esa-sceva/satcom-chunk-collection huggingface dataset:
# A Simplified Pointing Model for Alt-Az Telescopes
## 5 Conclusions  
In order to enhance the pointing accuracy of the PAST telescope, we have developed a new simplified version of the telescope pointing correction model. This greatly streamlines the previously complex telescope pointing model fitting equations, allowing us to quickly obtain a high-precision telescope pointing model. With this method, the pointing accuracy of the PAST telescope is better than 10 arcs, with an RMSE of 6.8 arcs for the azimuth axis and that of 3.8 arcs for the elevation axis.  
The primary influencing factors of the pointing error in the alt-az telescope include the levelness of the installation plane, whether the azimuth axis is accurately aligned with true south or north, and the vertical alignment between the azimuth and altitude axes. Therefore, to achieve a rapid and accurate pointing of the observation targets and improve observation efficiency, a pointing model for correction is required. All alt-az telescopes have the same mounting on a concrete pier; therefore, the simple model that has been proposed in this paper can be widely applied to other alt-az telescopes. In the next steps, we will extend this simplified pointing model calibration method to other alt-az telescopes at the Lenghu site and verify its reliability, stability, and generalizability. In addition, it has been proposed that, apart from the telescopes' inherent system errors, which are generally small, the telescope pointing model could be represented by two angles, one of which is the tilt angle between the mounting plane of the telescope, and the other is the rotation angle of the telescope in the mounting plane. In future work, we hope to try another, simpler model, that is, using the observed pointing data to calculate the above-mentioned two angles in order to establish the pointing model.  
Conceptualization, F.H. and Y.Z.; methodology, Y.Z., Y.W. ([PERSON]) and F.H.; software, F.H.; validation, Y.Z. and Y.W. ([PERSON]); formal analysis, Y.Z. and Y.W. ([PERSON]); investigation, Y.Z. and Y.W. ([PERSON]); resources, Y.Z.; data curation, Y.Z. and Y.W. ([PERSON]); writing--original draft preparation, Y.Z.; writing--review and editing, F.H., Z.Y., Z.R. and Y.W. ([PERSON]); visualization, F.H.; supervision, Z.Y.; project administration, F.H.; funding acquisition, F.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 42222408, 12233009, and 42304188. F.H. was funded by the Youth Innovation Promotion Association of CAS (Y2021027). Y.W. ([PERSON]) was funded by the Key Research Program of the Institute of Geology and Geophysics, CAS, grant number IGGCAS-201904.  
Not applicable.  
The telescope pointing data are available upon request through email to the corresponding author.  
We greatly appreciate the Astrometry.net system for providing the service of star identification (accessed on 1 September 2023).  
The authors declare no conflict of interest.
## 2 Sparse Direct Algorithms
### The Issue of Bit Compatibility  
In some applications, users want bit compatibility (reproducibility) in the sense that two runs of the solver on the same machine with identical input data should produce identical output. Not only is this an important aid in debugging and correctness checking, some industries (for example, nuclear and finance) require reproducible results to satisfy regulatory requirements. For sequential solvers, achieving bit compatibility is not a problem. However, enforcing bit compatibility can limit dynamic parallelism, and when designing a parallel sparse direct solver, the goal of efficiency potentially conflicts with that of bit compatibility.  
The critical issue is the way in which \(N\) numbers (or, more generally, matrices) are assembled  
\[sum=\sum_{j=1}^{N}S_{j}\]  
where the \(S_{j}\) are computed using one or more processors. The assembly is commutative but because of the potential rounding of the intermediate results, is not associative, so that the result \(sum\) depends on the order in which the \(S_{j}\) are assembled. A straightforward approach to achieving bit compatibility is to enforce a defined order on each assembly operation, independent of the number of processors. As discussed in [34], this is a viable approach for multifrontal codes and is the approach used by HSL_MA97. Our supernodal codes, HSL_MA86 and HSL_MA87, were designed with performance as a key objective and do not compute bit compatible results.  
