esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study investigates the antidiabetic and hypolipidemic potential of newly isolated _Lacticaseibacillus paracasei_ strains in mature adipocytes. Differentiated 3T3-L1 cells are treated with 10% cell-free supernatants (CFSs) from four autochthonous (wild) strains (M2.1, C8, C15, and P4) of _Lacticaseibacillus paracasei_. Glucose consumption, intracellular lipid deposition, lipolysis rates, and some gene expressions related to adipocyte insulin sensitivity are evaluated. The results show that all CFS-treated groups experienced a substantial increase in glucose uptake, indicating a promising potential for countering glucotoxicity and insulin resistance. The different strains had notable differences in metabolic pathway modulation. Generally, the P4 CFS supplementation seems to enhance insulin-dependent glucose inflow, while M2.1, C8, and C15 supernatants stimulate insulin-independent glucose consumption by mature adipocytes. M2.1 CFSs ameliorate the mature adipocyte buffer capacity by enhancing intracellular lipid accumulation and reducing the lipolysis rate--an advantageous therapeutic effect in overweight individuals subjected to substantial obesity-predisposing factors. Notably, C8 and C15 CFSs suppressed the gene expression of crucial adipocyte insulin sensitivity markers, indicating an unfavorable outcome risk with prolonged treatment. Overall, our findings suggest that M2.1 and P4 _Lacticaseibacillus paracasei_ strains may be implemented as nutraceuticals to counteract glucotoxicity and insulin resistance, potentially easing the health status of obese individuals.  
probiotics; glucose consumption; insulin sensitivity; lipolysis; lipid accumulation; 3T3-L1; cell-free supernatants; food additives +
Footnote †: journal: Applied Sciences
# Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber) 1
## 5 Our PQC Incorporation and Implementation
### Subscription Concealment Protection Schemes (Modified for Post-Quantum Scheme--Kyber Profile C)  
This specifies the protection schemes used for concealing the SUPI in 5G networks. Each protection scheme is uniquely identified by a protection scheme identification. The currently defined identifiers are as follows: 0x0 (null scheme or no protection), 0x1 (Profile A), and 0x2 (Profile B), where Profiles A and B are defined by 3 GPP and described briefly in Section 2.2.1.  
For our scheme, which we call _Profile C_, we use the same data field but assign different identifier encoding: 0x3 (Profile C).  
The output size for each protection scheme varies based on its cryptographic components. The null scheme produces an output equal to the input size (e.g., MSIN or NAI username). Profile A includes a 256-bit (32 bytes) public key and a 64-bit (8 bytes) MAC, in addition to the input size. Profile B consists of a 264-bit (33 bytes) public key, a 64-bit (8 bytes) MAC, and the input size. Profile C, which uses the Kyber-512 post-quantum algorithm, generates a larger output due to its quantum-resistant components: it includes an 800-byte public key, a 768-byte ciphertext (encapsulation output), and a 64-bit (8 bytes) MAC, resulting in a total output size of approximately 768 bytes plus the MAC and the input size.  
The maximum size of the scheme output for proprietary or quantum-resistant protection schemes shall not exceed 3000 octets + input size [6]. This maximum was chosen to accommodate future post-quantum encryption schemes, such as lattice-based KEMs, without violating SUCI transport constraints. The UE shall not transmit an SUCI whose scheme output exceeds the defined maximum. The network may reject any SUCI message that surpasses this size limit to ensure protocol compliance and processing efficiency.
## 5 Results
### Feature Extraction Results  
The fastText algorithm used in this work was configured to create vectors of length 100. Figure 4 presents visualized example vector representations of all templates in the dictionary, which were used to capture semantic trends in the data. The number of dimensions was reduced to two using a combination of the incremental principal components analysis (IPCA) and t-distributed stochastic neighbor embedding (t-SNE) algorithms, allowing word embeddings to be represented on a plane.  
Between vectors of log templates occurring in similar contexts, the Euclidean distance is smaller than the distance between those occurring in different contexts. The context of log templates is a semantic relationship between them in a sequence. For example, log templates with indexes 310 and 427 have respective text representations _\"external input interrupt (unit = 0x02 bit = 0x00): uncorrectable torus error\"_ and _\"Torus non-recoverable error DCRs follow\"_. Both log templates describe similar log messages related to non-recoverable torus error; therefore, the Euclidean distance between their vector representations is very small.
