esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Simulation and Result Analysis
### Simulation Results and Analysis
#### 5.2.2 Solution Results for Dataset R  
By subtracting the final assembly input sequence from the painting output sequence, the difference plot between the input and output sequences for dataset R (Figure 9) can be obtained. Based on the greedy strategy state transition algorithm, the calculation of the average number of iterations is illustrated in Figure 10. It can be observed that when the average number of iterations reaches 54, the score of the optimization plan will have already approached a stable state. At this point, the average computation time required is 28.13 s, and the optimal average score result for the multi-objective optimization model is 49.79.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
1\(\rightarrow\)4 & 6\(\rightarrow\)15 & 22\(\rightarrow\)25 & 31\(\rightarrow\)35 \\ \hline
44\(\rightarrow\)53 & 54\(\rightarrow\)63 & 56\(\rightarrow\)65 & 58\(\rightarrow\)67 \\
71\(\rightarrow\)80 & 97\(\rightarrow\)106 & 100\(\rightarrow\)106 & 102\(\rightarrow\)111 \\
107\(\rightarrow\)116 & 126\(\rightarrow\)135 & 134\(\rightarrow\)143 & 140\(\rightarrow\)159 \\
151\(\rightarrow\)158 & 166\(\rightarrow\)172 & 178\(\rightarrow\)187 & 204\(\rightarrow\)213 \\
226\(\rightarrow\)231 & 233\(\rightarrow\)242 & 234\(\rightarrow\)238 & 235\(\rightarrow\)244 \\
250\(\rightarrow\)264 & 255\(\rightarrow\)264 & 270\(\rightarrow\)272 & 283\(\rightarrow\)288 \\
292\(\rightarrow\)301 & 295\(\rightarrow\)304 & 312\(\rightarrow\)318 & \\ \hline \hline \end{tabular} A\(\rightarrow\)B represents the movement of a vehicle body from its original position in sequence A to a new position in sequence B.  
\end{table}
Table 1: Table of the movement of the painting production line sequence for dataset N in the PBS scheduling system.  
Figure 9: Difference plot between input and output sequences for dataset R.  
Based on Figure 9, it can be observed that the difference between the input and output sequences appears in the form of 38 peaks, indicating the number of times the painting production line sequence was moved while meeting the requirements of the final assembly production. The detailed vehicle movement process is presented in Table 2. In the table, \"2\(\rightarrow\)5\" represents the movement of vehicle body number 2 from its original position in the sequence to the new position of vehicle body number 5, \"5\(\rightarrow\)14\" represents the movement of vehicle body number 5 from its original position in sequence to the new position of vehicle body number 14, and so on.
# A Measure Based on Beamforming Power for Evaluation of Sound Field Reproduction Performance
## 4 Experimental Example
### Experiment with 2.5d Hoa  
The spatial error and the beam-power error were compared in the same room, yet with 2.5D HOA [6]. A circular array of 16 loudspeakers at equiangular positions was used, and the radius of the circle was 1.8 m (Figure 7). The loudspeakers had \(\pm\)3 dB deviation from the mean value in the entire frequency range of interest. The details of the filter used were described in Appendix B. The loudspeakers were modeled as monopoles.  
Two virtual source positions were aimed for reproduction as illustrated in Figure 7. A monopole is reproduced in each experiment as indicated by '\(a\)' and '\(b\)' in Figure 7. The position '\(a\)' is (1.8 m, 90 degrees), and the position '\(b\)' is (1.8 m, 101.25 degrees). The first position corresponds to that of the first loudspeaker, and the second one is at the middle point of the first and the second loudspeakers.  
Figure 9 compares the spatial error and the beam-power error for the desired monopole located at \(a\) (left) and \(b\) (right). As the position '\(a\)' coincide with the loudspeaker #1, one might expect a better reproduction performance than that for the position '\(b\)'. The spatial error, however, shows similar values and fluctuations with the frequency for those two cases. The beam-power error for position '\(a\)' turns out to be smaller than that for position '\(b\)'.  
Figure 8: Spatial error and beam-power error in the pre-experiment.  
Figure 9: Spatial error and beam-power error in reproduction of a monopole at ‘\(a\)’ (**left**) and ‘\(b\)’ (**right**).  
As explained in Figure 8, these peaks in the spatial error are ascribed to the floor reflection. On the other hand, the beam-power error is less affected by the floor reflection.
