esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Heat-to-Heat Applications
### Transcritical CO\({}_{2}\) Heat Pump Cycles  
Heat pumps operating at a transcritical cycle with CO\({}_{2}\) as the working fluid are attracting much attention especially in the domestic sector. The main reason is the high temperature lift of these type of heat pumps and their high volumetric heating capacity, making them suitable for hot water heating applications. This large lift is possible even with a single-stage cycle, reducing the system complexity and cost. However, they need low temperature in the evaporator side, so as to keep the boiling process within the two-phase region (practically lower than about 25-30 \({}^{\circ}\)C), which is usually supplied by  
Figure 4: CO\({}_{2}\) transcritical heat pump (**left**) and its cycle in a pressure-enthalpy chart (**right**).  
ambient air. This restricts their use in the industrial sector for heat recovery in cases where the heat source is from low-temperature wastewaters or from the rejected heat of refrigeration units [81].  
In general, heat production of CO\({}_{2}\) transcritical heat pumps is limited to a temperature below 100 \({}^{\circ}\)C, being suitable for a limited number of industrial processes, such as for drying applications [16; 17; 82]. Their performance is greatly increased when the CO\({}_{2}\) temperature at the gas coolers is reduced as much as possible [83], exploiting most of the thermal content of the high-pressure gas. Heat production at different temperatures in the gas coolers is also possible. At the same time, it is ensured that the working fluid after the expansion process is within the two-phase region.  
The experimental studies of this type of heat pumps for the production of process heat are limited. Although there are numerous experimental test-rigs for hot water heating mostly for buildings [84; 85; 86], its industrial application has not been examined so extensively [87]. This has mostly to do with the heat recovery process, requiring lower temperatures as the ones available in most waste heat recovery applications, as previously explained. Moreover, the maximum heat sink temperatures usually restricted below 100-120 \({}^{\circ}\)C, limit the possible applications [88] mostly for air preheating or drying purposes [16]. It should be mentioned that heat pumps based on subcritical cycles and with other refrigerants, both HFCs and HFOs, can reach higher temperatures of process heat well above 100 \({}^{\circ}\)C, reaching even 150 \({}^{\circ}\)C, enlarging their possible integration in industrial units [81; 89; 90].  
The CO\({}_{2}\) heat pump for heat recovery can be an air-source one, utilizing the thermal content of the outlet air from drying processes or from rejected heat from refrigeration units (possibly within the same unit for simultaneous heating and cooling production). Water/brine-source ones are scarcer due to the need to keep the evaporation temperature below the critical one. Ground-source systems have been also examined [91], showing a good potential for both heating and cooling applications, once they recover the heat from the ground [92].
# Three-Dimensional Assessment of Upper Limb Proprioception via a Wearable Exoskeleton
## 3 Results
### Proprioceptive Error Across Body Joints  
We investigated another aspect of the proprioceptive performance across the different body segments and how their distal and proximal positions influence such performance in the body.  
As depicted in Figure 2B, illustrating the Matching Error, for all conditions, it has been found a lower proprioceptive acuity for the distal body joint (elbow) than for the proximal one (shoulder).  
The rANOVA test (Table 3), performed on the Error Bias and the Matching Error, highlighted a significant effect of the DoF (Error Bias: \(F\) (2,18) = 8.6, \(p\) = 0.001; Matching Error: \(F\) (2,18) = 73.0, \(p\) < 0.001) and an interaction effect between the conditions and the DoFs (Error Bias: \(F\) (2,18) = 29.7, \(p\) < 0.001; Matching Error: \(F\) (2,18) = 17.2, \(p\) < 0.001). No significant difference has been found between the body sides (Error Bias: \(F\) (2,18) = 1.7, \(p\) = 0.213; Matching Error: \(F\) (2,18) = 1.5, \(p\) = 0.246). The post-hoc analysis between the DoFs for the Error Bias and the Matching Error is reported in Table 3. The proprioceptive error resulted significantly larger for the distal joint than for the proximal one, considering the same DoF (FE).
