esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 IoMT Authentication Taxonomy
### Authentication Credentials  
The authentication process asks for unique credentials from the entities to allow them to access the IoMT systems. This process can be performed through a third, trusted, party or directly between the communicating entities. Whatever the credentials used, it is necessary to consider their uniqueness, universality, and storability [60]. The credentials required for authentication are classified into four categories [61]. These types are depicted in Figure 10.  
Knowledge credentials refer to the information known by the entity, such as passwords, identification numbers, or user names. This type of credential is straightforward and does not need a technique for extracting its value. This authentication scheme facilitates cryptography, steganography, or shadow to hide such credentials [42; 62; 63; 64; 65; 66; 67]. [PERSON] et al. [68] facilitated a dynamic pseudo-identity and a collision-resistant cryptographic function to hide users' identities. [PERSON] et al. [65] used three steganography-based random iterations to anonymize the patient's identity and authenticate him.  
Possession credentials refer to the information possessed by the entity, such as a smart card or passport for user authentication, and radio waves for device authentication. The researchers in [25; 44; 45; 46] used Radio-Frequency Identification (RFID) technology as a possession credential to identify devices anonymously and perform the authentication process. Other device authentication schemes used Physically Unclonable Function (PUF) to identify IoMT devices [49; 52]. Authentication schemes in studies [35; 69; 70; 71; 72; 73; 74] required smart cards as credentials to identify the IoMT systems users. The possessed credential method requires a way to input its value and use it as an identifier [75].  
Inherence credentials refer to something inherited by the entity, such as face/voice recognition, fingerprints, or all kinds of biometrics for user authentication. Currently, user authentication in the IoMT system primarily depends on the user's biometric informa  
Figure 10: Authentication credentials.  
tion [76; 77; 78; 79; 80; 81]. A fuzzy extractor is adopted in studies [70; 76] to provide unique and storable credentials through biometric authentication.  
Context credential refers to something in the entity's context that can be used for authentication, such as location, time, or IP address. This kind of credential is the least adopted due to its low level of security, especially for healthcare applications. A good instance of context credential is the device's location, which can authenticate the related devices uniquely. [PERSON] et al. [82] developed a location-enabled authentication protocol utilizing fog computing and blockchain technology. Their scheme identifies a fog entity through its location and further connects a group of IoMT devices by a specific fog entity. That allows the IoMT system to authenticate fog entities and their related IoMT devices.
## 3 Results
### Wettability Test  
Examples of droplets obtained during contact angle determination are shown in Figure 7.  
Based on the results obtained for the mean contact angle value, it was observed that the samples in groups one and two have hydrophilic properties--as shown in Figure 8.  
Figure 8: Mean values of contact angle \(\theta_{\text{av}}\).  
Figure 6: Comparison of the Sa parameter.  
Figure 7: Determination of contact angles: (**a**) initial state—after sandblasting, (**b**) condition after treatment at 800 \({}^{\circ}\)C—after sandblasting, (**c**) initial condition—after polishing, (**d**) condition after treatment at 800 \({}^{\circ}\)C—after polishing.  
The surfaces after mechanical polishing are characterized by higher wettability. It can also be observed that the wettability of the samples in the initial state shows the greatest reduction in the contact angle value in relation to the surfaces after sandblasting. Tests carried out by the authors [36] indicate that the contact angle values for samples with sandblasted surfaces ranged from 75.78 \(\pm\) 2.26\({}^{\circ}\) to 92.82 \(\pm\) 3.14\({}^{\circ}\). For this study, the increase in contact angle was dependent on the laser power of the incremental printing process. The authors of publication [37], in contrast, indicate the hydrophilicity of all the samples made using additive technology. The mean value of the \(\theta_{\text{av}}\) angle was 63.81 \(\pm\) 7.55\({}^{\circ}\).
# Interdependent Transcription of a Natural Sense/Antisense Transcripts Pair (_SLC34A1_/Pfn3)
## 2 Results  
### The SLC34A1/PFN3 Locus  
The _SLC34A1_ gene is well conserved, and its exon/intron structure is identical in all mammals (Figure 1A). Moreover, three homologues (_SLC34A1_, _SLC34A2_ and _SLC34A3_) exist with comparable genomic and protein structures, and distinct tissue distribution that reflects their physiological function. _SLC34A1_ mRNA is detectable in human testis but at very low levels compared to the kidney (Figure 1B,C).  
