esa-sceva/satcom-chunk-collection huggingface dataset:
## 12 Does the MMS Operon Play an Important Role in Initiation?  
The macromolecular synthesis (MMS) operon is highly conserved [80]; in _E. coli_, it comprises three genes: _rpsU_, which encodes the S21 ribosomal protein, _dnaG_, which encodes the DNA primase involved in the initiation of chromosome replication, and _rpoD_, which encodes the principal sigma subunit of RNA polymerase (sigma70, the \"house-keeping\" sigma). This operon is subject to a complex pattern of internal and external regulation in which it is possible to regulate each of its three genes independently of the others. In a series of investigations of heterogeneous responses to environmental stresses in _Listeria monocytogenes_, it was found that acid and other stresses primarily selected for _rpsU_ variants, in some of which 116 genes were upregulated, mainly those controlled by the alternative stress sigma factor SigB; this leads to the hypothesis (1) that single amino acid substitutions in RpsU enable _L. monocytogenes_ to switch between high fitness-low stress resistance and low fitness-high stress resistance and (2) that RpsU interacts with the stressorsome, a stress-related hyperstructure responsible for integrating information about multiple environmental stresses and transmitting this as signals [81; 82]. How might this relate to hyperstructure dynamics? If the MMS operon exists as a hyperstructure based on coupled transcription-translation, speculative hypotheses that might be entertained include an MMS hyperstructure being physically associated with an initiation/replication hyperstructure or a ribosomal hyperstructure; such association could then supply newly synthesized proteins directly to the appropriate hyperstructure (in the case of the primase, for lagging strand synthesis). Alternatively, the association between the MMS hyperstructure and another hyperstructure could result in the sequestering of newly synthesized proteins leading, in _E. coli_, for example, to a reduction in the level of sigma70 thereby favouring the other sigma factors and the emergence of a stress-adapted phenotype.
# The Holistic Perspective of the INCISIVE Project--Artificial Intelligence in Screening Mammography
## 5 Description of AI Toolbox/Breast Cancer Tools
### Preliminary Results--Initial Analysis on Mammography
#### 5.2.2 Annotation Style  
The medical professionals from the DP institutions annotated a fraction of the uploaded MGs using a predefined annotation protocol and contour segmentation. As presented in Table 1, the lesions/regions in the breast could be marked as: benign, malignant, or suspicious/indeterminate. Based on the analysis of the manually segmented MG images, the proportion of malignant and suspicious labels varies between data providers (Table 4). This might stem from the fact that certain clinicians evaluated images blindly, unaware of the accompanying clinical data, in order to make a fair and comparable decision on those made by image-based AI models, while others might make annotations with awareness of the clinical background and biopsy result. This problem might be alleviated by information extraction from clinical metadata containing the biopsy label for each patient in case of the presence of malignant/suspected or benign lesions in the accompanying images. The biopsy results will be considered as ground truth in image segmentation and classification tasks.  
Another interesting research avenue is related to the different style of manual segmentation, as experts can be more or less precise when contouring the lesions, which directly influences all quantitative measures for automatic image segmentation, such as the DICE coefficient. In order to approach this problem, the usability and efficacy of unsupervised solutions to infer the ground truth and introduce the segmentation quality control will be explored [64]. Moreover, the harmonization of the annotation procedure and alignment of multiple experts will be additionally achieved through a more detailed consensus in MG image annotation, which will serve as a guideline in a data donorship schema.
# Learning Kernel Stein Discrepancy for Training Energy-Based Models
## 5 Training Unnormalized Models with KSD
### PT KSD GAN (Kernel Stein Discrepancy Generative Adversarial Network with a Pulling-Away Term)
Acting as the GAN's energy model, the discriminator, \(D(x)\) processes the data \(x\). Its primary responsibility is to differentiate between the real sample \(x_{i}\) and fake sample \(y_{j}\) generated by the generator. 3. Simultaneously, the generator \(G(z)\), creates synthetic samples based on an input noise vector \(z\). This noise vector serves as a seed, ensuring diversity in generated samples. The fake samples are subjected to the PT Kernel Stein Discrepancy module. The integration of KSD, a non-parametric measure of discrepancy between distributions, aims to quantitatively evaluate the deviation of the generated samples from the target distribution. This evaluation imparts valuable feedback to the generator, steering it towards optimal data synthesis. 5. The outcomes from the PT Kernel Stein Discrepancy and Mean Absolute Error modules are collated to furnish a composite gradient that guides the generator's training. By assimilating feedback not only from the discriminator but also from an external discrepancy measure (KSD), the generator's training becomes more informed, focused, and robust against mode collapse.
