esa-sceva/satcom-chunk-collection huggingface dataset:
# Slipping Trend Prediction Based on Improved Informer
## 3 Methodology  
The creep classification prediction method based on Informer is a method to obtain a multi-axle two-classification prediction model by training on locomotive operation data.  
This paper combines the advantages of Transformer and Informer to improve the output structure of the network, as shown in Figure 3. Using the same encoder-decoder structure as Transformer, the feature matrix containing position information is obtained by value coding and position coding. Traditional full self-attention requires computing the attention weights between each _query_ and all _keys_, resulting in a computational complexity of \(O(n^{2})\). When the sequence length is large, both the computational cost and memory consumption increase significantly. In contrast, ProbSparse self-attention leverages the long-tail distribution of attention weights by selecting only the top \(n\) key positions with the most significant weight differences for detailed computation, while averaging the remaining parts. This approach significantly reduces computational complexity and memory usage, while ensuring that the most critical information receives sufficient attention, thereby improving overall computational efficiency. Therefore, ProbSparse is employed in this paper to extract relevant features.  
Figure 3: Improved Informer algorithm diagram (Informer+). The left side is the encoder, and the right side is the decoder. For the input to the decoder, we use the output threshold to replace the official full zero or full one as the filling value, splice it with the slipping state at the previous moment, and send the whole into decoder.  
Figure 2: Adhesion characteristic curves of dry and wet rail surfaces.  
Through the ProbSparse self-attention mechanism, the feature matrix obtains the attention relations of different feature vectors at different positions, and is parallelly calculated by the multi-head method. After completing the self-attention mechanism, in order to reduce the number of network parameters, the distillation pool operation is carried out, and the multi-head ProbSparse self-attention mechanism is carried out again. The input to the decoder is the splicing of the predicted results at some moments before the predicted moment and the filling value of the predicted length. In order to prevent autoregression, the joint attention mechanism is carried out with encoder's temporal feature matrix after mask self-attention, and the slipping parameter information at different times is associated with the slipping state. Finally, the future time is predicted directly by full connection and the Sigmoid activation function.
# Optimal Operation of CCHP Smart Distribution Grid with Integration of Renewable Energy
## 2 Mathematical Model of the Components of the Proposed System
### Electric Subsystem
#### 2.1.4 Energy Storage Battery  
The following equation governs the operation of the SB [36]:  
\[W_{SB}{}^{t}=W_{SB}{}^{t-1}-\Delta t\cdot\left(\frac{P_{SB-dis}{}^{t}}{\eta_{ dis}}\right)+\Delta t\cdot P_{SB-ch}{}^{t}\cdot\eta_{ch} \tag{7}\]  
where \(W_{SB}{}^{t}\) and \(W_{SB}{}^{t-1}\) are the level of the SB charge at time \(t\) and \(t-1\), respectively. \(P_{SB-ch}{}^{t}\) and \(P_{SB-dis}{}^{t}\) are the delivering and absorbing power. \(\eta_{ch}\) and \(\eta_{dis}\) are the efficiencies. \(\Delta t\) is the time interval of operation. The expense of the SB operation is calculated as follows:  
\[C_{SB}{}^{t}=C_{d}\cdot P_{SB}{}^{t}\cdot\Delta t \tag{8}\]  
where \(C_{d}\) is the operation expense of the SB (EUR/kWh).
# Three Rapid Methods for Averaging GPS Segments
## 3 Three Rapid Methods
### Descriptors and Model Selector  
We calculate three descriptors to estimate the segment: source (S), median (M), and destination (D). We first select the endpoints from all trajectories and cluster them by k-means into two clusters. We denote these two clusters as source set and destination set for simplicity. As the method is invariant to the travel direction, the order of S and D can be chosen arbitrarily. Next, we analyze the trajectories and select the median (middle point) from every trajectory to get median set. Finally, we calculate the average (centroid) of each of those three sets to produce the triple (S, M, D). The process is summarized in Figure 5.  
