esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Description  
### Description of System Configuration  
A schematic diagram of the system under investigation is shown in Figure 1. Such system can be used to provide households with hot water, electricity, and exterior shading of windows.  
The PV/T panel for exterior shading of a south-facing window is connected to a wall-mounted hot water tank of 120 L. The PV/T panel is fixed with a certain tilt angle by triangle brackets. The PV/T panel and water tank are mounted on the exterior wall with expansion bolts. In order to ensure safety and durability, the selection of brackets and expansion bolts for the installation should meet relevant standards or regulations. The tank is near and above the PV/T panel to create a natural circulation between the PV/T panel and tank, which is called thermosyphon solar water heater systems. A gas water heater is used to provide auxiliary heat by the way of mixing the two flows from the tank and the gas water heater when solar radiation is insufficient.  
The investigated system is a stand-alone photovoltaic system. The excess electrical power from the PV/T panel is stored in batteries which ensure that the electricity can be supplied stably. The electricity produced by the PV/T panel is limited and mainly supplied for direct current (DC) lighting.
# A Comprehensive Survey on Real-Time Applications of WSN
## 2 Types and Requirements of WSN
### Terrestrial Wireless Sensor Network  
Terrestrial WSNs consist of numerous tiny sensor nodes. These nodes are randomly deployed in a specific area from where an ad hoc network is used for communication between the nodes. These nodes can be organized by optimal placement, grid placement, or 2D and 3 Dplacement models [13]. [PERSON] et al. (2009) investigated this for applications of the terrestrial wireless sensor network in Radio Frequency/Free Space Optics (RF/FSO) systems, including open research issues and challenges. The drawbacks of terrestrial applications are the effect of weather such as rain and snow on an optical wireless communication link. The author has proposed a method to increase the lifetime performance of the network [14].
# Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings
## 4 Experimental Results and Analysis
### Simulation Results and Analysis of The Rolling Bearing Rolling Body Faults
#### 4.2.2 Performance Comparison  
This section compared the experimental results of ADKPCA, Deep KPCA, Deep DKPCA, and Deep ADKPCA by analyzing the failure detection rate and detection delay of the incipient failure experimental results of rolling bearing rolling elements.  
Figure 12a shows the fault detection rate of the rolling bearing rolling body fault by ADKPCA, Deep KPCA, Deep DKPCA, and Deep ADKPCA method. The failure detection rate of the ADKPCA failure detection method was 57.14%, which proved that this method basically did not have incipient fault detection capability. The fault detection rate of Deep KPCA and Deep DKPCA was about 80%, compared with ADKPCA method the detection rate had increased, but the detection capability still  
Figure 11: Experimental results of the Deep DKPCA fault detection method for the rolling bearing rolling body fault. (**a**) The result of \(T_{21}^{2}\), (**b**) The result of \(Q_{21}\), (**c**) The result of \(T_{22}^{2}\), (**d**) The result of \(Q_{22}\), (**e**) The result of \(T_{23}^{2}\), (**f**) The result of \(Q_{23}\), (**g**) The result of \(T_{24}^{2}\) and (**h**) The result of \(Q_{24}\).  
needs to be improved. Deep ADKPCA had a fault detection rate of 85.71%, which was significantly higher than other methods, and had a certain incipient fault detection capability.  
Figure 12b shows the detection delay of the rolling bearing rolling body fault by ADKPCA, Deep KPCA, Deep DKPCA, and Deep ADKPCA method. The ADKPCA method detected a failure at the 9 th augmented data point, that is, at about the 27 th sampling point. The Deep KPCA and Deep DKPCA methods found failures at the 14 th and 5 th sampling points, respectively. These methods could quickly detect the incipient fault. The Deep ADKPCA method detected failures at the beginning of the inspection without a time delay. From Figure 9b we could obtain that \(Q_{21}\) was more sensitive to this fault, the fault was detected from the beginning of the fault without delay. It could be seen from the failure delay that the Deep ADKPCA method could detect the incipient failure of the rolling bearing outer ring earlier.  
