esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Experimental Results
#### 3.3.1 Case A  
In this case, \(\lambda=0.6\), \(\sigma_{m}=0.08\) m, \(m_{m}=0.4\) kg. Three experiments were performed with \(\beta=0.6,\ 0.8,\ 1\), respectively. The tip residual accelerations are shown in Figure 12.  
In case A, the frequency ratio is selected as 0.6. In this case, three typical experiments are conducted with three amplitude ratios. The measured tip accelerations are illustrated in Figure 12. The tip accelerations were recorded. It can be seen from Figure 12 that the vibration amplitude when \(\beta=0.6\) is the smallest. Vibration amplitude when \(\beta=0.8\) is slightly greater than that when \(\beta=0.6\). However, when the amplitude ratio is 1, the residual vibrations are much more obvious than before.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **1 st Mode Frequency** & **2 nd Mode Frequency** \\ \hline Measured & 2.7382 & 13.9487 \\ Calculated & 2.8081 & 13.643 \\ Error & 2.781\% & 2.19\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The first and second frequencies (rad/s).  
Figure 12: Measured accelerations of the flexible beam tip in Case A.
# Orbital-Based Automatic Multi-Layer Multi-Pass Welding Equipment for Small Assembly Plates
## 2 Design and Analysis of Rail-Mounted Automatic Welding Equipment  
### Mechanical Design of the Equipment's Core Mechanisms  
As shown in Figure 1, the rail-mounted automatic welding equipment is installed above the working area of small assembly plates. The overall structure includes a rail and base moving mechanism, a fixing mechanism, a quick-plug mechanism, and welding fixtures. The rail is connected to the small assembly work area through the fixing mechanism, and the base can move along the rail. Simultaneously, the base is equipped with a robotic arm and a wire feeder. This part of the design includes the quick-plug mechanism at the end of the robotic arm and a protective mechanism for the visual sensors. The quick-plugmechanism serves as a connection between the welding torch and the robotic arm, while the protective mechanism provides automatic opening and closing to protect the fragile sensors, enabling the robotic arm to drive the welding torch for automatic welding. Due to the robotic arm's compact structure, small size, and flexible movements, the equipment can perform various types of automated welding in the confined spaces of small assemblies.
## 3 Results and Discussion  
A historical database of energy demand from a private company was used to examine the accuracy of the algorithms explained above. All models in the present study were trained with the energy demand database for the whole of 2017 for 15 min resolution, whereas the whole database for 2018 with same resolution was used for the validation analysis. Thus, each database has a length of 35040 samples. In addition, the input vector used to develop the FFNN, RNN, and SVM models was made up of: season, the time of day the prediction was made, and energy demand values during the last 24 h with 15 min resolution. Both the proposed energy demand IPIF and all of the PPFs presented below were programmed in MATLAB(r).
## 1 Introduction
Also, a previous perceptual study of EL speech suggested that the intelligibility of EL speech would be decreased if the signal-to-noise ratio (SNR) decreased [19]. It is also noted that the low-energy EL speech can be easily masked by the different environment noises [12], the reduction of speech quality, furthermore, causes listener fatigue. Therefore, it is important to investigate a new efficiently algorithm to eliminate both additive noise and the radiated noise. This would not only improve the life quality of the laryngectomees, since the EL is the only equipment for them to communicate, but also increase the quality of the EL speech for better understanding, especially in environment noise and electronic noise (low SNR) conditions. Unlike white Gaussian noise, which has a flat spectrum, the spectrum of EL noise and the additive environment noise are not flat. Thus, the noise signal does not affect the speech signal uniformly over the whole spectrum. Some frequencies are affected more adversely than others. This means that this kind of noise is \"COLORED\". In order to prevent destructive subtraction of the speech while removing most of the residual noise, it is necessary to propose a non-linear approach to improve the subtraction procedure. Therefore, this investigation was motivated by the need to improve EL speech, especially in electronic environments. A multi-band spectral subtraction algorithm is proposed that takes into account the variation of signal-to-noise ratio across the speech spectrum using a different over-subtraction factor of each frequency band to reduce colored noise. According to the features of the EL speech, we also give some recommended over-subtraction factors to effectively improve the EL speech quality.
## 3 The Proposed Circuits
### Design of TR Gate  
The TR gate is another reversible gate consisting of three corresponding unique inputs and outputs [21]. Figure 9 shows the quantum implementation from this gate. As represented in Figure 9, the inputs bits (A, B, and C) are mapped to the outputs bits (P, Q, and R) one-by-one. Its quantum cost is calculated 4 [21]. The truth table of the TR gate represents in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Input** & & **Output** \\ \hline
**A** & **B** & **P** & **Q** \\
1 & 1 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Truth table of FG.  
