esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Vine Water Status, Leaf Gas Exchange and Leaf Hydraulic Conductivity  
In 2020 and 2021, the stem water potential (\(\Psi_{\mathrm{S}}\)) (and occasionally, leaf water potential, \(\Psi_{\mathrm{I}}\)) was determined monthly from the beginning of the fruit set (June) until harvest (September). Six to eight healthy, fully exposed, and expanded mature leaves from the main shoots in the middle-upper part of the vine canopy were taken per clone-treatment combination. Leaves were enclosed in aluminium foil and covered with plastic at least 2 h before midday measurement. \(\Psi_{\mathrm{S}}\) was measured at noon (12:00-13:30 p.m., local time) using a pressure chamber (Model 600; PMS Instrument Co., Albany, OR, USA). In 2020 and 2021, leaf gas exchange rates were measured monthly between 09:00 and 10:30 a.m. (early morning) and between 12:15-13:45 p.m., local time (midday), from June to September on selected clear and sunny days. Measurements were made on leaves that were similar to those used for the stem water potential measurements (one leaf on each of six or eight vines per clone-treatment combination, depending on the year). Gas exchange rates were measured with a portable photosynthesis measurement system (LI-6400, Li-Cor, Lincoln, NE, USA) equipped with a broadleaf chamber (6.0 cm\({}^{2}\)). During measurements, leaf temperature was between 23-37 \({}^{\circ}\)C, leaf-to-air vapor pressure deficit was between 1.4-5.3 kPa, and relative humidity was 30-50%. Molar air flow rate inside the leaf chamber was 500 \(\mathrm{\SIUnitSymbolMicro m}\mathrm{mol}^{-1}\). All of the measurements were taken at a reference CO\({}_{2}\) concentration similar to ambient (400 \(\mathrm{\SIUnitSymbolMicro m}\mathrm{mol}^{-1}\)) and at a saturating photosynthetic photon flux density (PPFD) of 1500 \(\mathrm{\SIUnitSymbolMicro m}\mathrm{mol}^{-2}\) s\({}^{-1}\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \multicolumn{13}{c}{Period 2018-2020} & \multicolumn{13}{c}{**Year 2021**} \\ \hline \hline
**Clone** & **All Clone (resp.)** & **4** & & **94** & & **188** & & **276** & & **350** & & **372** & & **373** \\ \hline Treatment & C & S & C & S & C & S & C & S & C & S & C & S & C & S \\ DahConductance from stem to leaf (Kleaf) was estimated with the evaporative flux method, using the following equation [23]:  
\[\text{K}_{\text{leaf}}=\text{E}_{\text{max}}/(\text{\Psi}_{\text{s}}-\text{\Psi} _{\text{I}})\]  
where \(\text{\Psi}_{\text{s}}\) is the midday stem water potential in a non-transpiring leaf, \(\text{\Psi}_{\text{I}}\) is the midday leaf water potential in a transpiring leaf, and \(\text{E}_{\text{max}}\) is the maximum transpiration rate (measured between 12:00-16:00 p.m.). Gradient of water potential in the leaves (\(\Delta\text{\Psi}\)) was calculated as the difference between \(\text{\Psi}_{\text{I}}\) and \(\text{\Psi}_{\text{s}}\).
