esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### State of the Art on XR and Accessibility for Cultural Heritage
Over the years, the scientific approach to simulative models of the past and their communication has been discussed to ensure academic rigour in visualisation methods. Linked to the topic of scientific data accessibility, open data and FAIR principles have been introduced since 2016 to provide guidelines and best practices for optimising data reuse in scientific research [30]. According to these principles, data should be:  
* Findable. Automatic and reliable retrieval of datasets depends on the persistent identifiers (PIDs), such as DOI, Handle or URN, and descriptive metadata, which should be recorded in repositories that machines can also index;
* Accessible. The data or the metadata should be stored in repositories that make them persistent over time and traceable on the network;
* Interoperable. The data format should be open and interpretable by various tools, including other databases. Metadata should also use a standardised language shared internationally by different indexing services;
* Re-usable. Data processing should conform to the standards or protocols recognised by the relevant scientific communities. Data and metadata should be described and documented so they can be replicated and/or combined in different contexts. The reuse of data and metadata should be declared under one or more open licences. In order to answer some of the questions related to XR technologies and accessibility, the e-Archeo project turned to previous scientific research conducted by the CNR ISPC, which will be discussed in more detail in the following paragraphs: a platform for VR content (ATON) [31]; a method for mapping levels of reliability, sources and interpretative processes on 3D reconstructive models (extended matrix) [32]; research in the field of universal design with particular reference to tangible user interfaces (TUI) [28]; guidelines for the metadata of cultural heritage datasets in the Zenodo platform [33]. Zenodo is an open access repository for research publications and data distributed under user-chosen creative commons licences. It aims to share scientific material and to archive it in the long term.
# Software-Defined NB-IoT Uplink Framework--The Design, Implementation and Use Cases
## 5 Uplink Implementation
### Modulation  
The next module fulfills the function of mapping transmitted bits to the binary phase shift keying (BPSK) or the quadrature phase shift keying (QPSK) symbols. The mapping patterns for each modulation type are presented in Tables 5 and 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**NPUSCH Format** & \(\mathbf{\Delta f}\) & \(\mathbf{N_{sc}^{RUI}}\) & \(\mathbf{N_{slots}^{UL}}\) \\ \hline  & 3.75 kHz & 1 & 16 \\ \multirow{2}{*}{1} & 15 kHz & 3 & 8 \\  & 6 & 4 \\  & 12 & 2 \\ \hline  & 3.75 kHz & 1 & 4 \\ \multirow{2}{*}{2} & 15 kHz & 1 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: NPUSCH parameters [2].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**b(i)** & **I** & **Q** \\ \hline
0 & \(1/\sqrt{2}\) & \(1/\sqrt{2}\) \\ \hline
1 & \(-1/\sqrt{2}\) & \(-1/\sqrt{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: BPSK modulation [2].
## 6 Discussion and Future Work
Several studies related to controller placement are related to pure SDN. We considered master controller placement in the incremental deployment of SoDIP6 networks over hybrid SDN/legacy networks. Defining the controller count during network migration depends upon the size of networks, consisting of the number of routers to be migrated. During the network migration, a (3+1) controller (three master/slave controllers and one backup controller) placement strategy is the reasonable starting point [34], since latency is an important parameter in SDN for better quality of service. Because the controller provides the operational life to the data plane, in which the flow setup time is dependent on switch-controller communication latency, we considered latency in the optimal path (i.e., shortest path) as the optimal latency to take the decision regarding controller placement using BFR. Finding the optimal placement of other controllers is not within the scope of this paper. The placement of other master/slave ONOS controllers for proper load balancing and resiliency in large carrier-grade networks during migration using other optimization approaches will be considered as future works.
