esa-sceva/satcom-chunk-collection huggingface dataset:
# Closed-Loop Robotic Arm Manipulation Based on Mixed Reality
## 6 Results and Discussion  
The operator awareness in a collaborative environment is of critical importance and in order to increase user awareness in such environments, hands-on experience and safety demonstrations are required. However, with the developed application, an increased user awareness has been accomplished with the AR/MR visualization of the safety zones as well as the robot's intentions. Regarding the number of errors, significant variation was observed among the users due to different experience levels, i.e., the shop-floor technicians  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Definition** \\ \hline Task Effectiveness & How successfully was the task accomplished \\ Neglect Tolerance & Deviation in time for the robot’s task when the operator is not attending the robot \\ Robot Attention Demand & The fraction of total task time that an operator must attend to the robot \\ Free Time & The fraction of total task time that an operator does not have to attend to the robot \\ Fan Out & Estimated number of number of robots that the operator can attend simultaneously \\ Interaction Effort & The total time for interaction plus the cognitive demands \\  & of interaction \\ \hline \hline \end{tabular}
\end{table}
Table 2: Metrics for user awareness in HMI.  
Figure 6: Assembly steps for experimental scenario 1.  
made fewer errors than the participating engineers. Despite that, for all the participants, there has been noted a reduction in the number of errors with the use of the developed application relative to the current controller. More specifically, the increase in the human operator's awareness, from approximately 51% to 81%, is one of the most important findings, as reflected in Figure 7.  
Figure 7: Results: (**a**) number of errors; (**b**) assembly time; (**c**) user awareness.  
An interesting finding was the time required for completion of the assembly task. Similar to the rest of the findings in this research work, the time recordings varied considerably between the users, which is also explained by the variance of experience of such operations. However, in order to measure this indicator, the users were allowed to familiarize themselves with the interface of the developed application. Another aspect that has been observed during the experiments is an increase in users' engagement, as less experienced users seemed more confident in experimenting with the robot in the collaborative cell in contrast to the current setup where the hardwired controller has to be used. Mostly, this was due to the wireless remote operation of the robot, which gave the participants the feeling of freedom in an unwanted situation. Finally, the total assembly time was decreased by approximately 24%, and user errors decreased by approximately 60%.
# Automated Design of CubeSats using Evolutionary Algorithm for Trade Space Selection
## 2 Knapsack Problem  
The knapsack problem is analogous to the packing of items into a knapsack, where items are selected from an available set such that the total value of the selected items is maximized while simultaneously not exceeding the knapsack's capacity [18]. It is a highly popular problem in the research field of constrained and combinatorial optimization. The most common problem being solved is the 0-1 knapsack problem, which restricts the number of copies, \(x_{i}\), of each kind of object, \(o_{i}\), to zero or one. Given a set of \(n\) objects \(O=(o_{1},\,o_{2},\ldots,\,o_{n})\), each with a weight, \(w_{i}\), and a value, \(v_{i}\), along with a maximum weight capacity, \(W\), of the knapsack, the objective is to find a subset, \(S\subseteq O\), in such a way that the weight sum over the objects in \(S\) does not exceed the knapsack capacity and yields a maximum value. The problem is mathematically expressed as Equation (1).  
\[KP=\left\{\begin{array}{c}\max\sum\limits_{i=1}^{n}x_{i}v_{i}\\ \text{subject to }\sum\limits_{i=1}^{n}x_{i}w_{i}\leq W\\ x_{i}\in\{0,\ 1\}\ \forall i\in\{1,\ldots,\ n\}\end{array}\right. \tag{1}\]  
Here, \(x_{i}\) represents the number of instances of item \(i\) to include in the knapsack. Informally, the problem is to maximize the sum of the values of the items in the knapsack so that the sum of the weights is less than or equal to the knapsack's capacity. The problem is a reduction of real-world resource allocation problems with constraints. The 0-1 KP is weakly NP-hard, which means it can be solved exactly by utilizing pseudo-polynomial time algorithms based on dynamic programming. There are also other algorithms that can solve it a very short amount of time such as greedy algorithms and core branch and bound algorithms. However, the difficulty of the problem increases as the number of variables and correlation between them is increased.
