esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Frameworks
### Fta  
The FTA involves the development of a fault tree, which is a logical model that shows how basic events interact to result in an overall undesirable event known as a 'TOP Event' [2]. Different causes of a TOP event are related through logic gates [10]. The OR-gate represents whether one event is sufficient, and the AND-gate denotes all factors required to happen simultaneously. The FTA is used to identify the causes of a failure and illustrate how these causes relate during failure analysis. The information obtained from the FTA can be used at input in RBDs. FTA is one of the commonly used reliability analysis techniques in learning from failure studies [2; 10], with demonstration of how this technique can be used to learn from disasters and major failures.
## 5 Discussion
### Limitations and Further Research  
Despite the valuable insights our study provided on the transformative role of emerging digital technologies in achieving the SDGs, several limitations must be highlighted. These limitations not only contextualize the research findings but also suggest directions for future research.  
First, the study's regional focus, limited to countries in the European Union, restricts the possibility of generalizing the research findings to other global contexts. The EU's economic, social, and infrastructural conditions may differ significantly from those in other regions, influencing the potential relationship between digital technologies and SDG progress. Future research should extend this analysis to include countries from different continents and with different economic levels to increase the universality of the conclusions.  
Although the DESI is a comprehensive measure of digital technology adoption, it may not encompass all dimensions of digital transformation relevant to sustainable development. Emerging technologies such as blockchain, quantum computing, and advancements in renewable energy technologies were not explicitly included in the DESI-based analysis. Future research should consider a broader range of digital innovations and their specific contributions to different SDGs.  
The analyses of digitalization's impact on sustainability highlight the mediating role of economic performance, measured by GDP per capita. However, GDP per capita may not fully capture the economic dynamics involved. Other economic indicators, such as income distribution, employment rates, and investment in research and development, could provide a more nuanced understanding of how economic factors mediate this relationship. Future studies should incorporate these additional economic variables to enrich the analysis.
# Multipath Data Storage by Third-Order Nonlinear Optical Properties in Zinc Oxide Nanostructures
## 1 Introduction
An optical logic gate was employed for identifying optical nonlinearities in the thin solid films that were studied. The modification in multiphotonic interactions related to the optical Kerr effect and two-photon absorption exhibited by the 2D nanostructures were analyzed. These findings could be a base for future research related to the development of all-optical interconnection functions. This article has analyzed the potential of engineering optical nonlinearities for instrumentation and processing signals in multipath configurations.
###### Abstract  
In this paper, the eight schemes for aircraft wireless sensor networks are investigated, which are single-hop array beamforming schemes (including analog beamforming (ABF), and digital beamforming (DBF)), non-cooperative schemes (including single-hop and multi-hop schemes), cooperative schemes (including amplify and forward (AF), decode and forward (DF)), and incremental cooperative schemes (incremental decode and forward (IDF), and incremental amplify and forward (IAF)). To set up the aircraft wireless communication environment, we design the aircraft channel model by referring to the experimental parameters of the ITU (International Telecommunication Union)-R M.2283, which is composed of path loss, shadowing fading, and multi-path fading channel responses. To evaluate the performance, the conditions energy consumption and throughput analysis are performed. Through simulation results, the incremental cooperative scheme outperformed by 66.8% better at spectral efficiency 2 than the DBF scheme in terms of the energy consumption metric. Whereas, in terms of throughput metric, overall SNR (signal-to-noise ratio) ranged from \(-20\) to \(30\) dB the beamforming scheme had the best performance in which the beamforming scheme at SNR 0 dB achieved 85.4% better than the multi-hop scheme. Finally, in terms of normalized throughput metric in low SNR range between \(-20\) and \(1\) dB the ABF scheme had the best performance over the others in which the ABF at SNR 0 dB achieved 75.4% better than the multi-hop scheme. Whereas, in high SNR range between 2 and 30 dB the IDF scheme had the best performance in which the IDF at SNR 10 dB achieved 62.2% better than the multi-hop scheme.  
