esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Image Acquisition System  
The image acquisition system was composed of a high-resolution monocular Nikon D300s camera (Nikon Inc., Melville, NY, USA) with Nikon AF-S DX Nikkor 18-55-mm 1:3.5-5.6 G lenses and a focal length of 20 mm. This camera offers many possibilities for remote control and external image triggering. In this paper, the aperture of the camera was set to F11 with ISO200; the exposition time was set to 1/250 s; and no exposure compensation was applied. The images acquired were saved as RGB jpeg images with \(4288\times 2848\) pixels, 24-bit color depth and a file size smaller than 6 MB. The artificial illumination was generated mainly with a ring flash mounted over the camera with the energy released configured to 10 Ws. This ring illuminator provides a bright and uniform illumination in the complete area of the images acquired. The complete illumination system was pointed 45\({}^{\circ}\) to the ceiling and mounted in an auxiliary utility vehicle in order to explore the vineyard and obtain high-resolution and high-quality images of high-quality red-table grapes during the night.  
The utility vehicle was driven by an expert human operator at a constant speed and at an approximate distance of 2 m from the vines of both sides. The vineyard images were sampled at a fixed interval assuming no overlap between consecutive images. Under such conditions, the average diameter of a mature individual red grape is 109 pixels and the average area of a cluster of grapes is 250,000 pixels. Figure 1 shows a typical vineyard image. The hypothesis is that the use of high-resolution and high-quality images in controlled illumination conditions during the night will simplify the detection of individual clusters of grapes, simplifying the estimation of the areas of the clusters and improving the vineyard yield estimate.  
The vineyard images analyzed in this paper were obtained during the harvesting period. The real-time analysis of the acquired images and the synchronization between the trigger applied to the camera and the displacement of the utility vehicle will be addressed in future works.  
Figure 1: Example vineyard image: \(4288\times 2844\) pixels.
## 3 Artificial Intelligence Approach
### Supervised ML
#### 3.1.7 K-Nearest Neighbor (KNN)  
K-nearest neighbor is a straightforward yet effective algorithm for classification and regression analysis. It simply classifies unknown food items by matching their spectral signature with the known ones. K-nearest neighbors assume that similar food items are located around one another in a feature space. It is not surprising then that the technique has been found quite useful in a variety of HSI-based food quality assessments [125]. KNN has been successful in finding applications in foodborne pathogen detection, classifying moldy tea leaves, and identifying adulteration in meats with the help of hyperspectraldata due to its high accuracy in non-invasively identifying bacterial strains [126]. [PERSON] et al. (2019) classified mold contamination levels in dried tea leaves using KNN with optimal wavelengths and roughness penalty smoothing methods [127]. KNN has also been applied to meat authenticity through the determination of adulteration in cooked mutton kebabs, which helped in distinguishing the mutton from the skim alternatives like chicken or pork [128]. While a method such as KNN may not give superior results to more complicated models, its simplicity and performance are good enough to make it a practical option when a rapid food quality assessment is needed.
# Tropical Cyclone Winds from WindSat, AMSR2, and SMAP: Comparison with the HWRF Model
## 1 Introduction
This study's companion paper [4] performed a case study that computed intensity (maximum wind speed) and radii of several selected TCs using the TC-winds and compared them with the storm parameters derived from operational storm forecasts and analyses. Our study aims to build on the results presented in [4] by comparing the TC-winds with surface wind fields from the Hurricane Weather Research and Forecasting (HWRF) model, a high-resolution operational storm forecast model that is routinely tested, evaluated, and upgraded [21; 22; 23; 24; 25]. The HWRF model provides an external data source to compare to the satellites for each tropical cyclone of interest. However, before direct comparisons between the TC-winds and HWRF can be performed, two preliminary steps are necessary: (1) the high resolution HWRF model must be resampled to a coarser resolution similar to that of the satellites so that the features resolved by HWRF are similar to those of the satellite radiometers, and (2) the HWRF winds at the model output times must be temporally interpolated to the time of the satellite overpass. Both of these steps are integral parts of the study and are discussed in greater detail later in this paper. The paper is organized as follows: Section 2 describes the data sets used in this study and provides a brief additional assessment of the performance of TC-winds presented in [4]. Section 3 describes the methodology for resampling the HWRF model to the same resolution as the satellites. This section also discusses temporal interpolation and how to address pitfalls associated with it. Section 4 presents the results of the comparison after the HWRF data were appropriately resampled and interpolated. Differences in the satellite-model comparisons between different ocean basins and the impact of rain are discussed. Section 5 concludes and summarizes the work.
