esa-sceva/satcom-chunk-collection huggingface dataset:
# Production and Characterization of Cu/CNT Nanocomposites
## 3 Results and Discussion
### Deformation Behavior during Cold Rolling
Since the average hardness values shown in Figure 13a were obtained randomly on all the sample surfaces, it does not fully reflect the possible heterogeneities in the deformed sample. For that reason, hardness profiles were obtained along the cross-section (normal to the rolling direction) of the samples rolled at 1.61 strain to understand the difference in results between the samples. Figure 16 shows the results for Cu and Cu/CNT samples. The hardness distribution, from the surface to the center, differs for the nanocomposite and the matrix at a strain of 1.61. A non-uniform distribution of deformation across the sample was observed for the nanocomposites. The presence of the CNTs and even the slight difference in texture evolution originate different stress states, which will cause heterogeneities and differences in the deformation of the samples. Previous work ([PERSON] et al., 2018) on the deformation behavior of Ni and Ni/CNTs has shown that the CNTs influence the hardness and texture evolution of nanocomposites. The Ni/CNT nanocomposites softened at minor strains (0.11 and 0.22), which was attributed to the Bauschinger effect. For larger strains, different textures were observed for Ni and Ni/CNTs,which was explained by the initial crystallographic orientations and the presence of CNTs in the nanocomposites. Figure 14: (**a**) Estimated GND density and (**b,c**) GND maps of the Cu and Cu/CNT samples, as-sintered and cold rolled to strains up to 0.11, 0.69, and 1.61, respectively. A higher magnification detailed is present for the higher strains. Figure 16: Hardness profiles of Cu and Cu/CNT samples after cold rolling to a strain of 1.61. Figure 15: ODFs of Cu and Cu/CNT samples, as-sintered and after cold rolling to strains of 0.11, 0.69 and 1.61, respectively. In these ODFs, the main possible texture components are marked.
## 2 Related Work
### TLS for IoT Networks and Cipher Suites  
Many IoT systems use TCP/IP communications, whose current best security alternative consists in using TLS [32].  
TLS is a standard protocol composed by the TLS Record Protocol and the TLS Handshake Protocol:  
* TLS Record Protocol provides connection privacy and reliability by using symmetric cryptography (e.g., Advanced Encryption Standard (AES) and RC4) and hash functions (e.g., SHA-1).
* TLS Handshake Protocol enables server and client authentication and helps to determine the encryption algorithm and the cryptographic keys used by the application protocol.  
Therefore, the TLS Record Protocol is responsible for securing the connection after the TLS Handshake Protocol establishes the parameters of the TLS session. The procedure is conditioned by the used cipher suite, whose name indicates the involved algorithms. For instance, the cipher suite ECDHE-RSA-AES128-GCM-SHA256 makes use of ECDHE-RSA for the key-exchange, AES128-GCM as block cipher and SHA256 as the hash function that preserves the integrity of the handshake messages.  
RSA and Elliptic Curve Diffie-Hellman Ephemeral (ECDHE) are the most popular cipher suites recommended for TLS [15]. The cipher suites based on RSA make use of it for the key-exchange. In contrast, ECDHE-based cipher suites use Ephemeral Diffie-Hellman based on Elliptic Curves for the same purpose.
