esa-sceva/satcom-chunk-collection huggingface dataset:
# The Synergy of Renewable Energy and Desalination:
## 6 Discussion
Hybrid architectures, such as physics-informed ANNs, integrate domain-specific equations such as energy balances and osmotic pressure models with data-driven layers, enhancing predictive accuracy while maintaining physical plausibility. By anticipating fluctuations in both energy supply and freshwater demand, the ANN can facilitate more efficient scheduling of desalination processes, leading to reduced operational costs and improved overall performance. The multivariate input capability of neural networks also supports holistic system control. For example, feedforward ANNs can simultaneously optimize pump schedules to reduce energy consumption, antifouling chemical dosing to minimize costs, and charge/discharge cycles of the storage to maximize round-trip efficiency by processing inputs like real-time salinity levels, energy prices, and weather forecasts. While ANNs demand substantial computational resources for training, advancements in edge computing and lightweight architecture such as TinyML now enable deployment on low-power embedded systems, making them viable for remote, off-grid desalination applications. Their ability to generalize across diverse operating conditions and learn from imperfect datasets positions ANNs as a cornerstone for achieving the reliability and efficiency targets of RES-driven desalination facilities outlined in this review.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 8 Conclusions  
Low power wide area networks (LPWANs) are being widely deployed to interconnect many IoT-based applications, particularly applications that require long transmission ranges, low device/development costs, low power consumption rates (i.e., long battery lifetimes), and high scalability, albeit at low data rates. Nevertheless, many of these LPWAN technologies are frequently susceptible to a number of persisting challenges, such as increased interference in the ISM band, spectral inefficiency, and low data rates. Consequently, there is need for new ideas and paradigms to address these problems for which cognitive radio (CR) suffices as a candidate solution. Thus, in this article, we have discussed the concept of deploying CR in LPWAN systems via a review of different state-of-the-art developers in LPWAN technologies, their respective competing standards, and several application areas; likewise for CR systems. Details regarding the development and integration of CR in LPWAN for IoT were considered on a general note, including the description of a simple network architecture for CR-LPWAN, potential benefits, and challenges. Essentially, this paper has examined the possibilities for CR-LPWAN systems. Presently, only a few LPWAN technologies provide full support for CR in their technologies; thus, this article intends to spur future research interests in the study area of CR-LPWAN systems.  
These authors A.J.O., A.M.A.-M., and G.P.H. contributed equally to this work. Conceptualization, A.J.O., A.M.A.-M., and G.P.H.; methodology, A.J.O.; writing--original draft preparation, A.J.O.; writing--review and editing, A.M.A.-M. and G.P.H.; supervision, A.M.A.-M. and G.P.H.; funding acquisition, A.M.A.-M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Council for Scientific and Industrial Research, Pretoria, South Africa, through the Smart Networks collaboration initiative and Internet of Things (IoT)-Factory Program (funded by the Department of Science and Innovation (DSI), South Africa).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project  
6 LoWPAN IPv6 for low power wireless personal area networks  
ADEMCO  alarm device manufacturing company  
AES  Advance Encryption Standard  
ARDIS  advanced radio data information service  
AS  application server  
ATA  adaptive threshold estimation algorithm
## 4 Conclusions  
This paper addresses the Close Enough TSP to find a sub-optimal trajectory in term of minimal distance for an UAV having to collect data from buried sensor nodes distributed in the environment. The problem is addressed in 2D with the representation of the communication ranges by circular disks. An algorithm is proposed in three successive steps: the first one redefines some target points in case of intersections that enables to reduce the number of nodes. The second one is an evolutionary algorithm enables to find a sub-optimal order to visit the different nodes. Finally, the third step reduces significantly the length of the trajectory through geometrical heuristics. The algorithm is applied on two scenarios with respectively 15 and 50 nodes. The results highlight the obtention of limited trajectory length for the UAV. In particular, if the evolutionary algorithm is applied directly without geometrical heuristics on the 50 nodes, the obtained route length was 15,249 m. That distance was reduced by 28.4% (10,910 m) if the complete algorithm is applied.  
