esa-sceva/satcom-chunk-collection huggingface dataset:
# An Environmental Perception Framework for Robotic Fish Formation Based on Machine Learning Methods
## 5 The Spacing Judgement of Two Robotic Fishes
### The Tandem Formation
#### 5.1.2 The Construction and Validation of Judgement Models  
Through the results on right side of Figure 16, we found that, at the different inlet flow velocity \(U\), the relationship between \(p2_{3}\) and the spacing \(dx\) is different. Thus, we also need to use the kinematic pressure \(p2_{7}\), which is insensitive to the spacing as a feature for eliminating the effect of inlet flow velocity. In this work, for the presetting appropriate spacing \(\Delta d\), we divide the data into two categories: the label is equal to 0 when the current spacing \(dx\) is less than \(\Delta d\), and the label is equal to 1 when the current spacing \(dx\) is greater than \(\Delta d\). We construct a binary classification model by using the method of neural networks [44; 45]. The model can be used to judge the relationship between the current spacing \(dx\) and the presetting appropriate spacing \(\Delta d\). It has one input layer, two hidden layers and one output layer, as shown in Figure 17. The input layer contains two features, \(p2_{3}\) and \(p2_{7}\). Each hidden layer has 128 neurons and the activation function is Rectified Linear Unit (ReLU). The output layer is the result of classification, which is equal to 0 or 1 and the activation function is Sigmoid. The loss function of the model is binary_crossentropy, and the optimizer is Adam. We used the data simulated by settings in Table 4 to train the classification model, and used the data simulated by settings in Table 6 to validate the model. We used several \(\Delta d\) to construct the judgement models, and the effects are shown in Table 9.  
Figure 16: The relationship between \(dx\) and \(p2_{i}\) at a certain \(U\).  
Figure 17: The neural network structure.  
Through the above neural network structure, we can train a judgement model to compare the current spacing \(dx\) with any \(\Delta d\) in a certain range.
## 2 Results
### Analysis of TG Content in 3T3-L1 Cells  
To investigate the effect of SH and UA extracts on modifying TG content, which is a significant marker of adipogenesis, 3T3-L1 cells were treated with the extracts for 8 d and then analyzed. The TG content in the control cells was measured as 904.2 \(\pm\) 35.4 mg/g of protein. However, in the cells treated with 250 and 500 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of SH extract, they amounted to 739.7 \(\pm\) 26.7 and 688.4 \(\pm\) 22.6 mg/g of protein, respectively; these differences were statistically significant (\(p\) < 0.01). Similarly, significant differences (\(p\) < 0.01) were noted in the case of the UA extract, with 705.9 \(\pm\) 20.9 and 651.3 \(\pm\) 29.0 mg/g of protein measured in the cells treated with 250 and 500 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of UA extract, respectively. Therefore, the results confirmed that the cells treated with both extracts showed inhibition of TG content in a concentration-dependent manner compared to the control cells (Figure 3).  
Figure 1: Effects of _S. horneri_ and _U. australis_ extracts on 3T3-L1 cell viability. (**A**) _S. horneri_ ethanol extract; (**B**) _[PERSON]. [PERSON]_ ethanol extract. The extracts were diluted to 0, 62.5, 125, 250, 500, and 1000 \(\mathrm{\SIUnitSymbolMicro g}\)/mL and used to treat 3T3-L1 cells for 48 h. Cell viability was measured using the WST-1 assay. Data are expressed as the mean \(\pm\) standard deviation (n = 3).  
Figure 3: Effects of _S. horneri_ and _U. australis_ treatment on triglyceride (TG) inhibition during adipocyte differentiation. (**A**) _[PERSON]. horneri_ ethanol extract; (**B**) _[PERSON]. [PERSON]_ ethanol extract. After 8 d of differentiation, the protein was extracted, and the intracellular TG content was determined using enzyme colorimetry. Data are expressed as the mean \(\pm\) standard deviation (n = 3). Significant differences: **\(p<\)**0.01 vs. control cells.  
Figure 2: Inhibition of adipocyte differentiation in 3T3-L1 cells treated with _S. horneri_ and _U. australis_. (**A**) _[PERSON]. horneri_ ethanol extract; (**B**) _[PERSON]. [PERSON]_ ethanol extract. After an 8-day differentiation process, the Oil Red O stain was used; the dye was eluted using 100% isopropanol, and optical density (OD) at 500 nm was measured. Data are expressed as the mean \(\pm\) standard deviation (n = 3). Significant differences: **\(p<\)**0.01 vs. control cells.
