esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Cost and Schedule Data Integration Algorithm Based on Big Data Technology
## 6 Conclusions  
This research suggested a novel method to integrate cost and schedule data, a long-lasting challenge in the construction industry, by adopting big data technology. The proposed cost-schedule data integration algorithm allows securing consistency and integrity in complicated, various, dynamic, and vast data of construction management. In addition, its flexible data structure provides the ease of data extraction and analysis and high efficiency in building and transforming data that reduce significant time and effort required for information managing in construction projects. It is expected that the proposed algorithm will contribute to the leap of the construction industry toward a data-driven industry. This research facilitates the accumulation of construction management knowledge, informed decision-making, and the creation of a higher value in the management.  
For future research, a new methodology for using different classification systems and extracting and inferring data according to the project types will be investigated to ensure its flexible usability. Other future work could include developing an additional module for editing the data collection mechanism to improve its applicability in different data structure environments, such as naming conventions and document structures. The proposed algorithm only uses the data of an ongoing construction project. It is supposed to be utilized in several construction firms in South Korea; more than 1,000 databases of individual projects are expected to be collected. Future research will focus on improving the integrated cost-schedule management by employing insights generated by applying statistical algorithms (i.e., machine learning, deep learning) to the accumulated databases.  
Conceptualization, D.C. and M.L.; algorithm development, D.C. and J.S.; software, M.L.; validation, M.L. and J.S.; formal analysis, D.C.; investigation, M.L.; resources, D.C. and J.S.; data curation, M.L.; writing--original draft preparation, J.S.; writing--review and editing, J.S.; funding acquisition, D.C. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 20 CTAP-C152263-02).  
The authors declare no conflict of interest.
# Recent Advances in Sample Preparation for Cosmetics and Personal Care Products Analysis
## 2 Sample Preparation Strategies for Cosmetics Analysis
### Direct Sample Dilution  
Direct dilution (or ready-to-inject) is still employed as a suitable option for simple formulations such as perfumes or liquid cosmetics, but it is not adequate for complex matrices and formulations such as most of the currently marketed cosmetics and personal care products, where a high number of compounds at different concentrations coexist. In addition, from a practical point of view, this approach can negatively affect the chromatographic system. Direct dilution was successfully employed for the determination of phthalates [18; 19] and for the simultaneous analysis of fragrance allergens, synthetic musks, phthalates and preservatives [20] from perfumes and simple liquid cosmetic matrices. Ethanol [18; 19] or ethyl acetate [20] were the most employed dilution solvents, used in volumes ranging between 1 and 10 mL. For multianalyte determination, the sample:solvent volume ratio was a critical parameter since target compounds can be present at very different concentration levels. In these cases, different dilutions, between 1:10 \(v/v\) and 1:1000 \(v/v\) can be required [20]. Analyses were directly performed by gas chromatography-mass spectrometry (GC-MS) for perfumes, whereas an additional evaporation and reconstitution step was required for the other cosmetics matrices [18]. In all cases, satisfactory performance in terms of recovery (between 80 and 128%) and precision was achieved.  
Figure 1: Sample preparation techniques for cosmetics analysis included in this review.
