esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation
### Average Energy Consumption for Location Service  
First, we compare the average energy consumption for location service. The location service is defined as receiving the location of all source nodes available in the network. Especially in SLS, if there is no node located on the crossing point of the two paths or one query message of the source node fails, the location service will continue until receiving all the location information of sinks. As the overhead to construct the grid (SDLS, ALS) or anchor nodes selection (SLS) is one time only, we do not include the results. Figure 6(a) shows a graph of each protocol's average energy consumption versus the number of nodes making the anchor system from 1 to 10, incrementing by 1. While TTDD consumes more energy than the other protocols, SLS performs better than ALS and SDLS, since TTDD constructs grid structures (anchor system) for each source node and both ALS and SDLS are quite complex compared to SLS. ALS always uses the border nodes as its anchor system, so energy consumption is concentrated around border nodes. Conversely, SDLS balances across nodes in the entire grid. Therefore, it prevents high energy consumption of specific nodes. When we consider the energy consumption for making an anchor system, SLS provides the smallest energy consumption. However, this method will be disadvantaged in another evaluation.  
Figure 6(b) shows the average energy consumption when nodes send a location query to find nodes to make an anchor system. Here, we consider that there are four nodes making the anchor system. TTDD requires more energy than the other protocols to make an anchor system; we can see that TTDD consumes more energy than the other protocols. TTDD also has to find the location of source nodes per each source node. However, both SDLS and ALS find node locations using global grid nodes with low energy consumption. The anchor system of ALS concentrates location information on border nodes. Thus, it frequently reaches the border nodes to obtain location information. Therefore, SDLS can obtain location information faster than ALS. As shown in the figure, we can see that ALS's energy consumption increases faster than for TTDD. In TTDD, a sink finds four fixed source nodes. If the sink must find more source nodes, the protocol will consume more energy. As in forming the anchor system, SLS has the least energy consumption. However, the energy consumption of SLS increases gradually with the number of nodes querying location if no the sensor node is located on the crossing point or one query message fails.  
Figure 6: Average energy consumption for location service.
###### Abstract
The software packages follow the recent trends in the development of radar applications and are in line with the recommendations of the OPERA program (Operational Programme for the Exchange of Weather Radar Information) of the EUMETNET (Network of European Meteorological Services) [9]. The developed software can indicate noticeable results in the fields of operative meteorological or hydrological practice, with the potential for use in aviation meteorology. The benefits of the \(qRad\) software are represented by improved radar composites to better aid air navigation and safety, whereas those of the \(qPrec\) software are manifested through enhanced QPE, which than can be translated into more realistic hydrographs as outputs from hydrological models, and the estimation/forecast of runway conditions in air traffic management.
## 3 Results and Discussion
### Characteristics of N and P Pollution
#### 3.4.3 Tp  
The average TP concentrations in the overlying water of the six rivers were obviously different in Figure 5c; the water qualities of Chuan Lake and Xinkai River were above level V, and their exceeding multiples were 2.984 and 3.161, respectively. The water qualities of West Lake, Jingshui River, Yitong River, and Yongchun River were, separately, level V, level IV, level II, and level II. Compared with NH\({}_{3}\)-N and TN, the pollution degree of TP was slightly lower. The TP content was still high in Kuancheng and Lvyuan District, but the distribution pattern of PT content was disparate from that of NH\({}_{3}\)-N and TN, which indicated that there might be other pollution sources. Superfluous TP content is still theimmediate cause of water eutrophication. At the same time, inappropriate TP concentration will also cause harm to human health; for example, direct contact with water which has high TP concentration will irritate and damage the skin.  
To summarize, pollutants from industry, agriculture, and residents' lives had caused quite serious N and P pollution to the major rivers in Changchun Urban, among which the TN and NH\({}_{3}\)-N pollution of Jingshui River near the industrial park was the most serious and should be treated as a key target for water pollution prevention. It should also be investigated whether the wastewater treatment process of nearby factories is qualified.
