esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Parallelization of Microscopic Traffic Simulation
## 2 Architecture and Processes
### Network and Traffic Lights  
The network model is composed of arrays that characterize the network edges, as depicted in Figure 1a. The network arrays stored in the global GPU memory are summarized in Table 2.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Feature** & **ParSim** & **MANTA/LPSim** \\ \hline Time & Discrete, typically 0.5 s & Discrete \\ Position & Floating point & 1 m resolution \\ Velocity & Floating point & 1 m/s resolution \\ Multiple-GPU & No * & Yes \\ Kernel implementation & Single kernel fusion & Multiple *** \\ Lane model & Spread \({}^{\dagger}\) & Mandatory maneuvers \({}^{\dagger\ddagger}\) \\ Traffic lights (TLs) & Edge based TL programs & Simplified TL ** \\ \hline \hline \end{tabular}  
* Not needed, even scenarios with tens of millions of trips run on a single GPU. ** Flashing red lights to mimic delays. *** Kernels launched with bootloader to reduce kernel loading time. \({}^{\dagger}\) Traffic spread over multiple lanes to increase edge capacity; no lane changes. \({}^{\dagger\ddagger}\) Realistic lane changes with gap acceptance for mandatory maneuvers.  
\end{table}
Table 1: Main features of ParSim compared with MANTA/LPSim.  
The edge is represented by vectors that contain information about length, the number of lanes, and maximum allowed speed. Each edge is divided into smaller geometrical segments, which are described by the vectors representing offsets, lengths, and angles, respectively. This information is used to determine the coordinates of all vehicles in the network. Each edge contains one or multiple indexed lanes that are parallel to the edge geometry. Vehicles run on either of them from the start to the end of the edge; the current simulator does not contain a lane-change model to allow for overtaking, for example.  
Traffic-light systems, of any complexity and extension, can be modeled: the principle is that during a red phase, a traffic-light logic (TLL) can block the exit of dedicated edges. TTLs are again defined by vectors, representing the phase durations and the index of blocked edges during each phase. However, lane-specific traffic lights, for example, for dedicated left or right turns, are currently not implemented.  
This network architecture is sufficiently memory efficient to allow even larger urban areas to fit in the memory of a single widely available GPU.
# Mouth Sounds: A Review of Acoustic Applications and Methodologies
## 4 Main Findings
### Emotion Recognition  
A trend in certain aspects was observed from research articles that addressed emotion recognition [49; 107; 125; 129]. Research studies used the discrete domain of emotions, categorizing them as anger, disgust, fear, happiness, sadness, and surprise. The most commonly extracted features were MFCC, pitch, formants, and energy. None of these studies selected the valence and arousal model, which relies on the appetitive system that seeks well-being, and the aversive system, which prevents individuals from entering dangerous situations [130; 131]. Emotion recognition in the continuous dimensions of valence and arousal could serve as an initial phase of emotion recognition by classifying emotions in positive and negative (valence) sensations, and between the level of calmness and excitation (arousal) [130; 131].  
Just one of the research articles included an analysis of the dimensions of valence, power, and arousal in their study, using a regression for sets of acoustic components [102]. The results showed that valence was the worst predictor, while power and arousal provided 50% of the variance accounted for by the predictive sets [102]. The acoustic parameters that power and arousal had in common were formant amplitude, dynamics, and low-frequency energy. Therefore, emotion recognition in the dimensions of valence, arousal, and power could indeed be part of future analysis for emotion recognition in voice recordings.
## 3 Results and Discussion
### Molecular Chain Motion  
The migration and movement behavior of molecular chains in the polymer system can be characterized by mean square displacement (MSD), namely, the average of the squared displacement of all the atoms within a period of time [27], which can be expressed by the following formula.  
\[MSD=\left\langle\left|r_{i}(t)-r_{i}(0)\right|^{2}\right\rangle \tag{2}\]  
where \(r_{i}(t)\) and \(r_{i}(0)\) represent the position of the \(i\)th atom at time t and the initial time, respectively [28].  
