esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Beam Shaping Method  
The greatest challenge in the integration of the measuring system into the LMD-w process lies in the aberration-free beam shaping of the measuring and process laser. In order to make the process directionally independent, a circular scanning around the coaxial wire was implemented. The minimum scanning-circle diameter of the measuring light is limited by the size of the wire or wire guide. The scanning field should also not be too large to be able to record enough data with adequate spacing at the maximum measurement frequency of 70 kHz.  
For the required beam shaping, a combination of Axicon and prism is used in the setup, shown in Figure 5. A similar optical setup for beam shaping has already been shown in a previous work on the laser process of 3D laser brazing [20]. The original Gaussian distribution of the collimated laser beam is transformed into a ring shape by the Axicon. The additional material, in this case the wire, can be guided in the light-free center of the ring. To insert the coaxial wire guide into the ring, the ring is divided into two halves by a pair of refractive prisms. The arrangement of the prism provides a parallel offset of the incident beam, whereby an opening of the circle is realized. At one opening of the two resulting semicircles, the wire is inserted coaxially into the processing head. Finally, an aspherical lens focuses the process and measuring laser onto the processing zone. Here, the wire is melted with the help of the process laser, resulting in a deposition on a workpiece. This functional principle reveals the importance of the optimal distance  
Figure 4: Coaxial wire-based laser metal deposition system (LMD-w) with an integrated optical coherence tomography (OCT) scanning head: (**A**) Coaxial wire supply head and schematic representation of the wire deposition with the circular laser pattern around the wire. (**B**) Overview of the entire system, including data visualization. (**C**) Superimposed schematic representation of the dichroic-mirror-based integration of the OCT scanning head and the double axicon lens module.  
between the focusing position of the process laser and the end of the wire. The optics used, such as optical prisms and Axicon, allow annular beam guidance, and thus flexible control of the LMD-w process head, which is independent of the measuring system.
# Task-Oriented Evaluation of the Feasible Kinematic Directional Capabilities for Robot Machining
## 3 Results
### Collaborative Robot UR5e
#### 3.3.1 Path Segment Feasiblity
If the main concern was the linear velocity (the value of the synchronization parameter \(h\) was high), as in case (i), the maximal linear velocity vector \(v_{\text{max}}\) in the direction \(\hat{u}_{T}\) practically coincides with the vector length of the translational manipulability polytope \(P_{strong}^{T}\) in the strong sense in the same direction. The maximal angular velocity \(\Omega_{\text{max}}\) was near zero since the flattest part of the surface was considered. On the other hand, the lowest value of the synchronization parameter \(h\) was when a greater change in orientation was required as compared to the change in position, as it was in case (ii). As a result, the maximal angular velocity \(\Omega_{\text{max}}\) was, in this case, equal to the vector length of the rotational manipulability polytope \(P_{strong}^{R}\) in the strong sense, and the maximal feasible linear velocity \(V_{\text{max}}\) was the lowest among all three cases. When the synchronized linear and angular motion was taken into account, as in case (iii), the maximum linear and angular velocities were higher, and its vectors exceeded the bounds of the translational (rotational) \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Path Segment** & \(V_{\text{max}}\) **(m/s)** & \(\Omega_{\text{max}}\) **(rad/s)** & \(\hat{\theta}\) **(rad/s)** \\ \hline (i) & \(1.1552\) & \(0.2588\) & \(\left[-0.9854,\,1.7508,\,-3.1416,\,1.2716,\,0.1298,\,-0.9807\right]^{T}\) \\ (ii) & \(0.0699\) & \(1.9391\) & \(\left[0.3208,\,-1.2202,\,2.4198,\,-3.1416,\,-0.7500,\,0.8459\right]^{T}\) \\ (iii) & \(2.4365\) & \(3.2688\) & \(\left[-3.1063,\,1.6537,\,-2.7480,\,-1.4495,\,2.2663,\,-3.1416\right]^{T}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Feasible directional kinematic capabilities for three different path segments (UR5e). Figure 9: Graphical representation of \(v_{\text{max}}\) and \(\omega_{\text{max}}\) and determined by the DTF method for the 6 DOF UR5e robot manipulator in relation to its manipulability polytopes within the translational subspace \(P_{\infty}^{T}\), \(P_{strong}^{T}\), and the rotational subspace \(P_{\infty}^{R}\) and \(P_{strong}^{R}\), respectively, for segment (i): (**a**) \(v_{\text{max}}\) and (**b**) \(\omega_{\text{max}}\); for segment (ii): (**c**) \(v_{\text{max}}\) and (**d**) \(\omega_{\text{max}}\); and for segment (iii): (**e**) \(v_{\text{max}}\) and (**f**) \(\omega_{\text{max}}\). manipulability polytope \(P_{strong}^{T}\) (\(P_{strong}^{R}\)) in the strong sense. Generally, however, they can never exceed the bounds of the translational (rotational) manipulability polytope \(P_{\infty}^{T}\) (\(P_{\infty}^{R}\)) in the weak sense.
