esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiservice-Based Traffic Scheduling for 5G Access Traffic Steering, Switching and Splitting
## 4 Simulation Results and Analysis
### Simulation Results
Unlike the even distribution of network users in the previous scenario, users were unevenly distributed close to the WLAN APs to simulate the indoor scenario. The other simulation parameters were the same as those shown in Figure 8, and the simulation results are shown in Figure 9. Evidently, the proposed MSTT algorithm always maintains the best network capacity of the three employed algorithms. Furthermore, the WLAN exhibits very similar network capacities for the ABC and \(F(0)\) power-allocation algorithms. However, the user-transmit powers are different; this finding fully reflects the high network capacity of the WLAN. Moreover, with increasing user-transmit power, some of the power allocated to the NR can increase the effective network capacity, owing to saturation of the WLAN, and sharing data on the NR may continue to increase the effective network capacity. However, the network capacity was lowest when all the power was allocated to the NR (i.e., the \(F(100)\) power-allocation algorithm) because users were too far away from the NR BS and the signal attenuation was too high. The network capacity was observed to degrade if the NR link continued to transmit data. Figure 8: Relationship between normalized effective network capacity and UE-transmit power for evenly distributed network users. Taken together, the simulation results shown in Figures 8 and 9 suggest that, although the use of a fixed-ratio (i.e., proportional) power-allocation algorithm can enhance the effective network capacity under certain conditions, it is difficult to define \(x\%\) that is suitable for all situations; thus, network robustness cannot always be guaranteed. Further, the relationship between the effective network capacity and a service, when one of the service QoS indices is fixed, is shown in Figure 10. The figure shows that the normalized effective network capacity decreases as the DQoS index of another service increases when a particular service QoS index is fixed. Furthermore, when two service QoS indices simultaneously increase, the total effective network capacity decreases rapidly.
# Growth of Zr/ZrO\({}_{2}\) Core-Shell Structures by Fast Thermal Oxidation
###### Abstract
Besides these health or environmental applications, the use of zirconium oxide is increasing in importance in several optoelectronic devices and systems requiring higher permittivities, such as energy storage devices or Resistive Random Access Memories [14]. An important goal for optoelectronic applications is to optimize the doping processes. There is a general agreement on the relevant role of defects and dopants on the optoelectronic properties [9, 15, 16, 17, 18]. Dopants do not only interact with intrinsic defects forming different complexes, but they can also dramatically modify the band structure of the material. ZrO\({}_{2}\) is a very wide bandgap material (>5 eV), and consequently, all processes involving photoexcitation require ultraviolet illumination. The introduction of a dopant such as Ni causes a narrowing of the bandgap below 2.7 eV [19], thus enabling its use in micro-/nanoelectronic devices. In the present work we report on the fabrication of core-shell structures (Zr/ZrO\({}_{2}\)) by thermal oxidation of the metal. Thermal oxidation occurs when an electrical current passes through a metal, this is called the Joule effect, while the metal is kept in an ambient atmosphere. This procedure has already been proven to be effective in different oxides such as ZnO or CuO, being also suitable for the growth of doped oxide shells [6,20]. However, fewer studies have been performed on other transition metal oxides such as ZrO\({}_{2}\). This method is simple and fast, introducing multiple possibilities for the fabrication of doped oxides and electronic composites. A detailed study of the growth conditions, morphology, crystalline structure and composition of the oxide layer has been carried out. In addition, its luminescence properties have been investigated, combining cathodo- and photoluminescence experiments to analyze the defect structure of the material.
# A Machine-Learning-Based Cyberattack Detector for a Cloud-Based SDN Controller
## 5 Analysis of the Attack Detector
### Training of the Machine-Learning Model for Cryptomining Detection  
The architecture employed to implement the model for the task of cryptomining detection utilized a Fully Connected Neural Network (FCNN) architecture. This approach accurately predicts the labels of the dataset with a high degree of accuracy. This architecture was chosen based on its superior performance in the context of the cryptomining attack as demonstrated in a previous study that performed an exhaustive comparison with other similar techniques [32].  
The TensorFlow library was used to train a FCNN-based classifier to predict whether a connection corresponded to cryptomining activity or not according to all features derived from Tstat statistics except for the IPs and ports, which were used only to label the dataset (class labels). Note that source and destination IPs and ports can be easily changed by the attacker; therefore, they do not provide significant information to the ML-based detector.  
The structure of the FCNN model is specified below. In particular, the model consists of a stack of three fully connected layers with 20, 30 and 10 neurons with ReLU activation followed by a fully connected layer with two neurons and SoftMax activation as output layer. The training hyperparameters are as follows: a batch size of 4096 and the Adam optimizer with a learning rate of 0.001. Furthermore, the early stopping technique is used to automatically terminate the training process if the validation loss does not improve for 20 epochs, restoring the model weights to those obtained in the epoch with the lowest validation loss after training is complete. For the validation procedure, 20% of the training data are reserved for the validation split. Finally, as a loss function, the categorical cross-entropy function is used.  