## 4 Results of Simulation
### Simulation Test of Optimal Parameters
#### 4.2.2 Simulation of Energy Consumption on Different Pulse Times  
This test is to evaluate the energy consumption generated by different pulse times \(T_{p}\) under the constant of \(C\) and \(T_{w}\), where setting \(C\) = 2.0 \(\times\) 10\({}^{-3}\) kg/m\({}^{3}\), \(T_{w}\) = 10 s, \(T_{p}\) = 0.1 \(\sim\) 0.3 s. The result can be observed that the filter has a service life of 559 days as \(T_{p}\) = 0.1 s, and different \(T_{p}\) values do not affect the service life of the filter until pressure drops to 150 mmAq, as shown as Figure 9. On the other hand, this shows that different \(T_{p}\) values have little effect on the energy consumption of the fan motor, as shown in Figure 9, but increasing \(T_{p}\) will increase the amount of energy consumption in monthly backwash compressed air. In the analysis of cost, this study found that a different \(T_{p}\) has little effect on the electricity cost from the fan motor and pulse-jet cleaning, as shown in Figure 9. It also does not affect the cost of the filter. Therefore, as long as the appropriate pulse pressure \(P_{clear}\) is set appropriately, the \(T_{p}\) will not affect the cleaning efficiency of the dust collector. For energy saving, this study considers \(T_{p}\) = 0.1 s to be the better parameter.  
Figure 8: The simulation of energy consumption on different pulse interval times. (**a**). The analysis of the pressure drop. (**b**). The analysis of energy saving. (**c**). The analysis of cost.
## 4 Results and Discussion
### Performance Evaluation and Validation of the ANFIS Models  
To validate the performance of the developed models, an extra set of 12 experiments was carried out. Table 4 presents the different combinations of input parameters that were considered arbitrarily from within the limits of this study, covering, however, underrepresented regions within the studied bounds of conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test No.** & **\(Vc\) (m/min)** & \(f\) (mm/rev)** & \(ap\) (mm) \\ \hline
1 & 145 & 0.06 & 0.90 \\
2 & 165 & 0.07 & 1.00 \\
3 & 180 & 0.095 & 1.75 \\
4 & 220 & 0.09 & 1.10 \\
5 & 245 & 0.065 & 0.80 \\
6 & 260 & 0.085 & 1.30 \\
7 & 175 & 0.06 & 1.60 \\
8 & 200 & 0.07 & 1.90 \\
9 & 125 & 0.10 & 1.40 \\
10 & 235 & 0.055 & 0.70 \\
11 & 155 & 0.09 & 1.15 \\
12 & 270 & 0.08 & 1.60 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Validation experiments with respect to the input combinations.  
Figure 7: The sensitivity analysis plots for \(Ra\) (**a**) and \(T\) (**b**).  
Validation results revealed high convergence between the experimental and the predicted values for both roughness and temperature, suggesting the robustness and high accuracy of the modeling procedure. Specifically, according to Figure 8a, the relative error for the first model was calculated to be less than 2% or \(-\)2% for most cases, with the exception of test number 10, where the error was computed close to 7%. In a similar way, Figure 8b compares the experimental temperature values with the predicted ones. It is shown that the errors vary between \(-\)8.8% and 7.9%.  
Figure 9 illustrates the distribution of the summation of the errors for the two models, including all data sets. Figure 9a depicts the errors for the surface roughness prediction  
Figure 8: Validation test comparison for Models I (**a**) and II (**b**).  
model, whereas Figure 9b is for the tool-tip temperature. It is evident that for both models, all errors related to the training process are within the central bin (close to zero error). The validation errors are well distributed between \(-0.05\) and \(0.05\) error bins. Similarly, the testing errors are within a reasonable range from the zero-error bin, with the exception of three instances that are close to \(-0.1\) and two instances that surpass the \(0.1\) limit. Likewise, the training error points for the temperature model are all gathered in the close-to-zero bin, as shown in Figure 8b. Both testing and validation error points are well distributed among the error bins, with only one instance from each data set being relatively far from the center.
# Design and Analysis: Servo-Tube-Powered Liquid Jet Injector for Drug Delivery Applications
## 3 Results and Discussions
### Pressure Profile Shaping for Large-Volume Injection  
The device was also verified for its ability to produce large-volume, pulsed injections. It is hypothesized that this technique can offer additional benefits as it will not overload the tissue with one large dose but deliver it in a gradual manner. This aids in increasing injection completeness as well as the accuracy of targeted tissue depth. Figure 15 illustrates the injector delivering a two-pulse injection with the first phase at 15 MPa and the second phase at 7.5 MPa, corresponding to half the pulse frequency of the first but on the same interval duration.  
