esa-sceva/satcom-chunk-collection huggingface dataset:
### Object Tracking
The experiments on tracking the centroid of a moving ball subjected to projectile motion, free-fall bouncing motion, and back-and-forth linear motion are conducted to show the efficiency and superiority of the proposed AKF-ALS algorithm. The rest of the paper is organized as follows. Section 2 formulates the VOT problem and introduces the KF and the ALS method. In Section 3, the AKF-ALS algorithm is developed to deal with the visual object tracking task with uncertain noise covariance. It utilizes the ALS method to achieve real-time noise covariance tuning and proposes a novel adaptive thresholding method to improve the robustness of the BS method. The computation complexity of the AKF-ALS algorithm is derived and a numerical analysis is conducted to show its efficiency. In Section 4, the proposed AKF-ALS algorithm is validated to outperform the Kalman filter given constant noise covariance through the experiments on tracking the centroid of a moving ball subjected to projectile motion, free-fall bouncing motion, and back-and-forth linear motion. Section 5 provides concluding remarks and a discussion of future work.
## 3 Data Pre-Processing and Feature Analysis  
### Data Description and Input Variable  
A large dataset with over 727,000 charging load events was collected in nine provinces in Canada (Figure 4) by FleetCarma Inc., in partnership with 10 electric utility companies and the University of Waterloo, over a three-year period using FleetCarma data loggers via the OBD II port of 1000 EVs. The collected data include charging loads of thirty-five vehicle models for three charging locations and three charging levels. The data include charging load start and end times, energy consumption, energy loss, and the state of charge of the battery at start time and end time.  
For the charging load prediction, two individual models were developed to process battery size and weather events into feature input parameters, and the full results have been published recently [25].
## 4 Results and Analyses
### Experiment 3: The Combined Effect of Augmentation and Normalization
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training** & **VTLN** & **VTLN\({}_{\text{model}}\)** & **CV** & **KidsTALC** \\ \hline \multirow{4}{*}{CV adults’ speech} & None & None & 5.1 & 67.2 \\ \cline{2-5}  & & VTLN\({}_{\text{CV}}\) & **5.1** & 66.5 \\  & Train \(\dagger\) Test & VTLN\({}_{\text{KTalc}}\) & 5.2 \({}^{*}\) & **65.2 \({}^{*}\)** \\  & & VTLN\({}_{\text{MultiV1}}\) & **5.1 \(\ddagger\)** & 65.9 \(\ddagger\)** \\ \cline{2-5}  & & VTLN\({}_{\text{CV}}\) & **5.2 \({}^{*}\)** & **64.4 \({}^{*}\)** \\ \cline{2-5}  & Test & VTLN\({}_{\text{kidsTALC}}\) & 5.5 \({}^{*}\) & 65.0 \({}^{*}\) \\  & & VTLN\({}_{\text{MultiV1}}\) & **5.2 \({}^{*}\)** & 64.5 \({}^{*}\) \\ \hline \hline \end{tabular} \(\dagger\)\(\dagger\)\(p\)\(<\) 0.05, \(\ddagger\)\(p\)\(<\) 0.01, and \({}^{*}\)\(p\)\(<\) 0.001.  
\end{table} 
Table 10: Results in %WER, with significance level, for the German ASR system trained with SP and SpecAug when VTLN was applied (1) during training and testing or (2) only during test. The lowest WERs for each speaker group are highlighted in bold for the two normalization approaches separately. Underline indicates the best result for the specific speaker group over all conditions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Training** & **VTLN** & **VTLN\({}_{\text{model}}\)** & **SetA** & **SetC1** & **SetC2** & **Average** \\ \hline \multirow{4}{*}{SetA Adults’ Speech} & None & None & 9.9 & **10.0** & 38.8 & 24.4 \\ \cline{2-5}  & & VTLN\({}_{\text{SetA}}\) & 9.8 & 10.2 & 38.1 \(\ddagger\) & 24.2 \\ \cline{2-5}  & Train \(\dagger\) Test & VTLN\({}_{\text{SetC1C2}}\) & 9.8 & 10.2 & 38.2 \(\ddagger\) & 24.2 \\ \cline{2-5}  & & VTLN\({}_{\text{MultiV1}}\) & **9.8** & **10.1** & **37.9 \({}^{*}\)** & **24.0** \\ \cline{2-5}  & & VTLN\({}_{\text{SetA}}\) & 9.9 \({}^{*}\) & 10.0 & 37.2 \({}^{*}\) & 23.6 \\ \cline{2-5}  & Test & VTLN\({}_{\text{SetC1C2}}\) & 10.2 \({}^{*}\) & 10.2 & 37.8 \({}^{*}\) & 24.0 \\ \cline{2-5}  & & VTLN\({}_{\text{MultiV1}}\) & 10.0 \({}^{*}\) & **10.0** & **37.1 \({}^{*}\)** & **23.5** \\ \hline \hline \end{tabular} \(\dagger\)\(\dagger\)\(p\)\(<\) 0.05, \(\ddagger\)\(p\)\(<\) 0.01, and \({}^{*}\)\(p\)\(<\) 0.001.  
