esa-sceva/satcom-chunk-collection huggingface dataset:
# A Joint Domain-Specific Pre-Training Method Based on Data Enhancement
## 3 Approach
### Joint Pre-Training Strategy  
Existing traditional pre-training methods usually separate the pre-training phase from the downstream fine-tuning phase. As a result, this causes a catastrophic forgetting problem when the model is trained in the downstream fine-tuning task by forgetting the previous knowledge learned by the model in the domain corpus. To alleviate the catastrophic forgetting problem arising from the downstream fine-tuning process, this paper first proposes a joint training strategy that aims to capture the grammatical rules specific to the domain corpus using repeated learning of the domain corpus while learning a specific task, as shown in Figure 1b. The strategy first loads the domain pre-training corpus \(D_{domain}\) and the downstream task fine-tuning corpus \(D_{task}\) into the memory simultaneously. In each training episode, the update of the BERT parameters in the model is completed by calculating the MLM loss function \(L_{MLM}\) for each sample in \(D_{domain}\). After learning all the pre-trained corpus, the downstream fine-tuning stage fine-tunes the parameters of the whole pre-trained model by calculating the loss \(L_{task}\) of each task sample for the downstream task corpus \(D_{task}\). Thus, the language model in the pre-training phase is used as an objective function for information enhancement combined with the task-specific optimization function to obtain the joint training loss function, represented as follows:  
\[L=\sum_{s\in D_{domain}}L_{MLM}(s)+\sum_{s\in D_{task}}L_{task}(s). \tag{2}\]  
Such a joint training approach enables the model to perform the required downstream tasks well on the one hand, and on the other hand, allows the model to review the knowledge in the domain corpus while learning the knowledge of the downstream tasks. The joint learning of the corpus and the downstream task allows the BERT model to capture as many domain-unique expression patterns as possible to alleviate the catastrophic forgetting problem arising from traditional methods.  
However, in such joint training, the size of the corpus data \(D_{domain}\) required for pre-training is very large and the quality of the data is uneven. Therefore, the joint training method can easily enable the model to learn the wrong information. At the same time, the pre-training in the joint training strategy leads to an increase in the computational resources required for model training because the pre-training corpus is so large, which makes it difficult to use the strategy for different downstream tasks and reduces the generalizability of the joint training method.
## 4 Simulation Setup
### Dataset Information  
The dataset presented in this paper includes data extracted from authentic GPS signals collected from real-time experiments [38]. These experiments involved generating malicious signals by simulating three different types of GPS spoofing attacks. An eight-channel GPS receiver was mounted on a vehicle moving at speeds ranging from 0 to 60 mph to emulate UAV flight, as well as in three static positions on top of different buildings, each with  
Figure 4: The proposed framework architecture, showing the flow from data pre-processing to classification via a GA-Optimized LSTM network.  
a different altitude, to emulate UAV hovering operation. Thirteen characteristics were recorded from eight parallel channels during the data gathering process. Three different forms of GPS spoofing attacks were simulated in addition to the real GPS signals: basic, intermediate, and sophisticated attacks. The dataset contains 510,000 samples, with 55% representing real signals and 45% representing fake signals.  
Simplistic Spoofing Attacks: In this case, the attacker does not know the position of the receiver; the spoofer creates a phony GPS signal that is out of sync with the real one. This results a larger variance in the pseudo-range measurement due to a stronger Doppler shift that is more than the typical range of \(\pm\)20 Hz. This kind of attack involves the attacker sending a high-power fake signal, which increases the C/N value and makes it easy to identify. Further, there are many inexpensive commercial GPS spoofing tools available to plan such an attack.  
Intermediate Spoofing Attacks: In this case, the genuine and spoofed signals have code phase alignment since the spoofing attacker is aware of the target receiver's location. In contrast to a straightforward spoofing assault, the intermediate attacker can precisely control the GPS-generated signal to take over the UAV. Furthermore, because the spoofer considers the detection system, a detection system based solely on signal characteristics is unable to identify the attack. To avoid exceeding typical ranges, the Doppler shift and pseudo-range are always kept under control. Nonetheless, the effects of the received spoofing signal can be identified by closely observing the carrier phase shift, correlator amplitude, and Time of the Week (TOW) information.  
