esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussions and Future Directions
### Challenges and Future Directions for ANN in CM
#### 5.2.4 The Introduction of Emerging Technologies  
In the future, CM will continue to undergo rapid digital transformation, with advanced technologies being developed and introduced. A promising research direction is to introduce emerging technologies as a supplement to ANN applications. The Internet of Things (IoT), as a network of interconnected devices such as sensors, drones, wearable and mobile devices, and radio frequency identification devices (RFID), can collect real-time data about the status of the project [91]. The integration of ANN and IoT can achieve AIoT, which is a new generation of IoT [92]. AIoT can remotely control the construction site, optimize project performance, and predict future conditions for maintenance plans [16]. However, the practical use of AIoT is still in the startup, so further research on the integration of ANN and IoT is essential to its boost.  
Similarly, research on the integration of ANN with Building Information Modeling (BIM) and Geographic Information Systems (GIS) should be given more attention. BIM and GIS can provide digital representations of the building itself and external environmental entities [93]. As a shared database, both of them can provide reliable data support for ANN. For instance, future studies can explore GIS-ANN for construction land survey, facility management and maintenance, disaster monitoring, and other purposes in the future [93]. [PERSON] et al. [94] proposed that the synergy among AIoT, BIM, and GIS will promote the efficiency of data collection, transmission, and processing. Ways to integrate these technologies is a challenge and will become a hot topic in future works.  
As a nascent technology, blockchain, namely a verified chain with blocks of information, serves as a decentralized, transparent, and comprehensive database to improve the built asset sustainability [95]. Blockchain can provide a secure and stable database by collecting large data from all stages of the projects and sharing data among different stakeholders. ANN can intelligently analyze the data and find hidden knowledge. The integration of ANN and blockchain will contribute greatly to the realization of smart cities, transportations, etc. which are still insufficiently developed and deserve more research attention.
# Advances in Chronic Kidney Disease in Africa
## 10 CKD Unknown (CKDu) in Africa  
While hypertension, diabetes, GN, and HIV are considered the major contributors to CKD globally and in Africa, there is a large proportion of people with CKD who do not have a clear cause for their kidney dysfunction, now termed CKD unknown (CKDu). In a South African rural population, CKDu was found in 32% of those surveyed [121]. A scoping review reports CKDu in Cameroon was detected in 13.5-17% of cases [122]. However, 3.4% of sugarcane plantation workers had CKD, and CKD was detected in 15.9% of first-degree relatives of people with CKD. Herbal medication and self-medication increased the odds of having CKD.  
A common theme for those detected with CKDu is employment in the agricultural sector, such as sugarcane plantations. This raises the hypothesis that heat stress, dehydration, strenuous labour, and the exposure to agricultural chemicals could be contributing to CKD in these people. Other theories include exposure to heavy metals, excess trace elements and pollutants in water or agricultural products [123]. In support of some of these theories, data from Africa have demonstrated that heat may contribute to CKD in sugarcane plantation workers [124], crop farming with hexachlorocyclohexane [125], and chemical exposure in cassava farmers where the use of unsafe farming practices increased the risk [126]. Contrary to the findings from other African countries, a study in Malawi reported a very low prevalence of CKDu (5%), with no added risk in those from a rural setting or from agricultural work [127].  
In Africa, it is essential to consider infectious diseases as potential contributing factors to the development of CKD. HIV is well described, but other infectious diseases need to be considered. A study from Tanzania demonstrated that a history of TB or schistosomiasisomiasis was associated with albuminuria, and prior TB was associated with impaired kidney function, aside from the usual causes [128]. In Uganda, a study in children with complicated malaria (cereb malaria or with severe anaemia) found that AKI was a risk factor for CKD at 1 year. Severe AKI was associated with a four-fold increased risk of CKD [129]. AKI may be underestimated as a contributing factor to CKDu if recurrent episodes are undetected, particularly in non-hospitalised infectious diseases and agricultural workers [129].  
Genetics may also contribute to CKDu in Africa [130]. APOL1 is well described, but other genetic variants may be implicated, such as polymorphisms of the econucleotide pyrophosphatase/phosphodiesterase 1 [131], transforming growth factor beta 1 (particularly in HIV) [132], heme oxygenase, glycine amidinottransferase, and haemoglobin beta [133].
