esa-sceva/satcom-chunk-collection huggingface dataset:
# A Road Truncation-Based Location Privacy-Preserving Method against Side-Weight Inference Attack
## 3 Preliminaries and Problem Definition
### LBS Query Message  
An original LBS query message is defined as \(Q=(u_{id},loc,C)\), where \(u_{id}\) denotes a user's identity; \(loc\) represents the user's location information, usually precise location coordinates \((x,y)\); \(C\) denotes the user's query content.  
To preserve location privacy, the message is transformed. The location information can be a circular area, a polygonal area composed of multiple points, or a collection of multiple edges.
# A3C-R: A QoS-Oriented Energy-Saving Routing Algorithm for Software-Defined Networks
## 3 Modeling of QoS and Energy-Efficient Routing
### Modeling of Energy-Efficient Routing
\[\phi_{f}=\int_{t_{1}}^{t_{2}}\sum_{w\in E_{a}}\mu(x_{e}(t))^{\pi}dt \tag{11}\]  
We use the delay, bandwidth, and packet loss rate in QoS as the main optimization goals and reduce network energy consumption. In order to facilitate the optimization training, we need to normalize the energy consumption and QoS data and use the method of Z-score [19] for processing. The Z-score method is a widely used statistical technique for data standardization. It transforms the original data into a standard normal distribution, which is beneficial for comparing data from different sources and detecting outliers. In our study, we used the Z-score method for two main purposes. First, to pre-process the experimental data. Given that the network performance data (such as delay, bandwidth utilization) collected from various sources and under different measurement conditions may have significant differences in scale and distribution, Z-score standardization helps to unify these data onto a common scale, ensuring more stable and accurate subsequent data analysis and model training. Second, we utilized the Z-score to identify outliers in the dataset. Data points with an absolute Z-value greater than 3 (or less than \(-3\)) are often considered as outliers. For such outliers, we conducted further investigations to determine whether they were caused by measurement errors or special network events, thus ensuring the quality of the data. The calculation process is shown in Formula (12). \[y^{*}=\frac{y-u}{\sigma} \tag{12}\]  
where \(y^{*}\) represents the target value, \(y\) represents the original data, \(u\) is the mean, and \(\sigma\) is the standard deviation. Therefore, we can use the Formula (13) to express the optimization process. \[\min(\delta\cdot\phi_{f}t+\beta\cdot Delay(P)t+\lambda\cdot\frac{1}{D(P)t}+ \gamma\cdot Loss(P)t) \tag{13}\]  
Among them, \(\phi_{f}t\), \(Delay(P)t\), \(D(P)t\), and \(Loss(P)t\) are normalized energy consumption, delay, bandwidth, and packet loss rate, respectively, and \(\delta\), \(\beta\), \(\lambda\), and \(\gamma\) are parameter weights. In order to ensure that the above traffic scheduling process is not affected by the environment, we stipulate that the traffic transmission process must be completed before the latest deadline, the bandwidth occupation of each link does not exceed the link capacity \(C\), and each flow is received by the destination node.
## 3 System Model Description and Problem Statement
### DQN Model for RAN-Level Slice Admission Control  
As depicted in Figure 4, the system employs a DQN-based resource allocation algorithm to handle incoming slice requests while ensuring optimal network utilization. The DQN agent is deployed as an xApp within the near-RT RIC. This strategic placement allows the agent to receive up-to-date network data--such as resource availability, active slice requests, and QoS metrics--enabling it to make prompt and informed decisions regarding slice admission control.  
Upon receiving a request, the DQN agent assigns it to the appropriate slice type by analyzing available network resources and QoS constraints. To maintain efficiency, the system dynamically updates the list of processed requests, releasing resources previously occupied by completed tasks before handling new slice requests. If sufficient resources are available, the request is admitted, and the required PRBs are allocated within the corresponding BWP1 or BWP2. The selection between BWP1 or BWP2 depends on the latency sensitivity of the service, which is based on a predefined threshold.  
