esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodologies and Research Framework
### Histogram of Oriented Gradients (HOG)  
One of the popular methodologies for feature extraction is the HOG descriptor, and this is very powerful for objects detection, especially human detection. The reason for employing the HOG descriptor is that this methodology can also be used in tasks related to autonomous vehicles [24]. This methodology is composed of five stages [25]: (i) normalize images; (ii) compute gradients in X (horizontal) and Y (vertical); (iii) compute gradients histograms; (iv) normalize blocks; and (v) collect HOG descriptors from overlapping blocks and cover into a feature vector.  
The first stage, image normalization, is required to provide better illumination invariance such as lighting, shadows, etc. by normalizing cells in the block. Note that the definition of the cells and blocks is that once an image window is divided into small or large spatial regions, these are called cells, which consist of multiple pixels and can be rectangular or circular, and blocks, which incorporate multiple cells, respectively. However, image normalization was not applied in this research because it does not make a big impact on performance [25]. The second stage focuses on computing image gradients in X and Y. This stage can be performed by capturing the image edge or structure based on the locally dominant color channel. By doing so, it is also possible to provide better resistance to illumination variations. The third stage is about computing gradients histograms by collecting the information of gradient orientation locally. As mentioned in the first step above, computing gradients histogram is based on the concept of cells; that is, a local 1D histogram of gradient or edge orientations over the pixels in the cell is accumulated for each cell, and the combined histogram forms the representation of the orientation histogram [25]. After that, the range of a gradient angle is divided into a fixed of bins. The fourth stage is to compute normalization by accumulating a measure of local histogram \"energy\" over blocks. Note that this refers to normalized cells in the block because each cell is shared by multiple blocks. For this reason, a cell with different normalizations possibly appears in a final output vector. The last stage is to collect HOG descriptors over the detection window, which refers to the normalized block descriptors in the previous stage. The detailed concept of the HOG descriptor is available in [25].  
The main objective of data preprocessing and feature extraction through the HOG descriptor is to make the input dataset for a deep learning model. Before applying the HOG descriptor, a size of all the images is fixed at 60 px \(\times\) 60 px because every image has a different size. Eleven orientations, sixteen pixels per cell, and two cells per block were used for feature extraction, thus the total length of the feature vector was 1188. Thus, there were 1188 pixels in a single image, which means that 3600 pixels were converted into 1188 HOG features for both vehicle and non-vehicle objects. Figure 7 shows an example of feature extraction through the HOG descriptor.
# RF Energy Harvesting Wireless Communications:
## 5 Intermittent Connection Problem in RF-EHWC Networks
### Impact of Intermittent Connections  
The state of RF-EHWC nodes switching between alive and dead causes frequent and unpredictable changes of the network topology, which can pose a grand challenge on the resilient routing design. Although extensive routing protocols and topology management methods have been developed for wireless sensor networks [80], most of the existing protocols are vulnerable to the intermittent connections among nodes. How to maintain reliable communications among RF-EHWC nodes for packet delivery is a crucial problem.
# A Sigfox Energy Consumption Model
## 4 Modeling Sigfox Device Current Consumption
### Unidirectional Transactions
In unidirectional transactions, the current consumed by the device and its lifetime are independent of uplink frame delivery success. However, \(E[l_{delivery}]\) depends on the Frame Loss Rate (FLR), and therefore frame losses have an impact on \(EC_{delivery\_uni}\). Let \(l_{Payload}\) be the frame payload size, and let \(FLR_{UL}\) denote the FLR at the base station for a single uplink frame transmission. The uplink data message will be correctly delivered if at least one of the corresponding three uplink frame transmissions is successfully received. Then, the expected amount of data delivered by the device per transaction is determined as:  
\[E\Big{[}l_{delivery}\Big{]}=l_{Payload}\cdot\Big{(}1-FLR_{UL}{}^{3}\Big{)} \tag{7}\]
# Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images
###### Abstract  
The accurate estimation of electricity consumption and its spatial distribution are important in electricity infrastructural planning and the achievement of the United Nations Sustainable Development Goal 7 (SDG7). Electricity consumption can be estimated based on its correlation with nighttime lights observed using remote sensing imagery. Since night-light images are easily affected by cloud cover, few previous studies have estimated electricity consumption in cloudy areas. Taking Cambodia as an example, the present study proposes a method for denoising night-light images in cloudy areas and estimating electricity consumption. The results show that an exponential model is superior to linear and power function models for modelling the relationship between total night-light data and electricity consumption in Cambodia. The month-specific substitution method is best for annual night-light image synthesis in cloudy areas. Cambodia's greatest electricity consumption occurs in its four most economically developed cities. Electricity consumption spreads outwards from these cities along the main transport routes to a large number of unelectrified areas.  
