esa-sceva/satcom-chunk-collection huggingface dataset:
# Enabling the Orchestration of IoT Slices through Edge and Cloud Microservice Platforms
## 2 Background and Related Work
### Network Slicing in IoT
By putting together multi-tenant applications with network slicing, it is possible to have dedicated and independent IoT business solutions for specific verticals, deployed on the same infrastructure and using the same underlying networks. This multi-tenancy is an important requirement for many IoT applications like augmented reality, smart sensor networks or big data analytics as it is already highlighted by [PERSON] et al in [16]. Network slicing introduces a high degree of flexibility to support devices with different requirements in terms of quality of service, as well as to optimize the network usage, according to IoTWF [4] and [PERSON] et al [40]. This flexibility is achieved with the help of virtualization techniques by providing independent networks over a shared infrastructure. Each one of these networks is used for distinct business purposes. However, this implies greater complexity for the network owner'soperation departments since it is yet another aspect to be configured and maintained. This complexity requires the introduction of a network slice orchestrator responsible for considering all the business and technical provisioning requirements. The orchestration of network slices is still at an early stage. However, the IoT slice resource orchestration included in [41] describes the possibility for the operators to define network slicing as a Service (NSaaS) and propose the introduction of an intelligent 5G network slice broker. This broker makes use of the information provided by IoT gateways to optimally reconfigure and provision network slices. Another proposal to orchestrate and federate network slices for vertical players is the 5G Transformer orchestrator described in [42]. Some other examples of network slicing orchestration as the one described by [PERSON] et al [43] are still in a very initial phase. This article considers multi-tenancy aspects at the edge and the cloud domains along with network slicing at the different transport networks involved in the device-to-edge and end-to-cloud communications. This enables a holistic view of the slicing concept applied to an IoT solution, where our IoT slice orchestrator is capable of providing the required resources (computation, networking, and storage) across all the different domains involved in the execution of an IoT service. However, although the system is designed for this purpose, the prototype implementation is mainly focused on the multi-tenancy aspects at the edge and the cloud, leaving the network slicing part for a future development, due to its complexity and the lack of maturity of network slicing solutions specially in the open source ecosystem.
# An Unequal Clustering and Multi-Hop Routing Protocol Based on Fuzzy Logic and Q-Learning in WSNs
## 5 Results and Discussion
### Performance Evaluation in Scenario 2  
#### 5.3.1 Residual Energy  
Figures 22-24 illustrate the comparison of the network residual energy among the EEUC, CHEF, and FQ-UCR protocols across different node counts. As depicted in Figure 22, both the EEUC and CHEF protocols exhausted their energy by round 900 when the network comprised 100 nodes. On the other hand, FQ-UCR continued beyond 2478 rounds, retaining over 50.1% of its energy. Figures 23 and 24 show the residual energy for the 200-node and 400-node networks, respectively. FQ-UCR retained 61.24% and 63.78% of its energy, achieving over 1080 and 1127 rounds of iterations, respectively, whereas the compared protocols depleted their energy sooner. A detailed comparison across different node counts clearly reveals that FQ-UCR outperformed the other two protocols in energy efficiency  
Figure 21: Comparative analysis based on throughput in Scenario 1.  
Figure 20: Comparative analysis based on network stability period with BS located at (100, 250).  
Figure 23: The figure compares the residual energy (in Joules) of 200 nodes, with the BS located at (100, 250), across different protocols.  
Figure 24: The figure compares the residual energy (in Joules) of 400 nodes, with the BS located at (100, 250), across different protocols.  
Figure 22: The figure compares the residual energy (in Joules) of 100 nodes, with the BS located at (100, 250), across different protocols.
