esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology for Calculating Transient Magnetic Field and Induced Voltage
### Calculation of Induced Voltage  
Figure 6 shows a conductor loop in the PV bracket system. The distribution of the magnetic flux density on the loop plane can be obtained by the algorithm stated above. The magnetic linkage through the loop is calculated by the surface integral [19]  
\[\mathbf{\Phi}(t)=\int_{S}\mathbf{B}\cdot\mathrm{d}S \tag{13}\]  
To evaluate the magnetic linkage, the loop plane is subdivided into a considerable number of subareas (see Figure 6). The magnetic flux density is regarded as having a uniform distribution on each subarea. Using such a subdivision, the magnetic linkage at a discrete time (\(t=k\Delta t\)) is approximately calculated by  
\[\Phi(k\Delta t)\approx\sum_{j=1}^{M}B_{j}(k\Delta t)\Delta S_{j}\cos\varphi_{ j}(k=1,2\cdots,N) \tag{14}\]  
where \(M\) is the total number of the subareas and \(N\) is the maximum number of the time steps. According to [PERSON]'s law of electromagnetic induction, the induced voltage in the loop is determined by the time derivative of the magnetic linkage [24]  
\[u(t)=\frac{d\Phi(t)}{dt} \tag{15}\]  
Considering (14), the induced voltage is calculated by  
\[u(k\Delta t)=\frac{\Phi(k\Delta t)-\Phi[(j-1)\Delta t]}{\Delta t}(k=1,2\cdots,N) \tag{16}\]
# Fire Performance of Self-Tapping Screws in Tall Mass-Timber Buildings
## 4 Design Proposal
### Determination of the Withdrawal Resistance of Self-Tapping Screws
When compared to similar average strengths of STS found in the literature at ambient conditions [24; 43], the average residual withdrawal strength per unit length does not seem to be influenced by the fire exposure duration, even when the head is initially unprotected (i.e., exposed to fire). The reduction of withdrawal resistance (see Table 4) was induced by the loss of length of penetration into the timber as it heats up due to the high heat-conduction of steel. However, the withdrawal resistance per unit length remains essentially the same as that at ambient conditions, regardless of the duration of fire exposure. The assumption that the temperature-dependent reduction of the timber strength is more significant in comparison to the temperature-dependent reduction of the steel fasteners is validated, which was also validated by the results of [PERSON] et al. [47]. When considering the reduction of the cross-section area and the mechanical properties of the wood fibers at a maximum of 100 \({}^{\circ}\)C to retain sufficient residual strength, the proposal presented herein is a conservative approach to evaluate the structural capacity of self-tapping screws, regardless of the fire exposure duration.
## 3 Results and Discussion
### Fatty Acid Methyl Esters Analysis (FAMEs)
The ability of diatoms to respond favorably to red light is given to the presence of various photoreceptors such as phytochromes and exclusive aureochromes that are involved in the RL signaling process in _P. tricornutum_[68; 69; 70]. In a different approach using spectroscopy, the occurrence of red-shifted fucoxanthin-chlorophyll protein (FCP) showing the fluorescence peak at 714 nm was observed [71]. It is considered that Phaeodactylum red-shifted FCP is involved in enhancing the light-harvesting ability of cells by the prolonged excited state lifetimes and the absorption spectrum extended to wavelengths longer than 700 nm [71]. In general, the clarity in the relationship between diatoms and light is still an open topic for researchers to study both fundamental as well as industrial applications. For the conclusion of this study, an effect of the specific RS light shift condition in the fatty acid composition was observed that can be important in applications in both human nutrition and renewable energy. Overall, it is interesting to note that changes in the specific wavelength during the stationary phase can impact the total accumulation of lipid and fatty acid composition.
