esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Home Energy Management System with Smart Rule-Based Controller
## 2 Proposed HEM System
### Appliance Monitoring and Controlling Modules
#### 2.2.4 Room Condition Monitoring Circuit  
The proposed HEM system includes a circuit for monitoring and transmitting data on the ambient conditions, such as the room temperature, humidity, illuminance, and CO2 concentration. The circuit is also capable of detecting motion in the room through a motion sensor and is thus able to provide the main controller with the inputs required by the decision-making algorithm. Figure 7 shows the circuit and employed sensors.  
Figure 6: LED light dimming circuit: (**a**) Schematic and (**b**) Circuit connections.  
Each sensor in Figure 7 has a specific purpose. For example, the room temperature and humidity are measured by the DHT22 (STmicroelectronics, Shanghai, China, 2000) sensing module, which updates its reading information every 2 s. When the condition monitoring circuit is used to monitor the internal temperature of a refrigerator or the water temperature in a water heater, the waterproof DS18B20 (Maxim Integrated, San Jose, CA, USA, 2005) temperature sensor is employed. All of the recorded data are initially compared with readings from commercial temperature and humidity loggers to ensure that all of the sensing modules are producing accurate and reliable readings for input to the algorithm. Further, the TEMT 6000 (Vishay Americas, Greenwich, CT, USA, 2004) light sensing module is used to continuously log the light intensity within the room because the user controls the light intensity inside the room during the HEM process. Here again, the readings are validated by comparison with the measurements of a commercial ST-1309 LUX (ATP Instrumentation, Ashby-de-la-Zouch, Leicestershire, UK, 2018) meter.  
Two other sensors are used to identify the occupancy status of the room, namely, a passive infra-red (PIR) motion detection sensor and an SEN 1059 CO2 sensor (DF Robot, Pudong, Shanghai China, 2018). The information acquired by these sensors are used by the algorithm to automatically control the relevant appliances. The PIR motion detection sensor continuously scans the space in front of it within its range, and then sets its output to 1 or 0, depending on whether it detects motion or not, respectively. However, because there might be the situation in which a person is within the room, but without detectable motion, a CO2 sensing module is used for augmentation. The SEN 1059 CO2 sensor utilizes an inverse relationship between CO2 concentration and voltage (26).  
After the development of all the necessary hardware for providing the main controller with the needed inputs, a rule-based HEM algorithm was developed and experiments were performed in order to verify the reliability of the proposed HEM system.  
Figure 7: Room condition monitoring circuit.
## 2 Method  
### Criteria for Judging Hearing Loss  
As early as the last century, people began studying methods to evaluate hearing impairment. This project adopts the judgment criteria called 85 A-weighted decibel recommended exposure level, which is acknowledged by the National Institute for Occupational Safety and Health [5]. However, it must be mentioned that whether 85 dB is a safe noise exposure level for the public is still controversial [6]. Several key terms used in this standard care explained below.  
Decibel (dB): unit of level when the base of the logarithm is the 10 th root of 10 and the quantities concerned are proportional to power (ANSI S1.1-1994: decibel).  
Dose: the amount of actual exposure relative to the amount of allowable exposure, for which values of 100% and above represent exposures that are hazardous.  
Time-weighted average (TWA): the averaging of different exposure levels during an exposure period.  
REL: recommended exposure limit.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Activity** & **Clean and Jerk** & **Snatch** & **Deadlift** & **Squat** \\ \hline Average Noise Level (dBA) & 100.17 & 100.43 & 94.09 & 96.29 \\ \hline Peak Value (dBA) & 101.1 & 102.3 & 95.2 & 97.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Top four activities causing excessive noises in the gym.  
