esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Routing Protocols for UAV Communication
### Optimized Link State Routing (OLSR)
The duplicate message hold time specifies the expiration time of a duplicate set table entry. In order to avoid handling duplicate messages received within this time frame, a duplicate set table is used. The duplicate message hold time in E OLSR is reduced from 30 s to 15 s. An analytical comparison between all routing protocols is illustrated in Table 2:
# Non-Contact High Voltage Measurement in the Online Partial Discharge Monitoring System
## 3 System Architecture for Non-Contact High Voltage Measurement
### Signal Conditioning and Data Acquisition Unit  
The low-cost DrDAQ signal acquisition card by Pico Technology with a sampling frequency of 1 MS/s in block mode or 100 kS/s in fast USB streaming mode, and a resolution of 8 bits is used to measure the voltage registered with a capacitive probe. The input resistance \(R_{\mathrm{s}}\) of the acquisition card is 1 M\(\Omega\), and its input capacitance \(C_{\mathrm{s}}\) is 17 pF. The capacitive probe is an element with a very low current efficiency and connecting it to an acquisition card with a 1 M\(\Omega\) input impedance causes a significant reduction of the signal amplitude. This problem was solved using a voltage follower based on the AD549 operational amplifier with a very high input impedance (\(10^{15}\)\(\Omega\)) and a low offset voltage (max. 0.5 mV). Additionally, this op-amp can safely drive a 4000 pF capacitance at unity gain. This allows the use of long coaxial cables without negative consequences, such as parasitic oscillations or signal attenuation. In order to protect the circuit against overvoltage, BAV20-type protective diodes are used at the input of the voltage follower. Figure 6 shows a comparison of voltage waveforms recorded in the measurement system with and without a voltage follower. It can be seen that in the case of a circuit without a voltage follower, the recorded signal is highly distorted and has about 15 times lower amplitude.  
Figure 5: Construction of a capacitive probe for non-contact high voltage measurement: (**a**) 3D schematic diagram; (**b**) technical drawing with dimensions in mm.  
The conditioned voltage signal is sampled by the analog-to-digital converter of the data acquisition (DAQ) card, and then (already in digital form) transmitted via a universal serial bus (USB) cable to an industrial computer that is part of the online PD monitoring system. The schematic diagram of the system for non-contact high voltage measurement is shown in Figure 7.
## 1 Introduction  
Pain is \"an unpleasant sensory and emotional experience associated with, or resembling that associated with, actual or potential tissue damage\" [1]. Pain classification has been recently revised in order to overcome the challenging and over-simplistic categorization in purely neuropathic or purely nociceptive pain [1]. In this scenario, the term \"mixed pain\" has been recently introduced in order to characterize the overlapping of nociceptive, neuropathic and neoplastic pain [2]. This complex clinical presentation could be phenotypically heterogeneous and characterized by a simultaneous presentation of nociceptive, neuropathic and neoplastic pain [2].  
The term \"mixed pain\" has been recently proposed to describe a condition characterized by the concomitant presence of specific symptoms referrable to both nociceptive and neuropathic pain [3]. Although mixed pain has not been mentioned in the recent IASP classification, the current literature on this topic is exponentially raising exploring its occurrence in several pathological conditions such as low back pain (LBP), osteoarthritis, postsurgical and cancer pain. Moreover, mixed pain in these chronic pathological conditions contributes to increased disability and impairment in terms of both physical function and health-related quality of life (HRQoL) [3].  
In this context, the Percutaneous Electrical Nerve Stimulation (PENS) is a mini-invasive neuromodulation technique that has been recently suggested as a promising therapeutic intervention for the management of mixed and refractory pain in different clinical conditions [4]. Moreover, this novel technique might be used to reduce both pain perception in several musculoskeletal diseases of Physical Medicine and Rehabilitation (PRM) interest, including LBP [5], total knee arthroplasty [6], chronic pain after amputation [7], opioid-resistant cancer pain [8] and overactive bladder in children [9]. Although PENS showed to be effective in reducing unspecified pain in several chronic pain conditions, there is still a lack of evidence in the literature about its role in the management of neuropathic or mixed pain in musculoskeletal disorders not responsive to pharmacological treatments.  
