esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Equations (2) and (3), however, represent rapid proton transfer reactions [27]. Tertiary amines, sterically hindered or not, on the other hand, owing to the lack of free hydrogen atom, are unable to react directly with CO\({}_{2}\) to form a carbamate. Instead, an alternative reaction occurs preferably to form a bicarbonate ion under humid conditions (Equation (4)) [28]. \[\text{RR}^{*}\text{RR}^{*}\text{N}+\text{CO}_{2}+\text{H}_{2}\text{O} \leftrightarrow\text{RR}^{*}\text{RR}^{*}\text{NH}^{+}+\text{HCO}_{3}^{-} \tag{4}\]  
where R, R\({}^{*}\) and R\({}^{**}\) could be either different or identical alkyl groups. Sterically hindered amines (SHAs), as originally defined by [PERSON] and [PERSON], can be identified from either of the following: (i) a primary amine in which the amino group is attached to a tertiary carbon atom or (ii) a secondary amine in which the amino group is attached to at least one secondary or tertiary carbon atom [29]. Based on this definition, it is difficult to identify tertiary amines with small bulkiness. In this review, we regard tertiary amines as SHAs due to their similar performance. Unlike unhindered amines, SHAs are characterized by the formation of carbamates with intermediate-to-low stability due to the bulkiness of the substitutions attached to the amino groups. In this way, SHAs may provide a faster reaction with CO\({}_{2}\) while they lower the heat of reaction. As such, membranes with SHAs might proceed with CO\({}_{2}\) transport (adsorption-hop-desorption) more effectively across the membrane thickness. In addition, as shown in Equations (3) and (4), humidity or water vapor is also an important factor that impacts the CO\({}_{2}\) transport behaviors within amine-containing membranes, which might make it more complicated to investigate the role of amine type in CO\({}_{2}\) transport behaviors. The effect of humidity on CO\({}_{2}\) transport behaviors was therefore excluded in this review.
## 4 Statistical Multi-Node MIMO Switching in Mobile WMSNs
### Algorithm Description  
In this section, we propose a statistical MN MIMO mode switching algorithm based on the capacity analysis described in Section 3. The basic idea of the proposed algorithm is the switching of MIMO mode based on the statistical channel quality to maximize the capacity. To find an appropriate switching point and an optimal MIMO mode, we define a multi-node (1) channel estimation, (2) MIMO mode and antenna set selection and (3) MIMO mode switching.  
Figure 5 illustrates the overall sequences of the proposed scheme in an MS and SENs. As shown in Figure 5, an MS periodically measures the link quality, including the average SNR and the spatial correlation for a part or all antenna sets from multiple SENs. To measure the link quality, the MS usually exploits the predetermined reference pilot signals that are orthogonally transmitted from transmit antennas [40]. By correlating the received pilot signals transmitted from different antennas for much longer than the channel coherence time, the transmit antenna correlation can be estimated, whereas the receive antenna correlation can be obtained by correlating received pilot symbols of different receive antennas transmitted from the same transmit antenna [41]. The average SNR can also be estimated without much complexity. Based on the channel estimation results, either an SEN or MS determines  
an appropriate antenna set and a MIMO mode. In the proposed algorithm, ergodic capacity expressions Equations (11) and (13) (or their bounds, Equations (12) and (16)) can be the cost functions to find an optimal antenna set and a MIMO transmission mode. If the SEN determines an antenna set and a MIMO mode, the MS should report the channel estimation result to the SEN. Otherwise, the MS selects an optimal antenna set and MIMO mode and sends a MIMO-mode-switching request to the SEN. To maximize the capacity, SN (or MN) SM or SN OSTBC transmission can be selected from Equations (11) and (13) (or their bounds, Equations (12) and (16)) by calculating the determinants of the correlation matrix, e.g., Equations (6) and (7). Specific SNR crossing-points will be presented in the following section with detailed derivation for the two-antenna case. After antenna and MIMO mode selection, the currently connected SEN notifies the selected antenna set and MIMO mode to the MS, and the MS forwards it to the target SEN by transmitting a Mode_Switching_REQ message only when the multi-node MIMO scheme is selected. If the target SEN has enough power to support the requested MIMO mode for the MS, then it sends a Mode_Switching_RSP message to the MS. Then, the MS sends a Mode_Switching_RSP message to the currently connected SEN with the confirmation of the selected MIMO mode and antenna set. Finally, the connected SEN and the target SEN update the MIMO mode and transmit the sensing data using the selected antenna and MIMO mode.  
