esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions
Devices such as the one presented here are a perfect fit towards this direction for the future, Internet-based agriculture, as it can provide farmers and agronomists with valuable remote monitoring of crop production and decision support, so critical decisions for crop growth and health are made on time. Our future plans include the in vivo evaluation of our prototype in the greenhouse, with various kinds of plants, to assure its performance in real conditions. The next milestone includes the generalization of this prototype to the two and the three dimensions, so it can cover larger surfaces and vertical farming, respectively, with almost the same robotic head, eliminating the need for one such device per row in a greenhouse. Conceptualization, Z.K., S.P. ([PERSON]), I.N.D. and S.P. ([PERSON]); methodology, Z.K. and S.P. ([PERSON]); software, Z.K. and S.P. ([PERSON]); validation, Z.K., S.P. ([PERSON]) and S.P. ([PERSON]); formal analysis, Z.K. ; investigation, Z.K., S.P. ([PERSON]), I.N.D. and D.M.P. ; resources, Z.K., S.P. ([PERSON]) and D.M.P. ; data curation, Z.K. ; writing--original draft preparation, Z.K. and S.P. ([PERSON]); writing--review and editing, Z.K., S.P. ([PERSON]), I.N.D., D.M.P. and S.P. ([PERSON]); visualization, Z.K. ; supervision, Z.K., I.N.D. and S.P. ([PERSON]); project administration, Z.K., I.N.D. and S.P. ([PERSON]); funding acquisition, Z.K., S.P. ([PERSON]), I.N.D. and S.P. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data used to support this study's findings are available from the corresponding authors upon logical request. This work was partially supported by Hellenic Mediterranean University internal funds in the framework of a two-year postdoctoral fellowship \"Multispectral image processing for the creation of a data collection and processing system with application in Precision Agriculture\", awarded to the first corresponding author. Also, the authors acknowledge partial financial support from the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH-CREATE-INNOVATE (project SOIIless culture UPgrade--SOUP code: T1 EDK-04171). The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Investigating the Key Aspects of a Smart City through Topic Modeling and Thematic Analysis
## 4 Discussion  
In this study, our objective was to delve into the various aspects of the smart city concept. To achieve this, we performed a topic modeling analysis across three distinct datasets. Some topics identified through the analyses can be interpreted as overarching themes, while others can be seen as subthemes of those. For instance, \"surveillance\" could be regarded as a subtheme of \"security\". Furthermore, there are topics that could be merged into a more comprehensive aspect. For instance, \"smart grid\", \"RES\", \"lighting\", and even  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Common** & **Abstracts** & **Blog Posts** & **Social Media Posts** \\ \hline Air pollution & Accessibility & Administration & Career \\ Bicycles & Ageing & Automation & Food \\ Buildings & Antennas & Banking & Green spaces \\ Carbon emissions & Artificial Intelligence & Business & Railroad \\ Climate & Authentication & Crime & Road safety \\ Electric vehicles & Autonomous driving & Disaster management & Shopping \\ Healthcare & Big data & Education & Sidewalks \\ Housing & Blockchain & E-goverance & Street art \\ Infrastructure & City architecture & Innovation & Suburban \\ IoT & Cyber-physical model & Investments & Urban planning \\ Noise pollution & Dashboard & Lighting & Urbanism \\ Parking space & Disaster management & Ports & \\ Public transportation & Edge computing & Railways & \\ Security & Education & RES & \\ Smart grid & E-goverance & Road safety & \\ Sustainability & Fiber-optical & Sanitation & \\ Traffic management & Indicators & Surveillance & \\ Waste management & Industry & Taxes & \\ Water management & Innovation & & \\ City & Intrusion detection & & \\  & Logistics & & \\  & Machine learning & \\  & Networks & & \\  & Participation & & \\  & Positioning & & \\  & Privacy & & \\  & Recommendations & & \\  & Semantics & & \\  & Sensors & & \\  & Social media & & \\  & Technology & & \\  & Tourism & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Common and identical topics in each dataset.  
\"electric vehicles\" or \"carbon emissions\" could be integrated into a broader aspect called \"energy management\" Similarly, \"electric vehicles\", along with \"traffic management\", \"parking space\", \"public transportation\", \"road safety\", and \"bicycles\" could collectively constitute a larger theme named \"mobility\".  