## 3 Materials and Methods  
### Image Acquisition and Participants  
The T1-weighted MRI scans and clinical information were acquired from the Japanese-Alzheimer's Disease Neuroimaging Initiative (J-ADNI) dataset. All participants were assessed using neuropsychological assessment tools for AD, including the MMSE [28] and the Clinical Dementia Rating (CDR) [29], and then divided into four groups according to the results: (1) HC individuals (N: 146, mean age \(\pm\) sd: 68.28 \(\pm\) 5.61, mean MMSE \(\pm\) sd: 29.11 \(\pm\) 1.20, mean CDR \(\pm\) sd: 0.00 \(\pm\) 0.00); (2) sMCI individuals who were stable in MCI stage at least for 36 months (N: 102, mean age \(\pm\) sd: 73.44 \(\pm\) 5.97,mean MMSE \(\pm\) sd: \(26.55\pm 1.81\), mean CDR \(\pm\) sd: \(0.50\pm 0.00\)); (3) pMCI individuals who converted from MCI to AD was within 6-36 months after baseline (N: 112, mean age \(\pm\) sd: \(73.62\pm 5.57\), mean MMSE \(\pm\) sd: \(26.08\pm 1.53\), mean CDR \(\pm\) sd: \(0.50\pm 0.00\)); and (4) AD individuals (N: 147, mean age \(\pm\) sd: \(74.07\pm 6.57\), mean MMSE \(\pm\) sd: \(22.55\pm 1.80\), mean CDR \(\pm\) sd: \(0.67\pm 0.23\)). Our study population included 507 participants at baseline. More details of J-ADNI dataset is described elsewhere [30].
# A Lightweight and Partitioned CNN Algorithm for Multi-Landslide Detection in Remote Sensing Images
## 6 Discussion  
Firstly, the detection results demonstrated that our proposed model exhibited higher precision and average precision (AP) compared to the other models, indicating its ability to achieve a relatively low false positive rate even in complex situations. However, it is worth mentioning that the model detected landslides with relatively low confidence.  
Secondly, regarding small target detection, the model designed in this study exhibited limitations in detecting small objects, which is a known challenge in detection tasks. To address this issue, further exploration in future work can involve incorporating multi-scale structures into the model and adopting more-advanced data enhancement methods.  
Thirdly, for landslide detection, the scarcity of datasets and the diversity of landslide types contributed to the challenges in achieving accurate identification. For instance, in the case of loess landslides, their colors often closely resemble the surrounding environment, making it difficult for experts to accurately annotate landslide datasets and extract distinctive features for model training.  
Lastly, in terms of integration and application in landslide prevention and control systems, the lightweight design of our model offers advantages such as reduced computational burden and memory consumption. These characteristics render it suitable for deployment on power-constrained devices, including unmanned aerial systems (UASs). Integrating the model with UASs would thereby enable efficient and comprehensive monitoring of areas prone to landslides. This integration demonstrates promising prospects in augmenting early warning systems, facilitating real-time monitoring for landslide prevention and management.
# On the Performance of LDPC-Coded Large Intelligent Antenna System
## 5 Conclusions  
This article presents a performance analysis of a LIS system, with and without LDPC coding, associated to the SC-FDE transmission, and using four different types of receivers: ZF, MMSE, MRC, and EGC.  
It was seen that one of the advantages of the LIS System relies on its improved performance, facilitating simplicity as well, namely due to the possibility of not using equalization. It was also seen that by increasing the number of the antennas that form the LIS, the performance improves, regardless whether LDPC codes are used or not.  
The ZF and MMSE are the receivers that perform closer to the MFB, whose performance curves are almost superimposed, while the EGC is the one that performs worst. It is worth noting that the ZF and MMSE receivers are more complex because, for each frequency component, the computation of the inverse channel matrix is required.  
It was seen that the MRC and EGC receivers may be used without equalization, without degrading the performance, while ZF and MMSE receivers cannot avoid equalization. This makes MRC and EGC even simpler, besides the possibility of avoiding the channel matrix inversion for each frequency component of the channel. Moreover, in this scenario, channel estimation is not required, which also facilitates the system.  
Finally, it was demonstrated that a system comprising LIS, associated with LDPC codes, and coupled with the SC-FDE transmission technique, exhibits performance optimization that improves with increased number of antennas. The MMSE receiver achieves the best overall performance, but this is achieved at the cost of a more computational demanding system, and where equalization cannot be avoided. When low complexity is a requirement, avoiding channel matrix inversion, equalization, and channel estimation, the MRC receiver is a good option to make cellular communications better in the future.