Figure 5: The procedure of a statistical multi-node MIMO switching.
## 4 APSP and Routing Tables
### Complexity
#### 4.7.2 Complexity of the Classical Analog  
For completeness, we provide here a characterization of the complexity of a closely related classical algorithm for APSP with routing tables in the CONGEST-CLIQUE, as proposed in [12], which has complexity \(\tilde{\mathcal{O}}(n^{1/3})\). In their framework, the approach to finding witness matrices requires \(\tilde{\mathcal{O}}(\log(n)^{3})\) calls to the distance product ([12] Section 3.4), and similar to our approach, \(\log(n)\) distance products are required. Their classical algorithm computes distance products in \(\tilde{\mathcal{O}}(n^{1/3})\) rounds, or under \(2\log n\) message bandwidth in up to  
\[20n^{1/3}\log(n)^{4}=:g(n) \tag{5}\]  
rounds, the details of which can be found in Appendix A.2. Then, \(g(n)>n\), up until about \(n\approx 2.6\cdot 10^{11}\). As with the quantum APSP, though this algorithm provides the best-known asymptotic complexity of \(\tilde{\mathcal{O}}(n^{1/3})\) in the classical CONGEST-CLIQUE, it also fails to give any real improvement over the trivial strategy across a very large regime of values of \(n\). Consequently, algorithms making use of this APSP algorithm, such as [2] or [3], suffer from the same problem of impracticality. However, the algorithm only requires \(4n^{4/3}\log(n)\log(nW)+n\log(n)\log(nW)\) memory per node, which is less than what is required for the trivial strategy, even for \(n\geq 4\).
# PEMFC RUL Prediction for Non-Stationary Time Series Based on Crossformer Model
## 4 Results and Discussion
### Results
#### 4.2.1 Network Model Comparative Experiment
Their performance on the DTW metric remains superior to that of other models, albeit with a slightly broader error range, indicating some variability, with the presence of outliers reflecting larger errors in specific samples. For the LSTM-ATTN, TCN, and LSTnet models, MSE values are comparatively higher, with a wider interquartile range, especially in the case of the TCN model, which exhibits greater uncertainty and variability. This reflects lower predictive accuracy for these models compared to the proposed model and other advanced models, particularly with less stable performance on certain samples. In summary, the proposed model demonstrates superior performance over other compared models in both the MSE and DTW metrics, exhibiting not only lower error values but also narrower error distributions, indicating greater stability and accuracy. This suggests that under experimental conditions, the proposed model is capable of providing more precise predictions with higher consistency and reliability. Figure 11: Model error analysis on DTW indicators. Figure 10: Model error analysis on MSE indicators. Table 2 presents the experimental results of six models on four fuel cell vehicle datasets with an input length of 48 and a corresponding prediction length of 64. The bolded results indicate the best performance. The results demonstrate that the De-stationary-Crossformer achieves the best performance in terms of MSE and DTW, exhibiting superior prediction accuracy and sequence shape learning capability, with minimal variation, thus ensuring greater stability across datasets. The Crossformer, with a variation rate of approximately 30%, effectively captures complex temporal dependencies but shows slightly higher variability compared to the De-stationary-Crossformer. In contrast, the Transformer performs moderately, while LSTM-ATTN, TCN, and LSTnet exhibit higher error values and larger variability, highlighting their limitations in aligning time series data and learning sequence shapes. These findings underscore the inadequacy of traditional models in handling non-stationary time series data, emphasizing the need for more robust and effective modeling approaches.
