esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Grid Architecture for Rural Distribution Networks: Application to a Spanish Pilot Network
## 3 Electrical Actuations to Evolve the Rural Grid
### Inclusion of New Protection and Monitoring Devices  
As previously indicated, the protection elements of the PN are not equipped with automatic reclosing, thus they are able to isolate only part of the electrical grid in case of a malfunction. Modern rural grids require a certain degree of flexibility in order to offer new electrical configurations and new modes of operation [35]. For instance, they should allow isolated operation and reconnection of some or any parts of the grid when various grid eventualities occur [30]. To do so, the overload LV lines should be independently managed in order to energize the islands through back-up energy resources. Therefore, it is proposed that the LV fuses of feeders be replaced with automated and remotely controlled Electrical Power Switches (EPSs), who say breakers. This in turn impacts the selectivity of LV lines during grid eventualities.  
Moreover, it is indispensable to have selectivity at the MV level in order to disconnect the PN from the rest of system and also to have the possibility of creating different electrical configurations. Consequently, some manual switch-disconnectors are automated and remotely controlled (hereinafter Automated Switch-Disconnectors (ASDs)).  
Furthermore, to overcome the lack of observability in rural grids, the PN is also updated with distributed monitoring devices (so-called Electrical Monitoring Units, EMUs). Such enhanced monitoring provides flexibility for grid operation.  
To integrate and remotely manage the new protection and monitoring devices, an element is included: the so-called Remote Terminal Unit (RTU). The RTU is a solution for substation or power stations automation to interface with all substation equipment that provides protection and control. The RTU delivers information to the Supervisory Control And Data Acquisition (SCADA) of the DSO grid and offers full capability for integrating and controlling devices through different communications protocols.  
In the particular case of the PN, five RTUs are installed, four of them are in charge of each SS where they are placed and one is in charge of the whole PN. They are used to integrate mainly all new smart technologies, and controlling, monitoring and protection devices with the telecommunications network, and command of the PN according to the control algorithms. Thus, they act as a multi-protocol bridge between the SCADA and controllable equipment [42].  
Figure 2: Electrical scheme of Pilot Network with new element after actuations. For the sake of completeness, acronyms not specified in the figure are: Local Energy Management System (LEMS), Local Controller (LC), and Transformer Controller (TC). The whole list of acronyms can be found in abbreviations section.
# A Review of Ku-Band GaN HEMT Power Amplifiers Development
## 2 Ku-Band GaN HEMT HPA Technology
### GaN HEMT Process
Breakdown voltages range from 75 to 200 V. Meanwhile, the 0.15 um process utilizes a somewhat lower drain voltage of 20 to 28 V compared with the 0.25 um process, with a minimum of 12 V for OMMICs using Si substrates. Breakdown voltages for this process range from 50 to 120 V. The lower drain voltage reduces the voltage swing of the power amplifier, which is a disadvantage for achieving high output power. However, the fT performance, which is two to three times higher than that of the 0.25 um process, is favorable for obtaining high power gain and improving efficiency. As shown in Table 3, the power-added efficiency (PAE) of over 50% at 30 GHz led to the expectation of better power efficiency compared with the 0.25 um process in the Ku-band. MACOM/France (formerly OMMIC)'s GaN-on-Si HEMT process has shown output power performance comparable to other processes, even with a drive voltage of 12 V. HPA designs using cascode or stacked-field effect transistor (FET) structures that enhance the voltage swing in this process are expected to yield higher output power and warrant further research [21; 22].
## 1 Introduction
It has also been shown that epoxidation of sesquiterpenes could be achieved using TBHP as oxidant, and the reactivity was compared to \(m\)CPBA [24]. As the substrates previously studied for [MoO\({}_{2}\)L-TBHP] epoxidations were fairly simple and without functional groups, it was interesting to investigate how this Mo-catalyzed epoxidation could work on the more complex substrates **1-4**. Figure 2: Imperanene (**1**), dimethyl-imperanene (**2**), Imperaneic acid (**3**), permeabilized imperaneic acid (**4**). The mechanism for the [MoO\({}_{2}\)L-TBHP] epoxidations can be explained through a Bartlett-like mechanism, implying the formation of a pentacoordinated species [MoO\({}_{2}\)L] followed by the coordination of TBHP (Scheme 1). The [MoO\({}_{2}\)L-TBHP] adduct was responsible of the oxygen transfer from the oxidant to the olefin. [19]  
The aim of this study was to evaluate the traditional \(m\)CPBA-reaction and compare it to the novel Mo-catalytic version for epoxidation of the presented substrates. Furthermore, the subsequent reactions of the epoxy-intermediates to form different norlignan structures were the target of investigation.
