esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Future CONERSTONE Platforms
### Pick-and-Place of Light Sources onto SOI
From a mechanical point of view, the accuracy of the flip-chip technique can be enhanced via the use of patterned registration markers [171], computer vision registration techniques and active alignment [172]. The design of both the III-V and SOI chips in the coupling region can also be optimized to improve the tolerance to misalignment and minimize the coupling losses [173; 174; 175]. Due to these approaches III/V devices flip-chip bonded to SOI have insertion loss as low as 1.1-1.5 dB [176; 177; 178]. As detailed in Section 3.1, CORNERSTONE will also offer PIC development on a silicon nitride platform because of the advantages offered by its wide transparency down to the visible spectral range and beyond. The SiN transparency range is well-covered by a variety of III-V compound semiconductors such as aluminium indium gallium nitride (AlInGaN) [179; 180], gallium indium phosphide (GaInP), aluminium gallium indium phosphide (AlGaInP) and aluminium gallium arsenide (AlGaAs) [181]. The integration of these III-V active devices on a SiN material platform could be of great impact to several applications in healthcare, imaging and quantum technologies [182]. However, SiN presents additional integration challenges due to its lower refractive index, i.e., \(n=2.0\) for the stoichiometric composition, compared to the III/V materials, in the range of \(n=3\)-\(3.6\). This is often mitigated using a mode-matching waveguide between the III/V and the SiN waveguides made by a material with an intermediate refractive index waveguide, such as silicon or polymer. Examples of integration of III/V devices on a SiN platform are reported in the literature for both flip-chip bonding [183; 184; 185] and transfer printing [186; 187; 188] with an insertion loss as low as 2.1 dB. CORNERSTONE will offer flip-chip bonding by metal-to-metal thermocompression with submicron alignment. Due to the large demand for telecoms applications, the initial focus will be on InP-based distributed feedback (DFB) lasers and InGaAs photodetectors integrated on an SOI platform operating at a wavelength of 1550 nm. This activity will be supported by the design of suitable waveguide geometries to mitigate the impact of misalignment tolerances and by the establishment of design rules that will ultimately be part of the CORNERSTONE PDK. A key feature of this model is that, provided compliance to design and fabrication rules is followed, users will be able to use the CORNERSTONE capability to integrate any III-V device, which adds a unique level of flexibility to support both established and emerging applications at non-telecom wavelengths.
# Movable Platform-Based Topology Detection for a Geographic Routing Wireless Sensor Network
## 2 Background
### Common MAC Protocols  
Currently researchers have brought up many MAC protocols [26] for different sensor network applications, but there is no any unified method to classify sensor network MAC protocols yet [27]. In this paper MAC protocols are roughly classified into three types: First, distributed control or central control; second, single sharing channel or multiple channels; and at last, fixed distribution channel or random visit channel. Although MAC protocols of WSN are application-oriented protocols, MAC protocols for single WSNs under each application scenario are different. For node hardware restrictions on WSN, however, we often get a chance to find the common points of all MAC protocols: First, for small power of node receiving and sending signals, when two nodes communicate,the ones within the communication range will definitely maintain their communication retreat mechanism, in order to ensure communication quality; second, as node communication environment is free, obstacles, climate or electromagnetic interference may cause packet loss during inter-code communication, handshake information mutual confirmation of information intactness when sending and receiving information becomes very necessary; third, due to small node memory and packet validity assurance, data packets shall be as small as possible and the information sending and receiving interval shall be reduced when a group of data is transported among nodes, in order to improve communication efficiency. Finally, nodes in WSN are not able to sense global information of network, and data packets will mark the codes or address of information destination node and routing node in MAC frames. In this paper, we took advantage of the common characteristics of MAC protocols, so that the platform can determine station address code (in early platform detection stage, destination node address of data packets is forcefully recognized as station address. As an increasing number of data packets are captured in the later stage, the address code that appears most frequently is station address code) and make clear data packet flow and the upstream and downstream relations of part of the nodes according to the handshake information it captures, even if the target network uses an unknown MAC protocol. Consequently, the platform's universality to non-cooperative target networks will be greatly enhanced.
