esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### LCA and System Boundary
The calculation includes equipment production processes necessary for the Starlink satellite assembly. Usually, for single-satellite manufacturing, the engineering model (EM), structural model (STM), and qualification model (EQM) are included because of their significant impact on midpoint indicators. As this research focuses on a constellation of satellites, STM, EM, and EQM are also excluded because of negligible contribution to the final midpoint indicator results. Once all resources have been defined for a specific impact category, the cumulative impact of the product system within that category can be approximated by aggregating the characterized emissions associated with that impact category. This method is iteratively applied to all impact categories within a methodology, often numbering 10 or more, to furnish an assessment of the potential environmental strain induced by a product across various categories. These evaluations of environmental strain are commonly referred to as midpoint indicators [40]. Figure 3 below represents the scope of the ESA's Life Cycle Inventory (LCI) database for equipment production) [10]. It covers upstream activities that include all necessary procedures for the external manufacturing of primary materials and/or sub-components utilized on the production premises. as well as core activities, which includes primary operations, spanning from the initial to the final stages, and encompassing all tasks undertaken by the organization responsible for generating the LCI. Figure 1: Space mission system boundary [10]. Figure 3: Scope of equipment production [10]. Figure 2: System boundary of a space segment for LCA in the C + D phase [10].
## 3 Experiment and Results  
### Sensor Fabrication  
The fabrication process of the SAWR is shown in Figure 11. First, we cleaned the ST-X quartz wafer, and the Al (thickness 250 nm) film was coated on the wafer surface using an E-beam evaporator (MODEL #6100, Johnsen Ultravac, ON, Canada). Second, the wafer's surface was spin-coated with a photoresist (AZ(r) 5214E, MicroChemicals GmbH, Ulm, Germany), then exposed and developed by a mask plate. The photoresist was cured to obtain the 311 MHz SAWR pattern, which protected the Al electrodes it covered. Third, the Al film was removed from the non-patterned area by wet etching, at which point only the pattern of electrodes was covered with cured photoresist. Finally, we removed the cured photoresist by soaking in the photoresist stripper, which gave us the metallization pattern of the designed SAWR. After the above process, we placed the wafer under the RF probe station for observation confirming, performance testing, dicing, and wire-bonding.  
Figure 10: Particle collection efficiency curves of the cascade impactor simulated by COMSOL MULTIPHYSICS.  
Figure 9: 3D simulation of velocity distribution of impactors: (**a**) LCP stage; (**b**) PM\({}_{2.5-10}\) stage; (**c**) PM\({}_{1-2.5}\) stage.  
According to the optimal response area of the SAWR, the designed result of the impactor, and the feasibility of machining, we used the commercial software SOLIDWORKS to draw 3D models and then manufactured each stage of the cascade impactor. A specially designed printed circuit board was used as an impaction plate in each stage to facilitate the signal acquisition of the SAW sensor. Upon cascade assembly, the 311 MHz SAWR was placed in the center of the impaction circuit board, and the impactor nozzle was directly facing the center of the chip to obtain the largest matching area. The assembled sensor is shown in Figure 12.
# Portable Protein and Fat Detector in Milk Based on Multi-Spectral Sensor and Machine Learning
## 2 Materials and Methods
### Model Training  
This section outlines the training of the milk composition prediction model, as shown in Figure 7. Based on the following experiments conducted on a personal computer, a predictive model for protein and fat in milk was successfully built using machine learning algorithms. The accuracy of the model was verified after parameter tuning and performance tests. It was successfully ported to a Raspberry Pi module, enabling real-time compositional analysis of the embedded system.
# Design Procedures for Sustainable Structural Concretes Using Wastes and Industrial By-Products
## 4 Discussion
### Physical Properties  
#### 4.3.1 Porosity  
Figure 18a shows the results obtained for water absorption by immersion at atmospheric pressure. Mixture FRC_EAFS demonstrated slightly higher open porosity than EAFS8D1 (12.1%) whereas REF2 shows a smaller open porosity compared with EAFS8D1 (25.1%). The average porosity of FRC_EAFS, EAFS8D1 and REF2 were, respectively, 16.2%,  
Figure 16: Evolution of distribution modulus, \(q\), with respect to the water/powder ratio, \(w/p\).  
