esa-sceva/satcom-chunk-collection huggingface dataset:
# Testing of Indoor Obstacle-Detection Prototypes Designed for Visually Impaired Persons
## 2 Materials and Methods
### Audio Conversion  
#### 2.2.1 PCM Audio Conversion  
Pulse Code Modulation (PCM) it a modulation method facilitating the conversion of an analog signal into a digital one. Employing PCM modulation enables the digitization of various analog information, encompassing voice, video, audio, telemetry, and more. To derive a PCM signal during transmission, the analog signal undergoes sampling at regular intervals.  
The audio segment of the prototype is designed to offer the user an extensive array of customization options for the received message. Primarily, users can choose the language in which messages are transmitted. Additionally, they have the flexibility to select different voices, opt for a familiar voice, or even use their own voice.  
To enable these modifications, recording the audio messages in the desired language and voice becomes imperative. This recording process can be effortlessly accomplished using any available software on a computer along with a microphone. In this study, the Audacity software was used for all the voice recordings. It is a open-source software accessible across. Subsequently, the audio file may need processing to eliminate unwanted noise or pauses between words in the message.  
The subsequent step involves converting the file with the following parameters:  
* Sample per second: 16 kHz
* Channel: Mono
* Format: 16-bit PCM  
The resulting file is exported in MP3 format and, utilizing an audio encoder, is transformed into a PCM signal that can be directly loaded into the detection program installed on the microchip. In practice, this modification can only be executed by an individual with access to the code.  
Considering the memory space required for each message, the number of messages essential for the prototype's operation, and the overall space available on the microchip (approximately 2048 bits), it was necessary to declare the messages in the program to be PROGMEN-type constants. Implementing the term PROGMEN ensures that these constants are stored during program execution in alternative memory spaces, rather than the SRAM (Static Random-Access Memory) memory type that is typically used.  
The notable advantage of this prototype lies in the fact that end-users can request any language or voice type. A person with access to the source code can seamlessly modify and upload the new version to meet specific requirements.
## 2 Fiber-Optic Distribution
### Experimental Results
Meanwhile, a key question regarding the impact of external excitation is whether single-tone perturbations can cause increase of total phase noise power, which can be calculated by integrating the power spectral density traces in Figures 2 and 3 over the entire frequency span. We compare the total noise powers with and without acoustic perturbations for both SMF and PMF at the four perturbation frequencies between 100 Hz and 1 kHz, where the suppression to low-frequency noise is most significant. The result is summarized in Table 1. Given the noisy nature of the traces, we see some fluctuations in the amount of change of total noise power. But for most cases, the percentage power change with acoustic perturbation range from about 5% to about 10%. Evidently, single-frequency perturbation leads to the increase of phase noise power. Figure 3: Excess phase noise spectra with (solid) and without (dashed) external acoustic perturbations with 80 m polarization maintaining fiber (PMF) being used. Measurement is done at four single-tone perturbation frequencies: **(a)** 100 Hz; **(b)** 200 Hz; **(c)** 500 Hz; and **(d)** 1 kHz.
# Communication Technologies in Emergency Situations
## 4 Emergency Communications Technologies--A Systematic Literature Review
### Results  
In total, 60 articles were first identified, of which 47 were left after eliminating duplicates, 34 were left after screening, and 27 were left after eligibility for the data extraction.  
After the data processing of these 27 articles, several technologies have been identified, as well as the target contribution for each work. In this section, these technologies will be described and the corresponding technologic challenges addressed.  
As introduced in Section 2 on Emergency Communications Systems and in the desk research of Section 3, three types of communications (with very different requirements) existing in an Emergency Scenario will be distinguished.  
First, there is a Short Range (SH) category including all the communications between the people/objects within the area of the emergency. This category includes, for example, the communication between survivors and FRs, which is crucial to alleviate post-disaster consequences and save lives.  
Second, the CIRS category includes all the communications related to the coordination of the rescue, as, for example, the communication from the CIRS to the people/objects of the emergency area or even the communication between several CIRSs.  
