esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Measuring System, Materials and Methods
### Methodology
#### 2.1.3 Features Extraction
Created predictors are entirely independent of each other. Defining amplitude (\(A\)) and offset (\(O\)) is usually straightforward. However, accurately reflecting the shape of the waveform presents a more complex challenge, as attributes of this nature can exhibit considerable variability and must be defined explicitly for each study. Building upon insights derived from studies on the approximation method, ACO decomposition characterizes the shape of the investigated waveform through a comparative analysis with a reference curve. This process facilitates the determination of a singular universal shape parameter cross-correlation (\(C\)), thereby enhancing the versatility of the ACO method. Attributes \(A\) and \(O\) are essential for effective identification. Parameter \(C\) facilitates the detection of various anomalies, including noise, outliers, and completely erroneous measurements (method error). The ACO decomposition retains nearly all the advantages of the approximation method and minimizes the number of features. Moreover, this method obviates the need to define a function that accurately reflects the waveform. A significant advantage of this method is its universality. Similarly to equal partition methods, it can be applied in nearly any situation (including as an intermediate step in constructing the target model). The only prerequisite is the availability of a reference measurement. Furthermore, ACO can serve both as an attribute extraction method in the classical identification process (where the studied waveform is matched to the most similar class from the training database) and as a pattern recognition method (in which extensive training databases are not required). In the case of investigating reinforced concrete structures using electromagnetic NDT methods, obtaining a representative database can be challenging or even impossible. The parameters associated with reinforcement are standardized solely in terms of mechanical properties rather than electromagnetic characteristics. The precise composition of reinforcing steel is typically proprietary information maintained by the manufacturer. Consequently, similar reinforcement bars from different producers may yield divergent results in magnetic testing. Additionally, variations in additives incorporated into concrete can further influence these outcomes. Therefore, a database compiled for the specific examination may prove ineffective when applied to other constructions. ACO decomposition is a referential method. Therefore, a comprehensive database is not required. This reference measurement can be obtained from a specially prepared sample or at a location where it has been confirmed that the object's parameters and materials align with the established design specifications. The acceptance test can be conducted by assessing how significantly a given measurement diverges from the reference measurement.
## 6 Calculation of Parameters for Mudslide Mitigation
### Mudslide Runup and Maximum Rise Height  
When a mudslide encounters an adverse slope, it can exhibit runup, which refers to the phenomenon of the flow continuing upslope due to its momentum. Additionally, when a mudslide encounters an obstacle, its kinetic energy is instantaneously converted into potential energy, causing the debris material to splash upwards, a phenomenon known as mudslide rise height. The maximum rise height and runup height can be estimated using Equation (16):  
\[\begin{array}{l}\Delta H=\frac{Vc^{2}}{2g}\\ \Delta Hc=\frac{\delta Vc^{2}}{2g}\approx 0.8\frac{Vc^{2}}{g}\end{array} \tag{16}\]  
where \(\Delta H\) represents the maximum mudslide rise height (m); \(\Delta Hc\) denotes the mudslide runup height (m); \(Vc\) indicates the average mudslide velocity (m/s); and \(b\) is a coefficient that depends on the slope of the obstacle. The calculated runup and rise heights are presented in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Location**} & \multicolumn{3}{c}{**Average Velocity of Mudslide**} & \multicolumn{3}{c}{**Mudslide Rise Height**} & \multicolumn{3}{c}{**Mudslide Runup Height**} \\  & \multicolumn{3}{c}{**Section**} & \multicolumn{3}{c}{**\(\Delta H\) (m)**} & \multicolumn{3}{c}{**\(\Delta H\) (m)**} \\ \cline{2-10}  & \(p\) = 1\% & \(p\) = 2\% & \(p\) = 3\% & \(p\) = 1\% & \(p\) = 2\% & \(p\) = 3\% & \(p\) = 1\% & \(p\) = 2\% & \(p\) = 3\% \\ \hline Main gully catchment & 1.02 & 0.46 & 0.53 & 0.11 & 0.02 & 0.03 & 0.08 & 0.02 & 0.02 \\ \hline Tributary gully catchment & 1.23 & 0.50 & 0.87 & 0.15 & 0.03 & 0.08 & 0.12 & 0.02 & 0.06 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Calculated mudslide rise heights and runup heights for the main gully and tributary gully catchments in Dashing Gully.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Location**} & \multicolumn{3}{c}{**Mudslide Specific Weight**} & \multicolumn{3}{c}{**Average Flow Velocity of Mudslide**} & \multicolumn{3}{c}{**Mudslide Flow Impact Pressure P**} \\  & \(\gamma c\) & \multicolumn{3}{c}{**Section \(Vc\) (m/s)**} & \multicolumn{3}{c}{**(kPa)**} \\ \cline{3-8}  & **(kN/m\({}^{3}\))** & \(p\) = 1\% & \(p\) = 2\% & \(p\) = 3\% & \(p\) = 1\% & \(p\) = 2\% & \(p\) = 3\% \\ \hline Main gully catchment & 15.37 & 1.02 & 0.46 & 0.53 & 2.14 & 0.44 & 0.58 \\ \hline Tributary gully catchment & 15.37 & 1.23 & 0.50 & 0.87 & 3.12 & 0.51 & 1.56 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Calculation of overall mudslide impact pressure at the main gully and tributary gully catchments in the Dashing Gully.
