esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This could be easily obtained by winding a secondary coil to the material (as shown in Figure 1), which provides the voltage V\({}_{\text{ref}}\). This latter metric can be used to detect the output trend and to trigger a Power Management Electronic circuit, adopted to better couple the VEH with the proper load, similarly to what is carried out in [24]. Scientific literature offers a significant number of papers on power electronic interfaces for piezoelectric EH (e.g., [25; 26; 27; 28; 29]), whereas there is still room for magnetostrictive VEHs, also because it is a young technology. Figure 1: Sketch of the elements composing a VEH with magnetostrictive material. The iron frame and permanent magnets provide a magnetic bias, and one or more coils can be wounded around the active material. Further details about a magnetostrictive VEH can be found in [2; 10; 15]. A novel use of the AC-DC boost converter can be beneficial in this case, as schematized in Figure 2. Indeed, as previously described, the inductor is already present in the harvester, and this represents an economical advantage reducing, at the same time, losses. Several hybrid buck-boost converters, typically exploiting one or more external inductors with high frequency switching technique, have been proposed for VEHs based on electromagnetic mechanism or piezoelectric materials [30; 31; 32; 33; 34], whereas very few or no conversion techniques are available for magnetostrictive devices. Indeed, because of the intrinsic nature of harvesters based on magnetostrictive materials, such types of energy conversion methods have not been successfully applicable, and a suitable control technique must be applied. In this paper, we propose a tailored switching technique for an AC-DC boost that is capable to self-adapt to the input voltage frequency in order to optimize the harvested power. As proof of concept, the switching technique is implemented in a digital low-cost controller as an Arduino board. This allows one to easily change the control parameters and find optimum working points. It is worth underlining that, in order to study the AC-DC boost behavior, it is not necessary to use a real magnetostrictive VEH as input to the boost. A particular toroidal transformer has been used in this case, as described in more detail in the next sections. One of the aims of this work is to exploit and investigate the circuit through a programmable board in order to have a versatile tool. Finally, the circuit has been experimentally tested, and results have been compared with simulations of an equivalent circuit in a _1 Spiec_ environment [35]. The paper is organized as described below. In Section 2, the AC-DC boost switching converter is introduced, and its operation is analyzed. Section 3 is devoted to the description of the experimental setup, while in Section 4, the experimental results are compared with the simulations and discussed with some figures of merit. Conclusions end the paper.
## 2 Experimental Investigation
### Modal Test of the Threadless Structure  
The swept-sine signal can ensure the repeatability of the experiment and is easily used to measure the natural frequency and frequency response function [20]. Therefore, the swept-sine signal was selected as the excitation signal. The swept range of 100 to 5000 Hz could cover the first three order frequencies of the structure. The swept speed and excitation level were 5 Hz/s and 0.1 N, respectively. Figure 3 illustrates the frequency response function (FRF) measured at location A\({}_{2}\) of the threadess specimen (L40). The first-, second-, and third-order modal frequencies are 938.26, 2669.86, and 4582.51 Hz, respectively. The obtained FRF has sharp peaks, and the peaks are symmetrical with respect to the resonance, indicating that frictional damping is negligible and the FRF has prominent linear characteristics. For comparison with the experimental results, we obtained the first three order frequencies of the threadless specimen (L40) using the MD/Nastran software, as indicated in the second row of Table 2. The errors between the test and simulation are 1.1, 1.3, and \(-\)0.2%, respectively. The difference can thus be considered negligible, which confirms that the threadless specimen has no nonlinear factors. Because of the negligible difference between the test and FEM in the threadless structure, we used the simulation results instead of experimental results to investigate the threadless structures (L30 and L50), as indicated in Table 2.  
Figure 2: Details of the test setup (source elaborated by authors).
