esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient System for Automatic Blood Type Determination Based on Image Matching Techniques
## 5 Results and Discussion
### Simulation Results for the ORB Algorithm  
In the ORB algorithm, the feature points were also extracted for both the input and the reference images in addition to their binary descriptors. The ORB algorithm proceeds by feature matching based on the Hamming distance between the corresponding descriptors. Figure 19 shows that there are no matching points between the sample image and the anti-A spot, 13 matching points with the anti-B spot, and 5 matching points with the anti-D spot. According to the experimental results, a minimum of two matching points between the reference image and the blood spot are required to detect the agglutination.  
Table 11 demonstrates the confusion matrix extracted from the results of ORB algorithm for the 1500 blood spots, in addition to the corresponding evaluation measures in Table 12. The results revealed a large improvement in all of the evaluation measures  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Measure** & **Accuracy** & **Error Rate** & **Sensitivity** & **Specificity** & **Precision** & **F-Score** \\ \hline Result & 86.8\% & 13.2\% & 79.5\% & 95.1\% & 94.9\% & 86.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Evaluation measures for the SURF algorithm over the 1500 patches.  
Figure 19: Feature points matching between a blood sample and the reference image for the three blood spots (anti-A, anti-B, and anti-D) using the ORB algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Actual Class/Predicted Class** & **Agglutination = Yes** & **Agglutination = No** & **Total** \\ \hline agglutination = yes & 635 & 164 & 799 \\ \hline agglutination = no & 34 & 667 & 701 \\ \hline Total & 669 & 831 & 1500 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Confusion matrix for the SURF algorithm over the 1500 patches.  
compared to the SDS, the SIFT, and the SURF algorithms. This improvement was due to the fact that the ORB algorithm detects only the major feature points that are mostly located inside the object of interest. However, the sensitivity was less than the specificity for the ORB algorithm. In some samples, relatively low sensitivity can be noticed because the ORB algorithm fails to detect positive agglutination, especially positive Rh agglutination, as shown in Table 13. For example, in detecting A+ group samples, 3 samples were predicted as A\(-\) and 10 samples as O\(-\). In detecting O+ group samples, 5 O+ samples were estimated falsely as O\(-\). To reduce the percentage of error in detecting positive agglutination, another reference image is added to optimize the ORB algorithm and results in better estimation of blood type, as detailed in Section 5.6. The accuracy of blood typing for the 500 blood samples is 94%, where it was able to determine the type of a total of 470 samples correctly. The average execution time per sample is 0.167 s. The results of the ORB algorithm showed a considerable improvement for both accuracy and processing time.
## 2 Materials and Methods
### Determination of Tea Polyphenol Content  
The tea polyphenol content of fresh tea leaves was measured by referring to the Chinese national standard GB/T 8313-2018 immediately after the spectral data were collected. Slightly modified, the specific steps were as follows: the fresh tea leaves were placed in the oven (DL-6 CH, Quanzhou Deli Agroforestrial Machinery Co., Ltd., Quanzhou, China) at 120 \({}^{\circ}\)C for 6 min for fixation, followed by drying at 90 \({}^{\circ}\)C to a constant weight. Tea samples from 10 drying tea leaves of the same tea variety was taken as one sample; it was put into a frozen grinding machine (JXCL-3 K, Shanghai Jingxin Industrial Development Co., Ltd., Shanghai, China) to grind for 3 min and uniformly sieved through an 80 mesh sieve to obtain tea powder. Tea powder (0.2000-0.2005 g) was taken in a 10 mL centrifuge tube, and 5 mL of 70% methanol was added after heating in an electric-heated thermostatic water bath (HWS26, Shanghai Yiheng Technology Instrument Co., Ltd., Shanghai, China). Then, the samples were extracted inside a constant temperature water bath at 70 \({}^{\circ}\)C for 10 min with shaking at 5 min intervals. After centrifugation at 6886\(\times\)\(g\) for 5 min in a refrigerated ultracentrifuge (Centrifuge 5430 R, Eppendorf Corporate, Hamburg, Germany),  
Figure 2: Portable non-destructive detecting device for tea polyphenols in fresh tea leaves: structure diagram (**a**), physical diagram (**b**), and internal diagram (**c**).  
the supernatant was transferred to a 10 mL volumetric flask and the extraction was repeated for the precipitated tea powder residue. Finally, the supernatant from the two times extracts were combined into a 10 mL volumetric flask, followed by aspirating the preheated 70% methanol using a dropper to dilute the volume to the mark, which was then shaken evenly.  
