esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Optimization in Cluster-Based Routing Protocols for Large-Area Wireless Sensor Networks
## 2 Preliminaries
### Cluster-Based Hierarchical Routing  
It is assumed that there are \(N\) sensor nodes deployed in a circular-shaped field with radius of \(L\) (m). The distribution of the sensor nodes is uniformly random within this area, and the deployment is independent for different nodes. All nodes are assumed to have the same sensing ability and the same initial amount of energy. The sensing data are forwarded to a remote base station (BS) by performing the employed routing protocol. The BS is assumed to be at the border or outside of the sensing area.  
In cluster-based routing protocols, time line is divided into rounds. Each round begins with the set-up phase, which is composed of CH selection and control message communications. A random number of CHs are elected in the set-up phase of each round. The number of CHs in a round is a random variable whose expectation is \(k\). Conventional algorithms have estimated the value of \(k\) to be 5 by rule of thumb. Rounds, \(r=0,1,2,\cdots\), are grouped together in such a manner that rounds from \(r=0\) to \(r=N/k-1\) form a group of rounds (GOR), rounds from \(r=N/k\) to \(r=2N/k-1\) form another GOR, and so on. Let \(G\) be the set of nodes that have not been CHs within current GOR. Then, in round \(r\), node \(i\) (\(i=1,2,\cdots N\)) chooses to become a CH with the probability of  
\[T_{i,r}=\left\{\begin{array}{cc}\frac{k}{N-k\cdot(r\bmod\frac{N}{k})},&i \in G\\ 0,&\text{otherwise}.\end{array}\right. \tag{3}\]  
According to Equation (3), each node serves as a CH one time in a GOR. CH selection is followed by three sequential steps of transmitting control messages: (i) CHs for this round broadcast advertisement (ADV) messages; (ii) non-CH nodes transmit join-request (Join-REQ) messages; and (iii) CHs send TDMA scheduling messages to their members. This ends the set-up phase of a round.  
The set-up phase is followed by the steady-state phase in which the CHs gather, aggregate, and route the data from sensors within their cluster. If a non-CH node has failed to join a cluster in a round, it transmits its DATA to the base station (BS) directly for that round. Each member node of a cluster transmits DATA to its CH. CH nodes then perform data aggregation and finally send the aggregated data to the BS.
# A Performance-Consumption Balanced Scheme of Multi-Hop Quantum Networks for Teleportation
## 3 The Scheme of Network Architecture and Purification
### Purification Circuit for Different States
#### 3.2.1 Purification of Bell Pairs  
The standard entanglement purification protocol (EPP) is the way to reduce the influence of bit-flip noise. A Bell pair is measured to improve the fidelity of another pair. To solve the phase-flip noise, quantum privacy amplification (QPA) was proposed in [29], shown in Figure 6.  
\(U_{A}\) and \(U_{B}\) are rotation operators for \(\theta\) about x-axes, noted as \(R_{x}(\theta)\)[30].  
\[R_{x}(\theta)=e^{-j\theta X/2}=\cos\frac{\theta}{2}I-j\sin\frac{\theta}{2}X, \tag{10}\]  
where \(\theta\) is \(-\pi/2\) for \(U_{A}\), and \(\pi/2\) for \(U_{B}\). Assume that the two Bell pairs are both \(\ket{\Phi^{+}}\) with the fidelity of 1 at first. After quantum distribution, \(U_{A}\) acts on the quantum state in the source node, while \(U_{B}\) acts on the state in the destination node.  
For bit-flip noise, assume that the error probability of each qubit is \(p\), where \(0<p<0.5\). Let \(q=1-(1-p)^{2}-p^{2}\). The two nodes share \(\ket{\Phi^{+}}\) with the probability \(1-q\). The Bell pairs pre-shared by two nodes with probability are shown in Table 1.  
\(\ket{\Phi^{+}}_{c}\) is the control pair, the other is target pair. Prepare two Bell pairs \(\ket{\Phi^{+}}_{c}\otimes\ket{\Phi^{+}}\) at first. Because of the noise, four possible scenarios will thus occur for the two Bell pairs before purification. We can obtain the final results as  
\[\begin{split}\ket{\Phi^{+}}_{c}\otimes\ket{\Phi^{+}}\xrightarrow{ \text{CNOT}  
measurement and prepare new pairs until the purification succeeds. Finally, we obtain \(\ket{\Phi^{+}}_{c}\) with probability \(\frac{(1-q)^{2}}{(1-q)^{2}+q^{2}}\).  