## 2 Results and Discussions
### Volatile Compound Composition
There are different pathways responsible for furan formation which are based on pyrolysis of sugars at high temperatures, decomposition to ascorbic acid and \begin{table}
\begin{tabular}{c c c c} \hline
**LRI \({}^{*}\)** & **Compound** & **HOC** & **FHOC** \\ \hline
921 & 3-Methyl butanal & - & 298.7 \(\pm\) 1.9 \\
1080 & Hexanal & 605.8 \(\pm\) 20.2 & - \\
1130 & 2-Butylfuran & - & 23.7 \(\pm\) 0.3 \\
1176 & 2-Heptanon & - & 171.5 \(\pm\) 4.5 \\
1228 & 2-Pentyfuran & - & 229.3 \(\pm\) 9.6 \\
1210 & Limonene & 1941.6 \(\pm\) 193.8 & - \\
1253 & 3-Octanone & - & 160.0 \(\pm\) 10.7 \\
1245 & 1-Pentanol & 293.1 \(\pm\) 20.3 & - \\
1273 & Methylpyrazine & - & 172.1 \(\pm\) 13.2 \\
1273 & o-Simen & 362.0 \(\pm\) 10.7 & - \\
1319 & 2,5-Dimethylpyrazine & - & 565.3 \(\pm\) 46.3 \\
1364 & 1-Hexanol & 309.0 \(\pm\) 3.1 & - \\
1395 & 2-Nonanone & & 1993.3 \(\pm\) 131.6 \\
1445 & Acetic acid & 2601.6 \(\pm\) 154.7 & - \\
1442 & 1-Octen-3-ol & - & 377.8 \(\pm\) 3.8 \\
1445 & 1-Heptanol & 106.0 \(\pm\) 3.5 & - \\
1487 & 2-Ethyl-1-hexanol & 220.1 \(\pm\) 11.6 & 50.6 \(\pm\) 2.6 \\
1506 & Pyroe & - & 571.4 \(\pm\) 2.5 \\
1525 & Benzaldehyde & - & 5537.6 \(\pm\) 18.6 \\
1571 & 2-Methylpropanoic acid & - & 29.1 \(\pm\) 0.4 \\
1600 & Benzonitrile & - & 229.4 \(\pm\) 2.1 \\
1672 & 3-Methylbutanoic acid & - & 520.4 \(\pm\) 9.2 \\
1731 & Pentanoic acid & 35.0 \(\pm\) 2.3 & - \\
1843 & Hexanoic acid & 142.1 \(\pm\) 0.1 & - \\
1868 & Benzyl alcohol & - & 84.4 \(\pm\) 6.5 \\
1890 & Phenylethyl alcohol & - & 42.1 \(\pm\) 4.0 \\
1934 & Heptanoic acid & 76.4 \(\pm\) 0.1 & - \\
2072 & Octanoic acid & 167.6 \(\pm\) 13.7 & - \\
2161 & Nonanoic acid & 132.7 \(\pm\) 13.4 & - \\
2079 & p-Cresol & - & 15.0 \(\pm\) 1.0 \\
2413 & Benzoic acid & - & 121.3 \(\pm\) 6.5 \\
2438 & Indol & - & 24.8 \(\pm\) 0.7 \\
2455 & Benzophenone & - & 141.4 \(\pm\) 10.3 \\ \hline \end{tabular}
\end{table} 
Table 6: Detected volatile compounds in HOC and FHOC (ug/kg dry weight). related compounds, Maillard-type reaction systems involving amino acids and reducing sugars, and oxidation of polyunsaturated fatty acids [94]. The formation of pyrazines and pyrroles is also associated with Maillard reactions, and it is known that their formation is associated with an increase in the amount of free amino acids [95]. Finally, another dominant compound in the headspace is 1-octen-3-ol, a compound frequently detected in fermented foods that can be produced by microorganisms through the metabolism of unsaturated fatty acids [96].
