esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experiments and Discussion
### Preliminary Experiment
#### 6.1.3 Experimental Conditions and Processes  
We used the same experimental data as used to create the spectrogram image data for learning from the Greatest Hits dataset. The material classes used are for six classes (cloth, metal, glass, plastic, paper, and wood). In the experiment, data were selected from three classes, including one correct answer class from these six classes, and the impact sound was presented to the subject.  
Each subject performed the experimental task a total of 60 times, to cover all classes. The procedure for each perception experiment was as follows. First, as shown in Figure 16, three cubes were arranged in each VR, and sound was generated by hitting them with the rod-shaped virtual object, shown in Figure 3. Figure 16 shows a scene where we presented the subjects with a wood texture image. A texture image, with a material class corresponding to the correct hitting sound, was presented in the cube, and each of the three cubes generated a hitting sound of a different material class. We also included a condition, where no vibration was generated when the cube was hit, so that the difference in feedback, due to the texture, could be discriminated only by the sound.  
In addition to the impact sound, the texture image of the material to be presented was selected from the dataset and presented for each material class, as shown in Figure 17 (cloth, glass, paper, plastic, wood, and metal, from left to right).  
In the experiment, the subjects wore an HMD (see Figure 2) and used the VR controller for menu panel operations and hitting operations in the virtual environment. The controller was suitable for the action of hitting, while holding a rod-shaped object, and the same action could be performed with the same controller, using either the left or right hand, regardless of the subject's dominant hand.
# A Survey on Machine Reading Comprehension--Tasks, Evaluation Metrics and Benchmark Datasets
## 2 Tasks  
### Definition of Typical MRC Tasks  
In our survey, machine reading comprehension is considered as a special research field, which includes some specific tasks, such as multi-modal machine reading comprehension, textual machine reading comprehension, and so forth. Since most of the existing machine reading comprehension tasks are in the form of question answering, the textual QA-based machine reading comprehension task is considered to be the typical machine reading comprehension task. According to previous review papers on MRC [14; 27], the definition of a typical MRC task is:  
**Definition 1**.: _Typical machine reading comprehension task could be formulated as a supervised learning problem. Given a collection of textual training examples \(\big{\{}\big{(}p_{i},q_{i},a_{i}\big{)}\big{\}}_{i=1}^{n}\), where \(p\) is a passage of text, and \(q\) is a question regarding the text \(p\). The goal of typical machine reading comprehension task is to learn a predictor \(f\) which takes a passage of text \(p\) and a corresponding question \(q\) as inputs and gives the answer \(a\) as output, which could be formulated as the following formula [14]:_  
\[a=f(p,q) \tag{1}\]  
_and it is necessary that a majority of native speakers would agree that the question \(q\) does regarding that text \(p\), and the answer \(a\) is a correct one which does not contain information irrelevant to that question._
# From 5G to 6G--Challenges, Technologies, and Applications
## 3 Sixth-Generation Network's Aspects
### Technical Improvements  
#### 3.3.1 Frame Design  
The diverse nature of future network requirements and applications stimulates the pace of innovating new solutions in networking. It is crucial to maintain high spectral and power efficiencies under massive network deployments. In [23], a new radio frame design was proposed to add flexibility to the system. The proposed design explores the idea of different waveforms co-existing in the same radio frame with different parameters, such as OFDM and filter bank multi-carrier (FBMC) modulation with different numbers of subcarriers and subcarrier spacing values, respectively. Furthermore, this flexibility can be taken one step further by giving freedom to adjusting the numerical aspectsof certain parts of the frame to suit a group of users. For instance, in OFDM, edge subcarriers can cause an out-of-band leakage, which can be mitigated by assigning a larger time window size to them compared to inner subcarriers (between consecutive OFDM symbols with shorter CPs). Moreover, this design is anticipated to offer reduced interference and enhanced non-orthogonal multiple access schemes (NOMAs).
