esa-sceva/satcom-chunk-collection huggingface dataset:
# A Networked Sensor System for the Analysis of Plot-Scale Hydrology
## 3 Results and Discussion
### WSN Challenges and Utility in Hydrology
#### 3.5.2 Node Maintenance  
The maintenance of the data collection equipment depends on knowing the status of each individual node or data logger. However, the data loggers used in this study are not available on-line and therefore it is not possible to monitor the data collected with them in real time. In addition, in order to collect the data, the researcher needs to commute to the location where the data logger is located. There are some disadvantages with this approach. First, if a wire is loose, then data from one or several sensors attached to the data logger is lost. Second, if the batteries are depleted, then the data logger stops working. Third, there is no way to be aware of those issues until the data is downloaded and examined. Lastly, downloading the data from a data logger is time consuming and does not scale to the case of several locations because every location has to be downloaded independently. Also, the data for a single data logger generates a number of separate files (i.e., one at each location and time of downloading) that require further processing before analysis--as is the case for the Decagon Devices EM50 data logger used in this study. On the other hand, the data collected from our WSN is stored directly and automatically in a relational database that is available through a web-based integrated network and data management system for heterogeneous WSN site called INDAMS [97] for online monitoring.  
One way to reduce the need for node maintenance is by using enclosures of high quality, even though they tend to be more expensive, their associated costs pays off in the long run as they are more resistant to environmental damage, less prone to water intrusion, easier to open and close, and, therefore, easier to maintain. In addition, high quality enclosures keep the sensing and communication equipment, and the batteries safer.
# Preparation and Characterization of CPCM for Thermal Energy Storage Unit
## 2 CPCM Preparation and Characterizing Instruments  
### Preparation of LA/EG CPCMs  
Firstly, analytically pure LA made by Tianjin Kemiou Chemical Reagent Co., Ltd. (Tianjin, China) and both kinds of EGs (carbon content 99%) with 100 mesh and 200 mesh made by Henan Liugong Graphite Co., Ltd. (Zhengzhou, China) are prepared. Because the temperature of the heating water from an ASHP is usually about 50 \({}^{\circ}\)C, LA with a phase transition temperature of 42.0-44.0 \({}^{\circ}\)C and a latent heat of 180 kJ/kg is selected (these characteristics are provided by the manufacturers), which agrees with the results of the literature [12; 18].  
Then, 10 kinds of CPCM samples are prepared by absorbing LA in EG. The mass ratios of LA:EG (100-mesh) of 6:4, 7:3, 8:2, 9:1, and 9.5:0.5 are named CPCM1 to CPCM5, respectively, and the same mass ratios of LA:EG (200-mesh) are named CPCM6 to CPCM10, respectively. The CPCM preparation process is shown as Figure 2.  
1. Weighing LA and EG by a HUAZHI 10,000-digit electronic balance (HUAZHI, Qingdao, China) with an accuracy of 0.0001 g and mixing them together.
2. Putting the mixture into a 60 \({}^{\circ}\)C constant temperature water bath (HH-6) and stirring it at a speed of 250 \(\mathrm{r}/\mathrm{min}\) for 5 min to melt evenly.
3. Transferring the mixture into a conical flask and adsorbing it for 10 min in the constant temperature water bath under a vacuum condition of \(-\)0.68 MPa.  
Finally, the CPCM is obtained by naturally cooling the mixture to room temperature. By the same process, 10 CPCMs are prepared.  
Figure 2: CPCM preparation process.
# A Statistical Performance Analysis of Named Data Ultra Dense Networks
## 5 Results
### Model Selection
#### 5.3.4 Final Model  
Due to the curve in the residual plot of RB of Model 3, \(RB^{2}\) was used in an initial attempt to fit the model. The model with \(RB^{2}\), however, returned a concave downward quadratic function, which did not agree with the fact that an increase in RB would increase the ISR. Therefore, the final model (Equation (10)) was instead fitted based on log(RB).  
\[ISR=46.67+(-10.40)NN_{F}+(-0.05)NI+(27.05)log(RB) \tag{10}\]  
Table 5 presents the coefficient estimates of the final model and their corresponding \(p\)-values. The AIC of the final model selected by stepAIC (AIC = 1276.49) was much smaller than those of the previous models.  