Therefore, this scoping review aimed at summarizing the current literature focusing on the mechanisms of action, clinical indications, safety and effectiveness of PENS treatment in patients affected by mixed chronic pain conditions in the rehabilitation field.
## 4 Simulated and Measured Results  
To verify the simulated results, an antenna prototype was constructed and tested using an Anritsu MS46322B vector network analyzer (VNA). Figure 12a,b shows photos of the prototype before and after assembly, respectively, with the latter featuring CAB.058 SMA(F)Jack SMA connectors. It is noteworthy that the simulation results at 5.5 GHz, 28 GHz, and 38 GHz were confirmed by the experimental results. The results at 0.13 GHz were validated through another numerical solver, the Finite Element Method in Ansys HFSS 3D full wave simulation, as the VNA maximum frequency is 50 GHz.  
Figure 11: Parametric study results on the reflection coefficient. (**a**) Effect of the cavity length, (**b**) effect of the slot 1 length, (**c**) effect of the slot 2 length, and (**d**) effect of the gap width.
# Analysis of Optimal Loading Angle in Dynamic Flattened Brazilian Disc Splitting Test for Concrete
## 5 Results and Analysis  
### Crack Development in Flattened BD Test  
Figure 14 illustrates the evolution of the strain field of the flattened BD specimen in the y direction during the dynamic loading extracted by DIC. By analyzing the splitting process of dynamic loadings of SCC, it is found that the prefabricated loading angle in the specimen has an obvious influence on the splitting process of SCC under dynamic load.  
When the loading angle is in the range of 0\({}^{\circ}\)-24\({}^{\circ}\), as shown in Figure 14a-d, the BD specimen starts to fracture at the loading point and expands from the other end along the loading direction. It does not meet the center cracking assumption required by the BD experiment [42], which was consistent with the results of the numerical simulations above for the conventional Brazilian disc analysis. Therefore, the loading angle \(\theta\) in the range of 20\({}^{\circ}\)-24\({}^{\circ}\) is not enough to effectively reduce the stress concentration at the end under the action of dynamic load.  
When the loading angle is 26\({}^{\circ}\), as shown in Figure 14e, the crack propagation of the specimen becomes independent from the impact speed. This loading angle can be regarded as a critical value representing the transition from non-center initiation to center initiation.  
Figure 12: Waveforms and dynamic stress equilibriums of specimens in a typical flattened BD test (impact speed: 5.1 m/s). (**a**) Raw strain signals obtained from the strain gauges attached on the bars. (**b**) Dynamic force balance at both ends of the specimen.  
Figure 13: Calculation of the loading rate.  
When the loading angle is in the range of 28\({}^{\circ}\)-30\({}^{\circ}\) (Figure 14f,g), despite the increase in impact speed, the concentrated zone of tensile strain begins to appear in the center of the flattened BD specimen and then expands to both ends, which satisfies the validity condition of the BD dynamic tensile test. The BD specimen O first reaches the critical tensile strain value when the concrete is damaged, breaks and quickly expands to a crack parallel to the loading direction towards both ends of the specimen.
## 2 Study Area and Methods  
### Overview of the Study Area  
Located in central China, Changsha is defined as an important city in the central region in the national development strategy. By July 2021, the population scale of Changsha's central urban area had exceeded 5 million, which is one of 17 mega cities in China (the population scale of central urban area is between 5-10 million, which is a mega city). Like other metropolises in developing countries, Changsha is undergoing rapid development and transformation, with an accelerated industrialization and urbanization, and from 1949 to 2016, the urban population of Changsha increased from 383,500 to 3.36 million, the regional GDP from CNY 2.87 million to CNY 8510.13 million, and the built-up area from 6.7 km\({}^{2}\) to 476.34 km\({}^{2}\). During this period, the city's master plan went through six revisions in 1979, 1996, 2003, 2008, 2013 and 2016, respectively. The respective land use maps are shown in Figure 1, the urban land areas are shown in Table 1 and the urban sprawl intensities of various urban land areas are shown in Table 2. Figure 1 and Table 1 show that urban construction land is increasing, agricultural and forestry land are decreasing, urban compactness shows a downward trend and is \"spread out like a pie\", and the urban shape index has had little change. The expansion intensity of residential land is relatively the largest, especially after 2008, as it is several times that of other types of land. The expansion intensity of commercial service land is relatively stable. The expansion intensity of industrial land continues to decline, and the expansion intensity of road traffic land is relatively stable. Changsha is an energy-deficient city with an insufficient capacity to supply for a continuously growing energy demand. 80% of its energy needs to be constantly adjusted. The self-produced energy capacity is weak, and the growth potential is limited. The potential for new energies is insufficient, and the carbon emissions effect from energy consumption is very likely to grow in the future. How to balance the carbon emissions caused by rapid urban expansion and the serious energy-deficiency are important issues that need to be addressed.  