## 2 Channel Model and Signal-to-Noise Ratio
### Effective Signal-Collection Area Model for Non-Imaging Angle-diversity Receivers
#### 2.2.3 Self-Orienting Receiver  
The third angle-diversity receiver considered is called a self-orienting receiver (see Figure 3c). The self-orienting receiver consists of a single-element detector which employs an optical front-end based on a lens [20; 21]. The receiver is mounted on an electromechanical orienting system controlled by a digital signal processor (DSP). An SNR estimator and a maximum search algorithm, which is based on a modified version of Simulated Annealing (SA), are used to automatically aim the receiver at the area of the room with the highest SNR [20; 21]. From the description above, we can deduce that the receiver is actually a modified version of a select-best angle diversity receiver, which allows operating with very narrow FOV. In general, the self-orienting receiver can be represented by the parameters \(p_{R}\), \(n_{R}\), FOV\({}_{\textrm{total}}\), \(A\), \(A_{R}\), and FOV, i.e., its position, orientation, total FOV, aperture area of the lens, physical area of photodetector and the FOV of the system formed by the detector and the optical front-end. Therefore, its effective signal-collection area can be expressed as:  
\[A_{eff}(\psi)=A_{R}T_{S}(\psi)g(\psi)cos(\psi)rect\left(\frac{\psi}{\pi/2}\right) \tag{19}\]  
where \(T_{S}(\psi)\) is the filter transmission and \(g(\psi)=G\) represents the optical front-end gain. Replacing the gain \(G\) by \(A/A_{R}\) in the expression, and assuming an ideal filter, the expression that defines the effective signal-collection area of the receiver can be expressed as:  
\[A_{eff}(\psi)=A_{R}\:G\:cos(\psi)rect\left(\frac{\psi}{\pi/2}\right)=A\:cos( \psi)rect\left(\frac{\psi}{\pi/2}\right) \tag{20}\]  
In keeping with the proposal made in [20; 21], we have assumed that the self-orienting receiver uses a positive lens system with an aperture diameter of 1.5 cm as an optical concentrator, along with a circular photodetector with area \(A_{R}\) = 3.53 mm\({}^{2}\) and responsivity \(R\) = 0.6 A/W to collect the signals received. The concentrator provides a high optical gain (\(G\) = 50) at the expense of a narrow FOV, which is only \(3^{\circ}\). This extremely narrow FOV allows us to use an ideal bandpass optical filter with a narrow spectral bandwidth (\(\Delta\lambda\) = 50 nm) for better optical noise rejection.
## 2 Analysis of the Operating Mechanism of a Hybrid Plant Station Primary Frequency Regulation
### Mathematical Model and Dynamic Characteristics Analysis of the Electric Hydrogen Production Device
The P2H device has more load reduction power when the \(N\) value is smaller; however, because of the limited climbing power, the actual load shedding power of P2H per unit period is lower, and the SCESS must assume excessive frequency regulation power. The maximum load-shedding power curve of P2H and the various electrolyzer opening numbers (\(N1>N2>N3\)) are displayed in Figure 3.  
As seen in Figure 4, the maximum load-shedding power value reduces as more electrolyzers are opened, but the load-shedding rate increases. However, additional load-shedding power is possible when many electrolyzers are opened. However, it restricts the rate of load-shedding and results in excessive energy release from the SCESS, making it unable to take part in the next frequency regulation operation. The N value should be adjusted depending on the hydrogen production state to achieve a better frequency regulation effect. In the low hydrogen production state, a smaller N value should be used to obtain more load-shedding power and meet the frequency regulation requirements. Conversely, in the high hydrogen production state, a larger N value should be used to increase the actual load reduction rate of P2H while still satisfying the frequency regulation requirements. Figure 4: Maximum load reduction power curve of P2H under different N values. Primary Frequency Regulation and Control Strategy of the Hybrid Plant Station Based on the EEMD Power Distribution Algorithm  
In the first two chapters, we researched the primary frequency regulation operation mechanism of a hybrid plant station and analyzed the dynamic characteristics of each unit. It is determined that the P2H device and the SCESS jointly assume the frequency regulation power. This chapter proposes a multi-energy cooperative frequency regulation control strategy for a hybrid plant station based on the EEMD algorithm. This strategy rationally allocates the primary frequency regulation power instructions of P2H equipment and SCESS equipment by analyzing the EEMD algorithm.
