esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of CubeSat Missions and Applications
## 4 The Use of CubeSats as Telecommunication and Asset-Monitoring Platforms
### Radio Communication CubeSat Technologies
Examples of CubeSat missions combining both AIS and ADS-B technologies are the Royal Australian Air Force (RAAF) M2-A and M2-B CubeSats that were jointly developed by the University of New South Wales and the Australian Defense Force (ADF). The two M2 CubeSats were initially launched as a single 12U CubeSat and would later separate into two 2U \(\times\) 2U \(\times\) 1.5U individual spacecraft. Their main mission is to demonstrate in-house developed technologies that would eventually offer the Royal Australian Air Force (RAAF) global AIS and ADS-B tracking capabilities [178; 179; 180]. Due to their characteristics, CubeSat platforms represent excellent choices for the creation of large-number, cost-effective constellations providing global AIS and ADS-B coverage, with perhaps the most notable example in that field being Spire's existing constellation of Lemur CubeSat platforms, which in addition to their GNSS-RO mission payloads are also equipped with AIS and ADS-B payloads. The CubeSats of this constellation are placed in a variety of different orbits in order for the constellation to be capable of covering areas such as oceans, polar regions, and other remote areas that would otherwise be near-impossible to cover with ground based systems [181; 182]. However, it should be noted that even though CubeSats dedicated to AIS and ADS-B missions are most definitely expected to rise over the following years, performing a variety of important roles in the space segment of such tracking networks, Air-Traffic services require a hardware performance by ADS-B systems that is considerably greater than what CubeSat sized hardware is currently capable of providing, especially in terms of RF signal power, something that poses a significant hurdle in their prospects of taking over the provision of such services from larger satellite platforms [183].
# Transmitters and Receivers for High Capacity Indoor Optical Wireless Communication
## 3 Receiver for Optical Wireless Communication
### Quad Photodiode Array  
#### 3.3.1 Description  
The 2 \(\times\) 2 photodiode array presented in [18] was designed and manufactured by VTEC Lasers & Sensors. The array has a diameter of 100 \(\upmu\)m. The individual photodiodes in the array have the shape of a 90\({}^{\circ}\) circular sector so that the whole array takes the shape of a circle. Each photodiode is connected to a separate Trans-Impedance Amplifier (TIA). This concept allows for an increase in the active area while maintaining a high bandwidth. Additionally, having four independently processed photodiodes enables the detection of the incoming beam direction. This photodiode array could be used simultaneously for receiving data and localizing and tracking of users.
## 5 Simulation Examples
### Resolution Performance versus Rotation Frequency Errors  
There are two inherent incident signals with DOAs are (90\({}^{\circ}\), 81\({}^{\circ}\)) and (90\({}^{\circ}\), 85\({}^{\circ}\)) respectively, both the signal frequencies are 6 GHz. The snapshots number is \(L\) = 100, and in theory, the rotation frequency is \(f_{z}\) = 15 Hz. However, in fact, the rotation velocity will be greater or smaller than the theory rotation frequency \(f_{z}\). Now, assume the actual rotation frequency is \(f^{{}^{\prime}}\), and let \(f^{{}^{\prime}}\) range from 14.3 to 15.5 Hz by the step 0.1 Hz in this simulation. So, the rotation frequency error is \(\Delta f=f^{{}^{\prime}}-f_{z}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Channel mismatch errors**} & \multicolumn{1}{c}{**5-elment UCA-MUSIC**} & \multicolumn{1}{c}{**8-elment**} \\
**[degree]** & & **R-MUSIC** \\ \hline
0 & 0.1414 & 0.2739 \\
5 & 0.2191 & 0.2162 \\
10 & 0.4817 & 0.4000 \\
15 & 0.5441 & 0.4427 \\
20 & 0.6132 & 0.4336 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The RMSE _versus_ different channel mismatch errors[degree]  
Figure 8: Successful resolution probability _versus_ different channel mismatch errors.  
Figure 9 shows the successful resolution probability _versus_ rotation frequency error when SNR = 4 dB, 8 dB, 10 dB and 20 dB, successively.  
