esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### TCLP Test for Contaminated Soil/Stabilized Soils  
The concentrations of As that leached with fluids after the TCLP extraction method were analyzed to provide a worst-case scenario (see Figure 7). The As concentration leached in the different soil leachates were: control soil (174.1 ug/L) > steel slag- (95.5 ug/L) > AMDS- (65.9 ug/L) > cement- (32.1 ug/L) > CMDS-stabilized soil (20.6 ug/L) (Figure 7A).  
Figure 6: The cumulative release of arsenic from contaminated (control) and stabilized soils as a function of L/S ratio by the column percolation test.  
Similarly, the mass of As released per unit of soil mass (in terms of mg/kg) was in the order: control soil (3.48 mg/kg), steel slag (1.91 mg/kg), AMDS (1.32 mg/kg), cement (0.64 mg/kg), and CMDS (0.41 mg/kg) (see Figure 7B). The results showed the leaching of As was greatly reduced by stabilization, and the efficiency of As stabilization when compared to the control was in the order: steel slag (45.2%), AMDS (62.2%), cement (81.6%), and CMDS (88.2%). None of the soil TCLP leachates exceeded the USEPA toxicity characteristic (TC) limit of 5 mg/L for hazardous waste [30].
## Performance of Magnetic-Superconductor Non-Contact Harmonic Drive for Cryogenic Space Applications
###### Abstract  
Harmonic drives are profusely used in aerospace mainly because of their compactness and large reduction ratio. However, their use in cryogenic environments is still a challenge. Lubrication and fatigue are non-trivial issues under these conditions. The objective of the Magnetic-Superconductor Cryogenic Non-contact Harmonic Drive (MAGDRIVE) project, funded by the EU Space FP7, is to design, build, and test a new concept of MAGDRIVE. Non-contact interactions among magnets, soft magnetic materials, and superconductors are efficiently used to provide a high reduction ratio gear that smoothly and naturally operates at cryogenic environments. The limiting elements of conventional harmonic drives (teeth, flexspline, and ball bearings) are substituted by contactless mechanical components (magnetic gear and superconducting magnetic bearings). The absence of contact between moving parts prevents wear, lubricants are no longer required, and the operational lifetime is greatly increased. This is the first mechanical reducer in mechanical engineering history without any contact between moving parts. In this paper, the test results of a -1:20 inverse reduction ratio MAGDRIVE prototype are reported. In these tests, successful operation at 40 K and 10\({}^{-3}\) Pa was demonstrated for more than 1.5 million input cycles. A maximum torque of 3 N m and an efficiency of 80% were demonstrated. The maximum tested input speed was 3000 rpm, six times the previous existing record for harmonic drives at cryogenic temperatures.  
Keywords:Magdrive; Harmonic drive; Magnetic gear; Cryogenics; Space mechanism; Contactless device; Superconducting magnetic bearings +
Footnote †: journal: Journal of Applied Mechanics and Physics
## 2 Background and Related Work of LPWAN
### Related Work to Applications of LPWAN  
In this sub-section, we summarize the current solutions based on similar networks in Section 2.1 for forests or environmental monitoring. In Refs. [43; 44], data communication networks that can inform about forest fires in real-time were developed that could use long-distance, wireless-communication-based tools, namely LoRa. In terms of data transmission, the use of LoRa as an early warning method for monitoring forest fires in the Riau province allowed data to be sent from sensors to a gateway as far as 30 miles away [45]. In Ref. [46], [PERSON] and [PERSON] designed a LoRaWAN Internet-of-things network for Smart Manufacture on Batam Island. LoRa acquires high recognition by providing a low cost for IoT modules and equipment, M2M, and other industrial needs [47]. In order to monitor the system conditions perfectly, the authors of Ref. [48] incorporated LoRaWan-based IoT technology into the smart field environment. In Ref. [49], the authors applied