esa-sceva/satcom-chunk-collection huggingface dataset:
# Second Harmonic Revisited: An Analytic Quantum Approach
## 2 Results
### Quantum Regime
#### 2.2.7. Squeezed State  
A pure single-mode squeezed vacuum state is expanded over the number-state basis as follows [13; 16]:  
\[|\zeta\rangle=\frac{1}{\sqrt{\mu}}\sum_{n=0}^{\infty}\left(\frac{v}{2\mu}\right) ^{n}\frac{\sqrt{(2n)!}}{n!}|2n\rangle \tag{54}\]  
where \(\mu=\cosh(r)\), \(\
u=e^{i\theta}\sinh(r)\), \(\theta\) and \(r\) being respectively the phase and the squeezing parameter. For the sake of simplicity, we set \(\theta=0\). The autocorrelation function for a squeezed state is given by  
\[g_{\rm F}^{(2)}=3+\frac{1}{N}. \tag{55}\]  
We found that, for such an input state, the SH autocorrelation function reads:  
\[g_{\rm SH}^{(2)}=\frac{3}{(1+3N)^{2}}[3+5N(5+7N)-\frac{4}{1+3N}(1+5N(9+43N^{2}+ 87N^{2}+56N^{3}))\tilde{\gamma}^{2}]+o(\tilde{\gamma}^{4}). \tag{56}\]  
Firstly, we remark that the zero order in Equation (56) is the usual first approximation of the SH autocorrelation function, which is obtained through Equation (43). It is provided by the first and second orders of the perturbative expansion of the moments, then it also contains the superposition processes \(\beta_{2}\) (Equation (37)). We note that, while for a multithermal state, this contribution does not apparently affect the output correlations (see Figure 3), Figure 4 shows that for a squeezed input state, these superposition processes help to increase correlations. This is why the zero order of the SH autocorrelation function is smaller if we omit the superposition processes (red dashed line in Figure 4).  
Moreover, we find that, at increasing input photon-number, \(g_{\rm SH}^{(2)}(0)\) is reduced (compare the two panels in Figure 4). The same result is found for the autocorrelation function of the squeezed state (see Equation (55)), with the difference that the asymptotic value in the SH case is larger: \(g_{\rm SH}^{(2)}(N\to\infty)\to\frac{35}{3}\). The fact that this value is the same as that of the zero order of the autocorrelation computed in the absence of back-conversion processes (dashed red line in Figure 4) is not accidental. From Equation (36), we know that the conversion efficiency is asymptotically smaller for higher orders, and we have superpositions just for \(k\geq 2\), so that their contribution is reduced as \(N\to\infty\).  
Finally, we point out that, in general, the multiple back-conversion events occurring for \(\tilde{\gamma}>0\) contribute to reducing correlations as usual.  
The high fluctuations retrieved for this SH field are a signature of the quantum properties of both the input state and the SHG process: since the squeezed state is a superposition of pairs of photons (Equation (54)) and the process always annihilates even numbers of pump photons, then SHG enhances the correlations of the input statistics.  
Figure 4: Squeezed input state. Solid blue line: SH autocorrelation function, from Equation (56). Dashed line: SH autocorrelation function in the absence of annihilation processes and superpositions.
## 3 Results and Discussion
### Effect of US on Chain Conformation of KGM  
As shown in Table 1, the hydrodynamic radius \(R_{\rm h}\) and zeta-average radius of gyration \(R_{\rm g}\) of KGM fractions decreased as the US treatment time was increased, indicating the size reduction of polymer aggregates. The three-dimensional shape of polymer in liquid can be related to the ratio, \(\rho=R_{\rm g}/R_{\rm h}\). Usually, \(\rho\) is about 0.77 for a uniform and non-draining sphere, 1.0-1.1 for a loosely connected hyper branched chain, 1.5-2.0 for a flexible chain in a good solvent, and \(>\)2.0 for an extended rigid chain [46]. The \(R_{\rm g}/R_{\rm h}\) ratio fluctuated in the initial 10-20 min and showed a notable increase after 30 min of US treatment, i.e., the ratio for KGM went from 1.74 to 1.93 (30 min).  
Intrinsic viscosity [\(\eta\)] is related to the average MW via the Mark-Houwink equation, [\(\eta\)] = K\(M^{\alpha}\). The exponent \(\alpha\) value usually lies in the range of 0.5-0.6 for random-coil polysaccharide chains, while \(\alpha\) in the range of 0.7-0.8 suggests that the polymer exists as a linear flexible chain in a good solvent. For a rigid or rod-like polymer, the chains are greatly extended in solution and the \(\alpha\) value could be as high as 2.0 [47; 48]. The \(\alpha\) value was 0.79 for KGM. The notable increase in the \(\alpha\) value of KGM after US treatment suggested that the polymer chains were greatly extended. KGM with a lower MW and viscosity was more homogeneous. After US treatment for over 2 min, the \(\alpha\) values of higher-MW sections of KGM fractions increased from 0.51 to 0.95.  