The descriptors are then used to decide whether the set can be described by a linear model. We compare the straight line SD against the two-segment line SMD, which makes detour via the median point M. The more the detour deviates from the direct connection, the less likely a simple linear model is suitable to describe the segment. The exact criterion is the angle \(\angle\)SDM and \(\angle\)DSM. If the cosine of the angle is less than a threshold value, we use a curvy model. We call the method as Rapid Segment (RapSeg), see Algorithm 1 and Figure 3.  
```
1:Input: GPS trajectories: X; threshold value: T
2:Output: representative: Y
3: S, M, D \(\leftarrow\) LineSegment(X);
4: IF \(\cos(\angle\)SDM) \(\leq\) T \(\parallel\cos(\angle\)DSM) \(\leq\) T:
5: Y = CurveSegment(X);
6: ELSE:
7: Y = [S, M, D];
```  
**Algorithm 1**RapSeg (X, T):  
Examples of sets and their descriptors are shown in Figure 6. The first four examples have an only slight change of direction, and the linear model is therefore applied. The four examples below did not meet the angle criterion, and the curvy model is applied. Examples are shown using the median segment.  
Figure 4: The general structure of the three methods.  
Figure 5: Flow diagram of selecting the three descriptors.
## 1 Introduction  
Biomethane potential (BMP) test is an anaerobic digestion carried out, normally, in batch reactors for a prolonged time in order to estimate the ultimate biomethane or biogas potential of a specific substrate. There is no defined volume for batch reactors but volumes 0.5-1 L are often used. The substrate and inoculum used during anaerobic digestion are characterized in terms of total and soluble chemical oxygen demand (COD), total and volatile solids (TS and VS) as well as various other parameters (Table 1). The theoretical biomethane potential is calculated using various chemical equation relationships and compared with the estimate from the BMP tests in terms of yield, such as L CH4/g VS or g CH4 COD/g feed COD. BMP tests are widely used due to their low cost, simplicity and repeatability. Even though BMP tests take a relatively long time, usually longer than 30 days [1], they are crucial in assessing design parameters for full-scale anaerobic reactors. Full-scale reactors,especially those that are high-rate, often face difficulty in achieving the full biomethane potential of particle-rich substrates due to slow degradation of solid particles. Significant parts of the organic substances contained in the substrate remain undigested, limiting the efficiency of the reactors. It is important to unlock the biomethane potential of such substrates. Particle-rich substrates such as the organic fraction of municipal solid waste (OFMSW) and manure are abundantly available resources that are prime candidates for anaerobic digestion and biomethane production. If the problem of slow solid disintegration were solved, the efficiency of high-rate digestion of particle-rich substrates such as manure would be greatly improved. Estimating the BMP of particle-rich substrates is one of the steps towards achieving that goal. In this article, we aim to clarify the effect of solid particle content on disintegration and hydrolysis of substrates by comparing batch test results from high-particle and low-particle substrates. The tests were carried out with and without the addition of enzyme to obtain a better understanding of the limiting factors in disintegration of particulates. Finally, we aim to establish a simple but adequate kinetic model that uses classification of complex particulates into fast and slow degrading fractions to accurately represent the disintegration of particle-rich substrates.
## Appendix A.2.1 Architecture
### Cell Global Identifiers  
In UMTS, cells are grouped into distinct administrative areas which depend on the specific service.  
As for the registration service, cells are assembled into location areas (LAs), like in the case of GSM. LAs are identified by a location area identifier (LAI), which pairs the PLMN identifier with the LAC (16 bits).  
As for data routing, we refer to routing areas (RAs). In general, an LA consists of one or more RA [42]. Each RA is identified by the routing area identifier (RAI), which pairs the LAI with the 8-bit Routing area code (RAC). LACs and RACs are broadcast on a periodic basis over the BCCH.  
As for the administrative organization of the network, as in GSM, UMTS cells are uniquely identified by means of the concatenation of MCC, MNC, LAC, and CI (16 bits) [43]. In addition, each cell can also be uniquely identified with respect to the architectural structure of the network by replacing the LAC with the 12-bit radio network controller identifier (RNC-ID), which identifies the corresponding RNC [29].
