esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, the effects of slag content, alkali equivalent and modulus on drying shrinkage and efflorescence of RPSG under normal temperature curing were studied. The drying shrinkage pattern and efflorescence pattern of RPSG were systematically analyzed. The main conclusions of this paper are as follows:  
(1) Slag significantly reduces the drying shrinkage and efflorescence of RPSG. Slag can promote RPSG to generate more hydration products, which causes the matrix to become denser and reduces the capillary porosity, thus significantly improving its drying shrinkage and efflorescence performance.  
(2) When the modulus is constant, the potential for the efflorescence of RPSG increases with increasing alkali equivalent. However, the relationship between drying shrinkage and alkali equivalent is not a simple linear relationship. At low alkali equivalents (6% in this paper), the drying shrinkage of RPSG increases with increasing alkali equivalents and decreases with increasing alkali equivalents at high alkali equivalents.  
(3) The drying shrinkage of RPSG increases with increasing modulus when the alkali equivalent is constant. In contrast, the degree of efflorescence decreases with increasing modulus.  
(4) Micro-analysis has shown that slag promotes the dissolution of the silica-aluminum composition in RPSG and the formation of hydrated gels such as NASH and CASH with low polymerization. This results in a slurry with smaller pores and a denser structure.  
The research in this paper shows that the effects of slag content, alkali equivalent and modulus on the drying shrinkage and efflorescence of RPSG are not identical. In this study, RP-S45-M1.3N6 is the best proportional design for RPSG. The drying shrinkage properties and efflorescence performance of RP-S45-M1.3N6 were significantly reduced compared with RP-S0-M1.3N6 (drying shrinkage was reduced by 76.32%, visual efflorescence was significantly alleviated, capillary porosity was reduced by 60.9% and early pH value was reduced by approximately 2.0). The research has demonstrated that geopolymers with excellent durability can be prepared by using slag and RP, which has a beneficial impact on promoting the resource utilization of RP.  
The main materials used in the prepared RPSG are solid wastes, which have complex and variable chemical compositions. The effects of the content of the main chemical components, such as CaO, Al\({}_{2}\)O\({}_{3}\) and SiO\({}_{2}\), on the properties of RPSG should be further investigated.  
Conceptualization, X.L.; methodology, X.L.; resources, X.L.; writing--original draft, X.L. and E.L.; supervision, X.L.; validation, E.L. and Y.F.; investigation, E.L. and Y.F.; data curation, E.L. and Y.F.; writing--review and editing, E.L. and Y.F. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.  
The authors declare no conflict of interest.
# Unobtrusive Respiratory Flow Monitoring Using a Thermopile Array: A Feasibility Study
## 2 Materials and Methods
### Evaluation  
The signal to Noise Ratio (SNR), Root Mean Squared Error (RMSE), and Relative Error (RE) have been used to evaluate the accuracy of the thermopile in detecting the respiration signal.  
SNR was evaluated according to:  
\[SNR=10 log_{10}\left(\frac{\sum\limits_{f_{k}=0.12}^{1.67}\left(U(f_{k})\ P(f_{k}) \right)}{\sum\limits_{f_{k}=0.12}^{1.67}\left(\left(1-U(f_{k})\right)\ P(f_{k}) \right)}\right), \tag{7}\]  
where \(P(f_{k})\) is the signal's spectral density estimated with the periodogram or AR method and \(U(f_{k})\) is a binary template window centered on the signal peak and first harmonic with a predefined width equal to 2 frequency bins. The first harmonic was included as the respiration signal is not a perfect sinusoid. The SNR was evaluated on each window, and the average of all the windows was used.  
RMSE has been evaluated using the impedance pneumography as the reference:  
\[RMSE=\sqrt{\frac{\sum\limits_{j=1}^{L}\left(\tilde{R}R(j)-RR_{ref}(j)\right) ^{2}}{L}}, \tag{8}\]  
where \(\tilde{R}R\) is the respiration rate estimated, \(RR_{ref}\) is the one detected by the impedance pneumography, and \(L\) is the number of windows evaluated. RE has also been estimated to allow a better comparison.  
