esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Disintegration of CM: Defining PEF Parameters
The results after PEF treatment are summarized in Figure 5. The highest charge density was found for low electric field strength and short pulse width, whereas the lowest charge density was found for high electric field strength and long pulse width. Other medium PEF parameters result in partially comparable values for charge density. Considering also the results of SSA and charge density, the favorable PEF parameters for CM disintegration can be defined as follows: E = 10.7 kV/cm, pulse width = 15 \(\upmu\)s, inlet temperature of 10 \({}^{\circ}\)C, W = 25 kJ/kg. Figure 4: Effect of PEF on SSA of rehydrated CM at different electric field strengths (10.7–20 kV/cm) and pulse width (15–25 \(\upmu\)s), applying a specific energy of 25 kJ/kg and an inlet temperature of 10 \({}^{\circ}\)C, \(\Delta\) SSA = SSA before PEF \(-\) SSA after PEF.
## 4 Conclusions and Loose Ends
### Security-Proofing--Concerns on IoT Network Reliance
#### 4.1.2 Compliance with Standards and Risk Mitigation  
Complying with established security standards and frameworks, such as those outlined by the International Organization for Standardization (ISO), provides a structured approach to securing IoT ecosystems. These standards offer guidelines on best practices for IoT security and risk mitigation, from device manufacturing to data transmission and storage. Security strategies should be adaptive, capable of evolving with the changing threat landscape and technological advancements in the IoT and in any other field.
## 5 Results
### Simulation Results of Benchmark Functions
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Test Problems** & **Statistic** & **IMOPSO** & **MOPSO** & **NSGA-II** & **MOEA/D** \\ \hline KUR & max & \(1.2663\times 10^{-1}\) & \(5.9534\times 10^{-2}\) & \(1.3012\times 10^{-2}\) & \(1.9458\times 10^{-2}\) \\  & min & \(5.3247\times 10^{-3}\) & \(2.6536\times 10^{-2}\) & \(9.8574\times 10^{-3}\) & \(9.5847\times 10^{-3}\) \\  & ave & \(1.0752\times 10^{-2}\) & \(4.0853\times 10^{-2}\) & \(1.1532\times 10^{-2}\) & \(1.2532\times 10^{-2}\) \\  & std & \(2.1964\times 10^{-2}\) & \(8.4175\times 10^{-3}\) & \(7.7454\times 10^{-4}\) & \(1.9686\times 10^{-3}\) \\ \hline ZDT1 & max & \(8.2362\times 10^{-4}\) & \(1.6213\times 10^{-2}\) & \(4.3132\times 10^{-3}\) & \(5.8247\times 10^{-3}\) \\  & min & \(3.0381\times 10^{-4}\) & \(3.9211\times 10^{-4}\) & \(2.0014\times 10^{-3}\) & \(1.4356\times 10^{-3}\) \\  & ave & \(5.8370\times 10^{-4}\) & \(9.5527\times 10^{-4}\) & \(2.1165\times 10^{-3}\) & \(3.1143\times 10^{-3}\) \\  & std & \(8.5002\times 10^{-5}\) & \(2.1325\times 10^{-3}\) & \(5.8285\times 10^{-4}\) & \(8.2006\times 10^{-4}\) \\ \hline ZDT2 & max & \(5.2434\times 10^{-4}\) & \(9.2800\times 10^{-4}\) & \(5.9324\times 10^{-3}\) & \(8.3252\times 10^{-3}\) \\  & min & \(3.3372\times 10^{-4}\) & \(0.0000\times 10^{-4}\) & \(1.8478\times 10^{-3}\) & \(3.6892\times 10^{-4}\) \\  & ave & \(3.6052\times 10^{-4}\) & \(4.1485\times 10^{-4}\) & \(3.4657\times 10^{-3}\) & \(3.3358\times 10^{-3}\) \\  & std & \(3.5078\times 10^{-5}\) & \(1.9041\times 10^{-4}\) & \(1.0549\times 10^{-4}\) & \(2.3145\times 