esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Development and Assessment of Predictive Models  
On the basis of exhaustive and selected covariate sets, two log-ratio transformations (i.e., ALR and ILR) were combined with random forest (RF) and Cubist, consisting of four models (i.e., ALR-RF, ILR-RF, ALR-Cubist, and ILR-Cubist) for soil PSFs prediction (Figure 2). RF is currently the most widely used machine learning approach in Digital Soil Mapping (DSM) [19; 24]. It is an ensemble learning algorithm that improves its accuracy and robustness by combining the predictions of a random population of regression trees [17; 21; 56]. At each split, it randomly selects predictor variables to grow trees, while on the tree level, it randomly selects samples from the training set [56]. Cubist has recently increased in popularity in DSM [18]. Cubist is a rule-based regression technique that builds multivariate linear regression models at the terminus of a tree [57]. At the terminal node of a given tree, the final model shows a collection of machine learning regression models for calculating predicted values [24]. RF and Cubist were implemented with randomForest [58] and Cubist [59] packages in the R environment. The prediction of transformed and back-transformed soil PSFs was assessed by the 10-fold cross-validation and evaluatedusing the coefficient of determination (\(R^{2}\)), Root Mean Square Error (_RMSE_), and bias. Bias was calculated as the observed mean minus the predicted mean. The others were calculated as follows:  
\[R^{2}=\frac{\sum_{i=1}^{n}(x_{i}^{\prime}-\overline{x_{i}})^{2}}{\left(x_{i}- \overline{x_{i}}\right)^{2}} \tag{7}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(x_{i}-x_{i}^{\prime}\right)^{2}} \tag{8}\]  
where \(x_{i}\) represents the observed PSF, \(x_{i}^{\prime}\) is the simulated PSF, \(\overline{x_{i}}\) is the mean of \(x_{i}\), and \(n\) represents the sample size. \(R^{2}\) and _RMSE_ are used to measure the data dispersion and reflect the difference between the predictions and the observations. Bias can be used to detect the over fitting of the predictive models.
## 4 Applications
### Wideband Bandpass Filters and Dual-band Components based on OSRRs and OCSRRs
**Figure 19.** Fabricated dual-band power divider and frequency response. (**a**) Top view. (**b**) Bottom view. (**c**) Power division (_S21_, _S31_) and matching (_S11_). The substrate is the _Rogers RO3010_ with thickness \(h=0.635\) mm and dielectric constant \(\varepsilon_{r}\)= 10.2. Dimensions are: \(l=9\) mm, \(W=4\) mm, \(G=0.74\) mm. For the open complementary split ring resonator: \(r_{ext}\)= 0.9 mm, \(c=0.2\) mm, \(d=0.2\) mm. For the open split ring resonators: \(r_{ext}\)= 1.5 mm, \(c=0.3\) mm, \(d=0.2\) mm. The wide metallic strip in the back substrate side has been added in order to enhance the shunt capacitance of the open complementary split ring resonator stage, as required to achieve the electrical characteristics of the device. The circuit parameters, referred to the circuits of Figure 13(c) and 14(c) are: \(C=0.2\) pF, \(L=0.25\) nH, \(C_{s}=0.66\) pF, \(L\)'\(s=3.74\) nH, \(C\)'\(p=2.99\) pF and \(L\)'\(p=0.83\) nH. From [43]; copyright (c) 2009, IEEE; reprinted with permission.