\end{table} 
Table 11: Results in %CER, with significance levels, for the Mandarin ASR system trained with SP and SpecAug when VTLN was applied (1) during training and testing or (2) only during test. The lowest CERs for each speaker group are highlighted in bold for the two normalization approaches separately. Underline indicates the best result for the specific speaker group over all conditions.
## 2 Materials and Methods  
### Experimental Design and Soil Sampling  
In October 2016, a field survey of bacteria from the rhizosphere of different scion-rootstock combinations was conducted. Grapevines were planted 11 years prior to the experiment in a vineyard at the Institute for Grapevine Breeding, Julius Kuhn-Institut in Siebeldingen, Germany. The vineyard was located in an area with sandy loam soil (type pararedzina). In 2016, the area received approximately 625 mm rainfall.  
Each of four scion cultivars (Calandro, Reberger, Felicia, and Villaris; from now on referred to as cultivars) were grafted onto each of four rootstock types (125 AA, 5 BB, Binova, and SO 4). All cultivars belong to the _Vitis vinifera_ species; Calandro and Reberger are red grape varieties, and Felicia and Villaris are white grapes. The rootstocks were all _Vitis berlandieri_ and _Vitis riparian_ crosses with low genetic variation [33; 34]. Grafted grapevines were placed in adjacent rows which were all subjected to cover cropping: every second row with a vineyard-specific mixture of various legumes and other forbs (Wolff-mix) and tilled via disc harmony in spring each year, and the remaining rows with grass and mulched three times between April and August, depending on the weather and growing conditions. Fertilization was conducted yearly with Entec 26 (26% N, 13% S) at a rate of 39 kg N/ha. For the duration of the experiment no irrigation took place.  
Rhizosphere soils were collected in a single day, from three randomly selected plants from each cultivar x rootstock combination, yielding a total of 48 samples. Samples were collected by removing roots with attached soil in the field using sterile metal forceps, cleaned with 70% ethanol between each sample to avoid cross-contamination. Samples were transported on ice and processed in the laboratory within 24 h. Sampled roots were shaken to remove loosely adhering soil, and rhizosphere soil was collected by removing the remaining firmly attached soil particles using a sterile disposable brush. Samples were stored at \(-20^{\circ}\) C for further DNA extraction.
## 6 Discussion  
Warming and drought stress can affect winter wheat's stomatal morphology and photosynthetic characteristics. Based on photosynthetic characteristics, the effects of warming and drought stress on the coupling effect of An and Tr were verified. Both warming and drought stress affected the stomatal structure of wheat leaves, as well as reducing the number and size of stomata in wheat leaves. This is consistent with the results of previous studies [1,2]. Under the conditions of temperature rise and water deficit, the higher the chlorophyll content of leaves, the higher their An [47,48]; this is because chlorophyll is conducive to better capturing light energy and converting it into chemical energy [49], thus increasing the possibility of An. In conclusion, leaf senescence was rapid under high temperature conditions during grain-filling and ripening, resulting in reduced An. [PERSON] and [PERSON] [50] and [PERSON] et al. [51] reported that genotypes with delayed senescence increased yield and transpiration efficiency under water limitation [52,53]. In addition, increasing temperature and drought stress brought forward and shortened the growth period of winter wheat, and there were significant differences in the An and Tr between increasing and not-increasing temperature. Gs is an important physiological factor regulating plant An under water deficit conditions [54,55], and drought stress reduced An and Tr [10,11]. Moreover, warming and hot, dry air accelerated leaf senescence during the grain-filling stage [56], resulting in reduced yield [57].  
