esa-sceva/satcom-chunk-collection huggingface dataset:
# Mitigating the Impact of Noise on Nonlinear Frequency Division Multiplexing +
## 5 Improved Modulations
### The b-Modulation with Exponential Mapping  
A slight modification of the NIS modulation, based on an exponential mapping, was proposed in [18] to allow the modulation of the continuous spectrum also in the defocusing regime (GVD parameter \(\beta_{2}>0\)), in which the constraint \(|\rho(\lambda)|<1\) must hold for any \(\lambda\in\mathbb{R}\).  
In the more practical focusing regime considered in this work (\(\beta_{2}>0\)), there is no such a constraint on the nonlinear spectrum, but there is an analogous constraint on the scattering coefficient \(b(\lambda)\)--the condition \(|b(\lambda)|<1\) mentioned above--which gives rise to the power limit issue with b-modulation. This issue can be addressed by employing an exponential mapping [32]. The resulting modulation technique--referred to as bExp in the following and sketched in Figure 3c--consists in mapping the linearly modulated signal \(s(t)\) to the scattering coefficient \(b(\lambda)\) according to [27; 32]  
\[b(\lambda)=\sqrt{1-\exp\left(-|S(\lambda/\pi)|^{2}\right)}\exp\left(j\text{ Arg}(S(\lambda/\pi))\right), \tag{5}\]  
where \(S(f)\) is the conventional Fourier transform of \(s(t)\). The nonlinear spectrum \(\rho(\lambda)\) is then obtained by Equations (2)-(4). The issues related to the power of the signal are significantly mitigated in this case, since \(|b(\lambda)|\leq 1\) by construction. This technique, as will be shown in the following, grants a significant performance improvement with respect to both NIS and b-modulation, though giving up on the control over the temporal duration of the optical signal. Figure 4c reports an example of a signal encoding \(N_{b}=16\) symbols with bExp-modulation, showing that the waveform acquires a tail on both sides.
# Risk-Based Virtual Power Plant Implementation Strategy for Smart Energy Communities
## 4 Results
### Cost Analysis  
Table 4 shows monthly costs according to VPP service. VPPs based on renewable resources cannot fully service demand due to the uncontrollable nature of renewables. Therefore, energy balancing is performed through transactions with the utility grid. It was assumed that the price of electricity purchased from the utility grid was $0.133/kWh, and the selling price was half that of the purchased electricity. This is the average price of electricity traded in the US in 2021 [36].  
In the results for **P0**, the cost to the utility grid was lowest. However, as a great deal of capacity was required to match the energy balance, the VPP cost increased. Therefore, the overall cost increased in the results of **P0** compared to the results of the risk-based approaches **P1** and **P2**. Moreover, in the results of **P1**, with a relaxation multiplier of \(\gamma=0.5\), the total cost was minimized.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Resource** & **Demand of Commercial SEC** & **Demand of Residential SEC** \\ \hline PV1 & 0.28 & \(-\)0.03 \\ PV2 & 0.66 & \(-\)0.03 \\ PV3 & 0.60 & \(-\)0.33 \\ PV4 & 0.62 & \(-\)0.30 \\ PV5 & 0.60 & \(-\)0.32 \\ PV6 & 0.60 & \(-\)0.32 \\ PV7 & 0.59 & \(-\)0.32 \\ PV8 & 0.39 & \(-\)0.27 \\ PV9 & 0.43 & \(-\)0.26 \\ PV10 & 0.39 & \(-\)0.28 \\ Wind1 & 0.03 & 0.16 \\ Wind2 & \(-\)0.16 & \(-\)0.13 \\ Wind3 & \(-\)0.42 & \(-\)0.14 \\ Wind4 & \(-\)0.08 & 0.03 \\ Wind5 & \(-\)0.07 & \(-\)0.03 \\ Wind6 & 0.13 & \(-\)0.02 \\ Wind7 & \(-\)0.34 & \(-\)0.24 \\ Wind8 & \(-\)0.35 & \(-\)0.16 \\ Wind9 & \(-\)0.24 & \(-\)0.13 \\ Wind10 & \(-\)0.48 & \(-\)0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Pearson’s linear correlation coefficient between demand and resources.  
