esa-sceva/satcom-chunk-collection huggingface dataset:
# Convolutional Adaptive Network for Link Prediction in Knowledge Bases
## 3 Proposed Method
### Outline of CANet
It adaptively recalibrates the channel dimension characteristic reaction by specifically modeling channel interdependence and enhancing the network capacity. **Projection and Matrix Multiplication**: Then, by flattening and translating the corresponding remapping feature maps into a \(d\)-dimensional space, the matrix \(\mathbf{W}\in\mathbb{R}^{N\cdot H\cdot W\times d}\) is allowed to establish the hidden layer. The tail entity embeddings \(t\) are multiplied with hidden layer vectors, and the CANet score function \(\psi(h,r,t)\) can be written as follows:  
\[\psi(h,r,t)=(\text{vec}(\text{ReLU}(\mathbf{\tilde{F}}))\cdot\mathbf{W}) \cdot\mathbf{t} \tag{9}\]  
where \(\text{vec}(x)\) denotes the flattening operation. **Sigmoid**: The plausibility of the triplet (\(h\), \(r\), \(t\)) can be written as follows:  
\[\rho(e_{s},r,e_{o})=\sigma(\psi(h,r,t)+b)\in(0,1) \tag{10}\]  
where \(\sigma(x)=1/1+\exp(-x)\) describes a mathematically interpretable prediction of whether the triplet (\(h\), \(r\), \(t\)) is valid or not, and \(b\) is the bias term. The scoring procedure in our method uses the 1-N scoring strategy to reduce the convolution operations and accelerate the computation. Unlike the 1-1 scoring, the pair (\(h\), \(r\)) is scored with respect to all tail entities simultaneously. In addition to the 1-1 scoring, the 1-N scoring allows for a substantial reduction in preparation and training time while still improving accuracy.
# Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset
## 6 Analysis and Experimentation
### Empirical Analysis
#### 6.2.3 HTML Analysis  
Job ads composed by ATS systems are written in WYSIWYG editors that output HTML markup. HTML markup inspection conducted on EMSCAD resulted in the following classification rules:  
1. **High emphasized word ratio.** Fraudulent job ads have a high ratio of emphasized text, text wrapped in <b>, <strong>, <em>, and heading HTML tags. Table 5 shows emphasized text statistics in core job fields obtained from our dataset. One can easily notice that fraudulent job descriptions have four times the ratio (40.51%) of legitimate ones (9.8%), whereas in fraudulent job requirements the ratio is tripled (26.38%) compared to legitimate job ads (8.71%).
2. **Absence of valid HTML list formatting in requirements or benefits.** Legit job ads enlist job requirements and benefits wrapped in HTML list elements defined by the <ul>, <ol> and <li> tags, whereas fraudulent ones tend to contain raw text lists separated with dashes or asterisks. With reference to our dataset, 55.78% of non-fraudulent job ads have HTML lists in job requirements and 6.89% in benefits in contrast to 28.00% and 3.11% of non fraudulent job ads in the same fields.
## 2 Related Works
### CFO Case Studies in Multicarrier Modulations
\begin{table}
\begin{tabular}{l l l} \hline
**Papers (Years)** & **Advantages** & **Limitations** \\ \hline
[24] (2006) & \begin{tabular}{l} Used one OFDM training symbol instead of two \\ symbols to perform the CFO estimation for which half \\ of the symbols (odd subcarriers) included \\ null-subcarriers. \\ \end{tabular} & \begin{tabular}{l} The estimation range was limited within two \\ subcarriers’ spacing and should be extended. \\ \end{tabular} \\ \hline  & \
\begin{tabular}{l} The use of some additional even null subcarriers \\ reduced the computation complexity of the Fourier \\ transform processing. \\ \end{tabular} &
\begin{tabular}{l} This even null subcarrier allocation was critical to the \\ system performance. \\ \end{tabular} \\ \hline
[7] (2012) & \begin{tabular}{l} Used the tripartite structure of P1 symbols to derive an \\ optimum ML synchronization for P1 symbol \\ \end{tabular} & \begin{tabular}{l} The P1 symbols were not known a priori by the \\ DVB-T2 receiver. Therefore, in the presence of deep \\ fading, the estimator should be limited. \\ \end{tabular} \\ \hline  & \begin{tabular}{l} Two estimators such as ML and pseudo-ML were \\ proposed. Their performance and their \\ implementation complexity were compared. \\ \end{tabular} & \begin{tabular}{l} DVB-T2 system performance was not evaluated using \\ these estimators. \\ \end{tabular} \\ \hline  & \
\begin{tabular}{l} Rayleigh channel and SFN fading channel \\ environments were considered. The Cramér–Rao \\ bound was estimated for both estimators. \\ \end{tabular} &
\begin{tabular}{l} TU6 channel and various SFN schemes were not \\ considered. It was not performed for the SFN case as \\ the CFO estimators were not suitable for the \\ SFN environment. \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 2: Literature review on CFO case study (with advantages and limitations) in multicarrier modulations.
