esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Example  
### Test Building  
In this study, superstructure is defined as a four-story regular 3D reinforced concrete building. The test building is a relatively basic model with uniform spans and story heights. Since the primary aim of this study is to carry out the numerous nonlinear time history for the optimization of seismic isolation parameters, a simple model was chosen. Thus, many earthquake ground motion records were used in the structural analysis, and a large number of iterations were completed in the optimization process. The test structure is symmetrical in plan, with equal 5 m in all spans and floor heights of 3 m on each floor (Figure 4).  
Figure 3: A general flowchart of the optimization algorithm developed with the GWO.  
The dimensions of the columns, beams, and slabs are modelled using TS500 and TBEC 2018 [9; 54]. The concrete, steel yield, and steel tensile strengths are 30 MPa, 420, and 500 MPa, respectively. Columns and beams are modelled using frame elements, while stories are modelled as a rigid diaphragm. All columns, beam dimensions, and slab thicknesses were selected as 600 \(\times\) 600 mm, 500 \(\times\) 300 mm, and 150 mm, respectively. The structure loads are defined as 10 kN/m\({}^{2}\) dead and 2 kN/m\({}^{2}\) live load on the slab distributed uniformly over the area. In addition to the slab loads, a 6 kN/m wall load was modelled on the beams. A load combination was defined as 0.3 times the live loads and 1 times the dead loads in accordance with TS500, excluding the weight of the elements (columns, beams, and slabs). Thus, the mass source used in time history analyses was obtained.  
The numerical model and analysis of the test building were carried out in the ETABS finite element program, which has proven itself in modeling reinforced concrete structures and base-isolated structures [51]. Rubber bearing isolators are modelled using a link element. In ETABS, the link connection type is used to define the behavior of linear or nonlinear elements. All nonlinearity was restricted to the seismic isolation systems, while the superstructure was considered to be elastic. The fast nonlinear analysis (FNA) method, which is used effectively and fastly for isolated buildings, was performed in nonlinear time history analyses.
# In Situ Annealing Behavior of Cu Thin Films Deposited over Co-W Diffusion Barrier Layers
## 3 Results
### Cu/Ta  
#### 3.2.1 10 \({}^{\circ}\)C/min Heating Rate  
The Cu/Ta stack annealed using the 10 \({}^{\circ}\)C/min heating rate exhibited the first signs of Cu-film dewetting as early as at the 200 \({}^{\circ}\)C stage, as seen in Figure 5. Contrary to what was observed for the Cu/Co-W system, however, dewetting did not seem to progress continuously after its onset under these experimental conditions. The area percentage of Ta substrate exposed remained rather uniform until the 450 \({}^{\circ}\)C stage, after which abnormal Cu-grain growth became more pronounced in association with the progression of dewetting. As the Cu atoms diffuse laterally to other grains to feed grain growth, the Cu-film thickness decreases leading to exposure of the substrate. The final 550 \({}^{\circ}\)C temperature exhibited a smaller exposed substrate area than any of the Co-W heating rate results, at roughly 60% of the area covered by Cu. Given that dewetting of the Cu film did not progress continuously as soon as the substrate was exposed, surface tension at the Cu film should be lower than observed for the Co-W systems, where dewetting progressed quickly once conditions for its onset were fulfilled. This difference in behavior between both systems may be attributed to different initial residual stresses in the Cu film, as a result of the PVD process of creating both the substrate and the Cu films. The results at each analyzed temperature also show considerable differences from those seen on the Co-W system, with the presence of only a few abnormally large Cu grains dispersed along a still visible Cu film covering the substrate.  
Figure 4: Surface of the Cu film deposited over a Co-W layer from 150 \({}^{\circ}\)C (**a**) to 550 \({}^{\circ}\)C (**i**), using a 30 \({}^{\circ}\)C/min. heating rate.  
