esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
###### Abstract  
As the number of intelligent devices in mining environments increases, the transmission time for large datasets, including equipment status and environmental parameters, also rises. This increase leads to longer response times for service requests, making it difficult to meet the equipment's real-time requirements. Edge computing effectively addresses the demands for low latency and high performance. However, the deployment of edge nodes can negatively affect overall service performance due to resource limitations and node heterogeneity. In this paper, we propose two node deployment strategies: an improved genetic algorithm (IBGA) for fixed device scenarios and an improved sand cat swarm optimization algorithm (ISCSO) for mobile device scenarios, both accounting for the mobility characteristics of the devices. Additionally, we developed a simulation platform based on a production line system and an intelligent patrol vehicle to evaluate the proposed method's effectiveness. The experimental results show that the IBGA and ISCSO algorithms effectively reduce task delay and deployment cost. Both deployment methods outperform the benchmark algorithms and offer better service quality assurance.  
edge node placement; multi-objective optimization; heuristic algorithms; smart mine +
Footnote †: journal: applied sciences
## 1 Introduction
We also discuss implications on spatial data accuracy (quantization error), temporal reduction and data exchange, also comparing to existing raw binary encoding and other lossless approaches.
## 5 Discussion
### RQ1: Positive and Negative Impacts of VR Games  
VR games and cultural heritage settings are in the process of converging, and their interaction can be mutually beneficial. VR games not only give players a glimpse of the past, but allow them to manipulate the virtual word, be active in it, interact with cultural content and other players and actively involve perception and imagination (S13). As presented above, the use of VR games in cultural heritage are associated with multiple learning gains, increased visit motivation, and dynamic engagement. The ability of VR to increase immersion allows people to experience culture with multiple senses and not only to hear or read about it.  
Multiple types of technologies can be used depending on the venues needs and multiple types of games and interactions can be implemented, covering the needs of diverse audiences. These applications allow people to experience culture alone or in groups, and also provide opportunities for personalized content and experiences, depending on users' needs, personalities, and interests. VR games seem to increase enjoyment and thus provide a more holistic experience that not only focuses on learning but also addresses other needs of cultural visitors, such as having fun, being active, and making meaningful interactions with cultural content and other visitors. Finally, VR games can allow researchers and museum staff to collect valuable visitor feedback.  
In the works reviewed, a few negative impacts were also reported. One is related to possible training needs of players before they can successfully use the virtual environment. However, a design that respects usability principles can lower such demands. Another is related to the motion sickens reported in some studies. To investigate motion sickness in VR games, different studies have used the Simulator Sickness Questionnaire (SSQ) as a measurement tool [78; 79]. Usability principles together with specific guidelines for the use of such games (e.g., time restrictions, etc.) can also prevent such symptoms. Different genre of environments may provide significant differences in the levels of motion sickness [80].
###### Abstract  
Considering that diurnal variation interferes with three-component magnetic surveys, which inevitably affects survey accuracy, exploring an interference compensation method of high-precision is particularly desirable. In this paper, a compensation method for diurnal variation is proposed, the procedure of which involves calibrating the magnetometer error and the misalignment error between magnetometer and non-magnetic theodolite. Meanwhile, the theodolite is used to adjust the attitude of the magnetometer to unify the observed diurnal variation into the geographic coordinate system. Thereafter, the feasibility and validity of the proposed method were verified by field experiments. The experimental results show that the average error of each component between the observed value of the proposed method and that of Changchun Geomagnetic station is less than 1.2 nT, which indicates that the proposed method achieves high observation accuracy. The proposed method can make up for the deficiency that traditional methods cannot meet the requirements of high accuracy diurnal variation compensation. With this method, it is possible for us to set up temporary diurnal variation observation station in areas with complex topography and harsh environment to assist aeromagnetic three-component survey.  
Keywords:diurnal variation; three-component magnetic survey; magnetic interference; tri-axial magnetometer; non-magnetic theodolite +
Footnote †: journal: Applied Sciences
## 4 Link Budget
### Link Budget under Realistic Atmospheric Conditions
#### 4.2.3 Orbit Simulation  
The existing orbits were considered to perform orbit simulations. The ISS is the first candidate, since there are most resources (e.g., enough energy supply) available onboard. The second one is the ICESat, because the onboard GLAS lidar was already used for some forest studies [68, 69, 70, 73, 74, 75]. The SPOT (Satellite for observation of Earth) could be another candidate, but its altitude (832 km) is too high for lidar measurements. Thus, the two existing orbits of ISS and ICESat were chosen for the simulation by using their respective orbital characteristics, because they are more realistic for the proposed mission. Note that the inclination of the ISS orbit does not permit measurements in the higher latitudes.  
