esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background
### Drivers to Waste Management Adoption in Construction Sector  
The main drivers for CDW management and minimization strategies and GPP implementation in the AECO sector are:  
* Legislative drivers: as presented in the Regulatory framework section, the implementation of environmentally sustainable practices is promoted at all levels. Consequently, Public Clients can encourage the introduction of sustainable practices by implementing GPP tender processes and by using their purchasing power to opt for environmentally friendly goods, services, and works. In addition, positive public applications can highlight the advantages and possible income deriving from selective demolition, waste separation, and the recycling as secondary raw materials, such as, as an example, the high-quality recycling of aluminum.
* Business drivers: companies are facing the need to adopt sustainable strategies in order to maintain and enhance their competitiveness. In addition, companies are starting to focus not only on economic value but also on the company's social responsibilities and stakeholder engagement [6].
* Managerial and technological drivers: Information Modelling and Management (IMM) approaches can allow companies to overcome the barriers of sustainable practice integration by reducing design errors and information asymmetry, improving collaboration, and supporting waste quantity measurement and control [39].  
\begin{table}
\begin{tabular}{c c} \hline
**Barriers** & **Effects on the Construction Process** \\ \hline Incomplete design documents and & Causes most errors and rework during the construction phase [40], consequently \\ information asymmetry between designers & triggering waste generation [41, 42] and increasing the risk of project time and cost \\ and contractors & overruns. \\ \hline Non-collaborative culture & One of the main causes of waste generation [43], producing an unclear definition \\  & of responsibilities and inconsistent tender documents [44]. The contractor is \\  & usually not involved in design choices, increasing the risk of waste generation \\  & during the construction phase [45]. \\ \hline Blame culture & Actors of the different stages blame other specialists for generating waste [46, 47] \\  & with a consequent shift in the responsibility for waste generation. \\ \hline Waste inevitability culture & All actors see waste generation as unavoidable [6]. In standard practice, owners \\  & and contractors have usually already paid for waste treatments, transportation, \\  & and landfilling; therefore, selective demolition and the adoption of the 3 Rs \\  & approach are seen as burdens. \\ \hline Difficulty of innovating & Difficult replication of good practices and innovative applications in future projects \\  & due to the temporary relationships between the construction process parties [48]. \\ \hline Lack of training in applying innovative & Difficult application of innovative methods and technologies by the contractor [6]. \\ \hline \end{tabular}
\end{table}
Table 1: Barriers for the implementation of sustainable practices in AECO industry.
## 5 Results
### PCA and HCA Results
#### 5.3.5 PCA-3 Results  
PCA-3 was conducted on 77 particle size classes that were present in the 158 samples. The first and second PCs account for 31% and 21% of the total variance, respectively (Figure 10). The first PC shows a positive correlation with fine-to-medium silt grain size classes, while it exhibits a negative correlation with coarse-silt-to-fine-sand grain size classes. The second PC distinguishes coarse grain size classes on the positive side from the finest grain size classes on the negative side. The interpretation of the first PC axis suggests a gradient from laminar to turbulent flow conditions, while the second PC axis represents the opposition between traction and suspension currents.  
Figure 10: Principal Component Analysis: (**a**) scree plot, (**b**) circle of correlation, and (**c**) biplot of first and second principal components with cluster groups.
## 6 Existing Routing Protocols
### PEGASIS (Power efficient Gathering Sensor Information System)  
PEGASIS [15] is a near optimal chain-based power efficient protocol based on LEACH [7]. According to this protocol, all the nodes have information about all other nodes and each has the capability of transmitting data to the base station directly. PEGASIS assumes that all the sensor nodes have the same level of energy and they are likely to die at the same time. Since all nodes are immobile and have global knowledge of the network, the chain can be constructed easily by using greedy algorithm. Chain creation is started at a node far from BS. Each node transmits and receives data from only one closest node of its neighbors. To locate the closest neighbor node, each node uses the signal strength to measure the distance from the neighbors and then adjusts the signal strength so the only one node cab is heard. Node passes token through the chain to leader from both sides. Each node fuses the received data with their own data at the time of constructing the chain. In each round, a randomly chosen node (leader) from the chain will transmit the aggregated data to the BS. Node i (mod N) is the leader in round i. The chain consists of those nodes that are closest to each other and form a path to the base station. The aggregated data is sent to the base station by the leader.  