## 1 Introduction  
The northern pike (_Esox lucius_), as the top predatory species of the fish community, represents the most socio-economic freshwater fish species. They can be found in the cold and temperate regions of the northern hemisphere [1]. The northern pike is popular with consumers because of its fast growth rate, high nutritional value, and excellent taste. However, the fish is highly susceptible to spoilage, as it is high in water content, rich in nutrients, and neutral in pH value and has high endogenous enzyme activity [2]. Previous studies have shown that the spoilage of fish is related to their microbiota composition [3, 4, 5, 6, 7]. Nevertheless, microbiota in fish alters dramatically with storage time, species, environment, processingoperation and other factors, so it is different at different storage stages [3; 5; 8; 9; 10]. The initial microbiota of fish consists of microorganisms from water, capture, treatment, equipment, workers and others. Previous studies have shown that the species composition of fresh grass carp [11], cod loin [12] and raw Atlantic cod [5] included _Acinetobacter_, _Aeromonas_, _Brevundimonas_, _Chryeobacterium_, _Cutibacterium_, _Empedobacter_, _Flavobacterium_, _Geobacter_, _Glutamicibacter_, _Janthinobacterium_, _Lactococcus_, _Photobacterium_, _Pseudomonas_, _Psychrobacter_, _Microbacterium_, _Monxella_, _Shewanella_ and _Soonwooa_. However, only some microorganisms are able to adapt to harsh environments, grow and reproduce. _Acinetobacter_, _Aeromonas_, _Chryeobacterium_, _Flavobacterium_, _Flavobacterium_, _Micrococcus_, _Macrooccus_, _Photobacterium_, _Pseudomonas_, _Psychrobacte_ and _Shewanella_ are the most common freshwater fish microorganisms [3; 13; 14; 15; 16] and can secrete some enzymes to soften fish, produce small molecule peptides [17], free amino acids, biogenic amines and other metabolites [18] and finally produce volatile organic compounds (VOCs) such as hydrocarbons, esters, aldehydes, ketones, acids and others [19], resulting in a decrease in the sensory quality of fish. The dominant microorganisms in the process of aquatic product spoilage are called SSOs (specific spoilage organisms). The number and community proportion of SSOs in the early storage period of aquatic products are very small, but with the extension of storage time, the number of SSOs increases rapidly, and the proportion of colonies increases obviously [20; 21]. Considering that, it is necessary to study the relationship between spoilage organic compounds and volatile organic compounds to identify spoilage markers and to control fish spoilage.  
There are no studies to research _Esox lucius_ microbiota using an HTS (high-throughput amplicon sequencing) approach and potential spoilage markers. Therefore, in this study, the diversity of the microbial community in samples during storage at 4 \({}^{\circ}\)C was evaluated by using HTS technology, the quality changes and VOCs of the samples were determined, and the correlation between microorganisms and volatile organic compounds was further analyzed in order to provide a reference for the preservation of fish fillets under cold storage conditions.
# Ensemble Clustering in GPS Velocities: A Case Study of Turkey
## 3 Results and Discussion  
### How Many Clusters?  
Conceptually speaking, determining the optimal number of clusters is usually performed subjectively. Depending on the distribution pattern, structure, and scale of the parameters in the data set, however, the choice of the best number of clusters poses a bottleneck problem. To determine the goodness of fit or validity of the clustering results, the gap statistic algorithm is used (Figure 4).  