Conversely, if resources are insufficient and the request is not latency-sensitive, the xApp issues a slice admission rejection report to the non-RT RIC, ensuring that rejected slices are properly logged.
# _Sponias mombin_ Seed Oil Compounds Identification by Raman Spectroscopy and NMR
## 2 Materials and Methods
### NMR Experiments  
\({}^{1}\)H-NMR and \({}^{13}\)C-NMR spectra were obtained from a Varian 500 MHz instrument. Experiments using samples (approx. 50 mg) in CDCl\({}_{3}\) with TMS as internal standard were carried out. Chemical shifts were recorded in ppm, using the solvent proton signal as standard. Data were analyzed by using the equipment software.  
\({}^{1}\)H-NMR (500 MHz, CDCl\({}_{3}\)): \(\delta\) 0.87-0.92 (m, terminal CH\({}_{3}\)), 1.25-1.41 (m, CH\({}_{2}\)), 1.59-1.66 (m, CH\({}_{2}\)-CH\({}_{2}\)-COO), 2.00-2.09 (m, CH\({}_{2}\)-CH = CH), 2.31-2.37(t, \(J\) = 6.0 Hz, CH\({}_{2}\)-COO), 2.77-2.80 (t, \(J\) = 3 Hz, HC HC =HC-CH\({}_{2}\)-CH = CH), 4.14-4.19 (dd, \(J_{1}\) = 9 Hz, \(J_{2}\) = 6 Hz, glyceryl CH\({}_{2}\)O), 4.29-4.33 (dd, \(J_{1}\) = 9 Hz, \(J_{2}\) = 3 Hz, glyceryl CH\({}_{2}\)O), 5.25-5.29 (m, glyceryl CHO), 5.30-5.41 (olefinic H).  
\({}^{13}\)CNNMR (150 MHz, CDCl\({}_{3}\)): \(\delta\) 14.02 (\(\alpha\)-CH\({}_{3}\)), 22.54 (\(\beta\)-CH\({}_{3}\)), 24.82 (C-3), 25.62 (C-11, dialyllic), 27.16 (overlapping signals: C8-11 (oleyl), C8-14 (linoleyl)), 29.07-29.68 (CH\({}_{2}\)n), 31.51 (C-16, linoleyl), 34.01 (\(\alpha\)- C-2), 34.17 (\(\beta\)-C-2), 62.09 (glyceryl CH\({}_{2}\)O), 68.91 (glycerylCHO), 127.88 (C-12, linoleyl), 128.06 (C-13, linoleyl), 128.06 (C-13), 129.68 (C-9, oelyl), 129.95 (C-9, linoleyl, 129.98 (\(\alpha\)-C-10, oelyl), 129.99 (\(\beta\)-C-10, oelyl), 130.20 (C-10, linoleyl), 172.79 (\(\alpha\)-C-1), 173.25 (\(\beta\)-C-1), 173.21 (\(\beta\)-C-1).  
To determine the relative percentage of saturated and unsaturated fatty acids in the oil using \({}^{1}\)H-NMR, we followed the methodology described by [PERSON] et al. [25], who established the following equations using the integrals of different proton environments: polyunsaturated fatty acids: _PUFA = F/E_; monounsaturated fatty acids: _MUIFA = [C/2E]-PUFA_; saturated fatty acids: _SFA = 1-[D/2E]_, where _D_: \(\alpha\)-allylic proton environment at \(\delta\) 2.05 ppm; _E_: integrations of the acyl group at \(\delta\) 2.34 ppm; _F_: protons attached to the bis-allylic carbon at \(\delta\) 2.79 ppm.