Persistence length (\(L_{\rm p}\)) is defined as the average projection length along the initial direction of the polymer chain and it is a useful index for the linear flexibility of polysaccharide chain [39; 49]. Based on the wormlike cylinder model for characterizing the conformation characteristics of polymers, \(L_{\rm p}\) can be computed using the Bohdanecky plot of \((M^{2}/[\eta])^{1/3}\) against \(M^{1/2}\) according to the following equation [50]:  
\[\left(\frac{M^{2}}{[\eta]}\right)^{1/3}=A_{0}M_{\rm L}\Phi_{0,\infty}{}^{-1/3} +B_{0}\Phi_{0,\infty}{}^{-1/3}\left(\frac{2L_{p}}{M_{\rm L}}\right)^{-1/2}M^{ 1/2}, \tag{3}\]  
where \(\Phi_{0,\infty}\) is equal to 2.86 \(\times\) 10\({}^{23}\) mol\({}^{-1}\), \(M_{\rm L}\) is the mass per unit contour length of 330 g\(\cdot\)mol\({}^{-1}\cdot\)nm\({}^{-1}\), and \(B_{0}\) can be replaced by the mean value of 1.10 [39]. The \(L_{\rm p}\) value was 10.3 nm for the original KGM sample, suggesting it had a flexible chain conformation, which is in good agreement with a previous study [39]. For KGM after 2 min of sonication, the Bohdanecky plot was divided into two sections according to Mark-Houwink plot (Figure S1, Supplementary Materials), with a \(L_{\rm p}\) value of 7.1 nm in higher-MW sections. As the sonication time exceeded 30 min, the \(L_{\rm p}\) value of KGM in the higher-MW region was still 7.5 nm.
###### Abstract  
The aim of the present investigation was to compare the acute non-localized post-activation performance enhancement (PAPE) of an exercise protocol involving either the upper or the lower body muscles. Twenty-four resistance trained men participated in the present study and were randomly assigned to an upper body (UB) or to a lower body (LB) group. Both groups tested for upper and lower body power (bench press throw (BPT) and countermovement jump power (CMJP) tests). Participants in the UB group were tested pre and post a high-intensity (HI) and a high-power (POW) bench press protocol while participants in the LB group performed a HI squat and a jump session (POW). A significant group \(\times\) time interaction was found for CMJP in HI (\(p\) = 0.012). Post hoc tests revealed that CMJP was elevated in UB group only (+1.6%; \(p\) = 0.025). No other significant interactions were detected. Results of this study indicate that a non-localized PAPE on the lower body may be induced by a HI bench press protocol while a HI squat protocol may not increase upper body power. In particular, the squat protocol performed in the present study (5 sets of 1 rep) may be too demanding to produce a non-localized PAPE.  
strength; cross-education; acute effects Article
# Study on the Increase of the Supporting Capacity of a Cement Milk Pile with Expansive Additives
## 4 Conclusions  
In this study, a method was reviewed to increase the bearing capacity of cement milk using expansive additives during the construction of bored piles. The following conclusions were obtained:  
1. Measuring segregation resistance showed an insignificant difference in settlement amounts due to expansive additive mixing when a 15% concentration was used. The change in the surplus water was also insignificant, showing no specific material separation tendency.
2. Compressive strength increased with age but decreased after expansive additive mixing in all specimens, except those with W/B of 70% and 5% expansive additive. However, usability was not an issue as all test specimens exceeded the standard strength of 0.5 N/mm\({}^{2}\) for filling material. It is also expected that the strength will increase under constraint conditions created in actual construction conditions.
3. Frictional resistance increased as up to a 10% expansive additive was mixed in but decreased at 15%. Specimens containing 10% expansive additive showed stable ductile behavior during settling and a tendency to plastically deform.
4. SEM image and porosity analysis revealed that as the age increased, monosulfate and C-S-H gels were densely composed, porosity decreased, and the specific surface area of pores increased, resulting in large numbers of small-scale pores. This trend increased with the mixing of an expansive additive, showing a denser microstructure.
