esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
Table 2 provides a summary of the GDMS results of the Ag recovered from the MSA-based mixtures through electrowinning. The results show that the Ag metal recovered from the solution has purity as high as \(\sim\)99.92% (3N2). However, large amounts of sulfur and chloride are present as impurities. Both sulfur and chloride are considered to degrade in MSA due to the high applied current density during the electrolytic recovery process. Sulfur is considered to electrochemically decompose in MSA due to the high applied current density during the electrospinning process. As for chloride, it is considered that MSA electrochemically decomposes from the MSA mixtures used as the electrolyte, and is converted into the corresponding sulfonyl chloride, methanesulfonyl, or mesyl chloride [28]. This outcome is significant in that it not only provides an environmentally friendly process that facilitates the recovery of Ag with high purity, but also demonstrates that the process is applicable to actual EoL PV modules. Figure 5: Microstructures of recovered Ag according to the variation in the concentration of the added water: (**a**) 0 M, (**b**) 5.0 M, and (**c**) 20.8 M.  
\[i_{p}=0.4463 nF\sqrt{\frac{nFD}{RT}}A\mathcal{C}\sqrt{\
u}, \tag{1}\]0 X-ray diffraction profile of deposited Ag.
## 7 Experimental Results
### Realtime Implementation
#### 7.1.2. Evacuation Path
The presented decision model is saved in the output display document. Initially, sensor nodes collect environmental data of the deployed location and send them wirelessly to dispatching nodes. The coordinator node then makes a decision based on the model as discussed and operates the output screen. Figure 27: Equivalent Diagram of Building with Nodes during Fire Accident. When there is no fire alarm signal from the end devices, the coordinator will send a command to the display module to display the normal direction or announce that there is no fire. The operating model of the evacuation guidance system in the absence of fire is shown in Figure 28. If there is a fire detection signal from the end devices, the coordinator sends a command to the output display module to display the direction according to the decision pattern to provide an optimal and safe exit for evacuees. The operating model of the exit guidance system is shown in Figure 29.  
Table 4 illustrates the comparative analysis of the proposed system with previous studies implemented for building fire evacuation paths. The comparative analysis was performed based on certain parameters, namely: hardware, data collecting device, network simulation analysis, communication technology, and user interface. From the comparative analysis, it is concluded that the proposed system is superior to the previous studies in terms of implementing fire detection with customized hardware based on an Atmega 328P microcontroller, 2.4 GHz, and LoRa communication. Moreover, a hybrid architecture is proposed for establishing IoT enabled evacuation system. Network simulation of Zigbee and evaluation metrics of LoRa were performed in this study. The proposed architecture was realized with the Blynk cloud server application, where the hardware data were logged from the hardware into the cloud server. The majority of the study implemented evacuation paths on the simulation platform and limited studies have utilized hardware for executing the evacuation system. [PERSON]'s algorithm was tested and recorded on the exit display hardware to implement the evacuation path in real-time. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Starting** & **Destination** & **Distance** & **Evacuation Route** \\ \cline{1-1}
**Node** & **Node** & & \\ \hline E1 & & 7.0 m & E1\(\rightarrow\)E0 \\ \cline{1-1} \cline{3-4} E2 & & 14.5 m & E2\(\rightarrow\)E1\(\rightarrow\)E0 \\ \cline{1-1} \cline{3-4} E3 & & 21.0 m & E3\(\rightarrow\)E2\(\rightarrow\)E1\(\rightarrow\)E0 \\ \cline{1-1} \cline{3-4} E12 & & 14.0 m & E  
Figure 28: Working Model of Exit Display When There is No Fire. Figure 29: Working Model for Exit Display when Sensor Detects Fire Event.
## 5 Network Layer
### Summary  
The adaptations at the network layer extend the performance improvement from local clusters to a network scope. Spectrum-aware routing protocols can exploit the availability of spectral information at lower layers to determine more appropriate paths across the network to avoid packet loss or optimize frequency utilization. This layer is also marked with the notable opportunity for network integration. The predominance of the Internet Protocol (IP) makes it a suitable universal language for miscellaneous _IoT_ product. Although IP is historically considered too heavyweight for devices such as wireless sensors, IPv6 over Low power Wireless Personal Area Networks (6 LoWPAN), a compact version of IPv6, provides a viable solution which fosters rapid implementation in WSNs. Since 6 LoWPAN is intrinsically compatible with IP, various low-power _IoT_ devices not only communicate in a way that is far more efficient than numerous one-to-one translations, but also connect seamlessly with the vast existing IP-based Internet. Technologies, including how the routing discussed is able to function normally since only minimal, if any, adjustments are needed, e.g., the type of identifier.