It can be seen that Deep ADKPCA method could quickly detect the fault than the other three methods, and its detection rate was also the higher among the four methods. In summary, Deep ADKPCA had good fault detection performance.
## 4 Calculation of Environmental Impact and Economic Effect  
### Environmental Impact of Automotive Parts Replacement  
Remanufactured automotive parts consume less energy, use fewer raw materials, and emit less CO\({}_{2}\), despite requiring additional steps such as machining, cleaning, and conditioning during the remanufacturing process. However, most components are reused as their life cycles have not yet ended. Therefore, less energy is required for remanufactured parts compared to new ones. This indicates that environmental impacts, such as CO\({}_{2}\) emissions, can be reduced by increasing the proportion of remanufactured automotive parts in replacement scenarios.  
The detailed remanufacturing process for each automotive part must be considered to conduct a life cycle assessment (LCA). Each process is related to the amount of energy used, material consumption, transportation, and labor. Additionally, the bill of materials (BOM) for each part should be examined. As previously noted, only a few components are replaced to restore the functionality of automotive parts. LCA requires information on which components are replaced and the amount of energy consumed. The environmental impact of remanufactured parts can be evaluated by considering the number of replaced components throughout their life cycles.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Parameters** & **Unit** \\ \hline Replacement probability & \(\gamma\) & \% \\ Standard age & \(T_{S}\) & Year \\ Vehicle age & \(T\) & Year \\ Life expectancy of the vehicles & \(T_{L}\) & Year \\ Inspection period & \(L_{P}\) & km \\ Avg. mileage per year & \(M\) & km/year \\ Replacement probability & \(\gamma\) & \% \\ Standard age & \(T_{S}\) & Year \\ Vehicle age & \(T\) & Year \\ Replacement preference & \(\alpha_{1}\) & \(\beta_{1}\) & \% \\ (\(T<T_{S}\) years) & (new) & (remanufactured) & \\ Replacement preference & \(\alpha_{2}\) & \(\beta_{2}\) & \% \\ (\(T>T_{S}\) years) & (new) & (remanufactured) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters to calculate the demand of automotive parts replacement.  
ReCiPe [26] is a recent and harmonized indicator metric available for life cycle impact assessment. Unlike other approaches (such as Eco-indicator 99, the EPS method, LIME, and Impact 2002+), ReCiPe does not include potential impacts from future resource extraction in the impact assessment. ReCiPe has been used to evaluate the environmental impact of both new and remanufactured automotive parts. Three impact categories are calculated: ecosystem quality, human health, and resource use.
# Multimodal Dynamic Journey-Planning +
## 5 Conclusions and Future Work  
In this work, a new model for multimodal route planning in schedule-based public transport systems, called Multimodal DTM, was presented that constitutes an extension of the dynamic timetable model (DTM), originally developed for unimodal journey-planning in [10]. An extensive experimental study showed that Multimodal DTM compares favorably with other state-of-the-art multimodal route planners.  
We are currently developing a mobile application for multimodal route planning, using Multimodal DTM as the core routing engine of the cloud-residing component. Our multimodal journey planner can also be combined with the mobile application developed in [34] that allows users to evaluate the routes suggested (by the planner). We are also working on developing multicriteria multimodal journeys with more (than two) traveling options/criteria.  
All authors contributed equally to this work.  
[PERSON] was partially supported by the Operational Program Competitiveness, Entrepreneurship and Innovation (co-financed by EU and Greek national funds), under contract no. T1 EDK-01572 (project TourisHub). [PERSON] was supported by the Hellenic Foundation for Research and Innovation and the General Secretariat for Research and Technology of Greece. [PERSON] was partially supported by the INTERREG V-A GR-IT programme 2014-2020 (project INVESTMENT) and the Operational Program Competitiveness, Entrepreneurship and Innovation (co-financed by EU and Greek national funds), under contract No. T1 EDK-01572 (project TourisHub).  
