esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The Form of Occurrence of Elements in Fuels
#### 3.2.2 Waste Wood from the Furniture Production  
The inorganic components contained in biomass and in biomass ash can be divided by their concentration into major (>1%), minor (1-0.1%), and trace (<0.1%) elements [46]. The bonding type of elements affects their release during the combustion process. Ash-forming elements (Al, Ca, Cl, Fe, K, Mg, P, Na, S, Mn, Si, and Ti) in biomass may be bound in an inorganic or organic component. The method of inorganic binding elements in biomass is described by [PERSON] et al. [47]. Inorganic elements Al, Ca, Cl, Fe, K, Mg, Mn, Na, P, S, and Si associated with organic matter are bound directly in organic groups, complexes, matrix, and structures. Elements Ca, K, Mg, Mn, and Na are bound in carbohydrates, Fe in chelates, Cu, Mg, Mn, Mo, N, and Ti in chlorophyll, Ni in citrates, P and Si in esters. Phosphorus is bound by phospholipids, phytates [48] and in proteins where Mg, Mn, N, P, and S can also be bound. Ca and Mg are bound in petcates and oxalates together with K, Mn, and Na. Fe, N, and S may occur in binding to organic S (amino acids, sulfate esters, sulfolipids, sulfonates, and proteins. Organo-metallic compounds are formed with Hg and K [47].  
In inorganic form, the biomass contains Ca, Mg, K, Na, Fe, and Mn, which are bound in carbonates, phosphates, and silicates, where Al and Ti also enter. Important inorganic phases are oxyhydroxides, which contain Ca, Mg, Fe, Mn, Al, and Ti. Elements Ca, Mg, K and Na. Ca, Na, K, and Al may be present in the form of sulfates. Moreover, in the original biomass, all inorganic elements also occur in a water-soluble form [43].  
The most significant source of trace elements contained in virgin wood is the application of preservatives. Pesticides used for forest protection may contain Hg, Cu, Pb, Zn, Cd, Ni, Mn, and As in the form of calcium arsenate or sodium arsenate [40]. Chemical compounds used for surface treatment (siccatives) contain Zn, Cd, Pb, Cu, and Cr [49]. The most common wood-preservatives include chromatoted copper arsenate (CCA), Cu preservative without Cr (ammoniacal copper quaternary--ACQ, copper azide, copper citrate, and copper ethanolamine), boron and Al compounds [50; 51]. Preservative-treated wood contains 1-2% of heavy metals [51]. Content of inorganic elements in WM, wood and wood treated with preservative collected from literature data are listed in Table 5.  
The contents of major elements in WM decreases in the following order: Ca > Si > P > Al > Fe = K > S > Ti; and for trace elements Mn > Zn >> Cu > Pb = Ni > Cr. Zn is an essential element in plants [44]. In untreated stem wood (spruce) is a concentration of Zn < 100 mg/kg d.m. [53]. WM fuel contains a Zn concentration of 200 mg/kg, indicating the presence of Zn of anthropogenic origin (siccatives). The anthropogenic contamination of WM is also evidenced by the increased Al concentration, which is about ten times higher than the average Al concentration in plants--0.02 wt.% and the Fe/Mn ratio, which is >1 [54].
