esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
## 5 Case Study
Though there are some industrial 3D imaging solutions such as Zivid 2 [151], they are more expensive than monocular cameras. The method proposed in [150] was a cost-effective solution as it created 3D model of the target object using a monocular camera and open source structure-from-motion (SfM) technique [152]. Moreover, because of the flexibility of the overall method, this technique can be used for creating digital model of the railway track without human involvement, as shown in Figure 18c, which can be beneficial \begin{table}
\begin{tabular}{c c} \hline \hline
**Sensor** & **Purpose in RIRS** \\ \hline IMU & Localization and navigation \\ \hline Wheel encoder & Localization and navigation \\ \hline PTZ environment camera & Environment awareness \\ \hline GPS & Localization, navigation, referencing defect position \\ \hline Monocular camera & Defect detection, close inspection of defect \\ \hline RGB-D camera & Defect detection, obstacle avoidance \\ \hline LiDAR & 3D perception of surroundings, obstacle avoidance \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Purpose of different sensors in RIRS. Figure 18: Reconstructed 3D model of railway track components; (**a**) distorted side view of a rail section from RGB-D camera (**b**) reconstructed 3D model using proposed technique (**c**) reconstructed 3D model of a railway track section using mobile manipulator and SfM technique [148]. for creating a digital twin of railway tracks, performing digital surveys or creating railway datasets for machine learning algorithms.
# A Novel Localization Technique Using Luminous Flux
## 2 System Configuration Method
### Indoor VLC Channel Modeling  
The following subsection outlines various models applied for both LoS and NLoS indoor VLC channels. For impulse response analysis, we consider a room wall as a surface acting as a receiver, with the received power as given in Equation (4).  
The propagation model is represented as follows:  
\[h(t;E,R,\lambda)=h^{(0)}(t;E,R,\lambda)+\sum\limits_{k=1}^{\infty}h^{(k)}(t;E,R,\lambda) \tag{5}\]  
where \(h^{(0)}\) is a component that represents the LoS and \(h^{(k)}\) is k-reflection for a source with wavelength \(\lambda\) and a system with R receivers.  
Using ray analysis, considering the second reflection, we get the received power as  
\[P_{\tau}^{(2)}=\ P_{t}\ \frac{\rho}{10} \tag{6}\]  
where received power is a product of reflection off the walls due to 10 rays (randomly chosen) and \(\rho\) is the reflection coefficient of the surfaces or walls.  
The probability density function of the rays is given as  
\[\frac{\mathrm{m}+1}{2\pi}\cos^{\mathrm{m}}(\alpha) \tag{7}\]  
where \(\mathrm{m}\) is the Lambertian order and \(\alpha\) is the angle between the z-axis and a random ray vector. As the rays propagate around the room and bounce off the walls with a probability density function in a particular direction, this is summed up to obtain the impulse response.
## 5 Simulation-Based Performance Evaluation  
### Tracking and Robustness Analysis Under WLTC Conditions  
To assess the tracking performance and robustness of the proposed DWMPC strategy, simulations were conducted under the WLTC driving cycle, which features frequent acceleration, deceleration, and abrupt speed changes, challenging scenarios for longitudinal control.  
As shown in Figure 5a,b, DWMPC achieves superior velocity tracking with rapid convergence and minimal steady-state error. The zoomed-in view in Figure 5a highlights its responsiveness to sharp speed changes without overshoot or delay. This is further supported by the relative velocity error in Figure 5b, where DWMPC maintains the narrowest error band and low-frequency oscillations, outperforming both fixed-MPC and PI controllers. These improvements are driven by the dynamic tuning of the velocity weight \(C_{1}\), which increases during high-urgency situations to enhance response and decreases during steady states to ensure smooth control.  
Figure 5: Longitudinal control performance comparison under the WLTC cycle. (**a**) Speed tracking; (**b**) relative speed error; (**c**) inter-vehicle distance; (**d**) distance tracking error. The proposed DWMPC consistently achieves faster convergence, lower overshoot, and better disturbance rejection compared to Fixed-MPC and PI controllers, highlighting its advantages in both accuracy and stability.  
