esa-sceva/satcom-chunk-collection huggingface dataset:
# Noise Source Identification in Training Facilities and Gyms
## 1 Introduction
General test standards for the measurement of room acoustic parameters cover performance spaces [14], ordinary rooms [15], and open plan offices [16]. [PERSON] and [PERSON] [17] proposed that measurements should be conducted using gym users' smartphones. This enables access to the \"real life gym\", not disturbed by measurement equipment. However, it is far from any standardized method. The third direction, although not popular and common in the sport business, is the real psycho comfort [18,19] of the gym users. Psychoacoustics, in comparison to the standard approach, which focuses on the determination of the sound pressure level and absorption of the impact energy or isolation of the vibration and noise energy, defines engineering measurement factors to measure human comfort specifically. The selected factors are listed and described below:  
* Loudness--measure of human perception of the sound pressure level with respect to the tone frequency. * Sharpness--measure of high-frequency tone components in the noise spectrum. * Booming--measure of low-frequency tones in the noise spectrum. * Roughness--measure of high-frequency modulation. * Fluctuation strength--measure of low-frequency modulation. * Tone to noise ratio--measure of the tonal component with respect to the background/noise. * Prominence ratio--measure of the characteristic band with respect to defined bands of the background/noise. * Tonality--measure of the tonal to non-tonal components of noise Dedicated Olympic weightlifting centers are not located close to offices and residential buildings as now, cross training and functional training fitness centers are more common. The rise in the popularity of dynamic and ballistic movements in these objects requires an assessment of its influence with respect to the dynamic and acoustic phenomena. The aim of this study was to identify and analyze major sources of noise and shock sources. The measurements taken covered both acoustics and vibrations. The authors present an assessment of the sound pressure level and impact generation on the floor by the selected dynamic and ballistic exercises.
# Study on Strength and Microstructure of Red Clay Reinforced by F1 Ionic Soil Stabilizer
## 3 Results and Discussion
### Effect of the Freeze-Thaw Cycle on Shear Strength Parameters of F1 Reinforced Red Clay  
Freeze-thaw cycles can compromise the structure of compacted soil, reducing its mechanical strength, stability, and load-bearing capacity [30]. To analyze the effect of freeze-thaw cycles on the strength of F1 reinforced red clay, the relationship curve between the shear strength and the number of freeze-thaw cycles under different dosages was drawn, as displayed in Figure 8.  
Figure 8: Relationship between shear strength and number of freeze-thaw cycles. (**a**) Cohesion. (**b**) Internal friction angle.  
Figure 7: Relationship between shear strength parameters of red clay and F1 content.  
Figure 8 shows that the F1 red clay's shear strength decreased as the sample went through successive freeze-thaw cycles. After 15 freeze-thaw cycles, the 0.3, 0.5, and 0.7 L/m\({}^{3}\) F1 clay samples still exhibited 3.5%, 18.4%, and 6.0% higher cohesion than the virgin red clay, respectively, and correspondingly 32.3%, 57.1%, and 48.2% higher internal friction angle, respectively. These measurements indicated that the F1 reinforcing effect survived at least 15 freeze-thaw cycles and that the 0.5 L/m\({}^{3}\) F1 doses provided the best reinforcement effect. The freeze-thaw cycles caused a corresponding expansion and contraction, which disrupted the intergranular interaction in the red clay and weakened the cementation and biting force between particles, resulting in lower cohesion and internal friction angle after each successive cycle. The F1 soil stabilizer can promote the flocculation and agglomeration of soil particles through ion exchange and hydrophobic action, increasing the bonding strength between particles, reducing water sensitivity, and mitigating the damaging effect of the water phase change in red clay.
