esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification, Control, and Characterization of Peristaltic Pumps in Hemodialysis Machines
## 2 Methodology
### Statistical Analysis and Validation of Results  
This section describes the statistical approach adopted to validate the accuracy and reliability of the results obtained in the study. This analysis aims to provide a quantitative basis that supports the superiority of the developed system compared to a commercial hemodialysis machine and to evaluate the performance of the RPM estimates against the flow values in mL/min.  
To evaluate the system's performance and the accuracy of the characterization functions that relate RPM to flow values in mL/min, three main metrics were employed. The RMSE was used to measure the magnitude of the estimation error, especially penalizing larger errors. The average error was used to evaluate the overall system performance in relation to the target values, while the standard deviation quantified the variability in the estimates, providing a measure of the system's stability.  
Another important metric was the calculation of precision ratios, defined as the relationship between the measured flow and the desired flow. These ratios evaluate the system's efficiency in maintaining flows close to the target, a critical characteristic in clinical applications where any deviation can impact patient safety.  
To determine whether the observed differences between the developed system and the commercial machine were statistically significant, a paired \(t\)-test focused on the errors was conducted. This test compares the average errors of both systems under the same experimental conditions, providing formal validation of the proposed system's superiority. Error measurements were collected for both the developed system and the commercial machine across the same flow ranges (20-600 mL/min), which will be detailed later Each flow setpoint included repeated measurements to ensure robustness and consistency in the results. For each flow range, the absolute error of the developed system was subtracted from the absolute error of the commercial machine to compute the error differences. These differences served as the basis for the paired \(t\)-test, which evaluated whether the mean of these differences was significantly different from zero.  
The analysis ensured accurate computation of the \(t\)-value and \(p\)-value. A \(p\)-value below 0.05 was considered evidence of statistically significant differences in the errors, validating the developed system's improved performance.  
Based on preliminary results, it is anticipated that the developed system will show significantly lower errors than the commercial machine, supporting its superior precision. Additionally, the ratios are expected to exhibit consistent values close to the ideal in both systems, highlighting their capability to maintain precise flows. This statistical approach complements the experimental measurements and reinforces confidence in the obtained results.
# Prefix Data Augmentation for Contrastive Learning of Unsupervised Sentence Embedding
## 5 Ablation Studies
### Determining Positive Prefixes  
The primary criterion for positive prefixes is that they should not distort the original semantics of the sentence. Before specific experiments, we deployed a semantic similarity calculation tool using the pipeline of the Transformers package. By adding prefixes to several example sentences and calculating their similarity to the original sentences, we selected sentence pairs with the highest semantic similarity to preliminarily determine several positive prefixes. For the convenience of later discussion, the abbreviations and meanings of these positive prefixes are explained here. one-[CLS], one-[MASK], one-um: a single [CLS], a single [MASK], and a single 'um' as prefixes, respectively. level-[CLS], level-[MASK], level-um: As sentence length increases, the number of prefix words also gradually increases, as demonstrated in the Table 8 outlining the specific rules. Subsequently, we used the corresponding positive prefixes for data augmentation in sentence embedding learning. The experimental results are shown in Table 9, where the positive prefix \"level-um\" achieved the best results. Additionally, using \"level-[CLS]\" as a positive prefix also yielded accurate results, whereas \"one-[MASK]\" performed poorly. Other prompting sentences led to a decrease in performance.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **STS-B** \\ \hline SimCSE \(\heartsuit\) & 82.5 \\ +PosPrd & 83.4 \\ +NegPrd & 82.9 \\ PrdSimCSE & 83.5 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Improvement on STS-B development sets produced by PosPrd or NegPrd over SimCSE. \(\heartsuit\): results from [13].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sentence Length** & **Number of Words Added as Prefix** \\ \hline \(<\)8 & 0 \\ \([8,16]\) & 1 \\ \([16,24]\) & 2 \\ \([24,32]\) & 3 \\ \(\geq\)32 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Relationship between sentence length and number of words added as prefix
