esa-sceva/satcom-chunk-collection huggingface dataset:
# Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM
## 5 Structure and Principle of the Discriminator  
If only the neural network is used for fall detection of the target, it is highly likely to be misjudged. For example, when the target lies down to rest, the posture of the target at this time is also in the fall state, as recognized by the model. Therefore, this paper designs a discriminator to combine physiological data with the posture results of the neural models to improve judgment accuracy and monitor the health of elderly people in real time.  
Figures 10 and 11 show the working principle diagram and structure of the discriminator. These mainly include the Arduino series Microcontroller Unit (MCU), Organic Light-Emitting Diode (OLED) module, MAX30102 module, buzzer, and indicator light. Arduino is the core control and the brain of the system, enabling the whole operation to proceed smoothly. As the main module of heart rate detection, MAX30102 detects the human body's heart rate in real time, as shown in Table 3, and feeds the detected data to the Arduino through the I2C port in real time, so that Arduino can respond appropriately. Arduino makes a comprehensive judgment that is reliant on the physiological data and the target's posture received from the neural network models via the Bluetooth module and serial port. Finally, the buzzer alarms as a reminder that the resulting output of the MCU is in the fall state. At this time, the monitoring system relays the target's situation to the mobile terminal of the guardian, as shown in Figure 12. The system can be made into a wearable bracelet. The MAX30102 module is located at the bottom of the bracelet and can be detected by attaching it to the wrist of elderly people. At the same time, a battery of between 3.3 V and 5 V is stored inside the bracelet for power supply, thus improving the usability and convenience of the bracelet.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Normal Heart Rate** & **Abnormal Heart Rate** \\ \hline
85 & 98 \\
84 & 99 \\
87 & 101 \\
81 & 105 \\
83 & 105 \\
89 & 108 \\
88 & 107 \\
85 & 109 \\
86 & 108 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Heart rates of people in different situations.  
Figure 11: Structure of discriminator.  
Figure 10: Working principle diagram of discriminator.
# Study on Electromagnetic Radiation Interference Caused by Rocket Fuel
## 3 Electromagnetic Radiation Analysis and Modelling
### Analysis and Modelling of the Energy-Domain Characteristics of the Electromagnetic Radiation  
Energy is the basis of electromagnetic activities, and almost all electromagnetic wave applications are based on the propagation of electromagnetic energy. The electromagnetic radiation energy is controlled with various modulation styles in the frequency, time, and space domains, which is also the fundamental reason for the mutual influence of various electromagnetic activities. When the power of an electromagnetic interference signal is greater than the operating frequency of an electronic device, interference will occur immediately. The energy-domain characteristic of the electromagnetic environment of a spacecraft reflects the distribution of electromagnetic signal intensity, and its typical performance is the fluctuation of power and the uneven distribution of energy flow. Ideally, electromagnetic radiation propagates in all directions in an infinite space, and the energy density at any point in space is only related to the distance of propagation, the influencing factor of which is the attenuation factor. However, in actual situations, due to the existence of various radiation propagation factors, the energy density of electromagnetic radiation in space will not be uniform [26].  
The energy-domain characteristics represent the change in electromagnetic signal power, usually expressed by the field intensity, which can reflect the distribution law of signal strength within a specific area, a specific time period, and a specific spectrum range. According to Equation (4), in a certain space of V\({}_{\Omega}\), time range of [\(t_{1}\),\(t_{2}\)], and frequency range of [\(t_{1}\),\(t_{2}\)], the signal strength can be expressed as Equation (13), where S expresses the average power density spectrum of the signal [27].  
\[\text{S}=\frac{1}{V_{\Omega}(t_{2}-t_{1})(f_{2}-f_{1})}\int_{\Omega}\int_{f_{1 }}^{f_{2}}\int_{t_{1}}^{t_{2}}S(\textbf{r},t,f)dtdfd\Omega \tag{13}\]
# Special Equipment Safety Supervision System Architecture Based on Blockchain Technology
## 4 Blockchain System Architecture for Safety Supervision of Special Equipment
### System Design
In this process, the client first submits transaction information to the orderer (ordering service node) through the Google Remote Procedure Call (gRPC) connection, then the orderer node that receives the request parses and checks the message, then broadcasts the message to the peers. The orderer also can maintain the consistency of the data of each node, and locally maintain the ledger (blockchain) structure for each channel. We adopted ordering service based on Raft consensus, which is provided in version 1.4.1 of Fabric. Its many advantages are why we adopted it; the Raft model can tolerate the crashes, and the system will run normally when there is a node failure, if it can ensure that more than half of the nodes are lost. Figure 3: System architecture. SDK refers to an integrated development kit, which contains a series of application programming interfaces, used by client applications to interact with a Hyperledger Fabric blockchain network. the nodes are running normally. The Raft ordering service is easier to set up and manage than the Kafka ordering service. As has been included in the fabric community, the implementation of Raft ordering service is more convenient for developers. The most typical data that will be input into the blockchain system are self-inspection results from equipment users and third-party inspection results issued by inspection agencies. Here, we take Table 3 as an example to describe the typical data. As can be seen, the size of a record is usually less than 20 kb, and the default block file size is around 70 kb when storing the ledger.