# A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment
###### Abstract  
Cloud computing has emerged as a high-performance computing environment with a large pool of abstracted, virtualized, flexible, and on-demand resources and services. Scheduling of scientific workflows in a distributed environment is a well-known NP-complete problem and therefore intractable with exact solutions. It becomes even more challenging in the cloud computing platform due to its dynamic and heterogeneous nature. The aim of this study is to optimize multi-objective scheduling of scientific workflows in a cloud computing environment based on the proposed metaheuristic-based algorithm, Hybrid Bio-inspired Metaheuristic for Multi-objective Optimization (HBMMO). The strong global exploration ability of the nature-inspired metaheuristic Symbiotic Organisms Search (SOS) is enhanced by involving an efficient list-scheduling heuristic, Predict Earliest Finish Time (PEFT), in the proposed algorithm to obtain better convergence and diversity of the approximate Pareto front in terms of reduced makespan, minimized cost, and efficient load balance of the Virtual Machines (VMs). The experiments using different scientific workflow applications highlight the effectiveness, practicality, and better performance of the proposed algorithm.  
cloud computing; metaheuristic; multi-objective optimization; scientific workflow scheduling; symbiotic organisms search +
Footnote †: journal: Applied Sciences
## 4 NFV and SFC Frameworks Taxonomy
### Resilience and Fault Recovery Frameworks
#### 4.3.1 Reinforce Framework  
[PERSON] et al. [90] proposed REINFORCE, a framework for achieving resiliency of DPDK-based NFs, which provides the check-pointing of applications that reduce the state of network functions to be replicated. REINFORCE provides failure recovery of network functions across the entire chain, with the detection of node and link failures within the shortest possible time. Packet processing overhead is minimised by the separation of network function behaviour into deterministic and non-deterministic, thus committing to check-pointing the states of standby NFs in non-deterministic scenarios.  
REINFORCE emphasises stateful network functions, which maintain the state of connections either globally or per-flow. The characterisation of state information enables the framework to decide whether flow updates are deterministic, which helps with the synchronisation of NFs that operate in a particular chain. The use of lazy check-pointing of the NF state and the replay of packets is used by REINFORCE to speed up the process of recovering from failures. Figure 13 depicts the architecture of the framework in which a chain-wide symmetry is maintained by nodes. NFV nodes can host multiple network functions, which can either be part of a service chain or a complete chain in a single NFV node. The NF manager is able to access the shared memory pool, while the process of time-stamping ingress packets is carried out at the beginning of the chain, and sent to the next NFV after logging.
# Discriminator-Enhanced Knowledge-Distillation Networks
## 4 Conclusions  
In this study, we introduce the discriminator-enhanced knowledge-distillation (Dis-KD) approach, a novel method for transferring knowledge from a teacher model to a student model, with the aim of improving model performance. This is achieved by utilizing evaluation results from a discriminator.  
Our experimental results on the QAC task demonstrate that Dis-KD significantly outperforms the baseline methods, with the distilled two-layer student model even surpassing the six-layer teacher model, and our method is easy to optimize and can be combined with other methods to consistently improve performance. And our work has several main contributions:  
1. We propose Dis-KD, a novel discriminator-enhanced knowledge-distillation framework, which can both enhance model accuracy and reduce parameter size.
2. To make the discriminator's signal suitable for generation tasks, we introduce an easy-to-implement discriminator loss, as direct training on the signal from the discriminator is not differentiable.
3. Our approach involves intelligently leveraging the loss of the discriminator as an evaluation signal for the entire sentence. By adopting this method, we can effectively overcome the over-correction problem, thereby significantly reducing the model size nearly threefold and improving inference by the same factor. Furthermore, it exceeds the performance of the original GPT-2 model in terms of the mean reciprocal rank (MRR).  
The innovative distillation framework integrates a discriminator--conceptualized as a long short-term memory (LSTM) network--from a generative adversarial network (GAN). The discriminator signal, represented as a scalar output from the LSTM, serves two crucial functions. First, it assesses the complete output, thereby mitigating the risk of overcorrection. Second, it calculates the loss between the student and teacher networks, essentially functioning as an auxiliary loss to assist in the training of the student network. Consequently, the student network's output should ideally closely mimic that of the teacher network, as both should elicit the same signal from the discriminator.  
