esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Image Clustering Based on Label Similarity and Maximizing Mutual Information across Views
## 3 Deep Image Clustering under Similarity and Mutual Information
### Clustering Module  
The relationship of pairwise images is binary, i.e., similar or dissimilar. Based on such observations, the clustering task can be smoothly recast into a binary pairwise classification model [26]. It is natural to use the loss for the classification task instead of the clustering objective function:  
\[minL_{DT}(w_{l})=\sum_{i=1}^{n}\sum_{j=1}^{n}L(r_{i,j},g(x_{i},x_{j};w_{l})) \tag{1}\]  
where \(L_{DT}(r_{i,j},g(x_{i},x_{j};w_{l}))\) represents the cross-entropy loss between the ground-truth similarity \(r_{i,j}\) and the estimated similarity \(g(x_{i},x_{j};w_{l})\), and \(w_{l}\) indicates the learnable parameter of the clustering module.  
In Equation (1), the ground-truth similarity \(r_{i,j}\) of samples \(x_{i}\) and \(x_{j}\) is unknown, and this problem needs to be solved. Previous methods used a high threshold to select pairwise similar (dissimilar) samples to assign \(r_{i,j}\), where \(r_{i,j}=1\) indicates that samples \(x_{i}\) and \(x_{j}\) belong to the same cluster and \(r_{i,j}=0\) otherwise [26; 27]. Due to the existence of the threshold, the similarity relationship between pairwise samples does not satisfy transitivity, i.e., \(r_{i,j}=1=r_{i,k}\
eq r_{j,k}=1\).  
This can lead to unstable network training. Different from the existing methods, this paper uses K-means as a flexible threshold selection strategy to construct \(r_{i,j}\):  
\[r_{i,j}=\left\{\begin{array}{ll}1,&\text{c}_{i}=\text{c}_{j}\;or\;i=j,\\ 0,&\;\;\;\;otherwise.\end{array}\right. \tag{2}\]  
\(r_{i,j}=1\) indicates that \(x_{i}\) and \(x_{j}\) belong to the same cluster c or the same sample, \(i=j\), and \(r_{i,j}=0\) otherwise. When pairwise samples belong to the same cluster, their similarity relationship must be transitive. The estimated similarity \(g(x_{i},x_{j};w_{l})\) can be formulated as:  
\[\begin{split}& g\big{(}x_{i},x_{j},w_{l}\big{)}=f_{1}(x_{i};w_{l })\cdot f_{l}(x_{j};w_{l})=l_{i}\cdot l_{j}\\ & s.t.\forall i||l_{i}||_{2}=1,\,0\leq l_{ih}\leq 1,\,h=1,\ldots,k. \end{split} \tag{3}\]  
where \(f_{l}\) is the clustering module, and the operator \"\(\cdot\)\" represents the dot product between two label features. If the optimal value of Equation (1) is attained, the learned label features are \(k\)-diverse one-hot vectors ideally [26]. Images can be directly clustered based on the learned class-level representations.
# FY3E GNOS II GNSS Reflectometry: Mission Review and First Results
The main characteristics of GNOS II are listed below:  
* Combination of GNSS RO and GNSS-R;
* GNSS-R with multiple GNSS systems (GPS-R, BDS-R and GAL-R);
* Cooperation with a microwave scatterometer on the same platform;
* Nearly global coverage for Earth observation;
* Operational global data latency of less than 3 hours. This paper gives an overview of the FY3E GNOS II payload and its GNSS-R product. Section 2 introduces the GNOS II instrument. Section 3 describes the L1 calibration, L2 wind speed retrieval algorithm and data products. Preliminary validation for the wind speed product is presented in Section 4. Summary and future perspective are presented in Section 5.
## 5 Prototype Characterization  
The element distribution of the reflectarray aperture was transferred to an _FR_4 PCB through chemical etching. The reflectarray antenna structure was manufactured assembling the ground plane and the _FR_4 board that contains the patches, with foam spacers to generate the intermediate air substrate, and it was installed in the commercial holding structure and feedarm for horn location.  
