esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Test Results and Discussion
### Update Strategy  
In contemporary engineering and technology fields, the revision of technical standards adheres to a certain periodic pattern, and some new standards are issued with technological advancements. Despite this, compared to other types of knowledge available on the web, standard documents are relatively small in scale and their content remains comparatively stable. The RAG-enhanced LLMs knowledge base updates primarily involve the newly added segmented text chunks and their corresponding vector representations, which are derived from domain-specific standards.  
As standards constitute the authoritative corpus of domain knowledge, overlapping validity periods often exist between successive versions, during which both remain applicable. Furthermore, outdated standards also embody the historical trajectory of technological development, providing invaluable references for research. Consequently, the update strategy of this knowledge base focuses on appending new documents and their respective text blocks, ensuring each newly added text block contains explicit version information of the standard. When fed into the large model, the most recent standard text blocks are assigned to be of higher priority to ensure that output results are based as much as possible on the latest available information. Specifically, concerning the vertical field of engineering technical specifications, the update mechanism of this knowledge base encompasses several key steps:  
1. Coexistence of Multiple Versions: Whenever a new version is released or an existing standard is revised, the system stores it as an independent entity rather than simply overwriting the previous version. This approach takes into account both practical application timeliness and compatibility, such as the concurrent execution phases between different versions, while facilitating the tracking of technological evolution. Each document is annotated with metadata recording its effective date, obsolescence status, and revision summary for subsequent querying and tracing purposes.
2. Granular Mapping: Every text block will be accompanied by a version identifier, establishing a detailed mapping relationship from version to clause, which aids in the precise identification and citation of specific content under particular versions.
3. Priority Setting: Upon feeding updated text blocks into the large model, a prioritization mechanism can be implemented to give higher weight to the most recent standard text blocks. This method not only ensures responses are grounded in the latest knowledge but also enhances the system's sensitivity and responsiveness to industry dynamic changes.  
The aforementioned update strategy aims to construct a dynamic knowledge base that reflects current industry requirements while preserving historical perspectives, thereby supporting users in acquiring the most accurate and cutting-edge technical information.  
Moreover, this strategy provides a framework for future research aimed at exploring more effective ways of managing and utilizing standard literature resources.
# Internet of Things Energy Consumption Optimization in Buildings: A Step toward Sustainability
## 3 Optimization Model
### Interface Node to Send Data Selection  
Taking into account the two criteria of energy and distance difference, the proposed technique selects the intermediate node from among all live nodes in the network that have optimal conditions. As an intermediate node, it is optimal to choose a node that does not deplete the energy of the entire network. The energy utilized to convey data to the interface node is determined by calculating the relationship between the energy reduction of the sending nodes and Equation (7).  
\[\begin{array}{l}|E_{ijBS}=E_{Tx}(l,d(si,sj))+E_{Rx}(l)+E_{Tx}(l,d(sj,BS))\\ |=l\Big{(}E_{\text{elec}}\ +E_{fs}d^{2}(si,j)\Big{)}+lE_{\text{elec}}\ +l\Big{(}E_{\text{elec}}\ +E_{fs}d^{2}(sj,BS)\Big{)}\\ |=3 lE_{\text{elec}}\ +lE_{fs}\big{(}d^{2}(si,sj)+d^{2}(sj,BS)\big{)}\end{array} \tag{7}\]  
where \(E_{ijBS}\) shows the energy used to send data from the \(i\) node through the \(j\) interface node to reach the base station. \(d(s_{i}s_{j})\) shows the distance from node \(i\) to node \(j\) and \(d(s_{i}BS)\) represents the distance of node \(i\) to the base station. The energy used to send information to the base station is obtained directly from Equation (8).  
\[\begin{array}{lcl}E_{iBS}&|=E_{Tx}(l,d(si,BS))+l\Big{(}E_{\text{elec}}\ +E_{fs}d^{2}(si,BS)\Big{)}\\ |=IE_{\text{elec}}\ +lE_{fs}\big{(}d^{2}(si,BS)\big{)}\end{array} \tag{8}\]  
The difference between these two quantities of energy consumption (Equation (9)) must be more than zero in order that the network's total energy does not drop when the interface node is chosen.  