In all cases, as the VPP was served, the unit price was reduced as compared to the cost of purchasing power from the utility grid. Particularly, in the case of the residential SEC, about 70% of the cost was saved compared to about a 55% reduction in the commercial SEC case. Figure 5 shows the demand purchasing ratios from the VPPs and the utility grid. In the case of the residential SEC, approximately 80% of the VPP was utilized similarly in all results, as shown in Figure 4(b). However, in the case of the commercial SEC, the utilization of the VPP varied according to risk priority, as in Figure 4(a).
# Multi-Relational Graph Convolution Network for Service Recommendation in Mashup Development
## 3 Multi-Relational GCN Based Service Recommendation Model
### Problem Definition  
In this section, we formulate our problem with necessary notations. Let \(M=\{m_{1},m_{2},\ldots,m_{NM}\}\) be a set of \(NM\) mashups, and \(S=\{s_{1},s_{2},\ldots,s_{NS}\}\) be a set of \(NS\) services. \(N=NM+NS\) represents the total number of mashups and services. \(MS=\{(m_{1},s_{1}),(m_{1},s_{2}),\ldots\}\) represents the set of mashup-service pairs that have a composition relation. For each mashup and service, we also collect its textual descriptions and tags. \(MD=\{(m_{1},d_{m_{1}}),(m_{2},d_{m_{2}}),\ldots,(m_{NM},d_{m_{NM}})\}\) denotes the set of mashups and their textual descriptions, where \(d_{m}=w_{1},w_{2},\ldots\) represents the sequence of words after pre-processing the descriptions of mashup \(m\). Similarly, \(SD=\{(s_{1},d_{s_{1}}),(s_{2},d_{s_{2}}),\ldots,(s_{NS},d_{s_{NS}})\}\) denotes the set of services and their textual descriptions. \(MT=\{(m_{1},T_{1}),(m_{2},T_{2}),\ldots,(m_{NM},T_{NM})\}\) represents the set of mashups and annotated tags, where \(T_{m}=\{t_{1},t_{2},\ldots\}\) is the set of tags of each mashup \(m\). Similarly, \(ST=\{(s_{1},T_{1}),(s_{2},T_{2}),\ldots,(s_{NS},T_{NS})\}\) represents the set of services and their annotated tags.  
The problem can be formulated as follows: Given mashup-service composition relation \(MS\), textual descriptions of mashups and services \(MD\) and \(SD\), tags of mashups and services \(MT\) and \(ST\), for a mashup \(m\), we aim to recommend a list of services that are likely to be composed by \(m\). In this paper, we consider the case where the mashup is newly created by the developer, i.e., no composition record is registered and only textual descriptions and tags are available. As a concrete example, a developer tries to develop a mashup that can make music recommendations and provide explanations by using linked data and semantic web techniques. He or she can input the requirement in the form of text, and specify a set of tags such as \"_music_\", \"_recommendation_\", \"_semantics_\" to refine the requirements. The system aims to recommend suitable services that can meet the developer's requirements. In this case, _Last.fm_ service which can retrieve music resources from _lastfm_ ([[https://www.last.fm/](https://www.last.fm/)]([https://www.last.fm/](https://www.last.fm/)), accessed on 21 December 2021), and _DBpedia_ service which organizes the music metadata as a structured knowledge web can be recommended.
## 4 Research on Critical Technologies
### Modulation and Demodulation Technologies
#### 4.1.2 Coherent Optical Communication Technology  
Coherent optical communication adds the dimension of phase to the dimensions involved in carrying information compared to IMDD systems. The transmitter in coherent optical communication usually has an externally modulated structure. The receiving end, on the other hand, usually consists of a photocoupler and a balanced photodetector (BPD). The transmitter side of coherent optical communication is roughly similar, while the receiver side can be categorized into heterodyne receivers and homodyne receivers according to the relationship between the LO wavelength and the wavelength of the optical carrier wave that carries the signal.  