## 3 Description of Related Problems  
### Traffic Signal Control  
Figure 1 shows the layout of a common crossover with isolated intersections. Each arm has vehicles that travel in three different directions: left, straight ahead, and right. There is a dedicated CAV lane on each arm for straight-ahead and left-turn CAVs. Right-turn traffic signals are not used to govern CAVs and HVs as they operate in the right-turn lane. There is a passing zone and an adjustment zone on each arm close to the intersection. A CAV variable virtual waiting zone is set in the intersection in front of the passing zone, and the precise physical location of the signal intersection determines the size of the virtual waiting zone. the direction of the virtual waiting zone is determined by the control system. The CAVs are adjusted by the adjustment zone to form a queue and wait to enter the passing zone and variable virtual waiting zone. The CAV queue then follows a planned trajectory through the intersection without stopping in the dedicated lane formed by the passing zone and the variable virtual waiting zone. The CAV queue composition, the time for each CAV queue to enter the dedicated CAV lane, the CAV speed planning through the passing zone and the variable virtual waiting zone, the intersection signal timing, and the variable virtual waiting zone switching time are all optimized in one framework. The CAV queue not only takes advantage of the network connection of the network-connected vehicles and shortens the vehicle spacing, but also simplifies the amount of computation for CAV trajectory optimization, laying a realistic foundation for the implementation of the variable virtual waiting zone. To examine whether the model is valid, presumptions are made as follows:  
1. HV does not change lanes in the adjustment zone and the passing zone, and the adjustment zone and passing zone are equipped with basic sensing devices, which can collect or predict the arrival information of CAV and HV at the intersection in the next signal cycle.
2. The number and location distribution of HVs can be detected by roadside infrastructure.
3. CAVs can change lanes and complete formations in the adjustment zone.
4. Each arm has a dedicated CAV lane.
5. A passing zone is allowed within the intersection.
# An Improved Hilbert-Huang Transform for Vibration-Based Damage Detection of Utility Timber Poles
## 5 Results and Discussion
### Improved HHT
#### 5.3.1 Mother Wavelet Selection  
Since discrete wavelet packet is selected for this study, mother wavelet like Meyer, Gaussian, Mexican hat, and other complex wavelets cannot be used [39]. For the analysis, a total 32 mother wavelet functions are considered as shown in Table 5. To select the  
Figure 13: Instantaneous frequencies of A90C1 Serviceable Pole 1 generated by original HHT.  
best mother wavelet, 12 accelerometer data of Serviceable Pole 1 are used in the analysis. First, the dominant wavelet coefficient is obtained based on the maximum energy level. Maximum energy level is found for the first level approximation coefficient (A1,0) for all the accelerometers. Next, the Shannon entropy value is computed for each accelerometer data with respect to each mother wavelet using the dominant wavelet coefficient (A1,0). Energy of the original raw signal from each accelerometer is also calculated. Then the ratio of the energy to Shannon entropy value (\(R(s)\)) is determined for each mother wavelet using Equation (21). Table 6 shows the sample results of 11 mother wavelet functions which have the highest energy to Shannon entropy value among 32 mother wavelets analysed for this study. From the analysis, it is found that bior5.5 wavelet has the maximum ratio of energy to Shannon entropy (\(R(s)\)) and that is why it is chosen as the best mother wavelet for our study.
# Ensuring Stable Operation of Wind Farms Connected to Distribution Networks
## 4 Results
}=\frac{C_{\text{add,cap}}\times U_{\text{d}}^{2}}{U_ {\text{d supercap}}^{2}}=\frac{0.17\times 800^{2}}{2.7^{2}}=14,924\text{ F}\]  
Given the obtained calculated capacitance value, we use five Maxwell 2.7 V 3000 F supercapacitors and one 10 kW DC-DC converter 2.7 V/800 V. The cost of a set of equipment for one wind turbine (five supercapacitors and one DC-DC converter) will be about $3000. Considering that six wind turbines are installed at the wind farm under consideration, the total cost of the equipment set will be $18,000, which is approximately 17 times cheaper than the cost of the CLR. The cost of equipment for the implementation of the proposed technical solution at each wind turbine is significantly lower than the cost of the CLR, and even more so than the cost of STATCOM. However, in order to maintain warranty obligations for the wind turbine, its implementation should be agreed with the wind turbine manufacturer. The considered four groups of methods make it possible to prevent unnecessary shutdowns of wind turbines in case of typical disturbances in distribution networks, but they vary in technical and economic efficiency. The first group provides for the reconstruction of relay protection devices and automation in the distribution network adjacent to the wind farm. This can be quite costly for the grid company, and it takes a long time to complete the renovation project and carry out the work. The second group provides for the activation of the wind turbine reactive current injection function, which is low-cost and highly efficient but often insufficient to solve the problem of excessive wind turbine shutdowns. The desired effect is achieved by the joint implementation of other methods. The third group provides for the installation of additional electrical devices at wind farms. This can be a costly solution for wind farm owners if a STATCOM installation is required, while placing a CLR is less expensive but technically and economically efficient in small wind farms with a small number of wind turbines. The fourth group requires changes in the hardware of wind turbine inverters. To implement these methods, it is crucial to have an agreement with the wind turbine manufacturer, especially if there are warranty obligations involved. The wind farm owner, however, can make this decision independently since it is low-cost  
Figure 10: Simplified scheme for connecting a supercapacitor with a DC-DC converter to the DC link of a wind turbine inverter. and highly efficient. The choice of one or a group of methods for a specific wind farm should be made in the design process based on the results of transient calculations and a feasibility study of the various options. For each specific wind farm, the set of effective methods can vary greatly.