## 1 Introduction  
High-throughput sequencing of taxonomically informative loci of microbial genomes by amplicon sequencing dramatically improved our understanding of microbial communities. Microbiome research expanded into all microbial habitats on earth, including the human intestine (e.g., [1]), soils (e.g., [2]), and deep-sea sediments (e.g., [3]). A range of bioinformatic tools and platforms as well as reference databases have been developed to enable the extraction of biological insight from the large amounts of data generated by multiplexed amplicon sequencing. The number of sequence counts per sample (sequencing depth) obtained from such sequencing runs can vary by orders of magnitude [4]. Those variations are technical artifacts caused by unequal pooling of samples prior to multiplexed sequencing runs and varying sequencing efficiencies. This contributes to biased estimates of several parameters assessed in microbiome analysis, such as alpha and beta diversity, and relative abundances of taxa.  
Fortunately, variations in sequencing depth can be computationally compensated by normalization of sequence counts per sample, a step that has become essential in processing amplicon sequencing data. Traditionally, rarefying was used for this. In 2014, however, [PERSON] and [PERSON] [4] demonstrated that rarefying is statistically inadmissible for the normalization of microbiome count data. Although the work of [PERSON] and [PERSON] [4] received a lot of attention, rarefying is still frequently used in current microbiome studies, likely due to a lack of suitable alternatives. This motivated us to develop the scaling with ranked subsampling (SRS) algorithm, which outperforms rarefying for diversity analysis and relative abundance estimates, as recently shown [5].  
Because unequal sampling depth is a problem inherent not only to microbiome research but to all studies based on ecological count data, we introduced SRS as a tool for the normalization of ecological count data and successfully applied it to microbiome analysis [5]. Yet, the implementation of SRS in bioinformatic platforms was missing.  
In this work, we introduce an R package ('SRS') and a QIIME 2 plugin ('q2-srs') for the normalization of microbiome count data using SRS. Furthermore, we improve the original SRS algorithm and add features to visualize and evaluate the results. Finally, we provide an example for microbial ecologists that aim to normalize microbiome count data obtained by amplicon sequencing.
## 4 Experimental Results and Discussions
### Lung Segmentation
\begin{table}
\begin{tabular}{l c c c} \hline
**Algorithms** & **IoU (\%)** & **DSC (\%)** & **Accuracy (\%)** \\ \hline FCN & 83.9 \(\pm\) 3.9 & 91.2 \(\pm\) 2.3 & 94.8 \(\pm\) 2.3 \\ Seg-VGG16 & 87.7 \(\pm\) 3.6 & 93.4 \(\pm\) 2.1 & 95.7 \(\pm\) 2.1 \\ Seg-VGG19 & 86.9 \(\pm\) 2.6 & 92.9 \(\pm\) 1.5 & 95.5 \(\pm\) 1.5 \\ U-Net & 91.6 \(\pm\) 3.0 & 95.6 \(\pm\) 1.7 & 97.4 \(\pm\) 1.7 \\ DLabv3+R18 & 94.7 \(\pm\) 2.0 & 97.3 \(\pm\) 1.1 & 98.3 \(\pm\) 1.1 \\ DLabv3+R50 & 94.5 \(\pm\) 1.9 & 97.1 \(\pm\) 1.0 & 98.3 \(\pm\) 1.0 \\ DLabv3+M & 94.9 \(\pm\) 1.8 & 97.4 \(\pm\) 1.0 & 98.4 \(\pm\) 1.0 \\ DLabv3+X & 96.1 \(\pm\) 0.8 & 98.0 \(\pm\) 0.4 & 98.8 \(\pm\) 0.4 \\ \hline \end{tabular}
\end{table} 
Table 4: Performance of deep semantic lung segmentation methods on JSRT dataset.