\[\begin{array}{l}|lE_{\text{elec}}\ +lE_{fs}\big{(}d^{2}(si,sj)+d^{2}(sj,BS) \big{)}-lE_{\text{elec}}\ +lE_{fs}\big{(}d^{2}(si_{,},BS)\big{)}>0\\ |2 lE_{\text{elec}}\ +lE_{fs}\big{(}d^{2}(si,sj)+d^{2}(sj,BS)-d^{2}(si_{,}, BS)\big{)}>0\\ |\big{(}d^{2}(si,sj)+d^{2}(sj,BS)-d^{2}(si,BS)\big{)}>-\Big{(}2E_{\text{elec}}\ /E_{fs}\Big{)}\end{array} \tag{9}\]  
The intricacy of these two-stage procedures is intimidating for engineers who seek to integrate search algorithms in production goods. Although some techniques' source codes are available, they may not be immediately implementable in a company's actual products. The complexity analysis comparison for optimization methods is presented in Table 1.
In order to enhance spatial analysis capability, the spatial multi-objective decision method was applied to the navigation risk estimation research. For instance, Fuzzy-AHP [21; 22] and the grey relationship analysis (GRA) method were used to weigh evaluation criteria, and a navigation risk evaluation model based on spatial multi-decision technology was constructed [23]. Questionnaire data and expert experience data have also been used [21; 22], which led to strong subjectivity, and the study by [22] regarding low cloud data was used to replace the sea fog. Therefore, to provide better navigation risk estimation in fog seasons, it is necessary to integrate sea fog detection technology into navigation risk estimation models, and calculate the weights of relevant criteria using objective data. The main aims of this paper are to: (1) develop a sea fog identification method model based on Himawari-8 remote sensing imaging data, and obtain a large historical sea fog data set of Bohai Sea and its surrounding waters. Then, the main fog season(s) and fog area(s) are distinguished by statistical analyses; (2) determine the criteria weights from accident data using an objective weight method to avoid subjective uncertainties in the weight determination, and use data of sea fog, wind, waves, currents, and water depth to construct a navigation risk estimation model based on spatial multi-decision technology; and (3) assess the navigation risk during each of the four seasons, divide the shipping routes into different navigation areas, and analyze the risks in the specific navigation areas during each season.
## 3 Design and Implementation
### Electronic Control  
This part mainly includes components and electrical circuits to control our solar tracker system. The latter can be defined as a microprocessor-type information processing unit to which internal peripherals have been added, allowing the assembly to be carried out without the need for external components [21]. An Arduino board is a brand that covers hardware-free boards on which there is a microcontroller. Their free license allowed us to program microcontrollers that exist in the different cards as we wanted to analyze and produce electrical signals that can be used for different tasks, including the control and management of electronic processes and instruments [21]. Microcontrollers have reduced performance but are small in size and consume little power. Although the Arduino card was chosen because of its open-source architecture and the ease of connecting to the computer with a simple USB cable. This cable provides both the card power supply and serial communication [33; 34; 35]. The Arduino card is programmed via a software interface in pseudo-C language. Once the program has been written, it is sent to the card, which can then operate independently (without connection to the computer). To display the numerical or textual information produced by the Arduino card, we connected it to a computer on the one hand and to a liquid crystal display (LCD) on the other hand to be able to operate autonomously away from the computer. In our prototype, we used a display compatible with the Hitachi HD44780 driver.
# A New Online Service for the Validation of Multi-GNSS Orbits Using SLR
## 1 Introduction
### Ranging the GNSS Constellation
However, the accuracy of the dataset is dependent on the station-satellite pair, especially in the case of different, large and flat LRA, which are used onboard the GNSS satellites. The characteristics of the equipment used at stations may impose systematic biases in reference to particular GNSS satellites, such as the Satellite Signature Effect (see Section 3.1).
