esa-sceva/satcom-chunk-collection huggingface dataset:
# Small Infrared Target Detection via a Mexican-Hat Distribution
## 4 Experimental Results and Analysis
### Influences of the Modified Top-Hat Transformation and the DoG  
This experiment adopts Seq.2 that has low target-background contrast to evaluate the influence of the modified top-hat transformation. According to [22] as well as our experimental results, we set the parameters of the modified top-hat transformation as \(L_{top-hat}=5\), \(\delta=1\), \(\alpha=1\), \(\beta=1.2\), \(\gamma=1.2\), and \(\varepsilon=5\). Here \(L_{top-hat}\) is the window size. \(\alpha\), \(\beta\) and \(\gamma\) are the coefficients of target enhancement operation. For the two thresholds of our method, we set \(th_{10}=0.9\) and \(th_{12}=0.5\). Figures 5-7 show some results with and without modified top-hat transformation. The raw IR images of these figuresinclude a small target located at the edge of clouds, a small target located at the sky background without clouds, and a small target near the edge of clouds, respectively. We can see that the modified top-hat transformation benefits to increasing the contrast between the target and the background. Therefore, the targets with low contrast can be detected easier with such a transformation.
# Contour Mission Flight Planning of UAV for Photogrammetric in Hillside Areas
## 2 Methodology
### Contour Mission Flight Planning
#### 2.2.4 S4: Next Route  
When calculating the next route, two parameters are considered, the adjacent routes spacing \(N\) and the elevation difference value \(D\). First, the system evaluates the change in elevation of the corresponding terrain based on the position of the previous route plus the \(N\) value. The system then determines the relationship between the elevation change and the elevation difference threshold \(D\) and decides whether to produce the next route in step S5 or S6. This approach ensures that the route is reasonable and the shooting distance is accurate. The decision N value is based on estimated image accuracy [33]. N value is the spacing between adjacent flight routes (m), as shown in Equation (3).  
\[N=GSD\times H(1-q) \tag{3}\]  
where \(H\) is image height (pixel), and \(q\) is sidelap (%).  
The \(D\) value is a threshold value of difference elevation (m). It is based on estimated image accuracy [33], as shown in Equation (4).  
\[D=\sqrt{\frac{GSD\times B\times\text{ f}}{\sigma_{\text{i}}}} \tag{4}\]  
where \(D\) is the threshold value of difference elevation, and \(\sigma_{\text{i}}\) is image measurement accuracy.
## 5 Results and Discussion
### The DOE Analysis
#### 5.2.1 The Effect of the Depth of the Defect and Width of the Defect on the Response  
Figure 4 shows the response surface 3D plots with contour plots on their bases for the percentage of the signal as a function of the depth of the defect and width of the defect. Figure 4a shows the results for brass, and Figure 4b shows the results for mild steel. Figure 4c shows the results for copper. In general, these plots are useful to visualize the effects of the process factors and their interactions on the percentage of the signal and the optimal process conditions. The designs provided the results, where the percentage of the signal increased with the increase in depth and width of the defect and conductivity of the material. Figure 4a shows that, for brass, the influence of the depth of the defect on the percentage of the signal was insignificant when the width of the defect was narrow. The influence of the depth of the defect on the percentage of the signal was significant when the width was increased.  
With mild steel, the increase in the percentage of the signal was evident, as shown in Figure 4b. The interactive effect of the size of the depth and size of the width on the percentage of signal was similar to that of brass, except that the obtained percentage of a signal was lower with brass. This result was explained by the negative effect of the type of material on the percentage of the signal formation. With the copper material, the percentage of signal continued to increase, as shown in Figure 4c. The  
Figure 4: 3D graphic surface optimization of the percentage of the signal as influenced by the width and the depth of the defect of (**a**) brass material; (**b**) mild steel material; and (**c**) copper material.  
effect of the depth of defect on the percentage of the signal was more significant at wide than at narrow defect widths.
