esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Analysis of Current Approaches to Quality Estimation for Neural Machine Translation
## 5 Question 1: Which mPLM Is Best for QE Tasks?
### Experimental Results for Question 2
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Pearson**} & \multicolumn{4}{c}{**MAE**} & \multicolumn{4}{c}{**RMSE**} \\ \cline{2-10}  & **Max** & **Min** & **Average** & **Avg Diff** & **Min** & **Max** & **Average** & **Min** & **Max** & **Average** \\ \hline XLM-R-base & 0.365 & 0.272 & 0.326 & \(-\)0.002 & 0.462 & 0.495 & 0.481 & 0.653 & 0.698 & 0.670 \\ \hline XLM-R-large & 0.394 & 0.260 & 0.330 & +0.032 & 0.447 & 0.508 & 0.479 & 0.644 & 0.729 & 0.681 \\ \hline mBERT & 0.402 & 0.106 & 0.278 & \(-\)0.104 & 0.453 & 0.553 & 0.498 & 0.648 & 0.762 & 0.700 \\ \hline mBART & 0.388 & 0.277 & 0.346 & \(-\)0.005 & 0.436 & 0.543 & 0.478 & 0.664 & 0.693 & 0.674 \\ \hline XLM-CLM & 0.268 & 0.147 & 0.197 & \(-\)0.056 & 0.483 & 0.515 & 0.502 & 0.688 & 0.714 & 0.698 \\ \hline XLM-MLM & 0.250 & 0.128 & 0.177 & \(-\)0.029 & 0.517 & 0.557 & 0.540 & 0.694 & 0.751 & 0.727 \\ \hline XLM-MLM-17 & 0.267 & 0.172 & 0.230 & \(-\)0.023 & 0.482 & 0.502 & 0.491 & 0.682 & 0.713 & 0.693 \\ \hline XLM-MLM-100 & 0.314 & 0.189 & 0.260 & +0.028 & 0.503 & 0.587 & 0.544 & 0.666 & 0.748 & 0.709 \\ \hline XLM-TLM & 0.234 & 0.141 & 0.193 & \(-\)0.201 & 0.563 & 1.115 & 0.896 & 0.739 & 1.237 & 1.061 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of mPLM finetuning with inverted inputs for the test set of WMT20 sub-task 1. Figure 1: Comparison of the average Pearson correlation coefficients of original and reverse order inputs in sub-tasks 1 and 2. We refer again to the question we asked. Does the input order of the source sentence and the MT output sentence affect the performance of the model? Through these experiments, we determined that the performance fluctuation of the input order varies depending on the sub-task. To the question, we can answer that the structure of the input is a factor that affects the performance of the model, and it must, therefore, be considered before conducting such experiments.
# Clustering Method of Large-Scale Battlefield Airspace Based on Multi A * in Airspace Grid System
## 2 Analysis of Key Problems
A * algorithm is suitable for static environments and has high planning efficiency; D * algorithm is applicable to path planning in dynamic environment with high accuracy of planning results. Considering that different path planning algorithms may affect the airspace clustering results, this paper first plans the airspace clustering process based on multiple A * algorithms, and compares multiple path planning algorithms in the experimental part to evaluate the speed and effect of different algorithms. The overall process method is divided into three steps:  
1. The core problem is abstractly likened to a path planning problem with multiple starting points/ endpoints, that is, the airspace projection projected onto a two-dimensional plane is approximately regarded as a ground obstacle. Using the Multi A * algorithm, by setting a relatively reasonable starting point and endpoint for many times, multiple clustering lines without contact and conflict with the obstacle (task airspace) can be planned. These clustering lines combine to divide the entire task area into multiple airspace clusters. 2. On the basis of the first step, the airspace objects and path data are coded using the grid system, and the path-path intersection points (i.e., the vertices of each airspace cluster) are calculated through grid coding. 3. Finally, the number of the cluster where the center of the airspace is located is judged by \"Angle Addition\", and the airspace clustering result is finally obtained.