# Accounting for Training Data Error in Machine Learning Applied to Earth Observations
## Guidelines and Recommendations
### Step 3: Minimize Collection-Related Errors
Assuming that researchers correctly specify their data selection and legend design when using higher spatial resolution imagery to create TD, image interpretation errors due to insufficient resolution should be minimized; however, special care should be given to borderline classes, or classes exhibiting a high degree of spatial and/or spectral variability due to land-cover mixtures within the pixel [127; 137; 138; 154; 219]. In such cases, we recommend that training polygons be created near the center of scene objects, where pixel mixing is likely to be minimized, e.g., [55]. Another important error-minimizing approach relates to cases in which TD comes from a process model, as in the surface flux example outlined above. Process models are also increasingly used to train crop yield mapping models, due to the difficulty of obtaining sufficiently large and reliable field-scale yield data for training [220]. To circumvent this challenge, the scalable yield mapping (SCYM) method [221; 222] uses a mechanistic crop model to simulate yields under various environmental and management conditions. The model's outputs then become inputs for training an empirical mapping model (typically ML), in which the simulated yield is the dependent variable and a subset of remotely retrievable model variables serve as predictors. TD errors in such cases can be minimized by rigorously calibrating the process model (itself a challenging task) using best practices from the relevant modeling literature, e.g., [223]. Alternatively, if modeled TD are necessary but careful calibration is not possible (e.g., because the data are pre-existing), then a merging approach such as triple collocation (Section 3.2) can help reduce training error.
## 1 Introduction
### Research Needs  
It has been demonstrated that off-grid PV-membrane systems can function from a varying RE source. The usage of SCs and LA batteries for storing energy has been elaborated in several studies, while the application of Li-ion batteries in off-grid PV systems--in particular, with the impacts on the SEC, water quality and quantity--needs further investigation due to its high energy intensity and large charging/discharging cycles. Furthermore, experiments comparing the performance of different energy storage options--SCs vs. Li-ion batteries vs. the reference (directly coupled) system without storage--need to be conducted. In this paper, the following research questions will be addressed:  
1. How does the addition of up to one day's worth of energy storage via Li-ion batteries affect water production and the SEC of a PV-membrane system operated under a variety of weather conditions (so-called \"partly cloudy\", \"sunny\" and \"very cloudy\" days)?
2. What are the effects of using different amounts of battery storage capacity (realized by limiting the initial SOC of the batteries) on the PV-membrane system?
3. What are the impacts of different energy storage options on the PV-membrane system when compared with SCs and Li-ion batteries?  
Previous research by the authors resulted in the design of a PV-membrane system coupled with SCs [40], which, in this work, was used to investigate the performance of such systems under real weather conditions. The system setup, including Li-ion batteries, was based on a modified version of the system described in Li et al. [40] by (i) reconfiguring the PV characteristics (PV maximum point voltage and current), (ii) adding a new charge controller to regulate the power from the PV to the Li-ion batteries and load (the pump and membrane system) and (iii) adding a DC/DC converter to boost the output voltage of the batteries to assure the operation of the pump. This setup was used to study the impacts of Li-ion batteries and then enable the comparison with SCs on the PV-membrane system performance under real solar days.
## 2 Materials and Methods
### Numerical Approach
#### 2.2.2 The SST-Based Model  
The DDES hybrid method is constructed based on the traditional RANS turbulence model equations. In the present work, the SST \(k\)-\(\omega\) turbulence model was employed, which was essentially formulated on the basis of the following two equations:  
\[\left\{\begin{array}{c}\frac{\partial(\rho k)}{\partial t}+\frac{\partial \big{(}\rho u_{k}\big{)}}{\partial x_{j}}=P-\beta^{*}\rho\omega k+\frac{ \partial}{\partial x_{j}}\Big{[}(\mu+\sigma_{k}\mu_{t})\frac{\partial k}{ \partial x_{j}}\Big{]}\\ \frac{\partial(\rho\omega)}{\partial t}+\frac{\partial\big{(}\rho u_{i}\omega \big{)}}{\partial x_{j}}=\frac{\rho^{\gamma}}{\mu_{t}}P-\beta^{*}\rho\omega^{ 2}+\frac{\partial}{\partial x_{j}}\Big{[}(\mu+\sigma_{\omega}\mu_{t})\frac{ \partial\omega}{\partial x_{j}}\Big{]}+2(1-F_{1})\frac{\rho\sigma_{\omega}}{ \omega}\cdot\frac{\partial k}{\partial x_{j}}\cdot\frac{\partial\omega}{ \partial x_{j}}\end{array}\right., \tag{3}\]  
where \(\rho\) represented the density, \(\mu_{j}\) denoted the velocity vector, and \(x_{j}\) signified the position vector. In addition, \(k\) and \(\omega\) denoted the turbulence kinetic energy and the specific rate of dissipation, respectively, whereas \(\mu\) and \(\mu_{t}\), in order, were the laminar and turbulent viscosity coefficients. The form of the production term \(P\), the function \(F_{1}\), and the values of the coefficients \(\beta^{*}\), \(\gamma\), \(\sigma_{k}\), and \(\sigma_{\omega 2}\) can be found in the literature pertinent to the SST-based model [25].