Based on Ball-Berry and Penman-Monteith models, the measured An and Tr of winter wheat were compared with simulated values under warming and drought stress conditions. It was concluded that warming and drought stress reduced the fitting effects of An and Tr. [PERSON] et al. [21] stated that Tr and An have a positive linear correlation, ensuring good linear coupling between An and Tr during diurnal variation. However, under the conditions of a high atmosphere and drought, the proximity of stomata inhibits water loss, leading to a decrease in photosynthesis. However, crops still undergo transpiration [26,27]. This behavior can be described as decoupling between An and Tr under heat stress. High temperatures can inhibit photosynthesis [31] and increase respiration [32], making plants vulnerable to heat stress [33]. To date, studies on individual trees have shown that there is uncoupling between An and Tr under elevated temperatures [26,27,34,35]. These studies suggest that photosynthesis decreases at extreme temperatures, but transpiration remains high. Although most of these studies report the disappearance of decoupling due to decreased transpiration resulting from soil drying, these conclusions are similar to the results of this study. However, there are few studies on the coupling effects of warming and drought stress on An and Tr in winter wheat, and further similar experimental studies are needed.
## 4 Experiments and Results
### Model Setup  
This paper selects the MIMIC dataset [22], a substantial physiological dataset that has been referred to in Section 3.1. The suggested model was put into practice via Python, using the sklearn library with help from additional scientific computing libraries, such as Matplotlib, NumPy, and scikit-learn, to carry out a variety of tasks that include pre-processing and model selection. The ConvLSTM approach uses the Adam optimizer as its optimization algorithm. The ConvLSTM has a four-layer network with two dropout layers ((filters = 64 in two layers), (kernel_size = 1), (padding = \"same\" in two layers), and (activation function = \"Relu\")) with a kernel size of 1. The model was trained with a batch size of 30 and using various epoch counts. Using a ratio of 70:30 for the training and testing partitions, the dataset was divided into training and testing datasets. Due to the fact that the dataset is huge, this split is more suitable for this particular dataset. Table 3 summarizes the basic settings of the model.  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **Value** \\ \hline Language & Python \\ \hline Libraries & pandas, NumPy, scikit-learn, Matplotlib, and Keras \\ \hline Training set & 70\% \\ \hline Test set & 30\% \\ \hline Layers & 4 \\ \hline Kernel\_size & 1 \\ \hline Activation function & Relu \\ \hline Filters & 64 \\ \hline Dropout & 2 \\ \hline Optimizer & Adam \\ \hline Number of epochs & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model setup.
# Rapid and Accurate PPA Prediction for the Template-Based Processor Design Methods
## 3 PPA Prediction Method
### Prediction Model Derivation
#### 3.2.1 Multivariate Linear Regression Prediction Model  
To build the ML-PM, we assume that each parameter is independent and has three individual weights for performance, power, and area. In detail, for each parameter \(p_{i}\), \(i\in(1,n)\), this paper assumes that there are weight \(w^{\prime}_{i}\), \(w^{\prime\prime}_{i}\), and \(w^{\prime\prime\prime}_{i}\), for performance, power, and area specifically, while \(w^{\prime}_{i}\), \(w^{\prime\prime}_{i}\), and \(w^{\prime\prime\prime}_{i}>0\). Taking performance (\(P_{1}\)) for instance, \(P_{1}\) is the performance value of the base parameter set. And \(P_{1}\) will turn to \(P_{1,m^{\prime}}\) and fellow equation holding when one parameter \(p_{m},m\in(1,n)\) is modified to other value in its value range.  
\[S=\ \frac{w^{\prime}_{m}\bullet(1+\beta_{m})+\sum_{i=1,j\
eq m}^{n}w^{\prime}_{i}} {\sum_{i=1}^{n}w^{\prime}_{i}} \tag{1}\]  
\[P^{\prime}_{1,m}=P_{1}\bullet S \tag{2}\]  
In the Equation (1), \(\beta_{m}\) is the influence rate of the modified parameter \(p_{m}\) on \(P_{1}\). And the change rate S can be represented as Equation (3), when multiple parameters \(J=\{p_{j}\ldots\}\), \(J\subseteq\{p_{1}p_{2}\ldots p_{n}\}\) are modified.  