Figure 16 illustrates the system's ability to deliver an injection corresponding to an injection volume of approximately 0.2 mL per pulse. In order to obtain the pulsing action, a delay of 50 ms was added in between each pulse, and the pulse magnitude, as well as the duration, remained consistent throughout the multiple profiles. Hence, the pulsed injections were delivered at a frequency of approximately 10 Hz. The chamber was filled once to its complete volume at the beginning of the pulse sequence, and therefore, the subsequent pulsed jets constituted a fraction of the total chamber volume; consequently, the jet behavior was shown the same for each pulse.  
The device exhibited a good level of repeatability, attaining a mean pressure of 13 MPa per pulse. Further investigation is necessary to determine the maximum frequency at which the pulses can be delivered without blending; in the current study, a gap of 30 ms was the minimum that could be used in order to see two distinct peaks. Moreover, the accuracy of the pulse length will also depend on the precision and speed displayed by the control loop and linear motor combination.  
Overall, the device appeared to perform well in shaping a pressure profile, with very repeatable average stagnation pressures. The peak pressures displayed greater variation, and future studies should focus on quantifying the effect of servo response time vs. fluid response. This can be achieved by building a custom amplifier that not only makes use of high-power-high-frequency IGBTs and control hardware capable of working on the order of MHz, but also considers the real-time pressure output in order to build a feed-forward model for the device so that it can be used more accurately.  
Figure 15: The stagnation pressure evolution for a two-pulse injection profile.
# A Framework to Quantify the Quality of Source Code Obfuscation
## 6 Experiment
### Experimental Process
#### 6.1.1 Experimental Design  
The experimental design involved the following steps:  
1. Selection of Dataset: we used 130 C/C++ source codes from the NIST Juliet Test Suite and the Obfuscation-Benchmarks dataset. These datasets were selected to ensure a diverse range of code characteristics.
2. Configuration of Obfuscation Tools: four obfuscation tools were used:  
* Stunnix C/C++ Obfuscator
* Semantic Designs C-GCC4 Obfuscator
* Tigress Obfuscator (with three levels of obfuscation: Level 1, Level 2, Level 3)
* Obfuscator LLVM  
Each tool was configured with specific options to apply distinct obfuscation techniques.
3. Application of Obfuscation Techniques: the selected datasets were obfuscated using the configured tools. Each obfuscated code was then used to generate the required binaries for analysis.
4. Measurement of Metrics: the following metrics were measured:  
* Potency: McCabe cyclomatic complexity, control flow graph size, control flow depth, program length, and instruction count.
* Resilience: symbolic execution time, code coverage, static analysis time, and code optimization.
* Cost: time overhead, space overhead, and file size.
5. Analysis and Comparison: the measured metrics from the obfuscated codes were compared against the baseline (non-obfuscated) codes to evaluate the impact of each obfuscation technique.
# Integrity Monitoring of GNSS Constellations with Only LEO-PNT Satellites +
## 2 Exploring the Options
### Option 3: LEO Satellite-Based Integrity  
As with the previous option, the LEO satellite collects MEO observables; however, with this option, an integrity monitoring algorithm is run on board the LEO satellite, sending integrity indicators, possibly per MEO, to the user. A voting algorithm would need to be used to modulate false alarms and missed detection probabilities.
# System Design and Reliability Improvement of Wireless Sensor Network in Plant Factory Scenario
## 3 Results and Discussions
### Assessment of Clustered Routing Protocols  
The clustering and cluster head deployment of 33 predefined sensor nodes were performed using the K-means algorithm. The quality of the clustering was comprehensively evaluated by three metrics, DBI, CHI, and SC (Figure 8), which were used to determine the optimal number of clusters. The results showed that when the number of clusters was set to 4, the DBI reached the minimum value, while the CHI and SC reached their maximum values. Therefore, the optimal number of clusters was determined to be 4 in this study.  