This algorithm has been implemented in a software developed in C++. The advantage of this algorithm is to have a low implementation cost. It is easily programmable without the need of external libraries. The inputs, displayed in a geo-referenced map, are the GPS coordinates of the target locations with their respective communication ranges. These coordinates are first converted into Cartesian ones, to apply next the algorithm. The evolutionary algorithm proposed in this paper (genetic algorithm), as well as the geometrical heuristics, are easy to program and deliver a sub-optimal solution in a few seconds. The output is the trajectory to be followed by the UAV, with the successive waypoints.
## 4 Prediction of SPT Blow Counts
### Prediction and Analysis of SPT Blow Counts  
In this paper, the strength parameters of soil before and after grouting are obtained by testing. Based on Equation (2) and corresponding parameters, the predicted SPT blow counts of various soil layers after grouting can be calculated, as shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & \(\alpha\) & \(\xi\) & \(\mu\) \\ \hline \��⃝1 & 100.0010 & 37.0370 & 0.1207 \\ ⃝2 & 100.0105 & 136.8877 & 0.2782 \\ ⃝3 & 100.0104 & 36.0832 & 0.0479 \\ ⃝4 & 100.0168 & 62.8278 & 0.1138 \\ ⃝1t & 99.9910 & 94.5084 & 0.2309 \\ ⃝4 & 99.9956 & 51.0261 & 0.1501 \\ ⃝5 & 100.0011 & 40.7986 & 0.1842 \\ ⃝6 & 99.9931 & 58.2229 & 0.2329 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The values of \(\alpha\), \(\xi\), and \(\mu\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Before Grouting**} & \multicolumn{2}{c}{**After Grouting**} & \multirow{2}{*}{**Improvement of SPT Blow**} \\ \cline{2-3} \cline{5-5}
**Layer** & \begin{tabular}{c} **the Ultimate Side Resistance** \\ **(kPa)** \\ \end{tabular} & \begin{tabular}{c} **Tested SPT Blow** \\ **Count** \\ \end{tabular} & \
\begin{tabular}{c} **the Ultimate Side** \\ **Resistance (kPa)** \\ \end{tabular} &
\begin{tabular}{c} **Predicted SPT Blow** \\ **Counts** \\ \end{tabular} \\ \hline ⃝1 & 35 & 8 & 37 & 9 & 12.5 \\ ⃝4 & 40 & 14 & 55 & 31 & 121.4 \\ ⃝5 & 45 & 13 & 76 & 22 & 69.2 \\ ⃝4 & 35 & 14 & & & \\ ⃝5 & 50 & 22 & 75 & 39 & 77.3 \\ ⃝6 & 60 & 19 & 90 & 45 & 136.8 \\ ⃝6 & 60 & 15 & 98 & 39 & 160.0 \\ ⃝6 & 65 & 20 & 100 & 60 & 200.0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The Predicted SPT blow count after grouting.  
It can be obtained from Table 7 that the SPT blow count variation characteristics with the ultimate side resistance are nonlinear. The SPT blow counts of cohesive soil increased by about 12.5-160%, and the SPT blow counts of cohesionless soil increased by about 69.2-200%. It can also be concluded that the post grouting technique has a more obvious improvement on the strength of cohesionless soil.
