esa-sceva/satcom-chunk-collection huggingface dataset:
# A Theoretical Framework to Determine RHP Zero Dynamics in Sequential Interacting Sub-Systems
## 1 Introduction  
From the control engineering point of view, the closed-loop performance in systems exhibiting NMP behavior presents serious limitations in achievable bandwidth. In this context, it is advantageous to know a priori the dynamics of the system at hand, before specifying any requirements of the closed-loop performance, e.g., settling time and overshoot. Generically, the research objectives for this topic lead towards: (i) developing control strategies able to deal with the NMP challenges [1; 2; 3; 4] and (ii) analyzing the behavior prior to the control design stage [5; 6; 7].  
This paper proposes a generally valid mathematical framework to determine the system dynamics for sequential interacting sub-systems. The study case at hand is a generalization for a particular class of systems composed of several sub-systems in a train architecture. This specific characteristic appears in supply-chain management [8], water distribution networks [9; 10], distillation columns [11], and other complex interactive processes [12; 13]. This knowledge is also important when deciding the coupling of the sub-system in a minimal information structure for distributed control [14]. The study that is presented in this paper can be regarded as a system analysis that must be performed prior to controller design, since NMP systems have different dynamics and different responses than MP ones.  
The motivation of this work is to provide a sound analysis of the system dynamics, for this particular class of interacting sub-systems, that can be used prior to the control design. This is done without using identification techniques, if the coupling degree between sub-systems is known.  
The main contribution of this paper is based on analysis made on a laboratory setup available at Ghent University and can be summarized as follows [15]:  
* it delivers a generic polynomial expression to characterize the information about the presence of transmission zeros of a MIMO system, employing the Smith-McMillan decomposition method and
* it proposes a computationally-efficient method to evaluate the solution of the polynomial by means of Descartes' rule of signs.  
Notation: \(\mathbb{R}[s]\) is the set of polynomials with coefficients in \(\mathbb{R}\); \(\mathbb{R}(s)\) is the set of _rational functions_; \(\mathbb{R}[s]^{m\times n}\) denotes the set of \(m\times n\) matrices, whose entries are polynomials; and \(a|b\) stands for \(a\) divides into \(b\).
## 5 Conclusions  
This study proposed a TOPSIS-AHP and fuzzy comprehensive evaluation method to evaluate the EMC of vehicular wireless communication systems. Using high-resolution satellite remote sensing images, urban geographic data were collected to build an accurate city model. A communication link model was established, considering key factors like building occlusion and diffraction loss for urban radio propagation analysis. Key EMC performance indicators were identified in terms of three dimensions: communication quality, anti-interference capability, and transmission reliability. A hierarchical evaluation model (target, criterion, and sub-criterion layers) was developed, with weights determined using TOPSIS (quantitative) and AHP (qualitative) methods. The system's EMC performance was then evaluated using FCE. The findings provide critical insights for optimizing vehicular communication systems and improving urban intelligent transportation efficiency. Overall, this study proposes an improved practical framework for performance evaluation and enhancement of wireless communications in complex urban environments, thereby providing sustainable support for vehicular R&D and urban transportation planning.  
Conceptualization, H.L., G.Z. and P.C.; methodology, G.Z.; software, G.Z. and F.W.; validation, S.Z. and Y.Q.; data curation, Q.X. and S.Z.; writing--original draft preparation, G.Z.; writing--review and editing, H.L. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Defense Research Project, grant number JZX7X201901 JY0048.  
Figure 12: The relationship between the scores and BERs of each evaluation scheme.
## 1 Introduction
Reference [15] designs a convex rank unconstrained semi-finite programming (RUSDP) algorithm to obtain a unified solution for TDOA-based near-field and far-field localization by assuming that the signal propagation velocity is unknown. However, traditional AOA localization has certain shortcomings [16]. Because of multipath transmission and other effects, AOA localization makes it difficult to lateralize coherent signals. In addition, the measurement accuracy of a single-method interference localization algorithm is often insufficient and has a large error. When more base stations are monitored, solving the position equation is more computationally intensive, and the equation is often nonlinear, requiring an optimal solution to be found. However, it is difficult to find accurate results with traditional methods. Therefore, we propose a joint AOA and TDOA interference localization scheme, where the direction measurement uses decoherence to solve the shortcoming of AOA being unable to measure coherent signals, and the least squares method is used to solve the system of position equations. Numerical simulations show that the scheme can achieve accurate interference localization and can be applied to various interference signals. Moreover, the physical verification further verifies the usability and reliability of the proposed scheme. The main contributions of this paper are as follows:  
* A decoherence-based AOA-solving algorithm is proposed, which effectively addresses the problem that coherent signals cannot be directionally measured and is applicable to narrowband interference. * A TDOA solving algorithm based on the least squares method is proposed, and the simulation results show that this solving method is better than the maximum likelihood estimation algorithm. * A joint AOA and TDOA localization algorithm is proposed, and the simulation results show that the joint localization algorithm is better than the single localization algorithm with higher accuracy and better robustness. This paper is structured as follows. Section 1 presents a literature review of the related field, Section 2 presents the principle of the proposed algorithm, Section 3 analyzes the results of the proposed algorithm, Section 4 analyzes the semi-physical simulation results of the proposed algorithm and Section 5 presents the conclusion.
