esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Characterization of Microcapsules
#### 3.2.5 Structural Characterization Using Fourier Transform Infrared Spectroscopy (FTIR)  
The FTIR spectra of dried samples are depicted in Figure 3. All the powders showed rather similar peaks with some differences in the intensity of the peaks. The peak around 3300 cm\({}^{-1}\) comes from the phenolic hydroxyl groups, and the peak at about 2900 cm\({}^{-1}\) comes from the aromatic C-H groups of the samples. The band at 1600 cm\({}^{-1}\) belongs to the C=C stretching vibration. The peaks at 1373 and 1451 cm\({}^{-1}\) represent the C-H bending vibration. The peak at 1013 cm\({}^{-1}\) refers to C-O-C stretching vibrations. The stretching vibrations of anhydrous glucose are lower than 1000 cm\({}^{-1}\) and the C-O bond stretching appears between 800 and 1500 cm\({}^{-1}\).  
The intensity of the peak around 3300 cm\({}^{-1}\) is different according to the tested sample. In fact, the intensity of this peak became slightly higher for powders prepared at pH 3. The modification of the absorbance intensity of this rather broad peak due to the O-H bond could also highlight the presence of hydrogen bonds between the molecules within the capsules. The intensity of the peak at around 3400 cm \({}^{-1}\) corresponding to residual humidity presented less intensity for FD powders. This result is in agreement with water content results that showed FD powders contain less humidity content.  
Figure 3: FTIR spectra of microcapsules.  
In addition, between powders with the same pH, those that were spray-dried showed a slightly higher intensity of peaks around 1000 cm\({}^{-1}\) (Figure 3). This suggests that spray-drying may improve the interactions between the different constituents of the microcapsules and at the oil-water interface of emulsions during water evaporation, resulting in better protection of the active compound. This result is similar to the study by [PERSON] et al. [34], who used maltodextrin and lecithin to encapsulate squid oil via spray-drying.
## 2 Related Works
Convolutional Neural Networks (CNNs) are the most frequently used deep learning model for the automated detection of lung tumors. However, for staging, CNN has been applied in very few studies. [PERSON] et al. (2018) [14] developed a CNN for T1-T2 or T3-T4 staging of lung tumors. Their staging referenced the seventh edition of the TNM system and tested with 472 FDG-PET/CT scans. Their proposed CNN was constructed using 2D patches that were cropped around the center of the tumor. Additionally, their CNN used two neural networks: (i) one as a feature extractor to extract the most relevant features from a single patch, and (ii) another as a classifier to perform classifications for all slices. They reported a high testing accuracy of 90%, but the outputs were presented as binarynumbers (T1-T2 with label = 0 and T3-T4 with label = 1), that is, their method cannot give an accurate result for each specific stage. Moreover, their approach is not fully automated, because their CNN requires manually cropped patches as inputs. Another tumor staging method using a double convolutional deep neural network (CDNN) was presented in [15]. They used 73 CT exams from the Image and Data Archive of the University of South Carolina and the Laboratory of Neuro Imaging (LONI) dataset. They divided the input exams into two piles: pile 1 (with cancer) and pile 2 (without cancer). Then, all slices in both piles were further grouped depending on the angles using the K-means algorithm. Subsequently, they built a double CDNN containing double convolution layers and an additional max pooling layer. They reported a prediction accuracy of 99.62%. Nonetheless, their CDNN output only decimal values between 0 (non-cancer) and 1 (cancer); hence, a threshold value was necessary to determine the possibility of tumor stages. The primary objective of this paper is to propose a CAD that is capable of not only the detection of tumor nodules, but also of their staging. We present two significant contributions in our work; the first is performing double-staged classification, and the second is the extraction of locational features. In the first contribution, we perform two stages of classifications: one for the detection task and another for the staging task. Performing double-staged classification helps to minimize false positives and makes the CAD more accurate and robust. In the second contribution, we extract the locational features to identify the stages of the lung tumor because the severity of lung cancer strongly depends on the sizes and locations of the tumor nodules. Moreover, knowing the exact locations of tumor nodules is implicitly beneficial for the surgical planning of higher staged tumors. The rest of this paper is organized as follow. Section 3 describes the details of the methods applied in this study. Section 4 presents the experimental results and performance evaluation of the proposed method. Finally, Section 5 presents the discussion and conclusions of the paper.
