esa-sceva/satcom-chunk-collection huggingface dataset:
# A Dynamic Service Reconfiguration Method for Satellite-Terrestrial Integrated Networks
## 6 Conclusions  
This paper provides a novel dynamic service migration and reconfiguration method to improve the service provisioning quality in STINs. Since the dynamics of user demand and the mobility of network topology in STINs, and the difficulty of solving this kind of large-scale formulated problem, we design a joint optimization model to minimize  
Figure 5: Comparison of the success rate of service reconfiguration: (**a**) arrival rate of service reconfiguration requests \(\lambda=0.05\); (**b**) arrival rate of service reconfiguration requests \(\lambda=0.2\).  
Figure 6: Comparison of algorithm running time.  
the end-to-end delay and migration cost in the service migration problem, and solve the problem using fuzzy logic combined with the quantum genetic algorithm, which can provide efficient and paralleled computing capability in solving the optimization problem. Simulation results reveal that the proposed algorithm has good performance in convergence, effectiveness, and network performances, and the impacts of different parameters also have been analyzed. The models provided in this paper could help researchers to improve their service provision schemes in STINs studies, and could also help the service providers of STINs to adjust their resource allocation scheme, in order to improve the resource utilization of STINs.  
As future work, we will explore the lightweight framework of the deep reinforcement learning approach that is suitable for the communication scenario in the urban area of STINs, which could significantly enhance the performance of the service deployment scheme or resource allocation method in STINs.  
Conceptualization, W.Q. and Y.L. ([PERSON]); methodology, W.Q.; software, W.Q.; validation, H.L. and Y.L. ([PERSON]); formal analysis, H.L.; investigation, W.Q. and H.L.; resources, Y.L. ([PERSON]); data curation, Y.L. ([PERSON]) and L.M.; writing--original draft preparation, W.Q.; writing--review and editing, W.Q. and L.M.; funding acquisition, Y.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the \"National Natural Science Foundation of China, grant number 62071483\" and the \"Military Science Research Project, grant number IJ20202C020344\".  
The authors declare no conflict of interest.
## 5 Simulation and Experimental Results
### Real-World Experimental Results
^{T}\]  
The joint position and velocities of the motion planning for Experiment 2 are presented in Figure 14. Additionally, Figure 15 illustrates the final joint configuration along with the joint motion results measured by the encoders. Figure 12: Time history with a cubic polynomial timing law for Experiment 1. (a) Joint positions. (b) Joint velocities. Figure 13: KUKA YouBot final joint configuration for Experiment 1. (a) Real-world final joint configuration. (b) Joint motion results measured by encoders. As observed in Figures 13b and 15b, the internal controller follows the given joint references given in Figures 12 and 14, respectively. This indicates that the manipulator reached the optimal joint configurations computed by the proposed approach. Furthermore, Figures 13 and 15 illustrate the real-world applicability of the proposed approach; the computed joint configurations are physically reachable. For Experiment 3, a runtime of T = 4 s is set. In this case, a desired end-effector pose is defined with a reachable position but an unreachable orientation. The desired pose is specified as follows:  
\[\textbf{T}_{d}= \begin{bmatrix}\textbf{R}_{d}&\textbf{t}_{d}\\ \textbf{0}&1\end{bmatrix} = \begin{bmatrix}0&0&1&0.2\\ 0&-1&0&0.3\\ 1&0&0&0.4\\ 0&0&0&1\end{bmatrix} \]  
The inverse kinematics solution for experiment 3 is  
\[\textbf{q}_{r}= \begin{bmatrix}56.31&83.19&-35.95&42.76&0.0028\end{bmatrix} ^{T}\]  
The initial joint configuration considered for the polynomial trajectory is as follows:  
\[\textbf{q}_{i}= \begin{bmatrix}0&90&0&90&0\end{bmatrix} ^{T}\]  
Figure 14: Time history with a cubic polynomial timing law for Experiment 2. (a) Joint positions. (b) Joint velocities. Figure 15: KUKA YouBot final joint configuration for Experiment 2. (a) Real-world final joint configuration. (b) Joint motion results measured by encoders. The joint positions and velocities for the motion planning are shown in Figure 16. Additionally, Figure 17 presents the final joint configuration along with the joint motion results measured by the encoders. The achieved end-effector pose is  
\[{}^{0}\mathbf{T}_{n}= \begin{bmatrix}0&0.832&0.554&0.2\\ 0&-0.554&0.832&0.3\\ 1&0&0&0.4\\ 0&0&0&1\end{bmatrix} \]  
As can be seen, the desired position is reached. The desired and current positions are practically the same. However, the desired and current orientations are only approximately aligned. As expected, the proposed approach prioritizes minimizing position errors over orientation errors, given that the desired orientation is not reachable. It is worth noting that the implementation in real-world experiments further demonstrates that the inverse kinematics solutions are physically feasible. The proposed algorithm was executed on a computer with an Intel i7 processor and 16 GB of RAM. The inverse kinematics estimation took less than one second for each tested point. If further speed is required, the performance of metaheuristic algorithms can be enhanced using parallel architectures and dedicated hardware, such as NVIDIA CUDA.
