esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Segmentation of Ethnomusicological Field Recordings
## 4 Discussion  
Although it seems that with the advent of deep learning, which is already solving many difficult classification problems, music-speech classification and segmentation should be a relatively trivial task, recent work shows that we are still quite far from a perfect solution. Audio materials can be very diverse, so developing a robust approach that would process broadcast recordings on one side, and field recordings, on the other side of the quality spectrum, is difficult. The type of music the algorithms are trained on--e.g., commercial music vs. folk music, is also important, as is the goal of segmentation. Segmenting broadcast recordings, where the goal may be to detect all broadcast music, even short clips in the presence of speech (as in the recent Mirex evaluation [10]), is a different task to segmenting field recordings, where division into broader units is required, and short local changes need to be largely ignored. Also, as we show in our analysis of a wide spectrum of field recordings, these may require a finer level of classification than simply speech and music.  
Therefore, we believe that research into specialized methods, such as ours, which is specifically targeted to segmentation of field recordings, is still needed. To disseminate our work, we make the dataset used for training our models, as well as the code for training and segmentation, available to the research community. To assist ethnomusicologists with their work, we also developed a segmentation tool, SeFiRe, which we make available to interested researchers. SeFiRe incorporates our classification and segmentation algorithms (as presented in this paper, as well as a music-speech segmenter [23]) to visualize and automatically segment field recordings according to their contents. The segmentation can also be manually corrected and exported.  
SeFiRe visualizes field recordings as color-coded waveforms, where the color matches the contents. The four classes, speech, solo singing, choir singing, and instrumental performances, are mapped into: Dark blue, light blue, green, and red color. The display color of each short audio excerpt is interpolated between these basic colors according to the probability that the excerpt belongs to the respective class. Thus, where the classification algorithm is uncertain between two classes (e.g., solo vs. choir singing), the color reflects the uncertainty and is placed somewhere between the two.  
The user interface of SeFiRe is presented in Figure 4. The user may load a new recording, play back and navigate the recording, and perform segmentation, whereby they can choose the algorithm as well as the sensitivity of segmentation, which influences the number of placed boundaries. The user may also manually correct, annotate, and store the resulting segmentation.  
Figure 4: The SeFiRe (Supplementary Materials) user interface.
# Collective Communication Performance Evaluation for Distributed Deep Learning Training
## 6 Experiments in the PyTorch Environment
### Parameter Server Subroutine  
#### 6.1.1 Broadcast  
The results of the broadcast subroutine in the parameter server method are presented in Figure 19. In the MPI_Bcast, it is evident that latency increases in the order of bare metal, singularity, single docker, and cross-docker environments. There were some peculiarities in latency as the number of GPUs increased. We found that it took less time on four GPUs than single-container latency on three GPUs. For GLOO_bcast, the latency in the bare metal and singularity environments differs by less than 3%. It can be seen that the gap between single docker and bare metal is relatively large.  
In the case of NCCL_bcast, the difference between bare metal and singularity is less than 1%, but the latency is approximately 19% higher in single docker, which is the same single-container environment as singularity. Moreover, when comparing bare metal to cross docker in a four-GPU environment, cross docker exhibited 89% higher latency.
## 4 Results and Discussion
### Second Part: The Influence of Plasticization Parameters with Moisture Content  
#### 4.2.1 Influence and Comparison of Product Part Forming under Different Back Pressure  
Figure 6 shows a comparison of product part samples forming under different back pressure value (5 Mpa, 10 Mpa and 15 Mpa) in which the moisture-absorbed PC material is molded at a 20 mm/s injection speed, with a barrel temperature of 300 \({}^{\circ}\)C, and plasticization parameters of 50 rpm screw rotational speed. With sufficient pressure holding time, this ensured that the molded product was completely filled without short shots. From the figure sample, it can be observed that when the plasticization parameter back pressure was 5 MPa (A10--Figure 6), the silver marks caused by the influence of water vapor had a larger area of distribution on the transparent product part surface. However, the surface defect seems to reduce with increase in backpressure (A13--10 Mpa, A16--15 Mpa). Similarly, Figure 7a shows the measured surface gloss value under different back pressure, showing the change of the surface gloss of the injected sample. The surface gloss of the sample will change due to moisture defects and, from the gloss measured values, it can be seen that with increase in back pressure, the area of the product's silver mark distribution has been significantly reduced, and the frequency of defects also gradually decreased. Thus, when the back pressure was 15 Mpa, the product was transparent (A16--Figure 6) with high gloss value, and no defects were found in the sample under this parameter. Furthermore, the back pressure acting on the melt plasticization process helped to avoid the uneven melt density caused by the rapid retreat of the plumger. Therefore, increasing the back pressure will extend the plumger retreat time during the plasticization stage, and at the same time, the material will be stored. Moreover, the heat generated by friction with the wall of the material tube (barrel) will also increase during the process, and the energy transfer can effectively destroy the bubble film mixed with water vapor in the melt. Figure 7b shows the comparison of the standard deviations of the product molding weight. In this group comparison, the product weight stability is shown to be maintained at a better level. This indicates that the back pressure during the plasticization process can improve the melt uniformity. However, excessive temperature rises caused by increase in back pressure may affect the quality of the melt.  