# UAV Image Small Object Detection Based on RSAD Algorithm
## 4 Results
### Comparison Experiment
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Backbone**} & \multicolumn{8}{c}{**AP/\%**} & \multirow{2}{*}{**r\(\mathbf{\mu}\)P**} \\ \cline{3-14}  & & **P1** & **P2** & **B1** & **C** & **V** & **T1** & **T2** & **A** & **B2** & **M** & \multicolumn{1}{c}{_\%_} \\ \hline Faster & & & & & & & & & & & & \\ R-CNN & ResNet-50 & 21.4 & **15.6** & 6.7 & 51.7 & 29.5 & 19.0 & 13.1 & 7.7 & 31.4 & 20.7 & 21.7 & 9.3 \\ Cascade & & & & & & & & & & & & & \\ R-CNN & ResNet-50 & **22.2** & 14.8 & 7.6 & 54.6 & 31.5 & 21.6 & 14.8 & 8.6 & 34.9 & **21.4** & 23.2 & 12.1 \\ RetinaNet & ResNet-50 & 13.0 & 7.9 & 1.4 & 45.5 & 19.9 & 11.5 & 6.3 & 4.2 & 17.8 & 11.8 & 13.9 & 16.7 \\ CenterNet & Hourglass-104 & 14.8 & 13.2 & 5.6 & 50.2 & 24.0 & 21.3 & 20.1 & 17.4 & 37.9 & 23.7 & 22.8 & 14.0 \\ YOLOv5s & CSPDarknet53 & 19.7 & 13.7 & 3.84 & **62.0** & 27.2 & 22.4 & 15.7 & 6.9 & 40.3 & 19.8 & 23.2 & 57.2 \\ YOLOv8n & CSPNet & 13.6 & 11.6 & 1.8 & 55.9 & 21.0 & 18.3 & 10.6 & 5.63 & 30.1 & 15.4 & 18.4 & **61.5** \\ SSD300 & VGG-16 & 13.6 & 11.3 & 9.1 & 42.6 & 25.8 & 26.1 & 13.2 & 6.8 & 37.9 & 12.6 & 19.9 & 55.0 \\ RSAD(ours) & ResNet-50 & 15.1 & 14.0 & **10.5** & 51.8 & **43.0** & **46.1** & **25.5** & **19.1** & **59.5** & 19.9 & **30.5** & 52.0 \\ \hline \hline \end{tabular}  
* note: image-size: 300 \(\times\) 300; bolded text is the optimal result; P1: pedestrian; P2: people; B1: bicycle; C: car; V: van; T1: truck; T2: tricycle; A: awning tricycle; B2: bus; M: motor.  
\end{table} 
Table 3: Results of different algorithms on VisDrone test dataset. Figure 10: Display of results ((**a**,**b**) are a comparison of the detection effect of YOLOv5 and RSAD models in daytime; (**c**,**d**) is a comparison of the detection effect of YOLOv5 and RSAD models for nighttime).
# Highly Sensitive Non-Enzymatic Detection of Glucose at MWCNT-CuBTC Composite Electrode
## 2 Materials and Methods  
### Materials  
Multiwall carbon nanotubes (MWCNTs) characterized by 90% purity, a 1.5 \(\upmu\)m length, an average diameter of 9.5 nm, and a surface area of around 250-300 m\({}^{2}\)/g, were produced by the catalytic carbon vapor deposition (CCVD) method and were supplied by Nanocyl (Belgium). Araldite@LY5052 and its corresponding hardener, Aradur@5052, which were supplied by Huntsman Advanced Materials (Basel, Switzerland), were used as a polymeric matrix. The details for the synthesis of CuBTC (BTC = 1,3,5 benzentricularboxilate) have been already reported by our group [25].  
A standard stock solution of 0.1 M of glucose was prepared daily from analytical grade Merck reagents using double distilated water. The supporting electrolyte for the characterization and application of the electrode material in detection processes was a 0.1 M NaOH solution, and it was freshly prepared from NaOH of analytical purity (Merck) with double distilated water.
# Low-Temperature Direct Bonding of SiC to Si via Plasma Activation
## 2 Experiments  
In the experiment, 4-inch double polished N-type 4H-SiC (thickness = 350 \(\upmu\)m) and single polished P-type Si (thickness = 500 \(\upmu\)m) substrate wafers were all sliced into 20 mm \(\times\) 20 mm samples. The Si wafer was 100 oriented, and the bonding face of the 4H-SiC was the Si face (0001 oriented).  
