esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### 3D-Printed Composite Characterization  
The obtained pieces were analyzed in a Perkin Elmer DSC (model: DSC8000, Waltham, MA, USA), where a heating-quenching-heating cycle was carried out. The first heating step was used to delete the thermal history of the material, and heated the material from \(-\)30 to 230 \({}^{\circ}\)C, with a heating rate of 20 \({}^{\circ}\)C/min; then, it was kept for 5 min at 230 \({}^{\circ}\)C, quenched with a heating rate of 20 \({}^{\circ}\)C/min until the temperature reached \(-\)30 \({}^{\circ}\)C, and kept for 5 min at this temperature. The second heating cycle ranged from \(-\)30 to 230 \({}^{\circ}\)C with a heating rate of 10 \({}^{\circ}\)C/min, and the quenching and second heating cycle were used to calculate the crystallization temperature (\(T_{c}\)), crystallization enthalpy (\(\Delta H_{c}\)), melting temperature (\(T_{m}\)), and melting enthalpy (\(\Delta H_{m}\)) to evaluate the crystallization process. In addition, the degree of crystallinity (\(X_{c}\)) was calculated using the following formula:  
\[X_{c}=(\Delta H_{m}/\Delta H^{c})\times 100 \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Composite Filament** & **GHP Content [phr]** & **Code** \\ \hline PP & 0 & PP \\ PP 1 phr GHP & 1 & PP-1 GHP \\ PP 3 phr GHP & 3 & PP-3 GHP \\ PP 5 phr GHP & 5 & PP-5 GHP \\ \hline \end{tabular} pbr: parts per hundred of resin.  
\end{table}
Table 1: Formulations and nomenclature for the 3D-printed PP-GHP composites.  
Figure 1: Scheme of process for obtaining 3D-printed specimens: (**a**) extruder with temperatures, (**b**) filaments obtained by extrusion, (**c**) 3D printing, (**d**) obtained 3D-printed composites.  
where \(\Delta H^{\circ}\) is the melting enthalpy per mass unit of the 100% crystalline PP, with a value of 207 J/g [37] and \(\Delta H_{m}\) corresponds to the melting enthalpy. Each sample was tested in duplicate.  
The thermogravimetric behavior was evaluated using a TA Instruments Q600 Simultaneous TGA/DSC (SDT) (New Castle, DE, USA) in a temperature range from 30 to 600 \({}^{\circ}\)C, with a heating rate of 10 \({}^{\circ}\)C/min in a N\({}_{2}\) atmosphere. The dynamic mechanical analysis (DMA) was carried out using a TA Instruments Q800 (New Castle, DE, USA) with a temperature range from \(-\)40 to 150 \({}^{\circ}\)C and a heating rate of 5 \({}^{\circ}\)C/min in the multifrequency mode with a frequency of 1 Hz and a dual-cantilever clamp. The X-ray diffraction (XRD) analysis was performed using an Empyrean Malvern Panalytical diffractometer (Malvern, UK), with 45 kV and 40 mA, a Bragg-Bretano configuration, CuK\(\alpha\)1 (\(\lambda\) = 1.5406 A) radiation, 0.0263\({}^{\circ}\)20, and an interval from 4 to 60\({}^{\circ}\)20.
# Energy-Efficient Partial LDPC Decoding for NAND Flash-Based Storage Systems
## 5 Theoretical Analysis
### Calculation of the Number of Iterations  
For a specific SNR, the LLR values of all variable nodes are distributed following the binomial distribution of \(N(\lambda^{(l)},2\lambda^{(l)})\)[16]. The mean of the LLR distribution \(\lambda^{(l)}\) is obtained from (20) by adjusting the column degree \(w_{c}\) according to the size of the partial H-matrix. To decide the success or failure of the decoding, the BER is estimated from the calculated LLR distribution.  