## 2 State of the Art
### A Range of Communications Solutions
It remains, however, difficult to cater for a full range of application requirements using a single defined stack. As well as this, maintaining a single stack philosophy may inhibit new protocol from being developed. The communications stack must remain malleable to change allowing layers to be swapped for competing protocol. Early attempts to achieve this [17] were not successful as competing MAC and routing protocol could not be compared effectively, leading to a position where the preferred protocol was not known. To this end, we believe that developing a standard for the evaluation process for protocol relating to smart cities is a crucial element required to accommodate new innovation while maintaining interoperability.
## 2 Multi-Fidelity Data Learning Methods  
The key starting point of an MF data learning method is to find the relationship between MF data. The learning methods for MF data mainly include the following two directions [6]: (1) the hierarchical way is used to organize HF and LF model information, which is called the multi-fidelity hierarchical model (MFHM) [78; 79; 80]. (2) The HF and LF model information is fused through surrogate model construction, which is called the multi-fidelity surrogate model (MFSM) [7]. This article focuses on MFSM. Surrogate models, also known as meta models, response surface models, or approximation models, are simplifications of the true model. The advantage of using surrogate models is that the physical meaning between model input values and response values can be ignored, and one can directly focus on the data themselves for mathematical modeling [81].  
MFSM can be divided into deterministic methods (DM) and non-deterministic methods (NDM) according to the estimation methods for parameters of MFSM [6]. A DM finds their parameters by minimizing the difference between data and functions [82; 83]. An NDM assumes that the function or parameter is uncertain and uses the sample to reduce the uncertainty [84]. The most popular in NDM is the Bayesian framework [9; 10; 85], where the posterior distribution of the model parameters depends on the likelihood distribution and prior distribution of unknown parameters. A common implementation method of the Bayesian framework is Gaussian process (GP) [86; 87; 88]. In the validation part, the NDM of GP is used. The reason for employing GP lies in its established track record of effectively handling multi-fidelity data and delivering favorable outcomes [89; 90; 91]. Statistics indicate that among the numerous methods used for constructing surrogate models, 37% utilize GP [92].  
In this paper, we utilize GP for subsequent experimental validation. The chosen task type is regression, for which we deliberately introduced various types of noise into a small and low-dimensional dataset. In the case of MF datasets with diverse noise types, traditional linear regression struggles to handle them effectively [93]. GP, being an NDM, provides estimates of uncertainty in predictions, which proves highly valuable when dealing with data containing diverse types of noise [94]. On the other hand, references [95; 96] found that NDMs tend to be more accurate compared to DMs. Regarding MFSM, whether a DM or NDM, the correction approaches for MFSM can be classified into three major categories: additive correction, multiplicative correction, and comprehensive correction. We describe each of these methodologies in the following sections.
# Prediction of Short-Shot Defects in Injection Molding by Transfer Learning
## 4 Discussion  
This study utilized transfer learning to perform secondary training on pre-trained neural network models using a limited amount of data, resulting in new neural network prediction models. The use of transfer learning significantly improved prediction accuracy compared to models that did not employ transfer learning. The original LT60 and LT100 models had validation accuracies of 88.5% and 91%, and overfitting occurred during training. However, the LT60 and LT100 transfer models with limited data achieved validation accuracies of 90.2% and 94.4%, respectively. This approach not only reduces the time and material costs of acquiring training data, but also enhances the predictive accuracy of the products. The results of this experiment demonstrate the potential of transfer learning to reduce model training costs in the field of injection molding. By leveraging pre-trained neural network models, effective secondary training can be conducted with limited data, leading to significant savings in time and resources while substantially enhancing the predictive performance of the model. In addition, through the integrated application of the model with an injection molding machine, personnel can instantly determine whether short-shots have occurred in the product as soon as the filling process is completed. There is no need to wait until the product has cooled down to perform the inspection, allowing the defect troubleshooting process to be initiated earlier, thereby enhancing overall production efficiency.  
In the future, further explorations can be done to investigate the effectiveness of transferring the model trained for predicting short-shots to predicting models for other types of product defects, expanding the application scope of transfer learning.  