## 5 Conclusions
Additionally, integrating machine learning techniques could refine the compression process, ensuring that diagnostically relevant features are preserved with even greater precision. The DCT-based computational unit designed for image compression presents an effective tool for medical image compression, with direct applications that can significantly enhance storage, transmission, and overall management of medical images. Its integration into healthcare systems promises to improve operational efficiencies, reduce costs, and ultimately contribute to better patient care outcomes. Conceptualization, T.M. and S.G.N. ; methodology, T.M. ; software, T.M. ; validation, T.M., S.G.N., T.P., and V.K.K. ; formal analysis, T.M. ; investigation, T.M., T.P., and S.G.N. ; resources, T.M. and S.G.N. ; writing--original draft preparation, T.M. ; writing--review and editing,T.P. and V.K.K. ; visualization, T.M. ; supervision, S.G.N. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data are contained within the article. **Acknowledgments:** The authors thank Manipal Academy of Higher Education for offering them the [PERSON] scholarship to complete this research. The authors would also like to thank the Department of Electronics and Communication Engineering at Manipal Institute of Technology for providing the necessary laboratory environment and resources. **Conflicts of Interest:** The authors declare no conflict of interest. **List of Abbreviations**  
ALU  Arithmetic Logic Unit  
ASIC  Application-Specific Integrated Circuits  
BEC  Binary to Excess 1 Converters  
CAD  Computer Aided Design  
CT  Computed Tomography  
CPU  Central Processing Unit  
CR  Compression ratio  
CSA  Carry Save Adder  
CSLA  Carry Select Adder  
DCT  Discrete Cosine Transform  
DIP  Digital Image Processing  
EDA  Electronic Design Automation  
ET  Error-Tolerant  
FOM  Figure of Merit  
GDI  Gate Diffusion Input  
GPP  General-Purpose Processors  
HWT  Haar Wavelet Transform  
IC  Integrated Circuit  
ICSBETA  Iterative Carry Save Selector-Based Error Tolerant Adder  
IDCT  Inverse Discrete Cosine Transform  
LPSBETA  Low-Power Selector-Based Error Tolerant Adder  
MAS  Multiplier Adder and Subtractor  
MRI  Magnetic resonance imaging  
OETA  Optimized Error Tolerant Adder  
PDA  Power Delay Area  
PDP  Power Delay Product  
PSNR  Peak Signal-to-Noise Ratio  
RCA  Ripple Carry Adder  
RLE  Run Length Encoding  
ROI  Region Of Interest  
RTL  Register Transfer Level  
SBETA  Selector-Based Error Tolerant Adder  
SSIM  Structural Similarity Index  
TSMC  Taiwan Semiconductor Manufacturing Company Limited  
2D  Two-Dimensional  
UT  Urdhva Tiryagbhyam  
VCD  Value Change Dump  
VLSI  Very Large-Scale Integration
## 3 Proposed Methodology
### Prediction Model  
In this study, to verify the performance according to the generated WBMs, a CNN-based classification model was used to test the classification accuracy according to each defect pattern. As noted in Section 2, the CNN is effective in classifying WBM defect patterns. We constructed a deep CNN model with three convolutional layers, three max-pooling layers, and two fully connected layers for the experiment. Table 4 shows the CNN structure and hyperparameters constructed for the experiments. In the CNN structure, all convolutional layers used the'same' padding and the ReLU function as an activation function. ReLU was used as the activation function of the first fully connected layer, and the softmax function was used as the activation function of the second fully connected layer.
## 1 Introduction
### Additional Design Equations for CSI  
The following equations are critical for proper CSI design [31].  
DC link current maximum ripple:  
\[\Delta i_{L,\text{max}}=\frac{V_{DC}\cdot T_{S}}{L_{DC}}(1-\frac{\sqrt{3}}{2} \cdot m) \tag{7}\]  
where \(T_{S}=1/fs\), \(V_{DC}\) is the input voltage, and \(L_{DC}\) is the DC link inductor.  
Output filter (LC) capacitor RMS current:  
\[i_{RMS}=I_{DC}\sqrt{m(\frac{2}{\pi}-\frac{m}{2})} \tag{8}\]  
Determining the maximum DC link ripple and the low-pass output filter's capacitance current precisely is essential to avoiding design errors in high-power applications where substantial ripple or capacitor stress could cause failure.