Assuming that the transmitted codeword are all zeros, the correctly decoded codeword has positive LLR values for all bit-positions, but the uncorrected codeword has some negative LLR values. Therefore, the ratio of the negative area to the total area of the distribution can be considered as the uncorrected BER for a specific number of iterations. Since the LLR values are binomially distributed, the BER after \(l\) iterations is calculated as  
\[\text{BER}^{(l)}=Q\Bigg{(}\frac{2\lambda^{(l)}-\lambda^{(l)}}{\sqrt{2\lambda^ {(l)}}}\Bigg{)}=Q\Bigg{(}\sqrt{\frac{\lambda^{(l)}}{2}}\Bigg{)}, \tag{21}\]  
where \(Q(x)\) is the Q-function of the given distribution,  
\[Q(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{\infty}e^{-\frac{\sigma^{2}}{2}}\,dt. \tag{22}\]  
The LLR distribution and the estimated BER for the (149, 61, 6) array LDPC code with an SNR of 5 dB is shown in Figure 5. The full H-matrix is used for decoding, which means that \(w_{c}\) is 6. As the number of iterations increases, the mean of the LLR distribution moves to the higher value, leading to a reduced BER. The estimated BER is used to compute the number of iterations needed to achieve successful decoding. It is assumed that the left tail of the BER distribution in Figure 5, which falls into the negative region, represents the proportion of errors relative to the total number of cases. The area of that tail was calculated using the Q-function, as described in (21) to determine the BER value. When the calculated BER is less than \(10^{-15}\) in a certain iteration, which is a criterion widely accepted in the storage market, the decoding is considered to be successful in that iteration.  
Figure 5: The probability distribution of LLR values and the estimated BERs of the (149, 61, 6) array LDPC code when the SNR is 5 dB.  
Therefore, we analyze the theoretical number of iterations needed to achieve successful decoding for a range of SNR. The numbers are depicted in Figure 6. Since the graphs look similar to the simulation results shown in Figure 2, the proposed decoding algorithm is consistent with the theoretical analysis. In addition, the existence of an initial partial H-matrix that provides the same error-correcting performance as the full H-matrix for a specific SNR is explained theoretically.
## 2 Materials and Methods
### Architecture of 6 mAPred-MSFF
#### 2.2.3 Feature Fusion Module  
Feature fusion, the combination of features from different layers or branches, is an omnipresent part of modern network architectures. It is often implemented via simple operations, such as summation or concatenation, but this might not be the best choice. We use a uniform and general scheme, namely attentional feature fusion (AFF) which is proposed by [PERSON] et al. [53]. The architecture of AFF is shown in Figure 1. Based on the multi-scale channel attention module \(M(X)\), the AFF can be expressed as follows:  
\[Z=M(X\oplus Y)\otimes X+(1-M(X\oplus Y))\otimes Y \tag{18}\]  
where \(Z\in R^{H\times W\times C}\) is the fused feature, and the element-wise summation of \(X\oplus Y\) is used as the initial integration for MS-CAM. \(M(X\oplus Y)\) and \(1-M(X\oplus Y)\) are the fusion weights for \(X\) and \(Y\), respectively. Please note that the fusion weights consist of real numbers between 0 and 1, which enable the network to conduct a soft selection or weighted averaging between \(X\) and \(Y\).
# Frequency-Dependent Schroeder Allpass Filters
## 2 Schroeder Allpass Filters
### Classic Schroeder Allpass  
Historically, delay feedback filters were one of the first means to create artificial reverberation due to their efficient implementation. However, they suffer from intense coloration (why they are also called comb filters). However, by merely introducing an additional feedforward path, [PERSON] and [PERSON] [1] were able to turn the comb filter into an allpass (see Figure 1).  
The corresponding transfer function  
\[H_{\text{Schroeder}}(z)=\frac{X(z)}{Y(z)}=\frac{g^{*}+z^{-m}}{1+gz^{-m}} \tag{13}\]  
is allpass (see (9)). Such a Schroeder allpass can be easily augmented by replacing the delay \(z^{-m}\) with an arbitrary allpass filter resulting in so-called nested allpasses [4]. The system poles \(p_{i}\) of a Schroeder allpass are the scaled roots of unity, i.e., for \(1\leq i\leq m\)  
\[p_{i}=g^{1/m}e^{\frac{2\pi i}{m}}. \tag{14}\]  
Therefore, the pole frequencies \(\arg(p_{i})\) are distributed uniformly, and the feedforward/-back gain \(g\) and delay \(m\) determine the pole magnitudes \(|p_{i}|\). The Schroeder allpass was then further generalized by [PERSON] [5].
## 3 The Proposed System's Architecture
### FB-ITS User Interface  
The adaptation model uses the information stored in the student model and knowledge domain model to adapt relevant learning materials. The output of this model is transferred to the system interface to be presented to the student. The adaptation model offers a pedagogical option to support the individual student during the learning process. The adaptive navigation support method is one of the common adaptation methods used in web-based educational systems to support a student to navigate in the learning environment by adapting to the preferences, goals, and knowledge of the individual student [12; 43]. This method is used here by the FB-ITS user interface to support a student during the learning process, which involves link hiding and link annotations.  