The basic process flow is demonstrated in Figure 1. The SiC and Si wafers were cleaned successively by piranh solution (H\({}_{2}\)SO\({}_{4}\):H\({}_{2}\)O\({}_{2}\)), RCA2 (HCl:H\({}_{2}\)O\({}_{2}\):H\({}_{2}\)O), and diluted HF. To obtain a hydrophilic surface, the Si wafers were cleaned by NH\({}_{4}\)OH:H\({}_{2}\)O\({}_{2}\):H\({}_{2}\)O in a ratio of 0.25:1:5 by parts, at 70 \({}^{\circ}\)C, for 10 min. After cleaning and drying, the wafers were immediately activated by O\({}_{2}\) plasma RIE. The RIE power and gas flow were set to 200 W and 50 sccm, respectively. In order to study the influence of the activation time on the bonding efficiency, the activation time was set to 0 s, 30 s, 60 s, 90 s, and 120 s, respectively. Following the plasma activation, the SiC and Si wafers were prebonded immediately. To reinforce the bonding strength, the bonded samples were annealed in the Suss MicroTec SBee Wafer Bonder. The tool pressure of 2500 mBar and a chamber pressure of 1 \(\times\) 10\({}^{-3}\) mBar were applied to the prebonded samples. To study the influence of annealing time, two groups of samples were annealed at 150 \({}^{\circ}\)C and 300 \({}^{\circ}\)C for 3 h, respectively.  
The surface roughness of the Si and SiC samples under different activation conditions was tested by atomic force microscopy (AFM). The surface chemical states were characterized by X-ray photoelectron spectroscopy (XPS). In addition, the bonded interface SiC/Si was observed by transmission electron microscopy (TEM).  
Figure 1: Schematic diagram of the O\({}_{2}\) plasma-activated bonding process.
## 2 Materials and Methods
### Data Preparation  
Data preparation involves various processes such as data division, data collection, and data normalization. In this study, the data used in training and testing the model have been obtained from Photovoltaic Geographical Information System (PVGIS) website [40]. The accuracy of this data set has been confirmed in literature as it has been used for other machine learning and deep learning tasks in existing works of literature [41; 42]. According to PVGIS, the following parameters; ambient temperature sun elevation, and wind speed at 10 m are required to determine/predict PV performance and solar irradiance and these parameters are represented as T\({}_{\text{amb}}\) (deg. C), A\({}_{\text{S}}\) (deg. ), W\({}_{10}\) (m/s), and Gi (W/m\({}^{2}\)) in this study. While the ambient temperature affects the intensity of solar radiation, sun elevation is directly related to solar irradiance [43]. Additionally, PVGIS uses information about the elevation of the terrain with a resolution of 3 arc seconds (about 90 m). The hourly measurement of these parameters for twelve years (2005-2016) will be used in the study. The data collected (105,192 rows) are divided into training and testing sets respectively. A total of 841,536 data points is used in this study for each location. The data split is done in  
Figure 2: Map of Africa highlighting the countries used as the case study. 9:1 proportion such that 90% of the data is used for training the hybrid neural networks and the remaining (10%) is used for testing the models. Data normalization is applied to improve accuracy and speed up the rate of convergence of gradient descent. If the data is not normalized, the network often encounters a model learning problem because the gradient descent becomes complex and does not converge swiftly. This is done in accordance with machine learning literature. Since, normalization is majorly to standardize the range of the values, the range \([0,1]\) is used because it is better at reducing computation complexities. In this study, each dimension of the dataset is normalized to values between 0 and 1 using Equations (1) and (2). \[X_{N}=\frac{X-\tilde{X}}{\tilde{X}-\tilde{X}} \tag{1}\]  
\[X=X_{N}\big{(}\hat{X}-\tilde{X}\big{)}+X \tag{2}\]  
where \(\tilde{X}\) and \(\tilde{X}\) represent the minimum and maximum values of variable \(X\) respectively. \(X_{N}\) is the normalized value of variable \(X\). The statistical summary of the data used in this study is tabulated in Table 2. This data will be insightful in evaluating the performance of all the models developed.
## 3 Security Aspects Regarding Wireless Body Area Framework
### Authentication and Key Agreement
#### 3.2.1 Physical Unclonable Functions  
One promising approach involves the use of Physical Unclonable Functions (PUFs) [13, 14]. PUFs are unique physical characteristics of electronic devices that can be used to generate cryptographic keys. They exploit the inherent and unavoidable variations that occur during the manufacturing of semiconductor circuits. The randomness and uniqueness of PUF-derived keys make them suitable for authentication in WBANs without the need for certificates [15]. In the system in Figure 1, the authentication between the master unit and a sensor node operates on a challenge-response mechanism. The device being authenticated, the sensor node, is challenged with a set of known input values (challenges), and the corresponding responses are recorded in the master unit. This database of challenges and responses is called a Challenge-Response Pair (CRP). During the authentication, the sensor node is challenged by the master node which then generates a response and compares it to the stored CRFs. If the response is close enough to a stored one, the device is deemed to be authentic. PUF authentication is secure because it is based on the physical characteristics of the device itself. These characteristics are unique to each device and cannot be replicated. Therefore, it is very difficult for an attacker to forge a PUF response. Physical Layer Security, in general, is a promising approach to enhancing the security of resource-constrained devices [16].
