esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Technologies: Internet of Medical Things
### Algorithms for IoMT Edge Devices
#### 3.2.2 Approximate Computing  
Approximate computing is a new paradigm shift in designing circuitry or writing a code that produce acceptable but inaccurate results [32]. This theory has been used for instance in reducing the bitwidth of arithmetic circuits when implementing digital signal processing algorithms using fixed-point representation [33].  
Approximate computing can be done at various abstraction levels: data, circuit, architectural, instruction, algorithm, and system level. An approximate circuit is achieved by reducing the number of partial products in designing multipliers [34] and the number of transistors in designing the mirror adder [35]. A comprehensive survey on the approximate arithmetic circuit is reported in [36]. In the biomedical domain, the work of [37] devised an approximate adder circuit by reducing the number of gates from five gates (two AND, two XOR, and one OR) to four gates (two OR, one AND, and one XOR gates). The approximate adder was then used to design an energy-efficient Baugh-Wooley multiplier. In [38], the authors used approximate adders and multipliers to design an energy-efficient wearable that implements the Pan-Tompkins algorithm for the detection of the QRS complex. The algorithm has five stages. First, the ECG signal is recorded and sampled using an ADC converter. Afterwards, the sampled signal is filtered using a low-pass filter followed by a high-pass filter. The output is then squared and integrated.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\ell_{1}\)-minimization & Basis pursuit \\ \cline{2-3}  & Quadratically constrained basis pursuit \\ \hline \multirow{2}{*}{Greedy methods} & Orthogonal matching pursuit \\ \cline{2-3}  & Compressive sampling matching pursuit \\ \hline \multirow{3}{*}{Thresholding methods} & Basic thresholding \\ \cline{2-3}  & Iterative hard thresholding \\ \cline{2-3}  & Hard thresholding pursuit \\ \hline \hline \end{tabular}
\end{table}
Table 5: Reconstruction algorithms.  
The algorithm is computation-intensive in which the optimization of the bit-width and the architecture of the arithmetic circuits play an inevitable role in reducing the overall power consumption. To accomplish this, the authors used five types of approximated full adders (Figure 5) and two architectures of the approximate multiplier. The approximation starts from the LSB and moves towards the MSB. In each iteration, both energy saving and accuracy of peak detection are determined. The process halts when the maximum number of LSBs has been approximated without affecting the structure similarity index measure (SSIM).  
Approximate memory and storage is another approach in the approximate computing topic. It is achieved by the following techniques as bit-trimming, voltage scaling, reducing the refresh rate for DRAM, and approximate compression for selected memory regions [39; 40; 41].
# Drone and Controller Detection and Localization: Trends and Challenges
## 3 UAV Detection Methods
### Radar
It further explains the detection method done using 5G millimeter-wave radar at rotors of UAVs. The high-resolution range profile (HRRP) can identify a UAV location, while micro-Doppler identifies the UAV. Moreover, the cepstrum method was used to extract any number and speed information of the detected UAV rotor. Multiple UAVs can be identified using the sinusoidal frequency modulation (SFM) parameter optimization method. The proposed method determines the following: the number of detected UAVs, the number of rotors, the rotation speed of all rotors, and the position of the UAVs. The proposed radar detection in [26] presents a UAV identification and detection study by providing a method for UAV tracking using the GPS-independent method, such as GPS signal failure, GPS signal interference, and satellite occlusion areas. HRRP technology and micro-Doppler provide a successful solution to detect and localize any rotating targets regardless of weather conditions. The presented simulated results showed high robustness and performance of the cepstrum method. Authors in [59] presented a passive radio drone detection system that uses goodness-of-fit (GoF) based spectrum sensing and the MUSIC algorithm to detect the transmitted signal of a drone and its controller and estimate the DOA. Once a signal is detected, the DOA is estimated at the detected frequency. The MDL algorithm detects the number of targets and whether the source is a drone or controller. The detection system detected drones and controllers from different manufacturers with good sensitivity. A challenge associated with UAV detection is the presence of aircraft and birds in the background [60; 61; 62]. Hence, clutter suppression and target detection algorithms are needed to overcome this complex issue, as stated in [26]. Rationally, object detection of possible UAVs comes first, followed by classification to separate UAVs from other detected objects. In addition, the purpose of these classifications and identifications can be used to extract many unique features of these UAVs [23]. As stated previously, the effects of Doppler radar are used to determine the velocity of a distant object more accurately. This is obtained from the radial component of a target velocity in relation to radar. Using stepped frequency waveform (SFW), an HRRP can be obtained. Due to HRRP and Doppler information from a wide-band Doppler radar, detected objects scanned using wide-band are identified and classified. Millimeter wave base stations and 5G network systems can be used as detection network channels for UAV detection using the data from the processing center of 5G base stations. The process includes extracting essential parameters from multipath locations through 5G bases. Many factors must be considered during the development to enhance the radar systems' performance, such as operating frequency, data acquisition, processing algorithms, classification techniques, and environmental clutter. The summary of reviewed studies of this technique is given in Table 3.