# Tuning the Cell and Biological Tissue Environment through Magneto-Active Materials
## 6 Magneto-Active Polymers in Biomedical Applications
### MAPs for Tissue Hyperthermia  
Another potential use of MAPs relies on their ability to introduce located heating, with great potential in cancer therapy. Chemotherapy is well known to present severe problems such as drug resistance and to be very toxic for the patient. Apart from the advantages that MAPs can provide for delivering drug at a specific site, these nanomaterials can also be used for hyperthermia treatment. Hyperthermia is an interesting approach for cancer treatment, since it has been proven that temperatures between 40 and 44 \({}^{\circ}\)C have been proven cytotoxic for cells in an environment with a low \(p\) = 2 and pH,conditions found specifically within tumour tissue due to insufficient blood perfusion. Under these conditions, radiotherapy is less effective, and systemically applied cytotoxic agents will reach cancer tissue in lower concentrations than well perfused areas, so hyperthermia shows itself as promising approach to target tumours [226]. For that, magneto-active nanoparticles can be employed to release energy under high frequency magnetic fields, thus killing malignant cells or release chemotherapeutics at the tumour site [188; 191; 194; 195; 196; 197]. This increase in temperature is achieved by subjecting the magnetic hydrogels to an alternating magnetic field, reaching temperatures as high as 45 \({}^{\circ}\)C. Many materials have been tested for this purpose, such as Poly (\(N\)-isopropylacrylamide) (PNIPA) [227], Poly(ethylene glycol) [228] or even double-network hydrogels composed by alginate and PNIPA [133], all of them including soft-magnetic iron oxide particles.
## 4 Factors to Study in VANETs
### Message Periodicity  
As mentioned previously, warning mode vehicles inform other vehicles about their status by sending warning messages periodically. Normal mode vehicles participate in the diffusion of these warning packets and, moreover, they also send periodic \(beacons\) with information such as their positions, speed, _etc_.  
Similarly to the number of warning vehicles, the more warning messages are sent at the same time, the more redundant rebroadcasts, channel contention, and message collisions there will be. Thus, message periodicity seems to be an important factor that offers a trade-off between performance and overhead.
# Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data
## 4 Results
### Spatio-Temporal Pattern of Abandoned Salinized Farmland  
The evolution of the detected area of abandoned salinized farmland from 1988 to 2019 is shown in Figure 12. The results show that both the area of abandoned salinized farmland and sand dunes have declined steadily in recent decades. The tense relationship between food demand and crop yield provoked the installation of irrigation systems (e.g., center-pivot sprinkler irrigation systems, Figure 6b) in the desert area. As a result, an increasing desert area was turned into an oasis in Hetao (area of desert decreased from 1.32 \(\times\) 10\({}^{5}\) ha to 0.48 \(\times\) 10\({}^{5}\) ha). In addition, the improved irrigation pattern and techniques, drainage system, and optimized cropping structure lowered the saline situation, allowing some of the abandoned salinized farmlands to be cropped again (the area of abandoned saline area was reduced from 0.45 \(\times\) 10\({}^{5}\) ha to 0.19 \(\times\) 10\({}^{5}\) ha). The spatial distribution of the abandoned salinized farmland in the last three decades is shown in Figure 13. Initially, the abandoned saline area was distributed relatively evenly across the study area. As increasingly highly saline land transformed into non-saline crop field, the abandoned salinized farmland vanished in both eastern and western Hetao. However, it can be seen clearly that there was a group of abandoned salinized farmland that has been in existence for 30 years in central Hetao. These wastelands are usually vast; therefore, it is difficult to leach the salt out of the fields. Moreover, the high potential evaporation in this wasteland made it a groundwater sink, gathering salt from the surrounding cropping area. The incoming salt ascended into the surface soil with the upward water flux, and it might have accumulated in the abandoned salinized farmland if effective leaching had failed to be deployed.  
Figure 11: _Cont._
## 2 Neural Network Architecture
### VCSEL-Neuron Modeling and Dynamic Regimes
Before presenting a layer-by-layer analysis, it is critical to highlight that the proposed neural structure relies on electro-optic synapses (Figure 1c); meaning that the optical output of each VCSEL is recorded by a photodiode (PD) with a bandwidth that is matched by spike duration. The electrical signal generated by the PDs is fed to an analogue driving circuit that weighs, sums and modifies the electrical bias of subsequent photonic neurons. This approach is considered very efficient in terms of bandwidth and flexibility, whereas it allows a straightforward implementation of neural excitation and inhibition. Moreover, it is by far more beneficial compared to power hungry digital solutions. In particular, a positive weight (excitation) corresponds to an increase in the forward bias of the VCSEL, while a negative weight (inhibition) is linked to a decrease in the bias current, driving the laser away from its threshold [9].