# ML-Empowered Microservice Workload Prediction by Dual-Regularized Matrix Factorization
## 4 Performance Evaluation
### Prediction Accuracy Comparison (RQ1)  
To evaluate the effectiveness of the proposed TDDRMF framework, we conduct multi-step prediction experiments for both \(T+1\) (1-step prediction) and \(T+10\) (10-step prediction) scenarios. The performance of TDDRMF is compared against several baseline models to assess prediction accuracy and computational efficiency.  
The experimental results for 1-step and 10-step predictions are summarized in Tables 2 and 3, respectively.  
The Q1 experiment evaluates various models in both single-step and multi-step prediction tasks, demonstrating that matrix factorization-based methods strike a good balance between computational efficiency and prediction accuracy.  
In single-step prediction tasks, although LSTM achieves the best precision with the lowest RMSE and highest \(R^{2}\), its training and prediction times are significantly higher than those of other models, highlighting its high computational cost. In contrast, TDDRMF maintains a lower computational time while achieving superior prediction accuracy compared to other matrix factorization methods, such as TAMF and TRMF. This can be attributed to the synergistic optimization of temporal regularization and nuclear norm constraints.  
In multi-step prediction tasks, the advantages of TDDRMF become even more apparent. It achieves the lowest training and prediction times among all models while delivering accuracy second only to LSTM, and it significantly outperforms traditional statistical models such as ARIMA and other matrix factorization methods. Notably, ARIMA exhibits anomalies in \(R^{2}\) values during multi-step prediction, indicating its limited applicability in dynamic workload scenarios.  
Overall, TDDRMF effectively balances computational efficiency and prediction accuracy through the joint optimization of temporal regularization and nuclear norm regularization. It demonstrates robust performance in both single-step and multi-step prediction tasks, providing an efficient and reliable solution for dynamic workload prediction.
## 4 Materials and Methods
### Data Preprocessing  
DICOM retrospective preprocessing is applied on CT slices in order to convert slice domain from imaging machine domain to CT Hounsfield Unit (HU) domain. Domain transformation relies on the differences in the absorption/attenuation coefficients of radiation (X-ray beam) within tissues. The HU value of a tissue can be computed based on the linear attenuation of the tissue (\(\mu\)) relative to the attenuation of water (\(\mu_{water}\)) and air (\(\mu_{air}\)) under standard temperature and pressure as expressed in Equation (1).  
\[HU=1000\times\frac{\mu-\mu_{water}}{\mu_{water}-\mu_{air}} \tag{1}\]  
The radiodensity of water is considered to be zero HU and air is \(-1000\) HU. In order to generate grayscale images during CT reconstruction, the intensity values are transformed  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**NSCLC Radiomics (Lung 1)** & & & & \\ \hline  & **ADC** & **SCC** & **NOS** & **LCC** \\ \hline I & 11 & 23 & 44 & 15 \\ II & 8 & 23 & 4 & 5 \\ IIIA & 14 & 44 & 15 & 36 \\ IIIB & 18 & 62 & NA & 57 \\ \hline \multicolumn{4}{c}{**NSCLC Radiomics-Genomics**} \\ \hline  & **ADC** & **SCC** & **NOS** & \\ \hline I & 5 & 9 & 25 \\ II & 23 & 23 & 9 \\ IIIA & 3 & 4 & NA \\ IIIB & 5 & 6 & NA \\ \hline \multicolumn{4}{c}{**NSCLC Radio-Genomics**} \\ \hline  & **ADC** & **SCC** & **NOS** & \\ \hline I & 73 & 18 & 2 \\ II & 22 & 12 & NA \\ IIIA & 15 & 5 & 1 \\ IIIB & 2 & NA & 1 \\ UD & 60 & NA & NA \\ \hline \multicolumn{4}{c}{UD: Unspecified} \\ \hline \end{tabular}
\end{table}
Table 1: Stage and Pathology (ADC, SCC, NOS and LCC) Distribution in the Non-Small Cell Lung Cancer Benchmark Datasets.  
