esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
A high consumer demand for natural flavour and aroma compounds has increased the pressure on traditional, natural sources of many natural additives [1]. Natural flavours and fragrances are traditionally sourced from herbs, spices, plants, and animals--usually in the form of essences, extracts, and oils. The organoleptic properties of such flavours and fragrances tend to be highly complex and difficult to reproduce realistically by synthetic means. As a result, commercial demand for such products often outstrips global supply and prices can be volatile.  
Microorganisms have been used to produce and enhance flavours in food and beverage products for centuries, for example, cheese, wine, and chocolate [2, 3]. In recent times, there has been a resurgence of using fermentation to produce consumer goods [4]. Various fungi and yeasts have been investigated for their potential to produce aroma compounds, including _Rhizopus_ spp., _Trichoderma_ spp., _Certocystis_ spp., _Saccharomyces_ spp. And _Hanseniaspora_ spp. [5, 6, 7, 8, 9, 10, 11, 12].  
Solid-state fermentation (SSF) is a promising method to produce natural flavours and fragrances using filamentous fungi, as it offers a complex growth medium that closely mimics the natural environment of these organisms. It is characterised by a fermentation carried out on solid particles, in the absence of free water [13]. Most often, this is a controlledferentation where either bacteria or yeast grow on the surface of the solid particles in a biofilm, or in the case of filamentous fungi, hyphae can penetrate the solid particles [13]. In such cases, microorganisms tend to produce more complex metabolites--including volatile molecules that contribute to aroma and flavour [14]. SSF has been traditionally used in food fermentation such as during cheese ripening, mushroom production, and part of soy source production.  
Many different aroma compounds have been produced through SSF on a range of substrates, including coffee husk, cassava bagasse, apple pomace, wheat bran, citrus pulp, and sugarcane bagasse [5; 7; 9; 10]. This presents an encouraging basis to investigate the potential of a range of agro-industrial by-products for use in SSF to produce flavour and aroma chemicals. In the present study, the volatile profiles of 36 axenic fermentations, using four different filamentous fungi and nine different food-grade agro-industrial by-products, were screened using headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry (HS-SPME-GC/MS). This enabled us to identify potential aroma compounds of commercial interest that can be produced naturally via fermentation and from a sustainable, often wasted, raw material.
## 2 Materials and Methods
### Methods
#### 2.2.2 SHAP Interpretable Method  
The SHAP method is an attribution analysis interpretation method based on game theory [36] and local interpretation [37] proposed by [PERSON] and [PERSON]. This method is specifically designed to estimate the marginal contribution of each feature to the model. In machine learning models, N groups with N type features are used to predict the output (N). In SHAP, the contribution \(\phi_{i}\) of each feature \(i\) to the model is allocated based on its marginal contribution. The Shapley value is the contribution of the feature value to the predicted value, which is obtained by combining all possible eigenvalues and summing them with a weighted sum. The formal definition of the Shapley value is as follows [38]:  
\[\phi_{i}=\underset{S\subseteq N\{i\}}{\sum}\frac{|S|!(n-|S|-1)!}{n!}[v(S \cup\{i\})-v(S)] \tag{7}\]  
The sum of the Shapley values of each feature is the contribution of the overall deviation from the average predicted value, and the overall explanatory model is as follows:  
\[g(z^{\prime})=\phi_{0}+\underset{j=1}{\overset{M}{\sum}}\phi_{j}z^{\prime}{}_ {j} \tag{8}\]  
where g denotes the interpretation model, \(z^{\prime}\in\{0,1\}^{M}\) manifests whether or not the matching feature could be noticed (1 or 0), M represents the count of features selected, \(\phi_{i}\in R\) is the attribution value (Shapley value) for each feature, and \(\phi_{0}\) is the constant of the interpretation model, generated by the predicted mean of all training data [38]. The final model prediction is obtained by summing the SHAP values for each input feature and subtracting the average prediction value. Shapley values provide a mathematically rigorous approach to feature importance interpretation, uniquely satisfying the properties of local accuracy and consistency, and TreeExplainer is a specialized interpreter for explaining tree models. In this study, we employ the SHAP algorithm to interpret the output of the ensemble tree model and quantitatively rank feature contributions.
