esa-sceva/satcom-chunk-collection huggingface dataset:
# Kolmogorov-Arnold Finance-Informed Neural Network in Option Pricing
## 7 Discussion
### Discussion on American Put Pricing
#### 7.2.2 Convergence
\begin{table}
\begin{tabular}{l r r r r r r} \hline \hline MEAN & \(\mathcal{L}_{total}\) & \(\mathcal{L}_{initial}\) & \(\mathcal{L}_{financial}\) & \(\mathcal{L}_{boundary}\) & \(\mathcal{L}_{boundary,0}\) & \(\mathcal{L}_{boundary,0}\) \\ \hline KAFIN & 80.5322 & 15.9445 & 0.2464 & 64.3412 & 34.6914 & 29.6498 \\ FINN REG & 1615.1479 & 163.6442 & 1.5037 & 146.5673 & 146.3641 & 0.7128 \\ FINN FFN & 2969.4533 & 2969.4227 & 0.0306 & 316.0591 & 2652.2803 & 1.0833 \\ FINN DNN & 3092.3232 & 3092.3232 & 0.0000 & 347.3228 & 2698.7922 & 46.2082 \\ FINN MLP & 2519.5703 & 2519.1787 & 0.3916 & 252.6796 & 2264.6677 & 1.8314 \\ \hline STD & \(\mathcal{L}_{total}\) & \(\mathcal{L}_{initial}\) & \(\mathcal{L}_{financial}\) & \(\mathcal{L}_{boundary}\) & \(\mathcal{L}_{boundary,0}\) & \(\mathcal{L}_{boundary,0}\) \\ \hline KAFIN & 1031.4969 & 212.0296 & 0.1620 & 822.9333 & 336.1925 & 605.8546 \\ FINN REG & 637.2571 & 637.6745 & 0.5911 & 101.2585 & 537.3467 & 2.3560 \\ FINN FFN & 246.4827 & 246.4926 & 0.0104 & 39.5853 & 207.5623 & 0.3016 \\ FINN DNN & 180.0077 & 180.0077 & 0.0000 & 22.8652 & 173.2816 & 14.6223 \\ FINN MLP & 320.4845 & 320.6253 & 0.1511 & 48.2389 & 269.3868 & 6.4497 \\ \hline MAX & \(\mathcal{L}_{total}\) & \(\mathcal{L}_{initial}\) & \(\mathcal{L}_{financial}\) & \(\mathcal{L}_{boundary}\) & \(\mathcal{L}_{boundary,0}\) & \(\mathcal{L}_{boundary,0}\) \\ \hline KAFIN & 0.4742 & 0.2592 & 0.0007 & 0.0090 & 0.0042 & 0.0001 \\ FINN REG & 1321.4523 & 1319.9526 & 0.0083 & 92.8221 & 1226.2039 & 0.0000 \\ FINN FFN & 2836.8435 & 2836.8066 & 0.0001 & 279.0123 & 2555.5266 & 0.0001 \\ FINN DNN & 2991.8159 & 2991.8159 & 0.0000 & 315.1060 & 2618.3367 & 0.0000 \\ FINN MLP & 2358.1545 & 2357.6838 & 0.0000 & 212.8911 & 2142.5896 & 0.0000 \\ \hline MIN & \(\mathcal{L}_{total}\) & \(\mathcal{L}_{initial}\) & \(\mathcal{L}_{financial}\) & \(\mathcal{L}_{boundary}\) & \(\mathcal{L}_{boundary,0}\) & \(\mathcal{L}_{boundary,0}\) \\ \hline KAFIN & 27,701.9805 & 6116.9302 & 3.3060 & 21,583.5708 & 3421.9819 & 18,161.8652 \\ FINN REG & 3975.9348 & 3975.9265 & 4.7604 & 492.7719 & 3481.0391 & 12.4286 \\ FINN FFN & 3893.2429 & 3893.2427 & 0.0384 & 476.4237 & 3428.1106 & 2.2759 \\ FINN DNN & 3894.0923 & 3894.0923 & 0.0000 & 462.3209 & 3440.4221 & 53.7341 \\ FINN MLP & 3868.3335 & 3868.3335 & 0.5005 & 462.6045 & 3414.2256 & 34.1587 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Loss values in American put option pricing with 2300 epoch training. Despite the occasional larger loss values as indicated by MAX and STD, KAFIN stands out as the most effective model in terms of achieving the lowest MIN total loss. This demonstrates that KAFIN excels in loss minimization, making it an ideal choice for tasks where achieving high accuracy in the best-case scenario is crucial.
