esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Analysis
### Experimental Parameter Settings
#### 3.2.1 Parameter Setting for Extraction of Deep Spatial-Temporal Features  
While training the 3D CNN, the video frame is normalized to \(112\times 112\) pixels and every 16 frames are treated as a basic unit to be input to the 3D CNN. During the training process, the iteration count is set to 2000.  
As for the parameters of the LSTM network, the dimension of the output of the first layer, the training batch for the overall network and the iteration count are set to 50, 30 and 500, respectively.  
During the test phrase, every 16 frames is fed as an input to the 3D CNN. The size of feature maps at the second convolutional layer is \(56\times 56\) and the number of channels is set as 64 in the network.  
The features extracted by the 3D CNN are reshaped to [16, 1, 200704], which serves as an input to the trained LSTM network. The features of the first layer of the LSTM network are extracted as the deep spatial-temporal features of a video.  
The deep spatial-temporal features are combined with the parameters in Table 1 to form an input feature parameters vector. For Waterloo Streaming QoE Database-III dataset, the influencing factors include: 50-dimensional deep spatial-temporal features; 12-dimensional features represent video quality; four-dimensional features represent video's fluency and two-dimensional features represent video's volatility. Total dimension of the input feature parameters vector is 68.  
For LIVE-NFLX-II dataset, the input features parameter vector has a total of 62 dimensions, including 50-dimensional deep spatial-temporal features; seven-dimensional features representing video quality (6 video quality levels); three-dimensional features representing video's fluency (no initial buffering time) and two-dimensional features representing video's volatility.
## 4 Performance Analysis
### Security Level  
The security level is a measure of the strength that a cryptographic primitive, such as a cipher or hash function, achieves. The security level of the suggested mechanism gets compared. Other current strategies are compared in order to reveal the efficiency of the suggested improved elliptic curve cryptography algorithm (IECCA).  
The level of security of various encryption methods is examined and contrasted in Figure 4. The analyzed techniques are DES, RSA, AES, and the suggested IECCA. For file size 20 MB, the level of security is 83% of IECCA, 78% of DES, 76% of RSA, and 70% of AES. Likewise, the security level is examined for 40, 60, 80, and 100 MB file sizes. The graph displays the suggested IECCA attains a higher level of security when contrasted with alternate encryption methods.
# A Research on Fault Diagnosis of a USV Thruster Based on PCA and Entropy
###### Abstract  
This study focuses on faults in the thrusters of unmanned surface vehicles, which are fatal to the integrity of their missions. As for the fault conditions, the breakage of the thruster blade and the entanglement of floating objects were selected, and a data-driven method was used to diagnose the faults. In the data-driven method, it is important to select the sensitive fault feature. In this study, vibration, current consumption, rotational speed and input voltage were selected as fault features. An experiment was conducted in an engineering water tank to obtain and analyze data on fault conditions to verify the validity of the selected features. In addition, a new fault diagnosis algorithm combining principal component analysis and Shannon entropy was applied for analyzing the correlations among fault features. This algorithm reduces the dimensionality of data while preserving their structure and characteristics, and diagnoses faults by quantifying entropy values. A fault is detected by comparing the entropy value and a predetermined threshold value, and is diagnosed by analyzing the entropy value and visualized 2D or 3D principal component results. Moreover, the fault diagnosis performance of the unmanned surface vehicle's thruster was verified by analyzing the results for each fault condition.  
USV; underwater thruster; fault diagnosis; PCA; Shannon entropy
# Early Neighbor Rejection-Aided Tabu Search Detection for Large MIMO Systems
## 2 Background  
### MIMO System Description  
In a MIMO system with \(N_{t}\) transmit and \(N_{r}\) receive antennas, the received signal vector \(\mathbf{\tilde{y}}=[\hat{y}_{1},\hat{y}_{2},\ldots,\hat{y}_{N_{r}}]^{T}\) is given by  
\[\mathbf{\tilde{y}}=\mathbf{\tilde{H}}\mathbf{\tilde{s}}_{t}+\mathbf{\tilde{n}}, \tag{1}\]  
where \(\mathbf{\tilde{s}}_{t}=[\hat{s}_{t,1},\hat{s}_{t,2},\ldots,\hat{s}_{t,N_{t}}]^{T}\) is the transmitted signal vector with each component independently drawn from a complex constellation, such as quadrature amplitude modulation (QAM), and \(\mathbf{\tilde{H}}\) denotes an \(N_{r}\times N_{t}\) channel matrix, where \(\hat{h}_{i,j}\) is the complex channel gain between the \(j\)th transmit antenna and \(i\)th receive antenna. Furthermore, \(\mathbf{\tilde{n}}=\left[\hat{n}_{1},\hat{n}_{2},\ldots\hat{n}_{N_{r}}\right]^ {T}\) is a noise vector consisting of independent and identically distributed additive white Gaussian noise (AWGN) samples with zero mean and unit variance. We assume that the channel matrix is perfectly known at the receiver.  