Measurements were performed in the anechoic chamber of the _High Technology and Homologation Centre (CATECHOM)_ of the University of Alcala (Figure 9). Due to the dimensions of the anechoic chamber (3.6 m), the maximum diameter of the antenna (40 cm) and the measurement frequency (11 GHz), measurements were carried out in near field and transformed to the far field. The gain was estimated employing a standard gain antenna and applying the Friis transmission formula and the corresponding near-field to far-field conversion factors [18].  
First, a near field elevation cut was made at design frequency (11 GHz) in order to characterize the antenna holding structure and to steer the main beam along the horizontal plane. After this calibration stage, full 3D RP measurements were carried out, for frequencies ranging from 10 GHz to 12.5 GHz.  
Measurements results are shown in Figure 10. The main cuts at the design frequency (Figure 10a) are similar to the simulated ones with beamwidths of 8.7\({}^{\circ}\) in azimuth plane and 5.2\({}^{\circ}\) in elevation. The maximum gain is above 19 dBi in the frequency range [10.5-12] GHz (Figure 10b), which means that the design antenna operates correctly in almost the full DVB-S band.  
Figure 8: Reflectarray modelled in HFSS and simulation results showing RP cuts.  
Figure 9: Manufactured reflectarray prototype assembly for characterization in the anechoic chamber.
# A Review: Thermal Stability of Methylammonium Lead Halide Based Perovskite Solar Cells
## 4 Impact of Thermal Stress on Various Layers and Possible Solutions
### Hole Transport Layers
A rapid degradation of the perovskite films was observed for all three HTLs, as evidenced by the photographs in Figure 9b. To achieve better thermal stability, organic HTLs were replaced with functionalized single-walled nanotubes (SWNT) embedded in an insulating polymer of polycarbonate (PC) and poly (methyl methacrylate) (PMMA). The absorption  
Figure 8: (**a**) Two-step process to form an insoluble and thermally stable hole extraction contact. (**b**) The performance of devices using Spiro-OMeTAD as the hole extraction contact tested at room temperature (light gray) and after annealing at 110 \({}^{\circ}\)C (dark gray). (**c**) The performance of devices using VNBP-MoO\({}_{3}\) tested at room temperature (light gray) and after annealing at 110 \({}^{\circ}\)C (dark gray). (**d**) Optical microscopy images of doped Spiro-OMeTAD and VNBP-MoO\({}_{3}\) films before and after annealing. (**e**) XRD patterns illustrating the evolution of perovskite content in the device active layer. Reprinted from [63], with permission. and XRD measurements revealed that the devices with spiro-OMeTAD degraded when heated at 80 \({}^{\circ}\)C for 96 h in ambient environment. In contrast, devices with PMMA and PMMA-SWNT showed less degradation in absorption spectra and no significant change in the XRD patterns, as shown in Figure 9d,e. Devices fabricated with P3 HT/SWNT-PMMA HTLs did not exhibit PCE reduction when annealed at 80 \({}^{\circ}\)C. Interestingly, the efficiency increased from 13.3% to 14.3% with an annealing time of 96 h, but a further increase in temperature led to a reduction in PCE (see Figure 9c), possibly due to the degradation of the perovskite layer at such high temperatures.
# The Performance of LiF:Mg-Ti for Proton Dosimetry within the Framework of the MoVe IT Project
## 2 Materials and Methods
### TLD Characterization and Irradiation  
The TLDs used were characterized and calibrated with photon, electron, and proton beams in the dose and energy range of interest for clinical applications. The calibration curves for all beams are reported in previously published studies [32; 33; 39]. According to the entrance energy into the bio-phantom used for the MoVe IT experiments, the corresponding calibration factor was applied to convert the responses of the TLDs to dose values. In order to obtain high spatial resolution dose information, the dosimeters were accommodated in a water-equivalent bio-phantom (Figure 1) designed to contain both TLDs and tumor cells in a gelatine developed for this purpose. The experimental setup ensured that the beam entered orthogonally to the bio-phantom. A schematic view and description of the irradiation configuration implemented in Trento for radiobiological experiments can be found in [24].  