## 3 2D Deconvolution for Obtaining Better Beam-Time Migration
### Conventional Plane-Wave Beamforming  
Considering a far-field source, the angle of incidence \(\widetilde{\theta}\) is shown in Figure 1. When using uniform horizontal array, as shown in Section 2, with interelement spacing denoted by \(d\), the received signal of the array can be expressed as  
\[\mathbf{X}_{S}(\omega)=\mathbf{p}_{a}S(\omega)+\mathbf{n}=\begin{bmatrix}p_{a,1}\\ p_{a,2}\\ \vdots\\ p_{a,N}\end{bmatrix}S(\omega)+\mathbf{n} \tag{5}\]  
where \(\mathbf{p}_{a}\) is the array response vector given as \(\mathbf{p}_{a}=[p_{a,1},p_{a,2},\cdots p_{a,N}]^{T}\), \(p_{a,n}=e^{-i\omega nd\sin\widetilde{\theta}/c}\), and \(\mathbf{n}\) is the noise vector.  
Conventional beamforming [12; 13; 14] multiplies the array signals \(\mathbf{X}_{S}(\omega)\) by a steering vector \(\mathbf{\mu}=[\mu_{1},\mu_{2},\cdots\mu_{N}]^{T}\) for the direction \(\varphi\), where \(\mu_{n}=(1/N)e^{-i\omega nd\sin\varphi/c}\). The beam power as a function of \(\sin\varphi\), which is obtained with conventional beamforming, is written as  
\[\mathbf{X}_{p}(\sin\varphi)=\left|\mathbf{X}_{S}(\omega)\mathbf{\mu}^{T} \right|^{2}=\left|\frac{S(\omega)}{N}\sum\limits_{n=1}^{N}e^{i2\pi n\frac{d}{ 4}\left(\sin\varphi-\sin\widetilde{\theta}\right)}\right|^{2}=\left|S(\omega )\right|^{2}\left|\frac{1}{N}\sum\limits_{n=1}^{N}e^{i2\pi n\frac{d}{4}\left( \sin\varphi-\sin\widetilde{\theta}\right)}\right|^{2} \tag{6}\]
## 2 Materials and Methods
### Proposed Method  
As shown in Figure 2, the proposed method consists of three parts: preprocessing, model, and output. First, the standard 12-lead ECG records are processed to filter out baseline wander and high-frequency noise. In addition, R-wave detection is performed on the pure ECG records, and sliding windows with the same number of heartbeats are generated based on the position of the R-wave. These sliding windows are an input to a bidirectional long short-term memory (Bi-LSTM) network with heartbeat-attention mechanism. Finally, real output is comprehensively determined by the prediction results of sliding windows.
## 2 Materials and Methods
### Universal GNSS Channel Validation
#### 2.2.1 Constellation Compatibility  
As summarized in Table 1, the four GNSS constellations provide similar (for GNSS compatibility), yet distinctive (for GNSS interoperability) modulation characteristics. On top of that, most signals have their own navigation message definition, including preamble synchronization, parity checking, framing, interleaving and encoding, all of which are defined in their respective Interface Control Documents (ICD), or Interface Specification (IS) [10; 11; 41; 42; 43; 44]. All four constellations have their own geodetic and timing systems, but provide (now or in a near future) information to relate with other GNSS. Such navigation message data fusion into a common solution add to the complexity of a  
Figure 9: Proposed Universal Channel High Level Architecture.  
universal receiver is outside the scope of this paper, rather focused on signal processing. In order to demonstrate the proposed universal channel compatibility with all constellations, at least one signal of each is acquired and tracked.
# An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar
## 2 Materials and Methods
### UAV Experimental Environment Selection and Radar Parameter Settings  
#### 2.2.1 Checking the Weather Conditions  
The UAV-based radial velocity validation of the radar should, as far as possible, be undertaken in clear weather and with a ground wind velocity of level 1-2, reducing the interference of the ambient wind field on the state of the UAV. This could ensure that the measurement results truly reflect the radar performance.
