esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Electronic Nose and Analysis
The first principal component PC1 indicates that the electronic response \(\Delta R/R_{max}\) parameter for all eight sensors and \(T_{ratio}\) for the TGS2600, TGS2610, and TGS2611 sensors successfully discriminated the Brazilian coffee from the others, especially from the coffees from Ethiopia, Guatema, and Costa  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Country of Origin**} \\ \hline
**Parameter** & **Brazil** & **Ethiopia** & **Guatemala** & **Costa Rica** & **Peru** \\ \hline
2602 \(\Delta R/R_{max}\) & 3.17 & 2.22 & 1.17 & 1.88 & 1.98 \\ SD & 0.06 & 0.04 & 0.02 & 0.04 & 0.04 \\ AS-MLV-P2 \(\Delta R/R_{max}\) & 2.13 & 1.33 * & 0.57 & 0.86 & 1.35 * \\ SD & 0.04 & 0.03 & 0.01 & 0.02 & 0.03 \\
2603 \(\Delta R/R_{max}\) & 1.77 & 1.36 & 0.65 & 1.05 & 1.11 \\ SD & 0.04 & 0.03 & 0.01 & 0.02 & 0.02 \\
2612 \(\Delta R/R_{max}\) & 0.30 & 0.05 * & 0.02 & 0.07 & 0.06 * \\ SD & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 \\
2610 \(\Delta R/R_{max}\) & 0.58 & 0.28 & 0.14 & 0.22 & 0.22 \\ SD & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 \\
2611 \(\Delta R/R_{max}\) & 0.49 & 0.26 & 0.12 & 0.20 & 0.17 \\ SD & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 \\
2620 \(\Delta R/R_{max}\) & 1.12 & 0.54 & 0.25 & 0.42 & 0.40 \\ SD & 0.02 & 0.01 & 0.01 & 0.01 & 0.01 \\
2602 \(T_{ratio}\) & 2.19 & 1.46 * & 1.80 & 1.38 & 1.48 * \\ SD & 0.04 & 0.03 & 0.04 & 0.03 & 0.03 \\ AS-MLV-P2 \(T_{ratio}\) & 2.29 & 6.15 & 2.57 & 1.27 & 3.11 \\ SD & 0.05 & 0.12 & 0.05 & 0.03 & 0.06 \\
2603 \(T_{ratio}\) & 2.33 & 1.50 * & 1.80 & 1.34 & 1.50 * \\ SD & 0.05 & 0.04 & 0.04 & 0.03 & 0.03 \\
2612 \(T_{ratio}\) & 2.35 & 2.58 & 3.00 & 1.40 & 2.82 \\ SD & 0.05 & 0.05 & 0.06 & 0.03 & 0.06 \\
2610 \(T_{ratio}\) & 8.80 & 3.20 & 3.00 & 1.32 & 3.39 \\ SD & 0.18 & 0.06 & 0.06 & 0.03 & 0.07 \\
2611 \(T_{ratio}\) & 8.00 & 3.00 * & 3.00 * & 1.44 & 3.21 \\ SD & 0.16 & 0.06 & 0.07 & 0.03 & 0.06 \\
2620 \(T_{ratio}\) & 3.14 & 1.73 & 3.97 & 1.22 & 2.50 \\ SD & 0.06 & 0.03 & 0.08 & 0.02 & 0.05 \\
2600 \(T_{ratio}\) & 2.79 & 1.78 & 2.30 & 1.27 & 1.62 \\ SD & 0.06 & 0.04 & 0.05 & 0.03 & 0. Rica. The Peruvian coffee had a similar composition of volatile groups to the coffee from Brazil, which consequently located it on the left side of the first principal component PC1, as in the chemometric analysis. The odor profile of the Brazilian and Peruvian coffees was characterized by the highest content of azines, which include pyridine, responsible for the characteristic bitter smell. Approximately 15 compounds containing a pyridine ring were detected in the aroma profile of the coffees [24]. Its content in the aroma profile was indicated by the negative PC1 values. The lowest levels of this compound were detected in the coffees from Costa Rica and Guatema. Consistent results for this parameter were obtained in the e-nose and chromatographic analyses of volatile compounds (Figures 3b, 4b and 5b).