## 4 Results  
### Confusion Matrix and F1 Score Results  
Figure 10 shows the confusion matrix of different machine learning methods, and labels 0-2 represent high sensitivity, low sensitivity, and medium sensitivity, respectively. When the number in the matrix is closer to 1, the model fits better. The XGBoost matrix is larger than that of the other models, which indicates that XGBoost outperforms ANN and NB. Furthermore, the matrix value of XGBoost without SMOTE is smaller than that of XGBoost with SMOTE.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameters** & **Value** \\ \hline XGBoost & n\_estimators & 360 \\  & learning\_rate & 0.002 \\  & max\_depth & 6 \\  & min\_child\_weight & 1 \\  & gamma & 0.2 \\  & colsample\_bytree & 0.5 \\  & subsample & 0.8 \\ ANN & learning\_rate\_init & 0.0001 \\  & activation & tanh \\  & hidden\_layer\_sizes & (100, 100, 100) \\  & max\_iter & 260 \\ NB & priors & 3 \\  & var\_smoothing & \(10^{-9}\) \\ XGBoost\_NoSmote & n\_estimators & 360 \\  & learning\_rate & 0.005 \\  & max\_depth & 5 \\  & min\_child\_weight & 1 \\  & gamma & 0.3 \\  & colsample\_bytree & 0.7 \\  & subsample & 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimal parameters of models.  
Figure 9: Flow chart of proposed method.  
Figure 11 presents the F1 score of different machine learning methods. An F1 score closer to 1 indicates that the model has better performance. Figure 11 shows that XGBoost achieved the best F1 score, Recall, and Precision (0.72, 0.72, and 0.73, respectively), followed by NB (0.68, 0.69, and 0.70, respectively), and XGBoost_NoSmote (0.68, 0.66, and 0.71, respectively). ANN had the worst performance in terms of the F1 score, Recall, and Precision (0.61, 0.62, and 0.63, respectively). Using SMOTE, the performance of XGBoost on the F1 score, Recall, and Precision improved by 6.9%, 9.1%, and 2.8%, respectively.
## 3 Opportunities for MEW in Tissue Engineering
In another study, [PERSON] and colleagues [98] performed melt electrospinning writing to fabricate box-shaped scaffolds for tendon and ligament application. The parameters were adjusted to achieve sinusoidal filaments to resemble the crimped structure of collagen I present in tendons and ligaments. An increase in tensile strength, elastic modulus, and elasticity as compared to straight fibers were observed. Besides these applications, [PERSON] et al. [99] fabricated microfluidic channels via melt electrospinning writing method. A master mold of PCL as a sacrificial layer was melt electrospun, followed by casting polydimethylsiloxane (PDMS) on the patterned PCL structure and further curing. Afterward, the PDMS layer was removed and inlet and outlet holes were punched. Another PDMS microchannel was bonded to the first layer by applying a hot press. By means of this method, they achieved a microfluidic channel in a straightforward and cost-effective manner with the ability to adjust width and depth of the channels. The current state of literature highlights the great potential of MEW in different areas of tissue engineering and scaffold fabrications, from hard to soft tissues with a wide range of materials according to the desired properties. Mechanical properties and biological response to the scaffolds could be simply tuned with adjusting the internal architecture. Moreover, since no toxic solvent is used, in situ fabrication of scaffolds for applications, such as wound dressings and bandages, could be realized [57]. The possibility of the incorporation of multi-nozzle setups could also be considered. Expansion of materials library, innovative hardware designs, and hence the ability to deliver more structural diversity make MEW a versatile approach to fabricate microstructures with controlled and desired properties. The balance between processability, mechanical properties of the scaffolds, and their biocompatibility can be considered as one of the main challenges in the further development of MEW. In this respect, the utilization of other established fabrication technologies together with MEW as a hybrid approach could be a promising strategy to overcome the shortfalls of MEW and broadening its potential. By the aid of a hybrid method, one can fabricate hierarchical structures in order to satisfy cellular and mechanical demand and fulfill the requirements for tissue engineering constructs, while other application criteria, such as mechanical durability and/or processing challenges of specific materials, could be addressed. By this way, a handful of techniques that are based on surface modification and/or inclusion of hydrogels within stiff polymer structures fabricated by MEW would make new opportunities in the field of scaffold fabrication. Although the application of MEW as a part of a hybrid bio-manufacturing strategy is not widely explored at the moment, a critical review on the current state of research in this area can provide insight on the possibilities for development of sophisticated multifunctional structures.