# FnmOS-ELM: A Flexible Neural Network Mixed Online Sequential Elm
## 4 Experimental Design and Result Analysis
### Hyper-Parameters on CIFAR-10 Dataset
#### 4.5.1 Impact of Batch Size D on Performance  
On the CIFAR-10 dataset, we examine the influence of hyper-parameters on the training effect in detail, and the performance of the fnmOS-ELM model on the classification problems mentioned above did not achieve the best performance. If the hyper-parameters can be adjusted to a more appropriate state, the classification accuracy will be higher.  
It is found that the batch size has a greater impact on the performance of fnmOS-ELM. The fnmOS-ELM uses the online learning method and learns data chunk-by-chunk. For traditional networks with gradient descent training, using a larger batch size will readily lead to the decline of the generalization performance, due to sharp minima. On the other hand, using a smaller batch size will lead to inherent noise, which will affect the speed of gradient variation [54]. Although the training process of the fnmOS-ELM does not update the parameters by gradient descent, the batch size has also a great influence on the test accuracy. In Equations. (2) and (5), when the batch size (\(D\)) affects the dimension of \(Y_{i0}\), it also affects the dimension of the \(H_{0}\) matrix. As shown in Figure 6a, when \(i~{}=~{}55\), the fnmOS-ELM replaces the fully connected layers in the network and the batch size has a large impact on the test accuracy. When \(i~{}=~{}65\), the fnmOS-ELM is used as the optimiser of the original VGG16 network and the batch size has a small impact on the classification effect on the test accuracy. Therefore, when using the fnmOS-ELM as an optimiser, a good performance can be achieved without repeatedly adjusting the batch size; when using the OS-ELM as a network classifier, it is beneficial to adjust the batch size to a suitable value.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Method** & **Trainable Parameters** & **Test Accuracy (\%)** \\ \hline \multirow{8}{*}{**CIFAR-10**} & ResNet-100 & 1.7 M & 93.57 \\  & ELU & \textgreater{}1 M & 93.45 \\  & RCNN & 0.67 M & 92.91 \\  & VGG16 & 15 M & 93.01 (\(\pm\)0.3) \\  & ELM-LRF & \textgreater{}10,000 & \textless{}85.30 \\  & CNN(VGG16)-ELM & \textgreater{}10,000 & \textless{}90.73 \\  & \(i\) = 55 & 6000 & 93.97 (\(\pm\)0.1) \\  & fnnmOS-ELM & \(i\) = 65 & 90 & 93.80 (\(\pm\)0.1) \\ \hline \multirow{8}{*}{**CIFAR-100**} & ELU & \textgreater{}1 M & 75.72 \\  & RCNN & 1.87 M & 68.25 \\  & NIN+APL & 0.67 M & 69.17 \\  & VGG16 & 15 M & 69.73 (\(\pm\)0.59) \\  & ELM-LRF & 0.1 M & \textless{}60.31 \\  & CNN(VGG16)-ELM & 0.1 M & \textless{}67.77 \\  & \(i\) = 55 & 85,000 & 70.64 (\(\pm\)0.1) \\  & fnnmOS-ELM & \(i\) = 65 & 7000 & 70.67 (\(\pm\)0.1) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Trainable parameters and test accuracy of several methods on the CIFAR-10 and CIFAR-100 datasets.