## 3 Proposed Method
### Process Design
#### 3.4.3 Generation Phase of Pedersen Commitment and Bulletproofs Range Proof for Relevant Amounts  
In order to achieve concealment of the transaction amount, both parties to the transaction calculate the Pedersen commitments for the relevant amounts separately and generate the range proofs for the corresponding amounts based on Bulletproofs. This process corresponds to steps 3, 4, and 5 in Figure 2, the specific process of which is described below.  
1. The transaction sender generates the commitments and range proofs for the relevant amounts
2. The transaction sender Bob selects random numbers \(r_{\text{b}_{1}}\), \(r_{\text{b}_{2}}\), and \(r_{\text{b}_{5}}\) as blind factors and generates commitments \(C_{1}\), \(C_{2}\), and \(C_{5}\) for input amounts \(v_{1}\), \(v_{2}\), and change amount \(v_{5}\), respectively: \[\begin{array}{rcl}Commit\left(v_{1},r_{\text{b}_{1}}\right)&=&r_{\text{b}_ {1}}\cdot\text{G}+v_{1}\cdot\text{H}\to C_{1}\\ Commit\left(v_{2},r_{\text{b}_{2}}\right)&=&r_{\text{b}_{2}}\cdot\text{G}+v_{2 }\cdot\text{H}\to C_{2}\\ Commit\left(v_{5},r_{\text{b}_{5}}\right)&=&r_{\text{b}_{5}}\cdot\text{G}+v_{5 }\cdot\text{H}\to C_{5}\end{array}\] (12)
3. [PERSON] generates the range proof \(\pi_{5}\) for the change amount \(v_{5}\) based on Bulletproofs: \[RangeProve\left(v_{5},r_{\text{b}_{5}},C_{5}\right)=\pi_{5}\] (13)
4. [PERSON] sends the commitments \(C_{\text{b}}=[C_{1},C_{2},C_{5}]\) and the blind factor \(r_{\text{b}}\) to the transaction receiver [PERSON], where \(r_{\text{b}}=r_{\text{b}_{1}}+r_{\text{b}_{2}}-r_{\text{b}_{5}}\).
