esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method  
Based on the investigation in Section 2, we can conclude that the W-DO has less accuracy as the number of simultaneously occurring sources increases. In order to eliminate the problem of poor  
Figure 2: Average ratio of overlapped time-frequency (ROTF) for 2–6 sources.  
separation quality caused by this phenomenon, we propose a multiple source separation method based on self-reduction of dimensionality by using a B-format microphone. After proposing an effective detection method of the active sources in a non-sparse bin, we conduct a statistical analysis on the active source number that is involved in the non-sparse components and the corresponding possibility. It is found that when there exist \(K\) sound sources simultaneously and the non-sparse components are mostly caused by less than \(K/2\) sound sources, it is very rare that the TF components of all sound sources are overlapped. Therefore, based on this phenomenon, we assume that when \(K\) sound sources simultaneously occur in a sound scene, the active source number corresponding to the non-sparse component does not exceed \(K/2\), which we call the half-K assumption. In addition, due to the recording characteristics of B-format microphone, we can get three linear equations with K source signals as independent variables. If the number of sound sources is greater than three, the linear equations will have multiple solutions, so the B-format microphone can only be used to separate the source signals of the scene with three sound sources. Based on the half-K assumption proposed in this paper, the B-format microphone can be used to solve the separation problem in the sound scene with six sound sources.  
The illustration of the proposed BSS scheme is shown in Figure 3. For the input mixture signals (four recording signals of the B-format microphone [21]), the DOA estimation can be obtained by a traditional localization procedure [21]. Thereafter, the sparse components recovery can be achieved by a clustering process of TF bins. The unprocessed non-sparse components are then obtained by masking the recovered sparse components from mixture signals. The half-K assumption provides a reduction of the dimensionality of the linear equations. By solving the linear equations, the non-sparse components will be effectively separated. Eventually, the final TF coefficients of each source will be recovered by the synthesis of sparse and non-sparse components.
## 3 Proposed Partial Nulling RBD
The IUI can be removed perfectly by excluding \(y_{q,m_{q}}\) from \(\mathbf{y}_{k}\). The received signal after IUI is removed is written as follows,  
\[{{\bf{H^{\prime}}}_{q}}={{\left[{{\bf{h}}_{q,1}^{T}\cdots{\bf{h}}_{q,m_{q}-1}^ {T}{\bf{h}}_{q,m_{q}+1}^{T}\cdots{\bf{h}}_{q,N_{q}}^{T}}\right]}^{T}}, \tag{29}\]2  
A case of \(L_{\text{total}}=N_{R}-l_{total}\) is considered for the sake of generalization. The received signal \(\mathbf{y^{\prime}}_{k}\) for the \(k\)th device after IUI is removed can be made by excluding received signal corresponding to the selected row vectors of the \(k\)th device. The advantage of the proposed scheme is that the diversity gain is achieved by reducing the number of total data streams. In addition, since the multiplication of the post-coder is not needed, the computational complexity at the receiver is low. However, since the proposed scheme excludes the whole row vectors from the channel, the loss of the channel gain can grow rapidly with the number of the excepted row vectors. Therefore, the limitation of the proposed scheme is the large loss of the channel gain.
## 2 Materials and Methods
### Method
#### 2.3.2 Gaussian Kernel Density Estimation  
To avoid overlap among scatter when visualizing data with a high scatter density, Gaussian kernel density estimation was introduced. This process counts the frequency (density) of occurrences of regional scatter and uses different colors to indicate the level of frequency. The density of points in the scatterplot is represented by a color bar. To performa kernel density estimation (KDE) on our data, we utilized the'scipy:stats.gaussian_kde' function from the SciPy library. The formula used is as follows:  
\[\mathrm{K(x)}=\frac{1}{\sqrt{2\pi}}\mathrm{e}^{-\frac{\mathrm{x}^{2}}{2}}, \tag{3}\]  
where x is the input value and \(\mathrm{K(x)}\) denotes the value of the Gaussian kernel function.  
\[\mathrm{\hat{f}(x)}=\frac{1}{\mathrm{nh}}\mathrm{\sum}_{i=1}^{n}\mathrm{K} \bigg{(}\frac{x-x_{i}}{h}\bigg{)}, \tag{4}\]  
where \(\mathrm{\hat{f}(x)}\) is the probability density function estimated at x, n is the sample size, \(\mathrm{K(x)}\) is the kernel function, and h represents the default value of the bandwidth, i.e., the default value of'scipy:stats.gaussian_kde' was used.  