# A Task-Centric Cooperative Sensing Scheme for Mobile Crowdsourcing Systems
## 4 Task-Centric Cooperative Sensing Scheme
### Improved Participant Selection Scheme
\(I_{A}\leftarrow I,I_{s}\leftarrow\{1,2,...,M\}\) \(\epsilon_{min}\leftarrow\infty,Y_{A}\leftarrow\textbf{Data}_{M},Y_{B} \leftarrow\emptyset\), iteration count \(r\gets 0\) priority queue \(A=[1,2,...,M]\) for time-periods.count \(1\) to \(N\) for\(i\gets 1\) to \(M\) \(r\gets r+1\)  average_grid_value \(\leftarrow\sum\limits_{1\leqslant j\leqslant M}y_{j}(t)/M\) for\(j\gets 1\) to \(M\) if\(\frac{average\_grid\_value-y_{j}(t)}{average\_grid\_value}>\) 0.2 queue_sort (\(M,j\)) /make a sort of residual power */ for\(k=1\) to \(M-r+1\), \(X\)\(Y_{A}\) matrix with the data set of \(I_{A}\) \(Y_{tmp}\leftarrow[Y_{B}\mid k^{th}\) column of Data matrix] \(\epsilon\left(k\right)\leftarrow\frac{|1|Y_{tmp}-X(X^{T}x)|^{-1}X^{T}x^{*}Y_{tmp}| |^{1}\bar{1}Y_{tmp}|^{1}\bar{1}\bar{1}\bar{1}}\) if\(\epsilon(k)<\epsilon_{min}\)then \(\epsilon_{min}\leftarrow\epsilon(k),k\gets k^{*}\)  Data = Data matrix without the \(k^{*}\) column if\(\epsilon_{min}>\) loss_threshold \(\textbf{then}\) Flag = True else Flag = False; Selected_id \(\leftarrow\)\(k^{*}\) endif \(Y=[\)\(Y\mid k^{*}\) column of Data matrix \(]\) \(I_{s}\leftarrow I_{s}\) without selected_id \(k^{*}\) \(C(r)=\epsilon_{min}\) end Output: selection set of least grids \(\leftarrow I_{s}\)
```  
**Algorithm 2**Improved Participant Selection Algorithm (IPSA). Discussions  
As we have known, one potential shortcoming of the TCPS algorithm is that the workload of individual users may not be balanced. Some users may never be selected throughout the entire MCS task cycle, thus resulting in unfairness. This problem has been handled in the IPSA, which sets a priority queue and continuously modifies the queue in every task cycle based on the residual power of the participants. All the participants have a chance to be selected to be set active mode in a task in this solution for the reason that if a participant has worked for a long time then its priority will be less than that of other participants which have never been selected. In such a selection strategy based on priority queues, we can always select the high-power participants in a certain task cycle to make sure every participant can join in the tasks. For another problem of resource consumption, representing a full utilization of resources, the IPSA takes some inaccurate sample data as a substitute for real sensing data and then uses the TCPS to find an optimal participant selection set among the other participants. In this way, we can further reduce the number of participants and save more power because we have gained some data for foundation. Although using these special sensing regions and inaccurate data will result in extra errors, we have proposed a solution to handle the offset errors and ensure that our selection strategy is based on sufficient data integrity.
###### Abstract
It allows the extraction and introduction of analytes into the GC inlet in one step. It is a rapid technique, which prevents the use of organic solvents and is characterized by a reduced operating cost due to the reuse of extraction fibers [25]. In a recent study conducted in our laboratory by [PERSON] et al., a HS-SPME/GC-MS method was developed to study the VOCs emitted by the fragrant flowers of three _Tillandsia_ species. First, two extraction methods were developed and allowed the identification of 30 volatile compounds from _T. xiphioides_[26]. Then, these two methods were used to study the aromatic profile of _T. caliginosa_ and _T. crocata_ and allowed to identify 65 compounds [27]. Before this work, only one pioneering study about the aromatic profile of the _Tillandsia_ genus was carried out in 1991 for _T. crocata_ species [28]. This led to a partial isolation of VOCs (seven volatile compounds) from its flowers, as part of a larger study on fragrance composition of flowers attracting male euglossine bees. For the present study, the two extraction methods developed in our laboratory by [PERSON] et al. [26] were used to obtain the aromatic profiles of 14 species and hybrids of _Tillandsia_ genus, belonging to a different subgenus [5]. These species are: _T. aeranthos, T. bergeri, T. albertiana, T. xivoides, T. ionantha, T. tenuifolia, T. palacena, T. cactiola, T. lorentziana, T. didisticha, T. bandensis, T. recurrata, hybrids of _T. aeranthos_ and _T. bergeri_ (_T. aeranthos_ x _T. bergeri_), and _T. aeranthos_ (_urguay_/, a form of _T. aeranthos_ (see Section 4.1). All the 14 species have scentless or faint-scented flower odors for humans. However, _T. bandensis_ and _T. palacena_ are considered to be faint-scented flowers for some people, while the 12 other species are considered scentless for humans. The objective of this study is to identify the VOCs of non-fragrant flowers of these 14 species and hybrids of the genus _Tillandsia_ using the HS-SPME/GC-MS method. The final objective is to confirm that non-fragrant _Tillandsia_ flowers also emit common VOCs. All the flowers are presented in Table 1.  
