esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results  
### DSF/Cu Irreversibly Reduces the Viability of NPC Cells  
MTT and LDH assays were performed to investigate the cytotoxic effects of DSF/Cu on four NPC cell lines: 5-8F, CNE2, S18 and 6-10B. The nonmalignant nasopharyngeal epithelial cells NP69-SV40T were used as non-tumor cell control. No obvious cell death was observed from 1 uM Cu-treated NPC and control cells (Figure S1A), whereas DSF/Cu exhibited a highly cytotoxic activity against these cells in a DSF concentration-dependent manner (Figure 1A, Figure S1B). In the four NPC cells lines (5-8F, CNE2, S18 and 6-10B), cell viability was significantly inhibited by treatment with DSF/Cu at 0.2 uM/1 uM, and the inhibition effects reached their peaks at 1.0 uM DSF.  
Interestingly, DSF/Cu also showed a vigorous cytotoxicity on the nonmalignant NP69 cells (Figure 1A). What is more, NP69 cells were more sensitive to lower doses of DSF (<0.2 uM) than the NPC cells. Furthermore, MTT and LDH assays (Figure 1B and Figure S1C) showed that when treated with a relatively high dose of DSF/Cu (1 uM/1 uM), the reduction of viability was observed in a time-dependent manner, and the inhibition rate was over 80% in these five cell lines at 24 h. These results indicated that DSF/Cu could decrease the cell viability sharply in both tumor and non-tumor cells.  
Furthermore, to determine whether the cytotoxic effect of DSF/Cu against NPC cells was reversible, DSF/Cu was removed after 0.5, 1 and 2 h of administration, and then drug-free media were added. As shown in Figure 1C and Figure S2, with 0.5 or 1 h incubation, 5-8F viability decreased significantly after 24 h of DSF/Cu withdrawal. Moreover, with 2 h of DSF/Cu incubation, cell viability after drug withdrawal was similar to those in the non-withdrawal group. Most of the cells died when cell viability was examined at 12 h. These results indicated that the cytotoxicity of DSF/Cu on NPC cells was irreversible.
## 5 Simulation Results and Discussion  
Five different benchmark test systems were applied to evaluate the performance of the proposed RLGWO algorithm. Its performance was also compared with that of several optimization techniques reported in the literature. The nature-inspired RLGWO was executed using MATLAB 7.1 (R2010a) on Intel (R) 1 GB RAM, 2.60 GHz CPU.  
Dissimilar to other metaheuristic computations that require suitable values for the algorithm's input parameters in order to improve their convergence rate, like particle swarm optimization (PSO) or evolutionary algorithms (ED), GWO has the advantage of being free from the initialization of input parameter. In this paper, the initial parameter for population size and the maximum number of iterations is selected as 50 and 200, respectively.
# A High-Density Crowd Counting Method Based on Convolutional Feature Fusion
## 3 Experiments
### Evaluation Metrics  
The mean absolute error (MAE) and mean square error (MSE) were used to evaluate performance [15; 16; 17]. The MAE reflects the accuracy of the prediction and the MSE reflects the robustness of the prediction. The detailed definitions are as follows:  
\[MAE=\frac{1}{M}\sum_{i=1}^{M}\lvert z_{i}-\hat{z}\rvert \tag{6}\]  
\[MSE=\sqrt{\frac{1}{M}\sum_{i=1}^{M}\left(z_{i}-\hat{z}_{i}\right)^{2}} \tag{7}\]  
where \(M\) is the number of test images, \(z_{i}\) and \(\hat{z}_{i}\) represent the ground truth head count and the estimated head count in the \(i\)-th image, respectively. With smaller values of MAE and MSE, the results will be better.