# Efficient Communication Model for a Smart Parking System with Multiple Data Consumers
## 4 Efficient Communication Model for an SPS with Multiple Data Consumers
### Hybrid Power Cycling for Mesh Devices  
Thread is an emerging routing protocol that is extensively used in smart home appliances [44]. The thread's communication model has mains-powered thread routers and duty-cycled sleepy end devices. Such a hybrid power cycling works for smart home mesh networks. However, the mains power is not suitable for mesh forwarders employed in a smart parking lot because a huge number of forwarders are required to cover the entire parking lot. However, the data collators are smaller in numbers and can be mains powered to maximize the packet reception rate of the data servers and also cater to high data volumes. They mains-powered devices do not switch off their radios. All other sensor nodes can have radio duty cycling to reduce energy consumption and switch off their radios most of the time except during packet transmissions. The BR, too, has its radio on so as to have a seamless connection to Internet. The positioning of BR makes it easier to extend the same to the data servers that are nearby. This hybrid power solution allows for lower energy consumption for the battery-powered nodes and ensures reliable occupancy data collection for the data servers.
## 4 Conclusions  
In this study, radiation protection parameters of silver-doped BiPbSrCaCuO superconductor samples were investigated at gamma energies of 59.5 to 1332 keV. The theoretical values of mass attenuation coefficients have been calculated in the energy range from 1 keV to 100 MeV by means of the WinXCom software. It was used to determine some shielding parameters such as HVL, MFP, Zeff, RPE, and kerma (\(k_{\gamma}\)) by using mass attenuation coefficient values. In addition, the effective removal cross-sections (\(\Sigma_{\rm R}\)) for the different alloys were calculated. Mass attenuation coefficients (MAC), HVL, MFP, Zeff, RPE and gamma-ray kerma coefficients (\(k_{\gamma}\)) were evaluated at 59.5, 122, 383, 511, 662, 1274, and 1332 keV gamma energies for BiPbSrCaCuO superconductors and seven elements using the EG54 Monte Carlo simulation package. The variation in the radiation shielding parameters for superconductors was computed in the in the energy range from 1 keV to 100 MeV utilizing the WinXCom program, and these were plotted. The values of the MAC (\(\mu/\rho\)), Zeff, RPE, and gamma-ray kerma (\(k_{\gamma}\)) coefficient are all dependent on the incident photon energy and the elemental composition of the material through which the gamma rays are passing.  
One potential application of superconductivity in particle physics is in the construction of superconducting magnets, which are used to bend and focus charged particle beams in particle accelerators. These magnets must be able to operate in high-radiation environments, where gamma rays can be produced as a result of the interaction between particles and materials. The mass attenuation coefficients of the materials used in the magnets are therefore important parameters to consider in the design and optimization of these magnets.  
As the silver (Ag) contribution increases for BiPbSrCaCuO superconducting ceramics, there is a serious decrease in the mass attenuation coefficients up to the first 500 keV. At higher energies, the mass attenuation coefficients drop less. The radiation shielding efficiency (RPE), the neutron removal cross-section (\(\Sigma\)R, cm\({}^{-1}\)), the maximum and semi-valent layer (HVL), and the mean free path (MFP) values were found to be small for BiPbSrCaCuO superconducting ceramics as the silver (Ag) contribution was increased.  
Figure 8: The gamma kerma coefficients of Ag-dopant BiPbSrCaCuO superconductor samples as a function of photon energy.  
Data from this study can guide further research and development of shielding materials for gamma ray and neutron shielding. This work will be useful in designing applications of superconductivity for use in high-energy particle physics experiments, where gamma rays can be produced as a result of the interaction between particles and materials.  
This research received no external funding.  
All data is available.  
The author declares no conflict of interest.