# Feasibility of Using Low-Cost Dual-Frequency GNSS Receivers for Land Surveying
## 1 Introduction
Currently, two dual-frequency receivers are available on the market, namely u-blox ZED-P9P ([[https://www.u-blox.com](https://www.u-blox.com)]([https://www.u-blox.com](https://www.u-blox.com))) and SkyTraQ PX1122R ([[https://www.skytraq.com.tw](https://www.skytraq.com.tw)]([https://www.skytraq.com.tw](https://www.skytraq.com.tw))). The former is capable of simultaneously tracking four GNSS (GPS, GLONASS, Galileo, and BeiDou). However, the positioning accuracy of low-cost receivers is worse than geodetic-grade receivers, thus increasing the convergence time of a real-time PPP solution [25]. If a low-cost patch antenna is used, it is needed to correct the carrier phase patterns [26]. Moreover, these receivers can be used for geodetic surveys in open sky conditions and short baselines [27]. Very few studies on the performance of a low-cost receiver combined with a low-cost antenna, used for precise relative and absolute positioning in static and kinematic mode, are available [28]. Although the manufacturers declare that their receivers enable a reliable positioning in RTK mode with a short convergence time, such a performance is limited to a favorable environment [24]. Therefore, this paper aims at evaluating the signal acquisition performance and accuracy of a low-cost dual-frequency multi-GNSS receiver, used for a relative (RTK and NRTK) and absolute static positioning in typical land-surveying applications, e.g., establishment of control points and cadastral surveying.
# Building Scalable and Secure Multicast Delivery Infrastructure in a Local Area Network
## 4 Core Based Multicast Tree
#### Step 3: Switches That Are More Than One-Hop Away from a Core Node  
As before, let \(S\) be a switch that wishes to join the CBT-delivery tree.  
1. \(S\) broadcasts a _CBT-Join_ message to every switch connected with \(S\), if it is received by a switch that is not a part of the CBT-delivery tree yet will ignore the message.
2. If the message is received by a switch \(S1\) that is already a part of the delivery tree, it will forward to its attached core through the reverse path.
3. Let \(C\) be the core node that receives the _CBT-Join_ message from \(S\) forwarded by \(S1\). If \(C\) receives the forward from multiple switches, it will process only the message with the best cost (shortest hop, least latency, etc.). Messages forwarded through other switches will be ignored. \(C\) then follows the same challenge-and-response scheme outlined in Step 2 to authenticate \(S\) and add to the delivery tree. The core also sends a copy of the _CBT-Join Confirmation_ message to \(S1\). Thus, \(S1\) knows that \(S\) is authenticated to join the CBT tree. The switch \(S\) then creates a reverse pointer to \(S1\), and \(S1\) creates a forwarding pointer to \(S\).
4. As before, if \(S\) received multiple challenges from different core nodes, it will process the one with the best cost. For all other core nodes, \(S\) will issue _CBT-join reset_ messages.  
The message flow diagram outlining this step is given in Figure 2.
## 5 Experimental Results
### Threshold Optimization Results
The optimization process reveals that a threshold range of 0.5 to 0.7 yields the highest \(F1\) scores, reaching values of 0.98 and 0.99. The improved performance within this range highlights the significant impact of SMOTE in reducing the effects of class imbalance in the dataset.
## 3 Potential Security Implications of IoT in 6G Environment
### Network- or Device-Level Vulnerabilities
#### 3.1.6 Lack of Standardized Security Protocols  
The rapid deployment of IoT devices and networks in 5G has outpaced the development of standardized and universally accepted security protocols. The diverse and specialized use cases of 6G will amplify the need for adaptable, unified, and scalable security frameworks [22].
## 3 Temporal sequence of PLAL processes  
The PLAL process can be grouped into four main stages [10; 11; 12], as represented in Figure 1:  
1. laser ablation and plasma production,
2. plasma expansion and shockwave propagation,
3. cavitation bubble expansion and compression,
4. NPs releasing in solution.  
Figure 1: Temporal sequence of PLAL processes with the employed diagnostic techniques. All the images in the sketch have been recorded in the VIS spectral range (400–650 nm).  
