esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Wireless Monitoring and Control of a Grid-Connected Photovoltaic System
## 4 Communication Module
### Wireless Signal Flow  
The wireless, grid-connected PV system has a coordinator node, WCC, which is responsible for linking the communications and receiving the sensor data sent by the WSPS and the WCU nodes. The WCC receives the voltage, \(V_{in}\), the boost converter input current, \(i_{in}\), the inductor current, \(i_{L}\), the temperature, \(T\), and the irradiance, \(G\), from the sensors of the WSPS every 7 ms. Then, the WCC applies the adaptive Lyapunov control and generates the control signal, the duty cycle, \(\gamma\), which is sent by the host to the WSPS. As for the data sent by the WCU, they are received every 0.1 ms. The values sent by the WCU are the DC/AC converter input voltage, \(V_{o}\), and the inverter output current, \(i_{grid}\). With those values, the control signal, the duty cycle for the inverter, \(\gamma_{ime}\), is obtained after applying the cascade PI control in the WCC. This information is depicted in Figure 8, in which the communication between the host node and the two different controllers is presented. The host node sends the signal values to the controllers and the controllers calculate the duty cycle required for the correct operation of the system. Then, the control signals are sent by the host node to the WSPS and WCU. The two figures at the bottom of Figure 8 are the DC/DC controller and the DC/AC controller, showing the signals required to obtain the control signals. In the case of the DC/DC controller, the signals are the irradiance, the temperature, the boost converter input voltage and current, the DC/DC converter output voltage, and the inductor current. For the DC/AC controller, the signals needed are the grid current and the inverter input voltage, \(V_{o}\).  
The WSPS node is responsible for controlling the boost converter to extract the maximum power from the PV modules. To this purpose, the values of the DC/DC converter input voltage, the boost converter input current, the inductor current, the irradiance, and the temperature must be read by means of analog to digital converters (ADCs). Those values are sampled and sent to the host node every 7 ms. Then, the WSPS node receives the control signal for the DC/DC converter.  
The WCU node controls the DC/AC converter and is responsible for maintaining the inverter input voltage given by the reference value. For that, the sensor signals, in this case, the DC/DC converter output voltage and the DC/AC converter inductor current, \(i_{grid}\), are sampled every 0.1 ms. The received signal is the control signal for the inverter, \(\gamma_{inv}\), as Figure 9 presents.
# Study of the Load-Bearing Characteristics of Bolts under Asymmetric Freezing Conditions
## 1 Introduction
The peak strength of the sample increased with decreasing exposure temperature and increasing normal stress change rate, and the \"m\" coefficient decreased with decreasing temperature. In the available research on frozen soil [24; 25; 26; 27; 28], some scholars studied the effects of freezing temperature, soil confining pressure, dry density and water content on the interfaces between frozen soil and structures. The results showed that the shear strength of the anchor-soil interface increased linearly with decreasing temperature and increasing normal stress. When the temperature rose, the anchorage bearing capacity was obviously lost. The pull-out resistance of the frozen soil anchor was several times or even more than ten times that of unfrozen soil. In anchoring projects, the axial force of the anchor in the initial freezing state significantly increased [29; 30]. When the temperature rose, the axial force decreased rapidly when the frozen soil melted, and it was difficult for the axial strength to recover to the level before freezing. Additionally, the anchoring force on soil after frozen soil thawed decreased year by year. [PERSON] [31] and [PERSON] [32] considered that every time the soil freezes and thaws, the underground structure inevitably produces relative dislocation with the surrounding soil, the frost heaving force has a creep effect on the underground structure, and its bearing capacity changes with creep displacement. The displacement of the structural damage position of the slope anchor in the seasonal freezing area occurs frequently due to freezing expansion and thawing settlement [33; 34; 35]. Damage to the anchor bolt by the pull-out force is caused by freezing and thawing cycles. [PERSON] et al. [36] considered that the ultimate pull-out bearing capacity of the bolt decreased with increasing number of freeze-thaw cycles, and freeze-thaw damage was relatively rapid. In the past, research on anchor bolts in frozen soil focused on the relationship between freezing parameters and shear strength but did not pay enough attention to the freezing method, and most studies adopted one way freezing. However, the transmission direction of a negative temperature load in nature often has a certain angle with respect to the structure. For example, in slope engineering in cold regions, the frozen front is constantly diffusing inwards from the slope surface, and the temperature gradient line intersects the anchor rod in a nonvertical direction. This leads to the anchor rod entering an asymmetric freezing state, and the two sides bear asymmetric frost heaving forces, as shown in Figure 1. In this study, the load-bearing characteristics of anchors under asymmetric freezing conditions were studied by indoor pull-out tests of frozen soil bolts.