Keywords:night-light image processing in cloudy areas; VIIRS/DNB; estimated electricity consumption; spatial patterns of electricity consumption; SDG7 +
Footnote †: journal: Applied
# Experimental Validation of Non-Marker Simple Image Displacement Measurements for Railway Bridges
## 3 Processing and Test Methods
### Image Processing Method
In the EEC, sub-pixel estimation is performed using the reference of the image translated by 0.5 pixels and estimated separately. The movement amount adjusted by 0.5 pixels is estimated from the movement amount of these images, and the bias to the integer value is offset by considering the average of the movement amount estimated from the original image (see reference [25] for details). In the displacement estimation by DIC and parabola fitting, the influence of the subset size \(L_{A}\) is frequently mentioned. In particular, if the texture of the surface to be measured is a random pattern, the accuracy is generally higher when the size \(L_{A}\) of the subset A is set to large value [19]. However, if the size \(L_{A}\) of subset A is increased, objects and backgrounds that move differently from the bridge will be mixed in the subset, and the accuracy will decrease. Moreover, as shown in the discussion in Section 5, the brightness variation of  
Figure 2: Outline of parabola fitting with 8 peripheral pixels. the measurement surface has a greater effect on the displacement estimation error than the effect of the subset size in the case of non-marker DIC. Considering these factors, the subset size \(L_{A}\) is based on 15 pixels, and the effect of brightness variation on the measurement surface accuracy is empirically analyzed. The obtained pixel-by-pixel displacement is converted from pixels to actual dimensions (m) using structural dimensions comparable to the drawing, such as the girder height at the measurement position. The effects of camera calibration [19] were investigated; however, the effects could not be confirmed, so their description is omitted from this study.
# WON-OCDMA System Based on MW-ZCC Codes for Applications in Optical Wireless Sensor Networks
## 6 Discussion  
The research presented in this study addresses the functionality gap between emerging optical sensor networks and urban SHM in order to leverage efficient remote vibration sensing of constructed sites. Despite the broad adoption of optical sensing in SHM, there are several challenges when it comes to monitoring various structures located in hard-to-reach areas. To address the needs, this paper proposes a new family of MW-ZCC codes for use in advanced monitoring systems for carrying information collected by optical sensors monitoring structural vibration. The proposed codes were investigated in WON-OCDMA system that does not require any traffic management or synchronisation.  
It was shown that the proposed approach could also be effectively implemented without any undesirable interference or cross talk on other simultaneous communication services, including remote video surveillance. The novel family of MW-ZCC codes can support high-performance optical transmission systems that need to provide QoS differentiation in free space applications. As shown, MW-ZCC codes are well suited for use in WON-OCDMA systems even when used as single-weight codes because of their low cross-correlation properties. Since they are easy to convert into codes with the power of two code-weigh, they are well suited to support the desired QoS differentiation.
## 2 Materials and Methods
### Data Processing and Features Extraction  
The signal processing and Feature Extraction steps were performed in the same way on both datasets described above. The initial step involved applying a band-pass filter to the EEG signal using a fifth-order Butterworth filter within the 2-30 Hz interval. Detection of blink artifacts employed the Reblinca method [51], and the correction was implemented by utilizing the ocular component estimated through a multi-channel Wiener Filter (MWF) [52]. Segmentation of EEG signals into 1-s epochs occurred, and artifact rejection was applied based on a threshold criterion of \(\pm\)80 \(\mu\)V [53]. This conservative threshold value was chosen over the default value of 100 \(\mu\)V suggested by the EEGlab toolbox, as a single criterion was adopted [54].  
