esa-sceva/satcom-chunk-collection huggingface dataset:
# Highly Efficient GaN Doherty Power Amplifier for N78 Sub-6 GHz Band 5G Applications
## 4 Implementation and Simulation Results of the N78 Band Sub-6 GHz DPA
### Simulated DPA Performance  
This section presents the results of the designed Doherty power amplifier, including curves of output power, power gain, and power-added efficiency (PAE). Figure 16 illustrates the simulated values of output power (Pout), amplifier gain, drain efficiency (DE), and power-added efficiency (PAE) plotted against the input power (Pin), which is scaled from 0 to 40 dBm. In Figure 16a, it can be observed that the large-signal gain during nominal operation is approximately 15.7 dB when the input power is at 10 dBm. To achieve a 3 dB compression point value (P3 dB) for our design, the Pin value needs to be set at 29.2 dBm. Consequently, the output power is approximately 41.8 dBm at P3 db when the Pin value is at 29.2 dBm. The simulated plot of both the drain efficiency and the power-added efficiency of the proposed DPA design is presented in Figure 16b, when getting 78.4% and 74.3% of the P3 dB, respectively. Compared with the efficiency characteristics of a non-Doherty power amplifier (class AB) bias conditions, we obtain a PAE at the P3 db point of 55% and 58% for DE, as shown in Figure 17. The corresponding intrinsic drain voltage and current waveforms of the designed DPA are depicted in Figure 18.  
Figure 16: The results of the designed DPA. (**a**) Large-signal gain and output power. (**b**) PAE and DE, in terms of input power.  
Figure 17: Drain efficiency and PAE versus input power for non-Doherty (class AB) bias conditions.
## 1 Introduction
### Co-Registration and Individual Tree Matching
#### Related Methods for Co-Registration and Tree Matching  
Alternative methods which might be suitable for co-registration and tree matching can be found in the fields of image pair registration, object recognition, or point set registration. These disciplines deal with finding a transformation function between two 2D or 3D point sets.  
In the case of non-distorted point sets, the usage of the random sample consensus (RANSAC) [22] technique (to find corresponding point pairs) and the application of Helmert transformations is sufficient [23; 24]. In the case of roughly preregistered point sets, the iterative closest point method is expedient [25].  
For both distorted and non-distorted point sets, algorithms which identify matching point pairs by iteratively applying affine transformations while minimizing some kind of an objective function are popular. Examples comprise the Softassign algorithm [26], the coherent point drift algorithm [27], or more recently, a rigid registration algorithm inspired by gravitational force fields [28]. A further solution is to describe each point by some kind of feature which makes it recognizable in the second point set. These features are defined by image statistics [29] or surface shape [30] in the proximity of the point. Relevant representatives use similarity measures [30] or machine learning [31] to find corresponding points based on the features.
## 1 Introduction
Moreover, continuous monitoring of effluent toxicity, along with chemical parameters, prior to discharge into the environment is critical for assessing the effectiveness of wastewater treatment systems and to more effectively ensure the protection of the receiving media. However, bioassays are an unrealistic representation of an ecosystem across a single organism. Indeed, it is difficult to predict the impact on other species as they have different sensitivities to the same pollutant. Thus, it is recommended to apply a battery of tests with organisms belonging to different trophic levels and with a wide range of sensitivities [18]. Hence, the aim of the present study was to evaluate the toxicity of textile effluents (untreated and treated) by performing a battery of ecotoxicity tests with species from different trophic levels. The toxicity tests were performed with the bacteria _Aliiubrio fischeri_, the microalgae _Raphitdocelis subcapitata_, the macrophtyte _Lema minor_ and the cladoceran _Daphnia magna_. The parameters tested were bioluminescence (_A. fischeri_), survival (_D. magna_), feed rate (_D. magna_) and growth (_R. subcapitata_ and _L. minor_).
## 5 Simulations Setup and Results
### Simulation Results
#### 5.2.3 Time Complexity versus Input Files  
In this experiment, we measure the time complexity in seconds versus the size of input files in KB (please refer to Figure 4). We proceed from measurement to measurement by increasing the size of the input files by 4 KB. The order of complexity is depicted in terms of the big-O notation. The simulation results show a linear increase for LAPP, a logarithmic increase for SEPPA and PPA, and quadratic time complexity for PPPAS and SPS.