A clustering method based on virtual grid was proposed in [8]. Coordination mechanisms among heterogeneous nodes were also introduced. [PERSON] _et al_. [9] proposed a grid-clustering routing protocol that provides scalable and efficient packet routing. A cluster grid construction scheme was presented to reduce energy consumption. In [10] an energy efficient framework for detecting events in sensor networks was presented. Clusters are used as local decision units. Cluster decisions are exchanged with one hop clusters that are likely to have been influenced by the event. An energy-efficient event notification scheme was also proposed. Event detection in grid-clustered sensor networks was investigated in [11]. Inter-cluster communications with some error corrections were used to improve event detection performance. A cellular approach to fault detection and recovery in sensor networks is presented in [12]. A virtual grid structure is used to detect energy-depleted nodes. In wireless sensor networks, in general, due to a strong tradeoff between event detection accuracy and false alarm rate it is difficult to maintain high event detection accuracy for relatively small events or/and high fault probabilities, unless the tradeoff is greatly lessened. In this paper, we present a grid-based, distributed, event detection scheme for wireless sensor networks, covering even relatively small event regions. To lessen the tradeoff the proposed scheme employs a smoothing filter to reduce the effect of transient faults. In addition, it maintains confidence levels of sensor nodes to isolate faulty nodes exhibiting errors for some extended period of time. Event detection locally at each grid based on the readings of its member nodes might achieve poor performance when an event region lies across multiple grids. To cope with this variations, a sensor network is divided into M \(\times\) N square grids, each of which is further divided into 2 \(\times\) 2 sub-grids. An event is detected by finding a square region of 2 \(\times\) 2 sub-grids, not necessarily in the same grid, that passed a predefined threshold. The rest of the paper is organized as follows. In Section 2, network structure, fault and event models are described. The proposed grid-based event detection scheme is presented in Section 3. Simulation results are shown in Section 4. Section 5 concludes the paper.
# Tamper and Clone-Resistant Authentication Scheme for Medical Image Systems
## 4 A Proposed New Secured Unclonable Medical Watermarking Scheme
### System Analysis: Benefits of Combining SUC and Watermarking
#### 4.3.2 Protocol 2: User-Server Authentication Protocol for Image Verification  
This sample generic protocol allows a user such as doctor \(D\) to request a patient's medical image from the TA server. Then, the TA server answers with the requested image as shown in Figure 12.  
Figure 11: Medical device-server authentication protocol for secured logging of a medical image transaction and image verification.  
Figure 12: User-server authentication protocol for exchanging registered medical images.  
Protocol.2 can proceed as follows:  
1. Doctor \(D\) randomly selects \(x_{D_{j}}\) and computes the corresponding cyphertext \(y_{D_{j}}\) by using its \(\text{SUC}_{D}\).
2. Doctor \(D\) asks the TA server to send the required medical image \(Ml_{1}\) of the patient SN as \(y_{D_{j}}\Big{|}\ E_{x_{D_{j}}}(SN,\ req\_MI_{1},ID_{D})\), where \(req\_MI_{1}\) is the request of the medical image \(Ml_{1}\) and \(ID_{D}\) is a public identifier of doctor \(D\).
3. TA server uses \(y_{D_{j}}\) to determine \(x_{D_{j}}\) from the device \(D\)'s secret record in DB.
4. TA server decrypts the received message \(E_{x_{D_{j}}}^{-1}E_{x_{D_{j}}}(SN,\ req\_MI_{1},ID_{D})=SN\), \(req\_MI_{1},ID_{D}\). If the decrypted \(ID_{D}\) matches the public identifier \(ID_{D}\) of doctor \(D\), the TA server registers the request of the medical image \(Ml_{1}\) and doctor \(D\) cannot deny using \(Ml_{1}\).
5. TA server answers with \(E_{x_{D_{j}}}(Ml_{1})\), where \(Ml_{1}\) is the medical image.
6. Doctor \(D\) decrypts the received message: \(E_{x_{D_{j}}}^{-1}E_{x_{D_{j}}}(Ml_{1})=Ml_{1}\). It should be noted that doctor \(D\) cannot generate or predict the signed watermark \(Z\) of \(Ml_{1}\) stored in DB (see Section 5). Therefore, doctor D cannot change and fake \(Ml_{1}\). This proposed protocol attains the following security functions:  