## 2 Experimental Program and Data Preparation
### Samples Preparation and Testing  
Sample preparation used three different aggregate gradations with 12.5 mm nominal maximum aggregate sizes to prepare the SMA mixtures, namely SMA type I, SMA type II and SMA type III. The mixture gradation and gradation limits were subjected to the AASHTO M325 testing and the results plotted in Figure 1. The SMA I samples were prepared with coarse and fine aggregates, mineral filler, Bitumen 60/70 and cellulose fibers, the SMA II samples were produced with similar aggregates, cellulose fibers and PMB I as binder, whereas the SMA III samples were using different weight percentages of coarse and fine aggregates, mineral filler, Bitumen 60/70 and cellulose fibers. Out of these, with the SMA I samples, the bitumen content is in a range from 5.4% to 7.0% and retained coarse aggregates on the 4.75 mm sieve was 76.1 wt.%; with the SMA II samples, the bitumen contents varied from 5.5% to 7.5% and the retained coarse aggregates on the 4.75 mm sieve is 71.17 wt.%; and with the SMA III samples, the bitumen content is in a range from 5.7% to 6.9% and the retained coarse aggregates on the 4.75 mm sieve is 74.2 wt%. In summarize, a number of 60 mixtures were prepared.  
For the experimental procedures, the aggregates were first dried in an oven at 105 \({}^{\circ}\)C to 110 \({}^{\circ}\)C until a constant mass was achieved, following the ASTM D6926 norms [29]. The separation of aggregates by dry-sieving was performed next in order to obtain the desired size fractions. The mixing process was conducted following the ASTM D6926 standard [29], using an asphalt mixer with 30 L capacity. Before adding cellulose fiber, the mixture of aggregates and mineral filler were homogeneously mixed together. The corresponding bitumen content was then added into the mixing machine and stirred for 120 s to assure the aggregates and mineral filler are well covered by bitumen [29]. The asphalt cements were heated in order to get the viscosities of 170 \(\pm\) 20 cP and 280 \(\pm\) 30 cP for mixing and compacting procedures, respectively [29]. The Marshall Automatic Compaction was next used for the compacting procedure. The SMA samples were compacted on two faces, using 50 blows of a 4536 g hammer falling from a 457.2 mm of height, following the ASTM D6927 procedure [30]. After cooling down to ambient  
Figure 1: Mixture gradation aggregate for the SMA samples.  
room temperature, the samples were extracted from the cylindrical mould and kept under laboratory conditions. The testing procedure was performed within 24 h after the compaction [30].  
The Marshall test was carried out with cylindrical specimens of 63.5 mm in height and 101.6 mm in diameter, a standard compaction hammer and a cylindrical mold. Marshall Tests were conducted as per ASTM D6927 [30], with an Automatic Stability Testing machine. Before each measurement, the specimen was placed in a hot water at 60\({}^{\circ}\)C for 40 min [30]. Data of the MS and MF were collected after each measurement, whereas the MQ was deduced as a ratio of MS and MF. The SMA materials and testing machine are shown in Figure 2.
