esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Positional Accuracy Test Results Depending on Flight Direction  
To investigate the effect of flight direction on positional accuracy, all other parameters were set the same, including the orientation of the camera perpendicular to the ground at a 90\({}^{\circ}\) angle; the flight direction relative to the study area was the only variable. This resulted in three cases (two, three, and six) where the direction of the flight was longitudinal, transversal, or a combination of both directions. As shown in Figure 9, the results indicated that when the angle of detection was 90\({}^{\circ}\), the accuracy obtained with the longitudinal flight direction (6 cm) was better than that with the transversal flight direction (12 cm). However, the best accuracy was generally obtained with the combined acquisition of the longitudinal and transversal flight directions (5 cm).  
Figure 8: The RMSEs for the last ten cases.  
Figure 9: The impact of flight direction (acquisition angle = 90\({}^{\circ}\)).  
On the other hand, if all parameters were set the same and the camera orientation in this test was set to 70\({}^{\circ}\), and the only variable was the direction of flight, then there were three cases (4, 5, and 11). In this case, the accuracy obtained with the longitudinal flight direction (36 cm) was very poor and the accuracy obtained with the transversal flight direction (12 cm) was the best compared with the first case (Figure 10). This was due to the different detection angles (90\({}^{\circ}\) and 70\({}^{\circ}\)). In addition, the accuracy was still the best when the longitudinal and transversal flight directions were combined (3 cm).
# Quantum Circuit Design of Toom 3-Way Multiplication
## 3 Proposed Method
### Quantum Circuit for Toom 3-Way Multiplication
Furthermore, special attention should be given to the division circuit since it is commonly known to be resource-expensive, whose cost can be as high or even higher than the multiplication itself, such as in [24]. Fortunately, the division required in the Toom-3 is an exact division, whose output is always an integer instead of a fraction, and there is no remainder. Secondly, it is a division by constant. Classical computation has employed several different techniques to make the operation efficient, such as by evaluating the division as a multiplication by the number's inverse. However, since extracting the value's information from the preceding quantum circuit is not straightforward, this approach is not favorable. Rather than using this approach, we employ another method that suits this special case of integer division, that is by treating it as a multiplication by the reciprocal of 3 followed by a binary shifting. Proposed by [PERSON] in 1991 [40], this approach has been adopted and widely used, such as in the GNU multi-precision library [19] and various patents. Regarding the case of Toom-3, we require an exact division by 3 circuit. Since 3 is a prime number, its reciprocal has a period (i.e., always repeats). If we convert its decimal fraction (0.333...) to binary, we obtain an unending 01010101. Thus, multiplying this reciprocal by a number will be equivalent to dividing the number by 3, adding \"\(1\)\" at the end for rounding function [19; 40]. Note that the result needs to be right-shifted by \((n+1)\)-bit to get the final result. For instance, let 27 be the dividend in a 16-bit system. Dividing it by 3 equals to multiplying it with the reciprocal of 3 (i.e., 0 xAAAB in hexadecimal), which yields 1179657. Shifting right the intermediate result by 17 will obtain 9, which is the exact quotient of 27/3. Then, for the quantum case, we can use the multiplication by constant circuit proposed in [24], whose Toffoli depth, Toffoli count, and qubit count are \(8n\), \(4n(n+1)\), and \(3n+1\), respectively. Note that the depth of this constant multiplication circuit is around four times of an adder (whose depth is bounded by \(2n\)[8; 11]), which is lower than \(n^{2}\) as in the base-case multiplication.
