esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 HELIOS Concept in the Real Estate Market--Methodology and Implementation
### Methodological Overview
#### 3.1.2 Research Methodology  
1. Application of NLP and LLMs in Real Estate Market Analysis: Data Collection: Utilising LLM models to process and analyze vast amounts of data from various sources, such as descriptions of completed transactions in the real estate market under scrutiny. Natural Language Processing: Interpreting property descriptions, market analyses, and forecasts to gain insights into factors influencing property prices. Historical Analysis: Examining historical property price data to forecast future trends.
2. Data Standardization and Unification: Information Aggregation: Automatic retrieval and aggregating data from various sources to achieve a coherent view of available properties. Data Extraction and Classification: Employing LLMs to extract essential information from unstructured textual data and classify it based on specified parameters. Attribute Notation Standardization: Unifying different notations of attribute values to enable their joint analysis.
3. Development of the HELIOS System: ETL Process (Extract, Transform, Load): Processing data to achieve a format suitable for subsequent stages of analysis. Early Verification: Incorporating feedback from real estate market experts to iteratively refine model accuracy. Property \"Fingerprint\" Creation: Utilising LLM technology to generate detailed profiles of properties.
4. Methodology for Classifying Homogenous Objects: Text-to-Vector Transformation: Utilizing the S-BERT model to transform property descriptions into vector representations of the text context. Vector Classification: Applying the self-organizing map (SOM) method to classify objects based on vectors. Detailed descriptions of the above elements, enabling a deeper understanding of the methodology and techniques employed in the HELIOS concept system, are provided in the following sections.
## 2 Baselines in the Literature
### Evaluation Criteria  
In order to evaluate the performance of a baselining methodology, evaluation criteria are required. The accuracy of the baseline is a key evaluation criterion and typically the only quantifiable criterion considered. However, it is not the only relevant criterion. Several other (qualitative) criteria should be considered as well, such as transparency, integrity, simplicity, inclusiveness, and proneness to manipulation.
# Refinement Orders for Quantitative Information Flow and Differential Privacy +
## 6 Application: Comparing DP Mechanisms
### Preliminaries
Such a mechanism satisfies \(\alpha\cdot d_{E}\)-privacy where \(\alpha\geq\frac{\varepsilon}{2}\) (which can be calculated exactly from the channel construction)._  
Note that the construction presented in Definition 12 uses the Euclidean distance metric since we only consider integer domains. The general construction of the exponential mechanism uses arbitrary domains and arbitrary metrics. Note that its parameter \(\varepsilon\) does not correspond to the true (best-case) \(\varepsilon\)-DP guarantee that it provides. We will denote by \(E^{\varepsilon}\) the exponential mechanism with 'true' privacy parameter \(\varepsilon\) rather than the reported one, as our intention is to capture the privacy guarantee provided by the channel in order to make reasonable comparisons. **Definition 13**. : _A randomized response mechanism is a channel \((\mathcal{X},\mathcal{Y},R^{\varepsilon})\), parametrized by \(\varepsilon\geq 0\) constructed as follows:_  
\[R^{\varepsilon}_{x,y} =\frac{e^{\varepsilon(1-d_{D}(x,y))}}{e^{\varepsilon}+n} \text{for all }x,y\in\mathcal{Y} \tag{31}\] \[R^{\varepsilon}_{x,y} =\frac{1}{n+1} \text{for all }x\
ot\in\mathcal{Y} \tag{32}\]  
_where \(n=\|\mathcal{Y}\|-1\) and \(d_{D}\) is the discrete metric (that is, \(d_{D}(x,x)=0\) and \(d_{D}(x,y)=1\) for \(x\in\mathcal{Y},x\
eq y\)). Such a mechanism satisfies \(\varepsilon\cdot d_{D}\)-privacy._  
We note that the randomized response mechanism also satisfies \(\varepsilon\cdot d_{E}\)-privacy. Intuitively, the randomized response mechanism returns the true answer with high probability and all other responses with equal probability. In the case where the input \(x\) lies outside \(\mathcal{Y}\) (that is, in 'over-truncated' mechanisms), all of the outputs (corresponding to the outlying inputs) have equal probability. **Example 7**. : _The following are examples of each of the mechanisms described above, represented as channel matrices. For this example, we set \(\varepsilon=\log(2)\) for the geometric and randomized response mechanisms, while, for the exponential mechanism, we use \(\varepsilon=\log(4)\)._ \begin{tabular}{|c|c c c|} \hline \(x_{2}\) & \(x_{3}\) \\ \hline \(x_{1}\) & \(1/2\) & \(1/4\) & \(1/4\) \\ \(x_{2}\) & \(1/4\) & \(1/2\) & \(1/4\) \\ \(x_{3}\) & \(1/4\) & \(1/4\) & \(1/2\) \\ \hline \end{tabular}   \\ \end{tabular}  
_Note that the exponential mechanism here actually satisfies \(\log(\frac{16}{7})\cdot d_{E}\)-privacy even though it is specified by \(\varepsilon=\log(4)\)._  
We now have three families of mechanisms which we can characterize by channels, and which satisfy \(\varepsilon\cdot d_{E}\)-privacy. For the remainder of this section, we will refer only to the \(\varepsilon\) parameter and take \(d_{E}\) as given, as we wish to understand the effect of changing \(\varepsilon\) (for a fixed metric) on the various leakage measures.
