esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating the Effects of Deep Excavation on Nearby Structures Through Numerical Simulation
## 3 Numerical Simulation Analysis and Verification
### Monitoring System  
The monitoring system for this project was based on the site's geological survey report and relevant structural drawings. Instruments were installed according to the stratigraphic distribution map and construction safety requirements, the monitoring items were as follows:  
1. Deformation Monitoring of the Diaphrag Wall: Four inclinometer tubes were installed in the wall with a depth of 32 m. During construction, an inclinometer was used to monitor the deformation of the diaphragm wall and its base.
2. Support Axial Force Monitoring: This was installed in the support system within the site. Four strain gauges were installed on each of the first to fifth levels of support, with one gauge on each side of the web, totaling 20 sets (40 gauges). These measured the axial force of the supports and estimated the eccentric force.
3. Rebar Stress Monitoring: To ensure the safety of the diaphragm wall structure, rebar gauges were installed on corresponding inner and outer main rebars in high-stress areas of the wall. Four diaphragm wall units were selected, each with 8 rebar gauges, totaling 32 gauges.
4. Settlement Monitoring Around the Site: Excavation caused the settlement of the ground around the site. Therefore, 40 settlement monitoring points were installed on the surrounding pavement and buildings, and a level was used to observe the settlement.
5. Heave Monitoring of the Central Columns: One heave monitoring point was installed on the central column to measure changes in heave during excavation.
6. Building Inclination Monitoring: Excavation often leads to ground settlement around the site, causing nearby buildings to crack and tilt. Therefore, nine inclination monitoring points were installed on important columns or walls of surrounding buildings to measure the inclination.
7. Groundwater Level Monitoring: Four electronic water level m were installed--one inside and three outside the site--to measure changes in groundwater levels.
8. Groundwater pressure monitoring: One electronic water pressure gauge was installed inside the site at a depth of 16 m to measure changes in groundwater pressure.
## 4 Proposed Video Latency Measurement Technique
### Latency Measurement  
This application node is responsible for various important functionalities, which include connection to the camera client, the starting sequence of RTSP packets, accepting and decoding video data, detecting events in the video and calculating the video latency. The latency measurement node also registers to the timestamp sender application node in order to be able to receive event ID and timestamp data, based on which the video latency is calculated. A pre-built FFmpeg library was used for handling the RTSP and decoding of the video. The image processing and event detection is done by utilizing the NI vision development module.  
As illustrated in Figure 8, the application is organised in four threads. The first thread utilises a pre-built FFMPEG library for starting the RTSP sequence and for decoding the video data. The output of Thread 1 is a sequence of images, which are fed to Thread 2. Data transfer between the threads is done by using queues in order to ensure that no data will be lost during the transfer. Thread 2 applies the IMAQ colour learn [51] function from the NI vision development module to each image in the sequence. The function outputs an image colour spectrum which contains the colour features present in the image. Whenever a visual event (which was generated by the timestamp sender application) is detected, the associated timestamp is captured and Thread 3 is notified. This thread is responsible for calculating the video latency, which is done by subtracting the timestamp at the moment the visual event was detected from the timestamp at the moment the same event was generated. The thread also logs the calculated latency data to a file. Times needed for the image to be processed by the application (overhead time) is also taken into account and subtracted from the final result. Thread 4 is responsible for registering to the timestamp sender application and receiving visual event data from it. The data that is received by Thread 4 is forwarded to Thread 3 so that the necessary calculation can be done.  
Figure 8: Latency measurement application architecture.