PEGASIS outperforms LEACH by eliminating the overhead of dynamic cluster information, minimizes the sum of distances and limits the number of transmission. Each node requires global  
Figure 3: Hierarchical clustering in TEEN and APTEEN Protocols.  
information about the network. This is a drawback of this protocol because at any time it can be collected from the network. PEGASIS is shown in Figure 4.
# Application of Wavelet Transform and Fractal Analysis for
###### Abstract
The research presented in this paper was carried out to answer two fundamental research questions. First, the authors wanted to check whether and to what extent it is possible to use wavelet and fractal analysis to analyse pH-metry waveforms in order to examine or determine new features/differences between the waveforms of healthy and sick people. If it turned out that there were measurable differences, visible only as a result of testing with the aforementioned methods, this would open the possibility of further exploring the nature of the differences, such as those in relation to the specific symptoms of reflux diseases. The second significant reason for undertaking this research work was to check whether it is possible to develop new and better methods of GERD diagnosis based on the analysis of the pH course--methods that could also be used in computer-aided diagnostic systems and self-diagnosis systems. Therefore, the authors of the paper put forward the thesis that the use of the computer methods of waveform analysis (wavelet analysis and spectral analysis) for esophageal pH courses would allow the development of new indicators enabling the diagnosis of gastroesophageal reflux diseases. The newly developed methods could be a viable alternative to the classical method of diagnosing reflux diseases.
## 2 Our Proposal
### Dynamic MSL Signs Classifier
As with other dynamic signs, the \(\alpha\) parameter allows for tolerance in movement errors. The movement of double letters is modeled by monitoring coordinate 0, representing the wrist, obtained from MediaPipe. The following two conditions must be met:  
1. Point \(M1\)'s horizontal position must be similar to point \(M0\) plus the width of the bounding box. This condition is shown in Figure 9(a), where the position of \(M1\) along the x-axis (purple arrow) must match the sum of the magnitudes of the orange arrow (bounding box width) and the green arrow (position of \(M0\)). 2. There should be no significant variation in the vertical movement. Figure 9(b) illustrates this condition, where the green arrow (representing \(M0\)'s position along the y-axis) should align with the purple arrow (representing \(M1\)'s position along the y-axis). Figure 9: Conditions for classifying the letter “k”. In summary, detecting double signs relies on analyzing the wrist's position, which remains consistent across different hand shapes. For a sign to be classified as a double letter, the wrist must move horizontally by a distance approximately equal to the width of the hand's bounding box. ```
Data: \(\overrightarrow{x}\)- Coordinates \(x\). \(\overrightarrow{y}\)- Coordinates \(y\). \(w_{bb}\)- width of bounding box. \(\alpha\)- Error tolerance. letter- String. Input:\(\overrightarrow{x}\), \(\overrightarrow{y}\), \(w_{bb}\), letter. Output: letter if letter is \(r\) or letter is \(l\)then \(M0\leftarrow(\overrightarrow{x}[0],\overrightarrow{y}[0])/*\)Coordinate of wrist */ \(w_{bb}\leftarrow\)width of bounding box detectionCount \(\gets 3\) whiledetectionCount \(>0\)do  get new frame \(\overrightarrow{x}\leftarrow\overrightarrow{x}\) of current frame \(\overrightarrow{y}\leftarrow\overrightarrow{y}\) of current frame \(M1\leftarrow(\overrightarrow{x}[0],\overrightarrow{y}[0])/*\)Coordinate of wrist */ /* First condition if\((M_{0x}+w_{bb})-(M0_{x}+w_{bb})*\alpha>M1_{x}>(M0_{x}+w_{bb})+(M0_{x}+w_{bb})*\alpha\)then \(detectionCount-detectionCount-1\) continue  end if /* Second condition if\(M1_{y}-M1_{y}*\alpha>M0_{y}>M1_{y}+M1_{y}*\alpha\)then \(detectionCount-detectionCount-1\) continue  end if  end for returnletter
```  
**Algorithm 3**Double letter detection  
It is important to note that the final classification outcome depends on the performance of the FC-NN (step 3), as it generates the confidence value for each frame. Due to the structure of the design, there are rare instances where a dynamic sign is correctly detected,  
Figure 10: Conditions for classifying double letters. but step 4, does not select this result because its confidence value is slightly lower than that of another letter in the list. Despite this limitation, the algorithm's overall performance remains suitable for its intended application, as demonstrated in the experimental results section. It should be emphasized that the movement behavior of signs has been specifically studied for MSL. While there are similarities with other sign languages, the movement patterns and hand configurations may differ.
