esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Design and Control of MMC STATCOM for Improving Power Quality Indicators
## 4 Modular Multilevel Converter-Based STATCOM (MMC STATCOM)
### Design of SM Capacitance  
The value of the capacitor affects the power quality indices such as the level of the THD and the voltage balance of the system. Many methods in literature have been introduced to determine the values of the capacitors. These methods can be divided into two main methods; the first is based on the idea that the average sum capacitor voltage per arm should be kept constant. The second is based on keeping constant the individual average capacitor voltages for the sub-modules for each arm. Both of the two methods are based on determining the required stored energy of the STATCOM-based MMC. The conclusion based on the literature review is that the first method is considered to be a better choice than the second one because using the first method improves the reliability of the system, and it is more convenient for the sub-module capacitor, as well as the semiconductor device, to perform under low voltage [20]. A general description of the first method has been presented.  
Based on the energy storage demands of the converter, the capacitance of the SM can be determined. The minimum value of the capacitance of the SM is calculated as follows [29]:  
\[C=\frac{2*N*E}{V_{dc}^{2}} \tag{24}\]where \(E\) denotes the lowest limit of energy storage for each arm. Due to the similarity between both arms, E for the upper arm can be expressed as reported in [25]:  
\[E=\frac{\Delta E}{k_{\max}^{2}-\max(\frac{n_{\omega}^{2}-\epsilon_{n\omega}k_{ \max}^{2}}{1-\epsilon_{n\omega}})}, \tag{25}\]  
where k\({}_{\max}\) refers to the capacitor voltage upper boundary. Typically, k\({}_{\max}\) = 1.1 is commonly used.  
Finally, as \(\Delta\)E and \(E\) are in direct dependence on the rated power of the converter, the demands for energy storage of the converter can be given as:  
\[W=\frac{6}{S_{n}}E_{nom} \tag{26}\]  
where \(W\) denotes the energy storage needed for each MVA rating. According to [24; 30; 31], the capacitance in each sub-module is selected so that the energy stored in all capacitors of the converter sub-modules is about 30-40 kJ/MVA. As the capacitance of the sub-module is not chosen yet, the ripple level of the sub-module capacitor voltage in the operating point is simply determined. There are two possible ways to obtain an anticipatory value of the voltage ripple. The first way is to assume a ripple level of \(\pm\)10%, whereas the second way is based on assuming a value for the capacitor in each sub-module in the range, which achieved 30-40 kJ/MVA stored energy, then performing simulation in the time domain to determine the level of the voltage ripple.
## 3 The Time Source Techniques
### Global Navigation Satellite Systems
#### 3.1.4 Reliability and Security  
Due to the wide applications of GPS in the power grid, its reliability is essential. GPS signals can be lost or impaired in several ways, with interruptions varying in duration depending on the failure mechanism and system resilience. These interruptions fall into two main categories [43]:  
* Natural: Space weather can disrupt GPS service for extended periods, potentially lasting days, or even cause permanent damage to satellite equipment. Additionally, collisions with other objects in orbit pose a significant risk to the satellites.
* Man-made: Interference can unintentionally disrupt GPS signals, such as radio frequencies near the GPS bandwidth causing signal interference. Additionally, intentional actions like jamming or spoofing of space-based positioning, navigation, and timing (PNT) communications can disrupt single or multiple GPS receivers, whether from stationary or mobile sources.  
While the probability of natural GPS interruptions is low, when they do occur, they can last for extended periods. However, jamming and spoofing present more pressing concerns [44]. Jamming aims to disrupt GPS signal reception by introducing sufficient noise at the same frequency to overpower the satellite signal, exploiting its inherently weak nature. Spoofing, on the other hand, involves transmitting a malicious or duplicate GPS signal to deceive the receiver [45]. These types of attacks are relatively easy to execute but can have profound impacts on critical systems.  