Figure 6: Product part appearance molding under different back pressure.
# Tertiary Waves Measured during 2017 Pohang Earthquake Using an Underwater Glider
## 3 Results  
### Noise Measurement of the Underwater Glider  
#### 3.1.1 Glider's Operation Noise Measurement at Laboratory Tank  
Sound pressure level (SPL) is usually expressed as twenty times the logarithm to base 10 of the ratio of the root-mean-square sound pressure to the reference sound pressure: SPL = 20 log (\(\hat{p}\)/\(p_{0}\)) where \(\hat{p}\) and \(p_{0}\) are the root-mean-square sound pressure and reference sound pressure, respectively. SPL has units of dB re 1 uPa\({}^{2}\). SPL values of the driving- and sensor-part noise sources are discussed.  
Noises from both the driving and sensor parts of the underwater glider were measured in a laboratory tank (Figure 2). The size of the tank is 20 m (W) \(\times\) 50 m (L) \(\times\) 10 m (D) and water depth in the tank was about 8 m. Since the water tank contained fresh water, the buoyancy of the underwater glider was adjusted in advance to be neutrally buoyant in fresh water. In order to record the noise of the underwater glider, a hydrophone was mounted on the underwater glider body. The reference value was \(-\)168 dB re 1 uPa\({}^{2}\). The gain and resolution of the recorder were 30 dB and 16 bits, respectively.  
Figure 1: Components of the Slocum Underwater Glider.  
Except for the tail, which was used for radio frequency communication with the operator, the underwater glider was fully submerged. Noise was generated by inputting an operation command arbitrarily to measure the SPL of each driving and sensor component (Figure 3). Fin noise measurement, however, was not possible because the tail was not submerged, to allow for radio frequency communication. The noise analysis of fin operation was obtained from the recorded data at sea, as described in the next section. The pumped CTD did not work due to the low electrical conductivity value of the fresh water used in the tank; so, the pumped CTD noise analysis is also described in the next section. The Piston noise level spectrum rises above the background level by more than 5 dB in all frequency bands up to 10 kHz (Figure 4a). The Pitch noise level likewise rises by more than 5 dB in frequency bands above 1 kHz (Figure 4b). The Air bladder noise level rises more than 10 dB at 155, 310, 463, 522, 552, 618 Hz and to a lesser extent in a broad frequency band (Figure 4c). The Altimeter noise level rises more than 5 dB at frequencies above 100 Hz (Figure 4d). To record noise from the flow between the underwater glider and the hydrophone, the glider performed a \"yo\" (descent and ascent) mission. By measuring the noise from repeated yo missions, it was confirmed that there was no flow noise caused by turbulence between the underwater glider body and the hydrophone.  
Figure 3: Recorded noise signal when the underwater glider operates in the laboratory tank.  
Figure 2: A underwater glider being prepared for immersion into a tank and diving.
###### Abstract  
This paper exploits for the first time the use of machine learning (ML) based techniques to identify complex structured light patterns under free space optics (FSO) jamming attacks for secure FSO-based applications. Five \(M\)-ary modulation schemes, construed using Laguerre and Hermite Gaussian (LG and HG) mode families, were used in this investigation. These include 8-ary LG, 8-ary superposition-LG, 16-ary HG, 16-ary LG and superposition-LG, and 32-ary LG and superposition-LG and HG formats. The work was conducted using experimental demonstrations for two different jammer positions. The convolutional neural network (CNN)-based ML method was utilized to differentiate between the stressed mode patterns. The experimental results show a 100% recognition accuracy for 8-ary LG, 8-ary superposition-LG, and 16-ary HG at 1, \(-2\), and \(-2\) dB signal-to-jammer ratios (SJR), respectively. For SJR values \(<\) 0 dB, the standard LG modes are the most affected by jamming and are not recommended for data transmission in such an environment. Besides, the accuracy of determining the jammer direction of arrival was investigated using CNN and a simpler classifier based on linear discriminant analysis (LDA). The results show that advanced networks (e.g., CNN) are required to achieve reliable performance of 100% direction determination accuracy, at \(-5\) dB SJR, as opposed to 97%, at 2 dB SJR, for a simple LDA classifier.  
