esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Model of Packet Size Adaptation Using Bounded Multi-Armed Bandits
### Bounded Multi-Armed Bandits  
The MAB proposed is composed of K arms, represented as \(1,2,\cdots,N\). An arm is chosen from a non-empty subset S(t) \(\subseteq\{1,2,\cdots,N\}\) to draw at each time step, and we pay a drawing cost ci. The cost budget is B. It means that the total cost is no more than this budget constraint B. When a nonnegative return is received, its distribution is associated with a particular arm. We assume that each arm support has a limited return distribution, because the reward value is usually restricted in an actual application. When the arm i is drawn, the agent receives the mean value of the reward \(\mu\)i. Maximizing the total of the rewards that one gets from drawing the arm is the goal, but the agent does not have the original knowledge \(\mu\)i of each arm, so it is necessary to learn these values to select a strategy to maximize total return. In view of this, the goal is to find the arm whose rewards is the most to draw, whose total reward can achieve the maximum expectation, no more than B [31].  
Formally, A is the arm-pulling algorithm, which can get the finite sequence. NBi(A) is the random variable which is the number of the arm i pulled by A. B is the budget limit. NBi(A) is the random variable, because A depends on the reward is observed. Therefore, we have:  
\[N_{i}^{B}(A)=\sum_{i}I\{i\in S^{d}(t)\} \tag{12}\]  
where S\({}^{d}\)(t) is the subset where _the agent_ chooses the arm i to draw. \(I\{i\in S^{d}(t)\}\) is the indicator function. In order to ensure that the sequence of total cost is no more than B, we have:  
\[P(\sum_{i=1}^{N}N_{i}^{B}(A)c_{i}\leq B)=1 \tag{13}\]  
where P (\(\bullet\)) represents the probability. Furthermore, it assumes that the agents draw each arm number which is no more than L\({}_{i}\). That is:  
\[\forall\text{i: P}(N_{i}^{B}(A)\leq L_{i})=1 \tag{14}\]Now, let \(G^{B}(A)\) be the total return which is got by using the A algorithm to draw the arms and the cost is no more than B. The expectation value of \(G^{B}(A)\) is:  
\[E[G^{B}(A)]=\sum_{i=1}^{N}E[N_{i}^{B}(A)]\mu_{i} \tag{15}\]  
Then, \(A^{*}\) represents the optimal algorithm. It maximizes the total return:  
\[A^{*}=\arg\max_{A}\sum_{i=1}^{N}E[N_{i}^{B}(A)]\mu_{i} \tag{16}\]  
In order to achieve the optimal algorithm \(A^{*}\), we have to understand in advance the value of \(\mu\)i, which is not saved in our case. Therefore, \(A^{*}\) is on behalf of the theory of the optimization algorithm which could not be achieved. However, the regret is defined as the difference between the expected total return and the optimal value \(A^{*}\)[40]. That is:  
\[R^{B}(A)=E[G^{B}(A^{*})]-E[G^{B}(A)] \tag{17}\]  
Here the purpose is to get a sequence of arm pulls which minimizes the regret of the above definition. It is a bounded multi-armed bandits problem. Since we limit \(L_{i}=\infty\mid S(t)\mid=1\), we get the budget restricted MAB. Moreover, when setting \(B=\infty\), we can obtain the standard MAB model.
# A Review of Small-Scale Vapor Compression Refrigeration Technologies
## 2 Small-Scale Refrigeration Technologies
### Active Technologies
#### 2.2.6 Absorption Cooling  
Absorption refrigeration systems have similarity to VCR systems, except for their use of a thermochemical rather than mechanical compressor, in addition to using two working fluids (Figure 7). The refrigerant plays the same role as in vapor compression: cooling in the desired area, while the absorbent captures the refrigerant gas from the evaporator to transfer it to the generator by using a pump. Once in the generator, the working fluids are separated by applying heat from an external source; the refrigerant flows to the condenser while the absorbent returns to the absorber. The most common working fluid pairs used in these systems are ammonia/water and water/lithium-bromide. The main applications of small-scale absorption refrigeration systems include electronics cooling, medical and laboratory applications, food and beverage, climate control, and solar-powered refrigerators and freezers.  