# Deep Learning-Based Acoustic Echo Cancellation for Surround Sound Systems
## 3 Signal Model  
Assuming there are \(L\) loudspeakers and one microphone in the near-end room, and there is an Ambisonics recording device in the far-end room, then the typical diagram of the AEC method is shown in Figure 2, and the microphone signal in the near-end room can be formulated as:  
\[\begin{split} y(n)&=\sum_{l=0}^{L}x_{l}^{D}(n)*h_{l} (n)+s(n)+v(n)\\ &=d(n)+s(n)+v(n),\end{split} \tag{2}\]  
where \(x_{l}^{D}(n)\) denotes the signals played back by the loudspeakers with \(l\) denoting the index of loudspeakers, which can be regarded as the D-format signals in the surround sound system. \(h_{l}(n)\) denotes the room impulse responses (RIRs) from the loudspeakers to the microphone in the near-end room and \(*\) denotes the linear convolution operation. The near-end speech signal is denoted as \(s(n)\), and \(v(n)\) represents the additive noise signal. The echo signals transmitted from the loudspeakers to the microphone are denoted as \(d(n)\). In the far-end room, as shown in Figure 2, the Ambisonics recordings are denoted as \(x_{m}^{B}(n)\), with \(m\) denoting the index of Ambisonics input channels, which are generated by the source signal \(r(n)\) via the acoustic paths characterized by the impulse responses \(g_{m}(n)\). \(x_{m}^{B}(n)\) can be decoded to the loudspeaker signals \(x_{l}^{D}(n)\) via a decoding matrix. The adaptive filtering-based AEC methods attempt to cancel out the echo signal \(d(n)\) from \(y(n)\) by subtracting the estimated echo signal \(\hat{d}(n)\) via the adaptive filters. However, it is difficult for a conventional AEC method to entirely reduce the echo signals and the residual echo signal inevitably exists in the estimated near-end signal \(\hat{s}(n)\). Besides, in the surround sound condition, the conventional AEC methods may suffer from the non-unique solution problem due to the high cross-correlation between the loudspeaker signals.
## 3 Non-Thermal Technologies in Food Processing
### UV Radiation
#### 3.2.2 Application in Meat and Fish Products  
[PERSON] et al. investigated UV-C light irradiation on beef, pork, and chicken meat in order to lower the population of _E. coli_. UV-C lamps reduce the number of bacteria by (1.0 \(\pm\) 0.2) log\({}_{10}\) CFU/mL in beef. Regarding chicken and pork, after 4 and 10 min of irradiation, the bacteria decrease by (1.6 \(\pm\) 0.7) log\({}_{10}\) CFU/mL and (1.6 \(\pm\) 0.4) log\({}_{10}\) CFU/mL, respectively [118].  
[PERSON] et al. analyzed which is the most suitable UV-C light intensity to inactivate _S. typhimurium_ inoculated in chicken breast meat, stored at 4 \({}^{\circ}\)C for 9 days, without damaging it. The group of meat treated with the highest UV-C intensity showed extended shelf life and lag phase as well as a decrease in the initial bacterial load. Some intensities revealed a more stable yellowness (b*) value than the other groups; the rise in the biogenic amines, tyramine, cadaverine, and putrescine contents registered for all the analyzed groups should not be considered as an indicator of bacterial growth but as a consequence of the UV treatment [126].  
Bacteria and microorganism can also attack fish meat. In this case, UV-light can be an easy method to inactivate these foodborne pathogens. [PERSON] et al. evaluated the effectiveness of UV-C treatment on vacuum-packaged smoked salmon during refrigerated storage (4 \(\pm\) 1 \({}^{\circ}\)C) against _L. monocytogenes, Listeria innocua, S. typhimurium, Salmonella enterica enteridia, S. aureus, E. coli_ O157:H7, _Aeromonas hydrophila_, and _Plesiomonas shigelloides_. Moreover, after 28 days of storage, the degree of lipid oxidation, color, and external appearance have been evaluated. A precise dosage of UV-C radiation permits inactivation in the range of (\(-\)0.5) and (\(-\)1.3) log\({}_{10}\) CFU units of the microbial population without important variations in the sensory quality properties of the treated products [127].