## 3 Motivation
### Impact of Correlation on Compression Efficiency  
In CVSNs, due to different sensing directions, video sensors have a different range of FoVs, and their observations have a different correlation. Video sensors with large overlapped FoVs are likely to report the same event concurrently, and they are likely to have high compressed sensing gains.  
Take Figure 1 as an example to illustrate how the correlation affects compression efficiency. In CVSNs, the application specifies which object it is interested in, and the sensors that can observe this object may be members belonging to the same cluster. The overlapped ratio of FoVs determines in a substantial probability whether video sensors can observe the object of interest. There exists a high correlation among observations of the nodes with large overlapped FoVs. If video sensors with large overlapped FoVs belong to the same cluster, it is possible to achieve a good video compression effect. If we randomly cluster video sensor nodes without consideration of the correlation of FoVs, some nodes that do not observe the object may appear in the cluster. Because there are no targets observed, they fail to participate in compression, which makes application requirements hard to be satisfied.
# A Deadline-Aware Scheduling and Forwarding Scheme in Wireless Sensor Networks
## 6 Performance Study
### Adaptation to Requirement Changes
#### 6.1.2 Effects of Delay Bound  
In order to examine the effects of delay bound, we kept all parameters as default values, except for the delay bound \(\xi\), which varies between 10 s and 50 s. In this scenario, the required DCSR, \(p_{s}\), is set to 95%, which means at least 95% of the packets are required to reach the sink within the delay bound. Figure 5a shows that DASF meets the required DCSR (95%) with different delay bounds, and the difference between the collected and required DCSRs is always similar. For instance, approximately 97% of the packets can be forwarded to the sink under different delay bound values. The reason is as follows. In this subsection, we keep the required DCSR unchanged over different values of the delay bound. In order to maintain the unchanged DCSR \(p_{s}=\frac{\xi-\mu}{\sigma}\), the average \(\mu\) and variance \(\sigma\) of \(Z^{\prime}\) should be changed. Since \(\mu\) and \(\sigma\) are a function of the duty cycle interval \(T\), as shown in Subsection 5.3, the algorithm tunes the value of \(T\), such that \(p_{s}\) remains immutable.  
Figure 5b illustrates that the calculated duty cycle interval tends to increase when the delay bound increases. As a result, nodes wake up less frequently, which leads to a longer E2E delay. As shown in Equation (12), the duty cycle interval growth causes the increased average E2E delay of the packets. For instance, when the delay bound is set from 10 s to 50 s, the calculated duty cycle interval linearly increases from 9.2 s to 46.2 s and the collected average E2E delay gradually increases from 4.9 s to 23.8 s. In terms of PDR, Figure 5b shows that our algorithm can achieve a high packet delivery ratio, made up of nearly 100% over different values of delay bound.  
Figure 4: Effects of required DCSR. (**a**) effects on achieved DCSR and average E2E delay, (**b**) effects on packet delivery ratio and duty cycle interval.  
Figure 5: Effects of the delay bound. (**a**) effects on achieved DCSR and average E2E delay, (**b**) effects on packet delivery ratio and duty cycle interval.
