esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Judgment of Hazardous Solid Waste Properties of Petrochemical Excess Sludge
#### 3.1.3 Analysis of Dioxins in Petrochemical Excess Sludge
\begin{table}
\begin{tabular}{r c c c c c c c c} \hline \hline \multirow{2}{*}{**Dixins**} & \multirow{2}{*}{**I-TEF**[35]} & \multicolumn{6}{c}{**Measured Concentration (ng/kg)**} & \multicolumn{3}{c}{**I-TEQ (ng-TEQ/kg)**} \\ \cline{3-10}  & & **Ah** & **Bl** & **Cq** & **Ah** & **Bl** & **Cq** \\ \hline  & 2,3/7.8-TCDF & \(\times\)0.1 & N.D. & N.D. & N.D. & N.D. & N.D. & N.D. \\  & 1,2,3/7.8-PeCDF & \(\times\)0.05 & N.D. & N.D. & N.D. & N.D. & N.D. & N.D. \\  & 2,3/4.7,8-PeCDF & \(\times\)0.5 & N.D. & N.D. & N.D. & N.D. & N.D. & N.D. \\ Polychlorinated & 1,2,3/4.7,8-H-NCDF & \(\times\)0.1 & N.D. & N.D. & N.D. & N.D. & N.D. \\ dibenzoturans & 2,3/4.7,8-H-NCDF & \(\times\)0.1 & N.D. & N.D. & N.D. & N.D. & N.D. \\ (FCDFs) & 2,3/4.6,7-H-NCDF & \(\times\)0.1 & N.D. & N.D. & N.D. & N.D. & N.D. \\  & 1,2,3/4.7,8-H-NCDF & \(\times\)0.1 & N.D. & N.D. & N.D. & N.D. & N.D. \\  & 1,2,3/4.7,8-H-NCDF & \(\times\)0.01 & N.D. & N.D. & N.D. & N.D. & N.D. \\  & 1,2,3/4.7,8-H-NCDF & \(\times\)0.01 & N.D. & N.D. & N.D. & N.D. & N.D. \\  & OCDF & \(\times\)0.001 & 39.56 & 172.31 & 157.44 & 0.04 & 0.17 & 0.16 \\ \hline  & 2,3/7.8-TCDD & \(\times\)1 & N.D. & N.D. & N.D. & N.D. & N.D. \\  & 1,2,3/7.8-TCDD & \(\times\)0.5 & N.D. & N.D. & N.D. & N.D. & N.D. \\ Polychlorinated & 1,2,3/4.7,8-H-NCDD & \(\times\)0.1 & N.D. & N.D. & N.D. & N.D. & N.D. \\ dibenzotoxins & 1,2,3/6.7-H-NCDD & \(\times\)0.1 & N.D. & N.D. & N.D. & N.D. & N.D. \\ (FCDDs) & 1,2,3/8.9-H-NCDD & \(\times\)0.1 & N.D. & N.D. & N.D. & N.D. & N.D. \\  & 1,2,3/4.6,7-H-NCDD & \(\times\)0.01 & N.D. & N.D. & N.D. & N.D. & N.D. \\  & OCDD & \(\times\)0.001 & 158.31 & 192.05 & 106.83 & 0.16 & 0.19 & 0.11 \\ Total dioxin (FCDD/Fs) & — & — & — & — & 0.20 & 0.36 & 0.27 \\ \hline \hline \end{tabular} Note: I-TEF international toxic equivalence factor; I-TEQ international toxic equivalence quantity. N.D.: not detected, below detection limit (detection limit \(>\)0.01 ng/kg)  
\end{table} 
Table 4: Dixon content in wet sludge of Ah, Bl, and Cq. The leaching toxicity of Ah, Bl and Cq wet sludges and their flammability, corrosivity, reactivity and dioxin content were evaluated. The result showed that Ah, Bl, and Cq wet sludges are not hazardous wastes according to Chinese National Standard GB 5085.7-2007 [46].
