esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The primary objective of this research is to propose and compile a specific protocol for photocatalytic measurements of modified TiO2 particles under visible-light irradiation. Nitrogen-modified titanium dioxide (N-TiO2) powder was synthesized by the sol-gel method and characterized by X-ray Diffraction Analysis (XRD), Field Emission Scanning Electron Microscopy (FESEM), Transmission Electron Microscopy (TEM), X-ray Photoelectron Spectroscopy (XPS), Raman Spectroscopy (micro-Raman), and Ultraviolet-visible Spectroscopy (UV-Vis). Photocatalytic tests were performed on a specially designed photocatalytic batch reactor to test the ability of the powder to degrade hazardous toxic compounds. Via the Taguchi method, nine experiments (I.9) were compiled to examine the factors that affect the photocatalytic activity of the nano-sized powder. The N-TiO2 particles were characterized by the dominance of the crystalline anatase phase, exhibiting crystals in the nano-scale. The Taguchi method was designed to control four selected parameters (pollutant selection among azo dyes, amount of catalyst to pollutant, distance of the photocatalytic cell from the radiation source, and time protocol) with three levels/options each. Conclusions were drawn regarding the way each parameter affects the final degradation of the pollutant. The parameter that proved to affect the degradation of the pollutant to a greater extent was the choice of pollutant, followed by the amount of catalyst. The other two factors almost slightly affect the process, with a similar percentage. Taking into account the abovementioned results, a photocatalytic protocol for testing TiO2 nano-powder activity under visible light irradiation is proposed by using a batch, horizontal, rectangular, vis-LED equipped reactor with reflective walls.  
TiO2 nanoparticles; N-doped; sol-gel method; Taguchi method; photocatalytic test; visible light irradiation; optimization; photocatalysis protocol +
Footnote †: journal: Applied Sciences  
15 December 2022  
13 December 2022  
31 December 2022  
15 December 2022  
3 January 2023  
1
# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 6 Conclusions  
In this paper, we introduced Siamese-SAM, an innovative change detection model that leverages a Siamese network architecture tailored for remote sensing image analysis. By incorporating the SAM as the encoder for each input image and introducing three specialized modules--the GIEM, DIEM, and DGIF--our model significantly enhances the utilization of both global contextual information and differential features.  
Our comparative experiments across three benchmarks (LEVIR-CD, SYSU-CD, GZ-CD) reveal that Siamese-SAM achieves state-of-the-art performance metrics: F1 scores of 92.67%, 82.61%, and 88.79%, along with IoU values of 86.34%, 70.17%, and 79.83%. The experimental results not only demonstrate superior performance over existing conventional approaches but also underscore the advantages of our methodology in the domain of remote sensing change detection.  
Furthermore, comprehensive ablation studies validate the importance of each proposed module in improving model performance, confirming the rationality and effectiveness of the Siamese-SAM framework. Despite these successes, we acknowledge certain limitations, such as dependence on high-quality training data and computational costs. Future research will focus on addressing these challenges, including exploring optimization techniques to improve computational efficiency and developing more effective cross-domain adaptation strategies to bridge the gap between natural images and remote sensing imagery.  
In summary, Siamese-SAM offers a powerful tool for remote sensing image change detection and shows significant potential for applications in environmental monitoring, disaster assessment, and urban expansion tracking. We believe this model will play a pivotal role in advancing both research and practical implementations in the field.  
Conceptualization, G.W. and Y.M.; methodology, Y.M.; software, G.W.; validation, G.W., Y.M. and Z.W.; formal analysis, G.W.; investigation, Z.W.; resources, Z.W.; data curation, Y.M.; writing--original draft preparation, Z.W.; writing--review and editing, Y.M. and G.W.; visualization, Z.W.; supervision, Z.W.; project administration, Z.W.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data and the code of this study are available from the corresponding author upon request.  
Thanks to Tongji University and the mentor for providing computing resources.  
The authors declare no conflicts of interest.
## 4 Discussion  
### Capability of Biodegradation UDMH  
In 1979, [PERSON] [32] selected the _Paracoccus denitrificans_ sp. named D-31 from the soil, and discussed its tolerance of UDMH. From then on, _Achromobacter_ sp., _Bacillus_ sp., _Pusedomonas_ sp. [35], and _Enterobacter cloace_ sp. [42] were used to research the toxicity and tolerance of UDMH. According to the study, [PERSON] [43] thought that UDMH was not suitable to be biodegraded because of the toxicity and inhibition effect of UDMH on the environmental bacteria, so UDMH in the environment was rarely adopted as a biodegradation method for a long time [44]. It is worth noting that these strains were screened from nature directly, rather than from the acclimated environment.  
