esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 UML Profile for Messaging Patterns
### Stereotypes for Structural Components and Messaging Protocols  
Two trends in communication can be observed. The first is derived from corporate solutions, and communication is related to the realization of a business process. In the second one, communication most often occurs between two services. The first type employs enterprise service buses, and the second type is intended for lighter message brokers. Therefore, stereotypes for both environments have been included in the profile. The abstract \(<<\)EnterpriseServiceBus\(>>\) stereotype has been proposed. The rest are concrete stereotypes that inherit from this abstract stereotype and represent actual frameworks.  
Figure 3: UML profile diagram with declared stereotypes for messaging systems.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Stereotype** & **Base type** & **Icon** \\ \hline Publish–Subscribe & \(<<\)PublishSubscribe & UML Action & \\ Channel & Channel\(>>\) & UML Action & \\ Channel Adapter & \(<<\)ChannelAdapter\(>>\) & UML Action & \\ Request–Reply & \(<<\)RequestReply\(>>\) & UML Action & \\ Saga & \(<<\)Saga\(>>\) & UML Action & \\ Durable Subscriber & \(<<\)DurableSubscriber\(>>\) & UML Action & \\ Envelope Wrapper & \(<<\)EnvelopeWrapper\(>>\) & UML Action & \\ Content Enricher & \(<<\)ContentEnricher\(>>\) & UML Action & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Stereotypes for selected messaging patterns.  
Figure 4 depicts the UML profile diagram with an inheritance hierarchy of stereotypes for structural components representing enterprise service buses.  
Similarly, for message queues, one abstract \(<<\)MessageQueue\(>>\) stereotype has been declared. Three concrete stereotypes stand for actual message broker frameworks: \(<<\)ActiveMQ\(>>\), \(<<\)RabbitMQ\(>>\), and \(<<\)ZeroMQ\(>>\). Table 4 shows stereotypes declared for message queues.  
In addition, there are various communication protocols, especially in the IoT area. That is why the stereotypes have been declared for the most commonly used IoT messaging protocols [25]. Table 5 shows stereotypes declared for messaging protocols.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Stereotype** & **Base Type** \\ \hline Apache Camel ActiveMQ & \(<<\)ActiveMQ\(>>\) & UML Component \\ RabbitMQ & \(<<\)RabbitMQ\(>>\) & UML Component \\ ZeroMQ & \(<<\)ZeroMQ\(>>\) & UML Component \\ \hline \hline \end{tabular}
\end{table}
Table 4: Stereotypes for message queues.  
Figure 4: Stereotypes for enterprise service buses.  
There are no icons declared for structural components, as components can be applied to various stereotypes. The icon would restrain its use in one context. Stereotypes for protocols mark connections between two modeling elements. In the paper, the _UML Control Flow_ type element connects two _UML Actions_ type constructs. Connections have a standard appearance in UML.
## 2 Materials and Methods
### Electrochemical Setup  
The electrolysis unit used in this work consists of a single-compartment glass beaker (handle capacity of 500 cm\({}^{3}\)) with a bi-polar electrode setup. Cuboid-shaped graphite electrodes were used as both anode and cathode electrodes with an effective surface area of 74.56 cm\({}^{2}\) (HLM, Carbograf, Raciborz, Poland). In all the electrochemical experiments, the space between the electrodes was kept at about 3 cm. A saturated calomel electrode (SCE) was used as the reference electrode. All experiments were performed at room temperature. The Cu/Fe mild steel galvanic cell was set up according to Ref. [18]; also, to avoid repetition of the results, only the effect of the electrodegradation of this setup is discussed in this article.  
Before the experiments, the graphite electrodes were first dipped in a 5% HCl solution to remove all the organic residuals from their surface. Then, the electrodes were submerged in acetone under ultra-sonication for 5 min before being rinsed with ultra-pure water (18.2 M\(\Omega\) cm) from the Spring 30 s (Hydrolab, Straszyn, Poland) purification system.  
