esa-sceva/satcom-chunk-collection huggingface dataset:
# On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks
## 6 RE-AEDG: The Proposed Scheme
\begin{cases}\eta_{c}&if\quad d^{\alpha}_{th}<|d_{j}-d_{i}|<d^{ \beta}_{th}\quad\forall\;\;i,j\;\epsilon\;N\\ \eta_{d}&if\quad|d_{j}-d_{i}|>d^{\beta}_{th}\quad\forall\;\;i,j\;\epsilon\;N \end{cases}  \tag{11}\]  
where \(d^{\alpha}_{th}\) and \(d^{\beta}_{th}\) (positive real numbers) are two thresholds that change according to current node density. Regarding nodes' density two possibilities arise here;  
* Node density is greater than the marginal value,
* Nodes density drops to the marginal value. Whereas, marginal value is equivalent to half of the total number of deployed nodes. Moreover, as stated earlier, the threshold values are dynamic in nature therefore; initially the difference between the two thresholds is kept low so that lesser nodes fulfills the eligible neighborhood criterion. This causes energy conservation as lesser nodes receive the information being sent by the source node. However, with the passage of time nodes deplete their energies and at that instant keeping lower difference among thresholds would not be a good approach. This is due to the fact that the source node is unable to find sufficient neighbors around and remains unattended. This situation could lead to a degraded reliability factor due to fewer or non availability of cooperating nodes. Hence, as a solution of sparsity we keep on increasing the threshold difference. Therefore, our proposed model is adaptive in this aspect. Mathematically,  
\[ \begin{cases}d_{th}^{\alpha}-d_{th}^{\beta}>\ \delta&\quad\quad\quad\quad if\quad N^{ \prime}\ \leq\ 1/3\ (N)\\ \Omega\ <\ d_{th}^{\alpha}-d_{th}^{\beta}<\ \delta&\quad\quad if\quad N^{\prime}\ >\ 1/3\ (N)\end{cases}  \tag{12}\]  
where, \(\omega\) and \(\delta\) are real numbers. For our proposed model, another important feature is the GWs involvement. GWs are nodes with relatively higher initial energy levels as compare to MNs. This is because of the reason that in our technique, each node transmits its own data packet as well as the data packets being generated by the predecessor nodes. Since data forwarding burden is highest on the chain heads which causes their rapid early energy depletion and leads to shorter network lifetime and stability period. Moreover, AUV also assists the process while moving on its predefined trajectory and collecting and forwarding data from GWs towards sink. AUV ensures data communication at minimized distance leading to network lifetime prolongation and it takes stops at specific positions known as sojourn locations for specific time called sojourn time.
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 4 Proposed Solution
### Proposed Design Requirements
#### 4.2.4 Idle Time  
The sender needs to know that how long a packet has to stay in the playout buffer of the receiver. When a receiver receives the packet, it puts it in the playout buffer where the packet stays until its  
\begin{table}
\begin{tabular}{c l l l} \hline
**Acronym** & **Definition** & **Acronym** & **Definition** \\ \hline \(APP\) & Application Packet & \(t_{p\text{-}aspr}\) & Playout time (in sender’s APP packet) \\ \(d_{ow}\) & One way delay & \(NTP_{SR}\) & NTP timestamp of SR \\ \(t_{r}\) & RTP packet’s received time & \(T_{s}\) & Reference clock capture time \(T_{s}\) for RTP packet \\ \(t_{s}\) & RTP Packet’s sent time & \(M_{bs}\) & \(M_{bs}\) is the media timestamp of the data packet \\ \(t_{i}\) & Idle time & \(M_{\sigma}\) & Media timestamp in last received RTCP-SR packet \\ \(t_{rb}\) & Removal Time from the buffer & \(TS_{sr}\) & Corresponding reference clock (NTP) timestamp \\ \(t_{p}\) & Playout time & \(R_{mts}\) & Nominal media timestamp clock rate (media clock \\  & & & ticks per second \\ \(t_{pr}\) & Processing time in the receiver & & \\ \(d_{off}\) & Playout-delay offset & & \\ \hline \end{tabular}
\end{table}
Table 1: Table of abbreviations.  
playout time arrives. If a packet arrives later than its playout time (delayed), it is treated in a different way depending upon the application and the media being transmitted.  