After sample pretreatment, 1 mL of tea sample and 5 mL of 10% Folin-Ciocalteu's phenol reagent were taken into a 20 mL test tube and incubated for 6 min. Then, 4 mL of 7.5% sodium carbonate was added and kept at room temperature under light-proof conditions for 30 min. Finally, in a 96-well microtiter plate, the absorbance value of 200 mL of the test solution was measured at 765 nm with the Tecan microplate reader (Infinite M200 Pro, Grodig, Austria), and the tea polyphenol content was calculated according to the standard curve established by the gallic acid standard (Beijing Solarbio Science & Technology Co., Ltd., Beijing, China). Each sample was performed in triplicate. After the measured value with the largest relative error to the remaining two chemical values was eliminated, the average of the remaining two values was recorded as the measured chemical value of the sample. Tea polyphenol content of fresh tea leaves were calculated by Equation (1) as follows:  
\[TP=\frac{1000\times X}{(86.334\times m)\times(1-0.01\times w)} \tag{1}\]  
where _TP_ is the tea polyphenols content (%) of the sample; \(X\) is the absorbance of the sample; \(m\) is the powder weight (g) of the sample; \(w\) is the dry matter content (%) of the sample.
## 3 Results and Discussion
(in the condensed phase) with respect to the thiolate exchange between a thiolate and a selenyl sulfide [53], the same trend in thermodynamics can be expected to hold true when comparing EbSe to its diselenide, in agreement with the findings of [PERSON] et al. [15]. Lastly, starting from the Pinhb obtained from EbSe, we investigated the mechanistic hypothesis of Se-C bond-breaking depicted in Scheme 3. The reaction appears to proceed as an aromatic nucleophilic substitution (S\({}_{\text{N}}\)Ar), but no Meisenheimer intermediate was located, and all attempted optimizations led back to either the reactants or to the products, both in the gas and the condensed phases. Thus, in agreement with previous studies on molecular models, the soft chalcogen-leaving group and the weakly activated aromatic ring seem to favor a concerted S\({}_{\text{N}}\)Ar mechanism [55]. Beside the level of theory used throughout this work, some other density functionals (i.e., M06-2X [56], M11 [57] and B3 LYP without Grimme dispersion) were tested to assess the non-existence of a Meisenheimer intermediate, as was done for model compounds in [55]. In all cases, the guess intermediate broke down. Interestingly, the step described in Scheme 3 appears to be energetically favorable (all the following energies are gas phase Gibbs free energies), leading to products (i.e., (Cys)-SSeH and salicanilide) that are located on the PES 12.33 kcal mol\({}^{-1}\) below the Pinhb with the addition of one water molecule. However, the mechanistic details of the Se-C bond-breaking of EbSe should be carefully investigated to assess the kinetic feasibility of such a pathway and the role of the catalytic pocket residues, especially as no evidence suggests the occurrence of a similar reaction in solution. Such an extensive investigation is beyond the scope of our work, which revolves around the preliminary well-documented Se-S bond formation. However, due to the importance of the topic of the use of organoselenium compounds as M\({}^{\text{pro}}\) inhibitors, the preliminary computation of one of the possible transition states involved in the process was carried out, i.e., for the attack of OH\({}^{-}\) to the aromatic ring, with the other water proton located on His41 (HIP). This transition state is situated at a rather high energy on the PES (+52.37 with respect to the Pinhb with the addition of a water molecule). This suggests that it is unlikely that such a reaction would proceed in a biological environment without the active participation of nearby residues, such as His41. Indeed, the direct participation of His41 in the Se-C bond breaking event was hypothesized in [20].
