esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Energy Harvesting Mechanism and Low Power Technology in Wireless Sensor Networks
## 4 Simulations and Results
### Energy Harvesting System
#### 4.1.2 Performance of the Bistable Piezoelectric Cantilever Oscillator  
(1) Simple and harmonic incentive  
In this experiment, we examined the power generation performance of a bistable piezoelectric cantilever oscillator composed of two magnets under simple harmonic incentive, with varying magnetization strength and load resistance. In Figure 7A, the voltage amplitude frequency curve is compared for different magnetization intensities. The system's voltage amplitude solution exhibits jumps as the excitation frequency changes. Furthermore, an increase in the nonlinear term coefficient results in a rightward shift of the curve, widening the response band of large motion and increasing the amplitude. Figure 7B displays the power generated at different impedances. It is noteworthy that the jump phenomenon's corresponding excitation frequency in the system's amplitude and frequency curve remains unchanged regardless of the resistance value. In the system, there is an issue with resistance matching. Specifically, the resistance value R = 30 k needs to be addressed and optimized.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Time**} & **Circuit Input** & **Power Supply** & **Power Supply** & **Power Supply** \\  & **Average Voltage** & **Average Voltage** & **Average Current** & **Voltage Deviation** \\ \hline
6:00 & 5.00 V & 3.33 V & 100.0 mA & 0.91\% \\
9:00 & 4.97 V & 3.30 V & 99.8 mA & 0\% \\
12:00 & 4.97 V & 3.29 V & 102.6 mA & 0.30\% \\
15:00 & 4.98 V & 3.31 V & 101.4 mA & 0.30\% \\
18:00 & 4.21 V & 3.28 V & 99.9 mA & 0.61\% \\
21:00 & 4.19 V & 3.27 V & 100.0 mA & 0.91\% \\
24:00 & 4.99 V & 3.32 V & 100.1 mA & 0.61\% \\
3:00 & 5.02 V & 3.34 V & 103.4 mA & 1.21\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measured data of energy harvesting system.  
Figure 6: Diagram of spatial–dimensional data fusion of nodes.  
2. Random incentive  
In this experiment, we examined the impact of various incentive strengths and load resistances on the response of a bistable piezoelectric cantilever subjected to random incentive. The excitation strength was set to 0.01-0.09 G2/Hz and the load resistance was 1 k. As shown in Figure 8A, the output voltage of the system increases proportionally with the excitation strength. The incentive strength is 0.06 G2/Hz, and Figure 8B shows the average power curve when the impedance changes. The figure clearly demonstrates that at a resistance of 19.1 k, the output power reaches its peak. This signifies that matching the impedance maximizes the output power of the system when subjected to random excitation.
## 4 Multimedia-Based Cross-Layer Optimization in VSNs
### Image-Based Cross-Layer Optimization  
When dealing with image compression, three very relevant characteristics will be typically present in the encoded images: unequal importance, error tolerance and constrained error propagation [19]. The unequal importance characteristic refers to the fact that different parts of compressed images have different perceptual and structural relevance. The error tolerance denotes that errors during transmission over the network may not prohibit the reconstruction of the original data, which may be performed even with some degradation. Finally, the constrained error propagation indicates that if some bits are corrupted, the neighboring bits are likely to become useless as well. Such characteristics are commonly considered by image-based cross-layer optimization solutions.  
Visual sensor networks for image-based monitoring are very attractive since the communication requirements are less stringent than video streaming monitoring applications. Analyzing the surveyed works, we can identify different approaches for cross-layer optimization. Among the proposed solutions, wavelet-based prioritization and progressive coding seem to be very suitable for wireless image sensor networks, and new investigations concerning cross-layer design exploiting such coding techniques should still arise.
# Urban Free-Space Optical Network Optimization
## 4 Results
### Results on Real-World Data  
Now, we present experimental results of the GA on real-world data. Here, we used the OSM open project data to create visibility graphs based on real building coordinates and LOS computation. The framework built for these experiments comprises of three steps:  
* Get urban data (location of high buildings in a city).
