esa-sceva/satcom-chunk-collection huggingface dataset:
# Techno-Economic Potential of Plasma-Based CO\({}_{2}\) Splitting in Power-to-Liquid Plants
###### Abstract  
Mitigating climate change requires the development of technologies that combine energy and transport sectors. One of them is the production of sustainable fuels from electricity and carbon dioxide (CO\({}_{2}\)) via power-to-liquid (PtL) plants. As one option for splitting CO\({}_{2}\), plasma-based processes promise a high potential due to their flexibility, scalability, and theoretically high efficiencies. This work includes a modeling and techno-economic analysis. A crucial element is the process of the joint project PlasmaFuel, in which two plasma technologies are included in a PtL plant to produce synthetically sulfur-free marine diesel. The results are divided into three scenarios, which differ in the use of different boundary conditions and thus represent different degrees of technology development. The evaluation results in process efficiencies from 16.5% for scenario 2018/20 to 27.5% for scenario 2050, and net production costs between EUR 8.5/L and EUR 3.5/L. Furthermore, the techno-economic potential is mapped in order to open up development steps in the direction of costs below EUR 2.0/L. The present work allows statements regarding system integration and the industrial use of the plasma-based process; moreover, conclusions can be drawn towards the most important levers in terms of process optimization.  
techno-economic analysis; reaction engineering; CO\({}_{2}\) conversion; dielectric barrier discharge; gliding arc discharge; plasma catalysis; sustainability; plasma processes; oxygen extraction Article  
Techno-Economic Potential of Plasma-Based CO\({}_{2}\) Splitting in Power-to-Liquid Plants  
1
## 5 Experimental Results
### Discussion
facilitates the monitoring of medical devices and patients' health signals within a hospital's network. TOCA-IoT can detect anomalies in data streams that indicate potential issues with equipment or data manipulation. The limitations of the proposed methodology are: (1) Linearity Assumption (LiNGAM): LiNGAM assumes linear relationships between variables. Network traffic data are often complex and non-linear. The assumption of linearity might limit the accuracy of the causal inference results, potentially leading to an incomplete or inaccurate understanding of the causal mechanisms. (2) SMOTE Limitations: while SMOTE addresses class imbalance, it can generate synthetic samples that are not representative of the true data distribution, potentially leading to overfitting or reduced generalization performance. (3) Threshold Optimization Dependence: the model's performance significantly depends on the effectiveness of the threshold optimization strategy. The choice of optimization metric (\(F1\) score in this case) influences the balance between precision and recall. A thorough sensitivity analysis of the chosen threshold is needed. (4) Computational Cost: the integration of SMOTE, LiNGAM, and random forest may incur significant computational costs, particularly when dealing with large datasets. Computational efficiency should be considered, especially when dealing with real-time anomaly detection.
# An All-Mach Number HLLC-Based Scheme for Multi-Phase Flow with Surface Tension
###### Abstract
For example, [PERSON] [27] is cited as the first to employ a geometric VoF method in conjunction with the Roe solver for a homogeneous two-phase flow without surface tension effects. Similarly, [PERSON] et al. [28] combined a geometric VoF method with a newly derived Godunov scheme for surface tension but did not employ the HLLC solver. [PERSON] et al. [22] used a similar geometric VoF method with HLLC. However, in the latter cited work, conserved variables were reconstructed and the VoF method was restricted to Cartesian grids. Hence in this work, we propose the use of VoF and HLLC. As opposed to the cited work, we advocate primitive variable reconstruction in the interests of consistency and accuracy. Extending earlier work, the Compressive Interface Capturing Scheme for Arbitrary Meshes (CICSAM) by [PERSON] et al. [34] is employed to ensure conservative sharp interface tracking on arbitrary meshes. Second-order flux reconstruction of primitive variables (\(\rho\), \(p\) and \(u\)) is ensured via the Monotone Upstream-centred Scheme for Conservation Laws (MUSCL) [41]. The use of this method ensures that shock waves are efficiently and accurately captured. Surface tension is accounted for via the continuum surface force (CSF) method where the interface curvature is reconstructed using the convolution method [4] on non-orthogonal meshes and height functions [2; 3] on Cartesian grids. The implementation of the developed algorithm is done in the Elemental(r)[24; 39; 42; 43] multi-physics platform. The developed solver is rigorously assessed via application to a range of benchmark test-cases from 1-D to 2-D.