###### Abstract  
One of the key reasons for the deterioration of antenna pointing accuracy for radio telescopes is the deformation and tilt of antenna alidades, which primarily result from track unevenness and thermal gradients. A high-precision inclinometer measurement system is installed to investigate the tilt of the antenna alidade and the pointing errors caused thermally. An environment control box with a leveling base was designed to reduce the interference of the external environment, which proved to be effective in guaranteeing the zero-point stability and repeat accuracy of the inclinometer. The tilt of the alidade caused by the track unevenness was measured by a test of slowly rotating the antenna along the azimuth at windless nighttime. A 5-day antenna stationary test and a 48 h astronomical pointing error measurement were performed, which proved the inclinometer measurement system is capable of measuring the thermally induced inclinations with acceptable accuracy. Through a preliminary compensation experiment, the pointing error is compensated from 37\({}^{\prime\prime}\) to 12\({}^{\prime\prime}\), which shows that the application of the system has a good effect on improving the pointing accuracy of the antenna. The system with high measurement accuracy, good system stability, and low computational complexity, proves an effective tool for the radio telescope to solve the problem of real-time measurement and compensation for antenna pointing errors.  
inclinometer; pointing errors; radio telescope; alidade; thermal gradients; track unevenness
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 3 Methodology  
The wireless system integrates several key methodologies to ensure efficient monitoring in real-time, low latency, and reliable data transmission.
## 2 Signal Model
### Compressive Array Signal Model  
The number of antennas in a massive MIMO system is typically much higher than the number of users or targets. Consequently, the impinging signals can be considered sparse in the spatial (angular) domain. Such sparsity property allows us to design an optimal CMM that projects the array receive signal to a lower-dimensional manifold with no or negligible information loss. In this manner, the array receive signal can be compressed significantly in the analog domain as shown in Figure 2. As a result, the number of front-end circuits in the analog domain and the computation burden in the digital domain can be significantly reduced.  
In this compressive sampling scheme, \(M\ll N\) linear projections of the RF received signal \(\mathbf{x}^{\rm RF}(t)\) are taken along the measurement kernels represented as row vectors \(\mathbf{\phi}_{m}=[\phi_{m,1},\phi_{m,2},\cdots,\phi_{m,N}]\in\mathbb{C}^{1\times N}\) with \(m=1,\cdots,M\). The \(m\)th compressed measurement of the RF received signal \(y_{m}^{\rm RF}(t)\) is the linear projection of the RF received signal \(\mathbf{x}^{\rm RF}(t)\) in the \(m\)th measurement kernel \(\mathbf{\phi}_{m}\), i.e.,  
\[y_{m}^{\rm RF}(t)=\left\langle\mathbf{\phi}_{m},\mathbf{x}^{\rm RF}(t)\right\rangle= \sum_{n=1}^{N}\mathbf{\phi}_{m,n}x_{n}^{\rm RF}(t), \tag{2}\]  
where \(x_{n}^{\rm RF}(t)\) is the \(n\)th element of vector \(\mathbf{x}^{\rm RF}(t)\).  
Stacking all \(M\) measurement kernels forms the CMM \(\mathbf{\Phi}=[\mathbf{\phi}_{1}^{\rm T},\mathbf{\phi}_{2}^{\rm T},\cdots,\mathbf{\phi}_{M}^{ \rm T}]^{\rm T}\). Matrix \(\mathbf{\Phi}\) is designed to be row orthonormal, i.e., \(\mathbf{\Phi}\mathbf{\Phi}^{\rm H}=\mathbf{I}_{M}\), to keep the noise power unchanged after applying the compression.  