## 6 Adaptation
### A Confidence Measure  
A fundamental requirement for sensor fusion is to obtain some kind of confidence measure for the decisions made. With this kind of information sensors can be be weighted based on the accuracy, or more sensors may be turned on if, for example, the uncertainty is high. We already described a likelihood based method that can be extended to the adaptation case easily: to obtain a confidence measure for the classifications after the adaptation, we will try to predict the probability of the user answering _yes_, given the current observed features and the adaptation data. To do this we use the [PERSON] theorem,  
\[P_{yes|\mathbf{L}}=\frac{p_{\mathbf{L}|yes}P_{yes}}{p_{\mathbf{L}}} \tag{20}\]  
where \(P_{yes|\mathbf{L}}\) is the probability we are looking for, that is, the probability of the user answering _yes_ given the likelihood value of the class that was selected. \(p_{\mathbf{L}|yes}\) is the (empirical) conditional density of the yes-likelihood. \(P_{yes}\) is the probability of successful detection after the adaptation. \(p_{\mathbf{L}}\) is the weighted combination of the _yes_ and _no_ likelihoods. The _no_ likelihood distribution enables us to normalize the posterior probability, which would be very difficult without the adaptation process [28]. It should be noted that in the case of the user answering _no_, we do not know the actual correct class. This is a drawback of the simple binary feedback method, but our method of predicting the probability of the _yes_ answers does not require the knowledge of the true class in the case of a negative answer. To avoid heavy computations on the mobile phone, the required distribution estimates can be computed on the server side. The necessary information to be transmitted to the server would be the \(L_{i}(yes)\) and \(L_{i}(no)\) values, and the server would return the new classifier parameters along with \(p_{\mathbf{L}|yes}\),\(P_{yes}\) and \(p_{\mathbf{L}}\).
## 1 Introduction
In this paper, the real-time implementation of this new method called OEUFS (Optimal Estimation using Uncombined Four-frequency Signals) in the CNES PPP demonstrator is presented. We investigate deeper the benefits of using this method through the PPP-Snapshot concept with the instantaneous convergence allowed by the four frequencies case, as exposed to what has been shown in [16]. Improvements are also achievable by adding high-rate Doppler measurements. The user-level implementation is then described. Since OEUFS involves optimal partial ambiguity resolution, the BIE (Best Integer Equivariant) method has been integrated. Some actual real-time positioning results are presented in good conditions (open sky) and in difficult environments, such as dense urban areas with narrow streets or medium urban areas with foliage. Despite the fact that the number of Galileo satellites and E6-compatible stations at the IGS RTS is growing, the real-time phase-biases production suffers from some limitations such as the lack of compatible stations or the fact that some BDS-3 real-time observables are not available. To overcome these drawbacks, we describe how a post-processed phase-biases solution can be designed and computed. The idea is to take an IGS solution (orbits, clocks, code biases, yaw...) and to compute a set of phase biases consistent with this solution using the majority of the stations available at the IGS (around 250). We can then obtain an optimal and accurate set of phase biases and better demonstrate the instantaneous ambiguity resolution. In this paper, we show how to compute such a solution. The main idea is to perform some carefully chosen phase-residuals combinations to solve for the ambiguities in these combinations, then to identify the corresponding biases, and finally to recover the individual phase biases by inverting a system of equations. The code and phase biases for all GNSS signals are computed except for the GLONASS phase biases. Indeed, the Frequency Division Multiple Access (FDMA) technique to distinguish among the signals broadcast by the Russian constellation prevents us from solving for the integer ambiguities. This process is done on a daily basis, and the products are freely available for test purposes. Finally, the combination of post-processed phase biases and the user software allows very accurate PPP solutions to the centimeter level. This article proposes some comparisons with other existing services, either academic or commercial. To demonstrate such a capacity, we propose an online positioning service. This service works similar to the other services available on the internet [17; 18; 19]: the user uploads a rinex file along with a small set of configuration parameters, such as static or kinematic positioning, and the service returns the PPP trajectory, in an NMEA file in the kinematic case or the cartesian coordinates in the static case. The majority of the features of the demonstrator, such as the multi-constellation and multi-frequency ambiguity resolution, are supported. The targeted accuracy for this service is the centimeter.
# Data Anonymization for Hiding Personal Tendency in Set-Valued Database Publication
## 1 Introduction
In addition, employing generalization technique such as full domain generalization in set-valued database leads significant reduction in data utility [4] and causes item loss in databases if several items from the same category appear together in one record [5]. As a result, data recipients cannot obtain maximum utility from the database. Suppression based data anonymization techniques [4; 6; 7] can be more realistic for hiding personal tendency since certain items in a record are removed so that there is no clue to find the suppressed items. Unfortunately, such a strategy may cause significant distortion in record structure and item loss in the database, making an anonymized database becomes less useful for data recipients. Here record structure refers to the composition or a set of items that construct the records in a database. The swapping technique moves values or items from a record to another record. The advantage is that it does not reduce the number of items as well as does not cause item loss in a database. Accordingly, we seek a solution to develop an anonymization algorithm based on swapping technique in order to overcome drawbacks of other techniques described above. Moreover, it can hide personal tendency by deeming a personal tendency as a tendency of a different individual. As a result, the individual's true tendency is hidden and cannot be easily identified. In this paper, we propose an algorithm that ensures to hide personal tendency while at the same time it does not excessively distort the record structure of an individual. To achieve these properties, we only select a category that has the highest item frequency in a record and swap its items to another record that has a minimum similarity. The rest of the paper is organized as follows. Section 2 describes several works in protection of individual information and current development of swapping technique. Sections 3 and 4 discuss the problem that we are going to solve and our proposed method, respectively. Lastly, Sections 5 and 6 provide experimental results and conclusion, respectively.