# On Alpha-Expansion-Based Graph-Cut Optimization for Decoder-Side Depth Estimation
## 4 Experimental Results
### Results
It is better to first provide a coarse proposal of the depth map and then refine it only with depth levels that are more probable (i.e., the neighboring depth levels and depth levels from neighboring segments). This scheme is the only one that provides quality gain, and, moreover, the runtime is still one of the shortest of all (only 5% longer than the fastest scheme)  
The comparison of estimated depth maps is visualized in Figures 3 and 4. As can be seen, most of the differences between depth maps are present on the edges of objects. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{4}{c}{\(n=2\)} & \multicolumn{4}{c}{\(n=4\)} \\ \cline{2-7}  & **BD-Rate** & **BD-Rate** & **Decoding and** & **BD-Rate** & **BD-Rate** & **Decoding and** \\  & **Y-PSNR** & **IV-PSNR** & **Rendering** & **Runtime** & **Y-PSNR** & **IV-PSNR** & **Rendering** \\ \hline ClassroomVideo & \(-\)7.1\% & \(-\)2.8\% & 75.5\% & 2.8\% & \(-\)0.6\% & 61.8\% \\ \hline Museum & \(-\)2.8\% & \(-\)0.9\% & 108.5\% & \(-\)4.8\% & \(-\)1.3\% & 93.2\% \\ \hline Chess & \(-\)99.2\% & \(-\)17.8\% & 99.8\% & 19.9\% & \(-\)12.0\% & 81.3\% \\ \hline Guitarist & 13.6\% & 2.4\% & 109.8\% & 76.1\% & 34.3\% & 87.6\% \\ \hline Hijack & 50.9\% & 110.6\% & 95.8\% & \(-\)11.4\% & 2.0\% & 86.2\% \\ \hline Cyberpunk & \(-\)2.4\% & \(-\)0.7\% & 90.7\% & 1.6\% & \(-\)3.3\% & 77.5\% \\ \hline Kitchen & 2.5\% & 2.7\% & 75.0\% & 3.8\% & 2.6\% & 52.6\% \\ \hline Cadillac & \(-\)1.2\% & \(-\)0.1\% & 76.3\% & 0.1\% & 1.5\% & 57.8\% \\ \hline Mirror & 1.9\% & 2.7\% & 77.1\% & 2.4\% & 2.3\% & 57.4\% \\ \hline Fan & \(-\)1.5\% & \(-\)0.8\% & 77.8\% & \(-\)2.0\% & \(-\)1.3\% & 59.9\% \\ \hline Group & 39.7\% & 31.2\% & 98.5\% & 44.3\% & 21.6\% & 84.6\% \\ \hline Dancing & 3.8\% & 2.4\% & 75.9\% & 7.4\% & 5.0\% & 58.6\% \\ \hline Painter & 0.6\% & 1.0\% & 70.4\% & 1.7\% & 1.5\% & 51.0\% \\ \hline Breakfast & \(-\)20.4\% & \(-\)12.9\% & 83.4\% & \(-\)11.2\% & \(-\)6.1\% & 66.5\% \\ \hline Barn & 1.4\% & \(-\)0.8\% & 77.4\% & 2.8\% & \(-\)1.4\% & 54.9\% \\ \hline Frog & 0.1\% & \(-\)0.0\% & 57.6\% & \(-\)0.2\% & \(-\)0.3\% & 37.7\% \\ \hline Carpark & 13.9\% & 7.9\% & 78.0\% & 8.3\% & 5.7\% & 55.6\% \\ \hline Street & 2.3\% & 0.4\% & 68.6\% & 17.0\% & 11.3\% & 53.0\% \\ \hline Fencing & 49.9\% & 20.8\% & 66.9\% & 147.7\% & 71.3\% & 43.8\% \\ \hline CBA Basketball & \(-\)5.3\% & \(-\)60.1\% & 79.9\% & 2.9\% & \(-\)0.2\% & 62.7\% \\ \hline MartialArts & 67.1\% & 19.7\% & 89.8\% & 56.7\% & 81.8\% & 68.4\% \\ \hline Average & 5.1\% & 5.0\% & 82.5\% & 17.4\% & 10.2\% & 64.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: State-of-the-art hierarchical alpha-expansion with \(n=2\) (anchor) and \(n=4\), compared to basic alpha-expansion.