Entering the 21 st century, as bioremediation technology became more mature, the treatment of microorganisms on the UDMH gained attention again. Actually, UDMH is a highly reactive compound that undergoes oxidation reduction reactions and acts as a potent reducing agent [8]. If bacteria that are UDMH tolerant could be found, the biodegradation of UDMH might be realized. The acclimated activated sludge method attempted to treat wastewater containing UDMH of 1000 mg\(\cdot\) L\({}^{-1}\), and the degrading percentage of UDMH (72 h) was high at 90% [45]. The EOW/MBR (Electrolyzed Oxidizing Water/Membrane Biological Reactor) technology succeeded in treating wastewater containing UDMH of 500 mg\(\cdot\) L\({}^{-1}\), which also proves that UDMH can be treated by biodegradation [46]. A biodegradation technology of bioaugmentation in the fixed-film bioreactor has been adopted by NASA to treat wastewater containing methylhydrazine and hydrazine fuel, which has similar properties to UDMH [47].  
Almost all studies have been focused on wastewater treatment; only [PERSON] and [PERSON] [35] studied the special strain biodegradation of hydrazine in soil, but the results showed that the UDMH in soil could not be degraded by adding bacteria. In this study, under the process of co-metabolism, P4 had good adaptability in soil, which could withstand 600 mg\(\cdot\)kg\({}^{-1}\) UDMH and exhibited high biodegradation activity. More importantly, the concentration of the intermediate products obviously decreased in this biodegradation process.
# Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
## 3 Review on Optimization Methods under Continuous Phase Shift
### GD Method  
Even with the MM method, the complexity order is quadratic. To further reduce the computational complexity to linear order, GD can be employed to find a stationary point of (5). The key observation is that the ultimate unknown variable in the feasible set \(\mathcal{F}\) is in fact \(\{\theta_{m}\}_{m=1}^{M}\) instead of \(\mathbf{e}\). Therefore, problem (5) can be recast into an unconstrained optimization problem as  
\[\min_{\mathbf{\Theta}}~{}f\Big{(}\mathbf{x}^{(n)},e^{i\mathbf{\Theta}}\Big{)}, ~{}~{}\text{s.t.}~{}~{}\mathbf{\Theta}=[\theta_{1},\dots,\theta_{M}]^{T}. \tag{9}\]  
By recasting the quadratic function \(f\Big{(}\mathbf{x}^{(n)},\mathbf{e}\Big{)}\) shown in Figure 4a as \(f\Big{(}\mathbf{x}^{(n)},e^{i\mathbf{\Theta}}\Big{)}\), a graphical demonstration of the GD method is illustrated in Figure 4b. Using a feasible initialization point \(\mathbf{\Theta}^{(0)}\), \(\mathbf{\Theta}^{(r+1)}\) can be obtained at the \((r+1)^{th}\) iteration based on \(\mathbf{\Theta}^{(r+1)}=\mathbf{\Theta}^{(r)}-b^{(r)}\
abla_{\mathbf{\Theta}} f\Big{(}\mathbf{x}^{(n)},e^{i\mathbf{\Theta}^{(r)}}\Big{)}\), where \(b^{(r)}\) is the step size. Since only gradient information is involved in each update, GD has a linear complexity order with respect to \(M\), and the final converged point is a stationary solution to (5).  
Another point to note is that, by expressing the objective function in terms of \(\mathbf{\Theta}\), many local minima are introduced compared to the objective function in terms of \(\mathbf{e}\). Therefore, the quality of the converged solution of the GD method highly depends on the initialization. Notice that, since this method directly optimizes with respect to \(\theta_{m}\), it is also applicable to model C3 where \(\beta_{m}\) is a function of \(\theta_{m}\). The only change in (9) is replacing \(e^{i\mathbf{\Theta}}\) with \([\beta_{1}(\theta_{1})e^{i\theta_{1}},\dots,\beta_{M}(\theta_{M})e^{i\theta_{ M}}]^{T}\).
# Robust Estimators in Geodetic Networks Based on a New Metaheuristic: Independent Vortices Search
## 5 Results and Discussion
### Results in Network Adjustments  
For better analysis of the behavior of each RE, we divided the results into three topics: the no outlier case (Package 01); scenarios with small outliers (Packages 02, 04, 06, and 08); and scenarios with outliers of great magnitude (Packages 03, 05, 07, and 09).
