esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Control Strategy
Firstly, the control algorithm discretises all the possible values of the output in velocity and stores them in a vector, \(\mathbf{u}\). Then, for each joint at a given instant \(k\), the algorithm checks if \(e_{k}\) is in the range \([e_{k-1}-\delta e,e_{k-1}+\delta e]\), where \(\delta e=e_{max}/n_{d}\), and consequently chooses the proper output \(u_{k+1}\) from the \(\mathbf{u}\) vector. The control algorithm runs on the real-time processor at a frequency of 100 Hz. ```
1:\(k=0\)
2:\(n_{d}\leftarrow\) discretisation step
3:\(v_{max}\leftarrow\) max actuator velocity
4:\(\delta v=v_{max}/n_{d}\)
5:\(\mathbf{u}=[-v_{max},-v_{max}+\delta v,\dots,v_{max}-\delta v,v_{max}]\)
6:\(e_{k}\leftarrow\) angular error
7:\(e_{max}\leftarrow\) angular error s. t. \(|e_{k}|\geq e_{max}\Rightarrow|u_{k+1}|=v_{max}\)
8:\(\delta e=e_{max}/n_{d}\)
9:\(i_{k}=0\)
10:while Application Rundo
11:\(i_{t}=\) floor(\(e_{k}/\delta e\))
12:if\(i_{t}>i_{k}\)then
13:\(i_{k+1}=i_{k}+1\)
14:elseif\(i_{t}<i_{k}-1\)then
15:\(i_{k+1}=i_{k}-1\)
16:else
17:\(i_{k+1}=i_{k}\)
18:endif
19:\(i_{k+1}=i_{k+1}+n_{d}\)
20:if\(i_{k+1}>2n_{d}\)then
21:\(i_{k+1}=2n_{d}\) {upper bound on \(\mathbf{u}\) elements}
22:endif
23:if\(i_{k+1}<0\)then
24:\(i_{k+1}=0\) {lower bound on \(\mathbf{u}\) elements}
25:endif
26:\(u_{k+1}=\mathbf{u}(i_{k+1})\)
27:\(k=k+1\)
28:endwhile
```  
**Algorithm 1** Control Algorithm
# Enhancing Periodontial Bone Loss Diagnosis Through Advanced AI Techniques
## 2 Materials and Methods  
### Proposed System  
In this research, we propose an AI-based system that comprises two robust models as two screening stages to enhance and facilitate the PBL diagnosis process using panoramic radiographs. Our proposed system pipeline is depicted in Figure 2, where it starts with the acquisition of the dataset, to be subsequently subjected to some preprocessing techniques. The processed and annotated data are fed into our primary model. This model performs binary classification to determine the presence of periodontal bone loss. If the image is classified as indicative of periodontitis, it is then passed to our secondary model, which is responsible for identifying the location of the periodontal bone loss as well as classifying its severity.  
The development of a sophisticated deep learning model for the diagnosis of periodontal bone loss in panoramic radiographs has been meticulously executed [19]. Employing advanced tools like TensorFlow and Keras [20], we incorporated the pre-trained MobileNetV2 model, as shown in Figure 3, for binary classification tasks. MobileNetV2 is particularly well-suited and fine-tuned for this task due to its efficient use of inverted residuals and linear bottlenecks [21], which maintain high accuracy while reducing computational complexity. Rather than utilizing the default output layer, we thoughtfully added custom layers to capture key features essential for disease detection. OpenCV is utilized for image processing, as shown in Figure 4, while NumPy and Pandas assist in managing and augmenting the data. Before inputting images into the model, they are resized to \(350\times 350\) pixels and brightened by 10% for improved clarity and consistency [22]. Visualizations are used to comprehend the dataset, track model performance, and evaluate the effects of augmentation. During training, we focused on accuracy and recall metrics, optimizing the model with the Adam optimizer and applying techniques such as zooming and flipping to enhance generalization.  
Moreover, we have seamlessly integrated YOLOv8, with its advanced architecture shown in Figure 5, as our secondary model to detect the location of periodontitis in the radiographs and classify the severity of each instance [23]. YOLOv8's single-stage detection framework, coupled with its anchor-free approach and Feature Pyramid Network (FPN), allows for real-time detection and accurate classification of periodontal disease severity [22,24]. It was customized, fine-tuned, and trained using a custom dataset of periodontal bone loss for 50 epochs with an image size of 640 \(\times\) 640. The main output of our developed YOLOv8 secondary model is localizing all existing PBL areas (one or more)  
Figure 2: Our proposed detection-classification system pipeline.