The kernel density distribution of the GPP was centralized, except for some data (including faults) that had a more specific distribution structure (Figure S1). The data points in the GPP were concentrated at higher values (\(>\)5 kg C\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\)), but most data points were distributed in the region of lower values (\(<\)5 kg C\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\)). These values were derived from temperature and precipitation data, but after different definitions, the data showed different distributions and properties. The distribution of the NDVI (Figure S2) with each type of index is more concentrated compared to the GPP.
## 3 Neural Network Based DPD Architecture
### Features of the NN Model
#### 3.1.3 Activation Functions  
The activation or capability of response of a node is not pre-defined. This is deduced with the help of an Activation. It does this by building a relationship between various weights and biases affecting the node and applying the relationship to the node as a function, hence determining responses. It also helps it learn the complex patterns of data. They transform the incoming signals in the node into an output signal which will be used in the next layer of the network or will be the output. The different types of activation functions are summarized in the table below (Table 3).  
When considering ReLu, the possibility of a vanishing gradient is lower, in contrast with the gradient of sigmoids, which grow smaller with an increase in the absolute value of x.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Obstacle Type** & **Brief Definition** & **Convergence Speed (Descending Order of Converging Speed)** \\ \hline \multirow{3}{*}{Saddle point} & Point on the surface of a function & \multirow{3}{*}{ADAM, RMSprop, AdaDelta,} \\  & which is not a local extremum of the function but the slopes in & \\  & orthogonal directions are all zero. & \\ \hline \multirow{3}{*}{Plateau region} & Plateau of a function is a part of its & \multirow{3}{*}{ADAM, Momentum} \\  & domain where the function has & \\  & constant value. & \\ \hline \multirow{2}{*}{Local minima} & Minimum of a function other than & \multirow{2}{*}{ADAM, RMSprop,} \\  & the global minimum (extremum). & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of Optimizers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Activation Function** & **Mathematical Representation** & **Brief Description** \\ \hline Binary Step & \((x)=\left\{\begin{array}{ll}0\ ;for\ x<0\\ 1\ ;for\ x\geq 0\\ y(x)=x\\ y(x)=\frac{1}{1+e^{-x}}\end{array}\right.\) & \multirow{3}{*}{\
\begin{tabular}{c} only has values 1 or 0 as output. \\ linear over entire range \((-\infty,\infty)\). \\ Range \((0,1)\) \\ Range \((0,1)\) \\ Range \((0,x)\) \\ Range \([0\) to \(\infty)\) \\ \end{tabular} \\ Leaky ReLu & \(f(x)=max(0,0.1*x\,\ x)\) & \multirow{3}{*}{
\begin{tabular}{c} Range \((-\infty,\infty)\). \\ Range \((0,1)\) and \\ sum of O/Ps \(=1\) \\ Range \((-1,1)\) \\ \end{tabular} } \\ \hline \end{tabular}
\end{table}
Table 3: Activation Functions.  
ReLu has a constant gradient, helping it learn faster making it the better choice, provided we can work in the positive range of input values.
## 2 Critical Infrastructures (CIs)
### Failure Types  
The various types of failures that arise from dependencies due to disruptions are grouped into distinct categories, which encompass cascading, escalating, and common-cause failures. These classifications are essential in understanding how the dependencies affect the functioning of interdependent infrastructures when faced with disruptions. By evaluating and categorizing these failures, researchers and practitioners can better identify the most effective strategies for mitigating their impact and enhancing the resilience of interconnected systems. Furthermore, this classification allows for a more accurate assessment of the potential risks and vulnerabilities associated with each type of failure, leading to more informed decision making in managing and protecting these vital infrastructures. Overall, understanding the different types of failures is crucial in developing comprehensive and robust strategies to ensure the stability and sustainability of interdependent critical infrastructures [21]:  
* **Cascading failure** is characterized as a situation where a disruption in one infrastructure (Infrastructure A) impacts one or more elements within another infrastructure (Infrastructure B), consequently leading to partial or total unavailability of Infrastructure B. An example of this type of failure is when an electrical power outage affects communication systems, causing disruptions or a complete loss of connectivity in those systems [25].
* **Escalating failure** is defined as a situation in which a disruption in one infrastructure exacerbates an independent disruption in another infrastructure, typically by increasing the severity of the functional loss or prolonging the recovery or repair time for the second infrastructure. For example, a disruption in an ICT system may amplify the impact of a separate disturbance in a road transport system, leading to more significant consequences or longer restoration times.
