esa-sceva/satcom-chunk-collection huggingface dataset:
# Software Operation Anomalies Diagnosis Method Based on a Multiple Time Windows Mixed Model
## 4 Experiments Design
### Experimental Design
#### 4.3.2 Comparison of Anomaly Detection Algorithms  
This experiment compares the performance of the proposed anomaly detection integrated algorithm based on a time window and 10 basic anomaly detection algorithms. Firstly, 10 basic detectors are used to train and detect each dataset to obtain anomaly scores. After obtaining the integrated algorithms through the model optimization algorithm introduced in Section 3, their results are fused from the perspective of accuracy and sensitivity and compared with the results of the basic detector. The datasets used in this experiment are the seven datasets introduced in Section 4.1. The data in this experiment are the average values obtained from five replicates.
## 3 Results and Discussion
### The Interaction Mechanism of ITEC-Al\({}^{3+}\)  
A Job's plot was drawn according to our previous method to obtain the complex ratio of ITEC-Al\({}^{3+}\)[47]. From Figure 4a, the fluorescent spectrum vertex reached the mole fraction of 0.7, which indicated a 1:2 stoichiometry between ITEC and Al\({}^{3+}\)[48; 49]. To further investigate interaction mechanism, the \({}^{1}\)H NMR spectra of ITEC and ITEC-Al\({}^{3+}\) were measured and showed in Figure 4b. The integral intensities of peaks from a to c at 7.71 ppm improve continually with the increase of Al\({}^{3+}\) concentrations, and the same phenomena is also found at 6.87, which indicates that the proton signal of HC=N will change with the addition of Al\({}^{3+}\). The coordination of Al\({}^{3+}\) with the N on amide bond can availably reduce their electron-donating ability, so the photoinduced electron transfer (PET) process from the N to the triphenylamine is suppressed, which leads to a large increase of fluorescence intensity. Based on the above studying, a possible sensing mechanism of ITEC toward Al\({}^{3+}\) has been proposed as shown in Figure 4c. At the same time, there was obvious blue fluorescence after adding Al\({}^{3+}\). In addition, a 2:1 stoichiometry between ITEC and Fe\({}^{3+}\) was found. The binding mode of ITEC and Fe\({}^{3+}\) was explored and detailed in Figure S6-S8 are supporting information.
# Anti-Cancer Drug Screening with Microfluidic Technology
## 2 Monodrug Screening on a Microscale Sample  
Based on the microfluidic design, different techniques are employed for monodrug screening. Among them, polymer-based and paper-based microfluidic platforms have been well studied.  
Paper-based microfluidics is a branch of microfluidics consisting of porous and hydrophilic materials like paper that wick fluid by capillary force. Compared to polymer-based microfluidics, the fabrication method of these devices is more straightforward and cost-effective. Most importantly, these platforms can be easily operated without a pump and other bulky, external accessories, making them ideal for point-of-care applications. Accordingly, paper-based microfluidic platforms have been widely used to develop inexpensive, portable, and quick diagnostic medical devices, mainly referred to as microfluidic paper-based analytical devices (microPADs) [29]. However, the absence of continuous medium perfusion limits the wide application of paper devices for cell culture and drug screening applications. Nevertheless, a novel wick-like paper-based microfluidic device has been recently developed to overcome this drawback [30]. This platform is used for medium self-driven perfusion during the three-dimensional cell culture and further anticancer drug screening.  
Polymer-based devices are mainly built from polydimethylsiloxane (PDMS) using soft lithography techniques. Some microchips take advantage of concentration gradient generators (CGGs) [23; 31; 32], while others set drug dosage manually on a microwell platform or automatically by diffusivity of the reagents. Moreover, they can use 2D or 3D cell cultures.  
Some of these microfluidic platforms for monodrug screening on a microscale sample are shown in Figure 1. The microwell platform shown in Figure 1a uses a 2D cell culture, and the nutrients/drugs diffuse from microchannels to microwells [33]. In contrast, in Figure 1b, an alginate 3D cell culture is injected by a micropillar chip onto a microwell platform [34].A microarray spotter dispensed the drug solutions onto the microwell chip. Figure 1c is a two-layer PDMS-based microfluidic device with an integrated cell culture membrane that can be used for both drug delivery and mechanotransduction [23]. It should be noted that the size of the membrane can be tailored based on the sample size. Figure 1d automatically shows generation of drug dosages based on the diffusion process onto a 3D biopsy-derived spheroids microwell platform [35]. Although 2D cell cultures are still used for most research, the 3D cell culture industry is more appropriate, especially in cancer research. In the following, we classified monodrug testing on microscale samples based on cell-culture type (two dimensional or three dimensional).
