus in the network, respectively.