esa-sceva/satcom-chunk-collection huggingface dataset:
# Stride-TCN for Energy Consumption Forecasting and Its Optimization
## 4 Experiments
### Experimental Results and Comparison
#### 4.2.2 Results and Analysis
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Model**} & \multicolumn{6}{c}{**Time Prediction**} \\ \hline  & **1 h** & **12 h** & **24 h** & **36 h** & **48 h** & **60 h** & **72 h** & **84 h** \\ \hline LSTM & 372,351 & 374,012 & 375,824 & 377,636 & 379,448 & 381,260 & 383,072 & 384,884 \\ GRU & 103,747 & 104,462 & 105,242 & 106,022 & 106,802 & 107,582 & 108,362 & 109,142 \\ TCN & 6433 & 88,652 & 1,037,720 & 580,004 & 353,968 & 355,516 & 587,270 & 589,268 \\ Heuristic-stride-TCN & 3521 & 3884 & 4280 & 4676 & 5072 & 5468 & 5864 & 6260 \\ \hline \hline \end{tabular}  \ \begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Model**} & \multicolumn{6}{c}{**Time Prediction**} \\ \hline  & **1 h** & **12 h** & **24 h** & **36 h** & **48 h** & **60 h** & **72 h** & **84 h** \\ \hline LSTM & 372,351 & 374,012 & 375,824 & 377,636 & 379,448 & 381,260 & 383,072 & 384,884 \\ GRU & 103,747 & 104,462 & 105,242 & 106,022 & 106,802 & 107,582 & 108,362 & 109,142 \\ TCN & 345,887 & 374,988 & 260,824 & 319,076 & 188,528 & 52,700 & 814,280 & 1,504,724 \\ Heuristic-stride-TCN & 3521 & 3884 & 4280 & 4676 & 5072 & 5468 & 5864 & 6260 \\ \hline \hline \end{tabular} 
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Model**} & \multicolumn{6}{c}{**Time Prediction**} \\ \hline  & **1 h** & **12 h** & **24 h** & **36 h** & **48 h** & **60 h** & **72 h** & **84 h** \\ \hline LSTM & 372,351 & 374,012 & 375,824 & 377,636 & 379,448 & 381,260 & 383,072 & 384,884 \\ GRU & 103,747 & 104,462 & 105,242 & 106,022 & 106,802 & 107,Figure 4 illustrates the correlation between performance and complexity between seven models on the CNU dataset, including the LSTM, GRU, and TCN baseline, together with our proposed stride-TCN and heuristic-stride-TCN. In most cases, the baseline TCN has the largest number of parameters, followed by the LSTM and GRU. On the contrary, the heuristic-stride-TCN model constantly reaches the smallest number of parameters. Besides, the family of the stride-TCN also has a relatively small number of parameters compared to baseline models. Figure 4 also demonstrates that using BO usually leads to a model with better performance but suffers the model's complexity trade-off. Another statement that can be drawn here is that the baseline models outperform stride-TCN only in the task of short-term forecasting since the difference in MSE between baseline and the proposed architecture is not trivial later. Generally, the results confirm that our proposed TCN architecture yields a family of lightweight models capable of being implemented on constrained resource devices. Last but not least, among models in the stride-TCN family, the heuristic-stride-TCN does favor not only less complexity but also the fastest in terms of training because the stride hyperparameter can be predefined by data's pattern instead of being searched by BO.
## 4 Syntax and Security Description of CLR-SS-AIBBE  
### Syntax of CLR-SS-AIBBE  
Inspired by refs. [50; 51; 53], a formal definition of CLR-SS-AIBBE is given.  
**Initialization algorithm: Start\((\varpi,l)\rightarrow(MP,MK)\)**. The algorithm inputs the maximum value \(l\) of users and security index \(\varpi\) as inputs. It generates the public parameter (or master public key), \(MP\), and the master private key, \(MK\). \(MP\) is open to all users. \(MK\) is kept as a secret.  
**Private key generation algorithm: KeyGen\((MP,MK,ID)\to SK_{ID}\)**. The algorithm inputs \(MP\), \(MK\) and one user's identity \(ID\). It obtains the user's private key  
\(SK_{ID}\)\(=(SK_{ID,0,1},SK_{ID,0,2})\).  
**Private key updation algorithm: KeyUpd\((MP,SK_{ID,k})\to SK_{ID,k+1}\)**. It inputs \(SK_{ID,k}\) and \(MP\). It obtains a new private key \(SK_{ID,k+1}\).  
