esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of an LED Strip Controller Based on a PWM Driver and a PIC Series Microcontroller
###### Abstract  
This research paper investigates an LED strip lighting system, whose LED lighting controller was developed based on the PIC24 FV32 KA302 microcontroller and PCA9685 PWM driver. During the study, various parameters of the system were evaluated, including the response time from the length of commands, the dependence of the PWM signal characteristics on the logic values generated by the microcontroller, and the dependence of the LED light flux and power on the percentage of the PWM signal and the power of the power supply channels. The results of the study revealed that the reaction time depended on the length of the sent command, where as the logic values of the microcontroller were changed from 1000 to 4000, the size of the PWM signal changed from 25 to 100%. The use of the IZC communication protocol, which is a master-slave architecture and uses data and synchronization lines, was also found to affect response times. When the percentage of the PWM signal was changed from 10 to 100%, the light flux of the LED strip changed from 100 to 1000 lm. These results reflect the advantages of applying microcontrollers and PWM drivers to LED control systems, emphasizing their flexibility, efficiency, and precise light control.  
LED controller; PWM modulation; microcontroller; LED strips; lighting control Article
# Optimizing Crop Yield and Reducing Energy Consumption in Greenhouse Control Using PSO-MPC Algorithm
## 3 Methodology  
An overview of the method is presented in Figure 1, which illustrates the three key components of the approach: the greenhouse climatic model, the biophysical model, and the particle swarm optimization algorithm. The greenhouse climatic model is the first component of the proposed approach. It is a model that simulates the climate conditions inside the greenhouse. This model takes into account various factors that affect the greenhouse environment, such as temperature, humidity, lighting, and air circulation. The greenhouse climate model provides a detailed understanding of climatic conditions inside the greenhouse, which is essential for optimizing the growing conditions for crops. The second component of the proposed approach is the biophysical model. This model simulates the growth of plants in the greenhouse. It takes into account various factors that affect plant growth, such as light intensity, temperature, humidity, and nutrient availability. The biophysical model provides a detailed understanding of how the plants are growing in the current conditions of the greenhouse. The third component of the proposed approach is the particle swarm optimization algorithm, which is used to optimize the control parameters in the greenhouse on the basis of both the greenhouse climatic model and the biophysical model. The algorithm uses a population of particles that move through the search space to find the optimal controllable parameters solution, to reduce the consumed energy while maximizing the crop yield.
# Efficient Retrieval of Music Recordings Using Graph-Based Index Structures
## 4 Experiments
### Retrieval Time
We observe that the runtime increases more than linearly with dimensionality \(K\) for the \(K\)-d tree. In contrast, the runtime for the HNSW graph increases only slightly with increasing dimensionality \(K\). Note the different scales of the vertical axes, which again underline the substantial search time improvements caused by the HNSW graph. We see that the HNSW approach requires nearly the same time for searching 1, 10, or 100 database items (resulting in overlapping curves). The reason for this is the parameter setting \(\
u^{\prime}=100\) (number of intermediate neighbor candidates, described in Section 3.2), which leads us to search internally for 100 neighbors anyway. To summarize, we can conclude that the dimensionality reduction approach (PCA or DNN) has only a minor influence on the runtime, which is expected. The dimensionality \(K\) of the index items has a substantial impact on the runtime. Still, the indexing approach (KD or HNSW) has the most important effect on the runtime. Our experiments show that, compared to \(K\)-d trees, the HNSW index is much faster and more stable concerning the dimensionality and the number of items to be indexed. This increase in retrieval efficiency comes without substantial loss in retrieval quality (as shown in Section 4.2), which makes the HNSW graph a powerful tool for music retrieval. Figure 5: Search runtimes (in ms) for the DNN- and PCA-based embedding approaches. Note the different scale of the vertical axes. The solid lines show the mean and the light areas show \(\pm\) the standard deviation around the mean for the repetitions of our experiment. Search using **(a)** the KD strategy and \(\mathbb{D}_{\text{sm}}\), **(b)** the HNSW strategy and \(\mathbb{D}_{\text{sm}}\), **(c)** the KD strategy and \(\mathbb{D}_{\text{lg}}\), and **(d)** the HNSW strategy and \(\mathbb{D}_{\text{lg}}\).