Receivers calculate this idle time using following equation.  
\[t_{i}=t_{rb}-t_{r} \tag{2}\]  
We can elaborate this formula in another way as:  
\[t_{i}=t_{p}-t_{r}-t_{pr} \tag{3}\]  
We use RTCP-APP packets to send information from receivers to senders and vice versa. Adding new fields to RTCP SR and RR packets will change the current protocol stack implementation.  
Sender calculates playout time, packs it in APP packet and sends it to the receiver(s).
## 5 Results
### Dropped Messages over Time  
Dropped messages refer to the loss of packets. In this case, it occurs due to the DCC mechanism as the channel becomes more saturated over time. The number of dropped messages was measured in two scenarios to see how the packet loss develops using one static and three dynamic channels.
## 2 Graph Theory  
### Improved Laplacian Matrix  
In a multi-agent system, agents communicate with each other by an undirected connected graph \(G(\Delta,\Gamma)\), which contains a vertex set \(\Delta\) and an edge set \(\Gamma\). In the edge set \(\Gamma\), the weight \(a_{ij}\) of edge \((i,j)\in\Gamma\) is equal to \(0\) or \(1\). That is, if agent \(j\) is connected to \(i\), \(a_{ij}=1\); if not, \(a_{ij}=0\). And the neighbor of agent \(i\) is denoted by \(N_{i}=\left\{j:a_{ij}\
eq 0\right\}\). In addition, the degree matrix \(D\) is a diagonal matrix and expressed by \(D=diag(d_{i})_{i=1,\cdots,n}\), where \(d_{i}=\sum\limits_{j=1}^{n}a_{ij}\). Therefore, Laplacian matrix \(L\) corresponding to \(G\) is expressed as \(L=D-A\), where \(A\)\(=\left(a_{ij}\right)_{n\times n}\) is the adjacency matrix of \(G\).  
Considering the location feature of each agent, an improved Laplacian matrix with the weight corresponding to each edge is adopted in this paper. In the edge set \(\Gamma\), \(a_{ij}\) is the reciprocal of relative distance between \(i\) and \(j\) in communication topology and implies the communication intensity of agent \(i\) and \(j\).
## 7 Discussion  
The study results reveal the limitations in auditory information browsing without visual support in terms of monitoring the playback position and understanding the current context. HearIt helps the users overcome these limitations by reducing the task completion time but maintaining accuracy. Furthermore, the users felt more comfortable with controlling the audio playback as what they wanted to. The users also became confident in their guesses about the following audio content to decide whether to keep playing.  
In this study, we focused on supporting information browsing, while information exploration should be a flexible combination of browsing and querying [38]. Further studies for supporting and balancing both browsing and querying would be helpful. For example, using the audio acceleration and bookmarking functions in [1] can be considered with the paragraph-level skip and the auditory cues. In addition, the current HearIt uses auditory feedback, but it is possible to utilize other modalities, such as leveraging haptic or tactile interaction. For example, [44] proposed wearable devices utilizing vibration that helps visually-impaired people communicate with information. We think that there is much potential for utilizing multimodal interaction in information exploration.  
In addition, the current topical cue can be improved in several ways. First of all, it is possible to examine the ideal number and structure of keywords for topical cue to save the time to play them. The keywords' extraction can also be improved, for example, utilizing text features with audio features (e.g., voice pitch [10]). Lastly, the current term weighting is based on the original text's static structure (e.g., paragraph), but it would be possible to use semantic structures by advanced text analysis techniques [45; 46].  