Note that for bit compatibility, the BLAS must produce bit compatible results. This can be achieved by choosing a BLAS library that guarantees this. Often the serial variant will do so, but our experiments with parallel variants show that while the Intel MKL BLAS are bit compatible, some other common BLAS libraries are not.
## 1 Introduction
### Blockchain Combined with Edge Computing in UAV Networks  
The blockchain is a very promising and emerging solution to resolve the security issue of EC in many practical applications, such as IoT [10], autonomous vehicles [11], smart homes [12], and healthcare [13]. However, owing to the heavy computational burden for solving the PoW, some resource-limited nodes, such as UAVs/mobile devices, need to use considerable processing power and sacrifice considerable time to participate in mining and consensus processes [14], which is indeed a major challenge in applying the blockchain to EC applications as well as UAVs and other mobile applications [2]. In this study, we investigate how such issues can be effectively addressed by employing competing miners in the blockchain.  
EC is very promising and useful to alleviate the computational burden of devices because computing tasks can be offloaded to the edge servers having more computational resources. Although the blockchain is being recognized as an appealing solution for EC (and vice versa), its potential benefits and synergetic aspects have not yet been fully revealed, especially in terms of latency and energy consumption. To the best of our knowledge, very few reports [2; 15; 16; 17] have studied the issue of the blockchain in EC. However, in those studies, the simplistic situation of only a single miner is considered. In addition, in those studies, the focus is not on improving the energy consumption and latency of the EC network, as we propose with our framework, but to improve the security and aid the blockchain with computational resources. In addition, we have found no current papers that study the issue of the amount of computational resources needed in a blockchain-empowered UAV network. Performing a blockchain operation requires a lot of computational resources, which are very limited on UAVs. Therefore, in this paper, we conduct research on how these issues could be resolved.
## 6 AC/DC Power Flow of a Multi-Infeed LCC-HVDC System
AC and DC systems are properly modeled. The specific control parameters are also considered for the fast decoupled power flow method. In ref. [59], a converter modeling for a simultaneous approach of power flow is explained. The converter buses are modeled as a constant-voltage-depended load. A problem associated with a converter tap position is discussed in [60]. The converter tap position in a power flow is a very essential parameter for convergence. Mostly, the tap position obtained lies outside the gear limit, and sometimes it makes an extinction angle to hit its limits. In all the available literature, the obtained tap position is continuous and turns discrete by regulating to the nearest discrete value. This technique initiates an error in other control parameters of a converter, especially the converter's extinction angle. An improved AC/DC continuous power flow method for stability and point of collapse is discussed in [61]. A technique describing a power flow method of AC and multiterminal DC systems is investigated in [62]. The DC converters are modeled as a current source in parallel with commutating resistance. A detailed analysis of an AC/DC power flow considering multi-infeed HVDC systems is reported in [63]. The advantages and disadvantages of both sequential and simultaneous approaches are highlighted in detail. The simultaneous approach does not have the ability to use existing AC power flow programs. The reason is obvious that the AC and DC systems in a simultaneous approach are solved together, which increases the size of calculations and also increases the complexity. The benefits of a sequential approach are much more than those of a simultaneous approach because the sequential approach has the ability to use existing programs of an AC power flow. All that has to be done is to integrate existing programs with new DC power flow methods. Moreover, due to a separate solution for both systems, the complexity of calculation also decreases. The sequential method is widely accepted in a power flow analysis of AC/DC systems, but it also has some challenges. Specifically, the sequential approach encounters a convergence issue when a DC system operates under reduced power. To deal with imaginary reactive power, the cosine of an extinction angle and a tap adjustment problem, a method is discussed that changes the state variables. The discrete value of a converter tap position is achieved by regulating to nearest discrete gear. In ref. [64], the AC/DC power flow for multiterminal HVDC systems is discussed by including the DC network. The method uses the simultaneous power flow approach, which extends the size of the Jacobian matrix and the complexity. A generic DC network representation is provided by considering the independent current source at each node. The converter transformer tap position adjustment problem remains unaddressed for quick convergence.
