esa-sceva/satcom-chunk-collection huggingface dataset:
# Escalator: An Autonomous Scheduling Scheme for Convergecast in TSCH
## 4 Proposed Autonomous Scheduling Scheme for Convergecast
### Baseline Slotframe Size Determination
The length of the slotframe and the number of channels should be relatively prime to allow each timeslot to communicate using all available channels [3; 11]. In addition, \(L(S_{Conv})\) and \(L(S_{Base})\) should be satisfying the following three conditions, so that all nodes can send one packet to the sink within two cycles of the convergecast slotframe, even if a collision occurs. First, when the remainder obtained by dividing \(L(S_{Conv})\) by \(L(S_{Base})\) is defined as \(m\) in Equation (8), \(m\) should not be equal to zero; because if \(m\) is zero, a specific timeslot of \(S_{Conv}\) at every cycle is suppressed by the shared timeslot of \(S_{Base}\). \[m=(L(S_{Conv})\text{ mod }L(S_{Base}))\
eq 0. \tag{8}\]  
As a second condition, if \(L(S_{Base})\) is greater than \(L(S_{Conv})\), \(L(S_{Base})\) should be greater than or equal to \(L(S_{Conv})+H_{max}(G)\) as in Equation (9). When \(L(S_{Base})\) is greater than or equal to \(L(S_{Conv})+H_{max}(G)\), if a collision occurs at timeslot \(ts\) of convergecast slotframe \(T\), then the next collision occurs after \(ts+H_{max}(G)-1\) timeslots of slotframe \(T+1\). Therefore, a collision does not occur in the timeslot section \([ts,ts+H_{max}(G)-1]\) of slotframe \(T+1\), so delayed packets in slotframe \(T\) could be transmitted to the sink. \[L(S_{Base})\geq(L(S_{Conv})+H_{max}(G)),\quad if\quad L(S_{Base})>L(S_{Conv}). \tag{9}\]  
Figure 7a shows an example where \(L(S_{Base})\) is greater than \(L(S_{Conv})\). In the example, a collision occurs at timeslot \(ts=1\) of slotframe \(T\) and timeslot \(ts=6\) of slotframe \(T+1\). Since collisions do not occur in timeslot Sections 1-5 of slotframe \(T+1\), the packets delayed at timeslot \(ts=1\) of slotframe \(T\) could be transmitted to the sink. Figure 6: Timeslots of the convergecast slotframe where collisions should be avoided. In the last condition, if \(L(S_{Base})\) is less than \(L(S_{Conv})\), then \(L(S_{Base})\) should be greater than \(H_{max}(G)+m-1\) as in Equation (10). If a collision occurs at \(ts\) timeslot of slotframe \(T\), collision will occur at \(ts+L(S_{Base})-m\) timeslot of slotframe \(T+1\). Since no collision should occur at \([ts,ts+H_{max}(G)-1]\) of slotframe \(T+1\), \(L(S_{Base})-m\) should be less than zero or greater than \(H_{max}(G)-1\). \(m\) cannot be larger than \(L(S_{Base})\) since \(m\) is the remainder obtained by dividing \(L(S_{Conv})\) by \(L(S_{Base})\) as in Equation (8). Therefore, \(L(S_{Base})\) should be larger than \(H_{max}(G)+m-1\). \[L(S_{Base})>H_{max}(G)+m-1,\quad if\quad L(S_{Base})<L(S_{Conv}). \tag{10}\]  
Figure 7b shows an example where \(L(S_{Base})\) is less than \(L(S_{Base})\). In the example, collisions occur at timeslot \(ts=1,12\) in slotframe \(T\) and timeslot \(ts=11\) in slotframe \(T+1\). Since there is no collision in the timeslot section [1; 5] of slotframe \(T+1\), a packet delayed at timeslot \(ts=1\) in slotframe \(T\) can be transmitted from slotframe \(T+1\) to the sink.