# Vegetation Classification in Urban Areas by Combining UAV-Based NDVI and Thermal Infrared Image
###### Abstract  
Vegetation has become very important decision-making information in promoting tasks such as urban regeneration, urban planning, environment, and landscaping. In the past, the vegetation index was calculated by combining images of various wavelength regions mainly acquired from the Landsat satellite's TM or ETM+ sensor. Recently, a technology using UAV-based multispectral images has been developed to obtain more rapid and precise vegetation information. NDVI is a method of calculating the vegetation index by combining the red and near-infrared bands, and is currently the most widely used. In this study, NDVI was calculated using UAV-based multispectral images to classify vegetation. However, among the areas analyzed using NDVI, there was a problem that areas coated with urethane, such as basketball courts and waterproof coating roofs, were classified as vegetation areas. In order to examine these problems, the reflectance of each land cover was investigated using the ASD FieldSpecet spectrometer. As a result of analyzing the spectrometer measurements, the NDVI values of basketball courts and waterproof coating roofs were similar to those of grass with slightly lower vegetation. To solve this problem, the temperature characteristics of the target site were analyzed using UAV-based thermal infrared images, and vegetation area was analyzed by combining the temperature information with NDVI. To evaluate the accuracy of the vegetation classification technology, 4409 verification points were selected, and kappa coefficients were analyzed for the method using only NDVI and the method using NDVI and thermal infrared images. Compared to the kappa coefficient of 0.830, which was analyzed by applying only NDVI, the kappa coefficient, which was analyzed by combining NDVI and thermal infrared images, was 0.934, which was higher. Therefore, it is very effective to apply a technology that classifies vegetation by combining NDVI and thermal infrared images in urban areas with many urethane-coated land cover such as basketball courts or waterproof coating roofs.  
vegetation index; UAV; multispectral image; thermal infrared image; spectrometer +
Footnote †: journal: Medical Imaging  
0000-0001-8896-2000][PERSON]  
0000-0002-313-5150][PERSON]  
0000-0002-0002-313-5150][PERSON]  
0000-0002-313-5150][PERSON]  
0000-0002-313-5150][PERSON]  
0000-0002-313-5150][PERSON]
# Business Process Configuration according to Data Dependency Specification
## 4 Conf-BP Framework: A Configurable System to Create Imperative BP
### Conf-BP Declarative Specification (ConfD-BP). A Formalization of the Language
#### 4.1.1 Sub-Process Description  
The _sub-process description_ includes the components associated with the activities, gateways, and control-flow which are known and can be represented in an imperative way. Taking \(\mathbb{A}\) as the finite set of activities {\(A_{1}\), \(\ldots\), \(A_{i}\), \(\ldots\), \(A_{n}\)} contained in a determined BP, the following definitions are introduced.  
**Definition 1**.: ACTIVITIES_DATA_INPUT(ADI) _and ACTIVITIES_DATA_OUTPUT(ADO) represent the sets containing, respectively, all the input data and output data involved in the execution of all the activities._  
Within the set \(\mathtt{ADI}\)/\(\mathtt{ADO}\), containing all the input (output) variables, it is possible to identify the input (output) variables of each activity, defined as follows.  
**Definition 2**.: DATA_INPUT \((A_{1})\) _and DATA_OUTPUT(\(A_{1}\)) describe the set of input and output data of an activity \((A_{1})\) involved in the sub-process, respectively, so that_  
\[\begin{array}{l}\forall A_{i},\mathtt{DATA\_INPUT}\ (A_{i})\subseteq\mathtt{ADI}, \text{where}\ \mathtt{ADI}\subset\mathbb{DF}\text{ and}\\ \forall A_{i},\mathtt{DATA\_OUTPUT}\ (A_{i})\subseteq\mathtt{ADO},\text{ where}\ \mathtt{ADO}\subset\mathbb{DF}\end{array}\]  
Since various activities can share the same data input (or output), then it is also possible that:  
\[\begin{array}{l}\mathtt{DATA\_INPUT}\ (A_{i})\cap\mathtt{DATA\_INPUT}\ (A_{j})\
eq\emptyset,\text{ for i}\
eq\text{j and}\\ \mathtt{DATA\_OUTPUT}\ (A_{i})\cap\mathtt{DATA\_OUTPUT}\ (A_{j})\
eq\emptyset,\text{ for i}\
eq\text{j} \end{array}\]  
The union of all the DATA_INPUT sets and all the DATA_OUTPUT sets of all the activities, constitute the sets \(\mathtt{ADI}\) and \(\mathtt{ADO}\) respectively, with non-repetitive elements.  