## Results and Discussion
### Volatile Profile of Different Peach Cultivars Using GC-IMS
#### 3.2.3 Similarity Analysis of Samples between Different Peach Cultivars Based on PCA  
PCA was performed on the volatile compound content (signal peak volume) of different cultivars of peaches using the Dynamic PCA plug-in, and the data were visualized to obtain a spatial-dimensional plot containing three principal components, where the X, Y and Z axes indicate the first, second and third principal components, respectively (Figure 6). The cumulative contribution rate of PC1, PC2 and PC3 was 65.7%. The results show that the eight peach cultivars were clearly distinguished based on the identified VOCs using GC-IMS. JC and JXIU are located on the right side of the PC1-axis (Figure 6a), indicating that the content of characteristic aroma compounds in these two cultivars were more similar, which might be caused by the fact that JC was an F1 offspring of JXIU. DTML, XFML, JG and JXIANG are close together and can be grouped together, while HH and HJML are located at the ends of the PC3 and PC2 axes, which means that they are relatively different from the other samples based on the VOC performance (Figure 6a). The difference between samples in a group is small, while the differences between groups are more obvious. Overall, the aroma profiles of JC, HJML and HH are more specific compared to the other samples. This result is highly consistent with that of the cluster analysis in Figure 3. The PCA loading plot showed that (E)-2-pentenal, 2-hexenal and pentanal highly contributed to the JC's aroma, and 1-hexanol, 3-methylbutanol highly contributed to the HH aroma. These results are consistent with the GC-IMS fingerprints. These findings suggest that the aroma profiles of these eight popular peaches are quite different for selecting strong/specific aroma cultivars.
# A Watermarking Protocol Based on Blockchain
## 1 Introduction
In fact, blockchain belongs to the category of distributed ledger technologies that enable commercial or network transaction data to be recorded in cryptographic chained blocks by employing several security technologies, such as cryptographic hash, digital signature, and distributed consensus mechanism. When they are appended to a chain, blocks are timestamped and linked in a way that makes them resilient to modifications. Therefore, they are considered to be trusted for transactions among web entities, and can be verified in a decentralized way by exploiting multiple web nodes to form a consensus on whether a transaction is valid or not. In addition, blockchain supports the so-called \"smart contracts\", which represent a way to automatically execute the terms of an agreement reached between distinct web entities. More precisely, a smart contract encapsulates a number of preset rules in the form of code, and sets corresponding trigger events under specific conditions: when the conditions are met, the terms of the agreement are automatically executed without control from a central authority [26; 27; 28; 29; 30; 31]. This paper presents a new watermarking protocol based on blockchain technology. The protocol is built on the experiences previously conducted with the protocols documented in [22; 23; 24], and follows the buyer friendly and mediated design approach. The main aim is to simplify the interaction scheme of the protocol by exploiting the blockchain technology, which makes it possible to better control the involvement of the TTP in the protocol. In fact, such an involvement has been further restricted in order to reduce the possibility of collusive actions from the TTP, making the developed protocol more secure and suited to the current web context. The paper is organized as follows. Section 2 reports on related work. Section 3 introduces the main challenges faced in developing the proposed protocol. Section 4 reports the basics of the proposed protocol, whereas Section 5 describes the protocol in detail. Section 6 analyzes the proposed protocol. Section 7 focuses on the main implementation aspects of the watermarking protocol. The final remarks are in Section 8.
## 5 Solid-Tank Design Problem
### Experiment Result and Discussion  
The stopping parameters for the solid-tank design problems are shown in Table 3; all other parameters remained the same as in Table 2. The maximum accepted step length \(\epsilon_{\text{MAX\_STEP}}\) was designed to be the target manufacturing accuracy of the design. The minimum accepted step length \(\epsilon_{\text{MIN\_STEP}}\) was designed to be the manufacturing error of the design. \(\epsilon_{\text{V}}\) ensured the accuracy of the stability of the solution to be within \(10^{-3}\).  
For each experiment, the method was assigned a random initial point within the constraints. Then, if the method was able to find a solution with unused function calls, it was assigned with a new initial point and began a new search. This process was repeated until the evaluation budget was exhausted.  
Each experiment was performed with three different profile settings: water, Flexy-Dos3D, and ClearViewTM. These represented three standard dosimeters used in gel dosimetry and each had unique optical parameters (index of refraction and linear attenuation coefficient). As such, we had six related but distinct case problems.  
To compare the performance of the DQL and Smart DQL methods, we ran the experiment with both methods. For each individual case, both methods were assigned the same list of initial points and evaluation budget. The optimum scores found by different methods for distinct profiles are shown in Table 4. Recall that values were between zero and one with the goal of maximizing these values.  