1. Doctor \(D\) cannot deny using the image generated by medical device \(A\).
2. The stored image in the TA server cannot be changed or faked later by doctor \(D\).
3. TA server knows undeniably \"who and when\" a user such as doctor \(D\) was using the medical image.
## 5 Conclusions  
This paper proposes a novel gesture recognition system using multiple sensors based on Earth's magnetic field and a 1D convolution neural network. Compared with traditional gesture recognition technology, gesture recognition technology based on magnetic sensors has numerous advantages, including increased accuracy in recognition and immunity to environmental interference. Our proposed system detects gestures by installing multiple magnetic field sensors on the fingertips and back of the palm, with data collected from these sensors classified using a 1D convolution neural network. To validate the system's efficacy, we conducted an empirical test using American Sign Language alphabet gestures, and the results demonstrated an exceptional recognition accuracy of nearly 97% under experimental conditions. Further investigation is required to effectively recognize more complex and dynamic gestures.  
The future holds vast application possibilities for magnetic-sensing gesture-recognition technology. Its exceptional precision and reliability make it ideal for various computer graphics applications such as virtual and augmented reality, providing a more natural and authentic human-computer interaction. Moreover, this technology can enhance the ease of device control for individuals in healthcare, smart homes, and other areas. Additionally, magnetic-sensing gesture-recognition technology can facilitate barrier-free technology, assistive tools, and other fields to offer greater convenience for individuals with disabilities.  
Conceptualization, X.C.; methodology, B.S.; software, B.S.; validation, B.S. and Z.H.; formal analysis, H.S.; investigation, X.C. and R.H.; resources, X.C.; data curation, H.S.; writing--original draft preparation, B.S.; writing--review and editing, X.C. and R.H.; visualization, B.S. and Z.H.; supervision, X.C.; project administration, R.H.; funding acquisition, X.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (51777010).  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Critical and Rare Metals in Greece
### Gallium (Ga), Germanium (Ge) and Indium (In)  
The rare metals gallium, germanium and indium are the most important commodities in the high-technology society, essential for electronic components. They are critical metals for the efficiency of utilization of recourses that are used and consumed in the order of millions of tones [34]. Moreover,germanium has major applications in optical materials and indium in liquid crystal displays (LCD). Presently gallium is produced as a by-product of bauxite processing and from the zinc processing residues, although the largest reserves are associated with phosphate ores [19]. Germanium and indium are recovered during the zinc production process.  
The epithermal polymetallic veins of the Rhodope province are the most promising ore types for Ga, Ge and In exploitation in Greece. The Agios Philippos mine at Kinki, western Thrace, is a polymetallic vein deposit with intermediate-high sulfidation epithermal affinities and contains an unusual mineralogy consisting of several Pb-As-Cu-Bi-Sn sulfosalts. It is hosted within Eocene sediments and displays spatial relationships to Upper Oligocene rhyolite porphyry exposed at the northern part of the mining area. Wurtzite is the dominant Zn-ore mineral in Agios Philippos, along with sphalerite, and is found in the form of veinlets and granular masses. It contains considerable amounts of In (reaching 3.5 wt %) and Ga (reaching 1.6 wt %), whereas Ge is present in smaller amount (below 0.3 wt %) [35].  
In Pefka/Evros the high-intermediate sulfidation epithermal mineralization is related to quartz veins hosted within andasitic and dacitic volcanic rocks. Bulk ore analyses (present study) revealed relatively high In content (up to 675 ppm), whereas Ga and Ge are low (max 17 ppm and 16 ppm respectively). Another potential deposit for future Ga exploration is the Lavrion intrusion-related deposit, especially the Pb-Zn-Cu bearing granitic dikes from Plaka deposit, where Ga reaches 326 ppm [36].  
Relatively lower contents of In are indicated for the Lavrion carbonate-replacement ore bodies where sphalerite concentrates contain up to 43 ppm and the bulk Cu-pyritic ores up to 72 ppm [18]. The Polykastro/Kilkis and Molaoi/Peloponsos (Kuroko-type) and the Thermes/Xanthi and Olympus/Chalkidiki (carbonate-replacement) deposits should be also evaluated for their In, Ge and Ga potential [3].  