# What Advanced Treatments Can Be Used to Minimize the Production of Sewage Sludge in WWTPs?
## 3 Advanced Treatments and Technologies
### Chemical Treatments
#### 3.1.1 Ozone and Chlorine  
Among the conventional chemical reagents, the most common are O\({}_{3}\) and chlorine [21]. Chemical oxidation can be considered an advanced technique applicable both in the water line, directly on the recirculation sludge, and in the sludge line, after the thickener and upstream of the stabilization [22]. The aim of this treatment is the partial oxidation and hydrolysis of the organic matter, which is transformed into smaller molecular-weight compounds and is therefore more easily biodegradable [23]. Among the main advantages there is the best settleability of the SS [24], the reduction of filamentous microorganisms and the simplicity of plant engineering and management. However, the costs related to investment and oxidizing dose, and the formation of by-products, in particular with chlorine, are the main disadvantages. As regards the performance related to ozonation, there is a reduction in SST from 30% to 99% in the water line and from 10% to 60% in the sludge line [25]. This great variability, in terms of performance, is influenced by the dosage of oxidizing agent. However, [PERSON] et al. [22] found that ozonation applied in the water line significantly reduced SS production but resulted in a slight decrease of biomass respiratory activity. [PERSON] et al. [26] studied the possible application in the sludge line and demonstrated the (non-linear) inverse relationship between ozone dose and specific particulate chemical oxygen demand (COD) solubilisation in the reactor. As concerns the chlorine dosage, the performances obtained by testing synthetic wastewater were lower than 65% for TSS [25]. [PERSON] et al. [17] studied chlorine application in a sequencing batch reactor (SBR) in order to minimize the SS production. The SBR treatments operate in batch and are composed of five stages: filling, reaction, settling, effluent and idle. The results showed a small dosage of chlorine, equal to 0.26 \(\beta_{\text{chlorine}}\)\(\beta_{\text{MLSS}}\)\({}^{-1}\) (MLSS--mixed liquor suspended solids), in return excess sludge to the reactor was able to reduce the yield coefficient by 50%. However, as in the case of ozonation applied in the water line, the COD removal percentage in the biological reactor decreased, particularly from 95% to 56%, due to partial inhibition of biomass caused by the reagent (O\({}_{3}\), chlorine) dosage. Therefore, the application of chemical oxidation in the water line should be carefully evaluated because even with a low concentration of reagents, partial inhibition of biomass could occur.
## 2 Materials and Methods
### Heavy Metals Analysis  
An analytical method was followed, \"General Rules for Atomic Absorption Spectrochemical Analysis\" (JIS K 0121:1993), with slight modification. Briefly, approximately 0.1 g of dried specimen was weighed and transferred into a Teflon container. The specimen was spiked with 3 mL of sulfuric acid and 3 mL of pure water and was decomposed under pressure and at a high temperature. The decomposed specimen was transferred into a 100 mL Teflon beaker. The decomposition container was rinsed with pure water and the rinse water was added into a specimen beaker. This combined specimen was evaporated to dryness, and a constant volume was obtained by adding 1% sulfuric acid (v/v). After sample preparation, the heavy metal concentration was analyzed by inductively coupled plasma atomic emission spectroscopy (ICP-AES) (ICPE-9000; Shimadzu, Kyoto, Japan). We analyzed six heavy metals: Pb, Cd, Cr, copper (Cu), zinc (Zn), and aluminum (Al).
## 3 Cellular Network Modeling
### GSM Networks  
As depicted in Figure 3, each GSM network operator (entity PLMN) is identified by attributes _mcc_ and _mmc_ and is characterized by the _ncc_.  
Figure 2: A high-level conceptual schema for cellular networks.  
As for the administrative part of the network, in GSM, administrative areas are divided into location areas (LAs) and routing areas (RAs); the former are uniquely identified within a PLMN by means of the corresponding code (attribute _lac_, to compose the _lai_), and the latter are identified within an LA by means of the attribute _rac_, or in other words, each RA is uniquely identified by the attribute _rai_, which is the combination of _lai_ and _rac_. Each cell can be identified within its LA by means of its attribute _ci_. Note how in the diagram we chose this \"administrative\" way of identifying a cell, considering the positioning application and the standard applied by all operators.  
Let us now turn to the physical unit of the network. A GSM PLMN deals with a set of base station controllers (BSCs), each of which is basically a switch in charge of frequency management among a set of base transceiver systems (BTSs), which are cellular towers whose radio signals cover one or more specific areas (entity cell). These components can be recognized through operator-defined attributes. For this reason, it is natural to specify a set of weak identifiers: the _bsc-id_ identifying a BSC within its PLMN, the _bts-id_ identifying a BTS within its BSC, and the _sector_ identifying the cell within its BTS. In addition, at the physical level, each cell can also be locally characterized by _bsic_ (that according to the standard definition is composed of _bcc_ and _ncc_) and _arfcn_. Finally, note how each BSC is related to its packet control unit (PCU).