# Efficient V2V Communications by Clustering-Based Collaborative Caching +
## 4 Simulation Evaluation
### Simulation Condition  
We assume that a vehicle requests road condition information and weather information. The provider of content is the vehicle closest to it. Vehicles form clusters in the first 10 s, during which they do not request content. After that, each vehicle sends an interest packet periodically to fetch content. The content requests follow the Zipf law [24], where the number of requests of the \(k\)-th most popular content is proportional to \(1/k\).  
Detailed simulation conditions, simulation scenarios, the values of parameters, and the sizes of interest/data packets and control messages are shown in Table 2, Table 3, Table 4, and Table 5, respectively. Figure 7 is the open street map used in Scenario 3, which simulates real roads and buildings around Chofu Station.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Variable Name** & **Value** \\ \hline \(l_{\text{neighbor}}\) & 300 ms \\ \(d_{\text{cluster}}\) & 150 m \\ \(v_{\text{cluster}}\) & 1.4 m/s \\ \(n_{\text{sync}}\) & 100 \\ \(t_{\text{break}}\) & 1 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Values of parameters.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**No.** & **Road Shape** & **Number of Vehicles** \\ \hline Scenario 1 & 4 km straight road & \(100\) \\ Scenario 2 & 4 km straight road & 200 \\ Scenario 3 & \(1.6\) km \(\times\) 1.6 km urban area & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation scenarios.  
Figure 7: The map used in Scenario 3 (around Chofu Station in Japan) (©OpenStreetMap contributors).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Packet Name** & **Size (Bytes)** \\ \hline Interest packet & 128 \\ Data packet & 512 \\ Content-Not-Found & 128 \\ Request-To-Cache & 128 \\ Sync (for \(n\) content) & \(24+12n\) \\ Beacon (clustering disabled) & 48 \\ Beacon (clustering enabled) & 80 \\ Merge request & 16 \\ Merge ack & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Sizes of interest/data packets and control messages.
## Materials and Methods
### Satellite Image Processing  
Landsat 8 Operational Land Imager (hereafter referred to as Landsat 8) imagery for Wales was downloaded from the USGS Global Visualisation Viewer ([[http://glovis.usgs.gov/index.shtml](http://glovis.usgs.gov/index.shtml)]([http://glovis.usgs.gov/index.shtml](http://glovis.usgs.gov/index.shtml))) for 2013 and 2014 (Table 1). The raw pixel values (digital numbers) were calibrated to top-of-atmosphere (TOA) reflectance [27]. For comparison, Landsat 8 Surface Reflectance Products were also downloaded from the USGS Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA) On Demand Interface ([[https://espa.cr.usgs.gov/](https://espa.cr.usgs.gov/)]([https://espa.cr.usgs.gov/](https://espa.cr.usgs.gov/))).  
Clouds were masked from the imagery by using the Quality Assessment (QA) band to identify pixels flagged as cloud or cirrus with a medium confidence level [27]. A buffer of 10 pixels was added to capture cloud edges. A geometry based cloud shadow mask was applied, which estimated the location of cloud shadow using the solar zenith and azimuth angle for the image and an assumed cloud height of 1.5 km, and adding a buffer of 10 pixels to account for variability in cloud height.  
The reflectance values in the red and near-infrared (NIR) bands, \(\rho_{Red}\) and \(\rho_{NIR}\), respectively, were used to produce NDVI images, where:  
\[NDVI=\frac{(\rho_{NIR}-\rho_{Red})}{(\rho_{NIR}+\rho_{Red})}, \tag{1}\]  
The NDVI values at the coordinates of each of the GMEP vegetation sampling quadrats were then extracted from the Landsat 8 imagery. Two 100% cloud free Landsat 5 TM surface reflectance NDVI images, acquired on 28 April 2011, were also used to illustrate what is possible under ideal conditions [28].