During the experimentation, it was observed that, although the model's accuracy using all available features was high, a considerable number of these features did not significantly contribute to the predictive performance. Therefore, in order to enhance the training efficiency and model inference, a random selection of the most commonly utilized features was made. This resulted in a reduction of the required input to ten features while maintaining a high F1 Score (>95%). The selected features are enumerated in Table 2, where it is worth noting that, if a feature has a CS (Client-Server) and SC (Server-Client) identifier, it is because it has been measured in both directions. On the other hand, if a feature has only one identifier, it is because it has been measured in the direction indicated by the identifier type (CS or SC).  
Additionally, to ensure that one variable did not dominate the results due to its scale, all data underwent standardization to achieve a mean of 0 and a standard deviation of 1. This standardization greatly improved the results obtained from the FCNN model.
# Complete Blood Cell Detection and Counting Based on Deep Neural Networks
## 2 Related Works
### Machine Learning-Based Methods  
With the rapid improvement of deep learning algorithms and computational resources, adopting deep learning for medical image analysis has become an effective and efficient approach to abstract information [PERSON] and [PERSON] (2018). Various related studies implemented machine learning techniques in medical research, such as computer-aided diagnosis, medical image disease detection, and image-guided therapy [PERSON] and [PERSON] (2018); [PERSON] and [PERSON] (2018). CNNs, with strong capabilities for learning and extracting features, have been widely used in several medical fields and achieved outstanding performances making CNNs also popular in the blood cell counting and identification domains.  
In 2013, [PERSON] et al. proposed a subcomponent system that classified WBCs into five different types in low-resolution images [PERSON] and [PERSON] (2018). The system employed two kinds of SVM and one CNN as classifiers, and the latter gained the best results. [PERSON] et al. proposed an ANN-based WBC classification system in 2014 [PERSON] et al. (2014). They segmented WBCs, extracted the effective features, and fed the features into three kinds of ANNs to recognize the category of WBCs. The obtained a high recognition rate, which could be 99.11% correct. Similarly, [PERSON] et al. proposed a system for automatically counting and classifying WBCs in 2014 [PERSON] et al. (2014). They segmented the blood cells into sub-images and fed them into an ANN classifier. Combined with PCA (Principal Component Analysis), the ANN classifier's success rate reached 95%.  
[PERSON] et al. later proposed a WBC classification system that used pre-trained models of ResNet and Inception [PERSON] and [PERSON] (2018) in 2018. The images were augmented, preprocessed by color distortion, and fed into ImageNet pre-trained ResNet and Inception networks to abstract useful features for recognition. ResNet V1 50 gained the best detection rate with an average of 100%, while ResNet V1 152 and ResNet 101 achieved 99.84% and 99.46%, respectively. [PERSON] and [PERSON] extended the range of identification, and the proposed system simultaneously classified RBCs and WBCs by ANN [PERSON] and [PERSON] (2018). They fed segmented images into CNN-based ANN and used the IoU (Intersection over Union) threshold to examine the overlaps and count each type of blood cell.
# ErrorExplainer: Automated Extraction of Error Contexts from Smart Contracts
## 9 Related Work  
### Error Handling in Smart Contracts  
Recent work has characterized how developers write and evolve error handling in smart contracts. [PERSON] et al. [9] systematically classified the real-world use of transaction-reverting statements into seven categories, including _address authority check_, _token verification_, _range check_, and so on. They showed that explicit require and revert checks appear more often than if statements in smart contracts. By removing those statements and re-analyzing the code with vulnerability detectors, they demonstrated the crucial roles of transaction-reverting statements.  
Two companion studies by [PERSON] et al. charted the adoption of Solidity error-handling features [27] and addressed the security problems when the transaction-reverting statements fail to protect against invalid states [28]. Using regular-expressionpattern matching, they highlight misuses such as missing validations for external-call return values.  
From a usability angle, [PERSON] et al. [2] investigated how end-users perceive the transfer risk in ERC-20 smart contracts. They found that 71% of participants misunderstood the transfer risks, and even Web3-proficient developers held similar misconceptions. In particular, mainstream crypto wallets, such as MetaMask, do not provide the reason for transfer failure errors, making it difficult for users to recognize the transfer risk.  
ErrorExplainer complements these studies by automatically linking a runtime failure to its exact revert code and returning a rich, human-readable error context, which addresses the discussed comprehensive gap.
