esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Communication Model
#### 2.3.1 Data Rate Characterization of the Cellular Access Link  
At 28 GHz, mmWave signal propagation exhibits minimal multipath effects owing to directive antennas. The communication link closely matches an additive white Gaussian noise (AWGN) channel [39]. Consequently, we do not consider multipath effect in mmWave communications. Furthermore, the interference in case of mmWave data transmission link occurs due to transmitters having LoS paths to the data-receiving UE. Thus, in case of downlink, interference is exhibited by the neighboring cells. We derive the signal-tointerference-plus-noise-ratio (SINR) at UE \(n\) for mmWave-based cellular access mode in the following expression:  
\[\Gamma_{mm}=\frac{P_{i}G_{t}(i,n)G_{r}(i,n)d_{i,n}^{-\delta}}{\sigma^{2}+\underbrace {\sum\limits_{j\
eq i}P_{j}G_{t}(j,n)G_{r}(j,n)d_{j,n}^{-\delta}}_{I}}, \tag{3}\]  
where \(P_{i}\) represents the transmission power of SBS \(i\). Both \(G_{t}(i,n)\) and \(G_{r}(i,n)\) represent the directional antenna gains of data-transmitting SBS \(i\) and data-receiving UE \(n\), respectively. \(d_{i,n}\) represents the distance from SBS \(i\) to UE \(n\) and \(\delta\) represents the path loss exponent. \(\sigma^{2}\) denotes the AWGN power density, and \(I\) denotes the inter-cell interference from all the other SBSs that are not associated with the UE \(n\). Similarly, both \(G_{t}(j,n)\) and \(G_{r}(j,n)\) represent the directional antenna gain of the interfering SBS \(j\) and data-receiving UE \(n\), respectively. The transmission rate provided by SBS \(i\) operating in mmWave mode is computed as  
\[R_{mm}=W\text{log}_{2}(1+\Gamma_{mm}). \tag{4}\]  
SBS and UE are assumed to be equipped with steerable directive antennas and perfect training is performed to accomplish successful data transmission. However, mmWave provides high bandwidth, so it is vulnerable to high propagation loss in case of NLoS communication. In situations where mmWave access connectivity is unavailable, data transmission is performed through \(\upmu\)Wave spectrum, leveraging its negligible blockage and penetration losses. When UE \(n\) is served by \(\upmu\)Wave band, the SINR is determined by  
\[\Gamma_{\mu}=\frac{P_{i}|h_{i,n}|^{2}d_{i,n}^{-\delta}}{\sigma^{2}+\sum\limits _{j\
eq i}P_{j}|h_{j,n}|^{2}d_{j,n}^{-\delta}}. \tag{5}\]  
where \(|h_{i,n}|^{2}\) denotes the channel fading coefficient from serving SBS \(i\) to receiving UE \(n\). \(|h_{j,n}|^{2}\) represents the channel fading coefficient from interfering SBS \(j\) to UE \(n\). The transmission rate is derived as  
\[R_{\mu}=W\text{log}_{2}\big{(}1+\Gamma_{\mu}\big{)}. \tag{6}\]
## 6 RF Feature Classifiers
### ML-Based Classification
#### 6.2.2 SVM Classifier  
As the problem we address here is a classification problem with two classes: spoofer absent (or \(\mathcal{H}_{0}\) hypothesis) versus spoofer present (or \(\mathcal{H}_{1}\) hypothesis), the most encountered ML classifier for a two-class problem is the support vector machine (SVM), as SVM is designed to maximize the margin between classes in such a two-class case. The SVM classifier could be versatile by using a kernel trick. Considering 2D points \((\mathbf{x},\mathbf{y})\), here, we list several popular kernels \(k(\mathbf{x},\mathbf{y})\):  
* Linear kernel: \(k(\mathbf{x},\mathbf{y})=\mathbf{x}\cdot\mathbf{y}\);
* Polynomial kernel: \(k(\mathbf{x},\mathbf{y})=(\mathbf{x}\cdot\mathbf{y})^{d}\), \(d\) is the exponent;  
Figure 14: An example based on Galileo E1 and spoofer simulated data: the first two principal components of PCA of spectrogram images are classified by KNN under a different number of nearest neighbours: 3 (**left**) and 10 (**right**).  
