esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Thermal recovery technology is generally suitable for shallow lays due to the higher thermal loss for the deep heavy-oil reservoirs. Non-thermal recovery technologies, such as the non-condensate gas injection technology, are not limited by the reservoir depth and could be extensively applied for the heavy-oil reservoir. Many experimental studies and field applications of non-condensate gas injection have been conducted in heavy-oil reservoirs. The injected non-condensate gas could achieve dynamic miscibility with heavy oil through multiple contacts, which has a significant viscosity-reduction effect under the reservoir conditions. In addition, the equipment involved in the gas injection operation is simple. There are many kinds of non-condensate gases, and common types of gases include N\({}_{2}\) and CO\({}_{2}\) due to abundant gas sources and lower prices. Moreover, CO\({}_{2}\) is a greenhouse gas and the injection of CO\({}_{2}\) into the reservoir would have environmental benefits. The non-thermodynamic method is to inject N\({}_{2}\) and CO\({}_{2}\) separately to produce heavy oil based on the mechanism of the volume expansion of crude oil to form elastic flooding and reduce crude oil viscosity and foamy oil flow. Steam injection recovery of the thermodynamics method has the disadvantages of large wellbore heat loss and inter-well steam channeling. The addition of N\({}_{2}\), CO\({}_{2}\), and other non-condensate gases to the steam could greatly improve the thermophysical properties of the injected fluid, and lead to higher expansion performance. After being injected into the reservoir, the viscosity of heavy oil could be effectively reduced, the seepage characteristics of heavy oil would be improved, and the reservoir development effect could be improved. Non-condensate gas injection stimulation technology can not only effectively improve oil recovery, but also help to achieve carbon neutrality, which has a very broad application prospect in the future oil recovery, energy utilization, environmental improvement, and other aspects.  
keywords: heavy-oil reservoir; non-condensable gases; production enhancement; hot fluids; enhanced oil recovery Article
## 2 Design Details of the Proposed System  
The overview of the proposed system is shown in Figure 1. Here, acoustic sensors are used to collect sound signals in urban environments. Then, these signals are processed by embedded processing boards through endpoint detection and the ADPCM method and are transferred to the server using wireless transmission modules. Finally, the remote server aggregates and stores the resulting data from multiple nodes for further analysis such as the feature extraction and event recognition.
# Reducing the Number of Sensors in the Data Glove for Recognition of Static Hand Gestures
###### Abstract  
Data glove devices, apart from being widely used in industry and entertainment, can also serve as a means for communication with the environment. This is possible thanks to the advancement in electronic technology and machine learning algorithms. In this paper, the results of the study using a designed data glove equipped with 10 piezoelectric sensors are reported, and the designed glove is validated on a recognition task of hand gestures based on 16 static signs of the Polish Sign Language (PSL) alphabet. The main result of the study is that recognition of 16 PSL static gestures is possible with a reduced number of piezoelectric sensors. This result has been achieved by applying the decision tree classifier that can rank the importance of the sensors for the recognition performance. Other machine learning algorithms were also tested, and it was showed that for the Support Vector Machines, k-NN and Bagged Trees classifiers, a recognition rate of the signs exceeding 90% can be achieved just for three preselected sensors. Such a result is important for a reduction in design complexity and costs of such a data glove with sustained reliability of the device.  
