esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
The AFT technique can avoid the analytical treatment of nonlinear terms through time-domain discretization [26], and this makes the HB-AFT method very effective in solving the strong nonlinear contact problem, which is different from the traditional harmonic balance method. In this paper, the periodic solution is called the period-\(n\) solution if the period of the response is \(n\) times the excited period. The solution process of the period-1 motion of Equation (1) is given below.  
Let \(\tau=\omega\cdot t\), then Equation (1) can be expressed as  
\[\omega^{2}\frac{d^{2}x(\tau)}{d\tau^{2}}+F(x(\tau))=A\cos\tau+W. \tag{4}\]  
where the nonlinear restoring force is  
\[F(x(\tau))=2\zeta\omega H(x(\tau))\frac{dx(\tau)}{d\tau}+H(x(\tau))\text{sgn} (x(\tau))|x(\tau)|^{\alpha}. \tag{5}\]  
To find the periodic solution of Equation (4) with period \(2\pi\), \(x(\tau)\) and \(F(\tau)\) can be expressed as  
\[x(\tau)=\mathbf{P}(1)+\sum_{k=1}^{K}\left[\mathbf{P}(2k)\cos(k\tau)-\mathbf{P}(2k+1)\sin( k\tau)\right] \tag{6}\]  
\[F(\tau)=\mathbf{Q}(1)+\sum_{k=1}^{K}\left[\mathbf{Q}(2k)\cos(k\tau)-\mathbf{Q}(2k+1)\sin( k\tau)\right] \tag{7}\]  
According to the process of harmonic balance, insert Equations (6) and (7) into Equation (4) and obtain the following \(2K+1\) algebraic relationships:  
\[\mathbf{g}(\mathbf{P},\mathbf{Q},\omega)=0 \tag{8}\]  
Equation (8) consists of \(2K+1\) algebraic equations but contains \(4K+2\) unknown harmonic coefficients denoted as \(\mathbf{P}\) and \(\mathbf{Q}\) in Equations (6) and (7). To obtain \(\mathbf{P}\) from Equation (8), the following AFT process is adopted.  
The time-domain discrete information of \(x(\tau)\) is given by the inverse discrete Fourier transform as,  
\[x(n)=\text{Real}\Bigg{\{}\mathbf{P}_{0}+\sum_{k=1}^{K}\mathbf{p}_{k}e^{i(2\pi kn/N)} \Bigg{\}} \tag{9}\]  
and the discrete information of the restoring force in time domain is  
\[\mathbf{F}(n)=\mathbf{F}(\mathbf{X}(n),\mathbf{X}^{\prime}(n),\omega) \tag{10}\]  
Then, by discrete Fourier transform, \(\mathbf{Q}\) can be expressed as  
\[\mathbf{Q}_{k}=\frac{\phi}{N}\sum_{n=0}^{N-1}\mathbf{F}(n)\;e^{i\left(-2\pi k\omega/N \right)} \tag{11}\]  
Herein, \(\phi_{k}=\phi\) (\(2k\))+ \(i\)\(\phi\) (\(2k\)); \(\phi_{0}=\phi\) (\(1\)), where \(\phi\) takes \(\mathbf{P}\) or \(\mathbf{Q}\) in Equations (9) and (11); \(N\) is the number of sampling points in a time domain period, and \(n=0,\,\ldots\), \(N-1\); \(\phi=1\) when \(n=0\), otherwise \(\phi=2\).  
Taking \(\mathbf{P}\) as an unknown variable and \(\omega\) as a control parameter, the arc-length continuation can be introduced to Equation (8) for automatically tracking \(\mathbf{P}\) and the frequency-response curve of system (4).  
Finally, the stability characteristics of the frequency-response curve can be obtained by Floquet theory [26].
## 3 Results and Discussion
### Adsorption of 4-Ethylphenol and 4-Ethylguaiacol  
Four commercial ACs, produced from coconut shell, were first tested, as received, in the removal of 4-EP and 4-EG from the liquid phase. In the first tests, the amount of 4-EP and 4-EG removed by the ACs was evaluated by spectrophotometric UV/VIS analysis. The 4-EP was analyzed at 221 and 271 nm and the 4-EG at 234 and 294 nm. To obtain more accuracy and reproducibility, in the following tests, the 4-EP and 4-EG quantification was conducted by HPLC, using the conditions stated in the experimental section.  