## 4 Numerical Results
### Multiple Linear Regression (MLR) Model Developed under Op1  
The constructed MLR model is:  
\[y=-0.8206+0.8522x_{1}+36.89x_{4}-0.1846x_{1}^{2}-307.0x_{4}^{2}-0.2002x_{1}x_ {2}-1.028x_{1}x_{3}+e \tag{51}\] \[(0.351)\ (0.133)\ (9.95)\ (0.0229)\ (82.1)\ (0.0812)\ (0.344)\ (0.116)\]In (51), as well as all in other MLR models, the values in brackets under the coefficients stand for the HC3 heteroskedasticity consistent estimates of the standard deviation (error). In the same fashion, the value under the residual \(e\) is its standard deviation as per (17). The performance measures are shown in the first column of Table 3. The diagnostics tests produce the following:  
(a) CODPA is never applied in Op1. That is why no outliers were identified and the model was constructed with a training set containing \(n=126\) observations.  
(b) The stepwise regression algorithm MSRA converged in 15 steps, all in phase one.  
(c) The ANOVA test for validity of the regression produced test statistics \(F_{stats}=66.7\) which resulted in \(p\)-\(value_{F}<2.2(10^{-16})\). The conclusion of the test is that the model is valid. The same results from the ANOVA test were acquired in the previous 14 steps of MSRA.  
(d) The expected error nullity test produced test statistics \(t_{stats,null}=5.22(10^{-4})\) resulting in a \(p\)-\(value_{null}=0.9996\). The conclusion of the test is that there is not enough statistical evidence to reject the hypothesis that the expected error in the regression model is zero. The same results for the expected error nullity test were acquired in the previous 14 steps of MSRA.  
(e) The heteroskedasticity testing algorithm MHTRA produced valid auxiliary regression with ANOVA resulting in \(p\)-\(value_{F}=6.62(10^{-4})\). However, its adjusted coefficient of determination is \(R_{adj}^{2}=0.135\). The conclusion of MHTRA is that the constructed regression is statistically significantly heteroskedastic, but the latter is practically insignificant. Similar conclusions for the heteroskedasticity were acquired in the previous 14 steps of MSRA.  
(f) The modified Jarque-Bera Monte-Carlo test for error normality produced test statistics \(JB_{stats,null}=2.98\) that resulted in \(p\)-\(value_{B}=0.170\). The conclusion of the test is that there is not enough statistical evidence to reject the hypothesis that the error in the regression model is normal. The same results for the modified Jarque-Bera Monte Carlo test were acquired in the previous 14 steps of MSRA.
## 3 Convolutional Neural Network
### Algorithm Flow and Experimental Environment  
Before the convolutional neural network is trained and tested, the image data are collected (through the CCD camera), preprocessed (e.g., scaling, graying, etc.), and divided (via the hold-out method). The algorithm flow chart is shown in Figure 4, it involves two parts, including the forward propagation of the data and the reverse propagation of the error [32]. Firstly, the training parameters of the network are set, the weight and bias of the network are initialized, and then the input feature map processed by the convolution layer, the pooling layer and the fully connected layer is transmitted to the output layer. During this process, the output of each layer is the input of the next layer. Then, the error between the actual output and the expected output is reversely transmitted using the back propagation (BP) algorithm, layer by layer. Next, this error is allocated to each layer, and the weight and bias of the network are adjusted until the convergence condition is satisfied, thus realizing the effective supervised training of the network.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Parameters Information** & **Variables** & \
\begin{tabular}{c} **Input Data** \\ **Dimension** \\ \end{tabular} &
\begin{tabular}{c} **Output Data** \\ **Dimension** \\ \end{tabular} \\ \hline Conv1 & 64 convolution kernels with 3 \(\times\) 3, stride is 1 & 640 & 28 \(\times\) 28 \(\times\) 1 & 28 \(\times\) 28 \(\times\) 64 \\ Pool1 & Pooling size 2 \(\times\) 2, stride is 2 & 0 & 28 \(\times\) 28 \(\times\) 64 & 14 \(\times\) 14 \(\times\) 64 \\ Conv2 & 128 convolution kernels with 3 \(\times\) 3, stride is 1 & 73,856 & 14 \(\times\) 14 \(\times\)64 & 14 \(\times\) 14 \(\times\) 128 \\ Pool2 & Pooling size 2 \(\times\) 2, stride is 2 & 0 & 14 \(\times\) 14 \(\times\) 128 & 7 \(\times\) 7 \(\times\) 128 \\ FC1 & 200 nodes & 1,254,600 & 1 \(\times\) 6272 & 1 \(\times\)200 \\ FC2 & 64 nodes & 12,864 & 1 \(\times\) 200 & 1 \(\times\) 64 \\ FC3 & 32 nodes & 2080 & 1 \(\times\) 64 & 1 \(\times\) 32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The configurations of the convolution, pooling, and fully connected (FC) layers.  
