esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Methods
### New Channel Access Mechanism
3:Listen to the channel;
4:if (Beacon not received) then
5: Wait for beacon;
6:else
7:\(CW_{i}=CW_{MAX}*\frac{B_{iMax}-B_{i,nww}}{B_{i,Max}}\);
8:\(R_{bck}=random(0,\ CW_{i})\);
9:if\((R_{time}\leq R_{bck}+T_{cca}+T_{pkt})\)then
10:\(NB=NB+1\);
11:if\((NB>NB_{max})\)then
12: Stop due to channel access failure;
13:else
14: go to step 1;
15:endif
16:else
17: Access channel after \(R_{bck}\) duration;
18:if (channel busy) then
19: Switch to power saving mode for \(random(0,T_{pkt})\) duration;
20:\(NB=NB+1\);
21:if\((NB>NB_{max})\)then
22: Stop due to channel access failure;
23:else
24: go to step 5;
25:endif
26:else
27: Start transmission and wait for ACK;
28:if (ACK is not received) then
29:\(R_{tms}=R_{tms}+1\);
30:if\(R_{tms}\leq MAX_{R_{tms}}\)then
31: Wait for next retransmission period and go to step 5;
32:else
33: Stop due to transmission failure;
34:endif
35:endif
36:endif
```  
**Algorithm 1** The modified CSMA/CA mechanism  
Our proposed channel access mechanism uses only one CCA and gives priority to the devices based on buffer status for which energy consumption and packet drop rate decreases significantly in the network. Again, only the collided devices can contend during the subsequent retransmission periods in different channels, which reduces the contention collision and interferences for which the reliability is increased significantly.
### Plastoquinone Synthesis  
The synthesis of the benzoquinone ring for all isoprenoids synthesized in chloroplasts, as described above, originates from L-Tyrosine (Figure 2). The resulting L-Tyrosine is converted by 4-hydroxyphenylpyruvate dioxygenase (HPPD) to homogenistate acid (HGA) via 4-hydroxyphenylpyruvate acid (HPPA). It is HGA that is the benzene quinone ring precursor for PQ and tocopheroids in plants. This is an important step in the synthesis of plastid tocopheroids in plants. The second key component in PQ synthesis, solanesyl diphosphate, is synthesized from geranylgeranyl diphosphate (GGDP) and IPP by a reaction catalyzed by solanesyl diphosphate synthase (SPS). This is one of the most important regulatory enzymes in PQ synthesis, since under exposure of Arabidopsis plants to increased light intensity, an accumulation of PQ was observed accompanied by the increase of transcript levels of the genes encoding the enzymes of plastoquinone biosynthesis and, first of all, the gene encoding SPS1 [48]. Condensation of HGA ring and side chain of solanesyl diphosphate is the first direct step in PQ synthesis via a methyl-solanesyl-benzoquinone intermediate. The formation of PQ and its product plastochromanol-8 (hereafter referred to as plastochromanol) occurs by tocopherol cyclase enzymes ( VTE3 and VTE1).
# Augmented and Virtual Reality Evolution and Future Tendency
## 3 Results
### Manuscript Publication Evolution
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Year** & **AR Number of Publications** & **Variation** & **VR Number of Publications** & **Variation** \\ \hline
2000 & 2300 & - & 14,100 & - \\
2001 &  
Summarizing this first study, after year 2013, both technologies start a decay stage until 2019, when both almost disappear from Gartner's hype cycle. However, AR is getting a new life as an \"AR cloud\", which is a new paradigm that uses cloud technologies with augmented reality works, and it is important to get focus on that too. With this information, we should not expect that virtual reality will increase its importance in the next coming years; however, AR tendency describes a fresh start with the inclusion of cloud technologies, so we should expect an important increase in the works and references in this field in the next years. The numerical data about the total number of publications in those years (not only the papers selected for this work) is shown in Table 2.  
The data presented in Table 2 is represented graphically in Figure 3. In both representations, it is important to observe the tendency detected in Gartner's hype cycle for AR and VR: the works related to both technologies are decreasing in the last two years. In Gartner's hype cycle, its decay phase started in 2013-2014 and its consequences are observed in the works and citations four years later. Because of this relation, there is a real possibility that, in the next 3-4 years, there will be an increase in the number of works and publications related to augmented reality due to the increased importance of AR due to the cloud technologies. On the other hand, these technologies are very close to the game industry, so it is important to focus the attention in this sector and study if the same evolution can be observed in this field. To do that, a deep searching process is done using the most famous platforms and devices (like Steam or Oculus Rift), looking for games that use one of them at least. The only way to look for that is using the Steam platform because other platforms do not allow to sort by year or to look for certain keywords. The evolution of publications in the game industry obtained is shown in Figure 5.  