[PERSON], researcher from Fudan University, successfully transmitted 100 Gbit/s and 125 Gbit/s PDM-QPSK signals in an RoF system at a frequency of 22 GHz using the real time processing of an optical transport network [21] and realized error-free transmission. Researchers, including [PERSON], from Universiti Teknologi, Malaysia, designed a transceiver system using differential quadrature phase shift keying, carrier-suppressed non-nulling modulation, dual binary coding, RoF, and dense WDM techniques. A constellation diagram, an eye diagram, the BER, the optical signal-to-noise ratio requirement, and the receiver's sensitivity were analyzed [22]. The results show that a maximum data rate of 792.64 Tbps was achieved using one-channel dense wavelength division multiplexing, and the maximum transmission distance was extended to 1600 km with a BER of \(10^{-12}\). [PERSON] et al. from King Saud University successfully realized the transmission of a 6 Gbaud orthogonal phase shift keying signal by fusing RoF and RoF-FSO and measured the relationship between the BER and error vector magnitude of the signal under different densities of smoke FSO channels [23]. The results show that the visibility range can reach one hundred meters under error-free transmission.
# Planning and Scheduling with Uncertainty in the Steel Sector: A Review
## 2 Uncertainty in Scheduling: Definition and Approaches
### Proactive Scheduling
#### 2.1.1 Stochastic Programming  
Stochastic programming consists on optimization models where the main variables are represented by discrete or continuous probabilistic distributions. The most relevant example within this category is the two-stage stochastic programming. In the first stage, the main driver decisions are taken before the incorporation of any uncertainty parameter, while in the second stage the infeasibilities raised by the realization of the uncertainty can be compensated by corrective recourse actions.  
Let \(Q(x,y,\xi)\) be the optimal solution of the stochastic model of the second stage, with \(\xi\) being the vector of uncertain parameters associated with the problem. These uncertain parameters can be represented by random variables with known distributions. The first stage model can be formulated incorporating the mathematical expectation (E) of \(Q(x,y,\xi)\) to the equation expressed in (1):  
\[\text{Min }\Big{\{}c^{T}x\ +\ d^{T}y\ +\ E[Q(x,\ y,\ \xi)]\Big{\}} \tag{2}\]Let \(z\) denote the decision variables for the second-stage problem, with the random variable \(q(\xi)\) representing the corresponding coefficient vectors. The second-stage model can be defined as:  
\[\text{Min }q(\xi)^{T}z \tag{3}\]  
Subject to: \(T(\xi)x\ +\ V(\xi)y\ +\ W(\xi)z\ \leq\ h(\xi)\ |\ z\ \geq 0\), where \(T\), \(V\), \(W\) correspond to random variables representing the constraint matrixes for the second stage problem associated with the decision variables \(x\), \(y\), \(z\) respectively. The maximum threshold for these constraints is established by the random variable \(h\).
# PTA-Sync: Packet-Train-Aided Time Synchronization for Underwater Acoustic Applications
## 2 Synchronization Protocol for UANets
### PTA-Sync Protocol
Figure 1:0  
The mean clock skew of UN, \(\overline{a}\), can be determined by obtaining the average of \(\hat{a}(j)\) as follows:  
Figure 1:1  
The clock offset of UN, \(\hat{b}\), can be determined using \(\overline{a}\) and Equation (1) as follows:  
Figure 1:2  
The entire operation procedure of PTA-Sync can be expressed as the following pseudo code (Algorithm 1):  
```
1:Get\(P_{UN}(1)\)
2:Get\(RT(j)\) and \(\mathbf{V}_{UN}(j)\), \(1\leq j\leq N\)
3:Get\(P_{CS}(j)\) and t(\(j\)) by parsing \(B_{j}\), \(1\leq j\leq N\)
4:Set\(j=1\),
5:while\(j\leq N\)do
6:if\(j>1\)
7:\(\Delta RT(j)=RT(j)-RT(j-1)\)
8:\(\Delta t(j)=t(j)-t(j-1)\)
9:\(\mathbf{P}_{UN}(j)=\mathbf{P}_{UN}(j-1)+\mathbf{V}_{UN}(j-1)\cdot\Delta t(j)\)
10:\(\hat{D}(j)=|\mathbf{P}_{CS}(j)-\mathbf{P}_{UN}(j)|/c\) // c is the underwater acoustic speed
11:\(\Delta D(j)=D(j)-D(j-1)\)
12:\(\hat{a}(j)=\frac{\Delta RT(j)}{\Delta t(j)+\Delta D(j)}\)
13:endif
14:\(j++\)
15:endwhile
16:\(\overline{a}=\text{mean}\{\hat{a}\}\)
17:\(\hat{b}=RT(1)-\overline{a}\cdot[t(1)+D(1)]\)
```  
**Algorithm 1**PTA-Sync
# Wind Speed Measurement via Visual Recognition of Wind-Induced Waving Light Stick Target
## 2 Theoretical Modeling  
### Imaging Formulation  
To measure horizontal wind speed, the proposed vision-based measurement system relies on (1) the corresponding (angular) displacement of the real-world light-stick target in the camera image plane and (2) the coefficient relating this displacement to wind speed magnitude. To achieve the first, the pinhole camera model is used to model the target such that the captured target's displacement in the image plane can be mapped to the real-world angular displacement of the target. Then, the target is calibrated in a wind tunnel experiment to determine the coefficient relating its angular displacement to the wind speed magnitude. In this way, the prevailing horizontal wind speed can be determined by the product of the angular displacement of the target as obtained by the camera, and the wind speed-displacement coefficient as obtained from the calibration experiment.
