esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification of Ratholes in Desert Steppe Based on UAV Hyperspectral Remote Sensing
## 1 Introduction
However, satellite remote sensing is limited by the external environment and the accuracy of its own sensors, meaning it cannot meet human expectations for small-feature surveys, such as rathole surveys [12; 13]. Manned aircraft, which are commonly used for low-altitude remote sensing, have limited flight altitudes due to their fixed-wing design, making it difficult to achieve low-altitude flight and meet the precision requirements for data collection. In summary, the data collection methods mentioned above that are based on the rapid location of rodent holes may involve some risk of data loss or omission during the data acquisition process. Nevertheless, they still offer some degree of feasibility for analysis in the context of rodent hole investigation. Field surveys based on low-altitude UAV remote sensing platforms are of great interest and popularity. The main reasons are as follows: (1) they offer the ability to enter hazardous areas for surveying; (2) they represent a new cost-effective way to detect small-scale spaces; and (3) they have the advantages of speed, efficiency, and simplicity of operation. UAVs depend on their unique advantageous conditions to fill the gap in using low-altitude remote sensing platforms in the field of small-scale investigation. Hyperspectral images have multiple continuous bands, which can be used to obtain both high temporal and spatial resolutions of the spectral data. Each pixel and band in the image contains rich spectral feature information and spatial information, helping to truly achieve \"spectrum integration\" and effectively express the real information of the measured object. At present, low-altitude UAV remote sensing platforms with hyperspectral imagers are widely used in the dynamic monitoring of large-area data in, for example, agriculture and forestry, as well as in the recovery monitoring of mining areas, but few studies have been reported on feature identification monitoring based on deserted steppes [14]. Desert steppes have harsh climatic conditions and extensive degradation areas, and the area of grassland rodent holes is narrow, with a relatively hidden location. In this study, in order to achieve the accurate identification of grassland ratholes in large areas,based on the advantages of UAVs, a hyperspectral imager was integrated with a UAV to collect the surface spectral information of a desert steppe. With desert steppe ratholes as the research object, relying on the unique spectral characteristics of ratholes, a method of rathole identification based on principal component analysis is proposed, which can quickly and accurately identify desert steppe ratholes with high accuracy. The proposed scientific method provides a technical means for the dynamic monitoring of grassland rodent activity areas and a sound grassland rodent monitoring system. It also contributes to curbing grassland rodent outbreaks at the source and controlling the threat of plague and other infectious diseases to human health.
## 2 Scott Transformer Model for Real-Time Simulation
Let the turn ratio of the primary side to the secondary side of the Scott transformer be \(k\) = (\(N_{1}/N_{2}\)). Then, the relationship between transformer voltage and current can be expressed as follows [3]:  
\[\left[\begin{array}{c}\overline{\textbf{V}_{\text{AB}}}\\ \overline{\textbf{V}_{\text{BC}}}\\ \overline{\textbf{V}_{\text{CA}}}\end{array}\right]=\left[\begin{array}{cc} \frac{\sqrt{3}k}{2}&-\frac{k}{2}\\ 0&k\\ -\frac{\sqrt{3}k}{2}&-\frac{k}{2}\end{array}\right]\left[\begin{array}{c} \overline{\textbf{V}_{\text{T}}}\\ \overline{\textbf{V}_{\text{M}}}\end{array}\right],\left[\begin{array}{c} \overline{\textbf{I}_{\text{A}}}\\ \overline{\textbf{I}_{\text{B}}}\\ \overline{\textbf{I}_{\text{C}}}\end{array}\right]=\left[\begin{array}{cc} \frac{2}{\sqrt{3}k}&0\\ -\frac{1}{\sqrt{3}k}&\frac{1}{k}\\ -\frac{1}{\sqrt{3}k}&-\frac{1}{k}\end{array}\right]\left[\begin{array}{c} \overline{\textbf{I}_{\text{T}}}\\ \overline{\textbf{I}_{\text{M}}}\end{array}\right] \tag{1}\]  
If the magnitude of three-phase current in Equation (1) is denoted as \(I\) and identical loads are connected to M- and T-phases, the induced current is derived as  
\[\left\{\begin{array}{c}\overline{\textbf{I}_{\text{T}}}=\frac{\sqrt{3}k}{2} \angle 0^{\circ}\\ \overline{\textbf{I}_{\text{M}}}=\frac{\sqrt{3}k}{2}\angle 90^{\circ}\end{array}\right. \tag{2}\]  
that is, currents of identical magnitude but the phase difference of \(90^{\circ}\) flow in the secondary side of the Scott transformer. The three-phase current of the primary side achieves the balanced state as follows:  
\[\left\{\begin{array}{c}\overline{\textbf{I}_{\text{A}}}=I\angle 0^{\circ}\\ \overline{\textbf{I}_{\text{B}}}=I\angle-120^{\circ}\\ \overline{\textbf{I}_{\text{C}}}=I\angle 120^{\circ}\end{array}\right. \tag{3}\]  
Figure 1: The basic structure of Scott transformer.  
