esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 4 Framework Architecture
### Energy Consumption in IoT-NDN  
It has already been stated that IoT devices are often very restricted in terms of energy capacity, memory, and computation power. In this paper, we want to focus specifically on reducing energy consumption. For that matter, we assume that most of the network's energy is consumed by network communication, not actual CPU computation. Ref. [29] argues that radio interfaces cause up to 50% of smartphones' energy consumption during typical usage. The canonical unit to measure energy is the _joule ()_, where \(1J=1W\cdot s=1V\cdot A\cdot s\)[30]. The _voltage_ (\(V\)) should be specified for a device, the _electric current_ (\(A\)) depends on the state in which the device currently is, and the _time_ (\(s\)) can be measured as the time span that the device takes on a certain state. These states refer to the different Wi-Fi states like idle or transmission. The total energy consumption per device is computed by summing up the energy consumption per state.  
More formally, let \(U_{dev}\) be a device's voltage, and \(I_{1},I_{2},\ldots,I_{n}\) the electric currents for the \(n\) different states. Let \(t_{1},t_{2},\ldots,t_{n}\) be the \(n\) time spans that the corresponding stateslast, where \(I_{j}\) corresponds to \(t_{j}\) and \(T\) is the total runtime of the experiment. Then the total energy consumed by a device \(E_{exp}\) can be computed like this:  
\[E_{exp}=\sum_{j=1}^{n}U_{dev}\cdot I_{j}\cdot t_{j}\text{, where}\sum_{j=1}^{n}t_{j}=T \tag{2}\]  
To evaluate the overall performance of a network, we compute the average energy consumption per node \(S_{avg}=\frac{\sum_{j=1}^{n}E_{exp_{j}}}{n}\), where \(n\) is the graph size and \(E_{exp_{j}}\) is the energy consumption of the \(i\)-th device.
## 3 Methodology
### Linking the Risks to the Processes of a Bridge Project Lifecycle
Applying the Pareto Principle enabled a strategic prioritization of processes, focusing efforts on those most impacted by risks to develop targeted mitigation plans. This prioritization emphasizes that the most effective way to manage critical risks in the bridge lifecycle is by prioritizing processes based on their cumulative risk impact. For instance, focusing on the process with the highest impact--risk allocation in contracts during the Procurement And Contracting phase--and utilizing precise risk management tools can establish a strong foundation that positively impacts other processes. Eleven vital risks (R3, R6, R9, R13, R20, R29, R41, R43, R45, R48, and R52) affect this process alone, underscoring the substantial impact that effective risk mitigation can have on the overall project. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**The Procurement and Contracting Phase**} \\ \hline
**Main Process** & **Sub Process** & **Associated Risks** \\ \hline
**Tendering and Bidding** & - Invitation to tender & R3, R52, R34, R48, R43, R29 \\ \cline{2-3}  & - Bid preparation and submission & R20, R7, R6, R45, R33, R28, R29 \\ \cline{2-3}  & - Bid evaluation and scoring & R5, R9, R33, R48, R52, R20, R41 \\ \hline
**Contract Drafting and Negotiation** & - Contract scope development & R3, R48, R33, R52, R34, R29 \\ \cline{2-3}
**Negotiation** & - Negotiation of terms (cost, timelines, penalties) & R20, R7, R6, R45, R9, R52, R41 \\ \cline{2-3}  & - Risk-sharing clauses & R20, R43, R45, R6, R9, R13, R29 \\ \hline
**Legal and Regulatory** & - Developed procedures for compliance & R21, R43, R52, R51, R19, R40, R24, \\ \cline{2-3}
**Compliance** & with laws and standards & R41, R25, R22, R28 \\ \hline
**Supplier Selection and** & - Supplier pre-qualification & R31, R3, R48, R13, R29, R24 \\ \cline{2-3}
**Supplier Selection and** & - Supplier performance assessment & R12, R13, R41, R9, R29, R33 \\ \cline{2-3}
**Qualification** & - Sustainability criteria evaluation & R40, R19, R22, R43, R28, R50 \\ \hline
**Budget Estimation** & - Detailed cost estimation & R20, R7, R45, R6, R29, R33, R3 \\ \cline{2-3}  & - Contingency planning & R20, R37, R52, R29, R25, R40 \\ \cline{2-3}  & - Risk-adjusted budget allocation & R20, R7, R45, R6, R41, R43, R34 \\ \hline
**Risk Allocation in Contracts** & - Assignment of risks to stakeholders (e.g., owner, contractor) & R3, R6, R20, R48, R52, R13, R29 \\ \cline{2-3}  & - Development of risk-sharing mechanisms (insurance) & R20, R43, R45, R6, R9, R41, R52 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The analysis of the Procurement and Contracting phase and its associated risks.
