esa-sceva/satcom-chunk-collection huggingface dataset:
# Topology Optimization and Testing of Connecting Rod Based on Static and Dynamic Analyses
## 1 Introduction
In addition to the static TO, this study validated an optimal design under dynamic load and fatigue life analysis, ensuring that the connecting rod was stable under an irregular load and moment of motion. Furthermore, material restrictions were introduced to ensure that optimized designs could be manufactured using traditional production methods such as forging and casting. These material savings can contribute to fuel efficiency and improved engine performance. An optimized design was evaluated by transitional analysis and fatigue under realistic load conditions to ensure that it remained structurally sound under operational stress. The results demonstrate that, when combined with rigorous structural validation, topology optimization can significantly improve the performance of components while reducing unnecessary material use. This paper is structured in the following way: Section 1 (Introduction) explains the problem and objective of this research along with a description of the proposed solution; Section 2 (Materials and Methodology) describes the properties of the chosen material and the design of the IC engine components; Section 3 (Computational Analysis) describes the methodologies used; Section 4 (Results) includes a discussion and comparison of the results; Section 5 (Conclusion) explains the outcome of the research and future scope of this work; and the References section contains the bibliography of this work.
## 2 Methodology and Fundamentals
### Inventory Management  
Inventory management plans and controls stocks. It supplies the stored goods based on the expected sales [29] and fixes the repositioning point and the safety inventory. Inventory management includes the functions of demand planning, inventory planning, and procurement planning [30]. Other instruments are order planning defining the optimal quantity and frequency, as well as the selection of the order policy [31]. This refers to the management of existing inventory at present, and its main task is the definition of the optimal order point.  
Inventory planning includes the determination of the necessary safety stock (SS) and the stock for the release of the supply order [32]. This requires consistent forecasting and inventory management from demand planning [32].  
Procurement planning takes care of procurement initiation, order quantity selection, and lot size selection. In different supply policies, the two parameters, quantity and frequency of the order, can be variable or constant [33]. If the four characteristics of the two parameters--size and frequency of the order--are combined, there are four different procurement ordering strategies [31]. In the reporter point method, the inventory level is checked after each stock movement to assess if the remaining inventory is below the reorder point. If this is the case, then an order is placed [34]. A third method, called the control rhythm, arises from another combination of parameters. These are the policies (t, s, q)--(t, s, S) [35]. The three methods used here are for make-to-stock (MTS), while a fourth method depends on the demand and is, therefore, make-to-order (MTO).  
There are many available formulas to select the optimal lot size that is prescribed in many new studies. Although these formulas are available, few companies attempt to arrive at an explicit quantitative balance of inventory and procurement costs [36]. The economic order quantity (EOQ) plays an important role in the total profit of the factory in all respects [37]. A constant order quantity (q), the most widely used, in practice, is the classical EOQwith the Andler-Harris formula for the optimal lot size [38] which minimizes the total cost of the inventory [37]. The optimal lot size is always calculated for a certain period [38]. The economic order quantity (EOQ) used in the paper finds the optimal quantity to order among three components: the cost per unit, the procurement order cost, and the inventory holding costs [39] as it can be seen in detail in Appendix A. This order quantity strategy can be applied with a dynamic calculation in each period or with a discrete calculation valid for a period, such as a year. For a variable order quantity, the inventory is filled to a target level [33]. This is one of the options for the dynamic or feedback-dependent model [32].
## 5 Issues in Data Anomaly Classification  
### Ambiguous Data Investigation  
Ambiguous classes were investigated based on the results of the CNN-based classification. Data samples from both _Trend-ambiguous_ and _Drift-ambiguous_ were tested using the trained CNN model. Note that this analysis does not evaluate the performance of the CNN-based classification but discusses the effects of intrinsic ambiguity in the measured data. The classification results for _Trend-ambiguous_ and _Drift-ambiguous_ conditions are shown in Figures 17 and 18, respectively. In the case of _Trend-ambiguous_, the data were mostly classified into _Normal_, _Trend_, and _Drift_ classes. Similarly, in the case of _Drift-ambiguous_, most of the data were classified into _Normal_ and _Drift_ classes, whereas few were classified into  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Labels** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & \\ \hline
**Class** & _Normal_ & _Missing_ & _Minor_ & _Outlier_ & _Square_ & _Trend_ & _Drift_ & **Total** \\ \hline
**Quantity** & 20,000 & 5105 & 5091 & 4536 & 5000 & 5883 & 2425 & **48,040** \\
**Train** & 1000 & 1000 & 1000 & 1000 & 1000 & 1000 & 1000 & **7000** \\
**Test** & 19,000 & 4105 & 4091 & 3536 & 4000 & 4883 & 1425 & **41,040** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training and test dataset information for the database.  
