esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
## 4 Results
### UAV Real-Time Object Detection Evaluation
#### 4.7.1 Accuracy  
According to our review, ten evaluation metrics were used in the literature. As Figure 10 shows, the accuracy was used as the highest number as an evaluation metric. Accuracy is defined as the ratio of the number of correctly predicted samples to the total number of predicted samples. Although there are 23 studies using accuracy as an evaluation criterion for model performance, it is often criticized for failing to reflect the proportion of true positives, and it is questionable whether the detection results are comprehensive. In addition, precision, recall, F-score, and mAP, which appear more than 10 times, can better present the model's performance by considering true positives, true negatives, false positives, and false negatives. These are the most commonly used metrics for object detection; we also have some other metrics that can evaluate the model to some extent.  
The average accuracy represents the average recall rate of each class in multi-object detection. Compared with accuracy, this metric can better reflect the impact of class imbalance. AUC means the area under the curve of an ROC. However, since an ROC is not easy to measure, the average precision is equivalent to calculating the area under the precision/recall curve. Meanwhile, the IoU is a simple measurement standard for object detection, which represents the intersection ratio of the ground truth and the prediction, and the mIoU is the average of each class.
## 3 Challenges
### Platform Heterogeneity  
Sensors and actuators do not share the same level of resource constraints, as mentioned above. Possibly designed using different technologies and with different goals, they are different from each other in many aspects such as computing/communication capabilities, functionality, and number. In a large-scale system of systems, the hardware and networking technologies used in the underlying WSANs may differ from one subsystem to another. This is true because of the lack of relevant standards dedicated to WSANs and hence commercially available products often have disparate features. This platform heterogeneity makes it very difficult to make full use of the resources available in the integrated system. Consequently, resource efficiency cannot be maximized in many situations. In addition, the platform heterogeneity also makes it challenging to achieve real-time and reliable communication between different nodes.
## 4 Performance Evaluation and Comparison  
Section 4 is divided into three sub-sections. Firstly, the performance of the proposed \(K_{CS}\) is analyzed. Afterwards, it is compared with five other kernels using the feature extraction approach. Finally, performance comparison between proposed and related work is discussed.
## 4 Tree-Based Protocols
### Manchester Coding
#### 4.4.3 Optimal Query Tracking Tree Protocol
However, OQTT may incorrectly estimate the tag number. The estimation error however is negligible, only producing an imperceptible difference in the final result of the number of queries. According to the literature, e.g., [58], this protocol provides an efficiency of approximately 0.614 and is one of the most efficient anti-collision protocols for tag identification. Although the slot efficiency obtained by OQTT is very high, the preprocessing increases the energy consumption of the protocol, especially in dense tag environments [58]. * A modification of the OQTT is presented in [59] called Optimal Binary Tracking Tree (OBTT). This modification implements the estimator of the OQTT with a simple Binary Tree (BT) protocol [60]. The estimator establishes the inital upper bound for tags' counters. This produces a separation of the existing tags in groups avoiding excessive responses in the beginning of the interrogation procedure.
# BLE Signal Reception and Localization Performance with Varying Receiver and Beacon Setups
## 2 BLE Signal Collection Setup
### Preliminary Investigations  
The setup described in the next subsections is informed by preliminary investigations aimed at assessing the operational efficacy of the gateway (GW) and beacon components. In the preliminary test, the gateway was positioned at ground level, while the beacon was manually held at a distance from the tester, who moved through the room with strategically placed beacons. The results indicated that the GW effectively detected signals at short distances but struggled to distinguish signals at medium and long ranges. Notably, the  
Figure 1: BLE signal mapping equipment and network.  
number of signal measurements was higher when the beacon was in close proximity, suggesting that antenna positioning influenced signal characteristics. Suboptimal alignment, due to the gateway being placed on the floor and oriented towards the ceiling, caused signal degradation and loss as the tester moved further away.  