The dosimeters were arranged in single or double rows (named row 1 and row 2). Eight dosimeters were not irradiated and were used to correct the TLD measures for the background signal.  
The experiments reported in this study were performed between 2018 and 2020 and are described below. In the first irradiation stage (July 2018), only one bio-phantom was irradiated by a proton beam with an initial energy of 150 MeV (about 80 MeV on the phantom entrance) at a Bragg peak dose of 1.5 Gy. In the second irradiation stage (October 2018), two TLD rows were used, each positioned in a bio-phantom and irradiated one-by-one. The experimental setup of the first bio-phantom irradiation was the same as that adopted in the July experiment, while in the second bio-phantom the energy of the entrance proton beam was deliberately increased to observe the shift of the Bragg peak position in the TLD row. In the third and fourth irradiation stages (March and October 2020), one single TLD row was irradiated at the dose level with Bragg peaks of 1.5 Gy and 0.8 Gy, respectively.
## 2 Applications in Chemistry
### Transesterification of Cyclic Carbonates  
The transesterification of cyclic carbonates, especially ethylene carbonate **1c**, to give non cyclic carbonates has attracted much attention, due to their role as building blocks for the production of chemical intermediates in the polymer industry [49].  
[PERSON] and co-workers [50] reported the synthesis of dimethyl carbonate **16a** via transesterification of ethylene carbonate **1c** using an excess of methanol (1:15) over 4 h with ionic liquid catalysts supported on the mesoporous cellular foam (MCF) (Scheme 15). In this work, 4-diazobicyclo[2.2.2]octane (DABCO) provided the highest yield of dimethyl carbonate **16a** (84%), while the heterogeneous catalyst [DABCO]OH@MCF gave around 77% yield for the expected product. The ionic-liquid-supported catalyst was re-used four times with only 8% decrease of **16a** yield. At higher temperatures (up to 150 \({}^{\circ}\)C), decomposition of ethylene carbonate **1c** or degradation of the immobilized species was observed, causing a decrease in **16a** yield. Furthermore, the chemically immobilized [DABCO]OH@MCF catalyst proved to be more stable than the physically impregnated DABCO/MCF.  
In a closely related study, [PERSON] and [PERSON] [51] developed the synthesis of a series of bifunctional, DABCO-based, hydroxyl-functionalized ILs, which were applied in the conversion of carbon dioxide into valuable chemicals, such as cyclic carbonates, the transesterification of which was also considered. Here, transesterification of ethylene carbonate **1c** with methanol to prepare dimethyl carbonate **16a** proceeded at a moderate temperature in the presence of different ionic liquids. The [DABCO-PDO][I], functionalized with propane diol functional groups, gave the highest conversion rate and yield for dimethyl carbonate **16a**, with 93% yield after 4 h at 80 \({}^{\circ}\)C (Scheme 15). One major advantage of this protocol is that no transition metal, solvent, or additive is used.
