esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Modeling of Motorized Spindle System with Unbalanced Mass and Spindle Inclination
## 2 Dynamic Modeling
### Dynamic Modeling of Motorized Spindle with Unbalanced Mass Fault  
Unbalanced mass fault is the simplest and most common fault form of the motorized spindle. When an unbalanced mass fault occurs, the center line of the mass and the center line of the rotation are in the same plane, and the distance between them is \(r\), which is called eccentricity. Therefore, the unbalanced force will be generated at this time:  
\[F=U\omega^{2} \tag{10}\]  
where \(U=mr\), m is the unbalanced mass.  
Under the influence of the unbalanced force, the force state of the disc will change as shown in Figure 3. The D'Alembert equation is then re-established:  
\[\begin{cases}M^{R}=M^{L}-\left(J_{d}-J_{p}\frac{\Omega}{\omega}\right)\omega^ {2}\theta+k_{0}\theta+U\omega^{2}\\ Q^{R}=m\omega^{2}y+Q^{L}-k_{r}y\end{cases} \tag{11}\]  
Figure 3: Stress diagram of rigid disc under unbalanced mass fault.  
It is expressed in the form of a matrix to obtain  
\[\begin{bmatrix}y\\ \theta\\ M\\ Q\end{bmatrix}^{L}_{i+1}=\begin{bmatrix}1&l&\frac{l^{2}}{2 EI}&\frac{l^{3}}{6 EI}(1-v) \\ 0&1&\frac{l}{EI}&\frac{l^{2}}{2 EI}\\ 0&0&1&1\\ 0&0&0&1\\ \end{bmatrix}_{i}\times\begin{bmatrix}1&0&0&0\\ 0&1&0&0\\ 0&k_{\theta}-(J_{d}-J_{p}\frac{\Omega}{\omega})\omega^{2}&1&0\\ m\omega^{2}-k_{r}&0&0&1\\ \end{bmatrix}_{i}\begin{bmatrix}y\\ \theta\\ M\\ Q\end{bmatrix}^{L}_{i}+\begin{bmatrix}0\\ 0\\ 0\\ 0\\ l_{i}\end{bmatrix}\omega^{2} \tag{12}\]  
\[\begin{bmatrix}y\\ \theta\\ M\\ Q\end{bmatrix}^{L}_{i+1}=\begin{bmatrix}1+\frac{l^{3}}{6 EI}(1-v)\big{(}m\omega^{ 2}-k_{r}\big{)}&l+\frac{l^{2}}{2 EI}\Big{(}k_{\theta}-(J_{d}-J_{p}\frac{\Omega} {\omega})\omega^{2}\Big{)}&\frac{l^{2}}{2 EI}&\frac{l^{3}}{6 EI}(1-v)\\ \frac{l^{2}}{2 EI}\big{(}m\omega^{2}-k_{r}\big{)}&1+\frac{l^{2}}{EI}\big{(}k_{ \theta}-(J_{d}-J_{p}\frac{\Omega}{\omega})\omega^{2}\big{)}&\frac{l}{EI}&\frac {l^{2}}{2 EI}\\ l\big{(}m\omega^{2}-k_{r}\big{)}&k_{\theta}-(J_{d}-J_{p}\frac{\Omega}{\omega}) \omega^{2}&1&l\\ m\omega^{2}-k_{r}&0&0&1\\ \end{bmatrix}_{i}\begin{bmatrix}y\\ \theta\\ M\\ Q\end{bmatrix}^{L}_{i}+\begin{bmatrix}\alpha l\\ \alpha\\ 0\\ 0\\ \end{bmatrix}_{i} \tag{13}\]  
As the motorized spindle without fault, for convenience of description, Equation (13) can be written as \(\mathbf{Z}_{i+1}^{L}=\mathbf{T}_{i}\mathbf{Z}_{i}^{L}+\mathbf{H}_{i}\), where \(\mathbf{T}_{i}\) is the transfer matrix between units and the \(\mathbf{H}_{i}\) is unbalance vector.
