esa-sceva/satcom-chunk-collection huggingface dataset:
# Mixed Reality: A Reconsideration Based on Mixed Objects and Geospatial Modalities
## 4 MR Demonstration: Mixed Objects and Geospatial Modalities
### Geospatial Input Linking Modalities
#### 4.1.1. Object Input Devices  
1. Smartphone Camera. The simplest input device for real-time capturing of the real-world scene during an observer's existence on-site experiencing MR is a smartphone of normal capabilities: a Xiaomi Redmi Note 7 with an Octa-core CPU (4 \(\times\) 2.2 GHz Kryo 260 Gold and 4 \(\times\) 1.8 GHz Kryo 260 Silver) an Adreno 512 GPU, and 3 GB RAM. The data acquired is the real-time capturing of the real world in 2D by the camera sensor.
2. Visual Tag with QR Code. Typical smartphone devices have an Inertial Navigation System (INS) with gyroscope, accelerometer, and magnetometer [30] to detect changes in rotation, velocity, and azimuth (direction of view) respectively. A major issue is the smartphone initialization in terms of position and azimuth. Global Positioning Systems (GPS) through GNSS antenna receivers may result in poor accuracy that is  
Figure 7: MR experience in the field: the virtual helicopter is partially covered by the monument.  
increased with bad weather conditions and their use is limited to outdoor environments. Further, accuracy in azimuth may vary on different devices. To handle the peculiarities of any potential mixed environment (indoor, outdoor, or lack of sensor calibration) we have selected a marker-based positioning and azimuth system using QR Codes, as shown in Figure 8. These QR Codes contain stringified JSON data that describe the perceived digital world's ID, the observation point ID, the position in x, y, z coordinates, and the azimuth.
3. UAVs properly equipped for capturing the area. UAVs have become the proper technology for capturing an area and acquiring high-resolution images to be used for creating the 3D mapping of the area via 3D mapping methods such as photogrammetry. For the purposes of the demonstration, we used free 3D photogrammetrically mapped models of the area as shown below.
# Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval
## 3 The Proposed DSAH Framework
### Label Alignment Scheme  
As described above, labels contain rich semantic information, directly converting the complex label vectors into binary semantic matrix will cause the loss of semantic information. The results of [PERSON]'s work [52] demonstrate that the ordinary least squares  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Explanations** \\ \hline \(\mathbf{X}^{(t)}\in\mathbb{R}^{d_{m}\times n}\) & Features of heterogeneous modalities \\ \(\mathbf{L}\in\mathbb{R}^{c\times n}\) & Label information \\ \(\mathbf{B}\in\{-1,1\}^{k\times n}\) & Hash codes \\ \(\mathbf{P}_{t}\in\mathbb{R}^{k\times q}\) & Hash mapping matrix \\ \(\mathbf{D}\in\mathbb{R}^{c\times c}\) & Projection matrix for label information \\ \(\mathbf{V}\in\{-1,1\}^{k\times n}\) & Auxiliary matrix \\ \(\mathbf{J}_{b}\in\mathbf{R}^{k\times n}\) & Auxiliary matrix \\ \(d_{m}\) & Dimension of modality \(\mathbf{X}^{(m)}\) \\ \(n\) & Number of instances \\ \(c\) & Number of categories \\ \(q\) & Number of Kernelized features \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations.  
