esa-sceva/satcom-chunk-collection huggingface dataset:
## 4. Study Regions of Interest
### Metropolitan Area of Atlanta
In this way, the so-called _Detrended Rate Matrix_ was computed, in which only the LST increase effectively due to the LC variation is considered, and obviously, the values on the main diagonal are zero. It is worth underlining once more that the lower triangle cells (2,1), (3,1) and (3,2) (according to the description given for the _Rate Matrix_ and represented in Figure 5) of the _Detrended Rate Matrix_ represent respectively the net effects of temperature increase due to the LC changes from _Cultivated_ to _Urbanized_, from _Forest/Shrubland_ to _Urbanized_ and from _Forest/Shrubland_ to _Cultivated_. On the contrary, the _Detrended Rate Matrix_ upper triangle cells (1,2), (1,3) and (2,3) represent the net effects of the inverse LC changes. Figure 8: The ROI around the Georgia State University weatherSTEM station, Peoplestown (GA), considered for the LST vs. air temperature comparison (the star denotes the location of the permanent weather station). Figure 9: Comparison of the LST retrieved from the Landsat data with the air temperature measured at the Georgia State University weather station. Hence, the values obtained for the _Detrended Rate Matrix_ are shown in Figure 7b, where all the changes represented in the lower triangle of the matrix are positive (0.03 \({}^{\circ}\)C/year for _Forest/Shrubland_ to _Cultivated_ LC change) or strongly positive (0.08 \({}^{\circ}\)C/year for _Cultivated_ to _Urbanized_ LC change and 0.12 \({}^{\circ}\)C/year for _Forest/Shrubland_ to _Urbanized_ LC change). Moreover, as regards the reverse LC changes from _Urbanized_ to the _Cultivated_ or _Forest/Shrubland_ classes, it is possible to notice a decreasing LST trend, as expected (Figure 7b); anyway, given the small number of pixels involved in those two transformations, the results are less significant. It is worth underlining that the meaning of the _Rate Matrix_ is doubtful for the discussed possible calibration issues of the procedure implemented in CE to retrieve the LST, whereas the _Detrended Rate Matrix_ is clearly able to highlight the connection between LC changes and the increase of urban LST and SUHI. Therefore, only the _Detrended Rate Matrix_ has been considered significant to represent the results of the long-term monitoring of the impact of LC changes on the SUHI. Finally, since the number of available images is slightly unbalanced between the warmer and the colder periods (Table 1), the analysis was repeated considering them separately. However, the results (Figures 10 and 11) suggest that the seasonal effect does not have a significant influence on the results in the case of the Atlanta MA.
# UAV Formation Trajectory Planning Algorithms: A Review
## 6 Problems of UAV Formation Trajectory Planning Algorithm
### Algorithm Real-Time Problems  
Realistic environmental information is usually time-varying. Regarding UAV formation trajectory planning in an unknown environment, the success rate of trajectory planning strategies used by traditional algorithms and local trajectory planning algorithms in the face of emergencies and dynamic environments is low; in addition, the amount of calculation is large, and the trajectory is not optimal. It is difficult for drones to complete real-time trajectory updates.
# Real-Time Visual Feedback in Singing Pedagogy: Current Trends and Future Directions
## 4 The Resonatory System
### Intonation  
Nowadays, there is a vast number of software and cell phone applications to display, in real-time, the \(f_{\mathrm{o}}\) contour and thus keep a visual track of pitches [57; 58]. Tracking \(f_{\mathrm{o}}\) contour is relevant in voice pedagogy, as intonation is used as an expressive tool [73; 74]. Most of these applications quantify \(f_{\mathrm{o}}\) in semitones, thus disregarding finer pitch effects. This excludes the possibility of monitoring microtonal effects, which are used in many singing styles [74; 75; 76]. The VoceVista Video Pro software, by contrast, can also visualize micro-intonation effects; Figure 20 shows an example of this. Each semitone is marked by a white line and the \(f_{\mathrm{o}}\) contour by the blue curve. The fifth tone (red rectangle) is centered between the pitches C4 and C#4.