IoT technology to propose a novel forestry management system in the Fushan Botanical Garden in Taiwan. Their system techniques included the following: 1. Transmission of sensing data on forest information using the wireless network communication technology of a low-power wide-area network (LPWAN) such as LoRa or an NB-IoT. 2. Application of different sensing techniques to process forest resources and monitor the microclimate changes in a forest. In Ref. [50], the authors presented the design and implementation of a LoRa-based forest-fire-monitoring system. The LoRa-based forest-fire-monitoring system technology was based on wireless technology that can transmit data across the forest. To detect the presence of a fire, Arduino Uno was used as a microcontroller that regulates the input from the AMG8833 sensor and GPS Ubox 6M. In Ref. [51], the authors built a LoRa-based smart agricultural management and monitoring system using a WSN in rural areas to replace the current technology of the agricultural monitoring system. In Ref. [52], the authors proposed the design and implementation of a LoRa-based wireless sensor network for monitoring the quality of water in coastal areas, rivers, and ditches with the aim of generating an observatory of water quality for the monitored areas. A comparison between the proposed solution with other methods is listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Technical Protocol**} & \multirow{2}{*}{**Promoter**} & **Established Year** & **Number of Base Station Connections** & **Frequency Band Used** & **Transmission Distance** & **Transmission Speed** & **Technical Category** \\ \hline \multirow{3}{*}{Sigfox} & \multirow{3}{*}{Sigfox} & \multirow{3}{*}{2009} & \multirow{3}{*}{1,000,000} & ISM band, sub-1 GHz & Urban area, 10 km; 100 bps & Terminal device to front-end
## 4 Numerical Results
### Description of Test Cases
In order to describe ship's real motion, the dimensionless excitation period of the scaled tank and the real ship should be set to the same value. According to [63], the excitation period of the scaled tank \(T_{m}\) can be determined by:  
\[T_{m}\cdot\sqrt{\frac{\mathcal{S}}{H_{m}}}=T_{r}\cdot\sqrt{\frac{\mathcal{S}} {H_{r}}} \tag{14}\]  
where \(g\) is the gravitational acceleration; and \(H_{m}\):\(H_{r}\) is the scaled ratio, which is set to 1:50 in this paper. In this paper, the sloshing-induced dynamic force is numerically computed in four characteristic cases, which are the combinations of different filling ratio, maximum rolling angle, and rolling period, as described in Table 5. It should also be noted that two different rolling periods are adopted to carry out numerical simulation. The first one, 1.45 s, is obtained by Equations (12)-(14) and thus is the ship's undamped natural period under the LOADA02 condition. The other one, 1.80 s, is selected to simulate a more violent sloshing phenomenon. Meanwhile, the main calculation parameters used in the MPS method are described in Table 6.  
All the cases have been performed in an Intel (R) Core (TM) i7-11700F 2.5 GHz single processor machine. The step size of the simulation is \(5\times 10^{-4}\) s. The relationship among the number of particles, computation time, and performed time steps for each case is shown in Table 7. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Draft** & **Trim** & **Displacement** & **T.C.G.** & **V.C.G.** & **GM** \\
**[m]** & **[m]** & **[t]** & **[m]** & **[m]** & **[m]** \\ \hline
14.001 & \(-\)0.149 & 260,089.5 & 0.059 & 15.342 & 17.27 \\ \hline \end{tabular}
\end{table} 
Table 4: Parameters of “PACIFIC VISION” under load condition LOADA02, from [61]. \begin{table}
\begin{tabular}{c c c c c} \hline
**ID** & **Filling Height** & **Filling Ratio** & **Rolling Angle** & **Period** \\
**[mm]** & **[\%]** & **[\%]** & **[\({}^{\circ}\)]** & **[s]** \\ \hline
1 & 100 & 18.8 & 10 & 1.45 s \\
2 & 300 & 54.5 & 10 & 1.45 s \\
3 & 100 & 18.8 & 20 & 1.45 s \\
4 & 100 & 18.8 & 20 & 1.80 s \\ \hline \end{tabular}
\end{table} 
Table 5: Description of the numerically computation cases.