Figure 8: FG gate (a) logic diagram, (b) block diagram, and (c) QCA layout.  
Figure 7: The quantum implementation FG.  
Figure 9: The quantum implementation TR gate.  
The logic and block diagram and QCA implementation for TR are represented in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Input** & & & **Output** & \\ \hline
**A** & **B** & **C** & **P** & **Q** & **R** \\
1 & 1 & 0 & 1 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Truth table of TR gate.  
Figure 10: TR gate (**a**) logic diagram, (**b**) block diagram, and (**c**) QCA layout.  
The following logic equations are the TR gate equations that correspond to Table 3, as follows:  
\[P=A \tag{6}\]  
\[Q=M\big{(}M(A^{\prime},B,0),M(A,B^{\prime},0),1\big{)}=A\oplus B \tag{7}\]  
\[R=M(M\Big{(}\big{(}M(A,B^{\prime},0)\big{)}^{\prime},C,0\Big{)},M\big{(}M(A,B^{ \prime},0),C^{\prime},0\big{)},1=AB^{\prime}\oplus C \tag{8}\]
## 8 LLC inductance and Magnetics  
Magnetizing inductance with a small value is necessary to achieve ZVS in the LLC resonant converter. In order to have a more miniaturized structure, many researchers have proposed topologies to utilize the LLC transformer leakage inductance as a resonant inductance instead of an external inductance [105; 106; 107; 108; 109]. Thus, an optimized design of the transformer would be necessary to avoid excessive losses.  
In order to achieve higher power density and lower magnetic core loss, an integrated LLC transformer with stacked planar cores has been proposed in [17; 110]. This technique can achieve flux cancellation between cells. Also, PCB winding is shaped near the airgap to reduce the fringing loss. PCB winding transformers can improve the reliability for the automated manufacturing process in MHz LLC converters [30; 112; 113].  
Magnetic integration is necessary for three phase LLC converters, because they need three individual inductors and three individual transformers which degrade the LLC converter benefits in power density and cost [11].  
Another integrated transformer with \"\(\infty\)\" winding structure is proposed in [114]. In order to further reduce terminal loss and winding loss caused by leakage flux, the SR devices are mounted on the PCB board [114; 115]. The matrix transformer is another solution proposed in the literature for flux cancellation, core size reduction, and loss reduction [17; 31; 116; 117]. A numerical loss analysis, as given in [25] for matrix transformer selection, in an LLC converter benefits from the GaN HEMT transistor. Parallel connection of GaN HEMTs, in addition to the matrix transformer, is suggested in [31] to achieve more output current, but it is difficult to drive them synchronously. It makes the system vulnerable to unbalanced current and current overshoot, resulting in lower reliability.  
Considering high switching frequency in the converter, fringing loss due to the airgap cannot be neglected anymore [17]. The shaped winding and gapped or notched winding are proposed to reduce the fringing effect in the transformer [118; 119].  
The magnetic components for LLC converters operating below 100 kHz still face some challenges in regard to size and complexity [11]. With the help of GaN HEMT devices, the switching frequency pushed to the MHz range and other problems will arise like skin effect, proximity effect, and fringing effect [6; 120; 121]. Also, the PCB transformer solution, as proposed in the literature, will intensify these effects [112]. A test-based method for core loss and copper loss evaluation is proposed in [120] to optimize the transformer in the MHz LLC converter.
# Data-Driven Packet Loss Estimation for Node Healthy Sensing in Decentralized Cluster
## 3 System Overview and Problem Formulation
### System Overview
#### 3.1.1 Gossip Based Membership  
In order to correctly cooperate with other nodes in the cluster, each member in the cluster holds a table that contains the states of all the members in the cluster and uses Gossip protocol to make this table globally consistent. The status of the member consists of two elements, i.e., working state and reachability state. These two elements stand for whether the node is working and whether the node can be reached, respectively. To make the state globally consistent, a node periodically exchanges its state to a random neighbor. If their exchanged states are different, the node with state of older version will update its state to the newer version. To add or remove a member, a node can simply modify its member table. After that, it gossips its member states to other nodes. When the member state meets the consistency, this add or remove action is finished.