## 3 Results and Discussions
### The Soaking Method

Table 1: Influence of the wood species and impregnation method on the uptake of three preservative solutions and Tukey’s significant difference among the mean uptake values for each of the preservative products utilized. Mean values that share the same upper-case letter are not statistically different. solvent [19]. Moreover, considering the sole Belocid, stone pine sapwood soaked for 480 min showed values of uptake not different from the statistical point of view, from been treated with the vacuum method. Stone pine sapwood soaked for 480 min in Silvanolin showed an uptake evidently higher (254.60 \(\pm\) 116.31 kg/m\({}^{3}\)) and not statistically different from that of Scots pine and beech treated with the vacuum method (respectively, 370.87 \(\pm\) 29.55 and 281.86 \(\pm\) 81.19 kg/m\({}^{3}\)). Of note, the result showed a very high variability in uptake (cf. standard deviation) that might have been responsible for a lack in statistical difference in the obtained values. It can be assumed that the higher uptake may have been due to a high sensitivity to the ethanolamine component of Silvanolin of stone pine wood. Ethanolamine is a wood-swelling agent that allows for better penetration of copper-based wood preservatives [31; 32]. Figure 1 shows the radial and tangential penetration of Silvanolin in the different wood species and the applied impregnation method. In general, the penetration depth increased with a longer immersion time. It is expected that the vacuum and the empty-cell method lead to a better penetration, which is comparable with the results of the Silvanolin uptake presented before. The higher the uptake, the higher the penetration. Therefore, the highest uptake and penetration were found in the samples impregnated with the full-cell method. This was because the Silvanolin penetrates the entire sapwood cross-section of stone pine. Such a pronounced penetration was only found in the sapwood of stone pine in the case of full-cell and vacuum treatment. However, the sapwood of Scots pine was only fully penetrated in the full-cell method. For the different immersion times, the penetration depth in the sapwood of stone pine increased after 480 min of immersion (3.70 mm) compared to 60 min of immersion (1.30 mm). In beech, spruce, and Scots pine, the penetration depths of Silvanolin after 480 min of immersion were 0.88 mm, 0.85 mm, and 1.30 mm, respectively. On the other hand, comparable penetration was achieved on the sapwood of stone pine after 60 min. The penetration data are consistent with the uptake data, and the results prove that stone pine sapwood is very permeable and copper-ethanolamine systems such as Silvanolin are an excellent choice for treatment. Figure 1: Radial and tangential penetration of Silvanolin preservative solution for the different impregnation methods.
## 6 Security Analysis
### Computational and Communication Costs
#### 7.2.3 Communication Cost  
We have recorded and analyzed the communication performance under varying conditions and compared our proposed scheme against existing methods, including SA [16], Versa [17], and ESVFL [43]. The comparative results are depicted in Figure 9. These results clearly demonstrate that our proposed scheme exhibits a significant advantage in terms of communication cost. Specifically, our scheme achieves reductions in communication cost of at least 47%, 69%, and 75% compared to SA, Versa, and ESVFL, respectively. This superior performance stems from the fact that in SA and Versa, each user must transmit a large volume of parameters to either the cloud server or other users, thereby increasing communication overhead. Similarly, in ESVFL, each user is required to send four encrypted gradients, which also contributes to higher communication burdens. Notably, our scheme demonstrates remarkable stability in communication cost, unaffected by an increase in the number of users, with variations primarily influenced by changes in gradient quantities.  
Similarly, we evaluated the communication cost per user when users drop out. As shown in 1. Please confirm whether there are repeated formulas in paper. 2. The format of the formulas needs to be consistent. Please check/confirm Figure (b)b, there was no significant fluctuation in the communication overhead per user as the proportion of dropout users increased. This stability is attributed to the fact that, irrespective of fluctuations in the user dropout rate, each active user is required to transfer merely two auxiliary pieces of information in the worst-case scenario, a process consuming less than 0.05 MB of communication resources. Consequently, despite a rise in the dropout user ratio, the communication cost on a single user did not experience a significant escalation.  
Figure 8: Computational cost comparison of each user. (a) \(|\mathcal{U}|=100\), with different numbers of gradients. (b) \(|\mathcal{G}|=1\times 10^{4}\), with different numbers of users.
## 2 Materials and Methods  
### Samples Collection  
Lists of registered rural households were provided by the municipal agencies of Arta, Epirus, Greece. From these lists and with the aid of random numbers a series of households were located as sampling points. In every household the aim of the study was explained to the owner through a telephone conversation the previous day and on the day of the visit he was asked to sacrifice between one and three birds depending on the size of the flock (3-15 chickens, 15-40 chickens, and more than 40). In case of absence or denial, the team contacted and visited the next listed household. The days chosen for visits were always Thursdays and Fridays because according to the local culinary tradition of the area, these are the days for slaughtering in order for the bird's meat to season until Sunday, the day of its consumption in the family table. We decided that a maximum of 20 households for every group of combined criteria (see below) would be sufficient. All owners sacrificed the birds by cervical dislocation and immediately afterward our team took the necessary samples aseptically using sterile forceps and scissors as follows: (a) approximately 30 g of chicken skin, (b) 100 g of the pectoralis muscle, and (c) one swab of the visceral cavity and the matching liver. The samples were placed in a sterile polyethylene bag or small bottles containing a small quantity of 0.1% peptone water (Oxoid, Basingstoke, England) just to avoid dryness, and the samples were transported to the laboratory in insulated boxes with ice packs and processed within 1 h. A consent document was obtained from the head of each household for participation in this study.  