# Hybrid CNN-NLP Model for Detecting LSB Steganography in Digital Images
## 1 Introduction
### Paper Approach and Organization  
Contemporary approaches to steganalysis primarily focus on detecting statistical patterns, often overlooking the semantic coherence of the extracted content. However, this issue is critical in forensic scenarios, where the volume of digital evidence is substantial, and progressive analysis demands prioritizing semantically relevant messages by minimizing irrelevant data, particularly by reducing false positives. While numerous tools are available for digital evidence processing, few integrate steganalytic capabilities, and, to date, none has incorporated post-detection linguistic validation. In response to this gap, the model proposed in this study is designed to perform efficient steganalysis on digital images subjected to LSB steganography and subsequently evaluate the semantic coherence of the recovered message, thereby enhancing the interpretability and evidentiary value of the extracted information.  
The document is organized as follows: First, Section 2 introduces the methodology employed, while Section 3 presents the CNN-NLP architecture design for LSB steganography detection. Section 4 details the results and comparison with existing models. Finally, the discussion and conclusions are presented in Sections 5 and 6.
# Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies
## 3 Evolving Computing Paradigms and Related Concepts
### Multi-Access Edge Computing (MEC)  
The Mobile Edge Computing standard has been constituted by the European Telecommunications Standards Institute (ETSI) [72], which offers cloud computing and IT functionalities within the Radio Access Network (RAN) in the vicinity of mobile subscribers [71]. As of 2017, ETSI renamed \"Mobile Edge Computing\" to \"Multi-Access Edge Computing\" due to increasing interest in MEC by non-cellular operators [73; 74]. MEC is a continuation of mobile computing via edge computing, delivering computation and storage to energy and resource-constrained mobile devices [2].  
With Multi-Access Edge Computing, a cloud server is deployed at the cellular network's base stations. It executes tasks such as enhancing the application's performance and minimizing network congestion, bandwidth use, and latency for subscribers that cannot be achieved with a conventional network architecture [75; 76; 77]. Even though the processing and memory capabilities of mobile devices improve, they are still not sufficient to handle compute-intensive tasks, which has led to MCC and MEC models [78]. The MEC architecture is portrayed in Figure 5. MEC considerably reduces the process duration and energy demands of mobile devices by setting up computational and other resources close to the base stations [79]. As base stations serve as crucial access points to several IoT devices, end devices could be directly serviced with just one hop through MEC [80]. MEC showcases a low latency, proximity to the user, location awareness, and geographical distribution. However, it restraints the need to install a dedicated MEC server for MEC services. With the rise in the demand for resources over time, scaling is another major challenge [81]. MEC exhibits the reliability, energy efficiency, and a low latency suitable to diverse applications [82] and outperforms MCC comparably [83].
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## The Proposed Scheme
### Server Spoofing Attack  
Assume that an attacker \(A\) has obtained all the transmitted messages \(\{H_{i},R_{c},L_{i}\}\) and intends to masquerade as a server to deceive the client. In this case, \(A\) needs to construct a legal ACCEPT message \(\{L_{i}^{\prime}\}\). Unfortunately, without the server's secret key \(k_{s}\), \(A\) cannot decrypt \(H_{i}\) to acquire \(ID_{i}\), which is used in \(L_{i}^{\prime}\). Therefore, our proposed scheme resists server spoofing attacks.
## 2 Related Works
### Hybrid CNN and LSTM/BiLSTM Models  
Traffic flow prediction plays a key role in intelligent transportation systems. With the rapid development of deep learning technology, many studies have been devoted to exploring how to build more accurate and effective prediction models. In terms of hybrid models, [PERSON] et al. [11] used the CNN's powerful spatial feature extraction ability to accurately capture the spatial layout and traffic distribution characteristics of traffic networks. At the same time, an LSTM was used to finely process time series data and deeply integrate spatial and temporal features, which effectively improved the prediction accuracy and provided strong support for traffic management decisions. The traffic flow prediction model based on deep learning proposed by [PERSON] et al. [12] not only considers the historical traffic flow data but also introduces the reflection time as an additional input feature, which improves the prediction accuracy. [PERSON] et al. [13] studied the influence of reflection time on traffic flow prediction in depth and found that the reasonable setting of reflection time parameters can significantly improve the prediction performance of the model. With the two-way propagation mechanism of a BiLSTM, [PERSON] et al. [14] made the model more fully understand the complex change pattern of traffic flow and provided a reliable prediction basis for long-term traffic planning. [PERSON] et al. [15] applied it to the field of short-term traffic flow prediction, creatively combining multi-source information, because traffic flow changes are constrained by road networks and many external factors (such as weather conditions or holiday arrangements). The model proposed by [PERSON] et al. [16] can automatically learn to reflect the influence of time on traffic flow prediction and adjust the prediction results accordingly, which improves the prediction accuracy and enhances the interpretability of the model. [PERSON] et al. [17] demonstrated the excellent ability of LSTM in capturing the time series characteristics of traffic data and laid a solid foundation for subsequent research. [PERSON] et al. [18] skillfully combined the spatial feature extraction ability of CNN with the time series modeling advantages of LSTM and optimized the short-term traffic volume prediction depth to significantly improve the prediction accuracy.