## 2 Materials and Methods
### MLS Accuracy Evaluation
#### 2.7.1 Comparison with Checkpoints  
Custom software was developed to compare point clouds and measured checkpoints with higher accuracy, calculate deviations, and visualise them. It allows working in the European Terrestrial Reference System 89 (ETRS89) and in the System of the Unified Trigonometrical Cadastral Network (S-JTSK) together with the heights given in the Bpv system (Baltic Vertical after Adjustment). Since the measurements were carried out on the territory of the Czech Republic, S-JTSK and Bpv were chosen as the main coordinate systems of the results.  
The developed software allows the calculation of coordinate differences, coordinate standard deviations (Equation (2)), root mean square deviations (Equation (1)), visualisation of these values in bar charts, and visualisation of deviations depending on the distance of the control points.  
\[\text{RMS}=\sqrt{\frac{\sum_{i=1}^{n}\Delta x_{i}^{2}}{n}}, \tag{1}\]  
Figure 4: RiPRECISION software principle [33].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Point Cloud Name** & **Record** &
\begin{tabular}{c} **Type of Control-Point** \\ **Data Set** \\ \end{tabular} & **Point Spacing [m]** \\ \hline MLS\_4 & Record\_1 & GNSS & 250 \\ MLS\_5 & Record\_1 & GNSS & 1000 \\ MLS\_6 & Record\_1 & Untied & - \\ MLS\_7 & Record\_2 & Adjusted (TS) & 50 \\ MLS\_8 & Record\_2 & GNSS & 1000 \\ MLS\_9 & Record\_2 & Adjusted & - \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Cont._where \(\Delta x_{i}=X_{i}-\hat{X}_{i}\), \(X_{i}\) are the coordinates of the check point, \(\hat{X}_{i}\) are the coordinates of the point obtained from the point cloud.  
\[\text{StDev}=\sqrt{\frac{\sum_{i=1}^{n}v_{i}^{2}}{n-1}}, \tag{2}\]  
where \(v_{i}=\Delta\overline{X}-\Delta X_{i}\), \(\Delta X_{i}\) are the coordinators differences between the check points and the points obtained from the cloud, \(\Delta\overline{X}\) is their average.
## 3 Proposed Schemes
### Cooperative Delay Minimization Routing (CoDMR) Scheme
Information from the lower two minor sink nodes is broadcasted towards the upper two minor sink nodes, from where the information is further broadcasted towards the top major sink. The below Figure 7 indicates the flow chart of both the protocols. While Algorithm 1 clearly describes the path setup, relay selection and data forwarding for both the schemes. Figure 7: Flow chart of DMR and CoDMR. ```
1\(RL_{n}\):Relay node
2\(BER\):Bit Error Rate
3\(SR_{n}\):Source node
4\(TR\):Transmission range
5\(DT_{n}\):Destination node
6\(N\):Total number of rounds
7\(S_{major}\):Major sink node
8\(S_{minor}\):Minor sink node
9\(H_{p}\):Transmitted hello packet
10\(d\):distance from sensor node to minor sink nodes
11 Nodes transmits hello packet= then
12 Find neighbors
13 Neighbors found= true
14 if\(d<TR\)= then
15 Find \(S_{minor}\)
16\(S_{minor}\) found
17 Send packet to \(S_{minor}\)= true
18 elseif\(S_{minor}\) is not exist in the \(TR\)= then
19 Find \(DT_{n}\)
20 Calculate \(d\) for \(DT_{n}\) selection
21\(DT_{n}\) found= true
22 if BER < 0.5= then
23 Find \(RL_{n}\) found= true
24 Packet send to \(RL_{n}\)
25 while
26 Checks \(S_{minor}\)
27 Packet reached to \(S_{minor}\)
28 Packet forward to \(S_{major}\)= true
29 endif
30 endwhile
31 break
```  
**Algorithm 1**Information sharing, path set up and data forwarding
## 2 Principle of 2D ERT  
The ERT uses two current electrodes (C1, C2) and two potential electrodes (P1, P2) for measurement, as shown in Figure 1. In Figure 1a, the low frequency AC is conducted from the current electrodes to the stratum, in different geometric positions; the potential difference and current are measured by using potential electrodes and current electrodes, and then the apparent resistivity is theoretically calculated by electrostatics, expressed as Equation (1) [45]:  