Figure 16: Confusion matrix for the unseen test dataset.  
_Trend_ and _Minor_ classes by the trained CNN. These ambiguous classes not only make data labeling difficult but also considerably degrade the classification accuracy of CNN-based methods. Class ambiguity also applies to the rule-based classification. Perfect thresholds that can distinguish ambiguous classes from _Normal_ classes do not exist.
## 4 Experiment and Evaluation
### Comparative Experiment  
In order to simulate researchers retrieving marine knowledge about the South China Sea, we compared the generation results of two models: Qwen2-72B and the BM-RAGAM. For the sake of comparison, we asked the same question to both models, as shown in Appendix A.  
We performed a qualitative analysis according to the generation of the Qwen2-72B basic model and the BM-RAGAM. The results are as follows:  
Depth and specificity: The Qwen2-72B basic model responded in great detail, discussing mesoscale processes (such as the vorticity and ocean currents) and their effects on various aspects of the South China Sea. In addition, it highlighted the complexity of this topic and pointed out that multiple factors need to be considered when analyzing the mesoscale variability. The BM-RAGAM's responses were equally detailed, but more focused on the spatiotemporal variability, discussing specific factors such as the thermocline depth, the salinity, and regional differences to provide a comprehensive overview of how mesoscale features evolve over time.  
Structural clarity: The structure of the Qwen2-72B basic model was clear, allowing readers to smoothly understand different aspects of the mesoscale variability. It effectively introduced concepts, provided context, and cited specific journals as references for further reading. The BM-RAGAM interpreted the content comprehensively, but it was less segmented and mainly expressed the content.  
Suitable groups: The Qwen2-72B basic model generated content more suitable for the general public, and specific journals were listed. The content generated by the BM-RAGAM was suitable for participants in the field to retrieve literature information and provide professional reference materials.  
To summarize these findings, Table 6 highlights the key qualitative differences between the two models.  
\begin{table}
\begin{tabular}{c c c} \hline
**Aspect** & **Qwen2-72B** & **BM-RAGAM** \\ \hline \multirow{4}{*}{Depth and specificity} & Discussed mesoscale processes & \multirow{2}{*}{Focused on spatiotemporal variability with detailed discussions on thermocline depth and salinity.} \\  & broadly (e.g., vorticity, ocean currents) and highlighted & \\  & complex interdependencies. & \\ \hline \multirow{4}{*}{Structural clarity} & Well-segmented and clear; & Less segmented; integrated information comprehensively, but may have reduced clarity for general readers. \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \end{tabular}
\end{table}
Table 6: The key qualitative differences between the two models.  
\begin{table}
\begin{tabular}{c c} \hline
**Category** & **Configuration** \\ \hline Operating system & Windows11 \\ CPU & 12 th Gen Intel® Core (TM) i7-12650H \\ GPU & NVIDIA GeForce RTX 4060 \\ Apipost & 8.1.1.0 \\ ChromaDB & 0.5.4 \\ \hline \end{tabular}
\end{table}
Table 5: The experimental environment’s software–hardware.
## 3 2 DOF Systems with Hysteretic Dampers  
The phenomena of nonlinear modal interactions in a multidegree-of-freedom system can be suitably investigated by means of a 2 DOF oscillator [7; 24]. The analysis was developed for two different realizations: (a) top configuration (Figure 3a), where the second element was hysteretic, and the goal was to reduce the response of the main mass \(m_{1}\) and (b) base configuration (Figure 3b), where the first element was hysteretic, and the goal was to reduce the action transmitted from the ground to the superstructure represented by mass \(m_{2}\) and the elastic second element.  
The equations of motion which govern the forced vibrations of the two systems are two dynamic equilibrium equations of the two masses and the differential equation which describes the hysteretic restoring force, leading to a five first-order differential equations in the state space.  