Keywords:data glove; gesture recognition; haptic interfaces; piezoelectric sensors; sign language +
Footnote †: journal: Applied
## 7 Conclusions  
This study proposed biased-power allocation (Bi-PA) and shared antenna selection (SAS) algorithms intending to improve the performance of SM-LDM systems. The proposed Bi-PA adaptively adjusted power ratios based on signal-to-interference-plus-noise ratios (SINRs) and squared minimum Euclidean distances (SMEDs) between modulation constellations to improve BER fairness, eliminate the error floor problem, and achieve nearly identical BERs for both layers. SAS simplifies the SM-LDM system by allowing diverse layers to cooperate on antenna selection, increasing the sum-rate while simultaneously enhancing BER performance and fairness. According to the BER results, the SAS provides gains ranging from 7 to 15 dB for _LL_, while _UL_ performance ranges from slight gain to minor loss. The numerical findings demonstrated the advantages of the Bi-PA and SAS methods, such as higher achievable rates, sum-rate, spectral efficiency, and reduced inter-layer interference. By utilizing the benefits of SM, SAS, and Bi-PA, our proposed system satisfies the rigorous SWaP-C reduction criteria for a MIMO RF front-end. These findings show the potential of the proposed strategies for improving the efficiency, fairness, and BER performance of SM-LDM systems in broadcasting scenarios. Although our study considers that the Bi-PA is compatible with various RF chain architectures, future work could focus on investigating the optimal RF chain architecture designs for SM-LDM systems. It could consider things such as power consumption, hardware complexity, and compatibility with emerging technologies.  
Conceptualization, M.A.-A., M.K. and E.S.; methodology, M.A.-A. and E.S.; software, M.A.-A. and M.K.; validation, M.A.-A. and E.S.; formal analysis, M.A.-A. and E.S.; writing--original draft preparation, M.A.-A.; writing--review and editing, M.A.-A., M.K. and S.C.; supervision, M.K. and S.C.; project administration, M.K.; funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deanship of Scientific Research at Prince Sattam bin Abdulaziz University, Project No. (2022/01/20627) KSA.  
Not applicable.  
This project was supported by the Deanship of Scientific Research at Prince Sattam bin Abdulaziz University Project No. (PSAU/2022/01/20627), KSA.  
The authors declare no conflict of interest.
# Dynamic Observer Modeling and Minimum-Variance Self-Tuning Control of EDM Interelectrode Gap
###### Abstract  
The electric discharge machining (EDM) interelectrode gap directly determines the discharge state, which affects the machining efficiency, workpiece surface quality, and the tool wear rate. The measurement of the real-time varying interelectrode gap during machining is extremely difficult, and so obtaining an accurate mathematical model of the dynamic interelectrode gap will make EDM gap control possible. Based on p-type single-crystal silicon EDM, a flat-plate capacitance model is introduced to analyze the time-domain characteristics of the inter-electrode voltage in the breakdown delay phase. Further, we theoretically established a physical model of the interelectrode spacing \(d\) and the charging time constant \(\tau\) of the plate capacitor. The least-squares fitting of the experimental data was used to determine the model coefficients, and in combination with the actual machining process, a minimum-variance self-tuning controller was designed to control the interelectrode gap in real time. The experimental verification results show that the established physical model can correctly predict the interelectrode gap in the actual machining process. The minimum-variance self-tuning controller improves machining stability, and eliminates the occurrence of the short-circuit state.  
EDM; plate capacitor; interelectrode gap; modeling; control Article
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 5 Simulation Results
### UC1: Rural Environment
#### 5.1.3 Rural Backhaul Capacity  
The ENs' serving capacity determines how efficiently the resource allocation process is. In our simulations, 100% of the links support more than 1 Gbps, and their maximum supported bit rate depends on the EN locations. This means that the \(p_{y0}\) supported bit rate is 3.6 Gbps and 3.2 Gbps for FA and LP. The \(p_{90}\) for the joint design (FL) is 2.8 Gbps due to the optimisation function that maximises the user coverage, increasing the required ENs and reducing the supported bit rate of each. This is further evidenced by the fact that each EN's provided capacity is only 500 Mbps for FL, but these values are 950 Mbps and 800 Mbps for FA and LP scenarios, respectively. Since more than 50% of links use less than 10% of the supported capacity, link efficiency is also relatively low. Comparatively, the FL scenario's \(p_{90}\) efficiency is 35% compared to 42% for the FA and 62% for the LP, demonstrating that the system's efficiency is sacrificed in order to meet the coverage criteria.
