esa-sceva/satcom-chunk-collection huggingface dataset:
# Innovative Antibacterial Polymer Coatings
## 2 Antibacterial Coatings
### Photoactive Antibacterial Coatings
#### 2.4.1 Titanium Dioxide-Based Photocatalytic Coatings  
Photocatalytic TiO\({}_{2}\) is one of the most effective light-activated biocidal materials used for creating antibacterial coatings. The photocatalytic properties are due to surface defects in nanosized TiO\({}_{2}\) crystals. It has been found that both crystalline forms of TiO\({}_{2}\) (anatase and rutile) exhibit photocatalytic behavior. In the presence of water, oxygen, and ultraviolet light, the surface of TiO\({}_{2}\) nanocrystals continuously generates hydroxyl radicals (-OH), superoxide radical anions (O\({}_{2}\)-), hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), and other highly reactive oxygen species by converting water and oxygen at light-activated catalytic sites (Figure 7) [66; 67; 68].  
The half-life of these compounds is only milliseconds, allowing them to diffuse only submicron distances. When reactive oxygen species (ROS) are released, they can transform other molecules within a certain distance, leading to the generation of secondary reactive species that have longer lifespans. This significantly extends the reactive range of the initially released ROS. Research has shown that bacteria, such as _E. coli_, can be completely degraded over time when they are attached to a reactive TiO\({}_{2}\) surface in the presence  
Figure 7: Schematic representation of ROS formation during TiO\({}_{2}\) photocatalysis. Adapted from ref. [68] with permission from Elsevier, Copyright 2021.  
of UV light [66]. Despite its potential, TiO\({}_{2}\) faces three main challenges that hinder its universal use:  
* Degrades any organic material over time
* Requires the presence of water
* Needs UV light exposure  
TiO\({}_{2}\) can be effectively utilized only in inorganic environments, such as on surfaces like glass, calcium carbonate, or silicon dioxide, and it requires the presence of water and UV light. The first two challenges can be addressed by designing porous inorganic coatings that incorporate TiO\({}_{2}\) nanoparticles [66]. Activating photocatalytic coatings made from pure titanium dioxide (TiO\({}_{2}\)) necessitates ultraviolet (UV) radiation, which limits their effectiveness for indoor applications. When using UV radiation alongside TiO\({}_{2}\), it is crucial to manage the exposure carefully to differentiate between cell death resulting from the radiation and that caused by the coating itself.  
Titanium dioxide can be doped with small amounts of various metals, such as silver, copper, iron, or manganese, to overcome the limitations of UV light. Additionally, non-metals like carbon or nitrogen are also used for doping TiO\({}_{2}\)[66; 67; 68; 69]. The resulting shift in absorption makes these photocatalytic composites antimicrobial under visible light. Currently, the catalytic activity of these composites under visible light is still significantly lower than that in the UV range. Alternative metal oxides that absorb visible light, such as NiO/SrBi\({}_{2}\)O\({}_{4}\), have demonstrated effectiveness in killing pathogenic bacteria [66].
# Router Activation Heuristics for Energy-Saving ECMP and Valiant Routing in Data Center Networks
## 6 Simulation Experiments and Performance Evaluation
### Estimation of Maximum Network Bandwidth  
The first series of experiments was conducted to assess the maximum available bisection bandwidth of both network topologies. In these experiments, each pair of servers in the network transmitted the same amount of data, generated at a constant rate throughout the simulation. We assumed that nodes remained active until the end of the simulation, i.e., no activity control heuristics were used. The ECMP routing algorithm was used without the activation of any energy-saving heuristics. Figures 3 and 4 show the packet loss percentage depending on flow size for respective topologies. As seen in the case of one server, the heuristic solution is consistent with the exact one obtained using a mathematical model and CPLEX MIP solver.  
Figure 3: Packet loss for fat-tree and varying flow size.  
Packet loss was not present for flows up to 16 MB, while for larger flows it was present, due to the insufficient link capacity. For the same reason, the mathematical programming task for the respective setup was infeasible, conventionally denoted by the 100% loss level. It can be seen that the advantage of heuristics is the possibility of determining an approximate solution also for cases not supported by the mathematical model. In the case of two servers, a solution using heuristics achieves slightly better QoS for the butterfly topology. It is an obvious result of the greater freedom of choosing paths at the lower level of the graph built with a significantly larger number of devices than in the case of the fat-tree. Since the losses for 8 MB flow and fat-tree networks are relatively small and completely absent in the case of one server, this value was selected for further experiments.