## 3 Results and Discussion
There could be one reason for this observation, the loss of SERS activity of AgNPs as a result of undesired reactions with HCl during the first 24 h. Once all HCl is consumed, the SERS activity of AgNPs continues to be intact upon mixing reaction mixture with the AgNPs colloidal suspension at 30 and 48 h. Thus, as seen, several bands appear at 30 h, and more bands at 48 h.  
Figure 4: (**a**) Possible degradation sites in PMT structure. It degrades via oxidation of its five-member ring of pyrrolopyrimidine moiety through water loss and its rearrangement, and/or hydrolysis of amide bond as shown above, (**b**) potential degradation products (DPs) of PMT under NaOH, HCl and H\({}_{2}\)O\({}_{2}\) exposures. Figure 7 shows the SERS spectra of PMT exposed to H\({}_{2}\)O\({}_{2}\) for increasing durations. According to the PMT degradations studies, PMT is not stable as it is exposed to H\({}_{2}\)O\({}_{2}\)[14, 17, 18, 19]. PMT was degraded by H\({}_{2}\)O\({}_{2}\) as it is an oxidizing agent and the exposure generates DPs such as oxidative dimers and lactams [18, 20]. [PERSON] et al. reported that when PMT was exposed to 3% H\({}_{2}\)O\({}_{2}\) for 30 min, 17.15% of PMT degraded [20]. As in Figure 7, it was only possible to obtain a meaningful SERS spectrum after 30 h exposure time. This could be due to the undecomposed H\({}_{2}\)O\({}_{2}\) at earlier exposure times (24, 12, and 6 h), which can cause dissolution of AgNPs, and a result the loss of the SERS activity. The new bands appeared at increased exposure times (30 and 48 h) at 752, 953, 1011, 1055, 1448 cm\({}^{-1}\) can be assigned to pyrimidine ring breathing, C=O stretching, hydrogen bonds of pyrimidine-water, CH in plane bending, and H\({}_{2}\)C=CH-CH\({}_{3}\) symmetric deformation, respectively [28, 35]. On the other hand, after 48 h exposure, while both bands at 752 and 1448 cm\({}^{-1}\) disappeared, a new band occurred at 1096 cm\({}^{-1}\). This can be explained with oxidation of carbon atoms in CO, CN or CH bonds. Figure 5: SERS spectra of PMT exposed to 0.1 M NaOH for a duration of 6–48 h.  
Figure 6: SERS spectra of PMT exposed to 0.1 M HCl for a duration of 6–48 h.  
Since the same concentration of HCl and H\({}_{2}\)O\({}_{2}\) is used for all exposure times, at increasing exposure times, the HCl and H\({}_{2}\)O\({}_{2}\) left in the drug and degradation product mixture react with the AgNPs mixed with this causing the loss of SERS activity. This could be due to dissolution of AgNPs into its ions or precipitation of AgNPs as result of change in surface charge. When the spectra of PMT exposed to HCl and H\({}_{2}\)O\({}_{2}\) from 30 to 48 h are compared, the same bands at 1051, 1100, 1437 cm\({}^{-1}\) (Table S1) with little shifts are observed. This explains that the oxidative dimer and lactams are the DPs of PMT for both conditions.
