esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Improved Symmetric Algorithm  
This section mainly introduces the concept of improved symmetric algorithms, and how to apply it to automated palletizing systems. In recent years, robotic arms have gradually replaced artificial object stacking. However, the operation of the robotic arm requires time for training. When palletizing, the placement of objects cannot overlap, and a safe distance must be maintained. The simplest way to palletize is to place all objects in the same direction until the objects cannot be arranged. However, in most practical situations, placing all objects in the same direction of arrangement will cause a lot of waste of effective area. The arrangement in the same direction would be less stable on the stack of objects than the staggered arrangement. An improved symmetric algorithm is developed that can be applied practically by referring to other algorithm theories to replace the programming and planning stack requirements of the robotic arm, and use the staggered arrangement to improve the stability of the stack. The algorithm developed in this paper is implemented on a real workstation.  
The main purpose of this paper is to place the largest quantity of goods in the effective area; therefore, the concept and detailed calculation process of the algorithm used in this system will be introduced. The goal of the algorithm is to replace the current built-in stack program of the robotic arm to achieve better area utilization. Before developing an algorithm, system requirements and user requirements need to be understood. A palletizing algorithm is developed based on the requirements to determine the input and output data to the system. The requirements for this system are as follows:  
* Input data: the length, width, and height of the effective area, and the size of the arranged objects;
* Output data: object arrangement result, object arrangement coordinate list;
* Combine the arrangement of vertical and horizontal to increase the utilization rate of effective area;
* Use staggered stacking to increase the stability of objects when palletizing;
* The size of the space between objects can be set.
## 5 System Implementation
### Static Rendering Technology  
To reduce the network bandwidth of the server and improve off-line processing ability, the geometry information was stored in the IndexDB database of the browser, which can be updated according to the timestamp of the record to ensure the real-time performance of the data interaction. Furthermore, in order to improve the rendering efficiency of the browser, static rendering technology was employed, i.e., the static drawn image was simply maintained when the rendered image was not triggered by users, such that the load of CPU and GPU could be greatly reduced.
## 3 Methodology and Models
### Model Development  
Main Model with Attention Mechanism  
The primary model used in the GREENIA framework was designed to capture both the temporal dependencies and the complex relationships between meteorological features that influence energy production. The architecture integrates GRUs with an attention mechanism, allowing the model to focus on the most relevant time steps and features for accurate forecasting. In Figure 5 a representation of the main ML model that was utilized for the prediction of the energy production is presented.  
* GRU Layer: A GRU layer was employed to process the sequential data, leveraging its ability to learn long-term dependencies within the time series. This layer was crucial for capturing the trends and patterns in the meteorological data, such as wind speed and temperature variations, that are critical for accurate energy production forecasts.
* Attention Layer: To enhance the model's predictive power, an attention mechanism was applied on top of the GRU layer. The attention layer dynamically assigns weights to different time steps and features, enabling the model to focus on the most relevant portions of the input sequence. This allowed the model to prioritize key moments in the data that have a significant impact on energy production, improving the interpretability and accuracy of the predictions.
* Weights: The output of the GRU layer, along with the attention weights, was processed through time-mixing weights--learnable parameters that adapt the importance of different time steps and features. These weights help the model effectively handle varying temporal patterns and feature interactions, enhancing its flexibility in dealing with different types of meteorological sequences.
* Fully Connected Layers with Swish Activation: The processed sequence data, influenced by the attention layer, was passed through several fully connected layers, each followed by the Swish activation function. This architecture allowed the model to capture nonlinear interactions between features, further refining its predictive capabilities. The Swish activation function was chosen for its ability to improve model performance through smoother gradient flow during backpropagation.
* Output Layer: The final fully connected layer condensed the information into a single output, representing the predicted energy production for the given time period. The model was designed to forecast energy production across different regions and energy types, ensuring accurate and reliable predictions that could be used to guide strategic renewable energy deployment.  
Figure 5: GRU with attention layer (Swish activation function).
## 6 Numerical Examples
### Tiling Examples Where All Solutions Are Sought
#### 6.2.4 Case 4  
We give an example of the monohedral polyomino type, where the application of colouring techniques does not split the full (uncoloured) polyomino tiling problem into multiple pariamino tiling subproblems, and there is _no_ reduction in complexity.  
