esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Mapping Study on Research in Anemia Assessment with Non-Invasive Devices
## 4 Results
### Temporal and Geographical Distribution of the Publications  
Due to the impartial nature of the classification of distributions, the interaction among the reviewers was necessary just to verify the mined data.  
Figure 3 shows how the number of non-invasive anemia estimation papers published annually varied from 2014 to 2020.  
A significant number of non-invasive anemia estimation studies have been published since 2014 (168 papers). The publication rate seems to remain approximately stable over the years, even if during 2016 there is a slight drop in publications.  
The graph in Figure 4 allows to clearly view the geographical distribution of the documents, the countries that have focused mostly on research on the non-invasive estimation of anemia are represented, each with the relative number of articles published: in case an article had had authors from more than one country, this would have been considered a product of the country of origin of the majority of the authors; in the extreme case where there is no clear majority between nationality, then the nation of the first author will win the authorship of that document.  
India, USA, and China are producing the most (41, 35, and 17 papers, respectively). Regarding them, an explanation could be that they are huge countries, with very dynamic economies, and in two of them anemia is a particularly felt problem, especially in pregnant women and preschool children [2],  
Figure 3: Papers distribution by year.  
the USA could also be stimulated by the particular interest in reducing healthcare costs. Only three European countries are listed. Germany is the first (9 papers), Italy and Spain (5 papers published each).
# SC-FDE Layer for Sensor Networks in Remote Areas Using NVIS Communications
## 7 Conclusions  
In this work we presented an analysis of the SC-FDE with the objective of improving the performance of NVIS communications following the STANAG and MIL-STD standards.  
This study was carried out using two NVIS nodes 100 km apart. From the results of these transmissions, we can conclude that the SC-FDE together with an MMSE equalization obtains a much higher success rate than OFDM studied previously under the same conditions [20], making it much more efficient. As seen in the plots, this physical layer achieved a success rate of 93% BER \(10^{-3}\) for the SC-FDE using the appropriate CR values at the expense of needing higher E\({}_{\text{b}}\)N\({}_{0}\). With the same probability, the ODFM under the same conditions could only meet BER \(10^{-2}\). Apart from that, taking the best configuration of both, the OFDM needed a CR of 9 dB and the SC-FDE needed 3 dB less signal saturation. Moreover, by having the CP, the protection against the multipath effect is quite relevant. In addition, the CP set at the limits can be 33% more efficient in the case of no having multipath and slightly better if the symbol used to equalize has fewer pilot symbols. Finally, this physical layer will allow the use of low power amplifiers for battery-powered sensors, or use compact antennas with controlled losses of up to 10 dB. As an application, this protocol can be used as a platform for transmitting data collected from IoT devices and is suitable for USN thanks to the defined low transmitting powers. A disadvantage to take into account is that the bitrate in this type of communication is not particularly high, but it is not critical because remote sensors do not need high rates, but rather a strong communication robustness.  
In future works, an exhaustive study of OFDM and SC-FDE together with their multiple access techniques (OFDMA and SC-FDMA) will be conducted, optimizing this physical layer, in order to try to define a complete uplink and downlink of NVIS technology. We will also study different bandwidths outside the standards in order to achieve higher bitrates.  
Investigation, T.G., J.P., J.M., J.L.P. and D.B.; methodology, J.L.P. and D.B.; software, T.G., J.P. and J.M.; supervision, A.Z., J.M.M., J.N., J.L.P. and D.B.; validation, J.L.P. and D.B.;  
Figure 10: 25 W SC-FDE CR sweep CDF.  
writing--original draft, T.G., J.P. and J.M.; writing--review & editing, T.G., J.P., J.M., J.N., J.M.M., A.Z., J.L.P. and D.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish Ministry on Science, Innovation and University, the Investigation State Agency and the European Regional Development Fund (ERDF) under the grant number RTI2018-097066-B-I00 (MCIU/ AEI/FEDER, UE) for the project \"NVIS SENSOR NETWORK FOR THE SOUTH SHETLAND ISLANDS ARCHIPELAGO\" (SHETLAND-NET). This work received funding from the \"Agencia de Gestio d'Ajuts Universitatsi de Recerca (AGAUR)\" of \"Generalitat de Catalunya\" (grant identification \"2017 SGR 977\").  
