esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation and Characterization of Alginate Extracted from Brown Seaweed Collected in the Red Sea
###### Abstract
\({}^{1}\)H-NMR and FTIR techniques are the main techniques applied in the examination of alginates' composition and structural patterns [2; 24]. Nowadays, alginate is one of the most exploited seaweed polysaccharides by various types of industry, and thus it is important to find and analyze new raw sources to extract alginate around the globe so as to meet the high demand for alginate, mainly sodium alginate [26]. Despite the existence of many studies regarding the nutritional content and antimicrobial and antioxidant activities of the Red Sea seaweeds [27; 28; 29], there is almost no information available about the quantity and quality of the alginates in the Red Sea brown seaweeds, which could be a new source of raw material to be exploited by the alginate extraction industry. In this context, this study focused on the assessment of the most dominant (high biomass available) brown seaweeds in the Red Sea, in order to analyze their alginate yield. In addition, there was a characterization of the extracted alginate to guarantee the purity rate and the possibility to be incorporated into the food industry.
# Thermophysical Property Measurements with the Finite Bar
## 3 Experimental Setup
### Characteristic Time Windows of the Measurements  
Before the measurement is performed, uniform temperature of the bar must be ensured. This is done by keeping the sample at room temperature for several hours before the measurement is performed. Furthermore, this offers the advantage of keeping the error due to diabatic effects small in the small Fourier-Number limit. The measurements itself can be divided into three phases:  
1. Sensor alignment and calibration
2. Constant heating phase
3. Cooling phase  
The necessary measurements for the determination of the thermophysical properties take place during the constant power heating phase. Measurement without heating before and afterward are necessary to calibrate the sensors and correct the diabatic effects. The heating phase itself can be divided into three characteristic phases:  
1. The phase of small Fourier-Numbers, where the temperature rise at the adiabatic end has not started (Small Fo window).
2. Transient phase in which an asymptotic temperature profile is slowly established.
3. The phase of large Fourier-Numbers, where the asymptotic temperature profile is stable (Large Fo window).  
Figure 3 shows the temperature measurements without calibration corrections on the sample bar AL7075. On the left side, the small time window for the small Fourier-Number limit is marked. In the middle, the time window for the large Fourier-Number limit and the different curves for the sensors can be seen. The processed data of the cooling phase lies in the cooling window, which ranges approx. from 1000 s up to the end of the measurement.  
Figure 2: One side heated one dimensional bar with temperature measurement.
# Cooperative Buffering Schemes for Time-Shifted Live Streaming of Distributed Appliances
## 4 Experimental Results  
To show the effectiveness of our cooperative buffering schemes for time-shifted live streaming of PVRs, we performed extensive simulations in a PeerSim P2P simulator. We set backbone network bandwidth to 10 Gbps and set the ratio of the bandwidth between a PVR and a router so that the portions of 100 Mbps, 50 Mbps, 20 Mbps, and 10 Mbps could be 10%, 50%, 20%, and 20%, respectively. The maximum number of neighbors of each PVR and the minimum required number of neighbors to playback without quality degradation, i.e., \(MIN_{nb}\), were set to six and four, respectively. The inter-arrival and inter-leaving rates followed a Poisson distribution with a mean of 3400 s. The playback rate of each program is 720 Kbps, and it is divided at a rate of three chunks per second. The size of each chunk was 30 KB, and each PVR buffers at least 45 chunks corresponding to 15 s before initial playback. However, note that a PVR can receive 45 chunks more quickly, i.e., without waiting for 15 s, if it runs under good conditions. Table 2 shows the simulation parameters used in this section.  
To compare the performance of our proposed schemes according to the buffer size, we changed it to 150, 300, and 450 s. To reflect the fact that a large portion of playback requests are concentrated near the live broadcasting time and the others are dispersed over the entire time-shifting hours, we used three types of playback request distributions according to the degree of concentration of playback requests around the live broadcast time: _distribution 1_ (the highest concentration degree), _distribution 2_ (the middle one), and _distribution 3_ (the lowest one), as shown in Figure 5. To show the performance of the schemes according to the number of participating PVRs, we also varied it between 1200 and 2400.  
In this section, we compare the performance of our proposed buffering schemes based on \(D\_DP\) with different \(w\) values and \(R\_RC\) with each other in terms of playback continuity and startup delay while varying the parameters of buffer size, playback request distribution, and the number of PVRs.