# ATP and NAD+
## 3 Results
### Mitochondrial ATP Production  
To test whether PD was associated with mitochondrial dysfunction in the skeletal muscle the ATPmax was assessed in the FDI and TA muscles in the PD subjects and compared with data from healthy age-matched controls from previously published clinical trials [31; 32; 33]. In the TA, the ATPmax was significantly reduced in the PD subjects relative to the control group (Figure 2A, Table 2), while there was no difference in ATPmax in the FDI between the PD and control subject groups (Figure 2B, Table 2). Since [PERSON]'s disease symptoms frequently present asymmetrically, especially at the stage assessed in this study, we performed a secondary analysis to test whether the predominantly affected side was more impacted than the less affected side of the body. An analysis was done on the subgroup who reported their symptoms starting on the right vs. left (all our evaluations were done on the right) (Figure S1A). There was no difference in the ATPmax values when compared in this way, suggesting that the decline in ATPmax was not due to a change in muscle activity based on PD symptoms.  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **N = 33 (100\%)** \\ \hline Characteristics & \\ \hline Male, n (\%) & 24 (73\%) \\ \hline Female, n (\%) & 9 (27\%) \\ \hline Age, mean & 72.5 \\ \hline Symptom Severity & \\ \hline Years since PD diagnosis, mean & 7.56 \\ \hline [PERSON] \& [PERSON] 2, n (\%) & 6 (22\%) \\ \hline [PERSON] \& [PERSON] 2.5, n (\%) & 11 (40.7\%) \\ \hline [PERSON] \& [PERSON] 3, n (\%) & 10 (37\%) \\ \hline UPDRS-Part 1, mean \(\pm\) SD & 2.85 \(\pm\) 1.94 \\ \hline UPDRS-Part 2, mean \(\pm\) SD & 10.93 \(\pm\) 4.98 \\ \hline UPDRS-Part 3, mean \(\pm\) SD & 16.74 \(\pm\) 7.07 \\ \hline UPDRS-Part 4, mean \(\pm\) SD & 4.04 \(\pm\) 2.69 \\ \hline UPDRS total, mean \(\pm\) SD & 34.59 \(\pm\) 12.62 \\ \hline PRO-PD & \\ \hline Mean (SD) & 815 (423) \\ \hline Minimum & 275 \\ \hline Maximum & 1763 \\ \hline Quality of Life & \\ \hline Excellent & 5 (18.5\%) \\ \hline Very Good & 10 (37\%) \\ \hline Good & 9 (33.3\%) \\ \hline Fair & 3 (11.1\%) \\ \hline Poor & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample descriptive statistics.  
Figure 2: In vivo mitochondrial ATP production. Muscle maximum ATP production (ATPmax) measured using \({}^{31}\)P MRS in the (**A**) leg (tibialis anterior, TA) and (**B**) hand (first dorsal interosseus, FDI). Solid line indicates mean for each group. ** \(p<0.01\).
## 3 Theoretical Model and Computational Analysis of Aberrations in Double Prism Compensation Devices
### Aberration Result Verification  
In addition to combining the aberration coefficients of different planar symmetric systems, aberration coefficients can also be extracted from the optical path differences and ray aberrations of a few real rays [23; 24]. The results of these two methods correspond to each other but are slightly different. Currently, it is straightforward to establish compensation device models for various states using computer simulation software. Simulation software has the ability to track real light rays and provide actual aberrations. The theoretical results, calculated using the  
Figure 5: Aberrations generated on each surface. (**a-d**) represent the magnitude and direction of aberrations produced by the four surfaces respectively. expressions, can then be compared with actual ray-tracing data to verify the accuracy and effectiveness of the results. The aberration methods calculated from theory and data fitting can be written into ZEMAX macros, making it convenient to apply them to different adjustment states of compensation devices. We have tested the system under various adjustment states using this macro, and the results confirm that the expression is accurate. Among these aberrations, distortion is purely a mapping error and will not cause image blurring. Therefore, distortion can be corrected through post-processing of the image. In some applications, post-processing may not be possible, or distortion should be minimized to reduce post-processing errors. For this reason, we specifically emphasize the types of distortions in the aberration field. The distortion satisfies the following equation:  
\[
## 4. Machining History Analysis and Optimization
### Linear Interpolation Based on User-Input Maximum Load  
Cutting conditions include cutting depth, cutting width, feed rate (F) and spindle RPM (S). The feed rate is calculated with feed per tooth (\(f_{t}\)) and RPM using the following equation:  
\[F\ =\ \ S\ \times\ f_{t}\ \times\ N_{t} \tag{7}\]  
In Equation (7), \(N_{t}\) refers to the number of tool teeth. In terms of spindle RPM, the tool RPM in Equation (8) is decided according to the property values of materials. If the line speed at the tip of the tool which encounters a material exceeds the limit of the material, wear is rapidly accelerated. In contrast, a sufficient level of torque and power should be kept to remove the materials.  
\[D\ \times\ \pi\ \times S\ \leq\ Limit_{sped} \tag{8}\]  
\[a\ \times\ b\ \times Coeff_{c}\ <\ T\!orque(S) \tag{9}\]  
Figure 11. Data schema for storage of synchronization data.  