5. The transaction receiver generates the commitments and range proofs for the relevant amounts
6. After receiving the commitments from [PERSON], [PERSON] first verifies the correctness of the commitments: \[C_{1}+C_{2}=C_{5}+r_{\text{b}}\cdot\text{G}+v_{4}\cdot\text{H}\] (14) where \(C_{1}+C_{2}\) is [PERSON]'s commitment to the input of the transaction; \(C_{5}\) is [PERSON]'s commitment to the change amount; and \(r_{\text{b}}\cdot\text{G}+v_{4}\cdot\text{H}\) is [PERSON]'s commitment to transfer the amount to [PERSON], i.e., to verify that the input and output of the transaction are balanced: \(v_{1}+v_{2}=v_{4}+v_{5}\);
7. [PERSON] chooses the random number \(r_{\text{a}}\), generates the Pedersen commitment \(C_{4}\) for the payment amount \(v_{4}\), and computes \(K\): \[\begin{array}{rcl}Commit\left(v_{4},r_{\text{a}}\right)&=&r_{\text{a}}\cdot \text{G}+v_{4}\cdot\text{H}\to C_{4}\\ K&=&\left(r_{\text{b}}-r_{\text{a}}\right)\cdot\text{G}\end{array}\] (15)
8. [PERSON] generates the range proof \(\pi_{4}\) of payment amount \(v_{4}\) based on Bulletproofs: \[RangeProve\left(v_{4},r_{\text{a}},C_{4}\right)=\pi_{4}\] (16)
## 3 Results and Discussion
### Stability Appraisal and Uncertainty of Machine Learning Models
At the seasonal scale, except for the RF and bagging models, the other models also had higher RMSE (0.5-1.2 mm day\({}^{-1}\)) in the early-growing and mid-growing seasons, which increased from 31% to 137% relative to the RF and bagging models. In other seasons, except for Scenario 6, the RMSE of all the models was within 0.5 mm day\({}^{-1}\). Meanwhile, the differences in RMSE values between the RF and bagging models were within 10%. These results also suggest that the tree-based EL (RF and bagging algorithms) exhibit good potential for tea plantation \(\text{ET}_{\text{a}}\) estimation with a higher stability. However, the results also indicate high uncertainty for estimating daily tea plantation \(\text{ET}_{\text{a}}\) in the sub-interval of 6-8 mm day\({}^{-1}\) (Figures 7 and 8) largely due to the significant underestimation of \(\text{ET}_{\text{a}}\) by machine learning models (Figure 4). Many similar results for the underestimation of \(\text{ET}_{\text{a}}\) in high-value areas have been reported using machine learningin humid region ecosystems [32; 48; 68]. Notably, daily tea plantation \(\rm ET_{a}\) in the sub-interval of 6-8 mm day\({}^{-1}\) is concentrated in the early- and mid-growing seasons. In our study site, energy conditions are the key control factors of daily tea plantation \(\rm ET_{a}\). However, during the early-growing and mid-growing seasons, the plant physiology limit for \(\rm ET_{a}\) increased due to high temperatures and heat stress. Our observation results in the tea plantation flux site also captured the physiology limit for \(\rm ET_{a}\), where canopy conductance decreased, limiting water loss and promoting water use efficiency (WUE) with high heat stress during the two seasons [6; 73]. These results revealed that physiological responses to high temperatures and heat stress would reduce \(\rm ET_{a}\), even if \(\rm R_{n}\) was high. However, as discussed above, a significant positive correlation relationship existed between \(\rm R_{n}\) and \(\rm ET_{a}\) for tea plantations (Table 2), which is the basis for the daily tea plantation \(\rm ET_{a}\) estimation by our machine learning models. However, physiological limits might lead to low \(\rm ET_{a}\) under high \(\rm R_{n}\) conditions with high temperatures and heat stress. In addition, the extreme climate events and human disturbance altered the stationary process of meteorological and vegetation ecological data, which might lead to higher RMSE values for predicting tea plantation \(\rm ET_{a}\) in the two seasons. Hence, we speculated that the underestimation of \(\rm ET_{a}\) for our models was primarily due to the physiological limitations of tea plantations and the nonstationary nature of the data during the early-growing and mid-growing seasons.
## 2 The HEPD-02 Detector
### Electronics  
The electronic boards used for HEPD-02 are listed below:  
* HV-CTRL (High-Voltage Control), which is designed for the control of the high voltages of the PMTs;
* LV-CTRL (Low-Voltage Control), which controls the power of the other boards;
* T-DAQ (Tracker Data Acquisition), which is used for the readout of the MAP sensors of the tracker;
* PMT&T (PMT readout and Trigger), which is designed for the acquisition and digitization of the PMT signals and the generation of the trigger signal for the detector;
* DPCU (Data Processing and Control Unit), which controls all the subsystems and manages the communication toward the satellite.  
All the subsystems provide HOT/COLD redundancy and communicate with DPCU via SpaceWire Lite protocol.  
In this contribution, the PMT&T board will be described in detail, while a brief description of the DPCU and T-DAQ boards follows.
# An Efficient Location Verification Scheme for Static Wireless Sensor Networks
## 3 System and Attack Models
### System Model  
It is assumed that the networks consist of homogeneous sensors. The sensors are assumed to be static and have the same capability in terms of transmission range, computation power and storage. Assumptions for the network are as follows:  
* The network consists of \(N\) sensors, and the \(i\)-th sensor is defined as \(n_{i}\), where \(i=1,2,...,N\).