## 5 Optimal Sensor Placement and Validation Results Based on QR Pivoting
### Experimental Results of the Proposed Optimal Sensor Placement
In addition, the reconstruction error of the QR pivoting-based placement has an upward trend with the increase of 4-mode dimension, which supports the conclusion in Section 4.2. Next we will explore the effect of sensor increments on reconstruction errors in our experiments. When the number of basis vectors is taken to three, the reconstruction errors with different sensors are shown in Figure 29. It is obvious that when the sensors exceed three, the error decreases significantly, which supports the conclusion in Section 4.2, i.e., once the sensors exceed the number of basis vectors, the solution of the linear equation can be converted into an oversampling problem. Significantly, even if the number of anemometers randomly arranged reaches 15, the reconstruction error is still higher than that of 10 anemometers optimally arranged based on QR pivoting. Therefore the ability of optimal placement based on QR pivoting to effectively reduce the number of required sensors has been experimentally verified. In addition, to visualize the differences between the CFD results and the wind tunnel results (containing the reconstruction results and the measurements), we counted the measurements of ten verification points, the CFD results, the reconstruction results and the error of each point in Table 9. Notably the CFD calculation results are based on the inlet velocity profile fitted by 116 measurements in Section 3.2. The difference between the velocity values of each group is clear. Firstly, there is only one point in the QR-pivoting  
Figure 28: Reconstruction errors with different numbers of basis vectors. \begin{table}
\begin{tabular}{c c} \hline \hline
**Sensor Distribution** & **Relative Reconstruction Error (\%)** \\ \hline QR Pivoting & 5.04 \\
10 random sensors & 7.89 \\
15 random sensors & 6.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Relative reconstruction errors (%) for three sensor distributions. group with a relative error exceeding 10%, and the remaining errors are almost lower than 5% (the minimum error is only 0.74%). Secondly, the reconstruction error obtained by two groups of randomly arranged sensors is generally large and unstable, especially when ten sensors are employed. Finally, the average relative error between the CFD simulation values and the ten verification points is 9.35%, which seems to have no advantages over the reconstruction method. However, considering the measurement error of the sensor and the fitting error of the inlet wind speed, although the final calculation result is not as accurate as that of the reconstruction, the CFD calculation does not require the support of the wind field database, which saves the time of offline database construction and is more suitable for application without sufficient wind field data. In general, Table 9 strongly demonstrates that the proposed optimized reconstruction algorithm incorporating QR pivoting has a higher practical value than CFD in velocity field reconstruction with sufficient wind data.
## 3 Research Method and Procedures  
This study is divided into three stages: requirements assessment, software selection, and platform construction and development. First, according to the research background and motivation, the research topic and purpose are introduced, then, the relevant literature is discussed to define the research framework, object, and scope. Based on the needs of this research, the conservation of historical buildings and the application of the hybrid modeling technology and integrated information platform are discussed (Figure 2).  
Figure 1: Research trend of integrated hybrid modeling technology (sources: [9; 28; 29; 30; 31; 32; 33; 34]).  
This study integrated a variety of digital platform software tools, chose Game Technology as the development platform, used procedural modeling to build the scene platform of historical blocks, and explored the possibility of Information and Communication Technology (ICT) to promote the re-evolution of the digital process in the field of cultural heritage conservation. Old city blocks and historical buildings in Taiwan were taken as the exhibition objects. Considering that SketchUp is commonly used as a tool to build 3D historical models in the field of cultural heritage, ArcGIS and SketchUp were selected to build the main 2D and 3D information models. Taking Unity3D as the game engine development software, and aiming at information visualization, the interaction of historical buildings, the automatic update technology of the historical building model in the game engine, and the 3D historical building model and 3D-GIS historical block geographic information were integrated through the Unity platform and the third-party game engine, in order to optimize historical building environment information viewing (Figure 3). In order to expand the research on the application ability of cultural heritage display, real-time architectural visualization further creates the interactive game script of the exclusive old city model in the game engine environment through interactive programming.  
Figure 2: Integrated hybrid approach.  
Based on the literature review, procedural modeling was selected as the research method in this study. Procedural modeling is divided into two stages: information modeling and programming integration (Figure 4). In the procedural modeling stage, the most suitable research tools were selected for this study. GIS digitization and Sketchup modeling were fitted to the game engine to conduct information visualization and the interactivity of 3D historical building models, respectively, and after the prototype was completed, professionals were invited to conduct user evaluation. Finally, the research results were collated, and the user evaluation results were used as reference for conclusions and future research directions.
