esa-sceva/satcom-chunk-collection huggingface dataset:
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
## 3 Motivation  
As commented before, the complexity of assessing audio-visual quality lies, among other factors, in the little understanding there is on how the auditory and visual stimuli are perceived, and also at what stage, and how, the human perceptual system integrates them. This reflects the gaps between neurophysiology and computer science in terms of quality assessment. Undoubtedly, there is still a great demand for studies that investigate the complexity involving the integration of multimodal stimuli, as stated by [PERSON] and [PERSON] in a recent survey [6]. Unfortunately, many studies that assess the audio-visual quality consider impairments only in the video component and leave the audio unimpaired, i.e. ; subjects are presented with audio-visual content but with no distortion in the audio component. As for the ones that do include audio distortions, they are usually limited to weak distortions caused by audio compression. Table 2 presents a summary of some of the most important audio-visual databases and their corresponding perceptual experiments. As it can be observed, most of the available audio-visual material is limited in terms of audio distortion. These conditions make it difficult reaching to more significant conclusions in terms of visual and auditory stimuli integration. In this study, we aim to use a wider collection of audio and video distortions, which are not commonly included in the available audio-visual databases. Such distortions were selected by the researchers based on previous studies from the literature and a particular interest in studying some specific types of distortions. Three types of visual distortions were selected: video coding, packet loss, and frame freezing. As for the audio component, four types of distortions were selected: background noise, clipping, echo, and chop. For each type of distortion, different levels of degradation were selected by audio and video
## 5 Conclusions  
The present study findings provide preliminary information on the antibacterial activity of C-CaOH\({}_{2}\) NPs against aerobic and anaerobic strains. The C-CaOH\({}_{2}\) NPs demonstrated dose-dependent antibacterial efficacy against all tested pathogens. This antibacterial activity is considered intermediate compared to the control antibiotic. Moreover, the best recorded ZOIs were at 100 and 150 mg/mL concentrations in all bacteria. The exhibited inhibitory efficacy was better against the Gram-negative aerobes than the Gram-positive strains. Furthermore, the MIC and MBC values for aerobes indicate higher potency of C-CaOH\({}_{2}\) NPs against them. Conversely, higher MIC and MBC values were required to induce inhibitory and bactericidal effects against the anaerobic strain (_P. micra_). Finally, the C-CaOH\({}_{2}\) NPs intermediate activity was not superior when compared to other CaOH\({}_{2}\) NPs reported in the literature. This study is the first to report the antibacterial potential of green-synthesized C-CaOH\({}_{2}\) NPs. The C-CaOH\({}_{2}\) NPs may serve as an antibacterial agent for biomedical applications, such as metal implants coatings and root canal medication. However, further investigations are required to address this study limitation and optimize the outcome.  
Conceptualization, H.S.A. and O.G.A.; methodology, H.S.A. and N.R.A.; validation, S.D.; formal analysis, H.S.A.; investigation, H.S.A. and S.D.; resources, M.S.A. and O.G.A.; data curation, H.S.A.; writing--original draft preparation, H.S.A.; writing--review and editing, M.G.A.; visualization, N.R.A.; supervision, M.G.A.; project administration, M.S.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology
## Abbreviations  
\begin{tabular}{l l} CR & Cognitive radio \\ _SU_ & Secondary users \\ _PU_ & Primary users \\ MBSS & Multiband spectrum sensing \\ SNR & Signal-to-noise ratio \\ IoT & Internet of things \\ ED & Energy detector \\ MRA & Multiresolution analysis \\ ML & Machine learning \\ HFD & Higuchi fractal dimension \\ SDR & Software-defined radio \\ USRP & Universal software radio peripheral \\ MIMO & Multiple-input multiple-output \\ Tx & Transmitters \\ Rx & Receivers \\ PSD & Power spectral density \\ MSPS & Mega samples per second \\ \end{tabular}
## 3 Design  
In this section, we present the design of our novel consensus protocol. From the above description about consensus protocol, we found three existing problems of the current popular consensus protocols: efficiency, centralization, and privacy issues. Therefore, we ask the following research questions:  
1. How to improve the efficiency of the blockchain system from the perspective of consensus protocol and reduce the waste of energy?
2. How to guarantee the democracy or fairness during the selection of the new block packer to avoid the centralized control by one or a small group of nodes while reducing the ratio of malicious nodes being selected?