###### Abstract  
To solve the problem that the channel conditions in asymmetric cooperative FSO communication systems are not fully utilized, and the data reliability deteriorates due to high-order modulation, we proposed a layered modulation, joint physical-layer network coding scheme. In this scheme, we first designate the data priority of the information to be transmitted at the source node. Then, the transmission power of different proportions is allocated to the data based on its priority. Then, the modulated data is sent to each node, and physical-layer network coding is performed on the received data at the relay node. Finally, the relay node sends the encoded information to the destination node, and the destination node recovers the original information using the physical-layer network coding scheme. The simulation results showed that when the average signal-to-noise ratio of the channel was 15 dB, the BER of the cooperative FSO communication system could be reduced to below \(10^{-8}\). In the strong atmospheric turbulence channel, the cooperative FSO communication system can obtain a signal-to-noise ratio gain of about 1.5 dB. Under strong atmospheric turbulence, this scheme could also improve the average channel capacity performance of a cooperative FSO communication system.  
c pacs: 22.22.6912, 22.6912 +
Footnote †: journal: Sensors
# A Review of Photothermal Detection Techniques for Gas Sensing Applications
## 4 Intra-Cavity PTS Gas Sensors
### PTS in an Intracavity He-Ne Laser Configuration  
One of the first attempts to utilize an intracavity photothermal gas detection configuration was reported in the work by [PERSON] et al. in 1986 [PERSON] et al. (1986). The core of the sensors was a linear cavity HeNe laser operating at \(\sim\)638.8 nm, with a layout as depicted in Figure 10.  
The _probe_ laser resonator was formed by two mirrors--M1 and M2, producing a cavity length of 50 cm. The gas sample was delivered with an open-ended gas cell (ST on the schematic) directly to the _probe_ laser resonator. The cell was 20 cm long. In this experiment, a tunable 0.8 W CO\({}_{2}\) laser was used as a _pump_ source to excite sulfur hexafluoride (SF\({}_{6}\)). The beam of the _pump_ source was aligned to coincide with the HeNe beam at a single point in the resonator. The PT effect induced by the _pump_ laser was deflecting the beam of the HeNe laser and thus causing a change in the output power. The _pump_ laser was chopper-modulated at 42.7 Hz and the PT signal was registered using an LIA. Authors calculated an NEC of 3 ppb. Although reaching a very good sensitivity, this configuration of a PT gas sensor would not have any particular applications in out-of-lab sensors, mainly due to the severe sensitivity to mechanical and acoustic noise.
###### Abstract  
In autonomous driving, object detection is considered a base step to many subsequent processes. However, object detection is challenged by loss in visibility caused by rain. Rainfall occurs in two main forms, which are streaks and streaks accumulations. Each degradation type imposes different effect on the captured videos; therefore, they cannot be mitigated in the same way. We propose a lightweight network which mitigates both types of rain degradation in real-time, without negatively affecting the object-detection task. The proposed network consists of two different modules which are used progressively. The first one is a progressive ResNet for rain streaks removal, while the second one is a transmission-guided lightweight network for rain streak accumulation removal. The network has been tested on synthetic and real rainy datasets and has been compared with state-of-the-art (SOTA) networks. Additionally, time performance evaluation has been performed to ensure real-time performance. Finally, the effect of the developed deraining network has been tested on YOLO object-detection network. The proposed network exceeded SOTA by 1.12 dB in PSNR on the average result of multiple synthetic datasets with 2.29\(\times\) speedup. Finally, it can be observed that the inclusion of different lightweight stages works favorably for real-time applications and could be updated to mitigate different degradation factors such as snow and sun blare.  