Moreover, signals collected in Dataset A have also been compared by assessing the number of pixels involved in the respiration signal. This was performed by evaluating the absolute value of the correlation coefficient between the detected respiration signal and the signals contained in other pixels. If the correlation was higher than 0.7, then the pixel was considered to contain respiration signal as well and was added to the pixel count.  
The temperature variation detected by the thermopile due to respiration was also considered; this was obtained by using the range of the signal, i.e., the maximum amplitude minus the minimum amplitude. Both amplitude and the number of pixels have been estimated on each window and then averaged for the entire measurement.
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 3 Algorithmic Aspects of WGA Algorithms
### Methodological Underpinnings  
The methodological underpinnings of WGA algorithms can be broadly categorized into heuristic and exact methods. Heuristic methods, such as MUMmer and BLASTZ, generate approximate alignments rapidly but may not always produce the optimal alignment [57; 58]. These methods are often used for the initial stage of alignment to identify regions of similarity, which are then refined using more accurate methods [59].  
Exact methods, on the other hand, guarantee finding the optimal alignment but are typically slower and require more computational resources. Such methods, including those based on dynamic programming, are often used for refining alignments or for aligning smaller regions of the genome [60; 61].  
Recent advancements in WGA algorithms have seen the development of methods that strike a balance between speed and accuracy. For instance, tools like Cactus and Progressive Cactus utilize a combination of heuristic and exact methods to generate accurate alignments quickly [62]. These tools use heuristic methods for the initial alignment and then apply exact methods to refine the alignment, resulting in a balance of speed and accuracy.  
WGA algorithms also vary in their ability to handle genomic rearrangements, such as inversions, duplications, and translocations. Some algorithms, like Mauve and Progressive Cactus, have built-in features for handling these rearrangements [27]. Other algorithms may require additional steps or tools to correctly align regions with genomic rearrangements [27].  
Finally, the choice of a suitable WGA algorithm often depends on the specific requirements of the research question. For instance, studies focusing on closely related species may benefit from algorithms that excel at aligning highly similar sequences, while studies on divergent species may require algorithms that can handle high levels of sequence divergence [63; 64].  
In summary, the algorithmic aspects of WGA algorithms, from their performance characteristics to their methodological underpinnings, are key factors influencing their suitability for different research applications. Continued advancements in these aspects will undoubtedly contribute to the ongoing evolution of the field of whole-genome alignment [65; 66].
## 3 Case Study
### Parameter Selection  
The output and input parameters are summarized in Tables 1 and 2, respectively. The selection of input parameters is based on engineering experience and previous studies on shield tunneling [5]. The key concerns for operators include the cutter rotational speed and the advance rate. The cutter rotational speed refers to the number of revolutions the cutterhead completes per unit of time. This should be set within a suitable range to ensure that the shield machine can effectively cut the palm surface. If not, the shield machine may fall into an unstable condition. The advance rate is the distance that the shield machine tunnels per unit of time, with excessively fast advance rates potentially resulting in a loss of control over the shield attitude. The total thrust refers to the force used by the shield machine to overcome various resistances along the moving path. The torque is the rotational force generated by the cutterhead of the shield machine. An inappropriate total thrust and torque may result in deviations of the shield attitude. The oil pressure is the hydraulic pressure, which is directly used to control the shield attitude. In addition, the jack stroke, grouting quantity, and slurry inlet flow may also affect the shield attitude. Hence, the above influential factors are chosen as input parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Maximum** & **Minimum** & **Average** & **Unit** \\ \hline Horizontal deviation of shield head (HDSH) & 45 & \(-100\) & \(-4\) & mm \\ Horizontal deviation of shield tail (HDST) & 75 & \(-137\) & 4 & mm \\ Vertical deviation of shield head (VDSH) & \(-26\) & \(-69\) & \(-54\) & mm \\ Vertical deviation of shield tail (VDST) & 51 & \(-50\) & 0.82 & mm \\ Total thrust & 76,000 & 34,423 & 61,814 & kN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Output parameters.  
Figure 5: Project layout.
## 4 Use Case: Application Scenario  
The applicability of SWIFTNET design strategy is vast e.g., it can be applied to any event detection application such as, seismic monitoring [57], soil moisture measurement for rainfall detection [25], wildfire monitoring [58], flood warning system [13], _etc_. For easy testing and for the implementation of our approach, we present a wildfire monitoring application as a use-case.