# Extending the Quality of Secure Service Model to Multi-Hop Networks
###### Abstract  
Rarely are communications networks point-to-point. In most cases, transceiver relay stations exist between transmitter and receiver end-points. These relay stations, while essential for controlling cost and adding flexibility to network architectures, reduce the overall security of the respective network. In an effort to quantify that reduction, we extend the Quality of Secure Service (QoSS) model to these complex networks, specifically multi-hop networks. In this approach, the quantification of security is based upon probabilities that adversarial listeners and disruptors gain access to or manipulate transmitted data on one or more of these multi-hop channels. Message fragmentation and duplication across available channels provides a security performance trade-space, with its consequent QoSS. This work explores that trade-space and the corresponding QoS model to describe it.  
communication model; security; metrics; probability; confidentiality; integrity +
Footnote †: journal:  
0000-0001-5000-5000][PERSON]  
0000-0002-0002-3899][PERSON]
## 3 Literature Review
### Grasp Task  
Many techniques have been developed to improve the grasp task, the authors in [4] give an overview of present advances with the focus on options to learn, detect, and grasp objects. A software architecture for service robots manipulating objects in human environments [2] was presented by the respected authors. Learning synergies between pushing and grasping with self-supervised deep-RL allowed us to discover and learn how pushing can help rearrange cluttered objects to make space for arms and fingers from scratch through model-free deep-RL [18]. Another technique that was developed with deep-RL is thinking while moving. The action is completed with the time evolution. However, this mechanism requires a controlled system. Such as when the robot must decide on the following activities while still performing the previous action [7]. A further scalable deep-RL for vision-based robotic manipulation was researched, in which a study of the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach in the context of grasping was carried out [19]. There is also a multitasking-oriented robot arm motion planning scheme. The system is based on deep-RL, and twin synchro-control, which illustrates a technique that combines deep reinforcement learning (DRL) with digital twin technology to control humanoid robot arms [20]. Accelerating deep continuous RL through task simplification is a novel method for improving the learning process by task simplification inspired by the Goldilocks effect.  
The process is known for developmental psychology to solve the problem of collecting the enormous amount of required training samples in a realistic time that surpasses the possibilities of many robotic platforms [21]. Unity 3D machine learning-based research on robot arm control includes DRL strategies to train the robotic arm through machine learning, using the reward function for intelligent control of the robotic arm [22]. The authors in [13] introduced a new approach to promote the exchange of objects between robots and humans. The authors in [23] propose, via the grasping for stacking network (GSN), a system that jointly learns the grasping and the stacking policies. This (GSN) enables a robotic arm to pick boxes from a table and put them on a platform properly. The grasp task has also been developed through different kinds of methods, such as in [24], which proposes a method of detecting fruits and automatically harvesting using a robot arm. There are other developed techniques based on inverse kinematics, such as [3], which proposes a methodology to generate joint-space trajectories of stable configurations for solving inverse kinematics using deep reinforcement learning (RL).
## 3 Simulation Verification and Analysis
The THD analysis has been conducted considering the transmitting and receiving coil currents under variable coupling and variable load conditions. Figure 6 shows the waveform and THD of the current through the receiver coil under variable mutual inductance conditions. The results show that the current waveform is basically sinusoidal and the THD value is less than 5%. Finally, the simulation results indicate that the proposed coordinated control method can ensure constant voltage output while maintaining high transmission efficiency under the conditions of WPT load resistance and mutual inductance variations. Therefore, the effectiveness of the proposed WPT coordinated control method of constant voltage output and maximum transmission efficiency has been preliminarily verified.
# Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
## 2 Performance Parameters
### Meteorological Parameters  
Meteorological parameters (such as solar irradiance, ambient and panel temperature, soiling, humidity, clouds, wind, rain, and dust) play an oversized role in each module's energy output [18]. Statistics from \"World Energy Data\" explain that energy consumption with renewable energy resources reaches 23.6% of world energy consumption [19]. Installation configuration of solar panels (such as PV array size, module configuration, and panel orientation in reference to sun direction) also affects each module's energy output [20; 21]. Solar arrays are installed facing south at a certain angle to capture optimum solar irradiance. Depending upon solar installation configuration, varying meteorological parameters can affect the overall output of a solar plant. In the meteorological parameters, which include solar irradiance and panel temperature, these two parameters affect the solar panel's energy mostly [9; 10; 22]. A major portion of solar radiation is absorbed by solar  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Meteorological Parameters** & **Electrical Parameters** \\ \hline \hline  & **1. Photovoltaic Array:** \\  & Output Voltage (\(V_{A}\)), Output Current (\(I_{A}\)), \\  & Output Power (\(P_{A}\)), Output Energy (E), \\  & Duty Cycle (DC) \\ Total irradiance (G\({}_{i}\)), \\  & Ambient temperature (T\({}_{amb}\)), \\  & Module temperature (T\({}_{mod}\)), \\  & Wind speed (WS), \\  & Wind direction (WD), \\  & Humidity (H), \\  & Bareometric pressure (x), \\  & Dust (D), Light Intensity (LI), \\  & Rain (R), \\  & UV radiations (Uv), \\  & Angle of Tilt (A\({}_{T}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Meteorological and electro-physical parameters affecting solar plant’s energy output.  
panels instead of converted to electrical energy and is absorbed by the solar panel, thus increasing the panel's temperature [21; 23]. This increase in the solar panel's temperature affects its current-voltage characteristics, known as the IV curve [12]. The solar panel's output current is directly proportional to solar irradiance with little effect on the output voltage [8; 10; 12]. The panel's temperature has a positive impact on the panel's output current while having a negative impact on output voltage [8; 10; 21; 23; 21].  