## 2 The Intelligent Fault Diagnosis System Model
### Network Model of Intelligent Fault Diagnosis System  
The network of intelligent fault diagnosis system supporting offloading contains \(M\) MEC servers and \(K\) intelligent terminals. Let \(\mathcal{M}=\{1,\cdots,M\}\) and \(\mathcal{K}=\{1,\cdots,K\}\) be the index sets of the MEC servers and the intelligent terminals, respectively. Part of the diagnostic data will be offloaded to the MEC server, assuming that the MEC server has more computing power than the intelligent terminal. The system time is divided into consecutive time frames with equal time period \(\tau_{0}\) and the time indexed by \(t\in T=\{0,1,\cdots\}\). The channel state information between the \(m\)-th MEC server and the \(k\)-th intelligent terminal is denoted as \(h_{m,k}\), and the task size at intelligent terminal \(k\) is marked as \(C_{k}\). The channel state  
Figure 3: The framework of the intelligent mechanical fault diagnosis system in this paper, which contains three parts: intelligent terminal, agent server, and MEC servers.  
information of the MEC network \(\{h_{m,k}(t)\}\) and the task arrival \(C_{k}(t)\) at each intelligent terminal change for each time interval \(t\in T\). In order to save the energy consumption of intelligent terminals and MEC servers and reduce the task processing latency, the central agent node needs to determine the task ratio of local execution content size and offloading content size, as well as the power allocation ratio of local task processing and data transmission. The power splitting of the MEC server among multiple smart terminals should be determined if one MEC server is selected to help handle tasks from multiple intelligent terminals. The communication model and the computational model are described in detail below.
## 3 Our Contribution
### UWSN Channel Model
#### 3.1.3 Channel Gain Characterization
Thus,  
\[\overline{G}=\sum_{\mathrm{P}}\mathrm{G}_{\mathrm{P}}\overline{h}_{\mathrm{P} }^{2}\mathrm{e}^{\frac{\mathrm{E}^{2}}{\overline{\gamma}}\sigma_{\mathrm{l}_{ \mathrm{P}}}^{2}/2} \tag{56}\]  
\[=\sum_{\mathrm{P}}\mathrm{G}_{\mathrm{P}}^{2}\overline{h}_{\mathrm{P}}^{4} \mathrm{e}^{\frac{\mathrm{E}^{2}}{\overline{\gamma}}\sigma_{\mathrm{l}_{ \mathrm{P}}}^{2}}\left(\mathrm{e}^{\frac{\mathrm{E}^{2}}{\overline{\gamma}} \sigma_{\mathrm{l}_{\mathrm{P}}}^{2}}-1\right) \tag{57}\]  
For very small zero-mean Gaussian path length displacements, the gain is (in dB),  
\[\mathrm{g}(\mathrm{t})=\overline{g}+\mathrm{\Delta}\mathrm{g}(\mathrm{t}) \tag{58}\]  
where  
\[\overline{g}=10\left[2\log_{10}\overline{G}-\frac{1}{2}\log_{10}\left( \overline{G}^{2}+\sigma_{\mathrm{G}}^{2}\right)\right] \tag{59}\]  
In (58), \(\mathrm{\Delta}\mathrm{g}(\mathrm{t})\) refers the zero-mean Gaussian with variance  
\[\mathrm{\widetilde{G}}(\mathrm{t})=\frac{1}{\mathrm{B}}\int_{\mathrm{f}_{0}}^{ \mathrm{f}_{0}+\mathrm{B}}\big{|}\mathrm{H}(\mathrm{f},\mathrm{t})\big{|}^{2 }\mathrm{d}\mathrm{f} \tag{51}\]0  
The gain model, as depicted in (58), refers to a fixed channel geometry characterizing the nominal attenuation at the distance \(\mathrm{d}=\overline{l}_{0}\). However, due to movement over time, scale \(\overline{g}\) varies accordingly. It is expected that \(\overline{g}\) maintains a log-distance relationship, given as (58):  
\[\mathrm{\widetilde{G}}(\mathrm{t})=\frac{1}{\mathrm{B}}\int_{\mathrm{f}_{0}}^{ \mathrm{f}_{0}+\mathrm{B}}\big{|}\mathrm{H}(\mathrm{f},\mathrm{t})\big{|}^{2 }\mathrm{d}\mathrm{f} \tag{51}\]1  