The complex system model (1) can be transformed to its equivalent real signal model \(\mathbf{y}=\mathbf{H}\mathbf{s}_{t}+\mathbf{n}\), i.e.,  
\[\left[\begin{array}{c}\Re(\mathbf{\tilde{y}})\\ \Im(\mathbf{\tilde{y}})\end{array}\right]=\left[\begin{array}{cc}\Re( \mathbf{\tilde{H}})&-\Im(\mathbf{\tilde{H}})\\ \Im(\mathbf{\tilde{H}})&\Re(\mathbf{\tilde{H}})\end{array}\right]\left[ \begin{array}{c}\Re(\mathbf{\tilde{s}}_{t})\\ \Im(\mathbf{\tilde{s}}_{t})\end{array}\right]+\left[\begin{array}{c}\Re( \mathbf{\tilde{n}})\\ \Im(\mathbf{\tilde{n}})\end{array}\right]. \tag{2}\]In (2), the dimension of the matrices and vectors doubles compared to (1), i.e., \(\mathbf{s}_{t}\) contains \(N=2N_{t}\) elements, and the \((M\times 1)\)-received signal vector \(\mathbf{y}\), \((M\times N)\)-channel matrix \(\mathbf{H}\), and \((M\times 1)\)-noise signal vector \(\mathbf{n}\) also double their dimensions, such that \(M=2N_{r}\).  
Based on the signal model (2), the optimal solution \(\mathbf{\hat{s}}_{\text{ML}}\) can be determined as  
\[\mathbf{\hat{s}}_{\text{ML}}=\arg\min_{\mathbf{s}\in\Omega^{N}}\|\mathbf{y}- \mathbf{H}\mathbf{s}\|^{2}, \tag{3}\]  
where \(\|\mathbf{y}-\mathbf{H}\mathbf{s}\|^{2}\) is the ML metric value for \(\mathbf{s}\), and \(\Omega\) denotes the set of real entries in the constellation, e.g., \(\Omega=\{-3,-1,1,3\}\) in the case of 16-QAM.
## 5 Application of Marine Accident Occurrence Prediction Model and Results
### Test for Predicting Marine Accident Probability  
In Section 5.3, we discussed the training of a model for predicting the probability of MTSA-related accidents during coastal passenger ship operations based on the SMOTE-applied training set. In this section, the trained model is applied to the 2022 MTSA-related route information for testing, as described in Section 4.2. This process was performed to estimate the probability of related accidents, assuming that the respective project site did not perform MTSA. The MTSA-related route information was divided into individual groups consisting of ship-route-sailing information, and the predicted probabilities of MTSA-related accident occurrences were derived for each group after applying the model. To ensure the accuracy of the experimental results, probabilities below 0.005 were set to zero and the remaining values were rounded to the third decimal place. The distribution of the predicted MTSA-related accident probabilities for the 101 data groups is illustrated in Figure 11.  
Figure 10: Illustration of feature importance.
