esa-sceva/satcom-chunk-collection huggingface dataset:
# Geometric Processing and Accuracy Verification of Zhuhai-1 Hyperspectral Satellites
###### Abstract  
The second batch of Zhuhai-1 microsatellites was successfully launched on 26 April 2018. The batch included four Orbita hyperspectral satellites (referred to as OHS-A, OHS-B, OHS-C, and OHS-D) and one video satellite (OVS-2A), which have excellent hyperspectral data acquisition abilities. For the first time in China, a number of hyperspectral satellite networks have been realized. To ensure the application of hyperspectral remote sensing data, a series of on-orbit geometry processing and accuracy verification studies has been carried out on the \"Zhuhai-1\" hyperspectral camera since the satellite was launched. This paper presents the geometric processing methods involved in the production of Zhuhai-1 hyperspectral satellite basic products, including geometric calibration and basic product production algorithms. The OHS images were used to perform on-orbit geometric calibration, and the calibration accuracy was better than 0.5 pixels. The registration accuracy of the image spectrum of the basic product after calibration, the single orientation accuracy, and the accuracy of the regional network adjustment were evaluated. The spectral registration accuracy of the OHS basic products is 0.3-0.5 pixels, which is equivalent to the spectral band calibration accuracy. The single orientation accuracy is better than 1.5 pixels and the regional network adjustment accuracy is better than 1.2 pixels. The generated area orthoimages meet the seamless edge requirements, which verifies that the OHS basic product image has good regional mapping capabilities and can meet the application requirements.  
\"Zhuhai-1\" satellite constellation; hyperspectral; geometric calibration; band-to-band registration; interior orientation determination accuracy; block adjustment 2019 11 996 23 April 2019
## 2 System Model  
To show the expected gain, a good system model should be defined first [17]. We consider a single isolated cell with one BS and \(K\) terminals/users. The BS has \(N_{t}\) TX antennas, and each terminal has only one receiver (RX) antenna. The received signal vector at terminals can be represented as follows:  
\[\mathbf{y} = \sqrt{P_{tx}}\mathbf{H}\mathbf{s}+\mathbf{n}, \tag{1}\]  
where \(\mathbf{y}\) is the \(K\times 1\) received vector for \(K\) terminals, \(P_{tx}\) is the total TX power for downlink, \(\mathbf{H}\) is the \(K\times N_{t}\) small scale i.i.d. Rayleigh fading channel matrix between \(N_{t}\) BS antennas and \(K\) terminals, \(\mathbf{s}\) is the \(N_{t}\times 1\) TX signal vector, and \(\mathbf{n}\) is the \(K\times 1\) additive white Gaussian noise (AWGN) vector at the terminals. As a simple rule of thumb, if \(N_{t}>10K\), we say that the system is LSAS.
## 4 Spatiotemporal Feature Extraction Techniques
### Autoencoders and Other Enhanced Methods  
In the field of feature extraction, several studies have proposed improved methods based on autoencoders. For example, using an enhanced sparse autoencoder for latent feature extraction has shown promising performance in designing network IDS [24]. Additionally, feature extraction methods that combine word embedding models with TF-IDF and Word2 Vec have demonstrated improved classification performance in intrusion detection, making these approaches more competitive among existing feature extraction methods [60].
## 4 Implementation and Discussion of Results
### LSTM Deep Learning (Setup and Training)
#### 4.2.1 Selecting the Nodes, Layers, and Hyper-Parameters of LSTM  
Multiple layers are layered one on top of the other in Keras, and the model must be initialized as sequential. Depending on the dataset, excellent forecasting outcomes can be acquired using a trial-and-error technique to choose nodes and layers. The optimal model can be selected depending on the value of the loss function. In this analysis, the loss function is used to calculate the error of forecasting for optimizing the hyperparameters of the LSTM models, which is the mean squared error (MSE). In the spectrum of normalization and reshaping, the datasets are normalized and reshaped between \([0,1]\). The original dataset is split into two parts: training and testing. Two hidden LSTM layers were chosen because they are sufficient for recognizing complicated patterns. To avoid over-fitting problems, the dropout layer is designated to disregard some neurons throughout the training phase. The dropout layer has a value of 0.2 and is introduced after each layer of the LSTM to maintain the model's accuracy. The parameter settings in the experiment include the learning rate, which was set to 0.0005; the number of time steps, which was set to 7; the number of features, which was set to 16; and the number of hidden units, which was set to 200. The activation function employed is ReLU. The data sequence is organized in an appropriate format that can be analyzed. A sequence of data points is included in every training sample. The sequence of data is provided to the LSTM layers to forecast future COVID-19 cases. Afterward, the output of the last time step is passed to the next input sequence and so on.
