esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobile Robot Tracking with Deep Learning Models under the Specific Environments
## 3 Method
### Different Environments  
The second test data set containing 661 images is created and defined as test dataset two. The images in test data set two have never been selected from the images in the target robot data set and test data set one. The targets in these images are captured from different angles and distances to simulate different mobilities. Practical occlusion is simulated for out-of-range scenarios. The images of the data set also contain images taken under harsh lighting or noisy conditions. The practical occlusion is about 10% to 30% of the target.  
Furthermore, the deep learning models have different performances in detecting the object at different light intensities which can be seen in Table 1: Testing results for deep learning models' brightness. One image was selected from our testing data set. Then we change the exposure of the image from \(-5\) to \(5\). The Faster RCNN and the SSD models can detect the object in the darkest and lightest environment. However, the Faster RCNN model produces multiple detection results for the same target. The YOLOv5 model can detect the object in the lightest environment, but it cannot detect the object in the darkest environment.
# Enhancement of Conventional Beat Tracking System Using Teager-Kaiser Energy Operator
## 2 Dataset and Methods
### System Evaluation  
During the analysis, we first used the reference dataset to determine the accuracy of the GT and ABD estimation. We computed the GT of each track by the proposed beat tracking method using both the proposed onset detection function (DS (default system)), and the same onset detection function with the TKEO (TS-system with the TKEO). Then we compared the reference values (annotation of the dataset) of each tested track with values estimated by the DS and the TS. The reference tempo was obtained as the number 60 (BPM definition) divided by the median of time differences between consecutive beat time positions. Then we calculated the median (Me) and the mean value (\(\bar{x}\)) of time differences of consecutive beats in all recordings and also in which the average was less than 1 s. This represents the ABD of tracks that were close to the reference tempo (some recordings achieved more than \(\sim\)20 BPM difference in the GT when tested; they were excluded for the extended ABD testing).  
Next, we analysed the string quartet database. First, all 33 recordings were divided into six segments with a relatively steady tempo and then all motifs were tested by the TS and the DS to estimate the GT. We computed the reference EAT of all segments of each interpretation (Table 1) by calculating the number of quarter notes (Table 2) and dividing them by the time length of each recording. The complete table is in Table 11. Finally, the EAT and the computed GT were compared. Systems were implemented using Python language (especially NumPy and LibROSA packages).
## 8 Conclusions  
In this paper, we reviewed articles published from January 2016 to December 2023 that utilized remote sensing and ML algorithms for predicting and mapping soil salinity. This review yields the following insights:  
1. Most literature focuses on depths up to 60 cm within the surface layer. However, soil salinity perturbations in crop root zones at greater depths (1-2 m) have not been adequately studied.
2. Freely available satellite sensors are extensively employed. In contrast, commercial satellites and high-end Chinese satellite sensors (e.g., GF series and ZY series) have received comparatively less attention in soil salinity mapping.
3. Traditional ML algorithms (e.g., RF, SVM) have been thoroughly applied. However, improved ML algorithms such as LGBM, GBRT, and XGBoost, particularly deep learning algorithms, remain underexplored.
4. Current studies on the fusion of different satellite sensors primarily focus on optical and microwave data. However, the integration of hyperspectral data (low spatial resolution) with multispectral data (high spatial resolution) has yet to be investigated in the realm of soil salinity mapping.
5. The relationship between metadata and prediction accuracy indicates that validation accuracy, sample size, number of variables, and average sample salinity correlate with modeling accuracy, while factors such as sampling depth, variable type, sampling time, and maximum salinity do not appear to influence modeling accuracy.  
Current research predominantly centers on predicting soil salinity at large scales in oases. Nevertheless, prediction accuracy in agricultural fields remains low due to vegetation disturbances. A widely accepted method for real-time observation of soil salinity dynamics at multiple depths in agricultural settings has yet to be established.  
