esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Characterization of the Activated Carbon
#### 2.4.2 Adsorption Isotherms  
Any adsorption system's isotherm is an equation which relates to the amount of adsorbate on the adsorbent surface and the adsorbent's concentration or partial pressure at constant temperature [73]. The most used adsorption isotherms model contaminants for removal are the Langmuir isotherm, Freundlich isotherm, Temkin isotherm, and BET (Brunauer-Emmett-Teller) isotherm which are used to gain extensive knowledge on the relationships between the adsorbent surface and the adsorbate [74; 75]. Two classic isotherm equations, namely Langmuir and Freundlich, were selected in this study to determine the isotherm parameters.  
Langmuir adsorption is made up of four assumptions. The adsorbent's surface is homogenous, implying that practically all binding sites are equal. Adsorbed molecules do not encounter each other. The method of adsorption is similar in all situations, where a monolayer is always assumed to be formed. It has been developed to clarify gas-solid adsorption where monolayer adsorption is directly proportionate to the fraction of the adsorbent surface, which is opened, while desorption is proportional to the portion of the adsorbent surface covered. The Langmuir isotherm is given as [76; 77].  
\[\text{Qe}=\frac{\text{Qm.KJ.Ce}}{1+\text{KJ.Ce}} \tag{5}\]  
where Ce is adsorbate's concentration at equilibrium (mg/L), Qm is quantity of molecules adsorbed on the adsorbent's surface at any time (mg/g), and KJ is the Langmuir constant (L/mg). When Ce/Qe is plotted against Ce, a straight line with a slope of 1/Qm and an intercept of 1/KJ Qm is obtained.  
Freundlich isotherm maintains multi-layer as well as heterogeneous molecular adsorption and gives an interpretation that describes the heterogeneity of the surface, and furthermore, the exponential function of the active site and their energy [78; 79]. The mathematical expression of Freundlich isotherm is:  
\[\text{Qe}=\text{K}f.\text{Ce}^{1/n} \tag{6}\]  
where K_f_ is Freundlich constant or adsorption capacity (L/mg), n represents the extent of heterogeneity in the surface and furthermore characterizes how the adsorbate is distributed on the adsorbent surface. In addition, the exponent (1/n) indicates the absorbent system's favorability and efficiency. As ln (Qe) is plotted against ln (Ce), a straight line with a slope of 1/n and an intercept of ln (K_f_) emerges.
# Representing and Inferring Massive Network Traffic Condition: A Case Study in Nashville, Tennessee
## 2 Materials and Methods  
### Data and Study Area  
The radar detector system (RDS) in Nashville consists of 349 detectors covering 564 directional links. The geographic locations of the detectors used in this study and the shape of input data matrix are displayed in Figure 1. Figure 1 displays where Nashville, TN, is in the US, and Figure 1 shows the location of all detectors in Nashville, TN.  
The objective of this study is to represent the traffic condition of a massive network, and traffic speed is one of the most widely used and most intuitive characteristics to reflect the traffic condition at a specific location. Thus, traffic speed data were sampled from 9 July 2023, to 29 July 2023. The first two weeks of data were used as training data to select the most representative detectors and learn the relationship matrix, while the remaining one week was used to evaluate the performance of the proposed framework on new data. Figure 2 shows the input data matrix.  
The raw data, updated each 30 s, were first aggregated to 5 min to reduce randomness and variability, which will greatly reduce the computational cost as well. We will also implement an online application scenario by applying the relationship matrix learned from 5 min data to 30 s raw data in a near \"real-time\" manner.
# On the Energy Efficiency of On-Off Keying Transmitters with Two Distinct Types of Batteries
## 5 Simulation Results
### Impact of B on BECPB  
In this part, we use equation Equation (19) and Equation (22) to compute BECPB when \(B\) increases from 3 kHz to 35 kHz with \(\gamma=0.015\) and \(d=60\) m for the two types of batteries. The results are shown in Figure 5. From the results, we can see the impact of \(B\) on the BECPB, and compare the BECPB for different batteries. Moreover, we can justify if the theoretical analysis agrees with the evaluated results.  