## 4 Cellular Component of the CRC microenvironment
### Tumor-Associated Macrophages (TAMs)
In CRC, macrophages can induce apoptosis through the interaction of the Fas ligand with tumor cells [111]. This indicates that macrophages effectively fight the tumor through direct contact [98], but dependently on the tumor stage. In a study by [PERSON] et al., a high percentage of M1 macrophages, dependent on the CRC stage, gave a higher likelihood of patient survival, and this was not disrupted even by the accompanying infiltration of M2 macrophages [94]. In turn, recent in vitro studies have shown that cancer cells promote the polarization of macrophages towards M2 to inhibit inflammation and escape from immune surveillance. Lactate secreted by cancer cells was found to be involved in this process, confirming the immunosuppressive role of the acidic tumor microenvironment [112]. Other mediators of M2 polarization include IL-4, IL-6, IL-10, TGF-\(\beta\), EGF, and cancer-derived exosomes, which mediate the exchange of malignant traits within the tumor [88; 113]. TAMs are considered inducers of angiogenesis in CRC for two main reasons. First, TAMs themselves secrete vascular factors like VEGF [98; 114]. Second, they can stimulate colorectal cancer cells to release VEGF-A, recognized as the most potent inducer of angiogenesis and lymphangiogenesis [95; 114]. VEGF-A is acknowledged as a mitogen for vascular endothelial cells, promoting their proliferation and mediating the regeneration of blood vessels during embryonic development. However, in cancer, VEGF serves as a supportive factor for tumor vascularization, leading to metastasis to distant sites. Additionally, angiogenesis is enhanced by the characteristic hypoxic tumor environment, which increases as the tumor develops [114; 115; 116]. Macrophages also increase the level of pro-angiogenic IL-8 through the production of TNF-\(\alpha\) and IL-1\(\alpha\)[117; 118]. The angiogenic effect of TAMs in CRC was confirmed by studies on patient samples, which showed that macrophage infiltration is associated with an increase in the production of angiogenic growth factors, such as VEGF. Furthermore, TAM infiltration correlates with the density of blood vessels in tumors. This means that, under the influence of TAMs-cancer interactions, the number of microvessels increases, which facilitates further cancer invasion and distant metastases [104; 119].
## 3 Related Works on Models Compression
### Quantization  
Quantization reduces the model to lower representation allowing the reduction of computational time and low memory footprint. The quantization techniques can be grouped into two main categories: Scalar and Vector Quantization, and Fixed-Point Quantization.  
Scalar and vector quantization techniques are mainly used in data compression. This technique makes use of a codebook and a set of quantization codes to represent the original data. Since the size of the codebook is much smaller than the original data, the original data could be effectively compressed by quantization. Inspired by this, scalar or vector quantization approaches consist of representing the parameters or weights applied to a deep lattice for compression. In [44] the authors applied k-means clustering to weights or perform product quantization and obtained a very good balance between model size and recognition accuracy. They achieved a factor of 16-24 network compression with only 1% loss of accuracy on the ImageNet classification task.  
The Fixed-Point Quantization decreases computational complexity by decreasing the number of bits used in float representation. The precision of the result is also decreased, but in a controlled fashion. Quantization may be done during training (prequantization) or after (postquantization). In prequantization, a floating-point model is trained directly using fixed-point representation training techniques. An alternative approach to prequantization consists of training the floating-point model, and then using quantization training techniques at the fine-tuning step. Postquantization consists of converting a pretrained floating-point model to a fixed-point representation, and inferences are drawn using a fixed-point computation. The model is used without any retraining or fine-tuning step.  
Quantization techniques achieve high compression ratios and accuracies but also require appropriate software approach or sophisticated hardware to support inference.