# Context-Aware Data Dissemination for ICN-Based Vehicular Ad Hoc Networks
## 3 Context-Aware Packet Forwarding  
As discussed above, previous work [20; 21; 22] has analyzed the advantages of using ICN techniques in VANETs and extended the basic NDN architecture to implement the name-based data dissemination for VANETs. In our work, we keep the following methods used in previous work: (i) Packets are received and broadcast over the same wireless network interface; (ii) a timer is used to control how long a packet will wait at an intermediate node before being forwarded; (iii) the CS (Content Store) remains in VANETs as for normal NDN operation in the wired environment.  
A problem inherent in previous adaptations of NDN for vehicular environments is that they have the potential to cause broadcast storms in the network, leading to performance degradation and the waste of network resources. Furthermore, the mobility characteristics and associated topological consequences observed by vehicles in regular traffic have not been considered, which may lead to inefficient packet forwarding. In order to suppress the broadcast storms and increase the transmission efficiency, we introduce a context-aware packet-forwarding mechanism for ICN-based VANETs. In general, four factors are considered during the packet forwarding:  
* The relative distance to the content holder of the candidate forwarding nodes.
* The number of neighboring vehicles (their density) at the time of data forwarding.
* The location similarity with respect to the content holder of neighboring vehicles.
* The priority of the content being requested or sent in response.  
Figure 1 shows the general idea of the context-aware packet-forwarding method. A distance-based forwarding control algorithm is used in order to reduce the total number of packets in the network. Here, the distance between two vehicular nodes is converted to the deferral time for forwarding packets. The node with the shortest deferral time will forward the packet first, which will stop the deferral timer and suppress the packet forwarding at the nearby nodes. This can reduce the total number of packets in the network considerably. However, this can reduce the successful data delivery ratio and/or the data response time too. In order to maintain a high successful data delivery ratio and reduce the data response time, we limit the suppression of the packet forwarding to within a certain scope, determined by the location of the vehicles, as well as the density of the vehicles in the area, and the priority of the content being forwarded. We will describe the control algorithms more fully in the following sub-sections.  
In this paper, we follow the terminology used in NDN in so far as we call the messages transmitted in the network \"packets\", for example, Interest packet, Data packet, Hello, and Response packet.
## 3 Materials and Methods
### Development Environment and Tools
#### 3.3.3 Data Processing Frameworks
Consent procedures are designed to ensure transparency and user rights, including access, rectification, and data deletion upon request. Using Python, HTML, CSS, PHP, SQL, and JavaScript for embedded programming, along with ICIPRO for UI and cloud integration, enables scalable and efficient software frameworks for real-time data exchange in connected health systems. Throughout its sensor network, the Monit4 Healthy system uses an efficient data processing framework that maximizes computing efficiency and minimizes energy usage. The technology preprocesses raw physiological signals locally before sending only relevant, organized data to the cloud through the use of edge computing at the device level. This approach ensures real-time sensitivity without using a large amount of power by effectively reducing bandwidth usage, network congestion, and cloud computing requirements. The Monit4 Healthy system successfully establishes a balance between dependable data transfer and energy economy by integrating low-power transmission protocols like BLE for short-range communication. Through these procedures, the system optimizes total power consumption, preserves device battery life, and addresses key issues in latency-sensitive biomedical applications, therefore mitigating the typical limits of IoT health monitoring.
# Analysis of Power Allocation for NOMA-Based D2D Communications Using GADIA
## 6 Results and Discussion
### D2D System Model
For determining the actual behaviour of GADIA, it is required to run the simulation multiple times and analyze the mean performance afterwards. In this regard, we have repeated the experiments 200 times to obtain reliable results. To better emphasize this, we study the mean interference received by D2D users by applying three different approaches, i.e., GADIA controlled frequency allocation, random frequency allocation, and without using any frequency sharing. As it can be readily checked from Figure 17, the average interference for GADIA-based algorithm for urban scenarios is about \(-74\) dBm which is much smaller than other resource allocation schemes. Figure 16: Mean interference level in urban environment along GADIA iterations. Figure 15: Mean interference level in rural environment along GADIA iterations.
