esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, for offshore oilfields, productivity evaluation primarily relies on DST tests and lacks segmented productivity test data, hence making it impossible to identify the effective production layer segments of buried hills. Furthermore, accurately calculating reservoir parameters is challenging due to the complex fracture seepage patterns present in metamorphic buried hill reservoirs. The prediction of productivity is not highly reliable due to the lack of accurate determination regarding effective producing layers and reservoir parameters. This paper focuses on predicting well productivity in fractured reservoirs of metamorphic rock buried hills. Through the analysis of geological data, combined with imaging logging and array acoustic logging, it has obvious advantages in fracture identification and evaluation. A fracture validity index has been constructed using fracture parameters such as aperture, porosity and density to provide a quantitative assessment of reservoir validity. Following an accurate description of the effective reservoirs within the buried hill and an analysis of the seepage characteristics of fractures, a reservoir permeability index is established. Using the real productivity coefficient, a productivity evaluation chart for the buried hill reservoir is produced. The validity of this method is demonstrated through comparison with tested data obtained from actual gas wells. This presents a novel concept for appraising and predicting the productivity of the efficient reservoir of metamorphic rock in the research site.
# Effect of Temperatures on Polyphenols during Extraction
## 1 Introduction
Many studies have shown that at drying temperatures above 80 \({}^{\circ}\)C, the polyphenolic yield decreases [19; 28; 29; 30; 31; 32; 33; 34; 35]. For this reason, polyphenols are regularly regarded as heat-labile compounds. However, pressurised liquid extraction (PLE) studies have shown high phenolic yield at temperatures well above 100 \({}^{\circ}\)C. Generally, an increase in extraction temperature (up to 180-200 \({}^{\circ}\)C) correlates to an increase in phenolic content and antioxidant activity [36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53]. As this conflicts with findings from studies using traditional extractions, this topic requires further investigation. The aim of this paper was to examine the variety of complex mechanisms and factors that could explain the variance in the behaviour of polyphenols at high temperatures. Note:  
PLE: Pressurised liquid extraction, subcritical water extraction, hot water extraction  
TSE: Traditional solvent extraction  
TPC: Total phenolic content  
TFC: Total flavonoid content  
AOA: Antioxidant activity
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 4 Wireless Technologies  
After considering various network topologies, the focus now shifts to a thorough analysis of the selected wireless technologies. This examination prioritizes criteria such as network structure, transmission techniques, frequency bands, and security measures to validate their compatibility with the project's requirements.
## 3 Results and Discussion  
### Protein Yield and Composition  
The protein contents of the generated extracts isolated from _Spirulina platensis_ and _Isochrysis galbana T-Iso_ were calculated, and the results are shown in Table 1. The extracts contained 85.50 \(\pm\) 4.90% and 71.90 \(\pm\) 8.60% protein, respectively. The _Spirulina_ sp. and _Isochrysis_ sp. extracts contained 3.27 \(\pm\) 4.90% and 2.87 \(\pm\) 1.27% lipid and consisted of 2.10 \(\pm\) 0.40% and 1.70 \(\pm\) 1.10% ash, respectively. The quantity of extractable protein was 17.15 \(\pm\) 1.5 g protein/100 g from the _Spirulina_ sp. biomass and 23.18 \(\pm\) 10.60 g protein/100 g biomass from the _Isochrysis_ sp. biomass, respectively, using the traditional extraction method. These results are lower than the protein yields described in previous studies, which reported the level of protein obtainable from _Spirulina platensis_ to be 55-77% of the dry weight of the alga. This may be due to the method used to cultivate _Spirulina_ sp. and _Isochrysis_ sp. for use in this work. It may also be due to a different nitrogen conversion factor being used in previous calculations, which resulted in a higher estimation of protein in these studies. It is well documented that protein measurement methods can yield very different results [21; 22; 23]. In addition, the use of sonication to burst the cell wall may have involved a probe instead of a sonication bath, as used in this work.