# A New Route for Low Pressure and Temperature
## 1 Introduction
HCL resins (also called Davankov resins) are microporous polymers prepared from a wide range of aromatic monomers, by crosslinking of a linear precursor or a light crosslinked gel-type polymer matrix, via a two-step or one-pot synthetic procedure [46; 47; 48]. These are an exciting class of materials with a high ability to adsorb chemicals [49]. Nevertheless, in the past years, the application of NPs loaded HCL has been limited by complex synthetic procedures [46]. This work concerns the preparation of an innovative nanocatalyst, namely PtRu/MoS\({}_{2}\) nanoparticles, the development of a new route for dispersion of this nanocatalyst in a high surface area HCL resin and the test of this system for the removal of phenols in water in extremely mild conditions. In particular, uniform size, opportunely surface-modified PtRu/MoS\({}_{2}\) nanoparticles, constituted of PtRu alloy NPs partially covered by MoS\({}_{2}\) nanosheets, were obtained through a scalable and reproducible 'wet chemistry' synthetic approach [50; 51]. For what concerns the resin, here we propose the synthesis of styrene-based HCL resin via a modified Davankov synthetic strategy, in which a precursor polymer is prepared by bulk polymerization and then hyper-crosslinked by Friedel-Crafts reaction [52]. Through this procedure, microporous nanocomposites were already realized, containing homogeneously dispersed nanomaterials such as carbon nanotubes, graphene nanoplatelets, or graphene oxide [47; 52]. Therefore, this procedure was applied to the preparation of microporous nanocomposites containing PtRu/MoS\({}_{2}\) NPs, allowing them to obtain a high surface area nanocomposite containing homogeneously dispersed nanoparticles. The final aim of this work was to investigate the catalytic activity of PtRu alloy NPs and their MoS\({}_{2}\) nanosheets protective layer, stabilized and dispersed in an HCL porous resin, for phenol CWAO at low air pressure and 95 \({}^{\circ}\)C. It is worth noticing that, although the preparation of the nanocatalyst and nanocomposite will require effort to implement production lines at an industrial level, the reduced amount of catalyst, which is in fact produced in mild conditions and through a scalable process [53; 54; 55]; the fact that the synthesis of hyper-cross-linked resins is an established low-cost mass production process [48; 52; 56; 57], here proposed to prepare in a one-step the catalytic nanocomposites; and, finally the mild conditions required by the nano-composite for pollutant removal, make this approach promising. Moreover, excellent removal performance was obtained. This is due to a combination of factors: the preservation of the hierarchical structure of the resin, enabling easy access to the pores; the NPs confinement and reagents pre-concentration in the adsorbent resin; PtRu alloy activity; the MoS\({}_{2}\) highly defective nanosheets protecting the catalyst surface against carbon polymerization and poisoning.
## 2 A Comparative Analysis of IoT Receiver Architectures  
Direct conversion and low-IF represent the primary receiver architectures for BLE protocol IoT applications, as shown in Figure 2[7]. Direct conversion receivers down-convert RF signals to baseband, offering a simpler design and lower power consumption, but suffer from DC offset and flicker noise issues [8]. Low-IF receivers convert to a non-zero intermediate frequency, providing better immunity to these issues at the cost of higher power consumption and complexity [9].  
For 2.4 GHz ISM band BLE applications, architecture choice significantly impacts system performance. Direct conversion receivers particularly struggle with I/Q mismatch and DC offset issues, affecting packet reception rates [10].  
Low-IF receivers provide superior DC offset immunity through non-zero intermediate frequency processing--crucial for IoT applications where power constraints limit DC offset compensation [11]. This architecture inherently guards against flicker noise, essential for narrow-band BLE protocols with DC-concentrated baseband signals [12].  