## 5 Results and Discussion  
### Testing and Evaluation  
To evaluate the developed framework, an exploratory sequential mixed method is adopted, involving stakeholders (\(n=24\)) from different stages of prefabricated building design, including designers, component manufacturers, construction contractors, owners, and customers. The main testing contents include the following:  
* System Usability Scale (SUS): 10 Likert-scale questions scored out of 5 [41], encompassing positive and negative aspects.
* Objective Performance Evaluation: Through controlled experiments comparing this method with two existing traditional design methods, the usability of this method is quantitatively analyzed. It mainly compares and examines the number of design errors detected in collision tests and the number of design errors or queries raised in drawing reviews during the design evaluation phase of prefabricated buildings.
* Open-Ended Interview Theme Analysis: Following the principle of voluntariness, 15 stakeholders who had participated in the survey questionnaire were invited for open-ended interviews and theme analysis, aiming to evaluate the usefulness, functionality, spatial experience, and challenges of the method.  
Before the test, participants were required to complete the introductory part of the questionnaire, which captured their background information such as occupation, age, gender, experience in construction projects, and experience with virtual reality (Table 2). Additionally, participants were briefly introduced to the functionalities of BIM and VR-integrated technology, including the user interface and other interactive features. Subsequently, each participant was given 20 min to test the interior design, scene setup, material selection, and design review tasks.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Participant ID** & **Gender** & **Age a** & **Role** & **Previous VR Exp.** & **Previous BIM Exp.** & **Construction Experience (Unit: Year)** & **Participate in the Interview** \\ \hline PA 1 & M & 36 & Owner & \(\surd\) & \(\surd\) & 14 & \(\surd\) \\ PA 2 & F & 43 & Owner & \(\surd\) & \(\surd\) & 23 & \\ \hline PA 3 & M & 39 & Owner & \(\times\) & \(\surd\) & 16 & \\ PA 4 & F & 52 & Owner & \(\times\) & \(\surd\) & 27 & \(\surd\) \\ \hline PA 5 & F & 33 & Designer— Architecture & \(\surd\) & \(\surd\) & 8 & \(\surd\) \\  & & & Designer & & & & \\ PA 6 & F & 55 & —Structural Engineering & & & & \\ PA 7 & M & 44 & Designer & \(\times\) & \(\surd\) & 20 & \(\surd\) \\  & & & —Architecture & & & & \\ PA 8 & M & 31 & Designer & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Background information of test and evaluation participants.
## 2 Scheme of Proposed Control Model  
Figure 1 illustrates the proposed control scheme of the VSWG used to enhance the transient stability. The permanent magnet synchronous generator (PMSG) is connected to the grid through a power converter and a transformer. The power converter has a general control structure through which the active power and the reactive power are controlled according to each corresponding reference value. When the absolute value of deviation of the grid frequency, \(|\Delta f|\), becomes less than or equal to 0.2 Hz, which is the target value of the frequency deviation limit in Japan, \(\Delta P_{\textit{ref}}\) is set to 0, and hence the active power reference, \(P_{\textit{ref}}\), is equal to \(P_{\textit{mpt}}\), which is calculated according to the maximum power point tracking (MPPT) algorithm. When \(|\Delta f|\) becomes greater than 0.2 Hz, \(P_{\textit{ref}}\) is equal to the sum of -\(\Delta P_{\textit{ref}}\) and \(P_{\textit{mpt}}\). \(\Delta P_{\textit{ref}}\) is determined by multiplying \(\Delta f\) by two variable proportional gains \(K_{p1}\) and  
As described above, the reactive power control has higher priority than the active power control. Therefore, the maximum value of \(P_{ref}\) is calculated by the following equation:  
\[P_{ref\,\,lim}=\sqrt{VA^{2}-Q_{ref}^{2}} \tag{2}\]
# A Smartphone-Based Mobility Assistant Using Depth Imaging for Visually Impaired and Blind
## 4 Results and Discussions
### Evaluation of Obstacle Detection Module  
The obstacle detection module calculates distances per pixel from 0 m to 8 m away via depth estimation and are represented in different colors as shown in Figure 8. Red and orange areas mean closer to the camera with a range of less than 1.7 m, yellow and green areas are greater than 1.7 m, and, lastly, blue areas are greater or equal to 2 m.  
