esa-sceva/satcom-chunk-collection huggingface dataset:
# How Automated Machine Learning Can Improve Business
## 3 Results and Discussion
### Impact of AutoML on Business Operations
#### 3.4.1 Efficiency and Productivity  
AutoML significantly enhances efficiency and productivity within organizations by automating many of the labor-intensive tasks involved in machine learning model development. Traditional machine learning workflows require extensive manual effort, from data preprocessing and feature engineering to model selection and hyperparameter tuning [47]. These tasks can be time-consuming and require specialized knowledge, often leading to bottlenecks in the development process. AutoML addresses these challenges by automating these stages, enabling businesses to build and deploy models faster and with less human intervention [48,49]. This automation speeds up the time-to-market for new products and services and frees up valuable resources, allowing data scientists and engineers to focus on more strategic tasks.  
The ability to quickly iterate and refine models through AutoML also leads to more accurate and reliable outcomes, further improving productivity [50]. In addition, AutoML's capacity to handle large volumes of data and explore a wide range of models in parallel means that businesses can scale their operations more effectively, tackling more projects in less time and with greater consistency [3].  
This efficiency translates into competitive advantages, as companies can respond more rapidly to market changes and customer needs, ultimately driving growth and innovation.
## 2 Related Works
### Related Research on In Situ Data  
In situ data and its underlying concept have been widely applied across various domains, including high-performance computing and distributed systems.  
[PERSON] et al. proposed the In situ Server System, which brings computing resources closer to data sources to preprocess part or all of the in situ data, thereby accelerating data response and storage efficiency [16]. Cisco introduced the concept of fog computing, leveraging devices near data sources (such as routers) to directly process in situ data, aiming to reduce dependence on the cloud. Although this approach has achieved certain success in alleviating network traffic, its focus remains on network optimization, and the computational capabilities and applicability of in situ computing are still relatively limited [17]. In the field of high-performance computing (HPC), large-scale compute-intensive tasks are common, particularly in scientific simulations and big data analysis. These scenarios often involve massive datasets requiring complex computations within limited timeframes. To reduce I/O overhead and enhance processing efficiency, the concept of in situ computing has been introduced to efficiently process large-scale data without incurring additional transmission costs [18; 19].  
In summary, existing research demonstrates the significant value of in situ computing and its concepts in various fields, particularly in improving data processing efficiency and reducing system burden. However, for distributed terminal data in smart grid scenarios, systematic studies on efficient in situ data storage and management remain lacking. Based on the InS system architecture, this paper proposes an in situ data management method for smart grids, aiming to optimize data access efficiency at the terminal side, reduce overall system latency, and further promote the intelligent evolution of smart grid data management.
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 4 Limitations and Challenges  
Despite the numerous advantages and growing adoption of MALDI-TOF MS in food microbiology, the technique is not without its limitations. These constraints span technical, operational, and biological aspects that can impact the reliability, resolution, and scope of microbial identification in complex food systems. A nuanced understanding of these challenges is essential for the appropriate application of the technology and for guiding future developments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Application Type** & **Food Matrix** & **Target Organism(s)** & **MALDI-TOF MS Role** & **Reference** \\ \hline Routine pathogen & & & & \\ identification & & & & \\ Quality control during & & & & \\ production & & & & \\ Post-processing & & & & \\ contamination detection & & & & \\ Detection of spoilage & & & & \\ bacteria & & & & \\ \hline Microbiota profiling & & & & \\ \hline Outbreak-source tracing & & & & \\ \hline Seafood pathogen & & & & \\ monitoring & & & & \\ Hygiene monitoring & & & & \\ Protection of resistant & & & & \\ strains & & & & \\ Rapid detection of & & & & \\ outbreaks & & & & \\ Rapid detection of _Listaria_ & & & & \\ _monocytogenes_ & & & & \\ Detection of food & & & & \\ adulterations & & & & \\ Detection of _Cronobacter_ & & & & \\ spp. & & & & \\ Identification of atypical & & & & \\ _Listeria_ spp. & & & & \\ Discrimination of & & & & \\ _Salmonella Enteritidis_ & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected Applications of MALDI-TOF MS for the Detection of Foodborne Pathogens.