However, it is worth noting that not all themes express the same perspective. Upon examining the derived topics, we can discern three distinct perspectives within the smart city ecosystem: city applications, technology, and socio-economic considerations. For instance, \"water management\" represents a smart city application, \"edge computing\" pertains to a technological aspect, and \"innovation\" addresses a socio-economic perspective. Additionally, certain themes may hold significance across multiple perspectives. For instance, \"security\" is a theme that holds relevance within all perspectives.
# A Proof-of-Multiple-State Consensus Mechanism for Mobile Nodes in Internet of Vehicles
## 7 Conclusions  
This paper analyzes the current research landscape on blockchain technology in the Internet of Vehicles (IoV) and presents a blockchain solution that uses the PoMS consensus algorithm. This solution is designed to optimize the selection of relay nodes for message dissemination within the IoV. Using a machine learning model, we predict the upcoming driving states of mobile vehicles and, based on these predictions, calculate the state values of all nodes participating in the consensus. In addition to evaluating node performance, the algorithm takes into account vehicles' tuning tendencies to strategically select high-quality relay nodes to improve wireless communication between vehicles. We evaluate the theoretical performance of the proposed consensus algorithm by analyzing transmission reception rate, delay, throughput and security. Simulation results show that our consensus algorithm has significantly lower packet loss rates and consensus delays compared to similar current proposals for vehicular networks in high-speed scenarios, highlighting its effectiveness in rapidly changing and complex environments.  
Future research directions include improving vehicle node behavior evaluation by integrating more specific indicators into consensus selection processes that go beyond historical records, or using methods similar to those in [31] to improve vehicle speed prediction models with other factors to improve prediction stability and accuracy, while enhancing system safety to best adapt to driving conditions.  
Conceptualization, R.C. and G.L.; Data curation, R.C. and G.L.; Formal analysis, R.C. and Z.S.; Funding acquisition, F.Z., C.L. and C.Y.; Investigation, R.C., F.Z., Z.S., G.L. and C.Y.; Methodology, R.C.; Project administration, F.Z., C.L. and C.Y.; Resources, F.Z. and C.L.; Software, R.C.; Supervision, F.Z., C.L. and C.Y.; Validation, R.C., C.L. and C.Y.; Visualization, R.C.; Writing-original draft, R.C.; Writing-review & editing, R.C., C.L. and C.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by grants from the Guangxi Natural Science Foundation (2023 GXNSFAA026294) and the National Natural Science Foundation of China (62362013).  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Breast Cancer Detection in the Equivocal Mammograms by AMAN Method
## 3 Proposed Framework--AMAN
### Dataset Descriptions  
The authors collected all mammography examinations after receiving ethical approval from the Standing Committee for Research Ethics on Living Creatures (SCRELC) at Imam Abdulrahman bin Faisal University (IAU). These examinations are from King Fahad Hospital of the University (KFUH), affiliated with IAU in Khobar, Kingdom of Saudi Arabia. The dataset consists of 1802 craniocaudal and mediolateral oblique views of bilateral breast images: 474 benign, 284 malignant, and 1044 normal.  
In our contribution, the authors expanded our dataset by adding 114 files of reported findings detected on mammography by the hospital's radiologist; these reports follow the BI-RADS lexicon according to the American College of Radiology (ACR) classification. The reports include 44 benign, 65 malignant, and 5 normal examinations. Figures 5 and 6 show the number of mammograms and reports per class, respectively. Furthermore, no earlier research has used or published this data. Each patient has two different views of the right and left breast (MLO): mediolateral oblique and cranial caudal (CC). This resulted in four images for each patient. The authors gathered data from patients who had already had digital mammograms recently, considering the variety in terms of classes such as normal, benign, and malignant.  
Figure 4: View detection result sequence diagram.  
Figure 5: Mammograms per each class.  
In this paper, two approaches have been investigated to provide a solid decision to the radiologists at the most suspicious and complicated stage: BIRADS-4. The first approach applies deep learning models to mammogram images for further classification, while the second approach applies machine learning models to clinical reports to extract the relevant features, contributing to a more consistent interpretation of screening mammography findings. The experiments with both approaches will be explained in the following sub-sections.