## 3 Simulated Experiment
### Engineering Application Experiments and Results
When k = 1, the maximum RE values of the parameter inversion results are 3.78%, 5.65%, and 3.83%, respectively, and the RMSE values are 1.6 mm, 9.2 mm, and 5.0 mm, respectively. When k = 0.1, the maximum RE values of the parameter inversion results are 2.67%, 4.61%, and 1.24%, respectively, and the RMSE values are 1.4 mm, 2.8 mm, and 1.7 mm, respectively. When k = 0.01, the maximum RE values of the parameter inversion results are 2.94%, 3.24%, and 3.77%, respectively, and the RMSE values are 7.5 mm, 3.9 mm, and 5.8 mm, respectively. When k = 0.001, the maximum RE values of the parameter inversion results are 5.29%, 4.88% and 3.07%, respectively, and the RMSE values are 12.9 mm, 4.8 mm and 2.8 mm, respectively. From these results, it can be concluded that the accuracy of the parameter inversion is relatively limited due to the change in k. As k decreases, the overall trend of the accuracy of parameter inversion shows a pattern of first decreasing, then increasing. When the value of k is set to 0.1, the proposed method has the highest accuracy in inverting the parameters of the PIM. Therefore, in the aforementioned simulation experiments, k was selected as 0.1.
## 1 Introduction  
Over the last decade, new goals have been established in sample preparation, such as the use of smaller initial samples sizes, improving the selectivity of the extraction process, and minimizing the amount of glassware and organic solvents used [1]. In this regard, the solid-phase micro-extraction technique (SPME) [2], where the analytes are retained in a micro-extraction phase and then desorbed for their analytical determination as concentrated extracts, has undergone huge development. The use of SPME has been widespread; however, it is not free of some disadvantages, such as difficulties for direct immersion of the fibre coating in complex matrices that can damage the sorbent, as well as the limited selectivity of the available sorbents.  
Molecular imprinting is a well-known technology that provides improvement to the selectivity of the extraction process. Molecularly imprinted polymers (MIPs) are synthetic materials obtained by copolymerizing a monomer with a cross-linker in the presence of a template molecule. Removal of the template once polymerization is complete leaves cavities that are complementary in size, shape and functionality to the analyte, allowing its further selective rebinding in a complex matrix. Thus, MIPs can selectivity rebind the template and related compounds that are separated from matrix-interfering compounds [3; 4; 5].  
[PERSON] et al. described for the first time the preparation of a MIP-coated silica fibre for the SPME of brombuterol from human urine [6]. Later, [PERSON] and coworkers [7; 8] and [PERSON] and [PERSON] [9] proposed another approach, where the MIP fibre is prepared inside fused silica capillaries. Just recently, the use of a hollow fibre (HF) membrane to protect the MIPs was described in the literature [10]. This approach has the advantage that the organic solvent is immobilized within the pores of the membrane of the HF, which contains the MIP polymer in its lumen. In this way, a supported liquid membrane between the sample and the MIP is created, and is used to protect the polymer from the sample matrix. Thus, the analytes are firstly extracted from the samples into the organic solvent, where the MIPs subsequently perform selective recognition. The polymer inside the HF can be in the shape of a monolith, as described for the analysis of thiabendazole in orange juices [11] and triazine pesticides in lake water [12]. Alternatively, MIP can be prepared as microspheres to be introduced into the lumen of the HF; this methodology was developed in our laboratory, and has been used for the determination of sulfonamides [13] and triazines [14] in environmental waters.  
In the present work, MIP microspheres were prepared by precipitation polymerization, packed inside the hollow fibre, and used for selective extraction of triazines in soil sample complex extracts. MLRs of triazines in these sample have not been established; however, several data can be found in the literature [15] estimating the PNCEs (Predicted Non-Effect Concentration) in the low ng g\({}^{-1}\) concentration range.
## 7 Conclusions  
In this paper, we propose a fine-grained probe-scheduling approach based on information feedback to identify large-scale IoT devices. First, we model the probe surface as three layers for IoT devices and define their relationships. Then, we improve the policy gradient algorithm to optimize the probe policy and generate the optimal probe sequence for the target device. We implement a prototype system and evaluate its effectiveness through real-world experiments. Our approach can achieve success rates of 96.89%, 93.43%, and 83.71% for device brand, model, and firmware version, respectively, and it reduces the identification time by 55.96%.  
Conceptualization, C.L. and B.Y.; methodology, C.L.; software, W.X.; validation, C.L. and B.W.; data curation, C.L.; writing--original draft preparation, W.P.; writing--review and editing, C.L., B.Y., W.X., B.W. and W.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the the Natural Science Foundation of China (61902416, 61902412) and Natural Science Foundation of Hunan Province in China (2019J)[50729].  
