esa-sceva/satcom-chunk-collection huggingface dataset:
# NeuChain+: A Sharding Permissioned Blockchain System with Ordering-Free Consensus
## 7 Evaluation
### Cross-Shard Transaction Processing Overhead  
In order to evaluate the overhead of cross-shard transaction processing based on the deterministic execution designed in this paper, the time consumption of the transaction execution phase was tested using the peak performance under different numbers of shards (setup is the same as in Section 7.4).  
The experimental results are shown in Figure 11. In the no-sharding situation, benefiting from deterministic execution, honest nodes could achieve consensus without communication coordination, resulting in an execution phase overhead of only 39.6 ms. After the introduction of sharding, the execution phase overhead increased by 43.9 ms, reaching 83.5 ms. This increase is attributed to the synchronization point that is difficult to optimize at _cross-reserve_. It is necessary to ensure the receipt of \(f+1\) copies of cross-shard reserve tables from all other shards before detecting conflicts for cross-shard transactions.  
Figure 10: Single-node storage space under different shards (SmallBank).  
Figure 9: Performance comparison of scaling the number of nodes (SmallBank).  
Furthermore, we also evaluated the impact of pipelined read sets transmission optimization on overhead. Under two, three, and four shards, the overhead was reduced by 4.8 ms, 5.8 ms, and 7.3 ms, respectively. Due to its optimization on generating read-write sets during the execution of cross-shard transactions, the effectiveness of this optimization was more obvious as the proportion of cross-shard transactions increased. Note that there are no cross-shard transactions under no sharding, so the optimization had no effect.  
Although cross-shard transaction processing brings unavoidable time overhead, the results of continuing to increase the number of shards indicate that the overhead of cross-shard transaction processing based on deterministic execution is relatively fixed and almost does not increase with the increase in the number of shards and the proportion of cross-shard transactions. We believe that such an increase in time overhead is acceptable given the advantages of high scalability brought by sharding.  
**Summary of results.** Experimental results show that NeuChain+ addresses the scalability issues of nonsmarding and previous sharding blockchains. Under 16 nodes, the throughput was 2.9\(\times\) that of NeuChain, and the storage overhead was only 30.9% of NeuChain. Compared to Meepo, NeuChain+ in the situation of storage sharding in each node and nontrustworthiness among all nodes, achieved 2.8\(\times\) higher throughput and 61% lower latency. Compared with nonsmarding blockchains under eight shards, NeuChain+ outperformed state-of-the-art blockchain systems with 1.7-75.3\(\times\) throughput.
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 4 Performance Evaluation
### Experiments
#### 4.2.3 Performance Evaluation for the Replacement Scenario  
Battery limitations mean drone flight time is generally less than 20 min [1]. Thus, replacement functionality is essential to prolong drone system service time. If remaining battery energy becomes low, GCS should send a return-to-home command to the depleted drone and assign a new drone to that service area. If a drone experiences failure due to any problem, e.g., collision or hardware malfunction, a new drone should be deployed to perform the designated mission. In these situations, it is important for drones to update routing tables with minimum overhead to preserve connectivity.  
Figure 10(b) shows the drone replacement scenario where the user located in Drone 4's service area sends 1 Mbps UDP data using _ipperf_ to Drone 1. Drones 5 and 6 were waiting for future replacement. At \(T_{1}\), Drone 3 returned to the base station, and Drone 5 replaced it, and Drone 6 replaced Drone 2 at \(T_{2}\). We measured UDP throughput of GCS-routing, DSDV, and OLSR, as shown in Figure 13.  
Figure 11: Drone deployments in mobility and replacement scenarios.  
Figure 12: Mobility experiment throughput.  
Network topology changed at \(T_{1}\) and \(T_{2}\), so connections were disconnected until new routes were established. Table 2 shows network disconnected time for each replacement. GCS-routing takes approximately 1 s to update routes between the two drones on average, whereas DSDV takes 17.5 s, significantly longer than both OLSR and GCS-routing. This is because the DSDV HELLO interval was 4 s, whereas OLSR interval was 2 s. Since OLSR detects events every 0.1 s, it can minimize delay to set up the routing table again. However, OLSR network disconnected time was 3 times that of GCS-routing. Thus, GCS-routing immediately reacted to network topology changes and stabilized the new network very quickly. The drone network supervisor loses drone control during network disconnected time, which is fatal for real-time applications. If drones remain uncontrolled for a long period, they could collide with obstacles or other drones.  
