esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Analysis of the Beam-Column Resistance Compared to Methods by European Standards
## 3 Verification According to European Standards
### Resistance of Beam-Columns with IPE 100 Type of Cross-Section
#### 3.1.3 Axial Compression and Minor-Axis Bending  
The resistance of beam-columns with an IPE type of cross-section with out-of-plane flexural buckling is presented in this section. Warping torsion does not occur in this loading combination, so the boundary conditions were modelled with the method of rigid plates in the end cross-sections. The lower lateral rigidity of members with this type of section induces lateral deformations in the same direction as the bending moment, so no additional adjustments were needed in the model. The Figures 26-30. presents the interaction curves for out-of-plane buckling mode with an IPE type of cross-section.  
Figure 26: Interaction curves for weak axis flexural buckling of beam-columns with IPE 100 cross-section.  
Figure 25: Interaction curves for laterally unrestrained beam-columns with IPE 100 cross-section with free warping.  
Figure 28: Interaction curves for weak axis flexural buckling of beam-columns with IPE 100 cross-section.  
Figure 27: Interaction curves for weak axis flexural buckling of beam-columns with IPE 100 cross-section.  
From the presented diagrams it can be concluded that with relative slenderness \(\overline{\lambda}_{z}=0.5\) the methods of Eurocode 3 provided similar results in case of pin-ended beam-columns. The deviations seem to be much higher for pinned-fixed beam-columns, where the Method A provided too conservative results.  
The accuracy of the method of Eurocode 9 was higher with increased slenderness, similar to methods of Eurocode 3. However, unlike the previous methods, the resistances were mostly on the unsafe side for lower slenderness ratios. The positive effect of coefficients \(\omega_{x}\) and \(\omega_{xLT}\) in the case of the pinned-fixed beam column showed up again, as the accuracy was higher for stockier members.  
On another note, the more convex behaviour of the interaction curve for slenderness \(\overline{\lambda}_{z}=0.5\) can be a consequence of the higher plastic capacity of the weak axis of an IPE cross-section.
## 2 Theoretical Background
### Relationship between Void Ratio and Dry Density in Gravel-Sand Mixtures
#### 2.2.1 Test Materials and Setup  
[PERSON] et al. (2010) [20] conducted experiments on gravel-mixed D-G-S samples obtained from Shimonoseki City, Japan, using a large-scale, one-dimensional compression tester. The maximum grain size of the samples used in their test was 9.52 mm. Gravel was determined as particles larger than 2 mm, as per Japanese standards, and was separated from the rest of the soil. The specific gravity of gravel (\(G_{g}\)) and specific gravity of sand (\(G_{s}\)) of the materials are 2.650 and 2.685, respectively. The density of sand is believed to be larger than gravel due to natural cracks within D-G-S, which are more noticeable as the particle size is enlarged [8; 9; 22].  
The test setup is shown in Figure 2. A cylindrical mold with a diameter of 150 mm and a height of 150 mm was used. Sand and gravel were mixed at gravel contents of 0, 20, 30, 40, 50, 80, and 100%. The samples were generally well-graded, especially at low levels of water content. The grain-size distribution curve based on the Japanese Geotechnical Society (2000) [23] and the properties of the samples are shown in Figure 3 and Table 1, respectively, which shows that the specimens are generally well-graded, especially at low levels of gravel content. The test specimens were compacted in the mold in 3 layers, with the water content set at 13%, in which 21 blows were performed for each layer using a 2.5 kg rammer falling from a height of 30 cm. Compression tests were then conducted by slowly increasing the vertical load at a rate of 0.5 mm/min up to a maximum compressive stress of 10 MPa.  
\begin{table}
\begin{tabular}{c c c c c c}
**Specimen** & \(d_{50}\) **(mm)** & **Fines Content, \(F_{c}\) (\%)** & **Uniformity Coefficient, \(C_{u}\)** & **Curvature Coefficient, \(C_{c}\)** & \(G_{e}(P)\) \\ \hline \(P(0\%)\) & 0.67 & 4.78 & 7.11 & 1.16 & 2.650 \\ \(P(20\%)\) & 1.00 & 3.83 & 8.71 & 1.20 & 2.682 \\ \(P(30\%)\) & 1.18 & 3.35 & 9.70 & 1.31 & 2.678 \\ \(P(40\%)\) & 1.40 & 2.87 & 10.90 & 1.22 & 2.674 \\ \(P(50\%)\) & 2.00 & 2.39 & 9.48 & 1.52 & 2.670 \\ \(P(80\%)\) & 2.18 & 0.95 & 3.84 & 2.89 & 2.685 \\ \(P(100\%)\) & 2.38 & 0 & 1.35 & 0.82 & 2.690 \\ \end{tabular}
\end{table}
Table 1: Physical properties of soils used by [PERSON] et al. (2010) [20].