All electrochemical experiments (CV and AC impedance spectroscopy tests) were performed using the AUTM204 + FRA32M Multi-Autolab potentiostat/galvanostat system (Metrohm Autolab B.V., Opacz-Kolonia, Poland). Otherwise, electrolyte pH and conductivity evaluations were carried-out with pHenomenal(r) pH 1100 L and pHenomenal(r) CO 3100 L meters from VWR (Gdansk, Poland), correspondingly. The electrochemical impedance spectroscopy measurements were performed at an AC signal of 5 mV over the frequency range swept between 1.0 \(\times\) 10\({}^{5}\) and 0.5 \(\times\) 10\({}^{-1}\) Hz, whereas cyclic voltammetry (CV) experiments were performed at a sweep rate of 50 mV s\({}^{-1}\). To perform all electrochemical experiments, the instruments were controlled by Nova 2.1 software for  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Chemical Compound** & **Structural Formula** & **Empirical Formula** & **CAS Number** & **Molecular Weight** \\  & & & & **[g \(\times\) mol\({}^{-1}\)]** \\ Acid Violet 90 & & & & \\  & & & & \\ Acid Red 357 & & & & \\  & & & & \\  & & & & \\ \end{tabular}
\end{table}
Table 1: Specification of the dyes used in the research [31; 32].  
Windows (Metrohm Autolab B.V., Opacz-Kolonia, Poland). Furthermore, all recorded cyclic voltammetry and impedance data were analyzed using the ZView 2.9 and Nova 2.3 software packages, respectively.  
Electrochemical oxidation of the azo dyes in synthetic wastewater was considered the primary treatment process, with the following biological polishing. Initially, the one-hour electrolysis process was conducted in continuous galvanostatic mode, where the applied current density was at a level of 0.29 mA cm\({}^{-2}\). Then, about 3 cm\({}^{3}\) of electrolytic solution was taken for UV-VIS analysis after 60 min of electrolysis.
# An Improved Forwarding of Diverse Events with Mobile Sinks in Underwater Wireless Sensor Networks
## 5 ESDR: Event Segregation Based Delay Sensitive Routing
### Improvements in Terms of Forwarding Function  
The forwarding function is the criteria for a node to decide whether to forward the delay sensitive packets or not. Forwarding decisions are made by each node irrespective of its own type. This means a normal node; which forwards its own packets to mobile sinks and do not broadcast to the surface sink, receives a delay sensitive packet it would be eligible for forwarding and forwards the packet if it comes out to be the best forwarding node. Likewise with iAMCTD, the forwarding function in ESDR is defined differently for different depth nodes. The \(SQI\) based forwarding function is redefined to ensure the comparable values of the forwarding function.  
\[SQI^{\prime}=\frac{\log_{10}(LSNR)\times E_{r}\times R_{T}}{l_{i}} \tag{18}\]  
The \(SQI\) based forwarding function range is always in interval [0, 491.91]. The upper limit is far less than defined in previous schemes and is more comparable to the range of \(ECF\) and \(DDF\) based forwarding function. \(ECF\) based forwarding function \(F_{F}\) is directly proportional to \(E_{r}\) and inversely proportional to depth difference previous and current node depths \(D_{p}-D_{c}\) is defined below:  
\[ECF^{\prime}=[p_{v}\times\frac{R_{T}}{D_{p}-D_{c}}]E_{r} \tag{19}\]  
where \(R_{T}\) is transmission range \(E_{r}\) is remaining energy of a certain node. Forwarding function is directly dependent on remaining energy weighted by a factor given in square brackets whose range is described as follows: Neighbour selection process and depth threshold ensure that \(D_{th}<(D_{p}-D_{c})<R_{T}\). Initially \(D_{th}\) is set to 60 m and is subsequently changed to 40 m and 20 m after certain percentage of deaths, and \(R_{T}\) is constant =100. So for above mentioned values factor in square brackets in Equation (19) has range, from 1 to 1.66 for \(d_{th}=60\), from 1 to 2.5 for \(d_{th}=40\) and from 1 to 5 for \(d_{th}=60\). This eventually gives value of forwarding function always between 0 to \(5\times E_{o}\) which is comparable with the range of \(SQI^{\prime}\). \(DDF\) is also modified taking newly defined and linearized version of \(SQI\).  
