esa-sceva/satcom-chunk-collection huggingface dataset:
# Semi-Coprime Array with Staggered Beam-Steering of Sub-Arrays
## 4 Parametric Analysis
### Effect of SLA for Fixed \(\Delta\) and \(L\)  
The simulations were carried out to study the effect of SLA with three different cases of \(\theta_{0}\) (0\({}^{\circ}\), 30\({}^{\circ}\) and 60\({}^{\circ}\)). The \(\Delta\) was fixed at 0.3\({}^{\circ}\) and the number of antenna elements (\(L\)) is fixed at 14. Figure 6(a) shows the results for SLA vs. HPBW and directivity. The figure shows that with an increase in SLA, HPBW and directivity increase slightly. Increasing SLA causes widening of the SA1 and SA2 main lobes, thus increasing their overlapping region and consequently increasing the HPBW and reducing the power loss, i.e., increasing the PSLR. Increasing HPBW and PSLR have opposite effects on directivity; however, the PSLR has a slightly dominant effect, and therefore the directivity increases slightly in Figure 6(a). The effect of SLA on the PSLR and power loss is shown in Figure 6(b). As mentioned above, an increase in SLA causes reduced power loss and enhanced PSLR due to increased overlapping of the SA1 and SA2 main lobes. However, when SLA is increased beyond a certain value, the grating lobes of SA1 and SA2 become so wide that they start to overlap, resulting in high side lobes and poor PSLR. Hence, SLA can be increased, to achieve an enhanced PSLR, up to a certain limit where the grating lobes start to overlap.
## 6 Performance Evaluation and Discussion
### Potential Simplification of the 3D Vision Setup  
While the current system utilizes three laser-line scanners to achieve robust 3D reconstruction, recent advances in compact depth cameras have opened the possibility of replacing the setup with multiple Intel RealSense D405 modules. This hardware simplification is currently under investigation and could lead to a more cost-effective and mechanically compact solution in future implementations.
# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 1 Introduction
This approach solves the problem of generating an ML model, which can be created by the end user's elected device or by the so-called \"fusion center\" or a \"central server\". ML has also shown utility in cooperative sensing, where multiple sensing nodes attempt to establish the state of the spectrum [16; 17]. To share SUs' sensing results or collected data with the fusion center and to receive a spectrum occupancy decision, bidirectional wireless communication will be implemented between each SU and the fusion center. These communications will be supported by a real wireless channel and, accordingly, they can be affected by imperfect transmission. We will take these imperfections into account when evaluating the performance of the proposed approaches. Following these observations and taking them into account, we propose three distinct schemes of a cooperative spectrum sensing (CSS) algorithm based on a deep neural network (DNN) model. Our approaches are specifically designed for a multi-antenna, multi-SU scenario, aiming to leverage the capabilities of both one-dimensional convolutional neural networks (1 DCNN) and long short-term memory (LSTM) networks at the SU level. By combining these networks, we can effectively extract both local and global features from sequential data, enabling the learning of group-level PU activity patterns for accurate classification. We opt for the 1 DCNN due to its remarkable ability to extract local features from sequential data. Additionally, the incorporation of LSTM layers enhances the extraction of global correlations, further improving the overall performance of the model. To evaluate the effectiveness of our proposed model, we consider the impact of both imperfect reporting and imperfect detection channels, demonstrating its robustness in challenging scenarios. The rest of this paper is structured as follows. In Section 2, we introduce the related work for this paper. An overview of the SS principle and adopted DL algorithms is provided in Section 3. Section 4 presents a detailed description of the proposed model based on DL. In Section 5, we present the verification of this solution, and we discuss the computer simulation details. Finally, we summarize this contribution in Section 6.
