esa-sceva/satcom-chunk-collection huggingface dataset:
# Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities
## 3 The Compression Framework
### Reduction of the Primary Data
#### 3.1.3 Quantization of the Latent Space  
Merely obtaining a latent space is not sufficient to obtain good compression. Entropy encoding of the latent space--also known as quantization--is required to efficiently store the bottleneck layer coefficients. Quantization is therefore an important step in our current pipeline. We use PQ [36] to convert the floating-point latent space coefficients into integers. PQ is a vector quantization [37] technique that breaks a high dimensional vector into numerous lower dimensional vectors and quantizes them independently using a simple clustering algorithm such as K-means. This effectively creates a Cartesian product of smaller subspaces to represent the original vector space (hence the term 'product' quantizer). Although very simple, the advantage of this approach is that the number of vectors that can be represented accurately by the codebook can be quite large with respect to the size of the codebook. The size of the quantized data depends on the number of bits that a PQ dictionary uses to represent codewords in the codebook (PQ bits) and the dimension of input vectors. Assume we have an \(L\)-dimensional vector consisting of floating-point numbers from an AE encoder, and let \(B\) be the PQ bit encoding. Then, each floating-pointnumber can be represented by \(B\) bits, and therefore the size of the quantized vector is \(L\times B\) bits.
# Real-Time Remote Sensing of the _Lobesia botrana_ Moth Using a Wireless Acoustic Detection Sensor
## 3 Results
### Field Test  
A final field validation with the complete system was performed. The system was installed near a vineyard in Maria Pinto in the Casablanca Valley, Chile, 70 km to the west of Santiago, for two weeks in January 2017. This is a valley affected by _Lobesia botrana_. The unit had a pheromone trap close to the microphone, as shown in Figure 13. The system was connected to the internet through an access point. It registered 9 events (26 January: 7 counts; 28 January: 1 count; 31 January: 1 count). These results were congruent with the register from the field traps from the Servicio Agricola y Ganadero (Chilean Agricultural Service) [46].  
Figure 11: Power spectrum average of the detected signals and background noise obtained with a neural network analysis.  
Figure 12: Experimental setup diagram for the detection range characterization.
# Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach
###### Abstract  
Recognising the Bangla alphabet remains a significant challenge within the fields of computational linguistics and artificial intelligence, primarily due to the script's inherent structural complexity and wide variability in writing styles. The Bangla script is characterised by intricate ligatures, overlapping diacritics, and visually similar graphemes, all of which complicate automated recognition tasks. Despite ongoing advancements in deep learning (DL), machine learning (ML), and image processing (IP), accurately identifying Bangla characters continues to be a demanding and unresolved issue. A key limitation lies in the absence of robust detection frameworks capable of accommodating the script's complex visual patterns and nuances. To address this gap, we propose an enhanced object detection model based on the YOLOv11 architecture, incorporating a ResNet50 backbone for improved feature extraction. The YOLOv11 framework is particularly effective in capturing discriminative features from input images, enabling real-time detection with high precision. This is especially beneficial in overcoming challenges such as character overlap and stylistic diversity, which often hinder conventional recognition techniques. Our approach was evaluated on a custom dataset comprising 50 primary Bangla characters (including vowels and consonants) along with 10 numerical digits. The proposed model achieved a recognition confidence of 99.9%, markedly outperforming existing methods in terms of accuracy and robustness. This work underscores the potential of single-shot detection models for the recognition of complex scripts such as Bangla. Beyond its technical contributions, the model has practical implications in areas including the digitisation of historical documents, the development of educational tools, and the advancement of inclusive multilingual technologies. By effectively addressing the unique challenges posed by the Bangla script, this research contributes meaningfully to both computational linguistics and the preservation of linguistic heritage.  
Bangla alphabet; Bangla character; YOLOv11; deep learning; ResNet50 2025, 15, 6326
## 6 Discussion  
In this study, we reviewed various statistical distributions commonly employed for the interpretation of insulation BD strength data. We then devised a methodology for selecting statistical distributions based on aging temperature and time, enabling a reliable interpretation of insulation BD strength data. This approach entails the assessment of R\({}^{2}\) and MD values to evaluate the suitability of different statistical distributions. In existing research, only a single factor, such as the coefficient of relationship or coefficient of determination, is considered for the method of selecting statistical distributions. However, in this study, the coefficient of determination and MD were judged by combining two factors.  