# Think Twice: A Post-Processing Approach for the Chinese Spelling Error Correction
## 3 Motivation  
In the research field of Chinese Spelling Error Correction, we usually test the algorithm's capabilities on some public datasets, which contain many errors (such as SIGHAN [5; 25; 26]). So far, the Alibaba team proposed SpellGCN, which obtained the SOTA F1 score on SIGHAN13-15 (the character level correction F1 scores are respectively 93.1%, 85.6%, and 89.4%). Unexpectedly, when we applied this model to correct erroneous texts in reality, it produced many unnecessary corrections. After careful analysis of the wrong samples, we believe that the difference of text error rate (the percentage of error sentences in the text) caused this result. In machine learning theory, the Independent and Identically Distributed (IID) assumption is often made for training and test datasets to imply that all samples belong to the same distribution. However, to make the training process more efficient, the CSC algorithm training data may contain many more errors than usual, which will break the IID assumption and cause poor performance during inference.  
To illustrate this phenomenon, we performed a detailed test of SpellGCN on _Triple Door_'s manuscript. _Triple Door_[27] is a famous novel, written by [PERSON]. However, its originality has been widely questioned. After a renowned debate with [PERSON] in 2012, [PERSON] published _Triple Door_'s handwritten manuscript to prove his originality. There are still a number of spelling errors in that manuscript, which need proofreading before it can be published. Proofreading before the publication of novels or news articles is the most common application scenario of Chinese Spelling Error Correction. As the only published unedited original manuscript of a modern book to the best of our knowledge, we consider _Triple Door_'s manuscript as a great dataset to test the CSC algorithm's real application performance. First, we selected the first 10 pages of the manuscript, including 17 typos. Then, we divided the text into 110 sentences (error rate of 0.15), 16 of which contained errors. The average length of these sentences was 37.4. Next, we used SpellGCN to correct these sentences. We kept the hyperparameter settings of the model consistent with its official open-sourced implementation. During this experiment, we used Precision, Recall, F1 score and False Positive Rate (FPR) as the metrics.  
We chose the model's result index on the SIGHAN15 test set (error rate of 0.5) as a comparison, containing 1100 sentences of which 550 sentences contained errors. As shown in Table 1, when SpellGCN performed spelling error correction on _Triple Door_, the result index was far lower than the result obtained by the model on the SIGHAN15 test set, and the FPR value was closer to twice that of the latter. The uneven data distribution and the low error rate caused this phenomenon. Improving the performance of the algorithm model in actual error correction tasks will promote the application of such methods in the field of Chinese Spelling Error Correction.
# Wind Turbines Optimal Operation at Time Variable Wind Speeds
## 2 Wind Turbine's Mathematical Model  
The study presented in this paper is based on the classical mathematical model of the wind turbine, as given in [3]. It allows for the optimal values of the PMSG's angular speed, \(\omega_{optim}\), to be determined, so that the captured wind energy is maximum.  
The characteristic of the wind turbine's power, \(P_{WT}(\omega,S_{w})\), which also represents the wind turbine's mathematical model, has, in general, the below shape  
\[P_{WT}(\omega,S_{w})=a\Big{(}\frac{S_{w}}{\omega}-b\Big{)}e^{-c\frac{S_{w}}{ \omega}}S_{w}^{3} \tag{1}\]  
where \(S_{w}\) is the wind speed, \(\omega\) is the PMSG's angular speed, and the parameters \(a,\ b,\ c\) are specific to the used wind turbine, and can be determined from the data sheet provided by the wind turbine's manufacturer.  
For time-varying wind speeds, \(S_{w}(t)\), the wind turbine's power, \(P_{WT}\), varies between a minimum value and a maximum value, depending on the PMSG's angular speed value, \(\omega\).  