\[S=\ \frac{\sum_{j,p_{j}\in J}^{n}w^{\prime}_{j}\bullet(1+\beta_{j})+\sum_{i,p_{ i}\in J}^{n}w^{\prime}_{i}}{\sum_{i=1}^{n}w^{\prime}_{i}} \tag{3}\]  
Although neither the \(\beta_{i}\) nor the \(w^{\prime}_{i}\) are known, the values of \(P_{1}\) and \(P^{\prime}_{1,j}\) are already known in the former preprocess, therefore, \(w^{\prime}_{j}\) and \(\beta_{j}\) can be replaced via \(\Delta P_{1,j}\) as the Equation (4) below.  
\[\Delta P_{1,j}=\ P^{\prime}_{1,j}-\ P_{1}=\ \frac{w^{\prime}_{j}\bullet\beta_{j}} {\sum_{i=1}^{n}w^{\prime}_{i}}\bullet P_{1} \tag{4}\]  
Based on the Equations (3) and (4), the change rate S can be calculated via available information, as the Equation (6) below.  
\[S=1+\frac{\sum_{j,p_{j}\in J}^{n}\Delta P_{1,j}}{P_{1}} \tag{5}\]  
And \(P_{1,J^{\prime}}\) can be calculated as Equation (6), when multiple parameters \(J=\{p_{j}\ldots\}\), \(J\subseteq\{p_{1}p_{2}\ldots p_{n}\}\) are modified.  
\[P^{\prime}_{1,J}=P_{1}\bullet S=P_{1}+\sum_{j,p_{j}\in J}^{n}\Delta P_{1,j} \tag{6}\]  
As the Equations (7) and (8) show separately, the power \((P_{2})\) and the area \((A_{1})\) can be calculated in the same way.  
\[P^{\prime}_{2,J}=P_{2}+\sum_{j,p_{j}\in J}^{n}\Delta P_{2,j} \tag{7}\]  
\[A^{\prime}_{1,J}=A_{1}+\sum_{j,p_{j}\in J}^{n}\Delta A_{1,j} \tag{8}\]
## 3 Ermost
### Sniffer
#### 3.1.2 Packet Capture  
After running the sniffer election detailed in Section 3.1.1, sniffers start capturing packets, where each sniffer captures, in promiscuous mode, packets sent by the target nodes that it monitors, that is, nodes which were selected by this election mechanism.  
As mentioned in Section 3, the aggregation of headers is optional. When the aggregation of headers is not used, the sniffer generates, for each captured packet, a monitoring message containing its address (sniffer address), a timestamp and the bytes of the captured packet. This monitoring message is sent to the local monitor through the monitoring network using multihop routing. The format of the packet sent by the sniffer is shown in Figure 4. The header is inserted by the link layer protocol of the MicaZ platform and has fixed length of 05 bytes. The payload carries the monitoring message. The maximum payload size on the MicaZ platform is 29 bytes. Therefore, if the number of bytes of the monitoring message is larger than this value, the sniffer has to send more than one packet to the local monitor.  
Figure 4: Format of the packet sent by sniffers without using aggregation of headers.  
When the aggregation of headers is used, only the information present in the headers of captured packets is sent by the sniffer to the local monitor. In this case, the headers of several packets may be sent in the same monitoring message, hence reducing the overhead of transmission and consequently reducing the energy consumption of the sniffers. The format of the packet sent by the sniffer is shown in Figure 5. The monitoring message carries the timestamp and the header of **N** captured packets. The maximum size of **N** depends on the size of header of the packets sent by the target nodes, because the monitoring message cannot be higher than the maximum payload size in the MicaZ platform (29 bytes).
# Radio Wave Propagation and WSN Deployment in Complex Utility Tunnel Environments +
## 4 WSN Deployment
### Scatterers Analysis: Human Presence within the Tunnel
Figure 15 presents the obtained estimation results in terms of the RF power level distribution differences between the case without a human presence and the case study with a three-worker team at different locations along the tunnel. From the results, a mild growth variation can be observed when comparing the three-worker team case study with the single-worker case study, and therefore, it can be stated that the previous estimated 20-dB margin is enough for a proper RF planification when considering teams of up to three workers within the tunnel. Figure 13: RF power level distribution differences between the case without a human presence and the cases with human beings at different locations along the tunnel. In general, the obtained results regarding the study of the human presence within the tunnel show that a margin of about approximately 20 extra dBs is required in order to preserve the received RF power level above the sensitivity level, thus ensuring the wireless communication links between nodes, even when up to a three-worker team is considered within the tunnel. This extra margin corresponds to no need of extra range reduction estimated in the previous subsection analysis, as can be seen in Figure 16.  