* The participants in the proposed location verification can play one of the following roles, i.e., claimant and verifier. A claimant is a sensor who claims its location, and a verifier is the one who checks the validity of the claimed location.
* The communication range of all sensors is the same and defined as \(r\).
* \(n_{i}\) emits data packet, \(data_{i}=\{s_{i},L_{c}^{i},\mathit{nonce}_{i}\}\), where \(s_{i}\) is sensed data, \(L_{c}^{i}\) is the claimed location and \(\mathit{nonce}_{i}\) is the random value. \(L_{c}^{i}\) and \(\mathit{nonce}_{i}\) will be used in the location verification process.
* All sensors are able to get their true location, \(L_{\mathit{true}}\), using the localization, and we assume that there is no localization error.
* An honest sensor reports its \(L_{c}\), and it is the same as \(L_{\mathit{true}}\).
* The network is dense enough, and the location of sensors is determined by a uniform distribution.
* Time synchronization is achieved using any proper technologies, e.g., [23]. This is an essential and basic function that makes the sensed data more meaningful in WSNs.
## 4 Review: Slant Links Rain Fade Models
### Physical Models
#### 4.3.3 Physical-Mathematical (PM) Model  
The PM model [95] is based on experimental 1 min time series data, and it is modeled as two layers of precipitation phenomena and the cell-based rain structure. It is based on the CCIR model, along with the application of the SST. The model was tested experimentally with a long-term frequency of 11.6 GHz and an SST velocity speed of 10.6 m/s at three Italian cities; it showed \(-10.6\%\) absolute error, 7.6% STD, and 13% RMS error, with an outage probability range of 0.005%. The detailed procedure of the PM model is as follows:  
\[A(x_{0})=k_{A}\int_{0}^{L_{A}}R^{\kappa_{A}}(x_{0}+\Delta x_{0},\zeta)d\xi+k_{B }r^{\kappa_{B}}\int_{L_{A}}^{L_{B}}R^{\kappa_{B}}(x_{0},\zeta)d\zeta \tag{41}\]  
An illustration of these parameters is given in Figure 2.  
\[L_{A} =\frac{H_{A}-H_{S}}{\sin\theta} \tag{42}\] \[L_{B} =\frac{H_{B}-H_{S}}{\sin\theta}\] (43) \[\Delta L =L_{B}-L_{A}=h/\sin(\theta) \tag{44}\]  
Along the horizontal axis, the speed of the wind is given by:\[v(\theta)=v/\cos(\theta) \tag{45}\]  
Transforming Equation (45) into a space spectrum, and with further analysis, Equations (46) and (47), yield the attenuation for the elevation angles \(\theta\
eq 90^{\circ}\) and \(\theta=90^{\circ}\), respectively.  
\[\lim_{v\rightarrow\infty}A(t) =k_{A}R^{a_{B}}(t)L_{A}+r^{a_{B}}a_{B}R^{b_{B}}(t)(L_{\mathbf{B}} -L_{A}),\ for\ \theta\
eq 90^{\circ} \tag{46}\] \[A(t) =k_{A}R^{a_{B}}(t)(H_{A}-H_{\mathrm{S}})+r^{a_{B}}k_{\mathrm{B}}R^ {a_{B}}(t)h,\ for\ \theta=90^{\circ} \tag{47}\]  
The result reveals that the SST technique demonstrates the smallest RMS and average error rate compared to [32; 116; 117; 118; 119; 120; 121]. The detailed steps of the PM model are given in Algorithm 17.  
```
1 Compute \(k\) and \(\alpha\) /* In both layer */
2 Compute \(L_{A}\), and \(L_{B}\)
3 Compute \(\Delta x\)
4 if\(\theta\
eq 90^{\circ}\)then
5 Compute \(A(t)\) /* Equation (46) */
6 if\(\theta=90^{\circ}\)then
7 Compute \(A(t)\) /* Equation (47) */
8 Return \(A(t)\)
```  
**Algorithm 17**PM Model [95]  
Advantages  
It is the first model that comes with the practical melting and rain layer consideration and applying a 1 min rainfall rate (to distance). The method uses the SST, which can also be used to calculate fade duration and its changes.  
