esa-sceva/satcom-chunk-collection huggingface dataset:
# 25 Years of Bluetooth Technology
## 5 Bluetooth Security
### Security Risks Specific to Bluetooth Low Energy  
The main security threats related to the process of pairing and to BLE as such are passive identity tracking, MITM, and eavesdropping.  
Inactive snooping occurs when a third device eavesdrops information transmitted amongst paired devices. BLE mitigates the threat by implementing various encryption types such as Advanced Encryption Standard (AES)-Counter with Cipher Block Chaining Message Authentication Code (CBC-MAC) (CCM) cryptography, etc.  
MITM attacks in BLE occur when a malicious device, mimics other legitimate devices and pairs with them. In this scenario, the BLE Generic Access Profile (GAP) connects to the malicious device which in turn routes the communication between the two other devices. Legitimate devices believe they are connected to each other while they are actually connected to the malicious user's device. This enables the intruding device to capture other users' data and also alter and re-insert new data in a transmission.  
Identity tracking occurs when a mischievous user associates the address of a BLE device with a certain user and then tracks that user. BLE mitigates this threat by intermittently altering the address of the device [43].
# Ad-RuLer: A Novel Rule-Driven Data Synthesis Technique for Imbalanced Classification
## 3 Method
### Overview of Existing Resampling Methods  
Before delving into the details of the Ad-RuLer method, we provide an overview of several prevalent resampling techniques. These techniques will later serve as benchmarks for a comparative performance assessment with the Ad-RuLer method in subsequent experimental sections. These methods include SMOTE, ADASYN, Tomek-links, Borderline-SMOTE, and KmeansSMOTE.  
* SMOTE: This oversampling technique generates new synthetic samples by interpolating functionity class instances, aiming to increase the representation of the underrepresented class. While widely applicable, SMOTE can introduce suboptimal samples in the presence of noise or significant class overlap.
* ADASYN: This algorithm is an extension of SMOTE. ADASYN adjusts the number of synthetic samples according to the learning difficulty of individual minority class samples. It generates more synthetic data for those samples that are harder to learn, potentially leading to an improved classifier performance.
* Tomek-links: This algorithm enhances class boundaries by identifying and removing Tomek-links, which are pairs of the nearest neighboring samples that belong to different classes. This method refines the dataset rather than augmenting it with new samples, potentially improving classifier boundaries without changing class distributions.
* Borderline-SMOTE: This technique focuses on the border regions around minority class samples, oversampling those likely to be misclassified. Borderline-SMOTE attempts to directly enhance the quality of the classification boundary.
* KMeansSMOTE: This method integrates K-Means clustering with SMOTE to perform oversampling within each identified cluster of the minority class, thereby preserving the inherent clustering structure of the data while synthesizing new samples.  
For a more detailed introduction to these resampling methods, please refer to [22, 25, 26, 29, 30]. Distinct from the above-mentioned methods, the Ad-RuLer approach focuses on the rule-based structure inherent within the dataset. It employs an iterative process to pairwise compare various rules, ultimately generating new rules that satisfy the predefined conditions. These rules are then transformed into synthetic minority class samples, thus providing a new perspective and novel solutions to addressing the challenges associated with imbalanced datasets.
# A TSVD-Based Method for Forest Height Inversion from Single-Baseline PolInSAR Data
## 6 Conclusions  
A TSVD-based method has been proposed in this paper for forest height inversion from single-baseline PolInSAR data. Differing from the traditional three-stage method, the new method estimates the pure volume coherence intuitively from the complex interferometric coherence, and has the capacity to adjust the contributions of the polarizations in the estimation of the model parameters. The TSVD-based method was first applied in forest height inversion from simulated PolInSAR data generated in PolSARpro. The results demonstrated that the TSVD-based method significantly improves the inversion results when compared to the three-stage method. This was also confirmed with airborne E-SAR P-band data obtained over a mixed boreal forest. The inverted forest heights obtained by TSVD showed an improvement in RMSE of 48.6% when compared to the results of the three-stage method. The phase heights of the estimated pure volume coherence were also well improved when compared to the results of the three-stage method.  