## 2 Measurements and Quality Control Processes
### Net Longwave Radiation  
The hourly net longwave irradiance, as indicated earlier, can be calculated from the respective difference of the downward and upward longwave irradiances. The hourly evolution of the calculated \(R_{nl}\) irradiances are shown in Figure 6c, while Table 5 and Figure 12 show the monthly daily net longwave radiation. Equation (16) is applied to estimate downward irradiance values by using the coefficients obtained from the best performance Brunt model, whereas Equation (20) is used to estimate the upward longwave irradiance using the local calibrated coefficients. The scatterplot of the relationship between the estimated and calculated \(R_{nl}\) is shown in Figure 19a. As indicated in the graph, the dispersions of the points are close to the fitted line with R\({}^{2}\) = 0.815.  
The net all-day downward longwave irradiance can be estimated from the difference of Equation (18) and the upward longwave irradiance estimated by Equation (19). The scatterplot of the relationship between the estimated and measured \(R_{nl}\) is shown in Figure 19b. In this case, the coefficient of determination is R\({}^{2}\) = 0.683. The low coefficient of determination is attributed to the weak relationship obtained from the downward irradiance. Therefore, the prediction of \(R_{nl}\) is better for the daytime rather the all-day interval.  
[PERSON] et al. [46] proposed an empirical equation to estimate the daily net longwave radiation (\(R_{nl}\)) given by  
\[R_{nl}=\sigma_{SB}*T_{a}^{4}*(0.34-0.14*e^{0.5})*(1.35*G/G_{c}-0.35) \tag{21}\]  
where \(\sigma_{SB}=4.903*10^{-9}\) MJ m\({}^{-2}\) K\({}^{-4}\) d\({}^{-1}\) (Stefan-Boltzman constant), \(T_{a}\) is the mean daily air temperature in K, \(e\) is the mean daily vapor pressure in kPa, \(G\) is the measured daily global solar radiation in MJ m\({}^{-2}\), and \(G_{c}\) is the calculated daily clear-sky radiation (MJ m\({}^{-2}\)). The above equation is used by WMO for the estimation of the reference evapotranspiration. Using all the available data, the above formula overestimates the net longwave radiation during the most months except the summer period (Figure 20).  
Figure 19: Comparison of measured and estimated hourly net longwave irradiances using the local calibrated coefficients at Athalassa for the period 2016–2017 (validation dataset), during **(a)** daytime and **(b)** all day intervals.
## 3 Results and Discussion
### Thermo-Mechanical Properties
#### 3.2.1 Tensile Strength
Tensile Strengths of Recycled Polystyrene and Virgin Polystyrenes (HIPS 7240 and GPPS 1540)  
Figure 4a-d shows the tensile stress vs. strain curves of recycled PS and virgin polystyrenes (HIPS 7240 and GPPS 1540) under different test temperature conditions of 24 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C. The behavior of elongation as a function of the stress applied to recycled PS, GPPS 1540, and HIPS 7240 at temperatures of 24 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C are as shown in Figure 4a-d respectively. The materials' elongation before breaking showed that recycled PS and HIPS 7240 have the characteristic of non-brittle materials and are classified as tough materials with yield points. Therefore, the thermo-mechanical properties of recycled PS and HIPS 7240 are comparable. HIPS 7240 and recycled PS had a decrease in elongation at break of 8.3% and 22.2%, respectively, when the temperature increased from 24 \({}^{\circ}\)C to 80 \({}^{\circ}\)C. The elongation to failure was considerably higher for the HIPS 7240 than for recycled PS at 80 \({}^{\circ}\)C as shown in Figure 4d, due to the presence of polybutadiene rubber in the HIPS 7240. On the other hand, GPPS 1540 was an exceptionally brittle thermoplastic with elongation to failure of 1% as shown in Figure 4a-d. Figure 5 shows comparisons of the tensile strengths between recycled PS and virgin polystyrenes (HIPS 7240 and GPPS 1540) at different test temperatures of 24 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C.  