* **Common-cause failure** happens when two or more infrastructure systems experience a disruption simultaneously due to a single underlying cause. This could be because the infrastructures are physically located in close proximity and depend on shared resources, such as power or communication lines. Alternatively, it could be due to an external factor that impacts multiple systems at once, such as a natural disaster. When such failures occur, it can be challenging to manage and recover from as it affects several systems simultaneously and requires a coordinated response from various organizations. For example, if a natural disaster hits an area, it could lead to the failure of all physical infrastructures in that region, thereby causing significant disruptions.
# Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids
## 4 Q-RPL: Q-Learning-Based Routing Protocol Design
### Integration into RPL
We have established a threshold of 10% below the smart meters' receiver sensitivity as a reasonable indicator of a poor link quality. This dynamic response to changing link qualities, facilitated by the integration of RSSI monitoring, significantly boosts the algorithm's capability to adapt and maintain effective routing paths in varying network conditions. Algorithm 2 presents a pseudocode that outlines each step of the enhanced parent selection process for RPL. Starting with how the next-hop node is chosen based on the  
Figure 4: Architecture of the Q-RPL algorithm. \(\epsilon\)-greedy policy, followed by the reward calculation, the update of the Q-values, and finally, the monitoring mechanism to detect nodes with decaying link quality. ```
0:\(\mathcal{K}\), a set of \(n\) candidate parents of node \(i\), where \(P_{k}\) is the current_preferred_parent. \(Q(s^{i},a^{i})\). Initialize \(\epsilon\), \(\epsilon_{min}\), \(\epsilon_{decay}\), \(\alpha\), \(\gamma\), threshold. for each new routing decision do  Choose next-hop based on \(\epsilon\)-greedy policy:  Choose a random number \(x\), \(x\in[0,1]\) if\(x<\epsilon\)then  Choose a random action \(a^{i}\): \(\pi(s^{i})=a^{i}\sim\mathrm{Uniform}(\mathcal{A}^{i}(s^{i}))\) \(P_{k}\leftarrow\pi(s^{i})\) else  Choose the best-known action \(a^{i}\): \(\pi(s^{i})=\underset{a^{i}\in\mathcal{A}}{\text{arg max}}\,Q(s^{i},a^{i})\) iftie in Q-values then  Identify all actions \(a^{i}\) with max Q-value: \(\mathcal{E}\)  Choose the action with the lowest ETX value: \(\pi(s^{i})=\underset{a^{i}\in\mathcal{E}}{\text{arg min}}\,\mathrm{ETX}(a^{i})\) endif \(P_{k}\leftarrow\pi(s^{i})\) endif  Transmit packet to chosen next-hop: \(P_{k}\) Observe outcome and calculate reward: if\(packetfail\)then \(r=-2\) else  get \(\rightarrow\) re-transmission attempt (\(m\)) \(r=2-m\) endif Update Q-value for the state-action pair: \(Q(s^{i},a^{i})\leftarrow(1-\alpha)Q(s^{i},a^{i})+\alpha[r+\gamma\max_{a^{i}}Q (s^{i},a^{i})]\) Update \(\epsilon\): \(\epsilon\leftarrow\max(\epsilon\times\epsilon_{decay},\epsilon_{min})\) endfor Monitor RSSI of nodes in \(\mathcal{K}\): if\(RSSI_{k}<threshold\), \(k\in\mathcal{K}\)then if\(\epsilon=\epsilon_{min}\) AND \(k=P_{k}\)then  trigger exploration by resetting \(\epsilon\) to initial value else  remove \(k\) from \(\mathcal{K}\) endif  endif
```  
**Algorithm 2** Enhanced Parent Selection Algorithm for RPL.
## 2 Theory and Methods
### GPEF Compensation Method
As can be seen in Equations (10) and (11), during the iteration, missing detector elements are estimated from \(\mathbf{MsLA}^{n}\) in the Fourier space. Then, the missing views of \(\mathbf{A}\) usually blurred by the artifacts are estimated by the missing detectors and compensated by weighted addition to this part of re-estimated image. Implementing the extrapolation in the frequency domain will guarantee the speed and accuracy of the compensation method.
## 5 Discussion
### Stability and Reliability of Results  
In our analysis, we assessed the accuracy and stability of the parameter inversion results obtained using the improved modular vector method and other intelligent algorithms. To account for the fluctuation observed in the inversion results of genetic algorithms, particle swarm optimization algorithms, and simulated annealing algorithms, we conducted 20 experiments for each of these three algorithms in our analysis. By comparing the optimal and average values of the inversion results, we enhanced the reliability of our experimental findings.
## 4 Development of Propulsion Power Prediction Model
### Model Learning  
The SVR algorithm was applied to the training dataset. In this study, RBF was chosen for the kernel function. The RBF is a widely adopted kernel function because the number of hyper parameters is small compared to that of other models without losing too much prediction performance.  
