esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminary Process Analysis  
The research was based on determining the process potential and fulfilling the process function through preliminary process capability indices and sigma levels in order to identify all influential factors of process variation. Before determining the key variables in experimental planning, it was necessary to gain insight into the actual state of the printing process. Process analysis was essential to verify whether the process was in a state of statistical control and whether it followed a normal distribution, indicating stability, which allows for the prediction of its performance. When the process is under control, there is a lower likelihood that the observed process parameters will exceed the specified control limits. It was also necessary to verify if the process was properly centered in relation to the already defined and monitored Critical Product Characteristics (CPCs).  
In addition to controlling the characteristics related to the functional requirements of the product, it was important to monitor the factors influencing the process. These included the paper temperature (\({}^{\circ}\)C) and humidity (%), the ink viscosity (mPa\(\cdot\)s) and ink conductivity (\(\mu\)S/cm), the hardness (dH), and the alcohol content in the dampening solution (% vol.). The paper temperature and humidity are defined as controlled constants, while the ink viscosity, dampening solution conductivity, hardness, and the alcohol content in the dampening solution are defined as absolute constants. Therefore, it was not necessary to define LSL (Lower Specification Limit), USL (Upper Specification Limit), or TV (Target Value) for them.  
This approach is in line with the principles of the Six Sigma methodology, which emphasizes process stability and control as a prerequisite for reducing variability and improving quality. Constant values for these parameters minimized the influence of uncontrolled factors on the process. This allowed the analysis to focus on optimizing key variables and ensuring process performance within predefined quality standards.  
It is important to note that the frequency and sample size of 100 measurements were based on the preliminary process capability assessment, which is carried out at the beginning of the process or after a relatively short monitoring period, where a sample of at least 100 units is taken.
## 4 Experiments and Results
### The Power Consumption in Transmission Part (\(P_{send}\))  
The main objective of this experiment is to investigate and analyze the impact of delay time on the waiting period for the next cycle. In addition, this experiment aims to compare the power consumption of polling and deep sleep techniques. This experiment is designed based on the diagram shown in Figure 5, where the values of \(t_{u}\), \(t_{c}\), and \(t_{s}\) are fixed at 1.00 s, 0.10 s, and 1.00 s, respectively. On the other hand, the value of \(t_{w}\) is varied systematically to cover a range of delay times, including 15, 30, 60, 90, 120, and 150 s. The IoT devices process 40 times per round for each test case and repeat for ten rounds. This experiment employs two different techniques, polling and deep sleep, to simulate the delay functions. In the polling technique, the system continuously checks for the next cycle without entering sleep mode, which consumes more power. In contrast, in the deep sleep technique, the system enters a low-power sleep mode until the next cycle arrives. By comparing the power consumption of these two techniques, the experiment can determine which technique is more energy-efficient for waiting periods of varying lengths. To ensure the reliability of the experimental results, each test case was repeated ten times. This repetition enables the average power consumption to be calculated, thereby mitigating the impact of random fluctuations in the data. Through this experiment, we aim to identify an optimal technique for power consumption minimization during idle periods, thereby contributing to the design of more energy-efficient systems. This study compares the long-term current consumption of two techniques: delay with polling and deep sleep. The results, presented as the average of accumulated current consumption, are detailed in Table 4.  
Table 4 provides a comparative analysis of the current consumption (in amperes) of two techniques--polling and deep sleep--across various time intervals, measured in seconds (15 s, 30 s, 60 s, 90 s, 120 s, and 150 s). Each measurement was repeated ten times to ensure reliability, and the results are presented with their means and standard deviations. For the polling technique, the average current consumption decreases from 5.92 A at 15 s to 3.83 A at 150 s, with relatively low standard deviations indicating consistent results. Similarly, the deep sleep technique shows a decrease in current consumption from 5.64 A at 15 s to 3.20 A at 150 s, though with slightly higher standard deviations, indicating more variability in the measurements. Overall, both techniques reduce current consumption over longer intervals, with the deep sleep technique generally being more energy-efficient. This
## 2 Preliminaries  
### Fundamental Abstractions of SDNs  
A Software-Defined Network (SDN) refers to a new generation in the evolution of computer networks. SDN was launched in 2008. It was standardized by the Open Network Foundation [7] and implemented by a number of original equipment manufacturers (HP, CISCO, IBM, Juniper, NEC, and Ericsson).  
