esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Preparation of the Electrochemical Biosensor  
A bare gold electrode was polished twice to a mirror-like surface with 0.3 mm and 0.05 mm alumina slurry and rinsed thoroughly with sterilized ultrapure water between each polishing step. Then, it was washed successively by piranha solution (H\({}_{2}\)SO\({}_{4}\):H\({}_{2}\)O\({}_{2}\) = 3:1) and sterilized ultrapure water in an ultrasonic bath, and dried in the air. The polished electrode was electrochemically cleaned and characterized by potential cycling between \(-\)1.0 V and 1.0 V in 1.0 M H\({}_{2}\)SO\({}_{4}\) until a stable cyclic voltammogram for the cleaned gold electrode was obtained. Then, 10 \(\upmu\)L of 2 \(\upmu\)mol/L thiol-modified capture probe solution was dropped onto the surface of the gold electrode and incubated overnight at 4 \({}^{\circ}\)C. After rinsing with washing buffer, the electrode was immersed into 100 mL MCH (2.0 mM) for 1 h to obtain capture probe-modified gold electrode. Subsequently, the modified electrode was immersed and slightly shook in 0.1 mg/mL salmon sperm DNA and 1% BSA solution (each for 15 min) to avoid nonspecific adsorption [37]. After that, the treated gold electrode was rinsed with washing buffer, and 10 \(\upmu\)L of 3.0 \(\upmu\)M aptamer probe solution was dropped on its surface and incubated for 1 h at 37 \({}^{\circ}\)C to obtain the electrochemical biosensor.
# A Reliability Engineering Approach for Managing Risks in CubeSats
## 3 Materials and Methods
### Step 2. Identify the Failure Modes  
For each considered item--and its interfaces--it is necessary to identify the potential failure modes that can affect system performance, also considering their effects on systems. This step relies on the capability of the analysts to explore potential combinations of failure modes. Naturally, this capability does not reside in a single individual, thus implying the need to involve both fresh-eyes and experienced researchers in order to gather meaningful insights from collective collaborative knowledge and experiences. This step needs both teamwork and timely information exchange in order to ensure that the knowledge about the system is embedded in the reliability analyses. In case of systems made up of many different parts, it is necessary to develop a consistent coding system that uniquely identifies parts and related failures, in order to increase the comprehensibility of the analyses.  
In this context, considering the current lack in literature of a standard labeling system, this paper presents a coding system specifically developed to deal with the need of a CubeSat environment. The system has been developed to ease the exchange of information between different researchersand provide a formal and unequivocal representation of the knowledge relevant for the analysis. The coding system is developed through a 5-parts alphanumeric code, which synthetically relates the failure modes to system components, as follows:  
XXX-A.B-CCC#DDDD  
where:  
XXX--represents the sub-system being analyzed (coded in a three-letters acronym)  
A--represents the failure mode of the sub-system (coded in a numeric scale)  
B--represents the effect on the system (coded in a numeric scale)  
CCC--represents the propagation of the failure mode in the system (coded in a three-letters acronym)  
#--\"-\" if CCC affects a subsystem, \" |\" if CCC affects a mechanical interface, \"/\" if CCC affects an electrical/data interface:  
DDDD--represents the specific fail, with respect to the part level (coded in an alphanumeric acronym).
# Multi-Point Time-Synchronized Waveform Recording for the Analysis of Wide-Area Harmonic Propagation
## 4 Results
### Separate Operation of the EV Charger
Figure 6: Comparison of magnetizing current spectra versus MV current through Substation A. Test sequence A.2 — MG set supply. Furthermore, after analyzing the results presented in Figure 3, a conclusion can be drawn about the influence of harmonic voltage propagated from the upstream grid. A notable observation is a dissolution of voltage harmonics at 1150 Hz and 1250 Hz which again provoked change of harmonic currents at these frequencies. On the other hand, an increase of voltages at 250 Hz and at 350 Hz coincided with the change of EV charger current at these frequencies. While, as it was mentioned before, higher frequency order harmonic voltages reduced because of the higher short-circuit power capacity of the public grid, the change of spectral components at 250 Hz and 350 Hz was provoked by propagated background distortion interacting with the operation of the studied harmonic sources.
