esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 A Real Case Study
### The Number of Needed Machines  
The first step in our model is to calculate the number of machines needed to produce these 17 product types. As proposed, applying the FFD algorithm needs the following steps:  
**Step 1**: Rank the product types in descending order according to (2). The ranking criterion and the ranking are given in Table 1.  
**Step 2**: Assign the product type that is ranked first to the first machine. In our case, product \(j=5\) that was ranked first was assigned to machine 1, see Table 2.  
**Step 3**: Try to assign the product type that is ranked second to the first machine. In our case, product \(j=8\) that was ranked second can also be assigned to machine 1, \(\left(\left(T_{s}+\frac{\lambda_{5}}{9\%}\right)+\left(T_{s}+\frac{\lambda_{ 4}}{9\%}\right)=0.4878+0.4609=0.9487<1\right)\), see Table 2.  
**Step 4**: Do step 3, with the necessary adjustments, until all the product types have been assigned to any machine. Similarly, all the product types were assigned, see Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Products**} & \multirow{2}{*}{
\begin{tabular}{c} **Ranking** \\ **Criterion** \\ \end{tabular} } & \multirow{2}{*}{**Ranking**} & \multirow{2}{*}{**M1**} & \multirow{2}{*}{**M2**} & \multirow{2}{*}{**M3**} & \multirow{2}{*}{**M4**} & \multirow{2}{*}{**M5**} \\ \hline \(j\) & \(\left(T_{s}+\frac{\lambda_{j}}{9\%}\right)\) & \(R_{j}\) & \(i=1\) & \(i=2\) & \(i=3\) & \(i=4\) & \(i=5\) \\ \hline
1 & 0.4177 & 3 & & 0.4177 & & \\ \hline
2 & 0.3742 & 5 & & & 0.3742 & & \\ \hline
3 & 0.1268 & 14 & & & & 0.1268 \\ \hline
4 & 0.3635 & 6 & & & 0.3635 & \\ \hline
5 & 0.4878 & 1 & 0.4878 & & & \\ \hline
6 & 0.2794 & 8 & & & 0.2794 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Assignment of the 17 product types among the machines.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Products**} & \multirow{2}{*}{\begin{tabular}{c} **Deman** \\ **Rate** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Production** \\ **Rate** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Setup** \\ **time** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Production** \\ **Time in Months** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Ranking** \\ **Criterion** \\ \end{tabular} } \\ \hline \(j\) & \(\lambda_{j}\) & \(\psi_{j}\) & \(TS_{j}\) & \(T\psi_{j}\) & \(\left(T_{s}+\frac{\lambda_{j}}{9\%}\right)\) & \(R_{j}\) \\ \hline
1 & 2265 & 5824 & 0.0288 & 0.3889 & 0.4177 & 3 \\ \hline
2 & 18  
In our case, the lower bound of the number of machines needed is: \(LB=\left[\sum\limits_{j=1}^{17}\left(T_{s}+\frac{\lambda_{j}}{\psi_{j}}\right) \right]^{+}=[4.5344]^{+}=5\). Therefore, our heuristic solution that gives 5 machines is also optimal. i.e., \(M^{*}=5\). The internal order of the product type on each machine is preserved to the next step.
## 3 Experimental Results
The correlation between the total ion VMI signal and the XUV intensity is exhibited in (**h**); (**c**,**f**,**i**) show electron VMI data, with the average detector image (left part) and inverted image (right part) in (**c**). The photoelectron kinetic energy spectrum is displayed in panel (**f**) with the attributed electronic molecular channels and the color-coded corresponding harmonic orders. The correlation between the total electron VMI signal and the XUV intensity is shown in (**i**). The slight bend observed for low intensities in the correlation plots is likely caused by a non-linear behavior of the photodiode in this signal region.
# Negotiation-Free Encryption for Securing Vehicular Unicasting Communication
## 6 Performance Evaluation
### Comparison and Analysis of Two Methods
We define the formula of average transmission bandwidth as  
\[bandwidth=\frac{1}{N}\sum_{i=1}^{n}(\frac{DataLenth}{Delay}) \tag{16}\]  
\(N\) is the number of messages, \(DataLength\) is the data length in transmission, and Delay is the average delay. Then, we compared the performance of our proposed Negotiation-free encryption method and IPSec, the result is shown in Table 5. We send a 20-byte payload with 411 bytes of data using IPSec. The average delay is 310 ms and the average bandwidth is 1.3 bytes/s. Using the NFEM, we also send a 20-byte payload with 127 bytes of data and the average delay is 150 ms. Therefore, the average bandwidth is 0.84 bytes/s. Obviously, the NFEMs greatly reduce the bandwidth overhead. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Transmission Method** & **3 DES192** & **AES128** & **AES256** \\ \hline IPSec & 309.7 ms & 311 ms & 310 ms \\ Negotiation-free encryption method & 149.8 ms & 150.5 ms & 150 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average delay.