The _PFN_ gene family contains four members, with profilin 1 and 2 displaying close similarity (>60% homology) but PFN3 and PFN4 only sharing <40% and <20% amino acids with other Profilin isoforms [27]. The predicted 3D structures of all isoforms, however, are clearly related ([[https://alphafold.ebi.ac.uk/](https://alphafold.ebi.ac.uk/)]([https://alphafold.ebi.ac.uk/](https://alphafold.ebi.ac.uk/)), accessed on 10 December 2021). What separates _PFN3_ from the other _PFN_ genes are the poor phylogenetic conservation, the lack of introns and mRNA expression restricted to testes (Figure 1A,B). In addition, large scale proteomics approaches did not detect PFN3 protein in any of the assessed tissues, including testis (Figure 1B). In previous work, we cloned _PFN3_-related 3\({}^{\prime}\)RACE products from human and mice testes and only found alternatively spliced transcripts that resulted in a truncated _PFN3_ open reading frame and ran into the _SLC34A1_ gene, generating a natural antisense transcript (Figure 1A) [7; 23]. Hence, the non-protein coding antisense transcript constitutes a significant proportion of the _PFN3_ transcriptional output.  
There is currently no epithelial human cell line with an active _SLC34A1/PFN3_ locus; the only cell line that expresses _Slc34A1_ is derived from opossum kidney (OK cells) [28]. Moreover, primary cells from human kidneys lose the expression of the transporter within about two weeks in culture [29].
## 3 Methods
### Spatial Probability Distribution Encoder
The aggregated anchor features are adaptively distributed to surrounding points based on their feature similarity, facilitating interaction and feature sharing within the local region. A group of \(k_{n}\) points is selected around each anchor to form local clusters, where the anchor feature is denoted as \(f_{an}\) and the local context features as \(f_{loc}\). The dispatched local context features \(f_{loc}^{\prime}\) are computed as follows. Figure 7: The architecture of spatial distribution encoder. The confidence distribution encoder takes the spatial positions of matches as input. Cluster-based graph mapping enables the more effective aggregation of spatial position information within the matching space. The FPS aggregation dispatch mechanism facilitates the efficient transmission of local information to the global context through aggregation and dispatch processes, while also accelerating computation. \[(Q_{d},K_{d},V_{d})=Linear_{(Q,K,V)}(f_{an},f_{loc},f_{loc}) \tag{3}\] \[f^{\prime}_{loc}=softMax(\frac{Q_{d}K^{\prime}_{d}}{\sqrt{dim}})V_{ d},\]  
and sampling preference \(p_{spa}\in\mathbb{R}^{B\times L\times N}\) is obtained for the data point set by passing \(F_{fps}\) through several \(1\times 1\) MLP layers. **Multi-Perspective Block. ** Since both clustering and sampling operations involve randomness, a single poor sampling or clustering result may lead to suboptimal results. To promote diversity in sampling, the spatial encoder is executed in parallel with \(L\) heads. Additionally, the confidence distribution probabilities are combined with the sampling preference probabilities. A hyperparameter \(\delta\) is introduced to balance the two weights and derive the final sampling probability \(p=p_{con}+\delta p_{spa}\).
## 2 Signal Analysis
### Elevation, C/N\({}_{0}\) and Biases
based on the C/N\({}_{0}\)-dependent weighting function \(w(S_{j}^{s})=1/(1+b_{j}\times 10^{-0.1\times S_{j}^{s}})\) (see also [6]), such that:  
\[(\sigma_{j}^{1s})^{2} = 2\sigma_{j,0}^{2}\times\big{(}1+b_{j}\times 10^{-0.1\times S_{j} ^{1s}}\big{)} \tag{12}\] \[= \sigma_{j,0}^{2}\times\big{(}2+b_{j}\times 10^{-0.1\times S_{j} ^{1}}+b_{j}\times 10^{-0.1\times S_{j}^{s}}\big{)}\]  
where the coefficient \(b_{j}\) varies with the frequency, and \(S_{j}^{s}\) denotes the between-receiver C/N\({}_{0}\) for satellite \(s\) on frequency \(j\). Based on Equation (12):  
\[10^{-0.1\times S_{j}^{1s}}=\frac{10^{-0.1\times S_{j}^{1}}+10^{-0.1\times S_{j }^{s}}}{2} \tag{13}\]  
so that \(S_{j}^{1s}\) can be obtained with:  
\[S_{j}^{1s}=-10\times\log_{10}\bigg{(}\frac{10^{-0.1\times S_{j}^{1}}+10^{-0.1 \times S_{j}^{s}}}{2}\bigg{)} \tag{14}\]  
where the between-receiver C/N\({}_{0}\)\(S_{j}^{s}\) can be obtained, similar to Equation (14), with:  