## 8 Conclusions  
We developed a new approach to solve large-scale combinatorial optimization problems, focusing on the Travelling Salesman Problem (TSP), using hybrid parallel computing and quantum platforms. While a quantum computer has the potential to solve large combinatorial optimization problems fast, it requires \(n^{2}\) qubits for TSP-like optimization. Due to the relatively small number of qubits available in current quantum computers, a hybrid approach, in which sub-graphs of a TSP tour are executed on a quantum computer, and the results from the quantum optimization are combined for further optimization of the whole tour, is a viable approach. Since the simple optimization of aggregated locally optimized sub-graphs is prone to be trapped in a local minimum, we developed a parallel ACO with periodic pheromone exchange between ant colonies. Further, we repeated the above process by creating different sub-graphs from the previously optimized iteration, so that the boundaries of the adjacent sub-graphs changed, which avoiding obtaining a sub-optimal solution.  
Our results are encouraging. We obtained optimum results for benchmarks with less than 150 nodes, and within 1% of the optimal solution for benchmarks with a few thousand nodes. The execution time required by our parallel ACO algorithm increases significantly for TSPs with a large number of nodes, as many iterations are needed to arrive at a good solution. If the sub-graphs are solved using the ACO, instead of a quantum computer, the execution time is of the order of a few days for problems involving a few thousand nodes. Thus, quantum computing can optimally solve some NP-hard problems, such as TSP. However, as the qubit size increases in the newer quantum processors, and quantum computers become widely accessible, our hybrid approach presented herein will allow solving relatively large optimization problems.  
Recently, the use of AI techniques to advance the optimization of the TSP has been attempted via Graph Neural Networks (GNNs), e.g., as reported in a work [36] that attempted to speed up local search via GNNs. This is needed to keep the real-time execution constraint. Our future work will attempt to use a GNN-based local search in our hybrid framework to further improve the execution time in place of the \(k\)-opt based local search that we currently use.  
Conceptualization, B.G., A.M. and K.E.; methodology, software, B.G. and A.M.; validation, analysis, resources, visualization, data curation, B.G.; writing--original draft  
Figure 14: High-level iterative process of partitioning, optimization, assembly optimization with our HPACO algorithm.  
preparation, B.G.; writing--review and editing, A.M. and K.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
# Kolmogorov-Arnold Networks for Reduced-Order Modeling in Unsteady Aerodynamics and Aeroelasticity
## 2 Reduced-Order Modeling
### Kolmogorov-Arnold Gated Recurrent Networks
The function \(\sigma(\cdot)\) denotes the activation function, which is set to the sigmoid function in this work. The vector \(p_{t}\) is defined as the concatenation of outputs obtained from multiple KAN layers, formally expressed as follows:  
\[p_{t}=\text{Concat}[\tilde{p}_{1,t},\tilde{p}_{2,t},\dots,\tilde{p}_{L,t}] \tag{7}\]  
Subsequently, the candidate hidden state \(\tilde{h}_{t}\) is computed via  
\[\tilde{h}_{t}=\tanh(W_{h}p_{t}+U_{h}(r_{t}\odot h_{t-1})+b_{h})\]  
where \(\tilde{h}_{t}\) represents the internal memory state. Finally, the hidden state \(h_{t}\) is updated according to the following equation:  
\[h_{t}=z_{t}\odot h_{t-1}+(1-z_{t})\odot\tilde{h}_{t} \tag{8}\]  
Through this design, KAGRN combines KARN with gating mechanisms, where the Kolmogorov-Arnold recurrent layers provide enhanced nonlinear representational capabilities essential for modeling complex input-output relationships, and the gating mechanisms effectively mitigate vanishing gradients and dynamically manage information flow, enabling the network to better capture and retain long-term temporal dependencies. This innovative architecture exhibits superior generalization performance compared to conventional approaches while maintaining robust modeling capabilities, allowing for a more accurate and efficient representation of the dynamic characteristics inherent in unsteady aerodynamics and aeroelasticity. In addition to its ability to represent complex unsteady aerodynamic characteristics with high accuracy, KAGRN also offers a distinctive interpretability advantage that is particularly valuable in the context of reduced-order modeling. Within this setting, the KAN sublayer provides an explicit functional decomposition: each learned one-dimensional mapping can be analyzed independently, enabling practitioners to directly understand how specific displacement components influence the predicted aerodynamic force coefficients. The shapes of these mappings often reveal physically meaningful nonlinear behaviors that would remain obscured in conventional black-box models. In this sense, the KAN serves as a compact, data-driven basis that maps structural motion to aerodynamic response in a more transparent and physically interpretable manner than conventional approaches. Together, these attributes demonstrate that KAGRN not only achieves high predictive accuracy but also enhances model transparency, offering valuable insights into the underlying dynamics. This combination of accuracy and interpretability makes KAGRN particularly suited for scientific analysis and engineering deployment.