The revisit cycle of both orbits was chosen to be 26 days, as for the SPOT mission, which is dedicated to surface survey. The simulated orbits are shown in Figure 13, with 1 day's revolutions for the global area and 26 days' revolutions for the 2 selected areas.
# Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN
###### Abstract  
The PASCAL VOC Challenge performance has been significantly boosted by the prevalently CNN-based pipelines like Faster R-CNN. However, directly applying the Faster R-CNN to the small remote sensing objects usually renders poor performance. To address this issue, this paper investigates on how to modify Faster R-CNN for the task of small object detection in optical remote sensing images. First of all, we not only modify the RPN stage of Faster R-CNN by setting appropriate anchors but also leverage a single high-level feature map of a fine resolution by designing a similar architecture adopting top-down and skip connections. In addition, we incorporate context information to further boost small remote sensing object detection performance while we apply a simple sampling strategy to solve the issue about the imbalanced numbers of images between different classes. At last, we introduce a simple yet effective data augmentation method named 'random rotation' during training. Experimental results show that our modified Faster R-CNN algorithm improves the mean average precision by a large margin on detecting small remote sensing objects.  
Keywords:object detection; modified faster R-CNN; remote sensing; feature pyramid +
Footnote †: journal: Applied Sciences
# Path Planning of Underground Robots via Improved A* and Dynamic Window Approach
## 2 Improved A* Algorithm
### Adaptive B-Spline Curve Smoothing
Figure 8: Path smoothing flow chart. The comprehensive score is  
\[score=w_{L}\ \cdot L_{norm}\ +w_{C}\ \cdot C_{norm}\ +w_{F}\ \cdot F_{norm}\ +w_{R}\ \cdot R_{norm} \tag{8}\]  
Select the \(k\) value according to the range of _score_. \[k=\left\{ \begin{array}{l}3\ \text{if score}<0.25\\ 4\ \text{if}\ 0.25\leq\text{score}<0.5\\ 5\ \text{if}\ 0.5\leq\text{score}<0.75\\ 6\ \text{if}\ \text{score}\ \geq 0.75\end{array} \right. \tag{9}\]  
The smoothing factor is calculated by the following formula  
\[s=score\times(\text{max\_deviation})^{2} \tag{10}\]  
After determining the order and smoothing factor of the B-spline curve, the corresponding degree of the spline curves can be selected to smooth the global route. However, it is necessary to evaluate whether the smoothed path exceeds the preset maximum deviation limit, which is designed to control the degree of deviation of the smoothed path from the original path, thereby ensuring the accuracy and feasibility of the path. The maximum deviation is defined as the maximum distance from the point on the smooth path to the nearest point on the original path. The specific deviation calculation formula is  
\[D=\max_{p^{\prime}\in\text{smooth path}}\biggl{(}\min_{p\in\text{original path}}\bigl{\|}p^{\prime}-p\bigr{\|}\biggr{)} \tag{11}\]  
\(p^{\prime}\) is the point on the smooth path. \(p\) is the point on the original path. \(\|p^{\prime}-p\|\) denotes the Euclidean distance between two points. Finally, it is necessary to verify whether the smoothed path collides with the obstacle. If a collision occurs, the system will resume using the original path; if no collision occurs, the smoothed path is accepted and adopted.
## 4 Empirical Assessment Using Real-World Data
### Experimental Design  
This section presents a detailed methodology for the real-world assessment of the proposed machine learning-based hurdle model for analyzing zero-inflated crash frequency data. The evaluation focuses on three key areas: the performance of the classification stage, the performance of the regression stage, and the overall model results. Figure 3 provides a comprehensive overview of the entire process, including data preprocessing, model training, and evaluation. The real-world assessment aims to validate the model's practical applicability and robustness in predicting crash occurrences and frequencies. This assessment is conducted in a structured manner, ensuring that each stage of the model is rigorously tested and compared against several benchmark models.
