esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Controller and Observer
### Objective Sliding Mode Control (OSMC)
\begin{array}{ll}\mathbf{\tau}&=\mathbf{\tau}_{\text{eq}}+\mathbf{\tau}_{\text{sw}}\\ &=M_{0}\bigg{(}\bar{\mathbf{q}}_{r}+-\frac{amf(\mathbf{e})^{m-1}f(\mathbf{e})^{\prime}\dot{ \mathbf{e}}+ef(\mathbf{e})^{m}+emf(\mathbf{e})^{m-1}f(\mathbf{e})^{\prime}\dot{\mathbf{e}}+\dot{e}f (\mathbf{e})^{n}}{\big{(}1+\beta nf\big{(}\dot{\mathbf{e}}\big{)}^{n-1}f(\mathbf{e})^{ \prime}\dot{\mathbf{e}}+\dot{e}nf\big{(}\dot{\mathbf{e}}\big{)}^{n-1}f(\mathbf{e})^{\prime }\big{)}}\bigg{)}\\ &+\mathbf{V}_{0}+\mathbf{G}_{0}+\text{dst}\Big{(}\delta_{1}^{-1}\mathbf{s}\Big{)}+c_{3}M_{ 0}\mathbf{s}\end{array}  \tag{32}\]  
Now, clarify that the denominator should be different to zero to avoid singularities in the control law. Proof. : In (32), the denominator can be expressed as  
\[1+\beta n\bigg{(}\dot{e}+\frac{a\dot{e}}{\exp\big{(}\dot{e}\big{)}^{2}}\bigg{)} ^{n-1}\bigg{(}1+\frac{a-2a\dot{e}^{2}}{\exp\big{(}\dot{e}\big{)}^{2}}\bigg{)} +\dot{e}n\bigg{(}\dot{e}+\frac{a\dot{e}}{\exp\big{(}\dot{e}\big{)}^{2}}\bigg{)} ^{n-1}\bigg{(}1+\frac{a-2a\dot{e}^{2}}{\exp\big{(}\dot{e}\big{)}^{2}}\bigg{)} \tag{33}\]  
With the following parameter settings \(\alpha>0\), \(\beta>0\), \(1<n<2\), that the denominator is different to zero. \(\square\)  
Theorem 1.: _For the multi-joint manipulator system described in Equation (1), the control law formulated in Equation (24) ensures that the system error converges to zero within a finite time [30]._  
Proof of Theorem 1.: The Lyapunov function is constructed as  
\[V_{2}=\frac{1}{2}s^{\text{T}}\mathbf{s} \tag{34}\]  
Combining (9) and (10) yields the following derivation:  
\[\begin{array}{ll}\dot{V}_{2}&=s^{\text{T}}\dot{\mathbf{s}}\\ &=s^{\text{T}}\Bigg{(} \begin{array}{c}\ddot{\mathbf{e}}+amf(\mathbf{e})^{m-1}f(\mathbf{ e})^{\prime}\dot{\mathbf{e}}+ef(\mathbf{e})^{m}+emf(\mathbf{e})^{m-1}f(\mathbf{e})^{\prime}\dot{ \mathbf{e}}\\ +\beta nf\big{(}\dot{\mathbf{e}}\big{)}^{n-1}f(\mathbf{e})^{\prime}\ddot{\mathbf{e}}+\dot{ e}f\big{(}\dot{\mathbf{e}}\big{)}^{n}+\dot{e}nf\big{(}\dot{\mathbf{e}}\big{)}^{n-1}f(\mathbf{e})^{ \prime}\ddot{\mathbf{e}}\\ &\leq s^{\text{T}}\Big{(}\mathbf{D}-\Big{(}\text{dst}\Big{(}\delta_{1}^{-1}\mathbf{s} \Big{)}+c_{3}M_{0}\mathbf{s}\Big{)}\text{sgn}(\mathbf{s})\Big{)}\\ &=\Big{(}\mathbf{D}-\Big{(}\text{dst}\Big{(}\delta_{1}^{-1}\mathbf{s}\Big{)}+c_{3}M_{ 0}\mathbf{s}\Big{)}\Big{)}\|\mathbf{s}\|\\ &=\sqrt{2}\Big{(}\mathbf{D}-\Big{(}\text{dst}\Big{(}\delta_{1}^{-1}\mathbf{s}\Big{)}+ c_{3}M_{0}\mathbf{s}\Big{)}\Big{)}\big{)}V^{\frac{1}{2}}\end{array}  \tag{35}\]  
When \(\mathbf{s}=0\), \(\dot{V}_{2}=0\). At this point, the system can be seen to be asymptotically stable. Lemma 1 states that the sliding surface s will converge to the origin within a finite time interval \(t_{s}\). \[t_{s}\leq\frac{\sqrt{2}V_{20}^{\frac{1}{2}}}{\Big{\|}\text{dst}\Big{(}\delta_{ 1}^{-1}\mathbf{s}\Big{)}+c_{3}M_{0}\mathbf{s}\Big{\|}-D_{E}} \tag{36}\]  
where \(V_{20}\) is the initial value of \(V_{2}\). \(\square\)  
Ultimately, it is ensured that the system error converges within a finite time frame. \(t=t_{e}+t_{\sigma}+t_{s}\).
