esa-sceva/satcom-chunk-collection huggingface dataset:
# LoRaWAN Behaviour Analysis through Dataset Traffic Investigation
## 2 LoRaWAN in a Nutshell
### LoRa  
LoRa is the physical layer developed by Semtech [3], enabling the LoRaWAN end-device to achieve long-range communication through radio frequency modulation. LoRa modulation is based on chirp spread spectrum (CSS) [4]. It offers immunity to multi-path and tolerate small frequency offset caused by Doppler effect, making LoRa ideal for communication in urban environments. A chirp is characterized by a frequency excursion all over the bandwidth. The base chirp, or up-chirp, is characterized by linear increasing frequency, starting from the value \(f_{min}\) and ending with a value \(f_{max}\). Instead the chirp that starts with frequency \(f_{max}\) and ends with \(f_{min}\) is called down-chirp. For any digital bit sequence the LoRa device will produce a time shifted chirp to the respect of the base-chirp. In the CSS modulation the number of different symbols/chirps depends on the Spreading Factor (_SF_), \(N_{Symb}=2^{SF}\). The value of the \(SF\) is also equal to the number of encoded bits per symbol. For example a modulated signal with \(SF\) = 7 can produce 128 distinguishable symbols each of one encoding a different sequence of 7 bits. Symbols are organized in frames (also named packets in the following). The frame structure is reported in Section 2.3. Packets with different spreading factors are orthogonal, appearing as a noise to each other. Therefore, two packets that arrive at the same time on the same receive channel at different spreading factors will not collide and, both will be demodulated by the gateway. However, two packets with the same spreading factor arriving at the same time on the same channel result in a collision. According to LoRa specification the \(SF\) may assume 6 different values, from 7 to 12. The bit rate \(R_{b}\) depends on the \(SF\). Given a bandwidth BW and a coding rate CR, the \(R_{b}\) is given by the following expression:\[R_{b}=SF\cdot\frac{BW}{2^{SF}}\frac{4}{4+CR}bit/s \tag{1}\]  
The higher the \(SF\) the lower the bit rate. If the same message is transmitted with two consecutive values spreading factors, e.g., 7 and 8, the time needed to transmit the message roughly doubles. The LoRa modulation characteristic vary for each region. In Europe, there are channels with bandwidth of 125 kHz and 250 kHz. LoRa transmits in the ISM band that in Europe is set in the frequency range 863-870 MHZ.
## 1 Introduction
First, it develops five new converters for measurements that support 13-digit timestamps with harmonic inclusion capabilities and creates new datasets in the NILMTK-DF format. Additionally, it implements comprehensive disaggregation software for a systematic algorithm evaluation and comparison. It also analyzes the impact of the harmonic inclusion and other considerations on NILM metrics. Finally, it designs and validates open-source measurement hardware to ensure the results are replicable. This study provides an in-depth evaluation of open hardware platforms and compares five household appliances using the Non-Intrusive Load Monitoring Toolkit (NILMTK) metrics. The findings highlight the importance of standardized tools like the NILMTK in assessing NILM performance and underscore the need for accessible hardware and software platforms to foster reproducibility and innovation. This article is structured to present a comprehensive investigation of NILM, after the Section 1, which sets the context of NILM and the ongoing challenges, Section 3 describes the instruments (the OpenZmeter oZm v3 platform), the datasets used (DSUALM10H and DSUALM10), the software tools and evaluation metrics (NILMTK v0.4.0 and NILMTK-Contrib, with MAE, RMSE, F1-score, and NDE), the algorithms tested (including traditional methods such as CO and FHMM, and deep neural networks such as DAE and RNN), and the experimental conditions (sampling rate, harmonic content, and power filters). Then, in Section 4, the results of the evaluation of the algorithms under these conditions are presented, analyzing the influence of experimental factors and execution times. Finally, the Section 5 summarizes the main findings and their implications for future research.
# A Review of Experimental Techniques for Measuring Micro- to Nano-Particle-Laden Gas Flows
## 4 Conclusions  
This article reviews measurement techniques for both carrier phase and dispersed particles from the nanoscale to the microscale in size, with a focus on the developments of various techniques over the last 20 years. It also draws relationships and makes comparisons among these techniques. We provide a systematic description and classification of these optical and electrical methods and describe the parameters they measure, such as particle velocity, size, composition and concentration. A more detailed review is made of some of the most important measurement techniques, includingPIV, PDPA and light scattering intensity for micro-particles, as well as the SMPS, FMPS and ELPI for nano-particles. The principles, development histories and applications of these techniques are covered. In general, their spatial and time resolutions appear to be the main factors affecting the scope of these techniques.  