5. Regarding economic feasibility, the number of required piles was reduced as the expansive additive was mixed into W/B of 83% specimens at ratios up to 10% when the greatest frictional resistance increase was achieved. Specifically, a 10% expansive additive ratio reduced the number of required piles by up to 34.84%, providing  
Figure 8: Pile reduction percentage and construction cost projections based on the mixing ratio of expansive additives.  
the greatest economic feasibility by reducing the total construction cost by as much as 33.24%.  
Conceptualization, H.C. ([PERSON]) and K.L.; methodology, H.C. ([PERSON]) and K.L.; investigation, H.C. ([PERSON]), T.L., H.C. ([PERSON]) and K.L.; writing--original draft preparation, H.C. ([PERSON]) and K.L.; writing--review and editing, H.C. ([PERSON]), T.L., H.C. ([PERSON]), K.L. and D.-E.L.; funding acquisition, H.C. ([PERSON]) and D.-E.L. All authors have read and agreed to the published version of the manuscript.  
This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (nos. 2018R1A5A1025137 and 2021R1C1C1011347).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 7 Results and Discussions
### Par  
PAR plays an important role in the optimal scheduling of smart home appliances. Due to high PAR, the utility faces huge peak loads during peak hours, and the rest of the day, most of the generating units remain idle. Therefore, researchers try to reduce PAR for economical load dispatch in smart grids. Figure 10 shows that, in the case of a single home for a single day and thirty homes for a single day, MFO performed better than FA, while in the case of a single home for thirty days and thirty homes for thirty days, FA showed better results than MFO. In our proposed hybrid model, we tried to not only schedule appliances optimally, economically and having maximum end-user comfort, but also gave the lowest PAR, for the benefit of utility, and hence, to further increase the end-user comfort level.  
Figure 7: Total cost for un-scheduled, and GA, MFO, ACO, CSA, FA and TG-MFO Scheduled load (**a**) Total cost for a single home for 1 day. (**b**) Total cost for single home for 30 days. (**c**) Total cost for 30 homes for 1 day. (**d**)Total cost for 30 homes for 30 days.  
A comparison of the proposed algorithm with state-of-the-art research works in the smart grid environment for energy optimization and end-user comfort is depicted in Table 8. Most of the existing techniques have used trade-offs between user comfort and bill minimization.  
Figure 8: The hourly load for un-scheduled and GA-, MFO-, ACO-, CSA-, FA- and TG-MFO-scheduled load. (**a**) The hourly load for a single home. (**b**) The hourly load for 30 homes.  
Figure 9: Energy demand curves of the scheduled and the un-scheduled load along with RES and BSUs.  
Table 9 shows the performance of the proposed TG-MFO algorithm, compared to unscheduled load and scheduled with the GA, MFO, ACO, FA and CSA algorithms.  
Figure 10: PAR for un-scheduled and GA-, MFO-, ACO-, CSA-, FA- and TG-MFO-scheduled load. (**a**) PAR for a single home. (**b**) PAR for 30 homes.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Techniques** & **No. of Homes/Days** &
\begin{tabular}{l} **Cost** \\ **Reduction** \\ \end{tabular} & **Waiting Time** \\ \hline [PERSON] et al. [5] & & 34\% & 1.76 h \\ \hline [PERSON] et al. [8] & 30 days & 20\% & 7.64 h \\ \hline TLGO [14] & & 33\% & 1.83 h \\ TLBO & & 31.5\% & 2.14 h \\ GA & & 31\% & 2.37 h \\ \hline [PERSON] et al. [23] & & & 9.6 (dis-satisfaction level) \\  & & & 20.2= \\  & & & 30.9= \\ \hline [PERSON] et al. [25] & & 33\% & 1.65 to 1.70 (Annoyance rate) \\ \hline [PERSON]. [PERSON] et al. [28] & 10 Home appliances & 38\% & 73.32 s \\  & 11 & & 76.28 s \\  & 12 & & 86.83 s \\  & 13 & & 94.39 s \\  & 14 & & 107.58 s \\ \hline TG-MFO (Proposed) & 1 home for 1 day & 32.25\% & 0.62 h \\  & 1 home for 30 days & 19.39\% & 0.48 h \\  & 30 homes for 1 day & 43.98\% & 0.38 h \\  & 30 homes for 30 days & 49.96\% & 0.26 h \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of TG-MFO with the state-of-the-art work.  
Table 10 shows the runtime of the proposed algorithms using an Intel (R) Core (TM) i5 processor, with 4.00 GB of installed memory (RAM) and the 32-bit Windows 7 Operating system.