# Factorial Design Analysis for Localization Algorithms
## 4 Results
### Path-Loss Exponent  
This experiment uses the log-normal shadowing model, which was used in [19; 22] to evaluate the localization algorithms in the same evaluation scenario proposed for this study, as it is the most commonly used due to its simplicity and its fidelity to real Wireless Sensor and Actuator Network (WSAN) scenarios. In this model, two propagation settings are proposed with variation in the path-loss exponent \(\eta\), since the frequency bands that operate in the IEEE 802.15.4 standard are a parameter that has no impact on any WSN localization scenario. The path-loss exponent \(\eta\) was considered between 1.5 and 5 for this experiment, that being the typical range for WSN applications [37]. Inside a building with line of sight, the path-loss exponent \(\eta=1.5\), is considered, while in obstructed in building where there is not line of sight, the path-loss exponent \(\eta=5\), is considered [49].
## 4 Numerical Modeling
### Calculation Methodology  
The pile foundation system was evaluated using PLAXIS 2D 2021 finite element software [17]. PLAXIS is a finite element program, developed for the analysis of deformation, stability, and groundwater flow in geotechnical engineering. It is a program for geotechnical applications in which soil models are used to simulate soil behavior. PLAXIS (plane strain model) was used to predict the total and horizontal displacements of the pile as well as stress distribution in and around the soil-pile foundation system. The staged construction method is applied in calculations with the Plaxis program. This method provides a more realistic solution to the problem in stages. While calculating the bearing capacity of the piles, the plastic solution method was chosen from the calculation types available in the program. Plastic calculation should be selected to carry out an elastic-plastic deformation analysis. The calculation is performed according to the small deformation theory. This type of calculation is appropriate in most practical geotechnical applications. Then, stability analyses of the pile foundation were conducted using the SLOPE/W program [18]. Finally, the system was modeled in 3D using CSI SAP2000 V21 software [19] and reinforced concrete calculations were conducted for piles and the slab /foundation.
# Estimation of Gross Primary Productivity (GPP)
## 2 Materials and Methods
### Extracting Phenology Indicators from GPP and VIs
#### 2.4.2 Threshold Definitions  
The extraction of phenological metrics (SOS and EOS) from VI time-series is highly uncertain. Large discrepancies in the detection of SOS and EOS have been found by comparing different retrieval methods [65; 66; 67; 89]. We tested three state-of-the-art thresholds (first derivative, percentile and percentage) to perceive their differences and understand which one is the ideal threshold for GPP and VIs.  
_First derivative._ The SOS occurs when the first derivative value of the time-series curve changes from negative to positive, while for EOS is when it changes from positive to negative [90; 91; 92]. The max and minimum of the first derivative correspond to the faster greening and declining (senescence) rates respectively [93].  
_Percentile._ The percentile method does not have a universal definition. The two data sets for SOS and EOS were ordered from lowest to highest and the percentile was calculated as [94]:  
\[\textit{Percentile}(X)=\frac{X}{100}N \tag{2}\]  
where \(X\) is the reference percentage and \(N\) is the number of data points [95; 96].  
_Percentage._ The percentage considers the difference between the highest and lowest values. The data of each data set were first ordered from lowest to highest, similar to the percentile method [97]. The percentage was calculated as:  
\[\textit{Percentage}(X)=\left((MaximumN-MinimumN)\frac{X}{100}\right)+MinimumN \tag{3}\]  
where MaximumN and MinimumN are the highest and lowest values among the time-series data (e.g., GPP or VIs) and \(X\) is the selected percentage (e.g., 10%, 20%). After applying the equation to both data sets, we selected the corresponding value among the data set (e.g., parameters such as GPP or VIs) equal to or larger than the result of this equation. The days of the year associated with this value were selected as SOS and EOS for that time-series (e.g., GPP or VIs).  
For both percentile and percentage threshold methods, the reference percentage \(X\) (in Equations (2) and (3)) indicated the point in time when a defined fraction of the seasonal amplitude has been reached.  