# Advanced Hybrid Beamforming Technique in MU-MIMO Systems
###### Abstract  
This paper proposes a high performance wireless commmunication technology in MU-MIMO systems. The millimeter wave (mmWave) communication technology was considered for the future wireless communication systems such as the fifth-generation new radio (5G NR). In 5G NR, the mmWave communication technology was studied to increase the use of wide bandwidth and the data rate. Therefore, MU-MIMO systems can be used in mmWave. To decrease the complexity of conventional digital beamforming system, the hybrid beamforming system was studied. In particular, the proposed hybrid beamforming system improves the error performance and average sum rate in partially connected structure (PCS) hybrid beamforming system. The proposed PCS hybrid beamforming system forms variously combined beam patterns using the information of azimuth and elevation angles for the multi-paths according to the number of bits. In addition, the azimuth and elevation angles among the formed beam patterns are estimated according to the received signal strength (RSS). In the simulation results, the proposed PCS hybrid beamforming system has better error performance and the average sum rate than the conventional hybrid beamforming system.  
Keywords:mmWave; FCS hybrid beamforming; PCS hybrid beamforming; RSS +
Footnote †: journal: Applied Sciences
## 3 Results
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & \multicolumn{2}{l}{**HB Group**} & \multicolumn{2}{l}{**Control Group**} & \\ \cline{2-5}
**Outcomes** & & \multicolumn{2}{l}{**Differences (\(p\)-Value)**} & \multicolumn{2}{l}{**Differences (\(p\)-Value)**} & \multicolumn{2}{l}{**Between-Groups**} \\  & & & & & & \\ \hline IR Strength (\%BW) & & & & & \\ TS & 14.60 (3.66) & 0.77 (\(-0.30\); 1.84); & 12.61 (5.28) & 0.16 (\(-0.75\); 1.08); & 1.99 (\(-0.40\); 4.40) \\  & & 0.152 & & 0.716 & \(p=0.102\) \\ Non-TS & 13.83 (2.80) & & 12.44 (6.07) & & 1.39 (\(-1.10\); 3.88) \\  & & & & & \(p=0.269\) \\ ER Strength (\%BW) & & & & & \\ TS & 13.09 (3.18) & \(-0.94\) (\(-1.93\); 0.04); & 12.85 (5.36) & 0.62 (\(-0.46\); 1.72); & 0.23 (\(-2.09\); 2.56) \\  & & 0.061 & & 0.250 & \(p=0.839\) \\ Non-TS & 14.03 (3.18) & & 12.22 (5.11) & & 1.81 (\(-0.44\); 4.06) \\  & & & & & \(p=0.113\) \\  & & & & & \\ TS & 93.01 (26.38) & \(-3.01\) (\(-10.44\); 4.42); & 105.19 (24.05) & \(-3.85\) (\(-12.04\); 4.32); & \(-12.18\)(\(-25.59\); \(-1.23\)) \\  & & 0.414 & & 0.342 & \(p=0.009\) \\ Non-TS & 96.02 (21.14) & & 109.05 (26.73) & & \(-13.01\)(\(-25.79\); \(-0.25\)) \\  & & & & & \(p=0.025\) \\ Extensibility & & & & & \\ TS & \(-11.60\) (8.68) & \(-8.73\) (\(-11.12\); \(-6.34\)); & \(-3.86\) (8.40) & \(-4.35\) (\(-6.47\); \(-2.23\)); & \(-7.83\) (\(-12.42\); 3.23) \\  & & \(<\)0.001 & & \(<\)0.001 & \(p=0.006\) \\ Non-TS & \(-2.87\) (7.58) & & 0.50 (7.71) & & \(-3.25\) (\(-7.35\); 0.83) \\  & & & & & \(p=0.116\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Analysis of isometric strength and extensibility.
