esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion on Sensitivity Parameters
### Effect of \(S_{h}\)  
The effect of \(S_{h}\) on the drawdown difference is presented in Figure 10. When the groundwater drawdown inside the pit ranges from 2 m to 18 m with an increment of 2 m \(S_{h}\)/\(H_{0}\) ranges from 0.063 to 0.563. Since the groundwater outside the waterproof curtain cannot be recharged to the foundation pit in time, the groundwater level outside the waterproof curtain will be at a high level, and the drawdown difference will gradually increase.  
When \(L\)/\(H_{0}\) decreases from 0.844 (Figure 10a) to 0.688 (Figure 10b), the seepage path of groundwater is also decreased. As a result, groundwater outside the waterproof curtain can quickly flow into the pit, and the drawdown difference gradually decreases. Figure 10a shows that as \(S_{h}\)/\(H_{0}\) increases from 0.063 to 0.563, \(L\)/\(H_{0}\) = 0.844 causes the largest range of groundwater difference, and \(\Delta\)/\(H_{0}\) increases from 0.043 to 0.268 when \(t\) = 192 h, \(\Delta\)/\(H_{0}\) increases by 0.225.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Names** & \(S_{h}\)**/\(H_{0}\)** & \(R_{w}\)**/\(H_{0}\)** & \(L\)**/\(H_{0}\)** \\ \hline Effect of \(S_{h}\) & (0.063–0.563) & 0.313 & 0.688 \\  & & & 0.531 \\ \hline Effect of \(R_{w}\) & 0.375 & (0.063–1.250) & 0.844 \\  & & (0.063–1.250) & 0.688 \\  & & & 0.531 \\ \hline Effect of \(L\) & 0.375 & 0.625 & (0.438–0.938) \\  & & 0.938 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Combination of the sensitivity parameters (\(H_{0}\) = 32 m).  
Figure 9: Groundwater drawdown outside the pit at different locations with \(S_{w}\) equal to 12 m.  
As shown in Figure 10c, when the \(S_{h}\)/\(H_{0}\) exceeds \(L\)/\(H_{0}\), the groundwater difference decreases, which can be explained by a change of dewatering patterns and water level distribution according to [PERSON] et al. (2014). When \(S_{h}\)/\(H_{0}\) \(>\)\(L\)/\(H_{0}\), the effect of the waterproof curtain extending the seepage path becomes weaker, and it is not appropriate to adopt the scheme of groundwater drawdown being greater than the length of the waterproof curtain.
###### Abstract  
This study aimed to assess the effect of real-time vertical ground reaction force (VGRF) biofeedback on sagittal plane hip and knee joint biomechanics and extensor muscle activities in older adults. Fifteen healthy older adults (71 \(\pm\) 5.8 years) walked on a treadmill while instructed to increase their first peak of VGRF via biofeedback. Whole-body kinetic and kinematic data and electromyography data for the vastus lateralis and gluteus maximus muscles were recorded. A one-way repeated measure ANOVA followed by post hoc analysis was conducted. Results showed increases in peak VGRF (20.95%), knee extension torque (73.7%), knee flexion angle (53.8%), and vastus lateralis muscle activity (72.1%) during the loading response, with percentage changes calculated as the mean of acquisition and recall trials relative to baseline walking. In contrast, no significant effect on peak hip extension torque and hip flexion angle over time was observed. These findings suggest that biofeedback can induce greater vertical support forces production with increased knee extension torque and extensor muscle activity. In addition, older adults adopted higher vertical support without increasing hip joint torque, potentially aiding in mitigating age-related distal-to-proximal joint torque redistribution. These findings suggest that VGRF biofeedback could potentially be an effective intervention to enhance knee extensor activation and mobility in older adults without increasing hip load.  
vertical ground reaction force; biofeedback; older adults; knee torque; hip torque; muscle activity Article
## 4 Results and Discussions
### Product Quality  
Views of the survey respondents regarding if the use of Agile has improved the quality of the delivered product, have been plotted in Figure 9. This figure shows that 13% of the respondents are of the view that Agile has a \"Much Higher\" effect on product quality improvement, 55% termed it as \"Higher\", 16% think that it does not affect product quality, 11% say its effect is \"Somewhat Lower\" and 4% say that the effect on product quality is \"Much Lower\".