**Acknowledgments:** The work presented in the paper was supported by the Nature Science Foundation of China (Nos. 41531068, 41474008, 41574006, 41674012). The work also was supported by Hunan Provincial Department of Education Science Research Key Project (No. 15A074).  
**Author Contributions:** [PERSON] conceived the idea, designed and performed the experiments, and wrote and revised the paper; [PERSON] analyzed the PolInSAR experiments and revised the paper; [PERSON]  
Figure 11: Forest heights extracted by the least-squares method from the pure volume coherence estimated by the three-stage method.  
analyzed the experimental results and revised the paper; [PERSON] analyzed the experimental results; and [PERSON] performed the simulated experiments.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 5 Results Analysis
### Pure Overturning Moment
#### 5.3.2 Deformation of Bearing Inner Raceway  
Figure 12 shows the deformation of a non-circular raceway relative to a circular raceway, where Figures 12a,b and 12c,d represent the radial and axial deformation, respectively. Compared with Figure 11, it can be seen that when the contact force before optimization is greater than the optimized contact force, in the radial diagram, the deformation of the non-circular raceway is \"outward\" relative to that of the circular raceway (\(\Delta_{i,r}>0\)), and the deformation trend of the deformation and the contact force is the same, which increase with the increase in overturning moment (inner lower: \(1.43\times 10^{-2}\), inner upper: \(1.429\times 10^{-2}\)).  
When the contact force before optimization is less than that after optimization, the radial diagram shows that the non-circular raceway deforms \"inward\" relative to the circular raceway (\(\Delta_{i,r}<0\)), and the deformation also increases with the increase in overturning moment (inner lower: \(1.44\times 10^{-2}\), inner upper: \(1.193\times 10^{-2}\)).  
From the axial graph Figure 12c,d, it can be seen that when the contact force before optimization is greater than the optimized contact force, the inner and lower raceways have a \"downward\" deformation relative to the circular raceway (\(\Delta_{i,a}<0\)), and the inner upper raceway has an \"upward\" deformation relative to the circular raceway (\(\Delta_{i,a}>0\)). The deformation also increases with the increase in overturning moment (inner lower: \(1.322\times 10^{-2}\), inner upper: \(1.991\times 10^{-2}\)).  
When the contact force before optimization is less than the contact force after optimization, the inner and lower raceways have \"upward\" deformation (\(\Delta_{i,a}>0\)) relative to  
Figure 11: Contact force distribution before and after optimization: (**a**) contact force \(Q_{1}\); and (**b**) contact force \(Q_{2}\).  
the circular raceway, and the inner and upper raceways have \"downward\" deformation (\(\Delta_{i,a}<0\)) relative to the circular raceway. The deformation also increases with the increase in overturning moment (inner lower: \(1.322\times 10^{-4}\), inner upper: \(1.991\times 10^{-3}\)).
# Passive Power Line Communication Filter Design and Benchmarking Using Scattering Parameters
## 6 Conclusions and Future Work  
In conclusion, this paper has presented comprehensive benchmarking, design methods, and actual measurements for the development and evaluation of power line filters, with a specific focus on the CENELEC A communication band.  
We have designed a power line filter using an S-parameters simulator and real passive components and detailed information regarding the component selection and filter construction has been provided. Practical validation of the simulated values was achieved through actual measurements. Benchmarking against three commercially available filters shows that our filter is the best suited for filtering below 150 kHz.  
Our filter has superior characteristics, such as the best insertion loss versus frequency, three times higher attenuation at 50 kHz (\(-\)130 dB) compared to the best commercially available filter (\(-\)40 dB), and a power consumption of 0.6 W which is comparable to the most power-efficient commercial filter (0.5 W). Additionally, our filter has the second-best input and output impedance of 3.6 \(\Omega\) within the frequency range of 35-95 kHz.  
The proposed filter has the maximum rated current limit of only 30 A, which makes it suitable as a type 1 filter but unsuitable as a type 2 filter (separating two sections of the power grid) as defined per Section 2.1. It is of interest to design a type 2 filter using the design methods presented in this paper.  