Both datasets were treated and processed in the same way. The only difference is represented by the AFz channel instead of the FPz channel for the Realistic Dataset because the AFz channel was not available in the Controlled dataset.
# VLC Network Design for High Mobility Users in Urban Tunnels
## 4 Proposed VLC Network for High Mobility
### VLC Network Topology
In the IEEE 802.15.7 standard, there is a superframe configuration to support the user's mobility. In this configuration, multiple cells work coordinately transmitting the beacon at the same time and using a common CAP. The CFP is assigned per cell sequentially to avoid interruptions. In contrast, in our proposed system design, each site uses the superframe configuration for a single site. BI is 2 ms long and four times SD with a Beacon Order of 7 and SuperOrder of 5. Thus, each AP is active just a quarter of the time. The rest of the time is used in turns by neighbors APs. So, the SD shifts on the time to work as Time Division Multiple Access (TDMA), see Figure 6. These actions allow coexistence between contiguous APs and the user, reducing the system throughput as commented before. Figure 5: Layer topology diagram.
## 2 Materials and Methods
### The Communication Simulator  
Now that the point in space where the UFMC performance is studied is set, the communication channel simulator gives the information of the amount of optical power received and can also give us the information about the channel's impulse response (CIR). The CIR is a key parameter to assess the available coherence bandwidth and is defined according to the delay between the LoS main peak (the first ray reaching the receiver) and  
Figure 6: 3-D environment simulated when using the channel simulator for the NIRKON streetlight hanging on a wall.  
Figure 7: Raw point cloud of the IES file of the real radiation pattern of a streetlight for two points of view.  
the second peak (the reflected ray on a wall) reaching the receiver. Given that the light wave travels at the speed of light, and the measures of the scenario, if the LoS ray of light arrives at a time of \(1.5\times 10^{-8}\) and the first reflection (due to the proximity of the wall to the light) is approximately \(1.53\times 10^{-8}\), the difference between both rays, the main signal and its reflection, gives place to a bandwidth of approximately 3000 MHz, which is more than enough for the envisioned scenario (See Equation (1)). Thus, the available coherence bandwidth is sufficient to apply the flat channel hypothesis.  
\[BW=\frac{1}{\Delta t}=\frac{1}{0.03\times 10^{-8}}=33.33\times 10^{8}\;\mathrm{Hz} \tag{1}\]  
Furthermore, OFDM for VLC has been standardized in ITU G.9991 standard [30], and the framework of this standard was chosen in this paper to generate the waveforms. The standard proposes a set of parameters for bandwidths of 50, 100, or 200 MHz, which fit our scenario well given the available bandwidth computed above. Using the parameters of the G.9991 standard chosen is thus consistent.  
A point-to-point communication simulator as illustrated in Figure 4 is used in this study. Both OFDM and UFMC modulation schemes are implemented in this simulator to estimate the system's performance in terms of bit error rate (BER) and spectral efficiency (bit/s/Hz). The paper's use of OFDM simulation is interesting, especially as it is widely standardized. What sets it apart is comparing OFDM with UFMC, a 5G candidate waveform that was not selected. Furthermore, the inclusion of a realistic lamp pattern adds originality to the study. This section highlights the key parameters for OFDM and UFMC that were studied. On top of the use of UFMC, the originality of this study is the use of realistic LED optical spatial distributions and real photodiode models in the scenario under study. The rest of this section describes the working principles of UFMC and how to generate a VLC compatible signal, namely, a real-valued and positive signal. It then presents the various key parameters used in the simulation.
# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
## 6 Proposed Methodology
### Hybrid Deep Learning Model
* Testing Data: Features like gender, residence type, BMI, glucose levels, hypertension, heart disease, smoking status, and work type are among the testing data used by the stroke detection and prediction system [92]. It is preprocessed using encoding and normalization to guarantee consistency with the training data. Using metrics like accuracy, precision, recall, and F1-score, performance is assessed by comparing the predictions for stroke risk levels (low, medium, and high) made from the processed data with the actual values [73]. * Combined Prediction: The results of the RNN, LSTM, and CNN parts are converged to make a solitary expectation. This convergence of results permits the framework to consider each model's particular assets transient investigation from RNN and LSTM, and complex static element extraction from CNN [90; 93]. The joint forecast gives a reasonable and intensive assessment of the patient's gamble level; it is substantial and exact to ensure that the discoveries. * Risk Classification: The combined prediction classifies individuals into one of three stroke risk levels [80]:
* Low Risk: Patients with few indications of stroke risk who may essentially require occasional assessment and solid way of life direction. * Medium Risk: Patients with moderate gamble factors require more forceful measures, for example, way of life changes and ordinary wellbeing checks. * High risk: patients have impressive gamble signs and require fast clinical consideration, as well as possibly progressed demonstrative or safeguard intercessions. This categorization enables healthcare practitioners to customize therapies to individual risk levels, encouraging early detection and effective resource allocation. * Performance Metrics  
Five measurable factors were used in this review to assess the presentation and helpfulness of the classifiers: exactness, accuracy, review/awareness (review and responsiveness  
Figure 6: Flowchart of the hybrid deep learning model. are similar in parallel order), F1-score, and AUC bend [81]. Coming up next are the meanings of the measurable boundaries. \[Accuracy=\frac{TP+TN}{TP+FP+FN+TN} \tag{1}\]  
\[Precision=\frac{TP}{TP+FN} \tag{2}\]  
\[Recall/Sensitivity=\frac{TP}{TP+FN} \tag{3}\]  
\[F1\ Score=\frac{2*(Recall*Precision)}{(Recall+precision)} \tag{4}\]  
where,  
\(TP\) = true positive,  
\(FN\) = false negative,  
\(FP\) = false positive,  
\(TN\) = true negative.
# Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
## 2 Problem Formulation
### Model of the Agent  
#### 2.3.1 Dynamics  
The agent considered here is a multicopter, i.e., a holonomic UAV capable of flying in any direction and keeping a fixed position. The high level control generates a commanded velocity in the \(\{x,y\}\) plane, defined by two variables, a reference velocity \(v_{n}\) and its heading \(\psi_{c}\). The actual velocity vector is modeled as:  
\[\mathbf{v}(t)=v(t)\left(\sin(\psi_{c}(t)),\cos(\psi_{c}(t))\right) \tag{5}\]  
where \(v(t)\) is assumed to be dependent on the change of the flying direction:  
\[\frac{v(t)}{v_{n}}=\left\{\begin{array}{ll}1-\frac{|\Delta\psi|}{2\pi}\left( 1-\cos\left(2\pi\frac{t-t_{d}}{t_{o}}\right)\right)&\text{if }t-t_{d}\leq t_{v}\\ 1&\text{if }t-t_{d}>t_{v}\end{array}\right. \tag{6}\]  
where \(t_{d}\) is the time instant at which the agent changes its direction and \(t_{v}\) is the characteristic time for the velocity reduction due to the change in the flying direction (\(\Delta\psi\)).  
Figure 2: (**a**) Plain scenario; (**b**) probability distribution scenario; (**c**) obstacles scenario; (**d**) probability distribution and obstacles scenario. Examples of scenarios with the four types considered. The color bar represents the concentration of pheromones (see Section 3), proportional to the probability distribution of the targets’ location. The colored circles represent the initial position of the agents. The black dots are the targets to be detected. Obstacles have been colored in dark blue.