###### Abstract  
Global dynamic analysis of a 700-m-long SFT section considered in the South Sea of Korea is carried out for survival random wave and seismic excitations. To solve the tunnel-mooring coupled hydro-elastic responses, in-house time-domain-simulation computer program is developed. The hydro-elastic equation of motion for the tunnel and mooring is based on rod-theory-based finite element formulation with Galerkin method with fully coupled full matrix. The dummy-connection-mass method is devised to conveniently connect objects and mooring lines with linear and rotational springs. Hydrodynamic forces on a submerged floating tunnel (SFT) are evaluated by the modified Morison equation for a moving object so that the hydrodynamic forces by wave or seismic excitations can be computed at its instantaneous positions at every time step. In the case of seabed earthquake, both the dynamic effect transferred through mooring lines and the seawater-fluctuation-induced seaquake effect are considered. For validation purposes, the hydro-elastic analysis results by the developed numerical simulation code is compared with those by a commercial program, OrcaFlex, which shows excellent agreement between them. For the given design condition, extreme storm waves cause higher hydro-elastic responses and mooring tensions than those of the severe seismic case.  
submerged floating tunnel (SFT); mooring line; coupled dynamics; hydro-elastic responses; wet natural frequencies; mooring tension; seismic excitation; wave excitation; seaquake +
Footnote †: journal: Applied Sciences
# Star Identification Algorithm Based on Dynamic Distance Ratio Matching
## 5 Experiment and Analysis
### Focal Length Error  
In the simulation, the focal length value \(f\) is modified to simulate changes in focal length. The accuracy of each identification algorithm is then statistically analyzed under varying focal lengths to assess the robustness of the identification algorithms to focal length changes.  
Based on a focal length of 58.4563 mm, each algorithm's identification rates are tested with a step of 0.5 mm, under a range of errors from \(-3\) mm to \(+3\) mm, as shown in Figure 10.  
As seen in Figure 10, the proposed identification algorithm, which uses distance ratios as features, demonstrates a clear advantage under focal length errors. Next, a detailed analysis of the impact of focal length variations on each algorithm will be provided.  
The improved triangle algorithm and pyramid algorithm, both of which rely on angular distances between stars for identification, show a significant drop in identification rates to below 5% with slight variations in focal length. Analyzing the reasons behind this reveals that these algorithms depend on the coordinates of the star points in the image coordinate system and the focal length to obtain the direction vectors. Therefore, when there is a deviation between the actual focal length and the set focal length, these algorithms quickly become ineffective.  
In the radial axial algorithm, the distances between each neighboring star and the reference star to be identified are measured and used as radial features, with fixed intervals for differentiation. However, as the focal length changes, the positions of the star points also shift, causing variations in the distances between the reference star and its neighboring stars. This ultimately prevents correct radial matching, leading to identification failures. The figure illustrates that as the focal length error increases, the identification rate of this algorithm sharply declines.  
The grid algorithm achieves identification by dividing star points into grids. When the focal length changes, the shape of the entire star map also shifts, causing the star points to continue to diverge outward from the field center. When the divergence error of these star points exceeds the interval of each grid in the grid algorithm, effective identification becomes impossible. The figure shows that as the focal length error gradually increases, the identification accuracy of the grid algorithm declines to below 50%.  
Figure 10: The relationship between focal length and star identification rate.  
The distance ratio algorithm is nearly unaffected by changes in focal length, maintaining an identification rate of over 98%. This effectively demonstrates the algorithm's validity and indicates that it possesses strong robustness in dealing with variations in focal length.