## 3 Results
### Change in Isokinetic Muscle Function of Trunk
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Time**} & \multicolumn{4}{c}{**Group**} & \multirow{2}{*}{**Post Hoc**} \\ \cline{3-4} \cline{6-7}  & & **ST (\(n=8\)) a** & & **TT (\(n=8\)) b** & **NA (\(n=8\)) c** & \multirow{2}{*}{G: 0.001} & \multirow{2}{*}{a, b \(>\) c} \\ \hline \multirow{2}{*}{\begin{tabular}{c} 30\({}^{\circ}\)/s \\ Extensor (\%BW) \\ \end{tabular} } & Pre & 280.7 \(\pm\) 47.2 & 312.0 \(\pm\) 30.9 & 247.3 \(\pm\) 27.1 & G: \(<\) 0.001 & \multirow{2}{*}{a, b \(>\) c} \\  & Post & 355.2 \(\pm\) 31.8 * & 376.3 \(\pm\) 32.5 * & 268.0 \(\pm\) 15.9 & G \(\times\) T: 0.075 & \\ \hline \multirow{2}{*}{\begin{tabular}{c} 30\({}^{\circ}\)/s \\ flexor (\%BW) \\ \end{tabular} } & Pre & 261.5 \(\pm\) 46.5 & 300.5 \(\pm\) 27.8 & 277.1 \(\pm\) 42.7 & G: 0.103 & \multirow{2}{*}{-} \\  & Post & 328.5 \(\pm\) 30.4 * & 354.7 \(\pm\) 30.5 * & 312.1 \(\pm\) 25.0 ** & G \(\times\) T: 0.342 & \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} 120\({}^{\circ}\)/s \\ extensor (\%BW) \\ \end{tabular} } & Pre & 222.2 \(\pm\) 56.9 & 227.0 \(\pm\) 74.1 & 161.0 \(\pm\) 54.6 & G: 0.012 & \multirow{2}{*}{b \(>\) c} \\  & Post & 263.3 \(\pm\) 56.5 * & 333.0 \(\pm\) 46.4 * & 212.0 \(\pm\) 45.4 * & G \(\times\) T: 0.186 & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} 120\({}^{\circ}\)/s \\ flexor (\%BW) \\ \end{tabular} } & Pre & 304.3 \(\pm\) 44.0 & 259.3 \(\pm\) 81.0 & 258.1 \(\pm\) 110.4 & G: 0.021 & \multirow{2}{*}{-} \\  & Post & 405.8 \(\pm\) 68.7 ** & 403.8 \(\pm\) 38.8 ** & 311.1 \(\pm\) 88.6 ** & G \(\times\) T: 0.021 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Measurements of isokinetic muscle function of trunk parameters by group and time.
## 3 Results and Discussion
### Removal Efficiencies of Solids and COD
#### 3.3.1 Total and Volatile Solids
Globally, the removal efficiencies of TS and VS registered for all three HRTs tested in this work were higher than the values reported in the literature, which can be attributed to the higher biodegradability of the OFMSW tested in the present work when compared to the synthetic organic substrates generically used in the previously published papers.
## 4 Passive TMD Control Tuned by Genetic Algorithms  
In this section, the TMD device damping coefficient \(d_{TMD}\) and spring stiffness \(k_{TMD}\) are optimized using a metaheuristic method, Genetic Algorithm (GA). To enable a comparison between the results presented here and those reported in other studies, the TMD mass has been set to 20,000 kg, consistent with [11], as it aligns with the TMD described in that publication.  
The GA generates the initial tuning parameters for the passive TMD. These are the stiffness \(k_{gTMD}\) and damping, \(d_{gTMD}\). These coefficients are stored in the simulation as \(k_{TMD},\ d_{TMD}\). Subsequently, the simulation is executed (\(run_{sim}=1\)). Upon completion, the tower top displacement (TTD) is analyzed, focusing on the standard deviation of this signal, \(\sigma_{TTD}\), which acts as the fitness function for the GA. This process is repeated iteratively until the optimization convergence criterion is satisfied (Figure 3).  
Figure 4 shows the execution scheme of the passive TMD after the optimization process is complete. It can be observed that the TMD parameters cannot be externally adjusted.  
In the optimization process, an initial population of 75 individuals is used for the GA. Each individual comprises two variables to be optimized (chromosomes): the spring constant \(k_{TMD}\) and damping coefficient \(d_{TMD}\). Genetic operators, crossover and mutation, are applied with probabilities of 0.05 * Population and 0.01, respectively. The stop criterion is triggered when the average change in the cost function remains below the value \(10^{-6}\) for at least 15 consecutive generations. The complete GA configuration is detailed in Table 2. The fitness function is defined as the standard deviation of the fore-aft tower top displacement (TTD), denoted as \(\sigma_{\text{TTD-FA}}\).  