# Optical Angle Sensor Technology Based on the Optical Frequency Comb Laser
###### Abstract  
A mode-locked femtosecond laser, which is often referred to as the optical frequency comb, has increasing applications in various industrial fields, including production engineering, in the last two decades. Many efforts have been made so far to apply the mode-locked femtosecond laser to the absolute distance measurement. In recent years, a mode-locked femtosecond laser has increasing application in angle measurement, where the unique characteristics of the mode-locked femtosecond laser such as the stable optical frequencies, equally-spaced modes in frequency domain, and the ultra-short pulse trains with a high peak power are utilized to achieve precision and stable angle measurement. In this review article, some of the optical angle sensor techniques based on the mode-locked femtosecond laser are introduced. First, the angle scale comb, which can be generated by combining the dispersive characteristic of a scale grating and the discretized modes in a mode-locked femtosecond laser, is introduced. Some of the mode-locked femtosecond laser autocollimators, which have been realized by combining the concept of the angle scale comb with the laser autocollimation, are also explained. Angle measurement techniques based on the absolute distance measurements, lateral chromatic aberration, and second harmonic generation (SHG) are also introduced.  
optical angle sensor; mode-locked femtosecond laser; optical frequency comb; laser autocollimation; diffraction grating; absolute angle measurement; nonlinear optics; second harmonic generation +
Footnote †: journal: Applied Physics Letters
## 5 Discussion
### Limitations and Future Directions
#### 5.6.1 Model Optimization and Practical Deployment  
The excellent performance of MS-RRBR hinges on its complex multi-model collaborative structure. Although this complexity strengthens the model's overall recognition capabilities, it also demands substantial computational resources, potentially hindering real-time processing and large-scale applications. Therefore, designing lightweight models or leveraging techniques like model quantization to reduce computational complexity and memory usage becomes a key focus of future research, ensuring that our proposed method can operate effectively in resource-constrained environments.
# Render U-Net: A Unique Perspective on Render to Explore Accurate Medical Image Segmentation
## 3 Methodology
### Nested U-Net Architecture  
U-Net and its variants are the popular backbone in semantic segmentation experiments. UNet++ [19] and ANU-Net [20] are two representative architectures. The nested U-Net architecture of Render U-Net referred to these works. The structure of the Render U-Net is shown in Figure 2.  
We see in Figure 2 that the structure of Render U-Net has perfect symmetry, showing an inverted pyramid shape. In this inverted pyramid structure, the convolutional blocks are nested from top to bottom. This nested structure design allows the network to train only one shared encoder for feature extraction and four decoders for feature recovery.  
In the encoder, convolutional blocks transfer features from top to bottom. In the decoders, the detailed information is restored by point-sampling from the deeper layer in a bottom-up manner. This point-sampling is a kind of rendering method, which predicts the mask from low resolution to high resolution by selecting uncertain points. Furthermore, dense skip connection under attention selection is set up between each convolution block, which strengthens the effectiveness of semantic information transmission. In this way, concatenation layers fuse the semantic information from different levels.  
The nested architecture can extract features of different depths. The deep supervision (Section 3.4) allows the network to spontaneously select the features that are most suitable for the task, thereby avoiding a complex selection of features. In addition, the attention mechanism (Section 3.2) and the point-sampling method (Section 3.3) can be integrated in this architecture.  
Figure 1: Overview of methodologies in Render U-Net. Nested architecture is analyzed in Section 3.1. Attention mechanism is analyzed in Section 3.2. The point-sampling method is analyzed in Section 3.3. Deep supervision is analyzed in Section 3.4. Model pruning is analyzed in Section 3.5.