Limitations  
The model needs a 1 min rainfall rate and needs a storm translation speed, which may not be readily available worldwide.
## 3 Results
### Frost Damage in Climber Roses  
The frost damage varied in scale for different years and cultivars; however, none of the plants suffered complete frost damage (no signs of regeneration) (Table 2). 'Mme Plantier' was the only cultivar without frost damage in any year. Low frost damage in all remaining cultivars of climbers was observed after the mild winter seasons of 2006/2007, 2007/2008, and 2013/2014. Short-term (1-2 day) drops in minimal temperature exceeding \(-20~{}^{\circ}\)C (2002/2003, 2005/2006, 2009/2010, 2010/2011, and 2011/2012) or a few days of long-term minimal temperature not exceeding \(-20~{}^{\circ}\)C (2012/2013) caused significant frost damage (5-6 points). In the seasons from 2014/2015 to 2016/2017, minimal temperatures below \(-20~{}^{\circ}\)C were not recorded, and the frost damage in shrubs was evaluated at 1-3 points and was limited to darkened vascular bundles on shoots or dying one-year shoot tips (Table 2).  
Considering all the years of observation, low frost resistance was typical for 'Dr Robert Huey' and 'Duc de Constantin' in contrast to 'Alberic Barbier', 'Albertin', 'Chaplin's Pink Climber', 'Orange Triumph clg', and 'Venusta Pendula' (Table 2). The frost damage to 'Alberic Barbier', 'Albertin', 'Chaplin's Pink Climber', 'Orange Triumph clg', and 'Venusta Pendula' after the winter seasons 2000-2006 and 2009-2013 resulted in shoots completely dying out to the ground level, darkened vascular bundles on shoots, or darkening buds. The buds that began growth on damaged shoots in March and April died out in May. The shoots were damaged to the ground surface (5 and 6 points on the scale) and the shrubs were pruned low. 'Dr Robert Huey', 'Duc de Constantin', 'Gerbe Rose', and 'Paul's Scarlet Climber' also required strong pruning after the winter season in 2000-2006 and 2009-2013 (Table 2, Figure 6).  
Correlation analysis of the climber roses for frost damage showed a strict relationship between average monthly temperature and values of frost damage in all cultivars. The decreasing of average temperature in October, January, and February correlated with an increase of frost damage in 8 out of 11 cultivars ('Alberic Barbier', 'Albertin', 'Chaplin's Pink Climber', 'Gerbe Rose', 'Orange Triumph clg', 'Paul's Scarlet Climber', 'Venusta Pendula', and 'Zephirine Drouhin') (Table 3) with a high tendency to frost damage (Table 2), while the decreasing of average temperature in November, January, February, and March had the same effect on the remaining three cultivars ('Mme Plantier', 'Duc de Constantin', and 'Dr Robert de Huey') (Table 3) with a lower tendency to frost damage (Table 2). Increased average temperatures in December and April also correlated with an increase in frost damage in 'Mme Plantier', 'Duc de Constantin', and 'Dr Robert de Huey'. Decreasing the average air temperature in the following month caused higher frost damage (Table 3), but a decrease of the low minimum temperature was significant only in December and March (Table 4).  
Figure 6: The height (cm) of climber roses after spring pruning and at the end of October in the following years.
