esa-sceva/satcom-chunk-collection huggingface dataset:
# Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image Analysis
## 4 Experimental Results and Analysis
### Comparison
The reason is that the grayscale difference between neighboring pixels in a low-contrast environment is extremely small. The Sobel is less sensitive to such small grayscale changes, and it is difficult to accurately locate the edge position. Then, the phenomenon of edge loss and edge blurring occurs. The Canny primarily comprises Gaussian filtering, gradient magnitude computation, non-maximum value suppression, and double-threshold detection. In this method, the gradient magnitude  
Figure 9: The results of detected edge images in terms of F1-score using different methods. computation is used to identify edges. The gradient magnitude computation step is also based on the grayscale difference between pixels. A weak contrast makes the gradient magnitude overall at a low level. With low contrast, weaker edges are easily suppressed due to the magnitude not reaching the threshold, resulting in incomplete edge detection. CIED shows significant advantages in cases of different contrasts, especially when the contrast is low. It extracts edge information by analyzing the three MSB planes and is almost independent of contrast variations since it does not depend on gray level differences. Not only that, but also when the contrast is reduced, it may also suppress disturbing factors such as noise to a certain extent. This process helps the CIED to recognize the edges better and thus achieve stable edge detection. It is indicated that CIED is robust to contrast-invariant. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Evaluation Metrics** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** & **100\%** & **110\%** & **120\%** & **130\%** & **140\%** & **Average** \\ \hline Accuracy—Sobel & 0.975 & 0.965 & 0.954 & 0.940 & 0.924 & 0.905 & 0.882 & 0.858 & 0.829 & 0.809 & 0.905 \\ Accuracy—Canny & 0.990 & 0.989 & 0.988 & 0.986 & 0.983 & 0.981 & 0.979 & 0.976 & 0.974 & 0.972 & **0.982** \\ Accuracy—CIED & 0.975 & 0.984 & 0.972 & 0.969 & 0.964 & 0.975 & 0.956 & 0.970 & 0.966 & 0.963 & 0.969 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The comparison result of detected edge images at different contrast levels using Sobel, Canny, and the CIED. Figure 10: The demonstration of detected edge images at different contrast levels using Sobel, Canny, and CIED methods.
## 3 Experimental Proceeding  
The process of measuring an electrical or physical phenomenon is called data acquisition (DAQ). A DAQ system is normally composed by one or more sensors, a DAQ measurement device or hardware and a computer with programmable software as shown in Figure 2 [4].  
Figure 1: Laminar flowmeter.  
Figure 2: Parts of a data acquisition (DAQ) system.  
The DAQ system used to automate the laminar flowmeters in this work was composed by: two sensors (an absolute pressure and temperature sensor and a differential pressure sensor), an Arduino microcontroller board and a computer (Figure 2).  
In Figure 3 the solution's is presented with all the components: The MEMS capture the three input variables (\(p\), \(T\) and \(\Delta p\)). The Arduino acquires the signals from the sensors, stabilizes them if necessary and converts them. These signals are sent to the computer where they are processed according to information programmed in LabVIEW and finally the desired outputs (\(Q\), \(Q_{a}\) and \(Qstd\)) are obtained.  
A MS5611-01 BA03 barometric pressure sensor, with a stainless steel cap [12] was used, to measure pressure and temperature (see Figure 3). This is a new generation of a high resolution altimeter sensor, which the specifications are presented in Table 1. A differential and gauge, integrated pressure sensor, MPXV5004 DP, was used to measure the pressure drop [13], and the respective specifications are presented in Table 2. The MPXV5004 DP MEMS combines a highly sensitive implanted strain gauge with advanced micromachining techniques, thin-film metallization and bipolar processing to provide an accurate, high level analog output sign that is proportional to the applied pressure [13]. The data acquisition system used the micro controller board Arduino Leonardo [14] based on the Atmega32u4 microprocessor [15]. The Arduino Leonardo has 20 digital input/output pins, 16 MHz crystal oscillator, a micro USB connection [14]. In Table 3 are presented the Atmega32u4 principal specifications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor Performance (\(V_{S}\)= 3 V)** & **Minimum** & **Maximum** & **Unit** \\ \hline Pressure Range & 10 & 1200 & mbar \\ Pressure Accuracy (25 \({}^{\circ}\)C; 750 mbar) & \(-\)1.5 & +1.5 & mbar \\ Error Band ([\(-\)20, +85] \({}^{\circ}\)C, [450, 1100] mbar) & \(-\)2.5 & +2.5 & mbar \\ Temperature Range & \(-\)40 & +85 & \({}^{\circ}\)C \\ Temperature Accuracy & \(-\)0.8 & +0.8 & \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: MS5611-01 BA03 technical data.  