# Mapping a Guided Image Filter on the HARP Reconfigurable Architecture Using OpenCL
## 5 Guided Image Filtering  
Guided Image Filtering is an image processing technique in which an input image is smoothed based on a guidance image, while preserving edges [15]. During filtering, the structure information of the guidance image is transferred to the filtered image. The filtering output is locally a linear transform of the guidance image. An example application of this algorithm is the enhancement of LIght Detection And Ranging (LIDAR) data. By scanning an environment using LIDAR, a point cloud, consisting of (x,y,z)-coordinates, is obtained. This point cloud can act as the filtering input in the guided image filter algorithm. By making use of a color image of the scene as the guidance image, the LIDAR data can be denoised.
## 3 Experiments and Analysis  
### Imaging a Coin with a Smartphone-Based PDU  
In this experiment, we used the B-Camera with a smartphone-based PDU. The object was a nickel coin (Rp.1000), which possesses a ferromagnetic property. In the first experiment, the coin was located at the center of the array, as shown in the left part of Figure 3a. The magnitude field distribution expressed in Equation (2) was displayed on a smartphone screen, as shown in the right part of Figure 3a. The colors represent the field density in gauss (G), whose values are indicated in the color bar. The image showed the circular distribution of the field, which was strong at the center of the array. This image was consistent with the position of the object.  
In the second experiment, the coin was located in the corner of the array. It should be noted that in normal usage, the array looks downward; therefore, the left-right positions are exchanged. The left part of Figure 3b shows the position of the object in the array, whereas the right part shows the field distribution. This figure shows that the image of the object was as expected, indicating that the device worked properly.  
Figure 2: The system with a larger sensor array consisting of 320 sensors in 20 subarrays. Each subarray had \(4\times 4\) sensors, a multiplexer and a slave microcontroller connected to the master microcontroller via a Serial Peripheral Interface (SPI) bus. This configuration allowed faster data collection and simultaneous operation for all subarrays.
## Materials and Methods
### Concept of Operations
The ASACAS evaluates the situation in real time at a cycle frequency (ASACAS runs at 1 Hz), so it updates the maneuver suggested to the pilot continuously, which means that if the pilot waits too much and the suggested maneuver is no longer applicable, it is automatically substituted by the ASACAS with a new applicable maneuver. The \"latency\" is referred to the overall time that is needed for the reception of traffic info by the ADS-B IN for processing by the ASACAS and the elaboration of the suggested maneuver, as well as  
Figure 1: Automatic separation assurance collision avoidance system (ASACAS) concept of operations. ATC: air traffic control service provision facility; GCC: remotely piloted aircraft systems (RPAS) ground control station; FCC: flight control computer; ADS-B: automatic dependent surveillance-broadcast. the time needed for the maneuver evaluation by the remote pilot and implementation. In general, the latency contribution due to the ASACAS's computation time is much lower (1 s usually when working at 1 Hz because in all the performed real-time simulations, the system was demonstrated to be able to provide a maneuver in 1 s) than the time needed by the pilot for evaluation and implementation. Considering that the human-related time is a variable not predictable, the ASACAS was designed to automatically perform continuous updates of maneuvers, as indicated above, so that the proposed maneuvers is immediately substituted with a new one when the old is no longer applicable, therefore overcoming the problem of a possible long evaluation time by the remote pilot. This concept is also applied after the clearance and implementation of the maneuver by the remote pilot because the current situation is always evaluated by the ASACAS at cycle frequency (1 Hz) during the flight--even during the maneuver execution--in order to be able to provide a new maneuver when needed.
## 3 Methods and Data  
We set out to create a model of measured flow characteristics that is spatially accurate in 3D and contains flow velocities and 3D flow directions of a spatially continuous area as a time-integrated snapshot of the measurement period of about two hours.  