## 3 Research Design
### Conceptual Model for Project Resilience Assessment  
#### 3.1.1 The Concept of Project Resilience  
The definitions of project resilience adopted are different due to the different focuses of different studies [49]. In the process perspective, project resilience is defined as the capacity of a project to withstand and recover from disruptions. This can also be considered a form of dynamic resilience. In this perspective, project resilience is viewed as a dynamic developmental process that includes the ability to prepare beforehand, the ability to react during the event, and the ability to recover after the event. A comprehensive assessment of the three phases of capability constitutes an assessment of project resilience.  
In the capability perspective, project resilience is defined as the internal strengths and advantageous resources of the project, also known as static resilience. This perspective emphasizes the adaptability and resilience of the project itself and how these capabilities can be effectively utilized to maintain the stability and functionality of the project. It focuses more on the recovery result of the project, as the project itself has certain resilience capabilities that help the project restore its performance after a disruption. Therefore, assessing resilience from a capacity perspective can be assessed using performance indicators from the recovery phase.  
This paper also addresses the recovery phase and therefore employs the capability perspective to assess project resilience. In the capability perspective, project resilience is often defined as the capacity of a project to prepare for, cope with, and mitigate the effects of disruptions in order to ensure successful project delivery. Building on this definition,  
Figure 1: Research framework.  
this paper proposes that project resilience is the capacity to enable a project to proactively adapt and maintain its functionality throughout its response to disruption.
# Mechanical Performance of Built-Up Columns
## 3 Test Results
### Load-Strain Curves  
For ease of description, the four tubes (T1, T2, T3 and T4) belonging to the same built-up column are numbered. Figure 9 plots the strain of tubes of S1 versus the load. It is specified that the compression strain is negative and the tensile strain is positive. It can be seen that the curves kept growing linearly  
Figure 8: Load-lateral displacement curves: (**a**) Lateral displacement at quarter-length point of S1; (**b**) Lateral displacement at half-length point of S1 and S4.  
Figure 7: Load-strain curves of connectors: (**a**) S1; (**b**) S2; (**c**) S3; (**d**) S4.  
at the initial stage and the load dropped especially fast after the curve reached the peak point. The tube T1 and T3 kept in compression zone throughout the loading process. The tubes T2 and T4 went into tensile zone in the post-buckling stage. In addition, it can be found that not all the four tubes generated strain at the initial stage of loading. When the load didn't exceed 20 kN, the value of strain for tubes (except tube T1) was small, which means that the four tubes are not subjected to load at the same time. According to Table 2, the yield stress of the tube is 375 MPa and the Young's modulus is 204 GPa. Therefore, the yield strain is 1850 \(\mu\epsilon\) derived from 375 MPa divided by 204 GPa. It can be known from Figure 9 that most of the strain of four tubes are smaller than the yield strain during the whole loading process. Moreover, only a portion of the strain exceeds 1850 \(\mu\epsilon\) in the post-buckling phase. That is to say, there are very few cross-section areas that went into the plastic zone. The above shows that the four tubes lost stability before the strain of the tubes reached the yield value. The bearing capacity of the steel tube has not been fully utilized during the loading process. Therefore, it is necessary to improve the structure so as to maximize the bearing capacity of the four tubes.  
From the above test results and analysis, it can be concluded that changing the spacing between tubes, replacing the C-shaped connectors with loops formed by steel strips and setting the transverse diaphragm have no effect on improving the bearing capacity of built-up columns. Therefore, it is necessary to further study the influence of more factors on the bearing capacity performance of built-up columns by means of the finite element method.
## 2 Experimental Procedure
### Developing an ASR System with Kaldi  
Until now, in our previous works, we have always used gASR for the automatic assessment of pre/post-tests as a complement to or replacement for subjective assessment. Since gASR works as a black-box and does not allow us to obtain details on the quality of each individual speech fragment, we decided to develop an in-house ASR system of our own using Kaldi (kASR). In this subsection, we present the ASR pipeline that we implemented for the kASR system and we provide details about its general architecture and specific parameters.  