## 5 Validation of the Proposed CARLA+
### Experiments
#### 5.2.1 Controlled Settings  
This experiment aims to model the dynamics between rain, the number of vehicles, and their dynamic speed in the environment. More specifically, a model is developed that can dynamically change the traffic count and speed of the vehicles based on the presence of rain in the environment. Our assumption is that when it is raining, the vehicle count and speed of vehicles will be low, and when it is not raining, it will be high.  
Therefore, for this setting, a relatively simple PGM model is designed to validate the effect of rain on the dynamic traffic count and vehicle speed. Three random variables are selected, namely, Rain, Traffic and Speed, having the possible states as shown in Table 4. The Traffic and Speed variables have a dependency on Rain, and their CPDs are defined manually, as shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Random Variable** & \multicolumn{2}{c}{**States**} \\ \hline Rain & NO\_RAIN & RAIN \\ \hline Traffic & LOW & HEAVY \\ \hline Speed & LOW & HIGH \\ \hline \hline \end{tabular}
\end{table}
Table 4: Random variables and their states.  
Figure 7: Bayesian network of controlled settings along their CPDs.  
Firstly, the environment manager reads the weather configuration files and updates the simulation weather accordingly. Then, based on the amount of rain in the simulation environment, the PGM model predicts the state of Traffic and Speed. Finally, a _Poisson distribution_ is used to randomize the actual number of vehicles to spawn and the speed of those vehicles. The value from the predicted state is passed onto the _Poisson function_ as the expected number of events occurring in a fixed time interval, which would return a sample from the parameterized _Poisson distribution_.  
The notion of LOW and HEAVY can be set by the user in the configuration files as well. For example, in one simulation, 20 vehicles might be considered LOW Traffic while in another, it is HEAVY Traffic. These values vary based on the dynamic simulation environment and experiment settings and can be configured easily via configuration files.  
This minimal and bare-bones experiment aims to showcase the effectiveness and usefulness of CARLA+ even in the simplest of scenarios. Simple rules and dependencies are used to model the dynamic environment, which would then generate values from random distributions to better model the randomness inherent in the real world. Given that we use a _Poisson_ distribution to select the actual value, instead of a hard-coded one, we ran the experiment 1200 times for each of the scenarios, i.e., NO_RAIN and RAIN to better showcase the effectiveness of this approach. As can be seen in Figure 8, the resulting graph shows that in the case of NO_RAIN, the Speed and Traffic is low, while in the case of RAIN, their values are generally high. This shows that even such simple rules and models can be used to create dynamic environments easily using the proposed CARLA+.
# Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron
## 4 Proposed Research Methodology  
Our proposed DDoS attack classification methodology (MLP classifier) algorithm is shown in Algorithm 1. The flowchart for the proposed system is shown in Figure 6. The main features of HTTP like GET and POST between other techniques, like TRACE, HEAD, DELETE, CONNECT, OPTIONS, and PUT, are studied. Ordinary legitimate clients have no more than 15-20 HTTP GET and POST demands per IP address. Bots become intelligent and imitate human behavior.  
Usually, the same bots have the same HTTP request and spend the same time, same packet frame size and use increasing DDoS attack strategies to achieve their goal. Algorithm 1 captures the number of HTTP GET and POST request duration and packet size within 160 s.  
The next feature states that the number of IP addresses is recorded and compared with the IP address list of the anonymous proxy server. The malicious user has launched multiple attacks through proxy servers to hide/backtrack his bots by performing a proxy DDoS attack, while a legitimate user mostly uses the real IP address to access the URL.  
To gain access to the web server, the malicious user has employed several bots. Every cooperative bot opens a separate port whenever it reaches a specific destination port number, forwarding a large number of requests to the victim's web server. Long periods of time pass without any port connections being closed. A typical legitimate client opens ports, transmits data, and then shuts the connections. It was observed that the port numbers of authentic clients rarely varied. However, malevolent machines frequently have different port numbers that rise sequentially. The source port number's initial value was generated haphazardly. It has also been noted that the source port numbers in many DDoS attack tools begin with an arbitrary port number. Just one more digit than the one before it makes up the next port number. They establish a stable mapping to the destination server's port numbers.  
The use of several bots to create a bot network is inherent in the head character of the bot or its master. Many vendor bots employ various manipulating codes to bring down a web server. It is well known that a client-side bot runs its code with a fixed log size and session duration.
