esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To improve our understanding of the carbon balance, it is significant to study long-term variations of all components of carbon exchange and their driving factors. Gross primary production (GPP), respiration (Re), and net ecosystem productivity (NEP) from the hourly to the annual sums in a subtropical coniferous forest in China during 2003-2017 were calculated using empirical models developed previously in terms of PAR (photosynthetically active radiation), and meteorological parameters, GPP, Re, and NEP were calculated. The calculated GPP, Re, and NEP were in reasonable agreement with the observations, and their seasonal and interannual variations were well reproduced. The model-estimated annual sums of GPP and Re over 2003-2017 were larger than the observations of 11.38% and 5.52%, respectively, and the model-simulated NEP was lower by 34.99%. The GPP, Re, and NEP showed clear interannual variations, and both the calculated and the observed annual sums of GPPs increased on average by 1.04% and 0.93%, respectively, while the Re values increased by 4.57% and 1.06% between 2003 and 2017. The calculated and the observed annual sums of NEPs/NEEs (net ecosystem exchange) decreased/increased by 1.04%/0.93%, respectively, which exhibited an increase of the carbon sink at the experimental site. During the period 2003-2017, the annual averages of PAR and the air temperature decreased by 0.28% and 0.02%, respectively, while the annual average water vapor pressure increased by 0.87%. The increase in water vapor contributed to the increases of GPP, Re, and NEP in 2003-2017. Good linear and non-linear relationships were found between the monthly calculated GPP and the satellite solar-induced fluorescence (SIF) and then applied to compute GPP with relative biases of annual sums of GPP of 5.20% and 4.88%, respectively. Large amounts of CO\({}_{2}\) were produced in a clean atmosphere, indicating a clean atmospheric environment will enhance CO\({}_{2}\) storage in plants, i.e., clean atmosphere is beneficial to human health and carbon sink, as well as slowing down climate warming.  
CO\({}_{2}\) balance; gross primary production; respiration; net ecosystem productivity; empirical model; solar-induced fluorescence
## 2 The Transmitting and Receiving System of the Composite Signal
### The Reception of the Composite Signal  
The receiving process of the composite signal is shown in Figure 3. The reception of the BOC signal is divided into two processes: acquisition and tracking. Acquisition realizes the rough estimation of the BOC signal PN code offset and Doppler frequency, and tracking achieves high accuracy estimation of the BOC signal parameters.  
Since the BOC signal and the BPSK signal are at the same frequency, the two signals have the same Doppler frequency. In addition, the BOC signal has better anti-interference performance due to the use of a PN code. Therefore, we use the parameter of the BOC signal to assist the demodulation of the BPSK signal in Section 3.
## 1 Introduction
This HDL-based detector integrates a 1D-CNN model with a Bi-LSTM model; the 1D-CNN model captures spatial information from received signal, while the Bi-LSTM model processes these features. This combined approach improves detection performance even in the presence of CSI uncertainty. * The channel data and received signal are preprocessed using OFDM-IM domain knowledge before being fed into the HDL model. This preprocessing step enhances the HDL-based detector's accuracy in detecting active subcarrier indices. Additionally, we apply several different equalizers to the received signal and evaluate their comparative performance. * We assess the BER performance of the proposed HDL-based detector at various SNR levels and explore its performance with different optimizers. Additionally, we calculate the SE and throughput of the model to further demonstrate its effectiveness. The results indicate that the proposed HDL detector significantly enhances detection performance in OFDM-IM systems. The remainder of this paper is organized as follows: the system modeling is introduced in Section 2; a thorough discussion of the suggested model, including details on offline training and online testing protocols, is provided in Section 3; the simulation results are presented in Section 4 and the complexity in Section 5; finally, the conclusions are presented in Section 6.
## 3 Results and Discussion
### Influencing Factors  
#### 3.3.1 Effect of Initial Ni\({}^{2+}\) Concentration  
Figure 5 shows the effect of the initial solution concentration on Ni\({}^{2+}\) adsorption over time by nZVI@ATP. When the reaction was conducted for 12 h, the removal rate of the Ni\({}^{2+}\) by the nZVI@ATP at an initial solution concentration of 100 mg/L reached 95.65%; however, the removal rate reached only 67.75% at an initial concentration of 300 mg/L. As the reaction proceeded, the removal rates of each group gradually tended to equilibrate after 2 h. The corresponding adsorption capacities were 95.65, 143.86, and 203.27 mg/g at initial solution concentrations of 100, 200, and 300 mg/L, respectively.  