## 3 Results and Discussion
### Hardened-State Properties of Mortars  
#### 3.2.1 Compressive Strength  
The compressive strength results between 7 days and 365 days of curing are shown in Figure 4a. For ages up to 91 days, the binary mortars that incorporate between 5% and 10% of RC show a compressive strength that is 11.5% and 23.9% (Table 5) lower than that of the conventional mortar, respectively, due to the dilution effect and low reactivity of the mortar. RC [15] and the mortars that incorporate between 10% and 20% of FA also decrease their compressive strength between 9.2% and 21.7%, respectively, with respect to the conventional mortar because the clinker dilution effect still prevailed over the pozzolanicity of FA [38]. Regarding the compressive strength of the ternary mortars (simultaneous incorporation of RC and FA), they experienced a reduction between 6.1% (mix M-5/10) and 31.6% (mix M10/20) of this property compared to that of the conventional mortar. According to these results, the simultaneous use of RC and FA compared to the binary mortars, which only contain RC or FA, improves the compressive strength of mortars. These findings can be explained by the fact that RC complements the reactivity of active SCMs, as in the case of FA [39]. These results have also been previously observed in past investigations on ternary mortars that incorporate limestone powder (10-20%) and FA (10-30%) [40] or recycled concrete powder (10-40%) in combination with SF and FA (15-30%) [19]. At 365 days of curing, the ternary mortars that incorporate 5% RC and 10% FA show similar compressive strengths (<2.5%) to that of the conventional mortar, and the compressive strength of mortars with 10% RC and 20% FA show decreases of less than 10%. This result is directly related to the changes induced by the simultaneous combination of the two additions (RC and FA), specifically in the refinement of the pore structure [19]. Finally, Table 6 shows that the individual and/or simultaneous incorporation of RC and FA as additions to the cement did not alter the logarithmic relationship between curing age and compressive strength. As seen there, the correlation coefficients (R\({}^{2}\)) were higher than 0.92 and similar to the R\({}^{2}\) > 0.94 found for mortars that incorporated other additions, such as ash from biomass-fuelled power plants [4] or ornamental granite industry sludge [6], that replaced cement. Figure 3: (**a**) Air content as a function of consistency (**b**) and variation in density with OPC replacement in the mixes.
# Neural Network Exploration for Keyword Spotting on Edge Devices
## 3 Method
### Quantization  
After training, each neural network was evaluated in terms of model size, number of parameters, latency, memory consumption, and accuracy using different quantization methods. TensorFlow Lite [29] facilitates the conversion of large TensorFlow models into smaller, space-efficient FlatBuffers. This converted model can easily be deployed on resource-constrained systems. The performance of the converted TensorFlow Lite FlatBuffer models was evaluated with four available post-training quantization methods: 16-bit floating-point quantization, dynamic range quantization, integer quantization with floating-point fallback, and full-integer quantization [30].  
* The 16-bit floating-point quantization utilizes 16-bit floating-point values for weights and 32-bit floating-point values for operations.
* Dynamic range quantization allows the model to mix integer and floating-point operations when possible (falling back to 32-bit floating-point operations when necessary) while the weights are stored as 8-bit integers. The weights are quantized post training, and the activations are quantized dynamically at inference in this method.
* Integer quantization with floating-point fallback utilizes integers for weights and operations. If an integer operation is not available, the 32-bit floating-point equivalent is substituted. This results in a smaller model and increased inference speed. For convenience, this scheme is shortened hereafter as _integer-float quantization_.
* Full-integer quantization utilizes only 8-bit integers for weights and operations, removing the dependency on floating-point values. This method achieves further latency improvements, reduction in peak memory usage, and compatibility with integer-only hardware devices or accelerators.  
After the TensorFlow Lite models were saved, the accuracy, memory consumption, latency, and size of the model files were recorded. The collected results were saved in a CSV file and evaluated on the same Google Cloud Platform virtual machine. The evaluation results are presented and elaborated in Section 4.
## 6 Proposed Method
### Intra Cluster Communication  
In addition to the main cluster head (MCH) a new backup cluster head (BCH) is created in each cluster to effectively utilize the one hop communication process. TDMA and CSMA are used for the data communication in this section. TDMA handles the time slot allocation process among the nodes in the network. As the results all the nodes will transmit the data in the particular instant of time. At the initial condition, the MCH selects its sink node (MCH or BCH) for each and every node inside the cluster. The major step of intra cluster communication is given as follows,  
* [noitemsep,nolistsep]
* Sink node determination
* Time slot determination of each node
* Data transmission
* Aggregation
## 5 Triple-Frequency Medium RTK Performance Analysis
### Computation Cost Time  
For high-frequency terminals, such as those operating at 10 Hz, the computational time of the RTK algorithm is an essential consideration. Excessive computational power consumption will lead to an increase in hardware costs. Considering the advantage of accelerated convergence through triple-frequency observations, this section compares the computational time of Scheme 2 and Scheme 3. Based on the Intel i7-10700 processor with a clock frequency of 3.8 GHz, the processing time of 48 sessions for the 6 baselines is shown in Figure 13.  
