esa-sceva/satcom-chunk-collection huggingface dataset:
## Part B: Network Design for Energy Optimization
### The Model and its Combinatorial Abstraction
This sensor can operate for 3 intervals. If we start it at \(\tau_{1}\) then the element (\(r_{i5},\tau_{1}\)) \"covers\" the elements (\(r_{i5},\tau_{2}\)), (\(r_{i5},\tau_{3}\)) but also (\(r_{j7},\tau_{1}\)), (\(r_{j7},\tau_{2}\)), (\(r_{j7},\tau_{3}\)) and (\(r_{k30},\tau_{1}\)) (\(r_{k30},\tau_{2}\)) (\(r_{k30},\tau_{3}\)). (b) A sensor is attached to a moving object that moves from \(r_{11}\) to \(r_{12}\) to \(r_{13}\) and then \(r_{34}\), \(r_{35}\), \(r_{36}\). The sensor lasts 6 intervals and our moving object starts at \(\tau_{5}\). Then, element (\(r_{11},\tau_{5}\)) \"covers\" elements (\(r_{12},\tau_{6}\)), (\(r_{13},\tau_{7}\)) and also (\(r_{34},\tau_{8}\)), (\(r_{35},\tau_{9}\)), (\(r_{36},\tau_{10}\)) (and itself, of course). In general, each placement of a certain sensor activated at a certain time and operating for a certain time, corresponds to a _set of \"covered\" elements_. Each such \"set\" has _a certain cost_ e.g. it is more expensive if the sensor's battery is such that the sensor lasts for a long time. Also it is more expensive if its sensing range is large and can \"see\" more route pieces. **Definition 12**: A redundant monitoring design (RMD) D is a set of possible choices of \(q\leq n\) sensors\(\sigma_{1},\ldots,\sigma_{q}\), each with a placement act \(A(\sigma_{i})\) of the form \"deploy\" and the associated cost of the placement act. Each RMD results in a family of sets \(\Sigma_{1},\ldots,\Sigma_{q}\), each having a cost \(c(\Sigma_{i})>0\) and each being a subset of our universal set of elements \(U=\{e:e\) is a pair \((r_{ij},\tau_{m})\}\). For _feasibility_ of the RMD we can require:  
(a) that the union of all \(\Sigma_{i}\) is \(U\). (b) that each \(e\) in \(U\) belongs to at least 3 \(\Sigma_{i}\) sets initially. However, this is not necessary for our method, since the method itself will discover an infeasible RMD. **Definition 13**: Given are an instance of an RMD of sets \(\Sigma_{1},\ldots,\Sigma_{q}\) on the universe \(U\) of elements related to the domain \(S\) and the period \(T\), and also a cost \(c(\Sigma_{i})\geq 0\) for each \(\Sigma_{i}\). Then, an _optimal_ final monitoring decision (Optimal FMD) is a sub-collection, \(F\), of sets \(\{\Sigma_{t_{1}},\ldots,\Sigma_{t_{q^{\prime}}}\}\) (where \(q^{\prime}\leq q\) and each \(t_{i}\in\{1,2,\ldots,q\}\)), whose total cost \(c(F)=\sum_{j=1}^{q^{\prime}}c(\Sigma_{t_{j}})\) is minimum, and such that (i) \(\cup_{i}\sum_{t_{i}}=U\) (ii) each \(e\) in \(U\) belongs to at least 3 sets in the sub-collection \(F\). We note that we can construct several RMDs for each run of our Algorithm, get a close to optimal solution (FMD) for each and select the best among them.
