esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### The Case of Non-Ideal Signal
The same procedure is applied to all \(\beta_{i}\) in the set \(\Omega_{c}\) to retrieve all \(\Pi^{*}_{i}\left|\forall\beta_{i}\in\Omega_{c}\right.\). The final set of sensor nodes for pruning the positioning area is \(\Gamma=\left\{c,\,e,\,i,\,k,\,m,\,p\right\}\), as shown in Figure 4\(g\). The positioning area pruned by the sensor nodes in the set \(\Gamma\) is shown in Figure 4\(h\). CAPPS employs the position-aware nodes and the reference points collected by the mobile anchor that are able to detect an unknown node in order to obtain the approximate location of that unknown node. Next, CAPPS uses the sensor nodes that are not able to sense an unknown node in order to refine the possible area. The centroid point (CP) mechanism employs the position-aware nodes or reference points that can receive signal from an unknown node and their one-hop neighbor nodes that cannot detect the unknown node to find the approximate location of the unknown node. CAPPS uses the CP mechanism to filter the points in order to compute the possible area of the unknown node, thus refining the size of the positioning area. Among the existing methods that use a mobile anchor to locate the unknown nodes, both the unknown nodes and the mobile anchor use the same communication range. Thus, the proposed i-SCAN and MARS methods also apply this parameter. However, if the communication range is not the same, the smaller of the two communication ranges between the unknown nodes and the mobile anchor must be applied in order to achieve a uniform communication range for positioning. It is also necessary to use the minimum communication range when computing the upper and down boundary of the covered area. Therefore, it can be ensured that the communication range of the mobile anchor is able to cover all unknown nodes as the mobile anchor is moving along its trajectory. In addition, almost all methods using a mobile anchor to position the unknown nodes set the deployment area as a rectangle. If this is not the case, the practical area can be enclosed with an outer rectangle before applying mobile anchor positioning algorithms. Therefore, these positioning methods can also be used in non-rectangular scenarios.
## 8 Expected altimeter performances
### Measurements objectives and principle
#### Performance of the instrument acquisition phase  
Let us first characterize the acquisition duration. In the nominal acquisition mode, with a search range of \(\pm\) 25 km, the acquisition duration can last up to 2.8 s. This can be very critical because about 18 km in the along-track ground direction is covered during this time. Coupling the search phase with DORIS/DIODE outputs, this acquisition duration will decrease down to 400 ms only. Such a large improvement can prove very useful in particular to detect small continental water areas.  
Figure 8: Pointing errors effect on Ka-band waveforms. (Horizontal axis: Range gate number; Vertical axis: Return Power)
# Development of Practical Design Approaches for Water Distribution Systems
## 3 Model Formulation
### Performance Measures  
To quantitatively determine the design performance from a practical approach, the individual simulation is repeated 50 times, with each simulation using more than 10,000 iterations for each problem. The number of individuals run is determined by the sensitivity analysis increasing the number of runs (e.g., 5, 10, 20, 30, 50, 70, 100). Since the optimal design of WDSs using the metaheuristic has an uncertainty of the quality of solutions, for objective performance evaluation, the sensitivity analysis is necessary. As a result of sensitivity analysis, at least 50 run times are needed to show stable performance, and if the individual run is less than 50 times, the design generated by the optimization has a large variance. For this reason, this study selected the number of individual runs as 50 times, and the final solution is taken as a mean value from 50 run times, rather than the accidental best solution to show the stable and objective results.  
In addition, the simulation times also are tracked for evaluating the performance and running speed simultaneously. To verify the proposed approach, this study applies the pipe continuity index (PCI), which evaluates the number that satisfies for the pipe size continuity. PCI is calculated using the rate between the total number of pipes and the number of pipes satisfying the pipe size continuity constraint by following Equation (6). When pipe size continuity is satisfied perfectly in a WDSs design, it becomes 1. On the other hand, if pipe size continuity is not satisfied, the index decreases relatively. How many pipes have a pipe size continuity problem? When all pipes violate the continuity constraint, the index would be as 0.  
\[PCI=\ \frac{NP_{con}}{NP_{total}} \tag{6}\]where \(NP_{con}\) is the number of pipes satisfying the pipe size continuity and \(NP_{total}\) is the total number of pipes.