Our kASR system uses a standard context-dependent triphone system with a simple Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) [21], adapted from existing Kaldi recipes [22]. Although recent studies report excellent outcomes from neural models with Kaldi [23], we did not find relevant differences in preliminary runs due to, mainly, the small size of the training corpus (described in Section 2.4). After collecting and preparing the speech data for training and testing, the first step is to extract acoustic features from the audio utterances and training monophone models. These features are Mel frequency cepstral coefficients (MFCCs) with per-speaker cepstral mean and variance statistics. Since Kaldi is based on a finite-state transducer-based framework to build language models from the raw text, we use the SRILM toolkit for building a 2-g language model [24].  
To train a model, monophone GMMs are first iteratively trained and used to generate a basic alignment. Triphone GMMs are then trained to take the surrounding phonetic context into account, in addition to clustering of triphones to combat sparsity. The triphone models are used to generate alignments, which are then used for learning acoustic feature transforms on a per-speaker basis in order to make them more suited to speakers in other datasets [25]. In our case, we set 2000 total Gaussian components for the monophone training. Then, we realigned and retrained these models four times (tri4) with 5 states per HMM. In particular, in the first triphone pass, we used MFCCs, delta, and delta-delta features (2500 leaves and 15,000 Gaussian components); in the second triphone pass, we included linear discriminant analysis (LDA) and Maximum Likelihood Linear Transform (MLLT) with 3500 leaves and 20,000 Gaussian components; the third triphone pass combined LDA and MLLT with 4200 leaves and 40,000 Gaussian components, and the final step (tri4) included LDA, MLLT, and speaker adaptive training (SAT) with 5000 leaves and 50,000 Gaussian components. The language model was a bigram with 164 unique words (same probability) for the lexicon, 26 nonsilence phones, and the standard SIL and UNK phones.
# Long-Term Stability of Er:YAG Laser Non-Surgical Periodontial Treatment
## 1 Introduction  
The evaluation of the clinical effectiveness of mechanical periodontal therapy is based on the evaluation of the elimination of the inflammatory process, which consists of the reduction of periodontal pocket bleeding, reduction of pocket depth and gain of clinical attachment [1]. The main goal of periodontal therapy is to preserve natural dentition by achieving and maintaining a healthy periodontitis. That is why the first step in the treatment of periodontitis, as a result of polynicrobial infection, is the removal of dental biofilm and calculus deposits, aiming at reducing periodontal pathogens to levels compatible with periodontal health [2]. Various tools have been developed for professional mechanical plaque removal. Hand and ultrasonic instrumentation have proven efficacy in non-surgical periodontal treatment [3]. Different types of lasers have also been used in the treatment of periodontitis, with only the Erbium family having the ability to ablate hard and soft tissues and thus remove calculus as well [4, 5]. The Er:YAG laser has also proved its antibacterial effectiveness [6, 7].  
Despite the use of lasers in the last 25 years, the results in terms of their clinical effectiveness are quite contradictory [8, 9].  
For the first time, the clinical efficacy of the Er:YAG laser was studied by [PERSON] et al. in 1996 [10]. They instrument the root surfaces of 60 teeth in 60 patients with 40 mJ energyand 10 Hz pulse frequency using a 600 \(\upmu\)m diameter tip. The authors report successful removal of supra- and subgingival calculus in 95% of cases. Although the surfaces appear rough after treatment, the researchers report a decrease in pocket depth. As no side effects or complications are identified during the study, the authors conclude that the Er:YAG laser is safe and effective in clinical application. Later, a number of studies regarding the monotherapy or additional use of the Er:YAG laser in the treatment of periodontitis are conducted. Most of them are short-term and do not give clear information about the long-term clinical effectiveness of the Er:YAG laser in non-surgical periodontal treatment [11; 12; 13].  
