esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Power Scalable Digital Baseband
### Proposed Architecture and Functioning
The counter counts number of samples and the tracking begins when the counter reaches the count _track_address_. Once the synchronization is done (_sync_success_) is raised, all synchronization blocks turn off and receiver data-path (NCO, Matched filters, decimator, demodulator and detector) adjusts itself to new settings of \(Q_{dig}\) and \(f_{s}\). **Figure 4.**: Preamble and timing for various synchronization units. Figure shows how various synchronization blocks work in tandem. \(clk_{1}\) = \(30\,\mathrm{MHz}\), \(Q_{dig1}\) = \(8\)-bit. \(clk_{2}\)—1 to 30\(\,\mathrm{MHz}\). \(Q_{dig2}\)—1 to 8 bits. Changing sampling frequency requires the estimates for synchronization computed during preamble to be preserved. Values of the estimate depend on the sampling frequency [13]. The frequency estimate needs to be scaled and the phase continuity has to be preserved. The path from ADC output to the input of the demodulator has a latency of a number of clock cycles due to FIR, CORDIC pipelines, decimator, _etc._, as shown in Figure 6. When the sampling frequency of the receiver is changed after the packet preamble, the delay elements in these contain samples sampled at the highest sampling frequency used during preamble. The receiver is very sensitive to timing error when the sampling frequency is very low. For, e.g., for sampling frequency of 2 Msps, every pulse is sampled twice. In such a case, an error of one sample results in offset by half a pulse. Hence the delay across the data-path needs to be carefully accounted, particularly, when the sampling frequency is low. While changing \(Q_{dig}\) and \(f_{s}\), it is proposed to discard all samples in delay elements across the receiver. This is due to the fact that the samples in delay elements across the receiver is sampled at higher sampling frequency than the new assigned \(Q_{dig}\) and \(f_{s}\) for the data duration. Delay elements are reset when the _adap_ctrl_ goes high. As shown in Figure 6, once the _sync_succ_ goes high, demodulator waits until the _sample_index_ reaches _start_index_. Value of _start_index_ is equal to number of clock cycle delay from output of ADC to demodulator.
## 6 Discussion
### Benefits
#### 6.1.1 Better Data and Machine Learning Model Sharing  
In the logically and physically centralized architecture of KDN, a centralized entity involves collecting a large dataset from multiple nodes and training just one artificial intelligence agent. However, the collection of such data is difficult due to bandwidth constraints, large overhead and costs in communication, and difficulty in aggregating heterogeneous data. Alternatively, in the distributed control architecture of KDN, machine learning model training is distributed, which is more compatible with decentralized blockchain technology. Thus, data can be shared on a blockchain for training ML agents. Furthermore, the trained ML models can also be shared using the blockchain. The use of collaborative federated learning using blockchain to protect the immutability of ML prototypes and thwart model contamination assaults is clear through frameworks like DBF-B [375]. Thus, the data employed for ML model training can be guaranteed to originate from legitimate users thanks to the digital signature verification, and as every piece of data inserted into the blockchain is verified using cryptographic hash functions, the data cannot be tampered with by third parties. Furthermore, blockchains can enable automatic data cleansing using consensus and smart contracts to automatically identify erroneous data, thus improving the accuracy of the data. Thus, blockchain enables accurate and trustworthy machine learning and model sharing.
# Advances in Ionospheric Space Weather by Using FORMOSAT-7/COSMIC-2 GNSS Radio Occultations
## 2 Intense GNSS RO Soundings of the Ionosphere
On the other hand, F7/C2 sampled the same region during the same period with more than four times the corresponding number of F3/C occultations (Figure 1c). Note that the locations marked in Figure 1b,c correspond to the F-peak altitude (hmF2), and the actual latitude coverage of F7/C2 may be slightly larger when considering the tangent locations of the entire profile for each occultation event. This paper made use of these additional available measurements to re-examine the previously reported major results by F3/C.