regression is sensitive to the boundary contour. Inspired by the work in [53], we consider \(\ell_{p,q}\) norm instead of \(\ell_{2}\) norm to handle the problem, the \(\ell_{p,q}\) norm can be formulated as  
\[\min_{\mathbf{E}}||\mathbf{E}||_{p,q}=\min_{\mathbf{E}}\,(\sum_{i=1}^{c}(\sum_{ j=1}^{n}|\mathbf{E}_{ij}|^{p})^{q/p})^{1/q}, \tag{3}\]  
where \(\mathbf{E}=\mathbf{R}^{\top}\mathbf{B}-\mathbf{L}\) and \(\mathbf{R}\in\mathbb{R}^{k\times c}\) is the semantic projection matrix. It is easy to find that when \(p=q=2\), Equation (3) is a standard Frobenius norm. However, in order to improve the robustness of the model for outliers and the sparsity of the label alignment matrix, we need to redefine the values of \(p,q\). In general, the sparsity of the model can be guaranteed when the constraint conditions satisfy \(p\geq 2\) and \(0\leq q\leq 2\). Therefore, in the paper, we set \(p=2\) and \(q=1\), as if \(q=0\), the problem is not convex. Then, we can rewrite the Equation (3) as \(\min_{\mathbf{E}}||\mathbf{E}||_{2,1}\). After some algebraic manipulations, we obtain  
\[\min_{\mathbf{R},\mathbf{B}}\,tr(\mathbf{E}^{\top}\mathbf{D}\mathbf{E}), \tag{4}\]  
where \(\mathbf{D}\in\mathbb{R}^{c\times c}\) is the diagonal matrix, and the \(i\)-th element of \(\mathbf{D}\) is defined as \(d_{ii}=\frac{1}{2||e_{(i)}||_{2}}\), where \(e_{(i)}\) is the \(i\)-th row of \(\mathbf{E}\).
## 3 Materials and Methods
### Loss Functions
#### 3.5.3 Unified Focal Loss  
The Unified Focal loss, proposed by [PERSON] et al. [42], generalizes Dice-based and cross-entropy-based losses to handle class imbalance. First, the authors modify the Asymmetric Focal loss [80], which removes the focal parameter for the component of the loss that relates to a rare class \(r\)[42], by adding a \(\delta\) parameter to handle class imbalance. They define this modified Asymmetric Focal loss as follows:  
\[L_{maF}=-\frac{\delta}{N}y_{ir}\log(p_{t},r)-\frac{1-\delta}{N}\sum_{c\
eq r }(1-p_{t,c})^{\gamma}\log(p_{t},r), \tag{4}\]  
where the added \(\delta\) term controls the relative contribution of positive and negative examples [42], \(N\) is the total number of samples, \(y\) refers to the ground truth class, and \(pt\) is defined as follows:  
\[p_{t}=\begin{cases}-\log(p),&\text{if }y=1\\ -\log(1-p),&\text{otherwise},\end{cases} \tag{5}\]  
where \(p\) is the estimated probability for the class \(y=1\)[81].  
Then, [PERSON] et al. propose a modified version of the Tversky Index [79], replacing its \(\alpha\) and a \(\beta\) coefficients with a single \(\delta\) term as follows:  
\[mTI=\frac{\sum_{i=1}^{N}p_{0i}g_{0i}}{\sum_{i=1}^{N}p_{0i}g_{0i}+\delta\sum_{ i=1}^{N}p_{0i}g_{1i}+(1-\delta)\sum_{i=1}^{N}p_{1i}g_{0i}}, \tag{6}\]  
where \(p_{0i}\) is the probability that a pixel \(i\) belongs to the foreground class and \(p_{1i}\) the probability of a pixel belonging to the background class.  
Next, the authors remove the focal parameter for the component of the Focal Tversky loss function that relates to the background, thus preserving the enhancement of the rare class \(r\)[42], and define the Asymmetric Focal Tversky loss as follows:  
\[L_{aFT}=\sum_{c\
eq r}(1-mTI)+\sum_{c\
eq r}(1-mTI)^{1-\gamma}. \tag{7}\]  
With the proposed \(L_{maF}\) and \(L_{aFT}\) loss functions, [PERSON] et al. define the Unified Focal loss as follows:  
\[L_{\text{UF}}=\lambda L_{maF}+(1-\lambda)L_{aFT}, \tag{8}\]  
where \(\lambda\) is in the range between zero and one and determines the relative weighting of the two losses [42]. Finally, the authors note that their proposed Unified Focal loss generalizes Dice-based and cross-entropy-based losses into a single framework, as the Dice and cross-entropy losses can be recovered by setting the hyperparameters \(\lambda\), \(\delta\), and \(\gamma\) to certain values.