10^{-4}\) \\ \hline ZDT3 & max & \(1.1263\times 10^{-4}\) & \(1.2586\times 10^{-3}\) & \(2.3123\times 10^{-3}\) & \(5.2532\times 10^{-2}\) \\  & min & \(5.5677\times 10^{-4}\) & \(7.4425\times 10^{-4}\) & \(1.5547\times 10^{-3}\) & \(5.7817\times 10^{-4}\) \\  & ave & \(7.8125\times 10^{-4}\) & \(9.4334\times 10^{-4}\) & \(1.8564\times 10^{-3}\) & \(3.2984\times 10^{-3}\) \\  & std & \(9.7499\times 10^{-5}\) & \(1.4741\times 10^{-4}\) & \(1.9552\times 10^{-4}\) & \(9.5547\times 10^{-3}\) \\ \hline ZDT4 & max & \(5.0733\times 10^{-4}\) & \(2.2014\times 10^{1}\) & \(1.4857\times 10^{-3}\) & \(1.4225\times 10^{-3}\) \\  & min & \(3.7921\times 10^{-4}\) & \(1.1057\times 10^{-2}\) & \(5.2122\times 10^{-4}\) & \(3.5120\times 10^{-4}\) \\  & ave & \(4.5216\times 10^{-4}\) & \(5.6523\times 10^{0}\) & \(8.6015\times 10^{-4}\) & \(5.6826\times 10^{-4}\) \\  & std & \(3.3182\times 10^{-5}\) & \(6.4916\times 10^{0}\) & \(2.4718\times 10^{-4}\) & \(1.9736\times 10^{-4}\) \\ \hline ZDT6 & max & \(5.4012\times 10^{-3}\) & \(1.4771\times 10^{-1}\) & \(4.8123\times 10^{-2}\) & \(3.3601\times 10^{-2}\) \\  & min & \(2.5875\times 10^{-4}\) & \(2.5815\times 10^{-4}\) & \(2.5027\times 10^{-4}\) & \(2.6774\times 10^{-4}\) \\  & ave & \(4.6869\times 10^{-4}\) & \(1.7124\times 10^{-2}\) & \(1.9012\times 10^{-3}\) & \(1.7445\times 10^{-3}\) \\  & std & \(9.2828\times 10^{-4}\) & \(4.3962\times 10^{-2}\) & \(8.7443\times 10^{-3}\) & \(6.2142\times 10^{-3}\) \\ \hline \end{tabular}
\end{table}
# Removal of Phosphate from Aqueous Solution by
## 3 Results and Discussion
### Adsorption Mechanisms and Analysis
#### 2 Ligand exchange  
It was displayed that the phosphate adsorption process occurs chemically according to the movement of O-H peaks in the FTIR pattern of the zeolite-biochar composite after phosphate adsorption and the generation of amorphous thin-film calcium and magnesium phosphate compounds in TEM-EDS. This is because the surface of the zeolite-biocarbon composite was covered with MgO and CaO particles, which can be used as the primary adsorption sites for removing phosphate. When it comes into interface with water, hydroxylation of the metal oxide surface can occur, and phosphate replaces the hydroxyl group for ligand exchange [75], resulting in the formation of an amorphous calcium and magnesium phosphate compound.  
\[\text{Ca(Mg)O-OH}_{2}\text{${}^{+}$}+\text{H}_{2}\text{PO}_{4}\text{${}^{-}$} \rightarrow\text{Ca(Mg)O-H}_{2}\text{PO}_{4}+\text{H}_{2}\text{O}\text{ (0.12 $<$ pH $<$ 9.21)}\]  
\[\text{2 Ca(Mg)O-OH}_{2}\text{${}^{+}$}+\text{HPO}_{4}\text{${}^{2-}$} \rightarrow\text{2(Ca(Mg)O)-HPO}_{4}+\text{2H}_{2}\text{O}\text{ (5.21 $<$ pH $<$ 10.67)}\]  
\[\text{3 Ca(Mg)O-OH}_{2}\text{${}^{+}$}+\text{PO}_{4}\text{${}^{3-}$} \rightarrow\text{3(Ca(Mg)O)-PO}_{4}+\text{3H}_{2}\text{O}\text{ (10.67 $<$ pH $<$ 12)}\]  
In addition, it has been shown that metal oxides can generate surface precipitation with phosphates through hydrogen or chemical bonding [76; 77; 78]. Although no solid precipitation was seen for any of the three materials in this study, an amorphous thin-film material was discovered, which may be connected to the lower phosphate content or the shorter aging period of the precipitation [79].  