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 2 Key Concepts in Modeling Over-the-Sea Channels
### Important Definitions and Parameters
**Power delay profile (PDP):** Using the PDP, we can find out the intensity of a signal received through a multipath channel. It is usually measured as a function of time. PDP can be used to measure some other channel parameters, such as delay spread, etc. It is possible to obtain PDP using the spatial average of the complex baseband channel impulse response. **Mean excess delay:** The mean excess delay and root mean square (RMS) delay spread are multipath channel parameters that can be extracted from PDP. The exact first moment of PDP can be denoted as mean excess delay. Mathematical formulation of mean excess delay is as follows:  
\[\overline{\tau}=\ \frac{\sum_{k}\alpha_{k}^{2}\tau_{k}}{\sum_{k}\alpha_{k}^{2}}, \tag{1}\]  
where \(\alpha_{k}\) is the magnitude of the received signal, and \(\tau_{k}\) is the delay between the occurrences of the received multipath waveform after receiving the initial impulse. The RMS delay spread can be defined as the square root of the second central moment of the power delay profile. RMS delay spread (\(\sigma_{\tau}\)) can be formulated as:  
\[\sigma_{\tau}=\ \sqrt{\tau^{2}-(\overline{\tau})^{2}} \tag{2}\]  
Figure 6: An example of a beyond non-line-of-sight (bLoS) propagation pathway. Atmospheric ducts:Atmospheric ducts are horizontal layers in the lower atmosphere. In atmospheric ducts, radio signals are ducted and they always tend to follow the curvature of the Earth. Waves are bent by atmospheric refraction in such scenarios. The characteristics and formation of such ducts are subject to refractivity of a certain atmosphere. There are four types of atmospheric ducts. They were classified into four types based on their formation process and refractivity profile. These are: (a) evaporation ducts, (b) surface-based ducts, (c) elevated ducts, and (d) surface ducts. A surface duct mainly occurs due to temperature inversion. The evaporation duct is a special version of the surface duct. The evaporation duct is a refractive layer that results from the rapid decrease in humidity level with altitude. This takes place above water bodies where the atmospheric surface layer exists. Elevated ducts are formed due to a high-density air layer. They commence at a high altitude and continue upwards. These ducts primarily affect very high frequencies. Bending of a ray like this occurs under super-refractive conditions. Surface-based ducts occur when the air in the upper layer is warm and dry compared to the air on/near the surface. Figure 7 shows a transceiver platform in an elevated evaporation duct.
# Secure Mimo Communication System with Frequency Hopping Aided OFDM-DCSK Modulation
## 3 Performance Analysis
### Ber Performance
 \tag{29}\]  
For the AWGN channel, we set \(h_{k}=h=1\). Then, we can derive the expression of theoretical BER as:  
\[ \begin{split} BER_{AWGN}&=\frac{1}{2}\text{erfc}( \sqrt{\frac{\Gamma}{2}})\\ &=\frac{1}{2}\text{erfc}(\sqrt{\frac{8(\frac{E_{b}}{N_{0}})^{2}} {(\beta+6\frac{E_{b}}{N_{0}})}}).\end{split}  \tag{30}\]
## 4 Experiments and Results
### Effects of Joint Decoding with LID  
As shown in Table 4, joint LID decoding improves LLS, but has no effect on ALS-share. For ALS-share, the LID task and the ASR task use the same decoder, which makes the result of the LID task more closely related to that of the ASR task. In contrast, as for LLS, the LID task is relatively independent of the ASR task. Therefore, LID decoder training in LLS is more capable of correcting language errors in the ASR task. Under the configuration of the LLS method, CTC joint training for the LID task and LID joint decoding, the final system achieves an MER of (30.95%), obtaining up to a (19.35%) and (8.86%) relative MER reduction compared to the RNN-based CTC-Attention baseline system (38.38%) and the CTC-Transformer baseline system (33.96%) respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Baseline System** & **MER(\%)** \\ \hline GMM-HMM & 39.6 \\ RNN-based CTC-Attention & 38.38 \\ CTC-Transformer & **33.96** \\ \hline \hline \end{tabular}
\end{table}
Table 2: MER(%) of different framework CSSR baseline systems.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**MTL Strategy** & **LID-CTC** & **MER(\%)** \\ \hline no LID task & no & 33.96 \\ \hline LLS & no & 32.24 \\  & yes & **31.37** \\ \hline ALS-share & no & 32.79 \\  & yes & 31.84 \\ \hline ALS-indep & no & 34.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: MER(%) of ASR task corresponding to three MTL implementations: LLS, ALS-share and ALS-indep.