## 1 Introduction
[PERSON] et al. [37] have also reported about the difficulties in rearing bacterium in the soil because of various reasons like substances in high amounts disabling the bacteria in urea hydrolysis. Mechanism of enzymatic activity can also be beneficial in heavy metal remediation [38,39]. In-situ application of the EICP technique has vast innovative possibilities because of its ease of implementation in environmental applications and construction like bioremediation and bioccmentation [40]. Urease enzyme is extensively found in nature and present in different forms in plants, soils, fungi and mammals [41, 42, 43, 44]. Urease used in this study is extracted from jack bean, technically termed as _Canavalia ensiformis_[42]. Use of non-fat milk powder is found to create nucleation sites in soil mass; these nucleation sites facilitate the precipitation of CaCO\({}_{3}\) by urease enzyme anddelays the process of precipitation of CaCO\({}_{3}\), and; this delay in precipitation is beneficial for precipitate morphology [21]. Soil stabilization is usually carried out using conventional stabilizers like cement, lime, fly ash, fibers to enhance the geotechnical properties [45]. The potential applicability of bio-cementation for various field applications has not been explored [46]. The current study relies on the applicability of EICP method in reducing the permeability of soil and its efficacy in restricting the contaminant mobility [47]. It can effectively reduce the built of water near building pits due to seepage [48]. Apart from permeability, the swelling phenomenon of soils due to varying moisture content(s) is also a major concern for majority of civil engineering applications [49]. In majority of the cases, the swell induced stresses overtake their respective soil bearing capacities leading to catastrophic disasters and economic deficits [50]. Moreover, rapid industrialization has led to the pollution of soil and water resulting in accumulation of high concentration of heavy metals which are known to intrude into the cell cycle of living organisms leading to cancer [51]. In lieu of this, there is an urgent need to look for suitable sustainable remedies which can reduce the toxicity of these heavy metals in soils. In the present study, soils exhibiting different mineralogy are tested for their sorption capacities and removal efficiencies along with swell and permeability characteristics using EICP method. This sustainable study relying on sorption and desorption, will open new horizons in contaminant remediation.
# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
## 4 Applications Related to the Water Cycle  
In spite of the numerous limitations of satellite precipitation products, their global coverage, relatively easy access, and documented data format have encouraged their use in a number of applications related to the water cycle. The increasing time span of space-based precipitation data records has given rise to applications that would otherwise not be conceivable, contributing to discoveries related to hydrological and land processes, climate, and ocean freshwater budget, and contributing to the possible solution of societal issues [249]. The planning of new satellite missions nowadays will have to meet end-user application requirements to inform policy and enhance decision-making at the local to global scales [250; 251]. Let's now briefly address some key applications of satellite data products that show the importance of this data source for science and society.
# Nanofiltration of Simulated Acid Mine Drainage: Effect of pH and Membrane Charge
## 1 Introduction
### Effect of Feed Solution pH  
An NF membrane exhibits an amphoteric behavior and the membrane charge varies according to the feed solution pH that it is exposed to. This was demonstrated in a pioneering study by [PERSON] and [PERSON] [15], in which the zeta potential of Desal 5 DK membrane was expressed as a function of the solution pH. The isoelectric point (IEP) of the membrane corresponds to the intersection of the curve with the \(x\)-axis where the zeta potential or net membrane charge is zero. This usually corresponds to the minimum rejection, where size exclusion is the only active separation mechanism [16]. Below the IEP, a positive zeta potential is observed indicating a positive membrane surface charge and vice versa for pH >IEP.  
Based on this behavior, the separation performance in terms of permeate flux and solute rejection can be altered. [PERSON] et al. [17] showed that commercial polyamide thin film composite (TFC) membranes exhibit a more open polymeric structure (resulting in higher permeate flux and lower solutes rejection) under alkaline conditions. However, an opposite observation was made by [PERSON] et al. [18] with the permeation of sodium chloride solution using a polyester composite NF membrane. This study found that the electrostatic repulsion between the (-COO-) groups on the membrane surface and the OH- groups in the feed solution at high pH results in pore shrinkage which consequently decreases the permeate flux. A higher flux was also observed at low pH due to the conversion of amino groups to RH3N+, which increases the overall hydrophilicity of the membrane and enlarged its pores [19] due to a reduced electrostatic repulsion brought about by the decreased H+ concentration in the feed solution. This discrepancy in reported findings shows that the effect of pH on the separation performance depends largely on the membrane chemistry.  
The objective of the present study was to investigate the relationship between ions rejection, feed pH and membrane charge by characterizing the performance of a commercial NF membrane for single salt and mixed salt solutions at different pH values. These salts contain ions found in a typical AMD solution. Subsequently, ion rejection performance for a model AMD at different pH values was systematically investigated and characterized.