While the application is capable of detecting distances up to 8 m, a distance of less than 2 m is considered and tested as it has been observed that a distance of greater than 2 m is not practical for detection and evasion for VIB people and creates complications in navigating. In Figure 9, depth image distances of \(<\)1.7 m, \(>\)1.7 m, and \(\geq\)2 m are displayed and evaluated. It has been observed that at a distance of 1.6 m, the detection capability is strong and was confirmed as an optimal range for the VIB to detect the obstacle as the walking cane was just centimeters away from the object, allowing confirmation of the detected obstacle at a safe range.  
Figure 8: Depth map color distance representations with red distance as \(<\)1.7 m, yellow or green \(>\) 1.7 m, and blue \(\geq\) 2 m.  
\begin{table}
\begin{tabular}{c|c} \hline
**Activated Functions** & **Battery Life Duration** \\ \hline Camera Only & 8 h 43 min \\ Camera + Bluetooth & 7 h 17 min \\ Camera + Bluetooth + Internet & 4 h 11 min \\ Camera + Bluetooth + Internet + Location Services & 3 h 26 min \\ \hline \end{tabular}
\end{table}
Table 3: Battery life duration evaluation based on activated functions while running the mobile application.  
The smartphone is placed with the backside facing outside and placed vertically inside a sling pouch. During the testing, the participants were blindfolded and given a walking can as it is recommended to use this together with the application to determine their direction and the terrain of the environment safely as seen in Figure 10. The obstacle course had some natural obstacles such as light posts, elevated areas, trees, and others. We also included randomly placed obstacles along the way to further test the system.  
The obstacle detection test results are listed in Table 4. This includes the finish time, the number of obstacles successfully detected and evaded, and the number of obstacles that were not detected by the application. The five participants were asked to navigate at their own pace. It was observed that when the person was walking too fast, the process time of the application could not keep up with the user's quick movement as some parts of the area take time to focus and update the distance calculation.  
Figure 10: Blindfolded participant **(left)** with walking cane walking through the obstacle course **(right)** with randomly placed obstacles along the way.  
Figure 9: Depth map comparison of fan obstacle at different distances.
## 2 Materials and Methods  
### Materials  
The compost-derived wastewater used in this study was generated from a commercial Agro compost. Its initial physico-chemical characteristics, expressed as the average values \(\pm\) standard deviation, were pH 3.95 \(\pm\) 0.11, electrical conductivity 1900 \(\pm\) 18.88 \(\upmu\)S/cm, turbidity 251 \(\pm\) 12.72 NTU, chemical oxygen demand (COD) 1642 \(\pm\) 105 mg O\({}_{2}\)/L, and total solids (TS) concentration of 3.08 \(\pm\) 0.03 g/L [30]. Electrodes used in this study were made of aluminum alloy AA2007 (2000 series), predominantly composed of aluminum (92.58%) and copper (3.84%). A detailed elemental composition of the alloy is available in previously published sources [15]. Zeolite (Z) used in this study is synthetic, obtainedfrom Alfa Aesar and labeled as NaX. The crushed zeolite NaX was sieved on a Retsch AS 200 digit laboratory sieve shaker (Retsch GmbH, Haan, Germany), using sieves of various mesh sizes and an amplitude of 2.5 mm. In this research, particles with a size below 0.04 mm were used. XRD analysis confirmed its classification within the Faujasite (FAU) framework. Characterization data, including PXRD and SEM-EDS, were previously reported [17]. Elemental analysis of the zeolite conducted through SEM/EDS mapping and the acquisition of a map sum spectrum revealed a composition primarily comprising silicon, aluminum, and oxygen, alongside exchangeable cations such as sodium, potassium, calcium, and magnesium, yielding a Si/Al ratio of approximately 2.07 [17]. Based on elemental data obtained, the estimated cation exchange capacity (CEC) for the zeolite is approximately 6.22 meq/g. The density of the zeolite is 2.0605 g/cm\({}^{3}\). It was measured using the Anton Paar Ultrapyc 5000 gas pycnometer (Graz, Austria), by applying Boyle's Law and the gas displacement principle with helium as the analysis gas.
