esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Default-Sets
#### 5.2.2 Side Note Project Workflow Manager (PWM) and Revit Workflow Manager (RWM)  
The entire BIM2 BEM flow project comprises a three-stage workflow, enabled by three applications. (I) a web-based application for defining and managing properties and their mapping to BIM and simulation tools (YAPS); (II) a web-based application for defining workflows (PWM), each representing a clearly defined and complete use case; and (III) a Revit plugin (Revit Workflow Manager--RWM) to integrate and manage information within the BIM model and ensure the export of a validated IFC. While this publication focuses on the database (YAPS), the PWM and RWM applications are briefly explained to provide an overall picture of the work.  
Thanks to the described database functionality, a property library can now appear, as shown in Table 6.  
A parameter in the library includes mappings to a BIM tool (Revit) and multiple simulation tools (e.g., IES VE, DALEC, and PHPP), as well as several default sets that can represent different standards (e.g., DGNB Gold and PH standard). A workflow can now be defined using the PWM, specifying the following:  
* The responsible BIM person and their chosen tool;
* The responsible simulation person and their chosen tool;
* The use case;
* The project phase (currently optional but reserved for future functionality).In addition, the YAPS library to be used must be selected (see Table 7).  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Library** & **Parameter** & **BIM (Revit)** & **IES VE** & **DALEC** & **PHPP** & **DGNB Gold** & **PH Standard** \\ \hline \multirow{6}{*}{**Control**} & Thermal & Thermal & & & & & \\  & Transmittance & Transmittance & U-Value & U-Wert & & & \\  & Coefficient & Coefficient & & & & & \\ \cline{2-8}  & Visible Light & Visible Light & & & & & & \\  & Transmittance & Transmittance & Light Trans & VLT & Transmission Licht & 0.65 & 0.7 \\ \cline{2-8}  & Shading & \multirow{2}{*}{Reflectance 15\({}^{\circ}\)} & Shading & \multirow{2}{*}{SH\_R\_15} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} \\  & Reflectance 15\({}^{\circ}\) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Property library and default sets.  
Figure 19: Exemplary list of threshold values.  
The user can now log in to RWM and will see only the workflows assigned to their account. When a user imports a workflow into the BIM model, only properties required by that workflow (e.g., the simulation tool--in this example, DALEC)) are imported. The user can then populate these properties either manually or automatically using values from the default set. Referring to Tables 6 and 7, only the properties \"Thermal Transmittance Coefficient\" and \"Visible Light Transmittance\" are imported. By selecting the PH Standard default set, these values are filled as 0.6 \(\left[\mathrm{W}/\left(\mathrm{m}^{2}\mathrm{\cdot}\mathrm{K}\right)\right]\) and 0.7 [-], respectively, and the parameters are exported to the IFC via the mapping as \"U-Wert\" and \"VLT\".
## 3 Results  
### Temperature Measurements of Wafer-Baking Plates with Infrared Laser Thermometer  
The temperature values of the baking plates made of GG-25 material, numbered 1, 10, 17, 30, and 37, and the baking plates made of GJV-350 plate material, numbered 6, 15, 20, 26, and 43, were measured by infrared laser thermometer and thermal camera. Table 2 shows the temperature values obtained from infrared laser thermometer measurements of wafer-baking plates. Temperature measurements were taken three times each from the specified baking plates.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Baking Plate Temperature Measurement**} \\ \hline \multirow{2}{*}{**GG-25**} & **1 st Measurement** & **2 nd Measurement** & **3 rd Measurement** & **Arithmetic Mean** \\  & (°C) & (°C) & (°C) & (°C) \\ \hline \multirow{2}{*}{**Plate No. 1**} & 144 & 145.4 & 142.8 & 144.1 \\ \cline{2-5}  & 154 & 146.9 & 151.5 & 150.8 \\ \hline \multirow{2}{*}{**Plate No. 17**} & 146 & 146.4 & 138.6 & 143.7 \\ \cline{2-5}  & 138.7 & 142.8 & 143.6 & 141.7 \\ \hline \multirow{2}{*}{**Plate No. 37**} & 164.2 & 161.1 & 164.4 & 163.2 \\ \cline{2-5}  & \multicolumn{4}{c}{**Baking Plate Temperature Measurement**} \\ \hline \multirow{2}{*}{**GJV-350**} & **1 st Measurement** & **2 nd Measurement** & **3 rd Measurement** & **Arithmetic Mean** \\  & (°C) & (°C) & (°C) & (°C) \\ \hline \multirow{2}{*}{**Plate No. 6**} & 145 & 147.2 & 142.7 & 145.0 \\ \hline \multirow{2}{*}{**Plate No. 15**} & 146 & 143.7 & 140 & 143.2 \\ \cline{2-5}  & 149 & 145.7 & 145 & 146.6 \\ \hline \multirow{2}{*}{**Plate No. 26**} & 139 & 144.1 & 142 & 141.7 \\ \cline{2-5}  & 148.7 & 146 & 142 & 145.6 \\ \hline \end{tabular}
\end{table}
Table 2: Temperature measurement results of wafer-baking plates (infrared laser thermometer).  