The power grid relies on accurate time signals for a range of applications, and disruptions to GPS threaten the availability and functionality of these systems, potentially compromising grid operations [18]. As reported in [46], approximately 70% of PMUs have encountered issues related to GPS signal loss.
## 2 System Model and Problem Formulation
### Problem Formulation  
The mission completion time, \(T_{all}\), is converted into \(T\) time slots with different size durations, \(\delta_{t}\), \(t\in\{1,2,3,\ldots,T\}\), as shown in Figure 2. Each time slot, \(\delta_{t}\), contains two parts: \(\delta_{f,t}\) is the flight time, and \(\delta_{h,t}\) is the hovering time, i.e., \(\delta_{t}=\delta_{f,t}+\delta_{h,t}\). During the flight time, \(\delta_{f,t}\), the UAV flies a fixed distance, \(l\), and chooses the direction of flight according to the policy. The UAV's position at the \(t+1\)-th time slot, \(\delta_{t+1}\), is expressed as  
\[\begin{split} x(t+1)&=x(t)+lcos(\theta_{t}),\\ y(t+1)&=y(t)+lsin(\theta_{t}),\end{split} \tag{6}\]  
Figure 1: UAV-assisted data collection system without precise location information being known.  
where \(\theta_{t}\in\{0,\pi/2,\pi,3\pi/2\}\) represents the angle between the flight direction of the UAV and the x-axis on the XY plane.  
The corresponding uplink signal-to-noise ratio, \(SNR_{k,t}\), between the UAV and the \(k\)-th SN at the \(t\)-th time slot is denoted as  
\[SNR_{k,t}=\frac{p_{k,t}|h_{k,t}|^{2}}{P_{N}}, \tag{7}\]  
where \(P_{N}\) represents the power of additive white Gaussian noise (AWGN) at the UAV receiver, and \(|h_{k,t}|^{2}\) is the channel power gain during the hovering time, \(\delta_{h,t}\). To save the power cost of SNs and ensure service quality during data transmission, we set a pre-defined SNR threshold \(SNR_{th}\), where the \(k\)-th SN can be woken up and served by the UAV only if \(SNR_{k,t}\geq SNR_{th}\). So the binary parameter, \(e_{k,t}\), is defined as  
\[e_{k,t}=\begin{cases}1,&SNR_{k,t}\geq SNR_{th},\\ 0,&\text{otherwise},\end{cases} \tag{8}\]  
We set the side length, \(l\), and \(SNR_{th}\) so that the UAV can only collect the information carried by the SNs, which are in the same cell as it. Then, the UAV's hovering time at the \(t\)-th time slot is defined as  
\[\delta_{h,t}=\frac{e_{k,t}D_{data}}{R_{k}(t)}+t_{rec}, \tag{9}\]  
where \(D_{data}\) denotes the data size of the information carried by \(k\)-th SN to be collected, and \(t_{rec}\) represents the fixed time for the UAV to receive the response signal from the SNs.  