## 3 System Model and Problem Formulation
### Problem Formulation  
Since the throughput of the network system and the QoE of the user are very representative optimization indicators in the communication network scenario, they are used as forward optimization indicators in the optimization problem. Because we want to obtain the impact of the number of dual-connection users on throughput and QoE, the additional energy consumption of the system, represented by the number of dual-connection users, is taken into account. In order to meet the QoE of SBS users with insufficient bandwidth and to improve the total throughput of the system, and at the same time reduce the extra system energy consumption caused by dual connections as much as possible, we define a network slicing resource allocation problem based on DC. The optimization objective of the problem is to maximize the total utility of the system. The utility is defined as  
\[\max_{B_{i,k}^{n}}P=\alpha\sum\limits_{i=0}^{I-1}R_{i}+\sum\limits_{n=0}^{N} \beta_{n}\cdot\mathrm{QoE}_{n}-\eta\varphi \tag{8}\]\[s.t.\;\text{C}_{1}:x_{i,k}=\begin{cases}1,&\text{the useriassociate withBS }k\\ 0,&\text{otherwise.}\end{cases},\] \[\text{C}_{2}:\sum\limits_{j=0}^{k}x_{i,j}\leq 2,\] \[\text{C}_{3}:\lambda_{i}=\begin{cases}1,&\sum\limits_{j=0}^{k}x_{ i,j}=2,\\ 0,&\text{otherwise}\end{cases},\] \[\text{C}_{4}:W_{k}=\sum\limits_{n=0}^{N}B_{i,k}^{n},\] \[\text{C}_{5}:\text{y}_{q_{m}}=\begin{cases}1,&r_{u_{n}}\geq\overline {r}_{n}\;\&\;l_{p_{u_{n}}}\leq\overline{l}_{n}\\ 0,&\text{otherwise}\end{cases},\] \[s.t.\;\text{C}\]  
where \(\alpha\), \(\beta=[\beta_{1},\beta_{2},\cdots,\beta_{n}]\) and \(\eta\) are the importance coefficients for adjusting system throughput, QoE and extra energy consumption, respectively. A maximum of two BSs connected to each user is indicated in constraint C\({}_{2}\). The constraint C\({}_{4}\) denotes that the sum of bandwidth owned by each slice in the BS does not exceed \(W_{k}\). The constraint C\({}_{5}\) represents that the user can successfully receive packets when both the rate and delay are satisfied.\(\overline{r}_{n}\) denotes the rate limit of service \(n\) and \(\overline{l}_{n}\) is the delay limit of service \(n\).
# An Ensemble Approach for Speaker Identification from Audio Files in Noisy Environments
###### Abstract  
Automatic noise-robust speaker identification is essential in various applications, including forensic analysis, e-commerce, smartphones, and security systems. Audio files containing suspect speech often include background noise, as they are typically not recorded in soundproof environments. To this end, we address the challenges of noise robustness and accuracy in speaker identification systems. An ensemble approach is proposed combining two different neural network architectures including an RNN and DNN using softmax. This approach enhances the system's ability to identify speakers even in noisy environments accurately. Using softmax, we combine voice activity detection (VAD) with a multilayer perceptron (MLP). The VAD component aims to remove noisy frames from the recording. The softmax function addresses these residual traces by assigning a higher probability to the speaker's voice compared to the noise. We tested our proposed solution on the Kaggle speaker recognition dataset and compared it to two baseline systems. Experimental results show that our approach outperforms the baseline systems, achieving a 3.6% and 5.8% increase in test accuracy. Additionally, we compared the proposed MLP system with Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) classifiers. The results demonstrate that the MLP with VAD and softmax outperforms the LSTM by 23.2% and the BiLSTM by 6.6% in test accuracy.  
