esa-sceva/satcom-chunk-collection huggingface dataset:
# Cognitive Radio Network Technology for IoT-Enabled Devices +
## 3 Cognitive Radio Network Based on IoT
### Cognitive Radio Network Technology for IoT  
In recent years, researchers' attention has largely been focused on the three fields, communication, opportunistic, and sensing. However, IoT will not be capable of evaluating its capacity to deal with potentials of developing difficulties without extensive cognitive capabilities [23]. It is currently clear that both intra- and inter-cognitive communication (INTRACC and INTERCC, respectively) are crucial for the Internet of things. In INTRACC mode, cognitive objects share the same set of abilities, whereas in INTERCC  
Figure 4: Five-layer IoT architecture.  
mode, they have a unique sets of skills. Cognitive radio-based IoT frameworks are being researched. Future IoT items should think, learn, and recognize both social as well as physical worlds [24; 25]. These objects should feature observation action cycles, intelligent decision making and knowledge discovery, enormous data analytics, and on-demand service provisioning. Hence, IoT will require cognitive radio network integration. The following causes necessitate it:  
* Wireless methods will become increasingly important in the future for transmitting data between sensors and cloud servers. According of their limited range, wireless solutions like Bluetooth and Zigbee have made way for the CRNs-based IoT paradigm [23; 25];
* The situation will increase because of the massive number of IoT objects and it will be tough to provide bandwidth to such a large number of devices. Concurrently, the growing population of licensed users will provide challenges for those who lack proper authorization for this purpose. Bandwidth acquisition costs will also be expensive. This encourages us to look outside the box for solutions, and CRNs might be that resolution;
* Interference issues will arise when the number of IoT objects increases and they are moved around. IoT objects utilizing a CRN can opportunistically seek out channels with low interference to improve upon communication;
* Spectrum sharing is not possible with current wireless communication technologies. Spectrum sharing problems will be an issue for future cellular networks as well. This advantage of cognitive radio technology bodes well for the future, when more machines will compete for limited spectrum. However, the implementation of spectrum-sharing areas should take into account regulatory, business, and technological frameworks.
# Trends and Opportunities in the Dairy Industry: A2 Milk and Processing Methods
## 5 Milk Processing
### Non-Thermal Methods
#### 5.2.5 Cold Plasma--Nonthermal Plasma (NTP)  
Cold plasma or nonthermal plasma is a quasi-neutral ionized gas (partially or entirely) consisting of charged particles--ions, free radicals, atoms, molecules, and some radiation, exhibiting a resultant neutral charge. Its most active elements are reactive oxygen species (ROS) and reactive nitrogen species (RNS) [113]. Furthermore, in concert with free radicals, the generation of ultraviolet radiation by plasma results in the oxidation of phospholipids and proteins located on the plasma membrane, thereby damaging nucleic acids. This ultimately leads to the death of micro-organisms. Conversely, reactive oxygen species can lower the pH of foods and modify the properties--viscosity, solubility, and water absorption--of polysaccharides and proteins [114]. Atmospheric cold plasma (ACP) is a state of partially ionized gas in which ionization is about 5% or less of the gas and is maintained at approximately room temperature. Exposing food to ionizing radiation is an innovative way to inactivate micro-organisms in the food industry [115; 113]. Its benefits include short processing times (from a few seconds to minutes), efficiency at room temperature, which is essential for heat-sensitive products--such as milk--and low energy consumption, which makes it an environmentally friendly method. A processing time of at most 5 min is recommended due to color changes associated with oxygen in the raw material and oxidation of proteins and fats [115].  
[PERSON] et al. [116] extended the shelf life of milk to 15 days using NTP for 10 min, where TPC was above 6 CFU/mL and titratable acidity was below 0.18% according to standards. However, the treatment released free lipids into the milk, which underwent oxidation reactions. No significant effect on nutritional value or xanthine oxidase activity was observed, but the milk changed color more strongly than the control samples. The team noted that this is a worthwhile technology to develop--data on different types of milk are not available.