The influence of the pH solution, the concentration of each compound, equilibrium time, textural characteristics, and physical form of the ACs (power and granulated), on the phenolic compound's removals was evaluated. The increase in the solution acidity promotes the adsorption of 4-EP and 4-EG, primarily when the ACs presented a basic surface character; this is in agreement with previous work, which included phenol and para-nitrophenol adsorption on ACs [32]. The decision to perform the adsorption of 4-EP and 4-EG in an acid solution was also supported by the fact that wines are acidic, presenting a pH closer to 3.
# Portable Protein and Fat Detector in Milk Based on Multi-Spectral Sensor and Machine Learning
## 4 Discussion  
### Near-Infrared Spectroscopy Analysis Using Machine Learning  
The objective of this study was to improve the performance of traditional NIR milk composition detection methods through machine learning algorithms and sensor technology. Infrared spectroscopy, which is the method for detecting milk components, is fast and capable of multi-component analysis and non-destructive testing. The NIR wavelength is generally selected in the range of 400-1000 nm [34; 35; 36], and wavelength data are generally acquired through a spectrometer to obtain spectral data over the entire wavelength range. However, the instrument structure using spectrometer technology is complex and expensive. The portable milk composition detection instrument uses several typical NIR wavelengths for milk detection, requiring multiple infrared receiving units combined with a single wavelength filter to achieve multi-channel infrared measurement. The new type of multi-wavelength sensor used in this article simultaneously obtains wavelength data from six channels corresponding to representative wavelengths (610, 680, 730, 760, 810, 860 nm) in the range of 400-1000 nm, which cover the infrared characteristics of protein and fat in milk. Thus, an NIR detection method for milk is proposed.  
In previous studies, the partial least-squares method was used for regression of spectral data to measure the fat and protein contents in milk [36; 37; 38]. The coefficients of determination (\(R^{2}\)) of better protein and fat prediction models were 0.974 and 0.973, respectively [36]. We use the machine learning algorithm XGBoost [28], which is a powerful ensemble learning algorithm that can better fit the nonlinear relationship between wavelengths and components, significantly reduce the model bias, and increase the model accuracy. With sufficient training with a large quantity of sample data and adjustment of model parameters, the prediction model exhibited a good generalization ability and no overfitting [39], indicating that the measurement results were accurate.  
Figure 9: Protein model prediction results for Test Set 2 (**a**) and fat model prediction results for Test Set 2 (**b**).
## 4 Results and Analyses
### Model Validation and Quantitative Analysis  
In this study, validations of the mapping results were performed by the area under the curves of the two models. The landslide susceptibility index values were sorted in descending order and divided into 30 classes of accumulated area ratio percentages. The success rate curve and the predictive rate curve were created with the landslide training dataset and the testing dataset, respectively.  
To compare the result quantitatively, the AUC (Area Under Curve) were re-calculated, in which the total area is 100 means perfect success rate and prediction rate. The results of the training dataset  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Area/km\({}^{2}\)** & \begin{tabular}{c} **Area of** \\ **Classification/(\%)** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Landslides** \\ \end{tabular} & \
\begin{tabular}{c} **LND/km\({}^{2}\)** \\ \end{tabular} &
\begin{tabular}{c} **LAD/(\%)** \\ \end{tabular} \\ \hline
**Very low** & 5.92 & 1.00 & 3 & 0.50 & 0.19 \\
**Low** & 332.96 & 56.61 & 103 & 0.30 & 0.12 \\
**Moderate** & 133.98 & 22.78 & 587 & 4.38 & 1.83 \\
**High** & 108.31 & 18.41 & 5484 & 50.62 & 10.72 \\
**Very high** & 6.88 & 1.17 & 505 & 73.29 & 17.41 \\ \hline \end{tabular} Note: LND is landslide number density; LAD is landslide area density.  
\end{table}
Table 4: Statistics of different landslide susceptibility classes (SVM).  
Figure 14: Histogram showing relative distribution and landslide occurrence of various susceptibility classifications of SVM. LND is landslide number density. LAD is landslide area density.  