Figure 4: Variation of zeta potential of ATP and nZVI@ATP with pH.  
Figure 5: Adsorption results of different initial concentrations on Ni\({}^{2+}\) over time by nZVI@ATP (initial pH of 5; temperature of 298 K; dosing amount of 1 g/L; and stirring time of 12 h).
## 3 First Application: AE Events in a Simulated Fracture Process
### Model Description  
This simulation described a concrete-made, 316 mm long by 36 mm thick square plate, as shown in Figure 2 with its prescribed boundary conditions. The figure also includes the positions of virtual AE sensors, simulating accelerometers aligned with the direction normal of the plate's median plane. The corresponding LDEM model comprised \(79\times 79\times 9\) cubic cells, with \(L_{n}=4\) mm. All other relevant parameters are shown in Table 1.  
Figure 2: DEM model of the plate submitted to pure-shear stress, with boundary conditions and AE sensor positions.  
In Table 1, \(d_{eq}\) = 0.022 m was obtained by assuming \(\sigma_{t}\) = 10 MPa, \(G_{f}\) = 70 N m\({}^{-1}\), \(E\) = 32 GPa, and \(d\) = 0.316 m, in Equation (8), yielding \(s\) = 0.27. Therefore, replacing \(s\) in Equation (9) resulted in the adopted \(d_{eq}\). As that parameter was much smaller than the plate's characteristic dimension \(d\) = 0.316 m, brittle behavior was expected. We also observed that the chosen \(\sigma_{t}\) was slightly higher than the real value for concrete, as it was selected to emphasize the model's fragile behavior and to facilitate the identification of AE events.  
For the random field, the coefficient of variation was determined according to [48], which showed that \(CV(G_{f(solid)})=CV(G_{f})/2.5\) for the LDEM's cubic arrangement. Therefore, \(CV(G_{f(solid)})=100/2.5\) = 40% in the present case. The correlation lengths \(l_{cx}\), \(l_{cy}\), and \(l_{cz}\) were considered equal to the cubic module's side. Random mesh variability was introduced as the normally distributed perturbation with \(CV_{p}\) = 2.5%. This was based on [56], which stated that this value had to be smaller than 2.75% and suggested 2.5% as a typical value for concrete. The introduction of randomness in the LDEM was discussed in more general terms by [50].
## 4 Discussion Based on Practical Examples  
### A Short Description of the Structures  
To back up the above descriptions with practical examples, the paper cites the results of the basic chemical tests of concrete in selected industrial support structures. The cited results are compared with the actual technical condition of the structures in question.  
All the structures were built in the second half of the 20 th century and their age (and operation period) at the time of the examinations was 45-55 years.  
The first case was a support structure of a WR-25 power boiler. The structure consisted of a bottom slab (on piles) and a spatial frame resting on it. The frame consisted of nine columns and a top grid tied with a plate with openings. This structure is hereinafter referred to as A.  
In the second case, the analysis covered the support structures of four power boilers--three of the WLM-5 type and one of the WR-10 type. Each of the identical support structures of WLM-5 boilers consisted of a bottom slab with two flat frames fixed in it. Each frame consisted of two columns joined by a lock with brackets and the locks were joined by a plate. Those structures are hereinafter referred to as B1, B2, and B3. The support structure of the WR-10 boiler was similar, but each frame contained three columns. This one is hereinafter referred to as B4.  
In the third case, the analysis covered two identical foundations of WLM-25 boilers which were geometrically similar to the first foundation (A), but their dimensions were different. Those structures are hereinafter referred to as C1 and C2.  
The fourth case was slightly different in terms of construction because it was a pipeline support system in a steel plant. The trestle bridge was designed and built as an RC one, structurally joined with an open water channel. In structural terms, the trestle was an RC spatial frame consisting of two-levelcolumns joined by longitudinal and transverse locks; the top tier beams were joined by a plate. At an unknown time, the top longitudinal beams were confined in a steel sheet housing. In the past, the columns were additionally concreted and confined in steel elements; those reinforcements did not reach the foundation level. Most of the structural elements bore traces of past repairs made using PCC materials. The structure is marked as D.  
All the presented facilities are RC monolithic structures operated in industrial conditions, including exposure to process water. All those structures can also be divided into an underground part and a part above the ground. This division is very important here because it distinguishes between two zones characterized by varying exposure to the external corrosive impact.