The following points can be observed:  
* The BECPB for the ideal battery decreases with \(B\), while the BECPB for each realistic battery first decreases and then increases with \(B\). There exists the optimal bandwidth corresponding to the minimum BECPB.
* The BECPB for each realistic batteries is always larger than that for the ideal battery. With the increase of \(d\), the BECPB gap gradually expands.
* The optimal bandwidth for the battery with IDC-based BUF is 9 kHz, which is smaller than that with MDC-based BUF, namely 13 kHz. When \(B=9\) kHz, the BECPB for the battery with IDC-based BUF is 8% higher than the ideal battery. Similarly, the BECPB for the battery with MDC-based BUF is about 6% higher when \(B=13\) kHz. The above observations are consistent with the theoretical analysis made in Section 4.3.
# Anomaly Detection Method Considering PLC Control Logic Structure for ICS Cyber Threat Detection
###### Abstract  
Anomaly detection systems are being studied to detect cyberattacks in industrial control systems (ICSs). Existing ICS anomaly detection systems monitor network packets or operational data. However, these anomaly detection systems cannot detect control logic targeted attacks such as Stuxnet. Control logic tampering detection studies also exist, but they detect code modifications rather than determining whether the logic is normal. These tampering detection methods classify control logic as abnormal if any code modifications occur, even if the logic represents normal behavior. For this reason, this paper proposes an anomaly detection method that considers the structure of control logic. The proposed embedding method performs embedding based on control logic Instruction List (IL) code. The opcode and operand of IL code use separate embedding models. The embedded vectors are then sequentially combined to preserve the IL structure. The proposed method was validated using Long Short-Term Memory (LSTM), LSTM-Autoencoder, and Transformer models with a dataset of normal and malicious control logic. All models achieved an anomaly detection performance with an F1 score of at least 0.81. Additionally, models adopting the proposed embedding method outperformed those using conventional embedding methods by 0.088259. The proposed control logic anomaly detection method enables the model to learn the context and structure of control logic and identify code with inherent vulnerabilities.  
Keywords:industrial control systems; anomaly detection; control logic; programmable logic controller cyber security +
Footnote †: journal: Applied sciences
## 3 Experiments
### Simulation Results and Experimental Verification
The urea loss rate shows a continuously increasing trend. After starting the spray, it first increases at a faster rate to around 4.5% and then slows down. At 50 ms ASOI, the urea quality loss rate reached 5.6%, but at this point, the urea loss rate has not yet reached stability, indicating that the urea loss rate will continue to increase with the spray process until it stabilizes. The crystallization situation inside the SCR system mixer in the experiment is compared with the area where the urea film appears in the simulation in Figure 8. A considerable amount of urea crystals have formed on the inner wall of the porous tube. At a location closer to the injector, the crystals exhibit a \"spiral band\" distribution. At a distance from the injector, there is a relatively small amount of urea crystals present, exhibiting a \"dot  
Figure 3:0 Flow line distribution in the mixing section of the SCR system. Figure 3:1 Accumulation of urea and urea film mass and changes in urea loss rate over time. like\" distribution. UWS forms a urea film on the inner wall of the mixing tube. Due to the accumulation rate of the urea film being lower than the consumption rate, the urea film continues to accumulate and gradually solidifies into crystals in a low exhaust temperature environment. At the location near the injector, the eddy current has not yet fully developed, resulting in a weaker blowing effect of the airflow, forming a \"spiral strip\" distribution of crystals. With the development of an eddy current, the blowing effect of the airflow in the rear section of the mixing tube is strengthened, and in addition, UWS droplets are relatively dispersed, ultimately forming a \"dot-like\" distribution of crystals. Compared with the simulation results in the previous section, the distribution position of crystals is basically consistent with the formation position of urea film, and there is also a similarity in the distribution morphology, indicating that the crystallization risk prediction method used in this chapter for the SCR system of diesel engines is reasonable.