# Efficient Transmit Antenna Subset Selection for Multiuser Space-Time Line Code Systems
## 3 TAS Selection Algorithms
### Proposed Incremental TAS Selection Algorithm
Here, \(\mathbf{H}(:,q)\) and \(\mathbf{H}_{0}(:,q)\) denote the \(q\)-th column vector of the updated channel matrix \(\mathbf{H}\) and \(\mathbf{H}_{0}=\mathbf{Q}_{2K}^{T}\mathbf{H}^{*}\), respectively, which are associated with the transmit antennas remained after completing each step. It should be pointed out that in Algorithm 2, a \(2\times 2\) matrix inverse operation is performed in each incremental step, which mainly contributes to lower complexity compared to theconventional greedy-based algorithm. The idea of the proposed algorithm is to find a TAS by reducing the complexity for the matrix inverse operation needed at each step. ```
Inputs: \(\mathbf{H},\mathbf{Q}_{2K},N_{T},N_{S},K\)
1:\(\mathbf{H}_{Q}=\mathbf{Q}_{2K}^{T}\mathbf{H}^{*}\)
2:\(\mathbf{\hat{V}}_{0}=\mathbf{I}_{2K}\)
3:for\(k=1,2,\cdots,N_{S}\)
4:for\(q=1,2,\cdots,(N_{T}-k+1)\)
5:\(\mathbf{\hat{H}}_{k,q}=\left[\mathbf{H}(\cdot,q)\,\mathbf{H}_{Q}(\cdot,q)\right]\)
6:\(\mathbf{\hat{O}}_{k,q}=\mathbf{\hat{V}}_{k-1}\mathbf{\hat{H}}_{k,q}\)
7:\(\lambda_{k,q}=Tr\left[\mathbf{\hat{V}}_{k-1}-\mathbf{\hat{O}}_{k,q}\left( \mathbf{I}_{2}+\mathbf{\hat{H}}_{k,q}^{H}\mathbf{\hat{O}}_{k,q}\right)^{-1} \mathbf{\hat{O}}_{k,q}^{H}\right]\)
8:end
9:\(\mathbf{\hat{q}}=\underset{q}{\text{ arg min }\lambda_{k,q}}\)
10:\(\mathbf{\hat{V}}_{k}=\mathbf{\hat{V}}_{k-1}-\mathbf{\hat{O}}_{k,q}\left( \mathbf{I}_{2}+\mathbf{\hat{H}}_{k,q}^{H}\mathbf{\hat{O}}_{k,q}\right)^{-1} \mathbf{\hat{O}}_{k,q}^{H}\)
11:\(\mathbf{H}=\mathbf{H}(\cdot,[1:(q-1)\,\,(q+1):\text{end}])\)
12:\(\mathbf{H}_{Q}=\mathbf{H}_{Q}(\cdot,[1:(q-1)\,\,(q+1):\text{end}])\)
13:end
14:\(\mathbf{H}_{S}=\mathbf{H}\)
15:Output:\(\mathbf{H}_{S}\)
```  
**Algorithm 2** SNR-based efficient incremental TAS selection algorithm.
# Internal Flow Prediction in Arbitrary Shaped Channel Using Stream-Wise Bidirectional LSTM
## 3 Experiments
### Comparison Methods  
The effectiveness of the SB-LSTM module was evaluated on two baseline models, ED and U-Net. These two models were adopted because they were used to train internal flow datasets in [1] and [27] and have also been referenced in many other papers regarding obtaining steady state solutions. ED consists of an encoder and decoder, and U-Net is an extension of ED with skip connections. In both models, the SB-LSTM module is attached at the latent space, and the structural comparison of the four different methods is depicted in Figure 8.  
Moreover, the number of model parameters of the four different methods is listed in Table 1. In both ED and U-Net, the model capacity is increased to 4,348,192 parameters with relative increment ratios of 34.67% and 22.60%, respectively. The significant growth in parameters occurs due to the use of the fully connected operation in the input gate, output gate, and forget gate of LSTM. Furthermore, since the network that we use utilizes two LSTM models, one for backward and one for forward processing, the increase in the number of parameters mentioned earlier is doubled. Despite the larger model capacity, overfitting did not occur during training and the models with the SB-LSTM module showed better performance, which is comprehensively analyzed in Sections 4.1-4.3.
###### Abstract  
This study aims to establish a probabilistic capacity model of a wall pier under various damage states, and the seismic vulnerability of a typical wall pier bridge is studied. The finite element analysis of the wall pier is carried out by using the layered shell element, and its accuracy is verified through the comparison with the experimental results. A series of wall pier samples are generated based on the survey data, and the corresponding finite element models are established. The hysteresis analysis is implemented to obtain the displacement drift ratio of each seismic performance point. A candidate capacity model with various factors is proposed, and the unknown parameters are estimated and filtered by the Bayesian method. One hundred and twenty bridge samples of a benchmark bridge are generated by considering the uncertainty of parameters, and the finite element models are established. The bridge samples and ground motions were matched by one-to-one correspondence for the nonlinear time history analysis, and seismic vulnerability models of bridge components and system are obtained. The results showed that the in-plane capacity of wall piers is mainly affected by axial compression ratio, shear span ratio, and vertical reinforcement ratio. The wall pier shows excellent behavior in the earthquakes. The capacity models of wall piers can be used for evaluating the damage states of wall piers, and obtaining the seismic vulnerability model of wall piers bridges to be used for future seismic risk assessment and retrofit prioritization.  