speaker identification; audio; voice activity detection; deep neural network; recurrent neural network; spectrogram +
Footnote †: journal: applied sciences  
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# A Simplified Query-Only Attention for Encoder-Based Transformer Models
## 3 Materials and Methods
### Evaluation  
We evaluated our proposed method on an EEG conformer model and compared its performance to the original attention mechanism. We applied the same training and testing methods with the EEG conformer study for consistency [26]. For dataset I, the first session of the experiment was used for model training, and the second was used for testing. The EEG signals were segmented from 2 to 6 s based on the visual cue for each trial that corresponded to movement imagination. For dataset II, the first three sessions were used for training, and the last two sessions were used for testing. The signals were epoched from 3 to 7 s based on the visual cue for each trial. The epoched signals were used as input for the EEG conformer model for both datasets I and II. The data augmentation method was used because a large dataset was required to train the model. Segmentation and reconstruction (S&R) were performed in the time domain for the data augmentation. The number of segments, \(N\)s, in S&R was 8. An Adam optimizer was used to train the EEG conformer. The parameters were 0.0002, 0.5, and 0.999 for the learning rate, \(\beta\)1, and \(\beta\)2, respectively. The number of self-attention computations (\(N\)) and multi-heads were 6 and 10, respectively. The number of epochs and the batch size were 2000 and 72, respectively. We evaluated the prediction accuracy for each epoch of training and testing. The average accuracy of all the epochs and a kappa value were shown for evaluation. A paired-sample \(t\)-test was performed between the prediction accuracies of the previous and the proposed methods. The kappa value can be calculated using Equation (3).  
\[kappa=\frac{p_{o}-p_{e}}{1-p_{e}} \tag{3}\]  
where \(p_{o}\) denotes the average accuracy of all the epochs and \(p_{e}\) means the accuracy of the random guesses.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 3 State-of-the-Art
### State-of-the-Art in the Image Aspect
#### 3.2.3 Transforms  
Image transforms are of critical importance in chaotic image encryption. Some of them are studied below.  
Wavelet  
The wavelet transform is a popular method of permutating the cells of a 2D matrix and can yield a significant increase in encryption effectiveness [103]. To fulfill the chaos requirement of good encryption, several chaos sources have been paired with the wavelet transform including an improved 3D cat map [104], a 1D logistic map [105], a 3D logistic map [106], the Arnold map [107,108], and a logistic sequence [108]. Other algorithms utilizing variations of the standard wavelet transform such as the Integer Wavelet Transform (IWT) have also been proposed [109].  
The zigzag transform is capable of rearranging the cells of a 2D matrix to heavily decrease the correlation between adjacent pixels, an important property when considering image encryption. [PERSON] et al. [110] proposed an algorithm for image encryption utilizing a more complicated implementation of the transform that yields better security.  
[PERSON] et al. [111] proposed an image encryption algorithm utilizing the Discrete Fraction Cosine Transform (DFrCT), which has additional benefits over the standard Discrete Cosine Transform (DCT) that make it more suitable for image encryption.  
The contourlet transform provides a method of decorrelating the cells of a 2D matrix and was designed to improve upon the shortcomings of the wavelet transform when dealing with natural images. [PERSON] et al. [112] proposed an image encryption algorithm utilizing the transform, which has some desirable attack resistances, for example against JPEG compression.  
Linear Canonical Transform (LCT)  
[PERSON] et al. [113] proposed an image encryption algorithm utilizing LCT that is both speedy in execution and also boasts a large keyspace to protect against brute-force attacks.
## 6 Experiments on SBS Mitigation and Parametric Amplification with a DOF
### Comparison of Conversion Efficiency (CE)  
The efficacy of using a nonlinear fiber as a parametric amplifier is often quantified by its conversion efficiency (CE) of converting an input signal to a conjugate idler through four wave mixing (FWM) involving the pump. Thus CE is basically the ratio of the output idler power to the input signal power. To find the CE of the DOF used, we perform an experiment schematically represented in Figure 17. A degenerate pump and a signal are launched into the nonlinear fiber (HNLF or DOF) and the power of the input signal as well as the output idler are recorded for different pump powers and pump-signal detuning, using an optical spectrum analyzer (OSA).  
We compared the experimental results, with a numerical NLSE model introduced in Section 4.2. The fiber parameters for the DOF and the HNLF are same as in the previous subsection. The nonlinear coefficient \(\gamma\) for the DOF and HNLF used are, 3.5 and 11.1 (W km)\({}^{-1}\).  