Not applicable.  
**Data Availability Statement:** We will release all data and the analysis script to replicate the results of this work and to encourage further studies: [[https://github.com/sherlocklchen/real-IoT-device-assets](https://github.com/sherlocklchen/real-IoT-device-assets)]([https://github.com/sherlocklchen/real-IoT-device-assets](https://github.com/sherlocklchen/real-IoT-device-assets)).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 7: Time efficiency compared with another work: (**a**) success rate of brand; (**b**) success rate of model; (**c**) success rate of version.
# 6G Goal-Oriented Communications: How to Coexist with Legacy Systems?
###### Abstract  
6G will connect heterogeneous intelligent agents to make them natively operate complex cooperative tasks. When connecting intelligence, two main research questions arise to identify how artificial intelligence and machine learning models behave depending on _(i)_ their input data quality, affected by errors induced by interference and additive noise during wireless communication; _(ii)_ their contextual effectiveness and resilience to interpret and exploit the meaning behind the data. Both questions are within the realm of _semantic_ and _goal-oriented_ communications. With this paper, we investigate how to effectively share communication spectrum resources between a legacy communication system (i.e., data-oriented) and a new goal-oriented edge intelligence one. Specifically, we address the scenario of an enhanced Mobile Broadband (eMBB) service, i.e., a user uploading a video stream to a radio access point, interfering with an edge inference system, in which a user uploads images to a Mobile Edge Host that runs a classification task. Our objective is to achieve, through cooperation, the highest eMBB service data rate, subject to a targeted _goal effectiveness_ of the edge inference service, namely the probability of confident inference on time. We first formalize a general definition of a goal in the context of wireless communications. This includes the goal effectiveness, (i.e., the goal achievability rate, or the probability of achieving the goal), as well as _goal cost_ (i.e., the network resource consumption needed to achieve the goal with target effectiveness). We argue and show, through numerical evaluations, that communication reliability and goal effectiveness are not straightforwardly linked. Then, after a performance evaluation aiming to clarify the difference between communication performance and goal effectiveness, a long-term optimization problem is formulated and solved via Lyapunov stochastic network optimization tools to guarantee the desired target performance. Finally, our numerical results assess the advantages of the proposed optimization and the superiority of the goal-oriented strategy against baseline 5G-compliant legacy approaches, under both stationary and non-stationary communication (and computation) environments.  
Keywords:goal-oriented communications; 6G; connecting intelligence; edge inference; edge artificial intelligence +
Footnote †: journal: _et
## 2 Materials and Methods
### Predictive Algorithm Selection  
#### 2.2.1 Latin Hypercube Sampling  
To address the inefficiency of conventional Monte Carlo (MC) methods in high-dimensional spaces, Latin Hypercube Sampling (LHS) employs a stratified sampling strategy to achieve superior spatial coverage uniformity with fewer samples. As the dimensionality increases, LHS maintains a uniform distribution across all dimensions, thereby mitigating the \"curse of dimensionality\" inherent to MC methods at higher dimensions [40; 41; 42]. As illustrated in Figure 3, significant disparities exist between simple random sampling and LHS. To ensure sampling consistency and generalizability, this study adopts LHS to alleviate computational burdens.  
The LHS workflow proceeds as follows:  
* For each input variable, partition its cumulative probability range into N equiprobable intervals based on its probability distribution.
* Randomly draw one cumulative probability value from each interval, yielding N stratified samples per variable.
* Convert the sampled cumulative probabilities to actual variable values using the inverse cumulative distribution function.
* Perform random pairwise permutations of sampled values across variables to eliminate spurious correlations.
* The resultant N samples form a multidimensional input space with minimized spatial clustering.  
In this study, 400 groups of unrepeated samples are generated through Latin hypercubic sampling based on the number of relevant cleaning parameter variables and the corresponding value range of each variable [29], and the parameterization process of model construction, meshing, and numerical simulation is adopted for each group of samples, to obtain the objective function values corresponding to the samples in each group, i.e., the evaluation indexes (the peak wall shear stress \(\tau_{\text{max}}\), action area \(A_{\text{eff}}\), and cleaning water consumption \(Q_{\text{total}}\)); all the samples and corresponding objective function values are jointly established into a database of the aerated jet cleaning model for subsequent structural optimization work. All the samples and the corresponding objective function values are jointly established into a database of aerated jet cleaning models for subsequent structural optimization work.  