###### Abstract  
Accurate dynamic model and response prediction of industrial robots (IRs) are prerequisites for production optimization before actual operation. In this study, a hybrid-driven dynamic position prediction (HDPP) approach integrating a parametric dynamic model (PDM) and learning-based residual error compensators (RECs) is developed to estimate the actual position of a robot end-effector based on the reference input trajectory. Firstly, a PDM consisting of a flexible dynamic model of the mechanical system and a servo system model is constructed as the primary predictor in HDPP. Meanwhile, a reinforcement learning (RL)-based parameter identification method is presented to obtain independent dynamic parameters, which integrates a CAD model, least squares estimation, and RL. Then, an REC based on the temporal convolutional network long short-term memory (TCN-LSTM) is proposed for each joint to compensate for the residual error after PDM prediction. A TCN is employed as the input of LSTM to extract and compress the discontinuous features, which can enhance the compensator's accuracy and stability. Additionally, a dynamics-integrated (DI) dataset construction scheme is developed for network training to boost the prediction accuracy. Finally, a series of experiments and comparative analysis are preformed to validate the performance of HDPP in terms of prediction accuracy and stability.  
robot dynamic position prediction; hybrid-driven model; parametric dynamic model; reinforcement learning-based parameter identification; TCN-LSTM network +
Footnote †: journal: Applied sciences
# A Novel Approach for Software Defect prediction Based on the Power Law Function
###### Abstract  
Power law describes a common behavior in which a few factors play decisive roles in one thing. Most software defects occur in very few instances. In this study, we proposed a novel approach that adopts power law function characteristics for software defect prediction. The first step in this approach is to establish the power law function of the majority of metrics in a software system. Following this, the power law function's maximal curvature value is applied as the threshold value for determining higher metric values. Furthermore, the total number of higher metric values is counted in each instance. Finally, the statistical data are clustered into different categories as defect-free and defect-prone instances. Case studies and a comparison were conducted based on twelve public datasets of Promise, SoftLab, and ReLink by using five different algorithms. The results indicate that the precision, recall, and F-measure values obtained by the proposed approach are the most optimal among the tested five algorithms, the average values of recall and F-measure were improved by 14.3% and 6.0%, respectively. Furthermore, the complexity of the proposed approach based on the power law function is \(O(2n)\), which is the lowest among the tested five algorithms. The proposed approach is thus demonstrated to be feasible and highly efficient at software defect prediction with unlabeled datasets.  
software defect prediction; power law function; unlabeled data; curvature +
Footnote †: journal: Applied Sciences
## 6 Simulation Environment
### Simulation Parameters  
The simulations are conducted within a 3D underwater region, measuring 10 km \(\times\) 10 km \(\times\) 10 km. The sum of acoustic sensor nodes deployed in this environment varied between 100 and 500, which was distributed randomly yet strategically to ensure comprehensive coverage of the region. These acoustic sensor nodes are considered homogenous with an initial energy of 100 joules and a communication range of 2000 m. To facilitate communication, nine surface sink nodes are affixed on the water surface equidistant from each other to optimize data collection. The acoustic sensor nodes, subject to water current dynamics, exhibit horizontal mobility with the water flow at speeds ranging from 1 to 3 m per second. However, they are inhibited from abandoning the networking region. The data payload size is fixed to 200 bytes. A TDMA-based approach is assumed to handle potential packet collision and ensure efficient communication between nodes. This prevents simultaneous transmissions, thus avoiding interference and collisions regardless of node density. The packet transmission rate is influenced by the channel parameters, specifically the bandwidth (4 kHz) and channel bit rate (10 kbps), which define the theoretical data-handling capacity of the network. These simulation settings related to network dimensions, sinks, acoustic sensor nodes, and their movement within the defined region allowed the study of various performance metrics.
## 4 Development of Numerical Model Database  
Given the lack of dynamic loading test results, the training data for the random forest algorithm were based on the results of the proposed numerical model. For this paper, a total of 930 \(\times\) 4 = 3720 numerical models were established to develop the numerical model database. Four levels of strain rate (\(10^{-5}\)/s, \(10^{-3}\)/s, \(10^{-2}\)/s, \(10^{-1}\)/s) were taken into account. Under each strain rate level, 930 numerical models of columns were established considering different structural parameters, such as the concrete compressive strength (30-50 MPa), the reinforcement yielding strength (335-500 MPa), the shear span ratio (1.67-3.83), the axial load ratio (0.2-0.6) and the stirrup spacing (50-50 mm). It is noted that the above structural parameters were uniformly distributed on the corresponding interval and randomly assigned to form the basic structural design parameters for a numerical model of the columns. Although the numerical models were designed for the Chinese building design code specifically, the proposed method and research conclusions can be extended to foreign situations and other materials.  