## 3 Ground Response Analyses and Input Motions
### One-Dimensional Analyses  
In this study, each Vs profile collected by the SDMT investigation was used to determine the response of these complex deposits. The computer code STRATA [14] performs an iterative 1D analysis to track the variation in the normalised shear modulus (G/G\({}_{0}\)) and damping ratio (D) values with shear strain. It assumes one-dimensional soil deposit conditions, simplifying the soil layers as horizontal and of infinite lateral extent.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**GU** & **Type** & **g** & **V\({}_{\text{S}}\)** & **D\({}_{\text{B}}\)** & **G/G\({}_{0}\)(g)** \\  & & **(KN/m3)** & **(m/s)** & **(\%)** & **D(g) Curve** \\ \hline FM1 & Fill material & 18 & 250 & 1 & [PERSON] (1998) [10] \\ FM2 & Fill material & 18 & 500 & 1 & [PERSON] (1998) [10] \\ FM3 & Fill material & 18 & 300 & 1 & [PERSON] (1998) [10] \\ GR1 & Sandy gravel & 20 & 700 & 1 & [PERSON] (2010) [11] \\ GR2 & Sandy gravel & 20 & 600 & 1 & [PERSON] (2010) [11] \\ SL1 & Silty sand & 19 & 400 & 1 & [PERSON] (2001) [12] \\ SL1 & Silty sand & 19 & 700 & 1 & [PERSON] (2001) [12] \\ LA1 & Clayey deposit & 19 & 400 & 1 & [PERSON] (2013) [13] \\ LA2 & Clayey deposit & 19 & 800 & 1 & [PERSON] (2013) [13] \\ BR & Limestone & 24 & 1250 & 0.5 & Elastic \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical and dynamical soil parameters of the geotechnical units.  
Figure 3: (**a**) Superimposed profiles of V\({}_{\text{S}}\) measured by SDMT in backfilled boreholes and (**b**) profiles appropriately averaged for the A–A’ section ground response analyses (Figure 2). The sites with V\({}_{\text{S}}\) inversion profiles are indicated in the circles.  
Therefore, the code operates an equivalent-linear site response analysis: the nonlinear response of the soil is approximated by modifying the linear elastic properties of the soil based on the induced strain level. Since the induced strain depends on the soil properties, the strain-compatible shear modulus and damping ratio values were iteratively recalculated based on the computed strain. Simulations were conducted using the time-series method, where the input acceleration time history was provided, and the input Fourier amplitude spectrum was computed from this time series using the fast Fourier transform (FFT).  
Boundary conditions were applied to the bedrock, which was modelled as an elastic base to simulate the absorption of waves reflected from the surface.
## 2 Results and Discussion
### Distribution of ARR VOCs In Vivo in Rats
#### 2.2.3 Distribution of Main Compounds Identified In Vivo in Rats  
The peak areas of all identified compounds of ARR in the urine, feces, blood, and eight organs (brain, heart, lung, spleen, kidney, liver, stomach, and small intestine) were calculated and sorted, and the constituents with max peak areas were selected. In the powder-treated group, the five main constituents in the blood and organ samples were 3,5-DMT, 2,3,5-TMT, 3,4,5-TMT, methyleugenol, and safrole. The main compounds in the urine were kakuol, eugenol, borneol, dihydroteugenol, and M127 (kakuol isomer). The sum of their peak areas accounts for at least 75% of the total peak areas (calculated by normalization areas) of all compounds identified in each sample. In the feces, 35-DMT, borneol, 3,4,5-TMT, 2,3,5-TMT, and safrole were the main constituents detected, and their peak area accounts for 65% of the total peak areas. Except dihydroenenol, which is a metabolite, these compounds were original constituents of ARR: 3,5-DMT, 2,3,5-TMT, 3,4,5-TMT, methyleugenol, safrole, kakuol, eugenol, borneol, and M127.  
