esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Physical Layer of OFDM Communication System
The DFT transformation is given by:  
\[Y(k)=\frac{1}{N}\sum_{n=0}^{N-1}y(n)e^{\frac{-\beta\pi\mathrm{k}n}{N}}. \tag{4}\]  
The symbol \(Y(k)\) undergoes channel estimation and channel equalization to produce an estimate of the transmit frequency-domain IQ data, \(\hat{X}(k)\). The channel estimation compares the received data sequence and pilot symbols with the previously known ones to obtain an estimate of the Channel Impulse Response (CIR) and perform channel correction. Channel estimation techniques used in the experiment will be further discussed in Section 3.1.2. This estimate is then demodulated into soft bits (log-likelihood), \(\widetilde{b}\), which are further converted into binary output bits, \(\tilde{b}\), by the channel decoder before passing it to the next communication layer. For source coding and decoding, variable length coding (VLC) is used instead of full-length coding (FLC) as it reduces the code length and does not concern errors in padded bits as opposed to FLC [45]. It is simpler than probabilistic coding techniques and avoids synchronization problems at the receiver, improving the visual perception quality of the recovered images. However, its limitation is the additional overhead and inefficiency due to sending side information (code length) for each transmission (each image in our case). The extent of this paper is focused on the lower PHY while the source and channel coding are interesting directions for the future.
# All-Optical Modulation and Ultrafast Switching in MWIR with Sub-Wavelength Structured Silicon
## 3 Optical Model
In Equation (2), the second term represents the 2D Maxwell-Garnett mixing rule, while the third term stands for the Drude contribution of the free carriers. The model and the fitting procedure were presented in more detail in previous publications on nanoporous silicon [24; 25; 26; 27].
## 4 UAV Communication Network and Technology
### Communication Protocols in UAV Networks
At the same  
Figure 23: System model to evaluate the coverage performance of sub-links (U2U) compared to mobile infrastructure links (U2I). time, a basic model of the UAV mobile Internet is proposed, introducing three transmission modes (U2N, U2U, and U2D) for U2X communication. To reduce the computational load on the BS, some of the sensing data can be transferred to cell edge UAVs with computing capabilities using the U2U mode. After data processing, cell edge UAVs transmit the data to the target UEs directly via the U2D mode. MEC with U2X communication reduces the computational pressure of the BS and enhances the QoS transmitted to cell edge UEs by reducing the transmission distance. The application of sub-terahertz (<THz) transmission in future 6G networks has the potential to create synergies with the ubiquitous deployment of nanodrones in various applications. However, applications that deploy a swarm of UAVs for long periods of time, combined with wireless charging and the ability to efficiently guide and control individual nano-drones with limited batteries, are critical. In [77], the authors explore the convergence of sub-THz communications, radiated wireless power transfer, and 3D radar imaging in future 6G networks. Based on this, they propose a method using a sub-THz on-off keying (RZ-OOK) signal modulation scheme to transmit power and joint radar imaging (JPTRI), while maintaining ultra-low latency communication to control the UAV swarm. The results demonstrate that the proposed JPTRI method remains highly efficient as the number of UAVs in the swarm increases, maintaining continuous ultra-low latency control, consistent average DC power, and improved 3-dimensional positioning of the UAV swarm.
###### Abstract  
In vehicular ad hoc networks (VANETs), due to the fast-moving mobile nodes, the topology changes frequently. This dynamically changing topology produces congestion and instability. To overcome this issue, privacy-preserving optimization-based cluster head selection (P2O-ACH) is proposed. One of the major drawbacks analyzed in the earlier cluster-based VANETs is that it creates a maximum number of clusters for communication that leads to an increase in energy consumption which reflects in a degradation of the performance. In this paper, enhanced rider optimization algorithm (ROA)-based CH selection is performed and that optimally selects the CH so that effective clusters are created. By analyzing this, the behavior of the bypass rider's CH is chosen, and this forms the optimized clusters, and during the process of transmission, privacy-preserving mobility patterns are used to secure the network from all kinds of malfunctions which are performed by the new vehicle blending and migration process. The proposed P2O-ACH is simulated using NS-2, and for performance analysis, two scenarios are taken, which contain a varying number of vehicles and varying speeds. For a varying number of vehicles and speeds, the considered parameters are energy efficiency, energy consumption, network lifetime, packet delivery ratio, packet loss, network latency, network throughput, and routing overhead. From the results, it is understood that the proposed method performed better when compared with earlier work, such as GWO-CH, ACO-SCRS, and QMM-VANET.  