## 2 Related Work
### Extrinsic Calibration  
Extrinsic calibration, the process of aligning sensor outputs to a common, vehicle-fixed reference frame, is critical for the consistent interpretation of 3D data. In the automotive domain, this calibration step is well-established. Early methods include offline marker-based calibration [25] and online self-calibration techniques leveraging vehicle speed and motion data [26]. More recent developments have introduced vision-based methods that utilize image features [27,28], as well as deep learning approaches for automated calibration [29].  
In contrast, the railway domain remains comparatively underexplored. Recent works [30,31] address the alignment of multi-sensor systems, such as camera-LiDAR setups or multi-camera configurations, using vehicle-fixed coordinate frames, thereby underscoring the relevance of such frames. However, there remains a notable gap in solutions focused on monocular setups. This is particularly limiting for interpreting 3D reconstructions in tasks such as estimating vegetation spread along railway tracks. Our proposed method addresses this gap by offering a robust monocular calibration approach, establishing a benchmark for purely monocular vision-based systems in the railway domain.
## 2 Methodology and Fundamentals
### MRP Systems  
Starting from the production program, the productive elements, such as production means, labor, and materials, necessary for the manufacture of primary needs are made available within the framework of the planning of production needs [40]. In this way, the planning of the production factors ensures the capacity to implement the production program through adequate supply programs [27]. These can be made based on consumption- or production-oriented planning. While the consumption-oriented determination of secondary needs is conducted through forecasts, the secondary needs are based on a production schedule-oriented requirements planning, calculated by disaggregating the product structure of primary needs considering the relationships between quantities. Through the planning of production factors, the gross secondary requirements are determined based on the production plan and through disaggregation of the bill of materials, as well as a net comparison with the warehouse inventory [27]. The net comparison is made dynamically by considering the planned inflows and outflows for demand at a certain time point. Through inventory management, gross secondary requirements are covered through raw materials stock. The determination of net dependent requirements generates supply orders. Supply orders are grouped into a supply program, either produced within the company itself or subcontracted. Based on the supply orders that are in the subcontracting program and considering the information from the purchasing departments in relation to the available contracts, the planning of the production factors is defined, such as the calculation of the procurement order quantity. In this process, secondary needs are grouped for a given period to determine optimal order quantities from the economic point of view through the use of methods for their calculation [27].  
Materials requirements planning (MRP) is an inventory management system designed to help production managers schedule and order items dependent on demand. Demand-dependent items are components of the finished product such as raw materials, components, and sub-assemblies or the amount of inventory required depending on the level of production of the final product [41]. MRPs have undergone various developments of MRP and closed-loop MRP, including manufacturing resource planning (MRP II), advanced planning, and scheduling systems (APS), and ERP enterprise resource planning systems [4]. Materials requirements planning assumes that demand and delivery times are deterministic. Unfortunately, most production systems are stochastic. Therefore, the deterministic assumptions of the MRP are usually too restrictive [4].
# Determination of the Equivalent Thickness of a Taiko Wafer Using ANSYS Finite Element Analysis
## 3 Determination of the Moment of Inertia  
By following reference [33] along with the schematic of Figure 1, it is possible to gain an expression of the flexural rigidities \(E_{s}I_{sub}(z_{B})\) and \(E_{s}I_{ring}(z_{B})\). Indeed, the total bending moment per unit length \(M_{sub}^{T}\) acting in the substrate region coated with the FSM film can be expressed as:\[\frac{M_{sub}^{T}}{\kappa}=E_{f}\int_{h_{s}+z_{B}}^{h_{s}+z_{B}+h_{f}}z^{2}dz+E_{s }\int_{z_{B}}^{h_{s}+z_{B}}z^{2}dz=\frac{E_{f}}{3}\bigg{[}\left(h_{s}+z_{B}+h_{f }\right)^{3}-\left(h_{s}+z_{B}\right)^{3}\bigg{]}+\frac{E_{s}}{3}\Big{[}\left(h _{s}+z_{B}\right)^{3}-z_{B}{}^{3}\Big{]} \tag{10}\]  
Hence, the flexural rigidity of the substrate region, corrected by the Poisson coefficients of the thin film and of the substrate is:  
\[\frac{E_{s}I_{sub}}{1-v_{s}}=\frac{E_{f}}{3\Big{(}1-v_{f}\Big{)}}\bigg{[} \left(h_{s}+z_{B}+h_{f}\right)^{3}-\left(h_{s}+z_{B}\right)^{3}\bigg{]}+\frac{ E_{s}}{3(1-v_{s})}\Big{[}\left(h_{s}+z_{B}\right)^{3}-z_{B}{}^{3}\Big{]} \tag{11}\]  
\[\text{If }h_{f}=0,\frac{E_{s}I_{sub}}{1-v_{s}}=\frac{E_{s}}{3(1-v_{s})}\Big{[} \left(h_{s}+z_{B}\right)^{3}-z_{B}{}^{3}\Big{]}.\text{ In the limit }H\to h_{s},\text{ since }h_{s}+z_{B}=h_{s}/2,\]  
\(I_{sub}\) becomes \(\frac{h_{s}^{3}}{12}\), which is what we expect.  