## 3 Data and Method
### InSAR Calculation in Cloud Platform  
The time-series analysis algorithm of the InSAR processing system in this study is built upon the SBAS algorithm, with the main workflow illustrated in Figure 2. This system integrates InSAR GPU-assisted processing technology and a fast InSAR time-series analysis method with full resolution [19]. The massive InSAR data processing system operates on a cloud platform that provides open and free online services available at [[https://aiearth.aliyun.com](https://aiearth.aliyun.com)]([https://aiearth.aliyun.com](https://aiearth.aliyun.com)) (accessed on 3 April 2024) [14]. Users can directly utilize the system in a web browser without the need to download, install any software, or configure complex software environments. Furthermore, the utilization of cloud computing and distributed processing technologies ensures the efficient and reliability of large-scale InSAR processing, with low hardware requirements. The user interface is easy to use without requiring programming skills or specialized knowledge of InSAR. This allows researchers from various disciplines to conveniently utilize the system for further analysis. Through a web-based Graphical User Interface (GUI), users can select the area of interest (AOI), configure start and end times for data processing, and adjust basic interferometry processing parameters easily and customarily. These interferometric parameters include maximum and minimum temporal baselines, valid value ratios of corresponding points in the interferograms, and filtering parameters. The system optimizes the InSAR processing workflow by simplifying processing parameters, which reduces human intervention and increases processing efficiency. InSAR processing and deformation result displays from massive Sentinel-1 data can be promptly completed on the cloud, with exportable computed results for further analysis. This approach addresses the challenges in traditional InSAR technology, such as data downloading, storage, parameter setting, and limitations in computing resources, thereby meeting the demands of diverse natural disaster surveys and emergency monitoring efforts.  
Figure 2: Main workflow of InSAR surface deformation analysis tool based on the cloud platform.
# DRLLA: Deep Reinforcement Learning for Link Adaptation
## 5 Results
### NLOS Channel  
Figure 6 shows the result of using the NAC approach on an NLOS channel with a static SNR level of 15 dB, fading channel effects, and attenuation caused by a 10 m path loss simulation. The agent chose a combination of MCS5, MCS6, and MCS7 with theoretical data rates of 18 Mbps, 24 Mbps, and 27 Mbps, respectively. The achieved effective data rate is approximately 18.75 Mbps. A combination of MCSs is uncommon for typical LA implementations on almost non-changing channel states. However, in this experiment, the agent estimates that a more granular differentiation is beneficial to achieve a high effective data rate.  
The experiments on the NLOS channel show similar results to the outcome produced on the AWGN channel, despite the added Rayleigh fading and attenuation. By looking at the reward, results in Figure 6 show an effective data rate of approximately 18.75 Mbps and outperform results in Figure 4, which cover an effective data rate of approximately 15.3 Mbps, as the learned model proposes a combination of several MCSs per episode rather than recommending a single one. This is likely thanks to a decrease in the learning rate in this experiment from \(10^{-2}\) to \(10^{-3}\).  
Figure 5: The behavior of the NAC reinforcement learning approach on an AWGN channel with a sweeping SNR level between 0 dB and 50 dB.  
Figure 6: The behavior of the NAC reinforcement learning approach on a Rayleigh NLOS channel at a static SNR of 15 dB.
## 4 Control Strategies in Greenhouses
### Actuators Used in Greenhouses  
In greenhouses, several actuators are used to automatically regulate the environment, ensuring optimal conditions for plant growth. These devices work in conjunction with sensors to keep the greenhouse's internal environment under control. Below are the main actuators used in greenhouses:  
1. Automated Irrigation Systems: Control the quantity and frequency of irrigation based on soil moisture data [239].
2. Fans and Ventilation Systems: Regulate air circulation and ventilation within the greenhouse to maintain adequate temperature and humidity [240].
3. Heaters: Maintain the ideal temperature in cold climates, activating automatically when the temperature drops below a certain level [241].
4. Shading Systems: Control the amount of sunlight that enters the greenhouse, protecting plants from excess light and heat [242].
5. Grow Lamps: Provide supplemental lighting, especially during periods of low natural light, to ensure plants receive enough light for photosynthesis [243].
6. \(CO_{2}\) dosers: Adjust carbon dioxide levels in the environment to optimize plant growth [244].