# Graphene-Based Materials for the Separator Functionalization of Lithium-Ion/Metal/Sulfur Batteries
## 2 Preparation Methods of Graphene-Based Materials
### The Use of Graphene-Based Materials for the Separator of a Lithium-Metal Battery
(**A**) (**i**) Suppression effect of NSG-coated PE separator on lithium dendrites: PE separator (left) and NSG-coated PE separator (right); (**ii**) SEM images of lithium electrodes after 200 cycles with (**iii**) PE and (**iv**) NSG separators; (**v**) Charge/discharge curves of the lithium-metal batteries with NSG separators after different cycle numbers; (**vi**) Rate capability and (**vii**) discharge capacities of the lithium-metal batteries with different separators [124]. Reproduced with permission: Copyright 2015, American Chemical Society. (**B**) (**i**) Procedure followed to prepare the bilayer \(\text{BN}_{\text{x}}\text{Gr}_{\text{y}}/\text{PP}\) separator; SEM image of the Li dendrites after 100 cycles using (**ii**) bare PP membrane, and (**iii**) separator modified with \(\text{BN}_{\text{0}}\text{Gr}_{\text{50}}\); (**iv**) rate capability, (**v**) EIS information, (**vi**) Charge/discharge curves of the lithium-metal batteries with \(\text{BN}_{\text{x}}\text{Gr}_{\text{y}}/\text{PP}\) separators after different cycle numbers and (**vii**) cycle performance of different separators [128]. Reproduced with permission: Copyright 2021, Elsevier. (**C**) (**i**) Schematic illustration of the synthesis of GO-g-PAM composites; SEM images of Li metal anodes assembled with (**ii**) PP and (**iii**) GO-g-PAM@PP separators after 100 cycles; (**iv**) Nyquist plots and of symmetric  
Li i Li cells with different separators; (**v**) LSV curves of asymmetric Li stainless-steel cells with different separators; Galvanostatic curves of Li i Li Ti Ti S O I I2  cells with (**vi**) GO-g-PAM@PP and (**vii**) PP separators at 3 C [134]. Reproduced with permission: Copyright 2019, Springer Nature.
## 5 Conclusions  
In this paper, we investigated the impact of implementing different filtering methods to our ACBC scheme under a typical mMTC scenario: the traffic model 2. The latter leads to severe congestion when no access control scheme is implemented.  
Based on the behavior described in Section 4, it is safe to claim that our ACBC scheme can operate efficiently with several filtering methods, and its performance is compromised only if this process is omitted. That is, when compared to the optimal configuration of a static ACB scheme, our ACBC scheme can reduce the 95 th percentile of access delay by more than 26%, while increasing the success probability and reducing the number of preamble transmissions with numerous filtering methods.  
Figure 8: **(a)** Number of UE arrivals \(a(i)\) and successful accesses \(s(i)\) per RAO with the RLS algorithm, and barring rate \(p_{\mathrm{acb}}(j)\) calculated at the \(j\)th SIB2 for a single simulation run and \(r=54\) under the traffic model 2 with the **(b)** RLS, ALE LMS, and PALE LMS and **(c)** LPF with \(\rho\in\{0,2\}\). Results derived from a single simulation.  
One of the main advantages of selecting an adaptive algorithm, such as the LMS and the RLS, is the \"pulling\" effect provided by setting the desired response to 1. By doing so, the access delay of UEs is further reduced. The latter is of utmost importance under scenarios in which the number of UE accesses is considerably low when compared to the RAP capacity, such as the traffic model 1. In these scenarios, an access control scheme is not needed and increasing the access delay of UEs may be counterproductive. On the other hand, correctly configuring the LMS and RLS algorithms is not straightforward and different methods to reduce the access delay in these scenarios exist. For example, the mean barring time can be set to be a function of the barring rate, hence reducing the barring time as the barring rate increases. Furthermore, if the access delay is not relevant at all, implementing a simple FIR filter in our ACBC scheme would be equally effective as implementing an adaptive algorithm, but the former eliminates the difficulty of selecting correct configuration parameters.  
Conceptualization, I.L.-M., M.A.R.-H. and V.P.; Methodology, I.L.-M. and M.A.R.-H.; Project administration, V.P.; Software, I.L.-M.; Supervision, V.P. and J.M.-B.; Visualization, I.L.-M.  
This research has been supported in part by the Ministry of Economy and Competitiveness of Spain under Grant TIN2013-47272-C2-1-R and Grant TEC2015-71932-REDT. The research of [PERSON] was partially funded by grant 383936 CONACYT-GEM 2014.  
The authors declare no conflict of interest.