Figure 4: The algorithm flow chart.  
The experimental environment is described in Table 2.
## 2 Antenna Modelling
### Antenna's Efficiency Percentages  
A basic antenna efficiency calculation compares radiated or received power to input power. The antenna efficiency formula is Radiated Power/Input Power \(\times\) 100% = Efficiency (%). An antenna is 80% efficient if it radiates 80% of its input power. Performance and energy usage depend on antenna efficiency in real-world applications: Signal Quality: High-efficiency antennas broadcast and receive signals more efficiently, improving signal quality, range, and communication reliability. Low power consumption makes efficient antennas appropriate for battery-operated systems. They save electricity and battery life. Cost-effectiveness: High-efficiency antennas maximise input power utility, eliminating the need for extra amplification or signal processing equipment. Efficiency percentages show how successfully the antenna transforms electrical energy into electromagnetic waves and can help design applications.
# Time Slotted Channel Hopping and ContikiMAC for IPv6 Multicast-Enabled Wireless Sensor Networks
## 4 Simulation Settings
All the presented networks were built using the Zolertia Z1 mote in the Cooja simulator. The power consumption of the Zolertia Z1 radio could be found in its datasheet and equaled 56.4 mW (3V \(\times\) 8.8 mA) for receiving, 52.2 mW (3V \(\times\) 17.4 mA) for sending, and 0.0015 mW (3V \(\times\) 0.5 uA) when it was in low power mode [21]. Note that the default schedule of TSCH was adapted by adding a new rule dedicated to LL broadcast data traffic, as mentioned in Section 3. Table 1 summarizes the characteristics of the obtained RPL trees or topologies and the simulations scenarios. Figure 4: The RPL trees or topologies formed without interference between nodes of the same rank. (**a**) RPL tree showing a linear topology, in which each node, apart from the leaf node has one child. (**b**) RPL tree showing a topology in which each node, apart from the leaf nodes, has 2 children. (**c**) RPL tree showing a topology in which each node, apart from the leaf nodes, has 3 children. Figure 5: The RPL trees or topologies formed with interference between nodes of the same rank. (**a**) RPL tree showing a topology in which each node, apart from the leaf nodes, has 3 children. (**b**) RPL tree showing a topology in which each node, apart from the leaf nodes, has 4 children. (**c**) RPL tree showing a star topology with the root node placed in the center. Figure 6: The RPL trees or topologies formed with a random placement of nodes. (**a**) The root node is placed in the center of the network. This topology is 3 hops deep. (**b**) The root node is placed at the top of the topology. This topology is 4 hops deep.
## 3 Nonlinear Dynamic Inversion Control Law
### Altitude Control Loop  
The altitude control loop is designed using a PI controller. The saturation limit is used to constrain the altitude error according to the flight envelope specification of Tiger-Trainer UAV model given in [21]. The classical PI controller is tuned offline with conventional tuning method [11], followed by fine-tuning to obtain the best tracking performances. The two cascaded PI controller is used to increase the robustness and to track the rate of change of climbing, as shown in Figure 4.  
The altitude control loop equation is given in Equation (26) shows that the UAV altitude is controlled by executing a certain angle of attack. The error between the desired altitude and current altitude is controlled using PI controller.  
\[\alpha_{c}=(h_{c}-h)\left(k_{ph}+\frac{K_{ih}}{s}\right)+(\dot{h}_{c}-\dot{h}) \left(k_{ph}+\frac{K_{ih}}{s}\right) \tag{26}\]  
where \(h_{c}\) is the desired altitude with its derivative \(\dot{h}_{c}\), and \(h\) is the current altitude with its derivative \(\dot{h}\)