**Acknowledgments:** We thank the anonymous reviewers whose comments helped us to improve the paper. The last author is indebted to [PERSON] for various interesting discussions.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funding agents had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Travel Modes**} & **Update** \\ \cline{3-5}  & & **Bus** & **Train** & **[μs]** \\ \hline \multirow{3}{*}{Berlin} & TE-UH [10] & \(\bullet\) & \(\bullet\) & 249.3 \\  & DTM-U [10] & \(\bullet\) & \(\bullet\) & 85.7 \\  & MDTM-U & \(\bullet\) & \(\bullet\) & 87.2 \\ \hline \multirow{3}{*}{London} & TE-UH [10] & \(\bullet\) & \(\bullet\) & 484.6 \\  & DTM-U [10] & \(\bullet\) & \(\bullet\) & 148.1 \\ \cline{1-1}  & MDTM-U & \(\bullet\) & \(\bullet\) & 163.1 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison among update algorithms for the TE-red, DTM, and MDTM models.
# Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors
## 3 Methodology and Experimental Procedure
### Experimental Procedure  
The experimental procedure followed to perform this work was organised in two steps:  
* Step 1: use of a dynamic platform simulator to design the algorithms for fusing the data coming from UWB sensors, odometry and IMU.
* Step 2: use a vehicle, an RC-Car, to collect data in a real and representative scenario to validate the proposed positioning system.  
These steps are explained in more detail in the following.
## 5 Conclusions  
In this paper, an analytical model for internal spur gear with tooth wear is proposed. On the basis of this, the new mesh relationship of internal spur gear pair with non-uniform wear is obtained. To deduce the multi-tooth mesh relationship, the multi-tooth mesh rule is proposed. Based on the multi-tooth mesh rule and the new mesh relationship, the influence of different wear depths on mesh stiffness and USTE is discussed. Furthermore, the mesh stiffness is verified by finite element method. Through the analysis, the main conclusions are summarized as follows:  
1. An analytical model for internal spur gears with tooth wear is proposed, based on which the new mesh relationship of tooth wear is derived. With the proposed multi-tooth mesh rule and new mesh relationship, the evaluation method of the influence of tooth wear on mesh stiffness and USTE is presented.
2. The case of non-uniform wear illustrates that tooth wear mainly affects the meshing ranges of mesh stiffness in a single tooth and double teeth and has relatively less effect on the amplitude of the meshing stiffness.
3. The multi-tooth mesh stiffness of the internal spur gear calculated by the proposed analytical model has been verified by the FE models. The meshing ranges of the two in single-tooth-pair and double-tooth-pair mesh period match well. The maximum error is 6.4% and 3.2% in double-tooth meshing range and single-tooth meshing range, respectively, for the prefect pair. The maximum error for the worn gear pair is 9.8% in the double-tooth meshing range and 4.6% in the single-tooth meshing range.
4. According to the new mesh relationship and multi-tooth mesh rule, USTE is comprehensively analyzed. The results indicate that the USTE varies periodically as the  
Figure 20: USTE of non-uniform wear with different wear depths.  
gear pair meshing and its amplitude grows with the increase of the wear depth. The effectiveness of the proposed model is verified by the finite element method.  
Furthermore, the proposed model for the internal spur gears with tooth wear is also instructive for the dynamic modeling and analysis of the gear transmission systems of helicopters with multiple faults. These topics will be discussed in our coming works.  
Formal analysis, Y.W.; methodology, K.L. and Z.S.; investigation, Y.W.; validation, Z.S.; writing--original draft preparation, K.L.; writing--review and editing, B.Q.; supervision, X.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Technical Field Funding of Foundation Strengthening Project under the grant number 2021-JCQ-JJ-0238, the National Natural Science Foundation of China under the grant number 52075414 and the China Postdoctoral Science Foundation under the grant number No. 2021M702595.  
Not applicable.  
The authors declare no conflict of interest.