## 3 Results and Discussion
### Feasibility Analysis of the Model
#### 3.1.2 Model Validation  
To enhance the model's accuracy, calibration and verification are undertaken based on steady-state pressure drop experimental data for a clean GPF, as described in Ref. [41]. Various flow conditions without soot load are chosen for validation, keeping the GPF structural parameters and inlet conditions shown in Table 2 consistent to establish the relationship between GPF pressure drop and inlet velocity, resulting in the curve depicted in Figure 3. The viscous resistance and inertial resistance are determined as \(6.5\times 10^{12}\) m\({}^{-2}\) and \(2\times 10^{8}\) m\({}^{-2}\), respectively. The simulated GPF pressure drop value represents the difference between the average pressure at the inlet and outlet interface. While simplifications and assumptions inherent to the current model may introduce some error compared to experimental values, it is noteworthy that the simulated pressure drop error remains below 5%. Therefore, it is concluded that this model accurately represents experimental results and will provide a solid foundation for future simulation studies.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Initial Parameters** & **Number** \\ \hline Inlet velocity (m\(\cdot\)s\({}^{-1}\)) & 11 \\ Outlet pressure & Atmospheric pressure \\ Porosity & 0.5 \\ Viscous resistance (m\({}^{-2}\)) & \(6.5\times 10^{12}\) \\ Inertial resistance (m\({}^{-1}\)) & \(2\times 10^{8}\) \\ Exhaust density (kg\(\cdot\)m\({}^{-3}\)) & 1 \\ Dynamic viscosity (Pa\(\cdot\)s) & \(2.9\times 10^{-5}\) \\ Re & 417 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial condition.  
Figure 2: Grid independence test and verification.
# Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review
## 3 Sensors
### Microphone  
Engagement is indeed a multifaceted phenomenon. One may be found with eyes closed and motionless whilst being highly engaged in a phone call with plug-in headphones. Speech and dialogue open up a whole new realm for human engagement estimation. Hands-free phone calls mark a great emancipation for drivers yet engender risks of diverted attention [117]. Whoever leads the floor by actively conversing often exhibits a high level of engagement [118], no matter whether in class or online. Using vocal wake words is, by far, the most popular way to awaken and initiate interactions with smart devices, autonomous agents, and robots [12; 19; 119]. The microphone is the device to accomplish these tasks. The sound source direction, distance, and multi-speaker separation can all be utilized together in engagement-related design [82]. A microphone array can be leveraged for triangulation [120], i.e., to obtain arriving angle of sound via analyzing the Time-Difference of Arrival (TDOA) of each microphone unit. Distance estimation is also achievable with sound amplitude attenuation modelling [121].  
A Speech Activity Detection (SAD) module [89] is often adopted to distinguish speech cues from non-speech acoustic cues. The latter refers to any acoustic cue other than speech itself, such as background noise which reveals speakers' contextual and environmental settings to an extent. Speech cues contain many features: pitch, pitch contours, speech rate, voice intensity, and, most importantly, semantics, which directly relates to speakers' interest, enthusiasm, and immersion in the subject matter [21; 25]. It is possible to use even lower-level acoustic features of fundamental frequency, log-energy, and Mel-Frequency Cepstrum Coefficients (MFCCs) for engagement estimation [78] with the aid of sound analysis toolkits like openSMILE [122].  
A common paradigm for analyzing multiparty conversational engagement resorts to who leads a conversation [123]. This requires understanding who controls the floor and detecting whenever floor exchanges take place [82]. Considering conversational semantics, an adjacency pair [33], i.e., an utterance following a previous one in response, typically manifests a high engagement level. Backchannel [33] reflects engagement in a similar vein, e.g., a verbal communication of \"yeah\" or gestural communication of nodding usually resonating to highly engaged interactions.  
Nevertheless, speech and audio sensing is regarded among the most privacy-unfriendly means, second to none even including cameras [124]. Speech and non-speech acoustic cues can disclose sensitive attributes [125] about users' personality traits, gender, health condition, geographical origin, socioeconomic status, moods, etc. Users have the right to be informed about the use of microphones and how acquired data are to be stored and processed. Hence, it is compulsory to seek consent before deploying microphones for engagement estimation.