GCS-routing also provided more uniform network service quality than the other protocols, as shown in Table 3. DSDV and OLSR standard deviations were relatively high due to the HELLO messages. Thus, GCS-routing exhibited superior network stability and would be more appropriate for controlling drone networks. The video of the experiment can be found at [13].
## Materials and Methods
### Experimental Setup  
Air-dried soil samples were sieved to 5 mm and pre-incubated at 45% water filling pore space (WFPS) at 20 \({}^{\circ}\)C for 4 days. NZ\({}_{\text{p}}\) and CZ\({}_{\text{p}}\) were applied to the soil at 10 wt% and tested as treatments (NZ and CZ, respectively) versus a control (CNTR). All the treatments received 486 ug g\({}^{-1}\) of NH\({}_{4}\)NO\({}_{3}\) equivalent to 170 kg-N ha\({}^{-1}\), which reflects the fertilization rate used in NVZ according to the Nitrate Directive 91/676/EEC. The same fertilization rate was also used in previous field experiments of the ZeoLIFE project [28] and in laboratory studies involving the same soil and zeolites [22]. As an additional factor, in a separate batch, each treatment received 2.916 u g\({}^{-1}\) of the nitrification inhibitor DMPP in liquid form, corresponding to 0.6% of the fertilizer added, according to [35; 38]. This dosage also reflects the amount of DMPP that is commonly contained in granulated formulations [51; 54].The tested treatments thus resulted in the following six combinations in four replicates:  
* unamended soil (CNTR);
* unamended soil (CNTR) + DMPP;
* soil with 10 wt% of NZ\({}_{\text{p}}\) (NZ);
* soil with 10 wt% of NZ\({}_{\text{p}}\) (NZ) + DMPP;
* soil with 10% of CZ\({}_{\text{p}}\) (CZ);
* soil with 10% of CZ\({}_{\text{p}}\) (CZ) + DMPP.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **pH** & **EC** & **Total N** & **Total C** & **Total OC** \\  & **mS cm\({}^{-1}\)** & **g kg\({}^{-1}\)** & **g kg\({}^{-1}\)** & **g kg\({}^{-1}\)** & **g kg\({}^{-1}\)** \\ \hline CNTR & 7.59 \(\pm\) 0.05 a & 1.44 \(\pm\) 0.04 c & 2.09 \(\pm\) 0.10 c & 33.47 \(\pm\) 0.76 a & 21.04 \(\pm\) 0.23 a & \\ NZ\({}_{\text{p}}\) & 7.58 \(\pm\) 0.05 a & 0.54 \(\pm\) 0.07 e & 0.10 \(\pm\) 0.01 e & 0.80 \(\pm\) 0.10 e & nd \\ CZ\({}_{\text{p}}\) & 6.95 \(\pm\) 0.09 b & 15.03 \(\pm\) 0.63 a & 4.27 \(\pm\) 0.12 a & 1.53 \(\pm\) 0.08 d & 0.08 \(\pm\) 0.01 d & \\ NZ & 7.55 \(\pm\) 0.03 a & 1.34 \(\pm\) 0.02 d & 1.76 \(\pm\) 0.01 d & 27.69 \(\pm\) 0.75 c & 16.48 \(\pm\) 0.03 c & \\ CZ & 7.63 \(\pm\) 0.04 a & 2.56 \(\pm\) 0.11 b & 2.27 \(\pm\) 0.07 b & 29.78 \(\pm\) 0.78 b & 18.10 \(\pm\) 0.01 b & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic zeolites and soil properties. NZ\({}_{\text{p}}\) and CZ\({}_{\text{p}}\) refer to pristine chabazite zeolite tuff (with no soil added) in natural and NH\({}_{4}\)+-enriched state, CNTR is the unamended soil, NZ is the soil + 10% wt of NZ\({}_{\text{p}}\) while CZ is the soil amended with 10 wt% of CZ\({}_{\text{p}}\). EC is electrical conductivity (mS cm\({}^{-1}\)). Values are expressed as mean \(\pm\) standard deviation (3 replicates). Different superscript letters express significant differences as a result of ANOVA and Tukey-HSD statistical tests (\(p<0.05\)). nd = not detected.
