esa-sceva/satcom-chunk-collection huggingface dataset:
# On a Framework for Federated Cluster Analysis
## 2 Materials and Methods
### The Federated Fuzzy Clustering Framework
#### 2.2.2 Federated Fuzzy Davies-Bouldin Index  
As described in the introduction (Section 1.1, Figure 1), there is a need for federated cluster validation metrics. It is not enough to calculate metrics such as the Davies-Bouldin index locally and draw conclusions from there. Therefore, we formulate a federated version of the index. In the federated setting, the global server does not have access to the clients' data. That means that it cannot carry out the calculation of the soft Davies-Bouldin index \(\bar{R}\) directly. Specifically, the central server can not directly calculate the cluster spread \(S_{i}^{f}\) (Equation (12)) that requires the calculation of distances between cluster centers \(c_{i}\) and data points \(x_{j}\). Through a simple transformation we see that sharing the data points is not required:  
\[S_{i}^{f} =U_{i}(\frac{1}{N}\sum_{j=1}^{N}||x_{j}-c_{i}||^{q})^{\frac{1}{q}} \tag{15}\] \[=U_{i}(\frac{1}{\sum_{l=1}^{P}N_{l}}\sum_{l=1}^{P}\sum_{j=1}^{N_{ l}}||x_{j}^{(l)}-c_{i}||^{q})^{\frac{1}{q}}\] (16) \[U_{i} =\frac{1}{\sum_{l=1}^{P}N_{l}}\sum_{l=1}^{P}\sum_{j=1}^{N_{l}}u_{ ij}^{(l)} \tag{17}\]  
Hence, for the calculation of \(S_{i}^{f}\), each client \(l\) needs to share its number of data points \(N_{l}\), its local local cluster spread \(\sum_{j=1}^{N_{l}}||x_{j}^{(l)}-c_{i}||^{q}\), and its local average assignment vectors \(\sum_{j=1}^{N_{l}}u_{ij}^{(l)}\) for \(i=1,\ldots,K\). With that information, the global server can calculate \(S_{i}^{f}\). Since the global server calculates (and knows) the cluster centers, it can also calculate \(M_{ij}\), i.e., the distances between centers \(c_{i}\) and \(c_{j}\) (Equation (11)), and, finally, \(R_{ij}\) for all \((i,j)\), \(R_{i}\) and the index \(\bar{R}\). Note that the federated and nonfederated versions of the Davies-Bouldin index produce the same result given \(X=\bigcup_{l=1}^{N}X^{(l)}\), and the nonfederated and federated cluster centers are the same. Generally, the first assumption holds in our experiments while the second one is the subject of study and cannot be guaranteed. In fact, due to different convergence criteria in the nonfederated and federated fuzzy \(c\)-means algorithms, the centers are often different. Generally, however, we expect federated clustering and nonfederated algorithms to converge to similar centers.
# Computer Vision Algorithms on a Raspberry Pi 4 for Automated Depalletizing
## 3 Methodology
### Scale-Invariant Feature Transform (SIFT)  
The scale-invariant feature transform (SIFT) is a robust algorithm for keypoint detection and feature matching in image processing [4]. This algorithm extracts the features of an object considering different scales, rotations, illumination, and geometric transformations. SIFT has been proven to be the most widely used algorithm in object recognition. It works in four phases as follows:  
* Scale-space Extrema Detection;
* Keypoint Localization;
* Orientation Assignment;
* Keypoint Descriptor.  
SIFT builds a multi-resolution pyramid over the input image and has proven to be very robust to noise and invariant to scaling, rotation, translation, and (to some extent) illumination changes.  
In the code developed for this application, there are a few preprocessing steps applied to the images before performing object detection. The implementation of SIFT in this research followed these steps:  
1. **SIFT Feature Extraction**: SIFT key points are detected and descriptors are extracted from key points in both the template and target images as histograms of local intensity gradients at multiple scales using differences of Gaussians.
2. **Looping Over Image Regions**: The code iterates over different regions.
3. **Matching and selection**: Matches between descriptors are selected, with a minimum match count.  
Figure 5: Pattern matching object detection technique tested on the matchboxes image.  
4. **Filtering Matches**: A ratio test is applied to filter out good matches from the initial set of matches comparing the distances between the nearest descriptor and the second one in each descriptor.
