esa-sceva/satcom-chunk-collection huggingface dataset:
# A Vision-Based Micro-Manipulation System
## 1 Introduction  
Micro-manipulation systems play a critical role in various scientific and industrial applications, enabling precise handling and control of microscopic objects. These systems have gained increasing importance in diverse fields such as biotechnology, materials science, and microelectronics, where the need for manipulating microscopic objects is constantly growing [1; 2; 3]. Precise micro-manipulation is essential, whether it involves the assembly of microelectronic components, the handling of biological samples, or the fabrication of micro-scale structures. However, achieving accurate manipulation at the micro-scale poses unique challenges, primarily due to the minuscule size of the objects involved.  
In recent years, the integration of vision-based technologies into micro-manipulation systems has opened new paths for enhancing their capabilities, enabling more efficient and versatile manipulation of micro-scale objects. While micro-manipulation has traditionally relied on manual control or open-loop control systems, these methods are limited in their precision and adaptability. In particular, when dealing with living cells, a key challenge arises in their detection and recognition. Living cells are often immersed in non-transparent growth media, making visual recognition and precise manipulation a difficult task. Additionally, variations in image quality can be influenced by factors such as the nature of the object material, calibration parameters, and ambient light conditions.  
Existing systems for biological object manipulation are frequently manually controlled, and many of these systems are tested not with biological specimens, but with polymer or metal micro-objects [4]. User participation is currently required to determine the positionof the object and the distance to the object being manipulated. This limitation not only hampers efficiency, but also introduces potential inaccuracies in the manipulation process.  
Recognizing the growing demand for automated living cell manipulation, this research article focuses on different components that could complement a robotic micro-manipulation system, such as object detection, recognition, and precise positioning using visual recognition and machine learning techniques. By integrating advanced image enhancement [5], state-of-the-art visual recognition using YOLOv7 [6], and a purpose-built micro-manipulator, our system aims to address the challenges associated with micro-scale object manipulation, particularly in the context of living cell manipulation. The following sections provide an in-depth exploration of the key components of our Vision-based Micro-Manipulation System, its operational details, and the results obtained through rigorous testing and experimentation, as well as a brief look into a functional, complete system. Additionally, we discuss the implications of this research and its potential applications in various fields, including biotechnology, healthcare, and materials science.
# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 4 Result Discussion and Analysis
### Results and Discussion
#### 4.2.2 IoD Path Optimality  
A path optimality is an indication of safety and cost-effectiveness for UAV operation in the flying environment. A convergence curve, reflecting solution optimality, provides insight into solution quality. The results of convergence curves for all algorithms, averaged over 30 independent replications, are depicted in Figure 5. We observe varying energy levels for different drones, attributable to distinct environmental constraints. For instance, UAV1 and UAV5 exhibit the lowest fitness value due to light obstacles along their paths, enabling them to fly relatively far from obstacles. Conversely, UAV2, UAV3, and UAV4 encounter obstacles between their start and end points, requiring avoidance of obstacle-laden paths and incurring high costs. This illustrates that IPSO generally yields lower energy values across most UAV paths.  
Figure 4: Two-dimensional view for path planning for the four approaches: (**a**) PSO, (**b**) IPSO, (**c**) ABC, and (**d**) GA. The drones fly from starting locations to their destinations.