Compared to the fuzzy-MPC approach in [20], which relies on rule-based weight assignment, DWMPC achieves faster response and lower tracking errors by continuously adapting weights based on driving context.  
In terms of spacing control, Figure 5c,d shows that DWMPC maintains a stable inter-vehicle distance with low fluctuation. Other controllers tend to over- or under-compensate, especially under high-frequency variations. The corresponding distance error plot (Figure 5d) shows that DWMPC consistently keeps the error close to zero with reduced amplitude. This robustness benefits from the coordinated adjustment of weights \(C_{2}\) and \(C_{3}\): \(C_{2}\) increases in critical moments to enhance braking response, while a lower \(C_{3}\) reduces the control penalty, enabling more decisive actions. These mechanisms allow DWMPC to balance safety and stability under dynamic driving conditions.
## 3 Materials and Methods
### Stage 3--Production Demand Forecasting  
This step consists of performing the production demand forecast. The Holt-Winters model was used because it has many uses in demands that present variations, seasonality, and trends. It contains triple exponential smoothing without recalculating seasonal factors from scratch [24; 25]. The software R Project x64 (version 3.2.4, R Core Team, Vienna, Austria) and Excel developed the models. The R Core Team [26] developed the R Cran softwareto read and model the demand data. The TSA, MASS, tseries, and forecast packages were used for the modeling.  
The first manipulation step is plotting the correlogram and performing an initial analysis to identify the components of randomness, trend, and seasonality. When the series is random, autocorrelations close to zero are observed. However, there are drops or positive peaks in the values when there is a trend or seasonality. Therefore, if the data show a trend, it is necessary to carry out the differentiation process to remove this component and then repeat the initial analysis of the differentiated series.  
This repetition can take place until the trend component is removed. To prove the assumption of the removal of the trend component, it is necessary to carry out a hypothesis test at 0.05 of significance (Augmented Dickkey-Fuller) [27]. This test is based on the following hypotheses: H0: non-stationary process (null hypothesis); H1: stationary process (alternative hypothesis).  
The series is broken down into trend and seasonality in the second step, and the graph is displayed for verification. After this procedure, the database is divided into testing and training, in which 87% train (fit) the model and 13% test the model's prediction quality.  
The training process involves a series of steps. First, there is the adjustment of the Holt-Winters model, which can be additive or multiplicative, and, after that, an eight-step forward prediction is performed with these two approaches. Next, the forecasted models must be analyzed to choose the best among them. This analysis is performed from the calculation of forecast errors. Finally, with the results in hand, the prediction made by the model that presents the best performance is chosen.  
Then, it is necessary to carry out graphical tests of normality and autocorrelation. The most commonly used test is the Shapiro-Wilk test [28]. The model's simplifying hypotheses are H0: residuals are not normal (null hypothesis); H1: residuals are normal (alternative hypothesis). If the \(p\)-value of the test is less than 5%, the null hypothesis is rejected so that the model meets the normality assumption.
# Risk Management for the Reliability of Robotic Assisted Treatment of Non-resectable Liver Tumors
## 2 Materials and Methods
### Definition of the Medical Task
#### 2.1.1. Remote Center of Motion Concept for the Guiding of the I-US Probe  
In 1995, [PERSON] [14] introduced the concept of the remote center of motion (RCM), defining it as the point of entrance in the abdominal cavity, a fixed point which should not be displaced during the medical task. In minimally invasive surgery (MIS), it is used for instrument insertion into the body, and for our procedure this concept is used for the manipulation of the I-US probe. Based on Figure 3, it can be stated that with respect to point B (RCM), the instrument can achieve four independent motions:  
* In spherical coordinates: two rotations that would position the point E on a surface of a \"sphere\" with radius BE, one translation along the A-B-E segment or the longitudinal axis of the instrument, and one rotation around the same axis;
* In Cartesian coordinates: three translations which enable the positioning of the point E in space with respect to the point B and one rotation around the longitudinal axis of the instrument.  
