esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Cloud Computing Security
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Classification of Attack** & **Description** & **Attack Name** \\ \hline \multirow{6}{*}{Denial of Service} & \multirow{6}{*}{Large amount of data traffic is generated by the attacker to obstruct the availability of services} & SMURF: ICMP: generating echo request to an intending IP address. \\  & & LAND: transferring spoofed SYN packets with the same source and destination IP address. SYN Flood: reducing storage efficiency through IP spoofed packets. \\  & & Teardrop: exploiting flaw TCP/IP stacks. \\ \hline \multirow{6}{*}{Distributed Denial of Service} & \multirow{2}{*}{A DDoS is the distributed form of DoS where the system is flooded in a distributed manner.} & HTTP Flooding: exploiting legitimate HTTP POST or GET requests. \\  & & Zero Day Attacks: exploiting security loopholes unknown to CSPs. \\ \hline \multirow{3}{*}{Remote to Local} & \multirow{2}{*}{Attacker compromises the system by executing commands that grants access to the system.} & SPY: installations that runs a machine for phishing purposes. \\  & & Password Guess. \\  & & IMAP: finding a vulnerable IMAP Mail server. \\ \hline \multirow{3}{*}{User to Root} & \multirow{3}{*}{Attacker gains root access to destroy the system.} & Rootkits: Offering privileged access while masking its existence. \\  & & Buffer Overflowing \\ \hline \multirow{3}{*}{Probing} & \multirow{3}{*}{Breaching the PII of a victim} & Ports Sweeping. \\  & & NMAP: port scanning. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Common security attacks. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Attack Name** & **Description** & **Affected Layer** \\ \hline \multirow{6}{*}{Service Injection} & \multirow{6}{*}{This attack affects the integrity of services at the application and VM level. This is accomplished through the injection of malicious services into legitimate identification files. This, in turn, provides malicious services instead of legal services.} \\  & & Impedes on availability of service by compromising legitimate VMs through direct or indirect host machine flooding. \\ \hline \multirow{3}{*}{Hypervisor and VM Attack} & By compromising the hypervisor, the intruder gains access to a users VM, through the escape of a virtualisation layer. \\  & & Accessing data transfer or communication to users. These affect the integrity and confidentiality of the message. \\ \hline \multirow{3}{*}{Man in the Middle} & \multirow{3}{*}{This attack affects the data privacy and availability of service. This is accomplished by the compromise of a valid VM, by providing rights to access resources.} \\  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Intrusion detection security threats to cloud computing.
## 3 Methodology
### Component Complementarity Analysis
#### 3.7.4 Analysis of DRANet  
DRANet improves the network's ability to recognize rain marks while recovering background details by utilizing spatial attention and channel attention mechanisms. The network efficiently recovers the details of the image while removing rain marks through residual learning. DRANet utilizes residual learning and attention mechanism to recover the background details, as shown in Equation (16).  
\[I_{restord}=I_{input}-I_{rain}+Attention(I_{input}) \tag{16}\]  
where \(I_{rain}\) is the estimated rain trace map, and the attention mechanism selectively recovers background details through spatial and channel features.  
\[I_{restord}=I_{input}-I_{rain}+Attention(I_{input}) \tag{17}\]  
In Equation (17), \(\sigma\) denotes the activation function, and \(W_{1}\), \(W_{2}\) are the learned weights. In this way, the model is able to effectively recover the background details while removing the rain marks.  
Overall, the key to the success of the network lies in the complementarity between the individual components. Each module plays a specific role: the RSENet extracts rain traces, helping the network to focus on the most relevant features, the IMARM enhances the feature extraction process through a multi-scale attention mechanism, ensuring that details and global contextual information are captured efficiently and effectively, and the U-Former improves the capture of long-range dependencies and reduces computational complexity through the structure of the Transformer model. DRANet ensures that the background details are recovered while removing the rain traces, avoiding the introduction of artifacts. These components complement each other, and each module plays a role in different tasks (rain trace extraction, feature enhancement, global dependency capture, detail recovery), thus improving the performance of the whole rain removal process.