## 3 Results and Discussion  
### Characterization of TPCB  
#### 3.1.1 Chemical Structure of TPCB  
Figure 2 shows the FTIR spectrum of TPCB. Because of the simple graphite-like molecular structure of TPCB, most of the chemical bonds in TPCB are non-polar bonds. The infrared activity non-polar bonds are very weak [26; 27], so the FTIR spectrum is simple, with four different absorption bands. The relatively wide domain absorption band at 3438 cm\({}^{-1}\) is caused by the stretching vibration of -OH of H\({}_{2}\)O, because TPCB can absorb water from the air. The absorption bands at 1450 cm\({}^{-1}\) and 1604 cm\({}^{-1}\)[28; 29] are assigned to the stretching vibration of the sp\({}^{2}\) hybrid C=C, which is caused by the carbon atoms in the graphite-like sheets of TPCB. The absorption band at 1104 cm\({}^{-1}\) is caused by the stretching vibration of C-O [30], which is due to the structural defects (sp\({}^{3}\) hybrid carbon atoms) in the graphite-like structures.
## 8 Experiments
### Results
#### 8.3.3 Transformer Models  
The results obtained with transformer models are collected in Tables 12 and 13. Character-based transformers had better results than LSTM models for the datasets GosVL, Gos, and Artur_J. For other datasets, LSTM models were slightly better. The results of  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **WER [\%]** & **CER [\%]** & **BLEU** & **chrF** \\ \hline GosVL & **2.97** & **0.81** & 92.99 & 97.75 \\ Gos & **5.14** & **1.77** & 88.88 & 95.04 \\ [PERSON]\_J & **1.96** & **0.51** & 95.45 & 98.50 \\ [PERSON]\_N & **3.55** & **1.07** & 91.72 & 96.85 \\ [PERSON]\_P & **2.22** & **0.54** & 94.67 & 98.46 \\ [PERSON] & **2.84** & **0.84** & 93.52 & 97.66 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Evaluation of character-based LSTM models. Results that surpass the baseline are highlighted in bold.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{2}{c}{**Vocabulary Size = 5000**} & \multicolumn{2}{c}{**Vocabulary Size = 1000**} & \multicolumn{2}{c}{**Vocabulary Size = 500**} \\
**Dataset** & **WER [\%]** & **CER [\%]** & **WER [\%]** & **CER [\%]** & **WER [\%]** & **CER [\%]** \\ \hline [PERSON] & 7.84 & 5.95 & 6.11 & 4.03 & 10.16 & 7.95 \\ [PERSON] & 6.15 & **2.75** & 6.19 & **2.67** & 6.39 & **2.89** \\ [PERSON] & 3.26 & **1.43** & 3.06 & **1.22** & 2.76 & **0.97** \\ [PERSON] & 7.95 & 4.11 & 4.94 & **1.98** & 5.58 & **2.20** \\ [PERSON] & 4.44 & **1.82** & 3.87 & **1.27** & 3.58 & **1.27** \\ [PERSON] & 4.45 & **1.77** & 4.22 & **1.62** & 4.00 & **1.53** \\ \hline \hline \end{tabular}
\end{table}
Table 11: Evaluation of subword-based LSTM models. Results that surpass the baseline are highlighted in bold.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{2}{c}{**Vocabulary Size = 5000**} & \multicolumn{2}{c}{**Vocabulary Size = 1000**} & \multicolumn{2}{c}{**Vocabulary Size = 500**} \\
**Dataset** & **WER [\%]** & **CER [\%]** & **WER [\%]** & **WER [\%]** & **WER [\%]** & **CER [\%]** \\ \hline [PERSON] & 3.92 & 2.46 & 3.83 & 2.44 & 4.08 & 2.48 \\ [PERSON] & **5.18** & **2.06** & **5.17** & **2.02** & 5.36 & **2.06** \\ [PERSON]\_J & 2.62 & **0.83** & 2.48 & **0.79** & 2.40 & **0.76** \\ [PERSON]\_N & **4.23** & **1.49** & **3.96** & **1.39** & **3.99** & **1.4** \\ [PERSON]\_P & 3.51 & **1.06** & 3.04 & **0.89** & 3.03 & **0.88** \\ [PERSON] & 3.99 & **1.56** & 3.88 & **1.41** & 4.01 & **1.44** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Evaluation of subword-based statistical models. Results that surpass the baseline are highlighted in bold.  
subword transformer models were worse than those of character-based transformer models and also worse than subword-based LSTM models, regardless of the vocabulary size.  
To summarize the results of the conducted experiments, we can conclude that using characters as the basic units is not always the best choice when using SMT models. However, it is by far the best option for neural architectures. LSTM and transformer architectures had comparable results.
## 5 Challenges, Open Research Problems, and Future of WBANs
### Mobility  
The purpose of WBANs and health monitoring systems is to let people go about their life while accessing high-quality medical services. The use of sensor networks for this purpose is not new [102, 103]. However, the emergence of WBANs has enabled the development of applications to ensure and promote the mobility of users. Thus, the WBANs technologies enable ubiquitous of healthcare systems. Mobility requires the development of multi-hop, multi-modal, _ad hoc_ sensor networks which bring the problems mentioned in the previous subsections along with the problem of location awareness.