Figure 11: Mechanism of phosphate adsorption by zeolite-biochar composite (PZC 7:3).  
The presence of Cl\({}^{-}\) in the EDS of zeolite-biocarbon composites after adsorption of phosphate indicates its involvement in the adsorption process, as well as the possibility of ion exchange between phosphate and chloride ions at the adsorption site. [PERSON] et al. [43] evinced that the higher the Cl\({}^{-}\) content in the material, the more actively the ion exchange mechanism is involved. The relatively low chloride ion content in this study suggests that the adsorption process was successful.
# Unraveling the Biosynthesis of Carvacrol in Different Tissues of _Origanum vulgarae_
## 3 Discussion  
### Volatile Organic Compound Profiles in Different Tissues of O. vulgar 'HS'  
The most volatile monoter genes found in _Origanum_ are p-cymene, thymol, carvacrol, and their precursor \(\gamma\)-terpinene. The biosynthetic precursors of \(\gamma\)-terpinene and p-cymene not only determine the taste and smell of the plant, but also its properties, such as antioxidant and antimicrobial activities [18]. The aim of the present study was to use _O. vulgar_ 'HS' as a source of carvacrol (with a very high content), which can fight against human pathogenic bacteria and fungi, as well as foodborne microbial pathogens. The accumulation of terpen presented tissue-specific patterns in flowers, leaves, and stems of _Origanum_ species [19]. In _O. vulgar_ 'HS', carvacrol was the main component presenting in PEs (94.40 \(\pm\) 1.23%), SEs (96.92 \(\pm\) 0.85%), BRs (96.07 \(\pm\) 0.67%), LEs (84.71 \(\pm\) 1.59%), whereas the main components in SIs were p-cymene (65.44 \(\pm\) 5.77%) and carvacrol (13.06 \(\pm\) 6.74%) (Table S1). Other monoterenes and sesquiterpenes (sabinene, \(\beta\)-caryophyllene, etc.) had lower levels in _O. vulgar_ 'HS', as consistent with VOC composition identified in leaves of _O. vulgar_ L [20]. Our study shows that _O. vulgar_ 'HS' is a carvacrol-rich species whose VOCs exhibit tissue dependence.
# A Geospatial Framework of Food Demand Mapping
## 2 Materials and Methods
### Food Map Generation  
In this study, the spatial distribution of eating habits across Lithuania will be analyzed using a geospatial approach. The methodology involves dividing Lithuania into 60,000 points and interpolating survey data. The number of points can be adjusted based on the required spatial resolution and the sample size, which might impact the validity of the map. This flexibility allows for more precise and accurate representations of food demand, catering to specific research needs and ensuring that the spatial analysis aligns with the available data. Adjusting the number of points helps in balancing the granularity of the analysis with the practical limitations of survey data, ultimately enhancing the reliability and applicability of the generated maps.  
Our main aim is to analyze the spatial distribution of eating habits across Lithuania, employing a geospatial approach. Our methodology involves dividing the country into 60,000 points and utilizing spatial interpolation with survey data. As already discussed in the previous sections, a well-established deterministic approach for scattered data interpolation is Shepard's operator. This operator relies on a weighted average of values at data points and, owing to its simplicity, finds frequent application in interpolating natural processes like groundwater depth or pollution, as documented in [33].  