Keywords:beamforming; cooperative communication; aircraft channel model; wireless sensor network +
Footnote †: journal: Applied Sciences
# A Novel Video Transmission Latency Measurement Method for Intelligent Cloud Computing
## 4 Evaluation
### Method 2: Remote Online Measurement  
The method requires a camera, a display, a flash, a codec, and a CC3030 before the measurement. In this experiment, the flashing light flickered once every second for 200 ms. When using the camera to capture video, we must ensure that the flashing light is in the capture picture.  
When the PPS signal from GPS arrives, the lights start flashing and CC3030 also starts the timing operation. The flash video picture captured by the camera is transmitted and the reconstructed video is obtained at the decoding end. The reconstructed video is input to CC3030 and the FPGA in the device obtains the flashing picture and stops the timing operation after finding it. Finally, the measurement results are printed on the reconstructed video and displayed on the screen.  
The frequency of flashing light in this measurement is 1 s. The frequency of the flash determines the range of the measured latency. If the frequency of the flashing light is less than the latency of the transmission system when the FPGA searches for the flashing picture, the latency measurement error will occur due to the stacked flashing picture of the system. Therefore, it is necessary to change the flashing frequency when measuring the transmission system with large latency.  
Figure 10a shows the latency result of directly inputting the flash picture collected by the camera into CC3030 without going through the codec, which is the latency of the camera. As can be seen, the video acquisition format is 720P60 HZ and the time latency of the camera used for measurement is 34.9 ms.  
Figure 10: (**a**) Camera delay measurement result; (**b**) Method 2 Delay measurement results.  
Figure 9: (**a**) Delay measurement of H.264 codec; (**b**) Delay measurement of H.265 codec. The Chinese characters in the two pictures are “School of Information Engineering, Jimei University”.  
Figure 10b shows the latency result after the video source is transmitted by the codec. The codec selected for measurement is the H.264 video codec and the video acquisition format is 720P60 HZ. It can be seen that the latency generated in the video transmission process is 96.4 ms and 61.5 ms can be obtained after removing the latency caused by the camera video acquisition.
## 4 Experiment
### Feature Performance Analysis
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{8}{c}{**Prompt**} \\ \cline{2-10}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **Ave.** \\ \hline SLF & 0.802 & 0.652 & 0.665 & 0.768 & 0.774 & 0.691 & 0.732 & 0.612 & 0.712 \\ DOC & 0.775 & 0.687 & 0.683 & 0.788 & 0.810 & 0.807 & 0.805 & 0.614 & 0.746 \\ SEN & 0.796 & 0.695 & 0.701 & 0.779 & 0.801 & 0.793 & 0.792 & 0.678 & 0.755 \\ DOC-SEN & 0.808 & 0.709 & 0.705 & 0.798 & 0.819 & 0.814 & 0.807 & 0.682 & 0.768 \\ DOC-SEN-SLF & 0.839 & 0.742 & 0.732 & 0.812 & 0.821 & 0.817 & 0.815 & 0.721 & 0.788 \\ MSSF & **0.846** & **0.748** & **0.737** & **0.820** & **0.826** & **0.825** & **0.820** & **0.725** & **0.793** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation Experiment Results. The bold number is the best performance for each prompt. Figure 5: Effectiveness analysis. Feature weights of shallow linguistic features on each dataset. The brighter the color, the greater the influence on the final score. sentence vectors by obtaining word vectors and then averaging them did not yield excellent results. Since the SBERT-based model obtained excellent performance by pre-training in the STS task and by fine-tuning the mechanism, its result is substantially better than InferSent and Universal Sentence Encoder (USE). We analyzed the following four reasons that make MSSF work best:  
* It generates contextually aware sentence embeddings. This allows SBERT to capture the contextual meaning of sentences and include word order and dependencies, rather than just averaging out the word vectors in the sentence, resulting in more accurate embeddings. * It benefits from the large-scale pre-training of BERT models. It is typically pre-trained on vast amounts of text data, allowing it to learn a broad range of language patterns. This extensive pre-training contributes to the model's ability to generalize well across different tasks and domains. * It allows fine-tuning on specific downstream tasks. This fine-tuning process adapts the pre-trained model to the specific task, resulting in improved performance. On the other hand, InferSent and USE are not designed for easy fine-tuning and lack this adaptability. * It can be used to calculate the semantic similarity between sentences. The more accurate measure of similarity can be obtained by sentences embedding in a high-dimension and computing the cosine similarity between them. It can better understand the semantics of sentences and improve the capability of sentence vectorization.