The way we represent spatial flow data in 3D is as a LiDAR-like (light detection and ranging) point cloud [28] that contains flow parameters as scalar fields. Unlike a LiDAR point cloud, which generally aims to represent primarily the surface of a 3D object, the point clouds we create here contain data from the entire water column. Furthermore, unlike a LiDAR point cloud, this one is semi-systematic in its structure, that is the points are irregularly spaced, but they are vertically stacked, albeit at varying vertical spacing.  
Creating multidimensional flow data point clouds involves a range of field methods and data post-processing. Figure 2 shows the main components of the set-up in the field and how the field measurements relate to the final data product. The process is described in detail below.
## 2 Data Collection
### Rate of TEC Index  
The rate of TEC (_ROT_) index and its standard deviation, known as _ROTI_, can also identify small-scale ionospheric irregularities. The unit of the rate of TEC (_ROT_) is TECU/min, where 1 TECU refers to \(10^{16}\) electrons/\(m^{2}\). Different research works [30; 31; 32] pointed out that the existence of some correlations between _ROTI_ and ionospheric scintillation indices such as the S4 and _ROTI_ index can be employed for predicting the occurrence of ionospheric scintillation created by ionospheric irregularities [33]. The fundamental formula for _ROT_ measurement is as follows [34]:  
\[ROT(i)\ =\ \frac{L_{\text{GF}}(i)\ -\ L_{\text{GF}}(i\ -\ 1)}{\Delta t\ \times\ 10^{16}\ \times\ 40.3\ \times\ (\frac{1}{f_{1} ^{2}}\ -\ \frac{1}{f_{2}^{2}})} \tag{4}\]  
where \(f_{1}\) and \(f_{2}\) represent the \(L_{1}\) and \(L_{2}\) GPS signal frequencies, respectively; \(\Delta t\) indicates the time interval in minutes; and \(L_{\text{GF}}\) shows the wavelength of a geometry-free linear combination, which can be calculated as shown below:  
\[L_{\text{GF}}=L_{1}(i)\times\lambda_{1}-L_{2}(i)\times\lambda_{2} \tag{5}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) represent the signal wavelength of \(L_{1}\) and \(L_{2}\). The ROTI estimated within a time interval is represented as follows:  
\[ROTI\ =\ \sqrt{<ROT^{2}>\ -\ <ROT>^{2}} \tag{6}\]  
_ROTI_ values employed in this research were averaged in five minutes, using the mean of the _ROTI_[31]. For the same satellite receiver link, plasma bubble irregularities can cause both TEC fluctuation and scintillation. _ROTI_ values have a positive correlation with S4 values; therefore, the _ROTI_ is implemented to identify the occurrence of ionospheric scintillation. Previous studies showed that _ROTI_ values are positively correlated with the S4 in different longitude sectors, although the _ROTI_ and scintillation represent the irregularities with different scale sizes [33; 35].
## 3 Results and Discussion
### Cu\({}^{2+}\) Distinguishing EZM Enantiomers
#### 3.3.2 The Binding Form of Ternary Complexes  
CID of [Cu\({}^{\Pi}\)(\(\beta\)-CD)(EZM)\({}_{2}\)-H]\({}^{2+}\) cluster ion typically yields several different fragment ions such as [Cu\({}^{\Pi}\)(\(\beta\)-CD)(EZM)-H]\({}^{+}\), [Cu\({}^{\Pi}\)(\(\beta\)-CD)-H]\({}^{+}\) and the neutral EZM, and there was obvious difference between EZM enantiomers in MS\({}^{2}\) spectra. As shown in Figure 3, the \(m/z\) of precursor ion is 1007.79, which yields four major fragment ions: two dimers corresponding to the loss of only one neutral EZM and two EZM (single charged at \(m/z\) 1605.44 and 1196.29, respectively); one protonated ion of EZM at \(m/z\) 410.16 and one product ion at \(m/z\) 392.15 via the loss of H\({}_{2}\)O from EZM. It can be seen that the EZM product ion's intensity of \(RSR\) is higher than \(SRS\), which indicates that \(RSR\) tends to lose one neutral EZM more easily than \(SRS\). In other words, [Cu\({}^{\Pi}\)(\(\beta\)-CD)(\(SRS\)-EZM)\({}_{2}\)-H]\({}^{2+}\) should have a stronger affinity than [Cu\({}^{\Pi}\)(\(\beta\)-CD)(\(RSR\)-EZM)\({}_{2}\)-H]\({}^{2+}\). Therefore, when the same collision energy is performed on the two clusters, [Cu\({}^{\Pi}\)(\(\beta\)-CD)(\(RSR\)-EZM)\({}_{2}\)-H]\({}^{2+}\) will produce a higher abundance of EZM fragment and the two EZM enantiomers can be differentiated.  