Finally, we note that the results of this study should be carefully interpreted. The number of participants in the pilot study may not be sufficient to prove the general effectiveness. In addition, we conducted the lab-controlled experiment, so observing the in-situ behavior with the methods would be necessary further. Nevertheless, we believe that our study results show the feasibility of the proposed method and directions for further improvement.
## 2 Materials and Methods
### Determination of the Biogenic Amines Content  
The extraction and determination of BAs in non-fermented and fermented lentil samples followed the procedures developed by [PERSON] et al. (2018). Perchloric acid (0.4 mol/L, 10 mL) containing a known amount of 1,7-diaminoheptane, as an internal standard (ISTD), was added to 3 g of sample, and the mixture was homogenized with Ultra-Turrax apparatus (IKA Labortechnik, Staufen, Germany) and centrifuged at 3000\(\times\)\(g\) for 10 min at room temperature. The residue was extracted again with an equal volume of 0.4 mol/L perchloric acid. Both supernatants were combined, and the final volume was adjusted to 30 mL with 0.4 mol/L perchloric acid. The extract was filtered through a Whatman No. 1 paper filter. One millilitre of extract or standard solution was mixed with 200 mL of 2 mol/L NaOH solution and 300 mL of saturated NaHCO\({}_{3}\) solution. Two millilitres of 10 mg/mL solution of 5-(dimethylamino)naphthalene-1-sulfonyl chloride (dansyl chloride) in acetone was added to the mixture and incubated at 40 \({}^{\circ}\)C for 45 min. Residual dansyl chloride was removed by the addition of 100 mL of 25 mg/L aqueous ammonia solution. After incubation at room temperature for 30 min, the mixture was adjusted to 5 mL with acetonitrile. Finally, the mixture was centrifuged at 3000\(\times\)\(g\) for 5 min at room temperature; the supernatant was then filtered through a 0.2 \(\upmu\)m filter (Millipore, nylon, Bedford, MA, USA) and stored at 25 \({}^{\circ}\)C until HPLC analysis. An Agilent 1200 HPLC system (Carlsbad, CA, USA) equipped with a diode array detector (DAD) and Chemstation liquid chromatography (LC) software (version B.04.03) was employed in combination with a Chromolith C\({}_{18}\) HPLC column (diode array detector (DAD) 100 mm \(\times\) 4.6 mm-\(\upphi\)\(\times\) 4 \(\upmu\)m-\(\upphi\) particle size, Merck KGaA/EMD Chemicals, Darmstadt, Germany). Ammonium acetate (0.1 mol/L) and acetonitrile were used as the mobile phases at the flow-rate of 0.45 mL/min. The injected sample volume was 10 \(\upmu\)L and the biogenic amines were monitored at 254 nm wavelength (\(\uplambda\)). The detection limits for standard amine solutions were approximately 0.1 mg/kg.
## 3 Results  
The default cold pain detection threshold of 19 participants watching a black screen was 18.0 \(\pm\) 8.2 \({}^{\circ}\)C. When watching a virtual agent with painful, unhappy, and neutral facial expressions, cold pain detection thresholds were 21.1 \(\pm\) 5.7 \({}^{\circ}\)C 19.4 \(\pm\) 6.9 \({}^{\circ}\)C and 18.5 \(\pm\) 7.7 \({}^{\circ}\)C, respectively (Table 1). Cold pain detection thresholds were thus quite-variable. Changes of cold pain detection thresholds watching a virtual agent with painful, unhappy, and neutral facial expressions were 3.1 \(\pm\) 5.4, \(-\)1.7 \(\pm\) 4.2 and \(-\)0.9 \(\pm\) 2.6, respectively. Pain intensities of the different facial expressions (painful, unhappy, and neutral) were rated as 6.6 \(\pm\) 2.3, 5.2 \(\pm\) 2.0 and 2.7 \(\pm\) 2.6, respectively. These pain ratings for three different facial expressions were varied among the participants (Table 1). Following facial expressions were rated in order of severity: Painful \(>\) unhappy \(>\) neutral (\(n=10\)); Unhappy \(>\) painful \(>\) neutral (\(n=3\)); Painful \(=\) unhappy \(>\) neutral (\(n=2\)); Painful \(>\) neutral \(>\) unhappy (\(n=2\)); Unhappy \(>\) neutral \(>\) painful (\(n=1\)); and painful \(=\) neutral \(>\) unhappy (\(n=1\)). Adjusted by the respective pain intensities, only when watching the painful expression of a virtual agent did the cold pain detection threshold increase significantly (Figure 1).  