As shown in Figure 9, when \(f^{\prime}=f_{z}\) and the SNR\(>\)8 dB, the successful resolution probability of the R-MUSIC is 100%; However, when the actual rotation frequency \(f^{\prime}\) is larger or smaller than \(f_{z}\), the successful resolution probability will decrease. The larger the \(\left|\Delta f\right|\) is, the lower the successful resolution probability is. On the other hand, when SNR = 4 dB, the successful rotation frequency (\(>\)60%) ranges from \(15-0.3\sim 15+0.3\) Hz; When SNR = 10 dB, the successful rotation frequency ranges from \(15-0.4\sim 15+0.4\) Hz; When SNR = 20 dB, the successful rotation frequency ranges from \(15-0.5\sim 15+0.4\) Hz; that is to say, with increases in SNR, the rotation array system can tolerate wider rotation frequency error range. However, when \(\Delta f\) is larger than a certain value, even increasing SNR would not improve resolution. That is because according to Equation (15), a larger rotation frequency error will cause a Doppler frequency shift, which has a great influence on the estimation and resolution performance of the proposed algorithm.
# Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System
## 4 Design and Performance Simulation of the Adaptive RC-NB-LDPC Coding Scheme
### Design, Analysis, and Simulation of RC-NB-LDPC Codes  
The multi-rate LDPC scheme of DVB-S2 is a typical paradigm of the LDPC codes applied in communication standards, which is designed with full consideration of business diversity requirements and has good adaptability. It supports 11 code rates from 1/4 to 9/10, in both normal and short FEC frame modes. The use of ACM is another significant feature of DVB-S2, which provides more accurate channel protection and dynamic connection adaptability for specific receiving terminals according to specific propagation conditions. While improving the original channel transmission capacity, it greatly expands the service range and directly improves network coverage and the user transmission rate. This provides a reference for the ultimate user experience of B5G in the future.  
In this section, we first discuss the construction method of RC-NB-LDPC codes, and some parameters such as code length and code rate all refer to the DVB-S2 standard [49]. In addition, we analyze the decoding threshold of the LDPC code by using the EXIT chart [50]. Finally, we simulate the BER performance of the proposed LDPC codes.
# Thinger.io: An Open Source Platform for Deploying Data Fusion Applications in IoT Environments
## 3 Thinger.io Overview
### Research Efforts at Thinger.io
#### Modeling Simplicity  
Finally, one of the key aspects of this platform, and the main focus of this paper, relates to the ability of modeling information in devices in an easy way. There are several IoT solutions out there that require an extensive coding to do simple tasks like sending a sensor reading, or parsing an input command on the device. This coding usually involves managing HTTP requests, adding headers, parsing input data, etc., so a simple task usually requires dozens of lines of code.  
In this case, Thinger.io provides client libraries that simplify modeling any kind of device resources, like input resources for actuation over the device; output resources to send information generated in the device; resources that require an input and generate an output; or execution functions that can be triggered remotely. As such, it is possible to cover multiple use cases, in a simple way, requiring just a small coding.  
Just to provide a comparative example, Figure 4 presents a sample code from the Particle platform, which is a state-of-the-art IoT vendor, known for its simplicity. In this example, the _Setup_ code registers a function in the cloud, called _led_, that allows an led to be turned on and off remotely. The body of this function, called _ledControl_, just receives a _String_ command, that needs to be parsed to extract both the pin number and the desired state. So, a simple requirement like turning on/off a digital pin becomes complex to code, leading to the possibility of undesired bugs being included.  
Figure 3: From the device model to REST API. Thinger.io allows device models to be defined that are automatically converted to REST APIs and can be consumed from any external application in real time.  
Additionally, Thinger.io provides a modeling language based on Protoson, which is just like JSON but much more efficient. Modelling the same functionality that this one described for the Particle platform requires just a couple of simple lines. This example is provided in Figure 5, where a resource is defined called _led_, which exposes an input function that just does a _digitalWrite_ over the parameters given in _pin_ and _state_.  
Moreover, such simple definition in the device code, as described in the interoperability subsection, automatically generates a REST API endpoint that can be used from the Cloud, including also the expected fields to execute the command, which are extracted automatically from the model definition. This endpoint can be easily tested from the console client provided by Thinger.io, as shown in Figure 6.  