Based on the numerical results, the bearing capacity and displacement at yielding point, the ultimate point and the failure point were determined using the same approach illustrated in Section 3.2. In the database, the numerical samples obtained under strain rate levels (\(10^{-3}\)/s, \(10^{-2}\)/s, \(10^{-1}\)/s) were treated as the dynamic mechanical behavior parameters, whereas the numerical samples under strain rate level of \(10^{-5}\)/s were considered as the corresponding static mechanical behavior parameters. Then, the values of \(DMC_{F_{y}}\),\(DMC_{F_{x}}\) and \(DMC_{y}\) were calculated by employing Equations (1)-(3). The predictive models of \(DMC\) were developed by 80% of the \(DMC\) values. In that case, the total number of training data were 930 \(\times\) 3 \(\times\) 80% = 2232.
# Mobile Network Operators' Assessment Based on Drive-Test Campaign in Urban Area for iPerf Scenario
## 2 Drive Test Campaign
### Measurement Testbed  
Drive tests were realized using the mobile testbed installed on special cars' equipped with a set of professional measuring R&S equipment (see Figure 3[35]), such as [33; 35]:  
* R&S Smart Benchmarker Rel. v20.3.95;
* Measuring terminals (UEs) based on Samsung Galaxy S21 + 5G (SM-G996 BDS) (Suwon, Republic of Korea) working as active test probes;
* R&S SwissQual QualiPoc software(Rel. 20.3.121);
* Passive Radio Frequency (RF) scanner R&S TSME6 (Munich, Germany);
* Global Positioning System (GPS) receiver with the GPS antenna.  
The R&S Smart Benchmarker Rel. v20.3.95 worked as the main control, synchronization, and measurement unit that controlled and managed all devices of the test station within the test campaigns. The used measuring equipment was fully operational and performed reliable measurements, which were checked and verified during pretests. The measuring stations were operated by qualified, experienced personnel from the Systemics-PAB company. The measurement terminals were used to ensure a fair assessment of all MNOs, enabling the aggregation of all frequency bands used by the measured networks. The main aim of using the passive scanner was to measure the QoS metrics that represented the quality and power of radio reference signals, e.g., RSRP, SINR, or RSRQ. The scanner supports all frequency bands used in tested mobile networks.  
The measurements, data processing, and data analysis were carried out under standards defined by ETSI [36; 37; 38; 39; 40; 41; 42] and best industry practices described by R&S in [43; 44]. During the tests, controlled measurement conditions were ensured:  
* The measuring terminals were in conditions with a uniform, controlled temperature;
* The parameters of the measurement terminals affecting the measurement quality, such as processor load or processor/battery temperature, were continuously monitored;  
The equipment was mounted in special housings in the roof box at a height of about 1.8 m.
## 2 Materials and Methods
### Data Processing and Data Analysis
This non-parametric test is appropriate for comparing two independent samples when the normality of the data cannot be assumed. Our analysis targeted the detection of a medium effect size (Cohen's d = 0.5), with a significance level (alpha) of 0.05 and a desired power of 0.8 (beta = 0.2). These parameters indicated an approximate sample size of 77 trials per group for achieving sufficient statistical power. Our dataset consisted of measurements from 13 participants, but we analyzed 111 strikes (events, movements), with each represented by a single, unique dataset row. Variables include total ground reaction force (Total_GRF) and the acceleration of the fist (afist), forearm ( aforearm), and arm (aarm). This sample exceeds the estimated requirement from our power analysis, providing confidence that our study has the power to reliably detect the specified effect size. Figure 4: An example graph of impact force and acceleration during one impact. The vertical straight line marks a point of capturing values of acceleration for maximum force value.
## 4 Experimental Results
### Dataset  
Three datasets are employed to validate the performance of the lightweight UAV detection model. Primary evaluations, including precision, latency, and comprehensive performance assessments, are conducted using the VisDrone dataset, a standard in UAV lightweight model testing. The COCO dataset, with its larger objects and greater variety of categories, provides a different perspective to test the model's generalization capabilities. Finally, the Traffic-Net dataset is utilized to specifically validate the model's effectiveness in real-world traffic scenarios, demonstrating its practical application in UAV-based traffic management.  