Conceptualization, F.W. and Y.W.; funding acquisition, F.W.; software, X.G.; methodology, F.W. and Y.W.; investigation, L.L., R.L., X.L., H.H., L.L. and Y.W.; writing--original draft, F.W.W and Y.W.; writing--review and editing, F.W., Y.W., L.H. and L.L.; project administration, F.W. and Y.W.; Resources, C.B.; formal analysis, J.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 42101363. This project was also supported by the Research Initiation Fund of Chengdu University (2081923044 and 2081923045).  
We are very proud to have been invited to participate in the environmental assessment project, and we also thank the truck-mounted drilling operators and samplers who worked in difficult conditions.  
The authors declare no conflict of interest.
## 2 Theoretical Background as the Basis for Model Development
### Capacity Planning and Maintenance  
In this research paper it was considered the core capacity planning tasks and its relationship with maintenance management. First, based on sales information the production master program (PMP) determines what products should be produced and in what quantities at the following planning intervals [26] (p. 57). The PMP is subdivided into partial processes of sales planning, primary gross needs planning, primary net requirements planning, and approximate resource planning [21] (pp. 41-43). The result of the PMP is a coordinated production program when considering the productive capacities and sales expectations of the company [21] (p. 185). In the approximate planning of the resources it is verified if the sales planning and the production programs can be carried out with the available resources according to the type, quantity, and date on which they are planned and compared with available resources. In this context, resources are personnel, facilities, tools and material [21] (p. 43).  
Process programming provides temporary relationships between production orders. There are three methods: progressive programming, regressive programming, and midpoint programming. The programming of the process can be carried out considering finite or infinite capacities. Capacity is the output of an installation for a period of time. Capacity calculation for a production system depends on maintenance in regard of machine availability. Moreover to calculate the total output of the production system the quality and performance rates have to be added. For ETO manufacturers capacity planning vary normally a lot depending on the product type due to the differences in terms of time needed.  
The capacity demand is then compared with the supply of available capacity. Based on this there are two options in case the required capacity exceeds the offer. The first is to adjust the capacity by increasing it thanks to overtime or special shifts. The second is to postpone demand peaks by shifting manufacturing orders to later points in time [21] (pp. 48-50).  
Sequencing of orders is carried out later with the help of criteria selected as priority rules or minimization of equipment preparation times. The release of production orders is carried out taking into account detailed resource planning and detailed programming. In doing so, defined release rules or methods are used, such as load-oriented order release [21] (pp. 55-57).
## 3 Results and Discussion
### Saturation Magnetization Analysis  
The saturation magnetization of these magnetic polystyrene microspheres was measured by an MPMS-3 VSM magnetometer. Figure 4 presents the hysteresis curves of Fe\({}_{3}\)O\({}_{4}\) and Fe\({}_{3}\)O\({}_{4}\)@PS. As shown in the figure, the hysteresis loops of Fe\({}_{3}\)O\({}_{4}\) and Fe\({}_{3}\)O\({}_{4}\)@PS all pass through the origin, indicating that the above materials have no hysteresis and are superparamagnetic at room temperature. The saturation magnetization (Ms = 65.524 emu/g) of Fe\({}_{3}\)O\({}_{4}\) is significantly higher than that of Fe\({}_{3}\)O\({}_{4}\)@PS (Ms = 9.962 emu/g). This indicates that the saturation magnetization decreases continuously with the gradual increase in non-magnetic materials outside the magnetic core, which results in the weakening of magnetism. During further functionalization, the saturation magnetization of these magnetic polystyrene microspheres decreases to Ms = 4.267 emu/g with the increase of surface sulfonic groups. However, Fe\({}_{3}\)O\({}_{4}\)@PS and Fe\({}_{3}\)O\({}_{4}\)@PS can still be easily separated from the solution under an external magnetic field, which meets the requirements of pretreatment experiments.
# On the Performance of LDPC-Coded Large Intelligent Antenna System
## 1 Introduction
### Objective, Contribution, and Organization  
Previous work considered using the block transmission techniques Orthogonal Frequency Division Multiplexing (OFDM) and SC-FDE and their combination in MIMO or LIS systems [17; 18; 19]. The research published in [18] studies the performance of LDPC-Coded m-MIMO associated with non-orthogonal multiple access techniques. The novelty of this article relies on studying the performance of the LIS system with LDPC codes and SC-FDE transmission.  