## 3 Platform for Real-Life Testing of Communication Protocols
### Monitor Messaging
#### 3.3.2 Monitoring Messaging Methods  
To develop the implemented framework, we explored a few messaging approaches, two of which are based on the protocol buffer library [50], while a third is a baseline implementation that solely uses PMT. The PMT method sends messages as pmt::dict (see Figure 11). Both PROTO-based methods differ in the way they transmit the message between the monitoring message generator and the probe, as well as how they set the probe-specific fields in the message:  
* The first method serializes the proto message immediately after building the message in the GNU radio block working thread and passes the serialized data to the probe as pmt::blob. This method is known as PROTO-BLOB.
* The second method passes the proto message object as pmt::any (i.e., boost::any), and this method is known as PROTO-ANY.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fields** & **Mandatory** & **Filled by** & **Description** \\ \hline Timestamp & Yes & GNU radio block & Timestamp when the message was built. \\ Probe queue size & No & Monitor probe & GNU radio message passing API queue size. \\ Probe message counter & No & Monitor probe & Number of messages sent. \\ Payload & Yes & GNU radio block & Monitoring data. \\ Payload ID & Yes & GNU radio block & Indicates the payload type for the parser. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structure of the monitoring messages.  
Figure 11: The generation and transfer of monitoring messages in the messaging system for the implemented messaging methods.  
Figure 11 shows how messages flow through the three components of the messaging system using the three messaging methods (see Figure 10) and two programming languages.
## 4 Discussion
### Project Validation by SHAP Value Force Plot  
In order to verify the feasibility of the model, this study selects two projects (projects A and B) from the projects that have been researched but not put into production in the past five years for verification and verification. SHAP value force plot gives us interpretability for single model predictions that can be used for error analysis to find explanations for specific instance predictions. In the plot, the significance of features can be demonstrated by the length of the bar in the SHAP values. Longer bars mean higher SHAP values, and variables are more important. Among them, features that will increase the SHAP value (resulting in production) are displayed in red, and features that will decrease the SHAP value (resulting in non-production) are displayed in blue.  
For instance, Figure 12a shows that due to the short Estimated Mine Life, Low In Situ Value (Measured Indicated Excl Reserves), and High Country Risk Score Legal Current, even if the Reserve (Ore Tonnage) is high, the model estimates that the project is more likely to be non-productive (probability f(x) = 0.47).In Figure 12b, the project is forecast to be production-ready. Although the project has two risk characteristics: low Stripping Ratio (Stripping Ratio = 1.75) and Low In Situ Value (Measured Indicated Excl Reserves), its Estimated Mine Life, Study Year, and MillHead Grade are all at a high level, which puts the project at a production-ready level.  
Figure 11: Distribution and correlation of important features (SHAP value dependence plot). (**a**) the distribution of Estimated Mine Life; (**b**) the correlation between Estimated Mine Life and Cash Costs per oz; (**c**) the distribution of Stripping Ratio; (**d**) the correlation between Stripping Ratio and MillHead Grade.