SDN is an emerging paradigm that relies on decoupling the tightly coupled implementation of the network control logic and network devices. In fact, the control logic (control plane) is separated from network devices (data plane) and is implemented in a logically centralized controller [8]. Thus, SDN mainly consists of three planes: the application plane, the control plane, and the data plane. Figure 1 depicts a simplified view of the SDN architecture. The application plane is presented as a set of network applications that implement network control logic (e.g., firewall, traffic engineering, load-balancer, etc.) leveraging a northbound interface, e.g., REST API [9], which offers universal network abstraction data models and functionality to developers [1,8]. This set of network applications also either explicitly or directly notifies the network behavior to the control plane by means of a northbound interface. The former is presented by a logically centralized controller, which is also named the Network Operating System (NOS), and supports the translation of the network requirements and the desired behavior of the application plane to the data plane based on a southbound interface, e.g., OpenFlow [2] and ForCES [3]. Indeed, a southbound interface formalizes the way in which the control plane and the data plane communicate. Finally, the data plane is presented by the set of network devices, e.g., switches and routers, which remain a set of simple forwarding devices [1,8].
# Feature Map Analysis of Neural Networks for the Application of Vacant Parking Slot Detection
## 2 Materials and Methods
### Data Acquisition  
NNs with an outstanding classification performance were needed to obtain the correct feature analysis outcome, and an appropriate dataset was required to make outstanding NNs. Public datasets, such as PKLot [33] and CNRPark [25], have been used in many studies. However, the datasets were inappropriate for our experiments because they comprise parking slot images with small sizes or extensive occlusion caused by lampposts and trees. On the other hand, it has been demonstrated that the classification performance of NNs is affected by ROIs of a specific parking slot [34; 35]. For these reasons, a specific dataset was needed. A camera was placed on the wall of our campus building to acquire images of the outer parking lot. The dataset was composed of 2700 cropped parking slot images. The parking lot in front of the campus building is shown in Figure 2.  
Among all the parking slots, only 20 slots (P1 to P20 in Figure 2) in the middle of the parking lot were employed to collect the dataset. Five different ROIs were applied to each parking slot to collect parking slot images in various views, and 27 images per ROI were obtained from the video frames. Thus, 135 images per slot were acquired, and 2700 images were collected. Example images of five ROIs are shown in Figure 3.  
ROI initialization was conducted based on the following sequences: first, the four edge coordinates of each parking slot were found. These were matched to the four edges of the ROI level 0 of each slot. As shown in Figure 2, higher ROI levels were obtained by moving  
Figure 3: Sample images corresponding to the five selected ROIs. (**a**) ROI level 0; (**b**) ROI level 1; (**c**) ROI level 2; (**d**) ROI level 3; (**e**) ROI level 4.  
Figure 2: Dataset acquisition environment. (**a**) Parking lot in front of the campus building; (**b**) parking slot numbers and examples of ROIs distinguished based on height from the ground.  
the four edges of ROI level 0 in the +Y direction. However, critical information could be lost when the edges excessively move in the +Y direction. For instance, only unrelated information will remain if the edges move too much. Likewise, occlusion by adjacent vehicles increases unnecessary information. Moving the ROI in the X direction disturbs the classification by enhancing the occlusion. Thus, moving the ROI in the X direction was not considered. Therefore, ROI level 4 was set to prevent excessive motion just after ROI level 0 was found. When the four edges of ROI level 0 moved in the +Y direction by the difference between the Y coordinates of the lowest and rightmost edges of ROI level 0, the coordinates of the moved four edges corresponded to the four edges of ROI level 4. Suppose that 'k' is the difference between the Y coordinates of the lowest and rightmost edges of ROI level 0. Then, ROI levels 1, 2, and 3 are obtained by moving the four edges of ROI level 0 in +Y direction by \(\frac{k}{4}\), \(\frac{2k}{4}\), and \(\frac{3k}{4}\).
# Effect of Calined Marble Powder and Magnetized Water on the Performance of Cement-Based Composites
## 2 Experimental Details  
### Materials  
In the preparation of cement-based composites, standard sand defined in EN 196-1 [27] was utilized as an aggregate. CEM 1 42.5 R Portland cement, RMP, and CMP were utilized as binders. RMP was obtained by grinding waste marble fragments sourced from marble-processing facilities. Prior to its incorporation into the mixtures, RMP was sieved through a 75 \(\upmu\)m mesh, and the material that successfully passed through was utilized in the mixtures. CMP was obtained by calcining RMP at 1000 \({}^{\circ}\)C for a duration of 3 h. The calcination time and temperature were determined based on studies conducted in the literature [5; 28]. The production of CMP and RMP is illustrated in Figure 1. Table 1 presents the chemical oxide analysis and physical properties of PC, RMP, and CMP.  
