esa-sceva/satcom-chunk-collection huggingface dataset:
# Wavefront Shaping and Its Application to Enhance Photoacoustic Imaging
###### Abstract  
Since its introduction to the field in mid-1990s, photoacoustic imaging has become a fast-developing biomedical imaging modality with many promising potentials. By converting absorbed diffused light energy into not-so-diffused ultrasonic waves, the reconstruction of the ultrasonic waves from the targeted area in photoacoustic imaging leads to a high-contrast sensing of optical absorption with ultrasonic resolution in deep tissue, overcoming the optical diffusion limit from the signal detection perspective. The generation of photoacoustic signals, however, is still throttled by the attenuation of photon flux due to the strong diffusion effect of light in tissue. Recently, optical wavefront shaping has demonstrated that multiply scattered light could be manipulated so as to refocus inside a complex medium, opening up new hope to tackle the fundamental limitation. In this paper, the principle and recent development of photoacoustic imaging and optical wavefront shaping are briefly introduced. Then we describe how photoacoustic signals can be used as a guide star for in-tissue optical focusing, and how such focusing can be exploited for further enhancing photoacoustic imaging in terms of sensitivity and penetration depth. Finally, the existing challenges and further directions towards in vivo applications are discussed.  
photoacoustic imaging; optical scattering; optical diffusion limit; penetration depth; wavefront shaping; iterative optimization; transmission matrix; Grueneisen effect; noninvasive internal guide star
## 4 Experimental Results and Discussion
### Experimental Results and Comparative Evaluation
#### 4.3.2 Comparison of Different Testing Methods
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Pixels** & **P/\%** & **R/\%** & **[EMAIL_ADDRESS]\%** & **GFLOPs/G** \\ \hline YOLOv8n [37] & 640 \(\times\) 640 & 92.3 & 91.4 & 94.8 & 12.4 \\ YOLOv8\_FasterNet [38] & 640 \(\times\) 640 & 94.5 & 93.4 & 95.3 & 9.2 \\ YOLOv8\_Gsconv [39] & 640 \(\times\) 640 & 96.6 & 94.0 & 97.9 & 11.6 \\ YOLOv8\_fastC2f [40] & 640 \(\times\) 640 & 97.0 & 93.5 & 96.2 & 11.1 \\ YOLOv8\_Biformer [41] & 640 \(\times\) 640 & 94.4 & 96.7 & 98.4 & 12.7 \\ YOLOv8\_Adv & 640 \(\times\) 640 & 97.3 & 96.6 & 98.7 & 10.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative experiments of YOLOv8_Adv to other models. Figure 12: mAP curve comparison of various models. performance with the lowest computational load of 9.2 GFLOPs. This result indicates that YOLOv8_FasterNet is more advantageous in resource-limited application scenarios, making it suitable for implementing object detection tasks on devices with constrained computing capabilities. Overall, these data reflect the differences in efficiency and accuracy among the various models, providing strong evidence for selecting models that are suitable for specific application needs, while emphasizing the superior performance demonstrated by the YOLOv8_Adv model.
## 1 Introduction
As a consequence, ASIC-based commercial firewalls are usually expensive and their design is a highly protected intellectual property, hindering the possibility of users knowing what a security-critical device, placed in their network, is doing. From this point of view, a Field Programmable Gate Array (FPGA) implementation has significant advantages in terms of faster design, lower costs, in particular for the realization of a small number of prototypes, and openness. The objective of our research is the development of a hardware firewall on an FPGA to check the safety of Ethernet traffic among universities and research centers. The protection of scientific data is an essential aspect of ensuring data security since cyber-attacks and data breaches are increasing in number in higher education institutions and universities [27]. The designed firewall should be able to support high data rates (at least 1 Gb/s) and must feature a high configurability, where the single user is able to define the features of the data that must be allowed or blocked. In the current stage of development, our research group has focused on the design of a stateless firewall, but future works on this research project will be aimed at the integration of stateful packet inspection and/or machine-learning algorithms for security threat detection. In this paper, a hardware firewall based on an FPGA is presented. The firewall is implemented on a Xilinx KC705 development board that integrates the Kintex-7 XC7K325T-2 FFG900C FPGA. It is designed to work at a maximum data throughput of 1 Gb/s, but preliminary simulations have shown that the system performance can be extended to a higher data throughput of 10 Gb/s. The system features a highly configurable set of rules to discriminate between safe and potentially dangerous data that can be defined according to a whitelist or blacklist approach. It also calculates a set of statistics (such as the number of received/transmitted packets, the packet length, etc.) that can be downloaded to a PC usingthe USB-UART interface for further data analysis. Experimental results have shown that the designed firewall can achieve a data throughput of 0.982 Gb/s with a latency of 627 ns, and the percent of packet loss due to memory resource exhaustion is negligible (3.29\(\cdot\)10\({}^{-5}\)%). The results of the paper represent the continuation of the research line presented in [17] and [18], where a packet sniffer was designed on an FPGA for network data throughputs of 1 Gb/s and 10 Gb/s, respectively. The paper is organized as follows. In Section 2, a short review of the related works of hardware firewalls implemented on an FPGA is presented. In Section 3, the design of the proposed hardware firewall and its main features are presented. In Section 4, the experimental results achieved with the proposed firewall are presented along with preliminary simulations to extend the device data throughput to 10 Gb/s. In Section 5, the performance of the designed firewall is compared with other firewalls from the literature. Finally, concluding remarks are presented in Section 6.