The low-intermediate-frequency processing enables effective channel filtering and adjacent channel rejection, improving selectivity in dense IoT deployments [9]. Low-IF better handles I/Q imbalance versus direct conversion, enhancing demodulation reliability for complex modulation schemes [13]. Additionally, the absence of DC offset simplifies automatic gain control implementation, improving dynamic range handling [14]. However, to avoid corrupting the desired signal, the image signal needs to be suppressed. While complex bandpass filters are effective for image rejection in low-IF receivers [15], they often lead to increased power consumption [16]. The integration of bandpass quadrature sigma-delta modulators presents a promising solution to reduce this power demand while maintaining performance.
# Dynamic Investigations of Shared Bicycle Operators' Competition Based on Profit Maximization
## 5 Discussion  
Given the reality that there are typically two types of shared bicycles available between residential areas and the nearest bus or subway stations, this paper has constructed a dynamic adjustment model aimed at maximizing the profits of shared bicycle operators.  
One of the most intriguing discoveries of our research is the identification of a novel chaotic attractor, which we have termed the \"shared bicycle attractor\". This finding suggests that overly rapid adjustments by operators can lead to a chaotic state within the market, which can be detrimental to the long-term growth and stability of the operators. This insight underscores the importance of strategic planning and the need for operators to consider the potential systemic effects of their decisions.  
The complexity of the shared bicycle market is further highlighted by our numerical simulations, which have underscored the intricate and sometimes unpredictable nature of the system. These simulations have been instrumental in visualizing the potential outcomes of different strategic decisions by the operators.  
Our exploration of the market impact of comfort loss costs, discounts, and distance has revealed that these factors can significantly influence an operator's competitive position. The findings indicate that substantial discounts and comfort-related costs can erode an operator's market share. This underscores the necessity for relentless technological innovation and user comfort enhancement to maintain a competitive edge.  
Our study presented modeling processes and analytical methods not limited to the specific context of shared bicycles. These methodologies are designed to be versatile, allowing for their application to other goods or services that exhibit substitutability. By employing these methods, researchers and practitioners can effectively analyze the impact of operator discounts, as well as other market factors, on competitive dynamics.  
Although the model proposed in this paper provides some useful insights for the profit maximization decision making of shared bicycle operators, the following assumptions should be relaxed in further research:  
(1) The model considers all users as homogeneous, but user behavior may vary based on personal preferences, income levels, and usage habits. Future research can introduce heterogeneity in user behavior to improve the predictive ability of models.  
(2) In the construction of the model, this paper only considers the competition between two shared bicycle operators, but in fact, it is possible for three operators to coexist. Future research can extend to the impact of operators' decisions on the system when three operators compete.  
(3) This study examines the outcomes of competition in the potential demand for shared bicycles. Future research could extend its scope to explore scenarios involving price competition and collusion.  
Figure 5: Sensitivity of initial values under Cournot competition: (a) \(x_{1}\); (b) \(x_{2}\).
## 3 2D-DOA Total Variation Regularization
### Total Variation Regularization
#### 3.1.3 2D-DOA Total Variation Regularization Model  
When Equation (19) is substituted into Equation (14), the sparse DOA estimation model can be transformed into a 2D-DOA total variation regularization model:  
\[\begin{array}{rl}\overset{\wedge}{s}&=\underset{s}{\arg\min}\big{\|}y-\overline{ A}s\big{\|}_{2}^{2}+\mu_{1}\|D_{\text{tv}}S_{z}\|_{1}\\ &=\underset{s}{\arg\min}\big{\|}y-\overline{A}s\big{\|}_{2}^{2}+\mu_{1}\|D_{ \text{tv}}S\|_{1}+\mu_{1}\big{\|}D_{\text{tv}}S^{T}\big{\|}_{1}\end{array} \tag{20}\]  
In order to ensure the sparsity of the target solution \(s\), the LASSO and two-dimensional total variation are combined based on Equation (20), which improves the sparsity of the coefficients (i.e., there are few active coefficients in the solution) and the sparsity of the continuous coefficient difference (i.e., the sparsity of the sparse contour flatness). As a result, the proposed method is suitable for the applications involving block sparsity or mixed peaks and flat regions. Therefore, the final 2D-DOA total variation sparse estimation model is expressed as:  
\[\overset{\wedge}{s}=\underset{s}{\arg\min}\big{\|}y-\overline{A}s\big{\|}_{2} ^{2}+\mu_{1}\big{\|}D_{\text{tv}}S_{Z}\big{\|}_{1}+\mu_{2}\|s\|_{1} \tag{21}\]  
where \(\mu_{1}\) and \(\mu_{2}\) are regular parameter.  
