esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Cutting Force Modelling in Micro-Milling Aluminum Alloy LF 21 Considering Tool Wear
## 3 An Improved Cutting Force Model in Micro-Milling LF 21 Considering Tool Wear
### Parameter Identification of the Micro-Milling Force Model
The \(V_{c}\) and \(V_{s}\) corresponding to the tooth involved in cutting at different tooth angles can be solved according to the cutting force model. The simulation curve of the cutter rotation is shown in Figure 17. The peak cutting force in y-direction corresponds to the tooth angle of the end of the blade bottom at 2.379 radians. Where, \(V_{cy}=-4.1442\times 10^{-18}\)m\({}^{3}\), \(V_{sy}=4.2738\times 10^{-18}\)m\({}^{3}\). Similarly, the peak value of x-direction cutting force corresponds to the tooth angle at the bottom of the blade at 1.566 radian. Where, \(V_{cx}=2.5790\times 10^{-19}\)m\({}^{3}\), \(V_{xx}=5.9234\times 10^{-18}\)m\({}^{3}\). Assume that the maximum cutting layer area corresponds to the axial force peak value. Figure 18 shows the results obtained from the studies of maximum cutting layer area corresponds to the z-direction cutting force peak value when the tooth angle at the bottom of the blade is 1.6085 radian. Where, \(A_{a}=5.8904\times 10^{-11}\)m\({}^{2}\), \(V_{a}=5.9305\times 10^{-18}\)m\({}^{3}\). Figure 17: Decomposition of interference volume in plough area. Figure 18: Simulation of cutting layer area. The experimental design and results of determination of coefficients of micro-milling force model dominated by shear effect are shown in Table 7.  
Coefficients \(A_{c}\), \(A_{s}\), \(V_{c}\) and \(V_{s}\) of the force model dominated by shear effect are solved, respectively, by the least square method. \(K_{tc}=-6.43155\)\(\times\) 108, \(K_{tp}=4.53989\)\(\times\) 1016, \(K_{fc}=2.00831\)\(\times\) 108, \(K_{fp}=-1.94050\)\(\times\) 1016, \(K_{ac}=2.37296\)\(\times\) 108 and \(K_{qp}=6.39313\)\(\times\) 1015. Similarly, the experimental results of determination of coefficients of micro-milling force model dominated by plough effect are shown in Table 8.  
The least square method is also used to estimate the coefficients of the force model dominated by plough effect, and the values of \(V_{c}\) and \(V_{s}\) are successively solved where, \(K_{tpp}=4.03217\)\(\times\) 1016, \(K_{fpp}=-1.06732\)\(\times\) 1016 and \(K_{qpp}=1.18242\)\(\times\) 1016.
# Smartphone-Based Photogrammetry for the 3D Modeling of a Geomorphological Structure
## 2 Case Study  
The present research was aimed at the three-dimensional reconstruction of a portion of the Cave of Bossea (CN), Italy. The Cave of Bossea, is considered among the most beautiful and geologically interesting caves in Europe, since it also has an underground karst laboratory managed by the Bossea Scientific Station of Italian Alpine Club (Club Alpino Italiano--CAI) of Cuneo and by the Department of Environmental, Land and Infrastructure Engineering of the Politecnico di Torino (Italy), with the collaboration of the Cuneo Department of Piedmont Regional Agency for the Protection of the Environment (Agenzia Regionale per la Protezione Ambientale--ARPA) and the Radiation Section of the ARPA Valle d'Aosta. This laboratory is of national importance for the study of water circulation in carbonate rocks, of the organization and evolution of karst aquifers, of genetic and lithogenic speleological processes, of atmospheric constituents, of the microclimate, and of the energy balance of the underground environment [35].  
The preliminary test related to the proposed methodology was performed at the Geomatics Laboratory of DIATI (Department of Environmental Engineering, Land and Infrastructure) of the Politecnico di Torino, using a sample of calcareous material extracted from the Cave of Bossea (Figure 1).  