The values obtained with this optimization are spring stiffness \(k_{TMD}=4868\) N/m and damping coefficient \(d_{TMD}=2419\) Ns/m, respectively.
## Broadband Spectral Amplitude Control in High-Order Harmonic Generation  
**[PERSON]**  
UPC-Polytechnic University of Catalonia, Colom 11, 08222 Terrassa (Barcelona), Spain;  
E-Mail: [EMAIL_ADDRESS]; Tel.: +34-93-7398137; Fax: +34-93-7398000  
_Received: 26 October 2012; in revised form: 1 December 2012 / Accepted: 7 December 2012 / Published: 19 December 2012_  
**Abstract:** A technique for broadband spectral amplitude control of light pulses produced in high-order harmonic generation (HHG) is presented. It has been shown elsewhere that broadband spectral phase control in HHG is achievable using a computerized feedback loop scheme by coherently adding a filtered region of the HHG emission to the intense IR driving pulse with optimal attenuation and time delay parameters. In the present study, further computational evidence of the capabilities of this control scheme is provided by considering the spectral amplitude in a broadband region of the HHG spectrum as the control target for the production of isolated attosecond pulses. Different spectral widths and central photon energies are examined, such as a spectral width of 30 eV centered at 36 eV, well in the plateau, and a width of 20 eV centered at 60 eV in the cutoff region. An iterative procedure of the method is implemented and optimal isolated single cycle pulses at a central photon energy of 36 eV are obtained. This control scheme is a fundamental tool that can be implemented for amplitude and phase shaping of any suitable spectral region in HHG.  
**Keywords:** ultrashort pulses; attosecond pulses; coherent control; high-order harmonic generation; strong field
# Dynamic Packet Duplication for Industrial URLLC
## 4 Tests  
The proposed scheme was tested in a simulated 5G network, using ns-3, which is a free and open-source network simulator that is very popular in research [36]. In particular, the 5G-LENA module [37] was selected to conduct the simulations. This module focuses on the new 3 GPP NR specifications and includes numerology support, frequency division multiplexing of numerology and an OFDMA-based scheduler. It also includes beamforming and HARQ feedback implementation.  
In this section, the simulation scenario along with the KPI recollection phase to train the model are described.
## 2 Methodology
### Network Models
#### 2.2.2 Model Selection
combined a random forest machine learning classifier with MobileNet, InceptionV3, and Xception to construct an efficient CNN model, confirming the model's effectiveness in identifying road cracks on real concrete surfaces [34]. In the detection task, [PERSON] et al. used VGG16 as the basic convolutional network to propose a road crack detection method based on a multi-feature layer convolutional neural network, thereby improving the accuracy of road crack identification [35]. [PERSON] et al. used ResNet50 and YOLO v2 networks for feature extraction and identification, and the results show that this method had excellent performance and advantages in asphalt pavement water damage detection and location [18]. Based on the above analysis, this paper tests and evaluates 18 network models derived from 8 network architectures, namely AlexNet, InceptionV3, VGG (VGG11, VGG13, VGG16, and VGG19), ResNet (ResNet18, ResNet34, ResNet50, and ResNet101), DenseNet (DenseNet121 and DenseNet169), MobileNet (MobileNetV2 and MobileNetV3), SqueezeNet (SqueezeNet1_0 and SqueezeNet1_1), and EfficientNet (EfficientNetB2 and EfficientNetB3).
## 2 Magnetizer Modeling and Simulation Analysis
### Analysis of the Feasibility of Large Lift-Off  
The finite element method was adopted to simulate the MFL signal of the defected wire rope at a large lift-off distance. The solution type was magnetostatic. The diameter of the wire rope was set to 22 mm. Its material was X52, steel used for oil and gas pipeline transmissions, and a cut was set in the middle part of the wire rope as the break whose length was, respectively, 2 mm, 4 mm, 6 mm, 8 mm, and 10 mm. For the sensor head, the inner diameter, outer diameter, and thickness of all three permanent magnets are, respectively, 85 mm, 120 mm, and 10 mm. The permanent magnets are made of NdFe52 material and the coercivity is set to 955 kA/m. The remanence of them is 1.44 T, and the relative permeability along the axial direction is 1.21 [26]. The model is shown in Figure 3. To make a better simulation for a real situation, an air gap between the 2 half circles was set to 10 mm. The axial component and radial component of magnetic flux density are, respectively, extracted at a distance of 30 mm from the outer surface of the wire rope. Finally, MFL signals of both axial and radial components are collected as two characteristic curves shown in Figure 4a,b.  