# Impedance-Based Stability Analysis of Grid-Connected Inverters under the Unbalanced Grid Condition
## 2 Impedance Modeling of the DSOGI-PLL-Based Inverter and Verification
### Impedance Modeling under the Three-Phase Balanced Condition
#### 2.2.6 Impedance Model of the Inverter under the Balanced Condition  
Since these voltage and current components are coupled together, Equation (13) can be transformed into the following matrix expression:  
\[\begin{bmatrix}\mathbf{V}_{\text{p}}\\ \mathbf{V}_{\text{c}}^{*}\end{bmatrix}=\begin{bmatrix}Z_{11}&Z_{12}\\ Z_{21}&Z_{22}\end{bmatrix}\begin{bmatrix}\mathbf{I}_{\text{p}}\\ \mathbf{I}_{\text{c}}^{*}\end{bmatrix} \tag{14}\]  
Combining with the coefficients in Equation (13), the elements of the above impedance matrix can be obtained as follows:  
\[\begin{bmatrix}Z_{11}&Z_{12}\\ Z_{21}&Z_{22}\end{bmatrix}=\begin{bmatrix}\frac{B_{1}C_{2}-B_{2}C_{1}}{A_{1} B_{2}-A_{2}B_{1}}&\frac{B_{1}D_{2}-B_{2}D_{1}}{A_{1}B_{2}-A_{2}B_{1}}\\ \frac{A_{2}C_{1}-A_{1}C_{2}}{A_{1}B_{2}-A_{2}B_{1}}&\frac{A_{2}D_{1}-A_{1}D_{2 }}{A_{1}B_{2}-A_{2}B_{1}}\end{bmatrix} \tag{15}\]
###### Abstract  
Robots have been predominantly controlled using conventional control methods that require prior knowledge of the robots' kinematic and dynamic models. These controllers can be challenging to tune and cannot directly adapt to changes in kinematic structure or dynamic properties. On the other hand, model-learning controllers can overcome such challenges. Our recently proposed model-learning orientation controller has shown promising ability to simultaneously control a three-degrees-of-freedom robot manipulator's end-effector pose. However, this controller does not perform optimally with robots of higher degrees-of-freedom nor does it resolve redundancies. The research presented in this paper extends the state-of-the-art kinematic-model-free controller to perform pose control of hyper-redundant robot manipulators and resolve redundancies by tracking and controlling multiple points along the robot's serial chain. The results show that with more control points, the controller is able to reach desired poses in fewer steps, yielding an improvement of up to 66%, and capable of achieving complex configurations. The algorithm was validated by running the simulation 100 times, and it was found that, in 82% of the times, the robot successfully reached the desired target pose within 150 steps.  
kinematic-model-free control; model-learning control; redundancy resolution; multi-point tracking; adaptive control Article  
[PERSON], [PERSON], [PERSON], P  
task space without any predefined kinodynamic model of the robot. However, this controller underperforms, taking longer than required to converge to the desired pose, when higher-degrees-of-freedom robots are to be controlled, leaving redundancies unresolved, as well.  
The Kinematic-Model-Free (KMF) Multi-Point controller presented in this paper extends the state-of-the-art kinematic-model-free orientation controller, enabling the control of hyper-redundant robot manipulators and resolving redundancies in these manipulators by tracking and controlling multiple points along the kinematic chain of such robots.
## 5 Evaluation and Discussion
However, a notable limitation of the point cloud-only model is its lack of rich texture and appearance cues, which are often essential for distinguishing between activities with subtle motion differences but similar spatial configurations. This limitation can hinder the model's ability to differentiate between contextually similar actions, especially when spatial geometry alone is insufficient for classification. The comparison results in Table 6 clearly indicate that he proposed a two-stream CNN-LSTM model, demonstrated superior performance in excavator activity recognition compared to single-stream approaches, as evidenced by the comparative metrics. When trained on multi-stream input data, the model achieved an accuracy of 94.67%, an F1-score of 94.78, and a loss of only 5.33%, outperforming both RGB-only (90.67% accuracy, 9.33% loss) and PC-only (92.00% accuracy, 8.00% loss) configurations. This improvement highlights the complementary strengths of RGB and point cloud data, where RGB provides rich spatial features and point cloud enhances depth perception and robustness to occlusions and lighting variations. The balanced precision (94.90%) and recall (94.67%) further indicate that the model effectively mitigates misclassifications, particularly in challenging scenarios such as distinguishing between \"Approach\" and \"Leveling\" activities, where motion patterns often overlap. These results align with the paper's hypothesis that multi-modal fusion addresses the limitations of single-source data, such as fixed viewpoints in RGB and sparse textures in point clouds. Notably, the point cloud-only model (92.00% accuracy) surpassed the RGB-only model (90.67% accuracy), reinforcing the value of 3D geometric features in recognizing complex excavator kinematics. Although the performance of the point cloud-only model surpassed the RGB-based model, the two-stream architecture achieved the best overall performance. This confirms the synergistic benefit of integrating RGB's rich imagery with point clouds' structural robustness for enhanced excavator activity recognition. Furthermore, compared to recent work in Table 1, the proposed method sets a new benchmark, validating its potential for real-time construction monitoring and digital twin integration. Nevertheless, the model's performance may degrade in multi-excavator environments, suggesting future enhancements with IMU sensor fusion or attention mechanisms to improve scalability. Overall, this study confirms that two-stream learning significantly advances activity recognition accuracy while addressing critical challenges in dynamic construction sites.
# Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation
## 4 Experiments
### Experimental Setup  
#### 4.1.1 Datasets  
We conduct experiments on three benchmark datasets: Foursquare, Gowalla, and Meituan. The statistics of the datasets are shown in Table 1.  
* Foursquare [19]: The Foursquare dataset consists of check-in data generated on Foursquare from April 2012 to September 2013. Following [19], we removed users with less than 10 interactions and POIs with less than 10 interactions. After preprocessing, it contained 1,196,248 check-ins between 24,941 users and 28,593 POIs.
* Gowalla [19]: The Gowalla dataset consists of check-in data generated on Gowalla from February 2009 to October 2010. As was done in [19], we removed users with less than 15 interactions and POIs with less than 10 interactions. After preprocessing, it contained 1,278,274 check-ins between 18,737 users and 32,510 POIs.
* Meituan \"[[https://www.biendata.xyz/competition/smp2021_1/](https://www.biendata.xyz/competition/smp2021_1/)]([https://www.biendata.xyz/competition/smp2021_1/](https://www.biendata.xyz/competition/smp2021_1/)) (accessed on 28 July 2023)\": The Meituan dataset consists of check-in data generated on the Meituan APP from 1 st March 2021 to 28 th March 2021. We removed users with less than 10 interactions and POIs without location information. After preprocessing, it contained 602,331 check-ins between 38,904 users and 3182 POIs.  
For each dataset, we chose the oldest 70% of interactions of each user as the training data and the newest 20% of interactions as the test data. The remaining 10% were used as validation data.
## 3 Results and Discussion
### Biological Evaluations
The antiviral activity was evaluated as previously described for the fractions by infecting Vero cells infected with ZIKV\({}^{\text{GFP}}\) at an MOI of 1 and treating simultaneously with the compounds. The mitochondrial activity of the cells was assessed 48 h post-treatment using an MTT assay to determine cell viability. Both CC\({}_{50}\) and IC\({}_{50}\) values, showcased in Table 4, illustrate the compounds' effectiveness. Compounds **2-4**, **7-8**, and **13**, with IC\({}_{50}\) values exceeding 100 uM, were not further explored. The selectivity index (SI)--the ratio of cell toxicity (CC\({}_{50}\)) to antiviral activity (IC\({}_{50}\))--was calculated for compounds with notable antiviral effects. An SI of 1 or below is deemed unsatisfactory, suggesting greater toxicity than specific antiviral action. The positive control (isoquercitrin) exhibited strong antiviral activity (IC\({}_{50}\) = 4.22 uM) and low toxicity (CC\({}_{50}\) = 96.26 uM), hence a high SI. Notably, betulinic acid (**11**) and corosolic acid (**10**) from Fraction F5 displayed significant activity (IC\({}_{50}\)s of 20.21 uM and 26.80 uM, respectively). Although betulinic acid (**11**) has been previously reported as an antiviral agent [23; 24], corosolic acid (**10**) showed a lower SI (1.16) compared to the positive control, suggesting its antiviral properties may be closely tied to its cytotoxicity. Emerging structure-activity relationships reveal that corosolic acid (IC\({}_{50}\) 26.80) is more active than its oleanane counterpart, mashinic acid (IC\({}_{50}\) 82.97), hinting at steric factors affecting activity. However, this theory could not be fully investigated as the corresponding ursane-oleane pair (asiatic acid (**5**) and arjulonic acid (**6**)) were not isolated separately. Among the lupane triterpenenes, betulinic (**11**), betulinic (**12**), and the novel psiloxylic acid (**1**) vary only at position 4, featuring alcohol, ketone, and carboxylic acid groups, respectively. Betulinic acid (**11**), with its alcohol group, shows higher antiviral activity and comparable cell toxicity to its ketone analogue (**12**). The new compound **1**, an oxidized and ring-opened form of compound **12**, exhibits lower cytotoxicity but also diminished antiviral activity. These initial findings lay the groundwork for optimizing a lupane derivative that could potentially be more active and less toxic [25].