The optimal statistical distribution for interpreting insulation breakdown data can vary depending on whether the data are collected before or after aging. This underscores the significance of considering aging conditions when analyzing the data. Accordingly, before degradation, the log-normal distribution was identified as the most suitable, with an R\({}^{2}\) value of 0.92. The Weibull distribution was determined to be the most suitable choice at degradation temperatures of 110 \({}^{\circ}\)C for 720 h or more and of 130 \({}^{\circ}\)C for 480 h or more. In order to review the reliability through degradation, it is necessary to review degradation conditions under various conditions. The degradation condition of this study was that the temperature was selected in consideration of the maximum allowable temperature of PP and thermal stress was applied for 960 h, but degradation may not be sufficient. For this reason, the breakdown strength tends to increase, even though thermal stress is applied in Figure 9.  
In this study, contrary to previous studies, we evaluated various statistical distributions using R\({}^{2}\) and MD values to interpret insulation BD strength data. Nevertheless, the results pertaining to the selection of an appropriate statistical distribution based on degradation temperature and time are specific to the materials and electrode structure studied. Different conditions may necessitate a distinct statistical evaluation. The analysis method of this paper can be generalized and applied to other materials. For example, XLPE, an insulator commonly used in power cables, will be described. Since the maximum allowable temperature of XLPE is 90 \({}^{\circ}\)C, the degradation of XLPE will proceed more rapidly under  
Figure 9: Weibull box chart of BD strength according to degradation conditions.  
the same degradation conditions as in this study, and the time for the Weibull distribution to be suitable can be expected to be faster than PP.
## 4 Results
### Reliability and Convergent Validity  
SmartPLS (version 3.3.3) analysis was used to assess the model's validity, reliability, and path coefficients. [PERSON] and [PERSON] (2016) and [PERSON] (2016) suggested composite reliability and [PERSON]'s alpha for reliability analysis. For model validity testing, convergent and discriminant validity were applied. The average variance extracted (AVE), created by [PERSON] and [PERSON] (2016), may be used to assess convergent validity by determining how correctly the latent model reflects the scale items ([PERSON], 2016). [PERSON]'s alpha > 0.7, rho_A > 0.7, composite reliability > 0.8, and AVE > 0.5 were found to fulfill all essential reliability and validity standards in the final structural model. The detailed findings are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Construct**} & \multicolumn{2}{c}{**Number of Items**} & \multicolumn{2}{c}{**[PERSON]’s Alpha**} & \multicolumn{2}{c}{**AVE**} & \multicolumn{2}{c}{**Composite Reliability**} \\ \cline{2-9}  & **Initial** & **Final** & **Initial** & **Final** & **Initial** & **Final** & **Initial** & **Final** \\  & **Model** & **Model** & **Model** & **Model** & **Model** & **Model** & **Model** & **Model** \\ \hline ANX & 8 & 7 & 0.859 & 0.868 & 0.511 & 0.561 & 0.891 & 0.899 \\ HAP & 7 & 6 & 0.884 & 0.856 & 0.594 & 0.639 & 0.910 & 0.913 \\ DEJ & 5 & 5 & 0.769 & 0.769 & 0.518 & 0.518 & 0.841 & 0.841 \\ ANG & 4 & 4 & 0.794 & 0.794 & 0.620 & 0.620 & 0.866 & 0.866 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Reliability and convergent validity results.  
Figure 2: A preliminary structural model considering external loads.
