esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Human Health Risks Assessment  
In total, 92 out of 651 (-14%) records of BaP in bivalves exceed the limit of 0.005 mg/kg set by the EU [11] (Table 1), with most of these records (57) being on Mediterranean mussels caught in the Tyrrhenian Sea. Conversely, none of the 26 records of PAH4 in bivalves exceed the limit of 0.03 mg/kg set by the EU [11] (Table 1).  
In this database, assuming a consumption rate equal to the average per capita consumption in Italy (FAOSTAT, 2018), ELCR values range from a minimum of 1.36 \(\times\) 10\({}^{-10}\) to a maximum of 4.52 \(\times\) 10\({}^{-4}\), which is reached by a record of dibenzo(a,i)pyrene (DaiP) in Mediterranean mussel. A total of 324 out of 5275 records (-6%) exceeds the threshold value of 10\({}^{-5}\) for ELCR. Records exceeding ELCR threshold value are mostly on Mediterranean mussel (225), while dibenzo(a,h)anthracene (DahA) is the compound most frequently associated with such high ELCR values.  
THQ values range from a minimum of 2.2 \(\times\) 10\({}^{-9}\) to a maximum of \(\sim\)0.13, which is reached by a record of BaP in Mediterranean mussel. Therefore, none of the records in this database exceeds the threshold value of 1 for THQ. THQ distributions for some of the most consumed species in Italy [55] are reported in Figure 6. As can be seen, among those included in the graph, European anchovy (_Engraulis encrasicolus_), blue mussel (_Mytitlus edulis_) and European hake (_Merluccius merluccius_) are the first three species showing the highest median THQ.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Period** & **PAHs Class 1** & **C\({}_{\text{mean}}\) 2** & **C\({}_{\text{min}}\) 3** & **C\({}_{\text{max}}\) 4** & **Depth\({}_{\text{min}}\) 5** & **Depth\({}_{\text{max}}\) 6** & **Year\({}_{\text{min}}\) 7** & **Year\({}_{\text{max}}\) 8** & **Lat\({}_{\text{min}}\) 9** & **Lat\({}_{\text{max}}\) 10** \\ \hline \multirow{3}{*}{Autumn}{/Winter} & LMW & 0.00434 & 0.00000 & 0.12000 & 0.10 & 49.02 & 1999 & 2017 & 38.54 & 43.50 \\  & MMW & 0.02649 & 0.00000 & 0.96000 & 0.10 & 49.02 & 1999 & 2017 & 38.54 & 43.50 \\  & HMW & 0.0953 & 0.00000 & 0.20237 & 0.10 & 49.02 & 1999 & 2017 & 38.54 & 43.50 \\ \hline \multirow{3}{*}{Spring/Summer} & LMW & 0.00685 & 0.00000 & 0.20000 & 0.19 & 108.00 & 1981 & 2017 & 38.54 & 44.42 \\  & MMW & 0.01234 & 0.00000 & 1.05960 & 0.19 & 108.00 & 1981 & 2017 & 38.97 & 44.42 \\  & HMW & 0.00766 & 0.00000 & 0.18040 & 0.19 & 108.00 & 1981 & 2017 & 38.54 & 44.42 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary statistics on LMW (low molecular weight), MMW (middle molecular weight) and HMW (high molecular weight) PAHs in Mediterranean mussels caught in different seasons in the Tyrrhenian Sea. Concentrations are in mg/kg wet weight; sampling depth is in meters; latitude are in decimal degrees. Reported statistics were calculated after missing data removal from concentration, sampling depth, sampling year and latitude columns.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 7 Challenges and Future Research Directions
### Time/Frequency/Space Dimensions  
In FANETs, there are still serious challenges in the time, frequency, and space domains that should be addressed carefully. For instance, the time delay propagation of FANETs can vary from 1 ms to several milliseconds according to their altitudes. Therefore, transceivers should be able to dynamically adjust the uplink time advance parameters within a large range [3]. The time delay also impacts the HARQ-ACK process [173], although the HARQ-ACK can be disabled in specific scenarios at the cost of reliability. In the frequency domain, because of the fast and variable speed of a large number of UAVs, large Doppler frequency shifts may require complex frequency pre-compensation techniques at the receivers [83]. Moreover, there is a need for multiplexing and dynamic spectrum sharing to control the interference in highly varying network topologies. Likewise, in the space domain, frequent handover, location prediction, and trajectory optimization are challenging issues that require further research with respect to the specific application requirements.