# Characteristics of BeiDou-3 Experimental Satellite Clocks
## 5 Frequency Spectrum Analysis  
NUFFT are used to further study the periodicity in the residual sequence with gaps [32; 33]. An open source code downloaded from the literature [34] is used. Figure 7 shows the amplitude spectrum BeiDou-3 of the new atomic clock frequency standard as well as the BeiDou-2 Rb frequency standard computed by the global clock solution. The horizontal axis represents the cycles per revolution (CPR).  
Figure 7 shows the amplitude spectra of the GEO (top), IGSO (middle), and MEO (bottom) satellite clocks. The GEO satellites have the largest frequency amplitude. There are two stronger harmonics, 24-h (1 CPR) and 12-h (2 CPR), and two lesser harmonics, 8-h (3 CPR) and 6-h (4 CPR) in the top plot. The spectrogram of C04 is as complex as expected, and its performance is consistent with the frequency variation shown in Figure 4.  
For the IGSO satellites, the amplitude spectrum of the PHM clock carried on C32 is visibly smaller than that of the Rb clock carried on the others IGSO satellites. According to the existing research [29], the IGSO satellites of BeiDou-2 have three obvious cycles (i.e., 24-h (1 CPR), 12-h (2 CPR), and 8-h (3 CPR)). However, there is no obvious 24-h period in C32 detected by NUFFT. Presently, it is hard to explain this phenomenon with insufficient measurements of BeiDou-3. On the other hand, C31 shows two strong harmonics of 24-h and 8-h, and a lesser harmonic of 12-h, similar to the BeiDou-2 IGSO satellites.  
The bottom plot of Figure 7 shows that there are common periods of MEO satellites around 12.9-h (1 CPR) and 6.45-h (2 CPR), and the magnitudes are smaller than that of GEO and IGSO. Also, we found that C33 and C34 are noisier than the other MEO satellites, and they have a unique period around 0.92 CPR. Combined with the stability analysis in Figure 6, inadequate measurements may lead to these results.  
The periodicity of the clock is closely related to the orbital error. Furthermore, an obvious period of C11 appears near 10.05 CPR, similar to the results of the authors of [12]. For the spectrum analysis results obtained by NUFFT, the PHM clock has a great improvement, while the new Rb clock did not show a marked improvement, which needs further evaluation and analysis using increasing tracking stations in the future.
# Obstacle Avoidance Drone by Deep Reinforcement Learning and Its Racing with Human Pilot
## 2 Related Work
### Drone Navigation with Supervised Learning  
Supervised learning can be promising with a large amount of labeled data. Almost all of the studies on applying supervised learning are based on convolutional neural network (CNN), so that their model can effectively extract features within vision inputs from various environments. It has been shown that a drone can be trained to navigate indoor, where the surrounding area is comparably small [13]. By focusing more on negative data, such as crashing and collision of a drone, it has been shown that a network can effectively avoid collision [14]. A recent study shows that a drone navigates through the corridors within a building using two well-trained CNNs: one is for depth map and the other for RGB input [15]. It is also shown that a well-trained CNN has such a small footprint that it is possible to embed on a nano quadrotor for controlling its flying trajectory [16]. The other possible way for the drone navigation is to combine a CNN with a long short-term memory(LSTM) for training within a Gazebo [17] environment, consisting of a block, a wall, and overhang in a room [18]. More recently, by utilizing a simulation environment during training, a CNN-based model for real-time navigation in a drone-racing track is proposed using micro UAV [19]. This has shown that a deep neural network not only controls a drone in real-time with vision input but also a network trained in a virtual environment can effectively maneuver a drone in the real-world. Instead of training a network in simulation first, many studies made use of data from the real-world directly to train networks. By using vision data collected from forests, a network drives a drone for forestry purposes [2]. Similarly, by using a large amount of data collected in an urban area, it has been shown that a drone can navigate robustly by avoiding obstacles appearing in a city [20]. These improvements in controlling the drone using supervised learning lead to broad applications for the society along with technologies such as the Internet of Things (IoT) [21; 22]. Even though the studies mentioned above have successfully applied supervised learning to meet the needs of diverse applications, manual labeling remains a burden for researchers. Moreover, as the data is highly oriented towards a specific environment, it is necessary to go through the entire process again whenever there is a need to apply a model to a different environment. On the other hand, research based on RL try to overcome such an issue.