## 2 Methods and Data
### Experimental Design
#### 2.3.3 Experiment on the SM Dataset  
For the SM dataset, the soil moisture observations from the Agrometeorological Information Service of the Rural Development Administration (RDA) [34] were used to evaluate the predictive performance. After excluding some data observed along coastal lines and islands, 40 RDA observations were used as test data. Unlike the synthetic Landsat dataset, the error information of the input SM dataset was unknown. To explore the impact of input errors on the prediction results, the accuracy of the coarse resolution SM data was also computed using the RDA observations to assess the quality of the input data.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Case** & **LR** & **RF** & **SVR** \\ \hline C1 & Trend component only & C1\_LR & C1\_RF & C1\_SVR \\ C2 & ATPRK & C2\_LR & C2\_RF & C2\_SVR \\ C3 & ATPRK with trend normalization & - & C3\_RF & C3\_SVR \\ \hline \end{tabular}
\end{table}
Table 2: List of spatial downscaling result cases considered for three regression models (LR: linear regression; RF: random forest; SVR: support vector regression; ATPRK: area-to-point regression kriging).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Case** & **Description** \\ \hline E0 & Error-free standard input \\ E1 & Noisy input (noise standard deviation = 5) \\ E2 & Noisy input (noise standard deviation = 10) \\ E3 & Noisy input (noise standard deviation = 20) \\ \hline \end{tabular}
\end{table}
Table 3: List of different input cases used for spatial downscaling of the Landsat dataset.
## 6 Lab Experimental Validation
### Signal Analysis  
In order to alleviate the computational burden in VMD, we resample all the experimental signals at a new sampling frequency of 3200 Hz. The new effective frequency band (0-1600 Hz) covers the first five harmonics of the gear meshing frequency. The new resampled signal still contains the gear fault information, because gear fault information is mainly carried by the gear meshing vibration and its harmonics. According to the proposed criterion, we set the number of IMFs to be separated \(K=5\), in the following analyses.  
Figure 4: Planetary gearbox experimental system (1, drive motor; 2, encoder; 3, planetary gearbox; 4, accelerometer; 5, magnetic powder brake).  
Figure 5: Gear fault: (**a**) Sun gear localized fault; (**b**) Planet gear localized fault; and (**c**) Ring gear localized fault.
## 4 Performance Evaluations
### Experiment Results
#### 4.2.3 Performance Results According to the Ratio of Intra to Inter Latency  
In Section 4.2.3, we focus on experiments designed to study the performance effects associated with varying ratios of intra to inter latency. The primary difference between the proposed PBFT method and DLM-PBFT is whether location-based clustering is applied or not. Consequently, as the ratio of intra to inter latency increases, the proposed PBFT method is expected to outperform DLM-PBFT. Figure 7 presents the performance results for both the proposed PBFT method and DLM-PBFT, according to the ratio of inter latency to intra latency, where the total number of nodes (\(N_{total}\)) is 280. Experiments were conducted with intra to inter latency ratios set at 1:2, 1:4, and 1:8, respectively. In the experiments, the intra latency was set to constant at 0.03 s, while the inter latency was set to 0.063 s for a 1:2 ratio, 0.126 s for a 1:4 ratio, and 0.252 s for a 1:8 ratio.  
Numerous communications occur during the consensus process in a blockchain network that operates based on P2P communication without relying on third parties or central  
Figure 6: Results according to the maximum number of child groups of the proposed PBFT. (**a**) Total number of nodes (\(N_{total}\)) is 120. (**b**) Total number of nodes (\(N_{total}\)) is 160. (**c**) Total number of nodes (\(N_{total}\)) is 200. (**d**) Total number of nodes (\(N_{total}\)) is 240. (**e**) Total number of nodes (\(N_{total}\)) is 280. (**f**) Total number of nodes (\(N_{total}\)) is 300.  
institutions. Therefore, the latency incurred in each communication affects the overall consensus processing time. The experimental results depicted in Figure 6(a),b demonstrate that the overall consensus processing time also increases as inter-latency increases.  
However, when comparing scenarios with identical delay ratios in Figure 6(a),b, the proposed PBFT method outperformed DLM-PBFT in consensus message processing time. This means the proposed method's location-based clustering reduces the time each group needs for consensus, offering superior performance over DLM-PBFT. Furthermore, the proposed PBFT method consistently demonstrated stable performance despite continuous changes in the delay ratio. In contrast, DLM-PBFT exhibited performance degradation as the delay ratio increased.
