esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of 1-Bit Digital Reconfigurable Reflective Metasurface for Beam-Scanning
## 4 Conclusions  
In this paper, a novel 1-bit RRM at C-band is simulated and fabricated aiming at achieving the beam-scanning characteristic in wider broadband and decreasing the design difficulty of RRM. The 1-bit element RRM cell, with only one varactor diode, works between 3 GHz and 4 GHz and provides two states with 180\({}^{\circ}\) phase difference as coding \"0\" and coding \"1\". Then, we design a RRM working at 3.5 GHz. The measured gain of the proposed RRM is 18 dB and 3 dB bandwidth is 16.6%. Besides, the 1-bit digital RRM can achieve the beam-scanning. This demonstrates that the beam-scanning performance can be achieved by controlling the coding of the RRM. The measurement results are in good agreement with the simulation results, which shows that the design of the 1-bit digital RRM is effective. Compared with the state-of the-art, the 1-bit digital RRM is a better choice in practical implementations owing to the easier bias voltage control. That is to say, the number of the bias voltage only needs to be two to obtain the coding \"1\" and coding \"0\". The 1-bit digital RRM is feasible to achieve beam-scanning and it is preferable to implement, especially in large-scale antenna designs due to stable phase states, system simplification and low cost. In the future, the 1-bit or multi-bit digital RRM will play an essential role in many realms with its unique features.  
This work is supported by National Natural Science Foundation of China under Contract No. 51477126, and supported by Technology Explorer and Innovation Research Project, and Fundamental Research Funds for the Central Universities (K5051202051 and SPSZ021409). All authors contributed substantially to the reported work. [PERSON] was the originator of the idea of this study and revised the paper; [PERSON] provided the main instructions of experiments; [PERSON] performed the experiments, analyzed the data and wrote the paper.  
The authors declare no conflict of interest.
## 2 Materials and Methods
Spectrograph Andor with a lens-free optical signal transmission system was used as a recorder in the Raman spectroscopy system (WITec, Germany), which allows to reduce losses. Electron-multiplying charge-coupled device (EMCCD) matrix with cooling down to \(-60\)\({}^{\circ}\)C was used for signal detection, which reduces dark noise by an order of magnitude. The adjustment of the measurement site was performed using an optical microscope tuning system and a precision piezoelectric microscope table. The spectra were processed with the WITec Project software. All the SERS spectra are firstly baseline-corrected using polynomial method.
## 1 Introduction
Relatively little information is available on the relationship between volatile compounds and flavor scores by the consumers of beef. Such studies are made more difficult because the aroma compounds responsible for beef flavor are present at very low concentrations, which causes them to be difficult to determine. However, it has been demonstrated that the related classes of aroma compounds respond together to factors affecting beef quality and that some classes of compounds are positively and negatively associated with the consumer liking of flavor [24]. It was proposed to monitor these \"marker compounds\", which have been shown to be associated with desirable or undesirable flavor characteristics. The determination of \"marker compounds\" may help to explain differences between the flavors of different muscles prepared by different cooking methods. The roasting and stewing of beef are extensively practiced cooking methods in Poland. Their widespread use is attributed to their alignment with culinary culture, the utilization of more economical cuts of meat, adherence to culinary knowledge traditions, adaptation to climatic conditions, and the preservation of nutritional value. These factors collectively contribute to the popularity of these cooking methods in Polish cuisine, forming integral elements of the culinary heritage of the country. Meat Standards Australia (MSA) is a grading system used in Australia to provide a grade for beef cuts based on their predicted eating quality [2]. The MSA protocols for assessing beef cooked by various methods, outlined by [PERSON], [PERSON] et al. [25], were applied to evaluate the quality of Polish beef in the study described in this manuscript, which is part of a broader project (POIG.01.03.01-00-204/09 Optimizing Beef Production in Poland According to \"from Fork to Farm\"). The ProOptibeef project conducted in Poland to improve the eating quality of Polish beef used the MSA methodology to evaluate the consumer acceptance of a wide range of beef muscles and cooking methods [5]. The aim (and novelty) of the study was to elucidate the relationship between consumers' scores for beef flavor and the volatile compounds that contribute to it. A variety of methods, including the monitoring of marker compounds for different compound classes, was used to evaluate the effect of two different cooking techniques (roasting and stewing) on the volatile compounds collected from three different muscles and to identify any relationship between these volatiles and the flavor liking scores from consumer panels.