## 2 Power Internet and High Voltage Transmission Line Condition Monitoring Intelligent Sensor Design
### Smart Grid High-Voltage Transmission Line State Data Acquisition Technology
#### 2.2.3 Data Collection Based on Orthogonal Discrete Multiwavelet Transform Algorithm
The decomposed function is:  
\[\varphi(x)=\sqrt{2}{\sum_{m}}H_{m}\varphi(2x-m). \tag{7}\]  
In the decomposed function, its spatial decomposition coefficients also need to be spatially transformed by data translation and scaling, and the final double-scale function is represented by the following function:  
\[\frac{1}{\sqrt{2}}\varphi\Big{(}\frac{x}{2}-k\Big{)}=\sum_{m}H_{m}\varphi(x-m- 2k), \tag{8}\]  
where \(\varphi\) denotes the angle coefficient of the dual scale function, according to:  
\[\varphi_{J,k}(x)=\big{[}\varphi_{1,J,k}(x),\cdots,\varphi_{r,J,k}(x)\big{]}^{T}, \tag{9}\]  
where \(1\) denotes the angle coefficient of the dual scale function, according to:  
\[\varphi_{1,k}(x)=\sum_{m}H_{m}\varphi_{0,2k+m}(x). \tag{10}\]  
Through the exposition of Equations (1)-(10), it is possible to output the multiwavelet function space-shifting initial function, that is, the scale function, which is expressed through the functional relationship equation using the following equation:  
\[C_{1,k}^{T}=\sum_{m}H_{m-2k}(x)C_{0,m}. \tag{11}\]  
Due to the complexity of the above equation, a simplification process is performed to facilitate the calculation to obtain:  
\[C_{J,k}=\big{\langle}f(x),\varphi_{J,k}\big{\rangle}^{T}=\sum_{m}H_{m-2k}C_{j -1,m}. \tag{12}\]The simplified wavelet function is expressed through the following equation:  
\[g_{J}(x)=\sum_{k}C_{J,k}^{T}\psi_{J,k}. \tag{4}\]0  
The spatial decomposition of the moving signal using the data space projection approach again results in the output of the following function:  
\[g_{J}(x)=\sum_{k}C_{J,k}^{T}\psi_{J,k}. \tag{4}\]1  
The above equation indicates that the spatially shifted original signal can be obtained from the summation of functions on space, i.e., it can be expressed as  
\[g_{J}(x)=\sum_{k}C_{J,k}^{T}\psi_{J,k}. \tag{4}\]2  
According to the derivation process of the scale function, the spatial decomposition existence relation is obtained and expressed as:  
\[g_{J}(x)=\sum_{k}C_{J,k}^{T}\psi_{J,k}. \tag{4}\]3  
Substituting the above equation into the spatial shift decomposition formula, we finally obtain the following:  
\[g_{J}(x)=\sum_{k}C_{J,k}^{T}\psi_{J,k}. \tag{4}\]4  
The orthogonal transformation of the scale space function and the wavelet function can then result in a reconstructed expression for the multiwavelet function, expressed by the following function:  
\[g_{J}(x)=\sum_{k}C_{J,k}^{T}\psi_{J,k}. \tag{4}\]5  
Edge computing can complete the calculation using data from the device data collected from multiple channels, and the obtained data will be processed to complete the composition of the power IoT.
# Shape Optimization of Discontinuous Armature Arrangement PMLSM for Reduction of Thrust Ripple
## 2 Structure of Discontinuous PMLSM and Analysis of Characters  
### Investigated Model  
Figure 1a shows the PMLSM with a discontinuous armature arrangement that was adopted in this study. It has a 15-slot, 4-pole fractional slot structure, comprising an armature section and a mover section moving along the armature section. The armature has a discontinuous arrangement at regular intervals and comprises a laminated core and a coil wound through concentrated winding. The saturation magnetic flux density of the laminated core is 1.94 T. The mover comprises a permanent magnet magnetized in the opposite direction to the pole adjacent to the back iron. Figure 1b presents the force applied to the mover of PMLSM, including the normal force and attractive force due to the magnet's attraction force, and the detent force that occurs periodically between the permanent magnet and the teeth. If the detent force is the same as the moving direction of the mover, the force that accelerates the mover acts with a positive value, and when the detent force occurs in the opposite direction to the moving direction, the force that slows the mover acts with a negative value. For the characterization of the PMLSM, COMSOL Multiphysics was adopted, and as shown in Figure 2, a 2D FEA model comprising 73,246 elements  
was constructed. The PMLSM is shown in red lines, the rest of the domains are all air regions. The silicon steel armature core is modeled with a nonlinear B-H Curve. The voltage induced in the stator coils is calculated using open circuit configuration. The thickness of the armature is 80 mm. For detent force and back EMF analysis, the mover was moved 0.01 mm per step until it entered and exited the armature. To determine if magnet saturation occurred, the maximum flux density value of the circuit was recorded as the mover moved. The specifications of the PMLSM with a discontinuous armature arrangement are presented in Table 1.