# A Bivariate Volterra Series Model for the Design of Power Amplifier Digital Predistorters
## 3 PA Modeling Performance
### Signal Alignment  
Notice that a perfect time alignment of the envelope \(z(k)\) and the input \(x(k)\) is assumed in the bi-CKV model (10), i.e., the envelope delays \(\mathbf{q}_{m}^{\prime}\geq 0\). In case that the envelope is advanced with respect to \(x(k)\), the lower limit of \(\mathbf{q}_{m}^{\prime}\) must be a negative index. A second presumption is a perfect synchronization of the acquired output \(y(k)\) and the input. This signal alignment is commonly performed with a postprocessing procedure based on the best cross correlation between \(y(k)\) and \(x(k)\)[6]. An error in the timing coincidence can yield a raw signal \(\hat{y}(k)\) advanced in time, and the bi-CKV model would require noncausal terms with a dependence on \"future\" input samples. Alternatively, it is possible to refine the synchronization by delaying the coarse aligned signal \(\hat{y}(k)\), the number of samples necessary to optimize the NMSE. The procedure is illustrated in Figure 5, where the NMSE of a bi-CKV model with ninth-order and 10 samples of memory length are plotted for the raw output signal of the class AB PA under the same operating conditions. When repeating the modeling with the signal delayed one sampling time, the NMSE improvement is appreciable. The optimum result is given for a delay of two samples, with an error reduction of about 1 dB.  
Figure 4: NMSE results of the conventional and the bivariate Volterra models, for a class AB power amplifier operated at 3.6 GHz with an average output power of 27.4 dBm.  
Figure 5: NMSE results of the bi-CKV models for different input–output alignments for a class AB power amplifier operated at 3.6 GHz with an average output power of 27.4 dBm. The NMSE is optimized with a delay of 2.
## 5 Experimental Results
The voltage sag values of the MG\({}_{2}\) has approximately the same values of 83% and 96.1% during fault 1 and 2, respectively, as shown in Figure 7b. Under the second fault, fault2, similar to MG\({}_{1}\), MG\({}_{2}\) will be transmitted to islanded mode at t = 0.5 sec. Figure 9b shows the current signal of L2 during the ring topology. Figure 8: RMS Current signal at the main feeder. Figure 9: Current signal of L2: (**a**) MG\({}_{1}\), (**b**) MG\({}_{2}\). During the islanded mode, maintaining a reliable operation is one of the vital priorities that have to be covered. Therefore, in order to sense the accurate time of the MG transition to islanded mode, a current sensing system is built to sense the current signal of the main grid source. As shown previously in Figure 8, it can be noticed that \(T_{x}\) = 0.5 sec for both MG\({}_{1}\), and MG\({}_{2}\). For both MGs, as the main grid current (I\({}_{\text{grid}}\)) goes to 0 A at t = 0.5 sec, the power difference, \(\Delta P\) is calculated. In this example, L3 is assumed to be the most critical load, while L2 has lower priority than L1 (\(Pr_{L2}<Pr_{L1}\)), where \(Pr_{L2}\) is the priority of L2 and \(Pr_{L1}\) is the priority of L1. In MG\({}_{1}\), the gradual load shedding process starts its operation by disconnecting L1, since L2 has been already disconnected during the connected mode. By the load disconnection, \(\Delta P\) goes to a positive value, and hence, meets the power balance state, as shown in Figure 10a. Following Equation (9), the \(T_{b}\) parameter has the value of 0.06 s. While in MG\({}_{2}\), the load shedding starts by disconnecting L2. The \(\Delta P\) value is decreased, as shown in Figure 10b; however, it is still negative. Therefore, and based on the proposed algorithm, another disconnection operation of L1 is needed to meet the power balance state. Hence, the \(T_{b}\) parameter has the value of 0.12 s.  
Frequency is like the pulse of the power system, and its status indicates the imbalance between generation and real power demand. Figure 11 illustrates the frequency signals for MG\({}_{1}\) and MG\({}_{2}\), respectively. It can be seen that when the power balance state is reached, the frequency is maintained at its nominal value (50 Hz). Figure 10: Total power difference, \(\Delta P\): (**a**) MG1, (**b**) MG2. For comparing LoR of MG1 and MG2, under a fault scenario F. MG1 has higher LoP\({}_{\text{R}}\) (31%) as opposed to MG1 (0%) during the connected mode. Whereas, both MG1 and MG2 have almost the same voltage sag percentage (96%). Then following definition 4, LoR (MG2, F) > LoR (MG1, F). Thus, MG2 is more resilient to fault scenario F as compared to MG1.