Figure 3: New Violin plot for the histology box-plot against age with distribution of genders (red: male and blue: female).  
to HU CT values. Equation (2) is used for transformation, where \(m\) is the rescale slope and \(c\) is the rescale intercept. The rescale values are available as part of each CT slice metadata.  
\[HU=mx+c \tag{2}\]  
Following HU conversion, a binary image (mask) is generated for each slice depending on HU values. Multilevel thresholding is applied based on established HU spectral bands [27], where the lung HU values range from \(-900\) to \(-400\). The mask helps highlight the lung tissue area for further processing. In addition, Gaussian filter is applied on the original slices to remove the noise. The noise might have been generated from electromagnetic waves and heat or light from surroundings.
# Detection and Attribution of Greening and Land Degradation of Dryland Areas in China and America
## 3 Results
### Dryland Areas in America
#### 3.2.2 Dominant Factor Distribution  
In this section, we discover the regional dominant factor of American drylands (Figure 10). We attributed the vegetation growth to three main factors (CO\({}_{2}\), CC and LU). We found that most areas of American dryland were controlled by CO\({}_{2}\) and LU. Previous studies found that in some west areas such as New Mexico, grass predominated in the middle and end of the 19 th century. However, the grass biomass decreased to less than 7% until the end of the 20 th century due to overgrazing. This led to a shift in ecosystem structure and function here. Grass depletion led to nutrient loss and surface erodibility. The expanded shrub occupied the main plant biomass [37]. Additionally, the role of CC in vegetation change cannot be ignored. It is found that a change in the rain also contributed to the shift. The shrub expanded due to the temporal precipitation distribution characterized by a decrease in summer rains and the concomitant increase in winter rain [11].  
Figure 9: (**a**) Observed change of vegetation growth in American dryland area. Yellow means decrease in NDVI\({}_{m}\) and green means increase. Dotted areas suggest the change is significant (\(\alpha_{FDR}\) = 0.10). Areas that have large uncertainties are masked in white. (**b**) The change of NDVI\({}_{m}\) attributed to CO\({}_{2}\). (**c**) The change of NDVI\({}_{m}\) attributed to LU. (**d**) The change of NDVI\({}_{m}\) attributed to CC. (**e**) The change of NDVI\({}_{m}\) attributed to CV.
## 5 Experimental Findings
### Difficult Cases
#### 5.4.3 Case with Irregular Mass  
Opposed to what is reported in the literature [39], breast lesions may sometimes exhibit irregular shapes and borders with internal divisions. In these cases, breast masses could cause difficulties during the manual segmentation process: in fact, as the lesion contour becomes more irregular, the manual tracing of the ROI becomes more challenging. As a consequence, the manual segmentation of breast lesions with unusual shapes does not always match the effective lesion contour. Figure 15 exhibits one of this cases with unusual  
Figure 14: Case with a blurred boundary lesion: (**a**) entire axial DCE-MRI slice; (**b**) crop of the slice shown in (**a**). Comparison between manual ground-truth (green) and automated segmentation (red).  
Figure 13: Case with a low-contrast lesion: (**a**) entire axial DCE-MRI slice; (**b**) crop of the slice shown in (**a**). Comparison between manual ground-truth (green) and automatic segmentation (red).  
elongated breast lesion. The manual segmentation (green contour) completely cut the left portion in the upper part of the lesion which is, instead, properly included in the semi-automatic boundary (red contour). As a consequence, computer-assisted segmentation process results in a more precise mask that properly reproduces the narrowed-shape of lesion.