\[\begin{array}{l}\mathtt{ADI}=\{\mathtt{DATA\_INPUT}\ (A_{1})\cup\ldots\cup\mathtt{DATA\_INPUT} (A_{n})\}\\ \mathtt{ADO}=\{\mathtt{DATA\_OUTPUT}(A_{1})\cup\ldots\cup\mathtt{DATA\_OUTPUT}\ (A_{n})\} \end{array}\]  
Likewise, the specific variables that are inputs or outputs of the overall sub-process can be distinguished. They represent the information that flows from the customer to the sub-process and vice versa:  
**Definition 3**.: PROCESS_DATA_INPUT \((\mathtt{PDI})\) is the set of input variables of the sub-process, which determines the information provided and defined by the customer. \(\mathbb{PDI}\) is composed of a subset of variables of \(\mathtt{ADI}\).  
\[\mathbb{PDI}\subset\mathtt{ADI}\]  
**Definition 4**.: PROCESS_DATA_OUTPUT \((\mathtt{PDO})\) is the set of output variables of the sub-process. \(\mathbb{PDO}\) is composed of a subset of variables of \(\mathtt{ADO}\).  
\[\mathbb{PDO}\subset\mathtt{ADO}\]
# Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks
## 2 An Overview of The MPTCP Schedulers and Congestion Control Algorithms
### MPTCP Congestion Control Algorithms
#### 2.2.2 Opportunistic Linked Increase Algorithm  
While LIA provides a trade-off between optimal resource pooling and responsiveness, the opportunistic linked increase algorithm (OLIA) provides both simultaneously [35]. TheCWND decrease part of both LIA and OLIA are the same. OLIA adapts Kelly and Voice's algorithm to provide optimal resource pooling in the increase mechanism [36]. To ensure first responsiveness and non-flappiness, it measures the number of bytes transmitted since the last loss event and reacts to the loss event within the current window. However, OLIA becomes unresponsive to sudden network changes from time to time in a dynamically changing network scenario [37]. Moreover, as OLIA also bases its fairness mechanism on that of LIAs, OLIA also faces similar fairness issues like LIA [34].
## 4 Theoretical Optimization of Converter Inductor
### Application to the Three Sets of Inductors
#### 4.2.1 Losses in the Main Inductors  
Similar to Section 4.1.1, the losses in the main inductors have been calculated for each series and are presented in Figure 19 with respect to switching frequency. Purple curves represent the 47 uH values (as in Section 4.1). These curves show that due to the rising of _R\({}_{lr}\) and R\({}_{lr}\)_ with the frequency, there is a rise in the losses at low switching frequencies (left side of each curve). The losses for each inductors value will decrease with the frequency increase (leading to a reduction of the ripple current) down to losses due to _R\({}_{DC}\)_ These results are comparable to [18] as it shows the equilibrium between skin effect and losses due to current ripples. However, the interesting part of each value is where the loss into the inductor is lightly dependent on the Frequency-Ripple-Output inductor (F-R-Oi).  
Figure 18: Calculated total losses into the full-bridge converter considering all losses modelled in Section 3. These losses are considered for a 30 V to 20 V, 50 W converter equipped with 2 \(\times\) 47 uH main inductors.  
Decreasing the inductor value will decrease the losses at the same frequency if the losses due to \(R_{lr}+R_{lr}\) are not prevalent.
## 2 Materials and Methods
### Satellite Data
Four control points were selected to qualitatively inspect the water reflectance: water hyacinth (P1), near the water hyacinth (P2), at the mouth of the tributary river (P3), and at the center of the lake over clear waters (P4) (Figure 3a). The reflectance spectrum (Level-1C TOA, BOA Sen2 Cor, and BOA C2 RCC) was extracted using the tool \"pin manager\" and \"spectrum view\" in SNAP software. The Normalized Difference Vegetation Index (NDVI) was calculated in the QGIS software using the reflectance spectral bands (Level 2A BOA Sen2 Cor): band 4 (red) and band 8 (NIR), as follows:  
\[\text{NDVI}=[\text{band 8 $-$ band 4}]/[\text{band 8 $+$ band 4}]\]  
to investigate the seasonal variation in water hyacinth's biomass and cover. The water hyacinth cover was determined by NDVI values ranging from 0.6 to 0.95, using spectral reflectance image analysis, a true color composite overlaid for comparison, visual supervision, and field information. The average biomass, expressed in mean NDVI, and cover at \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Sensing Date** & **Mission** \\ \hline
1 & 17 January 2020 & Sentinel-2A \\
2 & 16 February 2020 & Sentinel-2A \\
3 & 22 March 2020 & Sentinel-2B \\
4 & 16 April 2020 & Sentinel-2A \\
5 & 11 May 2020 & Sentinel-2B \\
6 & 30 June 2020 & Sentinel-2B \\
7 & 10 July 2020 & Sentinel-2B \\
8 & 19 August 2020 & Sentinel-2B \\
9 & 18 September 2020 & Sentinel-2B \\
10 & 23 October 2020 & Sentinel-2A \\
11 & 12 November 2020 & Sentinel-2A \\
12 & 7 December 2020 & Sentinel-2B \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Acquisition date of the Sentinel-2A/B satellite images used during 2020 in Lake Tana. Acquisition time -07:50 am is referenced in Coordinated Universal Time (UTC). the northeastern part of Lake Tana in every image were measured using the QGIS \"Zonal statistics\" plugin. The water hyacinth biomass was multiplied by the total cover per image, and the total biomass cover change between images during 2020 was calculated.
