esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Field Testing and Validation
### AI Model's Performance  
The tests related to the performance of the AI were divided into two parts: functional tests and a 24 h uninterrupted test. The functional tests involved detecting objects being stationary or in motion at different locations (distance and angle from the direct line of sight). Firstly, the results of the distance and angle tests imply that the camera struggled with objects at a long distance and distortions near the edge of the frames. This is likely due to the wide field of view of the camera lens, generating optical distortion and chromatic aberrations. Finally, the movement tests aimed to simulate the scenario of an excavator moving and with its arm ready to dig. With the arm automated and moving up and down, the unit can detect it successfully.  
In the second testing phase, the unit was set up to run continuously for 24 h to collect data for false/true positive/negative analysis and luminescence tests. The luminescence tests were conducted to assess the daily operational time window when the unit is able to perform object recognition. Since the camera in the unit does not have night vision capability, the performance of the AI dropped after the camera went into the dark environment  
Figure 8: Field testing and validation setup for the AIoT device.  
(between 21:00 and 06:00) during the test. The device was still capable of detecting objects at dusk, at dawn, and in direct sunlight scenarios. It should be noted that the AIoT device was programmed to only forward the highest confidence level for the same object detected over the three consecutive images taken every 15 min by the device. For example, if the device detects an excavator in the sequence of images with confidence levels of 90%, 91%, and 95% in images 1, 2, and 3, the unit will only forward the alert with the confidence level of 95%.  
When considering all the categories that can be recognized by the AI, the unit achieved a true positive rate (detecting the right object) of 67.30%, a false positive rate (detecting the wrong object or missing a detection) of 32.70%, a true negative rate (not detecting an object when there is none) of 47.37%, and false positive rate (not detecting an object when there is one) of 52.63%. When only focusing the tests on the excavator category, the test results in the 24 h test had a false negative rate of 14.74% and a true negative rate of 85.26%. There is no true/false positive rate for this category due to missing data during the test. Figure 9 shows a few instances of the field tests where the model was able to correctly detect the excavator and also a few instances where the model confused the excavator with other categories (i.e., truck, cable plough). The field testing indicated that the AI needs more data coming from the sensors to recalibrate it as the images captured by the device present some optical and chromatic distortions. This explains the high rate of errors.
# Summertime Aerosol Radiative Effects and Their Dependence on Temperature over the Southeastern USA
## Abbreviations  
The following abbreviations and acronyms are used in this manuscript:  
\(\beta\) & Up-scatter fraction  
\(\phi\) & Mean daytime value of the secant of the solar zenith angle  
\(\omega\) & Single scattering albedo  
AATSR & Advanced Along-Track Scanning Radiometer  
ACCOMP & Atmospheric Chemistry and Climate model Intercomparison Project  
ADV & AATSR Dual-View algorithm  
AOO & Aerosol optical depth  
BENZ & Benzene  
BVOC & Biogenic volatile organic compounds  
\(C_{c}\) & Fractional cloud amount  
CCI & Climate change initiative  
CCN & Cloud condensation nuclei  
CMG & Climate Modelling Grid  
CONTROL & Simulation with all model schemes in use  
DJF & December-January-February  
DRE & Direct radiative effect  
DUE & Data User Element  
\(E_{a}\) & Activation enthalpy  
ENVSAT & Environmental satellite  
ERF & Effective radiative forcing  
ESA & European Space Agency  
ECMWF & European Centre for Medium-Range Weather Forecasts  
GAW & Global Atmosphere Watch  
GFED & Global Fire Emissions Database  
GISS & Goddard Institute for Space Studies  
GLYX & Glyoxalas  
IEFOX & Isoprene epoxydiols  
IGBP & International Geosphere Biosphere Programme  
IMPROVE & Interagency Monitoring of PROtected Visual Environments  
ISOP & Isoprene  
JIA & June-July-August  
\(k\) & Reaction coefficient for VOC oxidation  
\(k_{0}\) & Reference reaction coefficient  
LST & Land surface temperature  
MEGAN & Model of Emissions of Gases and Aerosols from Nature  
MISR & Multi-angle Imaging SpectroRadiometer  