In another test on the effect of distance on signal strength, two GWs were positioned at a height of 1 m on opposite walls, 7 m apart. The received RSSI was measured at various points between the GWs. The results showed a significant decline in signal strength as the distance increased, demonstrating the impact of distance on signal attenuation. Further analysis of RSSI stability and the body effect was conducted with GWs positioned on the ceiling. Four GWs were symmetrically arranged, and RSSI values were measured at various points, accounting for the body effect. The results showed stable and frequent signals, with variations in signal strength depending on the tester's orientation. The body effect was observed as a reduction in signal strength, particularly when the tester's body was positioned between the GW and the beacon.  
In the final test investigating the impact of body effects on signal data, involving GWs and beacons, observations revealed variations in signal fields at different angles and distances, with notable anomalies at longer ranges. The body effect was particularly significant when GWs were positioned in front of and behind the tester. Asymmetry and signal irregularities were observed in scenarios involving multiple GWs.
# Ship Type Selection and Cost Optimization of Marine Container Ships Based on Genetic Algorithm
## 2 Establishment of the Bi-Level Programming Model  
### Assumptions and Variable Descriptions  
In the context of international cargo transportation services provided by container liner shipping companies, the choice of ship type is typically based on cost optimization, aiming for rapid capital accumulation. However, with the increasing competition in the international shipping industry, especially in the contexts of the global production chain becoming more globalized and industrial decarbonization, the shipping industry must not only optimize operational costs but also ensure that carbon emissions meet national carbon neutrality and peak carbon emission requirements. To stand out in future high-standards competition and achieve sustainable development, container liner shipping companies must consider not only economic efficiency and cost reduction but also environmental concerns and the reduction of carbon emissions from large cargo ships. To address this, this paper employs a bi-level programming model for analysis. The upper-level programming (U) aims to minimize the operational cost of a single cargo transport task for the container liner shipping company, focusing on streamlining international cargo transport services. The lower-level programming (L) aims to minimize the carbon emissions of a single transport task, aiming to enhance customer satisfaction through optimized logistics services. The hierarchical structure of the bi-level programming model reflects the mutual influence between the upper and lower levels, and through multiple iterations, an optimal solution integrating cost and service quality is obtained [24]. The basic assumptions are as follows:  
1. The international cargo transport service covers only transportation and simple value-added services (e.g., sorting, packing, etc.), and costs are calculated per container.  
Logistics service costs consist only of fixed costs, transportation costs, and value-added service costs, with fixed unit prices for transportation and value-added services.
3. Different types of ships used by the shipping company correspond to fixed routes.
4. The transportation cost of logistics business is related only to the unit transportation cost of the ship and the shipment volume, while the value-added service cost is related only to the handling volume.  
The Settings of parameter variables are shown in Table 1.
## 3 System Design and Implementation
### ECG Monitoring System Implementation  
The received sensor data is further processed and interpreted by the ECG monitoring system running on the Android(tm) mobile device. In the ECG monitoring system, various algorithms are combined and implemented as mobile application software with the Java Android(tm) language to handle all the query processes in the WSN. The query processes handle the communication between sensor nodes and the ECG device, detect and differentiate the ECG sensory data, interpret, analyze and manipulate the sensory data packet, and then, they proceed to display the data graphically on a mobile screen in real time. Figure 5 shows the data visualization of the GUI of the ECG monitoring system. It includes an ECG waveform display, ECG waveform analysis, real time heart rate estimation and also queries buttons to handle the process to initiate and end a monitoring activity.  
The ECG monitoring system was developed and tested on an Android(tm) mobile platform development kit [10] as shown in Figure 6(a), running an ARM processor (S5 PC100X--ARM-CORTEX A8). This development kit comes with a sensor expansion board and an Android(tm)-based smart phone-alike mobile device. Through the serial port, the sensor expansion board is able to connect  
Figure 5: GUI of ECG monitoring system.  
Figure 6: Android(tm) mobile devices: (**a**) AchroHD (Huins Inc. Korea); (**b**) Galaxy S (Samsung Electronics Ltd., Korea).  
to different types of hardware devices and various development testing can be performed on it. The specifications of the development kit are similar to the specifications of Galaxy S in Figure 6(b), a market available Android(tm) based operating system. Both the AchroHD development kit and the Samsung Galaxy S running on Android(tm) OS version 2.1 were used to test the implementation of our approach.
