esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intelligent Method for C++ Test Case Synthesis Based on a Q-Learning Agent
## 2 Materials and Methods
### Reward Function  
In reinforcement learning-based TC synthesis, the structure of the reward function plays a crucial role. It determines which action sequences the agent will consider beneficial and which are undesirable. In our method, the reward function is multi-level and includes both an immediate reward after each action and a final reward for the entire completed TC. The first level governs local optimal growth, encouraging the agent to explore new API calls. This design allows the agent to learn after each step, rather than only at the end of the episode, which can be particularly effective when working with complex APIs. Moreover, it is important not only to achieve higher code coverage but also to ensure the structural efficiency of the resulting TC by eliminating redundant calls. We adapt the idea of semantic similarity from AFLNET [19], where function names and argument types are used as a heuristic to detect repetition. In our method, similarity is considered as equality of function names and the number of arguments, without comparing the actual values of the arguments.  
Depending on the outcome of executing a partially constructed TC, the stepwise reward is assigned as follows:  
* Increase in coverage \(\rightarrow\) +0.5;
* New execution traces discovered \(\rightarrow\) +0.5;
* Two semantically similar actions in a row \(\rightarrow-0.15\);
* Three semantically similar actions in a row \(\rightarrow-0.3\);
* No improvement in coverage \(\rightarrow-0.1\).  
Upon completing the TC, an additional global evaluation is applied based on the results achieved by the entire TC. This evaluation considers the relative increase in coverage or discovery of new traces compared to the original TC.  
The TC compression coefficient \(\vartheta\) is computed as  
\[\vartheta=\frac{N-N^{\prime}}{N} \tag{3}\]  
\(N\) is the original TC length and \(N^{\prime}\) is the length of the synthesized TC. After the TC is finalized, the agent's final reward per episode is computed using the compression coefficient \(\vartheta\) as follows:  
* Increased coverage \(\rightarrow\) +5.0\(\cdot\vartheta\);
* New execution traces discovered \(\rightarrow\) +5.0\(\cdot\vartheta\);
* No coverage improvement \(\rightarrow\) +0.0\(\cdot\vartheta\);
* Coverage degradation \(\rightarrow\) -5.0\(\cdot\vartheta\).  
Thus, positive rewards are assigned for increased code coverage, discovery of new execution traces, and synthesizing shorter TCs compared to the original ones. Penalties are imposed for repeating the same actions consecutively, stagnating coverage, or producing semantically equivalent yet inefficient operations. The reward is scaled based on the TC length, encouraging the agent to generate shorter and more efficient TC. Given how the reward is computed, we now proceed to examine the action selection policies, which govern the balance between exploration and exploitation during both training and application phases.
## 2 Background and Proposed System Model
### Glrt
#### 2.3.2 GLRT with EF and WEF  
For the GLRT with EF, denoted as GLRT-EF, the \(u\)-th SU also must compute the SCM and its eigenvalues, as it does for the GLRT-DF. Instead of making individual decisions, however, each SU sends its eigenvalues to the FC via CC for final computations and achievement of the cooperative decision on the occupation state of the sensed PU band. The eigenvalues are, therefore, the quantity derived from the received signal samples in this case. Then, in this case, the \(u\)-th SU must also compute (11) from (10) and send the eigenvalues of \(\mathbf{R}_{u}\) to the FC. The FC computes the GLRT-EF statistic as [20]:  
\[T_{\mathrm{GLRT}_{\mathrm{EF}}}=\frac{L\sum_{u=1}^{U}\lambda_{1,u}}{\sum_{u=1} ^{U}\sum_{l=1}^{L}\lambda_{l,u}}, \tag{13}\]  
with \(\lambda_{1,u}\geq\lambda_{2,u}\geq\cdots\geq\lambda_{L,u}\) being the eigenvalues of \(u\)-th SU received at the FC (remembering that the eigenvalues received at the FC are identical to those sent by the SUs since the CC is assumed to be error-free). If \(T_{\mathrm{GLRT}_{\mathrm{EF}}}>\gamma_{\mathrm{FC}}\), the FC decides as \(\mathcal{H}_{1}\), or the FC decides as \(\mathcal{H}_{0}\) if \(T_{\mathrm{GLRT}_{\mathrm{EF}}}<\gamma_{\mathrm{FC}}\), where \(\gamma_{\mathrm{FC}}\) denotes the decision threshold at the FC.  