Consider dataset \(X=(x_{1},x_{2},\ldots,x_{k})\), where \(x_{i}\in\mathbb{R}^{p}\) for \(1\leq i\leq k\) and \(p\geq 1\). The corresponding response values are given by \(Y=(y_{1},y_{2},\ldots,y_{k})\) where \(y_{i}\in R^{m}\), \(p\) and \(m\) denotes the number of features, and \(k\) represents the sample size. Let us define a linear extrapolator \(y:R^{p}\to R^{m}\) as follows:  
\[y(x)=Y^{T}u(x)\]where  
\[u(x)=\left\{\begin{array}{ll}\frac{w(x)}{1^{T}w(x)},&\quad if\ |x_{i}-x|\
eq 0\ \forall i\\ y_{i}\,&\quad if\ |x_{i}-x|=0\end{array}\right.\]  
where \(\mathbf{1}\) is a \(k\)--dimensional vector of ones, and the weights are determined by:  
\[w(x)=\left(\frac{1}{\left|x_{1}-x\right|^{\delta}},\frac{1}{\left|x_{2}-x \right|^{\delta}},\dots,\frac{1}{\left|x_{k}-x\right|^{\delta}}\right).\]  
with \(0\leq\delta\leq 1\). Note that the Shepard's extrapolator satisfies the condition \(\mathbf{1}^{T}u(x)=1\).
## 5 Data Imbalance
### Data Imbalance Handling Techniques
#### 5.3.3 Generative Adversarial Networks (GANs)  
Generative Adversarial Networks (GANs) have shown great potential in addressing data imbalance. GANs generate realistic minority class samples through adversarial training, significantly enhancing the diversity and representativeness of minority class data [93]. However, the training process for GANs is complex and requires many resources, and the model requires meticulous hyperparameter adjustment. Moreover, although GAN-generated samples are highly realistic, there is still a risk of mode collapse, where the diversity of the generated samples is insufficient, leading to a less effective representation of the minority class [94; 95].  
Table 6 shows a comparison of existing data imbalance handling techniques.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**ID** & **Method** & **Detection Accuracy** & **False Positive Rate** & **Dataset** & **Advantages** & **Challenges** & **Empirical Support** \\ \hline \multirow{4}{*}{1} & Generative Adversarial Network (GAN) & High accuracy in generating realistic data, improving minority classes. & Low due to high-quality synthetic data misclassification. & UNSW-NB15, NSI-KDD, CIC-IDS2017 & Produces realistic data, handles data & Complex training is sensitive to hyperparameters and dependent on original data & Complex training is sensitive to hyperparameters and dependent on original data & Complex training is & Complex training is sensitive to parameters, datasets only. & Complex training is sensitive to parameters, datasets only. & Complex training is sensitive to hyperparameters, false positives, limited & Complex training is sensitive, fixed & Complex training is sensitive to hyperparameters & Complex training is sensitive
# Modeling Activity-Time to Build Realistic Plannings in Population Synthesis in a Suburban Area
## 6 Algorithm for Time-Choices Setting
### Activity-End-Time: A Final Value  
#### 6.2.1 Select an End-Time Value  
After the end-time potential range is calculated, the next step is to to select an end-time value from that range. Based on the nature of the activity, two main cases are considered: home activity case and other activities case.  
The activity _home_ does not have a defined duration; departure time (home end-time) depends on the beginning (start-time) of the next activity \(B\). To calculate it, the home activity end-time distribution is updated as the next activity start-time distribution (see Equation (22)). For the remaining activities, the end-time distribution is kept the same (no updating process).  
\[D_{home}=ST_{B} \tag{22}\]  
After updating the activity end-time, the selection process of an end-time value is applied.  
First, all possible end-time proportions \(p\in P\) are selected, which satisfies the following constraints: it is a proportion from \(D_{A}\), and the intersection between \(p\) and the end-time range is not empty (see Equation (23)).  
\[(p\in D_{A})\wedge(p\cap[ET_{A},LT_{A}]\
eq\emptyset) \tag{23}\]  
Second, the selected proportions \(P\) are normalized. Due the intersection of the end-time range and \(P\), some proportions partially belong to the end-time range. An adjustment process is necessary to adapt the weight \(r\) of these partially proportions. This process considers the quantity of time from the proportion that belongs to end-time range. A belonging parameter \(b_{p}\) is defined for a proportion \(p\) as the ratio of \(p\), which belongs to \([ET_{A},LT_{A}]\) (see Equation (24)).  
\[b_{p}=\frac{|p\cap[ET_{A},LT_{A}]|}{|p|} \tag{24}\]  
The proportion weight is adjusted following Equation (25), and it is illustrated in Figure 20.  
\[r_{p}=b_{p}\times r_{p} \tag{25}\]  
Figure 19: Updated end-time range: the final end-time range is generated, which includes the constraints \(EVT\) and \(LLT\) on the initial end-time range; therefore, the range-bounds are updated.  