## 2 Data and Methods
### Ancillary Data
#### 2.2.1 Sea Ice Concentration (SIC)  
FMPL-2 T\({}_{B}\) observations at the ocean-ice boundaries can be ambiguous, because their values partially consist of open water and sea ice. This often leads to an underestimation of thin ice, especially at low-concentrated areas around sea ice margins. The Ocean and Sea Ice Satellite Application Facility (OSI-SAF) OSI-401-b product by EUMETSAT provides SIC maps using a dynamic tie-point algorithm applied to T\({}_{B}\) data from the Special Sensor Microwave Imager/Sounder (SSMIS) at 19 GHz and 37 GHz vertical, and at 37 GHz horizontal polarization [43]. The SIC maps are available on a daily basis at a 10 km Polar Stereographic grid projection true at 70 \({}^{\circ}\)N/S for both hemispheres, respectively, and images can bedownloaded from [[http://osisaf.met.no/p/ice/](http://osisaf.met.no/p/ice/)]([http://osisaf.met.no/p/ice/](http://osisaf.met.no/p/ice/)), (accessed on 07 February 2021). These maps were regridded to a 12.5 km EASE-Grid 2 to build a sea ice coverage mask. Only input data with a SIC > 15 % were considered for training the network.
## 4 Wireless Transmission in WBANs
### Chapter Summary  
In response to the special technical requirements of WBAN, this chapter not only introduced the IEEE 802.15.6 standard channel models, but also discussed the existing WBAN channel models in different scenarios, including the methods of channel model building approaches and their respective advantages. Finally, the three important parts of a WBAN, i.e., PHY, MAC and networking layers, were expounded on, and their respective requirements and corresponding solutions were put forward. Therefore, WBANs focus on energy consumption, bit error rate, QoS transmission efficiency, security, privacy, etc.
# Water Temperature and Salinity Measurement Using Frequency Comb
## 3 Materials and Methods  
Theoretically, the optical path length of MA1 \(L_{w}\) and MA2 \(L_{a}\) can be expressed as:  
\[L_{w}=(L_{0}+L_{2}+L_{3})n_{a}+2 dn_{g}+Dn_{w} \tag{1}\]  
Figure 1: Temperature and salinity measurement experiment setup. FC: Frequency Comb; Rb: Rubidium clock; BS1: Beam Splitter 1; M1: Mirror 1; RA: Reference Arm; PDP: Precision Displacement Platform; BS2: Beam Splitter 2; MA1: Measurement Arm 1; MA2: Measurement Arm 2; M5: Mirror 5; PD1: Photodetector1; Cw Laser: Continuous wave Laser; BS3: Beam Splitter 3; PD2: Photodetector 2; Scope: Oscilloscope; TS: Temperature Sensor; SS: Salinity Sensor; \(d\) is the thickness of the glass tank; \(D\) is the geometric distance of water; PD: Photodetector; \(L_{0}\), \(L_{1}\), \(L_{2}\), \(L_{3}\), \(L_{4}\) is the geometric distance of the optical path marked in the schematic.  