Figure 3: Simple random sampling and Latin hypercube sampling.
# Biosynthesized Zinc Oxide Nanoparticles Disrupt Established Biofilms of Pathogenic Bacteria
## 2 Materials and Methods
### Assays for the Inhibition of biofilms
#### 2.6.2 Inhibition of Biofilms on Solid Surface  
The effect of ZnO-NPs on the biofilm-forming ability of test bacteria on glass surface was studied using microscopic tools such as confocal microscopy, light microscopy, and scanning electron microscopy. The detailed procedure for each microscopic analysis is described below.  
The analysis of biofilm inhibition on glass surface was performed in a 24-well tissue culture plate [21]. The test bacteria were grown with respective highest sub-MIC of ZnO-NPs. The highest sub-MIC for _E. coli_, _S. aureus_, and _P. aeruginosa_ were 100, 100, and 200 \(\upmu\)g/mL, respectively. Briefly, ZnO-NPs were diluted to desired concentrations in LB broth in wells of a 24-well plate. Diluted log phase culture of bacteria was added as inoculum. Glass coverslips of 1 cm\({}^{2}\) were placed in slant position in each well, and plates were incubated at 24 h in a static incubator at 37 \({}^{\circ}\)C. The coverslips were removed and washed gently with buffer (phosphate) to remove the loosed attached cells. The biofilms were stained with crystal violet (0.1% aqueous solution) for 20 min followed by washing with the phosphate buffer. Finally, the glass coverslips were dried in air at room temperature and used for visualization. The visual inspection of biofilms was performed under light microscope equipped with a digital camera. The light microscopic images were taken at 40X.  
Confocal Microscopic Analysis of Biofilms Inhibition  
For confocal microscopic examination of biofilms, the bacteria were grown without and with ZnO-NPs as mentioned above in the light microscopic section. The biofilms of glass coverslips were given a gentle wash to eliminate the loosely attached bacteria cells and then stained with acridine orange (0.1%) for 30 min in the dark. The glass coverslips were washed to remove excess amount of the dye and then air-dried in the dark. The confocal microscopic analysis was performed using Zeiss LSM780 confocal laser scanning microscope, and the images were recorded at 63X.  
Scanning Electron Microscopic Analysis of Biofilms Inhibition  
For scanning electron microscopic (SEM) analysis of the biofilms inhibition, test bacteria were cultured, as mentioned above. After completion of incubation, glass coverslips were washed using autoclaved phosphate buffer. The biofilm of glass surface was fixed with 2.5% glutaraldehyde at 4 \({}^{\circ}\)C overnight. The biofilms were then dehydrated using ethanol gradient from 10% to 100% with each gradient for 15 min. Glass coverslips were dried followed by coating with gold. SEM images were taken using JOEL-JSM 6510 LV at 2000-2500X magnification and 15 kV.
## 2 Materials and Methods
### ABTS Radical Cation Scavenger  
The gelatin ABTS radical scavenging capacity was assessed by using a modified version of the method of Re [20]. A stock solution of the ABTS radical was prepared by mixing 5.0 mL of a 7 mM ABTS solution with 88 \(\upmu\)L of 139 mM potassium persulfate that was kept in the dark at room temperature for 18 h. An aliquot (0.5 mL) of the stock solution was diluted with a phosphate buffer (50 mM, pH 7.4) to prepare the working solution of the ABTS radical with an absorbance of 0.70 \(\pm\) 0.02 at 734 nm. The gelatin solutions (5% p/v) were prepared in the same buffer, and the buffer was used as a blank. For this analysis, a 20 \(\upmu\)L aliquot of the sample was mixed with 280 \(\upmu\)L of the ABTS radical working solution, which was incubated for 30 min at room temperature in the dark, and then the absorbance was measured at 734 nm by using a microplate reader (Thermo Scientific Multikskan Sky; USA). A standard curve of Trolox was used to express the results as Trolox equivalents/mg sample (TE/mg). Six replicates were carried out for each sample.