esa-sceva/satcom-chunk-collection huggingface dataset:
# Insights from the Experimentation of Named Data Networks in Mobile Wireless Environments
## 4 Experimentation Setup and Results  
To evaluate the performance of our NDN implementation in a real-world wireless environment, three scenarios have been designed. The first scenario addresses real experimentation in the city of Aveiro, using the ATCLL, while the second and third scenarios occur in laboratory environment for a more fine-grain validation of a set of network characteristics. In the first one, we evaluate the content dissemination through mobile nodes traveling around the city using a single radio access technology (detailed in Section 4.1). In the second scenario we study the impact of multiple wireless interfaces in the performance of the NDN. In this case ITS-G5 and Wi-Fi are used simultaneously for the communication between OBUs and RSUs (Section 4.2). Finally, in Section 4.3 we study how the network characteristics (the Maximum Transmission Unit (MTU), and consequently, the chunk size) impact the performance of the NDN. All the scenarios consider that mobile Consumers communicate with a single Producer strategically placed in the infrastructure. Such interaction occurs using the single-Data request communication paradigm, which is traditional in NDN.
# Bio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios
## 4 Cluster Head Formation
#### Fitness Function  
The cluster head (CH) selection is based on the residual energy (\(R_{S}\)), node distance \(N_{D}\), and network coverage (\(N_{C}\)).  
Residual energy: Residual energy is one of the vital factors in selecting a CH, as a CH requires more energy to perform tasks. The CH is responsible for receiving data from the node member and passing it to another CH or the BS. Therefore, the node with higher residual energy has a high chance of becoming a CH.  
\[R_{S}=\sum_{i=1}^{O_{opt}}\frac{1}{E_{CH_{i}}} \tag{10}\]  
In Equation (10), \(E_{CH_{i}}\) is the residual energy of the \(i\)-th cluster.  
Node Distance: This is also an essential factor in energy consumption. The shorter the distance between the CH and a node, the less energy is required for transmission. The equation for the distance between the node and the CH is  
\[N_{D}=\sum_{j=1}^{O_{opt}}\biggl{(}\sum_{i=1}^{N_{j}}distance(CH_{j},X_{i})/I_ {j}\biggr{)} \tag{11}\]  
Network coverage: Higher network coverage is achieved by assigning a CH to a node that is not covered by any other CH. This provides higher scalability and increases the lifetime of the network [46]. Network coverage is denoted by [36]  
\[N_{C}=\frac{(N*-D)-\sum_{D=1}^{D}C*M_{D}}{\sum_{D=1}^{D}C*M_{D}} \tag{12}\]  
The fitness function used for the BOA in this study is as follows:  
\[Fitness_{CH}=R_{S}*\beta 1+N_{D}*\beta 2+N_{C}\beta 3 \tag{13}\]  
where \(\sum_{i=1}^{3}1,\beta_{i}\in(0,1)\). In this study, we assume \(\beta 1\) is 0.50, \(\beta 2\) is 0.30, and \(\beta 3\) is 0.20.  
The optimal CH selection process is given in Algorithm 1.  
```
Define the objective function using Equation (13) Create a population of butterflies  Stimulus sensor modality \(c\), switch probability \(p\) and power exponent \(\alpha\) whileiteration < maximumdo for each butterfly in population: do  calculate fragrance using  update optimal butterfly population endfor for each butterfly in population: do  generate \(r\) (random number [0, 1]) if\(r<p\)then  move towards best butterfly using Equation (3) else  move randomly using Equation (2) endif endfor  update power exponent value \(\alpha\) endwhile  the optimal solution for selecting the CH
```  
**Algorithm 1** CH selection algorithm process using the BOA
# The Influence of Substrate and Strain on Protein Quality of _Pleurotus ostreatus_
## 3 Results and Discussion
### Protein Content and Extraction Yield  
The protein content (% m/m, dry mass) of the _P. ostreatus_ flours and PCs is presented in Table 1. The protein content differed significantly between the strains and the substrates. Regarding the mushroom flours, the highest and lowest values were recorded for AMRL150 cultivated on BOS (24.6 \(\pm\) 0.4%) and on WS (17.2 \(\pm\) 0.1%), respectively. The BOS substrate significantly enhanced the protein content of both _P. ostreatus_ strains. [PERSON] et al. (2023) [37] also reported that the maximum protein concentration for the carposomes of the same _P. ostreatus_ strains was achieved when cultivated on BOS and on beech wood shavings (BWS). Following the alkaline extraction at pH 12 and precipitation at the isoelectric point (pH 4), the produced PCs had almost doubled their protein content. The highest protein content was achieved in the PC from the AMRL150 and 144 strains cultivated on BOS (43.1 \(\pm\) 0.4% and 43.5 \(\pm\) 0.1%, respectively). The mean protein extraction yield (calculated using Equation (1)) was equal to 25 \(\pm\) 2%, which agrees with that reported in the literature. Specifically, [PERSON] et al. (2021) [18] found that for the same extraction ratio as in the present study (mushroom-to-solvent ratio of 1:10), the extraction yield reached 25% and by increasing the ratio from 1:10 to 1:20, no significant difference in the protein solubility was found.