Figure 4: Particle.io code example for enabling/disabling an arbitrary digital pin.  
Figure 5: Thinger.io code example for controlling an arbitrary digital pin.
## 5 Network and Data Stream
### Representation of Network Topology
At the same time, the states of the related nodes and the transmission success rates of the related links should be updated correspondingly. Removing nodes: This case refers to that a node starts to sleep in the network or a node is removed from the network. After a node is removed, the links between the removed node and its neighbor nodes should be first deleted, and then the network topological graph should be updated. At the same time, the states of the related nodes and the transmission success rates of the related links should be updated correspondingly.
# Microstructured Phononic Crystal Isolates from Ultrasonic Mechanical Vibrations
## 3 Fabrication and Characterization of the Phononic Crystal  
### Material and Writing Procedure  
To fabricate the phononic crystal described in Section 2, with typical cell dimensions spanning from 100 um to 200 um, we used the two-photon polymerization technique. In particular, the hybrid organic/inorganic negative photoresist SZ2080 was employed in this work for its good optical and mechanical properties and its good dimensional stability with negligible shrinkage.  
The set-up for the fabrication includes a femtosecond laser source (Toptica, FemtoFiber pro NIR) operating at its second harmonic and emitting mode-locked pulses at 780 nm wavelength. This source has a repetition rate of 80 MHz and a pulse length of about 80 fs. To generate the 3D photopolymerized structures, the beam scans the resin that has been previously dropcasted on a glass substrate in a layer-by-layer pattern. In particular, movements in the \(x\)-\(y\) plane were achieved with XY linear stages (ANT95-50-XY, Aerotech), while movements along the \(z\)-axis were performed using a linear stage (ANT130-035-L-ZS, Aerotech) that translates the 40\(\times\) objective lens (Carl Zeiss, N-Achroplan 40\(\times\) WI and 0.75 NA). Beam passage control was obtained with a fast galvanometric shutter, while beam intensity control was achieved with a polarizer and a waveplate. After the photopolymerization process, the samples were developed in a solvent to remove the unpolymerized resin.  
As shown in [44,45], the mechanical properties (in particular the Young's modulus) of the two-photon polymerized SZ2080 strongly depend on the different writing parameters: power of the laser, writing speed, distance between lines. Moreover, differently from [44], we here employ a water-immersion objective lens 40\(\times\) to speed-up the fabrication process, thus obtaining a polymerized SZ2080 with different stiffness with respect to [44].  
Figure 1: (**a**) Unit cell of the proposed phononic crystal, with \(a\) the dimension of the cubic cell. (**b**) Non-dimensional dispersion diagram with non-dimensional frequency \(f_{\mathrm{m}}=fa/v\). The irreducible Brillouin zone (IBZ) and the bandgap opening and closing modes are also reported. (**c**) Non-dimensional transmission diagram computed on a \(2\times 2\times 3\) phononic crystal. The input is prescribed as a \(z\)-axis displacement at the bottom surface of the glass support, while the output is measured as the average \(z\)-axis displacement of the top plate (black arrows).
# The Debris Flow Risk Prediction Model Based on PCA-Elman
## 2 Materials and Methods  
### Overview of the Study Area  
Yunnan, abbreviated as \"Yun\" or \"Dian\", is located in the southwestern frontier of China, spanning between 97\({}^{\circ}\)31\({}^{\prime}\) E and 106\({}^{\circ}\)11\({}^{\prime}\) E longitude, and 21\({}^{\circ}\)8\({}^{\prime}\) N and 29\({}^{\circ}\)15\({}^{\prime}\) N latitude. The Tropic of Cancer crosses the southern part of the province, which is characterized as a low-latitude inland region. The total land area of Yunnan is 394,100 km\({}^{2}\), accounting for 4.1% of China's total land area. It borders Guizhou Province and the Guangxi Zhuang Autonomous Region to the east, faces Sichuan Province across the Jinsha River to the north, adjoins the Tibet Autonomous Region to the northwest, shares a border with Myanmar to the west, and is adjacent to Laos and Vietnam to the south. Due to its complex topographical, climatic, and geological conditions, Yunnan is prone to debris flows. The province's terrain is predominantly mountainous and plateau, with a rich variation in slope gradients, generally exhibiting a stepped distribution from high in the northwest to low in the southeast, featuring steep mountains and significant slopes ranging from gentle (0-5\({}^{\circ}\)) to extremely steep (greater than 30\({}^{\circ}\)), providing favorable topographical conditions for debris flows. Yunnan's subtropical monsoon climate leads to uneven distribution of precipitation, with a concentrated rainy season, and the influence of the Bay of Bengal cyclones increases the frequency and intensity of heavy rainfall, supplying ample water sources for debris flows. Geologically, Yunnan Province is situated near a plate collision zone, with complex geological structures, active neotectonic movements, and frequent earthquakes, resulting in a fragmented surface and poor rock stability, providing a material basis for the occurrence of debris flows. With the advancement of urbanization and industrialization, large-scale construction projects have altered the original topography and hydrological conditions, increasing the risk of debris flows. Moreover, Yunnan's ecological environment is fragile, with low vegetation coverage, especially in mountainous and valley areas, where the lack of vegetation leads to severe soil erosion, supplying loose solid materials for debris flows.  