Finally, depending on the character of the emergency event, residents of the affected area can also be informed about the emergency by alerting, warning, and notification systems of the state. The Warning System (WS) category provides the required reliable performance of warning and notification systems for the population.
# Research on Collision Warning Method for Ship-Bridge Based on Safety Potential Field
## 3 Establishment of Safety Potential Field Model in Bridge Area
### Static Elemental Potential Energy Field
\(r_{d}\) is the position vector from the centre of static element i to any point in the channel area, as shown below:  
\[\left|r_{d}\right|=\sqrt{\left(x_{i}-x_{o}\right)^{2}+\left(y_{i}-y_{o}\right) ^{2}} \tag{3}\]  
where \((x_{0},y_{0})\) is the position coordinate of the static marker element O, and \((x_{i},y_{i})\) is the position coordinate of any point in the channel. The central region of the potential energy field is located in the geometric centre of the static marker element, and the field strength at the centre tends to be close to infinity, the farther away from the centre of the static marker, the smaller is the value of the potential energy, and, when the value is too small, it can be ignored and is considered that no impact on the bridge abutment will occur. According to the _Navigation Standard of Inland Waterway_, natural and canalised river water crossing a building axis in the normal direction and with the current flow angle should not be greater than \(5^{\circ}\), meaning that the ship and both sides of the bridge abutment between the rich width of the I and V channels can be taken as 0.6 times the width of the track zone, and the VI and VII channels can be taken as 0.5 times the width of the track zone, the rich width of the ship, and the bridge abutment between the rich width, as follows:  
\[d=\gamma_{2}\times(B_{s}+L\cdot\sin\alpha)(0.5<\gamma_{2}<0.6) \tag{4}\]  
where \(B_{S}\) is the width of the track zone, usually centred on the ship's track to either side; L is the length of the ship; \(\alpha\) is the flow pressure angle; the research object is a 70,000-tonne bulk carrier with a length of 90 m; and the shortest safe distance of the ship from the bridge abutment is found to be 8 m. In the text, the target object is considered to be in contact when the distance between the target object and the boundary of the static object markers (the bridge abutment) is 10 m, which is indicated by a red line around the static object markers, i.e., the event of collision against the bridge abutment has taken place. Additionally, a distance greater than 20 m indicates that the ship can pass safely, indicated by an orange line around the static object marker. The 2D and 3D potential energy fields generated by static elements such as piers are shown in Figure 2a,b below.
# Non-Parallel Articulatory-to-Acoustic Conversion Using Multiview-Based Time Warping
## 3 Multiview Temporal Alignment
### Latent-Space Similarity Metrics
#### 3.1.1 Canonical Correlation Analysis  
The first loss function we propose attempts to maximize the statistical correlation between pairs of aligned embedding vectors. Given a minibatch of \(N\) pairs of _aligned_ observations \(\mathcal{B}=\{(\mathbf{x}_{1},\mathbf{y}_{1}),\dots(\mathbf{x}_{N},\mathbf{y}_{N})\}\), the CCA loss function maximizes the correlation between the outputs of the DNNs, \(\mathbf{f}(\mathbf{x})\) and \(\mathbf{g}(\mathbf{y})\), as follows,  
\[(\Theta_{f}^{*},\Theta_{g}^{*})=\operatorname*{argmax}_{\Theta_{f},\Theta_{g} }\sum_{i=1}^{N}\operatorname{corr}(\mathbf{f}(\mathbf{x}_{i};\Theta_{f}),\mathbf{g}(\mathbf{y} _{i};\Theta_{g})). \tag{3}\]  
As detailed in [19], this equals to maximizing the following loss function,  
\[\mathcal{L}_{ccn}=\sqrt{\operatorname{tr}(\mathbf{T}^{\prime}\mathbf{T})}, \tag{4}\]  
where \(\operatorname{tr}(\cdot)\) is the trace operator and \(\mathbf{T}=\mathbf{\Sigma}_{xx}^{-1/2}\mathbf{\Sigma}_{xy}\mathbf{\Sigma}_{yy}^{-1/2}\). The covariance matrices \(\mathbf{\Sigma}_{xx}=\operatorname{cov}(\mathbf{f}(\mathbf{x}),\mathbf{f}(\mathbf{x}))\), \(\mathbf{\Sigma}_{xy}=\operatorname{cov}(\mathbf{f}(\mathbf{x}),\mathbf{g}(\mathbf{y}))\) and \(\mathbf{\Sigma}_{yy}=\operatorname{cov}(\mathbf{g}(\mathbf{y}),\mathbf{g}(\mathbf{y}))\) are estimated from the outputs of the DNNs. The DCTW algorithm described in [23] is a specific case of our TRANSIENCE algorithm when the CCA loss in (4) is used.  