## 3 Volatile Organic Compounds Emitted from WBPs
### Other Compounds  
Emissions of alcohols have been reported in different wood species; however, no clear formation mechanism was found in the literature. Methanol is among the most prominent alcohols emitted from several heat-treated hardwoods [48], while ethanol [65; 87], 1-octanol, and 2-methoxyphenyl have been found in heat-treated pine [65]. Comparisons between air-dried and heat-treated emissions from softwood (pine and spruce) and hardwood (aspen), compounds such as 1-hexanol, 1-pentanol and 1-penten-3-ol were found in air-dried aspen while heat-treated aspen samples resulted in low emissions of 1-butanol. 1-pentanol was also present in emissions from air-dried pine [66]. Fresh wood emissions have also included alcohols such as pentan-1-ol found in pine, pronoun-2-ol in larch, and 2-ethylhexanol in both larch and spruce [64].  
The thermal decomposition of lignin may also result in the formation of aliphatic and aromatic hydrocarbons, phenol, and phenolic compounds. [PERSON] et al. (1999) reported that some terpen, including \(\Delta\)3-carene, limonene, and camphene, can degrade into volatile aromatic compounds such as \(p\)-cymenene and \(o\)-cymene [106]. After thermal modification, vanillin and guiaacylacetone were identified in three Tunisian pine species [107], whereas anetol (or estragole) was found to be the most abundant aromatic compound emitted from southern yellow pine (_Pinus_ spp.) [65]. Other identified aromatic hydrocarbons in PB and laminated PB include C\({}_{10}\)-C\({}_{16}\) alkanes, benzene, toluene, naphthalene and their derivatives, \(p\)-xylene, and phenanthrene [77]. Furans and its derivatives--such as alkylfurans (ethyl-, propyl- and pentyl-)--were detected in hardwoods as products of thermal degradation of cellulose and other polysaccharides [108; 109]. Additionally, thermal degradation of hemicellulose may lead to the formation of esters, which occur through reactions between hydroxyl and carboxyl groups at high temperatures [59]. A summary of other volatile compounds found in different wood types is presented in Table 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Wood** & **Other Volatile Compounds Emitted *** & **References** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} _Pinus_ \\ \end{tabular} } & Pine & ethanol, pentan-1-ol, tridecane, toluene, & \multirow{4}{*}{[64; 65; 87; 91]} \\  & (_Pinus sylvestris, Pinus spp._) & \(o/m/p\)-xylene & \\ \cline{2-3}  & Spruce & ethanol, propan-2-ol, 2-ethylhexanol, & \multirow{4}{*}{[64; 91]} \\  & (_Pica abies_) & \(o/m/p\)-xylene & \\ \cline{2-3}  & Larch & ethanol, propan-2-ol, 2-ethylhexanol, 1-propene, & \\ \cline{1-1}  & (_Larix decidua, Larix sibrica, Larix_ & octadecane, \(o/m/p\)-xylene & \\ \cline{1-1}  & & &
## 3 Related Works: An Overview of Wireless Power Transfer
### Energy scavenging Sensor Nodes  
Energy scavenging (ES) refers to the conversion of energy from one form to another. Sensor nodes are powered by devices whose energies are derived from external sources in order to boost or prolong the network lifetime. Energy harvesting techniques have shown strong potentials to address the trade-offs between sensor-node lifetime and performance parameters, and to provide a lasting solution for the energy-hungry electronic devices [5]. The most popular technique of energy scavenging is the conversion of solar energy to electrical energy [3]. This type of energy harvestable source is greeted with several limitations that border around difficulty in controlling the intensity of direct sunlight with which may be affected by variations in weather conditions and seasonal patterns. For instance, the solar and wind sources of ES are limited by the durations and strengths attached to solar radiation or wind [5] which are subject to weather or seasonal constraints. Additionally, there may be inadequate sunlight at night to generate energy [6]. Other energy harvesting sources include thermal, mechanical vibrations, and RF energy. Among all, RF energy harvesting shows the most promising alternative to obtain energy for wireless devices even though it has the least power density [7]. This is because RF energy signals are pervasive whose systems have the potentials of harvesting sufficient energy to power many sensor devices. Hence, RF energy harvesters deliver more viable energy source for WSN and IoT-based applications. Figure 1 is a simplified block diagram of a typical energy scavenging system with three component parts: the energy source, the harvesting architecture, and the load.