Classification  
The samples were classified with respect to the following four criteria (see Discussion):1. The size of the flock: up to 15 birds (_Gallus domesticus_), 15-40 birds, and more than 40 birds to 60;
2. The presence or not in the same household of other poultry species like turkeys, ducks, etc;
3. The presence or not in the same household of small ruminants (sheep and goats) and pigs;
4. The administration of households' leftovers of plant origin (potatoes, tomatoes etc.) or the administration of industrial-grade concentrated feeds (corn, barley, etc.). From these 4 criteria, 15 study groups emerged (Table 1).
# Deep Anomaly Detection Based on Variational Deviation Network
## 5 Results
### Performance Evaluation Method
Therefore, the larger the AUC-PR, the better the algorithm performance. The study in [35] found that when the data in the dataset was out of balance, that is, the difference between positive and negative examples was very large, which is the same as the characteristics of anomaly detection data, this led to a smaller difference in AUC-ROC, and AUC-PR had more effective differences between performance algorithms. In order to further improve the accuracy of the evaluation index, we applied the method of calculating the average accuracy in [36] to calculate AUC-PR, where AUC-ROC was used as a reference index to further measure the superiority of the algorithm. In order to prove the superiority of V-DevNet, we added the Wilcoxon signed-rank test [37], where the idea of this method is to take the rank of the absolute value of the difference between the observed values and the central position assumed by the original hypothesis (or the two sets of sample values) and add them up separately according to different signs as its test statistic. In using the above evaluation method, the relevant AUC-PR and AUC-ROC indicators of the variational deviation network and the other five comparison methods are shown in Tables 1 and 2. In the above table, we can see that the variational deviation network in data 7 and 9 showed the best performance. Among them, under the AUC-ROC performance index, the variance deviation network with other experimental methods by ANOVA test, we found that the \(p\)-value was less than 0.05, so they were significantly different; by comparing the mean, we found that the performance of the variational deviation network was significantly higher than that of the deviation network (2%), REPEN (12%), Deep-SVDD (6%), FSNet (25%), iForest (33%). Under the AUC-PR performance index of the variance deviation network with other experimental methods by the ANOVA test, the \(p\)-value was also less than 0.05, so they were significantly different; by comparing the mean, our experiment found that the performance of the variational deviation network was significantly higher than that of the deviation network (6%), REPEN (130%), Deep-SVDD (30%), FSNet (124%), iForest (332%). The experimental results showed that we used variational self-coding to learn the reference scores with a data-driven approach and to guide the optimization of the deviation network with the reference scores, resulting in significantly higher experimental results than the other methods. In the study of time complexity, we found that the overall running time of V-DevNet grew linearly with the size and dimensionality of the data, which provides a strong guarantee for the use of the algorithm.
# A Realistic Approach of the Maximum Work Extraction from Solar Thermal Collectors
## 2 Review up Today  
### Maximum Work Extraction from Solar Energy  
The first way to express the maximum possible work extraction from solar irradiation uses the exergy analysis. Many researchers have investigated the exergy efficiency of the solar irradiation, and various literature equations about solar exergy have been suggested. Below, a brief literature review about the exergy flow of the solar irradiation (\(\eta_{ex}\)) is given. The ambient (reference) temperature (\(T_{amb}\)) can be taken at 298 K (for instance) and the sun temperature (\(T_{sun}\)) at 5770 K.  