## 2 Results and Discussion  
### Catalyst Preparation  
The palladium catalysts supported on the resins can be readily prepared upon ion-exchange of [Pd(NH\({}_{3}\))\({}_{4}\)]SO\({}_{4}\) with the sulfonated resins. To prevent the adventitious presence of halide ions no chloro- or bromo-metal precursors were used in the preparation of the catalysts. The ion-exchanged resins were then suspended in THF and treated with H\({}_{2}\) at 506 KPa and 60 \({}^{\circ}\)C fo 5 h to reduce Pd\({}^{\text{II}}\) to Pd\({}^{\text{0}}\) (Scheme 1).  
The sulfonated resins were prepared from a commercial macroreticular pSDVB (15% cross-linked, Spolchemie, Czech Republic, hereafter referred to as m-pSDVB) and from \(\mu\)-pDVB according to a standard procedure with concentrated H\({}_{2}\)SO\({}_{4}\)[44]. \(\mu\)-pDVB was prepared upon polymerization of technical grade DVB (85%, the complement to 100% is ethylbenzene) under'solvothermal' conditions which ensure the peculiar morphology described in the Introduction. The ion-exchange capacity of the supports ranged from ca 1.0 to 2.5 mmol\(\cdot\)g\({}^{-1}\) of a di-cation, corresponding to ca 100 to 250 mg of palladium, much more than needed to obtain a load of the supported metal of 5% (\(w/w\)) or lower. Some analytical data concerning the supports and the resin supported catalysts are reported in Table 1.  
The catalysts were generally used within 24 h from the final step of their preparation (reduction and drying). The leftovers of some of them were then divided into three batches, which were stored under different conditions before use. Coding of the different batches and their ageing histories are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Ion-Exchange Capacity of the Support** & **Pd** \\ \cline{2-4}  & **M\({}^{\text{+}}\)** & **M\({}^{\text{2+}}\)** & **(\%, \(w/w\))** \\ \hline Pd/\(\mu\)S-pSDVB10 & 5.0 & 2.5 & 1.0 \\ \hline Pd/\(\mu\)S-pDVB10 1 ** & 2.0 & 1.0 & 1.0 \\ \hline Pd/\(\mu\)S-pDVB35  & 3.2 & 1.6 & 3.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected analytical data on the resin-supported Pd catalysts employed in this work.  
One batch was stored for two weeks in a closed desiccator under air to protect it from environmental humidity. The second one was stored for three weeks in a dry-box under nitrogen atmosphere (O\({}_{2}\) < 10 ppm; H\({}_{2}\)O < 0.1 ppm). The third one was stored for four weeks under laboratory conditions and regenerated upon the very same reduction treatment used during its preparation.
# Communication Network Standards for Smart Grid Infrastructures
## 2 Network Standards That Support Communication in Smart Grid Infrastructures
### Condition Monitoring  
The electricity grid faces several challenges given the ever-increasing demand, the requirements for continuous improvement of its availability, and the provision of quality value-added services in the most efficient way. Key objectives in overcoming these challenges are the extension of the average life cycle of the equipment and the minimization of its maintenance costs. These objectives are based on smart grid functions such as real-time monitoring of the network, real-time infrastructure quality control, forecasting and early diagnosis of faults, predictive maintenance, etc. Specifically, a condition monitoring function provides all the technical information required for maintaining availability and, at the same time, for maximizing efficiency in the energy supply chain, thus helping to optimize the network by preventing unplanned downtime or hardware failures. The condition monitoring function also includes specialized processes such as monitoring of transformers (refrigeration, lubrication, contact opening speed, switch operating time, etc.), insulators, ground, wiring lines, ARRESTER mechanisms, etc. [52, 73, 74].  
Regarding telecommunications, the deployment of a system that meets the measurement and control procedures requirements is essential, as well as the description of uniform data models for all subsystems of the smart network. Although there is a lot of standardization activity in this field, the basic communication standards are IEC 61970 (EMS) & IEC 61968 and IEC 61850, which are supported by most wired and wireless communications technologies [34, 35].
## 5 Experimental Results
### Evaluation Metrics
#### 5.1.1 Peak-Signal-to-Noise Ratio  
PSNR is an articulation of the proportion between a signal's most extreme conceivable worth and the watermarked signal. The PSNR is normally represented in the logarithmic decibel scale. PSNR between the watermarked picture and cover picture is utilized to assess intangibility and is effortlessly characterized through the mean square error (MSE).  