## 9 Benefits and Limitations of This Work
### Limitations of This Work  
This work is based on queuing theory models and, hence, the probabilistic nature of this type of analysis raises many issues including the fact that, for this kind of approach to be useful, actual distributions for traffic as well as proper average rates of traffic along roads and on junctions must be obtained in real-time. It is assumed that these parameters are available via a modern VANET network which can monitor all the vehicles and road infrastructure in real-time. The cost of deploying such a network over a wide network may be prohibitive. Secondly, in this work, the exponential distribution is assumed at the junctions and along the links. This is a simplistic assumption and more work is needed to look at actual traffic distributions, which, we know, are dependent on the day, time of day, layout of the local infrastructure and length of the links between junctions. More work is needed to look at accurate distributions of arrival and service rates along links and at junctions.
## 2 Experimental Setup and Measurement
### Data Reduction  
The mass of accumulated frost can be calculated by Equations (1)-(3):  
\[m_{f}\ =\ \int_{0}^{t}\dot{m}_{a}(d_{in}-d_{out})dt \tag{1}\]  
\[\dot{m}_{a}\ =\ \rho_{a}\cdot\dot{V}_{a} \tag{2}\]  
\[\dot{V}_{a}\ =\ A\cdot u_{a} \tag{3}\]  
where \(\dot{m}_{a}\) is the air mass flow rate in kg\(\cdot\)s\({}^{-1}\); \(d_{in}\) and \(d_{out}\) are the air humidity ratio at the inlet and outlet, respectively, in kg\(\cdot\)kg\({}^{-1}\); \(\rho_{a}\) is the air density in kg\(\cdot\)m\({}^{-3}\); \(\dot{V}_{a}\) is the air volume flow rate in m\({}^{3}\cdot\)s\({}^{-1}\); \(A\) is the area of the air duct in m\({}^{2}\); and \(u_{a}\) is the air velocity in m\(\cdot\)s\({}^{-1}\).  
The air humidity ratio \(d\) can be obtained by the pressure and RH, as shown in Equation (4) [32]:  
\[d\ =\ 0.622\frac{\varphi p_{s}}{p-\varphi p_{s}} \tag{4}\]  
where \(\varphi\) is the air RH, \(p_{s}\) is the saturation pressure of water vapor, Pa.  
The coolant used in this study has no phase transition; the heat transfer on the coolant side can be calculated by Equation (5).  
\[\dot{Q}_{c}\ =\ \dot{m}_{c}c_{c,p}(T_{cin}-T_{out}) \tag{5}\]  
where \(\dot{m}_{c}\) is the coolant mass flow rate in kg\(\cdot\)s\({}^{-1}\); \(c_{c,p}\) is the specific heat of the coolant in kJ\(\cdot\)kg\({}^{-1}\cdot\)\({}^{\circ}\)C\({}^{-1}\); \(T_{cin}\) and \(T_{out}\) are the temperature of the coolant at the inlet and outlet, respectively. The air-side heat transfer and the coolant-side heat transfer were verified and their deviation was within \(\pm\)5%.  
