esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Edge Intelligence  
Edge Intelligence (EI) is a concept that defines the communication, computing, and storage capabilities of a specific infrastructure closest to the local unit user of a distributed network. Previous studies introduced the word \"Edge Intelligence\" [24]. \"Edge\" is an actual location representation in which data is generated and processed. In other words, the edge is the location of the control and computing devices. According to a recent study, devices equipped with small computers are changing to internet of things (IoT) and industrial internet of things (IIoT). These devices work with the addition of AI functions. Edge Intelligence performs edge computing in the analysis performed on AI and ML models. The intelligent edge generally breaks the existing client-server model, and the server has the ability to process, analyze, and protect data [25]. Edge Intelligence has three main entities: connection, computing, and control [26]. This Edge Intelligence allows manufacturing systems to refine and classify data to the cloud without additional connections. In addition, Edge Intelligence Devices (EID) interconnect a range of workers, managers, smart facilities, robots, and sensors, and enables extensive connections such as smart factories. Hyper-connected Edge Intelligence systems can collect, manage, analyze, and store large amounts of data through distributed computing. This local computing unit can be combined with a cloud system to improve or replace computing performance. Edge computing is mainly performed by a data collector in a production facility or by an edge server (or IIoT gateway). This feature applies to specific business services that require control of the insights calculated by these local units and may extend to the cloud.
# Skin Lesion Segmentation by U-Net with Adaptive Skip Connection and Structural Awareness
###### Abstract  
Skin lesion segmentation is one of the pivotal stages in the diagnosis of melanoma. Many methods have been proposed but, to date, this is still a challenging task. Variations in size and color, the fuzzy boundary and the low contrast between lesion and normal skin are the adverse factors for deficient or excessive delineation of lesions, or even inaccurate lesion location detection. In this paper, to counter these problems, we introduce a deep learning method based on U-Net architecture, which performs three tasks, namely lesion segmentation, boundary distance map regression and contour detection. The two auxiliary tasks provide an awareness of boundary and shape to the main encoder, which improves the object localization and pixel-wise classification in the transition region from lesion tissues to healthy tissues. Moreover, concerning the large variation in size, the Selective Kernel modules, which are placed in the skip connections, transfer the multi-receptive field features from the encoder to the decoder. Our methods are evaluated on three publicly available datasets: ISBI2016, ISBI 2017 and PH2. The extensive experimental results show the effectiveness of the proposed method in the task of skin lesion segmentation.  
medical image segmentation; skin lesion; multi-tasking network; selective feature engineering
# Cyber-Security Threats and Side-Channel Attacks for Digital Agriculture
## 3 Threats to Digital Agriculture
### Big Data and Machine Learning Threats  
A tremendous amount of data is generated from sensors and autonomous farming machines. Machine learning and artificial intelligence techniques provide a unique insight that can be used to improve food production and use the limited resources optimally. However, it raises concerns about the privacy and accuracy of data. Data compromise, falsification, or eavesdropping could skew the ML/AI algorithm, revealing the IP or creating data ownership tension between stakeholders [31].
# Preliminary Global NO2 Retrieval from EMI-II Onboard GF5B/DQ1 and Comparison to Tropopmi
## 3 Results and Validation
### Tropospheric Vcd
This method has been theoretically proven to be feasible and is widely applied in operational retrieval algorithms ([PERSON], 2004; [PERSON], 2004; [PERSON], 2004). Additionally, an empirical aerosol optical thickness value of 0.06 was adopted during the computation of the box-AMF LUT, which is consistent with the method used by [PERSON] et al. (2019) [PERSON] et al. (2019). We further calculated the box-AMF both with and without the presence  
Figure 7: Spatial distributions of tropospheric NO\({}_{2}\) VCD over the Arabian Peninsula and Iran region on 6 July 2022 (**top**), weekly average of 16–22 July 2022 (**middle**), and monthly average of 27 June–27 July 2022 (**bottom**) of GF5B and DQ1 EMI-II and of TROPOMI. of aerosols and assessed the effects of aerosols on the box-AMF. When the aerosol optical depth is set to 0.1, the absolute difference between the two calculations does not exceed 0.01. In extreme scenarios, the influence of aerosols on the box-AMF is limited to a maximum of 7%. Under typical conditions (surface pressure = 1013 hPa, albedo = 0.05, SZA = 0\({}^{\circ}\), VZA = 0\({}^{\circ}\), and RAA = 45\({}^{\circ}\)), the impact of aerosols on the box-AMF is approximately 0.39%.