# POL-MUX System for Noncoherent Optical Networks
## 4 Proposal of a Wide-Band Passive Optical Network (WDM-PDM-PON)  
Based on knowledge gained from previous measurements, a simulation model of a wide-band passive optical network based on a polarization multiplex in combination with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time [Hours]** & **RX [dBm]** & \(\Delta\) [dBm] & **BER [–]** & **T [Gbps]** \\ \hline
0 & \(-25.7\) & 0 & 0 & 10 \\
12 & \(-28.7\) & 3 & 0 & 10 \\
24 & \(-30.7\) & 5 & 0 & 10 \\
36 & \(-31.2\) & 5.5 & \(1.60\times 10^{-12}\) & 10 \\
48 & \(-31.7\) & 6 & \(9.50\times 10^{-12}\) & 10 \\
60 & \(-32.2\) & 6.5 & \(1.1\times 10^{-11}\) & 10 \\
72 & \(-32.7\) & 7 & \(3.10\times 10^{-10}\) & 9.999 \\
84 & \(-33.2\) & 7.5 & \(8.70\times 10^{-10}\) & 9.997 \\
96 & \(-33.7\) & 8 & \(1.40\times 10^{-9}\) & 9.986 \\
108 & \(-34.2\) & 8.5 & \(6.60\times 10^{-9}\) & 9.94 \\
120 & \(-34.7\) & 9 & \(1.90\times 10^{-8}\) & 9.816 \\ \hline \hline \end{tabular}
\end{table}
Table 1: BER and throughput changes in time.  
Figure 7: BER and data throughout on real optical network.  
a well-known wavelength division multiplex was designed. The system was designed in the VPItransmissionMaker(tm) Optical Systems simulation software [26].
# Multiobjective Optimization Design for a MR Damper Based on EBFNN and MOPSO
###### Abstract  
The structural parameters of the magnetorheological (MR) damper significantly affect the output damping force and dynamic range. This paper presents a design optimization method to improve the damping performance of a novel MR damper with a bended magnetic circuit and folded flow gap. The multiobjective optimization of the structural parameters of this MR damper was carried out based on the optimal Latin hypercube design (Opt LHD), ellipsoidal basis function neural network (EBFNN), and multiobjective particle swarm optimization (MOPSO). By using the Opt LHD and EBFNN, determination of the optimization variables on the structural parameters was conducted, and a prediction model was proposed for further optimization. Then, the MOPSO algorithm was adopted to obtain the optimal structure of the MR damper. The simulation and experimental results demonstrate that the damping performance indicators of the optimal MR damper were greatly improved. The simulation results show that the damping force increased from 4585 to 6917 N, and the gain was optimized by 50.8%. The dynamic range increased from 12.4 to 13.2, which was optimized by 6.4%. The experimental results show that the damping force and dynamic range of the optimal MR damper were increased to 7247 N and 13.8, respectively.  
MR damper; optimal design; EBFNN; MOPSO +
Footnote †: journal: Applied Sciences
## 5 Performance Evaluations
### Results and Discussions
#### 5.3.5 Total Energy Consumption  
In this experiment, total energy consumption of network in the proposed scheme and SEED were evaluated against the number of rounds. For normal traffic scenario, packet rate of 10 packets per second was used. Figure 9 depicts that the proposed scheme had lower energy consumption compared to SEED in all rounds. Due to frequent re-clustering in SEED, the control messages were transmitted frequently to rebuild the cluster, which increased energy consumption of the nodes. Another reason is that SEED did not consider the storage capability and computational capability of node in the CH declaration process. Therefore, in high traffic conditions, a CH may not store the incoming data packets which in turn creates buffer overflow. Due to buffer overflow packet drop ratio increases thereby result in high energy consumption. However, the proposed scheme considered the storage capacity and computational capability along with residual energy in the CH declaration process which in turn minimized the re-clustering frequency, meaning that if the residual energy of CH went below a certain threshold, the re-clustering process executed. This significantly reduced the overall energy consumption of nodes.  
In order to show the effect of bursty traffic on energy consumption, the packet rate of 200 packets per second was used. Results show that the proposed scheme significantly reduced the total energy consumption of network (around 60%) as compared to SEED. The reason is that in the proposed scheme CH shared its load with the neighboring high resource CHs which reduced the packet drop rate and packet re-transmissions. Therefore the overall energy consumption of network reduced. In contrast, SEED did not employ any mechanism such as CH-acquaintanceship and CH-friendship to optimize energy consumption of nodes in bursty traffic scenarios. Moreover, we also analyzed the performance of proposed scheme by varying the number of nodes in a cluster as shown in Figure 10. Results show that the proposed scheme outperformed the SEED protocol in each case and consumed on average of 50% less energy compared to SEED. In the proposed scheme, increase in the number of nodes and packets in a cluster did not have high impact on energy consumption. Since the CH had high storage and computational capability, the CH stored more data from child nodes and reduced the energy consumption due to frequent re-transmissions. Additionally, the CH-acquaintanceship and CH-friendship features of proposed scheme reduced the overall energy consumption.