As previously mentioned, finding the optimal parameter is a crucial step for learning the SVR model. For the SVR model with RBF kernel function, three hyper-parameters \(C\), \(\varepsilon\) and \(\sigma\) are required. In this study, to find the optimal hyper-parameter set, every possible parameter combination was validated by a K-fold cross validation approach. As a result, the optimal hyper parameter for the training  
Figure 3: An example of outlier detection using [PERSON]’s criterion; 75 observations were randomly chosen from the dataset. (**a**) Before outlier detection. (**b**) After outlier detection.  
dataset was C = 4950, \(\sigma\)= 0.6 and \(\varepsilon\)= 1.0. The experiment was conducted with Scikit-learn which is a famous open-sourced machine-learning library of python.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 3 Fair Editions of Decision Trees
### FFTree  
The authors of [10] proposed FFTree, a novel fair and flexible Decision Tree classifier that integrates multiple fairness criteria while preserving interpretability and classification performance. Unlike traditional fairness-aware models that focus on a single fairness definition, FFTree enables users to select and combine different fairness constraints (e.g., Disparate Impact, Disparate Mistreatment, and Disparate Treatment) according to the specific requirements of their application. In this algorithm, splits are selected using a modified information gain (IG) criterion that incorporates fairness constraints. A split is accepted only if it satisfies the predefined fairness conditions; otherwise, if no fair split is found, the node is converted into a leaf, thereby preventing unfair outcomes. The authors evaluate FFTree using the Adult Income Dataset and a proprietary real-world dataset from Intesa Sanpaolo Bank. The results demonstrate that FFTree achieves accuracy comparable to existing fairness-aware Decision Trees while offering greater flexibility in fairness optimization.
# Power Control Method for Energy Efficient Buffer-Aided Relay Systems
## 3 Achievable Rate of RN with a Buffer  
An RN employs a buffer that can reserve the information bits. The buffered bits are received from SN, yet not forwarded to DN if the channel condition of the second link is poor. The bits in the buffer are forwarded later once the channel condition changes to be good. Considering the limited buffer size, \(b\), and information causality, we can consider two cases as follows.  
* \(R_{s}(t)\geq R_{r}(t)\): RN forwards \(R_{r}(t)\) and reserves the remaining bits, i.e., \(R_{s}(t)-R_{r}(t)\) bits, at the buffer unless the buffer is full with \(b\) bits. Thus, the buffer status at time \(t\) will be \(\min\left(B(t-1)+\left(R_{s}(t)-R_{r}(t)\right)\right)\), \(b\)), where \(B(0)=0\).
* \(R_{s}(t)<R_{r}(t)\): RN forwards \(R_{s}(t)\). In this case, since RN can forward more bits up to \(R_{r}(t)-R_{s}(t)\), the number of forwarded bits will be \(R_{s}(t)+\min\left(B(t-1),\ R_{r}(t)-R_{s}(t)\right)\). Accordingly, the buffer status will be \(\max\left(B(t-1)-\left(R_{r}(t)-R_{s}(t)\right),0\right)\).  
Concretely, by virtue of the buffer, the achievable rate of the overall link with the buffer-aided RN and the buffer status at time \(t\) are written as follows.  
\[R_{B}(t) =\begin{cases}R_{r}(t),&\text{if }R_{s}(t)\geq R_{r}(t)\\ R_{s}(t)+\min\left(B(t-1),R_{r}(t)-R_{s}(t)\right),&\text{otherwise}\end{cases} \tag{5}\] \[B(t) =\begin{cases}\min\left(B(t-1)+\left(R_{s}(t)-R_{r}(t)\right),b \right),&\text{if }R_{s}(t)\geq R_{r}(t)\\ \max\left(B(t-1)-\left(R_{r}(t)-R_{s}(t)\right),0\right),&\text{otherwise}\end{cases} \tag{6}\]  
For example, Figure 2 shows the status of a buffer with \(b=2\) for \(T=500\) when \(\gamma_{sr}=10\) dB. From the results, it is clearly observed that the buffer operates as expected. When the first link is better than the second link, i.e., \(\gamma_{sr}\gg\gamma_{rd}\), the information amount delivered from SN to RN is greater than that forwarded from RN to DN. Thus, the buffer is almost always full when \(\gamma_{rd}=0\) dB, as shown in Figure 2a. On the other hand, as \(\gamma_{rd}\) increases to 10 dB and 20 dB, the information amount forwarded from RN to DN increases, resulting in the reduction of information bits in the buffer as shown in Figure 2b,c, respectively. When \(\gamma_{rd}=30\) dB, the buffer is almost empty as shown in Figure 2d. Here, we propose that the size of the buffer is critical for the achievable rate of the RN networks.
