esa-sceva/satcom-chunk-collection huggingface dataset:
# Configurable Hardware Core for IoT Object Detection  
[PERSON]  
1 INESC-ID, Instituto Superior Tecnico, Universidade de Lisboa, 1000-029 Lisboa, Portugal;  
[EMAIL_ADDRESS] (P.R.M.); [EMAIL_ADDRESS] (D.P.);  
[EMAIL_ADDRESS] (D.D.L.); [EMAIL_ADDRESS] (R.P.D.); [EMAIL_ADDRESS] (H.C.N.);  
[EMAIL_ADDRESS] (J.T.J.S)  
[PERSON]  
1 INESC-ID, Instituto Superior Tecnico, Universidade de Lisboa, 1000-029 Lisboa, Portugal;  
[EMAIL_ADDRESS] (P.R.M.); [EMAIL_ADDRESS] (D.P.);  
[EMAIL_ADDRESS] (D.D.L.); [EMAIL_ADDRESS] (R.P.D.); [EMAIL_ADDRESS] (H.C.N.);  
[EMAIL_ADDRESS] (J.T.J.S)  
[PERSON]  
1 INESC-ID, Instituto Superior Tecnico, Universidade de Lisboa, 1000-029 Lisboa, Portugal;  
1p00-310 Lisboa, Portugal  
than one-stage detectors but are computationally more demanding [18]. Therefore, in a distributed edge computing scenario the one-stage detectors are the preferred choice for their lower cost with good accuracy.  
Even the one-stage detectors require high computing power to achieve real-time performance [18]. For this reason, graphics processing units (GPUs) are generally used to deploy CNNs due to their high-performance floating-point computing. However, GPUs are power-hungry and cannot be deployed in edge devices with reduced energy [19]. Dedicated hardware solutions are therefore important for the successful deployment of CNN-based object detectors on edge devices [19]. Many recent works use field-programmable gate arrays (FPGAs) since they offer high computing power with high energy efficiency [20]. Besides, they also offer hardware flexibility, permitting a custom design for particular constraints of the system target.  
In this study, we proposed a configurable hardware core for object detection based on YOLO. To improve the performance of the core for object detection, the YOLO algorithm was first quantized from a floating point to a fixed point with a small precision drop in the mean average precision (mAP).  
The system was tested with Tiny-YOLOv3, a lightweight version of YOLOv3 [15]. The core was integrated into a full system-on-chip (SoC) and prototyped in a low-cost ZYNQ7020 FPGA. The solution with 16-bit quantization achieved 7 frames per second for 416 \(\times\) 416 sized images in the ZYNQ7020 with 31.5 mAP\({}_{50}\). The 8-bit quantization solution doubled the throughput but reduced the mAP\({}_{50}\) by 0.7 percentage points.  
The article is organized as follows. Section 2 introduces the background of CNNs and object detection with YOLO. Section 3 describes the architecture of the object detector accelerator. Section 4 describes the mapping process of Tiny-YOLOv3 on the proposed architecture. Section 5 presents the results of the solution for the two fixed-point representations and compares the proposed system with other computing platforms and other FPGA-based object detector works. Finally, Section 6 concludes the article.
# A Prediction Model of Marine Geomagnetic Diurnal Variation Using Machine Learning
## 2 Methodology
### Fusion Model  
Ensemble learning is widely popular for its capability to merge multiple weak learners, using sample and model weighting to achieve better generalization than single learning models [16]. In this study, daily variation data from multiple geomagnetic stations are learned by employing both SVM and RF algorithm models. To enhance the overall performance of the final learning model, the combination of multiple models requires a certain level of accuracy for each individual model while also leveraging their differences. Rather than a simple superposition, the models are integrated by establishing an error threshold through the comparison of errors on the validation set. When the error difference among different models falls below the threshold, the models are merged by assigning weights. Subsequently, the final calculated value is adjusted according to these assigned weights.  
\[MAE^{SVM}=\left|y^{SVM}-y_{true}\right| \tag{5}\]  
Figure 2: Flowchart illustrating the random forest algorithm process.  
\[MAE^{RF}=\left|y^{RF}-y_{true}\right| \tag{6}\]  
\[W^{SVM}=\frac{MAE^{RF}}{MAE^{SVM}+MAE^{RF}} \tag{7}\]  
\[W^{RF}=\frac{MAE^{SVM}}{MAE^{SVM}+MAE^{RF}} \tag{8}\]  
In Equations (5)-(8), where \(y^{SVM}\) is the calculated value, \(y^{RF}\) is the real value of the SVM model, \(y_{true}\) is the RF model on the validation set, \(MAE^{SVM}\) and \(MAE^{RF}\) represent the calculation error of the model, and \(W^{SVM}\) and \(W^{RF}\) represent the combined weight.  
