esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents a solution for enabling the coexistence of digitized radio-over-fiber (D-RoF) and analog radio-over-fiber (A-RoF) interfaces operating in the optical fronthaul of 5G mobile systems. In the first section, we formulate the need to introduce new technologies to the cloud/centralized radio access network (C-RAN) (Next Generation RAN (NG-RAN) in 5G systems). A proposition of construction of the optical remote radio head (O-RRH)/gNodeB--distributed unit (gNB-DU), which will enable the operation of digital Splits/Options and new proposed analog Splits/Options, is presented. The methods performing calculations of bit rate and optical bandwidth demand in the fronthaul/midhaul, with reference to the parameters of the new-radio-release-15 (NR-Rel-15) wireless interface and subsequent releases, towards the next generations, are presented. The bandwidth demands were calculated for selected Splits/Options, and the results are shown in diagrams. A special section is devoted to description of the results achieved and presenting potential applications of the proposed construction of a radio-photonic device as well as new Splits/Options of the next generation fronthaul/midhaul.  
fronthaul; midhaul; radio-over-fiber; optical gNB-DU; RRH; A-RoF; D-RoF; eCPRI; BBU; all-optical network +
Footnote †: journal: Applied Physics Letters  
_Applied_  
_Acticle_  
D-RoF and A-RoF Interfaces in an All-Optical  
Fronthaul of 5G Mobile Systems
# Investigation of Electro-Elastic Properties for LN Single Crystals at Low Temperature
## 2 Experimental Section  
Restricted by the crystal symmetry, the LN crystals had 3-m symmetry and possessed 2 dielectric permittivities, 6 elastic compliances and 4 piezoelectric coefficients, which are given as below:  
\[\varepsilon_{\bar{\eta}}=\left(\begin{array}{ccc}\varepsilon_{11}&0&0\\ 0&\varepsilon_{11}&0\\ 0&0&\varepsilon_{33}\\ \end{array}\right) \tag{1}\]  
\[s_{ij}=\left(\begin{array}{cccccc}s_{11}&s_{12}&s_{13}&s_{14}&0&0\\ s_{12}&s_{11}&s_{13}&-s_{14}&0&0\\ s_{13}&s_{13}&s_{33}&0&0&0\\ s_{14}&-s_{14}&0&s_{44}&0&0\\ 0&0&0&0&s_{44}&2s_{14}\\ 0&0&0&0&2s_{14}&2(s_{11}-s_{12})\\ \end{array}\right) \tag{2}\]  
\[d_{ij}=\left(\begin{array}{cccccc}0&0&0&0&d_{15}&-2d_{22}\\ -d_{22}&d_{22}&0&d_{15}&0&0\\ d_{31}&d_{31}&d_{33}&0&0&0\\ \end{array}\right) \tag{3}\]  
In this work, the cut samples were prepared from the high-quality LN single crystals, which were supplied by CETC Deqing Huaying Electronics Co., Ltd., Zhejiang, China. The crystal cut configuration was prepared based on our previous work described in [22], as showed in Figure 1. The precise orientations were carried out by X-ray diffraction crystal direction finder (YX-4 made by Liaodong Radioactive Instrument Company, Dandong, China), with the accuracy of \(<\)20\({}^{\prime\prime}\). In this experiment, all the samples were nearly the same dimensions with the same vibration mode. The dimensions of the prepared crystal cuts were 10 \(\times\) 10 \(\times\) 2 mm\({}^{3}\) for square plates (samples (a) and (b)), 4 \(\times\) 4 \(\times\) 12 mm\({}^{3}\) for Z bar (samples (c)), and 1.5 \(\times\) 4 \(\times\) 15 mm\({}^{3}\) for rectangular-shaped plates (samples (d) and (i)).  
