esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
## 2 Methodology
### Evaluation Metrics  
In regression analysis and predictive modeling, these metrics characterize the elements that influence how well the model fits the systems under study. Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE), mean absolute percentage error (MAPE), and squared mean root percentage error (RMSPE) are often employed metrics. The calculation of these metrics is described by Equations (42)-(46). Researchers and practitioners can better understand how well their models are producing the intended results by using these metrics, which each highlight distinct facets of a model's predictive ability. The key to developing a successful model is having a solid grasp of the measures that will produce accurate predictions [45]. When comparing the error between the expected and actual numbers, RMSPE and MAPE are frequently utilized. RMSPE places greater focus on significant mistakes that are susceptible to outliers because it is calculated using a quadratic scoring algorithm by squaring differences. Because it can detect big mistakes, it can be utilized in situations where big mistakes must be avoided. However, because MAPE averages the absolute error by each outlier, it is a linear measure of the absolute error and might be less susceptible to them. The performance analysis is mostly defined by KGE and NSE models, especially for water and environmental models. Kling-Gupta Efficiency is a composite measure that makes it possible to analyze factors related to bias, correlation, and discrepancies between estimated and real series. The degree to which the predicted value captures the variation in the observed data is determined by the symmetric measure for R\({}^{2}\). A robust explanation is indicated by values near to 1. These metrics provide a sound basis on which model performance evaluation and improvement can be based as shown in Table 8, therefore offering enhanced insight and decision-making in modeling [50].
## 3 Results and Discussion
### Simulations for Flexible Spacecraft
The frequency of the system is fixed, so the increasing frequency ratio heightened the rotation velocity of the eccentric mass, and also the amplitude of torques. As a result, values of accelerations increase with increasing frequency ratio. Figure 8a,b show the amplitudes of residual vibration in the frequency ratio-domain with selected amplitude ratios. Figure 6: Displacement amplitude results in two forms: (**a**) In surface representation; (**b**) In marker representation. Figure 7: Acceleration amplitude results in two forms: (**a**) In surface representation; (**b**) In marker representation. The variation tendencies in Figure 8a,b are analogous to those in Figure 4a,b. Situations with \(\beta=0.7\) in the frequency ratio-domain are much smaller than those for the other three selected amplitude ratios. Resonance is obvious at \(\lambda=1\) for all the amplitude ratios even though the amplitude of the synthetic torque is small. The fundamental harmonic of square torque is adopted to compensate the sinusoidal disturbance while the other orders of the harmonic become the new disturbances. Frequency of the second order harmonic meets the fundamental natural frequency of the system precisely, but amplitudes of remaining orders of the harmonic are so small to cause evident vibrations. As a result, resonances at \(\lambda=0.3\), which are shown in floating windows, are not obvious. Although higher Fourier series result in new disturbances, the main effect is from the first order. Figure 9a,b shows us how amplitudes vary in the amplitude ratio-domain under special frequency ratios. Results revealed by Figure 9a,b demonstrated the relationship between the amplitude ratio and the level of residual vibration. The results all have the feature that the displacement amplitude always has a minimum in the amplitude ratio-domain. According to data in Figure 9a, \(\beta=0.7\) is an optimal region for the compensation of disturbances. The optimal amplitude ratio for tip acceleration, shown in Figure 9b, appears to be somewhat different, but the best combination of the amplitude and frequency  
Figure 8: Results in the frequency ratio-domain: (**a**) Displacement amplitude results; (**b**) Acceleration amplitude results. Figure 9: Results for the amplitude ratio-domain: (**a**) Displacement amplitude results; (**b**) Acceleration amplitude results. ratio does exist despite that the values are different for frequency ratios. All the simulation results demonstrated this for the most frequently occurring situations.
# DNS-IdM: A Blockchain Identity Management System to Secure Personal Data Sharing in a Network
## 5 Security Analysis
### Authentication Protection: Preventing Phishing  
In traditional systems, there is no way for the user to authenticate the IdPs and RPs. The fact that makes phishing attacks possible is based on the ability of the attackers to convince users to reveal their credentials and personal data.  