## 3 NA-MEMD Method  
To further reduce the mode-mixing effect in an MEMD, a noise-assisted MEMD method, named NA-MEMD, was introduced by adding white noise. The method is different from the ensemble EMD method in that multiple realizations of white noise are added to the input signal before being decomposed via EMD [13]. The NA-MEMD method adds extra multivariate channels, such as \(l\) dimensional independent white noise to the original \(n\) dimensional multivariate signal, and then processes such an \(n+l\) dimensional composite signal via MEMD. The NA-MEMD algorithm is described as follows:  
Figure 1: IMFs of a synthetic signal obtained by MEMD.  
Figure 2: IMFs of the synthetic signal in Figure 1 obtained by standard EMD.  
1. Create an \(l\)-dimensional independent Gaussian white noise series with the same length as that of the input signal;
2. Generate an \(n+l\)-dimensional signal by adding the noise series created in Step 1 to the \(n\)-dimensional input multivariate;
3. Process the resulting \(n+l\)-dimensional multivariate signal via the MEMD algorithm, to obtain the multivariate IMFs;
4. Discard the \(l\)-dimensional IMFs corresponding to the noise and obtain a set of \(n\)-dimensional IMFs corresponding to the original signal from the resulting \((n+l)\)-variate IMFs.  
For adding Gaussian white noise, the NA-MEMD method has better binary filtering characteristics than the MEMD method. Both the mode mixing and misalignment problems were significantly reduced. Figure 3 illustrates the benefits of noise-assisted MEMD algorithms for reducing mode mixing via a bivariate signal example. The Z component signal contains a 20 Hz sinusoid and two intermittent signals with frequencies 8 Hz and 80 Hz. The X component was generated by rotating the Z component 90 degrees on the vertical direction. For convenience, we plotted only the IMFs of the Z component. We can see that mode mixing in a single channel is obvious for the standard EMD in Figure 2(a). A single-frequency scale mode exists in both IMFs. In addition, different frequency modes can also be seen in the same IMF. With two extra channels of white noise, the decomposition of the same bivariate signal by the proposed NA-MEMD method is shown in Figure 2(b). This demonstrates that mode mixing is significantly reduced, and each IMF only contains a single frequency mode.  
Figure 3: The EMD decompositions of a 20 Hz sinusoid contaminated with two 8 Hz and 80 Hz intermittent signals: (**a**) IMFs of EMD method; (**b**) IMFs of the NA-MEMD method.
## 3 Data
### Corinle Land Cover Data  
The Coordination of Information on the Environment Land Cover (CORINE) dataset provides a land cover classification for Europe with a resolution of 100 m, grouping the land types into five major groups: artificial surfaces (I), agricultural areas (II), forests and semi-natural areas (III), wetlands (IV), and water bodies (V), which are further characterized within 44 individual subcategories in total. The minimum mapping unit is 25 ha with a mapping accuracy of greater than 85%. The dataset is based on Sentinel-2 and Landsat-8 image classification using computer-assisted visual image interpretation tools (CAPI) [50; 51]. Focusing on agricultural areas for estimating soil moisture, all areas within the group \"agricultural areas\" are considered, while all other categories were masked. Thereby, all subcategories within the category \"agricultural land\" are considered as one class. The CORINE land cover dataset only distinguishes between individual cultivation types when there is long-term land use (e.g., olive trees, fruit trees, rice fields, pastures). Areas with crop rotations are placed in the subcategory \"complex cropping patterns\". In this respect, no information on the specific crop type is available for this study.
## 4 Related Works and Contemporary VSN Projects
### Sensor Network Virtualization Projects
#### 4.1.3 SensorPlanet  
SensorPlanet [44] is Nokia-initiated cooperation. It facilitates a global research framework on mobile device-centric large-scale wireless sensor networks. The outcomes of SensorPlanet are: (1) It gives a test platform that enables the collection of sensor data on a never seen scale, (2) It also provide central repository for sharing the collected sensor data for research purposes. The idea behind the initiative is that the participating universities will develop their own applications for example environment and traffic monitoring, urban and participatory sensing, wellness and navigation applications, _etc._ It will also share the collected data in order to conduct research on data analysis and mining, visualization, machine learning, _etc._  
Moreover, it is the goal to develop novel application ideas and use cases based on the learning and the research. The objectives these research frameworks are: (i) strengthening mobile device-centric Wireless Sensor Network research, establishing a new field within WSN; (ii) establishing an open source community around Wireless Sensor Networks; (iii) providing a forum for publishing early results. (iv) accelerating the concept of innovation and finding of new application areas; (v) creating an ecosystem for industry and academia collaboration; (vi) enabling research groups to design and implement large scale experiments; (vii) facilitating creation and sharing of large data sets; (viii) studying future interaction modes for accessing sensor data; (ix) developing novel abstraction and visualization methods for user friendly interaction; (x) investigating the ways of fusion methods where different sources provide different type of data on various levels of granularity and semantics.