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 1 Introduction
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & **Sigox** & **NB-IoT** & **LoRaWAN** & **WiFi** & **ZigBee** & **Bluetooth** \\ \hline Standards & Sigox & SGPP & LoRa Alliance & IEEE 802.11 & IEEE 802.154 & Bluetooth, SIG \\ Modulation & BPKS & QPSK & CSS & DSSG, GPOM & DSSG, GPSK & 2 GPSK \\ Frequencies & BSR-433 MHz, 868 MHz, 915 MHz & Licensed under LTE & SIR 433 MHz, 868 MHz, 915 MHz & BSR 2.4 GHz, 5 GHz & ISM: 868 MHz, 2.4 GHz & 2.4 GHz \\ Coverage & 10-40 km & 2-20 km & 1-10 km & 10-100 m & 10-100 m & 10-100 m \\ Bandwidth & 100 Hz & 200 kHz & 125 kHz, 250 kHz & 20 MHz, 40 MHz, 80 & 2.0 Hz & 1 MHz \\ TX Limit & 140 Packes per Day & Unlimited & Datry Cycle Lim. & Unlimited & Unlimited & Unlimited \\ Max Data Rate & 100 lps & 200 kbps & 50 kbps & Gpsps & 250 kbps at 2.4 GHz & 2 Mbps \\ Private & No & No & Yes & Yes & Yes \\ Depolyments & & & & & & \\ Energy & Low & Low & Low & High & Low & Low \\ Consumption & & & & & & \\ Security & Low & High & High & Low-High & High & Low-High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Sigofx, NB-IoT, LoRaWAN [3] and conventional networks. This paper is organized as follows; In Section 2, LoRaWAN technology is explained in detail with two main layers of the network Phy and MAC. In Section 3, different types of LoRaWAN applications are discussed and the most important use cases are highlighted. In Section 4, possible research focus areas are introduced that can help researchers to take action against and Section 5 concludes the paper.
# Operator and Graph Theoretic Techniques for Distinguishing Quantum States via One-Way LOCC
## 2 One-Way LOCC and Operator Relations
Example 1: As a very simple and illustrative example, consider the following two Bell basis two-qubit states:  
\[|\Phi_{0}\rangle = \frac{1}{\sqrt{2}}(|0\rangle_{A}|0\rangle_{B}+|1\rangle_{A}|1 \rangle_{B})\] \[|\Phi_{1}\rangle = \frac{1}{\sqrt{2}}(|0\rangle_{A}|1\rangle_{B}+|1\rangle_{A}|0 \rangle_{B})\]_This set is distinguishable, with the following measurement choices:_  
* _[PERSON]:_ \(A_{1}=|0\rangle\langle 0|\) _and_ \(A_{2}=|1\rangle\langle 1|\)_._
* _[PERSON]:_ \(\{B_{1,1},B_{1,2}\}=\{|0\rangle\langle 0|,|1\rangle\langle 1|\}=\{B_{2,1},B_{2,2}\}\)_._  
_If [PERSON] obtains outcome 0, then tells [PERSON], who after measurement obtains outcome 0, then the state is \(|\psi_{0}\rangle\). Similarly, it would be \(|\psi_{1}\rangle\) if [PERSON] measured a 1._  
Notationally, we shall let \(|\Phi\rangle\) be the standard _maximally entangled state_ on two-qudit space \(\mathbb{C}^{d}\otimes\mathbb{C}^{d}\); \(|\Phi\rangle=\frac{1}{\sqrt{d}}\big{(}|00\rangle+\ldots+|d-1\,d-1\rangle\big{)}\). We recall that every maximally entangled state on the two-qudit space is then of the form \((I\otimes V)|\Phi\rangle\), where \(V\) is unitary on \(\mathbb{C}^{d}\). The following result of [PERSON] [7] frames one-way LOCC distinguishability in terms of operator relations and was the starting point for our collaboration. **Theorem 1**. : _Let \(\{M_{i}\}\) be operators on \(\mathbb{C}^{d}\), and let \(\mathcal{S}=\{|\psi_{i}\rangle=(I\otimes M_{i})|\Phi\rangle\}\subseteq \mathbb{C}^{d}\otimes\mathbb{C}^{d}\) be a set of orthogonal states. Then, the following conditions are equivalent:_  
* _The elements of_ \(\mathcal{S}\) _can be distinguished with one-way LOCC._
* _There exists a set of states_ \(\{|\phi_{k}\rangle\}_{k=1}^{r}\subseteq\mathbb{C}^{d}\) _and positive numbers_ \(\{m_{k}\}\) _such that_ \(\sum_{k}m_{k}|\phi_{k}\rangle\langle\phi_{k}|=1\) _and for all_ \(k\) _and_ \(i\
eq j\)_,_ \[\langle\phi_{k}|M_{j}^{*}M_{i}|\phi_{k}\rangle=0.\]
* _There is a_ \(d\times r\) _partial isometry matrix_ \(W\) _such that_ \(WW^{*}=I_{d}\)_, and for all_ \(i\
eq j\)_, every diagonal entry of the_ \(r\times r\) _matrix_ \(W^{*}M_{j}^{*}M_{i}W\) _is equal to zero._  
Conceptually, the states \(|\phi_{k}\rangle\) are determined by [PERSON]'s (rank one) measurement operators, and the orthogonality of the states \(\{U_{i}|\phi_{k}\rangle\}_{i}\) for every \(k\), allows [PERSON] to distinguish \(i\). In the example above, note that \(|\Phi_{0}\rangle=(I_{2}\otimes I_{2})|\Phi\rangle\) and \(|\Phi_{1}\rangle=(I_{2}\otimes X)|\Phi\rangle\), where \(X\) is the single-qubit Pauli bit flip operator. Here, we have \(M_{1}=I_{2}\), \(M_{2}=X\), and \(d=2=r\). So, we can take \(W=I_{2}\); note that \(M_{j}^{*}M_{i}=X\) for \(i\
eq j\).