Moreover, the accuracy of the models was also validated by employing residual graphs. In Figure 6, the residual graph shows the residual on the vertical axis and the predictor variable on the horizontal axis. The residual plots for the final model showed adequate goodness of fit. There were no trends in the residual plots.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Estimate** & **Std. Error** & **\(t\)-Value** & **Pr (\textgreater{}l 1)** \\ \hline (Intercept) & 56.879840 & 3.074145 & 18.503 & \textless{}2 \(\times\) 10\({}^{-16}\) *** \\ NN & \(-0.261602\) & 0.058563 & \(-4.467\) & \(1.2\times 10^{-5}\) *** \\ NI & \(-0.052954\) & 0.003219 & \(-16.452\) & \textless{}2 \(\times\) 10\({}^{-16}\) *** \\ RB & 4.091370 & 0.126599 & 32.317 & \textless{}2 \(\times\) 10\({}^{-16}\) *** \\ \hline \end{tabular}
\end{table}
Table 4: Model 2 (ANOVA table).  
Figure 6: Residuals plots: (**a**) residuals vs. leverage, (**b**) NN residuals, (**c**) NI residuals, and (**d**) log(RB) residuals.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Estimate** & **Std. Error** & **\(t\)-value** & **Pr (>| f 1)** \\ \hline (Intercept) & 46.677446 & 2.535294 & 18.411 & \textless{}2 \(\times\)\(10^{-16}\) *** \\ factor(newNN)1 & \(-\)10.402083 & 1.732878 & \(-\)6.003 & 6.72 \(\times\)\(10^{-9}\) *** \\ NI & \(-\)0.052954 & 0.002685 & \(-\)19.725 & \textless{}2 \(\times\)\(10^{-16}\) *** \\ log(RB) & 27.051692 & 0.675387 & 40.054 & \textless{}2 \(\times\)\(10^{-16}\) *** \\ \hline \hline \end{tabular}
\begin{tabular}{c} Signif. codes: 0 \({}^{***\cdots}\); 0.001 \({}^{***\cdots}\); 0.01 \({}^{***\cdots}\); 0.05 \({}^{\cdots}\); 0.1 \({}^{***\cdots}\) 1; residual standard error: 12.01 on 252 degrees of freedom; multiple \(R\)-squared: 0.8895, adjusted \(R\)-squared: 0.8882; \(F\)-statistic: 676.5 on 3 and 252 DF, \(p\)-value: \(\textless{}2.2\times\)\(10^{-16}\); AIC: 1276.49.  
\end{table}
Table 5: Final Model (ANOVA table).
###### Abstract  
As a result of extensive penetration of wind farms into electricity grids, power systems face enormous challenges in daily operation because of the intermittent characteristics of wind energy. In particular, the load peak-valley gap has been dramatically widened in wind energy-integrated power systems. How to quickly and efficiently meet the peak-load demand has become an issue to practitioners. Previous literature has illustrated that the demand response (DR) is an important mechanism to direct customer usage behaviors and reduce the peak load at critical times. This paper introduces air-conditioning loads (ACLs) as a load shedding measure in the DR project. On the basis of the equivalent thermal parameter model for ACLs and the state-queue control method, a compensation cost calculation method for the ACL to shift peak load is proposed. As a result of the fluctuation and uncertainty of wind energy, a two-stage stochastic unit commitment (UC) model is developed to analyze the ACL users' response in the wind-integrated power system. A simulation study on residential and commercial ACLs has been performed on a 10-generator test system. The results illustrate the feasibility of the proposed stochastic programming strategy and that the system peak load can be effectively reduced through the participation of ACL users in DR projects.  
Keywords:stochastic unit commitment; demand response; air-conditioning load; peak-shaving +
Footnote †: journal: Applied Sciences
## 1 Introduction  
Al alloys can develop a thin oxide layer to restrain their further corrosion [1]. However, this thin layer is reactive in damp environments. The susceptibility of Al alloys to corrosion may be further enhanced due to the cathodic activities of their intermetallic particles, such as the Al\({}_{2}\)CuMg and Mg(ZnCuAl)\({}_{2}\) phase [2]. Many coating methods have been reported for fabricating surfaces on Al and its alloys, such as preparing a chromium [3] or superhydrophobic coating on the Al alloy [4]. However, these methods are still subject to some limitations, such as toxicity, a complicated procedure, and high cost.  
Metallic glass (MG) as an excellent coating material is attracting a lot of attention because of its ultra-high hardness and corrosion-resistant properties [5]. Generally, the formation of metallic glass needs extremely high cooling rates and the methods that are used to generate it vary from conventional liquid splat-quenching to pulsed laser quenching to avoid crystallization [6]. However, these methods have either low cooling rates for the MG formation or require expensive instruments.  