# Efficient V2V Communications by Clustering-Based Collaborative Caching +
## 3 The Proposed Method
### Cluster Construction and Maintenance
#### 3.1.3 Splitting/Merging Clusters  
If the distance between two adjacent CHs changes drastically, it is necessary to split a cluster into two (when the distance is too large) to avoid relay failure or to merge two clusters (when the distance is too short) to improve relay efficiency.  
To this end, a CEP CH ID is added to the beacons sent from CHs. A CEP CH is a neighbor CH of the sender CH that satisfies the following conditions.  
* The CEP CH, as a relay of the sender CH, has the shortest distance to the CH in either the front or the back of the CH.
* The connection between the CEP CH and the sender CH is predicted to break within \(t_{\text{break}}\).  
A beacon may contain at most two CEP CH IDs to notify the potential relay failure of the sender CH briefly. Figure 2 shows an example, where CH1, predicting that the connection between itself and CH2 will break soon, sends a beacon containing CH2's ID as a CEP CH ID.  
When a CM receives a beacon from any CH containing a CEP CH ID and it can communicate with both the sender CH and the CEP CH, it tries to leave its cluster immediately and create a new one. Because multiple CMs simultaneously receive a beacon with a CEP CH ID, each CM calculates its CH suitability according to (1). Here, \(N_{i}\) denotes the number of neighbors of vehicle \(i\), and \(\bar{d_{i}}\) is the average distance from vehicle \(i\) to its neighbors.  
\[s_{i}=\frac{N_{i}}{\bar{d_{i}}}. \tag{1}\]  
Then, the vehicle with the largest \(s\) becomes a new CH, and other vehicles choose to become its CMs or remain as the CMs of the previous clusters. Figure 3 shows that CH3 constructs a new cluster based on information in a beacon following the actions in Figure 2.  
Two clusters start to merge when all of the following conditions are satisfied.  
* The CHs of both clusters are moving in the same direction.
* A CH can communicate with all of the CHs that the other can.
* The distance between the two CHs is less than or equal to \(d_{\text{cluster}}\).
* The CH has not sent a merge request to another CH.  
Figure 2: CH sends a beacon containing a connection-expired-predicted CH ID.  
When CH1 decides to merge its cluster with CH2's, it sends a merge request to CH2 and schedules a merge-cancel timer. CH2 accepts the request if the following conditions are satisfied and ignores the request otherwise.  
* The cluster merging requirements are satisfied.
* CH2 is not requesting to merge clusters with another CH.  
Once CH2 accepts the request, it selects a new CH from CH1 and CH2 by calculating both of the CH's CH suitability according to (1) and choosing the CH with higher suitability. Then, it sends CH1 a merge ACK packet containing the information on whether CH2 becomes a CH or CM. All CMs are notified of the cluster merging by CH's beacon or the merge ACK packet.  
Cluster merging is canceled if the merge-cancel timer expires.
# Modeling and Analyzing Preemption-Based Service Prioritization in 5G Networks Slicing Framework
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPPThird Generation Partnership Project  
5G Fifth generation  
BE Best effort  
BG Best effort with minimum guaranteed  
BS Base station  
CN Core network  
DN Data network  
E2E End-to-End  
FOV Field of vision/view  
GB Guaranteed bit rate  
GSM Groupe Speciale Mobile  
IoT Internet of Things  
IoV Internet of Vehicles  
MNO Mobile network operator  
NS Network slicing  
NSI Network slice instance  
PP Pre-emption-based prioritization  
QoS Quality of Service  
QS Queueing system  
RAC Radio admission control  
RAT Radio access technology  
RR Resource reservation  
TN Transport network  
VoIP Voice over Internet Protocol  
VR Virtual reality
# The Effects of AI-Driven Face Restoration on Forensic Face Recognition
## 4 Results
### The Influence of Restoration Methods on the Recognition Performance of Face Recognition Systems
#### 4.2.2 Comparison of Four Different Restoration Methods for Blurriness Level 5  
Table 3 presents the comparison results of four different restoration methods for blurriness level 5.  
The result of the homogeneity of variance test indicated that the variances were homogeneous (F(3, 373) = 20.792, \(p=0.075\)). Similarly, the inter-group P-value showed significant differences among the four groups. Pairwise comparisons between different restoration methods revealed that, compared to restoration 4, restoration 2 obtained a higher quantitative value for face similarity comparison, while no significant difference was observed between restoration 1 and restoration 3 (\(p=0.183\)). Significant differences were found among the rest of the pairwise comparisons. By comparing Tables 2 and 3, we can conclude that restoration 2 achieved the best image restoration effect. In practical applications, it is necessary to choose the most suitable image restoration method based on specific circumstances and requirements.  