# Two-Dimensional Vortex Solitons in Spin-Orbit-Coupled Dipolar Bose-Einstein Condensates
## 3 Semi-Vortices in Spinor SOC BEC with Pure DDI  
In Equation (1), if \(\Omega,g,\gamma\) is set to be zero and \(\beta\) is chosen to be \(>0\) (Setting (i)), the system then corresponds to a spinor SOC BEC with pure DDI, and Equation (1) is reduced to:  
\[\begin{split} i\partial_{t}\psi_{+}&=-\frac{1}{2} \
abla^{2}\psi_{+}+\lambda\hat{D}^{[-]}\psi_{-}+\beta\psi_{+}\int d\mathbf{r}^{ \prime}R(\mathbf{r}-\mathbf{r}^{\prime})(|\psi_{+}(\mathbf{r}^{\prime})|^{2}+| \psi_{-}(\mathbf{r}^{\prime})|^{2}),\\ i\partial_{t}\psi_{-}&=-\frac{1}{2}\
abla^{2}\psi_{-} -\lambda\hat{D}^{[+]}\psi_{+}+\beta\psi_{-}\int d\mathbf{r}^{\prime}R(\mathbf{ r}-\mathbf{r}^{\prime})(|\psi_{+}(\mathbf{r}^{\prime})|^{2}+|\psi_{-}( \mathbf{r}^{\prime})|^{2}).\end{split} \tag{4}\]  
The DDI kernel follows Equation (3) and \(\cos^{2}\theta\equiv(x-x^{\prime})^{2}/|\mathbf{r}-\mathbf{r}^{\prime}|^{2}\), which means the dipoles are all polarized in the 2D plane along the positive direction of the \(x\) axis. In order to obtain 2D solitons, the size of the soliton needs to be much larger than the cutoff \(\epsilon\), which requires proper setting of \(\epsilon\) and \(\lambda\). In this section, \(\epsilon=0.05,\lambda=1\) is fixed [58].  
The SOC term in Equation (4) breaks the Galilean invariance of the system, while the SOC and DDI terms both break the conservation of the angular momentum of the system. Thus, for spinor SOC BEC with pure DDI, the system is invariant with respect to specular reflections in the \(x\)-\(y\) plane:  
\[x\rightarrow-x,\ t\rightarrow-t,\ \psi_{\pm}\rightarrow\pm\psi_{\pm}^{*} \tag{5}\] \[y\rightarrow-y,\ t\rightarrow-t,\ \psi_{\pm}\rightarrow\psi_{\pm}^{*} \tag{6}\]  
where \(*\) denotes the complex conjugate, which provides a combined symmetry of the system, which reads:  
\[(x,\ y)\ \rightarrow\ (-x,\ -y),\ (\psi_{+},\ \psi_{-})\ \rightarrow\ (\psi_{+},\ -\psi_{-}) \tag{7}\]  
The localized solution to Equation (4) is said to be symmetric if it keep this symmetry, otherwise it is said to be asymmetric [58]. Equation (4) can be numerically solved by the imaginary-time method [66; 67].
## 1 Introduction and Related Work  
The age of smart devices has arrived. With the increasingly powerful paradigm of the Internet of Things (IoT), smart city environments have become a pressing need for wireless technologies that enable efficient communication between distant objects [1, 2]. Long Range (LoRa) [3] technology stands out for its combined low-power requirements and long-range coverage compared to other IoT communication protocols such as Bluetooth, Wi-Fi, ZigBee, or GSM. Such features are owed to its chirped spread spectrum and large bandwidth, making it less prone to interference and distance-fading. Examples of previous works deploying LoRa include monitoring systems for energy [4], environmental [5], and road condition monitoring [6], and flood prevention [7], among many other applications [8, 9]. LoRa's characteristics have enabled implementations using very low-power mobile [10] and even battery-free [6, 11] devices. The main disadvantage of LoRa technology is the tradeoff between distance coverage and the data transfer rate. Strategies for tackling this limitation include LoRa variants such as the Carrier Sense Multiple Access (CSMA) mechanism, which has enabled long-range image transmission [12].  