This variation affects how sensor nodes participate in data collection, processing, and communication. Different sensor nodes may generate data at varying rates depending on their sensing capabilities and the environmental conditions they monitor. This variability can lead to uneven data traffic within the network, complicating the clustering and data aggregation processes. The proposed K-BCO algorithm aims to account for these differences to enhance data collection efficiency. The study also considers the importance of energy efficiency in WSNs. Heterogeneity in energy levels among sensor nodes means that some nodes may have more energy available for data transmission and processing than others. The K-BCO algorithm is designed to select cluster heads based on their energy status, ensuring that nodes with sufficient energy are chosen to lead data transmission. This approach helps to balance energy consumption across the network, prolonging the overall network lifetime. The physical arrangement of sensors can change over time, especially in situations where mobile sensors are involved. The K-BCO algorithm's ability to dynamically adjust cluster formations helps maintain connectivity and communication efficiency in such environments. WSNs have unique requirements regarding data accuracy, latency, and reliability. The heterogeneity of application needs means that a one-size-fits-all clustering approach may be ineffective. The K-BCO algorithm is designed to be adaptable, allowing it to cater to the specific demands of various applications while optimizing energy consumption and network performance. The sections of this paper are organized as follows: Section 2 (related works), Section 3 (theoretical framework), Section 4 (proposed clustering technique), Section 5 (experimental settings of WSN), Section 6 (simulation results), Section 7 (discussion of results), Section 8 (conclusions).
## 4 Experimental Results
### The Effect of the Number of Submodule Inputs on the DNFS
Therefore, the DNFS algorithm with five submodule inputs was researched further. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Methods** & **AIC** & **MAE** & **RMSE** & **SMAPE** & **Score** & **Layers** & **No. of Submodules** & **No. of Params** & **Time(s)** \\ \hline DNFS3-0 & \(-\)14,625.26 & 0.081153 & 0.105330 & 8.1929\% & 5 & 4 & 40 & **1690** & 76.9161 \\ DNFS4-0 & \(-\)14,451.25 & 0.078638 & 0.101860 & 7.8498\% & 7 & 4 & 30 & 1911 & 77.8035 \\ DNFS5-0 & \(-\)**14,761.41** & **0.073618** & **0.098158** & **7.5000\%** & **12** & **3** & **22** & 1904 & **76.7091** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance of DNFSs with different submodule inputs on Test Dataset No. 3. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Methods** & **AIC** & **MAE** & **RMSE** & **SMAPE** & **Score** & **Layers** & **No. of Submodules** & **No. of Params** & **Time(s)** \\ \hline DNFS3-0 & \(-\)**12,986.28** & 0.039451 & 0.051882 & 4.0123\% & 9 & 3 & 11 & **460** & **14.4406** \\ DNFS4-0 & \(-\)12,020.87 & 0.039588 & 0.052901 & 4.0713\% & 5 & 3 & 9 & 897 & 30.1497 \\ DNFS5-0 & \(-\)10,667.72 & **0.037904** & **0.051602** & **3.8449\%** & **10** & **2** & **6** & 1632 & 96.9152 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The performance of DNFSs with different submodule inputs on Test Dataset No. 1. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Methods** & **AIC** & **MAE** & **RMSE** & **SMAPE** & **Score** & **Layers** & **No. of Submodules** & **No. of Params** & **Time(s)** \\ \hline DNFS3-0 & \(-\)**12,986.28** & 0.039451 & 0.051882 & 4.0123\% & 9 & 3 & 11 & **460** & **14.4406** \\ DNFS4-0 & \(-\)12,020.87 & 0.039588 & 0.052901 & 4.0713\% & 5 & 3 & 9 & 897 & 30.1497 \\ DNFS5-0 & \(-\)10,667.72 & **0.037904** & **0.051602** & **3.8449\%** & **10** & **2** & **6** & 1632 & 96.9152 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance of DNFSs with different submodule inputs on Test Dataset No. 2. Figure 3: Prediction chart of DNFSs with different submodule inputs on the three test datasets. (**a**) Test Dataset No. 1. (**b**) Test Dataset No. 2. (**c**) Test Dataset No. 3.
## 3 Results
### Real-Time Classifier-in-a-Loop System  
Now there is enough evidence from our offline predictive analysis for machine learning algorithms to be used as an online/real-time classifier for electrode array steering during insertion. By this, we can achieve low force profiles during EA array insertion that could lead to less traumatic intraoperative behaviour. This online method is used for correction of the trajectory of the electrode array during the insertion based on the prediction of machine learning models.  