## 4 DASH Live Broadcast Traffic Model  
The DASH segment file is transmitted using the live broadcast scheme in this paper. The DASH live broadcast traffic model consists of the token bucket and leaky bucket. The proposed IP-based transmission structure in this paper uses several token buckets conceptually in consideration of several services (sessions). However, the total size and service bit rate of the token buckets cannot be larger than that of a leaky bucket. Therefore, in this paper, modeling is performed on the assumption that the total sizes of the token bucket and leaky bucket and the service bit rate are the same.
## 4 Discussion
### Three-Dimensional Printing Methods and Materials for the Gastrointestinal Tract  
According to current studies, FDM is the primary method for 3D printing the gastrointestinal tract. FDM is a popular 3D printing technology with rapid production, cost-efficiency, accessibility, broad material adaptability, and the ability to produce complex components [43,44]. Stereolithography printing has also been utilized to produce 3D models of the gastrointestinal tract, but its cost is higher than that of FDM. Furthermore, research on the technical differences between various 3D printing technologies for the gastrointestinal tract is limited, and data on printing times are scarce. Only four studies have reported printing times, ranging from 6 to 48 h [28,33,39,42]. This lack of detail affects the overall assessment of current gastrointestinal 3D printing technology.  
Silicone is the most commonly used printing material for gastrointestinal models that can provide tactile feedback to users. Several types of silicone suitable for 3D printing the gastrointestinal tract have been investigated. [PERSON] et al. compared three types of silicone for 3D printing: Ecoflex 0010, Ecoflex 0030, and Dragon Skin FX-Pro [19]. In that study, 10 silicone rectum models were produced using mixtures of different silicone types. The rectum model created with three parts Ecoflex 0030 and one part Slacker showed the best results in terms of haptic realism. In [PERSON]'s research, the tensile strengths and elongations, number of silicone coatings (0, 2, and 8 times), and specimen hardness (50, 60, and 70 Shore A) of three materials (Agilus, Elastic, and Flexa) were evaluated (Figure 5) [32]. [PERSON]'s research compared six silicone materials with different Shore hardnesses to develop 3D-printed models of the small bowel [35].  
PLA is often combined with silicone. Studies have documented the use of PLA to print molds and then silicone to prepare the final intestinal model [25,35,39]. Resin-printed models have been used in some case reports for presurgical planning [28,41].  
Apart from these materials, one study explored the application of PVA/PAM hydrogels in 3D-printed models. PVA/PAM hydrogels are an elastic and flexible material that closely simulates the physical characteristics of soft tissues such as the brain, liver, and gastrointestinal tract [42]. This research has provided potential innovative material for future 3D gastrointestinal printing.  
There is a dearth of studies comparing various printing materials, so examining more realistic printed materials is a potential research area for future studies.