Sophisticated Spoofing Attacks: In this case, the attacker simulates the GPS constellation by using several synchronized antennas. In this manner, the attacker can simultaneously fake multiple channels and take full control of the system. While achieving synchronization between numerous antennas is difficult, new Software-Defined Radio (SDR) technologies make it easier to achieve. However, due to multipath signals and the motion of the satellite and receiver, a correlator-based detection system might be misleading for such a crucial kind of attack.
## 2 Radio Resource Management
### Beam Hopping  
Time flexibility is an alternative scheme where the full available spectrum can be allocated to a subset of the overall spot beams, which are activated only for a specific portion of time. The set of illuminated beams can be changed over time slots according to a periodically repeating spatio-temporal transmission pattern. Co-channel interference is kept to a minimum, keeping the beams belonging to the same group with a minimum geographical separation. The main design problem of a BH system is to identify the optimal illumination pattern of the beams, i.e., which beams are activated when and for how long [30].  
The beam scheduling or beam illumination pattern formulation considers a specific time window \(T_{w}\) segmented into time slots of duration \(T_{s}\). In this case, we use \"snapshot\" to refer to a particular arrangement of illuminated and unilluminated beams. Within a BH window \(T_{w}\), a number of illuminated snapshots are are used. Denote \(\mathcal{G}\) as the set of \(\mathcal{G}\) possible snapshots and \(\mathcal{G}_{b}\subset\mathcal{G}\) as a set of snapshots with illuminated beam \(b\). In that sense, a beam illumination design can be formulated as a \(\max\min\) problem where the objective is to maximize the minimum value of the ratio between offered capacity and traffic demand in the entire set of illuminated beams, and \(\max\min\) is generally widely used for fairness purposes, such as fairness in satisfying user demand [8]:  
\[\max_{t_{g}}\min_{b}\{\frac{C_{g}^{b}\frac{\sum_{g\in G_{b}}t_{g}T_{s}}{T_{b}} \}{D_{g}^{b}}\} \tag{6}\]  
such that the following is the case:  
\[\sum_{g=1}^{\mathcal{G}}t_{g}T_{s}=T_{w} \tag{7}\]  
\[\{t_{1},t_{2},...T_{\mathcal{G}}\}\in\mathbb{I}, \tag{8}\]  
where \(C_{g}^{b}\) represents the offered capacity, \(D_{g}^{b}\) represents the traffic demand on the \(b\)th beam and \(t_{g}\) is the normalized illumination time for the \(g\)th snapshot.  
Based on its features, (6) is a mixed integer linear programming (MILP) problem. To simplify the proposed problem, a reformulation is usually carried out that relaxes the integers to non-negative continuous numbers. Then, the objective function is transformed from a max-min to maximization-only by introducing an auxiliary variable as explained in [30]. However, the illumination time per beam must be provided as a function of traffic demand, as well as taking into account the number of simultaneously active beams and user latency as explained in [8]. Therefore, an ML-assisted model is expected to help improve the optimization performance presented in Equation (6) without having to resort to the aforementioned simplifications.