Enhancing the algorithm's generalization ability is crucial. In cases where the training data yield suboptimal results for SVM and RF, a threshold of \(\theta\) is established to determine the necessity of model combination. If the relative difference between the two training models exceeds this threshold, the combination process is omitted. The decision-making approach is outlined as follows:  
\[\frac{\left|MAE^{SVM}-MAE^{RF}\right|}{\max(MAE^{SVM},MAE^{RF})}>\theta \tag{9}\]  
The algorithm flowchart is as follows:  
The SVM-RF algorithm in Figure 3 partitions the dataset into a training set, a validation set, and a test set as an initial step. The SVM model and the RF model are then trained individually on the training set, leading to the development of two separate models. Following this, the discrepancy between the predicted values and the actual values from both models on the validation set is calculated and compared to a predetermined threshold. If the disparity is lower than the specified threshold, the models are combined by assigning weights determined using Equations (7) and (8).  
Figure 3: Flowchart depicting the integrated SVM-RF hybrid algorithm process.
# A Poisson Process-Based Random Access Channel for 5G and Beyond Networks +
## 3 Existing Contention-Based Random-Access Mechanism
### Random Access Response (RAR) (eNB \(\to\) UE)  
The eNB calculates the power delay profile (PDP) of the obtained preamble on the PRACH. A predefined threshold is tested for this calculated PDP, and, if it is found to be greater than the threshold, it is called an active RA preamble. The eNB decodes the RNTI  
Figure 3: Contention-based random access (RA) procedure in a 5G network.  
for each active RA preamble to discover the RA slot where the preamble has been submitted. Later, the eNB sends a RAR message to the decoded UEs on the DL control message channel. The RAR message includes a timing advance (TA) instruction to synchronize eventual UL transmissions, a UL resource award for radio resource channel (RRC) requirements, and a short RNTI, which could be made stable at the collision resolution period (CRP). However, if different UEs transmit a similar preamble at a similar RA slot, a collision occurs.
## Results and Discussion
### Synthesis of Liquid-Phase Phosphine Reagent (2)
#### Liquid-Phase Wittig Reactions **in aqua**  
Reagent **2** was subsequently transformed into the polymer-supported phosphonium salt **12** suitable for Wittig olefination in aqueous solution. Organic chemistry in water has been the focus of considerable efforts in recent years [26-28]. The Wittig olefination reaction has also been much studied throughout the evolution of crosslinked polymer-supported reagents and considerable success has been achieved [29-34]. [PERSON] and [PERSON] [34] noted an inverse correlation between the yield of alkene obtained from polymer-supported Wittig reagents with aldehydes and ketones in solution and the degree of divinylbenzene (DVB) cross-linking in the resin. Progressing from 0.5-20% DVB crosslinking, _i.e._ from gel to macroporous resins, can result in a fall of 20% in the yield of alkenes. These results suggested that a soluble polymer-supported phosphonium salt may give excellent yields in the Wittig reaction.  
The physical properties of the PEG backbone are such that the PEG-supported phosphonium salt **12** is eminently water soluble and offer the tantalizing prospect of facilitating the first soluble polymer-supported Wittig olefination reactions _in aqua_. Treatment of PEG-supported phosphine **2** with benzyl-bromide, followed by precipitation into diethyl ether yielded the soluble polymer-supported benzyl tri-arylphosphonium salt **12** in 81% yield (based on the weight of polymer obtained). This polymeric phosphonium salt **12** is completely soluble in water at pH 7. The reaction of this novel Wittig salt (**12**) was then studied with a range of aldehydes (**11a** and **13a-d**) in aqueous sodium hydroxide solution (Scheme 2, Table 2).  
The isolated yields of the alkenes **14a-e** by this method were good to excellent, proving that the rate of reaction of the ylide with the substrate aldehydes is indeed much faster than decomposition to **9**. Isolation of the stilbenes (**14a-e**) was achieved by partitioning the reaction mixture with CH\({}_{2}\)Cl\({}_{2}\), followed by removal of the water by addition of anhydrous magnesium sulfate, and then precipitation of the polymer supported triphenylphosphine oxide byproduct **9** into diethyl ether and concentration of the etheric solution to dryness.  
To explore the scope of this reaction two components were varied: base strength and temperature. Increasing the base-strength (from 1 N to 2 N NaOH) neither affected the yield of stilbenes (**14a-e**) nor the \(E\):\(Z\) isomer distribution. However, elevating the temperature (up to 90 degC) resulted in improved yields of **14d** and as to be expected an increase in the \(E\):\(Z\) ratio for the stilbene products.