# An Innovative Approach to Anomaly Detection in Communication Networks Using Multifractal Analysis
## 2 Literature Review
According to [11], the authors proposed a method for detecting false attacks, as well as for locating the position of opponents performing attacks using a wireless forgery attack detector, which uses K-means cluster analysis. Presented studies using both 802.11 (WiFi) and 802.15.4 (ZigBee) networks showed that it is possible to detect wireless forgery with both a high detection frequency and a low false-positive rate, thus providing strong evidence for the effectiveness of the K-means forgery detector and the attack locator. Similar results are presented in [12]; their experimental results show that the proposed methods can achieve over 90 percent hit rate and precision when determining the number of attackers. Their localization results, using a representative set of algorithms, provide strong evidence of the high accuracy of localizing multiple adversaries. The Physical Layer (PHY) authentication test is presented in [13]. The authors, using information about the radio channel (such as received signal strength indicators), assessed the effectiveness of detecting forgery attacks in wireless networks. Both simulation and experimental results have validated the efficiency of the proposed strategies. An interesting and innovative approach to the protection of wireless networks is presented in [14]. The authors examined the influence of piloting falsification attacks on the estimation of the reverse link channel, assuming that base station (BS) knows the angle of arrival (AoAs) of legal channels. To compare the performance of the estimators, theoretical analysis and numerical simulations were used, which showed that even imperfect knowledge of AoA and the averaging power of forgery signals allows maximum likelihood estimator (MLE) and minimum mean square error estimator (MMSEE) to achieve a significant increase in performance compared to least squares estimator (LSE). The article [15] presents non-cryptographic mechanisms that are complementary to authentication and may detect forgery of devices with little or no dependence on cryptographic keys. Counterfeit-resistant relationships associated with transmitted packets and forgery-proof consistency checks have been introduced, which enable other network units to detect abnormal activity. The cyber-attack technologies are constantly being developed, and the number of new threats is steadily growing. In the article [4; 16], an innovative method of anomaly detection based on the estimation of self-similarity of systems and networks has been presented. Proposed in this article, the methods, in particular multifractal analysis, show sensitivity to any deviation in network traffic properties resulting from anomalies. The presented analysis allowed us to verify the theses and confirm the effectiveness of multifractal methods in detecting anomalies in the operation of any communication network. This shows that anomaly detection based on multifractality can be ideal for protecting critical data and maintaining the continuity of internet services, including IoT.
# Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens
## 1 Introduction  
### The Immune Aspects of Peptide-Based Cancer Vaccines  
The immune system plays a critical role in the detection and elimination of cancerous cells [1]. Immunogenicity refers to the capacity of a foreign biomolecule to induce a humoral and/or cell-mediated immune response within the host organism. Molecules that elicit an immune response leading to the formation of memory cells are classified as protective immunogens. In recent years, numerous immunotherapeutic approaches for cancer treatment have emerged, including immune checkpoint inhibitors, chimeric antigen receptor (CAR) T-cell therapy, and cancer vaccines.  
Cancer cells are distinguished by the presence of specific molecules known as antigens. These antigens are presented on the surface of cancer cells and bound to major histocompatibility complex (MHC) molecules [2]. When this complex is recognized by the T-lymphocytes, an immune response is initiated, and the antigen becomes immunogenic. This recognition mechanism forms the basis for the immune system's ability to identify and target cancerous cells for destruction.  
Peptide-based cancer vaccines have emerged as an important milestone in modern oncology research as the search for innovative cancer treatments continues to gain momentum. They operate by directing the immune system to recognize tumor antigens as foreign entities [3]. Despite the widely recognized safety and tolerability profiles in clinical contexts, none of these vaccines have yet secured approval from the drug agencies for human patientuse. Consequently, the realm of cancer vaccine research has garnered considerable scientific interest and investment. Substantial endeavors are now being directed towards optimizing the vaccine selection and production processes, with a central emphasis placed on the cautious selection of the most efficacious antigenic components. The main stage in the development of cancer vaccines lies in the strategy used for antigen selection [4]. In an ideal scenario, each prospective antigen would undergo isolation or synthesis, followed by an empirical assessment of its immunogenic properties. However, this approach is impeded by logistical challenges and practical constraints. Consequently, to streamline and rationalize the antigen discovery process, in silico prediction algorithms have emerged as innovative tools in recent years [5].