# A Survey on Bug Deduplication and Triage Methods from Multiple Points of View
## 6 Evaluation Methods and Results
\begin{table}
\begin{tabular}{l l l l} \hline
**Work** & **Methods** & **Effects** & **Dataset** \\ \hline HHNDBR & Low-dimensional space vector conversion & 2 M from nine datasets & 98.83\% accuracy and 97.08\% F1 score \\ \hline DABT & Bug dependency graph, LDA, TF-IDF, and SVM & 50\% bug fix time reduction & 16 k from DJT, 70 k from LibreOffice, and 112 k from Mozilla \\ \hline One-class classification-based triage & One-class SVM & \(\sim\)93\% average accuracy and \(\sim\)53\% average & 4 k from Platform and 20 k form Firefox \\ \cline{3-4}  & & recall rate & 4 k from Platform and 20 k form Firefox \\ \hline SusTriage & Multimodal learning & 69\% mean average precision improvement & \\  & & and 61\% mean reciprocal rank improvement & 16 k from Eclipse and 15 k from Mozilla \\ \hline Efficient feature extraction model & TF-IDF-based feature extraction & Android, Eclipse, Mozilla, and OpenOff & 97\% accuracy, precision, recall rate, and \\  & & & F1 score \\ \hline Triage in large-scale industrial contexts & TF-IDF and two-level classifier & Human resource reduction & 78 k reports \\ \hline Triage using CNN-LSTM & CNN and LSTM & 52.4\% accuracy & 383 k from Chromium, 314 k from Firefox, and 162k from Mozilla \\ \hline Multi-triage & AST extractor, context augmenter, text encoder, and AST encoder & 57\% accuracy for developer triage and 47\% accuracy for bug triage & 81.6 k from spaceto, azure-poweredshell, 112k Eclipse, device, and insuredLiftytoolkit-unity, monogame, nunit, realin-java, Ryan, and rayi \\ \hline Triage based on transfer learning & Transfer learning & 75.2\%, 82.7\%, 78.2\%, and 79.3\% accuracy for & 163 k from Chromium, 130 k from Mozilla \\  & & Chrome, Mozilla Core, Firefox, and a private & Core, 138 k from Mozilla Firefox, and 75 k from a private dataset \\ \hline MSDBT & LSTM & 0.5424 RR83, 0.6375 RR85, and 0.745 RR810 & 14 k from Mozilla, 10 k from Eclipse, 11 k from \\ \hline ST-DGNN & Joint random walk and graph recurrent convolutional neural network & \(\sim\)0.7 FI8k & 150 k from Eclipse and 170 k from Mozilla \\ \hline Deepcrash & frame2 vec, Bi-LSTM, and Rebucket & 80.72\% F score & 10 k from SAP hana and 47 k from Netbeans \\ \hline Triage using transformer & BERT and transfer learning & Over 60\% top-10 accuracy & Eclipse, Firefox, and NetBeans \\ \hline DENATURE & TF-IDF, SVM, and logistic regression & 45 k from Eclipse & 88.8\% accuracy \\ \hline XAI-based triage & XAI model & 208 K from Eclipse & Not mentioned \\ \hline CombinedIRDL & IR + ML & 1 M from Eclipse, Mozilla, and OpenOffice & 7.1\(\sim\)11.3\% precision improvement \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._
## 2 NDN Background and Related Work  
### Named Data Networking Architecture  
The communication philosophy of NDN is fundamentally based on three types of entities: consumers, producers, and routers. When a consumer wants to request content, it sends an Interest Packet (Input) containing the content's name. This Input is routed through the network towards the data producer, which responds with a Data packet containing the requested content. Each router in the NDN maintains three crucial tables: the Pending Interest Table (PIT), Forwarding Information Base (FIB), and Content Store (CS). These tables work together to optimize content delivery in NDN by efficiently managing the Input and Data packet within the network. Once receiving an Input, a router first checks its CS (a cache that stores data requested before) to see if it already has a copy of the requested data. If a match is found in the CS, the router immediately satisfies the received Input. If the CS check fails to find the desired data, implying that no cached copy of the required content exists, the router then performs a lookup on the PIT (which keeps track of all the Inputs that have been forwarded but have not yet been satisfied) to verify whether it has an entry for the received Input. If an entry is found in the PIT, the router will add the received interface ID to the PIT entry, and the Input will be dropped. Otherwise, the router then performs a name-based longest prefix match lookup in its FIB (which contains the forwarding information for each content name) to determine the interface to forward the Input toward the producer. If a match is found for the content name in one of the FIB entries, the router then creates a new PIT entry for the Input using the received interface ID and Input's content name, and the Input is forwarded upstream. This process is repeated until the Input reaches the producer that replies with a Data packet, which follows the reverse path stored in the PIT entries across the routers, taken by the Input, back to the consumer. In the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Acronyms** & **Definition** \\ \hline RVS & Rendezvous Server \\ RP & RemovePath \\ CP & CreatePath \\ CPD & CreatePathData \\ RSS & Received Signal Strength \\ KF & Kalman Filter \\ GM & Gauss–Markov \\ WMN & Wireless Mesh Network \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._case when no longest prefix match is found, then the Input is forwarded according to the forwarding strategy used.