## 3 Results and Discussion
### Texture Profile  
Texture profile analysis of different yogurt samples was carried out on the last day of storage and the results are presented in Figure 2. The textural parameters (i.e., hardness, adhesiveness, cohesiveness, stickiness, and gumminess) showed significant changes with the addition of different concentration of inulin and _S. boulardii_ compared with the control yogurt. Addition of _S. boulardii_ with starter culture decreased hardness in the probiotic yogurt S2, probably because of fermentation of available sugars by the yeast. However, addition of different concentrations of inulin increased the hardness (S3 from 24.80 to 26.80 g, an 8% increase; S4 from 24.80 to 31.30 g, a 26% increase; and S5 from 24.80 to 64.40, a 159% increase). Use of inulin increased the viscosity of yogurt samples by binding and orienting water that did not integrate into the protein network and inhibited wheying-off resulting in strong casein micelles aggregation. The same pattern was observed in terms of adhesiveness in probiotic and symbiotic yogurt samples. Cohesiveness of different yogurt samples was in the range from 0.49 to 1.01. Surprisingly, the highest cohesiveness was observed in S3 where 1% inulin was used. Stickiness and gumminess also showed significant variation among the samples compared with the control. The use of inulin increased gumminess but there was no particular trend.  
[PERSON] et al. (2016) added gelatin to camel milk yogurt and found a three-fold increase with 1% gelatin, and a seven-fold increase with 1.5% gelatin. [PERSON] et al. (2016) used 1% and 2% starch that significantly increased firmness and adhesiveness of yogurt. [PERSON] et al. (2017) studied different thickening agents such as starch and whey protein, and reported increased values in the texture profile of yogurt. Analogous results were reported by [PERSON] et al. (2018) with modified tapioca starch. [PERSON] et al. (2019) also reported improvement in hardness, springiness, and adhesiveness of corn yogurts produced by addition of gelatin. From the texture profile analysis described above, our study indicated that the symbiotic yogurt with 1% inulin exhibited better textural properties, in terms of hardness, adhesiveness, and gumminess, without negatively affecting the palatability of natural yogurt in comparison with the control and probiotic yogurts. Thus, addition of probiotic  
Figure 2: Texture profile analysis of the control plain yogurt (S1), probiotic yogurt with 0.5% _S. boulardii_ (S2), symbiotic yogurt with 0.5% _S. boulardii_ + 1% inulin (S3), 0.5% _S. boulardii_ + 1.5% inulin (S4), and 0.5% _S. boulardii_ + 2% inulin (S5). Different letters denote statistical differences (\(p<0.05\)).  
(_S. boulardii_) alone and with combination of prebiotic (inulin) in different concentrations potentially influenced the texture properties of yogurt.
# A Crowd Cooperative Spectrum Sensing Algorithm Using a Non-Ideal Channel
## 2 System Model  
The system model is shown in Figure 1. M SUs are evenly distributed within the coverage of base station. The base station releases the channel to be sensed and reward information. After SUs finish the sensing, they send the sensing results to the base station, and the base station fuse all the results.  