# Low-Complexity Pupil Tracking for Sunglasses-Wearing Faces for Glasses-Free 3D HUDs
## 2 Methods  
The proposed eye-tracking method, which works for bare faces and sunglasses-wearing faces, comprises two different modes. Two different machine-learning-based eye trackers, which include facial shape alignment and tracker checker modules, are adopted to deduce the eye center positions. The eye-tracking mode selection is based on an eye-area classifier, which determines whether a person is wearing sunglasses or not.  
Both eye-tracking modes use the face region as their input, which can be obtained via face detection or face region extraction using the frame-by-frame tracking of facial points. Depending on facial classification differentiators, such as whether a person is wearing sunglasses, corresponding shape aligners are applied to obtain the pupil center localization. In our previous work [23; 24], we developed an eye-tracking method for bare faces that involves 11-point eye-nose shape tracking based on the supervised descent method (SDM) [30] (for details of the algorithms for bare-face eye tracking, see References [23; 24]). For sunglasses-wearing faces, we propose an eye-position estimation method: the eye tracker uses nonoccluded face area tracking (nose-mouth-face boundary) combined with a supervised regression-based pupil position estimation method to identify the pupil centers. The basic components of our proposed eye-tracking method for people wearing sunglasses can be divided into two main stages: (1) nonoccluded face area tracking and (2) eye-center position estimation for occluded areas. A flowchart describing the proposed eye-tracking method for bare and sunglasses-wearing faces is presented in Figure 2.
# Displacement Patterns and Predictive Modeling of Slopes in the Bayan Obo Open-Pit Iron Mine
## 2 Data and Methodology
### Methodology
#### 2.3.1 Identification of Early Warning Units Based on the Clustering Algorithm
The top 20 principal components, accounting for a cumulative variance contribution of over 92.2%, were selected (Figure 4). This shows that most of the information is preserved with minimal loss. Using the PCA-reduced data, the 10 th nearest neighbor distance for each point was calculated via K-Nearest Neighbors. These distances were sorted and plotted as a curve. By observing the \"elbow\" in the curve, a suitable range for \(\varepsilon\) could be intuitively estimated. Candidate \(\varepsilon\) values were uniformly selected from the 50 th to 99 th percentiles of the sorted distances. The minimum number of samples MinPts was set between 30 and 90. For each parameter set, DBSCAN was run, and the resulting silhouette coefficient was calculated. The silhouette coefficient is defined as shown in Equation (7). \[\mathrm{O}_{\mathrm{(i)}}=\frac{\mathrm{w}_{\mathrm{(i)}}-\mathrm{u}_{ \mathrm{(i)}}}{\mathrm{max}\Big{\{}\mathrm{u}_{\mathrm{(i)}},\mathrm{w}_{ \mathrm{(i)}}\Big{\}}} \tag{7}\]  
where \(\mathrm{u}_{\mathrm{(i)}}\) and \(\mathrm{w}_{\mathrm{(i)}}\) represent the similarity between data point \(\mathrm{P}_{\mathrm{i}}\) and its current cluster and other clusters, respectively. The silhouette coefficient measures the similarity of each point to other points within its own cluster and its dissimilarity to points in other clusters. Its value ranges from \(-1\) to 1. The parameter selection is based on maximizing the overall silhouette coefficient. After parameter optimization, the clustering performance under different combinations of eps and MinPts was visualized using the silhouette coefficient heatmap, which helped verify the rationality of the parameter selection and marked the optimal parameter combination. Figures 5 and 6 show the K-distance graph and the silhouette coefficient heatmap. As shown in the figures, when eps is set to 3.94 and MinPts is set to 70, the silhouette coefficient approaches 1, indicating a better clustering performance. Figure 4: Cumulative variance explained rate curve.