## Scenario 2 Additional Bus Needed  
_SDCF_: 3 (heavier-than-usual congestion).  
_RTCVCF_: 30 (30 passengers at the station).  
Bus Capacity (\(k\)): up to 50 passengers.  
Decision Output: 1 (an additional bus is added).  
The _SDCF_ reflects heavier congestion, indicating a need for increased transport capacity. The _RTCVCF_ of 30 suggests that a significant number of passengers waiting. An additional bus is scheduled to accommodate the demand, ensuring the passengers do not waiting too long.  
**Scenario 3. Skipping a Scheduled Bus**  
_SDCF_: 1 (light congestion).  
_RTCVCF_: 10 (10 passengers at the station).  
Bus Capacity (\(k\)): up to 50 passengers.  
Decision Output: \(-1\) (scheduled bus is skipped).  
In this case, the _SDCF_ indicates light congestion, and the _RTCVCF_ shows only 10 passengers. Since the number of passengers is far below the bus capacity, the decision is made to skip the scheduled bus to avoid running an empty service, thus optimizing operational costs.  
**Scenario 4. High Congestion, Few Passengers**  
_SDCF_: 4 (heavy congestion).  
_RTCVCF_: 20 (20 passengers at the station).  
Bus Capacity (\(k\)): up to 50 passengers.  
Decision Output: 0 (bus departs as scheduled).  
Despite the heavy congestion indicated by the _SDCF_, the number of passengers is still manageable. The bus can depart on schedule to prevent passengers from experiencing delays due to traffic conditions, even if it is not fully occupied.  
**Scenario 5. Very High Congestion, Full Capacity**  
_SDCF_: 5 (peak hours).  
_RTCVCF_: 50 (50 passengers at the station).  
Bus Capacity (\(k\)): up to 50 passengers.  
Decision Output: 1 (an additional bus is added).  
In this scenario, the congestion level and the number of waiting passengers are at their peaks. An additional bus is scheduled to address the overwhelming demand and ensure all passengers can board without significant delays.  
Each of these scenarios illustrates the dynamic decision-making capabilities of the PUTSS algorithm in real-time urban transportation management. By analyzing congestion levels (_SDCF_) and passenger counts (_RTCVCF_), the algorithm can adjust bus schedules, optimizing service for passengers while minimizing operational costs for the transport provider.  
Table 4 shows the programmed scheduled departure times of a bus, along with the applied scheduled changes by the _SDCF_ and _RTCVCF_. The sixth column of Table 4 identifies the actions taken regarding the bus service, such as the bus departing as scheduled or the bus being canceled (Figure 4).  
Table 4 outlines a bus schedule operating at 15 min intervals, detailing each departure's status regarding bus capacity and congestion factors.  
The proposed algorithm is a demonstrative model for potentially integrating AI techniques to optimize public urban transportation fleet allocation. Municipalities can implement and improve it regardless of their size and bus fleet so that public transportation systems become more adaptive and responsive to real-time needs, reduce costs, and enhance sustainability, contributing to a greener and more environmentally friendly future.
## 3 Results
### Distribution of Algorithm Types by Source  
Figure 8 presents the comparative use of algorithm types across MDPI, Elsevier, and Google Scholar. Elsevier showed a dominance of deep learning models, MDPI a balanced distribution, and Google Scholar a mix including unspecified methods.  
The distribution of algorithmic strategies across different publication sources offers valuable insights into editorial priorities, disciplinary trends, and regional practices. Figure 8 summarizes the breakdown of algorithm types by source--MDPI, Elsevier, and Google Scholar--based on the categories established earlier: traditional, shallow learning, deep learning, hybrid, and unspecified.  
A clear trend emerges from the data:  
Elsevier exhibits the highest concentration of deep learning techniques, including CNNs, LSTMs, and advanced neural network architectures. This reflects Elsevier's focus on high-impact engineering and control journals, which often prioritize performance-driven, simulation-validated studies with application to industrial environments.  