Analogously, in the ring region it holds:  
\[\frac{E_{s}}{(1-v_{s}^{2})}\int_{-H+z_{B}+h_{s}}^{z_{B}+h_{s}}z^{2}dz=\frac{E_ {s}}{3(1-v_{s}^{2})}\Big{[}\left(z_{B}+h_{s}\right)^{3}-\left(-H+z_{B}+h_{s} \right)^{3}\Big{]} \tag{12}\]  
and the flexural rigidity in this region becomes,  
\[\frac{E_{s}}{(1-v_{s}^{2})}I_{ring}(z_{B})=\frac{E_{s}\Big{[}\left(z_{B}+h_{s} \right)^{3}-\left(-H+z_{B}+h_{s}\right)^{3}\Big{]}}{3(1-v_{s}^{2})} \tag{13}\]
## 3 Permanent Deformation Analysis
### Effect of Grading on the Permanent Deformation  
The relationship between \(\varepsilon\) and \(N\) with different gradings is displayed in Figure 9. As demonstrated in Figure 9: (1) \(\varepsilon\) increases approximate linear when \(N\) is less than 400. The growth rate of \(\varepsilon\) begins to slow down and gradually decreases when \(400\leq N\leq 1000\). \(\varepsilon\) tends to be stable when \(N\) is more than 1000. The abovementioned laws apply to the final stability relationship curve between \(\varepsilon\) and \(N\). Therefore, \(N=1000\) can be used as a criterion for reaching the stable stage of CGSF, which can guide the later test loading and coal gauge subgrade engineering. (2) Under the condition of maximum fine particle content (\(D_{f}=2.61\)), the coal gangue sample will fail (\(\varepsilon\) reaches 15%) rapidly when \(N\) is very small (\(N=535\)). Moreover, in these four groups of tests, \(\varepsilon\) of the coal gangue sample is the smallest when \(D_{f}=2.13\). Hence, the influence of grading, i.e., particle size distribution, on \(\varepsilon\) of coal gangue samples is significant. With the increase of the fractal dimension (\(D_{f}\)), \(\varepsilon\) first decreases and then increases, reflecting that there is an obvious optimal grading for coal gangue samples under cyclic loading. [PERSON] et al. (2016); [PERSON] et al. (2016) studied the compaction characteristics of coarse aggregates for embankment dams using the surface vibration compaction test and lateral compression test, which showed that there is an optimal grading of coarse aggregate. In addition, through the large-scale vibration compaction test and static shear test, the authors of ([PERSON] et al., 2017) also investigated the influence of grading on the compaction and strength of CGSF, and then the optimal grading, i.e., \(2.04\leq D_{f}\leq 2.55\), was captured using FMGE. The reason for this phenomenon is related to the contact relationship and interlocking relationship between coarse and fine particles. These conclusions are all consistent with this study.  
Figure 8: The relationship between \(\varepsilon\) and \(N\) with different confining pressure values.
# Deep Learning-Based Autoscaling Using Bidirectional Long Short-Term Memory for Kubernetes
## 6 Experiment and Evaluation
### Datasets
#### 6.1.1 NASA Dataset  
The NASA logs were collected by the NASA Kennedy Space Center WWW server in Florida for two months, and can be classified into two sub-logs [27]. The first log contains all HTTP requests made from 00:00:00 1 July 1995, to 23:59:59 31 July 1995 (total 31 days). The second log contains requests made from 00:00:00 1 August 1995, to 23:59:59 31 August 1995. These two logs contain 3,461,612 requests in total. Because of Hurricane Erin, the server was shut down, so there were no logs between 1 August 1995 14:52:01 and 3 August 1995 04:36:13. This dataset has been used in [15; 41; 42] and others to evaluate the autoscalers.  