The higher heating rate used for the Cu/Ta stack exhibited an initial dewetting stage at 200 \({}^{\circ}\)C, which expanded slightly at 250 \({}^{\circ}\)C but remained mostly stable until 450 \({}^{\circ}\)C, at the end of which the area of exposed substrate was nearing 22%. The two highest isothermal stages used showed large Cu islands formed after completely consuming the Cu film, with no considerable differences between the 500 and the 550 \({}^{\circ}\)C temperatures, thus indicating Cu dewetting was already mostly complete at 500 \({}^{\circ}\)C.
## 4 Results
### Relative Tectonic Activity Model (RTAM)  
Five different morphotectonic parameters were applied and quantified for the Wadi All-Lith sub-basins. These parameters were categorized by defining an average morphotectonic model, called the relative tectonic activity model (RTAM) (Table 3). Accordingly, this model was assigned three different classes of tectonic activity: class 1, indicating high levels of tectonic activity; class 2, describing moderate tectonic activity levels; and class 3, defining low levels of tectonic activity. The results suggest that class 1 was assigned for sub-basins 5, 6, 13, and 15, while class 3 was recorded for sub-basins 7, 11, and 14. Finally, sub-basins 1, 2, 3, 4, 8, 10, and 12 were denoted as moderate basins of tectonic activity (Table 3; Figure 8).  
Figure 6: Map illustrating hyposometric integral parameter (H\({}_{\rm i}\)) values and their tectonic classes.  
Figure 7: Map showing hyposometric curve parameter (H\({}_{\rm C}\)) values and their tectonic classes.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \
\begin{tabular}{c} **Sub-** \\ **Basins** \\ \end{tabular} & **Vf Class** & **Af Class** & **Bs Class** & **Hi Class** & **Hc Class** & **Average** &
\begin{tabular}{c} **RTAM** \\ _Class_ \\ \end{tabular} \\ \hline
1 & 2 & - & 2 & 2 & 3 & 1.8 & 2 \\
3 & 2 & 1 & 1 & 2 & 2 & 1.6 & 2 \\
4 & 2 & 1 & 1 & 1 & 2 & 1.4 & 2 \\
5 & 1 & 1 & 2 & 1 & 1 & 1.2 & 1 \\
6 & 2 & - & 2 & 1 & 1 & 1.2 & 1 \\
7 & 1 & 2 & 3 & 2 & 2 & 1.8 & 3 \\
8 & 1 & - & 3 & 2 & 2 & 1.4 & 2 \\
9 & 2 & 1 & 3 & 1 & 2 & 1.8 & 3 \\
10 & 1 & 2 & 2 & 1 & 1 & 1.4 & 2 \\
11 & 2 & - & 3 & 2 & 2 & 2.2 & 3 \\
12 & 1 & 1 & 3 & 1 & 2 & 1.6 & 2 \\
13 & 1 & - & 3 & 1 & 1 & 1.2 & 1 \\
14 & 1 & 3 & 3 & 3 & 2 & 2.2 & 3 \\
15 & 1 & - & 1 & 1 & 2 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Values and classes of morphometric parameters and relative tectonic activity model (RTAM).
## 3 System Model and Assumptions  
A single 5G C-RAN that has a BBU pool has been considered. Many RRHs are connected to the C-RAN through high-speed connections. In this paper, a single-cell area served by a single RRH through a 5G air interface is assumed. Each RRH serves many users, and each user's request is related to one of the specified 5G slices. The C-RAN is assumed to have a C number of BBUs and is virtually sliced into three slices, where each slice is isolated for a specific service type. The general components of the proposed system are shown in Figure 2. The first slice is dedicated to uRLLC services, while the second slice is dedicated to oMBB services, and finally, the third slice is dedicated to mMTC services. All the RRH connection requests are sent to the BBU pool and classified based on their slices. The arrival rate of each request type is assumed to follow a Poisson distribution and is denoted as \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\) for uRLLC, eMBB, and mMTC, respectively. The service time for all connection requests is assumed to be exponentially distributed with rates \(\upmu_{1}\), \(\upmu_{2}\), and \(\upmu_{3}\) for uRLLC, eMBB, and mMTC users, respectively. We use the variable C to represent the number of BBUs in the C-RAN BBU pool. The value of C will depend on the 5G bandwidth selected and is assumed to be statically identical. The connection is assumed to be millimeter wave with Giga speed.  
