esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Smart City Safety and Utilizing AI Expert Systems for Violence Detection
## 3 Results
### Detection of YOLO v7 Model and Pose Estimation Model  
This subsection shows the detection result of the YOLO v7 model and the pose estimation model's action for violent objects. Violent objects include baseball bats, knives, and pistols. A violent target object is detected using the different-color bounding box and the pose is estimated through MediaPipe key points. Each key point is estimated according to the body posture. The below figures show the prediction result using an object detection algorithm.  
Figure 4 shows the real-time prediction or test results of the object detection with bounding boxes on the objects in different colors using the unseen dataset from a dash camera. In the prediction result, different kinds of the condition are targeted. In Figure 4a,d, the green-color bounding boxing shows the target images that show the violent object, a baseball bat; Figure 4b shows the violent object, a gun, in the red bounding box; and the other figures, Figure 4c,e, also indicate that a knife was detected in yellow-color bounding boxes in the image frames with a different standing view. These results cover possible different conditions to check the performance of the proposed model. The further figures below display the pose estimation prediction results.  
In Figure 5, the pose estimation of the main three types of action is covered at the time of the development of MediaPipe. These actions are related to the object detected in the YOLO model. These are the types of actions: in Figure 5a, the first action shows that criminals are trying to attack with a baseball bat from the right side of the victim; in Figure 5b, the second action shows that a criminal is willing to attack with a knife in his left hand; Figure 5c shows the third action, which provides the action with a gun; Figure 5d shows that the criminal can be detected even from a flipped-back position; and Figure 5e shows the side portion of the criminal who is attacking. These are different scenarios in which a criminal is using violence to attack a victim. These test predictions were predicted in real time using real test data from a dash camera. All actions were trained  
Figure 4: The proposed YOLO v7 model—real-time predicted results in different attack-type conditions: (**a**,**d**) show a detected violent object, a baseball bat; (**b**) shows a detected violent object, a gun; (**c**,**e**) show a detected violent object, a knife.  
in the various possible kinds of positions and postures for highly accurate detection. Here, we snapped 100 videos of each action. Every position has been stored in the dataset of MediaPipe. Further, the LSTM classification method is used to classify the actions. In the next subsequent section of the model, evaluation metrics show the performance of YOLO v7 and the pose estimation model along with the LSTM performance.
## 4 MAC Layer in WMSN  
The design of highly efficient and reliable medium access control (MAC) protocols is critical in wireless sensor networking. Conventionally, the goal is to provide sufficient transmission capacity at the minimum energy cost under a moderate network load condition. A quick look into the existing MAC protocols for WSNs, as surveyed in [18], reveals that lack of standardization and application-specific diverse requirements has deprived WSNs from having a single de-facto standard MAC protocol.  
MAC in WMSNs is essential to coordinate the channel access among competing devices. Given the energy constraints of the small, battery powered sensor devices, it is desirable that the MAC layer provides reliable, error-free data transfer with minimum retransmissions while meeting the QoS requirements ((_i.e._, bit error rate, transmission rate, delay, fairness, _etc._) with efficient resource utilization. MAC layer attempts to address these issues by enforcing channel access, scheduling policies (Figure 2), and error control. Therefore, a proposal of MAC layer protocol for WMSNs should satisfy the following features:  
* maximize network throughput,
* enhance transmission reliability,
* minimize control overhead,
* be energy-efficient,
* and guarantee a certain level of QoS.