## 4 Conclusions  
Detecting fires in surveillance videos is crucial to taking prompt action and preventing damage and loss of life. Various machine learning and deep learning models have been proposed to detect fires in surveillance videos, but some face limitations regarding accuracy and computational efficiency. We proposed a highly efficient and effective fire detection model called E-EFNet to cope with these issues. The E-EFNet uses EfficientNetB0 for feature extraction, which is then passed to a densely connected, stacked encoding network for feature refinement, followed by classification. E-EFNet has achieved superior performance through massive evaluation compared to detailed ablation studies and existing methods. This architecture is designed to enhance accuracy and reduce false alarms, yielding results that outperform previous state-of-the-art models in both accuracy and false alarm rate. Furthermore, the time complexity analysis demonstrated that the proposed E-EFNet model achieved high FPS rates on both robust systems and resource-constrained edge devices. This capability makes it a promising solution for real-time fire detection in various IoT applications.  
Conceptualization, H.F. and B.J.; methodology, H.F.; validation, H.F., B.J. and S.B.A.K.; formal analysis, H.F. and B.J.; investigation, H.F.; data curation, B.J. and S.B.A.K.; writing--original draft preparation, H.F.; writing--review and editing, M.M.N. and B.J.; supervision, M.M.N.; project administration, H.F. and M.M.N.; funding acquisition, M.M.N. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the support of Prince Sultan University for paying the article processing charges (APCs) of this publication.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **FPS** & **Parameters (Millions)** & **Edge Device (FPS)** & **System Specifications** \\ \hline [PERSON] et al. [68] & 34 & 4.3 & 5 & Nvidia TITAN X GPU \\  & & 60 & & RPIB3+ \\ [PERSON] et al. [29] & 20 & & 4 & Nvidia TITAN X \\ [PERSON] et al. [65] & 60 & \(-\) & 3 & RPIB3+ \\ [PERSON] et al. [18] & 70 & \(-\) & - & - \\ [PERSON] et al. [71] & 20 & \(-\) & - & Dual-core CPU \\ DFAN [2] & 70 & 83.63 & 0.83 & GeForce-RTX-3090 \\ E-EFNet & 51 & 12.3 & 8 & RPIB3+ \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative Analysis of FPS Achieved by State-of-the-Art Models on Different Devices.
# Regulated Assembly of LPS, Its Structural Alterations and Cellular Response to LPS Defects
## 8 Regulated Structural Alterations in LPS
### Glycoform Switches
Glycoforms IV and V have the same molecular masses, but are structurally different due to the incorporation of P-EtN on the second Kdo by the RpoE-regulated EptB and attachment of Rha to the terminal third Kdo defining glycoform V. Without RpoE induction, EptB synthesis is silenced by the PhoP/Q-dependent MgrR sRNA (no incorporation of P-EtN on the second Kdo, instead the attachment of Rha on the second Kdo) and hence the synthesis of glycofom IV derivatives. One of the interesting structural features of glycoform V derivatives with a third Kdo is the concomitant truncation of the terminal disaccharide and the incorporation of P-EtN on the second Kdo with Rha on the third Kdo. The minimal LPS structure that can support incorporation of a third Kdo requires WaaO-mediated addition of glucose and hence serves as a branch point in determining switches between glycoform I and glycoform IV/V derivatives [62]. Primarily this switch is regulated by levels of WaaR and WaaZ, whose expression are regulated by induction of RpoE and PhoB/R TCS. Truncation of the terminal disaccharide suggested that the WaaR glycosyltransferase is limiting under RpoE-inducing conditions. This was indeed experimentally validated by observed repression of WaaR synthesis due to the RpoE-regulated RybB sRNA and to some extent by another RpoE-regulated sRNA MicA (Figure 2). At the same time, transcription of the _waaZ_ gene is induced under such conditions. Physiologically, switch to the synthesis of glycoform derivatives with a truncation in the outer core, hence lacking terminal heptose, might be important for escaping detection by host, since such derivatives cannot incorporate \(O\)-antigen. Importance of the \(O\)-antigen incorporation can be critical, as it becomes essential for survival in _Salmonella_ in the absence of the _rpoE_ gene [78]. Thus, a coordinated molecular programming ensures increased synthesis of the WaaZ Kdo transferase under RpoE-inducing conditions, and the EptB P-EtN transferase, the induction of BasS/R-dependent WaaS synthesis with a simultaneous repression of WaaR and MgrR synthesis.
