esa-sceva/satcom-chunk-collection huggingface dataset:
# Metal Additive Manufacturing for Satellites and Rockets
###### Abstract  
The emerging technology of 3D printing can not only be used for rapid prototyping, but will also play an important role in space exploration. Additive manufactured parts can be used in diverse space applications, such as magnetic shields, heat pipes, thrusters, etc. Three-dimensional printed parts offer reduced mass, high possible complexity, and fast printability of custom-made objects. On the other hand, materials which are not excessively damaged by the harsh conditions in space and are also printable by available technologies are not abundantly available. This review gives an overview of recent metal additive manufacturing technologies and their possible applications in space, with a focus on satellites and rockets, highlighting already applied technologies and materials and gives an outlook on possible future applications and challenges.  
p +
Footnote †: journal: Applied Sci. 2021  
1120366  
research studies and already applied. Finally, it summarizes the remaining challenges and indicates possible future applications.  
The paper is organized as follows. Starting with an overview of diverse metal additive manufacturing techniques, applications for microsatellites, such as thrusters, structural components, and antennas, are described together with a few other applications reported in the literature. Each section contains applications at the laboratory stage, where basic research is necessary to evaluate the potential for future applications, along with mature applications which are already used in actual satellites and rockets. Finally, future potential applications are described.
## 3 Results and Discussion
### Ethanol Production  
The ethanol production by \(\mathrm{POF^{-}}\) and \(\mathrm{POF^{+}}\) was relatively similar but pronounced in \(\mathrm{POF^{+}}\) within 24 h. After 72 h of active fermentation with \(\mathrm{POF^{-}}\), the ethanol yield was 4.0% (\(v/v\)), 3.9% (\(v/v\)), 3.8% (\(v/v\)), 3.6% (\(v/v\)), and 3.5% (\(v/v\)) in B100, R40B80, R60B40, R80B20, and R100, respectively. For \(\mathrm{POF^{+}}\), values of 4.1% (\(v/v\)), 3.7% (\(v/v\)), 3.7% (\(v/v\)), 3.6% (\(v/v\)), and 3.5% (\(v/v\)) were recorded in B100, R40B80, R60B40, R80B20, and R100, respectively. Substituting barley malt with Riceberry rice malt resulted in a low ethanol yield, unlike the use of unmalled rice as an adjunct, which increases ethanol yield. After 4 weeks of beer maturation, the alcohol content in B100, R40B60, and R60B40 was within 4.0-4.2% (\(v/v\)), and that in R80B20 and R100 was 3.6-3.8% (\(v/v\)), respectively, for both \(\mathrm{POF^{-}}\) and \(\mathrm{POF^{+}}\). In this study, beer of R100 had an ethanol content above 3.19-3.28% (\(v/v\)), which was reported in black rice beer by [PERSON] et al. [12] but was below the values (4.59-5.12% (\(v/v\))) reported by [PERSON] et al. [9] for malted rice beer.  
Figure 1: Dynamic of glucose and maltose during 72 h fermentation of composite wort: glucose metabolism by \(\mathrm{POF^{-}}\) (**A**) and \(\mathrm{POF^{+}}\) (**B**); maltose metabolism by \(\mathrm{POF^{-}}\) (**C**) and \(\mathrm{POF^{+}}\) (**D**). BRY 97 (_S. cerevisiae_), \(\mathrm{POF^{-}}\); and Belle Saison (_S. cerevisiae var. diastaticus_), \(\mathrm{POF^{+}}\).
## 3 Federated Learning and Blockchain: Brief Review
### Federated Learning
#### 3.1.1 Categories of Federated Learning  
In the literature, federated learning is categorized in two ways; one categorization is based on how the data are distributed [18], and the other is based on the network architecture [12]. The three data distribution categories are as follows:  
* Horizontal federated learning: Where the datasets have the exact same features but varying samples.
* Vertical federated learning: Where the sample space is the same, but the features are different.
* Federated transfer learning: Starts from a pre-trained model where the overlap of the samples space and features space is less.  
Based on how the devices are connected in the FL environment, it can be further classified as one of the following approaches:  
* Centralized approach: Where a global central model is updated by aggregating the clients' training parameters. This approach applies protocols to avoid malicious alien participation
* Decentralized approach: Where the clients' complete reliance on their neighbours to update the model removes the central authority. This approach requires absolute trust among clients.