## 4 System Model and Preliminaries
### Assumption  
The data communication pattern in ATLAS is receiver-driven [12], where a beacon packet (given by \(B\)) is used to initiate any sort of communications. The network is assumed to be time-synchronized and the beacon includes the timing information. Moreover, several control parameter such as traffic load status, requests for an explicit CFP slot for further data traffic are implicitly piggy-backed at the packets used for protocol operation.  
In ATLAS, all the devices communicate using an identical, half-duplex, low-cost single shared wireless radio. The routing process is circumvented in ATLAS, instead a static approach is used to generate a fixed route between each sensor to the gateway via one cluster-head. Hence, the routing layer tasks such as cluster formation and cluster-head selection are beyond the scope of this study, and protocols discussed in [4,19] can be adopted for this purpose. Note that the multi-hop path between sensors to the gateway is bi-directional.  
Figure 2: Network model for WBAN.
## 3 Scenario
### Virtual Scenario Description  
To generate the dataset, a virtual infrastructure was deployed using the ESXi 6.5 hypervisor. Through the vSwitch capacity provided by this hypervisor, an isolated Ethernet network, called the IoT network, hwas configured, so that the infrastructure is not interfered with by external or unwanted traffic. Five virtual machines with the Ubuntu 20.04 operating system were deployed in this network, four of which represent the four CoAP servers, called inRow, which in turn have four sensors each, while the fifth virtual machine implements the CoAP client functions and therefore implements the messages sent to each of the four sensors of each of the four CoAP servers, thus performing the function of a system monitor. Figure 1 shows the scenario with different virtual machines that contain a client and four CoAP servers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **IoT Network** & **Anomaly** & **ML Validation** \\ \hline MedBioT [32] & IoT devices & Mirai, BashLite, Torii & KNN, SVM, DT, and RF. \\ \hline \multirow{3}{*}{X-IloTID [40]} & \multirow{3}{*}{IIoT} & Weaponisation, exploitation, lateral movement, exfiltration, tampering, crypto-ransomware, ransom denial of service & DT, NB, KNN, SVM, LR, DNN, and GRU \\ \cline{3-4}  & & & NB, DT, LDA, AdaBoost, Ridge, Perceptron, Passive-Agressive, XGBoost, KNN, RF, LSVC, and SGD \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._The CoAP client message, sent by the client virtual machine, contains a CoAP packet built using the Scapy for Python tool [43], where the IP and UDP protocols are concatenated and the token value is randomly generated. Finally, the CoAP packet is sent asynchronously using the send method from the Scapy library. In each of the four virtual machines that simulate the operation of the InRows, a server CoAP that listens for messages from the CoAP client was implemented, using the sniff method of the Scapy library and performing a filtering process. For each message received, a reply is sent, which includes the temperature value for each node. The temperature values were calculated based on the time series implemented in [33].  
Each of the samples generated by the sensors is sent every five minutes to the client through a CoAP message that contains its node identifier as a clientID (using URI-Host and URI-Path values). The capture of the traffic exchange between the nodes is executed on the client. The connection information is stored to be latter tagged in the dataset via Scapy. Most abnormal packets are flagged on the server, except those that belong to the interception anomaly.
