esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Auto-HPIF Modeling Approaches
### Explicit Multi-Intent-Slot Task Module  
Inspired by [26], this section aims to decode the discourse representation H into a sequence of intuitive intents and slot labels using an intuitive intent decoder and slot decoder and then feed these token-level slot features and intent information to the next stage to guide downstream modules to interact with high-level intent categorization and precise slot-level details. Specifically, to determine whether the input contains multiple intents and extract task-relevant slot features, the model performs token-level multi-intent detection and slot filling using an intuitive multi-intent decoder and slot decoder. Subsequently, it passes this intent information and slot features to the next stage for additional processing. This involves employing a heterogeneous graph-based slot-intent interaction module to facilitate the integration of extracted intent and slot information and enhance the overall multi-intent prediction. As shown in part (b) of Figure 2, an intuitive intent decoder and slot decoder are introduced to explicitly perform the first intent prediction and slot label sequence generation for the input discourse and pass this information to the next stage as inputs for the implicit interaction.  
* **Intuitive Multiple-Intent Decoder.** In the experiments, a bidirectional LSTM is used as an intuitive intent decoder. The bidirectional LSTM obtains a more comprehensive understanding of the input sequence by inputting the input sequence into two LSTMs in temporal and inverse order, respectively, and combining their outputs in time steps where the hidden vector of the decoder at each decoding time step \(t\) is calculated as: \[\overset{\rightarrow}{h}_{t}^{(f)}=\textit{BiLSTM}(y_{t},\overset{ \rightarrow}{h}_{t-1}^{(f)})\] (5) \[\overset{\leftarrow}{h}_{t}^{(b)}=\textit{BiLSTM}(y_{t},\overset{ \leftarrow}{h}_{t+1})\] (6) \[h_{t}^{\Pi}=[\overset{\rightarrow}{h}_{t}^{(f)},\overset{ \leftarrow}{h}_{t}^{(b)}]\] (7) \[y_{t}^{\Pi}=\textit{sigmoid}(W_{t}^{1}(\sigma(W_{t}^{2}h_{t}^{\Pi}+b_{t}^{2})) +b_{t}^{1})\] (8) where \(\overset{\rightarrow}{h}_{t}^{(f)}\) is the previous hidden state of the forward LSTM unit, \(\overset{\leftarrow}{h}_{t}^{(b)}\) is the previous hidden state of the backward LSTM unit, and \(y_{t}^{\Pi}=\{I_{1},I_{2},\ldots I_{n}\}\) is the intention label information distribution for the t-th token in the discourse.
* **Intuitive Slot Decoder.** Similar to the intuitive intent decoder approach, the intuitive slot decoder uses a bidirectional LSTM. Subsequently, a softmax classifier is employed to produce the slot label distribution for each word. As shown in Equation (9), \(y_{t}^{IS}=[1_{1},I_{2},\cdots,I_{t}]\) is the distribution of slot labels generated for each word and \(h_{t}^{IS}\) is a specific feature of the slot-filling task. \[y_{t}^{IS}=\textit{softmax}(W_{t}^{1}(\sigma(W_{S}^{2}h_{t}^{IS}+b_{S}^{2})) +b_{S}^{1})\] (9)
# ChatGPT in Computer Science Education: A Case Study on a Database Administration Course
## Appendix A Test Carried Out Throughout the Course to Measure the Evolution of Knowledge  
**Table A1.** Test carried out throughout the course to measure the evolution of knowledge.  
**Question/Answers (The Correct Answer Is in Bold)**  
1. What statement is used in MySQL to load data from an external file?  
1. **LOAD DATA INFILE**  
2. **IMPORT DATA INFILE**  
3. **INSERT INTO**  
4. **Data loading must be done through third-party software, not directly from MySQL.  