# Small-UAV Radar Imaging System Performance with GPS and CDGPS Based Motion Compensation
## 3 Experimental Results
### First Test Case
The air-soil interface appears at z = 5.5 m in Figure 11a and at z = 4.5 m in Figure 11b and this positioning difference is associated to the altitude estimation bias between GPS and CDGPS. Tomographic reconstructions, depicted in Figure 12a,b, provide an approximate localization of the targets by using both GPS and CDGPS, but standalone GPS data do not allow to correctly reconstruct the air-soil interface profile (it is not flat in Figure 12a). \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Specification** \\ \hline Time Gating & Start Time 22 (ns) & End Time 39 (ns) \\ \hline Frequency range & \(f_{min}=\ 3.1\) (GHz) & \(f_{max}=\ 4.8\) (GHz) \\ \hline Frequency step & \multicolumn{2}{c}{30 (MHz)} \\ \hline \multirow{2}{*}{Imaging Domain} & \(x\)-axis size: 36.50 (m) & dx: 0.025 (m) \\ \cline{2-3}  & \(z\)-axis size: 3.45 (m) & dz: 0.025 (m) \\ \hline TSVD threshold (T) & \multicolumn{2}{c}{\(-\)20 (dB)} \\ \hline Subdomain Aperture (L) & \multicolumn{2}{c}{5 (m)} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Signal Processing Parameters: Track 1. Figure 10: Processed radargram Test 1-Track 2: **(a)** aligned ad interpolated radargram by exploiting GPS information and after filtering operations; **(b)** aligned and interpolated radargram by exploiting CDGPS information and after filtering operations. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Specification** \\ \hline Time Gating & Start Time 57 (ns) & End Time 87 (ns) \\ \hline Frequency range & \(f_{min}=\ 3.1\) (GHz) & \(f_{max}=\ 4.8\) (GHz) \\ \hline Frequency step & \multicolumn{2}{c}{20 (MHz)} \\ \hline \multirow{2}{*}{Imaging Domain} & \(x\)-axis size: 37.30 (m) & dx: 0.025 (m) \\ \cline{2-3}  & \(z\)-axis size: 5.65 (m) & dz: 0.025 (m) \\ \hline TSVD threshold (T) & \multicolumn{2}{c}{\(-\)15 (dB)} \\ \hline Subdomain Aperture (L) & \multicolumn{2}{c}{7 (m)} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Signal Processing Parameters: Track 2. Figure 11: Tomographic images Test 1-Track 1 obtained by Shift and Zoom Truncated Singular Value Decomposition (TSVD) algorithm: (**a**) GPS based motion compensation (MoCo); (**b**) CDGPS based MoCo. Figure 12: Tomographic images Test 1-Track 2 obtained by Shift and Zoom TSVD algorithm: (**a**) GPS based MoCo; (**b**) CDGPS based MoCo.
## 3 Results
### Learning Curves  
The anomalous learning curves for the model trained on Dataset A (Figure 3(a)), where the validation accuracy is higher than the training accuracy, are an immediate red flag. The ideal scenario is to see a small gap between the training and validation curves with the validation accuracy slightly lower than the training accuracy (as seen in Figure 3(b)). This gap reflects that the model is learning useful patterns from the training data while still being somewhat regularized, ensuring it can generalize well. When the two curves are nearly overlapping or, worse, when the validation accuracy exceeds the training accuracy, it is a red flag. It often that the validation procedure is contaminated or that the model suffers from improper regularization or data leakage. In this case, the validation set from Dataset A may have been overly \"friendly\" or unrepresentative of the true complexity present in the training examples, leading the model to appear as if it were generalizing better than it really was.  
This deceptive behavior means that the model's apparent in-domain performance (70% overall accuracy on Dataset A during training) is not a reliable indicator of its true generalization ability. When the model is later tested on an external dataset (Dataset B), the mismatch in data characteristics is exposed and the performance deteriorates significantly; dropping to 49.94% overall accuracy with similarly reduced Cohen's Kappa and Matthews CC values. Essentially, the inflated validation accuracy masked underlying issues such as overfitting to Dataset A's spurious patterns or artifacts. As a result, the model failed to adapt to the new data distribution in Dataset B.  
Figure 4: Learning curves for accuracy returned by the deep learning model using the Keras framework trained on Dataset A (**a**) and Dataset B (**b**).  
Figure 3: Confusion matrices illustrating the cross-dataset evaluation of the deep learning model for a five-class classification problem using the Keras framework. (**a**) Model trained on Dataset A and tested on Dataset B; (**b**) model trained on Dataset B and tested on Dataset A. Adapted from [43].  