MODIS & Moderate Resolution Imaging Spectroradiometer  
MTP & Monoterepens  
NASA GES & National Aeronautics and Space Administration Goddard Earth  
DISC & Sciences Data and Information Services Center  
noBIOSOA & Simulation without biogenic SOA precursor emissions  
ODR & Orthogonal Distance Regression  
OMI & Ozone Monitoring Instrument  
POM & Primary organic matter  
\(R\) & Gas constant  
r & Correlation coefficient  
\(R_{a}\) & Shortwave surface reflectance  
RMSE & Root-mean-square error  
\(S_{rad}\) & Incident solar radiation at the top of the atmosphere  
SALSA & Sectional Aerosol module for Large-Scale Applications  
SOA & Secondary organic aerosol  
SW10 & Southerly wind speed  
\(T\) & Temperature  
\(T_{min}\) & Aerosol-free atmospheric transmission  
TOA & Top-of-the-atmosphere  
TOL & Toluene  
U10 & Wind speed component (east-west direction) at 10 m altitude  
US & United States  
V10 & Wind speed component (north-south direction) at 10 m altitude  
VBS & Volatility basis set  
VOC & Volatile organic compounds  
XYL & Xylene
# Segmentation of Intracranial Hemorrhage Using Semi-Supervised Multi-Task Attention-Based U-Net
## 3 U-Net  
The difficulty of procuring large, high-quality datasets revolving around medical imagery created the need for a new type of network, u-nets. These networks are well suited for segmentation tasks, which was proven by the network's victory while in its infancy in the EMsegmentation challenge at ISBI2012 [38]. Without any fully-connected layers, the resulting segmentation map simply consists of pixels for which a full context is revealed in a given image, allowing for the seamless segmentation of large images through an overlap-tile strategy. These techniques work for high resolution images that otherwise would be difficult to analyze due to current GPU memory limitations [39].  
The architecture of u-nets is based on fully-convolutional networks, consisting of convolutional, deconvolutional, upsampling, and pooling layers. Each u-net is made up of a contracting encoder, which analyzes the entirety of the input image, and an expansive decoder, which produces a full-size segmentation [40,41]. More specifically, the encoder is a typical Convolutional Neural Network (CNN) with multiple convolutions followed by a Rectified Linear Unit (ReLU) and a 2 \(\times\) 2 max pooling operation. It is followed by the decoder, which combines feature information through a 2 \(\times\) 2 up-convolution [39]. Usually, these networks use 2D inputs and outputs, although other similar networks have been produced to operate on 3D data. One such network was described in a paper by [PERSON] et al. [40].  
The architecture of the u-net focuses on the following two objectives: (1) capturing and summarizing coarse-grained features and (2) using fine-grained information for inference. By adding a contracting and expanding autoencoder structure, the u-net can achieve the first objective despite its exponentially large receptive field (which grows by a factor of two per layer in the network). This allows the final layer of the network to have gradient access to a large window of the input and to compute a summary that is most informative for segmentation, which is the second objective. To take off the load of reconstruction, which many autoencoders treat as their objective function, and to leverage fine-scale information, residual connections of the u-net are then able to span across the encoder and decoder. This both allows the final layer to have access to the initial input and reduces the number of chain-rules required for gradient descent, which reduces the impact of the vanishing gradient problem.
# Rate of Penetration Prediction Method for Ultra-Deep Wells Based on LSTM-FNN
## 1 Introduction
However, the direct prediction of the ROP only after RNN or LSTM calculation is likely to lose other correlation characteristics. Therefore, the current intelligent prediction model of ROP still needs to be optimized. Based on the characteristics of deep well drilling and data correlation, this paper analyzes and optimizes the input parameters of a deep well ROP intelligent prediction model. At the same time, considering the time series correlation and other complex correlations between multiple parameters and the ROP, the existing LSTM structure is optimized, and an intelligent prediction model of the ROP based on an LSTM-FNN algorithm is established. The accuracy and mobility of the model are analyzed through the field actual drilling data, which verifies the reliability and superiority of the model established in this paper.