# Performance Evaluation of IM/DD FSO Communication System Under Dust Storm Conditions
## 3 System Model and Performance Analysis
### Outage Probability Analysis  
The probability of outage is a key performance metric in wireless and optical communication systems. It is defined as the probability that the instantaneous SNR falls below a predefined threshold \(\gamma_{\text{th}}\). Mathematically, the outage probability is expressed as follows:  
\[P_{\text{out}}=\text{Pr}[\gamma(I)<\gamma_{\text{th}}]. \tag{16}\]  
Substituting the definition of the instantaneous SNR, \(\gamma(I)=\eta I\), where \(\eta\) is the SNR scaling factor, we get the following:  
\[P_{\text{out}}=\text{Pr}[I<I_{0}],\quad\text{where }I_{0}=\frac{\gamma_{ \text{th}}}{\eta}. \tag{17}\]  
Equation (17) represents the cumulative distribution function (CDF) of the Beta distribution, which is given as follows:  
\[P_{\text{out}}=I_{I_{0}}(\alpha,\beta)=\frac{1}{B(\alpha,\beta)}\int_{0}^{I_{ 0}}t^{\alpha-1}(1-t)^{\beta-1}dt. \tag{18}\]  
This integral can be expressed in closed form using the Gaussian hypergeometric function \({}_{2}F_{1}(\cdot)\) as follows [17]:  
\[P_{\text{out}}=\frac{I_{0}^{\alpha}}{\alpha B(\alpha,\beta)}\cdot{}_{2}F_{1}( \alpha,1-\beta;\alpha+1;I_{0}), \tag{19}\]  
where \({}_{2}F_{1}(a,b;c;z)\) is the standard Gauss hypergeometric function that is defined as follows:  
\[{}_{2}F_{1}(a,b;c;z)=\sum_{n=0}^{\infty}\frac{(a)_{n}(b)_{n}}{(c)_{n}
# Study on Slump and Compressive Strength of Gangue Based on Aggregate Size Gradation
## 4 Experimental Results and Analysis
### Analysis of Slump Test Results of Aggregate Size Gradation  
During the filling process, filling paste slurry is usually transported by either artesian flow or pumping. Therefore, the filling paste must have a certain level of fluidity, and the slump is an important indicator. If the slump is extremely large, the slurry is prone to segregation. Conversely, if the slump is too small, it becomes difficult for the slurry to flow normally, particularly in a pump. To meet the flow performance requirements of the filling paste, the slump of the filling paste is generally required to be between 180 and 240 mm.  
As shown in Figure 5, the slump of the filling paste continuously decreased with a gradual increase in the proportion of fine aggregates. First, an increase in fine aggregate reduces the gap in the filling paste slurry, resulting in a more compact filling paste and reduced slump. Second, increasing the amount of fine aggregates increases the contact area between the aggregates, which in turn increases the internal friction force of the filling paste and reduces its fluidity. Therefore, an increase in this friction force resulted in a decrease in the slump of the filling paste. Furthermore, an increase in fine aggregates results in enhanced water absorption of the filling paste slurry, causing the free water in the slurry to transition into the adsorbed state. Consequently, the fluidity and plasticity of the filling paste decreased, leading to a reduction in its slump.
## 2 Materials and Methods
### Equipment and Instruments  
The instruments used were a JK-LB1700 multi-parameter controllable in situ precise drying experimental bench (Changchun Jida Scientific Instrument Equipment Co., Ltd.; Changchun, China), PQ-520 single-grain moisture meter for rice and wheat (Metller Toledo Group, Zurich, Switzerland), BLH-3250 huller (Zhejiang Bethlehem Instrument Equipment Co., Ltd.; Hangzhou, China), DGG-9250 GD electric constant-temperature blast-drying oven (Shanghai Senxin Experimental Instrument Co., Ltd., Shanghai, China), PL3002-IC electronic analysis balance (Mettler Toledo Instruments Ltd.; Zurich, Switzerland), FSJ-IIA Hammer Cyclone Mill (China Grain Reserve Management Group Co., Ltd. Beijing, China), Climacell Series Constant Temperature and relative humidity Chamber (Leica Instruments GMBH; Brunswick, Germany), OS-200 Orbital Oscillator (Hangzhou Aosheng Instrument Co., Ltd.; Hangzhou, China), TG16K Centrifuge (Changsha Dongwang Experimental Instrument Co., Ltd.; Changsha, China), and HS153 moisture analyzer (Mettler Toledo Group, Zurich, Switzerland).  
The test was conducted in the Changping pilot Test Base of the Scientific Research Institute of the National Food and Strategic Reserves Administration. The drying test device was developed independently [26]. The structure of the test device is shown in Figure 1. The test device can ensure that the temperature and flow rate of the drying medium inside the experimental chamber is consistent and uniform when the test requires that the material is located in an environment of high relative humidity conditions, and, if the evaporation of water from the grain itself cannot meet the requirements, the test device has the function of automatic humidification. The test device can control the temperature of the drying medium, relative humidity and flow rate and other parameters, monitoring temperature, relative humidity, moisture, wind speed, etc. (the details are shown in Figure 1d); can restore the grain in the dryer in environmental conditions simulating different factors and levels of grain drying; can be adjusted at any time in its micro-environmental drying parameters; and can be completed to approximate the level of in situ measurements of the test, such as constant temperature or temperature-varying forms of drying tests.
## 1 Introduction  
Deformation and fracture are some of the common problems affecting wood material as it is often used, nowadays as in the past, in the construction of structures and artefacts of cultural value and therefore in need of special care and conservation. Research on wood is conducted by using different non-destructive testing (NDT) techniques mainly aimed at timber grading and assessment of existing timber structures [1].  