Figure 1: Cold Pain Detection Thresholds while Watchng Three Different Facial Expressions of a Virtual Agent and a Black Screen. Average cold pain detection thresholds (squares) of the participants while watching three different facial expressions of a virtual agent and a black screen are shown. Error bars indicate standard deviations of the respective cold pain detection thresholds for the facial expressions. For the three facial expressions, participants mandatorily rated pain intensity on an 11-point numerical rating scale. Differences between the average default cold pain detection threshold of all participants and their cold pain detection thresholds while watching a black screen and the three facial expressions were compared using analysis of covariance (ANCOVA) with the respective pain intensities as covariates. Only when watching the painful expression of a virtual agent did the cold pain detection thresholds increase significantly. * Adjusted by pain intensities for facial expressions.
## 2 Chitosan Properties and Biomedical Application  
Chitosan is a cationic polysaccharide composed of a random composition of \(\beta\) (1\(\rightarrow\)4) linked 2-amino-2-deoxy-D-glucose (D-glucosamine) and 2-acetamido-2-deoxy-D-glucose(N-acetyl-D-glucosamine) units [16]. It has a structure which is similar to cellulose and it also has chelating properties. It is obtained from deacetylation of a polysaccharide found in crustacean shells known as chitin (Figure 1) [17]. It exhibits a positive charge on its surface resulting from the presence of primary amino groups on its polymer backbone. Its ability to form inter and intramolecular hydrogen bonding and its polycationic surface make it suitable for the development of formulations for intranasal administration [17]. It exhibits good biocompatibility and it is suitable for the design of targeted drug delivery systems for the treatment of neurodegenerative disorders. Its good biodegradability property affects its bioactivity on the blood-brain barrier at the molecular level, which is advantageous in therapies for neurological disorders [18; 19; 20]. It is also hydrophilic in nature, which makes it useful for biomedical applications [21]. It is soluble in acidic media and exhibits unique properties useful in drug delivery, such as mucoadhesion, good gelling capability, hydrophilic nature, and good permeation effects [22]. There are many challenges associated with the treatment of neurodegenerative diseases. Effective drug delivery to the brain requires understanding the physiology and anatomy of the blood-brain barrier and modifying the surface of the drug delivery systems with antibodies for brain delivery. Therefore, research on brain-targeted delivery systems is a significant approach that can overcome the problems associated with therapeutics used to treat neurodegenerative disorders. A major limitation in the treatment of neurological diseases is the inability of the conventional drug molecules to cross the blood-brain barrier.  
Figure 1: Chemical structure showing the preparation of chitosan by deacetylation of chitin.
# Multimodal Shot Prediction Based on Spatial-Temporal Interaction between Players in Soccer Videos
## 2 Proposed Multimodal Shot Prediction in Soccer Videos  
We explain the details of the proposed multimodal shot prediction method in soccer videos, as shown in Figure 2. In the proposed method, we use a graph with audio-visual features obtained from the input soccer videos as nodes. To enhance the feature representation capability, we construct a complete bipartite graph that introduces players' importance based on their field positions and teams into the graph edge. This is described in more detail in Section 2.1. After constructing the graph, the proposed method extracts latent features through GCRNN. In this way, our method can make predictions with latent features that incorporate relationships between connected nodes and between graphs of different time steps, which correspond to spatial-temporal relationships. This is discussed further in Section 2.2. As a final step, we achieve the probability predictions of shot occurrence while accounting for uncertainty with BNNs, which is explained in Section 2.3.