The change trend of the long-chain PC system's MSD with time in the packing stage is shown in Figure 8. It is observed that the MSD increases slowly in CIM. According to the free volume theory, the free volume of the polymer decreases at low temperature [29; 30]. Meanwhile, there is insufficient transition energy for the molecular chains. Thus, the molecular chains have difficulty overcoming the entanglement barrier and do not migrate in a wide range, whereas the MSD increases rapidly under the RHCM process. This indicates that the free volume and system energy rise with temperature increasing. Under a certain pressure, molecular chain segments cross the high potential energy region, overcome the viscous flow activation energy, and jump to the adjacent \"holes\". That is to say, the entirety of the chain motion of the macromolecule is realized by the successive transition and segmental displacement of chain segments. Furthermore, the motion morphology of molecular chains can be described by the Reptation model [31; 32]; that is, the molecular chain is restricted to move within a virtual tube composed of adjacent chains, and its motion is limited by the topology of other chains around it [33]. Its Reptation is realized by the Brownian motion of the chain segment. When a molecular chain wriggles along one end of a tube, a new section of tube is formed; meanwhile, a section of tube at the other end disappears. Thus, the chain moves completely to a new position, forming a new entanglement and completing the diffusion motion.  
\[\mathrm{D}=\frac{1}{6}\lim_{t\rightarrow\infty}\frac{d}{dt}\langle\left|r_{i} (t)-r_{i}(0)\right|^{2}\rangle \tag{3}\]  
The slope of the MSD curve becomes larger when the pressure of RHCM increases. According to the [PERSON] equation shown in Formula (3) [34; 35], the diffusion coefficient \(D\) is proportional to the slope of the MSD curve, which shows that increasing pressure can accelerate the migration and diffusion of polymer molecular chains and make the polymer flow better. As mentioned above, its filling rate is higher, and the replication capability of nanostructures is greatly improved. Therefore, the filling process of the polymer can be understood as a diffusion motion of the molecular chain along the direction of a tube under the combined action of Brownian motion and packing pressure. With the increase of temperature or pressure, the migration and diffusion of the molecular chain can be promoted, and the replication capability of nanostructure parts for mo  
Figure 8: The mean square displacement (MSD) curve of the long-chain PC system in the packing stage.
## 3 Results
### Overview
#### 3.2.2. Theme 2: Acceptability and Usefulness for Older Adults and Their Carers
Most of the studies considered the perspective of older adults [43]. Only one study considered the carers' perspective. Caers in this study reported that monitoring technologies appeared to address issue-specific risks without the ability to evolve and prevent future risks as the health condition of the assisted person got worse [43]. That is, the technology should detect non-emergency incidents. While focusing on present, observablerisks could promote safety and security, enabling assistive technologies to evolve and address foreseeable long-term risks is critical to sustaining such feelings. Two of the five studies identified that the stigma inflicted by the monitoring devices was a concern [41; 43]. Participants in one study considered smart home technologies themselves to signify frailty [41]. Similarly, participants in another study considered the negative association of technologies with 'disability' to be a barrier to adopting assistive technologies. Participants in the same study added that, to address stigmatisation, the design of the appearance of these sensors could perhaps be made less physically intrusive. Participants in another study reported they were uncomfortable in the presence of the sensing device because that led to behavioural change [43]. The example was specific to door sensors. Given that they were not in the habit of closing the door, they had to learn to close the door regularly to prevent the sensor from alarming. Although the result did not affect acceptability or perceived usefulness, the inconvenience was an issue the participant hoped future technologies could address. There is no doubt that negative social perceptions and individual living habits should be considered when considering the adoption experience. 2.3. The  
representing real-person footage with cartoons or sticky figures [43]. In the same study, older adults reported that content-release control functions similar to those in Facebook (which determined who could see your detailed personal information) could be added to the monitoring system to regulate data release to uphold security. Despite the privacy issues, one study remarked that ambient monitoring was better than wearable sensors and that the level of intrusion was less [44]. None of the studies explicitly acknowledged the association between assisted technologies' acceptability or perceived usefulness and privacy issues. These Themes are summarised in Figure 3.  
It is noteworthy also that none of the studies included questions about the monitoring device's perceived efficacy or effectiveness.