The equilibrium cycle analysis is a quick methodology to compare the cycle performance of various working pairs and useful for the working pair selection. An important parameter that is sometimes overlooked in such analyzes or not paid proper attention to is the effect of thermal mass. [PERSON] et al. [28] have recently pointed out that the thermal masses of individual components can considerably influence many adsorption heat pump systems' performance and should be carefully evaluated. [PERSON] et al. [28] utilized three metrics factors: mass ratio, specific thermal mass, and effective specific heat to discuss the influence on the system performance. Given the complexity of the actual adsorption system, it is rather difficult to determine the exact value of the thermal masses for the adsorbent and heat exchanger materials. Nevertheless, the adsorption system's performance with respect to the thermal mass ratio (adsorbent to heat exchanger mass) will be an important parameter for the working pair selection. Here, we attempted an improved equilibrium cycle analysis considering the thermal mass impacts to comprehensively estimate the performance of selected activated carbon/refrigerant pairs. MSC-30, formerly known as Maxsorb III, is well defined and widely accepted as the high-performance activated carbon; however, the amount of research data and information available about this material's performance with various refrigerants is still limited. Therefore, we performed a detailed measurement of MSC-30/R1234 yf pair's isotherms by the constant-volume-variable-pressure (CVVP) method at different temperatures to obtain the necessary data for the performance analysis. For comparison's sake, we adopted the isotherm data of MSC-30 with R134a from [PERSON] [29]. Using this working pair, [PERSON] [29] reported an experimental and theoretical study of the high-pressure adsorption chiller. MSC-30 and ACF A20 were selected as adsorbents, while R134a, R410a, and R507A were the adsorbates. Here, we focus on investigating the potential of MSC-30/R1234 yf for adsorption heating applications comparing with the MSC-30/R134a pair. It was noted that R1234 yf is considered a direct replacement for R134a for the automobile air-conditioning system. Thus, isotherm and performance data will be useful for the hybrid AC (mechanical+ adsorption) systems using a single refrigerant, unlike the work of [PERSON] et al. [18]. For the purpose of the energy performance analysis, we re-fitted the isotherm experimental data from our measurements and the data from [PERSON] [29] by the adsorption isotherm models of Type I and critically examined their suitability. We followed the conventional approach as well and used the more common models ([PERSON] model, Langmuir model) in contrast with more developed models such as the Toth model, D-R model, and D-A model.
## 3 Auto-Adaptive Multi-Objective Task Selection Approach
### Connectivity Utility  
Connectivity utility computes, optimistically, the connectivity level present in a location at a certain moment. The _connectivity_ utility function \(\Omega_{i}:T\times R^{M}\times S^{m\times n}\rightarrow[0,1]\) is defined as follows:  
\[\Omega_{i}(T_{j},R,E_{known})=\frac{log_{2}\left(\left(2^{\rho}-1\right)\cdot \frac{\left|\mathcal{N}_{i}(E_{known}(t),t)\right|}{M-1}+1\right)}{\rho} \tag{9}\]  
s.t.  
\[1\leq i\leq M=|R|,1\leq j\leq N=|T|\]  
Given the current state of the fleet \(R\) and the current environment knowledge \(E_{known}\), the function \(\Omega_{i}\) estimates the connectivity utility obtained by a robot \(R_{i}\) in case of selecting the task \(T_{j}\). Particularly, it is interesting to do so concerning the arrival time to \(T_{j}\). The current fleet state refers to both the location of the assigned tasks in case of assigned robots and the robot positions otherwise. The parameter \(\rho\) works as a shaping factor that could be used to tune the relation between connectivity level and utility. Note that the utility is decreasing in the number of robots, and may favour the adoption of MANET compliant connectivity techniques. In such networks, messages travel from source to destination members in more than one hop, where intermediate nodes forward messages until the destination is reached. The shape and usefulness of the \(\Omega\) function may be appreciated in Figure 6.
## 4 BER Computational Strategies for Decision Unit--Background Tutorial
### Tail Extrapolation Scheme  
We should keep in mind that the BER estimation obstacle, in essence, is a numerical integration problem if we regard the eye diagram (ED) in Figure 5, measured for an experimental system with SNR = 20 dB. It is possible to determine the worst case of the received bit sequence.  
Figure 5: Probability density function (PDF) tails obtained from an eye diagram (ED).  
When we regard the PDF of the eye section in lines A and B, the lower bound on the PDF (green line) is the worst-case bit sequence, and the small red area contains all of the bit errors. The BER of the given system can be thought of as the area under the tail of the probability density function.  
Generally, we could not depict the sort of distribution to which the slopes of the bathtub curve in ED belong. However, we may presume that the PDF file is affiliated with a specific class and then accomplish curve-fitting on the obtained data. That technique for estimating the bit error rate (BER) is known as the tail extrapolation (TE) method [95].  
When we set multiple thresholds for the lower bound, the number of times the decision metric surpasses every \(D_{th}\) is recorded, and a standard MC simulation can be executed. A wide category of PDFs is then detected. The tail region is typically identified by certain members of the Generalised Exponential Class (GEC) and is identified as follows:  
\[f_{v,\sigma,\ \mu}(x)=\frac{v}{2\,\sqrt{2}\ \Gamma(\frac{1}{p})}\ e^{-[\frac{x- \mu}{\sqrt{2}\,\sigma}]^{v}}\] (10a) where \[\Gamma(\cdot)\] is the gamma function, \[\mu\] is the mean of the distribution, and \[\sigma\] is related to the variance \[V_{v}\] through \[V_{v}=\frac{2\,\sigma^{2}\ \Gamma(\frac{3}{p})}{\Gamma(\frac{1}{p})}\] (10b) where the parameters ( \[v,\ \sigma,\ \mu\] ) are then adjusted to find the PDF that best fits the data sample; therefore, the BER could be estimated via the integral evaluation of the PDF for \[D_{th}\]. Nevertheless, which class of PDF and which \[D_{th}\] should be selected is not frequently clear. Generally, it is hard to evaluate the estimated BER accuracy [95].