Figure 3: Solution’s setup.
## 3 Wireless Communication Schemes for Aircraft
### IDF Scheme  
The IDF is the DF-based communication, which has three phases. In the first phase, the source node broadcasts to the relay and destination node. In the second phase, the destination node makes a decision whether to request the packet of the source node or not, and the destination node gives an ACK or a NACK to the relay node and the source node. In the third phase, according to the decision of the destination node, the relay node forwards the packet or keeps silent. Thus, the total energy consumption [42] of the IDF can be expressed by:  
\[\begin{split} E_{IDF}=&\left(1-\rho_{SD}\right)\frac{ \left(P_{AMP}+P_{TX}+2P_{RX}\right)\kappa}{\theta R_{b}}\\ &+\rho_{SD}\cdot\rho_{SR}\frac{\left(P_{AMP}+P_{TX}+2P_{RX} \right)\kappa}{\theta R_{b}}\\ &+\rho_{SD}\cdot\left(1-\rho_{SR}\right)\frac{\left(2P_{AMP}+2P_ {TX}+3P_{RX}\right)\kappa}{\theta R_{b}}.\end{split} \tag{30}\]  
where the first term shows when the destination node decodes the packet sent directly from the source node successfully, the second term shows the energy consumption when both of the relay node and the destination node fail to decode the packet from the source node, and the third term shows when the cooperative relaying works fully.  
The IDF throughput can be obtained by:  
\[T_{IDF}=R\left(1-\rho_{SD}\right)+\frac{R}{2}\rho_{SD}\left(1-\rho_{SR}\right) \left(1-\rho_{RD}\right) \tag{31}\]  
where the first term represents the throughput when the packet of the source is conveyed successfully to the destination node, and the second term represents the throughput when the packet is conveyed via the relay node to the destination node.
## 7 Applications in SDN-based VANETs
### Heterogeneous Support  
In the VANET-based correspondences framework, heterogeneity is a regular thing. Although the VANETs utilizing IEEE 802.11p (WiFi) and cell correspondence rely upon either V2V or V2I. Novel VANET applications require increasingly productive and savvy correspondence advances to decrease correspondence cost. The known and financially cheap innovation is the DSRC, but it is not appropriate for the V2I interchanges as its remote range for communication is short. Then again, for long-extended correspondences, 4G/5G cell systems are utilized at the same time and they are exorbitant. Others such as Wireless Access in Vehicular Environments (WAVE), Universal Mobile Telecommunication Service (UMTS), and Worldwide Interoperability for Microwave Access (Wi-MAX), are additionally accessible. In the SDN-based VANETs scientists are allowed to pick access innovations for the various layers of SDN-based VANETs, which empowers heterogeneous accessibility and enhances the utilization of transfer speed and the channel [43; 144; 145].
# Discussion on the Design of Sprayed Eco-Protection for Near-Slope Roads Along Multi-Level Slopes
## 7 Original Design Scheme
### Design Comparative Analysis
#### 7.4.2 Safety Comparative Analysis  
The original design neglected road layouts for maintenance phases and employed overly simplistic configurations, limiting equipment mobility and causing congestion risks. The current design enhances flexibility, supports simultaneous multi-equipment operations, and improves efficiency while mitigating safety hazards from congestion. Critical safety improvements:  
1. Curve radii: Original undersized radii hindered large equipment and risked collisions; current radii comply with minimum turning requirements for all vehicles.
2. Gradient design: Original steep gradients caused tire slippage; current gradients (e.g., 12%) ensure stable operation.
3. Turnaround areas: Original improperly sized areas impeded maneuvers; current dimensions (12 m \(\times\) 11.5 m) enable safe turning.  