Therefore, the problem of minimizing the mission time, \(T_{all}\), is depicted as  
\[\begin{array}{ll}\min\limits_{\theta_{t},\{x(t),y(t)\},\mathcal{T}}&\sum \limits_{t=1}^{T}\delta_{t}=T_{all}\\ s.t.&\sum\
olimits_{t=1}^{T}e_{k,t}=1,\forall k\\ &0\leq x(t)\leq U\times l,\forall t\\ &0\leq y(t)\leq U\times l,\forall t\\ &\theta_{t}\in\{0,\pi/2,\pi,3\pi/2\},\forall t\end{array} \tag{10}\]
## 5 Numerical Modeling
### The Case of Two Sources with Different Initial Signal Phases
For activation functions (18) and (20), it is clear that with phase values \((\varphi_{1},\ \varphi_{2})=(72^{\circ},\ -72^{\circ})\), the process was unable to converge to a global minimum according to the \(l_{0}\) norm, that is, to minimize the number of non-zero basic coefficients, although the loss function (6), for all options, was less than \(10^{-9}\) and practically did not change over time, which indicates that the minimum in the \(l_{1}\) norm has been reached. Thus, like the case of using activation functions (18) and (20) and phase \(\varphi=72^{\circ}\), the value of the loss function (6) turned out to be very small, but the non-zero basis coefficients were very different from the source. In addition, the convergence process slowed down. Activation function (19), unlike the previous ones, made it possible to achieve a global minimum according to the \(l_{0}\) norm, with an increase in time, in the cases considered, by 2-3 times. An example of a change in the number of non-zero basis coefficients over time is presented in Figure 6. The number of basis coefficients is plotted along the vertical axis, and normalized time is plotted along the horizontal axis. The number of base coefficients is shown in different colors, the values of the modules of which, normalized to the maximum value \(U=|X(t)|/\underset{i}{\max}|X(t)|\), exceed the following specified levels: \([10^{-14},\ 10^{-9},\ 10^{-4},\ 10^{-3},\ 10^{-2},\ 10^{-1}]\). (The legend is shown in the figure. )It can be seen that all the basic coefficients quickly tend to become zero, and, in the end, in addition to the two basic coefficients corresponding to the source data, there are only three more coefficients with a level exceeding \(10^{-9}\). Thus, like the case with a single source, all three versions of the activation functions (18)-(20) allow, at zero initial phases, the accurate representation of the data with basis functions; however, if the initial phases of the sources are not zero, then options (18) and (20) may give a result that does not correspond to their real position and amplitude. The variant of activation function (19) for the considered case, as well as for a single source, demonstrated resistance to phase changes.
# Learning-Based Traffic Scheduling in Non-Stationary Multipath 5G Non-Terrestrial Networks
## 1 Introduction
### Related Work
#### 1.1.3 LOS Prediction and Tracking  
Various LOS prediction and tracking methods have also been proposed. For example, in [19], the authors proposed a theoretical model that estimates the probability of cloud-free LOS (CFLOS) for satellite links based on the satellite elevation and the altitude of the ground station. [PERSON] et al. [20] proposed a method for detecting Non-Line-of-Sight (NLOS) using data from the Global Navigation Satellite System. In [21], the authors proposed an empirical model to estimate the LOS probability for satellite and HAP communications. In contrast to these physical and empirical methods, we propose an RL-based model for non-stationary scenarios in which LEO satellites move continuously, causing their elevation angles and consequently the LOS probability to change. Moreover, our model allows UE to estimate the traffic scheduling policy for multiple satellites, allowing for multiple parallel transmissions.
# Learning Motion Constraint-Based Spatio-Temporal Networks for Infrared Dim Target Detections
## 2 Materials and Methods
### Training Phase
#### 2.1.3 Learning Spatio-Temporal Constraint Module  
Traditional multiple-frame-based methods usually use fixed parameters to estimate the motion model. However, in real-world scenarios, the velocity and the movement direction of the target are uncertain. In response, the methods often need costs that are too high and that involve more calculations relative to real-time applications [26, 27]. Inspired by the success of the vision NLP (Nature Language Processing) model, we adopt Conv-LSTM's structure to learn the spatio-temporal constraints of the target. Benefiting from timing and modeling capabilities, the Conv-LSTM structure could automatically analyze the motion model of the original data.  
The basic LSTM structure is proposed for long-term predictions by its input gate, forget gate, and output gate. The workflow of the Conv-LSTM in this paper resembles a variable weighting machine. For the image sequence, the background and the target are fed into the input gate identically. Then, the forget gate decides the correlation between the current pixels and the previous pixels. Compared with the relatively stationary background, the trajectory features of the moving target are stored and transmitted to the next frame. The structure of the Conv-LSTM [28] is similar to the original LSTM structure except that the input and output are all 3D tensors, which represent the length of the sequence and the dimension of the single frame, respectively. In addition, the multiply operation is replaced with the convolution of the images. Thereupon, the memory cell is not only a sequence accumulator but a spatial feature extractor. The structure of STM is shown in Figure 7.  