## 5 Results
### LSMs by Two Ensemble Models  
The second phase of the analysis was to produce LSMs using an ensemble of two-models (Figure 7). Considering each of RF, ANN, LR and SVM model, a set of six ensemble models were derived, i.e., ANN-RF, ANN-SVM, SVM-RF, SVM-LR, LR-RF, and LR-ANN. and the derived LSMs showed varied results. The areal expansion of VH susceptibility class extracted by ANN-RF, ANN-SVM, SVM-RF, SVM-LR, LR-RF and LR-ANN ensemble model are 17.91%, 18.41%, 16.71%, 17.89%, 16.06%, and 20.09%  
Figure 6: Distribution of area under different landslide susceptibility classes (in %) of (**a**) individual models, (**b**) ensemble of two models, (**c**) ensemble of three models and (**d**) ensemble of four models.  
of the total area, respectively. Regarding high susceptibility category, the areas are 15.08%, 16.74%, 15.91%, 14.25%, 14.02%, and 12.45%, respectively for each of the models. The largest area under the moderate category was shown by the LR-ANN model (27.29%) followed by LR-RF (20.69%), SVM-RF (20.23%), SVM-LR (19.68%), ANN-SVM (18.65%), and ANN-RF (17.76%) models. Correspondingly, the total area under L and VL susceptibility classes of the LSMs was 49.25%, 46.2%, 47.15%, 48.17%, 49.23%, and 40.16% accordingly (Figure 6).
## 2 Theory
### Prediction Models
#### 2.2.4 Stacking  
Similar to averaging, the stacking method (STACK) consists in a weighted sum of the predictions of the \(A+1\) PLSR models (\(r\) = 0,..., \(A\) LVs):  
\[\hat{y}_{new,\text{stack}[A]}=\theta_{0}\hat{y}_{new,0}+\theta_{1}\hat{y}_{new,1}+\ldots+\theta_{A}\hat{y}_{new,A} \tag{4}\]  
but the coefficients \(\theta_{r}\) are now estimated from a \"top\" model ([24]). A K-fold CV is done on the training data \(\{X,\,y\}\) from which is computed the \(n\,\times\,(A+1)\) prediction matrix \(\hat{Y}_{CV}=\{\hat{y}_{CV,0},\hat{y}_{CV,1},\,\ldots,\,\hat{y}_{CV,A}\}\). The top model consists in regressing \(y\) to \(\hat{Y}_{CV}\).  
In this paper, the top model is an MLR (without intercept), but other types of models can be used [24]. Contrary to weights \(w_{r}\), the returned MLR coefficients \(\{\theta_{0},\,\theta_{1},\,\ldots,\,\theta_{A}\}\) used in Equation (4) are unbounded and can be negative.
# Effect of Excitation Beam Divergence on the Goos-Hanchen Shift Enhanced by Bloch Surface Waves
## 3 Simulation  
A one-dimensional photonic band-gap (PBG) structure that supports the Bloch surface wave (BSW) is simulated, where the structure is substrate/(HL)\({}^{9}\)/HL'. Here H, L and L' are corresponding  
Figure 1: Schematic diagram of the Goos–Hänchen (GH) shift.  
to the higher index layer, lower index layer and lower index buffer layer, respectively, as shown in Figure 2.  
The incident beam is set as 980 nm for TM-polarization. The dielectric materials are TiO\({}_{2}\) and SiO\({}_{2}\), for the high and low index layers, respectively, where their reflective indices are set as 2.30 and 1.45 and the thicknesses are 163 nm and 391 nm respectively. The buffer layer on top is a layer of low index dielectric with a thickness of 500 nm. The thickness of the high index, low index and buffer layerare designed according to the theory of the photonic band-gap [18]. The multilayer stack is deposited on a ZF10 glass substrate, where the reflective indexis set as 1.668. The external medium cladded is set to water with the reflective index of 1.33. According to our previous work [5], the loss of the TiO\({}_{2}\) and SiO\({}_{2}\) multilayer structure is set as \(10^{-4}\).  
Figure 3 show that reflectivity and phase under different incident angles, when BSW occurs, calculated using the Fresnel equations. For a focused beam with a beam waist size of \(w_{0}\), the beam divergence angle \(\theta\) could be calculated, and so is the \(k_{x}\) vector, which determines the integration range in Equation (3).  