Figure 13: Landslide susceptibility index (LSI) map and susceptibility class map of SVM (**a**) LSI map; (**b**) LSI class map.  
and the validation dataset were calculated, and the success rate curve and the predictive rate are shown in Figure 15. The results show that the two models have good prediction accuracy. For training set, the AUCs of LR and SVM are 0.847 and 0.909, respectively. For validating set, the AUC is 0.839 for LR, 0.871 for SVM. Overall, the prediction accuracy of the SVM is higher than LR.  
In order to determine the relationship between the landslide susceptibility index and landslide area percentage (LAP), the range of susceptibility index was divided into 32 equal intervals, and then the classification area within each level and its LAP were counted. Overall, the LAP increases rapidly with the increase of the susceptibility level for two models. As shown in Figure 16a, the classified area based on the LR decreases rapidly with the increasing of susceptibility level, most of the study area is in the interval of \(<\)0.1 and \(>\)0.9. The results of SVM show that most of the research area is in the intervals of 0.3-0.45 and 0.65-0.8 (Figure 16b). This is why the distribution area of very high (0.8-1) and very low (0-0.2) based on LR is larger (Figure 11b), but the assessment result of SVM concentrates in the classification area of the high (0.6-0.8) and low (0.2-0.4) (Figure 13b).
# A TCP Acceleration Algorithm for Aerospace-Ground Service Networks
## 3 Transmission Acceleration Using BoostTCP
### Fast Prediction-Based Packet Loss Judgment and Recovery Mechanism  
The standard TCP protocol stack determines packet loss in two ways, based on the number of Dup-ACKs received at the receiving end and based on the ACK timeout. When a large number of packets are lost, the ACK timeout has been frequently used to determine the timeout condition and initiate a retransmission. It should be noted that packet loss is frequently sporadic in modern networks, and it is not uncommon for multiple data packets to be lost concurrently on a connection. As a result, the standard TCP protocol frequently relies on timeouts to retransmit data to fill gaps, resulting in a waiting state of several seconds, which can even last up to ten seconds. As a result, the transmission may pause for an extended period or even disconnect entirely, which can affect the standard TCP efficiency significantly.  
In addition to the two methods used by standard TCP, the BoostTCP's packet loss judgment mechanism uses a dynamic self-learning algorithm to predict packet loss based on the network characteristics of the TCP connection path. The prediction algorithm considers network characteristic factors similar to those considered by the self-learning algorithm for BoostTCP congestion detection. The BoostTCP packet loss detection algorithm calculates a probability of loss for each packet sent but not confirmed by the other party's ACK. The probability changes as the transmission process continues. When the probability reaches a certain value, the algorithm considers the data packet lost and initiates retransmission immediately. This mechanism significantly reduces the likelihood of the TCP transmission relying on timeout and determining the packet loss, allowing it to fill holes faster, transmit data more smoothly, and achieve a higher average transmission rate. This packet loss-to-retransmission mechanism, which is faster than the standard TCP, is beneficial for maintaining faster and smoother data transmission in ultra-long-distance special-line optical fiber transmission networks.  
Due to the untimely packet loss detection of standard TCP, its transmission efficiency is frequently very low, and transmission quality is unstable, which is difficult to predict and impacts user experience. BoostTCP acceleration can predict packet loss in real time and recover the lost packets on time. The transmission is smoother and faster, significantly improving the user experience.
## 4 Discussion
### Recommendations  
The DEng change project has demonstrated that a water supply, specifically coming from a non-potable water source, can be used as a decarbonization strategy in Abu Dhabi, which could also be transferred to countries located in arid climates that have limited access to natural resources and depend on desalinated water.  
The following recommendations from the authors are therefore to encourage the local authority to develop a sustainable water conservation and reuse (SWC) strategy, forming the basis for a water protocol including an assessment methodology to measure the impact of decentralized buildings' non potable water systems GHG emissions and their financial viability with the design and development of a water:energy GHG metric in kgCO2e/m3, as summarized in Figure 7. This proposed calculation method, using equations [35; 51; 60; 61; 62; 69; 76; 77; 78; 79] from the literature, fills the knowledge gap with regard to assigning values of building water streams (Figure 1 in Appendix A) while accounting for the energy intensity of non-potable water supply sources for a decentralized water system.  