5. What statement is used in MySQL to load data from an external XML file?  
6. **The same statements or tools used to load files of other formats**  
7. **LOAD XML INFILE**  
8. **To load XML files, the file must be pre-transformed into another format compatible with MySQL.**  
1. **Define foreign keys before the loading process**  
1. **Makes the loading process faster and more reliable**  
2. **Makes the loading process more reliable but slower**  
3. **Makes the loading process faster but less reliable**  
4. **What keyword is used to skip the first row when loading a CSV file into a table?**  
1. **IGNORE FIRST ROW**  
2. **SKIP HEADER**  
3. **IGNORE 1 LINES**  
4. **SKIP 1 LINES**  
5. **What statement should be used in MySQL to define an index?**  
6. **CREATE INDEX indexName ON tableName**  
7. **CREATE INDEX ON tableName (column)**  
8. **CREATE INDEX ON tableName**  
9. **What is the beginning of the statement used in MySQL to define a unique index?**  
1. **CREATE UNQUE INDEX**  
2. **CREATE NOT NULL INDEX**  
3. **There is no direct statement for this; to indicate that the index is unique, the field must be defined as unique in the table definition**  
4. **None of the answers is correct**  
1. **The use of indexes**  
1. **Can improve the performance of certain queries**  
2. **Always improves the performance of certain queries**  
3. **Rarely improves the performance of certain queries**  
4. **Influences database space usage more than performance**  
1. **What statement should be used in MySQL to delete an index?**  
1. **DROP INDEX**  
2. **REMOVE INDEX**  
3. **DELETE INDEX**  
4. **DISABLE INDEX**  
1. **Regarding databases, what does the XAMPP software include?**  
1. **A MySQL DBMS and an IDE to manage it**  
2. **Mariable DBMS and an IDE to manage it**  
3. **Exclusively a MariaDB DBMS**  
4. **Exclusively a MySQL DBMS**  
10. **How are the ports of the DBMS configured in the XAMPP environment?**  
1. **The ports are predefined by default and cannot be modified through XAMPP**  
2. **Through the config.inc.php file**  
3. **Through the my.ini file**  
4. **Through the config.inc.php and my.ini files**
## 5 Experimental Results
### Cnn
#### 5.2.3 Activation Function Selection  
According to the above measurement results, the number of network layers of the CNN model was set as 5, the convolution kernel size was 1 \(\times\) 1, and the pooling method was max pooling. Four activation functions, including tanh, Sigmoid, ReLU, and Swish, were used to evaluate the recognition performance of the CNN models. The test results are shown in Figure 20. The activation function could enhance the learning ability of CNNs. As shown in Figure 20, ReLU had the highest recognition rate and the least training time compared to the other three activation functions. Therefore, this study used ReLU as the activation function for the CNN models.
## 3 Results and Discussion
### Properties of the Allylamine Plasma-Modified Ti Samples  
Figure 5 displays the RSM analysis results (contact angle) of the allylamine plasma-modified Ti sample. When the Ti was plasma-polymerized at power 80 W, deposition time 22 min, and chamber pressure 150 mTorr, the minimum contact angle of 70.15\({}^{\circ}\) was reached. Based on the RSM analysis results, it was found that the power, time, and pressure factors were proportional to the contact angle of the allylamine deposition surfaces during the allylamine plasma modification while deposition time had more of an influence than power and pressure within the parameters designed in this study. The contact angle values of the allylamine plasma-modified Ti surfaces varied between 70.15\({}^{\circ}\) and 88.26\({}^{\circ}\) in the designed parameters. Figure 6 shows the AFM image (1 \(\upmu\)m \(\times\) 1 \(\upmu\)m) of the allylamine plasma-modified sample at 80 W and 150 mTorr for 22 min. It presented an island-like surface with amino-group deposition. From a higher magnification of the cross-sectional view by TEM (Figure 7), there was a formation of an amino-group layer with a thickness of \(\sim\)21.4 nm on the Ti surface after this optimal parameter modification. Evidently, the RSM analysis results indicated that the allylamine plasma-modified Ti surfaces exhibited surface hydrophilicity, because their contact angle values were smaller than 90\({}^{\circ}\)[26; 27]. The amino-groups as a functional surface is a high-energy one, and hence could be fully wetted by most liquids [28]. The wettability reflects the surface binding energy of the Ti interface between the bone cells. A surface with a hydrophilic property could facilitate cell adhesion, proliferation, and bone tissue healing [29]. Afterward, it promoted osseointegration of the interface between the implants and bone tissue [6].  
Figure 5: The RSM analysis results (contact angle) of the allylamine plasma-modified Ti sample (80 W, 22 min, and 150 mTorr).  
Figure 6: AFM image of the allylamine plasma-modified sample (80 W, 22 min, and 150 mTorr).