Figure 9 presents the pictorial representation of one of the real-time experiments. In Figure 9A, the experimental setup is shown as the EA is inserted for 4 mm and data are collected, EA trajectory is towards the medial wall as EA can be seen inside the saline-filled ST model. A close-up of this trajectory is shown in (C). Once the recorded data vector is passed on to classifier A and classifier B, the controller makes the correction by moving the rotational stage of the actuator towards the right by 0.5\({}^{\circ}\), as shown in Figure 9B and in the close-up in (D). The trajectory could be towards the lateral wall, as shown in (E), and is corrected, as shown in (F).  
Table 3 shows the ensemble learning classification results for both offline and real-time predictions. Ensemble learning models (classifier A and classifier B) are trained and tested first using three machine learning algorithms. The classifier A ensemble model achieved accuracy of 89.05%, which is an increase from 87.4%, when the highest performing individual model was used for path prediction. For classifier B, cross-validated accuracy of 86.86% was achieved using ensemble learning, which is slightly higher than the highest performing individual SNN model. For online prediction, 30 iterations were performed to test both classifier ensemble models. Classifier A achieved 83.33% accuracy, whereas classifier B achieved slightly lower accuracy of 80%, when tested for online prediction and correction.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multirow{2}{*}{**Sub-Seq**} & \multicolumn{5}{c}{**Accuracy (\%)**} \\ \cline{3-6}  & & **SNN** & **SVM** & **KNN** & **RF** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **EN** \\ **EN** \\ **EN** \\ \end{tabular} } & 4 mm-2S/3C & **87.4** & 81.3 & 83.7 & 83.2 \\  & 3 mm-3S/3C & 82.4 & 76.4 & 76.9 & 78.3 \\  & 2 mm-4S/5C & 78.7 & 73.2 & 76.6 & 74.4 \\  & 1 mm-8S/10C & 64.2 & 67.1 & 68.2 & 65.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cross-validated binary class accuracies for straight/curved path prediction using EP specific subsequences. S—straight path, C—curved path.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ensemble Classification Model** & **Offline Accuracy (\%)** & **Real-Time Accuracy (\%)** \\ \hline Classifier A & 89.05 & 83.33 \\ Classifier B & 86.86 & 80.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Offline and real-time classification accuracy using ensemble of classifiers.
## 4 Results
### Comparison of LDS U-Net with Other Contemporary Models
#### 4.2.4 Evaluation of Computational Requirement  
In Table 4, the computational requirement of the contemporary models and the proposed model are compared in terms of the total number of parameters and program storage requirements. LDS U-Net architecture offers advantages such as (i) low computational requirements due to the usage of depthwise separable convolution layers; (ii) feature reuse enabled through the application of novel light dense block; and (iii) enhancement of the depth and width of the network, with no incremental computational budget, through the redesigned multi-scale inception layer. As shown in Table 4, these advantages of the proposed model translate to a lower parameter requirement and smaller memory footprint when compared to U-Net and Attention U-Net. Although MultiResUNet requires a fewer number of parameters than LDS U-Net, the overall segmentation performance of MultiResUNet was lower, as discussed in the previous sections.
# A Design of a Parcel Delivery System for Point to Point Delivery with IoT Technology
###### Abstract  
Recently, IoT technology, low power networking technology for IoT, and geopositioning systems are very useful tools for future applications. One of the applications is the logistics system, especially for the point to point delivery. The current parcel delivery system is delivered through the logistics hub despite latent economic disadvantages in terms of delivery time and delivery distance. The major disadvantage of current hub and spoke delivery system is that individual parcels must go to the logistics hub first even if the origin and destination are nearby or within a distance that can be delivered through the hub. These sorts of delivery are inefficient and increase total expense of delivery in terms of delivery time and delivery costs. We designed a new delivery system of point to point delivery using IoT technology and end-to-end IoT platform based on ThingPlug and LoRa technology for fast delivery and less cost of delivery. We designed an IoT device that can be part of parcels or attached to parcels, which have various functionalities including the capability to understand current delivery routes with the use of geopositioning systems. In addition, we addressed the difficulties with issues such as linking IoT device into IoT platforms and designing IoT device functionalities. We also showed methods to improve the efficiency of IoT based parcel delivery systems. The proposed system may improve economics of parcel delivery system by reducing travel distance for delivery and dissipation of delivery time.  