## 2 Materials and Methods
### Software and Measurement Equipment  
Measurements of spectral irradiance were taken with a _JETI Specbos 1201_ spectroradiometer (_JETI Technische Instrumente GmbH_, Jena, Germany) and analyzed through _JETI LiVal V6.14.2_ software on a connected personal computer. Spectral calculations were performed by the software, i.e., for CCT, color rendering index (CRI),otopic illuminance, and MEDI [24]. If not stated otherwise, MEDI values refer to a standard measurement with an unobstructed sensor (180\({}^{\circ}\)). MEDI values denoted as _with FOV_ in the Results Section stem from measurements with a field-of-view obstruction according to the standard _CIE S 026_[24]. Luminance distribution measurements were taken with an _LMK mobile air_ (Canon 70D camera with a 180\({}^{\circ}\) fisheye lens, calibrated by _TechnoTeam Bildverarbeitung GmbH_, Ilmenau, Germany). Pictures were analyzed with _LMK LabSoft_ software. Figures containing spectral measurements or derived parameters were produced with _R V4.1.0_ software [25]. Lighting simulations were performed with _RELUX V2019.1.1.0_ software (_Relux Informatik AG_, Muenchenstein, Switzerland). Numeric results were analyzed with _Microsoft Office Excel 2019_ software.
# Adaptive Feature Fusion for Small Object Detection
## 2 Materials and Methods
### Materials
#### 2.1.2 Feature Fusion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Chronological** & **Algorithm** & **Backbone** & **Method** \\ \hline
2015 & Faster R-CNN [3] & ResNet-50 & It improves the fully connected layer and implements the stitching of multi-task loss functions. \\
2016 & YOLOv1 [5] & GoogLeNet & It takes the entire graph as input to the network, regressing the location and category of the BBox directly at the output layer. \\  & & & The use of multi-feature mapping can be comparable to \\
2016 & SSD [6] & VGG16 & FasterRCNN in some scenarios, and the network optimization is simple. \\
2017 & Mask R-CNN [4] & VGG16/Resnet & It redesigned the backbone network structure and replaced RoI \\
2017 & YOLOv2 [7] & Darknet19 & It proposes a joint training method of object detection and classification using a new multi-scale training method. \\
2018 & YOLOv3 [8] & Darknet53 & It proposes multi-scale predictions. \\
2019 & Trident [31] & ResNet & It designed the Trident parallel network to increase the width of the network and proposed the concept of parallel network. \\
2019 & M2 Det [33] & VGG-16/ & It proposed a multi-layer feature pyramid network. \\
2020 & EfficientFet [34] & MobileNet & It proposed a two-way pyramid network, BiFPN. \\
2020 & YOLOv4 [37] & CSPDarkent & It adopted various training skills and improved the loss function and maximum suppression method. \\
2021 & EFPN [28] & Feature texture transfer (FTT) and foreground-background balance loss functions were designed to mitigate the area imbalance of the foreground and background. \\
2021 & TPH-YOLOv5 [9] & CSPDarknet & It integrated transformer prediction heads (TPH) and CBAM into YOLOv5. \\
2021 & YOLOX [38] & CSPDarknet & It decoupled the detection head and used dynamic \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Introduction to the mainstream algorithms of deep learning small object detection.
# Design Methodology and Application Dynamics of Compact Quasi-Zero Stiffness Isolators
## 3 Compact Design of QZS Isolation
### Magnetic Type
#### 3.2.2 Electromagnetic Coil-Based Magnetic Springs  
Figure 9 illustrates four types of QZS isolators with solenoid coil magnetic springs. [PERSON] et al. [56] made the structure more compact by nesting a toroidal permanent magnet in the electromagnetic coil winding. Also using a permanent magnet nested in the solenoid winding structure, [PERSON] et al. [57] developed a magnetic negative stiffness isolator based on Maxwell's normal stresses, which can produce a higher magnetic negative stiffness compared to a magnetic negative stiffness isolator with the same size and weight. Similarly, [PERSON] et al. [58] design a new magnetic circuit to greatly improve the adjustable range and capacity utilization of negative stiffness springs. However, the difference is that [PERSON] discarded the permanent magnet element to avoid the disadvantage that permanent magnets would demagnetize and be fragile at high temperatures. [PERSON] et al. [59] proposed a new compact, non-contact, and negative stiffness adjustable multi-layer electromagnetic spring, which can adjust the negative stiffness by controlling the current, and the 6-layer electromagnetic spring can generate variable stiffness from \(-9800\) N/m to \(9800\) N/m with only \(1\times 10^{-4}\) m\({}^{3}\) of compact volume.  