# Macroscopic Traffic-Flow Modelling Based on Gap-Filling Behavior of Heterogeneous Traffic
## 2 Macroscopic Models Based on Fluid Flow  
Developing traffic-flow models using the fluid flow has become the norm in the field of traffic engineering. In addition to the LWR and PW models, several other researchers also developed traffic-flow models using the fluid-flow theory. [PERSON] and [PERSON] [27] were one of the earliest to derive a traffic-flow model by conceptualizing the kinetic theory of gases. In their model, vehicles are treated as gas particles, while vehicle interaction is taken as analogous to the collision between gas particles. This model is suitable for dilute traffic conditions only. [PERSON] [28] presented another traffic-flow model, using the kinetic theory of gases. This model is also suitable for dilute traffic conditions only and assumes that the desired speeds of the drivers are independent of the local traffic conditions. [PERSON] [29] employed statistical techniques used in the kinetic theory of gases to develop a model that produces results which remain stable over a wide range of traffic densities. [PERSON] [20] used the ideal gas law and developed analogies of the gas parameters with traffic parameters. Accordingly, a driver's anticipation term was developed, which replaces the constant anticipation term in the PW model. [PERSON] numerically solved the PW model and the proposed model using the Roe scheme [30]. Both models were then implemented and simulated in MATLAB for performance evaluation. Similarly, [PERSON] et al. [31] also developed a traffic-flow model by deriving analogies between traffic flow and fluid flow, using Darcy's Law. This model attempts to characterize the heterogeneous traffic by replacing the constant anticipation term in the PW model. However, the values taken for traffic parameters in the proposed model also result in a constant term. The proposed model was simulated in MATLAB on a circular road, along with other models, and the results were compared.  
In this study, a macroscopic model is developed for heterogeneous traffic conditions based on the gap-filling behavior. The model is derived by developing analogies of the traffic parameters with gas parameters used in the Maxwell-Boltzmann equation for the effusion of gases [32]. The performance of the proposed and PW models is then compared by using numerical simulations on a straight road (with and without ingress traffic) and a circular road.  
The remainder of this paper is organized as follows. The development of the proposed model is presented in Section 3. The numerical solution of both the proposed and PW models is presented in Section 4. Finally, the numerical simulation of both models on straight (with and without ingress traffic) and circular roads is presented in Section 5, along with detailed discussion on each scenario. Section 6 draws pertinent conclusions and presents future work.
## 4 Case Study
### Identify Critical Quality Control Procedure  
Prefabricated parts' inspection and calibration: The removable flood wall used in this study was obtained from IBS Company, Germany, and has been applied in Germany, Austria, the UK, and China [5]. Table 1 shows the main components and materials.  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**Class**} & \multicolumn{1}{c}{**Model**} & \multicolumn{1}{c}{**Material**} \\ \hline Center post & MS100K-T05-1865 & Aluminum alloy EN AW 6005 \\ End post & E100K-T01-1865 & Aluminum alloy EN AW 6005 \\ Dam beam & DBAL100 \(\times\) 200 – 2.5 (width: 3000 mm) & Aluminum alloy EN AW 6063 T66 \\ Ground seal & B100K (width: 3000 mm) & PE(polethylene)/PU(polyurethane) \\ Pressing tool & VS 100K & Stainless-steel SS304 \\ Anchor plate & AP 100K-T05 & Stainless-steel SS304 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main structure parts and materials of removable floodwalls [5].  
The removable floodwall consists of two permanent installed components, one is the end post, and the other is the anchor plate. It also includes four removable components: logs, ground seal, pressing tool, and center post. All of them are prefabricated and were transported to the job site, as shown in Figure 3.  
Figure 3: Removable floodwall storage and install. (**a**) Prefabricated parts received and stored in the warehouse, (**b**) install the center post, and (**c**) install all the parts.  
It is essential to inspect the quality and dimension of the prefabricated components. In the preconstruction phase, the research first utilized laser scanning technology to verify the anchor plates' dimensions and then used the laser scanner to survey the construction site.  
The workflow of quality control for a removable floodwall system with 3D laser scanning and the BIM method is shown in Figure 4.