# A Trust-Based Predictive Model for Mobile Ad Hoc Network in Internet of Things +
## 1 Introduction
The term trust has been coined in the social science literature and adopted by distributed computing and mobile computing as well. Trust is a combination of opinion, belief prediction, and probability [3]. Trust is a measurement of reliability, utility, and availability, and it also improves the overall network functionalities like quality of services, reputation, availability, risk, and confidence [4]. Trust quantification and evaluation has been fostered in recent years for a wide range of applications. As discussed above, trust is a function of uncertainty which incurs the level of confidence about a node for a particular activity. If the node believes that its peer would definitely not perform some action in any circumstance, the node does not \"trust\" the latter, again if it believes it would complete the particular task, the trust level is high. In both cases there is no uncertainty. But if the node is not confident about the next move of the other then uncertainty occurs. Unfortunately, nodes involved in IoT-MANET show this kind of uncertainty in their behavior. In the present proposal, the level of trust can be measured by a continuous real number, referred to as the trust value. Trust is not necessarily symmetric. The fact that A trusts B does not necessarily mean that B also trusts A, where A and B are two entities. As trust is subjective probability [4], when a system converges to a trusted stage it is not well addressed. This paper deals with a distributed scheme to utilize trust evidences for secure data delivery in IoT-MANET. The trust-based framework is based on the notion of point-to-point trust to prevent malicious behaviors of MANET nodes. (a) **Contribution:** This paper discusses an evidence-based quantitative trust management scheme that supports both direct and indirect trust opinion. The direct trust is calculated using a quantitative model by taking consideration of trust parameters which are combined by \(\beta\) distribution. For calculating final or resultant trust, various recommendations are collected from other common neighbors, and combined using a ARMA/GARCH mathematical model of prediction [5]. Using the notion of multilevel prediction, trust is quantified with good accuracy. Through extensive simulation, the model is evaluated and compared with an existing trust management scheme [1]. The proposed model is lightweight in terms of computation and powerful in terms of flexibility and accuracy in managing trust in IoT-MANET. (b) **Organization of the paper:** The rest of the paper is organized as follows: Section 2 gives a brief note on the related research efforts in the area of trust and secure routing in MANET. In Section 3, we present our proposed trust model. Section 4 presents the simulation results of the proposed trust protocol along with existing protocols. Section 5 discuss about the security analysis, and finally conclusions are presented in Section 6.
# Multi-Layer QoE Learning System Implemented by Fiberhost
## 3 Multi-Layer QoE Metric Nature
### Iv Service Layer
A good reference can be the ITU-T recommendation P.1203 [32], which evaluates QoE in adaptive streaming based on stalls, bitrate of audio and video streams, and data associated with the codec used. Other algorithms that analyze quality or QoE include Video ATLAS [33], KSQI [34], or VMAF [35]. There are also algorithms that provide infrastructure improvement or adaptations based on the obtained QoE score, including SDNDASH [36], QDASH [37] or described by [PERSON] [38]. In our research, we aimed to acquire user engagement and assess video quality per user account on the system but also to assess the overall system delivering TV content per location. Our work is based on a continuous dataset that comes from linear TV content, rather than the finite number of video files.
# Task-Based Motion Planning Using Optimal Redundancy for a Minimally Actuated Robotic Arm
## 5 Simulation Results
### Pick and Place  
In this simulation, the entire algorithm was tested over a typical pick and place task while maneuvering around an obstacle. The positions of the targets are given by their (\(x\),\(y\)) coordinates in mm, where (500,800) is the pick, and (\(-700\),\(-300\)) is the place, when (0,0) represents the first (and fixed) joint of the arm. The obstacle was a vertical wall between the points (200,700) and (200,1100), and the arm's initial position corresponded to the case where all links are vertically aligned. First, an optimal assembly was generated after 21 workspace iterations (0.47 s), which considered joints 1-4 as active (magenta in Figure 7a) and left the last joint redundant. Secondly, a search graph was created with 935K nodes (Table 2) to achieve 95% full areal coverage with a 5 mm cover radius, when all configurations less than 50 mm from the obstacle were eliminated (safety offset). The search from the initial position to the target to pick (Figure 7b) required 127,267 node expansions, and the second search from the pick to place targets took 324,460 expansions. The number of expansions is a conventional criterion in heuristic searches, since it solely depends on the algorithmics, as opposed to runtime, which for the same search, can differ across different programs and environments. Thus, performance was tested on the number of expansions. For comparison, other heuristic searches that were executed on similar models such as in [18] without the minimal workspace as the search graph required expansions in the scale of millions, and often ran out of memory. This indicates that optimal path planning by using the minimal redundancy approach is at least ten times more effective than previous heuristic methods. To keep the search as efficient as possible, the path found by the A* search was adapted for a fully actuated manipulator. To do so, the last step in the algorithm was to separate the simultaneous movement into its component (Figure 7c, steps downsized by 20) to achieve a minimal redundant motion plan for a minimally actuated arm.
## 3 Results and Discussion
### Catalytic Activity: Transesterification of Glycerol with Ethylene Carbonate
#### 3.3.2 Effect of Molar Ratio  
As transcarbonation is a reversible process, it is usual to add an excess of the organic carbonate to ensure a high conversion of glycerol and, as a consequence, a high yield to glycerol carbonate and ethylene glycol, also increasing reaction rates in this solventless system [41; 42]. After studying the optimum conditions of load catalyst useful for the reactivity of ethylene carbonate with glycerol, we studied the effect of the molar ratio of these reagents effect at 100 \({}^{\circ}\)C, to also have a first evaluation of the temperature effect by comparing to the previous results.  