# Study on Temporal and Spatial Distribution of Landslides in the Upper Reaches of the Yellow River
## 3 Temporal and Spatial Distribution of Landslides in the Upper Reaches of the Yellow River
### Spatial Geometric Distribution Characteristics of Landslide
The residual volume peaks are mainly concentrated in four intervals: 0.5-4 \(\times\) 10\({}^{8}\) m\({}^{3}\), 6-8 \(\times\) 10\({}^{8}\) m\({}^{3}\), 10-12 \(\times\) 10\({}^{8}\) m\({}^{3}\) and 15-17 \(\times\) 10\({}^{8}\) m\({}^{3}\). The former two sections and the latter two sections belong to the category of super-large landslides and giant landslides, especially the giant landslides with \(>\)15 \(\times\) 10\({}^{8}\) m\({}^{3}\) are developed in the mudstone strata of the study area, such as the Xiazangtan landslide phase I on the right bank of the Yellow River in the Qunke-Jianzha Basin and the Xijitan landslide on the left bank of the Yellow River in the Guide Basin, which are not developed in other strata. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{3}{*}{**Category**} & \multirow{3}{*}{**Number**} & \multirow{3}{*}{**Average**} & \multicolumn{3}{c}{**Landslide Thickness Classification/m**} \\ \cline{3-6}  & & & **Shallow** & **Mid-Level** & **Deep-Seated** & **Ultra-Deep** \\  & & & **Landslide** & **Landslide** & **Landslide** & **Landslides** \\ \cline{3-6}  & & & **\textless{}10** & **10–25** & **25–50** & **>50** \\ \hline Number of & & & & & & \\ individuals & 116 & 39.6 & 0 & 13 & 78 & 25 \\ \hline \multicolumn{6}{c}{Percentage/\%} & 0 & 11.2 & 67.2 & 21.6 \\ \hline \end{tabular}
\end{table} 
Table 6: Statistical table of landslide thickness. Figure 6: Relationship between length and width of landslide. (**a**) Length-width ratio of landslide body; (**b**) The correlation coefficients between length and width.
## 4 Discussion
### Spatial Distribution Analysis of Drought Characteristics
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{3}{*}{**Correlation**} & **Before** & \multirow{3}{*}{**Cropland**} & \multirow{3}{*}{**Grassland**} & \multirow{3}{*}{**Grassland**} & \multirow{3}{*}{**Barren**} & \multirow{3}{*}{**Cropland**} & \multirow{3}{*}{**Barren**} \\  & **Conversion** & & & & & & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Drought frequency \\ Drought duration \\ Drought intensity \\ \end{tabular} } & 0.68 & 0.68 & 0.59 & \(-\)0.15 & 0.56 & \(-\)0.26 \\  & 0.93 & 0.92 & 0.85 & \(-\)0.21 & 0.81 & \(-\)0.44 \\  & 0.40 & 0.51 & 0.52 & \(-\)0.39 & 0.49 & \(-\)0.53 \\ \hline \end{tabular}
\end{table} 
Table 4: Correlation between meteorological and ecological droughts in converted land uses. response to drought can take months or even years to fully manifest, and this lag results in a longer duration of ecological drought. Third, long-term climate change will also affect the characteristics of ecological drought. Climate change has led to an increase in the frequency, intensity, and duration of drought events. In this context, the high frequency and intensity of meteorological droughts may further exacerbate ecosystem stress, making the cumulative effects of ecological droughts more significant. In recent years, the companies in the Shendong mining area have vigorously implemented ecological restoration projects and actively implemented the strategy of mining while restoring, which has increased the vegetation coverage rate and stabilized the change in ecological drought to a certain extent [62]. Furthermore, certain scholars have determined that global atmospheric circulation influences the characteristics of ecological drought at the local level, and phenomena such as El Nino may also affect the incidence of ecological drought in continental interiors [63].
## 4 Mitigating the Data Protection Risks
### Protection against Reconstruction Attacks  
With respect to the risk of reconstruction attacks, an approach that could be considered is to avoid using ML models that explicitly store the feature vectors, such as SVMs (support vector machines). If such ML models are used, it is suggested that the feature vectors are not given to the output module [42; 45].  
Since reconstruction attacks can also be seen as a specific instance of reidentification attacks (see Section 4.1), other well-known data protection engineering techniques, like DP mentioned above (and elaborated next), are also applicable in this case. Similarly, distributed (i.e., noncentralized) settings can also help address such threats, although it may still be necessary to employ other privacy techniques in such settings. In this respect, DP may again have an important role.
