esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Healthcare System: BSNCare+
### The Registration Phase  
At first, the body sensor \(BS_{i}\) sends its identity \(ID_{i}\) to the BSN-Care server, i.e., _Server_, as a registration request. Upon receiving the request from \(BS_{i}\), the _Server_ generates a random number \(N_{is}\) and uses its identity \(ID_{s}\) to compute a secret key \(K_{is}=H(ID_{s}\mid\mid N_{is}\mid\mid ID_{i})\). Next, the _Server_ calculates a set of un-linkable shadow identities \(SID=\{\mathit{sid}_{1},\mathit{sid}_{2},\dots\ \}\) for \(BS_{i}\), where each \(\mathit{sid}_{h}\in SID\) and \(\mathit{sid}_{h}=H(N_{h}\mid\mid K_{is})\). Note that \(N_{h}\) is a random number used for deriving each \(\mathit{sid}_{h}\) value. In addition, a track sequence number \(\mathit{Tr}_{\mathit{seq}}\) is created for the fast identification of \(BS_{i}\) and to prevent replay attacks as well. \(\mathit{Tr}_{\mathit{seq}}\) will be renewed and stored on both the _Server_ and the \(BS_{i}\) sides at each authentication session. In that case, the _Server_ is able to check the freshness of an incoming request from \(BS_{i}\), and to directly identify \(BS_{i}\) via \(\mathit{Tr}_{\mathit{seq}}\) in the backend database during the authentication session. If \(\mathit{Tr}_{\mathit{seq}}\) in the request is not maintained in the backend database, the _Server_ will reject the incoming request and terminate the connection. The _Server_ then asks \(BS_{i}\) to send a new request embedding one of the fresh shadow identities \(\mathit{sid}_{h}\) from the list of \(SID_{s}\) as an anonymous identity of \(BS_{i}\). Note that the used \(\mathit{sid}_{h}\) must be removed from the \(SID\) list at both the _Server_ side and the \(BS_{i}\) side after the authentication session.  
Finally, the _Server_ issues a security credential, i.e., (\(ID_{i}\), \(K_{is}\), \(SID\), \(\mathit{Tr}_{\mathit{seq}}\), \(H(.)\)) to \(BS_{i}\). Meanwhile, the _Server_ maintains a tuple (\(ID_{i}\), \(K_{is}\), \(SID\), \(\mathit{Tr}_{\mathit{seq}}\), \(H(.)\)) corresponding to \(BS_{i}\) at the backend database. Note that \(H(.)\) denotes a secure one-way hash function. On the other hand, the registration phase between the mobile gateway \(MG_{j}\) and the _Server_ is performed in a similar way. The \(MG_{j}\) sends its identity \(ID_{j}\) to the _Server_ as a registration request. Then, the _Server_ computes \(K_{js}\)= \(H(ID_{s}\mid\mid N_{js}\mid\mid ID_{j})\) with a newly generated random number \(N_{js}\), and shares a security credential, i.e., (\(ID_{j}\), \(K_{js}\), \(H(.)\)). The _Server_ maintains a tuple (\(ID_{j}\), \(K_{js}\), \(H(.)\)) corresponding to the \(MG_{j}\) in the backend database.