Figure 6 shows a phishing attempt on the DNS-IdM, assuming that the end goal of the attacker is to gain access to the banking service customized to the user. The very first step of the attacker may involve sending a malicious email containing a link to redirect the user to a legitimate-looking application asking for personal data attributes. Any information provided by the user can be utilized by the attacker to access the ledger through the proxy, to gain service ultimately. However, access to the ledger requires permissions, and it is restricted to the client address. Moreover, the adversary also fails to validate his/her claim unless he/she has access to users official documents, such as a bank card to scan. Therefore, the attacker fails from his/her attempt.  
The DNS-IdM is able to defend against phishing attacks as the proxy contract terminates any unwanted access from unregistered addresses. Addresses are mapped to identity attributes, and claims are validated before getting stamped by the identity. Hence, features such as restricted address-based authentication and the validation criteria secure the system against phishing attacks.
# Analysis and Design of Protection Device for Anchor Cable Pull-Out in High-Stress Roadways
## 5 Engineering Application  
Informed by the aforementioned studies, the 5 mm diameter spring protection device was selected for large-scale production and subsequently implemented in the 1511 (1) headentry roof anchor tests in Zhangji Mine. The roadway was supported by a network of anchor cables. Each anchor cable, a high-strength prestressed steel strand, measured \(\varphi 22\times 6300\) mm, with a spacing of 1250 mm and row spacing of 900 mm. The anchor bolt, made of screw steel, had dimensions of \(\varphi 20\times 2500\) mm, a spacing of 900 mm, and row spacing of 750 mm. A dedicated individual from the mine was assigned to routinely monitor the anchor cable and measure the tensile deformation produced by the protective device using a steel tape. The protective devices for engineering application are as shown in Figure 24.  
Notably, the average roof displacement registered was 31 mm, and only a single incident of anchor cable pull-out and ejection was recorded. Crucially, the cable remained contained through the spring protection device, underscoring its practical viability.
## 2 Research Motivation  
The principal objective of this paper is to provide the algorithms and methods necessary for MRs to operate effectively in unpredictable environments whilst taking time and energy constraints into consideration. MRs can navigate in their surroundings on their own without assistance. MRs have actuators, sensors, and embedded processing to sense their surroundings, provide conclusions, and perform operations. For MRs, choosing the best routes for traveling with avoidance of obstacles is an essential aspect of navigation. For AMRs, particularly those driven by power sources, energy consumption is an important factor. For an AMR to stay operational for a longer duration, recharge less frequently, and operate more effectively generally, the use of energy needs to be optimized. MRs can move by keeping cognizance of energy constraints and then find actions that utilize the lowest possible amount of energy despite accomplishing their targets. Also, time constraints can originate through the need to accomplish work in an allocated time or simply the necessity of responding fast to the variation in environmental conditions. MRs can decide how to utilize the available time most effectively by introducing time constraints into the navigation strategies. The ultimate motivation for this research is to address the concerns with conserving energy, battery limitations, time-sensitive assignments, and unpredictable conditions and to advocate ecological responsibility; studies on the optimization of time and energy constraints for the navigation of MRs have been performed. Thus, we can enhance the functionality, independence, and general feasibility of MR systems in many kinds of applications.
## 1 Introduction
Quasi-phase-match (QPM) in periodically poled crystals is an alternative way for SHG, such as MgO-doped periodically poled lithium niobate (MgO:PPLN), periodically poled KTiOPO\({}_{4}\) (PPKTP), periodically poled LiTaO\({}_{3}\) (PPLT), etc. Compared to BPM crystals, QPM crystals provide much a higher nonlinear coefficient and any spatial beam walk-off. To date, for green laser generation, most QPM studies have focused on the SHG by continuous wave, nanosecond, or picosecond pulses, and few works have reported the demonstration of watt-level femtosecond green lasers. For example, [PERSON] et al. reported a SHG quasi-Q-switched-mode-locked (QML) laser at 532 nm generated from a MgO:PPLN crystal, and the conversion efficiencies are \(\sim\)53% for quasi-QML pulses and \(\sim\)44% for continuous ML pulses, respectively. As the pulse duration of the laser was over 120 ps, a 2 cm long crystal produced an average power of \(\sim\)200 mW at 532 nm [23]. [PERSON] et al. obtained sub-200 fs green femtosecond lasers with over 40% conversion efficiency using a PPLT and PPKTP crystal, respectively, but the average power of the second harmonic light was limited to \(\sim\)200 mW [24; 25]. [PERSON]. [PERSON] et al. proposed a MgO:PPLN crystal based optical parametric oscillator (OPO) delivering visible femtosecond pulses (522-800 nm and 455-540 nm) with a conversion efficiency below 10% [26]. In this paper, we report an all-PM femtosecond fiber laser system followed by an efficient MgO:PPLN SHG module. The fundamental picosecond pulses are generated stably from a NALM mode-locked oscillator. As high as an 820 mW 20 MHz green laser is produced by a MgO:PPLN crystal, corresponding to conversion efficiency of 40%. To ensure the proper operation of the laser system, the laser induced damage threshold (LIDT) of the crystal is also investigated. The standard deviation of the pulse-to-pulse intensity is calculated as 0.007 (normalized), and the root-mean-square value (RMS) of the SHG average power is only 0.18% in an eight hour measurement. As far as we know, this is the first time a nearly Watt-level femtosecond green laser was realized in a QPM system. Such a meaningful achievement proofs a low-cost, compact, and robust system for the feasible application of TPP.