Figure 2: Experiment photography.  
\[L_{a}=(L_{0}+L_{1}+L_{2}+D+L_{4})n_{a}+2 dn_{g} \tag{2}\]  
Among these Equations, \(n_{g}\) is the group refractive index of glass. \(n_{w}\) stands for the group refractive index of the water. \(n_{a}\) represents the group refractive index of air. Please note that the MA1 optical path distance \(L_{w}\) is slightly longer than MA2 \(L_{a}\). Therefore, the optical path difference (\(OPD\)) between MA1 and MA2 can be calculated as:  
\[OPD=L_{w}-L_{a}=(L_{3}-L_{4}-L_{1})n_{a}+D(n_{w}-n_{a}) \tag{3}\]  
When water temperature or salinity changes respectively, the variation of water group refractive from \(n_{w}\) to \(n_{w1}\) will change the optical length of MA1 from \(L_{w}\) to \(L_{w1}\), which results in \(OPD\) changing into \(OPD1\).  
\[L_{w1}=(L_{0}+L_{2}+L_{3})n_{a}+2 dn_{g}+Dn_{w1} \tag{4}\]  
\[OPD1=L_{w1}-L_{a}=(L_{3}-L_{4}-L_{1})n_{a}+D(n_{w1}-n_{a}) \tag{5}\]  
\[L_{w1}-L_{w}=OPD1-OPD=D(n_{w1}-n_{w}) \tag{6}\]  
\[n_{w1}=\frac{L_{w1}-L_{w}}{D}+n_{w}=\frac{OPD1-OPD}{D}+n_{w} \tag{7}\]
## 1 Introduction  
Squeezed states and quantum entanglement [1] are essential resources for constructing quantum information networks, performing ultrasensitive quantum measurements, and implementing speedy quantum computing [2; 3; 4; 5; 6; 7; 8; 9]. For instance, squeezed (entangled) states are necessary in order to improve the sensitivities of spectroscopic measurement [10; 11], velocimetry [12], light detection and ranging equipment [13], gravitational wave detection [7; 8], and quantum key distribution [14; 15]. For future quantum networks, the quantum repeater protocol is indispensable for implementing the distribution of quantum-entangled states over long distances via quantum memory and entanglement swapping [16; 17; 18]. To this end, one desires a compact entangled source with a high degree of entanglement at the non-degenerate frequencies of 0.8 \(\upmu\)m and 1.5 \(\upmu\)m. The 0.8 \(\upmu\)m wavelength is compatible with the quantum memory device, which consists of alkaline atoms, and the 1.5 \(\upmu\)m wavelength is compatible with telecommunication optical fibers. In addition, such entangled sources are valuable to long-distance continuous variable (CV) quantum key distribution, allowing one to significantly extend the distribution distance and tolerate excess noise, as shown by [PERSON] et al. in Ref. [14].  
Non-degenerate optical parametric amplifiers (NOPAs) are powerful tools for preparing high quality CV quantum-entangled light fields. Various entangled states, including two-color [19; 20; 21; 22; 23; 24; 25] and three-color [26; 27; 28] CV quantum-entangled states, have been generated. We have utilized a single ring resonator, which combines both the NOPA and non-degenerate optical parametric oscillator (NOPO), and prepared quantum-entangled states with a large wavelength difference. In previous works, the generated two-color CV quantum-entangled states only produced 3.3 dB of quadrature squeezing [6], which is less attractive for quantum information processing, where a high degree of entanglement is desired. In this paper, we analyze the relevant factors, such as the total efficiency of the optical system and the fluctuation of the phase-locking, which are crucial to the degree of entanglement. Based on simulation results, we improved and optimized the experimental system and successfully increased the two-color CV entanglement from 3.3 dB to 6 dB, which is the best result using this design, to our knowledge.