where \(\mathrm{d}_{\mathrm{ref}}\) states a reference distance taken as 1 m. The other factors, \(\mathrm{g}_{0}\) and the path loss exponent \(\mathrm{k}_{0}\), can be calculated from averages of the gains retrieved at changing distances. The relationship between gain and distance is [52]  
\[\mathrm{\widetilde{G}}(\mathrm{t})=\frac{1}{\mathrm{B}}\int_{\mathrm{f}_{0}}^{ \mathrm{f}_{0}+\mathrm{B}}\big{|}\mathrm{H}(\mathrm{f},\mathrm{t})\big{|}^{2 }\mathrm{d}\mathrm{f} \tag{51}\]2  
Our proposed acoustic channel model addresses the multipath transmission, small and large-scale deviations, rough surfaces and resulting topology changes, Doppler variations,etc. This paper proposes a novel multipath transmission model for UWSN. The derived channel model has been used in this paper to model a multipath transmission where an enhanced random linear network coding (RLNC) scheme has been developed to perform data transmission. The discussion of the proposed RLNC algorithm for multipath transmission over UWSN is presented in following sections.
# Microalgal Growth in Aquaculture Effluent: Coupling Biomass Valorisation with Nutrients Removal
## 2 Materials and Methods
### Aquaculture Wastewater  
The wastewater from a trout farm aquaculture facility was characterised (Table 1) and used as a culture medium for the cultivation of microalgae. This effluent is discharged in the public wastewater collector system and subsequently treated in wastewater treatment plants as urban effluent.  
A multi-parameter analyser (Consort's C6010, Brussels, Belgium) was used to measure temperature, pH and conductivity. Turbidity was determined with a turbidimeter (Hanna Instruments HI88703, RI, Smithfield, VA, USA). The effluent composition in terms of different forms of carbon (DIC--dissolved inorganic carbon; DOC--dissolved organic carbon; TDC--total dissolved carbon) and nitrogen (as TN) were measured using a total organic carbon analyser. The Brucine method, used to quantify nitrate-nitrogen (N-NO\({}_{3}\)), is based on the reaction between brucine in an acidic medium, resulting in a yellow colouration whose absorbance was measured at 410 nm on a UV/vis spectrophotometer (Genesys 10 UV, Thermo Scientific, Waltham, MA, USA) [24]. Phosphate-phosphorus quantification (P-PO\({}_{4}\)) was performed by a reaction with ammonia molybdate, resulting in blue colouration. Then, the absorbance at 820 nm was determined in a UV/vis spectrophotometer (Genesys 10 UV, Thermo Scientific, Waltham, MA, USA) [25]. COD was quantified by the closed reflux method, which is based on the oxidation of organic compounds by potassium dichromate, in an acidic medium, at boiling temperature [26].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Values** & **Unit** \\ \hline pH & 7.53 \(\pm\) 0.01 & - \\ Conductivity & 1129 \(\pm\) 1 & \(\upmu\)S cm\({}^{-1}\) \\ Turbidity & 0.655 \(\pm\) 0.007 & NTU \\ TDC & 14.62 \(\pm\) 0.08 & mg C L\({}^{-1}\) \\ DOC & 5.86 \(\pm\) 0.43 & mg C L\({}^{-1}\) \\ DIC & 8.76 \(\pm\) 0.17 & mg C L\({}^{-1}\) \\ TN & 20.23 \(\pm\) 0.54 & mg N L\({}^{-1}\) \\ Nitrate-nitrogen (NO\({}_{3}\)-N) & 10.35 \(\pm\) 0.22 & mg N L\({}^{-1}\) \\ Phosphate-phosphorus (PO\({}_{4}\)-P) & 2.22 \(\pm\) 0.01 & mg P L\({}^{-1}\) \\ COD & 10.1 \(\pm\) 0.6 & mg O\({}_{2}\) L\({}^{-1}\) \\ \hline \hline \end{tabular} COD—chemical oxygen demand; DIC—dissolved inorganic carbon; DOC—dissolved organic carbon; TDC—total dissolved carbon; TN—total nitrogen; NTU—nephometric turbidity unit.  