###### Abstract  
This work proposes a new architectural approach to enhance the security, privacy, and scalability of VANETs through threshold key management and Privacy Enhancing Technologies (PETs), such as homomorphic encryption and secure multiparty computation, integrated with Decentralized Ledger Technologies (DLTs). These advanced mechanisms are employed to eliminate centralization and protect the privacy of transferred and processed information in VANETs, thereby addressing privacy concerns. We begin by discussing the weaknesses of existing VANET architectures concerning trust, privacy, and scalability and then introduce a new architectural framework that shifts from centralized to decentralized approaches. This transition applies a decentralized ledger mechanism to ensure correctness, reliability, accuracy, and security against various known attacks. The use of Layer 2 DLTs in our framework enhances key management, trust distribution, and data privacy, offering cost and speed advantages over Layer 1 DLTs, thereby enabling secure vehicle-to-everything (V2X) communication. The proposed framework is superior to other frameworks as it improves decentralized trust management, adopts more efficient PETs, and leverages Layer 2 DLT for scalability. The integration of multiparty threshold key management and homomorphic encryption also enhances data confidentiality and integrity, thus securing against various existing cryptographic attacks. Finally, we discuss potential future developments to improve the security and reliability of VANETs in the next generation of networks, including 5G networks.  
VANETs; Homomorphic Encryption; Decentralised Ledger Technologies; Layer 2 DLT
## 4 Results and Discussions
### Performance Metrics  
The evaluation of predictive performance in this study was conducted using metrics such as F1 score, accuracy, recall, and precision.  
The equations representing each evaluation index are shown below.  
\[\text{Recall}\left(\text{R}\right)=\frac{\text{True Positive}}{\left(\text{ True Positive}+\text{False Negative}\right)} \tag{15}\]  
\[\text{Precision}\left(\text{P}\right)=\frac{\text{True Positive}}{\left(\text{ True Positive}+\text{False Positive}\right)} \tag{16}\]  
\[\text{Accuracy}\left(\text{ACC}\right)=\frac{\text{TP}+\text{TN}}{\text{TP}+ \text{TN}+\text{FP}+\text{FN}} \tag{17}\]\[\text{F1 score}=2*\frac{\text{P}*\text{R}}{\text{P}+\text{R}} \tag{18}\]
## 3 Results  
### De Novo Assembly and Annotation Stats for C. keiskei  
RNA-sequencing libraries were constructed from the leaf, flower, and root to perform transcriptome analysis and study the distribution of terpenoid synthase genes and their by-products in different tissues of _C. keiskei_. The raw reads obtained from these libraries were deposited in the NCBI-SRA database under accession numbers SRR20982553-55. The highest number of unigenes (146,550) was obtained from the root, followed by the flower (116,434), and the lowest number of unigenes was observed in the leaf (72,044). The quality of the assembled transcripts was assessed using BUSCO, which revealed that the flower had 99% complete transcripts, while the leaf and root had 98% complete transcripts. The duplicated gene content was also identified. The overall assessment and annotation analysis indicated that the transcripts were of good quality (Supplementary Figure S2). The _A. thaliana_ database from TAIR was used for analysis and annotation, and approximately 20% of the genes from the transcriptome database were retained. Annotation of the protein family database (Pfam) annotated approximately 30% to 52.2% of the genes. KEGG analysis was performed to identify pathways associated with the unigenes, and 40% to 51.03% of the unigenes were successfully assigned to pathways. The contigs from the three transcriptomes were integrated and de novo assembled, resulting in a total of 237,129 unigenes. These assembled unigenes were then annotated using BLAST against three public databases (TAIR, KEGG and Pfam). Out of the total unigenes, 138,029 (35.17%)were successfully annotated, with at least one functional gene in the leaf, flower and root of _C. keiskei_ (Table 1).