Denote \(\mathbf{x}(t)\) as the baseband signal corresponding to \(\mathbf{x}^{\rm RF}(t)\). Note that vector \(\mathbf{x}(t)\) is not observed in the underlying system and is introduced solely for notational convenience. Then, the \(M\) compressed measurements in baseband yield \(\mathbf{y}(t)=[y_{1}(t),y_{2}(t),\cdots,y_{M}(t)]^{\rm T}\in\mathbb{C}^{M}\), which is given as  
\[\mathbf{y}(t)=\mathbf{\Phi}\mathbf{x}(t)=\mathbf{\Phi}\mathbf{A}(\theta)\mathbf{s}(t)+\mathbf{\Phi}\mathbf{n} (t), \tag{3}\]  
where \(\mathbf{\Phi}\mathbf{A}(\theta)\in\mathbb{C}^{M\times D}\) represents the compressed array manifold with significantly reduced dimension compared to \(\mathbf{A}(\theta)\).
## 4 Results
### Impact of Illumination Polarization
In order to fit the data, Equation (8) was used, leading to a qualitatively correct result for all three plots in Figure 3. As result of fitting, the sample permittivities could be extracted (see Table 1), illustrating two findings: firstly, the highly non-resonant nature of gold at 10.6 \(\upmu\)m can be reproduced by the fit. Secondly, the comparably small difference in sample permittivity for resonant excitation could be resolved successfully for 3C-SiC at 10.6 \(\upmu\)m and 10.7 \(\upmu\)m, respectively. The deviation between the values determined by us and the literature values is likely caused by inaccurate fitting for a single polarization line. In particular, as detailed in Section 2, the near-field interaction depends on, e.g., tip radius, modulation amplitude, and tip-sample distance in addition to sample permittivity. These important system parameters are not fitted reliably by the single-line measurement and need to be taken into account. To increase the accuracy further and thereby enable a more detailed polarization examination, in the following an analyzer will be added to the detection path.
## 2 Measurement Principles and Methods
### LSF Processing Methods  
The ESF can be transformed into the LSF by differentiating it from the discrete ESF. This process occurs post-noise reduction [40], as outlined in Equation (15), where the actual edge distribution overlays the noise interference.  
\[\begin{split} f^{\prime}_{ESF}(x)&=f_{ESF}(x)+f_{ noise}(x)\\ F\Big{\{}\frac{d}{dx}f^{\prime}_{ESF}(x)\Big{\}}& =F_{LSF}(j2\pi)+j2\pi F_{noise}(j2\pi)\end{split} \tag{15}\]  
Due to the spatial domain differentiation of the ESF, noise increasingly impacts the MTF values of the system. The noise coefficient escalates with rising frequencies, indicating a more pronounced noise effect. Therefore, it is necessary to smooth the LSF to mitigate this noise impact. For smoothing, Hamming windows are typically employed. This paper compares the efficacy of the Tukey and Hamming window functions in both time and frequency domains, as depicted in Figure 7.  
Figure 7 illustrates that in the time domain, the Tukey window (represented by a black line) features smooth transitions at the window's extremities. This characteristic diminishes signal abruptness, thereby aiding in the reduction in spectral leakage. In contrast, the Hamming window (represented by a red line) is generally smoother but exhibits more pronounced jumps at the window's onset and conclusion, potentially leading to increased spectral leakage [41]. In the frequency domain, the Tukey window typically exhibits lower sidelobe magnitudes (peaks outside the main peak) compared to the Hamming window, suggesting enhanced efficacy in minimizing spectral leakage. Moreover, the main sidelobes (central highest peaks) of the Hamming window [42] are marginally wider than those of the Tukey window, indicating a slight inferiority in frequency resolution. These distinctions imply that the Tukey window might be preferable for applications requiring a balance between time-frequency characteristics, reduced spectral leakage, and controlled sidelobe magnitude. Particularly in scenarios demanding precise frequency characteristic measurement or analysis, the Tukey window's attributes may offer superior performance. Therefore, this paper employs the Tukey window for smoothing purposes.
