esa-sceva/satcom-chunk-collection huggingface dataset:
Future Internet_**2015**, 7, 196-213; doi:10.3390/fi7020196  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
_Article_  
**Self-Optimization of Handover Parameters for Long-Term Evolution with Dual Wireless Mobile Relay Nodes**  
**[PERSON] *, [PERSON] and [PERSON] \({}^{\dagger}\)**  
Graduated School of Global Information and Telecommunication Studies, Waseda University,  
Bldg. No. 29-7, 1-3-10 Nishwaseda, Tokyo 169-0051, Japan;  
E-Mails: [EMAIL_ADDRESS] (K.Y.); [EMAIL_ADDRESS] (T.S.)  
\({}^{\dagger}\) Present Address: Department of Communications and Computer Engineering, School of Fundamental Science and Engineering, Waseda University, 66 Bldg., 802, 3-14-9 Ookubo, Tokyo 169-0072, Japan.  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +81-3-5286-2816; Fax: +81-3-5286-3832.  
Academic Editor: [PERSON]  
_Received: 4 March 2015 / Accepted: 29 May 2015 / Published: 11 June 2015_  
**Abstract:** In recent years, train passengers have been transferring increasing amounts of data using mobile devices. Wireless networks with mobile relay nodes support broadband wireless communications for passengers of such vehicles using backhaul links. However, the mobility management entity reuses the handover of existing user equipment, resulting in the handover of the Long-Term Evolution network being unsuitable for user equipment within the cabins of vehicles traveling at high speed. In this paper, we propose a self-optimizing handover hysteresis scheme with dual mobile relay nodes for wireless networks in high-speed mobile environments. The proposed mechanism tunes the hysteresis and cell individual offset handover parameters based on the velocity of the vehicle and the handover performance indicator, which affects the handover triggering decision and performance. The results of simulations conducted in which the performance of the proposed scheme was compared to that of an existing scheme show that the proposed scheme can reduce the number of radio link failures and service interruptions during handover procedures.  
**Keywords:** self-optimization; handover parameter; mobile relay node; cell individual offset; hysteresis
# Wavelet-Enhanced YOLO for Intelligent Detection of Welding Defects in X-Ray Films
## 3 Materials and Methods
### The Weld Defect Dataset and Pre-Processing  
In this study, we utilized a dataset comprising 7000 radiographic images of weld defects. Each image is annotated with defect locations, though specific defect class labels are not provided. The dataset was divided into three subsets: a training set containing 4900 images, a validation set with 700 images, and a test set of 1400 images, which was used to evaluate the model's overall performance in detecting all types of defects.  
Given the elongated nature of the weld images, a pre-processing strategy was implemented to segment the longer side of each image into smaller patches. Specifically, overlapping patches were extracted with a step size of 200 pixels, each with a fixed length of 640 pixels. These patches were then used for training, validation, and testing, ensuring that the model learns from different regions of the image. This approach increases input diversity and enhances the model's robustness to variations in defect location and image geometry.  
In addition to the general test set, we also created another test set consisting of 1400 defect images collected from on-site production environments, with each image containing only one type of defect. This set is designed to evaluate the model's ability to detect different defect categories. It includes seven defect classes: crack, lack of fusion, incomplete penetration, concavity, undercut, slag inclusion, and porosity, with 200 images per category. In order to facilitate a detailed performance comparison, defect images from each category are randomly sampled at a 10:1 ratio for manual assessment. This allows the model's predictions to be directly evaluated based on human detection.  
To maintain consistency, all images, including the cropped patches, were resized to 640 \(\times\) 640 pixels. Data augmentation techniques were applied to improve generalization, including random rotations (up to 30\({}^{\circ}\)), horizontal flipping, and random scaling. Furthermore, image normalization was performed by subtracting the mean and dividing by the standard deviation of pixel values across the dataset. These pre-processing steps collectively enhance the model's ability to detect defects across various scales and orientations.
