esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions and Future Scope  
This study involved the comprehensive empirical evaluation and optimization of an RNN-based HAR application in device mapping. We covered six RNN units, namely, the Vanilla RNN, Fast Gated RNN, Fast RNN, GRU, UGRNN, and LSTM. We used hold-out and cross-validation methods on eight datasets, namely, DSA19, SPHAR, Opportunity, PAMAP2, MHealth, USCHAD, WHARF, and WISDM. We trained and compressed the RNNs. We addressed over-fitting issues while training using dropout regularization. For compression, we used low-rank parameterization, iterative hard thresholding, and spare retraining methods. We found that efficient training and suitable compression methods are critical in optimizing RNNs for performance and memory efficiency. We performed inference on a Raspberry Pi.  
For finding the right set of hyperparameters for a model, NAS can be explored. Other compression techniques like the Kronecker Products can be explored, which, in the literature, have proved efficient in edge modeling studies.  
Conceptualization, V.S.L. and V.R.B.; methodology, V.S.L.; validation, V.S.L., V.R.B. and J.A.; formal analysis, V.S.L. and H.S.S.; investigation, V.S.L., V.R.B., J.A. and H.S.S. writing--original draft preparation, V.S.L.; writing--review and editing, V.S.L. and J.A.; visualization, V.R.B. and H.S.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The instance data and solutions will be made available upon request.  
We would like to thank the anonymous reviewers for providing valuable feedback for improving the technical quality and segmentation of this journal.  
Author [PERSON] was employed by the company ABB GISPL. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} RNN & Recurrent Neural Network \\ HAR & Human Activity Recognition \\ \end{tabular}  
Figure 14: Recurrent Neural Network or RNN inference time for 8 different datasets on Raspberry P (RPi). (**a**) Inference time of RNN models on RPi for Daily and Sports Activity Recognition (DSA-19), Smart Phone Human Activity Recognition (SPHAR), Opportunity, and Physical Activity Monitoring for Aging People (PAMAP2) datasets. (**b**) Inference time of RNN models on RPi for Mobile Health (MHEALTH), University of Southern California Human Activity Dataset (USCHAD), Wearable Human Activity Recognition Folder (WHARF), and Wireless Sensor Data Mining (WISDM) datasets.
## 1 Introduction
In the same year, [PERSON] et al. has demonstrated achieving higher efficiency of 20%, by using M-TiO\({}_{2}\) doped with Li\({}^{+}\) salt deposited on C-TiO\({}_{2}\) prepared using aerosol spray pyrolysis [8]. The aerosol spray pyrolysis technique also requires high temperature treatment by holding the sample at 450 \({}^{\circ}\)C.  
In 2021, the synthesis of high quality lithium based up-conversion nanoparticles was reported by [PERSON] et al. to expand the spectral absorption and enhance the performance of PSCs. They achieved a PCE of 19%, and a fill factor of 82% [13]. Recently, [PERSON] et al. has fabricated PSCs directly on lithium-based up-conversion thin films of LiYF\({}_{4}=\)Yb\({}^{3+}\), Er\({}^{3+}\) and achieved a PCE of 19.1% in comparison to pristine PSCs efficiency of 16.5%. These devices have shown a higher and more stable photovoltaic performance under continuous illumination without a cooling system [14]. In our study, DC-sputtering was used to deposit the C-TiO\({}_{2}\). The DC-sputtering is a well establish technique for high throughput manufacturing environment. It is a low temperature processing, which does not require holding the sample at elevated temperatures as compared with the aerosol spray pyrolysis of C-TiO\({}_{2}\) deposition method. Moreover, the perovskite film reported by [PERSON] et al. was based on MAPbI\({}_{3}\) (CH\({}_{3}\)NH\({}_{3}\)PbI\({}_{3}\)) [9]. We report here on Cesium Lead Iodide CsI\({}_{0.05}\)[([FAPbI\({}_{3}\))\({}_{0.85}\) (MAPbBr\({}_{3}\))\({}_{0.15}\)]\({}_{0.95}\) based perovskite films. There is no study, to our knowledge that have reported on compact TiO\({}_{2}\) deposited using DC sputtering with the M-TiO\({}_{2}\) layer doped with FK209 cobalt or lithium. Considerable improvement in the efficiency, fill factor (FF), current density (Jsc) and open circuit voltage (Voc) were observed with doping. Moreover, the stability and reproducibility of the fabricated PSCs are also influenced by doping.