# Applying Game Theory to Teach the PERT Scheduling Method
## 3 Use of the PERT Method in Construction Planning and Scheduling
### PERT Method (Program Evaluation and Review Technique)
#### 3.3.1 Elements of the PERT Diagram  
Like CPM (Critical Path Method), the PERT method allows for graphically drawing the diagram of the relationships between each activity and their interdependencies [81]. It consists of the following elements: (a) Arrows: These are used to represent an activity to be executed. The start of the arrow represents the beginning of the activity and is associated with the node, while the arrowhead at the end of it represents the direction, from right to left; (b) Nodes: An event or milestone that has no duration, where node i is the start of one or more activities, and node j is the end of an activity. The diagram can only have one start node and one end node. Activities are linked in the nodes; (c) Dummy Activities: Represented by an arrow with a dotted line, they indicate the dependencies between activities. These do not have a duration; (d) Float: The length of time that an activity can be delayed without causing a delay in the completion of the project.  
The PERT method, like CPM, was designed to provide valuable information for project managers and decision-makers. If the critical path is delayed in both tools, the same amount delays the project, and the float is the same in both cases.
## 5 Performance Evaluation
### Simulation Experiments
#### 5.1.1 Regular Deployment
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CPU TIME (m)** & & **DV-Hop** & **PDM** & **LSVR** & **ML-RELM** \\ \hline \multirow{4}{*}{40 anchors, 365 unknown nodes (C-shaped)} & **Average** & 1.0917 & 1.1244 & 1.5707 & 1.1027 \\  & **Median** & 1.0782 & 1.1081 & 1.5586 & 1.0858 \\  & **Worst** & 1.2618 & 1.3289 & 1.679 & 1.2232 \\  & **Standard deviation** & 0.0517 & 0.0697 & 0.0445 & 0.0456 \\ \hline \multirow{4}{*}{40 anchors, 447 unknown nodes (Z-shaped)} & **Average** & 2.2876 & 2.3103 & 2.7801 & 2.2796 \\  & **Median** & 2.3745 & 2.3779 & 2.8577 & 2.3926 \\  & **Worst** & 2.9239 & 2.7247 & 3.1042 & 2.5051 \\  & **Standard deviation** & 0.1730 & 0.1746 & 0.1933 & 0.1870 \\ \hline \multirow{4}{*}{40 anchors, 485 unknown nodes (S-shaped)} & **Average** & 2.4172 & 2.4079 & 2.8511 & 2.3901 \\  & **Median** & 2.3745 & 2.3779 & 2.8577 & 2.3926 \\ \cline{1-1}  & **Worst** & 2.9239 & 2.7247 & 3.1042 & 2.5051 \\ \cline{1-1}  & **Standard deviation** & 0.1534 & 0.1063 & 0.1024 & 0.0696 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Running time in the regular deployment. Figure 7: Average RMS for different algorithms in the regular deployment environment.
# Optimization of Sensor Placement for Modal Testing Using Machine Learning
## 2 A Review of Traditional Sensor Placement Techniques for Modal Testing
### Residual Kinetic Energy Method (RKE)
Similarly to \(\mathbf{R}\), the \(\mathbf{R}\mathbf{K}\mathbf{E}\) matrix is 0 at the rows corresponding to the sensor positions (measured DOF). The sum of the contribution of each degree of freedom is 1. Sensors should be placed at the locations of nodes with higher RKE values. The RKE matrix column will be much lower than 1 if that mode is already appropriately instrumented. By iterating through this matrix, the location where sensors should be placed can be determined, as well as the minimum number of sensor locations. This methodology works well when applied to existing analysis points to identify additional degrees of freedom that are under-measured by the initial sensor placement, and it has been adopted by NASA and others to meet NASA and Department of Defense standards for modal testing.