Other meteorological parameters are considered as second-order factors with a lesser impact on an individual solar panel's electricity output. Ambient pollution and panel soiling do affect a solar panel's electricity output [5]. These parameters are not significant in areas with ample rains but have to be countered with frequent cleaning in dryer regions.
###### Abstract  
The presence of isolated or multiple gingival recessions in the mandibular anterior region is a challenge for the clinician, as they may be associated with a shallow vestibule, high freenum insertion and/or little or no attached gingiva. Only limited evidence is available on the use of the double lateral sliding bridge flap technique with connective tissue graft (CTG) technique for treating gingival recessions in the mandibular anterior region. The aim of this study was to describe and evaluate the clinical and esthetic outcomes of the double lateral sliding bridge flap technique with CTG on isolated and multiple gingival recessions at the level of the mandibular incisors. Nine patients required treatment of gingival recessions in the mandibular incisors at the University of Salamanca (Spain) (seven females, two males; mean age: 27.9 \(\pm\) 6.9) with a total of 14 isolated (42.9%) and multiple (57.1%) Miller class II and III gingival recessions. After a mean follow-up of 36 months, the mean percentage of root coverage was 80.5% for all treated recessions. Statistically significant differences (_p_ < 0.05), were observed for reduction in recession depth, increased width of keratinized tissue and increased gingival thickness, this being dependent on the Miller class. The esthetic outcome was acceptable, with a final mean esthetic score of 7.4 out of 10. The double lateral sliding bridge flap surgical technique with CTG is an effective procedure for the coverage of isolated and multiple gingival recessions in the anterior mandibular region, as it offers satisfactory esthetic results.  
Keywords:girgival recession; connective tissue graft; root-coverage esthetic score; bridge flap technique; multiple gingival recession; localized gingival recession +
Footnote †: journal: Medical Imaging
# A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution
## 2 Methods  
### Ordered Subset Expectation Maximization (OSEM) Reconstruction  
In order to correct the motion during the reconstruction, an iterative reconstruction algorithm is needed. OSEM is used as the reconstruction algorithm [14; 22].  
\[\mu_{j}^{n+1}=\frac{\mu_{j}^{n}}{\sum_{i\in S_{b}}a_{ij}}\sum_{i\in S_{b}}a_{ij} \frac{y_{i}}{\sum_{l}a_{il}\mu_{l}^{n}}, \tag{1}\]  
where \(\mu_{j}^{n}\) is the estimated activity in voxel \(j\) after iteration \(n\), \(y_{i}\) is the measured sinogram, \(a_{ij}\) is the system matrix element representing the probability that a photon emitted from voxel \(j\) is detected in detector pixel \(i\), and \(S_{b}\) is one subset.
# MLUTNet: A Neural Network for Memory Based Reconfigurable Logic Device Architecture
## 2 Backgrounds  
### Low-Precision Neural Networks  
The neural network, is a popular structure used in approximate computing and machine learning. In a neural network, the basic operation that occurs layer by layer can be expressed as the following equation:  
\[\mathrm{z}=\sigma(\mathrm{w}\times\mathrm{a}+\mathrm{b}) \tag{1}\]  
where Equation (1) represents deep neural network mechanism. Variables z, w, a, and b represent the output tensor, the weight tensor, the activation tensor generated by the previous network layer and the bias tensor, respectively. \(\sigma\) is the non-linear activation function. The main component of the forward propagation process is matrix multiplication. The backward propagation, on the other hand, is a chain law operation that calculates the gradient of each weight parameter relative to the final loss function. This leads to a problem of computational cost.  
Assuming full precision for all parameters, the computational expense of a large number of matrix multiplications would be a non-negligible obstacle for implementation in hardware. Therefore, many works on low-precision neural networks have been proposed, the most representative of which is the binarized neural network (BNN) [8].  