## 4 Security Threats
### Distribution of Sharing Rules
#### 4.3.2 Altered/Distorted Rules Received Claim  
According to this attack, an adversarial node caught violating the rules in a DSA network may attempt to escape punishment using a slightly different claim. He may falsely claim that the rules or assignments he received were purposefully modified, or distorted. In other words, such a malicious node may claim that he always abides by the rules of the DSA network, but this time someone else infused specially modified rules to victimize him and lead him to act improperly. Obviously, each network should be able to protect against such situations by ensuring that no maliciously infused rules may be considered as valid by its nodes and, additionally, that no node can repudiate the receipt of the original ones [53]. Otherwise, an adversary could easily invert his position and pretend to be the victim, even if he is the offender.
# Confirmation of Final Bolt Tightening via Deep Learning-Based Image Processing
## 3 Overview of Proposed Method
### Bolt Extraction Module  
In the proposed system, an image including multiple bolt tightening points is used as an input image to increase the bolt tightening work efficiency. The tightening confirmation results for each bolt must be output; thus, it is necessary to detect each bolt in the input image and individually process each bolt.  
We use YOLOv3 [11]--an object detection method based on deep learning--to detect bolts in input images. In this method, the model is trained with coordinate information of the target to be detected and set as a label, and the output for each input image is an image that encloses the detection target in a rectangle. Still images are extracted from the input video frame-by-frame and are used as inputs for YOLOv3 to detect the bolts. A score of 0-1 is calculated for each extracted result. The higher the score, the higher the probability that a bolt was detected. The model was created by fine-tuning a publicly available trained YOLOv3 model. The training data used for fine-tuning are a set of images in which bolts are present as well as the coordinates of rectangles surrounding the bolts in the images. In this paper, 303 sets of data were used for fine-tuning.  
In YOLOv3, the identity of the detection result in one video frame is not guaranteed in the detection result of the next frame image. Therefore, each detected bolt must be judged to determine whether it is the same as the bolts detected in the previous and subsequent frame images.  
We use the coordinate information of the bolts in the detection results to identify each bolt detected in each frame image. From the coordinates \(x_{b_{b,1}},\ y_{b_{t,1}}\) of bolt \(b_{t,1}\) detected at frame \(f_{t}\) and the bolt coordinates of the detection result of the next frame \(f_{t+1}\), the bolt which has the coordinates with the shortest Euclidean distance to the coordinates \(x_{b_{t,1}},\ y_{b_{t,1}}\) is set as \(b_{t+1,1}\), and it is judged to be the same as the bolt \(b_{t,1}\) detected in the previous frame \(f_{t}\).  
In the input video, there may be frames where bolts are not detected, depending on the angle. In such cases, to avoid erroneously identifying other bolts that are clearly distant from the bolt coordinates of the previous frame as the same bolt, a threshold is set for the Euclidean distance between the coordinates of the bolt in the previous frame and the coordinates of the nearest bolt. If the bolt whose coordinates are farther than the threshold is the closest, it is assumed that the bolt in the previous frame does not exist.  
In the bolt extraction module, each detected bolt is extracted from the original image. Each bolt is allocated an ID and frame number indicating where the detection was conducted, and each extracted bolt would be saved as an image file (Figure 2).  
Figure 1: The process flow of the proposed method.