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 2 Literature Survey
This study's hybrid model recognizes Arabic Sign Language using deep learning, Convolutional Neural Networks (CNN), and LSTM classifiers. Sign language spatial and hand movement sequence data are collected by the model. A total of 500 dynamic gesture word films and 4000 Arabic Sign Language (ArSL) photos from a 20-word dataset showed the model's performance. The model's accuracy of 82.70% and accuracy of 94.40% implied that it could help Saudi Arabia's hearing-impaired communication. [PERSON] and [PERSON] [23] suggested a new boundary-adaptive learning-based encoder-decoder approach to Chinese sign language recognition (SLR). With the help of the boundary-adaptive encoder (BAE), this technique encodes the hierarchical structure inside sign language signals. A location-based window attention model makes the decoding step more efficient. Additionally, the approach incorporates both discrete and continuous SLR in Chinese through subword units in sign language. While the approach has shown competitive results on several major benchmarks, it is not yet lightweight or optimized for real-time performance. The study's primary limitation lies in its focus on enhancing efficiency. [PERSON] et al. [24] solved sign language machine translation problems with 3D motion capture and graph matching. Three-dimensional sign matching issues include identifying the same signs in multiple motion frames and extracting signs from non-sign hand movements. Two-phase graphs that match early estimates help solve these problems. Intra-graph matching searches databases for motion-intensive frames. Early-estimation model inter-graphs match motion-derived queries and datasets. With this method, graph-matching estimates sign faster with less frames. The model is evaluated on 350 Indian sign language terms with four variations for each sign by five signers at different hand speeds and motions. [PERSON] et al. [25] indicated that the public struggles to learn sign language for speech and hearing-impaired purposes. Sign language could aid interaction. Current approaches are expensive because they require worn sensors. A unique sensor-based-based hybrid deep neural net approach identifies Indian and Russian sign motions. The framework detects non-manual and manual co-articulations. Attention-based Bi-LSTM extracts temporal and sequential features, while a 3D deep neural net with atrous convolutions extracts spatial features. Modified autoencoders and hybrid attention modules select abstract and discriminative features. Models outperform present ones.
## 3 Results and Discussion
### Supervised Learning: Classification
As the test size increases, Logistic Regression shows a gradual decline in accuracy, indicating a potential sensitivity to data  
Figure 8: Accuracies of 90-sample dataset with (**a**) k-means method k = 5, then f = 4; (**b**) percentile method f = 4; (**c**) percentile method f = 5.  
size. Both Random Forest and XGBoost maintain relatively stable trends in accuracy across the range of test sizes. Random Forest performs between 0.41 and 0.52, while XGBoost achieves accuracies ranging from 0.44 to 0.61.
## 6 Application of Algorithm to Various Scenarios for Fault Identification: Case Studies
### Fault identification with Current Measurement on SE Injection Node  
To generalize the applicability of the proposed algorithm, the current was recorded at node 680 (PR-2 relay location) of the test system where solar power was injected during a phase-A to ground fault on node 671. The current was processed using the algorithm and the results are illustrated in Figure 12. A weight with a value of 5 was used for FI to detect the fault on the PR-2 relay location (node 680). Figure 12 illustrates that the fault index corresponding to the faulty phase (phase-A) is higher than the threshold whereas this FI has a lower magnitude corresponding to healthy phases (phase-B & C) than the threshold. Hence, the algorithm effectively detects the AG fault on node 671 by a current measurement on node 680 (point of SE injection).
# Impact of Cytochrome P450 Enzymes on the Phase I Metabolism of Drugs
## 2 Examples of Drugs Metabolized by CYPs
### Tamoxifen  
Tamoxifen (Figure 12) is a well-known anticancer drug belonging to selective estrogen receptor modulators (SERMs) used in the treatment of early and metastatic hormone receptor-positive breast cancer [89]. It is a prodrug, which is metabolized via the CYP enzymes to the more active primary metabolites \(N\)-desmethyl-tamoxifen, 4-hydroxy-tamoxifen and the secondary metabolite endoxifen (\(N\)-desmethyl-4-hydroxy-tamoxifen) that are responsible for the anticancer activity [90]. The main route of phase I tamoxifen metabolism is its demethylation to \(N\)-desmethyl-tamoxifen, catalyzed essentially by CYP3A4/5 [91], whereas the 4-hydroxy-tamoxifen is mainly produced by the action of CYP2D6. However, the formation of \(N\)-desmethyl-tamoxifen has also been reported to be catalyzed by CYP2D6 [92]. Single-nucleotide polymorphisms (SNPs) of CYP2D6 play a crucial role in the rate of tamoxifen metabolism, since they have a potential effect in the efficacy of tamoxifen-based therapies. It was recently suggested that pharmacogenomics may play essential roles in the advancement of adjuvant therapies with tamoxifen. The investigation of further SNPs and epigenetic changes of CYP2D6 affecting tamoxifen metabolism may be envisaged, which may enable the design of precision medicine [93]. The involvement of other CYPs, including CYP2C19, CYP1A1, CYP2A6 and CYP3A4, has also been  
Figure 11: The structure of omeprazole and its metabolites.  