5. **Homography Estimation**: a perspective transformation matrix \(M\) is estimated based the best-matched key points to align the template with the current region of interest.
6. **Perspective Transformation**: Transformation \(M\) is applied.
7. **Bounding Box Computation**
8. **Drawing and Visualization**: The bounding box and center of each detected object are visualized on the target image.  
The result of detection is shown in Figure 6.
# Semi-Supervised Manifold Alignment Using Parallel Deep Autoencoders
## 5 Experiments and Results
### Experiments on 2-, 3- and 4-Manifold Alignment
#### 5.2.1 Double Pendulum Datasets
Then, feature vectors for the 2D-2D, 2D-3D, and 3D-3D cases were defined as:  
2D-2D: \((e_{x},e_{y},\ \cos\theta_{1},\cos\theta_{2},\ \sin\theta_{1},\sin\theta_{2})\)  
2D-3D: \((e_{x},e_{y},e_{z},\ \cos\theta_{1},\ \cos\theta_{y^{\prime}},\cos\theta_{z^{ \prime}},\ \sin\theta_{1},\ \sin\theta_{y^{\prime}},\sin\theta_{z^{\prime}})\)  
3D-3D: \((e_{x},e_{y},e_{z},\ \cos\theta_{y},\cos\theta_{z},\ \cos\theta_{y^{\prime}},\cos \theta_{z^{\prime}},\ \sin\theta_{z},\ \sin\theta_{z},\sin\theta_{y},\ \sin\theta_{z^{ \prime}})\)  
The data points for the 2D double pendulums were generated using its equations of motion and then sampled at angular increments of \(10^{\circ}\) in \(\theta_{1}\) and \(\theta_{2}\) at both joints. There were \((360/10)^{2}=1296\) instances and six features, resulting in a dataset of size \(1296\times 6\) for each of the pendulums \(X\) and \(Y\). In the case of the 2D-3D motion, instances were sampled at angular increments of \(30^{\circ}\) at three angles \(\theta_{1}\), \(\theta_{y^{\prime}}\) and \(\theta_{z^{\prime}}\) of the corresponding joints. As a result, the number of instances was \((360/30)^{3}=1728\), and with the nine features, the size of the two datasets, \(X\) and \(Y\), was \(1728\times 9\). In the 3D-3D case, instances were sampled at angular increments of \(30^{\circ}\) in the rotational angles \(\theta_{y}\), \(\theta_{z}\), \(\theta_{y^{\prime}}\) and \(\theta_{z^{\prime}}\) at the corresponding joints. As a result, the number of instances was \((360/30)^{4}=20\),\(736\), and with the 11 features, the size for each of the two datasets \(X\) and \(Y\) was \(20\),\(736\times 11\). In order to challenge the robustness of the different alignment methods and to simulate potential real-world scenarios, two different types of noise were added in separate experiments to the clean datasets \(X\) and \(Y\). The first type of noise we refer to as \"actuator noise\", and it was added to the joint angles to imitate the noise at actuator joints in a real-world system. The range of actuator noise was incremented from \(0^{\circ}\) to \([-10^{\circ},10^{\circ}]\) in steps of \(2^{\circ}\). The second type of noise was added to the end-effector coordinates, and we refer to it as \"coordinate noise\". This noise could simulate, for example, the jittery motion of robot limbs. In the experiments, the coordinate noise range was increased from \(0.0\) to \([-1.0,1.0]\) in steps of size \(0.2\).
## 4 Discussion
### Conclusions Regarding Loading Bay Restrictions  
The evaluation of the effects of loading bay restrictions shows a heavy influence of the availability of these bays in combination with the current weather conditions (c.f. Section 3.2). Therefore, weather conditions induce major restrictions. Comparing the application of two vessels with a single vessel and a single loading bay, the efficiency per vessel decreases by up to 10% for scenarios with bad weather conditions. Nevertheless, if the weather is good, the results show no efficiency loss. The observed maximum bay use of only 0.6 for two vessels also supports the strong dependence on weather conditions. Thus, over the entire time, the bay was only used 60% of the time, leaving several time slots for additional loading operations. While the use increases to approximately 0.75 for three vessels on a single bay, the efficiency decreases by approximately 22% compared to the single vessel case, depending on the weather conditions. The results also show a decrease in the scenario with close to perfect weather conditions (month August). Finally, the use stagnates at this level, even if the project applies more vessels. These results demonstrate that the optimizer is not able to schedule more installation operations due to weather restrictions, despite being capable of loading more components. In consequence, the optimal number of vessels and bays shows a strong dependence on weather restrictions. Nevertheless, considering the high charter costs of these vessels, more than two vessels seem inefficient compared to the potential gains.