Figure 4: Evolution of augmented reality (AR) and virtual reality (VR) technologies in the hype cycle. Figure 5: Evolution of games published in Steam. As can be seen, the developed games tendency (see Figure 5) is similar to the manuscript publications in virtual reality (see Figure 3). Game development reaches a peak in 2017 and then starts to decrease. Because of the improvement of virtual reality HDM, it is possible that this tendency will change and become stable in a few years. To do this study in AR, the same problems are observed: Google Play and Oculus do not allow to sort information by year. Only some sporadic works in this area can be observed (like Pokemon Go), but too few works are obtained. Therefore, no tendency can be extracted from this information.
## 4 Experiments and Discussion
### Experimental Setup
#### 4.1.2 Evaluation Measure  
As we all know, one of the important characteristics of OSNs is the community structure [36]. Nodes in the same community are closely connected, interact frequently, and exchange information smoothly [37]. If the information diffusion model has a more realistic view of propagating information, it should have superior community detection accuracy [31]. Suppose that a person publishes a message on the OSN. As people in the same community have close relationships and higher levels of trust, they may tend to demonstrate a higher probability of receiving information after the propagating process. In this way, a message is given to different seed nodes randomly. Information is spread through the network according to the given information diffusion model. Each time a node accepts information from a neighbor node, the weight of the corresponding edge increases by one. When the process of information spread is over, we can obtain the weights of these edges. After that, these edges can be used to form a weighted network. This weighted network is used as the input of the community detection method. Note that the SLPA [38] algorithm is used for community detection in our experiments. If an information diffusion model can find better communities, then it would be a better information propagation model for OSNs.  
In order to evaluate the communities obtained, we use modularity [31] to measure the accuracy of the communities found in the experiment. This measure is the most popular qualitative measure for detecting communities in social network analysis. The higher the modularity is, the finer the discovered communities are. The higher the degree of modularity of the community detection results, the more accurate the community division is. The more accurate the community division, the closer the diffusion process simulated by the model is to reality. The definition of modularity follows:  
\[Q(P)=\sum_{s=1}^{k}\left[\frac{l_{s}}{L}-(\frac{d_{s}}{2L})^{2}\right] \tag{12}\]  
where \(P\) is the found partition, \(k\) refers to the number of communities, \(L\) is the total number of edges in the social network; \(l_{s}\) and \(d_{s}\) are the number of edges and degrees in community \(s\) according to \(P\), respectively.
## 2 Study Area and Datasets  
### Study Area  
As the largest freshwater lake in China, Poyang Lake is also one of the most important wetlands in the world, which has been recognized by the International Union for Conservation of Nature [37]. Five main tributaries (Ganjiang, Fuhe, Xiushui, Xinjiang, and Raohe) flow into Poyang Lake and then discharge into the Yangtze River from south to north, owing to a higher elevation in the south. Poyang Lake ungauged zone consists of the ungauged watersheds between seven stream flow gauging stations (Qiuin, Wanjabu, Waizhou, Lijiadu, Meigang, Hushan and Dufengkeng) of the five tributaries and the boundary of Poyang Lake, as illustrated in Figure 1a. The detailed method of drawing the watersheds in the ungauged zone is presented in [38]. SAR images have been repeatedly collected over Poyang Lake by Sentinel-1 sensors, and the overlapping area of these images acquired at different times is chosen as the study area (Figure 1b), which covers a majority of the ungauged zone. The region is characterized by a subtropical monsoon climate with wet season from April to September, and dry season from October to March [39]. The annual average temperature is 17.5 \({}^{\circ}\)C, and the annual average accumulative precipitation is approximately 1680 mm [40]. Due to the seasonality of precipitation and other meteorological conditions, Poyang Lake's inundation area varies significantly from \(>\)3100 km\({}^{2}\) during the wet season to \(<\)750 km\({}^{2}\) during the dry season [39; 41]. While the peripheral areas are mountainous, covered largely by dense forests, the alluvial plains surrounding the lake are mainly used as cultivated cropland. Floods and droughs have occurred frequently around the Poyang Lake in recent years, posing a great threat to natural and economical resources, or even to human lives. Watershed scale soil moisture product with high temporal resolution is essential for the predicting and monitoring of these disasters.
# Deep Learning-Powered System for Real-Time Digital Meter Reading on Edge Devices
## 2 Materials and Methods
### Automatic Meter Reading Pipeline
#### 2.2.2 Text Detection  
EAST [11] is one of the most commonly used algorithms in scene text detection, presenting a highly simplified pipeline and efficiency to perform inference at real-time speed. It uses a fully convolutional network (FCN), which outputs per-pixel predictions of words or text lines, followed by a non-maximum suppression (NMS) step on the geometric map as post-processing. The output layer is composed of two feature maps: the geometry map, which is used to generate the text bounding boxes and can assume different shapes--rotated box (RBOX) or quadrangle (QUAD)--and the scores map, which corresponds to the confidence of the geometry shape predicted at the same location. The loss function consists of a weighted sum of the score map and the geometry losses. Additionally, this method is commonly used for applications on edge devices, which is vital for the use case of this work.  