autonomous vehicles; weather conditions; object detection; rain removal Article  
A Lightweight Network for Real-Time Rain Streaks Accumulation Removal  
A Note: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI System
## 1 Introduction
The inference mechanism has two basic tasks: (1) determining the extent to which each rule is relevant to the current situation, as characterized by the inputs; and (2) drawing conclusions by using the current inputs and the information in the rule-base. The defuzzification process generates the output of fuzzy inference, which is a crisp value for a controlled system. The fuzzy recognition algorithm is simple and fast; thus, it is suitable for a real-time SSVEP-based BCI system. EEG signals are small, varying, and complex, so accurately recognizing EEG features is extremely difficult. Fuzzy sets can easily be installed in single-chip microprocessors. Single-chip fuzzy inference systems have been widely used in numerous applications, especially for real-time systems [14]. [PERSON] and [PERSON] combined wavelet methods and fuzzy algorithms to implement a bearing fault detection system [15]. An adaptive neurofuzzy inference system (ANFIS) was used to classify breast mass [16]. [PERSON] and [PERSON] used ANFIS to detect electrocardiographic (ECG) changes in patients with partial epilepsy [17] and classify EEG signals [18]. However, an ANFIS must collect a training database for parameter estimation of a neural network. The performance of an ANFIS would be degraded when its user's EEG data is not collected. Achieving real-time recognition for a BCI-based application is essential, as is adapting the system so that it can quickly fit a new user. In this study, a fuzzy tracking and control algorithm is proposed for helping subjects with ALS use a BCI remote control system. For accurate representation of EEG signal characteristics, a fast Fourier transform (FFT) was applied to extract the power spectrum, which involves the features for recognition. To quickly track the changes of EEG signal characteristics, a fuzzy tracking algorithm was developed. To improve the accuracy and stability of this BCI system, a fuzzy control algorithm is proposed. To help subjects with ALS operate remote-controlled devices, a BCI-based air swimmer drone vehicle was developed. The remainder of this paper is organized as follows. Section 2 describes the fuzzy tracking and control algorithms and discusses visual stimulation and signal acquisition. Section 3 presents the resultsof a series of experiments and evaluates the performance of our approach. Finally, conclusions are drawn in Section 4, and possible improvements for the future development of this work are discussed.
# Heat Transfer Enhancement of Nanofluids with Non-Spherical Nanoparticles: A Review
###### Abstract  
This article reviews the heat transfer enhancement of nanofluids with non-spherical nanoparticles. We divided the non-spherical nanoparticles suspended in nanofluids into three categories based on the dimension of geometric particle structure. Based on the measured data in experimental studies, we then evaluated the shape effect of non-spherical nanoparticles on thermal conductivity and convective heat transfer enhancement of nanofluids. Recent studies explored the numerical predictions and related heat transfer mechanisms. Due to large aspect ratios, thermal conductivity is abnormally enhanced only for nanofluids with carbon nanotubes/nanofibers/nanowires. The approximate enhancement effect exerted by three types of non-spherical nanoparticles on thermal conductivity was 4.5:2.5:1. Thermal conductivity enhancement per concentration was larger for nanorods/ellipsoids with small aspect ratios. The convective heat transfer coefficient was increased by suspending non-spherical nanoparticles in the base fluid. Consequently, no significant thermo-hydraulic performance was discovered for convective heat transfer of non-spherical nanoparticle nanofluid flow, specifically for turbulent flows, due to increased pumping power. However, the temperature and particle concentration effect on convective heat transfer remains unclear. In addition, no perfect model for predicting the thermal conductivity and convective heat transfer of non-spherical nanoparticle nanofluids has been reported.  