## 3 System Overall Process
### Shared Feature Encoder  
As shown in Figure 7, the diagram illustrates the feature extraction of the backbone network. The shared encoder primarily includes the feature extractor Backbone, which is a crucial component of the entire network in object detection and image analysis tasks. Its main function is to extract multi-level feature information from the input images. Through a series of convolutional operations and down-sampling processes, the Backbone extracts feature maps that encompass both low-level information such as edges and textures, as well as high-level features related to object semantics. These feature maps serve as the foundation for subsequent decoding modules to perform classification and localization.  
In this study, the convolutional layers of ResNet50 are used as the Backbone, responsible for extracting features from the input images layer by layer. By stacking residual blocks, the encoder gradually extracts the texture, edge, and semantic information of the targets from lower to higher layers, generating multi-scale deep feature maps. These feature maps provide rich feature representations for the classification and localization decoders.
# Fuzzy-Based Multi-Modal Query-Forwarding in Mini-Datacenters
## 6 Results and Discussion
### Query Forwarding Behavior Within Mini-Datacenter
#### 6.1.3 Fuzzy Logic Intelligence in Query Forwarding  
We have defined the query forwarding techniques based on the calculated performance from the fuzzy input variables. The proportion of overloaded gateways that have higher buffer occupancy compared to the total number of available gateways would give insight into the level of congestion across the gateways. If the performance is low (<0.2), then it is coarse-grain forwarding, and if it is medium, around 0.4, then it corresponds to fine-grain forwarding with minimal redundancy. For heavily overloaded buffers showing low performance, the forwarding technique is coarse-grain forwarding with no redundancy. For a gateway availability of 30% and overloading of 70%, the performance output is 0.17619, as demonstrated in Figure 12. Here, the forwarding technique is coarse-grain forwarding with a reliability of 0.8.  
Figure 11: Query forwarding with medium buffer occupancies.  
Figure 12: Performance for coarse-grain forwarding.  
For high performance, the forwarding technique is a fine-grain forwarding with higher redundancy. For a gateway availability of 80% and overloading of 60%, the performance output is 0.7851, as demonstrated in Figure 13. Here, the forwarding technique is fine with higher redundancy (50%). The reliability is 0.96.
## 4 Case Study: Clipped Area
### Niigata City  
Niigata City was previously characterized by rapid subsidence (\(\sim\)50 cm/yr) associated with groundwater pumping and natural gas extraction around 1960 [64]. Since then, prevention measures have been taken to prevent subsidence, including regulation of extraction and restoration of groundwater. As a result, the subsidence rates have decreased, although slow subsidence (\(\sim\)2 cm/yr or less) is still detected around the mouth of the Agano River using conducted dense leveling surveys (Figure 11a) [65].  
Here, we compare the vertical velocity between InSAR and leveling. We calculate the leveling-derived velocity at the benchmarks from five leveling surveys spanning September 2014 to September 2018. We project the InSAR-derived LOS velocity to vertical by taking into account the incidence angles under the assumption of no horizontal displacement. The reference point for both InSAR and leveling is set at benchmark II2025 (Figure 11a).  
The derived vertical velocity shows an obvious correlation between InSAR and leveling with a degree of scattering (Figure 11b). The average and STD of the differences (n = 185) are 0.7 and 2.5 mm/yr, respectively. The time series of InSAR at benchmark A, where the most rapid subsidence has been observed, shows almost constant subsidence at a rate of 15.0 mm/yr, which is also consistent with leveling (13.8 mm/yr; Figure 11c). Note that the leveling results consist of only five data points and that the associated velocity uncertainty could be quite large compared to the GNSS and InSAR results. Therefore, the uncertainty of the InSAR-derived velocity estimate could be smaller than 2.5 mm/yr.  