A Article  
Privacy-Preserving Mobility Model and Optimization-Based Advanced Cluster Head Selection (P2O-ACH) for Vehicular Ad Hoc Networks  
1
## 6 Conclusions  
The optimal covariate predictor and DA-ECMS are proposed in this paper. First, two indicators--namely the fuel cell stack voltage and the battery capacity--were selected to characterize the performance degradation of the hybrid energy sources, and a corresponding performance degradation model was constructed. Second, a speed prediction model was constructed based on the LSTM neural network, and an optimal covariate predictor was created based on the PMP. The prediction accuracies of the predictor model were 91.44% and 88.17% under the WLTP and CLTC, respectively. The fuel cell and battery performance degradation were then incorporated into the optimization objective of the A-ECMS to construct a DA-ECMS. Compared with the A-ECMS, the DA-ECMS achieved reductions in the fuel cell stack voltage degradation of 17.1%, 23.2%, and 16.6% under the WLTP, CLTC, and NEDC conditions, respectively, and the corresponding battery capacity degradation reductions were 5.1%, 11.1%, and 11.2%. In comparison with the A-ECMS, this sacrifice under the DA-ECMS was relatively small in terms of economy, despite the slight increases in the hydrogen consumption over 100 km of 2.7%, 6.6%, and 9.1%, respectively. Finally, the DA-ECMS was validated by HIL tests. Compared with the simulation, the fuel cell stack voltage degradation, the battery capacity degradation, and the 100-km hydrogen consumption of this strategy under WLTP conditions only increased by 4.5%, 2.7%, and 0.4%, which is within an acceptable error range. Therefore, the EMS established in this  
Figure 26: Equivalent hydrogen consumption of DA-ECMS in simulation and HIL test.  
Figure 25: SOC trajectories from simulation and HIL test.  
paper has good effectiveness and real-time performance capabilities. In the future, the prediction of optimal covariates will be performed using other neural networks and tested on real vehicles.  
Methodology, C.D.; Software, Y.W. and H.D.; Validation, Y.W. and H.D.; Formal analysis, Y.W.; Data curation, H.D.; Writing--original draft, P.Z. and Y.W.; Writing--review & editing, P.Z. and C.D.; Supervision, P.Z. and C.D.; Funding acquisition, P.Z. and C.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (52305069), Foshan Xianhu Laboratory of the Advanced Energy Science and Technology Guangdong Laboratory (XHRD2024-11233100-01), Key R&D project of Hubei Province, China (2021 AAA006).  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Measuring Redundancies in Sentinel-2 Bands  
To assess the quality of the spectral regression approaches, we will analyze the statistical dependence between all the pairs of Sentinel-2 bands. Instead of measuring correlations, which are limited to linear (Pearson correlation) or monotonic (Spearman correlation) dependencies, we will use the mutual information, \(I\). It measures dissimilarity between the joint distribution of a pair of variables and the product of the marginals. It is therefore a measure of the distance to general statistical independence:  
\[I(X;Y)=D_{KL}(P_{(X,Y)}\|P_{X}\otimes P_{Y}),\]  
where \(D_{KL}\) is the Kullback-Leibler divergence. The mutual information can also be written in terms of entropies (\(H\)) as follows:  
\[I(X;Y)=H(X,Y)-H(X|Y)-H(Y|X)=H(Y)-H(Y|X)=H(X)-H(X|Y),\]  
and it is therefore a measure of the amount of uncertainty about one variable once the other is known. The mutual information is positive, but it is not upper-bounded. Therefore, we use a normalized version using the entropies of each variable:  
\[I_{norm}(X;Y)=\frac{I(X;Y)}{\sqrt{H(X)H(Y)}}\]  
We will study this measure for both L1C and L2A data.