* Compute the city visibility graph (mainly the LOS between the buildings).
* Compute the Steiner graph with GA and the 2-factor approximation method.  
In these experiments, we used the urban data of Manhattan Island in New York City. Figures 27 and 28 show the results on real-world data in the form of histograms. The experiments were run on 100 urban graphs, each with 2000 vertices, of which 5 are blue (i.e., require communication).  
The histogram in Figure 27 shows that the GA does not always return the best solution. However, in most cases, the GA returns better solutions than the 2-factor approximation method. Moreover, the cost differences in favor of the GA tend to be higher than those in favor of the 2-factor approximation method. The histogram in Figure 28 reveals the clear superiority of the GA in run time over the 2-factor approximation method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Graph Size** & **Graph Density** & **Cost Reduction** \\ \hline \multirow{4}{*}{4000} & 0.05 & 33\% \\  & 0.1 & 38\% \\  & 0.15 & 20\% \\  & 0.2 & 41\% \\ \hline \multirow{4}{*}{8000} & 0.025 & 28\% \\  & 0.05 & 39\% \\  & 0.075 & 33\% \\  & 0.1 & 43\% \\ \hline \multirow{4}{*}{16,000} & 0.005 & 31\% \\  & 0.01 & 44\% \\ \cline{1-1}  & 0.015 & 67\% \\ \cline{1-1}  & 0.02 & 26\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cost reduction on large graphs with different densities.  
Figure 26: The cost reduction of the GA over 1000 generations on an example problem. The fixed red line represents the result of the 2-factor approximation method.  
We also report a series of experiments that we conducted on large-scale graphs (over 4000 vertices). The graphs were generated randomly, but the graph features (such as density and size) were fixed to simulate urban visibility graphs. From the results, we can conclude that for finding a solution to the Steiner problem on these graphs, the GA was efficient and within a reasonable run time. Because the Steiner problem is NP-hard, we could not test the GA method against the 2-factor approximation method (which demands \(O(V^{3})\) run time). However, the experiments did show that the GA can reach solutions with up to 80% cost reduction. This fact shows that the algorithm is more efficient than simply choosing a solution at random. Another feature of the GA method is the ability to produce \"anytime\" solutions, meaning we can decide when to stop the algorithm's run and get a solution. Given that running the GA on large graphs can take up to 1500 s, this anytime feature becomes especially helpful in dynamic conditions when weather or demand changes affect the urban network.
# Weight-Based PA-GPSR Protocol Improvement Method in VANET
## 5 Simulation and Performance Analysis
### Result Analysis
#### 5.2.1 Packet Loss Rate
This result occurs because the probability of data congestion caused by higher node density increases. At the same time, W-PAGPSR has packet survival time and MAC queue size, which leads to the loss of some data packets and increases the packet loss rate. Figure 9: Packet loss rate under different number of nodes and CBR connections; (**a**) 5 CBR connections; (**b**) 10 CBR connections; (**c**) 15 CBR connections; (**d**) 20 CBR connections.
# Adipose Stem Cell Response to Borophosphate Bioactive Glass  
[PERSON]  
1 Department of Biological Sciences, Missouri University of Science and Technology, Rolla, MO 65409, USA; naa8 [EMAIL_ADDRESS] (N.A.A.); [EMAIL_ADDRESS] (B.A.B.)
2 Department of Materials Science and Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA; [EMAIL_ADDRESS] (R.K.B.)
* Correspondence: [PERSON]ADDRESS]
* These authors contributed equally to this work.  