Figure 2: Scott connection using a single-phase transformer and a single-phase three-winding transformer.  
In other words, when an identical load is connected to the M- and T-phases of the Scott transformer, we obtain currents with equal magnitude and a phase difference of 90\({}^{\circ}\) flow in these phases. Accordingly, balanced currents with equal magnitude and a phase difference of 120\({}^{\circ}\) flow in the three-phase side are obtained.  
When it comes to industrial transformer certification, the characteristics of M- and T-phases must be tested separately. In the same manner, while configuring a Scott transformer with two transformers, each transformer's parameters must be set in detail to contain the characteristics of M- and T-phases, respectively.
## 3 Proposed Methodology of Monitoring
### The Feedback Control System  
Moreover, the feedback control system uses the three-dimensional mm level displacement data of Beidou GNSS real-time monitoring system. Based on the feedback of the automatically-measured data, the closed loop control of smart pump station and smart jack tensioning is thus achieved through controlling the horizontal displacement of the tower to the target state.  
The feedback control system in the paper mainly refers to the smart hydraulic jack tension system. It can automatically control the tension and release of the smart pump station and the smart jack through the control data received and processed by the workstation, as shown in Figure 6 below.  
Figure 4: Beidou GNSS satellite measurement system and supporting facilities. (**a**) Control room; (**b**) Beidou satellite station and demodulator.  
Figure 5: The working principle of the data monitoring system.  
Figure 3: Overall layout of lifting construction system.  
The feedback control system collects data based on Beidou data monitoring. The software automatically issues instructions, and realizes precise tension of wind cable and cable according to the feedback of the automatic measurement data of the displacement sensor on the smart pump, making the tower keep numerical state. In addition, it stores the data in the process of tension, automatically generates reports, and shows the historical data of tension at any time. Thus, it can eliminate the interference of human factors and effectively guarantee the quality of tensioning construction. The working principle is shown in Figure 7. Intelligent active control is to install the intelligent Jack after the initial tensioning of the designed active control cable wind, monitor the structural deviation through the GNSS automatic displacement acquisition system, judge the structural displacement by the controller, and send the command of Jack tensioning or retraction to the intelligent hydraulic pump station to realize the tensioning and retraction of cable force, so as to reduce the structural displacement. Figure 8 below shows the schematic diagram of top displacement smart active control.  
Figure 8: Schematic diagram of tower top displacement control.  
Figure 6: The feedback control system and supporting facilities.  
Figure 7: The working principle of smart tension system of hydraulic Jack.
# An Insulator Fault Diagnosis Method Based on Multi-Mechanism Optimization YOLOv8
## 3 Experimental and Analysis
### Experimental Environment and Parameter Settings  
In this paper, the environment is configured as follows: the operating system is Windows 10, the CPU model is Intel(R) Core(TM) i7-13700K (Intel, Santa Clara, CA, USA), the GPU model is NVIDIA GeForce RTX 3060 (NVIDIA, Santa Clara, CA, USA), and the version of Python used is 3.11, using pytorch 2.2.0 network framework. Additionally, the dataset was divided into training, validation, and test sets in an 8:1:1 ratio. The specific model training parameters are shown in Table 2.  
In Table 2, Image size denotes the dimensions of the input images, Epochs indicates the number of training iterations, and Batch size refers to the number of samples used for training per iteration. Given resource constraints, this paper selects a batch size of 16. The learning rate determines the magnitude of weight adjustments; an appropriate learning rate enables the model to converge quickly, while a rate that is too high or too low can result in unsuccessful training. This paper selects a default value of 0.01. Optimizer determines the strategy for updating model parameters. The chosen Stochastic Gradient Descent (SGD) is a widely used algorithm for optimizing deep learning models, which utilizes only a small subset of data per iteration, making SGD faster and more efficient compared to gradient descent that uses the entire dataset. Device specifies the training hardware setup, with a setting of 0 indicating the use of a GPU for model training.  