# Smart Ocean: A New Fast Deconvolved Beamforming Algorithm for Multibeam Sonar
## 2 Beamforming and Deconvolution
### Deconvolved Beamforming
#### 2.2.4 Accelerated R-L Algorithm and Fast Accelerated R-L Algorithm
for \(k=3\), \(4\),... \(\hat{S}_{k}^{\prime}=\hat{S}_{k}+\alpha_{k}(\hat{S}_{k}-\hat{S}_{k-1})\); Predict \(\mathcal{S}\) at the iteration time \(k\) \(\hat{H}_{S_{k}^{\prime}}=fft(\textbf{S}_{k}^{\prime})\); Calculate the Fourier transform of \(\textbf{S}_{k}^{\prime}\) \(\textbf{H}_{\hat{I}_{F_{k}^{\prime}}}=\textbf{H}_{p}\textbf{H}_{S_{k}^{\prime}}\); Calculate the Fourier transform of \(\textbf{F}_{k}^{\prime}\) \(F_{k}^{\prime}=ift(\textbf{H}_{\textbf{F}_{k}^{\prime}})\); Calculate the inverse Fourier transform of \(\textbf{H}_{\textbf{F}_{k}^{\prime}}\), \(ifft(\ldots)\) denotes the inverse fast Fourier transform operation. \(\textbf{H}_{temp}=fft(\frac{F}{F_{k}})\); \(\textbf{temp}=ifft(\textbf{H}_{p}^{*}\textbf{H}_{temp})\);\(\textbf{H}_{p}^{*}\) denotes the conjugate of \(\textbf{H}_{p}\) \(\hat{S}_{k+1}=\textbf{S}_{k}^{\prime}\textbf{temp}\); Estimate \(\mathcal{S}\) at the iteration time \(k+1\) \(g_{k}=\hat{S}_{k+1}-S_{k}^{\prime}\); Calculate the gradient of \(\mathcal{S}\) at the iteration time \(k\) \(\alpha_{k+1}=\frac{g_{k}^{T}\cdot\hat{S}_{k-1}}{g_{k-1}^{T}\cdot\hat{S}_{k-1}}\); Calculate the acceleration factor at the iteration time \(k+1\) }
```  
**Algorithm 4** Fast Accelerated R-L algorithm. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Multiplicative Times per Iteration** \\ \hline
1 & \(2N^{2}+N\) \\
2 & \(8N\log_{2}N+9N\) \\
3 & \(2N^{2}+4N\) \\
4 & \(8N\log_{2}N+12N\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The amount of calculation of four algorithms. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Fast** & **Accelerated** & **Computation Complexity** \\ \hline
1 & NO & NO & Most \\
2 & YES & NO & More \\
3 & NO & YES & Less \\
4 & YES & YES & Least \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The differences of the four algorithms.
## 3 Kalman Filtering for GPS/BDS Float Ambiguity Estimation
_{(m-1)\sim m} \tag{22}\]  
\(i\)th column The Kalman filter equations can be written as follows:  
\[\mathbf{P}_{k+1k}=\mathbf{F}_{k,k+1}\mathbf{P}_{k}\mathbf{F}_{k,k+1}^{r}+\mathbf{ Q}_{k} \tag{23}\]  
\[\mathbf{K}_{k+1}=\mathbf{P}_{k+ijk}\mathbf{H}^{T}\left(\mathbf{H}\mathbf{P}_{k+ ijk}\mathbf{H}^{T}+\mathbf{R}_{k}\right)^{-1} \tag{24}\]  
\[\mathbf{P}_{k+1}=\left(\mathbf{I}-\mathbf{K}_{k+1}\mathbf{H}\right)\mathbf{P} _{k+lk} \tag{25}\]  
\[\mathbf{X}_{k+lk}=\mathbf{F}_{k,k+1}\mathbf{X}_{k} \tag{26}\]  
\[\mathbf{X}_{k+1}=\mathbf{X}_{k+lk}+\mathbf{K}_{k+1}\left(\mathbf{Z}_{k+1}-h \left(\mathbf{X}_{k+lk}\right)\right) \tag{27}\]  
where \(\mathbf{P}_{k}\) is the VC matrix of the states at \(k\)th update, \(\mathbf{P}_{k,k+1}\) is the one-step estimation of \(\mathbf{P}_{k}\), \(\mathbf{K}\) is the Kalman gain matrix, \(\mathbf{Q}\) is the process noise VC matrix and can be modeled as Equation (28), where the position related terms are set to infinite (usually a large number in practice) to reduce the dependence on receiver dynamic model, and the ambiguity related terms are set to zero based on its time-invariant feature:  
\[\mathbf{Q}= \begin{bmatrix}\mathbf{\infty}_{3x3}&&\\ &\mathbf{O}_{mm}&&\\ &&\mathbf{O}_{mm}\end{bmatrix}  \tag{28}\]  
\(\mathbf{R}\) is the VC matrix of the DD measurements as given by Equation (29):  
\[\mathbf{R}= \begin{bmatrix}\mathbf{D}^{\text{GPS}}\mathbf{R}_{\text{SD}}^{ \text{\#GPS}}\left(\mathbf{D}^{\text{GPS}}\right)^{T}\\ &\mathbf{D}^{\text{BDS}}\mathbf{R}_{\text{SD}}^{\text{\#BDS}}\left(\mathbf{D} ^{\text{BDS}}\right)^{T}\\ &\mathbf{D}^{\text{GPS}}\mathbf{R}_{\text{SD}}^{\text{\#GPS}}\left(\mathbf{D} ^{\text{GPS}}\right)^{T}\\ &\mathbf{D}^{\text{BDS}}\mathbf{R}_{\text{SD}}^{\text{\#BDS}}\left(\mathbf{D} ^{\text{BDS}}\right)^{T}\end{bmatrix}  \tag{29}\]  