## 5 Results and Discussion
### Real-Field Experiment Measuring a Corona Discharge  
The experimental investigation demonstrated the practicality of the proposed sensor system for detecting corona discharge in real-field applications. During the tests, the UV corona sensor system effectively detected the UV signals generated by the corona discharges from a sphere-to-plane electrode setup at a distance of around \(\sim\)20 cm in real-time. Figure 12a illustrates the equipment and test arrangement tailored to simulate corona discharge phenomena typically encountered in field conditions, while a schematic layout of this setup is provided in Figure 12b. Figure 12c,d present the recorded detection results from 10 cm distance and 20 cm distance, respectively. The voltage applied to the corona discharge equipment was adjusted manually; as the applied voltage increased incrementally, the intensity of both the corona discharge and ultraviolet radiation intensified, resulting in observable frequency shifts within the SAW sensor. These shifts, recorded within a voltage range from 2 kV to 20 kV, highlight the sensor's robustness in monitoring corona discharge events. This experimental demonstration confirms the sensor's reliability in detecting corona discharge, underscoring its potential suitability for field-based applications.
# Evaluation of Deployment Challenges of Wireless Sensor Networks at Signalized Intersections
## 7 Conclusions  
In this work, the demands for modeling the radio channel in an urban city area for intelligent traffic light control systems are presented. The obtained results show the complexity of this type of scenario, with traffic lights, vehicles, people, buildings, vegetation and urban environment. It can be concluded that the morphology as well as the topology of the scenario play a key role in the estimation of radio signal propagation, due to the strong impact of multipath components in the overall loss mechanism of the propagating radio waves. The validation of the 3D Ray Launching algorithm has been done with a good match in the comparison with real measurements. The use of simulation tools,  
Figure 24: Relative humidity measured (**a**) and PIR activity detected (**b**).  
Figure 23: Parameters monitored by the WSN. (**a**) Temperature variation per hour; (**b**) Maximum, minimum and average temperatures per day; (**c**) Relative humidity per day; (**d**) Wind orientation and intensity.  
such as the 3D Ray Launching code, is a great aid for radioplanning analysis with the aim of deploying WSN within this type of environments. Moreover, the context aware scenario implementation using different wireless technologies can be properly designed with a detailed characterization and analysis of the radio wave propagation for different frequencies. The system has been tested by means of an ad hoc application and its corresponding platform, providing real time traffic monitoring within the scenario under analysis. Results show that the use of deterministic tools in WSN deployment can aid in providing optimal layouts in terms of coverage, capacity and energy efficiency of the network.  
The authors wish to acknowledge the financial support of project TEC2013-45585-C2-1-R, funded by the Spanish Ministry of Economy and Competiveness.  
[PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] conducted the characterization of wireless propagation mechanisms in the outdoor scenario, as well as the WSN campaign of measurements. [PERSON] and [PERSON] participated in the development of the WSN and the application for such scenario.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} WSN & Wireless Sensor Network \\ ITS & Intelligent Transportation Systems \\ ISM & Industrial, Scientific and Medical \\ V2V & Vehicle to Vehicle \\ V2I & Vehicle to Infrastructure \\ ICA & Intersection Control Agent \\ VANET & Vehicular Ad-Hoc Network \\ RFID & Radio Frequency Identification \\ RL & Ray Launching \\ RT & Ray Tracing \\ MoM & Method of Moments \\ FDTD & Finite Difference Time Domain \\ GO & Geometrical Optics \\ GTD & Geometrical Theory of Diffraction \\ UTD & Uniform Theory of Diffraction \\ BER & Bit Error Rate \\ BLE & Bluetooth Low Energy \\ WAVE & Wireless Access in Vehicular Environments \\ AR & Arrival Rate \\ PR & Processing Rate \\ CTh & Congestion Threshold \\ \end{tabular}
# A Circularly Polarized Non-Resonant Slotted Waveguide Antenna Array for Wide-Angle Scanning
## 4 Verification and Comparison  
The planar antenna was fabricated and measured in our work. During manufacturing, the antenna is divided into three layers: the waveguide layer, including the waveguide and ridge structure; the slot layer, including the slots and cylinders; and the circular polarizer  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Slot No.**} & **Eight-Element Array’s** & **Eight-Element Array’s** & **Nine-Element Array’s** \\  & **Cylinder Height** & **Slot Length** & **Cylinder Height** & **Slot Length** \\ \hline
1 & 1.7 & 21.6 & 1.6 & 21.6 \\
2 & 2.2 & 21.4 & 1.9 & 21.4 \\
3 & 2.8 & 21.4 & 2.5 & 21.4 \\
4 & 3.3 & 21.4 & 3.1 & 21.4 \\
5 & 3.9 & 21.1 & 3.4 & 21.1 \\
6 & 4.1 & 20.9 & 3.9 & 20.9 \\
7 & 3.8 & 20.9 & 4.0 & 20.9 \\
8 & 3.2 & 21.1 & 3.5 & 21.1 \\
9 & N/A & N/A & 3.1 & 21.4 \\ \hline \hline \end{tabular} N/A = Not Applicable.  