future internet; internet of things; LoRa; delivery system +
Footnote †: journal: Future Internet
# Molecular Regulatory Mechanisms Affecting Fruit Aroma
## 3 Main Synthetic Pathways
### Fatty Acid Synthesis Pathway  
Fatty acid-derived aroma volatiles are metabolized mainly through the lipoxygenase (LOX) pathway and the \(\alpha\)-/\(\beta\)-oxidation pathway, in which unsaturated fatty acids such as linoleic acid and linoleic acid are the core precursors [4]. In peach, the key enzymes such as LOX, hydroperoxidase lyase (HPL), ethanol dehydrogenase (ADH), and alcohol acyltransferase (AAT) have been shown to be involved in this metabolic process [31] (Figure 2c). Alcohols, as precursors to the synthesis of ester aroma compounds in plants, are catalyzed by AATs to convert various alcohols produced by fatty acid and amino acid pathways into ester aromas [32]. Lactones in plants are derived from fatty acids and are formed through dehydrogenation, epoxidation, hydration, hydroxylation, \(\beta\)-oxidation, and internal esterification with hydroxy-acetyl-CoA [33; 34; 35]. The key enzymes such as fatty acid desaturase (FAD), epoxide hydrolase (EPH), and acyl-CoA oxide (ACX) play crucial roles in this process [33; 34].  
Fatty acid derivatives include several small alcohols and aldehydes, and fatty acid oxidase is the main regulatory enzyme (Figure 2c). At the same time, volatile unsaturated fatty acids are also one of the main sources of aroma in vegetables, melons, and fruits. For example, the metabolism of major VOCs in pear aroma products is dependent on the fatty acid oxidase pathway [36]. However, there are differences in the fruit aroma components among grape varieties, and there are fatty acid substances such as ethyl acetate [37]. Someesters, alcohols, and acids were identified in the fruit aroma of passion fruit, and it was found that esters were the main components of the volatile aroma of passion fruit. Ethyl butyrate, ethyl caproate, and propyl acetate have shown high odor importance in fruits [38]. The expression of lipoxygenase in passion fruit has been shown to have a positive regulatory effect [39].
# Application of Computational Fluid Dynamics Analysis after Bimaxillary Orthognathic Surgery
## 5 Conclusions  
We confirmed that the airway change pattern analysis using CFD was effective. It is possible to simulate the air flow pattern according to the individual patient's airway shape and to predict the location of airway obstruction.  
After bimaxillary setback surgery, the airway decreases as a whole, especially at the velopharynx, oropharynx, and epiglottis. As a result, the flow rate in oropharynx and laryngopharynx increases and a substantial amount of negative pressure is generated. The greatest difference in speed and pressure occurs around the epiglottis, which causes the formation of strong vortex structures. The locations where the velocity and pressure gradients are large are at the velopharynx, oropharynx, and epiglottis levels with narrow cross sections. Wall shear stress is also observed at these locations. The velopharynx, oropharynx, and epiglottis are the structures most vulnerable to morphological changes, meaning that their structures are easy to observe.  
This study has some limitations. The number of subjects was small (\(n=1\)), and the use of CBCT taken in the standing position did not reflect the changes in airway patterns due to gravity. Another limitation is the lack of consideration of other factors that cause OSA. Follow-up studies with more patient groups are needed.  
Conceptualization, Y.-D.K. and E.Y.; methodology, H.S.; software, H.S.; validation, J.M.S. and E.Y.; investigation, N.-R.C. and H.S.; resources, Y.-D.K.; data curation, N.-R.C.; writing--original draft preparation, J.M.S.; writing--review and editing, E.Y.; visualization, H.S.; supervision, Y.-D.K.; project administration, Y.-D.K. 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.