Figure 7: Optimal deployment of sensor nodes.  
To ensure the reliability of the clustering routing protocol, a communication radius of 10 m was designated for cluster heads based on the output of the link quality prediction model. The structure of the multi-hop clustering network model is illustrated in Figure 9. Sensor nodes within the communication range of a cluster head established predefined data transmission paths directly with the cluster head. For sensor nodes located outside the communication range, predefined data transmission paths were established based on the sensor node with the shortest Euclidean distance within the same cluster. These nodes transmitted data to the cluster head using a multi-hop mechanism. During the experimental process, the size of the network and the quality of communication were considered, and both the clustering algorithm and cluster head selection had a significant impact on the system's performance. The K-means clustering algorithm effectively assigned nodes to appropriate clusters, reducing communication delays in the network. However, the selection of appropriate cluster heads is crucial for the stability and efficiency of the entire network. By using the link quality prediction model to assess the communication quality of the cluster heads, the influence of low-quality links was avoided, thus enhancing the reliability of data transmission and the overall performance of the system.  
In future research, efforts should focus not only on enhancing the reliability of wireless sensor networks to ensure accurate environmental monitoring but also on integrating advanced algorithms such as data fusion. Such algorithms can reduce the discrepancies between sensor data and the actual environmental conditions within plant factories. Additionally, addressing environmental noise and system disturbances from multiple perspectives is necessary to improve the quality of data acquisition and enhance the authenticity of collected data. These advancements will contribute to the development of more robust and reliable monitoring systems, ultimately optimizing greenhouse management and crop production.  
Figure 8: Determination of the number of clusters. SC refers to the Silhouette Coefficient, DBI stands for the Davies–Bouldin Index, and CHI denotes the Calinski–Harabasz Index.
# Modeling the Dynamic Behavior of a Pilot-Operated Solenoid Valve for an Ultra-High Pressure Vessel
## 5 Conclusions  
Dynamic behavior of the driving unit in a pilot-operated solenoid valve for an ultra-high pressure vessel is analyzed through experiments, simulation, and system identification. The analyzed results are modeled as an equation of motion. The new findings are as follows.  
1. The driving unit motion consists of two sequential movements. In Phase 1, only pilot plunger moves, and in Phase 2, pilot plunger and main plunger move together. The mathematical models for the driving unit are constructed.
2. Phase 1 has a displacement of 260 \(\upmu\)m over 5.3 ms, with an average velocity of 49.1 \(\upmu\)m/s. Phase 2 has a displacement of 970 \(\upmu\)m over 4.6 ms, with an average velocity of 210.9 mm/s. Although Phase 2 has a large mass and high damping coefficient, the average velocity is four times faster, so the influence of the attraction force is dominant.
3. The damping coefficient of each phase is estimated 0.001 N \(\cdot\) s/mm at Phase 1, 0.004 N \(\cdot\) s/mm at Phase 2 by comparing the experimental results and the simulation results through Matlab Simulink.  
Conceptualization, J.C. and J.H.A.; Formal analysis, J.C. and H.Y.K.; Methodology, J.C. and J.H.A.; Resources, J.H.A.; Software, J.C.; Supervision, J.H.A. and H.Y.K.; Validation, J.C.; Writing--original draft, J.C.; Writing--review & editing, J.H.A. and H.Y.K.; All authors have read and agreed to the published version of the manuscript.  
This work was supported by a two year research Grant of Pusan National University.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 9: Comparison of displacements between the test and simulation.
## 2 Materials and Methods
### Method Validation Parameters
#### 2.6.4 Linearity  
Linearity, defined as the ability of the method to obtain test results that are directly proportional to the analyte concentration within a specific range [37], was assessed in the range of 2.5-50 \(\mu\)g/mL (0.75-15 mg/g from 3000-fold dilution, or 7.5-150 mg/g for 300-fold dilution, in sample units) from three injections for each point on the curve using the least-squares method and by calculating the coefficient of determination R\({}^{2}\). An R\({}^{2}\) coefficient higher than 0.99 is considered an acceptable criterion of linearity.