We set \(X\) to 10% and 20% because thresholds between 10% to 25% provide more accurate estimates of phenological metrics as previous works have demonstrated [98; 99].
# Attempts to Obtain Material Based on Polyvinyl Alcohol with Barrier Properties against Water Vapor
## 3 Results and Discussion
### Water Vapor Permeability (WVP) of the Films  
The WVP of the prepared films was calculated using the formula in Section 2.5 (Equation (2)) and the results obtained were compared with the WVP values obtained by the untreated and unmodified PVA film and the PE film. These can be seen in Table 3.  
The WVP of untreated and unmodified PVA film was improved by all formulations used in this study.  
The best results were obtained with the heat treated PVA VIII film at 150 \({}^{\circ}\)C, which reduced the WVP of the unmodified and untreated PVA film from 3.92 \(\times\) 10\({}^{-10}\) g/s\(\cdot\)m\(\cdot\)Pa to 0.76 \(\times\) 10\({}^{-10}\) g/s\(\cdot\)m\(\cdot\)Pa. This film achieved WVP values 5.15 times lower than PVA's.  
Compared to the PE film, the best results were achieved with the same film, whose WVP value was 0.71 \(\times\) 10\({}^{-10}\) g/s\(\cdot\)m\(\cdot\)Pa higher, and with the PVA V film treated at 150 \({}^{\circ}\)C, which was 0.72 \(\times\) 10\({}^{-10}\) g/s\(\cdot\)m\(\cdot\)Pa higher than the PE film. WVP values close to those of the PE film are also measured from the thermally treated PVA VIII film at 125 \({}^{\circ}\)C, which had a WVP 0.79 \(\times\) 10\({}^{-10}\) g/s\(\cdot\)m\(\cdot\)Pa higher than the PE film, and from the PVA IX film treated to 125 \({}^{\circ}\)C whose WVP values were 0.9 \(\times\) 10\({}^{-10}\) g/s\(\cdot\)m\(\cdot\)Pa higher than the values recorded by the PE film. The WVP of the best performing film is 15 times higher than PE's, while untreated PVA film is 78 times higher than of PE's.  
Figure 11: FTIR spectra of the films.
# A Multi-Granularity Word Fusion Method for Chinese NER
## 2 Related Work
### Models with Multi-granularity Word Information  
The goal of using multi-granularity information is to improve character-based models. Lattice LSTM [16] applied a lattice structure for Chinese NER, showing good performance. FLAT [17] improved the results of Chinese NER with a shallow Flat-Lattice Transformer to handle the character-word graph. A-NER [18] is BiGRU-CRF model that combines a self-attention mechanism with multi-embeddings technology. It can extract richer linguistic information of characters from different granularities (e.g., radical, character, word) and find the correlations between characters in the sequence. CWPC_BiAtt [19] put forward a new comprehensive-embedding, considering three aspects, namely character-embedding, word-embedding, and pos-embedding and thus acquired their dependencies, based on which we propose a new Character-Word-Position Combined BiLSTM-Attention for the Chinese NER task. ZEN [5] enhanced Chinese BERT with a multi-layered N-gram encoder but is limited by the small size of the N-gram vocabulary.  
Recent works try to integrate the multi-granularity word information and pre-training language models by utilizing their respective strengths. PLTE [20] enhances self-attentionto incorporate lattice structure and introduces a porous mechanism to augment localness modeling while retaining the strength of capturing the rich long-term dependencies. ERNIE [3] exploits entity-level and word-level masking to integrate knowledge into BERT in an implicit way. Moreover, Character and N-gram features are concatenated for joint training. WMSEG [6] uses memory networks to incorporate wordhood information with several popular encoder-decoder combinations for CWS. Multi-metadata Embedding-based Cross-Transformer (MECT) [21] improves the performance of Chinese NER by fusing the structural information of Chinese characters, and then uses multi-metadata embedding in a two-stream Transformer to integrate Chinese character features with radical-level embedding.  
Our method is in line with the above approaches. We try to combine multi-granularity information and BERT. The most notable difference between our method and others is in the fusion approaches. Unlike many models that focus on characters (e.g., A-NER and CWPC_BiAtt), we pay more attention to word information and the fusion of word information with character information. Instead of concatenating various information, we design a precise strong-weak feedback attention to accomplish the fusion. It ensures that the output embeddings are of the same length as the original character embeddings, thus avoiding computational pressure.
