esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Motivation  
Organisations have the flexibility to choose from a variety of international standards and frameworks (ISFs) to steer the design, execution, and upkeep of their BC solutions. Variousviewpoints on BCM, as outlined by the most commonly employed frameworks, establish a set of requisites, practices, or activities designed to oversee the components of BC.  
Certain organisations postpone the implementation of their BCP, citing reasons such as design limitations, which may be attributed to technical or financial constraints or issues related to the interpretation of requirements [12], or limitations imposed by company policies and project timelines [13]. Hence, there exists a shortfall within organisations that have adopted and obtained certification in BCM standards, especially in the public sector [14]. Small or medium-sized enterprises (SMEs) are more susceptible to disasters due to their limited financial and human resources and poor technological capabilities to recover from disasters [15]. Other constraints felt in organisations are the lack of proactivity in BC planning and disaster recovery (DR) [16] that can lead to damage to reputation and market share, failure in customer service and business processes, regulatory responsibility, and increases in the system resume and restore times.  
These limitations highlight a perceived level of complexity when it comes to launching a BCM program and the essential expertise required for crafting and executing a BCP [17], as well as its ongoing maintenance.  
To address the challenges associated with constraint mitigation, it is essential to gain a comprehensive understanding of each identified component within the business continuity management system (BCMS). Additionally, it is crucial to review research findings that may uncover a broader range of constraints and strategic guidelines aimed at optimising the implementation of BCPs.
## 3 Results and Discussion  
### Characterization Studies  
Figure 2 illustrates the XRD pattern of the commercial SnO\({}_{2}\) particles. It depicts the peaks related to the crystalline planes of tetragonal SnO\({}_{2}\), matching JCPDS Card No. 41-1445 [36]. No peaks related to other phases or impurities were detected, indicating the high purity of the SnO\({}_{2}\) particles. Figure 2a-c present SEM images of the SnO\({}_{2}\) particles at various magnifications. Overall, they had an almost spherical shape, and the size of the individual particles was approximately 100 nm. However, they were agglomerated  
Figure 1: (**a**) Schematic of tubular sensor (**b**) fixing the tubular substrate before applying the sensing layer to it; (**c**) real image of the fabricated sensor on support.  
into larger particles. The agglomerates were not fully dense, and there were some pores, channels, and voids among the SnO\({}_{2}\) particles. Therefore, the target gas can easily diffuse into deep parts of the sensing material, expecting a relatively high response to H\({}_{2}\) gas.  
Next, the chemical composition of the particles was studied. Figure 3a presents a typical SEM image of the SnO\({}_{2}\) particles, and the corresponding SEM-EDS elemental mapping of Sn and O elements is presented in Figure 3b,c, respectively. The mentioned elements were uniformly distributed on whole parts of the SnO\({}_{2}\) particles. Furthermore, in Figure 3d, the corresponding SEM-EDS spectrum is presented. The weight percentages of O and Sn elements were 36.09 and 63.91%, respectively. Also, the atomic percentages of the mentioned elements were 80.73 and 19.27%, respectively.  
XPS measurements were performed to study the surface composition of the materials and the valence states of the elements up to a depth of 10 nm from the surface. Figure 4a  
Figure 3: (**a**) SEM micrograph; (**b**) EDS mapping of (**c**) “Sn” and “O” elements; (**d**) EDS spectrum of SnO\({}_{2}\) powders.  
Figure 2: (**a**) XRD pattern of commercial SnO\({}_{2}\) particles. (**b–d**) SEM images of commercial SnO\({}_{2}\) particles at different magnifications.  
presents the XPS survey of commercial SnO\({}_{2}\) particles. The peaks related to Sn and O elements were recorded. In addition, a peak related to carbon (from the environment) was observed. To have a better insight, we also studied the XPS core-level regions of OIs and Sn 3d. Figure 4b presents the OIs XPS core-level region, which exhibits a peak centered at 531 eV related to the presence of lattice oxygen (O\({}^{2-}\)) in SnO\({}_{2}\)[37]. Figure 4c indicates the Sn 3d XPS core level, in which two peaks of Sn 3d\({}_{5/2}\) and Sn 3d\({}_{3/2}\) are located at 487.5 and 497.5 eV, demonstrating that Sn is in the (IV) valence state [38]. Therefore, based on characterization results, commercial SnO\({}_{2}\) particles had almost a spherical morphology and desired crystallinity, without undesired phases and impurities.