The VC matrices of the SD GPS carrier phase and pseudorange measurements (\(\mathbf{R}_{\text{SD}}^{\text{\#}}\) and \(\mathbf{R}_{\text{SD}}^{\text{\#}}\)) are given in Equations (30) and (31) respectively, where \(\sigma\) is the variance of the original non-differenced base or rover measurements. The corresponding VC matrices of BDS can be obtained similarly:  
\[\mathbf{R}_{\text{SD}}^{\text{\#GPS}}= \begin{bmatrix}\left(\sigma_{1,b}^{ \text{\#GPS}}\right)^{2}+\left(\sigma_{1,r}^{\text{\#GPS}}\right)^{2}&&\\ &\ddots&\\ &&\left(\sigma_{m,b}^{\text{\#GPS}}\right)^{2}+\left(\sigma_{m,r}^{\text{\#GPS }}\right)^{2}\end{bmatrix}  \tag{30}\]  
positioning and calculating the satellite position with its ephemeris which is not discussed here; the reader can refer to any GPS textbooks such as [15].
## 6 Conclusions and Further Work  
The popularity of wireless communications for a wide range of use cases means that 5G technologies are likely to become increasingly ubiquitous over time, both in traditional MNO deployments but also in a range of other scenarios, from 5G NPN for supporting IIoT to community-owned networks. However, the vast range of technologies and techniques that represent 5G, both in the core and at the edge, make it very complicated to define exactly what 5G 'looks like' and how it performs in every situation.  
In Liverpool, a 5G network has been deployed by a consortium including the local council and healthcare providers, technology firms and academic institutions to support healthcare and community care services. This network leverages the existing CCTV fibre network and extends it using mmWave backhaul links into areas of the city where connectivity is required. These backhauls are built using 802.11 ad point-to-point links that offer up to multi-gigabit services but are susceptible to the restrictions of 60 Ghz communications.  
In an effort to understand this better, we built a simulator in MATLAB to model the behaviour of these links under a range of conditions, and compared our results to the real-world deployments to validate our findings. Our results show that some links conform to our model very well, while others are somewhat unpredictable. This difference could be due to manufacturing tolerances or variations in link quality over short distances. This is a valuable contribution to ongoing 5G deployment efforts that utilize this technology, as real-world characteristics for 60 GHz link performance will help to refine node placement and configuration. It also provides useful insights to the research community on the effectiveness of novel private and community 5G-utilizing heterogeneous technologies.  
Over time, we aim to continually improve our models through further validation to generalize our model such that it can be used as a general-purpose tool for network planning or as a basis for further work to examine network optimization. We would also be interested in evaluating the potential impact of 802.11 ay-based links in this environment and conducting a comparative analysis of their performance.  
Conceptualization, M.M. and A.R.; methodology, M.M., A.R. and O.T.; software, O.T and A.R.; validation, O.T. and A.R.; formal analysis, O.T. and A.R; investigation, M.M. and A.R; writing--original draft preparation, M.M. and A.R.; writing--review and editing, M.M. and A.R.; visualization, O.T.; supervision, M.M. and A.R; project administration, M.M.; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Not applicable, the study does not report any data.  
The authors declare no conflict of interest.
