esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Axial Modification on the Meshing Performance of Involute Developid Gear Pair
## 4 Meshing Performance Analysis of the Beveloid Gear Pair with Axial Modification
### Influence of Different Modification Lengths on Meshing Performance  
Figure 11 illustrates the distribution of the equivalent stress of the beveloid gear under the condition of the same maximum modification amount but different modification lengths. Figure 12 depicts the variance of the maximum equivalent stress of the tooth flank under varying modification lengths. The parameters of each modification scheme are presented in Table 3. It should be noted that Scheme 5 employs the same parameters as Scheme 2.  
Figure 11: _Cont._  
The results indicate that an increase in modification length results in a gradual concentration of the contact area towards the central position of the gear, accompanied by an expansion of the distribution range of tooth root stress. Additionally, there is a discernible decline in the maximum equivalent stress of the tooth flank.  
At the maximum modification of 10 \(\upmu\)m, no evidence of edge loading was observed on any of the tooth flanks. Combined with the results in Figure 12, the maximum equivalent stress of Scheme 4 is slightly smaller than that of the non-modified tooth. In Scheme 5, the maximum equivalent stress is further reduced compared with Scheme 4. However, the root stress range and the tooth contact stress range partially overlap. In Scheme 6, the maximum equivalent stress is reduced again on the basis of Scheme 5. Nevertheless, the overlap area between the tooth root stress range and the contact stress range is further increased.  
Based on a comprehensive analysis of the above, the modification length should be controlled within a reasonable range: if the modification length is too short, it will not  
Figure 11: Equivalent stress distribution under varying modification lengths. (**a**) Scheme 4; (**b**) Scheme 5 (same as Scheme 2); (**c**) Scheme 6.  
Figure 12: Maximum equivalent stress under varying modification lengths.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Schemes** & **Modification Parameter** & **Pinion** & **Gear** \\ \hline \multirow{2}{*}{Scheme 4} & Amount (\(\upmu\)m) & 10.0 & 10.0 \\  & Length (mm) & 2.6 & 2.4 \\ \hline \multirow{2}{*}{Scheme 5} & Amount (\(\upmu\)m) & 10.0 & 10.0 \\  & Length (mm) & 5.2 & 4.8 \\ \hline \multirow{2}{*}{Scheme 6} & Amount (\(\upmu\)m) & 10.0 & 10.0 \\  & Length (mm) & 7.8 & 7.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Modification scheme group (2).  
effectively reduce the edge load, whereas if it is too long, it may result in excessive load on the tooth root. The modification length adopted in Scheme 5 achieves a better effect in improving the meshing performance of the tooth face.
## 1 Introduction
### Background  
System identification is a process of developing mathematical models of dynamical systems based on observed data [1]. The dynamical system to be modeled can be a mechanical system, an industrial system or other types of system. Many identification methods have been proposed for the specific systems. For example, some identification techniques for mechanical systems can be found in [2; 3; 4]. In this paper, we study the identification of multivariable systems because most of the industrial processes can be modeled as multivariable systems.  
Multivariable systems are a class of systems which often present significant interactions between the multiple-input and multiple-output channels. A wealth of literature is available on effective identification algorithms for such systems. For example, an expectation-maximization technique was employed for the estimation of the linear time-invariant (LTI) multiple-input multiple-output (MIMO) systems [5]. A matchable-observable linear identification with zero-order oracle filter tuning method was developed for the LTI MIMO systems by comprising the problems of parameter estimation,filter tuning and model structure selection [6]. The methods proposed in [5; 6] are applicable for the MIMO systems without time-delays. In many industry processes, time-delays are usually unavoidable. Therefore, it is of great interest to study the identification of multivariable systems with unknown time-delays.  
For the estimation of time-delays in multivariable systems, several methods have been proposed. In [7], a parametric model is first estimated by using generalized orthonormal basis filters. Then, the time-delay estimates are generated by analyzing the simulated step response of the resulting model. However, it is required that the model structure is available a priori. To address this problem, a non-parametric method was proposed in [8]. The central idea is to break up a general \(m\times n\) MIMO system into \(mn\) decoupled single-input single-output systems using partial coherence functions, and then to apply the frequency-domain method to estimate the time-delay of each subsystem. Since the time-delay estimation procedure was repeated for \(mn\) times, this method requires a large amount of computation and is limited for high order systems. In this paper, we attempt to find a simple and efficient method to address the joint estimation of time-delays and parameters of multivariable systems.