## 7 Simulation Setup
The results analysis summarizes that when there are two patient nodes, the required time to select an optimized channel from the table takes 63 s. When the nodes are increased to a maximum of 20, the time has increased up to 410 s to select the idle channels for the increased OP-BNC nodes. Table 4 provides the comparison of the existing algorithm with the proposed EFPOC algorithm for a wide range of nodes from two to sixty. The time for calculating and choosing the best optimum channel is noted. It is observed that there is a linear increase in the time as the node increases. The proposed EFPOC has constantly less channel selection time, representing the improvement in the overall performance of the system and making it an energy-efficient algorithm. One more improvement measure is the rank-based method, which can handle up to 34 nodes in channel selection, whereas the \begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Values** \\ \hline Ls & 1024 bits \\ N & 15 to 25 \\ L\({}_{\text{h}}\) & 60 bits \\ T\({}_{\text{0}}\) & 0.9 \\ r & 15 Kbps \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulated Parameters. Figure 7: Number of Channels vs. Time (ms). proposed EFPOC algorithm can handle a maximum of 60 nodes, which is a more technical improvement achievement and makes it a more advantageous selection process. The simulation results also indicate the strength of the proposed EFPOC algorithm in Tier-1 device usage for the effective channel allocation strategy by the APs for the CRC within a WBAN environment, thereby indirectly increasing the control signal and reducing the lifetime of the battery. However, the proposed EFPOC algorithm does not require the additional BER calculation and control signal to communicate, thus extending the lifetime of the Tier-1 BNC device. It prolonged the network lifetime of the Tier-1 system by up to 46.47%.
# A Machine Learning Framework for Student Retention Policy Development: A Case Study
## 4 Experimental Results and Discussion
### Using Prediction Results to Enhance Student Retention Policies  
Our third research question examined how an HEI's student retention policy-making process can be supported. Using ML algorithms to identify students at risk of dropping out is the first step of the student retention process. The ultimate goal is not only to predict students at risk of dropping out but to generate or enable the generation of effective strategies or policies to prevent these students from dropping out. It should be noted that this does not exclude human intervention. Devising retention policies must align with the mission and strategic plan of an HEI and take into account budget and human resource-related constraints.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Models** & **Accuracy** & **Precision** & **Recall** & **Dropout F1** & **Grad F1** & **Macro F1** & **Weighted F1** \\ \hline
**Support Vector Classifier (SVC)** & 0.76 & 0.76 & 0.74 & 0.68 & 0.81 & 0.75 & 0.76 \\ \hline
**K-Nearest Neighbors (KNN)** & 0.66 & 0.64 & 0.63 & 0.54 & 0.72 & 0.63 & 0.65 \\ \hline
**Logistic Regression (LR)** & 0.60 & 0.30 & 0.50 & 0.0 & 1.0 & 0.38 & 0.45 \\ \hline
**Gaussian Naive Bayes (NB)** & 0.61 & 0.58 & 0.52 & 0.17 & 0.74 & 0.46 & 0.52 \\ \hline
**Artificial Neural Network (MLP)** & 0.81 & 0.81 & 0.79 & 0.75 & 0.85 & 0.80 & 0.81 \\ \hline
**Random Forest (RF)** & 0.81 & 0.80 & 0.78 & 0.74 & 0.85 & 0.79 & 0.80 \\ \hline
**Categorical Boosting (CatBoost)** & **0.82** & **0.82** & **0.80** & **0.76** & **0.86** & **0.81** & **0.82** \\ \hline
**Classification and Regression Trees (CART)** & 0.81 & 0.80 & 0.79 & 0.75 & 0.85 & 0.80 & 0.81 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance measures of ML models with optimal parameters and only the 5 most important features.
# A Review of Anomaly Detection in Spacecraft Telemetry Data
## 2 Anomaly Detection in Spacecraft Telemetry Data  
### Anomaly Detection  
Anomaly detection [13; 14], known also as outlier detection, refers to the procedure of identifying unexpected values or observations that differ significantly from the norm. A broad range of applications of anomaly detection in different domains exists, including industrial control systems [15], Internet of Things [16], medicine [17], and aerospace [18]. With regard to aerospace, one of the applications of anomaly detection is to identify novel or unusual events in spacecraft, enabling specialists to quickly identify abnormal events and take the appropriate corrective action.