# A Review of Early Injection Strategy in Premixed Combustion Engines
## 3 Injection Timing
### Two-Stage Early Injection
#### 3.2.1 First Injection Timing
Results showed that with advanced first injection timing, the maximum heat release rate of three of four fuels (diesel, B30, and G30, but not diesel/gasoline, diesel/n-butanol (DGB)) increased. Smoke emissions decreased due to the advanced first injection timing. NOx emissions decreased and then increased slightly before and after the first injection timing of 40\({}^{\circ}\) BTDC. On the contrary, CO and HC emissions increased with the advanced first injection timing. Figure 8 shows the effects of pilot injection strategies and fuel properties on in-cylinder pressure and heat release rate. [PERSON] et al. [85] studied the effects of pilot injection timing on the combustion noise and particle emissions of a diesel/natural gas dual-fueled engine at low load. In the study, the pilot injection timing was set at 30\({}^{\circ}\) to 5\({}^{\circ}\) BTDC. The results showed that with advanced pilot injection timing from 5\({}^{\circ}\) to 17\({}^{\circ}\) BTDC, BTE increased sharply, and then a similar tendency could not be observed. In terms of emissions, with advanced pilot injection timing, brake specific HC emissions (BSHC) and brake specific CO emissions (BSCO) notably decreased, while brake specific NOx emissions (BSNOx) significantly increased because the dual-fuel combustion phase was strongly controlled by the pilot injection timing, and advanced injection timing led to an earlier combustion phase. Table 4 shows a summary of the variation of performance and emissions of the PCCI engine after advancing the first injection timing. Advancing the first injection timing will generally decrease NOx and soot emissions and increase HC and CO emissions. Engine performance deteriorates with advanced injection timing due to the increased negative work and incomplete combustion. Figure 8: Effects of pilot injection strategies and fuel properties on in-cylinder pressure and heat release rate [84]. (**a**) pilot injection rate = 15%, different pilot-main interval, in-cylinder pressure and HRR curve with CA; (**b**)constant pilot-main interval, different pilot injection rate, in-cylinder and HRR curve with CA; (**c**) and (**d**) compares the in-cylinder pressure and heat release rate of four different fuels.
## 6 Future Research Directions
### Public Trust and Responsible Development  
Future research in AI-enabled 6G communication networks must prioritize building public trust and ensuring responsible development. This entails developing transparent and explainable AI models that clarify decision-making processes, detecting and mitigating biases to promote fairness, and establishing robust ethical governance frameworks. Security measures, including advanced encryption and real-time threat detection, are crucial for protecting user data and network integrity. Additionally, involving users in the design process can enhance usability and alignment with societal values. International standards and regulatory compliance frameworks will be essential to ensure ethical AI deployment across diverse applications and regions. Addressing these priorities will not only advance the capabilities of 6G networks but also uphold transparency, fairness, and security, fostering trust among users and stakeholders alike.
## 2 Wettability Measurement Methods
### Analytical Methods
#### 2.4.1 Lak Method  
[PERSON] et al. modified [PERSON]'s second rule, excluded the third, and combined the first and second rules to create a wettability index (\(I_{L}\)) for determining average wettability. The (\(I_{L}\)) value ranges from \(-1\) to \(+1\), with positive values indicating water-wet rocks and negative values indicating oil-wet rocks. The (\(I_{L}\)) values close to zero indicate rocks with neutral wettability. The wettability index (\(I_{L}\)) is defined using Equation (4) [36].  
\[I_{L}=\alpha\bigg{(}\frac{0.3-k_{riv@Sor}}{0.3}\bigg{)}+\beta\bigg{(}\frac{0.5 -k_{riv@Sor}}{0.5}\bigg{)}+\frac{CS- RCS}{1-S_{or}-S_{wir}}, \tag{4}\]  
Figure 9: TGA results of oil-wet carbonate samples after treatment with modified seawater: SW3S, SW3 Mg, and SW [34].  
where \((k_{rw@Sor})\) is the maximum relative permeability to water measured at residual oil saturation, \((CS)\) is the water saturation at the crossover point of the relative permeability curves, \((S_{wir})\) is the irreducible water saturation, and \((S_{or})\) is the residual oil saturation. Constants (\(\alpha\)) and (\(\beta\)) are determined with Equation (5).  