The maximum value of the wind turbine's power, \(P_{WTmax}\) is obtained from Equation (1), for the PMSG's optimal angular speed, \(\omega_{optim}\), by canceling the wind turbine's power derivative  
\[\frac{dP_{WT}}{d\omega}=\frac{d}{d\omega}\Big{(}a\Big{(}\frac{S_{w}}{\omega} -b\Big{)}e^{-c\frac{S_{w}}{\omega}}S_{w}^{3}\Big{)}=0 \tag{2}\]which yields to  
\[\omega_{optim}=\frac{c}{1+bc}\times S_{w}=k_{1}\times S_{w} \tag{3}\]  
where \(k_{1}\) represents the proportional constant, determined in [3],  
\[k_{1}=\frac{c}{1+bc} \tag{4}\]  
Furthermore, the wind turbine's power is zero for \(\omega=\omega_{max}\) (the maximum value of the PMSG's angular speed), which, employed in Equation (1), yields to  
\[P_{WT}=a\Big{(}\frac{S_{w}}{\omega_{\max}}-b\Big{)}e^{-c\frac{S_{w}}{\omega_{ \max}}}S_{w}^{3}=0 \tag{5}\]  
whose solution is  
\[\omega_{\max}=\frac{S_{w}}{b} \tag{6}\]  
Combining Equations (3) and (6), the ratio  
\[\frac{\omega_{optim}}{\omega_{\max}}=\frac{bc}{1+bc} \tag{7}\]  
which will be useful in determining the \(b\) and \(c\) coefficients.  
Finally, by replacing Equation (3) in Equation (1), the maximum value of the wind turbine's power, \(P_{WTmax}\), is obtained as  
\[\begin{array}{l}P_{WTmax}=a\Big{(}\frac{S_{w}}{\omega_{optim}}-b\Big{)}e^{-c \frac{S_{w}}{\omega_{optim}}}S_{w}^{3}=a\Big{(}\big{(}b+\frac{1}{c}\big{)}-b \Big{)}e^{-c(b+\frac{1}{c})}S_{w}^{3}\\ =\frac{a}{c}e^{-1-bc}S_{w}^{3}=k_{2}S_{w}^{3}\end{array} \tag{8}\]  
where  
\[k_{2}=\frac{a}{c}e^{-1-bc} \tag{9}\]  
represents another proportional constant, determined in [3].
## 4 Results and Discussion
### Refrigeration Cycle
The pressure varied from 0.00 MPa to 0.16 MPa in all processes, with an average difference of 0.03 MPa. The enthalpy ranged from a minimum of 2.81 kJ/kg to a maximum of 17.13 kJ/kg, with an average difference of 7.78 kJ/kg. This is because evaporation and condensation do not follow an ideal isobaric process in an actual refrigeration cycle. In the compression and expansion process, the isentropic and isenthalpic processes are the same as the theory, and the enthalpy difference at each point occurred due to superheating and subcooling in actual operation. However, it was confirmed that the difference was less than 5% of the error of pressure and enthalpy. Figure 9 shows the results of analyzing the pressure and enthalpy difference between the existing refrigeration cycle measurement results and the predicted results. Pressure and enthalpy have different units and ranges. Hence, the proposed refrigeration cycle prediction results were normalized and evaluated by calculating the distance function of the input and output points of each process of the refrigeration cycle. The difference between the input and output refrigerant pressure and enthalpy on the evaporator side and the input and output refrigerant pressure and enthalpy on the condenser side were large, as determined from the refrigerant temperature data of the evaporator. The distance function analysis between refrigeration cycles showed that the pressure had minimum and  
Figure 8: Range of accuracy analysis for the proposed method. The refrigerant pressure and enthalpy measurements in the data used to develop the refrigeration cycle measurement method had an error of \(\pm\)5%. The reliability of the proposed refrigeration cycle was evaluated by comparing the pressure and enthalpy with the maximum and minimum error values with a 5% error. The minimum and maximum pressures were 1.02 MPa and 2.67 MPa, respectively, and the minimum and maximum enthalpies were 237.94 kJ/kg and 491.45 kJ/kg, respectively. The difference between the pressure and enthalpy minimum and maximum values and their 5% tolerance was 0.05 MPa minimum and 0.13 MPa maximum for the pressure and 11.9 kJ/Kg minimum and 24.57 kJ/kg maximum for the enthalpy. In addition, a comparison of the distance function between the evaporation and condensation inlet and outlet points showed that the distance function has a minimum of 0.037 and a maximum of 0.077 when an error of 5% occurs in both pressure and enthalpy. The maximum distance function between the existing refrigeration cycle measurement results and the prediction results of the measurement method was smaller than the distance function value for a maximum error of 5%. Therefore, the proposed refrigeration cycle measurement method is reliable.
## 3 Case Study
### Training the Network with Time-Domain Data  
#### 3.1.1 Neural Network Training Process  
This study builds a multi-channel data recovery model and defines the network architecture. The LSTM network that contains an LSTM layer with a specified number of hidden units and a fully connected layer is created. The solver 'Adam' is used to train epochs and batch size based on user-specified size. The learning rate is specified as 0.03.  