# A Simple Convolutional Neural Network with Rule Extraction
## 3 Proposed Model
### The Interpretable CNN Architecture
Hence, given a sample \(S_{k}\) covered by \(R_{i}\) and its set of n-grams \(H\), each discriminative n-gram related to antecedent \(A_{ij}\) is found by characterizing the  
Figure 6: Flow of data in the interpretable CNN (see the text for more details). n-gram (in \(H\)) activating neuron \(m_{j}\) the most. Given \(R_{i}\) and \(S_{k}\), the following algorithm generates discriminative n-grams:  
1. \(\Gamma=\emptyset\)
2. For all \(A_{ij}\subset R_{i}\) loop (w.r.t variable \(j\))
3. \(G_{j}=\left\{g\in G\ \bigcap\ H\ \mid\ A_{ij}\text{ is true }\right\}\)
4. \(F_{j}=\left\{x\in\Re^{q}\ \mid\ q=\left|G_{j}\right|,x=act(G_{j})\text{ for neuron }m_{j}\right\}\)
5. \(f_{j}^{*}=\ \max(F_{j})\)
6. \(g_{j}^{*}=\left\{g\in G_{j}\ \mid\ f_{j}^{*}=act(g)\text{ for neuron }m_{j}\right\}\)
7. \(\Gamma=\Gamma\ \cup\ g_{j}^{*}\)
8. end loop  
For clarity let us explain the different steps of the algorithm specified above. First, its result will be stored in \(\Gamma\), which is initialized as an empty set. Secondly, a main loop that iterates on all rule antecedents is defined. Subsequently, in step three, for each rule antecedent \(A_{ij}\) we determine the n-grams of \(S_{k}\) (a sample) that makes \(A_{ij}\) true. Then, for each antecedent \(A_{ij}\) the purpose is to determine the n-gram involving the highest activation of neuron \(m_{j}\) in the M layer (steps four to six). For instance, with the antecedent \(f_{113}\geq 0.24\) of a particular rule generated from an interpretable CNN and a sentence given as \"a real movie, about real people, that gives us a rare glimpse into a culture most of us don't know\", four trigrams makes the antecedent true:  
1. \"culture most of\" (0.251715)
2. \"rare glimpse into\" (0.257523)
3. \"into a culture\" (0.273310)
4. \"us a rare\" (0.311517)  
the number after each trigram is the activation value of the 113 th neuron in the M layer. The fourth trigram provides the highest activation, hence it is the detected trigram. Generally, it is plausible to obtain a global view of each extracted rule by characterizing for each antecedent its winning n-grams with respect to all the covered samples. For a given rule antecedent and a given sample, it is useful to determine the linear contribution of each winning n-gram \(g_{j}^{*}\), with respect to the final classification. Specifically, this measure corresponds to the product of the filter activation in the M-layer multiplied by the weight connecting the neuron of highest activation in the output layer. The output neuron of strongest activation indicating the class, a positive value of this measure means that the n-gram contributes in favor of the classification, while a negative value is against it. The linear contribution is given as a function:  
\[LC(g_{j}^{*})=v_{k}\cdot act(g_{j}^{*}); \tag{12}\]  
with \(\text{act}(g_{j}^{*})\) designating the activation of a neuron \(m_{k}\) in the M layer and \(v_{k}\) corresponding to a weight between \(m_{k}\) and the output layer (the one related to the neuron of highest activation).
# Research on Energy Storage Optimization Operation Schedule in an Island System
## 2 Problem Formulation
### PSS Model  
In Equation (7), \(P_{ln}\) represents the output power of the battery. The values of discharge power of the battery \(P_{Bd}\) is positive, while the charging \(P_{Bc}\) is negative. In addition, the discharging efficiency \(\eta_{Ind}\) and the charging efficiency \(\eta_{Inc}\) are included.  
\[P_{ln}(t)=\eta_{Ind}P_{Bd}(t)+P_{Bc}(t)/\eta_{Inc} \tag{7}\]  
The battery's state-of-charge (SOC) is shown in Equation (8) and describes the relationship between the charging and discharging power of the battery. The inequality in (9) ensures that the SOC of the battery is within the minimum and maximum limits of the SOC. Finally, with \(B_{d}\) and \(B_{c}\) as the binary variables for the discharging and charging state of the battery, respectively, Equation (10) should be satisfied to ensure that the battery does not discharge and charge at the same time.  
\[SOC(t)=SOC(t-1)-\delta_{i}(\eta_{Ind}P_{Bd}(t)+P_{Bc}(t)/\eta_{Inc}) \tag{8}\]  
\[SOC_{min}\leq SOC(t)\leq SOC_{max} \tag{9}\]  
\[B_{d}(t)+B_{c}(t)\leq 1 \tag{10}\]In Inequalities (11) and (12), the power limitation of the charging and discharging power of the battery is formulated. \(P_{Bd\_max}\) and \(P_{Bc\_max}\) are the maximum values of battery discharging and charging power, respectively.  