# Microencapsulation of Polyphenols and Their Application in Food Technology
## 4 Microencapsulation of Polyphenols
### Encapsulation Carriers
#### 4.2.2 Proteins  
Proteins, such as casein and gelatine, are effective encapsulation carriers due to their amphiphilic nature, which allows them to interact with both hydrophobic and hydrophilic compounds. This characteristic makes them ideal for stabilising polyphenols, which are often hydrophobic and prone to oxidation [160; 162]. For instance, casein, a milk-derived protein, has been shown to encapsulate polyphenols effectively through methods such as spray drying and coacervation. Furthermore, casein molecules naturally form micelles in aqueous environments, which can trap polyphenols within their structure, providing physical and chemical protection. Moreover, casein can enhance the bioavailability of polyphenols by improving their solubility in the gastrointestinal tract [154; 162]. Similarly, gelatine, derived from collagen, is another protein frequently used for microencapsulation. Thanks to its ability to form strong gels and films, it is suitable for encapsulating polyphenols in both aqueous and lipid-based systems. Gelatine encapsulation not only protects polyphenols from oxidation but also enhances their controlled release by responding to environmental changes, such as temperature and pH. For instance, polyphenols encapsulated in gelatine can be released in a controlled manner during digestion, optimizing their bioactivity and improving their absorption in the gastrointestinal tract. [154; 157; 159].
## 3 Proposed Technique
### Phase Bias Estimation
\begin{cases}P_{i},&|\overline{v}_{i}|\leq c_{0}\\ P_{i}\frac{c_{0}}{|\overline{v}_{i}|}\left(\frac{c_{1}-|\overline{v}_{i}|}{c_{ 1}-c_{0}}\right)^{2},&c_{0}<|\overline{v}_{i}|\leq c_{1}\\ 0,&|\overline{v}_{i}|>c_{1}\end{cases}  \tag{24}\]  
where \(\overline{P}_{i}\) is the \(i\)-th diagonal of the equivalent weight matrix, \(P_{i}\) is the \(i\)-th diagonal of the original weight matrix, \(\overline{v}_{i}\) is the standardized residual expressed as \(\overline{v}_{i}=v_{i}/\sigma_{v_{i}}\), and \(\sigma_{v_{i}}\) is the standard deviation of \(v_{i}\). \(\overline{P}_{i}\) is a descending function with respect to the standardized residual, so that the effect of observation outliers can be reduced. \(c_{0}\) and \(c_{1}\) are the detection thresholds, which are generally taken as \(c_{0}=1.0\)-\(1.5\) and \(c_{1}=2.5\)-\(8.0\), respectively. It is worth noting that if \(\rho(\cdot)\) is chosen as the quadratic cost function, and the weight matrix is selected as the identity matrix, then the solution of Equation (22) is the averaging estimation, i.e., \(\hat{\psi}=\frac{1}{L}\sum_{l=1}^{L}\delta\tilde{\varphi}_{\mathbf{K}+L-l}\). This solution is intuitive but has no rejection capability for the observation outliers, as shown in the following simulations. It should also be noted that there is a trade-off in the selection of the window size \(L\). That is, increasing \(L\) has the advantage of attenuating the discriminator noise, which may result in a better phase bias estimate. However, it also increases the prediction error and the risk of tracking loop divergence.