Figure 3: Location of the acceleration sensors. Points 1–3 are on the rail, points 4–6 are on the track slab, and points 7–8 are on the tunnel wall. The circle is the point on the track slab, the star is the point on the railway track, and the triangle is the point on the tunnel wall.  
When a subway train passed, a set of 12.8 s vibration signals were generated, and then the collected eight-channel vibration signals were used as the training network. Channels 1-6 were used as test predictor variables (XTest), and Channels 7-8 were used as the response sequence (YTest). The LSTM network was used to predict the incomplete sequences that lacked data from Channels 7 and 8. The prediction results from the training network are shown in Figures 4 and 5. Figure 4 showcases the prediction of the missing data for Channel 7, while Figure 5 showcases that for Channel 8. It can be observed from the figures that this training network can correctly demonstrate the relationships between the data from Channels 1-6 and  
The user-specified parameters in the above neural network are optimized to maintain the model's training accuracy and speed. The optimization results are as follows:  
Effect of Epoch on Model Accuracy  
As shown in Figure 6, when the epoch value is less than 50, the RMSE value drops rapidly. The decline rate of the RMSE value slows down with the continuous increase in epoch value, and the RMSE region stabilizes when the epoch value exceeds 100. Therefore, 100 is selected as the epoch value for this model by considering the computational accuracy and efficiency.  
Effect of Batch Size on Model Accuracy  
An appropriate batch size selected in an LSTM can increase the model accuracy and reduce the model's training time. As shown in Figure 7, the value of the RMSE shows a trend of first decreasing and then increasing with the increase in batch size in this model. When the batch size is selected as 250, the value of the RMSE is the minimum, and so 250 is used as the value of the batch size for this model.  
Figure 6: Effect of different epochs on model accuracy.  
Figure 7: Effect of different batch sizes on model accuracy.  
Effect of the Number of Hidden Units in the LSTM Layer on Model Accuracy  
When the number of hidden units is less than 30, the RMSE of the training model drops rapidly, as shown in Figure 8. When the number of hidden units is up to 50, the RMSE value is the minimum. As the number of hidden units continues to increase, the RMSE value tends to increase slowly. Therefore, 50 is selected as the value of the number of hidden units in the LSTM layer for this model.
## 6 Evaluation and Analysis
### Simulation Results
#### 6.3.2 NET Layer Results  
This section evaluates the performance of three network routing protocols: DAAM, context-aware DAAM and AODV, with IEEE 802.15.4 unslotted CSMA/CA as the common MAC protocol. Similarly, the results are shown for normal and high traffic settings as summarized in Table 2.  
Figure 11 shows the PDR of the three network routing protocols. It is observed that the context-aware DAAM can achieve 26%, and 13%, improvement to AODV and original DAAM, respectively, under normal traffic. The improvement in PDR under high traffic scenario is 44%, and 18%, respectively. In DAAM, multiple node-disjoint paths can increase the success of the packets being delivered to the VB since any of the VB member nodes can accept the incoming sensor data packets. This avoids losses due to congestion when only a single node, e.g., a cluster head, is the destination for all sensor data packets. In addition, the context-aware DAAM makes the sensor data packets traversed through paths according to their data priority; this can further improve the packets delivery to the VB.  
Figure 11: PDR of NET protocols.  
Figure 10: Energy cost for a successful frame delivery by MAC protocols.  
Figure 12 compares the end-to-end delay among the three protocols. Under context-aware DAAM, the delay is reduced by 38%, and 32%, over AODV, and original DAAM, respectively, under normal traffic scenario, while it is reduced by 45%, and 35%, respectively, under high traffic scenario. The path diversity of DAAM significantly improves the delay performance of AODV. By further combining with AmI context information, the delay can be further reduced as low latency paths are used to transmit the high priority sensor data.  
Figure 13 shows the corresponding energy cost. It illustrates significant energy savings under high traffic scenario where the context-aware DAAM reduces the energy cost by 24%, and 15%, over AODV, and original DAAM, respectively. Compared to AODV, which only uses a single path to transmit all the data between a pair of nodes, more available paths from the DAAM based protocols allow the data to be delivered to any member nodes of a VB. This increases the PDR, as shown in Figure 11, which in turn improves the energy efficiency.  
Figure 12: End-to-end delay of NET protocols.  
Figure 13: Energy cost for a successful packet delivery of NET protocols.