Figure 2: Block diagram of proposed TRANSIENCE algorithm for time series alignment. First, feature vectors of two sequences to be aligned are projected into a common, latent space (represented with red dots in figure) using DNNs trained for each view. Then, DTW algorithm is used to align resulting sequences of embedding vectors and, hence, original sequences. To train parameters of DNNs, different loss functions are employed (see text for more details).
## 3 Case Study  
We conducted a real case study of a grain temperature field to validate our proposed method. The sensing data in our real case were collected from the grain temperature sensor networks in a national granary in central China at least every seven days from 31 January in one year to 4 March in the second year. Figure 5 presents the grain temperature sensor network of the granary. The sensor network consisted of 240 sensors, which were uniformly distributed in a traditional granary with the volume of \(46\) m \(\times\) \(26\) m \(\times\) \(6\) m. The sensors were deployed at the initial positions of 0.5 m, 0.5 m, and 0.3 m in the space domain and located at 5 m intervals in the \(x\) and \(y\) directions and 1.8 m intervals in the \(z\) direction. There were in total 17,280 sensing observations. We randomly selected training data from sensing data in the first 66 time points from 31 January in one year to 1 February in the second year, and set the remaining data as test data. The ratio of training data set to test data set was set as 3:1. To validate the prediction capability of our proposed model, we also retained the sensing data from 2 February to 4 March in the second year as the test data.  
The physical properties of stored grains were given in Table 1, and the attribute values were further incorporated into the thermodynamic model. In Table 1, thermal diffusivity in the vertical directions is greater than the transverse directions due to the faster airflow enhancing the heat transfer in vertical direction.  
The validation of the proposed field estimation approach contained three layers: modeling of the global temperature profile, parameter estimation of the proposed Gaussian process model and spatiotemporal field estimation.
###### Abstract  
Accurate prediction of natural gas purchase volumes is crucial for both the economy and the environment. It not only facilitates the rational allocation of resources for companies but also helps to reduce operational costs. Although existing prediction methods have achieved some success in addressing the nonlinear relationships in natural gas purchases, there remains potential for further improvement. To address this issue, a stacking ensemble learning model was developed to enhance the ability to handle complex nonlinear problems. This model integrates diverse algorithms and incorporates weather factors, while regionalizing characteristics of natural gas usage, thereby achieving accurate forecasts of natural gas purchase volumes. We selected three distinctly different base models--Informer, multiple linear regression (MLR), and support vector regression (SVR)--for our research. By conducting four different feature combination experiments for each base model, including weather, time, regional, and usage features, we constructed 12 foundational models. Subsequently, we integrated these base models using a meta-learner to form the final stacking ensemble model. The experimental results indicate that the stacking ensemble model outperforms individual models across key metrics, including \(R^{2}\), MRE, and RMSE. Notably, the \(R^{2}\) values improved by 4-15% compared to the 12 base models. The model was subsequently applied to predict natural gas purchase volumes in Pi County, Chengdu, China. In November 2024, a side-by-side comparison of the predicted and actual data revealed a maximum error of just 5.39%. This exceptional accuracy effectively meets forecasting requirements, underscoring the model's predictive strength in the energy sector.  
natural gas purchase prediction; stacking ensemble; Informer; multiple linear regression; support vector regression 2020 IEEE/RSJ International Conference on Intelligent Systems (IEEE), Vol., pp. 1-10. External Links: Document Cited by: SS1.