In addition, a ternary complex was detected as [Cu(EZM)\({}_{2}\)(\(\beta\)-CD)-H]\({}^{2+}\) whether in ion trap MS or in Q-TOF MS. After collision in the MS/MS, the [Cu(EZM)\({}_{2}\)(\(\beta\)-CD)-H]\({}^{2+}\) produced several fragments, among which the [EZM-H\({}_{2}\)O+H]\({}^{+}\) (\(m\)/z = 392.15) shows a distinct difference between \(SRS\)-EZM and \(RSR\)-EZM (Figure 3, Table 1). The recognition of \(SRS\)-EZM and \(RSR\)-EZM was based on the collision-induced dissociation of the diastereomeric complex method.  
Figure 3: The MS/MS chromatogram of [Cu(EZM)\({}_{2}\)(\(\beta\)-CD)]\({}^{2+}\) ternary complexes in different collision energy, 16 V is the best collision energy. (**A**) Collision energy from 10 V to 16 V. (**B**) Collision energy from 18 V to 20 V.
# A Location-Aware Waypoint-Based Routing Protocol for Airborne DTNs in Search and Rescue Scenarios
## 3 Related Work  
We explore the past research in terms of DTN routing. A brief description of classic DTN routing protocols is provided, as well as some related work that use location information to support the packet delivering in DTNs. Next, we discuss several proposals focused on geographic information in order to help the packet delivering.
## 5 Experimental Setup  
### Data Summary  
PTDP data from January to June 2021 from Narashino, Chiba, Japan, provided by Agoop Corp. [20], were used for this experiment. The target periods of data extraction comprise only workdays because people's behaviors on holidays are commonly different from those on workdays, which could affect classification accuracy. Narashino is in the region [139.59-140.50\({}^{\circ}\) E], [35.38-35.42\({}^{\circ}\) N], covering an area of 20.27 [km\({}^{2}\)], and as of January 2021, its population was 176,197 [43, 44]. The location data acquisition timing is unit time \(t_{\rm unit}=1\) [min]. Because IDs linked to mobile devices are reset at 12:00 a.m., each user's behavior can be identified only for a maximum of one day. In other words, the data length of trajectories \(T_{k},\forall k\in K\) is \(\mathcal{T}=60\times 24=1440\). Each ID \(k\) is associated with one age group among five-year age divisions. Although an age group is also linked to each mobile device, not all IDs have age group information, and some data are missing. Therefore, in this study, we construct and evaluate the ML model using only data for IDs with age group information.  
This study aims to determine whether the features generated by the sparse trajectory \(T_{k}\) can lead to accurate classifications. To facilitate this evaluation, we simplify the problem into a six-class classification problem, with each class comprising ages 0 to 19, 20 s, 30 s, 40 s, 50 s, and 60 s and over.  
Because people's behavior can vary depending on the season, we divided the data from January to June 2021 by month, i.e., \(1,2,\ldots,6\). Figure 5 shows the distribution of the data size by month and class; the figure indicates that the dataset is imbalanced. Additionally, Figure 6 shows the distribution of the data size for the missing value rate \(\theta_{k},\forall k\in K\) for each month. Figure 6 indicates that the distributions for all the periods are almost the same shape.  
Note that during the target period, the target area was under the outbreak of the novel coronavirus (COVID-19), and declarations of a state of emergency and a semistate of emergency were enforced from 8 January to 21 March and 20 April to 1 August 2021, respectively [45]. This is noted in the experimental results and discussion.  
Figure 4: (a) SDA. (b) Fine-tuning of the DNN obtained via SDA.