esa-sceva/satcom-chunk-collection huggingface dataset:
# A Guaranteed Approximation Algorithm for QoS Anypath Routing in WMNs
## 3 An An Anypath Routing Approximation Algorithm for QoS
### Design of HMAA Algorithm
#### 3.1.2 Detailed Procedure of HMAA Algorithm  
The detailed procedure of HMAA algorithm is shown in Algorithm 1.  
```
Input:\(\mathbf{G}(\mathbf{V},\mathbf{\widetilde{E}},\mathbf{\widetilde{W}},\mathbf{L}, \mathbf{p})\), the source node s, the destination node t Output: Anypath \({P_{s}}^{*}\)
1. for each \(v\in\mathbf{V}\)in \(\mathbf{G}(\mathbf{V},\mathbf{E},\mathbf{\widetilde{W}},\mathbf{L}, \mathbf{p})\) do
2. \(w_{m}(v)=\max_{2\leq i\leq\mathbf{K}}w_{i}(v)\);
3. \(\hat{w}_{m}(v)\leftarrow\infty\);
4. \(\mathbf{F}(v,P)\leftarrow\varnothing\);
5. end for
6. \(\hat{w}_{m}(t)=0\);
7. \(P\leftarrow\varnothing\);
8. \(Q\leftarrow\mathbf{V}\);
9. while \(Q\
eq\varnothing\) & \(w_{1}(s,P)\leq L_{1}\) do
10. \(u\leftarrow\text{Min}(Q)\);
11. \(P\gets P\cup\{u\}\);
12. \(\hat{w}(u,P)\leftarrow\max_{2\leq i\leq\mathbf{K}}\hat{w}_{i}(u)\);
13. for each incoming link \((v,u)\) in \(\mathbf{E}\) do
14. \(\mathbf{F}\leftarrow\mathbf{F}(v,P)\cup\{u\}\);
15. \(\hat{w}_{m^{\prime}}(v)\leftarrow\frac{w_{m}(v)}{p(v,\mathbf{F})}+\hat{w}_{m}( \mathbf{F})\);
16. if \(\hat{w}_{m^{\prime}}(v)\leq\hat{w}_{m}(v)\) then
17. \(\hat{w}_{m}(v)\leftarrow\hat{w}_{m^{\prime}}(v)\);
18. \(Q\leftarrow Q-\{u\}\);
19. \(\mathbf{\hat{F}}(v,P)\leftarrow\mathbf{F}\);
20. for \(i=2:\) K do
21. \(\hat{w}_{i}(v)\leftarrow\frac{w_{i}(v)}{p(v,\mathbf{F}(v,P))}+\hat{w}_{i}( \mathbf{F}(v,P))\);
22. end for
23. end if
24. end for
25. end while
26. if \(w_{1}(s,P)\leq L_{1}\)
27. \({P_{s}}^{*}\gets P\); OUTPUT \({P_{s}}^{*}\);
28. else Output NO Feasible Anypath;
29. end if
30. END
```  
**Algorithm 1** HMAA algorithm
# Research on the Accumulated Plastic Strain of Expansive Soil under Subway Loading
## 3 Analysis of Test Results
### Analysis of Accumulated Plastic Strain Behavior of Expansive Soil under Intermittent Loading
#### 3.2.3 Analysis of the Influence of Cyclic Stress Ratio on Accumulated Plastic Strain  
Figure 8 shows the relationship curve between accumulated plastic strain and number of cycles under the influence of different cyclic stress ratios. As observed from the graph, under consistent conditions, the strain behavior for varying cyclic stress ratios follows a similar pattern. Specifically, in the early stages of cyclic loading, expansive soil experiences a rapid increase in strain. With an increase in the number of cycles, the strain gradually diminishes, eventually stabilizing its growth. As the cyclic stress ratio increases, the strain magnitude for the same number of cycles also increases. Towards the end of the loading process, when the strain development curve approaches stability, the accumulated plastic strain for a cyclic stress ratio of \(\eta_{d}=0.2\) is approximately 2.8 times that of a cyclic stress ratio of \(\eta_{d}=0.1\); similarly, for a cyclic stress ratio of \(\eta_{d}=0.4\), the accumulated plastic strain is roughly 5.4 times that of a cyclic stress ratio of \(\eta_{d}=0.1\). This demonstrates that an elevated cyclic stress ratio significantly promotes strain development.  