We preprocessed the original dataset by aggregating all logs occurring in the same minute into one accumulative record. Thus, the dataset now represents the total workload for each minute. The preprocessed dataset is shown in Figure 4, which indicates a repetitive data pattern within a certain period.
# PowerBridge: Covert Air-Gap Exfiltration/Infiltration via Smart Plug
###### Abstract  
Power lines are commonly utilized for energy transmission, and they serve as a conduit for data exfiltration or infiltration in some specific scenarios. This paper explores the feasibility of establishing bidirectional communication between a modified plug and the equipment power line within an air-gapped network organization and with external entities. Bidirectional air-gap communication includes two scenarios, the data leak from air-gapped networks and the transmission of external data to air-gapped networks, namely, exfiltration and infiltration. In the exfiltration scenario, software in the air-gapped networks modulates and encodes data by manipulating the power consumption of the equipment during transmission, which is then sent outside through the power line. The device utilizes a smart plug power meter to record current fluctuations and subsequently decode any leaked data. In the infiltration scenario, a smart plug is used to control the power supply status of a device's power cord, enabling data encoding and decoding by turning the power supply on and off. The software in the air-gapped equipment captures and decodes the power supply status to infiltrate. We discuss relevant literature and provide scientific background on smart plugs and power line communication. We simulate the communication scenario, propose a communication scheme, and present data modulation techniques as well as a communication transmission protocol for air-gap channels. Our evaluation of the PowerBridge air-gap channels demonstrates that data can leak from the air-gapped computer into the power line at an approximate rate of 30 bps, which can be captured by the smart plug. Additionally, it is possible for data to penetrate from the smart plug into air-gapped networks at a speed exceeding 1 bps.  
air-gapped channel; smart plug; exfiltration; infiltration; laptop +
Footnote †: journal: Applied Sciences
## 1 Introduction
To achieve this, this research introduces a novel hybrid framework combining Long Short-Term Memory (LSTM) networks with Genetic Algorithm (GA) optimization. The approach leverages LSTM's strength in identifying intricate temporal patterns and the GA's capability to fine-tune model hyperparameters effectively. This framework is rigorously validated through simulation and a comparative analysis against established machine learning techniques, enabling a nuanced understanding of its efficacy. The outcomes of this study are anticipated to contribute to the development of resilient UAV systems that can detect and mitigate GPS spoofing attacks in real time, ensuring dependable and secure operations across dynamic environments. To guide this investigation, the following research questions are proposed:  
1. How does the proposed hybrid LSTM-Genetic Algorithm approach compare with alternative deep learning models such as the CNN-RNN and PCA-CNN-LSTM in terms of accuracy, adaptability, and robustness in detecting GPS spoofing attacks on UAVs? 2. What are the computational trade-offs of the hybrid LSTM-Genetic Algorithm model in real-time applications, particularly in terms of scalability and processing time compared to traditional and contemporary machine learning approaches? 3. Can expanding the Genetic Algorithm's search space to include atypical hyperparameter configurations significantly enhance the detection accuracy and adaptability of LSTM models for GPS spoofing prevention in dynamic environments? The remainder of this paper is organized as follows: Section 2 presents the literature review. The proposed Long Short-Term Memory (LSTM)-Genetic Algorithm (GA) model is introduced Section 3. Sections 4 and 5 demonstrate the simulation setup and results. Section 6 provides the discussion. Limitations of the work are provided in Section 7. Finally, Section 8 presents the summary and conclusions of this paper.
## 2 Background
### The Federated Learning Process  
Figure 1 depicts a typical FL training flow where a FL central server obtains a set of available clients \((c_{1},c_{2},\dots,c_{k})\) that meet certain criteria (i.e., availability) and subsequently broadcasts to the clients the training program and an initial model parameterization \(\mathbf{W}_{t_{0}}\) with \(t_{0}\) denoting the initial training round. At this point, the clients update the model locally \(\mathbf{w}_{c_{i},t_{1}}\), based on their data and current knowledge without exchanging (sensitive) data among themselves. The local data of each client and the number of samples \(s_{k}\in S\) used during local training can differ per client. When finished, the server collects an aggregate of the client updates, creating a new global model \(\mathbf{W}_{t_{1}}\). This process is repeated for several rounds until it reaches a max number of rounds or the convergence exceeds a certain accuracy/loss for early termination.