The validation of the NLSE based simulation model is achieved through comparing the CE as a function of pump-signal wavelength separation obtained from the experiment as shown in Figure 18a. On the other hand, in Figure 18b, we investigate the CE of the DOF (blue) and the HNLF (red) as a function of the input pump power, both through the simulation (dotted lines) and the experiment (filled circles). The input signal power is 7 dBm. Needless to say, the experimental data satisfactorily conform to the numerically predicted trend. From Figure 18b, we find that the DOF, similar to a standard HNLF, show a linear growth of CE with respect to the pump power (in a log-log scale). The 10 dB difference between the DOF and the HNLF in Figure 18b arise due to the combined effect  
Figure 16: (**a**) Scheme of the experimental setup for measuring the back reflected power from a nonlinear fiber due to SBS. EDFA: Erbium doped fiber amplifier; FUT: Fiber under test; PM: Powermeter. (**b**) Experimental data of reflectivity vs. input pump power for a standard HNLF (blue filled circles) and a DOF (red filled circles). Intersection of the dashed lines denote the location of 1% reflectivity of input power due to SBS. Fiber parameters are provided in the text.  
Figure 17: Experimental scheme for the measurement of CE of a nonlinear fiber (HNLF or DOF). OSA: Optical spectrum analyzer; FUT: Fiber under test; EDFA: Erbium doped fiber amplifier. The input and output spectra of the FUT illustrated with brown arrows.  
of their unequal nonlinear coefficients, average dispersion values and lengths. To confirm this hypothesis, we simulated the same curve for a hypothetical DOF (hDOF) with the same length, \(\gamma\) and \(\overline{\beta}_{2}\) as the HNLF (see green dashed curve in Figure 18b). Indeed, we find the CE curve of the hDOF to coincide with that of the HNLF, confirming the CE is almost unaffected due to the dispersion oscillation.
# Joint Cell Association and User Scheduling in Carrier Aggregated Heterogeneous Networks
## 3 Joint Cell Association Scheme for Multi-Flow Carrier Aggregation  
In this section, joint cell association is proposed for multi-flow carrier aggregation across different BS in different layers, which may provide with an upper bound on the network throughput maximization problem.
# Prototyping of Environmental Kit for Georeferenced Transient Outdoor Comfort Assessment
## 6 Validation of Environmental Measurements
### Solar Radiation  
The TCS34725 RGB sensor was chosen for the prototype based on the previously discussed arguments. The main incentive was to reduce the cost as much as possible. However, after comparing the data against the reference sensor, we realized the limitations of this sensor (34,196 lux). Therefore, the capacity of the RGB sensor was deemed insufficient for calculating the solar radiation on a sunny day.  
To elaborate, the reference pyranometer recorded between 500 W/m\({}^{2}\) and 560 W/m\({}^{2}\). On the other hand, the solar radiation readings from the prototype showed a constant 256 W/m\({}^{2}\) values, indicating the maximum limit of the sensor. The result of the test is displayed in Figure 18.  
This comparison concludes that our RGB sensor cannot be used in outdoor conditions. However, it can be suitable for calculations under 34,000 lux. The concept could be valid in presence UV and IR sensors provided that the limit of the sensor is beyond 120,000 lux.
# Land Suitability Mapping Using Geochemical and Spatial Analysis Methods
## 4 Results and Discussion
### Land Evaluation Suitability for Various Types of Use Based on Screening Values
#### 4.3.2 Cobalt  
All the sampling sites present Co content in soil higher than EPA (RS) and EPA (P,A,M), suggesting a potential risk to residents and ecological receptors (Figure 7B). In an investigation of land suitability of a Mediterranean site located in Evia island (Greece), [PERSON] [35] reported that Co content in topsoil was the limiting factor for residential land use. Moreover, an investigation of land suitability after the July 2018 western Attica wildfire (Kineta area, Greece) revealed that Co concentration in ash was also among the limiting factors for residential land use [34]. Cobalt concentration in soil of the study area exceeding the CCME (A) is observed in the 24.3% (3.6 km\({}^{2}\)), 28.3% (22.0 km\({}^{2}\)), 33.1% (2.2 km\({}^{2}\)) and 1.7% (0.03 km\({}^{2}\)) of the urban, agricultural, shrubby-vegetated and wetlands area, respectively (Figure 9A).  
Cobalt content in soil higher than CCME (R) and DEC (EI) is recorded in 14.5% (2.1 km\({}^{2}\)), 20.2% (15.7 km\({}^{2}\)) and 19.4% (1.3 km\({}^{2}\)) of the urban, agricultural and shrubby-vegetated areas, respectively (Figure 9B).  
The expected impacts of Co on plants are the reddish-purple coloration along chloride leaf margins, decrease in chlorophyll content and decrease in shoot growth and length [72; 83; 84]. According to [PERSON] et al. [85], the effects of Co on human health are (a) harmful, because it is related to an increase in the incidence of lymphatic and hematopoietic malignancies; and (b) beneficial, because Co is a part of Vitamin B12, which is essential to maintain human health.  