# Releasing Differentially Private Trajectories with Optimized Data Utility
## 5 Experimental Evaluations
### Privacy Metric  
_Location privacy attacks metric._ As we summarized in Section 2, location inference attack and de-anonymization attack are two of the most common attacks in the literature concerning location privacy protection. A common objective of both attacks [3; 35] is to infer individual actual locations from obfuscated locations. Before the attack, for any location \(l\in\mathcal{L}\), especially a sensitive location such as a hospital or workplace that the adversary is interested in, he or she has a prior probability distribution \(P_{l}\) regarding the individuals who visit \(l\). Since the distribution represents the preference of population on \(l\), it is assumed to be accessible to the adversary. After observing the obfuscated trajectories \(D_{O}\), the adversary could derive a posterior probability distribution \(P_{l|D_{O}}\) through the attack. It represents the preference of the individuals in \(D_{O}\) on \(l\), which is assumed to be inaccessible to the adversary before the attack. The difference between \(P_{l|D_{O}}\) and \(P_{l}\) indicates how much knowledge concerning the actual location \(l\) the adversary has obtained by observing the obfuscated dataset \(D_{O}\). It also implies the location privacy protection algorithm's vulnerability to both the location inference attacks and the de-anonymization attacks. A larger difference indicates a weaker privacy guarantee. We measure the difference by Jensen-Shannon divergence \(JS(P_{l}||P_{l|D_{O}})\), a widely used measurement of the difference between two probability distributions. 10% locations are randomly chosen from \(D_{A}\) uniformly at random and regarded as sensitive locations, denoted by \(\mathcal{S}\). We calculate the location privacy attacks metric, \(P(f)\), by taking the negative average of \(JS(P_{l}||P_{l|D_{O}})\) over all sensitive locations, as given by Equation (15). A larger \(P(f)\) indicates a stronger privacy guarantee.  
\[P(f)=-\frac{\sum_{l\in\mathcal{S}}JS(P_{l}||P_{l|D_{O}})}{|\mathcal{S}|} \tag{15}\]
# Assessment of IRNSS-Only Data Processing: Availability, Single-Frequency SPP and Short-Baseline RTK
## 2 Constellation and Signal of IRNSS  
The IRNSS-1I satellite was successfully launched in April 2018. Currently, the IRNSS constellation has eight in-orbit satellites: three GEO satellites and five IGSO satellites (see Table 1). Figure 1 displays the ground tracks of eight IRNSS satellites on 13 July 2019. The three satellites IRNSS-1C, IRNSS-1F, and IRNSS-1G are positioned at 83\({}^{\circ}\) E, 32.5\({}^{\circ}\) E, and 129.5\({}^{\circ}\) E over the equator, respectively, and their ground tracks all vary within 5\({}^{\circ}\) in latitude as the orbit inclination angle is not strictly zero. The repetition period of both GEO and IGSO satellites is 23 h and 56 min. The orbit inclination of the IGSO satellites is 29\({}^{\circ}\) (fluctuated by 2\({}^{\circ}\)). Regarding the longitude, the IRNSS-1B and IRNSS-1I satellites are in the orbit of 55\({}^{\circ}\) E, and the IRNSS-1A, IRNSS-1D, and IRNSS-1E satellites are in the orbit of 111.75\({}^{\circ}\) E. The approximate latitude range of ground tracks for the five IGSO satellites is 30\({}^{\circ}\) S-30\({}^{\circ}\) N.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Common Name** & **SVN** & **PRN** & **COSPAR ID** & **NORAD ID** & **Orbit Type** & **Launch Date** \\ \hline IRNSS-1A & 1001 & 101 & 2013-034A & 39199 & IGSO & 2013/07/01 \\ IRNSS-1B & 1002 & 102 & 2014-017A & 39635 & IGSO & 2014/04/04 \\ IRNSS-1C & 1003 & 103 & 2014-061A & 40269 & GEO & 2014/10/15 \\ IRNSS-1D & 1004 & 104 & 2015-018A & 40547 & IGSO & 2015/03/08 \\ IRNSS-1E & 1005 & 105 & 2016-003A & 41241 & IGSO & 2016/01/20 \\ IRNSS-1F & 1006 & 106 & 2016-015A & 41384 & GEO & 2016/03/10 \\ IRNSS-1G & 1007 & 107 & 2016-027A & 41469 & GEO & 2016/04/28 \\ IRNSS-1I & 1009 & 109 & 2018-035A & 43286 & IGSO & 2018/04/11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information about IRNSS constellation.  