Conceptualization, Z.-W.Z., H.-Y.Y. and Y.-H.T.; methodology, Z.-W.Z., H.-Y.Y. and B.-X.X.; software, Z.-W.Z., H.-Y.Y. and B.-X.X.; validation, B.-X.X., Y.-H.T., S.-C.C. and W.-R.J.; investigation, Y.-H.T., S.-C.C. and W.-R.J.; data curation, Z.-W.Z. and H.-Y.Y.; writing--original draft preparation, H.-Y.Y.; writing--review and editing, Z.-W.Z.; visualization, Z.-W.Z., H.-Y.Y. and B.-X.X.; supervision, Y.-H.T., S.-C.C. and W.-R.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data are not publicly available due to subsequent research needs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Number of Training Data** & **Number of Validation Data** & **Validation Set Accuracy** \\ \hline LT 60 without transfer learning & 1100 & 122 & 0.885 \\ LT 60 with transfer learning & 1100 & 122 & 0.902 \\ LT 100 without transfer learning & 576 & 144 & 0.910 \\ LT 100 with transfer learning & 576 & 144 & 0.944 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the models’ performance.  
Figure 20: Real-time short-shot prediction system operation process.
## 4 Performance and Discussion  
### Optical Bandwidth  
To confirm the optical bandwidth of the DBOSW modulator with the above geometric parameters, the relationships between Re(\(N_{\rm eff}\)) and Re(\(\Delta N_{\rm eff}\)) of the two modes as a function of the incident wavelength are investigated when \(\mu_{\rm c}\) ranges from 0.2 to 0.6 eV. As shown in Figure 12a, Re(\(N_{\rm eff}\)) decreases with the increment in wavelength. When \(\lambda\) = 1.1 \(\upmu\)m and \(\mu_{\rm c}\) = 0.6 eV, the maximum Re(\(N_{\rm eff}\)) of the TM mode and the complex mode is 1.7135 and 1.7163, respectively. When \(\lambda\) = 1.1 \(\upmu\)m and \(\mu_{\rm c}\) = 0.2 eV, Re(\(N_{\rm eff}\)) of the TM mode and complex mode is 1.7096 and 1.7127, respectively. When \(\lambda\) = 1.9 \(\upmu\)m and \(\mu_{\rm c}\) = 0.6 eV, Re(\(N_{\rm eff}\)) of the TM mode and complex mode is 1.6438 and 1.6427, respectively. When \(\lambda\) = 1.9 \(\upmu\)m and \(\mu_{\rm c}\) = 0.2 eV, Re(\(N_{\rm eff}\)) of the TM mode and complex mode is 1.6586 and 1.6574, respectively. It can be observed that a very low Re(\(\Delta N_{\rm eff}\)) of around 0.001 can be obtained within a wavelength range from 1.1 to 1.9 \(\upmu\)m.  
In Figure 12b, when the wavelength varies from 1.1 to 1.9 \(\upmu\)m, Im(\(N_{\rm eff}\)) of the TM mode quickly increases from 0.01207 to 0.02164 at \(\mu_{\rm c}\) = 0.2 eV; meanwhile, Im(\(N_{\rm eff}\)) of the complex mode increases from 0.01160 to 0.02177 at \(\mu_{\rm c}\) = 0.2 eV, which mainly results from the enhancement of the graphene-light interaction due to the increasing dielectric constant with the increment in optical wavelength. To be mentioned, the MPA change of both two modes is very limited within the wavelength range. The MDs of the TM mode are 0.40918 dB/\(\upmu\)m at \(\lambda\) = 1.1 \(\upmu\)m and 0.46931 dB/\(\upmu\)m at \(\lambda\) = 1.9 \(\upmu\)m, respectively. While, the MDs of the complex mode are 0.38416 dB/\(\upmu\)m at \(\lambda\) = 1.1 \(\upmu\)m and 0.46415 dB/\(\upmu\)m at \(\lambda\) = 1.9 \(\upmu\)m, respectively. Therefore, the proposed modulator shows good polarization-independence characteristics over a broad optical bandwidth.  
Figure 11: Re(\(N_{\rm eff}\)) and Re(\(\Delta N_{\rm eff}\)). Im(\(N_{\rm eff}\)) and PL of the TM mode and complex mode versus Femi level (chemical potential \(\mu_{\rm c}\)) from 0 to 1 eV are shown in (**a**) and (**b**), respectively.