# Intelligently Counting Agricultural Pests by Integrating SAM with FamNet
## 3 Experimental Results
### Evaluation Experiment on Different CAPM Architectures  
To evaluate the effectiveness of using SAM as an auxiliary tool for FamNet in this study, FamNet was used as the baseline model, and two sets of systems proposed in this paper were tested. System 1 involved the basic system that directly performed segmentation and used the TopK threshold matching approach. System 2 stored all detection results as historical knowledge vectors for the next round of predictions. System 3 stored the detection result with the highest accuracy as a historical knowledge vector for the next round of predictions. System 4 stored the detection result with the highest accuracy as a historical knowledge vector for the next round of predictions and included a forgetting mechanism.  
In the performance evaluation of CPM, this study found that storing the detection results as vectors for the next prediction as prompt information improved the accuracy to some extent. However, since FamNet is a few-shot model, it may not require excessive prompts. Too many prompts can actually lead to a decrease in performance. In System 3, when multiple instances were selected using TopK and matched based on a threshold, adding multiple prompts at once led to a faster decline in performance. In System 4, although a forgetting mechanism was added, it actually resulted in a decrease in performance. The study speculates that this is because the simple forgetting mechanism caused the system to forget the correct prompt knowledge and resulted in a higher proportion of incorrect prompts. The specific experimental results are shown in Table 2 and Figure 8. To provide a comprehensive view of the experiments conducted in this study, additional information regarding experimental visualizations can be found in the Appendix A.  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **MAE** \\ \cline{2-3}  & **0 Epoch** & **5 Epoch** & **25 Epoch** \\ \hline SAM + Matching based on threshold + FamNet & 4.21 & 4.21 \\ SAM + Matching based on threshold + Prompt & 4.21 & 2.67 & 11.64 \\ SAM + Matching based on threshold + Prompt & 4.21 & 3.90 & 4.46 \\ \multicolumn{3}{l}{based on Top + FamNet} \\ SAM + Matching based on threshold + Prompt & 4.21 & 2.67 & 3.65 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative experimental results between four different architectures.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & \multicolumn{3}{c}{**MAE**} \\ \cline{2-3}  & **0 Epoch** & **5 Epoch** & **25 Epoch** \\ \hline SAM + Matching based on threshold + FamNet & 4.21 & 4.21 & 4.21 \\ SAM + Matching based on threshold + Prompt & 4.21 & 2.67 & 11.64 \\ SAM + Matching based on threshold + Prompt & 4.21 & 3.90 & 4.46 \\ \multicolumn{3}{l}{based on Top + FamNet} \\ SAM + Matching based on threshold + Prompt & 4.21 & 2.67 & 3.65 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative experimental results between three different architectures.