###### Abstract  
Black soldier fly larvae (BSFL) (_Herntetia illucons_) reared on food waste streams are considered a sustainable source of protein in feed livestock diets. Recently, portable near-infrared spectroscopy (NIR) instruments have been assessed to monitor the consistency and quality of food waste streams used to feed black soldier fly larvae. During the application of NIR spectroscopy, sample presentation (e.g., drying, processing, particle size) plays an important role in the accuracy of the models developed (quantitative or qualitative analysis). The objective of this study was to evaluate the effect of sample presentation (number of larvae used during the scanning of BSFL) on the accuracy of classification models developed to trace the food waste stream (e.g., supermarket of childcare) used to feed the larvae. BSFL samples were sourced from two waste streams and scanned as half, 1, 2, or 3 larvae using an NIR portable instrument (MicroNIR, Viavi, Mliptias, California, USA). Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to analyze the NIR data and to classify the samples according to the waste stream. The main differences in the NIR spectra of the BSFL samples associated with the number of larvae scanned were observed around 1200 nm, mainly associated with the C-H overtones (lipids). The classification results showed that high classification rates (>93%) were obtained regardless of the number of larvae scanned, ranging from 93% (using 0.5 larvae) to 100% (using 1, 2, or 3 larvae samples). Overall, the number of larvae scanned had minimal to no effect on the accuracy of the LDA classification models. The present study demonstrated that a portable NIR instrument can be suitable for an initial rapid classification or determination of the origin of the waste stream used to feed the BSFL.  
black soldier fly; NIR; sample presentation; classification; waste +
Footnote †: journal: Applied Sciences
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Software
#### 4.1.4 Implementation Considerations  
Beyond technical capabilities, practical factors also influence software selection. Research by [PERSON] et al. [31] highlights key considerations, including cost-related aspects such as initial licensing fees, training requirements, maintenance, and integration expenses. Additionally, technical requirements such as hardware specifications, network connectivity, database compatibility, and security features play a crucial role in determining the feasibility of implementation within healthcare institutions.
# Applications of Mueller Matrix Polarimetry to Biological and Agricultural Diagnostics: A Review
## 4 Applications of MMP to Biology and Agriculture
### Characterization of Soils and Folders and Vegetation Monitoring  
MMP finds application in the characterization of bulk materials such as soils and folders. An MMP setup can be implemented as a unit for measuring the disperse composition in a robotic system that transforms soils into a stable, high-performance, organic, chemical bioreactor to better control the biomolecular interaction of nanoparticles and their adsorption by biological and mineral media [44]. MMs of microwave backscatter from bare soils are used to determine moisture content and surface irregularities [45]. Polarized IR laser scattering from sandy soil proved to be effective enough for evaluating the optical properties of sand particles and their size [46]. Information about vegetation properties can be obtained from the MM of vegetation-covered surfaces measured in bistatic scatter geometry by polarimetric radars [115; 116]. Vegetation biomass can be derived from microwave polarized scattering returns using neural networks which are trained with vegetation scattering models [70]. The IR spectra of the MM elements measured for organic polymer films showed the presence of characteristic spectral lines in the block-diagonal elements of MM [98; 99], which reveals the potential of IR MM spectrollipsometers for the analysis of organic compounds in soils and folders.
# Sensitivity of Common Vegetation Indices to the Canopy Structure of Field Crops
## Materials and Methods
### Vegetation Indices  
The VIs (Table 1) used in the study (NDVI, EVI, EVI2, MTVI2 and OSAVI) make use of a total of four spectral bands (blue, green, red, and NIR) available in most modern satellite remote sensing instruments. The corresponding wavelengths used in the study are blue: 470 nm, green: 550 nm, red: 680 nm and NIR: 800 nm. As these exact wavelengths were not available in the AISA data, we used the closest available wavelength. The actual wavelengths used in the analysis were 470 nm, 551 nm, 682 nm, and 795 nm, respectively.
