esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Morse Code  
The history of Morse code dates back to the 1830s. It is a digitalized electrical text transmitted by on-off tones, lights or radio. Unlike the binary system of computer communication, Morse code symbols are either a text character (letter or numeral) or a pause represented by a unique sequence of dots and dashes organized into five statuses [10]. Morse code relies on precise intervals of time between dots and dashes, between letters, and between words. In this work, the dot duration is the basic unit of time measurement in code transmission, the dash duration is three time units long [11; 12; 13; 14; 15], and the other three statuses take up 0.2, 5, 7 units respectively.  
As is known to us, many encoding systems are digitalized and use mature encoding/decoding algorithms, which are widely applied in the related door access system. However, they are accompanied with some security issues. On the contrary, the visible light Morse encoding application proposed in this paper, uses the Morse code that is more difficult to decoding than digital decoding.  
Moreover, it has limited usage scope as the visible light cannot penetrate the wall. Furthermore, being free from electromagnetic interference improves the system security and reliability.  
Morse code could be subdivided into either a long or short coding system by the use of English characters and their complexity. The shorter version uses only Arabic numbers, 0-9, and is simpler than its longer counterpart. Table 1 shows the Morse code and its representing number in the short coding system which we used in this article; while Table 2 is the standard Morse code.
# Jamming and Spoofing Techniques for Drone Neutralization: An Experimental Study
## 2 Background
### GNSS Fundamentals
#### 2.3.1 Radio Frequency Drone Spoofing  
Spoofing involves sending fraudulent communications that appear to originate from a known trusted source, aiming to deceive the recipient. Effective spoofing requires an understanding of the communication protocol stack beyond the physical layer. By simulating GPS signals, attackers can trick receivers into accepting false location data, compromising the integrity of location-based information [25]. A critical aspect of airspace security is No-Fly Zones (NFZs), designated areas where specific aircraft are prohibited from flying to mitigate airborne threats. Established by governmental bodies, NFZs enhance security through surveillance and preventive actions, particularly in high-profile events. Violating NFZs through spoofing can result in automatic landing or prevent takeoff altogether [26].
# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 7 The RINA Domain  
### Basic Concepts  
Current 5G approaches still model the network as a flat collection of physical devices forwarding data between interfaces, hiding the underlying complexity via overlays. This suffers of scalability issue on the one side and still maintain the complexity of managing the network infrastructure on the other side. A completely different focus has been taken in RINA.  
RINA is a back-to-basics approach learning from the experience with TCP/IP, which reminded us that from the earliest days, networking was viewed as Inter-Process Communication (IPC) [78]. Thus, RINA starts from the premise that networking is IPC and only IPC. In particular, networking provides the means by which application processes on separate systems communicate, generalising the model of local IPC. In contrast to the fixed, five-layer model of the Internet, where each layer provides a different function, RINA is based on a single type of layer, implementing only two protocols called Error and Flow Control Protocol (EFCP), and Common Distributed Application Protocol (CDAP), which is repeated as many times as required by the network designer. The layer is called a Distributed IPC Facility (DIF), which is a distributed application that provides IPC services over a given scope to the distributed applications above (which can be other DIFs or regular applications). These IPC services are defined by the DIF Application Programming Interface (API), which provides operations to: (i) allocate flows to other applications by specifying an application name and a set of characteristics for the flow (such as delay, loss, capacity), (ii) read/write data from/to the flows, and (iii) deallocate flows and free the resources associated with them.  
A key characteristic of RINA is its design based on the separation of all functions in mechanisms and policies, which dramatically simplifies networking. Although all DIFs implement the same two protocols (EFCP and CDAP), the specific operation of each DIF can be customised to its particular scope via programmable policies. In this way, the routing or packet forwarding policies configured in a backbone DIF can differ from those in a DC DIF, as their topological characteristics and dynamicity of the supported traffic can differ significantly.  
To succeed in our endeavor, in ALLIANCE we used several open-source implementations and tools made available by previous research projects and by the RINA research community such as the IRATI RINA Stack (an open-source implementation of RINA for OS/Linux systems) and iporinad (a daemon program which is able to tunnel IP traffic over a RINA network) [79,80].