The authors declare no conflict of interest.
# Autonomous Obstacle Avoidance with Improved Deep Reinforcement Learning Based on Dynamic Huber Loss
## 3 A DRL Framework Based on Dynamic Huber Loss and Soft Updates
### General Framework Design  
The network architecture for DRL in the UAV obstacle avoidance scenario is shown in Figure 1. The framework takes the information obtained in the UAV obstacle avoidance environment as the input to the network model, and the output of the neural network is the Q-values of all the actions in the current state, which is passed on to Q-Learning, which decides which action the UAV is going to take; the action includes four directions: up, down, left, right, and left. The UAV continuously acquires environmental interaction data and stores it in the experience pool. When enough data are accumulated in the experience pool, priority experience playback is used instead of the original random extraction, and the training data are given priority so that the data containing valid information are more likely to be extracted for training, thus improving the utilization of the data. The framework is a dual-network structure containing an online network and a target network. The online network output is the maximum Q that can be obtained by taking an action in the \(s_{t}\) state, i.e., \(\underset{a}{\max}Q(s_{t},a;\theta_{t})\). The output of the target network is the Q value obtained by putting the action with the maximum Q value from the online network into the target network in the \(s_{t+1}\) state, i.e., \(Q(s_{t+1},a^{-},\theta_{t}^{-})\). Use the online network to calculate the predicted value and the target network to calculate the target value. Update the online network parameters through the loss function and the Adam optimizer. Use the Soft update instead of the original Hard update so that the target network parameters can change gradually and smoothly. Repeat the above process until the optimal strategy is obtained.  
Figure 1: A DRL framework for autonomous obstacle avoidance of UAV.
## 5 Virtual Inertia Adaptive Control Strategy Based on TDC Algorithm
### Virtual Inertia Adaptive Control Strategy Based on ISRU  
Nonetheless, the TVSG adaptive control strategy solely accounts for the frequency change rate of the APL system, overlooking the impact of network frequency disturbances on the system's stability performance, and failing to address the instability issues arising from excessive \(J_{\omega}\) compensation. Consequently, building upon the traditional VSG adaptive control strategy, this paper introduces a virtual inertia adaptive control strategy that integrates the ISRU approach rate, further enhancing the VSG control's transient stability. The specific mathematical expression is presented as follows:  
\[J_{\omega}=\left\{\begin{array}{ll}J_{0},&\left|\frac{\text{d}\omega}{ \text{d}t}\right|\leq T_{\text{j}}\\ J_{0}+AK_{\text{j}}\big{(}\frac{\text{d}\omega}{\text{d}t}\cdot\frac{|\omega- \omega_{0}|}{\omega-\omega_{0}}\big{)},&\left|\frac{\text{d}\omega}{\text{d}t} \right|>T_{\text{j}}\\ A(x)=\frac{ax}{\sqrt{1+(ax)^{2}}}\end{array}\right. \tag{22}\]  
where \(a\) is the adjustment coefficient of the ISRU function; \(A(x)\) is the ISRU function.  
The variation trends of the ISRU function and its derivatives are illustrated in Figure 9. As a nonlinear function capable of facilitating soft switching continuous control, the ISRU function features a changing boundary that allows \(J_{\omega}\) to vary between \(J_{0}+K_{\text{j}}\) and \(J_{0}-K_{\text{j}}\), thereby mitigating the instability issues associated with excessive inertia compensation coefficients. In comparison to the traditional virtual inertia compensation value \(K_{\text{j}}[(\text{d}\omega\,|\,\omega-\omega_{0}|\,)/\text{d}t\,|\,\omega- \omega_{0}|\,)]\), the virtual inertia adaptive control strategy incorporating the characteristics of the ISRU function, when \(a=2\), is capable of suppressing frequency deviations more swiftly and enhancing the frequency recovery rate.