Figure 1: Land use change from 1949 to 2016.  
First of all, Changsha is one of the most representative cities that is experiencing rapid urban expansion and economic development, and the carbon emissions of energy consumption are increasing. Secondly, the data on energy consumption over the years in the urban statistical yearbook is relatively complete. Third, we have six consecutive urban land use data sets provided by the Changsha Survey and Design Institute. Based on the above reasons, we selected Changsha City as the case study area.
## 4 Experimental Results
### Lung Nodule Classification Result  
The proposed modified gradient boosting classifier is trained and tested with the given dataset. The entire dataset is divided into 75:25 for training and validating the proposed classifier. Out of 1018 CT scan images, 764 have been used to train the proposed classifier model, and the remaining 254 images have been used to validate the proposed model. A confusion matrix is generated for classifying the test dataset as benign class 1 nodule, benign class 2 nodule, non-nodules class 3, malignant class 4 nodule, and malignant class 5 nodule for the proposed method and is shown in Figure 7.  
Figure 5: (**a**) Boundary of benign class 1 nodule, (**b**) Boundary of benign class 2 nodule, (**c**) Boundary of malignant class 4 nodule, (**d**) Boundary of malignant class 5 nodule.  
Figure 6: Steerable Riesz wavelets for texture analysis.  
From Figure 7, it can be seen that the proposed classifier identifies the lung nodules in a correct class more accurately. Due to the similarity in the nature of the boundaries, the proposed system classifies malignant nodules as non-nodules, with 5.78%, and benign nodules as non-nodule, with 2.04%.  
The precision vs. recall and the ROC curve are generated based on the confusion matrix for the proposed modified gradient boosting classifier and are shown in Figure 8. From Figure 8a, it can be seen that the proposed modified gradient boosting classifier curve is steeper to the right corner of the graph, which indicates the better performance of the proposed method.  
The ROC curve, which is illustrated in Figure 8b, is the graph between the True Positive Rate (TPR) and the False Positive Rate (FPR). The blue line indicates the ROC curve of the classifier and diagonal line indicates the arbitrary classifier. From Figure 8b, it can be seen that the proposed modified gradient boosting classifier curve is steeper toward the left corner of the graph. From the performance analysis, it can be seen that the proposed classifier achieves a precision, recall, F1 score, and validation accuracy of 0.957, 0.91, 0.941, and 95.67%, respectively.
## Materials and Methods
### Composite Processing
If adjacent pixels along the edge vary by more than this amount, the original \"hard\" seamline at the scene boundary will be left intact. Since automatically selecting clear and non-turbid scenes is not always accurate, an additional manual quality control step was added to identify cases where sub-optimal scenes were selected in key areas. This resulted in the manual identification and replacement of 860 scenes out of the 38,642 used in the composite, mostly along the coast of Cuba and Jamaica. In most cases, the automated approach struggled to detect turbidity, so the hand  
Figure 3: Example of corrections applied during image compositing in Ile-à-Vache, Haiti: (**a**) Input surface reflectance data based on best quality scenes; (**b**) Normalization applied using MODIS reference data in addition to the physics-based atmospheric correction approach used in the PS surface reflectance scene product; (**c**) Seamline removal via a surface reconstruction method that smoothly shifts values near scene edges to match values at the boundary between scenes; (**d**) Manual quality control where the hand selection of scenes significantly reduced turbidity in near-coastal waters in the final composite. selection of scenes significantly reduced turbidity in near-coastal waters (Figure 3d). The final composite resulted in ~300 GB of seamless, non-cloudy, four-band dataset covering one million square kilometers with a spectral response approximately similar to MODIS (Figure 4). This composite reduced the need for scene-level corrections and served as the input for all subsequent processing steps and classification.