# Digital Shearing Speckle Pattern Interferometry Based on Rochon Prism and Its Application
## 4 Experiments and Discussion  
Here, we built a system to compare the performance of the two prisms under high power illumination. As shown in Figure 5, the system is based on digital shearing speckle pattern interferometry (DSSPI). The illuminant was a 532 nm green single longitudinal mode laser (MSL-FN-532-150 mW). The shearing devices were based on a WP and a RP respectively, the angles of divergence were 1.52\({}^{\circ}\) and 1.50\({}^{\circ}\), respectively  
Figure 4: Fringe pattern under different shearing distance: (**a**) \(\Delta x=10\) mm, (**b**) \(\Delta x=20\) mm, and (**c**) \(\Delta x=30\) mm.  
In the experiment, a positive temperature coefficient (PTC) resistance was applied as the heat source to load the displacement. By associating the heat flux with the voltage applied to the PTC ceramics, we were able to quantify the load and ensure that the same amount of displacement was achieved in each experiment.  
By changing the shearing device, we captured the speckle patterns under the same heating rate loaded, as shown in Figure 6. With a laser of power 150 mW, the images acquired with RP and WP are shown in Figure 6a,b, respectively.  
Figure 5: (**a**) The measurement scheme and (**b**) the setup of the measurement system.  
After ensuring that each experiment was loaded with the same amount of load, we established a model based on the finite element method (FEM) to simulate the theoretical value of the measured displacement, as shown in Figure 7a. By dividing the surface of the measured object into many grid structures, we decomposed the overall changes of the measured object into the sum of local changes. The theoretical displacement gradient on the object surface should be distributed as shown in Figure 7b.  
Figure 6: The speckle patterns and the fringe patterns generated by RP and WP: (**a**) the butterfly fringe patterns of RP; (**b**) the butterfly fringe patterns of WP.  
Figure 7: The model based on the finite element method (FEM) and the theoretical values: (**a**) displacement distribution, with the geometric center as the origin; and (**b**) the theoretical value of the displacement gradient along the x-axis.  
Using WP and RP as shearing devices, we measured the objects under the same FEM. As shown in Figure 8, the measurement accuracies of the system with WP or RP as shear devices were different. The error distribution of WP had an obvious trend with the x-axis, and the overall error was larger than RP. In Figure 8b, the maximum relative measurement error of RP is less than 4%; however, the maximum relative measurement error of WP is about 6% and has a systematic distribution trend.  
The experiment proves that the RP can compensate for the system error caused by background noise. The accuracy of the system is limited by other factors, such as airflow and stray light. However, the improvements in the shearing device provides the possibility of using the shearographic measurement system outdoors, where the power of illumination is generally high.
# Low-Carbon Logistics Distribution Vehicle Routing Optimization Based on INNC-GA
## 2 Problem Description and Basic Assumptions  
This study explores the vehicle routing problem in the context of low-carbon logistics and distribution. The scenario involves a logistics center overseeing a fleet of vehicles responsible for delivering goods to multiple customers. Customer demand and locations are predetermined, with time window requirements. The goal is to minimize vehicle and carbon emission costs while meeting operating time window, vehicle load, and travel speed constraints. The following assumptions are made for analytical and research purposes:  
1. The distribution center's location is predetermined, with all vehicles originating from and returning to this center upon task completion.
2. The types of vehicles in the distribution center are indistinguishable.
3. The requirements of each customer are known, and all are within the maximum load capacity of the vehicle.
4. It is strictly forbidden to violate the customer's time window and it is required that the delivery must be completed within the time window.
5. The weight of the cargo in each vehicle must not exceed its maximum load capacity.
6. Each customer should be served by a single vehicle, without any overlap.
7. The vehicles travel at a fixed speed, regardless of factors such as traffic congestion or environmental conditions.  
For clarity, Table 1 offers an explanation of the symbols utilized in the model.