# A Brief Survey of Fixed-Parameter Parallelism
## 8 Problems not Fixed-Parameter Parallel Tractable
### Problems that Are \(Nc\) but not FPT (so neither PNC nor FPP)  
By the definition of \(NC\), most problems for which a solution of size \(k\) is required are in \(NC\) for any particular value of \(k\), given that \(k\) is independent of the input size \(n\). We now consider problems that are in \(NC\) for any fixed value of the parameter \(k\), but not \(FPT\). Many \(XP\) problems fall in this class. Examples include \(k\)-Clique, \(k\)-Independent Set, and \(k\)-Dominating Set. In all of these cases, we can assign \(O(n^{k})\) processors, where every processor handles some subset of \(k\) elements from the input of size \(n\). Using a number of processors that is polynomial in \(n\), we can verify whether a subset of \(k\) elements forms a valid solution. We reemphasize that, as far as the \(NC\) definition is concerned, the value of \(k\) is treated as a constant since it is independent of \(n\).  
In fact, the above discussion is based on (and can be generalized into) the following simple observation: a problem parameterized by the solution size \(k\) is always in \(NC\) as long as, given a solution to the problem, it can be verified in \(NC\)-time.
## 2 Materials and Methods
### Technical--Organizational Details
These signals are issued for the location of ships in distress, lifeboats, or survivors. In this regard there are: Emergency Position Indicating Radio Beacons (EPIRB), Emergency Locator Transmitters (ELT), Personal Locator Beacons (PLB), and Maritime Survivor Locator Devices (MSLD). Common frequencies assigned to these devices are:  
* 121.5 MHz and 243 MHz with the use of emission class A3X (amplitude modulated with full carrier and both sidebands) with a modulation factor between 85 and 100%, not allowing overmodulation and N0N. The carrier must be identifiable separately from the 2 sidebands and the level of its carrier must be at least 30% above the sidebands. During the A3X broadcast, depending on the mode of variation of the audio signals that modulate the carrier, a distinction can be made between EPIRB, ELT, PLB or MSLD [18];
* 406.025 MHz using G1B emission class (phase modulation using \(\pm\) 1.1 + 0.1 radians peak, referenced to an unmodulated carrier) for satellite emergency position indicating radio beacons [19];
* 156.525 MHz for EPIRBs operating on VHF Channel 70 (G2B). Communications performed during search-and-rescue (SAR) operations are called \"on scene communication\" [20]. The frequencies used for these communications are 2182 kHz (J3E) and 158.6 MHz (G3E). In communications with SAR aircraft, in addition to the frequencies listed above, the following may be used: 3023 kHz, 4125 kHz, 5680 kHz, 121.5 MHz, 123.1 MHz and VHF channel 6 (156.30 MHz). For passenger ships it is mandatory to have radiocommunication equipment that can make calls on the aero-nautical air/sea VHF frequencies of 121.5 MHz and 123.1 MHz. To perform distress, emergency, and safety communications with seagoing vessels, aircraft will be able to communicate in J3E mode when using the carrier frequency 2182 kHz or 4125 kHz and in G3E mode when using the frequency 156.8 MHz and, optionally, frequency 156.3 MHz. Additionally, during SAR operations, the aircraft can use DSC equipment on the frequency 156.525 MHz and automatic identification system (AIS) equipment on the AIS frequencies 161.975 MHz and 162.025 MHz [17].
# Theoretical and Numerical Simulation Research on Fire of Large-Span Spatial Structures
## 2 Numerical Simulation Model Verification and Fire Source Condition Setting
### Numerical Analysis Method Validation
#### 2.1.2 Finite Element Simulation Verification Results  
In the analysis, the fire source power's ramp-up time curve was adjusted to account for factors influencing combustion, including oil pan ignition and metal melting and dripping. The thermocouple positions within the numerical model mirrored those in the experiments, enabling us to derive the air temperature-time curves. Figures 6 and 7 present a comparison of these curves between experimental data and numerical simulations for representative gas-phase thermocouples. The red curve is the simulated value and the black curve is the experimental value. It can be found that the simulated curve is consistent with the experimental curve, and the maximum temperature difference is not more than 10%. The reason may be the influence of wind in the real environment, which is not considered in the simulation. At the same time, because the temperature measuring points of the test and simulation adopt different acquisition time intervals, the simulated data show greater volatility. In general, the simulation curve is in good agreement with the experimental curve. Consequently, the established numerical model demonstrates its ability to accurately forecast the air temperature fields in Test 1 and Test 4, thereby validating the simulation's validity and precision.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Materials** & **Model Component** & **Density/(kg\(\cdot\)m\({}^{-3}\))** & **Specific** & **Conductivity/(kW\(\cdot\)m\({}^{-1.^{\circ}}\)C\({}^{-1}\))** \\ \hline Concrete & Ground & 2280 & 1.040 & 0.002 \\ \hline Brick & Wall & 750 & 1.040 & 0.004 \\ \hline Ceramic fiber & Fireproof fabric & 118 & 0.670 & 0.036 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermal properties of materials in numerical model [45].  