In summary, the proposed design advances construction efficiency, cost-effectiveness, and safety compliance, aligning with the industry's \"safety first\" principle.
# Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods
## 1 Introduction
Considering that the backpropagation neural network (BPNN) [28] and CNN [13] models are capable of simulating complex nonlinear problems and exhibit good computational efficiency, these two models are used to predict the EDP of structures subjected to earthquake, to reflect the destructive power of earthquake to structures. Specifically, in Section 2, the analysis procedure of the method is introduced; in Section 3, the details of the adopted structural model and the ground motion records are presented; in Section 4, two feature extraction methods of ground motions are proposed, and the BPNN is used to assess the earthquake destructive power to structures; in Section 5, the CNN is adopted to extract the features of ground \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Literatures** & **Simulation** & **Problem Type** & \begin{tabular}{c} **Computational** \\ **Workload** \\ \end{tabular} & **Machine Learning Methods** \\ \hline [PERSON] et al. [23; 24] & Structural damage states & Classification & Light & Discriminant analysis, k-nearest neighbors, decision trees, random forests \\ [PERSON] et al. [25] & \
\begin{tabular}{c} Structural \\ time-history \\ response \\ \end{tabular} &
\begin{tabular}{c} Time series \\ prediction \\ \end{tabular} & Moderate & LSTM network \\ This study & EDP & Value prediction & Light & BPNN, CNN \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of machine learning-based seismic response simulation methods. LSTM, long-short-term memory; BPNN, backpropagation neural network; CNN, convolutional neural network; EDP, engineering demand parameters. motions and predict the earthquake destructive power to structures. The proposed method aims to realize a rapid evaluation of earthquake destructive power to structures based on machine learning methods. It is expected to provide a useful reference for timely emergency response and disaster relief after the earthquake.
## Development of a Remote Monitoring System for Henhouse Environment Based on IoT Technology  
**[PERSON], [PERSON], [PERSON] *, [PERSON], [PERSON] and [PERSON]**  
Jiangsu Key Laboratory of Intelligent Agricultural Equipment, College of Engineering, Nanjing Agricultural University, Nanjing 210031, China; E-Mails: [EMAIL_ADDRESS] (H.L.);  
wanghuan_njau(@163.com (H.W.); [EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (Y.Q.);  
[EMAIL_ADDRESS] (F.H.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-025-58606690; Fax: +86-025-58606699  
Academic Editors: [PERSON] and [PERSON]  
_Received: 25 June 2015 / Accepted: 27 August 2015 /Published: 10 September 2015_  
**Abstract:** To monitor multiple environmental factors of henhouses in modern chicken farms, a henhouse online monitoring system based on wireless sensor network was developed using wireless sensor technology and computer network technology. Sensor data compensation and correction were designed to be achieved using software and data fitting methods, data reliable transmission achieved using a data loss recovery strategy, and data missing during monitoring addressed using a self-decision and online filling method. Operation test of the system showed that: The system was economic and reliable; it enabled wireless monitoring and Web display of the environmental factors of a henhouse; and the root mean square errors (RMSEs) between the estimated values from the self-decision and on-line filling method and experimental values of the four environmental factors were 0.1698, 3.0859, 77 and 0.094, respectively, indicative of high estimation accuracy. The system can provide support for modern management of henhouses and can be transplanted to related monitoring scenarios in the agricultural field.  
**Keywords:** henhouse environmental parameter; IoT technology; remote monitoring; loss recovery
## 2 Study Area and Methods
### Data Sources
#### 2.2.4 Other Data and Processing  
The preprocessing of each remote sensing datum mainly includes image stitching, projection, extraction, resampling and masking processes. First, in order to ensure the minimum area deformation of each datum, in this study, we projected all the remote sensing data into the WGS84 geographic coordinate system and the Albers equal-area conic projection spatial coordinate system, and we resampled the data to 1 km \(\times\) 1 km to ensure that all the remote sensing data could be efficiently matched spatially. All of the data preprocessing was carried out on ArcGIS (ESRI, Redlands, CA, USA) and ENVI (Exelis VIS, Boulder, CO, USA).
# Aveiro Canyon Head (Portugal) Submarine Slope Instability Assessment
## 2 Setting  
The Aveiro Submarine Canyon is located 50 km off the city of Aveiro (Figure 1) in the West Portuguese margin. Specifically, the studied area is located between the middle continental shelf (100 m depth) and the lower course of the canyon (3000 m depth).  