A qualitative heatmap was performed to identify in which species the different compounds were present. Then, a principal component analysis (PCA) was performed to profile the variations of compounds of two different species and their hybrids, _T. aeranthos_, _T. bergeri_, and _T. aeranthos_ x _T. bergeri_ (hybrids). Finally, another PCA analysis was performed including most of the _Tillandasia_ species studied in order to try to classify _Tillandasia_ species into groups corresponding to the subgenus groups, if possible. The aromatic profile of these _Tillandasia_ species has never been studied before and it is the first time an analysis and comparison of the profiles of volatile compounds between non-fragrant _Tillandasia_ species and fragment ones has been realized.
## 3 Bisection Constraint Method for Topology Optimization Considering Multiple-Loading Conditions
### Starting Point  
All optimization algorithms require users to supply a starting point denoted by \(x_{0}\). Users with knowledge of the application and the dataset may be in a good position to choose \(x_{0}\) to reasonably close the true solution. Otherwise, the starting point must be selected by an algorithm, either systematically or arbitrarily. The choice of the starting point is an important task that significantly affects the robustness of the algorithm. A poor choice of the starting point might even lead to a convergence failure [66].  
As shown in Figure 2, the result at the end of each outer loop is a solution that belongs to the Pareto set because it is obtained from one sub-TO problem, forming a trade-off relationship between compliances [58]. Therefore, this method is an algorithm for generating several Pareto solutions with different trade-off relationship setups for the compliances. However, after each outer loop, a Pareto solution is generated with better performance than the previous one (the maximum compliance becomes smaller). At this point, improving a structure that does not belong to the Pareto set holds no significance because it fails to represent any trade-off position of the compliances. In addition, starting at a point belonging to the Pareto front can increase the possibility of convergence to the true solution because it might be closer to the true solution than picking a point in an arbitrary manner. Thus, it is a good idea to start the algorithm at a point that belongs to the Pareto front, which is generated by an optimization algorithm.  
In general, a WS method with positive weights can be used to generate a solution which is Pareto optimal [40]. In the current article, an optimization algorithm using the TEWS method is used to generate the starting point for the proposed method. The TEWS method has several advantages, including simple formulation, low computational cost, and low time consumption, and, more importantly, its solution might be reasonably close to the true solution.  
As shown in Figure 1, the TEWS method is used in Stage 1 to generate the starting point for the main stage (Stage 2). Particularly, in Stage 1, a TO problem is solved using the objective function as the sum of the compliances under each loading condition:  
\[C(\mathbf{x})=\sum_{j=1}^{n_{L}}c_{j}(\mathbf{x}),\ j\in N_{L}=\{1,2,\ldots,n_{L}\}. \tag{14}\]  
The optimization problem in this stage is formulated as follows.  
\[\min_{\mathbf{x}}C(\mathbf{x}),\] (15a) subject to \[K(\mathbf{x})\mathbf{u}_{j}(\mathbf{x})=\mathbf{p}_{j},\ j=1,2,\ldots,n_{L} \tag{15b}\] \[\frac{V(\mathbf{x})}{V_{0}}=f,\] (15c) \[0<x_{min}\leq x_{i}\leq 1,\ \ i=1,2,\ldots,n. \tag{15d}\]  
It is straightforward to solve Problem (15), where the initial point is usually set as all elements in the design domain having the same density, \(x_{0}=f\). Stage 2 further improves the design in the sense of minimizing the maximum compliance, as explained in Section 3.1.