# IRS-Enabled Ultra-Low-Power Wireless Sensor Networks: Scheduling and Transmission Schemes
## 3 Transmission Scheme  
Leveraging the objective of WSNs to have minimal network infrastructure and reduce operational costs, this work assumes that the ISNs are a completely additive technology without any control links between the ISNs and BS. Because the system is without backhaul links, there is no need to perform extensive channel estimation at the BS, as the IRS cannot utilize this information to reconfigure accordingly. Hence, we avoid the caveat of channel estimation in IRS-assisted systems, which scales prohibitively with the dimensions of the system (number of antennas at the BS \(M\), number of IRS elements \(N\), and number of ISNs \(L\)). Instead, we propose a centralized and controlled access technique by which the BS assigns and activates a particular ISN to begin transmission.  
Scheduling sensors in a WSN requires a simple solution, especially considering the lack of control links between all the ISNs and the BS, as well as the lack of an RF front-end at the IRS. A scheduling algorithm is often implemented to manage the sensors in a collision-free manner [15]. The ISNs can be scheduled without any feedback using round-robin (RR) scheduling by having each take a turn sequentially. Other scheduling schemes require feedback, such as the OS scheme visualized in Figure 3. Below, we detail a general outline of this scheme, with this sequence illustrated in Figure 4:In a frame \(t_{f}\), the BS sweeps through all the ISNs with a known reference signal \(\mathbf{x}_{l}\), defined as \[\mathbf{x}_{l}=\sqrt{P_{l}}\mathbf{f}_{l},\] (7) in which \(\mathbf{f}_{l}\in\mathbb{C}^{M}\) is a beamforming vector designated for ISN \(l\) with unit norm \(\|\mathbf{f}_{l}\|^{2}=1\) and \(P_{l}\) is the power allocated for the reference signal catering to ISN \(l\). Moreover, each IRS pseudo-randomly chooses a phase configuration \(\mathbf{\Phi}_{l}\); thus, when the incident wave from the BS impinges on it, the IRS scatters it randomly towards the user.
2. The user computes the signal-to-noise ratio (SNR) \(\gamma_{l}\) defined in (13) and feeds it back to the BS.
3. After a full sweep illuminating each ISN, the BS now has \(\gamma_{l},l=1,\ldots,L\), corresponding to each ISN. The BS then schedules the IRS according to a certain performance metric, such as throughput or fairness, using a beacon beam to awaken it for modulation.
4. The ISN begins modulating onto the reference wave using BPSK.  
Note that the sweep through the ISNs is an overhead that scales with \(L\) (number of ISNs), and does not scale with \(N\) (number of IRS elements) or \(M\) (number of antennas at BS). In the following subsections, we look into the definition of asymptotic favorable propagation, the introduction of beacon beams, and the end-to-end transmission that enables this scheduling protocol.  
Figure 4: The sequence of scheduling and information transmission detailed for a particular frame \(t_{f}\).  
Figure 3: Flowchart for opportunistic scheduling in a certain frame index \(t_{f}\).
## 5 Conclusions  
MeznSat is a 3U CubeSat that uniquely combines a SWIR spectrometer with an RGB camera for monitoring the level of GHG in the atmosphere. MeznSat payload is a SWIR spectrometer that will be operating in conjunction with an RGB camera to achieve high geolocation accuracy. Simulations show that the three-year mission will achieve 46% payload footprint coverage that can be leveraged using attitude maneuvering. The paper shows that the minimum possible platform to fulfill the missions' goals is a 3U platform given the current technology status for the various subsystems.  
Available platform volume is the primary limitation found for the mission. It was a struggle to achieve an optimum design for the internal satellite configuration, given the dimensions of the various components, particularly the payloads and the ADCS. The mass budget shows a significant mass margin, and the power analysis shows a healthy power profile that is capable of handling additional pressure on the power system.  
The small FOV of the spectrometer derives a requirement for an accurate ADCS system, requiring reaction wheels for actuation and sun sensors, star tracker and magnetometer for sensing, aiming for pointing accuracy of 1 degree. Simulations of the ADCS system shows appropriate performance that meets the stringent mission requirements.  
Overall, the analysis presented in the paper included mass, volume, power, lifetime, payload coverage and pointing accuracy shows a feasible mission that uniquely combines SWIR payload with an RGB camera in a compact 3U platform. MeznSat is planned for launch in March 2020 into an orbit of 550 km.  