\[\begin{array}{l}\rho=K\frac{\Delta V}{L};\Delta V=V_{P1}-V_{P2}\\ K=\left(\frac{2\pi}{k_{1}^{2}-\frac{1}{k_{2}^{2}}-\frac{1}{k_{3}^{2}}+\frac{ 1}{k_{4}^{2}}}\right)\end{array} \tag{1}\]where K is the geometric parameter of the electrode array, \(\Delta\)V is the potential difference measured by potential electrodes, I is the input current strength, R1 is equal to the distance between C1 and P1, R2 is equal to the distance between C2 and P1, R3 is equal to the distance between C1 and P2, and R4 is equal to the distance between C2 and P2. The apparent resistivity represents the resistivity measured by the electrical resistivity method when the medium is homogeneous [45]. During the measurement in real strata, the resistivity of the subject may be heterogeneous, and the resistivity measured by changing the electrode spacing and location thus change. Therefore, if tens of electrodes are laid equidistantly on the earth's surface at first, the pseudo-section can be obtained by continuously changing the spacing and horizontal movement of the aforesaid paired current electrodes and potential electrodes. Figure 1b shows the pseudo-section measured by using a Wenner arrangement of electrodes [46]. Finally, the resistivity profile representing strata can be obtained by inverse computation analysis. The inverse analysis generally uses the optimized method. The inverse calculation software first estimates the initial value of the inverse calculation model by measuring the pseudo-section of the resistivity. After the initial model of inverse calculation is given, the difference between the response values and the measured data of the model is minimized by multiple operations. Thus, the result of this inverse calculation is the resistivity profile of real strata.
## 3 Experimental Measurement  
### Design of Methodology for Experimental Measurement of Deformations in Selected Places  
The advantage of experimental measurement is that it allows the consideration of real boundary conditions including constrains, dynamics loading and other operational influences [13; 14; 15; 16]. The strain gauge method was proposed for experimental measurements. At selected locations, values of reduced stresses were recalculated from relative values and were compared.  
A strain gauge method was proposed for experimental measurements. The aim was to compare values of measured strain in selected places with values determined by numerical modelling. The application points of strain gauges were placed symmetrically along the axis parallel to the longer side of the roof (Figure 6). Since the stress in question was not known in advance, strain gauges RY91-10/120 were used. The deformation sensitivity constant was \(\mathrm{k_{a}}=1.98\pm 1\%\), \(\mathrm{k_{b}}=2.04\pm 1\%\) and \(\mathrm{k_{c}}=2.05\pm 1\%\). Each grid was connected in a quarter-bridge. A Quantum MX840 strain gauge (manufactured by HBM) with Catman software evaluation was used for operational measurements. Since a quasi-static load was considered, a sampling frequency of 50 Hz was set. The aim was to compare values of measured reduced stresses in selected places with values determined by numerical modelling.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Measuring Place**} & \multicolumn{3}{c}{**Deviation Interval of Measured Values (\%)**} \\ \cline{2-4}  & **2/1** & **3/2** & **4/2** \\ \hline
1–20 & \(<\)1.32; 9.09\(>\) & Not evaluated & \(<\)–9.72; 0.0\(>\) \\
21–38 & \(<\)–7.0,2; 8.22\(>\) & \(<\)–46.88; 18.52\(>\) & \(<\)–17.91; 0.00\(>\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Displacement deviations between individual stages of antenna mounting.