The gastrointestinal absorption of Co involves mechanisms common with Fe\({}^{2+}\) since people with Fe\({}^{2+}\) deficiency show an increased Co absorption [86]. After absorption, Co is mainly accumulated to the heart, spleen, serum, liver and kidneys.
# Context-Aware Data Dissemination for ICN-Based Vehicular Ad Hoc Networks
## 3 Context-Aware Packet Forwarding
### Density of Vehicles  
The calculation of both deferral time and suppression angle are related to the density of the vehicle distribution at a certain time and a certain location. Each node can sense the current density of nearby vehicles, i.e., vehicles within the scope of radio transmission. For example, through onboard camera, radar, and image recognition techniques, the number of connected vehicles in a certain area can be determined.  
In this paper, we use Hello packets to detect the numbers of the connected vehicles nearby. Each vehicular node broadcasts a Hello packet periodically. All vehicles that hear the Hello packet will respond to it with a Response packet. Figure 8 shows the format of Hello and Response packet.  
Since the radio transmission range of the vehicular node is known, the density at time \(t\) of node \(i\) can be calculated using Equation (5), namely:  
\[\rho_{i,t}=\frac{N_{i,t}}{\pi R_{i}{}^{2}} \tag{5}\]  
for simplicity, we use \(\rho\) to denote the density omitting \(i\) and \(t\) when no ambiguity will be introduced.  
The period \(T\) for a vehicle to broadcast Hello depends on the velocity and the acceleration. The higher the velocity and the acceleration, the greater the location change of the vehicle; Therefore, the smaller T should be. We define T as Equation (6), where \(v_{i,t}\) and \(a_{i,t}\) are the speed and acceleration at time \(t\) for node \(i\); \(\lambda\) is a constant, which can be adjusted and optimized according to the practical environment. Thus, the scope of \(T\) is [0, \(\lambda\)].  
\[T_{i,t}=\frac{\lambda}{1+\mathrm{e}^{\frac{-1}{\pi i,t}a_{i,t}}} \tag{6}\]
## 3 Results
### Bacterial Community Diversity in PM-EFB  
In total, 709,230 sequences were obtained from the sequencing of 12 EFB samples, with an average length of 382.16 bp for all the samples, and the samples contained 48,510-68,658 sequences. The sequencing coverage of each sample was greater than 99.9%, and the sparse curve was close to saturation, thus indicating that the sequencing depth of the amplicon library adequately reflected the composition of the bacterial communities in the evaluated samples (Table S1). The Shannon diversity index was the highest (3.37) in the P13-1, which contained PM and was not submitted to heat-shock treatment. Conversely, the Shannon diversity index was decreased significantly (1.48) in the P14-1, which contained PM and was heat-treated, thus indicating that the heat shock inactivated a large number of thermo-sensitive microorganisms in the PM. Except for the sample P13-6, which showed a rapid increase, the overall trend in the Shannon indexes in the samples of the two treatments within rounds 2-6 of the enrichment was an initial increase followed by a decrease. The Shannon indexes of the round-3 samples were the highest, at 2.87 (P13-3) and 2.84 (P14-3), respectively. Moreover, the coefficient of variation of the Shannon indexes in the two samples from the same round was smaller than the difference between the rounds (Table S1 and Figure 2A).  
Furthermore, a PCoA analysis based on the ASV data showed that the 12 samples grouped into four distinguishable clusters (Figure 2B), i.e., P13-2 and P14-2 clustered together; P13-1, P13-3, and P14-3 clustered together; P14-1, P13-4, and P14-4 clustered together; and P14-5, P14-6, P13-5, and P13-6 clustered together. Considering the influence of the PM and the heat shock on the round-1 samples, only around 2-6 samples were analyzed. It was found that the samples from the same enrichment round and submitted to different treatments tended to cluster together. Thus, it can be hypothesized that the heat-shock pretreatment affected the succession pattern of the microbial community to a lesser extent than the enrichment rounds. Moreover, based on the differences in acid production between the different enrichment rounds, the enrichment process was divided into three stages, i.e., the acclimation stage (round 2), the main fermentation stage (rounds 3-4), and the late fermentation stage (rounds 5-6).  
Figure 2: Microbial community diversities of EFB samples in different enrichment rounds. (**A**) Changes in Shannon diversity indexes of microorganisms in different enrichment cycles. (**B**) Principal coordinates analysis (PCoA) of microbial communities in different enrichment stages.