\[\begin{array}{l}k_{rw@Sor}<0.3,\;then\;\alpha=0.5,\;\beta=0\\ If\{0.3\leq k_{rw@Sor}\leq 0.5,\;then\;\alpha=\beta=0\\ k_{rw@Sor}>0.5,\;then\;\alpha=0,\;\beta=0.5\end{array} \tag{5}\]  
\(RCS\) is the half of the connected pore space where the wettability alteration occurs and is determined using Equation (6) as follows:  
\[RCS=\frac{1}{2}+\frac{S_{wir}-S_{or}}{2}. \tag{6}\]
###### Abstract  
Expressive clinical terminologies are of utmost importance for achieving a semantically interoperable data exchange and reuse in healthcare. SNOMED CT, widely respected as the most comprehensive terminology in medicine, provides formal concept definitions based on description logic which not only allows for advanced querying of SNOMED-CT-coded data but also for flexibly augmenting its 350,000 concepts by allowing a controlled combination of these. This ability for postcoordination largely increases the expressivity of the terminology but correlates with an intrinsic complexity. Complicated by the current lack of tooling support, postcoordination is widely either ignored or applied in an error-prone way. To help facilitate the adoption of postcoordination, we implemented a web application that guides users through the creation of postcoordinated expressions (PCEs) while ensuring adherence to syntactic and semantic constraints. Our approach was largely facilitated by making use of the extensive SNOMED CT specifications as well as advanced HL7 FHIR Terminology Services. Qualitative evaluations confirmed the usability of the developed application and the correctness of the PCEs created with it.  
semantic interoperability; clinical terminology; SNOMED CT; biomedical ontology; postcoordination; expression templates +
Footnote †: journal: Medical Informatics, University of Luebeck, 23562 Luebeck, Germany
Grammar as the syntax specification and its Concept Model to sanction reasonable combinations of concepts. Adhering to these constraints is necessary to generate correct PCEs but correlates with the challenges inherent in powerful yet complex instruments. Due to a current lack of adequate tooling support, responsibility is passed on to the user, requiring intricate expertise of the aforementioned specifications. Thus, uptake of postcoordination is often hesitant [8] and error-prone.  
Mirroring the international trend, the same issue is becoming apparent in Germany's recent adoption of SNOMED CT. After joining SNOMED International in 2021, SNOMED CT concepts have been eagerly introduced in various national initiatives and standardization endeavors [9]. Postcoordination, on the other hand, is either avoided altogether, as in the case of the Medical Informatics Initiative (MII) [10], or used as a last resort, such as in the Medical Information Objects (MIOs) [11] of the National Association of Statutory Health Insurance Physicians (NASHIP, in German: Kassenarzthiche Bundesvereinigung, KBV) [12]. Qualitative evaluation of the postcoordinated expressions employed in the latter revealed several inaccuracies, thus showing the necessity for software supporting the creation process.  
To help overcome these barriers and facilitate the international adoption of postcoordination, we aimed to develop an integrated web application guiding the user through the creation process of postcoordinated expressions while ensuring syntactic and semantic correctness.
# Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset
## 5 Discussion  
In this article, the authors presented a study of the effectiveness of power insulator detection in digital images with the application of a limited training dataset. However, the detection efficiency is also affected by other problems such as the following:  
* Flat and limited (unidirectional) views of objects. For aerial photographs taken perpendicular to the ground, objects of interest are relatively small and have fewer elements, mainly in flat and rectangular form. They usually include shots from above, omitting many important features of those objects in other planes.
* Large sizes of digital images. Currently, aerial imaging provides visual material at very high resolutions, which allows capturing increasingly more details, but at the same time introduces problems related to the use of sufficient computing power necessary to process them. These problems are eliminated by applying various methods of pre-processing for aerial photography. However, their proper selection requires a lot of research to determine the effectiveness of various solutions dedicated to specific technical problems.
* Overlapping objects. Objects may be occluded by other objects of the same type, which causes, e.g., inaccuracies when labeling data.  
* Replication of the same objects in different digital images. The same object can occur in two separate images, which can lead to double detection and errors when recognizing objects in images.  
Directions for further research include the verification of the results obtained in limited datasets captured during other inspection flights and effectiveness analysis for other methods of detecting objects in digital images. It is also appropriate to examine the influence of augmentation of images used in the process of training a convolutional network on detection efficiency.  
Conceptualization, M.T.; methodology, M.T. and P.M.; resources, J.O.; software, P.M. and J.O.; supervision, M.T.; validation, P.M.; visualization, J.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.