Figure 11: DD ionosphere delay time series of HZDQ-HZLA. Each colored line represents a satellite.  
Figure 12: (**a**) The positioning error in the Up direction for HZDQ-HZLA, using dual-frequency BDS B1I/B3I and (**b**) The positioning error in the Up direction for WHJ\(\times\)WHJA, using dual-frequency BDS B1I/B3I. The red dots represent fixed solutions, while the blue dots represent float solutions.  
Taking the baseline HZDQ-HZLA as an example, except for sessions 29, 34, and 36 (which exceed 40 s), all other sessions take less than 40 s for Scheme 2. In contrast, for Scheme 3, most sessions exceed 40 s, with session 34 being the longest at over 80 s. For other baselines, most sessions in Scheme 2 are also completed in under 40 s, whereas, in Scheme 3, the majority of sessions require more than 40 s. Table 3 provides statistics on the average computation time for the 48 sessions of the 6 baselines. Compared to Scheme 3, Scheme 2 has an average time consumption reduction of 8.26 s, or approximately 20%. The scheme that utilizes triple-frequency-only observations has a lower computation time, being more beneficial for high-frequency GNSS terminals.  
Figure 13: The processing time of 48 sessions for 6 baselines when using Scheme 2 and Scheme 3.
# Research on Real-Time Model of Turboshaft Engine with Surge Process
## 5 Conclusions  
In this paper, a novel real-time model of turboshaft engine with surge process based on instability mechanism is proposed. The CLM of a turboshaft engine and the extended MG model are established. Then the surge characteristic is embedded into CLM via regarding the compression system described by the MG model as the compressor component in the CLM. On this basis, a real-time surge model of turboshaft engine is proposed by building up a coupling relationship of compressor speed, flow, and pressure between the extended MG model and CLM. The main conclusions are as follows:  
1. Stability analysis and characteristic line extension of classical MG model are carried out to establish the extended MG model which can be matched with CLM.
2. The influence of engine rotor inertia and thermal inertia are considered when establishing the coupling relationship between compressor speed, flow and pressure. The fluctuating flow and pressure are carried into the component level model after the smoothing filter, which can ensure that the change of speed and temperature is in accordance with actual test conditions.
3. The real-time surge model proposed combines the extended MG model's ability to simulate the dynamic process of compressor surge with CLM's ability to simulate full envelope steady-state and dynamic process to realize the simulation of the whole engine surge process.
4. The aero-engine surge detection method [29] which has benefitted dozens of core engine surge tests is applied to the real-time surge model to verify the confidence of the simulated surge signal.
5. The modeling methods can be extended to other kinds of aeroengine to provide the model basis for surge detection and anti-surge control to avoid high risk and cost surge tests, so it has important practical significance for the development of active stability control.  
Conceptualization, T.Z.; methodology, X.Z.; programming, X.Z.; validation, X.Z. and L.L.; visualization, L.L.; writing--original draft preparation, X.Z.; writing--review and editing, T.Z. supervision, T.Z.; funding acquisition, T.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 51976089.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 5 Increase the Upper Limit of PV Capacity: Reactive Power Control
### Comparative Analysis of PV Uplift and System Loss Uplift  
For the proposed method, Figure 5 is again the verification object. After the bus TS reaches the upper limit of node voltage (105%), gradually increase the PV penetration rate, while attempting to adjust the reactive power absorption of PV to ensure that the voltage of TS remains below 105%.  
Define \(\Delta P_{\text{PV}}/P_{\text{PV-0}}\) as the proportion of increase in PV capacity and \(\Delta P_{loss}/P_{loss\text{-0}}\) as the proportion of system loss increase.  