# A Nonlinear CFD/Multibody Incremental-Dynamic Model for A Constrained Mechanism
## 1 Introduction
However, a disadvantage of Conceptual Separation would be that it cannot be simply integrated into standard trajectory simulation software for performing efficient and seamless end-to-end simulations of launch vehicle trajectories because it is tied to ADAMS [4; 18]. Therefore, the development of a new methodology is required to be implemented into a two-link mechanism. This study was aimed to propose a nonlinear CFD/multibody incremental-dynamic method to simulate a constrained two-link mechanism moving in the air. The simulations were conducted on the basis of Newton-Euler equations and the extracted equations were then input to ANSYS Fluent(r) software as the UDF. To check the accuracy of the UDF, the coupled-CFD equations were solved in Matlab(r) software using fourth order Runge-Kutta method [24] for an arbitrary external force field, and the outcomes were compared to the UDF results of ANSYS Fluent(r), which were solved by forward finite-difference. Since the fourth order Runge-Kutta method produces results with an error of order 4 and forward finite-difference leads to the error order 1, the method applied in Matlab(r) is considered as a benchmark test for the UDF results of ANSYS Fluent(r). An arbitrary external force field was selected because, unlike ANSYS Fluent(r), Matlab(r) does not have a solver for aerodynamic equations, so it cannot compute aerodynamic forces. Then the verified method was applied for a real aerodynamic force field using UDF of ANSYS Fluent(r). To the best of the authors' knowledge, since ANSYS Fluent(r) is unable to analyze the dynamics of a CFD/multibody mechanism under the constraint forces and momentums, the proposed method in this study can well incorporate into the software to accurately analyze the nonlinear dynamics of any complex multibody mechanisms, i.e., flying robots, airplanes, birds, flies, under various initial and boundary conditions. Eventually, the dynamic and aerodynamic equations were coupled in UDF, as the proposed method simulates the whole components of a multibody system straightforward and does not need to apart the components for simulation.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Predictive** & \multicolumn{2}{c}{**Input**} & \multicolumn{1}{c}{**Stages**} & \multicolumn{1}{c}{**RMSE**} & \multicolumn{1}{c}{**MAE**} & \multicolumn{1}{c}{**LMI**} & \multicolumn{1}{c}{**CC**} & \multicolumn{1}{c}{**WI**} & \multicolumn{1}{c}{**SRMSE**} \\
**Models** & **Combination** & & & & & & & & & \\ \hline ANFIS-DC & Model-1 & Train & 0.378 & 0.281 & 0.538 & 0.862 & 0.920 & 21.299 \\ ANFIS-DE & Model-1 & Test & 0.414 & 0.304 & 0.509 & 0.859 & 0.913 & 24.914 \\ ANFIS-DE & Model-2 & Train & 0.383 & 0.286 & 0.529 & 0.858 & 0.917 & 21.583 \\ ANFIS-DE & Model-2 & Test & 0.988 & 0.403 & 0.350 & 0.292 & 0.608 & 59.465 \\ ANFIS-DE & Model-3 & Train & 0.408 & 0.314 & 0.485 & 0.838 & 0.903 & 23.023 \\ ANFIS-DE & Model-3 & Test & 0.459 & 0.357 & 0.424 & 0.827 & 0.883 & 27.628 \\ ANFIS-DE & Model-4 & Train & 0.382 & 0.283 & 0.535 & 0.859 & 0.919 & 21.521 \\ ANFIS-DE & Model-4 & Test & 0.414 & 0.306 & 0.506 & 0.859 & 0.913 & 24.931 \\ ANFIS-DE & Model-5 & Test & 0.417 & 0.303 & 0.511 & 0.857 & 0.912 & 25.092 \\ ANFIS-DE & Model-6 & Train & 0.574 & 0.469 & 0.229 & 0.637 & 0.741 & 32.372 \\ ANFIS-DE & Model-6 & Test & 0.646 & 0.518 & 0.164 & 0.600 & 0.734 & 38.882 \\ ANFIS-DE & Model-7 & Train & 0.356 & 0.263 & 0.568 & 0.883 & 0.929 & 20.337 \\ ANFIS-DE & Model-7 & Test & 0.407 & 0.297 & 0.523 & 0.868 & 0.917 & 25.371 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The prediction performance of the hybrid ANFIS-DE model for all proposed input combinations over the training and testing stage. The boldface denotes the best modeling results.