FB-ITS uses drop-down menus to help the student to navigate and browse the course materials. The drop-down menu is more helpful since it guides the student to learn Excel topics step-by-step. This menu can be dynamically updated in terms of font color, based on the current knowledge state of the student and the student is not able to proceed to the next topic until she/he masters the pre-requisite topics by hiding the links for topics that are not yet ready to be learned. Moreover, through the drop-down menu, FB-ITS can notify the student about which topic is ready to be learned and which is not. The link annotation is used for highlighting each topic with an appropriate color based on the student's knowledge level. In addition to the navigation support method, an adaptive presentation mechanism is used by FB-ITS to present the contents of a course topic in various ways, such as text, images, and videos, in order to meet the individual student needs and preferences and to understand the topic effectively.  
The FB-ITS is a completely web-based system that can be accessed through a web browser. A student interacts with the proposed system via the user interface. In order to use the system for the first time, a student has to create an account through the registration page. When a student logs into the system for the first time, she/he is required to complete a pre-test to determine her/his prior knowledge of Excel. The pre-test consists of 22 multiple-choice questions. It is possible for the student not to answer any questions if she/he has no relevant knowledge.  
The home page of the system appears after finishing the pre-test as shown in Figure 5. In this page, a student can navigate the course materials from the left dropdown menu. The content of the lesson is displayed as text and images. Furthermore, the system provides a video lecture to help a student understand the topic with further explanation, which meets the student's preferences. After the student finishes reading the lesson, she/he has to answer the related test questions. Then, the system will provide feedback according to the test score.
# A Multi-Strategy Improved Honey Badger Algorithm for Engineering Design Problems
## 4 Application
### Pressure Vessel Design Problems  
The pressure vessel design problem is a classical optimization problem that involves minimizing the cost of a pressure vessel while satisfying specific constraints. The mathematical model for the four parameters of this problem is given in Equation (20), and a diagram of the pressure vessel design problem is given in Figure 10.  
\[\begin{array}{ll}\textit{Consider}&\overset{\rightarrow}{x}=[x_{1},x_{2},x_ {3},x_{4}]=[T_{s},\ T_{h},R,L]\\ \textit{Minimize}&f(\overset{\rightarrow}{x})=0.6224x_{1}x_{3}x_{4}+1.7781x _{2}x_{3}^{2}+3.1661x_{1}^{2}x_{4}+19.84x_{1}^{2}x_{3}\\ \textit{Subject}\ \ \textit{to}&g_{1}(\overset{\rightarrow}{x})=-x_{1}+0.0193x_{3} \leq 0\\ &g_{2}(\overset{\rightarrow}{x})=-x_{2}+0.00954x_{3}\leq 0\\ &g_{3}(\overset{\rightarrow}{x})=-\pi x_{3}^{2}x_{4}-\frac{4}{3}\pi x_{3}^{3}+ 1296000\leq 0\\ &g_{4}(\overset{\rightarrow}{x})=x_{4}-240\leq 0\\ \textit{Variable}\ \ \ \textit{range}&x_{1},x_{2}\in[0,99];x_{3},x_{4}\in[10,200]\\ \end{array} \tag{20}\]  
As shown in the experimental results in Table 10, the optimal values obtained by MIHBA are less than the other three algorithms, indicating that MIHBA obtains better values and performs better in the pressure vessel design problems. In this problem, the Optimal cost of MIHBA is reduced by 11.45% compared to HBA.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Optimum Value**} & **Optimal** \\ \cline{2-5}  & \(\mathbf{T_{s}}\) & \(\mathbf{T_{h}}\) & \(\mathbf{R}\) & \(\mathbf{L}\) & **Cost** \\ \hline PSO & 8.8652 \(\times\) 10\({}^{-1}\) & 4.3823 \(\times\) 10\({}^{-1}\) & 4.5933 \(\times\) 10\({}^{1}\) & 1.3428 \(\times\) 10\({}^{2}\) & 6.0978 \(\times\) 10\({}^{3}\) \\ DBO & 9.8781 \(\times\) 10\({}^{-1}\) & 4.8827 \(\times\) 10\({}^{-1}\) & 5.1182 \(\times\) 10\({}^{1}\) & 8.9237 \(\times\) 10\({}^{1}\) & 6.3489 \(\times\) 10\({}^{3}\) \\ HBA & 1.0888 \(\times\) 10\({}^{0}\) & 5.4029 \(\times\) 10\({}^{-1}\) & 5.6377 \(\times\) 10\({}^{1}\) & 5.4621 \(\times\) 10\({}^{1}\) & 6.6714 \(\times\) 10\({}^{3}\) \\ MIHBA & 7.9079 \(\times\) 10\({}^{-1}\) & 3.9089 \(\times\) 10\({}^{-1}\) & 4.0974 \(\times\) 10\({}^{1}\) & 1.9109 \(\times\) 10\({}^{2}\) & 5.9073 \(\times\) 10\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of comparative experiments on pressure vessel design problems.  