The Impact of Blur Level and Restoration Method on the Recognition Performance of the Face Recognition System  
Two-way ANOVA was used to compare the effect of the ambiguity level and restoration method on recognition performance. The results indicated that the main effect is statistically significant, meaning that both factors have a significant impact on recognition performance. Additionally, the P-value for the interaction effect between blur level and restoration method was less than the significance level (\(p<0.05\)), indicating a significant interaction between these two factors. Next, we analyzed the data from each face recognition system to determine the specific differences between the groups.
# Protection against Shiga Toxins
## 2 Compounds that Protect Cells against Stx
### Furin Inhibitors  
Furin is a type I transmembrane serine protease that activates precursors of different physiologically important proteins. Although furin is mainly located in the Golgi and _trans_-Golgi network, it also circulates through the endosomal system to the cell surface and back to the Golgi [179], and may also be secreted as a soluble truncated active enzyme [180]. In addition to its physiological role, furin activates numerous toxic proteins, including Stxs [95]. Moreover, furin has been implicated in various human diseases, including cancer, osteoarthritis, atherosclerosis, diabetes and neurodegenerative disorders [181; 182; 183; 184]. Although the complete knockout of the _fur_ gene, which encodes furin, is embryonically lethal to mice, the specific inhibition of furin by polyarginine inhibitors has been shown to be well tolerated in adult animals [185; 186], indicating that short term inhibition of furin might be exploited for treatment. Thus, various furin inhibitors have been developed and investigated for their potential therapeutic applications during the last years [184; 187; 111; 112; 183]. For instance, the furin inhibitor hexa-d-arginine amide has been demonstrated to improve the survival of mice challenged with _Pseudomonas aeruginosa_ exotoxin A, which requires furin-mediated cleavage for toxicity [185]. Later, the same compound was shown to delay anthrax toxin-induced toxemia both in cells and in rats [186], supporting the therapeutic potential of furin inhibitors for the treatment of infectious diseases.  
A number of peptidomimetic furin inhibitors have been developed and optimized to improve the activity and stability of the compounds [111], and some of these compounds have been found to protect against Shiga toxin without showing significant toxicity to cells [111]. The most effective analogue for the protection against Shiga toxin was 4-(guanidinomethyl)phenylacetyl-Arg-Val-Arg-4-amidinobenzylamide (called No. 24 in the original article), which reduced HEp-2 cells sensitivity to Shiga toxin by approx. 6-fold, when added to the cells 30 min before a 4 h incubation with unnicked Shiga toxin [111]. However, it has not yet been investigated whether cells are protected against longer challenges with the toxin. It has been shown that the Shiga toxin may be processed by other cellular proteases, but more slowly [95; 39]. Thus, it is still not clear whether furin inhibitors help to prevent or treat HUS in humans infected with Stx-producing bacteria.
# Thrust and Pressure Control in a Solid Propulsion System via Reinforcement Learning
## 2 Model and Algorithm
### Reinforcement Learning Trainer  
The PPO algorithm consists of three neural networks: \(\text{Actor}_{\text{learn}}\), \(\text{Actor}_{\text{choose}}\), and \(\text{Critic}\). It aims to improve strategy actions by computing action mean and variance and then selecting the final action via random sampling. Advantage clipping limits strategy updates to ensure robustness [26]. The basic PPO structure is shown in Figure 4.  
Figure 3: Control system model of the SADPS (solid line: gas path; dashed line: signal path).  
The \(\pi_{\theta_{old}}(a_{t}|s_{t})\) chooses the action based on the \(s_{t}\) and receives a reward. The \(V_{\phi}(s_{t})\) evaluates state values \(V_{t}\) and outputs the TD error. During updates, \(\widehat{A}_{t}\) is backpropagated and clipped within the range of \((1-\epsilon,1+\epsilon)\) to refine both the Critic and Actor\({}_{\text{learn}}\) networks. Periodically, the Actor\({}_{\text{learn}}\) parameters are copied to Actor\({}_{\text{choose}}\) for better action selection.  
To accelerate training, we applied the following techniques to improve the efficiency of PPO:  
* **Network Simplification: We replaced the neural network hidden layers of widths 256 and 128 with those of widths 64 and 64, thereby reducing the parameters by 87% and the inference time by over 20% without compromising control effectiveness.**
* **Generalized Advantage Estimation**[27]: We utilized generalized advantage estimation during network weight updates. Calculating the mean and variance of the advantage function across the entire batch made the training process smoother and more stable.