# Multi-Hop Routing Protocols for Oil Pipeline Leak Detection Systems
## 2 Related Work
### Multi-Hop Routing Protocols for Leak Detection Systems
After that, CH aggregates and relays the data to the base station through other CHs. This protocol increases the range of transmission without consuming more power. However, although it provides scalability and low energy, it increases network traffic. Additionally, the author in [39] introduced a Distributed Algorithm for Multi-Hop Communication (MH-LEACH). This protocol reduces energy by decreasing the transmission ranges. The CH sends the data packets to the nearest neighbor MH-LEACH has low power consumption, at the cost of generated network traffic volumes. Table 3 shows a comparison of Hierarchical Multi-Hop Routing Protocols. For inter and intra-communication, each protocol may apply single-hop or multi-hop routing. The route from the node to the leader node is an intra-communication, and the inter communication is from the leader node and the base station.
# In Operando Health Monitoring for Lithium-Ion Batteries in Electric Propulsion Using Deep Learning
## 2 Development of BMS
### Sensor Measurement
#### 2.1.1 Current and Voltage Measurement  
The current measurement was performed using a shunt resistor inserted into the current path called a high-side current sensor. It measures the current between the positive terminal of the power supply to the load. A current-voltage sensor INA219 was chosen due to its extensive monitoring capabilities, including the ability to handle variable pack voltages, extreme high and low voltages of the battery (from 0 to 26 V), and a standard current measurement range of \(\pm\)3.2 A; the chosen sensor provides current resolution of 8 mA and uses inter-integrated circuit communication [32; 33]. To accommodate real-time scenarios where the current discharge from the battery exceeds \(\pm\)3.2 A, the default 0.1 \(\Omega\) current shunt resistor was substituted with a 0.01 \(\Omega\) resistor. This adjustment enabled the system to accommodate a maximum current range of \(\pm\)32 A. The current-voltage sensor's maximum voltage differential of 0.32 V drove the choice of the 0.01 \(\Omega\) resistor, corresponding to a maximum current range of \(\pm\)32 A when divided by the voltage differential according to Equations (1)-(4). A Datasheet of the INA 219 current and voltage sensor was used for programming this sensor to develop our BMS.  
\[V_{shunt}=V_{Int}^{+}-V_{In}^{-} \tag{1}\]  
\[V_{shunt_{max}}=0.32\;\mathrm{V} \tag{2}\]  
\[R_{shunt}=0.01\;\Omega \tag{3}\]  
\[Maximum\;Possible\;Current\;(I)=\frac{V_{shunt}}{R_{shunt}}=\pm 32\;\mathrm{A} \tag{4}\]
## 5 The Hybrid Predictor
### Random Forest Model  
The second component of our hybrid predictor is the Random Forest (RF) model. This model is used to correct the forecasting errors produced by ARIMA by detecting non-linear patterns in the incoming data. These non-linear patterns often arise from factors that influence workload but do not follow a clear temporal sequence--factors that ARIMAis unable to capture. Such factors may include external events, user behavior, or other contextual variables. To address these limitations, the RF model operates on an engineered feature set defined as \(X_{t}=[h_{t},w_{t},r_{t-1},r_{t-2}]\), which includes:  
* Time-based features: These features capture periodic patterns or contextual influences that ARIMA cannot model directly, thereby accounting for time-sensitive external events.
* Lagged residuals: \(r_{t-1},r_{t-2},\ldots\) represent historical prediction errors that ARIMA fails to capture, such as sudden workload spikes. Incorporating these residuals allows the RF model to compensate for ARIMA's linearity assumptions.  