From the above derivation process, it can be seen that the method (2 DTV-CES) does not need the traditional decoherence processing at the cost of losing the antenna aperture, and does not estimate the azimuth angle and the pitch angle respectively, therefore, the angle pairing is avoided totally and the algorithm is simplified sharply.
## 3 Results and Discussion
### Statistical Optimization Results  
The results of Grey relational generation (GRG), Grey relational coefficient (GRC) and Grey relational grade (GR*grade) for the mechanical properties are shown in Tables 4 and 5 for mono-reinforced and hybrid composites, respectively, while the obtained main effects plots are presented in Figure 6. The higher grade shows that the experiment is nearer to optimum composition. Based on these results, it can be observed that multi-response optimal performance of the mono-reinforced composite can be achieved by using 82% UPT and 18% CPH while the hybrid composites are 79% UPT and 18% CPH representing samples C1 and E2, respectively.
###### Abstract
This study uses satellite-based precipitation, LST, and gross primary production (GPP) to identify the influence of local climate on crop growth. For better spatiotemporally continuous investigations, GPP is used as a surrogate variable representing agricultural production [21]. Maize and soybean yields are also used to identify the influence of climate conditions. This study aims to identify the influence of climate conditions from high-resolution satellite images and to understand the role of climate conditions affecting regional maize and soybean production. Because anomalous climate conditions such as unusually hot and dry summers cause significant modulation in the US crop yield via excessive heat stress [6; 18; 22], this study focused on July-August. Another goal is to investigate whether the climate-US maize and soybean yield relationship exhibits regional dependency. The sensitivity of maize and soybean yield to temperature and precipitation depends on local factors, including irrigation rates, dry soil conditions, and farming methods [23; 24; 25]. These considerations strongly suggest that detailed investigations of regional characteristics of the climate-maize and soybean yield relationship are necessary. A better understanding of the relative role of regional temperature and precipitation on crop productivity will greatly benefit a wide range of agricultural applications. The specific objectives of this paper are to: (i) examine correlations between satellite-based temperature and precipitation indices and GPP data at various scales (i.e., pixel, county, and state levels); (ii) identify what causes the regionally dependent sensitivity of GPP to the meteorological conditions; and (iii) examine the extent to which GPP can explain maize and soybean yield in the census.
## 3 Experiment Setup and Evaluation
### Results of Rock Detection  
Figure 10 shows the results of the Viola-Jones algorithm against the different types of features used. It is observed that the Haar-like and HOG features present many FPs; however, the LBP feature effectively identifies the rock characteristics. Figure 11 shows the results of the ACF algorithm before the different experiments were conducted. Similar to the Viola-Jones algorithm, many false positives were observed for all variants.  
Figures 12 and 13 show the results of rock detection in a test image from the database and in an image that does not belong to the database. This was done to analyze the generalization capacity of these algorithms for images with different characteristics from the training images. The size of the images that were not from the database was \(3264\times 2448\) px. These two images correspond to a normal scenario without visual pollution.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **ADAM** \\ \hline Epochs & 10 \\ Batch size & 8 \\ Learning rate & 0.001 \\ Weight decay & 0.0001 \\ Gradient decay & 0.9 \\ Squared gradient decay & 0.999 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters of the ADAM optimizer.  
Figure 10: Results of the Viola-Jones detector. (**a**) Feature Haar-like. (**b**) Feature LBP. (**c**) Feature HOG.  
It is observed that the algorithms manage to detect rocks, but they have the problem of presenting many false positives, except for YOLO v4. The YOLO v4 algorithm effectively detects rocks even in images that do not belong to the database, and this demonstrates the generalization capacity obtained.