This article is divided as follows: Section 2 details system and signal characterization; Section 3 analyses performance outcomes; Section 4 summarizes this article; and Section 5 presents future research.
## 5 Conclusions  
This paper provides a thorough numerical analysis and reliability assessment of DS-CFCST stub columns. The FE model developed accurately predicts the ultimate bearing capacity and confinement behavior under axial compression, validated against experimental results. Key findings include:  
* FE model results show good agreement with experimental data, evidenced by a mean value of \(N_{\text{u,Exp}}/N_{\text{u,FE}}\) at 1.008 and a CV of 0.040. Load-axial strain curves from the FE analysis align closely with experimental observations, confirming the model's validity.
* The strength of the infilled DS concrete, steel yield strength, and steel ratio significantly influence the ultimate bearing capacity of DS-CFCST stub columns. Conversely, the DS replacement ratio has a minor impact on load-bearing capacity.
* This study highlights the diminishing effect of DS replacement ratio on steel tube confinement efficacy. However, the relative influence of this factor is minimal compared to other design parameters.
* A simplified, straightforward formula for predicting the axial load-bearing capacity of DS-CFCST columns is proposed. This formula, incorporating a steel tube shape confinement factor, aligns well with experimental and FE-derived results, offering a more accurate and simpler alternative to existing design code formulas.
* Reliability analysis supports balanced design approaches, considering material strengths, reinforcement ratios, loading conditions, and DS replacement ratio. This ensures adherence to the target reliability index, emphasizing the necessity of adequate safety margins against failure in structural systems.  
S.I.S., investigation, methodology, formal analysis, writing--original draft preparation, visualization, validation. F.-X.D., conceptualization, supervision, writing--review and editing, project administration, funding acquisition, and resources. M.W., supervision, writing--review and editing, project administration, funding acquisition, and resources. F.L., supervision, writing--review and editing, project administration, funding acquisition, data curation,and resources. K.A., visualization, validation, data curation, and resources. H.X., validation, data curation, and resources. H.X., validation, data curation, and resources. All authors have read and agreed to the published version of the manuscript.  
The research work was financially supported by the National Natural Science Foundation of China (Grant No. 52008400) and the Natural Science Foundation of Hunan (Grant No. 2019 JJ20029). Additionally, this work received support from the High-Performance Computing Center of Central South University.  
All data that support the findings of this study are available from the corresponding author upon reasonable request.  
Authors [PERSON] and [PERSON] were employed by the company China Construction Third Engineering Bureau Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Travel Speed Reliability  
### Travel Speed Reliability Evaluation Model  
Travel speed reliability of the road section refers to the probability that the travel speed of the road section is larger or equal to the travel speed reliability evaluation threshold \(v_{*}\) in time interval \(T\) under certain road traffic conditions [23, 24, 25], as shown in Equation (10). There are two main types of travel speed reliability evaluation threshold values taken, one is a fixed speed value [23, 24, 25] and the other is a variable speed value [23].  
\[R_{T,m,v_{*}}=P\{v_{T,m,i}\geq v_{*}\}=\frac{N}{n} \tag{10}\]  
where \(R_{T,m,v_{*}}\) denotes the travel speed reliability of road section \(m\) in time interval \(T\), \(v_{*}\) denotes the travel speed reliability evaluation threshold (km/h), and \(N\) denotes the number of sample records of the travel speed of road section \(m\) larger or equal to the travel speed reliability evaluation threshold \(v_{*}\) in time interval \(T\).  
(1) Fixed speed value as the travel speed reliability evaluation threshold  
Some studies (road service level reliability) have suggested that the travel speed reliability evaluation threshold can be taken as the minimum average travel speed of the road corresponding to different road service levels [23,24], as shown in Equation (11).  
\[v_{*}=v_{s} \tag{11}\]  
where \(v_{s}\) denotes the minimum average travel speed of the road corresponding to road service level s (km/h).  