# TBM Enclosure Rock Grade Prediction Method Based on Multi-Source Feature Fusion
## 2 Methodologies
### Kolmogorov-Arnold Network (KAN)  
This paper proposes an enhancement to the nonlinear mapping capability of a model by introducing a Kolmogorov-Arnold network (KAN). This is designed to assist a subsequent TBM enclosure grade prediction model in performing effective feature fusion and transformation. In contrast to traditional multilayer perceptrons (MLPs) that perform nonlinear spatial transformations at each level, KANs only perform nonlinear transformations on each pair of bases individually and then combine them into a multidimensional space. This approach effectively demonstrates that a high-dimensional function can be reduced to learning a polynomial number of one-dimensional functions. When represented as a neural network graph, this is equivalent to a two-layer neural network. The difference is that there is no more linear combination, and the input features are directly activated nonlinearly. It is important to note that these activation functions are not predetermined; they have the capacity to self-learn.  
The multi-layer KAN network architecture is illustrated in Figure 3. In the \"activation layer\", a number of B-spline functions with different control points are used to fit functions of arbitrary shapes.  
To construct deep KANs, one simply needs to define for each KAN layer a function matrix:  
\[\Phi=\{\phi_{q,p}\},p=1,2,\cdots,n_{\text{inv}},q=1,2\cdots,n_{\text{out}} \tag{4}\]  
Figure 2: Schematic diagram of the basic CNN architecture.  
Multiple stacking should be performed. In a manner akin to traditional multilayer perceptual machines, neural networks are predicated on the establishment of in-out to output mapping relationships, that is to say, the construction of transformations (matrices) from input to output layers:  
\[x_{l+1}=\underbrace{\begin{pmatrix}\mathbf{\phi}_{l,1,1}(\cdot)&\mathbf{\phi}_{l,1,2}( \cdot)&\cdots&\mathbf{\phi}_{l,1,n_{l}}(\cdot)\\ \mathbf{\phi}_{l,2,1}(\cdot)&\mathbf{\phi}_{l,2,2}(\cdot)&\cdots&\mathbf{\phi}_{l,2,n_{l}}( \cdot)\\ \vdots&\vdots&&\vdots\\ \mathbf{\phi}_{l,n_{l+1}}(\cdot)&\mathbf{\phi}_{l,n_{l+1},2}(\cdot)&\cdots&\mathbf{\phi}_{ l,n_{l+1},n_{l}}(\cdot)\end{pmatrix}}_{\mathbf{\phi}_{l}}\mathbf{x}_{l} \tag{5}\]  
The function matrix \(\mathbf{\phi}_{l}\) corresponds to the first layer of the KAN. In general, a KAN network can be regarded as a single-layer composition. For a given input \(x_{0}\in\mathbb{R}^{n_{0}}\), the output of the KAN is computed as  
\[\text{KAN}(x)=(\Phi_{L-1}\circ\Phi_{L-2}\circ\cdots\circ\Phi_{1}\circ\Phi_{0})x \tag{6}\]
## 4 Load-Similar Node Distribution
### Load-Similar Node Distribution Strategy  
The above analysis showed that the average load per node increases with the decrease of distance from the sink. To address the energy hole problem in the MOL-enabled PMRC-based WSN, we propose a novel load-similar node distribution strategy. The underlying principle is that if the sensor nodes are deployed in the area according to the load distribution (that is, more nodes will be deployed in the range where the average load is higher), then the load among different layers in the sensor network tends to be balanced. During the deployment of the network, the location of a node will be determined as follows:  
1. Compute \(L_{1}\), the average load per node at the outer border of layer 1;
2. Randomly generate a polar coordinate, \(p(\zeta\), \(\theta)\), centered at the sink. If \(\zeta\), _i.e._, the distance between point \(p\) and the sink is less than the transmission range \(r\), then deploy a node at location \(p\), and return to Step 1 for the deployment of the next node; otherwise continue with the following steps;
3. Randomly generate a value \(L^{\prime}\) between 0 and \(L_{l}\);
4. Compute \(L_{p}\), the average load per node at location \(p\). If \(L_{p}>L^{\prime}\), then deploy a node at location \(p\); otherwise return to Step 1 to repeat the process, until all nodes have been deployed.  
Figure 6: Pseudocode of load-similar node deployment.  