# Evaluation of Tooth Movement Accuracy with the F22 Aligner System: A Retrospective Study
###### Abstract  
Background: To investigate the accuracy of an F22 Aligner system, considering the amount of prescribed movement, tooth type, grip points, sex and age. Methods: Digital models of 120 patients (64 females and 56 males; mean age 35.2 years \(\pm\) 7.4) affected by mild-to-moderate Class I malocclusion and treated via F22 Aligners, retrospectively selected from the University of Ferrara Orthodontics Clinic's electronic database; post-treatment models were generated, and three angular values per tooth and four linear intra-arch measurements per arch were acquired. For angular measurements, planned (T1) and achieved (T2) values were obtained thorough digital model superimpositions. Linear measurements were acquired from pre-treatment, reference and post-treatment models. Statistical comparisons were performed to assess accuracy among tooth types and prescribed movements, tooth type, grip points, sex and age were investigated via chi-squared automatic interaction detection regression trees. Results: Mean accuracy for inclination and angulation were 86.76% and 88.01%, respectively, whereas rotation was less accurate (61.59%), especially for rounded teeth. All variables investigated influenced accuracy, with the exception of inclination, which was only influenced by age. Regarding linear measurements, good expansive capacity was shown, except for the distance between mandibular second premolars. Conclusions: F22 aligners are a viable solution for the treatment of Class I malocclusion of mild-to-moderate complexity, although clinicians should bear in mind the lower predictability of rotation, as well as the influence of the variables investigated.  
clear aligners; accuracy; digital superimposition; clear aligner therapy +
Footnote †: journal: Applied sciences
## 2 Related Work  
In the past few years, automatic image velocimetry methodologies for river monitoring have blossomed [4]. Most of such approaches stem from traditional large scale particle image velocimetry (LSPIV) [5], particle tracking velocimetry (PTV) [6], optical flow [7], and exhibit diverse sensitivity to flow regime and seeding density. The OTV approach combines automated feature detection, tracking through the Lucas-Kanade algorithm, and trajectory-based filtering that retain the most realistic trajectories which pertain to objects transiting in the field of view [1]. Features such as corners and junctions are detected through the Fast from Accelerated Segment Test (FAST) algorithm. Such features are then tracked with the pyramidal Lucas-Kanade sparse first-order differential technique. Images are sub-sampled up to four levels, and out of all trajectories, only those which exhibit a minimum length and inclination with respect to the stream cross-section are retained for further processing. OTV does not rely on the deployment of tracers in the field of view, and the length and angle of the trajectory need to be defined by the user, as well as the maximum number of trajectories. The method works well in conditions where surface features are sparse, and where flows are unsteady [1]. Recently, OTV has been optimized by leveraging its parallel processing capability to facilitate in situ deployment [3].  
Similar methodologies include the KLT-IV v1.0 that offers a user-friendly graphical interface for the determination of river flow velocity and river discharge using videos acquired from a variety of fixed and mobile platforms [8]. Likewise, the Surface Structure Image Velocimetry (SSIV) uses a cross-correlation algorithm similar to LSPIV and further introduces a filter which mitigates the effects of shadows and glare as well as the need for a densely seeded flow surface [9]. While the SSIV commercial software is available as a smartphone app and for permanent monitoring implementations, self-powered setups featuring automatic surface flow velocity measurement at the edge in remote environments are still untapped. In this vein, the OpenRiverCam initiative is an open-source and low cost web-software stack with API to establish and maintain river rating curves in small to medium sized streams based on LSPIV [10]. Further initiatives aimed at creating a network of monitoring stations for streamflow monitoring include [11], where simple camera setups enable water level estimations in the upland area of a headwater river basin.