## 1 Introduction
Although this is convenient to standardize \"doses\" of therapeutic agents and seemingly control one variable of the regenerative paradigm, researchers often fail to recognize the inherent variability of cell potency from donor to donor, the effects of culture conditions/passage number, or the value of a heterogenous population, including multiple progenitor phenotypes (mesenchymal, hematopoietic, endothelial, etc.). The clinical translation of an in vitro expanded cell approach is further in question due to regulatory restrictions of the United States FDA and other agencies. The usage of autologous cells at the point-of-care is an appealing alternative, with less regulatory requirements and a decreased risk of cell contamination or rejection. A growing amount of data has suggested strong correlations in clinical outcomes with non-union fracture, degenerative disc disease, rotator cuff tear, avascular necrosis, and other orthopedic injuries, based on the concentration of MSCs present in bone marrow at physiological levels, or concentrated up to 10\(\times\)[17; 18; 19]. The effects of non-cultured MSC concentration on bone formation when combined with HA granular particles are presently unreported in the literature. In the present study, we investigated the combination of autologous bone marrow aspirate with porous or non-porous HA granules. Both of these materials were prepared with a collagen-derived carrier to quickly absorb the bone marrow and form a moldable putty, to pack into bilateral, critically-sized femoral chondyle defects in adult rabbits. An aliquot of each animal's bone marrow was evaluated for nucleated cell concentration and MSC content to retrospectively examine the effects of MSC concentration in fresh marrow, as well as the effects of granular porosity with identical cell populations in the same animal.
## 2 Design of the Shaking-Table Test of the Pile-Soil-Layer Interval Seismic Structure
### Preparation of Model Soil  
The test foundation was prepared with uniform silty clay. A 20-mm-thick rubber bag was placed in the soil box to prevent the water and silty clay added during the filling of the soil box from flowing out of the cracks in the soil box. Moreover, in [20], the authors verified that the rubber bag can better eliminate the boundary effect of the soil box through the shaking-table test.  
The total height of the model soil was 1.4 m. Before the test, the water content and compactness of the model foundation were controlled using the layering compaction method and a simultaneous geotechnical test. The filling of the model foundation was divided into eight layers, with the two bottom layers measuring 250 mm and the remaining six layers measuring 150 mm. After filling each layer of the foundation soil, a large amount of water was added and allowed to stand for seven days to retain the model soil in a plastic state. Then, the foundation soil was sampled and geotechnical tests were conducted to measure parameters, such as water content, density, and plastic liquid limit. With reference to the test results in [21], the mechanical property parameters of each layer of the foundation soil were controlled to ensure that the model foundation conforms to the test conditions of the soft soil foundation. Then, the next layer of soil was filled. When manually filling the bottom two layers of the foundation soil, electric impact ramming was used to consolidate the soil so that the bottom model soil layer could meet the requirements of being the supporting layer of the pile foundation. Figure 10 shows the soil mass developed through electric-impact ramming. In practical engineering, the soil around the pile was compacted with a rubber hammer to simulate the compaction of the soil at the pile side.  
The Poisson's ratio of the model foundation soil was 0.35 according to the Engineering Geology Manual of China [22]. In addition, the shear wave velocity of the soil was approximately 60 m/s according to the results of the white-noise test obtained from the shaking table below, and the elastic modulus of the soil was 20 MPa according to reference [23]. The physical indexes of soil samples are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Species** & \begin{tabular}{c} **Density/** \\ **(kg\(\cdot\)m\({}^{-3}\))** \\ \end{tabular} & \begin{tabular}{c} **Moisture** \\ **Content/\%** \\ \end{tabular} & \begin{tabular}{c} **Liquid Limit/** \\ **\%** \\ \end{tabular} & \begin{tabular}{c} **Plastic Limit/** \\ **\%** \\ \end{tabular} & \begin{tabular}{c} **Status** \\ \end{tabular} & \
\begin{tabular}{c} **Friction Angle/** \\ \({}^{\circ}\) \\ \end{tabular} &
\begin{tabular}{c} **Cohesion/MPa** \\ \end{tabular} \\ \hline clay & 1760 & 29 & 18.93 & 40.66 & plasticity & 17 & 0.022 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical indicators of the soil sample.  