Air side heat transfer rate:  
\[\dot{Q}_{a}\ =\ \dot{m}_{a}(h_{a,in}-h_{a,out}) \tag{6}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Range** \\ \hline Ambient air temperature & 2 \({}^{\circ}\)C \\ RH & 80\% and 90\% \\ Inlet coolant temperature & \(-7\)\({}^{\circ}\)C and \(-10\)\({}^{\circ}\)C \\ Frontal air velocity & 0.6 m/s, 1 m/s, and 1.5 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental conditions.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Instrument Name** & **Rang** & **Accuracy** \\ \hline T type thermocouple & \(-200\)-\(350\)\({}^{\circ}\)C & \(\pm 0.5\)\({}^{\circ}\)C \\ Hot-wire anemometer & 0-\(99\) m\(\cdot\)s\({}^{-1}\) & \(\pm 0.1\) m\(\cdot\)s\({}^{-1}\) \\ Air pressure transmitter & 0-\(200\) Pa & \(\pm 0.4\) Pa \\ Air temperature sensor & \(-40\)-\(80\)\({}^{\circ}\)C & \(\pm 0.5\)\({}^{\circ}\)C \\ Humidity transmitter & 0\%-\(100\)\% RH & \(\pm 2\)\% RH \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specifications of the measurement instrument.  
where \(h_{a,in}\) is the air-specific enthalpy at the inlet of the air duct in kJ\(\cdot\)kg\({}^{-1}\), \(h_{a,out}\) is the air-specific enthalpy at the outlet of the air duct in kJ\(\cdot\)kg\({}^{-1}\). It can be calculated by Equation (7) [15]:  
\[h_{a}\ =\ 1.005T_{a}+d(2051+1.86T_{a}) \tag{7}\]  
The heat transfer capacity is defined as the total heat transfer over the whole frosting cycle, which is expressed as [32]:  
\[Q_{h,ca}\ =\ \int_{0}^{frestingduration}Q_{h} \tag{8}\]
# Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN
## 5 Experimentation and Results
### Effect of Content Item Number  
This section examines the impact of the number of content items on caching performance. The number of content items ranged from 600 to 1000. Figure 3 illustrates how the caching performance varied with the number of items in the GEANT [17] network, where routers with caching capability had a uniform cache size of one item. The GEANT network is a well-known real-world topology comprising 53 nodes and 74 edges. Within the network are 13 source nodes responsible for content production, 32 router nodes, and 8 receiver nodes that initiate requests. However, it is worth noting that only router nodes with a degree higher than 2 have cache capabilities, which amounts to 19 nodes in this case.  
Figure 3 demonstrates that GNN-DDQN consistently outperformed all other caching strategies across different numbers of distinct content items. GNN-DDQN achieved a maximum improvement of 34.42% in CHR, 4.76% in ALT, 3.77% in APS, and 5.21% in ALL compared to MLP-DDQN. On average, GNN-DDQN surpassed LCD and PROB_CACHE by 41.33% and 103.92% in CHR, respectively. It also achieved significantly lower ALT, APS, and ALL than LCD and PROB_CACHE. Furthermore, the performance gap  
between GNN-DDQN and LCE and CL4M was even more pronounced regarding all evaluation metrics.  
Overall, GNN-DDQN consistently exhibited exceptional caching performance regardless of the number of content items. Its superiority over MLP-DDQN stemmed from its ability to facilitate cooperative caching among neighbouring router nodes. By efficiently utilizing the caching space of all router nodes, GNN-DDQN enhanced the network performance. Additionally, GNN-DDQN outperformed traditional caching algorithms by quickly capturing user preferences and proactively placing popular content on appropriate router nodes. Consequently, the cache hit ratio improved, alleviating network traffic congestion.