## 2 Test Samples and Experimental Methods Description
### Experimental Methods Description
#### 2.3.2. Durability against Alkali-Silica Reactivity  
Alkali-silica reactivity (ASR) autoclaving test qualifies with certainty the no reactivity of different aggregates and it showed the best performance [38,43]. This test was carried out in this work in accordance with NF P-P18-594 [44]. First, sand with granularity between 0.16 mm and 5 mm was obtained. Second, this sand was washed and dried at (80 \(\pm\) 5) \({}^{\circ}\)C; and its particles were classified according to their sizes, as indicated in Table 4. Afterwards, NaOH was added to water till 4% mass content of Na\({}_{2}\)O related to mass of cement. Each composite was then prepared by mixing different components as given in Table 4 (Figure 5a). The obtained mixture was immediately poured in three prismatic molds where on both their sides were fitted stainless steel studs to measure dimensional variations (Figure 5b,c). Furthermore, these molds, each with dimensions of 40 \(\times\) 40 \(\times\) 160 mm\({}^{3}\), were placed in a chamber with relative humidity of 90% and temperature of (20 \(\pm\) 1) \({}^{\circ}\)C for 24 h. After demolding operation (Figure 5d), the mortar specimens were immersed in clean tape water at (20 \(\pm\) 1) \({}^{\circ}\)C for 48 h. After this time, the initial lengths \(L_{0i}\) (\(i\) = 1, 2, 3) were measured with comparator with an accuracy of 0.001 mm (Figure 5e,f). Next, the test samples were put in autoclave (Figure 5g) with a relative pressure of (0.15 \(\pm\) 0.01) MPa and temperature of (127 \(\pm\) 2) \({}^{\circ}\)C during 5 h \(\pm\) 30 min. Finally, after 18 h \(\pm\) 30 min of rest in ambient temperature (Figure 5h), the final lengths of these samples were measured \(L_{1i}\) (\(i\) = 1, 2, 3).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **RM** & **MRWG10** & **MRWG20** & **MRWG30** & **MRWG50** \\ \hline \multirow{6}{*}{Composite} & & 0.16–0.31 & 120 & 108 & 96 & 84 & 60 \\  & Sand classified & 0.31–0.63 & 120 & 108 & 96 & 84 & 60 \\  & with grain size & 0.63–1.25 & 300 & 270 & 240 & 210 & 150 \\  & (g) & 1.25–2.50 & 300 & 270 & 240 & 210 & 150 \\  & & 2.50–5.00 & 360 & 324 & 288 & 252 & 180 \\  & Waste glass (g) & 0 & 111.94 & 223.88 & 335.82 & 559.70 \\  & Cement (g) & 600 & 600 & 600 & 600 & 600 \\  & Water with NaOH Addition (L) & 300 & 300 & 300 & 300 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Formulation of each mortar-based composite for ASR test.  
Figure 4: Euromex isotope optical microscopic of thin blade samples: (**a**) after ASR test of MRWG50 and (**b**) at 90 days of cured CRWG50.  
The relative strain (%) was obtained as being the arithmetic mean value of different strains.  
\[\overline{\epsilon}=\sum_{i=1}^{3}\epsilon_{i} \tag{1}\]  
where:  
\[\epsilon_{i}=\frac{L_{1i}-L_{0i}}{L_{0}}\,,\;\text{L}_{0}\;=160\;\text{mm} \tag{2}\]
# Earth Observations for Sustainable Development Goals
## 6 Other Applications  
An article in this Special Issue reviews the opportunities of using remote sensing technologies in addressing the persistent global challenges related to the artisanal and small-scale gold mining sector [10]. Case studies performed in the Democratic Republic of Congo and in Colombia using Open Data Cube demonstrate the identification and quantification of impacts of gold mining on land degradation and water turbidity. The article encourages governments to adopt remote sensing methods into their small-scale gold mining monitoring plans and policies.  
Many SDGs indicators are related to human activities. Some of these activities are visible as small features that can be detected by remote sensing imagery, can be counted, and the evolution of their quantities with time can be studied. A paper in this special issue discusses the current capabilities and a modification of the YOLOv5 methodology for extracting objects from a bird's eye perspective of satellite photographs [11]. The modified YOLOv5 improves the mean Average Precision (mAP) by 0.071 on the DOTA dataset. The proposed method does not fully utilize spectral information, and the subsequent fusion of RGB images with multispectral data can be explored in the future.  
In most papers of this Special Issue, the computation of SDG indicators using remote sensing data shows great potential. The use of remote sensing generates needs and requirements for in situ data that can be used for calibrating and validating remote sensing products and numerical models. It is important to collect these requirements in a systematic and centralized way to secure the continuous production of the necessary in situ data [12]. A database to collect in situ data requirements is proposed as an additional component for the GEOSS infrastructure.  