# Path Planning of Inspection Robot Based on Improved Ant Colony Algorithm
###### Abstract
(2) We introduce an adaptive pheromone update mechanism [20] that takes into account the difference between the pheromone levels of the best and worst paths, thereby increasing the likelihood of discovering a globally optimal path and preventing stagnation in local optima. (3) We apply a triangle pruning method to reduce redundant turning points in the planned path, making the route more efficient and easier for the robot to follow. Through extensive simulation experiments in different grid-based scenarios, our improved algorithm demonstrates significant advantages in terms of path length, iteration times, and reduced inflection points, outperforming traditional ACO and other benchmark algorithms. These contributions represent a substantial step forward in improving the efficiency and reliability of path planning for logistics robots. The improved ant colony optimization (ACO) algorithm [21] has a wide range of practical applications across various domains. First, in computer networks, ACO can be employed for dynamic routing to optimize data packet transmission paths, thereby improving overall network performance. Second, in the manufacturing and service industries, the algorithm is useful for task scheduling and resource allocation, enhancing efficiency in production processes. Moreover, the improved ACO is highly effective in solving the Traveling Salesman Problem (TSP) [22], enabling logistics companies to optimize delivery routes and reduce transportation costs. Additionally, in the field of image processing, ACO contributes to image segmentation and feature extraction, resulting in enhanced image quality and improved recognition accuracy. Finally, in electric load scheduling and power grid optimization, the algorithm plays a critical role in resource allocation, reducing energy consumption and improving system stability.
## 2 The Tools--Chemometrics and Vibrational Spectroscopy
### Vibrational Spectroscopy
Analysis of such fingerprints is the basis of many applications of MIR spectroscopy in food analysis [41; 42; 51; 52; 53]. In recent years, modern IR instruments have incorporated sampling accessories such as attenuated total reflectance (ATR) [41; 51; 52; 53; 42]. The incorporation of ATR has boosted the use of mid infrared (MIR) spectroscopy in food analysis, allowing for the development of new applications as it removes any necessity for sample preparation, as the ATR accessory extends the path length assessed by the instrument significantly [41; 42; 51; 52; 53]. Spectra that originate in the MIR region are characterised by intense and more informative bands or peaks of the molecules present in the food matrix (fundamental frequencies) [41; 42; 51; 52; 53; 54; 55]. Raman spectroscopy, based on the inelastic scattering of monochromatic when interacting with molecules, is gaining an increasingly wider area of applications in different industries (e.g., pharmaceutical, food) [56; 57; 58; 59]. Raman spectroscopy provides rich information of the molecule's vibrational modes, as well as it is characteristic structure [56; 57; 58; 59]. Although only a small amount of the incident light is inelastically scattered, resulting in the Raman scattering being a naturally weak effect, the weak counterpart of the incident light energy is modified by the molecular vibrations of the scattering sample [56; 57; 58; 59]. Therefore, the observed vibrational responses provide the analyst with enough information about the chemical composition of the sample [56; 57; 58; 59]. In recent years, measuring the Raman spectra of complex samples (e.g., muscles, cereals, wine) in situ became more accessible due to the release of high resolution and portable instruments that provide good detection capabilities [56; 57; 58; 59]. Regardless of the method or technique used (chemometric and spectroscopy) during the process of developing the application, important questions must be addressed depending on the objective or target. For example, if the application targets only what is present in the sample (e.g., is something present in the sample?) the objective of the application will be focused on detection; if the application aims to confirm what is in the samples (e.g., is this what we think it is? ); the application will focus on classification, if the application questions the environment of the target molecule/atom (e.g., what group does this belong to?) the application targets what is present in the sample and will focus on identification (e.g., what is this?) (see Figure 2).
## 5 Importance of Ubiquitous Cellular Coverage in Hospitals
### Role in Telemedicine  
Telemedicine has emerged as a transformative tool in healthcare, enabling remote consultations, patient monitoring, and real-time collaboration among specialists. Its adoption has accelerated in recent years, driven by the need to reduce patient flow in physical facilities and optimize hospital resources. However, the success of telemedicine depends heavily on reliable cellular coverage.  
**Remote Consultations--**Telemedicine allows patients to consult with healthcare providers from the comfort of their homes, reducing the burden on hospital infrastructure. For example, patients with chronic conditions can receive regular check-ups without traveling to the hospital. Weak cellular coverage can disrupt video calls, cause delays in data sharing, or lead to dropped connections, undermining the effectiveness of remote consultations.  