# Advanced Micro/Nanocapsules for Self-Healing Coatings
## 5 Types of Self-Healing Capsules
### Optimization of Micro/Nanocapsule Design for Self-Healing Coatings
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Types of Self-Healing** & **Description** & **Self-Healing Mechanisms** & **Applications** \\ \hline Core–shell microcapsules & These capsules have a core that contains the healing agent, surrounded by a protective shell. & Upon shell rupture, the core releases the agent to fill cracks or gaps. & Used in structural materials, protective coatings, and smart textiles. \\ \hline Multicore capsules & These capsules contain multiple cores within a single shell, each filled with different healing agents or components. & Upon damage, multiple heating agents are released sequentially or simultaneously to enhance the repair process or to address different types of damage. & Used in complex systems where multiple types of damage might occur. \\ \hline Encapsulated liquid & These capsules are filled with liquid metals, which can flow and solidify to repair damage. & When a crack forms, the liquid metal flows into the damaged area, filling the voids, and solidifies to restore the material’s integrity. & Used in batteries, electronic circuits, and advanced materials requiring rapid and robust healing. \\ \hline \multirow{4}{*}{Temperature-responsive capsules} & These capsules are triggered by changes in temperature. & When the material is exposed to a certain temperature range, the capsule releases its healing agent to address thermal damage. & Used in high-temperature environments, such as in automotive or aerospace applications. \\ \hline \multirow{4}{*}{pH-responsive capsules} & These capsules are designed to release their healing agent in response to changes in pH levels. & When the material’s environment becomes more acidic or basic due to corrosion or damage, the capsules respond by releasing the healing agent. & Used in materials exposed in harsh environments and corrosion-resistant coatings. \\ \hline \multirow{4}{*}{Polymer capsules} & These capsules contain polymer precursors that can form a solid polymer when released. & Upon capsule rupture, the polymer precursors are released, and through chemical reactions, a solid polymer is created that heals the crack. & Used in adhesives, coatings, and structural polymers. \\ \hline \multirow{4}{*}{Emulsion-based capsules} & These capsules contain emulsions that can release healing agents when triggered. & The emulsion can release the healing agent either through a gradual diffusion process or by capsule rupture. & Used in paints and self-healing coatings, where gradual healing over time is desired. \\ \hline \multirow{4}{*}{Time-delayed capsules} & These capsules are designed to release their healing agent after a specific time delay. & The capsule slowly degrades or reacts over time, eventually releasing the healing agent. & Used in systems where immediate repair is not necessary or where gradual healing is needed. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 6 Conclusions  
The proposed method in this study focuses on enhancing wind power prediction accuracy under extreme cold-wave conditions. For cold wave scenario identification, a refined cold wave identification criterion is developed by incorporating the operational characteristics of wind turbines under extreme weather, which reduces the scenario recognition time to an hourly level compared with the criteria in [36]. Considering the limited sample size of cold wave events that may compromise classification accuracy, existing studies [26; 27; 28] address this issue through sample generation techniques. However, the concurrent inclusion of generated samples in both training and test sets introduces potential evaluation biases. To mitigate this, the proposed method exclusively integrates generated samples into the training set. While current research has achieved cold wave scenario recognition through meteorological pattern classification and weather evolution analysis [23; 24; 25], insufficient attention has been paid to integrating cold wave forecasting into wind power prediction frameworks. This study bridges this gap by synergizing cold wave identification with wind power prediction, thereby improving forecasting performance under cold-wave conditions.  
The developed methodology is particularly applicable to wind farms in cold wave-affected regions where extreme low temperatures significantly influence power generation. Limitations include the following: (1) the current framework exclusively addresses cold wave impacts without considering other extreme weather scenarios (gales or blizzards); (2) the proposed cold wave criterion, optimized for regional weather patterns in the study area, requires parameter adjustments when applied to geographically distinct regions; (3) although sample generation enhancement constitutes a key contribution, the academic community has yet to establish robust evaluation criteria for determining the substitutability of generated samples for original data. Future research should prioritize developing standardized methods for synthetic sample feasibility assessment.  
Conceptualization, Z.L. and R.C.; methodology, Y.S.; software, D.S.; validation, Z.L., R.C. and Y.J.; formal analysis, D.S. and Y.S.; investigation, Z.L.; resources, R.C.; data curation, Y.S. and D.S.; writing--original draft preparation, Y.S.; writing--review and editing, D.S.; visualization, Z.L.; supervision, Z.L.; project administration, R.C.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
The research was funded by a State Grid Corporation Limited Science and Technology Project Grant (Project No. 4000-202355381A-2-3-XG).  
Not applicable.  
The datasets presented in this article are unavailable due to privacy restrictions.  
Authors [PERSON], [PERSON] were employed by State Grid Corporation of China. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Clinical Applications of Low-Intensity Pulsed Ultrasound and Its Underlying Mechanisms in Dentistry
###### Abstract  
Low-intensity pulsed ultrasound (LIPUS) serves as a non-invasive treatment tool that reaches the lesion site in the form of ultrasound. Due to its low toxicity, low thermal effect, and low immunogenicity, LIPUS has attracted wide interest in disease treatment. It has been demonstrated that LIPUS can activate multiple signal pathways in the shape of sound wave and one of the most acknowledged downstream response components is integrin/focal adhesion kinase (FAK) complex. In recent years, the functions of LIPUS in bone regeneration, bone healing, bone mass maintenance, and cellular metabolism were found. Various oral diseases and their treatments mainly involve hard/soft tissue regeneration and reconstruction, including periodontitis, orthodontic tooth movement (OTM), dental implant, mandibular deficiency, and dentin-pulp complex injury. Thus, more and more researchers pay close attention to the application prospects of LIPUS in stomatology. We searched these articles in PubMed with keywords LIPUS, temporomandibular joint (TMJ), periodontitis, orthodontics, and pulp, then classified the retrieved literature in the past five years by disease type. In this review, the function effects and possible mechanisms of LIPUS in periodontal tissue regeneration, orthodontic treatment, implant osseointegration, TMJ bone formation/cartilage protection, and dentin-pulp complex repair after injury will be summarized. The challenges LIPUS faced and the research limitations of LIPUS will also be elucidated. Therefore, this paper intends to provide new insights into oral disease treatments, explore the optimal application specification of LIPUS, and probe the future research orientation and the prospect of LIPUS in the dental field.  