# Thermal Effects on Photovoltaic Array Performance: Experimentation, Modeling, and Simulation
## 1 Introduction
[21] then developed a fast transient thermal model that accounts for transient climatic conditions and thermal mass of module, which improves energy production prediction. [PERSON] et al. [22] proposed new correlations based on experimental data, while accounting for variations in irradiance, wind speed, and ambient temperature. These correlations can be used to predict the performance of PV systems. As previously mentioned, although generalized models remain elusive, new correlations based on reliable experimental data need to be developed as PV becomes more widely adopted and integrated with complex flow regimes and building structures. This work aims to study a flat-roof mounted PV array on an industrial building to examine how wind speed, wind direction, and ambient temperature affect array temperature \begin{table}
\begin{tabular}{l l} \hline
**Correlation** & **Reference** \\ \hline \(h=5.74V^{0.8}L^{-0.2}\) & [PERSON] [6] \\ \(h=16.21V^{0.452}\) & [PERSON] and [PERSON] [7] \\ \(h=8.55+2.56V\) & [PERSON] et al. [8] \\ \(h=5.7+3.8V\) & [PERSON] et al. [9] \\ \(h=8.7+9.4V\) & [PERSON] et al. [10] \\ \(Nu=0.86 Re^{0.5}Pr^{1/3}\) & [PERSON] [11] \\ \(Nu=1.067 Re^{0.466}Pr^{1/3}\) & [PERSON] [12] \\ \hline \end{tabular}
\end{table} 
Table 1: Reported PV flat plate heat transfer coefficient (\(h\)) and Nusselt number correlations. \(V\) is wind speed in m/s. and production efficiency. A new empirical correlation fit is proposed in this work, which is not available in the literature to the best of the authors' knowledge. These experimental results are then compared to CFD simulations, as well as an existing empirical model available in PVsyst, an industry-standard PV design package. The remainder of the paper is structured as follows: Section 2 describes the experimental setup; Section 3 discusses the thermal model used in PVsyst; Section 4 focuses on the CFD model; Section 5 summarizes the experimental and numerical results in Sections 5.1 and 5.2, respectively.
## 1 Introduction  
Given the momentum taken by the space sector for lunar exploration both by private and governmental actors, scientific and commercial initiatives are expected to expand in the 2020s, led by the European Space Agency (ESA)'s Moonlight initiative and National Aeronautics and Space Administration (NASA)'s Artemis program [1]. In this framework, the development of guidance, navigation, and control solutions being independent of tracking and controlling ground segments is strongly desired for lunar exploration. In particular, the south pole region of the Moon is attracting attention from the perspective of human activity bases and exploration [2; 3; 4; 5]. In this context, the ESA and NASA together with the Italian Space Agency (ASI) plan to fly in-orbit demonstration missions around the Moon. These missions are the Surrey Satellite Technology Ltd (SSTL) Lunar Pathfinder which is planned to be flying the NaviMoon receiver in 2025, [6], and the Firefly Blue Ghost Mission 1 flying a multi-constellation Global Navigation Satellite System (GNSS) receiver payloadin 2024 in the framework of the Lunar GNSS Receiver Experiment (LuGRE) [7]. Both will track Galileo E1, E5a, and GPS L1 C/A, L5 signals and will return pseudorange, carrier phase and Doppler measurements. The success of these two missions will then contribute to the extension of the Space Service Volume (SSV) to the cis-lunar space. SSV is defined as the volume in space currently spanning from 3000 km to 36,000 km above the Earth's surface, in which the availability and performance of GNSS signals are characterized [8].  
In addition to being equipped with a navigation system and to being on or around the Moon at the same time [9], these missions will often be interacting as well, [10], potentially relaying communications for one another or act as distributed systems, given their effectiveness and cost efficiency when compared to monolithic missions [11]. The interactions between these various space objects around the Moon justify the need for developing inter-spacecraft ranging techniques applicable to the lunar vicinity. The family of Differential GNSS (DGNSS) ranging techniques have been used a lot in the past decades both on Earth and in space to improve the position of a specific user, given their non-invasiveness as they rely solely on a communication link among the interacting GNSS users.