Figure 10: (**a**) Landsat 8 image of the Echigo Plain. (**b**) LOS velocity. Black dots denote GNSS stations. A black square denotes the reference point (960566).
# LoRa 2.4 GHz Communication Link and Range
## 5 Range Versus Data Rate: Results  
Figures 2-4 show the maximum communication range and corresponding data rate at each combination of SF and bandwidth for the free space and indoor and urban environments, respectively. In all cases, the highest possible data rate decreases in a logarithmic way when the communication range between the transmitter and receiver increases.  
Using the Free Space Path Loss model, it is found that a 2.4 GHz LoRa signal can travel up to 133 km in free space and still be received properly. Obviously, this is only a theoretical range and cannot be realized in real-world environments.  
The performance of a more realistic indoor path loss model has been visualized in Figure 3. When transmitting with a spreading factor of 12 and the lowest bandwidth (i.e., 203 kHz), the path loss equals 142.5 dB. Consequently, a maximum communication range of 74 m can be achieved. Furthermore, the highest possible data rate at that range becomes 0.595 kbit/s. At the other extreme, the highest achievable data rate of 253.91 kbit/s is possible at a range of up to 18 m.  
Finally, the communication range of the urban ECC-33 path loss model varies from 3 m at the highest achievable data rate of 253.91 kbit/s to 443 m at the lowest possible data rate of 0.595 kbit/s.  
Figure 3: Communication range and data rate for every combination of spreading factor (SF) and bandwidth (BW) in an indoor environment.  
Figure 2: Communication range and data rate for every combination of spreading factor (SF) and bandwidth (BW) in a free space line of sight (LoS) environment.
# An Improved Circumferential Fourier Fit (CFF) Method for Blade Tip Timing Measurements
## 1 Introduction
An improved formulation for the calculation of the TOA was proposed by [PERSON] and [PERSON], who proved the BTT analysis technique has limitations regarding synchronous response, in that the blade vibration was not considered in the  
Figure 1: (**a**) Under-sampling of asynchronous vibration and (**b**) under-sampling of synchronous vibration. TOA [32]. In this paper, an improved CFF (ICFF) method is proposed, which considers the influence of blade vibration when measuring the ToA of the blade tip. The ICFF is more robust with more probes employed to complete the parameter identification rather than the single-parameters or the improved single-parameters method that just used one probe. Compared to the AR method that requests the probes are equally spaced installed, the ICFF method has not got so strict limitations for probe layout. Moreover, the ICFF introduced into the EO traversed thought that proposed by the literature [31] and a more precision calculation method proposed by the literature [32]. These improvements are more consistent with reality. A 12-blade assembly simulator and experimental data were used to evaluate the performance of ICFF. In both simulated and experimental tests, the ICFF performed better than TCFF.
# Performance Evaluation of GFDM Channel Estimation Using DFT for Tactile Internet Application
## 1 Introduction
### Notations  
Vector sign \(\overset{\rightarrow}{X}\) denotes column vector and boldface \(\mathbf{X}\) indicates matrices. Time domain representation is denoted by lowercase letter; meanwhile, frequency domain representation is indicated by uppercase letter. The transpose and Hermitian transpose of \(\mathbf{X}\) are, respectively, denoted by \(\mathbf{X}^{T}\) and \(\mathbf{X}^{H}\). \(\mathbb{E}[\cdot]\) is the expectation operator; \(\sqrt{\mathbf{X}}\) is the element-wise absolute square root of matrix \(\mathbf{X}\). The indices \(k\) and \(m\) refer to subcarrier and time slot respectively.