MSE permits the analysis of the valid pixel upsides of a unique image to a corrupted image. The higher the PSNR values, the higher the quality of the watermarked image:  
\[MSE=\frac{\sum_{m=0}^{M-1}\sum_{n=0}^{N-1}\left(I_{m,n}-WMI_{m,n}\right)^{2}}{ I_{m,n}}\]  
\[PSNR_{I,WMI}=20 log_{10}\left(\frac{max_{I}}{\sqrt{MSE}}\right) \tag{1}\]  
The \(MAX_{ML}\) refers to the extreme value of the image that is possible.
# Secure and Transparent Space Exploration Data Management Using a Hybrid Blockchain Model
## 4 Implementation and Experimental Analysis
### Experiment Scenarios
#### 4.2.1 Public Blockchain Data Integrity Verification  
The objective is to verify that the public blockchain can detect any tampering of data by using stored hashes, thereby ensuring the integrity of public data. We took a sample EVA mission log entry (considered as D_pub) and had it go through the system's public data pipeline. The entry was hashed (H(D_pub)) and recorded on the Ethereum blockchain via our smart contract. Later, to test integrity, we simulated a scenario where a user retrieves the mission log and verifies its authenticity. The user calls the smart contract to obtain the official hash from the blockchain, and then computes a fresh hash of the local copy of the data. We performed two sub-tests, (a) using an unaltered copy of the data, and (b) using a deliberately modified copy.  
In case (a), the hash computed by the user exactly matched the hash stored on the blockchain. The smart contract's verification function confirmed the match, indicating that the data had not been altered. In case (b), as expected, the hash of the modified data did not match the blockchain's hash. The discrepancy was immediately flagged, effectively detecting the tampering. Throughout multiple trials with different log entries, the system consistently detected even minor modifications. This experiment demonstrated that the public blockchain serves as a reliable integrity ledger, that is, as long as the hash on-chain and the data off-chain are compared, any unauthorized changes to the public data are caught. There were no false positives or false negatives observed. This confirms that our approach of storing hashes on the public blockchain provides robust data integrity verification for all stakeholders.
## 3 Results and Analysis
### Analysis of the Single-SAR Imaging Simulation Results
Therefore, without considering factors such as the rotation of the Earth and curvature of the ground and taking the subsatellite point as the origin, the projections of the SAR azimuth and range were the x and y axes, and the SAR imaging coordinate system was established. The reason for establishing the coordinate system in this way is that the azimuth and range are perpendicular to each other. Other parameters of the SAR system are the same as those in Table 1.  
Figure 2: Two-dimensional (2D) PM wavenumber spectrum as follows: (**a**) 2D PM wavenumber spectrum at a wind speed of 10 m/s and a sampling pitch of 8 m, (**b**) 2D PM wavenumber spectrum at a wind speed of 15 m/s and a sampling pitch of 8 m, and (**c**) 2D PM wavenumber spectrum at a wind speed of 10 m/s and a sampling distance of 16 m.  
Figure 4: Ocean surface backscatter coefficient as follows: (**a**) wind speed of 10 m/s and (**b**) wind speed of 15 m/s. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Numerical Value** \\ \hline Platform height & 10 km \\ Speed & 200 m/s \\ Observation angle of incidence & 30\({}^{\circ}\) \\ Carrier frequency & 3 GHz \\ Pulse duration & 5 \(\upmu\)s \\ Chirp frequency modulation bandwidth & 18.75 MHz \\ \hline \hline \end{tabular}
\end{table} 
Table 1: System parameters. Figure 3: 2D ocean surface diagram as follows: (**a**) 2D sea surface at a wind speed of 10 m/s, (**b**) top view of (**a**), (**c**) 2D sea surface at a wind speed of 15 m/s, and (**d**) top view of (**c**). The color bars on the right side of the figure represent the height fluctuations of the ocean surface. The ocean surface SAR images obtained by the RD algorithm are shown in Figure 6. The direction of the wave stripe in the figure represents the direction of wave travel, and this direction is the same as those of the initial input parameters of the simulation.
