esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Gait Smoothness Metrics in Healthy Elderly and Young People
## 2 Materials and Methods
### Signal Processing  
The IMU-acquired signals were filtered using a low-pass second-order Butterworth filter, with a cut-off frequency of 20 Hz. This choice was determined by the human gait movement frequencies typically ranging from 0.1 to 15 Hz [28] and the 20 Hz theoretical maximum frequency outlined in the formula for SPARC analysis of the FFT spectrum [13]. The root mean square (RMS) values for both acceleration and angular velocity were then computed. Specifically, the analysis focused on the gait phase while excluding the turning phase within each transition. The intervals delineated by the peaks observed in the angular velocity signal along the V direction, indicative of turns made by the subject along the walk, were identified, and all the signals were then segmented according to these markers. Afterward, the peaks in the AP linear acceleration were detected and utilized to segment the signal between two consecutive peaks, delineating it as a step within the gait phase. Gait phases displaying abnormal walking patterns were omitted from the analysis. The minimum number of steps realized considering all the gait phases of each participant was seven. Consequently, the gait segment starting from the first and ending at the eighth step within each gait phase for all individuals was selected for evaluating smoothness. Subsequently, smoothness was assessed by applying SPARC, LDLJ and HR metrics to  
Figure 1: Position of the IMU on the trunk and IMU reference system.  
the chosen gait segment and then averaged for each participant. Statistical analysis was performed to compare data among smoothness assessment between diverse groups and velocity tasks. Customized MATLAB R2023b(r) routines were developed to execute all signal processing and to perform statistical analysis.
## 2 Materials and Methods
### Electrochemical Methods  
Electrochemical experiments were performed using a Gill AC potentiostat (ACM Instruments, Grange-over-Sands, UK) controlled by a computer. Before each experiment, the bronze electrode was left for 1 h in the test solution to attain a steady-state condition. Polarization curves were recorded at a sweep rate of 10 mV/min, in the potential range of \(\pm\)250 mV with respect to the open-circuit potential. Electrochemical impedance spectroscopy (EIS) measurements were performed at the open-circuit potential, in the frequency range from 10 kHz to 10 mHz, at 5 points per decade, and at an AC voltage amplitude of \(\pm\)10 mV. The impedance parameters were calculated by fitting experimental results to equivalent electrical circuits using ZSimpwin 3.2 software.  
For each experiment, 100 mL of electrolyte was used. The measurements were performed at the room temperature (25 \({}^{\circ}\)C), and at least two replicates were conducted for each experimental condition.  
The corrosion inhibition efficiency values were estimated from the EIS and polarization measurements, according to the following equations:  
\[\eta_{\mathrm{imp}}(\%)=\frac{R_{\mathrm{P}}-R_{\mathrm{P}}^{0}}{R_{\mathrm{P }}}\cdot 100 \tag{1}\]  
\[\eta_{\mathrm{pol}}(\%)=\frac{i_{\mathrm{corr}}^{0}-i_{\mathrm{corr}}}{i_{ \mathrm{corr}}^{0}} \tag{2}\]  
where \(R_{\mathrm{P}}\) and \(R_{\mathrm{P}}^{0}\) represent the polarization resistances in solutions with and without an inhibitor, respectively. Similarly, \(i_{\mathrm{corr}}^{0}\) and \(i_{\mathrm{corr}}\) are the values of the corrosion current densities, calculated in the absence and in the presence of the drug, respectively.