Figure 10: Soil formed through electric-impact ramming.
## 3 Prediction of Short-Term Land Surface Phenology Using Machine Learning
### Prediction of Short-Term Vegetation Phenology Using Machine Learning
For instance, multiple studies indicate that higher temperatures can enhance plant metabolism and increase photosynthetic rates, thereby boosting net primary productivity [103]. However, this relationship is complex; while earlier onset of spring can enhance productivity, it may also lead to mismatches between crop development and optimal growing conditions, particularly if subsequent weather patterns are unfavorable. Additionally, changes in precipitation patterns and increased variability in humidity and soil moisture can disrupt the timing of phenological events, potentially leading to lower yields or crop failure [104]. The interplay of these climatic factors necessitates adaptive agricultural practices, such as altering sowing dates and selecting heat-resistant cultivars [105], to mitigate adverse effects on crop phenology and ensure food security in a changing climate. These models frequently utilize high-resolution remote sensing data to assess vegetation greenness and phenological transitions across various ecosystems, including forests and grasslands. Previous studies have demonstrated high accuracy in recognizing and counting crop features using machine learning, such as wheat spikes and lettuce heads [106], which are critical for assessing phenological stages. Additionally, machine learning models  
Figure 2: The number of cropland suitability studies indexed in Web of Science Core Collection (WoSCC) based on topic search of “vegetation phenology” and “machine learning”. can integrate multiple environmental variables, such as temperature, precipitation, and soil properties [98], to predict phenological events like flowering and harvest timing more effectively than traditional methods. These predictive capabilities facilitate timely agricultural interventions, optimizing yield by aligning farming practices with the anticipated growth cycles of crops. Moreover, the continuous feedback loop created by machine learning allows for the refinement of models based on real-time data, enhancing prediction accuracy [107]. Thus, the application of machine learning in monitoring vegetation phenology represents a significant advancement in precision agriculture, enabling farmers to respond proactively to climatic variations. Consequently, machine learning provides a robust framework for understanding and predicting how different environmental factors influence vegetation phenology across diverse geographic areas.
# Software Fault Localization Based on SALSA Algorithm
## 3 Proposed Approach
### Data Pre-Processing
#### 3.1.2 Obtain Statement Suspicion Values Based on Statistical Formulas  
After obtaining the spectrum of the faulty version of the program, the next step is to calculate the suspicion values of program statements using statistical formulas. SBFL employs statistical formulas to compute the suspicion scores for each code entity, which may include formulas such as Tarantula and Ochiai. When calculating suspicion values, it is necessary to select the appropriate statistical formula based on specific circumstances. Different statistical formulas consider various factors when calculating suspicion values, so the right formula must be chosen based on different scenarios. Table 2 lists some commonly used formulas. The four parameters used in the formulas need to be calculated based on the spectrum matrix, and Table 3 demonstrates the meaning of each parameter.