## 2 Related Works
### Traditional Distance Estimation  
Typical photos taken from a monocular camera are shown in two dimensions that would require extra information for distance estimation. Distance estimation (also known as depth estimation) is an inverse problem [17] that tries to measure the distance between 3D objects from insufficient information provided in the 2D view.  
The earliest algorithms for depth estimation were developed based on stereo vision. Researchers utilize geometry to constrain and replicate the idea of stereopsis mathematically. [PERSON] and [PERSON] [18] conducted a comparative evaluation of the best-performing stereo algorithms at that time. Meanwhile, [PERSON] et al. [19] developed methods to estimate the distance from a monocular camera. They investigated the possibility of performing distance control to an accuracy level sufficient for an Adaptive Cruise Control system. A single camera is installed in a vehicle using the laws of perspective to estimate the distance based on a constrained environment: the camera is at a known height from a planar surface in the near distance and the objects of interest (the other vehicles) lie on that plane. A radar is equipped for obtaining the ground truth. The results show that both distance and relative velocity can be estimated from a single camera and the actual error lies mostly within the theoretical bounds. [PERSON] et al. [20] also proposed a distance estimation method for vision-based forward collision warning systems with a monocular camera. The system estimates the virtual horizon from information on the size and position of vehicles in the image, which is obtained by an object detection algorithm and calculates the distance from vehicle position in the image with the virtual horizon even when the road inclination varies continuously or lane markings are not seen. To enable the distance estimation in vehicles, [PERSON] and [PERSON] [21] also proposed a system to determine the distance between two vehicles using two low-resolution cameras and one of the vehicle's rear LED lights. Since the poses of the two cameras are pre-determined, the distances between the LED and the cameras, as well as the vehicle-to-vehicle distance can be calculated based on the pinhole model of the camera as the focal lengths of the cameras are known. The research also proposes a resolution compensation method to reduce the estimation error by a low-resolution camera. Moreover, [PERSON] et al. [22] proposed an integrated system that combines vehicle detection, lane detection, and vehicle distance estimation. The proposed algorithm does not require calibrating the camera or measuring the camera pose in advance as they estimate the focal length from three vanishing points and utilize lane markers with the associated 3D constraint to estimate the camera pose. The SVM with Radial Basis Function (RBF) kernel is chosen to be the classifier of vehicle detection and Canny edge detection and Hough transform are employed for the lane detection.
## 2 Materials and Methods
### Ground Reaction Force Asymmetry Index  
The GRF AI test measured the difference in the vertical ground reaction force at seat-off between the right and left lower extremities. To measure vertical GRFs, three low-cost force plates (Vernier Software & Technology, Beaverton, OR, USA), about 600 dollars each, were used. The dimensions of the force plates used were 28 \(\times\) 32 \(\times\) 5 cm. Therefore, the size of the force plate was sufficient for each foot and both buttocks of the participant. Data were recorded in N and obtained 100 times per second.  
For the measurement, participants removed their shoes and placed each foot on the force plate of the corresponding side while sitting on a force plate on the motorized table, adjusted according to each participant's height. Before STS, the participant spread both feet to shoulder width. Table height was adjusted by examiner such that the participant's ankle joint dorsiflexion was at 15\({}^{\circ}\), the knee joint flexion was at 105\({}^{\circ}\), and the thigh was parallel to the ground. The examiner ensured that the participant's trunk was vertical and that the table only touched approximately one third of the participant's thigh. Because the participant was sitting on the force plate, weight bearing on the Bobath table was minimal while their hands were touching the wall.  
For the GRF AI of the maximal weight shift to the right, participants slightly touched the wall with their hands at chest level and then stood upright while maximally moving weight to the right. The procedure was identical for the GRF AI of the maximal weight shift to the left, except participants maximally shifted their weight to the left while standing. Participants were instructed to keep both feet stable on the force plates during the test to enhance safety. To prevent sudden movement, a metronome was used during the test and participants were instructed to smoothly perform each action for 2 s. The GRF data of the lower extremities were obtained when the GRF of the buttocks reached zero, that is, when the buttocks were detached from the force plate (seat-off) (Figure 1). Hip and knee extension torques reach their peak values around the instant of seat-off [23].  