Despite the various effective techniques referred to in this paper, there are still several challenges to be addressed. Now that non-intrusive diagnostic techniques for the carrier phase use particles as small as the tracer particles, the influence of these tracers on the fluids needs to be considered. In particular, this needs to be addressed when micro-channel flow has sub-microparticles as tracers and when high-speed flow requires very high tracer concentrations to satisfy the camera sensitivity. Theoretical models for many of these techniques still need to be developed. For micro- or larger particles, the extension of existing particle characterization methods to non-spherical particles and non-homogeneous particles is very important for both engineering and scientific applications. For submicron- and nano-sized particles, there is a strong demand for the development of online techniques and in situ (freezing) sampling methods, which can reproduce the exact particle spatial distribution, without being affected by particle-particle interactions and particle deposition.  
This work was jointly supported by the National Natural Science Foundation of China (Grant No. 11602266, No. 11632016, No. 11132008, No. 11402259) and the Natural Science Foundation of Zhejiang Province (Grant No. LQ15A020003). [PERSON] and [PERSON] conceived of and designed the content of the major measurement techniques. [PERSON] performed the evaluation of the referred literature. [PERSON] and [PERSON] analyzed the references, the relationships among different measurement techniques and the trends of them. [PERSON] contributed references and important suggestions for revision. [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Oscillation Suppression Method of Digital Proportional Valve Based on Fuzzy Intelligent PID Control
## 5 Simulation Results and Discussion
### Effectiveness Analysis
#### 5.1.2 Sinusoidal Input Tracking Response  
To verify the dynamic response performance of the proposed method to sinusoidal input, the reference input \(x\left(t\right)\) = 3.5 sin (0.5\(\pi\times t\)) was used. The controller parameters are the same as those in the step response, and the dynamic response results of the DPV are obtained, as shown in Figure 13. As is evident from Figure 13, the main spool produces irregular oscillation in the process of following the sinusoidal change signal under the existing control method. The proposed method could adjust the control input of the HSVB in real time according to the movement direction, position error, and error change rate. Therefore, the pressure in the two control chambers of the main spool can follow the input state changes, and the oscillation is suppressed effectively. The position tracking error is maintained at a relatively low level. Under stable operating conditions, the maximum tracking error is less than 3.1%. Moreover, compared with simply using the PID control method, this error is reduced by 41%. Furthermore, an integral square error analysis was conducted on the proposed control method and the PID of half-bridge control method within the time interval ranging from 3 s to 5 s, with the results presented in Table 5. When the input is a sinusoidal signal, the tracking error of the proposed method is much smaller than that of the PID of half-bridge control method. Compared with the existing control  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Oscillate** & **Rising Time (ms)** & **Overshoot (\%)** &
\begin{tabular}{c} **Steady State Error** \\ **(mm)** \\ \end{tabular} & **ISE Value** \\ \hline PID of full-bridge complementary & yes & 128 & 1.12 & - & - \\ PID of half-bridge control & no & 116 & 4.32 & 0.243 & 379.796 \\ Proposed & no & 121 & 3.19 & 0.016 & 17.945 \\ \hline \hline \end{tabular}  
* In this text, “-” means that the value has not been calculated.  
\end{table}
Table 4: Performance indices for the studied valve subjected to the step input response.  
methods, the proposed method can improve the response speed and control accuracy for sinusoidal input.
# Coordinated Multi-UAV Reconnaissance Scheme for Multiple Targets
## 4 Proposed Method
### Division of Decision Variables  
A solution should include the reconnaissance task set \(p\), reconnaissance sequence set \(q\), as well as reconnaissance time set \(t\). Each \(solution=p\cup q\cup t\), and each decision vector is explained as follows:  
1. The task assignment set of the UAV. Different UAVs are assigned to different tasks, and the task set \(p_{i}\) defined as \(p_{i}=\{p_{i1},p_{i2},\ldots,p_{i|p_{i}|}\}\) stores the tasks assigned to the \(i\)-th UAV. Here, \(|p_{i}|\) represents the number of tasks in the task set.