# First In-Orbit Validation of Interferometric GNSS-R Altimetry: Mission Overview and Initial Results
## 2 Mission Overview  
### Payload Design  
The NSSC iGNSS-R altimeter operates in a near-polar orbit at \(\sim\)900 km altitude, demonstrating innovative spaceborne iGNSS-R capabilities for high-precision SSH measurements, and is compatible with the GPS, BDS, and GAL systems.  
The primary operating mode is the interferometric altimetry mode, though it also supports the local code correlation Delay-Doppler Mapping (DDM) mode and the carrier phase altimetry mode. The interferometric altimetry mode and the local code DDM mode share the same high-gain beam-scanning antenna, which features a back-to-back dual-layer structure. The sky-facing antenna, whose beams are steered toward GNSS satellite positions, which are computed by the receiver using ephemeris data derived from the navigation positioning antenna, is used to receive the direct dual-frequency signals from GNSS satellites, while the ground-facing antenna--whose beams are continuously aligned with dynamically computed specular reflection points, which are determined in real-time based on the GNSS satellite positions, the receiver positions, and the Earth's surface topography, in order to optimally capture reflected dual-frequency GNSS signals--is used to receive the reflected dual-frequency signals. Each layer of the antenna structure consists of 45 phased array elements, as shown in Figure 1a, with a maximum scanning range of 45\({}^{\circ}\), capable of simultaneously tracking signals from up to four GNSS satellites. The reflected signals received by the beam-scanning antenna are divided into two parts. The first part is directly correlated with the direct signals in the GNSS-R receiver to generate interferometric waveforms. The other part is correlated with the local code to generate the DDM, which serves as an auxiliary measurement for the NSSC iGNSS-R altimeter, primarily used to calibrate the pointing direction of the beam-scanning antenna and to provide the GNSS-reflected signal power. During operation, the receiver prioritizes tracking high-elevation satellites within a 35\({}^{\circ}\) incidence angle range, allowing for a maximum cross-track observation swath of approximately 1100 km, as shown in Figure 1b. To the best of the authors' knowledge, this is the first launched GNSS-R satellite to employ a beam-scanning antenna. The carrier phase mode utilizes an independent left-hand and right-hand circularly polarized dual-frequency antenna, primarily for verifying low-elevation carrier phase altimetry.  
This study primarily focuses on the interferometric altimetry mode, and on interferometric SSH retrieval and accuracy assessment utilizing the dual-frequency interferometric waveforms generated by this mode.
## 4 Realization of the Optimization of the Measurement Points of the Deck Modal Test of STTGB
### Optimization Results of the Number of Bridge Deck Measurement Points  
To introduce the modal confidence matrix, see Equation (9) and take Equation (10) as the fitness function. The numerical simulation was conducted to optimize the number of measuring points on the bridge deck. The first seven modal shape data points of the bridge deck were selected as the training data. Based on the optimization strategy of the number of measuring points in Section 3.4, the variation curve of the obtained value with the number of measurement points is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Structural Steel (Q235)** \\ \hline Density & 7850 kg/m\({}^{3}\) \\ [PERSON]’s modulus & \(2\times 10^{11}\) Pa \\ Poisson’s ratio & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Structural material parameters for the STTGB.  
Figure 5: Finite element settings for STTGB. (**a**) Constraint position; (**b**) finite element model.  
As can be seen from Figure 6, when the number of measurement points increases to 28, the value of \(f_{max}\) basically stops changing, and it is believed that the extra measurement points above 28 do not have much impact on the \(f_{max}\). When the number of measurement points is less than 28, the value of \(f_{max}\) keeps decreasing as a whole, with small fluctuations in the middle but with little impact. Therefore, based on the above analysis, it is determined that the optimal number of deck modal test points of STTGB is 28.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Order** & **Calculated Modal Frequencies [Hz]** & **Modal Vibration Pattern** & **Description of the Vibration Pattern** \\ \hline
1 & 18.037 & 1 st order bending \\
2 & 27.455 & 1 st order twisting \\
3 & 37.271 & 2 nd order bending \\
4 & 53.726 & 2 nd order twisting \\
5 & 79.738 & 3 rd order twisting \\
6 & 85.554 & 4 th order bending \\
7 & 95.439 & Combined 2 nd order bending and twisting deformation \\ \hline \hline \end{tabular}
\end{table}
Table 3: The calculated modal frequencies and vibration patterns of the STTGB deck.