# Internet of Things and Distributed Computing Systems in Business Models
## 4 Theoretical Framework
### IoT and CDS Impact on Business Models
#### 4.4.3 Enhanced Operational Efficiency  
IoT and Distributed Computing Systems enhance operational efficiency across various business functions. For instance, businesses utilize IoT devices to continuously monitor and report on various operational parameters [55]. This provides them unprecedented visibility into their processes. In addition, [PERSON] et al. [56] indicate that this real-time monitoring allows organizations to identify and address inefficiencies promptly, ensuring smoother operations and reducing wastage. Distributed computing systems complement IoT by providing the computational power needed to process the huge number of information created by IoT machines [57; 58]. These systems can distribute the computational load across multiple nodes, ensuring efficient data processing without bottlenecks. For instance, a large manufacturing plant can use distributed systems to aggregate data from hundreds of IoT sensors, analyze it to spot regularities, and provide actionable data to operators and decision-makers [59]. This decentralized approach to information sorting guarantees rapid responses to operational issues.
###### Abstract
The binding affinities of each of the peptides and their conjugates was compared on the basis of molecular docking abilities with ER-\(\alpha\) (PDB ID: 3 OS8), Epidermal Growth Factor Receptor (EGFR) (PDB ID: 2 RGP), and peroxisome proliferatorator-activated receptor alpha (PPAR-\(\alpha\)) (PDB ID: 2P54) in order to evaluate potential targeting abilities of these conjugates. We further conducted molecular docking studies with the unmutated original peptide (pep) sequence as a point of comparison. In several cases, the novel conjugates displayed comparable or improved binding affinities for ER compared to ER's native ligand estradiol [31]. The stability and compactness of the receptor-docked ligands were examined using MD simulations and average binding energies were estimated using MMGBSA calculations from the trajectories. Overall, in this study, we were able to refine the targeting ability of the peptide Y-H-W-Y-G-Y-T-P-Q-N by inducing point mutations, conjugating it to polyphenols, and assessing targeting of ER-\(\alpha\), PPAR-\(\alpha\) and EGFR. We found that several peptide conjugates possessed comparable targeting abilities for all three receptors, while many of the mutated peptides and conjugates exhibited improved binding. Thus, we have determined for the first time that the tumor targeting peptide, its single-point mutated peptides, and some of the novel diterpene/polyphenol-peptide conjugates may be beneficial for developing drugs for targeting of ER-\(\alpha\) and EGFR. The capability of these peptides and peptide conjugates to bind to PPAR-\(\alpha\) also indicates their potential for targeting several other types of cancer cells. Overall, the conjugates that exhibited the most promising binding interactions for all three receptors were Rosmarinate-Y-H-W-Y-G-Y-T-P-Q-N, Gallate-F-H-W-Y-G-Y-T-P-Q-N and Gallate-Y-I-W-Y-G-Y-T-P-Q-N. \begin{table}
\begin{tabular}{c c} \hline Original Peptide (Pep) & YHWYGYTPQN \\ \hline Mutation 1 (Mut1) & FHWYGYTPQN \\ \hline Mutation 2 (Mut2) & YIWYGYTPQN \\ \hline Mutation 3 (Mut3) & YHWYGYTHQN \\ \hline Mutation 4 (Mut4) & YHWYGYTPQD \\ \hline \end{tabular}
\end{table} 
Table 1: Peptide Sequences. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**A** & **B** & **C** & **D** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Structures of Polyphenol-peptide Conjugates with Pep and Mut1-Mut4. Sequences from top to bottom. (Column A = carnosic amide derived conjugates, Column B = Chlorogenic amide conjugates; Column C = gallic amide conjugates and Column D = Rosmaritic amide derived conjugates).