Pulsed electrical discharge (PED) treatment based on the spark erosion on a metal surface has been applied widely as the traditional metal cutting method [7]. Recently, PED has been employed to fabricate Zr-, Ti-, and Fe-based amorphous layers on bulk metal as wear- and corrosion-resistant coatings [8] because of its extremely high heating (up to 10,000 \({}^{\circ}\)C) and ultra-high cooling rates. Notably, the intense heating and cooling during PED treatment can also cause element distribution changeson the alloy's surface [9; 10], which could lead to significant changes in its physical and chemical properties, such as surface hardness, hydrophobicity, and corrosion resistance [11; 12; 13]. However, the PED-induced element distribution changes on metal surfaces have seldom been studied.  
The Al-Cu-Mg-(Zn) alloys have been widely used in aerospace and military industries due to their superior combination of high specific strength and fracture toughness but weak glass-forming ability [14]. Herein, PED treatments were adopted to fabricate metallic glasses (MGs) and study their composition and element changes on the surface of Al-Cu-Mg-(Zn) alloys. Significant Al and Cu element segregation has been found in the vitrified coatings on their surfaces. Surface element evaporation has been found and proven to be the main reason for the abnormal element segregation through the PED process. In addition, enhancement of the microhardness and corrosion resistance of Al-Cu-Mg-(Zn) alloys has been obtained after PED treatment. This work provides a green and cheap way for the element proportions and distribution control in wear- and corrosion-resistant Al-based MG layers through PED treatment.
# Throughput/Area Optimized Architecture for Elliptic-Curve Diffie-Hellman Protocol
## 3 Proposed ECDH Architecture
### Dedicated FSM Controllers
#### 3.5.2 Controller-2  
It is responsible for generating control signals for the computation of \(Q=d\times P\) and \(SK=d\times Q\) using Algorithm 1. The mathematical formulations are shown earlier in Figure 2. As presented in Figure 4, the three steps ((i) affine to projective conversions, (ii) PM in projective coordinates and (iii) reconversions from projective to affine) of Algorithm 1 constitute a total of 88 states (states 0 to 87). The details for the corresponding states are as follows:  
1. The initial state (state 0) is idle. When _Controller-2_ receives the _start_ signal (as 1) from _Controller-1_, it starts generating the control signals for projective to affine conversions. It requires 10 states from state 1 to 10 such that each state takes one clock cycle for computation. In other words, the proposed ECDH architecture takes 10 clock cycles for affine to projective conversions.
2. Fourteen instructions of _PAdd_ (seven) and _PDbl_ (seven) functions of Algorithm 1 are operated in seventeen states (state 11 to 27). The reason for more states is the two-stage pipelining. The details of pipelining are given in Table 1 of Section 3.4). During each state, the value for an inspected key bit, i.e., \(d_{i}\), is checked. When the inspected value for \(d_{i}\) becomes 1, the \(if\) part from Algorithm 1 is implemented. Otherwise, the \(else\) part is operated. These 17 states are operated in 17 clock cycles and are repeated until the condition for the \(loop\) statement of Algorithm 1 becomes true. When it becomes true, the processor switches control from PM to the reconversions step.
3. The states from 28 to 87 are responsible for projective to affine conversions. In states 28 to 67, a polynomial inversion is computed. For one inversion computation, our ECDH design takes \(2(10+m-1)\) clock cycles. As shown in Algorithm 1, the projective to affine conversions require two inversion computations. Therefore, the cost for two inversion operations is \(4(10+m-1)\). Moreover, some additional states are needed (from 68 to 87) to accomplish the remaining operations of projective to affine conversions.  
To summarize, the total number of clock cycles for the computation of \(Q=d\times P\) and \(SK=d\times Q\) are calculated using Equation (2). It reveals that the affine to projective conversion requires 10 clock cycles. The PM computation in projective coordinates requires \(17(m-1)\) clock cycles. The projective to affine conversion process requires \(4(10+m-1)\) cycles for two inversion computations and additional 20 cycles for the completion of the remaining operations in the process. Consequently, the proposed ECDH architecture over \(GF(2^{m})\) with \(m=233\) requires 4942 clock cycles for one PM computation (i.e., \(Q=d\times P\), and \(SK=d\times Q\)).  