# Metrology for Measuring Bumps in a Protection Layer Based on Phase Shifting Fringe Projection
## 6 Conclusions  
This paper presents a novel designed module that works with an improved frame rate and is easily compatible with the hardware system by adjusting the ROI. We explored a measurement algorithm solution for eliminating the effects of PI or other material layers on top of the surface, which can influence the measurement accuracy of bump heights/coplanarity. The spectroscopic reflection model and FT analysis method clearly identified the air-PI layer, PI layer-oxide, and oxide film-silicon substrate interfaces; they are useful for accurately determining the thickness of each layer and eliminating their influence on the bump height measurement accuracy.  
Future work will involve the stage system integration combining a planar XY table with a 300 mm \(\times\) 300 mm travel range and a rotational stage with 360\({}^{\circ}\) angle adjustment range. A fringe projecting module with a selectable fringe density and adjustable projecting angles will be tested to flexibly and optimally introduce the phase-shifting algorithm to bump measurements for a wide range of heights and pitches. Multiple reflectometer sensors forming an array will be designed and implemented in the system to accommodate the line scan FOV for feedback on multiple sites with varying PL thickness. We will also focus on a measurement solution for high-density micro-bumps only a few microns in diameter to meet future advanced packaging market needs.  
Conceptualization, Y.-S.K.; methodology, P.-Y.C. and C.-H.C.; software, C.-W.L. and Y.-C.C.; validation, P.-Y.C. and H.-W.L.; writing--original draft preparation, Y.-S.K.; writing--review and editing, Y.-S.K. All authors have read and agreed to the published version of the manuscript. This research was funded by the MOEA (Ministry of Economic Affairs; grant No. 107-EC-17-A-24-15197) and the Advanced Semiconductor Technologies Project within the ITRI.  
Figure 15: (**a**) Cross-section SEM results for a Sn bump on PL: bump height of 163 \(\upmu\)m, PL thickness of 25.1 \(\upmu\)m (right side)–27.1 \(\upmu\)m (left side), and oxide layer thickness of 5.95 \(\upmu\)m. (**b**) Schematic labeling the material in (**a**).  
**Acknowledgments:** The authors thank [PERSON] at the National Measurement Laboratory/CMS/ITRI (Center for Measurement Standards/ Industrial Technology Research Institute) for his great help on collecting stylus measurement data and [PERSON] and [PERSON] at CMS for their help with collecting reflectometer spectra.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# An Efficient Grid-Based Geocasting Scheme for Wireless Sensor Networks
## 4 Simulation Results
### Number of Rounds versus Number of Nodes  
Next, we discuss the influence of the impact of the changes in the number of nodes. The number of nodes starts from 200 nodes and increases by 200 each time up to 1200 nodes. In Figure 9, the number of rounds for transmission that the WSN performed is shown under various numbers of nodes. In the simulation, as the number of nodes increases, the number of rounds that can be performed by each scheme also increases. Among them, the number of rounds performed by the proposed GB-FERMA is the best. For 1200 sensor nodes with two different initial power levels, the number of rounds performed by GB-FERMA is about 1.2 times of FERMA-QL, 1.5 times of FERMA, and 2.1 times of GEAR.