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 6 Speech Technology in Industry 4.0
### Speech in IoT Applications  
SmartHear system [226] is a hearing aid system designed to reduce the effects of noise, reverberation, and signal quality degradation when the speaker and listener are separated by a large distance, such as in an auditorium. It was created primarily to assist students with hearing loss or foreign students who were studying in a noisy environment. A smartphone records the audio signal, which is then transmitted to the listener via Bluetooth or WiFi. Therefore, the room's noise and reverberation can be effectively eliminated, and understanding the spoken information requires less cognitive load from the listener. As an added convenience, automatic speech recognition (ASR) is integrated, allowing the listener to follow the written text on the screen. The system was evaluated subjectively as well as objectively, and the results show that the degradation caused by the speaker's distance can be eliminated using SmartHear.  
The smart campus system described in [227] is an application that combines a number of novel technologies. The conversational dialogue system is based on the Chinese word  
Figure 15: System structure  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Problems** & **Techniques** & **Contribution** & **Ref.** \\ \hline \multirow{3}{*}{Teaching cooperative robots is a complex process} & Audio-visual fusion & \multirow{3}{*}{Novel method to teach} & \multirow{3}{*}{[222]} \\  & based on text, intent & & \\ \cline{1-1} \cline{3-4}  & detection, maximum & & \\ \cline{1-1} \cline{3-4}  & entropy intent to control & & \\ \cline{1-1} \cline{3-4}  & mapping & & \\ \hline \multirow{3}{*}{Speech-controlled medical robots} & HIWIN robotic & An accuracy of up to 3.1\({}^{\circ}\) & \multirow{3}{*}{[225]} \\  & endoscope holder with & & \\ \cline{1-1} \cline{3-4}  & speech recognition & & \\ \hline \multirow{3}{*}{Control robots by using speech} & Web-based control, speech recognition, context free grammars & Quasi-natural language commands & [224] \\ \cline{1-1} \cline{3-4}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Summary of speech technology in robotic application.  
embedding solution, which improves dialogue toleration and semantic interpretation significantly. Emotional labeling of the conversation is also performed, and it is used as an input feature by the neural network-based dialogue engine. Several emotional scoring methods were trained and tested, and GoogLeNet outperformed the others with over 95% accuracy. In domain conversations were used to train the dialogue engine. The integrated system provides information about a Taiwanese university campus via an easy-to-use audio interface.
## 3 Impact of Cold Plasma on Packaging Properties
### Thermal Properties
The etching effect and the loss of moisture from the film surface result in the modification of T\({}_{\text{m}}\) and \(\Delta\)H\({}_{\text{m}}\) as the presence of water can act plasticizer and increase the chain mobility. Similarly, the application of other gases like O\({}_{2}\), N\({}_{2}\), and EtOH-Ar increased the T\({}_{\text{m}}\) and \(\Delta\)H\({}_{\text{m}}\) with the exception of Ar gas, which had no significant impact on the thermal properties of the films [78]. In another study, [PERSON], [PERSON] [109] investigated the impact of cold plasma treatment with different gases (O\({}_{2}\), N\({}_{2}\), air, He, and Ar) on the edible film prepared from defatted soybean meal. It was reported that the application of He and Ar increased the T\({}_{\text{g}}\) compared to the untreated film, which may have been due to the introduction of radicals at the polymer surface resulting in the formation of a cross-linked network and leading to the increase in T\({}_{\text{g}}\). On the other hand, T\({}_{\text{g}}\) decreased in films after O\({}_{2}\) cold plasma treatment due to the amalgamation of oxygen-containing functional groups at the biopolymer surface, enhancing the surface molecular mobility and resulting in the observed decrease in T\({}_{\text{g}}\) values [109]. A few studies reported that plasma treatment had no impact on the thermal properties of biopolymer-based film from PLA [110], nisin-coated PLA [48], and zein [75]. Other studies reported the negative impact of cold plasma treatment on the thermal properties of biopolymer-based films. [PERSON], [PERSON] [84] studied the effect of dielectric barrier discharge (DBD) plasma treatment on sodium caseinate film and showed that T\({}_{\text{g}}\) of the plasma-treated film decreased as compared to the untreated film. The decrease in T\({}_{\text{g}}\) can be ascribed to chemical etching due to the breakdown of the chemical bonds, chain scission, or chemical degradation of the plasma-treated biopolymer. Similarly, [PERSON], [PERSON] [107] studied the impact of glow discharge plasma on fish protein films. The DSC result showed that the application of plasma for 2 and 5 min decreased T\({}_{\text{g}}\), T\({}_{\text{m}}\), and \(\Delta\)H\({}_{\text{m}}\) of the film significantly as compared to the untreated film. The changes in these thermal parameters suggest that the biopolymer matrix is affected.