In order to control the noise exposure levels and durations, the combination of exposure level (\(L\)) and duration (\(T\)) can be calculated as follows:  
\[T\ (\min)=\frac{480}{2^{(L-85)/3}}. \tag{1}\]  
According to Equation (1), the exchange rate is 3. In other words, when the noise increases by 3 dB, the duration is halved [7]. Considering that daily noise exposure consists of periods of different noise levels, this can be calculated as follows:  
\[D=\left(\frac{C_{1}}{T_{1}}+\frac{C_{2}}{T_{2}}+\frac{C_{n}}{T_{n}}\right) \times 100, \tag{2}\]  
where \(C_{n}\) is the total time of exposure at a specified noise level, and \(T_{n}\) is the exposure duration for which noise at this level becomes hazardous.  
Usually, for noise, given an 85 dBA exposure limit and a 3 dB exchange rate, the _TWA_ is calculated as follows:  
\[TWA=10.0\times Log\left(\frac{D}{100}\right)+85, \tag{3}\]  
where \(D\) is the dose.  
Using Equation (3), the daily dose can be converted into an 8 h _TWA_. This method provides a unified judgment standard for hearing loss. In order to more intuitively show the relationships between exposure level (\(L\)) and duration (\(T\)), daily noise dose, and 8 h _TWA_, Figures 1 and 2 [5] are provided.  
Figure 1: Combinations of noise exposure levels and durations not to be equaled or exceeded.
# Detecting Selected Instruments in the Sound Signal
## 3 Data Pre-Processing
### Considered Features  
The popularity of _MFCC_ coefficients results from the multitude of possibilities they offer. First of all, they allow the extraction of a lot of different information from the signal, such as the timbre of the voice, the way the instrument is played, the type of instrument, or the musical genre. Therefore, this method is well suited for speech recognition systems, instruments, and other parameters related to music and sound. In the context of the topic of this work, it is important that they are a mathematical representation of the actual reception of sound by the listener. The disadvantage of the described algorithm is its high level of complexity--the path to obtaining the final matrix from the input signal is multi-stage and requires some experience in signal analysis. Moreover, _MFCC_ coefficients are characterized by low resistance to noise [4].  
Mel-cepstral parameters are great for extracting the necessary information from a sound signal, so instruments can be classified based on them. The choice of model and classifier remains crucial. While implementing this work, we decided to use artificial neural networks.  
One of the basic phases of using artificial neural networks is data pre-processing. In this case, this means converting the audio file into a matrix of _MFCC_ coefficients. An arti  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Instrument** & **Number of Recordings—Samples** \\ \hline clarinet & 1311 \\ \hline electric guitar & 1854 \\ \hline female voice & 1744 \\ \hline flute & 3555 \\ \hline piano & 6032 \\ \hline tenor saxophone & 477 \\ \hline trumpet & 627 \\ \hline violin & 5971 \\ \hline in total & 21,571 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of recordings for each instrument.  
ficial neural network has a permanent structure, so to function properly, it must be ensured that the dimensions of the training, validation, and test data samples are the same. The use of the dataset (Section 3.1) ensures that the sound signals have the same waveform length. Otherwise, a procedure that would equalize the duration of all recordings should be implemented, preferably without losing valuable information. Due to the fact that the set used has recordings lasting 2972 milliseconds and the sampling frequency is 22,050 Hz, assuming the default and recommended value of the _hop length_ parameter is 512, it is possible to calculate the width of the matrix taken at the network input. (Equations (1) and (2))  
\[width\,of\,\,\,the\,\,\,matrix=\frac{sample\,rate\cdot duration}{hop\,length} \tag{1}\]  
\[\frac{22,050\cdot 2.972}{512}\approx 128 \tag{2}\]  
The height of the mentioned matrix is equal to 13, i.e., the number of _MFCC_ parameters extracted from the signal, which constitute a set representing frequency power in different ranges, and in this work, the full frequency spectrum is divided into 13 _MFCC_.  
In summary, the input data fed to the first layer of the network have dimensions of \(128\times 13\).