# Biochar Affects Heavy Metal Uptake in Plants through Interactions in the Rhizosphere
## 3 Results
### Plant Growth and Metal Uptake
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Metal**} & \multirow{2}{*}{**Plant Part**} & \multicolumn{6}{c}{**Unpolluted Soil**} & \multicolumn{6}{c}{**Weakly Polinted**} & \multicolumn{6}{c}{**Highly Polinted**} \\ \cline{3-14}  & & \multicolumn{3}{c}{**Control**} & \multicolumn{3}{c}{**Biochar**} & \multicolumn{3}{c}{**Control**} & \multicolumn{3}{c}{**Biochar**} & \multicolumn{3}{c}{**Control**} & \multicolumn{3}{c}{**Biochar**} \\ \cline{3-14}  & & & \multicolumn{3}{c}{**SEM**} & \multicolumn{3}{c}{**SEM**} & \multicolumn{3}{c}{**SEM**} & \multicolumn{3}{c}{**SEM**} & \multicolumn{3}{c}{**SEM**} & \multicolumn{3}{c}{**SEM**} & \multicolumn{3}{c}{**SEM**} \\ \hline Cd & Root & 3.18\({}^{*}\) & a & 0.35 & 3.16 & a & 0.37 & 3.51 & a & 0.34 & 4.15 & a & 0.60 & 19.3 & & **1** & 4.87 & b & 0.71 \\  & Shoot & 1.27 & b & 0.18 & 1.15 & b & 0.12 & 1.07 & b & 0.13 & 1.10 & b & 0.12 & 7.17 & a & 1.14 & 1.30 & c & 0.09 \\ Cu & Root & 27.3 & a & 3.00 & 23.1 & a & 3.19 & 132 & b & 3.29 & 190 & a & 21.7 & 155 & & \(n\) = 1.4 & 43 & a & 57.9 \\  & Shoot & 7.50 & b & 0.35 & 5.67 & b & 0.54 & 14.3 & c & 0.82 & 12.0 & c & 0.71 & 38.3 & a & 4.08 & 13.2 & b & 0.82 \\ Pb & Root & 23.7 & a & 4.05 & 19.4 & a & 4.06 & 32.5 & a & 13.4 & 24.4 & ab & 4.70 & 206 & \(n\) = 1 & 35.4 & b & 2.22 \\  & Shoot & 17.0 & ab & 2.12 & 9.17 & b & 1.47 & 8.17 & b & 1.63 & 6.00 & b & 2.55 & 73.3 & a & 10.8 & 6.67 & c & 1.08 \\ Zn & Root & 41.9 & ab & 0.10 & 64.3 & a & 20.1 & 39.0 & a & 3.71 & 37.9 & a & 7.24 & 93.3 & a & \(n\) = 1 & 77.3 & a & 11.6 \\  & Shoot & 28.5 & b & 2.45 & 28.5 & b & 0.61 & 37.3 & a & 1.95 & 34.0 & a & 2.83 & 105 & a & 26.7 & 49.3 & b & 2.35 \\ \hline \hline \multicolumn{14}{l}{\({}^{*}\)Mean values (\(n=3\)) followed by the same letters are not significantly different (\(\phi<0.05\), Tukey’s test), \({}^{*}\) The three replicates were pooled due to very low plant mass in the control treatment.} \\ \end{tabular}
\end{table} 
Table 3: Metal concentrations (mg/kg) in roots and shoots of barley grown in three differently polluted soils amended or not with biochar (2% by weight). Calculated TF showed that metals taken up by barley were largely retained in roots, as shown by general TF values <1 (Table 4). Biochar application consistently reduced TF of plants grown in all three soils. Biochar application significantly decreased Zn and Cu transfer to shoots in barley grown on HP soil, but had no effect on Cd and Pb translocation in barley. On less polluted soils, no significant effect on TF was observed.