# An Approach to Speed up Single-Frequency PPP Convergence with Quad-Constellation GNSS and GIM
## 4 Single-Frequency PPP Processing Results and Analysis
### Convergence Performance Assessment
During the converging procedure of the PPP position filter, the size of position errors over time can also reflect the converging speed of the position filter. Figure 7 illustrates the RMS statistical values of position solutions for all stations and sessions at the end of 15 min, 30 min, 60 min, and 180 min sessions, respectively. From the statistical results of 15 min, it is noted that the positioning errors sharply decrease after introducing GIM data at the beginning of the filter processing. Undoubtedly, the GIM data plays a crucial role in accelerating the position filter convergence in the early processing stage. Progressively, the contribution of GIM data is lowered since the IHD pseudo-observables have been assigned smaller and smaller weights over time because of its limited accuracy. As a result, the GIM data quality has not caused significant effect on the converged position accuracy, as seen from the statistical results after 60 min and 180 min. In terms of the RMS statistics of positioning errors at the end of 180 min, our proposed single-frequency PPP approach can achieve a position accuracy of 0.072 m, 0.032 m, and 0.153 m in the east, north, and up directions, respectively. Overall, the proposed approach can not only speed up the filter convergence, but also achieve the high accuracy of position solutions. \begin{table}
\begin{tabular}{c c c c c c} \hline  & & **GPS** & **GPS/GLO** & **GPS/GLO/BDS/GAL** & **GPS/GLO/BDS/GAL+GIM** \\ \hline \multirow{3}{*}{Convergence Time} & East & 68 & 42 (38\%) & 41 (40\%) & 30 (56\%) \\  & North & 43 & 25 (42\%) & 23 (47\%) & 23 (47\%) \\  & Up & 93 & 61 (34\%) & 61 (34\%) & 55 (41\%) \\ \hline \end{tabular}
\end{table} 
Table 2: Average convergence time for single-frequency PPP (min). Figure 6: Distribution of convergence time for GPS-only, GPS/GLONASS, GPS/GLONASS/BDS/Galileo, and GPS/GLONASS/BDS/Galileo plus GIM single-frequency PPP using datasets at 12 MGEX stations over seven days. Figure 7: RMS statistics of positioning errors for different single-frequency PPP processing scenarios at the end of 15 min, 30 min, 60 min, and 180 min.
# Semi-Supervised Learning for Intrusion Detection in Large Computer Networks
## 1 Introduction  
In our interconnected and digitized world, the security of digital networks has evolved from a concern to a necessity. The uninterrupted exchange of information across these networks underpins the operations of businesses, governments, and individuals alike. However, this connectivity introduces vulnerabilities that cyber attackers can exploit to compromise data integrity, confidentiality, and availability. Network intrusion in the form of unauthorized access to or manipulation of network resources remains a persistent threat, jeopardizing the trust upon which modern communication systems are built. The rapid evolution of technology has led to an increase in the complexity and scale of digital networks. As organizations migrate their operations to the cloud, adopt Internet of Things (IoT) devices, and process larger volumes of data, the attack surface for potential threats expands exponentially. The consequences of successful network breaches are far-reaching, encompassing financial loss, reputational damage, and even risks to public safety. High-profile breaches of financial institutions, government agencies, and multinational corporations underscore the urgency of safeguarding network infrastructure against malicious activities. As traditional methods of intrusion detection struggle to keep pace [1], there is a critical need for innovative solutions that can effectively address these evolving threats.  
The present study aims to fill this gap by investigating the application of semi-supervised machine learning techniques for intrusion detection in large network environments.  