# Explainable Artificial Intelligence Approach for Improving Head-Mounted Fault Display Systems
## 2 Explicative Head-Mounted Fault Display System Design
### Fault Diagnoser Model Computation
#### 2.3.1 LSTM Diagnoser Model  
Figure 3 provides a basic diagram of the structure of a long short-term memory (LSTM) unit. At first glance, the diagram may seem complex. However, focusing on the inputs and outputs clarifies its function. The network has three primary inputs: \(x_{t}\), the input at the current time step; \(h_{t-1}\), the output from the previous LSTM unit; and \(c_{t-1}\), the \"memory\" from the previous unit, which is arguably the most crucial input. Regarding outputs, \(h_{t}\) is the current unit's output, while \(c_{t}\) represents the current memory state. Therefore, for instance, for one single unit, the model decides by considering the current input, previous output, and previous memory. It generates a new output and alters its memory. The performed computation follows the following equations:  
\[\begin{split} f_{t}&=\sigma_{c}(W_{f}\times x_{t}+ U_{f}\times h_{t-1}+b_{f})\\ i_{t}&=\sigma_{g}(W_{i}\times x_{t}+U_{i}\times h_{t -1}+b_{i})\\ h_{t}^{\prime}&=\sigma_{c}(W_{h}\times x_{t}+U_{h} \times h_{t-1}+b_{h})\\ c_{t}^{\prime}&=\sigma_{c}(W_{c}\times x_{t}+U_{c} \times h_{t-1}+b_{c})\\ c_{t}&=f_{t}c_{t-1}+i_{t}c_{t}^{\prime}\\ h_{t}&=h_{t}^{\prime}\sigma_{c}(c_{t})\end{split} \tag{1}\]  
where \(\sigma_{g}\) and \(\sigma_{c}\) are, respectively, a sigmoid and an activation function \"_tanh_\". Let us notice that the repeated module structure can vary depending on the application as argued into [35] and the reference herein. Then, one has a model considering \(x_{t}^{n}\) training inputs, where \(n=\{1,2,\ldots,n_{x}\}\), with different past memories of order \(q=\{1,2,\ldots,n_{q}\}\). Furthermore, several key points should be noted from the equations:  
* These equations apply to a single time step only. They need to be recalculated for each subsequent time step.  
* The weight matrices (\(W_{f}\), \(W_{i}\), \(W_{o}\), \(W_{c}\), \(U_{f}\), \(U_{i}\), \(U_{h}\), \(U_{c}\)) and biases (\(b_{f}\), \(b_{i}\), \(b_{h}\), \(b_{c}\)) are fixed and do not vary with time. This implies that the same weight matrices are used across different time steps to compute the outputs. The core concept of an LSTM network is to learn a standard representation of input data that allows for the reconstruction of the original input with minimal information loss. This is particularly useful for time series data, as LSTMs can capture temporal dependencies. Additionally, LSTM models are adept at modeling long-term dependencies and memory in time series data, making them well suited for tasks such as anomaly detection in dynamic systems like wind energy. Notably, studies such as [9; 10] have highlighted the effectiveness of the LSTM model in capturing long-term dependencies by selectively updating and retrieving information, thus integrating past behavior into the diagnostic model to predict future conditions (normal or abnormal).
# Statistical Power Analysis in Reliability Demonstration Testing: The Probability of Test Success
## 1 Introduction  
Expenditures play an essential role in planning reliability demonstration tests. Resources are finite, and must be used in the most efficient way. Reliability demonstration tests should therefore use expenditures in such a way that they have the greatest effect, that is, the maximum promise of successful reliability demonstration.
# A Low Power IoT Sensor Node Architecture for Waste Management Within Smart Cities Context
## 3 Related Works
Such a node is mainly made up of the following components: an ultrasonic sensor and a temperature sensor (and their front-end electronics), a microcontroller, a GSM/GPRS module and a 14,000 mAh battery. The authors estimated the sensor node lifetime as 691 days by actuating a similar policy compared with the one that will be described in Section 5.1: all the elements are turned on only when necessary apart from the microcontroller which operates in a sleep mode when it is idle. That figure is higher than the one that will be found out in Section 5.2 due to the usage of a battery having a far bigger capacity than the one employed in the solution we are proposing. Unfortunately, suchlike battery was not available during the realization of the sensor node we are showing, otherwise its lifetime could have roughly been four time longer. The study [61] reports a wearable sensor node for indoor environmental monitoring whose aim is to supervise the level of harmful gases within industrial scenarios. That node is formed by the following main building blocks: a microcontroller, a temperature and humidity sensor and a CO\({}_{2}\) concentration sensor (along with their conditioning electronics), a ZigBee module, a couple of voltage regulator and a 800 mAh battery. The wearable node lifetime estimate is slightly more than 5 days. This result is pretty scant with respect to the one of the node we are describing (see Section 5.2) principally owing to two reasons: since the battery has to be a small-sized one in favour of wearability, only a small amount of power is available; the wearable sensor node is equipped with a not so optimal energy-saving policy, indeed none of the components are switched off every time that they are not necessary (only the microcontroller operates in sleep mode when it is idle whose waking signal is provided, as an interrupt, by the ZigBee module, therefore the latter has to be constantly turned on).