**Continuous Patient Monitoring--**Telemedicine enables healthcare professionals to monitor patients recovering from surgery or managing chronic illnesses. Devices such as wearable sensors transmit data to remote monitoring systems, allowing doctors to trackpatient progress in real-time. Poor coverage can result in gaps in monitoring, delaying interventions, and increasing the risk of complications.  
**Collaborative Care--Telemedicine facilitates real-time collaboration among specialists, even when they are in different locations. For instance, a surgeon in one hospital can consult with a radiologist in another during a complex procedure. Reliable cellular coverage ensures seamless communication and data sharing, enabling better decision-making and improved patient outcomes.**  
**In areas with poor coverage, telemedicine services can be severely compromised, leading to delayed diagnoses, reduced access to care, and diminished patient satisfaction. Reliable connectivity is essential to ensure uninterrupted video calls and real-time data transmission [24], and can be severely compromised, leading to delayed diagnoses, reduced access to care, and diminished patient satisfaction. Addressing these coverage gaps is critical for hospitals aiming to expand their telemedicine capabilities.**
## 2 Significance of Data Monetization Architecture
### Hybrid Consensus  
In this section, we explain the novel concept of hybrid consensus proposed in Substrate, which comprises two consensus phases as follows:  
* Block Authoring: It processes the transactions or extrinsic values and packages them into blocks by the set of validators for each discrete time slot. This is usually performed by a chosen validator in a round-robin selection by Authority Round (AuRa) consensus or Blind Assignment for a Blockchain Extension (BABE) scheme. These blocks are then subjected to the addition as agreed chain storage where a block header contains a reference to its parent or a previous block.
* Block Finalization: A previously authored blockchain contains a chain of blocks and can be subjected to forks where two blocks refer to a single parent block. So to resolve the fork or finalize the chain, we need an additional algorithm to select the best chain, which is the longest chain with higher weightage as in Greediest Heaviest Observed Sub-Tree-based Recursive Ancestor-Deriving Prefix Agreement (GRANDPA). Thisensures a deterministic finality where a chain can never be compromised, as only block authoring offers a probabilistic finality.  
Cross-Consensus Messaging (XCMP)  
In addition, the Substrate framework is also inter-operable, with a mechanism for communicating between chains called Cross-Consensus Message Passing (XCMP), where any custom message can be exchanged between the chains or parachains as called in Substrate terminology. They are classified into asynchronous, which is characterized by a request-response without any time guarantees, absolute, where the message is exchanged in order and efficiently, asymmetric, where there is no response back to the sender, and agnostic, which has no assumption about the message passed. In our architecture, we do not include these messaging systems as we construct a single unified chain focusing on consensus performance with the capability of extending between chains or parachains in the next version.
###### Abstract  
High-Static-Low-Dynamic Stiffness (HSLDS) isolators have been extensively studied, primarily considering continuous stiffness and viscous damping, often overlooking stiffness discontinuities and dry friction forces. This paper aims to provide a more accurate model of real systems by investigating the dynamic behavior of HSLDS isolators, including piecewise nonlinear-linear stiffness, viscous damping, and dry friction. The equation of motion is analyzed using the Krylov-Bogoliubov-Mitropolsky (KBM) averaging method, deriving approximate analytical expressions to evaluate the frequency response curves and stability boundaries near primary resonance conditions. The model is validated by comparing the approximate solution with direct numerical integration and [PERSON]'s closed-form solution. A parametric analysis explores the impact of key parameters through amplitude-frequency diagrams and critical forcing boundaries. A numerical example is presented, demonstrating how the present method can be used to identify critical dynamic conditions, such as saddle-node bifurcations and activation of the piecewise restoring force nonlinearity. Results confirm the reliability of the KBM method in dealing with piecewise restoring forces while highlighting its limitations in case of high dry friction. This study offers an approximate yet effective approach for evaluating the system's dynamic behavior, providing insights that could facilitate the design of isolation mounts and serve as benchmarks for future research.  
HSLDS; vibration; nonlinear dynamics; piecewise stiffness; coulomb friction; averaging method +
Footnote †: journal: Applied Sciences
## 5 Results and Discussion
### General Characteristics
#### 5.1.2. Profitability of IS Adoption (RQ2)  
Of the 83 companies that use IS the 82 of them answered this question. The estimate of this research, based on the respondents sample is that 92.7% of the companies find IS adoption profitable at a high or at least a satisfactory level (Figure 3). On the other hand, there is just one company that considers IS adoption as non-profitable at all. Another 6.1% of the companies have only recently installed the IS so they cannot provide yet a clear image regarding the results of the implementation.  