PLAL starts with the interaction of the laser pulse with the target. The laser pulse energy is converted into electron excitation in the material and the generation of charged particles (electrons and ions) occurs by multiphoton ionization. Because of these processes, the target material is ejected in a few tens of fs. In the case of ns irradiation, the laser pulse continues to interact with the ablated material and in particular with the free electrons, releasing further energy and heating the electron and, consequently, inducing the plasma.  
The plasma reaches a temperature of several thousands of Kelvin and consequently, it transfers part of its energy to the surrounding liquid. In these conditions, a thin layer of vapor at high temperature is suddenly formed. As a consequence of the high temperature, the vapor pressure is very high and this layer starts to expand at supersonic speed inducing the cavitation bubble. When the bubble reaches its equilibrium with the surrounding liquid, the expansion stops and the liquid mass accumulated at the border of the bubble compresses the vapor inducing the collapse stage. If enough energy has been stored in the bubble, the latter rebounds, inducing further expansion/compression stages. Recently, it has been shown that the compression stage plays an important role in the release of the formed NPs into the solution because the electrostatic pressure of the NP cloud inside the bubble becomes greater than the pressure of the liquid at the border [8].
## 1 Introduction
In addition, there are some blind SLM PAPR reduction schemes [11; 12; 13; 14] which do not need the transmission of SI. Unfortunately, they have huge complexity at both the transmitter and receiver and are not applicable in practical OFDM systems. In this paper, we present a low-complexity blind SLM PAPR reduction scheme for OFDM signals. The proposed method is mainly focused on reducing the computational complexity of the SLM PAPR reduction scheme while avoiding the transmission of SI. Based on deriving the general time domain form of the conventional SLM PAPR reduction method, the proposed method properly designs the phase conversion vectors in the time domain at the transmitter as well as using the equivalent channel to perform the frequency domain equalization (FDE) at the receiver without SI transmission. As compared to the conversion vector-based SLM PAPR reduction method in [10], the proposed scheme has low computational complexity in PAPR reduction without the transmission of SI and obtains great robustness of BER performance. The rest of this paper is organized as follows. The OFDM system model is introduced in Section 2. The new blind SLM PAPR reduction method is presented based on the proposed phase conversion vectors in the time domain and the computational complexity is analyzed in Section 3. The simulation results to illustrate the performance of the proposed scheme are presented in Section 4. Finally, the conclusions are drawn in Section 5.
## 4 Simulation Results
### Comparison with Different Methods  
Here, we run the simulation 50 times for each localization error in Equation (29), then the average localization error can be obtained. We compare the five algorithms as shown in Table 4 which lists the maximum localization error, the minimum localization error, the average localization error and the standard deviation of the five algorithms.  
The localization error of the Anchor-based algorithm is susceptible to the error of the estimated distances in relation to the coordinates of the fixed anchors, so the maximum localization error, the minimum localization error and the standard deviation of the Anchor-based algorithm are larger than the other four algorithms. Utilizing geometrical relationships, the AUV-aid method performs localization coarsely, so its average localization error is the biggest. We can see that the average localization error of the ACFL is 4.56, which is larger than that of the AFLA and the PCFL according to the simulation results. Table 4 shows that the PCFL algorithm has better localization performance and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Average Error(m)** & **Max Error(m)** & **Min Error(m)** & **Standard Deviation(m)** \\ \hline Anchor-based & 5.59 & 16.86 & 2.81 & 4.06 \\ AUV-aid & 9.82 & 7.72 & 1.56 & 2.69 \\ AFLA & 2.63 & 13.89 & 0.31 & 1.8 \\ ACFL & 4.56 & 10.22 & 0.51 & 2.06 \\ PCFL & 1.83 & 5.01 & 0.14 & 0.87 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of localization errors.  
Figure 10: Runtime.  
smaller localization error, compared with the other four algorithms. The average localization error of PCFL can decline by about 30.4% than AFLA method.