## 3 DIKWP Semantic Interaction for Unidirectional Communication Impairments
### Understanding Objects--Semantic Recognition
#### 3.2.1 Visual Object Recognition
With repeated exposure to specific shapes, the infant prefers more complex and familiar shapes. d. Enhanced Detail Recognition Period  
\[\forall x\in\Omega,t\in DetailRecognition,(ShapeComplexity(x)=\theta_{3}) \Rightarrow RecognizeEdges\in I(t) \tag{4}\]The notation \(\theta_{3}>\theta_{2}\) indicates the increasing ability to analyze the edge characteristics of shapes. Infants begin to recognize shapes' edge features and details, including the initial differentiation between \"smooth edges\" and the presence or absence of sharp corners. **B. Shape Recognition Logic**  
\[ \begin{split} IfShape(x)&=IsRound(x)\& \&\&\&\mathit{RollsEasily}(x)ThenShape=\textit{\"Circle\"}\\ IfShape(x)&=HasEdges(x)\&\&\&\mathit{CornersEqual}(x)ThenShape=\textit{\"Square}/\mathit{Rectangle\"}\end{split}  \tag{5}\]  
The algorithm is shown in Algorithm 1. ```
0:\(\Omega,t,I(t),\lambda_{1}-\lambda_{4},\theta_{1}-\theta_{3}\)
0:shape if\(t\) is in \(\lambda_{1}\)then for each object o in \(\Omega\)do if\(Contrast(o)>0\) and \(Motion(o,t)\)then  add\(\mathit{DetectContrast}(o)\) and \(\mathit{TrackMotion}(o,t)\) to \(I(t)\) endif endfor endif if if\(t\) is in \(\lambda_{2}\)then for each object o in \(\Omega\)do if\(ShapeComplexity(o)<\theta_{1}\)then  add\(\mathit{Notice}(o)\) to \(I(t)\) endif endfor endif if\(t\) is in \(\lambda_{3}\)then for each object o in \(\Omega\)do if\(ShapeComplexity(o)<\theta_{2}\) and \(\mathit{Exposure}(o,t)>\theta_{1}\)then  add\(\mathit{Prefer}(o)\) to \(I(t)\) endif endfor endif if if\(t\) is in \(\lambda_{4}\)then for each object o in \(\Omega\)do if\(ShapeComplexity(o)\leftarrow\theta_{3}\)then  add\(\mathit{RecognizeEdges}(o)\) to \(I(t)\) endif endfor endif
```  
**Algorithm 1** Infant Vision Data-Based Shape Semantic Recognition. We map the visual observations to the DIKWP model. Concepts such as \"color\" and \"shape\" are considered concepts in the data domain, with the same or different semantics existing as data semantics. Regarding information, the infant's vision refers to the perceptible differences, including \"color differences\". The exploration of the semantics of \"color differences\" is as follows:  
\(Input(\textit{eyes})=x_{1},x_{2}\). **Processing**  
We define the functions and symbols in advance. \(ColorDifference(c_{1},c_{2})\): The degree of difference between two colors, \(c_{1}\) and \(c_{2}\). Thus,  
\(ColorDifference(c_{1},c_{2})=Color(x_{1})-Color(x_{2})\)  
\(LearnColorDisT(c_{1},c_{2},t)\Leftarrow Exposure(c_{1},c_{2},t)>n\wedge Color Difference(c_{1},c_{2})>\delta\)Here, \(Exposure\) represents the number of times the infant is exposed to two colors simultaneously or sequentially. \(n\) is the minimum number of exposures required to distinguish between them, and \(\delta\) is the threshold for color difference. When infants can distinguish between several primary colors, they begin to form semantic associations of colors. A schematic of an infant's visual semantics for information is shown in Figure 2.
# AutoFL: Towards AutoML in a Federated Learning Context
## 5 Evaluation
### Experimental Setup  
Our experimental setup consisted of different types of computing nodes with different resource availability.  
* _Laptop_: An HP ZBook Power laptop with an Intel Core i7-11800H running at 2.30 GHz, 32 GB of memory, and an NVIDIA T1200 GPU for deep learning. This device is used for small-scale centralized federation simulations and benchmark purposes.
* _Pi4_: A Raspberry Pi 4 with a Broadcom BCM2711, Quad-Core Cortex-A72 (ARM v8) 64-bit SoC running at 1.8 GHz, and 4 GB of memory. This device is used in a federation with heterogeneous resources.
* _JetsonTX2_: An NVIDIA Jetson TX2 development board with a Dual-Core NVIDIA Denver 2, a 64-Bit CPU Quad-Core ARM Cortex-A57, with 8 GB of memory. The device is also equipped with a 256-core NVIDIA Pascal architecture GPU. This device is used in a federation with heterogeneous resources.
* _Server_: A high-end server with an AMD EPYC 7502 32-Core processor running at 3.32 GHz, and 256 GB of memory. This machine is used for larger-scale centralized federation simulations and in a federation with heterogeneous resources.
