esa-sceva/satcom-chunk-collection huggingface dataset:
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 5 Distributed Protocols
### Ald-Mac  
The neighbor discovery mechanism is a great choice for directional propagation systems, as it leads to the establishment of connections without redundancy transmission in random directions. Accordingly, a reinforcement learning (RL) based MAC protocol called adaptive learning directional medium access control (ALD-MAC) [70] was proposed to enable implicit cooperation between different nodes in the mmWave communication systems by combining a neighbor discovery algorithm with RL. In ALD-MAC, the channel access period is divided into a set of fixed-length frames. Each frame is further subdivided into a number of slots. Each node attempts to send a packet in the designated sector in each slot, and each slot's duration is set to be long enough to deliver a packet with the maximum size. The JFI is also used to evaluate the performance index for ALD-MAC. For a node count of 10, 20, and 30 ALD-MAC has an index of 0.91, 0.87, and 0.80, respectively.  
* Advantages: To gain a deeper knowledge of the network, ALD-MAC protocol provides implicit cooperation among diverse nodes. In addition, simulation results have demonstrated that ALD-MAC outperforms some traditional directional protocols, such as Directional Slotted ALOHA and MD-MAC.
* Disadvantages: RL is a powerful deployment tool for unknown environment applications, however, applying RL in real-life MAC applications has its drawbacks. Since the ALD-MAC protocol strongly depends on the neighbor discovery, RL might perform considerably poorly in discovering neighbors in the initial stage of the protocol execution because RL learns as it iterates in the environment by trial and error.
# Satellite Quantum Communications When Man-in-the-Middle Attacks Are Excluded
## 1 Introduction
While most QKD schemes use two bases for encoding, in our PKD schemes only a single encoding basis is used since mixed bases are primarily a mechanism to detect man-in-the-middle attacks. This avoids the basis reconciliation sifting stage and so is more efficient in its use of photons. [PERSON] et al. have previously discussed the opportunity for simplified cryptography schemes that fall between QKD and laser communications [6]. As they explain QKD (and PKD) fit within the wider field of physical layer security methods. These encompass such techniques as wiretap channels [7] and radio frequency equivalents [8], to laser-based methods that actively quantify the amount of information an eavesdropper can receive [9]. Our exploration of PKD here builds on the ideas put forward by [PERSON] et al. In this paper we look at the asymptotic secret key rates at high losses (i.e., long distance free space links) for three QKD schemes using three different kinds of transmitter hardware, and compare this to three simplified PKD schemes that use similar hardware. The simplified schemes are primarily targeted at satellite applications and all assume that a covert man-in-the-middle attack has been assessed to be non-existent. As expected, they are all found to remain effective at higher losses than their QKD counterparts.
###### Abstract  
With widespread non-linear loads and the increasing penetration of distributed generations in the power system, harmonic pollution has become a great concern. The causes of harmonic pollution not only include the integer harmonics, but also interharmonics, which exacerbate the complexity of harmonic analysis. In addition, the output variability of highly non-linear loads and renewables such as electric arc furnaces and photovoltaic solar or wind generation may lead to weakly time-varying harmonics and interharmonics in both frequency and magnitude. These features present challenges for accurate assessment of associated power-quality (PQ) disturbances. To tackle such increasing time-varying PQ problems, a hybrid detection method using synchrosqueezing wavelet transform (SSWT) is proposed. The proposed method first obtains the proper parameter values for the mother wavelet according to numerical computations. The wavelet transform-based synchrosqueezing and a clustering method are applied to determine each frequency component of the waveform under assessment. The time-domain waveform and the associated magnitude of each frequency component is then reconstructed by the inverse SSWT operation. The novelty of the proposed method is that it can decompose the measured waveform containing both harmonics and interharmonics into intrinsic mode functions without the need for fundamental frequency detection. Compared to other time-frequency analysis methods, SSWT has better anti-noise and higher resolution of time-frequency curves; even the measured signal has close frequency components. Simulation results and actual measurement validations show that the proposed method is effective and relatively accurate in time-varying harmonic and interharmonic detection and is suitable for applications in power networks and microgrids that have high penetration of renewables or non-linear loads causing time-varying voltage or current waveforms.  