## 4 Results of Simulation
### Simulation Test of Optimal Parameters
#### 4.2.1 Simulation of Energy Consumption on Different Pulse Interval Times  
This test is to evaluate the energy consumption generated by different pulse interval times \(T_{w}\) under the constant of dust concentration \(C\) and pulse time \(T_{p}\), where setting \(C=2.0\times 10^{-3}\) kg/m\({}^{3}\), \(T_{w}=5\sim 30\) s, \(T_{p}=0.1\) s. When \(T_{w}=5\) s-\(747\) days (the filter has a service life of \(747\)\(T_{w}\) days), it obtains \(T_{w}=10\) s-\(559\) days, \(T_{w}=15\) s-\(474\) days, \(T_{w}=20\) s-\(422\) days, \(T_{w}=25\) s-\(386\) days, and \(T_{w}=30\) s-\(359\) days, as shown in Figure 8a. According to the results, it is found that \(T_{w}\) is getting longer, and the service life of the filter will be shorter with the pressure drop to \(150\) mmAq. In the analysis of energy consumption, as shown in Figure 8b, we can observe that a different \(T_{w}\) has little effect on the monthly energy consumption of the fan motor. Conversely, a decrease in the \(T_{w}\) causes an increase in the number of cleanings, which will significantly increase the energy consumption of the compressed air for backwashing. In the analysis of cost, as shown in Figure 8c, we can observe that a different \(T_{w}\) has little effect on the electricity cost of the fan motor and the high-pressure backwash air due to the change from the fan motor mainly manifested by \(P_{Equip}\) and \(P_{pipe}\). Based on the results, the parameter of \(T_{w}\) will not be considered on energy saving but for cost, and \(T_{w}=5\) s is the best parameter setting for the simulation. However,  
Figure 7: The changes in cleaning efficiency and pressure drop in the zones.  
considering that \(T_{w}\) is too short, it will cause frictional damage when the pressure drop is not to 150 mmAq. Therefore, according to this, the study recommended that the parameter \(T_{w}\) = 10 s may be a better choice based on the actual use conditions to reduce the cost of cleaning by half.
## 3 Review Results
### Note on the Methodologies Applied in the Studies
#### 3.9.3 Analytical Techniques  
**Partial Least Squares Structural Equation Modeling (PLS-SEM):**  
Structural Equation Modeling (SEM) is a widely used analytical technique in these studies. SEM allows researchers to test complex relationships between observed and latent variables, providing a robust framework for understanding the factors influencing ChatGPT adoption. Various software tools like AMOS and SmartPLS are commonly employed for SEM analysis.  
PLS-SEM is frequently used in the analysis of survey data, particularly when the research focuses on predicting key target constructs and developing theoretical models. This technique is well suited for handling complex models with multiple constructs and indicators [4; 5; 27].  
**Thematic Analysis:**  
In qualitative studies, thematic analysis is often used to identify and analyze patterns or themes within interview data. This method involves coding the data and organizing them into meaningful categories that reflect the key factors influencing the adoption of ChatGPT [15; 21].  
**Artificial Neural Network (ANN) and Deep Neural Network (DNN):**  
In some innovative studies, hybrid approaches using an ANN, DNN, and classification algorithms have been used alongside PLS to enhance the predictive accuracy and robustness of the models. These advanced techniques provide deeper insights into the complex relationships between variables.  
The methodologies employed in studies on ChatGPT adoption in higher education are diverse, encompassing cross-sectional and longitudinal designs, quantitative and qualitative approaches, and a range of data collection and analytical techniques. The predominant use of survey-based data collection and SEM/PLS-SEM for analysis reflects the focus on identifying the key predictors and understanding the structural relationships influencing adoption. The integration of mixed-methods approaches and advanced analytical techniques like ANN and DNN highlights the evolving nature of the research in this field, aimed at providing comprehensive and nuanced insights into the adoption dynamics of ChatGPT in educational settings [50].
## 2 LDPC Codes
### CCSDS Code  
The check matrix of CCSDS codes consists of a submatrix of \(M=k/4=n/8\), where \(n,k\) denote the code word length and the original bit length, respectively. Let \(I_{M}\) denote the identity matrix, \(0_{M}\) denote the zero matrix, \(\Phi^{k}\) denote the identity matrix cyclically shifted right by \(k\) bits and \(\oplus\) denote the modulo-two addition of matrix elements at the corresponding positions. The check matrix can then be written as  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Spectrum** & **Spreading Mode** & **Codes \(\mid\)Modulation** \\ \hline \multirow{3}{*}{DSSS} & CCSDS(512,256) & \multirow{3}{*}{BPSK} \\  & CCSDS(256,128) & \\  & CCSDS(128,64) & \\ \hline \multirow{3}{*}{M-ary SS} & CCSDS(512,256) & \multirow{3}{*}{BPSK} \\  & CCSDS(256,128) & \\ \cline{1-1}  & CCSDS(128,64) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spread-spectrum coding system research content.  