## 3 A Case Study  
In this paper, a detached domestic building in the UK of approximately 30 years in age, occupied by four people is analysed. Daily electricity consumption in this paper was randomly generated by the CREST electricity model [37]. In order to simulate statistically realistic historical data, 50 groups of each month's electricity consumption time series were generated with this model is used test the proposed algorithm. The electricity tariff is obtained in [38], which proposes a dynamic system of tariffs which vary on a half hourly timescale. Daily heat consumption in this building was generatedby the model developed by Strathclyde University [34]. The daily gas price is based on current UK domestic gas price which is around 5 pence/kWh. Figures 6 and 7 are used to show daily energy tariffs and average energy demands.  
The Energy Hub model of this building is shown in Figure 2. In this building, the boiler burns gas to generate heat. Additionally, a high cost, high efficiency energy generator--a fuel cell CHP unit is used to generate heat and electricity together. Moreover, redundant electricity and heat can be stored in the batteries and water tank storage system to improve energy efficiency and reduce daily operation cost. The output power of CHP is 3 kW and its heat conversion efficiency is 66% and electricity conversion efficiency 22%. For heat boiler, gas to heat conversion efficiency is 88%. The overall efficiency of the battery system is 80% [39] and the standby loss for battery system is 3% per month [3]. The overall efficiency of heat storage system is 75% [40] and standby loss is 15% a day.  
It is worth noting that this paper is designed to verify the efficacy of the proposed algorithm for a domestic user who has already installed the aforementioned infrastructures, with no need to consider the optimal capacity for each infrastructure, because the rated capacities of the aforementioned infrastructures used in this paper are based on the references of [41; 42], in which many constraints, for example, battery SOC, are considered when sizing CHP and batteries. Additionally, the depreciation of energy infrastructures is not considered in this paper.  
Figure 6: Electricity and gas tariffs.  
Figure 7: An example of daily heat demand and Electrical Demand.
# A Survey on Shortest Unique Substring Queries
## 5 Approximate-SUS Queries  
### Motivation  
In molecular biology, shortest unique substrings found in DNA sequences can be used to compare similar organisms and determine unique patterns. It also helps to design polymer chain reaction (PCR) [15; 1]. If we just consider exact shortest unique substrings while comparing distinct organisms, possible patterns might be disregarded due to errors or mutations. In this section, we discuss the approximate_ version of SUS problem which is proposed by [PERSON] et al. [3], in which the uniqueness constraint is more strict. In this variant of SUS problem, the unique substrings are allowed for up to \(k\) mismatches. This version can be applied in computational biology, where factors such as genetic mutation and experimental error make approximate string matching necessary [9]. Another useful application of this approximate version is in computing average common substring, which has been considered as an approach to phylogenomic reconstruction [37; 38]. In order to estimate the evolutionary distance between pairs of primate genomes, [PERSON] et al. [39] showed that adding a similar \(k\)-mismatch parameter to average common substring finding equation leads to better results [9]. A \(k\)-mismatch shortest unique substring covering a position \(p\), denoted by \(\mathsf{SUS}_{p}^{k}\), is a \(k\)-mismatch unique substring \(\mathcal{S}[i,j]\) such that satisfies the condition \(i\leq p\leq j\) and there is no other \(k\)-mismatch unique substring with shorter length which satisfies the condition. Note that, similar to the definition of \(k\)-mismatch SUS in Section 2.1, we also consider the Hamming distance for the \(k\)-mismatch SUS problem. The problem is formally defined as follows:  
**Problem 4**.: \(k\)_-mismatch_ SUS _Queries_  
_Input: String \(\mathcal{S}[1,n]\) and integer \(k\) Output: Two integer arrays \(A\) and \(B\) s.t \(\mathcal{S}[A[i]\cdots B[i]]\) is the rightmost \(\mathsf{SUS}_{i}^{k}\) for every index \(i\)_  
**Example 3**.: If \(\mathcal{S}=\mathtt{caabaaddaacaddaaaabac}\), \(\mathsf{SUS}_{5}^{1}=\mathcal{S}[4,7]=baad\). Note that \(\mathsf{SUS}_{5}^{0}=\mathcal{S}[4,6]=baa\) as we saw in Example 1. Since there exists a substring caa such that \(H(baa,caa)=1\), \(\mathcal{S}[4,6]=baa\) cannot be a \(\mathsf{SUS}_{5}^{1}\).