## 5 Recent Applications Using Selective Enrichment
### Food Analysis  
[PERSON] _et al._ reported in 2012 a method for the fast determination of trace dimethyl fumarate in milk with NIR spectroscopy following fluidized bed enrichment [82]. The motivation for the work carried out was to improve the sensitivity in NIR spectroscopy. Macroporous styrene resin HZ-816 was used as adsorbent material, and 1L solution of dimethyl fumarate was run through the material for concentration. The milk sample was pretreated to remove interfering matters such as protein, fat and then passed through the material for enrichment. After this, diffuse reflection NIR spectra were measured for the analyte concentration on the material without any elution process. The enrichment and spectral measurement procedures were easy to operate. NIR spectra in 900-1.700 nm were collected for dimethyl fumarate solutions in the concentration range of 0.506-5.060 microgram/mL and then used for multivariate calibration with partial least square (PLS) regression. Spectral pretreatment methods such as multiplicative scatter correction, first derivative, second derivative, and their combinations were carried out to select the optimal PLS model. Root mean square error of cross-validation calculated by leave-one-out cross-validation is 0.430 ug/mL. Measuring ten samples in an independent test-sample set showed a maximum deviation of 5.3%. This application clearly demonstrates how NIR coupled with on-line enrichment can be expanded for the determination of trace analytes even for applications in real liquid samples like milk.  
In 2012 [PERSON] _et al._ published an article about the determination of trace diisooctylphtalate in drink by NIR spectroscopy coupled with membrane enrichment technique [83]. Thereby, the plasticizer diisooctylphthalate (DEHP) containing solutions were passed through and concentrated with the polyethersulfone (PES) membrane and then the surface was detected directly. DEHP could be detected in a concentration range of 0.5-5.0 mg/L with a value for SEP being 0.232 mg/L.  
Figure 5: Principle of material-enhanced infrared spectroscopy (MEIRS).  
[PERSON] and [PERSON] have introduced a flow through sensor benefiting on the selective enrichment employing silica C18 for the selective analysis of caffeine down to a level of 1.8 mg L\({}^{-1}\)[84]. The principle of this work is based on a previous paper published by [PERSON] _et al._ in 1999 [85].  
[PERSON] _et al._ reported a novel ATR sensor that integrates SPE and IR detection by the design of a flow cell enabling the online coupling with a sequential injection system. As a stationary phase LiChroult EN was located in the sensitive element of the ATR without using any external coating. This new technical attempt was evaluated for the qualitative and quantitative analysis of caffeine in soft drinks. The authors reported that the LOD was 7 ug/mL and additionally the method showed high precision, with a RSD of 4 percent. In this article not only the advantages but also the disadvantages of this novel approach have been discussed in great detail [86].
# Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction
## 2 Method and Model Establishment
### Frequency Band Feature Extraction Method of UAV Sound
#### 2.2.1 Pre-Processing of Sound Signal  
Typically, UAV sounds propagating through air experience distance dependent energy attenuation, with different frequency components decaying unevenly. High-frequency components attenuate more severely over distance compared to low-frequency components. To better compensate for high-frequency attenuation losses, pre-emphasis (Equation (5)) is applied. It is worth noting that we do not use a microphone array, so \(x(n)=X(k),k=n\).  
\[y(n)=x(n)-ax(n-1) \tag{5}\]  
where \(x\) represents the original input sound signal, y denotes the pre-emphasized signal, and a is the pre-emphasis coefficient, usually \(a=0.97\).
###### Abstract  
Fifth-generation networks promise wide availability of wireless communication with inherent security features. The 5G standards also outline access for different applications requiring low latency, machine-to-machine communication, or mobile broadband. These networks can be advantageous to numerous applications that require widespread and diverse communications. One such application is found in smart grids. Smart grid networks, and Operational Technology (OT) networks in general, utilize a variety of communication protocols for low-latency control, data monitoring, and reporting at every level. Transitioning these network communications from wired Wide Area Networks (WANs) to wireless communication through 5G can provide additional benefits to their security and network configurability. However, introducing these wireless capabilities may also result in a degradation of network latency. In this paper, we propose utilizing 5G for smart grid communications, and we evaluate the latency impacts of encapsulating GOOSE, Modbus, and DNP3 for transmission over a 5G network. The OpenAirInterface open-source library is utilized to deploy an in-lab 5G Core Network and gNB for testing with off-the-shelf User Equipment (UE). This creates an effective 5G test platform for experimenting with different OT protocols such as GOOSE. The results are validated by measuring two different Intelligent Electronic Devices' contact closure times for each network configuration. These tests are also conducted for varying packet sizes in order to isolate different sources of network latency. Our study outlines the latency impact of communication over 5G for time-critical and non-critical applications regarding their transition toward private 5G-based OT network implementations. The conducted experiments illustrate that in the case of GOOSE packets, simple encapsulation may exceed the protocol's time-critical nature, and, therefore, additional measures must be taken to ensure a viable transition of GOOSE to 5G services. However, non-critical applications are shown to be viable for migration to 5G.  