\end{table}
Table 1: Characterisation of the aquaculture effluent used in this study.
# Enhancing Propaganda Detection in Arabic News Context Through Multi-Task Learning
## 3 Methodology  
This section presents the framework of the MTL model proposed in the current study. The model integrated seven distinct task-weighting schemes to optimize the learning process across three interrelated tasks, propaganda detection (primary task), sentiment analysis, and emotion detection (auxiliary tasks), with the aim of improving propaganda detection. Further, two models, PSMTL and PEMTL, were developed. Although the primary focus was propaganda detection, the inclusion of auxiliary tasks, sentiment analysis, and emotion detection enhanced the model's understanding of textual data, consequently improving its primary task performance. Figure 1 provides a high-level workflow for the proposed MTL model. The workflow was divided into three main parts: the input layer, the shared layer, and the task-specific layer. Sections 3.2-3.5 explain each part in detail.  
The parallel architecture, in which all tasks shared a backbone model but had separate output layers, was the proposed MTL models' core architecture. The backbone model involved fine-tuning the AraBERT model [46], which encoded text news and labels as hidden representations. The best-performing pretrained model presented in [22] motivated the selection of this model. Although the Hugging-Face Transformers library, designed for training BERT-based models, currently supports single-task models, it does not natively accommodate modular task heads. To address this limitation, a hard-parameter sharing strategy was implemented. In this approach, the parameters of the shared layers were shared between all related tasks. Each task had its own output layers, referred to as task heads. This approach enabled the model to learn a shared feature representation, which effectively supported the simultaneous modeling of all tasks [9]. As Figure 1 illustrates, the proposed model followed a pipeline consisting of three components: input, shared, and task-specific layers. Algorithm 1 shows the main design steps of the proposed MTL for Arabic propaganda detection. The following subsections provide a comprehensive description of each component and the mathematical formulation of the problem.  
```
0: D: A raw dataset containing Arabic documents, Tasks: A set {Propaganda Detection, Sentiment Detection, Emotion Detection}, w: Initial weights for task losses Output: Preds: Predictions for propaganda labels
1: Preds \(\leftarrow\)\(\emptyset\)
2:for all d \(\in\) D do
3: cleaned_d \(\leftarrow\) preprocess(d) // Cleaning and normalization
4: tokens_d \(\leftarrow\) tokenize(cleaned_d) // Tokenization via AraBERT tokenizer
5: input_ids, attention_masks, token_type_ids \(\leftarrow\) tokens_d
6:for task \(\in\) Tasks do
7: Ytask \(\leftarrow\) generate_labels(d, task) // Propaganda, Sentiment, Emotion labels
```  
**Algorithm 1**The Proposed MTL for Arabic Propaganda Detection  
Figure 1: The workflow of the proposed MTL framework.
# Data-Driven Damage Classification Using Guided Waves in Pipe Structures
## 4 Experimental Investigations
### Dataset Preparation of Guided Wave Signals  
A total of 520 guided wave signals were obtained following the above process. All signals can be divided into five categories, as listed in Table 1. They were associated with five cases of no damage, corrosion, dent led by impact, groove and pipe end, respectively. To reduce the effect of measurement noise, the received signals were averaged over 50 repetitions of the experiment within 1 min.  
Ten signals for each type were plotted in Figure 5, in which the available signals were intercepted as 100 \(\mu\)s length from their original test signals at the sampling frequency of 1 MHz. For every damage type, the signals were obtained from different sizes of damage and different lengths of pipes to simulate as many damage states as possible. Furthermore, they were measured at different times of day, which means that the experiments were conducted under different environmental conditions. This increased the robustness of damage classification. Moreover, the signals were normalized within the full range; therefore, the amplitudes of intercepted signals were very different in Figure 5.  
It can be seen from the time-domain signals of guided waves that the wave packets have very complicated characteristics under different damage types and degrees. The reason is the multiple mode conversions and interaction between guided waves and different damages, which increase the difficulty of signal interpretation. By using a single signal feature, it is difficult to distinguish the signals under different working conditions. Therefore, this paper uses the neural network to further process the signal. For conventional signal analysis, some features are usually extracted for further analysis, such as the amplitude and location of the peak in the time domain, the time-of-flight, the peak frequency, the average frequency, and other statistic features. While for the neural network, the input dataset is the original signals without processing, and the time-consuming is not seriously affected during network training and testing.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Signal Type** & **Number** & **Typical Picture** \\ \hline No damage & 70 & \\ Corrosion & 70 & \\ Dent led by impact & 100 & \\ Groove & 80 & \\ Pipe end & 200 & \\ \hline \hline \end{tabular} Note: The yellow circle in the typical picture of groove stands for the groove position.  