Keywords:nanofluids; non-spherical nanoparticles; heat transfer enhancement; thermal conductivity; convective heat transfer +
Footnote †: journal:  
_sciences_
# Enhanced Quasi Type-1 PLL-Based Multi-Functional Control of Single-Phase Dynamic Voltage Restorer
## 3 Reference Signal Generator  
In this Section, reference voltage \(v_{c}^{\text{ref}}\) will be generated from the measured grid voltage \(v_{g}\). For this purpose, let us consider the sing-phase grid voltage signal model in time-domain as provided below:  
\[v_{g}(t)=v_{0}+\underbrace{V_{p}\sin\left(\theta_{g}\right)}_{v_{g}^{\text{ ref}}}, \tag{8}\]  
Figure 1: Topology of the considered DVR.  
where \(|v_{0}|\geq 0\), \(V_{p}\), and \(\theta_{g}=\int\omega_{g}dt,\theta_{g}(0)=\phi\) are the DC offset, amplitude, and instantaneous phase with \(\omega_{g}\) being the grid frequency, and \(\phi\) is the initial phase angle. The frequency is \(\omega_{g}=\omega_{n}+\delta_{\omega}\), where \(\omega_{n}=100\pi\) is the nominal frequency and \(\delta_{\omega}\) is the deviation from the nominal frequency. For efficient operation of the DVR, the reference voltage should be in phase with the instantaneous phase of the grid voltage \(\theta_{g}\). Thus, the process of extracting \(\theta_{g}\) from the measured voltage \(v_{g}\) is considered in this section. For this purpose, a PLL-based approach has been considered in this study. Details are provided below.
## 4 CNN-Based Signal Classification Model
### Training Result  
In a total of 55 iterations of training process, the loss function and training accuracy curve are shown in the Figure 23. It can be seen that after the first 40 iterations of training, the loss function curve gradually decreased to a lower level. However, in the 41 st training process, both the loss curve and the accuracy curve showed great changes which means the model has been overfitted. The second negative learning completed the correction of the over-fitting, and the loss function and accuracy curve returned to a normal level. The test results of the finally trained model on the test set are shown in the Table 3.
# A Construction Method for a Dynamic Weighted Protein Network Using Multi-Level Embedding
[15] employed a hierarchical compression strategy to condense the inputted protein interaction network into a multi-layer PPI network, learning different granularity levels of protein embeddings through network embedding methods and constructing a weighted PPI network using the similarity of protein embeddings. [PERSON] et al. [16] used network embedding to learn the vector representation of gene ontology (GO) terms, mapped GO term vector sequences to protein vector representations using long short-term memory (LSTM) encoding, and predicted whether two proteins interact using a feed-forward neural network. Within the protein network, each node contains multiple features, such as protein structural domain information, GO annotation information, and gene expression profiles, which significantly impact the performance and function of PPINs [17]. Therefore, we propose a construction method of dynamic weighted protein network through multi-level embedding (DWPNMLE), which makes full use of the topological structure and biological information to study the construction of protein networks and combines variational graph auto-encoders (VGAEs) with deep attributed network embedding (DANE) for dual embedding to build the final dynamic weighted protein interaction network. This method can fully mine the high-order structural information and node attribute information of PPINs. VGAE and DANE are combined because, in recent years, research has proven that attributed network embedding methods are very effective in generating vector representations of nodes in networks [18]. In the transformed vector space, the topological proximity between nodes and the affinity of node attributes is maintained. The essence of the dynamic construction of protein networks is the prediction of interactions between proteins, which can be seen as a label-propagation problem on complex biological networks. VGAE and DANE have shown excellent performance in the feature representation of nodes on graph-structured data, which can mine the network's high-order structural information as well as maintain the attribute information of the nodes, and have significant application value in the graph-structured data problems of bioinformatics [19; 20]. The experimental results show that the DWPNMLE proposed in this paper has advantages regarding F1-measure, robustness, and algorithm efficiency when applied to four different complex identification algorithms, compared with the other two most advanced dynamic protein network construction methods.