* _Client_: Twenty client machines with either an Intel Core i5-4570S CPU running at 2.90 GHz or an Intel Core i5-6500 CPU running at 3.20 GHz, and 8 GB of memory. These machines are used for experiments in a homogeneous federation.  
All machines and edge devices were connected to a wired Gigabit network and typically run a Ubuntu 20.04 or 22.04 LTS Linux operating system with a recent Miniconda Python 3.10 stack ([[https://repo.anaconda.com/miniconda/](https://repo.anaconda.com/miniconda/)]([https://repo.anaconda.com/miniconda/](https://repo.anaconda.com/miniconda/)), accessed on 4 May 2023). The Jetson TX2 board ran the latest supported operating system, i.e., Ubuntu 18.04 LTS, while the Raspberry Pi 4 ran the Debian Bullseye 11.7 operating system. The two edge devices both ran an Aarch64 version of Miniforge Python 3.10 ([[https://github.com/conda-forge/miniforge](https://github.com/conda-forge/miniforge)]([https://github.com/conda-forge/miniforge](https://github.com/conda-forge/miniforge)), accessed on 4 May 2023).
# Auralization of High-Order Directional Sources from First-Order RIR Measurements
## 3 Listening Experiment--Comparative Study
### Auralization Techniques--Virtualizations of the IKO
_SRD ARIR and real IKO (Sr):_ The SRD ARIR method as proposed in Section 2 only requires first-order loudspeaker and microphone arrays for measuring the _Room Transfer Function_, on the source and receiver sides respectively. Thus, the SRD method is rather hardware efficient with a theoretical minimum of 4 channels for the source and the receiver. Here we used our 6-channel Cubelet and the tetrahedral ST450 as source and receiver arrays, respectively. Note that the first-order RIR measurements ([[https://phaidra.kug.ac.at/o:104376](https://phaidra.kug.ac.at/o:104376)]([https://phaidra.kug.ac.at/o:104376](https://phaidra.kug.ac.at/o:104376))) as well as high resolution directivity measurements of the Cubelet are available online ([[https://phaidra.kug.ac.at/o:104374](https://phaidra.kug.ac.at/o:104374)]([https://phaidra.kug.ac.at/o:104374](https://phaidra.kug.ac.at/o:104374))). In a next processing step, the resolution is upscaled from first order to any higher order, see Equation (8) and the detailed description throughout Section 2.2. Therefore, both the source and receiver side are modular and permit exchange with any directivity pattern. In the experiment we inserted KU100 HRIRs with 5 th order resolution of a MagLS decoder [18], and at the source side the true measured directivity of the IKO are used. The _Directivity_ of the 20 loudspeakers is represented using an order \(N\) representation of the directional IRs from every loudspeaker to every microphone of a surrounding microphone array. We use IRs measured using an equiangular grid of \(18\times 36\) zenith and azimuth angles, respectively. With 648 sampling points on the sphere we set \(N\leq 17\). The high resolution directional IRs of the IKO are available online ([[https://phaidra.kug.ac.at/o:75316](https://phaidra.kug.ac.at/o:75316)]([https://phaidra.kug.ac.at/o:75316](https://phaidra.kug.ac.at/o:75316))). _SRD ARIR and ideal 3 rd-order directivity (Si):_ While the _Room Transfer Function_ is represented by a SRD ARIR as well (same as for \(Sr\)), the source Directivity is assumed to be an ideal 3 rd-order directivity instead of the real IKO, here. Thus, the directivity is synthesized by multiplying the encoded signals with a frequency-independent diagonal matrix containing the max-\(r_{\mathrm{E}}\) weights [44; 45] up to order \(N_{B}\).