## 3 Identification of Cable Tension Using TFR Techniques
### Identification of Time-Varying Cable Tension  
Based on the results of the reassignment spectrogram \(\overline{P}_{a}^{g}(t,\omega)\), the time-varying frequencies of cables are identified with the following process: First, the time-varying frequency on each sampling point \(j\) is picked up from the peak value point, where \(N\) is the number of the sampling points. Subsequently, the basic material parameters of the cable, such as length, mass per unit length, etc., need to be tested or referred to in the design data. Finally, the cable tension can be identified via measured frequency based on the relationship between the tension and frequency. Particularly, the string theory is used to calculate the tension in this study:  
\[T=4 mL^{2}\frac{{f_{k}}^{2}}{k^{2}}\ (k=1,2,\cdots,\infty), \tag{12}\]  
where \(T\), \(m\), \(L\), \(f_{k}\)', and \(k\) are taut tension, mass per unit length, length, the \(k\)-th frequency of sampling point \(j\), and frequency order of the cable, respectively. In Equation (12), \(m\) and \(L\) are constant parameters for specific cables. Thus, cable tension \(T\) can be explicitly represented as a function with respect to \(f_{k}\)' and \(k\).  
The detailed implementation process of the TFR-based algorithm is combined with the signal processing theory and mechanics theory. The identification algorithm can be divided into several steps, as shown in Figure 1.  
Step 1: Measure the dynamic response of cables from health monitoring systems;  
Step 2: Choose a suitable length of the window function \(g(t)\), calculate three STFT spectrograms \(F_{a}^{g}(t,\omega)\), \(F_{a}^{g^{\prime}}(t,\omega)\), and \(F_{a}^{tg}(t,\omega)\);  
Step 3: Calculate the time-frequency reassignment spectrogram \(\overline{P}_{a}^{g}(t,\omega)\) from three STFTs and compare the time-frequency resolution using the different length of window function in parameter studies;Step 4: Identify the time-varying frequency from the reassignment spectrogram \(\overline{P}_{a}^{S}(t,\omega)\) by picking the peak ridge; Step 5: Calculate the time-varying tension based on the vibration theory of cables by Equation (12).
## 2 State of the Art  
The IoT network domain is highly fractured. This applies to higher level standardisation [6] down to the direction, design and evaluation of new protocol and communications ideas. In order to have productive discussion it is necessary to narrow the focus to a more specific domain or use case. Much of the discussion will be focussed on IoT for smart cities, and in particular communications networks for BEMS. These systems incorporate building wide sensor and actuator networks primarily for collecting environmental data but increasingly for effecting change to the environment. Section 2.1 discusses the relevant communications solutions in this area. While each solution presents a unique method or performance profile the section stresses the inconsistencies in evaluation methods which leads to difficulties in comparing the solutions. Section 2.2 discusses the current techniques used to model and understand the underlying communications network. Many of these models focus on modelling the communication channel in IoT networks. Less work has been directed at modelling the performance of communications solutions as a whole in the network. Typically these performance measurements have been presented using empirical testing and evaluation of individual or sets of protocol in research papers directed at showcasing the abilities of a particular solution. We believe there is a significant absence within the literature with regard to the provision of standardised testing and overall performance modelling.  
While nearly all papers evaluating newly proposed communications solutions agree that it is important to test the solution over a range of varying physical features of a network, there is no consensus or standardisation of evaluation methods. This article aims to shift the perspective from evaluation of communications solutions in isolated testing environments to a standardised evaluation process after which direct comparisons can be made.