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Channel Equalization
#### 3.2.5 Postequalization DBMLP  
To further improve the utility of NN equalizers, researchers had proposed a modified double-branch multilayer perceptron (DBMLP) postequilibrium algorithm [44] to further reduce the consumption of energy and computational resources. DBMLP reconstructed the MLP postequalization algorithm using the structure of the Volterra series postequalization algorithm as a template. DBMLP combines the advantages of linear adaptive filters and MLP, which can improve the BER performance of the algorithm while reducing the complexity of the algorithm by 74.1%. The core structure of DBMLP is two branches of linear and nonlinear ones. In the DBMLP structure, a CNN with a convolutional layer and a dense-layer structure to simulate the linear distortion in the signal bandwidth is the first branch. In addition, a hollow MLP with an airlift layer and two dense-layer structuresto simulate the nonlinear distortion outside the signal bandwidth is the second branch. The nonlinearity of the output of the first branch is corrected by the output of the second branch, and the hollow layer can ignore the effect of the intermediate signal on the signals on both sides.  
To further reduce power consumption and complexity, a pruning algorithm based on DBMLP is proposed [58]. The algorithm performs the operation of pruning by setting the smaller absolute value of weights of the connections to be pruned to 0 based on sparsity. The weights of the linear branch are not prunable while the nonlinear ones are prunable. The experimental results confirm the superiority of this approach.
## 5 GNSS Implementation
The results of the pre-processing are fed into the model to obtain the best estimate of the output system. The above solutions indicate the need for additional sensors built on rolling stock, while minimizing the development of costly trackside infrastructure. GNSS positioning introduces a new possibility for providing the required performance that has not been possible to implement until now. With conventional solutions for detecting and locating rail vehicles on the rail infrastructure, ETCS requires the use of physical balises, which are fixed in the track with their exact location determined. The use of GNSS, i.e., the use of an embedded wireless system, combined with the use of digital maps, makes it possible to dispense with the use of physical balises and use so-called virtual balises, whose location is specified on a digital map. For the system to work like a physical balise, it should detect when a train reaches a virtual balise previously identified and stored in the virtual balise map database. Before migrating toward virtual balises, the system must guarantee that the train ahead will pass (in its entirety) through the virtual balise to ensure that the track between the locations in question is free. The above is one of the bases for justifying the implementation of a GNSS. Reducing the use of trackside equipment has a positive effect on financial aspects, as well as on the environment. When implementing this type of solution, however, it should be borne in mind that railway operating rules, regulations, and standards are not structured in terms of operations in this regard [26; 103].
## 3 Experimental and Numerical Methods
### Finite Element Model
Thus, we built a two-dimensional FEM using a Partial Differential Equation (PDE) solver in the coefficient form [46], and solved the pair of wave Equations (35) and (36). Figure 4 illustrates the geometry of the numerical model. The load application was simulated by making the entire Boundary 1 displaced in the positive \(x\)-direction at a constant rate. Boundary 2 and 3 were the free boundaries. Boundary 4 was fixed in both \(x\) and \(y\) directions. To assimilate previous experiments [13], we used the set of parameters shown in Table 1. This combination of \(G\) and \(\rho\) yields the transverse wave velocity of 10 cm/min., which is of the same order as the experimental values reported in ref. [13]. The pulling rate of 35 \(\upmu\)m/s is also of the same order as these experiments.
# Throughput and Packet Loss Probability Analysis of Long Range Wide Area Network
###### Abstract  
Long Range Wide Area Network (LoRaWAN) is the one of the promising low power wide area network (LPWAN) technologies at present and is expected to grow in the foreseeable future as a tool to provide connectivity among small things. In this paper, we present a simple analytical model to compute the throughput and packet loss probability of Medium Access Control (MAC) for Class-A of LoRaWAN. This analysis results can be used as a reference for deploying the appropriate number of end-devices (EDs) that can be accepted in a gateway (GW) while maximizing network throughput or guaranteeing the packet loss rate of EDs.  