Figure 13: An example of KNN for three nearest neighbours.  
* Sigmoid kernel: \(k(\mathbf{x},\mathbf{y})=\tanh a\mathbf{x}\cdot\mathbf{y}+b\), \(a>0\) and \(b<0\);
* Gaussian kernel (also known as an rbf kernel): \(k(\mathbf{x},\mathbf{y})=e^{\left(-\frac{|\mathbf{x}-\mathbf{y}|^{2}}{2\
u^{2}} \right)}\). Typically, a Gaussian kernel takes best into account the irregular boundary in the I/Q GNSS datasets. As the dimensions of raw I/Q data are typically huge, some forms of dimensionality reduction can be typically employed. One such form is known under the name of **Principal components analysis (PCA)**.  
PCA is a common method to pre-process data for the purpose of reducing the dimension of the target dataset before the classifications. The first few principal components implies the most dominant features existing in the dataset, whilst using PCA is an effective way to improve the classification performance.  
For example, Figure 15 demonstrates the first 20 th components in the spectrogram images of a Galileo E1 and a spoofer (also based on Galileo E1 signal specifications), respectively. The plots are shown for a very high CNR level (100 dB-Hz) for illustration purposes. The PCA levels are clearly distinct in the two plots of Figure 15, pointing out the fact that the various transmitter HW features can indeed differentiate between the transmitter types to some extent by further processing via SVM for example.
# A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning
## 2 State-of-the-Art Techniques
### Work Related to the VLC Channel
#### 2.1.3 Work Related to FSK in Underground Channels  
As previously stated, the majority of the literature on VLC in general has used OOK and laboratory level tests at very short distances with white light. On the ground, this results in large, power-hungry drivers. Therefore, it might be interesting to include the frequencies or wavelengths of the chosen colours [45]. There are few FSK applications for VLC which, by definition, has a longer range and lower power consumption. In [46], an advertising panel that uses FSK to communicate with a cell phone application is discussed. [PERSON] [47] described a lab-scale VLC system comprised of a single-stage buck-boost power factor correction converter operating in discontinuous drive mode with dimming capability. [PERSON] [48] described a system for vehicle-to-vehicle communication using FSK. The only studies on FSK modulation applied to underground mining are presented in [49; 50; 51], which all involved testing in a mine tunnel.  
The models discussed previously are extremely rigid and linearly conceptualised. Quantum objects, on the other hand, do not need to have their properties defined; a beam of light can arrive at the photodetector not only in a straight line in a coherent manner but also via other angles in an incoherent manner [52]. Dust, for instance, can be modelled as large particles colliding with waves, which cause collisions that deflect the waves, but photon jets arrive at the photodetector because they are aligned. If these become stuck in the detector and clogged by dust, leaving a few holes that convert the photon jets back into waves, all of the previously discussed models become invalid. According to [PERSON] [53], infrastructure maintenance and cleanliness appear to be more important than simple model validation.
# Wideband Ring-Monopole Flexible Antenna with Stub for WLAN/C-Band/X-Band Applications
## 3 Antenna Fabrication and Analysis
### Antenna Fabrication and Antenna Characteristics
The reason that the gain of the antenna is not constant is that the radiation pattern is distorted as the antenna operates in a higher-order mode at high frequencies. Although the graph is not smooth, the effect on the application is small because the tendency does not change. Table 2 shows a comparison with the antenna parameters of the references. The proposed antenna had the smallest physical size of the flexible antennas operating in the WLAN/C-band/X-band.