IRNSS can broadcast signals on two frequencies, namely L5 and S, based on code division multiple access (CDMA) technology (detailed in Table 2). The suitable frequencies for navigation of near 1575 MHz have almost completely been occupied by GPS, GLONASS, Galileo, BDS, and QZSS. Thus, the L1 frequency is not selected for IRNSS. For ease of interoperability of IRNSS with other satellite systems, the L5 signal with a center frequency of 1176.450 MHz is selected as the first signal for IRNSS. IRNSS also chooses an S signal with a center frequency of 2492.028 MHz as its second signal. Compared with the L5 signal, the S signal is less affected by the ionospheric delay due to its larger frequency. IRNSS uses differently modulated L5 and S signals, with a BPSK (1) modulation for SPS users and a BOC (5, 2) modulation for RS users.
# Interclass Interference Suppression in Multi-Class Problems
## 3 Interclass Interference Suppression Methods  
### All-Features-Based Approach  
In the feature selection process of multi-classifiers, the essential features of a certain class are often deleted as redundant features, which causes interclass inference. Therefore, an intuitive solution to interclass inference suppression is to forgo feature selection and retain all features. This idea is intuitive and can eliminate interclass inference completely. However, this method goes against the conventional way that is widely adopted in the machine learning field, and it also seriously increases the computational cost. In the field of machine learning, many studies have shown that feature selection is an important way to improve the generalization performance of learning machines. As a result, for many machine learning methods, feature selection has become an indispensable part in the learning process [52]. Feature selection can greatly reduce the dimension of input space by removing those unimportant or redundant features, thus reducing the complexity of functions implemented by machine learning methods. Feature selection can generally improve the generalization performance of machine learning methods. Therefore, reserving all attributes are not very suitable to suppress the interclass inference.
# Derivation and Verification of Laminated Composite T-Beam Theory
## 2 Derivation of the Formula for Laminated T beam
### Stress and Strain of Nonsymmetric Laminated Composite T-Beam  
#### 2.5.1 Narrow Flange  
The sum of the moments on the web is given in Equation (61).  
The sum of the flange moment is derived from Equation (44) and is expressed as:  
\[M_{flange}=b(N_{x}Z_{1}+M_{x})=b\left[\left(\frac{F_{44}}{\Delta}Z_{1}^{2}- \frac{F_{14}}{\Delta}Z_{1}\right)+\left(\frac{-F_{41}}{\Delta}Z_{1}+\frac{F_{ 11}}{\Delta}\right)\right]\kappa_{x} \tag{74}\]  
Therefore, the moment of the beam is calculated by combining Equations (61) and (74).  
\[M_{beam}=M_{web}+M_{flange}=\left[\left(\frac{h^{3}}{12A_{11,w}^{-1}}+\frac{Z_{ c}^{2}h}{A_{11,w}^{-1}}\right)+b\left(\begin{array}{c}\frac{F_{44}}{\Delta}Z_{1}^{ 2}-\frac{F_{14}}{\Delta}Z_{1}\\ +\left(\frac{-F_{41}}{\Delta}Z_{1}+\frac{F_{11}}{\Delta}\right)\end{array} \right)\right]\kappa_{x}=EI_{eff}\kappa_{x} \tag{75}\]  
Consequently, the effective stiffness of the narrow flange is expressed as:  
\[EI_{eff}=\left(\frac{h^{3}}{12A_{11,w}^{-1}}+\frac{Z_{c}^{2}h}{A_{11,w}^{-1}} \right)+b\left[\left(\frac{F_{44}Z_{1}}{\Delta}-\frac{F_{14}}{\Delta}\right)Z _{1}+\left(\frac{-F_{41}Z_{1}}{\Delta}+\frac{F_{11}}{\Delta}\right)\right] \tag{76}\]  
The strain of the beam in the x-axis direction is given by Equation (65).  