3. How to protect the privacy of the blockchain network participants when the information being transmitted is sensitive?  
To answer these questions, we proposed a novel consensus protocol called Delegated Proof of Secret Sharing (DPoSS), which is suitable for the application scenario of an IoT environment. DPoSS is designed based on the following assumptions:  
1. The IoT network includes many edge devices and more sensors connected to the edge devices. The blockchain network consists of only those edge devices; sensors are not included as the nodes of the blockchain network.
2. Each node in the blockchain network has it own pair of a public key and a private key. The node's public key is available to all other nodes, and its private key is known only to itself and the control center, if established.
3. Our proposed DPoSS relies on one group of delegate nodes in the blockchain network instead of all nodes, as in the case of PoW. With DPoSS, every node has the opportunity to be elected as a delegate, denoted as \"teller\"; all other unelected nodes are called \"fishers\".
4. Sensors are not able to vote for delegates due to the constraints on the abilities of computation power, battery, and transmission bandwidth.  
Basically, there are two kinds of nodes in the blockchain network during the processing of consensus: regular nodes (fishers) and delegate nodes (tellers). We do not define and confine the principle to choose or screen the tellers for the various applications of the IoT network because there are many options available, such as random choosing and PoW-like method, in which the first several nodes successfully figure out one simple problem will be the tellers. Meanwhile, there is also no requirement of the number of tellers. The appropriate number of tellers depends on the threshold of secret sharing. For the convenience of discussion, we denote the number of all nodes (fishers and tellers) as \(m\) and denote the number of tellers as \(n\).
## 4 Results
### Restoration Results of Graben Denudation in Changling Formation
By analyzing the regional geological background and sedimentary facies maps, it was determined that the water depth in the Changling Graben was approximately 40 m, corresponding to a residual thickness of about 2200 m, resulting in a paleo-water depth error impact of about 1.8%. Additionally, the dynamic coupling of tectonic activity and sedimentary environments may alter the original stratigraphic distribution patterns. This study integrated tectonic-sedimentary responses to minimize human cognitive errors as much as possible, enhancing the credibility of the research results. The structural characteristics and distribution of denudation thickness within the Changling Graben demonstrate a distinct inheritability in the faults that have evolved across different periods. The principal faults, which are long-term syndepositional and control the scale of development in various troughs, have directed the denudation range to evolve inherently from the base upwards. The Shenzijing and Hashituo regions stand out as the main denudation areas, with the Shahezi Formation recording the peak denudation values. The Yingcheng Formation exhibits a generally elevated degree of denudation, implying that the late-stage inversion and compression during the Yingcheng epoch were particularly forceful, impacting a substantial portion of the study area and resulting in a considerable uplift range. As we progress to the Denglouku Formation, denudation is notably reduced, yet the spatial extent of denudation expands, suggesting that the compressional stress during the Denglouku period was comparatively weaker. The concentration of denudation on the eastern side points to a more pronounced effect of compressive stress in this region, which also explains the greater uplift on the eastern side compared to the western side due to compressional forces. Moreover, the extent of denudation demonstrates an inverse relationship with fault scale: larger faults have a broader impact, while the influence of smaller faults is more limited, to the extent that their effect can be largely overlooked.
## 4 Simulation Results
### Convergence Analysis  
In this paper, to evaluate the performance of proposed scheme, the sum-rate in (10) was adopted. Figure 3 shows the sum-rate according to the number of iterations in Algorithm 2. One channel realization was considered in this simulation. The optimization algorithm of the RS power-splitting ratio can converge in 10 iterations. Moreover, it can be seen that the sum-rate performances were optimized with a gain of about 10 by the algorithm.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline The number of transmit antennas & \(N_{T}\) & 8 \\ The number of UEs & \(N_{K}\) & 3 \\ Characteristic impedance & \(Z_{0}\) & 50 \(\Omega\) \\ Path-loss exponent & \(\beta\) & 2.2 \\ Reference path loss & \(L_{0}\) & 30 dB \\ Transmit power & \(P_{T}\) & 40 dBm \\ Noise power & \(\sigma_{1}^{2}=\sigma_{2}^{2}=\cdots=\sigma_{N_{K}}^{2}\) & \(-80\) dBm \\ Initial RS power-splitting ratio & \(\alpha\) & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 3: Sum-rate versus iteration with \(N_{G}=4\) and \(N_{I}=16\).