## 4 Results
### Unit Bedload Transport  
Unit bedload rates were computed at the six verticals along the cross-section P1, where HS samples were collected, and MB tests were performed with ADCP. Despite the highest resolution in water velocities obtained with SmartPulseHD (automatically variable frequency: 1.0 MHz and/or 3.0 MHz), the moving-bed velocities measured with this feature were very similar to those obtained with the two manual configuration schemes (1.0 MHz and 3.0 MHz fixed frequencies). The average standard deviation of measurements performed on 31 January 2018 (Figure 10), for example, was 0.019 m/s. On that day, the highest standard deviation happened in Vertical 3 (Std Dev = 0.044 m/s).  
Figure 11 presents the unit bedload rates estimated with HS versus the depth-averaged water velocities measured at each vertical. The low correlation of the plotted data (R = 0.29 for 31 January 2018 and R = 0.44 for 1 February 2018) reinforces the high uncertainties associated with the HS sampl  
Figure 10: Moving-bed velocities measured with ADCP on 31 January 2018 at the six verticals of cross-section P1, in Taquari River.  
Figure 9: (**a**) Underwater DEM (1 February 2018) of the region where the modified ISSDOTv2 method was applied; (**b**) difference of DEMs showing regions of aggradation (+) and degradation (\(-\)) between the surveys.  
Comparing the unit bedload rates based on ADCP-BTe and ADCP-BTvr (Figure 12), there is no trend where one method yields higher bedload rates than the other. For example, at some verticals, the ADCP-BTe unit rates were higher than the ADCP-BTvr estimates, but at other verticals, the opposite happened. This suggests that a compensation of differences exists, influenced by the necessary parameters to apply Equation (1) in each vertical: the thickness of the bedload layer (\(\delta_{b}\)) and the sediment concentration in the bedload layer (\(c_{b}\)). In general, the estimates of \(c_{b_{-}\varpi}\) were lower than \(c_{b_{-}e}\) (which was constant), with an average ratio of 35%. On the other hand, estimates of \(\delta_{b_{-}\varpi}\) were in average twice of \(\delta_{b_{-}e}\), with higher deviations observed at the verticals where flow velocities and bedload transport were more intense.
# ICN-Based Enhanced Content Delivery for CDN
## 3 Proposed Design
### Details
#### 3.4.4 Large Content Chunking
Multiple chunks can be retrieved concurrently from different cache replica routers so that the download latency can be reduced.
## 1 Introduction
### Contribution  
To fulfill the somewhat contradictory requirement of enabling the collection unit to verify the integrity of aggregated meter readings without revealing any individual measurements, we propose a novel aggregation scheme with authentication capability for smart metering. In the proposed scheme, metering data on the routing path is aggregated by exploiting a symmetric homomorphic encryption in such a way that the collection unit can obtain the aggregated data without individual plain measurements being revealed to it or intermediate smart meters en route. The collection unit can verify the integrity of the aggregated measurements by adopting an identity-based sequential aggregate signature mechanism. The identity-based sequential aggregate signature scheme enables signers (that is, smart meters) to attest to their different readings while permitting savings on bandwidth and storage in comparison to public-key solutions. Signatures are aggregated one-by-one as the aggregate-so-far moves along the path, as is natural in the routing-based data aggregation application we consider. In addition, the verification process does not reveal plain metering data to the collection unit since individual data is obfuscated from the collection unit's view.  
Thus, the proposed scheme is secure against any active attacks such as false data injection or data modification attacks launched by outside adversaries or intermediate compromised smart meters on the routing path. In addition, the proposed scheme enables the collection unit to obtain aggregated data, verify its integrity, and authenticate the participating smart meters without any knowledge of individual plain measurements. Therefore, the proposed scheme is also secure against passive attacks such as eavesdropping by a curious collection unit or smart meters in the smart grid network, which preserves user privacy. As opposed to the previous schemes, the proposed scheme enables the collection unit to verify integrity of the aggregated data as a whole (rather than independent verification for each individual measurement) by exploiting the aggregate signature. The efficiency analysis and experimental result show that the proposed scheme is more efficient than the previous schemes [3,5,6,21] while still guaranteeing both user privacy and aggregated data integrity.