## Current Trends in Wireless Mesh Sensor Networks:  
A Review of Competing Approaches  
[PERSON], [PERSON] *, [PERSON]  
and [PERSON]  
Department of Information and Communication Technologies, Technical University of Cartagena (UPT), Campus Muralla del Mar, Cartagena E-30202, Spain; E-Mails: [EMAIL_ADDRESS] (D.R.-H.);  
[EMAIL_ADDRESS] (F.G.-S.); [EMAIL_ADDRESS] (J.G.-H.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-968-326-538; Fax: +34-968-325-973.  
Received: 27 March 2013; in revised form: 30 April 2013 / Accepted: 2 May 2013 /  
_Published: 10 May 2013_  
**Abstract:** Finding a complete mesh-based solution for low-rate wireless personal area networks (LR-WPANs) is still an open issue. To cope with this concern, different competing approaches have emerged in the Wireless Mesh Sensor Networks (WMSNs) field in the last few years. They are usually supported by the IEEE 802.15.4 standard, the most commonly adopted LR-WPAN recommendation for point-to-point topologies. In this work, we review the most relevant and up-to-date WMSN solutions that extend the IEEE 802.15.4 standard to multi-hop mesh networks. To conduct this review, we start by identifying the most significant WMSN requirements (_i.e.,_ interoperability, robustness, scalability, mobility or energy-efficiency) that reveal the benefits and shortcomings of each proposal. Then, we re-examine thoroughly the group of proposals following different design guidelines which are usually considered by end-users and developers. Among all of the approaches reviewed, we highlight the IEEE 802.15.5 standard, a recent recommendation that, in its LR-WPAN version, fully satisfies the greatest number of WMSN requirements. As a result, IEEE 802.15.5 can be an appropriate solution for a wide-range of applications, unlike the majority of the remaining solutions reviewed, which are usually designed to solve particular problems, for instance in the home, building and industrial sectors. In this sense, a description of IEEE 802.15.5 is also included, paying special attention to its efficient energy-saving mechanisms. Finally, possible improvements of this recommendation are pointed out in order to offer hints for future research.  
keywords: wireless mesh sensor networks; mesh solutions and review; IEEE 802.15.4; IEEE 802.15.5
# A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners
## 2 Materials and Methods
### Processing and Evaluation of the Acquired Data
#### 2.6.3 The Overall Accuracy of Individual Clouds After the Initial Transformation  
The overall root mean square error between the test and the reference clouds (RMSE\({}_{\text{OVERALL}}\)) was determined as follows: For each point of the test cloud, an irregular triangular area network (TIN) was constructed from the 15 nearest points of the reference cloud, and the distance of the point relative to this TIN surface was determined.
# Development and Evaluation of a Fermented Pistachio-Based Beverage Obtained by Colloidal Mill
## 3 Results and Discussion
### Microbiological Analysis, pH, and Acid and Sugar Content of the Fermented Pistachio-Based Beverage  
The fresh pistachio-based beverages were then inoculated with six strains of lactic acid bacteria (_Leuc. pseudomesenteroides_ PD4, _L. plantarum_ PT1, _C. kinchi_ PU2, _L. plantarum_ PV2, _C. alimentarius_ PG3, _L. paraplantarum_ PN4) previously selected according to different technological characteristics as reported by [PERSON] et al. (2023) [12].  
Figure 2 shows the microbial counts at time zero (inoculation), after 24 h fermentation at 28 \({}^{\circ}\)C (1 day), and during storage of the beverage at 4 \({}^{\circ}\)C for 30 days.  
