esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficiency of Using a Simulator for Ultrasound and Laser Dose Calculation in Physiotherapy
## 1 Introduction  
In 2007, [PERSON] [1] defined simulation as \"a technique, not a technology, to replace or augment real experiences by guided experiences evoking or replicating substantial aspects of the real world in a totally interactive way\". Although today it is complicated to find standards allowing the analysis of the actual quality of simulators used in place of actual clinical practice [2], the use of learning applications in digital environments allows any student to autonomously practice with no space or time limits [3]. Previous experience reports satisfactory results, albeit with other types of simulators and specific pathologies [4, 5, 6]. Some of them even make it possible to assess the student's learning competences in much more specific ways than those currently used [7].  
In the context of the lockdowns due to COVID-19, the e-learning has become the sole tool allowing learning objectives to be achieved successfully. However, for some subjects,training aided only by this type of tool encounters much difficulty, especially because of the experimental nature of such subjects. This is the case of subjects such as electrotherapy, which is part of the undergraduate degree in Physiotherapy, in which determining doses of ultrasound or laser to be applied to a patient requires skilled practitioners in such processes in order to make the applied treatment effective. The students gain these skills by repetitively solving cases; an action which requires the appropriate \"teacher-student\" continuous interaction in terms of time and space, and which is not likely to take place under the current conditions. This is the reason a simulator has been created for estimating ultrasound and laser doses in order to assess its efficiency in the learning process of a group of students.  
Therefore, the aim of this study is to assess the efficiency of a simulator for estimating dose calculation via ultrasound and laser based on surveys and a written test in a group of students.
## 1 Introduction
When working with simple devices, a realistic simulation model should include: (a) a phase shift due to incorrect positioning; (b) a frequency-dependent phase shift; (c) unknown complex permittivity of the nearby floor affecting multipath propagation; (d) limited bandwidth; and (e) measurements with limited quality because of the number of bits in the analog-digital converter (ADC). In summary, the purpose of this work is to introduce a low-cost free-space transmission setup with separate commercial transmission and reception hardware, and to present a comparison between experimental data and a 2D model simulated by the finite element method (FEM) [22]. Explicit contributions include the description of a customized free-space solution, which is based on simple programmable hardware in the 0.85-1.55 GHz range, as well as a discussion of the computational model that provides data for comparison with the experimental data. This comparison provides insight into the roles of the different parameters involved in the measurement and estimation procedures and may be used as a reference for further works. This paper is organized as follows: Following the introduction, we descibe the methodology, where the theoretical concepts and experimental details are described, both for the simulation and measurement. This is followed by the results and discussion, where a parameter study is presented and measurements and optimization are shown. Finally, we conclude with general comments about our experience with this setup and about the proposed approach.
## 3 Results and Discussion
### Reinforcement Learning  
Reinforcement learning (RL) is a sub-branch of machine learning that allows computer programs to make decisions by varying the experimental settings and learning from mistakes to achieve the most optimal results in a \"trial and error\" process by maximizing the reward function. RL has witnessed success in complex systems, such as power plants that include multiple interfaces of process control in their operation. The process of RL-based decision-making allows researchers to predict the behavioral characteristics of power plants and better formulate control processes to reduce emissions.  
Researchers have observed that RL has been used to evaluate the carbon emissions of power plants in recent years. The authors of Ref. [35] developed a model-based offline reinforcement learning (RL) framework, which utilizes historical operational data and estimated an improvement of a mere 0.5% in the combustion efficiency of 600 megawatts (MW), which is validated against experiment results. The authors of [36] designed a reinforcement learning framework combining long short-term memory convolutional networks and deep Q-networks to achieve a balance between emission control and boiler efficiency in the field of combustion optimization. The reinforcement learning optimizer contributes to the online development of policies based on static and dynamics stages, which results in a 5% improvement in combustion efficiency and a 15% reduction in NOx emissions compared to experimental data.  