As future work, we propose to benchmark additional power line filter using the test methods presented in Sections 3 and 4. In addition, it is of interest for us to design and evaluate a hybrid between resonant and \(n\)th order filters with the scope of reducing the order of the filter while maintaining the attenuation below 150 kHz.  
Conceptualization, S.A. and R.V.; Methodology, S.A. and R.V.; Resources, S.A. and R.V.; Validation, S.A.; Writing--original draft, S.A.; Writing--review and editing, S.A. and R.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# An Optimal Sizing Design Approach of Hybrid Energy Sources for Various Electric Vehicles
## 2 Development of Global Search Algorithm for HEESs  
### The Configuration of HEESs  
According to our literature review, the four configurations of HEESs are separated as shown in Figure 1. They are: (1) active, (2) passive, (3) active-passive type 1 and (4) active-passive type 2. For the active type (Figure 1a), two DC/DC converters are equippedat the rear parts of dual energy sources, respectively. By properly controlling the control unit, the energy management is determined by two converters while the output voltages are stabilized at the downstream of the converters. The drawbacks of (1) are the extra cost and occupied volume of the converters. For passive type, the in/out energy flows of two energy sources are determined only by output voltages of the two sources without the energy management. Though the cost is lowest and the structure is simplest, the state-of-health (SOH) of both sources might decrease rapidly due to improperly drawn currents or lack of SOC control. For active-passive type 1, one DC/DC converter is at the output of the Li3 battery. As the output current (power) of the DC/DC converter is controlled while the output voltage is stabilized, the output power of the LTO is passively given. For active-passive type 2, one DC/DC is at the output of the LTO battery. As the output current (power) of the DC/DC is controlled while the output voltage is stabilized, the output power of the Li3 is passively given. Both active-passive types exhibit the same performance as that of the active type. However, the overall system efficiency is higher because the extra input/output power loss of the second converter is absent. Moreover, the system cost and occupied volume and weight are lower due to the single DC/DC converter. Though the cost is higher than that of the passive type, the SOH of dual energy sources can be extended to lower the cost. Of these four types, two active-passive types were chosen for system sizing optimization. The position of the single DC/DC converter was selected using the proposed GSA. Note that the system efficiency of hybrid energy sources is significantly higher than that of the single source, especially at the EoL period [23]. Hence, the single-source system was not considered in this research.
## 4 Intelligent Prediction of Link States
### Effective Additive Attention (EAA)  
The EAA consists of four hierarchical stages of different scales\(\{\frac{1}{4},\frac{1}{8},\frac{1}{16},\frac{1}{32}\}\), each of which is consistent and composed of a Convolutional Encoder and a SwiftFormer encoder [15], with a downsampling layer introduced between two consecutive phases to halve the spatial size and increase the feature latitude. The structure of EAA is shown in Figure 1. The Convolutional Encoder consists of a 3 \(\times\) 3 depth convolution [16] and two point-state convolutions [17]. The initial module of the SwiftFormer encoder consists of a 3 \(\times\) 3 depth convolution followed by a point-state convolution [16], followed by an efficient additive attention module. Finally, the output features are fed into a linear module that consists of two \(1\times 1\) point-state convolution layers, batch normalization, and GeLU activation [15].  
Figure 1: Structure of EAA.  
In SwiftFormer' s attention module, the input embedding matrix \(x\) is transformed into a query (\(Q\)) and a key (\(K\)) via two matrices \(W_{q}\), \(W_{k}\), where \(Q\), \(K\in R^{n\times d}\), \(W_{q}\), \(W_{k}\in R^{d\times d}\), \(n\) is the length of tokens, and \(d\) is the dimension of embedding vectors. Next, the query matrix \(Q\) is multiplied with the learnable parameter vector \(w_{a}\in R^{d}\) to learn the attention weight of the query and generate the global attention query vector \(\alpha\in R^{n}\), as in Equation (6):  
\[\alpha=Q\cdot w_{\alpha}/\sqrt{d}, \tag{6}\]  
The query matrix is then pooled based on the learned attention weights, resulting in a single global query vector \(q\in R^{d}\), as follows in Equation (7):  
\[q=\sum_{i=1}^{n}\alpha_{i}\cdot Q_{i}, \tag{7}\]  
Next, the interaction between the global query vector \(q\in R^{d}\) and the key matrix \(K\in R^{n\times d}\) is encoded to form a global context via element-level multiplication (\(R^{n\times d}\)). A linear transformation layer for query-key interactions is employed to learn the hidden representation of the tokens. The output of the attention module \(\hat{x}\) can be described using Equation (8):  
\[\widehat{x}=\widehat{Q}+T(K\cdot q), \tag{8}\]  
where \(\hat{Q}\) denotes the normalized query matrix and \(T\) denotes the linear transformation [15].