Figure 6: Details of the network’s design. Parameter \(t\) is the length of the input sequence. \(H_{t}\) is the output of the single cell of Conv-LSTM, and \(C_{t}\) is the status of the single cell. The outputs are multiple scale feature maps.  
Figure 7: The workflow of STM in the proposed model. The STM has 3 Conv-LSTM layers followed by 1 pooling layer. It takes multiple frames as inputs, such as \(t\) frames. \(t\) is related to the targeted motion speed. We set \(t\) to 5 in this paper.  
Multiple Conv-LSTM layers could improve the memory ability of the network [28]. Considering the size of the target and the resolution of the dataset images, we chose 3 layers to learn the sequence's motion feature. C\({}_{\text{ij}}\) implies the cell status of the timeline. H\({}_{\text{ij}}\) is the output of the single cell. \(i\) denotes the network layer, and \(j\) denotes the specific unit. _Pool_ is the pooling layer. It is employed to refine the learning result. The output of the STM is the time-weighted feature layer, which is displayed by the pink square.
# Evaluating Novel Speech Transcription Architectures on the Spanish RTVE2020 Database +
## 5 Results and Resources
### Processing Time and Resources  
The decoding processes of the transcription systems were performed on an Intel Xeon CPU E5-2683v4 2.10 GHz 7 xGPU server with 256 GB DDR4 2400 MHz RAM memory. The GPUs used for decoding correspond to an NVIDIA Geforce GTX 3090 Ti 24 GB graphics acceleration card.  
The following Table 6 presents the processing time and computational resources needed by each ASR system for the decoding of the 55 h and 40 min of the RTVE2020 dataset. It should be noted that the first four systems were decoded using only one CPU core, whilst the Wav2 vec2.0 based system took advantage of 40 CPU cores.  
In terms of RAM memory, the Kaldi based and the Quartznet based architectures occupied a similar memory space, mainly related to the size of the language model. In contrast, the Wav2 vec2.0 based system took much more memory space since the pre-trained model was composed of 300 million parameters. Even though the Multistream CNN based system outperforms the CNN-TDNN-F in quality (see Table 5), it is worth noting that it took almost as long to decode the whole RTVE2020 database, even though the acoustic model was much bigger and the decodings were performed using only one CPU core. It could be sped up by segmenting the original waveform through a Speech Activity Detection (SAD) module and processing the segments in parallel using several CPU threads. However, the SAD module could have additionally introduced new errors when performing this segmentation that would have irreparably impacted the final result. Finally, it should be mentioned how competitive the latency of the Wav2 vec2.0 based system was (RTF of 0.14) despite the big size of the model.
## 3 Proposed SPMIPv6 Protocol
### Application Scenarios of IP-WSN based on SPMIPv6
#### 3.3.2 Structural Monitoring  
Intelligent monitoring in facility management can make safety checks and periodic monitoring of the architecture status highly efficient. Powered nodes can be included in the design phase of a construction or battery-equipped nodes can be added afterwards. All nodes are static and manually deployed. Some data such as normal room temperature is not critical for security protection, but event-driven emergency data must be handled in very critical manner.