non-linear load; renewables integration; power quality; clustering method +
Footnote †: journal: Applied Sciences
# Quality-Aware Resource Model Discovery
## 3 Quality-Aware Resource Model Discovery
### Overview  
This section presents the whole structure of the proposed methodology, i.e., quality-aware resource model discovery, as depicted in Figure 1. Our approach includes five steps: model generation, superior and inferior case identification, integrated model construction, statistical analysis on resources, and model simplification. It first takes an event log as input and produces a resource-oriented transition system representing the resource behaviors described in the log. Second, based on quality data, we identify the cases recorded as relatively superior or inferior to others. Afterwards, the extracted resource model and the identified superior and inferior cases are combined into a quality-aware resource model that distinguishes the high- and low-quality-generating resource paths. The discovered model, however, may be remarkably complicated since plenty of resources are generally engaged in a business process. Thus, the fourth step performs a series of statistical analyses on the performance of resources to determine resources that have high impacts on the quality. Lastly, we simplify the discovered model using the results of the statistical analysis.  
Before presenting how to implement the quality-aware resource model, we define an event log in Section 3.1.1.
# Micro Vibration Measurement with Microscopic Speckle Interferometry Based on Orthogonal Phase
## 5 Conclusions  
We propose a micro-device vibration measurement method based on microscopic speckle interferometry combined with orthogonal characteristics. We have measured vibrations with frequency from 0.5 Hz to 20 Hz used the experimental device, and the maximum amplitude that can be measured is 60 nm. Our measurement method has two advantages: 1. It does not require scanning for imaging, so it can directly achieve real-time full-field measurement; 2. The measurement principle and equipment is simple, so there is no need to introduce a stroboscopic light source, heterodyne device or additional signal modulation devices.  
This method enriches the measurement range of optical vibration measurement technology to a certain extent. The main reason for limiting the upper limit of the measurable vibration frequency is the acquisition frequency of the camera. For example, the acquisition frequency of Mini-AX200 camera produced by Photron Company can reach 160,000 frames/s, which is 400 times that of our camera. In theory, the upper limit of the vibration frequency that can be measured with this camera can reach 8 kHz. This theoretical derivation needs to be verified by further experiments.  
Figure 10: (**a**) The speckle image collected by CCD; (**b,c**) In the traversal process, the light intensity curves in two windows satisfying the orthogonal characteristics; (**d**) The surface distribution.  
**Author Contributions:** Study design, data analysis, and writing--original draft preparation: [PERSON]; project administration, writing--review and editing: [PERSON]; software: [PERSON]; visualization: [PERSON]; supervision: [PERSON]; investigation: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 52075034.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Polygonal Model to Design and Fabricate Ion-Exchanged Diffraction Gratings
## 5 Conclusions  
We present a simple polygonal model to describe the phase profile of ion-exchanged gratings. Its simplicity makes it easy to calculate the transmission function of gratings with such a phase profile. From this transmission function and the experimental efficiencies of three gratings with approximate phase steps \(\pi/2\), \(\pi\), and \(3\pi/2\), we characterized the ion-exchange process through simple functions for the intrinsic parameters of the model. In order to show the potential of this model, we designed and fabricated a diffraction grating with suppressed zero order and a three splitter. The cancellation of the zero order was very good: only 0.5% of the power remains at this order after diffraction. As for the three splitter, its phase profile is close to the optimal one, and the difference between the efficiencies of its first orders is 3%. Moreover, this value can be further improved by lowering the temperature of the ion-exchange process. Finally, this polygonal model could be useful to design more complex diffractive elements by increasing the number of segments. It could also be generalized to the two-dimensional case.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(m\) & \(|t_{m}^{2}|_{exp}\) & \(|t_{m}^{2}|_{Equation}(\gamma)\) \\ \hline
0 & 27.1\% & 33.3\% \\ \(\pm 1\) & 30.1\% & 28.65\% \\ \(\pm 2\) & 3.5\% & 2.15\% \\ \(\pm 3\) & 1.6\% & 1.55\% \\ \(\pm 4\) & 0.7\% & 0.5\% \\ \(\pm 5\) & 0.3\% & 0.25\% \\ \(\pm 6\) & 0.1\% & 0.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental and predicted diffraction efficiencies for \(\lambda_{0}=632.8\) nm of the grating designed to be a three splitter.  
Figure 8: Images of a red pencil (**left**) through the grating with suppressed zero order (**middle**) and the three splitter (**right**).  