(2) Variable speed value (a multiple of the average travel speed of the road segment) as the travel speed reliability evaluation threshold  
A previous study (travel speed reliability of car commuters) suggested that the travel speed reliability evaluation threshold can be taken as the expected acceptable travel speed of the car commuter, which is equal to 0.71 times the average travel speed of the car commuter, and the average travel speed of the car commuter can be taken as the average travel speed of the road [23], as shown in Equations (12) and (13).  
\[v_{*}=\overline{v}_{EATS}=0.71\times\overline{v}_{cc} \tag{12}\]  
Figure 10: Travel speed range of the Beijing 3 rd Ring Road: (**a**) Inner Ring; (**b**) Outer Ring.  
\[\overline{v}_{cc}=\overline{v}_{T,m} \tag{13}\]  
where \(\overline{v}_{EATS}\) denotes the expected acceptable travel speed of the car commuter (km/h), and \(\overline{v}_{cc}\) denotes the average travel speed of the car commuter (km/h).  
The evaluation thresholds of the road service level reliability and the travel speed reliability of car commuters were selected to analyze the travel speed reliability of the Beijing 3 rd Ring Road, as shown in Table 3. The minimum average travel speed of the road corresponding to medium traffic congestion, light traffic congestion, and smooth conditions were selected (see Table 2) as the evaluation thresholds of road service level reliability based on the current Chinese code \"Urban road traffic performance index (DB11/T 785--2011)\" [17] and \"Evaluation methods for road traffic congestion levels (GA/T 115--2020)\" [18].
## 3 Materials and Methods
### Attention Mechanism
#### 3.2.2 CA Mechanism  
Coordinate Attention (CA) is a lightweight attention module characterized by considering channel dimensions while incorporating spatial position information, elevating ordinary cross-dimensional fusion to the connection between channels and spatial positions [16]. The structure is shown in Figure 2.  
Figure 2: CA network structure diagram.  
The CA mechanism performs global average pooling operations along vertical and horizontal directions on the input tensor, generating two one-dimensional feature vectors to capture spatial feature information from different directions. The pooled features from both directions are concatenated to construct a feature representation containing complete spatial positional information. The concatenated features undergo convolution, batch normalization, and Rectified Linear Unit (ReLU) activation processing to further enhance feature representation capability. The processed feature maps are split along horizontal and vertical directions, with each direction activated through sigmoid functions to generate direction-aware attention weights, which are then applied to the original input features through element-wise multiplication, resulting in enhanced feature outputs that fuse spatial features from different directions with channel information.
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 3 The NDMR Approach
### NDMR Operations
#### 3.2.2 Frame Forwarding  
To send a data frame to the destination, the source transmits the frame containing the address of the source and destination. Neighbors of the source located in its transmission range can hear the frame. Only two of the neighbors that belong to the two node-disjoint paths have a route entry that matches the frame and they re-transmit it. When an intermediate node receives the data frame, it makes a forwarding decision by looking up its route table. Each entry of the route table contains information about the source, the destination, and the next hop. If the intermediate node has a route entry that matches the source and destination nodes of the received frame, the intermediate node will transmit the data frame to the next hop of the route entry. Otherwise, it discards the frame. Eventually, two identical copies of the data frame arrive at the destination through the two node-disjoint paths. The destination receives the first copy, sends it to upper layers and then discards the duplicate.  