## 4 Results and Discussion
### Seasonal effects  
It is important to recognize that seasonal effects will affect predicted RSL. Even with the most satisfactory NN models, the seasonal effect was obvious. For example, the living lettuce harvested during February to April was predicted to have RSL of 3.4 days (sd=0.28, n=2) on the 5 th day of storage, while that harvested during June to October had predicted RSL of 11.6 days (sd=0.17, n=3). This seasonal difference in RSL observed under variable temperatures was also similar to variability in TSL observed under constant temperatures (Fig. 1). This variation demonstrates the need for considering such factors as seasonal effects and including them in any modeling system. Therefore, in commercial deployment of NN models, different models would be required for different seasons.
## 3 Methodology
### Dimensionality Reduction  
Dimensionality refers to the quantity of features included in the dataset [50]. Dimensionality reduction is crucial for converting high-dimensional data into a more manageable low-dimensional representation [51, 52]. It has become indispensable in many domains, as its primary goal is to eliminate unimportant, redundant, and noisy features from the dataset, thereby reducing memory requirements [50] and computational complexity [53]. Algorithms that struggle with high dimensionality can benefit significantly from this process, improving their efficiency and accuracy [50]. Dimensionality reduction methods are commonly categorized into two main types: feature selection and feature extraction.
## 2 Materials and Methods
### Method Validation
#### 2.5.4 Precision  
The degree of consistency among individual test results when the method is repeatedly applied to multiple samples under the same prescribed conditions but at different times, on different instruments, and/or by different laboratories is defined as its precision. In this study intra-day and inter-day precision were assessed to validate the method. For this, triplicate analyses of three different concentrations of the respective fat-soluble vitamin samples were carried out either on the same day or over three days using 150, 200, and 250 ng/band for vitamin D\({}_{2}\), D\({}_{3}\), and E and 30, 40, and 50 ng/band for vitamin K\({}_{1}\). Precision was expressed as the percent relative standard deviation (%RSD) of the results obtained for peak areas for vitamin D\({}_{2}\) and D\({}_{3}\) as well as peak heights for vitamin E and K\({}_{1}\).
## 3 Results  
### Calculation Results and Analysis of Land Use Carbon Emissions  
#### 3.1.1 Analysis of Calculation Results of Carbon Emissions from Land Use  
By analyzing the land use change data of the study area in 2010, 2015, and 2020, we compute the carbon emissions resulting from land use. As demonstrated in Table 2, the carbon emissions in this region witnessed an upward trend at first and then a downward one. The carbon emissions increased from 1144.62 \(\times\) 10\({}^{4}\) t in 2010 to 1512.26 \(\times\) 10\({}^{4}\) t in 2015, marking an increase of 367.64 \(\times\) 10\({}^{4}\) t. The growth in this stage may be closely related to economic development, accelerated urbanization, and land use change, which reflects that the management and control measures for carbon emissions in this period have not been fully put in place. From 2015 to 2020, land use carbon emissions decreased slightly from 1512.26 \(\times\) 10\({}^{4}\) t to 1497.01 \(\times\) 10\({}^{4}\) t, with a reduction of 15.25 \(\times\) 10\({}^{4}\) t. This downward trend suggests that a range of effective policies and measures have been implemented for carbon emission control in the study area, leading to an improvement in the region's carbon emission situation to some extent.