# On Applicability of Network Coding Technique for 6 LoWPAN-based Sensor Networks
###### Abstract  
In this paper, the applicability of the network coding technique in 6 LoWPAN-based sensor multihop networks is examined. The 6 LoWPAN is one of the standards proposed for the Internet of Things architecture. Thus, we can expect the significant growth of traffic in such networks, which can lead to overload and decrease in the sensor network lifetime. The authors propose the inter-session network coding mechanism that can be implemented in resource-limited sensor modes. The solution reduces the overall traffic in the network, and in consequence, the energy consumption is decreased. Used procedures take into account deep header compressions of the native 6 LoWPAN packets and the hop-by-hop changes of the header structure. Applied simplifications reduce signaling traffic that is typically occurring in network coding deployments, keeping the solution usefulness for the wireless sensor networks with limited resources. The authors validate the proposed procedures in terms of end-to-end packet delay, packet loss ratio, traffic in the air, total energy consumption, and network lifetime. The solution has been tested in a real wireless sensor network. The results confirm the efficiency of the proposed technique, mostly in delay-tolerant sensor networks.  
network coding; wireless sensor networks; 6 LoWPAN; delay-tolerant sensor networks; Internet of Things +
Footnote †: journal: Sensors
## Appendix A Evaluation and Assessment Metrics  
The terms \"true positives\" (TP) and \"false negatives\" (FN) refer to the number of events belonging to a specific class that were correctly and incorrectly classified, respectively. \"True negatives\" (TN) represent the number of events that were correctly identified as not belonging to a particular class. Finally, \"false positives\" (FP) indicate the number of events that were incorrectly categorized as belonging to a specific category.  
The accuracy metric represents the overall effectiveness of the model. However, it can be misleading in the case of unbalanced datasets. Therefore, the additional metrics provided below should be used in conjunction with the accuracy metric.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\] (A1)  
Precision is a measure of the model's capacity to avoid false positive classifications, which is of paramount importance in the context of object detection, where misidentification can have significant and detrimental consequences.  
\[Precision=\frac{TP}{TP+FP}\] (A2)  
The recall represents the model's capacity to identify all instances of true positive cases.  
\[Recall=\frac{TP}{TP+FN}\] (A3)  
The F1-score represents the harmonic mean of precision and sensitivity, thereby facilitating a balanced assessment of the model.  
\[F1=2\cdot\frac{Precision\cdot Recall}{Precision+Recall}\] (A4)  
The resulting model achieved a high level of precision, with a value of 0.79, as determined by plotting the precision, recall, and F1-score metrics (Figure A1) for confidence threshold = 0.85. This is in agreement with the results of other studies in the field of building roof detection using deep neural networks. The low standard deviation between the different test areas is indicative of the stability and generalizability of the model.
# Fiber Monitoring System Applied to Railway Bridge Structures in a Near-Fault Region
## 2 Measurement Theory
### Separating the Temperature Effect on Fiber  
From Equation (2), there are two variables that contribute to the Brillouin frequency shift: the strain (\(\epsilon\)) and the temperature (T) [19]. Further calculations must be made to separate the effects of the two variables. One option is to nullify the effect of temperature change. We can choose the measured results with the same temperature conditions as the measured initial values. This could be achieved by choosing the same month to calculate for each year to minimize the temperature difference or by taking the same temperature results for the next month. This way, \(\Delta\)T = 0, Equation (3), can be simplified to  
\[\Delta\epsilon=\frac{\Delta v_{B}}{C_{\epsilon}} \tag{4}\]  
Another option would be to input temperature into the equation for every measurement. There are a few options to acquire temperature values. For the first option, the system server could search for temperature data from nearby weather stations given by the Central Weather Administration. With reference to the initial temperature condition, \(\Delta T\) could be calculated, and the strain difference could be calculated from Equation (3). The second option is the installation of a thermometer that is linked to the server. The system server can read the thermometer as it simultaneously measures the frequency shift. The third option is to install a temperature-sensing fiber. This kind of fiber is designed to not be affected by strain within a certain limit. Its structure is specially designed, for example, oil-filled fiber cable and gel-filled fiber cable. Therefore, since the strain is zero, Equation (2) becomes \(\Delta T=\Delta v_{B}/C_{T}\). The coefficient of temperature \(C_{T}\) can be obtained by the fiber supplier or measured through an experiment. The strain difference can then be calculated from Equation (3). The last option is to coil up the sensing fiber for several meters. This is synonymous with option three, but the fiber needs to be installed and fixed in such a way as to not induce stress and only have the effect of thermal expansion present. The thermal expansion coefficient \(\alpha\) of the outer layer of the fiber cable must be considered. The equation for calculating the temperature difference is as follows: \(\Delta T=\Delta v_{B}/(C_{T}+\alpha\cdot C_{\epsilon})\). If the thermal expansion coefficient is minimal, we use the same equation as the third option for obtaining \(\Delta T\) and then input \(\Delta T\) to calculate the strain difference from Equation (3). For this research, the last option for installing thermal sensing fiber is used, as it gives a more direct reading of the temperature of the structure because it is in contact with the structure.