free space optics; structured light; jamming; machine learning +
Footnote †: journal: _Photonics_
## 1 Introduction  
Field-programmable gate arrays (FPGAs) are eminently utilized to develop the latest complementary metal oxide semiconductor (CMOS) technology due to their high volume density, scalability, and ability to cope with the highest performance demands for digital and mixed-mode analog applications. However, after the rapid downscaling in FPGAs, the device encounters challenges, including increased noise sensitivity, manufacturing variability, and reliability concerns [1; 2; 3; 4]. These factors escalate the degradation of the chip performance, which eventually reduces the lifespan of the product. Numerous reports elaborate on delays that occur in FPGAs, which are detrimental to chip performance [5; 6; 7; 8; 9; 10]. The optimization of delays has been studied by using key testing parameters, such as lookup tables (LUTs) and algorithm analysis [11; 12]. [PERSON] et al. studied the dependency of application types with fault delay [13]. The prediction of interconnect delays was studied by [PERSON] et al. [14]. It was deduced that the timing model is beneficial for the physical synthesis flow of an FPGA design [15; 16].  
It has been reported that the delay is the effect of a negative bias temperature instability (NBTI), which is a critical factor of FPGA degradation [17; 18; 19]. [PERSON] et al. [20] developed a framework that caused a delay of less than 500 ms. Previous studies primarily concentrated on the NOR gate chains, which are found to reduce delay degradation [21]. The effects of delay on switching activity were discovered by [PERSON] et al. [22], who reported on the analysis of the reduction of the circuit speed. Meanwhile, [PERSON] et al. [23] believed that the delay component is a temperature-activated process in irradiated devices. [PERSON] et al. [24] observed that the delay is caused by an increase in the current and subsequent redistribution of the voltage. The nominal delay degradation for the ISCAS Benchmark indicates that the delay degradation in semiconductor devices can be as low as 617 ps [25].  
Seven output ranges of a phase-detection aging sensor with a lifetime prediction table for different FPGAs manufacturers are proposed to guide system designers and industrial players through simulation and experimental validation. After that, an automatic clock correction method was proposed and validated through experiments to stabilize the system's performance with the best clock signal for the entire FPGA system to counter the aging degradation and maintain the system's performance.  
This paper is organized as follows. First, Section 2 describes the lifetime reliability sensing in FPGAs and Section 3 describes the methodology of analyzing aging sensors with multiple point detection. Then, Section 4 compares multiple types of delay frequencies, which was validated through the experimental work presented in Section 5. Finally, the conclusion is presented in Section 6.
## 5 Conclusions and Outlook  
Synthesizing the results and findings of the study, we conducted an in-depth performance comparison and detailed analysis of a multi-intent recognition task for the Winter Olympics problem. Through a comprehensive understanding of the performances of different models, we found that each model had different advantages and disadvantages in dealing with long text and contextual relevance. In particular, in facing the context of long text and contextual relevance, such as the Winter Olympics multi-intent problem, the TextCNN model focused more on local key features, while the BiLSTM-Multi-heads attention model was better at capturing the semantic features of long-distance text. In terms of model fusion, we used the self-constructed BCNBLMATT model, which combined the advantages of both the BiLSTM-Multi-heads attention and TextCNN models, and the model achieved a significant performance improvement compared to a single model in terms of the Macro_P, Macro_R, and Macro_F1 values, respectively, reaching 1.2%, 2.52%, and 2.61%. This highlights the superiority of the Multi-model fusion strategy, which extracted the semantic features of the problem text more comprehensively and successfully meets the challenges of complex tasks. In addition, we verified the effectiveness of the Multi-head Attention mechanism in improving the Macro_F1 value by 1.9% compared to the conventional Attention mechanism, highlighting the importance of choosing the Multi-head Attention mechanism when solving multi-intent problems.  