## 2 Main Preparation Methods
To overcome this limitation, tolerances in the geometry or properties should be determined and implemented that will, in turn, impact on the synthesis process and final cost of the noble metals nanofluids. In the case of a nanofluid designed to attain improved optical properties, the tolerances should be tight, given that a minor shift in the morphology of the nanoparticles may drastically alter the optical features of the nanofluid. A droplet reactor was developed by the researchers [PERSON] et al. [13] to synthesize large volumes of silver nanofluids for a beam-splitting solar collector. A mineral oil flows in the reactor as the carrier liquid. Then, a silver solution and 0.5 mM AgNO\({}_{3}\) aqueous solution were mixed. This mixture is then deposited, dropwise, into the carrier oil downstream. The droplets of controlled size are then suspended in the carrier flow to ensure a uniform, continuous, controlled reaction along the remaining length of the droplet reactor. By controlling the molar ratio of the mixture, the size of the droplets, and the reaction time in the reactor (e.g., its length vs. flowrate), a well-controlled, continuous production process can be achieved. A droplet reactor of this type should incorporate several tight turns to agitate the droplets and force them to mix. Nonetheless, a sonication procedure can also be integrated in the system. The ascorbic acid reduces the AgNO\({}_{3}\) over time and the Ag\({}^{+}\) ions reduce onto the silver seeds. As the reaction takes place, the nanoparticles get larger, which is indicated by the change in color of the droplets as they flow through the system. After the aqueous droplets leave the mixing channel, they can be collected and separated from the carrier oil because of the intrinsic immiscible nature of the two solvents. The fluid is then centrifuged at 10,000 rpm for 100 min and re-dispersed in water for direct use. The droplet reactor successfully synthesized silver nanoplates. Other large-scale preparation methods like the one described are most welcome to be applied in the noble metals nanofluids fabrication.
# A Novel Object Detection Method of Pointer Meter Based on Improved YOLOv4-Tiny
## 3 Experiments and Discussions
### Experimental Environment and Settings  
(1) Description of the Dataset  
Due to the lack of public datasets for pointer meters, pointer meter images were collected from the web by using web crawlers and from real scenes by using mobile phones, so a high-standard dataset was created. Firstly, web images were scraped by using \"pointer meter\" and \"pointer gauge\" as keywords. However, since these images varied in quality and had duplicates, the repetitive and poor-quality images were filtered out manually. The remaining images were still insufficient for deep learning purposes, so they were supplemented by the photos of pointer meters taken by mobile phones in realistic settings. Then, the two sources of images were mixed and the dataset images were labeled as \"meter\" by using LabelImg software to generate YOLO format labels. Finally, some images were randomly cropped, dynamic fuzzy data were enhanced, and a dataset containing 2000 pointer meter images with 2575 pointer meter targets was obtained. In addition, all the data were randomly sampled and divided into training set, validation set, and test set at a ratio of 8:1:1.  
(2) Experimental parameters and environment  
To ensure the fairness of the experiment, the initial training parameters of each group of experiments were set to be the same, the size of all input images was adjusted to 608 \(\times\) 608 \(\times\) 3, each training batch was set to 16, the num_works was set to 2, all models were trained for 200 epochs, the initial learning rate was set to 0.01 and CosineAnnealing was used to reduce the learning rate, the minimum learning rate was set to 0.0001, SGD was selected as the optimizer, the mosaic data enhancement method was enabled for the first 70% of epochs, the confidence threshold was set to 0.5, and the IoU threshold was set to 0.3. The computational resources required for the experiment are shown in Table 1.
# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
###### Abstract  
Mobile broadband (MBB) services in Lagos, Nigeria are marred with poor signal quality and inconsistent user experience, which can result in frustrated end-users and lost revenue for service providers. With the introduction of 5G, it is becoming more necessary for 4G LTE users to find ways of maximizing the technology while they await the installation and implementation of the new 5G networks. A comprehensive analysis of the quality of 4G LTE MBB services in three different locations in Lagos is performed. Minimal optimization techniques using particle swarm optimization (PSO) are used to propose solutions to the identified problems. A methodology that involves data collection, statistical analysis, and optimization techniques is adopted to measure key performance indicators (KPIs) for MBB services in the three locations: UNILAG, Igorodu, and Oniru VI. The measured KPIs include reference signal received power (RSRP), reference signal received quality (RSRQ), received signal strength indicator (RSSI), and signal-to-noise ratio (SINR). Specific statistical analysis was performed, and the mean, standard deviation, skewness, and kurtosis were calculated for the measured KPIs. Additionally, the probability distribution functions for each KPI were plotted to infer the quality of MBB services in each location. Subsequently, the PSO algorithm was used to optimize the KPIs in each location, and the results were compared with the measured data to evaluate the effectiveness of the optimization. Generally, the optimization process results in an improvement in the quality of service (QoS) in the investigated environments. Findings also indicated that a single KPI, such as RSRP, is insufficient for assessing the quality of MBB services as perceived by end-users. Therefore, multiple KPIs should be considered instead, including RSRQ and RSSI. In order to improve MBB performance in Lagos, recommendations require mapping and replanning of network routes and hardware design. Additionally, it is clear that there is a significant difference in user experience between locations with good and poor reception and that consistency in signal values does not necessarily indicate a good user experience. Therefore, this study provides valuable insights and solutions for improving the quality of MBB services in Lagos and can help service providers better understand the needs and expectations of their end users.  
4G LTE; key performance indicators; particle swarm optimization; probability density function; quality of service; optimization; dense urban environment +
Footnote †: journal:  
0023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 3 Results and Discussion
### Performance Tests of the Membranes for the Removal of Emulsified Oil
#### 3.5.2 Image Characterization of the Water/Oil Solutions before and after Treatment  
Figure 9 displays VIPA microscopic photograph of the oil droplets in the feed emulsion and permeate solutions. To visualize the oil emulsion in water, the oil emulsion was dyed with Oil Red EGN. Prior to filtration, the VIPA microscopic picture of the filtration exhibits the presence of oil droplets in the emulsion as observed in Figure 9a. As demonstrated in Figure 9d, oil droplets are not visible in the VIPA microscopic image after filtration, which indicates TNF/CNF membrane has removed the emulsified oil droplet from the feed.
# Enhancing Detection of Arabic Social Spam Using Data Augmentation and Machine Learning
###### Abstract  
In recent years, people have tended to use online social platforms, such as Twitter and Facebook, to communicate with families and friends, read the latest news, and discuss social issues. As a result, spam content can easily spread across them. Spam detection is considered one of the important tasks in text analysis. Previous spam detection research focused on English content, with less attention to other languages, such as Arabic, where labeled data are often hard to obtain. In this paper, an integrated framework for Twitter spam detection is proposed to overcome this problem. This framework integrates data augmentation, natural language processing, and supervised machine learning algorithms to overcome the problems of detection of Arabic spam on the Twitter platform. The word embedding technique is employed to augment the data using pre-trained word embedding vectors. Different machine learning techniques were applied, such as SVM, Naive Bayes, and Logistic Regression for spam detection. To prove the effectiveness of this model, a real-life data set for Arabic tweets have been collected and labeled. The results show that an overall improvement in the use of data augmentation increased the macro F1 score from 58% to 89%, with an overall accuracy of 92%, which outperform the current state of the art.  
data augmentation; machine learning; spam detection; online social networks; Arabic spam +
Footnote †: journal: 2022, 12, 11388. [[https://doi.org/10.3390/app122211388](https://doi.org/10.3390/app122211388)]([https://doi.org/10.3390/app122211388](https://doi.org/10.3390/app122211388))
## 4 Discussion
### Effects of Additional Information on Model Calibration and Validation  
Including additional information, in particular S2-indices, S1-indices, and S1-soil moisture, was intended to increase the models' predictive performance by providing new variables that distinguish the varying soil conditions better on different acquisition dates. This additional information had more influence during calibration than validation and on the DNN models than on the RF models (e.g., when including S2-indices, mean RPIQ improved from 2.02 (\(\pm\)0.15) to 2.59 (\(\pm\)1.30) when calibrating the DNN model and from 1.50 (\(\pm\)0.13) to 1.57 (\(\pm\)0.07) when calibrating the RF model) (Figure 9). It likely had more influence during calibration than validation because the validation dataset contained spectral information acquired on dates when soil surface conditions of the sampling points in the training dataset were unknown. In fact, some of the sampling points were bare soil on only a few dates (e.g., 1-3), while others were bare soil on 5-18 dates, which gave the machine-learning algorithms, especially DNN, more opportunities to learn. This may also explain the instability in performance of calibrated DNN models \"a + b\", \"a + c\", and \"a + e\" among the iterations (mean RPIQ = 2.59 (\(\pm\)1.30), 2.53 (\(\pm\)1.49), and 2.89 (\(\pm\)2.16), respectively). Thus, the number of dates retained for each sampling point during model calibration strongly influenced model performance. The additional information may have had stronger effects on the DNN models due to their ability to learn and extract more representative features through their hidden layers and neurons [105; 107]. Thus, the validation performance of DNN models that included additional information, varied from 2.41 to 2.95 g\(\cdot\)kg\({}^{-1}\) for RMSEP and from 1.29 to 1.42 for RPD; however, the performance of RF models was almost stable (RMSEP = 3.34-3.59 g\(\cdot\)kg\({}^{-1}\) and RPD = 0.95-1.02).