# Annotated-VocalSet: A Singing Voice Dataset
### A Review on Annotating Methods
#### 1.2.3 Semi-Automatic Annotation  
The fundamental frequencies in the Erkomaishvili dataset [5] were automatically computed within the user-specified regions using an F0 estimation algorithm similar to Melodia [26]. Then, the annotator could guide the estimation process. Moreover, the tool's audiovisual feedback mechanisms helped the annotator validate and correct the computed F0-trajectories.  
For annotating F0 in the MASTmelody dataset [17], a software tool, Melodia [26], was used. Then, since the pitch contour was not error-free, they altered the pitch contours manually.  
To manually annotate beats in the Dagstuhl ChoirSet dataset [2], the Sonic Visualiser tool [20] was employed. An expert annotator corrected the annotations that a non-professional musician had created. To synchronise notes with MIDI files, they employed the dynamic time warping approach presented by [27; 28], using the beat annotations as anchor points for the alignment. Therefore, after the synchronisation, they had each note's onset, offset, and MIDI pitch. Regarding F0 annotation, they applied the PYIN [23] and CREPE [29] to estimate them. In addition, they also used a tool, Tony [30], to edit pitch contours manually.  
Similarly, the F0s in the Vocadito dataset [4] were estimated by [PERSON] [30], which is based on the PYIN [23] algorithm, and then an expert edited the estimated pitch contours. They used a similar approach for extracting notes; Tony was used first to estimate the onset and offset of the notes. Then, two experts corrected any errors identified in these extracted notes. Finally, for the lyrics, they manually added the words that the singers sang without considering the timing of the words.  
As can be seen from the related work in annotations, the current automatic tools are not error-free, and humans need to review their results and alter any incorrect annotations. Thus, we used the same approach; first, a tool automatically annotated the dataset and then manual intervention was used to alter any incorrect annotations, as explained in the following section.
## 4 IEC61850 Multicast Communication for Wide-Area Applications  
State-of-the-art wide-area applications are implemented in accordance with IEC61850 standards to provide interoperability. This standard was originally defined for substation automation systems in which substation data are modeled with the standard information model (IEC61850-7-1) and mapped to the standardized messages: Sampled Values (SV) and Generic Object-Oriented Substation Events (GOOSE). While SV messages (IEC 61850-9-2) are used for transmitting digitized measurement values, GOOSE messages (IEC61850-8-1) are mostly used for protection/interlocking purposes. The scope of these messages can be extended from a substation local-automation system to a wide-area automation system. However, there is a technical challenge in using SV/GOOSE messages in wide-area applications.  
From a data-networking point of view, SV/GOOSE messages are OSI model (ISO/IEC 7498-1) Layer 2 multicast messages. Thus, they were originally designed to be non-routable and only to be exchanged locally inside a substation LAN. The technical challenge is that remote (inter-substation) communication of these messages is also needed in wide-area applications, which involve distributed IEDs communicating with SV/GOOSE messages over a WAN. Therefore, non-routable SV/GOOSE messages need to be routed over the WAN using IP-based communication and routing protocols.  
The challenge can be overcome by using tunneling and encapsulation techniques [28], for instance L2 TP and Ethernet over Multiprotocol Label Switching (EoMPLS). The difficulty with this is that these techniques, used to enable communication of SV/GOOSE over WAN, are Point-to-Point solutions [29]. They are only capable of connecting two locations at the same time. For example, they can connect two substations to each other or one substation to a control center, but that is it. These solutions do not cover applications of SV/GOOSE over WAN in Point-to-Multipoint scenarios (e.g., from one substation to multiple substations), and this is what is often required in wide-area applications. To address these issues, IEC 61850-90-5 defined a routable version of SV/GOOSE, i.e., R-SV/R-GOOSE, in which OSI model Layer 3 multicast messages not only extend the application of SV/GOOSE from LAN to WAN but also support Point-to-Multipoint wide-area scenarios.