In Equation (9), '\(Coeff_{c}\)' refers to the specific cutting resistance of a material by the unit area, while '\(Torque(S)\)' represents a torque when the RPM is '\(S\)'. If machining history is floated on the time domain, it can check tool position, machining load, and chatter existence, as shown in Figure 12. Changes in feed rates can affect machining load. In addition, adjusting spindle speed can remove chatter. In an overload section, the machining load should be reduced by decreasing the feed rates. In a no-load section, on the contrary, an increase in feed rates has no influence on the machining quality. In terms of chatter, a very short violation can be ignored. However, if it lasts '\(\Delta t\)' seconds or longer, the speed should be adjusted to chatter avoidance RPM.  
First, for uniformization of the machining load, maximum and minimum load ranges should be set. In terms of machining load optimization at a random time (t) on the time domain, feed rates are adjusted by linear interpolation with an equation shown in Figure 12. For chatter removal, a new RPM is set by Equation (5). The new feed rates and RPM should meet Equations (7)-(9).
## 3 Results and Discussion
### Comparison and Evaluation of Prototype DPIs
#### 3.2.4 Comparison of Engine HC and NO\({}_{x}\) Emission Results  
Figure 19 shows the diesel engine experimental results using the same conditions applied in previous chapters. The speed of the engine was maintained at 1200 rpm, and the injection timing was set to BTDC 10 CA in a single injection case and BTDC 15 CA and 5 CA in a two-injection stage (pilot/main) injection case. The fuel injected was 3 mg and 7 mg with the pilot and main injections, respectively. This was done to maintain the total fuel mass with the single injection experiment. By fixing the pilot injection timing at BTDC 15 CA, it was observed that the combustion characteristics and performance were influenced by the injection timing of the main injection. First, it was observed that HC and NO\({}_{x}\) emissions for the conventional type and the Type 3 DPI were different, regardless of single and two-injection stages. As shown in Figure 19a, in the case of the Type 3 DPI at the injection quantity of 7 mg/stroke, the HC emission increased by about 8 ppm, but the NO\({}_{x}\) value decreased by about 23 ppm, which is more than 50% lower than the conventional type DPI. However when the injection timing of BTDC 10 CA differs for the two-injection stage, as shown in Figure 19b. The NO\({}_{x}\) emission value is about 109 ppm, which is about 4.5 times more than the difference. The reason for this is that when the injection angle of the conventional type DPI is small, the injector response is relatively slow and any special distinction between the pilot injection stage and the main injection stage is not clear. Therefore, it is expected to have a similar effect to that of injecting at the injection stroke. Conversely, Type 3 can exhibit low NO\({}_{x}\) emission values with a relatively fast injection response and spray speed. This shows that HC and NO\({}_{x}\) emissions are significantly reduced in a two-stage injection scheme. The premixed combustion phase and the mixing controlled combustion phase were mostly affected by the injection rate, as stated in Section 3.2.3. It was observed that most of the fuel was injected rapidly following the injection signal at BTDC 15 CA, due to the characteristics of the DPI, a fast SOI, and a high injection rate. Therefore, the premixed combustion phase was promoted. Also, the rest of the remaining fuel was injected after the ignition. Therefore, the combustion was continuous, until the controlled combustion phase. As a result, the air-fuel mixture was saturated, and some fuel in the cylinder was not able to participate in combustion, remaining in an unburned condition. Later, the unburned fuel was used in the late combustion phase. Therefore, a multiple injection strategy could be advantageous in combustion performance assessments. In this sense, a Type 3 DPI specified as Case 3-8 (three plate springs + one thick pressure plate + one thin pressure plate) is a very useful measure for a FIE (fuel injection equipment) system in a newly clean CRDi diesel engine.
# Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments
## 5 Key Challenges and Future Research Directions
### Security Issues  
The overall security of the entire sensor system is complex and is required in every layer, especially the network and the application layer. This is because cyber-attack can lead to stealing and loss of data from the cloud, manipulation of computational results, etc. These results could be as catastrophic as the collapse of the entire grid. Thus, breach or loss of data could affect the power security and stability of the power grid [157]. Ref. [3] suggests resolving the issue of cybersecurity with blockchain technology where all the parameters can be retained in blockchains and accessed through blockchain protocols [158]. Another option could be the design of advanced and secured protocols with authentication and authorization as methods for access and control [73].