# Study of Fast and Reliable Time Transfer Methods Using Low Earth Orbit Enhancement
###### Abstract  
The Global Navigation Satellite System (GNSS) can be utilized for long-distance and high-precision time transmission. With the ongoing development of low Earth orbit (LEO) satellites and the rapidly changing geometric relationships between them, the convergence rate of ambiguity parameters in Precise Point Positioning (PPP) algorithms has increased, enabling fast and reliable time transfer. In this paper, GPS is used as an experimental case, the LEO satellite constellation is designed, and simulated LEO observation data are generated. Then, using the GPS observation data provided by IGS, a LEO-enhanced PPP model is established. The LEO-augmented PPP model is employed to facilitate faster and more reliable high-precision time transfer. The application of the LEO-augmented PPP model to time transfer is examined and discussed through experimental examples. These examples show multiple types of time transfer links, and the experimental outcomes are uniform. GPS + LEO is compared with exclusive GPS time transfer schemes. The clock offset of the time transfer link for the GPS + LEO scheme converges more swiftly, meaning that the time required for the clock offset to reach a stable level is the briefest. In this paper, standard deviation is employed to assess stability, and Allan deviation is utilized to assess frequency stability. The results show that the clock offset stability and frequency stability achieved by the GPS + LEO scheme are superior within the convergence time range. Controlled experiments with different numbers of satellites for LEO enhancement indicate that time transfer performance can be improved by increasing the number of satellites. As a result, augmenting GPS tracking data with LEO observations enhances the time transfer service compared to GPS alone.  
low Earth orbit satellite; precise point positioning; global navigation satellite system; time transfer 2024
# Mechanical Performance Characterization of Lignin-Modified Asphal Mixture
## 4 Results and Discussion  
### Abrasion Resistance  
The averages of Cantabro loss calculated by Equation (1) for the control mix and mix with lignin are shown in Figure 4. The SMA-LP had a Cantabro loss of 10.4%, which was lower that of SMA-C with the Cantabro loss of 13.5%. Compared with the control mix, the lignin powder-modified asphalt binder reduced the Cantabro loss values significantly, which demonstrated that the SMA-LP showed a stronger abrasion resistance. The reason for the improvement in abrasion resistance could be attributed to the increase in the adhesion force between lignin-modified asphalt and aggregate. By contrast, the SMA-LF had a higher Cantabro loss than SMA-C. The reason accounting for this finding is that the addition of lignin fiber with a relatively large size had a negative effect on the adhesion and bonding between asphalt and aggregates. The addition of lignin fiber also absorbed an amount of asphalt, which is usually believed to decrease the abrasion resistance [10]. The decrease of effective asphalt content in the mixture will degrade the abrasion resistance.
## 5 Analyses
### Performance Evaluation
#### 5.3.2 Communication Cost Analysis  
In this part now, we will present the communication overhead of the proposed scheme against the related schemes [2; 13; 23; 27; 55] by borrowing experiment results from [17] to account for transmission cost for sending packets from vehicle to RSUs in V2I or V2V communication in VANETs, the sizes of elements in \(G_{1}\) and \(G\) are 128 bytes and 40 bytes respectively. In addition, the elements in \(Z_{q}^{*}\), the hash function value and timestamps are of the sizes 20 bytes, 20 bytes and 4 bytes respectively. We will consider the message traffic load for signatures only.  