Fidelity of entanglement states before purification is described as \(F_{initial}\),  
\[F_{initial}^{2}=(1-q)(\left\langle\Phi^{+}|\Phi^{+}\right\rangle)^{2}+q\left\langle \Phi^{+}|\[PERSON]^{+}\right\rangle\left\langle\[PERSON]^{+}|\[PERSON]^{+}\right\rangle=1-q. \tag{12}\]  
Fidelity after purification is \(F_{purify}\),  
\[F_{purify}^{2}= \frac{(1-q)^{2}}{(1-q)^{2}+q^{2}}\left\langle\[PERSON]^{+}|\[PERSON]^{+} \right\rangle\left\langle\[PERSON]^{+}|\[PERSON]^{+}\right\rangle+\frac{q^{2}}{(1-q)^{2 }+q^{2}}\left\langle\[PERSON]^{+}|\[PERSON]^{+}\right\rangle\left\langle\[PERSON]^{+}|\[PERSON]^{+}\right\rangle \tag{13}\] \[= \frac{(1-q)^{2}}{(1-q)^{2}+q^{2}}.\]  
For phase-flip noise, the calculation and result are consistent thanks to the rotation operators.
## 2 Results
A comparison of the EXAFS signal in k-space of the MCR-ALS pure spectral components with selected experimental spectra shows good agreement and reduced noise level for MCR-ALS which improves fitting accuracy, see Figure A3. Component #1 can be fitted in quite straightforward manner based on the published structure of TiO\({}_{2}\) anatase [25], using a simple single scattering approach. Best fitting agreement was achieved when including two elongated bonds in the first oxygen shell, in agreement with the typical axially elongated octahedral coordination of titanium in anatase [18; 25]. Analogous to Component #1, a first coordination shell with similar configuration (two different bond lengths with respective populations of 4 + 2 neighbours) is also observed for the other three components. The bond lengths values of Components #2 are very similar to those of Component #1, but they become more elongated for component #3, corresponding to the completely reduced sample at the end of the first discharge (EOD). On the other hand, the bond distances decrease again for Component #4, which corresponds to the sample after one complete discharge/charge cycle (EOC), even though they do not reach the values of the initial state. Our findings suggest that the initial state is not recovered after one electrochemical cycle. This is in good agreement with the absence of anatase features reformation upon desodation and irreversible crystal structural rearrangements evinced in an in situ XRD study by [PERSON] and coworkers [4]. Going from Component #1 to #3, a noticeable increase of the Debye-Waller factors is observed for both shells, in line with a gradual increase of the disorder and the presence of larger distributions of bond lengths for increasingly sodiated species. Figure 3: Fourier transform of EXAFS signal in R-space of the four MCR-ALS pure components (blue). Top curve in each plot corresponds to magnitude and bottom curve to real-part contribution. Fit of the real-space EXAFS signal (red) and the range in which the fit was carried out (green). Comparing Component #3 and #4, the Debye-Waller factors of the first two shells decrease slightly, reaching values similar to those obtained for Component #2. For the three outer shells, similar bond length as for pristine state are found, within the error range. A rise in bond length and of the Debye-Waller \(\sigma^{2}\) factors is observed with increasing sodiation, reflecting the rise of disorder upon sodium uptake. It should be noted that due their practically identical electronic structure, sodium and oxygen neighbours are virtually impossible to distinguish by EXAFS. Therefore the outer shell of component 4 has been marked as \"Ti-Na (O\({}_{3}\))\" to account for this ambiguity, see Table 1.
# PROFEE: A Probabilistic-Feedback Based Speed Rate Adaption for IEEE 802.11 bc
## 5 Experiments
### Probability Selection Algorithm  
Something to extract from Figure 7 is that each of the three estimators performs better in accuracy and convergence for a particular probability, which is the basis of the probability selector algorithm. Now, we will show the algorithm's operation that searches for a probability that provides the best estimation of the number of STAs receiving messages correctly/incorrectly, employing the silence estimator only.  
Figure 8 shows three plots of simulations in ns-3 of the probability selector algorithm (\(p_{NACK}\)) for 10, 100, and 1000 STAs positioned within a 100 m disk and the AP uses MCS(5) to transmit messages. The silence percentage (%Silences) bounds \(\left[prop_{NACK}^{inf},prop_{NACK}^{sup}\right]\) for these experiments were set between 15% and 45% as discussed before in Section 3. The red line represents the actual number of STAs that did not receive the messages correctly (which may vary over time depending on channel variations at the physical layer). The green line represents the estimation of the number of STAs that did not receive the messages correctly up to the given message (that is, using the number of silences up to that time) using the silence estimator. Every 2000 messages, the algorithm changes \(p_{NACK}\) according to Algorithm 1, and at this time, the counter of silence events restarts. This operation generates significant estimation errors after the counter restarts since it could involve divisions by a small number, as defined in Equation (7) and (8).  