# A Novel Method of Local Anode Effect Prediction for Large Aluminum Reduction Cell
## 4 Prediction of Local Anode Effect Based on Analysis of Anode Guide Current Fluctuation
### Tracking Prediction of Non-Fluctuating Anode Current Based on ARIMA Model  
The local anode effect occurs 5-15 min before the current fluctuation of one or several guide rods. But under the working condition that the local alumina concentration of the contact part between the anode rod and the electrolyte decreases (according to expert experience [16], when the aluminum concentration is lower than 1.5%, the electrolyte balance will be broken, and the anode effect is early to occur), it takes some time to spread to other nearby anode rods. So tracking the current changes of other anode rods in the same area that have not yet fluctuated will provide an important reference for anode effect prediction.  
The differential ARIMA model is an algorithm for forecasting short-term, medium, and long-term time series information [23]. \(ARIMA(p,d,q)\) model is essentially a combination of difference operation and \(ARIMA(p,q)\) model. The basic process is as follows: first, the obtained time series data are checked for stationarity, and then the non-stationary time series data is converted into the stationary time series data by the \(d\)-difference operation; then, the order of stationary time series data are determined and the parameters are estimated to obtain the values of \(p,q\); finally, \(ARIMA(p,q)\) model is used to predict the time series.  
Considering the algorithm computation, operability, real-time and other factors, if the anode rod current is detected to fluctuate, the ARIMA model is used to track and predict the current of the anode guide rods that do not fluctuate in the area where the anode rods are located. First, the stationary data is obtained by performing d-difference operation on the obtained time series current; after that, the \(ARIMA(p,q)\) is fitted to a fixed order to determine the \(p\) and \(q\) parameters; then, the ARMA model is used to track the current sequence of the anode rod without fluctuation and output the current prediction value for a certain time in the future. If the error between the current prediction value and the actual current is greater than the set threshold value, it is considered that the current of the anode rod tracked has fluctuated; finally, the local anode effect level is determined according to the number of non-fluctuating anode rods changing into fluctuating anode rods in this area.  
The ARMA model of the original anode rod current after stationarity treatment is as follows:  
\[I_{t}=\varphi_{1}I_{t-1}+\varphi_{2}I_{t-2}+\cdots+\varphi_{p}I_{t-p}+\varepsilon _{t}-\theta_{1}\varepsilon_{t-1}-\cdots-\theta_{q}\varepsilon_{t-q} \tag{17}\]  
where, \(I_{t}\) is the original anode rod current signal; \(p\), \(q\) is the order of ARMA model, which is determined by the autocorrelation coefficient and partial correlation function of the sequence; \(\varphi_{p}\), \(\theta_{q}\) are the model coefficient; \(\varepsilon_{t}\) is the mean square error of the current sequence.
# Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery
## 4 Methodology
### Generating Multi-Index Images  
The suitable index combination selection was performed by an experimental analysis of the various combinations of indices as components of the multi-index dataset. For the built-up component, the existing built-up indices and NDTI were examined. Detailed evaluations of the existing built-up indices and the NDTI are provided in Sections 4.3 and 4.4.  