We compute the non-linear residual \(\hat{r_{t}}\) using Random Forest regression [29], which averages predictions across \(B\) decision trees:  
\[\hat{r_{t}}=f_{rf}(X_{t})=\tfrac{1}{B}\sum\limits_{b=1}^{B}T_{b}(X_{t}) \tag{8}\]  
where \(T_{b}\) is the \(b\)-th decision tree. The final prediction of the hybrid model is the combination of ARIMA's linear forecast and the RF's correction of non-linear residuals:  
\[Y_{t}=\underbrace{f_{linear}(Y_{t-1},Y_{t-2},\ldots)}_{ARIMA}+\underbrace{f_{ non-linear}(X_{t})}_{RE}+\epsilon_{t} \tag{9}\]  
where \(\epsilon_{t}\) represents undefined errors. The main advantage of this hybrid approach--coupling ARIMA with RF--lies in the RF's ability to reduce the variance that ARIMA fails to model. This benefit is expressed as:  
\[Var\Big{(}Y_{t}-\hat{Y}_{t}^{Hybrid}\Big{)}<Var\big{(}Y_{t}-\hat{Y}_{t}^{ARIMA }\big{)} \tag{10}\]  
while ARIMA-RF hybrids exist in forecasting the literature, we design our predictor to suit the serverless environment specifically through incorporating RF with explicit time-based features that represent external events based on the domain knowledge, in addition to SLA-triggered retraining when cold starts exceed 5% or prediction RMSE is greater than 10%. These adaptations are essential for serverless, where infrastructure dynamics dominate performance.
## 4 Discussion and Conclusions  
In this paper, we propose the EG method for mining relatively important nodes based on the greedy strategy of edge importance. This method measures the importance of the edges of known important nodes, and uses the most important edges to find nodes that are closely related to the current important nodes. The EG method does not calculate or consider network paths, and thus avoids the limitations of some methods that consider path information only. By comparing with the existing methods of mining relatively important nodes, such as NN, RD and Katz, based on the indicator of \(AUC\), we proved the performance and the feasibility of the EG algorithm.  
Although the adaptability of our proposed algorithm to different networks has been discussed, we still consider it necessary to further analyze the impact network topology has on an algorithm in a more detailed and systematic way, in order to obtain better guidance for the use of the EG algorithm. The EG method is designed for undirected networks, and does not consider applications in directed or weighted networks. Extending the EG method to directed graphs, weighted graphs, or even time series networks could be another possible future direction. Lastly, it seems that the EG method does not possess a rigorous physical or mathematical meaning; some theoretical work can be carried out surrounding this issue in the future.  
Conceptualization, J.L. and C.Y.; methodology, J.L.; software, J.W.; validation, J.L., C.Y. and H.W.; formal analysis, J.L.; investigation, J.W.; resources, N.Z.; data curation, C.Y.; writing--original draft preparation, J.L. and H.W.; writing--review and editing, J.L. and H.W.; visualization, J.W.; supervision, N.Z.; project administration, N.Z.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Special Plan of Yunnan Province Major Science and Technology Plan (202102 AA100021), the National Natural Science Foundation of China (62066048), the Yunnan Natural Science Foundation Project (202101 AT070167) and the Open Foundation of Key Laboratory in Software Engineering of Yunnan Province (20205E311).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Micromachining in Powder-Mixed Micro Electrical Discharge Machining
## 5 Variation of Power-mixed \(\upmu\)-EDM
### Power-Mixed \(\mu\)-EDM Combined with Tool and Workpiece Vibration  
The introduction of powder into the dielectric fluid has indeed improved the machining time [77] and surface quality. However, due to the presence of powder in the dielectric fluid which may induce short circuits, [PERSON] et al. [78] used a probe-type vibrator to assist the fabrication of deep microholes of ceramic material during the \(\upmu\)-EDM machining process. The results showed that utilizing ultrasonic vibration only is insignificant to improve the depth of microholes. Thus, carbon nanofibers were added into the dielectric fluid to enhance the performance of \(\upmu\)-EDM in terms of material-removal rate, maximum machining depth, surface topography, hole geometry and process stability. Ultrasonic vibration enhances the stirring effect to uniformly distribute the carbon nanofibers and flush out the debris from the machining gap. [PERSON] et al. [79] aided the workpiece vibration on nano graphite powder-mixed dielectric fluid of \(\upmu\)-EDM processes, as illustrated in Figure 10. Several powder concentrations (0 g/L, 5 g/L, 10 g/L, 15 g/L, 20 g/L) were used to investigate the effect of workpiece vibration in improving the performance of powder-mixed \(\upmu\)-EDM processes. The addition of powder at high concentrations may lead to unstable discharge and increase the occurrence of short-circuits during the machining process. The results showed that the highest machining time was achieved by combining workpiece vibration and nano graphite-powder-mixed dielectric fluid at concentration of 20 g/L. The improvement was due to workpiece vibration which reduced the arcing and short-circuiting caused by the excessive powder concentration.  