In the case of the TC scheme, Figure 3a, the equations of motion are:  
\[m_{1}\frac{d^{2}x_{1}}{dt^{2}}+k_{1}x_{1}-k_{2}(x_{2}-x_{1})+c_{1}\frac{dx_{1} }{dt}-z(x_{2}-x_{1})=-m_{1}a_{g}\sin(\Omega t) \tag{8}\]  
\[m_{2}\frac{d^{2}x_{2}}{dt^{2}}+k_{2}(x_{2}-x_{1})+z(x_{2}-x_{1})=-m_{2}a_{g} \sin(\Omega t) \tag{9}\]  
\[\frac{dz}{dt}=\bigg{\{}k_{d}-\bigg{[}\gamma+\beta\text{sgn}\bigg{(}z\frac{d(x _{2}-x_{1})}{dt}\bigg{)}\bigg{]}|z|^{n}\bigg{\}}\frac{d(x_{2}-x_{1})}{dt} \tag{10}\]  
where the hysteretic component is a function of the relative displacement between the two masses \((x_{2}-x_{1})\) and acts on both masses.  
In the case of the BC scheme, Figure 3b, the equations of motion are:  
\[m_{1}\frac{d^{2}x_{1}}{dt^{2}}+k_{1}x_{1}-k_{2}(x_{2}-x_{1})+z(x_{1})=-m_{1}a _{g}\sin(\Omega t) \tag{11}\]  
\[m_{2}\frac{d^{2}x_{2}}{dt^{2}}+k_{2}(x_{2}-x_{1})=-m_{2}a_{g}\sin(\Omega t) \tag{12}\]  
\[\frac{dz}{dt}=\bigg{\{}k_{d}-\bigg{[}\gamma+\beta\text{sgn}\bigg{(}z\frac{dx_ {1}}{dt}\bigg{)}\bigg{]}|z|^{n}\bigg{\}}\frac{dx_{1}}{dt} \tag{13}\]  
where the hysteretic component acts on the mass \(m_{1}\) only. The initial and post-elastic stiffnesses of the hysteretic element of the 2 DOF oscillators are:  
\[k_{A}=k_{i}+k_{d},\;k_{B}=k_{i} \tag{14}\]  
where \(i=1,2\) for the base and top configurations, respectively.  
Figure 3: 2 DOF system schemes: (**a**) top-hysteresis (TC); (**b**) base-hysteresis configurations (BC), examples of modes at small (A) and large (B) amplitudes; (**c**) TC2; (**d**) BC1.
# A Blockchain-Assisted Security Protocol for Group Handover of MTC Devices in 5G Wireless Networks
###### Abstract
These encompass the absence of reciprocal authentication, deficiencies in Key Forwarding Security (KFS), and a heightened vulnerability to Denial of Service (DoS) attacks. Moreover, due to the ultra-dense nature of 5G networks with a larger number of cells, the handover events occur more frequently, leading to increased signaling when a group of MTCDs simultaneously handover from a service base station to a target base station. Current research has not delivered an efficient and secure solution for MTCD group authentication in 5G wireless networks. Blockchain technology offers decentralization to enhance data security and privacy while shifting authentication from centralized servers to base stations, effectively countering both DoS and Distributed Denial of Service (DDoS) attacks. To address the abovementioned shortcomings, we designed a blockchain-assisted security protocol for group handover (BSPGH) for MTCDs in 5G wireless networks. By uniquely combining blockchain technology with group authentication in 5G wireless networks, the protocol ensures secure handover authentication while remaining simple to deploy with high efficiency. The specific contributions made in this paper can be summarized as follows:  
1. We have devised the BSPGH protocol, leveraging the capabilities of blockchain technology. This protocol guarantees the preservation of all security attributes while remaining in alignment with the architecture of the 5G wireless network specified by the 3 GPP standard and ensuring its suitability for the MTCD handover scenarios. 2. The proposed BSPGH protocol harnesses the blockchain to establish a decentralized public key management system. It directly culminates in the realization of mutual authentication between base stations and MTCDs. It adeptly streamlines the handover authentication procedure, curtails the volume of interaction messages, safeguards against single points of failure, and fortifies resistance against both DoS and DDoS attacks. 3. The BSPGH solution is built upon the Reliable Malicious KGC-Resistant Certificateless Aggregate Signature (RelCLAS) algorithm and undergoes a formal assessment using the Burrows-Abadi-Needham (BAN) logic and Scyther Tool. It can achieve mutual authentication with key negotiation, anonymity, traceability, perfect forward and backward secrecy, resilience against DoS attacks, and defense against impersonation attacks, etc. The structure of the remaining paper is as follows. Section 2 provides an overview of the existing handover authentication schemes. Section 3 explains the background knowledge. Section 4 explains the system model and attack model. Section 5 describes the details of the proposed protocol. Section 6 presents the security analysis of the protocol. Section 7 evaluates the performance of the solution. Finally, Section 8 concludes the paper with a summary.