# Empowering Healthcare: TinyML for Precise Lung Disease Classification
## 3 Materials and Methods  
In this section, we describe the various methods employed to acquire, preprocess, and build models for distinguishing between normal and asthma conditions. The development process was conducted by using Edge Impulse, a platform specifically designed for training models, adjusting hyperparameters, and optimizing them for operation on various edge devices. Figure 1 depicts our approach, which involves the standard stages found in traditional machine learning projects, with added steps for real-time processing on the microcontroller:  
* Data collection and preparation: Lung sound data were acquired from a publicly accessible dataset, divided into 80% for training and 20% for testing. These data were then processed by using bandpass filtering to reduce external noise.
* The lung sound data were uploaded to the Edge Impulse platform [36] and segmented into 5-second windows, with MFCC features extracted from each window.
* A custom CNN, a CNN proposed by Edge Impulse, and a custom LSTM were created, trained, and optimized for deployment on a microcontroller.
* The three models were tested by using the test data, and performance metrics were computed.
## 5 Results and Discussion
### General Characteristics
#### 5.1.4 Barriers of IS Adoption (RQ4)  
In the tenth question of the questionnaire the respondents were asked to assess the importance of specific obstacles to the IS adoption, by rating them on the five-point Likert scale (1: \"the least important\" and 5: \"extremely important\"). That question was answered by all the respondents whether they have adopted an IS or not. The six inhibitors that were explicitly mentioned in the questionnaire are those that are described in Section 3.4. Figure 5 contains the average ratings of the six barriers. All barriers--but one--have a score that puts them between being of \"medium importance\" and \"very important\". Most important barrier is considered to be the \"cost of IS\" that the companies seek to adopt. Employee's opposition does not seem to be much of an important barrier with an average score of 1.77.  
The cost of an IS system seems to be one of the most important--if not the most important--barrier in other studies, too, not only because of the cost to buy it, but also due to the additional costs--implementation costs, employee training, upgrading existing facilities, maintenance etc.--it might bring along. Those must be taken into consideration especially in SMEs [6; 32; 49; 50; 51]. Economic crisis might have a negative impact on a company's efforts to apply innovations which might lead to problems in its financial performance [41]. [PERSON] et al. [6] suggest that despite the financial crisis that Greece has been hit IT might play a crucial role in the country's attempts to recover and develop. In the current research companies seem to be reluctant enough to adopt a new system when the one they already have is sufficient enough or it satisfies them completely. Several researchers acknowledge that sometimes employees are reluctant to change and they oppose adopting new technologies, especially if they believe the new process is not going to be better than the one they already use [5; 31; 33]. It is important for the companies to communicate--especially with the lower-level employees--the changes they need to be done and try to motivate them to go along and not resist.  
In literature there is also another barrier that is considered important and seems to be verified by the current research. That is the size of the company in regards to its employees [14,15]. The larger the company the greater the possibility of using--more advanced--IS (e.g., ERP). Small-medium companies that are able to do the job in other ways are not probable to adopt IS or they continue to use an older kind of IS (e.g., MRP) [10]. In the current research all the companies that are not using IS, are those with no more than 50 employees. On the other hand [PERSON] et al. [12] suggest that the size of the company might not be an issue regarding the company's decision to adopt IT since the companies could use IT systems that are most suitable to them, such as less complex ERP suites and not ERP that is mostly used and can be afforded by larger companies.