## 2 BGSe Crystal Phase-Matching Characteristics  
For the BGSe crystal, the phase-matching characteristics can be calculated by the following Sellmeier equations (S-E) [21]:  
\[n_{x}^{2}=6.72431+\frac{0.26375}{\lambda^{2}-0.04248}+\frac{602.97}{\lambda^{2 }-749.87} \tag{1}\]\[n_{y}^{2}=6.86603+\frac{0.26816}{\lambda^{2}-0.04259}+\frac{682.97}{\lambda^{2}-781.78} \tag{2}\]  
\[n_{y}^{2}=7.16709+\frac{0.32681}{\lambda^{2}-0.066973}+\frac{731.86}{\lambda^{2} -790.16} \tag{3}\]  
where \(\lambda\) is in micrometers. The phase-matching angles of BGSe-OPO pumped by 1.064 \(\upmu\)m are shown in Figure 1a. It can be seen that the BGSe crystal can satisfy the type-I and type-II phase-matching conditions in the \(x\)-\(z\) and \(y\)-\(z\) principal planes. In the monoclinic class, \(m\) symmetry crystals, one of the principal dielectric axes (\(xyz\)), are always parallel with the \(b\) crystallographic axis. In our paper, for BGSe, it is \(x\equiv b\), so the corresponding expressions for the effective nonlinearity coefficients of BGSe read [20]:  
\[d_{eff-I}(x-z)=d_{16}\cos^{2}\theta+d_{23}\sin^{2}\theta \tag{4}\]  
\[d_{eff-II}(x-z)=-d_{24}\sin\theta \tag{5}\]  
in the \(x\)-\(z\) principal plane and  
\[d_{eff-I}(y-z)=\pm d_{16}\cos\theta-d_{15}\sin\theta \tag{6}\]  
\[d_{eff-II}(y-z)\approx 0 \tag{7}\]  
in the \(y\)-\(z\) principal plane, where the tensor components \(d_{15}\), \(d_{16}\), \(d_{23}\), and \(d_{24}\) can be found in [15]. Moreover, we take all the (\(\theta\), \(\varphi\)) on the phase-matching curves to calculate the \(d_{\mathit{off}}\), which are shown in Figure 1b. It was confirmed that the suitable phase-matching directions of the BGSe crystals are located in \(x\)-\(z\) and \(y\)-\(z\) principal planes. In Figure 1b, type-I phase-matched BGSe in the \(x\)-\(z\) principal plane and type-II phase-matched BGSe in \(y\)-\(z\) principal plane have approximative \(d_{\mathit{off}}\), but there was a wider accept angle and accept bandwidth in the \(x\)-\(z\) principal plane. Therefore, in our experiment, BGSe crystal was cut at \(\theta\) = 47.1\({}^{\circ}\) and \(\varphi\) = 0\({}^{\circ}\), whose \(d_{\mathit{off}}\) is about twice that of AGS crystal (\(\sim\)15.8 pm/V).
# Synchronous Remote Calibration for Electricity Meters: Application and Optimization
## 2 Related Work
### Remote Calibration Systems
[26] introduced a scheme for remote calibration of digital input meters, featuring an on-site calibration device that outputs digital sampling values to the meter under test, with signal waveforms scheduled by a remote master station. [PERSON] et al. [27] developed a remote calibration system for DC EV chargers' metering devices, using real-time pulse comparison for verification. Studies on remote calibration systems typically consider single calibration laboratories handling tasks in a centralized manner, which can become inefficient in large-scale operations. Distributed remote calibration [10], though potentially more efficient and scalable, remains underexplored.
# Application of Digital Image Analysis for Assessment of Star Content and Distribution in Potatoes
## 4 Conclusions  
The use of digital image analysis facilitates the assessment of both the starch content and distribution in potatoes. The high coefficient of correlation between the starch contents assessed chemically and the mean pixel brightness determined by digital image analysis allows its accurate estimation with the latter method. The tested method enabled us to distinguish between five of the six potato varieties used in this study. It is noteworthy that the chemical method for the starch content assessment in potatoes allowed us to distinguish only three groups of cultivars. Interestingly, the pattern of starch distribution in the potatoes is cultivar-specific and makes it possible to distinguish between the varieties better than only on the basis of the total starch content. Digital image analysis can be used for fast estimation if the material delivered to a potato-processing plant is homogeneous in terms of potato variety and if the potatoes were subject to long-term freezing or not. Simultaneously, the assessment of the starch content and distribution in potatoes by using digital image analysis is markedly cheaper and less time-consuming than chemical methods.  
Conceptualization, T.B. and H.B.; methodology, T.B.; software, T.B.; validation, T.B., H.B., and W.D.; formal analysis, T.B.; investigation, H.B. and B.R.; resources, T.B.; data curation, T.B.; writing--original draft preparation, T.B. and W.D.; writing--review and editing, T.B. and W.D.; visualization, T.B.; supervision, T.B.; project administration, H.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
There are no conflicts of interest regarding the publication of this paper.
# Disentangled Feature Learning for Noise-Invariant Speech Enhancement
## 5 Conclusions  
In this paper, we proposed a novel speech enhancement method in which speech and noise latent features were disentangled via adversarial learning. In order to explore the disentangled representation which has not been exploited in the conventional speech enhancement algorithms, we designed a model using GRLs. The proposed architecture is composed of five sub-networks where the decoders and the disentanglers were trained in an adversarial manner to encourage the encoder to produce noise-invariant features. The speech latent features generated by the encoder reduced the variability among different noise types while retaining the speech information intact. Experimental results showed that the proposed model outperformed the conventional DNN-based speech enhancement algorithms in terms of various measurements in both the matched and mismatched noise conditions. Moreover, the proposed model achieved more competitive noise-invariant property through disentangled feature learning. Visualization of the speech latent features demonstrated that the proposed method was able to disentangle speech attributes from the noisy speech in the latent feature space.  