# Energy Consumption Analysis of LPWAN Technologies and Lifetime Estimation for IoT Application
## 4 Approach, LPWAN Communication Technology, and Energy Consumption Analysis
### LPWAN Communication Technology
#### 4.2.2 Dash7
Thus gateway needs to pull the data from an endpoint, in contrast to push where the node voluntarily does the data dissemination. The concept of adhoc synchronisation is the key enabler of PULL mode communication. The adhoc synchronization lets the node to synchronize for commands from the gateway at any random time. When a gateway wants to read data from the end-device, it floods the channel with a background frame which contains a time value. This time value specifies the time when the gateway will send a foreground frame with the actual data. Any node receiving this background frame will go to sleep after switching on a timer with the previously received time value. When the timer triggers, the device will wake up and receive the foreground frame. We profiled the energy consumption of DASH7 in both the PUSH and PULL modes and are presented in Tables 2-4. The end-devices were configured to transmit in LOW_RATE mode with transmission power +14 dBm. The current consumption in PUSH mode as captured by the power  
Figure 10: DASH7 message frame structure. Figure 9: DASH7 network architecture. analyzer is shown in Figure 12. State 1 and 9 in the shown scope view represents the low power sleep mode of the device. DASH7 uses polite spectrum access and hence performs Clear Channel Assessment (CCA) before transmitting a packet. Current consumed in CCA operation is shown by states 3 and 5. Once CCA succeeds, end-device enters transmission mode, which is state 6 to disseminate the packet. Once the transmission is done the end-device enters state 7 where it waits for an Acknowledgement (ACK) from the gateway. Finally, after receiving an ACK in state 8, it goes back to sleep mode. The reception of an ACK is optional and happens if the end-device has explicitly requested for an ACK from the gateway. The PULL mode of data transmission is shown in Figures 13 and 14. It follows a similar current consumption pattern except that there are periodic peaks caused by radio wake-ups. Figure 13 shows a background, foreground, and data transmission process. Where as, Figure 14 shows a detailed view of a packet transmission. In one of the wake-up periods, a background frame is received by the node which is marked by state 2. The background frame contains the time period of state 3 in which the device go backs to low power mode. The reception of a foreground frame happens in state 4 and in state 6 the node transmits the data to the gateway.
# Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review
## 1 Introduction
Different breast image classification methods have been used to assist doctors in reading and interpreting medical images, such as traditional computer-aided diagnosis/detection (CAD) systems [8; 30; 31; 32] based on machine learning (ML) [33; 34; 35], or based on modern CAD-deep learning (DL) system [36; 37; 38; 39; 40; 41; 42]. The goal of CAD is to increase the accuracy and sensitivity rates to support radiologists in their diagnosis decisions [43; 44]. Recently, [PERSON] et al. [45] developed a CAD system for screening mammography readings that demonstrated an approximately 92% accuracy in the classification. Likewise, other studies [46; 47] used several CNNs for mass detection in mammography's and ultrasounds [48; 49; 50]. In general, DL-CAD systems focus on CNNs, which is the most popular model used for intelligent image analysis and for detecting cancer with good performance [51; 52]. With CNNs, it is possible to automate the feature extraction process as an internal part of the network, thus minimizing human interference. DL-CAD systems have added broader meaning with this approach, distinguishing it from traditional CAD methods. The next-generation technologies based on the DL-CAD system solve problems that are hard to solve with traditional CAD [12; 33]. These problems include learning from complex data [53; 54], image recognition [55], medical diagnosis [56; 57], and image enhancement [58]. In using such techniques, the image analysis includes preprocessing, segmentation (selection of a region of interest--ROI), feature extraction/selection, and classification. In this review, we summarize recent upgrades and improvements in new DL-CAD systems for breast cancer detection/diagnosis using mammograms and ultrasound imaging and then describe the principal findings in the classification process. The following research questions were used as the guidelines for this article:  
* How the new DL-CAD systems provide breast imaging classification in comparison with the traditional CAD system? * Which artificial neural networks implemented in DL-CAD systems give better performance regarding breast tumor classification? * Which are the main DL-CAD architectures used for breast tumor diagnosis/detection? * What are the performance metrics used for evaluating DL-CAD systems?
## 4 Empirical Study
### Experimental Results
#### 4.4.2 Parameter Learning and Analysis  
To learn from discrete data, Bnlearn provides two methods for parameter learning: Maximum Likelihood estimation and Bayesian posterior probability estimation [31]. These two methods are the most popular and effective methods for finding the 'best fitting' model from a specific class of models based on a particular dataset, but how they go about it is somewhat different. Maximum Likelihood estimation views the parameters as quantities whose values are fixed but unknown, and estimates parameter values by maximizing the likelihood (probability) of observing the actual training examples. However, Bayesian posterior probability estimation views parameters as random variables having some known prior distribution and transforms the parameters' prior distribution into posterior distribution by observing the actual training examples. Moreover, in Bayesian posterior probability estimation, when considering new samples, the posterior density function gains sharper performance, guaranteeing the accuracy of the nearby peak parameters.  