The model performance evaluation metrics used are Recall (\(R\)), Precision (\(P\)), Average Precision (\(AP\)), and mean Average Precision (\(mAP\)). The calculation equation is (5) [26].  
\[\left\{\begin{array}{l}P=\dfrac{TP}{TP+FP}\\ R=\dfrac{TP}{TP+FN}\\ AP=\int_{0}^{1}PRdr\\ mAP=\dfrac{1}{n}\sum\limits_{i=0}^{n}AP_{i}\end{array}\right. \tag{5}\]  
In the equation, \(P\) represents precision, \(R\) denotes recall, \(\mathit{TP}\) is the number of true positive samples correctly identified, \(\mathit{FP}\) is the number of negative samples incorrectly identified as positive, and \(\mathit{FN}\) is the number of positive samples incorrectly identified as negative. \(\mathit{AP}\) stands for Average Precision, and mAP represents the mean Average Precision across all target classes in the dataset. A higher mAP value indicates better model performance.
# Mitigating the Impact of Noise on Nonlinear Frequency Division Multiplexing +
## 7 Discussion
### Computational Complexity  
In recent years, many advances have been made in the development of efficient FNFT and BNFT algorithms (see, e.g., [5] and references therein). However, we are still a long way from the computational simplicity and stability of algorithms such as the FFT. Moreover, some techniques discussed in this work, such as the DF-BNFT in Section 4.4, use the FNFT or BNFT algorithms several times per each block of received symbols, further increasing the computational complexity. This is another important aspect that needs to be carefully investigated, with the aim of reducing the complexity of the NFT algorithms and of the related NFDM modulation and detection technique. However, such a complex issue is of secondary importance compared to the performance issue that is discussed above and which, according to our findings, is not yet solved.
## 2 Related Works
Reference [23] utilized DRL agents with convolutional neural networks to improve the performance of the routing configuration in complex networks and enhance the performance of QoS-aware routing. Reference [24] proposed an off-line training routing solution combining the Deep Q-Network (DQN) and LSTM algorithms, which used batch reinforcement learning methods to learn the optimal control strategies from pre-collected transition samples without interacting with the system. Reference [25] employed a framework combining imitation learning and deep reinforcement learning, effectively reducing the instability of RL algorithms. After several years of development, multi-agent deep reinforcement learning technology is one of the important technological approaches to solve decision control problems in complex environments. It has been successfully applied in various fields, such as game confrontation, robot obstacle avoidance, drone formation control, and traffic signal management. Gradually, it is becoming a key method to study the emergence of swarm intelligence in multi-agent systems. [PERSON] et al. [26] introduced the Medium-Access Control (MAC) methods from the communication domain into multi-agent deep reinforcement learning, proposed a Scheduled Communication model, which optimized the transmission patterns of information and enabled agents to have full-time communication capability. A multi-agent team from Tsinghua University combined the Minimax principle with the MADDPG algorithm to propose the M3 DDPG algorithm [27]. The Minimax principle is used to estimate the worst-casescenario where the behaviors of all agents in the environment are completely hostile, and the agent strategies are continuously updated based on this estimated worst-case scenario, which improves the robustness of the agent learning strategies and ensures the effectiveness of learning. Reference [28] proposed a real-time distributed learning method in mobile edge networks. It employed an MADRL model to realize the independent routing decisions for each edge node, overcoming the limitations of traditional turn-based DRL methods and improving the transmission packet ratio and effective throughput. In summary, the feature expression capabilities and autonomous learning mechanisms of the multi-agent deep reinforcement learning model can be used to calculate the optimal routing strategies for traffic and perform real-time forwarding. Therefore, this paper proposes the traffic scheduling method MAFS, which intelligently realizes the packet processing logic of the data plane and the centralized decision-making of the control plane.
## 2 Analysis of Signal Power and Noise
### Phase Measurement Noise
Taking into account ionosphere-free combination coefficients, it can be expected that RMS of DIF for the GPS system should be higher, but only by about 5% (assuming the same precision at all frequencies). However, in the case of IAWA this deterioration is about 25%, suggesting that Galileo phase observations are less noisy. These findings need further investigation.