**Encryption algorithm: \(\textbf{Encrypt}(MP,M,S)\to CT\)**. The algorithm inputs \(MP\), the message \(M\) and an identity set \(S=\{ID_{1},\ldots,ID_{d}\}\) (\(d\leq l\)) and obtains \((Hdr,CK)\), where \(CK\) is a symmetric key, and \(Hdr\) is called the header. When the broadcaster is going to send the ciphertext of the message, \(M\), to the users in \(S\), the broadcaster obtains \(C\) by encrypting the \(M\) by \(CK\), which generates the ciphertext \(CT=(C,Hdr)\) and broadcasts \((C,Hdr,S)\).  
**Decryption algorithm 1: \(\textbf{Decrypt}(MP,SK_{ID,k,1},S,CT)\to CT^{\prime}\)**. The algorithm inputs the master public key \(MP\), private key \(SK_{ID,k,1}\), users' identity set \(S\) and ciphertext\(CT\). First, it divides \(CT\) into \((C,Hdr)\). If \(ID_{i}\in S\), the algorithm uses \(Hdr\) to produce some part related to the plaintext.  
**Decryption algorithm 2: Decrypt\(2(MP,SK_{ID_{i},k,2},S,CT^{\prime})\to M\)**. The algorithm inputs the master public key \(MP\), private key \(SK_{ID_{i},k,2}\), users' identity set \(S\) and ciphertext \(CT^{\prime}\). If \(ID_{i}\in S\), it first calculates the \(CK\). Then, the plaintext message is recovered by decrypting \(C\).  
**Semi-functional private key generation algorithm: KeyGenSF\((MP,MK,ID)\rightarrow\widetilde{SK_{ID}}\).** It inputs \(MP\), \(MK\) and an identity \(ID\). It outputs the semi-functional private key \(\widetilde{SK_{ID}}\).  
**Semi-functional encryption algorithm: EncryptSF\((MP,M,S)\rightarrow\widetilde{CT}\)**. The algorithm inputs \(MP\), \(S\) and \(M\). Semi-functional ciphertext \(\widetilde{CT}\) is generated.  
The first three algorithms are run by the private key generation center, and other algorithms are run by the user. The last two algorithms are only used for the security proof. Both decryption algorithm 1 and decryption algorithm 2 are executed by the data user. They are usually executed on two components and then transmit information through a secure channel. Each component operates independently and suffers from side-channel attacks. In this way, security can be enhanced.
## 3 Results and Discussion  
### Evaluation of Temperature Control Performance Using CFD Analysis  
CFD analysis was performed to design the flow path of the multi-gas temperature-controllable plasma jet. Figure 3a,b show the temperature distribution in the cross-section of the plasma jet when a TCF of \(-30\)\({}^{\circ}\)C and \(90\)\({}^{\circ}\)C flows in the TCF channel, respectively. When a TCF of \(-30\)\({}^{\circ}\)C was supplied, the average temperature of the plasma jet body decreased to \(-29.1\)\({}^{\circ}\)C. The gas temperature 2 mm from the plasma outlet was \(-23.0\)\({}^{\circ}\)C. When a TCF of \(90\)\({}^{\circ}\)C was supplied, the temperature of the plasma body was \(89.5\)\({}^{\circ}\)C. The gas temperature at 2 mm from the plasma outlet was \(82.1\)\({}^{\circ}\)C. In each case, it was shown that the gas temperature approached the room temperature of 24 \({}^{\circ}\)C as the distance from the plasma outlet increased.  
Figure 3c shows the relationship between the TCF temperature and gas temperature at a distance of 2 mm from the plasma outlet. As the TCF temperature increased, the gas temperature increased proportionally. The CFD analysis shows that the gas temperature  
Figure 3: Evaluation of plasma jet body using CFD analysis: (**a**) temperature distribution at temperature control fluid (TCF) of \(-30\)\({}^{\circ}\)C; (**b**) temperature distribution at TCF of 90 \({}^{\circ}\)C; (**c**) gas temperature at a distance of 2 mm from the plasma outlet calculated by CFD.  
before the plasma generation can be easily changed using a plasma jet body containing a TCF channel. It has been reported that the gas temperature of the plasma can be changed by varying the gas temperature before the plasma generation [15]. The gas temperature of the plasma can also be controlled by varying the TCF temperature used in the designed plasma jet.