Figure 8a shows that for 10 STAs, the probability selector algorithm increased the value of \(p_{NACK}\) until it reached 0.1 at the end of Frame 4, which provides a 56% of silence events. Notice that even if this percentage is higher than the upper limit (45%), 0.1 is the maximum probability allowed by the algorithm. Due to the reduced number of STAs in this simulation, the error is of, at most, one STA in a steady state. On the other hand, Figure 8b shows the results for 100 STAs (the number of STAs that did not receive the messages correctly is around 70). For 100 STAs, \(p_{NACK}\) started at 0.005 in Frame 1 and ended at 0.015 in Frame 5, where the percentage of silences reached 18.4%, within the desired range. Finally, Figure 8c shows the results for 1000 STAs. In this case, the value of \(p_{NACK}\) went up and down in various Frames until it reached 0.0015 at the end of Frame 4, achieving a 33% of silence events, which is between the 15-45% bounds. From these plots, it is possible to notice that the final value of \(p_{NACK}\) selected by the algorithm corresponds to approximately \(\frac{1}{n_{\text{NACK}}}\), which occurred without the AP having any previous knowledge about the number of STAs it is serving.  
Figure 8: _Cont._
# Wavelet-Based Optimum Identification of Vehicle Axles Using Bridge Measurements
## 5 The Selection of Mother Wavelet  
The above results were found using the Daubechies mother wavelet function, 'db2'. Two other mother wavelet functions are tested here, db5 and db8, using the same signal as before (Figure 3). The results are illustrated in Figure 6, where it can be seen that there are now multiple closely spaced peaks corresponding to the passage of each axle. Since different mother wavelet functions generate different wavelet coefficients with different local patterns of peaks, a correlation coefficient is proposed to identify the mother wavelet function that most closely matches the original signal.  
A correlation factor between the original signal and its wavelet coefficient is presented as:  
\[r=\frac{\sum(x-\overline{x})(y-\overline{y})}{\sqrt{\sum(x-\overline{x})^{2}(y -\overline{y})^{2}}} \tag{7}\]  
where x is the original signal, y is the wavelet coefficient at the optimum scale and \(\overline{x}\), \(\overline{y}\) are their respective mean values.  
Considering the Daubechies wavelet family, the correlation coefficients are calculated for nine mother wavelets, each at its optimum scale, and illustrated in Figure 7. It can be seen that the 'db2' function has the maximum correlation coefficient.  
Figure 5: Wavelet coefficient at scale 5.  
Figure 6: Optimum axle identification using different Daubechies mother wavelet functions: (a) db5, scale = 11, (b) db8, scale = 13.
# JSSTR: A Joint Server Selection and Traffic Routing Algorithm for the Software-Defined Data Center
## 4 System Model and Problem Statement
### Joint Server Selection and Traffic Routing Problem  
The joint server selection and traffic routing problem is described and formulated in this section. In that problem, the traffic refers to the data transmission between the user and the destination server. To make the calculated routing path have a low cost while the network and server load balancing are realized, the fitness of the server and routing path is defined as \(sf=l_{i}\) and \(pf=\sum\limits_{e_{i,j}\in E}(c_{i,j}+l_{i,j})\). Then, the joint server selection and traffic routing problem can be formulated as follows.  
Minimize:  
\[\sum\limits_{e_{i,j}\in E}x_{i,j}*(c_{i,j}+l_{i,j})+\sum\limits_{s_{i}\in S}y _{i}*l_{i} \tag{1}\]  
Subject to:  
\[tb_{i,j}-ub_{i,j}\geq b_{k} \tag{2}\]  
\[tr_{i}-ur_{i}\geq r_{k} \tag{3}\]  
\[\sum\limits_{y_{i}\in S}y_{i}\leq 1 \tag{4}\]  
\[x_{i,j},y_{i}\in\{0,1\} \tag{5}\]  
\(x_{i,j}\) is defined to indicate whether the link \(e_{i,j}\) is chosen as the routing path, and \(y_{i}\) represents whether the server \(s_{i}\) is chosen as the destination server. Equations (2) and (3) ensure that the remaining resource of the chosen routing path and destination server is enough for the request. Equation (4) guarantees that the request will be responded to by at most one server. Equation (5) indicates that a link or a server will either be chosen or not.  