# An Intelligent Collaborative Charging System for Open-Pit Mines
## 4 Technical Implementation Scheme for Intelligent Collaborative Charging in Open-Pit Mines  
### Optimal Travel Route Planning and Transportation Allocation for Blasting Trucks  
According to the mining design, blasting design, and production plan of the mine, information such as the blasting area range of the open-pit mine, the coordinates of the blast hole orifice, and the charge design of the blast hole is efficiently and accurately transmitted to the on-board terminal of the explosive vehicle. The calculation and control system of the on-board terminal of the explosive vehicle plans the optimal path for the explosive vehicle to transport explosives based on the obtained information, including the driving route and parking position. The explosive vehicle is equipped with flowmeters and solenoid valves. After the explosive vehicle has stopped at the parking point and the loading robot has located the holes, the explosive vehicle compares the position information of the blast holes with the position information of each blast hole in the blasting design database to complete the matching of the blast holes to be loaded and obtain the designed explosive quantity for the blast holes. The explosive supply is opened through the solenoid valve,and the amount of explosive supplied is controlled by the flowmeter, precisely rationed and delivered to the loading robot as needed.
# Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review
## 2 Dynamic Low-Emission Zones
### LEZ and Green Urban Planning  
Urban greening enhances Low-Emission Zones (LEZs) by mitigating air pollution, reducing urban heat islands, and improving environmental quality. Trees act as natural air filters, capturing pollutants like PM2.5 and NO\({}_{\text{x}}\), complementing emission reductions. Cities such as London, Madrid, and Milan have integrated extensive tree-planting initiatives into their LEZ policies [10].  
Green spaces also regulate microclimates, lowering temperatures and reducing energy consumption for cooling, indirectly cutting emissions [11]. In Oslo and Paris, urban greening promotes active mobility, encouraging walking and cycling while improving urban aesthetics [12]. Additionally, trees absorb noise pollution, as seen in Berlin, where LEZ-adjacent vegetation improves urban livability [13].  
Furthermore, urban trees serve as carbon sinks, supporting long-term decarbonization strategies, as exemplified by Stockholm [14]. However, improper tree placement can trap pollutants in urban canyons, and some species release volatile organic compounds (VOCs), requiring careful planning [15].  
Integrating urban forestry with LEZ policies fosters sustainable, low-carbon cities, reinforcing emission reduction efforts while enhancing urban resilience and public health. Future urban policies should consider synergistic strategies that align LEZs, green infrastructure, and sustainable mobility.
## 2 Diagnosis Model Description
### Fundamental Equations of the Diagnostic Model  
#### 2.2.1 Engine Energy Balance  
Equation (1) represents the thermodynamic model inside the combustion chamber. The model relates the variables of temperature, pressure, and mass inside the chamber as a function of the crankshaft angle.  
\[\frac{\mathrm{d}\mathrm{U}}{\mathrm{d}\theta}=\frac{\mathrm{d}\mathrm{Q}}{ \mathrm{d}\theta}-\frac{\mathrm{d}\mathrm{W}}{\mathrm{d}\theta}+\sum_{\mathrm{ i}}\frac{\mathrm{d}\mathrm{h}_{\mathrm{i}}}{\mathrm{d}\theta} \tag{1}\]  
where the subscript i is the input or output of the system.  