# Corrosion Fatigue Assessment of Bridge Cables Based on Equivalent Initial Flaw Size Model
## 4 Corrosion Fatigue Lifespan Assessment of Parallel Wire Cable
### Load Effect on Corrosion Fatigue Lifespan
In the corrosive fatigue environment, metals often do not exhibit a clear fatigue strength [50]. Steel wires with initial cracks can still experience corrosion fatigue failure albeit over a longer period [51] of time. Therefore, the threshold effect is neglected when considering crack propagation. When measuring microscopic defects in steel wires with tensile strengths greater than 1800 MPa, [PERSON] et al. [51] noted that such initial defects were roughly in the range [2.5 \(\upmu\)m, 13 \(\upmu\)m]. To facilitate comparing the effects of different defect sizes on the lifespan, these initial defects are treated as equivalent cracks for calculation purposes. Five levels of initial crack depths are selected for comparison: 2.5 \(\upmu\)m, 5 \(\upmu\)m, 7.5 \(\upmu\)m, 10 \(\upmu\)m, and 13 \(\upmu\)m. The calculations are conducted with an equivalent stress amplitude of 23.65 MPa while keeping the environmental conditions consistent across different operating conditions. The conversion coefficient \(\tau\) is set to 0.1985, and the fatigue crack growth rate modification factor Cor is taken as 1. As shown in Figure 17, the corresponding lifespan for defect sizes of 2.5 \(\upmu\)m, 5 \(\upmu\)m, 7.5 \(\upmu\)m, 10 \(\upmu\)m, and 13 \(\upmu\)m are 594, 420, 339, 289, and 248 years, respectively, with a decreasing rate of lifespan reduction. Additionally, the crack evolution starts with a very  
Figure 16: Evolution of the defect depth in each wire of bridge cable. small size and maintains a relatively long stable period. During this time, the crack depth does not exhibit significant changes, but the pit growth depth noticeably increases. In this stage, due to the slow crack propagation rate, the crack tip tends to degrade into a pit. However, as the pit growth rate gradually decreases, in the later stages of crack growth, excessive stress intensity factors will rapidly lead to fracture propagation. Furthermore, the crack propagation relationships were further compared when the fatigue crack growth rate modification factor \(C_{cor}\) was taken as 1, 3, and 5, as shown in \[N_{eq}=\sum n_{i} \tag{30}\]0 The calculations revealed that the lifespan with \(C_{cor}\) = 1 is approximately 250 years, which is a value much larger than the design reference period for bridges (100 years). However, when \(C_{cor}\) reaches 2, the crack propagation lifespan will be less than 100 years, and when \(C_{cor}\) reaches 3, the remaining lifespan is 57 years. While it is not clear how \(C_{cor}\) relates to the environment, it at least indicates that under extreme conditions, corrosion fatigue fracture can still occur within the design reference period for bridge cable wires subjected to small stress amplitudes. \[N_{eq}=\sum n_{i} \tag{30}\]1 Effect of different initial defect depths on lifespan. \[N_{eq}=\sum n_{i} \tag{30}\]2 Relationship between \(C_{cor}\) and the amount of crack extension.