Essentially, the routing path calculation problem in the joint server selection and traffic routing problem belong to the Multi-Commodity Flow (MCF) problem, which is a well-known NP-hardproblem. However, the joint server selection and traffic routing problem is more complex than the MCF problem as it needs to choose one server from a number of servers. Moreover, the joint server selection and traffic routing problem is also a combinatorial optimization problem, which makes it even harder. In conclusion, the joint server selection and traffic routing problem belongs to the NP-hard problems, which means that there is no polynomial-time solutions for it. Therefore, in the next session, a heuristic method is designed to find the approximate optimal solution of the joint server selection and traffic routing problem in polynomial time.
# Weak and Dynamic GNSS Signal Tracking Strategies for Flight Missions in the Space Service Volume
## 4 INS-Assisted Open Loop Tracking Strategy
### Structure of INS-Assisted Tracking Strategy
\tag{28}\]  
It is obvious that the estimation of \(\delta\phi_{0}\) and \(\delta f\) are tightly coupled. The PLL discriminator output \(\delta\phi_{0}\) is influenced by \(\delta f\), while the FLL discriminator output \(\delta f\) is influenced by \(\delta\phi_{0}\). The calculation results obtained directly from \(I_{P}\) and \(Q_{P}\) in Equation (28) are unreliable, so a non-coherent MLE-based FLL discriminator is applied in the architecture shown in Figure 4 to settle the problem.
# Robust Optimization for Household Load Scheduling with Uncertain Parameters
## Simulation Result
### Schedules with Different Robust Levels  
When the robust parameter takes different values, different solutions with different levels of conservatism can be obtained. For customers, different levels of conservatism make it possible to make trade-offs between the economy and comfort. In order to compare the comfort and economy of the schedules with different robust levels, the comfort is quantitatively described by the violation rate of the comfort constraints (see [25]), which is calculated through Monte Carlo simulation where all uncertain parameters are assumed to follow uniform distribution. Without loss of generality, the robust level \(\alpha\) (the ratio of the robust parameter to its maximum value), is defined to unify the levels of conservatism for all comfort constraints. Table 4 shows the economy and comfort of the schedules with different robust levels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Robust Level \(\alpha\)**} & **Violation Rate for the** & **Violation Rate for the** & **Electricity Bill/Economy** \\  & **Confort Constraints of AC** & **Confort Constraints of WH** & **(Vuan)** \\ \hline
1 & 0 & 0 & 12.75 \\
0.8 & 0.003 & 0.008 & 12.48 \\
0.6 & 0.024 & 0.075 & 12.20 \\
0.4 & 0.062 & 0.113 & 11.70 \\
0.2 & 0.181 & 0.643 & 11.11 \\
0 & 0.517 & 1.000 & 9.81 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The economy and violation rate of schedules with different robust levels.  
Figure 6: The complete robust schedules of household appliances.  
As shown in Table 4, with the robust level increasing, the violation rates for the comfort constraints of the AC and WH go on declining but the electricity bill is rising. This result demonstrates that the customers can give up some comfort to save more money. Various schedules with different robust levels are offered to customers.
# LoRA-Tuned Multimodal RAG System for Technical Manual QA: A Case Study on Hyundai Staria
## 3 Data Construction Methodology
### QA, RAG, Multi-Turn Dataset Composition Methods and Characteristics  
In this study, we constructed three types of datasets for the training and evaluation of multimodal technical question-answering systems: document-based retrieval-augmented question answering (RAG QA), Multi-Turn question answering (Multi-Turn QA), and single-turn question answering (Simple QA). Each dataset was designed to evaluate response structure, document consistency, and dialogue flow maintenance capabilities, reflecting realistic technical support scenarios across automotive and other equipment domains.  
First, the RAG QA dataset consists of 5065 question-answer pairs, each containing three elements: question, context, and answer. The context is a text segment semantically matched to the query using the BAAI-bge-m3 embedding model from the Staria technical manual. This dataset is used for training and evaluating Retrieval-Augmented Generation (RAG) models, enhancing contextual alignment and answer generation quality.  
Second, the Multi-Turn QA dataset includes 275 conversation scenarios, with each item containing multiple consecutive question-answer pairs (qa list) associated with a single manual segment. The average number of dialogue turns is approximately 3.2, following common inquiry patterns such as symptom description \(\rightarrow\) clarifying question \(\rightarrow\) additional explanation \(\rightarrow\) resolution. This dataset is used to assess systems' abilities in context retention, pronoun resolution, and procedural reasoning. Initial responses were generated using GPT-4 and refined manually by researchers for accuracy and fluency.  