## 4 Results and Analysis
### RF Measurements  
Figure 13 shows the spectrograms of the recorded signals, reported at the antenna connector output, for the ISM 2.4 GHz band and for the 862-922 MHz band. The spectrograms show the spectral power density of the ground-produced ISM noise that was measured during the HAB flights. It can be seen that some frequency ranges are characterized by the near absence of interferent emissions, while others are associated with relatively high RF power. In particular, no significant emissions were recorded in the band 900-917 MHz (US-licensed band) and in the band 2.485-2.5 GHz, of common use in Europe. Such sub-intervals of the ISM bands are therefore potentially suited for a dedicated IoT application making use of the LEO satellite without little or no risk of being affected by ground-generated interference.  
Figure 11: Environmental temperature and temperature inside of the payload gondola during the flight.  
Figure 12: Measured external temperature as a function of altitude (blue line). The temperature predicted by the Standard Atmosphere model is shown for the sake of comparison (red line).  
The interferences measured at 32 km can be appropriately scaled via software in order to estimate the interferences collected by a spacecraft in orbit at an altitude of 600 km (as in a typical LEO small satellite mission). This provides useful indications for the design of a future IoT constellation in LEO.
# SNR-Based GNSS-R for Coastal Sea-Level Altimetry
###### Abstract
SNR-based GNSS-R can be especially useful in regions with large land subsidence or uplift. This is because a COTS GNSS station can provide both vertical land motion and sea level variations. In this contribution, SNR observations from a newly installed GNSS station in the Haiti Coast Guard Base Port-au-Prince tide gauge station (Figure 1) are used to retrieve sea levels in the Caribbean Sea. Haiti lies right on the boundary of the Caribbean and North American plates. [13] reported a 19 mm/yr left-lateral relative motion between the Caribbean and North American plates. Haiti, the most vulnerable country to climate change in Latin America and the Caribbean, is in the center of the hurricane belt and has experienced six major storms in the past 30 years [14]. The West and South Departments of this small island nation lie in the path of the strongest tropical storms [14]. Hurricane Matthew in 2016, with a Category 4 strength, devastated the Southwestern part of the country and caused the sea level to rise, flooding the bay of Port-au-Prince with up to 1 m of water [15]. A mean average of 1.8 mm/yr was reported as the sea level rise rate in the Caribbean region over the last 60 years [16]. A sea level rise of between 0.05 m and 0.22 m is estimated for 2030 in the Caribbean [14]. The accuracy and precision of SNR-based GNSS-R sea level altimetry in the Haiti Coast Guard Base Port-au-Prince tide gauge station (POPR) are evaluated using co-located radar tide gauge records. In addition, the performance of the in situ tide gauge station is assessed using pressure and radar sensors over 2014, when two tide gauge sensors were operational. Finally, the GNSS-R-derived tidal constituents are compared with those of the radar tide gauge sensor from 10 October 2020 to 1 March 2021. The rest of the paper provides a general description of the geodetic GNSS-R footprint and physically-based forward model and statistically-rigorous inverse model in Sections 2 and 3, respectively. Then, the study area is presented in Section 4. We proceed to assess the geodetic GNSS-R altimetry results in more detail in Section 5. Sea levels in ITRF2014 are explained in Section 6. Finally, the paper is ended by conclusions in Section 7.
# Parallel Implementation on FPGA of Support Vector Machines Using Stochastic Gradient Descent
## 5 Comparison with the State of the Art
In [10] a hardware implementation on FPGA of the HogBatch algorithm for SGD computation is used, which employs systolic arrays for improved scalability and performance results. The results were obtained with the RCV1-V1-test dataset, which contains 47,236 features and logistic regression was used as the model; the cited proposal requires at least 13.75 ms with 32 Processing Elements (PEs) to compute each sample, resulting in the analysis of 72 sps. The proposal presented in this paper would need 971 us (1029 sps) based on the results from the XOR float-point implementation, 45.7 us (21,881 sps) if the XOR fixed-point implementation is considered and 278 us (3597 sps) with the Iris float-point. Generating speedups of 14.160\(\times\), 300.875\(\times\) and 49.460\(\times\). In [11] the use of low precision data is evaluated regarding throughput and statistical efficiency for SGD applied to linear models on FPGA and CPUs. The authors present results of throughput and statistical efficiency for different number of bits on linear regression. From the results shown in the work the design that is most similar to this paper uses 32 bits fixed-point hardware. The design consists of a linear regression SGD, capable of processing 3 Giga Numbers Per Second (GNPS) using a model of 48 kB, with a sample of 1500 features executed in 16 us (62,500 sps). The proposal presented in this paper presents a speedup of 0.517\(\times\), needing 30.9 us per sample (32,362 sps) using the results  
Figure 14: Linear regression curve for execution time per sample for varying numbers of kernels \(k\), Iris dataset floating-point precision. from the XOR float-point implementation, 1.5 \(\upmu\)s (66,6666 sps) with speedup of 10.666\(\times\) considering to the XOR fixed-point and 8.9 \(\upmu\)s (112,359 sps) as well as speedup of 1.797\(\times\) with the extrapolation from the Iris float-point implementation.