In this case, we evaluated three molar ratios: 1.5:1, 2:1 and 3:1 (ethylene carbonate/glycerol) to see the effect of in the experimental conditions previously established for the catalyst concentration, \(-2\)% of raw clay (0.36 g) at 100 \({}^{\circ}\)C. In the case of using a molar ratio of 3:1, the conversion of glycerol attained its maximum value of 94% at 4 h, which can be appreciated in Figure 11A. Therefore, the excess of the carbonate reagent is of importance to ensure a high conversion of glycerol and a relatively high average reaction rate during the 4 h reaction. Figure 11B shows that the initial reaction rate is influenced by the molar ratio of the reactants; it can be seen that the fastest reaction will take place when the EC/Gly molar ratio is equal to 1.5:1. Therefore, a larger excess of ethylene carbonate means an excessive dilution of glycerol at zero time that reflects on the reaction rate.
# Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography
## 3 Methods and Materials
### Zaslavsky Map  
The Zaslavsky map is a two-dimensional chaotic map for studying dynamical systems and encryption. It is effective in enhancing image security through chaos-based operations [28].  
\[x_{n}+1=[x_{n}+v(1+ny_{n})+e^{\mu}cos(2\Pi x_{n})]mod1\]  
\[y_{n}+1=\mathrm{e}^{-x}[y_{n}+ecos(2\Pi x_{n})]\]  
\(X_{n}\) is the position or horizontal coordinate of the system at the iteration.  
\(Y_{n}\) is momentum or a variable related to the rate of change; it is diluted by (e to the minus x end superscript), indicating gradual deceleration or dampening.  
Where \(x_{n}\), \(y_{n}=0.1\). This map has the following features:  
Chaos generation, where the map creates a chaotic attractor under suitable parameter values (\(\
u=4\), \(\varepsilon=2.3\), \(\tau=3\));  
Sensitivity to initial conditions, which are highly dependent on starting values, is crucial for ensuring security;  
Applicability in image encryption to confuse and diffuse pixel data [28].
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 4 Experimental Results and Analysis  
In this section, we will explain the datasets used for the experiment, the process of the experiment, and discuss the results. First, the dataset was divided into three parts, i.e., the training set, validation set, and the testing set with their respective proportions being 7:2:1. We applied different versions of the YOLO model, SSD, and Faster R-CNN to train the dataset and compared the results. Next, we used the Sentinel-1 simulated images for ship detection experiments and evaluated the accuracy of the results. Finally, we tested our proposed method on real images from HISEA-1.
# Simplified Polydispersion Analysis of Small-Angle Scattering Data
## 4 Simplified Polydispersion Analysis
Last but not least, the uncertainty of the \(n\)-th momentum of the lognormal SDF can be calculated by the propagation of the estimated uncertainties of the three parameters defining the SDF:  
\[\frac{d\Sigma}{d\Omega}(0)=(\frac{4}{3}\pi)^{2}(\Delta\rho)^{2}N_{o}R_{o}^{7} exp(\frac{49}{2}\sigma^{2})\sigma\sqrt{2\pi} \tag{22}\]5  
Hence, with the determination of the three quantities \(R_{g*}\), \(\frac{d\Sigma}{d\Omega}(0)\), and \(A_{p}\) from the experimental SCS, it is possible to uniquely define the SDF of polydisperse inhomogeneities through Equations (27)-(29). Provided the experimental SCS fulfils the condition of a \(Q\) range wide enough to contain the Guinier, as well as the Porod approximations, this is possible by performing a simple analysis of the experimental SCS, consisting of interpolating straight lines in the \(ln\frac{d\Sigma}{d\Omega}-Q^{2}\) plot at low \(Q\) values for the Guinier approximation and in the \(log\frac{d\Sigma}{d\Omega}-logQ\) plot at large \(Q\) values for the Porod approximation. In the \(ln\frac{d\Sigma}{d\Omega}-Q^{2}\) plot, the SCS is fit with a straight line \(ln\frac{d\Sigma}{d\Omega}=aQ^{2}+b\), where \(a=-R_{g}^{2}/3\) and \(b=ln\frac{d\Sigma}{d\Omega}(0)\). Similarly, the power law in Equation (14) turns into a straight line in the \(log\frac{d\Sigma}{d\Omega}-logQ\) plot, and therefore, the SCS can be fit with the straight line \(log\frac{d\Sigma}{d\Omega}=c+dlog(Q)\), where \(c=log(A_{p})\) and \(d\) is the coefficient of the power law, ideally equal to \(-4\) for sharp interface. The presence of an incoherent background in the SCSs affects the application of the SPA by not allowing a linear fit in the \(log\frac{d\Sigma}{d\Omega}-logQ\) plot at large \(Q\) values for the Porod approximation. In this case, the SCS shall be fit against the following function with a power law and constant background \(B\),  