\[H_{64\times 128}=\left[\begin{array}{llllll}I_{M}\oplus\[PERSON]^{7}&\[PERSON]^{2}& \[PERSON]^{14}&\[PERSON]^{6}&0_{M}&\[PERSON]^{0}&\[PERSON]^{13}&I_{M}\\ \[PERSON]^{6}&I_{M}\oplus\[PERSON]^{15}&\[PERSON]^{0}&\[PERSON]^{1}&I_{M}&0_{M}&\[PERSON]^{0}&\[PERSON]^{7}\\ \[PERSON]^{4}&\[PERSON]^{1}&I_{M}\oplus\[PERSON]^{15}&\[PERSON]^{14}&\[PERSON]^{11}&I_{M}&0_{M}&\[PERSON]^{3 }\\ \[PERSON]^{0}&\[PERSON]^{1}&\[PERSON]^{9}&I_{M}\oplus\[PERSON]^{13}&\[PERSON]^{14}&\[PERSON]^{1}&I_{M}&0_{M} \\ \[PERSON]^{16}&\[PERSON]^{0}&I_{M}\oplus\[PERSON]^{55}&\[PERSON]^{27}&\[PERSON]^{56}&I_{M}&0_{M}&\[PERSON]^{ 43}\\ \[PERSON]^{35}&\[PERSON]^{56}&\[PERSON]^{62}&I_{M}\oplus\[PERSON]^{11}&\[PERSON]^{58}&\[PERSON]^{3}&I_{M}&0_{ M}\end{array}\right] \tag{1}\]  
From the check matrix of the CCSDS code, its generator matrix can be calculated by setting  
\[H=\begin{bmatrix}Q&P\end{bmatrix} \tag{4}\]  
where \(Q\), \(P\) denote the submatrix of the first \(4M\) and last \(4M\) columns of the check matrix, respectively. Then, we have  
\[G=\begin{bmatrix}I_{4M}W\end{bmatrix} \tag{5}\]  
where \(W\) can be calculated from  
\[W=\begin{pmatrix}P^{-1}Q\end{pmatrix}^{T} \tag{6}\]
## 2 Materials and Methods
### Ring Resonator
Antennas designed with add-drop ring resonators can adjust their operating frequency range according to restrictions specified by regulations. Hence, the antenna can adapt to different frequency bands by reconfiguring the resonance properties of the ring resonator. Antenna performance parameters such as bandwidth, gain, and radiation efficiency can be improved by using add-drop ring resonators. The add-drop ring resonator has the advantage of providing communication with different standards simultaneously without a significant increase in the antenna footprint.
###### Abstract  
Background: Scarce evidence is available regarding the occurrence and prevalence of hard/soft tissue deficiencies among patients undergoing surgically assisted rapid maxillary expansion (SARME) as compared to non-surgical rapid maxillary expansion (NSRME) approaches. The purpose of this study is to evaluate the effect of NSRME and SARME upon the periodontal structures. Methods: A literature search was performed to identify studies that fulfilled pre-established eligibility criteria, evaluating changes in the periodontium (e.g., probing depths) and hard/soft tissue deficiencies (e.g., bone levels, gingival recession) within patients undergoing NSRME and SARME procedures. Results: A total of 21 articles were included in the present review. Four of them evaluated the outcomes of both NSRME and SARME procedures, while six and eleven studies analyzed NSRME alone and SARME alone, respectively. The incidence of hard (e.g., changes in buccal bone width/height) and soft tissue deficiencies (e.g., gingival recession, keratinized tissue, and clinical attachment level) is slightly increased among patients undergoing maxillary expansion with worsened outcomes during NSRME procedures. Conclusions: The impact of NSRME and SARME upon the periodontium remains inconclusive.  
Keywords:palatal expansion technique; rapid maxillary expansion; transpalatal distraction osteogenesis; transverse maxillary distraction; periodontium +
Footnote †: journal:  
maximilomandibular jawbones via orthognathic surgery can be considered when treating severe skeletal discrepancies; however, the increased cost, risk of morbidity, and need for hospitalization create demand for alternative treatment modalities [12].  
Rapid maxillary expansion was introduced as a simpler and less invasive alternative when compared to orthognathic surgery [13]. Among adult patients, mild transverse discrepancies can be camouflaged by orthopedic and orthodontic forces alone, while more severe transverse discrepancies require surgical intervention [14; 15; 16]. Aside from orthognathic surgery, surgically assisted rapid maxillary expansion (SARME) has been indicated as the preferred treatment for skeletally mature patients with a large transverse discrepancy and who are not in need of additional surgical jaw movements [14; 17].  
Although orthopedic expansion can successfully treat maxillary deficiencies, a complete understanding of the impact of dental expansion and craniofacial modifications upon the periodontitis remains unclear. Adverse outcomes, such as osseous dehiscence (DEH) [18], vertical bone defects [19], root resorption [20], and mucoginyal deformities/conditions [21], have been reported.  