# Impact of Intracavity Power Variations toward Ultrashort Pulse Generation
## 2 Graphene Nanoplatelet Powder  
The preparation of GNP powder has been described in [47]. The expanded graphite (3772, Asbury Carbons, Inc., Asbury, NJ, USA) is thermally exfoliated for ten seconds at 950 \({}^{\circ}\)C. To exfoliate expanded graphite, 0.1 g of the expanded fluffy powder is steeped for 24 h in 100 mL of a mixture containing sulfuric acid (V(H\({}_{2}\)SO\({}_{4}\))) and nitric acid (V(HNO\({}_{3}\))). The treated expanded graphite is then rinsed ten times in deionized water using the centrifuge technique to eliminate extra acids. To exfoliate further, the processed expandable graphite is ultrasonicated utilizing a probe sonicator (QSonica Q700) in ethanol solution (0.1 mg/mL). The sonicated sample is centrifuged at 5000 rpm, and the residue is over-dried at 60 \({}^{\circ}\)C for 12 h to produce GNP powders.  
Figure 1 compiles the material characterization of the synthesized GNP powder. Figure 1a depicts the absorption spectrum of the GNP powder using a Shimadzu UV-1601 UV-visible spectrophotometer. The conventional graphite material's p-plasmon activation provides a maximum absorbance of 260 nm [47]. Furthermore, the gapless material transition of graphene has extensive absorbance at 270 nm [47]. The absorption peak of GNP powder seen at 267 nm, based on experimental data, reveals that this substance is formed of many layers. As shown in Figure 1b, a Renishaw inVia Raman microscope with laser excitation at 532 nm is employed to obtain the Raman spectrum of GNP powder. The GNP powder is determined to have a multi-layered structure since the I\({}_{2D}\)/I\({}_{G}\) ratio is less than two (0.53) [48].
# Potentiometric Biosensor Based on Artificial Antibodies for an Alzheimer Biomarker Detection
## 3 Results and Discussion
### FTIR Analysis  
FTIR analysis is useful to evaluate the chemical structure of the nanomaterial and to identify existing compounds by surface analysis. The FTIR / ATR spectra were plotted for SWCNT, SWCNT-COOH, MIP and NIP (Figure 3).  
As observed in Figure 3, the SWCNT does not present relevant visible peaks; however, after acidic treatment, many new peaks appear. The peak observed for SWCNT-COOH at 1701 cm\({}^{-1}\) can be attributed to the C=O stretch of the carboxylic group, whereas the peak at 1545 cm\({}^{-1}\) can be related to the C=C stretch of SWCNTs and the peak at 1150 cm\({}^{-1}\) to the C-O stretch [68; 69]. These bands may be related to the chemical oxidation of SWCNT.  
Both MIP and NIP materials showed intense peaks at 1723, 1539, 1444, 1087.67 and 856 cm\({}^{-1}\), respectively. These peaks have been attributed to the carboxylic/acetone groups, C=C stretch and C-O/C=O stretch bonds. Although these peaks are also present in the SWCNT; however, they showed a peak shift to the left or right. This characteristic allows us to confirm the modification of the surface after polymerization.  
Figure 3: FTIR spectra of SWCNT, CNT-COOH and MIP and NIP.