# BFE-Net: Bidirectional Multi-Scale Feature Enhancement for Small Object Detection
## 4 Experiments
### Main Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Backbone** & **AP** & \(\text{AP}_{\text{50}}\) & \(\text{AP}_{\text{75}}\) & \(\text{AP}_{\text{S}}\) & \(\text{AP}_{\text{M}}\) & \(\text{AP}_{\text{L}}\) \\ \hline Two-stage methods & & & & & & & & \\ DeNet [31] & ResNet-101 & 33.8 & 53.4 & 36.1 & 12.3 & 36.1 & 50.8 \\ CoupleNet [26] & ResNet-101 & 34.4 & 54.8 & 37.2 & 13.4 & 38.1 & 50.8 \\ Faster R-CNN by G-RMI [32] & Inception-ResNet-v2 & 34.7 & 55.5 & 36.7 & 13.5 & 38.1 & 52.0 \\ Faster R-CNN +++ [19] & ResNet-101 & 34.9 & 55.7 & 37.4 & 15.6 & 38.7 & 50.9 \\ Faster R-CNN w FPN [16] & ResNet-101 & 36.2 & 59.1 & 39.0 & 18.2 & 39.0 & 48.2 \\ Mask R-CNN [5] & ResNet-101 & 38.2 & 60.3 & 41.7 & 20.1 & 41.1 & 50.2 \\ Cascade R-CNN [24] & ResNet-101 & 42.8 & 62.1 & 46.3 & 23.7 & 45.5 & 55.2 \\ \hline One-stage methods & & & & & & & \\ YOLOv2 [8] & DarkNet-19 & 21.6 & 44.0 & 19.2 & 5.0 & 22.4 & 35.5 \\ SSD513 [6] & ResNet-101 & 31.2 & 50.4 & 33.3 & 10.2 & 34.5 & 49.8 \\ YOLOv3 [9] & DarkNet-53 & 33.0 & 57.9 & 34.4 & 18.3 & 35.4 & 51.1 \\ DSSD513 [13] & ResNet-101 & 33.2 & 53.3 & 35.2 & 13.0 & 35.4 & 51.1 \\ RetinaNet [11] & ResNet-50 & 32.5 & 50.9 & 34.8 & 13.9 & 35.8 & 46.7 \\ EfficientDet-D0 [12] & EfficientNet & 34.6 & 53.0 & 37.1 & - & - & - \\ RetinaNet [11] & ResNet-101 & 34.4 & 53.1 & 36.8 & 14.7 & 38.5 & 49.1 \\ RefineDet512 [15] & ResNet-101 & 36.4 & 57.5 & 39.5 & 16.6 & 39.9 & 51.4 \\ \hline Ours & & & & & & & \\ BFE-Net & ResNet-50 & 34.6 & 52.9 & 37.2 & 16.7 & 37.3 & 47.8 \\ BFE-Net & ResNet-101 & 36.3 & 54.4 & 38.5 & 17.9 & 39.6 & 50.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: BFE-Net vs. other two-stage and one-stage detectors on COCO test-dev.
## 3 IoT-JFML to FFH Proposal
#### IOT-JFML Architecture to FFH  
The IoT module combined with JFML library provides an IoT infrastructure to generate intelligent IoT solutions under the IEEE std 1855-2016. These advantages were applied in the previous FFH proposal. Figure 2 illustrates a description of the proposed system.  
The architecture includes JFML instance, FML file, sensors, actuators, and the broker to act as a conductor between all the elements of the system. The expert knowledge according  
Figure 1: Distribution of the sensors in the construction site.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor** & **Information** & **Range** \\ \hline BLE beacon (\(B_{i}\)) & RSSI & 0 to \(-\)94 dB \\ \hline BLE Receiver (\(R\)) & Distances & 0 to 6 m \\ \hline Altimeter (\(Alt\)) & Altitude & 0 to 200 m \\ \hline Anemometer (\(Anm\)) & Wind velocity & 0 to 150 km/h \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sensors description [65].  
to the IEEE std 1855-2016 for FFH is represented by the FML file. The sensors and actuators supply information that pass through the MQTT broker which are used for the JFML to make the inference based on the expert knowledge represented in the FML file.  
The MQTT broker is a Mosquito instance [55] due to it versatility and easy integration with the elements; the JFML library runs on an external computer or in the cloud. The broker and wireless capabilities allow communication between the elements. A common procedure of the communication process is described as follows:  
1. Sensors provide data, which publish them into \"input\" topics. Then, the sensors should be related with their input variables. For instance, the sensor \(Anm\) publishes data into the topic _\"input/\(Anm\)\"_. Similarly, the sensor \(Alt\) publishes data into the topic _\"input/\(Alt\)\"_, etc.