2. The reconnaissance sequence of the UAV on the task set. Each UAV should execute the tasks in the corresponding task set \(p_{i}\) with the sequence of its own reconnaissance sequence \(q_{i}=\{q_{i1},q_{i2},\ldots,q_{i|p_{i}|}\}\), where \(q_{i}\) means the reconnaissance sequence of the \(i\)-th UAV, and \(q_{i|p_{i}|}\) refers to the \(|p_{i}|\)-th task to be scouted by the \(i\)-th UAV. The reconnaissance sequence length is the same as the task set length, i.e., \(|q_{i}|=|p_{i}|\).
3. The reconnaissance time of the UAV on the task area. The reconnaissance time of a task in the reconnaissance sequence is denoted by \(t_{i}=\{t_{i1},t_{i2}\ldots,t_{i|q_{i}|}\}\), where \(i\) represents the UAV's serial number. \(t_{ij}\) represents the reconnaissance time for the task area of the \(j\)-th reconnaissance conducted by the \(i\)-th UAV.
# Bar-Wave Calibration of Acoustic Emission Sensors
## 3 Results and Discussion
### AE Sensor Response
#### 3.2.5 PAC S9220 Sensor  
This sensor is a nominally 6 mm diameter, 900 kHz design for broadband applications. Results of bar-wave calibration tests are given in Figure 13. The blue curve is from the present displacement sensitivity test, while the red curve was reanalyzed from the previous test data for Figure 13b in reference [8]. The dashed green curve is the normally incident wave displacement sensitivity (Figure 10b in reference [4]), while the dotted blue curve is for the bar-wave velocity calibration. Again, the red curve from the previous study showed a decrease below 120 kHz due to radial resonance effect. This low frequency decrease needs to be ignored, but it is shown for completeness.  
A comparison between the blue and dashed green curves points to a distinction between the guided-wave response and normally incident wave sensitivity. The latter gives a peak response at 900 kHz, while the former peaks at a low frequency. Such a difference always emerged in all the sensor evaluation so far. In the bar-wave (blue) curve, a dip is observed at 400 kHz as in V103 (Figure 12). The aperture nulling effect for this smaller sensor is expected here when the bar wave propagates at 2 mm/\(\upmu\)s. A few modes exist in the calculated dispersion curve (Figure 5c).  
Figure 12: Receiving sensitivity of Olympus V103. The displacement receiving sensitivities to bar waves are shown in blue curve for the present work and in red curve from the old data (reproduced with permission from [8]. Copyright AE Group, 2010). Green curve is the displacement receiving sensitivity to normally incident waves [4].
# Enhancement of Optical Chirality Using Metasurfaces for Enantiomer-Selective Molecular Sensing
###### Abstract  
Circular dichroism (CD) is a physical property observed in chiral molecules by inducing the difference of absorption between left- and right-handed circularly polarized light (CPL). Circular dichroism spectroscopy is widely used in the field of chemistry and biology to distinguish the enantiomers, which typically show either positive or severe side effects in biological applications depending on the molecular structures' chirality. To effectively detect the chirality of molecules, diverse designs of nanostructured platforms are proposed based on optical resonances that can enhance the optical chirality and amplify the signal of circular dichroism. However, the underlying physics between the optical chirality and the resonance in a nanostructure is largely unexplored, and thus designing rules for optimal chiral detection is still elusive. Here, we carry out an in-depth analysis of chiral enhancement (C enhancement) in nanostructured surfaces to find the relationship between optical resonances and chirality. Based on the relations, we optimize the nanostructured metasurface to induce effective chiral detection of enantiomers for diverse conditions of molecule distribution. We believe that the proposed designing rules and physics pave the important pathway to enhance the optical chirality for effective circular dichroism spectroscopy.  