## 4 Floor Heave Control with Anti-Shear Pile Technology
### Effectiveness of Anti-Shear Pile Method
In these piles, five main steel bars for longitudinal reinforcement and spiral lateral reinforcement stirrups were used (Figure 15c). The entry floor heave after pile installation with different schemes is shown in Figure 16. The form of the floor contour is the same for all pile variants. Steel piles have the best counteraction to heaving (the maximum floor heave is 361 mm). The worst variant is the \"steel tube\" (the maximum floor heave is 399 mm). The effectiveness of the \"filling pile\" depends on the class of concrete. Thus, the floor heave for the B-20 class is 397 mm, and for the B-60 class, it is 386 mm. An increase in the class of concrete leads to a decrease in the size of the vertical post-peak strain zone in the entry floor (Figure 17b,c) and a decrease in floor uplift. The localization and size of the post-peak strain zone for the steel tube pile and filling pile with B-20 concrete do not differ (Figure 17a,b). However, the cost of piles varies significantly. A comparison of costs for one set of piles (2 \(\times\) 2 m) for different types is shown in Table 4. The prices are correct for Ukraine as of March 2024. Figure 14: The entry floor heave and horizontal strain distributions in surrounding rock before and after pile installation at the 2 nd simulation stage. Figure 11.0 The entry floor heave after pile installation at the 3 rd simulation stage with different schemes. Figure 11.1 File design in numerical simulation: (**a**) steel pile; (**b**) steel tube; (**c**) filling pile. 1—steel tube (diameter 200 mm); 2—concrete; 3—main steel bars (5 × 14 mm); 4—lateral reinforcement; stirrups (spiral length 14 m, diameter 6 mm). Filling piles have a minimal cost, which gives them an advantage. So, with the 2 pile 5+2(B-60) scheme, the cost of the pile is 3.57 times less than with the 2 pile 5+2 scheme. Installation of the filling pile is easier, since the weight of reinforcement elements is less, which also reduces transportation costs. In addition, the installation of reinforcement of filling piles, unlike the installation of tubes, can be carried out without significant labor costs. Thus, filling piles are more beneficial for floor heave control. The design of the proposed pile is shown in Figure 15c. Optimization of pile reinforcement design requires additional research and is not studied in this article.
## 1 Introduction  
Recently, thermally activated delayed fluorescence (TADF) organic light-emitting diodes (OLEDs) have attracted significant research interest because a maximum internal quantum efficiency as high as 100% can be theoretically achieved without the need for rare and heavy metals, such as iridium [1; 2; 3; 4; 5]. As the injected holes and electrons recombine, singlet and triplet excitons are formed at a ratio of 1:3 [6; 7; 8; 9]. TADF emitters up-convert triplet excitons into singlet excitons by the reverse intersystem crossing process when the energy split (\(\Delta\)EST) between the singlet (S\({}_{1}\)) and triplet (T\({}_{1}\)) excited states is sufficiently small [10; 11].  
Blue TADF OLEDs with a maximum external quantum efficiency (EQE) of over 20% have been consistently reported in recent literature [12; 13; 14]. Although TADF OLEDs have demonstrated high efficiencies, their poor stability is a significant drawback compared to phosphorescent OLEDs. Blue devices can be considered even less stable because they have a shorter lifetime than red or green devices. Many researchers have attempted to address the stability problem by focusing on the lifetime and roll-off characteristics of OLEDs. The approaches detailed in these studies include optimizing the host/dopant ratio and synthesizing new materials for blue TADF emitters [14; 15; 16; 17].  
Despite significant efforts to improve the stability of blue TADF OLEDs, there is a paucity of quantitative analyses of their degradation. Measuring J-V and EL characteristics, along with the infrared imaging method, is the most representative OLED degradation characteristic analysis technique; however, it is difficult to analyze the degradation characteristics according to the light-emitting mechanism of OLED. These methods also sometimes prevent the measured OLED pixels from being used again. On the other hand, it is possible to quantitatively analyze the degradation characteristics of the aged device considering the charge accumulation feature at the organic interfaces using C-V characteristics and the Cole-Cole plot [18; 19]. In this study, an impedance analyzer, which is non-destructive measuring equipment, was used to investigate the degradation features of blue TADF OLEDs by measuring the capacitance-voltage (C-V) characteristics.  