\[\frac{d\Sigma}{d\Omega}(0)=(\frac{4}{3}\pi)^{2}(\Delta\rho)^{2}N_{o}R_{o}^{7} exp(\frac{49}{2}\sigma^{2})\sigma\sqrt{2\pi} \tag{22}\]6  
the linear interpolation no longer being possible. In the \(\frac{d\Sigma}{d\Omega}-Q\) plot, the SCS is fit with the straight line \(\frac{d\Sigma}{d\Omega}=\frac{c}{Q^{4}}\) with \(c=A_{p}\), plus the incoherent background \(B\). The parameters \(a\), \(b\), and \(c\) are then used for the SPA, with their estimated uncertainties \(\sigma_{a}\), \(\sigma_{b}\), and \(\sigma_{c}\). Interpolations of the SCS can be performed with many user-friendly commercial software packages that usually include fitting capabilities with predefined and user-defined fitting functions. Alternatively, one can also consider using a user-defined function in commercial software to interpolate the SCS with a non-linear fit by directly optimizing the physical quantities \(\frac{d\Sigma}{d\Omega}(0)\), \(R_{g*}\), and \(A_{p}\).
## 5 Performance Evaluation Criteria  
The results of applying the proposed model architecture on the data set are evaluated through accuracy, precision, recall/sensitivity, and F-Measure criteria calculated from confusion matrix values. A confusion matrix for a typical two-value classification problem is presented in Figure 3.  
The values of accuracy, precision, recall, specificity, and F1-score are determined based on the true positive value, true negative value, false positive value, and false negative value.  
Figure 3: A confusion matrix of the proposed model architecture.  
Figure 2: A block diagram of the proposed model architecture.  
* TP (true positive): the actual observation indicates that domestic violence has occurred and the ML algorithm detects domestic violence from the given data (i.e., the detection result is true positive).
* TN (true negative): the actual observation indicates that domestic violence has occurred whereas the ML algorithm cannot detect domestic violence from the given data (i.e., the detection result is a true negative).
* FP (false positive): the actual observation indicates that no domestic violence occurred and the ML algorithm indicates that no domestic violence is detected from the given data (i.e., the detection result is a false positive).
* FN (false negative): the actual observation indicates that no domestic violence occurred whereas the ML algorithm detects domestic violence from the given data (i.e., the detection result is a false negative).  
Accuracy is one of the important classification evaluation criteria that is defined as follows:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{5}\]  
Precision is the ratio between the true positives and all the positives. The precision is defined as follows:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{6}\]  
Recall/sensitivity is the true positive rate. It measures how frequently the experiment detects the domestic violence from the given data when the actual domestic violence has occurred. The recall is defined as follows:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{7}\]  
The F1-score is a measure of a model's accuracy on a data set. It is used to evaluate binary classification systems, which classify examples into 'positive' or 'negative'. The F-score is a way to combine the precision and recall of the model, and it is defined as the harmonic mean of the model's precision and recall. The F1-score is defined as follows:  
\[\text{F1-score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{8}\]  
Specificity is the ability of an experiment to classify the non-domestic violence case correctly. It is the ability to correctly classify the non-domestic violence case. The specificity is calculated as follows:  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{9}\]