# A Lightweight Detection Method for Meretirix Based on an Improved YOLOv8 Algorithm
## 5 Conclusions  
This paper proposes a lightweight YOLOv8 algorithm (YOLOv8-RFD) based on a deep reversible architecture, which improves detection accuracy while reducing resource consumption. The aim is to efficiently detect and recognize clams in resource-constrained environments. The experimental results validate the applicability of the algorithm in clam detection. The main conclusions are as follows:  
1. In the feature extraction stage, RevColNet is introduced as the backbone network, replacing the original deep convolutional structure. This network enhances the representation ability for clam targets through its efficient feature aggregation mechanism. It exhibits stronger feature learning ability when handling complex background interference and multi-scale targets, while significantly reducing redundant computation. The introduction of RevCol alone reduces the number of parameters from 3.16 M to 2.28 M, while maintaining the mAP@50:95 without any degradation. It is suitable for target detection tasks in complex background conditions.
2. In terms of model lightweighting, the C2f-Faster lightweight convolution module is adopted to replace the traditional feature fusion unit. This module optimizes the computation path and parameter reuse strategy, reducing the GFLOPs to 6.3 with minimal loss in accuracy.
3. In the design of the detection head, the DyHead dynamic detection head is introduced. Through the dynamic allocation mechanism of cross-layer feature weights, the model's adaptability to multi-scale targets is enhanced. Compared with the traditional fixed weight allocation method, this approach captures target edge information more  
Figure 9: Comparison of detection results between YOLOv8 and YOLOv8-RFD: (**a**) original image; (**b**) YOLOv8 detection rendering; (**c**) YOLOv8-RFD detection rendering.  
accurately, further reducing false detection rates and enabling small target detection in dense scenes.  
Conceptualization, Z.T. and S.H.; methodology, Z.T.; software, S.H.; validation, S.H. and X.Y.; formal analysis, X.H.; investigation, Z.T.; resources, Z.T.; data curation, S.H.; writing--original draft preparation, S.H.; writing--review and editing, Z.T.; visualization, S.H.; supervision, Z.T.; project administration, Z.T.; funding acquisition, Z.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (14 th Five-Year Plan), grant number 2023 YFD2400800.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 3 Motivation and Contributions to the Literature  
In summary, in the literature, there are different kinds of deep learning models, but the most used and successful models are MLP, RNN, LSTM, CNN, DBNs, and autoencoders [36; 37]. Throughout the literature, financial time series forecasting is the most used data since this problem is investigated by the finance industry, individual investors, and academia, and experiments have been conducted by using various deep learning environments such as Tensorflow, Keras, Pytorch, Theano, and Matlab [8]. Some researchers claimed they used Python, but they do not provide detailed information about the development environment and model architecture, which makes it impossible to present a full comparison chart. Thus, for us, one of the main motivations is to constitute a study that presents the prediction performance of popular deep learning models by clearly stating the model's architecture, development environment, and corresponding codes.  
The main contributions of this paper can be summarized as follows:  
* Although deep learning models have been widely used for trend prediction and time series forecasting tasks, there are only a few comprehensive studies that present the performance comparison of the models. Moreover, the works in these reviews do not provide enough information for a proper comparison. Thus, in this study, we present some literature for time series forecasting and investigate the effectiveness of the most used deep learning models on univariate time series data with the same or similar complexity models.
* Long short-term memory networks and recurrent neural networks are the kinds of networks designed to memorize previous samples. They are the most used models in time series prediction tasks and are known as the silver bullet for this kind of task. However, we present that wavenet-based CNN models can also extract useful knowledge from datasets with the dimension of time and yield producing more precise prediction results than LSTM models.