# Anaerobic Digestion of Duckweed Used to Remediate Water Contaminated with Zinc and Ammonium
## 4 Discussion
### Phytoremediation of Zn/NH\({}_{4}\)\({}^{+}\)-Polluted Water by Duckweed
#### 4.1.2 Effect of NH\({}_{4}\)+ on Zn Phytoremediation and Duckweed Growth  
The direct assimilation of NH\({}_{4}\)+ by duckweed and the biodegradation by its rhizosphere microorganisms synergistically purify wastewater. However, Zn recovery was inhibited in the presence of NH\({}_{4}\)+-N. The pH was very low at the end of incubation in the presence of NH\({}_{4}\)+-N, particularly in the incubations with 5.0 mg/L Zn and 40 mg/L NH\({}_{4}\)+-N (Figure 6c). This was likely because NH\({}_{4}\)+-N was absorbed onto the root cells, exchanging a hydrogen ion (H\({}^{+}\)) from the functional groups on duckweed, thus adding an acidic component to water. In addition, ammonium is converted to nitrate by the action of nitriifying microorganisms. In the process, protons are released to the medium, reducing pH. In this condition, Zn absorption onto plants decreases because of the competition between Zn and H\({}^{+}\) ions for uptake sites on the root surface [60; 61]. In addition, acid conditions are beneficial for Zn precipitation in the presence of NH\({}_{4}\)+-N [58], which led to a better Zn removal efficiency in the treatment with a NH\({}_{4}\)+-N concentration of 40 mg/L (T9) compared to the 20 mg/L NH\({}_{4}\)+-N incubation (T6).  
Overall, the above results demonstrate a clear antagonism between Zn and NH\({}_{4}\)+-N in duckweed systems. While NH\({}_{4}\)+-N can stimulate growth and enhance stress tolerance, it compromises Zn uptake and phytoremediation efficiency. The balance between nutrient supplementation and metal stress is critical--optimal phytoremediation may require the careful tuning of nutrient inputs to avoid suppressing metal accumulation.  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Plant** & **Growth Medium** & **Temperature (\({}^{\circ}\)C)** & \begin{tabular}{c} **Exposure Time** \\ **(d)** \\ \end{tabular} & \begin{tabular}{c} **Zn Concentration** \\ **(mg/L)** \\ \end{tabular} & \
\begin{tabular}{c} **Removal** \\ **Efficiency (\%)** \\ \end{tabular} & **Reference** \\ \hline \multirow{4}{*}{_Lemma minor_} & Synthetic media, i.e., & & & & \\  & Quarter Coic and & 25 & 4 & 10 & \textasciasci{}70–80\% & [53] \\  & Lessaint solution & & & & & \\ \hline _Lemma trisulca,_ & & & & & & \\ _Lemma minuta_ & & & & & & \\ _Lemma minor_ & & & & & & \\ \hline _Lemma gibba_ & Quarter Coic solution &
\begin{tabular}{c} Ambient \\ temperature \\ \end{tabular} & 7 & 18, 14, 10 and 6 & 61–71\% & [58] \\ \hline Hybrid of _Lemma minor_ and _Lemma turionifera_ & & 18 & 7 & 2.5 and 5.0 & \textasci{}82\% & This study \\ \hline \hline \end{tabular}
\end{table}
Table 3: Zn removal efficiency of duckweed reported in different studies using synthetic and real wastewater.
# P-Wave Reflection Approximation of a Thin Bed and Its Application
###### Abstract  
\"Thin-bed\" reservoirs have become important targets of seismic exploration and exploitation. However, traditional amplitude versus offset/amplitude versus angle (AVO/AVA) technologies, for example, those based on Zoeppritz equations and their approximations for a single interface, are not sufficiently accurate for thin-bed stratigraphy. Analytic solutions of thin-bed reflectivity may become practical for thin-bed AVO analysis and inversion. Therefore, a linear analytic approximation of thin-bed P-wave reflectivity is developed under small-incidence and thin-bed assumptions. Numerical simulations show that the amplitude approximation errors are usually smaller than 10% for incidence angles less than 20 degrees, and the thin-bed thicknesses are less than one-tenth of the P-wave wavelength. Based on the least-squares approach, the inversion strategy is proposed using the approximate formula. A synthetic data test shows that the proposed inversion method can produce more accurate thin-bed properties than that based on the Zoeppritz equations, which reveals the potential of the inversion method based on the linear analytic approximate formula in the fine characterization of thin reservoirs.  
thin bed; P-wave response; approximated formula; application Article