# On-Board Decentralized Observation Planning for LEO Satellite Constellations
## 3 Improved CBBA
### Main Procedure of ICBBA
```
procedureICBBA (current satellite index \(i\), satellite index set \(\mathcal{I}=\{1,\ 2,\ \ldots,N\}\), task index set \(\mathcal{J}=\{1,\ 2,\ \ldots,M\}\), observation availability mask index sets \(\{\mathcal{K}_{i1},\ \mathcal{K}_{i2},\ \ldots,\mathcal{K}_{iM}\}\), communication availability mask index sets \(\{\mathcal{L}_{i1},\ \mathcal{L}_{i2},\ \ldots,\mathcal{L}_{iN}\}\)  Initialize \(T=0\), \(\mathbf{b}_{i}(T)=\varnothing\), \(y_{ij}(T)=0\)\(\forall j\in\mathcal{J}\), \(z_{ij}(T)=-1\)\(\forall j\in\mathcal{J}\), \(s_{ii^{\prime}}=0\)\(\forall i^{\prime}\in\mathcal{I}\), \(conf_{i}=0\) while termination condition is not reached do /* Execute the procedure if the earliest task execution start time or the running time threshold set in advance has not been reached */ if\(conf_{i}=0\)then \(T=T+1\) /* Record the iteration time */ \(\mathbf{b}_{i}(T)\), \(\mathbf{y}_{i}(T)\), \(\mathbf{z}_{i}(T)\leftarrow\) Bundle  Construction(\(i\), \(\mathcal{J}\), \(\{\mathcal{K}_{i1},\ \mathcal{K}_{i2},\ \ldots,\mathcal{K}_{iM}\}\), \(\mathbf{b}_{i}(T-1)\), \(\mathbf{y}_{i}(T-1)\), \(\mathbf{z}_{i}(T-1)\)) /* Construct the bundle and update the planning information */ \(t\leftarrow\) Time Reading() /* Record the current time */ \(\mathcal{N}_{i}^{t}\leftarrow\) Neighbor Getting(\(t,\ i,\ \{\mathcal{L}_{i1},\ \mathcal{L}_{i2},\ \ldots,\mathcal{L}_{iN}\}\)) /* Calculate the communication availability */ for\(i^{\prime}\) in \(\mathcal{N}_{i}^{t}\)do  Planning Information Sending(\(i,\ i^{\prime}\), \(\mathbf{y}_{i}(T)\), \(\mathbf{z}_{i}(T)\), \(\mathbf{s}_{i}\)) /*Send the planning information to each neighbor */ endfor if neighbor \(sat_{i^{\prime}}\) is sending planning information to \(sat_{i}\)then  Planning Information Receiving(\(i,\ i^{\prime}\), \(\mathbf{y}_{i^{\prime}}\), \(\mathbf{z}_{i^{\prime}}\), \(\mathbf{s}_{i^{\prime}}\)) /*Receive the planning information from the communicating neighbor */ \(t\leftarrow\) Time Reading() /* Record the time of current communication */ \(conf_{i^{\prime}}\), \(\mathbf{b}_{i}(T)\), \(\mathbf{y}_{i}(T)\), \(\mathbf{z}_{i}(T)\), \(\mathbf{s}_{i}\leftarrow\) Consensus  Check(\(t\), \(i^{\prime}\), \(\mathcal{I}\), \(\mathcal{J}\), \(\{\mathcal{K}_{i1},\ \mathcal{K}_{i2},\ \ldots,\mathcal{K}_{iM}\}\), \(\mathbf{b}_{i}(T)\), \(\mathbf{y}_{i}(T)\), \(\mathbf{z}_{i}(T)\), \(\mathbf{s}_{i^{\prime}}\), \(\mathbf{y}_{i^{\prime}}\), \(\mathbf{z}_{i^{\prime}}\), \(\mathbf{s}_{i^{\prime}}\)) /*  Check the consensus between satellite itself and its neighbor and update the planning information */ endif endwhile endprocedure
```  
**Algorithm 1** ICBBA for \(sat_{i}\)
# An Overview of CNN-Based Image Analysis in Solar Cells, Photovoltaic Modules, and Power Plants
## 4 Discussion  
In most cases, the subject of application for CNN models was provided by images of solar cells. In studies by [PERSON] [65], [PERSON] et al. [73], [PERSON] et al. [88], [PERSON] [90], [PERSON] et al. [63], [PERSON] et al. [67], [PERSON] et al. [75] [PERSON] and [PERSON] [76], [PERSON] et al. [78], [PERSON] [82], [PERSON] and [PERSON] [93], [PERSON] [94], and [PERSON] and [PERSON] [44], models were used for both detection and classification tasks, and most often for crack defects. Photovoltaic modules were examined by [PERSON] et al. [69], [PERSON] et al. [89], [PERSON] et al. [79], [PERSON] et al. [66], [PERSON] et al. [72], [PERSON] and [PERSON] [18], and [PERSON] et al. [96]. Furthermore, the power plant level application of CNN was presented by [PERSON] and [PERSON] [91], [PERSON] et al. [68], [PERSON] et al. [77], [PERSON] et al. [95], [PERSON] et al. [20], and [PERSON] et al. [97].  