# Towards Flexible Integration of 5G and IIoT Technologies in Industry 4.0: A Practical Use Case
## 2 Related Work
### Security for IIoT Applications
[30] proposed the Authorization Framework for the IoT, which adapts eXtensible Access Control Markup Language (XACML) for IoT applications. This proposal defines very expressive security policies, but they are too heavy to be implemented in the most constrained devices. Similarly, [PERSON] and [PERSON] [31] proposed the Usage-based access control (UCON) adapted to IoT. However, these two approaches are based on a centralized architecture, where a central server performs all the access control processes, so they do not offer any expressiveness as they do not take into account any local condition of the devices. Among the solutions based on a distributed architecture, [PERSON] et al. [32] proposed Distributed Capability-Based Access Control (DCapBAC) for IoT, which offers capability-based access control, exchanging tokens that contain information about the users and their permissions. However, this mechanism has been developed using Java, and, as its framework is rather heavy, it is not suitable for most constrained devices. Another possible solution is the Delegated CoAP Authentication and authorization Framework (DCAF), proposed in [33], which uses security policies that consider local conditions of the sensors. However, the implementation of this mechanism requires the establishment of DTLS channels, and this increases the overhead of the messages exchanged, wasting the resources of the devices. Moreover, this solution uses Concise Binary Object Representation [34] (CBOR) to encode and compress the policies, and as it is a generic compressor, it does not offer a high enough compression rate. To deal with some of the aforementioned problems, the Ladon [35] protocol was developed, an access control solution feasible even in the most constrained devices. This protocol performs the authentication and E2E authorization of the users wanting to access a resource in the sensors. However, the security policies it uses are rather static, reducing the expressiveness of the process. As an evolution of Ladon, Hidra [36] allows for the enforcement of highly expressive policy-based security policies which also include local context conditions, such as battery level. For this reason, this work proposes to extend the applicability scenarios of Hidra by integrating it in a 5G-enabled industrial environment as an orchestrated NFV network service. In this way, the IIoT applications can implement robust access control mechanisms in a fast and flexible way.
# Monolitic Hybrid Transmitter-Receiver Lens for Rotary On-Axis Communications
## 4 Discussion
### Full-Channel Performance
The solid angle of incidence is much smaller and the incident ray bundle exhibits a smaller divergence. The larger the distance, the better the design approach with the edge-ray principle works. Another issue is the nonrotationally symmetric shadowing effect of the TX lens part that manifests itself as a local minimum around \((x,y)=(-2.5\,\text{mm},0\,\text{mm})\) in Figure 8a,c. The variation \(\Delta P_{\text{PD}}=3.29\) dB is not crucial for data transmission. Assuming a misalignment of \(-2.5\) mm and a rotation speed of 10 000 rpm, the link moves 9.42 um over the surface in Figure 8a during one bit-duration of 1 ns (1 Gbit/s). The change of \(P_{\text{PD}}\) over a sequence of bits is small enough and does not influence the transmission drastically. Ideally, the receiver circuit includes a decision-point control mechanism for continuous adaptation of the decision level to improve the BER [26].
# On the Determination of Meshed Distribution Networks Operational Points after Reinforcement
## 4 Conclusions  
This paper proposes an innovative probabilistic method for evaluating the potential performance of meshed and radial distribution lines, improving electric energy distribution grids reinforcement decisions. It is developed as additional to the existing distribution networks optimization methods, offering a tool for performance evaluation. Furthermore, it serves to the creation of operational points to be used for demand management, if electric vehicle charging is completely controlled. This method is a useful tool for network planners, operators and large users, since they are more able to estimate the robustness of a given network after reinforcement and its capability to host electric vehicle loads. The operational points are loading patterns for the transformers of the low voltage distribution network that minimize voltage drop and maximize total loading. They are calculated for increasing production of the connected to the line distributed generators. Other distribution network lines are expected to have similar behavior. They shall present specific operational patterns and their reinforcement could be also evaluated using this method. Future work includes the investigation of the adoption of the appropriate protection schemes that will ensure the reliable and uninterruptable operation of the network.  
Conceptualization, methodology, V.V.; writing--original draft preparation, writing--review and editing, S.L.; validation, C.A.C.; resources, data curation, G.S.  
The authors acknowledge financial support for the open source publication of this work from the Special Account for Research of ASPETE through the funding program \"Strengthening research of ASPETE faculty members\" under the project \"DECA\".  
The calculation resources for this research were provided by Okeanos high performance cloud computing [33]. This work was also supported by computational time granted from the Greek Research & Technology Network (GRNET) in the National HPC facility--ARIS--under project ID pa171102 [39]. The authors would like to thank the unknown reviewers, whose comments have improved the quality of this work.