# On Coding and Decoding Reconfigurable Radiation Pattern Modulation Symbols
###### Abstract  
In this paper, we propose the theoretical framework for a reconfigurable radiation pattern modulation (RRFM) scheme, which is reminiscent of the index modulation technique. In the proposed scheme, information is encoded using far-field radiation patterns generated by a set of programmable radiating elements. A considerable effort has been invested to allow for high transmission of the reconfigurable radiation pattern symbols; yet, the receiving system has received little attention and has always been considered ideal. Depending on the number of receivers and their respective positions, two variables are considered here for data transmission: the sampling resolution and the fraction of the covered space by the receiving antennas. Hence, we quantitatively investigate their effect on the bit-error-rate (BER) by making use of a limited number of measurements that approximate the behavior of the system under real-field conditions.  
antennas; arrays; digital communications; modulation techniques; wave-matter interaction; radiation patterns +
Footnote †: journal: Electronics
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 4 Comparison of Forest AGB Maps over Large Regions
### Europe  
Among the eight forest AGB maps covering Europe, AGB in [PERSON] et al. (2019) was greater than the other seven datasets in Western Europe, while [PERSON] and [PERSON] (2017) showed almost the highest AGB in most parts of Europe, especially in Russia (Figure 1). The frequency distributions of these AGB maps with 10 Mg/ha bins showed that in [PERSON] et al. (2018) biomass was in the range of 30-50 Mg/ha, and half of the pixels had AGB ranging from 40 Mg/ha to 50 Mg/ha (Figure 2). The frequency distributions were similar in three global forest AGB datasets including that of [PERSON] et al. (2019), [PERSON] et al. (2010) and [PERSON] et al. (2018). The majority of the pixels in the datasets of [PERSON] et al. (2010) and [PERSON] et al. (2018) had AGB ranging from 70 Mg/ha to 80 Mg/ha, and for [PERSON] et al. (2019), it shifted to 120 \(\sim\) 130 Mg/ha. Distributions in both [PERSON] et al. (2012) and [PERSON] and [PERSON] (2017) approximated a bell shape, but the frequency peak was offset to higher AGB for the [PERSON] and [PERSON] (2017) dataset compared to [PERSON] et al. (2012).  
According to the global land cover climatology dataset, regions shown in Figure 1 were mainly evergreen needleleaf forest, mixed forest, and woody savannas. The FN index showed that most forest AGB maps were consistent with each other in the evergreen needleleaf forests and mixed forests over Europe. However, there was spatial disagreement in AGB between [PERSON] et al. (2018) and [PERSON] and [PERSON] (2017), between [PERSON] et al. (2018) and [PERSON] et al. (2019), and between [PERSON] et al. (2010) and [PERSON] and [PERSON] (2017) in evergreen needleleaf forest. Furthermore, there was also spatial disagreement in AGB between [PERSON] et al. (2018) and [PERSON] and [PERSON] (2017), and between [PERSON] et al. (2018) and [PERSON] et al. (2018) in mixed forest (Figure 3). However, the eight AGB datasets were almost entirely inconsistent in woody savannas, suggesting large discrepancies in woody savanna estimates over Europe.  