## 4 Results and Discussion
### Influence of the Relative Position between Pipeline and Tunnel  
#### 4.2.1 Pipeline Horizontal Distance  
Figure 7 shows the vertical deformation curve of the top of the pipeline at different horizontal positions. The abscissa in the figure was the Y coordinate corresponding to the monitoring point on the pipeline in the model, that was, the distance from the tunnel portal. There were 9 pipelines arranged between X = \(-25\) m and X = \(25\) m. The relative positions and numbering of pipelines are shown in Figure 5a. Figure 7 indicates that except for several monitoring points at the tunnel tail, the settlement of pipeline 1 at other locations was generally greater than that of other pipelines, indicating that the pipeline deformation above the central axis of the double-arch tunnel, that is, the central axis of the central pilot tunnel, was most affected by the tunnel excavation. On the basis of the analysis of the changing trend of a curve, it was found that the settlement of the pipeline at the portal section was greater than that at the middle part of the tunnel, and greater than that at the tail part of the tunnel. This is because the support structure was not perfect during the excavation of the portal section, and the surrounding strata had a large displacement after the release of surrounding rock stress. With the continuous progress of the excavation and support process, when the excavation surface advanced to the middle part of the tunnel and the tail part of the tunnel, the primary lining, secondary lining and invert arch and other supporting structures had been relatively perfect, so the settlement of underground pipeline was worth controlling to a certain extent. It also can be seen the settlement of the pipeline converged at the end of the tunnel. The analysis believed that the end excavation of the tunnel has been completed, and there was no subsequent excavation to disturb the surrounding rock, so the pipeline settlement here was small.  
Figure 6: Vertical settlement of the vault of the right tunnel.  
To more intuitively reflect the influence of pipeline horizontal distance changes on pipeline deformation, the maximum settlement of the pipeline at different positions was taken out and plotted, as shown in Figure 8. The maximum vertical deformation of the pipeline above the central axis of the double-arch tunnel was significantly higher than that of the pipeline at other positions. When the distance between the underground pipeline and the central axis of the tunnel gradually increased, the maximum settlement value of the pipeline kept decreasing.  
Figure 8: Maximum vertical deformation of the pipeline at different horizontal positions.  
Figure 7: Vertical deformation of pipelines at different horizontal positions.
## 7 Long-Range Underground Monitoring
### Technology Description  
#### 7.2.1 LoRa  
LoRa (long range) and LoRaWAN(r) describe a low-power wide-area network (LP-WAN) protocol used for IoT applications. The term LoRa refers to the proprietary physical layer by Semtech (also called LoRaPHY), and LoRaWAN describes the MAC Layer specification. LoRaWAN(r) is an open MAC protocol specification which is managed by the LoRa Alliance(r). The LPWAN operates in ISM sub-GHz frequency bands with data rates between 0.3 to 37.5 Kbps [38; 87; 88].  
LoRa LPWAN is used in Smart City applications with various use-cases in agriculture [89], traffic monitoring [90], environmental monitoring [91], waste management [92], industry process monitoring and energy monitoring [93].  
In mining, LoRa technology has successfully been implemented for conveyor belt monitoring. Live sensor data transmitted via LoRa can help to detect rips in the belt and trigger a shut-off [39].  
The network consists of three devices present: end-devices, gateways, and network servers. One or multiple gateways handle the received packets from the end devices and requested downlink packets from the network server. The network server connects to IP-based (cloud) applications which process the received data [38; 94]. The topology of the devices within the LPWAN network follows a star-of-stars topology with gateways being the central points of the network [88; 94]. Due to the topology of the network, dynamic extension of the coverage of the network is done by adding more gateways to the network which need to be connected to a (wired) IP-based network, which limits the ability to extend the network dynamically in quickly changing environments. To overcome the star-of-stars topology limitations not allowing for Device-to-Device communication, [95] summarize recent research findings on multi-hop LoRa networks allowing for extended coverage, improved scalability, network reliability and energy efficiency. [96] propose a LoRa Mesh protocol allowing for device-to-device communication and meshed network architecture. In [97], a linear underground LoRa multi-hop node architecture with an ad-hoc transmission protocol optimizing for node wake-up times with the aim of energy consumption minimization is presented. Similar work on developing a linear multi-hop LoRa network is done in [20].  
The focus of this work is on investigating signal strength in underground environments with and without line-of-sight of communication nodes and probabilistic evaluations of successful packet delivery within the multi-hop network.  
Ref. [98] investigate transmission ranges of Lora compared to Wi-Fi in underground environments with the result that LoRa has up to 2/3 higher range than Wi-Fi. In [99], LoRa is used in an open-cast mine environment propagating real-time data.