The strain of the flange is expressed as Equation (77) by combining Equations (12) and (43).  
\[\left\{\begin{array}{c}\varepsilon_{x}\\ \varepsilon_{y}\\ \gamma_{xy}\end{array}\right\}=\left\{\begin{array}{c}F_{11}N_{x}+F_{14}M_{ x}\\ F_{21}N_{x}+F_{24}M_{x}\\ F_{51}N_{x}+F_{34}M_{x}\end{array}\right\}+\xi\left\{\begin{array}{c}\kappa _{x}\\ \kappa_{y}\\ \kappa_{xy}\end{array}\right\} \tag{77}\]
# Purification of Produced Water by Solvents to Enhance Oil Recovery and Reuse Separated Droplets
## 4 Discussion
### Alcohols
#### 4.2.2 Polyhydric Alcohols  
Glycols, possessing two OH groups and with two, three, or six carbon atoms, are slightly less effective than monohydric alcohols in oil recovery, as shown in Figure 3c. However, as with glycerin, the introduction of a third OH group leads to a stronger affinity for the aqueous phase over the oily phase. Despite high concentrations of salts in the water, particularly sodium chloride, glycerin dissolves well and becomes solvated. This solvated glycerin can encapsulate oil droplets, thereby increasing their zeta potential. Consequently, this results in electrostatic repulsion that prevents droplet aggregation and coalescence, potentially even stabilizing the emulsion. Indeed, the addition of just 5 mL of glycerin to 100 mL of produced water achieves a mere 2.2% oil recovery. When 25 mL is added, the recovery is virtually non-existent, at only 0.1%.  
To assess the influence of the number of hydroxyl groups for a constant carbon number, four different C3 alcohols were compared: 1-propanol, 2-propanol (1 OH), propylene glycol (2 OH), and glycerin (3 OH). As expected, and in line with the previous paragraph, alcohols with a single OH group are the most effective (regardless of whether the OH is in the 1 or 2 position), followed by propylene glycol (with 2 OH), and almost complete inefficacy for glycerin with 3 OH (Figure 3d,e).  
Except for glycerin, the effectiveness of alcohols is high, though it does not reach the desired standards of near-complete purification of produced water. However, in their favour are highly advantageous factors such as widespread industrial availability, very low cost, and easy recyclability. Ethylene glycol and methanol are exceptions, as they are toxic to higher life forms. Nonetheless, the other alcohols analysed are minimally toxic to humans and the environment. If near-complete purification is not required, they could be a good alternative for industrial applications.
## 2 Literature Review
### LCA (Life-Cycle Assessment)  
Life-cycle assessment (LCA) is a technique to assess the environmental impacts associated with all the stages of a product's life from-cradle-to-grave (i.e., from raw material extraction through materials processing, manufacture, distribution, use, repair and maintenance, and disposal or recycling) [5; 15].  
For LCA CO\({}_{2}\) emission evaluation of tunnel project, the authors divided the life cycle of tunnel into four stages. (Materials production; construction; use and maintenance; and end of life), each of which consists of the following:  
1. Materials production: this phase includes the process of the manufacturing and processing of raw materials; the building materials to be charged into the building consume resources, and the energy required for production, such as the production of products [5].
2. Construction: this phase includes all the activities necessary for tunnel construction, such as earthworks, concrete pouring, material transport etc.
3. Use and maintenance: this phase includes the operation for using, repairing and manage the tunnel project until it is demolished, such as, repairment of tunnel road pavement, maintenance of lighting and ventilation condition inside the tunnel, etc.
4. End of life: the last phase occurs when the tunnel reaches the end of its useful life. According to the tunnel management strategy, the tunnel may be recovered or demolished, and removed materials may be recycled or left in the field, increasing the burden on the environment [7].  
The life-cycle assessment (LCA) of tunnel CO\({}_{2}\) emission includes four stages, as shown in Figure 1.  
In this study, the authors selected stage 1 and stage 2 as the research scope, which are directly related with construction process.
## 3 Proposed System  
The proposed system consists of four modules: the HaEx segmentation module, optic disc localization module, macula localization module, and finally the DME grading module that uses all the information provided by the previous three modules. The input image obtained from ophthalmoscopy is pre-processed to crop the unwanted background which is introduced into three modules: the HaEx segmentation module, optic disc localization module, and macular localization module. Each module executes different tasks with the intention of combining the results to create an interpretable diagnosis decision to help ophthalmologists determine the grade of DME from eye fundus images that are the input of our system. Figure 1 shows a diagram of the proposed system with all modules and their relationship. Each module will be described in detail in the following subsections.