## 2 Materials and Methods
### Study Zones  
We have selected three different tidally controlled coastal areas in Great Britain, Ireland, and Spain which include sand and gravel beaches backed up by three different environments: low-lying dunes and marsh (Bull Island in Ireland); steep, rocky cliff (Start Bay in Great Britain); and high, sandy dunes (Salinas in Spain) (Figure 2). All three study areas also capture different thermodynamic behaviors and cloud coverage conditions. The details of each study site are described in detail here.  
Figure 1: Overall methodology used to assess shoreline change from SAR satellite imagery in three macro-tidal coastal environments. SAR-S1 data, combined with auxiliary data, are used to extract SAR-SLs, distances from the reference line (RL), and annual change rates. Interpretation of SAR-SLs is performed for three different locations.  
In all the selected locations, cloud cover represents the main limitation to utilizing optical data for monitoring coastal evolution. There are only a limited number of optical images available for each site, making it extremely challenging to build a sufficiently extensive time series. Although SAR images cannot directly be compared to optical images due to speckle noise and geometry artifacts, they provide reliable temporal sampling (20 images per month for the S1 constellation). In Table 1, we compare the data availability between the optical satellite Sentinel-2 (S2) and the SAR S1 missions, both of which have been developed by the European Space Agency (ESA) as part of the Copernicus program.
# Communication Technologies for Smart Grid: A Comprehensive Survey
## 4 Challenges of Smart Grid Communication
### Reliable Transmission  
Reliable transmission of information with high QoS is one of the most prioritized requirements for SG communications. It will greatly improve the system robustness and reliability by harnessing the modern and secure communication protocols, the communication technologies, faster and more robust control devices and Intelligent Electronic Devices (IEDs) for the entire grid from substation and feeder to customer resources [105]. As the use of communication systems in other scenarios, there are many challenges to achieve robust transmission because of limited bandwidth, limited power, or adverse transmission environment (interference, high path loss, etc.) [106; 107; 108; 109]. As discussed in the previous sections, both wireless and wired communication technique consists important parts of the SG communication with its own advantages and disadvantages. In many cases, a hybrid communication technology mixed with wired and wireless solutions can be used in order to provide higher level of system reliability, robustness and availability [111].
## 3 Methodology for the Inertia Estimation
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Characteristic** & **Optimization** & **ROCOF** & **Load Disturbance and** & **Fith Order** & **Methodology** \\  & **Methods**[16] & [17] & **Frequency Deviation**[23] & **Polynomial**[29] & **Used** \\ \hline Simplicity of the algorithm. & – & – & – & + & + \\ Online implementation. & – & – & + & + & + \\ Accuracy on inertia estimation on the first oscillation. & – & – & – & – & + \\ Percentage of error. & \textless{}5\% & \textless{}13\% & \textless{}10\% & \textless{}25\% & \textless{}45\% \\ Less number of parameters used. & – & + & – & + & + \\ Robustness of the inertia estimation against & – & – & – & + & + \\ variations in the system. & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison among methodologies for the inertia estimation. Figure 1: Virtual inertia concept in wind power plants. \[\Delta P_{E(t)}=P_{E(t)}-P_{E0} \tag{5}\]  
The swing equation can be developed with [11]:  
\[\frac{2H}{\omega_{0}}\frac{\partial^{2}\Delta\theta}{\partial t^{2}}=P_{E0}-P_{E }(t)-K_{D}\frac{\partial\Delta\theta}{\partial t} \tag{6}\]  
It can be rewritten as:  
\[\frac{2H}{\omega_{0}}\frac{\partial^{2}\Delta\theta(t)}{\partial t^{2}}+K_{D} \frac{\partial\Delta\theta(t)}{\partial t}=P_{E0}-P_{E}(t) \tag{7}\]  
Another consideration is to assume \(K_{D}=0\) in a short time after a disturbance. Furthermore, as can be noted, this methodology is suitable for discrete measurements; in this way, using finite difference equations and applying a fourth-order central finite-difference scheme [32], Equation (7) can be expressed as:  
\[H=\frac{12h^{2}*\omega_{0}*(P_{E0}-P_{E}(t))}{2*(-\theta_{i+2}+16\theta_{i+1}- 30\theta_{i}+16\theta_{i-1}-\theta_{i-2})} \tag{8}\]  
where \(h\) is the integration step size of the finite-difference approach, \(\theta\) is the vector of the bus-voltage angles, and \(i\) is the \(i^{\text{th}}\) element of this vector. The implementation of the methodology considers the following conditions: (a) the detection of the beginning of the disturbance, (b) the detection of the peak value of the first angular oscillation, and (c) assuming the knowledge of the active power provided by the generation units [11]. Finally, the bus-voltage angle at the generation buses is provided by the PMUs, and the total generated active power is directly measured at the generation stations.
# Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems
## 4 Model Development and Training
### Data Preprocessing  
In the process of monitoring sensor and thermal image data, there are instances when data may be missing and create some gaps due to various reasons, for example, sensor malfunctions, data transmission problems, and others, that are not explored in this article. These gaps can lead to inaccuracies in forecasting results if not properly addressed before training the model. Due to this, in both the sensor data and thermal images, it was decided to group the sensor measurements by hourly mean. This approach allowed for a more consistent and reliable dataset, compensating for the missing data points that can improve the model forecasting capabilities.  
The sensor data were loaded, and the time column was converted to datetime format, enabling it to be set as the \"DataFrame\" index. This allowed the resampling of the data to an hourly mean. The processed data were then converted into a dictionary format and saved as a file, ensuring effective retrieval for model implementation. Similarly, the preprocessing of thermal image data involved parsing timestamps from filenames in a specified directory and organizing the data by hourly averages. Each image file, stored as a CSV, was read, and its data were aggregated based on the parsed hourly timestamp. This approach allowed for the calculation of mean values for each hour by stacking the matrices from the same time and averaging them. The processed data, now represented as mean matrices for each timestamp, was then saved in a dictionary that was used for model training.  
Data from each sensor were preprocessed to address issues such as missing values and inconsistencies. The thermal images along with sensor reading values were normalized using min-max normalization [24] to ensure uniformity in scales across all images and readings before they are used in the model for sensor measurement forecasting.
## 2 Materials and Methods
### Cell Viability Assay  
Cell viability was evaluated using an MTT assay. Briefly, glioma cells were seeded into 96-well microtiter plates (Nunclon TM, Nunc, Germany) at a density of 8 \(\times\) 10\({}^{4}\) cells/well. After 24 h cells were treated with the peel extract at concentration of 0.5, 1, 5, 10, 20, 40, 80, 100, 150, 200 and 400 \(\upmu\)mL. After an incubation time of 48 h, the medium was replaced by a fresh one containing 0.5 mg/mL of MTT (Sigma, Deisenhofen, Germany). After an incubation time of 2 h at 37 \({}^{\circ}\)C, unreacted dye was removed, and the purple formazan product was dissolved in 200 \(\upmu\)L/well of dimethylsulfoxide (Merck, Darmstadt, Germany) and absorbance was read at 570 nm by a Microplate Spectrophotometer (Thermo Scientific Multiskan SkyHigh, Carlsbad, CA, USA). The data were expressed as absorbance relative to untreated cells in the same experiment and standardized to 100%. All data points were performed in triplicate. Results are the average of three independent experiments.
## 9 Future Research Directions and Challenges
### Experimental Setup and Realistic Simulation  
Real experiments and setups for the use of 5G with robots are lacking in the state-of-the-art. This is critical, as the actual setup's outcome does not necessarily match the predictions and expectations. Two examples are the offloading and handover in the 5G network. In the uplink, which is crucial for offloading, the data rate in 5G NR is supposed to be significantly improved, but [122] recorded the maximum of 67 Mbit/s in the uplink for a flying drone, showing no improvement compared to 4G. The impact of handover is also worth further research, especially for high-mobility scenarios. Ref. [122] shows that the handover rate between LTE and 5G is too high, which is unacceptable.