## 1 Introduction
It can be seen that studies in relation to construction error evaluation and adjustment of both large-span structures and spatial pre-stress structures are underway and have made certain research achievements. Yet, there is still a lack of research regarding the construction errors analysis, pre-stress deviation adjustment, and evaluation of adjustment results for the flexible cable-bar tensile structure. Considering the fact that there was a lack of an efficient method to analyze and evaluate the construction errors of the cable-bar tensile structure, and especially no proper adjustment method was available to correct these errors, based on the previous basic work by the authors, the member length deviation was adopted as the variable to propose a method to correct the pre-stress deviations. Firstly, based on the equilibrium equation, physical equation, and geometric equation for pin-joint structures, a relationship between the pre-stress deviation and member length deviation was built. On this basis, a correction method was devised for the pre-stress deviation under various schemes, and an evaluation of the efficiency for pre-stress deviation correction was proposed by using measured data. Finally, a 5-m diameter cable-bar tensile structure model was designed and constructed for simulation, in which the pre-stress deviation of the measuring point was adjusted by adjusting the length of the outer diagonal cable in stages. Then, the theoretical values and the measured values of the measuring points during the adjustment process were compared and evaluated. On this basis, the adjustment effects of different adjustment schemes on the pre-stress deviation and shape deviation were further analyzed, compared, and evaluated. Thus, this paper proposes a theoretical foundation for the construction errors analysis, pre-stress deviation adjustment, and evaluation of adjustment results of actual pretension structure, and offers valuable insights into both theoretical research and actual engineering application.
# Reliability of Automated Intracranial Volume Measurements by Synthetic Brain MRI in Children
## 2 Materials and Methods
### SyMRI Volume Measurements  
The archived MDME sequences were sent to the hospital's syngo.via (version VB60A_HP06) server (Siemens Healthineers, Erlangen, Germany), which was equipped with the SyMRI software package version 11.2.11 (SyntheticMR AB, Linkoping, Sweden). All studies were automatically analyzed using this package. The absolute values for cerebrospinal fluid (CSF), brain parenchyma (BPV), and intracranial volume (ICV) were retrieved together with the respective data points' standard deviations (SDs). The data were transferred to a table and saved by the examiners. Figure 2 shows reference curves calculated by the SyMRI software.
## 2 Literature Review
### The Working Principles of Aerial Vehicle Guided Agriculture System  
The information flows from the aerial vehicle (drone) to the agriculture manager (Figure 1). The flow of information occurs with the help of the Internet of Things. The IoT enables precision farming, which involves using data to make informed decisions about crop management. This includes optimizing fertilizer application, irrigation, and pest control. From the Internet of Things device, the aerial vehicle captures the status of the agriculture system. The micron troller helps to send the status of the soils to the drone. Before the information flows, the aerial vehicle is made stable with the help of fuzzy PID controllers (optimal fuzzy PID controllers). A fuzzy logic controller uses the outputs of the aerial vehicle parameters as an input. Based on the provided input aerial vehicle parameters, the fuzzy logic controllers' statements (on/off) are then used as the input for the PID controllers.. This process is cyclically repeated.
## 2 Related Work and Technologies
### Weightless-N/NWave  
NWave technology uses advanced demodulation techniques to allow a network to coexist with other radio technologies without additional noise. This proprietary technology is particularly aimed at the smart parking sensor monitoring market, where it has found a considerable market niche [40].  
In [40], a comparative study of the three LoRa technologies, Xbee Pro (XBee868) and NWave, with LoRa technology appearing to have a slight advantage, is also presented. In the context of this study, specialized hardware was created to incorporate the different technologies and provide quantitative and qualitative scientific information related to data rates, success rates, modes of energy transmission and energy consumption and communication ranges.