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 3 Materials and Methods
### Polynomial Regression
#### 3.6.3 Noise Figure vs. Tilt  
In EDFAs, the gain _tilt_ refers to the slope of the gain curve relative to frequency. This parameter can be adjusted to address wavelength-dependent gain variations, which cause different power levels among channels traveling through the fiber at various frequencies. Factors contributing to this include the amplifier's limited gain bandwidth and potential gain saturation. Tilt control enables the shaping of the amplifier's gain profile, helping to mitigate these unwanted effects and achieve more uniform amplification across the frequency spectrum. The gain in an EDFA can be modeled as follows [11]:  
\[G(f)=G+\frac{T}{B}(f-f_{0})+g(f) \tag{14}\]  
where \(G\) is the target gain, \(T\) is the target tilt, \(B\) is the amplifier bandwidth, \(f_{0}\) is the center frequency, and \(g(f)\) represents the gain ripple.  
Tilt primarily affects channel gain, and its influence varies with frequency. As a result, different outcomes can be expected for low and high channel indices. As shown in Figure 10, the noise figure generally decreases as tilt increases. This effect is more pronounced at lower target gains and diminishes at higher gains. In the previous discussion, it was noted that higher gain typically results in a lower noise figure. For lower channel frequencies, gain increases with increasing tilt, whereas, for higher channel frequencies, the opposite occurs. Typically, the target gain value is achieved with tilt values close to 0 or slightly negative. This trend also applies to \(P_{ASE_{AMP}}\). The variation in channel gain due to tilt remains consistent across different input powers, target gains, and channel frequencies. However, \(P_{ASE_{AMP}}\) shows a more negative slope with respect to tilt when the target gain is low. This results in minimal variation for low frequencies, where the positive slope is flattened, and a more pronounced negative variation for high frequencies, where the slope is already negative. Therefore, the difference with gain is more distinct at low gains, while the contributions tend to balance each other more at higher gains. For this reason, a third-degree polynomial was found to be an appropriate fitting function for the noise figure's dependency on target tilt.  
Figure 9: Noise Figure vs. channel gain, at various input parameter configurations.
# Physics-Informed Graph Neural Networks for Attack Path Prediction
## Appendix B K-Fold Cross Validation  
K-fold cross-validation is a resampling technique used to evaluate the performance of a model by partitioning the dataset into \(K\) equal-sized subset \"folds\". The model is trained on \(K-1\) folds and tested on the remaining fold, repeating the process \(K\) times with a different test fold each time. This method helps to mitigate overfitting and provides a more robust estimate of model performance. The value of \(K\) affects the bias-variance trade-off. A small \(K\) results in higher bias but lower variance, as the training set is smaller in each fold. A larger \(K\) reduces bias by using more data for training but increases variance due to the smaller test sets.
## 2 Problem Formulation and Simulation Methods
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Mesh Size** & \(\mathbf{C_{f}}\) & **Mesh Size** & \(\mathbf{C_{s}}\) & \(\mathbf{C_{w}}\) & \(\mathbf{C_{f}}\) \\ \hline
130 \(\times\) 40 \(\times\) 60 & 0.001307 & & & 0.1 & 0.32 & 0.001294 \\
140 \(\times\) 50 \(\times\) 70 & 0.001291 & 150 \(\times\) 60 \(\times\) 80 & & & \\
150 \(\times\) 60 \(\times\) 80 & 0.001294 & 150 \(\times\) 60 \(\times\) 80 & & & \\
160 \(\times\) 70 \(\times\) 90 & 0.001288 & & 0.18 & 0.55 & 0.001288 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Grid and Wale constant independence validation. \(C_{f}\) is drag coefficient on the bottom wall, \(C_{s}\) is Smagorinsky constant, \(C_{w}\) is Wale constant. Figure 3: Mean streamwise velocity normalized with \(u_{\tau}\). The dashed yellow line is the theoretical prediction \(u^{+}=y^{+}\) in the viscous sublayer. The dashed blue line is the fit \(u^{+}=2.44\ln\left(y^{+}\right)+6.5\) in log-layer region. Our simulation model is established and consistent with the experimental model. The sound pressure profile is calculated, as shown in Figure 5.  