## 4 Conclusions  
Hydrothermal carbonization of grape seeds is a promising way of valorization of that biomass waste from the wine industry. It allows obtaining chars of fairly good properties to be used as improved solid fuel. Further activation of those chars leads to higher value-added products, activated carbons. Activation with FeCl\({}_{3}\), H\({}_{3}\)PO\({}_{4}\) and KOH led to high BET surface area carbons of predominantly microporous texture, which can be useful adsorbents for liquid-phase applications, as in water pollutant  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **pH Slurry** & \(q_{L}\) **(mg\(\cdot\)g\({}^{-1}\))** & \(K_{L}\) **(L\(\cdot\)mg\({}^{-1}\))** & \(R^{2}\) \\ \hline GSHC-KOH-3 & 7.6 & 650.8 & 0.055 & 0.983 \\ GSHC-FeCl\({}_{3}\)-3 & 8.1 & 147.4 & 0.295 & 0.963 \\ GSHC-H\({}_{3}\)PO\({}_{4}\)-3 & 2.2 & 128.6 & 0.069 & 0.988 \\ Commercial AC & 7.7 & 221.8 & 0.296 & 0.951 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Langmuir parameters for SMX adsorption on the activated carbons of Figure 5.  
Figure 5: Adsorption isotherms of SMX on GSAC-KOH-3, GSAC-FeCl\({}_{3}\)-3, GSAC-H\({}_{3}\)PO\({}_{4}\)-3 and commercial activated carbon at 20 \({}^{\circ}\)C (symbols: experimental values; short dot lines: fitting to the Langmuir equation). Error bars are included.  
removal. In fact, a fairly high adsorption capacity was experimentally found with sulfamethoxazole, an emerging contaminant, as the target compound.  
The following are available online at [[http://www.mdpi.com/2076-3417/9/23/5127/s1](http://www.mdpi.com/2076-3417/9/23/5127/s1)]([http://www.mdpi.com/2076-3417/9/23/5127/s1](http://www.mdpi.com/2076-3417/9/23/5127/s1)): Figure S1: N\({}_{2}\) adsorption-desorption isotherms at 77 K of hydrocarbons, Figure S2: CO\({}_{2}\) adsorption isotherms at 273 K of hydrocarbons, Figure S3: N\({}_{2}\) adsorption-desorption isotherms at 77 K of activated carbons not included in Figure 2.  
Conceptualization, E.D., J.J.R. and A.F.M.; methodology, E.D., F.J.M. and J.V.; investigation, E.D., F.J.M. and J.V.; writing--original draft preparation, E.D. and F.J.M.; writing--review and editing, E.D., J.J.R. and A.F.M.; supervision, J.V., J.J.R. and A.F.M.; funding acquisition, E.D., J.J.R. and A.F.M.  
The authors greatly appreciate financial support from the Spanish MINECO (CTM2016-76564-R). Comunidad de Madrid (S2018/EMT-4344) and UAM-Santander (2017/EEUU/07). [PERSON] wishes to thank the Comunidad de Madrid (PEJ16/AMB/AI-1327) for a research grant.  
The authors declare no conflict of interest.