Assuming an ideal gas behavior, Equation (1) can be expressed, as shown in Equation (2).  
\[\mathrm{m}_{\mathrm{comb}}\cdot\mathrm{C}_{\mathrm{v}}\frac{\mathrm{d}\mathrm{ T}}{\mathrm{d}\theta}+\mathrm{u}_{\mathrm{f}}\frac{\mathrm{d}\mathrm{m}_{ \mathrm{f}}}{\mathrm{d}\theta}=\frac{\mathrm{d}\mathrm{Q}_{\mathrm{c}}}{ \mathrm{d}\theta}-\frac{\mathrm{d}\mathrm{Q}_{\mathrm{r}}}{\mathrm{d}\theta}- \frac{\mathrm{m}_{\mathrm{comb}}\cdot\mathrm{R}_{\mathrm{comb}}\cdot\mathrm{ T}}{\mathrm{V}}\cdot\frac{\mathrm{d}\mathrm{V}}{\mathrm{d}\theta}+\sum_{\mathrm{i}}( \mathrm{h}_{\mathrm{i}}\cdot\frac{\mathrm{d}\mathrm{m}_{\mathrm{comb},\mathrm{ i}}}{\mathrm{d}\theta}) \tag{2}\]where \(Q_{c}\) and \(Q_{r}\) is the heat released from the chemical process during the combustion process and the transfer of heat through the cylinder walls.  
The diagnostic model in this paper is based on the calculation of the heat release rate caused by the combustion process. Therefore, the calculation is made for the process release rate, based on Equations (2) and (3) shows the result obtained.  
\[HRR=\frac{P.\frac{dV}{d\theta}+m_{comb}\cdot C_{v}\cdot\frac{\Delta T}{\Delta \theta}+\frac{dQ_{r}}{d\theta}-\frac{dm_{f}}{d\theta}\cdot\left[h_{f(T_{inj}) }-u_{f}\right]+\frac{dm_{bb}}{d\theta}\cdot R_{comb}\cdot T}{m_{comb}\cdot LHV _{f}} \tag{3}\]  
where each term indicates the following:  
\begin{tabular}{l l} \(P.\frac{dV}{d\theta}\) & Work is done inside the combustion chamber. \\ \(m_{comb}\cdot C_{v}\cdot\frac{\Delta T}{\Delta\theta}\) & Change of internal energy in the combustion chamber. \\ \(\frac{dQ_{r}}{d\theta}\) & Heat rejected by convection of the combustion chamber. \\ \(\frac{dm_{f}}{d\theta}\) & Energy associated with fuel injection and vaporization. \\ \(\frac{dm_{we}}{d\theta}\cdot R_{comb}\cdot T\) & Flow work associated with losses due to leaks in the combustion chamber. \\ \(m_{comb}\cdot LHV_{f}\) & It is the total energy generated by the fuel injected during the cycle. \\ \end{tabular}  
The temperature (T) inside the combustion chamber is calculated with Equation (4).  
\[T=\frac{P\cdot V}{m_{comb}\cdot R_{comb}} \tag{4}\]  
where \(P,~{}V,~{}m_{comb}\) and \(R_{comb}\), are mean combustion chamber pressure, combustion chamber volume, the mass of combustion chamber gas, and ideal gas constant, respectively.