Compared with the full-precision general neural networks, the BNN makes two major changes: (1) 1-bit representation is utilized to replace the full-precision parameters. To guarantee correct gradient descent process, the gradient values and not-well-trained weight values are still calculated and stored in full-precision; but after the training process, the weight values are stored in 1-bit binary value; (2) Complex floating multiplication operations of weight values and activation values are replaced by XNOR-Bit-count operations, as the Equation (2). This could save significant amount of resource of computation in hardware implementation.  
\[a=\mathrm{popcount}\Big{(}\mathrm{xnor}\Big{(}a^{b},w^{b}\Big{)}\Big{)} \tag{2}\]  
As a result, although the BNN inevitably suffer from the performance degradation, because of largely saving the parameter complexity and computing cost, it is still regardedas a promising technique for deploying deep models on resource-limited devices. Many related works have flourished, for example, the XNORNet [9] succeeds to extend it to the convolutional neural network structure.
## 4 High Average Power Short-Pulse Thin Disk Lasers at HiLASE
### Beamline C
#### 4.3.1 Second and Fourth Harmonic Generation  
Owing to numerous and constantly increasing applications of visible, ultraviolet and deep ultraviolet high-energy picosecond pulses, we stepped up to the generation of these wavelengths. Precise microprocessing and inspection of metal and ceramic materials, sterilization of surfaces, photolithography and information storage are some examples of their various industrial, medical and scientific applications. In our case, the motivation has been the preparation of a deep UV laser source for efficient electron emission from photocathodes and to offer unique capabilities of laser-induced damage testing (LIDT) in this wavelength range.  
The deep UV is generated by cascaded conversion of the fundamental wavelength of 1030 nm in nonlinear crystals. The borate crystals LBO (lithium triborate) and BBO were used for the second harmonic generation (515 nm) and fourth harmonic generation (258 nm), respectively. Both crystals have a relatively high effective nonlinear coefficient. The LBO shows a lower walk-off angle and better thermal conductivity. The BBO has a better transparency and is phase-matchable in the deep UV. Our first-in-touch experiments with the harmonics generation were realized on a 20 mm-long LBO crystal for the frequency doubling and a 3 mm-long BBO crystal for the frequency quadrupling. In both cases, the critical phase-matching was used. The parameters of the fundamental beam (Beamline C) were 25 W of average output power and 1.8 ps of pulse duration, _i.e._, 0.25 mJ of pulse energy at a 100-kHz repetition rate. The conversion efficiency into the second harmonic was about 40%; therefore, the input into the BBO crystal was 11 W. The conversion efficiency of the second harmonic into the fourth was 18%, which gives 2 W of average power at the fourth harmonic [41]. In the following attempts, we have substantially improved our experimental setup, and at present, 50 W at the fundamental wavelength were converted into the second harmonic at a conversion efficiency of about 63%. Thus, much higher fourth harmonic production can be expected.  
Figure 9: Dependence of the continuous-wave (CW) output power and optical-to-optical efficiency on the pump power for the second amplifier of Beamline C.
## 3 Results
### Factors Influencing Vegetation Growth Changes in the Mining Area
#### 3.3.3. Effects of Land Use Change on Vegetation Growth  
Land use change has an important impact on vegetation growth. Through monitoring land use changes in the Huojitu mining area from 1999 to 2023 (Figure 6a,b), it was found that land use in this area has undergone great changes. The land use area (Figure 16) clearly exhibits a noticeable pattern of change across all types. During 1999-2023, grassland and forest land had the greatest changes. The change of forest land area from 1999 to 2023 was 42.29 km\({}^{2}\), and the forest land area increased from 0.13 km\({}^{2}\) in 1999 to 42.42 km\({}^{2}\) in 2023. The forest land conversion accounted for 37.76% of the total area while the grassland changed 56.62 km\({}^{2}\). The grassland area decreased from 90.12 km\({}^{2}\) in 1999 to 33.57 km\({}^{2}\) in 2023, totaling 29.97%. At the same time, the changes in built-up areas and croplands were also noticeable as +14.48 km\({}^{2}\) and \(-1.99\) km\({}^{2}\), respectively. The changes in water bodies and unoccupied lands were not significant.  
ArcGIS 10.8 [54] was used to visualize land use changes in the mining area from 1999 to 2023 using the map fusion method (Figure 17a). In total, there were 17 different types of land use conversions, some of which were very small in size. Therefore, this study conducted an overlay analysis on the maps depicting significant land use transformation types (Figure 17a) and vegetation growth trends (Figure 13b) between 1999 and 2023. It then determined the contribution rates of each relevant transformation type to vegetation changes (Figure 17b). The contribution rate of significant land use conversions to vegetation degradation was 13.43%, while the total contribution rate to vegetation improvement was 46.45%. This indicates that during the study period, land use changes had a substantial impact on vegetation growth. Among the conversions, the conversion of grassland to forest land contributed most significantly to vegetation improvement, with a contribution rate of 29.85%. Conversely, the conversion of grassland to built-up area had the highest contribution rate to vegetation degradation at 9.26%.  
Figure 16. Chord diagram of land use type Changes in Huojitu mining area from 1999 to 2023.