\[\text{PM}_{\text{cycles}}=10+17(m-1)+4(10+m-1)+20 \tag{2}\]
## Result and Discussion
### Water Penetration  
To examine the water penetration resistance of each specimen, the water penetration test was conducted in accordance with KS F 4919 [31] \"cement mixed polymer waterproof material\". The penetration depth was measured three times at the same atmospheric pressure using the \(\mathcal{O}100\times 30\) mm specimens prepared for the water penetration test, and the average values were calculated. The test results showed that ZWR had the lowest penetration depth followed by OPC, Sand-Co, Silica-Co, FA-Co, and WR-In. For the ZWR specimen, the penetration depth difference was approximately \(\pm 2\) cm, and the amount of water absorbed was \(\pm 3\) g. Table 12 shows the test results, and Figure 10 shows the results in a graph.  
Figure 9: Result of the contact angle test.  
The test results showed that ZWR absorbed little water and the specimen exhibited no significant weight change. This appears to be because ZWR was evenly dispersed inside and the ZWR powder filled the micropores, thereby inhibiting water penetration. Meanwhile, for other specimens, it was judged that silane/siloxane did not penetrate into the mixture. Some of the water-repellent active ingredient attached only to the surface inhibited the bonding of cement, and it increased pores as it was left in pores that were not filled with hydration products.  
Water penetrated all the specimens except the ZWR and OPC specimens, and they could not withstand an atmospheric pressure of 0.3 N/mm\({}^{2}\). Figure 11 shows icoducts35 Jcts35 8 de41 hode3 dtai  
Figure 10: Result of water penetration test graph.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline Name. & \multicolumn{4}{c}{Penetration Depth (mm)} & \multicolumn{4}{c}{Weight Difference (g)} & \multicolumn{4}{c}{Normalized Values} \\  & & & & & & & & & \\ \hline - & 1 st & 2 nd & 3 rd & Average & 1 st & 2 nd & 3 rd & Average & - \\ \hline OPC & 18.13 & 15.34 & 14.41 & 15.96 & 13.1 & 14.5 & 12.7 & 14.09 & 100 \\ \hline Sand–Co & 30 & 30 & 30 & All penetration & 25.1 & 22.6 & 27.8 & 25.1 & 188 \\ \hline Silica–Co & 30 & 30 & 30 & All penetration & 38.3 & 34.6 & 36.4 & 36.4 & 188 \\ \hline FA–Co & 30 & 30 & 30 & All penetration & 27.5 & 21.7 & 24.3 & 24.5 & 188 \\ \hline WR–In & 30 & 30 & 30 & All penetration & 31.5 & 31.6 & 34.3 & 32.5 & 188 \\ \hline ZWR & 2.9 & 2.8 & 2.3 & 2.7 & 2.1 & 1.6 & 1.5 & 1.7 & 16.91 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Result of water penetration test.
# Hyperspectral Imaging for Detecting Plastic Debris on Shoreline Sands to Support Recycling
###### Abstract  
Environmental pollution from plastic debris is raising concerns not only for the vulnerability of marine species to ingestion but also for potential human health hazards posed by small particles, known as microplastics. In this context, marine areas suffer from a lack of constant shoreline cleanups to remove accumulated debris, preventing their degradation and fragmentation. To establish optimal strategies for streamlining plastic recovery and recycling operations, it is important to have a system for recognizing plastic debris on the beach and, more specifically, for identifying the type of polymer and mapping (e.g., topologically assessing) the distribution of plastic debris on shoreline sands. This study aims to provide an operative tool finalized to perform an in situ detection, analysis, and characterization of plastic debris present in the coastal environment (i.e., beaches), adopting a near-infrared (NIR)-based hyperspectral imaging (HSI) approach. In more detail, the possibility of identifying and classifying polymers of plastic debris by NIR-HSI in three different areas along the Pontine coastline of the Lazio region (Latina, Italy) was investigated. The study focused on three distinct beaches (i.e., Foce Verde, Capo Portiere, and Sabaudia), each characterized by a different type of sand. For each location, the adopted approach allowed for the systematic classification of the various types of plastic waste found. Three Partial Least Squares Discriminant Analysis (PLS-DA) classification models were developed using a cascade detection strategy. The first model was designed to distinguish plastics from other materials in sand samples, the second to detect plastic particles in the sand, and the third to classify the type of polymer composing each identified plastic particle. Obtained results showed that, on the one hand, plastics were correctly detected from sand and other materials (i.e., sensitivity = 0.892-1.000 and specificity = 0.090-0.996), and on the other, the recognition of polymer type was satisfactory, according to the performance statistical parameters (i.e., sensitivity = 1.000 and specificity = 0.991-1.000). This research highlights the potential of the NIR-HSI approach as a reliable, non-invasive method for plastic debris monitoring and polymer classification. Its scalability and adaptability suggest possible future integration into mobile systems, enabling large-scale monitoring and efficient debris management.  