## 6 Conclusions  
This study has proposed a modified 3-DOF spherical motor with an improved electromagnetic structure and a particular controlling strategy. The proposed motor has a compact diameter of 28 mm. Moreover, it has a movable angle of \(\pm 25^{\circ}\) along the \(X\)- and \(Y\)-axes and that of \(\pm 5^{\circ}\) along the \(Z\)-axis. In the structural design, eight symmetrical coils  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rotation Angle** & \begin{tabular}{c} **Total Torque Output** \\ **of Original Motor** \\ **(mN-m)** \\ \end{tabular} & \begin{tabular}{c} **Total Torque Output** \\ **of Proposed Motor** \\ **(mN-m)** \\ \end{tabular} & \begin{tabular}{c} **Total Torque Density** \\ **of Original Motor** \\ **(kN-m/m3)** \\ \end{tabular} & \
\begin{tabular}{c} **Total Torque Density** \\ **Density of Proposed** \\ **Motor (kN-m/m3)** \\ \end{tabular} &
\begin{tabular}{c} **Improvement** \\ **Percentage (\%)** \\ \end{tabular} \\ \hline \hline \multicolumn{5}{c}{Rotate from \(-25^{\circ}\) to \(+25^{\circ}\) in a counter-clockwise direction} \\ \hline \hline \(-25\) & 6.29 & 9.01 & 0.55 & 0.78 & 43.13 \\ \(-20\) & 7.86 & 9.72 & 0.68 & 0.85 & 23.56 \\ \(-15\) & 8.60 & 9.38 & 0.75 & 0.82 & 8.99 \\ \(-10\) & 8.78 & 8.34 & 0.76 & 0.73 & \(-4.95\) \\ \(-5\) & 8.56 & 6 with specific coil combination are used instead of conventional four symmetrical coils. The performance of the proposed 3-DOF spherical motor has been evaluated by means of a 3D FEM. The numerical results have shown that the proposed 3-DOF spherical motor has higher torque output in the large rotation angle (average improvement percentage of 5.22%) when compared to the original 3-DOF spherical motor [25]. As a result, the proposed 3-DOF spherical motor provides a promising solution for a wide application of the humanoid and autonomous systems, such as joints and eyeballs of the humanoid, unmanned aerial vehicles, autonomous cars, walking robots, and other fields.
# Microheater Topology for Advanced Gas Sensor Applications with Carbyne-Enriched Nanomaterials
## 2 Materials and Methods
Bonding was conducted with a semi-automatic ultrasonic wire bonder F&S Bondtec at conditions during packaging of the sensor device as follows: gold wire supplied via ball-wedge \"stitch-ball\" method (preliminary formation  
Figure 1: Images of the fabricated gas sensors with three different geometries of the microheaters: (a) small meander; (b) large meander; (c) dense rectangular. of a ball for better stabilization and higher strength of the bond) at bond time of 50 ms, ultrasonic power of 50 W, bond force of 70 g, and substrate temperature of 100 \({}^{\circ}\)C.  
Impedance of the samples vs. frequency for the three types of samples was measured using an impedance analyzer (Hioki IM3570, Ueda, Japan) in four terminal mode at a bias voltage of 1 V and frequency range 100 Hz-120 kHz (the accuracy for this frequency range is Z: \(\pm\)0.08%; \(\theta\): \(\pm\)0.05\({}^{\circ}\); Cs, Rs: \(\pm\)0.05%; see Figure 2a). The initial value for the sensor impedance before heating was 6.8 G\(\Omega\) with a phase angle shift varying in the range of +209\({}^{\circ}\div-213^{\circ}\), the contact resistance of the electrodes was 4.6 \(\Omega\), and the interface capacitance between the electrodes and the gas-sensitive layer was 11.9 pF. The contact resistance and interface capacitance between the metal electrodes and carbyne-enriched detecting layer were monitored before and after desorption, activated with the different microheaters, to track initiation of local burnout due to potential irregular distribution of the thermal flow. The details for the functional testing of the SAW samples at different ethanol concentrations are described in our previous study [23] and shown in Figure 2b. In our previous study, it was found that a linear response in the sensor can be expected in the concentration range between 180 ppm and 680 ppm. The hysteresis curves for all microheater topologies were derived by exposing each sensor to increasing concentrations of the target vapor and measuring its response at each point. Afterwards, the microheater was activated for the desorption process, and the sensor exposure was repeated again to check to what degree its initial state was restored. The most informative parameter of the SAW sensor (time delay between the input and the output signal at a certain concentration) was tracked for change and expressed in percentage, aiming for the change to be as small as possible. The extracted information is new, because of the novel implemented carbyne-enriched material, highlighted theoretically as a perspective for sensing application, but still with insufficient practical implementation examples.