We employed the EAST model pre-trained on ICDAR 2013 [12] and ICDAR 2015 [13] datasets with ResNet-50 [14] as the backbone. This model is fine-tuned with the SINATRA dataset. For data augmentation of the input images, we apply random crops, Gaussian blur, zoom, translations, and rotations, and adjust the contrast and brightness. The images are resized to \(512\times 512\), and the batch size is 6. The Adam optimizer is adopted with a learning rate \(1\times 10^{-4}\). Regarding the outputs, we work with the RBOX geometry due to its superior performance demonstrated in [11].  
Succeeding the text boxes detection, the selection of important information (values for reading) takes place in the template matching step (Figure 4). As mentioned before, the values extracted from the UC are related to ORP and OD; the value from the FM is the flow rate. This procedure is based on the graphical structure of the screen content; hence, the projection of the screen onto a normal plane being vital. The rectangular regions containing the desired text are obtained by averaging the ground truth locations, thus resulting in templates for each equipment. Then, the intersection over union (IoU) between the detected and the template's regions is found, and the text boxes with IoU higher than our empirically set threshold of 15% are selected.
###### Abstract  
Background: Biomedical devices and implants are adversely affected by biofilm-associated infections that pose serious public health issues. Biosurfactants (BSs) can combat pathogenic biofilms through their antimicrobial, antibioticfilm and antiadhesive capabilities. The objective of our research was to produce biosurfactant (BS) from _Lactobacillus acidophilus_ NCIM 2903 and investigate its antibioticfilm, antiadhesive potential using microfluidics strategies by mimicking the micro-environment of biofilm. Methods: Antibioticfilm and antiadhesive potential was effectively evaluated using different methods like microfluidics assay, catheter assay, polydimethysiloxane (PDMS) disc assay. Along with this chemical and physical characteristics of BS were also evaluated. Results: Cell free biosurfactant (CFBS) obtained was found to be effective against biofilm which was validated through the microfluidic (MF) or Lab on Chip (LOC) approach. The potency of CFBS was also evaluated on catheter tubing and PDMS surfaces (representative bioimplants). The efficacy of CFBS was also demonstrated through the reduction in surface tension, interfacial tension, contact angle and low critical micelle concentration. Conclusion: CFBS was found to be a potent antimicrobial and antib Biofilm agent. We believe that perhaps this is the first report on demonstrating the inhibiting effect of _Lactobacillus_ spp. derived CFBS against selected bacteria via LOC approach. These findings can be explored to design various BSs based formulations exhibiting antimicrobial, antibioticfilm and antiadhesive potential for biomedical applications.  
biofilm; bioimplant; biosurfactant; _Lactobacillus_; microfluidics.
## 2 Predictive Approaches for Sending Marketing Campaigns
### Predictive Approaches Based on Regression Classifiers  
[PERSON] et al. [24] predict the impact of business campaigns by estimating the percentage of subscribers who interact with the communications received. The model considers as input (i) the email hyperlinks, (ii) the click-through rates, (iii) the time between an email sent, and (iv) a subscriber's action time. They use regression algorithms to estimate click-through rates. To understand the impact of the messages and their content, they also use Natural Language Processing [25]. Subscribers are segmented by the service provider of each client company with a clustering algorithm.  
[PERSON] et al. [26] present models for sending marketing communications to subscribers, optimizing the moment to repurchase a product. The communications are sent by SMS, through a Rabbit MQ protocol [27], which enables sending and receiving mobile messages. The first model applies regression algorithms to calculate the number of days between the last purchase and the next possible purchase for the subscriber. This model uses as input (i) the open messages rates, (ii) purchase transactions, (iii) the participation frequency, and (iv) the click-through rates. The second proposed model is based on the date predicted by the first model to establish an approximate date for the automatic reminder of the repurchase. XGBoost [28] has the best performance of all the implemented algorithms, with 95% of confidence level. A limitation regarding this project is the small size of the dataset they worked on.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 5 Data Validation
### Data Validation: Comparison with Laboratory Data
#### 5.1.1 Methodology and Data Sources  
The validation study used two independent data sources:  
* Experimental data (system measurements): Collected using the Raspberry Pi-based multi-sensor platform, measuring pH, total dissolved solids (TDSs), and turbidity at regular time intervals.