Figure 5: Numerical model of aluminum alloy reticulated shell fire test.
## 3 Materials and Methods
### Convolutional Neural Network (CNN) Architectures
#### 3.4.3 ResNet-50 CNN Architecture  
ResNet-50 is a well-known version of the Residual Network (ResNet) architecture, recognized for its effectiveness and depth in training complex CNNs. It gained attention for its success in addressing challenges related to training deep neural networks, particularly in large-scale image recognition tasks [47]. ResNet-50 is distinguished by its implementation of residual learning, which allows deep networks to learn from residual information, representing the discrepancy between predicted and observed outputs. Known for its innovative architecture and substantial depth, ResNet-50 has established itself as a fundamental tool in image recognition, consistently outperforming a wide range of computer vision tasks [47; 48].  
The ResNet-50 model, composed of 48 convolutional layers, one max pooling layer, and one average pooling layer, exemplifies the efficiency of the ResNet framework. This design enables convolutional neural networks to operate effectively even with very deep architectures--a challenge that earlier models often struggled to overcome. The ResNet framework, with its strategic incorporation of shortcut connections and residual functions, plays a pivotal role in reducing training errors in deep neural networks. This reassures us of the model's reliability. Additionally, this architecture allows for the bypassing of specific layers through direct connection sets, further enhancing its adaptability. Table 2 illustrates the architectural details of ResNet-50.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Layer Type** & **Output Shape** & **Number of Filters** & **Kernel Size** & **Stride** \\ \hline Input & 227 \(\times\) 227 \(\times\) 3 & - & - & - \\ Convolutional 1 & 55 \(\times\) 55 \(\times\) 96 & 96 & 11 \(\times\) 11 & 4 \\ Max Pooling 1 & 27 \(\times\) 27 \(\times\) 96 & - & 3 \(\times\) 3 & 2 \\ Convolutional 2 & 27 \(\times\) 27 \(\times\) 256 & 256 & 5 \(\times\) 5 & 1 \\ Max Pooling 2 & 13 \(\times\) 13 \(\times\) 256 & - & 3 \(\times\) 3 & 2 \\ Convolutional 3 & 13 \(\times\) 13 \(\times\) 384 & 384 & 3 \(\times\) 3 & 1 \\ Convolutional 4 & 13 \(\times\) 13 \(\times\) 384 & 384 & 3 \(\times\) 3 & 1 \\ Convolutional 5 & 13 \(\times\) 13 \(\times\) 256 & 256 & 3 \(\times\) 3 & 1 \\ Max Pooling 3 & 6 \(\times\) 256 & - & 3 \(\times\) 3 & 2 \\ Fully Connected 1 & 4096 & - & - & - \\ Fully Connected 2 & 4096 & - & - & - \\ Fully Connected 3 & 1000 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: AlexNet’s CNN architecture [30].
## 5 Conclusions  
In this study, we propose a Multi-Agent Reinforcement Learning-Based Joint Centralized-Distributed Routing (MARL-JR) algorithm for Low Earth Orbit (LEO) satellite constellations. To address the highly dynamic network states and link instability inherent in satellite networks, we employ a distributed algorithmic framework with periodic link-state broadcasting. This framework enables the rapid detection of topological changes and timely adjustments to routing strategies, significantly enhancing algorithmic robustness. To mitigate computational overload on satellite nodes and improve initial performance, centralized pre-training is utilized to initialize Q-tables, thereby reducing computational demands and enhancing early-stage performance. Additionally, to tackle uneven traffic distribution in LEO networks, a residual load factor is introduced to prevent congestion and packet loss due to overloaded nodes. The simulation results demonstrate that, compared to conventional centralized (DR-BM) and distributed (QR) algorithms, MARL-JR achieves superior performance in terms of lower average latency, reduced packet loss rate, and a more balanced load distribution.  
Furthermore, MARL-JR exhibits exceptional resilience in handling link failures. Future research will focus on extending the reinforcement learning framework to diverse satellite network scenarios and complex link connectivity conditions to further enhance its applicability and performance.  