## 7 Results and Performance Evaluation
### Performance Evaluation

Table 10: Comparison of TDR% (proposed model) and ‘Detection Rate %’( model by [PERSON] _et al._). The asterisk mark (*) shows those readings where the model by [PERSON] _et al._ is better than the proposed model. Elsewhere, the proposed model’s performance is either matching or better. \begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|} \hline \multicolumn{2}{|c|}{**Type of jammer**} & \multicolumn{3}{c|}{**FDR% (proposed model) and ‘False Positive Rate \%’( model by [PERSON] _et al._)**} \\ \hline \multirow{3}{*}{**Proposed model**} & \multirow{3}{*}{**[PERSON] _et al._**} & **jnr** & \multicolumn{2}{c|}{**jnr**} & \multicolumn{2}{c|}{**jnr**} \\  & & **25\%** & \multicolumn{2}{c|}{**50\%**} & \multicolumn{2}{c|}{**100\%**} \\ \cline{3-8}  & & **Proposed** & **[PERSON]** & **Proposed** & **[PERSON]** & **Proposed** & **[PERSON]** \\ \hline \multirow{3}{*}{RADECH} & Random (bad) & 0.7 & 0.8 & 0.4 & 0.51 & 0 & 0 \\  & - & 0.6 & - & 0.3 & - & 0 & - \\  & - & 0.5 & 0.57 & 0.3 & 0.4 & 0 & 0 \\  & - & 0.35 & 0.38 & 0.02 & 0.04 & 0 & 0 \\  & - & 0.28 & 0.3 & 0.12 & 0.13 & 0 & 0 \\  & - & 0.06 & - & 0.05 & - & 0 & - \\  & - & 0.04 & 0.05 & 0.03 & 0.03 & 0 & 0 \\  & - & 0.02 & Not clear & 0.02 & Not clear & 0 & 0 \\  & - & 0.02 & Not clear & 0.01 & Not clear & 0 & 0 \\  & - & 0.01 & Not clear & 0.01 & Not clear & 0 & 0 \\ \hline \end{tabular}
\end{table} 
Table 11: Comparison of FDR% (proposed model) and ‘False Positive Rate %’( model by [PERSON] _et al._). The proposed model’s performance values are either better or matching with those of the model by [PERSON] _et al._not communicate at all, or communicate only very rarely. In such a scenario, the PDPT could be very low, and in the worst case it could be zero. However, the SNR may or may not be low. Therefore, both PDPT and SNR will definitely have some value as defined by Equation 2 and Table 2. Accordingly, one of the nine rules given in the rule base under Section 5.1.1.2 (most probably, either of Rule 1, or Rule 4, or Rule 7) will be invoked, and the JI will be computed accordingly without any prejudice to the accuracy of the jamming detection. We thus conclude that the method is effective for all types of traffic patterns.
# Adaptive 3D Imaging for Moving Targets Based on a SIMO InISAR Imaging System in 0.2 THz Band
## 6 Conclusions  
In this paper, a scheme to achieve 3D images of moving targets in THz band is proposed base on a SIMO InISAR imaging system. Based on the phase-comparison monopulse technique, the angular information of the targets is determined using the phase response difference in different receiving channels, which enables a real-time tracking of the moving targets with high accuracy. The continuous observation during tracking provides a foundation for high-resolution imaging. A combined motion compensation method was developed to accomplish the ISAR imaging, for the purpose of maintaining a good coherence among the images between different channels. The proposed scheme and methods were validated by the proof-of-principle experiments with a 0.2 THz SIMO system in the laboratory environment. The moving targets were tracked with a high angular accuracy and stability, based on which well-focused ISAR images and clear 3D images are obtained. Compared with the current THz imaging systems, this paper attempts acquiring the 3D images for moving targets for the first time. The promising results shown in the paper reveal the potential applications in tracking and imaging non-cooperative moving targets in many areas, like the supervision and identification of military targets or space debris.  