This collection of articles addresses some of the knowledge gaps in the field of 'Earth Observations for Sustainable Development Goals' in general, and the potential for complementing the statistical methodologies for computing SDGs indicators. We hope this will encourage further investigation in this area and thus improve the performance of remote sensing technologies and data analysis techniques, as well as widen applications in research related to sustainable development, monitoring, and impact assessment.  
**Author Contributions:** Conceptualization, J.M., writing--original draft preparation; J.M., writing; A.Z. and I.S., review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been partially funded by e-shape and WQeMS, which received funding from the European Union's Horizon 2020 research and innovation Programme under Grant Agreement No. 820852 and No. 101004157. This work was also supported by the Catalan Government (SGR2021 00554).  
**Acknowledgments:** I thank all the authors for their contributions and all the reviewers for their valuable comments and feedback.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Advanced Intelligent Frame Generation Algorithm
```
0: Average packet arrival rate \(\lambda\); number of iterations G; decay frequency \(\alpha\); policy update period \(T_{c}\); discount factor \(\gamma\); clipping parameter \(c\); number of policy updates per training episode \(n_{update}\); policy optimization network learning rate \(\rho\); training sample size N
0: Average frame generation time \(\Phi(S_{w})\), frame multiplexing efficiency \(E(eff_{m})\), average packet delay \(\vartheta(T_{d})\), throughput Initialize network simulation environment based on network parameters and create simulation topology if\(U_{\text{integerValue}}=1\) (indicating real-time business) then  Generate a data stream that follows a Poisson distribution based on the real-time business requirements  Initialize network parameters \(\theta\)  Use Algorithm 1 to obtain the optimal frame generation time threshold \(T_{h}\)  Set the real-time frame generation threshold \(T_{th}=T_{h}\) and set maximum MPDU length for the transmission frame \(L_{th}=L_{h}\) if the number of packets arriving within \(T_{1}\) exceeds the threshold \(n\)=\(N\)then  Encapsulate n packets into the MPDU packet area  Set \(T_{th}=T_{1}\)  Calculate \(\Phi(S_{w})\), \(E(eff_{m})\) and \(\vartheta(T_{d})\) by using Equations (4), (5) and (6), respectively else if the number of packets arriving in T \(n\)<\(N\)then  Encapsulate packets into the MPDU packet area  Fill the idle packets, and set \(T_{th}=T\)  Calculate \(\Phi(S_{w})\), \(E(eff_{m})\) and \(\vartheta(T_{d})\) by using Equations (4), (5) and (6) respectively  End the time slot, get the reward \(r_{t}\) and the new state \(s_{t+1}\)  Release the current frame and send it to the virtual channel scheduler endif elseif\(U_{\text{integerValue}}=2\) (indicating non-real-time business) then  Generate a data stream that conforms to a self-similar traffic model, in accordance with the characteristics of non-real-time service requirements  Initialize \(\theta\)  Set \(L_{th}=L_{h}\), and observe the MPDU packet area status if the arriving packet length \(L=L_{h}\), MPDU packet area is filled then  Encapsulate the data into the transmission frame and calculate \(\Phi(S_{w})\) and \(\vartheta(T_{d})\) for the HEFG module by using Equations (8)-(11) endif endif  The iteration is completed. Return \(\Phi(S_{w})\) for both delay-sensitive and non-delay-sensitive queues, \(E(eff_{m})\) of the delay-sensitive queue, and \(\vartheta(T_{d})\) for both delay-sensitive and non-delay-sensitive queues
```  
**Algorithm 2** Advanced Intelligent Frame Generation Algorithm for Differentiated QoS Demands in Advanced Orbiting Systems
## 3 Cognitive Radio for Smart City
### Cognitive M2M  
Smart systems are characterized by a large diversity of devices and machines that have to be interconnected and able to exchange information autonomously in order to make the environment smart. This makes M2M communications a dominant paradigm, especially in contexts such as in-home applications, vehicular telematics, healthcare, and public safety [33; 34; 35; 36]. However, M2M communications present multiple differences with respect to current Human-to-Human (H2H) information production, processing, and exchange. M2M communications are characterized by massive transmissions, small bursty traffic, low