Absorption refrigeration systems are more complex and involve the use of more components than VCR. They also require large heat input to the generator, have high gas pressure ratio, provide intermittent instead of continuous cooling, and have limited cooling capacity due to slow compressor operation [15]. [PERSON] and [PERSON] [38] presented an absorption heat pump for miniaturized or mobile applications with up to 300 W cooling capacity.
# A Comprehensive Review on Music Transcription
## 4 Challenges for Music Transcription
### Instrumentation  
The type of instrument used in a recording can significantly affect the transcription process due to its distinct characteristics, timbres, and playing techniques associated with different instruments [87]. For example, transcribing piano music involves handling multiple simultaneous notes across a wide range [88; 89]. Each musical instrument consists of distinctive timbres, which arise from the combination of the physical attributes and material and playing techniques of singers. Transcribing instruments with diverse timbral characteristics requires advanced algorithms to accurately capture and improve the performance of music transcription. In addition to this, instruments have varying pitch ranges which influence the frequency range of the audio signal. The handling for this problem requires more sophisticated transcription algorithms that must be effective in tracking pitch variations across the spectrum.
# Near-Infrared Quartz-Enhanced Photoacoustic Sensor for H\({}_{2}\)S Detection in Biogas
## 4 Conclusions  
A sensitive H\({}_{2}\)S trace gas sensor based on the QEPAS technique was realized. With Watt-level high-power lasers used as photoacoustic exciting laser, a QEPAS supertophone composed of a QTF with 800 \(\upmu\)m-prong spacing and tubes with diameters of 2.6 mm prevents the generation of large noise contribution caused by the light touching tubes. In this condition, the ultimate noise level of the sensor is determined only by the QTF Johnson noise. The impact of tube position with respect to bare custom QTF on signal amplitude, background noise and SNR of the QEPAS sensor was investigated. The analysis allowed to determine the position of tubes maximizing the SNR, which is useful in the assembly of the QEPAS supertophone. With a laser power of 1.6 W, the sensor achieved an H\({}_{2}\)S detection sensitivity of 330 ppb at atmospheric pressure and room temperature, corresponding to an NMEA of \(3.15\times 10^{-9}\) W\(\cdot\)cm\({}^{-1}\)/(Hz)\({}^{1/2}\). To demonstrate the capability of the reported sensor system for in-situ measurements, the calibrated QEPAS sensor was used to measure H\({}_{2}\)S in a biogas sample from a household biogas digester.  
F.Z. and H.W., methodology; L.D., software; Y.G., L.Y., Y.Y., J.L., and J.R., experiments, software and data curation; S.d.R. and A.Z., formal analysis; P.P. and L.D., investigation and data curation; F.Z. and H.W., writing--original draft preparation; L.D., writing--review and editing; H.W. and L.D., supervision and project administration. This research was funded by National Key Research and Development Program of China (No. 2017 YFA0304203); National Natural Science Foundation of China (Grants #61622503, 61805132, 61575113, 11434007); Scientific Research Foundation (No. 227545028); Changjiang Scholars and Innovative Research Team in University of Ministry of Education of China (No. IRT_17R70); 111 project (Grant No. D18001), Outstanding Innovative Teams of Higher Learning Institutions of Shanxi; Shanxi \"1331 Project\"key subjects construction; Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (No. 2019L0028); National Training Program of Innovation and Entrepreneurship for Undergraduates (201910108001, 2019017287); Applied basic research program of Shanxi province (No. 201801D221174). The authors declare no conflict of interest.