# FPSO Hull Structures with Sandwich Plate System in Cargo Tanks
## 1 Introduction  
The FPSO structure is based on stiffened panels and the classification societies demand three kinds of inspections for safety reasons: annually, every 2.5 years (intermediate) and every 5 years (Special Periodical Survey). However, these surveys are a time-consuming and risky activity, requiring, for instance, the entrance of people inside the tanks for cleaning and thickness gauging. This activity can be dangerous and time-consuming because it involves confined and high places. Another reason is that this survey is conducted while the FPSO is at sea in full operation. It may take 1 to 1.5 years just to install the risers on the vessel. In other words, there is no possibility to move it to a dry dock and perform the inspection there, as it would turn financially unattractive. Thus, the FPSO crew needs to first reallocate the liquids from the tank, which will be inspected, to the others from the vessel without jeopardizing the oil and gas production. The oil tank to be inspected may be out of operation for up to 10 or 15 days. The storage of the produced oil in this period shall be rearranged in the remaining tanks. This rearrangement can be quite tricky in the earlier years after the beginning of the operation because it is when the production reaches its peak. Therefore, the faster the crew can clean it and inspect it, the less time the production capacity will be reduced and, therefore, the more revenue the FPSO will generate.  
In this context, new technologies are arising to try to overcome or minimize this problem. One of them is the use of sandwich plates in offshore structures. A sandwich structure is a fabricated material that consists of two steel plates joined to either side of a low-density core material or structure [1; 2]. For example, the sandwich plate system (SPS) was shown in [3]. The last type of panel is made of two steel plates bonded to acompact polyurethane elastomer core. These sandwich panels are mainly used as a side shell protection against collision from tugs or supply vessels that approach the FPSO during its operation.  
This study focuses on the research for the design and construction of hulls of FPSOs with sandwich plates without stiffeners in the bottom plate and longitudinal bulkheads of cargo tanks. The main goal is to have the plates of the longitudinal bulkhead and bottom of the cargo tanks free of stiffeners to allow remote cleaning and thickness inspection of bottom and bulkhead plates using autonomous equipment. Therefore, these structures will be replaced by sandwich panel systems (SPS). The steel scantling design to be used is shown in Figure 1, and the thickness of the panels will be determined later.
# Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
## 2 System Model and Conventional Optimization Approach
### Optimization Problem Formulation  
The Tx selects \(U\) antennas from \(N_{t}\) antennas to allocate one antenna to each user. Then, \(U\) independent data streams are transferred to the \(U\) users, i.e., data stream \(x_{i}\) is delivered to user \(i\) and is estimated by \(\tilde{x}_{i}\). For a certain antenna allocation scheme indexed by \(l\), let \(s_{i}^{(l)}\) denote the index of the antenna allocated to user \(i\), where \(s_{i}^{(l)}\in\{1,2,\cdots,N_{t}\}\) and \(s_{i}^{(l)}\
eq s_{j}^{(l)}\) if \(i\
eq j\,(i,j\in\{1,2,\cdots,U\})\). The corresponding index vector of the \(U\) allocated antennas is then defined as \(\mathbf{s}_{l}\triangleq[s_{1}^{(l)},s_{2}^{(l)},\cdots,s_{U}^{(l)}]^{T}\), where the superscript \(T\) represents the transpose of a vector or matrix, and index \(l\in\mathcal{L}\) is used to denote the antenna allocation scheme. Here, we define \(\mathcal{L}\triangleq\{1,2,\cdots,L\}\) as a set of antenna allocation schemes, where the number of all available \(\mathbf{s}_{l}\) (all valid antenna allocation schemes), \(L\), is given by  
\[L\triangleq\frac{N_{t}!}{(N_{t}-U)!}. \tag{1}\]  
Figure 1: System model of the considered multiuser communication network consisting of one transmitter with \(N_{t}\) antennas and \(U\) users with a single antenna.  