Unfortunately, a more extensive communication coverage range makes it difficult to accurately predict the signal strength within the covered area, which is crucial for an efficient network installation and hotspot deployment. Thus, understanding the propagation of LoRa signals is immensely relevant nowadays, at the birth of smart cities and in the future, when IoT-enriched technologies promise to prevail.  
In this work, we predict the coverage map of a LoRa network with simulated Received Signal Strength Indicator (RSSI) maps obtained using a recently developed electric-field propagation method known as the Oscillator Finite-Difference Time-Domain (O-FDTD) [13]. We propose to use the dielectric permittivity of the absorbing buildings as a single parameter to model the presence of concrete walls, using real-world building distribution data extracted from Google Maps. In this manner, the low computational cost O-FDTD simulation method provides high-resolution cityscape LoRa RSSI maps. Also, we develop a simple experimental procedure to determine the coverage area based on monitoring the time of arrival of LoRa packets sent with a constant period by a moving transmitter. We use the area surrounding the university campus as the test environment. First, we compare the path-loss characteristics of the O-FDTD simulations with the conventional Free-Space Path Loss (FSPL) model. Then, we overlay the experimental and simulated coverage areas for comparison and calculate the area overlap. We obtain a good agreement between the predicted and characterized coverage area and find that the assumption of a single dielectric permittivity for all buildings within the analyzed area provides accurate mappings of the LoRa cityscape signal coverage, its local shadow regions, and hot spots.
# Wireless Sensor Networks for Building Information Modeling
## 3 BIMERR Wireless Sensor Network Topology  
As described in the previous sections, the Gateway of each apartment makes the sensor-data streams available to the BIMERR Middleware via the Internet. Regarding the connection of the Gateways to the Internet, the following two technical solutions were employed:  
1. WSN Architecture 1: In this pilot case, all Gateways were connected via Ethernet Cat 6 cables to a common Switch, and the Switch was connected to the Internet via a wireless 4G LTE Gateway. For the needs of local data processing of the sensor-data streams, a mini PC (Raspberry Pi 4 Model B with 4 GB RAM) was connected to the Switch. This PC was configured and remotely controlled for the needs of the project.
2. WSN Architecture 2: In these pilot cases, each Gateway had an autonomous internet connection either via a 4G LTE Gateway or via the apartment's Internet Router (depending on the consent of the apartment's tenants). Again, for local data processing of the sensor-data streams, a remotely controlled mini PC (Raspberry Pi 4 Model B with 2 GB RAM) was connected either to a local Switch or the Internet Router of the apartment.  
The two aforementioned WSN architectures, regarding Internet connectivity, are depicted in Figures 3 and 4, respectively.  
Figure 2: Z-Wave mesh network concept.  
Figure 4: WSN Architecture 2.  
Figure 3: WSN Architecture 1.  
Regarding the installation of an apartment's Gateway, the proposed location was the main corridor, i.e., the space of each apartment with a direct view of all other rooms. In this manner, the Gateway can have, in most cases, Line-of-Sight (LOS) communication with at least one sensor in each room, but even if LOS is not achieved due to the existence of walls and household obstacles, the distance between the Gateway and the sensors is the shortest possible. This was the recommended setup, inasmuch as this could be achieved during installation. This setup was finally achieved for the WSN pilot sites in Spain and Greece. However, this was not the case for the Polish pilot buildings, where the apartment Gateways should be placed at the staircase, just outside of, and very close to, the main entrance of each apartment. LOS conditions were not met in this case; however, since apartments are not very large, the frequency used by the Z-Wave was low (868.42 MHz) and the Z-Wave network was a mesh network, so data transfers between the Gateways and the sensors were accomplished with adequate signal strength (RSS). This was also justified with field tests prior to the installations.  