## 6 Conclusions  
The 3D-printing technology provides a broad possibility for manufacturing. The topology optimization technology provides an important way to design high-performance lightweight structures. This paper proposed a combination of topology optimization and 3D-printing technology for designing and manufacturing of an aerospace bracket considering the fatigue performance. In this approach, the fatigue damage problem of the bracket in cyclic loading was transformed into a topology optimization problem that controls the bracket with minimal strain energy under a variety of extreme operating conditions, based on the real operating environment of the bracket. Under the guidance of the topology optimization result considering fatigue performance, a new aerospace bracket was redesigned by CAD software, and fabricated by SLM. The mass of the fabricated new bracket was reduced by 37% compared with the original bracket structure. Both the numerical simulation and fatigue test were implemented to verify the reliability of the new bracket structure. The numerical simulation shows that the new bracket has a reasonable stress distribution compared with the original bracket. The fatigue test perfectly fulfills the engineering specifications proposed at the beginning of the bracket design. Both approaches validate the effectiveness of our proposed new design method. This work indicates that the integration of the proposed topology optimization design method and additive manufacturing can be a powerful tool for the design of lightweight structures considering the fatigue performance.  
Conceptualization, Q.W. and C.W. and Z.L.; methodology, Q.W. and Z.L.; software, Z.L.; validation, Y.C. and C.W.; investigation, Y.S. and P.G.; resources, Y.Y. and Z.L.; writing--original draft preparation, Y.C.; writing--review and editing, Y.C. and Z.L.; formal analysis, Y.C. and C.W.; visualization, Y.S. and P.G; supervision, Q.W. and Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Foundation of China (grant Nos.51675506); the Foundation for Excellent Young Scholars of Jilin Province, China (grant number 20190103015 JH).  
Not applicable.  
If readers want to obtain original data in the paper, please contact with correspondence Z.L.  
The fatigue test in this work was implemented by the AECC Beijing Institute of Aeronautical Materials (Beijing, China) and AVIC Touchstone Testing Innovation (DaChang) Company (Langfang City, Hebei Province, China).  
The authors declare no conflict of interest.  
Figure 17: The numerical simulation result of reconstructed geometry subjected to 100 kN vertical load.
## 3 Proposal of Ground Station Direction Detection Method Using Color Markers
### Color Marker Detection Algorithm
#### 3.1.3 RCM Detection  
The blob coordinates \(B_{n}\big{(}x_{gn},y_{gn}\big{)}\) that best match the pattern of the predicted marker coordinate points \(M_{i}(x_{i},y_{i})\) are calculated by matching the blob coordinates on the acquired image with the predicted marker coordinate points in the image plane calculated so far. For example, consider the case of nine color markers; each marker is placed, with the RCM at the center, and spaced at equal intervals, as shown in Figure 4. Consider a graph a data structure representing the relationship between markers. The data structures representing the connection between adjacent color markers can be said to be an undirected graph \(\mathrm{G}=(\mathrm{V},\mathrm{E})\), represented by a finite set V of marker coordinates and a finite set E of vectors connecting them.  
We focus on each of the blob coordinates \(B_{n}\) obtained in Section 3.1.1, respectively, compare the predicted marker placement with the surrounding blob coordinate placement, assuming that \(B_{n}\) is the RCM, and compute the error. Recursively calculate the sum \(L_{n,0}\) of the errors for the entire graph, assuming coordinates \(V_{1}=B_{n}\) in Equation (6).  
Figure 4: An undirected graph consisting of 9 markers and 12 lines connecting them.  
Figure 3: Relationship between the space-fixed coordinate system \(\mathrm{O_{s}}\), the camera coordinate system \(\mathrm{O_{c}}\), and the marker coordinates.  
\[L_{n,\,i}=\min(B_{m}-(V_{i}+(M_{i+1}-M_{i})))+L_{n,i+1} \tag{6}\]  
where \(B_{m}\) is the set of blob coordinates excluding the blob coordinate of interest \(B_{n}\). \((M_{i+1}-M_{i})\) is a vector indicating the coordinate direction of the next node calculated from the marker coordinate points obtained from the initial value calculation of the marker coordinate point set. Note that \(L_{n,i+1}=0\) when \(i\geq 9\).  
The above calculations are applied to all blob coordinates, and the blob coordinate with the smallest sum of errors in the entire graph is identified to be the RCM.