Now, suppose that the antenna allocation scheme is given by \(\mathbf{s}_{l}\). We can then compute the resultant data rate of user \(i\) as follows [50]:  
\[R_{i}(\mathbf{s}_{l})=\log_{2}\left(1+\frac{P_{\text{TX}}\mathcal{S}_{i,s_{i}^{( l)}}}{N_{0}+\sum_{l\
eq l}^{ll}P_{\text{TX}}\mathcal{S}_{i,s_{i}^{(l)}}} \right), \tag{2}\]  
where \(P_{\text{TX}}\) is the transmit power, \(N_{0}\) is the noise variance, \(P_{\text{TX}}\mathcal{S}_{i,s_{i}^{(l)}}\) is the power of the desired signal to user \(i\), and \(\sum_{l=1}^{U}P_{\text{TX}}\mathcal{S}_{i,s_{i}^{(l)}}\) is the power of interference signals from the antennas allocated to other users. Then, the sum rate of the system for the antenna allocation \(\mathbf{s}_{l}\) is given by  
\[R_{\text{sum}}(\mathbf{s}_{l})=\sum_{i=1}^{U}R_{i}(\mathbf{s}_{l}). \tag{3}\]  
The optimization problem is then formulated as follows:  
\[\underset{l\in\mathcal{L}}{\text{maximize}}\quad R_{\text{sum}}(\mathbf{s}_{ l}). \tag{4}\]
## 2 Design and Fabrication  
### Vertical and Horizontal Resistor Designs  
The device schematics of vertical and horizontal GeTe resistors are shown in Figures 1 and 2, respectively. Figure 1a shows the top view of a vertical resistor containing a GeTe thin layer sandwiched between two gold contacts. These metal contacts are electrically isolated from each other by a 100-nm thick Si\({}_{3}\)N\({}_{4}\) film (Figure 1b). The optical microscope image of a'ready to test' vertical resistor is shown in Figure 1c. Here, via hole is at the location where the GeTe thin film connects from the top and to bottom electrodes. Figure 1c shows that the top metal contact completely covers the whole via hole despite some misalignment. Therefore, we believe this slight misalignment is more cosmetic in nature and does not adversely affect the transport characteristics. In comparison to vertical devices,the horizontal resistors consist of a narrow bridge of GeTe connecting two gold contact pads as shown in Figure 2a. Similar to a vertical resistor, a Si\({}_{3}\)N\({}_{4}\) layer separates top gold contact pads from the bottom conduction layer (Figure 2b). This bottom conduction layer is essential for fast heat dissipation during the melt-quench process and to promote uniform crystallization. The optical microscope image of a'ready to test' horizontal resistor is shown in Figure 2c. Irrespective of design, the resistance of all the resistors can be expressed by  
\[R=\rho\frac{L}{A} \tag{1}\]  
where \(\rho\) is GeTe resistivity, \(L\) the inter-electrode distance, and \(A\) the cross-sectional area of the testing resistor. Here, it is noteworthy to mention that '\(L\)' represents GeTe layer thickness and separation between two gold pads in vertical and horizontal configuration, respectively. Similarly, '\(A\)' represents the size of the via and the product of the GeTe layer thickness with the distance between the contact pads for vertical and horizontal resistors, respectively. Here, each design size was linearly scaled to maintain the measurement consistency and to establish a relationship between phase transition and voltage pulse. Vertical resistors had cross-sectional areas ranging from 3 \(\times\) 3 \(\upmu\)m\({}^{2}\) to 15 \(\times\) 15 \(\upmu\)m\({}^{2}\) with 90 nm, 150 nm, and 400 nm interelectrode distances. On the other hand, horizontal resistors have cross-sectional areas ranging from 5 \(\times\) 0.09 \(\upmu\)m\({}^{2}\) to 10 \(\times\) 0.4 \(\upmu\)m\({}^{2}\) with inter-electrode distances of 5 \(\upmu\)m and 10 \(\upmu\)m.  