## 4 Anomalies and Sources
### Unrecognized AGV Failure  
As a first example for the source of an anomaly that can cause an AGV to stop, a set of well-known anomalies that could already appear in traditional factories without a direct link to cyber-attacks is considered: the malfunctioning AGV is suffering from an unrecognized failure of its hardware or software. The specifics of such an anomaly can vary, e.g., the motor of the AGV might be broken, the sensors for self-localization on the factory floor might be blocked or defective, the locally stored data on the factory map might be lost or the AGV might repeatedly try and fail to exchange goods with a machine. Since such anomalies are already well understood and mitigation methods include adding redundant sensors, integration of additional degradation modes in case a component or service is unavailable or adding timeouts for the process of exchanging goods, the detailed identification of the source of the system failure is out of the scope for this paper. Instead, the focus is on the general identification and mitigation of situations in which the AGV is not moving while its controller does not react to the system failure, such that the AGV still participates in the fleet communication as if it were operational.
# Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients
## 3 Fuzzy C-Means Clustering with Multiple Fuzzification Coefficients (MC-FCM)  
### The Fuzzification Coefficients  
Each element is assigned its own fuzzification coefficient. The fuzzification coefficient of an element is calculated based on the concentration of other elements surrounding that element. If the concentration is high, meaning a high chance to create a cluster, the fuzzification coefficient will be small for faster convergence. If the concentration is low, the fuzzification coefficient will be large to increase the ability to select a cluster through iterations.  
With each element \(X_{i}\), \(1\leq i\leq N\), let \(\delta_{ij}\) be the distance between the elements \(X_{i}\) and \(X_{j}\), \(1\leq j\leq N\). In the dataset, we can treat the total distance between \(X_{i}\) and \(N/C\) elements with the closest distance to \(X_{i}\) as a heuristic metric for the concentration surrounding \(X_{i}\), notated \(\delta_{i}^{*}\). Afterward, the fuzzification coefficient \(m_{i}\) can be calculated from \(\delta_{i}^{*}\), as shown below.  
**Summary of steps for computing \(m_{i}\)**  
**Input:** the dataset \(X=\{X_{1},X_{2},\ldots,X_{N}\}\), the interval for fuzzification coefficients \([m_{L},m_{U}]\), \(m_{L}>1\) and a parameter \(\alpha\).  
**Output:** the fuzzification coefficient \(m_{i}\) for each element \(X_{i}\)  
**Steps:**  
* Step 1: Calculate the distance between two elements \(\delta_{ij}=\|X_{i}-X_{j}\|\), \(1\leq i\leq N\); \(1\leq j\leq N\).
* Step 2: Rearrange \(\delta_{ij}\) with index \(j\), we have \(\Delta_{i}=\left\{\delta_{i1}^{\prime},\delta_{i2}^{\prime},\ldots,\delta_{iN} ^{\prime}\right\}\) in non-decreasing order. Calculate \(\delta_{i}^{*}=\sum\limits_{j\leq N}\delta_{ij}^{\prime}\).
* Step 3: Calculate \(\delta_{\min}=\min\limits_{i=1,\ldots,N}\{\delta_{i}^{*}\}\) and \(\delta_{\max}=\max\limits_{i=1,\ldots,N}\{\delta_{i}^{*}\}\). For each \(X_{i}\), \(1\leq i\leq N\), calculate \[m_{i}=m_{L}+(m_{U}-m_{L})\left(\frac{\delta_{i}-\delta_{\min}}{\delta_{\max}- \delta_{\min}}\right)^{\alpha}\] (4)  
The algorithm to calculate \(m_{i}\) is carried out once before clustering, with a complexity of \(O(N^{2})\). The parameter \(\alpha\) is utilized to adjust the mapping of the distance between \(X_{i}\) and its neighboring elements with the exponential parameter in the range of \([m_{L},m_{U}]\). If \(\alpha=1\), it indicates linear mapping; if \(\alpha<1\), the distribution is skewed toward \(m_{U}\); and if \(\alpha>1\), the distribution skewed toward \(m_{L}\).