## 3 A Faster Provable Sieving Algorithm in \(\ell_{p}\) Norm
### Linear Sieve
In [49], it has been shown that if we divide \([-R,R]\) from one corner--i.e., place one small hypercube at one corner of the larger hypercube \(B_{n}^{(\infty)}(R)\)--then \(O\Big{(}\Big{(}\Big{[}\frac{2}{\gamma}\Big{]}\Big{)}^{n}\Big{)}\) copies of hypercubes of radius \(r\) suffices. Suppose in one sieving iteration, we have a set \(S\) of lattice vectors of length at most \(R\); i.e., they all lie in \(B_{n}^{(p)}(R)\) (Figure 2(a)). We would like to combine points so that we are left with vectors in \(B_{n}^{(p)}(\gamma R)\). We divide each axis into intervals of length \(y=\frac{\gamma R}{n^{1/p}}\) and store in an ordered set (\(\mathcal{I}\)) co-ordinates of one corner of the resulting hypercubes that have a non-zero intersection with \(B_{n}^{(p)}(R)\) (Figure 2(b)). Note that this can be done in a time of \(O(nN_{h})\), where \(N_{h}\) is the maximum number of hypercube translates as described in Lemma 5. We maintain a list \(\mathcal{C}\) of pairs, where the first entry of each pair is an \(n\)-tuple in \(\mathcal{I}\) (let us call it \"index-tuple\") and the second one, initialized as empty set, is for storing a center pair. Given \(\mathbf{y}\), we map it to its index-tuple \(I_{\mathbf{y}}\) as follows: we calculate the interval in which each of its co-ordinates belong (steps 10-13 in Algorithm 2). This can be done in \(O(n)\) time. This is equivalent to storing information about the hypercube (in Figure 2(b)) in which it belongs or is mapped to. We can access \(\mathcal{C}[I_{\mathbf{y}}]\) in constant time. For each \((\mathbf{e},\mathbf{y})\in S\), if there exists a \((\mathbf{e}_{\mathbf{c}},\mathbf{c})\in\mathcal{C}[I_{\mathbf{y}}]\)--i.e., \(I_{\mathbf{y}}=I_{\mathbf{c}}\) (implying \(\|\mathbf{y}-\mathbf{c}\|_{p}\leq\gamma R\))--then we add \((\mathbf{e},\mathbf{y}-\mathbf{c}+\mathbf{e}_{\mathbf{c}})\) to the output set \(S^{\prime}\) (Figure 2(c)). Otherwise, we add vector pair \((\mathbf{e},\mathbf{y})\) to \(\mathcal{C}[I_{\mathbf{y}}]\) as a center pair. This implies that if there exists a center in the hypercube, then we perform subtraction operations to obtain a shorter vector. Otherwise, we make \((\mathbf{e},\mathbf{y})\) the center for its hypercube. Finally, we return \(S^{\prime}\). More details of this sieving procedure (Linear Sieve) can be found in Algorithm 2.
# Spatially-Precise Video Editing with Reference Imitation: A Region-Aware Cross-Modal Framework
## 3 Materials and Methods
### Local Editing Module  
After obtaining the canonical image through the above methods, the next step is to utilize the local editing module to edit it with the help of a pre-trained diffusion model, so as to achieve the goal of video editing. As shown in Figure 2, the local editing module is based on a meticulously de-signed dual diffusion model architecture. This architecture is mainly composed of two important components: the source image U-Net and the correlation auxiliary U-Net.  
The selection of reference data typically involves user-supplied reference images containing the desired texture or content. These images undergo a preprocessing pipeline to ensure compatibility with the subsequent editing framework. First, non-square images are padded to square dimensions to standardize their geometry. Second, they are resized to the target resolution while strictly preserving the aspect ratio to avoid spatial distortion. Additionally, data augmentation techniques--including Gaussian blur and random perspective transformation--are applied to enhance feature robustness and model generalization. Finally, style and content features are extracted using a CLIP visual encoder, and these features are projected into a dimensional space compatible with the UNet architecture through a linear resampler, ensuring seamless integration with the downstream network. Collectively, these steps facilitate the effective extraction and utilization of reference image features for precise manipulation of source images.  
If multiple similar objects appear in the video, our current method relies entirely on user-specified regions to constrain editing. That is, the user manually selects the target object to be edited. No automatic instance segmentation is applied at this stage. However, in future work, we plan to explore the integration of instance segmentation techniques to better distinguish between visually similar and spatially close objects, thereby improving localization accuracy and edit precision.