The uRLLC (e.g., with a smart drive) has the highest priority, and the mMTC has the lowest priority (e.g., smart city applications). eMBB has a lower priority than the uRLLC (e.g., in smartphones). The arrival of an uRLLC can interrupt any in-service lower-priority requests if all BBUs are busy. In addition, the arrival of eMBB can interrupt any in-service mMTC if all BBUs are busy. The proposed model has two queues with finite lengths for eMBB and mMTC requests. The queued requests can wait until the resources are available.
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 9 Case Study
### Case Study 1: Neural Cleanse and TABOR  
Neural Cleanse [12] and TABOR [13] initially utilizes reverse engineering techniques to generate embedded triggers for backdoor detection. We applied both Neural Cleanse and TABOR to defend against all-band BadNets and sub-band BadNets attacks with the EuroSat dataset. Figure 5 shows the reverse-engineered triggers of varying sizes. The first and second rows of Figure 5 display the triggers reverse-engineered by Neural Cleanse and TABOR, respectively, for the all-bands BadNets attack. Notably, neither method could accurately recreate the planted backdoor triggers. The triggers generated by TABOR (Figure 5b) were less precise compared to those generated by Neural Cleanse, as depicted in Figure 5a. Similar observations apply to the settings of less-important-band and important-band BadNets attacks, as shown in Figure 5c-f. Both Neural Cleanse and TABOR were least effective in defending against the important-band BadNets attack, as evidenced in Figure 5e,f.  
We then utilized the generated triggers to unlearn the backdoored models and computed the ASR of these models after unlearning. During the unlearning process, we embedded the generated triggers into clean images and fine-tuned the models using these poisoned images (but with correct labels), and the results are presented in Table 7. A lower ASR after unlearning indicates that the unlearning process was successful. We observed that the important-band BadNets attack was the most challenging to defend against using Neural Cleanse and TABOR, with most cases still maintaining very high ASRs. The all-band BadNets attack ranked second in difficulty, followed by the less-important-band BadNets attack. Overall, even the less-important-band BadNets attack posed a significant threat to Neural Cleanse and TABOR, maintaining relatively high ASRs.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**TS** & **All-Bands** & \multicolumn{3}{c}{**Less-Important-Bands**} & \multicolumn{2}{c}{**Important-Bands**} \\ \hline  & **NC** & **TABOR** & **NC** & **TABOR** & **NC** & **TABOR** \\ \hline \(2\times 2\) & 0.51 & 0.29 & 0.29 & 0.12 & 0.14 & 0.12 \\ \(3\times 3\) & 1.00 & 1.00 & 0.32 & 0.73 & 0.72 & 0.90 \\ \(4\times 4\) & 0.18 & 0.92 & 0.24 & 0.11 & 0.98 & 1.00 \\ \(5\times 5\) & 0.10 & 0.75 & 0.54 & 0.17 & 0.73 & 1.00 \\ \(6\times 6\) & 0.77 & 1.00 & 0.31 & 0.21 & 0.95 & 1.00 \\ \(7\times 7\) & 0.32 & 0.41 & 0.15 & 0.26 & 1.00 & 1.00 \\ \(8\times 8\) & 0.15 & 0.09 & 0.11 & 0.13 & 1.00 & 1.00 \\ \(9\times 9\) & 0.10 & 0.10 & 0.11 & 0.10 & 1.00 & 1.00 \\ \(10\times 10\) & 0.12 & 0.10 & 0.10 & 0.10 & 1.00 & 0.92 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Attack success rate (ASR) of different backdoored models after unlearning with reversed-engineered triggers by Neural Cleanse (NC) and TABOR with the ‘River’ class being the target class. TS stands for ‘trigger size’. A larger ASR is a failure.  