For a quasi-collimated beam (\(w_{0}=2580\) um, beam divergence angle = 0.007\({}^{\circ}\), or a larger beam), as shown in Figure 3, the corresponding angular range is relatively small. Within this small angular range, the reflectivity intensity changes little, and the phase change is approximately linear. As the radius decreases, the angular range is broadened, where the reflectivity intensity cannot be regarded as one single value and the higher order components other than linear approximation for the phase change cannot be neglected. The simulation results of seven different waistradiusesare shown in Figure 4. The simulated GH shift values are obtained by numerically calculating the reflected field distribution of a Gaussian beamand the result is calculated using Equation (3). From Figure 4a, we note that the larger the waistradius, the narrower the peak of the GH shift, and the closer the corresponding GH shift is to the Artmann estimation. Also, as shown in Figure 4b, the calculated GH shift is approaching the estimation calculated through the Artmann equation as the waistradius increases, in an exponential-likehood manner.  
Figure 3: Reflectivity and phase under different incident angles.  
Figure 2: (**a**) photograph of the fabricated 1D PBG structure on a ZF 10 glass substrate (**b**) structure of 1D PBG.
## 4 Discussion
### Choosing Appropriate Models: Practical Scenarios  
An exhaustive guide to model selection was not the main objective of the present study. Nevertheless, the results obtained allow some practical recommendations. If a high recall is a priority, the SVM or a carefully tuned ensemble method (e.g., CatBoost or XGBoost) can be used to capture as many positive instances as possible. In scenarios requiringbalanced performance in terms of other metrics, CatBoost and XGBoost have consistently achieved good results, including robust ROC-AUC values. In situations where limited computational resources are available, logistic regression proved to be particularly suitable due to its short training and inference times compared to the other algorithms evaluated. Therefore, practitioners are advised to base model selection on the specific performance metrics of interest (e.g., precision, recall, F1, ROC-AUC), available computational capacity, and the inherent distribution of the dataset.
# Matchability and Uncertainty-Aware Iterative Disparity Refinement for Stereo Matching
## 4 Experiments
### Ablation Study
#### 4.3.1 Ablation of the MUD Module  
The MUD module is proposed in Section 3.1 and is designed to reduce the susceptibility to mismatched pixels. The matchability mask and disparity uncertainty of each iteration can be decoded from the hidden state using the MUD. In row 3 of Table 1, the modified joint disparity-uncertainty estimation proposed in Section 3.1.2 is evaluated. Compared to the original method [17] (row 2), our method reduces the disparity EPE by about 15.4% and the uncertainty APE by 41.2%. It alleviates the unnecessary decrease in disparity accuracy during joint training. Following this, in rows 4 and 9 of Table 1, the MUD module is evaluated. Compared to the uncertainty-aware only method (row 3), the disparity EPE and uncertainty APE is reduced by about 12.5% and 11.3%, respectively. Compared to the baseline (row 1, 8), the EPE and Bad1.0 Occ are reduced by about 8% and 10.9%, respectively. This demonstrates the improved robustness in ill-posed regions.  
Notably, the contribution of the MUD module is not only for the improvements in quantitative evaluation results, but also for enhancing the interpretability of models through the estimated matchability mask and disparity uncertainty shown in Figure 7d,f. Both the occluded and textureless regions are masked out in the matchability mask. The disparity error converted from the estimated uncertainty keeps highly consistent with the error map. With a specified error threshold (1 px in Figure 7f), flying pixels at depth discontinuity boundaries can be easily filtered out, as shown in Figure 7g,h.
## 3 A Corpus for Routing Evaluation in OppNets
### Quality Requirements  
The corpus aims to be a fair field for evaluating OppNet routing algorithms, providing a set of scenarios that can emulate real-world environments due to their characteristics. This article presented a corpus creation methodology depicted in Figure 3 and explained it in detail in Section 3.5. In addition, the corpus creation methodology presented in this research pursues the following requirements: Coverage, scope, quality and usability.  
* Coverage: the coverage of the corpus should have representativeness for real-world environments, considering a significant difference between scenarios.
* Scope: the scope of the corpus should be the performance evaluation of routing algorithms in OppNets.
* Quality: the quality of each scenario of the corpus should be guaranteed by analyzing the representativeness and diversity among other scenarios.