Figure 1:0  
where \(S_{j,r}^{s}\) and \(S_{j,r}^{s}\) represent the C/N\({}_{0}\) of the reference and the rover receivers for satellite \(s\) on frequency \(j\), respectively. The RMS of the DD residuals is computed within small C/N\({}_{0}\) intervals (\(S_{j}^{1s}\)) of 0.3 dBHz starting from the minimum \(S_{j}^{1s}\) above the lower bound of 12 dBHz, and is shown in the top panels of Figure 1:1 In general, a higher correlation between the RMS and the C/N\({}_{0}\) can be observed in the suburban scenario. It can also be observed that the signals on L2W (the red and magenta lines in the top panels) generally experience lower C/N\({}_{0}\) than those on L1C (the blue and green lines in the top panels). This agrees with the results shown, e.g., in [37] that the C/N\({}_{0}\) for the L2 P(\(\Upsilon\)) signals are lower than those for the L1 C/A and L5 signals, which is related to the lower minimum received power of L2. Based on the analysis above, the weighting functions \(w_{\phi,j}^{s}\) and \(w_{p,j}^{s}\) are in this study considered as a function of both the elevation angles \(\theta^{s}\) and the C/N\({}_{0}\) for phase \(S_{j}^{s}\) in the form:  
Figure 1:2 Figure 1:3  
where the parameters \(a_{\phi}\) and \(a_{p}\) are used to adjust the elevation impact on the weights, and \(b_{\phi,j}\) and \(b_{p,j}\) are used to adjust the impact of the C/N\({}_{0}\) on the weighting functions. In this contribution, to evaluate the weighting model as will be discussed in Section 2.2, \(a_{\phi}\) and \(a_{p}\) are empirically searched from 0 to 16 with a step of 2, and \(b_{\phi,j}\) and \(b_{p,j}\) are searched for 0, 100, 500, 1000, 4000, 7000 and \(10^{4}\) Hz to find the most appropriate coefficients. This study does not attempt to search for higher coefficients for \(b_{\phi,j}\) and \(b_{p,j}\), as the weighting functions \(1/(1+b_{\phi,j}\times 10^{-0.1\times S_{j}^{s}})\) and \(1/(1+b_{p,j}\times 10^{-0.1\times S_{j}^{s}})\) are expected to be around one at a \(S_{j}^{s}\) of 54 dBHz, which is characterised as the maximum possible signal power strength according to [36].
# A Novel Relay Selection Scheme Based on Q-Learning in Multi-Hop Wireless Networks
## 2 System Model and Conventional Selection Schemes
### Best Harmonic Mean Scheme  
The last relay selection scheme is a best harmonic mean scheme. The BHM relay selection scheme is generally known as one of the optimal relay selection schemes. The CSI is necessary in order to calculate the harmonic mean of all relays. In summary, the source should obtain the harmonic mean of two channels by using the CSI. This scheme uses a magnitude of two channels between source-relay and relay-destination. The \(m\)-th harmonic mean is calculated as follows,  
\[h_{BHM}(m)=\sum_{k=1}^{K}\frac{1}{\left(\left|H_{S,R_{m}}^{k}\right|^{-2}+ \left|H_{R_{m},D}^{k}\right|^{-2}\right)}, \tag{3}\]  
where the \(k\) means the sub-carrier index. The \(\left|H_{S,R_{m}}^{k}\right|^{2}\) means the channel magnitude between the source and the \(m\)-th relay. The \(\left|H_{R_{m},D}^{k}\right|^{2}\) means the channel magnitude between the \(m\)-th relay and the destination. The source compares with each harmonic mean and selects the relay that has the largest harmonic mean. Since the BHM scheme is an optimal relay selection scheme, it provides very high reliability. However, since the source needs to know all the channel information, it is difficult to use in a practical system.
# Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers
## 4 Experimental Results
### Ablation Study  
In the previous study, we developed a composite model, ELP-Net, integrating both the spatial-temporal GNN module and the transformer module. To further assess the contributions of these components, the following analysis presents an ablation study on ELP-Net, examining the specific impacts of each module individually and in combination with the overall model performance. We define the variants of ELP-Net by removing different components as follows.  
* Base model: a version of the model without transformer and GNN modules.
* +Transformer: the base model with the addition of transformer module.
* +GNN: the base model with the GNN module added.
* Full model: the complete ELP-Net, with both the transformer and GNN modules.  