Figure 17: Evolution of the mechanical behavior with respect to the water/powder ratio, \(w/p\), at 28 days: (**a**) characteristic compressive strength,\(f_{\delta\delta}\); (**b**) average elasticity modulus, \(E_{cm}\).  
14.5% and 11.6%. Previous studies indicated porosity values for ordinary concretes between 10.4-20.2% [61; 62] and for concretes made with fine recycled concrete aggregates between 13.2-16.5%, being 17-46% higher than their reference concrete [63]. This allows us to conclude that the addition of industrial by-products and recycled fibers did not cause a significant increase in the open porosity, in the context of water absorption, and that the slight increase observed with respect to the reference mixture may be associated to the highly porous nature of EAFS aggregates.  
Figure 18b presents the test results of water absorption by immersion under vacuum, which indicates that the average open porosity of the EAFS8D1 mixture was 21.8%. Comparing the results obtained in vacuum with the values of open porosity obtained at atmospheric pressure, a higher porosity was obtained as expected. Prior studies showed results of water absorption by immersion under vacuum varying between 11.9-17.9% for ordinary concretes [61; 64] and between 9-13% for concretes with bottom ashes or fly ashes as cement replacement [65]. In the present case, the open porosity under vacuum showed slightly higher values, which can again be justified due to the porous nature of EAFS aggregates. The study of alkali-activated ceramic/slag-based mortar showed an open porosity under vacuum of 23.26% [66], which emphasizes that the use of porous materials leads to an open porosity under vacuum higher than other current materials.
# Simultaneous Transmission of Photonic Services over One Fiber with an ITU 100 GHz Grid
## 5 Conclusions  
This article introduces the concept of photonic service and its definition. To use photonic services, it is crucial to keep their parameters stable with regard to the reserved bandwidth across the spectrum throughout the optical path. The transmission of an accurate time and a stable frequency is often described and implemented separately, although these are special applications that have very specific requirements to be fulfilled. Next, we also presented our own sensor system based on the principle of phase OTDR, as well as a system for accurate time transmission. Both of these systems were used during the measurements. As the WDM principle has been known for several decades, we can expect an increase in the use of measuring the simultaneous transmission of photonic services in a single fiber. However, photonic service considerations are no longer only about the transmission of data signals or special applications. In the future, it will be necessary to take into account the deployment of sensor systems for critical infrastructure protection. Infrastructure protection is currently a highly sought after topic in the Czech Republic and worldwide, and with the falling prices of optical fiber sensors, this trend is likely to continue. Figure 5: Measurement results for G.655 (on the **left** side) and a combination of G.652D and G.6533 fibers (on the **right** side). Although the ITU grid defines the 100 GHz spacing between services as sufficient, our measurements show the exact opposite. The spacing between services without a sensor system is quite sufficient; however, with the use of high-power sensor systems, service interaction may occur. Our results also show that the influence among the services is also demonstrated by the type of optical fiber being used. While G.652D and G.655 fibers are considerably less susceptible to service interactions, if critical infrastructure is made up of G.653 fibers, higher service spacing should definitely be considered. Our future work will continue with another higher modulation format with data transmission (150G with DP-8 QAM and 200G with DP-8 QAM), and we will also address a stable frequency evaluation and a higher output power of a sensor system. Conceptualization P.M.; Investigation P.M.; Funding acquisition P.M. and J.V. ; Measurement T.H. and P.M.; Methodology P.M.; Project administration P.M.; Resources J.V. and V.S. ; Supervision P.M.; Validation P.M. and J.V. ; Writing--original draft T.H. and P.M. The presented research has been supported by the project of the Ministry of Interior under grant no. VI20152020045, \"E-infrastructure CESNET--modernisation\", registration no. CZ.02.1.01/0.0/0.0/16 013/0001797, and by the National Sustainability Program under grant LO1401. For the research, the infrastructure of the SIX Center was used. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:
## 5 Proposed mMTC Device Grouping Algorithms
### Reinforcement Learning-Based Device Grouping Algorithm
#### 5.2.2 Lagrange Relaxation
```
1:Inputs:
2:\(S=\{s_{1},s_{2},\dots,s_{k}\}\): State set representing device characteristics
3:\(A\): Action set for device grouping
4:\(\pi\): Policy function mapping \(S\) to \(A\)
5:\(C(s,a)\leq 0\): Constraints on states and actions
6:\(\lambda\): Lagrange multiplier
7: Initialize RL model parameters \(\theta\), policy \(\pi\), state set \(S\), action set \(A\), and \(\lambda\). 8:for each device in the network do
9: Observe state \(s\in S\). 10: Determine action \(a^{*}=\pi_{\theta}(s)\) for device grouping. 11: Execute \(a^{*}\), grouping the device accordingly. 12: Update \(S\) with new device groupings. 13:endfor
14:for each action \(a^{*}\) during grouping do
15: Evaluate grouping effectiveness and constraint satisfaction. 16: Calculate reward \(R(s,a)\) and penalty \(\lambda C(s,a)\). 17: Update \(\theta\) using the gradient of \(R(s,a)+\lambda C(s,a)\). 18: Adjust \(\lambda\) based on constraint violations. 19:endfor
20:Output: Optimized device groupings with minimal unallocated space, adhering to constraints. ```  
**Algorithm 1** Lagrangian RL for Device Grouping
# A Gradient-Based Cuckoo Search Algorithm for a Reservoir-Generation Scheduling Problem
## 5 Case Study
### Reservoir Scheduling in Normal Years  
In this section, we evaluate the performance of GCS in normal years. Streamflows of the Pingshan station in Figure 9 were chosen as the input of the LHGS model. The parameters and other conditions were the same as those in Section 5.3.  
As shown in Table 5, GCS gains more power generation than the other three methods. The average power generation obtained by GCS is 210.5, 220.5, 218.1 and 215.9 billion kWh in the four normal years. Compared with IHS, KGSA and CS, the average increases are 5.8%, 8.6% and 3.3%, respectively. The standard deviation obtained by GCS is also smaller than that obtained by the other three methods. Figure 10 shows the convergence process of the four algorithms in the case of the year 1997 as an example. It can be seen from Figure 10 that GCS has the fastest convergence speed. As in Section 5.3, KGSA does not perform well compared with the other methods.  
Figure 8: The optimal results obtained by GCS in wet years: (**a**) Wudongde; (**b**) Baihetan; (**c**) Xiluodu; and (**d**) Xiangjiaba.
# Multi-Parametric Analysis of Reliability and Energy Consumption in IoT: A Deep Learning Approach
## 5 Conclusions and Future Work  
In this work, machine learning is adopted to predict reliability in the form of \(PDR\), and \(EC\). A public dataset containing performance measurements for more than 48,000 combinations of different stack parameters' (including \(OF\), \(AQS\), \(AT\), \(IAT\), \(TP\), \(QS\), \(PS\), \(DT\), \(RD\), and \(MT\), etc.) configurations is used to evaluate the prediction accuracy of different regression models. It turned out that deep learning performed well enough to grasp the relationship between these parameters and target metrics, and achieved an accuracy of up to 98%. It strengthens the fact that deep learning has a significant potential for performance predictions in wireless scenarios (IEEE 802.15.4 in this case). Certain features like \(TP\), \(DT\), \(AT\), \(OF\), and \(PS\) contributed significantly in prediction accuracy. Because many features in predicting \(PDR\) and \(EC\) are common, it can be concluded that both \(PDR\) and \(EC\) should be jointly considered. With these prediction results, deep learning offers a more practical solution compared to the legacy NP-hard optimization problems. Moreover, the computational load is on the server-side, thus easing the resource-constrained user devices. This study also vitalizes the importance of deep learning in predicting other performance metrics, and to design a comprehensive QoS solution for WSNs and the IoT, which is difficult to realize using conventional mathematical approaches. In the future, the surge in this domain will be broadened by including parameters like MAC and routing protocols, number of nodes, topology, and interference, etc. Including more variables will potentially help the learning process in better understanding the relationship between those variables and performance metrics, thus potentially yielding even better prediction accuracy and more general solutions. The aim is to collect comprehensive datasets under diverse scenarios to extend research in this direction.  