Several studies examining the results of Er:YAG laser treatment over a longer period of time report promising data. [PERSON] et al. demonstrate that the Er:YAG laser is a suitable alternative to conventional mechanical cleaning, as its application in patients with chronic periodontitis demonstrates a significantly greater reduction in bleeding on probing and in gain of clinical attachment compared to manual instrumentation [14]. The authors confirm this result up to 2 years after the initial instrumentation [15]. Other authors who have observed treatment outcomes over a longer period also report a significant reduction in clinical data after instrumentation [16; 17].  
The aim of the present study is to determine the long-term clinical efficacy of Er:YAG laser monotherapy in the treatment of moderate chronic periodontitis and to compare it with conventional instrumentation with Gracey curettes.
## 4 Trng
### Performance Analysis  
(a) Entropy: Entropy of TRNG defines the randomness of the generated data. Entropy can be calculated with the following equation [29]:  
\[E=-\sum_{i=2}^{2}p_{i}log_{2}(p_{i}) \tag{1}\]  
where p1 and p2 are probability of 1 s and 0 s respectively in a n-bit long data stream. For an ideal TRNG, the entropy is 1. We have calculated entropy of all chips column-wise and the average entropy is shown in Figure 13(a). It is evident that, columns 0, 1, 2, and 7 show poor randomness (explained in previous section) and therefore needs to be masked. After masking, the proposed TRNG offers an entropy of 0.95.  
(b) Repeatability: Repeatability is another important metric to evaluate TRNG performance. Ideally, a TRNG should only repeat when all other possible cases are already covered. For example, for a 4-bit TRNG, the generated value should only repeat when all the 16 possible values are generated. However, practically this is very difficult to achieve. In the proposed TRNG algorithm, we are able to get all possible outcomes with small standard deviation (0.0062) and the correlation coefficient is calculated as 0.05. Figure 13(b) shows the outcomes of 10,000 cases for the proposed TRNG.  
Figure 13: (**a**) Circuit and (**b**) timing waveform to implement specific write time selection for achieving desired switching probability.  
The energy, bandwidth and area of the proposed TRNG are found to be 0.41 pJ/bit, 0.12 Gbit/s and 0.769 um\({}^{2}\)/bit (including all peripheral circuits). We have also tested our TRNG outputs with NIST STS randomness test (summarized in Table 3). It is evident from the results that the proposed TRNG achieves excellent quality. Table 4 benchmarks the proposed TRNG with existing MRAM/STTRAM TRNG. We can observe that the proposed PUF is comparable to prior works and its energy (bandwidth) is significantly lower (higher) than others.  
Figure 14: (**a**) Average entropy of all chips measured column-wise; (**b**) repeatability of TRNG outcomes out of 10,000 responses.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**NIST Statistical Test** & \(\mathbf{p}\)**-Value** & **Proportion** & **Result** \\ \hline Frequency & 0.349865 & 199/200 & Pass \\ Block Frequency & 0.257217 & 199/200 & Pass \\ Cumulative Sums & 0.393322 & 199/200 & Pass \\ Discrete Fourier Transform & 0.476393 & 199/200 & Pass \\ Approximate Entropy & 0.844361 & 200/200 & Pass \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of NIST Suite Statistical Result.
## 7 Proportional--Integral--Derivative (PID) Controller Enhanced with Deep Learning (DL)
### System Vibration Control Under Impulse and Random Disturbances
#### 7.4.1 Control System Affected by Impulse Disturbance  
In the scenario where an impulse disturbance occurs at time 0 s, the system's behavior is analyzed. The reference input is a unit step with amplitude zero, and Figure 19 presents the resulting output signal to observe how the system reacts to this disturbance.  