Among NDT techniques, acoustic emission (AE) has recently been used to detect mechanical stresses in existing timber structures. An overview of the application of AE on wooden materials was already provided by [PERSON] [2] and [PERSON] [3]. The aim of our review is to analyse the state of the art of the AE NDT technique applied to monitor the occurrence of fracture events in softwoods, specifically Scots pine, focusing on the influence of grain angle and tree ring density on such occurrence. First, the general framework of NDT used on wood samples was initially defined by means of a preliminary survey, designed to study the influence of high/low tree ring density and grain angle direction on the outcomes obtained through the different methods. After that, the same influencing factors in relation to the AE parameters detected during experiments on wood samples, were investigated in a systematic literature review.  
The density parameter as a focus of this work was defined as the ratio between the dry weight of wood divided by the green volume of the same wood (expressed in g/cm\({}^{3}\)) [4]. In addition, the tree ring width (TRW) was investigated, as one of the main parameters used in dendrochronology for the reconstruction of climate variability of the past (tree rings usually grow wider in warm, wet years and they are thinner in years when it is cold and dry). Finally, the wood grain angle as the angle between the wood fibre direction and the vector in the axial direction (as shown in Figure 1), was studied together with the density as influencing factors in the AE detection.  
The paper is structured into three sections: following the introduction, the second section (i.e., Methodology) describes the search strategy based on the three-step process that led to the screening of the documents analysed in this review. In the third section (i.e., Results and Discussion), papers were organized by outcomes and into geographical regions, year of publication and subject areas of the research. Discussion on the outcomes is provided here. Then, the last section (i.e., Conclusions) evaluates the aspects mentioned above and identifies gaps in the application of AE on structural health and condition monitoring of softwood material, that might be faced and filled in the future.
# Estimating Airway Resistance from Forced Expiration in Spirometry
## 3 Results  
### Baseline Characteristics  
#### 3.1.1 Study Population  
The baseline characteristics of the cohort are shown in Table 1. This cohort comprised of 63 GOLD I, 111 GOLD II, 92 GOLD III, 61 GOLD IV, and 135 healthy smokers. In this cohort, FEV1/FVC decreased with severity of airflow limitation from healthy (74 \(\pm\) 4%) to GOLD I (64 \(\pm\) 4%) until GOLD IV (30 \(\pm\) 6%). In addition, Raw increased with severity of airflow limitation while other PFT parameters such as DLCO and air-trapping index (RV/TLC) also showed expected patterns. The extent of emphysema as quantified in CT by % below \(-\)950 HU and 15 th percentile HU increased considerably with airflow limitation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Healthy** & **GOLD I** & **GOLD II** & **GOLD III** & **GOLD IV** & \(p\) \\ \hline N & 135 & 63 & 111 & 92 & 61 & \\
**Age (years)** & 62 (6) & 63 (6) & 68 (8) & 69 (9) & 63 (7) & \textless{}0.001 \\
**Sex (female \%)** & 21 & 24 & 23 & 20 & 26 & 0.9 \\
**Smoking** & 42 (21) & 50 (28) & 49 (23) & 49 (24) & 52 (25) & 0.06 \\
**FEV1 (\%pred)** & 97 (13) & 93 (10) & 65 (9) & 40 (5) & 23 (5) & \textless{}0.001 \\
**FVC (\%pred)** & 103 (14) & 115 (15) & 95 (16) & 82 (15) & 62 (14) & \textless{}0.001 \\
**FEV1/FVC (\%)** & 74 (4) & 64 (4) & 55 (9) & 39 (8) & 30 (6) & \textless{}0.001 \\
**Raw (kPa/I/s)** & 0.32 (0.10) & 0.35 (0.12) & 0.44 (0.12) & 0.60 (0.16) & 0.82 (0.29) & \textless{}0.001 \\
**sGaw (I/(kPa s))** & 0.9 (0.2) & 0.75 (0.21) & 0.55 (0.18) & 0.34 (0.11) & 0.22 (0.07) & 0.01 \\
**RV/TLC (\%)** & 37 (6) & 38 (6) & 47 (7) & 57 (8) & 68 (8) & \textless{}0.001 \\
**DLCO** & 7.7 (1.8) & 7.1 (2.2) & 5.5 (2.0) & 4.2 (1.4) & 2.9 (1) & \textless{}0.001 \\
**(mmol/min/kPa)** & & & & & & \\
**15 th percentile (HU)** & \textless{}935 (41) & \textless{}938 (34) & \textless{}943 (49) & \textless{}953 (39) & \textless{}968 (44) & \textless{}0.001 \\
**\% below -950 HU** & 10 (12) & 11 (11) & 15 (16) & 19 (16) & 26 (17) & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Baseline characteristics of the Leuven COPD cohort with N = 462. Values are Mean (SD).