## 5 Experiments
### Experimental Results and Comparative Analysis  
Table 2 presents the performance comparison between DSTGFCN and the baseline models for 15 min (short-term), 30 min (mid-term), and 60 min (long-term) predictions on the METR-LA and PEMS-BAY datasets. Table 3 displays the performance comparison between DSTGFCN and the baseline models for average one-hour predictions on the four traffic flow datasets. Our proposed model demonstrated superior predictive performance in traffic speed and traffic flow prediction tasks. The traditional statistical method ARIMA performed the worst, failing to capture the nonlinear relationships in the traffic sequences. FC-LSTM, being a classical recurrent neural network, effectively extracted nonlinear features from sequences. However, it only modeled temporal correlations and overlooked the spatial correlations in the traffic road network, resulting in lower accuracy than the graphbased models. This finding highlights the importance of modeling spatial correlations to achieve accurate predictions. STGCN and DCRNN are typical spatio-temporal data prediction models. Both models consider spatial factors, which lead to improved prediction accuracy. However, these methods only utilize adjacency matrices defined by spatial distances for graph convolution operations, which may only partially capture spatial relationships. GW-Net, AGCRN, and STG-NCDE employ adaptive adjacency matrices to explore further hidden spatial features, which can be understood as learning the optimal graph topology of the traffic road network. Although they demonstrated excellent performance, these models still employed static graphs when modeling spatial correlations and did not consider the dynamic variations of spatial relationships. DSTAGCN connects the graphs of the recent and past time frames to construct a dynamic adjacency matrix, resulting in improved prediction capabilities compared to static graphs. Compared to the models above, DSTGFCN fully extracted the dynamic features from the traffic data and combined dynamic spatial features from multiple time frames to generate the dynamic adjacency matrix more efficiently and appropriately. As a result, DSTGFCN exhibited excellent predictive capabilities. Figure 5 visualizes the prediction errors of DSTGFCN and two other baselines at each time step on the PEMS04 and PEMS08 datasets. The error growth rates of AGCRN and
Entropy_**2013**, _15_, 2218-2245; doi:10.3390/e15062218  
_Entropy_**ISSN 1099-4300**  
www.mdpi.com/journal/entropy  
Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction  
**[PERSON] *, [PERSON] and [PERSON]**  
NATO Science and Technology Organization (STO), Centre for Maritime Research and  
Experimentation (CMRE), Viale San Bartolomeo 400, 19126, La Spezia, Italy;  
E-Mails: [PERSON]L_ADDRESS] (M.V.); [PERSON]L_ADDRESS] (K.B.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +39-0187-527-349; Fax: +39-0187-527-354.  
_Received: 1 March 2013; in revised form: 10 May 2013 / Accepted: 29 May 2013 / Published: 4 June 2013_  
**Abstract:** Understanding maritime traffic patterns is key to Maritime Situational Awareness applications, in particular, to classify and predict activities. Facilitated by the recent build-up of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers, ship movement information is becoming increasingly available, both in coastal areas and open waters. The resulting amount of information is increasingly overwhelming to human operators, requiring the aid of automatic processing to synthesize the behaviors of interest in a clear and effective way. Although AIS data are only legally required for larger vessels, their use is growing, and they can be effectively used to infer different levels of contextual information, from the characterization of ports and off-shore platforms to spatial and temporal distributions of routes. An unsupervised and incremental learning approach to the extraction of maritime movement patterns is presented here to convert from raw data to information supporting decisions. This is a basis for automatically detecting anomalies and projecting current trajectories and patterns into the future. The proposed methodology, called TREAD (Traffic Route Extraction and Anomaly Detection) was developed for different levels of intermittency (_i.e._, sensor coverage and performance), persistence (_i.e._, time lag between subsequent observations) and data sources (_i.e._, ground-based and space-based receivers).  
**Keywords:** maritime situational awareness; knowledge discovery; maritime route extraction; route prediction; anomaly detection
# Insider Threat Detection Using Machine Learning Approach
## 2 Literature Review  
Insider threat detection is a broadly researched topic; a variety of solutions have been proposed: specifically, different learning techniques to facilitate early, more accurate threat detection. To discover present research gaps and potential future research domains, an analytical review of the various approaches to insider threat identification is required.  
Over the past two decades, researchers have investigated insider threat detection and prevention using anomaly-based approaches. These techniques \"learn\" from normal data only to detect anomalous instances that deviate from expected instances; this approach has remained the most popular method in the literature. Anomaly-based detection is based on one major assumption: that an attacker's actions differ from normal patterns of actions. Specifically, some of the common behaviours associated with insider threats include (i) the collection of large datasets and (ii) uploading files that originate from outside the organisation's website [13]. One crucial shortcoming of this traditional approach to anomaly detection is that once the baseline has been fully modelled, anything outside this threshold will be considered a potential threat; this causes an abundance of false positives [14]. Moreover, classification-based insider threat detection represents an alternative research method; it \"learns\" from normal and anomalous data to determine the decision boundary that distinguishes normal from anomalous incidences.  
This section provides an up-to-date, comprehensive survey of recent approaches that address insider threat detection: (i) machine learning (ML) and deep learning (DL) approaches (either anomaly-based [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27], and (ii) classification-based approaches [2; 14; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42]).