* Usability: the corpus should be easy to use, and the scenarios should be adaptable to simulation software, where the evaluation of the performance of algorithms in OppNets is carried out.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**N\({}^{\circ}\)** & **Characteristic** & **Measurement Directive** & **Ref.** \\ \hline
8 & Centrality & Betweenness centrality computed as number of connections held by each node & [2] \\ \hline
9 & Inter-contact time & Elapsed time each node has between contacts & [52] \\ \hline
10 & Contact time & Elapsed time of the connection between two nodes & [52] \\ \hline
11 & Contact time per minute & Contact-time within a period of one minute & [53] \\ \hline
12 & Contact node ratio & Node contact ratio & [54] \\ \hline
13 & Popularity & Unique peer-connections a node has & [55] \\ \hline
14 & Window centrality & Centrality during a period & [56] \\ \hline
15 & Encounters & Number of connections a node has & [57] \\ \hline
16 & Sociability & Ratio of the number of contacts a node has to the total number of nodes & [58] \\ \hline
17 & Total encounters & Summation of the number of connections within nodes & [57] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Indirect scenario characteristics measurement directives with references.  
Figure 3: Corpus creation methodology with backtracking stage for scenario selection assuring purpose, coverage, scope, quality and usability requirements.
###### Abstract
On the whole, a novel diagnosis method based on semi-supervised clustering and SVDD with adaptive parameter optimization and improved decision strategy is proposed in this study. Firstly, VMD is applied to decompose the vibration signals into sets of IMFs, and the decomposing mode number \(K\) is preset with central frequency observation method. Then FuzzyEn values of all IMFs are calculated to construct the feature vectors of corresponding samples with different fault types. Afterwards, training samples are clustered by SSFCM for information mining. Subsequently, SVDD with improved decision strategy (ID-SVDD) that combines with k-nearest neighbor is proposed to establish diagnostic model. Meanwhile, the optimal parameters \(C\) and \(\sigma\) for ID-SVDD is searched by the proposed adaptive sine cosine algorithm (ASCA). Finally, the engineering application and contrastive analysis indicate the availability and superiority of the proposed method. The paper is organized as follows: Section 2 is dedicated to the basic knowledge of VMD, FuzzyEn, SSFCM, and SVDD. Section 3 introduces the proposed fault diagnosis method based on SSFCM and ASCA-optimized ID-SVDD. Section 4 presents the engineering application and comparative analysis, the experimental results of which demonstrate the superiority of the proposed method. Some discussion about the method presented in this paper is in Section 5. The conclusion is summarized in Section 6.
## 2 EBPSK Modulation and Transmission System  
EBPSK [6] is a modulation method with high frequency spectra efficiency, which is defined as following:\[g_{0}(t)=A\cos 2\pi\!\!f_{c}t,0\leq t<T\]  
\[g_{1}(t)=\left\{\begin{array}{cc}B\cos(2\pi\!\!f_{c}t+\theta),&0\leq t<\tau,0 \leq\theta\leq\pi\\ A\cos 2\pi\!\!f_{c}t,&\tau\leq t<T\end{array}\right. \tag{1}\]  
where \(g_{0}\left(t\right)\) and \(g_{1}(t)\) represents the waveform modulated by \"0\" or \"1\", respectively, \(T=2\pi\,N/\omega_{c}=NT_{c}\) is the symbol width of data, \(T_{c}=2\pi\big{/}\omega_{c}=1\big{/}f_{c}\) is the carrier cycle, namely \(T\) lasts \(N\geq 1\) cycles of carrier; \(T\) is the symbol width of data; \(\theta\) is the phase jumping angle or modulating angle, and \(\tau\) is the time length of the \(\theta\) jumping lasting. The paper just considers the case of \(A=B=1\).  
In EBPSK modulation, the modulated waveforms corresponding to \"0\" and \"1\" have a very tiny difference, and the most parts of the waveform are sine components, therefore, having compact power spectra [10], _i.e.,_ high throughput efficiency, and is difficult to detect. The problem faced is how to design a UNB receiver filter to save or improve phase jumping information. The traditional IIR or FIR filter can erase the phase information, so that \"0\" and \"1\" information cannot be distinguished. A special UNB impacting filter [8] will be used in EBPSK system, which can transform the tiny phase information into the higher impulse amplitude at the phase jumping point corresponding to code \"1\" than the amplitude of \"0\" [11], that is to say, amplify the signal characters as much as possible and remove the maximum noise and other interferences. Consequently, in the case of precise synchronization, we can use directly amplitude detection at the jumping point [12] or energy accumulation of tailing to judge \"0\" and \"1\", which causes the structure of receiver much simplifier than that based on phase locked loop [6]. Therefore, the UNB transmission system can be digitally implemented, since the EBPSK has covered both the traditional BPSK and most UNB modulations, which is beneficial for chip integration.