This phenomenon is primarily due to the considerable influence of cyclic dynamic loading on soil deformation. Under smaller loads, the soil experiences elastic deformation without generating plastic strain. However, as external loads increase progressively, plastic strain accumulates within the soil. Consequently, an increase in the cyclic stress ratio leads to a noticeable rise in strain. In comparison to the effect of static bias stress, it is evident that a higher cyclic stress ratio has a slightly greater influence on promoting strain accumulation.  
Figure 8: Development curves of accumulated plastic strain with number of cycles under different cyclic stress ratios. (The sample numbers are S10, S11, S12, S22, S23, S24). (**a**) \(\Delta T/T=1\)\(\sigma_{s}=30\) kPa; (**b**) \(\Delta T/T=10\)\(\sigma_{s}=30\) kPa.  
Additionally, it is evident from the graph that at lower cycles, the slope of strain at any given point increases with higher cyclic stress ratios. This can be attributed to the immediate surge in strain caused by initial instantaneous loading. Moreover, the cycles required for the accumulated plastic strain development curve to transition from rapid growth to gradual growth vary with different cyclic stress ratios. A larger cyclic stress ratio corresponds to a higher number of cycles needed for the strain development curve to enter the gradual growth phase. This trend arises from the fact that larger cyclic stresses make it more challenging for a strain to enter a stable growth phase, hence necessitating a higher number of cycles.
## 3 Results and Discussion
### Circuit Walking Path Results
#### 3.3.1 Percent Residual Activity on Boots from Circuit Walking  
Figure 16 shows the average %Res of Sr-85 contaminated particles that remained on boots after each experiment, including the NSCs and surfaces stabilized with CaCl\({}_{2}\), Phos-Chek MVP-Fx, and Soil\({}_{2}\)O. Table 11 shows the p-values from the \(t\)-tests performed on the data. The \(p\)-values indicate the probability that the groupings of data were from the same population, i.e., statistically similar.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Step Number** & **Control** & **Soil\({}_{2}\)O** & **CaCl\({}_{2}\)** & **MVP-Fx** \\ \hline
1 & ND\({}^{1}\) & ND & ND & ND \\ \hline
2 & 8\% & 4\% & 0.6\% & 1\% \\ \hline
3 & 3\% & 0.8\% & 3\% & 0.8\% \\ \hline
4 & 10\% & 2\% & 6\% & 0.9\% \\ \hline
5 & 1\% & 0.2\% & NC & 1\% \\ \hline
6 & 4\% & ND & 0.3\% & ND \\ \hline
7 & ND & 2\% & ND & ND \\ \hline
8 & NC\({}^{2}\) & ND & 2\% & ND \\ \hline \end{tabular} \({}^{1}\) ND = Not detected. \({}^{2}\) NC = Not collected because no particles were deposited.  
\end{table}
Table 10: Percent transfer of walking experiments to sand and non-sand.  
Figure 15: Percent Transfer for Walking Experiment on the Sand Walking Path.  
The potential for residual appeared to be highest for the sand surface, as indicated by the control %Res. However, the sand results were determined from the 5 min continuous walking experiment, so the results are not exactly comparable. Interpreting the efficacy values in the figure in combination with the _p_-values from the table reveals the following for specific surfaces.  
_Asphalt._ Soil\({}_{2}\)O (0.2 \(\pm\) 0.2 %Res) and MVP-Fx (ND %Res) resulted in significantly decreased %Res compared to the NSC (2 \(\pm\) 0.3 %T), while CaCl\({}_{2}\) (0.8 \(\pm\) 0.7 %Res) did not. MVP-Fx (ND %Res) demonstrated significantly lower %Res than CaCl\({}_{2}\) (0.8 \(\pm\) 0.7 %Res) and Soil\({}_{2}\)O (0.2 \(\pm\) 0.2 %Res).  