In cases where the training dataset is very large, the results of the parameter learning are the same using these two methods. However, in reality, and especially in our case, the training set is rather small. Hence, we should consider the choice of these two methods from two perspectives of computational complexity and computational veracity. On the one hand, Maximum Likelihood estimation with differential calculus has lower computational complexity than Bayesian posterior probability estimation with multiple integrals. On the other hand, Bayesian posterior probability estimation normally exhibits better computational veracity than Maximum Likelihood estimation.  
Since the training set is not big, Bayesian posterior probability estimation performs better in this case. We present an example to analyze experimental results of the parameter learning. For instance, Figure 7 shows the conditional probability distribution of the 'Cargo Theft Risk' node using the structure learning algorithm with HC, the score function with k2, the parameter learning algorithm with Bayesian posterior probability estimation and five as the maximum number of father nodes.  
Figure 7: The conditional probability distribution of ‘Cargo Theft Risk’ node using the combination of structure learning algorithm with HC, the score function with k2, the parameter learning algorithm with Bayesian posterior probability estimation and five as the maximum number of father nodes.  
The subfigure in the bottom right corner (highlight in the red and dotted rectangle) of Figure 7 indicates that the cargo theft risk is highest when 'Cargo Weight Deviation' equals 2, 'Means of Payments' equals 2, 'Reformed or Not' equals 1, 'Arrival time' equals 2 and 'Weather' equals 1. In other words, this means that when a reformed truck arrives at the port between 10 p.m. and 6 a.m. to pick up a delivery of prepaid cargo and the cargo weight deviates greatly from the normal value, then there is a significant fraud risk of bulk cargo theft.
# SD-BROV: An Enhanced BGP Hijacking Protection with Route Validation in Software-Defined eXchange
## 4 Implementation and Evaluation
### BGP Hijacking Protection Evaluation
#### 4.3.2 Experiment: Direct Affection  
At the beginning, when the legitimate AS first advertised their BGP routes in SDX, such routes had received by BGP speaker. BGP speaker then advertised the best routes to SDX application as well. After that, SDX application validated the routes received from the BGP speaker according to RPKI database before installing MP2 SP intents. In Figures 8 and 9, the route 80.83.176.0/20 from AS 34868 and 208.65.152.0/22 from YouTube were valid. Therefore, SDX application installed corresponding intents and established the datapath. By completing these steps, end-hosts from these two networks will get reachability through the SDX.  
Figure 8: Received routes for SDX application validating.  
Figure 7: Hijacking in SDX with direct connection.  
In order to prove that the hijacking prefix will not affect the legit traffic exchange destined to a BGP peer network in the SDX, a route 208.65.153.0/24 from Pakistan Telecom AS 17557 intended to hijack the YouTube traffic, and SDX application received this route. However, the validation result showed an \"invalid\" status. As a result, there was no intent created for this hijacking prefix (see Figure 10).  
In this scene, invalid BGP route advertisement kept propagating to the eBGP peers in the SDX through the normal eBGP peering session. Figures 11 and 12 show the contents of the BGP routing table of BGP speaker and Client Network router. Even though the YouTube router received the invalid BGP route, traffic was still forwarded to the legitimate destination. As expected, traffic flow to YouTube 208.65.152.0/22 was not affected. The ping test result shows that even the BGP prefix of hijack targeting network, the constant reachability was still fine (see Figure 13).  
root@clientrouter:=#ping -I 80.83.176.1 208.65.153.101 -c 5 PING 208.65.153.101 (208.65.153.101) from 80.83.176.1 : 56(84) bytes of data.  
64 bytes from 208.65.153.101: icmp_seq=1 ttl=64 time=234 ms  
64 bytes from 208.65.153.101: icmp_seq=2 ttl=64 time=234 ms  
64 bytes from 208.65.153.101: icmp_seq=4 ttl=64 time=234 ms  
64 bytes from 208.65.153.101: icmp_seq=5 ttl=64 time=234 ms  
... 208.65.153.101 ping statistics... 5 packets transmitted, 5 received, 0% packet loss, time 4000 ms  
rtt min/avg/max/dev = 234.052/234.320/234.667/0.240 ms
## 3 Results
### Monte Carlo vs. \"Classical\" Approaches  
Firstly, comparisons are made between the MC evaluations and all the \"classical\" approaches, i.e., the mono-compartmental MIRD approach (MIRD, using the personalized lung mass), LED, and SVOX using a previously published ST VSV kernel [26] (SVOX_ST). All results are presented in a correlation plot shown in Figure 4. This plot also includes the results obtained by rescaling the SVOX_ST data by the voxel's local density according to Equation (3) (SVOX_L).  