In summary, Yunnan Province's ecological and geological environment is characterized by complex topography and geomorphology, variable climate conditions, active geological structures, frequent human activities, and a fragile ecological environment. The interaction of these factors has led to the high incidence and prevalence of debris flow disasters in the region, making it one of the provinces in China with the widest area affected by debris flows. Therefore, constructing a debris flow risk prediction model based on data from Yunnan Province ensures that the model is typical, representative, and practically significant. The spatial geographic location and terrain slope of Yunnan Province are depicted in (Figure 1).
# The BCL Method for DSSC: Basis and Applications +
## 5 The \(\mathbf{I_{T}}\) Parameter: A Measure of the Electronic Density on the Anchor Group
### Theoretical Background  
#### 6.1.1 The Global Efficiency Index  
To understand these results, we proposed building a theoretical index (GEF), the Global Efficiency Index, calculated by employing data obtained from computational simulations of the ruthenium dyes anchored to the titanium dioxide surface. In this sense, a DSSC can be de described as part of a general class of donor-linker-acceptor (DoAc) systems, where an electron transfer process occurs from a donor to an acceptor via an organic linker. In the present model, the donor is ruthenium complex, the acceptor is a [Ti(OH)\({}_{3}\)]\({}^{+}\) unit and the linker is a the carboxylate moiety.  
In the previous work, the donor group was located on the ruthenium complex, while the acceptor was centered on the anchor ligand. Through this model, a general set of equations was derived to calculate how much of the absorbed energy by the dye could reach the anchor ligand and be available for injection.  
The model went further, by now considering the ruthenium complex (D) bound to a model of the semiconductor, a Ti(OH)\({}_{3}\) group, forming a stable adduct labeled, [D-Ti(OH)\({}_{3}\)]. In this way, the charge of the carboxidated group is neutralized by a Ti(OH)\({}_{3}\)\({}^{+}\) unit, both constituting the transferring center to inject electronic density to the semiconductor. In the DolAc architecture, the acceptor and donor groups are linked by the carboxylate moiety, and charge will flow from the SOMO of the excited ruthenium dye to the LUMO of Ti(OH)\({}_{3}\).  
The Global Efficiency Index (GEI) is calculated as the product of three independent parameters, **GEI = F\({}_{1}\)F\({}_{2}\)F\({}_{3}\)**, that take into account the efficiency of the main three chemical process governing the overall cycle of the DSSC.  
D-Ti(OH)\({}_{3}-+\) hu \(\rightarrow\) D\({}^{*}\)-Ti(OH)\({}_{3}\) (F1);  
D\({}^{*}\)-Ti(OH)\({}_{3}\)\(\rightarrow\) D\({}^{*}\)-Ti(OH)\({}_{3}\)(\(-\)) (F2);  
D\({}^{*}\)-Ti(OH)\({}_{3}+\) R \(\rightarrow\) D\({}^{*}\)-Ti(OH)\({}_{3}\) + R (F3).  
Figure 22: (**a**) IPCE (**b**) APCE for the series of complexes under study.  