In [27], the vehicle broadcast the message \((ID_{i},vpk_{i},M_{i},t_{i},\sigma_{i}=(R_{i},S_{i}))\) to RSUs, where \(ID_{i},vpk_{i},R_{i},S_{i}\in G\) and \(t_{i}\) is a timestamp. Therefore, the communication overhead is \(3\times 40+4=124\) bytes. In [13] the vehicle sends the message \((ID_{i},vpk_{i},Q_{ID_{i}},\sigma_{i}=(R_{i},S_{i}),t_{i})\) to RSUs or AS, where \(ID_{i},vpk_{i},Q_{ID_{i}},R_{i}\in G\), \(S_{i}\in Z_{q}^{*}\) and \(t_{i}\) is the timestamp. Thus, the communication load on the network is \(4\times 40+20+4=184\) bytes. In [55], the vehicle sends \((ID_{i},m_{i},upk_{i},signature(U_{i},V_{i}))\) to RSU, which requires the bandwidth size of \(4\times 40+20+4=184\) bytes. Whereas, in [54] the message sent from a vehicle to RSU is \((PS_{j},PS1_{j},P_{i},PP_{i},\sigma_{i}=(U_{i},V_{ijk}))\), where \(PS_{j},PS1_{j},P_{i},PP_{i},U_{i},V_{ijk}\in G\). Therefore, the communication overhead is \(6\times 128=768\) bytes. In the proposed, ECLAS, scheme a vehicle sends traffic related signed message \((ID_{i},Q_{ID_{i}},vpk_{i},M_{i},t_{i},\sigma_{i})\) to the verifier where \(ID_{i}\in G\). Therefore, the total communication overhead is \(4\times 40+20+4=184\) bytes. The proposed scheme has less communication overhead load than [27; 54] and is on a par with the schemes in [46; 51; 55] as outlined in Table 5.  
Figure 3: Verification Time Delays and Number of Signatures Relationship.  
However, these comparable works are found to be insecure in different aspects, like in [13], which so far has a decent efficient output, it was discovered that the scheme is insecure in [23; 27].
# An Introduction of a Modular Framework for Securing 5G Networks and Beyond
## 4 An Integrated Security Solution
### Federated Security in 5G  
This section covers the integration of federated security into 5G as well as how the UE performs SSO. The benefits of adopting FId in mobile communications and how it eliminates the need for the UE to continually authenticate and authorise services, including in roaming scenarios, were presented in [55], which also discussed federated identity management in 5G. When the suggested solution in this article is implemented in a 5G-enabled D2D communications network, the tokens and caching data of the security processes are reduced when the UE needs to re-authenticate to the network or perform handover authentication while roaming but due to SSO, tokens and caching data of the security processes are reduced.  
Next, the following steps will show how federated security is used in 5G communication:  
* Step 1: Following network authentication, the UE requests the SP for service authorisation;
* Step 2: The UE is forwarded by the SP through SMF to the identity provider (IdP), which creates and assigns the FId to the UE;
* Step 3: Using the UE's identity token, the IdP and UE carry out federated authentication operations.
* Step 4: The UE uses an identity token to ask the SP for an access token, which the SP-AAA then issues along with a refresh token. SSO has now been accomplished;
* Step 5: If the access token is legitimate, the SS will grant the UE's request for access to the service;
* Step 6: Using the cached access token, the UE requests caching and sharing authorisation with other UE after getting access to the service.  
An integrated security solution addressing security risks at various levels of the system model is provided by the interface between the underlying security protocols of the security framework using supported security context, as shown in Figure 7.
# Antioxidant and Antimicrobial Properties of Selected Phytogenics for Sustainable Poultry Production
## 4 Selected Plant Sources of Antimicrobial and Antioxidant Compounds  
### Moringa oleifera  
_Moringa oleifera_ is a medium-sized perennial, evergreen, and deciduous tree from the family _Moringaceae_ that grows quickly in a variety of soil types and hot, humid, and dry tropical and subtropical climates [42]. Out of the 13 species in this family, _M. oleifera_ is the most popular and widely utilized species [43]. Owing to its nutritional profile, _M. oleifera_ is prized globally for its contribution to the livestock industry and food and pharmaceutical sectors [44]. _M. oleifera_ is rich in essential nutrients, such as lipids, proteins, minerals, and vitamins [45]. Reports have indicated that the leaves have the highest protein value (250 to 270 g/kg CP) [42], with some protein being available in the seeds, roots, and flowers [46]. Furthermore, _M. oleifera_ is also a great source of well-balanced amino acids and vitamins, of which the amount of vitamin A is about 10 times more than in carrots and pumpkins and the amount of vitamin C is 7 times more than in oranges [47]. According to [PERSON] [48], morning leaves contain vitamins A, B and B-Complexes, C (ascorbic acid), E (\(\alpha\)-tocopherol), K, and pro-vitamin A in the form of beta-carotene, along with minerals such as sulfur, calcium, phosphorus, potassium, manganese, and iron. Moringa products are also rich in monounsaturated fatty acids [45] and polyunsaturated fatty acids, such as linoleic acid, \(\alpha\)-linolenic acid, arachidonic acid, stearic acid, oleic acid, and palmitic acids [49].  