## 3 Results
### Test Usefulness  
The TE for SJ for both male and female participants was greater than the presumed SWC; consequently, these measures were rated as \"marginal.\" In contrast, TE for CMJ and CMJAS for both genders were similar or lower than SWC and was rated as \"OK\" and \"good\".  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Male (n = 26)** & **Female (n = 18)** \\ \hline Age (years) & 30.1 \(\pm\) 10.6 & 28.9 \(\pm\) 5.6 \\ Height (cm) & 178.2 \(\pm\) 16.2 & 169.6 \(\pm\) 6.2 \\ Weight (kg) & 85.9 \(\pm\) 23.8 & 60.5 \(\pm\) 8.7 \\ Leg length (cm) & 108.1 \(\pm\) 4.7 & 106.1 \(\pm\) 4.5 \\ Years of training & 10.5 \(\pm\) 7.6 & 9.8 \(\pm\) 6.6 \\ Training hours per week & 6.2 \(\pm\) 2.1 & 3.9 \(\pm\) 1.1 \\ \hline \hline \end{tabular} Note: Values are expressed as mean \(\pm\) SD.  
\end{table}
Table 1: Descriptive characteristics.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **SJ** & **CMJ** & **CMJAS** \\ \hline
**Test (cm)** & 29.6 \(\pm\) 6.0 & 31.9 \(\pm\) 6.6 & 39.4 \(\pm\) 9.7 \\
**Retest (cm)** & 30.8 \(\pm\) 6.6 & 34.2 \(\pm\) 6.9 & 39.7 \(\pm\) 10.0 \\
**ES** & 0.19 (trivial) & 0.34 (small) & 0.03 (trivial) \\
**Diff (95% CI)** & 1.2 (0.4; 2.1) & 2.3 (1.6; 2.9) & 0.3 (\(-\)0.5;1.1) \\
**ICC (95% CI)** & 0.93 (0.86;0.96) & 0.96 (0.93; 0.97) & 0.97 (0.95; 0.99) \\
**TE (95% CI)** & 1.8 (1.5;2.3) & 1.3 (1.1;1.7) & 2.0 (1.6;2.6) \\
**CV% (95% CI)** & 5.8 (4.7; 7.6) & 4.1 (3.4; 5.5) & 5.0 (4.0; 6.6) \\
**SWC\%** & 1.2 (4.3\%) & 1.3 (4.0\%) & 2.0 (5.3\%) \\
**Rating** & marginal & OK & OK \\ \hline \hline \end{tabular} Abbreviations: SJ, squat jump; CMJ, countermovement jump; ES, effect size; Diff, difference; CI, confidence interval; ICC, intraclass correlation coefficient; TE, typical error; CV, coefficient of variation; SWC, smallest worthwhile change.  
\end{table}
Table 2: Test-retest reliability and usefulness of My Jump 2 in male recreationally active adults.
## 3 Proposed Routing Algorithm  
The proposed algorithm (RA) is different from previous works that try to improve DD by using the additional control session. The novelty of the proposed routing algorithm is that RA is designed to remove the burden of additional control session. By adopting cross-layer design instead of the additional control session, RA can deal with the lossy situation without additional overhead.  
The previous algorithms need an additional control session to deal with lossy situation within a transport layer which controls the error recovery process such as the retransmission of control packets, rerouting, and error correction process. The additional control session can recover the lossy situation, but it imposes an additional overhead and complexity on the original features. Furthermore, their modification of DD without consideration of the radio information may give disadvantages to break originality and to burden overhead and complexity.  
DD already has the traditional control session at the application layer [23] and this functionality is used to ease the failed route problem. This functionality of DD operates well in most lossy situation, but DD suffers severely from communication overhead due to the flooding packets of the additional control session. It is obvious that the additional control session results in degradation of the communication throughput and transmission delay.  
Furthermore, as the authors of [24] pointed out, the inability of the previous algorithms of wired networks is not suitable for wireless networks to correctly identify the cause of packet losses. It is important to know the details of current status of wireless network, but the early works did not consider the radio information which can identify the cause of errors reflecting current status in lossy WSNs. In the previous works, since the network layer has no method to know the details of the current network condition, the packet should be transmitted from low layers to a network layer so that the routing algorithm is able to utilize the radio information.  