## 4 Results and Analysis
### Limitations and Future Work
#### 4.3.2 Future Work  
As research and development for 6G networks progress, future iterations of ONSSO should explore compatibility with 6G architectures, particularly the integration of AI-native network management and quantum communication technologies. ONSSO's machine learning models should evolve to accommodate the ultra-dense and ultra-low-latency environments expected in 6G.  
Security Enhancements  
Addressing the security concerns identified earlier, future work should focus on integrating adversarial defence mechanisms and blockchain-based solutions to ensure the integrity and trustworthiness of machine learning models within the ONSSO framework. These solutions could help mitigate the risks of data tampering or malicious model manipulation.  
Real-World Deployment and Testing  
The next step for ONSSO is to move beyond simulation environments and test the framework in real-world 5G networks. Collaborations with industry stakeholders and telecom providers could facilitate pilot projects where ONSSO is deployed in live network environments to validate its performance under real-world traffic, user behaviour, and network conditions.
## 4 Problem Formulation  
In the mobile system, the UE experience during offloading decisions is determined by both latency and energy budget. A utility function must be designed as a trade-off between the time and energy budgets to make the appropriate decision when the BS is to offload and perform HO due to the UE mobility. The total time cost for task \(m\) can be calculated as the sum of execution time, task offloading time, and HO time, as in (10):  
\[T^{tot}_{m,\eta}=\sum_{k=1}^{k_{m}}(t^{e}_{m,\eta}+t^{o}_{m,\eta})+t^{h}_{m, \eta}. \tag{10}\]  
The total time cost can be written as (11):  
\[T^{tot}_{m,\eta}=\sum_{k=1}^{k_{m}}(t^{e}_{m,\eta}+t^{o}_{m,\eta}+\tau^{h}_{m }\mathbb{H}\{x\}). \tag{11}\]  
This study only considers the task offloading energy cost of UEs. The total energy consumption of UE for offloading task m is calculated by  
\[E^{tot}_{m,\eta}=\sum_{k=1}^{k_{m}}e^{o}_{m,\eta}. \tag{12}\]The decision-making process cannot predict the UE trajectory after the computation deadline time \(t_{m}\) while considering UE mobility during task offloading. Therefore, the total time cost needs to be less than the time constraint \(t_{m}\) presented as  
\[T_{m,\gamma}^{tot}\leq t_{m},\forall m\in M \tag{13}\]  
Eventually, the UE's limited energy budget will restrict the sum of the energy cost \(M\) tasks to be less than energy constraint \(\rho\). The total UE energy is constrained as below:  
\[\sum_{m=1}^{M}E_{m,\gamma}^{tot}\leq\rho. \tag{14}\]  
As previously noted in Equation (1), all candidate BS \(\eta\) for task \(m\) should be within coverage area with a radius of \(R\), denoted as (15):  
\[\eta_{m}^{k}\in\mathcal{A}(m),\forall k\in\{1,2,\ldots,k_{m}\}. \tag{15}\]  
Our target is to determine the optimal offloading decision to minimize the total time cost within a limited energy budget for all tasks \(M\). The problem is formulated as follows (16):  
\[\text{GP:}\min_{\eta}\frac{1}{M}\sum_{m=1}^{M}T_{m,\eta}^{tot} \tag{16}\]  
where \(\eta\in\{\eta_{1},\eta_{2},\ldots,\eta_{M}\}\) is the variable vectors to be optimised. Constraint (13) ensures that the task will be executed on time (subject to the computation deadline time \(t_{m}\)). Constraint (14) guarantees that the energy consumption is below the energy budget \(\rho\). Additionally, constraint (15) chooses all the candidate BSs that can serve task \(m\).  
Due to the non-linearity of the optimisation problem presented in (16) and the complexity of other variables, the GP is a mixed-integer non-linear programming (MINLP) problem [61]. An online algorithm is proposed to address the UEs' trajectory during task offloading.