There are several aspects that affect the financial gains due to ERP implementation which leads to controversial results depending on the company [40]. Larger companies showed better results in financial measures than smaller companies. According to [PERSON] and [PERSON] [41] CRM adoption does not affect the organization's financial performance. Additionally, some companies although they spent enormous amounts of money to implement an IS they had no positive results regarding their business performance [42]. One the other hand other studies [43] concluded that ERP adoption influenced positively the firm's profitability. Similarly, [PERSON] and [PERSON] [44] were led to the conclusion that IT adoption leads to better performances regarding profit margins.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Information System** & **Number of Companies** & **Percentage** \\ \hline ERP & 70 & 85.4\% \\ WMS & 32 & 39.0\% \\ OMS & 31 & 37.8\% \\ CRM & 29 & 35.4\% \\ MES & 21 & 25.6\% \\ MRP & 17 & 20.7\% \\ SCM & 11 & 13.4\% \\ SRM & 11 & 13.4\% \\ TMS & 8 & 9.8\% \\ DRP & 6 & 7.3\% \\ APS & 6 & 7.3\% \\ Other & 3 & 3.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information systems in use.  
Figure 2: Last time the companies invested in IS.
# Combination Analysis of Future Polar-Type Gravity Mission and GRACE Follow-On
## 2 Numerical Simulation Scheme
### Mathematical Model for Gravity Field Recovery
To recover \(\delta\mathbf{u}\) with observations of multiple arcs, such as one month, the arc-specific parameters \(\delta\mathbf{w}_{j}\) should be pre-eliminated arc-wise, forming the reduced normal equation. After that, reduced normal equations are summed up to \(J\) arcs, and then the SHC parameter \(\delta\mathbf{u}\) is obtained by solving Equation (10),  
\[\widetilde{\mathbf{N}}_{J}\cdot\delta\mathbf{u}=\widetilde{\mathbf{R}}_{J} \tag{10}\]  
where \(\widetilde{\mathbf{N}}_{J}=\sum\limits_{\overline{J}}\widetilde{N}_{j}\) and \(\widetilde{\mathbf{R}}_{J}=\sum\limits_{\overline{J}}\widetilde{\mathbf{R}}_{j}\). \(\widetilde{\mathbf{N}}_{j}\) and \(\widetilde{\mathbf{R}}_{j}\) are coefficient matrix and the right-hand side of the reduced normal equation of arc \(j\), with \(\widetilde{\mathbf{N}}_{j}=\mathbf{N}_{\mathbf{u}_{j}\mathbf{u}_{j}}-\mathbf{N}^{T}_{\mathbf{u}_{j}\mathbf{ u}_{j}}\mathbf{N}^{-1}_{\mathbf{w}_{j}\mathbf{u}_{j}}\mathbf{N}_{\mathbf{w}_{j}\mathbf{u}_{j}}\) and \(\widetilde{\mathbf{R}}_{j}=\mathbf{R}_{\mathbf{u}_{j}}-\mathbf{N}^{T}_{\mathbf{u}_{j}\mathbf{u}_{j}\mathbf{ u}_{j}}\mathbf{R}_{\mathbf{w}_{j}}\)[48]. As regard to the GFO and FPG combination, it can be carried out on NEQ level by Equation (11),  
\[(\widetilde{\mathbf{N}}^{GFO}_{J}+\widetilde{\mathbf{N}}^{FPG}_{J})\cdot\delta\mathbf{u}=( \widetilde{\mathbf{R}}^{GFO}_{J}+\widetilde{\mathbf{R}}^{FPG}_{J}) \tag{11}\]  
where \(\widetilde{\mathbf{N}}^{GFO}_{J}\), \(\widetilde{\mathbf{R}}^{GFO}_{J}\) and \(\widetilde{\mathbf{N}}^{FPG}_{J}\), \(\widetilde{\mathbf{R}}^{FPG}_{J}\) are established by Equation (10) of GFO and FPG, respectively. It should be mentioned here that the same variance of unit weight must be employed in constructing the weight matrices of GFO and FPG, otherwise the two normal equations cannot be simply summed up as in Equation (11).