# Evaluation of CYGNSS Observations for Snow Properties, a Case Study in Tibetan Plateau, China
## 3 Results and Analysis
### Comparison of Surface Reflectivity and Parameters on the Tibetan Plateau
For other commonly used evaluation metrics, such as leading edge slope (LES) and signal to noise ratio (SNR), we provide a brief discussion in Appendix A. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time** & **Snow Depth (cm)** & **Surface Reflectivity (dB)** & **Soil Temperature (\({}^{\circ}\)C)** \\ \hline \multicolumn{3}{l}{(a) Mixed–Forest} \\ \hline
2018.11 & 0.30817 & –27.36039 & –0.43541 \\
2018.12 & 0.75975 & –26.51822 & –0.72392 \\
2019.01 & 1.15806 & –25.96435 & –1.34392 \\
2019.02 & 0.99447 & –27.32706 & –0.82510 \\ \hline \multicolumn{3}{l}{(b) Open–Shrubland (Desert)} \\ \hline
2018.11 & 1.98258 & –35.25758 & –3.73655 \\
2018.12 & 3.66645 & –33.51377 & –7.68061 \\
2019.01 & 4.54840 & –32.83397 & –9.16413 \\
2019.02 & 4.04246 & –34.33123 & –7.49004 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The mean values of snow depth, SR, and soil temperature for the four different land cover types over a four month period. Figure 8: The November 2018 to February 2019 monthly average soil temperature on Tibetan Plateau. (a) November 2018, (b) December 2018, (c) January 2019, (d) February 2019.
## 3 Results and Discussion  
### Characterization of Raw Leachates  
The characterization results of the raw leachates are listed in Table 3. The crude leachate had a relatively high electrical conductivity of 8.5 \(\pm\) 0.3 mS cm\({}^{-1}\), which was directly attributed to the high charge of dissolved anions and cations, both as dissolved organic matter and in the form of inorganic ions [7]. The inorganic ions present included Cl\({}^{-}\). The concentration of Cl\({}^{-}\) in the crude leachates was 6700 \(\pm\) 200 mg L\({}^{-1}\). It is important to note that the Cl\({}^{-}\) content in leachates is usually not mitigated by soil and is highly mobile [55]. Cl\({}^{-}\) content can be used as a strong indicator of pollution [56]. The high amount of Cl\({}^{-}\) in the leachate can be attributed to the particular mixture of MSWs in the landfill site [57]. These MSWs contain significant amounts of soluble salts from anthropogenic sources, such as food waste from hotels, restaurants, and homes [30]. Although we must not forget that the Bordo Poniente landfill is located in a highly saline area, it also contributed to the high value of Cl\({}^{-}\) present in the leachates analyzed. This high conductivity prevented the addition of any additional electrolytes during the EC treatment. The total COD value was 3400 \(\pm\) 100 mg L\({}^{-1}\). Significant differences were found when comparing the results obtained with other stabilized leachates. [PERSON] et al. [58] obtained a COD of 536 mg L\({}^{-1}\). [PERSON] and [PERSON] [59] found a COD of 12,300 mg L\({}^{-1}\). Even in a previous investigation carried out by the same working group at the same sampling site, a COD of 2100 \(\pm\) 100 mg L\({}^{-1}\) was obtained [60]. These distinct differences between leachates are caused by the variability in their composition and production [61; 62]. Owing to the MSW composition, landfill practices, ambient temperature, and age of the MSW, leachate composition varies considerably [7]. The DOC value of the crude leachates was 1200 \(\pm\) 50 mg L\({}^{-1}\). The determination of organic carbon in samples is considered a more environmentally friendly parameter with respect to the use of chemicals and is more accurate in the case of wastewater. It should be noted that DOC determination may underestimate the levels of organic carbon in municipal and industrial wastewater [63]. Although leachates came from a landfill closed in 1994, the DOC content could mainly include high-molecular-weight humic compounds [24]. Humic compounds included HA and FA, which were two main classes. HA was soluble only in alkaline media, whereas FA was soluble under all pH conditions [64]. The pH obtained was 8.4 \(\pm\) 0.2, which was consistent with other investigations of stabilized leachates. [PERSON] and [PERSON] [59] and [PERSON] et al. [60] found a pH of 8.9 in stabilized leachates. The alkaline pH values for stabilized leachates are justified by the acetogenic and methanogenic stages of MSW in a sanitary landfill, as discussed in the literature [65]. At the general level, stabilized leachates have a pH value greater than 7.0 because of the microbiological reduc