As shown in Figures 3 and 5, our simulation results are greatly consistent with the experiment results. Therefore, the accuracy of the simulation method is proven. The same size and coordinates as the flow field should be maintained to accurately interpolate the flow field data into the sound field as a sound source. The sound calculation domain is shown in Figure 6.  
Figure 4: Diagram of [PERSON] experiment. Figure 5: Comparison of the simulation results and [PERSON] experiment data. The black line is simulation results. The red line is [PERSON] experiment data. Figure 6: Schematic of sound simulation domain. The size of center source area is \(7\delta\times 2\delta\times 3.5\delta\), same as the flow field. The streamwise and spanwise directions of the sound source area extend \(2.5\delta\) as the sound propagation area. The upper and lower boundaries are impervious boundary, and the other boundary is infinite element domain to simulate no-reflection boundary. A total of 10 hydrophones are set in the sound field to monitor the sound pressure, to analyze the influence of the superhydrophobic surface on the flow noise. The hydrophone settings are shown in Figure 7.
# AI-Driven Innovations in Software Engineering: A Review of Current Practices and Future Directions
## 3 AI Applications in Software Engineering
### Deployment and Maintenance
#### 3.5.2 Automated Monitoring and Alert Systems  
AI enhances system monitoring by automating the detection of irregularities in deployed applications. Intelligent alert systems, powered by AI, can distinguish between normal operational fluctuations and significant issues, thereby reducing false alarms and enabling teams to focus on critical events.  
Integrating AI into continuous integration/continuous deployment (CI/CD) pipelines can automate post-deployment monitoring. AI systems can assess system logs, identify anomalies, and promptly notify the development team, ensuring the swift resolution of production issues.  
For instance, developers may enhance their CI/CD pipelines by incorporating AI-generated unit tests, which are created on the fly and added to the codebase during deployment. These tests run upon deployment and in subsequent deployments, improving test coverage and reliability. Additionally, AI can be employed to monitor production system logs for a defined period after deployment, enabling the early detection of anomalies. This integration of AI into the deployment process results in a more efficient and robust system, reducing the likelihood of undetected issues impacting end-users.  
Advanced AI techniques, such as machine learning-based anomaly detection and natural language processing for log analysis, can process large volumes of system data to identify subtle patterns indicative of potential issues [57]. By automating these processes, AI reduces the manual effort required for monitoring and enables real-time responsiveness to operational problems.
# Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network
## 3 Results and Discussion  
### Crack Detection Results of the YOLO_v2  
The testing images described in Section 2.3 were input into the trained YOLO_v2, the detection results of using 11 CNN models as the feature extractors are illustrated in Table 3. The feature extractor based on six transfer learning models achieved superior detection results, the _AP_ values were higher than 0.6. Among them,'resnet18' achieved the best detection results with the _AP_ value reaching 0.89, and 'googlenet' (_AP_ = 0.84) and'mobilenetv2' (_AP_ = 0.87) also had comparable _AP_ values. The performance of some feature extractors (i.e., 'alexnet','resnet50', 'vgg16') was unsatisfactory, and the _AP_ value tended to 0.  
The computational cost was an important index to evaluate whether the model was suitable for fast and real-time detection. In this paper, the computation time of the 11 models was recorded and illustrated in Table 3. The 'alexnet' took the least computation time, the'squeeze' and'resnet18' were ranked second and third respectively in terms of the computation time; the 'inceptionresnetv2' used the most computation time which was more than 10 times that of 'alexnet'. For a certain series of networks, the computational cost would increase with the increase of network complexity, e.g., in the series of'resnet18', resnet50', and'resnet101'. On the whole,'resnet18' was the best detector in the trade-off between detection precision and computational cost.