The application of this method not only compresses the model size but also empowers the student network to achieve superior performance using fewer parameters than the teacher network. The proposed approach presents a promising strategy for enhancing the training of deep neural networks, with potential applicability across diverse domains.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Latency** \\ \hline Dis-KD & 26.1 ms \\ GPT-2(2) & 26.0 ms \\ GPT-2(6) & 74.3 ms \\ LSTM & 34.1 ms \\ MCG & 0.18 ms \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of query per millisecond (ms/query).
## 2 Peculiarities of Implemented ML Pipelines for IR and THz Absorption Spectra Analysis
### Classification
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Idea** & **Advantages** & **Disadvantages** & **Interpretability** & **Ref.** \\ \hline ELM-AE is a variant of & & & & & \\ single-hidden-layer autoencoder. & & & & & \\ The ELM-AE is based on & & & & & \\ construction of the set of three & & & & & \\ input data representations: (1) & & & & & \\ decoding–the input feature space & & & & & \\ is transformed to the & & & & & \\ low-dimensional feature space; (2) & & & Finding optimal weights & & \\  & encoding–this low-dimensional & Very fast training. & and biases for the & “Black box” & [66; 67] \\ ELM-AE & feature space is equivalently & & hidden layer is a & model. & [66; 67] \\  & mapped to the high-dimensional & & nontrivial problem. & & \\ feature space; & & & & \\  & (3) autoencoding–the input & & & & \\ features are equivalently mapped & & & & \\  & from the original feature space to & & & \\  & the equal-dimensional & & & \\  & feature space. & & & \\ \hline ANN & & & ANNs are prone to & & \\  & & & & overfitting. It is hard to & \\  & & & & find the optimal & \\  & & tunable weights and arranged in & which are & configuration of layers & “Black box” & \\  & layers to learn patterns from & characterized by & and weights. ANNs & model. & \\  & input data and provide & & slightly different & require a lot of & \\  & output responses. & shapes of spectra. & annotated data for & & \\ \hline CNN & Same as ANN, but 2D & & Excellent & & \\  & convolution masks parameters & performance in & & & \\  & are optimized instead of weights & image analysis. & Same as for ANN. & “Black box” & [58] \\  & for neurons outputs. & & & & model. & \\ \hline SVM & SVM is based on the finding the & & & & \\  & maximum margin hyperplane, & & & & \\  & which separates two classes. The & & It is hard to separate & Linear model & \\  & support vectors are data points, & & slightly different spectra. & can be & interpreted. \\  & which are placed on boundaries & & SVM fails when the & \\  & of this hyperplane. & & number of frequencies is & “Black box” & [69; 70; 71] \\  & In kernel SVM, data are & & much larger the volume & model for & \\  & preliminarily projected into & & of the dataset. & & non-linear \\  & higher dimensional space, where & & & \\  & classes are linearly separable. & & & \\ \hline RF is a combination of decision & & & & \\  & tree classifiers trained & & & \\  & individually. Their joint use & & & \\  & increases accuracy & & & \\  & of classification. & & & \\ RF & Initial set of spectra can be & & & \\  & divided into two subsets, based & & and overfitting. RF & \\  & on computed threshold value for & & can handle & be significant. & \\  & the frequency. A sequence of such & & missing data. & \\  & splits provides separation of & & & \\  & initial data into classes. & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification methods.
## 4 Experiments
### Experimental Setup
#### 4.1.2 Experimental Scenarios  
The primary objective of this study is to determine how the proposed C-STS mechanism affects the stair-climbing performance of a curved-spoke wheel and to validate its effectiveness in mitigating abrupt deceleration during the DCS phase. To that end, we measured the wheel's CoR linear velocity through video capture and subsequent trajectory analysis, comparing the results for both the suspension-equipped platform and the one without it. In all tests, the platform remained stationary at a point 300 mm away from the first step, ensuring the wheel's center of rotation started from a consistent initial position.  