As part of CNN implementation in large PV power plants, IR and RGB imaging modes are very useful to detect failure sources and thus have a higher chance of detecting solar panel damage and PV power plants' performance degradation or possible failure, resulting in power generation interruptions. This will also allow faster and more efficient intervention and decision-making by operators in case of problems. It is even more important that the detection is quick and accurate. The use of UAVs has made it possible to carry out a rapid condition assessment, which could even be improved. It should also be taken into account that imaging technologies may also improve, providing researchers with better-quality databases. Data augmentation is a key tactic in fault detection, especially when it comes to deep learning used for solar cell analytics.  
The photovoltaic industry is expected to grow even faster than what has been seen so far. Therefore, it is essential that the development of CNN technologies follow this trend. Real-time detection is believed to play an extremely important role in this because the faster any potential faults are identified, the quicker the response time during manufacturing and PV plant inspections. Studies by [PERSON] et al. [73] and [PERSON] et al. [77] numerically demonstrate that the detection of scratches and cracks remains below 85% mAP, while in the case of black core, finger interruption, and broken grid, 85% to 90% mAP can be achieved. [PERSON] et al. [68] found that YOLO methods, unlike hierarchical classification, are well suited for detection and classification even for UAV-based images.
## 3 MFCS Model
### Hydrogen Supply Subsystem  
As shown in Figure 3, the MFCS hydrogen supply subsystem consists of a hydrogen tank, hand valve, pressure-reducing valve components, ejector, proportional valve, common rail, and hydrogen circulation pump, with the functions of hydrogen supply and circulation. The common rail includes an inlet common rail and outlet common rail. The function of the inlet common rail is to ensure the stability of the different stack inlet pressures, which is referenced to the high-pressure common rail technology [22]. The function of the out common rail is to ensure the same circulating pressure because only one hydrogen-circulating pump is set up.  
An active hydrogen circulation pump and passive ejector are used as hydrogen circulation equipment for hydrogen circulation. During the circulation of anode exhaust gas, the anode channel pressure is stabilized by a hydrogen discharge valve, while the transmembrane transport of nitrogen is considered to ensure the hydrogen concentration. For the MFCS hydrogen supply subsystem, the rate of hydrogen utilization is an interest point in hydrogen circulation. The PEM fuel cell hydrogen cycle diagram is shown in Figure 4. \(x_{1}\), \(x_{2}\), \(x_{3}\), \(x_{4}\), and \(x_{5}\) denote the hydrogen discharged from the hydrogen tank, the hydrogen involved in electrochemical reactions, the hydrogen discharged from PEM fuel cells, the hydrogen recycled from the ejector and hydrogen-circulating pump, and the hydrogen discharged from the discharge valve, respectively.  
\[\left\{\begin{array}{l}x_{1}+x_{4}=\lambda_{\rm H_{2}}\cdot x_{2}\\ x_{3}=x_{1}+x_{4}-x_{2}\\ x_{4}=k_{\rm H_{2}}\cdot x_{3}\\ x_{5}=x_{3}-x_{4}=(1-k_{\rm H_{2}})\cdot x_{3}\end{array}\right., \tag{5}\]  
where \(\lambda_{\rm H_{2}}\) denotes the excess coefficient of hydrogen, \(k_{\rm H_{2}}\) denotes the circulation factor for hydrogen, and \(x_{2}\) can be calculated according to the load current and [PERSON]'s equation.  
Then, the real-time hydrogen utilization is the ratio of the current electrochemically reacted hydrogen to the current inlet gas from the hydrogen tank, and it can be expressed as  
\[\eta_{\text{H}_{2}}=\frac{x_{2}}{x_{1}}=\frac{1}{\lambda_{\text{H}_{2}}(1-k_{ \text{H}_{2}})+k_{\text{H}_{2}}}, \tag{6}\]  
where \(\eta_{\text{H}_{2}}\) denotes the real-time hydrogen utilization. Of course, the utilization rate does not consider the parasitic power used during hydrogen circulation.
## 3 Methodology
### Model Architecture
#### 3.2.1 Long Short-Term Memory (LSTM)  
LSTM networks were specifically designed to address the vanishing gradient problem encountered in traditional RNNs. This issue limits the RNN's ability to retain and propagate relevant information across long sequences, hampering its capacity to capture temporal dependencies effectively [5; 6].  