## 5 Experimental Verification
### Dynamic Conditions  
The Rx1 position is changed to analyze the effects of dynamic conditions, the Rx2, Rx3 and Rx4 positions are kept constant. The induced voltage at load-1 varying from 6.4 V to 4.2 V for the variation of separation from 3 c.m to 7 c.m as shown in Figure 18a,b at 1 MHz. The load-2, load-3 and load-4 are designed with the resonant frequency of 1.5 MHz, 2 MHz, 3 MHz respectively. The non-resonant  
Figure 17: Measured load voltage of selective wireless power transfer (WPT) with two identical Rx resonant frequency (**a**) \(V_{L1}\), \(V_{L2}\) at \(f_{T1}=f_{T2}=1\) MHz (**b**) \(V_{L3}\), \(V_{L4}\) at \(f_{T1}=f_{T2}=1\) MHz (**c**) \(V_{L3}\), \(V_{L4}\) at \(f_{T3}=f_{T4}=2\) MHz (**d**) \(V_{L1}\), \(V_{L2}\) at \(f_{T3}=f_{T4}=2\) MHz.  
load voltages are less than 0.45 V as shown in Figure 18a,b. The effect of cross coupling on each Rx is reduced, and the frequency splitting also eliminated for the closer coupling distance of 3 c.m. The measured frequency of the load-1 voltage at 3 c.m is of 1 MHz, and the frequency deviation is not present in the load-1.  
The comparative analysis of the proposed method with the existing design in the literature is presented in Table 5. It is observed that the proposed design for the Rx WPT system with identical or non-identical operating frequency gives a better and simple solution to eliminate frequency splitting and cross coupling with improved PTE.
## 3 Results
### Bottom Electrodes  
In a traditional multi-layer structure, an additional bottom electrode with floating potential often helps improve the \(K^{2}\) of the resonator [35; 36; 37]. For the proposed structure in this study, introducing a bottom electrode with floating potential formed a structure called hybrid quasi-SAW/BAW configuration [31], illustrated by Figure 7a. For simplicity, the two modes in the new structure with the bottom electrode are named as quasi-Rayleigh and quasi-Sezawa mode. For further study on the impacts of the thickness and material of the bottom electrode, different conditions were considered for each mode, and these specific situations were consistent with those of the previous section, as the metallization ratio of the two modes was still 0.5. Figure 7b shows that the \(V_{\rm p}\) value goes down with the growth of the bottom electrode thickness for both modes. This is due to the fact that the acoustic velocity of the bottom metal electrode is much lower than SiC, and the high acoustic velocity of the substrate cannot be better taken advantage of when surface acoustic waves propagate. The acoustic velocity of the hybrid quasi-SAW/BAW structure must be lower than that of the same structure without a bottom electrode. Figure 7c depicts the \(K^{2}\) of the quasi-Rayleigh mode, which decreases monotonically with growing bottom electrode thickness, while the \(K^{2}\) of the quasi-Sezawa mode exhibits the contrary trend. Compared to the same etched structure without a bottom electrode, \(K^{2}\) of the hybrid quasi-SAW/BAW configuration does not always increase. For the quasi-Rayleigh mode, bottom Cu electrode exhibits a better behavior than Al, and the maximum \(K^{2}\) value of  
Figure 6: \(V_{\rm p}\) and \(K^{2}\) as functions of metallization ratio \(2w/\lambda\) of (**a**) quasi-Rayleigh mode and (**b**) quasi-Sezawa mode.  