Conceptualization, P.M., A.-H.J.; Writing--original draft preparation, A.-H.J., P.M., Z.A.A., M.A.; Writing--review and editing, A.-H.J., P.M., M.A., A.A.M.; Project administration, A.A.M., P.M., A.-H.J.; Software, A.-H.J., M.A.; Investigation, A.-H.J., P.M., Z.A.A., M.A.  
This research was funded by the UAE SPACE AGENCY, grant number R01-2016-001.  
The authors would like to thank all students involved as well as the UAE Space Agency for sponsoring the MeznSat program.  
The authors declare no conflict of interest.  
Figure 20: Results of the ADCS control using Y-Thomson controller.
## 2 System Overview  
The proposed approach can be formulated as follows: Given a set of convex or/and non-convex cultivated land areas--denoted as parcels--isolated without the possibility of establishing a communication infrastructure among them (see Figure 2), a system that will be able to monitor a set of biophysical interesting variables in near real-time (_i.e._, frequently enough to prevent crop damages) and dispatch the information collected to a predefined end-user(s) or base-station(s) should be defined.  
Hence, from the communications point of view, each parcel will address a cluster that can be set up using one or several WSN nodes. Clusters are represented as a unique access point, since inside the cluster, nodes communicate among them using a mesh topology.  
Figure 1: Active node surrounded of dense foliage from the vines.  
Figure 2: Several crop parcels where a desired aerial trajectory is depicted.  
In order to cope with the limitations of the conventional WSN, such as short communication range, energy consumption, limited processing and storage capacity and low bandwidth, a hybrid system made up of three different elements is proposed:  
1. Sensing system (WSN). It is made up of a split ground static wireless sensor network, which monitors the crop acquiring information in an intelligent manner (_i.e._, optimal sampling, storing and routing). This will be described in detail in Section 2.1.
2. Mobile node. Stationary nodes are combined with a mobile node carried by a mini aerial vehicle, employed as a remote dynamic data collector, analyzed in Section 2.2.
3. Long distance communications. Finally, the system is endowed with a packet oriented mobile data service connection, which provides a long distance communication channel. This is presented in Section 2.3.  
A scheme of the system layout over the parcels is shown in Figure 3.
## 3 Results
### \(R\) model  
Table 5 gives the coefficients of \(R\) model before and after calibration, where the change in \(b_{2}\) was large. The training accuracy of the calculated \(R_{nd}\) from the \(C_{d}\) estimated from \(R\) model (named as \(R_{nd-Cd-RM}\)) yielded an R\({}^{2}\) of 0.89, RMSE of 21.55 Wm\({}^{-2}\), and Bias of 2.05 Wm\({}^{-2}\).  
Figure 5 presents the scatter plots between the \(R_{nd-Cd-RM}\) estimates before and after model calibration and the validation samples, respectively. From Figure 5b, the accuracy of the calibrated \(R\) model improved a lot, with an RMSE of 21.36 Wm\({}^{-2}\) and Bias of 1.33 Wm\({}^{-2}\). Compared to the results of the original model (Figure 5a), the rRMSE decreased by 19.02%, and the magnitude of the Bias decreased by 38.26 Wm\({}^{-2}\). However, although the overestimation tendency of the original \(R\) model was revised after calibration to a certain extent, the tendency of underestimation was still observed at high values (>150 Wm\({}^{-2}\)).  
In summary, the two existing models for \(C_{d}\) estimation demonstrated a reasonable relationship between \(C_{d}\) and other parameters, but the two models should be calibrated with more comprehensive samples before application.
## 2 Materials and Methods  
### Walls Description and Material Characterization  
In Figure 1 is presented the horizontal cross-sections of the partition and facade load-bearing LSF walls that are studied in this paper. The partition LSF wall is 126.5 mm thick and possesses vertical steel studs spaced 400 mm apart (with a web of 90 mm, a flange of 43 mm, a lip return of 15 mm and a thickness of 1.5 mm (C90 \(\times\) 43 \(\times\) 15 \(\times\) 1.5)), Mineral Wool (MW) filling the entirety of the 90 mm air cavity, one oriented strand board (OSB) structural sheathing panel 12 mm thick on both sides of the steel studs, and a gypsum plasterboard (GPB) sheathing layer 12.5 mm thick on the inner surface. The facade LSF wall, besides having the same configuration as the partition wall, also has on the outer surface an internal thermal insulation composite system (ETICS), with expanded polystyrene (EPS) insulation 50 mm thick and a 5 mm layer of ETICS finishing, adding up to a 181.5 mm wall thickness. It should be noted that the LSF wall configurations evaluated in this study are existing ones, which are widely recognized in the field. They are usual LSF wall configurations for cold frame (Figure 1a) and hybrid (Figure 1b) construction systems [17], and have been already studied in previous research [20; 21; 22].  