## 2 Related Literature
The paper [19] is devoted to the dynamic facility location problem with respect to the agricultural machinery maintenance network that is designed to ensure prompt and reliable responses to agricultural machinery during a harvest period. A busy farming season was divided into several time periods in which the problem was to determine where to locate temporary maintenance stations. This problem was formulated as a mixed-integer linear program to minimize the total service mileage between maintenance stations and demand points. To solve the mixed-integer program, an algorithm based on Benders decomposition was developed. The model and algorithms were illustrated by application to of a real-world problem in China. The computation determined an optimized facility location-allocation plan and demonstrated the advantage of implementing contiguity constraints. The paper [20] presents the potential for using software for optimization of machinery park equipment in sustainable agriculture. The developed algorithms enable selection of agricultural equipment to perform planned agricultural works. Using this software, the desired economic effects and advantages can be achieved, and the possible risk related to the purchase of agriculture equipment can be minimized. Agricultural producers more frequently rely on computer programmers to support their activity. The frequently used software consists of different applications assisting current activity by producing financial and reporting documents. The software enables producers to record the operations performed, the inputs purchased, and the levels of agricultural production. Computer programs for scheduling, planning and designing production are less used. This is because there are more programs available for reporting and balancing than for planning and scheduling. The use of applications belonging to the latter group may give farmers competitive advantages and help them to avoid mistakes in their decision making. The increased availability of agricultural technology programs, whose basic function is to select machinery park agricultural equipment, would allow producers to carry out simulations and check the results of the planned decisions. Summarizing the above survey, one can conclude that planning of the machine fleet is important for sustainable agriculture. Its basic objectives are as follows: ensuring the suitable selection of crops and crop rotations, planning fertilization, and controlling fertilization and livestock density. The realization of these objectives and maintaining appropriate production profitability is possible with suitably selected agricultural machines. One can assume that in the future, the significance of algorithms and programs for the management and scheduling of agricultural machinery will increase.
## 2 Bending Mechanism of the Connection between a Porcelain Bushing and Flange  
The interconnection between the UHV porcelain bushing and flanges is shown in Figure 1. Based on the structural analysis of the connecting joint between the flange and porcelain bushing, a bending load-bearing model was developed, as shown in Figure 2a. Specifically, the bending moment load is transferred from the porcelain bushing to the cement, and then to the flange member.  
When the bending stiffness of joints connecting porcelain bushings and flanges is replaced by an equivalent beam unit, the current code for the seismic design of electrical  
Figure 1: Interconnection between UHV porcelain bushing and flanges.  
Figure 2: Joint connecting the porcelain bushing and flange. (**a**) Bending deformation of the flange and porcelain bushing connecting joint; (**b**) Porcelain bushing bending deformation; (**c**) Cement mortar extrusion deformation; (**d**) Flange member ring wall bending deformation.  
installations in China provides the formula for calculating the bending stiffness of the flange connected with the porcelain bushing:  
\[K_{c}\ =\ \frac{\beta d_{c}h_{c}^{2}}{t_{e}} \tag{1}\]  
where \(K_{c}\) is the bending stiffness of the joint (N\(\cdot\)m/rad), \(d_{c}\) is the outer diameter of the porcelain bushing (m), \(h_{c}\) is the height of the cement bond in the flange (m), \(t_{e}\) is the thickness of the cement filling between the flange and the porcelain bushing (m), and \(\beta\) is a coefficient, defined as \(\beta\) = 6.54 \(\times\) 10\({}^{7}\).  
In Equation (1), all quantities to the right of the equal sign are expressed in meters (m), whereas stiffness \(K_{c}\) is expressed in (Nm/rad). It is obvious that the physical concept of the formula is not clear. It cannot reasonably reveal the bending mechanism of joints connecting porcelain bushings and flanges. Therefore, the authors investigated the mechanical bending model of the joint and proposed a new formula for the equivalent bending stiffness of the joint.  
According to the analysis, it can be assumed that the deformation, \(\theta\), of the flange and porcelain bushing connecting joint consists of the porcelain bushing bending deformation, \(\theta_{1}\); cement mortar extrusion deformation, \(\theta_{2}\); and flange member ring wall bending deformation, \(\theta_{3}\), as shown in Figure 2b-d, respectively. Therefore,  
\[\theta\ =\ \theta_{1}+\theta_{2}+\theta_{3} \tag{2}\]  
The bending stiffness, \(K_{c}\), of the joint is composed of the bending stiffness of the porcelain bushing, \(K_{c1}\); bending stiffness of the cement bond, \(K_{c2}\); and bending stiffness of the flange member ring wall, \(K_{c3}\):  
\[\frac{1}{K_{c}}\ =\ \frac{1}{K_{c1}}+\frac{1}{K_{c2}}+\frac{1}{K_{c3}} \tag{3}\]
# Covert Channel Communication as an Emerging Security Threat in 2.5D/3D Integrated Systems
## 3 Types of Physical Covert Channels
### Power-Based Channels
#### 3.2.1 PowerHammer  
PowerHammer is the first work to introduce the power-based (current flow-based) covert channel [32]. In this work, binary information is encoded by increasing and decreasing the current flow, which is then propagated through the power lines, and intercepted by an attacker. The receiver in PowerHammer is a current probe connected to a small computer (for demodulation). The probe is attached to the power line (line-level attack) feeding the computer at the electrical outlet or even the main electric panel (phase-level attack).  