## 3 Results  
We want to examine Equation (25) for the typical values of the single mode OF characteristics. We choose the standard telecommunication OF suitable for transmission of a signal with wavelength 1550 nm (like SMF-28). Then \(\alpha=-0.2\) dB/km, \(c=3\cdot 10^{5}\) km/s, \(\beta=1.5\). We do not assume \(\Delta\)as it will be considered as a further parameter (this parameter can be determined with standard polarimetric measurements). According to Equation (28) for these parameters we get \(\gamma_{H}=1829\) Hz. For temperature of 300 K and wavelength 1550 nm we estimate \(n_{T}=10^{-13}\). Parameter \(\theta\) can be determined with standard polarimetric measurements based on the system's construction. Thus, it is not a typical parameter and its value may be arbitrary. Parameter \(\sigma\) is determined with standard polarimetric measurements as well for a particular electro-optical modulator or is defined in a technical passport. We acknowledge that the value of this parameter may vary significantly as it is dependent on the type of nonlinear crystal used in particular modulator and modulator design in general. As a result, for some modulators the value of the modulation indices ratio may be close to one, for others close to zero.  
Equation (25) seems to be a bit complicated for direct analysis. Thus, we would like to introduce approximated expression (second order of \(t\)) for Equation (25) as follows:  
\[Q_{app}(t)=\frac{t^{2}}{64\mu^{2}(1-\sigma)^{2}}\left(\gamma_{H}\left(1-\Delta \right)n_{T}\cot\left(\theta\right)+\mu\gamma_{H}\Delta(1-\sigma)^{2}\sin(2 \theta)+\gamma_{H}n_{T}\tan\left(\theta\right)\right) \tag{29}\]  
This approximation works well (relative divergence is less than few percents) up to around 150 km (recalculating propagation time into length of the OF by \(L=\frac{ct}{B}\)). We estimate the maximal contribution to the QBER at 150 km by choosing appropriate \(\theta\) that maximizes the value for different \(\Delta\). The obtained dependence is shown in Figure 1. Contribution of dichroism was found to be weak; even for extreme values of \(\Delta\) it is no more than 3%. Typical values of \(\Delta\) are about \(\pm 0.1\) then the maximal expected contribution will be no greater than tenth percents. Despite that in case of fine tuning of the system (e.g., 1-1.5% QBER) even the small contribution is worth being concerned. Obviously in general the contribution of imperfect devices will be more tangible. Imperfect spectral filtration provides constant contribution to the QBER and detector's dark count become crucial at high distances. Typical tolerable QBER for SCW QKD systems is about 5-7%. The upper bound for QBER is in general defined by hardware and software characteristics, hence it may vary.  
Figure 1: Dependence of approximate maximal value of quantum bit error rate (QBER) on \(\Delta\) that characterized dichroism at 150 km OF length for several values of \(\sigma\). For even smaller values of \(\sigma\) dependence does not sufficiently increase QBER contribution. Typical values of \(\Delta\) are about \(\pm 0.1\) so we may conclude that the considered effect is weak.