To sum up, the fundamental compact design of the magnetic structure lies in how to improve the magnetic intensity density, and the current research mostly focuses on exploring the two directions of the new permanent magnet array arrangement and magnetic circuit optimization. However, the magnetic structure is prone to unstable magnetic field strength, which leads to the failure of positive and negative stiffness matching and the decrease in vibration isolation performance and even may play a negative role. Therefore, the introduction of active control strategies to realize precise regulation is a key research direction for the follow-up.  
Figure 9: QZS isolator with solenoid Coil Magnetic Spring: (**a**) Electromagnetic QZS model [56]; (**b**) Magnetic negative stiffness configuration based on Maxwell’s magnetic normal stress [57]; (**c**) Adjustable negative stiffness spring consisting of a stator, a mover, and coils using [PERSON]’s normal stresses [58]; (**d**) Multilayer electromagnetic spring model [59].
# Inorganic Salt Hydrate for Thermal Energy Storage
## 5 Application
### Salt Hydrates for Air Conditioning System  
Apart from applications in freezers, eutectic salt hydrates are also popularly applied to air condition system due to their similar temperature with the evaporation of the mainly refrigeration host and high density compared with water. There are following characteristics of air conditioning cold energy storage technologies with eutectic salts.  
1. Transfer the electricity peak and time of power consumption
2. Capacity and power of refrigeration equipment can be reduced by 30-50%
3. Improve efficiency of operation and utilization  
Figure 6[90] showed several air conditioning system by various media with chilled water, ice and PCMs. Nanoparticles like TiO2 and Al2O3[88; 89; 90; 91] are often added into PCMs to increase melting rate and reduce problems of refrigerating mediums such as environmental pollution, low thermal conductivity, large size and high installing and running cost. [PERSON] et al. [92] found that LiCl/H2O and NH3/H2O pairs have good performance among several absorbent/refrigerant pairs, which can be used for air conditioning application and subzero applications, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Salt Solution** & **Melting Temperature (\({}^{\circ}\)C)** & **Heat of Fusion (J/g)** & **Reference** \\ \hline
30.5\% Al(NO3)3/H2O & \(-\)30.6 & 131.5 & [88; 89] \\
32.3\% NH4F/H2O & \(-\)28.1 & 199.1 & [88] \\
21.5\% KF/H2O & \(-\)21.6 & 225.2 & [88] \\
22.4\% NaCl/H2O & \(-\)21.2 & 222 & [89] \\
21.5\% NH4 Cl/H2O & \(-\)16 & 289 & [89] \\
39.7\% (NH4)SO4/H2O & \(-\)18.5 & 269 & [88] \\
36.8\% K2 HPO4/H2O & \(-\)13.5 & 189 & [88] \\
22.1\% BaCl2/H2O & \(-\)7.7 & 102 & [89] \\
27.2\% ZnSO4/H2O & \(-\)6.5 & 208 & [89] \\
18.63\% MgSO4/H2O & \(-\)4.8 & 84.96 & [89] \\
3.9\% NaF/H2O & \(-\)3.5 & 309.2 & [89] \\
5.9\% Na2 CO3/H2O & \(-\)2.1 & 281 & [88] \\
6.49\% K2 SO4/H2O & \(-\)1.55 & 268.8 & [89] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Melting temperatures and heat of fusion of salt solutions for refrigeration systems.
## 4 System Performance Analysis
### System Bit Error Rate Analysis
The bit error rate for low-priority data from source node \(S_{i}\) to relay node \(R\) can be expressed as:  
\[P\Big{\{}\varepsilon_{S_{i}R}^{S_{i}}|\lambda\,\Big{\}}=\frac{1}{2}\overline{ P}_{e}(a_{1},b_{1};\lambda,M,\overline{\gamma})-\frac{1}{4}\overline{P}_{e}(a_ {2},b_{2};\lambda,M,\overline{\gamma})+\frac{1}{4}\overline{P}_{e}(a_{3},b_{3} ;\lambda,M,\overline{\gamma}) \tag{22}\]  