power, low cost, and low mobility. Moreover, while H2H communications access the network following resource scheduling policies based on traditional bandwidth request mechanisms, M2M communications need to be separately considered. Indeed, these are characterized by a high number of nodes that exchange low data rate information. Hence, the spectrum usage is very limited and short in time. However, the main challenge for M2M communications is due to the high number of involved devices, which excessively increases the access requests to the network and, without countermeasures, gives rise to network congestion. Indeed, even if each device transmits only a very small amount of data, the huge number of connected devices will dramatically increase the signaling overhead, thus leading to a network overload and deteriorating the QoS of other H2H applications. A viable solution is to develop an autonomous M2M communication system that shares the resources with other H2H communication systems without causing congestion. This is a new paradigm, called Cognitive M2M (CM2M) communications [37; 38]. The exploitation of CR technology for setting up M2M communications is a promising trend. Indeed, a cognitive approach can lead to the definition of a scenario where M2M nodes are constrained to send data in specific bands and time intervals, reducing the congestion issues. CM2M communications concern with the presence of a primary H2H communication system that coexists with a secondary CM2M system in an almost transparent way, avoiding interference and introducing low performance degradation thanks to the cognitive approach. The cognitive engine of an M2M network can operate in a distributed manner (among all the devices of the network) or in a centralized manner at the network gateway, which is in general supposed to be a more powerful node. A more detailed discussion on CM2M solutions is provided in Section 4.
## 3 Results and Discussion
### TFC Membrane Performance in Conditions Imitating Flue Gas Separation
#### 3.5.1 Influence of Relative Humidity  
In dry conditions (Figure 8a), the CO\({}_{2}\) permeance moderately decreases with increasing feed pressure following the DMM sorption model. This behavior confirms the solubility-controlled CO\({}_{2}\) transport for all investigated materials, except for **P9 IL6 Zn1**, the CO\({}_{2}\) permeance of which remains stable over the whole range of applied pressures. Importantly, CO\({}_{2}\) permeance in all blend membranes is at least 50 % higher when compared to pure PIL (**P9 IL0 Zn0**). The CO\({}_{2}\)/N\({}_{2}\) selectivity improves at higher feed pressures for all membranes except for **P9 IL6 Zn1**. The exceptional behavior of **P9 IL6 Zn1** correlates well with the solubility driven separation process, as the solubility of N\({}_{2}\) increases rapidly with increasing partial pressure of N\({}_{2}\) (1-6 bar range) according to Figure 3b. **P9 IL6 Zn0** and **P9 IL6 Zn9** achieve \(\alpha_{\text{CO${}_{2}$/N${}_{2}$}}\) values comparable to the parent PIL material at sensibly higher overall permeance.  
In humidified conditions (Figure 8b), the CO\({}_{2}\) permeance shows a different trend in comparison to the dry conditions. For pure PIL, CO\({}_{2}\) permeance hardly changed, while the composite materials show a two-fold permeance increase from the initial value observed for the parent PIL. This suggests that humidified conditions not only provide additional transport mechanism for CO\({}_{2}\) molecules, referred to as internal sweep [33], but also allow water molecules to adsorb in the material matrix, plasticize it and make it more rubber-like [17].  
Remarkably, 90 % relative humidity enhances \(\alpha_{\text{CO${}_{2}$/N${}_{2}$}}\) for all investigated materials by at least 10 %. Here again, **P9 IL6 Zn9** exhibits a comparable CO\({}_{2}\)/N\({}_{2}\) selectivity to pure PIL, while permeating two times more CO\({}_{2}\) over the broad range of feed pressures. This phenomenon may signify that in a hydrated state, the IL and Zn[Tf\({}_{2}\)N]\({}_{2}\) constituents play an even more significant role in CO\({}_{2}\) sorption. This observation supports the hypothesis that incorporation of additives containing [Tf\({}_{2}\)N]\({}^{-}\) anions, strongly interacting with CO\({}_{2}\) molecules, and Zn\({}^{2+}\) cations, provides additional CO\({}_{2}\) separation benefits by encouraging the facilitated transport mechanism in the PIL/IL composites.