## 3 Results Analysis
### Statistical Significance Analysis  
To quantitatively assess the comparative performance improvement of the TTHCA algorithm, a chi-square test of independence was undertaken to verify whether the wormhole detection rates and FP results are statistically significant compared with MHA and DelPHI. The detailed chi-square test results for all four wormhole variants are presented in Appendix, with a summary provided in Table 2, where the hypotheses are defined as follows:  
\begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline \multicolumn{2}{|c|}{Wormhole variants} & \multirow{2}{*}{Result type} & \multirow{2}{*}{TTHCA _vs._ MHA} & \multirow{2}{*}{TTHCA _vs._ DelPHI} \\ \cline{1-1} \cline{5-5} \cline{7-7} \multicolumn{1}{|c|}{Type} & & Length (hops) & & \\ \hline HM I-B & \multirow{2}{*}{3–6} & \multirow{2}{*}{Wormhole} & \multirow{2}{*}{\(H_{0}\) = False} & \multirow{2}{*}{\(H_{0}\) = False} \\ \cline{4-5} \cline{7-7} \multicolumn{1}{|c|}{HM O-B} & & & & \\ \cline{4-5} \cline{7-7} \multicolumn{1}{|c|}{PM I-B} & & FP & \(H_{0}\) = False & \(H_{0}\) = False \\ \hline \multirow{4}{*}{PM O-B} & \multirow{2}{*}{3} & \multirow{2}{*}{Wormhole} & \multirow{2}{*}{\(H_{0}\) = True} & \multirow{2}{*}{\(H_{0}\) = False} \\ \cline{4-5} \cline{7-7} \multicolumn{1}{|c|}{} & & FP & \(H_{0}\) = False & \(H_{0}\) = False \\ \cline{4-5} \cline{7-7} \multicolumn{1}{|c|}{} & \multirow{2}{*}{4–6} & \multirow{2}{*}{Wormhole} & \multirow{2}{*}{\(H_{0}\) = False} & \multirow{2}{*}{\(H_{0}\) = False} \\ \cline{4-5} \cline{7-7} \multicolumn{1}{|c|}{} & & FP & \(H_{0}\) = False & \(H_{0}\) = False \\ \hline \end{tabular}
\end{table}
Table 2: Statistical significance test results for wormhole detection and FP performance.  
The results conclusively prove that the difference in wormhole detection rates observed between TTHCA and DelPHI is statistically significant for all wormhole variants. Similarly for the TTHCA and MHA case, all observed wormhole detection performance differences are also significant except for the 3-hop PM O-B wormhole, where the null hypothesis was upheld for the reasons discussed earlier (see Figure 7). In terms of the FP occurrence performance, the null hypothesis is rejected in all cases.
## 2 Materials and Methods  
### Experimental Database of RCDBs  
#### 2.1.1 Database Description  
This research presents a comprehensive database of 950 experimental specimens of RCDBs failed in shear, collected from 69 earlier publications published between 1951 and 2024 [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37  
* Specimens with only rectangular cross-sections were considered (a/d \(\leq\) 2.5).
* Specimens were cast using low to high-strength concrete.
* Specimens with different web reinforcements were considered.
* Specimens with only deformed steel bars were considered (no limit on yield strength).
* Specimens were tested under three-point or four-point or monotonic loads.
* Specimens with axial or prestressed or post-tensioned loading were not considered.
* Specim's ultimate shear strength is less than 2000 kN.
* Specimens with only adequate information were recorded.  
Based on web reinforcement, the database includes four different types of RCDBs: 412 WOR (RCDBs without reinforcement), 42 WHR (RCDBs with horizontal reinforcement), 268 WVR (RCDBs with vertical reinforcement), and 228 WHVR (RCDBs with horizontal and vertical reinforcement) (see Figure 2).
## 6 Magnetic Properties
### Power Loss
#### 6.2.3 Anomalous Loss  
At first, the discrepancy between practical loss and theoretical loss was attributed to flux harmonics, arising from the non-linear B-H relationship. However, [PERSON] et al. [8] showed that flux harmonics alone could not fully account for the additional loss when  
Figure 10: Schematic diagram of the eddy currents, in the case of uniform magnetic fields [59].  
modelling thin ferromagnetic laminations. To address this phenomenon, [PERSON] et al. [9] introduced the concept of domain wall bowing. According to this theory, the centre of the domain wall moves more slowly than the surface, leading to a difference between practical and theoretical loss, which was termed anomalous loss.  