# On the Use of Transformer-Based Models for Intent Detection Using Clustering Algorithms
## 3 Materials and Methods
### Evaluation Metrics  
Four standard evaluation metrics were examined for benchmarking the models. The first was homogeneity, which is a metric used in clustering that determines if the points ofeach cluster belong to a unique class (given the ground truth label). The score ranges from 0 to 1, with 1 indicating all clusters containing data points from a single class. This metric is defined as [42]  
\[\text{Homogeneity}=1-\frac{E(C|K)}{E(C)}, \tag{1}\]  
where \(E(C|K)\) is the conditional entropy of the class distribution given the cluster assignments, and \(E(C)\) is the entropy of the class distribution. Completeness was the second metric, and it assessed whether the data points that belonged to the same class were placed in the same cluster. This metric also reports a value that ranges from 0 to 1, and is given by [42]  
\[\text{Completeness}=1-\frac{E(K|C)}{E(K)}, \tag{2}\]  
where \(E(K|C)\) is the conditional entropy of the cluster assignments given the class distribution, and \(E(K)\) is the cluster assignment entropy.  
The V-Measure was the third metric, and it evaluates the correctness of cluster assignments by analyzing conditional entropy. A higher V-Measure value indicates greater similarity. It is seen as the harmonic mean of the stated previous metrics through [42].  
\[\text{V}-\text{Measure}=2\cdot\frac{\text{Homogeneity}\cdot\text{Completeness}}{ \text{Homogeneity}+\text{Completeness}}. \tag{3}\]  
Accuracy is a metric commonly used to evaluate classification models, but it can also be used to evaluate clustering algorithms. However, the Hungarian method was employed to address the correspondence problem since the cluster labels do not have any inherent meaning (the labels specify the group to which each sample belongs). Similar to what was conducted for CDAC development, the clustering accuracy was also measured for the studied ensemble methods [14]. The calculation of accuracy is given by the number of correct predictions divided by the total number of predictions.
# A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities
###### Abstract  
The smart energy system, viewed as an \"Energy Internet\", consists of the intelligent integration of decentralized sustainable energy sources, efficient distribution, and optimized power consumption. That implies the fault diagnosis for a smart energy system should be of low complexity. In this paper, we propose a Strong Tracking Unscented Kalman Filter (\(STUKF\)) and modified Bayes' classification-based Modified Three Sigma test (\(MTS\)), abbreviated as \(SFBT\), for smart energy networks. The theoretical analysis and simulations indicate that \(SFBT\) detects faults with a high accuracy and a low complexity of \(O(n)\).  
Keywords:fault diagnosis; low-complexity; energy internet; smart city +
Footnote †: journal:
# Estimation of the Block Adjustment Error in UAV Photogrammetric Flights in Flat Areas
Chains of GCPs located across the block should be used for altimetric control to reduce altimetric error; another option is to make cross strips at the ends of the block or every certain number of models, to minimise altimetric error, or in the case of surveys with several flights. Therefore, the strips are stabilised in the direction of the flight, producing a more stable solution [22]. The number of GCPs directly influences the model's accuracy [23; 24; 25; 26; 27; 28]. In this sense, to increase the accuracy of the bundle block adjustment, it is recommended to understand the behaviour of the planimetric and altimetric errors based on the number of GCPs. For altimetry, the GCPs should be distributed across the flight lines along chains or cross stripson the edges of the block [2; 23]. However, currently, there is usually no distinction between planimetric control and altimetric control, and surveyors measure full XYZ coordinates. Besides, the absence of GCPs makes it challenging to detect coarse global errors [23; 29]. The influence of the number of GCPs has been recently studied for UAVs based on the area studied. Table 1 summarises the error reported in different contributions, showing how a greater density of GCPs per hectare (ha) may not directly increase the accuracy of the photogrammetric bundle block adjustment. The main aim of this study is to demonstrate how the flight plan influences the results obtained with UAV-DAP by applying aerial triangulation with bundle block adjustment.