\end{table}
Table 2: Detailed parameters of the planar array (unit: mm).  
Figure 15: The near E-field curves and its envelope line of the adjacent eight- and nine-element linear arrays.  
layer, including the circular polarizer and isolation gap surface. The planar antenna was manufactured using CNC machining technology. During assembly, a conductive adhesive was applied to bond the waveguide layer and slot layer. This prevents the deterioration in the electrical performance caused by electromagnetic leakage resulting from the interlayer gaps; the circular polarizer layer and waveguide layer were mechanically connected using screws, which were distributed around the periphery of the prototype. The mechanical strength is the primary consideration when using screw connections, as the three layers are heavy and cannot withstand the required strength solely through adhesive bonding. The measurement was conducted using the far-field method in a 9 m x 9 m microwave anechoic chamber. The assembled antenna is depicted in Figure 16a, with dimensions of 370 mm x 240 mm x 52 mm.  
The excitation method warrants mention. Although this antenna is intended for phased scan array applications, constructing a complete phased array system for measurement purposes is impractical. Instead, in this study, the phase delay was achieved using nine coaxial cables of varying lengths, ensuring approximate phase differences of 144 degrees. These cables were connected to a power divider and the antenna, as depicted in Figure 16b. The experimental setup is illustrated in Figure 16c, showing a 12\({}^{\circ}\) inclined support being employed to accommodate the deviated main lobe in the elevation plane.
# Analysis of Blur Measure Operators for Single Image Blur Segmentation
## 3 Material and Method
### Methodology  
In this section, we present a unified framework for the blur detection and segmentation. The blur measure operators investigated in this study have been mentioned in the next section. We have used the publicly available code of the blur segmentation method originally proposed by [PERSON] and [PERSON] [14]. However, we have introduced a little modification in the blur map classification step of this framework such that we have employed Interquartile Range (IQR) of the initial blur map for its classification instead of some fixed thresholds. This is more compelling choice and its advantage has been proven by better results. All of the steps of the blur segmentation methodology have been performed at three different scales to account for the varying sizes of the blur as suggested in [11] and followed in [14]. Figure 2 depicts the key steps involved in the blur segmentation process by taking the first image of Figure 1 as an example.
## 2 Materials and Methods
### Statistical Evaluation of Measurement Results  
Statistical evaluation of measurement results was performed for both Arm I--AdM and Arm I--CvM with the use of CoM and NeM. In each case, 11 measurements described in chapter 2.5 were performed, while each group of 11 measurements formed one set. The results were processed according to Figure 9 as follows:  
1. _Verification that the selection of measured values comes from a population with a normal distribution._  
* Check outlier method of comparing the distance of the minimum and maximum from the first and third quartiles.
* Implementation of the Grubbs test for outlier and removal of 1 to 3 extreme values to maintain the required 8 values, as in the following tests the required number of required values is >7.
* Verified groups of measurements come from a population with a normal distribution performed by the Shapiro-Wilk normality test. If the hypothesis was rejected, the Kolmogorov-Smirnov normality test or the Pearson chi-square normality test was performed. If the hypothesis was rejected in all cases, then the measurements do not come from a population with a normal distribution. Otherwise, we do not reject the hypothesis of a normal distribution.