# Semantic 3D Reconstruction for Robotic Manipulators with an Eye-In-Hand Vision System
## 3 Overview of the Proposed Method
### Object Recognition and Fusion Segmentation  
The semantic segmentation algorithm is the basis of map stitching. Pictures and point clouds are segmented by neural networks and geometric features, respectively, and finally the two parts are fused together to generate semantic information. Therefore, this algorithm includes three parts: 2D semantic segmentation, point cloud segmentation, and semantic fusion.
# Financial Optimization of a Solar-Driven Organic Rankine Cycle
## 4 Conclusions  
The objective of this work is the optimization of a solar-driven organic Rankine cycle that operates with toluene as the working fluid. Parabolic trough collectors coupled to a sensible storage system are used in order to feed the ORC for electricity production. The system is examined for all the years in the location of Athens in Greece. The analysis is conducted with a dynamic model that is programmed in FORTRAN. The thermodynamic data of the ORC have been taken by a developed thermodynamic model in Engineering Equation Solver (EES). The optimization variables are the collecting area and the storage tank volume. There are different optimization criteria that are mainly financial, as well as the system energy efficiency. Moreover, a multi-objective optimization procedure is performed. The most important conclusions of this work are summarized below:  
* The maximum system energy efficiency is found at 15.38%, and, in this case, the collecting area is 140 m\({}^{2}\) and the storage tank volume is 12 m\({}^{3}\). * The maximum net present value is 123 k\(\xi\) and is found for a 220-m\({}^{2}\) collecting area and a 14-m\({}^{3}\) storage tank volume. * The minimum payback period is 8.37 years and is found for a 160-m\({}^{2}\) collecting area and a 14-m\({}^{3}\) storage tank, while, for the same design point, the levelized cost of electricity is minimized at 0.0969 kWh\({}^{-1}\). * The multi-objective optimization procedure proved that the optimum design is for a 160-m\({}^{2}\) collecting area and a 14-m\({}^{3}\) storage tank. Moreover, this design point is the optimum according to the payback period minimization and LCOR minimization criteria. Thus, this design is selected as the overall optimum choice. * The monthly analysis indicates that higher electricity is produced during the summer and especially in July and in August. Moreover, the use of higher collecting areas leads to significant enhancement in electricity production, mainly in the winter period. **Author Contributions:** Supervision, writing--review and editing, writing--original draft preparation, C.T. ; conceptualization, methodology, investigation, writing--review and editing, writing--original draft preparation, C.T. ; E.B. All authors have read and agreed to the published version of the manuscript. **Funding:** This research is co-financed by Greece and the European Union (European Social Fund ESF) through the Operational Programme, Human Resources Development, Education and Lifelong Learning, in the context of the project \"Reinforcement of Postdoctoral Researchers 2 nd Cycle\" (MIS-5033021), implemented by the State Scholarships Foundation (IKY). **Acknowledgments:** [PERSON] would like to thanks the State Scholarships Foundation (IKY) for its financial support (MIS-5033021). **Conflicts of Interest:** The authors declare no conflicts of interest. **Nomenclature**
## 4 Overview of the Proactive Transmission of Replicated Frames (PTRF) Mechanism
### PTRF Operation
#### 4.2.3 Approach C: Link-Based Estimation and Replication  
The main characteristic of this approach is that each port of each talker and bridge may produce a different number of replicas. We must recall that each talker output port in approach A and also each bridge output port in approach B generates the same number of replicas and that this number is calculated to achieve a given reliability for the largest path between a talker and listeners. Conversely in approach C, the number of replicas is calculated independently for each link taking into consideration its unreliability. This allows saving bandwidth, since it makes it possible to reduce the number of replicas in links that exhibit adequate reliability. The main trade-off of approach C is that, like in B, bridges need to implement PTRF.  
In Figure 2(c) we see that, as we anticipated, the number of replicas varies in each link, transmitting three replicas in link 1, two replicas in link 2 and three replicas again in link 3. Specifically, talker \(T_{1}\) transmits three replicas of frame \(f_{1,1}\), \(B_{1}\) receives the three replicas correctly, it uses replica \(r_{1,1}\) to create a new set of two replicas and ignores replicas \(r_{1,2}\) and \(r_{1,3}\). Now, \(r_{1,1}\) is affected by a temporary fault in link \(l_{2}\) and dropped upon reception by \(B_{2}\). Thus, \(B_{2}\) only receives \(r_{1,2}\) and uses it to create a new set of three replicas. Finally, \(r_{1,3}\)  
Figure 3: Behaviour of the three approaches of PTRF in the presence of temporary faults in the links. Reproduced as in [11]. (**a**) Behaviour of the approach A in the presence of temporary faults in the links. (**b**) Behaviour of the approach B in the presence of temporary faults in the links. (**c**) Behaviour of the approach C in the presence of temporary faults in the links.  
is affected by a fault in link \(l_{3}\) and it is dropped by listener \(L_{1}\). Nonetheless, \(L_{1}\) correctly receives replicas \(r_{1,1}\) and \(r_{1,2}\), delivers replica \(r_{1,1}\) to the application and ignores \(r_{1,2}\).