A plot of \(log_{e}(W_{k})\) versus the number of clusters \(k\) for this data is shown in Figure 4a. The null hypothesis in Figure 4a elementally represents the average of 20 different random reference data sets, and the error bars (two standard deviations on either side of the plotted point) correspond to the scatter of those individual reference sets about that mean value. The \(log(W_{k}^{obs})\) values in Figure 4a fall significantly (more than two standard deviations) below the null hypothesis, indicating that clustering is significant for \(k\geq 1\). Figure 4b is a plot of the gap statistic (the difference between the two curves in Figure 4a) for this data; the error bars in Figure 4b refer to two standard deviations on either side of the plotted point. The gap statistic increases as the number of clusters \(k\) is increased from \(k=1\) to 5,  
Figure 4: The gap statistic algorithm: (**a**) Plot of \(log_{e}(W_{k})\) as a function of \(k\), the number of clusters; (**b**) the difference between the two curves in (**a**).  
but it is seen that there is no significant increase beyond \(k=5\). Therefore, it can be revealed that \(k=5\) is the optimum choice of \(k\). This value is similar to the other internal indicator results regarding the determination of the optimum number of clusters [7; 15]. For instance, ref. [15] the clustered velocity field of Turkey with the optimum number of clusters that best represent the data, 5 as they found with the Silhouette Index. Moreover, in [7], the authors used various optimum cluster number determination algorithms such as Elbow, Davies-Bouldin, GAP, and Silhouette to detect the best \(fk\) value that represents the data and they found the value as 5 for this region in a combined data set.
## 3 Results and Discussion
### Fouling Behavior on the PAN-NF Membrane after Backwashing  
#### 3.2.1 Effect of Physical Backwashing on Organic Fouling  
Backwashing is commonly utilized in most HF membrane filtration systems to reduce fouling in cross-flow applications [13; 41; 42; 43]. The modified PAN-NF HF membrane was evaluated for organic fouling with HA and SA. Figure 4a indicates the flux recovery of the modified PAN-NF membrane after physical backwashing. The flux recovered by 20% after HA filtration and 10% after SA filtration. The flux recovery efficiency of the PAN-NF HF membrane did not improve because both HA and SA formed a gel-like sticky fouling layer on the membrane surface [44; 45]. These types of fouling on the membrane surface possibly reduced the flux performance of the PAN-NF HF membrane, and additional force was required to remove the fouling layer from the membrane surface. Therefore, conventional physical backwashing resulted in a lower backwashing effect, and thus, the flux of the PAN-NF HF membrane was not fully recovered after organic fouling by NOM.  
Figure 3: Normalized permeate flux as a function of time for modified and pristine PAN-NF membranes as a function of time. Operating conditions: (**a**) HA and (**b**) SA = 5 mg/L; pressure = 5 bar; stirring speed = 300 rpm; cross-flow velocity = 1 L/min; conductivity = 300 μs/cm; temperature = 25 \({}^{\circ}\)C. The permeate and concentrate were recirculated back to the feed tank during filtration.
## 3 Materials and Methods
### Data Preprocessing  
The meteorological data (average temperature, lowest temperature, highest temperature, average cloudiness and precipitation level), atmospheric information data (sulfurous acid gas, carbon monoxide, ozone, nitrogen dioxide, and micro dust levels), and regional identifiers were selected as candidates for independent variables. Data such as atmospheric information and meteorological information have high uncertainty, so it is necessary to select variables through weighting and correlation analysis [31; 32]. Therefore, for the variables other than the regional identifier, which serves as a simple identifier rather than a numeric number, multicollinearity was evaluated using the variance inflation factor (VIF) index, and the variables that were not in violation were defined as the final independent variables. The lowest temperature and the average temperature had a VIF index of 10 or above and were correlated with the highest temperature; thus, they were excluded. The final independent variables selected included regional identifier, highest temperature, average cloudiness, precipitation level, sulfurous acid gas level, carbon monoxide level, ozone level, nitrogen dioxide level, and micro dust level. Day and season were also included in the final list of independent variables. The independent and dependent variables for each age group are listed in Table 2.