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **e1** & **e2** & **e3** & **e4** & **e5** & **e6** & **e7** & **Result** \\ \hline T1 & 1 & 1 & 1 & 0 & 0 & 1 & 0 & 1 \\ T2 & 1 & 1 & 0 & 1 & 0 & 0 & 1 & 1 \\ T3 & 0 & 0 & 1 & 1 & 0 & 0 & 1 & 0 \\ T4 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ T5 & 0 & 0 & 1 & 0 & 1 & 0 & 1 & 0 \\ T6 & 1 & 1 & 0 & 0 & 1 & 1 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spectrum in matrix form.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Formula** \\ \hline \hline Dstar & \(\frac{\text{N}_{\text{eq}}^{2}}{\text{N}_{\text{eq}}^{+}\text{N}_{\text{ sat}}}\) \\ Jaccard & \(\frac{\text{N}_{\text{af}}}{\text{N}_{\text{af}}^{+}\text{N}_{\text{art}}^{+}+ \text{N}_{\text{ep}}}\) \\ Tarantula & \(\frac{\text{N}_{\text{af}}}{\text{N}_{\text{af}}^{+}\text{N}_{\text{af}}^{+}+ \text{N}_{\text{ep}}^{-}\text{N}_{\text{ep}}}\) \\ Ochiai & \(\frac{\text{N}_{\text{af}}+\text{N}_{\text{af}}^{-}\text{N}_{\text{af}}^{-}+ \text{N}_{\text{ep}}}{\sqrt{(\text{N}_{\text{ef}}+\text{N}_{\text{af}}^{-})( \text{N}_{\text{ef}}+\text{N}_{\text{ep}})}}\) \\ Ochiai2 & \(\frac{\text{N}_{\text{af}}\cdot\text{N}_{\text{ep}})\cdot(\text{N}_{\text{af}} \cdot\text{N}_{\text{ap}})\cdot(\text{N}_{\text{af}}\cdot\text{N}_{\text{ap}}) \cdot(\text{N}_{\text{af}}\cdot\text{N}_{\text{ap}})}\cdot(\text{N}_{\text{af}} \cdot\text{N}_{\text{ap}})\) \\ OP2 & \(\frac{\text{N}_{\text{ef}}\cdot\text{N}_{\text{ep}}+\text{N}_{\text{ap}}+1}{ \text{N}_{\text{ef}}^{-}+\text{N}_{\text{ep}}^{-}+\text{N}_{\text{ep}}^{-}+ \text{N}_{\text{ep}}^{-}+\text{N}_{\text{ep}}^{-}+\text{N}_{\text{ep}}^{-}+ \text{N}_{\text{ep}}^{-}+\text{N}_{\text{ep}}^{-}+\text{N}_{\text{ep}}^{-}+\text{N }_{\text{ep}}^{-}+\text{N}_{\text
## 4 Safety Evaluation of Shaft Lining
### Model Calculation Result Analysis  
#### 4.3.1 Calculation of Safety Factor of S1 ZK0 + 660 m Section lining  
(1) Calculate the lining safety factor according to Method 1.  
For the Method 1 loading plan, the simulation results of the S1 ZK0 + 660 m section are shown in Figure 22.  
It can be seen from Figure 22, that under the action of non-uniform pressure, the maximum bending moment of the lining was 257.4 kN m, and the corresponding axial force was 7198 kN. Under the action of uniform pressure, the maximum bending moment of the lining was 41.2 kN\(\cdot\)m, and the corresponding axial force was 6775 kN. The bending moment and axial force were substituted into the formula to obtain the safety factor of the lining structure, as shown in Table 4.  
It can be seen from Table 4 that the safety factors of the S1 ZK0 + 660 m lining section under non-uniform pressure and uniform pressure are 1.9 and 2.1, respectively, and their values were both greater than the minimum values specified in the mine design code, thus indicating that the lining design meets the safety requirements.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Non-Uniform** & **Uniform** \\ \hline Bending moment (kN-m) & 257.4 & 41.2 \\ Axial force (kN) & 7198 & 6775 \\ Calculate the safety factor & 1.9 & 2.1 \\ Specified safety factor & 1.1 & 1.35 \\ \hline \hline \end{tabular}
\end{table}
Table 4: S1 ZK0 + 660 m section lining safety factor calculation value, according to Method 1.  
Figure 22: Simulated internal force of S1 ZK0 + 660 m section. (**a**) Non-uniform axial force diagram; (**b**) Non-uniform bending moment diagram; (**c**) Uniform axial force diagram; (**d**) Uniform bending moment diagram.  
(2) Calculate the lining safety factor according to Method 2.  
For the Method 2 loading plan, the simulation results of the S1 ZK0 + 660 m section are shown in Figure 23 and Table 5.  
It can be seen from Table 5 that the safety factors of S1 ZK0 + 660 m lining section under non-uniform pressure and uniform pressure are 1.72 and 1.88, respectively, and their values are all greater than the minimum value specified in the mine design code, thus indicating that the lining design meets the safety requirements.