# Tightly-Coupled Data Compression for Efficient Face Alignment
## 5 Results
### Quantitative Experimental Results  
As shown in Figure 2, the face features can be divided into five parts: face contour, eyebrows, eyes, nose, and mouth. Face contour contains points No. 1 to No. 17. Eyebrows contain points No. 18  
Figure 8: Face alignment results for different methods on the 300 W dataset [12]. The left column shows the results with our compressed training model. The center column shows the results by using the SDM algorithm with an uncompressed training model [6]. The right column shows the results with the 2D-FAN [22].  
to No. 27. Eyes contain points No. 37 to No. 48. Nose contains points No. 28 to No. 36. Mouth contains points No. 49 to No. 68. The average values of the normalized mean errors in the five parts, respectively, on the 300 W test dataset were estimated. We compared our compressed training model against the uncompressed training model and deep learning based method. The results are shown in Figure 9. From the figure it can be concluded that our proposed compressed training model can achieve very close feature points compared with the uncompressed training model. The deep learning based method performs slightly better. However, the differences of the normalized mean errors on the 300 W test dataset between our proposed method and the deep learning based method are all below 1% for all five parts, which is acceptable considering the high computational cost and memory storage usage of the deep learning based method.  
Similar to the work in [22], a subset was chosen from the 300 W test dataset whose yaw angles are between 0 and 30 degrees. Experiments on this subset with the above three methods were conducted. The results are shown in Figure 10. From this figure, we can find that for moderate head poses, which are the typical scenarios for mobile applications, all three methods can achieve lower normalized errors and generate very similar results, especially for eyes and face contour parts. These two parts are very important for AR (Augmented Reality) based applications. The differences of the normalized mean errors between our proposed method and deep learning based method were less than 0.6% for most parts and even achieved 0.3% for the eyes regions. This proves the effectiveness of our proposed algorithm.  
Figure 11 shows the cumulative error distribution curve of our proposed method and the uncompressed training model SDM [6]. It is obvious that the two curves are very close to each  
Figure 10: Normalized mean errors of our compressed training model SDM compared with the uncompressed training model SDM [6] and deep learning based method [22] on a subset of the 300 W test dataset [12] whose yaw angles are between 0 and 30 degrees.  
Figure 9: Normalized mean errors of our compressed training model SDM compared with the uncompressed training model SDM [6] and deep learning based method [22] on the 300 W test set [12].  
other. This again confirms the similar performance of both methods despite our training model being much smaller.
## 3 WSAN System Models  
The WSAN control application model employed in this work is described in this section. The control application considered is a building-temperature control system as illustrated in Figure 1. To measure and control the temperature of a room over a wireless sensor network is the main purpose of this control system. There are three major components in Figure 1: a controller and an actuator, a plant/a physical system, and a wireless sensor network indicated by block numbers #1, #2, and #3, respectively. The controller is responsible for commanding the heating/cooling actuator based on the room temperature measuring by sensor nodes. The room temperature behavior is characterized by its physical model. In our scenario, the source node ID 0 is placed in the room and detects the temperature every sampling period. The measured room temperature is then encapsulated in a data packet and sent to the sink node ID 1 via wireless multi-hop communications. The controller calculates the error between the desired and the measured temperature values before finding an appropriate supply air temperature command for the actuator. This wireless closed loop control system continues until the temperature in the room reaches the desired target and still has to operate to maintain the control objective. Details of the physical model and the controller studied in our work are described below.