# An Optimization Routing Algorithm Based on Segment Routing in Software-Defined Networks
## 2 Related work
### Traffic Engineering for Segment Routing in SDN  
Traffic engineering refers to a series of methods to optimize the performance of a network [8]. The goal of traffic engineering is to dynamically analyze and predict the data flow in the network. For different types of networks, traffic engineering technology has been evolving to meet different needs. With the development of network technology, users have higher performance and reliability requirements for the application services of networks. So, the stability and cost of networks are very important. SDN shows its advantage in effectively solving this problem. SDN provides a better foundation for traffic engineering. First, SDN provides a centralized global view, and the controller can get the global network status in real time. Second, SDN provides a programmable data layer interface, and the operator can dynamically redistribute the network resource. In addition, the switches of multiple device vendors use a unified programming interface to provide full openness. The idea of SR is to divide an end-to-end path into multiple segments. Each segment is identified by an SID [21]. If the network state changes or the policy needs to be changed, the SDN controller recalculates the path and updates its segmented list at the previous first hop or segment changed router.  
Our work first focuses on optimizing the link weight for saving path cost and load balance. Then, it chooses the first \(k\) shortest paths to get a shorter segment list. Because a longer path means a longer segment list, more energy consumption. Finally, we use the comprehensive evaluation model to select the path with the best comprehensive evaluation value from the first \(k\) shortest paths, that is, the best path for comprehensive performance. First, we introduce several traditional routing algorithms. The shortest path first (SPF) [12] algorithm uses the shortest path algorithm to calculate the path. The SPF algorithm does not consider if the bandwidth of the selected path satisfies the need of the actual application. The idea of the widest shortest path (WSP) [14] is to find a path with the largest remaining bandwidth in all min-hop paths. The shortest widest path (SWP) algorithm [13] is to find the shortest path with the largest remaining bandwidth. The idea of the minimum interference routing algorithm (MIRA) [15] is to minimize the impact on the establishment of link requests for future nodes. [PERSON] and [PERSON] in [16] propose a routing algorithm for SDN with SR. The algorithm can meet the bandwidth requirement of routing requests and reduce the extra cost of packet header size. However, the algorithm does not consider preferences of users to evaluate the link performance of a network.
## 3 The Proposed Model
### Bi-Directional Context Using Deep Reinforcement Learning
#### 3.2.1 Reward Definition  
Our work proposes two rewards to generate desired responses and ensure the conversation's responses have a specific goal. An emerging problem with encoder-decoder-based models is that they often generate highly possible responses but are incoherent or irrelevant to the dialogue history. To avoid inappropriate responses in a conversation, we use consecutive turns to define our forward-looking functions. By utilizing the mutual information between the action \(a\) and the preceding utterance in the conversation, we can ensure the appropriateness of the generated responses. Let \(r_{1}\) denote the reward obtained for each action. Let \(h_{t}\) and \(h_{t+1}\) denote the representations obtained from the agents for two consecutive turns \(s_{t}\) and \(s_{t+1}\). The cosine similarity between \(h_{t}\) and \(h_{t+1}\) gives the first reward at the current state \(s_{t}\):  
\[r_{1}=cos(h_{t},h_{t+1})=cos(\frac{h_{t}.h_{t+1}}{\|h_{t}\|\|h_{t+1}\|}) \tag{6}\]  
In addition to the first reward, we suggest the second reward to encourage the model to contribute new information in each conversation. This reward leverages the intralinguistic relations in the sentence to detect changes in the content, thereby helping to maintain coherence and sustainability in the conversation. To achieve this purpose, we define the second reward, denoted as \(r_{2}\), as the minimum cumulative distance between the words in turn \(s_{t}\) and all the words in the subsequent turn \(s_{t+1}\).  
\[r_{2}=\frac{1}{N_{s_{t}}}\sum_{w_{p}\in s}\min_{w_{q}\in s_{t+1}}cos(\frac{w_ {p}.w_{q}}{\|w_{p}\|\|w_{q}\|})) \tag{7}\]  
where \(N_{s_{t}}\) denotes the number of tokens in sentence \(s_{t}\), and \(w_{p}\), \(w_{q}\) are embedding vectors of the words in sentences \(s_{t}\) and \(s_{t+1}\), respectively. The final reward for an action \(a_{t}\) at the current state \(s_{t}\) is a weighted sum of the rewards computed by  
\[r_{t}=\lambda_{1}r_{1}+\lambda_{2}r_{2} \tag{8}\]  
where \(\lambda_{1}+\lambda_{2}=1\). We set \(\lambda_{1}=0.5\) and \(\lambda_{2}=0.5\).