**Keywords: wall pier bridge; capacity model; Bayesian method; seismic vulnerability; uncertainty**
## 4 Discussion
### Limitations of the Research  
Although this study addresses a real and pressing safety issue related to construction sites, particularly concerning pedestrians, there are still several limitations. First, the study relied solely on image-based evaluations without incorporating the direct observation of pedestrian behavior in real-world settings. While the controlled nature of image-based surveys enables the systematic assessment of visual factors, it limits our ability to capture dynamic pedestrian responses to evolving construction environments. For example, [PERSON] et al. [49] analyzed video footage recorded by camera-equipped drones to extract pedestrian trajectories and detect abnormal behaviors, including sudden changes in direction, abrupt stops, or unexpected accelerations. In this context, future studies should aim to develop citywide real-time surveillance systems capable of detecting unsafe pedestrian behaviors induced by construction activities.  
Second, this study simplified the range of visual factors influencing pedestrians' perceived risk by focusing primarily on flat, paved sidewalks and considering only the areas occupied by obstructions and barriers. Other important attributes, such as the obstruction type, barrier height, and barrier color, were not evaluated. However, previous studies have found that the color and shape of safety signs can significantly influence pedestrians' risk-taking behavior and perceived risk [50]. In addition, common visual elements in urban environments, including scaffolding and warning signs, were not incorporated into the analysis. Therefore, to achieve a more in-depth understanding of pedestrian behavior and to improve the generalizability of the findings, future studies should expand the model to account for a wider variety of environmental features and visual factors.  
Third, although the SAM was effective in generating segmentation masks with minimal user input, the current study still required manual verification and occasional corrections to ensure segmentation accuracy. Its performance under complex real-world conditions, particularly for potential applications in real-time surveillance systems, has not yet been validated. To address this limitation, future studies will propose advanced models aimed at enhancing accuracy by leveraging few-shot learning algorithms, which enable models to generalize effectively from only a small number of labeled examples [51].  
Lastly, although the study analyzed 2562 responses (evaluations of 61 images by 41 participants), a larger sample could enhance the generalizability of the findings. In addition, [PERSON] et al. [31] have demonstrated demographic differences in pedestrians' perception of risk, such as older adults exhibiting greater dissatisfaction when encountering obstacles that impede walking, or women expressing heightened concerns in environments with inadequate lighting. Considering these points, future studies should aim to recruit a larger and more demographically diverse sample to better capture potential variations in perceived risk related to factors such as age, gender, and mobility.
## 6 Isolated Empirical Studies on ML-based xApp Vulnerabilities
### Implications for O-RAN Security  
The proposed threat model provides a structured approach to understanding and mitigating security risks in O-RAN. By incorporating both individual and correlated vulnerabilities, the model enables  
* **Quantitative Risk Assessment:** estimating the overall impact of potential attacks;
* **Prioritization of Mitigation Efforts:** focusing on high-impact vulnerabilities and attack chains;
* **Design of Defense Mechanisms:** developing countermeasures tailored to specific attack scenarios.  
**Examples of Mitigations:**
* **Robust Aggregation:** To mitigate data poisoning, trimmed mean aggregation is employed, reducing the impact of outliers:  
Figure 1: Directed graph of cross-layer attack chains showing synergy coefficients (\(\delta\)) between vulnerabilities. Edge weights correspond to values derived from DAWN dataset simulations in Table 6. Dashed lines indicate cross-layer interactions between ML and system-level vulnerabilities.  
\[W_{g}^{\prime}=\frac{1}{n}\left(\sum_{i\in\mathcal{B}}W_{i}+\sum_{j\in M}\text{ Trim}(W_{j}^{\prime})\right),\] (7) where Trim\((W_{j}^{\prime})\) discards extreme values to enhance robustness.
* **Zero Trust Architecture (ZTA):** To prevent unauthorized access, continuous authentication and micro-segmentation are enforced. Anomaly detection is based on \[\lambda_{\text{thresh}}=\mu+3\sigma,\] (8) where \(\lambda_{\text{thresh}}\) defines the threshold for detecting rogue xApps.