**Example 6**.: _Find all ways of tiling the 16 \(\times\) 18 rectangle with_  
_all orientations permitted. The area of \(R\) is 288, so we need 48 copies of the hexomino. The associated ILP file, constructed with the program LPMAKE_POLYONINOS_FIQUE7, has \(n=892\) unknowns, \(m=288\) constraints, and \(f=609\) free variables. CPLEY found 217,266 solutions in 45.55 s._  
_Using colouring techniques, we wish to tile the checkerboard coloured region \(\widehat{R}\) shown in Figure 18a using 48 pariaminoes from the set_  
\[\{P_{1}^{+}\}=\bigg{\{}\]  
Figure 17: Target region and a tiling solution for Example 5: **(a)** The target region \(\widehat{R}\), a checkerboard coloured diamond shaped region with a single ‘hole’ in the middle; **(b)** Successful tiling of the target region \(\widehat{R}\) using 28 copies of an L-shaped trimino. The 20 pariominoes \(P_{1}^{+}\) are coloured light-blue and the 8 pariominoes \(P_{1}^{-}\) are coloured dark-blue, corresponding to an optimal solution of the single pariomino tiling subproblem.  
all orientations permitted. We note here that \(P_{1}^{+}=P_{1}^{-}\) and so we only tile with copies of a single parionino._  
_As the hexomino and \(R\) have zero parity, the parity equation (see Theorem 1) is trivially satisfied and we obtain a single parionino tiling subproblem. We constructed the ILP file in this case, using the program \(\texttt{LPMAKE\_PARIONIHOES\_FIGURE7}\). Solving this subproblem in CPLEX with \((m,n,f)=(288,892,609)\) yielded 217,266 solutions in 50.75 s, i.e., an increase in the runtime of approximately 11.42% compared to the full (uncoloured) tiling problem. Thus we do not observe a potential speedup. An optimal solution of the parionino tiling subproblem is illustrated in Figure 18b._  
_The parionino tiling subproblem is essentially equivalent to the full polyomino tiling problem. Both formulations have the same binary matrices and no constraints of the form (4). Thus, the difference in the time taken for CPLEX to solve the polyomino tiling problem and the parionino tiling subproblem is simply because of differences in the ordering of the unknowns in the binary linear systems. This results from the different order in which the binary matrices are constructed by our MATLAB programs for the polyomino and parionino tiling cases. This situation also applies to any monohedral tiling problem where \(P_{1}^{+}=P_{1}^{-}\) (so \(p_{1}=0\)), for example, any pure domino tiling problem._
## 5 Experiments
### Data Preparation  
The flame image monitoring device (CCD camera and cooling system) was installed in the cement rotary kiln of a cement plant, as shown in Figure 10. A color CCD camera (resolution: 1920 \(\times\) 1080; refresh rate: 30 FPS) was used to collect the burning data, and the subsequent detection of the burning state was carried out. A cooling system was used to reduce the temperature of the camera's operating environment to ensure the normal operation of the equipment. The resolution of the collected image is 1920 \(\times\) 1080, which is sufficient to reflect the detailed distribution characteristics, texture characteristics, and color characteristics of the flame.  
The data collected on site were manually annotated by experts, classifying the combustion state in the combustion zone into three categories: normal sintering, under-sintering, and over-sintering. The on-site images of each condition are shown in Figure 11. The distribution of data samples collected by the monitoring device is shown in Table 1.  
The data in the table indicate a severe imbalance in sample distribution, and the data volume is completely insufficient to support deep learning training. Therefore, it is necessary to expand the data samples to achieve a balanced sample distribution to meet the needs of deep learning training.  
Due to the high-temperature environment inside the furnace, some images have significant noise and require denoising processing. In this study, a Gaussian filter was used for denoising. Through analysis of the sample data, it was found that the contribution of the furnace wall edges to the classification is very small. Therefore, the images were  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Normal Sintering** & **Over-Sintering** & **Under-Sintering** & **Total** \\ \hline Quantity & 1538 & 165 & 142 & 1845 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of combustion state data.  
Figure 11: (**a**) Under-sintering; (**b**) normal sintering; (**c**) over-sintering.  
Figure 10: Cement rotary kiln combustion detection system.  
cropped around the discharge port to 1080 \(\times\) 1080. This not only reduces network training time but also reduces the interference of some unimportant factors.
# A Real-Time Water Level and Discharge Monitoring Station: A Case Study of the Sakarya River
## 2 Materials and Methods
### Software and Connections
The calculated distance value is then transmitted to the server following this conversion. Figure 6: Php Functions. To further illustrate the interaction between the Arduino and the ultrasonic distance sensor, a general flowchart of the connection and data processing steps is provided below (Figure 7).