# \"In Vivo\" Validation of 3D-Printed Innovative Surgical Template for Lumbar Spinal Arthrodesign.
## 6 Conclusions  
This paper is focused on the design of a novel custom-made surgical template for the optimization of pedicle screw insertion during arthrodesis surgeries. The design is based on both RE and RP techniques, but the surgeon remains responsible for choosing the entry point of the screws.  
To summarize briefly, the time spent on the insertion of pedicle screws via the proposed surgical template was cut down by about 63%, while the number of X-ray shots was reduced by about 92%. Precision in surgery outcomes was the same or higher in all of the tests performed.  
Future developments will concern the treatment of more complex vertebral shapes. These will entail the introduction of innovative devices for anchoring the surgical template on the vertebra. A new trial has been planned to surgically treat cases with hard scoliosis.  
Finally, the template approach will allow a new methodology to be developed for mini-invasive template-based surgery that will bring new methods for percutaneous approaches.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/17/5977/s1](http://www.mdpi.com/2076-3417/10/17/5977/s1)]([http://www.mdpi.com/2076-3417/10/17/5977/s1](http://www.mdpi.com/2076-3417/10/17/5977/s1)).  
All authors contributed to the study conception and the design of the presented medical device. In vivo trial was performed by N.N. Data collection and analysis were performed by E.C., and N.C. The first draft of the manuscript was written by A.N. and F.N. and all authors commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare that they have no conflict of interest.  
**Compliance with Ethical Standards:** All procedures were performed in accordance with the ethical standards of current Italian law and were in line with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The surgery-trial was approved by the Ethical Review Board of the San Giovanni di Dio e Ruggi d'Aragona University Hospital in Salerno, where the trial/tests were conducted. The trial itself and the proposed procedure have been approved by the Ethical Committee of Italian Health Ministry (Campania regional district). The principal investigator, responsible for the clinical trial, is Dr. [PERSON], co-author of this paper.  
**Informed Consent**: All participants were informed about the experiment protocol, the nature of the medical implements used and risks and benefits linked to the new surgical device. Informed consent was signed by the patients.
## 3 Background
### HSR Issues  
The forwarding rule of the standard HSR protocol is that nodes forward unicast frames from one port to other ports, unless they already sent the same frame in that direction. A node will not forward a frame that it injected into the ring [1].  
Figure 1: HSR example of single-ring topology for unicast traffic.  
HSR protocol has no issues with unicast frames in single-ring networks. However, HSR protocol has a serious drawback in connected-ring networks that is HSR generates too much unnecessary redundant unicast traffic. The drawback is caused by the following issues:  
1. Forwarding frames into all DANH rings, except the destination DANH ring.
2. Forwarding frames into all QuadBox rings.
3. Doubling and circulating frames in all the rings.  
This drawback of HSR degrades the network performance and may cause network congestion and delay. Therefore, several techniques for reducing HSR unicast traffic have been proposed to solve the problem of HSR. These techniques can be classified into the following two categories: traffic filtering-based methods and predefined-paths based methods. Traffic filtering-based techniques prune unicast traffic for rings in HSR networks, and preventing the traffic from doubling and circulating in the rings. Predefined path-based techniques establish two separate paths for each connection pair of terminal nodes. These dual paths are then used to forward unicast frames between each connection pair of nodes, instead of duplicating and forwarding frames to all parts of the networks as in the standard HSR protocol.  
We will describe these traffic filtering-based techniques and predefined path-based techniques in Sections 4 and 5, respectively.  
Figure 2: HSR example of connected-ring topology for unicast traffic.