# Multi-User Detection Based on Improved Cheetah Optimization Algorithm
## 3 Results and Discussion
### Performance Testing of the Algorithm
Neglecting the effect of the proximity ratio between the users, setting the SNR to 5 dB, and other basic experimental conditions as above, the relationship between the number of users and the BER analyzed in this experiment is shown in Figure 5d. From Figure 5d, we observe that although the BER of the HCO algorithm grows from 0.039 to 0.042 as the system's user count increases, the evolution of the BER of the HCO algorithm grows more gently with an increase in the number of users than the other algorithms. The BER is kept at the lowest level, indicating that the HCO algorithm is minimally affected by growth in the number of users and has the largest communication capacity.
## 4 Discussion
### Microsphere Size Function of the Pickering Emulsions Preparation Conditions  
We have investigated the effect of ultrasonication time on the polymer microsphere size obtained from Pickering emulsion polymerization, by keeping the same intensity, the same monomer (MM), and the same nanoparticle (NP-Gly), and the data are presented in Figure 3. For the other monomers, the data are presented in SI, Figures S9 and S10.  
The size distributions of the microspheres were constructed by measuring the diameters of ca. 500 microspheres from the SEM images with the ImageJ software; the SEM images were taken from different regions of the sample at a magnification of 80\(\times\). The size distribution for the microspheres obtained is rather broad, probably due to the non-uniform distribution of the acoustic field, causing different cavitation intensities in the sample volume. From Figure 3, a general trend can be seen, in the size distribution of the PBM polymer microspheres obtained. The size of the obtained PBM microspheres decreases only very slightly with the increase in the ultrasonication time, from 80 \(\upmu\)m at 15 s to 60 \(\upmu\)m at 45 s. This aspect is important in understanding the effect of the ultrasonication time in  
Figure 3: The PBM microsphere size distribution obtained by Pickering emulsion polymerization of BMand stabilized by NP-Gly at different emulsion ultrasonication times.  
controlling the size of the Pickering emulsion colloidosomes and the resulting polymer microspheres. The fact that the size of the microspheres are smaller with the increase in the ultrasonication time is an expected result, and was experimentally demonstrated in surfactant-free emulsion polymerization [42]. Moreover, it can seem that the size of the polymer microspheres depends only slightly on the ultrasonication time, but as we will see next to a much lesser extent than the on the surface and interfacial energy of the NPs, that it ultimately determines the quality and size of the microspheres (colloidosomes) obtained.
# Identification and Isolation Techniques for Plant Growth Inhibitors in Rice
###### Abstract  
Plant growth inhibitors (PGIs) in rice (_Oryza sativa_), or rice alleolochemicals, are secondary metabolites that are either exudated by rice plants to cope with natural competitors or produced during the decomposition of rice by-products in the paddy fields. Of these, the major groups of rice PGs include phenolics, flavonoids, terpenoids, alkaloids, steroids, and fatty acids, which also exhibit potential medicinal and pharmaceutical properties. Recently, the exploitation of rice PGs has attracted considerable attention from scientists worldwide. The biosynthesis, exudation, and release of PGs are dependent on environmental conditions, relevant gene expression, and biodiversity among rice varieties. Along with the mechanism clarification, numerous analytical methods have been improved to effectively support the identification and isolation of rice PGs during the last few decades. This paper provides an overview of rice PGs and techniques used for determining and extracting those compounds from rice. In particular, the features, advantages, and limitations of conventional and upgraded extraction methods are comprehensively reported and discussed. The conventional extraction methods have been gradually replaced by advanced techniques consisting of pressurized liquid extraction (PLE), microwave-assisted extraction (MAE), and solid-phase extraction (SPE). Meanwhile, thin-layer chromatography (TLC), liquid chromatography (LC), gas chromatography (GC), mass spectrometry (MS), nuclear magnetic resonance (NMR), high-resolution mass spectrometry (HR-MS), infrared spectroscopy (IR), near-infrared spectroscopy (NIRS), and X-ray crystallography are major tools for rice PGI identification and confirmation. With smart agriculture becoming more prevalent, the statistics of rice PGs and extraction methods will help to provide useful datasets for building an autonomous model for safer weed control. Conceivably, the efficient exploitation of rice PGs will not only help to increase the yield and economic value of rice but may also pave the way for research directions on the development of smart and sustainable rice farming methods.  
rice; alleolochemicals; extraction; isolation; identification; chromatography; smart agriculture 2023