# Bio-Inspired Dielectric Resonator Antenna for Wideband Sub-6 GHz Range
## 5 Conclusions  
In this work, the design of a bio-inspired Spiral shell DRA (SsDRA) implemented by means of Gielis' superformula was proposed. The antenna was thoroughly optimized by properly setting Gielis' superformula parameters considering two aspects: antenna performance and fabrication feasibility via Additive Manufacturing processes. In particular, the numerical analysis shows that optimized SsDRA exhibits very wide bandwidth behavior, up to 2 GHz, comprising two main frequencies: 3.5 GHz and 5.5 GHz. This characteristic made the proposed SsDRA feasible for applications in the sub-6 GHz range. Moreover, the calculated gain is satisfactory, as 3.7 dB can be found at 3.5 GHz, while a gain of 5 dB is calculated at 5.5 GHz. From the fabrication viewpoint, the evaluation of the optimized SsDRA design proved that an accurate AM process is required to realize the micro-features characterizing the spiral rounds of the antenna; in particular, as the minimum size exhibited by the design is in the order of few tens of microns, stereolithography (SLA) is the best candidate to fabricate the SsDRA prototype.  
Conceptualization, L.M. and M.G.; software, L.M., I.M., G.N. and M.G.; investigation, L.M., I.M., G.N., V.B., V.M., A.D. and M.G.; writing--original draft preparation, L.M., I.M., G.N., V.M., V.B., A.D. and M.G.; writing--review and editing, I.M., G.N., V.M., A.D. and M.G.; supervision and project administration, A.D. and M.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Legal Status of Mega-Constellations under International Space Law
### International Telecommunications Union in Regulating Mega-Constellations  
Mega-constellations also fall within the scope of the activities of the International Telecommunications Union (ITU) [20], since it manages space telecommunications through the equitable and rational distribution of terrestrial frequencies and specific applications for geostationary orbit. As one of the UN's specialized agencies responsible for issues that concern information and communication technologies, it coordinates the shared global use of the radio spectrum, promotes international cooperation in assigning satellite orbits, aims to improve the telecommunication infrastructure in the developing world, and assists in the development and coordination of worldwide technical standards. It also recognizes the sovereign right of each state to regulate its telecommunications. The ITU's regulatory framework aims to promote equitable access to these resources, which is particularly important given the rapid increase in the number of satellites being launched into low Earth orbits (LEOs) by companies like SpaceX, OneWeb, and Amazon [2,5], as well as the GuoWang (SatNet) mega-constellation [21,22]. The ITU has faced some legal and regulatory challenges, such as the following.
# AI, IoT and Remote Sensing in Precision Agriculture
## 2 An Overview of Published Articles  
This Special Issue brings together six cutting-edge research articles that explore innovative approaches to address critical challenges in precision agriculture, leveraging advanced technologies, such as the Internet of Things (IoT), machine learning (ML), and deep learning (DL). These studies collectively highlight the transformative potential of data-driven methodologies in enhancing agricultural productivity, sustainability, and decision making, offering solutions to some of the most pressing issues in modern farming.  
The articles cover a wide range of agricultural challenges, from crop yield prediction and phenology forecasting to aquaculture management and automated quality assessment. By employing advanced modeling techniques, remote sensing, and hybrid optimization methods, the research demonstrates how digital tools can revolutionize agricultural practices. These contributions emphasize the importance of interdisciplinary collaboration and technological innovation in building resilient and efficient systems capable of adapting to a rapidly changing world.  
Together, these studies showcase the diverse applications of IoT, ML, and DL in tackling agricultural challenges. By integrating cutting-edge technologies with domain-specific expertise, this Special Issue advances the development of more efficient, sustainable, and resilient agricultural systems, paving the way for future innovations in the field.  
While the contributions gathered in this Special Issue represent significant advancements in the field of precision agriculture, they also underscore the need for further discussion and research to fully grasp the complexities inherent in modeling agricultural systems. The integration of technologies such as IoT, machine learning, and deep learning has opened new avenues for innovation, ensuring their accessibility and addressing the multifaceted interactions between environmental, climatic, and socioeconomic factors. Nonetheless, challenges still remain in scaling these solutions. It is my hope that this Special Issue will serve as a catalyst for stimulating new research initiatives, fostering interdisciplinary collaboration, and promoting actionable strategies to address the pressing challenges of modern agriculture. By building on these foundations, we can move closer to developing sustainable, efficient, and resilient agricultural systems that meet the needs of a growing global population.