The WSN topology for the apartments of each pilot site is depicted in Figures 5-7. In these figures, \"G\" stands for the Gateway and \"S\" stands for any Z-Wave sensor of the WSN. For simplicity reasons, not all equipment and sensors installed in the apartments are depicted. A detailed analysis of the exact type and quantity of the equipment and sensors installed per apartment is provided in Section 4.  
Figure 5: WSN topology of the pilot apartments in Poland.
# Monitoring of Ionospheric Anomalies Using GNSS Observations to Detect Earthquake Precursors
###### Abstract  
The study of the Earth's ionosphere is a topic that has increased in relevance over the past few decades. The ability to predict the ionosphere's behavior, as well as to mitigate the effects of its rapid changes, is a matter of primary importance in satellite communications, positioning, and navigation applications at present. Ionosphere perturbations can be produced by geomagnetic storms correlated with the solar activity or by earthquakes, volcanic activities, and so on. The monitoring of space weather is achieved through analyzing the Vertical Total Electron Content (VTEC) and its anomalies by means of time series, maps, and other derived parameters. In this study, we outline a strategy to estimate the VTEC in real-time, its rate of change, and the corresponding Signal-to-Noise Ratio (SNR) based on dual-frequency GNSS Doppler observations. We describe how to compute these parameters from GNSS data for a regional network using Adjusted Spherical Harmonic Analysis (ASHA) applied to a local model. The proposed method was tested to study ionospheric anomalies for two seismic events: the 2015 Nepal and 2023 Turkey earthquakes. In both cases, anomalies were detected in the maps of the differential VTEC (DTEC), differential VTEC rate, and SNR of the VTEC produced close to the earthquake zone. The robustness of the results is strongly related to the availability of a dense Ionosphere Pierce Point (IPP) cloud on the ionospheric layer and surrounding the studied area. At present, the distribution of Continuously Operating Reference Stations (CORSs) around the world is insufficiently dense and homogeneous in certain regions (e.g., the oceans). Robustness can be improved by increasing the number of CORSs and developing new models involving measurements over ocean surfaces.  
GNSS; ionosphere; ionospheric anomalies; earthquake; TEC; VTEC; DTEC; SNR; space weather monitoring. Article
## 1 Introduction
In this study, a hybrid approach combining PSO and quasi-Newton was chosen because of the PSO algorithm's ability to explore a large search space and quasi-Newton's efficiency in finding the optimal solution in a local search space. Additionally, quasi-Newton methods are capable of handling non-linear optimization problems and can converge to the global minimum, even when the function being optimized is non-convex. Furthermore, quasi-Newton methods do not require the computation of second-order partial derivatives, making them computationally more efficient than traditional Newton methods. This is particularly important when dealing with optimization problems involving large-scale systems. Therefore, this paper presents a novel hybrid optimization method called HPSO-QN for identifying the mechanical parameters of a 2 MM system. The method combines PSO for global exploration with quasi-Newton for local exploitation and estimates the mechanical parameters, such as motor and load inertias, shaft stiffness, and viscous and Coulomb friction coefficients on both the motor and load sides of the system. This work represents a significant step forward from previous studies [2; 3] that used various variants of PSO for the parameter identification of a 2 MM simulation. The most effective variant of PSO was carefully selected and incorporated into the hybrid framework. We then explored different versions of local search with the quasi-Newton method to reach the globally optimal solution. The proposed method was validated using experimental results from a 2 MM system, and its estimated results were compared with those identified using the frequency response function (FRF) method presented in [1]. The remainder of this paper is organized as follows. Section 2 presents the mathematical model of the 2 MM system. Section 3 presents the proposed identification method. Section 4 describes the detailed experimental setup of the study. Section 5 focuses on the analysis and discussion of the results obtained. Finally, conclusions are drawn in Section 6.