7. Automatic Ventanas and Blinds: Automatically adjust to control ventilation and sunlight based on sensor data [235].  
Many of these sensors and actuators are integrated into automated control systems that use algorithms and artificial intelligence to optimize the greenhouse environment in real-time. These systems can automatically adjust environmental parameters based on collected data, ensuring optimal conditions for plant growth and increasing operational efficiency [234].  
Control technology in greenhouses encompasses various forms of automation and monitoring, including sensor networks for real-time monitoring and accurate data, use of artificial intelligence to optimize the environment and predict problems, automated irrigation systems for efficient water use, automated ventilation control for temperature and humidity regulation, light control technology to maximize photosynthesis, and automatic control approaches with algorithms and continuous feedback [245].
## 5 Conclusions  
The diverse handwriting styles of individual writers make text line extraction from handwritten documents a difficult task. In this paper, a novel method based on the size variations of the written alphabet due to different handwriting styles is proposed to extract the text lines from handwritten Malayalam documents. Various thresholds are developed to perform text line extraction by measuring the average height and width of written characters in a document image. Therefore, these thresholds vary dynamically with each handwriting style. In the proposed technique, horizontal projection (HP) values are used to identify the positions at which to perform text line extraction. The two main problems encountered while using horizontal projection values are the extracted line segments with overlapped multiple lines and the varying gaps due to different handwriting styles between two characters when one character is written below another, resulting in the segmentation of such lines into two separate lines. These are addressed and solved effectively using the proposed method. Overall, 85.507% of the extracted text lines from the newly created LIPI database of Malayalam handwritten document images perfectly match the ground truth lines when evaluated using the metric MatchScore. Moreover, the technique proposed in this paper outperforms language-independent text line extraction algorithms like A* Path Planning and the piecewise painting algorithm on the LIPI database. Due to the unavailability of a Malayalam handwritten document image database, a new database of 402 images is created and is named LIPI. Another major contribution is the ground truth images created for the 7535 text lines in the document images. The proposed method is an initial step in digitizing Malayalam handwritten documents, which will be highly beneficial in enabling individuals to share handwritten documents in their local language.  
Conceptualization, P.P.V. and D.S.; Data curation, P.P.V. and D.S.; Formal analysis, P.P.V. and D.S.; Investigation, P.P.V. and D.S.; Methodology, P.P.V. and D.S.; Project administration, P.P.V. and D.S.; Resources, P.P.V. and D.S.; Software, P.P.V.; Supervision, D.S.; Validation, P.P.V. and D.S.; Visualization, P.P.V. and D.S.; Writing--original draft, P.P.V. and D.S.; Writing--review and editing, P.P.V. and D.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The database presented in this work is available in [[https://github.com/pearlsypv/LIPI-Database](https://github.com/pearlsypv/LIPI-Database)]([https://github.com/pearlsypv/LIPI-Database](https://github.com/pearlsypv/LIPI-Database)), accessed on 20 Ausgust 2023.  
The authors declare no conflict of interest.
# A Review of the Influence of Steel Furnace Slag Type on the Properties of Cementitious Composites
## 1 Introduction
### SFS Composition  
Because the various steelmaking processes are different from one another, the resultant SFS chemistry and mineralogy changes. In general, the bulk oxide chemistry of most SFSs consists of CaO, MgO, SiO\({}_{2}\), and FeO, along with some Al\({}_{2}\)O\({}_{3}\) and MnO [5; 20; 21], as shown in Table 1. The mineralogy of the SFS is dependent on the steelmaking process and fluxing agents but is also dependent on the cooling process; the molten SFS can be cooled by air, water spray, water or air quenching, box chilling, and so forth [20]. The cooling process can influence SFS properties and composition, such as degree of crystallinity, particle size, and free CaO and MgO contents [5; 20; 22; 23; 24].  