# Image-Based Malicious Network Traffic Detection Framework: Data-Centric Approach
## 4 Experimental Results
### Effectiveness of Feature Engineering
**} & \multicolumn{2}{c}{**Time**} & \multicolumn{2}{c}{**Time**} & \multicolumn{2}{c}{**Time**} & \multicolumn{2}{c}{**Time**} & \multicolumn{2}{c}{**Time**} & \multicolumn{2}{c}{**Time**} & \multicolumn{2}{c}{**Time**} \\  & & & **After** & & & & & & & & & & & & & \\  & & & & & & & & & & & & & & & \\ \hline Ours & NSL-KDD & InfoGain & \(41\to 36\) & \(12.2\) & CNN & \(98.98\) & \(99.06\) & \(+0.08\) & \(926\) & \(764\) & \(+17.47\) & \(3.71\) & \(2.99\) & \(+19.44\) \\
[54] & NSL-KDD & Autoencoder & \(41\to 10\) & \(75.6\) & Extra Trees & \(99.40\) & \(99.20\) & \(-0.20\) & - & - & - & - & - & - & - \\
[62] & NSL-KDD & LNNLS-KH & \(122\to 7\) & \(94.3\) & KNN & \(91.22\) & \(96.12\) & \(+4.90\) & \(3574\) & \(4171.8\) & \(-16.7\) & \(41.35\) & \(34.38\) & \(+16.8\) \\
[55] & NSL-KDD & PCA-ADASYN & \(121\to 28\) & \(76.9\) & SGAN & \(94.20\) & \(99.34\) & \(+51.44\) & - & - & - & \(11.6\) & \(9.8\) & \(+15.5\) \\
[56] & NSL-KDD & PCA & \(41\to 7\) & \(82.9\) & Naive Bayes & \(77.50\) & \(80.60\) & \(+4.10\) & \(86.7\) & \(396.5\) & \(-357.7\) & - & - & - & - \\
[57] & NSL-KDD & mRMR & \(39\to 10\) & \(75.4\) & MLP & \(85.73\) & \(85.73\) & \(0.00\) & \(207.3\) & \(141.3\) & \(+31.8\) & \(8.1\) & \(5.9\) & \(+27.2\) \\
[58] & UNSW- & InfoGain & \(52\to 20\) & \(61.5\) & DT & \(88.40\) & \(88.14\) & \(-0.26\) & - & - & - & - & - & - \\
[59] & UNSW- & \multirow{2}{*}{RefiF} & \multirow{2}{*}{\(73\to 9\)} & \multirow{2}{*}{\(87.7\)} & \multirow{2}{*}{LR} & \multirow{2}{*}{\(86.10\)} & \multirow{2}{*}{\(86.10\)} & \multirow{2}{*}{\(86.10\)} & \multirow{2}{*}{\(86.10\)} & \multirow{2}{*}{\(0.00\)} & \multirow{2}{*}{\(74.7\)} & \multirow{2}{*}{\(89.3\)} & \multirow{2}{*}{\(-19.5\)} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} \\  & & & & & & & & & & & & & \\
[60] & CICIDS2017 & PCA & \(78\to 10\) & \(87.2\) & CNN & \(97.50\) & \(97.20\) & \(-0.30\) & - & - & +80.0 & \(18.0\) & \(6.0\) & \(+66.7\) \\
[61] & CICIDS2017 & PCA & \(100\sim 51\) & \(49.0\) & DT & \(98.60\) & \(98.60\) & \(0.00\) & - & - & - & - & - & - \\ \hline \hline \multicolumn{11}{c}{Note: Dim. (Before.-After): Number of features before and after reduction. Red. (\(\square\)): Dimensionality reduction ratio: Acc. (\(\square\)): Absolute change in accuracy accuracy accuracy accuracy accuracy accuracy accuracy accuracy accuracy accuracy. Train Time, A(\(\square\)): Relative change in training time. Infer. Time A(\(\square\)): Relative change in inference time. -\(\times\) indicates improvement. -\(\times\) indicates degradation. References: [PERSON] et al., 2025 [54]; [PERSON] et al., 2021 [62]; [PERSON] et al., 2025 [55]; [PERSON] et al., 2022 [56]; [PERSON] et al., 2020 [60]; [PERSON] et al., 2025 [66]; [PERSON] et al., 2019 [61]; [PERSON] et al., 2024 [68]; [PERSON] et al., 2020 [94]. &
## 4 System Economic Analysis  
At present, equipment costs have become one of the main factors restricting PV power generation [41; 42]. In order to prove the practicability of the proposed DC microgrid, the cost of test equipment will be counted. The main economic investment in this project comes from the initial microgrid system build, and the test build physical selling price is shown in Table 6.  