Scare evidence is available regarding the occurrence and prevalence of hard/soft tissue deficiencies among patients undergoing SARME as compared to non-surgical rapid maxillary expansion (NSRME) approaches. Thus, the purpose of this systematic review was to evaluate the effect of NSRME and SARME upon the periodontal structures.
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 3 Privacy Techniques
### Pseudonymization  
Pseudonymization is a privacy-preserving technique used in FRSs to protect the privacy of users by replacing their identifiable attributes with a pseudonym [113; 114]. It is a method of masking the true identity of a user while allowing his/her to be identified by his/her pseudonym. Pseudonymization can protect a user's sensitive attributes, such as age, gender, and location, as well as his/her explicit and implicit preferences. Several studies have been conducted on this matter [115; 116; 117].  
The benefits of using the pseudonymization technique in FRSs include improved privacy for users, as their data are not stored, and only their pseudonym is used to identify them [118]. Furthermore, pseudonymization also allows for better scalability of an FRS as the amount of data stored is reduced and the data can be divided into smaller chunks [119]. However, there are also some limitations to using pseudonymization in FRSs. One of the main drawbacks is that it can be difficult to accurately link a user's pseudonym with his/her true identity and preferences. Furthermore, pseudonymized data can also be vulnerable to dictionary attacks and re-identification attacks.
## 5 Materials and Methods
### Suggested Empirical Correlation for \(V_{\rm s}\)-SPT-N(\(N_{60}\))  
In this study, in addition to the correlation between the V\({}_{\rm s}\) and SPT-N being examined, the correlation between the V\({}_{\rm s}\) and energy-corrected SPT-N(N\({}_{60}\)) values was investigated, and an equation was established for sandy soils. During SPT tests, the SPT-N blow counts are corrected via impact energy corrections. A donut-type hammer is used in Turkey, and the hammer is dropped through the application of two cycles to the rope. After the 60% energy correction of the standard rod energy, the blow counts are shown with the symbol N\({}_{60}\), and the energy-corrected SPT-N blow counts are determined by the following equation:  
\[N_{60}=N\frac{E_{r}}{60} \tag{3}\]  
where N is the blow count obtained directly from the SPT experiment, and the energy ratio (E\({}_{r}\)) is 45.  
The equation and correlation coefficient (r) for the sandy alluvial soil in the study area, obtained as a result of regression analysis between the V\({}_{\rm s}\) (m/s) and N\({}_{60}\) values, are as follows:  
\[V_{\rm s}=83{N_{60}}^{0.343}\left(\rm{R}^{2}=0.303-r=0.55\right)\text{ sandy alluvial soil} \tag{4}\]  
The regression analysis graph between V\({}_{\rm s}\) and N\({}_{60}\) is given in Figure 8.  
Figure 8: Correlations between V\({}_{\rm s}\) and SPT-N\({}_{60}\) for sandy alluvial soil.  
Figure 7: Comparison of correlations suggested in this study and previous studies for V\({}_{\rm s}\) and SPT-N for sandy soil [1; 2; 3; 5; 7; 21; 22; 23; 25; 27; 29; 31; 32; 33; 43; 44; 47; 48; 50; 51].
# A Novel Nonlinear Magnetic Equivalent Circuit Model for Magnetic Flux Leakage System
## 5 Testing the MEC Model with FEA
### FEA Studies
This effect becomes more pronounced when the \(w_{e}\) value approaches the material's thickness \(w_{m}\). As a result, the flux densities in the pole regions increase, leading to an exponential increase in the horizontal leakage flux within the slot. A similar effect emerges with the variation in the \(w_{s}\) parameter. Since the \(w_{s}\) dimension determines the distance between the poles, it changes the air reluctance of the horizontal leakage flux. While a decrease in \(w_{s}\) contributes positively to the flux density in the test material, it also increases the horizontal leakage flux, depending on the pole flux densities. Forming near saturation flux density in the tested sample can be seen as the main reason for this dilemma. Ultimately, the variation in \(w_{e}\) and \(w_{s}\) dimensions alters the character of the leakage flux within the slot, which is more critical for the system. An increase in horizontal leakage flux particularly affects the sensor measurement region directly, leading to adverse effects such as an increase in the DC level in the defect signal, shape distortions, and signal noise. Figure 1c illustrates an example of these distortions.