The basic situation is that the PV power supply operates at its voltage limit considering the unity power factor. At this point, the PV power and system losses are represented as \(P_{\text{PV-0}}\) and \(P_{Loss\text{-0}}\), respectively. When allowing PV to provide more electricity by absorbing reactive power, the value of \(P_{Loss}\) should be determined and compared with \(P_{Loss\text{-0}}\) because in some cases, the increase in system losses will exceed the increase in PV injection power, i.e., \((P_{Loss}-P_{loss\text{-0}})/P_{Loss\text{-0}}>(P_{\text{PV}}-P_{\text{PV-0}})/P _{\text{PV-0}}\).  
According to the analysis in Section 5.1, PV inverters can continue to increase PV capacity while maintaining the voltage within limits by absorbing reactive power. Figure 17 demonstrates the correctness of this analysis. The data as shown in Table 6. Furthermore, the amount of reactive power absorbed is not proportional to the increase in PV capacity. As can be observed from Figure 17, when the PV capacity increases by 13.75%, the amount of reactive power absorbed increases by 71.2%. This is unacceptable for practical systems. Therefore, it is crucial to use the method proposed in Section 5.1 to increase the PV upper limit within limits in special demand situations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **PV Capacity** \\ **(kw)** \\ \end{tabular} & \begin{tabular}{c} **Q\({}_{\text{PV-Theory}}\)** \\ **(kvar)** \\ \end{tabular} & \begin{tabular}{c} **Q\({}_{\text{PV-Verify}}\)** \\ **(kvar)** \\ \end{tabular} & \begin{tabular}{c} **P\({}_{loss\text{-verify}}\)** \\ **(kw)** \\ \end{tabular} & \
\begin{tabular}{c} **\(\Delta P_{\text{PV}}/P_{\text{PV-0}}\)** \\ **(%)** \\ \end{tabular} &
\begin{tabular}{c} **\(\Delta P_{\text{loss}}/P_{loss\text{-0}}\)** \\ **(%)** \\ \end{tabular} \\ \hline
164 & 3.91 & 4 & 6.6232 & 2.5 & \(-\)0.15 \\
166 & 14.5 & 15 & 6.9932 & 3.75 & 5.4 \\
169 & 25.8 & 27 & 7.5032 & 5.625 & 13.1 \\
172 & 37.6 & 38 & 8.0932 & 7.5 & 22.0 \\
175 & 50.4 & 51 & 8.9232 & 9.375 & 34.5 \\
## 3 Precise Calculation of the Pre-Close Wind Volume Coefficient  
As mentioned earlier, the pre-close wind volume compensates for the overshooting that occurs after the wind has been closed. The pre-close wind volume should be as close as possible to the amount of overshooting to ensure that the compensation is appropriate. The calculation of the pre-close wind volume is performed according to Equation (14). Therefore, after using the UKF method to accurately estimate the aluminum output speed, the pre-close wind volume coefficient is calculated to precisely obtain the pre-close wind volume:  
\[M=cv \tag{14}\]  
where \(c\) represents the pre-close wind volume coefficient, which is typically set to 5 based on experience; \(v\) denotes the aluminum output speed at the time the wind is closed; and \(M\) is the pre-close wind volume.  
After each aluminum output, the programmable controller calculates the aluminum deviation. A deviation greater than 0 indicates under-compensation. A deviation less than 0 indicates over-compensation. To adjust the parameter \(c\) so that the compensation is correct, the programmable controller can be used to find the optimal value of \(c\) after each aluminum output based on the deviation, as shown in Equation (15):  
\[A=\widetilde{cv}=cv+e \tag{15}\]  
where \(\widetilde{c}\) is the optimal value of \(c\); \(A\) is the overshoot after closing the valve; and \(e\) is the aluminum deviation.  
By performing a transformation of Equation (15), the optimal value \(\widetilde{c}\) of parameter \(c\) can be calculated using Equation (16), as shown below:  
\[\widetilde{c}=c+\frac{e}{v} \tag{16}\]  
Based on Equation (16), after each aluminum output, an optimal value \(\widetilde{c}\) for parameter \(c\) can be calculated. Considering that the optimal value \(\widetilde{c}\) obtained from Equation (16) may be affected by interference, the calculated parameter is filtered using Average Filter (AF), as shown in Equation (17):  
\[\widetilde{c}=\frac{1}{n}\sum_{i=1}^{n}\widetilde{c_{i}} \tag{17}\]  
Using the filtered value of \(\widetilde{c}\), the parameter \(c\) is set in the pre-close wind volume calculation formula.  