Adjusting CPU workload controls the power consumption, i.e., overloading the CPU with jobs results in more current consumption. PowerHammer regulates the workload of each core separately in order to increase stealthiness of the attack (cores being used for normal operation are not interrupted). PowerHammer also controls the amount of cores used in the attack, which gives flexibility to manipulate the amplitude and modulate the current consumption to encode data. A carrier wave is generated by applying a workload at full power consumption for half a period and no power consumption for the other half, where the time period determines the frequency of the generated carrier wave. Frequency-shift keying modulation is used for encoding data. Data is transmitted in frames consisting of 44 bits, with a preamble, payload and cyclic redundancy check (used for error detection) [32]. The transmitting program (that controls the workload of the cores) requires no special or elevated privileges (e.g., root or administrative) and contains basic CPU operations, which do not imply malicious behavior, therefore making it difficult to detect.  
The authors measured the current consumption of a PC, a low power IoT device (Raspberry Pi), and a server. They determined that the PC was highly susceptible to this type of attack. The current probe used was a split core current transformer connected to a laptop computer. The probe was secured around the power line directly connected to the device or inside the main electrical service panel of the entire floor. The probe measured the amount of supply current passing through. With the malicious program changing the workload from 2 cores to 8 cores, the amount of current drawn increased from 2.5 mA to 19 mA in the power-line level attack [32]. The PC maintained transmitted bit rates of 333 bps, 500 bps and 1000 bps without errors (0% BER) [32]. The Raspberry Pi only achieved low bit rates of 1 bps and 10 bps with a BER of 1.9% and 4.8%, respectively [32]. The results showed that desktop computers could be used to transmit a considerable amount of information (such as images, documents) and that low power devices (like the Raspberry Pi) were relevant for the transmission of small amounts of data (such as passwords). Phase-level attacks were demonstrated to have higher amounts of interference and, thus, the bit transmission rate was much lower, averaging approximately 10 bps for a laptop [32].
# Characteristic Analysis and Experiment of Adaptive Fiber Optic Current Sensor Technology
## 3 Impact of Temperature Drift on a Fiber Optic Current Sensor
### Comsol Simulation Models  
#### 3.1.1 Geometric Model  
We adopted wave optics and a magnetic field module in simulation. When using wave optics to analyze the optical field characteristics, the length of sensing units should be close to the wavelength 1310 nm, so usually in the micron scale. The three-dimensional equivalent model of optical sensing units is shown in Figure 3. The cylinder with a smaller radius is used as a model of the conductor. The optical fiber was selected as the sensing unit in a COMSOL simulation and the micro-arc is built to simulate a bending fiber. Its bending radius is 15 cm, the central angle is 4.87\({}^{\circ}\) and the fiber core radius is 9 \(\upmu\)m. The cylinder with the larger radius is with the air region as the simulation domain to solve magnetic fields.
## 2 Theoretical Framework and Methods
### Analysis Process
#### 2.3.1 Defining the Resilience Evaluation Space and Grading Criteria  
The resilience indicator system for flood disasters at subway stations is divided into three levels: the goal level, the criterion level, and the indicator level. The goal level is the resilience of subway stations to flood disasters, while the criterion level mainly considers three aspects of resilience: resistance, recovery, and adaptability. The indicator level consists of the specific characteristics of resilience to flood disasters at subway stations. The division of resilience levels is an important tool for assessing and enhancing the adaptability and recovery capabilities of systems, organizations, or communities when facing challenges. By categorizing resilience into levels, entities can self-assess against standards, identify their strengths and weaknesses, and thus make targeted improvements. The division of resilience levels is not only a technical assessment tool but also an important means of promoting social progress and sustainable development [15; 39; 40].  
Based on the characteristics of resilience to flood disasters at subway stations and referencing the \"Subway Design Code GB50157-2013\" [41], \"Waterproofing Technical Code for Underground Engineering GB50158-2008\" [42], \"Outdoor Drainage Design Code GB50014-2021\" [43], and relevant literature [44; 45; 46], five levels of resilience are defined, as shown in Table 2.  
Figure 2: Specific analysis process.