## 1 Introduction  
Over the few last years, Internet of Things (IoT) technology has become critical in industries and activity sectors, offering a large diversity of application domains and communication protocols. The most challenging requirement of IoT is connectivity, which is critical in regions where terrestrial networks are either too sparsely populated (deserts, seas, tropical forests, etc.) or unreachable by terrestrial networks; these locations are called white zones [1]. IoT connectivity can be extended to undeveloped and hard to reach areas, to allow global IoT coverage, namely Internet of Everything Everywhere (loEE) [2].  
Today, there are many technologies that ensure connectivity. However, they differ in cost, power consumption, and coverage range. Bluetooth, Wi-Fi, and ZigBee technologies are optimal solutions for short range applications, with low cost and low power consumption. For long range applications with low data rates, Low Power Wide Area Network (LPWAN) technology is the best solution, especially for applications where high data rates are not needed for low power consumption and cost deployment.
# Hyperspectral Data: Efficient and Secure Transmission
## 2 An Efficient and Secure Transmission Framework
### An Effective Implementation of the Proposed Framework  
For an effective implementation of the proposed framework, we considered the reversible watermarking scheme, described in Section 2.1.1, and the multiband lossless compression of hyperspectral images scheme (LMBHI), the compression algorithm is addressed in Section 2.1.2. However, other approaches can be efficiently used for an effective implementation of our proposal.
# Joint Object Detection and Multi-Object Tracking Based on Hypergraph Matching
## 4 Methodology
### Feature Extraction and Object Detection  
To encompass a wider range of low-level and high-level features, and to adjust the receptive field for tracking objects of different shapes and scales, we use Deep Layer Aggregation (DLA) [40] as the backbone to extract feature maps from the input image \(\mathbf{\mathsf{X}}^{t}\) to obtain the basic feature map \(\mathbf{\mathsf{F}}^{t}\).  
We employ the two-stage detector Faster R-CNN [52] as our detection component, merging the public detection results of the current frame with the tracking results from the previous frames as the candidate proposal \(\mathbb{P}^{t}\). The corresponding detection results, \(\mathbb{B}^{t}=\left\{\mathbf{B}^{t}_{1},\mathbf{B}^{t}_{2},\dots,\mathbf{B}^{t}_{n}\right\}\), are generated by the detector \(\mathcal{D}(\cdot)\), where \(n\) represents the number of detected objects, and \(\mathbf{B}^{t}_{i}\) represents the corresponding candidate bboxes.
## 5 Results and Discussions
The obtained graph is simulated for the generated turbulence attenuation with the mean value of 3.43 dB, and it is compared with the measured BER curves at 1 Gbps and 10 Gbps by assuming ideal atmospheric conditions (no scintillations). It is evident from Figure 12b that the implemented atmospheric turbulence at 1 Gbps data rate leads to a  
Figure 12: Testbed measurement results including (**a**) BER in terms of 1 Gbps and 10 Gbps data rates at no turbulence and (**b**) BER in terms of 1 Gbps in clear-air atmospheric scintillations. lowered gradient of BER curve for BER \(<10^{-3}\) in comparison to no turbulence conditions, which results in steadily increased power loss reaching a value of 4.23 dB at BER \(=10^{-8}\). Consequently, the considered satellite-to-ground FSO link that operates in continental turbulence scintillations and 1 Gbps data throughput is outperformed by the evaluated 10 Gbps scenario when no turbulence conditions are considered. The maximum difference, which depends on the performance of the selected 10 Gbps MZM, is up to 2.03 dB. Although room for further comparison with real measurements is left, such atmospheric data are still not available for Vienna, Austria, scenario to the best of the authors' knowledge. Finally, the six measured eye diagrams with DSAV334A (\(-17\) dBm, \(-27\) dBm, \(-29\) dBm, \(-31\) dBm, \(-33\) dBm, and \(-37\) dBm received optical power) that are shown in Figure 13 support the results completely. While the eye is gradually closing, there is still eye-opening even at \(-33\) dBm that confirms the improved performance when data throughput drops. The eye is already wholly closed at \(-37\) dBm.