# A Modified Methodology for Generating Indoor Navigation Models
## 5 Conclusions  
The main advantage of the proposed method for the automatic generation of a navigation network based on topological and semantic relations is its simplicity. The described solution relies on the MAT algorithm, which is widely used to construct indoor navigation models. The application of the midpoints of zigzagging lines minimizes network zigzagging. Therefore, the generated axis produces non-ambiguous solutions in geometrically regular spaces. Additional edges are generated at turns to develop routes resembling natural paths.  
The MPRSS, MPRSSE, and MPRSSEM methods for generating route network models were presented based on a simple example in Section 3 and were successfully verified in a larger object (Section 4).  
A manually generated model (Figure 8a), a model generated based on a previously described method [39], and a model generated with the use of the modified methodology (Figure 8c) are  
Figure 14: Paths generated in the MPRSS network model: (**a**) a network model generated with the MPRSS method for a larger number of base points in corridor space and (**b–d**) examples of paths generated between selected points.  
Figure 13: Verification of the adopted methodology for a larger number of points in corridor space: (**a**) zigzagging in corridor space based on TIN edges, (**b**) generation of midpoints in segments of the zigzag line, (**c**) segmentation of corridor space, (**d**), location of segment centroids (N), (**e**) location of midpoints (P\({}_{\text{Mid}}\)), and (**f**) model of corridor space based on midpoints.  
presented and compared in Figure 8. The results of this study indicate that automatic methods are more effective. In the modified method, midpoints were applied to denote segment nodes, which considerably improved the quality of the resulting network. The MPRSSE algorithm was modified to obtain the MPRSSEM algorithm, which supported the generation of more extensive network models with the shortest routes resembling natural paths.  
In the future, the proposed method will be tested in other sites in multi-planar space. Corridor networks in buildings will be modeled in 3D for different user groups (people and robots, such as drones). The first reports describing such solutions have already been published [42].  
[PERSON] developed the research concept and the methodology and wrote the paper; [PERSON] generated the network based on centroids; [PERSON] generated the network based on midpoints from the topological data base.  
This research was financed as part of a statutory research project of the Faculty of Geodesy, Geospatial and Civil Engineering of the University of Warmia and Mazury in Olsztyn, Poland, entitled \"Geoinformation from the theoretical, analytical and practical perspective\", (No. 28.610.033300, timeline: 2017-2020).  
The authors do not have any competing financial, professional, or personal interests.