## 3 Results and Discussion
### Xanthine Oxidase Assay  
Although not being as active as the positive control allopurnol, some tendencies in the structure activity relationship of the synthesized compounds can be noticed, as seen in Figure 2.  
Comparing compounds **2-6** where hydroxy and methoxy substitutions are present, the \(p\)- substitution can be inferred as a significant requirement for this products, as only \(p\)-OH and \(p\)-OMe products showed activity. This was also the case for compounds **7** and **8**, with hydroxy and methoxy groups as substitutions, while having a _para_ substitution besides a _meta_ one, allowed them to show activity.  
It appears that not only the \(p\)- position is of importance, but also that the functionality in these synthesized compounds must be of -OH or -OMe type, bearing an oxygen as a heteroatom bonded to the aromatic ring. Products **10** and **11** also have substitutions in this position, but with nitrogen as the heteroatom (an amine and nitro group, respectively) and in their case, the _para_ position with a nitrogenated group showed no activity. For the synthesized products, the interaction with xanthine oxidase, instead of being similar to the one for allopurnol, which interacts with one of its aromatic nitrogen to bond with molybdenum in the catalytic site of the enzyme [40], could be similar to the topiroxostat one. This inhibitor interacts with the xanthine oxidase molybdenum with its oxygen in a covalent bond [41]. While compound **10** has its nitrogen in a tertiary amine, and **11** in a nitro group, it could be more difficult for them to bond with the Mo center of the enzyme, favoring in our products the presence of oxygen over nitrogen.  
Product **3**, having a \(p\)-OH group and no other substitution that diminishes its activity, resulted in the most active compound from the synthesized ones. Between the hydroxy and methoxy substitutions, it appeared as the first one favored the inhibition activity over xanthine oxidase. Compound **3** with a \(p\)-OH substitution showed an IC\({}_{50}\) of 85.8 \(\mu\)g/mL, while **4**, which has a \(p\)-OMe, showed almost double the IC\({}_{50}\); again, between **7** and **8**, we could see that the methoxy version was less potent than the hydroxy one. This can be related with the observation made for different products with alcohol groups in their structure such as polyphenols, which can form hydrogen bonds with XO via their hydroxyl groups [14,42].  
However, the exception to the structure activity relationship discussed was **12**, having an \(o\)-NO\({}_{2}\), which lacked a _para_ position and oxygen heteroatom functionality. This compound was achieved  
Figure 1: Percentage of acetylcholinesterasecholinesterase inhibition of triphenyl imidazole compounds **1–13** (150 \(\mu\)g/mL). Galantamine served as the reference compound.  
as one of the few products with xanthine oxidase inhibition, although it showed the second lowest activity. Further _ortho_ nitrogen containing products must be synthesized to expand this analysis.
## 2 Recent Application of Hyperspectral in Food Authentication
### Cereals  
When considering food security in the cereal industry, it is necessary to use reliable and efficient methods to ensure the authenticity and the origin of raw materials and foods [14]. Hyperspectral imaging was explored (900-1700 nm) in order to identify and quantify the levels of adulteration in Irish organic wheat flour (OWF) with other commercial flours, such as wheat (WF), cassava, (CaF) and corn (CoF) [14]. Second derivative (2 nd Der) and standard normal variate (SNV) were used to pre-process the acquired spectra before modelling, where PLS regression and PCR were used for quantitative analysis of the levels of adulteration or contamination [14]. The authors of this study indicated that the best results were obtained by the use of pre-processing methods, such as first-derivative and mean centering iteration algorithm [14]. The R\({}^{2}\) and RMSEP for the prediction of OWF adulterated with CoF, CaF, and WF were 0.97 (0.03), 0.98 (0.026) and 0.97 (0.038), respectively [14]. These results indicatedthat HSI integrated with multivariate analysis has the potential to authenticate the admixtures in specific wheat flour in the range of 3-75% (\(w\)/\(w\)) [14].  