Assume the total number of nodes is \(N\), the width of the network area is \(2R\), and the transmission range is \(r\), then Figure 6 illustrates the pseudocode of load-similar node deployment. When \(N=400\), \(R=125\) m, \(r=40\) m, and the sink is located at the center of the area, an example of node deployment generated by Matlab is shown in Figure 7, where Figure 7(a) shows the load-similar node deployment and Figure 7(b) shows the uniform node deployment. It shows in Figure 7(a) that the node density is higher in the area close to the sink.  
Compared with the non-uniform node distribution strategy proposed in [14], the node deployment in our strategy is very straightforward. The network formation and routing is simply based on the PMRC structure with the MOL scheme. Hence, there is no need to deploy the forwarding nodes deliberately. In addition, using the cluster structure, each node simply sends its data to its cluster head. This is a sharp contrast to the \(q\)-Switch routing [14], where each node needs to select one relay node with the most residual energy out of up to \(q\) possible forwarding nodes each time before it actually sends out its data. This puts extra requirement that the forwarding nodes periodically broadcast their residual energy, which will consume extra energy.
# Positioning Information Privacy in Intelligent Transportation Systems:
## 4 Location Determination with Mutual Base Station Authentication
### Vehicle-Centered Approach  
First, we consider the case where the calculation is carried out on the vehicle side. In this case, the vehicle needs to contact at least three BSs, calculate the time of the signal traveling to the base station, and then, using verifiable multilateration method [63], calculate its coordinates. The P-AF-V protocol for exchanging information with the base station is shown in Figure 7.  
Figure 6: Distance-bounding protocol with base station authentication.  
After receiving the message _nonce_ from the base station, the vehicle chooses a random number \(\delta\), which determines the delay in response to the base station, waits for this time interval and sends a _commitment_ message to the base station afterwards. Thus, the base station does not know the random delay time chosen by the vehicle and, therefore, does not have an ability to determine the real time \(t_{VS}\) of the signal passing to the vehicle and back.  
Accordingly, the station transmits its miscalculated time of passing the signal \(\overline{t_{VS}}=t_{VS}+\delta\) to the vehicle, which can, in turn, calculate the real time of the signal passing through it \(t_{VS}=\overline{t_{VS}}-\delta\). It is worth noting that the vehicle must quickly obtain information from at least three different stations, calculate the distance to a particular station, and then calculate the coordinates.
# The Development of a Prototype Solution for Detecting Wear and Tear in Pedestrian Crossings
### Performance Evaluation
#### 2.3.1 Benchmark Scenario  
The presented YOLOv4-tiny model was trained and tested on the Google Colab platform [15]. An Intel(R) Xeon(R) CPU @ 2.00 GHz, 12 GB RAM, and a Tesla T4 GPU were used to perform the training on the Google Colab platform. This model was then tested on a Raspberry PI 5 with a Broadcom BCM2712 2.4 GHz quad-core CPU with a VideoCore VII GPU and 8 GB of RAM.
# Adaptive Thermal-Aware Routing Protocol for Wireless Body Area Network
###### Abstract  
The recent advancement in information technology and evolving of the (IoT) shifted the traditional medical approach to patient-oriented approach (e.g., Telemedicine/Telemonitoring). IoT permits several services including sensing, processing and communicating information with physical and bio-medical constraints. Wireless Body Area Network (WBAN) handles the issues pertaining to the medical purposes in the form of sensor nodes and connected network. The WBAN takes human physiological data as an input to subsequently monitor the patient conditions that are transferred to other IoT components for analysis. Such monitoring and analysis demand a cohesive routing approach to ensure the safe and in-time transfer of data. The temperature rise of bio-medical sensor nodes makes the entire routing operation very crucial because the temperature of implanted nodes rises and ultimately damages body tissues. This needs dispersion in data transmission among different nodes by opting various available routes while avoiding temperature rise. In this paper, we present Adaptive Thermal-Aware Routing algorithm for WBAN. The ATAR is designed to overcome the temperature rise issue of implanted bio-medical sensors nodes. The new protocol is based on Multi-Ring Routing approach to find an alternative route in the case of increasing temperature. The simulation results indicate that proposed protocol is more efficient in terms of temperature rise and throughput than existing approaches.  
Keywords:internet of things; WBAN; thermal-aware routing protocol; healthcare; biomedical sensors +
Footnote †: journal: Medical Informatics