## 1 Introduction
Our previous studies concerning the flake-like structures yielded very promising results. The synthesis and characterization of mesoporous silica flakes obtained on a graphene oxide template for use as a carrier of anticancer drug was presented. The mesoporous silica flakes were able to pack significant amounts of the drug, and its release was long-term [24]. In addition, both the pristine flakes and the complex with the drug have been tested for biocompatibility and cytotoxicity. Their biodistribution in internal organs has been also evaluated. The cytotoxicity of the nanoflakes-methotrexate (MTX) complex in reference to MTX showed similar cytotoxic potential against cancer cells. These data have provided useful information for designing drug delivery systems, which may improve anticancer efficacy and decrease side effects [25]. The obtained results inspired the authors to create a multifunctional structure. Functionalization with targeting molecules allows fully exploiting the potential of mesoporous silica flakes in anticancer drug delivery. In this contribution, we present the synthesis and characterization of mesoporous silica flakes obtained on two different templates: graphene oxide and double-layer magnesium and aluminum hydroxide. These matrixes were removed before functionalization with folic acid and anticancer drug loading. Polic acid was used as the targeting molecule, because some of the cancer cells have over-expressed folate receptors on their surfaces and are able to attract nanostructures functionalized with folic acid [26]. Methotrexate as a model anticancer drug was used. It is one of the earliest anticancer drugs and is extensively used in lymphoma, acute lymphoblastic leukemia, and osteosarcoma, among others. By inhibiting metabolic pathways, it prevents the synthesis of purine bases, which are necessary elements in the structure of DNA. The consequence is a disorder in the synthesis of DNA as well as RNA and proteins, which leads to the inhibition of cell division and to apoptotic cell death [27]. In this work, the physicochemical properties of mesoporous silica flakes produced on two types of matrices functionalized with folic acid were checked and compared. The effect of this functionalization on the ability to load and release the drug has also been tested.
## 3 Problem Definitions for the Point-to-Point Network and Dynamic Scheduling
### Multilevel Point-to-Point Network with Dynamic Scheduling  
Because there are many depots and several vehicles can be at each location, scheduling is required for safe operation. The multivehicle point-to-point network with dynamic AADS proposed in this paper is illustrated in Figure 3. There are two vehicles, UAM1 and UAM2, at the vertiport VD that need to go to other locations via different routes and then to return to the original vertiport, shown in Figure 3a. In addition, a time-space network is presented in Figure 3b that is used for the scheduling and safety of the UAMs. The other vehicles at a vertiport can only leave after the first vehicle leaves and need to wait for a specific time to ensure separation, using the FIFO rule [9]. The arrival times of two vehicles at a vertiport should not be the same, and the departure time from the vertiports should be the same. To control the arrival times at each vertiport, the speed is considered to be  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Problem**} & \multicolumn{2}{c}{**Depature Station**} & \multicolumn{2}{c}{**Vehicles at Each Departure Station**} & \multicolumn{2}{c}{**Network Type**} & \multicolumn{2}{c}{**Time Window**} \\ \cline{2-9}  & **Single** & **Multiple** & **Single** & **Multiple** & **Hub-and-Spoke** & **Point-to-Point** & **Fixed** & **Variable** \\ \hline TSP & ✓ & & ✓ & & ✓ & & \\ mTSP & ✓ & & ✓ & ✓ & & & \\ MD-mTSP & ✓ & & ✓ & ✓ & & & \\ TSPTW & ✓ & & ✓ & ✓ & & ✓ \\ mTSPTW & ✓ & & ✓ & ✓ & & ✓ \\ MDmTSPTW & & ✓ & ✓ & ✓ & ✓ & & ✓ \\ \hline VRP & ✓ & & ✓ & ✓ & ✓ & & \\ MDVRP & ✓ & & ✓ & ✓ & ✓ & & \\ VRFTW & ✓ & & ✓ & ✓ & ✓ & ✓ \\ MDVRPITW & ✓ & & ✓ & ✓ & & ✓ \\ \hline
**Proposed** & & & ✓ & ✓ & & ✓ & & ✓ \\
**method** & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the travelling salesman and vehicle routing problems, as well as the proposed problem. (✓ mean formulation supported characteristics).  
variable for the dynamic arrival of different vehicles, as a variable speed is applied for rescheduling by airlines [21] and in the electric VRP [22]. The departure time of two vehicles from a vertiport should not be same. To control the departure time, a variable waiting time is used instead of a fixed, predetermined time stamp as in the existing routing problem. By varying the waiting time at each vertiport, a dynamic departure schedule is achieved. Between each pair of vertiports, the mission flight times and corridors are predicted to satisfy the operation time horizon, as well as the departure and arrival time constraints. This problem is formulated in the following section, and the constraints are explained.