Figure 10: Pressure vessel design problem map.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Optimum Value**} & **Optimal** \\ \cline{2-5}  & \(\mathbf{N_{1}}\) & \(\mathbf{N_{2}}\) & \(\mathbf{N_{3}}\) & \(\mathbf{N_{4}}\) \\ \hline PSO & 46 & 26 & 12 & 47 & 9.9216 \(\times\) 10\({}^{-10}\) \\ DBO & 60 & 12 & 13 & 18 & 2.7265 \(\times\) 10\({}^{-8}\) \\ HBA & 54 & 12 & 37 & 57 & 8.8876 \(\times\) 10\({}^{-10}\) \\ MIHBA & 43 & 19 & 16 & 49 & 2.7009 \(\times\) 10\({}^{-12}\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparative experimental results for gear train design problems.
## 3 Finite Element Modeling  
### Modelling of Protection Barrier Wall  
The dimension of the model was taken from the code book to analyze the current practice for the minimum requirement of protection walls during a hydrogen gas explosion. The compressive strength of the concrete was 35 MPa. The height of the TNT from the ground level is 0.55 m. The distance between TNT and the protection wall was 1.6 m to 3.6 m to understand the behavior of the explosion effect on the protection wall under different distances. TNT equivalent method was used to convert the hydrogen gas tank explosion [26; 28; 29]. The weight of TNT was 69.7 kg at 75% limits for combustions in the air by volume. With the TNT equivalent, it was possible to predict the damage on the protection wall from simulation. The widely used concrete model implemented in LS-DYNA, the Karagozian & Case concrete material model, was used. Later the thickness of the protection wall increase from 0.12 m to 0.16 m and 0.2 m. Figure 4 shows the dimension of the foundation with different wall thickness.  
Figure 4: (**a**) KGS FP127 model dimension with 120 mm protection wall thickness; (**b**) Model dimension with 160 mm protection wall thickness; (**c**) Model dimension with 200 mm protection wall thickness.  
Figure 3: Pressure and time relation at the free air blast wave.  
To improve the effect of blast load on the protection wall, H-beam was used as a composite structure. The dimension of the H-beam for wall thickness of 120 mm is breadth = 240 mm, height = 160 mm, thickness = 10 mm. The compressive strength of the concrete was 35 MPa. H-beam was installed at an interval of 2 m. For a 10 m length of protection, all 4 numbers of H-beam can be placed. For the second case, the thickness of the wall was increased from 120 mm to 200 mm to understand the behavior of the barrier wall under hydrogen gas tank explosion, and only the barrier wall was considered for the simulation. Protection walls with a reinforced concrete structure and steel-concrete composite beams were analyzed under blast load. The position of the H-beam is shown in Figure 5. 16 mm of reinforcement was placed inside of protection wall. A detail of reinforcement is shown in Figure 6. The clear cover for the protection wall was 20 mm.
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
## 5 Graph-Based RA in D2D Networks
### Joint Power and Channel Allocation Methods  
A joint power and channel allocation method for the scenario of a variable number of CUEs and DUEs in the coverage of a single macro BS is developed in [51]. It allows for multiple RBs to be allocated for a single (D2D or cellular) communication link, and for an RB to be utilized by different links. To this end, an interference graph is employed, its vertices representing the CUEs and DUEs, while its edges represent their interference relationships (for each edge, the weight is a vector of interference levels on all RBs). Each vertex has attributes that characterize their current RB and power allocation, as well as their desired RBs. The algorithm forms clusters of CUEs and DUEs that use the same RBs, such that the cluster's throughput is maximized. It has been found that this solution is scalable due to its weak dependence on the number of vertices, while \(R_{T}\) increases. The authors of [52] also study the joint power and channel allocation problem in a similar scenario, with the aim of achieving the minimum throughput requirements of both CUEs and DUEs. First, the power allocation is performed separately under the consideration that each node can only occupy a single channel. Then, through a tripartite graph connecting the channels, CUEs, and DUEs, the channel distribution is chosen so as to maximize the edges' weights (achievable throughput per allocation) for both the CUEs and DUEs. This is performed by applying the Hungarian algorithm on a tree topology that represents all possible channel allocations via its branches. The results indicate that due to the limitation of each link to only utilize a single channel, there is a critical number of CUEs, which if  
Figure 5: Scenario of D2D networks and summary of methods for graph-based RA.  