LIPUS; periodontal regeneration; orthodontics; implants; TMJ; dentin-pulp complex sciences
## 1 Introduction
Among them, the BB84 protocol [14] and the MDI-QKD protocol [15] are representative protocols in quantum key distribution. The BB84 protocol utilizes the principles of quantum mechanics to achieve secure key distribution by sending and detecting quantum bits. Based on two mutually orthogonal quantum states, the BB84 protocol detects the presence of potential eavesdroppers (often referred to as Eve) by exploiting the properties of quantum states, and verifies the correctness of the bits through a public classical channel. Ultimately, [PERSON] and [PERSON] can negotiate a shared secret key by eliminating errors and eavesdropping. The MDI-QKD (Measurement Device Independent Quantum Key Distribution) protocol is an advanced quantum key distribution protocol that offers higher security and robustness. Unlike traditional QKD protocols, the MDI-QKD protocol does not require [PERSON] and [PERSON] to trust each other's measurement devices. The MDI-QKD protocol eliminates the need for device trust by introducing auxiliary photons shared by both signaling parties for quantum state measurements. In the MDI-QKD protocol, [PERSON] and [PERSON] communicate using their own quantum signals carryings quantum bits (typically polarization states) and perform joint measurements using a shared auxiliary photon. Through public interactions over a classical channel, [PERSON] and [PERSON] can extract a secure key. Currently, quantum key distribution (QKD) technology still has some limitations in certain aspects. Firstly, generating quantum bits in quantum key distribution is challenging, as it involves manipulating and measuring quantum states. Especially for long-distance quantum links, maintaining the integrity and stability of quantum bits becomes more difficult and requires complex technical means and devices. Secondly, the implementation of QKD technology heavily relies on physical devices and laboratory environments, which are costly and difficult to deploy on a large scale. Therefore, quantum keys produced through QKD are precious and rare compared to traditional keys. Lastly, it is worth studying how to design network architectures and algorithms to improve the utilization efficiency of quantum keys [16; 17].
# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 5 Results and Analysis
### Night Readings
The limitations due to the structural formation of the platform and water gate placed the system's depth readings at a slight disadvantage, especially during the daylight testing period.
## 3 An Implementation of the Proposed Architecture  
In this section, a minimal implementation of the proposed architecture is used to demonstrate the advantages of the proposed architecture. As shown in Figure 9, a simplified context-aware smart classroom, which is named as H204, is designed based on the proposed smart classroom prototype. A function of a sample control application is used for illustrating the operation procedures in the proposed technology integration model, and a context-aware energy saving application is designed based on the proposed model. The network configuration in this implementation is following the proposed supporting measures. Finally, a physical model house is demonstrated to explain the concept of virtual smart classrooms. Devices in H204 are list as follows:  
* A motorized projector screen
* Four light sets which can be controlled separately
* Doors with electromagnetic locks
* RFID based door access control
* An exhaust fan
* An air conditioner
* A sensor for CO2 level and temperature
* A wall mount interactive board
* Three Raspberry Pi boards for device control
* An IP NAT gateway
* A Web Server in the university network  
The control path line in Figure 9 represents that a device is controlled by the connected Raspberry Pi. A controlled device does not need to connect to the controlling Raspberry Pi directly. The ways that Raspberry Pi communicate with the controlled devices are varied depending on the controlled devices. Network configurations, such as Internet Protocol (IP) address, of Raspberry Pi boards are also shown in Figure 9. The devices with Ethernet control interface or connected to an Ethernet bridge are not illustrated in Figure 9. Three Raspberry Pi boards are used in the sample smart classroom. Network configurations, the devices controlled by Raspberry Pi boards in Figure 9, and the way that Raspberry Pi boards control the connected devices are described as follows:  
Figure 9: A smart classroom designed and configured based on the proposed architecture.  
* Raspberry Pi 01: IP address is 192.168.50.2. The projector, projector screen, all four light sets are controlled by Raspberry Pi 01.
* Raspberry Pi 02: IP address is 192.168.50.3. The interactive board and the environment sensors are controlled by Raspberry Pi 02.
* Raspberry Pi 03: IP address is 192.168.50.4. The RFID tag reader is connected to Raspberry Pi 03. Doors, air conditioner, and exhaust fan are controlled by Raspberry Pi 03.