The three processes labeled F1, F2, and F3 are related to three fundamental processes governing the efficiency of a sensitized solar cell: the harvested energy (F1), the injected energy (F2) and the regeneration efficiency (F3).  
In the following, a description of the F1, F2, and F3 parameters is presented.
## 1 _Article_
Figure 1: (**a**) Breakwater at the outer port of Punta Langosteira, A Coruña, Spain. (**b**) Breakwater sections and parameters representing vertical, composite, and sloping breakwaters. Figure 1a (source [6]) shows a sloping breakwater with all the previously explained elements. This image corresponds to the outer port of A Coruna, Spain, where we carried out the primary fieldwork for this work. Even a properly designed breakwater cannot protect the ships anchored there from all the possible wave-caused phenomena, i.e., a breakwater protects up to a point; it is designed to protect against phenomena up to a given magnitude. However, budget and physical constraints do not allow a breakwater to be built to protect ships against every wave-caused phenomenon. The main disrupting wave-related event that could hinder the protection offered by a breakwater is wave overtopping. Wave overtopping is a dangerous phenomenon that occurs when waves meet a submerged reef or structure or an emerged reef or structure with a height up to double the approximate wave height. The latter case is the one that affects a port's breakwater and the one studied in this work. During an overtopping, two processes occur: wave transmission and water passing over the structure. This work focuses on measuring the passing of water over the structure (a breakwater in this case). This process can occur in three different ways, either independently of each other or combined:  
* Green Water is the solid (continuous) step of a given volume of water above the breakwater's crown wall due to the wave's rise (wave run-up) above the exposed surface of the said breakwater. * White Water occurs when the wave breaks against the seaside slope. This event creates so much turbulence that air is entrained into the water body, forming a bubbly or aerated and unstable current and water springs that reach the protected area of the structure either by the wave's impulse or as a result of the wind. * Aerosol is generated by the wind passing by the crest of the waves near the breakwater. Aerosol is not an especially meaningful event in terms of the damage it can produce, even in the case of storms. This case is the least dangerous of the three, and its impact on the normal development of port activities is negligible. Regarding the damage they could cause, the more critical overtopping types are green and white water. So, this work focused on these overtopping types. Figure 2 shows an example of White Water overtopping captured in the outer port of Punta Langosteira, A Coruna, as part of this work. Figure 2: Overtopping example. Outer port of Punta Langosteira, A Coruna, Spain.
## 2 Materials and Methods
### MOD15 and MOD17 Products  
The Leaf Area Index (LAI), one of the six layers of the MOD15 product, was used to evaluate the relationship between satellite data and surface phenological patterns. The MOD15 algorithm uses daily surface reflectance values (MOD09) and data from a radiative transfer model, which are stored in a two-dimensional Lookup Table (LUT) [62]. MOD17 contains three layers, corresponding to gross primary production (GPP), net photosynthesis (PSnet) and PSnet pixel quality, with a 500 m spatial resolution. GPP is calculated on a daily scale, but the products are available with the sum for an 8-day period. The GPP derived from the MOD17 algorithm (GPPmod) is based on the linear relationship between absorbed photosynthetically active radiation and light use efficiency (LUE) [63] and is obtained according to Equation (8).  
\[GPP=\varepsilon\ X\ APAR \tag{8}\]  
where \(\varepsilon\) is the light use efficiency (LUE) and APAR is the absorbed photosynthetically active radiation. Although there are a large number of satellite environmental data measurement platforms [64; 65], the MODIS data were chosen because of the ease of NASA's specific data site tool, which provides various satellite information for these and other land measurement points, with varied environmental information available at: [[https://modis.ornl.gov/sites/?id=br_para_caxiuana_forest_almeirim#product_table](https://modis.ornl.gov/sites/?id=br_para_caxiuana_forest_almeirim#product_table)]([https://modis.ornl.gov/sites/?id=br_para_caxiuana_forest_almeirim#product_table](https://modis.ornl.gov/sites/?id=br_para_caxiuana_forest_almeirim#product_table)), accessed on 25 January 2023.  
The NDVI and EVI data are averaged every 16 days, while LAI values are estimated every 8 days. In order to compare them with other variables, they were averaged over the same period as the NDVI, EVI and LAI values.