Siclate and borate bioactive glasses have been reported to create alkaline conditions by rapidly releasing ions when reacting in aqueous solution. At certain levels, this alkaline solution can negatively affect cell viability. Adding phosphate ions to the glass composition can control the degradation rate of bioactive glasses and create a neutral pH environment. This study evaluated a series of borophosphate bioactive glasses (BPBGs) with nominal molar compositions 16 Na2O-24 CaO-xB2O3-(60-x)P2O5, where x = 0, 40, or 60. The phosphate (X0) glass (PBG) produced an acidic solution when dissolved in water; the borate (X60) glass (BBG) produced an alkaline solution, and the BPBG glass produced a pH-neutral solution. These three glasses were evaluated using adipose stem cells (ASCs), a cell population known for their therapeutic abilities. The effects of each glass on the pH of cell culture, ions released during degradation, and on ASC functions, including viability, migration, angiogenic ability, differentiation, and protein secretions, were evaluated. The X40 BPBG created a physiologically neutral pH in cell culture media after 24 h. The X0 phosphate glass promoted ASC migration, while the highly alkaline X60 borate increased the angiogenic ability of ASCs. These results indicate that BPBG can be used safely in cell culture studies and customized for specific biomedical applications.
## 3 Simulation Results
### Annual PV Real Power Injection for Different Power Factor Control Abilities  
The annual PV real power injection for different power factor control abilities are depicted in Figure 7. It can be seen from Figure 7 that (1) when the PV capacity is greater than 1500 kWp, the PV inverter with power factor control ability will exceed the PV without the ability to adjust power factor (_pf_ =1.0) in annual real power injection; (2) for PV capacity of 3500 kWp, the real power injection of PV with ability of power factor 0.9 or 0.85 is more than that of the PV with ability of power factor 0.95; (3) for PV inverters with the ability of power factor 0.9 and 0.85, their annual real power injection are very close; thus, the smart PV inverter with power factor control ability _pf_ = 0.9 will be sufficient to play the role to increase the PV power injection to the distribution system.  
Figure 6: Maximal PV real power injection for control abilities _pf_ = 1.0, 0.95, 0.9, and 0.85.
## 5 Multi-Connectivity over IEEE 802.11 ay Links
### Equilibrium Conditions  
A point \((\mathbf{w^{\star}},\mathbf{p^{\star}})\) is said to be an equilibrium of (25) and (26) if it satisfies, for all links,  
\[\Big{(}\frac{3((\tau-q_{t})/\tau)}{2w_{t}^{2}}-p_{t}\Big{)}_{w_{t}}^{+}=0, \frac{1}{m_{t}}\Big{(}\lambda_{t}-\theta_{t}\Big{)}_{p_{t}}^{+}=0\ \ \text{or}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline TRA Protocol & TCP/MPTCP & TCP Header & 20 bytes \\ Aggregation & MSDU/MPDU & A-MSDU bytes & 7935 \\ IP Header & 20 bytes & A-MPDU bytes & 4,194,303 \\ Payload size & 1460 bytes & Propagation delay & 6 ms \\ Block Ack Size & 1024 frames & No. of Transmits & 27 sectors \\ Sector Azimuth & \(-80^{\circ}20^{\circ}80^{\circ}\) & Congestion window & 1 (initial) \\ Sector Elevation & \(-45^{\circ}\):\(80^{\circ}45^{\circ}\) & Transmit Power & 12 dBm \\ MPTCP & OLIA/BALIA & file size & small/bulk \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network and Protocol Parameters.  
Figure 5: Temporal evolution of 5 TCP Reno vs. 5 MPTCP (_LIA and LD-MPSP_) users’ 802.11_ay_ throughputs obtained from 0 s to 200 s over seven links EDMG-SC ay networks with spatial streaming (up to eight) with two different experiments. Experiment 1: observe that LIA cannot compete well with TCP (see \(\mathrm{tc}_{\mathrm{LIA}}\)), Experiment 2: our algorithm (LD-MPSP) performs better than LIA when competing with TCP (see \(\mathrm{tc}_{\mathrm{LD-mpsp}}\)) flows (simulation is summarized in Table 2).  