As shown in Table 3, adding the transformer module alone results in an average performance improvement of 4.5%. This indicates the ability of the transformer to extract effective feature patterns from the time-series data of piece attribute features. By capturing the temporal evolution of the pieces' attributes, the transformer helps the model better understand the dynamics of the pieces over time, leading to more accurate predictions of their future positions.  
Figure 10: Effect of network depth on the performance of ELP-Net.  
Introducing the GNN module alone resulted in an average performance improvement of 4.3%. This means that the GNN successfully captured the key spatial information contained in the spatial situational data, such as the relative distances between pieces, mutual attack ranges, and cluster structures. These spatial factors significantly influence the changes in piece positions. By iteratively propagating and aggregating information, the GNN allows each piece to incorporate features from its surrounding environment. This enriched spatial relationship information enables the model to make more informed predictions about piece positions.  
When both the GNN and transformer modules are added simultaneously, the model achieves an average performance improvement of 6.4%. This improvement surpasses the gains that are achieved using either module alone, highlighting the synergistic effect between the two modules. The transformer focuses on the temporal patterns of the piece attribute features over time, while the GNN concentrates on the position relationships and their dynamic evolutions in the spatial situational data. This combination allows for a more comprehensive understanding of the factors affecting position changes, enabling the model to make more accurate and reliable predictions.
## Appendix A Methods of ET Partitioning  
### The Modified Relaxed Eddy Accumulation (MREA) Method  
The MREA method uses conditional sampling in multiple scalar quadrants [15; 16] by considering the assumption of turbulent transport similarity for nonstomatal processes [16] combined with the relaxed eddy accumulation method [44; 45] to filter the information about soil components carried in the jet stream and to directly separate soil evaporation or soil respiration from the eddy observations. The conditional sampling process selects the first quadrant of data where both \(c^{\prime}\) and \(q^{\prime}\) are greater than 0 under the condition of \(w^{\prime}>0\)The data satisfying this condition are the key information carried in the jet regarding the nonstomatal process [16]. Evaporation is calculated by the following equation:  
\[E_{MREA}=\beta\sigma_{w}\frac{\sum_{i=1}^{N}h_{i}q^{\prime}}{\sum_{i=1}^{N}I_{h,w +}} \tag{1}\]  
where \(N\) is the number of time series samples; \(\sigma_{w}\) is the standard deviation of vertical wind speed; and \(\beta\) is the similarity constant. To further constrain the information about photosynthesis and transpiration, a hyperbolic threshold (\(H\)) is introduced, resulting in \(I_{h}\) and \(I_{h,w+}\) as indicator functions.  
\[I_{h}=\left\{\begin{array}{l}1\to if\ c^{\prime}>0,q^{\prime}>0,w^{ \prime}>0,\left|\frac{c^{\prime}}{\sigma_{c}}\right|>\left|\frac{H\sigma_{w}}{ q^{\prime}}\right|\\ 0\to otherwise\end{array}\right. \tag{2}\]  
\[I_{h,w+}\left\{\begin{array}{l}1\to if\ w^{\prime}>0,\left|\frac{q^{ \prime}}{\sigma_{q}}\right|>\left|\frac{H\sigma_{w}}{c^{\prime}}\right|,\left| \frac{c^{\prime}}{\sigma_{c}}\right|>\left|\frac{H\sigma_{w}}{q^{\prime}} \right|\\ 0\to otherwise\end{array}\right. \tag{3}\]  
Regarding transpiration and photosynthesis, which are directly related to stomatal processes, the separation process was completed by calculating the residual terms of ET vs. EMREA and Fc vs. RMREA at the observed point locations. We used a recent correction assuming that the separation results are insensitive to the hyperbolic threshold (\(H=0\)) and rejected \(E_{MREA}>\ET\) to ensure that the separation results are biophysically meaningful; on the other hand, the number of sampling points was strictly controlled, i.e., the proportion of valid data in quadrants I and II was greater than 20% and the data in quadrant I were greater than or equal to 5% [17].