Internet of Things; low power wide area network; long range wide area network; Markov chain model +
Footnote †: journal: Applied Sciences  
1
# An In-Network Cooperative Storage Schema Based on Neighbor Offloading in a Programmable Data Plane
## 4 In-Network Cooperative Storage Mechanism
### Neighbor Selection Mechanism
```
0:\(\text{rtt}=\{rtt(1)_{i},rtt(2)_{i},\ldots rtt(k)_{i}\}\), current rtt of each neighbor interface; \(bandwidth=\{bandwidth(1)_{i},bandwidth(2)_{i},\ldots bandwidth(k)_{i}\}\), current bandwidth of each neighbor interface; \(w_{IO}=\{w_{IO}(1)_{i},w_{IO}(2)_{i},\ldots w_{IO}(k)_{i}\}\), current writing throughput of each neighbor; Output: state, target_list
1: Initialize RTT0 = 0, \(\alpha\) = 0.875, \(\beta\) = 0.1
2: While k < K do
3: calculate RTT(k) \(\leftarrow\) Equation (1)
4: calculate neighbor_weight(k) \(\leftarrow\) Equation (2)
5: EndWhile
6: calculate \(\text{average\_RTT}=\frac{1}{k}*\frac{\text{K}}{1}RTT(k)\)
7: calculate \(\text{self\_weight}\leftarrow\) Equation (3)
8: calculate normalized value \(\gamma\) \(\leftarrow\) Equation (4)
9:if\(\gamma<threshold\), then
10: state \(\leftarrow\) \"cooperative\"
11: sortedlist \(\leftarrow\) sort (neighbor_list i) by weight desc
12: sublist \(\leftarrow\) subisist (0, number_threshold-1) from sortedlist
13: target_list \(\leftarrow\) sublist
14:else
15: state \(\leftarrow\) \"local_storage\"
16: EndIf
17: Return state, target_list
```  
**Algorithm 1** Neighbor selection algorithm.
## 4 Multicast Routing Approaches in DTN
### Social-Aware Multicast Routing in DTN
#### 4.1.3 Discussion
[42] studied the impact of node selfishness in a DTN multicast context and how the multicast group size affects the performance of the multicast routing performance.
## 6 The Framework
### Functional Model  
Initially, a customer sends their request, which consists of a customized ERP business process in terms of functional and QoS needs to a _Semantic Context Broker_ Agent who plays the role of a mediator. Here, the _Semantic Context Broker_ Agent is a core agent that calls the _SCwA-IFCCS-3S_ Agent that is responsible for launching several _ERP-ICS_ and _ERP-FCS_ agents, which can migrate to IoT devices and Fog servers to look for the requested services. These agents search for local IoT and Fog services with the help of a knowledge-agent able to make partial or full services compositions of requested ERP business processes.  
The _ERP-CCS-3S_ Agent, from all the possible composite services, selects the best solution (single composite service or a set of composite services) that optimizes service qualities and energy cost. The _ERP-CCS-3S_ Agent uses a customer's preferences to select an optimal composite service. Once the optimal composite service has been selected and the ERP business process has been deployed, the deployment results are sent back to the customer. If the _SCwA-IFCCS Agent_ does not find any local services that match the customer's needs, it transmits the customer's query to the _ERP-CCS Agent_ to discover and compose services involved in the business process on Cloud servers.  
The Context Supervisor Agent is responsible for handling the problems encountered during the execution of the various services (such as changes of user's needs and/or customer preferences) and incoming new customer's needs in order to continuously provide an optimal composite service.  
* If it is a simple service update (replacing Cloud service by another IoT service), a _SCwA-IFCCS-3S_ Agent will replace it with another equivalent IoT/Fog service based on the Knowledge agent.