# Kinetics and Thermodynamics of Efficient Phosphorus Removal by a Composite Fiber
## 2 Materials and Methods
### Static Adsorption and Phosphorus Removal Experiment of ZSFB Composite Fiber
#### 2.1.2 Adsorption Thermodynamics  
According to adsorption thermodynamics, the adsorption mechanism can be further investigated. The most commonly used adsorption thermodynamic model is the van 't Hoff model, and the Gibbs free energy (\(\Delta G^{0}\), the part of the internal energy that can be converted into external work), the standard enthalpy of reaction (\(\Delta H^{0}\), the enthalpy change in a system by a given chemical reaction when all reactants and products are in their standard states) and the entropy of the system (\(\Delta S^{0}\), a measure of the unavailable energy in a closed thermodynamic system [14]) can be calculated using the van 't Hoff equation. The equations are as follows:  
\[\ln K_{D}=-\Delta H^{0}/RT+\Delta S^{0}/R, \tag{5}\]  
\[\Delta G^{0}=\Delta H^{0}-T\Delta S^{0}, \tag{6}\]  
\[\Delta G^{0}=-RT\ln K_{D}. \tag{7}\]  
Among them, \(K_{D}\) is the equilibrium constant, \(R\) is the gas constant (8.314 J/mol\(\cdot\)K) and T is the absolute temperature (K). According to the calculation, if the value of \(\Delta G^{0}\) is a negative, the adsorption reaction can conduct spontaneously, and the larger the absolute value of \(\Delta G^{0}\), the higher the degree of spontaneous reaction.
## 4 Evaluation Method
### Weight Calculation Method
#### 4.1.3 Analytic Network Process (ANP)  
The ANP methodology is based on [PERSON]'s [115] decision-making framework tailored for dependent hierarchies. Compared to AHP, the hierarchical configuration of ANP is significantly more intricate. ANP features a complex hierarchy that includes loops and feedback mechanisms among the structures, as well as interrelatedness and dominance among components within the same hierarchy. When it comes to assessing factors related to geological suitability in UUS, the ANP method proves to be more effective for determining the relative significance of each evaluation criterion, given that its weighting mechanism is superior to that of the AHP method [52]. The essential calculations involved in the ANP procedure consist of: (1) creating the network structure model for the evaluation criteria; (2) creating the network structure model for the evaluation criteria; (3) creating the network structure model for the evaluation criteria; (4) creating  
(2) developing the unweighted supermatrix; (3) computing the weighted supermatrix; and (4) obtaining the limit supermatrix to derive the final weights.  
The ANP effectively handles interdependence and feedback relationships, making it highly suitable for intricate decision-making contexts. Its flexibility permits application to a wide range of decision-making challenges, with the model structure being adjustable, as required. Employing a network framework and pairwise comparisons enhances the intuitiveness of the decision-making methodology. Nonetheless, gathering data for the ANP presents difficulties, requiring the participation of several experts for pairwise comparisons, which can be both labor-intensive and challenging. Furthermore, the ANP faces challenges regarding consistency; pairwise comparisons may show inconsistencies and demand thorough testing, thereby raising the overall complexity. In larger networks, the intricacy of calculations and analyses can increase substantially, often requiring professional software assistance [116] (Figure 5).  
In order to enhance the advantages of the ANP and mitigate its limitations, this technique is commonly used in conjunction with various other methods. However, there are relatively few examples of its application. In evaluating the geological appropriateness of subterranean urban areas in Hangzhou [52], the ANP was applied to establish the subjective weight of each assessment indicator. The objective weights were determined by criteria importance through intercriteria correlation (CRITIC), while the combined weights were obtained through game theory (Nash equilibrium model) (Figure 6e and Table 1)
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 5 The Layered Architecture of the IoV and Emerging Technologies in Smart Cities
### Perception Layer  
This layer is very important in the data acquisition process. At the bottom level of IoV architecture, this layer collects real-time data from sensors, cameras, and other embedded devices present in the vehicle. Data from vehicle speed, location, environmental conditions, and more are gathered by these sensors. The data are then converted to digital formats and further processed in the upper layers of the IoV architecture.
# Conversational AI over Military Scenarios Using Intent Detection and Response Generation
## 3 Methodology
### Slot Filling and Intent Detection
#### 3.2.1 Slot Filling  
The slot-filling task, in contrast, was defined as a sequence labeling problem, that is, an NER problem. We trained used five kinds of entities by the CRF model. Considering the amount of data and the implementation of integrating multiple modules, we choose the CRF-based method as the baseline for slot filling.  