The upper course of the Aveiro Canyon stands out from the other canyons that characterize the Portuguese Margin, due to its morphology and location. The upper course of the canyon presents a rather large and wide amphitheatre-shaped morphology, contrasting with the V-shape of the others Portuguese canyons. The Aveiro canyon starts in the middle shelf, below a depth of 110 m. Its head is about 60 km wide, being dissected by a dense network of smaller tributary valleys or runoff lines (Figure 1). It extends for over 150 km through the Valle-Inclan saddle until it reaches the Iberian Abyssal Plain at depths of 4000 m.  
The continental shelf nearby the canyon head is characterized by flatter bottom morphology with a very smooth slope, around 0.3% where some smaller reliefs are also observed, such as the Pontal da Galega [20,21]. Geologically, the structure of the continental shelf is characterized by a strong tectonic control. [PERSON] (2004) [21] reports the presence of (1) rotational gravitational slip faults formed during the Mesozoic rifting; and (2) filling of structural depressions by a thick tertiary sedimentary body (Eocene and Neogene bioclastic and detrital limestone formations).  
Given the regional setting of the canyon, there is no relationship between the origin and evolution of this structure with erosional processes, namely those associated with the nearby fluvial systems. In fact, its origin and the characteristics of the upper canyon was attributed to the geologic structure of the west Portuguese margin, specifically to the presence of a right lateral strike-slip fault (WNW-ESE direction), also with normal movement component in which the North block is the footwall [18]. It is  
Figure 1: Location of the study area—submarine Aveiro Canyon.  
likely that the fault, formed during the Variscan orogen and reactivated in younger tectonic phases played an important role in the canyon's origin and modelling [22].
# Multi-Objective Gate Allocation Problem Based on Multi-Commodity Network Flow Model
###### Abstract  
Gate allocation has always been a fundamental but critical issue in the daily operation of airports, which is related to service quality and schedule efficiency. In order to obtain reasonable and efficient gate allocation results, in this paper, a multi-commodity network flow model is proposed to describe the gate allocation process in flight flow, based on which a multi-objective optimization model is constructed. It not only comprehensively considers the flight information of aircraft arrivals and departures, but also integrates the broader interests of passengers, airlines, and airports. To solve it, a linear weighting technique is applied. In addition, K-means cluster analysis is used to explore different weight combinations, and on this basis, the idle time of the gate is introduced as a performance evaluation index to guide the selection of the final weight. By analyzing the optimization results of actual operation data, the proposed model significantly balances the interests of multiple parties and the number of flights at each gate and has a relatively high gate-utilization rate. It can provide rich decision support and a reasonable allocation scheme for airport management to a certain extent.  
airport operations; gate allocation; multi-commodity network flow; binary integer linear programming; idle time +
Footnote †: journal: Applied Soft. 2022, 12, 9849. [[https://doi.org/10.3390/app1299849](https://doi.org/10.3390/app1299849)]([https://doi.org/10.3390/app1299849](https://doi.org/10.3390/app1299849))
# A Comparative Study of 3D UE Positioning in 5G New Radio with a Single Station
## Appendix A Zadoff-Chu Base Sequence and Resource Mapping Rules
### Sub-Carrier and Time-Slot Mapping
\] (A7)  
All in all, the Zadoff-Chu sequences are arranged into specific subcarriers and time slots to form the SRS OFDM symbols with the rules defined in the standard; the final SRS information carrying OFDM signal \(\mathbf{S}_{\mathbf{s}\mathbf{r}}\in\mathbb{C}^{\mathbf{N}_{c}^{\text{RF}} \mathbf{N}_{RB,UL}\times W}\) contains the total SRS sequence \(\mathbf{r}_{zc}\in\mathbb{C}^{\mathbf{M}_{w,b}^{\text{SRS}}\times\mathbf{N}_ {\mathbf{s}\mathbf{s}\mathbf{s}}^{\text{SRS}}}\). The \(\mathbf{N}_{RB,UL}\) means the number of RBs allocated to the UEs, and the value depends on the UEs' total bandwidth and subcarrier spacing. Although it varies from case to case, 3 GPP [25] has defined the maximum and minimum values of \(\mathbf{N}_{RB,UL}\) to be 20 and 275, while the subcarrier spacing is less than 240 kHz.