## 3 Background and Theory
### Higher-Order Spectra Features  
The third feature extraction technique consists of Higher-Order Spectra (HOS) features [28], which are considered frequency-domain characteristics. These features are derived from the power spectral density (PSD), providing insights into power fluctuations within EEG signals and aiding in examining the Euclidean distribution of electrical activity across frequencies. In this analysis, skewness and kurtosis are selected as key HOS features. Skewness indicates the symmetry of the PSD around its mean, while kurtosis assesses the shape of the PSD distribution, focusing on the presence of heavy tails or potential outliers. These features are computed using Equations (11) and (12):  
\[\begin{split}\mathit{Skewness}_{HOS}&=\frac{\frac{ 1}{N}{\sum_{i=1}^{N}}{\left({Pxx_{i}-P\overline{x}x}\right)^{3}}}{\left({ \sqrt{\frac{1}{N}{\sum_{i=1}^{N}}{\left({Pxx_{i}-P\overline{x}x}\right)^{2}} }}\right)^{3}}\\ &\mathit{Kurtosis}_{HOS}=\frac{\frac{1}{N}{\sum_{i=1}^{N}}{\left( {Pxx_{i}-P\overline{x}x}\right)^{4}}}{\left({\frac{1}{N}{\sum_{i=1}^{N}}{\left( {Pxx_{i}-P\overline{x}x}\right)^{2}}}\right)^{2}}-3\end{split} \tag{12}\]  
where the following apply:  
* \(Pxx_{i}\) represents the individual power spectral density values;
* \(P\overline{x}x\) represents their mean;
* \(N\) denotes the number of observations in the PSD.
## 3 CPN Models
### Basic Models
#### 3.1.3 Iteration Statements  
Due to their particularities, the loop statements (_while_, _do-while_ and _for_) have different CPN models. The statement _while_ has a control that (1) checks if an expression is true and (2) then executes the body (in case the Boolean expression is true). When the body terminates, the control checks the expression again. This cycle ends when the expression returns false. Similarly to the command  
Figure 4: CPN model of the commands _if-then-else_ and _switch_.  
_if-then-else_, the command _while_ also has an expression that must be evaluated in order to decide what to do next. Hence, a probability is associated to the control return, _i.e._, the probability of the loop repeats. Similarly to the selection statements (Section 3.1.2), the application developer must associate a probability (commands _while_ and _do-while_) or a number of times the body runs (statement _for_) to the loop control.  
Figure 5 shows the CPN model for representing the command _while_. The transition _c_l_ is similar to one adopted in the command _if-then-else_ and decides whether the loops repeats (the token moves from place _control_ to _body_) or not (token moves to _next_). The place _body_ models the command(s) executed in the body. Similarly to the selection statements, this model may have dead transitions when the probability associated to the commands is zero.  
Figure 5: CPN model of the command _while_.  
Figure 6: CPN model of the command _do-while_.  
Similarly to the previous commands, the power consumption of command _while_ is also calculated using the functions addEnergy(), calcTime() and addResourceEnergy().  
The CPN model for representing the command _do-while_ is very similar to the command _while_. The only and fundamental difference between them is that the _do-while_ ensures that the body will be executed at least once. This feature means that this model has not dead transitions. Figure 6 depicts the proposed CPN model.  
Finally, the last loop command, namely _for_, consists of four parts: initialization, control, step and body. The first three ones may be modelled as shown in Section 3.1.1, _i.e._, by using the assignment, logical and increment/decrement operators. The body part is similar to those presented in the previous loop commands.  
Figure 7 illustrates the proposed CPN model for representing the command _for_. The places _assign_, _control_, _inc_ and _body_ model by the initialization, control, step and body, respectively. The initialization is performed before the loop itself and occurs only once. The transition \(c\)1_ decides the number of times (N) the loop must repeat based on a value set by the application developer. This model has dead transitions in the case a probability associated to the command is equal to zero.  
Figure 7: CPN model of the command _for_.