## 3 Results
### Evaluation of Compliance with the Content of Determined Mineral Components in Infant Formulas
#### 3.4.1 Conventional Initial and Follow-On Infant Formulas
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**Specialist IFs (mg/100 kcal)**} \\ \cline{2-13}
**Minerals by Commission** & \multicolumn{2}{c}{**Values**} & \multicolumn{2}{c}{**V-**} & \multicolumn{2}{c}{**V-**} & \multicolumn{2}{c}{**V-**} & \multicolumn{2}{c}{**V-**} & \multicolumn{2}{c}{**V-**} \\
**Defeated Regulation** & & & & & & & & & & & & \\ \cline{2-13}
**Ca** & 50 & 140 & 148.36 & 148.55 & 78.79 & 74.23 & 114.49 & 115.51 & 119.12 & 118.25 & 116.47 & 99.44 & 123.53 & 120.27 \\ Mg & 5 & 15 & 10 & **12.49** & 8.94 & **6.39** & 12.32 & **9.77** & 11.91 & 9.58 & 10.59 & **8** & 10 & **6.73** \\ Na & 25 & 60 & 46.42 & 46.34 & 32.58 & 32.18 & 40.87 & 34.19 & 35.29 & 32.77 & 44.26 & **33.12** & 50 & 51.19 \\ K & 80 & 160 & 144.03 & 144.07 & 108.33 & 99.36 & 127.68 & 121.14 & 138.24 & 137.15 & 110.12 & 99.03 & 108.82 & **160.43** \\ P & 30 & 100 & 62.54 & 64.38 & 62.12 & 59.27 & 81.74 & 78.27 & 69.12 & 67.8 & 75.81 & 67.98 & 79.41 & 77.23 \\ Fe & 0.45 & 2 & 1.39 & **1.03** & 0.98 & **0.77** & 1.23 & 1.09 & 1.59 & 1.29 & 1.51 & **1.14** & 1.03 & **0.79** \\ Zn & 0.75 & 1.25 & 1.42 & 1.35 & 0.77 & 0.76 & 0.91 & 0.91 & 1.09 & 1.28 & 1.1 & 0.95 & 0.74 & 0.77 \\ Cu & 0.06 & 0.1 & 0.09 & 0.08 & 0.08 & 0.07 & 0.07 & 0.08 & 0.07 & **0.09** & 0.08 & **0.14** & 0.07 & 0.07 \\ Mn & 0.001 & 0.1 & 0.044 & 0.038 & 0 & 0.01 & 0.057 & 0.028 & 0.03 & **0.023** & 0.00 & **0.06** & 0.0205 & 0.018 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The mineral content of specialist infant formulas expressed in mg/100 kcal and the corresponding value declared on the label by the manufacturer, and the minimum and maximum levels recommended by applicable regulations.
# A Short-Term Forecasting Method of Inter-Frequency Clock Bias for GPS Block IIF Satellites
## 2 Methods
### Trible-Frequency Uncombined PPP
\(\widetilde{N}_{r,i}^{s,G}(i=1,2,3)\) is an ambiguity at the corresponding frequency and absorbs the time-invariance part of the UPD, receiver differential code bias and the IF combination of the CHD in the receiver and satellite; \(IFB_{r,C}\) is the satellite-independent inter-frequency bias parameter and, as for the triple-frequency PPP, the hardware biases on the L5 pseudo-range cannot be completely absorbed into the ionospheric delay, which was lumped in with the differential code biases between the L1 and L2 pseudo-ranges. \(IFCB_{r}^{s,G}\) is the IFCB related to the satellite (i.e., the difference between the current clock products computed with L1/L2 and the satellite clocks computed with L1/L5), and it is noticed for the new L5 signal provided by GPS Block IIF satellites. In order to avoid the increase in computational burden and consumption of time, the best strategy is to pre-estimate or forecast the IFCB when one executes triple- or multi-frequency PPP.
## 5 Conclusions  
This study has showcased that utilizing a markerless needle tracking method working without external cameras is feasible, yet requires an individual approach for particular tools. One of the biggest challenges in obtaining needle 3D pose is high distortion of the point cloud obtained from the HMD depth camera. However, such an approach is still more useful and universal than standard marker-based solutions. In addition, superimposing a 3D-reconstructed CT/MR scan containing the highlighted planned trajectory onto the patient's body provides relevant information for the operator.  