Figure 1: (**a**) Exciting coil magnetizer; (**b**) Yoke type magnetizer.  
Figure 2: Open permanent magnetizing methods.  
The peak-peak value of the radial component and the peak-zero value of the axial component are, respectively, measured from the two curves above. To intuitively observe the relationship between the MFL signal and the length of the broken wire, Figure 5 is drawn below. In Figure 5, the B denotes the magnetic flux density of the leakage magnetic field, and the eigenvalue of the MFL signal is calculated from the peak-peak value of the radial component illustrated in Figure 4a.  
According to the above simulation analysis, MFL can be detected from the defect at a distance of 30 mm from the surface of the wire rope. Despite that, the amplitude of  
Figure 4: (**a**) Radial component of MFL; (**b**) Axial component of MFL.  
Figure 5: Relationship between the MFL signal and the length of the broken wire.  
Figure 3: Mesh on the model.  
the signal is weak. More specifically, useful information could easily be overwhelmed or vulnerable by noise from other resources.
## 4 Evaluation
### Evaluation Methodology
#### 4.1.2 Performance Evaluation Metrics  
This section introduces the metrics, namely transmission overhead, throughput, average energy consumption and average delivery delay for the performance evaluation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Total simulation time (days) & 5 \\ Warm-up period (days) & 1 \\ Cool-down period (days) & 1 \\ Wireless communication interface & Bluetooth \\ Transmission range (m) & 10 \\ Transmission rate (MBps) & 2 \\ Buffer size (MB) & 10 \\ Message size (KB) & 10 to 15 \\ Message TTL (days) & 1 \\ Number of nodes & 1349 \\ Ave. message generation rate/node & 1 message/h \\ Battery capacity (Joules) & 4800 \\ Receive/transmit energy (mW/s) & 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters for the performance evaluation.  
Figure 14: The Skudai simulation area (map data provided by OpenStreetMap, 2015).  
**Transmission overhead.** As shown in Equation (10), the transmission overhead represents the average number of transmissions required to deliver a message copy; where \(T\) is the total number of message transmissions, and \(V\) is the number of times messages were delivered. This is equivalent to the cost of delivering messages with a forwarding algorithm, since every transmission consumes energy on user devices.  
\[\textit{Transmission overhead}=(T-V)/V \tag{10}\]  
**Throughput.** As shown in Equation (11), throughput is the ratio between \(d\), the total number of messages successfully delivered at their respective destinations, and \(g\), the total number of messages generated. This signifies the message delivery efficiency of a forwarding algorithm, within assigned TTL.  
\[\textit{Throughput}=d/g \tag{11}\]  
**Average energy consumption.** Energy consumption (in Joules) is the amount of initial energy expended at the end of the simulation. As shown in Equation (12), the average energy consumption is the mean energy consumed by nodes in the network, where \(n\) is the total number of nodes, and \(E_{i}\) and \(e_{i}\) are the initial energy and the remaining energy of the \(i\)th node, respectively. This gives an insight on how routing impacts on resource consumption.  
\[\textit{Average energy consumption}=\sum_{i=1}^{n}(E_{i}-e_{i})/n \tag{12}\]  
**Average delivery delay.** Delivery delay is the time elapsed between message generation and delivery. As shown in Equation (13), the average delivery delay is the mean delivery delay in the network, where \(G_{i}\) and \(D_{i}\) are the generation time and the delivery time of the \(i\)th message, respectively. This gives an insight on how long it takes to deliver a message.  
\[\textit{Average delivery delay}=\sum_{i=1}^{d}(D_{i}-G_{i})/d \tag{13}\]
# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
## 2 Materials and Methods
### Methods  
In this study, forest inventory data, remote sensing data and a machine learning model were used to implement remote sensing retrieval of forest stand age information in the Loess Plateau. The main technical route is shown in Figure 2. For this study, the relationship between spectral information and forest stand age was quantitatively described, and a remote sensing inversion model of forest stand age with strong spatiotemporal suitability, supported by a machine learning algorithm, was established; this was followed by accuracy verification and error evaluation of remote sensing data of forest stand age in the Loess Plateau.