The crystal samples were vacuum-sputtered with platinum films (200 nm) as electrodes on the two parallel surfaces. The measurement of electro-elastic properties was carried out by the impedance method. The dielectric permittivities \(\varepsilon_{11}^{T}/\varepsilon_{0}\) and \(\varepsilon_{33}^{T}/\varepsilon_{0}\) were obtained by recording the capacitance of the X- and Z square plates using a multi-frequency LCR meter at 1 kHz. The resonant and anti-resonant frequencies were measured by an impedance analyzer connected to a temperature chamber. Based on the measured capacitances, resonant frequencies and anti-resonant frequencies of different crystal cuts and the electro-elastic constants were obtained. Moreover, the variations of electro-elastic characteristics were determined by Equation (4) with the temperature range from \(-150\)\({}^{\circ}\)C to 150 \({}^{\circ}\)C:  
\[Variation_{X}=\frac{X(T)-X(T_{0})}{X(T_{0})} \tag{4}\]  
where \(X(T)\) represents the dielectric permittivity, elastic compliance, electromechanical coupling factor and piezoelectric coefficient at arbitrary temperature; \(X(T_{0})\) represents these parameters at \(-150\)\({}^{\circ}\)C.
# Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives
###### Abstract  
Optical and thermal remote sensing data have been an important tool in geological exploration for certain deposit types. However, the present economic and technological advances demand the adaptation of the remote sensing data and image processing techniques to the exploration of other raw materials like lithium (Li). A bibliometric analysis, using a systematic review approach, was made to understand the recent interest in the application of remote sensing methods in Li exploration. A review of the application studies and developments in this field was also made. Throughout the paper, the addressed topics include: (i) achievements made in Li exploration using remote sensing methods; (ii) the main weaknesses of the approaches; (iii) how to overcome these difficulties; and (iv) the expected research perspectives. We expect that the number of studies concerning this topic will increase in the near future and that remote sensing will become an integrated and fundamental tool in Li exploration.  
satellite data; image processing algorithms; pegmatite; brine; lithological mapping; mineral alteration mapping; geobotanical mapping +
Footnote †: journal: Applied Sciences
# Control of Flexible Robot by Harmonic Functions
## 1 Introduction
Modelling flexibility in underactuated systems by a rotational spring is presented in [34], and a similar flexibility model is investigated in [35], where the spring stiffness is varied, and a point stabilization control is developed. A model with linear springs is investigated in [36], where input-output linearization is performed along with a PID controller. Sliding mode control is used to control the flexible robotic arm in [37]. The current work aims to introduce the invariant control strategy to the structures with compliancy (chain of flexible robotic arms). The nonlinear system with flexible modes is obtained using the procedure presented in [38]. An invariant control is presented in [2]. The application of such control to the underactuated system (double pendulum, double pendulum on a cart) is shown in [14; 39]. The current work does not include the flexible structures and does not deal with trajectory tracking. This paper aims to introduce the control strategy to the structures with compliancy--in our case, the flexible robotic arm. Another contribution of the paper is in its presentation of a control strategy which forces the undesired movements of the system into the desired system endpoint trajectory. The paper is organized as follows. The exact input-output linearization is presented in the Section 2 for underactuated systems. Next, the coordinates are separated into actuated and unactuated. Then, consecutive output parameterization is introduced. Section 2.1 introduces invariant control with harmonic functions and the natural motion used for the Eigen frequency evaluation. Section 3 introduces the presented method to flexible robotic arm: modelling and parameterization with input-output linearization is applied to the flexible robot. Next, the particular control application to the flexible robotic arm and simulation results are presented. Section 4 concludes the work.
# Pulsed Thermography Dataset for Training Deep Learning Models
## 2 Related Work
### Deep-Learning-Based Methods Used in Thermography for NDE  
In recent years, the use of deep-learning-based methods has shown significant improvement in data classification and segmentation for PT data. In [20], Mask-RCNN has been applied to a synthetic experimental PT dataset to detect abnormal regions. In [19], authors have employed an U-Net model to segment the defect regions on curved CFRP samples using PT technique. In [27], Faster-RCNN models with Inception V2 and Inception Resnet V2 architectures are trained to identify defects within composite materials using thermographic images. Average precision is used to assess the models' performance. The results show that the model with Inception V2 outperforms the one with Inception Resnet V2. A hydrogen deep neural network that incorporates temporal and spatial information on the infrared data to detect the defects in composite and coating material is proposed in [28]. In [29], the authors use PT to detect the impact damage within the composite samples. They employ a support vector machine to classify the defective areas from the healthy area. They report the classification results ranging from 78.7 % to 93.5 %. In [30], recurrent neural networks and feed-forward neural networks are investigated to detect defects within non-planar CFRP specimens. The results indicate that a long short-term memory neural network outperforms a feed-forward neural network when dealing with time-dependent information. A feed-forward neural network to classify the defects into different categories is proposed [31]. Two models are trained on raw data and Thermographic Signal Reconstruction (TSR) coefficients, respectively. The model using TSR coefficients outperforms the other using raw data.  