Figure 5: Triggers generated by different defense methods for all-band and sub-band BadNets attacks.
## 1 Introduction
The drawbacks inherent to music recommender systems with respect to time dynamics are addressed in this work. The proposed method manages the evolution of user preferences in a continuous way by using implicit ratings and avoiding time windows. Our proposal also exploits user listening behavior to improve recommendations. The validation of this approach has been performed through a case study, where a music dataset is used to recommend songs, both when the context is not considered and when the time of day context is taken into account. The research presented in this paper is an extension of previous work in which contextual aspects were not addressed [2]. The main contributions of this work are the following:  
* A method to induce music implicit ratings that addresses the evolution of user preferences. * A way of incorporating time-dependent user listening behavior in user-based collaborative filtering models. * An approach for providing context-aware recommendations that combine the evolution of user preferences, user listening behavior and the time of day as a contextual factor. To conclude this section, we refer to other important aspects of streaming services that are worthy of consideration, although an in-depth analysis is beyond the scope of this work. The first is related to the security of cloud services used on-demand in the context of smart cities. Different security issues must be implemented for maintaining a trusted environment [3]. Privacy is the second aspect to be considered, which is also a crucial issue of trusted cloud services. The growing concern about preserving the privacy of users demands the implementation of mechanisms to prevent access to the identity of users and their personal information [4]. These two aspects are especially important in applications such as recommender systems that handle personal data and user behavior information, even though these are treated anonymously. The rest of the paper is organized as follows. Section 2 is dedicated to introducing background information and describing related work. In Section 3, the proposed approach for addressing time-awareness in the area of music is presented. The experimental study and its results are reported in Section 4. Finally, the conclusions are given in Section 5.
# Extension of PAR Models under Local All-Sky Conditions to Different Climatic Zones
## 3 Methodology  
The dataset was distributed into three categories of sky conditions based on the clearness index, \(k_{t}\), [28] and the values adapted by [PERSON] [34] considering clear [0.65, 1), partial (0.35, 0.65), and overcast (0 \(<\) 0.35] skies. Figure 3 shows the Frequency Of Occurrence (FOC) of the different sky types in Burgos during the experimental campaign, which extended from April 2019 to February 2021. As can be seen, in Burgos, the clear sky conditions were prevalent except from November to January, when cloudy sky conditions occur more frequently.  
\begin{table}
\begin{tabular}{c c c c} \hline
**MI** & **MI** & **Expression** & **Ref.** \\ \hline \(RaGH\) & Global Horizontal Irradiance & recorded & \\ \hline \(k_{d}\) & Horizontal diffuse fraction & \(k_{d}=\frac{RaDH}{RaGH}\) & [30] \\ \hline \(Q_{p}\) & Photosynthetic photon flux density & recorded & \\ \hline \(PAR\) & Photosynthetically active radiation & \(PAR=Q_{p}/4.57\)\(\mu\)mol\(\cdot\)J\({}^{-1}\) & [26] \\ \hline \(k_{t}\) & Clearness index & \(k_{t}=\frac{RaGH}{RaG_{t}\cdot cosZ_{s}}\) & [28] \\ \hline \(T\) & Air temperature & recorded & \\ \hline \(p\) & Pressure & recorded & \\ \hline \(T_{d}\) & Dew point temperature & \(T_{d}=\frac{35.859\cdot log_{p}-21.48.496}{log_{p}\cdot k_{t}-10.2858}\) & [29] \\ \hline \(cosZ\) & Solar azimuth cosine & \(cosZ=sin\delta\cdot sinb+cos\delta\cdot cos\phi\cdot cos\omega\) & [28] \\ \hline \(\varepsilon\) & [PERSON]’s clearness index & \(\varepsilon=\frac{RaDH_{d}\cdot sinb+kZ_{s}^{2}}{1+k\cdot Z_{s}^{2}}\) & [31] \\ \hline \(\Delta\) & [PERSON]’s Brightness factor & \(\Delta=\frac{m\cdot RaDH}{B_{sc}\cdot Q_{2}\cdot cosZ_{s}}\) & [31] \\ \hline \end{tabular}
\begin{tabular}{c c} \(\varepsilon_{0}=1+0.033\cdot cos[2\cdot\tau\cdot d_{d_{s}}/365]\) is the average value of the orbital eccentricity of the Earth. \(d_{s}\) is the day of the year. \(B_{sc}\) is the extraterrestrial irradiance constant (\(1361.1\) W\(\cdot\)m\({}^{-2}\) [33]). \(k=1.04\) (or \(5.56\times 10^{-6}\) if \(Z_{s}\) is expressed in degrees). \(Z_{s}\) is the angle between the sky zenith and sun. \(\delta\), \(\phi\), \(\omega\) are the respective declination, hour angle, and geographic latitude of the specific location.  