# Susyfos: A Modular and Extendable Open Malware Analysis Platform
## 4 Classification
### Static and Dynamic Classifiers  
Sisyfos currently includes classifiers that exploit machine learning techniques. We have developed two different classifiers, one based on static features and one based on dynamic ones. We evaluate the effectiveness of these classifiers through a small dataset that contains 6000 Windows Portable Executable samples which include both malware and benign samples. The samples originate from VirusShare, a popular web repository of malware files. The VirusShare malware samples are augmented with benign installation files of well-known benign programs, in order to construct a balanced dataset of samples. We verified the labels of our dataset using the VirusTotal API [23]. The samples are then processed by the platform to produce feature matrices. We divide our dataset into two subsets, one containing 5000 samples used for training and one containing 1000 samples used for testing. Figure 5 shows the distribution of our dataset. Our current classification method uses approximately 500 features derived from static analysis and approximately 150 derived from dynamic analysis. Examples of such features are data extracted from the different sections of the executable, different matching YARA rules, DNS and HTTP requests, files dropped, downloaded files or detected PowerShell commands that are suspicious.  
Figure 5: Sample distribution in our training and test subsets.  
In order to identify the most efficient machine learning algorithm for our dataset, we use TPOT [34], an open source tool, which executes different algorithms and tries different values for the hyper-parameters and calculates the optimal combination. For our specific dataset, the optimal algorithm calculated by TPOT is the gradient boosting classifier. Table 1 presents the optimal hyper-parameters for our case and Table 2 presents the results of our static and dynamic model.
## 5 Experimental Results
### Security Analysis
#### 5.6.5 Resistance to Known Attacks  
* MITM attacks: Without an explicit authentication mechanism, the protocol may be vulnerable to MITM attacks. To mitigate this risk, enhancements such as pre-shared keys or certificate-based validation can be incorporated, ensuring that attackers cannot intercept or alter public keys during transmission. These measures strengthen the protocol's ability to verify the authenticity of communicating parties.
* Replay attacks: Incorporating time-sensitive parameters or nonces into the key exchange process effectively prevents replay attacks, where attackers attempt to reuse old keys to impersonate legitimate parties. These additions enhance the protocol's robustness in real-world deployments by ensuring that each key exchange session remains unique and resistant to duplication.
* SCAs: While the protocol's mathematical foundation ensures robust cryptographic security, its implementation on resource-constrained IoT devices may leave it vulnerable to SCAs, such as power analysis or electromagnetic leakage. To address this, the proposed dynamic key replacement mechanism mitigates the risk of SCAs by dynamically updating encryption keys after each cryptographic operation. This process ensures that even if partial information is leaked through physical signals, it becomes obsolete before it can be exploited. As a result, the mechanism significantly enhances the protocol's resilience against SCAs, providing stronger protection for IoT devices in practical deployments.
## 3 Experiment and Results  
### Experimental Setup  
In this section, a system with CPA-PEC is built and shown in Figure 2. The eddy current metal material analysis experimental platform mainly consists of the CPA-PEC probe, the signal generation module, the signal amplification module, the signal receiving module, the 2D scanning platform, the computer and the analysis software of the upper computer.  
The signal generation module is used to control the excitation coil to send out the ramp excitation signal. A microcontroller-controlled gate circuit is used for generating a periodic square wave. Then, the CPA-PEC voltage signal is generated by charging and discharging to a capacitor. After that, the voltage on the capacitor is converted to a current signal which is connected to the excitation coil. The receiving coil is used to collect the characteristic signal of metal induced eddy current field when the probe is close to the samples. The  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Constant Parameter** & **Value** \\ \hline \(k_{1}\) (A/us) & \(\sim\)0.09 \\ \hline \(\mu_{0}\) (N/A\({}^{2}\)) & \(4\pi\times 10^{-7}\) \\ \hline \(N_{1}\) & 36 \\ \hline \(N_{2}\) & 360 \\ \hline \(e\) & \(\sim\)2.718 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The constant parameters used in this paper.  
signal is fed into a signal amplification and filtering system and then converted into a digital signal by a 10-bit AD.  