The primary aim of this work is to evaluate the effectiveness of these methods through extensive experimentation on large, complex, diverse, and imbalanced datasets such as CIC-DDoS2019 and UNSW-NB15. The secondary aim is to compare the proposed models with traditional fully-supervised models. By comparing the performance of the proposed semi-supervised models with that of traditional fully-supervised models, this research seeks to demonstrate the advantages of semi-supervised learning for intrusion detection. The principal conclusions highlight the potential for these methods to achieve high accuracy even with limited labeled data, offering a promising direction for future research and practical applications in cybersecurity.
## 2 Materials and Methods
### Characterization of MCC
#### 2.3.3 Crystallinity  
X-ray diffraction was employed to assess the crystallinity of both cassava pulp and MCC. The sample, in powdered form, was placed onto a sample holder and flattened to ensure uniform exposure to X-rays. The analysis was performed at room temperature using an X-ray diffractometer (D8-Advance Bruker AXS GmbH, Ettlingen, Germany) with a monochromatic CuK radiation source (\(\lambda\) = 0.1539 nm). The measurement was performed in step-scan mode using a 0.2\({}^{\circ}\) angle range from 5\({}^{\circ}\) to 40\({}^{\circ}\), a step size of 0.02, and a scanning time of 0.5 min. The crystallinity index (CrI) was calculated using the Segal technique [26], as shown in Equation (1).  
\[\text{CrI }(\%)= \frac{(I_{200}\ -\ I_{am})}{I_{200}}\times 100 \tag{1}\]  
\(I_{200}\) is the maximum intensity of the (200) reflection at 20 of 22\({}^{\circ}\), and \(I_{am}\) is the lowest intensity of diffraction at 26 of 18\({}^{\circ}\) for the amorphous region.
## 4 Counting the Metal-Protein Interactions with Bioinformatics: Metallproteomes  
In contrast to the few proteins controlling cobalt metabolism, the number of proteins controlling iron or zinc metabolism is at least an order of magnitude greater, including dozens of proteins to fulfil the numerous functions that these metal ions have in catalysis, structure and regulation of proteins [9]. Bioinformatics made it possible to identify metal-binding sites in proteins because such sites are recognized in the primary structure (sequence) by signatures with specific amino acids that provide the donor ligands, with a characteristic number of amino acids between these ligands [18]. This approach provided, for the first time, rather accurate estimates of the total number of proteins whose functions depend on a metal ion, thus highlighting the importance of metalloprotein among proteins [19]. About 50% of all proteins depend on a metal ion for function [20]. This estimate does not consider the roles of metals in other aspects such as the synthesis of proteins, protein-protein interactions or cellular translocations of proteins. A figure of 3-5% of all human proteins being dependent on iron has been advanced, even if iron in the form of heme is excluded, and at least 10% depend on zinc, amounting to over 3000 zinc proteins [21; 22]. All the additional factors necessary for the synthesis of this high number of metalloprotein also reflect the investment of energy by organisms in obtaining and controlling metal ions. The million-fold increase in concentrations of iron in cells compared to the typical marine environment also attests to the high entropic costs. Bacteria, fungi and plants have biosynthetic pathways for a great variety of low-molecular-weight chelating molecules, called siderophores in the case of iron and amounting to an estimated 500 different compounds [23]. Siderophores assist in overcoming this million-fold gradient by scavenging and acquiring iron from an environment where iron(III) solubility is extremely low, e.g., the solubility product of Fe(OH)\({}_{3}\) is 10\({}^{-39}\). The coordination chemistry of siderophores has inspired the synthesis of pharmaceuticals that can be employed to treat disorders of iron metabolism or serve as antibiotics  
The proteins controlling metal homeostasis have mutations, many of which give rise to genetic diseases linked to inborn errors of metal metabolism. Though not specifically called intrinsic factors in the case of other metals, these proteins regulating metal ions add enormous significance to metallobiochemistry for medicine (Table 1). In the case of copper, for example, [PERSON]'s disease, a treatable copper overload condition, and [PERSON] disease, a fatal copper deficiency, are both due to mutations of copper transporter proteins (ATP7B and A, respectively). The number of genetic diseases of iron or zinc metabolism is much higher and still increasing. Only one genetic disease resulting from zinc deficiency, a defect in the zinc transporter Zip4 responsible for zinc uptake in the intestine and leading to acrodermatitis enteropathica, had clinical significance
## 4 Experimental Settings
### Evaluation Metrics
#### 4.4.2 AUC Index  
The area under the curve (AUC) is a performance metric for binary classification models, representing the area under the receiver operating characteristic (ROC) curve. It measures the model's ability to discriminate between positive and negative classes:  
\[\text{AUC}=\int_{0}^{1}\text{TPR}\times\text{FPR}^{\prime}\,\text{dFPR} \tag{34}\]  
where TPR (true positive rate) is the recall, and FPR (false positive rate) is the ratio of false positives to true negatives. An AUC of 1 indicates perfect discrimination, while 0.5 suggests no discriminative ability, akin to random guessing.