As the use of generative adversarial networks (GANs) has become increasingly popular, they have also been applied to processing thermographic data. A GAN-based thermal image argumentation method is applied to thermographic data to enhance the visibility of defects in CFRP components in [32]. In [33], the authors introduce IRT-GAN, which is trained on a large augmented numerical thermographic dataset. The dataset is fully simulated for composite materials with different defect types, sizes, and depths. The model takes TSR coefficients as input and is able to produce images for defect segmentation.
# DLSTM-Based Successive Cancellation Flipping Decoder for Short Polar Codes
## 3 The Proposed SC Flipping Algorithm
### DLSTM-Based Robustness Mechanism  
Current the neural network decoders are specific, such as certain block length or signal-to-noise ratios (SNR). To make the algorithm robust, we designed a DLSTM network architecture that is compatible with multiple block lengths. For different block length  
Figure 9: Diagram of two-bit flipping selection.  
polar codes \(P_{1}(N_{1},K_{1})\), \(P_{2}(N_{2},K_{2})\), \(P_{3}(N_{3},K_{3})\), and \(N_{1}>N_{2}>N_{3}\), we make the following updates to maintain the integrity of the input shape. The length \(N_{1}\) of the longest codewords is taken as the standard, and padding is performed for codewords smaller than this length. One-hot encoding is done directly for labels with length \(K_{1}\). After making the length uniform for polar codes with variable length, the padding part is learned during the training, which affects the prediction results. To reduce the impact, a mask layer is added before the DLSTM layer for skipping the respective time step. During the DLSTM training, the part that is masked is not involved in the calculation. Figure 10 presents this training process. To obtain better generalization capability, we adopt an early stopping criterion. The training is stopped if the accuracy of the validation set does not improve.
## 3 Results and Discussion
### Environmental Fluctuation Analysis for Cold Chain  
#### 3.2.1 Temperature Change along with Time  
The temperature fluctuation for the fresh peach and nectarine cold chain is described in Figure 4. The black shadowed area is the projection of the three-dimensional curve, which stands for the temperature variation with the change of time. The segment S1 represents the normal transportation stage after the fresh fruit were harvested in the orchard. The temperature in the segment S1, which mainly varied with the ambient temperature, ranged from about 25.8 \({}^{\circ}\)C to 27.7 \({}^{\circ}\)C. The temperature slowly rose because of the influence of the ambient temperature and the energy released by the respiration of fresh fruit. The segment S2 represents the process of unloading the fruit, and the temperature ranged from 26.5 \({}^{\circ}\)C to 27.60 \({}^{\circ}\)C. The quality management of fresh fruit cold chain demanded a quick fruit unloading.  
Figure 3: Process and information flow of the peach and nectarine cold chain.  
The segment S3 represents the cold storage of fresh fruit at 4 \({}^{\circ}\)C refrigeration temperature; at this point the temperature was quickly reduced from 26.08 \({}^{\circ}\)C to 9.4 \({}^{\circ}\)C. There was a small decrease in temperature in the loading segment, represented by S4. The temperature in the segment S5 started to rise slowly, while the fresh fruit were transported to the market. During the S6, the temperature rose rapidly from 8 \({}^{\circ}\)C to the ambient temperature of 23.3 \({}^{\circ}\)C.
# Investigating Master-Slave Architecture for Underwater Wireless Sensor Network
## 4 System Model
### Packet Types in ERPMSA-UWSN
#### 4.6.1 Division of Potential Forwarding Zone (PFZ) into PFN levels  
In this scheme, the division of PFZ into different PFZ levels is based on the depth difference of the source node and the PFN zone. To achieve optimal transmission of the data packet, we tend to access the master node closer to the sink. For this purpose, PFZ is divided into two equal regions based on the depth difference as shown in Figure 3. As the transmission range is 2000 m, the region where the depth difference is between 1001 m to 2000 m is considered to be PFZ1, while from 1 m to 1000 m, it is considered to be PFZ2. This 3D division results in a truncated hemisphere with an upper cap (PFZ1).