# Experimental Investigation of the BIPV System under Sanluurfa Meteorological Conditions
## 2 Materials and Methods
### Calculation Method  
Electric energy production values (\(P\)) of the PV panels were recorded every 15 min. The unit of \(P\) was W (Watts). Along with power, the total produced energy values, such as daily and monthly in the study, were found using Equation (1); the unit was Wh [42].  
\[E\left(\mathrm{kWh}\right)=P(\mathrm{kW}).\ h\ (\mathrm{hour}) \tag{1}\]  
In general, the electrical efficiencies (\(\eta e\)) of PV panels are calculated using Equation (2). Here, the P value represents the daily average power (watt), \(I_{T}\) represents the daily average amount of solar radiation received per unit area, and its unit is W/m\({}^{2}\), A represents the panel area, and its unit is m\({}^{2}\)[43].  
\[\eta_{e}=\frac{P}{I_{T}.A} \tag{2}\]  
The capacities of bifacial PV panels were determined on the basis of the front face of the panel under standard test conditions (STC), just as in monofacial panels. A new rating was made to account for the effect of the back surface. The number known as bifaciality, denoted by \(B\), is the ratio of the energy produced by the back surface of the panel to the energy produced by the front surface as measured under STC. The bifaciality ratio (\(B\)) is given by Equations (3) and (4) below [44]. In this study, the bifaciality ratio was used to indicate the percentage increase in production of the bifacial panel compared with the monofacial panel on the south facade, both having the same production potential.  
\[B=E_{total}/E_{front} \tag{3}\]  
\[E_{total}=E_{front}(1+B) \tag{4}\]
## 4 Parametric Analysis of CSPSWs
### Influence of Different Steel Sheet Width/Height Ratio on Energy Dissipation Capacity of CSPSWs  
As shown in Figure 23, finite element simulations were conducted using the four models from Table 3, TSCPSPW-C, TCSPSW-w/H-0.5, TCSPSW-w/H-1.0, and TCSPSW-w/H-1.5, with width/height ratios of 0.5, 0.68, 1.0, and 1.5, respectively. Compared to the other three models, the E\({}_{0}\) of the TCSPSW-w/H-1.5 model began to increase at the stage of \(\Delta\)/H = 0.25%, indicating that the model gradually entered the elastoplastic stage and started to dissipate energy (although the amount of energy dissipation was relatively small). The loading stage at which the other three models entered the elastoplastic stage also advanced with the increase in the width/height ratio. The width/height ratios of the models TSCPSW-C, TCSPSW-w/H-0.5, TCSPSW-w/H-1.0, and TCSPSW-w/H-1.5 increased progressively, and, correspondingly, the energy dissipation coefficients (E\({}_{0}\)) and energy dissipation of the models also increased to varying degrees. This shows that the energy dissipation efficiency and the amount of energy dissipation of the models increase with the increase in the steel plate width/height ratio, showing a positive correlation. At the stage of \(\Delta\)/H = 2.0%, the E\({}_{0}\) of the steel plate model with a width/height ratio of 1.5 (TCSPSW-w/H-1.5) was the highest, at 1.06, which was 27.37% higher than that of the model with the lowest E\({}_{0}\) (TCSPSW-w/H-0.5). The energy dissipation of the steel plate model with a width/height ratio of 1.5 (TCSPSW-w/H-1.5) was the highest at the stage of \(\Delta\)/H = 2.0%, reaching 127.15 kJ, which was 299.99% more than that of the model with the lowest energy dissipation (TCSPSW-w/H-0.5). It can be seen that the width/height ratio of the steel plate has a very significant impact on the energy dissipation capacity of the models.  
Figure 23: _Cont._  
Figure 22: Stress comparison diagram of different plate thickness models: (**a**) TCSPSW—t—4, (**b**) TCSPSW—C, (**c**) TCSPSW—t—8, (**d**) TCSPSW—t—10.  
Figure 24 shows the stress cloud diagrams of the four models with different width/height ratios. The stress concentration areas of the four models are all distributed at the valleys of the corrugated steel plates near the free ends on both sides. Changing the width/height ratio has little impact on the stress magnitude or failure mode of the models.