Figure 14: Inclusion of 3 computational human body models in the considered scenario for the 3D-RL simulation, emulating a maintenance team working in the utility tunnels. Figure 15: RF power level distribution difference between the case without people and the cases with a work team of 3 people at different locations along the tunnel. However, since the sensitivity value of ZigBee is \(-100\) dBm, in this case, a reduction of the distance between nodes is desirable in order to assure reliable wireless communication at every circumstance. This restriction was applied for the design of the WSN presented in the following subsection.
# Biomaterials for Ophthalmic Applications
## 2 Biomaterials in Ophthalmology
### Intracular Lenses  
Cataract is the clouding of the lens and is the leading cause of blindness in the world. It is due to changes in lens proteins, a natural phenomenon that occurs during aging, but can be accentuated by various factors, such as exposure to UV rays or smoking. It is treated by replacing the biological lens with a polymer-based substitute [1]. The most common procedure is phacoemulsification (FACO), creating a small incision in the cornea through which a probe is used to break the cloudy lens which is removed by suction. An intraocular lens artificial device (IOL) is then placed in the intact capsular bag [1; 62].  
The first IOL was in PMMA [63; 64] a rigid, non-collapsible and hydrophobic polymer requiring a large incision. [PERSON] invented the first foldable silicone lens that can be implanted through a 3 mm incision [65].  
An ideal IOL should provide the patient with good vision for a long period of time and the surgeon should be able to implant it easily, without causing complications [63]. The material and its design must allow for a low degree of postoperative inflammation and the production process must be relatively simple for the IOL to be accessible [66]. Currently, all IOLs include a chromophore in their composition to block UV light. Blue light is harmful and can cause damage to the retina due to increased oxidative stress [66].  
IOLs can be divided into the following two groups: an acrylic/methacrylate polymer comprising hydrophobic acrylic, hydrophilic acrylic or hydrogel (rigid or flexible) and hydrophobic (flexible) silicone/silicone elastomers. Non-folding PMMA lenses from the group of acrylic polymers are almost non-existent in Europe and the USA due to the size of the incision needed for implantation [43; 45; 67; 68].
## 5 Results  
### Tension-Compression Tests Results  
Results of the tension-compression tests for specimen WF-8 are plotted in Figure 11. A continuous curve can be seen on the Cole-Cole diagram, which indicates that the material is rheologically simple and that the time-temperature superposition principle (TTSP) is valid. The master curve of the norm of the complex modulus is plotted at a reference temperature (T\({}_{\text{ref}}\)) of 15 \({}^{\circ}\)C in Figure 11. The 2S2P1D model was fitted to the experimental data and is also plotted in Figure 11. A very good agreement between the experimental data and the 2S2P1D model can be observed.  
Figure 11: Tension–compression test results and fitted 2S2P1D model for specimen WF-8: (**a**) Cole–Cole diagram; and (**b**) master curve for the norm of the complex modulus at 15 \({}^{\circ}\)C.  
Figure 10: Summary and differences between the two proposed methods of back-analysis of the dynamic tests.  
The values of the 2S2P1D model and WLF equation constants obtained from the tension-compression tests are given for all specimens in Table 4. The constants k, h, \(\delta\), \(\beta\), C\({}_{1}\) and C\({}_{2}\) have the same values for a given material because they are only depending on the bitumen and not on the granular skeleton as shown in previous research [27; 37; 38].  
The measurements were recorded with a sampling frequency of 1 MHz by using a MATLAB application which was specifically developed for this test. Measurements were performed at five temperatures (\(-\)20, 0, 15, 35 and 50 \({}^{\circ}\)C) and five impacts were applied at each temperature. The applied force and the acceleration were recorded for each impact. The experimental data in time domain were then converted in frequency domain with a 1 Hz resolution using the Fast Fourier Transform (FFT). Figure 3 shows an example of the signals in time and frequency domains for specimen GB5-3 at 14.7 \({}^{\circ}\)C (measured temperature with a probe at the surface of the specimen).