# Optimal Base Station Density of Dense Network:
## 3 The Expected Link Rate of the SmBS Tier
### The Expected Link Rate of the SmBS Tier  
With the Equations (4), (5) and (7), we can get the expected link rate of the SmBS tier based on the Shannon formula. The expression of the expected link rate of the SmBS tier is  
\[R=A_{\mathrm{m}}\int\limits_{\eta}^{\infty}\int\limits_{\eta}^{\infty}f_{I} \left(I\right)\,W_{\mathrm{m}}\log\left(1+\frac{P_{\mathrm{m}}r^{-\alpha}}{I+ \sigma^{2}}\right)f_{R_{\mathrm{m}}}\left(r\right)drdI, \tag{8}\]  
where \(\eta\to 0\). \(\sigma^{2}\) is the power of noise. Considering the propagation environment where the small cells are deployed, according to [28], we assume \(\alpha=4\). Thus, we have  
\[R=\pi^{2}W_{\mathrm{m}}\frac{\lambda_{\mathrm{m}}^{3}P_{\mathrm{m}}^{1/2}}{ \mathcal{C}\lambda_{\mathrm{M}}+\lambda_{\mathrm{m}}}\int\limits_{\eta}^{ \infty}\frac{\mathrm{e}^{-\frac{\pi^{2}P_{\mathrm{m}}a_{0}^{2}}{I}}}{I^{3/2}}r \exp(-\pi\lambda_{\mathrm{m}}r^{2})\log\left(1+\frac{P_{\mathrm{m}}r^{-4}}{I +\sigma^{2}}\right)drdI. \tag{9}\]  
It is observed that the link rate is the function of SmBS density. Thus, by selecting the reasonable deployment density, the link rate can be optimized.
# From Genomics to AI: Revolutionizing Precision Medicine in Oncology
## 4 Genomics in Cancer Treatment
### Immunotherapy  
Genomic insights have also played a central role in revolutionizing cancer immunotherapy, which consists of the use of the body's immune system to recognize and destroy cancer cells. One of the most significant contributions of genomics in immunotherapy has been the identification of genomic biomarkers that predict a tumor's probability of responding to immune checkpoint inhibitors [48]. Tumors that exhibit high microsatellite instability (MSI-H) or high tumor mutational burden (TMB) are more likely to respond favorably to immune checkpoint inhibitors, such as pembrolizumab or nivolumab [49]. These drugs block the interaction between immune checkpoint proteins (e.g., PD-1/PD-L1), which are often used by cancer cells to evade immune detection. The increased mutation rate in MSI-H tumors or tumors with a high TMB leads to the expression of neoantigens (tumor-specific antigen) that the immune system can recognize as foreign, thereby improving the response to immune therapy [50].  
High TMB results in the generation of a larger number of neoantigens, which are presented by antigen-presenting cells to T cells, particularly cytotoxic CD8+ lymphocytes. This stimulates a stronger adaptive immune response within the tumor microenvironment, creating an \"inflamed\" state with increased infiltration of immune cells. Immune checkpoint inhibitors work by reinvigorating these exhausted T cells, allowing them to effectively attack cancer cells. In contrast, tumors with low TMB tend to have fewer neoantigens and a less immunogenic microenvironment, making them less responsive to checkpoint blockade therapies [51].  
Additionally, neoantigens, which are novel protein fragments produced by tumor-specific somatic mutations and not found in normal tissues, are becoming increasingly important in the development of personalized cancer vaccines. These vaccines are designed to stimulate an immune response specifically against neoantigens expressed on tumor cells, offering a customized treatment approach for individual patients based on the genetic profile of their cancer [52]. Immunotherapy, supported by genomic insights, is expanding the options available for patients, especially those with cancers previously deemed difficult to treat, such as melanoma and non-small cell lung cancer.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Gene** & **Cancer Type** & **Mutation Effect** & **Targeted Therapy** \\ \hline _EGFR_ & NSCLC & Constitutive activation & Erlotinib, psimertinib \\ _BRCA1/2_ & Breast, ovarian & Impaired DNA repair & PARP inhibitors \\ _ALK_ & NSCLC & Gene fusion & Crizotinib, alectinib \\ _KRAS_ & Colorectal, pancreatic & Uncontrolled proliferation & KRAS G12C inhibitors \\ _TP53_ & Many cancers & Loss of tumor suppressor & No targeted therapy yet \\ \hline \end{tabular}
\end{table}
Table 3: Key oncogenes involved in cancer progression, their associated mutation effects, and current targeted therapies. This summary highlights the impact of genomic profiling in guiding personalized treatment decisions across various cancer types.