## 3 Results and Discussion  
### Characterization of the Zeolite Samples  
#### 3.1.1 SEM and EDS Analysis  
The SEM images of freshly prepared Z-A and iron-Z-A are shown in Figure 1a,b, respectively. In the SEM image, both samples look complete hexahedron shape and well crystallized. The crystals of Iron-Z-A was found to be rough in contrast to Z-A. The roughness on the surface of iron-Z-A acts as evidence for iron (oxide). The SEM pictures of iron-Z-A after adsorption and desorption experiments are portrayed in Figure 1c,d. It can be clearly observed that the adsorption-desorption of phosphate did not affect the hexahedron shape of the iron-Z-A. A lump of adsorbed mass can be seen on the surface of PO\({}_{4}\)\({}^{3-}\) adsorbed iron-Z-A (Figure 1c). Similarly, a cleaner surface of iron-Z-A can be seen in PO\({}_{4}\)\({}^{3-}\) desorbed iron-Z-A (Figure 1d), as the lump from the surface is removed. PO\({}_{4}\)\({}^{3-}\) desorbed Iron-Z-A looks very much similar to freshly prepared Iron-Z-A in Figure 1b. It hints at the proper adsorption and desorption of PO\({}_{4}\)\({}^{3-}\) by Iron-Z-A.  
Based on EDS, the atomic compositions of all the four samples were analyzed and are given in Supplementary Table S1. The EDS result shows the absence and presence of iron in Z-A and Iron-Z-A, respectively. The atomic weight% values of phosphorus in phosphate adsorbed Iron-Z-A and phosphate desorbed also confirm the adsorption and desorption of PO\({}_{4}\)\({}^{3-}\) by Iron-Z-A.
# Rateless Coded Uplink Transmission Design for Multi-User C-RAN
## 3 Rateless Coded Uplink Transmission Scheme
### Scalar Quantizer at the RRH  
In order to meet the limited fronthaul capacity, the baseband signal should be compressed at the RRH before being uploaded to the BBU pool. Here, we use a scalar quantizer with uniform quantization intervals. The number of quantization bits is determined by the fronthaul capacity limitation. Due to the concern of practicality, the quantization thresholds only depend on the number of quantization bits and the statistics of the channel gain.  
According to (1), the base band signal at RRH \(j\) is  
\[y_{j}=h_{j1}\sqrt{p}x_{1}+h_{j2}\sqrt{p}x_{2}+n_{j},\ j=1,2, \tag{3}\]  
whose variance is calculated by:  
\[D\big{(}y_{j}\big{)}=E\big{(}y_{j}^{2}\big{)}-E^{2}\big{(}y_{j}\big{)}=P\sigma _{j1}^{2}+P\sigma_{j2}^{2}+\sigma_{0}^{2}, \tag{4}\]  
where \(\sigma_{\bar{\mu}}^{2}\) is the variance of the channel gain from user \(i\) to RRH \(j\).  
According to [40], it can be regarded that the value of \(y_{j}\) is almost distributed in the range of \(\left(-3\sqrt{D\big{(}y_{j}\big{)}},3\sqrt{D\big{(}y_{j}\big{)}}\right)\). Let the number of quantization bits be \(b\) and \(M=2^{b}\). Then, the signal \(y_{j}\) at RRH \(j\) is quantized into \(\hat{y}_{j}\) according to the following rule:  
\[\hat{y}_{j}=Q_{j}\big{(}y_{j}\big{)}=\left\{\begin{array}{l}q_{j1},\ -\infty<y_{j}<-3\sqrt{D\big{(}y_{j}\big{)}}+\Delta_{j}\\ q_{jk},\ -3\sqrt{D\big{(}y_{j}\big{)}}+(k-1)\Delta_{j}\leq y_{j}<-3\sqrt{D \big{(}y_{j}\big{)}}+k\Delta_{j},\ k=2,\ldots,M-1\\ q_{jM},3\sqrt{D\big{(}y_{j}\big{)}}-\Delta_{j}\leq y_{j}<\infty\end{array}\right. \tag{5}\]where \(\Delta_{j}=\frac{3\sqrt{D(y_{j})}}{M}\) is the quantization interval and \(q_{jk}=-3\sqrt{D\big{(}y_{j}\big{)}}+\Big{(}\ k-\frac{1}{2}\Big{)}\Delta,\ k=1, \ldots,M\), for the quantizer at RRH \(j\).