[PERSON] proposed the main idea of research, performed the experimentation and wrote the manuscript. [PERSON] and [PERSON] guided with the design of experiment, provided critical feedback, and helped in shaping the research, presentation of the results and the manuscript. [PERSON] organized and refined the manuscript.  
This research was funded by the Research Grant of Kwangwoon University in 2018.  
The authors declare no conflict of interest.
# Quality Tests of Hybrid Joint-Clinching and Adhesive--Case Study
## 4 Conclusions  
The conducted tests confirmed that it is reasonable to perform hybrid joints for steel sheets and hybrid adhesives. The average shear force of adhesive joints was 476 N, while the average force for a sheet metal clinching joint was 965 N. The difference between these forces is almost 500 N. Significantly higher strength than the one found in the adhesive and clinching joints was obtained for the hybrid joint. For the specimens in which the clinching joint was developed immediately after the adhesive joint, the strength was 312% higher than for the adhesive joint and 154% higher for the clinching joint. The study showed that the timing of the clinching joint is important. Preferably, the joint should be developed immediately after the adhesive bond. This increases the mechanical strength by 37%. If the splices are made immediately after the adhesive bonds, the adhesive bond is much thinner. For the hybrid joints in which the clinching was performed after the adhesive joints were fully formed, the thickness of the adhesive was, on average, 322 um, while in those specimens in which the sheet metal clinching was performed right after the adhesive joints were formed, it is about eight times less and amounts to 40.6 um. The thickness of the adhesive affects the quality of the connection. Greater thickness reduces the quality of the connection. The proposed technology not only increases the strength of the bond but can also lead to a reduction in adhesive consumption. This is not only an economic aspect but also an ecological one. The ecological aspect results from both reduced adhesive consumption and the decreased weight of the finished product, which directly translates to the emission of harmful substances [34; 35]. In further research, it is planned to combine materials with different acoustic properties. Adhesives with low damping coefficients and aluminium sheets will be used.  
Conceptualisation, J.K.; methodology, J.K., W.M. and W.S.; software, K.S.; validation, J.K., W.M. and K.S.; formal analysis, J.K. and W.S.; investigation, J.K.; resources, J.K. and W.M.; data curation, J.K. and D.W.; writing--original draft preparation, J.K. and W.S.; writing--review and editing, J.K.; visualization, J.K. and D.W.; supervision, M.N.; project administration, K.S.; funding acquisition, K.S. and M.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Polish National Centre for Research and Development under the grant--decision no. DWP/TECHMATSTRATEG-III/136/2020.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Series 1** & **Series 2** & **Series 3** & **Series 4** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 4: Strength test results—shear force value (N).
# A Perspective on Ethernet in Automotive Communications--Current Status and Future Trends
## 6 Ongoing Developments
### Cut-through Forwarding  
The ongoing IEEE P802.1 DU project is a stand-alone document, separate from the IEEE 802.1Q, that defines Cut Through Forwarding (CTF) Bridges and CTF in Bridged Networks. The aim of the project is to provide connectivity in networks of mixed CTF and 802.1Q Bridges with CTF and Store-and-Forward (S&F) MACs.  
In CTF operation, frame transmission on a switch starts before frame reception has been completed. This creates some problem, for instance, how to handle frames with inconsistent Frame Check Sequence (FCS) due to errors. As the existing S&F switches conformant to IEEE 802, 802.1 AC, and 802.1Q standards could be non-conformant/incompatible with P802.1 DU, ongoing work is addressing the support of the IEEE 802 and 802.3 standards to the CTF operation and the conformance/compatibility with switches working in the S&F way.