## 5 Results and Analysis  
In reference to the dynamic equations and control structure presented in the above sections, the model of SCIM-FESS is developed in MATLAB/Simulink environment as shown in Figure 5. The mathematical relations developed for mechanical and electrical losses as well as the power electronics losses are also embedded in the model to account for the system losses. The SCIM-FESS is set to run for 20 min and dynamic analysis is performed during acceleration and deceleration states within a speed range of 10-20 k\(\mathrm{\text{r}}\)pm. During the acceleration state, a positive torque of 90 N\(\cdot\)m is applied to charge the flywheel. During the deceleration mode, a negative torque of 90 N\(\cdot\)m is applied where the flywheel delivers energy to the electric grid. The SCIM-FESS is designed to deliver 5 kWh of useable energy during which the DC-link voltage is set to 600 V and maintained by the GSC and MSC during different states of operation.
# Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images
## 2 Materials and Methods
### Index for the Quality Evaluation of Sharpened Images  
#### 2.5.1 Erreur Relative Globale Adimensionnelle de Synthese (ERGAS)  
In this study, an evaluation index was applied to perform a quantitative comparative analysis of the spectral and spatial information contained in the original and sharpened images. ERGAS is an evaluation index that measures the quality of spectral information [26]. ERGAS quantifies the size difference between two vectors; values approaching zero indicate greater similarity in the spectral characteristics of the original and sharpened images. ERGAS is calculated as shown in Equation (21).  
\[\text{ERGAS}=100\frac{h}{l}\sqrt{\frac{1}{K}\sum_{l=1}^{L}\left(\frac{RMSE^{2}(i )}{MEAN(i)}\right)} \tag{21}\]  
where \(h\) refers to the spatial resolution of the sharpened image, \(l\) is the spatial resolution of the original image, \(K\) is the number of bands in the sharpened image, \(MEAN(i)\) is the \(l-\)th band's mean value, and \(RMSE(i)\) refers to the mean square error between the original and sharpened images. \(RMSE(i)\) can be calculated using Equation (22):  
\[RMSE=\frac{1}{M\times N}\sqrt{\sum_{i,j=1}^{MN}\left(\widehat{MS^{L}}(i,j)-MS( i,j)\right)^{2}} \tag{22}\]  
where \(M\times N\) refers to the image size, \(\widehat{MS^{L}}(i,j)\) is the \(i,j\)th pixel value of the sharpened image, and \(MS(i,j)\) refers to the \(i,j\)th pixel value of the original image.
## 4 SMS-Based Telemetry Data Transmission Protocol
### Security  
Another major issue with medical telemetry is that we are working with people's lives and their ongoing wellbeing. When monitoring a patient, we must have a high level of trust in the quality of the data. To trust the data, we must ensure that what we transmit is what is received at the other end. Any data transmitted must be able to be validated at the destination to ensure that data integrity has been maintained. This implies the use of checksums to allow a recipient to check the content of a message and ensure it results in the same checksum value as is recorded in the message. Any difference between the checksum transmitted and the checksum calculated by the recipient implies an error in the transmission, and potential corruption in the data. A checksum must be used which will almost certainly result in an error being discovered as any transmission errors would require the data be discarded and retransmission requested.  
The transmission of personal medial data implies certain privacy concerns. Obviously, most people are unlikely to want their medical data to be open to the public. A telemetry protocol should support encryption or obfuscation of data so as to prevent it being useful to anyone other than the intended recipient. While SMS is not highly secure, SMS messages can be considered secure from the casual eavesdropper due to the encryption which is natively present in SMS. However, the user data segment of an SMS can also be independently encrypted using a user defined method, so there is no reason why an SMS cannot be used for the transmission of personal and private information with user data encryption suited to the matter at hand. SMS has disadvantages, however. Issues around delivery reliability, validity periods of messages, and the time-to-live for an SMS message, the delivery guarantees not made by the network, and the lack of formal delivery receipts must all be considered when examining potential applications for SMS. Furthermore, the security of information must be a concern in environments where personal or privileged data is being transferred. Therefore, for SMS to be used as the telemetry data transmission protocol, each of these issues needs to be examined, and an appropriate mitigation is implemented. If this is accomplished, there is no real reason why SMS cannot be used for the transmission of important data. This protocol supports application specific encryption, and also provides a mechanism to use multiple different encryption schemes for the user data through the use of an encryption type octet.