# Drone-Based Vibration Monitoring and Assessment of Structures
## 4 Experimental Validation and Results
### Sensor Package Validation  
This experiment aimed at validating the accuracy of acceleration data collected by the sensor package. For this purpose, the sensor package was manually mounted along with a PCB accelerometer onto the steel plate, which was excited by the electromagnetic shaker (as shown in Figure 6). Using a data acquisition module, the excitation signal was swept from 1-20 Hz over a test period of 50 s. Due to the limited sample size of the sensor package (74,000 samples), a sampling frequency of 1480 Hz was used as a result of the limited buffer size onboard the sensor package. The validation was conducted utilizing a frequency response function of the sensor package data over the reference PCB accelerometer, shown in Figure 9. The results indicated an exponentially increasing error as the frequency approached zero. At lower frequencies (<5 Hz), the acceleration signal was below the sensitivity of the sensor package (5400 LSB/g), resulting in the large error shown. This could be mitigated in future designs by utilizing a higher accuracy accelerometer and decreasing the damping caused by the sensor package frame.  
Figure 8: UAS deployment of the sensor package showing (**a**) acceleration of the sensor package, (**b**) displacement of the UAS, and (**c**) photos of the sensor deployment and retrieval experiment.
# A Review of Recent Progress of Carbon Capture, Utilization, and Storage (CCUS) in China
## 2 Overview of CCUS in China  
China's energy system is large in scale and diverse in demand. China's rapid economic development has resulted in an increase in CO2 emissions. According to the total annual CO2 emissions of some countries [55] from 1980 to 2020 as shown in Figure 1, China has arisen to the top one in terms of annual CO2 emissions in the world. To strike a balance between the carbon neutrality goal and energy security and economic development, China has been actively constructing a modern energy system that is dominated by renewable energy and complemented by nuclear and fossil energy. However, according to China's actual national conditions, high-carbon-based fossil fuels continue to account for up to 85% of the country's overall energy consumption. It is predicted that China's energy consumption will still primarily depend on fossil fuels by 2060. Therefore, CCUS is the only technology option to achieve net-zero CO\({}_{2}\) emissions from fossil energy sources.  
The Chinese government has consistently encouraged technical applications while prioritizing the development of CCUS technology. Research and development activities on CCUS have received substantial financial support and strong policy incentives from the government. In the mode of cooperation between industrial companies and universities or research institutes, China's CCUS integrated technology has reached the stage of industrial demonstration. However, the demonstration scale is still limited due to the cost of the technology. With the continuous improvement and advancement of technology, China's CCUS technology is expected to achieve low-cost large-scale demonstration applications in the near future [50].
## 3 Methodology
### Diffusion Pseudo-Label Matching
```
0: Unmatched clusters \(\mathfrak{U}=\left\{\mathfrak{X}_{i}\right\}_{i=1}^{K}\)
0: Matched clusters \(\mathfrak{M}=\left\{\left(\mathfrak{X}_{i},y_{i}^{g}\right)\right\}_{i=1}^{K}\)
1:for\(i=1,2,\ldots,K\)do
2: Select \(i\)-th cluster \(\mathfrak{X}_{i}\) from \(\mathfrak{U}\) with Equation (10);
3: Select \(N_{top}\) top confident samples \(\mathfrak{X}_{i}^{top}\) from \(\mathfrak{X}_{i}\) with Equation (11);
4: Compute class proportions \(\mathbf{p}_{i}^{c}\) based on highest classification probability of each \(\mathbf{x}_{i}\in\mathfrak{X}_{i}^{top}\) using CLIP;
5:endfor
6: Generate priority matrix \(\mathbf{P}^{c}=\left[\mathbf{p}_{1}^{c},\mathbf{p}_{2}^{c},\ldots,\mathbf{p}_{K}^{c}\right]^{ \mathsf{T}}\);
7:while\(\mathfrak{U}\
eq\varnothing\)do
8: Pick a cluster \(\mathfrak{X}_{m}\) from \(\mathfrak{U}\);
9: Select the highest priority true label \(y_{i}^{g}\) among unrequested matches with Equation (12);
10:if\(y_{i}^{g}\) has not been assigned then
11: Assign \(y_{i}^{g}\) to cluster \(\mathfrak{X}_{m}\);
12:else
13:\(y_{i}^{g}\) has been assigned to cluster \(\mathfrak{X}_{k}\);
14: Assign \(y_{i}^{g}\) to the cluster with higher priority between \(\mathfrak{X}_{m}\)and \(\mathfrak{X}_{k}\);
15: Add lower-priority cluster to unmatched clusters \(\mathfrak{U}\)
16:endif
17:endwhile
18:return Matched clusters \(\mathfrak{M}=\left\{\left(\mathfrak{X}_{i},y_{i}^{g}\right)\right\}_{i=1}^{K}\);
```  
**Algorithm 1** Pseudo-label matching