## 3 Structure and Operation of DL-Based Index Estimator
### Proposed DL-Based Index Estimator
#### 3.1.3 Cnn  
The CNN structure contains four sub-blocks, such as the previously presented DNN structures. However, instead of using the FCL in all sub-blocks, a batch normalization (BN), convolution layer (CL), and an activation function are employed in the first three sub-blocks while the last sub-block remains identical to those of the previously proposed structures. The CL contains 16 kernels with sizes of 1; the number of neurons in the last FCL is 16. Such as the SFC-DNN structure, the CNN structure receives the received time-domain signal \([\mathbf{y}_{1},\cdots,\mathbf{y}_{4N/n}]^{T}\) as the network input in the form of a matrix to exploit the underlying features of the received signals. It is well known that the CL can perform neighborhood filtering on the inputs to capture the input features [26]. Moreover, to ensure that the inputs in the consecutive layer follow the same distribution, to improve the speed of CNN training significantly, and to alleviate the initial parameter dependency of the learning process, BN is considered before using the CL in each sub-block.
## 3 Results and Discussion
#### 3.1.1 Biological Decay  
The tested retrofit measures reduce the increasing risk of biological attacks caused by climate change in Seville, which is more pronounced for RCP 8.5 in the far future (Figure 4a). On the other hand, Oslo is not prone to biological decay (Figure 4b) due to its lower indoor temperature and relative humidity when compared to the Mediterranean climate [12]. The only exception to this behavior occurs in the far future and if the world evolves according to scenario RCP 8.5 (Figure 4b). If this scenario prevails, then MRF amounts to values well above 1.0 in Oslo, which is a risk for the preservation of the artifacts. For these conditions, the MRF is 1.7 for the case without retrofit measure.  
Nonetheless, in both cases, i.e., Seville and Oslo in the far future for RCP 8.5, the efficiency of the application of the wall retrofit measures (Figure 4a,b, respectively) can be observed since the MRF decreases considerably and attains values lower than 1.0 for a wall calcium silicate board system thicker than 7.3 cm for Seville and 15.7 cm for Oslo. In addition, the inferior limits upon which it is worth applying the thermal insulation systems vary according to the type of insulation layer and climate change scenario, as will be shown and discussed further ahead (Table 2).  
In terms of the ceilings and roofs, the worrying situation is the far future for scenario RCP 8.5 [34], in which the MRF values are well above the 0.5 safety limit. However, the application of the retrofitted measures decreases this risk since it is responsible for lowering the MRF. This reduction is only significant for the first tested thickness for Seville, which leads to the belief that this occurs mainly due to the air tightening of the roof that is caused by the application of the insulation system. The exception is the mineral wool retrofit, which is only worth installing from a certain thickness onwards (Table 2). Finally, the replacement of the window system does not affect both climates substantially because high thermal inertia buildings, such as the case study, typically have low wall/window ratios [35].
