esa-sceva/satcom-chunk-collection huggingface dataset:
# Neural Network Exploration for Keyword Spotting on Edge Devices
## 2 Background
### Audio Processing  
One of the most critical steps in neural network development is preparing the training data. This step may be further broken down into two smaller steps: finding a suitable dataset and extracting features. The selected dataset must contain the relationship, pattern, or function that the neural network will learn. In the case of keyword spotting, this means finding a suitable collection of audio clips of people saying the desired keywords. This dataset is then processed to extract features before training a neural network.  
The Google Speech Commands dataset [17] is selected for training neural networks in this research. This dataset consists of 105,829 one-second audio recordings of 35 keywords, saved as 16-bit 16 kHz PCM Wave files. Since the dataset was collected through volunteer submissions, participants were not obligated to submit recordings of every keyword. Therefore, the number of recordings for each keyword varies. Nevertheless, the size of this dataset, in conjunction with the limited vocabulary, makes it an attractive choice for training neural networks for keyword spotting applications.  
A feature is a particular type of information on which the neural network is trained. In this research, the Wave files are processed into mel spectrograms that contain high-level features of the audio signal. Specifically, the short-time Fourier transform is first applied to the audio amplitude values to produce a spectrogram, which depicts the change in the power of different frequency components throughout the original audio. This allows the neural network to focus on classifying the higher-level spectrogram [20]. Additionally, the spectrogram can be treated as an image-like input rather than a sequence of amplitude values. Since humans perceive volume and frequency logarithmically rather than linearly, the spectrogram is further processed into a corresponding mel spectrogram with amplitude values converted to the decibel (dB) scale and frequency values converted to the Mel scale through a series of overlapping triangular filters in the frequency domain [20]. In our research, a mel filter bank with 40 filters is implemented.  
Please note that another feature representation, called mel-frequency cepstral coefficients (MFCCs), is also used often in speech recognition applications. The goal of MFCCs is to extract highly unrelated coefficients from the mel spectrogram through the discrete cosine transform [21]. While MFCCs provide a compact, high-level representation of the original audio recording, decreasing the computation required of the neural network, the increased network efficiency comes at the cost of increased preprocessing. Therefore, the target platform's resources and the neural network's use case must be considered when deciding which representation to use, especially when the desired use case is keyword spotting on low-power edge devices. In this research, the mel spectrogram feature is used.
## 2 Orbit Selection Approach
The latter optimization can be achieved by interleaving the sampling of the second pair at the equator in between the sampling of the first one, resulting in an effective doubling of the spatial resolution, which can be achieved for a given period. As mentioned before, the orbits used in several ESA studies were selected by the approach described in this section. For example, [31] based their simulations on orbits with inclinations of 70 and 89\({}^{\circ}\) and altitudes of 355 and 340 km, respectively, and a ground track shift of 1.3\({}^{\circ}\) every 7 days. In addition to the period of 7 days, the orbit with an inclination of 70\({}^{\circ}\) offered a near-homogeneity value of 1.1 at 19 days, whereas the orbit with an inclination of 89\({}^{\circ}\) offered a near-homogeneity value of 1.1 at 18 days. Thus, the sampling was optimized for the retrieval of weekly as well as 18-19-day gravity field models.
# Ant-Balanced Multiple Traveling Salesmen: ACO-BmTSP
## 2 Methodology
### Applying ACO to the BmTSP  
As real ants are able to find the shortest path between a food source and their nest without using visual mechanisms, but only by exploiting a trail of pheromones, they can be similarly applied to the mTSP, considering the nest as a single depot and the location of a food source as the location of other cities. The road network in the mTSP was modeled with a graph simulating the path traveled by real ants to find food. Figures 2 and 3 show an example of three salesmen traveling to twenty-three cities while using two different variants: the minisum and unbalanced work (Figure 2)--where only the total cost (in this case, the distance) was the aim considered--and the minimax (Figure 3)--where the work was more balanced, but the only goal to minimize the path with the maximum salesman cost.  