* Reference data (laboratory measurements): Obtained from the Macao Municipal Laboratory website and the Macao Statistics and Census Service's environmental statistics reports.  
To ensure proper comparison, data synchronization was performed by matching system-recorded values with the nearest corresponding laboratory measurement times-tamps (08:00, 14:00, and 20:00 daily).
## 3 Methodology  
The original noise signals collected by the sound equipment contain substantial interference and may lead to biases in the calculation. Therefore, we applied frequency domain transformation and band-stop filtering to remove the background noise. Considering the sound pressure level (SPL) more intuitively reflects the noise intensity perceived by the human ear than amplitude signals, we further converted the filtered sound signals into SPL metrics. However, SPL metrics only reflect the instantaneous noise intensity and cannot describe the noise characteristics over continuous periods. To address this issue, we developed a set of noise evaluation metrics, which includes frequency domain attributes, noise intensity attributes, and the number of specific sound events (e.g., high-frequency noise squeals), which provide a holistic perspective for noise assessment. Since the noise evaluation metrics only describe the current state of noise, we also employed noise pattern clustering algorithms to further investigate their sources and characteristics and provide customized noise control strategies. The framework we proposed for urban rail transit noise control is illustrated in Figure 1, and the specific mathematical model will be elaborated in detail in the subsequent section.
# Enhanced Trajectory Tracking via Disturbance-Observer-Based Modified Sliding Mode Control
## 3 Simulation and Discussion
### Simulations on Second-Order System  
The simulations were performed in MATLAB/Simulink. Different cases were studied to verify the performance of the designed logic (ESO-based PIDSMC) compared with the previous logic (ESO based SMC) [38]. The simulations were performed on a second-order system.  
\[\ddot{x}=u+f\big{(}x,\dot{x},t\big{)}+\Delta f\big{(}x,\dot{x},t\big{)}+d, \tag{62}\]  
where \(x\) is the system state, \(u\) is the control input, \(f\big{(}x,\dot{x},t\big{)}=5\dot{x}\mathrm{cos}(x)\), \(\Delta f\big{(}x,\dot{x},t\big{)}\) is uncertainty in the system dynamics, and \(d\) is the external disturbance. The system's known dynamics, its uncertainty, and its external disturbance were considered as perturbations:  
\[x_{3}=f\big{(}x,\dot{x},t\big{)}+\Delta f\big{(}x,\dot{x},t\big{)}+d, \tag{63}\]  
\[\ddot{x}=u+x_{3}. \tag{64}\]  
The controller's task is to reach the desired state in the presence of perturbation. The objective of the research was to minimize the effect of perturbation on the output error. A block diagram of the simulation is shown in Figure 6. The controller parameters are shown in Table 1. The control switching gain (\(K\) and \(K^{\prime}\)) was selected on the basis of Equation (44). The other controller parameters were selected based on a trial-and-error method.  
Three different cases were considered. The performances of the proposed and previous controllers are discussed in the next subsection.
# Internet of Things for Modern Energy Systems: State-of-the-Art, Challenges, and Open Issues
## 3 IoT Challenges in Energy Systems
### Supplying Energy for Sensors  
In IoT-driven environments, every object sends and receives information to other objects or a cloud of information. This means that a sensor should be used to sense the information for each individual object and an energy source should also be defined to supply each sensor. Considering thousands of these sensing nodes, a tremendous amount of energy would be needed to run the things. This is a serious challenge that IoT will face in the near future [91]. A new hierarchical architecture including three layers of hardware, middleware, and application layer is designed in [84] in order to save energy consumed by sensors in an IoT-based system. In this attempt, sensors are switched into sleep mode in certain conditions to save energy: (1) whenever it is not necessary to use them; (2) whenever using sensors will affect its battery life; or (3) when battery energy is lower than a threshold. In [92], a new framework is introduced for IoT systems in order to decrease human interaction in system management and decrease energy consumption. In this regard, the self-organized system is designed, which is able to optimize energy efficiency, which leads to more saving in energy used by sensors. This self-organized IoT framework is called Self-organized Things (SoT).  
Aside from software solutions for saving energy or optimizing energy efficiency used by sensors, there are methods to provide sufficient energy sources in hardware point of view. In [93], wireless energy harvesting is introduced as one of the best ways to supply energy for many sensors. In this regard, energy harvested from environmental sources like solar power is used to supply sensors.
###### Abstract
And, at the technology layer, communications are secured using cryptography, while endpoints are hardened via practices like operating system security configuration, application whitelisting, and anti-malware tools on machines [8]. Encryption, certificates, and VPNs protect communications while strong passwords, removable media controls, and system hardening counter endpoint risks. Furthermore, least privilege permissions, compliance auditing, configuration management, and redundancy in safety systems help reinforce defenses throughout the environment [9].