\end{table}
Table 1: Damage types and number of signals.
## 3 Design of the FBG Demodulation System  
### Optical System  
The proposed FBG demodulation system as shown in Figure 1. The laser in the C + L band emitted by the ASE light source (BBS, 1528-1603 nm) passes through the annulus into the FBG sensor to be measured. The light reflected by it is delivered to the LPG used for demodulation. In this case, the overlapping area between the reflected spectrum of the FBG sensor and the spectrum of the LPG sensor is considered the transmitted intensity. According to the edge filtering method, the change in transmitted intensity due to the change in the central wavelength of the FBG sensor caused by external perturbations is the key to dynamically interrogating the central wavelength of the FBG sensor. The transmitted intensity is captured by the optical power meter and fed back to the sensor demodulation neural network, which completes the absolute interrogation of the central wavelength of the FBG sensor in the case of horizontal stretching.
# Improving the Maritime Traffic Evaluation with the Course and Speed Model
## 3 Maritime Traffic Flow Model
Variables, such as tides, wave height, and water depth, can significantly influence a ship's motion. Other contributing factors include weather conditions, such as wind and fog, sea route structure, support systems, obstructions, qualifications, and the training of ship operators, their health, and the number of crew members. Safety encompasses the subjective risks perceived by ship operators due to interactions related to maritime traffic or traffic volume. Despite the uninterrupted flow, maritime  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Object** & **Target** & & **Input Data** & **Output** \\ \hline \multirow{2}{*}{ES} & \multirow{2}{*}{Mariner’s Stress} & Fairway, & \(\bullet\) & Mariner’s survey & \multirow{2}{*}{ES\({}_{\text{A}}\)} \\  & & Waterway & \(\bullet\) & Topographical condition & (0–1000) \\ \hline \multirow{4}{*}{PARK} & \multirow{2}{*}{Mariner’s Stress} & Fairway, & \(\bullet\) & External condition & \multirow{2}{*}{PARK} \\  & & Waterway, & \(\bullet\) & Mariner’s survey & \multirow{2}{*}{Value} \\  & & Waterway & \(\bullet\) & Topographical condition & (0–7) \\ \hline \multirow{4}{*}{BC} & \multirow{4}{*}{Collision Risk (Potential)} & Fairway, & \(\bullet\) & Ship collision avoidance & \multirow{4}{*}{BC} \\  & & & support model & & \\ \  
traffic can still experience collision scenarios, such as crossings, encounters, and overtaking, which are not common in road traffic. Additionally, faults or failures in vessels can lead to substantial delays and even paralyze ports. Other factors include the cargo volume of nearby ports and peak times. Spatiality and continuity focus on the ship's output, allowing for the assessment of the smoothness of maritime traffic flow from a macroscopic perspective. However, specificity and safety offer a microscopic perspective, assessing the risks of maritime traffic based on factors influencing ship behavior. These indices should complement each other to provide a comprehensive understanding of maritime traffic. (Figure 1).
# Parallel-Coupled-Line Bandpass Filter with Notch for Ultra-Wideband (UWB) Applications
###### Abstract  
A compact parallel-coupled-line microstrip bandpass filter with a very wideband passband and a narrow notched band is demonstrated in this paper. The presented bandpass filter was constructed from one section of three parallel-coupled-lines which has a length of a quarter wavelength at a midband frequency of 6.9 GHz. For the purpose of increasing the filter selectivity, the middle line is extended by a length of a quarter-wavelength and connected at one end to the ground plane. Therefore, a transmission zero was generated at each side of the passband which effectively improved the filter performance. In addition, a narrow notched band at a precise frequency inside the proposed passband was exhibited by placing a stepped-impedance resonator near the parallel-coupled-line for blocking the unwanted radio signal. The proposed filter was designed, simulated, fabricated, and measured. The fabricated filter has a very compact circuit size and its measured response shows an excellent agreement with the simulated results.  
microstrip filter; parallel-coupled-line filter; UWB filter; notched band; bandpass filter +
Footnote †: journal: Applied Physics Letters B  
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