# Effect of Temperature on Channel Compensation in Optical Camera Communication
## 6 Results
### Medium SNR Case  
Figures 11 and 12 show the SINR for a SNR of 20 dB. In this case, the penalty was still evident in the blue band. However, in the X3 sensor, the higher noise level started hiding the temperature effect on its performance. In the X3, the red and green channels still  
Figure 10: BER at different temperatures, compensating the channel using the CSI corresponding to 60 \({}^{\circ}\)C and with a high SNR value (30 dB). (**a**) Sony IMX249 and (**b**) Foveon X3. Dashed line, dotted line, and dash-dot line correspond to red, green, and blue, respectively.  
Figure 9: Bit error rate (BER) at different temperatures, compensating the channel using the CSI corresponding to 27 \({}^{\circ}\)C and with a high SNR value (30 dB). (**a**) Sony IMX249 and (**b**) Foveon X3. Dashed line, dotted line, and dash-dot line correspond to red, green, and blue, respectively.  
decreased in SINR when compensated with an obsolete CSI in temperature, although it was less noticeable in the IMX249.  
Figures 13 and 14 depict the BER performance. In this case, most values were below the FEC limit in the IMX249, while in the X3, most values were above the limit. As in the last case, BER values were only above the limit using the CSI corresponding to 27 \({}^{\circ}\)C compensating CSIs corresponding to temperatures higher than roughly 80 \({}^{\circ}\)C in the Sony device. On the other hand, the BER was above the FEC limit using the CSI corresponding to 27 \({}^{\circ}\)C to compensate CSIs corresponding to temperatures higher than roughly 32 \({}^{\circ}\)C in the Foveon device. For those cases where the CSI was at a temperature near the operating LED, temperature was used as a reference for compensating, all the values were below the FEC limit using the Bayer-based sensor, while the vast majority of them were above \(3.8\cdot 10^{-3}\) using the Foveon sensor. Likewise, the blue band was again the most affected by temperature.  
These results showed that Bayer-based sensors have an advantage over Foveon sensors, in which RGB spectral responses are considerably overlapped.  
Figure 11: SINR at different temperatures, compensating the channel using the CSI corresponding to 27 \({}^{\circ}\)C and with a medium SNR value (20 dB). (**a**) Sony IMX249 and (**b**) Foveon X3. Dashed line, dotted line, and dash-dot line correspond to red, green, and blue, respectively.  
Figure 12: SINR at different temperatures, compensating the channel using the CSI corresponding to 60 \({}^{\circ}\)C and with a medium SNR value (20 dB). (**a**) Sony IMX249 and (**b**) Foveon X3. Dashed line, dotted line, and dash-dot line correspond to red, green, and blue, respectively.
# Using Feedback Strategies in Simulated Annealing with Crystallization Heuristic and Applications
## 2 SA with Crystallization Heuristic and Feedback Strategies
### Additional Settings  
The initial temperature is an important parameter. The determination of the appropriate temperature is not a difficult task. It is recommended to fix a temperature that is high enough to ensure that SA is in the exploratory phase. If the temperature is high enough, the percentage of accepted candidates is 100%. Under this condition, SA performs only a random search and accepts everything. As the temperature decreases and reaches 80% of accepted candidates, this is the appropriate initial temperature. Alternative approaches for the determination of the initial temperature exist in the literature [PERSON] et al. (2018).  