* VisDrone Dataset [49]: The VisDrone dataset, utilized for the experiments, is developed by the AISKYEYE team at Tianjin University, China. It consists of 288 video clips with 261,908 frames and 10,209 static images, captured across 14 cities in China under varying conditions using different drone models. This dataset includes over 2.6 million bounding boxes annotating diverse objects such as pedestrians, vehicles, and bicycles in various urban and rural settings. Additional data attributes like scene visibility, object class, and occlusion are provided to enhance the robustness of object-detection algorithms tested.
* COCO Dataset [50]: The COCO dataset offers a rich diversity of 80 common object categories. The images in the COCO dataset come from a wide range of scenes and environments, such as indoor and outdoor settings, urban and rural areas, which enhances the model's ability to generalize across different contexts. Compared with the VisDrone dataset, the COCO dataset contains more object categories and larger targets, providing a diverse perspective to evaluate the detection model.
* Traffic-Net Dataset [51]: The Traffic-Net Dataset contains 4400 images across four classes--accident, dense traffic, fire, and sparse traffic--with 900 images for training and 200 for testing. Designed for training machine-learning models in real-time traffic-condition detection and monitoring, Traffic-Net enables systems to effectively perceive and respond to various traffic scenarios. By applying the model to this dataset, the effectiveness is evaluated in UAV-based traffic management.
## 5 Experiments and Results
### Experimental Settings  
Multi-Element Contextual Hypergraph Extractive Summarizer (MCHES) was implemented by using PyTorch. Our experiments were designed to assess the model's performance across a diverse set of datasets and compare it with several baseline models. Below, we describe the specific settings used in these experiments.  
The MCHES framework incorporates several novel components, each configured to optimize the summarization process:* **Preprocessing**: Texts were tokenized by using spaCy, and sentences were encoded by using pre-trained BERT-base embeddings to capture deep semantic features.
* **Hypergraph construction**: A hypergraph was constructed for each document, where nodes represent sentences and hyperedges encapsulate semantic, narrative, and discourse relations.
* **Model architecture**: The CHM and the HCA module are central to our framework, focusing on homogenizing features and applying dual-level attention, respectively. Training was conducted on an NVIDIA Tesla V100 GPU with the following specifications:
* **Optimizer**: Adam, with a learning rate of \(1\times 10^{-4}\).
* **Batch size**: 32 documents per batch to balance computational efficiency and training stability.
* **Early stopping**: employed based on the validation loss to prevent overfitting, with a patience parameter of 10 epochs.  
The baseline models were set up as follows:
* **Setup**: All baselines were implemented with their standard configurations as reported in their respective publications.
* **Preprocessing**: Uniform preprocessing was applied across all models for a fair comparison, including the same tokenization and embedding methods used for MCHES.
* **Hyperparameter tuning**: Each model was tuned individually on the validation set of each dataset to optimize performance.
## 3 Results and Discussion
### k-Fold Cross Validation  
This is a standardized method of estimating the performance of the evaluation of the prediction model. This is usually applied during a smaller number of data samples that are present as input. The k-fold cross-validation process contains a single parameter, k, which designates how many groups should be created from a given data sample. Here, the k-fold parameters were considered as 5, i.e., k = 5, which indicates that splitting the given data samples into 5 groups is known as the 5-fold cross-validation, as explained in Figure 18.  
Figure 16: (**a**) Accuracy and (**b**) validation loss for single-lead.  
Figure 17: (**a**) Accuracy and (**b**) validation loss for multi (12)-lead.  
Each split contains five-fold values, out of that, four are used for training and the remaining one is used for validation.  
Table 6 shows the detailed information about accuracy and standard deviation scores obtained during each split for the single-lead ECG data and twelve-lead ECG data. Results show the validation scores of Network-8, considering single-lead and 12-lead data. The average accuracies were obtained as 99.5% and 99.1%, respectively, for single-lead and 12-lead data. Hence, this is one of the best methods to deal with clinical lab datasets. The model shows good accuracy (of about 98.6%) during the validation phase for both single and 12-lead ECG data. The development of a graphical user interface (GUI) incorporating the current model for both types of data can be the main scope for future work (for better accessibility for healthcare experts).