smart grid; Modbus; DNP3; IEC-61850 GOOSE; industrial Internet of Things; latency; 5G
## 2 Materials and Methods
### Methodology of Corrosion Testing of Zinc Coatings  
Corrosion resistance tests were conducted in a neutral salt spray environment following ISO 9227 [26]. The experiments were performed in a Liebisch S400M-TR chamber, with each test cycle lasting 24 h. Each cycle consisted of the following:  
* A total of 16 h of continuous salt spray exposure.
* A period of 8 h for the natural evaporation of the deposited solution from the sample surface (ambient temperature: 20-24 \({}^{\circ}\)C, relative humidity: 40-60%).
* Regular photographic documentation and additional analyses.  
Test Parameters:  
* Salt fog environment temperature: 35 \(\pm\) 2 \({}^{\circ}\)C
* Sodium chloride concentration in distilled water: 50 \(\pm\) 5 g\(\cdot\)L\({}^{-1}\)
* pH of the sodium chloride solution (at 20 \(\pm\) 2 \({}^{\circ}\)C): 6.5-7.2
* Consumption rate of sodium chloride solution: 0.5-0.6 L\(\cdot\)h\({}^{-1}\)
* Air pressure at spray nozzles: 120 kPa
* Inclination angle of test samples: 20 \(\pm\) 2\({}^{\circ}\) (relative to the vertical plane)
* Number of test samples per set: 5  
The degree of sample degradation was evaluated based on the formation of red rust, indicating perforation of the anti-corrosion coating down to the steel substrate. This criterion was selected due to the distinct colour contrast between zinc corrosion products (white) and steel corrosion products (red/brown), allowing for accurate visual identification. To quantify corrosion damage, digital image analysis was performed using the ImageJ software (version 1.54) to determine the percentage of the surface area affected by red rust (Figure 5).  
Figure 4: Principle of microscopic imaging after the cupping test.  
Since ISO 10289 [27] requires that the edges of the samples be excluded from the evaluation within a 5 mm margin, the evaluated surface dimensions were 143 \(\times\) 55 mm (total area: 7865 mm\({}^{2}\)).
# PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks
## 5 Analysis  
### Expected Data Forwarding Probability  
Each node \(n_{i}\) has to forward its sensed and collected data according to an expected data forwarding probability \(\mathcal{FP}_{i}\). Let \(\partial_{i}=\sum_{l=1}^{m_{i}}\left(1-\tilde{h}\big{(}n_{i,l}\big{)}\right)^ {\alpha}\sum_{l=1}^{m_{i}}\left(1-\tilde{d}_{i,l}\right)^{\beta}\sum_{l=1}^{m _{i}}\overline{E}_{i,l}\ ^{\gamma}\) and \(\rho_{i}=\sum_{l=1}^{m_{i}}\left(1-\tilde{h}\big{(}n_{i,l}\big{)}\right)^{ \alpha}\left(1-\tilde{d}_{i,l}\right)^{\beta}\overline{E}_{i,l}\ ^{\gamma}\) and \(\omega_{i,j}=\left(1-\tilde{h}\big{(}n_{i,j}\big{)}\right)^{\alpha}\left(1- \tilde{d}_{i,j}\right)^{\beta}\overline{E}_{i,l}\ ^{\gamma}\).  
By replacing in Equations (11) and (12), we get: \(\tilde{\varphi}_{i,j}=\frac{\omega_{i,j}}{\partial_{i}}\) and \(\tilde{\varphi}_{i,j}=\frac{\omega_{i,j}}{\rho_{i}}\).  