By using the traffic distribution strategy, NDMR can provide fault tolerance capability for MANETs and WSNs.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 7 Other Metal-Oxide Nanomaterials
### CeO\({}_{2}\)-Based
The Eu/CeO\({}_{2}\) samples exhibited significantly greater photocatalytic activity than both undoped CeO\({}_{2}\) and P25 (Figure 26). Notably, the 4% Eu/CeO\({}_{2}\) sample exhibited the highest photocatalytic activity, with an 80% increase in degradation rate compared to undoped CeO\({}_{2}\) and P25. However, an excessive amount of Eu led to an abundance of oxygen vacancies, which can facilitate charge carrier recombination, thereby reducing photocatalytic activity. The enhanced photocatalytic degradation of formaldehyde by the Eu/CeO\({}_{2}\) samples was attributed to an increase in specific surface area and improved adsorption capacity. Cerium oxide is known for its unique redox behavior, where cerium can switch between Ce\({}^{3+}\) and Ce\({}^{4+}\) oxidation states. This redox flexibility facilitates the generation of reactive oxygen species (ROS), such as hydroxyl radicals (-OH), which are highly effective in degrading a variety of air pollutants, including VOCs, NOx, and ozone. CeO\({}_{2}\) is also highly stable under a range of environmental conditions, including exposure to UV light, high temperatures, and humidity. This stability ensures that CeO\({}_{2}\)-based photocatalysts maintain their effectiveness over extended periods, making them ideal for real-world applications in air purifiers and outdoor environments. Cerium is a relatively abundant element compared to other rare earth metals, and CeO\({}_{2}\) is inexpensive to synthesize. This makes CeO\({}_{2}\)-based photocatalysts a cost-effective option for large-scale air pollution remediation applications. Still, CeO\({}_{2}\)'s ability to capture visible light remains relatively limited in its unmodified form. To enhance its performance under natural sunlight or artificial light, CeO\({}_{2}\) typically needs to be modified through doping with other metals or by combining it with other photocatalysts, making the process more expensive and more likely to pose environmental risks such as leaching.
## 3 End-to-End Speech Synthesis  
### FastSpeech2  
Recently, non-autoregressive TTS models such as FastSpeech2 [5] have become popular due to their fast inference speed and stability. Compared with the original FastSpeech [4], FastSpeech2 is optimized to minimize the mean square error (MSE) \(\mathcal{L}_{\text{{REL}}}\) between the predicted and the ground-truth mel-spectrograms, instead of applying a teacher-student training. Moreover, the duration target is not extracted from the attention map of an autoregressive teacher model but the forced alignment of speech and text. Additionally, ref. [5] condition the prediction of mel-spectrogram on the variance information such as pitch and energy with a variance adaptor. The adaptor is trained to predict the variance information with an MSE loss \(\mathcal{L}_{\text{{VAR}}}\).  
In this work, we use FastSpeech2 as our acoustic model. It contains a phoneme encoder that transforms the input phoneme sequence \(\mathbf{p}\) to a hidden state sequence \(\mathbf{h}\), that is  
\[\mathbf{h}=Encode(\mathbf{p}) \tag{1}\]  
The hidden state sequence \(\mathbf{h}\) is then passed to a variance adaptor and a decoder which outputs the mel-spectrogram, that is  
\[\mathbf{y}=Decode(Adapt(\mathbf{h})) \tag{2}\]
###### Abstract  
Prognosis techniques for prediction of remaining useful life (RUL) are of crucial importance to the management of complex systems for they can lead to appropriate maintenance interventions and improvements in reliability. While various data-driven methods have been introduced to predict the remaining useful life (RUL) of machinery systems or batteries, no research has been reported on the remaining useful strength (RUS) prediction of silicon carbide fiber reinforced silicon carbide matrix (SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\)) materials with pivotal role in its potential usage as a structural material in nuclear reactors and turbine engines. Knowledge of its degradation process is of the utmost importance to the manufacturers. For this purpose, two approaches based on the machine-learning techniques of random-forest (RF) and convolutional neural network (CNN) are proposed to predict the RUS of SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\) using only acoustic emission (AE) signals generated during the material's stress applying process. Experimental results show that the CNN models achieved better predictive performance than the RF models but the latter with expert-engineered features achieves better prediction for AE signals in the early stage of degradation. Additionally, our results demonstrate that both models can correctly predict the SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\) RUS as evaluated by our robust testing method from which the best average root mean square error (RMSE) and Pearson correlation coefficient of 3.55 ksi units and 0.85 were obtained.  
remaining useful strength; remaining useful life; silicon carbide fiber reinforced silicon carbide matrix; acoustic emission; random-forest; convolutional neural network +
Footnote †: journal: Applied Sciences