The RL method can be very well-designed for handling non-deterministic environments, such as control process conditions for operations (e.g., optimizing the combustion process for carbon emissions in power plants for real-time decision-making) under uncertainties. This non-deterministic environment may include errors in measurement and communication, as well as a lack of complete knowledge of the measured quantity. However, when applying the RL method, one needs to pay close attention to the quality of the reward function, which is used to set goals for the learned skill and direct the agent toward the best policy (e.g., scaler feedback to show how well the agent is doing at a particular  
Figure 3: Machine learning model in the prediction of CO2/NOx emissions from power plants using a keyword network diagram.  
step). We found that few of the above-discussed studies thoroughly showed how the reward function is optimized and designed.
## 3 Experimental Results and Discussion
### Characteristics of Cutting Force in Jointed Rock Mass
#### 3.1.2 Cutting Force in the Direction Parallel to the Joint Plane  
Figure 10 shows the \(FC_{m}\) of each cut with respect to the distance from the initial midpoint between the joint planes in a series of cuts, extending to the midpoint of the next joint plane, under cutting conditions of a 6 mm cutting depth and a 6 mm line spacing. Regardless of the joint spacing, \(FC_{m}\) near the midpoint between joint planes remained similar, ranging from approximately 2 to 2.5 kN. As the distance from the midpoint increased, \(FC_{m}\) gradually decreased. For a joint spacing of 30 mm, \(FC_{m}\) reached a minimum value of 0.87 kN at a distance of around 18 mm, then increased to 2.95 kN at 30 mm. Similarly, for a joint spacing of 60 mm, \(FC_{m}\) reached a minimum of 1.14 kN at 30 mm and increased to 3.03 kN at 60 mm. In the case of a joint spacing of 90 mm, \(FC_{m}\) reached a minimum of 1.01 kN at 42 mm. It is noted that \(FC_{m}\) reached its minimum of approximately 1 kN near the joint planes, while at the next midpoint, \(FC_{m}\) increased to about 3 kN, which is comparable to the \(FC_{m}\) in intact rock.  
The variation in \(FC_{m}\) with respect to the distance between joint planes can be explained by the influence of joint planes on crack propagation. Many studies have found that joint planes act as physical barriers to crack propagation, causing the crack growth to either stop at the joint plane or change direction [11; 20; 21; 34]. Assuming all cracks induced by the conical pick result from tensile fracture, the results are represented as shown in Figure 11. When the joint spacing is sufficiently large and the conical pick cuts at the midpoint between joint planes, cracks develop similarly to those in intact rock, as shown in Figure 11a. The \(FC\) required to initiate crack formation can be simply expressed as follows:  
\[FC=(C_{m}+C_{s1}+C_{s2})\times\sigma_{t} \tag{3}\]  
where \(C_{m}\) is the length of the median crack and \(C_{s1}\) and \(C_{s2}\) are the lengths of the side cracks.  
On the other hand, when cutting near a joint plane (Figure 11b), cracks propagating from the conical pick toward the joint plane are blocked by the joint plane. This indicates that \(C_{s2}\) becomes shorter, which can be observed in Figure 12. Consequently, total crack length becomes shorter compared to the case in Figure 11a, ultimately leading to a reduction in \(FC\).  
Figure 10: \(FC_{m}\) for each individual cut when \(\alpha\) is 0 (\(p=6\) and \(s=6\)).