## 3 Methods and Materials  
### Study Area and Data Collection  
This research was carried out in Ethiopia to identify and prioritize Road Traffic Crash (RTC) hotspot regions, with a particular emphasis on the Oromia region (see Figure 1). RTC data of Ethiopia was obtained from the Ethiopian federal police commission, traffic police division. The commission records crash data for all regions and federal territories. Crash data of four consecutive years (from 2014 to 2017) were used for crash severity analysis and hot spot identification. All collision types acquired from the police commission (i.e., single-vehicle collision, multiple vehicle collision, collision with pedestrians, collision with animals and so on) were utilized for analysis. The Ethiopian Geospatial Information Agency (EGIA) provided a map of Ethiopia divided into regions and federal territories, as well as a map of the Oromia region, divided into zones and towns. In these maps, roadway networks and other important features were included as a shapefile.
# AspectNet: Aspect-Aware Anchor-Free Detector for Autonomous Driving
## 2 Related Work
### Anchor-Based Approaches  
Anchor-based approaches can be divided into two main types of pipelines: multi-stage detection and single-stage detection. Multi-stage detection methods filter regions of interest in the image and extract the foreground area from preset dense candidates, using region proposal algorithms [11; 12] in the first stage. The bounding boxes of objects are constantly regressed in the subsequent stages, such as the R-CNN series [1; 11; 12; 13], which is time consuming. Complex multi-stage models cannot meet the requirements of real-time detection. Different from multi-stage methods that use traditional sliding windows and proposals, single-stage detection methods inherit the ideas of anchor boxes but directly detect the object and regress the bounding boxes in a single-shot manner without any region proposals, which avoids the repeated calculation of the feature map and makes the anchor boxes directly on the feature map, which speeds up the detection process dramatically, for example, YOLOv3, RetinaNet, and SSD [14].  
These methods need to preset a large number of anchors manually, which increases the calculation in the network, and the excessive number of samples aggravates the imbalance of positive and negative samples, as described above. Additionally, the misaligned problem between anchors and features affects the network's performance. In previous studies, researchers often used IoU to determine the samples. For example, if the IoU between the anchor boxes and ground truth boxes of a sample is in [0.5, 1], then the sample is positive, whereas if the IoU is in [0, 0.5), then the sample is negative. The anchor with the maximum ground truth IoU is selected for matching. These hyperparameters need to be manually fine-tuned for different detection tasks, and detection tasks are susceptible to hyperparameter selection. The matching definition of positive and negative samples has a significant impact on the model's performance.
# Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities
## 3 Applications
### Research Potential for Applications  
The growing demand for more innovative CV applications have witnessed unprecedented and huge strides in recent years. In this section, we provide a precise look at some of the promising CV applications and direct the reader's attention to some unnoticed research scopes.
# Battery Life Prediction for Ensuring Robust Operation of IoT Devices in Remote Metering
## 5 Experimental Results and Discussion
The ability to accurately predict the primary point before a sharp voltage drop occurs ensures that a hardware-based low battery alert is triggered in time. Moreover, even when unexpected SOC estimation errors result in missing SOC states, the voltage drop still effectively activates the low battery alert. This capability substantially mitigates the risk of unexpected power loss and supports proactive battery management, thereby enhancing the overall reliability and operational stability of our system in real-world applications.
## 3 Methods
### Models Evaluation  
A predictive model will not be convincing without scientific validation, so existing data will need to be split into training and validation sets. Previous research derived performance measures for single-hold-out models by employing a single training set and an independent test set. In the study, 80% of the data sets were randomly chosen for training and 20% for validation in LSM, and the split was repeated 5 times. Finally, these models were evaluated using the average of 5 groups.  
Four evaluation measures--accuracy, sensitivity, specificity, and AUC--were combined to analyze the performance of the models. Accuracy, sensitivity, and specificity are calculated from the Confusion Matrix, which is an N \(\times\) N matrix (Table 1). The TP represents the number of landslides that have been correctly predicted as unstable, and TN represents the number of non-landslides that have been correctly predicted as stable, while FP represents the number of non-landslides that have been predicted incorrectly as unstable and FN represents the number of landslide units that have been predicted incorrectly as stable.  