# Performance Analysis of Mesh Based Enterprise Network Using RIP, EIGRP and OSPF Routing Protocols +
###### Abstract  
Computer network communication is quickly growing in this pandemic situation. Phone conferencing, video streaming and sharing file/printing are all made easier with communications technologies. Data transmitted in time with little interruption become a significant achievement of wireless sensor networks (WSNs). A massive network is interconnection computer networks in the globe connected by the Internet, and the Internet plays a critical role in WSNs. Data access is a key element of any enterprise network, and the routing protocol is used to transmit data or access data. Due to the growing use of WSNs, it is essential to know about the network structure, the routing protocol. The routing protocols must be used to route all data sent over the Internet between the source and the destination. Which chooses the optimum routes between any two nodes in an enterprise network. This research focused on how the routing table will determine the optimum path/route of data packets to be transmitted from source to destination. The performance of three routing protocols, Routing Information Protocol (RIP), Enhanced Interior Gateway Routing Protocol (EIGRP) and Open Shortest Path First (OSPF), is investigated in this research for the massive mesh based enterprise wireless sensor network. We also investigated the behaviors of end-to-end packet latency, convergence time on flapping connections and average point-to-point throughput (bits/sec) between network links. Finally, the simulation results are compared to the efficacy and performance of these protocols implemented in the wireless LAN and internet-based wireless sensor network.  
wireless sensor network; RIP; EIGRP; OSPF; redistribution; dynamic routing protocol +
Footnote †: Copyright © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Multilevel Regularization Method for Building Outlines
## 2 Materials and Methods
### Local Regularization of Building Outline-Based Convex Hull Grouping
#### 2.3.4 Regularization of Area-like Set Using the Hausdorff Distance Method  
For the building outline \(P\) extracted from Figure 11a, the results obtained after applying the regularization method given in Section 2.2 are shown in Figure 11b. The convex hull method is used to group the unregularized nodes, resulting in four area-like sets \(V_{i}\), \(V_{j}\), \(V_{k}\), and \(V_{t}\), where \(V_{i}\) = {\(P_{1}\), \(P_{2}\), \(P_{3}\), \(\ldots\), \(P_{228}\)}, \(V_{j}\) = {\(P_{229}\), \(P_{230}\), \(P_{231}\), \(\ldots\), \(P_{332}\)}, \(V_{k}\) = {\(P_{333}\), \(P_{334}\), \(P_{335}\), \(\ldots\), \(P_{573}\)}, and \(V_{t}\) = {\(P_{574}\), \(P_{575}\), \(P_{576}\), \(\ldots\), \(P_{675}\)}.  
Figure 11: Construction and regularization of area-like sets. (**a**) Building outline \(P\) extracted from remote sensing images and its node distribution; (**b**) Overall regularization results based on the Hausdorff distance method; (**c**) Extraction of area-like set using the convex hull method (Line AB is an edge of the convex hull of the building outline); (**d**) Iterative regularization of area-like set; (**e**) Local regularization results.  
Figure 10: Regularization process for a line-like set using the least squares method, the change in the number of nodes before and after regularization is shown in the green line. (**a**) Process of fitting the point set to a line; (**b**) Local outline of the building with a line-like set type; (**c**) Regularization results using the least squares method.  
The regularization process for the area-like set is as follows:  
1. Connect the starting and ending nodes of the area-like set to form a closed surface. Regularize it using the method described in Section 2.2. The difference here is that, when calculating the MBR and using the Hausdorff distance method for regularization, only the line segments composed of adjacent nodes that belong to the building outline are calculated and processed. Line segments that overlap with the convex hull outline and do not belong to the building outline are not considered. As shown in Figure 11c, during the regularization of \(V_{i}\), segment AB is not part of the building outline. Thus, including it in calculations may affect the regularization results.
2. Continue to calculate the convex hull for area-like set \(V\) and construct unregularized point sets by category. Figure 11d shows the regularization of area-like sets \(V_{i}\), \(V_{j}\), \(V_{k}\), and \(V_{t}\), as well as the continued use of the convex hull method to split and classify the unregularized portions of \(V_{i}\).
3. Iteratively execute the above steps. Once all nodes have been processed, local regularization results are obtained.