Figure 4: Correlation plot of the lungs’ \(\overline{AD}\), obtained from MC simulations with the Reference phantom (x-axis) with those from: mono-compartmental MIRD approach (MIRD), local energy deposition (LED), SVOX with ST kernel with corrections for tissue heterogeneities according to Equation (3) (SVOX_L), or without these corrections (SVOX_ST). For the SVOX_ST data, a previously  
Figure 3: Plot of the \(AD\) per unit decay to the target voxel (y-axis) versus the source-target voxel distance (x-axis) for the previously published ST kernel (\(9\times 9\times 9\)) [26] (data freely available on the website [[https://www.medphys.it/down_svoxel.htm](https://www.medphys.it/down_svoxel.htm)]([https://www.medphys.it/down_svoxel.htm](https://www.medphys.it/down_svoxel.htm)), accessed on 24 July 2024) and the lung kernel calculated in this work (\(32\times 32\times 32\)). The right plot reports the same data shown in the left plot up to a source-target of 20 mm.  
published VSV kernel for soft tissue (ST) was used [26] (data freely available on the website [[https://www.medphys.it/down_svoxel.htm](https://www.medphys.it/down_svoxel.htm)]([https://www.medphys.it/down_svoxel.htm](https://www.medphys.it/down_svoxel.htm)), accessed on 24 July 2024). For each dataset, each point in the plot is associated with an increasing \(LS\) value (10%, 20%, 30%, and 40%), and a line representing the linear interpolation of each dataset serves as a qualitative eye guide only.  
All tested methods severely underestimate the \(MC\) results. The RD computed according to Equation (6) is reported in Table 2, while the \(\overline{AD}\) of each approach is reported in Table 3. The \(RD\) values are approximately \(-40\%\) for MIRD, \(-27\%\) for LED, \(-88\%\) for SVOX_ST, and \(-28\%\) for SVOX_L.  
Considering the activity map with \(LS\) = 0% (no activity in the lungs), all the classical approaches intrinsically yield \(\overline{AD}\) = 0, whereas the \(MC\) simulation yields a dosimetric contribution of the radiation transport from the hepatic dome to the lungs, resulting in 0.20 Gy/GBq.  
The MIRD approach using a 1 kg lung mass resulted in smaller \(\overline{AD}\) to lungs (as the lung mass is 0.724 kg), with values ranging from 4.9 Gy/GBq to 19.8 Gy/GBq increasing LS, with a constant RD of \(-27.6\%\) with respect to the MIRD \(\overline{AD}\) obtained with personalized lung mass, due to the mass difference.
# Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar
## 4 Discussion
### Discrepancies among Datasets
Thus, the degree of forest consistency across datasets is relatively better. However, the tropical climate is more prevalent in central and southern Myanmar. The terrain is flatter, human activities are complex, the total amount of forest is reduced, and the landscape type is dominated by cropland and constructed land. In contrast, although northern Myanmar has a predominantly subtropical climate, temperatures and precipitation conditions slightly less favorable than those of the tropics. However, its high terrain allows for less human interference and dense, continuous forest growth. The high altitude also limits deforestation, so northern Myanmar has the best forest consistency. The actual forest changes due to the year's changes cannot be ignored. Figure 13 specifically depicts the differences between datasets due to actual forest changes. Figure 13: Examples of actual forest change in Myanmar, 2010–2020. (**a**) Differences between datasets; (**b**) Actual forest change during 4 years.
# Toward Improving the Reliability of Discrete Movement Recognition of sEMG Signals
## 4 Proposed Methods  
As mentioned above, ambiguity rejection and novelty rejection are two concerns of this study. Though complex models, such as DNNs [29], may produce highly confident predictions, they are more computationally expensive to train and require more data to learn the model [14]. Therefore, two simpler models are preferred in this study.