* If it is about more than one Cloud service, he calls _ERP-ICS_ and _ERP-FCS_ agents. It moves towards IoT devices and Fog servers, and then sends discovered services to ensure adaptation of business processes. The discovered services will be transmitted to the _ERP-CCS-3S_ Agent. The _ERP-CCS-3S_ Agent composes these services and selects the optimal composite service.
# A Novel Cross-Layer Routing Protocol Based on Network Coding for Underwater Sensor Networks
## 4 Design of Network Coding Routing Protocol
### Protocol Design Overview  
The overall design of our proposed protocol is presented in Figure 3. Firstly, an initial routing path is constructed. Here we apply receiver-side routing and the destination node is responsible for broadcasting beacon messages. The aim of initial routing construction is not only finding a reliable path to forward data to the destination node, but also including enough auxiliary nodes to assist the data transmission. The detailed design of initial routing construction is given in Section 4.2. At the beginning of data transmission, data packets are encoded with random network coding. Random network coding can ensure that the probability of sending repetitive data packets is low under the premise of lacking information exchange among relay nodes. Nodes can jointly decode messages from all neighborhood nodes. Related encoding and decoding design is given in Section 4.3. The relay nodes jointly decode messages from neighbourhood nodes and determine whether current routing path need to be updated or not. The transmit power is allocated according to the channel condition among nodes. When the transmit power is decreased to zero or increased beyond a predetermined threshold, nodes need to update the routing paths. In order to reduce the effect of partial adverse channel problems to the overall path, we only update the paths near the nodes which have adverse channel problems. This scheme can complete routing update in a short time and have less effect on the overall routing paths. Routing update and maintenance are described in Section 4.4.  
Figure 3: Overview of NCRP
# DIKWP-TRIZ: A Revolution on Traditional TRIZ Towards Invention for Artificial Consciousness
## 4 Mapping and Coverage Analysis of TRIZ Rules in DIKWP
### Mapping the 40 TRIZ Principles to the DIKWP Model
#### 4.1.7 Information (I) \(\rightarrow\) Information (I)  
This phase discusses the process of refining and reorganizing Information to enhance its quality and utility. This process involves not only optimizing the Information itself but also improving its structure and presentation to facilitate better understanding and effective communication. The following are several TRIZ principles related to this transformation process and their specific applications:  
* **Principle 13: The Other Way Round** In the transformation of Information to Information, principle 13 is mainly reflected in the reorganization of information flow or structure to promote a better understanding of complex concepts and more effective communication. For example, in the previous studies and improvement of business information flow [66; 67], stakeholders were able to better understand and use corresponding workflows, which promoted business development and improved efficiency.
* **Principle 17: Another Dimension** The cognitive subject can employ various forms of representation, such as charts and diagrams, to transform one type of differentiation into another, providing multiple perspectives and deeper insights into the Information. For example, in Data visualization, representing Information across different dimensions, such as through multidimensional scaling and graph visualizations, can help users better understand high-dimensional Data. A study on visualizing dimension coverage in exploratory analysis demonstrated that representing Data visually with multiple perspectives helps analysts form new questions and gain deeper insights into datasets [68]. In market research, combining bar charts and pie charts can enhance the analysis by offering different viewpoints on the same Data. For instance, visualizing research topics using a three-dimensional strategic diagram enabled researchers to gain a more nuanced understanding of interdisciplinary research areas and emerging trends [69]. This multi-faceted approach enhances the ability to analyze complex Data and facilitates more informed and strategic decision making.
# Blood Pressure Estimation by Photoplethysmogram Decomposition into Hyperbolic Secant Waves
## 2 Methods
### Pulse Wave Model for Blood Pressure Estimation
#### 2.1.3 Multiple Regression for the Relationship between the Features and Blood Pressure  
To determine the adequacy of the PDA features of PPG for blood pressure estimation, we performed a multiple regression between the features and the ground truth blood pressure. Up to eight features can be used in this study, and the feature combination depends on the number of features used. Since the number of features was small, we decided to adopt the set of features that indicated the best performance in all combinations of the features.