* **Learning Rate Adjustment: We set the initial learning rates for the Actor and Critic to \(1\times 10^{-4}\) and \(3\times 10^{-4}\), respectively. Then, we reduced them exponentially by an order of magnitude over 1000 training epochs for more stable convergence.**
* **Random Initialization and Gradient Clipping: We initialized the network parameters with samples from a standard normal distribution and applied gradient clipping once before updating the network.**
* **Activation Function Change**[28]: We replaced the ReLU with Tanh in the hidden layers to avoid the vanishing gradient problem associated with negative values.
## 2 Methods
### How to Calculate the PV Output by MS2E Method  
#### 2.3.1 Extraction of Atmospheric Parameters  
Sunlight undergoes various losses before reaching the ground surface. Among them, AOD mainly causes scattering and absorption of the short-wavelength range of the solar spectrum and precipitable water causes absorption of the long-wavelength range of the solar spectrum. The atmospheric transmittances for aerosol scattering and absorption and water vapor absorption were calculated using Equations (1) and (2),  
\[Ta\lambda=\ \exp(-\beta\cdot\lambda^{-\alpha_{n}}\cdot M), \tag{1}\]  
Figure 1: Three types of photovoltaic (PV) modules installed at University of Miyazaki.  
Figure 2: Spectro-radiometers installed at the University of Miyazaki.  
\[T_{\rm wA} =\ \exp\Biggl{(}\frac{-0.2385\cdot a_{\rm wA}\cdot W\cdot M}{\left(1+20.07\cdot a_{\rm wA}\cdot W\cdot M\right)^{0.45}}\Biggr{)}, \tag{2}\]  
where \(\beta\), \(W\), \(a_{\rm wA}\), and \(M\) represent the AODs at 500 nm wavelength, the precipitable water in the vertical path, water absorption coefficient as a function of wavelength, and air mass, respectively. When a single value of \(\alpha_{\rm n}\) is used to represent the rural aerosol model, the value should be \(\alpha_{\rm n}=1.140\)[21], which is used as the reference spectrum [22]. From [PERSON]'s spectral model, the direct spectrum (\(I_{\rm dA}\)) was obtained by Equation (3),  
\[I_{\rm dA}=H_{0\lambda}\cdot D\cdot T_{\rm aA}\cdot T_{\rm wA}\cdot T_{\rm rA} \cdot T_{\rm oA}\cdot T_{\rm m\lambda}, \tag{3}\]  
where \(H_{0\lambda}\) is the extraterrestrial solar spectrum and \(D\) is the correction factor for the earth-sun distance. \(T_{\rm aA}\), \(T_{\rm wA}\), \(T_{\rm rA}\), \(T_{\rm oA}\), and \(T_{\rm m\lambda}\) are the transmittance functions of the atmosphere at wavelength \(\lambda\) for Rayleigh scattering, aerosol scattering, water vapor absorption, ozone absorption, and mixed gas absorption, respectively. The global tilted spectrum was obtained by Equation (4),  
\[I_{\rm gA\ clear}=I_{\rm dA}\cdot\cos\theta+I_{\rm aA}\cdot\frac{1+\cos(t)}{2} +I_{\rm rA}\cdot\frac{1+\cos(t)}{2}+I_{\rm gA}\cdot\frac{1-\cos(t)}{2}, \tag{4}\]  
where \(I_{\rm aA}\), \(I_{\rm rA}\), and \(I_{\rm gA}\) are the spectrum on a horizontal surface at wavelength \(\lambda\) for aerosol scattering, Rayleigh scattering, and reflection at the ground, respectively. \(\theta\) is the incident angle, and \(t\) is the tilt angle.  
In our study, the AOD and precipitable water were extracted using a model that minimizes the deviation of the global spectrum between the experimental values measured and the values estimated using [PERSON]'s spectrum model, as shown in Equation (4). The estimated global spectrum is a function of the variables AOD and precipitable water. The deviation based on the expressions of R\({}^{2}\) was used.
## 3 Results and Discussion
### Creep Coefficient  
Figure 5 plots the measured creep strain for tested SCC mixtures, while Figure 6 plots the creep coefficient curves for these mixtures. Creep coefficient represents the ratio of the creep strain to elastic strain at a stress level of 40% of the average 28-day compressive strength. The first readings were recorded at the first day after loading. Statistical analysis was conducted to show whether there was a significant difference between predicted and measured creep coefficient ratios when different types of SCMs/fillers were used. Table 8 shows the results in terms of average and variance of predicted-to-measured creep coefficient ratio. Comparing the results of all models indicates that ACI 209 and AASHTO LRFD models slightly underestimate the creep coefficient, while CEB-FIP MC90-99 and GL2000 models significantly overestimate the creep coefficient.  