## Materials and Methods
### Data Analysis
#### 2.6.2 Development of Calibration Model  
After preprocessing of the spectra, the samples were randomly divided into two sample sets. A calibration sample set consisted of 60 samples. This set was used for developing the calibration model using a leave-one-out cross-validation method. A prediction sample set, consisting of 35 samples, was used for prediction purposes. In this study, partial least squares (PLS) regression multivariate analysis was used to develop a calibration model for MC, pH, and SSC of tomatoes prediction. The PLS regression approach is one of the most popular chemometric algorithms for calibration model development due to its simplicity and small volume of calculations. The preprocessed spectral data were linked to MC, pH, and SSC of tomatoes using a PLS regression analysis to develop a calibration model. In the development of all the calibration and prediction models, 20 PLS factors were set up as a maximum. All types of spectral preprocessing and the development of the calibration and prediction models for MC, pH, and SSC of tomatoes prediction were performed using The Unscrambler@software (version 9.8, CAMO, Oslo, Norway).
## 4 Simulations, Results and Discussion
### Equivalence Among Complex Wald, Rao Test and GIRT for Linear Model
#### 4.2.4 Complex Wald Test  
The real FIM of this problem has a special form, so Theorem 2 applies. The complex Wald test is \[T_{\hat{W}}(\hat{\kappa}) = 2(\bar{\theta}-\bar{\theta}_{0})^{H}\bar{\mathbf{(}}\hat{\theta} )(\bar{\theta}-\bar{\theta}_{0})\big{|}_{\bar{\theta}=\bar{\theta}_{1}} \tag{55}\] \[= 2[(\bar{\mathbf{H}}^{H}\bar{\mathbf{C}}^{-1}\bar{\mathbf{H}})^{- 1}\bar{\mathbf{H}}^{H}\bar{\mathbf{C}}^{-1}\hat{\mathbf{x}}]^{H}(\bar{\mathbf{ H}}^{H}\bar{\mathbf{C}}^{-1}\bar{\mathbf{H}})(\bar{\mathbf{H}}^{H}\bar{\mathbf{C}}^{- 1}\bar{\mathbf{H}})^{-1}\bar{\mathbf{H}}^{H}\bar{\mathbf{C}}^{-1}\hat{\mathbf{x}}\] \[= 2\mathbf{x}^{H}\bar{\mathbf{C}}^{-1}\bar{\mathbf{H}}(\bar{ \mathbf{H}}^{H}\bar{\mathbf{C}}^{-1}\bar{\mathbf{H}})^{-1}\bar{\mathbf{H}}^{H }\bar{\mathbf{C}}^{-1}\hat{\mathbf{x}}\]  
Compared with the results obtained by using GLRT and the complex Rao Test for the same problem, all three detectors are equivalent in this case. Next, we show the root cause of this equivalence.
# A Video-Based Fire Detection Using Deep Learning Models
## 3 Proposed Method
### Long Short-Term Memory (LSTM) Network for Fire Features in a Short-Term  
In general, it is not appropriate to detect and judge the fire without considering the temporal behavior. In the proposed method we aggregate the changes in the extracted spatial features using LSTM in a short period \(T_{LSTM}\), and try to determine whether it is a fire or a non-fire object. Here, we do not differentiate between flame and smoke. Because the SRoFs or non-fire objects in consecutive frames have been determined by Faster R-CNN, we merely temporally accumulate the spatial features within the corresponding bounding boxes in LSTM and determine whether the consecutive box is fire or not for the short-term. This important step in DTA in the proposed method is similar to a person's quick glance to detect a fire. We assume that the fire decision based on a glance depends on the short-term dynamic characteristics of the fire.  
In the proposed method, the LSTM network consists of two stages in which the number of memory cells in LSTM is determined experimentally. The short temporal features pooled through the LSTM network are used to make a short-term fire decision by two soft-max units, one for fire or the other for non-fire. Figure 7 shows the part of LSTM used to accumulate and decide a fire in a short time period.  
In our method, the LSTM network is separately trained using the weighted GAP spatial features of CNN in bounding boxes. That means the \(d\)-dimensional features for fire or non-fire objects in consecutive frames of video clips should be calculated and prepared sequentially for the training of LSTM. We construct the new video dataset which consists of the same video training data for Faster R-CNN and additional video data to supply sufficient training data. The consecutive \(d\)-dimensional spatial features calculated from the trained Faster R-CNN for a video from the video dataset are prepared as input streams for the LSTM training. The output label for the LSTM short-term decision is determined according to the annotation of a video clip.  
Figure 7: The long short-term memory (LSTM) network for fire detection.