_Concrete._ Only CaCl\({}_{2}\) (ND %Res) resulted in significantly decreased %Res compared to the NSC (1 \(\pm\) 0.4 %Res), while MVP-Fx (0.4 \(\pm\) 0.2 %Res) and Soil\({}_{2}\)O (0.6 \(\pm\) 0.1 %Res) did not, although MVP-Fx was close to being significantly different (_p_ = 0.056). CaCl\({}_{2}\) (ND %Res) resulted in significantly lower %Res compared to Soil\({}_{2}\)O (0.6 \(\pm\) 0.1 %Res).  
_Sand._ Note: There was a single experiment for each stabilization technology, and the SD was based on the right and left sides of the path [N = 2], so no _p_-values were calculated. MVP-Fx (ND %Res) and Soil\({}_{2}\)O (2 \(\pm\) 0 %Res) resulted in decreased %Res compared to the NSC (3 \(\pm\) 0.4 %Res). CaCl\({}_{2}\) (3 \(\pm\) 0.6 %Res) generated higher average %Res than NSC (3 \(\pm\) 0.4 %Res).
###### Abstract  
Thermal power units (TPUs) play a crucial role in accommodating the high penetration of renewable energy sources (RESs) like wind turbines (WTs) and photovoltaics (PVs). This paper proposes an evaluation framework to quantitatively analyze the flexibility potential of retrofitted TPUs in enhancing the accommodate capability of RESs through coupling integration and optimal scheduling. Firstly, the coordination framework for coupling TPUs with RESs is outlined, including a comprehensive analysis of benefits and implementation strategies. Secondly, an annual optimal scheduling model for TPUs and RESs is developed, incorporating deep peak regulation services, ladder-type constraints for retrofitted TPUs, and their operational characteristics before and after the coupling integration. Thirdly, indices to evaluate RES accommodation levels and TPU regulation capacities are proposed to quantify the performance of power sources. Finally, a real-world case study is conducted to demonstrate that integrating retrofitted TPUs with RESs through coupling significantly enhances RES utilization by 3.6% and boosts TPUs' downward regulation capabilities by 32%.  
thermal power unit; flexibility potential; renewable energy accommodation; coupling integration
# Fine-Tuning Large Language Models for Kazakh Text Simplification
## 4 Experiments
### Results
In Sentence 4, metaphorical language such as \"thorn\" (shengelden) is inserted, which confuses the meaning and diverges from the original description of a crowd surrounding houses. These findings highlight that, for morphologically rich, low-resource \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & \begin{tabular}{c} **BLEU CI** \\ **(Low)** \\ \end{tabular} & \begin{tabular}{c} **BLEU CI** \\ **(High)** \\ \end{tabular} & \begin{tabular}{c} **BLEU Std** \\ **(High)** \\ \end{tabular} & \begin{tabular}{c} **SARI CI** \\ **(Low)** \\ \end{tabular} & \
\begin{tabular}{c} **SARI CI** \\ **(High)** \\ \end{tabular} &
\begin{tabular}{c} **SARI Std** \\ **(High)** \\ \end{tabular} \\ \hline kazLLM-Llama-3.1-70B & 14.24 & 18.36 & 1.09\% & 41.88 & 43.82 & 0.50\% \\ kazLLM-Llama-3.1-8B & 14.58 & 19.48 & 1.28\% & 43.87 & 46.89 & 0.80\% \\ Sherkala-Llama-3.1-8B & 14.67 & 19.53 & 1.21\% & 42.69 & 45.72 & 0.75\% \\ Llama-3.2-3.2-3B (zero-shot) & 0.08 & 0.49 & 0.11\% & 34.64 & 38.25 & 0.92\% \\ Qwen2-72B (zero-shot) & 0.91 & 1.74 & 0.21\% & 36.48 & 40.09 & 0.90\% \\ Llama-3.3-70B (zero-shot) & 3.55 & 5.73 & 0.56\% & 38.62 & 41.31 & 0.69\% \\ KazSim (Llama-3.2-3B) & 15.18 & 20.13 & 1.26\% & 43.19 & 45.76 & 0.69\% \\ KazSim (Qwen2-72B) & 15.94 & 20.64 & 1.19\% & 46.17 & 49.07 & 0.74\% \\ KazSim (Llama-3.3-70B) & 17.80 & 22.47 & 1.18\% & 46.91 & 50.05 & 0.80\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The 95% confidence intervals (CI) and standard deviations (Std) for BLEU and SARI scores on the semi-manually annotated test set. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & \begin{tabular}{c} **English** \\ \end{tabular} & \