When it comes to technical solutions based on robotic systems, the RCM can be achieved in two ways: by using the tissue around the insertion point as guidance or by mechanically constraining that point in space. Additionally, a third category of RCM can be added that combines the simplicity of the first with the capabilities of the second, namely, architecturally constrained. The first approach imposes a simple mechanical construction at the anchor point of the instrument to the robotic guiding device (in the form of a 2-DOF, degrees of freedom, passive Cardan joint), but its usability is limited to the manipulation of instruments that do not come in intimate contact with the internal tissues (e.g., a laparoscopic camera). The second approach, where the RCM is mechanically constrained, imposes the use of an active 2-DOF joint at the anchor point of the instrument. This, in turn, determines a much more complicated construction of the robotic device but it enables the manipulation of  
Figure 3. The remote center of motion (RCM) concept [14].  
Figure 2. Typical operating room with the necessary medical equipment for the procedure.  
instruments that come into direct contact with the internal tissues (e.g., the surgical instruments). The third approach imposes the development of the robotic device in such a way that without the addition of any supplementary joint it would keep the location of the RCM fixed. Even though such an approach will impose the use of a positioning mechanism that would adjust the position of the RCM in space, an architectural constraint mechanism can be limited to only 2 DOF. This approach also has an increased safety aspect because after the insertion of the instrument inside the patient, the positioning mechanism will remain fixed, reducing to a minimum the pressure exerted on the tissue walls and eliminating the risk of unwanted motions that could harm the patient.
# An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks
## 2 Related Work  
In recent years, researchers have conducted in-depth research on the scheduling of spatial information network resources with the explosive growth of multimedia services and the continuous emergence of new space tasks. Presently, the scheduling methods of spatial information network resources are mainly based on machine learning, reinforcement learning, and game theory.
# Photodynamic Properties of CdSe/CdS Quantum Dots in Intracellular Media
## 2 Experimental
### Cell Culture and In Vitro Experiment  
The Jurkat cells were cultured in RPMI-1640 medium supplemented with 10% inactivated fetal bovine serum (FBS) and 50 \(\mathrm{\SIUnitSymbolMicro g}\)/mL gentamicin at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\) in a humidified atmosphere. The cell line was seeded at a density of \(5\times 10^{4}\) cells mL\({}^{-1}\).  
In order to study the uptake capacity of the CdSe/CdS quantum dots, the Jurkat cells at log phase were seeded into 24-well plates at the concentration of \(1\times 10^{4}\) cells mL\({}^{-1}\) and \(1\times 10^{6}\) cells mL\({}^{-1}\); then, they were incubated for an additional 24 h. Then, polyethylene glycol (PEG-1500) and CdSe/CdS QDs were added to the cell-seeded wells for 3 h and 6 h. PEG interacts with phospholipids, which are the main components of the cell membrane, leading to the cellular membrane loosing and fusing. Thus, in this study, we applied PEG for the purpose of enhancing the penetration capacity of NPs to trespass into the intracellular cytoplasm. At the experimental condition (RPMI-1640 culture medium pH = 7.1, incubation at 37 \({}^{\circ}\)C for 3h, 6 h, 9 h and 12 h), we did not observe interaction or absorbance of PEG on the NP surface. After the allotted time, the medium and cells were collected into 15-mL Falcon tubes. The tubes were subjected to centrifugation at 1000 rpm for 5 min to separate the cells from the cultured medium. The cells were then triply washed with sterile PBS (phosphate buffer saline) pH = 7. After being resuspended in PBS, the nanoparticle uptake capacity for all treated cells was estimated. Table 1 shows the sample name for the two cell concentrations before treatment.  
\begin{table}
\begin{tabular}{c c c} \hline
**Sample Name** & **Cell Concentration (cell/mL)** & **PEG** \\ \hline CL & \(1\times 10^{4}\) & No \\ CL-P & \(1\times 10^{4}\) & Yes \\ CH & \(1\times 10^{6}\) & No \\ CH-P & \(1\times 10^{6}\) & Yes \\ \hline \end{tabular}
\end{table}
Table 1: Sample note. PEG: polyethylene glycol.