## 4 Security Analysis of LEACH-Based Secure Routing Protocols and ESPDA
### Security Analysis of ESPDA  
The drawbacks of ESPDA protocol are explained in the following points:  
* The use of a static network key \(k\) is risky. If any intruder is able to successfully obtain knowledge about the key \(k\), then security of the protocol can be destroyed. Therefore rekeying for the network key \(k\) is needed to mitigate this problem.
* Because the static network key \(k\) is used to encrypt the \(r_{b}\), pattern seed and mapping permutations, ESPDA does not provide freshness for this information. It is not clear how the source authentication and message integrity are provided for ESPDA. Even though we assume the id of the sender is included in the message and MAC computation so that the message origination and integrity service are available to the receiver, this protocol is still vulnerable to replay attacks. Any intruder may rebroadcast the previously captured messages by distributing \(r_{b}\), pattern seed and mapping permutations in another different session. The nodes having different \(k_{i,b}\) with that of the BS will fail in the BS decryption process. Some nodes may have different pattern seeds than the rest of the cluster which results in inaccurate data aggregation. The CH is not able to reconstruct the proper order of data blocks because different mapping permutations were used by the member nodes. Therefore a dynamic session-dependent group key can be a possible solution for this drawback.
* The data integrity of pattern code messages from sensor nodes to the CH is not provided. Any intruder can alter the pattern code message and disrupt the data aggregation. The integrity of the message from sensor nodes cannot be verified at the CH but only at the BS. No provisioning of the early invalid data detection in the BS causes energy consumption in vain. A network key \(k\) can be used to provide data integrity for the message.
* The source authentication only exists between nodes and the BS, not between nodes and the CH. Any intruder may launch impersonation attacks in which any unauthenticated node mayimpersonate any legal node. Though the intruder does not have the legal secret key shared with the BS, it can send its pattern code to a CH. The CH runs the pattern comparison algorithm with the received pattern code and requests the actual data from that illegal node if its pattern code is unique. Because an illegal data insertion cannot be detected by the CH and reaches BS, it consumes network resources. Providing shared secret among nodes may be a possible solution.
* In the protocol, the CH requests their actual data from the selected nodes for multi-unicast communication. This can be expensive. One broadcast message with an authentication service may be a solution.
## 3 Market Research: Current State of Immersive Technologies in Industrial Design
### Available Resources of Immersive Tools for Industrial Design
#### 3.2.1 Hardware Analysis for Immersive Design
Specifically, wireless devices, while prioritising portability and accessibility, have implemented substantial improvements in resolution and processing capacity. Headsets such as 'Meta Quest 3' and 'Apple Vision Pro' incorporate RGB cameras, allowing MR experiences in addition to pure VR. This evolution makes these systems fully functional for the early stages of conceptual design, educational settings, collaborative review, and product presentation. Consequently, these hybrid devices have helped blur the boundaries between VR, AR, and MR. In the field of product design, the choice between a wired or wireless system must be guided by criteria of use, context, and budget. For tasks requiring high-precision modelling, functional simulation, or detailed validation in controlled environments, wired systems remain the most suitable option. However, for rapid ideation, 3D sketching, collaborative processes, or product presentations to clients, wireless devices represent a better solution. The selection of appropriate immersive hardware should be based on a technical assessment that considers the balance between visual quality, interactive response, mobility, and cost. Additionally, it must be in accordance with project objectives, user profiles, and operational environment, whether educational, professional, or research-orientated.