\end{table}
Table 1: Meteorological indices (MIs) measured in Burgos.  
Figure 3: Monthly Frequency Of Occurrence (FOC, %) of clear, partial, and cloudy sky conditions based on the clearness index, \(k_{t}\), in Burgos, Spain, between April 2019 and February 2021.
## 2 Methods
### Inverse Kinematics Solution Based on SOP  
Inverse kinematics in robotics refers to a computational approach used to determine the joint angles or displacements necessary for an end-effector to achieve a specified position and orientation. Common methods for solving inverse kinematics include the analytical method [22], numerical methods [23], optimization-based methods [24], machine learning methods [25], and sampling-based methods [26]. As previously detailed in our work [21], the Sequential Quadratic Programming (SQP) algorithm [27] is employed.  
The objective function for the SQP algorithm is formulated as the sum of the absolute values of the position and orientation errors between the desired pose matrix and the current pose matrix, as given below:  
\[\min_{\theta_{1},\theta_{2},\theta_{3},\theta_{4},\theta_{5},\theta^{\prime}} =\left|err_{orientation}\right|+\left|err_{position}\right| \tag{7}\]  
where \(err_{orientation}=err_{roll}+err_{pitch}+err_{yaux}\), \(err_{position}=err_{x}+err_{y}+err_{z}\).  
As demonstrated in the current study [21], a trajectory tracking simulation experiment was conducted to verify the accuracy of the inverse kinematics algorithm based on SQP. This experiment was implemented within the Robot Operating System (ROS) Melodic, running on Ubuntu 18.04, using C++. The results revealed position and orientation errors, with the maximum position error being \(9.47\times 10^{-2}\) mm and the maximum orientation error being \(8.82\times 10^{-2}\) degrees.
## 4 Conclusions  
In this study, the dynamic changes in the volatile components during steamed bread making were detected using the E-nose and HS-SPME-GC-MS, and the effects of _Wickerhamomyces anomalous_ co-fermented with _Saccharomyces cerevisiae_ on the volatile flavor profiles in dough or steamed bread were analyzed. The E-nose can rapidly detect and comprehensively analyze the volatile substances in different dough or steamed bread in this study or others [40] with different response values, and the response value of co-fermented dough or steamed bread was higher than that of _S. cerevisiae_ SQJ20 alone. Based on the HS-SPME-GC-MS non-targeted metabolomics analysis, 236 volatile compounds were detected in all samples. The dynamic changes in the volatile substances were analyzed during steamed bread making, with alcohols being the largest component, especially Phenylethyl alcohol. The effects of different yeasts on the formation of volatile components in dough were further compared. Compared with fermented dough by _S. cerevisiae_ SQJ20, more alcohols and esters substances were upregulated in co-fermented dough by _W. anomalous_ GZJ2 and _S. cerevisiae_ SQJ20, with the Phenylalanine metabolic pathway as the key KEGG pathway. The steaming process also caused significant changes in the volatile substances in co-fermented steamed bread. Finally, the key volatile flavor compounds in steamed breads were identified using the relative odor activity value method (ROAV). The addition of _W. anomalous_ GZJ2 improved the volatile flavor compounds of steamed bread, especially the aldehydes and alcohols, which can be quickly distinguished using the W2S detector of the E-nose. Moreover, the addition of _W. anomalous_ GZJ2 increased the content of total protein and free amino acids in steamed bread [9]. Therefore, the co-fermentation of _W. anomalous_ GZJ2 and _S. cerevisiae_ SQJ20 is expected to be used in the industrial production of flavored steamed bread.  