## 3 Experimental Setup
### Setup for the Reference Sensors  
The proposed ARNC algorithm requires effective reference signals to suppress road noise in the cabin. Thus, the reference sensors, the 3-axis accelerometers in this study, had to be attached at those locations where the most time-advanced road-tire interaction exists, such as the lower arms around each wheel of the car. The reference signals also had to be strongly correlated with the road noise heard at the driver's ear (the error microphone was installed) in the cabin, and this allows a higher coherence to be obtained in the primary path of the algorithm. In addition, the reference sensors must be located far enough from the engine in order to minimize the effect of the engine vibration.  
Figure 4: Comparison between the measured SPLs (at the driver’s ear position) of the road noise (dashed line) at 60 kph and the practically maximum anti-sound (solid line) by the ESG–cowl pair.  
Figure 3: Setup of the ESG–cowl pair.  
Each 3-axis accelerometer was attached at some candidate locations around the lower arm of each wheel to obtain the proper reference signals. A number of measurements to obtain the primary path data were carried out while the car was driven at 60 kph. The measured data of the paths between the accelerometer at each candidate location and the error microphone gave the best locations of the candidates for reference sensing after analyzing the multiple coherence functions. For the measurement, each sensor at the two front wheels or the two rear wheels was attached at the locations that were symmetrically identical. However, the attachment locations at the rear wheels were different from the front wheels.  
The properties of the primary path of the car including the reference signals are discussed in Section 4.2 in depth.
## 3 Results and Discussion
### Principal Component Analysis of SERS Spectra  
Furthermore, principal component analysis (PCA) was employed to distinguish cells from different experimental groups. Figure 6 shows the scores of two principal components (PCs), PC1 and PC2, calculated from SERS spectra of 20 cells with no drug treatment, 20 cells treated with cordycepin for 12 h, and 20 cells treated with cordycepin for 48 h. It was observed that three clusters were easily separated by PC1 and PC2, which possessed the contribution accounts of 73.9% and 16.7%, respectively. The variance of PC1 and PC2 were 1.71 and 0.28, respectively. The main discriminant was the PC1 score, where the control group had the highest scores while the cordycepin-48 h had the lowest scores. The PCA distribution area of the cordycepin-12 h group and the cordycepin-48 h group were larger than that of the control group. It indicated that the individual differences in the cordycepin-12 h group and the cordycepin-48 h group were more obvious, and the uniformity across cellular molecules was slightly worse than that of the control group. The results demonstrated that the control group, the cordycepin-12 h group, and the cordycepin-48 h group could be easily distinguished by SERS-based PCA.
## 1 Introduction
For power-scaling of FRLs, lower fiber loss and superior cw brightness and power make 0.9-1 \(\upmu\)m diodes a better choice, and devices based on MM pumping of MM fibers have recently been demonstrated [14]. However, these still operated relatively close to threshold, with output power of 3 W and slope efficiency of 35\(\%\) or less. Furthermore, while some beam quality improvement (beam cleanup) is possible in MM FRLs, the beam quality was still far from that obtained with double-clad fibers, which can readily generate diffraction-limited output. In this paper, we present results with an improved pumping system that allows us to operate higher above threshold and with fibers with higher loss than in our previous work [15]. We reached a record output power of 20 W at 1019 nm and a slope efficiency of 80\(\%\), in a comparatively short piece of a commercial MM fiber with low total propagation loss. However, the output was multimode. In order to improve the beam quality, we also used a double-clad Raman gain fiber, and obtained an output power of 6 W and a slope efficiency of 19\(\%\). The efficiency is lower because the loss of this fiber, fabricated in-house, is higher than that of state-of-the-art commercial MM fiber. Nevertheless, thanks to an improved beam quality (\(M^{2}\) = 1.9), the brightness improved by a factor of 9.6 over that of the pump beam. We believe this is the first fiber Raman laser cladding-pumped directly by diodes. Finally, we estimate the improvements possible with optimized and lower-loss laser configurations, as well as with improved Raman gain fibers and pump sources.