\begin{tabular}{c} **Prompt** \\ \end{tabular} &
\begin{tabular}{c} **Kazakh Prompt** \\ \end{tabular} \\ \hline  & BLEU & SARI & F1 & BLEU & SARI & F1 \\ \hline Sherkala-Llama-3.1-8B & 17.08 & 44.23 & 83.55 & 15.78 & 46.15 & 82.66 \\ kazLLM-Llama-3.1-70B & 16.34 & 42.85 & 83.72 & 16.22 & 42.30 & 83.62 \\ kazLLM-Llama-3.1-8B & 17.09 & 45.34 & 82.78 & 15.85 & 46.42 & 82.48 \\ \hline Llama-3.3-70B (zero-shot) & 4.53 & 39.95 & 72.08 & 8.26 & 40.73 & 76.42 \\ \hline KazSim (Llama-3.3-70B) & 20.32 & 48.42 & 84.27 & 21.03 & 48.78 & 84.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of model performance under English and Kazakh instruction prompts. Metrics reported on the semi-manual test set include BLEU, SARI, and F1. languages like Kazakh, using native-language prompts during instruction tuning can lead to more accurate and nuanced simplifications.
## 3 Results
### CDM Application and Comparison  
Regarding the applicability of the CDM, it was tested to identify if the results of overall survival analyses across the source system and CDM were similar. The calculated survival probabilities did not differ significantly from each other (\(p\)-value = 0.82) and thus the H0 hypothesis was accepted. Accordingly, the median survival of a patient with breast cancer was 164 months in CDM; the calculated median survival in the source system was two months shorter (Table 5). The percentage deviation in cohort size was 1.5%, with a larger cohort included in the source system. The Number at Risk distribution can be seen in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline
**Domain** & **Events per Episode** & **Total Events per Episode** \\ \hline Measurement & 8.23 & 820,013 \\ Procedure & 3.29 & 477,966 \\ Observation & 4.79 & 399,128 \\ Drug & 2.94 & 238,664 \\ Condition & 1.16 & 121,044 \\ \hline \end{tabular}
\end{table}
Table 4: Events per Episode and total events stored in the Episode_Event table.  
Figure 4: Comparison of overall survival of a breast cancer cohort between CDM and source system.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**System** & **N** & **Events** & **Median** & **Standard Error** & **0.95 Lower CL** & **0.95 Upper CL** \\ \hline CDM & 3588 & 784 & 164.2 & 0.02 & 155.0 & 175.9 \\ GTDS & 3644 & 806 & 162.6 & 0.02 & 155.0 & 172.1 \\ \hline \end{tabular}
\end{table}
Table 5: Descriptive statistics of overall survival of patients diagnosed with breast cancer.
# Research on Data Transmission of Laser Sensors for Reading Ruler
## 2 Encoding Rulers and Code Readers  
A coding ruler is a device that marks position information in the form of digital signals so that the code reader can convert the position or motion information into digital signals [5]. A coding ruler usually consists of some marked lines or grids, which interact with the code reader through optical, magnetic, or electromagnetic methods. As shown in Figure 1, when the coding ruler moves relative to the code reader, the code reader detects the changes in these lines or grids and generates corresponding digital signals.  
Using a digital code band to identify absolute position information brings remarkable convenience to the factory application of automatic positioning. Compared with the traditional measurement method, the positioning accuracy and long-term stability are greatly improved by directly reading the code band information, which effectively eliminates the possible adverse effects caused by measurement errors, time drift, temperature drift, and analog-to-digital conversion errors.  