Once the adjustment is done, a selection probability \(S_{p}\) is calculated for each proportion \(p\in P\) (see Equation (26)).  
\[S_{p}=\frac{r_{p}}{\sum_{x\in P}r_{x}} \tag{26}\]  
Third, an end-time proportion \(p^{*}\) is selected from \(P\) based on \(S\). Finally, a random value \(ed\) is selected from the selected proportion \(p^{*}\). \(ed\) has to satisfy the constraints of Equation (27).  
\[ed\in p^{*}\wedge ed\in[ET,LT] \tag{27}\]  
The process of selecting an end-time value is presented in Figure 21.
# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 6 Experimental Evaluation  
In order to validate the proposed solution, we conducted experiments integrating an approach that uses ML and decision-making methods to support NSSF DAF operations in multi-domain slicing environments. The methods proposed in this approach focus on the evaluation and dynamic mapping of adequate QoS requirements by the analysis of network traffic coming from the UEs and then composing a template to create slices in various formats (e.g, YAML, NETCONF, and JSON), allowing for their integration with 5G Core network functions, and therefore the use of coding, automation, and network service orchestration tools, such as Ansible, Chef, and Puppet. The NSSF DAF framework also facilitates integration with ONAP Istanbul/Honolulu and OSM 11 orchestrators.
# Secure Indoor Localization Based on Extracting Trusted Fingerprint
## 3 Localization Model
### Fingerprinting Localization  
The fingerprint-based localization process is usually divided into two stages: the offline sampling phase and online localization phase. The offline phase creates a fingerprint database. The specific content collects the RSSI at all reference points in the locating area. The RSSI of APs at a reference point are taken as the fingerprint. All the fingerprints of the reference points form a fingerprint database. Each piece of fingerprint information corresponds to the position information. The indoor localization environment is variable, so in order to reduce the interference of environmental factors, we continuously sample at each sampling point and calculate the average. According to the RSSI of the location to be positioned, the fingerprint matching algorithm is used to calculate the similarity degree and obtain fingerprints that have high similarity. Then, the reference position coordinates corresponding to the fingerprint are taken as the actual position of the user. The original fingerprint localization algorithm keeps the offline fingerprint database static, but for the variable environment, if the fingerprints in the database remain unchanged, the final localization results will have greater deviation.  
Fingerprint-based localization technology not only should consider the differences of the fingerprint between the online phase and offline phase, but also needs to consider the AP's physical security issues. If the AP is impaired due to external conditions and at the same time the AP is still used for localization, the localization accuracy will be affected by the AP. In addition, if there are new APs in the environment and the fingerprint of the APs is not updated in the fingerprint database, the localization error may also be increased if the APs are used for localization.  
Figure 2: Reference system architecture.
## 2 Hardware Design and Implementation
### Onsite Monitoring System  
Onsite monitoring system receives data from the sensing networks and displays the data via the LPG storage tank monitoring software. The onsite monitoring system consists of wireless receivers (as mentioned in the previous section), a demodulator, a computer, a network module, and an Uninterrupted Power Supply system (UPS). The wireless receiver receives temperature information from the wireless sensing node and sends configuration command to the wireless sensing node. The demodulator collects of data from the fiber optical sensors. The computer can store and display the monitored data received. In case of power failure, a UPS is introduced to grantee a reliable power source.
# Analysis of BDS/GPS Signals' Characteristics and Navigation Accuracy for a Geostationary Satellite
## 3 BDS/GPS Signal Characteristics
### Observations Quantity and Availability  
In two days from 12:00 on 17 May to 12:00 on 19 May 2020, a total of 512,819 GPS observations and 299,523 BDS observations have been obtained at 2 s intervals. The number of GPS satellites tracked includes: 12 IIF, 11 IIR, 7 IIR-M, and 2 III satellites. The percentage of the GPS observations for IIF, IIR, IR-M, III satellites, are counted to be 27.4%, 41.4%, 27.6%, and 3.6%, respectively. The average observations of GPS III and IIF satellites are significantly less than those of the other two types of GPS satellites.  