# Research on Mechanical Performance of Porous Asphalt Mixture with High-Viscosity Modified Asphalt
## 2 Preparation and Performance Analysis of High-Viscoelastic Modified Asphalt
### Preparation Process of High-Viscosity Modifier  
The high-viscosity elastomeric modifier enhances asphalt performance through synergistic molecular interactions among its components. An interpenetrating network structure is formed between SBS and resin components, wherein the polystyrene blocks establish a three-dimensional elastic skeleton via \(\pi\)-\(\pi\) stacking interactions with aromatic rings, while the polybutadiene segments immobilize light asphalt fractions through physical entanglement, thereby improving elastic recovery properties [18; 19]. Antioxidants and stabilizers collaboratively mitigate aging degradation: radical scavengers inhibit oxidative chain reactions by neutralizing free radicals, whereas sulfur-containing stabilizers modulate asphalt micelle stability through coordination bonds with asphaltene aggregates [20; 21]. The co-solvent maintains dynamic equilibrium between polymer swelling and asphalt compatibility, critically governing the thermodynamic stability and processing characteristics of the modified system.  
This article prepared high-viscoelastic modifiers A and B by controlling the content of the two materials. The specific ratios of the high-viscosity modifiers are shown in Table 1. The preparation of these modifiers involves the following steps: mixing, melting, extruding, cooling, and shearing [22; 23; 24]. The'mixing' step involves high-speed shearing and high-temperature stirring of the various raw materials. The'melting' step involves melting themixed raw materials at a high temperature to achieve an optimal plasticized state. The 'extruding, cooling, and shearing' steps involve extruding the plasticized mixture through a die, cooling and shaping it, and finally shearing it into a suitable shape [25; 26; 27; 28; 29]. The method of preparing the high-viscoelastic modifiers is described below:  
1. Shear all raw materials at high speed for 10 min, then transfer them to a high-temperature mixer and mix for 10 min.
2. Transfer the mixed materials to a twin-screw extruder for extrusion.
3. Place the molded material in 5 \({}^{\circ}\)C pure water to cool for 20 min.
4. Place the cooled material in a shearing device to shear it into granular high-viscoelastic modifiers A and B.
## 4 Experimental Results
### Comparison Methods  
For a thorough and relevant comparison, benchmarks from [17], which specialize in lightweight object-detection algorithms on Rockchip platforms, are employed. This includes models like YOLOv5s, YOLOv6n, YOLOv-nano, NanoDet_m, YOLO-fastestv1.1, YOLO-fastestv2, and FastestDet. YOLO-fastestv2 and FastestDet are selected to avoid redundancy and ensure the inclusion of high-performing models. Moreover, the inclusion of the high-performance, high-parameter YOLOv8s model provides a comprehensive assessment of the method against a spectrum of detection capabilities.
## 3 Proposed FFA-BiGRU Method
### Multi-Level Spatial Feature Learning  
In reference [32], the channel-spatial attention-based feature fusion module, FFA, is proposed, and it has shown great effectiveness in image processing. In order to extract the spatial features from the log Mel-spectrogram of music audio, we utilized the architecture of the FFA module as a multi-level spatial feature learning module in our method.  
As shown in Figure 1, the spatial feature learning module first passes the log Mel-spectrogram of music audio into a convolutional layer containing 64 filters with a filter size of 3 \(\times\) 3 to perform shallow feature extraction. It then feeds the output into three group architecture blocks to extract multi-level deep spatial features. The output features from the three group architecture blocks are first concatenated in the channel direction and further fused together through channel-spatial attention. After that, the fused features are passed through two convolutional layers with a filter size of 3 \(\times\) 3 to further extract the spatial features. To avoid losing useful information about the original log Mel-spectrogram, a global skip connection is introduced between the original input and the final output of the spatial feature learning module.  
In the spatial feature learning module, group architecture is a key component for extracting multi-level deep spatial music features. In particular, each group architecture consists of 19 basic blocks with local residual learning, and each basic block is a convolutional residual subnet with channel-spatial attention. Therefore, in the following subsections, we will introduce channel-spatial attention, group architecture (including its basic blocks), and the spatial feature fusion strategy successively.