## 3 Encapsulation Protocol
### Ssh
#### 3.4.1 Traditional Detection  
**Statistic Feature**  
It is possible to identify SSH tunnel traffic among the detection methods (in Table 6), as well as to classify passenger protocols and applications in traffic using statistical features. With fine granularity, [PERSON] [61] and [PERSON] [56] classified SSH tunnel traffic applications and services. [PERSON] [61] used AdaBoost and RIPPER to identify SSH tunnel traffic with 95% accuracy in public datasets. The classification covers 11 applications/services, including local tunnels, remote tunnels, FTP, and Shell, with the accuracy of 99% and false alarm rates of 0.7%. According to [PERSON] [56], the \(k\)-means clustering analysis is used for real-time traffic classification, which results in an accuracy of 99.5% for SSH tunnel traffic and 99.88% for SCP-SSH, SFTP-SSH, and HTTP-SSH passenger protocols in tunnels. Although these two methods are lightweight, their disadvantages include their confusion when there are similar applications or services in the tunnel traffic, such as HTTP and FTP, and the model will become less accurate as more protocols are added.
# An Improved Method Based on EEMD-LSTM to Predict Missing Measured Data of Structural Sensors
###### Abstract  
Time history testing using a shaking table is one of the most widely used methods for assessing the dynamic response of structures. In shaking-table experiments and on-site monitoring, acceleration sensors are facing problems of missing data due to the fact of measurement point failures, affecting the validity and accuracy of assessing the structural dynamic response. The original measured signals are decomposed by ensemble empirical mode decomposition (EEMD), and the widely used deep neural networks (DNNs), gated recurrent units (GRUs), and long short-term memory networks (LSTMs) are used to predict the subseries of the decomposed original measured signal data to help model and recover the irregular, periodic variations in the measured signal data. The raw acceleration data of a liquefied natural gas (LNG) storage tank in shaking-table experiments were used as an example to compare and discuss the method's performance for the complementation of missing measured signal data. The results of the measured signal data recovery showed that the hybrid method (EEMD based) proposed in this paper had a higher complementary performance compared with the traditional deep learning methods, while the EEMD-LSTM exhibited the best missing data complementary accuracy among all models. In addition, the effect of the number of prediction steps on the prediction accuracy of the EEMD-LSTM model is also discussed. This study not only provides a method to fuse EEMD and deep learning models to predict measured signal' missing data but also provides suggestions for the use of EEMD-LSTM models under different conditions.  
long short-term memory; ensemble empirical mode decomposition; time series predictions; imputation; deep learning +
Footnote †: journal: Medical Imaging  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] An Improved Method Based on EEMD-LSTM To Predict Missing Measured Data of Structural Sensors.  
**Applied**  
Received: 22 July 2022  
Accepted: 5 September 2022  
Published: 8 September 2022  
**Publisher:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
## 2 Application of Networked Mobile Sensors
### Military Applications
A new swarming concept within a military-like hierarchy is suggested in this work: simple small teams made up of two to four robots are constructed to accomplish a given mission while searching, tracking, carrying, and deploying abilities. This research provides a novel approach on the coordination control of networked robot teams in military field using the inherent benefits of control from emulating the distributive hierarchical nature of military operational theory.
# Dependence of PV Module Temperature on Incident Time-Dependent Solar Spectrum
## 3 Energy Balance Equation  
The approach for calculating the PV module temperature, which is based on a simplified energy balance Equation, involves equating the solar radiation absorbed by the module to the electrical output and the thermal losses is mentioned in [15; 16; 17]:  
\[P_{rad}\tau\alpha=\eta_{PV}P_{rad}+U_{PV}(T_{c}-T_{a}) \tag{3}\]  
where  
\(P_{rad}\tau\alpha\) is the part of beam and diffuse radiation absorbed by the PV module,  
\(\eta_{PV}P_{rad}\) is the electrical power produced by the PV module,  
\(U_{PV}(T_{c}-T_{a})\) is the thermal losses from the PV module to the surroundings:\(\eta_{PV}\) is the PV module efficiency given by:  
\[\eta_{PV}=\eta_{r}[1-\beta_{c}(T_{c}-T_{r})] \tag{4}\]  
\(\tau\) is the transmittance of the module cover (_glass_) for the beam and diffuse radiation,  
\(\alpha\) is the absorption coefficient of the cells,  
\(\eta_{r}\) is the module's efficiency at _standard test condition (STC)_,  
\(T_{r}\) is the reference temperature \(25^{\circ}C\), and  
\(\beta_{c}\) is the power temperature coefficient of the PV cell dependent on the cell technology.  