SUs use energy detection for spectrum sensing. The detection probability of SU \(i\) is given by [21]:  
\[p_{i}=Q((Q^{-1}(p_{f})-\sqrt{t_{i}f_{s}}\gamma_{i})/\sqrt{2\gamma_{i}+1}), \tag{1}\]  
where \(p_{f}\) is the target probability of false alarm, and \(\gamma_{i}\) represents the signal-to-noise ratio of SU \(i\). \(t_{i}\) indicates the sensing time. \(t_{i}f_{s}\) indicates the sampling points, and it can be written as \(n_{i}\). The sampling frequency is a constant value. Function \(Q\) is expressed as:  
\[Q(x)=\frac{1}{2\pi}\int_{x}^{\infty}e^{-y^{2}/2}dy. \tag{2}\]  
Take \(p(u_{1})\) as the probability of the existence of the PU, and take \(p(u_{0})\) as the probability that the PU does not exist. So the correct sensing probability of SU \(i\), \(p_{i}^{c}\), and the incorrect sensing probability of SU \(i\), \(p_{i}^{c}\), are given by:  
\[p_{i}^{c}=p(u_{1})p_{i}+p(u_{0})(1-p_{f}), \tag{3}\]  
\[p_{i}^{in}=p(u_{1})(1-p_{i})+p(u_{0})p_{f}, \tag{4}\]If the PU exist, the sensing result is 1, otherwise, the result is 0. When SUs sends the results of sensing to the base station, error codes will occur. So the correct probability of the results which is received by the base station from SU \(i\) is given by:  
\[p_{i}^{rc}=p_{i}^{c}(1-p_{e})+p_{i}^{in}p_{e}, \tag{5}\]  
and the incorrect probability is given by:  
\[p_{i}^{rin}=p_{i}^{c}p_{e}+p_{i}^{in}(1-p_{e}), \tag{6}\]  
where \(p_{e}\) is the transmission error rate. Assuming that SUs use 2 DPSK modulation, the error rate of the receiver using non-coherent demodulation is \(p_{e}=\frac{1}{2}e^{-r}\)[22], where \(r\) indicates the signal-to-noise ratio of SUs signals received by base station.  
In this paper, we adopt voting fusion method for data fusion. First set a threshold \(m\). A hypothesis will be true if more than \(m\) SUs support this hypothesis. The detection probability and false alarm probability of voting fusion is expressed by:  
\[p_{D}=\sum_{j=m}^{M}\sum_{\sum u_{i}=j}\prod_{i}{(p_{i})^{u_{i}}(1-p_{i})^{1-u_ {i}}}, \tag{7}\]  
\[p_{F}=\sum_{j=m}^{M}\sum_{\sum u_{i}=j}\prod_{i}{(p_{f})^{u_{i}}(1-p_{f})^{1-u_ {i}}}, \tag{8}\]  
where \(u_{i}\) indicates the sensing result of SU \(i\). When threshold \(m=M/2\), the performance of fusion is optimum [21].
# New Approaches in Dynamic Metrics for Lighting Control Systems: A Critical Review
## 1 Introduction
### Objectives  
The main objective of this study is to conduct a critical review of key static and dynamic daylighting metrics, with a particular focus on their implications for occupant health and well-being, as well as their role in achieving energy efficiency through reduced electric lighting and its potential impact on thermal loads through solar gains.  
Building upon this review, the research further aims to address several key aspects: first, to determine the sensitivity of each metric to various design parameters; second, to explore the correlation, utility, and complementarity among different metrics applied to a singular case study; and finally, to identify the inherent limitations associated with the information each metric provides.
# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 1 Introduction  
### The Need for Fruit Load Forecast  
This review considers advances in methods for the yield forecast of tree fruit. Such forecasts can be made at a tree, orchard, whole farm or regional level, depending on the management task. At a farm level, forecasts can inform grower decisions around in-season agronomic practices such as thinning, postharvest requirements such as harvest labor, transportation, storage, and requirements for consumables such as trays and inserts. Improved monitoring also offers great potential for the accumulation of reliable data across time. An experienced agronomist will be able to utilize such a resource, combining this information with knowledge of fruit and tree physiology to provide current season advice and to advise on management practices to optimize orchard development.  
Forecasts are also critical to the value chain to inform marketing strategy and practice. For example, a typical advertising campaign is planned over four weeks ahead of harvest. In general, the longer or more involved the value chain, the more demand for accurate forecasts.  
A general target for yield forecast is for an estimate within \(\pm 5\%\) to \(10\%\) of the actual yield, with greater accuracy required for industries tied to processing industries or to other markets of set size, e.g., export markets. For example, winemakers desire errors of no more than 2-3% on forecast production, while a tree fruit producer supplying multiple and easily accessed domestic markets might accept a 10-20% forecast error, depending on production volumes, or operate without a forecast.  