## 3 Thermo-Structural Numerical Model
### Numerical Solution Schemes  
An initial temperature of 20 \({}^{\circ}\)C is applied. On the non-exposed surface a constant convection coefficient of 9 W/(m\({}^{2}\) K) as well as radiation effect are applied following Eurocode 1 [41]. The heat flow exchange between the fire-exposed surface of the steel decking and the fire environment takes into account convection and radiation heat transfer as Eurocode 1 defines. The steel decking is subjected to standard fire conditions. Owing to the obstruction to direct fire exposure caused by the waves of the steel decking, two convection coefficients are used: 25 W/(m\({}^{2}\) K) for the web and 15 W/(m\({}^{2}\) K) for the lower and the upper flange, respectively [42]. Profile steel decking is usually made of galvanized cold-formed steel. The emissivity value of 0.7 specified in Eurocode 4 does not consider the lower emissivity value of the zinc used in the galvanization. Therefore, in this work, a constant emissivity value of 0.4 is used, following previous works [13].  
The thermal numerical analysis was divided into 160 steps, which were carefully set to reduce convergence difficulties. The duration of each load step was set individually. To set the duration of each step, two conditions were established: (1) a variation of 30 \({}^{\circ}\)C between consecutive steps and (2) a maximum duration of 20 s for each step. The environmental temperature for each step was equal to the furnace temperature during the experimental tests.  
A sequential thermo-structural model was used to analyze the stress distribution of specimens under fire conditions. The temperature field results of the thermal analysis were used as the predefined field of the thermo-structural analysis (see Figure 10(b)). In the same position where the support is placed, the vertical displacement is constrained, and the load is applied at the top surface of the slab (see Figure 10(a)).  
Due to the complexity of the analysis increasing as the temperature rises, three loading steps were used for solving structural numerical analysis. These load steps gradually increase the load to make the convergence of the model easier.  
For this purpose, the first load step has a minimum step of 0.001 s and a maximum step of 0.1 s. In the second load step, the minimum step time defined is 0.001 s and the maximum is 0.05 s. In the third load step, the minimum time defined is 0.001 s and the maximum time is 1 s.
###### Abstract  
There are thousands or even tens of thousands of satellites in Low Earth Orbit (LEO). How to ensure the economic sustainability of LEO satellite constellation construction is an important issue currently. In this article, we envision integrating the popular and promising Internet of Things (IoT) technology with LEO satellite constellations to indirectly provide economic support for LEO satellite construction through paid IoT services. Of course, this can also bring benefits to the development of IoT. LEO Satellites can provide networks for IoT products in areas with difficult conditions, such as deserts, oceans, etc., and Satellite Edge Computing (SEC) can help to reduce the service latency of IoT. Many IoT products rely on Convolutional Neural Networks (CNNs) to provide services, and it is difficult to perform CNN inference on an edge server solely. Therefore, in this article, we use edge-distributed inference to enable the IoT services in the SEC scenario. How to perform edge-distributed inference to shorten inference time is a challenge. To shorten the inference latency of CNN, we propose a framework based on a joint partition, named EDIJP. We use a joint partition method combining data partition and model partition for distributed partition. We model the data partition as a Linear Programming (LP) problem. To address the challenge of trading off computation latency and communication latency, we designed an iterative algorithm to obtain the final partitioning result. By maintaining the original structure and parameters, our framework ensures that the inference accuracy will not be affected. We simulated the SEC environment, based on two popular CNN models, VGG16 and AlexNet, the performance of our method is varified. Compared with local inference, EdgeFlow, and CoEdge, the inference latency by using EDIJP is shorter.  