An additional indicator of model validity is the area under the receiver operating characteristic curve (AUROC). The value of AUROC ranges from 0.5-1; the larger the value, the better the generalization ability of the model and prediction performance. Finally, the natural breaks method was applied to reclassify landslide susceptibility into five classes: very low, low, moderate, high, and very high.
# A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks
## 5 Performance Evaluation
### Simulation Results
#### 5.2.2 Multiple Attackers
In practice, the network operation time is much greater than our maximum simulation time (three hours). Therefore, when there are inside packet drop attackers in the network, the pure GRP and the beta GRP will waste a lot of energy on unsuccessful packet transmissions. Figure 16: Packet delivery rate PDR (multiple attackers). (**a**) Three attackers (5%). (**b**) Five attackers (10%). (**c**) Eight attackers (15%). Figure 14: The number of packet drops due to attacks N\({}_{\text{A}}\) (multiple attackers). (**a**) Three attackers (5%). (**b**) Five attackers (10%). (**c**) Eight attackers (15%). Figure 15: The number of packet drops due to non-attacks N\({}_{\text{NA}}\) (multiple attackers). (**a**) Three attackers (5%). (**b**) Five attackers (10%). (**c**) Eight attackers (15%).
# From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
## 4 QAOA Mappings: Strings
### Example: MinGraphColoring
#### 4.4.3 Compilation in One-Hot Encoding  
We now give partial compilations of the elements of the mapping to qubits using the one-hot encoding.  
Mixer.In the one-hot encoding, the controlled-swap mixing Hamiltonian can be written as:  
\[\begin{split} H_{\mathrm{CS},\
u,\{a,b\}}^{(\mathrm{comp})}& =(X_{\
u,a}X_{\
u,b}+Y_{\
u,a}Y_{\
u,b})\,H_{\mathbf{NOR}(x_{\mathrm{ abhd}(v),\{a,b\}})}\\ &=2^{-D_{\mathrm{p}}-1}\left(X_{\
u,a}X_{\
u,b}+Y_{\
u,a}Y_{\
u,b} \right)\prod_{w\in\mathrm{nbhd}(v)}\prod_{c\in\{a,b\}}\left(I_{w,c}+Z_{w,c} \right)\end{split} \tag{41}\]  
with the corresponding unitary written as:  
\[U_{\mathrm{CS},\
u,\{a,b\}}^{(\mathrm{comp})}\left(\beta\right)=\Lambda_{ \mathbf{NOR}(x_{\mathrm{abhd}(v),\{a,b\}})}\left(e^{-i\beta\left(X_{\
u,a}X_{ \
u,b}+Y_{\
u,a}Y_{\
u,b}\right)}\right), \tag{42}\]  
where, for a string doubly indexed \(\mathbf{y}=\left(y_{i,j}\right)_{i,j}\), \(\mathbf{y}_{A,B}=\left(\left(y_{i,j}\right)_{i\in A}\right)_{j\in B}\) denotes the substring consisting the characters \(y_{i,j}\) for which \(i\in A\) and \(j\in B\), in lexicographical ordering of the two indices. In particular, \(\mathbf{x}_{\mathrm{nbhd}(v),\{a,b\}}\) indicates the bits corresponding to coloring the neighbors of \(v\) either color \(a\) or color \(b\). The \(\Lambda_{\mathbf{NOR}(x_{\mathrm{abhd}(v),\{a,b\}})}\) dictates that none of the neighbors of \(v\) take value \(a\) or \(b\) for the swap to be performed. Each \(U_{\mathrm{CS},\
u,\{a,b\}}^{(\mathrm{comp})}\) is a controlled gate with \(2D_{\mathrm{p}}\) control qubits and two target qubits. Altogether, the full mixing Hamiltonian can be implemented using \(\kappa(\kappa-1)n/2\) controlled gates on no more than \(D_{G}+2\) qubits.  