## 4 The Dataset Usage
### Data Extraction and Feature Selection
#### 4.1.5 Comparison with Existing Studies  
Table 5 presents a comparative analysis of various machine learning-based vulnerability detection approaches in Android applications, highlighting key aspects such as dataset size, feature selection, and overall performance. While prior studies have achieved promising results, most of them relied on relatively small datasets, limited feature sets, or specific code metrics, potentially restricting their generalizability. In contrast, our study utilizes a significantly larger dataset comprising 426,246 code snippets with an equal distribution of vulnerable and non-vulnerable samples. Furthermore, our approach, employing SVM and Logistic Regression classifiers, outperforms previous methods, achieving a detection accuracy of 98.71%. These results highlight the effectiveness of our methodology in identi  
Figure 4: Comparison of Machine Learning models for vulnerability detection.
## 3 Using ML for Water Activities
### Water Quality and Streamflow Management
#### 3.2.2 Water Management and Predictions  
* **Water management simulations[255]**: Rule-based reservoir management models (RMM) were augmented with ML, specifically long short-term memory (LSTM), for reservoir simulations. These hybrid models improved accuracy and forecasting in large-scale water management.
* **ML in inland water science[256]**: This chapter explored the integration of ML with limnological knowledge, enhancing the accuracy and interpretability of models in inland water science, particularly in predicting water quality and quantity.
* **Waste separation for a circular economy[257]**: To combat environmental pollution, this study proposed waste separation techniques involving sensor-equipped con veyor belts. This approach contributed to recycling and organic manure production, promoting a circular economy.
* **Water demand prediction in Brazil**[258]: A novel hybrid model, combining support vector regression (SVR) and artificial neural networks (ANNs), excelled in predicting water demand for reservoirs supplying the Metropolitan Region of Salvador, Brazil. This advancement enhanced water resource management.
# Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks
## 3 Proposed Architecture
### ResNet  
At each time step, a 3D feature map is followed by a residual network (ResNet, [37]). Based on the needs of lipreading architecture design, we use the 50-layer ResNet version, which was proposed for ImageNet [47]. Its main innovation is residual learning, so that a deeper convolutional network can be trained. The most critical solution to achieve residual learning is short connections. What we need to emphasize is that we did not use its pre-trained weights on ImageNet [47], as they optimize completely different tasks and evaluate other protocols. The weight initialization we adopt is standard random initialization, and the random parameters obey Gaussian distribution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layers** & **Output Size** & **Kernel/Stride/Pad** \\ \hline Input & N \(\times\) 112 \(\times\) 112 \(\times\) 3 & \\ batchnorm & N \(\times\) 112 \(\times\) 112 \(\times\) 3 & \\ \hline
3D-Conv & N \(\times\) 112 \(\times\) 112 \(\times\) 32 & 3 \(\times\) 3 \(\times\) 3/1, 1, 1/1, 1, 1 \\ batchnorm/relu & N \(\times\) 112 \(\times\) 112 \(\times\) 32 & \\
3D-Pool & N \(\times\) 56 \(\times\) 56 \(\times\) 32 & 1 \(\times\) 2 \(\times\) 2/1, 2, 2 \\ \hline ResNet50 [37] & N \(\times\) 2048 & \\ \hline TCN(1) & N \(\times\) 512 & 5/3/2 \\ TCN(2) & N \(\times\) 512 & 5/3/2 \\ \hline Linear & N \(\times\) L & \\ Softmax & N \(\times\) L & \\ \hline CTC loss & 1 & \\ \hline \hline \end{tabular} N is the number of frames in video; L is the number of labels.  