Hyperspectral imaging has also been evaluated for the prediction of the origin of rice [15]. The authors of this study analysed rice samples (\(n\) = 240) from four geographic regions in China using a HSI system [15]. The highest accuracy in terms of classification (>91%) was reported using a combination of spectral, morphological, and texture properties that were measured using instrumental methods [15]. The study demonstrated the suitability of HSI as a tool to rapidly identify the origin of rice from four Chinese regions [15].
## 7 Numerical Simulations and Data Analysis
### Numerical Simulations Study  
Since we cannot compare the performances of the different methods theoretically, some simulation results to compare the performances of the different methods are presented. We mainly compute the biases and mean square errors (MSEs) of the MLEs, AMLEs and Bayes estimates. The asymptotic C.I. of \(R\) and two C.I.s based on the non-parametric bootstrap methods are obtained. We also conduct the Bayes estimates and HPD credible intervals of \(R\). Here, we assume that \(a1=a2=b1=b2=0.0001\). We consider sample sizes \((n,m)=(10,10),\ (20,15),\ (25,25),\ (30,40),\ (50,50)\). For parameter values, we assume that \(\theta_{2}=1\,\theta_{1}=0.5,\ 1,\ 1.5,\ 2,\ 3\) and \(\alpha=2\). All the results are based on the 1000 replications. For the bootstrap methods, we compute the confidence intervals based on 300 resampling. The Bayes estimates are based on 1000 sampling, namely, \(M=1000\). In each case, the nominal level for the C.I.s or the credible intervals is 0.95. We also obtain the average biases and MSEs of the MLEs, AMLEs and Bayes estimates over 1000 replications in Table 1. From Table 1, we can find that the Bayes estimates are almost as efficient as the MLEs and the AMLEs for all sample sizes. Interestingly, in most of the cases, the MSEs of the Bayes estimate are smaller than the MSEs of the MLEs or AMLEs. We can find that the biases and MSEs of the MLEs and AMLEs are very close. As the sample size \((n,m)\) increases, the MSEs of the estimates decrease as expected. Table 2 reports the results of 95% asymptotic C.I. of \(R\), we also obtain C.I.s based on the bootstrap methods and the HPD credible interval. We represent the results of the average confidence/credible lengths and the coverage probabilities. From Table 2, the coverage probabilities reach the nominal level 95% with the increase of the sample sizes. We observe that the MLE method is the most valid procedure to obtain the confidence intervals. The AMLEs and the Bayes estimates are the second best confidence intervals. Interestingly, we find that the HPD credible intervals provide the most highest coverage probabilities. The Boot-\(p\) confidence intervals perform better than Boot-\(t\) confidence intervals, in terms of coverage probabilities. One point we should know is that the bootstrap method depends on the number of resampling. For small sample sizes \((n,m)\), the coverage probabilities for the MLEs and AMLEs are less than the nominal levels, with the increase of sample sizes \((n,m)\), they perform well.