## 3 Results
### Metal(loid) Concentrations
#### 3.2.1 Einsied Reservoir
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Geogenic Background Values (Median) (mg/kg) for Einsiedel Soils (LfULG 2015 [43])**} \\
**Layer** & **As** & **Cd** & **Cr** & **Cu** & **Hg** & **Ni** & **Pb** & **Zn** & **Co** & **V** \\ \hline toss & 65 & 0.13 & 34 & 18 & 0.14 & 19 & 86 & 68 & 6.6 & 53 \\ sus & 33 & 0.10 & 41 & 17 & 0.09 & 25 & 31 & 81 & 10.0 & 57 \\ \hline \multicolumn{8}{c}{Metal(loid)s (median \(\pm\) average absolute deviation) in soils of the Einsied Reservoir (mg/kg), \(n\) = 12} \\ \hline toss & 24.4 \(\pm\) 2.8 & 0.11 \(\pm\) 0.07 & 16.8 \(\pm\) 2.6 & 14.6 \(\pm\) 2.5 & 0.13 \(\pm\) 0.07 & 16.7 \(\pm\) 3.0 & 78.1 \(\pm\) 5.6 & 42.4 \(\pm\) 8.1 & **8.4 \(\pm\) 1.8** & 36.0 \(\pm\) 6.8 \\ sus & 15.2 \(\pm\) 3.6 & 0.08 \(\pm\) 0.06 & 19.8 \(\pm\) 3.0 & 12.2 \(\pm\) 2.6 & **0.11 \(\pm\) 0.03** & 19.4 \(\pm\) 2.1 & **43.9 \(\pm\) 8.7** & 44.1 \(\pm\) 5.7 & **10.8 \(\pm\) 1.9** & 38.4 \(\pm\) 6.6 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison between the metal(loid) background values for Saxon top- (tos) and subsoils (sus) on phyllite (land use: forestry) [43] and the measured metal(loid) concentrations of the soil samples within the Einsiedel Reservoir catchment. Values in bold exceeded the background concentrations. Figure 5: Vertical gradients (in cm below the sediment surface) of pH, organic matter (estimated as LOI) and the concentrations (mg/kg) of Cd, Co, Ni, Zn and As in reservoir sediments, exemplarily shown by the sediment cores ES\(-\)S, ES\(-\)II and ES\(-\)I.  
Figure 6: Metal(loid) concentrations in topsoil samples (tos), subsoil samples (sus) and brook sediments (bks) from the Einsiedel Reservoir catchment.
## 2 Materials and Methods
### Test Site  
The experiment was conducted in an open space on a university campus. The test site was inside a RFZ where a flight of 60 m AGL was prohibited for any privately owned UAV. The participants were aligned in three rows (o in Figure 2). The take-off spot (\(\bullet\) in Figure 2) of the drone was 3.5 m in front of the first row of the participants. The distance between each participant and the next one was approximately 0.5 m. After the drone took off, it was navigated to one of the predetermined locations ( in Figure 3) in the air 40 m AGL and was hovering there. The horizontal distance between the hovering position and the take-off spots was between 150 to 500 m. Five hundred meter was taken as the farthest testing distance because none of our researchers could see the drone at this distance in a pilot trial. The distances and visual angles of the drone with and without the protector at these hovering locations are shown in Table 1.  
The experiment started at 09:00 am and lasted for approximately two hours. The weather was cloudy. The wind speed on the ground, reported by the UAV Forecast(tm) app, was 8 km/h. This corresponds to a wind of level 2 (light breeze) in the Beaufort Wind Scale [29]. The wind direction was easterly. The relative humidity and temperature were 47% and 31 \({}^{\circ}\)C, respectively. The background noise and illuminance at the takeoff spot on the ground were 52 dB and 293 lux, respectively. They were measured using a sound level meter (Smart(r) Sensor, AS 824, Honk Kong, China) and a light meter (Prokit's Industries(r) Co. LTD, MT-4617 LED-C, New Taipei City, Taiwan) before the drone was started. The visibility was 10.3 km [30]. The sun was on the east and 86.68\({}^{\circ}\) to the south and 49.44\({}^{\circ}\) above the horizon at 09:00 [31]. The participants were facing to the south west without glare in their visual field. The drone navigation path and all the hovering locations were higher than all the plants and buildings in the testing site. The background of the drone's hovering locations during the experiment, observed from the participants' area, was the cloudy sky.