Most triplets and polarons are mainly accumulated at the interface of organic layers due to differences in energy levels and carrier mobility between them; and these cause annihilation and quenching phenomena between excitons and polarons, which are the representative reasons for device degradation. In this study, the hole transfer layer (HTL) structure and thickness of the exciton blocking layer (ExBL) were varied. Both independent variables are adjacent to the emission layer (EML) interface, where most carriers accumulate.
## 3 Analysis of Numerical Simulation Result
### Analysis of TBM Disc Cutter Model
#### 3.4.2 Impact Wear Estimated from Disc Cutter  
Here, the impact wear was calculated from the sum of normal contact forces between particles and each TBM disc cutter. Figure 19(a) presents the gradual accumulation of average total normal contact energy in all disc cutters, as the TBM advanced during the simulation.  
Figure 19: Average compressive force on disc cutters depending on their location.  
Figure 20: Average normal cumulative contact energy in all disc cutters with respect to (**a**) TBM advance and (**b**) cutterhead RPM.  
Figure 20b presents the variation of the average total normal accumulative contact energy in all disc cutters. It clearly increased as the cutterhead RPM rose.  
Figure 21 and Table 9 show the normal cumulative contact energy in each disc cutter after excavation. The gauge disc cutters (shown in red) at the transition zone of the cutterhead have higher energy, while those centrally placed (shown in blue) have lower energy compared with the rest. Overall, the figure shows that a disc cutter's wear depended significantly on its position on the cutterhead and the layout pattern. Therefore, a gauge disc cutter in the transition zone of the cutterhead was expected to be replaced frequently during the excavation [3; 4; 5; 6; 7; 8].
## 2 Problem Statement and Mathematical Models  
The portfolio selection problem deals with how to allocate the pre-given capital to a set of securities with the maximized returns. When all of the parameters are deterministic variables in this process, this classical problem can be essentially formulated as a linear programming model with the selection ratio constraints. In order to characterize this problem, we first introduce some notations in the formulation process.  
\(S\): total number of involved securities;  
\(s\): index of involved securities, \(s\in\{1,2,\ldots,S\}\);  
\(r_{s}\): return of the \(s\)-th security;  
\(x_{s}\): the investment ratio for security \(s\), which is a decision variable.  
If all of the parameters in this process are constant, we can formulate this problem as the following linear programming model.  
\[\left\{\begin{array}{l}\max r_{1}x_{1}+r_{2}x_{2}+\ldots+r_{S}x_{S}\\ s.t.\\ x_{1}+x_{2}+\cdots+x_{S}=1\\ x_{s}\geq 0,s\in\{1,2,\ldots,S\}\end{array}\right. \tag{1}\]  
In this model, the objective function is to maximize the total return among different feasible investment strategies. The constraint ensures that the sum of the investment proportions should be unity, and each proportion is larger than or equal to zero. Obviously, if all of the parameters are pre-given constants, it is typically a linear programming model and can be solved by the simplex method. Note that the portfolio selection plan is usually made before the return can be fulfilled. Thus, the actual return of each security is practically uncertain. In the following discussion, we particularly treat the return of each security as a fuzzy variable to suitably describe the practical uncertainty. Theoretically, if we consider the uncertainty in the portfolio selection problem, this problem should be formulated as a robust or reliable optimization programming with fuzzy parameters. For the completeness of this study, we shall introduce the basic knowledge in fuzzy set theory in the following discussion.
# Head-Related Transfer Functions for Dynamic Listeners in Virtual Reality
## 5 Experiment II: Dynamic 6-DoF Virtual Reality
### Results  
The data from the subjective experiments are analyzed by non-parametric Friedman tests, with the participant defined as a random effect repeated across measurements. The effect sizes are calculated using the [PERSON]'s W metric, which describes the level of agreement among the participants. To interpret the effect size, we refer to the guidelines laid out by [PERSON] (2007): \(0.1\leq\text{W}<0.3\) (small effect), \(0.3\leq\text{W}<0.5\) (moderate effect), and \(\text{W}\geq 0.5\) (large effect).  