# FPN-SE-ResNet Model for Accurate Diagnosis of Kidney Tumors Using CT Images
## 2 Materials and Methods
### Proposed Solution  
The study introduces a methodology that utilizes the KiTS19 database to accurately segment tumors and kidneys on CT slices [67]. The method involves two primary stages: data preparation, where CT volumes are scaled and normalized to enhance data consistency and quality, and segmentation using a SE-ResNet FPN model to delineate tumors and kidneys accurately. After converting the volumes from DICOM to PNG format, the segmentation process employs a SE-ResNet FPN model to delineate the kidneys and tumor, streamlining the segmentation stage. The SE-ResNet family is an excellent choice for the encoder component, while the FPN acts as the decoder, utilizing multiscale feature maps. The method typically calculates intersection-over-union (IoU) scores for various predictions and selects the segmentation with the IoU score and F1 score to determine the optimal segmentation. The segmentation models library is an open-source resource comprising cutting-edge deep-learning models designed for image segmentation tasks, encompassing state-of-the-art architectures for semantic, instance, and panoptic segmentation, such as U-Net, PSPNet, FPN, and LinkNet. The segmentation models library offers the advantage of transfer learning and fine-tuning on custom datasets, making it a valuable tool for researchers and practitioners in image segmentation tasks. It is located on GitHub at \"[[https://github.com/qubvel/segmentation_models](https://github.com/qubvel/segmentation_models)]([https://github.com/qubvel/segmentation_models](https://github.com/qubvel/segmentation_models))\" (accessed on 25 March 2023).
# Modeling and Analysis of RFI Impacts on Imaging between Geosynchronous SAR and Low Earth Orbit SAR
## 2 Accurate Modeling of SINR in the Presence of RFI
### RFI Power Affecting the Pixel after SAR Processing
#### 2.4.1 GEO-TO-LEO RFI
In this case, \(k_{aSAR}\) is the FM rate of the LEO SAR in the azimuth direction, \(T_{SAR}\) is the integration time of the LEO SAR, and \(k_{ai}\) is the FM rate of the GEO-to-LEO RFI in the azimuth direction, which can be written as  
\[k_{ai}=v_{LEO}^{2}/(\lambda_{LEO}R_{LEO})+v_{GEO}^{2}/(\lambda_{LEO}R_{GEO}) \tag{15}\]  
where \(v_{GEO}\) and \(R_{GEO}\) are the velocity and slant range of the GEO SAR, respectively. \(v_{LEO}\) and \(R_{LEO}\) are the velocity and slant range of the LEO SAR, respectively. \begin{table}
\begin{tabular}{c c} \hline
**Index** & **Operation** \\ \hline Step 1 & Generate a chirp signal \(s_{1}\)with pulse width \(T_{sSAR}\), FM rate \(k_{sSAR}\)and length \(N=T_{sSAR}\times PRF_{SAR}\) \\ Step 2 & Generate a chirp signal \(s_{1}\)with pulse width \(T_{sSAR}\), FM rate \(k_{ai}\) and length N \\ Step 3 & \(s_{2}(i)=0,if\ i\) mod \(times\
eq 0,i=1,\cdots,N\), where \(times=PRF_{LEO}/PRF_{GEO}\) \\ Step 4 & \(Operate\) match filtering on signal \(z\)gto get \(G_{a}=\) \\ \(E\Big{\{}\left|\text{ifififif}(s_{1})\times\text{conj}(\text{fft}(s_{2})) \right|\Big{\}}\), where \(E\{\cdot\}\) represents average. \\ \hline \end{tabular}
\end{table} 
Table 1: Steps of the numerical simulation. Therefore, the gain obtained by GEO-to-LEO RFI in SAR processing can be written as  
\[G_{RFI}^{G-L}=G_{r}^{G-L}G_{a}^{G-L}=G_{a}^{G-L}f_{sLEO}^{2}/|k_{GEO}-k_{LEO}| \tag{16}\]
## 5 Emerging Trends and Future Directions
### Future Opportunities and Challenges in the Field  
Artificial Intelligence (AI) and Machine Learning (ML) are playing a pivotal role in advancing data analysis, particularly in the context of IoT. In the maritime industry, they offer significant potential for automating and enhancing the analysis of data generated by IoT devices [93].  
As the integration of IoT in the maritime industry continues to grow, so too does the potential for data analysis. However, with these opportunities come a set of challenges that must be addressed.  
* Opportunities: The future of data analysis in maritime IoT holds immense opportunities. Advanced analytics can lead to more efficient operations, improved safety, enhanced decision-making, predictive capabilities, and compliance with regulations. Moreover, as data analysis techniques continue to evolve, they can uncover new insights, fostering innovation and continuous improvement in maritime operations.
* Challenges: Despite the potential, several challenges need to be tackled. Ensuring data quality, dealing with the complexity of big data analysis, protecting data security and privacy, managing the cost of implementing advanced analytics, and addressing interoperability issues are all significant hurdles. Furthermore, as the field continues to advance, keeping up with the latest techniques and technologies will be a constant challenge.  