# Numerical Investigation on the Principle of Energy Separation in the Vortex Tube
###### Abstract  
A vortex tube is a simple device that separates incoming gas into cold and hot gases. Several theories have been proposed to explain the principle of energy separation in the vortex tube. Existing theories have advantages and defects. Recently, various studies employed numerical simulations to study the flow in the vortex tube. As a result, flow structures were well clarified. However, in most cases, the temperature separation was partially discussed based on the temperature distribution in the radial direction. This study aimed to explain the principle of energy separation in the vortex tube. To do so, the relative contributions of the radial pressure gradient, axial pressure gradient, and viscous dissipation to the temperature separation were investigated using numerical simulations and energy equation analysis. The results indicated that the axial pressure gradient in the near-axis region was the major contributor to an increase in hot exit temperature. In addition, the reason for the increase in hot exit temperature with an increase in cold mass fraction was explained. The reason for the decrease in cold exit temperature with an increase in cold mass fraction was also explained.  
vortex tube; energy separation; numerical simulation +
Footnote †: journal: Applied Sciences
# Experimental Study on Multi-Cell Counting Using an Inertial Microfluidic Device
## 3 Results and Discussion
### Statistical Analysis of Percent Recovery
Flow rate showed a strong inverse relationship with recovery, where increasing flow rates from 1 \(\upmu\)L/min (63.84%) to 15 \(\upmu\)L/min (32.32%) resulted in progressively lower recovery, consistent with greater cell loss due to shear forces at higher flow velocities. Similarly, channel size significantly affected recovery, with narrower channels (150 \(\upmu\)m) achieving substantially higher recovery (69.46%) than wider channels (250 \(\upmu\)m: 32.28%), supporting the importance of physical confinement for optimal cell capture and retention. These graphical representations of the ANOVA results provide clear visualization of both the magnitude and statistical significance of each parameter's effect on cell recovery, as shown in Figure 10. The ANOVA results revealed statistically significant differences in percent recovery for microscope type (\(p<0.05\)), flow rate (\(p<0.05\)), and channel size (\(p<0.05\)), while cell size (\(p=0.683\)) and concentration ratio (\(p=0.196\)) showed no significant effects. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Factor** & **F** & **Sig. (\(p\)-Value)** \\ \hline Microscope type & 79.453 & 0.000 \\ Flow rate & 51.007 & 0.000 \\ Channel size & 131.357 & 0.000 \\ Cell size & 0.169 & 0.683 \\ Concentration ratio & 1.712 & 0.196 \\ Cell size \(\times\) concentration ratio & 19.010 & 0.000 \\ Microscope \(\times\) flow rate & 3.322 & 0.025 \\ Cell size \(\times\) flow rate & 2.667 & 0.055 \\ Flow rate \(\times\) channel size & 1.884 & 0.097 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the ANOVA results. Figure 10: Effects of various experimental parameters on the cell recovery percentage. (**a**) Microscope type, (**b**) flow rate, (**c**) channel size, (**d**) cell size, and (**e**) concentration ratio.
## 2 Materials and Methods
### Accuracy Evaluation Method
#### 2.3.3 Closure Error  
In order to further verify the effectiveness of the whole-network adjustment method, time synchronization performance is tested according to the closure errors of clock offset. The commonly used closure errors of clock offset include the inter-satellite station closure error and three-satellite closure error. The inter-satellite station closure error is shown in Figure 3.  
Supposing the satellite \(i\) is the node satellite, the clock offset of satellite \(j\) can be observed indirectly. If there exist the two-way clock offset \(\Delta T_{i}\) between satellite \(i\) and ground station, and \(\Delta T_{ij}\) between satellite \(i\) and satellite \(j\) in the same time period, the clock offset of satellite \(j\) can be obtained as:  
\[\Delta T_{j}^{\prime}=\Delta T_{ij}+\Delta T_{i} \tag{9}\]  
The difference between \(\Delta T_{j}^{\prime}\) and satellite-ground two-way time synchronization observed value \(\Delta T_{j}\) of satellite \(j\) is calculated to obtain the residual of the indirect clock offset. Since it is difficult to find the observed values of \(\Delta T_{i}\), \(\Delta T_{ij}\) and \(\Delta T_{j}\) in the same time period,  
Figure 3: Schematic diagram of inter-satellite station closure error.  
one day of data are acquired. \(\Delta T_{i}\) and \(\Delta T_{ij}\) are fitted to obtain the indirect fitting parameter of \(\Delta T_{j}^{\prime}\) and then calculate the fitting residual of \(\Delta T_{j}\) within the direct observation period.