# IoT-Chain and Monitoring-Chain Using Multilevel Blockchain for IoT Security
## 7 Conclusions  
We have proposed a lightweight blockchain and multilevel blockchain structure for IoT security. The challenges of existing blockchains, such as increasing the blockchain capacity, the consensus algorithm's calculation amount, the network communication volume, and privacy not being guaranteed, can be overcome through this blockchain structure. To solve the problem of increasing the blockchain capacity, the \(IC\) that can be operated in the IoT is stored in the \(DFS\) for each specific round, and the returned \(Address_{ic}\) is delivered to \(Network_{mc}\) and stored to save the \(IC\). To solve the problem of excessive computation and network traffic of the existing consensus algorithm, the \(VRF\)-based leader node election method, which is a random function, is used, and the average delay time was only 0.004 s. A stable and tamper-resistant embedding method was applied by constructing a multi-chain architecture and establishing a reliable metadata delivery consensus method and export node between chains (IoT-Chain-Monitoring-Chain). We have proposed a consensus algorithm that is suitable for IoT devices that produce data and generate transactions. It was confirmed that the block propagation time was not affected by the data size. Moreover, in the case of the \(IC\) export, when generating Schnorr signatures during the consensus process, the delay time was not affected by the number of nodes. As a result of the total time measurement analysis of the export, only the delay time of the Schnorr signature increased linearly according to the number of nodes. Furthermore, it was confirmed that the upload time to the \(DFS\) and the transfer time from \(Network_{ic}\) to \(Network_{mc}\) were constant. After distributing the \(IC\) to solve the privacy problem, \(Address_{ic}\) was stored in the \(MC\) that was implemented based on the private blockchain HF so that only users who have been granted access to data can access the data. The results of the TPS measurement demonstrated that one of the performance indicators of the blockchain system, \(Network_{ic}\), is suited to devices similar to actual IoT devices. As a result of constructing and measuring the \(TPS\), it was proven to maintain more than 1000 tps. In a future study, the Schnorr signature and data transmission delay time from \(Network_{ic}\) to \(Network_{mc}\) will be reduced to decrease the export time, and a study will be conducted on configuring the optimal route of the routing table of \(Node_{export}\).  
Conceptualization, D.N.; methodology, D.N.; programming, D.N.; writing--original draft preparation, D.N.; supervision and review, S.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Bisa Research Grant of Keimpung University in 2021.  
**Institutional Review Board Statement:** Not applicable.  
Figure 16: IoT-Chain TPS measurement.
## 3 Results and Discussion
### Real Wastewater  
#### 3.3.1 Photo-Fenton  
The scavengers present in the water, such as phosphate, carbonates or organic matter, reduce the removal efficiency of the photo-Fenton process. [PERSON] et al. (2016) found that oxytetracycline elimination was inhibited up to 20% by the presence of 10 mM of phosphates or 10 mM of carbonates, and the tetracycline removal was around 10% lower in their presence. A similar trend was found by [PERSON] et al. (2016), who reported that diazepm, fluoxetine, paracetamol and progesterone removal decreased between 40 and 60%, although other compounds such as furosemide and dipyrone did not show a significant difference in elimination.  
This phenomenon also happened in our investigations, where 5 mg L\({}^{-1}\) of iron and 40 mg L\({}^{-1}\) of H\({}_{2}\)O\({}_{2}\) with 500 W MP lamps was tested. A total of 72% of the target compound was removed after 20 min of the photo-Fenton process was applied for real wastewater at optimum conditions (Figure S3). The 80% removal was achieved in less than 30 min of the process, meaning less than 36 s of irradiation. Iron remained dissolved in the solution during the whole experiment, while H\({}_{2}\)O\({}_{2}\) was consumed by 10 mg L\({}^{-1}\).
###### Abstract
Given that the seepage and failure processes of rocks are time-dependent, studying the seepage characteristics of rocks in relation to time and fracture is also essential. Such investigations can improve stability and rock slope predictions by identifying the seepage characteristics of low-permeability rocks during the fracture process. In this study, a non-Darcy steady-state seepage test was conducted on limestone samples after the peak by using the triaxial seepage test system produced by TOP INDUSTRIE Company in France. The relationship between the seepage velocity and pressure gradient and the curve of axial strain and water head pressure under different hydrostatic pressures after the peak were obtained, and regular results were achieved. Unlike in the numerical test method, in the physical test method, the specimen is wrapped in a closed cylinder. The entire failure process of the specimen cannot be observed, and even the occurrence of failure cannot be judged by the naked eye. Therefore, this study used a special numerical simulation method to establish a plane model of non-uniform rock sample seepage, and the whole failure process was investigated from the meso perspective. The specific research contents are as follows. (1) Limestone is dense in texture, and its permeability is mainly fracture permeability. Therefore, prefabricated weak surface and prefabricated weak zone models were adopted in the current study, and the seepage channel was formed by axial loading. The stress state change of the rock samples and the evolution law of permeability in the process of damage and failure under high osmotic pressure were studied. The analysis focused on the variation of the stress-strain-permeability coefficient during the whole process and of the permeability law during crack initiation, propagation, and coalescence. (2) The prefabricated weak surface was similar to the crack formed by artificial splitting, so the biting effect of the crack surface was worse than that of the natural crack,and the roughness and fluctuation of the natural crack surface could not be effectively simulated. Therefore, the shear crack formed by the force of the complete model was also regarded as the seepage channel to simulate the permeability characteristics of the natural crack. (3) Rock as a brittle material exhibits the acoustic emission phenomenon during its failure process. The failure of rock samples can be predicted by monitoring and collecting acoustic emission information during the failure process. By collecting acoustic emission information during the failure process of the three models mentioned above, the failure and seepage catastrophe of the samples in this study were predicted. (4) Through numerical simulation, the whole stress process, micro-fracture distribution, crack development direction, and permeability of the rock samples were revealed to vividly and concretely reflect the crack situation. The results can provide a reference for the real-time control and prevention of accidents.