**Author Contributions**: Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON], [PERSON], [PERSON], and J.L.; software, [PERSON] and [PERSON]; validation, [PERSON] and [PERSON]; visualization, [PERSON] and [PERSON]; investigation, [PERSON], [PERSON], [PERSON], and J.L.; writing, original draft preparation, [PERSON]; writing, review and editing, [PERSON], [PERSON], [PERSON], and [PERSON] All authors read and agreed to the published version of the manuscript.  
**Funding**: This research was funded by Xunta de Galicia, Conselleria de Educacion, Universidades e FP, Grant GRC number ED431C2018/11, and Ministerio de Economia, Industria y Competitividad, Gobierno de Espana, Grant Number AYA2016-78773-C2-2-P.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Data is contained within the article.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 5 Internet of Medical Things
### IoMT Systems Architecture
#### 5.3.1 Physical Layer  
The physical layer serves as the foundation of the IoMT architecture, encompassing a range of devices and sensors utilized for data collection from patients. It consists of wearable, implantable, or non-invasive devices that collect data from patients. These devices can include wearables such as smartwatches, implantable devices such as pacemakers, or non-invasive sensors for monitoring vital signs. The physical layer is responsible for gathering and transmitting the patient's health data to the rest of the IoMT architecture.
## 3 Results
### Sociodemographic Data of AMPH-add Participants
#### 3.2.3 Macrophage Inflammatory Protein 4 (MIP-4) Level  
Interestingly, the MIP-4 level was significantly (_p_ = 0.012) increased in the serum of AMPH-adds compared to corresponding control healthy subjects. The serum MIP-4 level increased from 439.84 \(\pm\) 17.19 to 597.59 \(\pm\) 61.31 ng/mL (Figure 3) & (Table 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Control (_n_ = 19)** & **AMPH-add (_n_ = 17)** & _p_**-Value** \\ \hline ACT (ng/mL) & 163,406.31 \(\pm\) 60,769.13 & 834,027.99 \(\pm\) 329,296.57 & 0.037 \({}^{*}\) \\ \hline PEDF (ng/mL) & 27,709.64 \(\pm\) 1672.57 & 51,804.41 \(\pm\) 3205.92 & 0.001 \({}^{***}\) \\ \hline MIP-4 (ng/mL) & 439.84 \(\pm\) 17.19 & 597.59 \(\pm\) 61.31 & 0.012 \({}^{*}\) \\ \hline \hline \end{tabular} Data are expressed as mean \(\pm\) standard error. Difference between patients and control was examined using unpaired Student’s _t_-test. \(\alpha\)1-anti chymotrypsin (ACT) and pigment epithelium-derived factor (PEDF) versus macrophage inflammatory protein-4 (MIP-4). \({}^{*}\): \(p\) < 0.05; ***: \(p\) < 0.001.  
\end{table}
Table 2: Biomarkers and proinflammatory cytokines from AMPH-adds’ serum and control subjects.  
Figure 3: Comparison of MIP-4 serum level between AMPH-add and control. Data are expressed as mean \(\pm\) standard error. Statistical analysis was carried out using unpaired Student’s _t_-test. *: statistically significant at \(p\) < 0.05.  
Figure 2: Comparison of PEDF serum level between AMPH-add and control. Data are expressed as mean \(\pm\) standard error. Statistical analysis was carried out using unpaired Student’s _t_-test. ***: statistically significant at \(p\) < 0.001.  
Comparison of Liver Function Enzymes among Serum of AMPH-adds' and Control Subjects 3.3.1. Alanine Aminotransferase (ALT) Level  
The ALT level was significantly (\(p\) < 0.001) decreased in the serum of AMPH-adds compared to the corresponding normal control group. The serum ALT level decreased from 34.36 \(\pm\) 2.21 to 11.94 \(\pm\) 0.824 U/L (Figure 4a).