During the calcination process, the material undergoes thermal decomposition, which can cause changes in the particle size distribution. RMP typically has a larger particle size compared to Portland cement. After calcination, the particles of CMP may be finer, as some of the larger particles break down and undergo shrinkage. This can lead to a higher surface area of CMP, which, in turn, increases its reactivity when used as a partial replacement for cement. The finer particles help in occupying the voids in the cement matrix, thereby improving the packing density.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Chemical Composition (\%)** & **PC** & **RMP** & **CMP** \\ \hline SiO\({}_{2}\) & 16.51 & 9.12 & 9.2 \\ Al\({}_{2}\)O\({}_{3}\) & 3.89 & 0.86 & 0.7 \\ Fe\({}_{2}\)O\({}_{3}\) & 3.07 & 1.03 & 1.0 \\ MgO & 1.33 & 9.83 & 9.8 \\ CaO & 64.91 & 67.12 & 72.2 \\ Na\({}_{2}\)O & 1.33 & 0.13 & 0.1 \\ K\({}_{2}\)O & 0.90 & 0.24 & 0.2 \\ SO\({}_{3}\) & 3.95 & 0.18 & 0.1 \\
**Physical properties** & 3.19 & 2.52 & 2.68 \\ Bline fineness (cm\({}^{2}\)/g) & 3341 & 2895 & 3150 \\ Loss on ignition (LOI) & 2.10 & 11.2 & 9.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition and physical properties of PC, RMP, and CMP.  
Figure 1: Production stages of RMP and CMP.  
To identify the mineral phases of RMP and CMP, X-ray diffraction (XRD) analysis results are presented. The primary components of RMP are calcite and dolomite, which have low reactivity in cementitious applications. However, after calcination at 1000 \({}^{\circ}\)C, RMP transformed into CMP, with significant amounts of lime (CaO) and magnesium oxide (MgO), which significantly increased the reactivity of the material and contributed to improved cementitious properties.  
Two distinct types of water were used to mix water in the cement-based composites: TW and MW. MW was produced by exposing TW to a magnetic field with a strength of 0.6 Tesla. MW is obtained by circulating TW within the setup shown in Figure 2 for 30 min.
## 2 Speaker Identification Related Work
### Data Augmentation  
Data Augmentation (DA) is a key ingredient of the state of the art systems for image and speech recognition as it is a common strategy adopted to increase the amount of training data. It can also act as a regularizer to prevent overfitting [31] and to improve the performance in imbalanced class problems [32], making the whole process more robust. Due to the scarcity of data we mentioned in Section 1.3, this is a very good match for our case as the database we are using is quite small and has a noticeable imbalance. By using DA, we can increase the amount of data available and deal with the lack of balance between classes [33].  
As for the SI system to be designed, it should be adapted to what we expect Bindi to find in a real world situation: the goal of our system is to detect the users speaking even when their voices present stressed conditions. For this reason, we would be facing a mismatch learning problem in which we may only have neutral utterances available for training gathered in an initial Bindi setup--given that the possibility of forcing the user to be stressed is difficult--whereas the real environment operating conditions would contain both neutral and stressed samples together. Our approach to solve this problem relies in the use of DA techniques to artificially synthesize stressed samples from neutral speech by applying slight modifications, and then adding these new synthetically stressed samples of speech to the training set, to ultimately build a stress-robust SI system.