The findings for collecting water and energy data to produce the calculations can be summarized as follows:  
* A water balance to allow for the comparison of an onsite system as opposed to the use of municipal desalinated potable water. The water consumption records are to be collected and analyzed daily via sub flow meters connected to the EMCS. Additionally, these water data are to be compared with water bills to cross check the accuracy of the collected data and establish cost savings. This action research benefit is to help provide an account for the effect of non-potable water on landscape for water efficiency and water deficit offset feasibility analysis [6; 28; 51; 52].
* Establishing an irrigation water budget to create a baseline for LI irrigation demand against a suitable irrigation rate and irrigation controller schedule [6; 28].
* Conducting water and soil quality tests and building water system audits for implementation of a soil water strategy that could reduce substantial water consumption [6; 28].
* In addition to air conditioning condensate water, account for additional non potable water supply suitable for reuse as makeup water by conducting a building water audit [6; 51; 52].
* Accounting for the building operational boundaries for equipment electrical consumption serving the water systems [6; 51].  
These recommendations apply to the design and construction industry as well as to the building operations and maintenance industry who are willing to improve their outdoor water conservation programs to save on cost and minimize environmental impact.
# A Trustworthy Communication Hub for Cyber-Physical Systems
## 4 Building Blocks of the CPS Communication Hub  
### Trust Process  
An essential element related of the CPS hub from the owners' point of view is the trustworthiness. It refers here to all the capabilities required for ensuring trustability of the physical and logical resources, exposed information and their application under control of the owner. The basic trust enabling procedures in the CPS hub are shown in the Figure 6, and clarified in the following.  
Mutual _authentication_ is required in order to identify and bind the owner with the related communication space in the CPS hub. Authentication is also required to bind the physical and logical resources (e.g., servers and device resources) of the owner into the communication space of the owner. When an SP is an owner, its back-office services system may be a resource in the SP communication space. Such a resource, the back-office service system of the SP, usually also has specific authentication relying on its own allowed user space. It is obvious that a separate authentication is required between the referred resource of the SP and the customers of the SP.  
The _security policies (profiles)_ of the owners related to the access, use of their physical and logical resources, and the exposed information need to be _defined_ by the owner. The policy definitions may include, for example, rules according to which the access to the resources of the owner may be allowed for the other parties. Such rules may include conditions related to, for example, situation, time, environment, and level of trustworthiness, among others.  
The capabilities for creation and maintenance of online trust and value exchange relationships between the owners are required for keeping the grid of agreements in \"real-time\" according to the dynamic relationship situation (_online agreement negotiation_).  
_Authorization_ refers to the process for formally ensuring the identity of the owner/user access control rights for the stakeholder in the authentication, security policy profile definition, online agreement negotiation, and in the use of physical/logical resources.  
The physical resources (A-C... ) shown in Figure 6 can be any physical resource, embedded device/product and, for example, a server, which is owned by a single company (\"owner\"), CPS service provider (SP). Such a server of the SP usually has its own authentication and authorization services to ensure that only their customers have access to the services that their server provides. A specific owner  
Figure 6: Trust services of CPS hub.  
may be a customer of many SPs and, therefore, multiple authentication and authorization processes are required for enabling trustworthiness.
# Anomaly Detection in 6G Networks Using Machine Learning Methods
## 6 Experimental Results and Discussion
### Analysis, Discussions and Evaluations  
The categorization technique, number of FS, FS, FAR, and DR, as well as the accuracy of the reimplemented method, were all analyzed and contrasted with earlier studies. During testing, the proposal's detection accuracy was 90%, but during training, it was 99%. It produced a higher DR with a lower FAR than the single-stage technique.  
Additionally, it regularly achieved the highest accuracy, DR, and FAR levels compared to past research.  
The reimplemented system's accuracy, DR, FAR, the number of FS, FS, and classification methods were all studied and contrasted with prior studies. The proposal's detection accuracy was 99% during training and 90% during testing. It produced a greater FAR for the DR, which is lower than for the single-stage technique. In addition, compared to past investigations, it consistently achieved the highest levels of accuracy, DR, and FAR. This trade-off is further explained in Table 9.