# Biological Properties of Sandalwood Oil and Microbial Synthesis of Its Major Sesquietrepenoids
## 5 Metabolic Engineering Strategies to Increase the Yield of Santalene and Santalol in S. cerevisiae
### Manipulation of NADH and NADPH Cofactors  
NADPH is a crucial enzymatic cofactor essential for numerous cellular processes. Both the enzyme tHMG1 and the Cytochrome P450 enzymes (CYPs) rely on the presence of NADPH to carry out their biological functions, and as a result, increasing the levels of NADPH within the cell can lead to improved terpenoid biosynthesis in the yeast species _S. cerevisiae_[104]. The process that generates \(\alpha\)-santalene involves the overall generation of NADH and the utilization of NADPH. Manipulating the equilibrium of NADH and NADPH co-factors to address restrictions imposed by cellular redox conditions is a widely recognized approach in metabolic engineering to improve the synthesis of sesquiterpenes [105]. Increasing NADPH through altering the ratio of NADH and NADPH gives more advantages to product production. Likewise, enhancing the supply of the NADPH-reduced cofactor by removing the NADPH-consuming process involving glutamate dehydrogenase, as documented with the deletion of GDH1, has been utilized formerly to improve product yield [106]. Similarly, increasing the expression of GDH2's NAD-dependent glutamate dehydrogenase activated a distinct ammonium consumption route. This resulted in increased NADH flow during the metabolic construction process and a shift in cofactor balance within the yeast [107]. [PERSON] et al. performed genetic alterations by deleting GDH1 and increasing GDH2 to minimize NADPH use during ammonium absorption. This intervention significantly increased \(\alpha\)-santalene synthesis in the engineered strain of _S. cerevisiae_[88]. Also, overexpressing 2,3-butanediol dehydrogenase mutant is one successful strategy for increasing NADPH supply in _S. cerevisiae_[108; 109].
# SHPB Experiment and MPM Simulation on Dynamic Properties of Unsaturated Clay Under Impact Loading
## 3 MPM Simulation on SHPB Test of Unsaturated Clay
### Numerical Model of SHPB Test  
Figure 8 shows the model description of the SHPB test. The incident bar (defined as a rigid body) and the soil specimen (defined as a flexible body) are discretized by finite material points. The spacing distances between MPs for the bar and soil are set as 5 mm and 1 mm, respectively. The background mesh size is set as 5 mm \(\times\) 5 mm \(\times\) 5 mm. The mass of the incident bar equals its actual mass in the SHPB test. The density, Young's modulus, and Poisson's ratio of the SHPB bar are 2750 kg/m\({}^{3}\), 75 GPa, and 0.3, respectively. The material parameters for the unsaturated soil used in the MPM simulation are summarized in Table 3.  
To simulate the loading condition in the SHPB test, the incident bar moves with a constant velocity to impact the soil specimen. By implementing the rigid-flexible contact algorithm proposed by [PERSON] et al. [46], the velocity of the rigid body can be automatically adjusted and the corresponding contact force can be updated, thereby achieving the nonlinear contact process. The initial velocity of the rigid boy was determined through trial calculation until the contact force matched the front force (or stress) of the specimen obtained in the SHPB test.  
To replicate the stress state of the sample as accurately as possible, the front surface between the incident bar and the soil sample is set as a free boundary, while the end surface of the soil sample was set as a fixed boundary. Furthermore, a slip boundary is applied to the square background mesh tangent to the cylinder specimen.
## 3 Results  
### Bulk Wave Technique Results  
#### 3.1.1 Immersion Techniques  
Through-Transmission Immersion Ultrasonic Inspection  
Through-transmission immersion ultrasonic testing with bulk wave results are shown in Figure 11. The A-scans indicate where there is debonding--the ultrasonic bulk wave transmission to the receiver is significantly reduced (Figure 11a). While C-scan results mostly agree with the A-scans, it can be observed that the defect at the far left (defect number 1) is not as consistent as the other defects (Figure 11b). The slice in the middle of the bonding shows normalized amplitude change where there is debonding and no debonding.  