# Toward Vulnerability Detection for Ethereum Smart Contracts Using Graph-Matching Network
## 4 Methodology
### Abstract Semantic Graph Generation
#### 4.2.1 ASG from Source Code
An example ASG of the \(Simple\) contract is illustrated at the bottom of Figure 4. Note that sequential edges from the control flow are omitted in the example ASG since the sequence number of each node is self-explained. ```
0: Source code of smart contract, \(S\)
0: Abstract semantic graph, \(ASG\)
1: Parse the source code of \(S\) to an abstract syntax tree \(AST\)
2: Initialize \(ASG\) with \(AST\) in statement-level granularity
3:\(VariableSet\)\(\leftarrow\)\(\emptyset\)
4:for each node \(v\) in \(AST\)do\(\triangleright\) Add edges of control flow
5:if\(v\) is in the control flow then\(\triangleright\) such as for, while, if, etc. 6: Add control edge(s) to \(ASG\)
7:else
8: Add sequential edge(s) to \(ASG\)
9:endif
10:if\(v\) is a variable definition then
11: Add \(var\)(s) in statement \(v\) to the set of VariableSet
12:endif
13:endfor
14:for each \(var\)in \(VariableSet\)do\(\triangleright\) Add edges of data flow
15:if\(var\)is in a control path of \(ASG\)then
16: Add data edge(s) between each pair of nodes that both contain \(var\)
17:endif
18:endfor
19:return\(ASG\)
```  
**Algorithm 1** ASG generation from source code.
## 1 Introduction
### Superconductor Applications for Vehicles  
#### 1.1.1 Automotive Applications  
As for the main application this study considers, magnetic levitation has recently become a research topic. [PERSON] found it remarkable that some metallic elements, forming a magnetic field close to the surface, will expel the magnetic field lines when cooled down to reach the superconductivity state [9]. It has been discovered that intense currents will  
Figure 1: Superconductive characteristic of mercury [2].  
enclose the superconductor surface during the superconductive state and, in the presence of a magnetic field [10], cause it to expel the magnetic field lines, as shown below in Figure 2.  
Most conventional transport vehicles use certain amounts of fossil fuels. Despite the recent increase in the efficiency of internal combustion engines, the sustained use of non-renewable energy sources will eventually deplete the corresponding deposits, such as the crude oil used in manufacturing fossil fuels.  
Certain research studies mention an electric vehicle prototype equipped with a superconductive motor. This prototype was developed to test the superconducting material windings to verify the potential limitations of these materials. It was verified that the superconducting motor prototype has an output torque of 70 Newton-meters at a power of 18 kilowatts and a maximum speed of 70 km per hour. The maximum torque can be reached at low angular velocities, and, therefore, a smooth start and acceleration are possible. The prototype was tested over six months and reported no problems [11].  
The schematic diagram of such a vehicle is shown below in Figure 3. The schematic of a conventional electric vehicle is shown on the left side, while, on the right side, the figure shows a possible implementation for a superconductive electric vehicle. To increase the efficiency of the electric motor, a cryogenic cooling system, and a superconducting material were used to build the cooling mechanism, and to make the windings of the electric motor, respectively.  
Figure 3: A comparison between a conventional electric vehicle and a vehicle using a superconductive electric motor. (**a**) Block diagram for conventional electric vehicles; (**b**) Possible topology for superconductive motor electric vehicles.  
Figure 2: Magnetic field lines around normal state conductor and superconductive state conductor. (**a**) A temperature higher than the critical temperature of a superconductor does not influence magnetic field lines; (**b**) During the superconductive state, magnetic field lines induce a surface current through the superconductor. This leads to magnetic levitation, since incident magnetic field lines are expelled.  
A common aspect of this superconductive electric vehicle is that it uses stored electric energy to convert into kinetic energy by means of the electric motor. Creating a superconductor is the key to developing low-cost, highly efficient electric motors. Due to recent improvements in high-temperature superconductor performance and adiabatic cooling technology, it is possible to implement such electric motors.