# Environmental-Based Speed Recommendation for Future Smart Cars +
## 3 Proposed Approach
### Road Status Estimation Based on Weather Information  
Weather conditions can directly affect the behavior of the vehicle on the road. For example, driving on a rainy day can affect the stopping distance of a car, or it can influence the way a driver performs a maneuver. It is more likely that a driver can lose control of the car, if there's water or snow or ice on a road surface. In order to quantify the effect of weather conditions on the road, we utilize the concept of Road Surface Index (RSI), as presented in [41]. RSI is defined as a quantitative relation between the weather condition and the friction level on the road and describes the different surface condition categories. RSI depends on the road status which is affected by weather characteristics. RSI values vary from 0.1 (minimum value--ice covered) to 1.0 (maximum value--bare and dry surface). In the proposed work, we modify the approach in [41] and we identify four _road classes_ regarding the status of the road: (i) Dry; (ii) Wet; (iii) Snow; and (iv) Ice. Accordingly, the RSI values for each class are: (i) \([0.75-1.0]\), (ii) \([0.25-0.83]\), (iii) \([0.15-0.25]\), (iv) \([0.1-0.2]\).  
In order to estimate the status of the road surface, we utilized information from cloud and RSU services. However, the calculation of RSI as a closed formula based on weather information is inherently complex as it is a multi-dimensional and stochastic problem. In fact, the actual value of RSI depends on a variety of factors and the accurate calculation of RSI is not in the scope of this work. As a result, we utilize an RSI-speed correlation presented in [42] that estimates the value of RSI with respect to speed while avoiding direct calculation of RSI.
# Robust U-Nets for Fetal R-Peak Identification in Electrocardiography
## 4 Evaluation Results of Proposed Method
### Effect of Threshold \(Thr_{1}\) on Performance  
The R-peak detection stage of the proposed method incorporates a tunable threshold, \(Thr_{1}\) to filter out weak or false-positive predictions. This threshold acts as a critical decision boundary, directly controlling the TP, FP, and FN counts and influencing trade-off between SEN and PPV. To quantify this relationship and determine the optimal threshold value, we evaluated \(Thr_{1}\) across a range of 0.1 to 0.9 in steps of 0.2 on the eight datasets introduced in Section 3.1.  
The results show that a higher threshold discards marginal predictions, thereby reducing the risk of FP but increasing the risk of FN. Lower thresholds retain weak predictions and classify them as R-peaks, resulting in admitting more FPs. Figure 4 clearly shows that PPV is positively correlated with \(Thr_{1}\), and SEN is negatively correlated with \(Thr_{1}\). The F1-score, as a compromise between PPV and SEN, peaked at \(Thr_{1}\) = 0.3 across all SNRs, indicating that this threshold optimally balances PPV and SEN. Therefore, this work sets \(Thr_{1}\) to 0.3 to prioritize clinical reliability by minimizing missed R-peaks (FN reduction), which is critical for applications such as heart rate monitoring and arrhythmia detection.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Source** & **Method** & **Filtering Approach** & **Feature Enhancement** & **Detection Mechanism** \\ \hline Neurokit2, 2