Overall, by deeply exploring the performance of the BCNBLMATT model and cleverly integrating the advantages of different models, this study provides a more comprehensive understanding of solving the multi-intent recognition task for Winter Olympic problems. These findings have a guiding significance for academic research and provide strong insights for practical applications. The future research direction can consider integrating the BCNBLMATT model into the Winter Olympics Q&A model to solve more complex problems, provide more rapid, convenient, and accurate information query services for the Winter Olympics, and provide strong support for the development of China's sports industry.  
Conceptualization, P.L. and Q.L.; methodology, Q.L.; software, Q.L.; validation, Q.L., P.L. and Z.C.; formal analysis, Q.L.; investigation, Q.L. and Z.C.; resources, Q.L. and Z.C.; writing\(-\)original draft preparation, Q.L.; writing\(-\)review and editing, Q.L.; supervision, P.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the national key research and development program of China grant number 2020 YFF0305300, and the National Natural Science Foundation grant number 61762029.  
The authors declare no conflict of interest.
# Monte Carlo Analysis of the Intensification Factor of Design Response Spectra for Hoisted Loads
## 3 Dynamic Model of the Hoist Load  
Cranes are well known devices and consist of a structure supporting the pivot point that is then connected to the hoisted load via one or more vertical ropes, usually folded back via some pulleys located near the hook. The motors are located either close to the pivot point or at the base of the support structure and enable the motion of the pivot point, usually in a plane, and the winding or unwinding of the ropes.  
The resulting system has vertical dynamics, given by the elasticity of the ropes, and horizontal pendulum-like dynamics. We neglect here this horizontal dynamics because the natural frequency is low, e.g., below 0.25 Hz for lengths equal or above 5 m, which are typical for research infrastructures. The reader can see, for instance, the cranes used in the high-luminosity upgrade of the LHC at CERN [15] (see Section 17.6.4 of the technical report). Low natural frequencies are decoupled from the typical resonances of the structure and are not excited in a significant way. The crane trolley should also be assumed blocked during a seismic event due to the use of emergency braking.  
Considering only the vertical dynamics, Figure 3, and assuming that the motion of the pivot point is given by the supporting structure and is determined by the design of  
Figure 2: Qualitative shape of the floor RS used for the Monte Carlo simulation. Letters A, B and C refer to the three parts of the piecewise function in Equation (5).  
the support structure, the simplified motion equation of a hoist load with mass \(m\), in the non-inertial frame of the pivot, is as follows:  
\[ms(t)=-k(s(t)-l_{0})-cs(t)-a_{rs}(t)+mg \tag{6}\]  
where \(s(t)\) is the difference between the motion of the hoist load and the one of the pivot, \(x(t)-y(t)\). The input acceleration \(a_{rs}(t)\) is equal to \(\dot{y}(t)\). The stiffness and damping are non-linear functions:  
\[k=\begin{cases}0&s(t)\leq l_{0}\\ K&s(t)>l_{0}\end{cases} \tag{7}\]  
\[c=\begin{cases}0&s(t)\leq l_{0}\\ C&s(t)>l_{0}\end{cases} \tag{8}\]  
This means that the hoist load is in free fall when the crane rope is not in tension. The parameter \(l_{0}\) is the length of the rope without loads and \(g\) is the gravity. The typical elastic modulus of a wire rope is 91 GPa [16]. The construction concept of a crane of this type sees 4 ropes in parallel, with a diameter between 15 mm and 20 mm for weights up to 20 tons. The filling factor of the rope is about 75%. The stiffness \(K\) of the rope in tension is then about 700 kN/m for a length of 40 m.  
The damping \(C\) is defined in terms of the damping ratio, with the usual  
\[C=2\zeta\omega_{n} \tag{9}\]  
where \(\zeta\) is the damping ratio and \(\omega_{n}\) the natural frequency in rad/s.  
The simple dynamic model is required for running a large number of simulations with a Monte Carlo approach.