# On the Use of LoRa Technology for Logic Selectivity in MV Distribution Networks  
[PERSON]  
1 Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Universita di Napoli Federico II, 80125 Naples, Italy; [EMAIL_ADDRESS] (L.A.); [EMAIL_ADDRESS] (F.B.)  
[PERSON]  
1 Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Universita di Napoli Federico II, 80125 Naples, Italy; [EMAIL_ADDRESS] (L.A.); [EMAIL_ADDRESS] (F.B.)  
[PERSON]  
1 Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Universita di Napoli Federico II, 80125 Naples, Italy; [EMAIL_ADDRESS] (L.A.); [EMAIL_ADDRESS] (F.B.)  
[PERSON]  
2 Dipartimento di Ingegneria Industriale, Universita di Napoli Federico II, 80125 Naples, Italy; [EMAIL_ADDRESS] (Correspondence: [EMAIL_ADDRESS]; Tel.: +39-081-768-3912 22 These authors contributed equally to this work.2D2D2D2D2D2D2D2D2D2D2D2D2D2D2D2D2D2D2D2D2D2D2D2D2D2D2D2D2D2D2D2D22D2D2D2D2D2D2D2D22D2D22D2D2D2D22D2D22D2D2D22D2D2D22D22D22D22D22D2D22D  
In the past, the MV grid communication infrastructure was implemented and exploited only for remote monitoring purposes; low-cost installation and low-speed solutions, such as GSM, were thus preferred. To implement logic selectivity, infrastructures able to guarantee shorter transmission times must be employed. In some areas, the electrical network is being equipped with optical fiber, which guarantees very high communication speeds, but, on the other hand, it is characterized by high installation costs.  
To overcome this limitation, in this paper, the authors start from their knowledge on electrical systems [6; 7; 8] and analyze the possibility of using a communication platform based on LoRa technology. The term LoRa stands for Long Range and refers to a wireless communication system that combines the simplicity of installation, which is typical of wireless networks, with a high coverage range and adequate transmission speed in areas where the installation of optical fiber is inconvenient from an economic point of view [9].  
The paper is organized as follows. In Section 2, the theoretical fundamentals of logic selectivity are given. In Section 4, the constraints imposed by logic selectivity approach are examined, together with the limitations of the existing communication technologies. In Section 3, the characteristics of LoRa technology are presented, and in Section 5, the feasibility of implementing logic selectivity exploiting LoRa is discussed. The demonstrator realized for the experimental assessment is presented in Section 6. In Section 7, the experimental tests carried out, according to different types of faults and different locations, and the their results are reported. Concluding remarks are discussed, finally, in Section 8.
## 2 Preliminaries
### Measurement Model  
Denote the positions of the two spacecrafts in the inertial coordinate system as \((x_{1},y_{1},z_{1})\) and \((x_{2},y_{2},z_{2})\), and denote their relative position vector as  
\[\begin{split} x&=x_{1}-x_{2}\\ y&=y_{1}-y_{2}\\ z&=z_{1}-z_{2}\end{split} \tag{2}\]  
The inter-satellite range can be simply expressed as  
\[\rho=\sqrt{x^{2}+y^{2}+z^{2}} \tag{3}\]  
In the discussion section, the inter-satellite angle measurement data is also used. It is a simple fact that  
\[\delta=\arcsin\frac{z}{\rho}\ ;\ \alpha=\arctan\frac{y}{x} \tag{4}\]  
where \(\alpha\) is the right ascension, and \(\delta\) is the declination.  
To simulate the observation errors, we use the White Gaussian Noise as the measurement error. The standard deviation is set as 0.05 m for the range data and 5 arc-seconds for the angle data. One remark is made here. The two satellites are very close to each other. This fact leads to a negligible light time delay in the current study.
# Energy-Efficient Edge Optimization Embedded System Using Graph Theory with 2-Tiered Security
## 3 Problem Statement  
Researchers conclude that IoT systems are extensively used in several smart applications, including healthcare, smart homes, security surveillance, and the military. However, the limited resources explicitly reduce the performance results in terms of integrity and response interval. Furthermore, it is clear that a variety of solutions have been proposed to secure the IoT platform, but doing so results in more messages passing over the communication links, which negatively impacts the platform's sustainability. Additionally, the majority of the solutions do not make intelligent decisions, which usually results in retransmissions and incurs network congestion. Based on these constraints, our proposed system integrates edge computing with SDN controllers, which provide security using blockchain technology and intelligence utilizing graph theory.