reported [94]. Endoxifen and 4-hydroxy-tamoxifen have 100-fold higher affinity for the target estrogen receptor and 30- to 100-fold higher potency than tamoxifen. The secondary metabolite, endoxifen, is mostly responsible for the tamoxifen's anticancer activity [95].
# Quantum Recurrent Neural Networks: Predicting the Dynamics of Oscillatory and Chaotic Systems
## 3 Methodology
### Gated Recurrent Units (GRU)  
Gated Recurrent Unit (GRU) is a variation of recurrent neural networks that aims to solve the vanishing gradient problem, similar to LSTM. GRUs simplify the architecture seen in LSTM by combining certain gates and states, which often results in more efficient training for certain types of problems. The structure of a single GRU is shown in Figure 3.  
The GRU architecture is built around two gates: the update gate and the reset gate. These gates determine how much of the past information needs to be passed along to the future. The key equations defining a GRU are:  
\[z_{t} =\sigma(W_{z}\cdot[h_{t-1},x_{t}]+b_{z}), \tag{20}\] \[r_{t} =\sigma(W_{r}\cdot[h_{t-1},x_{t}]+b_{r}),\] (21) \[\tilde{h}_{t} =\tanh(W\cdot[r_{t}*h_{t-1},x_{t}]+b),\] (22) \[h_{t} =(1-z_{t})*h_{t-1}+z_{t}*\tilde{h}_{t}. \tag{23}\]  
In Equations (20)-(23), \(z_{t}\) and \(r_{t}\) represent the activations of the update and reset gates at time \(t\), respectively. \(h_{t}\) is the hidden state at time \(t\), and \(\tilde{h}_{t}\) is the candidate hidden state. \(W\) and \(b\) are the weights and biases associated with the respective gates and hidden state updates. The symbol \(*\) denotes element-wise multiplication, and \(\sigma\) represents the sigmoid activation function.  
GRUs provide an efficient alternative to LSTM and are particularly useful in modeling sequences where LSTM's complex structure may not be necessary.  
We need to consider the hardware realization of two activation functions here, which are the _tanh_ and _sigmoid_ functions. On classical hardware, _tanh_ and _sigmoid_ functions are computed directly, introducing nonlinearity in neural networks crucial for learning complex patterns:  
Figure 3: Structure of a single unit of classical GRU.  
\[\tanh(x) =\frac{e^{2x}-1}{e^{2x}+1}, \tag{24}\] \[\sigma(x) =\frac{1}{1+e^{-x}}. \tag{25}\]  
Classical computers execute these operations efficiently using their processing capabilities. However, quantum hardware employs a different approach due to its linear operation nature, making direct computation of _tanh_ and _sigmoid_ functions non-trivial. The encoding and variational layers in the VQCs allow the quantum-based models to catch the nonlinear trends across data inputs. Additionally, the measurement layer outputs values within the range of \([-1,1]\), which can then be processed classically to implement these nonlinear functions.