2020
# Addition of Industrial Hemp (_Cannabis sativa_ L.) Dry Inflorescence in Beer Production
## 3 Results and Discussion
### Wort and Beer Analysis
Bitterness was not affected at all with the addition of hemp inflorescence at any stage of brewing. This is interesting since industrial hemp belongs to the same family as hops and contains bitter substances. However, as reported by [PERSON] [26], they are mostly variety-dependent. \begin{table}
\begin{tabular}{l c c c c} \hline
**Quality Indicator** & **Unit** & **Control** & **Hemp Inflorescence** & **Dry Hemping** \\ \hline Specific gravity & mg/L & 1.01015 \({}^{\text{a}}\)\(\pm\) 0.00004 & 1.01071 \({}^{\text{b}}\)\(\pm\) 0.00009 & 1.01018 \({}^{\text{a}}\)\(\pm\) 0.00010 \\ Real extract & \% & 4.90 \({}^{\text{a}}\)\(\pm\) 0.04 & 5.05 \({}^{\text{b}}\)\(\pm\) 0.10 & 4.85 \({}^{\text{a}}\)\(\pm\) 0.03 \\ Original extract & \({}^{\circ}\)P & 14.49 \({}^{\text{a}}\)\(\pm\) 0.50 & 14.88 \({}^{\text{b}}\)\(\pm\) 0.02 & 14.52 \({}^{\text{a}}\)\(\pm\) 0.07 \\ Apparent extract & \% & 2.83 \({}^{\text{a}}\)\(\pm\) 0.05 & 2.67 \({}^{\text{b}}\)\(\pm\) 0.03 & 2.61 \({}^{\text{a}}\)\(\pm\) 0.04 \\ Alcohol content & \% & 6.19 \({}^{\text{a}}\)\(\pm\) 0.12 & 6.59 \({}^{\text{b}}\)\(\pm\) 0.06 & 6.37 \({}^{\text{a}}\)\(\pm\) 0.05 \\ Polyphenol content & mg/L & 180 \({}^{\text{b}}\)\(\pm\) 2 & 339 \({}^{\text{a}}\)\(\pm\) 3 & 272 \({}^{\text{b}}\)\(\pm\) 1 \\ Color & EBC & 7.0 \({}^{\text{b}}\)\(\pm\) 0.1 & 7.5 \({}^{\text{a}}\)\(\pm\) 0.1 & 7.2 \({}^{\text{b}}\)\(\pm\) 0.3 \\ pH & & 4.06 \({}^{\text{b}}\)\(\pm\) 0.02 & 4.09 \({}^{\text{a}}\)\(\pm\) 0.06 & 4.11 \({}^{\text{b}}\)\(\pm\) 0.02 \\ Bitterness & IBU & 25 \({}^{\text{a}}\)\(\pm\) 1 & 25 \({}^{\text{a}}\)\(\pm\) 1 & 25 \({}^{\text{a}}\)\(\pm\) 2 \\ CO\({}_{2}\) & g/L & 1.18 \({}^{\text{a}}\)\(\pm\) 0.03 & 1.07 \({}^{\text{b}}\)\(\pm\) 0.02 & 1.05 \({}^{\text{b}}\)\(\pm\) 0.01 \\ O\({}_{2}\) & ppb & 4014 \({}^{\text{a}}\)\(\pm\) 2 & 3807 \({}^{\text{c}}\)\(\pm\) 5 & 4006 \({}^{\text{b}}\)\(\pm\) 2 \\ \hline \end{tabular}  
* a–b: Means within rows with different superscripts are significantly different (\(p\) < 0.05); IBU—international bitterness unit. EBC—European brewing convention.  
\end{table} 
Table 2: Basic physical–chemical indicators of produced beers. The levels of CO2 were lowest in the dry \"hemped\" sample, 1.05 g/L, while the control contained 1.18 g/L. In the sample with the addition of inflorescence the level of CO2 was 1.07 g/L. The discrepancies are probably related to the forced carbonation process; however, this should be further conducted. Oxygen levels also varied among samples. The sample with the highest levels of O2 (4014 ppb) was the control sample. The sample which underwent dry \"hemping\" had 4006 ppb. This can potentially be related to the fact that dry hemping entails the opening of the fermenter to insert the dry hemp infloresences and thus allows fresh air to enter the container. Lowest levels of oxygen were detected in the sample with the addition of infloresences during boiling, at 3807 ppb. This could be due to the higher levels of polyphenic substances that have antioxidative activity.