During the preprocessing phase, the CkipTagger ([[https://github.com/ckiplab/ckiptagger](https://github.com/ckiplab/ckiptagger)]([https://github.com/ckiplab/ckiptagger](https://github.com/ckiplab/ckiptagger)) (accessed on 31 January 2022)) tool was used for Chinese word segmentation and part-of-speech (POS) tagging for the user's query. The CRF toolkit was performed to train five NER models. Five types of slots related to military missions were defined: the military unit and location, the name of military personnel (including job titles and ranks), the name of military event tasks, the name of the weapon, and time. As shown in Table 3, There are six types of features: POS tagging, vocabulary, specific terms, verbs, quantifiers, and punctuation. We match them to entities for vocabulary and specific terms, and the vocabulary source is the Military Dictionary. For verbs, quantifiers, and punctuation, we use them to determine the boundaries before and after entities. In summary, we trained five CRF models to predict five entities (i.e., location/unit, person, event, weapon, and time) for slot filling. The CRF model was then used to estimate the conditional probability of the sequence, as shown in Equation (1).  
\[P(y|x)=\frac{1}{Z(x)}\prod\
olimits_{t=1}^{T}exp\sum\
olimits_{k=1}^{K}\theta _{k}f_{k}(y_{t},y_{t-1},x_{t}) \tag{1}\]  
If it is assumed that \(x\) and \(y\) are random variables, given an observed sequence \(X\), \(P(y|x)\) is the conditional probability distribution of the hidden sequence \(Y\), whose probability estimate in the state t depends on that in the state \(t-1\). \(Z(x)\) is a normalization function for normalizing the value of \(P(y|x)\).  
Figure 2: Flowchart of slot filling and intent detection.
# Preprocessing for Multi-Dimensional Enhancement and Reconstruction in Neural Video Compression
## 3 Experiments and Results
### Implementation Details  
We use the Adam optimizer [22] to train the MDER preprocessing method, with a learning rate of \(1\times 10^{-3}\) and the batch size set to 64. To enhance the dataset's diversity and improve the model's generalization capabilities, we applied data augmentation prior to training. Specifically, the original images from the training set were randomly cropped into patches of size 128 \(\times\) 128, followed by random flipping, with an equal probability of 25% for no flipping, vertical flipping, horizontal flipping, and mirror flipping. The entire MDER is based on PyTorch [23], which requires about 3 days of training on an NVIDIA GeForce RTX 2080 Ti (NVIDIA, Santa Clara, CA, USA). During the deployment phase, the coding frame was processed by MDER, and then the preprocessed frame was coded by neural video codecs. Our MDER preprocessing method achieves inference performance of 1080p@49 FPS and 720p@62 FPS on a single NVIDIA GeForce RTX 2080 Ti.
# An Improvement of the Load Transfer Method for Energy Piles Under Thermo-Mechanical Loads
###### Abstract  
The energy pile integrates shallow geothermal energy extraction with underground structural engineering, thereby expanding the functional applications and scope of pile foundations. Due to its widespread adoption, research on energy pile analysis theory has advanced significantly. Among existing analytical methods, the load transfer method is widely employed owing to its computational simplicity and readily obtainable parameters. However, current load transfer models for energy piles remain imperfect, primarily because their results often fail to accurately reflect real-world loading conditions. This study investigates the underlying causes of this discrepancy and proposes an iterative method to eliminate unbalanced forces at the pile head, based on the displacement coordination algorithm for energy pile load transfer. The calculated results at the pile head show an 18% reduction in error compared to previous studies. The average error compared with field test results is within 20%, with consistent trend patterns, confirming the feasibility of the proposed method. Computational results demonstrate that the proposed method effectively captures the combined effects of mechanical load and temperature variations on the bearing behavior of energy piles. It should be noted that this paper focuses specifically on improving the temperature-dependent load transfer method for energy piles. Consequently, the conventional load transfer method and results under purely mechanical loading are not discussed herein.  