Participants performed nine STS performances, three times in each STS method (three with even weight distribution, three with maximal weight shift to the right side, and three with maximal weight shift to the left side). Each standing method was alternately performed. A 1-min rest was given between trials to prevent fatigue.  
Figure 1: Force plate graph of left-side weight shifting during the sit-to-stand performance. Red line, buttock force plate; blue line, right foot force plate; green line, left foot force plate.  
GRF AI is the value obtained by shifting the weight to the right and left performing STS. The GRF AI was defined as the GRF value of each side divided by the sum of both sides. The equation of the GRF AI was as follows:  
GRF AI at maximal weight shift to right (GRF AI Rt) = right GRF/(right GRF + left GRF)  
GRF AI at maximal weight shift to left (GRF AI Lt) = left GRF/(right GRF + left GRF).
## 2 Materials and Methods  
### Instrumentation and Experimental Methods  
All the experiments were carried out in different Spanish food industries. As previously explained, the analyses were carried out using six different food matrices (butter, flour, milk, yogurt, olive oil, and olives), with the analyte studied in most of the examples being different (see Table 1). The measurement procedure was made in every case with the AONIR (AOTECH S.L. [34]) integrated solution for real-time NIR measurements, including an NIR sensor, a measurement platform, and the precise software to integrate the hardware with the model outcome for posterior real-time monitoring of the parameters that were going to be measured in this work. The spectrometer was configured equally in all cases so that NIR reflectance was registered in each food matrix in a wavelength range from 900 to 1670 nm (125 wavelengths, accounting for a pixel resolution of 6 nm and a spectral resolution of 12 nm) and 50 readings per spectrum with an integration time of 10.8 ms. Nevertheless, the number of samples, the sample replicates, and the spectral ones were not the same in all cases given the determining factor that the measurements were made in different industries where it is necessary to adapt to the times of reception of samples, production times, and availability, among others. The state of the samples was also considered; for example, as can be seen in Table 1, it was decided to average the eight spectra recorded for each olive sample, considering that the distribution of the agrochemicals could not be homogeneous. The same applies to the powdery-nature flour, for which replicates are higher than in the other matrices. In some cases, the samples were even measured on different days. The details of samples and sample replicates, along with the final data matrix for each case, can be found in Table 1.  
After the NIR measurements, the samples were sent to an accredited laboratory in order to be measured using a certified reference method (the one indicated in Table 1 for each analyte in each food matrix).  
As mentioned above, the AONIR platform developed by AOTECH S.L. [34] was used to obtain the recorded spectra in a.csv file, while PLS-Toolbox [35] working under MATLAB version 9.9.0 (R2020b) [36] was employed for fitting the PLS models. The decision limits and the capabilities of detection were calculated using the DETARCHI [31] program, whereas CD\(\alpha\) and CD\(\beta\) were estimated using a handmade program also working under MATLAB [36], which generalizes DETARCHI [31] to all the cases described in this work. These programs avoid using a table of the non-central t-distribution and automate obtaining graphs and CC\(\alpha\), CC\(\beta\), CD\(\alpha\), and CD\(\beta\) values for any number of replicates and the values of the probabilities of false compliance and false non-compliance.
## 4 Results
### Characteristics of 3D In Situ Stress  
Based on the simulation results, the in situ stress of the Fengcheng Formation in the study area can be thoroughly analyzed. Generally speaking, the alterations and distribution of the in situ stress state are intricate. As shown in Figure 15, the simulation results conform to the acoustic emission experiment results. Most of the \(\sigma_{\rm hmin}\) exceeds 78 MPa, with the majority of \(\sigma_{\rm hmax}\) exceeding 90 MPa. The variation tendencies in \(\sigma_{\rm hmax}\) and \(\sigma_{\rm hmin}\) are identical, both displaying low values in the north and high values in the south, with the lowest stress magnitude located in the western part of the study area. The maximum horizontal principal stress is mainly concentrated between 98 and 124 MPa, accounting for 68.7% of the stress distribution (Figure 16). Similarly, the minimum horizontal principal stress is mainly centered between 85 and 100 MPa, making up 60.3% of the stress distribution (Figure 16). The orientation of \(\sigma_{\rm hmax}\) is NE, and the strike of \(\sigma_{\rm hmin}\) is approximately perpendicular to that of \(\sigma_{\rm hmax}\) (Figure 17). To sum up, the in situ stress state in the study area changes rapidly, with the orientation of horizontal principal stress varying near certain faults.