Besides these two LSF walls arrangements, another two simplified configurations were assessed to evaluate the relevance of the inhomogeneity of the material layer's thermal conductivity. Thus, in these two additional wall configurations, the steel frames were removed to obtain a partition and a facade wall with homogeneous layers.  
The thicknesses of each material layer and the corresponding thermal conductivities of these materials are listed in Table 1.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Material** & \(\mathbf{t}\) & \(\mathbf{\lambda}\) & **Ref.** \\  & **(mm)** & **(W/(m\(\cdot\)K))** & **Ref.** \\ \hline Gypsum Plaster Board (GPB) & 12.5 & 0.175 & [24] \\ Oriented Strand Board (OSB) & 12.0 & 0.100 & [25] \\ Mineral Wool (MW) & 90.0 & 0.035 & [26] \\ Steel Studs (C90 \(\times\) 43 \(\times\) 15 \(\times\) 1.5) & 90.0 & 50.000 & [19] \\ ETICS 2 Insulation (EPS 1) & 50.0 & 0.036 & [27] \\ ETICS 2 Finish & 5.0 & 0.450 & [28] \\ \hline \hline \end{tabular}  
* EPS–expanded polystyrene; 2 ETICS—external thermal insulation composite system.  
\end{table}
Table 1: Thickness, \(t\), and thermal conductivity, \(\lambda\), of materials [21].  
Figure 1: Evaluated load-bearing LSF walls’ horizontal cross-sections: (**a**) Partition; (**b**) facade LSF wall.
## 3 Wave Head Calibration Based on Parameter Optimization MVMD and Morphological Gradient
### Mpa
The larger the kurtosis value of the intrinsic mode function component, the richer the traveling wave characteristic information. The formula for calculating the kurtosis value \(k_{m}\) of the \(m\)th intrinsic mode function component obtained by MVMD is shown in Formula (15). \[k_{m}=\frac{\sum\limits_{n=1}^{N}\left[\text{IMF}_{m}(n)-\mu_{\text{IMF}_{m}} \right]^{4}}{(N-1)\sigma^{4}_{\text{IMF}_{m}}} \tag{15}\]  
In the formula, \(\text{IMF}_{m}(n)\) is the \(m\)th intrinsic mode function component obtained by decomposing the traveling wave signal by MVMD. \(\mu_{\text{IMF}_{m}}\), \(\sigma_{\text{IMF}_{m}}\), and \(N\) are the mean value, standard deviation, and the number of sampling points of the IMF\({}_{m}\) component, respectively.
# Communication-Efficient Vertical Federated Learning
## 1 Introduction
As a result, clients can collaborate by sharing compressed (latent) representations of their raw data without jeopardizing their privacy and security. The whole iteration is just performed once, which reduces the amount of communication needed significantly. Furthermore, our contributions include extensive experiments using the proposed approach on four benchmark datasets and comparing its performance to a centralized machine learning model. The rest of the paper is organized as follows: Section 2 discusses the existing methods designed to reduce the communication overhead in FL environment, Section 3 elaborates on our proposed method in detail, Section 4 explains the experimental setup of the performed experiments, Section 5 demonstrates the results of the experiments and provides a brief analysis. Finally, Section 6 concludes the paper by mentioning the overall observations from the experiments, while also providing insights for possible future improvements.