# Advances in Remote Sensing-Based Disaster Monitoring and Assessment
## 1 Overview of Contributions
Defining the phenomena of debris flow is challenging because of its drastic ignition and difficult access. A two-stage data analysis process with anomaly detection and debris flow identification was implemented in the framework. Signals were detected using a state-of-the-art machine learning approach, convolutional neural networks. The network of connected sensors was able to provide a process of debris flow from the initial to final stages. The system developed suggested an alternative method to detect the disaster and the related analytical method. [PERSON] et al. (2011) developed machine learning models to estimate the total precipitable water (TPW) from Himawari-8 data using the ERA-Interim TPW as a reference for Northeast Asia under the clear sky condition. The radiative transfer model was used for cloud screening. TPW, a column of water vapor content in the atmosphere, can be a critical variable to delineate hydrological conditions. It is also related to the intensity of disasters regarding the convective available potential energy (CAPE). Machine learning methods, RF, extreme gradient boosting (XGB), and deep neural network (DNN) were evaluated and compared. The DNN result outperformed the other models when validated using ERA-Interim and radiosonde observation (RAOB) data. TPWs retrieved from geostationary satellite images with a 10 min interval can provide valuable input to a disaster management system focusing on heavy rains and floods. The authors declare no conflict of interest.
# Na-Doping Effects on Thermoelectric Properties of Cu\({}_{2-x}\)Se Nanoplates
## 1 Introduction  
Thermoelectrics (TEs) is one of the most fascinating topics in the field of sustainable energy utilization due to its ability of direct conversion between electricity and heat based on either Seebeck or Peltier effects [1; 2; 3]. The efficiency of thermoelectric conversion is mostly dependent on the materials' dimensionless figure of merit (\(ZT\)) that is defined as \(ZT=(S^{2}\sigma/\kappa_{tot})T\), where \(S\) is the Seebeck coefficient (or thermopower), \(\sigma\) is the electrical conductivity, \(\kappa_{tot}\) is the thermal conductivity (\(\kappa_{tot}=\kappa_{dec.}+\kappa_{latt.}\), where \(\kappa_{dec.}\) and \(\kappa_{latt.}\) are the electronic and lattice contributions, respectively), and \(T\) is the temperature in Kelvin [4]. High-performance TE materials require a large Seebeck coefficient, low thermal conductivity, and high electrical conductivity. It is difficult, however, to concurrently improve all of the parameters because these three parameters are interrelated by carrier concentration. Recently, several strategies and concepts indicated that higher \(ZT\) values could be obtained in nanomaterials by a tailoring of the band structure and the phonon scattering [5; 6; 7; 8; 9; 10; 11]. Therefore, the pursuit of different TE materials using low-dimensional systems has recently become an active research field.  
Recently, the copper chalcogenides Cu\({}_{2-x}\)Q (Q = S, Se, Te) have attracted great attention as a promising TE material due to not only their exceptional thermal- and electrical-transport properties, but also their unique features of being environmentally benign, earth-abundant composition, and low cost. Therefore, they can be a potential candidate as a medium-temperature thermoelectric material. \(ZT>2\) has been identified in these binary copper chalcogenides. (\(ZT\) values of 1.7-1.9 for Cu\({}_{2-x}\)S [12; 13; 14; 15; 16; 17], 1.3-2.1 for Cu\({}_{2-x}\)Se [14; 15; 16; 17], and 0.3-1.1 for Cu\({}_{2-x}\)Te [18; 19; 20]). Chemical dopinghas been widely adopted to tune the carrier concentration and improve the \(ZT\) of thermoelectric materials [21; 22; 23; 24; 25; 26; 27].  
Following this idea, in the present study, Cu\({}_{2-x}\)Se nanocrystals were successfully synthesized via an aqueous approach. The morphologies of Cu\({}_{2-x}\)Se can be tunable from plate to wire by changing synthetic conditions. A sodium doped Cu\({}_{2-x}\)Se nanoplate was prepared. After consolidating the nanopowders using spark plasma sintering (SPS), an evaluation of the thermoelectric properties was performed.
# Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs
## 2 Related Works
Therefore, the VBF scheme does not perform well in sparse networks [16]. The performance of the VBF scheme is improved in the hop by hop VBF (HH-VBF) protocol by changing the direction of the virtual pipeline hop by hop. However, the unchanged pipeline radius restricts the network performance in the sparse node deployment because the probability of the void hole is not eliminated effectively. In the case of a small pipeline radius, the current forwarding node does not find potential forwarders in its effective transmission range. In other case, when the pipeline radius is large, packet duplication occurs due to the large number of forwarders in the pipeline [17]. This problem is further optimized in adaptive hop by hop vector-based forwarding (AHH-VBF). In [18], the pipeline radius (PR) and transmission radius (TR) are adaptively changed hop by hop taking the neighbor node distribution into account. Moreover, the adaptive transmission radius is intended to avoid unwanted energy consumption. It is noteworthy that we have taken the motivation from the AHH-VBF scheme. In this paper, 2 hop-AHH-VBF and QF-AHH-VBF adopt the adaptive PR and TR features of AHH-VBF; however, the proposed schemes are different in the following aspects. 1. There is an inefficient void hole avoidance mechanism opted for in AHH-VBF that is further modified in the proposed scheme. 2. Forwarder selection in 2 hop-AHH-VBF is based on the potential neighbor number (PNN) of the forwarder node in the area towards the destination (ATD). Hence, it aims to optimize the forwarder selection criterion from the AHH-VBF adopted. 3. In QF-AHH-VBF, every node computes the composite priority function (\(F_{p}\)) to elect itself as a qualified node. The node that secures high \(F_{p}\) among the PNN of a sender node is assigned the lowest holding time. Eventually, network throughput and end-to-end delay are achieved opting for this mechanism.
# Field Trial and Performance Evaluation of Soybean-Based Bio-Fog Seals for Asphalt Rejuvenation
## 2 Materials and Methods  
### Field Testing Sites  
This study evaluated the performance of bio-based fog seals (SESO and BioMag) applied to asphalt pavements on three low-volume road sections in Minnesota and was a collaborative effort of MnDOT, Iowa State University (ISU), and the National Road Research Alliance (NRRA).  
All test sections were approximately one year old at the time of treatment in July 2021. The sections are located in two sites: MnROAD Test Road, a pavement test track, operated by the Minnesota Department of Transportation Materials and Road Research division, and the local St. Michael County Road (15 th Street). The MnROAD test track has two sections, one constructed with 58-28S binder and the other with 58-34H. The St. Michael County Road utilized 58-34H binder grade. All test sections included control sections as well as sections treated with SESO and BioMag (Figure 3).  
Figure 2: Concept of rejuvenating asphalt mixture, (**a**) bio-fog seal application, (**b**) diffusion of the rejuvenator into the matrix, (**c**) changes in the asphalt binder properties, such as decrease in the viscosity, (**d**) microcrack filling and sealing.
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 3 Detailed Description of the Proposed Localization Scheme
### Determining the Perpendicular Distance from a Node to the Trajectory
#### 3.3.2 Data Processing for Curves Used for Comparison
The vector formed by the location of each feature point is  
\[\tilde{\mathbf{X}}=[\tilde{x}_{1},\tilde{x}_{2},\ldots,\tilde{x}_{NT}] \tag{33}\]  
We use the obtained regression function in Section 3.2 to predict each element in \(\tilde{\mathbf{X}}\), and obtain the RSSI value vector of the feature point, which can be expressed as  
\[\tilde{\mathbf{R}}=[\tilde{r}_{1},\tilde{r}_{2},\ldots,\tilde{r}_{NT}] \tag{34}\]  
We select the broadcast point of the R2 + R3 of the reference RSSI curve as the location of the feature point, and the RSSI value corresponding to the broadcast point as the value of the feature point. The location vector \(\overline{\mathbf{X}}\) and RSSI value vector \(\overline{\mathbf{R}}\) formed by the feature points on the R2 + R3 of the \(i\)th reference RSSI curve are  
\[ \begin{cases}\overline{\mathbf{X}}&=[x_{m-(N_{t}-1)},x_{m-(N_{t}-2)},\ldots,x_ {m}]\\ \overline{\mathbf{R}}_{i}&=[r_{i(m-(N_{t}-1))},r_{i(m-(N_{t}-2))},\ldots,r_{ im}]\end{cases}  \tag{35}\]  
\(\tilde{\mathbf{R}}\) and \(\overline{\mathbf{R}}_{i}\) are used for similarity comparison. When the node projection coincides with the midpoint, \(\tilde{\mathbf{R}}\) and \(\overline{\mathbf{R}}_{i}\) are  
\[ \begin{cases}\tilde{\mathbf{R}}&=[\widehat{r}_{1},\widehat{r}_{2},\ldots, \widehat{r}_{m}]\\ \overline{\mathbf{R}}_{i}&=[r_{i1},r_{i2},\ldots,r_{im}]\end{cases}  \tag{36}\]  
When the projection of the node is to the right of the midpoint, some equations that differ from the projection on the left of the midpoint are  
\[NL =\left\lfloor(L/2)/\Delta L\right\rfloor \tag{37}\] \[NR =\left\lfloor(L/2-L_{off})/\Delta L\right\rfloor\] (38) \[\overline{\mathbf{R}}_{i} =[r_{i1},r_{i2},\ldots,r_{i(m-(NT-1))}] \tag{39}\]Figure 8: Three positional relationships between the projection of the node and the midpoint of the trajectory: (**a**) the projection of the node is on the left of the midpoint; (**b**) the node projection coincides with the midpoint; and (**c**) the projection of the node is on the right of the midpoint.