In contrast, the model trained on Dataset B, which did not suffer from such deceptive learning curves, maintained higher performance even when tested on Dataset A (68.36% accuracy). This comparison underscores that a reliable validation strategy, reflected by proper learning curves where training accuracy at least matches or exceeds validation accuracy, is critical to developing models that generalize well across datasets. Thus, the \"bad-behaving\" learning curves for the model from Dataset A are directly linked to its poor cross-dataset performance: they reveal that the validation metrics were misleading, and that the model had not truly learned robust features but rather had adapted to quirks in its in-domain data.
## 4 Channel Modeling
### Indoor Physical Channel Model
#### 4.1.1 BS-RIS Channel
The LoS component of the channel matrix \(\mathbf{H}\) can be formulated as follows:  
\[\mathbf{H}_{\text{LoS}}=I_{h}(d_{BS-RIS})\sqrt{G_{e}\left(\theta_{\text{LoS} }^{BS-RIS}\right)L_{\text{LoS}}^{BS-RIS}}e^{j\pi}\mathbf{a}\Big{(}\phi_{\text{ LoS}}^{BS-RIS},\theta_{\text{LoS}}^{BS-RIS}\Big{)}\mathbf{a}^{T}\Big{(}\phi_{\text{ LoS}}^{BS},\theta_{\text{LoS}}^{BS}\Big{)} \tag{18}\]  
where \(I_{h}(d_{BS-RIS})\) follows a Bernoulli distribution containing values from the set \(\{0,1\}\), which indicates the presence of a LoS link between BS and RIS separated by a distance of \(d_{BS-RIS}\)\(\eta\sim\ \mathcal{U}[0,2\pi]\) is a random phase term. \(L_{\text{LoS}}^{BS-RIS}\) indicates attenuation of the LoS link calculated using (17) for the separation distance \(d_{BS-RIS}\). \(G_{e}\left(\theta_{\text{LoS}}^{BS-RIS}\right)\) is the RIS element gain for the LoS link between BS and RIS. \(\textbf{a}\left(\phi_{\text{LoS}}^{BS-RIS},\theta_{\text{LoS}}^{BS-RIS}\right)\) and \(\textbf{a}\left(\phi_{\text{LoS}}^{BS},\theta_{\text{LoS}}^{BS}\right)\) are the corresponding array response vectors for angles \(\phi_{\text{LoS}}^{BS-RIS},\theta_{\text{LoS}}^{BS-RIS}\) and \(\phi_{\text{LoS}}^{BS},\theta_{\text{LoS}}^{BS}\), respectively. To determine the LoS probability \(P(I_{h}=1)=\mu\), the 5G model in [25; 61] can be used as follows:  
\[\mu= \begin{cases}1,&d_{BS-RIS}\leq 1.2\\ \exp\left(-\frac{d_{BS-RIS}-1.2}{4\gamma}\right),&1.2<d_{BS-RIS}\leq 6.5\\ 0.32\exp\left(-\frac{d_{BS-RIS}-6.5}{32.6}\right),&d_{BS-RIS}>6.5\end{cases}  \tag{19}\]
# On the Development of a Neural Network Architecture for Magnetometer-Based UXO Classification
## 5 Results
### Resilience Analysis
Finally, we tested the models' resilience to a linear signal drift, with results shown in Figure 6. Consistent with the previous experiments, the Random Forest classifier's performance degrades sharply with even a minimal drift, while the neural network maintains its predictive power. For the neural network, a drift of 0.01 causes a minor decrease of about one percentage point in balanced accuracy. Even with a substantial drift of 0.1, the total performance drop is only about three percentage points. This proves the quality of the designed neural network, especially in relation to the classical machine learning methods. Figure 4: The impact of additive noise on model performance. Subfigures (**a–e**) show example signals with increasing levels of noise. Subfigure (**f**) plots the balanced accuracy of the neural network (blue) and Random Forest (orange) as a function of the noise amplitude. Figure 5: The impact of time warp augmentation on model performance. (**a**) Examples of signals distorted by different max speed ratios. (**b**) Balanced accuracy as a function of the max speed ratio. An important question arises regarding the potential for further mitigating the impact of signal perturbations on neural network performance. One promising approach to address this challenge is data augmentation, specifically by generating additional synthetic data samples. This strategy is particularly apt given the nonlinear dependency of magnetic field amplitude changes on sensor trajectory parameters, including altitude and azimuthal direction relative to the target object. A complementary approach involves including such augmented data (data that was used for resistance evaluation) within the training set. However, careful consideration is required to prevent performance degradation, especially when perturbations become excessively significant. While a comprehensive exploration of these augmentation strategies falls outside the scope of this article, it constitutes a direction for our future research.