In our future work, we will further develop novel structures and training techniques for a better representation of disentangled speech and noise features than the current model. In addition, we will consider a model that can disentangle the various factors that occur in voice communication systems.  
Conceptualization, S.H.B. and N.S.K.; methodology, S.H.B.; software, S.H.B.; validation, S.H.B. and N.S.K.; formal analysis, S.H.B.; investigation, S.H.B.; data curation, S.H.B. and I.C.; writing--original draft preparation, S.H.B.; writing--review and editing, N.S.K.; visualization, S.H.B. and I.C.; supervision, N.S.K.; project administration, S.H.B. and N.S.K.; funding acquisition, N.S.K.  
Figure 8: Visualization of speech latent feature (\(\mathbf{z}_{s}\)) using t-SNE in the mismatched noise condition.  
Figure 7: Visualization of speech latent feature (\(\mathbf{z}_{s}\)) using t-distributed stochastic neighbor embedding (t-SNE) in the matched noise condition.  
**Funding:** This work was supported by the research fund of Signal Intelligence Research Center supervised by Defense Acquisition Program Administration and Agency for Defense Development of Korea.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 7 Outcomes  
RQ1: What Is the Comprehensive Taxonomy of Anomaly Intrusion Detection in IoT Using Deep Learning Techniques?  
Recently, various studies have explored the application of anomaly detection in IoT using deep learning. For better insight, taxonomy is shown in Figure 5 to pinpoint all existing techniques and requirements of anomaly intrusion detection in IoT, using deep learning techniques.  
Figure 4: Selected distribution studies by journals.  
Figure 3: Selected distribution studies by years.  
learning techniques. The IDS are commonly categorized as supervised, unsupervised, and semi-supervised.  
* Supervised: in a supervised manner, anomalies detecting labeled datasets by constructing the network or system is normal behavior. Supervised anomaly detection techniques can leverage the measurement of distance as well as the density of clusters for the detection of intrusions.
* Unsupervised: in an unsupervised manner, the approach assumes a greater frequency of normal behaviors, thus leading to the establishment of the model on assumptions, wherein there is no need for any labeled data for training.
* Semi-supervised: in a semi-supervised manner, the algorithm is trained upon a combination of labeled and unlabeled data.
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 5 Performance Results and Evaluation
### Data Transmission and Network Scalability  
The system demonstrated the scalability and reliability necessary for large industrial deployments through REM optimization. With REM-guided node placement, the network achieved zero packet loss, even when additional nodes were added. These results highlight the system's ability to maintain high performance in complex layouts, making it suitable for dynamic and large-scale industrial environments requiring continuous monitoring for fires. To enable remote access, the system leverages port forwarding to route external HTTP requests to the Raspberry Pi's local IP address via the router's static IP address. This is accomplished by configuring the Wi-Fi router to forward incoming requests from the static IP address of the Wi-Fi router to the correct local IP and port of the Node-RED server, allowing users to access the Node-RED dashboard from anywhere via the custom URL (e.g., [[http://mcsllab.onthewifi.com:33891/ui](http://mcsllab.onthewifi.com:33891/ui)]([http://mcsllab.onthewifi.com:33891/ui](http://mcsllab.onthewifi.com:33891/ui)), accessed on 31 st October 2024). When a user enters the URL in a browser, the router directs the request to the Raspberry Pi where the Node-RED server is running. This port forwarding setup ensures real-time access to the dashboard, allowing users to remotely monitor the environmental data, make timely decisions, and respond to alerts from any location.  
Instances of fire detection in Area 1 were flagged in different threads (see \"Fire detection in thread 1\" and \"Fire detection in thread 2\" in Figure 11). Each thread represents a separate communication channel within the multi-threaded architecture, designed to enhance responsiveness and reliability by ensuring that fire alerts are transmitted promptly, even if one thread encounters latency or temporary disruption. This multi-threaded approach, combined with the ESP-NOW and MQTT protocols, significantly improves data transmission speed and network resilience. The system's interface provides users with clear, real-time alerts, allowing for rapid response and monitoring of multiple zones simultaneously. This configuration ensures that no alerts are missed, supporting robust data monitoring and quick decision making, essential for fire detection in industrial and large-scale environments.