Figure 5: Measured creep strain versus time after loading for all SCC mixtures.  
Figure 6: Measured creep coefficient versus time after loading for all SCC mixtures.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & \\
**SCMFiller** & & & & & & & & & & & & & & \\
**Prediction Model** & & & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Statistical analysis results of creep predictions for SCC with different types of SCM/ filler.  
Figure 7 also plots the measured and predicted creep coefficient using different models regardless of the type of SCM/filler. The slope of the line of best fit for data points presents the level of prediction accuracy of each model (slope of 1.0 is the highest accuracy). The coefficient of determination, R\({}^{2}\), represents the goodness of fit (the higher R\({}^{2}\), the lower the scatter of the model predictions). This figure indicates that AASHTO LRFD model has the lowest scatter in its predictions, while the GL2000 model has the highest. Table 8 also indicates that in model prediction models, the type of SCM/filler does not have a significant effect on creep coefficient with the exception of mixtures with limestone powder (FLP) that induce higher creep strains, which is in agreement with [PERSON] (2018). Therefore, it is recommended to use a modification factor greater than 1.0 to adjust creep coefficient prediction for SCC mixtures with limestone powder. The value of this modification factor varies depending on the model used (e.g., 1.2 for AASHTO LRFD model).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline  & & \multicolumn{8}{c}{**Ratio of Predicted-to-Measured Drying Shrinkage Strain**} \\ \cline{2-13}  & & \multicolumn{2}{c}{**F**} & \multicolumn{2}{c}{**FLP**} & \multicolumn{2}{c}{**S**} & \multicolumn{2}{c}{**C**} & \multicolumn{2}{c}{**All Mixtures**} \\ \cline{2-13}  
\\ \cline{2-13}
# A Survey on Pump and Dump Detection in the Cryptocurrency Market Using Machine Learning
## 2 Background and Related Work
### Related Works
In the absence of regulation and standard know-your-customer (KYC) procedures, exchanges play an important role in market manipulation. In the study, the authors suggest that using the identified market vulnerabilities to develop regulation approaches can be beneficial. Nevertheless, the study has some limitations, including a possible omission of important studies and subjective selection of results. Lastly, Table 1 summarizes the main scopes of these three studies compared to this survey. Accordingly, [PERSON] and [PERSON] (2010) and [PERSON] et al. (2011) focused on detecting manipulations only in the stock markets. However, our survey aims to focus on the studies that detected a unique type of market manipulation called P&D in the cryptocurrency markets. Moreover, although [PERSON] et al. (2013) provided an overview of market manipulations in cryptocurrency, including P&D, they did not focus on the methods to detect these manipulations. On the other hand, our study focused on the methods used to detect P&D. Besides studies addressing P&D detection in cryptocurrency markets, studies have been conducted across various financial markets addressing market manipulation detection in a broader context. Several approaches have been explored, including supervised and unsupervised DL techniques [22; 23], transformer models [24], immune-inspired dendritic cell algorithms [25; 26], Markov models [27], and other ML approaches [28; 29; 30; 31; 32; 33]. Models trained with supervised DL have been used to detect and classify manipulative trading behavior, whereas models trained with unsupervised DL focus on clustering and anomaly detection. Transformer models leverage self-attention mechanisms to capture dependencies between trading events, while immune-inspired dendritic cell algorithms mimic the immune system to detect anomalous activities. P&D detection techniques in cryptocurrency markets can become more comprehensive and effective by incorporating insights from these approaches or using transfer learning to finetune the previous models for detecting specific market manipulations like P&D.
## 3 Material and Methods
### ECG Signal Preprocessing  
ECG preprocessing involves steps including baseline removal, noise removal and heartbeat segmentation.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Database Name** & **Published** & **No. of Subjects** & **Signal (Time-Period)** & **Sampling Frequency** & **Total Records** & **Resolution** \\ \hline ECG-ID database & 6 March 2014 & 90 & 20 s & 500 Hz & 310 & 12 bit \\ MIT-BIH database & 24 February 2005 & 48 & 30 min & 360 Hz & 48 & 11 bit \\ NSRDB database & 3 August 1999 & 18 & 1 h 30 min & 128 Hz & 18 & 11 bit \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the ECG databases, including signal length, sampling frequency, resolution, published date and total records.  
Figure 1: Flow diagram of the complete methodology, including preprocessing and segmentation steps, feature level fusion of DWT and 1D-CRNN.