Among the six individual tests, the Smart DQL method found a solution with a higher score for five of them under the same evaluation budget and initial points. In two cases (\(n=8\) water and \(n=8\) ClearViewTM), the Smart DQL method found a significant improvement. For the only case where DQL returned a higher score (\(n=8\) FlexyDos3D), the improvement was only 0.003. This showed that the Smart DQL method was more reliable in this application than the DQL method. The experiment results agreed with our conclusion from the performance benchmark.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(\epsilon_{\
abla}\) & \(2\times 10^{-3}\) \\ \(\epsilon_{\text{MAX\_STEP}}\) & 0.5 \\ \(\epsilon_{\text{MIN\_STEP}}\) & 0.001 \\ MAX\_Search (\(n=5\)) & 5000 \\ MAX\_Search (\(n=8\)) & 8000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The Stopping Parameters for the Solid-Tank Design ProblemsWe were not able to identify a uniform design that was competitive for all profiles. The optimal design of the solid tank varied for different models and among different profiles. We noticed that in the eight-variable design, the method tried to minimize the block length \(x_{bl}\) and maximize the laser position \(x_{lp}\) for both FlexyDos3D and ClearViewTM; this suggested that further improvement may be gained by extending the range for these parameters.
###### Abstract  
X-Ray radiation sensors that work at room temperature are in demand. In this study, a novel, low-cost real-time X-ray radiation sensor based on SnO2 nanowires (NWs) was designed and tested. Networked SnO2 NWs were produced via the vapor-liquid-solid technique. X-ray diffraction (XRD), transmission electron microscopy (TEM) and field emission scanning electron microscopy (SEM) analyses were used to explore the crystallinity and morphology of synthesized SnO2 NWs. The fabricated sensor was exposed to X-rays (80 kV, 0.0-2.00 mA) and the leakage current variations were recorded at room temperature. The SnO2 NWs sensor showed a high and relatively linear response with respect to the X-ray intensity. The X-ray sensing results show the potential of networked SnO2 NWs as novel X-ray sensors.  
Keywords:SnO2; nanowire; radiation; X-ray sensors +
Footnote †: journal: Applied Physics Letters
# Joint Power and Time Allocation in Hybrid NoMA/OMA IoT Networks for Two-Way Communications
## 2 System Model  
We consider two-way communications between an AP and \(K\) IoDs. All the nodes are equipped with single antenna and work in half duplex mode. The IoDs are assumed to have limited processing capability, and the traffic on the UL and DL are asymmetric. To adapt to the transmission scenarios, we propose a scalable TDD transmission scheme by combing NoMA and TDMA. As the associated transmission protocol in Figure 1 showed, the transmission time is divided into two proportions of \(\tau\) and \(1-\tau\) for the UL and DL, respectively. On the UL, NoMA is applied by exploiting the AP's strong processing capability, and on the DL, the IoDs with limited processing capability cannot afford MUD for NoMA, and TDMA is thus adopted. Furthermore, the transmission time for TDD, and that for TDMA, is scalable and can be adjusted according to different traffic requirements on the UL and DL.  
Figure 1: Transmission protocol for the proposed hybrid NoMA/OMA scheme.  
consists of two parts, with the former and the latter being contributions from the UL and DL transmissions, respectively. Due to the coupling of power and time variables in the objective function and the multi-user interference (MUI) in the UL transmission rate constraints, the optimization problem is non-linear and non-convex and thus is in need of a numerical efficient solution.
## 3 Results and Discussion  
Surface Functionalization by 4-Carboxybenzenediamine Tetrafluoroborate and Quasi-Vertical Growth of ZnO  
Scheme 2 displays the general three-step design of IIPs. To do so, we functionalized the surface of the gold electrode with carboxyphenyl groups from the corresponding diazonium salt (Scheme 2A, (1)) and then deposited (by spin-coating) ZnO nanoparticles for the vertical growth of the ZnO nanorods (Scheme 2A, (2)). The second step consisted of the formation of the ion-imprinted polypyrrole by electropolymerization of the pyrrole in the presence of L-cysteine (chelator) and Hg\({}^{2+}\) template (Scheme 2B).  
The different steps followed for the formation of ionic cavities are illustrated in Scheme 2C [75]. Removal of the template by washing, adapted to the nature of the functional monomer-template bond, leaves artificial sites capable of selective rebinding of the template.  
The electroreduction of the diazonium salt carried out by cyclic voltammetry in 20 cycles in the range \(-\)1.0 to 0.0 V (vs. Ag/AgCl) gave a virtually zero reduction current (Figure 1A) resulting from a passivation of the electrode following the grafting of diazonium groups (formation of gold-diazo covalent bonds). The electroreduction peak is centered at \(-\)0.2 V (vs. Ag/AgCl). This step of surface functionalization by the diazonium salt makes it possible to tether ZnO NPs to the surface on the one hand and enables vertical growth of ZnO NRs on the other hand. We have shown in a previous work that the growth of ZnO NRs on a surface not modified by diazonium salts gives an orientation of the NRs in all directions and in a random manner [47].  