###### Abstract  
In fiber optic communication systems, the dispersion and nonlinear interaction of optical signals are critical to modeling fiber optic communication, and the regular perturbation (RP) model is a simplified modeling method composed of parallel branches, which has obvious advantages in deep learning backpropagation. In this paper, we propose a simplified single-mode fiber signal transmission model based on the RP model, which significantly improves the fitting accuracy of the model for dispersion and nonlinear interactions at the same complexity by adding trainable parameters to the standard RP model. We explain in the paper that this improvement is applicable to dual-polarization systems and still effective under the conditions of large launch power, without dispersion management, and containing amplified spontaneous emission (ASE) noise. The model uses the standard split-step Fourier method (SSFM) to generate labels and updates parameters through gradient descent method. When transmitting a dual-polarization signal with a launch power of 13 dBm, the modified regular perturbation (MRP) model proposed in the paper can reduce the fitting errors by more than 75% compared to the standard RP model after transmitting through a 120 km standard single-mode fiber.  
nonlinear Schrodinger equation; regular perturbation model; fiber nonlinearity; learnable parameters; single-span dual-polarization systems +
Footnote †: journal: _Photonics_
## 4 Experimental Results
### Calculation of the Irradiance Responsivity  
The calculation of the irradiance responsivity \(Re\) was carried out using the quasi-optical setup shown in Figure 4. Apart from the long pass filter, in our responsivity measurement, we need to characterize only the relationship between the GDD and the LTF without any medium between them. The irradiance responsivity measurement was performed by modifying the MMW source output power from 0-100 percent in leaps of 5%. To do that we used the user-controlled attenuation (UCA) input of the MMW source in order to control the source attenuation by providing different voltages in its input where 0 V is zero attenuation and 5 V is max attenuation. To improve accuracy, 40 samples of \(f_{mmw}\) (output frequency of LTF when exposed to MMW radiation) were performed, then a subtraction was performed to each pair of consecutive samples, one with the MMW source in ON mode and one with it in OFF mode, which yielded 20 \(\Delta f_{mmw}\) (change in consecutive samples of output frequency of LTF when exposed to MMW radiation) samples that were averaged to produce a single sample of \(\Delta f_{mmw}\) as shown in Figure 11.  
Figure 11: Variation in the output frequency of LTF in accordance to the UCA input.  
Each sample was taken in \(T_{gate}\)= 100 ms. The results from the responsivity measurement are shown in Figure 12, where \(P_{GDD}\) is the amount of MMW radiation power hitting the cross-section of the GDD.  
The irradiance responsivity is given by calculating the slope of the graph as given below:  
(a) With filter  
\[Re=\frac{(278-116.9)}{(2390.8-1195.4)\times 10^{-6}}=134.77\;\mathrm{KHz}/W\]  
Since only 0.1% of the MMW source power reaches the GDD, the irradiance responsivity is \(Re\times\frac{1}{0.001}\).  
Hence \(Re\) = 134.77 MHz/W (with filter).  
(b) Without filter  
\[Re=\frac{(236.9-99.05)}{(2390.8-1195.4)\times 10^{-6}}=115.32\;\mathrm{KHz}/W\]  
Since only 0.1% of the MMW source power reaches the GDD the irradiance responsivity is \(Re\times\frac{1}{0.001}\).  
Hence \(R_{e}\) = 115.32 MHz/W (without filter).
# Measuring the Centrality of DNS Infrastructure in the Wild
## 6 Name Server Centrality Analysis
### Dataset  
**TLD zone file.** Our goal in this section is to conduct a large-scale study of the name server usage. To ensure the coverage of domain names, we collected 210,446,494 domain names from 1138 gTLD zone files by the Centralized Zone Data Service (CZDS) [38] on October 19, 2021. While the zone file is dynamic, our goal is to measure the overall usage of name servers, so the zone files we used are enough to answer the above questions. Taken together, as far as we know, our dataset represents the most comprehensive dataset for centrality studies of authoritative name servers.