Figure 5: Temperature measurement of baking plates during period of wafer production and thickness measurement of the wafer sheet.  
From the data obtained from Table 2, it can be seen that the GG-25 casting baking plates have a temperature value between 141.7 and 163.2 \({}^{\circ}\)C. There is a temperature difference of 21.5 \({}^{\circ}\)C between the GG-25 casting baking plates. The GJV-350 casting baking plates have a temperature value between 141.7 and 146.6 \({}^{\circ}\)C. The GJV-350 casting baking plates have a temperature difference of 5 \({}^{\circ}\)C between them. It can be said that the temperatures of GJV-350 cast-iron baking plates are more stable. It is thought that the low temperature difference between the baking plates has a direct effect on wafer color and quality.
## 2 Related Work
### Exploration of Spectral Information  
The most important characteristic of hyperspectral images is that they contain hundreds of spectral bands of information, which provide special information for target classification and identification that other kinds of images do not have. But there is a great deal of information redundancy in these highly correlated spectral bands, and some spectral bands may not carry discriminant information for classification. The methodology of exploring spectral information has progressed along with the development of HSI classification.  
Extracting critical spectral bands is a primary strategy to reduce redundant information and enhance the discriminability of the spectral features. Principal component analysis (PAC) [20] is first employed to extract the main components of the spectrum. Then, the processed HSI is utilized for the subsequent classification task. In the CNN-based methods, the attention mechanism can be used to achieve the spectral selection task [21; 22; 23]. These methods demonstrate that simply selecting a dozen well-defined spectral bands is sufficient to obtain considerable results [24; 25]. But directly discarding the spectral band is not conducive to the full use of spectral information [2]. Another approach is to extract the spectral information step by step using 1D convolutional kernels in the spectral dimension or 3D convolutional kernels in the spatial-spectral dimension. This approach does not miss any spectral information, but it is time-consuming and vulnerable to redundant  
Figure 1: The pipelines of patch-based classification and pixel-based classification for HSI.  
Figure 2: An example of the information leakage problem.  
information. In addition, due to the limitation of the size of the convolution kernel, such methods can only extract the adjacent spectral information.
## 2 Background and Related Work
### Neural Machine Translation
#### 2.1.4 NMT Model Training  
When training an NMT model, the first step is to transfer the words to vectors, i.e., word embedding. The most frequently used words in one language will be chosen, and the remaining words are treated as unknown words. To overcome the problem of unknown words, the most common practice is subword tokenization with methods such as byte-pair encoding (BPE) [25], word-piece model (WPM) [26], or sentence-piece model (SPM) [27].  
During the training time, the encoder-decoder model is fed by a parallel corpus. The learning objective is to minimize the cross-entropy loss between the predicted target words and the actual target words in the reference. The model parameters are initialized randomly. The training process could be formulated as updating its parameters periodically until obtaining the minimum loss of the neural network. This loss minimization is an optimization problem, and we can use gradient descent methods such as SGD, Adam, ADAGRAD, and Adafactor [28]. Among them, Adam is able to train models very fast, but it suffers in converge speed. In contrast, SGD can converge better, but it requires a long time for training. Designing a learning schedule that combines several optimizers can help train a model efficiently [29].  