# Practical Grammar Compression Based on Maximal Repeats +
## 6 Conclusions
We defined the greatest size difference of any two possible grammars that can be generated by RePair for a given text, naming it GSDRP. We demonstrated that a lower bound of GSDRP is \(\frac{1}{6}(\sqrt{6n+1}+13)\) for a given text of length \(n\). We left improving the lower bound and showing an upper bound of GSDRP as our future work. We estimated the effectiveness of the compression using the size of the generated grammars instead of the length of the output bits. Reducing the grammar size has important implications since the majority of the existing text algorithms applied to grammar-compressed texts, including grammar-based self indexes [21; 22], edit distance computation [23], [PERSON]q\)-gram mining [24; 25], and pattern matching [26; 27; 28], have time/space complexities that are dependent on the input grammar size. For instance, the compressed indexes proposed by [PERSON] and [PERSON] [21; 22] can be directly built on MR-RePair grammar-compressed texts. Algorithms specifically designed for _straight-line programs_ (_SLPs_), which are text compressions with grammars in Chomsky normal form, can also be easily modified to work on grammars that are not in Chomsky normal form similar to MR-RePair grammars. Hence, MR-RePair serves as a base for practical improvements of these algorithms. From the viewpoint of storing data more compactly, developing a method for encoding constructed grammars is another important issue. Implementing an efficient encoding method for MR-RePair is one of our future tasks. Conceptualization, I.F. and T.T. ; methodology, I.F., T.T., Y.N., S.I., and H.B. ; software, T.K. ; writing--original draft preparation, I.F. and T.K. ; writing--review and editing, I.F., T.T., Y.N., S.I., H.B., and T.K. ; All authors have read and agreed to the published version of the manuscript. This work was supported by JSPS KAKENHI Grant Numbers JP18K18002, JP17H01697, JP16H02783, JP18H04098, and JP18K11149, JST CREST Grant Number JPMJCR1402, and JST PRESTO Grant Number JPMJPR1922. The authors would like to thank the people who provided the source codes. The authors declare no conflict of interest.
## 4 Results and Discussion
Measurement of hydrochemical indicators of salinity is easier and cheaper way to have an insight into real situation in given time but for better understanding of salinity processes, the experimental mathematical models are needed. In order to explain natural salinization processes that occur on case study area, precipitation values are collected from Croatian Meteorological and Hydrological Service and shown on Figure 9[34]. Comparison between Figure 9 where precipitation values are shown, and chloride concentrations on Figures 6-8 shows that shortly after the highest values of precipitation, the chloride concentration raises. On 26 August 2018 at Policnik station, precipitation in amount of 102.5 mm was measured, and at Boljkovac and the Golubinka well chloride concentrations began to rise. Regarding sulphate concentrations on Jezerce and Bokanjac wells, it can be seen that they are on the rise in days when precipitation values are above 20 mm. Figure 9: Precipitation values on Zadar and Policnik measurement station. Additionally, comparisons of the values of electrical conductivity in opposition to chloride concentrations for Bokanjac, Jezerce, and Boljkovac are made. Golubinka spring have different conditions of the salinization and it is not included into comparison given on Figure 10.  
The greatest impact of salinization from the observed water body was recorded at the Boljkovac pumping station. This was confirmed by operational monitoring [13; 24] through which the value of electrical conductivity was measured by the depth of the aquifer every ten minutes. The Jezerce pumping station is located close to the salinity zone, which is confirmed by slightly higher values of electrical conductivity. The farthest from the sea, and thus from the influence of salinization, is the Bokanjac (B-4 and B-5) pumping station, where the values of electrical conductivity are slightly higher than in other wells, but there is no significant influence of salinization. The analyzed parameters indicate that the Bokanjac pumping station is located in the freshwater zone (Figure 10), but salinization is also possible during long dry periods and in conditions of over pumping for higher consumption demand. Jezerce is also in the freshwater zone, but close to the mixed zone, and at the Jezerce water supply facility increasing of chloride and sulphate concentrations in groundwater occurs temporarily. Boljkovac is under the intrusion of sea water influence (Figure 10), and almost every year strong salinization occurs on the water supply facility.