* Energy Sources: This is usually characterized along the axes of controllable and non-controllable energy sources. A controllable energy source provides harvestable energy when he need arises whereas a non-controllable energy source is expected to harvest energy whenever available. Energy sources can be generally classified into two: ambient energy sources (relate to the energy sources from the environment such as solar energy, wind energy and RF energy) and human power (relate to energy sources as a result of the movement of body parts such as finger motion, walking, paddling) [3].
* Energy Harvesting (EH) Architectures: This can be divided into two--Harvest-Use and Harvest-Store-Use [3]. Harvest-Use depicts a timely scavenging of energy for use while Harvest-Store-Use harvests energy whenever it is available and stores the harvested energy for future use.
* Energy Conversion Mechanisms: This denotes the mechanisms for harvesting electrical energy from other energy sources. The selection of the type of energy conversion mechanism depends on the choice of energy source.  
Figure 1: Block diagram of an EH System.
# Infrared Multi-Scale Small-Target Detection Algorithm Based on Feature Pyramid Network
## 2 Materials and Methods
### Local Semantic Stitching Module  
The structure of the local semantic stitching module is shown in the upper part of Figure 3, and is used to extract the features of the local block of the feature map, referred to as LSS. For the feature map, \(X\in R^{\times H\times W}\), obtained through feature extraction at a given scale, \(b\), \(X\) is divided into \(b\times b\) blocks to obtain \(b^{2}\) feature maps \(d^{i}\in R^{\mathbb{C}\times\frac{H}{b}\times\frac{W}{b}}\) and \(i\in\{1,2,\ldots,b^{2}\}\). The \(b^{2}\) feature map \(d^{i}\) is processed by non-local blocks to obtain the \(b^{2}\) feature blocks \(e^{i}\in R^{\mathbb{C}\times\frac{H}{b}\times\frac{W}{b}}\) and \(i\in\{1,2,\ldots,b^{2}\}\). Subsequently, the \(b^{2}\) feature blocks are merged based on the position of the feature map \(X\) to obtain the feature map \(Q\in R^{\mathbb{C}\times H\times W}\)  
Figure 3: The CSE module.  
Figure 2: The PSC module.  
with the semantic splicing information of the local blocks. The non-local block captures long-distance dependencies by calculating the similarity between each position in the feature graph \(d\) and all other positions. The operations in the process are described by Equations (1) and (2):  
\[Q_{k}^{i}=\beta{\sum_{j=1}^{HW/b^{2}}\omega_{kj}^{i}\varphi\Big{(}X_{k}^{i} \Big{)}+X_{k}^{i}} \tag{1}\]  
\[\omega_{kj}^{i}=\frac{\exp\left(\theta\big{(}X_{k}^{i}\big{)}^{\top}\phi\left(X_ {j}^{i}\right)\right)}{\sum_{j=1}^{HW/b^{2}}\exp\left(\theta\big{(}X_{k}^{i} \big{)}^{\top}\phi\left(X_{j}^{i}\right)\right)} \tag{2}\]  
where \(Q_{k}^{i}\) represents \(k\) elements, \(\beta\) represents the scalar that can be learned, \(\omega_{kj}^{i}\) represents the element of the \(k\)th row and \(j\)th column of the coefficient matrix \(\omega^{j}\), and \(\varphi(.)\), \(\theta(.)\), and \(\phi(.)\) are all \(1\times 1\) convolution operations.
## Materials and Methods
### QCM-D Measurement  
Phosphate buffer was filtered through membrane of 0.22 um and then ultrasonic degassed for 20 min. The QCM-D (Q-SENSE E4, Biolin Scientific AB, Goteborg, Sweden) measurement was conducted at 37 \({}^{\circ}\)C, and PST chip was cast into the measure chamber. Phosphate buffer was injected into the chamber. When the baseline was stabilized, a diluted lipase solution was injected into the chamber using a flow rate of 50 \(\mu\)L/min. This process simulated the immobilization procedure. Adsorption time of lipase on PST chip was 90 min from the optimized results of pre-experiments. Then, the chip was rinsed by the phosphate buffer, and the desorbed lipase molecules were rinsed out. When the baseline was stabilized again, \(p\)-NPP was injected into the chamber to simulate the hydrolysis reaction of \(p\)-NPP catalyzed by the immobilized lipase. The \(p\)-NPP mixture (0.05 mg/mL) comprised \(p\)-NPP (0.05 mg), acetone (12.5 \(\mu\)L), and phosphate buffer (987.5 \(\mu\)L, 0.01 mol/L, pH 7.2).  