11.82% can be achieved when the thickness is negligible. At the same time, the \(V_{\rm p}\) of the quasi-Rayleigh mode is 1975.8 m/s, which indicates adding a bottom electrode is a feasible choice to further increase the \(K^{2}\) of the quasi-Rayleigh mode. For the quasi-Sezawa mode, a bottom Al electrode can achieve higher \(K^{2}\) than Cu, and the \(K^{2}\) value can be boosted to 9.26% when the normalized thickness of the bottom Al electrode is 0.1, however, at the same time, the value of \(V_{\rm p}\) decreases to 4754.8 m/s. Compared to the structure without a bottom electrode, the trade-off between the \(V_{\rm p}\) and \(K^{2}\) of the quasi-Sezawa mode should be taken into consideration. A specific choice of bottom electrode material and thickness can significantly improve the \(K^{2}\) by 36.6%, though at the cost of sacrificing \(V_{\rm p}\) of 12.1% compared to the case when the top Al electrode thickness \(t_{1}/\lambda=0.11\), the etching ratio \(d/h=0.7\) and ZnO thickness \(h/\lambda=0.6\) based on the partially etched ZnO/SiC structure without a bottom electrode.
## 6 Experimental Results  
### FPGA Implementation Results  
IO configuration and project constraints for the Nexys4 DDR FPGA development board were generated alongside the Verilog RTL for the SoC as a \".xdc\" file. The design was synthesized and implemented using Vivado 2021.2, with the resource utilization  
Figure 11: SoC architecture with lightweight cryptographic and hash function accelerator cores.  
shown in Table 5. There were no DRC violations, and the STA (static timing analysis) report was cleaned. The clock source for the Nexys4 DDR was from an onboard 100 MHz crystal, whereas the VexRiscv core SoC was driven by an MMCM (Xilinx Mixed-Mode Clock Manager) at 75 MHz. The choice of using a frequency of 75 MHz in the SoC, rather than aiming for the highest frequency, was made with the goal of better representing the system as an IoTs device. This frequency was chosen since it offers a compromise between efficiency and performance, which is crucial for Internet of Things devices. By limiting the operation to 75 MHz, the SoC can already perform the necessary computations, which will be discussed later, while still being able to conserve energy if needed.
# Bricks Using Clay Mixed with Powder and Ashes from Lignocellulosic Biomass: A Review
## 5 The Effect of Lignocellulosic Biomass Ash By-Products on Fired Bricks
### Effect on Physical Properties
#### 5.1.2 Loss of Ignition  
Loss of ignition is the loss of mass as a result of the bricks firing. It was determined by measuring the loss of mass of the sample between the drying and firing stages. It is expressed in percentages and calculated according to the following formula:  
\[\text{Loss of ignition }(\%)=\frac{m_{\text{dried}}-m_{\text{fired}}}{m_{\text{ dried }}}, \tag{2}\]Bricks made of lignocellulosic ash lose weight due to the decomposition of organic matter and calcium carbonates, as well as the dehydration of water in the clay. When olive bottom ash (OPBA) was added in amounts ranging from 10% to 50%, the loss on ignition rose from 11.25% to 20.6%, as opposed to 10.99% for the control brick [60]. For the bricks elaborated by the waste of olive pomace fly ash (CVLL) by [61], the loss on ignition went from 12.89% for the control brick to 21.99% for 25% of CVLL addition. For [62], the addition of 30% of olive stone ash (OSA) resulted in a loss of ignition of 21.9% because the ash contained a significant amount of unburned material. Additionally, Ref. [63] demonstrated that as the cooking temperature rises, the loss of ignition of rice husk ash waste (RHA) increases even more. Additionally, in all of the studies that were conducted, it was discovered that the loss on ignition of lignocellulosic ash bricks increases with increasing the percentage of added ash in the mixture. This finding suggested that, if the sintering process is not carried out at sufficient heating rates, using these ashes to create fired bricks could result in a significant volume reduction with deformation or breakage of the bricks. Thus, to maintain good performance, the loss on ignition should remain below 15% [71].  
Figure 3 illustrates that all ignition loss values are less than 15%. Due to the differences in the amounts of water used in shaping, the variations in calcium carbonate content, and the presence of organic implicates in the ashes (Table 2), it is not possible to compare the loss of ignition between these various ashes.