## 2 Literature Review
### Summary of Existing SER Models
#### 2.1.2 Deep Learning Approaches  
Deep learning models have revolutionized SER by enabling the direct extraction of features from raw audio signals. Convolutional Neural Networks (CNNs) are particularly effective in processing spectrograms of audio signals. Studies have demonstrated that CNNs outperform traditional methods by focusing on emotionally salient regions in speech, achieving a WA of 64.78% and a UA of 60.89% [15]. Recurrent Neural Networks (RNNs) and their variants, such as Long Short-Term Memory (LSTM) networks, capture the temporal dynamics of speech. Research using a Bidirectional Long Short-Term Memory (Bi-LSTM) model achieved a WA of 52.13% and a UA of 57.91% by incorporating an Elaboration Likelihood Model [16]. Experiments on the effects of variable sequence lengths  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
for LSTM-based SER systems showed that LSTM models perform best with 3-s speech segments, achieving a WA of 64.16% and a UA of 60.02% [17]. Comparisons of various deep learning architectures found that LSTM-RNN models achieved a WA of 61.71% and a UA of 58.05% [18].  
Applying CNNs to spectrograms of variable-length speech segments demonstrated improved emotion recognition performance, effectively mitigating confusion between specific emotions in the IEMOCAP dataset [19]. By combining CNNs and BiLSTM, methods processed speech input into smaller segments of 3 s, achieving a WA of 68.8% and a UA of 59.4% [20].  
Advanced methodologies such as the use of data augmentation and domain adaptation techniques were also utilized for underrepresented emotions in the IEMOCAP dataset. Generative Adversarial Networks (GANs) generated synthetic spectrograms for underrepresented emotions, achieving a UA of 53.6% [21]. Residual adapter architectures leveraging a large speaker dataset to overcome limitations of small emotion datasets achieved a WA of 72.73% and a UA of 67.58% [22]. Lightweight fully convolutional networks designed for SER in resource-constrained systems reported a WA of 60.35% and a UA of 63.98% [23].  
These findings, along with results from other methods, are detailed in Table 2, which summarizes various deep learning approaches evaluated on the IEMOCAP dataset.
## 1 Introduction
### Research Gap and Motivation  
In order to provide application maintenance in the offshore outsourcing context, the IT experts and decision makers select one of the three models, which are onshore, nearshoreand offshore. Experts typically choose the model based on the nature of the project and the needs of the customer. However, the vendors and clients lack an adequate decision support system that can assist them in making suitable sourcing decisions. Similarly, experts lack a decision support mechanism that allows them to rank the available alternatives for the offshore outsourcing decision of application maintenance.  
As a result, the current study presents a multi-criteria decision support system that assists IT experts and practitioners in selecting the most suitable and appropriate sourcing alternative. Thus, the appropriate model selection would result in a successful offshore outsourcing decision. Furthermore, the following goals inspired us to conduct this research:  
* Due to the involvement of multiple factors and sub factors the multi-criteria decision making is a big challenge in application maintenance offshore outsourcing;
* Application maintenance offshoring has been a hot research topic for the academicians and scholars since the last two decades, which motivated us to develop a multi-criteria decision support system;
* Another reason for the present research is that there is a gap between the literature and the proposed work. Using systematic literature reviews, we first identified a list of influencing factors. The factors have already been established and reported in the previous studies [2; 3]. Second, we performed an empirical study [1] in the outsourcing industry that evaluated the identified influencing factors. Third, a multi-criteria decision making model, based on the identified critical success factors is proposed for the sourcing decision of application maintenance in the current paper. To the best of our knowledge, no such approach has ever been used to identify the critical success factors of application maintenance offshoring and to tackle the challenges in making the sourcing decisions.  
The rest of the paper is organized as follows: Section 2 presents the research background, which includes a detailed discussion of MCDM problems and proposed models in GSD and other domains, Section 3 shows the proposed research methodology that consists of a systematic literature review, empirical study and AHP technique for the proposed MCDM model. Similarly, Sections 4 and 5 presents our results and conclusions, respectively.
# Cross-Layer and Energy-Aware AODV Routing Protocol for Flying Ad-Hoc Networks
## 4 Materials and Methods
### Cross-Layer and Energy-Aware-AODV (CLEA-AODV) Protocol  
In order to improve the overall performance of the FANET network, we propose the Cross-Layer and Energy-Aware AODV (CLEA-AODV) routing protocol, which mainly concentrates both the data layer and the network layer to enhance the performance in a better way. The cross-layer model is subdivided into two main concepts in our work. The  
Figure 3: Route maintenance.  