###### Abstract
Since SBSs in caching networks are equipped with cache units, some contents are periodically placed or replaced in the cache units and the content caching function must be taken into account when performing SBS state control. In this paper, we study the joint optimization problem of SBS state control and user association based on energy consumption and latency. First, three states of SBSs in caching networks are defined based on whether they can keep the content cache updated or not. Then, to address the drawback of relying on threshold settings when determining the state of SBSs through traffic load, this paper designs a relaxed threshold determination method, where the threshold settings only determine the initial state of SBSs and provide initial options for subsequent optimization algorithms, thus reducing the complexity. At the same time, in order to eliminate the impact on the access users when the switched-off SBS wakes up, a SBS state asynchronous switching mechanism is proposed to ensure that the users who cut to the waking SBS can carry out communication services normally. Finally, a joint optimization model of SBS state control and user association based on energy consumption and time delay is constructed to admit and correct the initial state of SBSs to maximize the system gain and obtain the final state strategy of each SBS in the next period. The rest of the paper is organized as follows: Section 2 introduces the system model, including the energy consumption model and the cost of SBS state switching; Section 3 designs a relaxed threshold setting method, and the initial state policy of the SBS is determined by long short-term memory (LSTM) traffic prediction; Section 4 uses the constructed objective function to make a final SBS state decision, which is admitted and modified the initial SBS state by the constructed objective function; Section 5 verifies the performance of the joint optimization algorithm by experimental simulation; and Section 6 summarizes the work of this paper.
## 5 Testing Scope and Results
For easier transparency of the results, values that deviate from the recommended limits are written in red. Additionally, the fluids used for testing are also listed fully, while, in the following Tables, only their designations are given. Based on the obtained results, we can give a first assessment that there is no unified behaviour regarding the compatibility of materials with ionic liquids. As can be seen from Table 5, in the case of FPM/FKM, the compatibility with the classical ionic liquid EMIM EtSO4 is poor, while in the case of other HILs it is better. The HIL B2002a stands out here since changes in swell, shrinkage and hardness deviate minimally from the given limit values. At the same time, it should be added that the limit values listed in the tables are given on the basis of various recommendations given in the literature (see Section 3.2 and the cited sources) and apply mainly to the most commonly used mineral oil. The latter was also taken as a starting point and the HIL values were compared with them. It is also evident that the properties deteriorate with the length of the thermal load, even in the case of mineral oil. In the case of using the POM material (Table 6), the compatibility with all tested fluids is good. Thus, even in the case of ionic hydraulic fluids, there is no other option than to check each type of fluid for compatibility with the seal materials.
# A Framework to Quantify the Quality of Source Code Obfuscation
## 8 Related Works  
In software obfuscation, the McCabe metric [58], known for measuring the complexity of a program, has been widely used to assess the potency of various obfuscation techniques. In related works, researchers have leveraged this metric to provide insight into how obfuscation impacts the comprehensibility and complexity of code.  
[PERSON] et al. [5] employed [PERSON]'s cyclomatic complexity metric to assess the quality of obfuscated code. By utilizing [PERSON]'s cyclomatic complexity metric, they suggested a methodology for gauging Potency, defined as the extent to which code is challenging for humans to comprehend. Moreover, the authors identified four key categories to assess obfuscation quality: Potency, Resilience, Cost, and Stealth, and they provided detailed descriptions for each. [PERSON] et al. [59] explored how different obfuscation methods affect the cyclomatic complexity of code, as measured by the McCabe metric. The authors applied various obfuscation techniques to a set of software samples, analyzed the resulting increase in complexity, and offered a quantitative assessment of obfuscation effectiveness. [PERSON] et al. [60] focused on the correlation between obfuscation and code metrics, including [PERSON]'s cyclomatic complexity. The authors demonstrated that higher post-obfuscation McCabe scores indicate greater difficulty in understanding and maintaining the code, thus proving the effectiveness of obfuscation in protecting software. [PERSON] et al. [61] presented a comparative analysis of several obfuscation techniques applied to Java bytecode. The authors used the McCabe metric to measure the increase in complexity after obfuscation, providing a detailed evaluation of each technique's potency.  
Symbolic execution attacks [33], which involve analyzing software by treating inputs as symbolic variables rather than concrete values, have been used in various studies to measure the resilience of obfuscation techniques. These works assess how well obfuscation can withstand such advanced analysis methods as performed by a given technique.  
[PERSON] et al. [33] presented a framework for evaluating the strength of different obfuscation techniques against symbolic execution attacks. The authors developed a series of tests using symbolic execution to probe obfuscated code and measure its ability to resist analysis, providing valuable insight into the effectiveness of various obfuscation strategies. [PERSON] et al. [62] employed symbolic execution to analyze the resilience of several popular obfuscation methods. The authors' findings highlighted the varying degrees of protection offered by different techniques and identified specific weaknesses attackers could exploit using symbolic execution.