It is essential for the maritime industry to recognize and address these challenges to fully harness the potential of data analysis in IoT. Collaboration between industry, academia, and regulators will be key to developing effective solutions and moving the field forward.
## 3 SATLD for MGSS Considering Waiting Time
### Simulated Annealing Algorithm with Tabu List and Start Time Decision
Output: A solution \(\mathcal{S}\) that includes the number of the ground station and the specific start time for each request, a total waiting time for all request \(R^{\prime}\gets sort\_By\_tDur(R)\); \(\mathcal{S}\leftarrow\mathcal{O}\); \(satisfiedNumber\gets 0\); waitingTime\(\leftarrow\) 0; for\(r\) in \(R^{\prime}\)do \(A^{\prime}\gets sort\_by\_tBeg(A)\); \(\mathcal{A}\gets chooseAccessWindowSet(A^{\prime})\); for\(a\) in \(\mathcal{A}\)do if(\(a\) meets the requirements of \(r\))then \(satisfiedNumber+=1\); if\(tArrival_{r}\leq ST(a)\)then \(g,y_{r}\gets gs_{a},ST(a)\);  end if else \(g,y_{r}\gets gs_{a},tArrival_{r}\);  end if \(A^{\prime}\gets update(A^{\prime},a,r,g,y_{r})\); \(\mathcal{S}\leftarrow(g,y_{r})\); break ;  end for  end for \(waitingTime\gets update(waitingTime)\)  end for
```  
**Algorithm 3**Downlink scheduling based on requirement time. ```
Input: Downlink request set \(R\), Ground station set \(G\), Satellite set \(S\), Access window set \(A\), Maximum number of iterations \(Max\_Iter\), Tabu list length \(L_{t}\) Output: A solution \(\mathcal{S}\) that includes the number of the ground station and the specific start time for each request, a total waiting time for all request \(\mathcal{S}\gets initial(R,G,S,A)\); \(waiting\_Time,satisfiedNumber\gets fitval(\mathcal{S})\); \(iter\gets 0\); whileiter\(<Max\_iter\)do \(\mathcal{S}^{\prime}\gets neighborTL(\mathcal{S})\); \(waiting\_Time^{\prime},satisfiedNumber^{\prime}\gets fitval(\mathcal{S}^{ \prime})\); \(\Delta W_{\mathcal{T}}=waiting\_Time^{\prime}-waiting\_Time\); if\(\Delta W_{\mathcal{T}}\leq 0\)then \(\mathcal{S}\leftarrow\mathcal{S}^{\prime}\); \(waiting\_Time,satisfiedNumber\gets waitingTime^{\prime},satisfiedNumber^{\prime}\);  end if  end while else if\(e^{-\Delta W_{\mathcal{T}}/\mathcal{T}}>random(0,1)\)then \(\mathcal{S}\leftarrow\mathcal{S}^{\prime}\); \(waiting\_Time,satisfiedNumber\gets waitingTime^{\prime},satisfiedNumber^{\prime}\);  end if  end if \(iter\gets iter+1\); \(iter\gets T\times\delta\);  end while
```  
**Algorithm 4**Simulated annealing with tabu list and start time decision (SATLD).
# A Distributed Sensor System Based on Cloud-Edge-End Network for Industrial Internet of Things +
## 2 Related Works and Research Gaps  
Multisensor information fusion technology has been widely adopted in industrial data processing [14; 15]. The use of data fusion for data collection and processing has proven to be an effective solution for improving datasets. Deep learning, a complex machine learning algorithm, has made significant contributions to information fusion, data mining, and related fields [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. By implementing complex machine learning algorithms through deep learning, improved performance can be achieved. Recently, deep learning has been utilized in developing and processing industrial data. In [28], data fusion technology is employed for processing industrial data, but the method does not guarantee an improvement in dataset accuracy. In [29; 30], an end-to-end processing method for industrial datasets based on the convolution neural network is designed, but the method has a low convergence rate and difficulty in processing large data. In [31], a data monitoring algorithm based on the capsule neural network is presented, and in [32], a multiscale convolution recursive neural network is used for multi-industry data monitoring. Finally, in [33], a new mobile edge framework based on the Industrial Internet of Things is proposed.