\[0\leq P_{Bd}(t)\leq P_{Bd\_max}B_{d}(t) \tag{11}\]  
\[-P_{Bc\_max}B_{c}(t)\leq P_{Bc}(t)\leq 0 \tag{12}\]
###### Abstract  
Life cycle assessment (LCA) is a methodology used to analyze the environmental impacts of a product. Initially, it was applied to buildings only, but recently, it has also been applied to entire neighborhood. This expansion from individual buildings to the neighborhood scale requires additional inputs, such as the impacts of public spaces, transportation, public network systems, and roads. Additionally, conducting an LCA for a neighborhood requires software tools to simulate the neighborhood and databases to store the environmental impacts of all the components integrated into the neighborhood. This paper presents an LCA case study of a neighborhood in Dijon, France. The study aims to analyze the thermal impacts of buildings and the neighborhood with single-, double-, and triple-pane windows. The second part of the study involves two LCA studies using the 1996 version of ecinvent. The first study is a continuation of the thermal simulation analysis, while the second study compares concrete, masonry, and timber-based construction designs for the neighborhoods. The results show that the heating demands inside the buildings are substantially reduced when transitioning from single- to triple-pane, while the cooling demands show the opposite effect. Furthermore, doubling the width of double-pane windows resulted in less profound environmental damage for the construction, use, and demolition phases compared to single-pane windows, with only the renovation phase being more damaging. Additionally, the comparison between different construction scenarios shows that the timber-based variant is slightly advantageous from an environmental point of view compared to the concrete and masonry designs.  
life cycle assessment; environmental impacts; neighborhood; sustainability; construction scenarios +
Footnote †: journal: applied sciences  
0000-0001-9993-3227][PERSON]  
1 Builders Lab, Builders Ecole d'ingenieurs, COMUE Normandie Universite, 1 Rue Pierre et Marie Curie, 14610 Epron, France 1
# Anomaly Monitoring of Process Based on Recurrent Timeliness Rules (AMP-RTR)
## 4 Experimental Results
### Summary of the Experiment  
Since the AMP-RTR model needs to process each generated data in turn, the time complexity of the model is \(0(n)\). In addition, the spatial complexity of the model is mainly divided into two parts: one is to store the timeliness score value after each iteration update, and the spatial complexity of this part is \(0(n)\); the other part uses \(R(A)\) to record the times greater than the set threshold \(N(A)\), and the spatial complexity of this part is \(0(1)\), so the spatial complexity of this model should be \(0(n)+0(1)=0(n)\), where \(n\) represents the size of the data volume.  
In summary, we tested the robustness of the AMP-RTR model by performing 10 sets of simulation experiments on a synthetic dataset; we then performed four sets of simulation experiments on the semiconductor dataset to verify the applicability of the AMP-RTR model; finally, we performed a set 15 simulation experiments by splitting the original semiconductor dataset into four, validating the performance of the AMP-RTR model when accounting for multivariate properties.
# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
## 5 Performance Evaluation
### Impacts of Parameters
#### 5.1.2 Impacts of \(\gamma\)  
Similarly, the impacts of \(\gamma\) is shown in Table 9. In this experiment, we set the value of \(\alpha\) to 0.5. As can be observed, the network lifetime gradually decreases when \(\gamma\) increases. This is because \(\gamma\) is the weight of the predicted maximal Q-value in the future. The greater the \(\gamma\), the more importantly the future prediction information contributes to the agent's action. When \(\gamma\) is significantly small, the role of the future prediction (i.e., \(Q_{max}\)) in the Q-value is minor. Increasing \(\gamma\) helps agents exploit more future information in making action decisions, thus improving the decision's goodness, thereby extending the network lifetime. However, when \(\gamma\) is significantly large, e.g., more than 0.6, increasing \(\gamma\) will eliminate the impacts of the current Q-value in making a decision. In other words, the agent tends to ignore all experiences learned so far and relies primarily on the future prediction. As the future prediction does not entirely correct, the performance of the Fuzzy Q-learning downgrades severely. From the experiment results, the optimal value of \(\gamma\) is from 0.4 to 0.5.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Factor** & **Value** \\ \hline \(\lambda\) & 4.32 \(\times\) 10\({}^{-4}\) \\ \(\beta\) & 0.2316 \\ Initial energy of the MC & 100 J \\ Battery capacity of MC & 500 J \\ The velocity of the MC & 5 m/s \\ Initial energy of sensors & 10 J \\ Battery capacity of sensors & 10 J \\ \(E_{th}\) & 4 J \\ Sensing range & 7.5 m \\ Transmission range & 15 m \\ Number of sensors & 200–400 \\ Number of targets & 100–300 \\ Per second packet generation probability & 0.05–0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 7: System parameters.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \(\alpha\) & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** & **0.8** \\ \hline Network lifetime (10\({}^{3}\) s) & 235.042 & 246.601 & 246.268 & 243.493 & 243.719 & 244.183 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The impact of \(\alpha\) on the network lifetime.