## 3 Results and Discussion
### Importance of Socioeconomic Classification (ACORN)  
Table 1 summarizes the performance achieved for each model following the premised from the previous paragraph.  
\begin{table}
\begin{tabular}{c c c c} \hline
**ACORN** & **Mean** & **RSME** & \(R^{2}\) \\ \hline ALL & 1782.39 & 294.6 & 0.76 \\ A & 91.18 & 16.03 &  
The row under the name ALL corresponds to the accumulated forecast for the studied population as a whole while the other ones correspond to each ACORN group. Moderate performance is obtained for model ALL attending to \(R^{2}\), although the value of RMSE is the highest in the table, indicating the model is not able to predict accurately the energy demand of certain part of the population. Therefore, the classification in ACORN groups is seen as an appropriate technique to better cover the consumers' behaviour. Regarding \(R^{2}\) values for these ACORN models, those with more than 80% of variance explained are ACORN C, H and N. Most ACORN groups have acceptable results around 70% (ACORN A, D, F, G, I, K, L, M, O, P and Q). This technique is not successful for groups E and especially J, with very low \(R^{2}\) values, although they do not have the highest errors based on RMSE. Attending to this last parameter, the lowest errors < 10) are registered for Acorns U, B, C, I, M, N and O while ACORN E has the highest one (110). The other groups register intermediate RMSE values, around 1-30.  
For deeper exploration of results, RF prediction residuals are represented in Figure 3. It was expected that the more grouped the data (ALL), the better the results, as uncertainties and outliers are masked and the common trend stands out. However, the errors in ALL are remarkable, especially in the tails and show a clear bias, which reflects the customers behave differently and a unique model is not suitable for covering the proposed objectives. Therefore, the hypothesis of dividing the customers in groups according to their socioeconomic characteristics (ACORN classes) appears as a wise alternative to reduce uncertainty.  
When divided by ACORNs, the previous bias disappears and only a slight deviation is detected for most groups, the previous bias disappears, residuals are random and follow a normal distribution and only a slight deviation for extreme data is detected. Therefore, only these grouped data are considered for further analysis.  
This becomes clearer when comparing the histogram of the residuals for all the customers and the one for the sum of the residuals of each Figure 4 (ACORN), this last histogram being sharper and with higher values than the first one, indicating the errors are  
Figure 3: Residuals q-q Plot for each ACORN group and all the customers (ALL).  
closer to zero. Furthermore, a Kolmogorov-Smirnov non-parametric test of the residuals has been peformed, as the data do not follow a normal distribution, rejecting the null hypothesis and thus confirming the distribution of errors is different in both forecasts.
# Autonomous Agent Navigation Model Based on Artificial Potential Fields Assisted by Heuristics
## 4 Experiments
### Artificial Potential Fields in a Multi-Agent Environment  
In order to verify the intended behavior of the APF, six agents where placed symmetrically with only one APF in order to keep a 1 m distance from the rest of the agents. The parameters used were \(a=4\), \(K_{1}=1.5\), \(k_{2}=0.8\).  
In Figure 10 a the initial position of six numbered agents is shown. In Figure 10b, dotted lines describe 1 m diameter circles to verify that the condition of 1 m was met. The agents form a circle without having the explicit instruction of doing so. Each agent considers the force generated by all the agents in sight. As a consequence, the local minima are located 1 m away from the agents at the sides, but it is not possible to satisfy the 1 m condition regarding the other agents because that would imply a decrease in the distance between the closest agents. The result is an emergent stable formation.  
In Figure 11a, the resultant force on agent 2 is shown. Notice that the force is not 0; therefore, the agent is not exactly at the equilibrium point. However, it is close enough to generate forces too low to be able to cause movement in the agent. The effect of forces can be observed in Figure 11b, where the speed is almost 0. This result implies that, while it is true that agents can reach a stable formation., if the number of agents increases, the  
Figure 10: (**a**) Start position (**left**). (**b**) Final position (**right**).  
complexity in the interaction of forces also increases, yielding non-zero scenarios that must be considered in the heuristics. For instance, if the agent is in a local minimum but there are forces caused by agents located farther, the latter ones can be ignored to give priority to the closest ones.
# On a Framework for Federated Cluster Analysis
## 3 Results
### Evaluation Method  
There are two main questions we want to answer with our evaluation.  
1. How reliably can the framework detect the correct number of clusters in federated datasets?
2. How good is the federated clustering result, and how does it compare to an assumed central clustering result?  
To answer the first question, we first need to define the \"correct\" number of clusters. In the first two sets of experiments (Sections 3.2.1 and 3.2.2), we know the ground truth number of clusters, because the data are artificially created by sampling from Gaussian distributions. Given the ground truth number of clusters \(K_{true}\) and the detected number of clusters \(K_{det}^{i}\) in experiment \(i\), we simply count how often the correct number of clusters was found and report the percentage of correct numbers:  
\[p_{correct}=\frac{\sum_{i=1}^{N_{exp}}\mathbbm{1}_{\{K_{det}^{i}=K_{true}\}}} {N_{exp}}, \tag{18}\]  
where \(N_{exp}\) is the total number of experiments. Moreover, we want to study how federated clustering compares to nonfederated clustering. Therefore, we report \(p_{correct}\) for the federated dataset and for the same centralized dataset.  