## 3 Results
### Perspective on the Use of Bioristor to Monitor Leaf Mineral Accumulation  
This study also examined whether the OECT sensor might be potentially used for estimation of nutrients (i.e., Ca and K) accumulation in newly growing leaves. Considering that Jw might causes the non-diffusive xylem load of ions at root scale and their solubilization in the sap (as discussed above), the sensitivity of the bioristor to changes of Jw (Figures 9 and 10) anticipates the promising capability of the bioristor to precisely estimate ions accumulation in plant organs. Apart from concentration of nutrients in the feeding xylem sap, nutrients accumulation depends on several factors including xylem-phloem mobility of the nutrient, growth of the organ, use of nutrients along the transportation path [24]. Within the experimental period (17 days) leaf area significantly increased by approx. 30% and 15% of the initial value in trees with regular (control) and reduced Jw, respectively (Figure 11A). That increment of leaf area corresponded to approx. 21 g dry matter per tree of new leaves (control) while it was significantly 50% lower (Student's _t_-test, \(p\) = 0.05) in trees under reduced Jw. The Ca and K nutrient content in that dry matter was determined destructively (dry matter analysis) and through the OECT based estimates (Figure 11B) showing a good agreement between the two methods. The non-linearity between the two methods might be explained considering that the nutrient accumulation depends on several factors (see above), while OECT-based method here adopted employed only the ion sap concentration and water flux density carrying the nutrients. Coefficients used to convert R values in Ca an K molar concentrations were retrieved from [15] who measured under lab condition the values of R in response to salt solutions of known concentrations, of course these coefficients need to be validated in in vivo systems. Improved version of the bioristor accounting for in vivo condition would refine the sensor output, however, it might be preliminarily stated that the bioristor could be a promising tool for monitoring of nutrients accumulation within a precision agriculture domain.  
Figure 11: (**A**) Average leaf area per plant (\(\pm\) SE) measured at beginning and at the end of the experiment in trees under regular (control) and reduced Jw. The increment of leaf area has been determined as the difference between initial and final values. Comparing the increment of leaf area between treatments, * indicates statistically significant differences. (**B**) Accumulation rate (mg day 1) of Ca (\(\bullet\)) and K (\(\Circle\)) as determined through the OECT values of sap concentration and through the chemical analysis of the dry mater of new developed leaves with a 2 weeks’ period. Note that dash circled values have been sampled from trees under reduced Jw.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 2 Energy-Efficient IoT
### Challenges of Achieving Energy Efficiency in the IoT
#### 2.2.3 Dynamic Atmospheres  
IoT devices are often deployed in dynamic environments, where their operating conditions and network statuses are constantly changing [19]. This dynamism significantly influences the effectiveness of energy management strategies: first, IoT devices transmitting data rely on networks, the quality and availability of which fluctuate depending on the location, time, and other factors. Second, high network latency or low bandwidth environments may compel devices to make multiple attempts to transmit data, thereby increasing energy consumption [20]. Third, an IoT device may switch its operating state, such as transitioning from a low-power mode to a high-power mode, according to current operational requirements [21]. Therefore, it is obvious that adjusting the states of IoT devices in a timely manner while meeting performance requirements becomes increasingly challenging.
## 6 SAR for Antennas on Human Body
### Different Techniques to Reduce SAR Value
#### 6.1.1 Reduction Using Electromagnetic Band Gap (EBG)  
The EBG's material properties include the stopband and delayed reaction of the regular structure. EBG structures use high-impedance surfaces in a frequency band to limit surface wave propagation and change radiation patterns. The EBG structure decreases the surface electromagnetic radiation on the body. EBG architecture improves PMC antennas [97]. The peak specific absorption rate (SAR) is 0.0545 W/kg with EBG structural geometry at 2.4 GHz, compared to 5.41 W/kg without an EBG [98]. EBG structures' periodic dimension is half the stopband wavelength, limiting their size for low-frequency applications [92]. This work introduces a small dual EBG. Two circular rings with slots and inner patches are used in the design. Simulations provide the reflection phase and coefficient, which are crucial design performance indicators. Figure 13 shows HFSS SAR values for the EBG antenna on a human arm and male torso model. The EBG array arrangement reduces the SAR to tolerable levels in simulations [99].  
Advantages: EBG structures offer a significant advantage in reducing the specific absorption rate (SAR) by effectively suppressing surface waves and channeling electromagnetic radiation away from the body. EBG structures offer a compelling choice for wearable electronics as they can reduce the energy consumed by human tissues.  
Disadvantages: EBG structures are susceptible to environmental conditions such as humidity, temperature, and mechanical stress. These factors might change the electromag  
Figure 12: Various SAR reduction techniques for wearable antennas.  
netic properties of the electromagnetic band gap (EBG), which might affect its ability to lower the specific absorption rate (SAR) and keep the antenna working well.
# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 4 Conventional Image Watermarking Schemes
### Spatial Domain
[76] proposed a robust watermarking scheme in the spatial domain to minimize the security vulnerabilities in the watermarked image. The fundamental idea behind this work is to embed a compressed color watermark into the luminance channel of the host image using a quantization process with pseudo-random steps. Both methods show good results in terms of robustness and time complexity. In general, watermarking methods based on the spatial domain remain sensitive to several types of attacks, especially synchronization attacks. The primary advantages of spatial domain-based methods are their low computational cost, large watermark capacity, and improved efficiency, which favor their use in real-time watermarking applications. However, spatial domain techniques perform well only when the image is not subjected to any type of distortions, making them less robust to several attacks, such as noise and compression. Table 1 summarizes an overview of the spatial domain-based state-of-the-art schemes.
## 3 Results and Discussion
### Concentrations of Studied APIs in Muddy Creek  
Of the 61 APIs in the presented method, 31 were detected in at least one sample from Iowa (Table S12). The highest number of APIs was detected in the WWTP effluent sample (\(n=31\)) and the lowest (\(n=16\)) in the stream sample collected upstream of the WWTP discharge point. The fluoroquinolones ciprofloxacin and enrofloxacin were the only APIs detected above quantification levels in the upstream sample (54.3 and 185.6 ng/L respectively), potentially due to upstream agricultural pressures or other urban sources (e.g., leaking sewer lines or septic systems). The total API concentration was lowest upstream of the WWTP (239.9 ng/L), highest in the WWTP effluent (10,373 ng/L) and attenuated (from 5561.5 to 3699.4 ng/L) with increasing distance downstream from the WWTP outfall (Figure 5) as one would expect in an effluent-dominated system [25]. At the time of sampling, 55% of Muddy Creek flow downstream from the WWTP consisted of sewage plant effluent. The composition of the detected APIs in Muddy Creek was clearly defined by those in the WWTP effluent and was dominated by antihistamines > antidepressants > antieipileptics > and antihyperglycemics (Figure 5). The API detected at the highest concentration was the antihistamine fexofenadine at a maximum concentration of 1644 ng/L in the effluent and 926 ng/L in surface water collected downstream of the WWTP (Table S4). This was closely followed by the antidepressant desvenlafaxine in the WWTP effluent with a concentration of 1617 ng/L. Interestingly, this chemical is also an active metabolite of the antidepressant venlafaxine, which was detected at 681.3 ng/L in the same effluent sample.  
Figure 4: Sum concentrations of the 19 quantified active pharmaceutical ingredients (APIs) determined by analysis at the CoEMS and USGS respectively with their prioritisation rank determined by total concentration.
# Designing an Adaptive Underwater Visible Light Communication System +
## 4 System Hardware  
The block diagram for the adaptive UVLC system can be seen in Figure 4 below. A modulated 4-PPM signal is created in MATLAB R2023b, which is connected to USRP NI-2920 (manufactured in Austin, TX, USA). The USRP transmits the signal to the Li-Fi transmitter, which converts the electrical signal into light of wavelength 470 nm. The light signal received by the Li-Fi receiver is converted to an electrical signal and sent to MATLAB at the receiving end by the USRP. The two USRPs are connected via loopback. Also, the MATLAB at the receiving end programs the Arduino, which controls the servo motor to change the direction of the Li-Fi Rx to the strongest signal direction for adaptive beam reception.  
The Li-Fi R&D kit, which includes both the transmitter and receiver modules from HYPERION Technologie (manufactured in Istanbul, Turkey), was used in the system. This module provides a 20 MHz bandwidth and has a 170-degree detection field of view. A 1-watt blue LED is employed to transmit the signal through water. The analog signal from the USRP is sent to an optical transmitter front-end, which adds a DC bias, converts the electrical signal into blue light, and then transmits the resulting optical signal through the underwater optical channel [17].