\[DDF^{\prime}=\frac{SQI^{\prime}\times l_{i}}{D_{i}} \tag{20}\]  
\(SQI\) based forwarding function is employed when depth of nodes is less than \(D^{\prime}_{1}=250\) m, \(ECF\) based forwarding decision is used if node is in between \(D^{\prime}_{1}=250\) m and \(D^{\prime}_{2}=750\) m. Nodes with depth more than \(D^{\prime}_{2}\) forward the packet using \(DDF\) based forwarding function.
## 2 Materials and Methods
### Age Prediction Using Machine Learning Models  
The data used for the present analysis consisted of the matrix of the bucketed 1D NMR spectra (675 \(\times\) 479) and the matrix of the quantified metabolites and lipoproteins (675 \(\times\) 140) in serum samples, as described in the previous sections (Sections 2.4 and 2.5).  
The metabolite and lipoprotein matrix as well as the spectra matrix were normalized using the Z-score transformation (subtracting the mean and scaling by the standard deviation).  
To perform data analysis, we used three well-known and validated machine learning methodologies: support vector machine (SVM) with linear kernel, generalized linear models (glmnet with ElasticNet regression), and PLS. SVM was implemented with a linear kernel using the scikit library in python version 3.1. ElasticNet and PLS models were implemented using R version 3.2.1. with glmnet package version 4.1.1 and PLS package version 2.7.3. Hyperparameters for SVM and ElasticNet were optimized using cross-validation. The number of PLS components was set to 6 after a few preliminary trials. The Klemera and Doubal method was implemented using the R function \"TrueTrait\" included in the package WGCNA (R package version 1.69).  
To evaluate the algorithms, cross-validation was employed. Root mean squared error (RMSE) and R\({}^{2}\) for all the methods were reported.  
To build the machine learning models, we trained the algorithms using the controls (Ctr, non-PD subjects) of the PROPAG-AGEING cohort, both with the dataset of binned spectra and with the dataset of metabolite/lipoprotein input features. The control cohort that the training set was composed of included healthy controls (Hs), siblings with risk scores of less than 10% (Sib), centenarians (Cent), and centenarians' offspring (CentOs). The distribution of individuals per cohort is reported in Table 1. In particular, the training set is composed of 420 subjects. Each model trained using this dataset was subsequently tested in the dn\({}^{2}\)PD and advPD cohorts, which were used as test sets. The performance of the models for chronological age prediction was assessed in both controls (training) and patients (test). The rationale was to use the discrepancy between the actual chronological age and the predicted chronological age as a proxy to estimate the health status of the diseased individuals, in the hypothesis that PD patients are (from a metabolic point of view) more like aged controls than younger individuals (i.e., they may have a higher biological than chronological age).  
The overall representation of the machine learning pipeline is reported in Figure 1.
# An Efficient Distributed Coverage Hole Detection Protocol for Wireless Sensor Networks
## 4 Proposed Hole Detection Protocol  
In this section, a _distributed coverage hole detection_ (DCHD) algorithm is designed to detect the bounded or non-bounded coverage holes present in the monitoring region. This scheme is proposed not only to solve the flaws of the perimeter-based coverage hole detection approaches, but also to consider the CIP to find a hole, which can reduce the time complexity of the coverage hole detection. In the first phase of the scheme, each sensor finds out its CIP set, as described in Section 4.1. In the second phase, each sensor also verifies if any of its points of intersection belong to a CP set or not, as described in Section 4.2. In the third phase, as described in Section 4.3, each sensor localizes and separates its CIP, which is used to determine the bounded or unbounded coverage hole. In the fourth phase, a sensor collaborates with its one-hop neighbors in the clock-wise direction and connect to its CIP to detect the presence of a coverage hole, as described in Section 4.4.
## 4 Experiment and Analysis
### Ablation Experiments  
To comprehensively evaluate the effectiveness of different configurations in the RT-DETR model for classroom behavior detection, we conducted ablation experiments, including enhancements such as backbone network replacement, learned positional encoding (LPE), and dynamic upsampling (DySample). The results are summarized in Table 3.  