Thereinto \(a_{1}=0\), \(b_{1}=1\); \(a_{2}=2\), \(b_{2}=3\); and \(a_{3}=2\), \(b_{3}=1\). We can set \(\mu=\overline{\gamma}_{ij}/\overline{\gamma}_{S_{i}R}\) to the channel asymmetric parameter between the adjacent source nodes \(S_{i}\) through \(S_{j}\) and the source node \(S_{i}\) to the relay node \(R\) link, thereinto \(i,j\in\{1,2,3,4\}\). By bringing in Equations (21) and (22) for parameters \(a\) and \(b\) and introducing asymmetric parameters of the channel, you can get high and low priority data about the bit error rate expressions for asymmetric parameters:  
\[P\Big{\{}\varepsilon_{S_{i}R}^{S_{i}}|\lambda,\mu\,\Big{\}}=\frac{1}{4}\Big{(} \overline{P}_{e}\Big{(}1,0;\lambda,M,\mu\overline{\gamma}_{S_{i}R}\Big{)}+ \overline{P}_{e}\Big{(}1,2;\lambda,M,\mu\overline{\gamma}_{S_{i}R}\Big{)} \Big{)} \tag{23}\]  
Figure 3: 4/16-QAM modulated in-phase subflow. \[P\Big{\{}\varepsilon_{S_{i}R}^{S_{i}}|\lambda,\mu\Big{\}}=\frac{1}{2}\overline{P}_{e }\Big{(}0,1;\lambda,M,\mu\overline{\gamma}_{S_{i}R}\Big{)}-\frac{1}{4}\overline{ P}_{e}\Big{(}2,3;\lambda,M,\mu\overline{\gamma}_{S_{i}R}\Big{)}+\frac{1}{4} \overline{P}_{e}\Big{(}2,1;\lambda,M,\mu\overline{\gamma}_{S_{i}R}\Big{)} \tag{24}\]  
where \(\overline{\gamma}_{S_{i}R}\) is the average signal-to-noise ratio from source node \(S_{i}\) to relay node \(R\). The total bit error rate of a single source node can be defined as the probability that the node will not be able to successfully receive information transmitted by other nodes. Taking source node \(S_{1}\) as an example, the bit error rate expression from other source nodes to source node \(S_{1}\) is:  
\[P_{S_{2}S_{1}}=\frac{1}{2}P\Big{\{}\varepsilon_{S_{2}S_{1}}^{S_{ 2}}|\lambda,\mu\Big{\}}+ \tag{25}\] \[P_{S_{4}S_{1}}=\frac{1}{2}P\Big{\{}\varepsilon_{S_{4}S_{1}}^{S_{ 2}}|\lambda,\mu\Big{\}}+\] (26) \[P_{S_{3}S_{1}}=1-\frac{1}{2}\Bigg{[}\begin{array}{c}\Big{(}1-P \Big{\{}\varepsilon_{S_{1}R}^{S_{1}}|\lambda\Big{\}}\Big{)}\Big{(}1-P\Big{\{} \varepsilon_{S_{3}R}^{S_{3}}|\lambda\Big{\}}\Big{)}\Big{(}1-P\Big{\{} \varepsilon_{RS_{1}}^{J_{h}}|\lambda\Big{\}}\Big{)}\Big{(}1-P\Big{\{} \varepsilon_{RS_{1}}^{J_{h}}|\lambda\Big{\}}\Big{)}+\\ \Big{(}1-P\Big{\{}\varepsilon_{S_{3}R}^{S_{3}}|\lambda\Big{\}}\Big{(}1-P\Big{\{} \varepsilon_{S_{1}R}^{S_{1}}|\lambda\Big{\}}\Big{)}\Big{(}1-P\Big{\{} \varepsilon_{RS_{1}}^{J_{h}}|\lambda\Big{\}}\Big{)}\Big{)}\Big{(}1-P\Big{\{} \varepsilon_{RS_{1}}^{J_{h}}|\lambda\Big{\}}\Big{)}\Bigg{]} \tag{27}\]  
The total bit error rate of source node \(S_{1}\) can be expressed as:  
\[P(\lambda,\mu)=1-\big{(}1-P_{S_{2}S_{1}}\big{)}\big{(}1-P_{S_{3}S_{1}}\big{)} \big{(}1-P_{S_{4}S_{1}}\big{)} \tag{28}\]
## 5 Policy-based Adaptive QoS Management: CNQF Runtime Validation
### Validation Tests  
In order to validate the CNQF prototype for adaptive policy-based QoS management we performed some tests on the Linux-based testbed depicted in Figure 2 where the prototype was deployed. High level policies were entered into the policy editing tool and the effects on the underlying network traffic were observed. The behavior of selected traffic flows under observation indicated whether the adaptive policies were successfully applied or not. First, the initial static configuration policies were applied via the GUI policy tool. This set of policies was intended to configure the network initially with DiffServ capabilities. Next, another set of policies for runtime dynamic reconfiguration to govern QoS control of selected flows in response to network measurements were entered. These allowed us to validate the CNQF prototype capability for DiffServ configuration as well as adaptive QoS management capability of the implementation.