The sample measurements were about \(60\times 7\times 3\) cm and aroused particular interest for its conformation in the study of water circulation in carbonate rocks. Moreover, it presents clear radiometric variations due to the characteristic components of the water that has been deposited over time and which represent the main study aspect of the object. The test was performed in optimal light and environmental conditions, in order to evaluate the potentialities of the procedures: this means that all images were collected in a controlled environment, both in terms of environmental (stable temperature of 25 \({}^{\circ}\)C and constant humidity of about 55%) and lighting conditions: the sample was settled on a non-reflective table and illuminated with professional lighting.  
Subsequently, the work aimed to stress the methodology in a real underground cave environment, with variable levels of illumination and critical situations of humidity or temperature. To this purpose, three walls inside the cave were selected (Figure 2 and Table 1). The three chosen surfaces, characterized by the typical irregularity of these environments, are distinguished by the different lighting, punctual  
Figure 1: The calcareous rock sample used for the test.  
in the first case, scarce in the second and diffused in the last one. Furthermore, due to a parapet that limits movement for safety reasons, it is not possible to get close enough to the various areas in order to inspect the various inlets with more detail. Moreover, most of the surfaces are moistened by a thin layer of water, which increases considerably in the most humid periods, and which makes them very reflective.
# A Novel UWB Pulse Expander Using an Integrated Microstrip Splitter, Delay Lines, and a Combiner
## 3 Experimental Validation
### Testing of Pulse Combiner  
Next, testing how well the pulse splitter can act as a combiner was necessary. For this reason, the combiner testing, with the same device described in Section 3.1, was performed by generating three identical waveforms, as shown in Figure 12, using three output channels of the AWG. AWG channels were connected to ports 2 to 4 of the combiner. The output of the combiner (port 1) was connected to the DSO. Accordingly, the resulting waveform can be seen in Figure 16, as follows:  
Additionally, the combiner was tested by offsetting the input waveforms by 150 ps relative to each other, as seen in Figure 17. This test simulates the waveforms at the output of the delay lines and allows us to confirm the capabilities of the combiner with three ideal input pulses at its input. The resulting output waveform can be seen in Figure 18.  
Figure 16: Measured (**a**) waveform, and (**b**) spectral density of the output pulse (duration 225 ps) of the pulse combiner for the test with three identical input pulses.  
Figure 17: DSO captured input pulse waveforms of the combiner with a 150 ps time shift between the pulses.  
Figure 15: Measured output waveform (225 ps duration) at each output port of the pulse splitter.  
The results show excellent performance when combining the pulses with no visible distortions or ripples in the waveform.
## 4 Equalizer Design Techniques
### Least-Squares Optimization Methods  
Mixed-phase equalization requires the approximation of the inverse of a non-minimum phase response, which is acausal. In order to approximate an acausal impulse response, it was proposed in [76] to add a delay in the response of the equalizer and to design the equalizer by minimizing a least-squares error criterion. The approach proposed in [76] was thereafter followed and improved upon by many researchers, for both single-position and multiple-position equalization [77] (see Section 6). Mixed-phase equalization requires the introduction of a delay in the equalizer. This delay should be kept as low as possible (on the order of a few milliseconds according to the backward masking characteristics of the ear [46]), since it can give rise to annoying artifacts in the form of pre-ringing or pre-echo effects. At the same time, the delay should be sufficiently long to obtain reasonable mixed-phase equalization. The least-squares optimization has been the key ingredient of many adaptive solutions, starting from the seminal paper of [77], as detailed in Section 7. Other least-squares optimization criteria considering further constraints have also been proposed; e.g., deconvolution with regularization [51], room response reshaping [78], Kautz filters [55], and short filters [79].  
The main limitations of the least-squares methods are the high sensitivity to the peaks and notches of the room response, the non-uniform distribution of errors in the spectrum, and the possibility of pre-ringing or pre-echo artifacts caused by the equalizer delay.
# Two-Factor Authentication Scheme for Mobile Money: A Review of Threat Models and Countermeasures
## 2 Related Work
### Threat Models
#### 2.7.5 Attacks against Availability  
This category is where an adversary denies mobile money users and agents access to the mobile money application server or bank server, thus rendering the service unavailable. Attacks against availability include DoS and distributed DoS (DDoS) attacks and mobile phone theft.  