The trained recurrent neural network (RNN) is employed to tune the PID controller by processing the system's output under impulse disturbance. The network predicts the optimal PID parameters required to reduce and mitigate the cantilever beam's vibrations. By analyzing the relationship between system inputs and outputs, it adjusts the PID gains, ensuring an effective control response.  
The PID parameters tuned by the network are summarized in Table 4, demonstrating the model's ability to infer suitable PID parameters across varying test conditions. This adaptability showcases the network's capacity to fine-tune the PID controller to match the system's specific requirements.  
As shown in Figure 19, the status of the beam is monitored based on this data, the network tunes the PID controller to generate the control signal seen in Figure 20. This control signal is tailored to counteract the disturbance by adjusting the PID parameters, allowing the controller to effectively stabilize the beam's vibrations effectively.  
Figure 19: Voltage Response of PZT Sensor before Vibration Control (under Impulse Disturbance).  
Figure 21 highlights the system's response after the deep learning-tuned PID controller is applied. The figures illustrate that the optimized PID gains provide improved vibration control, as the tuning adapts to disturbances. This approach emphasizes the advantage of integrating deep learning with traditional PID control, leading to more precise and adaptive vibration mitigation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(K_{p}\) & \(K_{i}\) & \(K_{d}\) \\ \hline
324.3 & \(-2887.8\) & 269.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PID Controller Parameters Tuned Using Deep Learning of the System Affected by Impulse Disturbance.  
Figure 20: System Control Signal.  
Figure 22 compares the system's performance before and after the PID controller was tuned using deep learning. The beam's vibrations are significantly reduced, especially in the first second. With the deep learning-tuned PID controller, the system reaches a settling time of 1.15 s, showing improved stability. The Integral Time Absolute Error (ITAE) is reduced to an impressively low 0.7326, indicating the system's efficiency in minimizing both the magnitude and duration of the error. Additionally, the mean squared error (MSE) of 3.4370 \(\times 10^{-14}\) further highlights the high predictive accuracy of the deep learning approach in optimizing the PID controller, resulting in more effective vibration control.
## 4 Experimental Procedure
### Round Robin Framework  
All seven bearing test cases were incorporated into each RUL estimation process in a round robin framework that seeks to maximise the data set as well as mitigate problems relating to over-fitting. The experimentation process involved allocating six bearing signals as training datasets to teach the ML algorithms. The seventh bearing test-case was used for testing purposes. Once RUL estimations were obtained for each of the out-of-sample test signals, the bearing was added back to the in-sample testing pool, and the next sequential bearing was transferred to the testing pool. The ML prediction model was retrained, and this was iterated until RUL estimations had been obtained for all seven bearing test cases.  
The incorporation of this framework to train and test the performance of each prediction algorithm greatly reduces over-fitting as we are only using out-of-sample test signals. All model training data comprises of signals from a completely different bearing for each test case. This gives an extremely accurate interpretation of how the models would perform  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(k\)**-NN Method** & **Distance Weightings** & \(k\)**Value** \\ \hline (Fine) & Euclidean & 1 \\ (Medium) & Euclidean & 10 \\ (Coarse) & Euclidean & 100 \\ (Cosine) & Cosine & 10 \\ (Cubic) & Cubic & 10 \\ (Weighted) & Weighted & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(k\)-NN method parameters.  
in a real-world application using signals from different bearings used to train the models in every case.  
The classification process involves dealing with a multi-class and multi-label classification model, which comprises five temporal wear-state classes to be estimated for thousands of consecutive time samples. A moving-average (MA) filtering technique is incorporated to smooth out any undesirable whipsawing or erratic transitioning between the five temporal wear-state classes. The MA technique involves taking a window length of nine discrete-time samples, consisting of the current target prediction, the previous four predicted targets and the following four predictions. The mode of the nine predicted values is then assigned to the current target sample. In a real-time application, these nine samples comprise a 40 s temporal time period. This short time period is extremely negligible over a bearing's lifetime, which is typically years for a real system.