Most reported studies focused on solving the mTSP by using a minisum formulation, and only very few aimed to specifically minimize the maximum distance traveled by all of the salesmen [41]. However, the best solutions showed that the salesmen's paths were not  
Figure 3: Balanced work in the mTSP.  
Figure 2: Unbalanced work in the mTSP.  
equitable or balanced. A small percentage of salesmen traveled most of the paths, while the rest traveled very short distances. To try to eliminate this disadvantage, this study focused on an attempt to achieve a single function that consisted of a combination of the shortest total path (minisum) and a minimization of the maximum subpath (minimax) in order to obtain balanced paths for each salesman. The mTSP was called balanced (BmTSP) when each salesman visited approximately the same number of cities or had the same tour cost. Therefore, the BmTSP was used, and the equitable distribution of some types of resources was important. Some authors formulated this problem by minimizing the maximum tour values. In terms of optimization, this function presents some gaps. If the 'lower' tours--the tours with lower distance values--change, the objective value could remain the same. Therefore, this change is not reflected in the objective function and does not give any clues for driving the search in the right direction.
# Remote Sensing for International Peace and Security: Its Role and Implications
## 5 Future Perspectives
### Technology Development Perspective
#### 5.1.1 Use of New Sensors  
The remote sensing applications for international peace and security rely on the availability of accurate, reliable, fast, and high-resolution data from sensors. Advancements in sensor technology include the development of miniaturized sensors that reliably and accurately capture data with high spatial and temporal resolution. The terrestrial LiDAR sensors, which have been widely used in 3D city modeling, transport infrastructure mapping, vegetation mapping, and utility surveying, etc. will be effective tools for monitoring international peace and security. Such sensors are capable of collecting a 3D point cloud, which can be processed by state-of-the-art registration methods to reconstruct 3D surfaces. The 3D models are developed from 3D surfaces using computer graphics techniques. The 3D models help trace the location and extent of territories, avoid international border conflicts, and, hence, contribute toward peace and security [117]. The micro-electromechanical system (MEMS) technology [72], 3D sensors, and sensor fusion [118] are some of the notable areas, which are augmenting the remote sensing technology toward its role in international peace and security.
## 3 Results and Discussion  
### Optimal Parameters for the Size and Position of the Excitation Source  
The simulation calculation shows that the vibration displacement in the Y direction of the execution end is the largest, and the maximum amplitude of the execution end of the original model is 0.5838 mm. The results obtained with the settings in Table 1 show that the maximum vibration amplitude of the execution end is found at 16 Hz. The corresponding main effect diagrams and interaction diagrams are shown in Figures 11 and 12, respectively. The main effects plot (Figure 11) compares the difference between the horizontal means of the torque of the five motors. The horizontal coordinates are the variation level of each motor's torque, and the longitudinal coordinates are the differences of the response means. The interaction plot (Figure 12) illustrates how the relationship between one moment and the continuous response depends on the value of another moment. This figure shows the horizontal mean of one torque on the \(x\)-axis and a separate line for each level of the other torque.  
Figures 11 and 12 indicate that Moment 3 has the most significant influence on the amplitude displacement value at the end of the controllable mechanism welding robot, whereas Moment 1 and Moment 2 have no effect, and Moment 4 and Moment 5 have no significant effect.  
The optimization target of the amplitude displacement value of the execution end is set to 0 mm, and solved using the response optimizer in Minitab. The resulting optimization plot is shown in Figure 13.  
The optimization graphic results in Figure 13 are provided in table form in Table 5.  
Figure 13 and Table 5 show that when Moment 1 to Moment 5 are 2400 N\(\cdot\)mm, 2400 N\(\cdot\)mm, 1300 N\(\cdot\)mm, 600 N\(\cdot\)mm and 280 N\(\cdot\)mm, respectively, the amplitude response displacement of the end of the controllable mechanism welding robot is at a minimum. Compared with the initial amplitude response displacement of 0.5838 mm, the amplitude response displacement decreases by 51%.  