The selected LAB were inoculated at approximately 6 log cfu/mL and reached high counts after 24 h of fermentation (1 day) at 28 \({}^{\circ}\)C, with values ranging from 8.5 log cfu/mL (_C. alimentarius_ G3) to 10 log cfu/mL (_L. plantarum_ T1), demonstrating that pistachio is a good growth source for LAB.  
The pistachio-based beverage at time zero had a pH value of 6.39 \(\pm\) 0.09, and lactic and acetic acids and ethanol were not detected. The concentrations of sucrose, glucose, and fructose were 1.55 \(\pm\) 0.08, 0.26 \(\pm\) 0.02, and 0.19 \(\pm\) 0.08 (g/L), respectively. The characteristics of the pistachio-based beverage after fermentation and after 30 days of storage at 4 \({}^{\circ}\)C are shown in Table 4.  
After 24 h of fermentation, all beverages showed strong acidification, reaching pH values between 3.84 and 3.92 (PV2 and PT1, respectively). The production of lactic acid was between 7.58 g/L and 10.94 (PT1 and PN4, respectively). Acetic acid values were below 0.5 g/L in all the samples. A low amount of ethanol was found only in the sample PD4  
Figure 2: Microbial count of lactic acid bacteria in fermented pistachio-based beverages at inoculum (time zero), after 24 h fermentation (1 day), and after 5, 12, 21, and 30 days of storage at 4 \({}^{\circ}\)C. The labels PD4, PG3, PN4, PT1, PU2, PV2 are fermented pistachio-based beverages inoculated respectively with _Leuc. pseudomesenteroides_ PD4, _C. alimentarius_ PG3, _L. paraplantarum_ PN4, _L. plantarum_ PT1, _C. kinchi_ PU2, _L. plantarum_ PV2.  
(0.2 g/L). After 30 days of storage at 4 \({}^{\circ}\)C, the pH values remained more or less similar and lactic acid values remained roughly similar for PU2 and PV2 and slightly increased for PD4, PG3, PN4, and PT1. Acetic acid also increased very little in some samples after storage.  
Glucose and sucrose were almost completely utilized by all the strains in the beverages after 24 h of fermentation. However, in regard to fructose, after 24 h only PG3 and PT1 strains showed a significant reduction. After 30 days of storage, all the sugars analyzed were not detected. Thus, despite cold storage, the strains continued to have fermentation activity, albeit very slowly.
# A Multi-Granularity Word Fusion Method for Chinese NER
## 1 Introduction
Some models introduce multi-granularity word information (3; 5; 6), and yet they reduce word segmentation errors and noise through model training, which is implicit. We hope to deal with them in an explicit way. To address the above problems, we propose a Multi-granularity Word fusion method for Chinese NER (short for MW-NER). We integrate character information and multi-granularity word information into our model, which is to enrich the semantics of sentences. To explicitly deal with word segmentation errors and noise, we design the strong-weak feedback attention mechanism. Specifically, according to semantic information, we firstly classify information into three kinds-strong information, moderate information, and weak information. Since the character information has clear semantics and a fixed size, it is strong. The word information has some errors and less clear semantic information, so it is moderate. The phrase information is weak since it contains rich semantics and various size, and also contains many potential errors. In our model, we use N-grams to represent phrase information. These embeddings of the three kinds of information are generated by the language model. Next, strong information is injected in the strong-weak feedback attention mechanism. The strong-weak feedback attention mechanism integrates strong information into moderate and weak information, and then feeds the moderate and weak information back to strong information. The rich semantic interaction could reduce word segmentation errors and noise in an explicit way. Experimental results show that our method outperforms most baselines and achieves the state-of-the-art methods in performance. (themethod's code is accessible on [[https://github.com/gaojianchina/mwner](https://github.com/gaojianchina/mwner)]([https://github.com/gaojianchina/mwner](https://github.com/gaojianchina/mwner)) (accessed on 8 February 2023))). Our contributions of this paper can be concluded as follows:  
* We introduce multi-granularity word information into the model, and use the strong-weak feedback attention mechanism to integrate this information, which could obtain rich semantic information and reduce word segmentation errors and noise. To make it clear, we classify the multi-granularity word information to strong, moderate and weak information. * We use the language model to generate character embeddings, word embeddings, and N-grams embeddings, which follows the closure property of the embedding space. * We conducted numerous experiments, including comparing the effects of various tokenizers, the effects of two fusion strategies, and an ablation study. Finally, we present the experimental results on four Chinese NER benchmark datasets. Experiments show our proposed method outperforms other methods and achieves the performance of the state-of-the-art methods over Chinese NER benchmark datasets.