At specific frequencies, such as 50 and 60 Hz, anomalous loss can constitute up to 50% of the total loss [60]. The magnitude of anomalous losses is influenced by the grain size of the material, with a smaller grain size leading to reduced domain width. The domain width is determined by two energies: the magnetostatic energy and the domain wall energy. Minimizing the combined effect of these energies determines the domain width, which is directly proportional to the square root of the grain size [61]. A smaller domain width means that the wall covers a shorter distance in the same amount of time, ultimately resulting in reduced wall velocity.
# An Audio Watermarking Algorithm Based on Adversarial Perturbation +
## 3 The Proposed Methodology
### Watermark Embedding and Attack Simulation Layer
#### 3.4.3 Watermark Embedding  
Watermark embedding is achieved by finding the \(\delta\) that minimizes the loss function \(\mathcal{L}\).  
\[\tilde{\delta}=\underset{\delta}{\text{argmin}}\ \mathcal{L} \tag{16}\]  
Since all simulated attacks in \(\mathcal{T}\) and all modules in the watermarking system are differentiable, a gradient descent-based optimizer can be adopted to solve Equation (16). The watermarked audio is \(\mathbf{x}_{w}(n)=\mathbf{x}(n)+\tilde{\delta}(n)\).  
Watermark embedding is an iterative process. If the imperceptibility constraint is too strong at the early stage of the iteration, it may cause the watermark to sacrifice robustness. Therefore, we dynamically adjusted the importance of imperceptibility versus robustness during the iterative process. In detail, robustness is more important in the first stage of the iteration, enabling AAW to focus more on watermark embedding. At this stage, we set the parameter \(\lambda=0.5\). In the second stage, we balance the imperceptibility and robustness of the watermark, so \(\lambda=1\). Finally, in the third stage, the imperceptibility is slightly strengthened, so \(\lambda=1.5\). The iterations in this paper lasted a total of 300 loops, of which the first stage lasted 150 loops and the second and third stages lasted 100 and 50 loops, respectively.
## 3 The Effect of Coupling  
### Extension of Existence Regime  
The rogue wave solution in Equation (2) is nonsingular if and only if \(a\) never vanishes. Since \(\Omega_{0}\) cannot be real, this is equivalent to having a non-real root for the cubic polynomial \(p_{3}(x)=x^{3}+2\Big{(}\delta^{2}+3\sigma\rho^{2}\Big{)}x^{2}+\delta^{4}x+2 \delta^{4}\sigma\rho^{2}\). By considering the discriminant of \(p_{3}(x)\), complex roots (and hence rogue waves) will exist for \(0<\delta^{2}<16.9\,\sigma\rho^{2}\). One highlight of the present work is that this constraint is much less restrictive than the corresponding existence condition for the Manakov system with two components, namely, \(0<\delta^{2}<4\sigma\rho^{2}\)[31]. We conjecture that the addition of more components to coupled systems may in general induce further modulation instabilities and enhance the existence of rogue waves. This hypothesis obviously must be tested for other dynamical systems in the future.  
Moreover, the incorporation of the third component increases the complexity of the geometry of the wave profiles. Analytically, the dispersion relation is expressed as a higher order algebraic polynomial and will allow multiple rogue wave solutions under the same input physical parameters. From Equation (3), if \(a+ib\) is a root of the dispersion relation, then \(a-ib\) will also be admissible and provides another rogue wave solution. This phenomenon was also observed in other multi-component system [38; 39; 40; 41]. Such multi-rogue-wave scenarios are not allowed in the two-component Manakov system.