## 3 Principal Component Analysis  
We consider a cloud \(C=\{P_{1},P_{2},\ldots,P_{N}\}\subset\mathbb{R}^{3}\) of \(N\) 3D points. We compute the mass point \(P\in\mathbb{R}^{3}\) and the covariant matrix \(M\in\mathbb{R}^{3\times 3}\),  
\[P=\frac{1}{N}\sum_{i=1}^{N}P_{i},\quad M_{jk}=\frac{1}{N}\sum_{i=1}^{N}(P_{i} -P)_{j}(P_{i}-T)_{k}.\]  
Since \(M\) is real-valued, symmetric, and positive definite, it has three positive eigenvalues,\[0<\lambda_{3}\leq\lambda_{2}\leq\lambda_{1}\]  
and the related eigenvectors can be formed into an orthonormal system \((v_{1},v_{2},v_{3})\), i.e., \(v_{i}\cdot v_{j}=\delta_{ij}\), where \(\delta_{ij}\) is the Dirac delta function. In fact, the vectors  
\[\widetilde{\lambda}_{1}\,v_{1},\ \widetilde{\lambda}_{2}\,v_{2},\ \widetilde{\lambda}_{3}\,v_{3},\ \text{where}\ \widetilde{\lambda}_{i}=2\,\sqrt{\lambda_{i}}, \tag{1}\]  
form semi-axes of in a sense best-fitted ellipsoid; see Figure 2.  
Notice that (1) is motivated by the fact that \(\lambda_{i}\), \(i=1,2,3\), are eigenvalues of the covariant matrix of the ellipsoidal point cloud of \(N=N_{1}\times N_{2}\) points  
\[P_{1}=\begin{pmatrix}\widetilde{\lambda}_{1}\,\sin\theta_{i_{1}}\,\cos\theta_ {i_{2}}\\ \widetilde{\lambda}_{2}\,\sin\theta_{i_{1}}\,\sin\theta_{i_{2}}\\ \widetilde{\lambda}_{3}\,\cos\theta_{i_{1}}\end{pmatrix},\]  
where \(\mathbf{i}=(i_{1},i_{2})\) is a multi-index; \(\theta_{i_{1}}=\frac{i_{1}}{N_{1}}\pi\), \(i_{1}=1,2,\ldots,N_{1}\); and \(\varphi_{i_{2}}=\frac{i_{2}}{N_{2}}2\pi\), \(i_{2}=1,2,\ldots,N_{2}\). For completeness, we recall that the eigenvalues are computed by [PERSON]'s formulae:  
\[\{\lambda_{1},\lambda_{2},\lambda_{3}\}=\bigg{\{}2\,r\,\cos\frac{\phi}{3}- \frac{a}{3},-2\,r\,\cos\frac{\phi\pm\pi}{3}-\frac{a}{3}\bigg{\}},\]  
where \(\phi=\arccos\left(-\frac{q}{2r^{3}}\right)\); \(r=-\frac{1}{3}\sqrt{-a^{2}+3b}\); \(q=-\frac{2a^{3}}{2r^{2}}+\frac{ab}{3}-c\); and \(a=\operatorname{tr}M=M_{11}+M_{22}+M_{33}=\lambda_{1}+\lambda_{2}+\lambda_{3}\), \(b=\frac{1}{2}\big{(}(\operatorname{tr}M)^{2}-\operatorname{tr}M^{2}\big{)}= \lambda_{1}\lambda_{2}+\lambda_{1}\lambda_{3}+\lambda_{2}\lambda_{3}\), and \(c=\det M=\lambda_{1}\lambda_{2}\lambda_{3}\) are the three principle matrix invariants.  
In the case of a dominant axis, \((1-\varepsilon)\widetilde{\lambda}_{1}>\widetilde{\lambda}_{2}\), where \(\varepsilon\in(0,1)\) is a relative tolerance, we divide the point cloud \(C\) into sub-clouds \(C_{1}\) and \(C_{2}\) by a cutting plane, which has the normal direction \(v_{1}\) and contains the following mass point:  
\[C_{1}=\{P_{i}\in C:\ (P_{i}-P)\cdot v_{1}\geq 0\},\quad C_{2}=C\setminus C_{1}.\]  
We apply PCA recursively to \(C_{1}\) and \(C_{2}\); see Figure 3. This way, we proceed and construct a subdivision binary tree. The vertices of the tree are associated with the length of the ellipsoid semi-axes (1). The leaves satisfy \(\widetilde{\lambda}_{1}\approx\widetilde{\lambda}_{2}\) and represent objects such as balls or squab cylinders. In Figure 4, we depict a subdivision of a point cloud and the related tree.  
Figure 2: (**a**) Model; (**b**) its point cloud and the axes of the best-fitted ellipsoid.