The eigenvalue weighting scheme, denoted as GLRT-WEF, confers robustness to GLRT-EF against the adopted DN model in [20] since it gives more weight to eigenvaluesof SUs affected by lower estimated noise powers. Then it is also used here under the DN model proposed in (2). The weight for the eigenvalues of the \(u\)-th SU is given by:  
\[g_{u}=\frac{1}{\vartheta_{u}^{2}}=\left(\frac{1}{L-1}\sum_{l=2}^{L}\lambda_{l,u }\right)^{-1}, \tag{14}\]  
where \(\vartheta_{u}^{2}=\frac{1}{L-1}\sum_{l=2}^{L}\lambda_{l,u}\) is the maximum likelihood estimate of the noise power of the \(u\)-th SU assuming the presence of a PU signal [6; 27]. Notice that \(\vartheta_{u}^{2}\) is the mean of the \(L-1\) ordered eigenvalues \(\lambda_{2,u}\geq\lambda_{3,u}\geq\cdots\geq\lambda_{L,u}\) of the \(u\)-th SCM \(\mathbf{R}_{u}\in\mathbb{C}^{L\times L}\) computed in (11). Thus, according to [20], the test statistic for the GLRT-WEF, after some tedious manipulations, can be written as:  
\[T_{\text{GLRT}_{\text{WEF}}}=L\left(1+\frac{U}{\sum_{u=1}^{U}\frac{\lambda_{1,u }}{\sum_{l=2}^{L}\lambda_{l,u}}}\right)^{-1}. \tag{15}\]
## 3 Experiments and Results
### Scenario 1: Academic Features Only  
The first scenario only includes academic features, and the performance of the obtained prediction models are reported in Table 6. The best performance by the end of term 1, is the LR method with term performance represented _by courses_, whether single or cumulative, with 65.6%. When the prediction is performed later, the best performance is systematically obtained again with the LR method, but with the term performance represented _by cumulative GPA_. Please note that GNB shows the same best performance for the terms 5 and 6, and second best for the terms 3 and 4. Looking at Figure 5b,d, we observe that performance of the cumulative models are improving from 65.6% (term 1) to 94.9% (term 6) with decreasing prediction window size. Finally, Figure 5a,c show that among the models using only one past term, performance reaches a pick mostly when the prediction is performed by the end of term 4 or term 5.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**ML Algorithm**} & **Term 1** & **Term 2** & **Term 3** & **Term 4** & **Term 5** & **Term 6** \\ \hline \multirow{4}{*}{**Single**} & \multirow{4}{*}{**Course**} & LR & **0.656** & 0.673 & 0.746 & 0.809 & 0.812 & 0.774 \\ \cline{3-8}  & & RFC & 0.630 & 0.623 & 0.670 & 0.774 & 0.760 & 0.749 \\ \cline{2-8}  & & GNB & 0.542 & 0.625 & 0.745 & 0.804 & 0.796 & 0.760 \\ \cline{2-8}  & & LR & 0.639 & 0.652 & 0.741 & 0.8027 & 0.778 & 0.717 \\ \cline{2-8}  & & RFC & 0.626 & 0.607 & 0.695 & 0.785 & 0.778 & 0.717 \\ \cline{2-8}  & & GNB & 0.531 & 0.638 & 0.741 & 0.816 & 0.826 & 0.766 \\ \hline \multirow{4}{*}{**Cumulative**} & \multirow{4}{*}{**Course**} & LR & **0.656** & 0.702 & 0.781 & 0.856 & **0.918** & 0.936 \\ \cline{2-8}  & & RFC & 0.630 & 0.666 & 0.760 & 0.843 & 0.893 & 0.907 \\ \cline{2-8}  & & GNB & 0.543 & 0.655 & 0.744 & 0.8
## 3 Results and Discussion
### Evaluation of the WRF Simulations
The analysis also revealed RMSE values in the order of 0.5-2.4 ms\({}^{-1}\) at the ten examined stations, with r values of 0.43 to 0.87 [27]. Furthermore, the WRF model represented the wind direction at 5 stations around the Urmia Lake fairly well, despite some biases in the frequency and wind intensity at each direction. The optimization and evaluation of the WRF model for wind energy resource assessment and mapping in Iran hold significant industrial relevance within the renewable energy sector [121]. The region possesses vast untapped potential for wind energy development, but accurate assessment of wind resources is a crucial step for successful project planning, financing, and operation [122; 123; 124]. Through the optimization of the WRF model specifically for the Iranian territory, we can improve the accuracy of wind resource assessment and provide valuable insights into the spatial distribution and variability of wind energy resources. This information is of paramount importance for scientists, developers, investors and policymakers involved in wind energy projects, enabling informed decision-making and resource optimization. Furthermore, the current approach for mapping wind energy resources in Iran takes into account