# Parameter Identification Methods for Hyperelastic and Hyper-Viscoelastic Models
###### Abstract  
In this paper, the Ogden model is employed to characterize the hyperelastic properties of rubber, and on the basis of a pattern search algorithm and the Levenberg-Marquardt algorithm, a professional method that can realize the comprehensive fitting of the uniaxial tension, biaxial tension, planar tension, and simple shear experimental data of hyperelastic materials was developed. The experiment data from [PERSON] (1944) was fitted very well, and the determined parameters by using this method were proven correct and practical in the numerical verification in ANSYS. Then, the constitutive model of the hyper-viscoelastic materials combining the Ogden model with the generalized Maxwell model was explained in detail, and the parameter identification method was also proposed by using the pattern search method. Then, three groups of relaxation tests of uniaxial tension and four groups of simple shear tests with different loading velocities were conducted to obtain the corresponding virtual experiment data. After discussing the constraints and initial setting values for the undetermined parameters, these virtual data of different loading histories were respectively employed to identify the parameters in the hyper-elastic model, and the accuracy and the reliability of the estimated parameters were also verified in ANSYS.  
parameter identification; Ogden model; pattern search algorithm; Levenberg-Marquardt algorithm; generalized Maxwell model; numerical verification Article
## 3 Results and Discussion
### ESI-MS of Uranyl Complexes with Glycine, Aspartic Acid, Cysteine, and Histidine
#### 3.3.1 Competitive CID and Reactivity of Cationic Uranyl Mixed Amino Acid Complexes  
Table 3 summarizes the results of competitive CID experiments in positive ion mode.  
Figure 7: CID mass spectrum of [UO\({}_{2}\)(Cl)\({}_{2}\)(Gly-H)]\({}^{-}\).  
All mixed complexes involving Gly showed mainly elimination of Gly, together with less important channels, as shown in Figures S38-S40 for [UO\({}_{2}\)(AA-H)(Gly)\({}_{2}\)]\({}^{+}\) and Figures S41-S43 for [UO\({}_{2}\)(AA-H)(AA)(Gly)]\({}^{+}\), where the indicated deprotonation is based on considerations discussed above. Again observed were loss of NH\({}_{3}\) in Cys-containing complexes and loss of CO\({}_{2}\) in a few cases. Interestingly, loss of AA was also detected for Asp and Cys in [UO\({}_{2}\)(AA-H)(AA)(Gly)]\({}^{+}\), with the presence of two AA ligands presumably statistically enhancing the probability of this channel.  
For the other amino acids, in alternative isomers [UO\({}_{2}\)(AA-H)(AA)(AA\({}^{\prime}\))]\({}^{+}\) or [UO\({}_{2}\)(AA\({}^{\prime}\)-H)(AA)\({}_{2}\)]\({}^{+}\), only AA or AA\({}^{\prime}\) losses were observed, as shown in Figures S44-S49. A clear pattern resulted, with Cys elimination from Asp/Cys and Cys/His complexes and Asp elimination from Asp/His complexes. Although this result for Asp/His casts doubt on the tentative assumption that the most acidic amino acid is deprotonated, it is possible that ligand rearrangement during CID, from Asp-H to Asp, favors Asp elimination.  
The reaction of [UO\({}_{2}\)(AA\({}^{\prime}\)-H)(AA)]\({}^{+}\) with background water was observed in most cases, with its fast nature precluding comparisons between amino acids. The overall general picture for the mixed complexes indicates preferred fragmentation involving loss of the amino acid with lower proton affinity: PA[Gly] \(<\) PA[Cys] \(<\) PA[Asp] \(<\) PA[His] (see Table S3) [53]. This suggests that PA is a good indicator of the strength of interactions of uranyl cations with neutral amino acids in gas-phase complexes.
# Controlling Conditions of the One-Dimensional Consolidation Test on Peat Soil
## 6 Effect of Specim Height  
The specimen height had a noticeable influence on the peat soil consolidation. The standard presently stipulates one-dimensional consolidation sample dimensions of 61.8 mm in diameter and 20 mm in height. Consolidation tests were conducted according to JTG 3430-2020 [32] on peat samples with diameters of 61.8 mm and heights of 20, 25, 30, 35, and 40 mm. The strain of the peat soil samples with different heights were compared (Figure 7), and the optimum sample height was explored.  
Figure 5: Percentage change in LOI and TN content. (**a**) Percentage decrease in LOI (**b**) Percentage increase in TN content. (Note: Method 1 is spraying thymol solution, method 2 is immersing in thymol solution).  
Figure 6: Post-test samples.  