## 3 Model Uncertainty Parameters
### The Uncertainty Factor of the Vibration Isolation System
The definition of mechanical parameters for steel damping devices remains unclear and lacks a singular consensus despite years of engineering practice in seismic mitigation. This paper proposes a method for determining the mechanical parameters of steel damping devices based on the principle of equal areas. However, different data processing methods can lead to variations in the parameter values of the model. Based on previous studies on dampers and the differences in data processing [21], it is hypothesized that the experimental curve perfectly aligns with the target design curve, characterized by a design yield displacement of 30 mm and a yield force of 250 kN. The yield displacement of the steel damping device is estimated to vary within a range of 31 to 36 mm, accounting for assembly gaps of 1 to 2 mm typical in hinging and contact devices. Therefore, considering the uncertainty from assembly gaps, the yield displacement of the steel damping device ranges between 32 mm and 38 mm, with the yield force ranging from 252 kN to 278 kN. For the bridge case in this study, utilizing eight combined steel damping units, the components exhibit a parallel relationship in their mechanical behavior; thus, the distribution range for the overall yield force of the steel damping system is from 1936 kN to 2064 kN. The hardening coefficient \(\rho\) varies between 1.4% and 3.0%. All the aforementioned distributions are presumed to follow a uniform distribution. It is important to note that the discrepancies in the parameters of the steel damping devices arise from the same experimental curve based on different equivalence rules, reflecting the uncertainties introduced during the data processing of experimental results, that is, the differences between actual conditions and theoretical calculations. In the context of FEM, this is accomplished by adjusting the yield displacement, yield force, and hardening parameters of the bilinear model of bearing. Regarding the gap at the beam ends, following [PERSON] [2] and considering the specific conditions of this bridge case, it is assumed that when subjected to a temperature change of \(\pm\)20 \({}^{\circ}\)C, the maximum expansion is calculated as \(\pm\)20 \({}^{\circ}\)C \(\times\) 10\({}^{-5}\)/\({}^{\circ}\)C \(\times\) 250 m = \(\pm\)5 cm. Thus, the beam gap is considered to uniformly distribute between 11 cm and 21 cm (16 \(\pm\) 5 cm). In FEM, this is realized by changing the gap in the collision model. The parameter distribution characteristics of the collision stiffness are derived from the values referenced in [PERSON] [2], as detailed in Table 2.
# Two-Stage Optimal Task Scheduling for Smart Home Environment Using Fog Computing Infrastructures
## 4 Performance Analysis and Evaluation Matrix
### Simulation Setup  
The numerical simulation was performed over both adversarial and legitimate models by publishing both ideal and intruder devices on the network. The intruders may steal or hack the consumer device for their own purposes or jam the network to perform a distributed denial of service attack. The trust values and communication track of both legitimate and fake devices were recorded after every specific interval of time as S(t). Let N(I) represent the number of ideal consumer electronic devices communicating in the network and N(M) denote the number of devices hacked by the intruders in the network. Table 5 illustrates the types of sensors and the units of measurement.  
Three performance parameters, latency, energy consumption, and network usage, were considered for the comparison of the two-stage task scheduling (2-ST) algorithm with the hybrid heuristic algorithm [12] in both the fog and cloud environment. Fog nodes are intermediate nodes and helpful for providing the minimum latency period for time-sensitive data, rather than processing the data in the cloud. Fog computing supports latency-sensitive real-time applications, and thus, the latency is considered one of the  
\begin{table}
\begin{tabular}{l l} \hline \hline
**IoT Sensors’ Parameters** & **Values** \\ \hline Number of IoT sensors & 7 \\ \hline RAM & 1 KB \\ \hline Processing capacity & 4 MIPS \\ \hline \hline \end{tabular}
\end{table}
Table 4: Smart home IoT sensors’ features.  
important performance parameters. Secondly, the energy consumption reduces the power utilization of the fog nodes, which further reduces the overall electricity bill for the smart home environment. Energy consumption was also compared for both the cloud and the non-cloud environment and is discussed in Section 5. The last performance parameter considered in this study was the network usage. An increase in the number of smart devices in the smart home leads to network congestion, but the concepts of resource management and task management play very important roles in the allocation of tasks on the different fog nodes in order to reduce the network congestion.
# Ten Years of TerraSAR-X Operations
## 4 Dedicated Mission Campaigns
### Modifications Induced by TanDEM-X Science Phase  
After completion of the TanDEM-X global DEM data acquisition phase, the time period from 9 October 2014 until 2 February 2016 was dedicated to the achievement of the secondary TanDEM-X mission goal, the generation of radar products for a number of new science and technology related applications. Specific flight formation configurations, which realized various along-track and across-track baseline conditions, were chosen in response to the needs of given applications. A second DRA campaign was conducted. This had a number of direct impacts onto the TerraSAR-X mission operation and led to further modifications inside the ground segment [12; 22].