# Autonomous Pointing Control of a Large Satellite Antenna Subject to Parametric Uncertainty
## 3 Autonomous Controller Design
### PD Plus Structural Filter Design
The SF can effectively remove the vibration signals embedded in the attitude dynamics. **Remark 2**. : _The closed-loop system with controller from Equation (13) has a gain margin of 60.2 dB and phase margin of 155 deg, which is therefore stable. For the proposed notch filter presented in the controller from Equation (13), the maximum magnitude gain can be obtained by \(-20\log_{10}\frac{\xi_{2}}{\xi_{p}}\) which occurs at \(\omega_{z}\). The filter damping ratios will also determine the effective notch region and system settling time._
# A Performance Prediction Method Utilizing Time-Dependent Subsystem Transfers between Family Systems
## 3 Cluster, Family, and Subsystems
### Construction of Family System
\[d_{ji}=\left\|s_{j}-\mu_{i}\right\|_{2}=\sqrt{\sum_{n=1}^{n}\left|s_{j\mu}- \mu_{ii}\right|^{2}} \tag{5}\]  
Step 3: Divide the subsystem \(s\) into corresponding family systems based on the nearest average vector. Step 4: Calculate the new average vector \(\mu^{\prime}_{i}\), if \(\mu^{\prime}_{i}\
eq\mu_{i}\); make \(\mu^{\prime}_{i}=\mu_{i}\) and repeat the second step. Step 5: If \(\mu^{\prime}_{i}=\mu_{i}\), output the results of the family system partition. To avoid excessive iteration time, set a maximum number of cycles. If the program reaches this maximum number of cycles, it will terminate and output the result. The focus of this study does not lie in the method for calculating the distance between the subsystem and the average vector. Therefore, as long as the distance testing method satisfies the four conditions outlined in Equations (6)-(9), various distance measurement methods commonly employed in machine learning can be flexibly utilized during the second step. \[\text{distance}\big{(}x_{i},x_{j}\big{)}\geq 0 \tag{6}\]  
\[\text{distance}\big{(}x_{i},x_{j}\big{)}=0\text{ iff }x_{i}=x_{j} \tag{7}\]  
\[\text{distance}\big{(}x_{i},x_{j}\big{)}=\text{distance}\big{(}x_{j},x_{i} \big{)} \tag{8}\]  
\[\text{distance}\big{(}x_{i},x_{j}\big{)}\leq\text{distance}\big{(}x_{i},x_{k} \big{)}+\text{distance}\big{(}x_{j},x_{k}\big{)} \tag{9}\]  
The determination of the \(k\) value is a crucial operation in the process of partitioning a family system. In theory, \(k\) can take any integer value between \(1\) and \(n\). When \(k=1\), it  
Figure 1: Flow chart of family system division. signifies that all subsystems within the cluster system are grouped into a single family system, exhibiting similarity. Conversely, when \(k=n\), each subsystem is assigned to an individual family system with varying performance. Meanwhile, the computational complexity of the family system partitioning process will also be influenced by variations in the value of \(k\). Therefore, when determining the optimal value for \(k\), it is imperative to consider factors such as subsystem similarity, program computation requirements, and the number of reference samples necessary for subsequent reliability analysis processes. It should be noted that the number of subsystems within each family system may not necessarily be equal; rather, they tend to differ based on performance similarities among cluster system subsystems.
## 4 Parallel Performance and Application in Actual Scenarios  
Overall, we ran three sets of tests. The setup of the tests is shown in Table 1.  
For the first test, we used three versions of the code for comparison. The code versions were (a) the original LICOM, (b) semi-optimized LICOM, in which optimization methods B(I), B(III), and C were employed, and (c) fully optimized LICOM. The hardware environments were the \"Era\" supercomputer and the \"Tianhe II\" supercomputer. For the second test, the fully optimized code was run on the \"Tianhe III\" supercomputer. For the third test, the fully optimized code was run on the \"Era\" supercomputer for a long term in a real scenario. For all of the tests on the three different hardware platforms, the same test case with a global resolution of 10 km \(\times\) 10 km was used. The simulation started from 1993. The detailed configurations are listed in Table 2. These parameters controlled the run of LICOM and described the real scenario in our test. An elapsed time of one model day was used as an indicator for comparison. We ran each particular test at least five times to calculate the average value. The simulation year per day, as a widely used indicator of running speed, was calculated from the elapsed time of one model day.