The crystalline composition of SFS also varies. One report noted that typical BOF slag compositions by weight consist of 30%-60% dicalcium silicate (with 1%-3% P\({}_{2}\)O\({}_{5}\) molar substitution), 0%-30% tricalcium silicate, 0%-10% free CaO (with 2%-10% MnO and 5%-15% FeO molar substitutions), 10%-40% wustite (with 10%-20% MnO, 10%-30% CaO, and 5%-20% MgO molar substitutions), and 5%-20% dicalcium ferrite [21]. One partial survey of the literature found that the commonly identified minerals in EAF slags are dicalcium silicate, merwinite, gehlenite, iron oxides (wustite, hematite, and magnetite), mayenite, brownmillerite, and pericase [25]. The same survey found that the commonly identified minerals in BOF slags are dicalcium silicate, tricalcium silicate, wustite, lime, dicalcium ferrite, portlandite, and calcite [25]. While LMF slag compositions have not been commonly reported, studies have shown dicalcium silicate, tricalcium silicate, wustite, mayenite, pericase, bredigite, and merwinite to be present [9; 10; 26; 27].  
One of the factors limiting the use of SFS is the expansion potential. SFS often contains free CaO and free MgO, which expand by 92% and 120%, respectively, when reacted with water [28]. The typical free CaO content ranges for BOF and EAF slags are 1%-10% and 0%-4%, respectively [21]. The free CaO and free MgO are present primarily due to the fluxing agents and the furnace refractory lining [20].
## 5 Experimental Results
### Experimental Procedure and Methods
#### 5.3.2 Outdoor Testing Scenario and Results  
The outdoor experimental evaluation took place in the Stefan cel Mare University of Suceava parking lot, on a mostly sunny day, with clouds occasionally covering the sky. As suggested in Figure 14, the VLC emitter had a SE-NW orientation, whereas the VLC receiver was orientated toward the VLC emitter. In such circumstances, the VLC receiver is exposed to a strong and direct sunlight component, perturbing its functionality. Again, the outdoor testing scenario respected the methodology presented in Section 5.1. However, as the experiments were conducted in uncontrolled conditions, with the sun traveling the sky, changing the incidence angle of parasitic light, while the clouds were occasionally reducing its intensity, the test results were influenced by these factors.  
Figure 13: Oscilloscope print screens presenting the signal reconstruction process at the VLC receiver level: Channel 1 (yellow) contains the output of the optical receiver adjusted at 20 dB gain; Channel 2 (cyan) contains the output of the band-pass filter; Channel 3 (magenta) shows the output of the amplification blocks; Channel 4 (green) displays the reconstructed signal which is used for the data decoding process: (**a**) 1% duty cycle; (**b**) 10% duty cycle; (**c**) 40% duty cycle.  
The experimental results in outdoor conditions are summarized in Figure 15. In this case, the communication distance reached 50 m. These results suggest that in strong sunlight conditions, with parasitic irradiance at the VLC receiver level reaching 50,000 \(\upmu\)W/cm\({}^{2}\), the decrease of the duty cycle affects the link performances in terms of communication range, BER, and reliability. Thus, in such conditions, light dimming to 1-2% almost halves the communication range, while increasing the BER with three orders of magnitude. To have a clearer effect concerning the light dimming effect, Figure 15 includes for comparison purposes the results of a different experiment [43] performed without light dimming, on a less sunny day, with OOK modulation, Manchester coding, and a data rate of 100 kb/s. These comparative results show that the V2V VLC can provide a communication range of over 70 m. This comparison reconfirms the negative effect of light dimming on the SNR level.  
Figure 14: Outdoor experimental setup.  
Figure 15: Summary of the V2V experimental BER results showing the influence of duty cycle reduction in outdoor sunny conditions. * The experimental results illustrating the 50% duty cycle have been introduced as a reference. These experiments have been performed in friendlier conditions, while using OOK modulation, Manchester coding, and a 100 kb/s data rate. Complete details concerning the experiments and the setup are available in [43].  
Last but not least, it should be pointed out that although the \(\pm 20^{\circ}\) VLC receiver FOV and the narrow-band optical filter significantly contributed to the SNR improvement, exposure to strong optical noise sources still affects the VLC link performances, especially in the cases when a low duty cycle is used.