# Superimposed Perfect Binary Array-Aided Channel Estimation for OTFS Systems
## 3 Superimposed Perfect Binary Array-Aided OTFS Channel Estimator
### Design of the PBA
A new DQPBA \(\mathbf{C}^{\prime}\in\mathbb{C}^{N\times M}\) can be constructed as follows: \[c^{\prime}[i,j]=\left\{ \begin{array}{cc}b[(i-j)_{N^{\prime}}],(i-j)\geq 0 \\ -b[(i-j)_{N^{\prime}},j],(i-j)<0\end{array} \right.,\] (29) where \(c^{\prime}[i,j]\) denotes the \((i,j)\)-th element of \(\mathbf{C}^{\prime}\), \(i\in[0,s-1]\) and \(j\in[0,M-1]\). * Suppose that \(\mathbf{A}\in\mathbb{C}^{N\times M}\) and \(\mathbf{C}\in\mathbb{C}^{N\times M}\) are PBA and DQPBA, respectively. Then, \(\mathbf{A}^{\mathbf{T}}\in\mathbb{C}^{M\times N}\) and \(\mathbf{C}^{\mathbf{T}}\in\mathbb{C}^{M\times N}\) are the new PBA and DQPBA, respectively, where the superscript \(\left(\cdot\right)^{\mathsf{T}}\) denotes the transpose of the matrix. Utilizing the methods above, we can construct the required PBA for the superimposed array \(\mathbf{P}\). We provide an example of generating a \(\mathbf{P}\in\mathbb{C}^{8\times 6}\) that satisfies (18). For PBA \(\mathbf{A}\in\mathbb{C}^{4\times 4}\) and QPBA \(\mathbf{B}\in\mathbb{C}^{4\times 4}\) given as \[\mathbf{A}= \begin{bmatrix}1&1&1&-1\\ 1&1&1&-1\\ 1&1&1&-1\\ -1&-1&-1&1\end{bmatrix} ,\quad\mathbf{B}= \begin{bmatrix}1&1&1&1\\ 1&1&-1&-1\\ 1&-1&1&-1\\ 1&1&-1&-1\end{bmatrix} \] Utilizing the iterative construction method (i), after one iteration, \(\mathbf{P}\in\mathbb{C}^{8\times 8}\) can be obtained as \[\mathbf{P}= \begin{bmatrix}1&1&1&1&1&1&-1&1\\ 1&1&1&1&1&-1&-1\\ 1&1&1&-1&1&-1&-1\\ -1&1&-1&1&-1&-1&-1\\ 1&-1&1&-1&1&-1&-1\\ 1&-1&1&1&1&-1&1\\ -1&-1&-1&-1&-1&1&1\end{bmatrix} .\] (30)  
The autocorrelation of \(\mathbf{P}\in\mathbb{C}^{8\times 8}\) given in (30) is calculated as \(\text{sum}\Big{(}\mathbf{P}\circ\mathbf{P}_{k,j,l_{j}}^{*}\Big{)}\), where \(k_{j},l_{j}\in[0,7]\). The autocorrelation result is shown in Figure 3. It can be seen that \(\text{sum}\Big{(}\mathbf{P}\circ\mathbf{P}_{k_{j},l_{j}}^{*}\Big{)}\
eq 0\) if and only if \((k_{j},l_{j})=(0,0)\). Therefore, the autocorrelation of \(\mathbf{P}\) satisfies (18), which means it is a PBA that can be used in the proposed method. For OTFS systems, the number of Doppler bins, \(M\), is typically greater than the number of delay bins, \(N\). Therefore, \(\mathbf{T}=\text{PBA}(N,N)\) is selected in this paper. Moreover, to ensure the autocorrelation of the superimposed array \(\mathbf{P}\), the frame structure should satisfy \(M=n_{\text{PBA}}N\), where \(n_{\text{PBA}}\) is a positive integer. In summary, to satisfy the ideal autocorrelation condition in (18), the superimposed array \(\mathbf{P}\) should be composed of \(n_{\text{PBA}}\) identical PBAs and can be expressed as  
Figure 3: Autocorrelation value of \(\mathbf{P}\) given in (30). \[\mathbf{P}=[\underbrace{\mathbf{T}\;\mathbf{T}\;\mathbf{T}\;...\;\mathbf{T}}_{n_{ \text{PRA}}}]. \tag{31}\]  
It is easy to prove that the superimposed array \(\mathbf{P}\) in (31) is a PBA.
## 2 Analysis of Previous Studies
### Risk Prediction Using Algorithms  
It was found that weather conditions significantly influence the road conditions for automobiles. Accordingly, several studies have reported the development of models that predict risk using ML based on basic data, such as road and weather condition data collected from each country. Random forest models are widely used among ML algorithms for reasons such as unnecessary data-scale transformations and superior model performance, leading to high accuracy.  
[PERSON] et al. developed a model using ML algorithms to identify accident hotspots by collecting data on accident dates, road types, and weather conditions. They devised methods for drivers on the Brazilian Federal Highway to alert them to potential highway risk areas in advance [23].  