This study has proven that using a mixed reality navigation system in biopsy procedures is beneficial for physicians at any level of expertise. The most valuable test results are those finding a significant decrease in the number of punctures needed and reduced procedure time, which together can lead to faster patient recovery and minimize the risk of complications. The most crucial step for operators is to precisely inject the needle as intended, as even minor mistakes can lead to a major offset. Our system addresses this issue by allowing the operator to meticulously plan the procedure and by guiding them to align the needle with the selected trajectory.  
Conceptualization, M.T., M.S. and A.S.; methodology, M.T., M.S. and A.S.; software, M.T.; validation, M.K., S.D. and A.S.; resources, A.S.; writing--original draft preparation, M.T., M.S. and A.S.; writing--review and editing, M.T., M.S., M.K., S.D. and A.S.; visualization, M.T. and M.S.; supervision, A.S.; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Ministry of Science and Higher Education in Poland under the Implementation Doctorate Programme, project number DWD/6/0235/2022. Preliminary research work was funded by the the National Centre for Research and Development under the Smart Growth Operational Programme 2014-2020, project number POIR.01.01.01-00-1115/21.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author A.S. upon reasonable request.  
We extend our thanks to [PERSON] for support in conducting tests and to [PERSON] for creating drawings depicting system schematics.  
[PERSON], [PERSON], and [PERSON] are MedApp S.A. employees; MedApp S.A. is the company that produces the CarnaLife Holo solution.
## 3 Proposed Circuit with ASK Modulation  
Based on the analysis presented in Section 2, the proposed circuit in this study for WPDT with ASK modulation is illustrated in Figure 8. The proposed circuit extends upon the foundation of Figure 2 by incorporating separate components for data modulation and demodulation (represented by the orange and blue dashed boxes, respectively). Additionally, the green dashed box can be regarded as the AC load \(R_{\text{L}}\). The data modulation process  
Figure 7: Class-E PA efficiency with different \(\varphi\).  
involves the switching devices \(S_{2}\) and \(S_{3}\), as well as inductor \(L_{\mathrm{a}}\). On the other hand, the data demodulation stage comprises the envelope detector, an RC filter, and a comparator. The circuit operation principle is illustrated below.
## 5 Experiments and Discussion
### Numerical Simulation Analysis of Single Vehicle
#### 5.1.2 Safety Distance Threshold Simulation of Avoiding a Side Crash  
Side crashes may occur between the \(LV\) and \(MV\), the \(MV\) and \(MV\), and the \(MV\) and \(RV\), as shown in Figure 2g. Figure 8 shows the critical distances \(D_{max}(\theta)\) and \(D_{min}(\theta)\) between the ego vehicle and the front vehicle in different motion states. The velocity difference between the two vehicles was represented using the horizontal axis and the distance was represented using the vertical axis. It can be seen from the results of the simulations that with the increase in velocity difference \(\Delta v\), the critical distance \(D_{max}(\theta)\) and \(D_{min}(\theta)\) gradually increased and the feasible gap between \(D_{max}(\theta)\) and \(D_{min}(\theta)\) gradually increased too. The string angle \(\theta\) made some difference in the distance value and the trend remained the same as in the last section. The critical distances reduced with the decrease in the ego vehicle acceleration \(a_{e}\), the decrease in the front vehicle velocity \(v_{f}\), and the increase in the front vehicle acceleration \(a_{f}\).
## 5 Simulation and Experimental Verification
The adjustment time for suppressing the white Gaussian noise of the designed fuzzy PID controller is about 0.6 s while the adjustment time of the conventional PID controller is about 1.5 s. In summary, the anti-interference performance of the fuzzy PID controller is better than that of the conventional PID controller, and it is more suitable for the torsional vibration control of the flexible-joint robot system. Then the fuzzy PID controller is validated on the flexible-joint robot experimental system which is shown in Figure 2. By setting the target speed of joint II as 2000\({}^{\circ}\)/s, the control effect of the designed fuzzy PID controller is shown in Figure 10.  