# Modeling and Performance of the IEEE 802.11p Broadcasting for Intra-Platoon Communication
## 4 Analytical Model
### MAC Layer Queuing Process Model
\tag{17}\]  
According to M/G/1/K model, the probability that a vehicle has not transmitted data packets in a slot time can be derived is  
\[q_{em}=\frac{p_{0}^{que}}{p_{0}^{que}+\lambda T_{sv}} \tag{18}\]  
where \(\lambda T_{sv}\) denotes the probability of the channel being occupied, and \(T_{sv}\) is the average packet service time. We can then apply the transfer-function approach, in which the packet transmission process is characterized by a linear system [37], as shown in Figure 3. Binary exponential back-off is performed when the channel is detected idle. A packet is sent after a back-off procedure, the probability that packets collision is \(p_{p}\), and the probability of frame error is \(p_{f}-p_{p}\), and the probability that packets successful transmission is \(1-p_{f}\). Each stage is considered as a subsystem of the service system, and the delay \(t\) generated in the time domain is represented by transfer function \(Z^{t}\) in the \(Z\)-domain. The service system is the cascade of multi-subsystems which means that system output is the superposition of multiple delay factors \(Z^{t}\). The transfer function of the packet transmission process is derived as  
\[H(z)=[p_{p}H_{c}(z)+(p_{f}-p_{p})H_{e}(z)+(1-p_{f})H_{s}(z)]\frac{1}{CW+1}\sum _{n=0}^{CW}H_{b}^{n}(z) \tag{19}\]  
where \(H_{c}(z)=z^{T_{e}}\) represents the time distribution transfer function of packets transmission failure caused by transmission conflict. \(H_{s}(z)=z^{T_{s}}\) and \(H_{e}(z)=z^{T_{s}}\) represent the time distribution transfer function of successful packets delivery and the time distribution transfer function of packets transmission failure due to channel errors, respectively. The time distribution transfer function of the back-off procedure \(H_{b}(z)\) is given by  
\[ \begin{split} H_{b}(z)&=[1-(1-q_{em})p]^{N-1}z^{ \tau}+[1-(1-q_{em})p]^{N-2}(N-1)(1-q_{em})pz^{T_{s}}\\ &+\{1-[1-(1-q_{em})p]^{N-1}-[1-(1-q_{em})p]^{N-2}(N-1)(1-q_{em}) p\}z^{T_{c}}\end{split}  \tag{20}\]  
Figure 3: MAC service time linear system diagram. In Equation (20), the coefficient of \(z^{\tau}\) and \(z^{T_{c}}\) are the probability of channel idle and channel conflict. While the coefficient of \(z^{T_{s}}\) represents the probability that one of the \(N-1\) vehicles transfer packets successfully. The back-off time is uniformly chosen in the range \([0,CW]\) where \(CW\) is the size of the contention window. When the channel is idle in a slot time, we can postulate that the back-off counters of all nodes decrease by one. The state of channels is divided into three: (1) idle; (2) busy; and (3) transmission successful. Thus, the back-off time is the mean value of the unit slot, conflict consumption time and successful transmission time. According to packets transmission process, the probability generating function of service time distribution can be expressed as \(H(z)=\Sigma_{T=0}^{T_{m}}p_{st}^{T}z^{T\tau}\). Combined with Equations (18) and (19), discrete probability \(p_{st}^{T}\) can be obtained.
# A Comparison of Different Topic Modeling Methods through a Real Case Study of Italian Customer Care
## 1 Introduction
### Motivation Example  
The present study concerns the analysis of conversation transcriptions between customers and agents in a customer care call center. The objective is to support the analysis of text transcription of human-to-human conversations, to obtain reports on customer problems and complaints, and on how an agent has solved them. The aim is to provide customer care service with a high level of efficiency and user satisfaction.  
From the studies in the literature, the highest increase in the topics concerning the analysis of spoken dialogues is evident, as highlighted in the papers [18; 19]. Topic modeling is the fundamental element within the study addressed in this paper, for which a state-of-the-art review is in [20; 21].  
One of the main difficulties concerning Natural Language Processing (NLP) related to real-world dialogues is the interpretation of the textual content from the unpredictability of human responses and behaviors [22]. In our specific case, an additional difficulty is due to the processing of dialogues in the Italian natural language, which is still not adequately faced in the literature.  
Until now, most representative text pre-processing tools and language models are based on English. Therefore, the majority of data sources used in text-based research are also documented in English. Current NLP methods and algorithms mainly focus on several high-resource languages. Other languages, such as Italian, have been undeserved by NLP approaches due to the lack of datasets and support. These challenges make the development of NLP models expensive and time-consuming. Therefore, it is essential to develop research projects at the country level to create NLP applications for domestic languages. The present research study aims to contribute to the literature by developing an NLP application--specifically, a TM algorithm--for the Italian natural language. This work can be an asset for grounding applications of topic modeling and can be inspiring for similar case studies in the area of customer care quality. A few case studies concerning text analysis in the Italian language appeared in the recent literature and can be found in [23; 24; 25].  
The ultimate goal is to investigate the most performing method in the TM field to adapt it to our case study. This aim would make it possible to improve the customer experience by optimizing the quality of responses to customer requests by operators. In particular, the Topic Visualization tools are valid in the investigation of useful outputs coming from TM [26].