Training is done for a large number of iterations till the model converges. That is, the model evaluation does not change by a significant amount over iterations. In the implementation, we will refine the parameters after it processes a batch of training samples. We have to take care of the hyperparameter tuning, including learning rate, number of layers, and so on.
# Time-Optimal Trajectory Planning for Woodworking Manipulators Using an Improved PSO Algorithm
## 3 Experiments and Results
### Simulation Results  
According to the simulation curve in Figure 12, the displacement and velocity curves of each joint are smooth and continuous, and there is no sudden change in the acceleration, and none exceeds the limit range. In the velocity and acceleration curves, the maximum velocity and acceleration of the joint tend toward the maximum limit value, indicating improved joint operation efficiency. In conclusion, the above results proved the successful of the time optimal trajectory planning method proposed in this paper. It not only improved the operational efficiency but also effectively ensured the continuity and stability of the manipulator.  
Figure 13 depicts three trajectories from starting point A to ending point D of the woodworking manipulator end-effector under optimal time and a simulation model of the woodworking manipulator by simplifying the woodworking manipulator into a series of connecting rods in the MATLAB Robotics Toolbox [42]. As shown in the following figure, the connecting rod can stretch and rotate to simulate the movement and rotation of the four joints of the woodworking manipulator. The red curve represents the end-effector trajectory of the woodworking manipulator and the four black dots marked represent the four path points A, B, C, and D in the Cartesian space. Figure 13a-d shows the entire operation process in detail. The trajectory of the end-effector passes through all paths point stably without interruption. Our results proved that the proposed algorithm achieved the time-optimal trajectory planning of the woodworking manipulator while ensuring stability and operational accuracy. In other words, we demonstrated that the proposed method is feasible.  
Figure 12: Woodworking manipulator displacement, velocity, and acceleration of each joint **(a)** Joint displacement running curve; **(b)** joint velocity running curve; **(c)** joint acceleration running curve.
# Attosecond Pulse Shaping by Multilayer Mirrors
## 1 Introduction  
Multilayer-based optics have been developed into well-established optical components for steering [1], focusing [2], polarizing [3] and spectral filtering [4] of extreme ultraviolet (EUV)/soft X-ray radiation since its first description by [PERSON] in 1976 [5].  
Their significant advantage compared with single-surface grazing incidence optics (working in total external reflection) lies in an increased glancing angle (even up to near-normal incidence), larger collection angle, better imaging performance (due to decreased astigmatism), and tailored spectral filtering. However, in many cases, multilayer reflectors do not reach the high reflectance values of grazing incidence mirrors and are limited to peak reflectances slightly above 70% at best, mainly due to absorption and scattering losses [6].  
Due to remarkable improvements in various deposition techniques, such as electron beam evaporation [7; 8], magnetron [9; 10; 11] and ion beam sputtering [12; 13; 14], or pulsed laser deposition [15; 16; 17], numerous multilayer properties can be realized. Nowadays, multilayers consist of various metal, semiconductor, or dielectric materials with layer thicknesses ranging down to less than 1 nm, layer numbers of many hundreds to thousands, and layer precisions in terms of thickness and interface accuracies on an atomic length scale (0.1 nm).  
Most multilayer optics development in the past was directed toward improved reflectivities for various multilayer material combinations and various photon energies from extended ultraviolet (EUV) to the soft X-ray regime. Such multilayer optics have been employed as imaging devices in solar astronomy [18], EUV lithography [19] and microscopy [20], plasma diagnostics [21], synchrotron radiation optics [22], and many more. Ultrashort soft X-ray radiation bursts with a few femtoseconds (\(10^{-15}\) s) to attosecond (\(10^{-18}\) s) pulse durations have become accessible over the past few years. This was possible due to advanced laser technology in high harmonic generation (HHG) [23] or advanced electron accelerator technology in free electron lasers (FEL) [24]. The application of multilayer optics to the formation and handling of these pulses has only recently been discussed in the literature; however, it is gaining increasing attention from the scientific community.  
This article aims to review the status of (our) multilayer attosecond optics development in these fields.