* DoS and DDoS attacks: DoS attack is where attackers overwhelm mobile money server with fake traffic to block requests from legitimate users requesting to access the services. The DDoS attack, on the other hand, is a kind of attack where adversaries overwhelm the mobile money server from different sources, thus making it difficult to stop the attacks. The goal of DoS and DDoS attacks is to make mobile money services unavailable by flooding servers with an immense amount of data to make it busy and unable to provide services to legitimate users [11; 45]. Additionally, when DoS and DDoS occur, mobile money agents, banks, and MMSPs lose money, and mobile money users cannot access their mobile wallet accounts [9; 11; 14; 63].
* Mobile phone theft: According to [PERSON] et al. [23] and [PERSON] et al. [11], when an attacker steals the mobile money user's or agent's mobile phone, the SIM card that has a wallet account becomes unavailable. It also results in loss of data and service access [64]. Moreover, an attacker can swap SIM cards for those of the mobile money users and agents and take over the victims' e-wallet account, thus making it unavailable for legitimate users [14; 50; 62].  
Figure 5 summarises the classification of threat models in mobile money's 2 FA scheme.  
Figure 5: Classification of threat models in mobile money’s two-factor authentication (2 FA) scheme.
## 3 Database Collection and Processing
### Preparation of the Data for AI Modeling
#### 3.2.2 Testing and Training Databases  
There are two types of databases required for the implementation of mathematical models, namely, training databases and testing databases. For all three mathematical models, training and testing were randomly divided. Figure 6 shows the main division of the database, where 80% of the main database was used for training and 20% for testing.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Variable** & **Observations** & **Minimum** & **Maximum** & **Mean** & **Std. Deviation** \\ \hline CBR & 27 & 0.900 & 16.700 & 6.856 & 4.013 \\ LL (soil) & 27 & 26.120 & 55.000 & 46.391 & 12.660 \\ PI (soil) & 27 & 8.830 & 34.000 & 26.239 & 11.399 \\ \% Sludge & 27 & 0.000 & 100.000 & 11.667 & 20.438 \\ Compaction (number of blows) & 27 & 10.000 & 65.000 & 31.667 & 19.513 \\ OMC (soil) & 27 & 18.000 & 22.500 & 21.111 & 2.021 \\ MDD (soil) & 27 & 1.560 & 1.725 & 1.596 & 0.048 \\ OMC (mixture) & 27 & 18.000 & 41.500 & 21.315 & 4.545 \\ MDD (mixture) & 27 & 1.060 & 1.746 & 1.577 & 0.146 \\ G\({}_{\text{s}}\) (soil) & 27 & 2.170 & 2.750 & 2.616 & 0.243 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The statistical information of the variables in the database.  
Figure 6: Distribution of (**a**) training and (**b**) testing databases.  
A random database division resulted in two test and experiment databases that were considered fixed, and were used in all three mathematical models. The statistical parameters for the training and test databases, are shown in Tables 2 and 3, respectively. The statistical information, including the minimum, maximum, mean, and standard deviation, is very similar for both databases. This can increase the accuracy of the network in predicting the output.
## 2 Developed Methodology
### Supervised Pose Refiner
Users can also manually halt tracking; adjust the pose using the global pose input (Section 2.3) when the tracker completely loses track; or resume tracking to refine the pose from the initial pose (\(T_{i}\)). With user supervision on the initial pose and during pose refinement, our modified SLET algorithm benefits from both the human dexterity and machine-like precision of the refiner to enhance the accuracy of the tool head's pose. Figure 10: Given that the monocular sensor is mounted (**1**) and fixed (**2**) on the board of the power tool, the tool head remains static in the background (**3**). TTool’s refiner can adjust for the tracked tool head’s correct pose during sudden movements and vibrations. ```
Data: Image frame, 3D model Result: Object pose update  Calculate the average probability, \(\langle P(h_{ij}|C)\rangle\); if\(\langle P(h_{ij}|C)\rangle<0.00625\)then  Reset: Set object pose to initial pose \(T_{i}\)  end if elseif\(\langle P(h_{ij}|C)\rangle<0.1\)then  Freeze: Do nothing  end if else  Tracking: Update object pose to \(T_{cm}\)  end
```  
**Algorithm 1**Pose Update  
Once the user validates the pose refinement, TTool's 6 DoF detection is terminated, and woodworking fabrication can start. At this moment, the physical tool head is perfectly aligned with its corresponding 3D model. Most importantly, any occlusions and cluttering of the targeted object (e.g., a drill bit piercing into the wood, protection metal component hiding the circular saw blade, and simple wood chipping) do not affect the object locking achieved by TTool. This is a fundamental condition of AR fabrication, since feedback can also be computed when the tool head pierces or cuts through the wood (e.g., providing depth monitoring on when to interrupt the drilling operation), as shown in Figure 11.  