The flexible design of stainless steel and plastic compression digital code tape enables it to bend in a certain range, which is suitable not only for straight line positioning but also for curve positioning scenes. This feature greatly broadens the application scope of  
Figure 1: Working schematic diagram of code reader and coding ruler.  
automatic positioning technology and enables more complex production environments to be effectively dealt with. Many types of equipment can operate simultaneously on the same bar code belt with the help of code reading and positioning technology. This not only improves the overall work efficiency but also the intelligent and efficient operation of the production line, laying a solid foundation for further improving the automation level of the factory [6].  
A code reader is a device used to decode the signals on a coding ruler and convert them into digital signals. It usually includes a photoelectric sensor, magnetic sensor, or other sensors. Once the marks on the coding ruler are detected, the code reader converts them into digital signals and sends them to the computer or control system for processing.  
The code reader usually contains the decoding algorithm corresponding to the coding scheme on the coding scale, and different coding methods correspond to different decoding algorithms. The accuracy and real-time performance of the positioning system can be improved by researching and designing a more universal and lightweight coding and decoding algorithm.
## 3 Classification of Attacks on RPL-Based IoT
### RPL-Specific Attacks
#### 3.2.2 Version Attacks  
This attack exploits RPL's Global Repair feature, manipulating the version number of the DODAG to induce topology inconsistency and routing loops. In the absence of mechanisms ensuring version number integrity, a malicious node can initiate the global repair procedure by transmitting a DIO message with a higher version number [43]. The primary objective of version number attacks is to deplete node resources and impede packet delivery.
# Flow Characteristics and Loss Models of the Transonic Radial Turbine
## 4 Loss Models
### Tip Clearance Loss  
A clearance gap must be provided between the rotor and its shroud, and consequently, leakage from the rotor blade pressure to suction surfaces occurs. Typically, the clearance gap can be separated into radial clearance and axial clearance. [PERSON] and [PERSON] showed the effect of tip clearance on the turbine efficiency and the exit flow conditions. It representsthat the radial clearance has more impact on the performance of a turbine. Equation (3) proposed by [PERSON] [6] is a typical formulation of this mechanism, where \(U_{d}\) is the velocity of the blade tip, \(Z_{r}\) is the number of blades, \(\varepsilon_{x}\) is the axial clearance, \(\varepsilon_{r}\) is the radial clearance, \(K_{x}=0.4\), \(K_{r}=0.75\)\(K_{c}=-0.3\), \(C_{x}=\frac{1-r_{d}/r_{4}}{C_{md}\bar{q}_{4}}\), and \(C_{r}=\left(\frac{r_{d}}{r_{4}}\right)\frac{\varepsilon_{r}-b_{4}}{C_{md}\bar {q}_{6}\bar{b}_{6}}\)  
\[L_{clr}=\frac{U_{d}^{3}Z}{8\pi}\Big{(}K_{x}\varepsilon_{x}C_{x}+K_{r} \varepsilon_{r}C_{r}+K_{c}\sqrt{\varepsilon_{x}\varepsilon_{r}C_{x}C_{r}} \Big{)} \tag{3}\]
## 3 Results and Discussions
### Influence of Crumb Rubber on Pervious Concrete Properties
#### 3.1.3 Flexural Strength  
As can be seen in Figure 7, the effect of rubber content on the flexural strength of RMPC is the same as on its effect on compressive strength. Figure 7 illustrates that the adverse impact of rubber on flexural strength also comes from rubber content and rubber particle size. Flexural strength decreases with the increasing rubber content. When rubber content changes from 0% to 8%, the flexural strength of the CCR-modified mixture decreases from 4.84 MPa to 2.87 MPa (40.7% loss), and the FCR-modified mixture decreases from 4.84 MPa to 4.01 MPa (17.1% loss), respectively. When the incorporation level is the same, FCR-modified mixtures have a slightly higher flexural strength than CCR, which means that FCR is relatively advantageous for flexural strength. The influence mechanism of rubber on flexural strength is consistent with compressive strength. The relationship between compressive strength and flexural strength of RMPC is listed in Figure 8. The linear regression equation can be used to describe the relationship well. The linear relationship between compressive strength and flexural strength of pervious concrete is the same as ordinary concrete.  
Figure 6: Compressive strength of RMPC versus rubber content.