In the decoction-treated group, 3,4,5-TMT, 2,3,5-TMT, and kakuol were the main constituents identified in the brain, heart, lung, spleen, and liver, with a total content higher than 65% in each sample. Especially, the content of 3,4,5-TMT was higher than 50% in the brain, heart, lung, and spleen. In the kidney, stomach, and small intestine, 3,4,5-TMT and kakuol had the highest content, accounting for 34.0% and 19.2% in the kidney, 23.7% and 30.0% in the stomach, and 17.3% and 28.4% in the intestine, respectively; however, the content of 2,3,5-TMT decreased to 2%-3% in these three organs. Eugenol replaced 2,3,5-TMT as the main constituent in the kidney and intestine (14.2% and 15.3%), and M148 (2',4'-dimethoxy-3'-methylpropiophenone) became a substitute in the stomach (17.4%). In the blood, 3,4,5-TMT, 2',4'-dimethoxy-3'-methylpropiophenone, and 2,3,5-TMT were the main absorbed constituents, with contents of 65.5%, 9.5%, and 6.9%, respectively. Kakuol, eugenol, and borneol were the main constituents identified in the urine, with contents of 37.79%, 23.67%,and 6.67%, respectively. In the feces, the main constituents were kakuol (41.2%), borneol (31.0%), and thymol (6.5%).  
The ratios of peak areas of 3,5-DMT, safrole, methyleugenol, 2,3,5-TMT, 3,4,5-TMT, eugenol, and kakuol between the powder- and decoction-treated groups are shown in Table 4, and the values of the peak area of all identified compounds are shown in Supplementary Materials Tables S2-S23.
# Internal Backpressure for Onboard Crospoint-Buffered Switches with Port Multiplexing
## 5 Simulation and Evaluation
### Comparison of Different Queue Management Mechanisms  
Ultimately, a comparative analysis is conducted to evaluate the performance of different queue management mechanisms. In this simulation experiment, several queue management schemes are set up, including fully shared buffering, static threshold management (ST) [44], dynamic threshold management (DT) [45; 46], and a no-BP approach. Notably, DT assigns a dynamically adjustable threshold value to each output queue, where this threshold is proportional to the remaining space in the buffer region of each OAM, as delineated by the following formula:  
\[T(t)=\alpha\big{(}B_{OAM}-Q_{OAM}(t)\big{)}=\alpha\Bigg{(}B_{OAM}-\sum_{i=0}^{ k-1}Q_{OAM}^{i}(t)\Bigg{)}. \tag{7}\]  
In this formula, \(T(t)\) signifies the threshold value, and \(\alpha\) is the dynamic threshold parameter, indicating that the buffer threshold is proportional to the remaining space in the shared buffer. According to [47], an appropriate value of \(\alpha\) is 1 for most cases. Therefore, this parameter is also set to 1 in our simulations. \(B_{OAM}\) represents the buffer size of a  
Figure 9: (**a**) Influence of \(\omega\) on packet delay of each BP mechanism under stochastic scenario. (**b**) Influence of \(\omega\) on packet loss rate of each BP mechanism under stochastic scenario. (**c**) Influence of \(\omega\) on throughput of each BP mechanism under stochastic scenario.  
specific OAM, and \(Q(t)_{OAM}\) denotes the aggregate queue length. In scenarios involving no-BP, since no output queue configuration exists in each OAM, it needs no output queue management considerations.  
Illustrated in Figure 10a,b, under disparate queue management methodologies, the simulation results indicate that an increase in \(\omega\) corresponds to increased challenges in buffer management for the system. In contrast, the BP load balance mechanism proposed in this paper, predicated on a dynamic threshold management strategy, demonstrates a superior capacity to govern queue resources through real-time threshold modulation, thus significantly reducing the packet loss rate and achieving high throughput. The findings indicate that, under the dynamic threshold strategy at an \(\omega\) of 0.4, the packet loss rate hovers at approximately 10%, marking a substantial reduction of 44% relative to the strategy of no-BP. At the same time, throughput will reach 89%, which is a 9% performance improvement compared to other BP mechanisms.