The localization accuracy was found significantly different for the different HRTF sets, when the audio object was placed in the scene, \(\chi^{2}_{(4,N=12)}=29.6\), \(p<0.001\), or the audio object was hand-held \(\chi^{2}_{(4,N=12)}=37.6\), \(p<0.001\). The corresponding effect sizes are \(W=0.62\) and \(W=0.78\), respectively. Similarly, the localization accuracy was found significantly different for the different HRTF sets, when the audio object was above the listener, \(\chi^{2}_{(4,N=8)}=24.8\), \(p<0.001\), or when the audio object was below the listener \(\chi^{2}_{(4,N=8)}=22.5\), \(p<0.001\). The corresponding effect sizes are \(W=0.77\) and \(W=0.70\), respectively. Post-hoc pairwise permutation tests revealed no significant differences between the measured, modeled, and generic HRTFs in any of these scenes. These results are displayed graphically in the first four panels a, b, c, and d, in Figure 5.  
The timbral fidelity was found significantly different for the different HRTF sets, when listening to an anechoic cello sample, \(\chi^{2}_{(4,N=12)}=29.8\), \(p<0.001\), or when listening to an anechoic castanets sample \(\chi^{2}_{(4,N=12)}=35.4\), \(p<0.001\). The corresponding effect sizes are \(W=0.62\) and \(W=0.74\), respectively. These results are shown in Figure 5 panels e and f. Post-hoc tests revealed all the other conditions to be rated significantly lower than the hidden reference _No HRTF_ in both scenes. In the _Cello_ scene, the generic HRTFs were rated significantly higher than the measured or modeled HRTFs. In _Castanets_, the measured HRTFs were rated significantly lower than all the other conditions.  
The overall audio quality was found significantly different for the different HRTF sets, when experiencing the park scene, \(\chi^{2}_{(4,N=12)}=27.3\), \(p<0.001\), or the fountain music scene \(\chi^{2}_{(4,N=12)}=29.6\), \(p<0.001\). The corresponding effect sizes are \(W=0.57\) and \(W=0.62\), respectively. The last panels g and h, in Figure 5 display these results. In _Park_, all of the HRTF sets, even the _Generic horizontal_, were rated equally and above the _No HRTF_ condition according to post-hoc tests. In _Fountain_, no difference was found between measured, modeled, and generic HRTFs, but the _Generic horizontal_ was significantly lower than _Generic_.
# Single Trace Side Channel Analysis on NTRU Implementation
## 3 Proposed Single Trace Side Channel Analysis on NTRU Implementation
### NTRU Open Source
#### 3.1.2 Experiment  
Figure 2 is a full trace of the NTRU Open Source porting on an KLA-SCARF AVR, captured in Lecroy HDO6104A oscilloscope with 250 M sampling rate [12; 31]. The parameters for the experiment are \(N=50\), \(d_{F_{1}}=8\), \(d_{F_{2}}=8\), \(d_{F_{3}}=6\) and the private key is as follows.  
\[b = \{0x03,0x01,0x1e,0x11,0x05,0x06,0x1a,0x0e,0x13,0x01,0x28,0x23,0x10,0x29,0x22,0x0c,\] \[0x07,0x08,0x0b,0x15,0x1b,0x25,0x2e,0x2c,0x18,0x21,0x17,0x2f,0x19,0x04,0x30,0x00,\] \[0x02,0x0f,0x27,0x2d,0x12,0x2a,0x2b,0x14,0x1c,0x1f,0x26,0x20\}\]  
We choose these values as we considered them to be suitable in the experimental environment. The first 16 entries of \(b\) represent \(F_{1}\), the next 16 values represent \(F_{2}\) and the rest of the values represent \(F_{3}\).  
The first step for analysis is discovering a reference trace \(R\) by SPA (Figure 3). The length of \(R\) is calculated by dividing the full trace length by the total number of operations. After that, the correlation coefficient can be calculated from the trace using the reference. Figure 1 is a part of the result containing the high peaks and the following intervals. There are two indices tagged on each peak, one represents an order of the high peak and the other is a distance between the previous high peak. The 31 th peak has different distance than others, so the first degree where coefficient is \(-1\) is \(50-31=19=0x13\). With this process, we can recover \(F_{1},F_{2},F_{3}\), and the private key.  
Figure 1: Result: High peaks of the addition and a distance value between the peaks.  
Figure 2: Full trace of NTRU Open Source: The top figure is a raw trace, the middle figure is a filtered trace, and the bottom two are enlarged figure of the filtered trace.
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 3 Discussion
### User Flexibility  
According to the Bluetooth Special Interest Group (SIG), the majority of the Bluetooth-based smartphones will support BLE by 2018. This will offer great flexibility to end users, as a BLE-enabled smartphone can potentially be utilized as an access point. ZigBee needs a ZigBee-enabled device as an access point (currently there are no mobile phones with ZigBee capabilities).