Figure 11: An example of (**a**) drilling and (**b**) sawing operations with TTool: (**a**) 1. The pose of the drill bit is detected via TTool. (**a**) 2,3. Once the drill bit is locked to its 3D model, piercing the wood or any other form of occlusion does not influence object locking. (**a**) 4. Once extracted, TTool can be re-initialized to swiftly refine the pose of the drill bit.
## 2 Problem Formulation  
GNSS positioning adopts the principle of trilateration to determine the position of a receiver by measuring the distance between the receiver and multiple satellites. However, the GNSS positioning process may be affected by various error factors. Therefore, accurately estimating the GNSS positioning error is challenging.  
In an Earth-centered, Earth-fixed (ECEF) coordinate system, assuming that there are \(n\) satellites, the coordinates of the satellites are \(S_{i}=(x_{i},y_{i},z_{i})\), where the value of \(i\) ranges from 1 to \(n\). The coordinates of the receiver are \(P=(x,y,z)\). Different positioning methods are utilized in the GNSS positioning process, and different observation equations are obtained. The most common GNSS positioning method is pseudorange measurement positioning, which is achieved by measuring the geometric distance from the receiver to the satellite and various error factors. If each error factor is regarded as an independent random variable, the observation equation of the pseudorange measurement model is  
\[P_{i}=\left\|P-S_{i}\right\|+\varsigma\Lambda t+T_{i}+I_{i}+M_{i}+\varepsilon_ {i} \tag{1}\]  
where \(\left\|P-S_{i}\right\|\) denotes the geometric distance from the receiver to satellite \(i\), \(\Delta t\) expresses the receiver clock error, \(T_{i}\) means the tropospheric delay, \(I_{i}  
The main application scenario of the proposed method is the positioning needs of daily electronic devices, such as smartphones. Although the positioning accuracy of code observation is lower than that of carrier phase observation, the positioning time required for code observation is shorter than that of carrier phase observation, and it is less affected by environmental changes. Therefore, code observation is more suitable for the application scenario of smartphone positioning.
## 6 Conclusions and Future Work  
In this paper, we developed both hybrid and end-to-end ASR approaches exploring some techniques to improve the performance of Speech-to-Text tasks under IberSpeech-RTVE 2020 Challenge. We showed that Hybrid DNN-HMMs can be adapted to the TV show domain by means of multi-condition data augmentation. The addition of reverberated data to the training data decreased the WER significantly (10% relative). A WER of 7.96% was achieved in better conditions. We demonstrated that the lack of data augmentation techniques could be the main reason of the gap between the Kaldi-based hybrid system and PyChain-based system. Moreover, a higher volume of data used to train the end-to-end system could contribute to increasing the ASR performance. However, other easy-to-apply techniques, such as DAT, can overcome this gap, yielding improvements in end-to-end ASR systems. We found that using DAT, acoustic features invariant to different TV domains can be learned, achieving a WER improvement of 2.87%.  
The IberSpeech-RTVE 2020 Challenge has provided some findings pointing out that end-to-end approaches are close to being competitive (between 28% and 40% WER) by using more than 600 h of speech. However, they are still far from the hybrid models.  