Figure 1: Remote sensing-based forest biomass maps across Europe.  
Figure 2: Frequency distribution of AGB with 10 Mg/ha bins in eight maps covering Europe.
# Joint Radar and Communications Waveform Design Based on Complementary Sequence Sets
## 3 JRC-CWG and DR-JRC-CWG Design
### JRC-CWG Design with Low Sidelobe Level
#### 3.1.2 JRC-CWG Design Algorithm
Step 1: Initialize \(\mathbf{\Theta}_{g}^{(0)}=\Big{[}\mathbf{\Theta}_{g,1}^{(0)},\dots,\mathbf{\Theta}_{g,M_{ r}}^{(0)}\Big{]}\), let \(k_{0}=0,\alpha_{0}=0\), and the search direction \(\tilde{\mathbf{V}}_{d}^{(0)}=\mathbf{0}_{N\times M_{r}}\). Step 2: Calculate the gradient matrix \(\tilde{\mathbf{G}}_{g}^{(k_{0})}=\Big{[}\tilde{\mathbf{g}}_{g,1}^{(k_{0})}, \tilde{\mathbf{g}}_{g,2}^{(k_{0})}\dots\tilde{\mathbf{g}}_{g,M_{r}}^{(k_{0})} \Big{]}\) using (16), where \(\tilde{\mathbf{g}}_{g,m}^{(k_{0})}=\partial D\big{[}\mathbf{\Theta}_{g}^{(k_{0})} \big{]}/\partial\theta_{g,m}^{(k_{0})}\). Step 3: Calculate the search direction \(\tilde{\mathbf{V}}_{d}^{(k_{0}+1)}=-\tilde{\mathbf{G}}_{g}^{(k_{0})}+\kappa_ {k_{0}}\tilde{\mathbf{V}}_{d}^{(k_{0})}\). Step 4: Compute \(\mathbf{\Theta}_{g}^{(k_{0}+1)}=\mathbf{\Theta}_{g}^{(k_{0})}+\lambda_{k_{0}}\tilde{ \mathbf{V}}_{d}^{(k_{0}+1)}\), where \(\lambda_{k_{0}}\) is the optimal solution that minimizes \(D\big{[}\mathbf{\Theta}_{g}^{(k_{0}+1)}\big{]}\), which is obtained with the line search method [45]. Step 5: If \(D\big{[}\mathbf{\Theta}_{g}^{(k_{0}+1)}\big{]}-D\big{[}\mathbf{\Theta}_{g}^{(k_{0})} \Big{]}\big{]}\leq\varepsilon\), go to Output, else (1) Let \(k_{0}=k_{0}+1\); (2) Calculate the gradient matrix \(\tilde{\mathbf{G}}_{g}^{(k_{0})}=\Big{[}\tilde{\mathbf{g}}_{g,1}^{(k_{0})}, \tilde{\mathbf{g}}_{g,2}^{(k_{0})},\dots\tilde{\mathbf{g}}_{g,M_{r}}^{(k_{0})} \Big{]}\) using (16), where \(\tilde{\mathbf{g}}_{g,m}^{(k_{0})}=\partial D\big{[}\mathbf{\Theta}_{g}^{(k_{0})} \big{]}/\partial\theta_{g,m}^{(k_{0})}\); (3) Calculate \(\kappa_{k_{0}}=\|\tilde{\mathbf{G}}_{g}^{(k_{0})}\|_{F}^{2}/\|\tilde{\mathbf{G }}_{g}^{(k_{0}-1)}\|_{F}^{2}\); (4) Turn to Step 3. Output: The phase of WTOs \(\mathbf{\Theta}_{g}^{(k_{0}+1)}\). ```  
**Algorithm 1**The JRC-CWG design algorithm.