X.D.: Visualization, Investigation, Writing. M.Y.: Conceptualization, Methodology, Visualization, Writing. H.G.: Methodology, Investigation. S.L.: Methodology. S.C.: Visualization. L.W.: Investigation. L.S.: Investigation, Writing--Review and Editing, Funding Acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by grants from the Guizhou Provincial Science and Technology Support ([2022] general 150) and the Zhenjiang Modern Agriculture Key R & D Project (grant no. NY2023008).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors thank Professor [PERSON] (Jiangsu University) for help in the electronic nose instrument.  
The authors declare that they have no competing interests.
## 3 System Model
### Communication Model
#### 3.4.2 Ground-to-Satellite Communication  
Satellite links experience more severe propagation conditions, modeled using the Rician-Shadowed fading distribution to capture atmospheric effects and shadowing. The uplink rate from IoTD \(k\) to satellite \(n\) is  
\[R_{k,n}^{up}(t)=B_{n}\log_{2}\left(1+\frac{h_{k,n}(t)P_{k}(t)}{\sum_{i\in \mathcal{K}\setminus\{k\}}h_{j,n}(t)P_{j}(t)+N_{0}}\right), \tag{10}\]  
where \(h_{k,n}(t)\) represents the channel power gain between the IoTD \(k\) and the LEO satellite \(n\) following the Racian-Shadowed fading model [31], and \(B_{n}\) indicates the channel bandwidth of LEO satellite \(n\). For the downlink, the large propagation distance typically eliminates inter-satellite interference at the IoTD. Then, the downlink transmission rate \(R_{k,n}^{dl}\) can be calculated as  
\[R_{k,n}^{dl}(t)=B_{n}\log_{2}\left(1+\frac{h_{k,n}(t)P_{n}(t)}{N_{0}}\right). \tag{11}\]
# Application of Artificial Intelligence in the Practice of Medicine
## 1 Introduction  
Advancements in artificial intelligence (AI) based on machine and deep learning are transforming certain medical disciplines. When combined with the rapid progress in high-performance computing, AI-based systems have enhanced the accuracy of diagnostics and the efficiency of therapeutics in many specializations. Advanced AI algorithms can extract features from a significant amount of healthcare data and then apply them to clinical practice. Furthermore, depending on feedback, the algorithm's accuracy is improved by its self-correcting abilities. Consequently, an AI-based healthcare support system can help physicians deliver optimal patient care by reducing diagnostic and therapeutic errors that unavoidably occur in human-based clinical practice [1]. In addition, such AI-based systems can extract meaningful information from a large patient population's data to draw real-time conclusions related to health risk alarms and health outcome projections.  
According to experts, diverse healthcare sectors including chronic illness management and clinical decision-making can expect to be substantially impacted by AI. While AI algorithms are still in the early stages of deployment, they show promise in fields including radiology, pathology, ophthalmology, and cardiology [2]. Such progress poses interesting questions about whether AI will eventually displace clinicians, enhance their professional prospects, or some combination of both.  
This Special Issue's objective is to advance research into a wide range of multidisciplinary perspectives on AI theory and its applications in medicine, medically oriented human biology, and general healthcare. The topics covered include (but are not limited to) AI in biomedicine and clinical medicine, machine learning-based decision support, robotic surgery, data analytics and mining, laboratory information systems, and AI in medical education. We stress the practical aspects of each study, emphasizing the importance of including a clinical evaluation of the utility and potential impact of the work.