## 2 Experimental Methodology
### Characterization of the Nanofluids  
The thermal conductivity (\(k_{NF}\)), dynamic viscosity (\(\mu_{NF}\)), density (\(\rho_{NF}\)), and specific heat capacity (\(C_{pNF}\)) of the prepared TiO\({}_{2}\)-W NFs were measured as functions of temperature and NP volume concentration. The \(k_{NF}\) was determined by the transient hot wire method using a KD2 Pro Thermal Analyzer (Decagon Devices Inc., Pullman, WA, USA). The single-needle sensor probe (model KS1, \(\pm\)5% from 0.2-2 W m\({}^{-1}\)\({}^{\circ}\)C\({}^{-1}\)) was calibrated with base fluid (i.e., deionized water) and glycerin before measuring each NF batch. The test temperatures (25, 35, and 45 \({}^{\circ}\)C) were achieved by immersing the sample in a 1 kW automatic heating bath circulator (setpoint \(\pm\) 0.03 \({}^{\circ}\)C, model COIRO C-B27, Julabo Inc., Allentown, PA, USA). A 45 mL sample was taken and poured into a glass vial with a PTFE screw cap and a silicon pad. The KS1 sensor probe was inserted into the center of the vial, which was then immersed in the heating bath for 20 min. Each reported value of the thermal conductivity was obtained by averaging the results of five measurements. The experimental setup for measuring \(\mu_{NF}\) consisted of a cone-and-plate viscometer (model DV2 TLVCP, \(\pm\)1.0%, spindle: CPA-40Z, Brookfield Engineering Laboratories, Inc., Middleborough, MA, USA) and a circulating temperature bath (model TC150). The viscometer was calibrated by the manufacturer, and the calibration was validated by measuring deionized water prior to each NF testing. Measurements were performed at 25, 35, and 45 \({}^{\circ}\)C and replicated five times. Additional measurements were performed using a reverse-flow Cannon-Fenske viscometer (size 25) as a control. The \(\rho_{NF}\) was determined using glass hydrometers ranging from 0.9-1.0 and 1.0-1.1 with graduation marks in 0.000 (accuracy \(\pm\) 0.10%). A sample of each NF was taken in a measuring cylinder and immersed in the heating bath utilized for the \(k_{NF}\) measurements. The test temperatures were 25, 35, and 45 \({}^{\circ}\)C. The hydrometer was immersed into each NF sample and the reading was taken after reaching thermal equilibrium. The observed readings were then corrected for each test temperature. The measurements were repeated at least once to verify the repeatability of the \(\rho_{NF}\) values. The \(C_{pNF}\) was determined by differential scanning calorimetry (DSC) analysis (modelDSC 3, measurement uncertainty \(\pm\)3.5%, Mettler Toledo, Greifensee, CH) using the standard protocol ASTM E1269 [32]. A sapphire disk was used as reference material. The thermal cycle was as follows: Isothermal heating for 4 min at 20 \({}^{\circ}\)C, heating from 20 to 100 \({}^{\circ}\)C at 20 \({}^{\circ}\)C/min, isothermal heating for 4 min at 100 \({}^{\circ}\)C. The measurements were replicated at least twice for all samples, and the STARe software from Mettler Toledo was used for data analysis.
## 5 Simulation Results and Analysis
### Performance Analysis on the Rayleigh Fading Channel  
To further analyze the performance of our proposed AMR approach for the worst-case scenario, i.e., a Rayleigh fading channel, the same set of simulations were repeated, and the results are presented in Table 5. A behavior of the AMR algorithm similar to that in the case of the AWGN channel was observed, showing an increase in PRA at higher SNRs and with larger numbers of samples. The PRA, however, was greatly reduced owing to the absence of the dominant path (non-line-of-sight scenario). Nevertheless, our proposed AMR approach showed good accuracy for all of the combinations of different SNRs and the sample size. The lowest PRA of 80% at an SNR of 0 dB was observed for the QAM signal, and it increased to 99.5% by increasing the number of samples to 4096. The average PRA for BPSK, QPSK, QAM, 16-QAM, and 64-QAM was 95.9%, 97%, 91%, 94.25%, and 96%, respectively, at an SNR of 0 dB.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**PRA of 64-QAM**} \\ \hline
**No. of Samples** & **0 dB** & **5 dB** & **10 dB** \\ \hline
512 & 98 & 99 & 100 \\
1024 & 99.95 & 100 & 100 \\
2048 & 100 & 100 & 100 \\
4096 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**PRA for BPSK**} \\ \hline
**No. of Samples** & **0 dB** & **5 dB** & **10 dB** \\ \hline
512 & 92.5 & 94.7 & 96.1 \\
1024 & 95 & 97.2 & 98 \\
2048 & 97.5 & 98.2 & 99 \\
4096 & 98.5 & 99.5 & 100 \\ \hline \hline \multicolumn{3}{c}{**PRA for QPSK**} \\ \hline
**No. of Samples** & **0 dB** & **5 dB** & **10 dB** \\ \hline
512 & 94.2 & 96.5 & 97 \\
1024 & 96.7 & 98 & 99.5 \\
2048 & 98 & 99 & 100 \\
4096 & 99 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Percentage recognition accuracy on the Rayleigh channel.