# Measuring Maritime Paint Thickness under Water Using THz Cross-Correlation Spectroscopy
## 4 Conclusions  
This study demonstrated the concept of using THz-CCS to measure paint thickness underwater. A 3D height profile scan of a paint sample revealed that the average thickness of the paint sample across its width was 230.0 \(\upmu\)m. To displace water, a silicone contact patch was created by 3D-printed molds, and it proved to have the correct firmness and an acceptable attenuation coefficient. The silicone contact patch was used in combination with a TOPAS plastic disc to create a water-displacing mechanism. The contact patch proved to be able to displace practically all water. The THz radiation was guided through the TOPAS plastic and the contact patch to interact with the paint and ultimately carry paint information back to a receiver in a reflection setup. Finally, a spectroscopic model was used in comparison to a simple time-of-flight analysis to determine the thickness of a maritime paint layer. The best fit of the spectroscopic model was able to determine the single-layer thickness of paint submerged in water with 1.2% deviation from the 3D height profile scan.  
We expect our results to pave the way for the development of a novel THz-CCS-based device that can monitor and help maintain antifouling layers on ships. This will in turn help the shipping industry reach the emission reduction goals that they have set forward.  
J.H.-N.: writing, data analysis, investigation, and visualizations. J.O.K.: writing, data analysis, investigation, and visualizations. T.B.: supervision, conceptualization, and fundraising. B.H.M.: software and algorithm development, writing, review and editing. S.J.L.: supervision, conceptualization, review and editing, and project administration. P.U.J.: review and editing. All authors have read and agreed to the published version of the manuscript.  
This research has been funded by The Danish Maritime Foundation and DTU Electro.  
Not applicable.  
Data Availability StatementData used in this study will be available upon request.  
This project was solely possible due to having access to the THz-CCS system designed and provided by DTU Electro, as well as the paint samples provided by Jotun.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{table}
\begin{tabular}{c c} \hline
**Type of Measurement** & **Thickness (\(\upmu\)m)** \\ \hline Estimated DFT based on the amount of wet paint applied & 200 \\ \hline Average DFT from Alicona 3D scan & 230.0 \(\pm\) 9.4 \\ \hline DFT from THz-CCS measurement without water & 235.0 \(\pm\) 0.2 \\ \hline DFT from THz-CCS measurement in water & 232.8\({}^{+6.3}_{-3.1}\) \\ \hline \end{tabular}
\end{table}
Table 1: The Alicona 3D thickness denotes the average and standard deviation of DFTs from the cross-sectional point measurements. The THz-CCS measurements were single-point measurements with 68% confidence limits.
## 5 Numerical Results
### Implementation of the Suggested Method on a 20-Unit System Considering Transmission Loss  
This section presents the comparison of the best cost, _CPU_, and \(N_{\textit{fs}}\) from the proposed IALO and other methods with a load power of 2500 MW. Data of this system was taken from [39]. The obtained results for this study are given in Table 4. When compared to IA [39], the suggested method was more powerful for all criteria, such as the best cost with an _ic_ % of 0.016%, _CPU_, and \(N_{\textit{fs}}\). The suggested method was much faster when comparing 4500 and 20,000 fitness evaluations. The outstanding performance cannot be seen when the proposed method was compared to the other remaining methods, but the proposed method was still evaluated to be more powerful. In fact, the best costs of RCSA [29], CBOA [41], and ED-SHO [45] were equal to that of the suggested method, but RCSA, CBOA, and ED-SHO methods used higher values for \(N_{\textit{fs}}\) equaling 10,000, 12,000, and 100,000, respectively. These values were much higher than the 4500 of the IALO. Other values such as the mean cost and the standard deviation reveal the stable search ability of the proposed method since the mean cost and the best cost of the proposed method are approximately the same as those of the other methods, and the standard deviation is nearly zero. The proposed method used the lowest number of fitness evaluations, but it reached the same best cost and the most stable ability. As a result, it can be concluded that the IALO is a potential method for the system with 20 units considering quadratic fuel cost function and power loss constraint.
# Time-Sensitive Networking in IEEE 802.11 be: On the Way to Low-Latency WiFi 7
## 3 IEEE 802.11 be
### PHY Layer  
The ongoing release of the 6 GHz band throughout the world will be of great benefit to WiFi dense scenarios, not only due to the additional 1.2 GHz of available spectrum, but also to the resulting interference reduction among networks/BSSs. The incorporation of the 6 GHz band into IEEE 802.11 be will also encompass channels as wide as 320 MHz, thus enabling higher transmission rates.  