###### Abstract  
Watermelon produces many byproducts (watermelon rind and skin) even though those components contain various bioactive compounds, including citrulline. This study evaluated the citrulline concentration, total phenolic content, and antioxidant activity (DPPH and FRAP assays) of different parts of watermelon and investigated the bioavailability of citrulline from different parts of watermelon using an in vitro human intestinal epithelial Caco-2 cell monolayer model. Solid-phase extracted watermelon flesh, rind, and skin samples were treated on a Caco-2 cell monolayer for 1, 2, and 4 h. The collected basolateral solution at each time point was analyzed for the percentage of citrulline transport. Watermelon flesh had the highest citrulline content, but the watermelon skin had the highest total phenolic content and antioxidant activity compared to other watermelon parts. The citrulline bioavailability showed greater % transport in watermelon skin than in watermelon flesh, rind, and L-citrulline standard. It may be due to the different food matrices of watermelon parts. This suggests that the utilization of watermelon by-products such as skin would help develop value-added products with better bioavailability of citrulline. However, since this study was conducted with an in vitro cell model, more extensive research with in vivo studies will be needed.  
waternalon; citrulline; bioavailability; watermelon flesh; watermelon skin; Caco-2 cells +
Footnote †: journal:  
utilization [14; 15; 16]. Converting watermelon by-products with high bioactive value to value-added products would benefit functional food industries and the human diet and have a better environmental impact with less food waste [3].  
Citrulline is a non-essential non-proteinogenic amino acid, and watermelon is one of the rich sources of citrulline [17]. Watermelon find has especially shown to have greater citrulline content than watermelon flesh [12; 18]. Citrusiline is a product of the nitric oxide (NO) cycle and a precursor of arginine [19]. Since arginine is a conditionally essential amino acid related to the NO system, citrulline has a potential role in vasodilation and cardiovascular functions [20; 21; 22]. citrulline also showed hydroxyl radical scavenging activities [23]. Thus, many studies have been on the therapeutic activities of citrulline in watermelon [24; 25; 26]. However, the bioavailability of citrulline needs to be considered since it depends on its intestinal absorption and different food matrix [24; 27; 28; 29]. There have been some studies on the bioavailability of citrulline in watermelon [27; 30; 31], but little is known about the bioavailability of citrulline from different watermelon parts. Therefore, this study evaluated the chemical composition of different watermelon parts (flesh, rind, and skin) and investigated the intestinal uptake of citrulline from watermelon flesh, rind, and skin using the differentiated monolayer human intestinal Caco-2 cell model.