## 5 Discussions and Conclusions
; funding acquisition, Q.C. ; formal analysis, visualization, J.L. ; writing--review and editing, W.L. and Y.M. ; data curation, G.W. and Y.W. All authors have read and agreed to the published version of the manuscript. Funding: This work was funded by \"the National Key Research and Development Program\" (2018 YFC1506704), \"National Natural Science Foundation of China\" (41475094 and 42305090). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Research data from this study will be made available on request ([EMAIL_ADDRESS]). Acknowledgments: The authors sincerely appreciate the support of the WRF ([[https://www.mmm.ucar.edu/models/wrf](https://www.mmm.ucar.edu/models/wrf)]([https://www.mmm.ucar.edu/models/wrf](https://www.mmm.ucar.edu/models/wrf)), last accessed on 15 June 2024) official website and FLEXPART official website ([[https://www.flexpart.eu/](https://www.flexpart.eu/)]([https://www.flexpart.eu/](https://www.flexpart.eu/)), last accessed on 18 June 2024) for providing model support and NCEP for providing FNL data ([[https://rda.ucar.edu/dataset/](https://rda.ucar.edu/dataset/)]([https://rda.ucar.edu/dataset/](https://rda.ucar.edu/dataset/)), last accessed on 15 June 2024) support, which made this work possible. Conflicts of Interest: The authors declare to no conflicts of interest. Abbreviations The following abbreviations are used in this manuscript: WRF Weather research and forecasting (WRF Model) FLEXPART Flexible particle dispersion model (FLEXPART Model) NECP National Centers for Environmental Prediction FNL Final Operational Global Analysis data RMSE Root mean square error MAE Mean absolute error
# Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model
## 2 Model Principles and Methods
### Combined Early Warning Model of PCA-PSO-ELM  
Since the factors affecting the occurrence of rock bursts are not completely independent, this work performs dimension reduction and feature information extraction from the sample data before predicting the grade of the rock burst. The extracted principal components not only eliminate the correlation between the different factors, but also simplify the network structure and improve the model performance. Meanwhile, due to the randomness of the input weights and the thresholds of the hidden layer of the ELM neural network, it has a significant impact on the identification and generalization ability of the ELM. In order to enhance the performance of the ELM, this paper adopted the PSO algorithm to optimize the input weights and the hidden layer thresholds of the ELM, obtaining its optimal parameter combination, and finally established a rock burst prediction model in accordance with the PCA-PSO-ELM combined algorithm. The model flowchart is exhibited in Figure 3.  
Figure 3: PCA–PSO–ELM model flowchart.
## 5 Advantages, Limitations, and Future Research Directions of the Model
### Model Limitations and Future Research Directions  
(1) We only performed simple preprocessing on the text data. We tried to find a better preprocessing method to filter out irrelevant information while minimizing the loss of effective text information.  
(2) We chose CLIP's text encoder, but it can only process texts with a maximum length of 77 tokens. For longer texts, we had to use truncation, which resulted in the loss of effective text information.  
(3) We used the simply preprocessed text as prompts for image generation. Due to the loss of effective information in the preprocessed text and the ambiguity in how the image generation model interprets words, this approach often produces erroneous and low-quality images.  
(4) The fusion method we chose cannot fully integrate text and image features.  
Next, we will conduct a detailed analysis of it from three aspects: preprocessing method selection, generated image quality, and fusion method.
## 4 OTA Control Methods
### Microbiological methods  
Carboxypeptidase as an enzyme capable of destroying OTA [261] and the use of atoxigenic _A. niger strains as ofoxypeptidase sources has been suggested [278]. Other enzymes that can be obtained from _A. niger strains_ and can efficiently degrade OTA are lipases [279], an crude enzyme preparation [180] and a metalloenzyme [281]. A carboxypeptidase present in _Phaffia rhodozyma_ can also degrade OTA up to 90% [264]. Moreover, certain bacteria belonging to _Streptococcus, Bifidobacterium, Lactobacillus, Butyribrio, Phenylobacterium, Pleurotus, Saccharomyces, Bacillus_ and _Acinetobacter_ genera [282] and certain fungi belonging to _Aspergillus_ (_A. fumigatus, A. niger, A. carbonarius, A. japonica, A. versicolor, A. wenti_ and _A. ochraceus_), _Alternaria, Botrytis, Cladosporium, Phaffia, Penicillum_ and _Rhizopus_ (_R. stolonifer_ and _R. oryzae_) genera [264,271,283], are able to degrade OTA _in vitro_ up to more than 95%. Moreover, some of them have shown detoxifying properties in _in vivo_ assays [282].  
Microbiological control of OTA is also important during wine manufacturing. OTA content increases until the malolactic fermentation step preceding bottling of wine. During this time the mycotoxin level diminishes, probably due to its adsorption on the _Saccharomyces_ yeast surface, to its interaction with metabolites produced by yeast or its degradation by the lactic bacteria still present in wine [284; 285; 286]. Grape washing, cooking and pressing can also promote the OTA content drop [53; 287]. During vine fruits production, efficient drying and turning over are required because in these stages, moisture and sugar content stimulates _Aspergillus_ section Nigri development and OTA synthesis [288].