# Recent Advances in the Heterologous Biosynthesis of Natural Products from _Streptomyces_
## 4 Host Engineering Approaches  
### Host Cleaning  
Analysis of the _Streptomyces_ genome has demonstrated that its genomes contain many BGCs; therefore, it is required that the genome be minimized by deleting unnecessary BGCs. The advantages of a minimal genome include faster growth of the host strains, a low background of native secondary metabolites, absence of biological activity, and a high chance of successful heterologous expression of BGCs [102]. Clean host strains are used for the characterization and modification of the BGCs, activation of the silent BGCs, and increasing the titer of target compounds. _Streptomyces_ host strains were engineered by deleting or expressing the expression of unwanted BGCs, and the resulting engineered strains were employed as hosts for the integration of heterologous BGCs, leading to the production of the target products [103]. Several methods are available to develop clean hosts, such as a PCR-targeted system [104], site-specific recombination using Cre-_loxP_[105, 106], magonuclease I-SceI [107], and clustered regularly interspaced short palindromic repeat (CRISPR)/Cas genome editing tools [108]. A PCR-targeted system is a useful tool for gene knockout. This system contains a selectable gene, two _FRT_ or _loxP_ sites with 40-50 bp of homologous extension flank at both ends, and a \(\lambda\) recombinant system. [PERSON] et al. successfully deleted 10 regulator genes in _S. coelicolor_ by using a PCR-targeted method [109]. Site-specific recombination using Cre/_loxP_ is applied in the knockout and/or insertion of huge BGC regions in many _Streptomyces_ spp. The Cre-_loxP_ system needs a double crossover to introduce the _loxP_ site into the genome and the expression of Cre recombinase to delete target genes. The Cre-loxP system was successfully used to delete 1.4 Mb of _S. avermitilis_'s genome [110]. Meganuclease I-SceI is an endonu
# Throughput/Area Optimized Architecture for Elliptic-Curve Diffie-Hellman Protocol
## 1 Introduction
### Outcomes and Significance  
We have implemented the proposed ECDH architecture over \(GF(2^{233})\) in Verilog (HDL) using the Vivado IDE (Integrated Design Environment) tool. The implementation results after place-and-route are provided on various 28 nm (Virtex-7, Artix-7, Spartan-7 and Kintex-7) technologies. The minimum hardware resources (5102 slices, 12339 look-up tables and 2459 flip-flops) are achieved for the Virtex-7 device when compared to Spartan-7, Artix-7 and Kintex-7 devices. In addition to the hardware resources, the times (or latency) to generate one public key on Virtex-7, Spartan-7, Artix-7 and Kintex-7 are 15.54 us, 15.83 us, 15.63 us and 16.25 us. For the same FPGA devices, the times to generate one shared key are 31.08 us, 31.68 us, 31.28 us and 32.51 us. The highest value (6.30) is acquired for Virtex-7 FPGA. For Spartan-7, Artix-7 and Kintex-7 devices, the figure-of-merit values are 5.60, 5.36 and 5.04. The proposed architecture is 302.7 and 132.88 times faster in terms of latency when compared to coprocessor architectures of [35, 38]. Similarly, it is 963.3 and 2.76 times faster in the context of as compared to [35, 38]. The achieved results and performance comparison ascertain the importance of our proposed architecture in all those shared key generation applications which require high speed with a low area.  
The remainder of this article is formulated as: Section 2 presents the related mathematical background. The proposed high-speed and low-area accelerator architecture for the ECDH protocol is described in Section 3. The implementation results and comparison to state-of-the-art are given in Section 4. Finally, Section 5 concludes the article.
## 4 Benchmark Techniques
### Clipping-Based Techniques
#### 4.2.2 Simplified Clipping and Filtering  
SCF mimics the powerful PAPR reduction capability of the ICF-based mechanism while dispensing the need for iterative operation. As in ICF, SCF algorithm [8] starts by applying IFFT operation to generate the time-domain OFDM signal, \(x[n]\), from the modulated symbol stream sequence, \(X[k]\), and then proceeds to compute a clipped version,  
Figure 1: Top-level view of ICF.  
\(\hat{x}[n]\), with maximum amplitude \(R\), according to the same criterion. However, the operation is then modified to achieve almost the same performance in a single iteration of signal processing. The clipped signal, \(\hat{x}[n]\), is then subtracted from the unclipped OFDM signal, \(x[n]\), to produce a clipping noise signal, \(d[n]\), which is then converted to frequency-domain representation, \(D[k]\), by applying FFT operation as shown in Figure 2. To suppress OoB emissions, \(D[k]\) is then filtered to yield \(\hat{D}[k]\) with nulled \((\eta-1)N\) trailing components. A crucial step giving SCF its ability to come very close to ICF performance without the need for multiple iterations was then performed. The frequency-domain clipping of the original modulated sequence, \(X[k]\), is accomplished by scaling the filtered noise signal, \(\hat{D}[k]\), and subtracting it from \(X[k]\), to obtain \(\hat{X}[k]\) which is finally converted to the time domain sequence \(\hat{x}[n]\) by applying IFFT operation. The clipped signal, \(\hat{X}[k]\), was computed as  
\[\hat{X}[k]=X[k]-\rho\hat{D}[k]\,, \tag{5}\]  
where the scaling coefficient, \(\rho\), is calculated as  
\[\rho=\frac{1-(1-\mu)^{1.5\lambda}}{1-(1-\mu)^{1.5}}\,, \tag{6}\]  
where the factor \(\lambda\) was used to establish an equivalency for the iterative nature of ICF and takes the same value as the number of iterations used by ICF. The parameter, \(\mu\), is a function of the maximum allowable amplitude, \(R\), and the standard deviation, \(\sigma\), of the unclipped time-domain OFDM signal, \(x[n]\), and is computed as  
\[\mu=\sqrt{\frac{8}{3\pi}}\Big{(}\frac{\sigma}{R}\Big{)}. \tag{7}\]
# CSRLoan: Cold Start Loan Recommendation with Semantic-Enhanced Neural Matrix Factorization
## 5 Experiment
### Hyper-Parameter Studies  
To answer **RQ3**, in this subsection, we evaluate the influences of the hyper-parameter's settings. Specifically, we analyze the impacts of two key parameters of CSRLoan, i.e., the dimension \(d\) of memory representation and the trade-off factor \(\alpha\) of the two losses in the joint objective.  