The stress level for the tested sample materials decreased with increasing temperature at the same strain rate. The trend observed is because the molecules of the thermoplastic  
Figure 4: Stress vs. strain behavior for HIPS 7240, GPPS 1540, and recycled PS at 24 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C.  
thermal mobility to move [49], and subsequently lead to a reduction of the material's tensile strength due to weakening forces of secondary valence bonding. Recycled PS had a slightly lower tensile strength than virgin PS (HIPS 7240) by 2.8-5.6% as the temperature increased from 24 \({}^{\circ}\)C to 80 \({}^{\circ}\)C. A similar experiment was done by [PERSON] et al. [39] to determine how the amount of reprocessing affects tensile strengths of HIPS, and it was found that the tensile strength decreased with the amount of reprocessing. Thermo-mechanical degradation of the material produced by continuous grinding, drying, and hot pressing contributed to the decrease in tensile strength. Also, the decline was associated with other degrading agents such as water, light, oxidation, contamination by residues such as inorganic, organic or biological residues [39]. On the other hand, GPPS 1540 had higher tensile strengths than recycled PS by 45.0-62.6% because the crystallization behavior of amorphous GPPS 1540 increases yield stress [18; 19; 20; 38].
## 5 Discussions and Future Perspectives
### State of the Art
However, although there are proposals that employ loss functions based on Sobel filters aiming to maintain structural details, the results obtained so far have not proven promising [156]. * Diffusion Models (DMs): Recently, new diffusion-based architectures have emerged to address stability and training issues found in other generative methods. By iteratively adjusting pixel estimates, these models allow for the progressive refinement of reconstructed images. In general, Diffusion Models applied to HDR reconstruction use a loss function based on the L2 loss. The standard diffusion loss, different from the traditional approach that uses information from the input image and the neural network output, compares the noise added to the input data with the noise predicted by the network [3; 25]. In HDR video, approaches based on optical flow and attention blocks have effectively reduced ghosting artifacts and maintained temporal consistency [30; 33; 34].
# Beyond REM: A New Approach to the Use of Image Classifiers for the Management of 6G Networks
## 3 Proposed System  
This section presents a broad framework aimed at addressing the current limitations in applying DL image classifier methods for cellular management, as outlined in Figure 1. Initially, the system collects UE traces that include both network and localization data. Network data--such as radio conditions and the performance experienced by the UEs--are directly available as part of the network's management and control plane structure. These measurements, in combination with the location data, are hereinafter referred to as Localized Measurements (LMs). In contrast, localization data, increasingly integrated into cellular network management and control planes, can be sourced from cellular network-based localization (directly available by the operator), Global Navigation Satellite Systems (GNSS), or additional third-party Localization Services (LCS) [26].  
The ensuing subsections provide an in-depth description of the proposed method for both the generation of NSIs and their subsequent DL inference analysis, especially highlighting its application to failure diagnosis.  
After data acquisition, the next step involves processing this information to create the proposed Network Synthetic Images (NSI). These image-like data structures are integral to applying DL image analysis mechanisms. Subsequently, these algorithms, based on the patterns identified in the NSIs, enable the execution of various network management tasks  
Figure 1: Framework for NSI use and application.  
such as failure identification/diagnosis, early error state detection, and network planning and optimization.
## 6 Experimental Results and Discussion
### Results Obtained Using GBD  
Tables 4 and 5 present the accuracies obtained using SVM, k-NN and Logistic Regression classifiers, trained with the feature vectors created using the GBD described in Section 6.2. The tables are divided according to the descriptor obtained by the closing and opening morphological operations. Table 4 represents, respectively, Area Closing (AC), Area Closing Binary (BinAC), Structural Closing (SC), Structural Closing Binary (BinSC), Reconstruction Closing (RC) and Reconstruction Closing Binary (BinRC). Table 5 represents, respectively, Area Opening (AO), Area Opening Binary (BinAO), Structural Opening (SO), Structural Opening Binary (BinSO), Reconstruction Opening (RO) and Reconstruction Opening Binary (BinRO).  
The accuracies achieved with the vectors extracted using the Closing operation, as shown in Table 4, in almost all classifiers are superior to the accuracies achieved with the Opening vectors, as shown in Table 5. It is noticeable that the Area Closing Binary (BinAC) achieved the best results when compared to other morphological filters, reaching the 96.67% mark using SVM and 95.83% using k-NN (\(k=5\)) classifier.  
The Reconstruction Opening (RO) vector, as shown in Table 5, obtained the lowest accuracies in all experiments, 50.83%, with the Logistic Regression classifier.  