2. _Descriptive statistics._  
* The following descriptive statistics are used to describe the measurement groups for each setting. Position statistics--average, median, minimum, maximum. The median is the mean value in the data sorted by size, and together with the minimum and maximum values, they give us a view of the data on how densely they are distributed.
* Statistics on the variability of results--range, standard deviation. While range is the difference between the minimum and maximum value and only expresses the width of the data occurrence, standard deviation refers to the data density around the average.
* Other statistics show the shape of the frequency curve, skewness about the location of the furthest sampling value from the arithmetic mean, and kurtosis about the density of data around the mean.
3. _Comparison of measurement pairs._  
* For Arm I--AdM, Arm I--CvM, where the other mode settings were the same, the Two Sample \(t\)-test was used for the null hypothesis (difference in means is equal to 0) \(\mu\_1=\mu\_2\). To calculate the test statistic t, it was necessary to verify whether var.equal applies. This was preceded by an F test to compare two variances with the ratio of variances equal to 1.
# Research on Routing Algorithm of Construction Robot Cluster Enhanced Ad Hoc Network
## 5 Conclusions  
In order to improve the connectivity in the process of building self-organized communication networks among construction robots, an enhanced self-organized network routing algorithm for construction robot clusters was proposed. A topology optimization algorithm based on location prediction and robot node priority was proposed to address the problem of broken network routing paths due to complex environments and unstable links. The routing path establishment process for construction robot application environments was studied. The main results of the research work in this paper are as follows:  
1. A self-organizing network routing algorithm was designed with the connectivity rate as the core index, which facilitates the selection of optimal paths in the route selection process by adding a connectivity rate feedback mechanism to the route discovery process;
2. Convergent routing path quality metrics quantitatively represent the link state of a routing path at a certain point in time and further optimize the path selection mechanism based on the node's energy state, balancing relative distance, link hops, remaining energy, and load, so that the network eventually achieves a connectivity rate of 97% at 100 nodes;
3. The topology prediction was also carried out based on the current position of the nodes and the direction of the moving speed, and the links with poor quality were eliminated. In contrast, the topology and communication strategy were further optimized by setting the priority of the construction robot nodes.  
The simulation results also show that the algorithm can effectively improve the link quality and network connectivity and improve the self-adaptability and reliability of routing protocols, which performed better than the other two algorithms, and has specific significance for building self-organized networks in construction robotics application scenarios.  
Methodology, N.W.; Software, N.W.; Validation, N.W.; Data curation, D.C.; Writing--original draft, N.W.; Writing--review & editing, N.W., L.Z. and D.C.; Project administration, L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Open Fund Project of Shaanxi Provincial Key Laboratory of Geotechnical and Underground Space Engineering (YT202004) and Shaanxi Provincial Department of Education Services Local Special Plan Project (22 JC043).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 14: End-to-end delay at different standstill times [36].
## 2 Model and Algorithm  
### The Model  
We denote the fixed \(d\)-dimensional subspace of \(\mathbb{R}^{n}\) as \(\mathcal{S}\). In application of interest we always suppose \(d\ll n\), say \(\mathcal{S}\) is a fixed low-rank subspace. Let the columns of an \(n\times d\) matrix \(U\) be orthonormal and span \(\mathcal{S}\). \(\{\mathcal{A}\,|\,\mathbb{R}^{n}\rightarrow\mathbb{R}^{m},m<n\}\) is a linear compressive measurement operator which should satisfy the restricted isometry property (RIP) [2] with constant \(\delta_{K}\)  
\[\left(1-\delta_{K}\right)\|\,x\|_{2}^{2}\leq\|\,\,\mathcal{A}(x)\|_{2}^{2} \leq\left(1+\delta_{K}\right)\|\,x\|_{2}^{2},\forall\|\,\,x\|_{0}\leq K \tag{2}\]  
We want to recover the \(K\)-sparse signal \(s\in\mathbb{R}^{n}\) buried in the low-rank background signal \(l=Uw\) and small Gaussian noise corruption \(\xi\) from the compressive measurement \(v\) with small Gaussian measurement noise \(\epsilon\) as Equation (1), here, \(K\)-sparse means \(\|\,s\|_{0}\leq K\).