The cooling schedule also is relevant for convergence [PERSON] et al. (2018). Several proposals exist in the literature; a good review can be found in [PERSON] et al. (2018). The EIT and airplane design applications use \(\alpha=0.98\). For the benchmark tests, the adaptive cooling schedule explained in [PERSON] et al. (2018) is used. In this case, \(\alpha\) is determined for each temperature and depends on the objective function's standard deviation during the specific temperature.  
The SA is terminated when the global condition is reached. The global condition represents the frozen state--it is a temperature where no candidate is accepted. In the EIT and airplane design applications, a small percentage of accepted solutions are used instead (such as \(1\%\)). In the benchmark tests, the algorithm is terminated when the number of objective function evaluations is reached.  
The second loop is terminated when the thermal equilibrium is reached at a specific temperature. The thermal equilibrium depends on the number of variables, which is considered \(N\). For a specific temperature, the thermal equilibrium is reached when the objective function is evaluated \(5N\) times or the number of accepted candidates reaches \(2.5N\). The number of accepted candidates is half the number of evaluated objective functions. For some applications, it is possible for the number of objective function evaluations to be greater than \(5N\).  
The value of \(\Delta r_{k}\) can be easily fixed considering the variable boundaries, and it is defined by  
\[\Delta r_{k}=\frac{x_{k,\max}-x_{k,\min}}{4} \tag{4}\]  
where \([x_{k,\min},x_{k,\max}]\) are the boundaries for variable \(k\). Finally, \(c_{\max}\) is defined as equal to 20.
# Continuous Camera-Based Premature-Infant Monitoring Algorithms for NICU
## 3 Methodology
### Top-Level Classification Block
#### 3.1.1 Feature Extraction and Object Detection
\[F_{8} =\log_{10}\left(\frac{1}{N}\sum_{i=1}^{N}\left(\sum_{f=5}^{39}S_{ i}(f)\right)\right) \tag{5}\] \[F_{9} =\log_{10}\left(\frac{1}{N}\sum_{i=1}^{N}\left(\frac{\sum_{f=40} ^{120}S_{i}(f)}{\sum_{f=121}^{201}S_{i}(f)}\right)\right) \tag{6}\]  
_Static object analysis._ A YOLO-v3 network is applied to the individual frames. The detection task determines the sought object types and their location. Our solution is primarily used for detecting the infants. Besides, other typical objects are recognized such as hands, arms and caring artifacts. The presence of such classes in the frame are concatenated to the aforementioned features, resulting in a 13-dimensional vector. Note that the output of this detector (more precisely the bounding box for the baby) is reused in the pulse network.
# Deducing Energy Consumer Behavior from Smart Meter Data +
## 4 Proposed Framework
### Load Disaggregation Algorithm (LDA)
#### 4.3.2 Features  
For data disaggregation it is common to use method based on machine learning and optimization. The approach is to first extract features from the dataset, and then train or validate advanced statistical models by using these features. In NILM, the features can be sub categorized in steady state, transient state and non-traditional features.  
Steady state features is features that can be extracted when the signal is in a stable state. Features that are often extracted in this state is \"Power Change\" which is the jump in power or reactive power usage. Time and frequency analysis is often used on the voltage and power signals of the meters. It is shown that analyzing the harmonics in signals is a powerful way of making appliance detection [43]. The trajectory between voltage and power usage is a good feature. This can be used to detect the reactive power of a signal of inductive loads. Some researchers have shown that different appliances makes different noise profiles on the main line. Unfortunately, this kind of recognition require a high sampling rate in the kHz or MHz area [44]. The transient state of an appliance is a short state that comes when an appliance switches between steady states. In this state, there often is appliance semi-unique waveforms in the current and voltage domain. The noise generated on the mainline in the transition is also a good indication of the appliances type. There also exists a series of non-traditional features that is used in special types of algorithms. One of them is based on matching the power usage profile to geometrical figures, and using the series of figures to recognize an appliance [42]. Many of the above features requires a high sample rate in kHz or MHz span in order to be effective. This kind of resolution is often not available since it is a normal smart meters that are providing the data. For a smart meter, a sample rate at 1 Hz would be considered fast, and for many applications this is down to 0.03 Hz or slower. It is, therefore, common only to use the steady state features, and most of the time only the power change feature, which looks at changes in power, reactive power, current or voltage.