# Application of the Residue Method in Steam Superheater Fault Detection
## 3 A Mathematical Modelling Method of the Proposed System
### The Equivalent Electronic Model of Superheat
To achieve this, it was necessary to linearise the mathematical model of the entire system as defined by Equations (18)-(26) and extract the transfer matrix around the steady state point at constant input sizes (F\({}_{\text{g}}\) = ct., F\({}_{\text{ai}}\) = ct., T\({}_{\text{gi}}\) = ct., T\({}_{\text{ai}}\) = ct.):  
\[ \begin{bmatrix}T_{a2}(s)\\ T_{a3}(s)\end{bmatrix} = \begin{bmatrix}H_{11}(s)&H_{12}(s)\\ H_{21}(s)&H_{22}(s)\end{bmatrix} \cdot \begin{bmatrix}W_{inj1}(s)\\ W_{inj2}(s)\end{bmatrix} , \tag{33}\]  
and for the steady state, the equations are:  
\[T_{a20}^{*}=k_{11}W_{inj10}^{*}+k_{12}W_{inj20}^{*}, \tag{34}\]\[T_{a30}^{*}=k_{21}W_{inj10}^{*}+k_{22}W_{inj20}^{*} \tag{35}\]  
In any complex process, there is always a possibility of defects or issues arising in various places or modes of operation. For instance, the control valves on the \(\mathrm{W_{inj1}}\) and \(\mathrm{W_{inj2}}\) injection streams may face blocking or hysteresis when working. Similarly, unusual fuel combustion or incorrect depression control in the firebox can lead to gas temperature or flow \(\mathrm{F_{g}}\) variations. Additionally, changes in steam flow to the turbine and heat transfer coefficients due to debris outside or inside superheater pipes can also cause problems. Therefore, it is crucial to monitor and address any such issues promptly to ensure the smooth functioning of the process. To avoid issues, it is important to know the appropriate action channels and how they are affected. Figure 6 shows processes with different time constants. SA1, SA2, and SA3 take longer, while SG1, SG2, and SG3 take less time. By analyzing the block diagram, one can identify the pairs that directly affect the measurable output: \(W_{inj1}\) and \(T_{a2}\), along with \(W_{inj2}\) and \(T_{a3}\). This information is crucial for quickly and effectively addressing any faults. To detect faults in the steam temperature \(T_{a2}\) and \(T_{a3}\), the blocks in Figure 10 will be used. The actuators AC1 and AC2 are linked to the injection flow \(W_{inj1}\) and \(W_{inj2}\). The deviation vector DE is a matrix function that is based on the fault vector DE. \[DE= \begin{bmatrix}DE_{1}\\ DE_{2}\end{bmatrix} = \begin{bmatrix}T_{a2}-T_{a2m}\\ T_{a3}-T_{a3m}\end{bmatrix} =F\bigg{(} \begin{bmatrix}W_{inj1}\\ W_{inj2}\end{bmatrix} \bigg{)}. \tag{36}\]  
Figure 9: Steam superheaters’ electronic simulator.
## 4 Results
### Case Study Application to Singtel Building
#### 4.4.4 Potential Impacts on Facility Planning  
A hypothetical redesign guided by the model's recommendations pointed to a 15% decrease in total fixtures while preserving a maximum 60-s wait for 90% of restroom users. This finding highlights how data-driven, context-specific predictions can cut costs and reduce space usage without sacrificing convenience.
## 3 Methods
### Artificial Neural Network (ANN)  
The ANN has been one of the most widely used ML algorithms for wide range of remote sensing applications. ANN is a computational model that is inspired by the human brain. ANN is formed by a collection of interconnected units (neurons) that learn from experience by modifying connections (weights) [56; 57]. ANN usually consists of an input layer, hidden layer, and output layer. The backpropagation (BP) ANN used in this study implements a multilayer perceptron (MLP) algorithm that trains with a BP algorithm, while an MLP refers to a feedforward network that generalizes the standard perceptron by having a hidden layer that resides between the input and output layers. It has been demonstrated that MLP can approximate any continuous function to an arbitrary degree of accuracy, given a sufficiently large but finite number of hidden neurons [56; 58]. In this study, the input layer and output layer are referred to as the Sentinel-2 spectral bands (reflectances) and the cotton LAI, respectively.  
The major tuning hyperparameters for ANN are the number of hidden layers and the number of neurons in the hidden layer. Here, we used the one-hidden-layer network because it was demonstrated to be powerful enough to approximate any measurable function to any desired degree of accuracy [59]. Many studies have been dedicated to the investigation of the optimal number of neurons in the hidden layer, and several empirical equations have been proposed [60; 61; 62]. In this study, the number of neurons in the hidden layer is determined by the following equation [62]:  
\[N_{h}=\sqrt{(m+2)N}+2\sqrt{\frac{N}{m+2}} \tag{1}\]  
where \(N_{h}\) is the number of neurons in the hidden layer, \(m\) specifies the number of layers, and \(N\) denotes the number of input neurons. The number of neurons in the hidden layer was set to ten in this study according to Equation (1). In the input layer, the input variables include 10 spectral bands. Other important hyperparameters were optimized using GridSearch and 5-fold cross-validation. The remainder of hyperparameters for the ANN model remain defaults. The structure of the neural network used in this study is presented in Figure 4. The hyperparameter values adopted in this study are listed in Table 4.  
Figure 4: The architecture of backpropagation (BP) neural networks used in this study for cotton LAI retrieval.