## 2 Numerical Methodology
### Virtual Wind Tunnel Analysis and Boundary Conditions  
The computational analysis consists of lattice structures shown in Figure 3. The dimensions of the numerical wind tunnel were selected as 3 m \(\times\) 2 m\(\times\) 6 m (width \(\times\) height \(\times\) length) to observe better analysis because it gives a suitable environment depending on turbine size, investigating the wake effect inside in virtual wind tunnel. Two cases were chosen to see the effect of several cells on the solutions: lattice structures with element numbers of about 2,500,000 and 5,000,000. In this simulation, an inlet wind velocity of 12 m/s is applied to predict the aerodynamic characteristics with various turbulence models in accordance with the reference [15]. An adaptive wake refinement technique observed detailed wake development around the blades. The XFlow engine incorporates an automatic scale adaptation feature that adjusts the resolved scales based on user-defined criteria. This adaptive process enhances the solution accuracy in regions near the walls by refining the mesh, effectively capturing strong gradients within the flow. Additionally, the engine dynamically adapts as the flow progresses to refine the wake region, ensuring an accurate representation of the evolving flow patterns. This behavior is illustrated in Figure 4, showcasing the adaptability and effectiveness of the XFlow engine in optimizing the solution quality throughout the computational domain.  
Figure 3: Cell Configuration of Darrieus Vertical Axis Wind Turbine (5,000,000 lattice cells).  
The design parameters of the turbine and boundary conditions were specified according to the reference [15]. One of the performance parameters of wind turbines is the tip-speed ratio (TSR), which is the blade-tip speed against wind speed defined in Equation (10). Typically, the power performance of the rotor can be presented by variation of power coefficient with TSR. In a wind turbine, power obtains by multiplying the torque generated on the blades by the aerodynamic forces with the rotation speed. The torque coefficient (\(C_{Q}\)) and power coefficient (\(C_{P}\)) were calculated using Equations (11) and (12), respectively [15].  
\[TSR=\frac{Tip\;Speed}{Wind\;Speed}=\frac{\omega\times R}{U_{\infty}} \tag{11}\]  
\[C_{Q}=\frac{Q}{0.5\rho ARU_{\infty}} \tag{12}\]  
In Equation (11), \(Q\) is torque [Nm], \(\rho\) denotes the air density [kg/m\({}^{3}\)], and \(A\) is the cross-sectional area of the rotor [m\({}^{2}\)] [15].  
\[C_{P}=\frac{Q\omega}{0.5\rho AU_{\infty}{}^{3}} \tag{13}\]  
To investigate the performance characteristics, three tip-speed ratios (TSRs) of 1, 1.2 and 1.6 were chosen for the calculations. Ref. [15] obtained the maximum power coefficient at TSR of 1.2 in their experimental results.