## 2 Study Area and Flood Events
### Study Area
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Code** & **Description of Category** & **Area \% 2006** & **Area \% 2018** \\ \hline
112 & Discontinuous urban fabric & 0.706\% & 0.706\% \\ \hline
121 & Industrial or commercial units & 0.011\% & 0.011\% \\ \hline
211 & Non-irrigated arable land & 7.347\% & 7.346\% \\ \hline
223 & Olive groves & 1.672\% & 1.672\% \\ \hline
241 & Annual crops associated with permanent crops & 1.984\% & 1.984\% \\ \hline
242 & Complex cultivation patterns & 14.939\% & 14.955\% \\ \hline
243 & Land principally occupied by agriculture, with significant areas of natural vegetation & 15.632\% & 15.630\% \\ \hline
311 & Broad-leaved forest & 1.178\% & 1.178\% \\ \hline
312 & Coniferous forest & 14.015\% & 9.706\% \\ \hline
313 & Mixed forest & 0.380\% & 0.380\% \\ \hline
321 & Natural grasslands & 3.595\% & 4.846\% \\ \hline
322 & Moors and heathland & 0.001\% & 0.001\% \\ \hline
323 & Sleepphyllous vegetation & 19.383\% & 19.093\% \\ \hline
324 & Transitional woodland-shrub & 18.341\% & 20.123\% \\ \hline
331 & Beaches, dunes, sands & 0.447\% & 0.447\% \\ \hline
332 & Bare rocks & - & 0.120\% \\ \hline
333 & Sparsely vegetated areas & 0.368\% & 1.804\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Analysis of the land use percentages according to the CORINE Land Cover data (2006 and 2018) in the Lilas River basin. Figure 3: The images illustrate the land use patterns within the Lilas River basin before the 2009 flood (**a**) and the 2020 flood (**b**). The description of the land use codes is as follows: 112 Discontinuous urban fabric, 121 Industrial or commercial units, 211 Non-irrigated arable land, 223 Olive groves, 241 Annual crops associated with permanent crops, 242 Complex cultivation patterns, 243 Land principally occupied by agriculture, with significant areas of natural vegetation, 311 Broad-leaved forest, 312 Coniferous forest, 313 Mixed forest, 321 Natural grasslands, 322 Moors and heathland, 323 Scleroophyllous vegetation, 324 Transitional woodland-shrub, 331 Beaches, dunes, sands, 332 Bare rocks, and 333 Sparsely vegetated areas.
## 2 System Description
### Sensitivity Analysis
#### 2.3.1 Magnetic Field Sensor Measurement Sensitivity Analysis
In summary, considering the operating environment of the sensors and omitting smaller noise terms, the equivalent input voltage noise (\(e_{IM}\)) of the inductive magnetic field sensor is given by  
\[e_{IM}(f)=\sqrt{e_{Rsc}{}^{2}+e_{w1}{}^{2}+[(i_{w})\times|R_{sc}+j2\pi fL_{pc} |]^{2}}, \tag{7}\]  
The equivalent input magnetic field noise (\(b_{IM}\)) is then  
\[b_{IM}(f)=\frac{e_{IM}}{|2\pi f\mu_{app}NS|} \tag{8}\]Based on the above analysis and the parameters provided in Section 2.1, an inductive magnetic field sensor is designed in this project to serve as the magnetic field antenna for a long-wave broadband receiver,with its noise level shown in Figure 7.  
Magnetic field sensors must be tested in an environment free from magnetic noise. In this study, a magnetically shielded room, isolated from urban electromagnetic interference, with a grounded enclosure was used. The chamber is made of high-permeability and high-inductance materials, providing a shielding factor greater than 90 dB at frequencies above 10 Hz, which effectively shields against low-frequency electric and magnetic field noise. The sensor under test was placed at the center of the shielded room, and measurements were taken using a digital signal analyzer (Agilent, Santa Clara, CA, USA, 35,670 A) outside the room. The results are shown in Figure 8.  
Figure 8: Magnetic field antenna noise test results: 765 mV at 10 kHz, with a conversion factor of 100 mV/nT, corresponding to a noise level of 7.65/T/\(\sqrt{\mathrm{Hz}}\). Figure 7: Equivalent noise level of the sensor. As shown, the sensor's noise power spectral density is less than \(8 fT/\sqrt{\mathrm{Hz}}@10 kHz\), meeting the design requirements.