## 4 Results
### Maintenance 5.0 Metrics: Resilient Physical Assets
#### 4.3.1 Resilience Using Real-Time Data  
Resilience is defined as the area between the metric used to control the process in normal behavior and the variation that said metric undergoes due to random causes, unrelated to the process. The resilience area includes the three typical steps of variance behavior (see Figure 11): (1) increasing the value of the process metric where symptoms that a variance exists are detected; (2) stabilization of the value of the process metric, where the maximum value of the variation is detected and (3) decrease in the value of the process metric, where the variation is mitigated. We use a cycloid function with initial and final slopes equal to zero to show a smooth variation of the metric rising or falling, also known as raised cosine [130; 131].  
\[P(t)=A+B\cos(\omega t+\varphi);\textit{Rised}\ \textit{Cosine} \tag{1}\]The maximum performance value curve (raised cosine) is shown in Equation (1). It can be applied to the symptoms, detection and mitigation intervals as follows: (a) \(P_{Symptoms}(t)=A+B\cos(\omega t+\varphi)\); _Rised Cosine_; (b) \(P_{Detection}(t)=A+B;MaximumValue\); and (c) \(P_{Mitigation}(t)=A+B\cos(\omega t+\varphi)\); _Rised Cosine_; where B is the cosine amplitude expressed as: \(\text{B}=(Max-No\_failure\;level)/2\); A is the raised cosine and is expressed as \(A=No\_failure\;level+B\); the angular frequency is \(\omega=\pi/(t_{1}-t_{0})\) and the phase angle is \(\varphi=\pi t_{1}/(t_{1}-t_{0})\). The formula for calculating resilience in real time is presented in Equation (2) (area between the variation curve and the level of normal behavior.  
\[\begin{array}{ll}\text{{Resilience}}&=\int_{t_{0}}^{t_{1}}\Big{[}P_{ Symptoms}(t)-P_{No\_failure\;level}(t)\Big{]}dt\\ &+\int_{t_{1}}^{t_{2}}\Big{[}P_{Detection}(t)-P_{No\_failure\;level}(t)_{n} \Big{]}dt\\ &+\int_{t_{2}}^{t_{3}}\Big{[}P_{Mitigation}(t)-P_{No\_failure\;level}(t) \Big{]}dt\end{array} \tag{2}\]  
Figure 12 presents other maintenance performance metrics that are related to resilience calculation. \(N\) corresponds to the number of failures in an interval: \((t_{33}<=t<=t_{01})\)  
On one hand, Figure 12 presents \(MTBF=\frac{\sum_{i=1}^{N-1}\tau BE_{i}}{N-1}\) in the interval \((t_{0b}-t_{0a})\), has an \(N=3\)\((N-1=2)\) with two intervals: \((t_{02}-t_{01})\)\(y\)\((t_{03}-t_{02})\). On the other hand, \(MTTF=\frac{\sum_{i=1}^{N}TTF_{i}}{N}\) in the interval \((t_{1a}-t_{0a})\) has an \(N=3\), with three intervals: \((t_{11}-t_{01}),(t_{12}-t_{02})\)\(y\)\((t_{13}-t_{03})\). Finally, \(MTTR=\frac{\sum_{i=1}^{N}TTR_{i}}{N}\) in interval \((t_{3a}-t_{2a})\), has an \(N=3\), with three intervals: \((t_{31}-t_{21})\), \((t_{32}-t_{22})\)\(y\)\((t_{33}-t_{23})\).