## 2 Materials and Methods
### The Maximum Likelihood Estimator for 2D FBM  
Through 2D FGN, its corresponding PDF can be obtained. For the PDF, there are 2 cases--1 contains the known variance, the other the unknown variance. The case with the known variance is superior in accuracy to the case with the unknown variance because we do not need to estimate the variance (each estimation will result in an error). In the paper, we only consider the case with the unknown variance because it is the most common form. Therefore, the PDF is expressed as follows:  
\[\begin{split} p\bigg{(}\overset{\rightarrow}{\mathbf{X}};\;H, \sigma^{2}\bigg{)}=\frac{1}{(2\pi)^{N_{1}/2}|\mathbf{R}|^{1/2}}\exp\bigg{\{}- \frac{1}{2}\overset{\rightarrow}{\mathbf{X}}^{T}\mathbf{R}^{-1}\overset{\rightarrow} {\mathbf{X}}\bigg{\}}\\ =\frac{1}{(2\pi)^{N_{1}/2}\big{|}\sigma^{2}\overline{\mathbf{R}} \big{|}^{1/2}}\exp\bigg{\{}-\frac{1}{2\sigma^{2}}\overset{\rightarrow}{\mathbf{X }}^{T}\overline{\mathbf{R}}^{-1}\overset{\rightarrow}{\mathbf{X}}\bigg{\}},\end{split} \tag{8}\]  
where  
\[\mathbf{R}=\sigma^{2}\overline{\mathbf{R}}.\]  
The PDF contains 2 parameters--the unknown variance and the Hurst exponent--that needed to be estimated by the MLE. For convenience, we first take the logarithm of PDF, then maximize the log-likelihood function \(\log p\bigg{(}\overset{\rightarrow}{\mathbf{X}};\;H,\sigma^{2}\bigg{)}\) with respect to \(\sigma^{2}\), and finally obtain  
\[\log p\bigg{(}\overset{\rightarrow}{\mathbf{X}};\;H,\sigma^{2}\bigg{)}=-\frac{N_ {1}}{2}\log(2\pi)-\frac{N_{1}}{2}\log\Big{(}\sigma^{2}\Big{)}-\frac{1}{2}\log \lvert\overline{\mathbf{R}}\rvert-\frac{N_{1}}{2}, \tag{9}\]  
where  
\[\hat{\sigma}^{2}=\frac{1}{N_{1}}\overset{\rightarrow}{\mathbf{X}}^{T}\overline{ \mathbf{R}}^{-1}\overset{\rightarrow}{\mathbf{X}}.\]Omitting 2 constants and the common coefficient of 0.5, we obtain a compact form as follows:  
\[\max_{H}\biggl{[}\log p\left(\overset{\rightarrow}{\mathbf{X}};\ H,\phi^{2} \right)\biggr{]}=\max_{H}\biggl{[}-N_{1}\log\biggl{(}\frac{1}{N_{1}}\overset{ \rightarrow}{\mathbf{X}}\overset{\rightarrow}{\mathbf{R}}^{-1}\overset{ \rightarrow}{\mathbf{X}}\biggr{)}-\log|\overline{\mathbf{R}}|\biggr{]} \tag{10}\]  
From the structure of \(R\) or its corresponding \(\overline{R}\), we know that the log-likelihood function contains the Hurst exponent in an inseparable or implicit way; it is impossible to maximize the log-likelihood function by taking a direct derivative with respect to the Hurst exponent. Instead, the golden section search [33,34] is adopted to find out the optimal estimate of the Hurst exponent.  
[PERSON] [22] has proposed two MLEs for 2D FBM--an iterative MLE and an efficient MLE. In the paper, we adopt the efficient MLE for 2D FBM and simply call it the MLE for 2D FBM.
# Hybrid Empirical and Variational Mode Decomposition of Vibratory Signals
## 3 Hybrid Methodology EMD-VMD
### Step 3: Application of VMD Using Calculated Gain \(K\)  
If mode mixing is detected, the methodology transitions to VMD for refined decomposition. VMD separates the signal into modes by solving a variational problem that minimizes the bandwidth of each mode:  
1. Initialization: The number of modes \(K\) is estimated using EMD preprocessing, detecting the number of intrinsic mode functions and the mixing present in these signals.  
Figure 8: Residual signal \(r_{1}(t)\) after extracting the first IMF \(c_{1}(t)\), used as the input for further decomposition steps.  