Figure 1: GeTe vertical resistor designs: L-Edit (**a**) top; and (**b**) side view of a resistor; (**c**) optical microscope image of vertical resistor with 3 \(\upmu\)m diameter GeTe via hole.  
Figure 2: GeTe horizontal resistor designs: L-Edit (**a**) top; and (**b**) side view of a resistor; (**c**) optical microscope image of a resistor with 5 \(\upmu\)m inter-electrode distance.
## 6 Conclusions  
We have presented the challenging research directions we are following to achieve practical quantum 3D imaging: minimizing the acquisition speed without renouncing to high SNR, high resolution, and large DOF [1; 2; 3; 4; 5]. Our work represents a significant advance with respect to the state-of-the-art of both classical and quantum imaging, as it enhances the performances of plenoptic imaging and dramatically speeds up quantum imaging, thus facilitating the real-world deployment of quantum plenoptic cameras.  
This ambitious goal will be facilitated by working toward the extension of the reach of quantum imaging to other fields of science, and opening the way to new opportunities and applications, including the prospects of offering new medical diagnostic tools, such as 3D microscopes for biomedical imaging, as well as novel devices (quantum digital cameras enhanced by 3D imaging, refocusing, and distance detection capabilities) for security, space imaging, and industrial inspection. The collaboration crosses the traditional boundaries between the involved disciplines: quantum imaging, ultra-fast cameras, low-level programming of GPU, compressive sensing, quantum information theory, and signal processing.  
Conceptualization, M.D., Z.H., J.R., S.B., P.M., E.C. and C.B.; methodology, F.V.P., G.M., M.P. ([PERSON]), B.S., S.B., P.M., E.C. and C.B.; software, F.S. ([PERSON]), G.M., L.M., M.P. ([PERSON]), M.I. ([PERSON]), I.P., F.S. ([PERSON]), P.M., A.U. and M.W.; firmware, P.M., A.U. and M.W.; validation, F.D.L., G.M., M.I. ([PERSON]), P.M., A.U. and M.W.; formal analysis, F.D.L., M.I. ([PERSON]), I.P. and F.S. ([PERSON]); setup implementation, P.M., A.U. and M.W.; setup design, F.D.L., D.G. and S.V.; writing--original draft preparation, F.V.P., I.P., F.S. ([PERSON]), S.B. and C.B.; writing--review and editing, F.D.L., F.V.P., I.P., F.S. ([PERSON]) and C.B.; supervision, M.D., A.G., F.V.P., C.A., L.A., E.C. and C.B.; project administration, M.D., C.A. and L.A.; funding acquisition, M.D., C.A., M.I. ([PERSON]) and C.B. All authors have read and agreed to the published version of the manuscript.  
Project Qu3D is supported by the Italian Istituto Nazionale di Fisica Nucleare, the Swiss National Science Foundation (grant 20 QZT21_187716 \"Quantum 3D Imaging at high speed and high resolution\"), the Greek General Secretariat for Research and Technology, the Czech Ministry of Education, Youth and Sports, under the QuantERA program, which has received funding from the European Union's Horizon 2020 research and innovation program.  
Not applicable.  