# Mid-Flexion Instability in Total Knee Arthroplasty: Insights from Robotic-Assisted Surgery
## 5 Risk Factors for MFI
This conclusion could be explained by anatomical studies by [PERSON] et al., where the primary result of posterior cruciate ligament sacrifice was creating a larger flexion gap [28]. However, [PERSON] et al. demonstrated no difference in the mid-flexion range between PS and CR implants [24]. Interestingly, examining CR TKA, [PERSON] et al. compared mid-flexion rotational laxity between two models of CR TKA: symmetrical surface design with neutral joint line obliquity and asymmetrical surface design with varus joint line obliquity [29]. They demonstrated that asymmetrical surface design reduced rotational laxity at the mid-flexion range in CR TKA [29]. Recently, attention has emerged towards the conformity of the articular surface, with high-conformity knees leading to decreased anterior femoral translation and reduced MFI [9; 30]. In this regard, the same authors underlined that paradoxical anterior femoral translation (AFT) relies not only on the articular conformity of the prosthesis but also on the patient activity, as previously cited, since in low-conformity knees, the sit-to-stand activity leads to the highest level of paradoxical AFT, in moderate-conformity knees the crossover and pivot turn held the same outcome being the worst-case activity, and finally, all activities produced a relatively small AFT in high conformity knees [30]. Additionally, posterior condylar offset (PCO) influences MFI [1; 9]. [PERSON] et al. described how upsizing and downsizing the femoral component changes the PCO, leading to MFI [21]. Concerning the radius-of-curvature of the femoral component and MFI, results are contradictory [1; 9; 31]. Despite introducing a single-radius design to avoid transient ligament laxity and instability during knee mid-flexion, [PERSON] et al. did not demonstrate enhanced mid-range stability of this type of implant over the multi-radius of curvature design. Consequently, they suggested the unknown ligament laxity during surgery as driving to mid-range instability [31]. As reported by [PERSON] et al., the impact of TKA alignment on MFI is controversial [9; 32; 33]. [PERSON] et al. supported anatomic alignment leading to lateral joint space laxity in the mid-flexion range. In contrast, mechanical alignment was associated with a lateral joint space opening by 2-3 mm throughout the knee range of motion [32]. In contrast, in another study, no difference in coronal plane stability in the mid-flexion range was seen between kinematically or mechanically aligned cadaveric knees [33].
## 3 Analysis of Accuracy and Performance
### Analysis of VLF Amplitude and Phase Characteristics
#### 3.2.2 An Analysis of the Amplitude Attenuation, Phase-Distribution, and Polarization
2.3 The Amplitude Attenuation and Phase-Distribution Characteristics of VLF Electromagnetic Waves with Different Frequencies When Propagating in the Ionosphere  
VLF electromagnetic waves with frequencies of 10 kHz, 20 kHz, and 30 kHz were adopted to study the influence of sinusoidal electromagnetic waves of different frequencies on the amplitude and phase, as shown in Figure 10a,b. Figure 8: (a) The variation curves of \(E_{r}\) and \(E_{\theta}\) with distance; (b) the curve of polarization angle varying with distance. Figure 9: The phase-change curve of the horizontal dipole source in the horizontal direction at ground level. Figure 10a shows the variation curves of the \(E_{r}\) value with the horizontal distance when the horizontal dipole sources of different frequencies are placed on the ground, and Figure 10b gives the variation curves of the phase of Er of the vertical electric dipole sources of different frequencies when placed on the ground with the vertical height distance. For VLF electromagnetic waves, when electromagnetic waves of different frequencies propagate in the horizontal direction, the higher the frequency, the greater the amplitude value. The higher the frequency, the greater the influence of the ionosphere on the phase of electromagnetic waves with lower radiation frequencies, causing phase anomalies at lower heights. The opposite is true for electromagnetic waves with relatively higher frequencies.
###### Abstract  
In recent years, multi-access edge computing (MEC) has become a promising technology used in 5G networks based on its ability to offload computational tasks from mobile devices (MDs) to edge servers in order to address MD-specific limitations. Despite considerable research on computation offloading in 5G networks, this activity in multi-tier multi-MEC server systems continues to attract attention. Here, we investigated a two-tier computation-offloading strategy for multi-user multi-MEC servers in heterogeneous networks. For this scenario, we formulated a joint resource-allocation and computation-offloading decision strategy to minimize the total computing overhead of MDs, including completion time and energy consumption. The optimization problem was formulated as a mixed-integer nonlinear program problem of NP-hard complexity. Under complex optimization and various application constraints, we divided the original problem into two subproblems: decisions of resource allocation and computation offloading. We developed an efficient, low-complexity algorithm using particle swarm optimization capable of high-quality solutions and guaranteed convergence, with a high-level heuristic (i.e., meta-heuristic) that performed well at solving a challenging optimization problem. Simulation results indicated that the proposed algorithm significantly reduced the total computing overhead of MDs relative to several baseline methods while guaranteeing to converge to stable solutions.  
caption offloading; heterogeneous networks; multi-access edge computing; particle swarm optimization; resource allocation +
Footnote †: journal: Applied Sciences