# Increased Motor Time in the Lower Extremity Is Related to Fall History in Older Women
## 4 Discussion
### MT in Relation to Muscle Strength  
The motor time showed a close relationship with the peak extensor force in the lower extremity, which confirmed our assumption that a longer motor time may co-vary with reduced strength. Theoretically, the MT and peak extensor force (PEF) are both measuring parameters for working muscles. The MT is the time duration of peripheral processing for motor preparation, representing the recruiting speed of muscle fibers under neural drive [32], and PEF in fast squats is a strength variable, reflecting the maximal output of contracting muscles in the lower extremities [33; 34]. Mechanically, motor preparation and ensuing muscle contraction are both determined by the rate of neural firing and properties of these muscle fibers [32]. Explosive movements require more rapid rates of cross-bridge cycling, in which the number and size of type II muscle fibers are highly related to rapid force production. Older adults experience serious muscle atrophy, especially in type II fibers, as they increase in age [35; 36]. And further, this undesirable muscle atrophy was confirmed as the primary factor not only in the marked increase in MT [8; 10], but also in the significant decrease in force output [37]. Fortunately, both muscle strength [38; 39]and motor processing speed [40; 41] could be improved with active physical activity in older adults. In addition, the present study revealed that the peak extensor force in the non-dominant leg was a strong predictor for the motor time. This indicates that a lower capacity for explosive force output in the non-dominant leg may be highly related to longer latency for neuromuscular recruitment. This result is supported, since the non-dominant leg performs a more stabilizing or supporting role in human motor movements compared to the dominant leg [42]. And the extension force in the lower extremity, especially the non-dominant leg, has been closely related to dynamic balance ability and history of falls [43].  
This advocates for the establishment of exercise guidelines for fall prevention in older adults, showing that explosive strength training could be used not only to increase muscle strength and power [18; 44] but also to reduce MT latency. However, in some Asian countries, gentle movement exercises such as tai-chi and yoga are very popular and regarded as a one-size-fits-all exercise to maintain older adults' health, whereas strength training exercises are typically given little-to-no attention in this cohort. Based on the findings presented in this paper, we strongly recommend that older adults, especially those with fall history, incorporate a strength training element into their current exercise regimen, to improve both their explosive muscular strength and MT latency. And, as proposed in previous studies, exercise programs that combine resistance training with balance and functional exercises are more effective in reducing falls [45; 46].
## 7 Simulation and Results
### Performance Comparison  
We compare our approach with maodv-sim [14]. Authors in [14] present a routing protocol that enhances the reliability of WSNs by using the technique to find out reliable emergency paths from source to the destination. They have modified the underlying AODV routing protocol to enhance the reliability. To perform an accurate comparison, we have benchmarked the simulation environment as defined in [14]. A total of 50 nodes were deployed in a terrain of 500m\(\times\)500m. We have used standard MAC Protocol of IEEE 802.15.4, _i.e._ CSMA/CA, as underlying MAC protocol. AODV is used as a routing protocol. The comparison is performed on Average end-to-end latency and Average packet success ratio. We benchmark their best results of reliability achieved by the maodv-sim routing protocol [14]. Node failure is simulated by shutting down the nodes randomly. We compare our suggested SBR with the maodv-sim routing.  
Figure 23 shows the average packet delivery ratio plotted against the percentage of failing nodes in the entire network. We notice that with less failed nodes, SBR gives almost the same ratio of packet delivery as claimed by [14]. However, as percentage of node failure increases our scheme outperforms the maodv-sim. Please note that in case of maodv-sim there is a sharp decline in the packet delivery ratio after the node failure rate reaches 15%. On the other hand, the packet delivery ratio in SBR approach is rather stable. Even though maodv-sim uses emergency paths to enhance the reliability but  
Figure 23: Average Packet Success Ratio comparison of SBR with maodv-sim [14].  
as more nodes start to fail the performance declines many fold. This decline in maodv-sim performance is due to the fact that there is a high probability that emergency path would fail with failing of the next hop nodes and the chances of reaching the destination would be grim. In our scheme the routing from source to the sink includes more reliable gateway assisted routing paths. It makes routing less dependent on the intermediate sensor nodes. Hence, the probability that the failure of sensor nodes will affect hotline based routing is much less in comparison to the routing technique explained in [14]. We notice that at 20% node failure hotline based approach gives almost 10% better packet success ratio as compared to maodv-sim.  