# FL-SDUAN: A Fuzzy Logic-Based Routing Scheme for Software-Defined Underwater Acoustic Networks
## 5 Experimental Results and Analysis
### The Comparisons of Throughput  
Throughout is also an essential metric for routing algorithms. The throughput of the five algorithms at different underwater signal rates is shown in Figure 13. The maximum signal rate of the experimental design is 2000 bps, and the throughput of all five algorithms tended to increase as the signal rate increased. FCO-MST had the highest throughput at lower underwater rate conditions (<1350 bps). However, as the underwater signal rate increased (>1400 bps), FPO-MST had the highest throughput, while [PERSON]'s algorithm had the lowest throughput, and the EEHC and LEACH algorithms had comparable throughputs.  
We also compared the throughput of the five algorithms for different numbers of underwater nodes, and the results are shown in Figure 14. The throughput of all algorithms decreased as the number of underwater nodes increased. In UAN, as the number of nodes increased, the probability of interference in the signals transmitted between nodes increased. On the contrary, FCO-MST and FPO-MST are based on SDN architecture and achieved the separation of data and control communication. They use fuzzy logic for routing decisions, which reduced the delays caused by communication detection and state switching. In summary, the proposed algorithms have high overall throughput. The FCO-MST algorithm is based on cut-set optimization and is suitable for large-scale hydroacoustic networks. In comparison, FPO-MST focuses on path optimization and is more fit for small-scale networks. Therefore, the throughput of FPO-MST was higher than that of FCO-MST when the number of nodes was small (<100), and FCO-MST had a good throughput when the number of nodes increased to \(117\).  
Figure 12: Comparison of E2 EL under different buffer utilization.  
Figure 13: The comparison of throughput under different transmission rates.
## 4 Conclusions  
By correlating experimental results with some theoretical considerations, we showed that the spectral energy distribution of the laser-accelerated protons stopped in the second CR-39 detector, of the detection stack, can be determined. The correspondence between the proton track diameter and the incident energy on the second detector is established by knowing that only protons with energies higher than 10.5 MeV can fully deposit their energy in the second CR-39 detector. The energy of protons stopped in the first CR-39 detector was also evaluated from the proton track diameter dependence on proton incident energy.  
Calibration of 1 mm thick CR-39 detector was performed both experimentally and theoretically by finding an analytical expression of the calibration curve. Using the analytical expression of the calibration curve in the 1-9 MeV energy range, we estimate individually the proton energy from the track diameter measured by AFM in each detector that makes up the irradiated stack. From the histograms of the counted track diameters on the front side of the second CR-39, we estimated the energy distribution of laser-accelerated protons.  
The data obtained after the microscopy analysis of the surfaces of the post-etched CR-39 detectors was discussed to explain all issues that occur after exposure of CR-39 type solid-state nuclear detectors to laser-accelerated proton beams. Only individual tracks with circular or elliptic shapes observed on the front side of the both detectors, with very well-defined edges and diameters in the 0.8-8 \(\upmu\)m range (theoretically established), are assigned to protons.  
The method proposed in this paper can be applied for dose assessment in high energy particle environments such as space, where astronauts dose records can be performed using CR-39 detectors. Using our approach, the energy distribution of ionizing particles extracted from each detector that compose a stack allows the assessment of ionizing particle type (neutrons, protons or heavier ions) as a function of its track shape in correlation with the incident energy on each detector surface. The correct evaluation of the type and energy of the ionizing particle ensure a better associated dose risk assessment.  
Conceptualization, A.G., B.B., M.S., M.G. E.S., and A.C.; methodology, M.S., B.B., E.S., M.S., and I.B.; software, A.C. and E.S.; validation, A.G., M.S., B.B., E.S., M.S., I.B., A.C., B.M., and M.G.; investigation, A.B., E.S., B.B. M.S., I.B., A.G, and B.M.; A.G., B.B., E.S., A.C. and M.S. writing--original draft preparation; B.M., M.G. and A.C. writing--review and editing.  
Work has been funded by European Space Agency within the ESA contract No.4000121912/17/NL/CBi.  
We acknowledge support from the CETAL-PW facility at the National Institute for Laser, Plasma and Radiation Physics, Magurele, Romania.  
The authors declare no conflict of interest.