Yield forecast has multiple components, including the at-harvest fruit number, size and quality, and harvest timing. Fruit number is the primary determinant of yield, while fruit size and quality are key attributes in terms of consumer acceptability, and therefore, achievable pricing. In-field measurements allow for the forward estimation of the proportions of fruit class distributions on these attributes at harvest, and thus for market planning. The timing of fruit maturation is determined by the timing of flowering and subsequent growth conditions. The combination of harvest timing and fruit load information can be used to estimate a weekly harvest schedule. Advances in techniques for the forecasting of each of these components of yield are presented in the current review.
## 2 Materials and Methods
### Proposed Unified Performance Metric
#### 2.3.2 Formulation of the Unified Performance Metric  
The development of a unified metric for WSN performance begins with normalizing the component metrics discussed in Section 2.3.1 using the standard normalization formula given in Equation (11). Normalizing provides each component with a metric standard scale and ensures that each parameter contributes proportionally to the unified metric without being skewed to its original scale.  
\[X_{norm}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{11}\]  
This is followed by creating a fitness function using the four components metric in Section 2.3.1: delay, distance, residual energy, and trust. We use Equation (12) as the fitness function that defines the overall performance of the WSN for the different CH and routing path selection algorithms.  
\[Fit=\lambda_{1}\xi_{norm}+\lambda_{2}D_{norm}+\lambda_{3}\theta_{norm}+ \lambda_{4}T_{norm} \tag{12}\]  
where  
\(\xi_{norm}\) is the normalized residual energy;  
\(D_{norm}\) is the normalized distance;  
\(\theta_{norm}\) is the normalized delay;  
\(T_{norm}\) is the normalized trust value;  
\(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\), \(\lambda_{4}\) are constant coefficients having values of 0.25, \(-\)0.25, \(-\)0.25, and 0.25, respectively.
# Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi
## 2 Methodology
### From Hidden Layer to Output Layer  
The vector \(h^{\prime}\) is then passed through the output layer ('FC2'), which transforms the 20-dimensional input into a 2-dimensional output suitable for binary classification. Let the weight matrix of the output layer be \(W_{2}\in\mathbb{R}^{2\times 20}\) and the bias vector be \(b_{2}\in\mathbb{R}^{2}\). The final output \(y\) can be expressed as:  
\[y=W_{2}\times h^{\prime}+b_{2} \tag{3}\]  
During the forward-direction of the model, the input data that first passed the first full connection layer (FC1) and activated the function ReLU are applied to the output of the hidden layer. Then, the final prediction result is output through the output layer (FC2).  
In order to enable the model to accurately identify the mode of health and bearing faults, the method of monitoring learning was adopted for the training of model parameters. The labeling dataset was used for training and optimizing the weight and bias of the model by minimizing the loss function, so that the prediction results of the model were as close to the real label as possible.  
The structure of neural network models is simple and effective, with less parameters and computing complexity, and it can operate efficiently on edge equipment such as a Raspberry Pi. Through this model, we can realize the real-time detection of wind turbine bearing faults and provide fast and accurate predictive results to provide feasible solutions for reducing maintenance costs and improving the availability and efficiency of wind turbines.
## 3 Path Following Control Strategy  
### Structure of the Proposed Control Strategy  
A novel path following control strategy is shown in Figure 3. We can see from Figure 3 that it consists mainly of the fuzzy predictor line-of-sight (FPLOS) guidance and the composite control strategy. The proposed FPLOS guidance strategy can not only provide the desired heading angle, but also compensate for the unknown sideslip angle. The proposed composite control strategy consists of two parts: the first part is that TLC controller ensures that the actual heading tracks the guidance heading; the second part is that the compensation controller is capable of solving unmodeled dynamics, multiple disturbances, and rudder saturation by using the NN, reduced-order LESO, and auxiliary system, respectively. Meanwhile, a nonlinear tracking differentiator (NTD) provides a satisfactory differentiation and filtering effect. In the control loop, \(\mu_{m}\), \(\mu_{l}\), and \(\mu_{n}\) represent the outputs of NN, LESO, and auxiliary system, respectively; \(\mu_{r}\) is a robust term.  
Figure 3: The block diagram of the proposed control strategy for the USV.