## 3 Data and Methods
### DEM Generation with the Ames Stereo Pipeline  
All SPOT6 tristereo scenes are processed in the ASP version 3.0 [25] using a custom pipeline to generate DEMs, such as the example shown in Figure 2B. A shell-script version of the processing is available on the GitHub page associated with this paper: https://  
Figure 2: Same area shown in Figure 1B overlain with SPOT6 data: (**A**) SPOT6 panchromatic (PAN) B-scene shown in grayscale as 12-bit digital numbers (values of 0-4095) and converted to top-of-atmosphere radiance (units of W/m\({}^{2}\)/sr/\(\upmu\)m) by dividing the digital number by the gain constant value of 10.4186 and adding the bias constant value of 0, both found in the SPOT6 metadata. Note the spectral range of the SPOT6 PAN channel is 0.450-0.745 \(\upmu\)m. Boxes and labels indicate the areas of subsequent figures in the main text and Supplemental Material; (**B**) topography from SPOT6 DEM BM-ck25 (cf. Table 1) following ASP processing. Holes in the DEM are shown by gray colors, particularly prominent on alluvial fans where image texture is low.  
github.com/UP-RS-ESP/Ames-Stereo-Pipeline-SPOT, last accessed 28 September 2022. The ASP is a flexible tool for DEM generation first developed by NASA's Ames Research Center for the Mars Pathfinder Mission. It can handle robotic, satellite, aerial, and historical stereo imagery and is open-source, and mostly automated, requiring little manual input aside from initial pipeline design and testing [23; 24; 25]. Other studies have detailed the robustness and flexibility of the ASP for a variety of terrestrial satellite and aerial data for primarily cryospheric applications (e.g., [7; 23; 34; 35]), and these studies are complementary to ours, which is concerned with snow- and ice-free mountain landscapes. We report on the main algorithms and parameters tested in this study, which can largely be found in the ASP manual: [[https://stereopipeline.readthedocs.io/en/latest/](https://stereopipeline.readthedocs.io/en/latest/)]([https://stereopipeline.readthedocs.io/en/latest/](https://stereopipeline.readthedocs.io/en/latest/)), last accessed 28 September 2022 [25]. We do not detail all of the myriad parameters in the ASP, but we do qualitatively mention some relevant tests we carried out. We conclude this section with step-by-step details of our pipeline, but we emphasize that similar findings and observations can be reproduced by other stereo-matching software packages.
## 4 Machine Learning Algorithms
### Validation and Comparison of the Models  
In this study, we evaluated model accuracy using the following metrics: Sensitivity, specificity, accuracy, kappa, root-mean-square deviation (RMSE), and area under the curve (AUC). There are four types of possible significance, i.e., true positive (TP), false positive (FP), true negative (TN), and false-negative (FN). TP is the number of expected landslides that are truly landslides. FP is the number of expected landslides that are non-landslides. TN denotes the number of expected non-landslides that are truly non-landslide, whereas FN is the number of non-landslides. Better predictive ability is indicated by higher values of sensitivity, specificity, AUC, and accuracy and the lower values of RMSE [21]. A kappa index value of 1 indicates an ideal model, whereas a value of \(-1\) signifies a non-reliable model. The mentioned metrics are expressed as follows:  
\[Sensitivity=\frac{TN}{TN+FP} \tag{21}\]  
\[Specificity=\frac{TN}{TP+FN} \tag{22}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{23}\]  
\[\text{Kappa}=\frac{O-E}{1-E} \tag{24}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}{(X_{\text{predicted}}-X_{\text{ actual}})^{2}}} \tag{25}\]  
where \(O\) and \(E\) are the observed and expected agreement, respectively, \(X_{\text{predicted}}\) and \(X_{\text{actual}}\) are the predicted and observed values of the ith instance from models, and \(n\) is the number of instances.  
The receiver operating characteristic (ROC) curve has been used to test the overall performance of LSM methods [93]. The area under the ROC curve is the statistical summary of the overall performance of models [72]. The x, and y axes of the AUC are, respectively, the sensitivity and 100-specificity. The values of AUC range from 0 to 1, with values closer to 1 indicating a better predictive ability; an AUC value of 1 indicates perfect model performance [94]. The schematic diagram of methodology is illustrated in Figure 4.
# The Customized Automatic Processing Framework for HY-2A Satellite Marine Advanced Products
## 3 The Components of HY-2A Capf
### The On-Demand Customized Processing Subsystem
The communication between the GUI layer and the services layer is based on Asynchronous Javascript and XML (AJAX) technology, whereas the communication between the services layer and the data repository layer depends on Representational State Transfer (REST) web services. The functions provided by the services layer are developed using Java programming language. The subsystem uses JSON and XML formats to exchange data between the server and a browser.