# The Effect of Data Transformation on Singular Spectrum Analysis for Forecasting
###### Abstract  
Data transformations are an important tool for improving the accuracy of forecasts from time series models. Historically, the impact of transformations have been evaluated on the forecasting performance of different parametric and nonparametric forecasting models. However, researchers have overlooked the evaluation of this factor in relation to the nonparametric forecasting model of Singular Spectrum Analysis (SSA). In this paper, we focus entirely on the impact of data transformations in the form of standardisation and logarithmic transformations on the forecasting performance of SSA when applied to 100 different datasets with different characteristics. Our findings indicate that data transformations have a significant impact on SSA forecasts at particular sampling frequencies.  
logarithmic transformation; standardisation; forecasting; singular spectrum analysis 14 January 2020; 17 April 2020; 7 May 2020
# Analysis of Nanoindentation Test Results of Asphalt Mixture with Different Gradations
## 1 Introduction
However, a comparison of the research findings published earlier reveals that the sample preparation procedure of asphalt materials is more complicated, and the test parameters are not the same when the nanoindentation test was applied in each study. Various factors highly influence the results of nanoindentation tests on asphalt materials due to the precision of nanoindentation test. [PERSON] et al. were the first to investigate the effect of indenter size on the test material in nanoindentation testing and found that the larger the radius of the indenter, the more plastic deformation caused by indentation load in the test [21]. [PERSON] et al. systematically studied the application of the nanoindentation technique in asphalt materials. The effects of loading rate and creep sati time on the nanoindentation test results were investigated, and the nanoindentation test parameters and settings applicable to asphalt materials were determined. The hardness and Young's modulus values measured at a satiation time of about 200 s were found stabilized [22]. According to [PERSON]'s test results, in addition to the maximum load, loading rate, and other test parameters, the nanoindentation test results of asphalt materials and asphalt mixtures are also affected by other factors, such as the composition of the asphalt material, interfacial attributes, and the gradation of the asphalt mixture [23]. The above research results show that asphalt mixtures are multi-phase composites with complex aggregate-asphalt interface properties, which are influenced by asphalt, aggregate, and mutual interaction. However, they ignore the influence of the gradation on the reliability of the nanoindentation results. For asphalt mixtures with varying gradations, the interface composition and structure characteristics are different, and the measured nanoindentation data will also appear significant differences. Therefore, it is necessary to investigate the suitability of different gradation asphalt mixture for nanoindentation. In addition, considering the preparation process of asphalt mixture, nanoindentation specimens must be polished to meet surface flatness requirements. For materials such as metals and bones, the surface disturbance generated by polishing has less impact on the measurement of mechanical properties of the samples. For asphalt materials, however,such disturbance is highly probable to damage the surface of the specimens and have an unpredictable impact on the subsequent mechanical property tests. Therefore, the purpose of this paper is to investigate the variability and deviation of the test results of nanoindentation for asphalt mixtures with different gradations. According to the variability of the test results, the influence of different RFAs corresponding to different gradations of mixtures on the nanoindentation test results is discussed. On this basis, the influence of polishing is considered to guide further on the subsequent performance testing of nanoindentation for asphalt mixtures.
# Research Progress and Typical Case of Open-Pit to Underground Mining in China
## 2 Analysis of Key Technologies for the Transition from Open-Pit to Underground Mining
### Analysis of Mine Development Systems in the Transition from Open-Pit to Underground Mining
#### 2.1.2 Partially Integrated Development System  
The partially integrated development system integrates operations by sharing certain resources, such as transportation routes, ventilation facilities, or connecting tunnels, between open-pit and underground operations. This system is particularly suitable for mines with complex geological conditions and extended transition periods. Mines such as Palabora [16], Ekati [17], and Grasberg [18], both in China and abroad, implement this model. By sharing resources, the partially integrated development system effectively balances construction costs with operational efficiency, making it adaptable to the transformation requirements of different geological conditions.