# Radiometric Calibration of the Near-Infrared Bands of GF-5-02/DPC for Water Vapor Retrieval
## 2 Materials
### Ground-Based PWV Data  
AERONET and SuomiNET are two ground-based networks that have been in continuous operation for several decades [14; 15]. Both networks provide high-quality observations of water vapor, which serve as crucial reference data for assessing the quality of remote sensing PWV data [12; 16]. In this work, AERONET data are used to develop the calibration model used for the DPC and to assess the quality of DPC PWV data, and SuomiNET data are employed to assess the accuracy of DPC PWV data. The observations from 229 AERONET sites and 187 SuomiNET sites are utilized in this study. Their spatial distribution is illustrated in Figure 2. The matching results between AERONET data and DPC data are randomly split into two sets in a 7:3 ratio. Among them, 70% of the data is used for calibration, and the remaining 30% of the data is used for the assessment of calibration results.  
Figure 1: The SRFs of the 865 nm bands of the DPC and POSP and the 910 nm band of the DPC.  
Figure 2: Geographical distribution of AERONET and SuomiNET sites. Each solid blue dot represents an AERONET site, and each solid red dot represents a SuomiNET site.
# Effects of Vibration on Adjacent Pipelines under Blasting Excavation
###### Abstract  
Exploring a pipeline's response to blast vibration during tunnel excavation is critical for ensuring the safety of the pipeline. In this paper, the vibration monitoring and numerical simulation methods are used to evaluate the dynamic response of ground soil and pipelines to blasts. The attenuation law of peak particle velocity (\(PPV\)) and the distribution characteristics of peak effective stress (PES) in pipe sections under different working conditions are studied. The following findings are recorded: (1) A three-dimensional model considering in situ stress is established, and it is found the triangular equivalent load simulation blast effect method used in this paper can effectively reflect the impact of blasting on pipelines. The simulation error is controlled at 7.69%. (2) The ground \(PPV\) of each monitoring point decays continuously with the increase in horizontal and axial distance, and the cavity enlargement effect is exhibited above the excavation area. The oncoming blast side \(PPV\) of the pipe section is more significant than that behind the blast side. (3) When the blast vibration is transmitted to the pipe, there are differences in the \(PPV\) and PES distribution characteristics across the pipe cross-section. The \(PPV\) is greater in the lower part of the pipe section, while the PES value is greater in the upper part of the pipe section. The maximum PES of 1.53 MPa is significantly lower than the safety threshold (\(\leq\)4.6 MPa) at the hazardous-section-monitoring point. (4) A pipeline \(PPV\) prediction model is proposed to guide subsequent blasting program development. An empirical formula for the safety criterion applicable to this study is proposed for the scientific implementation of safety assessments for subsequent construction. This safety evaluation framework can be used as a reference for similar projects.  