As future work, besides data augmentation, we believe that exploring speech-enhancement techniques could help to close the performance gap between hybrid and end-to-end systems. In addition, unsupervised machine learning methods (e.g., clustering) or automatic perceptual speech quality methods (e.g., PESQ) could contribute to a more accurate TV show classification prior to DAT. Finally, we also believe that the combination of DAT with a self-supervised approach could be useful to achieve significant improvements in ASR systems through robust embeddings trained even without supervised data.  
Conceptualization, J.M.P.-C., F.M.E.-C. and L.A.H.-G.; data curation, J.M.P.-C.; formal analysis, J.M.P.-C., F.M.E.-C. and L.A.H.-G.; investigation, J.M.P.-C., F.M.E.-C. and L.A.H.-G.; methodology, J.M.P.-C.; software, J.M.P.-C. and F.M.E.-C.; supervision, F.M.E.-C. and L.A.H.-G.; validation, J.M.P.-C. and F.M.E.-C.; writing-original draft, J.M.P.-C.; writing-review & editing, F.M.E.-C. and L.A.H.-G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
[leftmargin=*]
**Data Availability Statement:**: RTVE 2020 database was used under a license agreement. It is available upon request in [[http://catedarturve.unizares/rtvedatabase.html](http://catedarturve.unizares/rtvedatabase.html)]([http://catedarturve.unizares/rtvedatabase.html](http://catedarturve.unizares/rtvedatabase.html)), accessed on 14 January 2022.  
The authors declare no conflict of interest.
# Experimental Study of a Signal Modulation Method to Improve eLORAN Data Channel Communications
## 2 Materials and Methods
### Ninth Pulse Modulation  
The ninth pulse technology for data transmission is the 32-ary PPM scheme. An additional pulse is inserted after the eighth pulse of the traditional LORAN pulse group. The 32 state PPM is used to represent the time delay from the zero symbol offset. Each signal position is defined by a 5-bit symbol, the first two digits indicate rough delayed, and the last three digits indicate fine delay. Figure 3 shows the ninth pulse pattern. In this way, the data transmission rate is 5 bits/GRI [15,27,28].  
The zero sign offset of the additional pulse is 1000 \(\upmu\)s. The delay time of the remaining 31 symbols relative to the zero symbol is microseconds of a specific time. The ideal delays are given by the formula:  
\[d_{i}=1.25 mod(i,8)+50.625 floor(i/8) \tag{1}\]  
The actual delays are the ideal values shifted to coincide with the ticks of a 5-MHz clock. Table 2 lists the symbol \(i\) and corresponding time delay \(d_{i}\) with respect to the zero symbol [15].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**i = [0, 7]**} & \multicolumn{2}{c}{**i = [8, 15]**} & \multicolumn{2}{c}{**i = [16, 23]**} & \multicolumn{2}{c}{**i = [24, 31]**} \\ \hline
0 & 0.0 & 8 & 50.6 & 16 & 101.2 & 24 & 151.8 \\ \hline
1 & 1.2 & 9 & 51.8 & 17 & 102.6 & 25 & 153.2 \\ \hline
2 & 2.6 & 10 & 53.2 & 18 & 103.8 & 26 & 154.4 \\ \hline
3 & 3.8 & 11 & 54.4 & 19 & 105.0 & 27 & 155.6 \\ \hline
4 & 5.0 & 12 & 55.6 & 20 & 106.2 & 28 & 156.8 \\ \hline
5 & 6.2 & 13 & 56.8 & 21 & 107.6 & 29 & 158.2 \\ \hline
6 & 7.6 & 14 & 58.2 & 22 & 108.8 & 30 & 159.4 \\ \hline
7 & 8.8 & 15 & 59.4 & 23 & 110.0 & 31 & 160.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Symbol delays from zero-symbol offset (\(\upmu\)s).  
Figure 3: Ninth pulse pattern.  
Figure 2: 3S-PPM pattern.  
Figure 4 shows the ninth pulse modulation given in [27]. All modulation messages consist of three components: 4-bit for type, 41-bit for payload, and 75-bit for parity component. The message sending rate is 5 bits/GRI. The time length of the messages is 24 GRI (maximum of approximately 2.4 s).