On the other hand, RA can provide the method to utilize the radio information across the layers between the MAC and the network layers using cross-layer design. RA updates the radio information into the neighbor table. The neighbor table is shared between MAC and network layer to decide a route with best gradient. When the network becomes unstable in lossy WSNs, RA also suffers from the lossy situation. However, RA is able to restore the route in according to the variant network status. RA can provide reliable data collecting service without additional overhead and complexity for optimizing the route in lossy WSNs.
# Resource Indexing and Querying in Large Connected Environments
## 5 Proposed Approach
### Device Indexing vs. Device Networking  
Before going into detail, we thought it important to distinguish between resource indexing and device networking. In fact, _device networking_ refers to the capability of devices to establish physical communication with one another by employing various communication methods and protocols, such as WSN (wireless sensor networks), LAN (local area networks), and WLAN (wireless local area networks). Meanwhile, _device indexing_ refers to the capability of devices to establish communication with one another, guided by an indexing protocol that takes into account specific criteria and conditions. These criteria can include device semantics, physical location, and device capacities. Of course, device indexing inherently relies on device networking, necessitating a physical connection for devices to establish communication. Consequently, we need to ensure the existence of aphysical link between two devices before indexing them. In this paper, our primary focus is on device indexing. We assume that there is always a physical connection between each pair of devices that want to communicate. Device networking, along with its associated challenges, including issues related to physical connections being lost, will be addressed separately in future works.
## 3 Proposed Method
### Mixed-Scale Frequency Domain Feedforward Network  
Historically, the research and design of Feedforward Neural Networks (FNNs) often focused on enhancing performance by introducing intricate convolutional structures. However, this approach overlooked the significance of correlations between multiscale image features and their advantageous impact on image restoration. Contemporary studies have substantiated the effectiveness of multiscale approaches in image processing tasks. Beyond multiscale features, correlations between frequency domains also exert a noticeable influence on the image restoration process. Analogous to the principles of attention mechanisms, not all high-frequency and low-frequency information in the frequency domain proves beneficial for image restoration. Therefore, considering the amalgamation of these concerns, we introduce the Mixed-Scale Frequency Domain Feedforward Network (MDFN), and the specific structures are presented in Figure 1 as shown.  
According to the depiction of MDFN in Figure 1, given an input tensor X, subsequent to being processed through parallel branches of 3 \(\times\) 3 and 5 \(\times\) 5 convolutions, the image is transformed into the frequency domain. Then, an adaptable parameter matrix is incorporated to dynamically retain useful frequency domain information while discarding irrelevant frequency domain information. This process achieves efficient information filtering and retention. Taking the 3 \(\times\) 3 branch as an example, the operational principle of MDFN is defined as follows:  
\[F_{3\times 3}=\text{Conv}_{3\times 3}(\text{Conv}_{1\times 1}(\text{LN}(X))) \tag{14}\]  
where \(F_{3\times 3}\) represents the feature tensor after undergoing 1 \(\times\) 1 and 3 \(\times\) 3 convolutions. Subsequently, the DCT is applied to transform it into the frequency domain, utilizing a learnable parameter matrix to control the retention of information:  
\[F_{3\times 3}^{det}=\text{ReLU}(D^{-1}(W\otimes D(F_{3\times 3}))) \tag{15}\]  
The 5 \(\times\) 5 branch in the parallel processing follows a similar process to the previously mentioned 3 \(\times\) 3 branch. After obtaining the results from these two parallel branches, we proceed to obtain the final output of the network module through the following steps:  
\[MDFN\_OUT=\text{Conv}_{1\times 1}(Concat(F_{3\times 3}^{det},F_{5\times 5}^{det }))+X \tag{16}\]
## 4 Simulation Results and Discussion
### Simulation Results of the Scattered Sound Field and the Loudspeaker Placement for Each Method  
For the first simulation, the sound pressure amplitude of the scattered sound field and the results of the selected loudspeaker placement for each method from 400 Hz to 1000 Hz are shown in Figure 2. The first row shows scattered sound fields from 400 Hz to 1000 Hz, the second row shows Uni-RLS-based placement, the third row shows CLASSO-RLS-based placement, the fourth row shows LASSO-RLS-\(\gamma_{C}\)-based placement, and the fifth row shows LASSO-RLS-\(\gamma_{NL}\)-based placement.  