The key components of an LSTM cell are as follows:  
* Cell State (C_t): Serving as the memory component, the cell state enables the LSTM to retain essential information over time. It selectively regulates the information to be stored or discarded through specialized gates, allowing the network to learn long-term dependencies from the data.
* Input Gate (i_t): The input gate controls the flow of new information into the cell state. It decides which values from the current input and the previous hidden state should be incorporated into the cell state.
* Forget Gate (f_t): The forget gate determines which information from the cell state should be forgotten. It allows the LSTM to selectively discard irrelevant or outdated information from previous time steps.
* Output Gate (o_t): The output gate governs the filtering of the cell state's information to compute the current hidden state. It regulates the information to be propagated to the next time step.  
The mathematical transformations involved in the LSTM cell's internal operations are formulated as follows:  
* **Input Gate (i_t):** \[i_{t}=\textit{sigmoid}\Big{(}W_{i}*\Big{[}h_{(t-1)},x_{t}\Big{]}+b_{i}\Big{)}\]
* **Forget Gate (f_t):** \[f_{t}=\textit{sigmoid}\Big{(}W_{f}*\Big{[}h_{(t-1)},x_{t}\Big{]}+b_{f}\Big{)}\]
* **Output Gate (o_t):** \[O_{t}=\textit{sigmoid}\Big{(}W_{o}*\Big{[}h_{(t-1)},x_{t}\Big{]}+b_{o}\Big{)}\]* **Cell State Update:** \[u_{t}=thanh\Big{(}W_{c}*\Big{[}h_{\{t-1\}},x_{t}\Big{]}+b_{c}\Big{)}\]
* **Hidden State (h_t):** \[h_{t}=o_{t}*\tanh(u_{t})\]
and Bing have agreed to let OSM use their aerial imagery for the purposes of volunteers tracing the outline of objects. However, the concept of local knowledge is at the very core of VGI and OSM. [PERSON] _et al_. [12] describe results in their paper which shows volunteers with local knowledge classified roads, from high resolution aerial imagery, with over \(92\%\) accuracy on average, irrespective of surveying background and always better than professional surveyors without local knowledge. The combination of highly motivated volunteers with detailed local knowledge [13] has been instrumental in seeing OSM grow to become a very large global database of spatial data, frequently edited, and continually growing in size. We feel that one of the most exciting aspects of OSM is the collaborative editing and development of the OSM database by contributors. This provides motivation for this research work. As will be outlined in Section 2 all current studies into OSM, in the literature, only consider the most currently available version of the OSM database. This literature discusses: quality evaluation, accuracy measurement, or applications. Every few months OSM makes the \"planet.osm/full\" file available which includes almost all OSM data ever collected [14]. The key motivation of this paper is to use this historical data to investigate if there are any special characteristics that may be observed from analysis of \"heavily edited\" objects. After the literature review section of the paper (Section 2) we show how the full history for spatial objects can be extracted and processed from the \"planet.osm/full\" file (Section 3). In this section we also outline carefully how we selected our dataset of \"heavily edited\" objects. In Section 4 we provide the results of analysis performed on these objects. This includes: rates of contribution (Section 4.2), editing and tagging (Section 4.3 and Section 4.5), changes to object geometry (Section 4.6). Access to the historical trial of edits for objects allows use to investigate how these features have evolved from their first version to their current version. The full OSM history data for the UK and Ireland is extracted from the \"planet.osm/full\" file and is used as the case-study data. Section 5 closes the paper with some conclusions from the analysis performed and opportunities for further research.
## 2 Understanding the Jargon: Equipment and Data Products  
The acronym LiDAR stands for Light Detection and Ranging and for obvious reasons it is also called Optical Radar. The military community commonly uses the acronym LADAR, for Laser Detection and Ranging, to be specific about the type of light source used. Today the term LiDAR is used loosely and serves as an umbrella for different kinds of technologies and applications that range from using satellites to map atmospheric density, composition, and wind speed and direction; to close range 3D mapping of minute artifacts at micrometric resolution. For a historical description on the technology, nomenclature and diverse applications refer to [24].  
In this paper our focus will be on the use of topographic mapping LiDAR from airborne platforms, commonly referred to as airborne laser scanning or ALS [1]. However, even within the smaller umbrella of ALS there are multiple types of LiDAR units that have or can be used for archeological applications, and each one has specific advantages or disadvantages with regard to providing information to answer a given research question for a given type of environment. For example a single stop LiDAR system might prove very efficient in mapping the thinly vegetated surface of a desert, but would be nearly useless in mapping the surface of the ground in an area covered by jungle canopy [25].