energy piles; thermo-mechanical loads; load transfer method; the neutral surface Article
# Efficient Implementation of Multilayer Perceptrons: Reducing Execution Time and Memory Consumption
## 3 Results
### Operation Time
#### 3.2.2 Computer Server  
All these tests were carried out at CESGA in the HPC finisterrae 2 using the thin-node partition, which has an Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50 GHz (Intel, Santa Clara, CA, USA), and using the GCC compiler in version 7.2.0 [22; 23]. These times have been taken without using any of the optimization options provided by the compiler. The results can be checked in Table 5, and Figure 6 shows the increased speed. The times obtained by using compiler optimization options have also been analyzed. Specifically, the following parameters were used: \"-O3-fast-math-funroll-loops-free-vectorize-march=native\". The results can be checked in Table 5, and Figure 6 shows the increased speed. As was the case for the laptop runs, the server will follow a very similar behavior. With a minimal improvement of 130% that can be seen in the simplest case for the cancer problem in Table 5, the improvements in computational speed are impressively increased with higher network complexity and with the use of optimization in the compiler. It is interesting to remark that, contrary to what one might think, a greater improvement in times resulted when the executions were carried out on the laptop. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{5}{c}{**No Optimization Flags**} & \multicolumn{3}{c}{**Using Optimization Flags**} \\ \cline{3-8}  & **Network** & **Traditional** & **Proposed** & **Speed Up** & **Traditional** & **Proposed** & **Speed Up** \\  & **Topology** & **Approach** & **Approach** & & **Approach** & & \\ \hline \multirow{4}{*}{**Trained**} & 4, 5, 3 & 0.4108 s & 0.2084 s & 1.9712x & 0.1295 s & 0.0604 s & 2.1440x \(\uparrow\) \\  & 4, 5, 7, 3 & 1.1077 s & 0.4105 s & 2.6468x & 0.3152 s & 0.1075 s & 2.9320x \(\uparrow\) \\  & 4, 4, 5, 5, 3 & 1.3989 s & 0.4470 s & 3.1295x & 0.3942 s & 0.1147 s & 3.4367x \(\uparrow\) \\ \hline \multirow{4}{*}{**Trained**} & 9, 7, 1 & 0.5568 s & 0.3690 s & 1.5089x & 0.1459 s & 0.0942 s & 1.5488x \(\uparrow\) \\  & 9, 7, 3, 1 & 0.8677 s & 0.4695 s & 1.8481x & 0.2295 s & 0.1117 s & 2.0546x \(\uparrow\) \\  & 9, 12, 8, 4, 1 & 2.9614 s & 1.1292 s & 2.6225x & 0.8527 s & 0.2204 s & 3.8688x \(\uparrow\) \\ \hline \multirow{4}{*}{**Trained**} & 34, 9, 1 & 1.6810 s & 1.3907 s & 1.2087x & 0.5276 s & 0.2569 s & 2.0537x \(\uparrow\) \\  & 34, 9, 4, 1 & 2.3799 s & 1.5479 s & 1.5375x & 0.6511 s & 0.2823 s & 2.3064x \(\uparrow\) \\ \cline{1-1}  & 34, 12, 8, 4, 1 & 4.9259 s & 2.3174 s & 2.1256x & 1.4572 s & 0.4120 s & 3.5368x \(\uparrow\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance in network execution on a personal computer without using compiler optimization flags and using “-O3-fast-math-funroll-loops-free-vectorize-march=native”.
# The Challenges of Advanced CMOS Process from 2D to 3D
## 2 Lithography of Nano-Scaled Transistors  
To create sharp patterns is the first priority for a state-of-art lithography. The most developed technique for 20 nm and 14 nm node devices is 193 nm ArF immersion with multiple patterning [8]. For 7 nm node, 193 nm immersion with self-aligned double patterning (SADP) and self-aligned quadruple patterning (SAQP) techniques will be used [9].  
SADP is a technique which applied spacer transfer process for small pitch whereas SAQP is used twice self-aligned double patterning to create the extremely narrow shapes and lines. There are different challenges with multiple patterning, such as edge placement error, pitch walking and high cost [10]. Therefore, for 7 nm devices, extreme ultraviolet (EUV) lithography and 193 nm immersion with multi-patterning are predicted to be used in near future. The benefit to implement such expensive lithography technique as EUV is to replace some of most complex multiple patterning layers but it is expected that ArF immersion will still keep being used for some of the other critical layers. There is a growing consensus that EUV introduction in volume manufacturing will take place starting from 2018. Table 1 summarizes the layout to be used for the final steps in the roadmap, 10 nm and 7 nm nodes [11].
## 3 Results
### Titanium (Plate)  
In total, 6 of the 41 studies provided data for titanium (plate) in relation to infection complication risk as per the selection criteria. Implant number varied between 26 and 174 implants.  
Infection rates varied between 0.00% and 14.17%, with an average infection rate of 8.31%, a standard deviation of 4.58% and a relative risk of 0.79.  