In addition, _M. oleifera_ contains a myriad of phytochemicals, such as flavonoids, quercetin, apigenin, kaempfer, luteolin, myricetin glycosides, and polyphenols, which exhibit antioxidant, antimicrobial, anticarcinogenic, anti-inflammatory, and medicinal activities [50; 51]. However, the leaves also contain antimutritutional substances, such as alkaloids, carotenoids, glucosinolates, isothicyanates, saponins, tocopherols, tannins, oxalates, phytates, trypsin and amylase inhibitors, lectins, and cyanogenic glucosides [52], which may affect their utility in poultry diets and induce different responses. For example, [PERSON] et al. [53] found that adding 10% and 20% _M. oleifera_ leaf meal in place of sunflower seed meal enhanced feed intake and dry matter intake while lowering the egg mass output in laying hens. However, [PERSON] et al. [54] also reported that the inclusion of 1.2% _M. oelerera_ leaf powder in broiler feeds improved the intestinal microarchitecture and cellular count, which in turn promoted gut health.
# Impact of Heart Rate Fragmentation on the Assessment of Heart Rate Variability +
## 2 Methods
### ECG Data Base  
We used the ALLSTAR Holter electrocardiogram (ECG) database [2,13]. From this database, the data used for the present study were extracted as follows. The ALLSTAR database included 430,169 data of 24-h Holter ECG data recorded between November 2007 and February 2016, from which the candidate ECG data were selected by the following inclusion criteria:  
1. Record length \(>\)21.6 h (90% of 24 h).
2. Cardiac rhythm is in sinus rhythm for \(>\)19.2 h (80% of 24 h).  
ECG data were excluded from the analysis if ECG showed at least one of the followings:  
1. Evidence of artificial pacemaker implantation
2. Ventricular or supraventricular ectopic beats \(>\)3000 per 24 h
3. Atrial fibrillation for \(>\)10% of recorded beats
4. Atrial flutter for \(>\)10% of recorded beats
5. ST-T abnormality suggesting myocardial ischemia  
Among 430,169 data, 294,323 data (73.4%) of 24-h R-R interval time series were extracted by these criteria. From these data, 3917 data were randomly selected (up to 100 data per every 5 year old from ages 0 to 100 for each sex) for the present study by the Survey Select procedure of Statistical Analysis System (SAS Institute, Cary, NC, USA).  
Figure 5: R-R interval trend grams and power spectrum of the same case as Figure 4. HRF causes frequent inflection points in R-R interval (upper panel) and forms a spectral peak often in HF band (lower panel).