# Cellular and Virtualization Technologies for UAVs:
## 3 Testbed Components Description
### Virtualization in UAVs: The Power of Containers in Aerial Networks
In consequence, this testbed will introduce UAVs able to generate and send video traffic to a particular machine or client, either localized inside the testbed (video-consumer in Figure 2) or outside. This application will be deployed as a single container running in the worker nodes located at the RPi, where the video traffic will be generated. These nodes are represented as the UAVs in Figure 2. Afterwards, this video will be sent to a specific location, either to nodes located inside the cluster (e.g., the video-consumer) or located outside the cluster (e.g., a PC at a remote location). If the UAV goes offline at any moment in time, either due to a battery replacement cycle or connectivity issue, K8s will try to instantiate this application in a new UAV or, if no others are available, wait until one of them is available to re-deploy the container again, showing the potential of K8s to reduce service cut-offs in aerial networks.
## 2 Materials and Methods
### Data Extraction  
The sEMG data were normalized using the maximal voluntary isometric contraction (MVIC). The mean muscle activation of the biceps brachii was subsequently computed for both the concentric and eccentric phases. Data acquisition and offline analysis were performed using the MIOTEC Suite(tm) software (v. 1.0; MIOTEC(tm); Biomedical Equipment, Porto Alegre, RS, Brazil). To determine the onset of elbow movement, the concentric-eccentric transition, and the termination of the motion, all trials were video-recorded in synchronization with the sEMG system. Temporal markers corresponding to movement onset, phase transition, and movement end were manually assigned based on the video footage. The EMG signals for each contraction phase were then segmented according to these markers. Neuromuscular efficiency was assessed by calculating the ratio between the mean normalized sEMG activity and the corresponding force level (%MVIC: 20%, 40%, and 60%). This metric was expressed as %sEMG per kilogram-force (%sEMG/kgf). In this context, lower values indicate better neuromuscular efficiency, reflecting the muscle's ability to generate the required force with reduced electrical activity, suggestive of more optimized neuromuscular performance [9].
## 4 Generalized Outage Probability Expression  
The condition of an outage ensues when either \(\gamma_{1}\) or \(\gamma_{2}\), or both, fall below a certain specific threshold, \(\gamma_{th}\). Therefore, the outage probability \(p_{out}(\gamma_{th})\) for a dual-hop DF relay-based system can be calculated as [17]  
\[p_{out}(\gamma_{th})=p(\gamma_{1}\leq\gamma_{th})\|p(\gamma_{2}\leq\gamma_{th}) \tag{1}\]  
After some mathematical manipulations, (1) can be simplified as  
\[p_{out}(\gamma_{th})=p(min(\gamma_{1},\gamma_{2})\leq\gamma_{th}) \tag{2}\]  
Now, using the relation \((p(\gamma_{1})\leq\gamma_{th})+(p(\gamma_{1})>\gamma_{th})=1\), \(p_{out}(\gamma_{th})\) in (2) can be rewritten as  
\[p_{out}(\gamma_{th})=1-p(\gamma_{1}>\gamma_{th})p(\gamma_{2}>\gamma_{th}) \tag{3}\]  
Representing \(p(\gamma_{1}>\gamma_{th})\) in the integral form leads to  
\[p_{out}(\gamma_{th})=1-\int_{\gamma_{th}}^{\infty}p_{\gamma_{1}}(\gamma_{1})d \gamma_{1}\int_{\gamma_{th}}^{\infty}p_{\gamma_{2}}(\gamma_{2})d\gamma_{2} \tag{4}\]  
Using the definition of the CDF of a random variable, (4) can be simplified as  
\[p_{out}(\gamma_{th})=1-\bar{P}_{\gamma_{1}}(\gamma_{th})\bar{P}_{\gamma_{2}}( \gamma_{th}) \tag{5}\]  
where \(\bar{P}_{\gamma_{1}}(\gamma_{th})\) and \(\bar{P}_{\gamma_{2}}(\gamma_{th})\) are the complementary cumulative distribution functions (CCDF) of \(p_{\gamma_{1}}(\gamma_{1})\) and \(p_{\gamma_{2}}(\gamma_{2})\) evaluated at \(\gamma_{th}\). Using (5), the outage probability of a DF-aided relay communication system can be calculated for any fading scenario using the CDF of the SNR of the fading channel. In order to illustrate the calculation of the outage probability using (5), some cases involving different fading scenarios are explained in Section 4.