Titanium is a versatile metal and, in the field of cranioplasty, finds usage in a number of different structural forms. Titanium is considered a bioactive metal, with strong potential of osseointegration given appropriate porosity and surface texture [41; 42]. Titanium plate, however, has no porosity and does not provide a surface adequate for tissue ingrowth; it is given both an ISF ranking and a TAB ranking of 0. Titanium mesh, however, does provide a greater degree of integration potential [43],  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & (a) & \\ \hline
**Paper** & **Number of Implants** & **Number of Infections** & **Average Infection Rate (\%)** \\ \hline
[16] & 77 & 2 & 2.60 \\
[33] & 108 & 12 & 11.11 \\
[34] & 12 & 2 & 16.67 \\
[35] & 218 & 16 & 7.34 \\ Total: & 415 & 32 & 7.71 \\ Standard Deviation: & - & - & 5.21 \\ Relative Risk: & 0.73 & - & - \\ \hline  & & & \\ \hline  & & & \\ \hline
[36] & 26 & 0 & 0.00 \\
[27] & 65 & 7 & 10.77 \\
[37] & 95 & 7 & 7.37 \\
[38] & 151 & 6 & 3.97 \\
[39] & 127 & 18 & 14.17 \\
[40] & 174 & 15 & 8.62 \\ Total: & 638 & 53 & 8.31 \\ Standard Deviation: & - & - & 4.58 \\ Relative Risk: & 0.79 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: (a) Titanium mesh; (b) Titanium plate.  
and is given an ISF ranking of 1. Like titanium plate, the smooth nature of the mesh itself does not promote ingrowth into the material, but rather into the surrounding space. As such, a TAB ranking of 0 is provided to titanium mesh.
# Convolutional Neural Networks: A Roundup and Benchmark of Their Pooling Layer Variants
## 2 Materials and Methods
### Let the Pooling Begin
#### 2.3.6 Mixed, Gated, and Tree Pooling  
Mixed pooling [19] combines Max and Average pooling, selecting one of these two methods, outperforming both of them when used separately. [PERSON] et al. proposed two different variants along with the base one: mixed Max-Average pooling, and gated Max-Average pooling, along with an alternative method for tree pooling. An overview of the three methods can be seen in Figure 4.  
In **mixed Max-Average pooling**, a parameter \(a\) is learned and can be different per the whole network, per layer, or per pooling region. Then, the output of the pooling layer is computed by Equation (6):  
\[f_{\text{mix}}(x)=af_{\text{Max}}(x)+(1-a)f_{\text{avg}}(x) \tag{6}\]  
where:  
\[\begin{array}{ll}x&:\text{the input to be pooled;}\\ a&:\text{a learned parameter;}\\ \sigma(w^{\text{T}}x)&:\text{a sigmoid function, }1/(1+exp(-w^{\text{T}}x)). \end{array}\]  
In **gated Max-Average pooling**, a mask of weights is learned and the inner product of that mask with the pooled region passed through a sigmoid function is used to decide whether to use Max or Average pooling. This mask can differ per network, layer, or region. The output is then calculated as described in Equation (7). According to the method's paper [19], in a comparison between this method and mixed Max-Average pooling, it appears that the gated variant performs consistently better.  
\[f_{gate}(x)=\sigma(w^{\text{T}}x)f_{\text{Max}}(x)+(1-\sigma(w^{\text{T}}x)) f_{\text{avg}}(x) \tag{7}\]  
where:  
Figure 4: A schematic comparison of the three proposed operations in [19]: (**a**) mixed Max–Average pooling, (**b**) gated Max–Average pooling and (**c**) tree pooling with 3-level binary tree.  
\[\begin{array}{ll}x&:\text{the input to be pooled;}\\ w&:\text{the learned mask of weights;}\\ T&:\text{the transpose operator;}\\ \sigma(w^{T}x)&:\text{a sigmoid function, }1/(1+exp(-w^{T}x)).\end{array}\]  
A third alternative was proposed in the same paper for **tree pooling**, where a binary tree is used and the pooling filters are learned. The tree level is a pre-defined parameter, and each node holds a learned pooling filter. Furthermore, gating masks are used in a similar way as described for gated pooling previously. Thus, the pooling result for each node is described by the function (8), and the output of the pooling method is the calculated output for the root node.  
\[f_{m}(x)=\begin{cases}v_{m}^{T}&\text{if leaf node}\\ \sigma(w_{m}^{T}x)f_{m,left}(x)+(1-\sigma(w_{m}^{T}x))f_{m,right}(x)&\text{if internal node}\end{cases} \tag{8}\]  
where:  
\[\begin{array}{ll}\
u&:\text{the learned filter for each node;}\\ w&:\text{the learned mask of weights;}\\ m&:\text{the tree node index;}\\ T&:\text{the transpose operator;}\\ \sigma(w_{m}^{T}x)&:\text{a sigmoid function, }1/(1+exp(-w_{m}^{T}x)).\end{array}\]