**Influence of embedding dimension \(d\).** For dimension \(d\), we change \(d\) from 2 to 128 in CSRLoan. The performance of loan recommendation is shown in Figure 5. With the increase in \(d\), we find that the performance first increases and then decreases. One potential reason is that the memory provides too little information when the \(d\) is too small, while it can include more irrelevant information when it is too large.  
**Influence of trade-off hyperparameter \(\alpha\).** For \(\alpha\), we search \(\alpha\) from 0.1 to 0.9. The results are shown in Figure 6. The changing pattern is quite similar to \(d\), which indicates that both the pre-training strategy and default risk prediction modules are critical for the task.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Method** & **HR@5** & **NDCG@5** & **HR@10** & **NDCG@10** \\ \hline \multirow{2}{*}{General} & MF-BPR & 0.02134 & 0.01841 & 0.05263 & 0.03991 \\  & CML & 0.04630 & 0.03877 & 0.1518 & 0.1247 \\ \hline \multirow{2}{*}{Loan Rec} & FAR & 0.04261 & 0.02974 & 0.0993 & 0.04971 \\  & MBR & 0.05216 & 0.03234 & 0.1377 & 0.0860 \\ \hline \multirow{2}{*}{Ablations} & NMF & 0.0841 & 0.0710 & 0.2432 & 0.1221 \\  & CSRLoan \({}_{pre^{-}}\) & 0.0922 & 0.07082 & 0.2310 & 0.1778 \\  & CSRLoan \({}_{risk^{-}}\) & 0.1021 & 0.08113 & **0.2530** & 0.1809 \\ \hline Our & CSRLoan & **0.1150** & **0.0970** & 0.2233 & **0.1928** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of different methods in terms of HR@5, NDCG@5, HR@10, and NDCG@10. The bold values indicate the best performances.
# A Dynamic Assessment Methodology for Accident Occurrence Probabilities of Gas Distribution Station
## 2 Methodology
### Dynamic Bayesian Network Analysis
#### 2.3.2 Predictive Analysis  
The primary aim of BN predictive analysis is to determine the occurrence probabilities of UEs, specifically critical events (CEs) and outcome events (OEs). This is accomplished through deductive reasoning, also known as forward prediction, which relies on the available probabilities of REs, conditional probability tables (CPTs), and the topology of the network structure. The probability distribution for a given UE, denoted as \(P(UE=1)\), is calculated using Equation (16) [33]. Upon the acquisition of new observed failure evidence pertaining to REs, a dynamic predictive analysis can be performed to obtain updated probabilities of UEs.  
\[P(UE=1)=\sum_{i=1}^{2^{n}}[P(UE=1|X_{1}=x_{1},X_{2}=x_{2},\cdots,X_{n}=x_{n}) \times P(X_{1}=x_{1},X_{1}=x_{2},\cdots,X_{n}=x_{n})] \tag{16}\]  
where \(x_{i}=\{x_{i}^{1},x_{i}^{2},\cdots,x_{i}^{Q_{i}}\}\), \(i=1,2,\cdots,n\); \(Q_{i}\) is the number of the states for a single RE \(X_{i}\); \(n\) is the number of the REs; and \(x_{i}\) is the state of RE \(X_{i}\). Since each RE has two states \"Yes/No\", \(n\) REs contribute to \(2^{n}\) combinations; \(P(UE=1\,|\,X_{1}=x_{1},\,X_{2}=x_{2},\ldots,\,X_{n}=x_{n})\) denotes the CPT of UE; and \(P(X_{1}=x_{1},\,X_{2}=x_{2},\ldots,\,X_{n}=x_{n})\) is the joint probability distribution of \(X_{i}\).