\[w_{t}\geq 0,\frac{3((\tau-q_{t})/\tau)}{2w_{t}^{2}}\leq p_{t},\text{ and }\frac{3((\tau-q_{t})/\tau)}{2w_{t}^{2}}=p_{t},w_{t}>0\]  
equivalently,  
\[p_{t}\geq 0,\lambda_{t}\leq\theta_{t},\text{ and }\lambda_{t}=\theta_{t}\text{ if }p_{t}>0. \tag{27}\]  
Any finite equilibrium \((\mathbf{w^{\star},p^{\star}})\) must have \(p_{t}>0\) for all links \(t\). We always focus on finite equilibria and interpreting (27) in terms of \(w_{\text{max}}^{t}\) provides us with an important insight about the _bandwidth delay product_ (\(C_{t}\)) of the network circuit link (\(w^{\text{max}}\approx C_{t}\)),  
\[p_{t}=\frac{8((\tau-q_{t})/\tau)}{3(w_{\text{max}}^{2})^{2}}\approx\frac{8(( \tau-q_{t})/\tau)}{3{C_{t}}^{2}} \tag{28}\]
# Capacity-Operation Collaborative Optimization for Wind-Solar-Hydrogen Multi-Energy Supply System
## 2 System Description
### Operation Strategy  
A carefully orchestrated energy management strategy is vital for handling the inherent fluctuations of renewable energy sources such as wind and solar and to adapt to changes in user load demands effectively. The WSH-MES system is engineered to offer an encompassing solution to these complexities. In this framework, power generated with PV panels and WP systems serves as the primary electricity source. Their output is directed to meet immediate user demands, thus minimizing any need to rely on non-renewable sources, as indicated in Figure 1.  
When an excess of power is generated with these renewable sources, it triggers a set of energy storage solutions. Initially, the surplus electricity is used for electrolysis to produce hydrogen, which is then stored in the HST system. The HST serves as a robust chemical energy storage solution, also highlighted in Figure 2. Should there still be excess power after the HST system is at full capacity, this additional power is converted into thermal energy for subsequent storage in the TES system. The TES system is designed to store energy that can later be used for heating applications.  
Figure 2: Operation strategy of the WSH-MES system.  
When the PV and WP systems fall short in generating enough power to meet demand, a PEMFC becomes operational, supplying rapid peak power by using stored hydrogen from the hydrogen tank (HT). Further, if a deficit still exists after using the PEMFC, the CSP system contributes to balancing the energy equation. Only when all these systems are inadequate to meet the energy demands is power sourced from the governmental grid.  
This operating strategy not only ensures that the renewable energy generated is used in the most efficient manner but also offers layered energy storage solutions. It accommodates storage limitations and provides a holistic response to the challenges of energy supply and demand. All these components and their interactions are visually represented in Figures 1 and 2 for a more intuitive understanding.
# S-Band Doppler Wave Radar System
## 4 Results Comparison and Analysis
### Average Wave Period  
Figures 14 and 15 show a comparison of the average wave period between MORSE and the buoy from 00:00 22 March to 19:00 30 March 2015.  
Although the overall trend is similar, the results from MORSE are relatively higher at different levels, especially at the beginning and end of the entire data duration. In addition, certain MORSE data points present much greater deviation from the buoy data during the afternoon and at midnight of March 27. From the scatter plot we can see that those data points with large bias usually have low wind velocity. The average wave period is not as accurate as the significant wave height. The correlation coefficient is 0.3797, the mean error is 0.3938 s, the standard deviation error is 0.6206 s, and the root mean square error is 0.7389 s.  
Figure 14: Average wave period measured by MORSE and the buoy.  
Figure 13: Scatter plot of significant wave height measured by MORSE and the buoy.
# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 5 Algorithm Development: GNSS/Vision Integration
### Pseudorange Observation  
The pseudorange observation between the user and a satellite is related to the user position and clock states as:  
\[\rho_{i}=\mid r_{i}-r_{u}\mid+\mathit{c}dt+\mu_{\rho_{i}} \tag{12}\]  
where:  
\(\rho_{i}\) is the pseudorange of the \(i^{\text{th}}\) satellite;  
\(r_{i}\) denotes the \(i^{\text{th}}\) satellite's position at the transmission time;  
\(r_{u}\) represents the user position at the reception time;  
\(\mathit{c}dt\) is the receiver clock bias;  
\(\mu_{\rho_{i}}\) denotes the sum of all errors on the measurement;  
\(\mid\bullet\mid\) denotes the magnitude of a vector.  
Given an a priori estimate of the state, an estimate of the clock bias and the errors, the estimate of the pseudorange measurement is given by:  
\[\hat{p}_{i}=\mid r_{i}-\hat{p}_{u}\mid+\mathit{c}\hat{d}t+\hat{p}_{\rho_{i}} \tag{13}\]  
where all (\(\hat{\cdot}\)) represent the corresponding estimates of the parameters as defined in Equation (12).  
Figure 10: The proposed GNSS/vision tightly-coupled integration’s general architecture.  
Thus, the residual is expressed as:  
\[\delta\rho_{i}=\rho_{i}-\hat{\rho}_{i}=\begin{bmatrix}\mathbf{\hat{1}}_{i}^{ \mathsf{T}}&1\end{bmatrix}\begin{bmatrix}r_{u}-\hat{\tau}_{u}\\ c(dt-\hat{d}t)\end{bmatrix}+\mu_{\rho_{i}}-\hat{\eta}_{\rho_{i}} \tag{14}\]  
where \(\mathbf{\hat{1}}_{i}^{\mathsf{T}}=\dfrac{r_{i}-\hat{\tau}_{u}}{\big{|}\ r_{i}- \hat{\tau}_{u}\big{|}}\) is the unit vector from the estimated user position to the satellite.  
Likewise, the pseudorange rate observation is given by the following:  
\[\dot{\rho}_{i}=(\
u_{i}-\
u_{u})\ \bullet\ \dfrac{r_{i}-r_{u}}{\big{|}\ r_{i}-r_{u} \big{|}}+d+\mu_{\dot{\rho}_{i}} \tag{15}\]  
where:  
\(\
u_{i}\) and \(\
u_{u}\) are the \(i\)th satellite and user velocities, respectively, expressed in the Earth-Centred Earth-Fixed (ECEF) coordinate frame;  
\(d\) is the receiver clock drift in m/s;  
\(\mu_{\dot{\rho}_{i}}\) represents the ensemble of errors in the measurement in m/s;  
(\(\bullet\)) denotes the dot product.  
The estimate of the pseudorange rate is expressed as:  
\[\hat{\rho}_{i}=(\
u_{i}-\hat{\
u}_{u})\ \bullet\ \mathbf{\hat{1}}_{i}+\hat{d}+ \hat{\eta}_{\dot{\rho}_{i}} \tag{16}\]  
where (\(\hat{\cdot}\)), as previously, represents the estimates of the corresponding parameters.  