\end{table}
Table 1: The detailed of the proposed lipreading architecture.  
Figure 3: Schematic diagram of convolution structure, (**a**) 2D convolution operation, (**b**) 3D convolution operation.
# Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means
## 4 Experimental Results and Discussion
### Qualitative Comparison  
We compared our method with other state-of-the-art detection methods, including the EADL method [5] and the automatic band selection method [6], and detection carried out by human inspectors (the defects are marked by experienced factory workers). In each process, we used the parameters suggested in the original papers and followed the instructions provided in the authors' code distributions. The comparison results are shown in Figure 17, where the input original seven texture types' fabric defect images are given in Figure 17a. From top to bottom, they are plain fabric, twill fabric, star-patterned fabric, box-patterned fabric, dot-patterned fabric, statistical-texture fabric, and striped fabric respectively. The columns in Figure 17b-e are the defection results by our method, the ground-truth images from segmentation carried out by human inspectors, the Tsai [6], and the EADL method. It is found that the detection results using the EADL method [5] are located accuracy than using the automatic band selection method [6]. Figure 17b is the detection result using our method, it can be seen that the detection results are consistent with those carried out by human inspectors and outperform other methods.  
Figure 16: The computational time complexity curve line.  
Figure 15: Statistical-texture defect of detection results.
## 3 The Proposed Scheme for WSP Minimization
### Spectrum and Power Allocation Based on Newton's Method for a Matched PL-SL PAIR
Specifically, if we initialized an \(x_{0}\in(0,1)\) and obtained \(x_{k}\) after \(k\) iterations, a more approximate \(x_{k+1}\) is given by using [PERSON]'s method as outlined below:  
\[p_{\rho}^{low}=max\Big{\{}p_{min},\Big{(}2^{Q_{\rho}/\theta}-1\Big{)}n_{0}/g_{ \rho}\Big{\}}. \tag{8}\]1  
When the stopping criterion holds, i.e., \(|x_{k+1}-x_{k}|\leq\varepsilon\), \(x_{k+1}\) is obtained as an approximation for \(x^{*}\), where \(\varepsilon\) is a predefined step tolerance. * **Case 2** If \(p_{\pi}^{law}=p_{min}\) and \(p_{s}^{law}=[2^{Q_{s}/(1-\theta)}-1]n_{0}/g_{s}\), defining \[p_{\rho}^{low}=max\Big{\{}p_{min},\Big{(}2^{Q_{\rho}/\theta}-1\Big{)}n_{0}/g_{ \rho}\Big{\}}. \tag{8}\]2 (19)  
we can rewrite the objective function in Equation (10) as follows:  
\[p_{\rho}^{low}=max\Big{\{}p_{min},\Big{(}2^{Q_{\rho}/\theta}-1\Big{)}n_{0}/g_{ \rho}\Big{\}}. \tag{8}\]3  
The first and second derivatives of \(f_{3}(x)\) are \(f_{3}^{\prime}(x)=\mu p_{min}\) and \(f_{3}^{\prime\prime}(x)=0\). Similarly, \(w^{\prime\prime}(x)>0\). \(w(x)\) is still convex. In this case, \(w^{\prime}(0^{+})=\mu p_{min}+\frac{\
u n_{0}}{g_{s}}[2^{Q_{s}}(Q_{s}\ln 2-1)+1]\) and \(w^{\prime}(1^{-})=+\infty\). If \(w^{\prime}(0^{+})<0\), the numerical solution of \(x^{*}\) can be found using [PERSON]'s method described in case 1. If \(w^{\prime}(0^{+})\geq 0\), \(w^{\prime}(x)>0\) holds within \((0,1)\). In order to minimize (20), \(\theta\) should take its minimum. Thus, \(\theta^{*}=\Delta\theta\). * **Case 3** If \(p_{\pi}^{law}=(2^{Q_{\pi}/\theta}-1)n_{0}/g_{\pi}\) and \(p_{s}^{law}=p_{min}\), defining \[p_{\rho}^{low}=max\Big{\{}p_{min},\Big{(}2^{Q_{\rho}/\theta}-1\Big{)}n_{0}/g_{ \rho}\Big{\}}. \tag{8}\]4 (21)  