Figure 10: Workpiece vibration-assisted-powder-mixed \(\upmu\)-EDM.  
Figure 9: Ultrasonic vibration of the dielectric assisted powder-mixed \(\upmu\)-EDM.  
Finally, Figure 11 shows a simple illustration to improve the understanding of the variation of powder-mixed \(\upmu\)-EDM in machining processes.
## 4 Materials and Methods
### Virulence Gene Detection and Phylogenetic Group Determination  
The UPEC collection was tested for the presence of virulence factors: FimH, Sfa, PapC, IbeA, HlyA, CNF1, Iss, SitA, the presence of Ag43 antigen, and phylogenetic group membership, which were determined by PCR. All amplified products were visualized in 1% agarose in 0.5%TAE buffer (20 mM Tris pH 8.3, 0.5 mM EDTA, 10 mM acetic acid). RotiSafe dye (CarlRoth) was used to visualize the DNA. The details of primer (Sigma Aldrich) sequences and predicted sizes of amplified products are given in Table 4. UPEC isolates were assigned to separate groups (A, B1, B2, C, D, E, and F), according to [PERSON]'s classification. The presence or absence of \(arpA/chuA/yjaA/TspE4.C2 genes was assessed using quadruplex PCR. Separate primers ArpAgEp and trpAgpC were used to determine allele-specific E and C phylogenies, respectively. PCR amplifications were performed in an Eppendorf TM Mastercyler proS thermocycler under the following conditions: initial denaturation at 94 \({}^{\circ}\)C for 4 min, 30 cycles of 5 s at 94 \({}^{\circ}\)C, and 20 s at 57 \({}^{\circ}\)C (E-group), or 59 \({}^{\circ}\)C (quadruplex and C-group), 72 \({}^{\circ}\)C for 1 min, and final extension at 72 \({}^{\circ}\)C for 5 min. Based on the genotypes obtained, the strains were grouped following the principles described by [PERSON] et al. in 2013 [PERSON] et al. (2013). The presence of virulence factors encoding fimbrie P (\(papC\)), fimbrie type 1 (_fimH_), fimbrie S (_sfa_), cytotoxic necrotizing factor 1 (_cntf_), hemolysin (_hlyA_), and aerobactin (_aer_) was determined by PCR, where reaction conditions included initial denaturation for 5 min at 94 \({}^{\circ}\)C, 30 cycles of 30 s at 94 \({}^{\circ}\)C, 30 s at 60 \({}^{\circ}\)C, and 5 min at 72 \({}^{\circ}\)C, with a final elongation for 10 min at 72 \({}^{\circ}\)C. The reaction conditions for the _iss_, _sitA_, _ibeA_, and _agn_43 primers were as described previously ([PERSON] et al., 2013; [PERSON] et al., 2013).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**PCR Reaction** & **ID** & **Target** & **Primer Sequence (5-3)** & **Final Concn. (\#M)** & **Product** & **Reference** \\ \hline \multirow{8}{*}{Quadruplex} & chuA.1b & \multirow{3}{*}{_dynA_} & \(S^{\prime}\)-ATGCGTACCGGACGAACCA-3\({}^{\prime}\) & & \multirow{3}{*}{288} & \\ \cline{2-6}  & chuA.2 & & & & & \\ \cline{2-6}  & yjaA.1b & \multirow{3}{*}{_yiA_} & \(S^{\prime}\)-CAAACGTG
## 2 CFD Simulation and FMT Validation
### FMT Validation  
A flow model test was constructed to verify the CFD simulation results. Figure 3 indicates the platform of FMT experiments.  
Figure 3: The platform of flow model test (FMT) experiments.  
Figure 2: Meshing geometry of the representative area.  