A discrete wavelet transform model is used to transform the signal. The eddy current characteristic index is calculated by the discrete signal characteristic analysis model, which can quickly identify the metal to be measured. The experimental data are based on 100 replicate measurement sampling with the CPA-PEC probe. The detection results of metal samples Cu, Fe, Al, St, Fe-K162, Fe-K163 and Fe-K240 were analyzed by feature extraction of the data. The proportion of the elements in metallic alloys Fe-K162, Fe-K163 and Fe-K240 are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Fe-K162** & **Fe-K163** & **Fe-K240** \\ \hline Fe & 93.97\% & 93.994\% & 94.326\% \\ \hline C & 3.21\% & 3.30\% & 3.81\% \\ \hline Si & 1.28\% & 1.25\% & 1.23\% \\ \hline Mn & 1.05\% & 1.16\% & 0.319\% \\ \hline P & 0.075\% & 0.065\% & 0.044\% \\ \hline S & 0.045\% & 0.055\% & 0.026\% \\ \hline Cr & 0.24\% & 0.1\% & 0.037\% \\ \hline Ni & 0.09\% & 0.036\% & 0.009\% \\ \hline Mo & 0.001\% & 0.002\% & 0.026\% \\ \hline V & 0.012\% & 0.011\% & 0.017\% \\ \hline Cu & 0.014\% & 0.012\% & 0.142\% \\ \hline Ti & 0.013\% & 0.015\% & 0.014\% \\ \hline \end{tabular}
\end{table}
Table 2: Proportion of the elements in metallic alloys.  
Figure 2: Photograph of the experimental setup.  
The amplitude of the ramp signal is 825 mV, the width of the signal is 20 us and the repeat frequency is 1 kHz.
## 2 Fundamental Principles of Quasi-Zero Stiffness
### Several Types of Negative Stiffness Actuator
#### 2.3.1 Air Spring as Negative Stiffness Actuator  
This mechanism represents an innovative elastic vibration isolation mechanism, leveraging the compressibility of air within a sealed airbag. This mechanism necessitates the reservation of a predetermined amount of energy in advance and exhibits prompt responsiveness to changes in load-bearing. [PERSON] [49] introduced a pneumatic active vibration isolation system designed for low-excitation frequencies. This system employs a radial basis function neural network model to determine the optimal gain of the auxiliary controllers, thereby mitigating the vibration and jitter phenomena during the control process.  
[PERSON] [50] introduced the design and optimization of a pneumatic vehicle seat suspension system aimed at enhancing driver comfort in off-road vehicles, employing the negative stiffness structure illustrated in Figure 9a. The seat system consists of a pair of double-acting pneumatic linear actuators installed in front and behind a centralized pneumatic spring. Figure 9b illustrates the analysis of the dynamic response of a pneumatic QZS vibration isolator utilizing a wedge mechanism, as presented by [PERSON] et al. [51]. Adjusting the load achieved the objective of modifying the dynamic stiffness of the system. The study revealed that the QZS vibration isolator demonstrates complex dynamic responses and a phenomenon of multi-solution coexistence when analyzing the dynamics with the fifth-order Runge-Kutta algorithm. This study offered a novel concept for the design of vibration isolation mechanisms. [PERSON] [52] conducted an analysis of the dynamics of the pneumatic QZS vibration isolator under overload conditions, revealing that mass detuning leads to a more complex nonlinear response of the QZS isolator, which becomes increasingly sensitive to changes in the external excitation frequency, amplitude, and damping ratio. This study enhances the precision of determining the operating frequency of the pneumatic QZS vibration isolator and presents a novel approach for designing the subsequent structure of the pneumatic QZS vibration isolator, contributing to advancements in the field of vibration isolation technology.  
Figure 8: Different structures of the cam-roller as a negative stiffness mechanism: (**a**) sketch of structure of the cam-roller–spring-rod proposed by [PERSON] [46]; and (**b**) sketch of the structure of the cam-roller proposed by [PERSON] [48].  
The QZS vibration isolator, designed with air springs, demonstrates effective vibration amplitude reduction, avoidance of system resonance intervals, friction reduction resulting from linear spring articulation, and enhanced adjustability and controllability of negative stiffness to some degree. Nevertheless, air springs are plagued by drawbacks such as poor airtightness, restricted mounting options, and the necessity of an additional power unit for active control, factors that have constrained the widespread adoption of air-spring-type QZS vibration isolators in engineering applications.