###### Abstract  
The effects of helium cold atmospheric pressure plasma (He-CAPP) jet on _Porphylomonas gingivalis_ (HW24D-1) biofilm, on human gingival fibroblasts (HGF) and human gingival keratinocytes (OBA-9) were assessed. Standardized suspension of _P. gingivalis_ was obtained, and biofilms were grown anaerobically for 48 h. After exposition to He-CAPP, the biofilm viability was evaluated by XTT assay. HGF were grown at 37 \({}^{\circ}\)C, in an CO\({}_{2}\) chamber in DMEM, while OBA-9 cells were cultured in keratinocyte serum-free medium. After 24 h, plates were exposed to He-CAPP for 1 to 7 min. Plasma was generated using a commercial AC power supply with amplitude modulated signal (voltage amplitude of 20 kVp-p, frequency of 31.0 kHz and duty cycle of 22%). The corresponding discharge power was 0.6W at He flow rate of 1 L/min. DNA damage was accessed by static cytometry. Data were analyzed by GraphPad Prism (_p_ < 0.05). Significant reductions in _P. gingivalis_ viability in relation to non-treated groups were detected (_p_ < 0.0001), directly proportional to exposure time. Treated groups were slightly aneuploid after 5- and 7-min treatment in HGF, and for 3 min in OBA-9 cells, with 1.2 DNA index mean. Helium cold atmospheric pressure plasma jet showed inhibitory effect on _P. gingivalis_ mature biofilm and was not genotoxic for epithelial gingival cells and human oral fibroblasts.  
cold plasma; periodontitis; _Porphylomonas gingivalis_; genetic toxicity test 4.0/.
# Real-Time Penetration Recognition Based on Voltage Signal in K-TIG Welding
## 4 Verification  
To further verify the potential influence and monitoring capability of the characteristic frequency and strength of arc voltage on the welding melt state, the present study manipulates the welding speed during the welding process to modulate the heat input and obtain diverse melting states. To ensure welding-current consistency, it is imperative to have a constant rate of 525 A. In the first experiment, incomplete joint penetration occurred during welding due to current increments in the step state. The penetration state was eventually restored as the welding speed decreased after a certain period of time. In the second experiment, welding current was increased periodically, resulting in both a dynamic welding condition that maintained the penetration state and a dynamic welding condition that altered the penetration state. Both conditions were subjected to data collection and subsequent analysis. Figures 8 and 9 depict the front and back sides of the welding seam and the power spectrum intensity tracking of characteristic signals.  
This study demonstrates the possibility of transforming the incomplete joint penetration state, which occurs during the welding process, into a normal melt state by adjusting the welding speed. Moreover, the transformation process can be characterized and evaluated by monitoring the strength change of the characteristic frequency spectrum signal. The experiment confirms the feasibility of closed-loop control of welding quality by monitoring the strength of the characteristic frequency spectrum signal of the welding electrical signal. Specifically, when the welding parameters are adjusted without changing the melt state, the monitored characteristic frequency spectrum signal remains unchanged. However, when the welding parameters are adjusted to alter the welding melt state, a transformation of the characteristic frequency spectrum signal can be observed. Therefore, by monitoringthe characteristic frequency spectrum signal and adjusting the welding parameters, it is possible to maintain the welding process in a complete joint penetration state and achieve closed-loop control of welding melt quality. This approach provides a reliable method for enhancing the consistency and stability of welding melt quality and improving the efficiency of welding production.  
Figure 8: Power spectrum intensity tracking of characteristic signals under step change of the welding speed.  
Figure 9: Power spectrum intensity tracking of characteristic signals under step increase of the welding speed.