## 2 Results and Discussion
### Organic Compound Degradation and Biosurfactant Production
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Gene** & **Accession Number** & **KO Numbers** & **Gene Product** & **Activity** \\ \hline _ladA_ & MBG8560652.1 & K20938 & long-chain alkane monooxygenase (EC:1.14.14.28) & \begin{tabular}{c} Aliphatic compounds’ \\ degradation \\ \end{tabular} \\ \hline _paxG_ & MBG8562545.1 & K00448 & \begin{tabular}{c} Protocatechuate 3.4-dioxygenase. Alpha subunit \\ (EC:1.13.11.3) \\ \end{tabular} \\ _paxH_ & MBG8562546.1 & K00449 & \begin{tabular}{c} Protocatechuate 3.4-dioxygenase. Beta subunit \\ (EC:1.13.11.3) \\ \end{tabular} \\ _paxQ_ & MBG856076.1 & K02623 & Lysr family transcriptional regulator. Pca operon transcriptional activator \\ _paxC_ & MBG8560139.1 & K01607 & 4-carboxymouncolactone decarboxylase (EC:4.1.1.44) \\ _paxD_ & MBG8560138.1 & K01055 & 3-oxoadipate end-lactamase (EC:3.1.1.24) \\ _paxR_ & MBG8560131.1 & K02624 & Iclr family transcriptional regulator. Pca regulon regulatory protein \\ _pcaK_ & MBG8560132.1 & K08195 & \begin{tabular}{c} MFS transporter. AAHS family. \\ 4-hydroxybenzoate transporter \\ \end{tabular} \\ _paxF_ & MBG8560135.1 & K07823 & 3-oxoadiply-coa tholase (EC:2.3.1.174) \\ _pcaT_ & MBG8560136.1 & K02625 & MFS transporter. MHS family. Dicarboxylic acid transporter pcat & Aromatic compounds’ degradation \\ _paxB_ & MBG8560137.1 & K01857 & 3-carboxy-cis.cis-muconate cycloisomerase (EC:5.5.1.2) \\ _hpaF_ & MBG8558716.1 & K01826 & 5-carboxymethyl-2-hydroxyunconate isomerase (EC:5.3.3.10) \\ _hpaF_ & \begin{tabular}{c} MBG8558890.1 \\ \end{tabular} & K02510 & \begin{tabular}{c} 4-hydroxy-2-oxoheptanedideae aldolase \\ (EC:4.1.2.52) \\ \end{tabular} \\ _hpaH_ & MBG8558891.1 & K02509 & 2-oxo-hept-3-ene-1.7-dioirdates (EC:4.2.1.-) \\ _hpaF_ & MBG8558893.1 & K01826 & 5-carboxymethyl-2-hydroxyunconate isomerase (EC:5.3.3.10) \\ _hpaD_ & MBG8558894.1 & K00455 & 3.4-dihydroxyphenylacetate 2.3-dioxygenase (EC:1.13.11.15) \\ & & & 5-carboxymethyl-2-hydroxyunconic- \\ _hpaE_ & MBG8558895.1 & K00151 & semialdehyde dehydrogenase (EC:1.2.1.60) \\ & & & 5-oxopent-3-ene-1.2.5-tricarboxylate \\ _hpaG_ & MBG8558896.1 & K05921 & \begin{tabular}{c} decarboxylase/2-hydroxyhepta-2.4-diene-1.7- \\ dioate isomerase (EC:4.1.1.68 \\ 5.3.3.-) \\ \end{tabular} \\ _hpaA_ & MBG8558898.1 & K02508 & 4-hydroxyphenylacetate 3-monooxygenase (EC:1.13.11.3) \\ _paxF_ & MBG8559324.1 & K01692 & Enoyl-coa hydratase (EC:4.2.1.17) \\ _paxX_ & MBG8559346.1 & K02616 & \begin{tabular}{c} Preparylectic acid degradation \\ regulatory protein \\ \end{tabular} \\ _paxY_ & MBG8559347.1 & K02617 & \
\begin{tabular}{c} Preparylectic acid degradation \\ regulatory protein \\ \end{tabular} \\ _paxG_ & MBG8559349.1 & K15866 &
\begin{tabular}{c} Preparylectic acid degradation \\ (1.2-epoxy-1.2-dihydrophenyl)acetyl-coa \\ isomerase (EC:5.3.3.18) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Genes involved in organic compounds’ degradation.