## 3 Theoretical Background of a New Phase-Sensitive Technique
In order to extract this information it is possible to calculate the theoretical backward reflected Power, as a function of the distance \(d\) between the waveguide tip and the sample, and to perform the best fit of the experimental data, which is obtained by mechanically changing the distance, to this theoretical formula. The typical curve shows peaks spaced half of the radiation wavelength, due to the interference of the two components of the radiation inside the waveguide. This periodical behavior is damped when the distance \(d\) between the waveguide and the sample is increased, because, due to the natural diffraction broadening, the amount of reflected radiation that is injected back into the waveguide decreases with \(d\). By using this method, it is possible to obtain the values for \(\beta\) and \(\varphi_{s}\). Data in Figure 4 are the results of the reflection of an aluminum plate, and these data make a good fit of the theoretical model with parameters \(w_{0}=0.82\) mm, \(\alpha=0.28\), \(\beta=0.97\), \(\varphi_{s}=-3.14\),\(\varphi_{r}=-1.54\). The phase shift is not -\(\pi\) for a dielectric material, and the best fit can then be used to find the real and imaginary part of its refraction index. In Figure 5, the experimental data for the blue cobalt oil (BCO) on the cyclic olefin polymer (COP) plate and the best fit are reported. Parameters for obtaining the best fit allow for predicting optical information regarding the material. The THz Free Electron Laser (FEL) source, which operates between 100 and 150 GHz, is an ideal source for studies in art conservation. In fact, due to its peculiar pulse time structure, the FEL is able to deliver to the sample a very low average power and a very high peak power. This avoids any rise in the heating of the sample, which could result in damages while making it possible to have a very high S/N ratio thanks to the high peak power of the source. The main advantage of this method with respect to the conventional THz-TDS imaging technique involves a faster acquisition of the image and the possibility of obtaining information about the optical properties of the sample by analyzing the phase of the reflected radiation. The mechanical scan of the distance between the sample and the probe head can be replaced by the electronic scan of the source frequency, speeding up the data acquisition time. Comparing this alternative technique to other THz imaging techniques operating with CW sources, we notice that the physical mechanism  
Figure 4: Behavior of the reflected signal as a function of the distance from the sample. Experimental points (dots) are compared with the theoretical model (solid line). Figure 5: Experimental data for blue cobalt oil (BCO) (dots) and best fit (solid). that is responsible of the so called \"etalon effect\" [35], that is sometimes seen as a limit of coherent source imaging, producing annoying interference fringes on the image, is now used to obtain optical information about the sample.
# Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management
## 2 Materials and Methods
### Optical Data Based Classification
#### 2.3.4 Validation  
Validation data for the 2016 inland excess water period were derived from a pan-sharpened multispectral SPOT-7 image. Water surfaces were extracted using an automated clustering method (ISODATA with 60 classes), combined with manual selection of water classes. From the classified raster, gaps and separated water pixels smaller or equal to the mapping resolution (\(10\times 10\) m) were removed using the Sieve method. Permanent water bodies, linear infrastructures, built-up areas and shadows were also removed. In the last step, the validation data were resampled and aligned to the processing grid of Sentinel-2 data (Figure 5).  
The 2018 validation was based on an orthophoto mosaic covering an area of 20 km\({}^{2}\) in the northwest part of the study area. It was visually interpreted, and inland excess water patches were manually digitized. Polygons were filtered by deleting objects smaller than 100 m\({}^{2}\) and resampled and aligned to the processing grid (Figure 5). The rasterized validation datasets and the result inland excess water maps were compared using a cross-tabulation method. The producer's and user's accuracy and omission and commission error were calculated for the water and no water classes.
## 2 Context of the Experiments  
### R&D Center of Cadarache  
The experiments take place in the Cadarache research center of the French Alternative Energies and Atomic Energy Commission, or CEA (the French acronym for \"Commissariat a l'Energie Atomique et aux Energies Alternatives\"). It is a 60 year-old research center located near Aix-en-Provence. This center is spread over 1600 Ha of which 900 Ha are fenced (by a 22 km fence). Cadarache center consists of 480 buildings, including office buildings but also research laboratories. The center is directly connected to the electricity network. The CEA manages its own water network, heat network, and medium voltage electricity distribution network (made up of 18 15-kV loops). The research center plays the role of Distribution System Operator (DSO). A public lighting network and an EVCI are connected to this power network. Two thousand five hundred CEA employees work at the Cadarache site, as well as partner companies. In total, around 5000 people work at Cadarache. The CEA offers a private bus service to its employees for commuting. Thus, Cadarache center can be seen as a small town privately owned and managed by the CEA.  