For the vegetation cover component, the red-edge-based normalized difference vegetation index (NDVIre), and two well-known vegetation indices, the soil-adjusted vegetation index (SAVI) ([PERSON], 2014), and NDVI ([PERSON], 2014), were examined. [PERSON] et al. (2016), had first evaluated the NDVIre by analyzing hyperspectral reflectance data. [PERSON] et al. (2014) and [PERSON] et al. (2015) had first tested its applicability on the designed Sentinel-2 wavelength portions, before the launch of satellite mission, using data from several ESA field campaigns over agricultural sites. The results of both studies demonstrated that the application of this index to the Sentinel-2 red B4 (665 nm) and the new red-edge  
Figure 2: Spectral reflectance curves of different land cover/use (LCU) types, according to the Sentinel-2A image bands (top of atmosphere reflectance values derived from 12-bit resolution satellite image).  
B5 (705 nm) bands, provided high correlations during estimation of the leaf area index and the chlorophyll content. [PERSON] et al. (2019) and [PERSON] et al. (2019) introduced the operational use of this index for the Worldview-2 satellite imagery. To the best of our knowledge, this research is a novel evaluation of the NDVIre on operational Sentinel-2A imagery.  
Lastly, for the water body component, the normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) were examined ([PERSON] and [PERSON], 2016; [PERSON] et al., 2019). Table 1 summarizes the spectral indices that were used to produce the multi-index images, according to the three main LCU categories.  
The formulas related to the above spectral indices were:  
\[\text{NDTI}=((\text{SWIR 1}-\text{SWIR 2}))/((\text{SWIR 1}+\text{SWIR 2})) \tag{1}\]  
\[\text{NDVIre}=((\text{RedEdge 1}-\text{Red}))/((\text{RedEdge 1}+\text{Red})) \tag{2}\]  
\[\text{NDVI}=((\text{NIR}-\text{Red}))/((\text{NIR}+\text{Red})) \tag{3}\]  
\[\text{SAVI}=((\text{NIR}-\text{Red}))/((\text{NIR}+\text{Red}+0.5))\times 1.5 \tag{4}\]  
\[\text{NDWI}=((\text{Green}-\text{NIR}))/((\text{Green}+\text{NIR})) \tag{5}\]  
\[\text{MNDWI}=((\text{Green}-\text{SWIR 1}))/((\text{Green}+\text{SWIR 1})) \tag{6}\]
## 2 Background and Methods
### Evaluation of In-Car Navigation  
A variety of studies have examined the effectiveness of in-car navigation. In 2009, Tom-Tom users were asked about the use of their navigation device [10]. In 2011, a Hungarian-founded company, NNG--which developed the iGO Navigation Engine--used a questionnaire to evaluate its system [11]. A Romanian study examined traffic in reference to navigation systems [12].  
With the increased use of smartphones, free navigation applications have become popular. [PERSON] et al., (2015) examined if drivers are willing to sacrifice some of the affordances of modern navigation systems in order to prolong the phone's battery life. The study also acquired data about the prevalence of phone-based GPSs and voice/visual preference [13]. To get information on prototypes of Google Maps Navigation during real-world usage, an Android-based feedback mechanism was developed [14]. According to the 41 participants, most used the built-in car navigation system as a supplemental application [15].
## 4 Simulation Results and Analysis
### Computational Complexity Analysis
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithms** & **Computational Complexity** \\ \hline JAS-OUS & \(\mathcal{O}(\sum\limits_{i=1}^{N_{t}-N_{RF}}(UK^{2}-K^{3})(N_{t}-i))\) \\ \hline JAS-CPUS & \(\mathcal{O}(\sum\limits_{i=1}^{N_{t}-N_{RF}}((U+K^{2})(N_{t}-i)+K^{3}))\) \\ \hline JAS-SUS & \(\mathcal{O}(\sum\limits_{i=1}^{N_{t}-N_{RF}}(UK^{2}-K^{3})(N_{t}-i))\) \\ \hline Exhaustive Search & \(C_{N_{t}}^{N_{RF}}C_{U}^{K}(K^{2}N_{RF}+K^{3})\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Computational complexity comparison. Although the proposed JAS-OUS algorithm takes more simulation time than the JAS-CPUS algorithm, it achieves better system sum rate. Therefore, JAS-OUS achieves a compromise between computational complexity and communication efficiency. Further, the proposed JAS-PFUS performs user scheduling in all time slots to let more users participate in communication at the cost of more simulation time.
# Dynamic Cooperative Spectrum Sensing Based on Deep Multi-User Reinforcement Learning
## 2 System Model  
### Cooperative Spectrum Sensing Model  
In cooperative spectrum sensing, all cognitive radios measure the licensed spectrums and make independent decisions. The cooperative spectrum sensing system is illustrated in Figure 1. The cognitive radio network usually consists of several PUs and SUs, where the SUs aim to share multichannel spectrum resources with PUs without interrupting the PUs. SUs exchange their sensing information with each other, and each SU maximizes its own spectrum sensing performance. Because the channel environment is unknown, SU can only make a decision after each attempt to sense a different channel.  