Phosphate ores represent the largest reserves of several million tons, generally with a low Ga-content of 0.01%-0.1% [19]. The Neogene phosphate deposits of Greece contain low Ga content in general. The highest concentrations have been referred in the Vegora lacustrine basins of Vegora (Vegora-Komnina), 40 km northwest from Veria, where Ga in the phosphatic rocks reaches 15.9 ppm [37], and they cannot be considered as potential sources for future exploitation.
# AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks
###### Abstract  
With the development of 5G technology, high definition video and internet of things, the capacity demand for optical networks has been increasing dramatically. To fulfill the capacity demand, low-margin optical network is attracting attentions. Therefore, planning tools with higher accuracy are needed and accurate models for quality of transmission (QoT) and impairments are the key elements to achieve this. Moreover, since the margin is low, maintaining the reliability of the optical network is also essential and optical performance monitoring (OPM) is desired. With OPM, controllers can adapt the configuration of the physical layer and detect anomalies. However, considering the heterogeneity of the modern optical network, it is difficult to build such accurate modeling and monitoring tools using traditional analytical methods. Fortunately, data-driven artificial intelligence (AI) provides a promising path. In this paper, we firstly discuss the requirements for adopting AI approaches in optical networks. Then, we review various recent progress of AI-based QoT/impairments modeling and monitoring schemes. We categorize these proposed methods by their functions and summarize advantages and challenges of adopting AI methods for these tasks. We discuss the problems remained for deploying AI-based methods to a practical system and present some possible directions for future investigation.  
Keywords:optical transmission; optical networks; machine learning; artificial intelligence; quality of transmission; optical performance monitoring; failure management +
Footnote †: journal: Applied Sciences
# Multi-Dimensional Feature Fusion and Enhanced Attention Streaming Movie Prediction Algorithm
## 3 Fflstma
### Lstm  
The LSTM network model is a special form of an RNN, which integrates memory units and gating mechanisms to cope with the problem of gradient disappearance and gradient explosion encountered by traditional RNNs when dealing with long sequences of data. The schematic structure of the LSTM model is illustrated in Figure 2 below.  
The core of the LSTM model, i.e., the cell state, is labeled with the letter C in the diagram. This state runs throughout the time series and is responsible for storing and transferring information. The cell state is updated at each time step to capture and preserve the long-term dependencies within the sequence. Memory cell states are represented by \(C_{t-1}\) at the previous time step and \(C_{t}\) at the current time step. Information flow is controlled by the LSTM model's gating mechanism. The three gates that comprise an LSTM cell are the input, forget, and output gates. To ascertain the degree of information flow, all three gates employ a sigmoid activation function, whose output spans from 0 to 1, signifying the proportion of information that passes through.  
Input Gate: accountable for controlling the entry of fresh data into the memory cell. This gate determines which data should be stored in the memory cell based on the current input and the hidden state of the previous instant.  
Forgetting gate: responsible for determining whether the information in the memory unit of the previous moment should be continuously retained to the memory unit of the current moment. Through the processing of the forgetting gate, LSTM can selectively discard unimportant past information.  
Output gate: decides if the data in the memory cell at that precise time are transferred to the concealed state. This gate generates the present moment's output based on the present input value, the state of the memory cell, and the concealed state of the previous moment.  
Memory unit: in charge of storing the intrinsic states of neurons as the central part of LSTM. Long-term dependencies in the sequence data can be captured and maintained by LSTM thanks to its ability to save, read, reset, and update the long-history data.  
At moment t, the LSTM neural network [26] is defined by Equation (2) as follows:  
\[\left\{\begin{array}{l}f=sigmoid(Wf\cdot[h_{t-1},x_{t}]+bf)\\ i_{t}=sigmoid(W_{i}\cdot[h_{t-1},x_{t}]+b_{i})\\ O_{t}=sigmoid(W_{0}\cdot[h_{t-1},x_{t}]+b_{0})\\ c_{t}^{\prime}=tanh(W_{c}\cdot[h_{t-1},x_{t}]+b_{c})\\ c_{t}=f_{t}\times c_{t-1}+i_{t}*c_{t}^{\prime}\\ h_{t}=o_{t}\times tanh(c_{t})\end{array}\right. \tag{2}\]  
where sigmoid and tanh are the activation functions of the two neural networks, respectively, and \(W^{*}\) stands for the recursive connection weights of their respective thresholds.