Pulse-Echo Immersion Ultrasonic Inspection Results  
The results obtained by the pulse-echo immersion technique are shown in Figure 12. According to the A-scan results, the interface reflection is not easily separable from the multiple reflections (Figure 12a). The window in the time domain has been selected to create C-scans according to the time of flight calculations; however, multiple reflections needed to be considered. The C-scan results show  
Figure 11: Immersion ultrasonic through-transmission results: (**a**) A-scan; (**b**) C-scan; (**c**) slice from the middle of the defects.  
the defect position, however, the contrast between the defected zone and the sound area is not so clear (Figure 12b). The slice represents the normalized amplitude difference in the middle of the bondline. The higher values indicate the defect presence since the ultrasonic pulse-echo amplitude increases at the debonding due to impedance difference.
###### Abstract  
Noise vibration and harshness (NVH) development often takes place in the later development phases. Shifting the optimization to the early digital development phase enables more parameters to participate in the optimization and leads to a more holistic development process. Digital NVH development often modifies system and component frequency response functions (FRFs) using finite element (FE) simulation. Currently, the often manual process of creating new FE models for modified designs makes a systematic evaluation of many designs difficult and time-consuming. In this paper, we take on these difficulties and use both a Direct Morphing approach and a Box Morphing approach to automatically adopt the first existing FE models to modified designs. We use the generated simulation results to fit metamodels describing the correlation between geometrical parameters and characteristic FRF values. These metamodels provide an easy and fast to use tool for designers to consider NVH demands. In a simulation example, we demonstrate the capabilities by modifying the kinematic hard points of a vehicle suspension and using them to modify the noise transfer sensitivity. We show that the metamodels can lead the digital design process to intuitively and specifically reduce characteristic component FRF values by changing the location of the component hard points.  
m m  
component. The component FRFs are part of the system transfer behavior from vibration source to receiver.  
In this paper, we modify FRFs of a vehicle suspension component by changing its geometry in the early, digital design phase. We use a Direct Morphing-inspired and a Box Morphing approach to automatically generate changed geometries originating from an initial FE geometry. We compare both approaches regarding effort, time and results. Both approaches generate a large data base of simulation results which is used to create metamodels to describe geometry influences on FRF characteristics. These metamodels offer a simple tool in the design phase to determine directions of design changes. Finally, we use an automated procedure to adjust and optimize the component's FRFs, eigenfrequencies or stiffness by changing the component's geometry. This approach enables a first NVH optimal design in the early, digital design phase and preserves later hardware testing to fine-tune NVH performance.  
The structure of this paper is presented in Figure 1. In Sections:sota, we present the state of the art concerning NVH development in early design phases and morphing approaches. In Section 3, we introduce our approach. First, we present both morphing approaches; second, the usage of metamodels. In Section 4, we perform a study on a component of a vehicle suspension in which we reduce the transferred force at a specific frequency, using our approach. In this section, we compare both morphing approaches and their application.
###### Abstract  
This study utilized a multi-stage constant current (MSCC) charge protocol to identify the optimal current pattern (OCP) for effectively charging lithium-ion batteries (LiBs) using a Dandelion optimizer (DO). A Thevenin equivalent circuit model (ECM) was implemented to simulate an actual LiB with the ECM parameters estimated from the offline time response data obtained through a hybrid pulse power characterization (HPPC) test. For the first time, DO was applied to metaheuristic optimization algorithms (MOAs) to determine the OCP within the MSCC protocol. A composite objective function that incorporates both charging time and charging temperature was constructed to facilitate the use of DO in obtaining the OCP. To verify the performance of the proposed method, various algorithms, including the constant current-constant voltage (CC-CV) technique, formula method (FM), particle swarm optimization (PSO), war strategy optimization (WSO), jellyfish search algorithm (JSA), grey wolf optimization (GWO), beluga whale optimization (BWO), levy flight distribution algorithm (LFDA), and African gorilla troops optimizer (AGTO), were introduced. Based on the OCP extracted from the simulations using these MOAs for the specified ECM model, a charging experiment was conducted on the Panasonic NCR18650 PF LiB to evaluate the charging performance in terms of charging time, temperature, and efficiency. The results demonstrate that the proposed DO technique offers superior charging performance compared to other charging methods.  
optimized multi-stage constant current charging protocol; time-domain battery equivalent circuit model; Dandelion metaheuristic optimizer; charging time; charging temperature; multi-objective optimization
## 4 Experimental Results
### Ablation Study  
#### 4.5.1 Magnitude Criteria Weighted w.r.t. Similarity Criteria  
The proposed algorithm uses both magnitude and similarity criteria for filter selection during pruning. Therefore it is required to pass the parameter defining the pruning ratio for magnitude-based pruning and similarity-based pruning to match the final pruning ratio. This is defined by \(w_{mag}\) parameter which ranges from 0 to 1. If we set \(w_{mag}\) to 0, only similarity-based criteria is used for filter selection. Similarly, if we set \(w_{mag}\) to 1, only magnitude criteria is used for filter selection. We experiment with different values of \(w_{mag}\) for VGGNet pruning on the CIFAR-10 dataset, which is show in Figure 1. It is found that setting \(w_{mag}\) value around 0.5 produce the best results. Therefore, for all experiments in this paper, we set \(w_{mag}\) to 0.5, which means we set equal weight for magnitude criteria and similarity criteria.