## 5 Discussion
In colder climates, energy efficiency efforts should prioritise enhanced insulation and optimised heating systems: thus, IoT systems could focus on thermal retention and reducing heating losses. Conversely, in tropical climates, the emphasis would shift towards cooling systems, dehumidification, and natural ventilation strategies. For both cases, IoT-driven monitoring can dynamically manage energy use by responding to specific environmental conditions and building needs. Adapting this methodology to every region will require customised algorithms and sensor configurations that account for local climatic and cultural factors. Further research should aim to validate the scalability of the proposed IoT-driven methodology in diverse climatic regions by exploring the following: (1) extreme climate adaptations: how applying adaptive IoT ecosystems in extreme environments (arid or polar regions) to test the robustness and flexibility of this approach; (2) cultural and regional variations: how socio-cultural factors influence energy use and thermal comfort preferences, and how IoT ecosystems can accommodate these variations; and (3) technological enhancements: how integrating renewable energy sources (such as photovoltaic panels) with IoT ecosystems to further optimise energy efficiency in varying climatic contexts.
## 3 Results and Discussion
### Grain-Entrainment Loss Rate
When the forward speed is relatively low and the feed rate is relatively small, the smaller threshing gap makes the grain separation ability stronger. At this time, the entrainment loss rate with threshing drum adjustment type of 10 mm is lower than that with concave adjustment type of 10 mm. However, with the increase of feed rate, the thickness of the grain layer within the thresher device increases, and the possibility for the threshed grains to penetrate the grain layer becomes lower, which results the gradual increase of entrainment loss rate. Therefore, it eventually leads to the phenomenon that the entrainment loss rate with threshing drum adjustment type of 10 mm has a higher amplification and then exceeds that with concave adjustment type of 10 mm. Under low speed, the entrainment loss rate with threshing drum adjustment type of 30 mm was higher than that with concave adjustment type of 30 mm. With the increase of forward speed, both amplification trends were relatively low. When the forward speed was too large, the amplification of the entrainment loss rate with concave adjustment type of 30 mm became larger and exceeds the entrainment loss rate with threshing drum adjustment type of 30 mm within the section from 1.4 to 1.6 m/s. This is because that within the thresher device, the threshing gap with threshing drum adjustment type of 30 mm is higher than that with concave adjustment type of 30 mm. Although the feed rate is relatively low, the overall threshing gap within the thresher device is relatively large, and the relatively smaller threshing spaces on both sides of the drum with concave adjustment type of 30 mm make the separation ability of the grain stronger. Therefore, the entrainment loss rate is lower than that with threshing drum adjustment type of 30 mm. However, with the increase of feed rate, the thickness of the grain layer become larger, relatively speaking, the grain layer with concave adjustment type of 30 mm is thicker, and the possibility for the threshed grain to penetrate the grain layer decreases, which results in the great increase of its entrainment loss rate. [PERSON] et al. has studied the loss rate of grain under different gaps and rotation speeds. They found that when the threshing gap is too small, the entrainment loss rate increase with the increase of the rotation speed of the drum. When the threshing gap was increased, the entrainment loss rate would decrease with the increase of the rotation speed of the drum. However, when the rotation of the drum was relatively low, the entrainment loss rate would increase gradually with the increase of threshing gap. When the rotation speed was increased, the entrainment loss rate would decrease gradually with the increase of threshing gap. It can be interpreted that with the increase of the rotation speed of the drum, the centrifugal force of the material within the thresher device will increase, and the thickness of the material layer will become smaller. This is beneficial for grain separation; therefore, the entrainment loss rate will become smaller gradually.
# Demand Prediction Using a Soft-Computing Approach: A Case Study of Automotive Industry
## 3 Proposed Model  
In this paper, it is proposed the incorporation techniques of fuzzy logic and artificial neural networks, fed by the information obtained from surveys. The demand forecast is one of the problems generated from information or inefficient methods and required improvement. Changes in demand levels do not conform to historical values on most occasions. The objective is to adjust demand projections when projected values do not contemplate a disruptive change in the industry [40]. Therefore, the use of artificial intelligence techniques can generate more robust alternatives in new demand forecasting integrating more than one historical variable.  
This project is made from the experts' opinions; this is achieved through the information gathering tools. As mentioned before, there is a wide variety of tools focused on gathering information, the Delphi method generates quality information and it allows for convergence in opinions through consensus perspectives [9]. The data collected are transformed by fuzzy logic numerical values based on the convergence of opinions and then it is implemented in a time series artificial neural network.  