# Nano-/Microcapsules, Liposomes, and Micelles in
## 2 Spherical Nanoparticles
### Nanocapsules  
#### 2.1.1 Design, Shape, and Size  
The process of encapsulation entails enclosing or coating the active substance (core) with a carrier material (such as wall material, coating, external phase, or membrane) in order to generate capsules or particles at either a macrometric or nanometric scale. The encapsulation process produces macrocapsules (measuring more than 1000 \(\upmu\)m), microcapsules (measuring 1-1000 \(\upmu\)m), and nanocapsules (measuring less than 1 \(\upmu\)m), which have a profound impact on enhancing product effectiveness [53]. Nanoencapsulation has become increasingly popular in recent times as a result of nanocapsules' distinct characteristics, including enhanced bioavailability, high encapsulation efficiency, and loading capacity (due to their larger surface area), as well as their sustained release profile and ability to  
Figure 1: Schematic diagram of the structure of spherical nanoparticles.  
mask undesirable flavours. Additionally, nanocapsules offer improved stability, reduced particle size, and a narrower particle distribution [54]. The shape and size of the capsules are determined by the methodology and the polysaccharide properties. For instance, the nanocapsules resulting from spray drying exhibit diverse diameters and spherical forms. Moreover, they possess a concave structure due to speedy moisture loss following cooling [55].  
Nanocapsule core-shell structures can be categorized into inorganic/inorganic, organic/organic, inorganic/organic, and organic/inorganic based on the type of material and coating. The properties of core-shell microparticles, including morphology, size, and structure, greatly influence their application in the food industry [56].
# Isolation and Characterization of a Novel Cyanophage Encoding Multiple Auxiliary Metabolic Genes
## 2 Materials and Methods
### Genomic Analysis  
One library with insert size of approximately 300 bp was constructed and sequenced on the Illumina Hiseq4000 platform using HiSeq PE Cluster Kit v4 cBot (Illumina, San Diego, CA, USA). Raw reads were trimmed using Trimmomatic v.0.36 with custom settings (ILLUMiNACLIP: TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:40) [29]. Then, the reads were assembled using IDBA-UD v.1.1.3 with a list of sequential k-mer sizes from 51 to 87 with a 4-kmer step [30]. A binning approach developed by [PERSON] et al. was modified to cluster assembled sequences and to remove cellular DNA sequences that might be derived from bacterial hosts. Sequencing reads associated with non-bacterial bins were extracted using Perl scripts for subsequent reassembly [31]. BBMap ([[https://sourceforge.net/projects/bbmap/](https://sourceforge.net/projects/bbmap/)]([https://sourceforge.net/projects/bbmap/](https://sourceforge.net/projects/bbmap/)), accessed on 24 July 2018) with the command of bbmorm.sh was used to reduce the sequencing coverage of reads to 100\(\times\). Then the normalized reads were corrected and reassembled using SPAdes with a customized kmer list (33; 55; 77; 99) [32]. Finally, only one scaffold >1 kb of the reassembled draft genome was retained. A termini analysis, using a CLC Genomics Workbench (version 3.6.1) with default parameters, was used to identify the phage's genome termini and genome packaging with high-throughput sequencing data [33]. The open reading frames (ORFs) were predicted by the online server GeneMarks (version 4.25), and the tRNA sequences were identified by tRNAscan-SE v.2.0 [34]. Translated ORFs were annotated through a BLASTP search against the National Center for Biotechnology Information (NCBI) database with \(e\)-values \(\leq 10^{-5}\). Genome mapping was visualized based on the genome annotations using Java Operon. For phage taxonomy, vConTACT2 v0.9.19 was employed to compare cyanophage S-SZBM1 gene contents against the ProkaryoticViralRefSeq99 (v99) database with default parameters, and the phages predicted to be related with cyanophage S-SZBM1 were determined by similarity score > 1 [35]. Complete amino acid profiles of cyanophage S-SZBM1 and its related phages were submitted to the virus classification and tree building online resource (VICTOR) ([[https://ggdc.dsmz.de/victor.php](https://ggdc.dsmz.de/victor.php)]([https://ggdc.dsmz.de/victor.php](https://ggdc.dsmz.de/victor.php)), accessed on 14 July 2021) for phylogenetic analysis, and the recommended settings of genome BLAST distance phylogeny (GBDP) method were used [36]. In addition, intergenomic nucleotide sequence similarity and aligned genome fractions within the imported phages were plotted with Virus Intergenomic Distance Calculator (VIRIDIC) under recommended configuration [37]. The complete genome sequence of cyanophage S-SZBM1 had been submitted to the GenBank database under the accession number OL473597.