# IGBT Overvoltage Protection Based on Dynamic Voltage Feedback and Active Clamping
## 3 Overvoltage Suppression Circuit Combining an Active Clamp and Dynamic Feedback  
### Circuit Design  
As an improvement on the traditional overvoltage suppression circuit of an active clamp, the design idea of the overvoltage suppression circuit in this paper is as follows: (1) The overvoltage suppression circuit is composed of two independent parts: an active clamp and dynamic voltage feedback; (2) a capacitor is used to feedback the dynamic voltage rise rate; (3) the active clamp circuit is improved and the overvoltage feedback loop is increased; (4) a voltage source \(V_{CC}\) is added to the IGBT gate to prevent the gate voltage from being too high due to feedback, and the gate voltage is clamped.  
In summary, the designed active clamp and dynamic voltage feedback combined with the overvoltage suppression schematic diagram are shown in Figure 3. This figure includes an overvoltage suppression circuit and IGBT gate drive circuit. The overvoltage  
Figure 2: IGBT turn-off process curve.  
suppression circuit consists of the following parts: dynamic voltage rise rate feedback circuit composed of transient suppression diode \(Z_{s1}\) and capacitor \(C_{1}\), and improved active clamping circuit composed of transient suppression diodes \(Z_{s2}\), \(Z_{s3}\), \(Z_{s4}\), capacitor \(C_{2}\) and resistor \(R_{5}\); IGBT grid drive circuit: a push-pull circuit composed of \(T_{1}\) and \(T_{2}\) triodes serves as the drive power amplifier of the grid, \(R_{1}\) and \(R_{3}\) are current limiting protection resistors, and OC1 is optocoupler for photoelectric isolation protection. In addition, \(D_{1}\) and \(D_{2}\) are universal diodes to prevent the IGBT gate signal from being shunted to the overvoltage suppression circuit.
## 4 Discussion
### Prospective Use in Reactive Joining  
The structuring of RMF can be used to change the time-temperature distribution of heat released and enables the flow of molten material through the RMF plane in joining applications. Possible areas of application are diverse and extend across industries such as polymer manufacturing, automotive, aerospace, mechanical and electrical engineering, and household and housing technology. Potential users are primarily companies that have so far used adhesive bonding or welding of plastics in the manufacturing of their products and who need to avoid the disadvantages of these processes (e.g., pre-treatment, hardening times, thermal load) in the future. The first successful applications of reactive but still unstructured multilayers in plastic as well as hybrid plastic-metal joining with different thermoplastics (PC, PP, PA6) or fiber-reinforced plastics have already been reported [45]. Carbon fiber-reinforced plastic (CFRP) is bonded to pre-patterned aluminum (AA 6061) by applying reactive multilayers. The necessity of penetrating the foil interface with molten plastic is highlighted separately. Adhesion promoters were used, as well as structures introduced by laser writing into the metal partner to promote a hybrid bond. The compound has only limited adhesion properties since the reactive multilayers act as a barrier layer and hinder the plastic [46]. The advantages of an initial foil structure become clear once again. Hybrid lap joints between semi-crystalline polyamide 6 (PA6) and austenitic stainless steel (EN 1.4301) by means of Ni/Al RMFs were investigated [47]. The crack formations for this type of foil were identified, which allow penetration of the foil interface with plastic and thus have a positive effect on the overall composite.
###### Abstract  
In this study a prototype sub-picosecond laser was investigated for cutting and scribing of silicon wafers. The Yb:KYW laser used for this investigation, unlike ultrashort systems used previously, generates pulses of 650 fs, i.e., between the pico and femtosecond range. The laser was placed in a micromachining setup, involving a galvo scanner and a telecentric lens. A study of the influence of the processing parameters on the crater width, depth, and quality of machining was carried out. The optimal parameters were found to be 343 nm, 200 kHz, 7 mm/s, and 15 pattern repetitions. The experiments were performed using samples of a silicon wafer of 210-\(\upmu\)m thickness. The experimental results show that the sub-picosecond laser can be a promising and competitive tool for solar cell micromachining. In comparison to the commercially available ultrashort pulse laser systems, we find the sub-picosecond laser to be a more cost efficient and reliable source, than a femtosecond one. In addition, the prototype Yb:KYW design offers some unique parameters, such as repetition rate in the range of 100-400 kHz, UV wavelength or obtainable laser fluence close to the silicon ablation thresholds.  
sub-picosecond pulses; silicon wafers; photovoltaic cells; laser micromachining +
Footnote †: journal: Applied Sciences