Figure 1: The system of cooperative spectrum sensing.  
represents discount rate. The loss function is used to update the neural network parameters through the gradient back propagation of the neural network. The loss function is the mean square error between the value of the objective function and the value of the actual function, which is formulated in (2) and (3),  
\[L_{i}(\theta_{i})=E\bigg{[}(R_{t+1}+\ \gamma\ \underset{a\prime}{\max}Q(S_{t+1},a \prime;\theta_{i}^{-})-Q(S_{t},A_{t};\theta_{i}))^{2}\bigg{]}, \tag{2}\]  
\[\
abla_{\theta_{i}}L_{i}(\theta_{i})=E[(R_{t+1}+\ \gamma\ \underset{a\prime}{\max}\ Q(S_{t+1},a \prime;\theta_{i}^{-})-Q(S_{t},A_{t};\theta_{i}))\
abla_{\theta_{i}}Q(S_{t},A_ {t};\theta_{i})]\big{)}, \tag{3}\]  
where \(Q(S_{t+1},a\prime;\theta_{i}^{-})\) is the approximation function formed through old experience and \(Q(S_{t},A_{t};\theta_{i})\) is the approximation function formed by the new experience.  
The specific process of the DQN algorithm is shown in Algorithm 1.  
```
Input: State \(S\) Output: Action \(A\) BEGIN Step 1: Initialize experience memory \(D\), all parameters of the neural network and Q value of state-action pair Step 2: Initialize \(S\) as the first state of the current state sequence Step 3: Inputs the channel state \(S\) into the \(Q\) network, and utilizes the \(\varepsilon\)-greedy method to select a action \(A\) from the current output \(Q\) value Step 4: Perform action \(A\) and access the channel with the same value as \(A\) Step 5: Obtain the next state \(S\) and reward value \(R\) of the channel environment according to the state \(S\) and the executive action \(A\) Step 6: Store the sensing record \(<\)\(S\),\(A\), \(R\),\(Sr\)\(>\) in the experience memory \(D\) Step 7: Randomly sample m records from the experience memory \(D\), and update the \(Q\) neural network parameters according to (2) and (3) Step 8: Assign the value of \(S\) to \(S\), and repeat step 3 END
```  
**Algorithm 1** Dynamic multi-channel sensing based on DQN
# Evaluating the Impact of Mobility on Differentially Private Federated Learning +
## 3 Internet of Vehicles
### Federated Learning on Internet of Vehicles  
While the previous section lists studies on federated learning in vehicle scenarios, most of them simply utilize the scenarios as applications. Few address systematic perspectives of IoV. There remain many issues that need to be addressed before applying federated learning to real-world vehicles [9; 43]. We note that many of these issues are attributed to the mobility of vehicles, and the following are representative examples: (1) Since vehicles tend to only move on roads, training data between participating vehicles may be biased if model training is not considered. This can cause overfitting problems. (2) Due to the high mobility of vehicles, the characteristics of training data are extremely dynamic, and the amount of data also changes quickly. (3) When a coordinator selects participating vehicles randomly, it is possible that vehicles with very important information (e.g., those witnessing an accident) may not be selected as training nodes. (4) Because each vehicle has different motion characteristics and data distributions, the input data during training have non-independent and identically distributed (non-IID) characteristics. In order to deal with the problems, [PERSON] et al. [43; 44] investigate an interesting area: vehicular knowledge networking. They first examine the meaning and scope of knowledge on vehicular networks and propose a networking framework needed for describing, generating, storing, and sharing knowledge among vehicles. Using the framework, they show federated learning orchestration for a vehicular environment; a coordinator selects vehicles carefully by making use of vehicular knowledge to improve learning performance.