## 3 Key Parameter Design
### Buck Inductance, \(L_{b}\)  
Buck inductance can be calculated as:  
\[L_{b}=\frac{V_{b}(1-D)}{K_{ind}I_{b}f} \tag{16}\]  
where \(V_{b}\) is the voltage of buck output (V), in this case, \(V_{b}=22\) V; \(D\) is the duty cycle (%) in the interleaved dual-driver, \(D=\frac{1}{2}\frac{V_{b}}{V_{in}}=36.67\%\); \(K_{ind}\) is the ripple current rate, \(K_{ind}=0.3\), generally; \(I_{b}\) is the current of the buck output (A), and \(I_{b}=\frac{P_{b}}{V_{b}}=5.91\) A. Hence, \(L_{b}=78.6\) uH.
# Leveraging Digital Twin Technology for Enhanced Cybersecurity in Cyber-Physical Production Systems
## 1 Introduction
The framework enhances collaboration between the manufacturing and cybersecurity domains and bridges various organizational departments to ensure comprehensive monitoring and prediction of potential cybersecurity threats. Our approach leverages the capabilities of DT technology to support a simulation environment for vulnerability assessment across various attack scenarios without compromising the integrity of the physical system. Note that this paper follows a DT framework methodology as adopted in [16; 23; 24] to allow flexibility and modularity. The practicality efficacy of our proposed framework is substantiated through its application in a human-robot collaborative (HRC) assembly system, illustrating how DT can strengthen the cybersecurity posture of CPPS. This study emphasizes the utility of DT in component criticality analysis, vulnerability retrieval, and attack simulation, thus positioning DT technology as a pivotal instrument in advancing cybersecurity measures within the manufacturing sector. Our methods also show a capability of being integrated with existing solutions in practical settings. Our contributions are summarized as follows:  
* We present a flexible DT-centered framework that supports security assessment such as vulnerable component mitigation prioritization in CPPS without compromising operations. * We identify critical assets through comprehensive dependence rules within the cyber-physical layers. * We validate the framework's utility and effectiveness through an industrial case study involving an HRC assembly system, showcasing DT's potential to enhance CPPS cybersecurity. The structure of this paper is organized as follows: Section 2 firstly introduces CPPSs, outlines the threats these systems encounter, and then presents related works while highlighting existing research gaps. Section 3 details the proposed DT design and the associated dependency rules. Section 4 delves into a case study that demonstrates the applicability of the DT within an HRC system. The results of the case study are analyzed in Section 5. Finally, Section 6 concludes the paper, summarizing our findings and contributions.
# Establishing a Geological Knowledge Base for Braided River Deltas Using Google Earth
## 3 Results and Discussion
### Sedimentary Parameter Distribution Characteristics
#### 3.1.4 Angle Distribution  
Angles describe depositional structure dip. The analysis shows that Wax Lake, Yukon-Kuskokwim delta, Lena River, Po River, Poyang Lake, and Orinoco have 80% angles \(<\) 90\({}^{\circ}\), while Rufiji River has 80% angles \(<\) 120\({}^{\circ}\). Lena River and Poyang Lake exhibit 53% and 65% angles between 30 and 60\({}^{\circ}\), respectively. Most deltas (e.g., 65% in Poyang Lake, 53% in Lena River) show 30-60\({}^{\circ}\) angle concentrations, except Rufiji River, which shows higher angles (Figure 6).  
Figure 6: Angular distribution and comprehensive data of eight groups of braided river deltas.  
Angle variations relate to depositional slope and flow intensity. Moderate angles (30-60\({}^{\circ}\)) indicate balanced environments with frequent channel migration, while high angles (e.g., Rufiji River) suggest steep slopes or strong erosion similar to fan delta front structures. Braided river deltas on Songliao Basin's western slope exhibit high-angle sandbody stacking due to \"east-dipping steep slope\" settings.