Hence, the expected data forwarding probability for the node \(n_{i}\) is given by Equation (19).  
\[\mathcal{FP}_{i}=\sum_{j=1}^{m_{i}}\tilde{\varphi}_{i,j}\tilde{\varphi}_{i,j} =\sum_{j=1}^{m_{i}}\frac{\omega_{i,j}}{\partial_{i}}\left(\frac{\omega_{i,j}} {\rho_{i}}\right)=\frac{1}{\partial_{i}\rho_{i}}\sum_{j=1}^{m_{i}}{\omega_{i, j}}^{2}. \tag{19}\]
# Image Anomaly Detection Using Normal Data Only by Latent Space Resampling
## 3 Method  
This section describes the principles of our proposed method. The first step is to train a VQ-VAE using anomaly-free images as training data. When VQ-VAE can embed anomaly-free images into a compact discrete latent space and reconstruct high-quality outputs, we start performing the second step. All anomaly-free images are first encoded using the trained VQ-VAE to collect a latent code set, and then the probability distribution of this latent code set is estimated using PixelSNAIL. At the prediction stage, when the latent code of an input image is out of the distribution learned in the second step, PaxielSNAIL will conduct resampling operations on it. The resampled latent code is decoded as a restored image, which is used for anomaly detection by calculating the error with the directly reconstructed image. The overall work-flow of the proposed method is shown in Figure 1; in the following, we describe it in detail.
# Channel Measurement and Feasibility Test for Wireless Avionics Intra-Communications
## 1 Introduction
* Commercial antennas do not support the WAIC bands (4.2-4.4 GHz), and thus we self-produced monopole antennas to transmit and receive wireless signals at the WAIC bands (see Section 3). * We performed point-to-point wireless channel measurements and feasibility test of adopting IEEE 802.11 protocol in a real aircraft (KAI KT-1 Woongbi). (Specific reasons why we chose KAI KT-1 Woongbi as our test aircraft are explained in Section 3.) We obtained a power delay profile (PDP) and a root mean square (RMS) delay between measured points (see Sections 3 and 4). * We simulated the bit error rate (BER) performance using our measurement data when we considered a candidate sensor network scenario and reliable transmission techniques such as a maximum ratio combining (MRC) and space-time block coding (STBC) schemes (see Sections 5).
## 2 Selection of Ground Motion Records  
To explain the effect of different \(C_{y}\) and \(T\) on the damage potential ranking of ground motion, a total of 367 ground motions are obtained from the NGA-west2 database. The selected ground motions are classified based on the USGS site classification method and the classification result is shown in Table 1. The magnitude-epicenter distance distribution results of the selected ground motion records are shown in Figure 1. It can be concluded from the results that the magnitude ranges from 3.9 to 7.8. The magnitude of most ground motion records is above five, indicating that the selected ground motion records almost come from strong earthquakes. The epicenter distance is mainly larger than 30 km. It shows that most of the selected ground motion records belong to far-field ground motions.
# Performance Analysis of a WPCN-Based Underwater Acoustic Communication System
## 2 System Model
### Receiver
power \(P_{u}(i),\ \forall i\in\{1,2\cdots,I\}\) of the \(i^{th}\) subcarrier for the uplink information transmission can be expressed as:  
\[P_{u}(i)=\frac{\tau_{0}\eta\underset{k=1}{\overset{K}{\sum}}\left\{P_{d}(k)|H_{ d}(k)|^{2}+N[f_{d}(k)]\right\}}{I(1-\tau_{0})}. \tag{11}\]  
Let \(B=\frac{\tau_{0}\eta P_{d}}{I(1-\tau_{0})}\) and \(C(k)=\frac{\tau_{0}\eta N[f_{d}(k)]}{I(1-\tau_{0})}\). Then \(P_{u}(i)\) can be expressed as:  
\[P_{u}(i)=\underset{k=1}{\overset{K}{\sum}}[BH_{d}(k)+C(k)], \tag{12}\]  
where we also assume for simplicity that the transmit power of H-AP is equally allocated to each subcarrier for the downlink energy transfer in phase I, i.e., \(P_{d}(k)=P_{d}\), \(\forall k\in\{1,2,\ldots,K\}\).