# Molecular Imaging with \({}^{68}\)Ga Radio-Nanomaterials: Shedding Light on Nanoparticles
## 3 Radiolabeling Nanomaterials
### Surface Radiolabeling
#### 3.1.1. Chelator-Mediated Approach  
This approach is a classical way to produce radiolabeled nanoparticles. Chelate ligands are conjugated to the surface of the nanoparticle to form a coordination complex between nanoparticle and radionuclide. It involves several steps: nanoparticle synthesis, functionalization with chelate ligand and radionuclide incorporation. Although this method demands multi-step protocols and purification steps, it endows nanoparticle functionalization prior to radiolabeling. Chelate ligand choice will depend upon several factors: stability of chelator-nanoparticle bond and stability of coordination complex between chelator and radionuclide. If chelator detaches from nanoparticle surface in vivo, PET signal will be derived from the chelator-radioisotope complex rather that from the radiolabeled  
Figure 1: Nanoparticle radiolabeling approaches: (**a**) chelator approach; (**b**) chelator-free approach; and (**c**) nanoparticle core-doping. NP, nanoparticle; DOTA, 1,4,7,10-Tetraazacycldodecane-1,4,7,10-tetraacetic acid; NOTA, 1,4,7-triazacyclononane-1,4,7-trisacetic acid; DTPA, diethylenetriaminepentaacetic acid.  
nanoparticle. Therefore, chelate ligand and nanoparticle are preferably attached by a covalent bond. Presence of different cations in the bloodstream (Ca\({}^{2+}\) and Mg\({}^{2+}\)) may trigger transmetallation reactions, displacing radioisotope in the coordination complex. Again, in this case, signal recovered will come from the free radioisotope and not from the nanoradiotracer.  
Radioisotope choice determines chelate ligand used. Slower dissociation rates presented by macrocyclic ligands in comparison to their linear analogs bring about their preferential use. Common chelators for \({}^{68}\)Ga include a family of cyclic ligands based on heterocyclic N-dodecane or N-nonane moieties. Most commonly used ones are 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA); 1,4,7-triazacyclononane-1,4,7-triacetic acid (NOTA) and 1,4,7-triazacyclononane,1-glutaric acid-4,7-acetic acid (NODAGA). They have tetra- or triacetic acids that form extremely stable coordination complexes with \({}^{68}\)Ga (Figure 2).  
Although it has not been yet used to radiolabel nanoparticles, Tris(hydroxypyridinone) (THP) has been used as \({}^{68}\)Ga chelator in vivo. [PERSON] and coworkers used two THP conjugates, combined with the tumor-targeting peptide Arginine-Glycine-Aspartic (RGD), in murine models to detect U87 MG tumors. They managed to radiolabel these conjugates with over 95% radiochemical yield under ambient conditions in less than 5 min, achieving activities ranging from 60 to 80 MBq\(\cdot\)nmol\({}^{-1}\)[65].
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 2 Related Works  
### Overview of Localization Approaches  
Generally speaking, localization approaches can be classified into range-free and range-based [16]. Range-free approaches usually employ the local topology connectivity to estimate the location of the sensor node and obtain a coarser location [17]. The authors of [18] proposed a range-free method, named Area Localization Scheme (ALS). In ALS, anchor nodes broadcast acoustic signals that can be transmitted at varying power levels, underwater sensor nodes passively listen to the signals and record the information, and the sink node can use the information to localize the sensor within a certain area. In contrast, range-based approaches determine the location of sensor nodes using various ranging methods and provide an accurate location [19]. Ranging methods can be classified into four categories [20]: Time of Arrival (TOA) [21; 22; 23; 24], Time Difference of Arrival (TDOA) [25; 26; 27], Angle of Arrival (AOA) [28; 29; 30] and Received Signal Strength Indicator (RSSI) [31; 32; 33]. In underwater environments, distance information obtained by the TOA method is usually used, such as wide coverage positioning system (WPS) [34], GPS-less localization protocol (GPS-less) [35], motion-aware sensor localization (MASL) [36], underwater sparse positioning (USP) [37] and 3D underwater localization (3 DUL) [38]. TOA and TDOA methods require highly precise time synchronization of sensor nodes, which is hard to achieve due to the characteristics of acoustic signal when traveling in water [7]. The AOA method requires special devices for directional transmission and reception, which increase the cost of localization [19]. The RSSI method is cost-efficient and convenient because it does not require time synchronization and special devices. Since the RSSI value is vulnerable to environment disturbances, it is difficult to achieve accurate ranging by directly mapping the absolute RSSI value to the physical distance [9].