# Petri Net Modeling for Ising Model Formulation in Quantum Annealing
## 4 Binary Quadratic Net Construction from Problem Domain Petri Nets
### Application Example 1 (Marking-Based Construction): Traveling Salesman Problems  
We consider the traveling salesman problem as an example of a marking-based construction. Let \(N=(P,T,F,TS,FD)\) with \(P=\{P_{1},P_{2},\ldots,P_{n}\}\) and \(T=\{T_{i,j}|\forall(P_{i},P_{j})\in P\times P\}\) be a timed Petri net, where \(FD:T\rightarrow\mathbb{N}\) is a function that returns the firing duration. Each place \(P_{i}\) corresponds to a city, and the transition \(T_{i,j}\) denotes the path from \(P_{i}\) to \(P_{j}\). The firing of \(T_{i,j}\) indicates the movement from \(P_{i}\) to \(P_{j}\). The initial marking \(\mathbf{M}_{0}\) includes only one token at \(P_{1}\).  
Because the salesman visits each city only once from the problem definition,  
\[\sum_{k=0}^{|P|-1}M_{k}(P_{i})=1,\forall P_{i}\in P. \tag{48}\]  
In addition, because the salesman should be at one place for each step,  
\[\sum_{i=1}^{|P|}M_{k}(P_{i})=1,k=0,1,\ldots,|P|-1. \tag{49}\]  
The objective function is to minimize the total time to visit all cities and return to the starting place.  
\[distance=\sum_{i=1}^{|P|}\sum_{j=1}^{|P|}(FD(T_{i,j})\cdot\sum_{k=0}^{|P|-1} M_{k}(P_{i})M_{k+1}(P_{j})) \tag{50}\]By converting the constraints and objective function in the problem-domain Petri net into the binary quadratic nets, we have  
\[H_{\text{\emph{t}}isitingOnce}(\mathbf{M}) =\sum_{i=1}^{|P|}(\sum_{k=0}^{|P|-1}M(\hat{p}_{i}^{k})-1)^{2} \tag{51}\] \[H_{\text{\emph{s}}ingleness}(\mathbf{M}) =\sum_{k=0}^{|P|-1}(\sum_{i=1}^{|P|}M(\hat{p}_{i}^{k})-1)^{2}\] (52) \[H_{\text{\emph{distance}}}(\mathbf{M}) =\sum_{i=1}^{|P|}\sum_{j=1}^{|P|}(FD(T_{i,j})\cdot\sum_{k=0}^{|P|- 1}M(\hat{p}_{i}^{k})M(\hat{p}_{j}^{k+1})). \tag{53}\]  
The total binary quadratic net is as follows:  
\[H_{\text{\emph{t}}otal}(\mathbf{M})=A\cdot H_{\text{\emph{t}}visitingOnce}( \mathbf{M})+B\cdot H_{\text{\emph{s}}ingleness}(\mathbf{M})+\mathcal{C}\cdot H _{\text{\emph{distance}}}(\mathbf{M}), \tag{54}\]  
where \(A\), \(B\), and \(C\) denote the scale factors of the corresponding subnets. The formulation is equivalent to the QUBO model in [6].
## 5 Evacuation Modeling  
### Simulation Tool  
The people evacuation process was simulated using the agent-based egress calculation module of FDS, known as Evac (2017). This code treats each agent as an individual user with specific characteristics (e.g., walking speed) and escape strategy (e.g., choice of exit and escape route). Evac permits evacuees to move in a 2D space and considers the fact that environmental conditions resulting from a fire (i.e., concentration of toxic substances) can affect user behaviour such as walking speed. The code estimates the number of potential victims based on their exposure to toxic gases only. The initial position of tunnel users when the fire starts (i.e., t = 0), the number of users within each queued vehicle, walking speed, pre-movement time, and escape direction are the main input data to implement the Evac code.  
It is to be stressed that Evac does not take into account the effects of temperature and radiation on evacuees, which were computed in our study according to the procedure reported in [PERSON] et al. (2017).
## 4 Simulation Results
### Throughput Results  
The throughput of different services is compared in Figure 7. It can be seen from Figure 7 that the throughput of the proposed algorithm is larger than the throughput of LEACH and AODV, no matter what kind of service. The reason is that the proposed algorithm can achieve lower end-to-end delay and packet loss rate, enabling more data packets to be delivered in a certain time. With the increase in vehicle distance, the throughput gradually decreases, which is because the multiples hops influence the packet-delivery delay and the number of received packets. In addition, by observing the throughput of the three service types, it can be found that the strict security service has the smallest throughput, and the non-security service has the largest throughput, which is appropriate to the services' characteristics. Non-security services are usually games, video, and other high data-traffic services, and from the simulation of throughput it can be seen that the proposed algorithm can guarantee high throughput.