## 3 Results and Discussions
### Volatile Profile of the Mastic Gum Samples
#### 3.2.1 Monoterpenes  
Monoterpenes were the most dominant volatile composition detected in both mastic gum samples in the current study. It has been previously reported that the prenyltransferase enzyme activity present in the structure of plants plays a crucial role in the formation of the monoterene aroma compounds during the growth and maturation stages of many plants [24]. The analysis results in the current study revealed 11 and 10 aroma constituents, with total concentrations of 3862.1 and 5815.8 ug/kg in the MGT and MGG samples, respectively (Table 2). Comparison of the two samples showed that the MGG sample had a higher concentration of monoterenes compared to the MGT sample. Monoterpenes accounted for approximately 96% and 90% of the total aroma concentration in the MGT and MGG samples, respectively. Similarly, [PERSON] et al. [25] reported that the aroma profile of mastic gum oil consisted of 96.2% monoterenes. In parallel to our results, previous studies indicated that monoterenes were dominant in mastic gum and essential oils [11; 17; 18]. \begin{table}
\begin{tabular}{c c c c} \hline
**Color Properties** & **MGT** & **MGG** & \(p\)1  \\ \hline \(L\)* & 61.92 \(\pm\) 0.06 & 47.36 \(\pm\) 0.01 & ** \\ \(a\)* & \(-\)0.93 \(\pm\) 0.01 & \(-\)1.79 \(\pm\) 0.02 & ** \\ \(b\)* & 6.74 \(\pm\) 0.11 & 27.23 \(\pm\) 0.01 & ** \\ C* & 6.80 \(\pm\) 0.11 & 27.29 \(\pm\) 0.01 & ** \\ \(H^{\circ}\) & \(-\)82.14 \(\pm\) 0.21 & \(-\)86.23 \(\pm\) 0.03 & ** \\ \hline \end{tabular} Results are presented as the means \(\pm\) standard deviations of three replicates. 1 Statistical significances according to the \(t\)-test; ** significant at \(p<0.01\).  
\end{table} 
Table 1: The color properties of the mastic gum samples from Turkiye (MGT) and Greece (MGG).
# Efficient Verification of Cryptographic Protocols with Dynamic Epistemic Logic
## 6 Conclusions and Expectation  
We have introduced a formal language based on dynamic epistemic logic to verify the security of the cryptographic protocol. Especially, we use the action model to describe the execution process of a protocol. The updated model displays transformation between models. We have given the verification model and proved that this protocol meets these requirements. Compared with papers [7; 48], our method does not need to establish complex models or rules to derive the security of the protocol. We can formalize the whole protocol running process only through an action model. This process is simple, intuitive and efficient.  
In the future research, we will extend the frame of the language \(\mathcal{L}_{crypt}^{A,B}\) to make it more expressive and able to express different cryptographic protocols. We should build a complete and sound axiomatization for the extended language. In this protocol, we assumed the attacker is a passive attacker who only can eavesdrop the messages from the network. In the future study, we will assume the attacker is an active attacker and he has more abilities to launch active attacks against the cryptographic protocol, and we plan to build an automatic detection system for security verification including protocols with quantum cryptography as a general modeling tool.  
Conceptualization, X.C. and H.D.; methodology, X.C.; validation, X.C.; formal analysis, X.C.; investigation, X.C.; resources, H.D.; writing--original draft preparation, X.C.; writing--review and editing, H.D.; supervision, H.D.; project administration, X.C. and H.D.; funding acquisition, X.C. and H.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Project of Fundamental Research Funds for the Central Universities (No. XD)K2020B035), and Research Project of Education and Teaching Reform of Southwest University in 2020 (No. 2020Y055), and Special Project of Fundamental Research Funds for the Central Universities (No. SWU2009107), and Special Key Project of Technological Innovation and Application Development from Bureau of Science and Technology in Chongqing (No. cstc2019 jscx gksbx0103), and the Key Base Project of Chongqing under Grant 16 SKB040, and the Great Project of National Social Science Fund under Grant14 ZDB016.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.