# The Blockchain Oracle Problem in Decentralized Finance--A Multivocal Approach
## 5 Risks and Attack Vectors
### Flash-Loan Attacks  
As previously explained, flash loans are crypto loans repaid within the same block. This condition determines that there is virtually no limit to the amount of money borrowed [121]. As the transaction can influence the prices instantaneously, huge transactions can exponentially affect prices. If the oracles are not prepared to face the event, this condition can be exploited to manipulate the price of assets and collaterals [71]. Two articles from Peaster [7] and Tarasov [122], display a list of the most famous attacks in the DeFi space. In accordance with Thompson [4], they show that the most common and rewarding DeFi hack is the flash-loan attack. Due to that reason, Qureshi [71], commenting on the flash loan, declares that to date, the major outcome of flash loans is the enabling of flash-loan attacks. The b2x incident constitutes a typical example of the oracle problem in DeFi, and it is quite famous for being implemented multiple times on the same platform with success and even higher turnover. Basically, b2x smart contracts utilized the Kyber Decentralized Exchange price feed as an oracle. This dependency was exploited during the attack. The attack that happened on 18 February 2020 is, therefore, described and displayed in Figure 8.  
An attacker obtained a flash loan on the b2x of 7500 ETH, which was split into three parts (3518, 900, and 3082). The first part of 3518 ETH, was used to buy sUSD (on Synthetix), a synthetic USD token enabled by the Syntetiva protocol and which should always keep the price of $1. The sUSD were actually bought at the price of $1. However, a second part of the loaned ETH (900 ETH) was used to buy sUSD on Kyber, artificially inflating the price over $2. The b2x used Kyber as a price oracle, so on their pla  
Figure 8: Flowchart of 18 February 2020 b2x flash-loan attack scheme.  
priced at $2. The overpriced sUSD were then used as collateral on bZx to borrow ETH. Due to the inflated price of sUSD, the hacker was able to borrow 6796 ETH instead of 3518 ETH. The attacker then had a total pot of 6796 ETH plus 3082 ETH from the original loan for a total of 9878 ETH. In order for all these transactions to be validated, the initial loan had to be paid back so that the attacker was obliged to send 7500 ETH back to the smart contract. However, he could run away with the surplus of 2378 ETH, equal to $636,000 by that time [111]. The flash-loan attack is an important issue with oracle in DeFi, since the very presence of oracles causes it. As described, no collusion, bug, or data tampering was exploited, but only the oracles and their refresh rate. All the flash loans are performed more or less the same way as the one described here [123]. They are indeed used a lot because of their simplicity and, most of all, because they come basically at no cost. If they fail, only the transaction fees have to be paid, and if they succeed, then the turnover is priceless.
# Short-Term and Long-Term Travel Time Prediction Using Transformer-Based Techniques
## 4 Experimental Setup
### Parameter Settings  
In configuring our model's hyperparameters, a learning rate of 0.0001 and a batch size of 128 were set. We employed a multi-head attention mechanism with 4 heads in each encoder block. The architecture includes 2 layers each for both the encoder and the decoder. Additionally, the output dimension of the fully connected layer was set to 12, corresponding to a 1 h travel time prediction. Our experimental setup involved training the model for 15 epochs, and Mean Square Error (MSE) was utilized as the criterion for the loss function.
# RS-DETR: An Improved Remote Sensing Object Detection Model Based on RT-DETR
## 5 Conclusions  
With rapid advancements in remote sensing technology, both the quality and quantity of remote sensing images have greatly improved. However, while benefiting from high-quality images, remote sensing image object detection also faces challenges such as significant scale variations, dense target distribution, and the prevalence of small targets. These challenges may lead to inaccurate localization, missed detections, and false detections in practical remote sensing applications. To address these issues, this study proposes the RS-DETR model for remote sensing object detection by improving the RT-DETR algorithm. First, we introduce CGA into the AIFI module to enhance intra-scale feature interaction and increase computational efficiency. To address complex backgrounds and numerous small targets in remote sensing images, we propose EBiFPN to enhance cross-scale feature fusion. By incorporating low-level features rich in small target information, we improve small target detection accuracy and reduce false positives and missed detections. Additionally, applying the Focaler-GloU loss function as the localization regression loss reduces sensitivity to scale variations, enhances small object detection accuracy, and improves overall localization accuracy. Finally, experimental results on the SIMD and TGRS-HRRSD datasets indicate that our proposed algorithm achieves significant progress in remote sensing target detection, with mAP50 improving by 2.0% and 1.5% over RT-DETR, respectively. Furthermore, these results demonstrate advantages over other mainstream models, highlighting the advancement and effectiveness of the improved algorithm.  