# Microscopic Multiple Fatigue Crack Simulation and Macroscopic Damage Evolution of Concrete Beam
## 4 Fatigue Damage Analysis of Concrete Beams
### Macroscopic Damage Analysis  
Though S-N curve is enough for engineering use to estimate remaining life, it has some limits when it comes to evaluating the current performance state of structure. This is because fatigue life estimation based on S-N curve has an implicit assumption that the fatigue damage is linearly accumulated. In fact, the fatigue damage evolution is a nonlinear process. The period from microcracks propagation and coalescence to the formation of macroscopic cracks is relatively longer, while the speed from the initiation of macrocracks to the final rupture is relatively fast. In this sense, damage evolution based fatigue evaluation is required. According to [PERSON]'s concept of damage variable [36], damage is related to stiffness reduction [37; 38].  
\[D=1-\frac{\tilde{K}}{K_{0}} \tag{14}\]  
Here, \(D\) is the damage variable, \(K_{0}\) denotes the effective stiffness in defect-free state, and \(\tilde{K}\) is the stiffness in damaged state. Obviously, the larger \(D\) grows, especially near 1, the closer material or structure is to the failure state. For three-point bending beams, effective stiffness could be defined as  
\[\tilde{K}=\frac{F}{d}. \tag{15}\]  
Here, \(F\) and \(d\) are mid-span force and displacement respectively, which could be extracted from calculated database. In defect-free or damage-free status, \(d_{0}\) could be obtained as  
\[d_{0}=\frac{FL^{3}}{48 EI}, \tag{16}\]  
where \(E\) is the initial Young's modulus of concrete.  
Curves of damage evolution at different stress levels are plotted in Figure 12. It can be observed that the fatigue damage evolution of concrete can be roughly divided into three stages. When the cycle ratio \(n/N_{f}\) is less than 0.2, the damage value is relatively small, and the damage evolution rate is also slow. Growth rate of most microcracks fall in zone I. Crack coalescence has not occurred yet. With the increase of load cycles, damage evolution enters into a stable stage that the value of damage increases linearly with the number of load cycles. In this period, microscopic crack growth rate may vary from area to area (some areas in zone II, some still in zone I). Microcracks begin to link with others, gradually forming macroscopic cracks (crack coalescence). When cycle ratio reaches 0.8, structure enters into an unstable crack growth stage. Macroscopic cracks continue to propagate forward, merging small or microcracks along the rupture path. Correspondingly, the value of damage rapidly approaches the value of 1, finally leading to the fracture of beams.  
Figure 11: S-N curve obtained by numerical modeling.
# Random Segmentation: New Traffic Obfuscation against Packet-Size-Based Side-Channel Attacks
## 3 Threat Model  
We consider two observation points an adversary can exploit to collect encrypted WiFi traffic. In both scenarios, the attacker is physically located within the signal range of the victim's WiFi router or AP. The attacker can be one of the following:  
_Active Observer:_ The attacker can set a rogue AP with the same network name as the victim's network, which may lure IoT devices to connect to the rogue AP instead of the legitimate one. In this case, the attacker can observe and analyze the IP-level traffic of the connected IoT devices. We assume the observer can inspect the header of IP packets but does not know the device or break the encryption.  
_Passive Eavesdropper:_ The attacker can listen to the wireless channel to capture the encrypted WiFi traffic using a WiFi card in monitor mode ([[https://en.wikipedia.org/wiki/Monitor_mode](https://en.wikipedia.org/wiki/Monitor_mode)]([https://en.wikipedia.org/wiki/Monitor_mode](https://en.wikipedia.org/wiki/Monitor_mode)) (accessed on 15 May 2023)). Eavesdroppers are not required to access or join the network. The attacker is, therefore, very hard to detect.  
We assume the attacker can access the same IoT devices as the victim's network. The attacker can collect the encrypted traffic to build a profile that can be used to identify IoT devices with similar traffic patterns. Our adversary aims to infer the device (e.g., doorbell, sleep monitor, etc.) and then monitor for network events based on traffic pattern changes, e.g., a surge in doorbell traffic indicates the arrival of a visitor, a surge in sleep monitor traffic indicates a user is awake, etc. We consider device-fingerprinting attacks that operate on packet lengths and directions. Timing information falls beyond this paper's scope and, therefore, is not considered by our defense.