## 3 Experimental Investigation  
### Experimental Setup  
As shown in the Figure 2, the measurement system used for signal generation and data acquisition is mainly constituted by National Instruments (NI). The kit includes an arbitrary waveform generator card (PXI-5441), a 60 MS/s 8 channel digitizer (PXIe-5105) and a machine (PXIe-1073) for the cards to be inserted in. LabVIEW software is used to control the modular unit through a computer. Two identical wideband FUJI acoustic emission transducers (AE1045S), which have a very flat frequency response from 0.1 MHz to 1.5 MHz, are used as an actuator and a sensor. The response signal is promoted by a preamplifier (PXPA6) before acquisition. The galvanized steel wires under test have the same diameter of 7 mm and approximate length of 1 m. The transducers are coupled perpendicularly to the wire's cross section at two sides by the designed clamps.
## 3 Basic Model of Traffic Engineering Routing and Limiting
\begin{array}{l}\sum\limits_{j:E_{ij}\in X_{i,j}^{k}}\leq x_{i,j}^{k} -\sum\limits_{j:E_{ij}\in E}x_{j,i}^{k}=0;\;k\in K,\;R_{i}\
eq s_{k},d_{k};\\ \sum\limits_{j:E_{ij}\in X_{i,j}^{k}}\leq x_{i,j}^{k}-\sum\limits_{j:E_{ij}\in E }x_{j,i}^{k}=1-\beta^{k};\;k\in K,\;R_{i}=s_{k};\\ \sum\limits_{j:E_{ij}\in X_{i,j}^{k}}\leq x_{i,j}^{k}-\sum\limits_{j:E_{ij}\in E }x_{j,i}^{k}=\beta^{k}-1;\;k\in K,\;R_{i}=d_{k}.\end{array} \right. \tag{2}\]  
The following restrictions are imposed on variables \(\beta^{k}\) according to their physical content:  
\[0\leq\beta^{k}\leq 1. \tag{3}\]  
We denote by \(b^{k}=\lambda^{k}.\beta^{k}\) the intensity (rate) of the \(k\)th flow, with which packets will be limited (rejected) on the border router. The conditions for preventing overloading and providing load balancing in the network [21] have the following form:  
\[\sum\limits_{k\in K}\lambda^{k}.x_{i,j}^{k}\leq\alpha\cdot\varphi_{ij}\;\big{(} E_{i,j}\in E\big{)}, \tag{4}\]  
where \(\alpha\) is the additional control variable that obeys the following conditions:  
\[0\leq\alpha\leq\alpha_{TH}, \tag{5}\]  
where \(\alpha_{TH}\) is the \(\alpha\) threshold. The network requirements for the quality of service level determine its value.
## 4 Analytical Framework for Heterogeneous Service Provisioning
### Problem Overview and Healthcare Service Requirements  
In a typical smart healthcare system, service consumers (e.g., patients, caregivers, clinicians) interact with distributed service providers through a network comprising mist nodes, edge devices, and cloud infrastructure. The goal is to ensure that healthcare services are delivered at appropriate rates while meeting strict QoS constraints, especially for latency-critical applications such as emergency alerts or continuous glucose monitoring.  
Let \(S=\{s_{1},s_{2},\ldots,s_{n}\}\) denote the set of healthcare services, and let \(C_{s}=\{c_{s,1},c_{s,2},\ldots,c_{s,n}\}\) denote the set of consumers for each service \(s\in S\). Each consumer is connected to the service provider via a set of links \(T=\{t_{1},t_{2},\ldots,t_{m}\}\) with associated capacities \(C=\{c^{1},c^{2},\ldots,c^{m}\}\). The objective is to allocate network resources such that each consumer receives service \(s\) at an optimal delivery rate \(g_{s,i}\), measured in Mbps, while satisfying both rate and latency constraints.  
Services are broadly categorized into two types: (i) latency-sensitive services, such as real-time ECG monitoring or fall detection, which require immediate data processing and response, and (ii) latency-tolerant services, such as access to EHRs or non-urgent data synchronization, which can tolerate higher latency and lower bandwidth. This heterogeneous service demand motivates the development of a rate-adaptive and latency-aware provisioning model that dynamically adjusts service delivery rates based on network conditions, service criticality, and user context. In this work, we aim to jointly optimize the service delivery rate and latency using a utility-based convex optimization framework, further enhanced by ML techniques for traffic prediction and adaptive control.