# Energy-Aware Next-Generation Mobile Routing Chains with Fog Computing for Emerging Applications
## 5 Conclusions  
This work explores fog computing to propose a technique for energy-efficient mobile routing. The proposed algorithm provides data forwarding by exploring reliable search techniques and shortening the data transmission time. Moreover, the proposed algorithm provides the balancing of energy consumption among the devices, with the support of optimal criteria. The mobile device not only decreases the latency with the combination of fog nodes, but it also offers a lightweight authentication scheme for the node verifier. Another contribution of the proposed algorithm is protecting the data in the presence of attacks. The performance results demonstrate the significant outcomes of the proposed algorithm against existing work; however, it was observed that still, the proposed algorithm produces additional overhead and communication costs when the number of mobile devices increases. Furthermore, methods need to be designed for more efficient routing with the combination of deep learning models. In our future work, we aim to combine the intelligence and processing power of software-defined networks to cope with the efficient management of nodes and network resources. In addition, improved and lightweight intrusion detection systems require by exploring machine learning.  
Conceptualization, K.H. and M.S.; methodology, K.H. and M.S.; software, Z.U. and J.L.; validation, F.A.A., J.L.; formal analysis, K.H., Z.U.; investigation, F.A.A. and J.L.; resources, J.L. and M.S.; data curation, Z.U.; writing--original draft preparation, K.H. and F.A.A., M.S.; writing--review and editing, Z.U. and J.L.; visualization, Z.U. and J.L.; supervision, K.H. and J.L.; project administration, F.A.A. and J.L.; funding acquisition, F.A.A., J.L. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through project number: IFP22 UQU4170008 DSR265.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 9: (**a**) Energy consumption with message generation interval. (**b**) Node level energy consumption with mobile devices.
# A Synergistic Elixir-EDA-MQTT Framework for Advanced Smart Transportation Systems
## 2 Elixir Programming Language
### Elixir: Building on Erlang's Legacy  
Elixir, built on top of the BEAM virtual machine, inherits Erlang's core strengths. BEAM compiles Elixir code to bytecode for efficient execution. Lightweight processes and message-passing communication foster concurrency and fault tolerance, as failures in one process do not affect others. This inherent resilience is crucial for edge environments where reliability is paramount. Unlike imperative languages, like Java and C++, that rely on shared memory and heavyweight threads, Elixir's message-passing model avoids complex synchronization issues and performance bottlenecks associated with shared resource contention.  
Beyond inheriting Erlang's strengths, Elixir offers additional advantages for building scalable and maintainable edge applications:  
* Functional programming paradigm: Elixir encourages a side-effect-free programming style, where functions produce outputs solely based on their inputs, simplifying code comprehension and testing.
* Powerful tools and libraries: Elixir provides a rich ecosystem of libraries and tools designed for building robust and maintainable applications.  
These combined benefits make Elixir a compelling choice for developing reliable and performant edge computing frameworks. The next subsection delves deeper into Elixir's programming model and its specific advantages for edge development.
# Designing a C-ITS Communication Infrastructure for Traffic Signal Priority of Public Transport
## Materials
### Novelty and Innovation  
Although many of the aspects of the GLOSA and TSP services have already been extensively considered both scientifically and technically over the past 10 years or so, there have still not been any widespread implementation efforts. This is due to several reasons, and this is where the novelty of this paper begins. The following list represents the main aspects and innovations that were examined:  
* Range anxiety: The range anxiety studies were mostly theoretical. However, theoretical investigation is only one aspect. The novelty here is that these investigations took place theoretically and practically and were matched to suit the target system.
* The propagation models for radio communications in planning tools must be adapted to the G5 standard. This is often not considered or not sufficiently considered in the current tooling. Here, a new tool that permanently adjusts itself on the basis of the collected data is shown.
* So far, the C-ITS services have been described and evaluated individually. However, in interactions, much greater potential can be tapped. This aspect was thoroughly considered for the first time.