## 1 Introduction
A comparative analysis found that MLP autoencoders outperformed MLP, CNN, and LSTM architectures in detecting damage from vibration signals, achieving an AUC of 99.11% ([PERSON] et al., 2018). Additionally, research developed a hybrid health indicator using current and vibration signals to monitor bearings and stator windings in motors, demonstrating excellent performance across various load conditions ([PERSON] et al., 2018). CNN autoencoders were also used for the rapid identification of electrical damage in induction motors, achieving over 95% accuracy. In our previous works, this unsupervised methodology proved to be especially advantageous in sectors such as maritime, where instances of damage data are frequently limited ([PERSON] et al., 2018). To this end, PRISMOID aims to develop a modular and scalable digital twin platform capable of collecting large amounts of heterogenous data from different sensors and systems onboard and provide the software with the means to reliably use the data onboard and onshore for early warnings and decision support reasoning. The present research focuses on the anomaly detection framework developed during the project, which utilizes autoencoders in conjunction with the Mahalanobis distance to distinguish between healthy and compromised states by analyzing time series current data, with a specific focus on damage occurring in stator windings ([PERSON] et al., 2018). The proposed framework is validated by simulation data from an induction motor under various conditions. Its integration into aging vessels enhances management and maintenance, offering easy damage detection that optimizes energy losses, reduces downtime, and improves the CAPEX and environmental sustainability. This paper is organized as follows. Section 2 outlines the overall architecture of the digital twin platform, which includes the AI-driven framework. Section 3 details the simulation model for the motor, while Section 4 describes the proposed framework and Section 5 presents the validation results of the model. In Section 6, a discussion of the primary advantages of this approach and its applicability in business scenarios is offered, while Section 7 concludes with the key findings of the research.
# Robust Parking Block Segmentation from a Surveillance Camera Perspective
###### Abstract  
Parking block regions host dangerous behaviors that can be detected from a surveillance camera perspective. However, these regions are often occluded, subject to ground bumpiness or steep slopes, and thus they are hard to segment. Firstly, the paper proposes a pyramidal solution that takes advantage of satellite views of the same scene, based on a deep Convolutional Neural Network (CNN). Training a CNN from the surveillance camera perspective is rather impossible due to the combinatory explosion generated by multiple point-of-views. However, CNNs showed great promise on previous works over satellite images. Secondly, even though there are many datasets for occupancy detection in parking lots, none of them were designed to tackle the parking block segmentation problem directly. Given the lack of a suitable dataset, we also propose APKLOT, a dataset of roughly 7000 polygons for segmenting parking blocks from the satellite perspective and from the camera perspective. Moreover, our method achieves more than 50% intersection over union (IoU) in all the testing sets, that is, at both the satellite view and the camera view.  
deep learning; parking lot dataset; parking block segmentation; satellite dataset +
Footnote †: journal: Applied Sciences
# Analysis of Heavy Metal Sources in Xutuan Mining Area Based on APCS-MLR and PMF Model
## 3 Results and Discussion
### Analysis of the Potential Sources of the Sediment Heavy Metal Elements
#### 3.4.4 Comparison of APCS-MLR and PMF Results  
In our study, APCS-MLR simplified the complexities of six heavy metals into two dominant sources, efficiently underscoring natural inputs (72.5%). The results of the APCS-MLR analysis are similar to those of [PERSON] et al. [58]. APCS-MLR is used for rapid primary source screening, and PMF is used for fine analysis. PMF further decomposed the industrial contributions into specific human activities (mining: 26.4%, traffic: 27.05%). For Cd, APCS-MLR attributed 59% to industrial sources, while PMF traced its origin to coal mining (57.92%). This conclusion is roughly the same as the analysis results of heavy metal sources in sediments of the Tuohe subsidence section in the Huaibei mining area [47]. Slightly different is that this paper also analyzes the contribution of traffic pollution to heavy metals. Such synergistic analysis mitigates the risk of misinterpretation inherent in single-model approaches and aligns with the recommendations of [PERSON] al. [59]. APCS-MLR is particularly suited for datasets with strong collinearity among variables and provides intuitive source contribution ratios. However, its limitation lies in assuming linearity between factors and pollutants, which may oversimplify complex interactions. So, it only identified two sources of pollution. The reason for the difference in contribution rates between the APCS-MLR and PMF models is that the absolute principal component does not consider the uncertainty of the data in the calculation process, and there is a small amount of information loss. However, the uncertain data of the heavy metal content in the sediment of the PMF model are added in the calculation process and non-negative constraints are applied, which makes the final result more reasonable.
# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 6 Applications: XAI's Impact across Diverse Domains
### Healthcare  
XAI has emerged as a pivotal component within the healthcare sector, affording healthcare providers the ability to fathom the rationale behind AI algorithms' specific diagnostic conclusions or treatment recommendations. This fosters heightened assurance of the accuracy of these algorithms and consequently elevates the quality of decision-making and patient care. Consequently, XAI finds widespread utility in realms such as medical diagnosis, treatment recommendations, patient monitoring, and clinical decision support.
# A Tile-Based Multi-Core Hardware Architecture for Lossless Image Compression and Decompression
## 4 Validation and Analysis
### Performance of Proposed System
Additionally, an extra AXI-STREAM interface FIFO was implemented for the transmission of compressed information. At least 512 MB of high-speed memory, such as DDR2 or higher, is required for the implementation of this system. FLASH memory cards were selected as the external storage medium for data retention. * Energy consumption: The system power consumption was measured to be 2.585 W at room temperature. Under extreme low temperatures of \(-\)35 \({}^{\circ}\)C, power consumption was reduced to 2.37 W. At an extreme high temperature of 125 \({}^{\circ}\)C, power consumption was increased to 3.441 W.
* Impact of image patterns: The smoothness of images notably influenced the compression performance. Images with minimal grayscale variation yielded better compression ratios, while those with high variability performed less effectively. * Effect of tile size: Testing various tile sizes indicated a moderate increase in compression ratio with larger sizes. An optimal tile size of 16 \(\times\) 16 was identified, balancing compression efficiency with resource constraints. * Consistency: Since the proposed system is implemented using a lossless compression algorithm, the images before compression and after decompression are expected to be completely identical--that is, consistency should be preserved. Therefore, the decompressed images were compared with the original images on a one-to-one basis. Upon inspection, it was confirmed that the decompressed images were completely consistent with the original ones.
# The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges
## 2 From 1G to 5G
### The Fifth Generation (5G)
A notable challenge lies in transitioning from a management perspective rooted in quality of service (QoS) to one centered on QoE. The QoS viewpoint, being restrictive, confines network management to performance metrics like flow or packet priority, delay, jitter, bandwidth, etc., providing users with information about DiffServ/IntServ. Therefore, it remains crucial to further articulate the assumptions linked with QoE in a more comprehensive manner aligned with the functionalities and services provided by 5G [33]. The deployment of 5G networks will occur progressively, with the initial phase involving a modification in the radio interface. This involves linking 5G NR to the 4G network's EPC, termed the non-standalone phase. In the standalone phase, 5G NR will connect to the 5G core, enabling a dual-interface phone to execute handovers (or roaming) between the two networks. eMBMS utilizes MIMO technology to deliver multicast/broadcast media content, ensuring a superior quality of experience (QoE), including high-definition video streaming for consumers. The introduction of 5G NR brings novel functionalities aimed at enhancing QoE, incorporating the integration of network slicing, edge computing, and 5G QoS Flow concepts [10].
###### Abstract  
The cold forging backward extrusion is employed to produce parts that are characterized by better mechanical strength. However, in this process, punches are often prone to breakages because of the large forces encountered in deforming the steel billets. The service life of the punches is affected majorly by the geometrical attributes, the type of steel undergoing deformation, and hence the present investigation focuses on the applications of natural computing algorithms such as artificial neural network (ANN) and differential evolution (DE) optimization algorithm to study the differential influence on the forming behavior of different grades steel and enhance the punch service life. The AISI steel grades, such as AISI 1010, 1018, and 1045, employed extensively in the production of automotive components, have been compared in terms of forming behavior, such as effective stress, strain, strain rate, and punch force. The multi-layer feed-forward ANN architecture was utilized for process modeling with forming responses of finite element (FE) simulations that are strategically planned through the design of experiments (DoE) approach. Considerable variations were found for the effective stress and punch force amongst the steels, while marginal deviations were observed for effective strain and strain rates. Confirmatory experiments were conducted to validate the results of optimal combinations obtained through the DE optimization technique, and the deviations were observed to be in the acceptable range. The cold forging backward extruded components have also been examined for better mechanical soundness through microstructure and micro-hardness analysis that clearly revealed the mechanical integrity and strength enhancement within the forged components. The proposed study would assist the industries engaged in the production of cold-forged steel components in determining the appropriate values of variables to minimize the forming responses and, thus, help in enhancing the life of the tooling.  
Keywords:cold forging backward extrusion; finite element simulation; design of experiments; artificial neural network modeling; differential evolution optimization; micro-hardness +
Footnote †: journal: Applied Sciences