# Enhancements and Challenges in CoAP--A Survey
## 3 Applications of CoAP
### Streaming Services  
The blockwise transfer capability of CoAP enables the use of CoAP in the streaming services such as media streaming or video streaming. The application of CoAP in streaming services is mentioned further.
# Underwater Acoustic Target Tracking: A Review
## 5 Classification Underwater Acoustic Target Tracking Algorithms
### Sage-Husa Model
#### 5.3.2 Source-Driven Optimization Methods  
In general, underwater target tracking is realized by multiple sensors in which case the communication issues are inevitable. Due to the limited communication bandwidth in underwater channel, the amount of transmission message is restricted. The most effective way to reduce the length of communication message is quantization, which means compressing the raw measurements or state estimation into several bits of data. However, the quantized procedure degrades the performance of tracking accuracy for the loss of information. Recently, some studies are focused on addressing the problem of improving tracking performance with optimal quantization. The acoustic sensors used for underwater target tracking are equipped with limited energy and communication capability, which motivated the presence of effective sensor scheduling strategies. Tracking optimization means not only improving tracking accuracy but also considering the source constraints. Therefore, source-driven optimization methods including sensor scheduling strategies and quantized methods are presented here.
# Evaluating the Effectiveness of Large Language Models in Converting Clinical Data to FHIR Format
## 3 Related Works
### Large Language Models in Healthcare  
LLMs have been increasingly applied in healthcare for various tasks, including text summarization and information extraction [25]. Recent studies have demonstrated their potential in clinical text summarization, radiology report analysis, and extracting structured information from electronic health records [23; 24; 25]. Prompt engineering has emerged as a critical technique to enhance the performance of LLMs in these tasks. These studies are particularly relevant to our research as they highlight the effectiveness of prompt engineering and the potential of finetuned LLMs in healthcare applications.  
For instance, [PERSON] et al. [26] introduced chain-of-thought prompting, which improved LLM performance on multi-step reasoning problems. Additionally, [PERSON] et al. [27] demonstrated that zero-shot chain-of-thought prompting can enhance LLM capabilities without task-specific examples. These findings underscore the importance of prompt engineering in maximizing the potential of LLMs for diverse applications. Building on these insights, our study evaluates different prompting techniques to improve the accuracy and efficiency of converting unstructured clinical data into structured formats.  
The aim of this adapted Delphi study [28] from 2024 was to collect researchers' opinions on how LLMs might influence healthcare and on the strengths, weaknesses, opportunities, and threats of LLM use in healthcare. Participants offered several use cases, including supporting clinical tasks, documentation tasks, and medical research and education, and agreed that LLM-based systems can act as health assistants for patient education. The agreed-upon benefits included increased efficiency in data handling and extraction, improved automation of processes, improved quality of health care services and overall health outcomes, provision of personalized care, accelerated diagnosis and treatment processes, and improved interaction between patients and healthcare professionals.  
In their study, [PERSON] et al. [29] leveraged clinician feedback to learn from their preferences to tailor the model's outputs, ensuring that they aligned with the needs of clinical practice. Their experiments demonstrated that this approach improved upon the performance of the model post-supervised finetuning. Their findings underscored the potential of integrating direct clinician input into LLM training processes, paving the way for more accurate, relevant, and accessible tools for clinical text summarization. These insights are particularly relevant to our study as we evaluate the performance of a finetuned LLM in healthcare contexts.