chirality; chiral sensing; metasurface; circular dichroism; kerker effect; optical resonance Article
## 3 Results and Discussion
### The Impacts of Returning Biogas Slurry to the Field on the Yield of CO and Phosphorus Content
#### 3.4.2 The Impact of Biogas Slurry Application on Phosphorus Metabolism Functional Genes
The relative abundance of pstS decreased, and the application of digested slurry inhibited the microbial intake of Pi, which may weaken the competition for phosphorus between microorganisms, benefiting the absorption of phosphorus by CO. The results of this study indicate that the digested slurry also has an impact on the phosphorus deficiency response mechanism of microorganisms (Figure 5). It is worth noting that the read number of the phoR gene in the soil of CO planted in the field ranges from 3746 to 5142, which belongs to a relatively high abundance of genes. This functional gene can sense the external phosphorus content and activate downstream proteins (such as phoB) under low-phosphorus conditions to promote self-regulation, playing a role in helping microorganisms adapt to low-phosphorus environments [47; 48]. This study found that with increasing amounts of digested slurry, the abundance of the phoR gene in the soil gradually decreased. This indicates that the application of digested slurry increased the amount of available phosphorus in the soil, leading to a gradual decrease in the stress of low-phosphorus conditions, resulting in weakened expression of the phoR gene in microorganisms [49; 50]. From the perspective of the transformation relationship between inorganic phosphate and polyphosphate within biological cells, the application of the slurry can simultaneously increase the relative abundance of ppk1 and spoT, thereby accelerating the metabolic cycling rate of phosphorus within microbial cells. Relevant studies have shown that the application of organic manure can promote phosphorus metabolism within organisms and increase the overall phosphorus migration and transformation rates [51; 52], which is consistent with the results of this study.
## 3 Qualitative Analysis of the Application of AR in Industrial Assistance and Training
### Safety Training  
A multitude of potential risk factors exist in industrial environments, and in response to this and the recognised potential of AR, enhanced safety management systems are being implemented to protect workers.  
In the field of _visual guides_, virtual training systems have been developed that have improved current ways of teaching safety protocols related to manufacturing tasks [46; 50]. The adoption of AR panoramic viewers has enhanced the user interaction experience in safety training [58].  
With regard to _equipment handling_, there is a growing development of augmented tools designed to enhance the safety awareness and hazard identification capabilities of workers in industry [46; 50]. [PERSON] et al. [74] have developed an AR system designed to facilitate workers' immediate identification of hazards associated with machinery and equipment directly in the workplace.  
In _hazard identification_, several researchers [47; 58] have developed realistic and immersive environments, in which they have conducted controlled experiments that show an improvement in risk identification and management. These studies demonstrate that the utilisation of AR for safety training can enhance the risk identification and management abilities of industrial workers and professionals.  
In the context of _accident prevention_, while a great deal of research has been conducted on the development of systems and functions using AR, there is a notable lack of studies that critically analyse the impact and effectiveness of these tools in real-world contexts [62]. Among the limited body of research, a comprehensive and rigorous evaluation of an AR-assisted safety training system has been conducted, utilising advanced technologies such as fuzzy logic [76]. Furthermore, in order to expand the possibilities and applications of AR, several experts have investigated its combination with other emerging technologies, developing a safety assistance system using panoramic AR technology [74]. Finally, a recent study indicates that the utilisation of AR enhances the retention of knowledge, a crucial factor in ensuring long-term safety in industrial settings [86].
## 3 Results  
### Band-To-Band Re-Registration of Planet Dove Satellite Images  
Figure 3 shows the results of validation of the original band-to-band registration. Misregistration between RGB bands was observed in images with heavy cloud cover, images of the sea, and images taken by the next-generation sensor types (PS2.SD); this sensor took about 1% of all images analyzed. Misregistration was not observed in clear land images taken by the conventional sensor types (PS2). Therefore, no re-registration between RGB bands was performed for images taken with the PS2 sensors, and images taken by PS2.SD sensors were removed from the analysis. In many images, misregistration was seen between the RGB and NIR bands; these images were re-registered by matching the NIR band image to the RGB band image.
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## 4 Adaptability of the Formulation
### Detailed Analysis of Derived Equations
#### 4.5.2 Generalized Liouville Equation  
The modified Liouville equation is derived by substituting Equations (28) and (56) into the continuity equation for \(\rho\). Expanding the commutator:  
\[[\overline{F},\rho]=\frac{\partial\overline{F}}{\partial\mathbf{q}}\cdot \frac{\partial\rho}{\partial\mathbf{p}}-\frac{\partial\overline{F}}{\partial \mathbf{p}}\cdot\frac{\partial\rho}{\partial\mathbf{q}}, \tag{61}\]  
reveals how free-energy gradients (\(\
abla\overline{F}\)) redistribute \(\rho\) in phase space. This is exemplified in the quantum-to-classical transition shown in Figure 3.