# A Compendium of Parameterized Problems at Higher Levels of the Polynomial Hierarchy
## 3 Propositional Logic Problems
### Maximal Models  
The following \(\mathrm{FPT}^{\mathrm{NP}}[\mathrm{few}]\)-complete problems are based on various notions of (local) maximality for models of propositional formulas. Let \(\varphi\) be a (satisfiable) propositional formula, and let \(X\subseteq\mathrm{Var}(\varphi)\) be a subset of variables. Then, a truth assignment \(\alpha:\mathrm{Var}(\varphi)\to\{0,1\}\) is an _\(X\)-maximal model_ of \(\varphi\) if (i) \(\alpha\) satisfies \(\varphi\) and (ii) there is no truth assignment \(\beta:\mathrm{Var}(\varphi)\to\{0,1\}\) that satisfies \(\varphi\) and that sets more variables among \(X\) to true than \(\alpha\). Moreover, take an arbitrary ordering over the variables \(X\subseteq\mathrm{Var}(\varphi)\)--for the sake of presentation, let \(X=\{x_{1},\ldots,x_{k}\}\) and let the ordering \(<\) specify \(x_{1}<\cdots<x_{k}\). We say that a truth assignment \(\alpha:\mathrm{Var}(\varphi)\to\{0,1\}\) is the _lexicographically \(X\)-maximal model_ of \(\varphi\) (with respect to the given ordering) if (i) \(\alpha\) satisfies \(\varphi\) and (ii) each truth assignment \(\beta:\mathrm{Var}(\varphi)\to\{0,1\}\) that satisfies \(\varphi\) (with \(\alpha\
eq\beta\)) has the property that there is some \(1\leq\ell\leq k\) such that \(\alpha(x_{\ell})=1\) and \(\beta(x_{\ell})=0\) and for all \(1\leq\ell^{\prime}<\ell\) it holds that \(\alpha(x_{\ell^{\prime}})=\beta(x_{\ell^{\prime}})\).  
\begin{tabular}{|l|} \hline Local-Max-Model \\ _Instance:_ A satisfiable propositional formula \(\varphi\), a subset \(X\subseteq\mathrm{Var}(\varphi)\) of variables, and a variable \(w\in X\). \\ _Parameter:_\(|X|\). \\ _Question:_ Is there a \(X\)-maximal model of \(\varphi\) that sets \(w\) to true? \\ \hline
**Complexity:**\(\mathrm{FPT}^{\mathrm{NP}}[\mathrm{few}]\)-complete [7; 47]. \\ \hline \end{tabular}  
\begin{tabular}{|l|} \hline Local-Lex-Max-Model \\ _Instance:_ A satisfiable propositional formula \(\varphi\), a subset \(X\subseteq\mathrm{Var}(\varphi)\) of variables, and a variable \(w\in X\). \\ _Parameter:_\(|X|\). \\ _Question:_ Is there a lexicographically \(X\)-maximal model of \(\varphi\) that sets \(w\) to true? \\ \hline
**Complexity:**\(\mathrm{FPT}^{\mathrm{NP}}[\mathrm{few}]\)-complete [7]. \\ (The problem Local-Lex-Max-Model isn't considered explicitly in the literature. \(\mathrm{FPT}^{\mathrm{NP}}[\mathrm{few}]\)-completeness for this problem follows immediately from proofs in the literature--i.e., Propositions 73 and 74 in [7]).
# A Dual-Branch Speech Enhancement Model with Harmonic Repair
## 3 Experiments
### Spectrogram Analysis  
Figure 6 displays the spectrograms before and after enhancement via our proposed model. The spectrogram comes from a randomly selected ten-second audio in the DNS Challenge 2020 set. The red rectangular boxes in the image highlight the pure noise sections that are almost devoid of speech. In these sections, the proposed model effectively suppresses the background noise, as can be seen in the comparative spectrograms below. The pure noise sections, containing no human voice, are almost completely silent after noise reduction. The white and black dashed boxes in this figure indicate the effectiveness of harmonic restoration. In the white dashed box, the harmonic features are nearly invisible due to noise masking. After enhancement, the corresponding section in the spectrogram shows distinct comb-like harmonics, which indicates that the proposed model significantly restores the harmonics of speech during the enhancement process. This plays a key role in improving the clarity and distinguishability of the speech.
## 5 Problems and Development Trends
### Future Prospects and Trends
#### 5.2.3 Application of Green and Sustainable Technologies  
With the widespread adoption of environmental sustainability concepts, green aquaculture technologies are becoming an important direction in large yellow croaker breeding. By reducing feed waste, lowering antibiotic usage, and improving resource utilization efficiency, an environmentally friendly aquaculture system can be achieved [140]. The development and application of eco-friendly feed not only help alleviate water pollution but also contribute to improving the health levels and survival rates of fry. Combined with precise feeding systems and water environment control technologies, this can further optimize fry growth conditions and reduce energy consumption. Furthermore, natural immune regulation strategies based on immune stimulants and environmental adaptability screening are gradually replacing traditional chemical drugs, enhancing disease resistance and supporting the aquaculture industry's transition toward a green, low-carbon, and sustainable direction.