###### Abstract  
Silver nanoparticles ( AgNPsNPs) have increasingly gained attention owing to their distinctive physicochemical and biological properties. The objective of the investigation was to biologically synthesize AgNPs using plant extracts from _Barleria albostellata_. The synthesized AgNPs, obtained from _B. albostellata_ (leaves and stems), were characterized through various techniques including UV-visible spectroscopy, scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), energy-dispersive X-ray analysis, Fourier transform infrared (FTIR) spectral analysis, and nanoparticle tracking analysis (NTA). The antibacterial efficacy of the synthesized AgNPs was evaluated utilizing the disk diffusion method. The cytotoxicity effects of the synthesized AgNPs were determined using the MTT assay (3-[(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide]) in cervical cancer (HeLa), embryonic kidney (HEK293), and breast adenocarcinoma (MCF-7) cell lines. The results indicate that _B. albostellata_ extracts could serve as eco-friendly biofacories for the synthesis of AgNPs. UV-vis spectroscopy of the leaf and stem extracts revealed absorption peaks within the range of 400-450 nm, thereby confirming the synthesis of AgNPs. Elemental Ag was highest in the methanol leaf extracts (16.87 \(\pm\) 0.89%) and lowest in the powdered stem extracts (7.13 \(\pm\) 1.44%). Synthesized AgNPs were predominantly spherical in shape. HRTEM revealed that synthesized AgNPs from the methanolic stem extracts (34.32 \(\pm\) 16.99 nm) were larger in size, while those from the powdered stem extracts were smaller (16.57 \(\pm\) 5.55 nm). AgNPs synthesized from both the leaf and stem extracts exhibited zeta potential values between \(-\)8.8 and \(-\)32.1 mV, with hydrodynamics diameters ranging from 34.3 to 111.3 nm. FTIR spectroscopy confirmed the presence of various functional groups on the AgNPs. AgNPs synthesized from the leaf and stem extracts displayed significant antibacterial activity and were sensitive to Gram-negative and Gram-positive bacteria. AgNPs exhibited significant antibacterial activity (diameter of the zone of inhibition) against _Pseudomonas aeruginosa_ (21.67 \(\pm\) 2.87 mm) in the leaf methanolic extract. Synthesized AgNPs exhibited selective in vitro cytotoxicity against HEK293, HeLa, and MCF-7 cell lines. The IC\({}_{50}\) values of the AgNPs synthesized from the various extracts were all above 9 ug/mL. Significant cytotoxic levels (IC50 16.11 and 27.23 ug/mL) were observed for the MCF-7 cell line upon exposure to the methanolic leaf and stem AgNPs. This study recommends the use of medicinal plant extracts in producing economically effective AgNPs, due to their efficient capping. Overall, AgNPs synthesized from _B. albostellata_ extracts comprised novel antibacterial and anticancer agents, and warrant further investigation. Bio-synthesized AgNPs show great potential in the area of nanotechnology and may be used as an affordable, eco-friendly alternative for the delivery of conventional therapeutics.  
antibacterial activity; biofacories; biosynthesis; cytotoxicity; elemental Ag\({}^{+}\) +
# WLQRP: A Weighting Link-Quality-Based Routing Protocol for Underwater Sensor Networks
## 5 Simulation Results
### Results and Analysis
#### 5.3.5 Consumed Energy per Packet  
Figure 11 illustrates the contrast in average energy consumption of data packets between the two protocols as the number of nodes increases. Notably, the trend displayed in this graph is inverse to that observed in Figure 4. With an escalating node count, WLQRP demonstrates a progressive reduction in the average energy consumption of data packets, while VBF exhibits a corresponding increase. The energy consumption of HH-VBF is higher than that of VBF, which is reasonable because HH-VBF finds more paths, increasing energy consumption. As deduced previously, a higher number of packet transmissions leads to increased energy consumption. Consequently, considering the packet forwarding hop count, WLQRP experiences a decrease in the average energy consumption of data packets, whereas VBF encounters an increase.  
Figure 11: Consumed energy per packet.  
Figure 10: Residual energy ratio.
# Broadband Microstrip Antenna for 5G Wireless Systems Operating at 28 GHz
## 6 Optimization Process and Discussion of Simulation Results
The optimization process resulted in a final design of the antenna model, as shown in Figure 4. Dimensions of this design are shown in Table 2.  
The resulting antenna design was simulated with the use of FEKO software from the Altair company, which provided results for the following electrical parameters: reflection coefficient, standing wave ratio, input impedance, antenna gain, current distribution in the antenna, and radiation characteristics.