## 2 Hierarchical Dispatch Carrying Mechanism  
### Characteristics of Smart Grid Communication Services  
The application scenario of power 5G communication service is mainly divided into two production regions, the production control region (I) and the management information region (II). The production control large area service mainly includes control class traffic, such as distribution network differential protection, intelligent distributed feeder automation (FA), and precise load control, which require low delay control [22]. The management information region mainly includes acquisition- and application-type services, involving the acquisition of various terminal data, video monitoring, etc. The acquisition objects are extended to various types of IoT terminals and multimedia scenes, with a surge in the number of connections, which is in the millions [23]. At the same time, the acquired content tends to be video-based and high-definition, and the demand for return transmission has increased greatly [24]. For these diversified power 5G services, the three scenarios of enhanced Mobile Broadband (eMBB), massive Machine Type Communication (mMTC), and ultra-Reliable and Low-Latency Communication (uRLLC) in 5G network slicing can well provide service capability for them [25; 26]. The basic relationship between selected typical power 5G services and network slicing is shown in Table 1. The services of the two production regions involve three main categories, control, collection, and application, corresponding to the three application scenarios of 5G slicing. Based on the size of the specific feature data, they can be further categorized as general type and critical type.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Production Region**} & \multirow{2}{*}{**Traffic Category**} & \multirow{2}{*}{**Electric Power Service**} & \multicolumn{2}{c}{**Communication Requirements**} & \multirow{2}{*}{**NS**} & \multirow{2}{*}{**Hierarchical Classification**} \\ \cline{4-5} \cline{6-6}  & & & **Delay** & & **Bandwidth** \\ \hline \multirow{2}{*}{I} & \multirow{2}{*}{Control category} & Distribution network differential protection & \(\leq\)10 ms & \(\sim\)2 Mbps & \multirow{2}{*}{uRLLC} & Delay-critical Delay-general \\  & & Distribution automation & \(\leq\)100 ms & \(<\)10 Mbps & & Delay-general \\ \hline \multirow{2}{*}{II} & Collection category & Power information collection & \(<\)3 s & 10 kbps & mMTC & Connection-critical \\ \cline{2-6}  & \multirow{2}{*}{Application category} & Mobile operation & \(\leq\)300 ms & \(\geq\)4 Mbps & \multirow{2}{*}{eMBB} & Bandwidth-general Bandwidth-critical \\ \cline{1-1} \cline{5-6}  & & Robot inspection & \(\leq\)300 ms & 20–100 Mbps & & \\ \hline \end{tabular}
\end{table}
Table 1: Features of power 5G service.
# Predicting Fruit's Sweetness Using Artificial Intelligence--Case Study: Orange
## 1 Introduction
The color of the fruit is determined by the wavelength of light that is reflected from its surface. In biological materials, light differs greatly as a function of wavelength. These spectral differences provide a unique key for machine vision and image analysis. The search for modern food assessment methods aims to allow one to effectively control and evaluate food products. Therefore, in this context, artificial intelligence methods that are supported by computer analysis seem to be the right tool for use to support the various decision-making processes and processes that are related to food processing and preservation [16]. Moreover, it is important to select a noninvasive technology with the lowest possible cost when aiming to obtain high quality food products [17]. At the present time, modern methods are used, including image processing technology [18; 19; 20]. This technology makes it possible to transmit images from high-resolution cameras to a computer in order for them to be analyzed by special programs. Image processing technology is used in many areas, for example for the identification, dimensional measurements and quality assessment of kernels, tubers, vegetables and fruits [21; 22; 23; 24; 25; 26; 27], identification of pests [28], evaluation of the health of muscles and joints [28; 29] and the evaluation of drying processes [30; 31; 32], but it is also used in the analysis of fruit color and classification and the measurement of leaf area [33]. [PERSON] and [PERSON] [34] worked on a strawberry classification system that classified the fruit according to their shape, size and color, based on image processing technology. Furthermore, [PERSON] and [PERSON] used the image processing technique to classify pepper fruits based on their color [35]. Data analysis becomes a difficult task when using commercial software, which hampers the development of the science. The implementation of new algorithms and data analysis workflows is also slow. One solution to these problems is to use free programming environments such as Python or Octave in order to develop data processing algorithms. The Orange data mining tool allows the building of standard data processing workflows for various machine learning problems. Orange is also in the open source domain [36]. Research on the artificial intelligence system has become an interest of many scientists [37; 38; 39; 40; 41; 42; 43; 44] as it has a high level of potential for the future. One such innovation is the present attempt to use artificial neural networks for the non-invasive recognition of the flavor of oranges using a color space model. The aim of the present study is to check the relationship between the RGB values of orange fruits and their sweetness and to determine which algorithm has the highest prediction accuracy.