# Future Industrial Networks in Process Automation: Goals, Challenges, and Future Directions
## 2 A Case Study: Iggesund Paperboard
### Traffic Analysis
The variations are then an aggregate of the various protocol types shown on the right-hand side. An example of sporadic traffic is when the operators change the process views on their screens to a different section of the process. This change causes traffic bursts due to requests to start and stop subscriptions of information corresponding to the previous and new process view. Another example can be when the operators transmit and receive production data to the Manufacturing Execution System (MES) or other information to file servers or similar. Generally, this sporadic traffic has a larger packet payload, shown in Figure 8.  
Figure 6: Traffic per Virtual Local Area Network (VLAN); illustrated in logarithmic scale. Figure 7: Traffic between ACS00 Ms and Connectivity Servers in VLAN-G. The pie chart shows the proportion of consumed bandwidth by various communication protocols. Knowing about the diversity of OT network traffic, the main challenge would be finding appropriate methods to accommodate OT traffic together with unforeseeable IT network traffic in the same network while still guaranteeing real-time property for process automation applications. The following section will discuss possible technology enablers that aim to achieve such a goal.
## 5 Machine Learning Models
### Data Analysis
The process began by fixing the network and transfer functions. Multiple training functions or backpropagation algorithms were tested to identify the optimal one. Subsequently, various transfer functions were explored based on the selected training function. This sequential approach ensured that the most appropriate combinations were chosen. The experimentation continued by altering the network function, repeating the process, and recording the hyperparameter set that yielded the lowest mean absolute percentage error (\(MAPE\)), root mean square error (\(RMSE\)), and the highest coefficient of determination (\(R^{2}\)). Refer to Table 11 for the optimized values of the hyperparameters. Thus, in the current study, an accurate CDAF spectrum prediction is achieved using a two-layered feedforward neural network. Two layers are present, the output layer and the hidden layer. The accurate approximation of any function is possible in neural networks with a single hidden layer [83]. Therefore, when developing the ANN 5-10-1 model, the 10 neurons in the hidden layer were taken into account (Figure 12). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Hyperparameter** & **Optimal Value** \\ \hline \multirow{8}{*}{Artificial Neural Network (ANN)} & Number of neurons in the & \multirow{8}{*}{5} \\  & input layer & \\ \cline{1-1}  & Number of hidden layers & 1 \\ \cline{1-1}  & Number of output neuron & 1 \\ \cline{1-1}  & Number of neurons in the & 10 \\ \cline{1-1}  & hidden layer & \\ \cline{1-1}  & Training data & 70\% \\ \cline{1-1}  & Testing data & 30\% \\ \cline{1-1}  & Number of epochs & 1000 \\ \cline{1-1}  & Activation function & tanh \\ \cline{1-1}  & Loss functions & \(RMSE\), \(MAPE\) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Optimal configuration of the ANN model.
## 4 Methodology
### LSTM-Based RUL Prediction with Self-Attention  
#### 4.4.1 RUL Prediction Model Architecture  
How was engine RUL predicted? Once the engines are classified into distinct states (Healthy, Middle, and Unhealthy), separate LSTM models are trained for each state to predict RULs. A RUL prediction model designed whose architecture was based on the following layers: the Sequence Input Layer, with the size of the engine data clustered to one of the engine health states; the LSTM Layer, with 128 units with sequence output; followed by the Dropout Layer. The LSTM unit count (128) aligned with similar architectures in [39; 41]. One of the essential layers was the Self-Attention Layer (SAL), with eight heads and eight key dimensions. SAL was connected to the fully connected layer with 32 hidden units and ReLU activation layer, again followed by a fully connected layer and a regression head being fully connected layer with linear activation. Again, some neural network training options were based on the model performance metrics, starting from 100 epochs and 32 mini-batch sizes through 200 and 16, finishing with 400 epochs and a mini-batch size of 8. The initial learning rate was set at 0.001. Before neural network training, each engine state was divided into two subsets: training and testing, with a 0.8-to-0.2 division ratio. Beyond the 80/20 split, 5-fold cross-validation was performed per health state to ensure robustness. Mean RMSE variations were \(<\)5% across folds, confirming stability. For the GMM, cluster consistency was verified via repeated initialization (10 runs), yielding \(<\)3% label assignment variance.