Based on the above formula, a program can be designed to calculate the pre-close wind volume coefficient. The self-correction program will compute an optimal value for parameter \(c\) after each aluminum extraction and store it in the PLC memory. Then, by applying AF to multiple optimal values, parameter \(c\) in the pre-close wind volume calculation formula will be updated, reducing errors and achieving precise aluminum extraction.
# An Effect of White Space on Traditional Chinese Text-Reading on Smartphones +
## 5 Conclusions  
Design practitioners believe that white space provides relief in reading tedious text, reducing reading burden. However, readers who do not have design backgrounds oppose this opinion. This study explored the impacts of white space on reading Chinese essays on smartphones to resolve this dispute. The independent variables were Line spacing, Paragraph spacing, and Page spacing. Two types of performance measures predicting the impacts of the white spaces were an objective measure and three subjective measures. The objective measure was the reading time spent to find ten typos. Three subjective measures were ratings of effortless, comfort, aesthetic appeal and overall preference. The following are the conclusions of the findings.  
First, for Line spacing, the result demonstrated that participants felt that 3-F leading was more beautiful and comfortable than 2-F leading although 3-F leading took more time to read.  
Second, for Paragraph spacing, the result indicated that reading text with 2-L spacing was significantly faster than that with 1-L spacing; however, subjective ratings of the 1-L and 2-L paragraph spacing did not differ significantly on effortless, comfort, aesthetic appeal, and preference. Participants' satisfaction was only ordinary for these two kinds of paragraph spacing.  
Third, for Page spacing, the outcomes showed that too large (i.e., 1/2H in this study) page spacing size was not conducive to reading speeds. Participants felt that the smaller page spacing (i.e., 0H in this study) provided more ease, comfort, attractiveness and overall satisfaction.  
In conclusion, appropriate use of white space would increase user performance and satisfaction on reading a traditional Chinese text on smartphones. This study suggests that typographical design on smartphones may consider applying white spaces to the text to promote reading performance and subjective preferences, such as line spacing, paragraph spacing, or page spacing.  
**Author Contributions:** [PERSON]-M.H. conceived and designed the experiments, analyzed the data, and wrote the paper; [PERSON]-J.L. co-designed and programed the experiment application; and [PERSON]-C.T. performed the experiments and statistics analysis.  
**Funding:** This work was supported by The Ministry of Science and Technology, Taiwan, under Contract No. MOST-105-2221-E-150-034.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Research and Application of Semi-Supervised Category Dictionary Model Based on Transfer Learning
###### Abstract
It thus offers a balanced and effective strategy for leveraging target domain data, reducing the risk of negative impacts associated with excessive dependence on pseudo-labels. Furthermore, SSDT does not necessitate the introduction of additional classifiers; it solely requires the test set samples to recover the residuals on the category representation dictionary to obtain the predicted sample output. Subsequently, this paper delineates the structure of the proposed model and the strategy employed to optimize the objective function. Additionally, this model is applied to the practical task of bearing fault diagnosis. Comparative experiments with established models, such as 1-nearest neighbor (1-NN), sparse representation-based classifier (SRC), transfer component analysis (TCA), and geodesic flow kernel (GFK), illustrate that SSDT model effectively amalgamates the strengths of existing methods. To sum up, this paper proposed a new model SSDT to enhance the suitability of the sparse representation dictionary model within semi-supervised transfer learning contexts--particularly in addressing the labor-intensive task of manual labeling associated with large-scale data, and in managing intricate datasets. This model puts forward three strategies: utilizing semi-supervised learning methods to reduce the manual labor involved in labeling; employing transfer learning techniques to enhance the model's generalization capabilities while shortening training time; and implementing sparse dictionary optimization approaches to tackle challenges such as noise and other intricate features inherent in the data. This model's efficacy has been corroborated by applying it to both visual data and real production data, and by comparing its performance against other models. The SSDT showed promising results, thereby substantiating its practical potential and effective-ness. This method can a provide effective method for the classification of complex data in various fields.