## 2 Complex Load and Reliability Modeling
### Components of the Complex load  
Each Component of the Complex Load  
The complex load of the offshore derrick consists of gravity, the hook load, stand load, and wind load, as shown in Figure 1:  
(1) The weight of the crown block \(G_{\textit{crown block}}\) traveling block \(G_{\textit{traveling}}\), derrick \(G_{\textit{derrick}}\) and cracking board \(G_{\textit{tracking board}}\) is taken into account. (2) The weight lifted by the hook \(G_{\textit{hook}}\) is referred to as the hook load. (3) The stand load is the force applied to the derrick by the stand through the cracking board beam. Most of the existing research does not consider the stand load. In this model, the stand's gravity and wind load is fully considered, and the stand's horizontal component of gravity (1) and wind load (2) are referred to as [3]:  
\[P_{l}=\frac{1}{2}qln\cot\theta \tag{1}\]  
\[P_{w}=Wn^{{}^{\prime}}dl\sin\theta \tag{2}\]  
where \(q\) is the gravity per unit length of the stand (\(q=32\) kg/m), \(n\) is the number of stands (\(n=250\)), \(l\) is the length of the stand (\(l=28\) m) and \(\theta\) is the angle between the stand and drill floor (\(\theta=88^{\circ}\)). \(W\) is the calculated wind pressure defined by Equation (4). \(n^{{}^{\prime}}\) is number of stands per row on the racking board (\(n^{{}^{\prime}}=28\)). \(d\) is the diameter of the stand (\(d=5.5\) in).  
Figure 1: Simple mechanical analysis diagram of derrick.  
(4) The wind load is defined as the load on the derrick by the wind pressure, which is calculated by the equivalent static wind load equation; see [3]:  
\[W_{0}=\frac{v^{2}}{1.6} \tag{3}\]  
\[W=W_{0}k_{2}k_{p} \tag{4}\]  
\[P=WF \tag{5}\]  
where \(W_{0}\) is the basic wind pressure, calculated by the speed of the wind \(v\). \(k_{z}\) is wind the pressure coefficient and \(k_{p}\) is shape coefficient of the wind pressure. \(F\) is the area of the bearing wind load, m\({}^{2}\). The two coefficients and area \(F\) are listed in Table 1.  
The above components affect the strength and reliability of the offshore derrick together. In order to clearly reveal the influence of the complex load on the offshore derrick, three typical composite loads, which are applied in the research of Section 4, and other composite loads were designed in this model, such as: (1) gravity and hook load; (2) gravity, hook load and stand load; (3) gravity, hook load, stand load and back wind load; (4) other composite load.
# Quantifying Climate Change and Ecological Responses within the Yangtze River Basin, China
## 3 Results
### Correlations between Climatic Variations and Ecological Indicators  
Climate change has a significant impact on ecological indicators [46]. Correlation coefficients (r) between mean air temperature and annual precipitation and the various ecological indicators (annual mean NDVI, growing season mean EVI, annual LAI, annual total GPP, and annual total NPP) were calculated to determine the relationships. The results are presented in Figure 17a-d. The ecological indicators exhibit different responses to the climatic variations. The annual mean NDVI and annual mean air temperature showed a significant positive correlation (R\({}^{2}\) = 0.37, \(p\) < 0.05, Figure 17a); while mean growing season air temperature was moderate positive correlation with mean growing season EVI (R\({}^{2}\) = 0.24, Figure 17b); In addition, the annual mean air temperature had significant positive correlations with annual LAI (R\({}^{2}\) = 0.35, \(p\) < 0.05) and annual GPP (R\({}^{2}\) = 0.37, \(p\) < 0.05, Figure 17c); while annual mean air temperature was moderate positive correlation with annual NPP (R\({}^{2}\) = 0.28, Figure 17d). Moreover, we also used the partial regression to analyze the relationship between air temperature and precipitation (see Appendix A: Table 11), and that supported the conclusion by the linear regression analysis that temperature is more closely related to ecological factors than precipitation over the YRB.  
Figure 16: Spatial distribution of (**a**) the mean values of annual GPP and (**b**) the mean values of annual NPP; (c) linear trends of annual GPP and (**d**) linear trends of annual NPP in the YRB during 2000–2010.