# Phenolic Compound Composition and Biological Activities of Fractionated Soybean Pod Extract
## 2 Materials and Methods
### Determination of Polyphenol Content and Antioxidant Activity  
Determination of total polyphenols with Folin-Ciocalteu reagent was performed in accordance with the methodology proposed by [PERSON] et al. [15]. To perform the analysis, an extract with a 1 mg/mL concentration (0.25 mL) was mixed with 2.0 mL of 10% Folin-Ciocalteu reagent (\(v/v\)). After one minute, 1.0 mL of a 7.5% sodium carbonate solution (\(w/v\)) was added. The samples were thoroughly mixed and incubated at room temperature for 120 min. Absorbance was read at 740 nm against water as a blank sample. Calculation of the content of phenolic compounds was performed based on the calibration curve prepared for gallic acid and expressed in mg gallic acid/g of extract.  
The antioxidant activity of the extracts was determined with the DPPH radical (1,1-diphenyl-2-picrylhydrazyl) according to the method described by [PERSON] et al. [16] and slightly modified by [PERSON] et al. [15]. For analysis, 3.6 mL of a DPPH solution (0.1 mM methanol solution) was added to 0.4 mL of the extract. The control sample was a mixture of 0.4 mL of methanol and 3.6 mL of DPPH. The absorbance was measured at 515 nm.  
The antioxidant activity was also determined with the ABTS\({}^{\star\bullet}\) radical (2,2\({}^{\prime}\)-azino-bis (3-ethylbenzothazoline-6-sulfonic acid)) in line with the methodology described by [PERSON] et al. [17]. ABTS\({}^{\star\bullet}\) was generated via reaction of an ABTS aqueous solution (7 mM) with potassium persulfate (2.45 mM) in the dark for 12-16 h. The initial solution was diluted until absorbance reached 0.7 (\(\pm\)0.02) at 734 nm. The reaction mixture contained a 3 mL ABTS\({}^{\star\bullet}\) solution and a 0.1 mL extract. A solution with ethanol instead of the samples was used as a control. The absorbance was measured at 515 nm after 10 min at room temperature.  
Figure 1: Scheme of the experimental procedure.  
Antioxidant activity was assessed in a series of solutions obtained from dry extracts with a concentration range from 0.01 to 10 mg of extract/mL. Based on the linear relationship between the ability to neutralize the DPPH radical and the ABTS cation radical and the concentration of the extract, the IC\({}_{50}\) [mg/mL] value was calculated, which determines the concentration of the extract needed to neutralize 50% of radicals.
## 3 Application of the Proposed Method to the Fruit Variety Classification  
### Problem Statement  
The traditional grocery store has been evolving in recent decades to a supermarket and discount store concept, carrying all the goods shoppers often desire. These stores offer a very large number of products, both processed and partially processed, as well as fresh produce such as fruits and vegetables. Fresh product is typically sold per piece and by weight. As discussed earlier, the sale of produce may be burdensome for cashiers, because they must remember (or search for) the identification code of each item. In the case of self-service checkouts, the sale of fruits and vegetables is connected with the identification of the products species and varieties by buyers. Thus, the sales process in current use leads to longer customer service time, often causing errors (payments for bad products) and business losses.  
The published literature suggests that machine vision systems and machine learning methods allow for the construction of systems for automatic fruit and vegetable classification. In particular, deep learning methods have high classification accuracy for both training and testing images, mainly in the case of recognizing species of fruits and vegetables. Recognizing varieties of fruits and vegetables is more difficult due to highly similar color, structure, and shapes in the same class. In fact, the image of the identified object may differ from the learned pattern, resulting in classification errors.  
The primary problem addressed in this study is the following: Is it possible to build a machine vision system that can quickly classify the variety of fruits and vegetables together with providing the result certainty factor and, in the case of uncertainty, will notify about the set of the most probable classes?  
To tackle this question, a double-track method for fruit variety classification is proposed that uses the image classification methods on the example of images with a background, as well as the method of object detection allowing the detection of fruit objects that are also used for classification. Comparison of the classification results of different objects of the same image, using the weights of the results, will allow the calculation of the Certainty Factor (_CF_) regarding the proposed result of the classification.