Figure 1: Cyclic voltammograms corresponding to the electrografting of 4-carboxobenzenediazonium tetrafluoroborate. (**A**) Electroreduction, (**B**) passivation test in H\({}_{2}\)O/KCl 0.1 M solution, containing 0.01 M of K\({}_{3}\)[Fe(CN)\({}_{6}\)] and 0.01 M of K\({}_{4}\)[Fe(CN)\({}_{6}\)]: (1): as-received gold electrode; (2): cleaned electrode and (3): after electrografting of the diazonium salt. Scan rate: 100 mV/s, 20 cycles for electrografting.  
Passivation of the electrode was followed by cyclic voltammetry in the presence of 0.01 M of K\({}_{3}\)[Fe(CN)\({}_{6}\)] and 0.01 M of K\({}_{4}\)[Fe(CN)\({}_{6}\)]. Results presented in Figure 1B indicate that the electrode was passivated while keeping its conductive character (Figure 1B, red). The diazonium salt step makes it possible to achieve an almost homogeneous distribution of quasi-vertical growth of ZnO nanorods [47].
# Fourier Transform (FT) Analysis of the Stress as a Tool to Follow the Fatigue Behavior of Metals
## 3 Tensile Tests  
Firstly, tensile tests were performed on the materials at a strain rate of 3 mm/min. As it can be seen in Figure 1, CRS has the highest modulus and elongation at break, followed by brass and AL. Furthermore, only at very low strain (\(\varepsilon<0.5\%\)) are the stress and strain directly proportional to each other; i.e., the linear regime following [PERSON]'s law. At larger strains, the material response becomes nonlinear: the materials yield and break.
## 4 Towards Query Optimization to Maintain Bounded Congestion Continuously
### Maintaining Accurate Perception of \(x\)-Separation  
For any query scheme, the true location of a moving entity \(e_{i}\) at time \(t\), \(\zeta_{i}(t)\), may differ from its _perceived location_, \(\zeta_{i}(p_{i}(t))\), its location at the time of its most recent query. Let \(N_{i}(x,t)\) be \(e_{i}\) plus the set of \(x\) entities whose perceived locations at time \(t\) are closest to the perceived location of \(e_{i}\) at time \(t\). The _perceived \(x\)-separation_ of \(e_{i}\) at time \(t\), denoted \(\widetilde{\sigma}_{i}(x,t)\), is the separation between \(e_{i}\) and its perceived \(x\)th-nearest-neighbour at time \(t\), i.e., \(\widetilde{\sigma}_{i}(x,t)=\max_{x_{j}\in N_{i}(x,t)}\|\zeta_{i}(p_{i}(t))- \zeta_{j}(p_{j}(t))\|-2\). The _perceived \(x\)-radius_ of \(e_{i}\) at time \(t\), denoted \(\widetilde{r}_{i}(x,t)\), is just \(1+\widetilde{\sigma}_{i}(x,t)\).  
Since a scheme only knows the perceived locations of the entities, it is important that each entity \(e_{i}\) be probed sufficiently often that its perceived \(x\)-separation \(\widetilde{\sigma}_{i}(x,t)\) closely approximates its true \(x\)-separation \(\sigma_{i}(x,t)\) at all times \(t\). The following proposition, whose proof appears in the companion paper [3] (see also [14], Lemma 9) asserts that once a close relationship between perception and reality has been established, it can be sustained by ensuring that the time between queries to an entity is bounded by some small fraction of its perceived \(x\)-separation.  
**Proposition 1**: _Suppose that for some \(t_{0}\) and for all entities \(e_{i}\),_  
1. \(\sigma_{i}(x,p_{i}(t_{0}))/2\leq\widetilde{\sigma}_{i}(x,p_{i}(t_{0}))\leq 3 \sigma_{i}(x,p_{i}(t_{0}))/2\)_, [perception is close to reality for \(e_{i}\) at time \(p_{i}(t_{0})\)] and_
2. _for any_ \(t\geq t_{0}\)_,_ \(t-p_{i}(t)\leq\frac{c_{d,x}}{1+c_{d,x}}\widetilde{\sigma}_{i}(x,p_{i}(t))/12\)__ _[all queries are conducted promptly based on perception]._  
_Then for all entities \(e_{i}\), \(\sigma_{i}(x,t)/2\leq\widetilde{\sigma}_{i}(x,t)\leq 3\sigma_{i}(x,t)/2\), for all \(t\geq p_{i}(t_{0})\)._