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Family** & **Prec.** & **Rec.** & **F1** & **Family** & **Prec.** & **Rec.** & **F1** \\ \hline bamital & 99.63\% & 62.49\% & 75.31\% & orchard & 92.61\% & 19.95\% & 32.15\% \\ banjori & 78.91\% & 94.73\% & 86.04\% & pachrypt & 88.59\% & 87.07\% & 87.78\% \\ bazarloader & 98.26\% & 99.32\% & 98.79\% & phorpiex & 0.00\% & 0.00\% \\ chinad & 98.82\% & 99.38\% & 99.10\% & pitou & 75.70\% & 34.76\% & 45.16\% \\ conficker & 14.65\% & 0.58\% & 1.11\% & proslikefan & 0.00\% & 0.00\% \\ corebot & 96.39\% & 85.58\% & 90.65\% & pseudoman & 34.53\% & 2.74\% & 5.07\% \\ cryptolocker & 55.01\% & 16.25\% & 24.97\% & pushdo & 52.81\% & 21.77\% & 28.03\% \\ darkshell & 89.44\% & 82.97\% & 86.07\% & pushdotid & 20.60\% & 53.77\% & 29.73\% \\ darkwatchman & 85.77\% & 100.00\% & 92.34\% & pykspa2s & 45.83\% & 8.28\% & 13.14\% \\ dircrypt & 0.00\% & 0.00\% & 0.00\% & qadars & 99.40\% & 77.19\% & 86.89\% \\ dyre & 99.95\% & 100.00\% & 99.98\% & qakbot & 56.46\% & 59.50\% & 57.94\% \\ ebury & 94.38\% & 75.73\% & 84.02\% & qsnatch & 90.65\% & 98.76\% & 94.53\% \\ emotet & 0.00\% & 0.00\% & 0.00\% & ramdo & 79.18\% & 80.91\% & 79.95\% \\ flubot & 78.51\% & 92.21\% & 84.77\% & ramnit & 16.29\% & 8.57\% & 11.23\% \\ fobber & 0.00\% & 0.00\% & 0.00\% & ranbyus & 61.49\% & 89.22\% & 72.81\% \\ gameover & 99.83\% & 100.00\% & 99.91\% & rovnik & 87.07\% & 91.01\% & 88.98\% \\ gameover\_p2p & 95.75\% & 28.09\% & 43.31\% & shifu & 0.00\% & 0.00\% & 0.00\% \\ gozi & 68.27\% & 27.34\% & 38.61\% & simda & 55.89\% & 81.17\% & 66.20\% \\ granderoiro & 99.79\% & 95.87\% & 97.79\% & sphinx & 31.29\% & 34.51\% & 32.56\% \\ lockcy & 57.67\% & 2.65\% & 5.02\% & supropbox & 65.30\% & 82.98\% & 73.00\% \\ moyvtdd & 0.00\% & 0.00\% & 0.00\% & symmi & 95.10\% & 99.90\% & 97.44\% \\ metastealer & 99.98\% & 98.77\% & 99.37\% & tinba & 84.40\% & 99.51\% & 91.33\% \\ monerominer & 99.85\% & 100.00\% & 99.92\% & tinylfluf & 99.60\% & 99.82\% & 99.71\% \\ murofet & 71.48\% & 97.20\% & 82.36\% & tinynuke & 76.87\% & 100.00\% & 86.67\% \\ murofetweeky & 99.70\% & 98.93\% & 99.32\% & tufik & 0.00\% & 0.00\% & 0.00\% \\ mydoom & 0.00\% & 0.00\% & 0.00\% & urlzone & 93.93\% & 92.02\% & 92.97\% \\ necro & 100.00\% & 98.10\% & 99.04\% & vawtrak & 0.00\% & 0.00\% & 0.00\% \\ neucurs & 72.80\% & 66.00\% & 69.22\% & virtu & 99.48\% & 99.99\% & 99.73\% \\ ngioweb & 93.55\% & 71.35\% & 80.91\% & xhexhex & 98.97\% & 100.00\% & 99.48\% \\ nymaim & 0.00\% & 0.00\% & 0.00\% & zloader & 99.64\% & 99.73\% & 87.73\% \\ nymaim2 & 97.49\% & 36.81\% & 53.38\% & Average & 64.39\% & 58.22\% & 58.22\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Bilbo on classic training method precision, recall, and F1 score of each of the 61 DGA family.
# Pedestrian Counting Based on Piezoelectric Vibration Sensor
## 2 Related Work
### Sensor Selection  
In general, recent studies regarding vibration signal-based ubiquitous computing applications mainly use geophones (triaxial seismic sensor) [14; 15; 23; 29; 30] and piezoelectric sensors [27; 28; 31; 32].  