## 3 Results
### Technical Analysis
#### 3.2.5 Control Devices
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Control Device** & **Description** & **Efficacy** & **Efficiency** \\ \hline \multirow{3}{*}{[110]} & \multirow{3}{*}{_Controller (asynchronous motor)_} & Type-1 fuzzy control & \multirow{3}{*}{Simulations} & Results comparison without the control system \\  & & asynchronous motor to increase & & \\  & & power factor and regulate & & \\  & & voltage & & \\ \hline \multirow{3}{*}{[111]} & \multirow{3}{*}{_APF_} & Hybrid automata–fuzzy control & \multirow{3}{*}{Simulations} & Comparison with a simple PI \\  & & for THD and power factor & & \\  & & decrease & & \\ \hline \multirow{3}{*}{[112]} & \multirow{3}{*}{_STATCOM_} & Rise of STATCOM efficiency & \multirow{3}{*}{Simulations} & Results comparison without the fuzzy control of STATCOM and active disturbance rejection \\  & & through sub-synchronous & & \\  & & resonance using type-1 fuzzy & & \\  & & & control & \\ \hline \multirow{3}{*}{[113]} & \multirow{3}{*}{_UPQC_} & [PERSON] and Takagi-Sugeno & Laboratory & \multirow{3}{*}{Comparison with conventional modified PLL and TS-PLL} \\  & & fuzzy controllers and & & hardware in loop \\  & & phase-locked loop (PLL) control & & \\  & & Harmonics, unbalance, and & & real-time system & \\ \hline \multirow{3}{*}{[114]} & \multirow{3}{*}{_DVR_} & Self-tuned type-2 fuzzy PI & \multirow{3}{*}{Simulations} & Comparison with a type-1 fuzzy PI controller \\  & & controller for voltage sag and & & \\  & & THD alleviation & & \\ \hline \multirow{3}{*}{[115]} & \multirow{3}{*}{_UPFC_} & Type-1 fuzzy controls with active and reactive power variations & \multirow{3}{*}{Simulations} & Results comparison without the fuzzy control of UPFC and PID control \\ \cline{3-5}  & & & & control \\ \hline \multirow{3}{*}{[116]} & \multirow{3}{*}{_Controller (inverter)_} & Fuzzy supervisory with fuzzy PID control for voltage and current THD reduction & \multirow{3}{*}{Simulations} & Comparison with classic supervisory PID and classic supervisory FPID \\ \hline \multirow{3}{*}{[117]} & \multirow{3}{*}{_Controller (inverter)_} & Direct power control (DPC) & Simulations and & \multirow{3}{*}{Results comparison with the basic DPC} \\  & & based on type-1 fuzzy and fuzzy PI for THD diminishing & & \\  & & & & \\ \hline \multirow{3}{*}{[118]} & \multirow{3}{*}{_APF_} & Review about the control & \multirow{3}{*}{Simulations} & Comparison with a simple fixed-band hysteresis, adaptive hysteresis band, and fixed hysteresis band \\  & & & hysteresis method & \\ \hline \multirow{3}{*}{[16]} & \multirow{3}{*}{_Power line conditioner (PLC)_} & Line current harmonic & Software and & Comparison with a simple PI \\  & & distortions and power factor & hardware & and a gained scheduled \\ \cline{1-1}  & & Fuzzy PI controller & implementation & controller \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Relevant papers concerning control devices. Figure 7: Article distribution considering control devices.