# Competitive Analysis of Algorithms for an Online Distribution Problem
## 3 Online Algorithms with a Given Time of Acceptance
Theorem 3.2: \(\frac{ALG_{2}^{j}}{OPT}\leq max\{\frac{P}{p},\frac{p+hj}{p}\}\)_._  
Proof of Theorem 3.2: Since the producer accepts the minimum price offered from 0 to \(j\) at time period \(j\), the total cost obtained by applying algorithm \(\text{ALG}_{2}^{j}\) is \(P_{j}^{*}+hj\), where \(P_{j}^{*}=\min_{0\leq t\leq j}P_{t}\). Let us distinguish the following two cases. _Case 1: \(P_{t}\geq P_{j}^{*}\), \(\forall t>j\)_  
In this case, the optimal offline cost \(OPT\) is not lower than \(P_{j}^{*}\). Therefore, \(\frac{ALG_{2}^{j}}{OPT}\leq\frac{P_{j}^{*}+hj}{P_{j}^{*}}\leq\frac{p+hj}{p}\), since \(\frac{P_{j}^{*}+hj}{P_{j}^{*}}\) is a decreasing function in \(P_{j}^{*}\) and \(P_{j}^{*}\geq p\). _Case 2: \(P_{t}<P_{j}^{*}\) for some \(t>j\)_  
In this case, the optimal offline cost \(OPT\) is not lower than the minimum between \(P_{j}^{*}\) and \(p+hj\). Therefore, \(\frac{ALG_{2}^{j}}{OPT}\leq\max\{\frac{P_{j}^{*}+hj}{P_{j}^{*}},\frac{P_{j}^{ *}+hj}{p+hj}\}\leq\max\{\frac{p+hj}{p},\frac{P_{j}^{*}}{p}\}\leq\max\{\frac{p+ hj}{p},\frac{P}{p}\}\). We now show that this algorithm cannot have a better competitive ratio. Consider the following instance for Case 1: \(P_{t}=p\), \(\forall t\). The total cost generated by algorithm \(\text{ALG}_{2}^{j}\) is \(p+hj\), while the optimal offline cost is \(OPT=p\) (obtained by accepting the price offered at time 0). Therefore, in this instance, \(\frac{ALG_{2}^{j}}{OPT}=\frac{p+hj}{p}\). Consider now the following two instances for Case 2: \(P_{t}=p+\epsilon\) for \(0\leq t\leq j\), where \(\epsilon<h\), and \(P_{t}=p\), \(\forall t>j\). The total cost generated by algorithm \(\text{ALG}_{2}^{j}\) is \(p+e+hj\), while the optimal offline cost is \(OPT=p+\epsilon\) (obtained by accepting the price offered at time 0, since \(\epsilon<h\)). Therefore, in this instance, \(\frac{ALG_{2}^{j}}{OPT}=\frac{p+e+hj}{p+\epsilon}\rightarrow\frac{p+hj}{p}\) for \(\epsilon\to 0\). The second instance is the following: \(P_{t}=P\), \(\forall t\leq j\)\(P_{t}=p\), \(\forall t>j\), and \(h<\frac{p-p}{j+1}\). The total cost generated by algorithm \(\mathrm{ALG}_{2}^{j}\) is \(P+hj\), while the optimal offline cost is \(OPT=p+h(j+1)\) (obtained by accepting the price offered at time \(j+1\), as \(h<\frac{p-p}{j+1}\)); therefore, in this instance, \(\frac{ALG_{2}^{j}}{OPT}=\frac{P+hj}{p+h(j+1)}\rightarrow\frac{p}{p}\) for \(h\to 0\). Since the competitive ratio of algorithm \(\mathrm{ALG}_{1}^{j}\) is \(\frac{p+hj}{p}\), while it is \(\max\{\frac{p}{p},\frac{p+hj}{p}\}\) for \(\mathrm{ALG}_{2}^{j}\), the two algorithms have the same worst-case performance for \(j=0\), while the following theorem holds for \(j>0\). Theorem 4.1: _In the worst case, \(\mathrm{ALG}_{2}^{j}\) dominates \(\mathrm{ALG}_{1}^{j}\) when \(j>0\)._