## 2 Methodology for Identifying Unknown Impact Load
### Impact Load Reconstruction through Strain-Load Conversion  
A direct measurement of the impact force is not feasible in the general condition of manufacturing processes. Considering this, a small transducer was manufactured to accurately determine an arbitrary unknown impact load from strain signals. To measure  
Figure 1: Illustration of method to identify the external impact load acting on production equipment. (**a**) Schematic of process for determining impact load and application of reconstructed impact load; (**b**) procedure for calibrating impact load transducer using uniaxial compression test; (**c**) model for conversion from strain signal to impact load.  
the compressive strain signals, the axis of the strain gauges is aligned in the direction of the applied load. KFG-02-120-C1-11 uniaxial strain gauges of 120 \(\Omega\) from KYOWA are used in this study (Figure 1b). As shown in Figure 1b, the impact load transducer was calibrated under uniaxial compression conditions using a universal testing machine (UTM). The tests were performed at a testing rate of 10 mm/s. Five sets of loads were applied in the axial direction, and five points were recorded for each set. The specimen was loaded to 3 kN, and the force from the UTM and the output signals from the four strain gauges were recorded. A digital meter (System 7000 Strain Smart Data Acquisition System from Vishay Micro-Measurements) was used to record the resistance change of the strain gauge with a sampling rate of 2000 Hz. The average of the four strain gauge signals (\(\mu\varepsilon\)) was calibrated with the load data, as shown in Figure 1b. The developed converting model shows good agreement with the actual applied load (Figure 1c) and an R\({}^{2}\) value of 0.9945. Thus, the average of the strain values can be converted to a load as expressed in Equation (1):  
\[\text{Load}=6.82\cdot\text{Average Strain}+20.51 \tag{1}\]
## 7 Experimentation and Analysis
### Validation of Resource Optimization Methods  
#### 7.2.1 Environment Setup and Parameterization  
In this section, the system optimization model developed based on the GLSOAG is simulated and validated in order to demonstrate the usability of the designed unmanned emergency support system and the effectiveness of the system resource optimization method. The simulation parameters are set as shown in Table 7.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Function** & **F1** & **F2** & **F3** & **F4** & **F5** & **F6** \\ \hline SOA & 0.39195 & 0.39925 & 1.1094 & 0.40825 & 0.48161 & 0.40324 \\ GLSOAG & 0.498 & 0.50927 & 1.2398 & 0.49713 & 0.58244 & 0.51008 \\ PSO & 0.12593 & 0.13522 & 0.82269 & 0.12995 & 0.20624 & 0.12772 \\ BSA & 0.071708 & 0.080811 & 0.14069 & 0.067545 & 0.081211 & 0.069297 \\ \hline
**Function** & **F7** & **F8** & **F9** & **F10** & **F11** & **F12** \\ \hline SOA & 0.94493 & 0.53173 & 0.4826 & 0.50405 & 0.56276 & 1.8174 \\ GLSOAG & 1.0664 & 0.66205 & 0.51352 & 0.52581 & 0.59092 & 1.9678 \\ PSO & 0.65944 & 0.2263 & 0.227 & 0.24945 & 0.31196 & 1.5425 \\ BSA & 0.12505 & 0.078753 & 0.080009 & 0.080905 & 0.084555 & 0.21039 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Execution time of the algorithm on the test function.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Implications** \\ \hline \(D_{i}\) & Input data volume \\ \(C_{i}\) & Workload, i.e., CPU cycles required to complete the \(i\)st task \\ \(T_{i,local}\) & Processing latency of tasks at local terminal devices \\ \(f_{user,i}\) & Terminal device CPU clock cycle frequency \\ \(E_{j,local}\) & Calculating Energy Consumption for Local Devices \\ \(k_{u}\) & Terminal equipment energy factor \\ \(\delta\) & Latency Optimization Weighting Factors \\ \(F_{i,local}\) & Total task local processing overhead \\ \(W\) & Upstream bandwidth \\ \(w_{i}\) & Task \(i\) Upstream Transmission Link Sub-band Widths \\ \(P_{i}\) & Transmit power of the \(i\)th terminal device \\ \(H_{i}^{j}\) & Channel gain between terminal device \(i\) and edge node \(j\) \\ \(\sigma^{2}\) & Background noise variance \\ \(R_{i}^{j}\) & Transmission speed from terminal device \(i\) to edge node \(j\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Main parameter settings.