# _remote sensing_
## 4 Satellite Architecture and System Budgets  
The satellite architecture is based on two distinct modules hosting, respectively, the HORUS instrument and the satellite bus components, each one occupying one half of the satellite, corresponding to a 2 \(\times\) 1.5U CubeSat volume. The satellite CAD model (shown in Figure 6) shows the HORUS instrument in the lower 2 \(\times\) 1.5 Units and the satellite bus components in the upper 2 \(\times\) 1.5 Units.  
Deployable solar panels, each comprising three 1 \(\times\) 3U solar panel modules, are installed on the side of the spacecraft. For improved visibility of the spacecraft components, Cartesian projections are presented in Figure 7.  
The HORUS On-board Data Handling (OBDH) subsystem includes a high-performance microcontroller, which is in charge of system management, scheduling of the on-board operations, and execution of the scientific and housekeeping tasks. The on-board computer is integrated with native connectivity with a Software Defined Radio (SDR) for X-band down link of the scientific data at a high data rate. An X-band patch antenna is mounted on the CubeSat Nadir-pointing side. The Telemetry, Tracking and Command (TT&C) subsystem is based on an Ultra-High Frequency (UHF) link, with a dedicated radio and omnidirectional antenna. The Attitude Determination and Control Subsystem (ADCS), assuring a pointing accuracy of \(\pm\)0.1 degrees during operations, includes a star tracker, integrated solid-state gyroscopes and magnetometer, sun sensors on each of the six satellite sides, reaction wheels and magnetorquers. The control strategy with the four reaction wheels is obviously three-axis stabilization with zero-momentum control. The ADCS operation is managed by a dedicated processing unit. A miniature GNSS receiver provides for precise orbit determination and on-board timing for appropriate synchronization of the payload operation. The orbit maintenance is performed by a FEEP thrust  
Figure 6: HORUS CAD model.  
a volume of roughly 0.8U, propellant included. This subsystem will also perform the post-mission disposal (PMD) at the end of the spacecraft mission.  
The spacecraft electric power subsystem (EPS) includes two battery packs for a total energy storage of approximately 70 Wh and a photovoltaic system composed of body-mounted and deployable solar panels, with 16 advanced triple junction solar cells on the body-mounted panels and a total of 42 cells on the two deployable panel wings. In the orbital configuration, the solar panels point to the zenith. A dedicated solar array conditioning unit and related power distribution units manage and distribute the power. The satellite thermal control mainly relies on passive control.  
The spacecraft structure is manufactured in aluminum alloy Al7075, with an outer envelope of 340.5 \(\times\) 226.3 \(\times\) 100.0 mm, in compliance with the 6U CubeSat Design Specification [53].
# GNSS Signal Compression Acquisition Algorithm Based on Sensing Matrix Optimization
## 5 Conclusions  
In this paper, a GNSS signal compression acquisition algorithm based on sensing matrix optimization is proposed. A modified conjugate gradient method is employed to optimize the sensing matrix to improve the SNR of acquisition. The proposed algorithm requires fewer correlators than the conventional parallel-correlator-based techniques. The simulation results show that under the same SNR, the proposed algorithm can effectively improve the detection probability compared with the existing compression acquisition algorithms. In the future, the impact of large Doppler frequency and non-Gaussian noise on signal detection performance should be further studied.  
Conceptualization, [PERSON] proposed the main idea, finished the draft manuscript; [PERSON] conceived of the experiments and drew the figures and tables; Methodology, [PERSON]; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper; [PERSON]; [PERSON] and [PERSON] reviewed the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Chinese Academy of Sciences Youth Innovation Promotion Association, grant numbers Y202068, Wenzhou Major Scientific and Technological Innovation Projects of China, grant numbers ZG2021029, Scientific Research Project of Zhejiang Provincial Department of Education, grant numbers Y202146796.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
GNSSGlobal Navigation Satellite System  
FFTFast Fourier Transform  
CGConjugate Gradient acquisition algorithm  
GPGaussian Process acquisition algorithm  
ELADELAD acquisition algorithm  
SNRSignal-to-Noise Ratio  
ETFEquianz Tight Frame  
Figure 9: The detection probability of the proposed algorithm with different \(M/N\).