The determined parameters were \(\Delta f\) and \(\Delta D\). \(\Delta f\) represented the frequency change on the chip surface, which reflected the change of adsorption mass. \(\Delta D\) represented the change of dissipation factors, which reflected the change of adsorption viscoelastic. The \(\mid\!\Delta D/\Delta f\!\mid\) ratio reflected the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Microspheres** & **Average Pore Size** & **Total Pore Surface Area** & **Total Pore Volume** & **Porosity** \\  & **(nm)** & **(m\({}^{2}\)/g)** & **(cm\({}^{3}\)/g)** & **(\%)** \\ \hline PST-300 & 340.3 & 14.7 & 1.9 & 70.4 \\ PST-14 & 14.5 & 738.4 & 2.7 & 78.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural data of PST-300 and PST-14 microspheres.  
adsorption tightness, which was contrariwise proportional to the adsorption tightness. A large \(\left|\,\Delta D/\Delta f\,\right|\) ratio indicated an extended structure or loose binding between the interacting molecules [25].  
At the same time, the adsorption and the catalysis process was monitored, as a function over time, by simultaneously recording the shifts in the frequency (\(\Delta f\)) and in the energy dissipation (\(\Delta D\)) at the fundamental resonant frequency, along with the third, fifth, and seventh overtones, until the adsorption reached a steady-state. At this time, the long-term stability of the frequency was within 1 Hz, which was negligible when compared with the frequency shifts caused by adsorption. Normalized data obtained from different overtones were used in the calculation of adsorption mass, thickness, and viscoelastic properties of adsorbed layers using the Voigt model. Sauerbrey mass was calculated using the Sauerbrey equation [26]:  
\[M=-\ \left(C/n\right)\Delta f \tag{1}\]  
where \(\Delta f\), \(M\), and \(n\) represented frequency change, adsorbed mass per unit area, and overtone number, respectively. C was the mass sensitivity constant (17.7 ng/cm\({}^{2}\) Hz). The adsorption mass was inversely proportional to the \(\Delta f\) value.
## 5 Results and Analysis
### Indoor Performance of LoRaWAN  
The uplink and downlink packet statistics in Tables 1-3 constitute the RSSI and SNR records on a per RF link basis. The graphs in Figure 5a,b represent the RSSI and SNR for the link between the ground-floor showers sensor and the upper-floor gateway. This link was the only RF path with exciting features. Other RF paths were consistent over the test period.  
Deduplication: An analysis was conducted to identify any errors related to gateway backhaul latency and deduplication. This was performed by searching the server logs for traffic related to this site where there were successive \"gateway_count:1\" for the same mode for the same packet (same value in \"f_count_up\"). No deduplication errors were identified. However, there was an underlying 2.5% of all traffic where the mode packet was only forwarded by one gateway, excluding traffic for the high transmit rate mobile mode, which became 2.12%. Further analysis identified seven occasions, from 10 to 30 min, where one gateway or the other was not forwarding any messages. None of these occasions  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Uplink Packets** & **Lost Uplink** & **Lost ACK** \\ \hline Fixed Sensors & 1772 & 10 & 9 \\ \hline Movable Sensors & 70,305 & 16 & uknown * \\ \hline Total Up Packets & 87,577 & 26 & uknown \\ \hline Percent & - & 0.03\% & 0.05\% \\ \hline \hline \end{tabular}  
* Lost ACKs for the movable monitoring stations couldn’t be calculated from packet timing since the resend time interval and the normal send interval was the same.  
\end{table}
Table 1: summary of packets sent by sensors to network.  
Figure 5: RSSI (**A**) and SNR (**B**) for indoor transmissions from ground-floor shower room monitoring station to 3 rd floor gateway.  
were observed during regular business hours, although that did not preclude human intervention. On one occasion, one gateway was off for at least 24 min, then 20 min. Later on, the other gateway was off for 27 min. These periods account for 0.42% of \"gateway_count = 1\". There was no obvious explanation for the periods of gateway inactivity.  
Outdoor Range (Indoor Site): This site was not set up for outdoor monitoring, but both gateways could be accessed at the distant edge of the property at 650 m range. The top-floor gateway had the best reception at between \(-100\) and \(-110\) dBm, while the ground-floor gateway was below this level and not capturing as many packets. The range for the top-floor gateway was good, considering that the building had a metal outer skin.  
Channel Response by Frequency: Mean RSSI for each channel for each sensor to each gateway was calculated, but no significant variation between results for the different channel frequencies was identified. As the sensors operated on a fixed SF, it was impossible to analyse the combination of SF and channel performance.