Variational Problem Formulation: VMD seeks to decompose the signal \(x(t)\) into \(K\) modes \(u_{k}(t)\) by minimizing their bandwidth in the frequency domain [20]: \[\min_{\{u_{k}(t)\},\{\omega_{k}\}}\sum_{k=1}^{K}\left\{\left\|\partial_{t} \left[\left(\delta(t)+j\frac{1}{\pi t}\right)\ast u_{k}(t)\right]e^{-j\omega_{ k}t}\right\|_{2}^{2}+\alpha\|u_{k}(t)\|_{2}^{2}\right\}\] where - \(\alpha\) is a regularization parameter; - \(\delta(t)\) is the Dirac delta function; - \(u_{k}(t)\) and \(\omega_{k}\) are the modes and their central frequencies. This equation ensures that each mode has minimal bandwidth, which reduces mode mixing.
3. Iterative Decomposition: VMD iteratively updates the modes and their central frequencies. The equation adjusts the modes iteratively to ensure that they remain within specific frequency bands, and the modes are updated as \[\dot{u}_{k}^{n+1}(\omega)=\frac{\dot{x}(\omega)-\sum_{i\
eq k}\dot{u}_{i}^{n}( \omega)}{1+2\alpha(\omega-\omega_{k}^{n})^{2}}\] and the equation dynamically adjusts the central frequencies as follows: \[\omega_{k}^{n+1}=\frac{\int_{0}^{\infty}\omega|\dot{u}_{k}^{n+1}(\omega)|^{2} d\omega}{\int_{0}^{\infty}|\dot{u}_{k}^{n+1}(\omega)|^{2}d\omega}\]
4. Convergence and Extraction: The iterative process continues until convergence is achieved. The modes \(u_{k}(t)\) then provide a refined decomposition of the original signal, addressing mode mixing more effectively than EMD alone.
## 3 Sink-Based Centralized Transmission Scheduling
### Dealing with Frame Loss  
In real environments, signal strength dynamically fluctuates due to multipath fading, which leads to frame loss and degrades the frame delivery ratio. In SC-Sched, this loss is composed of two parts: one is the loss of WuCs, and the other is the loss of data frames.  
In the following, we improve SC-Sched to cope with both loss factors by using consecutive WuC transmissions and adding retransmission slots for data frames. WuCs are the one-way traffic over the asymmetric link and cannot be acknowledged. Therefore, we use consecutive transmissions to reduce the loss of WuCs.
# Drivers, Barriers and Benefits of the EU Ecolabel in European Companies' Perception
## 1 Introduction  
The objective of the EU Ecolabel Regulation (EC) 66/2010 is to establish a functioning, highly regarded Europe-wide voluntary ISO Type I Ecolabel [1], based on product and service requirements (so-called \"criteria\") that are appropriate for such an Ecolabel, aiming to encompass the top 10-20 percent of the market. Such criteria must be based on the latest scientific and technological considerations, be market-orientated, consider the most significant impacts and be simple to understand for consumers. The intention is that the EU Ecolabelling scheme will provide consumers with accurate, non-deceptive, science-based information on the environmental impacts of products and services, and will influence their purchasing decisions. Since its first release in 1992, a steadily growing number of companies have been awarded the EU Ecolabel. Indeed, EU Ecolabelled products numbers have increased steadily up to 44.711 as late as September 2015. However, the number of companies (licenses) stand at 2.031 in September 2015, which is modest for the whole of the European Union. Indeed, whilst generally increasing, the level of uptake for particular product groups is often very low and, for some others (such as a number of IT products), it is equal to zero. Figures 1 and 2 show the number of EU Ecolabel licenses and products/services in the period 2011-2015 (official data of the European Commission).  
These data suggest the persistence of a series of barriers against a much higher uptake of the EU Ecolabel and the perception among companies that the benefits deriving from the EU Ecolabel are insufficient and not always in line with the drivers that have pushed them to apply for the EU Ecolabel.
## 5 Conclusions  
Industrial waste heat sources are abundant and underutilized because of the typical long distance between industrial locations and the demand such as district energy networks. To overcome the distance limitation, a proposed system of mobile thermal energy storage can be used. To use the M-TES system in the SCE, it is essential to assess economic feasibility. The economic and environmental costs are determined based on $/MWh and tCO2e/MWh of M-TES considering both the heat demands and heat transport distances. M-TES has also been compared with other low carbon sources that the City is currently considering. M-TES can be used for delivering waste heat to district energy networks in urban centers. Based on the techno-economic and GHG reduction of multiple M-TES configurations, M-TES showed promising results when compared to other low-carbon energy sources, such as biomass energy and sewer heat recovery. The value of such thermal storage systems depended not only on the system-level ESD but also on the distance of the waste heat source from the DEN, schedule, and transportation mode.  