MDPI demonstrates a more balanced distribution of algorithm types. Many articles from MDPI integrated traditional statistical approaches (e.g., PCA, FDI, fuzzy logic) with emerging machine learning techniques. Additionally, this source featured a notable presence of hybrid models, which leverage both physical system knowledge and data-driven prediction.  
Google Scholar, as an aggregator of diverse publication types--including theses, conference papers, and preprints--had a higher proportion of unspecified or traditional methods. Several of these works focused on exploratory modeling or conceptual frameworks without rigorous algorithmic definition, often due to resource limitations or the preliminary nature of the research.  
Interestingly, while all three sources reported use of hybrid approaches, their proportion remained relatively low overall, highlighting a research gap in integrating physical knowledge and learning-based adaptability--a direction with high potential for practical deployment in power plant diagnostics.  
The comparative results indicate that source choice can influence not only publication format and audience but also the type of methods emphasized. These findings emphasize the importance of reviewing a broad range of sources to ensure methodological diversity and to capture both cutting-edge innovation and foundational principles in the field.
## 3 Results and Discussion  
To test the feasibility and practicality of this method, we conducted a series of experiments, including synthetic seismic records and real seismic records. First, we conducted synthetic experiments to verify the meanings of different parts of the loss function and the impact of coefficients of each part on the experimental results. Next, we carried out real experiments to validate the effectiveness of this method and compared it with traditional F-K filtering and curvelet filtering methods to demonstrate the superiority of the method.
## 8 Query/Data Protection Mechanism
### Energy Considerations  
A complete energy profile for the sending more must consider the processor, the radio, and the attached medical sensors. Of these components, the processor is used heavily during the initial ECC operations and in the encryption/MAC of outgoing packets. As discussed previously, the ECC key agreement consumes 7198 ms of CPU time on the more, while performing encryption/MAC on a single 100-byte packet consumes 44 ms of time.  
The radio and the sensors are used periodically to sample patient data and forward it to the base station. Based on the previous experiments, the time required for the radio to send a single a 100-byte packet is about 15 ms. In addition to the radio, the more's batteries would have to be used to power the attached biometric scanner and medical sensors. Because we have not yet integrated these sensors onto our motes, it is difficult for us to quantify their energy requirements. However, we note that the scanner is needed only once each time the key agreement protocol is run. Meanwhile, the medical sensors do not have to be continuously working; they might operate in a low-power sleep mode, being activated only when required to sample the patient's data.  
To get a rough estimate of energy consumption in a real-world deployment, we made the following assumptions: (1) the ECC key agreement protocol will be run once per 24 hours; and (2) the mote will send patient data at the rate of four 100-byte packets per second. The Tmote Sky draws 5.1 \(\mu\)A with the processor on standby and radio off, 1.8 mA with the processor alone on, and 19.5 mA with the processor on and the radio transmitting [23]. For the envisioned processor/radio usage, the mote's AA batteries (using a conservatively low estimate of 500 mAh) would be sufficient for about 14 days of operation. The majority (78.5%) of the power requirement comes from the radio, with 21.2% from CPU use during encryption/MAC and less than 1% from CPU use during the ECC key setup and idling.
# Wearable Flexible Antenna for UWB On-Body and Implant Communications
###### Abstract  
This paper describes the development and evaluation of an on-body flexible antenna designed for an in-body application, as well as on-body communications at ISM and UWB frequency bands. The evaluation is performed via electromagnetic simulations using the Dassault Simulia CST Studio Suite. A planar tissue layer model, as well as a human voxel model from the human abdominal area, are used to study the antenna characteristics next to human tissues. Power flow analysis is presented to understand the power flow on the body surface as well as within the tissues. Simulation results show that this wearable flexible antenna is suitable for in-body communications in the intestinal area, e.g., for capsule endoscopy, in the industrial, scientific, and medical (ISM) band and at lower ultra-wideband (UWB). At higher frequencies, the antenna is suitable for on-body communications as well as in-body communications with lower propagation depth requirements. Additionally, an antenna prototype has been prepared and the antenna performance is verified with several on-body measurements. The measurement results show a good match with the simulation results. The novelty of the proposed antenna is a compact size and the flexible substrate material, which makes it feasible and practical for several different medical diagnosis and monitoring applications.  