# Study of Annealing Temperature Effect on the Photovoltaic Performance of BiOI-Based Materials
## 3 Results and Discussion
### Photovoltaic Test
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Temperature** & \(J_{\rm sc}\) & \(V_{\rm oc}\) & **Fill Factor** &
\begin{tabular}{c} **Efficiency** \\ **(\%)** \\ \end{tabular} & \(R_{\rm sh}\) **(10\({}^{3}\times\Omega\) cm\({}^{2}\))** & \(R_{\rm s}\) **(10\({}^{3}\times\Omega\) cm\({}^{2}\))** \\ \hline
100 & 0.133 & 0.403 & 0.440 & 0.023 & 9.183 & 0.170 \\
200 & 0.408 & 0.449 & 0.349 & 0.064 & 11.49 & 0.156 \\
300 & 0.438 & 0.442 & 0.357 & 0.069 & 10.82 & 0.148 \\
450 & 0.009 & 0.139 & 0.320 & 0.0004 & 7.898 & 0.162 \\
550 & 0.073 & 0.256 & 0.276 & 0.0052 & 9.247 & 0.112 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Photovoltaic cell parameter of annealed BiOI at different temperatures. \(J_{\rm sc}\): short-circuit current; \(V_{\rm oc}\): open circuit voltage; \(R_{\rm sh}\): shunt resistance; \(R_{\rm s}\): series resistance. Figure 6: Current-voltage curve of annealed BiOI at different temperatures (**A**) and short-circuit current and open-circuit voltage as the function of temperature (**B**). Normally, due to the increase in bandgap energy, the \(V_{\rm{oc}}\) also improves since the thermodynamically open-circuit voltage limit is determined from the bandgap energy of materials. However, the annealing treatment resulted in a lower \(V_{\rm{oc}}\) value. A plummet drop of Voc existed for the annealed sample at 450 \({}^{\circ}\)C, which might have been influenced by shunt resistance (\(R_{\rm{sh}}\)). The small \(R_{\rm{sh}}\) value caused the low \(V_{\rm{oc}}\) in the photovoltaic device. Thus, the performance parameter in the photovoltaic device decreased. On the contrary, the higher \(R_{\rm{sh}}\) value had an impact on the Voc enhancement, and the higher \(R_{\rm{s}}\) will cause the lowering of the \(J_{\rm{sc}}\) value. The changing in shunt resistance produced another current way in a photovoltaic cell, which could have reduced its current flow and voltage. Therefore, the changing in its bandgap energy and a slight increase of its \(R_{\rm{sh}}\) value in comparison to others may have been responsible for the higher V\({}_{\rm{oc}}\) of annealed BiOI at 300 \({}^{\circ}\)C. The illustrated band structure of annealed BiOI is sketched in Figure 5B. Through this work, we expect that if a better quality of BiOI (coming from its morphology and material size) is used, cell performance will tend to be higher than our results. It has been reported that a single BiOI solar cell could reach a \(J_{\rm{sc}}\) value up to 2 mA/cm\({}^{2}\)[51]. Therefore, by using suitable electrons and holes transport materials, it can be suggested to apply the bismuth oxvoidides and its derivations, like Bi\({}_{7}\)O\({}_{9}\)I\({}_{3}\), for the absorber in the reported device structure [11].
# Blockchain-Based Information Management for Supply Chain Data-Platforms
## 2 Literature Review
### An Effective Combination: BC, IoT, and SCM  
In this section we explore the combinations between blockchain (BC), the internet of things (IoT), and SCM. Assuming blockchain can strengthen IoT security, it can ensure SC security through IoT systems [25]. According to [PERSON] [8], an IoT system in blockchain-based solutions validates the identities of individuals and assets, thereby enabling the use of blockchain in an SC to determine who is performing what actions. Additionally, these features facilitate a valid and effective measurement of outcomes and performances of key SCM processes.  
Furthermore, [PERSON] et al. [3] explored the role of IoT and its impact on SCM processes and applications. Thus, according to [PERSON] et al. [3], IoT for SCM will affect procurement, production planning, management of inventory, quality, and maintenance.  
IoT systems offer SCM an unprecedented visibility into all aspects of the SC [3]; however, the IoT for SCM is still in its early stage of development. In this instance, there are several risks correlated with the implementation of IoT in SCM [5]. According to [PERSON] and [PERSON] [5], these risks are divided in three clusters: (i) environmental; (ii) network-related; and (iii) organizational. These risks impact Industry 4.0 and either the IoT for Industry 4.0 or the IIoT [3; 4; 42] needs an adequate level of security to mitigate environmental, network, and organizational risks.  
[PERSON] et al. [17] defines IIoT as 'connecting and monitoring industrial objects and physical devices through the internet'.  