2. JFML is subscribed to all input topics to receive input data from the sensors and to assign them to the input variables. These input variables are defined in the FLS (represented in the FML file according to the IEEE std 1855-2016). For example, JFML is subscribed to the topics _\"input/\(Alt\)\"_, _\"input/\(Anm\)\"_, etc., to receive data from the sensors \(Alt\) and \(Anm\), respectively. These sensors are associated with the input variables _Altitude_ and _Wind velocity_, respectively.
3. The inference is carried out once all of the sensors have published their information and JFML has assigned these values to the input variables. Rules are activated according to the input values and the rule base defined in the FML file.
4. Once the inference process is finished, the output variables obtain values from the corresponding defuzzification method. Then, JFML publishes these values to \"output\" topics.
5. Actuators receive data, so they are subscribed to \"output\" topics. As a result, they must be associated with output variables.
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 6 Privacy-Preserving Architectures for Scalable IoT-Based Healthcare Systems
### Edge Computing for Real-Time Privacy Preservation  
The need to transmit raw data to remote servers is eliminated, as sensitive data computations can be conducted immediately on IoT devices or local gateways, benefiting from edge computing. Such computations can reduce privacy risks during data transmission and minimize data exposure. However, challenges such as optimizing computation-intensive tasks on resource-constrained devices and ensuring data integrity [17] require addressing.  
Figure 6: Privacy-preserving architectures for scalable IoT-based healthcare systems.  
The following section explains how edge computing can be leveraged in maintaining the confidentiality and efficiency of data handling while protecting patient privacy.  
* Advancing Privacy Preservation Through Edge Computing: Edge computing preserves patient data confidentiality by processing data closer to their source, aligning with the need for Data Protection Mechanisms [44].
* Mitigating Latency and Real-Time Analysis: By addressing real-time requirements, edge computing reduces the latency in data analysis crucial for healthcare decision-making [45].
* Addressing Resource Constraints in Edge Devices: Innovative approaches have optimized algorithms on resource-constrained edge devices, maintaining privacy without compromising performance.
* Ensuring Data Consistency and Security in Edge Environments: Strategies for ensuring data integrity and security in edge computing are connected with challenges discussed regarding Privacy-Preserving IoT Data Analytics [45].
* Decentralized Data Ownership and Control: Edge computing empowers individuals and healthcare institutions by processing data locally, aligning with the need for user-controlled privacy settings.
* Collaborative Edge Learning for Privacy-Preserving Analysis: Delving into collaborative edge learning, this approach bridges collaborative techniques and the localized processing capabilities of edge devices [46].
# Orderly Charging and Discharging Group Scheduling Strategy for Electric Vehicles
## 1 Introduction
Based on the proposed clustering strategy, a two-layer model for the real-time optimal scheduling of EV clusters based on the V2G mode is established for EVs, with the upper-layer model soliciting the charging and discharging power of EV clusters; in the lower-layer model, the charging and discharging plan of a single EV is optimized with the objective of minimizing the cost to the EV owner and making it as consistent as possible with the upper-layer scheduling plan. Through the collaborative work between the upper and lower models, the effective management of EV clusters and the load scheduling of the power grid is realized; the charging cost is reduced; and more flexibility and choices are provided for EV users and power system managers. The primary contributions are outlined as follows:  
* We propose the EV cluster segmentation strategy for the day-ahead-intraday multi-timescale; the strategy places EVs with similar attributes into the same cluster and adopts the unified expected completion time of the cluster instead of the expected completion time of each EV, to reduce the drawbacks brought about by the large differences in the entry and exit times of EVs. * Considering the response willingness and ability of vehicle owners, we establish a two-layer model for the real-time optimal scheduling of EV clusters based on the V2G model to achieve effective management of the EV clusters and the load scheduling of the power grid, to reduce charging costs, and to provide more flexibility and choices for EV users and power system managers.
# Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements
## 2 Related Works
Despite higher accuracy compared to Bluetooth, UWB-enabled scanner devices are currently limited, and BLE is still the most widely used wireless communication technology in indoor positioning applications across multiple industries. In large indoor environments such as warehouses, most localization and positioning applications use BLE and upgrading the existing infrastructure to UWB can be an expensive process. With the number of Bluetooth-enabled devices being much larger than UWB-enabled devices, Bluetooth is still the preferred choice in many indoor positioning applications in warehouses, shopping malls, etc. Therefore, in this research, we focus on BLE-based localization techniques.