## 3 Resource Allocation under Total Power Constraint
### Subcarrier Pairing  
By substituting the optimal power allocation into Equation (4), the dual function becomes  
\[L=\sum_{i=1}^{N}\sum_{j=1}^{N}\rho_{i,j}\varphi_{i,j}+\lambda_{1}P_{T}+\mu_{1} Q_{1}+\mu_{2}Q_{2} \tag{13}\]  
where \(\varphi_{i,j}\) is given by  
\[\begin{split}\varphi_{i,j}=&\log_{2}\left[\frac{ \sqrt{b_{j}}\left(a_{i}+g_{i}\right)}{a_{i}\sqrt{\lambda_{1}+d_{j}\mu_{2}}+ \sqrt{b_{j}\left(a_{i}+g_{i}\right)\left(\lambda_{1}+c_{i}\mu_{1}\right)-a_{i }g_{i}\left(\lambda_{1}+d_{j}\mu_{2}\right)}}\right]\\ &+\frac{\left[\sqrt{b_{j}\left(a_{i}+g_{i}\right)\left(\lambda_{1 }+c_{i}\mu_{1}\right)-a_{i}g_{i}\left(\lambda_{1}+d_{j}\mu_{2}\right)}+a_{i} \sqrt{\lambda_{1}+d_{j}\mu_{2}}\right]^{2}}{b_{j}\left(a_{i}+g_{i}\right)^{2} }\\ &-\frac{1}{2}\left(\log_{2}e+1+\log_{2}\ln 2\right)\end{split} \tag{14}\]  
when \(p_{s,i}^{\star}>0\) and \(b_{j}\left(\lambda_{1}+c_{i}\mu_{1}\right)>g_{i}\left(\lambda_{1}+d_{j}\mu_{2}\right)\) hold;  
\[\varphi_{i,j}=\log_{2}\left[\sqrt{\frac{g}{\lambda_{1}+c_{i}\mu_{1}}}\right] +\frac{\lambda_{1}+c_{i}\mu_{1}}{g}-\frac{1}{2}\left(\log_{2}e+1+\log_{2}\ln 2\right) \tag{15}\]  
when \(p_{s,i}^{\star}>0\) and \(b_{j}\left(\lambda_{1}+c_{i}\mu_{1}\right)\leq g_{i}\left(\lambda_{1}+d_{j}\mu_ {2}\right)\) hold; and \(\varphi_{i,j}=0\) when \(p_{s,i}^{\star}=0\) holds. By comparing the two Equations (14) and (15), it is not difficult to find that as long as \(x>y\) is satisfied, the value of the relaying link is greater than that of the direct link. This again proves that the selection criteria are correct. Now, only the subcarrier pairing variables are left. Obviously, the problem (13) is a typical linear assignment problem, which has been solved in [26]. Finally, dual variables have to be searched iteratively to satisfy the power and interference threshold constraints.
# Creation of Anatomically Correct and Optimized for 3D Printing Human Bones Models
## 1 Introduction  
Three-dimensional (3D) printing (also known as additive manufacturing) is a process of creating physical objects from their geometrical representation in a digital file by the successive addition of different materials [1, 2]. With cost-effective manufacturing for high productivity, 3D-printing technology has become more popular in medical education in recent years, and it is suitable for a variety of applications, including medical mouldages or anatomical models for educational purposes [3]. The benefit of anatomical models is that they can provide educational opportunities to learners who may otherwise not have access to original specimens [4, 5]. In addition, 3D printing is useful for anatomy teaching in creating anatomical models that are not available for sale or reflect real-life variability. Various created anatomical models can involve active student learning: from 3D scanning to working with a variety of 3D modeling software applications, to using 3D printers, and then preparing the final model [6]. The usage of 3D printed anatomical models is becoming not only just a tool for regular anatomical lectures and practical labs but is widely used by students and doctors in the clinical environment as well [7].  
Nowadays, 3D printing is being actively used in engineering and educational fields thanks to its great flexibility and the ability to create objects with complex sophisticated structures [8]. Medical schools, hospitals, and healthcare institutions can use the 3D models to improve clinical evaluation for several pathologies as well as bring new medical devices to market. Some of the most common applications in the medical industry are preclinical medical device testing and medical training models. For example, the insertion of a central venous catheter has been improved by using anatomical training kits [9]. Additionally,organ models play an important role in many types of major surgical procedures as well as the education and communication outside of surgery [10]. Three-dimensional (3D) models can also be used for surgical planning prior to operation, such as neurosurgery [11].  
The availability of 3D printers and improved segmentation algorithms have boosted the use of 3D printing in medicine, and this has led to an interest in many potential medical applications [12]. Models can be quickly adapted to a particular patient, quickly reshaped, and 3D printed. It provides a cheap alternative to general commercially available anatomical models. Thus, 3D printing can be used to teach anatomical structures [13]. It should be noted that there are still problems making this process available to wide-ranging users.  
This work describes the methodology for creating a correct and ready-to-print anatomical model for using radiological data. The whole workflow starting from the obtaining of radiological data until the physical 3D model is described in a step-by-step manner, along with programs required for the creation 3D model.