# All-in-Focused Image Combination in the Frequency Domain Using Light Field Images
## 3 Experimental Results
### All-in-Focused Image Combination
#### 3.2.4 On the Images of the 'Mouse' Dataset  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Criteria**} \\ \cline{2-3}  & **FMI** & \(\mathbf{Q^{AB/F}}\) \\ \hline Light Field Software & 0.9153 & 0.6917 \\ SML & 0.9293 & 0.7451 \\ DCT-STD & 0.9218 & 0.7626 \\ DCT-VAR-CV & 0.9294 & 0.7819 \\ SML-WHV & 0.9299 & 0.7548 \\ Agarwala’s Method & 0.9310 & 0.7863 \\ DCT-Sharp-CV & 0.9337 & 0.7917 \\ DCT-CORR-CV & 0.9340 & 0.7964 \\ DCT-SVD-CV & 0.9341 & 0.7971 \\ The Proposed Method & 0.9439 & 0.8077 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Objective evaluation of the image results (non-reference fusion metrics) for ’Mouse’ image.  
Figure 15: Comparison of ‘Mouse’ image results: (**a**) Light Field Software, (**b**) SML, (**c**) DCT-STD, (**d**) DCT-VAR-CV, (**e**) SML-WHV, (**f**) Agarwala’s method, (**g**) DCT-Sharp-CV, (**h**) DCT-CORR-CV, (**i**) DCT-SVD-CV, (**j**) The Proposed Method.
## 3 Results and Discussions
In the experiments for the natural latex samples with DRC in the range of 29.28-35.27%\(w/w\), the complex relative permittivity, the electrical conductivity, and the phase constant of the natural latex samples at different frequencies are calculated and shown in Figures 19-22, respectively. In Figure 19, when the DRC decreases due to the increasing water content in the natural latex samples, the real part of the complex relative permittivity increases over the frequency range of 0.5-4.0 GHz because of the high real part of the complex relative permittivity of water [23]. Therefore, the real part of the complex relative permittivity of natural latex samples increases as well. The maximum FOM1 is equal to 1.4462 (%\(w/w\))\({}^{-1}\) at the frequency of 0.5 GHz. The frequency of 0.5 GHz is the most suitable frequency when the real part of the complex relative permittivity is considered the indicator. Figure 20 shows the imaginary part of complex relative permittivity of the natural samples with different DRCs. The imaginary part of the complex relative permittivity increases when the DRC in the natural latex sample decreases. The maximum figure of merit is equal to 0.2759 (%\(w/w\))\({}^{-1}\) at the frequency of 4.0 GHz. Therefore, the frequency of 4.0 GHz is the most appropriate frequency when the imaginary part of the complex relative permittivity is considered the indicator. Figure 21 demonstrates the electrical conductivity of the natural latex samples with different DRCs. When the DRC in the natural latex sample decreases, the electrical conductivity is slightly changed in the frequency range of 0.5-1.0 GHz while the significant increase occurs in the higher frequency range of 1.0-4.0 GHz. The maximum FOM2 is equal to 0.0612 S/m * (%\(w/w\))\({}^{-1}\) at the frequency of 4.0 GHz. Therefore, when considering the electrical conductivity as an indicator, the frequency of 4.0 GHz is the optimum frequency. Figure 22 depicts the phase constant of the natural latex samples with different DRCs. The phase constant is inversely proportional to the DRC over the frequency range of 0.5-4.0 GHz. The significant increase of the phase constant occurs over the frequency range of 0.7-4.0 GHz. The maximum FOM3 is equal to 0.0093 rad/mm * (%\(w/w\))\({}^{-1}\) at the frequency of 4.0 GHz. When using the phase constant as an indicator, the frequency of 4.0 GHz is the most suitable frequency. Figure 11:4 The real part of complex relative permittivity of natural latex samples with different DRCs in the frequency range of 0.5–4.0 GHz at the temperature of 25 \({}^{\circ}\)C.  
Figure 11:5 Electrical conductivity of natural latex samples with different DRCs over the frequency range of 0.5–4.0 GHz at the temperature of 25 \({}^{\circ}\)C.  
Figure 11:6 The imaginary part of complex relative permittivity of natural latex samples with different DRCs in the frequency range of 0.5–4.0 GHz at the temperature of 25 \({}^{\circ}\)C.