## 3 Results and Discussion
### Physical-Chemical Characterization of the Waste Vitreous Enamels
#### 3.2.4 The Leaching Test  
In order to confirm the environmental safety of using mortar mixtures, the leaching test was performed on the mixtures with maximally allowed cement replacement, where the mortar mixtures were made with 30% of WEP, and 20% of WETM. The results are shown in Table 16.  
From obtained results, it can be seen that, according to the Rulebook which is applicable in Serbia, the concentrations of the released elements from both examined mortar mixtures were much lower than allowed.  
In order to fully confirm that the application of waste vitreous enamel is safe for the environment, the leaching test was continued and performed for the next 60 days. The concentration of released elements did not differ significantly in comparison with those shown in Table 16, for the whole examined time interval.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Released Element** & **Concentration, \(\mu\)g/dm3** & **\textasci{* Allowed Values, \(\mu\)g/dm3**} \\ \hline  & WEP & WETM \\ Cu & 34 & 5 & \textless{}100 \\ Zn & 21 & 15 & \textless{}1000 \\ Ni & 10 & 20 & \textless{}100 \\ Cd & 1 & 1 & \textless{}10 \\ Pb & 20 & 30 & \textless{}100 \\ Cr & 7 & 2 & \textless{}500 \\ Hg & 48 & 4 & \textless{}1 \\ As & 22 & 31 & \textless{}50 \\ Mg & 8 & 5 & - \\ Fe & 1270 & 31,000 & - \\ Co & \textless{}10 & \textless{}10 & - \\ Al & 2280 & 544 & - \\ Sn & 698 & 5 & - \\ Si & 470 & 420 & - \\ Mo & 1 & \textless{}1 & - \\ Sr & 600 & 140 & - \\ Ca & 100 & 400 & - \\ Mn & \textless{}1 & \textless{}1 & - \\ V & 10 & \textless{}10 & - \\ \hline \hline \end{tabular}  
* According to the Rulebook on permitted quantities of hazardous and harmful substances in soil and irrigation water and methods of their testing, “Sluzhen glasnik RS 23/1994”—applicable in Serbia. Dashes mean that the Rulebook does not prescribe limits.  
\end{table}
Table 16: The concentration of the released elements after the leaching test.  
Thus, it may be concluded that the application of the waste vitreous enamel as a partial replacement for cement in mortar and concrete productions is completely acceptable from ecological and human health safety aspects.
# Pico-Sat to Ground Control: Optimizing Download Link via Laser Communication
## 4 Results
### Link Budget for FSO Nano-Satellite
#### 4.1.4 FSO Overhead Analysis  
As in any satellite communication, FSO communication also requires a forward error correction (FEC) code. Moreover, a proper FSO protocol should have several modulation configurations that can be rapidly adapted to allow real-time modulation optimization, since any cloud in the sky may change the link budget significantly. Assuming standard on-off keying (OOK) protocol, the following parameters should be defined for each modulation: packet size (e.g., 100, 1000, 10,000 bytes), FEC overhead (e.g., 10%, 20%, 40%), and basic bit rate (e.g., 1, 10, 100, 1000 Mbps). Assuming a bit error rate (BER) of \(10^{-4}\), the use of very small packets (e.g., 100-byte packets) will be required in order to obtain a packet error rate (PER) lower than 10%. With the packet header overhead and the required FEC overhead (say 20%), the actual \"good-put\" speed is expected to be 70-75% of the theoretical FSO link, which is still significantly faster than an RF-based solution.
## Materials and Methods
### Evaluation of Soil Properties and Plant Growth  
Soil samples were collected in mid-June of each year to assess physical and chemical properties. Accordingly, mean emergence time (MET) was evaluated as follows: MET = (\(\Sigma\)n \(\times\) g)/N, where n is the number of seedlings emerging per day, g is the required number of days for emergence, and N is total number of emerged seeds [31].  