## 3 System Model
### User Connection Model  
In the multi-beam satellite communication system, multiple beams can be used to serve one user's packets. Therefore, the traditional two-dimensional resource grid cannot accurately describe the characteristics of multi-satellite and multi-beam connection with multiple connection modes, which leads to an increase in the complexity of resource allocation.  
In the multi-satellite multi-beam cooperative communication system, there are mainly four connection models between satellites and users, as shown at the time \(T_{1}\) in Figure 1. The first model is the single-satellite single-beam connection model, where the user \(u_{4}\) is only connected to a beam of the satellite \(s_{3}\). The second model is that the user connects to multiple beams of one satellite. The user \(u_{1}\) connects to two beams of the satellite \(s_{3}\). Another model is the multi-satellite single-beam connection model, where the user \(u_{2}\) connects to a beam of the satellite \(s_{3}\) and a beam of the satellite \(s_{2}\). The last model is the connection between the user and multi-satellite multi-beam. The user \(u_{3}\) is connected to a beam of the satellite \(s_{1}\) and two beams of the satellite \(s_{2}\) at the same time. In this case, the users' packets are transmitted simultaneously by a beam of the satellite \(s_{1}\) and two beams of the satellite \(s_{2}\) according to the result of resource allocation.  
Figure 2: Packet transmission process.
# Isoprenoids Production from Lipid-Extracted Microalgal Biomass Residues Using Engineered E. coli
## 2 Results and Discussion  
### Chemical Composition of LMBRs  
As is shown in Figure 1, in a given 100 g amount of LMBRs, using appropriate quantification methods, there are about 18.03 g of holocellulose, 48.12 g of protein, 24.35 g of ash, 0.28 g of lipid and 9.22 g of water. Compared to the total lipid content (30-36 g) of microalgae in the previous study [11], this suggestes that the lipid in LMBRs has been almost completely extracted. What is more encouraging is that no lignin was found, as this represents most unfavorable threat to lignocellulosic hydrolysis rejection, and therefore it must be removed prior to hydrolysis [12]. As we all know, to get rid of lignin during the process of pretreatment is rather costly and time-consuming as well. Hence, compared with lignocellulosic (rice straw, switchgrass, etc.), LMBRs (with its absence of lignin) is considered an ideal alternative as the biofuel-producing material [13].  
Figure 1: Chemical composition content of 100 g lipid extracted microalgae biomass.
# Sorghum Flour: A Valuable Ingredient for Bakery Industry?
## 3 Results and Discussion
### _Bread Quality_  
The six bread samples obtained from the mixtures of wheat and sorghum flowers with and without _L. plantarum_ and were analyzed compared to the control/trial sample of bread obtained only from wheat flour type 550 (ash = 0.55%), (Figure 6). The coding of the experimental bread samples was presented in Table 1. As it can be seen in Table 7, the porosity values of the bread samples obtained from the variants of mixtures are within limits of SR 878/1996 [38] (minimum 66%). The acidity of the bread samples does not exceed 1.6 degrees of acidity which is a value within the normal limits for wheat bread (max. 3.3%, SR 878-1996) [38]. The elasticity of the bread samples obtained from the flour mixtures was close, but significantly lower than that of the control sample [38].  
Figure 5: The influence of substitution level of sorghum flour on the Mixolab curves.  
Figure 6: Bread samples (**(a)** P1 = 70% wheat + 30% sorghum without _L. plantarum_; **(b)** P2 = 70% wheat + 30% sorghum with _L. plantarum_; **(c)** 65% wheat + 35% sorghum without _L. plantarum_; **(d)** P4 = 65% wheat + 35% sorghum with _L. plantarum_; **(e)** P5 = 65% wheat + 35% sorghum without _L. plantarum_; **(f)** P6 = 65% wheat + 35% sorghum with _L. plantarum_; **(g)** P7 = 100% wheat flour (type 550)–control sample).  