The second challenge is to evaluate the clustering result itself. On the one hand, we use the (federated) Davies-Bouldin index introduced in this work. Moreover, we calculate a \"knowledge gap\" metric whenever ground truth cluster centers are known:  
\[gap:=\sum_{k=1}^{K}\sqrt{\sum_{i}^{D}\frac{(\tilde{c}_{k}[i]-c_{k}[i])^{2}}{ Var(x[i])}}, \tag{19}\]  
between two sets of cluster centers \(\tilde{c}=(\tilde{c}_{1},\dots,\tilde{c}_{K})\), and \(c=(c_{1},\dots,c_{K})\) and \(Var(x[i])\) denotes the variance in the \(i\)-th dimension. This gives us a normalized measure of the distance between the cluster centers and another indication of whether the algorithm converged to a meaningful result.
## 3 Results and Discussions
### Thermal Conductivity  
The cooling and the heating DSC curves of the SSPCM (as a typical PCM example) are shown in Figure 2. From 0 to 30 \({}^{\circ}\)C, the SSPCM underwent a solid-liquid phase change, and the specific heat changed with temperature. Therefore, the thermal conductivity test of the SSPCM has to be carried out in a stable state at a temperature above 35 \({}^{\circ}\)C.  
Figure 3 shows the thermal conductivity of SSPCM filled with EGP, MWCNT, and hybrid filler. When a single filler was added, as show in Figure 3a, the thermal conductivity of SSPCM increased with the increasing EGP or MWCNT content. The thermal conductivity of PCM-xEGP composite increased greater than that of PCM-xMWCNT composite. At 9.09 wt %, the thermal conductivity of PCM-10 EGP is 0.575 W/mK, about 246% that of the SSPCM (0.234 W/mK). The thermal conductivity of PCM-10 MWCNT is 0.372 W/mK, about 159% that of the SSPCM. Therefore, EGP is more effective than MWCNT when serving as the thermally-conductive filler in SSPCM.  
MWCNT is a 1D flexible filler with high thermal conductivity and high aspect ratio. EGP is a 2D rigid filler also with high thermal conductivity and a high aspect ratio. When MWCNT and EGP were pretreated together and added into the SSPCM (total loading is 9.09 wt %), a synergistic effect was obtained, as shown in Figure 3b. The dashed line showed the calculated data from the addition law. When EGP content was more than 4.76 wt %, the measured data exceeded the theoretical value, demonstrating the existence of a synergistic effect. The greatest synergistic effect was obtained for PCM-EGP\({}_{\mathrm{8}}\)MWCNT\({}_{\mathrm{2}}\), with thermal conductivity of 0.674 W/mK.  
Figure 2: The cooling and the heating DSC curves of PCM: (a) cooling; and (b) heating.  
In order to explore whether PCM-EGP\({}_{8}\)MWCNT\({}_{2}\) had higher thermal conductivity than PCM-10 EGP and PCM-10 MWCNT at different temperatures, thermal conductivities at 35, 40, 45, and 50 \({}^{\circ}\)C were determined and are shown in Figure 4. It can be clearly seen that the thermal conductivity of PCM-EGP\({}_{8}\)MWCNT\({}_{2}\) was higher than that of PCM-10 EGP and PCM-10 MWCNT at different temperatures, demonstrating synergistic enhancement between EGP and MWCNT at all temperatures. In the 35-50 \({}^{\circ}\)C temperature range, the thermal conductivity remained constant. This is because the SSPCM was in a stable state from 35 to 50 \({}^{\circ}\)C.  
Meanwhile, the melting or crystallization temperature of SSPCMs filled with EGP or MWCNT were not changed, as shown in Figure 5, demonstrating that the SSPCM and fillers were physically mixed and no strong interaction occurred between them.  
Figure 4: Thermal conductivities of samples at different temperatures.  
Figure 5: Melting and crystallization temperatures of SSPCM filled with different concentrations of (**a**) EGP or (**b**) MWCNT.  
Figure 3: Thermal conductivities (at 35 \({}^{\circ}\)C) of SSPCM filled with (**a**) single filler and (**b**) hybrid filler.