[PERSON] performed theoretical study, conducted the experiments, processed the data and wrote the manuscript. [PERSON] designed the imaging system and revised the manuscript together with [PERSON], [PERSON] helped performing the experiments. [PERSON] provided the experiment equipment and funds for the research. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (2018 YFF01013004, 2017 YFA0701004, 2018 YFB2202500), and the National Natural Science Foundation of China (61671432, 61731020 and 61988102), and the Key project of equipment pre-research Fund (6140413010401), and the Science and Technology Key Project of Guangdong Province, China (2019B010157001), and the Key Program of Scientific and Technological Innovation from Chinese Academy of Sciences (KGFZD-135-18-029).  
Not applicable.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
Figure 13: ISAR and InISAR imaging results of the moving target in the right window. (**a**) 2D ISAR image, (**b**) 3D InISAR image.  
**Acknowledgments:** The authors would like to thank the editors and reviewers for their efforts to help the publication of this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Optimal Clustering in Stable Instances Using Combinations of Exact and Noisy Ordinal Queries
## 3 Clustering in Stable Instances
### Dynamic Programming
#### 3.4.1 Basic Notation and Adaptations  
First of all, observe that the algorithm in [3] applies to a more general problem in which we have a generic function \(g(u,d)\) applied to the distance function \(d\), and a function \(f(c)\) that indicates the cost of having \(c\) as a centroid. Therefore, we instantiate these two functions according to our problem, that is, we consider \(g(u,d)\equiv d\) and \(f(c)\equiv 0\).  
Before starting with the actual algorithm, we have to transform our tree \(T_{X}=(X,E_{X})\) in a binary tree \(B_{X}=(V_{B},E_{B})\) using the procedure described in [3] (select an arbitrary node as the root and then add nodes in a top-down fashion to get a binary tree). In the following, we use \(X\) as the set of the original points and \(U\) as the set of the points that we add so that \(V_{B}=X\cup U\). One can easily check that, for a node \(x\) that has \(c(x)\) children, we have to add \(c(x)-2\) nodes if \(x\) has more than two children and one if \(c(x)\) equals one. Note that the number of nodes is \(|V_{B}|=|X\cup U|=O(n)\), and thus we can still denote with \(n\) the input size and count the number of queries as a function of \(n\).  
We define \(L\) the set that contains all the leaves of \(B_{X}\), and we create two sequences \(S\) and \(S^{\prime}\) of the nodes in reverse breadth first order from the root, where \(S^{\prime}\) contains only the non leaf nodes.  
Then, we build a data structure \(Cost\) where, at each key \((u,j,c)\), we encode information to evaluate the optimal solution for partitioning the subtree \(B_{u}\) rooted at \(u\) into \(j\leq k\) clusters with \(c\) being the centroid of node \(u\) (here \(B_{u}\) is the set of nodes in subtree rooted at \(u\)). In particular, for every key \((u,j,c)\), the data structure \(Cost\) contains a set \(E(u,j,c)\) of edges of the tree that represents the connections of nodes in subtree \(B_{u}\) to their clusters in that particular solution, and an integer variable \(s(u,j,c)\) that represents how many of these centers are from \(U\), and thus how \"illegal\" is the solution (we have to add this variable since we set \(f\) to be always equal to zero). This is in contrast with the original dynamic programming in [3], where we associate only a single integer to every key.  
The last thing we have to do is to modify the output of the algorithm. In [3], it how to compute the total cost of the perfect clustering is the only thing mentioned. This of course can not be done with our model since we have no information about the absolute distance between the points. Thus, we have to further modify it in order to obtain the cluster centroid as output of the algorithm, so that we can easily compute the cluster by associating each point to the closest centroid. In order to achieve that, we create a second data structure \(Centroid\) indexed by keys \((u,k,c)\), where each entry contains a set of the best centroids for partitioning the sub tree rooted at \(u\) in \(k\) clusters.