The model of the tower type SCR-deNO\({}_{x}\) facility was made of transparent plexiglas with a scale of 1:30. A perforated plate was used to represent the pressure drop of the catalyst layers. Slices made of aluminum were used as the baffles, AIG, hybrid grid, and straightener. During the experiments, chemical reactions were neglected. Air and carbon dioxide (CO\({}_{2}\)) were substituted for flue gas and NH\({}_{3}\). A Testo-445 multi-function measuring instrument with different probes was used to measure the variables. A vane probe was used to measure the air velocity. Its measuring range is 0.6 to 40 m/s with a resolution of 0.01 m/s. The corresponding accuracy is \(\pm\)(0.2 m/s \(\pm\) 1.5% of measurement value) (+0.6 to +40 m/s). A CO\({}_{2}\) probe was used to measure the CO\({}_{2}\) concentration. Its measuring range is 0 to 10,000 ppm with a resolution of 1 ppm. The corresponding accuracy is \(\pm\)(500 ppm + 2% of measurement value) (0 to +5000 ppm) and \(\pm\)(100 ppm + 3% of measurement value) (+5000 to +10,000). In addition, a pressure probe was used to measure the pressure loss. Its measuring range is 0 to 100 hPa with the resolution of 0.01 hPa. The corresponding accuracy is \(\pm\)0.1 hPa (0 to +20 hPa) and \(\pm\)0.5% of measurement value (+20 to +100 hPa). Figure 4 indicates the 64 detection spots that are arranged as an 8 \(\times\) 8 matrix over the perforated plate.  
The experiments were repeated five times at room temperature while the air inlet velocities were modified to close to 14.75 m/s, which is in accordance with the case of 100% BMCR condition. The experimental results are shown in Appendix B. The average values of air velocity, CO\({}_{2}\) concentration, and pressure loss were calculated and recorded. Comparisons are presented between the results of the FMT experiments and CFD simulations as a validation of the optimal design.
## 4 Our Proposed Algorithm
### Datasets
#### 4.1.2 Spatial Compressibility  
In this section, we pick up two temperature subsets from LUCE-EPFL and CitySense that are representative of the other datasets to approximately estimate spatial marginal and conditional entropy. The cumulative distribution functions (CDF) are provided in Figures 1 and 2. In Figure 1, the red star curve represents CDF of marginal entropy, while the blue triangle curve is CDF of conditional entropy on LUCE-EPFL datasets. As can be seen in Figure 1, the values of marginal and conditional entropy are less than 3 bits. It is worth noting that conditional entropy is only about 0.7 bit. There is a big gap between the CDF curve of conditional entropy and the CDF curve of marginal entropy, which demonstrates that data storage space can be compressed considerably. Similarly, in Figure 2, the CDF of conditional entropy from CitySense is much smaller than that of marginal entropy. The same phenomenon with CitySense indicates the spatial compressibility of the sensor nodes' readings in WSNs. We especially study five different datasets divided into classes based on the physical conditions and calculate the marginal entropy per signal. The results are recorded in Table 2. In Table 2, the first column indicates the marginal temperature entropy of different datasets. The entropy range is 2.5 bit to 3.1 bit. The other columns are humidity, solar radiation, wind, water, light, and voltage. The minimum marginal entropy is 0.592 bit of DEI light, while the maximum is 3.256 bit of CitySense wind in general. Therefore, it is inadvisable to store sensor node readings with 32 bit or 64 bit, that is to say, sensor node readings are compressible to some extent.  
Figure 1: Spatial compressibility of LUCE-EPFL temperature.
## 5 Experimentation and Analysis
### Analysis of Experimental Arithmetic
#### 5.4.5 Comparison of Different Signal Decomposition Algorithms  
In order to avoid the chance of experiments, in this experiment, one week's data in four seasons without weather and other influencing factors are also selected and compared with the different signal decomposition algorithms. The experimental results show that the MVMO-LSTM prediction system outperforms the other traditional signal decomposition algorithms. Figure 9 and Table 4 summarize the various performance metrics of the proposed hybrid model. More in-depth details are provided below. Among them, the performance metrics of the improved model proposed in this study have been bolded in bold.  
1. Based on Figure 9 and Table 4, it can be concluded that the efficiency of the proposed hybrid prediction system is better than the LSTM model based on signal decomposition such as VMD, and the mean values of the evaluation metrics are RMSE = 0.1564 KW, MAE = 0.1216 KW, and R\({}^{2}\) = 99.20%, respectively. In addition, the model metrics with VMD were superior to the other four hybrid models as far as winter is concerned. The evaluation metrics were RMSE = 0.4572 KW, MAE = 0.3871 KW and R\({}^{2}\) = 96.02%.
2. In addition, the CEEMDAN-LSTM outperforms the other three hybrid models in all three seasons: spring, summer and fall. The average RMSE of the three seasons = 0.4012 KW. MAE = 0.3045 KW. R\({}^{2}\) = 97.89%. Due to the multi-strategy optimization of the dung beetle algorithm used in this study, the k-value of the VMD as well as the penalty factor were optimized. In addition to this, the parameters of LSTM are optimized. As for the MVMO-LSTM, this hybrid model is optimal in various experimental comparisons for various performance metrics. Thus, the proposed prediction model in this study provides better potential.