# Quantum Private Comparison Based on Four-Particle Cluster State
## 2 Materials and Methods  
The four-particle cluster state can be expressed as follows:  
\[\ket{\varphi}_{1234}=\frac{1}{2}(\ket{0000}+\ket{0011}+\ket{1100}+\ket{1111})_ {1234} \tag{1}\]  
From Equation (1), it can be inferred that qubits 1 and 2 are in identical states, while particles 3 and 4 also share the same states.  
The bit flip and phase shift operators can be represented by the following matrices:  
\[X=\begin{pmatrix}1&0\\ 0&1\end{pmatrix},Z=\begin{pmatrix}1&0\\ 0&-1\end{pmatrix} \tag{2}\]  
When applying the aforementioned operators to the states \(\ket{0}\) or \(\ket{1}\), the resulting transformations are as follows:  
\[\begin{cases}X|0\rangle=|1\rangle,X|1\rangle=|0\rangle\\ Z|0\rangle=|0\rangle,Z|1\rangle=-|1\rangle\end{cases} \tag{3}\]  
**Theorem 1**.: _When the same operations (e.g., bit flip or phase shift) are applied to qubits 1 and 2 of a four-particle cluster state, these qubits will be in the same state upon performing single-particle measurements. Similarly, the same applies to qubits 3 and 4 when identical operations are performed._  
Proof.: When the bit flip operation is applied to qubits 1 and 2 of the four-particle cluster state, we obtain  
\[\begin{array}{l}\ket{\varphi^{\prime}}_{1234}=(Z\otimes Z\otimes I\otimes I )\ket{\varphi}_{1234}\\ =\frac{1}{2}(Z\otimes Z\otimes I\otimes I)(\ket{0000}+\ket{0011}+\ket{1100}+ \ket{1111})_{1234}\\ =\left(\ket{0000}+\ket{0011}+\ket{1100}+\ket{1111}\right)_{1234}=\ket{\varphi}_ {1234}\end{array} \tag{4}\]  
Upon conducting single-particle measurements on the states represented in Equation (4), it follows that both qubits 1 and 2 are in the same state.  
Similarly, when the phase shift operation is applied to qubits 1 and 2, we have  
\[\begin{array}{l}\ket{\varphi^{\prime}}_{1234}=(X\otimes X\otimes I\otimes I )\ket{\varphi}_{1234}\\ =\frac{1}{2}(X\otimes X\otimes I\otimes I)(\ket{0000}+\ket{0011}+\ket{1100}+ \ket{1111})_{1234}\\ =\left(\ket{1100}+\ket{1111}+\ket{0000}+\ket{0011}\right)_{1234}=\ket{\varphi}_ {1234}\end{array} \tag{5}\]  
Again, performing single-particle measurements on the states in Equation (5) confirms that both qubits 1 and 2 are in the same state.  
By analogous reasoning, when the same operations are performed on qubits 3 and 4 of the four-particle cluster state, it results in these qubits also being in the same state upon single-particle measurements.  
Therefore, Theorem 1 is validated. \(\square\)
## 6 Results and Discussion  
We carried out trace-driven simulations to compare the FSF performance against the algorithms cited on the Section 5.3 by considering two different scenarios: (i) Increasing values of the message TTL, where the nodes have unlimited buffer size, that is, they can store their own messages, as well the messages addressed to other nodes with whom they accept to collaborate. When the TTL expires, FSF automatically drops the associated message from the buffer; (ii) Increasing buffer size, where the nodes have unlimited TTL, that is, there is no time limit for storing a message, but buffers have limited space storage.  
Following similar works found in the literature, message size varies from 512 KB to 1 MB, while buffer size varies from 10 MB to 60 MB, and TTL values varies from 5 to 96 h. The simulator used a uniform distribution to generate 1000 messages between a pair of nodes. Each experiment determined a 95% confidence interval for the estimated parameters. Both scenarios adopted the Drop Oldest algorithm [42] as the buffer management strategy.