The amplitude response curve of the execution end obtained after optimization is shown in Figure 14.  
Figure 14 reveals that when the excitation frequency is 16 Hz, the amplitude value of the actuator end is the largest, which is close to the optimization result of the response surface methodology of 0.2880 mm. The value is within the 95% confidence interval, which means that the response surface methodology is effective for the vibration optimization of the actuator end.
# Groundwater Storage Changes in China from Satellite Gravity: An Overview
## 1 Introduction
This study provides a quantification of GWS changes in the three main aquifers of China based on comprehensive comparisons of GRACE satellite data, ground well observations, and hydrological models. This paper is organized as follows. In Section 2, we briefly explain the GWS change estimation methods based on GRACE and the data used in this study. Section 3 presents GWS change estimates in three main aquifers of China. In Section 4, we discussed the uncertainties in GRACE-based GWS change estimates, taking the ITP as a case study. The study is summarized and concluded in Section 5. Figure 1: Map of China showing three main aquifer basins (i.e., (1) North China Aquifer System; (2) Song-Liao Basin; and (3) Tarim Basin). The boundaries of the three aquifers are based on the delineation of the world’s 37 large aquifer systems from the World-wide Hydrogeological Mapping and Assessment Programme (WHYMAP) [47]. The white areas denote glaciers from the Randolph Glacier Inventory (RGI 6.0) [48].
# Investigating the Learning Process of Folk Dances Using Mobile Augmented Reality
## 6 Discussion  
Participants' motion capture data were compared with ground truth data from professional dancers to measure similarity between their movements. Determination of ground truth data for motion capture is still an open research question. Human motion data obtained using an optical motion capture system are considered as gold standard in motion capture. In case of professional dancers, maximum size of the gap was set to 10 frames and after filtering all gaps where filled. From this we can conclude that the size of gaps was less or equal to 10. Since the frame rate of the system was 120 Hz, we can consider these data good enough to be used as ground truth.  
The prototype mobile AR application was designed to provide easier interaction and environment where participants could examine the dance from different angles and distance. This should facilitate learning. However, most participants did not use this advantage. In addition, interaction in AR is based on swipe gestures which was supposed to be natural for participants, since they all use smartphones in everyday life. Possible reasons why they struggled with commands is that the phone was placed in head-mounted cardboard. After explanation from the experimenter at the beginning, participants did not have any time to try and repeat commands but the learning phase started. Some participants spent significant amount of time intended for learning the dance on learning how to interact with the application. This is supported by results from PQ where score related to 'Possibility to Act' that includes questions like 'How much were you able to control events?' and 'How completely were you able to actively survey or search the environment using vision?' was in favor of application presented on projector screen.  
In our experiment some participants reported to feel some symptoms stronger after experiment but without significant difference. Since the folk dance, they had to learn, includes spinning, it could affect answers because participants that used projector screen also had to spin and they could experience vertigo or dizziness. This finding is in line with [43] which reported that only few participants experienced minimal discomfort. Moreover, [PERSON] et al. [17] reported that using AR/VR headset does not exacerbate motion sickness. It was indicated that variation of the latency over time can result with decrease in performance and increase in simulator sickness [44].  
For both testing modes (AR and projection screen), the main limitation was avatar visualization within the field of view. When users move in space, an avatar is not following them and it can happen that they cannot see avatar at some moments. Interaction through cutout in Google cardboard is another part to be improved. Users could not see the cutout while they were in AR and they had to remember where it was placed. Gesture recognition using camera could be better solution.