In this experiment, we used three different suspensions with torsional stiffness values of \(k_{t}=0.206\) N\(\cdot\)m/rad, \(k_{t}=0.547\) N\(\cdot\)m/rad, and \(k_{t}=0.909\) N\(\cdot\)m/rad. For convenience, we refer to them as the low-k suspension, middle-k suspension, and high-k suspension, while the configuration without a suspension is labeled basic. Table 5 summarizes the experimental scenarios: S1 involves a robot without any suspension (basic) climbing the stairs at low, medium, and high speeds. S2, S3, and S4 use the low-k, middle-k, and high-k suspensions, respectively, under the same three speed conditions. These different speeds were achieved by adjusting the PWM inputs to the motor driver. To ensure consistent data across all scenarios, each case was repeated three times, yielding a total of 36 stair-climbing trials.
# Elastic CRFs for Open-Ontology Slot Filling
## 2 Related Work  
One line of related work is zero-shot slot-filling learning [14]. The term _open ontology_ referred in this paper is a different name for zero-shot slot filling in spoken language understanding (SLU) for dialog systems. Zero-shot learning has been applied in various of SLU tasks. The authors of [15] leverage the intent embeddings to detect new intent labels which are not included in the training data. Additionally, ref. [12] exploits the slot label descriptions to parse the novel semantic frames for domain scaling and [16] extends the natural language generation module to generalize the responses into an unseen domain via latent action matching. The authors of [17] propose utilizing both the slot description and a small number of examples of slot values to enhance model robustness. In [18], the authors focus on multi-turn zero-shot slot filling in conversation. These studies have utilized the natural language descriptions of the labels, and by constructing the semantic encoder to take the label descriptions as inputs, any new labels in the testing phrase can still be predicted by the model. Our eCRFs also use this semantic encoder structure. However, unlike processing each label description separately in [12], eCRFs are trained and tested by jointly exploiting all possible slot descriptions at one time. Thus, they could capture relations between slot labels and relieve the burden of adjusting the oversampling ratio.  
Another line of related work is models for slot filling. CRFs have been extensively applied in traditional slot-filling tasks [19; 20], but are restricted by a fixed set of labels. With the progress of deep learning, state-of-art slot-filling methods usually utilize BiLSTM networks [9; 21; 22]. Extended models, such as encoder-decoder [5] and memory network [23] designs, are explored. More recently, ref. [24] proposes a coarse-to-fine approach (Coach) for cross-domain slot filling, which detects the value span boundary first and then predicts the specific fine types for the slot entities. With the advance of pre-trained models [25], there are also many work [26; 27; 28] that adapt the well-studied machine reading comprehension (MRC) framework to solve open-ontology slot filling. Motivated by the BiLSTM-CRF architecture [19; 29; 30], our eCRFs combine the representation power of deep neural networks and dependency modeling ability of CRFs, together with a newly designed potential function.
## 2 Study Area  
The Yeongcheon underground mine (37 deg4'14'', 128 deg18'46'') of Baek Kwang Mineral Products Co., located in Danyang-si, Chungcheongnam-do, South Korea, was selected as the study area (Figure 1). The mine comprises four levels; the average altitude of Levels 1, 2, 3 were 310, 250, and 280 m, respectively, while the lowest for Level 4 was 220 m. The mining method for room and pillar mining produces approximately 120 tons of limestone ore per year. The mine consists of four loading points underground (the IDs of which are 203, 233, 235 and 237) and a dumping area with a crusher on the surface. The ore was transported, using 30 ton dump trucks. The site's production manager assigns trucks to the loading point daily, accounting for the ore production goals and quality. Next, the truck driver departs to the point to load the ore. The ore is loaded and subsequently moved to the dumping area. The truck driver checks the amount of ore accumulated in the crusher; if the ore quantity does not exceed the crusher capacity, the ore is unloaded into the crusher, and if it exceeds the capacity, the ore is unloaded into the yard. This operation is repeated during the working hours.  