The CEA conducts research on solar thermal energy and on solar photovoltaics. There are therefore PV solar plants installed on the Cadarache site in two different locations, called the internal solar platform and the Megasol platform. On the internal platform, the CEA tests and evaluates innovative PV solutions ranging in size from modules to a few tens of kilowatts systems. On Megasol's platform, the CEA tests these innovative solutions on four PV plants totalizing 12 MWp, owned by industrial partners.
## 6 Conclusions  
We reviewed two methods of enhancing the Brillouin Stokes signal in PFGI-POFs for future practical applications as well as for detailed investigations of the BGS.  
Figure 10: (**a**) Normalized BGS at pump power of 14.5, 17.5, and 23.5 dBm. (**b**) Brillouin linewidth _vs._ pump power.  
In the first half of this paper, using an experimental setup similar to BOCDA system [16,17] based on the pump-probe technique, SBS in a PFGI-POF with 120-\(\upmu\)m core diameter was, for the first time, observed at 1.55 \(\upmu\)m. The BGS was detected with extremely high S/N ratio, even with a 1-m PFGI-POF, scrambled polarization state, and no averaging. We also investigated the BGS dependences on probe power and temperature, and confirmed that SBS in a PFGI-POF measured with this technique can also be utilized to develop high-accuracy temperature sensors.  
In the latter half, the influence of the core diameter and fiber length on the Stokes signal was investigated by use of PFGI-POFs with 62.5-\(\upmu\)m core diameter. First, using 5-m PFGI-POFs, we showed that extremely high Stokes power can be obtained compared to that of a PFGI-POF with 120-\(\upmu\)m core, and estimated the Brillouin threshold power to be 53.3 W. Then, we experimentally showed that using a PFGI-POF longer than \(\sim\)50 m is not an effective way to enhance the Stokes signal. We also theoretically predicted that it is difficult to decrease the Brillouin threshold power of PFGI-POFs at 1.55-\(\upmu\)m wavelength down to that of km-order-long silica SMFs even when their core diameter is sufficiently reduced to satisfy the single-mode condition. Finally, we investigated the Brillouin linewidth as a function of pump power, and confirmed the linewidth narrowing effect.  
Thus, the Brillouin Stokes signal has been drastically enhanced. It is also natural to predict that the Stokes signal will be further enhanced if we induce SBS by pump-probe technique using PFGI-POFs with 62.5-\(\upmu\)m core diameter or less. Therefore, we are sure that the first demonstration of distributed temperature sensing based on Brillouin scattering in PFGI-POFs will be possibly carried out in the near future, based on optical correlation-, time-, or frequency-domain technology. We believe our work to be a significant technological step toward the implementation of such sensing systems and other POF-Brillouin devices.
# An Image Processing-Based Method to Analyze Driver Visual Behavior Using Eye-Tracker Data
## 1 Introduction
The exact position on the screen may show different points of the scene according to simulation dynamics. The presented approach is based on an image processing technique that can recognize the boundaries of the road and, to them, the point where the driver is looking, whether inside or outside the road section. Our research is driven by the need to directly integrate eye-tracking data with simulation to analyze driver behavior comprehensively. The analysis of eye-tracker video recordings alone presents limitations due to the lack of correlation with the simulation environment. To overcome this, we propose a method that links eye-tracking data with simulation in virtual reality settings. This approach efficiently analyzes driver gaze data, even with devices lacking depth and lane data, offering advantages in understanding driver behavior and attention allocation. Examining gaze positions at road borders, our study aims to significantly enhance understanding of driver cognition and contribute to substantial improvements in road safety measures. The paper includes five other sections. In Section 2, a review of papers about the driver's visual behavior and its relationship with driving attention is carried out; in addition, the techniques used to analyze glance data are also presented here. In Section 3, the Methodology adopted for the analysis is described. The results are presented in Section 4 and then discussed in Section 5. Finally, in Section 6, the Conclusions are drawn.