## 2 Methods
### Time Varying Filtering Based Empirical Mode Decomposition
#### 2.1.1 Estimation of the Local Cut-Off Frequency  
In TVF-EMD method, B-spline approximation filter is chosen as a time-varying filter, which adopts polynomial splines to approximate the signal and can be represented as:  
\[g_{m}^{n}(t)\ =\ \left[p_{m}^{n}*x\right]_{\mathit{lm}}*b_{m}^{n}(t) \tag{2}\]  
where \(\left[.\right]_{\mathit{lm}}\) is the down-sampling operation; \(p_{m}^{n}\) is a pre-filter and \(p_{m}^{n}\ =\ \left[\left(\left[b_{m}^{n}\times b_{m}^{n}\right]_{\mathit{lm}} \right)-1\right]_{\mathit{lm}}\times b_{m}^{n}\), \(b_{m}^{n}(t)\ =\ \beta^{m}(t/m)\); \(\beta^{n}(t)\) denotes B-spline function; \(n\) stands for B-spline order; \(m\) represents the node; \(t\) is time; \(*\) represents convolution operation.  
According to Equation (2), the node \(m\) determines the local cut-off frequency of the B-spline time-varying filter. In practice, the nodes cannot be known. As a result, it is necessary to estimate the local cut-off frequency from the input signal. Then, the B-spline time-varying filter is constructed. The specific process is provided in Appendix C.
# Efficient Hair Damage Detection Using SEM Images Based on Convolutional Neural Network
## 5 Discussion  
We focused on how to detect hair damage autonomously and efficiently. We created a system based on artificial intelligence convolutional neural network that could autonomously detect the degree of hair damage. This could be used for personal daily care and maintenance, medical health diagnosis and other directions. We are currently developing a handheld machine that can detect the degree of hair damage, using the hair damage detection algorithm to quickly diagnose hair damage. When collecting hair samples, we only considered adults 20-60 years old, and did collect hair samples from minors or people over 60. The reason was that people in this age range care more about daily hair care and have more hair damage. In turn, mid helped our hair damage sample collection. We observed SEM (scanning electron microscope) images, considering the possible influence of hair color on the degree of hair damage, and we collected some brown, brown and red hair samples. After observation and analysis, we found that the relationship between hair color and hair damage is weak. When making the data set, we compared the microscopic characteristics of hair at 400\(\times\) and 800\(\times\) (Figure 10). First, we used the 400\(\times\) hair image under SEM observation. The display effect was not satisfactory, the hair surface texture could not be displayed accurately, and it was difficult to use for model training. Finally, we chose the 800\(\times\) hair image as the standard of the data set and made our own SEM hair data set.  
In the model, we explored the proposed attention mechanism model to find the best performance of the model. After many revisions, it is found that the dual attention mechanism module, especially the channel first attention method, has the highest efficiency in identifying hair damage features. Figure 11 shows the attention feature extraction of our attention mechanism module.  
Figure 10: Comparison of hair’s microscopic details under different magnifications.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attention Type** & **Top-1 Err. (\%)** \\ \hline Channel Attention & 5.03 \\ \hline Spatial Attention & 4.25 \\ \hline Channel \& Spatial Attention & 2.92 \\ \hline Spatial \& Channel Attention & 2.47 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification error (%) on SEM Hair dataset.
# Energy Saving in Wireless Sensor Networks via LEACH-Based, Energy-Efficient Routing Protocols
###### Abstract  
Wireless sensor networks are at the center of scientific interest thanks to their ever-growing range of applications. The main weakness of wireless sensor networks is the restricted lifetime of their sensor nodes due to limited energy capacity. The extension of the lifespan of sensor nodes is pursued in various ways. One of them is the usage of protocols that achieve energy-efficient routing. LEACH is one of the pioneering protocols of this type and has numerous descendants. This research article focuses on energy-efficient routing protocols that are based on LEACH. Specifically, a study of LEACH along with many of its successors is provided. In addition, a novel protocol of this kind, named T-LEACH\({}_{\text{SAS}}\) is introduced. This protocol combines the threshold-based approach for selecting cluster heads that was first introduced in T-LEACH, which is a well-known protocol, along with a mechanism for sleep-awake scheduling. The performance of T-LEACH\({}_{\text{SAS}}\) is compared against that of both LEACH and T-LEACH via simulation tests that confirm that T-LEACH\({}_{\text{SAS}}\) indeed provides a promising choice for energy-efficient routing in WSNs.  
wireless sensor networks; energy saving; energy efficient routing; hierarchical protocols; LEACH; T-LEACH; T-LEACH\({}_{\text{SAS}}\)