## 6 Results  
To obtain the experimental results, we use the Madrid-Las Tablas district's real urban traffic network, described in [34]. Different routing strategies are evaluated regarding MTTS and the effects of Dj-kSP and routing area variation are analyzed.  
As pointed out in (16) and (19), multiple variations can be applied to generate the corresponding TWM maps:  
* The number of \(K\) alternative paths considered for Dj-kSP. We vary on it, typically ranging between 3 and 10. Selecting a low K value will not create route variety, and using a higher number presumably is worthless as it will not find so many disjoint paths. Using a high K value has a significant performance penalty in the Dj-kSP algorithm with polynomial time complexity.
* The routing algorithm \(\phi\) is used to calculate the minimum cost route. In this work, we use the Dijkstra algorithm as it will not significantly affect the resulting maps. The routing algorithm performance affects the overall performance as the traffic assignment used during the optimization stage uses it extensively.
* The disjoint level criteria \(DjC_{n}\) used to evaluate the disjoint level of the paths. It is a traffic planning decision that conditions how routes are generated depending on the edge selection policy used. It is analyzed in the experimental section. This parameter also affects algorithm performance during the Dj-kSP calculation phase, as edge removal could take broader searches.
* The disjoint level threshold \(\phi\). We also vary it, with a typical value of 60%. This a traffic planning decision, where a high value of disjointness (>80%) probably will not produce the necessary variety of alternative routes, and a low value (<30%) will generate very overlapping routes. The combination of \(\phi\) and \(K\) directly affects the number of Dj-kSP and, consequently, the path flows to be obtained since there may not be enough alternative paths that meet the maximum overlap constraints.
* The similarity criterion (max/min Stretch, max/min Weight, max/min Crossing Paths) affects the performance of Dj-kSP calculation and the effectiveness of the routes found. Also, depending on the selected criterion, the ES-C algorithm finds different paths or restricts the ones found. Our experiments and the findings described by [16] suggest using the maxStretch or the maxWeight edge-removal criteria as they lead to better assignment.
* The distance of surrounding nodes to the nodes of each path flow, to define the routing areas. It should be greater than 1 to find some routing alternative; a typical value ranges from 2 to 5 connected nodes. It is possible to use higher values, but the routing areas probably would overlap, losing the disjointness properties. It does not affect the algorithm's performance.
* The different utility functions for the vehicle fleets. This item is not considered in the scope of this work.  
The map assignment to the vehicles and the static traffic (route) assignment processes also have different variation alternatives:  
* The adherence factor \(\psi\) (18) of the vehicles using TWM.
* The own GA resolution parameters.
## 2 Materials and Methods
### Gravity Frequency Shift
According to SFST, the GP difference between a ground site and a satellite is defined as follows [21]:  
\[\frac{\Delta\phi_{es}}{c^{2}}\equiv\frac{\phi_{s}-\phi_{e}}{c^{2}}=\frac{ \Delta f}{f_{e}}-\frac{v_{s}^{2}-v_{e}^{2}}{2c^{2}}-\sum_{i=1}^{4}q^{(i)}+ \Lambda f+O(c^{-5}), \tag{3}\]where \(\Delta\phi_{es}\) is the GP difference between the satellite and the ground station; \(v_{e}\) and \(v_{s}\) are velocities of the ground site and the satellite, respectively. The second term of the Equation's right side contains the second-order Doppler effect. \(q^{(i)}\) (\(i=1,2,3,4\)) are quantities associated with the velocities and positions of the ground site and satellite, vector potential, second Newtonian potential, and third- and fourth-order terms. \(\Lambda f\) shows the correction terms for ionospheric, tropospheric, and tidal effect; \(O(c^{-5})\) represents higher-order terms, which can be disregarded due to their negligible impact. For comprehensive details, please refer to [21]. The theoretical accuracy of Equation (3) is of the order \(10^{-19}\), demonstrating a significant improvement over the original formula used in the GP-A experiment, which had an accuracy of approximately \(10^{-15}\). With the stability of optical-atomic clocks (OACs) achieving a level of \(10^{-18}\), the SFST technique can yield a measurement of GP with precision to the order of a few centimeters [21].