## 1 Introduction
This paper describes an active SI cancellation technique for a SAW-less diversity receiver, extending the conference publication [23]. In this work, the design is extended including the down-conversion mixers and baseband trans-impedance amplifiers (TIAs). Furthermore, an expanded analysis of the system requirements, performance analysis and design details are reported. The active canceler was designed to adjust both its gain and phase to produce a signal that adds destructively to the SI signal, acting as a programmable vector modulator. The use of the SIC in the dual antenna system further increases the TX-RX isolation from below 30 dB to above 50 dB, as obtainable in a SAW-based system. This approach therefore, improves the effective receiver linearity. It was shown in [17] that in a typical multi-antenna handset configuration, the group delay of the SI path due to antenna coupling is typically in the order 2-3 ns and that, this limits the achievable cancellation to about 20 dB over a bandwidth of 10 MHz. Improved cancellation bandwidth may be achieved integrating an analog delay line in the canceller as proposed in [22] or using an additional digital cancellation path, as advocated in Figure 1, due to space limitations issues related to limited cancellation bandwidth as a result of larger antenna group delay are left for future work. This paper is organized as follows: Section 2 describes the system architecture and the system level requirements of the receiver chain. Section 3 describes the circuit implementation of the active SI canceler and the receiver chain. Section 4 discusses the simulation results. Section 5 concludes the paper.
## 2 Experimental Setup and Procedures
### Parameter Analysis and Comparison  
Analyzing the discharge time and voltage plateau curves, it can be observed that at 20, 30, 40, and 50 \({}^{\circ}\)C, the influence of high discharge rate to the CE is minor. However, under a high temperature environment, as the Ohmic heating from the increase in number of cycles and high discharge rate rapidly damages the internal materials of the battery; the capacity will quickly decrease. Under low temperature, the rate of electrochemical reactions lowers; by necessity the CE must also fall. However, under an environment of low temperature together with high discharge rate, Ohmic heating will offset some influence from low temperature, allowing the CE to increase slightly. It can be observed from Figure 2a-d that the discharge curves are not stable. However, as the number of cycles increases, due to uneven heating of internal materials of the battery, those close to the battery tip decay more quickly. Hence, there will be a much greater capacity fade compared to prediction.  
It can be seen that under the compound influence of environmental temperature and discharge rate, there will be huge changes to discharging time and capacity fade. From Tables 1-3, Figure 3a-e were constructed. From the figures, the influence of temperature and discharge rate on capacity can be clearly seen. The CE represents the battery performance, while the remaining capacity represents the life of the battery. These two are very important to battery safety. Hence, battery characteristics must be obtained from changes of the CE and remaining capacity under different temperatures, discharge rates, and numbers of cycles to establish a comprehensive battery model to ensure that the battery can operate safely.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & & & \multicolumn{4}{c}{\(SoH_{N}\) (\%)} & & & \\
**C Rate** & & **0.5 C** & & & **1 C** & & **2 C** & & & **3 C** & \\
**T (\({}^{\circ}\)C)** & **–10** & **0** & **10** & **–10** & **0** & **10** & **–10** & **0** & **10** & **–10** & **0** & **10** \\
**Cycle** & & & & & & & & & & & & \\ \hline
1 & 89.63 & 95.31 & 100.0 & 88.94 & 95.33 & 101.5 & 89.50 & 93.89 & 101.2 & 91.08 & 92.42 & 96.67 \\
100 & 83.63 & 92.58 & 97.64 & 83.64 & 88.92 & 99.08 & 80.11 & 88.67 & 97.61 & 77.83 & 86.58 & 93.58 \\
200 & 81.72 & 86.04 & 95.00 & 81.33 & 85.03 & 94.61 & 75.67 & 79.83 & 94.44 & 67.42 & 78.67 & 89.00 \\
300 & 75.31 & 81.93 & 91.85 & 71.56 & 73.06 & 91.83 & 65.78 & 70.72 & 92.39 & 46.75 & 70.67 & 83.83 \\
400 & 56.17 & 67.31 & 89.78 & 49.17 & 62.89 & 88.61 & 41.67 & 58.44 & 87.78 & 26.42 & 58.42 & 79.75 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Capacity change with 400 cycles under different rates of discharge; environmental temperatures \(-\)10 and 10 \({}^{\circ}\)C.