* NAT Gateway: The private network of the sample smart classroom is 192.168.50.0/24. The IP address of the network interface connecting to the private network is 192.168.50.1. The IP address of the network interface connecting to the university network is 10.0.0.1.
* Web Server: The web server is located in the university network. The IP address of the web server is 172.16.1.1
# Matched Filtering for MIMO Coherent Optical Communications with Mode-Dependent Loss Channels
###### Abstract  
The use of digital signal processors (DSP) to equalize coherent optical communication systems based on spatial division multiplexing (SDM) techniques is widespread in current optical receivers. However, most of DSP implementation approaches found in the literature assume a negligible mode-dependent loss (MDL). This paper is focused on the linear multiple-input multiple-output (MIMO) receiver designed to optimize the minimum mean square error (MMSE) for a coherent SDM optical communication system, without previous assumptions on receiver oversampling or analog front-end realizations. The influence of the roll-off factor of a generic pulse-amplitude modulation (PAM) transmitter on system performance is studied as well. As a main result of the proposed approach, the ability of a simple match filter (MF) based MIMO receiver to completely eliminate inter-symbol interference (ISI) and crosstalk for SDM systems under the assumption of negligible MDL is demonstrated. The performance of the linear MIMO fractionally-spaced equalizer (FSE) receiver for an SDM system with a MDL-impaired channel is then evaluated by numerical simulations using novel system performance indicators, in the form of signal to noise and distortion ratio (SNDR) loss, with respect to the case without MDL. System performance improvements by increasing the transmitter roll-off factor are also quantified.  
coherent optical communication; optical fiber communication; MIMO adaptive equalizer; matched filter; MMSE; spatial division multiplexing (SDM); polarization division multiplexing; fractional-spaced equalizer (FSE) +
Footnote †: journal: _Sensors_  
0
Footnote 0: Copyright** © 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 4 Methodology and Implementation
### System Model  
* IoT Environment: The system operates within an IoT framework consisting of various sensors and devices distributed across multiple locations. These devices are interconnected through a network and can collect, process, and transmit data;
* Sensors and Data Sources: Various types of sensors (e.g., temperature, humidity, motion, or light sensors) are employed. These sensors capture environmental data that exhibit inherent randomness and can be extracted to generate true random numbers;
* Data Processing Units: These are either embedded within the IoT devices or are standalone units (like edge computing devices) that perform initial data processing, including filtering, normalization, and preliminary randomness extraction from sensor data;
* Randomness Extraction Module: A dedicated module that applies statistical tests and extraction algorithms (e.g., hash functions) to raw sensor data to refine and ensure the quality of randomness;
* Communication Network: Connects IoT devices with each other and with central servers or cloud services. This network facilitates the transmission of sensor data, processed random numbers, and cryptographic keys/messages;
* Central Server/Cloud: Hosts higher-level data processing, storage, and management services. It may also perform additional security checks, distribute software updates, and manage cryptographic keys.
## 4 Proposed Method
### Overall Loss Function
#### 4.5.3 Loss of Sequential Outcome Decoder  
Based on the outputs from the predictive decoder and the classification decoder, we compute the loss for the sequential outcome decoder \(L_{s}(\theta)\) using the cross-entropy loss function:  
\[\mathcal{L}_{s}(\theta)=CrossEntropyLoss(P_{cg},S_{cg}) \tag{40}\]  
where the predicted edge probability matrix \(S_{cg}\in\mathbb{R}^{N\times N}\) is generated by Decoder 2:  
\[S_{cg}=\text{Decoder2}(h_{i},h_{g};\theta_{\text{dec2}}), \tag{41}\]  
* \(h_{i}\): Node embeddings (dimension \(d_{h}=128\)).
* \(h_{g}\): Graph embedding derived from average pooling of all node embeddings.
* \(\theta_{\text{dec2}}\): Trainable parameters of Decoder 2.  
The binary pseudo-label matrix \(P_{cg}\in\{0,1\}^{N\times N}\) is derived from the optimal path sequence generated by Decoder 1.  
To concurrently elevate the efficacy of both decoders, the ultimate training loss in our research is derived through the linear aggregation of losses originating from both decoders:  
\[\mathcal{L}_{\theta}=\mathcal{L}_{p}(\theta)+\mathcal{L}_{s}(\theta) \tag{42}\]  
where \(L_{p}(\theta)\)) represents the loss of the label generation decoder. By combining reinforcement learning with pseudo-label learning at each epoch, we simultaneously improved the performance of both the prediction encoder and the classification decoder. This not only increased the probability of finding the best option but also enhanced the efficiency of the entire process.