* With a fixed schedule of six trips/day and 360 days/year, an M-TES truck with a capacity of 10 tonnes and system level ESD of 0.7 MJ/kg can meet up to 7% of the SCE network anticipated demand for 2022.
* The levelized cost of energy of M-TES increases with distance and decreases with the energy storage density of the selected solution.
* The most efficient configuration of M-TES is achieved with the highest ESD and shortest distance between the industrial heat source and the DEN.
* Out of the three truck modes to be used with M-TES, the electric vehicles (EV) truck is slightly more competitive than the renewable natural gas (RNG) truck in BC. However, both RNG and electric trucks are better than diesel trucks in cost and GHG-avoided totals.
* As a result, when using RNG or electric trucks to move the tank between the IWH and DEN, M-TES is competitive with a distance range of 15-50 km.
* Diesel trucks are not efficient if the distance is more than 30 km.  
Conceptualization, M.S. and M.B.; Methodology, M.S.; Writing--Original Draft Preparation, M.S.; Review & Editing, E.K., J.O. and M.B.; Supervision, M.B.; Formal analysis, M.B. and M.S. All authors have read and agreed to the published version of the manuscript.  
The authors declare no conflict of interest.  
Not applicable.  
Restrictions apply to the availability of these data. Some data was obtained from City of Surrey and are available from the authors with the permission of City of Surrey.  
This research is supported by funding from the Pacific Institute for Climate Solutions (PICS) Opportunity (Grant No. 36170-50280) and NSERC Advancing Climate Change Science in Canada (Grant No. 536076-18). Financial support and valuable technical insights were provided by [PERSON], [PERSON], and [PERSON] of the City of Surrey.  
The authors declare no conflict of interest.
## 3 Dissimilarity Metrics Based on Neighboring Information  
The similarity and/or dissimilarity between two vehicles in a VANET can be calculated by employing local density metrics like the number of shared neighbor vehicles. A neighbor vehicle \(m\) is shared by two \(i\) and \(k\) vehicles (sender and receiver or vice-versa), if \(m\) is placed inside the wireless communication ranges of \(i\) and \(k\) (see Figure 1). It is worth indicating that the probability of a neighbor vehicle \(m\) of being within the overlapping area (IA in Figure 1) of both vehicles \(i\) and \(k\) depends on the Euclidean distance \(d\) between both vehicles and the density of vehicles in the VANET. In general, the size of IA decreases with the relative distance between two vehicles. Consequently, it is more probable to find more vehicles in the IA for a given VANET scenario. In addition, the dissimilarity between two vehicles can be defined as the contrary of the similarity. Notice that these definitions of similarity and dissimilarity between vehicles are also valid in case that the wireless communication range of vehicles is not an ideal circle as represented in Figure 1. Therefore, it is an important feature of the use of dissimilarity metrics, since they do not rely on wireless technology parameters or ideal circumstances. Consequently, they will adapt their performance to the real conditions in terms of connectivity among vehicles.  
In general, the dissimilarity of two vehicles can be a suitable estimation of the relative Euclidean distance between two vehicles [24; 25]. Most classical similarity/dissimilarity metrics are derived from the following general expression [27]:  
\[S_{ij}=\frac{a_{ik}}{a_{ik}+\lambda(a_{k}+a_{i})} \tag{6}\]  
where \(a_{ik}\) accounts for the number of neighbor vehicles shared by the vehicles \(i\) and \(k\), the term \(a_{i}\) determines the number of neighbor vehicles of the vehicle \(i\) that are not neighbors of the vehicle \(k\), and \(a_{k}\) is the number of neighbor vehicles of the vehicle \(k\) that are not neighbors of the vehicle \(i\). With respect to Figure 1, \(a_{ik}\) is the number of vehicles placed within the region \(IA\), \(a_{i}\) is the number of vehicles inside the \(I\) vehicle' communication range (I in Figure 1), and \(a_{k}\) is the number of vehicles within the region \(K\).