## 3 Results and Discussion
### Experimental Characterisation--Fresh Cell
#### 3.1.2 Thermal Response--Room and Cycling Temperatures  
The thermal response in terms of temperature increase of the cell 1 measured during the discharge process under various current rates 6 A, 20 A, 40 A (corresponding to 0.3C, 1C, 2C) at 25 \({}^{\circ}\)C and 35 \({}^{\circ}\)C at the top, middle and bottom regions are presented in Figure 10. The measurements were performed in a climate chamber (Clima Temperatur Systeme GmbH (CTS), Hechingen, Germany) in which the cell is subjected to forced convection. The highest temperature rises are established in the middle and bottom regions of the cell, with up to 25% increase. Although for low current rates similar evolutions are observed for both studied temperatures in the three regions, at high current rates more significant temperature rises are observed at room in comparison to the the cycling temperature. The differences in the temperature increases at the three top, middle and bottom regions indicate a non-uniform temperature distribution on the surface of the fresh cell. The temperature distribution originates from the non-uniform potential developed in the electrodes sheets leading to non-uniform current density. The heat generation being proportional to the square root of the current, is hence non-uniform and leads in turn to non-uniform temperature distribution at the surface of the battery cell. Further, differences in the temperature increase of an uncycled battery cell under room compared to the cycling temperature in Figure 10 are around 10%.  
Figure 9: Discharge capacity at 25 \({}^{\circ}\)C and 35 \({}^{\circ}\)C of the cell 1 at different current rates.
## 6 Conclusions  
Integer ambiguity resolution plays a crucial role in achieving the best positioning or orbit accuracy. Single receiver and double difference integer ambiguity resolution models are introduced in this contribution. One year of GRACE-FO data were analyzed to verify the orbit improvement with different ambiguity resolution strategies. In-flight antenna PCV maps were developed to further exploit the POD accuracy.  
With PCV corrections, along-track orbit accuracy of the SD AR solution is improved by 12.6%, and that of the DD AR solution is improved by 10.3%, which verifies the necessity of in-flight antenna calibration. With phase biases cancelled by the double difference operation, fixing rates of DD ambiguities are more than 96.7%. Similar performance is achieved by SD AR, which confirms the consistency of theoretical models and bias/clock products.  
JPL's orbited together with independent SLR and KBR measurements are used to assess the results of different AR strategies. Both SD AR and DD AR solutions improve the orbit accuracy. SD AR solution provides the best performance of absolute orbit. Compared with JPL's orbit, the RMS of the SD AR orbit differences is better than 6 mm and the SLRresidual RMS is less than 10 mm. The DD AR solution realizes the highest baseline accuracy and the STD of KBR residuals achieves 0.9 mm. In addition, sometimes there are fewer GPS satellites available, which leads to the reduction of orbit accuracy. With the application of multi-mode GNSS receivers and multi-GNSS phase bias products [70; 71], orbit accuracy based on single receiver ambiguity resolution can be further improved.  
Conceptualization, B.J. and Y.L.; methodology, B.J.; software, B.J. and K.J.; validation, B.J. and S.C.; writing--original draft preparation, B.J.; writing--review and editing, S.C.; supervision, Y.L. and Z.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 12033009.  
GRACE-FO data in this paper can be freely accessed at [[ftp://isdcftp.gz-potsdam.de/grace-fo/](ftp://isdcftp.gz-potsdam.de/grace-fo/)]([ftp://isdcftp.gz-potsdam.de/grace-fo/](ftp://isdcftp.gz-potsdam.de/grace-fo/)) (accessed on 17 October 2021). The GPS final orbit, clock, and OSB products can be found at [[ftp://ftp.aiub.unibe.ch/CODE/](ftp://ftp.aiub.unibe.ch/CODE/)]([ftp://ftp.aiub.unibe.ch/CODE/](ftp://ftp.aiub.unibe.ch/CODE/)) (accessed on 17 October 2021).  
The authors thank GFZ for providing GRACE-FO GPS data. The authors are grateful to the CODE for providing GPS products.  
The authors declare no conflict of interest.