blasting vibration; buried pipe; numerical model; safety criterion +
Footnote †: journal: Applied Sciences
## 3 Method
### Preprocessing  
In this paper, we use deep learning and machine learning to process physiological signal data separately, and achieve effective emotion detection. GSR is a non-stationary signal, and in this study, the signal is first decomposed by smoothing through empirical modal decomposition (EMD) to obtain the effective frequency, and then the low-pass butterworth filter is used to pre-process the GSR signal since the skin electrical signal changes slowly, and the effective frequency is between 0-0.3 Hz, the cutoff frequency of the low-pass filter is set to 0.5 Hz and the sampling frequency is 128 Hz, and then the SCR and SCL are decomposed. The ECG signal frequency is usually 0.05-100 Hz, firstly, the baseline drift is eliminated by discrete wavelet transform, which is to eliminate unnecessary low-frequency noise in the frequency range of 0.05 and 1 Hz, and then the end frequency is set to 1 Hz using butterworth high-pass filter which the sampling frequency is 128 Hz. Then to acquire the denoised ECG signal. The noise-reduced signal is first normalized by Z-Score (Equation (1)) using a sliding window of 2 s and an offset of 1 s to capture the subtle changes in emotional motion and derive the feature vectors. Then, the data enters the display or implicit feature extraction phase. The first one extract implicit features by convolutional networks through deep learning, and the second one, by machine learning methods, which extracts time and frequency domain manual features, in three steps of preprocessing, classification and multimodal fusion, respectively.  
\[\widetilde{X}=\frac{X-\mu}{\sigma}, \tag{1}\]  
where \(\widetilde{X}\) is the standardized data, \(\mu\) and \(\sigma\) are the mean and standard deviation of the data, respectively.
## 2 Regulatory Framework  
The issue of environmental aspects in public administration spending appeared for the first time in the Green Paper \"Public Procurement in the European Union\", a communication adopted by the European Commission on 27 November 1996 (COM (1996) 583) [6], which introduced the possibility of integrating environmental aspects into the definition of procedures for the purchase of goods and services and the execution of works. The Green Paper takes note of the role of the Public Administration as a consumer, which can condition the supplier market towards products and production processes that pursue sustainability.  
European Union legislators have implemented the Green Paper through a series of communications and directives. Communications COM (2001) 264 [7], and COM (2001) 274 [8], emphasized that EU member states should use public procurement to promote environmentally friendly products and services and that EU law offers the possibility of integrating environmental considerations into public procurement procedures. Communication COM (2003) 302 [9] explicitly requires member states to adopt National Action Plans for Green Public Procurement (GPP). The GPP is defined in the Communication COM (2008) 400 [10] as \"a process whereby public authorities seek to procure goods, services and works with a reduced environmental impact throughout their life cycle when compared to goods, services and works with the same primary function that would otherwise be procured\". GPP is a voluntary instrument so each Member State and public authorities can determine the extent to which they implement it. In Italy, the legislation has been implemented in the National Action Plan on Green Public Procurement (PAN GPP) [11], updated by the Ministerial Decree of 10 April 2013. From this plan comes the Minimum Environmental Criteria (CAM) that, as far as flooring is concerned, is currently being defined. Subsequently, the inclusion of the CAM within the tender documentation was introduced by Legislative Decree 50/2016 [12].  
In general, the regulatory framework discussed shows how public bodies and infrastructure managers are called upon to set environmental objectives and to do so by preparing criteria that are scientifically supported, easily verifiable, and that tend to encourage innovation in products and processes to reduce their impact on the environment. It is from these considerations that the present study and the formulation of the EAR environmental index proposed here originated.
## Materials and Methods
### Configuration of Control System
#### 2.1.2 System Flow  
The control system proposed in this study integrates the input gaze direction and environment information and determines the correct speed and direction of motion in real time. Figure 3 shows the system flow. The control system has four stages: input, environment recognition, integration by the fuzzy set theory, and output.  
Figure 3: System flow.  
Figure 2: (**a**) Sensor configuration; (**b**) The range of sensors; (**c**) RGB camera image obtained by RGB camera; (**d**) Environment recognition obtained by LRF; (**e**) World camera image obtained by Eye tracker; (**f**) Eye camera image obtained by Eye tracker.  
First, gaze point \((x_{g}^{e},y_{g}^{e})\) and environmental information are obtained as input. In case of the eye gaze input, the passenger is instructed to gaze in the intended direction of motion; as a result, the wheelchair moves in that direction.  
Second, the environment is recognized based on the environmental information obtained from the LRF. In this step, obstacles \((X_{o}^{w},Y_{o}^{w})\) and passages \((X_{p}^{w},Y_{p}^{w})\) through which the wheelchair can move are detected at approximately the same time.  
Finally, through use of this information, the direction of motion is determined based on the fuzzy set theory with due consideration of the direction in which the passenger wishes to move whilst ensuring safety and avoidance of obstacles.  
Section 2.2 describes the method to obtain gaze direction as input, while Section 2.3 describes the passage detection method using LRF. Section 2.4 describes the design of the motion control system based on the fuzzy set theory.