## 6 Experiments and Results
### Experiments
To select \(L_{x}\) and \(L_{z}\), a trade-off between an accurate heuristic and low computational cost should be considered. The parameters \(\sigma_{x}\) and \(\sigma_{z}\) allow the tuning of the convergence velocity of the likelihood probability distribution in the map. The parameter \(\mu\) can be estimated by evaluating the sensor reliability in the pollutant measurement. In addition, as explained before, the information of the sensor for the LMLS updating is binary. This information is obtained by passing the raw measurement of the sensor through a defined threshold. The threshold must be sufficiently high to evade the noise levels but without getting an insensitive binary sensor. Also, the wind vector is required to be estimated in each position but, as the method is probabilistic, a mean approximation of these effects over the environment is enough. On the part of the metaheuristic algorithm, the parameters \(T_{0}\), \(\Delta T\) and \(\epsilon\) must be adequately set. The temperature parameters \(T_{0}\) and \(\Delta T\) are selected such that temperature \(T\) must be maintained greater than zero and also its quick decrement must be avoided. The parameter \(\epsilon\) helps us to analyze whether the vehicle has found increasing pollutant gradients avoiding the noise levels. The analysis of the pollutant targeted and the sensor used would be indispensable for the gradient increasing definition.
# A Crowdsourcing Recommendation Model for Image Annotations in Cultural Heritage Platforms
## 3 Materials and Methods
### Data Representation Methods
#### 3.2.1 Preprocessing Techniques  
In the realm of machine learning, the algorithms that process text require the conversion of the data to a numeric form. This can be accomplished using well-known natural language processing (NLP) techniques aimed at generating scalar vectors from text input. One frequently used technique is the BoW, which builds a vector for a given text containing the number of occurrences of each word in the input document. These word frequencies permit us to evaluate the similarity of the documents for later use in tasks related to information retrieval, template matching, or document classification. The output of the BoW model is a frequency vector built by comparing each word to the previously created vocabulary, resulting in numeric data that usually serve as input for the machine learning algorithms [59]. Each element value forming the vector is scored based on this comparison, reflecting the appearance of the word in the text, in the simplest form using a Boolean value of 1 if it exists and 0 if it does not.  
Second, the TF-IDF representation is another measure that can be used to translate each term into a weight reflecting the relevance of a word with respect to a specific document [60]. The term frequency (TF) is the frequency of a given term appearing in a document, and it represents the level of importance of such a word in the document. However, some terms are never important in spite of appearing quite often in a given text because they are usually included in most texts ('is', 'this', 'and, 'the', and similar). As a result, the second part of the technique, inverse document frequency (IDF), is used to reduce the weight of terms appearing regularly in the document collection, so indirectly increasing the weight of less frequently used words. That is, the frequency assigned to a term is tuned to reflect how infrequently it appears in the other documents of the collection by combining both TF and IDF, proving a higher score for the words appearing frequently in a document but not in the others; that is, the relevance inside a document is balanced by the frequency of the term across all documents.
# Exploiting Global Structure Information to Improve Medical Image Segmentation
## 5 Conclusions  
In this paper, we propose a method to learn global anatomical structures in medical images by using a denoising convolutional autoencoder and constraining a segmentation network through a loss function such that the prediction of the segmentation model is performed in the learned anatomical feature space. Unlike previous studies in which anatomical priors are considered using a pre-trained autoencoder, we propose a single-stage approach in which the segmentation network and autoencoder are jointly learned. To demonstrate the advantages of the proposed method, extensive experiments were conducted on two medical image segmentation tasks: lung segmentation in CXRs and spinal cord segmentation in MRI images. The experimental results indicate that learning anatomical priors using the proposed method can help enhance the segmentation performance. In addition, to demonstrate the additional benefits of learning the anatomical structures, we investigated the domain robustness of the proposed method. The results indicate that the proposed method can enhance the robustness of segmentation networks against domain shifts. This domain robustness property will be particularly useful for other medical applications such as cranial implant design [31] or precise tooth segmentation [32] where understanding of anatomical structure is crucial to have reliable segmentation models. The findings highlight that the segmentation networks trained using the proposed method can effectively learn global anatomical structures commonly existing in medical images from various sources.  
Conceptualization, S.H.; methodology, J.H. and S.H.; software, J.H.; validation, J.H.; investigation, J.H.; resources, S.H.; writing--original draft preparation, J.H.; writing--review and editing, S.H.; supervision, S.H.; project administration, S.H.; funding acquisition, S.H. Both authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (NRF-2018R1D1A1A02086017) and also supported by Basic Science Research Program through the NRF funded by the Ministry of Education (NRF-2019R1A6A1A03032119).  
Not applicable.  
All datasets used for this study are publicly available. Refer to the description of each dataset.  