# Resource Mapping Allocation Scheme in 6G Satellite Twin Network
## 3 System Model of 6G Satellite Twin Network
### A Hierarchical Unified Feature Description Method
According to Theorem 1, when the performance index of the node \(p_{d_{n}}^{\mathrm{S}}\) meets the requirements of the communication system, the node \(d_{n}\) can be divided into the twin domain. The attributes of resources can be expressed as follows:  
**CPU \(\mathbf{cap}f_{j}^{\mathrm{S}}\)**: indicates the processor resource consumed when processing the function \(f_{j}^{\mathrm{S}}\). **Memory consumption \(\mathbf{mem}f_{j}^{\mathrm{S}}\)**: indicates the memory consumed when processing the function \(f_{j}^{\mathrm{S}}\). **Bandwidth \(\mathbf{ban}f_{j}^{\mathrm{S}}\)**: indicates the data rate when transferring the function \(f_{j}^{\mathrm{s}}\). **Function level \(\mathbf{level}f_{j}^{\mathrm{S}}\)**: determines the function level according to the function utilization, which has been defined before using it. **Function cost \(\mathbf{cost}f_{j}^{\mathrm{S}}\)**: When the execution time of a function exceeds the specified acceptable time, the cost of processing the function is increased. The computing system can be represented as \(G^{\mathrm{C}}=\left\{F^{\mathrm{C}},R^{\mathrm{C}},P^{\mathrm{C}}\right\}\). \(F^{\mathrm{C}}=\left\{f_{0}^{\mathrm{C}},f_{1}^{\mathrm{C}},\cdots,f_{J}^{ \mathrm{C}}\right\}\) denotes the set of functions running on this computing system. \(R^{\mathrm{C}}=\left\{ \begin{array}{cc}s_{11},s_{12},\ldots,s_{1q},\ldots,s_{ 1Q}\\ \cdots&\end{array} \right\}\) denotes the set of resources available in this computing system. The computing system consists of P servers, each of which is extended with Q docker containers. \(P^{\mathrm{C}}=\left\{p_{1}^{\mathrm{C}},\cdots,p_{Z}^{\mathrm{C}}\right\}\) denotes the performance indexes. According to Theorem 1, when the performance index of the node \(p_{d_{n}}^{\mathrm{C}}\) meets the requirements of the communication system, the node \(d_{n}\) can be divided into the computing domain. \(s_{pq}=\left\{\mathrm{caps}_{pq},\mathrm{mem}s_{pq},\mathrm{bans}_{pq}\right\}\)denotes the available resource in the docker container q. The attributes of \(s_{pq}\) can be expressed as follows:  
**CPU caps\({}_{pq}\)**: indicates the available processor resource of the resource \(s_{pq}\) in the docker container q. **Memory mem\({}_{pq}\)**: indicates the available memory of the resource \(s_{pq}\) in the docker container q. **Bandwidth bans\({}_{pq}\)**: indicates the data rate of the resource \(s_{pq}\) in the docker container q. **Resource cost costs\({}_{pq}\)**: indicates the cost of power, software, and equipment spent when processing.
# A New Method for Haul Road Design in Open-Pit Mines to Support Efficient Truck Haulage Operations
## 4 Case Study  
### Study Area and Data  
The proposed road design method for open-pit mines was tested by designing the road for an open-pit mine. Land containing a gold deposit located in the OO area was selected as the study area. The size of the study area was 430 m \(\times\) 980 m, and the gold-carrying ore body existed in an elongated shape in the N-S direction (see Figure 10). The average altitude above sea level is approximately 564 m, the terrain is relatively flat, and there is a mining town nearby. Thus, the area has the optimal infrastructure facilities required for mine development.  
Figure 10: Three-dimensional view of the study area. (**a**) Block model of gold deposits; (**b**) Gold ore body marking the grade of gold.  
Figure 9: Conceptual view of factors to be considered when generating a 3D haul road. (**a**) Cross section of a haul road showing the height (solid line) of the ground and the vertical layout (dashed line); (**b**) Road geometry showing a cross section of the road  
The block model of the ore body that exists in this area is composed of blocks with the size of 12.5 m \(\times\) 12.5 m \(\times\) 6 m. The number of blocks is 36 in the x-axis direction (easting), 80 in the y-axis direction (northing), and 31 in the z-axis direction (elevation). The blocks are divided into ore and waste and include information about the mass and grade of the ore. The average grade of the gold ore body is 0.073 g/ton.