Blockchain-based access management systems can address key IoT security challenges such as those associated with internet protocol address spoofing [25]. According to [PERSON] and [PERSON] [43], identity is a crucial property of IoT devices. Blockchain and decentralized identifiers (DID) can be used to trace back the origin of the device, which is especially important in security-relevant environments [43]. Consequently, blockchain may be used for the allocation and management of device identities, which will be an essential feature for future blockchain-based applications [43].  
A blockchain and IoT combination facilitates the sharing of the services and resources leading the marketplace between devices, paving the way for automation and a more secure way for innovation [20]. The blockchain-IoT combination is powerful and is set to transform many industries [20; 44]; for instance, IoT devices can conduct autonomous transactions through smart contracts [44], establishing machine-to-machine autonomous payments [45] and machine-to-machine autonomous communication and decision-making [3], and alter how IoT systems are used in business worldwide [20].  
Therefore, the combination between blockchain, IoT, and SCs might represent a performance improvement in information management for interlocked devices.
## 3 Methodology
### Development of Hybrid Encryption Technique
Single-point crossover is used, where a random crossover point within the key length (1-7 bits) combines segments from two parent keys to create an offspring, preserving diversity and high-fitness traits [36]. Mutation replaces a selected bit of the key with a new random value in the range \([0,255]\) at the rate of 10% to strike a balance between exploration and exploitation [37]. This ensures diversity and prevents premature convergence. These operators, applied iteratively over generations, enable the genetic algorithm to balance encryption strength, ciphertext randomness, and efficient message encoding while maintaining the decryption accuracy. RSA Encryption: RSA encryption is a public key encryption algorithm that uses two keys: a public key for encryption and a private key for decryption. This is based on the mathematical difficulties of factoring the product of two large primes [38; 39; 40; 41]. The integration of the RSA and the GA was designed such that the GA was executed as a pre-encoding step prior to the RSA encryption process, effectively introducing an additional layer of security. For plaintext, GA computes the necessary parameters while maintaining multiple criteria, as explained in the paragraph GA-Based Encryption for key generation and encrypts plaintext, as shown in Algorithm 1. This results in the formation of cryptographic information and structure. Attackers target cryptographic structures using attacks involving cryptanalysis processes [42] to recover keys and original information, necessitating additional security, which is ensured by RSA. The RSA encrypts the resulting cryptographic structure from the GA and stores keys (private and public) as objects that obscure its visibility against threats outside of the controller. Decryption was performed in the reverse order, with RSA executed before the GA, as shown in Figure 2.
# An Initial Load-Based Green Software Defined Network +
## 6 Performance Analysis
### Results
#### 6.3.1 The Performance Profile of ICWGA  
First, through the results for SND-Lib topologies, we observe the performance profile of ICWGA. Since the trend of results for the six topologies are similar, the results for three topologies are presented, and the results for the other three topologies are omitted.  
**(1). The improvement in energy saving ratio**  
In order to observe the performance in energy saving of ICWGA, we compare it with the solution of the MILP model which is solved using the IBM ILOG CPLEX Optimizer [24], and a simple uncoordinated sleeping scheme, in which the traffic is routed on the shortest path by using the Dijkstra routing algorithm and the idle links are put into sleep (dubbed simply as Dijkstra in the results). The initial status of the network is generated in a random way.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Topology** & \(\left|\mathbf{V}\right|\) & \(\left|\mathbf{E}\right|\) & **Link Density** & **Topology** & \(\left|\mathbf{V}\right|\) & \(\left|E\right|\) & **Link Density** \\ \hline Abilene & 12 & 30 & 2.5 & India & 35 & 160 & 4.4 \\ Newyork & 16 & 98 & 6.125 & Pioro & 40 & 178 & 4.45 \\ Janos-us & 26 & 84 & 3.23 & Germany & 50 & 176 & 3.52 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The SND-lib topologies sizes.  
Figure 3 shows the results for energy saving ratio of the different SND-lib topologies. It is observed that, for all the topologies, as \(\lambda\) increases the energy saving ratio decreases in all the three methods. The two green methods perform much better than the Dijkstra algorithm in energy saving. When \(\lambda=50\), compared with the Dijkstra algorithm, ICWGA achieves about 20% improvement in energy saving for the Abilene topology. Regarding the two relatively large topologies, compared with the Dijkstra algorithm, when \(\lambda=80\) ICWGA gets about 36% improvement in energy saving for the India topology, and 16% for the Germany topologies. In all the cases, these improvements are close to optimal solution achieved by solving the MILP model.