exceeded, the DUEs' outage will increase. Nevertheless, the proposed method prevents a dramatic decline in \(R_{T}\). Further, a joint power and channel allocation for underlay D2D communications, subject to the constraints of CUEs' throughput and secrecy data rate (the fraction of the data rate that has not been tampered with by a malicious D2D node), is developed in [53] to improve not only RA, but also the resilience to physical layer attacks from unauthorized DUEs. First, power allocation for all nodes is performed so as to satisfy the CUEs' throughput requirements. Then, the channels are allocated through a bipartite graph connecting the CUEs and D2D pairs, with the edges' weights representing the achievable SINR of the DUEs, if the CUEs' channels are shared. An exponential increase in the \(R_{T}\) with the number of both DUEs and CUEs, is reported.
# Remote Sensing of Ecosystem Water Use Efficiency in Different Ecozones of the North China Plain
## 2 Data and Methods
### Study Area  
The study area of this paper is the North China Plain, which mainly includes Henan Province, Hebei Province, Shanxi Province, Shandong Province, Beijing City and Tianjin City. In most areas of the North China Plain, the average temperature in July is 26-28 \({}^{\circ}\)C, and there is a lot of precipitation; in January, the average temperature is \(-5\)-4 \({}^{\circ}\)C, and the rainfall is less. Cultivated land and grassland vegetation types are mainly dominant in the study area, and they account for 57.44% and 29.33% of the total vegetation cover in the study area, respectively. The study area mainly includes the Bohai Sea saline meadow (BHSSM), Mongolian-Manchurian grassland (MGMG), Central China loess plateau mixed forests (CCLPMF), Ordos Plateau steppe (OPS), Huang He Plain mixed forests (HHPMF), Qin Ling Mountains deciduous forests (QLMDF), and Changjiang Plain evergreen forests (CJPEF) [39]. The spatial pattern of land use and ecosystem zoning locations in the North China Plain are shown in Figure 1.
# User Association Performance Trade-Offs in Integrated RF/mmWave/THz Communications
## 3 System Model
### Beamforming Model
#### 3.2.3 Beamwidth Adjustment:  
Array antennas dynamically adjust beamwidths to balance coverage vs. interference footprints.  
The dynamic beamwidth adjustment mechanism allows the beamwidth to be controlled via a parameter \(\beta\), where \(0<\beta\leq 1\). The nominal beamwidth of each beam is defined as \(BW_{nominal}\). The actual beamwidth formed is then given by \(BW_{actual}=\beta*BW_{nominal}\). A smaller value of \(\beta\) results in a narrower actual beamwidth. This affects the coverage area and beamforming gain differently. The coverage area provided by a beam is directly proportional to the actual beamwidth. Meanwhile, the beamforming gain is inversely proportional to the actual beamwidth. Therefore, selecting a smaller \(\beta\) increases the beamforming gain but reduces the coverage area serviced by that beam. This allows a tradeoff between the coverage and gain that can be exploited based on dynamic network conditions.
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 6 Limitation  
While the proposed SNNL-enhanced GAN framework demonstrates consistent improvements in minority-class detection, several limitations remain.  
First, the method assumes access to labelled samples from both majority and minority classes during training. This limits its applicability in unsupervised or purely anomaly-based intrusion detection settings, where only benign data is typically available. Extending the framework to semi-supervised or unsupervised scenarios remains an open research direction.  
Second, incorporating SNNL introduces moderate computational overhead. Although the increase in GPU memory usage is negligible, training time per epoch increases due to the additional pairwise similarity computations. While this overhead is acceptable for offline training, it may hinder deployment in latency-sensitive or resource-constrained environments.  
Finally, the effectiveness of SNNL-enhanced augmentation still depends on the generator's capacity to model complex minority-class distributions. In cases of extreme imbalance or data noise, GAN training may become unstable or suffer from mode collapse, limiting the benefits of SNNL. Further work is needed to improve robustness under such conditions.