Phase separator.Let \(U_{\mathrm{P},a}(\gamma)=e^{-i\gamma H_{\mathbf{NONE}(\mathbf{x})}}\), so that the phase separator Equation (40) can be written as \(U_{\mathrm{P}}(\gamma)=\prod_{a=1}^{\kappa}U_{\mathrm{P}a}(\gamma)\). Each partial phase separator can alternatively be written as:  
\[U_{\mathrm{P},a}=\Lambda_{\mathbf{NONE}(\mathbf{x},a)}\left(e^{-i\gamma} \right). \tag{43}\]  
Initial state.Any coloring can be prepared in depth 1 using \(n\) single-qubit \(X\) gates:  
\[\left|\mathbf{x}\right\rangle=\left(\prod_{i=1}^{n}X_{i,x_{i}}\right)\left|0 \right\rangle^{\otimes n\kappa}. \tag{44}\]
## 3 Multi-Objective Optimization Algorithms
### End-to-End Latency Feature  
The output of any sensor node is typically an analog signal, which the sensor node digitizes the data and stores it in the buffer (memory) of a sensor node, and after that, these data will be packetized and transmitted periodically. The sampling cycle and transmitting cycle of the wireless sensor are depicted in Figure 2[52]. The amount of time between the data packet is generated at the node and the packet is received by the coordinator node refers to the concept of end-to-end latency (_Te_). _Te_ can be defined in the following equation [52]:  
\[Te=T_{sam}+T_{I}, \tag{15}\]  
where \(T_{sam}\) is the sampling time, which refers to the amount of time that sensor node samples the signal until the number of samples reaches a certain size, and \(T_{I}\) is the end-to-end delay. The parameters of _Te_ are dependent on the parameters of end-to-end delay model. The results of end-to-end delay feature will play a significant role in determining the results of the _Te_ feature.  
The sampling time of IEEE 802.15.4 standard can be given as follows:  
\[T_{sam}=\frac{payload}{Samplingrate}, \tag{16}\]
# Effect of Food Emulsifiers on Aroma Release
###### Abstract  
This study aimed to determine the influence of different emulsifiers or xanthan-emulsifier systems on the release of aroma compounds. Solid-phase microextraction (SPME) and GC-MS were used to study the effects of varying concentrations of xanthan gum, sucrose fatty acid ester, Tween 80 and soybean lecithin on the release of seven aroma compounds. The effects of the emulsifier systems supplemented with xanthan gum on aroma release were also studied in the same way. The results showed varying degrees of influence of sucrose fatty acid ester, soybean lecithin, Tween 80 and xanthan gum on the release of aroma compounds. Compared with other aroma compounds, ethyl acetate was more likely to be conserved in the solution system, while the amount of limonene released was the highest among these seven aroma compounds. In conclusion, different emulsifiers and complexes showed different surface properties that tend to interact with different aroma molecules. The present studies showed that the composition and structure of emulsifiers and specific interactions between emulsifiers and aroma molecules have significant effects on aroma release.  
emulsifiers; xanthan gum; aroma release; SPME; GC-MS Article
## 4 Statistical Thermodynamic Entropy and Information Entropy
On the other hand, the effect of the external bias field is opposite: at fixed temperature there is the narrowing of the distributions of diameters with increasing \(H_{\text{ext}}\) leading to a lower number of events carrying more information and to a lower entropy encapsulating less information (see Figure 5b). Hence, via the analogy between Shannon's and Jaynes's entropy and the configurational thermodynamic entropy, the following arguments have been demonstrated: (1) room temperature magnetic skyrmions carry more information entropy than the ones at low temperature; (2) at fixed temperature, in the region of metastability, magnetic skyrmions that are not perturbed by an external bias field encapsulate more information entropy than the ones subject to \(H_{\text{ext}}\). These conclusions could be drawn already looking at Figure 5 but only within a statistical thermodynamic framework. Finally, a simple example of entropy data communication system using ferromagnetic materials hosting magnetic skyrmions is suggested. In information theory for data communication systems one should distinguish among: (1) a data source, (2) a communication channel and (3) a receiver. The receiver should interpret the data that come from the source observing and collecting them. For the ferromagnetic system studied, the data source of entropy information can be regarded as a thermal bath at a given \(T\) in contact with the ferromagnetic material (e.g., cylindrical dot) hosting the magnetic skyrmion exciting the thermal breathing mode that acts as a source of entropy. The communication channel can be thought of the ferromagnetic material itself where the magnetic skyrmion forms that is able to collect the entropy information bits coming from the magnetic skyrmion. The receiver could be a special device able to identify and collect the bits of entropy. It can be ideally supposed that the amount of entropy is less than the capacity of the communication channel leading to the communication of all the data to the receiver.