the region's unique geographical and climatic characteristics, including complex terrain, coastal effects, and desert environments. By considering all of these factors, we empower industry stakeholders to identify optimal locations for wind farm development, considering aspects such as land availability, proximity to transmission infrastructure, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multicolumn{2}{c}{**Mean Wind Speed (m/s)**} & \multicolumn{2}{c}{**Median Wind Speed (m/s)**} & \multicolumn{2}{c}{**WPD (Wm\({}^{-2}\))**} \\ \cline{2-7}  & **Model** & **Observation** & **Model** & **Observation** & **Model** & **Observation** \\ \hline Hajiabad & 5.068 & 4.458 & 4.358 & 3.420 & 198 & 169 \\ \hline Sheikh-Tape & 3.940 & 3.339 & 3.489 & 2.700 & 112 & 78 \\ \hline Koolhin & 6.544 & 6.963 & 6.131 & 6.500 & 339 & 432 \\ \hline Songhor & 4.328 & 4.691 & 3.467 & 4.000 & 151 & 183 \\ \hline Khalkhal & 6.152 & 6.664 & 6.155 & 6.200 & 258 & 416 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of the mean/median wind speed (m/s) and WPD (Wm\({}^{-2}\)) between WRF simulations and observations from SATBA wind masts at the height of 40 m.  
and potential environmental impacts. Our research contributes to overcoming the specific challenges associated with wind energy assessment and mapping in the region, supporting the growth and sustainability of the renewable energy industry.
## 2 Background
### Frenet Motion Planning  
The Frenet frame method consists of transforming the Cartesian coordinates \(x\) and \(y\) to a more dynamic reference frame \(n\) and \(t\), which are normal and tangential to the road vectors, respectively [49]. If a motion planning algorithm in a highway uses the traditional cartesian coordinate plane, it would have to parametrize \(x\) and \(y\) coordinates positions concerning time and solve for polynomials with significant coefficients. This is a challenging and computationally expensive task. Thus, the Frenet coordinate system uses vectors projected on the road's curves, treating the road as if it was all forward plane with no curves. Hence, by changing these vectors' magnitude, lane changing and braking and acceleration happen to be an isolated problem from the road following task. It helps to have two different easier problems: lane keeping and lane changing, instead of one more difficult situation. Frenet coordinate systems are preferred since these allow for lane-keeping using minimal computing effort. They also predict and calculate lane deviation in dodging for static or dynamic obstacles to then return to the lane's boundaries. The Frenet algorithm does not execute the prediction task, but its output can be easily used in many prediction algorithms. The Frenet coordinate system can also be taken for peripheral vehicles other than the ego. Still, its original purpose remains for the car to perform inertial odometry tasks. Much more can be exploited, but to get it to work, many sensor fusion tasks need to be performed to state the vehicle's center of mass since it will be the origin of the Frenet coordinate system. For example, in Figure 14, a route in which x and y coordinates are the curve's points, the vehicle doesn't see those coordinates. Instead, it transforms its coordinates to vectors T and N, so the car perceives itself as traveling in a straight line,only changing the N vector to keep the lane and vector T changing its speed. To update its N and T vector values, the following linear algebra equation is used:  
\[\left[\begin{array}{c}T^{\prime}\\ N^{\prime}\end{array}\right]=\left[\begin{array}{cc}0&\delta\\ -\delta&0\end{array}\right]\left[\begin{array}{c}T\\ N\end{array}\right].\]  
Since each car's Frenet route needs to be predicted, the T and N vectors are given by combining many algorithms, as discussed later in Section 3. The T and N derivatives are calculated with respect to the arc length, and thus the only missing value, and the one that needs to be predicted, is \(\delta\). Once \(\delta\) is predicted, the Frenet Frame can be calculated by solving for the \(x\) and \(y\) curve which is tangent to T and normal to N. The updated frame for each point of \(\delta\) will be 35 Hz as it is the vision algorithm frame rate and T and N will be captured using computer vision algorithms.  