## 4 Performance Evaluation of I\(k\)-Acm
If sinks are located in coordinates (200,0) and (300,0), 2406 nodes choose multicasting. In this case, energy saving increases to 32% and information loss increases to 0.90. Actually, there is a trade-off between data utility and energy consumption. If there is a need for energy minimization and some sinks can tolerate additional data losses, using multicasting when appropriate in some network topologies may be an efficient solution. Other than the location of sinks, another parameter that may effect the efficiency of hybrid method is the total size of WSN field. Figure 9 shows the overall performance results of WSN fields having different sizes and different sink locations. As the size of sensor field increases, hybrid method makes the network consume less energy. For example, when the sink coordinates are (0,0) and (0,500), energy saving is 0.22 for WSN size, 500 m \(\times\) 500 m, however gain rises to 0.35 for mode wider size, 1,000 m \(\times\) 1,000 m. Since, the distance between each sink is not different in both situations, ratio of group head nodes which choose multicasting increases in wider WSN fields. In field size 500 m \(\times\) 500 m, 1287 of 2500 group head nodes choose multicasting. On the other side, in field having sizes 1000 m \(\times\) 1000 m, 8787 of 10000 group head nodes select multicasting option. The cost of increasing the energy saving is the lower data utility since information loss increases to 0.88 from 0.79 in 1000 m\(\times\)1000 m sized field. In experiments having sink locations like (0,0), (0,500) and (0,0), (500,500), energy savings are nearly 3-6 sized fields which seem that hybrid method does not create any promising results. However, in wider sized area like 1000 m \(\times\) 1,000 m, hybrid method yields good results so that energy saving is above than 20%, If the sink locations are (200,0) and (300,0) for a WSN size 1,000 m \(\times\) 1,000 m, energy saving is 40% which is maximum gain among the all experiments. Total information loss rises to 0.91 from 0.66 if we compare hybrid with multipathing. \begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline
**Location of Sinks** & **Total Info** & **Number of Group** & **Number of Group** & **ES** \\ **(coordinates)** & **Loss For** & **Head Nodes Using** & **Head Nodes Using** & (\%) \\  & **(bits)** & **Multicasting Method** & **Multipathing Method** & \\ \hline (0,0),(500,500) & 0.73 & 716 & 1784 & 3 \\ (0,0),(500,0) & 0.79 & 1287 & 1213 & 6 \\ (100,0),(400,0) & 0.84 & 1813 & 687 & 14 \\ (150,0),(350,0) & 0.87 & 2137 & 363 & 22 \\ (200,0),(300,0) & 0.90 & 2406 & 94 & 32 \\ \hline \end{tabular}
\end{table} 
Table 4: Results of using hybrid and multipathing together with different sink locations.
## 2 Proposed Method
### Content Owner's Work
#### 2.1.3 Image Encryption  
After vacating room, the vacated image is encrypted by a stream cipher. Using the encryption key \(K_{\text{en}}\), eight pseudo-random, \(r_{i,j,1},r_{i,j,2},\ldots,r_{i,j,8}\), are generated for each pixel \(p_{i,j}\) in the vacated image. Decompose \(p_{i,j}\) into 8 bits \(b_{i,j,1},b_{i,j,2},\ldots,b_{i,j,8}\) as follows:  
\[b_{i,j,k}=p_{i,j}/2^{k-1}\text{ mod }2,\text{ where }k=1,\text{ }2,\text{ } \ldots,\text{ }8. \tag{5}\]  
\(p_{i,j}\) is encrypted into \(E_{i,j}\) as follows:  
\[e_{i,j,k}=b_{i,j,k}\oplus r_{i,j,k},k=1,2,\ldots 8 \tag{6}\]  
\[E_{i,j}=\sum_{k=1}^{8}e_{i,j,k}\times 2^{k-1} \tag{7}\]  
Figure 6: Example of the proposed method.  
After the image is encrypted, a certain number of the LSBs of the predictable pixels are replaced with capacity information to enable the data hider to obtain room for hiding data. Finally, the pre-processed encrypted image can be sent to the data hider for embedding the secret data.  
In the proposed method, an image sized \(H\times W\) is encrypted bit by bit with a pseudo-random binary sequence of length \(H\times W\times 8\). In the binary sequence, each bit has almost the same possibility of being 0 or 1. Therefore, without the encryption key, the attacker should test \(2^{H\times W\times 8}\) possible binary sequences to find out the correct decrypted image from the encrypted image. For the standard grayscale image sized \(512\times 512\), the number of possible binary sequences is \(2^{2097152}\)--it is almost impossible to test all possible sequences within an acceptable time. Therefore, the image encryption of the proposed method is strong enough to protect the content security.