## 8 tRNAs and Antibiotic Persistence and Resistance  
### Deacylated tRNA and the Stringent Response  
Nutritional stress or inhibition of the aminoacylation reaction of aaRSs results in the accumulation of deacylated tRNA. These uncharged tRNAs then bind to the A-site on the ribosome and are recognized by RelA and/or SpoT, which then trigger the stringent response mechanism [171; 172; 173; 174; 175; 176]. The resultant effect of the stringent response mechanism is elevated levels of alarmones, such as guanosine tetraphosphates (ppGpp) and guanosine pentaphosphates (ppGpp), which then inhibit RNA polymerase [171]. Thus inhibition of protein synthesis subsequently leads to down regulation of RNA synthesis [177]. Recently a novel compound relacin has been found that inhibits RelA and the subsequent production of (p)ppGpp [178]. The resultant effect of the stringent response leads to a series of physiological changes in the bacterium that aids in survival under adverse conditions, such as exposure to antibiotics. Several examples of persistence of microorganisms under stress conditions have been observed [179]. One of the effects involves formation of biofilms, which involves self-assembly into highly organized, surface attached and matrix encapsulated structures. Both, _Mycobacterium tuberculosis_ and _Mycobacterium smegmatis_ form biofilms under conditions of stress and can tolerate about 50 times the MIC of antituberculosis drugs, such as isoniazid and rifampicin [180; 181; 182; 183], thus making them less sensitive to these antibiotics. In some bacteria, such as the human pathogen _Pseudomonas aeruginosa_, quorum sensing gets elevated upon expression of the _relA_ gene [184]. Elevated levels of (p)ppGpp also results in increased virulence, pathogenesis and survival in the host. This has been demonstrated in _Pseudomonas_ sp., _Samonella_ sp., _Vibrio cholera_ and _Legionella_ sp. [185; 186; 187; 188]. Increase in the production of secondary metabolites and antibiotics has also been reported in some organisms, such as _Streptomyces coelicolor_[189]. Some other persistence responses include initiation of sporulation in _Myxococcus_ sp. due to increased levels of (p)ppGpp [190; 191] and formation of nodules in soil bacteria, such as Rhizobia, in response to nitrogen limitation [192; 193]. Targeting deacylated tRNA bound to the ribosome and/or interfering with the deacylated tRNA-dependent RelA activity is promising target to prevent the antibiotic persistence phenotype seen in a number of important human pathogens [178].
## 3 Proposed ML-Based Rotor Observer
### Learning Process
#### 3.3.1 Training Data and Validation Data  
A control system has a certain control range; this is also true in the present ML-based control system. The ML network must learn from training data that cover the entire range of motor speeds and torque control operations, including both speed and torque transitions. The speed variation commands and load variation profile for training data acquisition are shown in Figure 6a. Validation data were acquired using the profiles displayed in Figure 6b.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Flux & 0.0064 Wb & Rated Speed & 3000 rpm \\ Inductance (\(L_{d}=L_{q}\)) & 0.2 mH & Rated Voltage & 24 V \\ Resistance (\(R_{s}\)) & 0.36 Ohm & Rated Current & 7.1 A \\ Inertia Coefficient (\(J\)) & 7.06 \(\times\) 10\({}^{-6}\) Kg\(\cdot\)m\({}^{2}\) & Rated Torque & 0.27 Nm \\ Friction Coefficient (\(B\)) & 2.64 \(\times\) 10\({}^{-5}\) Kg\(\cdot\)m\({}^{2}\)/s & Pole Pair & 4 Pairs \\ \hline \hline \end{tabular}
\end{table}
Table 1: PMSM parameters.  
Figure 6: Speed commands and load profile settings for (**a**) training and (**b**) validation data acquisition.  
For the training and validation processes, learning data were recorded from a simulation. An example of the acquired data is presented in Figure 7. Data were recorded and saved as arrays; each data array contained six values: \(v_{x}\), \(v_{\beta}\), \(I_{x}\), \(I_{\beta}\), \(sin\)\(\theta\) and \(cos\)\(\theta\). The sizes of the training and validation data arrays were 660.000 and 100.000, respectively.