## 4 Numerical Experiments  
This section provides the numerical experiments of the methodology proposed in Section 3. The AVNS algorithm is implemented on Python 3.9.0 and on a laptop with an Intel i7 1065G7 CPU processor and 16 GB RAM. All experiment results report in tables are the average of running 30 times, and outliers removed based on the PauTa criterion. Due to the absence of suitable benchmark instances available for the dual-sourcing inventory routing problem, we use a CVRP benchmark set and adjust some information. Nodes of Augerat's CVRP benchmark instance set A is randomly generated in the range \(100\times 100\), where the first node is the central depot and the others are demand locations.  
These numerical experiment parameters' selection is based on the disaster relief background. We set the scale is 1:2 km. Trucks are land replenishment vehicles, and helicopters are aerial replenishment crafts. The land and aerial fleet size are set to be \(1+\lfloor n/15\rfloor\) and \(1+\lfloor n/30\rfloor\), respectively. For a truck, the speed is 60 km/h, the fixed cost is 500, the using cost is 50/h, and the capacity is 5000. For a helicopter, the speed is 260 km/h, the fixed cost is 10,000, the using cost is 250/h, and the capacity is 2000. For a demand location, the average demand \(\bar{d}=20\), the maximum deviation \(\bar{d}=10\), the unit holding cost is 0.05, and the unit shortage cost is 2. The planning horizon at each planning cycle is 24 h, that \(T=24\). In Augerat's benchmark instance set A, demands are generated in the interval \([1,30]\). We use the demand information as the initial inventory level, where the volume times 20 to fit the number scale.  
The score update parameters setting refers to [PERSON] et al. (2012), where \(\sigma_{1}=10\), \(\sigma_{2}=5\), and \(\sigma_{3}=2\)[40]. The starting temperature \(\tau_{start}\) is set to \(30,000\) and the cut-off temperature \(\tau_{cut}=0.01\). The cooling rate \(\phi\) is 0.995 which yields roughly 3000 iterations at each planning cycle. The segment capacity \(seg_{cap}=50\) and the reaction factor \(\eta=0.1\).
# Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data
## 5 Discussion  
### Validation of SVM Classification Model  
Water availability is the main limiting factor for crop growth in arid and semi-arid regions. There are large discrepancies between irrigated and rainfed wheat in Shanxi Province. Compared to irrigated wheat, rainfed wheat matures earlier and experiences inferior growth conditions. The peak NDVI and TIN values extracted from remote sensing data can realistically reflect these discrepancies. Therefore, these two feature vectors were used to establish an SVM classification model for irrigated and rainfed wheat. Quality and quantitative evaluations were conducted to validate the SVM classification model. The results showed that our proposed peak NDVI and TIN-based method has obvious advantages and obtains higher classification accuracy as compared to conventional NDVI time series-based methods.