## 4 Experimental Validation  
### Test Set-Up  
The experimental set-up is sketched in Figure 10. This arrangement is designed to trigger surface PD on a single-layer specimen (see sample in Figure 10) placed between two electrodes (high voltage, HV, and ground, GRD, both made of brass). In this study, 0.6 mm single-layer XLPE specimens are considered.  
Figure 10: Scheme of the PD measurement set-up. On the left, the main components of the supply circuit are represented (AC power supply feeding the HV transformer, controlled by a variac, which energizes the HV electrode). On the right, the components of the signal circuit are sketched: the HFCT sensor acquiring the PD signals coming from the ground electrode (GRD), connected to PD detector (PDA), and a PC running the SRI software.  
Figure 9: Electrode B: (**a**) PD inception and calculated fields for the gas model under the application of the normalized voltage \(V_{sim}\) of 1 kV, and (**b**) PD inception and calculated fields at the PDIV voltage equal to 3.3 kV. It can be seen that the intersection of the electric field profile and gas discharge model for electrode B is 0.38 mm from its triple point.  
The AC power supply is connected to an HV transformer, controlled by a variac. The low-voltage electrode is connected to the ground by means of a wire passing through a high-frequency current transformer (HFCT, bandwidth 100 kHz-50 MHz), which serves as the sensor for PD measurement. The HFCT is then connected to the above-described PD detector (PDA) interfaced to a PC running the innovative SRI (Separation, Recognition, Identification) software [5; 18; 19].  
Experiments were conducted in a climatized room (average temperature of 20 \({}^{\circ}\)C) under constant atmospheric pressure and humidity.
## 2 Corpus Collection and Preprocessing
### Textual Conversion and Data Cleaning
#### 2.2.4 Result Handling and Output  
The recognized text may require further post-processing, such as removing irrelevant characters, paragraph segmentation, etc., to improve readability and usability. Finally, the text is saved to a.txt document named according to the book's title based on the requirements.  
To evaluate the accuracy of OCR (Optical Character Recognition) using the cnocr library for Chinese text in images, the process begins by cleaning the reference text through the clean_text function to ensure accurate comparison. Next, the cnocr library is utilized to perform OCR on each image and extract the recognized text. Then, the SequenceMatcher from the difflib library is used to calculate the similarity between the OCR-recognized text and the reference text, resulting in a loss rate that reflects the degree of difference between the recognition result and the true text. Subsequently, the loss rate for each page is stored in a list and printed out for analysis. Finally, the average loss rate across all pages is computed, and a chart depicting the change in loss rate with respect to page numbers is drawn using the matplotlib library, visually exhibiting the trends in OCR recognition performance.  
Loss Calculation Formula:  
\[Loss\;Rate=1-\frac{Length\;of\;LCS}{max(len(A),len(B))}\times Adjustment\;Factor\]  
We have extracted 77 pages of images from the book titled \"The Historic Transition from Traditional Agriculture to Modern Agriculture\" using OCR technology, converted them into text, and compared the OCR-recognized text with the manually corrected data. We have calculated the loss rate for each page, and the data are shown in Figure 4. The average loss rate for this book is 0.0396. Using the same method, we have calculated the average loss rate for each of the 102 books, and the average loss rate for each book is shown in Figure 5. The average loss rate for all 102 books is 0.0304. With such a low recognition loss, using OCR technology to extract and recognize text from each page of a PDF, and manually checking and correcting the recognition errors, can greatly save time and cost in corpus processing.  
Figure 4: The loss incurred from using OCR technology to recognize the text in the book sample titled “The Historic Transition from Traditional Agriculture to Modern Agriculture”.