## 4 Performance Analysis of Islanding Detection Methods
### Degrading Power Quality  
Along with the generation requirement, power quality requirements have to be met by the DGs. Voltage fluctuation, frequency deviation, harmonic distortion, and electromagnetic interference are problems associated with power quality matter. Applying an is-landing detection scheme in the system can have a significant impact on degrading the power quality. For instance, passive methods do not degrade the power quality, while active methods can affect the power quality due to the injections and perturbation that active methods are based on.
## 3 Related Work  
### Anomaly Detection  
Modern semi-supervised AD models are commonly either hybrid models or one-class deep neural networks. The hybrid models are made up of two segments where deep learning models are usually used as feature extractors. The features learned by the deep models are then fed to conventional algorithms, such as one-class Support Vector Machine (SVM) classifiers [7]. Some studies have achieved very promising results using various hybrid models [6]. However, the hidden layers of the deep model use generic loss functions as opposed to anomaly-detection-specific functions, which could have a negative impact when the deep model acts as a feature extractor [7].  
One-class neural networks integrate the representation learning capabilities of deep networks with the one-class objective function, in order to develop boundaries that isolate the normal data instances from the anomalies. Two well-known examples of such models use a hypersphere (Deep Support Vector Data Description or Deep SVDD [42]) and a hyperplane (OC-NN [43]) as their one-class objective, respectively. The research findings indicate that one-class neural networks are among the state-of-the-art methods especially for complex datasets; however, training times can be lengthy, especially when working with high-dimensional data [7].  
One-class adversarial networks (OCANs) [44] are end-to-end one-class anomaly detectors that exploit the concept of bad GANs introduced in [45] to produce \"bad\" instances using normal data in the training set. \"Bad\" in this context means the generator distribution should not match the true data distribution. Hence, the data generated in bad GANs are supposed to be complementary to the training dataset as opposed to matching, in order to improve the generalization performance of a semi-supervised anomaly-detection algorithm.  
Unsupervised anomaly-detection approaches rely on the intrinsic properties within the data, such as distances or densities. Deep neural network architectures can be employed to identify these properties in the dataset [46]. A major advantage of unsupervised learning  
Figure 3: Examples of PVA. (**a**) An example of DTA. (**b**) An example of BSA.  
is its cost-effectiveness since there is no need for labeled data for the training process. Nevertheless, obtaining good performance with complex datasets is often very difficult. Unsupervised methods are also quite vulnerable to noise and faulty data and, thus, are generally outperformed by supervised or semi-supervised models. Furthermore, when unsupervised autoencoders are used for anomaly detection there are critical hyper-parameters, such as dimensionality reduction, to be tuned [7].
## 6 Conclusions  
A new semi-analytical solution was established in this study for the mechanical analysis of fully grouted passive bolts and rock masses in tunnels. Comparisons and parameter analyses were conducted to assess the behavior of the proposed solution. The main conclusions are as follows:  
(1) Comparisons with numerical results and in situ results indicate that the proposed solution could give a good estimation of the behaviors of passive bolts and rock masses in a deep circular tunnel.  
(2) Compared with the model that considered the decoupling at the rock-bol interface, a model that neglected the decoupling may significantly overestimate the reinforcement effectiveness of passive bolts, which may result in an unsafe design.  
(3) The displacement of the rock mass decreased with increasing shear stiffness before a critical value, beyond which, decoupling at the bolt-rock interface occurred, resulting in the reduction of the reinforcement effectiveness.  
(4) The interaction between the end plate and rock mass exerted a support pressure on the tunnel periphery and decreased the shear stress at the rock-bol interface, thus effectively improving the reinforcement effectiveness and avoiding decoupling. The maximum tensile force in the bolts in the case with an end plate was significantly larger than that in the case without an end plate.  
(5) To ensure the effectiveness and economy of passive bolts, the far end of the bolts should penetrate the elastic region, while it is not necessary to over-extend the bolts beyond the range of the plastic region. Increasing the bolt density can result in better effectiveness.  
(6) The tension force and shear stress both increased with the increasing bolt length and decreasing bolt density, while a large length or small density may cause the tension failure of the bolt and decoupling at the bolt-rock interface.  
Conceptualization, M.W.; methodology, X.Z.; software, J.T.; validation, X.Z. and J.T.; formal analysis, W.Y.; investigation, X.Z.; resources, J.T.; data curation, J.T.; writing--original draft preparation, X.Z.; writing--review and editing, M.W.; visualization, Z.W.; supervision, D.L.; project administration, J.T.; funding acquisition, M.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number No.51578458 and No.51878567 and No.51878568, and the China Railway Corporation Science and Technology Research and Development Program, grant number No.2017G007-H, No.2017G007-F and No.K2018G014.  
The authors declare no conflict of interest.