From Equations (15) and (16), we obtain the linearized pseudorange rate measurement given by the equation:  
\[\delta\dot{\rho}_{i}=\dot{\rho}_{i}-\hat{\rho}_{i}=\begin{bmatrix}-\mathbf{ \hat{1}}_{i}^{\mathsf{T}}&1\end{bmatrix}\begin{bmatrix}\
u_{u}-\hat{\
u}_{u} \\ d-\hat{d}\end{bmatrix}+\mu_{\dot{\rho}_{i}}-\hat{\eta}_{\dot{\rho}_{i}} \tag{17}\]  
From the above equations, the set of GNSS observations (\(\mathbf{z}_{GnsS}\)) is used in the form of measurements:  
\[\delta\mathbf{z}_{GnsS}=\begin{bmatrix}\delta\rho_{0}\\ \delta\rho_{1}\\ \vdots\\ \delta\rho_{n}\\ \delta\dot{\rho}_{0}\\ \delta\rho_{1}\\ \vdots\\ \delta\dot{\rho}_{n}\end{bmatrix} \tag{18}\]
# On Finding Optimal (Dynamic) Arborescences
## 2 Optimal Arborescences
### Tarjan Algorithm
#### 2.2.1 Initialization  
The data structures are initialized as follows. _queues_ is an array of heaps, which is initialized with an heap for each vertex \(v\) containing incident edges on \(v\). _roots_ is the list of vertices to be processed, which is initialized as \(V\). The forest \(F\) is initialized as empty, as well as the set _rset_. Four auxiliary arrays are also needed to build \(F\) and the optimal arborescence, namely, _inEdgeNode_--that for each vertex \(v\) stores a node of \(F\) associated with the minimum weight edge incident in \(v\), \(\pi\)--that stores the leaf nodes of \(F\), _cycleEdgeNode_--that stores for each representative cycle vertex \(v\) the list of cycle edge nodes in \(F\), and _max_--that stores for each strongly connected component the target of the maximum weight edge. These data structures are initialized as detailed in Algorithm 1.  
```
roots\(\leftarrow\emptyset\)\(\triangleright\) Set of vertices to process. for each \(v\in V\)do queues\([v]\leftarrow\textsc{INIT}(v,L[v])\)\(\triangleright\)\(L[v]\) refers to the list of edges incident in \(v\). SMAKE-SET\((v)\), WMAKE-SET\((v)\) roots\(\leftarrow\) roots\(\cup\{v\}\) \(\mathit{max}[v]\gets v\) inEdgeNode\([v]\leftarrow\)null \(\pi[v]\leftarrow\)null cycleEdgeNode\([v]\leftarrow\emptyset\) endfor \(F\leftarrow\emptyset\) rset\(\leftarrow\emptyset\)
```  
**Algorithm 1** Initialization of Tarjan algorithm.
# Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
## 4 Research on Machine Learning-Based IDSs
### Packet-Based Attack Detection
#### 4.1.1 Packet Parsing-Based Detection  
Various types of protocols are used in network communications, such as HTTP and DNS. These protocols have different formats; the packet parsing-based detection methods primarily focus on the protocol header fields. The usual practice is to extract the header fields using parsing tools (such as Wireshark or the Bro) and then to treat the values of the most important fields as feature vectors. Packet parsing-based detection methods apply to shallow models.  
The header fields provide basic packet information from which feature can be extracted used with using classification algorithms to detect attacks. [PERSON] et al. [40] proposed an SVM- and K-means-based packet detection method. They captured packets from a real enterprise network and parsed them with Bro. First, they grouped the packets according to protocol type. Then, they clustered the data with the K-means++ algorithm for the different protocol datasets. Thus, the original dataset was grouped into many clusters, where the data from any given cluster were homologous. Next, they extracted features from the packets and trained SVM models on each cluster. Their precision scores for HTTP, TCP, Wiki, Twitter, and E-mail protocols reached 99.6%, 92.9%, 99%, 96%, and 93%, respectively.  
In packet parsing-based detection, unsupervised learning is a common way to solve the high false alarm rate problem. [PERSON] et al. [41] proposed a fuzzy C-means based packet detection method. The fuzzy C mean algorithm introduces fuzzy logic into the standard K-means algorithm such that samples belong to a cluster with a membership degree rather than as a Boolean value such as 0 or 1. They used Snort to process the DARPA 2000 dataset, extracting Snort alerts, source IPs, destination IPs, source ports, destination ports, and timestamps. Then, they used this information to form feature vectors and distinguished false alerts from true alerts by clustering the packets. To reduce the influence of initialization, they ran the clustering algorithms ten times. The results showed that the fuzzy C-means algorithm reduced the false alarm rate by 16.58% and the missed alarm rate by 19.23%.