It is essential to state that the Frenet framework is mainly used for motion and path planning, and so the equation given above is not the only one.
## 4 Discussion
However, there was no damage seen by SEM in any of the specimens in the current study's protocol. The settings used in this study therefore represent safe and efficient laser settings for retrieval of lithium disilicate and zirconia crowns from natural teeth similar to the ones from implant abutments [23; 24; 25]. One difference was noted between the tooth and implant abutments; there seemed to be more cement left inside the zirconia crown retrieved from an implant abutment in the previous study [25] compared to those retrieved from the teeth. It is possible that the cement may adhere to natural tooth structure better than to the implant abutment made of zirconia. Unique to this study, the third crown retrieval attempt was made after some adjustment of the tooth. This was intended to simulate a clinical situation when a dentist may need to adjust the abutment tooth to refresh the dentin to optimize bonding, to remove staining or dental caries, to remove residual cement, or to replace a part of missing tooth structure. The increased cement gaps proved to have little effect on the crown retrieval time or the temperature in the pulpal chamber. Larger abutment surface areas appear to have a positive relationship with the crown retention and therefore translate into the longer retrieval time. This implies that the most important factor for crown retention is the remaining bonding surface area of the abutment tooth structure. There are some limitations to this study. First, the teeth selected for this study had good tooth structure and therefore allowed for an ideal preparation for a crown. In reality, teeth needing crowns have less optimal tooth structure rendering preparations less ideal, which may affect crown removal time. Second, attempts were made to simulate the oral cavity using a typodont mounted in a manikin head. In a live patient, cheeks and tongue can hinder the optimal access and direction of the laser irradiation. It may also be more challenging to get access and secure a crown tapping instrument at crown margins and at optimal angles. Finally, only one cement and one crown thickness (of 1-1.5 mm) were used in this study, different variations of cements and cementation protocols may alter the retrieval results. Future studies should focus on in vivo experiments to evaluate the retrieval time, different cementation protocols, different material thicknesses as well as clinical challenges and possibly patient's perceptions.
# Energy Efficiency Perspectives of PMR Networks +
## 5 Technical and Economic Considerations on Energy Efficiency of Next Generation PMR Systems
### Business Models for Mobile Broadband PMR
#### 5.1.5. Combine a G-MVNO with a Private Network (Hybrid Model)  
Given that the spectrum is a scarce resource in many regions, some agencies may elect to build a custom communications network dedicated exclusively to mission-critical services, while conducting less critical back-office operations through commercial operators using the G-MVNO model. This approach can be implemented relatively easily, since LTE is both a technology for commercial carriers as well as the new-generation platform for PMR. This model has the advantage of rapid deployment without having to wait for dedicated broadband spectrum to become available.  
It enables an agency to handle very high volumes of everyday data traffic while preserving a fully controlled, mission-critical core for emergency situations. It is flexible and positions the agency for future evolution to a fully dedicated network. It allows agencies to make investment efficient by combining a mix of commercial and dedicated spectrum terminals. In addition, when both options are available, the MNO network can be used to offload traffic from the private network that is not mission-critical, preserving key resources for mission-critical traffic and applications. Following this strategy means less than full control over the entire network and its coverage area, and may require mobile transmitters or antennas on some occasions. Developing a hybrid approach also introduces slightly greater complexity to design, operations and financial models, requiring critical consideration and coordination of these elements.  