The results obtained using vectors obtained by the granulometry operations were very divergent; AC, BinAC and BinSC performed even better than LPQ, and others such as SO and RO obtained very poor results. Concerning our **RQ2**, we can conclude it is possible to perform cancer identification exploring some granulometry filters described in Section 4, but not all of them.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **SVM (\%)** & **5-NN (\%)** & **REG (\%)** \\ \hline AC & 95.00 & 85.00 & 92.50 \\ BinAC & **96.67** & 95.83 & 88.33 \\ SC & 67.50 & 54.16 & 85.83 \\ BinSC & 71.66 & 75.83 & 92.50 \\ RC & 79.99 & 65.83 & 76.66 \\ BinRC & 82.50 & 70.83 & 75.83 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification accuracy using closing vectors.
# Blockchain Applications in Forestry: A Systematic Literature Review
## 4 Blockchain Applications in the Forestry Sector
### Forest Fire Detection  
Only Two (out of 21) included studies presented two blockchain-based IoT smart systems for forest fire surveillance. There are some similarities between these two included papers. Both studies are written by the same first author [PERSON] [50, 51]. Two smart forest fire detection systems have similar workflow and architecture with a few differences. This subsection analyzes and discusses these two blockchain-based IoT systems proposed for forest fire surveillance. Table 4 presents data extracted from these two articles. Based on the feature of these two articles, the categories in Table 4 have a slight difference from the tables in the previous two subsections.  
Blockchain-based Sybil-secured smart forest fire surveillance (BSSFFS) was proposed to transmit secured and Sybil-protected data in the forest wildfire [50]. The architecture of BSSFFS was composed of three layers: IoT layer, Fog layer, and Cloud layer. The IoT layer consists of IoT sensing devices (sensors) placed in forests. The sensors (including temperature sensor, fire sensor, humidity sensor, and smoke sensor) collect related data from forests. The IoT layer is connected to the fog nodes of the fog layer [50]. The fog nodes are physical devices to process real-time data. The fog nodes in the Fog layer preprocesses data collected from the IoT layer to predict the fire status in forests. The Fog nodes implement blockchain technology to perform real-time decision making and mining of transactions. [PERSON] et al. [50] also introduced an energy-efficient consensus algorithm called Proof-of-Confidence (PoC) to validate data. The Fog layer transfer mined data to the cloud layer. The cloud layer stores data and decrypt updated data. The Fog and Cloudlayer (cloud storage) form the blockchain network. The cloud performs decrypted data to predict forest fire and alert the fire department [50].  
Blockchain and EdgeDrone based secured data delivery for forest fire surveillance (BESDDFFS) was also developed for forest fire detection [51]. The architecture of BESDDFFS has three layers: IoT layer, edge layer, and cloud layer. The IoT layer in this system consists of drones to collect data. The drones are divided into leader drones (for adding data to blocks, collecting and processing data) and member drones (for data acquisition and data processing) to capture images of forests. The edge layer processes data at the local processing and routing station to trigger the alarm of the fire [51]. It communicates with both the edge layer and the cloud layer. The cloud layer performed on the Google cloud server reacts to the alarms generated from the edge layer [51]. Blockchain in the cloud layer validates the alarms. If the validation is successful, the system will alert the fire department immediately. If the validation is unsuccessful, the cloud layer will list the alarm in the buffer queue for subsequent alarms [51].
# A Novel Metasurface Lens Design for Synthesizing Plane Waves in Millimeter-Wave Bands
## 3 Metasurface Lens Design
### Metasurface Lens  
Different phase-shift elements were provided by the metasurface lens for the radiating wave from the feeding antenna to form a plane wave in a reduced distance. The phase difference between different phase-shift element positions could be determined as follows:  
\[\varphi_{i}=\frac{2\pi}{\lambda}r_{i}, \tag{3}\]  
where \(r_{i}\) was the distance between focus \(F\) and position \(i\), \(\varphi_{i}\) represented the phase distribution at position \(i\), and \(f\) was the focal length.  
As shown in  
The metasurface lens was symmetrical in vertical and horizontal directions with a radiating-aperture size of 0.4 m \(\times\) 0.4 m. The Jerusalem-cross element-based metasurface lens with a pertinent phase shift provided the required phase delay according to Equation (4).