Substituting Equation (4) into Equation (3) we get  
\[T_{c}=\frac{U_{PV}T_{a}+P_{rad}\tau\alpha-P_{rad}(\eta_{r}+\eta_{r}\beta_{c}T_ {r})}{U_{PV}-\eta_{r}\beta_{c}P_{rad}} \tag{5}\]  
The thermal coefficient losses, in \(W^{0}\cdot C^{-1}\cdot m^{-2}\) (per unit of module area), from the PV module to the surroundings is given in the present study, by [18]:  
\[U_{PV}=2\cdot(5.67+3.86\cdot V_{wind})\cdot W^{0}\cdot C^{-1}\cdot m^{-2} \tag{6}\]  
where \(V_{wind}\) is the wind speed in \(m/s\). Other expressions are mentioned in [16].  
The plane of array irradiance \(P_{rad}\) in \(W/m^{2}\) is measured or calculated at a given time for which the cell temperature is calculated.
# High-Precision Image Editing via Dual Attention Control in Diffusion Models Without Fine-Tuning
## 2 Related Work
### Image Editing Using Controllable Diffusion Models  
In the field of image editing, a technique called Prompt-to-Prompt [7] enables image content modification by changing the input text prompt. For example, it allowed transforming \"A squirrel eating a burger\" into \"A lion eating a burger\". The core of this method lies in guiding the image transformation through changes in descriptive text prompts. Additionally, SDEdit [9] introduced a method based on the Stochastic Differential Equation for guided image generation and editing. SDEdit provided some guidance in the original image, such as adding graffiti or new patches, and the model can edit the image according to the guidance provided by the user. The introduction of the Null-Text Inversion [40] technique further enhances editing capabilities. This approach allowed users to modify specific objects in an image through textual descriptions without needing to process the entire image. It simplified the editing process and significantly extended the application of diffusion models to real-world image editing scenarios. Another noteworthy research outcome is InstructPix2 Pix [6], which combined GPT-3's [41] powerful language understanding with SD's image generation capabilities to create datasets for training conditional diffusion models. Models trained in this way can modify images based on specific editing instructions, providing more flexible image editing functions. Similarly, DiffusionCLIP [36] and UniTune [37] achieved remarkable success in image editing using fine-tuning techniques. Meanwhile, the study titled _An Edit Friendly DDPM Noise Space: Inversion and Manipulations (Edit Friendly DDPM Inc)_[8] proposed an innovative idea with a new inversion algorithm that extracts an editable noise map from any image. This improvement reduces the time required for image editing in diffusion models.
# A Fast and Accurate Method for Computing the Microwave Heating of Moving Objects
## 4 Conclusions  
In this paper, we show a fast, flexible and accurate numerical method to simulate temperature-dependent moving objects coupling the electromagnetic field and thermal field. To avoid the temperature inheritance errors caused by mesh-mismatch, the cavity is rotated in the opposite of the actual rotation direction. More importantly, the dielectric properties of the potato are updated instantaneously with respect to the changing of temperature field during the whole heating simulation.  
To demonstrate the advantage of our method, firstly, a 3-D model with moving objects is presented and the simulated results are compared with those obtained by the ALE method, which is one of the most accurate methods and has been verified experimentally. Our proposed method is more efficient with an 89% decrease of the computation time with almost the same predictability.  
Secondly, using our method, the effects of discrete angles under different rotation speeds are also studied. The results show that the slower rotational speed is, the smaller the effective rotation angle is to ensure the accuracy of the simulation results.  
Finally, we also systematically investigate the influence of the rotational speed of the turntable on the microwave heating of two different shaped samples (i.e., cuboid and cylinder), which were infrequently studied by previous moving-mesh methods because this was too time-consuming. The results show that a widely used speed in domestic microwave ovens, 5 rpm, is indeed a good choice for improving the temperature uniformity with high energy efficiency. Our method may be helpful for optimizing microwave heating with heat-sensitive materials.  