# Weighted Matrix Factorization Recommendation Model Incorporating Social Trust
## 1 Introduction
People's choices and preferences are often influenced by other users in their social networks. For instance, when a user notices that a friend is particularly fond of a certain product, they often develop an interest in it too. By studying the social networks between users, such as friendships and trust, and combining these with traditional recommendation models, the accuracy and diversity of recommendations can be enhanced. This approach also helps alleviate issues, like data sparsity and cold starts, for new users. Currently, researchers have proposed several recommendation methods that integrate social networks. The core of these methods is to utilize the user relationships within social networks to enhance the performance of recommendation models. Some models also combine matrix factorization [8; 9], deep learning [10], and other elements to further improve the performance of recommendation models. However, most of the existing models mainly focus on how to enrich the available data information, such as by integrating social networks and user historical ratings and by deeply mining the explicit or implicit trust relationships among users in social networks. Yet, these models overlook the impact of the varying credibility of the added data information and the amount of noise data carried, which can affect the performance of recommendation models. To address the aforementioned issues, this paper proposes a novel trust-based weighted matrix factorization recommendation model, Trust-WMF. Initially, it uses social networks and user historical ratings to obtain two sets of predicted ratings. Then, employing an adaptive weight strategy, it dynamically merges the overlapping parts of these two sets of predictions to generate a third set. This approach ensures that a user's rating of an item is influenced not only by their own preferences but also by trust relationships within the social network. Subsequently, the matrix factorization model is improved to allow different parts of the ratings to have varying weights during training, enriching matrix information and reducing the impact of noise data. Finally, all three sets of predicted ratings are integrated into the improved weighted matrix factorization model to predict ratings for all unrated items. The main contributions of this paper are as follows:  
1. Before calculating the predicted ratings based on user historical ratings, two filtering layers are added to reduce the impact of noise data on the computation results. 2. Nonlinear fusion of similarity and trust relationships: A dynamically changing weight \(w\) is set to merge the similarity and trust relationships, where the variation of \(w\) is influenced by user historical ratings and the two filtering layers. 3. A new weighted matrix factorization model is proposed: By modifying the loss function of the existing matrix factorization model, this aims to assign different weights to different data during the matrix factorization process.
# Z-OFDM: A New High-Performance Solution for Underwater Acoustic Communication
## 3 Communication System Model
### Performance Analysis  
#### 3.2.1 Channel Capacity  
According to the definition of spectral resolution, an increase in resolution implies a decrease in the spacing of subcarriers, which means that a constant bandwidth can be synthesized by more subcarriers. For the same bandwidth, the channel capacity of the Z-OFDM system increases with the spectral refinement factor \(D\)[45].  
According to the definition given in the literature [46], the OFDM channel capacity can be expressed as  
\[\begin{split} C&=\sum_{l=0}^{k}C_{k}\\ &=\Delta f\sum_{k=1}^{N}log_{2}(1+\frac{P_{k}}{N_{0}})\end{split} \tag{18}\]  
where \(C_{k}\) is the information capacity of the \(k\)-th sub-channel, \(P_{k}\) is the average power of the sub-carriers under the first sub-channel.  
Assuming that the bandwidth of the UWA signal is constant, the relationship between the bandwidth, \(B\), the number of sub-carriers, and the sub-carrier interval should satisfy the description given in Equation (19).  
\[B=N\Delta f \tag{19}\]  
Combining Equations (2), (18), and (19), it is derived that the channel capacity of the Z-OFDM system can be described as  
\[C_{Z}=\sum_{k=1}^{ND}\frac{\Delta f}{D}log_{2}(1+\frac{P_{k}}{N_{0}}) \tag{20}\]  
The power of the channel noise in the \(k\)-th sub-channel is denoted as \(N_{0}\). Figure 3 shows the channel capacity of the Z-OFDM system over AWGN channel. From Figure 3, it can be concluded that the channel capacity of the Z-OFDM system increases as the spectral refinement factor \(D\) increases for the same \(\frac{E_{b}}{N_{0}}\). In contrast, the channel capacity of the FFT-based OFDM system is smaller than that of the Z-OFDM system.  
Figure 3: Average capacity of Z-OFDM system over AWGN channel.