## 3 Results and Discussion
### Relationship between Biological and Physicochemical Parameters and Nutrient Content
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Correlation Coefficient (r2)** & **Parameters** & **Correlation Coefficient (r2)** \\ \hline
**MBC** & \multicolumn{3}{c}{**FDA Activity**} \\ TOC & 0.798 ** & Fe & \(-\)0.585 * \\ C/N & 0.643 * & URS & \(-\)0.643 * \\ P & \(-\)0.585 * & H\({}^{\prime}\) & \(-\)0.832 *** \\ Fe & 0.646 * & & \\ B & 0.693 * & **URS Activity** & \\ Mn & 0.748 ** & K & 0.578 * \\ Zn & \(-\)0.823 *** & B & \(-\)0.702 ** \\ Cu & \(-\)0.631 * & Cu & 0.637 * \\ qCO\({}_{2}\) & \(-\)0.957 *** & H\({}^{\prime}\) & 0.714 ** \\ qFDA & \(-\)0.899 *** & & \\
**MBN** & \multicolumn{3}{c}{**qCO\({}_{2}\)**} \\   K & \(-\)0.707 ** & B & \(-\)0.798 ** \\ B & 0.753 ** & Mn & \(-\)0.624 * \\ Zn & \(-\)0.660 * & Zn & 0.810 *** \\ Cu & \(-\)0.791 ** & Cu & 0.650 * \\ C\({}_{\text{mic}}\)/N\({}_{\text{mic}}\) & \(-\)0.729 ** & qFDA & 0.892 *** \\ FDA activity & 0.723 ** & & \\ URS activity & \(-\)0.900 *** & **qFDA** & \\ H\({}^{\prime}\) & \(-\)0.701 ** & TOC & 0.712 ** \\  & & C/N & \(-\)0.580 * \\  & & Fe & \(-\)0.655 * \\ EC & 0.794 ** & Mn & \(-\)0.711 ** \\ FDA activity & \(-\)0.744 ** & Zn & 0.641 * \\ URS & 0.618 * & & \\ qFDA & \(-\)0.647 * & **H\({}^{\prime}\)** & \\ H\({}^{\prime}\) & 0.721 ** & Fe & 0.69 * \\  & & SEI & 0.832 *** \\
**DSH Activity** & \multicolumn{3}{c}{**SEI**} \\  & & TOC & 0.613 * \\  & & Fe & 0.803 ** \\  & & Mn & 0.639 * \\ \hline Only significant correlations are shown. The data include all biosolids samples collected in different seasons. *, ** and *** indicate \(p\) values less than 0.05, 0.01 and 0.001, respectively (n = 36). & \\ \end{tabular}
\end{table} 
Table 5: Pearson’s product moment correlation coefficient between biological and physicochemical parameters in the biosolids collected and evaluated in the UWWTP at different season of the year. biosolids (r = 0.798, \(p\leq 0.01\)), and a high MBN content would imply significant FDA microbial activity (r = 0.723, \(p\leq 0.01\)) in biosolids. Thus, these parameters could also be used as quality and functionality indicators of biosolids for use as organic amendment and predict the possible positive or negative effects of their addition to agricultural soils. Among the effects that could be highlighted could be the availability or loss of inorganic N in the soil, affecting the mineralisation processes of C and N as a consequence of the addition of a substrate with active microbial biomass in terms of MBC and MBN, as well as qCO\({}_{2}\) and qFDA, as has been described in the literature by [PERSON] et al. [105] and [PERSON] [111]. Another important effect to consider is a temporary increase of MBC in agricultural soils with the addition of biosolids as established by [PERSON] [109].