In the work produced by [35] a fuzzy artificial neural network application for forecasting demand is proposed. This system has a learning process of the artificial neural network based from the fuzzy rules. By other side, the work developed by [41] integrates experts' judgement combining genetic algorithms and neural networks.  
The proposed method consists of an approach made from three steps. The first one is gathering information that is recollected by the Delphi method which allows obtaining objective information through expert consensus. The next step, the data processing--in this step the experts' opinions are transformed by applying an artificial neural network combining with fuzzy logic. The fuzzy logic allows softening the opinions and behavior on weighted data. Finally, the system integrates the information and generates a demand forecast, see Figure 1.
# Autonomous Load Regulation Based Energy Balanced Routing in Reshargeable Wireless Sensor Networks
###### Abstract  
Recent progress in wireless charging technologies has greatly promoted the development of rechargeable wireless sensor networks (RWSN). The network lifetime of RWSN can be commonly extended through routing strategy and wireless charging technology. However, the node accepts the relay request of its neighbor unconditionally, and it cannot remove its overload on its own in a timely manner in traditional routing strategies. The energy balancing efficiency of the network may be limited by this passive mechanism, which poses a great challenge to obtaining optimal joint efficiency of routing and charging strategies. In this paper, we propose an autonomous load regulation mechanism-based energy balanced routing algorithm (ALRMR) for RWSN. In addition to an efficient framework of joint wireless energy transfer and multi-hop routing where the routing strategy is adapted to the charging scheme, an innovative load regulation mechanism is proposed. Under this mechanism, each node can actively adjust its own load by controlling its relay radius. The simulation demonstrates the advantages of our algorithm for energy balance efficiency and improving the network lifetime through the charging scheme and the innovative mechanism.  
autonomous load regulation; wireless energy transfer; rechargeable wireless sensor networks; routing algorithm +
Footnote †: journal: Applied  
efficiency of an MCV may be reduced due to the energy replenishment of a few high-load nodes over a long time period, which poses a great challenge to obtaining optimal joint efficiency of routing and charging strategies.  
Based on these observations, we propose an autonomous load regulation mechanism-based energy balanced routing algorithm (ALRMR) for rechargeable wireless sensor networks (RWSN) in this paper. The ALRMR connects the wireless energy transfer with multi-hop routing, where the routing is adapted to the charging scheme. Furthermore, an autonomous load regulation mechanism is proposed, where every node can actively adjust its own load by controlling its relay radius. The simulation demonstrates the advantages of our algorithm for energy balance efficiency and improving the lifetime through the charging scheme and autonomous load regulation mechanism.  
The main contribution of this paper is the proposal of the ALRMR. Moreover, its practicability is proven by simulation. The ALRMR improves energy balance efficiency and prolongs network lifetime through reasonable energy allocation and an autonomous load regulation mechanism.  
The rest of this paper is organized as follows: Section 2 introduces recent developments for charging and routing algorithms in WSN. Section 3 introduces the system model. Sections 4 and 5 describe our charging scheme and routing algorithm, respectively, in detail. Section 6 provides the simulation results and Section 7 presents a summary and future works.
## 2 Materials and Methods
### Expression Analyses  
One microgram of DNase-treated RNA was reverse-transcribed into cDNA using a SuperScript III First-Strand Synthesis System for RT-PCR (Invitrogen) and random hexamer primers. The resulting cDNA was diluted 3-fold, and the primers for _GbHMGS1_ (Table 1) were designed using Oligo 6.0. Semiquantitative PCR was performed with Taq Polymerase (Takara). QRT-PCR was performed with a ViiA 7 real-time PCR system using FastStart Universal SYBR Green Master with a ROX kit (Roche, Indianapolis, IN, USA) according to the manufacturer's manual. The relative expression levels between the target and the internal control elongation factor 1-alpha in _Populus_ were calculated [27]. The glyceraldehyde-3-phosphate dehydrogenase gene of ginkgo was used as a reference gene [3; 26], and the amplification results were analyzed using the 2\({}^{-\Delta\Delta\text{CT}}\) method [3].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Primer\_ID** & **Forward PCR Primer (5’-3’)** & **Reverse PCR Primer (5’-3’)** & **Product** \\  & & **Length (bp)** \\ \hline _GbHMGS1_ORF & ATGGCGCTCTCATCCAGAAAATG & CAATTGATCATG