# Temperature Load Mode of Bridge in Permafrost Region of Qinghai-Tibet Railway
## 1 Introduction
[PERSON] proposed a statistical method for establishing virtual distribution by using the reliability high moment theory and put forward the temperature action model suitable for bridge and track structures; the distribution law of uniform temperature spectrum was fitted by Fourier series, and the relationship between structure and atmospheric uniform temperature was established [25]. However, the above research is mostly aimed at specific bridge engineering examples and particular weather conditions, which is not suitable for the special geographical and climatic conditions of Qinghai-Tibet railway, and its application has great limitations. In some studies, the test cycle is short, makes it difficult to reflect the impact of climate conditions and date on the bridge temperature field, and there is a large error between the theoretical analysis results and the measured results. In short, the plateau's adaptability of temperature load mode in existing research and codes is not clear, and the mechanical behavior characteristics of bridge and track structures under complex plateau climate conditions need to be explored. Therefore, it is necessary to carry out the research on the bridge temperature load mode according to the special climatic conditions along the Qinghai-Tibet railway, so as to provide support for the service safety of CWR on the bridge. In order to study the temperature distribution characteristics and temperature effect of bridges in the permafrost area of Qinghai-Tibet railway, a long-term field test and finite element simulation analysis of the temperature field of the T-beam bridge in a representative section of the Qinghai-Tibet railway were carried out. Through one-year field monitoring, the variation laws of air temperature, beam temperature difference, and beam displacement were obtained. The heat exchange between the bridge and the outside world was transformed into heat flux boundary conditions, and the sunshine temperature field simulation model of the T-beam bridge of the Qinghai-Tibet railway was established based on the thermal analysis module of large-scale general finite element software ANSYS (Version: 19.2; ANSYS, Inc.; Canonsburg, PA, USA). The correctness of the finite element method was verified by the measured data. On this basis, the load mode of beam temperature difference and the temperature distribution law of the beam section in the permafrost area of the Qinghai-Tibet railway were analyzed, and the stress and displacement of the bridge and track under the action of beam temperature difference were studied. The temperature difference load mode proposed in this paper makes up for the deficiency that the relevant codes do not consider the special climatic conditions of the plateau. It can provide a reference for the design of CWR on a bridge in the plateau areas, such as the Sichuan-Tibet railway, and has strong engineering application value.
## 2 Security Considerations for Marine Network Protocols  
As with other networking protocols, marine network protocols are vulnerable against malicious attacks or unintentional human errors. Manufacturers must consider the extended Confidentiality, Integrity, and Availability (CIA) model that includes also Authentication, Authorization, and Non-repudiation from the creation to implementation and  
Figure 1: Network protocols utilized by a marine vessel and their interactions with other adjacent vessels or coastal services. National Marine Electronic Association (NMEA) OneNet communications require a gateway that translates packets for IP networks. Automated Identification System (AIS) is broadcasted to other ships via radio frequencies.  
maintenance of network device firmware. Other studies have conducted similar analyses using this methodology in order to evaluate the efficacy of protocols or products (e.g., Reference [9]). These are also summarized in Table 1.
# Malware Collusion Attack against SVM: Issues and Countermeasures
## 3 Motivation
### Collusion Apps to Evade SVM Detection
#### 3.3.1 SVM Based Malware Detection  
Initially, the SVM based detection method, as well as other machine learning methods, performs a static analysis on Android Apps to construct a feature space, denoted as \(D\). Android Apps are then mapped onto the feature space as follows. Let us assume that an Android App is represented as an object \(z\). We then denote with \(\Phi:Z\to X\) that maps an object \(z\) to a \(d\)-dimensional feature vector \(x=(x^{1},x^{2},\ldots,x^{d})^{T}\in X=\{0,1\}^{d}\), where each feature is set to 1 or 0 if the corresponding feature is present or not.  
As apps have been represented as feature vectors, we then can use SVM [23] to train a classification model. SVM aims to find a hyperplane that can separate two classes of samples with the maximal margin. Mathematically, it can be formulated as follows:  
\[\begin{array}{ll}\min_{w}&\frac{1}{2}\|w\|_{2}^{2}+\alpha\mathbf{1}^{\top} \xi\\ \text{s.t.}&\mathbf{1}-\xi-y\cdot(X^{\top}w+b)\leq 0,\\ &\xi\geq 0,\end{array} \tag{1}\]  
where \(X\in\mathbf{R}^{d\times n}\) is the observations, \(y\in Y=\{-1,+1\}\) is the label, and \(-1(+1)\) represents the legitimate (malicious) class. \(b\) is the offset, \(\xi\) is a slack variable, \(\mathbf{1}\) is a \(n\)-dimensional vector whose components are all 1, and \(\alpha\), \(\lambda\) are trade-off parameter between error and margin. After being trained on the dataset, the trained classification model \(f(x)=w^{T}\cdot x+b\) will be used as a malware detector. The label \(y^{c}=sign(f(x))\) is given by the classifier. We use \(y^{c}\) to refer to the label assigned by the classifier as opposed to the true label \(y\).