Figure 2: Route discovery.  
first one provides high energy efficiency to the network using Glow Swarm Optimization (GSO)-based Cluster Head selection. Second, an emergency Medium Access Control (MAC) model is constructed using Time Division Multiple Access (TDMA) and Carrier-Sense Multiple Access (CSMA) priority-based transmissions. The entire work of the protocol is explained in a sectional manner below. The structure of the proposed model is shown in Figure 4.
# Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks +
## 5 Topology Control  
UAVs in the network follow the BS-CAP mobility scheme to improve coverage and connectivity [7]. In this section, we discuss a topology control scheme to alter the trajectory of UAVs near the pipe region to maintain a robust pipe and a stable route. The proposed  
Figure 4: Illustration of a pipe around the current route (red links) from the target UAV to BS. The pipe consists of nodes (green nodes) that are up to 2-hop from the nodes along the route (red nodes).  
scheme, which combines both pipe-based routing (Section 4) and topology control, is called pipe routing with topology control (TC-Pipe).
# PFC Single-Phase AC/DC Boost Converters: Bridge, Semi-Bridgeless, and Bridgeless Topologies
## 3 Categorization of Semi-Bridgeless Boost Converter Topologies
### Conventional Semi-Bridgeless Boost Converter Topology  
Conventional or symmetrical SBBC topology is depicted in Figure 7[39; 40]. SBBC conventional topology can reduce conduction power losses since less semiconductors are required in the current path (current through of two semiconductors per cycle) if compared with BBC topologies [72; 89].  
The conventional SBBC topology has low Common-Mode (CM) and Differential-Mode (DM) noises in comparison with the conventional PFC BBC topology [34]. The DM noise and thermal stress in \(L\) can be reduced when it is added an inductor in the path of the neutral conductor [4; 38]. The inductors work in series in each semicycle; in this case, the CM noise can also be reduced if both inductors are coupled [90; 91].
## 5 Massive MIMO
### Classification of Applications
#### 5.1.2 Base Station
The SE for each user can obtain by dividing the top curve by the bottom curve and surprisingly the SE per user exist in the modest range of 1-2.5 bit/s/Hz [176][PERSON] Similarly, [PERSON] et al. [177] also evaluated the transmission features of mMIMO by using asymptotic eigenvalue distribution of a Wishart matrix and determined about 20% enhancement in the channel capacity by using spatial correlation. Moreover, MmWave base station 256-element antenna arrays and mobile antenna of 32-element arrays are already commercially available [44]. Furthermore, results in [178], show that the Massive MIMO is important to not only enhance the SE but surely able to be the driving force to achieve increased area throughput in 5G networks. The mMIMO technology is proven to be true for providing 10-fold or even fifty-fold enhancement in SE over IMT-Advanced by serving several users at the same time.
## 2 System Working Principle  
### System Structure  
The airborne laser communication system designed in this paper mainly comprises a signal transceiver system, an image tracking system, and a two-dimensional mirror control system. The overall structure is shown in Figure 1.  
The signal transceiver system includes a signal source, laser tube, modulation driver, transmitting antenna, APD photodetector, etc. The signal to be transmitted is loaded on the laser by the modulation driver, and the signal light is collimated and emitted through the transmitting antenna; the ground-side image tracking system includes a tracking camera, a two-dimensional aiming platform, and a host computer. When the position and attitude of the UAV deviate, the deflection angle of the gimbal can be adjusted in real-time to control the emission direction of the beam, and realize long-distance coarse alignment from the ground-side transmitting antenna to the airborne two-dimensional  
Figure 1: Structure diagram of airborne laser communication.  
mirror; The two-dimensional mirror control system is located on the airborne side and is mainly composed of a stepping motor and a subdivision driver, an optical mirror, an embedded controller, a through-hole four-quadrant detector(4-QD), and a signal processor. When the light beam reaches the surface of the UAV, the acquisition, pointing, and tracking functions of the light beam are completed by driving the two-dimensional mirror, and the precise alignment between the two-dimensional mirror and the through-hole four-quadrant detector is realized.