The authors declare no conflict of interest.
# Time Domain Analysis of NB-IoT Signals
## 4 Materials and Methods  
The purpose of the measurements carried out was twofold: on the one hand, to examine the trend over time of the NB-IoT signal and in particular of the single sub-carriers, identifying those on which the NRS is transmitted; on the other hand, to measure the received power during the transmission of the NRS in the time domain and compare it with what is obtained with a vector analyzer in code domain to verify the equivalence of methods.  
As known, the electric field \(E\) due to the measured signal is related to the power \(P\) (dBm) received by the analyzer by the relationship:  
\[E(V/m)=10^{(P+AF+CL-13,01)/20} \tag{3}\]  
where \(AF\) (dB/m) is the Antenna Factor and \(CL\) (dB) are the cable losses.  
The measurements were carried out in Pisa and surroundings, at some LTE800 base stations with NB-IoT signal implemented.  
The measurements were performed with a narrow band measurement chain consisting of a Rohde & Schwarz FSH8 spectrum analyzer (9 kHz \(\div\) 8 GHz) connected to a Rohde & Schwarz mod. TSEMF-B1 antenna (30 MHz \(\div\) 3 GHz). The analyzer is also equipped with a vector module for the analysis of NB-IoT signals in the code domain. This module allows the analysis and measurements on NB-IoT signals that use a single port (SISO). This configuration is actually implemented in the investigated cells. The module provides the measurement of the average power value received during the transmission of the NRS of the selected cell on a frame time basis. Cell selection may be done through introduction of its Cell_ID.  
On the other hand, the time domain measurements were performed with the spectrum analyzer in Span Zero mode. Here the analyzer operates in scope mode measuring, as afunction of time, power received by IF filter (RBW) set at the established analyzer center frequency. The acquisitions were carried out tuning the analyzer at each sub-carrier center frequency and using a width of the RBW filter equal to 10 kHz, the first available value lower than the bandwidth of the single sub-carriers, so that the received power was uniquely due to the analyzed one. Even if the used RBW value is lower than the channelization of the NB-IoT subcarriers, we expect an underestimation of no more than 0.7 dB on the received power obtained by Gaussian RBW filter compared to the one acquired with an ideal 15 kHz wide rectangular filter. In order to verify the trend of the sub-carriers over time, acquisitions of the received power were performed in Single Sweep mode with sweep times equal to 2 frame duration (20 ms), in order to display an even and an odd frame together. The RMS detector was used for acquisitions. The identification of the sub-carriers transmitting the NRS was carried out through prior acquisitions in the frequency domain (Figure 6).
## 4 Reliability Analysis of High-Pressure Tunnel  
### Determine Random Variables  
Through the sensitivity analysis of mechanical parameters, the variation law of the maximum principal stress in the secondary lining of the tunnel can be deeply understood. The reference coordinate system was converted to a cylindrical coordinate system to obtain the corresponding axial stress, and the relationship between the axial stress at the most dangerous point in the secondary lining and the mechanical parameters was plotted. As shown in Figure 7, a numerical model was used to demonstrate the relationship between the axial stress at the most dangerous point in the secondary lining and various parameters. When the elastic modulus of the surrounding rock (\(E_{1}\)) decreased from 15 GPa to 7.5 GPa, the axial stress increased by about 33%. There was a direct positive correlation between the most dangerous axial stress and \(v\), and the relationship curve was roughly in a nearly linear form. However, compared with the \(E_{1}\) parameter, the change in \(v\) has a limited effect on the axial stress. In the case of a low \(c\) value, the axial stress showed a high sensitivity to \(c\). When \(c\) gradually increased, the axial stress showed a downward trend. With the increase in \(\varphi\), the axial stress decreased gradually. In the range of 39\({}^{\circ}\)-44\({}^{\circ}\), the axial stress decreased by 3.09 MPa. The axial stress showed an approximately linear negative correlation with the elastic modulus of lining (\(E_{2}\)) and the lining compressive strength (\(\sigma_{c}\)) parameters, which was consistent with the assumption of the linear elastic constitutive model of concrete materials. It is worth noting that the \(E_{2}\) parameter had a relatively large change with the axial stress, and the change range of the \(\sigma_{c}\) parameter was relatively limited. In general, the results show that \(E_{1}\), \(c\), \(\varphi\), and \(E_{2}\) have a relatively significant impact on the stress of the tunnel structure. Therefore, these four mechanical parameters were selected as random variables for the reliability analysis of the high-pressure tunnel structure.  
Figure 6: The computational grid model. (**a**) A diagram of the overall model grid division. (**b**) A schematic diagram of the lining grid.