Figure 9: Comparison of the anti-interference performances of the designed fuzzy PID controller and the conventional PID controller: (**a**) step disturbance; (**b**) white Gaussian noise. It is seen from Figure 10 that the torsional vibration of the flexible-joint robot is effectively suppressed under the action of the designed fuzzy PID controller. After about 0.5 s, the rotational speed of joint II reaches the desired speed. On the other hand, with Figures 8 and 10 compared, one can obtain that the torsional vibration suppression times of the designed fuzzy PID controller on the experimental platform and the simulation platform are almost the same. Owing to the control parameters of the designed fuzzy PID controller for the experimental platform and the simulation platform are the same, the validity of the constructed system dynamics model and the parameter identification is further illustrated. In conclusion, the fuzzy PID controller designed in this paper can realize the effective control of the torsional vibration of the flexible-joint robot system.
## 5 Conclusions  
The current work shows the induction of mild to severe levels of colitis upon administration of different doses of DSS. Figure 1 depicts the variation in weight loss patterns when the mice were given different concentrations of DSS in their drinking water. The figure depicts the extent of weight loss, which was found to increase upon increasing the DSS concentration. Different doses of DSS, such as 1.5%, 2.5%, and 3.5%, had different effects on the DAI, as shown in the figure. The amounts of 2.5%, 2.5%, and 3.5% DSS showed clinical signs of the DAI in the early days of the experiment, whereas 1.5% DSS appears nearly normal on the last day of the investigation. The mice treated with DSS had shorter colon lengths than the healthy group, as shown in Figure 3. Blood and loose stools were observed in the colons treated with 2.5% DSS. The DSS-treated mice had significantly shorter colon lengths than the 1.5% and 3.5% DSS-treated mice, as depicted in Figure 4. The colon of a mouse was examined under 100\(\times\) fluorescent microscopy to confirm epithelial layer distortion, cryptic structure damage, and neutrophil infiltration. This damage was proportional to the increase in the DSS concentration. The microscopic histological scores assigned to the colons of the healthy mice and the 1.5%, 2.5%, and 3.5% DSS-treated mice are shown in Figure 6. It confirms that the microscopic scores increase with the increase in DSS concentration. Based on the above clinical signs, the severity of inflammation, and observations, 2.5% may be the most suitable concentration of DSS to induce reversible colitis in mice.  
Overall, the major benefit of using the dextran sulfate sodium (DSS)-induced model of colitis in inflammatory bowel disease (IBD) research is its ability to mimic the key features of human IBD, such as its chronic relapsing nature, mucosal inflammation, and the development of symptoms such as diarrhoea, abdominal pain, and rectal bleeding. This makes DSS a useful tool for investigating the underlying mechanisms of IBD, testing potential therapeutic strategies, and evaluating their efficacy prior to clinical trials in humans.  
Our work makes a very specific addition to the available literature by showing that changing the level of DSS can alter the disease's intensity from mild to severe; this can be very useful for researchers as ulcerative colitis is a dynamic disease that changes from remission to flare-ups in humans. Thus, considering the optimal dose and duration of DSS exposure for the induction of colitis can allow for better mechanistic studies and the development of effective therapeutics for ulcerative colitis.
# Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System
## 4 Case Studies
### Summary of Results  
The case studies presented demonstrate the feasibility and performance of the proposed dispersal method for deploying a single-plane constellation from a single launch. The results highlight the significance of selecting the disengagement velocity \(\Delta v\) and the insertion angle \(\theta_{initial}\), both of which directly influence phasing times.  
Simulations confirm that by adjusting \(\Delta v\) to within a given range, the phasing time can be significantly reduced. Additionally, the identification of a disengagement window, governed by the critical angle \(\theta_{thesh}\), provides flexibility, as planning for disengagement before this threshold is reached allows the system to tolerate operational delays without major performance degradation.  
In the case of the PQ8 architecture, the total phasing time was approximately \(255\pm 1.5\) days. This indicates that the proposed method is not only feasible but also particularly viable for missions involving small, mass-constrained satellite platforms. Collectively, the results demonstrate that launching a constellation as a single unit, with disengagement once in orbit and dispersing using differential drag control, is not only feasible but also scalable.