Relevant data are available from the authors upon reasonable request.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SPAD & Single-Photon Avalanche Diode \\ QPI & Quantum Plenoptic Imaging \\ fps & frames per second \\ GPU & Graphics Processing Unit \\ CPI & Correlation Plenoptic Imaging \\ SNR & Signal-to-Noise Ratio \\ \end{tabular}
## 1 Introduction
To evaluate the proposed method, we used the edge device: an MCU board with a 32-bit ARM Cortex-M4 processor. The custom-designed DSP-embedded I\({}^{2}\)S module was implemented in a Xilinx field-programmable gate array (FPGA). A TinyML program was modified as bare metal to implement an ML application on the MCU evaluation board. The audio data delivered to the edge device were transferred from the external FPGA via an I\({}^{2}\)S communication protocol. The baseline for comparison is that the I\({}^{2}\)S module delivers audio data, and the ML application, including the preprocessing of raw data, is run in the software. However, if preprocessing is included in the custom I\({}^{2}\)S module, then the protocol used for data transfer is the same as I\({}^{2}\)S, except that the data to be transferred have completed preprocessing. Moreover, the computation time and energy consumed in the two cases were compared. The rest of the paper is organized as follows. Section 2 describes related works about the TinyML approach and audio recognition using ML. Section 3 presents the overall structure of the hardware and software co-designed audio-classification system. Section 4 describes the system's implementation and applies the proposed method to a word-classification application using an embedded board. Section 5 concludes the paper. Figure 1: Proposed architecture overview for a custom hardware preprocessor coupled with a machine learning (ML) application.
# An Empirical Recommendation Framework to Support Location-Based Services +
## 4 Methodology
### Recommendation Generation
```
procedureRecommendation(\(U,P,N\))\(\triangleright\)\(U\) defines users, \(P\) defines each place location, \(N\) is the value of \(N*N\) grids for all\(person\) in \(U\)do for all\(poi\) in \(P\)do \(PoiPriority\leftarrow\) SimilarityScore \([person_{ien},priority]\)\(\triangleright\)\(n\leftarrow\) No of user in a group \(CentroidCartesianX\gets sum\) of Cartesian x of all users in the group \(CentroidCartesianY\gets sum\) of Cartesian y of all users in the group \(CentroidCartesianX\gets CentroidCartesianX\)\(\div\)\(length(U)\) \(CentroidCartesianY\gets CentroidCartesianY\)\(\div\)\(length(U)\) \(FinalScore\leftarrow\) Similarity between \(PoiPriority\) and POIs on centroid location\(*ratings\) return
```  
**Algorithm 4** Recommendation for a group of user. From Figure 6 we can see that there are five users in five different grids. First, we analyze each user's profile individually to find their POIs within a specific range. Let consider \(user_{1}\) has \(grid_{6}\), \(grid_{7}\), \(grids_{8}\), \(grid_{10}\), \(grid_{11}\), and \(grid_{12}\) to calculate the similarity. Similarly, \(user_{2}\) has \(grid_{5}\), \(grid_{6}\), \(grid_{7}\), \(grid_{9}\), \(grid_{10}\), and \(grid_{11}\); \(user_{3}\) has \(grid_{1}\), \(grid_{2}\), \(grid_{5}\), \(grid_{6}\), \(grid_{9}\), and \(grid_{10}\); \(user_{4}\) has \(grid_{2}\), \(grid_{3}\), \(grid_{4}\), \(grid_{6}\), \(grid_{7}\), and \(grids\); \(user_{5}\) has \(grid_{2}\), \(grid_{3}\), \(grid_{4}\), \(grid_{6}\), \(grid_{6}\), \(grid_{7}\), \(grids_{8}\), \(grid_{10}\), \(grid_{11}\), and \(grid_{12}\). First, we compare each user's interest profile with the POIs within the selected grids to identify the best matching. After finding each user's matched POIs, we calculate the majority voting to find the best matching POIs among the users. For example, we have two best matching POI such as 'Restaurant' and 'Shopping' category for the group. Then we try to find the 'Restaurant' category at an optimal distance from all the users of the group using their centroid location. From Figure 6 we can see that \(grid_{7}\) contains a 'Restaurant' which is located at an optimal distance. In case the first priority 'Restaurant' could not be found then we try to find the second priority 'Shopping' to make the final suggestion.