## 2 Results
### MICs and MBCs Determination  
As expected, the strain's biodiversity reflected different responses to treatment. The MICs results for the bacterial strains under this study comprised between \(<\)300 and 700 \(\mu\)L/mL (Table 3), with the same values after 24 and 48 h of incubation, thus confirming the stability of the antimicrobial effect. EAP solution displayed a strong antimicrobial activity in terms of MICs. As shown in Table 3, low MIC values were mainly detected, and, in detail, \(<\)300 (58% of the strains) and 400 \(\mu\)L/mL (38% of the strains). Only \(P\)_aeruginosa_ ATCC 27853 (strains S7) showed growth capability in the presence of higher concentrations of EAP solution, reaching a MIC value of 700 \(\mu\)L/mL. The MBCs' results (Table 3) confirmed the same trend detected for the MICs after 24 and 48 h, thus indicating that the inhibitory effect was due to the inactivation of the strains.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{N}^{\circ}\) & **COMPONENT** & **EAP (\%) 1** \\ \hline \multicolumn{3}{c}{**ORGANIC ACIDS**} \\ \hline
1 & lactic acid & 0.3 \\ \hline \multicolumn{3}{c}{**PHENOLIC ACIDS**} \\ \hline
2 & caffeic acid & 4.6 \\
3 & vanillic acid & 0.4 \\
4 & salicylic acid & 0.3 \\
5 & cinnamic acid & 1.0 \\
6 & 3-hydroxycinamic acid & 4.8 \\
7 & 3,4-dimethoxyacycinamic acid & 1.6 \\
8 & \(p\)-coumaric acid & 39.8 \\
9 & ferulic acid & 1.4 \\
10 & isoferulic acid & 15.7 \\ \hline \multicolumn{3}{c}{**FATTY ACIDS**} \\ \hline
11 & lauric acid & 0.6 \\
12 & palmitic acid & 0.8 \\
13 & \(\alpha\)-linolenic acid & 0.3 \\ \hline \multicolumn{3}{c}{**SUGARS**} \\ \hline
14 & D-glucose & 1.8 \\
15 & D-mannose & 2.1 \\
16 & D-arabofuranose & 0.1 \\
17 & D-tagofuranose & 2.0 \\
18 & L-sorbofuranose & 1.1 \\
19 & D-ribofuranose & 0.4 \\
20 & D-fructofuranose & 5.7 \\
21 & \(\alpha\)-arabinofuranoside & 0.5 \\
22 & methyl-\(\alpha\)-D-glucofuranoside & 0.8 \\
23 & D-arabinopyranose & 2.4 \\ \hline \multicolumn{3}{c}{**OTHERS**} \\ \hline
24 & vanillin & 4.5 \\
25 & benzylcinnamate & 0.7 \\ \hline \hline \end{tabular} 1  
\end{table}
Table 2: Chemical composition (percentage values) of EAP after derivatization.  
For the subsequent analyses, one representative strain of the whole set was chosen, and in detail, _L. monocytogenes_ ATCC 7644 (strain S5).
# Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery
## 2 Study Area  
Istanbul is the most populated and the largest city in Turkey, located at a latitude of 41\({}^{\circ}\)00\({}^{\prime}\)44.06\({}^{\prime\prime}\) N and a longitude of 28\({}^{\circ}\)58\({}^{\prime}\)33.66\({}^{\prime\prime}\) E, in the Northern Hemisphere, joining the two continents of Asia and Europe. Istanbul is also one of the largest metropolitan cities of Europe, covering approximately 5.500 km\({}^{2}\), with a population over 15 million in 2017, corresponding to 18% of the country [41]. The significant population growth that occurred due to the industrial development and unplanned urbanization, during the second half of the twentieth century, resulted in critical transformations of the structure and morphology of the city. The densely urbanized regions of Istanbul are located in its southern half. In the last decade, construction of new transportation infrastructures, such as the Yavuz Sultan Selim Bridge, the Northern Black Sea Highway, and a third airport, have affected the ecosystem and caused a dramatic increase in built-up areas, in the northern half of the city. Istanbul presents a complex pattern of various feature classes, including forest, water bodies, croplands, bare land, and heterogeneous urban areas, making it a suitable candidate for the purpose of this research work.  
The intensive urbanization in Istanbul and the change in LCU has attracted the attention of many researchers and several studies have been conducted to determine the LCU and its changes, using satellite images [42; 43; 44; 45; 46]. These studies have reported an intensive increase in urbanization--for different time-periods--that have resulted in the destruction of the natural landscape, by applying the traditional, pixel-based spectral image classification methods.  
The two test regions, Ankara and Konya, are located in the middle of the country. Ankara is the capital of Turkey and is the second most crowded city after Istanbul. Konya is also an important city in Turkey, with a high level of industrial activity, which is ranked seventh in the urbanization rate. Both of these regions include high- and low-density residential areas and industrial areas that are located between and surrounded by extensive bare land, which makes them good candidates to test the challenging bare-land-urban-area separation process. The locations of the main study area and test regions have been presented in Figure 1.