the objective function in Equation (10) can be rewritten as follows:  
\[p_{\rho}^{low}=max\Big{\{}p_{min},\Big{(}2^{Q_{\rho}/\theta}-1\Big{)}n_{0}/g_{ \rho}\Big{\}}. \tag{8}\]5  
The first and second derivatives of \(f_{4}(x)\) are given by \(f_{4}^{\prime}(x)=-\
u p_{min}\) and \(f_{4}^{\prime\prime}(x)=0\). \(w^{\prime\prime}(x)>0\) also holds. In this case, \(w^{\prime}(0^{+})=-\infty\) and \(w^{\prime}(1^{-})=-\
u p_{min}+\frac{\mu n_{0}}{g_{\pi}}[2^{Q_{\pi}}(1-Q_{\pi} \ln 2)-1]\). If \(w^{\prime}(1^When \(p_{\mu}^{low}=p_{s}^{low}=p_{min}\), the objective function in Equation (10) is rewritten as presented below:  
\[p_{\rho}^{low}=max\Big{\{}p_{min},\Big{(}2^{Q_{\rho}/\theta}-1\Big{)}n_{0}/g_{ \rho}\Big{\}}. \tag{8}\]6  
There are three possible optimal solutions for \(\theta\) as outlined below:  
If \(\mu>\
u\), \(\theta^{*}=\Delta\theta\);  
If \(\mu<\
u\), \(\theta^{*}=1-\Delta\theta\);  
If \(\mu=\
u\), then \(\theta^{*}\) can take any value within the spectrum-sharing factor levels. In summary, we discussed all possible optimal solutions regarding \(\theta\). Then, the corresponding optimal solutions for \(p_{\mu}\) and \(p_{s}\) can be obtained through Equations (8) and (9).
## 5 Experimental Results
### Experimental Results for CBA Algorithm
#### 5.1.2 Profit and Energy Consumption Results at Varied Arrival Rates  
Figure 5 displays the total value earned (profit) and the energy consumed by both HBA and CBA at the different arrival rates. At the high arrival rate, jobs arrived very frequently while at the low arrival rate the jobs arrived much less frequently. An interesting initial observation of the figure is that the value earned by HBA decreased as the arrival rate increased while the value earned by CBA increased instead. This was likely due to the poor estimation of value by the HBA algorithm: high schedule delays of jobs which arrived earlier but were delayed in preference of other jobs may still have lain at the peak of the value curves, earning less value when the computation time resulted in a value much further down the curve than the heuristic can predict. For the CBA algorithm, the increased arrival rate of jobs increased the total value as it had more options for allocation. It also tended to execute more jobs simultaneously as jobs with a very low schedule delay were often allocated to far fewer cores, likely due to significantly lower energy consumption compared to a relatively minor drop in value.  
At the low arrival rate, the value earned was slightly higher for CBA than HBA while the energy consumed was slightly higher for the latter approach. However, this difference did not seem to be very significant at \(<\)1% change in both cases. We believe that this change would be more significant for  
Figure 4: Value/energy for different confidence values.  
experiments over a longer period of time for a number of reasons: the overhead of initial exploration by CBA would be a smaller fraction of the total time and the algorithm would explore less frequently as jobs were executed more times. This effect occurred at all arrival rates but was more prevalent at the low arrival rate as the initial exploration took much more time.  
In general, HBA resulted in higher profit and lower energy consumption when compared to CBA. On an average, HBA optimized profit and energy consumption by 40% and 20%, respectively, compared to CBA.