A geophone [33] is deployed on the floor. It detects the velocity of movement of the floor and outputs a voltage signal. In contrast, the piezoelectric sensor measures changes in the pressure it bears. Although geophones can detect signals from three orthogonal axes in space, they are significantly larger than piezoelectric sensors in physical size. Furthermore, the price of a geophone [22] is 100 times higher than that of piezoelectric sensor [34]. In addition, the piezoelectric sensor has a simpler structure, higher sensitivity, wider frequency band, and larger dynamic range [35]. However, when it is used to detect floor vibration signals caused by pedestrian walking, there are issues with poor signal quality and low signal-to-noise ratio (SNR) [28]. The characteristics and physical parameters of different piezoelectric sensors are not strictly consistent and usually present significant individual-to-individual differences. The measurement error between different sensors is varied, and the SNR is not uniform. Furthermore, piezoelectric can only detect the signals perpendicular to the floor. Previous research [23] showed that using a triaxial seismometer can achieve an increased accuracy in a localization task by introducing signal arrive angle to a TDOA (time-difference-of-arrival) system. However, piezoelectric sensors provide less information than triaxial geophones sensors. Thus it is more challenging to achieve good results only with the signals perpendicular to the floor.  
To summarize, from a cost perspective the piezoelectric sensors are advantageous, especially in scenarios that require large-scale sensor deployment. However, there are more considerable challenges to get good results with the piezoelectric sensor-based approach. Our approach uses the piezoelectric sensors with a novel system design to overcome the limitations of this kind of sensors.
# Nanosponges for Water Treatment: Progress and Challenges
## 1 Introduction
Among the introduced nanosponges, especially derived from cyclodextrin, desirable attributes such as environmental friendliness, sustainability, non-toxicity, cost-effectiveness, and the capability of hosting different molecular agents have shown suitability for water treatment with up-scalable potentials [27]. There have been limited studies regarding the applications of molecular modeling methods for analyzing cyclodext, because of the flexibility and size of such molecules. Some limited calculations have been performed based on density functional theory (DFT) methods, with befittingly significant levels of theory and large basis sets. It appears that by uniting the results obtained from experimental and computational studies, the geometry of complexation of these structures can be better analyzed [28]. Notably, some crucial parameters such as adsorbent dosage, pH solution, isotherm, kinetics, thermodynamics, regeneration/desorption studies, recyclability, and adsorption mechanisms need to be further evaluated [29]. Herein, recent developments towards the application of nanosponges for water treatment and pollutant removal are deliberated, focusing on important challenges and future perspectives.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Block** & **Size** & **Maximum** & **Vertical** & & & & \multicolumn{3}{c}{**Air Quality Area on Building**} \\  & **Volume** & **(m\({}^{3}\))** & **Coverage** & **Height** & **Surface** & & & & **Envelope throughout the Year (\%)** & \\ \hline One-family & & & & & & & & & & & \\ residential & 28,374 & 33.76\% & 11 & 10,075 & Area (m\({}^{2}\)) & 7850 & 1904 & 230 & 76 & 15 & 0 \\ building block & & & & & Percent & & & & & & \\  & & & & & of total & 77.92\% & 18.90\% & 2.28\% & 0.75\% & 0.15\% & 0.00\% \\ Traditional block & & & & & & & & & & & \\ for city center & 55,169 & 71.04\% & 10.5 & 5736 & Area (m\({}^{2}\)) & 3123 & 1516 & 632 & 227 & 151 & 87 \\  & & & & & Percent & & & & & & \\   Multiple-tower & 55,330 & 10.35\% & 52 & 15,955 & Area (m\({}^{2}\)) & 13,886 & 1620 & 302 & 141 & 6 & 0 \\  & & & & Percent & & & & & & & \\  & & & & & of total & 87.03\% & 10.15\% & 1.89\% & 0.88\% & 0.04\% & 0.00\% \\ Slab city block & 56,350 & 20.26\% & 31 & 12,588 & Area (m\({}^{2}\)) & 10,247 & 1355 & 669 & 223 & 94 & 0 \\  & & & & Percent & & & & & & \\  & & & & & & of total & 81.40\% & 10.76\% & 5.31\% & 1.77\% & 0.75\% & 0.00\% \\ U-shaped & 54,256 & 29.28\% & 19 & 11,890 & Area (m\({}^{2}\)) & 10,296 & 1339 & 255 & 0 & 0 & 0 \\ city block & & & & & Percent & & & & & & \\  & & & & & Percent & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Air quality distribution throughout the year for eight different urban block types based on the method described in this study.