# Continuous Variable Quantum Secret Sharing with Fairness
## 3 Security Analysis
### Fairness Property  
In this protocol, the fairness property means [PERSON] and [PERSON] both can reconstruct the secret \(S\) or neither can recover it. According to the steps in recovery, the following situations would occur. (1) Suppose the position of the last secret \(S_{L}\) in message \(X\) is \(k\). If [PERSON] and [PERSON] are both honest and follow the recovery steps to exchange their shares or the fake shares released at \(j_{th}\) round, \(j>k+L_{2}\). After \((k+L_{2})_{th}\) round, the determine pointer \(P^{*}\) appears, which means the secret \(S\) is recovered and can be picked out in front of \(P^{*}\) with length \(L\). (2) If the fake shares released at \(j_{th}\) round, \(1\leq j<k\), the dishonest participant would be detected by message verification and the secret \(S\) has not been reconstructed before termination. (3) If the fake shares released at \(j_{th}\) round, \(k<j<k+L_{2}\), the dishonest participant is checked out. At this moment, the secret \(S\) has been reconstructed and \(P^{*}\) does not appear completely. So, they both has possibility to guess right position and picks out \(S\). (4) If the fake shares released at \(k_{th}\) round with probability \(1/L_{1}\), dishonest participant he or she would reconstruct \(S\) and other participants are failed, but he or she can not assure this is the secret, because the right position is unknown. After this round, dishonest participant would be found. (5) If the fake shares released at \((k+L_{2})_{th}\) round, dishonest participant, he or she, would reconstruct \(S\) and \(P^{*}\), also honest participant would guess the position of \(S\) and pick it out correctly with great probability. Above all situations, both participants can recover or cannot recover the secret simultaneously, except in situation (4), dishonest participant may have little more advantage to acquire the whole secret, but, when the length of checking sequence \(R\), \(L_{1}\), is great enough, the probability of situation (4) is close to zero.
## 3 Results and Discussions
### Analysis of Finite Element Calculation Results  
#### 3.2.1 Model Validation  
Figure 11 presents cloud diagrams showing the partial simulation results of chloride ion diffusion in concrete under the influence of stray currents and chloride salt. The different colors in the color scale of Figure 11 represent varying concentrations of chloride ions due to different depths of chloride ion intrusion.  
In these cloud diagrams, the red contour lines represent the limit boundary of chloride ion diffusion. Their maximum depth indicates the penetration depth of chloride ions in concrete at a specific time under different external chloride ion concentrations and voltages. Figure 12 shows a line graph comparing the simulated calculation values with experimental data for the depth of cathode chloride ion penetration. The experimental data are sourced  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group Name** & **Corrosion Current Density (\(\mu\)A/cm\({}^{2}\))** & **Corrosion Potential (V)** & \(\beta_{\text{a}}\) **(mV)** & \(\beta_{\text{c}}\) **(mV)** & **Corrosion Rate (g/(m\({}^{2}\)-h))** & **Steel Bar Loss Rate (mm/Year)** \\ \hline D-1 & 1.49 & \(-0.782\) & 284.76 & \(-34.15\) & \(1.18\times 10^{-2}\) & \(1.33\times 10^{-2}\) \\ \hline Sa-1 & 0.91 & \(-0.749\) & 200.88 & \(-38.51\) & \(0.95\times 10^{-2}\) & \(0.97\times 10^{-2}\) \\ \hline Sa-2 & 0.80 & \(-0.725\) & 239.57 & \(-35.  
from [21]. As can be seen from the figure, the simulated values are largely consistent with the experimental test values. Through a correlation analysis of these two sets of data, their Pearson correlation coefficient is found to be 0.9875. This demonstrates the accuracy and effectiveness of the ion diffusion model employed.