The simplest formula is the Jeter assumption, which practically assumes the Sun to be a heat reservoir. In this case, the maximum possible extracted work is calculated according to Equation (1), which is similar to the Carnot efficiency [13]:  
\[\eta_{ex}=1-\frac{T_{amb}}{T_{sun}} \tag{1}\]  
The next model was suggested by [PERSON]. This model assumes the Sun as a radiation reservoir, but it does not take into consideration the impact of the ambient in the final work production, and thus there is the term of \"4/3\" in this equation. Equation (2) gives the exergy efficiency of the solar irradiation, according to [PERSON] [13]:  
\[\eta_{ex}=1-\frac{4}{3}\cdot\frac{T_{amb}}{T_{sun}} \tag{2}\]  
[PERSON] [13] developed a model that assumes the Sun as a radiation reservoir, and, simultaneously, it takes into consideration the environmental situation.  
\[\eta_{ex}=1-\frac{4}{3}\cdot\frac{T_{amb}}{T_{sun}}+\frac{1}{3}\cdot\left( \frac{T_{amb}}{T_{sun}}\right)^{4} \tag{3}\]  
[PERSON] [14] suggested that the exergy of the solar irradiation has to include the half angle of the cone of the irradiation (\(\delta\)):  
\[\eta_{ex}=1-\frac{4}{3}\cdot\frac{T_{amb}}{T_{sun}}\cdot\left(1-\cos(\delta) \right)^{\frac{1}{4}}+\frac{1}{3}\cdot\left(\frac{T_{amb}}{T_{sun}}\right)^{4} \tag{4}\]  
[PERSON] [15] suggested a model for the exergy of the solar irradiation by taking into account the sun-shape, using a geometric factor (\(f\)):  
\[\eta_{ex}=1-\frac{4}{3}\cdot\frac{T_{amb}}{T_{sun}}+\frac{1}{3\cdot f}\cdot \left(\frac{T_{amb}}{T_{sun}}\right)^{4} \tag{5}\]
## 3 Methodology and Experimentation  
This section provides us the brief about the methodology followed along with different parameters used to carry out this evaluation, a description of the research protocol, an explanation of how measurements and calculations were made, and a statement of which statistical tests were used to analyze the data. This work analyses the performance of AODV, AOMD, and DSDV routing protocols in WBAN mobility models. For smooth conduction of the work, three major steps of simulation have been considered, which include the environment, parameters, and metrics of simulation as the experimental setup is fully based on simulation, which resulted in the performance of routing protocols with various mobility models. NS2 has been used for simulation-based work, as this NS2 software application replicates the similar behavior of a real-time network and can provide as accurate results as the real-time network will yield. This real-time application to accomplished by computing the interaction among various network elements such as links, points, nodes, switches, and routers. Various simulation parameters have also been taken into account as the number of  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Author**} & \multirow{2}{*}{**Year**} & \multicolumn{5}{c}{**Routing Protocols**} & \multicolumn{2}{c}{**Type of**} & \multirow{2}{*}{**Mobility Models**} & \multirow{2}{*}{**Performance Matrices**} \\ \cline{3-3} \cline{5-8}  & & **DSDV** & **AODV** & **AOMDV** & & & & **Outhers** \\ \hline [PERSON] et al. [24] & 2009 & ✓ & ✓ & & DSR & MANET & Random waypoint, Gauss Markov (GM), RPGM & E2E delay and mobile node speed \\ \hline [PERSON] et al. [17] & 2011 & ✓ & ✓ & & DSR, DYMO & MANET & Random Movement & PDR, E2E Delay, Throughput \\ \hline [PERSON] et al. [18] & 2013 & ✓ & ✓ & & DSR & MANET & RWM, RPGM & PDR, Throughput \\ \hline [PERSON] et al. [19] & 2011 & & ✓ & & OLSR, GPSR & VANET & Random Movement & Packet Loss Ratio, Route Failure \\ \hline [PERSON] et al. [20] & 2012 & & & & & DECA, DEMC, DEMC-R & WSN & RWM, RPGM, RVM & Packet Loss, PDR and Throughput \\ \hline [PERSON], [PERSON] et al. [21] & 2015 & ✓ & ✓ & ✓ & DSR & FANET & Random Mobility of Nodes & Packet Delivery Ratio, E2E Delay, PDR \\ \hline [PERSON] et al. [22] & 2018 & & ✓ & ✓ & & WSN & Random Node Movement & Throughput, E2E Delay \\ \hline [PERSON] and [PERSON] [23] & 2018 & & ✓ & & & WSN & Random Node Movement & Throughput, E2E Delay \\ \hline [PERSON] et al. [30] & 2019 & & ✓ & & & OLSR, GRP & MANET & FCM, RWM, SCM, HWM & Throughput, E2E Delay \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of Related Work.  
nodes, type of a channel, routing protocol, propagation model, etc., which can affect the simulation environment and also the result of the network.