As for the maximum number of spatial streams, it is expected to double its number from 8 in IEEE 802.11 ac/ax to 16 in IEEE 802.11 be, thus further benefiting from fundamental advantages of predominantly indoor WiFi operation: rich scattering, higher angular spreads, lower correlation, and diversity of channels with good propagation conditions.  
The maximum supported modulation size in IEEE 802.11 be is likewise expected to be boosted with the adoption of the 4096-QAM modulation, whose practical use, however, will only be feasible in combination with beamforming.  
All in all, new IEEE 802.11 be PHY features favor low-latency operation, as (1) wider available bandwidth results in faster transmissions and (2) more spatial streams turn into higher rates in the single-user (SU) mode and into more parallel transmissions (with less waiting time in the buffer) in the multi-user (MU) mode.
# Optimizing Weighted Fair Queuing with Deep Reinforcement Learning for Dynamic Bandwidth Allocation
## 6 Discussion
The integration of EWC for continual learning within the DRL algorithm enhances accumulative rewards over time by preserving effective policies from prior network states, leading to enhanced performance as the agent encounters new traffic patterns. By mitigating the problem of catastrophic forgetting, the WFQ continual-DRL algorithm is an effective strategy. 11. The real traffic in a network revealed a higher reward for two real traffic of various categories. On the other hand, they are more bursty with higher variance. Furthermore, they offer unpredictable patterns that the SAC with EWC for the proposed algorithm can exploit, resulting in better learning and higher cumulative rewards than simulation traffic. 12. Continual learning through EWC helps retain practical knowledge across similar queue behaviors in real traffic, enhancing generalization and performance, meaning that the traffic generated in simulation is more aggressive, indicating that the model can handle various patterns and categories of real traffic. Finally, integration of the EWC technique as a continual learning approach into DRL frameworks, realized here in the form of a new algorithm called WFQ continual-DRL for WFQ systems, facilitates adaptive dynamic bandwidth allocation, thereby enhancing the system's ability to maintain optimal performance across varying network conditions. This new approach enables more robust and reliable network management, effectively reducing packet loss and queue lengths and enhancing the overall quality of service (QoS), although in real world conditions.
## 1 Introduction
### Acceptance Phases for Long-Term Technology Acceptance  
To truly understand how people think and feel about technology, we must examine long-term use. After two months, researchers can observe ordinary use beyond the novelty effect [52]. After six months, most users have gone through all acceptance phases, and become fully acquainted with, and understand the technology. It becomes more than an accessory: it becomes a personal object through emotional attachment [9].  
There are many models of long-term technology acceptance. Some models more generally describe pre-acceptance (before using the technology), acceptance (early technology use), and post-acceptance (after initial acceptance (e.g., [53])). Other models include more detailed descriptions of acceptance. For example, the theory of domestication [54] explains the processes with which users give meaning and significance to the technology. The Technology Utilization Theory (TUT) is based on the importance of how technology is used and if it is being fully utilized by users, focusing on Post Usage [53]. The theory of diffusion of innovations [55] explains how users can reinvent or change technology while using it and the organizational process of structuring how technology and social structures change over time. It focuses on the technology's characteristics and environmental aspects rather than predictions of outcomes compared to other acceptance models [35; 56; 57]. The Domestic Robot Ecology (DRE) proposes physical and social space, social actors, and intended tasks to be influential factors of acceptance [58].  
In this paper, we used the Phased Framework of Technology Acceptance (PFTA; [9]) because it builds on prior frameworks (Theory of Domestication, Diffusion of Innovations, and TAM) and long-term study findings of acceptance of technology in participants' homes [52; 58; 59]. The PFTA has six phases of accepting interactive technology: Expectation, Encounter, Adoption, Adaptation, Integration, and Identification. A seventh phase, Non Use, describes when participants abandon use before or after full acceptance [9]. [PERSON] et al. [9] describes each phase and an approximate timeline (Table 1); however, they recognize that not all users reach acceptance phases at the same time, and some users get stuck in Non Use and stop progressing. These acceptance phases are useful in monitoring the progression, digression, and stagnation of a participant's acceptance of technology. We describe them in more detail below. To our knowledge, no other studies have used [PERSON]'s entire PFTA six acceptance phases [9].