Figure 24 shows the average end-to-end latency. The overall latency is much less in SBR as compared to maodv-sim [14]. As SBR involves more reliable hotline assisted path, from gateway to gateway, therefore the average end-to-end latency decrease as compared to the traditional routing approach adopted by maodv-sim. Authors in [14] claim that, in case of node failures, the use of pre-determined emergency paths decreases the overall end-to-end latency, nevertheless, performance of SBR outperforms the maodv-sim routing approach as shown in figure 24.
## 5 Results
### Tensile and Compression Results  
For each of the three designed and manufactured MRE patterns, tensile and compression experiments were performed, and the setup is described in Section 3. For the tensile test, this was carried out for two different instances of the same MRE arrays. Therefore, two results (Replicas 1 and 2) were obtained, as shown in Figure 21.  
Figure 21: Tensile tests. Electrical resistance vs applied force for three manufactured MRE transducers shown in Figure 3. For each transducer, two tests were carried out (Replicas 1 and 2). (**a**) Four lines in parallel; (**b**) four lines in \(90^{\circ}\); and (**c**) three lines in \(90^{\circ}/45^{\circ}\).  
Figure 20: Graph settings for the implemented dashboard. Figure modified from [39].  
Figure 21 shows that, for the four-parallel-line pattern,the electrical resistance was directly proportional to applied force, indicating the suitability of the MRE for use as a transducer. For the four-line 90\({}^{\circ}\) array,the behavior could not be linearly described because the electrical resistance did not vary in the same proportion as the applied force did. For the three-line 90\({}^{\circ}\)/45\({}^{\circ}\) array, Replica 1 demonstrated a quasilinear tendency from 3 to 30 N; however, Replica 2 showed erratic variation as the force increased.  
As shown in Figure 22, for the compression tests, the three-line 90\({}^{\circ}\)/45\({}^{\circ}\) array with horizontal resistance exhibited quasilinear behavior when the applied force was increased. Both replicas of this array presented this directly proportional dynamic throughout the testing range. For this set of tensile and compression experiments, the MRE array that was most suitable for use as a transducer was the three-line 90\({}^{\circ}\)/45\({}^{\circ}\) array with horizontal resistance.
# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 2 Related Works
### Literature Summary  
To conclude, existing learning-based methods have been highly successful in data-intensive object detection and tracking, but they are often hampered when the dataset is small. Moreover, multi-object detection and tracking for ships still face many challenges: (1) Most object detection and tracking methods focus on data association problems, which overly rely on the quality of detection results. Ship object detection often has small datasets with low annotation quality, which can frequently result in issues, such as frequent ID switches due to poor detection quality. (2) When tracking ships, the large-scale variation in ship scales can easily affect the accuracy of affine similarity in the network output, leading to frequent ID switches. (3) Long-term occlusion problems are that certain types of ships, such as Very Large Ore Carriers (VLOCs), often move at extremely low speeds, which may result in prolonged occlusion duration in ship multi-object detection.
## 4 Prediction Model Establishment
### Biological Evolution Algorithm: Genetic (GA) Algorithm  
Optimizing parameters can prevent underfitting in LSSVM learning, ensure the model's prediction performance, and enhance the accuracy of the predicted results. The genetic algorithm (GA) is a component of evolutionary computation. The theory was proposed by [PERSON] from the University of Michigan in 1975. It is a method of globally searching for the optimal solution by simulating [PERSON]'s genetic selection and natural elimination. The algorithm has a strong capability to optimize various functions. It is simple, universal, robust, and highly parallel. It is one of the most popular adaptive and global probabilistic search optimization algorithms [42]. The core process of genetic algorithms (GAs) to solve the problem includes coding (binary), genetic operations (selection, crossover, mutation), and fitness function. Firstly, the optimization parameters are binary coded, and the solution space is transformed into chromosome space. The initial population parameters, such as evolutionary algorithm, individual length, and population size, are established to determine the appropriate fitness function, and then the fitness of individuals in the population is calculated. Then, the population undergoes genetic operator operations, such as selection, crossover, and mutation. After iterative calculations, the population continues to evolve in the optimal direction in order to obtain the optimal solution [43]. Since the LSSVM model requires the optimization of two parameters (penalty factor \(\gamma\) and kernel parameter \(\sigma\)), the population dimension is 2. The specific operation of the GA optimization algorithm is as follows.  