Among the 33 BDS satellites, only C01, C02 and C05 of 3 BDS GEO satellites have no observations data. The percentage of the observations from 2 BDS GEO (C03 and C04), 7 BDS IGSO, and 21 BDS MEO satellites, are counted to be 21.7%, 9.1%, 69.2%, respectively. Although the number of BDS GEO satellites tracked is only two, the observations quantity of these two satellites accounts for a large proportion.  
According to results shown in Figure 3, the mean number of the GPS satellites tracked over two days is 7.4 and the mean number for the BDS satellites tracked is 4.3. Because each constellation in the receiver has only eight-channel hardware correlator, it can track up to eight satellites simultaneously. Considering the combination of GPS and BDS constellations, it remarkably increases the number of satellites tracked. The mean number for the GPS + BDS satellites tracked is 11.7. Figure 4 gives the tracking periods of the BDS satellites over two days. The BDS satellite of GEO C04 has the longest continuous tracking time, accounting for 68.9% of the two days. The continuous tracking time of the six BDS IGSO satellites is shorter than other GPS or BDS satellites.  
Figure 3: Number of the GPS, BDS, and GPS + BDS satellites tracked over two days.
## 2 Experimental Research
### Experimental Research Program  
While planning the experimental phase of the research, the initial assumption was made that the optimal composition of the cement screed could be defined through the analysis of component materials. Additionally, it was assumed that the optimal amount of micro-reinforcement directly influences the deformation properties of the cement screed.  
The composition of mortar mixes was designed based on the following initial conditions:  
* Constant ratio of binder to aggregate to avoid negative effects on certain physical-mechanical properties of the mortar;
* Consistency of cement screeds that should correspond to the description \"moist as soil\", leading to an assumed water-cement ratio of 0.38;
* Assumed density of freshly installed mortar;
* Addition of micro-reinforcement in the amount of 1.0 kg/m3;
* Use of electrofilter ash as a substitute for cement in the amount of 20%;
* Addition of plasticizer at a ratio of 1% by weight of cement.  
Testing of mortar-mix samples was conducted using prismatic samples of dimensions 4 \(\times\) 4 \(\times\) 16 cm. The following properties were analyzed within the research:  
* Consistency;
* Density.
* Hardened concrete
* Compressive strength;
* Flexural strength;
* Dynamic modulus of elasticity;
* Shrinkage.  
All series of mortar mixtures were made with the same type of cement, the same type and particle size distribution of aggregates, and potable water. The following materials were used for the preparation of the mentioned composites:  
* CEM I 42.5 R (Lafarge Beocin Cement Factory, 21300 Beocin, Serbia);
* Aggregate (\"Moravac\", fraction 0/4 mm, river Morava, Serbia);
* Fly ash (Kolubara Thermal Power Plant, 11563 Veliki Crnjani, Serbia);
* Polypropylene fibers (type: monofilament, wavy fibers, length 12 mm, Sika Srbija, 22310 Simanovci, Serbia);
* Plasticizer (\"Sika Estriplast\", Sika Srbija, 22310 Simanovci, Serbia).  
For the purposes of experimental research, according to the research program, the preparation and testing of the properties of two different types of mortar mixes for cement composites is foreseen. The groups are marked as follows:  
* A--mortar mixture with ordinary Portland cement as the binder;
* B--mortar mixture with electrofilter ash as a partial replacement for cement and superplasticizer in its composition.  
Individual types of mortar compositions are labeled with Roman numerals I and II, each number representing the designation of the mortar series within groups A and B:  
* A-I series--standard cement mixture;
* A-II series--cement mixture with the addition of microfibers;
* B-I series--cement mixture with electrofilter ash and superplasticizer;
* B-II series--cement mixture with electrofilter ash, superplasticizer, and the addition of microfibers.