## 7 Comparison Between the Cloud Gaming Architecture Leveraging Fog and the Proposed Model
From the comparison between the base model and the proposed model in terms of server traffic load and user coverage across clusters, several key conclusions can be drawn. * **Stability and efficiency in load distribution:** The proposed model shows a more homogeneous and efficient load distribution across servers, with fewer abrupt drops and better utilization of available resources. Although the minimum load is lower than in the base model, the overall stability is superior, indicating better load balancing and resource allocation optimization, especially when using fog nodes to distribute the load during critical moments. * **Better adaptability to variations in user coverage:** In terms of user coverage across clusters, the proposed model more efficiently manages fluctuations, maintaining a more stable overall coverage. Although the range is wider, reaching a minimum of 68%, the proposed model handles peaks and drops better, adapting to situations with greater variability compared to the base model, which has a narrower range but experiences more pronounced peaks in some clusters. * **Overall superior performance of the proposed model:** Overall, the proposed model outperforms the base model by providing greater robustness against fluctuations in traffic load and user coverage. The use of fog nodes as part of the load balancing strategy significantly contributes to more balanced distribution and a better user experience, making the proposed model a more resilient and efficient solution in scenarios with high demand and variability. Figure 9: The achieved percentage of user coverage in each game cluster as a function of time in seconds.
J. Sens. Actuator Netw._**2012**, \(1\), 272-298; doi:10.3390/jsan1030272  
_Journal of Sensor and Actuator Networks_  
ISSN 2224-2708  
www.mdpi.com/journal/jsan  
_Article_  
**Estimation of Physical Layer Performance in WSNs Exploiting the Method of Indirect Observations**  
**[PERSON] and [PERSON]**  
DIEE, University of Cagliari 09123, Cagliari, Italy; E-Mails: [EMAIL_ADDRESS] (G.C.);  
[EMAIL_ADDRESS] (L.A.)  
_Received: 15 September 2012; in revised form: 2 November 2012 / Accepted: 15 November 2012 / Published: 30 November 2012_  
**Abstract:** Wireless Sensor Networks (WSNs) are used in many industrial and consumer applications that are increasingly gaining impact in our day to day lives. Still great efforts are needed towards the definition of methodologies for their effective management. One big issue is the monitoring of the network status, which requires the definition of the performance indicators and methodologies and should be accurate and not intrusive at the same time. In this paper, we present a new process for the monitoring of the physical layer in WSNs making use of a completely passive methodology. From data sniffed by external nodes, we first estimate the position of the nodes by applying the Weighted Least Squares (WLS) to the method of indirect observations. The resulting node positions are then used to estimate the status of the communication links using the most appropriate propagation model. We performed a significant number of measurements on the field in both indoor and outdoor environments. From the experiments, we were able to achieve an accurate estimation of the channel links status with an average error lower than 1 dB, which is around 5 dB lower than the error introduced without the application of the proposed method.  
**Keywords:** Wireless Sensor Networks; passive monitoring; channel link performance; weighted least squares; ZigBee
# Interpolation Methods with Phase Control for Backprojection of Complex-Valued SAR Data +
## 5 Experimentation
### Measurement Setup  
The monostatic THz SAR imaging system was based on Rohde&Schwarz ZVA67 VNA, the operating frequency range of which is between 10 MHz and 67 GHz. The setup was further equipped with Rohde&Schwarz ZC330 frequency extender and the rectangular horn antenna, the parameters of which are described in ([13] Table 1, p. 578), which gave the opportunity to perform one-port measurements of the reflection coefficient in the frequency range [0.22, 0.33] THz. Note that the reflection coefficient measured in the frequency domain can then be transformed to the time domain and used for the SAR scene reconstruction. The frequency extender incorporated with the horn antenna as a transceiver was mounted on the mobile platform, which was shifted in the azimuthal (vertical) direction with a uniform step \(\Delta_{\xi}=1\) mm to form a synthetic aperture. The object under test was placed at the reference range distance \(R_{0}=2\) m from the platform antenna. The measurements were performed based on stop-and-go approximation: at each corresponding measurement position, a frequency sweep was transmitted and received, respectively. The total number of measurement and frequency points was 344 and 3001, respectively. The acquired raw data \(g(\xi,\tau)\) was filtered in the frequency domain with the cosine-tapered window (the cosine fraction \(\alpha=0.25\)) and time-gated [29] to suppress from the surrounding environment, outside of the range of interest [30]. The measurement setup parameters are summarized in Table 4.  