The QA generation process involved the following steps. First, technical manuals in PDF format were segmented by section. Each section was further divided into smaller segments (seg1, seg2,...). Sentences were then classified into types such as problem solving, component description, and configuration guidance using rule-based regular expressions, with GPT-assisted classification applied in ambiguous cases. Prompts were then customized according to each sentence type to generate realistic and diverse queries and responses.  
The QA types were organized into four categories: factual verification, procedural instruction, troubleshooting, and comparative analysis. All responses were designed to be purely text-based and grounded in the associated manual segment. To ensure response reliability and naturalness, we applied semantic similarity filtering, GPT summarization, and researcher-based validation.
# Condition Monitoring of Industrial Equipment Based on Multi-Variables State Estimate Technique
## 5 Validation and Discussion
### Validation of Blower Fan MSET Modeling
#### 5.1.1 Steady Working Condition  
In order to validate the effectiveness of the proposed method, traditional constant interval and access criteria method [18] are applied for comparison, which is shown as traditional method in the figures, and the size of memory matrix is also set as 200. It should be pointed out that in traditional method, the access criteria \(\varepsilon\) is a constant, and extreme condition vectors are also included in traditional method. The comparisons for proposed method and traditional method of horizontal and vertical vibration are shown in Figures 5 and 6, respectively, and the residual and similarity are compared.  
The dataset for validation in Figures 5 and 6 is relative steady and chosen from a normal working condition for blower fan, named normal condition I.  
Figure 4: Layout of variables data.  
and \(8.73\times 10^{-4}\) mm/s, respectively, that for traditional method are 0.0143 mm/s and 0.0238 mm/s, respectively. The similarity obtained according to Equation (17) also reflects the advantage of the proposed method, the similarity for the proposed method is above 0.97, including horizontal and vertical vibrations, that for most of samples are over 0.999, and the results for similarity comparisons prove the advantage of proposed method.  
For comprehensively evaluating the ability of characterizing normal state envelope space, the root mean square error (RMSE) and MAPE (mean absolute percentage error) are applied for comparison, the calculating formula are,  
\[\begin{array}{c}RMSE=\sqrt{\frac{\sum\limits_{i=1}^{n}{(y_{i}-\hat{y}_{i})^{ 2}}}{n}}\\ MAPE=\frac{1}{n}\sum\limits_{i=1}^{n}{\left|\frac{y_{i}-\hat{y}_{i}}{y_{i}} \right|}\end{array} \tag{21}\]  
where \(y_{i}\) and \(\hat{y}_{i}\) are observation and estimation values, respectively.  
The RMSE and MAPE comparison of proposed method and traditional method for normal condition I is shown in Table 4. In normal condition I, the RMSE and MAPE for proposed method of horizontal vibration \(x_{1}\) are 0.0045 mm/s and 0.45%, respectively, that for traditional method are 0.0265 mm/s and 2.12%, respectively. For vertical vibration \(x_{2}\), the RMSE and MAPE for proposed method are 0.0017 mm/s and 0.11%, that for traditional method are 0.0177 mm/s and 2.79%, respectively. The results reveal that while in a relative steady normal condition, the proposed method achieves a better estimation performance.
## 2 Materials and Methods
### Data Analysis  
All statistical analyses were computed using the SPSS package (SPSS v22.0 Inc., Chicago, IL, USA). All outcome measures showed normal distributions ([PERSON]'s normality test) and homogeneity of variances ([PERSON]'s test). A repeated measures ANOVA was performed to examine if there was any significant difference between orthotic conditions (D2, D6, D8, and control). Bonferroni Post-hoc comparisons were applied when any significant effect of orthosis was determined. The data were screened for sphericity using [PERSON]'s test. If sphericity was violated, significance of main effects was corrected through [PERSON]'s test. Alpha level was set at 0.05. The effect sizes for partial eta-squared (\(\eta_{p}\)\({}^{2}\)) and Cohen's d (\(d\)) were calculated and interpreted as small (0.01 \(\leq\)\(\eta_{p}\)\({}^{2}\) < 0.06; 0.2 \(\leq\)\(d\) < 0.5), medium (0.06 \(\leq\)\(\eta_{p}\)\({}^{2}\) < 0.14; 0.5 \(\leq\)\(d\) <0.8) and large (\(\eta_{p}\)\({}^{2}\)\(\geq\) 0.14; \(d\)\(\geq\) 0.5) [49].