The RT DETR-ResNet18, serving as the baseline model, demonstrated significant capabilities in various behavior classifications, achieving an overall accuracy of 87.2%. The introduction of learned positional encoding (LPE) improved the detection accuracy of specific categories such as \"Writing\" and \"Sleeping,\" raising the overall accuracy to 87.8%, with an increased frame rate of 60.2 FPS, indicating a notable enhancement in processing efficiency. Particularly, the RT DETR-ResNet18+LPE+DySample configuration achieved the highest accuracy in detecting \"Raising Hand\" and \"Writing\" behaviors, with an overall accuracy of 88.5%. This configuration also exhibited outstanding real-time processing capabilities, recording the highest frame rate of 57.6 FPS.  
The final model configuration, RT DETR-MobileNetV3+LPE+DySample, while not entirely surpassing its predecessors in accuracy, significantly reduced computational resource consumption, with only 9.67 M parameters and 26.3 GFLOPs. Despite an increased frame rate of 72.3 FPS, this configuration demonstrated strong potential for application scenarios where computational efficiency is critical, particularly suitable for deployment in mobile devices or embedded systems with limited resources.  
The ablation study highlighted the significant impact of various architectural enhancements on the performance of the RT-DETR model. Specifically, the RT DETR-MobileNetV3+LPE+DySample configuration effectively reduced the model's computational requirements and improved processing speed while maintaining reasonable accuracy, providing an excellent solution for real-time behavior detection in computationally constrained environments.
## 3 FMCW LiDAR Research Progress
### Progress in Detection Accuracy Research
With the continuous emergence of new materials and advanced technologies, more innovative solutions are expected to be developed in the future. These solutions not only need to improve the accuracy of ranging and speed measurement further but also must ensure stability in extreme environments. These technological advances will expand the application areas of FMCW LiDAR and meet a wider range of practical needs, thereby pushing the entire field to higher standards.
# Investigating Supercomputer Performance with Sustainability in the Era of Artificial Intelligence
## 2 Fundamentals
### Power Consumption and Energy Efficiency  
Supercomputers consume a tremendous amount of power (kW) for their computational operations, driven by the large number of interconnected cores and other factors. In some cases, powering a supercomputer can require up to 30 megawatts [20].  
The total amount of energy \(\gamma\) required to execute a given task is expressed as [17]  
\[\gamma=\int_{t_{a}}^{t_{b}}dt\beta(t)\]  
where the starting and ending of the task are represented by \(t_{a}\) and \(t_{b}\), respectively. The power consumption at time \(t\) is represented by \(\beta(t)\), and the average power drawn \(\overline{\beta}\) is expressed as  
\[\overline{\beta}=\frac{\gamma}{t_{a}-t_{b}}\]  
Power is collected at discrete values, \(t_{n}\), in practice at the time of execution. For instance, at each second, NVIDIA SMI time collects samples sufficient for the computation of the average power of samples, expressed as  
\[\overline{\beta}_{GPU}\simeq\frac{1}{N}\sum_{n=1}^{N}\beta_{GPU}\ (t_{n})\]  
where \(t_{1}\ =\ t_{n}\), \(t_{N}\ =\ t_{b}\), and \(N\) is the number of samples. The samples for rack monitoring are collected at an interval of approximately 7s at a lower frequency (this is not generally true for all systems; it varies according to the hardware _integrator_). There are a few seconds of variation between two samples. The energy is computed using the trapezoidal rule, taking measurement irregularities into account and mitigating roughness of the samples, as expressed as follows [17]:  
\[\gamma_{rack}\ \simeq\ \frac{1}{2}\sum_{n\ =\ 1}^{N}(t_{n\ +\ 1}-t_{n})[\beta_{rack}(t_{n\ +\ 1})\ +\ \beta_{rack}(t_{n})]\]  
\(\overline{\beta}_{rack}\) is the average rack power, and the average power consumption for non-GPU components is expressed as [17]  
\[\overline{\beta}_{non-GPU}=\overline{\beta}_{rack}-\overline{\beta}_{GPU}\]To promote EE in supercomputers, the Green500 list was created to rank the top-EE supercomputers globally. The Green500 ranks supercomputers based on the amount of computational performance delivered on the High-Performance Linpack benchmark per watt of power consumed by the HPC system. The EE is measured in gigaflops per watt (GFlops/watts) [19].