Figure 2a-c shows that scattered sound power is mainly distributed in the forward-scattered sound area (in the negative half-axis of the \(x\)-axis) and the backward-scattered sound area (in the positive half-axis of the \(x\)-axis). The amplitude of the forward-scattered sound increases with increasing frequency, and the region of forward scattered sound becomes larger along the negative half-axis of the \(x\)-axis.  
Figure 2d-f shows the uniformly placed secondary loudspeakers and the distribution of the secondary loudspeakers doesn't change with increasing frequency. Figure 2g-i shows that the selected loudspeakers of the proposed CLASSO-RLS algorithm are mainly distributed in the forward region of the cylinder (near the region of the forward-scattered sound) and the backward region of the cylinder (near the region of the backward-scattered sound). As the frequency increases, there are more secondary loudspeakers in the forward region and the secondary loudspeaker distribution in the backward region becomes more concentrated.  
Figure 2j-o illustrates that the selected loudspeakers of LASSO-RLS-\(\gamma_{C}\) method and those of the LASSO-RLS-\(\gamma_{NL}\) method are mainly distributed in the forward and backward regions of the cylinder. The distribution of the secondary loudspeakers changes with increasing frequency. Compared to the CLASSO-RLS method, there are more secondary loudspeakers in the forward region and the distribution of the secondary loudspeakers in the backward region is more concentrated.
## 3 Experiment
### Simulation Aging of the Fading Sample  
In this experiment, the optical simulation aging was conducted in a completely dark environment. The following steps were taken:  
1. Setting the Simulated Aging Box: The aging box was adjusted to simulate environmental parameters similar to those of the Mogao Grottoes. Considering the significant temperature and humidity differences between day and night at the grottoes, the temperature and humidity in the aging box were adjusted accordingly.  
2. External Isolation: A blackout curtain was used to isolate the aging box to prevent any external light variations from affecting the experiment.  
3. Arrangement and Illumination of the Fading Samples: The fading samples were placed below the light source in a circular arrangement. The height distance between the light source and the samples was adjusted to ensure that each sample received the same irradiance on its surface. Moreover, the distribution of the spectrum of the light source was periodically tested and replaced to prevent any abnormal phenomena, such as light decay, from affecting the accuracy of the aging experiment. The light source spectral distribution curve of the aging samples and experimental equipment is shown in Figure 8.  
The total number of cycles in this experiment was 102, with 10 h per cycle, for a total irradiation duration of 1020 h. An X-rite C64 integrating sphere spectrophotometer (X-Rite, Grand Rapids, MI, USA) was used to measure the colorimetric properties of each fading sample in the range of 400 to 700 nm under a D50 light source, 2-degree viewing angle, D/8\({}^{\circ}\) illumination geometry, and a 6 mm aperture. The measurement data were organized accordingly. Subsequently, the GWOAD-LSTM composite model was used for time series regression analysis of the hLC (hue, lightness, and chroma) values of the fading samples as the exposure time increased. This analysis aimed to predict the corresponding hLC variations of the six fading sample cards caused by the simulated sunlight exposure.  
After prolonged exposure to light, all six pigments exhibited uneven color patches on the surface. As shown in Figure 9, lead red and lithargite exhibited dark patches on their surfaces, particularly lithargite, which changed from its original bright yellow color to a deep brownish coffee color. Rouge showed the most severe damage, with partial cracking and significant fading. The cinnabar had a dark layer covering its entire surface. Azurite  
Figure 8: Spectral distribution curve of light source (**a**), and fading samples and experimental equipment display diagrams (**b**,**c**).  
had relatively minor changes, with a few white patches on the surface. Some areas of the malachite pigment transformed from white light green to dark green.
## 3 Background Theory
### Lightweight Cryptography Final Candidate  
Lightweight cryptography is an algorithm designed to meet the needs of cryptography in limited conditions that are not supported by current cryptography. In August 2018, NIST held a competition for the creation of lightweight cryptography standards. In the first round of the competition, there were 56 algorithm proposals submitted by researchers and practitioners. The algorithms were selected by NIST and became 32 algorithms in the second round and became 10 algorithms in the final round, which was announced in March 2021. The finalist candidates for lightweight cryptography included ASCON, Elephant, GIFT-COFB, Grain128-AEAD, ISAP, Photon- Beetle, Romulus, Sparkle, Tiny JAMBU, and Xoodyak. Of the 10 algorithms, there are three algorithms based on block ciphers, i.e., GIFT-COFB, Romulus, and Tiny JAMBU.