shoreline mapping; hyperspectral imaging; near-infrared spectroscopy; plastic debris; polymer identification; recycling +
Footnote †: journal: applied sciences
## 7 Datasets
### Aerospace  
In the aerospace field, MTSAD datasets are typically used to monitor the performance parameters and telemetry data of aircraft, satellites, spacecraft, and other related equipment. Aerospace datasets are crucial for predicting equipment failures and ensuring mission success. By conducting in-depth analysis of this time series data, anomaly detection algorithms can be developed and tested to enhance the reliability and performance of aerospace systems [108; 109]. For instance, the NASA Shuttle Valve Data [81] includes telemetry data from shuttle valves, which is used to analyze the health condition of the valves and predict potential malfunctions. The MSL (Mars Science Laboratory) dataset [25], collected by NASA, contains time-series data from 27 channels, each with 55 dimensions. The data is anonymized, standardized to a range of 0-1, and recorded every minute. Except for the telemetry values, which are continuous, most variables are binary (indicating whether commands were sent). An LSTM-based model can be used for anomaly detection in this dataset. [PERSON] et al. [25] proposed the LSTM-NDT, an unsupervised method for univariate time series anomaly detection, which serves as a non-parametric anomaly threshold method for NASA datasets.
## 4 Conclusions  
An increase in nanoparticle diameter and/or UV-Vis absorbance leads to an increase in colloidal density (mg/mL). The nanoparticle mean size and the density of a colloid are controlled by a combination of laser processing parameters. The most probable nanoparticle size distribution can be determined depending on the ablation time. An ablation time of 2 min always produces a bimodal distribution. The UV-Vis absorbance is directly proportional to ablation time and does not seem to be affected significantly by laser scan speed/fluence. An increase in scan speed tends to reduce the nanoparticle mean size while increasing the nanoparticle count according to DLS, UV-Vis and FESEM analyses. This is the first time that the influence of scan speed on metallic powder PLAL for nanoparticle synthesis has been reported. SEM images show the nanoparticles forming dendritic structures, a phenomenon attributed to the drop-casting/evaporation method implemented in sample preparation. The as-fabricated magnesium nanocolloids have the potential to be used in wearables and flexible sensors whereby they can be printed on flexible substrates for thermal insulation, conductivity enhancement, antibacterial effects or battery electrodes.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app112210974/s1](https://www.mdpi.com/article/10.3390/app112210974/s1)]([https://www.mdpi.com/article/10.3390/app112210974/s1](https://www.mdpi.com/article/10.3390/app112210974/s1)), Figure S1: Nanoparticle size distribution for sample 13A. A small number of partially fragmented particles (300-60 nm) is observed. Table S1: Nanoparticle size distribution per sample set. 3 types of distributions were observed (bimodal, positively skewed and normal).  
Conceptualisation, A.N., M.V. and D.B.; methodology, A.N., M.V. and D.B.; validation, M.V., S.C., B.F. and D.B.; formal analysis, M.V. and D.B.; investigation, A.N., M.V., S.C., B.F. and D.B.; resources, M.V., S.C., B.F. and D.B.; data curation, A.N., M.V. and D.B.; writing--original draft preparation, A.N.; writing--review and editing, A.N., M.V., S.C. and D.B.; visualisation, B.F.; supervision, M.V. and D.B.; project administration, M.V. and D.B.; funding acquisition, M.V., B.F. and D.B. All authors have read and agreed to the published version of the manuscript.  
This publication has emanated from research supported by a research grant from Science Foundation Ireland (SFI) under grant numbers 18/EPSRC-CDT/3584 and 16/RC/3872 and is co-funded under the European Regional Development Fund.  
Not applicable.  
This work is supported by I-Form, the Science Foundation Ireland Research Centre for Advanced Manufacturing. This work is also supported by Advanced Metallic Systems Centre of Doctoral Training (AMSCDT) which incorporates four universities, namely Dublin City University, University College Dublin, The University of Sheffield and The University of Manchester.  
The authors declare no conflict of interest.