# Topology Optimization of Anisotropic Materials with Smooth Fiber Orientation
###### Abstract  
In the concurrent optimization of topology and fiber orientation, the design of smooth fiber helps to maintain the stability of numerical calculation and the compatibility of the manufacturing process. However, the improvement of fiber continuity is often accompanied by a significant decrease in the overall structural stiffness. Aiming at this problem, this paper proposes a topology optimization method for anisotropic materials with smooth fiber orientation. This method improves the smoothness of fiber orientation and reduces stiffness loss by introducing a fiber angle constraint strategy and adaptive filtering technology. The fiber angle constraint strategy integrates the created angle constraint function into the Method of Moving Asymptotes (MMA) to complete the strong constraint of the angle. This strategy quantifies the continuity of the fiber and effectively improves the continuity of the fiber. At the same time, the application of adaptive filtering technology can adjust a reasonable fiber angle distribution on the basis of smoothing fibers, thereby enhancing the stiffness of the overall structure. In addition, this paper shows the complete optimization process and MATLAB code implementation and verifies the effectiveness of the method through a series of numerical examples, that is, on the basis of improving fiber continuity, the stiffness of the whole structure is guaranteed, and then the effective balance between the two is realized.  
topology optimization; concurrent optimization; fiber angle constraint; adaptive filtering; MATLAB implementation +
Footnote †: journal: Applied Physics Letters B
# A Network-Based Method for the EMU Train High-Level Maintenance Planning Problem
## 3 Methodology
### Time-State Network
It was assumed that the train-set can switch to maintenance state on the same day after operation ending, and vice versa, which is depicted by a deep-blue dash-dotted arc in Figure 3. The direction of the arc, denoted by \(a_{ij}^{k}\), signifies the state transition direction. In addition, \(a_{1,k}\) and \(a_{2,k}\), \(a_{1,k}\) and \(a_{3,k}\), \(a_{1,k}\) and \(a_{4,k}\) can be linked together, respectively, by \(a_{ij}^{k}\). However, it cannot be connected between the \(a_{2,k}\), \(a_{3,k}\) and \(a_{4,k}\). In other words, the state of a train-set could transform from available to maintenance, or from maintenance to available, but cannot transform from a level of maintenance to another level. HMP determines on which day the train-set should go to workshop and which level of maintenance the train-set should undertake. On the network, this process can be shown as a path by connecting different arcs throughout the whole planning horizon. Such as the path \(r_{0}\) (see in Figure 4):  
As shown in Figure 4, path \(r_{0}\) describes that a particular train-set is in an available state from the first day to the fourth day; then undergoes third-level maintenance according to maintenance requirements from the fifth day to the eighth day; after that, this train-set goes back to undertake transport tasks from the ninth day to the end of planning horizon. In this way, all the state and state transition processes for a train-set in the planning horizon are depicted by a path, which is decided by the time point from the available state transform to the maintenance state, the level of maintenance, and the length of maintenance service time. For any particular train-set, the level of maintenance and the length of maintenance service time are known in advance, and the key decision is the time point from the available state transform to the maintenance state. Therefore, the HMP problem could be converted into a pathway chosen problem on the time-state network. We optimized the latter to realize the optimization of the former in this paper. In addition, in order to show the specific state for a train-set intuitively on the network, we connect the time arcs in the same state to form an arc chain denoted by \(a_{i}^{hk}\), which is depicted by a solid line and painted with the homologous color. At the end of a planning horizon, let all the solid line arcs be directed to the hyper-node in each row. In Figure 3, a train-set is kept in an available state from \(v_{1,1}\) to \(v_{1,5}\), which can be depicted by a red solid line from \(v_{1,1}\) to \(v_{1,5}\). The length of this red line denotes the duration of available state for train-set. Similarly, this train-set is kept in third-level maintenance state from \(v_{2,5}\) to \(v_{2,9}\), which can be denoted by an orange solid line from \(v_{2,5}\) to \(v_{2,9}\). The length of this line represents the third-level maintenance service time. It should be pointed out that the Figure 3 is only a sketch map, whose time span do not presents the actual time. Figure 4: Path \(r_{0}\).