Bulk densities (BDs) were evaluated from oven-dried (at 105 \({}^{\circ}\)C for 24 h) soil sample weights and the soil corer volume, and they were further used to determine total porosity by assuming a particle density of 2.65 cm\({}^{-3}\). BD of the soil was calculated for a depth interval of 0-15 cm using the core method [32]. A gravimetric method was devised to record soil moisture at 55 DAS at a depth of 20 cm between sunflower rows across each plot. The moisture content of soil was recorded five days after each round of irrigation. BD multiplied by the gravimetric moisture content gave volumetric soil moisture. The Blanco-Canqui method [33] was followed to obtain saturated hydraulic conductivity (K\({}_{\text{sat}}\)). Core samples were taken from a depth of 0-15 cm along rows and across inter-row areas. Root depth was evaluated according to [PERSON] et al. [20].  
Chlorophyll was evaluated with the Minolta SPAD-502 hand-held device in fully expanded upper leaves at the flowering stage. Leaf area measurements were performed upon termination of the flowering stage. Observations on 10 plants selected at random were recorded to check for the impacts of different tillage systems and fertilizer treatments on phenological development of plants until maturity. Physiological maturity stage was targeted for harvesting the plants, as indicated by the color alteration of the back of the head (from green to yellow) and bracts (to brown). As far as harvesting was concerned, 10 plants were sampled at random from each replicate for each treatment before being quantified in terms of yield and yield components. Oil contents of the samples were extracted by a small expeller press and were further extracted by the Soxhlet extraction method where hexane served as the solvent [34]. Crop and soil properties were subjected to analysis of variance as described by [PERSON] and [PERSON] [35]. The procedure was implemented utilizing SAS Software (SAS Institute, Cary, NC, USA). The F-test was devised to indicate whether or not effects of the treatments were significant, while significance of the difference between means of the two treatments was evaluated by least significant difference (LSD) at 5% probability. We assessed the factor year and found no significant tillage \(\times\) year \(\times\) fertilizer type interactions.
# V2X Wireless Technology Identification Using Time-Frequency Analysis and Random Forest Classifier
## 4 Signal Identification
### Feature Vector Extraction  
The feature extraction procedure starts by applying empirical mode decomposition to the received signal \(y(t)\) using Equation (7). The obtained IMF components represent the original signal from high frequency to low frequency in different frequency bands. In addition, the first few IMFs are significant as they have the largest energy and contain the most important information from the I/Q signal. Therefore, the instantaneous frequencies of the prior \(K\) IMFs are then extracted using Equation (12). The value of \(K\) depends on the signal length and complexity. In practice, it is usually set between three and five [31; 32].  
Given \(N\) is the length of the signal of interest \(y(t)\), the number of elements of the feature vector made up of \(K\) IFs is equal to \(KN\), leading to a high-dimensional dataset. Consequently, dimensionality reduction is required in order to reduce the overall execution time and thus improve the classification model performance. In this context, singular value decomposition might be the most popular and efficient dimensionality reduction technique in machine learning. It comes from the field of linear algebra and consists of decomposing an \(m\times n\) matrix \(\mathbf{M}\) into three matrices \(\mathbf{U},\mathbf{\Sigma}\) and \(\mathbf{V}\) as follows:  
\[\mathbf{M}=\mathbf{U}\mathbf{\Sigma}\mathbf{V}^{T}, \tag{15}\]  
where \(\mathbf{U}\) and \(\mathbf{V}\) are two orthogonal matrices of dimensions \(m\times m\) and \(n\times n\), respectively, and \(\mathbf{\Sigma}\) is an \(m\times n\) diagonal matrix. The diagonal entries \(\sigma_{i}\)\((i=1,...,r)\) of \(\mathbf{\Sigma}\) are positive real values listed in descending order. They represent the singular values of \(\mathbf{M}\), while \(r\) is equal to its rank [33].  
By applying the SVD algorithm to the previously constructed feature vector, the most important structure in the raw data is preserved whilst reducing its dimension to \(1\times K\). Therefore, the obtained time-frequency feature vector that will be used for signal identification is given by:  
\[S=[\sigma_{1}(\omega),...,\sigma_{K}(\omega)]^{T}, \tag{16}\]  
where \(\sigma_{i}(\omega)\) is the singular value related to the instantaneous frequency of the \(i\)-th IMF.  
Figure 1: Pipeline of the proposed signal identification system.
# Application of Artificial Neural Networks in Assessing Mining Subsidence Risk
## 2 Methodology  
The overall study process is summarized in Figure 2 and is classified into four categories; collection and preprocessing of subsidence data, construction of ANN model and training, establishment of subsidence risk grade, and test of the model with new data.  
Figure 1: Typical examples of subsidence in this study.