# Micro-Blog Sentiment Classification Method Based on the Personality and Bagging Algorithm
## 3 Related Concepts  
### Ensemble Learning  
Ensemble Learning is widely used for classification and regression tasks. Its principal concept is simple: different methods are used to change the distribution of the original training samples to build multiple different classifiers, which are then combined linearly to get a more robust classifier. The two main types of integrated learning are boosting and bagging.  
Bagging is a popular integration approach that uses a bootstrapping algorithm to get multiple copies of the training set, which are then used to train different models. Voting schemes are then employed to incorporate projection and forecasting. Because the training sets are slightly different from each other, each model trained for these training sets would have different weights and focus, thus obtaining different generalization errors. By combining the models, the overall generalization error is expected to decrease to a certain extent. Figure 1 shows the flowchart of the bagging algorithm.
# On Combining DeepSnake and Global Saliency for Detection of Orchard Apples
###### Abstract  
For the fast detection and recognition of apple fruit targets, based on the real-time DeepSnake deep learning instance segmentation model, this paper provided an algorithm basis for the practical application and promotion of apple picking robots. Since the initial detection results have an important impact on the subsequent edge prediction, this paper proposed an automatic detection method for apple fruit targets in natural environments based on saliency detection and traditional color difference methods. Combined with the original image, the histogram backprojection algorithm was used to further optimize the salient image results. A dynamic adaptive overlapping target separation algorithm was proposed to locate the single target fruit and further to determine the initial contour for DeepSnake, in view of the possible overlapping fruit regions in the saliency map. Finally, the target fruit was labeled based on the segmentation results of the examples. In the experiment, 300 training datasets were used to train the DeepSnake model, and the self-built dataset containing 1036 pictures of apples in various situations under natural environment was tested. The detection accuracy of target fruits under non-overlapping shaded fruits, overlapping fruits, shaded branches and leaves, and poor illumination conditions were 99.12%, 94.78%, 90.71%, and 94.46% respectively. The comprehensive detection accuracy was 95.66%, and the average processing time was 0.42 s in 1036 test images, which showed that the proposed algorithm can effectively separate the overlapping fruits through a not-very-large training samples and realize the rapid and accurate detection of apple targets.  
image processing: target detection; saliency detection; DeepSnake instance segmentation; apple images +
Footnote †: journal: Applied Sciences
# DSP-Based 40 GB/s Lane Rate Next-Generation Access Networks +
## 3 Simulation Results
### Link Power Budget  
Figure 5 summarizes the achievable link power budgets of various 40 Gb/s PONs using different DSPs at typical fiber lengths ranging from 10 km to 40 km. The link power budget is calculated by considering the corresponding receiver power sensitivity at a BER of \(1\times 10^{-3}\), obtained in Figure 4 and for a 10 dBm of optical launch power. The vertical length of each bar in Figure 5 represents the corresponding link power budget, and it is explicitly marked with a value in dB. Comparatively, for fiber lengths beyond 10 km, there are some blank area with bars missing, which means that the link under such a receiver signal processing configuration fails, because the receiver BER cannot achieve \(1\times 10^{-3}\).  
At a 10 km SMF distance, optical Duobinary with 4-state MLSE offers the largest link power budget of 34.6 dB, which outperforms electrical Duobinary and PAM-4 by 2.1 dB and 5.7 dB, respectively even if a 4-state MLSE is used for the latter schemes. In addition, for each modulation scheme, the receiver power sensitivity shows very little variation among different receiver signal processing configurations, ranging from the case without equalization, to the case of MLSE, as indicated. This is because the chirp and fiber chromatic dispersions almost offset each other at such a distance. For the 20 km case, however, electrical Duobinary using 4-state MLSE achieves the best link power budget of 31.2 dB. Optical Duobinary with a 4-state MLSE gives rise to a similar link power budget of 30.5 dB. Optical Duobinary without equalization or by using only a linear equalization fails. PAM-4 is the only scheme that succeeds for all four DSP configurations, and the link power budget varies little among different DSP configurations. For 30 km and 40 km SMF cases, optical Duobinary fails even if a 4-state MLSE is considered. Electrical Duobinary with a 4-state MLSE brings about 31 dB and 28.2 dB link power budgets, which are the best for 30 km and 40 km cases, respectively. PAM-4 shows approximately a 3 dB worse link power budget in either case, compared to optical Duobinary. Both the electrical Duobinary and PAM-4 must incorporate equalizations, otherwise links fail.