# Bridge Assessment under Earthquake and Flood-Induced Scour
## 3 Case Study
### FE Modeling of Elements
#### 3.3.3. Abutments  
The abutments interact with the earthfill behind the back wall. Therefore, soil structure is considered in abutment modeling. The space between the abutment wall and girders is represented utilizing elastic-perfectly plastic gap components. The backfill soil on the abutment wall is modeled as suggested by Caltrans (2013) (2013), and for dynamic loading, the passive pressure will be raised by 50%. The abutment resistance in the bridge axis direction does not incorporate the influence of active soil pressure. Therefore, in the active direction, piles are the only resisting components. Spring elements are arranged in parallel along the bridge axis direction. The stiffness of the abutment wall is calculated using Equation (7).  
\[K_{\text{abut}}=K_{\text{i}}\cdot w\cdot\left(\frac{h}{1.7}\right) \tag{7}\]  
Given the parameters, \(w\) represents the width and \(h\) signifies the height of the backwall. The backfill soil stiffness is taken as \(K_{\text{i}}=14.35\) kN/mm/m as proposed by Caltrans. The maximum passive pressure of 239 kPa given by Caltrans is increased by 50%, and 368 kPa of soil pressure is utilized for dynamic and earthquake loads. This pressure is utilized to calculate the passive pressure force opposing movement at the abutment:  
\[F_{\text{abut}}=\left(\frac{h^{2}\cdot w\cdot 360}{1.7}\right) \tag{8}\]
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 4 Integration of Various RS Technologies
### Integration of the Vision-Based Technologies Together and with Other RTLS  
Using vision-based technologies such as photogrammetry, laser scanning, and their integration are appropriate choices to provide enough point cloud data in order to create 3D models of a site. More specifically, their integration enhances the point cloud data that are required for 3D reconstruction of the objects available on site. However, all these approaches have their limitations associated with their employment in a congested area and their drawbacks in modeling non-stationary and small objects such as furniture, equipment, etc. In this way, by integrating these vision-based technologies with a real-time locating systems (RTLS) technology such as robotic total station (RTS) and UWB, it is possible to overcome some of these problems. Table 4 provides a comparison between these integrated systems over the individual usage of photogrammetry or laser scanning. They are different in terms of the level of automation in data acquisition, cost, reliability, and scalability to be used in various projects.
## 3 Results and Discussion
### Volatiles Analysis
#### Changes of Volatiles in Kiwifruit Pulp Beverage after HHP
Because complex chemical reactions occurred when pulp beverage treated by HT, which could lead to losses of volatiles in fruit [44]. The content of ketones and acids were low in kiwifruit pulp beverage. However, acetic acid, pentyl ester, acetic acid, and hexyl ester were not detectable in treated samples. Figure 3: Changes in color of heat-treated and HHP-treated kiwifruit pulp beverage during storage at 4 °C for 40 days. (**A**) L*, the brightness value on a black (0) to white (100) scale; (**B**) a*, hue on a green (\(-\)) to red (\(+\)) scale; (**C**) b*, hue on a blue (\(-\)) to yellow (\(+\)) axis; (**D**) \(\Delta\)E, the total color difference between treated samples and untreated samples at the initial time. Heat treatment (85 °C for 10 min) (\(\blacksquare\)), HHP (high hydrostatic pressure, 400–600 MPa for 5–15 min): 400 MPa/5 min (\(\blacktriangledown\)), 400 MPa/10 min (\(\blacktriangle\)), 400 MPa/15 min (\(\blacktriangle\)), 500 MPa/5 min (\(\blacktriangledown\)), 500 MPa/10 min (\(\blacklo\)), 500 MPa/15 min (\(\blacklo\)), 600 MPa/5 min (\(\blacklo\)), 600 MPa/10 min (\(\blacktriangledown\)), 600 MPa/10 min (\(\blacklo\)), 600 MPa/15 min (\(\blacklo\)). All data were expressed as means \(\pm\) SD, \(n=3\). Because these volatiles were unstable that was influenced by heat treatment or HHP processing. Ethyl acetate has a pleasant ethereal-fruity and brandy-like aroma [43]. The level of ethyl acetate was not modified significantly after HHP. However, all treated samples were