###### Abstract
Due to significant application of DBR, a novel real-time production order release mechanism is presented in the current research to control standard parts of the variety of product models in buffer before CCR in MTO companies in each planning horizon. The planning and detailed scheduling of multi-item products in the production lines considering CCR and the continuous production order release information based on the CCR consumption rate in the planning horizon are novel applications of DBR for MTO companies [30], and it is presented in the current research. Furthermore, the novel customer order delivery control method is also presented in the current research to select the customer orders from an order pool and process on CCR based on the net profit in the current planning horizon. Moreover, the model is developed with heuristic steps for scheduling multi-item products from different orders in the production line which are downstream of the buffer and CCR for customer order delivery control. The proposed DBR method considers different conditions, based on which production order release is made for different product models to fill the buffer in each planning horizon. It also helps to obtain an optimal detailed schedule for MTO companies to complete customer orders on time and utilize the CCR to increase the throughput and profit in each planning horizon. Current research is novel in different aspects, which includes:  
* Introducing a novel DBR based production order release mechanism for mix model production;
* Presenting a novel strategy of optimum and flexible order delivery to maximize net profit in each planning horizon;
* Proposing a novel heuristic based on DBR for order releasing and multi-item scheduling in an MTO industry. The rest of the paper is organized as follows: Section 2 illustrates the problem description. Section 3 shows the method proposed in this research. Section 4 consists of computational experiments and results. Section 5 presents the conclusions and future directions of the research.
# A Method to Improve the Distribution of Observations in GNSS Water Vapor Tomography
## 3 Experiment and Analysis
### Comparison with SWV of Station HKSS  
To assess the performance of the proposed method, SWV achieved by GNSS observation data and meteorological data was compared with SWV calculated from tomographic results of different schemes. Table 4 shows the average MAE, STD, and RMS derived from the two schemes and the statistical results for 7 days. The results reveal that MAE, STD, and RMS values of Scheme #2 were lower than those of Scheme #1 for every day. The average MAE was decreased by 1.79 mm from 10.53 mm to 8.74 mm, whereas STD and RMS were reduced by 1.65 and 2.45 mm from 11.35/14.09 mm to 9.70/11.64 mm, respectively. This indicates that Scheme #2 had a higher average accuracy than Scheme #1.  
To specifically reveal the superiority of the proposed method (Scheme #2), the MAE, STD, and RMS of every tomographic solution in DOY 182, 2017 are shown in Figure 6. The evolution is developed from the perspective of each tomographic result of 30 mins. The advantage of the proposed method (Scheme #2) was evident in terms of smaller MAE, STD, and RMS for every tomographic solution of DOY 182, 2017. The maximum and minimum MAE/STD/RMS of Scheme #1 were 20.838/27.271/24.894 and 4.310/1.643/5.353 mm, respectively, and those of Scheme #2 were 19.518/22.599/20.955 and 3.499/0.683/4.119 mm, respectively.  
Figure 5: (a) Grayscale graph of the number of signal rays passing through each voxel in Solution #a; (b) Grayscale graph of the number of signal rays passing through each voxel in Solution #b.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Solution \#a**} & \multicolumn{3}{c}{**Solution \#b**} \\ \hline  & **Scheme \#1** & **Scheme \#2** & **Increment** & **Scheme \#1** & **Scheme \#2** & **Increment** \\ \hline
1 st layer & 12 & 25 & 10 & 18 & 22 & 4 \\
2 nd layer & 21 & 35 & 14 & 28 & 37 & 9 \\
3 rd layer & 26 & 39 & 13 & 38 & 43 & 5 \\
4 th layer & 31 & 40 & 9 & 43 & 46 & 3 \\
5 th layer & 34 & 44 & 10 & 42 & 46 & 4 \\
6 th layer & 35 & 47 & 12 & 47 & 49 & 2 \\
7 th layer & 37 & 46 & 9 & 50 & 52 & 2 \\
8 th layer & 41 & 49 & 8 & 52 & 53 & 1 \\
9 th layer & 43 & 49 & 6 & 54 & 55 & 1 \\
10 th layer & 46 & 49 & 3 & 50 & 56 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number and increment of voxels crossed by signal rays in different layers of Solutions #a and #b by two schemes.