## 4 Results and Evaluation
### LOSADA Representations  
Figure 4 presents the available results of LOSADA2016 and LOSADA2018 representations. The three representation-classifier pairs perform better on the datasets DDVHSM and Kaggle and have a lower performance on CSSRS. The combined representation with PBC4 cip performs better on DDVHSM and Kaggle with a mean performance of 0.9457 and 0.9586, respectively. On the other hand, the Ensemble of BOWs and Handcrafted features with logistic regression performs better on CSSRS with a mean performance of 0.6335.  
As seen in Figure 5, the same pattern is repeated when using AUC as the metric for performance. Nevertheless, using AUC, the Ensemble of BOWs and Handcrafted features with logistic regression performs better on both CSSRS and DDVHSM with a mean performance of 0.7357 and 0.9470, respectively. This contrasts with PBC4 cip, which has a mean performance of 0.72191061 and 0.9375.  
Figure 3: A comparison plot of the AUC for the proposed sentiment-based representations using PBC4 cip.  
Figure 2: A comparison plot of the F1 score for the proposed sentiment-based representations using PBC4 cip.
## 4 Results and Discussion
### Discussion  
The results obtained from the basic genetic algorithm and the adaptive genetic algorithm are shown in the figure below.  
In Figure 6, both sets of solutions satisfy the loading tasks at each shovel point in the open-pit mine as well as the consumption tasks at the crushing stations. Compared to the basic genetic algorithm, the adaptive genetic algorithm reduced the total transportation costs by CNY 761.61, the total waiting time by 4 h, the total transportation distance by 165.9 km, and the total number of battery swaps by 10. On average, the transportation cost per truck was reduced by CNY 25.59, the waiting time per truck was reduced by 8 min,  
Figure 5: Truck operation Gantt chart using the adaptive genetic algorithm.  
the transportation distance per truck was reduced by 5.53 km, and the number of battery swaps per truck was reduced by 0.34. The results obtained using the adaptive genetic algorithm outperformed those from the basic genetic algorithm across all four indicators: transportation costs, waiting time, number of battery swaps, and transportation distance. Specifically, the adaptive genetic algorithm reduced the total transportation costs and total waiting time by 5.6% and 17.4%, respectively, and decreased the number of battery swaps and transportation distance by 15.8% and 1.2%, respectively.  
Both solutions achieved the optimization of vehicle transportation scheduling under the new transportation mode of battery-swapping trucks in open-pit mines. The two solutions provided cross-validation, enhancing the diversity of the model's results and verifying the applicability and reliability of the multi-objective scheduling optimization model for battery-swapping trucks in open-pit mines. These results offer more reliable and optimized scheduling plans for vehicle scheduling under the new transportation mode.
## 3 Materials and Methods
### Resampling Strategies
#### 3.1.1 Smote  
The main drawbacks of random oversampling have been addressed by introducing some modifications in the original algorithm and evolving to more advanced approaches. One of the most popular is SMOTE (synthetic minority oversampling technique) [35], an oversampling method proposed for dealing with the overfitting problem, making the decision border of the minority class more general. In this method, instead of replicating instances of the minority class, new examples are generated by interpolating the existing ones. The steps of the algorithm are the following:  
1. Randomly select \(k\) nearest neighbors for each training instance \(x\) of the minority class.
2. Select a random point along the line segment between the instance \(x\) and the nearest neighbor and add it as a new instance to the training set.  
SMOTE is an algorithm especially efficient for highly imbalanced datasets and its performance can be improved combining it with undersampling techniques [35].