Figure 6: Measurement setup. A ground-based monostatic THz SAR imaging system was based on a vector network analyzer (VNA) and a transceiver, which was mounted on a mobile platform. The transceiver consisted of a frequency extender and a horn antenna to perform measurements in the frequency range [0.22, 0.33] THz. Here, \(R_{0}\) denotes the reference range distance between the platform antenna and the object under test (mannequin head).
# Potential Use of Paper Mill Sludge in Improving Soil Quality for Plant Growth
## 3 Results and Discussion
### Cationic Exchange Capacity of Soil Modifiers  
Figure 4 shows the cation exchange capacity (CEC) of the prepared soil modifiers, which measures their ability to hold cations such as calcium, magnesium, and potassium. When COP37 and PMS were mixed in a 5:5 ratio, the mixture exhibited a CEC of 4.2 meq./100 g. Similarly, when TOP55 and PMS were mixed in the same proportion, the resulting mixture had a CEM of 3.6 meq./100 g. Following their compost, the CEC of the COP37 and PMS mixture increased by 59%, while the CEC of the TOP55 and PMS mixture increased by 72%. These findings indicate that PMS likely played a role in enhancing the CEC of both mixtures by facilitating the decomposition of organic matter during the composoting process.  
The increase in CEC after composoting holds potential benefits for plant growth. Soils with high CECs can bind more cations, such as Ca\({}^{2+}\) or K\({}^{+}\), to the exchange sites located on the surfaces of clay and organic matter particles. Additionally, soils with increased CEC exhibit a higher buffering capacity, enhancing their ability to resist pH fluctuations [39].  
Figure 4: Cationic exchange capacity of soil modifiers before and after composoting.  
Figure 3: Electrical conductivity of soil modifiers before and after composoting.  
Consequently, these findings suggest that PMS might contribute to the observed rise in CEC after composting.
## 4 Technology Materials
### Secondary Raw Materials  
The exploitation of natural deposits and the production of primary raw materials most likely cannot be scaled up with the increasing demands. Depletion of (known) natural reserves, low abundance, and economic and environmental issues are obstacles in increasing the mine production. Secondary raw materials are produced by the recycling of end-of-life products and articles (e.g., waste), of by-products (e.g., from industrial production), and of materials after their initial use. The recovery of raw materials from anthropogenic resources is an important strategy to avoid severe shortage of commodities and to stabilize the materials supply chains. \"Urban mining\" of secondary raw materials has substantial economic and environmental impact as materials are used more efficiently and materials flows become manageable (circular economy [443], impact on materials flow cost accounting [444; 445]). The mining of urban resources is also more energy-efficient than the production of primary raw materials. The mining of gold (average Au concentration in ore ~5 g/ton) produces approx. 17,000 tons of CO\({}_{2}\) per ton of Au metal [446]. The CO\({}_{2}\) gas emission is in large part due to the energy required to extract the ore from deep lying natural deposits and to process it. Printed circuit boards (PCB) of computers and cell phones, on the other hand, have Au concentrations of 150 and 300 g/ton, respectively, and  
Figure 4: (**a**) European plastics demand distribution by resin type in million tons (Mt) in 2018. (**b**) Distribution of post-consumer plastic waste in Europe in 2018. Data taken from [441].  
are easily accessible [447]. Another example is the recycling of Cu and Al, which saves more than 85% and up to 95%, respectively, of the energy required to produce the primary raw materials by mining.  
The production of high-quality secondary raw materials from waste streams requires the inspection of the waste composition and the separation into different fractions and materials classes. In the recycling industry, various sensor technologies are employed in sensor-based sorting [448]. The sorting systems are using optical sensors (e.g., detecting the color or absorption/reflection/fluorescence of waste pieces) [449], X-ray transmission sensors (e.g., measuring the atomic density and element composition), magnetic and Eddy current sensors (e.g., detecting Fe and non-Fe metals), spectroscopic sensors (e.g., detecting specific absorption bands in the infrared (IR) and emission lines and bands in the ultraviolet (UV) and visible (VIS)), gamma ray-based sensors, and acoustic sensors.