The study area was equipped with a mine safety management system based on ICT. Owing to the installation of a wireless communication network, it was possible to implement a digital environment to track the location of equipment in real time, make voice calls, and monitor the working environment at an underground mine site where communication was not possible. Through the wireless communication network, the location of the production equipment in the mine was received in real time on the dashboard in the office. The data acquired through the safety management system were stored in real time. In this study, the location tracking data of the production equipment were used in the mine safety management system. When the production equipment passes by a wireless access point (AP) installed in the study area, its location is tracked and stored as tag-recognition data. Figure 2 shows the process of a truck being recognized by the wireless APs as it passes by them while transporting ore in an underground mine. The tag recognition packet data, stored as the truck passes, includes information regarding the data category, presence of an emergency, tag recognition sequence, ID of the recognized tag, and distance between the wireless APs and the tag. These data are transmitted once every second, and approximately 200,000 packets are stored in the web server per month; the size of the data packet is 20 bytes each.
## 5 Results  
This section presents the results of the interference analysis conducted based on the system model and mathematical frameworks detailed in the previous sections. We will explore various cases to quantify the impact of different interference sources on the GEO satellite system. The satellite uplink and downlink specifications are listed in Table 3.  
Figure 3: Far-field elevation pattern of the 1.2 m parabolic antenna at 14 GHz.  
Figure 2: Image of the 35-degree satellite footprint [21].  
Initially, we will establish a baseline by examining an ideal scenario devoid of interference. Subsequently, we will present the results obtained when considering adjacent satellite interference (ASI) and then the specific interference arising from MVDS transmissions.
# Application Research of Negative Pressure Wave Signal Denoising Method Based on VMD
## 2 Methodology
### Signal Noise Reduction Based on VMD  
#### 2.2.1 Signal Decomposition  
VMD decomposes the signal into \(k\) IMF, and the bandwidth of each component in the frequency domain has a specific sparsity. The constrained variational problem of VMD is [22]:  
\[\begin{split}\min_{\{u_{k}\},\{\omega_{k}\}}\biggl{\{}\sum \limits_{k}\Bigl{\|}\partial_{t}\Bigl{[}\Bigl{(}\delta(t)+\frac{j}{nt}\Bigr{)} *u_{k}(t)\Bigr{]}\mathrm{e}^{-j\omega_{k}t}\Bigr{\|}_{2}^{2}\biggr{\}}\\ \mathrm{s.t.}\sum\limits_{k}u_{k}=f\end{split} \tag{3}\]  
where, \(\{u_{k}\}=\{u_{1},\ldots,u_{k}\}\) is \(k\) IMF by decomposing the signal \(f\), and \(\{\omega_{k}\}=\{\omega_{1},\ldots,\omega_{k}\}\) is the center frequency corresponding to each component. In order to solve the constrained variational problem of Equation (3), the Lagrange multiplier \(\lambda\) and the quadratic penalty factor \(\alpha\) are introduced at the same time. The augmented Lagrange function is shown in Equation (4):  
\[\begin{split} L(\{u_{k}\},\{\omega_{k}\},\lambda)=\alpha\sum \limits_{k}\Bigl{\|}\partial_{t}\Bigl{[}\Bigl{(}\delta(t)+\frac{j}{nt}\Bigr{)} *u_{k}(t)\Bigr{]}\mathrm{e}^{-j\omega_{k}t}\Bigr{\|}_{2}^{2}\\ +\biggl{\|}f(t)-\sum\limits_{k}u_{k}(t)\Bigr{\|}_{2}^{2}+\biggl{\langle} \lambda(t),f(t)-\sum\limits_{k}u_{k}(t)\biggr{\rangle}\end{split} \tag{4}\]  
Alternate direction method of multipliers (ADMM) is used to obtain the saddle point of Equation (4), and iteratively update \(u_{k}\), \(\omega_{k}\) and \(\lambda\) to obtain the optimum value in the frequency domain, thereby obtaining each intrinsic mode function \(\{u_{k}\}\) and \(\{\omega_{k}\}\).