detected a remarkable reduction in the content of butyl acetate and butyl acetate were not detectable when samples treated by pressure greater than 400 MPa. The decrease in the content of butyl acetate after HHP treatment may be due to two reasons: (1) HHP activates or inactivates the enzymes that synthesize esters in fruit; (2) esters are hydrolyzed after HHP treatment [45]. Similarly, [PERSON] et al. [46] also reported the content of esters such as butyl acetate and isobutyl acetate in HHP-treated apple juice were lower than those of untreated juice. Figure 4: Loading plots after principal components analysis of the variables in the plane defined by the two first principal components (PC 1 and PC 2). Figure 5: Principal component analysis (PCA) plot of the volatiles of kiwifruit pulp beverage at different treatment conditions. Untreated (+); HT (85 \({}^{\circ}\)C for 10 min) (\(\blacksquare\)); 400 MPa/5 min (\(\blacksquare\)); 400 MPa/10 min (\(\star\)); 400 MPa/15 min (\(\blacksquare\)); 500 MPa/5 min (\(\blacksquare\)); 500 MPa/10 min (\(\blacksquare\)); 500 MPa/15 min (\(\blacktriangledown\)); 600 MPa/5 min (\(\bullet\)); 600 MPa/10 min (\(\blacktriangle\)); 600 MPa/15 min (\(\blacktriangledown\)).
## 5 Conclusions  
The operation of a cement grinding circuit is a complex and non-linear process, which makes it difficult to control, optimize and predict the outcome. Coarse return is one of the process parameters which has a major role in the control and optimization of the process, and its prediction is an essential factor of the process improvement. In this paper, an FCDNN architecture for the prediction of the coarse return is proposed. The result of the proposed model shows better accuracy in comparison with two widely used single hidden layer NN and LSTM architectures in the cement grinding process. The LSTM has a better performance than a single layer NN, but still not better than FCDNN. In future works, the LSTM, due to its capacity to deal with long time series prediction, will be further investigated and inserted into our prediction system.  
A one-month data collection campaign was run over of a real grinding process plant. The collected dataset contains the essential operating events capturing the variation of the operating conditions of the grinding circuit during the process cycle. It allows the prediction of the coarse return variables with high accuracy. The key variables affecting the coarse return were determined through the event-modeler technique (feature extraction technique), and the prediction model of the system was obtained by using an FCDNN identification.  
The proposed method is currently successfully being applied in a number of cement plants, enabling the operators to take corrective actions before the coarse return increase (both in autonomous and manual mode). The impact of the solution has improved efficiency  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method/Forecasting Ahead** & **1 min** & **5 min** & **10 min** & **15 min** \\ \hline A single layer NN & 8.99 & 14.07 & 23.73 & 23.85 \\ Stacked LSTM & 8 & 11.16 & 15.88 & 20.16 \\ \hline \hline \end{tabular}
\end{table}
Table 3: MAE between the actual and predicted coarse return on the test dataset in long short-term memory (LSTM) and single hidden layer neural network (NN) methods.  
resource use by 10% of resources, the plant stability, and the overall energy efficiency of the plant. The prediction of coarse return in a long horizon is one of the ways to achieve a better performance of cement plants. This is a clear example of the application of artificial intelligence in the cement industry and how it can benefit overall production.  
Conceptualization, M.D., F.S., P.S. and A.M.; Data curation, F.S. and P.S.; Formal analysis, M.D., S.D. and F.S.; Investigation, F.S.; Methodology, S.D. and M.D.; Project administration, M.D.; Resources, F.S.; Software, S.D. and F.S.; Supervision, F.S., P.S. and A.M.; Validation, S.D. and F.S.; Visualization, S.D. and F.S.; Writing--original draft, M.D.; Writing--review & editing, M.D., F.S., P.S. and A.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The actual data presented in this study are confidential and not allowed to be published.  
The authors declare no conflict of interest.