# Small-Scale Zero-Shot Collision Localization for Robots Using RL-CNN
## 5 Experiment Results and Discussion
### Experiment Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Hyper-Parameters** & **Settings** \\ \hline \multirow{8}{*}{RL-CNN} & Number of convolutional layers & 4 \\  & Kernel size & 3 \(\times\) 3 \\  & Striding and padding & 2, ”same” \\  & Learning rate & 1 \(\times\) 10\({}^{-6}\) \\  & Mini-batch size for training & 64 \\  & Optimizer & Adam \\  & Loss function & Cross-entropy \\ \hline \multirow{8}{*}{BN-DCNN} & Number of convolutional layers & 2 \\  & Kernel size & 3 \(\times\) 3 \\  & Striding and padding & 2, ”same” \\  & Learning rate & 1 \(\times\) 10\({}^{-5}\) \\  & Mini-batch size for training & 64 \\  & Optimizer & Adam \\  & Loss function & Cross-entropy \\ \hline \multirow{8}{*}{DCNN} & Number of convolutional layers & 2 \\  & Kernel size & 3 \(\times\) 3 \\  & Striding and padding & 2, ”same” \\  & Learning rate & 1 \(\times\) 10\({}^{-5}\) \\  & Mini-batch size for training & 64 \\  & Optimizer & Adam \\  & Loss function & Cross-entropy \\ \hline \multirow{8}{*}{BiLSTM} & Number of hidden units & 30 \\  & Learning rate & 1 \(\times\) 10\({}^{-5}\) \\  & Mini-batch size for training & 64 \\  & Optimizer & Adam \\  & Loss function & Cross-entropy \\ \hline \multirow{8}{*}{GRU} & Number of hidden units & 30 \\  & Learning rate & 1 \(\times\) 10\({}^{-5}\) \\  & Mini-batch size for training & 64 \\  & Optimizer & Adam \\  & Loss function & Cross-entropy \\ \hline \multirow{8}{*}{BiGRU} & Number of hidden units & 30 \\  & Learning rate & 1 \(\times\) 10\({}^{-5}\) \\  & Mini-batch size for training & 64 \\  & Optimizer & Adam \\  & Loss function & Cross-entropy \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The settings of the hyper-parameters of the proposed method and the compared methods. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **RL-CNN** & **DCNN** & **BN-DCNN** & **LSTM** & **BiLSTM** & **GRU** & **BiGRU** \\ \hline Case 1 & 85.33\% (+7.71\%) & 76.04\% & 77.62\% & 72.04\% & 72.37\% & 75.04\% & 75.24\% \\ Case 2 & 79.89\% (+1.41\%) & 71.08\% & 78.48\% & 63.26\% & 63.37\% & 65.87\% & 66.04\% \\ Case 3 & 82.96\% (+5.24\%) & 74.42\% & 77.72\% & 74.88\% & 74.02\% & 67.25\% & 64.80\% \\ Case 4 & 86.75\% (+7.77\%) & 72.37\% & 78.98\% & 68.06\% & 71.60\% & 68.61\% & 68.70\% \\ Case 5 & 76.08\% (+2.85\%) & 66.80\% & 73.23\% & 62.99\% & 66.52\% & 64.71\% & 65.70\% \\ Run Time & 4.32 ms & 1.77 ms & 2.24 ms & 4.32 ms & 8.12 ms & 4.22 ms & 7.71 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The prediction accuracy of different radial regions.
## 3 Proposed Design
### Application Traffic Patterns Identification
#### 3.1.5 Blood Pressure (BP)  
BP is the measurement of the pressure of circulating blood on the walls of vessels. During a cardiac cycle, BP fluctuates between a maximum (systolic) and a minimum (diastolic) pressure. BP readings have a significant relation with cardiovascular events; therefore, BP is an important parameter for monitoring activity of patients. Considering sampling rate 100 Hz and the sampling resolution 12 bits we have SIT = 10 ms; MPDS = 150 bytes/s; DNS after 10 ms including minimum header size = 16.5 bytes = 132 bits; Total data rate required by Pulse oximeter packet = 13.2 Kbps.
## 2 Materials and Methods
### Statistical Analysis  
The Critical Power Model (CPM) parameters (CP and W\({}^{\prime}\)) calculated at each 15 s time interval were subsequently compared to the parameters derived from the 3 min all-out test (\(CP_{3 MT}\) and \(W^{\prime}_{3 MT}\)). A one-way repeated-measures analysis of variance (ANOVA) model was used to assess differences between the CPM parameters at different time intervals and the corresponding 3 MT parameters. The Dunnett- Hsu post-hoc procedure was employed to control for Type I errors associated with multiple comparisons, determining whether there were any significant differences between the parameters estimated at each time interval and those from the 3 MT. An illustration of 60 s time duration compared to 3 MT is depicted in Figure 1.  
To evaluate the relationship between the shortened duration estimates and the 3 MT estimates, simple linear regressions were performed. In these regressions, the slope was tested against 1 rather than the typical value of 0 to determine the degree of deviation from identity between the estimates. Additionally, Bland-Altman plots were constructed to further investigate the level of agreement between the estimates from the shorter duration test and the 3 MT. The range of agreement was defined as the mean bias \(\pm\)2 standard deviations (SDs), and it was determined that 96% of values fell within these limits. The a priori acceptable limit of agreement was established at \(\pm\)5%, providing a benchmark for assessing the validity of the shorter duration estimates compared to the 3 MT.