flexible antenna; capsule endoscope; implant communications; in-body propagation; ISM-band; medical monitoring; on-body propagation; UWB +
Footnote †: journal:  
Article
# A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks
## 4 Applications of RL in FANET
### Flight Trajectory Selection
#### 4.2.5 Trajectory Optimization for UBS  
[PERSON] et al. [55] optimized the trajectory of a UBS using \(Q\)-learning to maximize the sum-rate for multiple users. The authors considered a scenario in which a UBS agent is flying at a fixed altitude to serve multiple ground users. A cuboid obstacle was also considered in this scenario. The UBS selects the flying trajectory toward the users while avoiding the obstacles using both table-based and neural network (NN) based Q-learning. Finally, the authors compared the results of table-based and NN-based Q-learning approaches, where NN-based Q-learning is more efficient and scalable.  
A similar approach was taken in [53], where [PERSON] et al. used UBSs to provide emergency radio coverage in disaster areas. The main goal of the approach was to provide an efficient emergency network while maximizing coverage, sum-rate, and avoiding obstacles and interference.
## 1 Introduction
In the work of [PERSON] and [PERSON] (2010), the active compounds from green macroalga _Ulva fasciata_ were extracted using a blender, mortar and pestle and then the obtained extract was tested on fenugreek (_Trigonella fenum-gracum_ L.) as a seaweed liquid fertilizer [20]. [PERSON] et al. (2010) for the production of algal filtrate used green macroalgae: _Ulva fasciata_, _Ulva fericulata_, and _Chaetomorpha_ sp. They examined its effect, as a liquid seaweed fertilizer, on the growth and yield of rice plants [21]. In the patent of [PERSON] (1990) it was found that the combination of four operations: (1) cryocomminution; (2) molecular milling; (3) turbodecantation; and (4) selective ultrafiltration made it possible to obtain a \"physiological filtrate\", characterized by certain properties such as being directly assimilable (it contains micro-elements, vitamins, micropeptides) and containing no residual alcohols originating from the extraction steps [22]. Another interesting approach is to use algal homogenate and filtrate not only as a biostimulant of plant growth, but also for seed soaking and coating. Seed soaking is a process in which seeds are immersed in appropriate solutions for an extended period of time in order to absorb nutrients, protectants, growth regulators, etc. [23]. Soaking of seeds (for example for 18-24 h) prior to sowing has been used in arid areas of the world to give cereal crops a \"head start\" in germination [4]. This seed treatment enhances fresh and dry weight, leaf area, plant height, leaf development [24], as well as seedling vigor and chlorophyll content and reduces harmful seed microflora and increases the level of plant defense enzymes [4]. In the case of seed coating, finely ground solids or liquids containing dissolved or suspended solids are used to form a more or less continuous layer that cover the natural seed coat [24; 25]. Seed coating provides delivery of desired quantities of compounds, for example micro- and macronutrients, growth regulators. Coating materials can influence also the microenvironment during germination by holding water around the seed or providing a source of oxygen [24]. The aim of the present paper was to produce a filtrate and homogenate from Baltic green macroalgae, determine their physico-chemical properties, and assess phytotoxicity and utilitarian characteristics in seedling growth tests on radish seeds. Radish was chosen because it can reach maturity in a short time period (up to 2 weeks). The method of the application of algal filtrate (soil and foliar) was examined. Algal filtrate was also used for seed soaking. The homogenate was applied for seed coating. Additionally, the obtained bio-products were enriched with Zn(II) ions in order to examine their ability to complex metal ions. Zinc was chosen as an example of metal ions, because it plays a crucial role in plant growth and metabolism [26]. This issue has not been discussed in details in the available literature yet. Finally, we chose the optimal dose and a method of application of the tested products and recommend them for future research.