## 2 Overview of Cyber Threats in EM
### Communication Media
#### 2.1.2 Cell Broadcast (CB)  
CB has been developed as part of the GSM standard. It is used for one-to-many messaging to specified mobile network cells as an alternative to the one-to-one SMS messaging. It utilises the existing mobile telecommunication infrastructure, but unlike SMS, it is not affected by traffic overload. In Japan and Korea, CB has been used for several years for public warning and communication between relief workers. Since 2005, European mobile network operators have started deploying the capability to transmit government text warnings via CB, and most recent mobile devices are CB-enabled.  
An initial approach for authentication of CB messages was provided in [22], where mobile stations' access to a cell broadcast message is limited through encryption before transmission. A more recent authentication mechanism that differentiates between logical broadcast channels has been presented in [23]. It specifies that a message control node receives the initiation request identifying a first message payload segment to be transmitted, determines the authentication group for which it is intended and a first encryption key associated with the authentication group. These mechanisms have been designed to reduce the likelihood of rogue messages being transmitted over CB, but would provide only limited protection against availability attacks.  
_Open issues in security of CB used in EM_: Considering that CB was introduced partly to address the bandwidth issues of SMS messaging during emergencies, it is significant to note that it does not include any provisions for addressing localised denial of service attacks, such as the ones that can be generated by cheap close proximity jammers. If the data and control message channels can be identified, then a control jamming attack can be launched, using significantly lower energy than what would be required to jam all communications channels [24]. If the attackers are already in the network, as insiders or following a successful masquerading attack, a technique detailed in [25] can be used to prevent mobile stations from communicating. It involves jamming only one in 51 frames on a control channel. Recently, [PERSON] _et al_. [26] have proposed a randomised distributed scheme that allows nodes to establish and maintain the control channel in the presence of an insider acting as a jammer, even if the latter has knowledge of the protocol specifics and of the cryptographic quantities used. Their approach, however, assumes that there is a sufficient number of participating nodes in order to be able to cooperatively identify the compromised ones. In an emergency situation where a large part of the infrastructure has been destroyed, this may not be the case. Defence against localised jamming of the CB channels during an emergency, especially from an insider, is an open issue for research.
# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
## 3 Algorithm
### Detection of Type 2 Vibrations
#### 3.5.2 Filtering of Scan Results  
Second, scan results were filtered for the most prominent vibrations (see Figure 8, right). Since scanning was conducted from bottom to top, only the lower part of corresponding samples was deleted in Step 3(b) to increase robustness of fitting. Consequently, samples \([\hat{x},\hat{y}]\) could be assigned to a different vibration in the next iterations.  
Hence, for each vibration \(m_{1}\), it was tested if there was a vibration \(m_{2}\) for which  
* At least 10% of samples \([\hat{x},\hat{y}]\) were assigned to both vibrations \(m_{1}\) and \(m_{2}\).
* A larger number of samples \([\hat{x},\hat{y}]\) was assigned to vibration \(m_{2}\).  
If these conditions were fulfilled, vibration \(m_{1}\) was deleted from the result set. The remaining vibrations \(V_{2,j}\) with \(j=1,...,J_{2}\) constituted the set of successfully scanned Type 2 vibrations (e.g., see Figure 9, right). Detected peaks \(\hat{p}_{i,n}\) corresponding to those vibrations are denoted as \(P_{v2}\). Overall, the number of peaks \(P_{all}\) as detected in Section 3.2 can be written as \(P_{all}=P_{v1}\bigcap P_{noise}\bigcap P_{v2}\bigcap P_{rem}\) with \(P_{rem}\) being those peaks, for which no significant vibrations could be extracted in Section 3.5. The joint set \(P_{v2}\bigcap P_{rem}\) is displayed in Figure 6 (right) in green colour.
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 2 VLC Transmitter Light Source
### Theoretical Background of LED for VLC Application
A \(\mu\)-LED array will be prepared for monochromatic and polychromatic emission [53; 54]. In this section, we review the characteristics and applications of \(\upmu\)-LED arrays in the field of VLC, and a brief benchmark for \(\upmu\)-LEDs used in VLC systems is shown in Figure 3[55; 56; 57; 58; 59; 60; 61; 62; 63; 64]. To utilize \(\upmu\)-LEDs in a VLC system, integrating multi-color emitters on a single chip is required. This can be achieved by combining blue \(\upmu\)-LEDs with color conversion materials (pc-LED) or RGB-LEDs.