Figure 10: Packet Delivery Rate (PDR) under varying link failure counts.  
**Author Contributions:** Conceptualization, L.X.; Data Curation, L.X.; Formal Analysis, L.X.; Funding Acquisition, S.Z.; Investigation, L.X.; Methodology, L.X.; Project Administration, S.Z.; Resources, S.Z.; Supervision, B.L.; Validation, L.X., B.L., S.Z. and Y.Z.; Visualization, L.X.; Writing--Original Draft, L.X.; Writing--Review and Editing, Y.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The relevant data in this paper can be obtained upon request ([EMAIL_ADDRESS]).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Experiments and Results
### Results
However, in few-shot environments, the BERT-LSTM-CRF model showed higher performance, which appears to be due to this being a relatively easier task, while the LDE model, specialized for nested NER, showed relatively lower performance. It is worth noting that SpanNER [14], which was specifically designed for low-resource flat NER, achieves significantly higher performance (71.1 \(\pm\) 0.4) on the CoNLL 2003 English dataset in the five-shot setting compared to our LDE model (42.53 \(\pm\) 7.04). This performance difference can be attributed to SpanNER's approach of directly learning from natural language descriptions of entity classes and its specialized architecture that separates entity span detection from class inference. While SpanNER excels at flat NER tasks, our LDE model offers advantages in nested NER scenarios, where the hierarchical relationships between entities present additional challenges. This comparison highlights the trade-offs between models optimized for specific NER architectures (flat vs. nested) and suggests that incorporating some of SpanNER's techniques for handling label semantics could further improve our model's performance on flat NER tasks.
## 6 Conclusions and Ongoing Work  
This paper introduced a quantum computer-based musical instrument. To the best of our knowledge, this is an unprecedented concept.  
Q1 Synth and _Spinnings_ are examples of how creative practices open the doors to new application pathways for quantum computing technology. Conversely, they illustrate how emerging quantum computing technology is leading to new approaches to musical instrument design and musical creativity.  
Currently, quantum hardware is commonly available only through the cloud. They are geared towards operating in batch processing regimes. But musical interaction requires real-time processing. With Q1 Synth, we started to tackle issues in real-time interaction with quantum computers.  
Developing the instrument as a Web application paves the way for a Quantum Internet of Things (QuIT); Figure 9 shows Q1 Synth running on an iPhone. Additionally, _Spinnings_ glimpsed at how QuIT might be harnessed for creative practices in the not-so-distant future.  
Figure 9: Towards a Quantum Internet of Things (QuIT): Q1 Synth running on a smart phone.  
Quantum computers need classical ones to function. Seamless integration and connectivity of different computing technologies will be of paramount importance to harness quantum computers for novel software solutions. Practical connectivity requirements for QISynth and _Spinnings_ were resolved through OSC-Qasm [26]. Although we have been developing OSC-Qasm to connect music programming environments with quantum hardware, the OSC protocol is generic enough to embrace domains other than music.  
Currently, we are working on _Spookings_, a musical performance with dozens of networked instruments geographically distributed, e.g., different cities.  
The qubits in _Spinnings_ do not interact with each other. For _Spookings_, we are advancing ways to entangle instruments, e.g., put instruments in a Bell state [12], whereby the measurement of one dictates the behaviour of another. This is a musical concept for group performance, which only quantum technology can truly afford. We intend to stage a performance of _Spookings_ in the Metaverse [33].  
A lite version of QISynth is freely available online [34]. As it works only in quantum simulation mode, it does not require a connection to a quantum hardware backend. This version makes sounds solely with the FM synthesis technique.  
Conceptualization, E.R.M.; methodology, E.R.M.; software, P.T. and P.V.I.; validation, E.R.M., P.T. and P.V.I; investigation, E.R.M., P.T. and P.V.I.; resources, E.R.M.; writing--original draft preparation, E.R.M.; writing--review and editing, E.R.M., P.T. and P.V.I.; supervision, E.R.M.; project administration, E.R.M.; funding acquisition, E.R.M. All authors have read and agreed to the published version of the manuscript.  
This work was partially funded by (a) the School of Culture and Society, University of Plymouth, UK and (b) QCS Hub, UK Quantum Technologies Programme, as part of the QuTune Project.  
Not Applicable.  
The authors declare no conflict of interest.