Step 1: firstly, the initial solution of the chromosome is generated, and the mutation and crossover factors are initialized.  
Step 2: determine the initial solution of the fitness function \(F_{i}^{k}=f(X_{i}^{k})\forall\)I and the optimal index of the chromosome.  
Step 3: randomly select two different genes (parameter values) from chromosome \(j\) to exchange and generate new high-quality individuals.  
Step 4: randomly select the \(j\) chromosome of the individual to introduce a small disturbance, mutate it into the optimal chromosome, maintain population diversity, and increase the possibility of covering the search space.  
Step 5: If the optimal chromosome is obtained in Step 4, the cycle is stopped; otherwise, the above steps are repeated. After multiple generations of evolution, the individual that is optimal or near-optimal is selected. This individual is then used as a parameter in the LSSVM model prediction after the decomposition operation, resulting in a more accurate performance prediction model.
## 4 Experimental Results and Comparisons
### Experiments on the Benchmark Dataset NEU
#### 4.1.2 The Effects of Training Data Volume/Complexity  
In this experiment, the original NEU dataset was enlarged to 9000 samples by adopting data-augmentation methods (e.g., flipping and add Gaussian noise). The training set contained 5400 samples, and the validation set and test set contained 1800 samples. The example images after data augmentation are shown in Figure 6. For comparison purposes, the hyperparameter settings are the same as in the previous section.  
The average defect classification accuracies of a standard Inception_v2 architecture and the proposed method with the original NEU dataset and the NEU dataset after data augmentation are compared in Table 3. It can be seen by comparing Tables 2 and 3 that the classification accuracy was improved through data augmentation for both the standard Inception_v2 and the proposed method. Moreover, in Table 3, the defects classification accuracy of the proposed method is relatively higher than that of the standard Inception_v2. A reasonable explanation is that, after data augmentation, the dataset volume increases and the features are more complex, which may provide more information for network learning; the results also demonstrate the strong robustness of the proposed method in a large-scale complex dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**NEU Dataset After Data Augmentation**} \\ \hline \multirow{2}{*}{**Accuracy (\%)**} & Standard Inception\_v2 & 88.47 \\  & Proposed method & 90.14 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The average classification accuracies of standard Inception_v2 and the proposed method with the NEU dataset after data augmentation.  
Figure 6: Example images after data augmentation: (**a**) the original images, (**b**) images after flipping 180\({}^{\circ}\), (**c**) images after flipping 90\({}^{\circ}\), and (**d**) images after adding Gaussian noise (radius = 3).  
Besides accuracy, the specificity and sensitivity are also two main statistical metrics to evaluate the performance of the model. The specificity represents the proportion of the negative samples that were correctly classified, and the sensitivity is the proportion of the positive samples that were correctly classified. The specificity and sensitivity are calculated as follows:  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{7}\]  
\[\text{Sensitivity}=\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{8}\]  
Table 4 provides the accuracy, sensitivity, and specificity of each defect of the proposed model. It can be seen from the table that the proposed model has both high defects classification sensitivity and specificity, so the results of the proposed method are significant, and the proposed method is effective.
# Replication-Based Dynamic Energy-Aware Resource Provisioning for Scientific Workflows
## 3 System Design and Model
### Parts of R-DEAR Strategy
#### 3.1.3 Workflow /Resource Information Provider (WRIP)  
The WRIP lists the computational and storage resources from the IaaS cloud platform. It also obtains workflow information from the workflow manager with resource requirements. The information also comprises the costs of computation and power consumption of the accessible resources.