[PERSON] et al. focused on the occurrence of risks, such as hydroplaning, owing to variations in road friction coefficients during rainy seasons caused by climate change. They estimatedthe importance of factors that contribute to accidents during rainy seasons and developed a traffic accident forecasting model using ML [24].  
[PERSON] et al. aimed to identify the factors affecting the frequency and damage caused by traffic accidents in Seoul City by collecting data and using algorithms to analyze the impact of these factors [7].  
[PERSON] et al. noted a high mortality rate owing to traffic accidents on highways during winter. They addressed the data imbalance using SMOTE and devised a winter highway risk prediction model using ML [8].
## 4 Experiments
### Implementation Details  
For a fair comparison with previous work, we adopted DeepLabV3+ [34] as a segmentation model, using ResNet [35] as the backbone network. The ResNet backbone network uses ImageNet [36] pre-trained weights as initial weights, and the weights of segmentation heads are randomly initialized. The experimental results are listed in Tables 1 and 2, containing all the results of our re-implementation using ResNet-50 as the backbone network. During the experiments on our model and all the re-implements, each mini-batch consisted of eight labeled images and eight unlabeled images due to hardware limitations in our environment, and the training epochs were set to 80. To train our model with Sync-BN [37], we used the stochastic gradient descent (SGD) optimizer and set the initial learning rate to 0.0025, moment to 0.9, and weight decay to 0.0001. In addition, we adopted a poly learning rate policy, in which the initial learning rate is multiplied by \((1-\frac{iter}{max\_iter})^{0.9}\) at each iteration. The crop size of the images was \(512\times 512\), with multi-scale data augmentation applied by randomly selecting scales (from 0.5, 0.75, 1, 1.25, 1.5, and 1.75). Additionally, considering that most SOTA studies have demonstrated performance improvement using CutMix, we apply CutMix after prediction used in [8] to our proposed method.  
We re-implemented previous SOTA methods in our environment. For fairness, batch size and training epochs were set to 8 and 80, respectively, which are the same settings as our model training, and all other settings are set to be the same as each study setting. All of our experiments were implemented using the PyTorch v1.12.0 framework [38] on servers with two NVIDIA GeForce RTX 3090.
## 2 Methods
### Physical and Mathematical Background
Finally, all thermal fluxes through solid walls, floor, roof, and windows and doors, including the effects of ventilation and solar irradiation, can be considered. The above-mentioned thermal flux rates contribute to the balance equation for every room, incorporating all relevant thermal gains and losses through integrating \(Q\) over appropriate time intervals. However, the time-dependent values of \(t\) for (6) are not known in advance, unlike \(t^{\bullet}\); one must include them in all computations using the appropriate interface relations. Notice that the modified version of (4) for the surface radiation always disturbs the originally linear system of partial differential equations, whose discretised version (for any fixed time step) in SW Stabil is expected to generate a system of linear algebraic equations. Fortunately, this contribution to the thermal flux rates is rarely dominating, thus, the backward Euler scheme, as given in Section 2.1, seems to be acceptable in practical calculations; otherwise, an inexact (less effective) Newton algorithm is available [66]. Another type of nonlinearity comes from the heating or air conditioning real-time control of thermal stability with prescribed temperature bounds; this is a non-trivial problem of the design and choice of some adaptive algorithms where some elements of artificial intelligence are welcome [67]. The computational simulations in this study took approximately 5 h and 33 min on a computer with 16 GB of RAM. The minimum hardware requirements were estimated at 4 GB of RAM, indicating that the method is computationally efficient and accessible even on standard workstations. The software (SW Stabil) is optimised for thermal flow simulations and employs a combined approach of the finite volume method and time discretisation. This optimisation enables robust data processing without the need for high-end hardware. In terms of scalability, the method shows potential for application on larger or more complex models. However, as the model size or the number of simulated zones increases, higher computational power and memory requirements can be expected. Simulations of buildings with highly intricate structures, a greater number of zones, or more detailed physical processes may require more advanced hardware and longer computation times. Nevertheless, the method has demonstrated its applicability for medium-complexity projects while maintaining accuracy and computational efficiency.