In this study, we proposed an improved algorithm to address challenges in remote sensing target detection. Experimental results show that the improved algorithm outperforms the baseline model in detection performance, though some shortcomings remain. In future research, we will tackle other challenges in remote sensing target detection, such as  
Figure 12: Comparison results of mAP50 metric.  
target ambiguity, to enhance the algorithm's robustness. Additionally, we aim to further refine the algorithm to reduce missed and false detections.  
Conceptualization, H.Z.; methodology, H.Z.; software, H.Z.; validation, H.Z., X.L. and Z.M.; formal analysis, H.Z.; investigation, H.Z.; resources, H.Z.; data curation, H.Z.; writing--original draft preparation, H.Z.; writing--review and editing, H.Z.; visualization, Z.M.; supervision, Z.M.; project administration, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Lamb Wave-Based FDM-PPM Method Data Transmission Scheme in Plate Structures
## 2 Lamb Wave Communication Method Based on FDM-PPM
### Construction of FDM-PPM Signals
#### 2.1.2 Position Modulation of Frequency-Division Multiplexing Signals  
Based on the subcarrier frequencies allocated to the sensor network nodes, the signal pulses are first modulated using FDM and then PPM, ultimately generating information-carrying FDM-PPM signals. Figure 2 illustrates the structure of the FDM-PPM sequence, where the numbers above the time axis correspond to the information content represented by different symbols. Parameter \(T_{ls}\) denotes the guard interval between each FDM-PPM signal, which is used to prevent mutual interference between signals. The time axis \(T_{s}\) is divided into \(N_{s}\) frames, each with a duration of \(T_{f}\). Each frame corresponds to a symbol, representing a signal modulated by frequency-division multiplexing.  
A complete frame structure comprises \(N_{ls}\) time slots, each with a duration of \(T_{ls}\). Within this structure, \(T_{ls}\) not only represents the width of the time slot but also has a close relationship with the position of the signal pulse. Figure 2a illustrates the signal structure of FDM-PPM after only undergoing FDM processing. In contrast, Figure 2b presents the signal structure of FDM-PPM for the same pulse signal after modulation with different phases and positions. The expression for the FDM-PPM signal is  
\[x_{FDMPPM}=x_{FDM}*\sum_{k=0}^{\infty}\delta(t-lT_{ls}) \tag{2}\]  
where \(x_{FDM}\) denotes the signal pulse modulated by PPM; \({}^{*}\) denotes the convolution operation; and \(1\in\{0,1,2,\ldots,N_{ls}\}\) denotes the number of time slots between the frame start  
Figure 1: Illustrations of subcarrier signal modulation: (**a**) the first carrier sequence, (**b**) the second carrier sequence, and (**c**) the carrier spectrum.  
Figure 2: Schematic of FDM-PPM sequence structure: (**a**) signal waveform modulation and (**b**) signal position modulation.  
position and the signal pulse. The number of bits represented by an FDM-PPM symbol, \(I_{\text{FDMMPPM}}\), is  
\[I_{\text{FDMMPPM}}=\log_{2}\left[2*\left(\sum_{k=0}^{1}2^{N-k}-1\right)*\left(N_{ ts}+1\right)\right] \tag{3}\]  
where \(N\) denotes the maximum number of subcarriers in a subcarrier sequence; and \(k\) denotes the number of adjacent subcarriers in a subcarrier sequence. The communication rate of the FDM-PPM system is  
\[R_{\text{FDMMPPM}}=\frac{1}{T_{s}}\log_{2}\left[2*\left(\sum_{k=0}^{1}2^{N-k}- 1\right)*\left(N_{ts}+1\right)\right] \tag{4}\]  
A flowchart of the Lamb wave FDM-PPM communication method is shown in Figure 3.