# Influence Maximization with Priority in Online Social Networks
## 5 Experiments
### Experimental Settings  
All the implementations are on Linux machine with configurations are \(2\times\) Intel(R) Xeon(R) CPU E5-2697 v4 @ 2.30 GHz and \(4\times 16\) GB DIMM ECC DDR4 @ 2400 MHz.  
_Algorithm comparisons._ Since IMP is an expansion of IM, we compare IGS algorithm with several state-of-the-art IM algorithms including: DSSA [15], BCT [2], OPIM-C [23]. In addition, we use the basic algorithm, Max degree (Degree), which is the common baseline for information diffusion problems. In IMP, there are two factors that impact the solution in practice: the budget (\(k\)) of selecting seed node and the priority set of nodes (\(U\)). As a result, these two factors also affect the algorithms. From the above observation, we conduct experiments under two settings: varies \(k\) and fixed \(T\); varies \(T\) and fixed \(k\).  
_The dataset._ For experimental purpose, we choose 5 types of databases from various resources: NetHept, NetPhy, DBLP are citation networks, Email-Enron is communication network [15] and Twitter. Retweet is online social networks [42]. The brief of these ones are described on Table 1. These databases are experimented because they are popular in information diffusion problems, especially used in the state-of-the-art algorithms what we are comparing.  
_Parameter Settings._ Graphs are formatted as each edge \(e=(u,v)\in E\) has the weight \(w(u,v)\) formulated as \(w(u,v)=\dfrac{1}{d_{in}(v)}\) where \(d_{in}(v)\) is the in-degree of node \(v\)[14; 15; 20].  
For the first case, \(k\) is assigned with 150, 160, 170, 180, 190 and 200, respectively, while \(T\) is fixed at 100. In addition, set \(U\) is generated with 200 nodes. With the second case, the value of \(k\) is fixed at 500. \(U\) set includes about 1000 nodes. We change the value of \(T\) increasing from 100 to 500. In all experiments, we keep \(\epsilon=0.1\), \(\delta=1/n\) according setting for IM algorithms [14; 15; 20] and \(\alpha=0.01\).
## 3 Results and Discussion
### Hydrophobicity of the GN/PSt Composites  
The wettability of the GN/PSt porous materials was quantified by their water contact angles. As shown in Figure 3, the WCA of the GN/PSt composite foams was higher than that of the pure foam prepared without GN or GN\({}_{0}\)/PSt (WCA--140.5 \(\pm\) 0.7\({}^{\circ}\)), suggesting that the addition of the GN nanosheets made the PSt-based foams more hydrophobic. The highest WCA was measured for the GN\({}_{4}\)/PSt composite monolith (WCA--150.9 \(\pm\) 0.6\({}^{\circ}\)), indicating that it was the most hydrophobic of the prepared materials.
## 3 Case Study Application
### WWTP Application and Modelling
To seize the impact of each element in loss of production, let us observe that, for example, Aerated Basin 4 and Cyclone Separator exhibit very similar expected availability indexes (97.03% and 97.04%, respectively), but very different P-OEI indexes (0.33% and 22.75%, respectively). This dramatic difference is mainly because of the EOI index they present, which relates to the fact that the Cyclone Separator is a single element connected in series to the system while the Aerated Basin 4 is set in a 40% overcapacity load-sharing configuration, which is part of one out of two subsystems within the Secondary Biological Treatment (ST). In fact, the EOI of the Cyclone Separator is 94.70%, while for Aerated Basin 4 the EOI is 1.38%. The latter suggests that the Aerated Basin 4 will barely impact production since it is highly probable that if the Aerated Basin 4 should fail, the others will be available and withstand the workload. Figure 4: Downstream Analysis: P-OEI and EOI. Figure 5 displays a scatter plot to better comprehend the concept of the P-OEI index, which, in simple words, can be described as the consequence (represented by the EOI index in the \(Y\)-Axis) times the frequency (represented by the unavailability in the \(X\)-Axis). Indeed, the Cyclone Separator (CS1) and the Disinfection Mixing Unit (DU1) present the highest indexes of P-OEI since their unavailability indexes are the highest among all the elements of the process even though they share similar expected operational impact (EOI) indexes with the Gravity Belts (GB1 and GB2). It is also important to note the difference between the equipment with very high EOI (CS1, DU1, GB2, and GB1) and most of the other pieces of equipment which present lower EOI indexes. This difference resides in the fact that the lower EOI indexes belong to subsystems under redundancy or load-sharing configurations, which dramatically reduces the criticality in the system [26] in contrast with the highest EOI indexes, which belong to subsystems without any kind of redundancy or overcapacity.