The recovery period can be estimated as in Equation (3)  
\[PB=\frac{Ci}{DCC\times AED\times 365} \tag{3}\]  
where \(PB\) represents the recovery period, \(Ci\) represents the initial investment, \(DCC\) represents the DC power consumption, and \(AED\) represents the local average electricity price.  
According to the above test results, it can be seen that the daily electricity generated by the monocrystalline PV panel with an area of 0.46 m\({}^{2}\) is 0.286 kWh. According to the electricity consumption statistics, the average daily DC power required per household is 1.43 kWh, so the area of PV panels required is 2.3 m\({}^{2}\). According to the market price of about 600 RMB/m\({}^{2}\), the initial investment per household is about 1380 yuan. The daily power generation is 1.43 kWh, the average price of electricity in the Qingdao area is 0.55 RMB per kWh, the daily saving is 0.79 RMB, and the cost recovery is calculated to be completed in about 4.8 years.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial** & & & & \\
**Number** & **Equipment Name** & **Quantity** & **Specification** & **Factory** & **Selling Price (RMB)** \\ \hline
1 & PV components & 1 (set) & Monocrystalline silicon (80 W) & Hekate & 545 \\
2 & PV battery & 1 (pc) & Colloidal battery (60 AH) & Hekate & 330 \\
3 & Circuit Boards & 4 (kinds) & 100 mm \(\times\) 10 mm & JiaLiChuang & 120 \\
4 & All kinds of electronic devices & 4 (sets) & Chips and Components & Your Cee & 300 \\
5 & Shipping Fee & & & 85 \\ Total & & & & 1380 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Breakdown of initial project investment.  
Figure 13: Physical diagram of the circuit system.
## 4 System Architecture
### Hermitian Mapping  
The technological features of a rolling-OFDM system are presented in this section. Instead of putting the data symbol directly into the IDFT block, as in conventional OFDM in radio frequency, each symbol must transit via the Hermitian block. After that, the signal is routed into the IFFT. The Hermitian block provides the unique task of ensuring that the IDFT's output is completely real.  
The Hermitian mapping process depicts in the equation below.  
\[X_{m}=X_{N-m}^{*}\text{ for }0<m<N\text{ and }X_{0},X_{1},X_{2}\ldots\ldots X_{N-1} \tag{5}\]  
\[X=[0,X_{1},X_{2}\ldots 0\ldots X_{2}^{*},X_{1}^{*}] \tag{6}\]
## 2 Materials and Method
### Methodology  
#### 2.2.1 Underground Thermal Behavior  
The geothermal heat exchanger reproduced in laboratory can be used to understand the thermal exchange that takes place in depth between the ground and the working fluid. Heat is provided through the working fluid which, in turn, increases the temperature of the surrounding ground (sandy material in the device). At the depth of the sensors located in the sand (in the middle of the total container height), heat is mainly transmitted by conduction. The sand temperature is measured at certain distances from the center of the borehole using the sensors installed in the cupper tubes. As explained in the already published research [33; 34], heat transmission is expressed in cylindrical coordinates and the heat transfer relation in a cylinder is defined as:  
\[\dot{Q}=\ -kA\frac{dT}{dr}\ \rightarrow\ \dot{Q}=2\pi kL\frac{T_{1}-T_{2}}{ln(r_{2}/r_{ 1})} \tag{1}\]  
where \(\dot{Q}\) is the heat transfer rate, \(k\) is the material thermal conductivity, \(L\) is the borehole length, \(dT/dr\) is the temperature differential in relation to the radius differential, \(T_{1}\), \(T_{2}\) are the temperatures in positions 1 and 2 and \(r_{1}\), \(r_{2}\) refer to the radius for positions 1 and 2.  