It can be seen in Table 7 that the porosity decreases with increasing degree of sorghum replacement (porosity values for P1 (75.7 \(\pm\) 0.45), P3 (71.8 \(\pm\) 0.35), P5 (68.6 \(\pm\) 0.55)). On the other hand, there is a negative influence of the addition of L. plantarum on the porosity, decreasing from P1 (75.7%) to P2 (71.3%), respectively P3 (71.8%) to P4 (66.7%), or from 68.6% (P5) to 64% (P6). In all cases of addition of sorghum, respectively _L. plantarum_, the porosity value was lower than that of the control sample (P7--77.8%). The same effects of adding sorghum can be seen on the elasticity since the value obtained in the case of the P7 control sample was considerably higher than all samples, with a value of 97%. The acidity was significantly influenced by the presence of _L. plantarum_, the values determined in samples P2 (1.2), P4 (1.2), and P6 (1.6) being significantly higher than the acidity of samples P1 (0.6), P3 (0.4), P5 (0.8), and P7 (1), respectively. The acidity of bread samples with the addition of _L. plantarum_ was much higher than that of samples without _L. plantarum_, however within normal limits according to the Romanian standards in force (SR 878:1996). Taking into account these elements, we can conclude that the P4 sample ensures a balance between the benefits brought by a high intake of sorghum and _L. plantarum_, respectively the reduction of the organoleptic characteristics of the samples.
## 2 Materials and Methods
### Pilot-Scale Pulsed Electric Field System  
The PEF system for seed treatment consists of a pulse generator, high-voltage (HV) power supply, HV switch, control PC, treatment chamber, and a conveyor. The treatmentchamber is equipped with two parallel steel electrodes, commonly referred to as parallel plates. The gap between these plates is adjusted according to the size of the seeds being processed. Once the electric field parameters--frequency, intensity, and treatment time--are configured, the seeds are introduced into the treatment chamber and exposed to the electric field at the specified dose and duration as they pass between the parallel plates along the vertical axis, driven by gravity after the system is activated. The electrode spacing is optimized to ensure effective electric field exposure, while a UV lamp positioned between the electrodes ionizes the surrounding air [13; 17; 18].  
In the pilot-scale PEF system, tomato seeds were treated with pulsed electric fields at frequencies of 110, 140, 160, and 180 Hz, with energy levels ranging from 1.07 to 17.28 J. The specific treatment durations and energy inputs are provided in Table 1.
# Face Stability Analysis for Tunnels under Steady Unsaturated Seepage and Inhomogeneity Conditions
## 6 Conclusions  
In this paper, a model of steady unsaturated seepage is integrated with an inhomogeneous soil model, the internal work rate and external work rate are calculated based on a log-spiral collapse model, and kinematic methods and strength reduction techniques are used to obtain the safety factor, which is finally obtained by an iterative method in order to find zero support pressure. Eventually, a framework is established for analyzing the face stability of a tunnel under conditions of steady unsaturated seepage and soil inhomogeneity. Under the conditions explored in this paper, the main findings are as follows:  
1. Under steady unsaturated seepage conditions, the flow rate should be stable. However, this paper compares steady seepage at different flow rates and found that tunnel face stability in clay is sensitive to changes in flow rate; the remaining soil types, i.e., silt, loess, and sand, are virtually unaffected by flow rate changes.
2. In most cases, tunnel faces in clay have the highest safety factor, followed by silt, while loess is third and sand has the lowest safety factor. It is worth mentioning that tunnel faces in clay have a lower safety factor than those in silt only in the case of high infiltration.
3. The influence of the cover-to-diameter ratio C/D of the tunnel on the stability of the tunnel face in various soil types is as follows: clay \(>\) silt \(>\) loess \(>\) sand.
4. The soil's inhomogeneity significantly affects the stability of the tunnel face. Different types of soils have different unsaturated seepage conditions, and coupled with the variation of flow rate, these also affect the stability of the tunnel face. Consideration of steady unsaturated seepage and inhomogeneity conditions in the design can effectively improve the stability of the tunnel face.  
This paper provides a methodology to analyze tunnel face stability in the conditions of steady unsaturated seepage and soils with conditions of inhomogeneity, however, the present work ignores many details, such as the effect of saturation, the inhomogeneous variations in layered soils, seismic effects, overconsolidation of soils, etc.; these conditions also deserve to be considered.  
Methodology, Y.X.; resources, D.Z. and H.L.; date curation, Y.X.; writing--original draft, Y.X.; writing--review and editing, D.Z. and H.L.; supervision, D.Z. and H.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data analyzed are available from the corresponding author on reasonable request. The data are not publicly available due to reasons of privacy.  
The authors declare no conflicts of interest.