# The Effect of Curing Conditions on Selected Properties of Recycled Aggregate Concrete
## 2 Materials and Methods  
### Materials  
Two types of cement were used, Portland cement CEM I 42.5R and blast-furnace slag (BFS) cement CEM III/A 42.5N--LH/HSR/NA. Manufacturer of both cements was: Lafarge S.A. Kujawvy Cement Plant, Piechcin, Poland. The properties of the binders are given in Table 1.  
Tap water (in accordance with the European standard EN-1008:2002 was applied for producing concrete mixes [38].  
The highly effective fluidifying admixture of a new-generation polycarboxylate ether superplasticizer was chosen for the concrete mixes in order to improve workability. The properties of the admixture were as follows: pH--6, specific density--1.07 kg/dm\({}^{3}\), solid content--30%, NaO\({}_{\text{eq}}\) \(<\) 0.8%.  
Two types of aggregate--natural (sand and gravel) coming from local sources and recycled concrete aggregate--were applied. The latter was prepared about 6 years before the beginning of the experiment by crushing three parent concretes with three different water-to-cement ratios. A laboratory jaw crusher was used for crushing. Next, the aggregate was separated on screens into fractions (4/6, 6/8, 8/12, and 12/16 mm) and placed in open boxes, located outside the building. A long storage period in variable thermal and moisture conditions contributed to the carbonation of the aggregate.  
The recipes and compressive strength of the parent concretes (PC) are given in Table 2. The recycled concrete aggregate (RCA) resulted from the mix of these three parent concretes, and was applied for the  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Characteristic**} & \multicolumn{2}{c}{**Cement Type \({}^{*}\)**} \\  & **CEM I** & **CEM III** \\ \hline Compressive strength [MPa] after: & & \\
2 days & 29.6 & 14.4 \\
28 days & 56.8 & 52.7 \\ \hline Blaine specific surface [m\({}^{2}\)/kg] & 387 & 467 \\ Ignition loss [\%] & 3.2 & 0.7 \\ Insoluble parts [\%] & 0.9 & 0.3 \\ \hline SO\({}_{3}\) [\%] & 2.7 & 2.2 \\ Cl\({}^{-}\) [\%] & 0.07 & 0.07 \\ Al\({}_{2}\)O\({}_{3}\) [\%] & 5.2 & 7.65 \\ Na\({}_{2}\)O eq [\%] & 0.61 & 0.7 \\ MgO [\%] & 1.2 & 1.4 \\ \hline C\({}_{3}\)S [\%] & 54.4 & 28.3 \\ C\({}_{3}\)A [\%] & 8.9 & 3.7 \\ \hline \multicolumn{3}{l}{\({}^{*}\) specifically: CEM I 42.5R and CEM III/A 42.5N—HSR/NA.} \\ \end{tabular}
\end{table}
Table 1: Characteristics of cements used for recycled aggregate concrete (RAC).  
investigations as 0%, 50% and 100% replacements of natural coarse aggregate. The final sieve curves of natural and recycled concrete aggregate compositions used in the experiment are presented in Figure 1.