## 2 Theoretical Background
### Vertical Coupling Vibration Model of Train-Rail-Bridge
#### 2.2.3. Rail Element Equation  
Similarly, the dynamic equation of the \(p\)th orbital element can be expressed as:  
\[\mathbf{M}_{\text{pr}}^{\text{e}}\ddot{\mathbf{z}}_{\text{pr}}^{\text{e}}+\left( \mathbf{C}_{\text{pr}}^{\text{e}}+\mathbf{C}_{\text{prr}}^{\text{e}}\right) \dot{\mathbf{z}}_{\text{pr}}^{\text{e}}+\left(\mathbf{K}_{\text{prr}}^{\text{e }}+\mathbf{K}_{\text{prr}}^{\text{e}}\right)\mathbf{z}_{\text{r}}^{\text{e}}- \mathbf{C}_{\text{pbr}}^{\text{e}}\dot{\mathbf{z}}_{\text{b}}^{\text{e}}+ \mathbf{K}_{\text{prb}}^{\text{e}}\dot{\mathbf{z}}_{\text{b}}^{\text{e}}=- \mathbf{N}_{\text{z}=x_{\text{w}}}^{\text{T}}F(t) \tag{12}\]  
\[\mathbf{C}_{\text{prr}}^{\text{e}}=c_{\text{rb}}\int_{(p-1)l_{r}}^{p\text{r}_{ r}}\mathbf{N}_{\text{pr}}^{\text{T}}\mathbf{N}_{\text{pr}}dx=c_{\text{rb}}\int_{0}^{l_{r}} \mathbf{N}_{r}^{\text{T}}\mathbf{N}_{\text{r}}dx \tag{13}\]  
\[\mathbf{C}_{\text{pbr}}^{\text{e}}=c_{\text{rb}}\int_{(p-1)l_{r}}^{p\text{r}_{ r}}\mathbf{N}_{\text{pr}}^{\text{T}}\mathbf{N}_{\text{b}}\text{d}x\text{ ; \quad}p=1,2,\cdots m \tag{14}\]  
where \(\mathbf{M}_{\text{pr}}^{\text{e}}\), \(\mathbf{C}_{\text{prr}}^{\text{e}}\), and \(\mathbf{K}_{\text{pr}}^{\text{e}}\) are the mass, damping, and stiffness matrices of the \(p\)th rail element, respectively. \(\mathbf{C}_{\text{prr}}^{\text{e}}\) and \(\mathbf{K}_{\text{prr}}^{\text{e}}\) are the additional damping and stiffness matrix of the rail element, respectively, caused by the elastic support of the \(p\)th rail element in the \(k\)th bridge element.  
Equation (15) can be obtained by substituting Equations (4)-(6) into Equation (12).  