Figure 7: Strain of specimens at different heights. **(a)** 12.5 kPa, **(b)** 25 kPa, **(c)** 50 kPa, **(d)** 100 kPa, **(e)** 200 kPa, **(f)** 400 kPa, **(g)** 800 kPa.  
The influence of sample height on the consolidation strain of mucky soil and peat was significantly different at the same load level, as shown in Figure 7. The strain of the mucky soil sample was the highest when the sample height was 20 mm, which decreased gradually when the sample height increased, which findings for Tianjin blow-fill soil [46]. The strain of peat soil first increases and then decreases when the sample height increases, and the strain is the largest when the height is 30 mm. When the load is greater than 100 kPa, the strain of the peaty soil (10% \(\leq Wu\leq\) 60%) samples with heights from 20 mm to 40 mm is the same, but the strain of peat (\(Wu\geq\) 60%) at the height of 30 mm is still the largest. It can be concluded that the strain of the peat soil with a height of 30 mm is greater than or equal to that of peat soil of other heights. Therefore, for peat soils, specimens that are 30 mm in height should be used for consolidation tests.
## 3 Methodology
### MDDA Augmented GNSS/INS Integration  
In a GNSS/INS fusion, IMU usually provides a higher data rate than the GNSS receivers. In our case, the GNSS receiver sampling rate was 1 Hz, with 100 Hz for the IMU. In this study, two integration models were employed to combine the RGNSSM with data from smartphone inertial sensors, and these models were further enhanced by the proposed MDDA.  
The architecture for LC integration, incorporating the MDDA module, is visually depicted in Figure 3. In this LC configuration, the module was positioned before the GNSS Kalman Filter (KF) to select the most suitable subset of satellite measurement. This subset is then used to compute the GNSS receiver's position and velocity.  
Similarly, in Figure 4 for TC integration, the module is placed after the tracking loop. It performs the task of selecting the optimal subset, which includes pseudorange and pseudorange rate data. The subsequent procedural step descriptions will elucidate these processes in greater detail.
## 1 Introduction
The communication range was up to 200 km in deep water. Low-density parity-check (LDPC) coded OFDM synthetic aperture communication was demonstrated in shallow water [19]. Combining multiple transmissions achieves an error-free reception, confirming the feasibility of coherent synthetic communications using OFDM. The source was towed at a speed of three knots at ranges between 600 m and 6 km while a vertical received array was moored in water 106 m deep [19]. A source diversity was considered for phase-coherent UWA communications for UUV with a single receiver element using distributed nodes of underwater acoustic networks [14]. An experiment on passive synthetic aperture time reversal communications was demonstrated via a shallow sea trial [20]. Motion compensation using all-phase fast Fourier transform (FFT) was presented for synthetic aperture spread spectrum underwater acoustic communication [21]. The contributions of this paper include the following: (1) we proposed a SC-FDE approach for synthetic aperture UWA communications; (2) we analyzed the performances of two receiver algorithms, FDE and hybrid time frequency domain equalizer (HTFDE) using data collected from a lake experiment; and (3) we analyzed and compared the influence of receiver element number and transmission number on the performance of the SC-FDE based synthetic aperture communication method. The rest of this paper is organized as follows. The system model is introduced in Section 1. Section 2 reviews two receiver algorithms of SC-FDE, including FDE and HTFDE. We then present experiment results in Section 3. Finally, we conclude the study in Section 4.
# Radiation-Balanced Lasers: History, Status, Potential
## 4 Development of Radiation-Balanced Lasers
### Athermal Fibre Lasers
#### 4.3.1 Athermal ZBLAN Fibre Lasers
(**b**) Similar graph for the athermal laser operation pumped at 1030 nm. In (**a,b**) on the left, the background absorption is \(4.278\times 10^{-2}\) m\({}^{-1}\). In (**a,b**) on the right, a 10-fold reduction is shown in the background absorption [43; 44]. Figure 15: Cross section of the multimode Yb-doped ZBLAN fibres used in [45]: (**a**) conventional multimode fibre, (**b**) double-D cladding-doped fibre, and (**c**) cladding-doped fibre with an octagonal inner cladding [45]. resulted in only a small fraction of the ions in the cladding being excited and contributing to the refrigeration process. An octagonal inner cladding of the fibre (Figure 15c) permitted to increase mode mixing and excite the cladding more uniformly. As a result, greater cooling was observed with this fibre.