Considering the above Equation (1) as an analogy to electricity circuits, the heat transfer rate could be expressed as the quotient of the thermal gradient in two positions (\(\Delta T\)) and the thermal resistance between them (\(R_{T}\)). In this way, the thermal resistance between two points can be calculated applying Equation (2) [34]:  
\[R_{T}=\frac{ln(r_{2}/r_{1})}{2\pi kL} \tag{2}\]  
After the corresponding calibration of the sensors that constitute the device and the validation of the heat transfer model [33], the experimental test consists of the circulation of the heated working fluid that comes from the thermal tank [35]. The system is then working and temperatures in the different locations of the sensors are registered. Once the stationary regime is reached, the calculation of the thermal resistance between two different positions and the ground thermal conductivity is possible. The estimation of this last property requires the calculation of the previously mentioned thermal resistance.  
Figure 3: Schema and images of the experimental setup.  
When obtaining this resistance, the heat transfer going through the model must be defined, and its determination is made according to the following Equation (3):  
\[\dot{Q}_{cond}=\dot{m}c_{p}(T_{i}-T_{o}) \tag{3}\]  
where \(\dot{Q}_{cond}\) is the conduction heat rate (W), \(\dot{m}\) is the mass flow rate, \(c_{p}\) is the working fluid specific heat and \(T_{i}\) and \(T_{o}\) are the inlet and outlet temperatures. For the solution of Equation (3), the mass flow rate is obtained from the product of the pump flow rate (measured by one of the sensors) and the fluid density. Finally, the thermal conductivity of the surrounding material is deduced as follows (Equation (4)):  
\[k=\frac{ln\left(\frac{r_{2}}{r_{1}}\right)}{2\pi R_{T}L} \tag{4}\]
## 2 Methodological and Operational Framework for CDTs
### Enablers for CDTs in Agile Supply Chains
#### CDT Governance Approach  
Our approach on CDT governance will be based on a similar deployment approach used to model DTs in the city of Cambridge [33]. The main aspects to consider are:  
* Define the context of operation (in which factory/supply chain hierarchical structure does the CDT belong?)
* Identify relevant stakeholders, CDTs and needs (physical asset, other involved assets/CDTs): Include appropriate external stakeholders where needed (e.g., customers who give input on the design aspects of a product manufactured by a particular production line CDT).
* Define interactions among stakeholders/CDTs: Clearly identify inputs/outputs, information sources and needs.
* Define roles per stakeholder/CDT: Who does what? Which are the decision-making power and authorizations? Is a particular CDT able to autonomously make decisions and initiate actions?
* Agree on liability and legislation issues: Agree on the ownership and liability of any malfunction of the CDT. Define any applicable legislation rule that affects the operation of the CDT.
* Define rules of collaboration, such as: Who owns data? Which are the norms/rules of collaboration/information exchange/processing and decision making? Which are the applicable information authorizations (access control policies, etc.)?
* Define required AI models: Agree on valuable insights that can be provided by forecasting future outcomes based on past data, or anomalies learnt based on past data. Explanations should be served along with those insights, to understand models' rationale behind a forecast or detected anomaly.
* Procedures/workflows: Given an information input and knowledge acquired (cognition) what is the process to be followed? How can cognition improve this process?
# Assessing the Applicability of Machine Learning Models for Robotic Emotion Monitoring: A Survey
## 2 Background
### Human Signal Sources
#### 2.1.4 Lungs Signals  
Lung signals are physiological data of great interest in numerous studies. Breathing has been proven to change in response to emotional changes [34]. Typically, respiration data comes in the form of respiration volume (RV) and respiration rate (RR) and can be recorded by a single device, such as a wearable strain sensor [35]. Their implementation varies widely, with some new methods even going contactless [36], but often a belt with several sensors is used, such as one by Neulog [37]. Oxygen saturation [38] can also contain information regarding emotional state. SpO\({}_{2}\) and HbO\({}_{2}\) are measures of oxygen saturation.