\[\mathbf{M}_{\text{pr}}^{\text{e}}\ddot{\mathbf{z}}_{\text{pr}}^{\text{e}}+\left( \mathbf{C}_{\text{pr}}^{\text{e}}+\mathbf{C}_{\text{prr}}^{\text{e}}\right) \dot{\mathbf{z}}_{\text{pr}}^{\text{e}}+\left(\mathbf{K}_{\text{prr}}^{\text{e}}+ \mathbf{K}_{\text{prr}}^{\text{e}}\right)\mathbf{z}_{\text{r}}^{\text{e}}-\mathbf{C}_{ \text{pbr}}^{\text{e}}\dot{\mathbf{z}}_{\text{b}}^{\text{e}}+\mathbf{K}_{\text{prb}}^{ \text{e}}\dot{\mathbf{z}}_{\text{b}}^{\text{e}}=-\mathbf{N}_{\text{z}=x_{\text{w}}}^{ \text{T}}F(t) \tag{15}\]  
\[\mathbf{K}_{\text{prw}}^{\text{e}}=a_{\text{in}}k_{\text{H}}\mathbf{N}_{\text{z}=x_{ \text{w}}}^{\text{T}}\mathbf{N}_{\text{z}=x_{\text{w}}}\]  
\[\mathbf{F}_{\text{prw}}^{\text{e}}=a_{\text{in}}k_{\text{H}}\mathbf{r}_{\text{w}}(x_{ \text{w}})\mathbf{N}_{\text{z}=x_{\text{w}}}^{\text{T}}\]where \(\mathbf{K}_{prw}^{e}\) is the rail additional stiffness matrix caused by wheel-rail contact, and \(\mathbf{F}_{prw}^{e}\) is the extra load vector caused by wheel-rail contact.
## 3 Results and Discussion
### Influence of the Size and Shape of Aluminum Granules on Hydrothermal Oxidation  
The size and shape of the aluminum granules can indeed have a significant impact on the oxidation process. This is because these factors largely determine the surface area of the aluminum that is in contact with water, which directly influences the reaction rate of the oxidation process.  
The reaction between aluminum and water is a surface-dependent process. Therefore, the larger the surface area of the aluminum that is exposed to water, the faster the reaction can proceed. Consequently, smaller granules, which have a larger surface-to-volume ratio compared to larger granules, are likely to oxidize more quickly. This is because more of the aluminum atoms are available for reaction with water.  
In terms of shape, irregular or non-spherical granules often have a greater surface area compared to spherical granules of the same volume. This could lead to faster oxidation rates in non-spherical granules. However, irregularly shaped granules may also have surface recesses or crevices where water may not readily reach, potentially slowing the oxidation process in these areas.  
Both these effects--granule size and shape--can lead to variability in the oxidation process, making it more challenging to predict and control. Therefore, to account for these effects, a correction factor or error value might need to be introduced when modeling or predicting the oxidation process based on granule size and shape.  
This correction factor or error value could be determined empirically, and the experimental results could then be compared with the theoretical predictions. By so doing, one could quantify the degree to which size and shape influence the oxidation process and then use this information to adjust the predictions accordingly.  
It should be noted that such a correction factor or error value would likely be specific to the conditions under which the oxidation process is being conducted. Factors such as temperature, pressure, and the presence of other substances (e.g., impurities in the aluminum or additives in the water) could also influence the oxidation process and would need to be accounted for when determining the correction factor or error value.  
Therefore, while the size and shape of aluminum granules can significantly influence the oxidation process, quantifying this influence is complex and requires careful experimental design and analysis. However, with such information, one could make more accurate predictions about the oxidation process and thus improve the efficiency and control of applications in which the hydrothermal oxidation of aluminum is used.
## 6 Comparisons and Performance Evaluation  
### Comparisons  
First, we describe some features as follows where these features are required for mobile users when they roam around the mobile networks.  
1. Hiding identity: Mobile users hide their real identities from the system operator, \(H\) and \(V\), and eavesdroppers.
2. No relation: It is difficult for the system to derive the relation between any two rounds of the communication of the same mobile user.
3. Secure channels: After performing mutual authentication between an anonymous mobile user and the system operator, they must establish a shared session key for the following communication activities.
4. Fair privacy: Fair privacy contains traceability and revokeability. If a crime happens, the police can trace the identities of related anonymous mobile users or the judge can revoke their privacy.
5. Credit-based chargeability: As mentioned in Section 3.5, the credit-based charging method is better than the debit-based one since the former (1) is the same as the practical situation in current GSM services; (2) can greatly reduce the relations between any two rounds of communication; and (3) is free from the problem of overspending.  
Figure 18: The oracles of the proof of Theorem 7.  
The comparisons between our proposed scheme and the others are summarized in Table 1. In Table 1, the authors of [6] also mentioned untraceability and revokeability, but they did not realize them in their scheme. We believe that realizing untraceability and revokeability is not trivial.