Public safety agencies embarking on an LTE deployment project must consider factors such as budget, regulatory issues, internal resource constraints, coverage and reliability targets, available spectrum (frequency band and bandwidth) and number of end users when choosing the best overall design and business model. Agencies should also have a plan for energy management because the wealth of robust data and growth of mobile traffic will offer new ways of conducting operations. Regardless of the model chosen, the network must be defined through an end-to-end service-centric approach.
# Diagnosing and Balancing Approaches of Bowed Rotating Systems: A Review
## 4 Discussion  
Perusing the prior sections, some cases can be raised in the practical study of the diagnosis and balance of bowed rotating devices:  
* The time-domain method, which has primarily been used in troubleshooting bowed rotors, is not widely applicable in the diagnosis of some other faults, such as cracks. As a result, forming a feature vector would be problematic if an intelligent method, such as neural networks, is desired to isolate several faults.
* In different investigations, the introduced symptoms of a bowed rotor system in the FFT plot were somewhat different. Increased 1X frequency components were stated to be a sign of a bow in some works, while 2X, 3X, and even 4X components were  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Researcher(s)** & **Investigated Fault(s)** & **Type(s) of Research** & **Symptom** & **Domain** \\ \hline [PERSON] and [PERSON] [4] & Unbalancing, shaft bow, and disc skew & Theoretical & & \\ [PERSON] [20] & Unbalancing, shaft bow & Theoretical & & \\ [PERSON] and [PERSON] [3] & Shaft bow & Theoretical & & \\ [PERSON] and [PERSON] [24] & Unbalancing, thermal shaft & Theoretical & & \\  & bow & experimental & & \\ [PERSON] and [PERSON] [25] & Thermal shaft bow & Theoretical & & \\ [PERSON], [PERSON], and [PERSON] [2] & Thermal shaft bow & Theoretical & & \\ [PERSON] et al. [27] & Shaft bow (geared system) & Theoretical & & \\ [PERSON] et al. [28] & Unbalancing and shaft bow & Theoretical & & \\  & (geared system with & Theoretical & & \\  & viscoelastic supports) & Theoretical & & \\  & (geared system with & Theoretical & & \\  &  
mentioned in others. It appears that the effects of various physical components, such as coupling (geared or flexible), on revealing higher components should be considered.
* Although the influences of a shaft bow on the behavior of some other faults, such as crack, rub, misalignment, and disc skew, were studied, the effects of this defect on other malfunctions, such as ball-bearing faults or mechanical losses, should be investigated further.
* Some straightening procedures are used in the industry, such as determining the location of the bow peak and straightening the rotor with a press machine or, in some cases, thermal methods, while they have not been studied in the academic context.
* While time-frequency-domain techniques, such as wavelet transformation, have become effective tools for diagnosing a variety of damages in rotating systems, the signs of a bent shaft in this domain have not yet received sufficient attention. Therefore, a thorough wavelet analysis of bowed rotor systems using CWT or discrete wavelet transformation (DWT) is worthwhile as prospective research.  
The diagnosis of bent shafts in rotor systems has largely relied on vibration analysis, but other methods, such as thermography, can also be used. Additionally, analyzing current signals can be used to study bowed induction motors, which is outside the scope of this study.
## 3 Test Results
### Excess Support Pressure  
#### 3.1.1 Support Pressure Ratio  
Define the support pressure ratio as the ratio of the support pressure to the initial pressure which theoretically equals the sum of static lateral earth and water pressure. The variation of support pressure ratio with the advancement of the faceplate is shown in Figure 6. The support pressure first increases rapidly and the shallower the buried depth, the easier to get support pressure increased which results greater the support pressure ratio. However, when the faceplate is advanced to a certain value, the support pressure still increases but at a reducing rate and the deeper the buried depth, the greater the support pressure ratio, which is the opposite of that before the certain value. The reason might be that the plastic deformation prevails in the soil when the tunnel face displacement reaches a certain value. Finally, the support pressure cannot increase due to the limitation of the apparatus. Nevertheless, it can still be seen that the support pressure tends to be stabilized.