Figure 14: Outline of potato and simulated temperature distributions on the top surface of the cylindrical meshed potato after microwave heating for 24 s with different rotation speeds (top view, unit: \({}^{\circ}\)C).  
Figure 15: The minimum (**a**), maximum (**b**) and average (**c**) temperatures of the cylindrical shaped mashed potato heated with different speeds.  
**Author Contributions:** Conceptualization, M.Z. and X.W.; Methodology, M.Z., Z.T. and X.J.; Software, M.Z.; Data Curation, Y.Z.; Writing - Original Draft Preparation, M.Z.; Writing - Review and Editing, Y.L. ([PERSON]) and Y.L. ([PERSON]); Supervision, Z.T. and XJ; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially supported by the Natural National Science Foundation of China (No. 61571186) and the Scientific Research Projects of Hunan Education Department (No.18C1446).  
**Acknowledgments:** The authors acknowledge [PERSON] from Jishou University and [PERSON] from Sichuan University for their help in using COMSOL Multiphysics to simulate microwave heating.  
**Conflicts of Interest:** The authors declare that they have no conflict of interest.
# Resource- and Neighbor-Aware Observation Transmission Scheme in Satellite Networks
## 4 Constrained Stochastic Game
### State Space  
Let \(\mathbf{S}=\prod\limits_{i}\mathbf{S}_{i}\) denote a global state space of players (i.e., observation satellites), where \(\mathbf{S}_{i}\) is a local state space of player \(i\) and \(\prod\) means the Cartesian product. \(\mathbf{S}_{i}\) can be defined as  
\[\mathbf{S}_{i}=\mathbf{M}_{i}\times\mathbf{G}_{i} \tag{1}\]  
where \(\mathbf{M}_{i}\) and \(\mathbf{G}_{i}\) denote the observation state space of observation satellite \(i\) and the ground station state space of observation satellite \(i\), respectively.  
Since \(\mathbf{M}_{i}\) can represent whether observation satellite \(i\) observes a target or not and the elapsed time from the observation, \(\mathbf{M}_{i}\) can be represented as  
\[\mathbf{M}_{i}=\{0,1,2,\ldots,M_{\max}\} \tag{2}\]  
where \(M_{i}(\in\mathbf{M}_{i})=0\) denotes that observation satellite \(i\) does not observe any target. In addition, \(M_{i}\) (\(>0\)) represents that observation satellite \(i\) observes a target and the elapsed time from the observation is \(M_{i}\). \(M_{\max}\) is the maximum elapsed time in our system model.  
Meanwhile, \(\mathbf{G}_{i}\) can be defined as  
\[\mathbf{G}_{i}=\{0,1\} \tag{3}\]  
where \(G_{i}(\in\mathbf{G}_{i})\) denotes whether observation satellite \(i\) can connect with the ground station or not. That is, if \(G_{i}=0\), observation satellite \(i\) cannot connect with the ground station. Otherwise, it can connect with the ground station.
## 2 Materials and Methods
### Configuration and Experiment of Weight Tracking System  
#### 2.3.1 Hardware/Software Configuration Diagram of Weight Tracking System by FSR Sensing  
The system configuration diagram for tracking the weight of an object to be carried by a construction worker using smart safety shoes is shown in Figure 1. The developed system consists of smart safety shoes with sensors attached, a mobile device part for collecting sensing data, and a web-based server computer for storing, preprocessing and analyzing such sensing data.  
The smart safety shoes consist of 7 pressure (FSR) sensors (manufacturer: Marvelex, model name: RA12P) on the left and right insoles, and Arduino Nano (manufacturer: SMG, model name: FT232 RL) chips on each shoe. The Arduino Nano converts analog signals into digital signals through Analog-to-Digital Converter (its parameter is raw data which has only magnitude value) and transmits sensor values to mobile devices through Bluetooth(module, OEM, HC-06 (DIP) firmware v.3.0, SZH-EK105). The transmitted sensing data values are primarily stored through an APP developed on a mobile device (manufacturer: Samsung, model name: Galaxy S8) possessed by the construction worker. The stored data values of L1-L7 and R1-R7 are transmitted to and collected on the web-based DB along with the measured time through a wireless network or LTE network as shown in Figure 1. This transmitted data is used for analysis to estimate the weight of an object carried by construction workers.