# Research on Demand-Based Scheduling Scheme of Urban Low-Altitude Logistics UAVs
## 4 Example Verification and Analysis
### Analysis of Algorithm Comparison  
In solving drone demand, there are numerous feasible solutions due to the large amount of sample data, and different algorithms can have different impacts on the results. In this paper, we use a controlled experiment method to analyze the effects of five algorithms: delivery distance first, delivery parcel volume first, random delivery, standard simulated annealing, and improved simulated annealing on the drone demand results. A total of 100 experiments were conducted separately, and each experiment was set for 10,000 iterations, of which the respective optimal solution was taken to represent the following results:  
Random distribution was carried out a total of one hundred times, and the minimum value was taken as the optimal solution and included in Table 7. After the random distribution experiment was completed, the maximum and minimum values of the respective distribution costs of the RN and BN routes were counted with the mean and variance as shown in the following table:  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Delivery Times** & **1 st** & **2 nd** & **3 rd** & **4 th** & **14 th** & **15 th** & **16 th** & **17 th** \\ \hline Drone 1 & 8:00-8:05 & 8:05-8:08 & 8:08-8:18 & 8:18-8:27 &... & 11:30-11:35 & & & \\ \hline Drone 25 & 8:00-8:15 & 8:15-8:20 & 8:20-8:23 &... & 11:34-11:47 & & & & \\ \hline... &... &... &... &... &... &... &... &... &... &... \\ \hline... &... &... &... &... &... &... &... &... &... &... \\ \hline Drone 39 & 8:00-8:15 & 8:15-8:27 & 8:27-8:37 &... & 10:48-10:59 & 10:59-11:02 & 11:02-11:09 &... & 11:21-11:30 & 11:30-11:41 \\ \hline... &... &... &... &... &... &... &... &... &... &... \\ \hline Drone 50 & 8:00-8:05 & 8:05-8:17 & 8:17-8:22 &... & 11:11-11:19 & 11:19-11:29 & 11:29-11:35 & & & \\ \hline \end{tabular}
\end{table}
Table 6: Drone Dispatch Flight Schedule (BN).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Delivery Times** & **1 st** & **2 nd** & **3 rd** & **4 th** & **14 th** & **15 th** & **16 th** & **17 th** \\ \hline Drone 1 & 8:00-8:05 & 8:05-8:08 & 8:08-8:18 & 8:18-8:27 &... & 11:30-11:35 & & & \\ \hline... &... &... &... &... &... &... &... &... &... &... \\ \hline Drone 21 & 8:00-8:02 & 8:02-8:08 &It can be seen from Table 7 that the improved simulated annealing algorithm adopted in this paper can obtain a more ideal delivery cost and delivery time than the other four algorithms, showing the accuracy and applicability of the algorithm. As seen in Table 8, the BN route itself is superior to the RN route, illustrating the feasibility of the aerial network constructed through the buildings. The average value of the distribution cost by the RN and BN routes illustrates that a better solution can be obtained by the improved heuristic algorithm, and the solutions obtained by the improved simulated annealing are improved by 8.4% and 5.1%, respectively, compared to the results of the randomized test, which illustrates the effectiveness of the improved simulated annealing algorithm.
# Mapping Mountain Landforms and Its Dynamics: Study Cases in Tropical Environments
## 3 Procedures, Material and Methods
### Mapping Procedures  
In the mapping process, it is important to follow a careful methodology, otherwise time will be wasted retaking steps endlessly. We can divide the whole cartography process into three: pre-mapping, fieldwork, and post-mapping [46]. The first step is to choose  
Figure 1: Location map of the study sites: Cerro Chirripó in south-east Costa Rica in the Talamanca Range and RbAMB (Reserva Biologica Alberto Manuel Brenes) in the north-west of the country in the Central Volcanic Range.  
the study area, and, depending on its extension and the desired detail, we will have to choose a suitable scale. Moreover, depending on the characteristics of the area or individual preferences, we will have to choose a legend that represents properly the geomorphic genetic groups, and the erosional/depositional landforms.  
Ideally, the process of a cartographic map has to be accompanied by several fieldwork campaigns, before, during, and after the mapping process. As the access to some high mountain areas is complicated and expensive, the support of aerial imagery is essential. Preferably, geomorphological maps should be first completed as a draft, using satellite, aerial or even UAV imagery. The better the resolution of the images, the more quality the final map will have. There are available satellite imagery covers around the world. The most used satellite images for cartography in high mountain areas are those from the Landsat\(-\)8 (an American Earth observation satellite launched by USGS (United States Geological Survey) and NASA (National Aeronautics and Space Administration), Sentinel\(-\)1 and Sentinel\(-\)2 (launched by ESA--the European Space Agency--for the Copernicus Program). In addition, some countries have a national plan of aerial flights that offers high quality aerial images, usually provided by national agencies. During recent decades, radar, LiDAR, and UAV technology have growth in technology and accessibility, as well as cost, which significantly improves the imagery options. The second phase is fieldwork, which is composed of campaigns and is key to checking and corroborating the features drawn on the map. Later, in the office, previously detected features that can be corrected or that represent new ones that were discovered during the fieldwork, are reviewed. Finally, when the map is finished, another fieldwork campaign carries out the last checks, and makes corrections if necessary.