# Towards the Internet of Smart Trains:
## 6 IoT-Enabled Services: From More Efficient Operations to New Business Models
### Train Control Systems
#### 6.4.4 Key Findings  
Train control systems include signaling and safety assurance processes. Level crossing control, dispatching video stream or on-board positioning systems are examples of improvements. Furthermore, opportunities for research exist on scheduling and maintenance planning and on event prediction, among other new activities, considering autonomous and semi-autonomous operation. Cyber security is another field to be studied with more depth, since cyber risks are exacerbated by the enormous quantity of data resulting from the increasing number of devices, processes and services integrated. Even more, there are several sources of vulnerabilities, whose countermeasures have to be designed.
# A Federated Generalized Linear Model for Privacy-Preserving Analysis
## 2 Materials and Methods
### Setup  
Our presented federated GLM algorithm is designed to run on a server-client architecture [6], as shown in Figure 1. In this scenario, (1) the user sends a task (i.e., the instruction to run a specific algorithm) to the server. When received, (2) the server manages and synchronizes the execution of the algorithm across all nodes (i.e., parties). In short, (3) each node accesses its own local data and executes the requested algorithm with the given parameters. Afterwards, the node outputs a set of intermediate results (often in the form of preliminary coefficients), which are sent back to the server. Then, using these, (4) the server computes a first version of the global solution and sends it back to the nodes, which use it to compute a new set of results. This process is repeated iteratively until (5) the global solution converges or after a fixed number of iterations, yielding the final version of the (federated) model.
# Fuzzy Adaptive Dynamic Surface Control with Constant Gain for Non-Affine Pure-Feedback Systems
## 2 Problem Formulation and Preliminaries
### Lemmas and Assumptions
Specifically, each gain term \(\psi_{i}\) is constrained to satisfy the following inequality: \(0<\psi\leq\psi_{i}\leq\widetilde{\psi}\), where \(\psi\) and \(\widetilde{\psi}\) represent strictly positive constants denoting the minimum and maximum bounds of the control gains, respectively._  
**Assumption 3** (see [6]). : _A boundedness condition is imposed on the external disturbance term \(d_{i}(t)\), which is assumed to be continuous but unknown. Specifically, the absolute value of the disturbance is constrained by \(|d_{i}(t)|\leq\widetilde{d}\in\Re^{+}\), where \(\widetilde{d}\) represents a known positive real constant that serves as the least upper bound for the disturbance magnitude. The upper bound value of disturbance is not used in controller design, so Assumption 3 is not strict._
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 6 Solution Proposal to the Hidden Node Problem
### Simulation Results  
To check the effectiveness of our proposed solution, we performed several simulations by applying it to our four defined scenarios. We employed the directed LOS channel model, maintaining the same simulation conditions we used when evaluating network performance with hidden nodes. Simulation results obtained with our solution are practically identical to those obtained from the simulations performed without hidden nodes in the network, for all scenarios and the whole network load values.  
To confirm the validity of our results, Figure 15 shows a comparative for goodput and message loss ratio values obtained from the simulation scenarios with hidden nodes in the network. In the first experiment labeled as (1) none of the devices in the network can detect transmissions of any other devices, therefore collisions are produced. Since no signaling mechanism is used to avoid collisions, maximum goodput does not reach 20%, see Figure 15a, and message loss rate increases rapidly, see Figure 15b.  
Results of the second experiment labeled as (2) have been obtained by applying our proposed solution. The coordinator sends a signaling pattern when it detects a transmission coming from one of nodes in the network. Thus, the rest of nodes in the network know that the channel is not free. In this case, goodput results are better than the results obtained in experiment (1). In addition, message loss rate is lower. The performance of the VLC network improves when our mechanism is used to avoid the hidden node problem. Results obtained in (2) are similar to those obtained from simulations performed without hidden nodes, see Figures 8 and 9.  
Figure 14: Transmission of out-of-band (**a**) and in-band (**b**) idle patterns by the coordinator for signaling transmission activity.