esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Hyper-redundant continuum manipulators present dexterous kinematic skills in complicated tasks and demonstrate promising potential in underground exploration, intra-cavity inspection, surgery, etc. However, the hyper-redundancy, which endows much dexterity and flexibility, brings a huge challenge to the kinematics solution and control of the continuum manipulators. Due to the pseudoinverse calculation of high-order Jacobian matrix or iteration, many inverse kinematic solution approaches of continuum manipulators are very time-consuming, which extremely limit their applicability in real-time control. Additionally, it is often difficult for the manipulators to perform the tasks well in complex scenarios due to lack of human intervention. Therefore, in this paper, a simplified kinematics model of a typical hyper-redundant manipulator is proposed based on its unique geometry relationships, where the mapping relationships between the actuators' rotation and the end-effector's position are derived through the analysis of its driving subsystem and motion subsystem, in particular the joint modules. To perform the tasks of manipulators with the help of operators, a teleoperation control scheme with modified wave transmission structure is designed to achieve the guaranteed stability and improved transparency, and the leader's trajectory and generated force feedback are the transmitted signals in the communication channel. Specifically, a virtual force feedback generation algorithm is developed in the teleoperation control scheme via the processing tracking errors, which can improve the operators' assistance and perception during the teleoperation process. The practical experiments with comparative wave variable structures in two different sets are implemented to verify the effectiveness of proposed kinematics model and control scheme.  
cable-driven; hyper-redundant; continuum manipulator; kinematics modeling; teleoperation control; wave variable +
Footnote †: journal: Applied Sciences
## 3 Methodology
### Activated-Carbon Method for Radon Measurement  
#### 3.2.1 Principle of Activated-Carbon Method for Radon Measurement  
Radon is the progeny of the uranium series and is the only inert gas and the progeny of interest because it can be migrated from the deep underground part to the ground surface, and it can display information on all kinds of changes in the deep formation. Radon is the intermediate product of radium decay, and its progenies 214 Pb and 210 Bi were the irradiators of \(\gamma\) rays.  
The activated-carbon radon detector identified the existence of radon by detecting the intensity of \(\gamma\) rays of radon progenies in soil. To be specific, the \(\gamma\) rays irradiated the Na1 (T1) scintillator in the detector, thereby generating photoelectric signals, which turned into weak electrical signals by using a photomultiplier. The pulses deemed acceptable by a single-chip computer formed through a preamplifier, main amplifier, and pulse-shaping circuit. The number of pulses within the unit of time reflected the intensity of the \(\gamma\) rays. The radon anomaly data were obtained by measuring the \(\gamma\) rays at different places, thereby laying a foundation for analyzing the underground geological conditions in work areas [32].  
The activated-carbon method for radon measurement is a static and accumulative radon-measurement method. To specify its principle, the activated-carbon adsorber was buried underground for a period of time, and then the adsorber was taken out and placed in an instrument (activated-carbon radon detector) to measure the intensity of \(\gamma\) rays radiated by the decay progenies of radon. Thus, the radon-gas concentration can be determined. The radon emanation elements migrated into the goaf and gathered in it, and then a radon anomaly area corresponding with the goaf morphology formed on the ground surface. Therefore, the position and range of the goaf in a coal mine can be inferred by measuring the radon-element concentration on the ground surface (i.e., measuring the intensity of rays released by radon and its decay progenies).
# The Effects of Signal System and Traffic Flow on the Sound Level
## 2 Site Measurement and Data Collection  
Measurements were conducted to determine the noise emission of moving vehicles on a road under various traffic conditions and scenarios. The measurement site was located at Yau King Lane in Tai Po in the New Territories where there is a carriageway with two road lanes serving two-way traffic. As the selected measurement site was situated at an open rural area with extremely low traffic flow, noise emission of the studied vehicles was solely recorded and irrelevant sound and noise were reduced to a minimum level. Influence due to other vehicles, reflections from road surfaces, human activities or noisy construction works on sound measurements and measurement results can be greatly avoided. In this research, a van and a coach were studied respectively as shown in Figure 1. Instruments used for data collection were six sound level meters. For the precision of measurements, all sound level meters were calibrated on the basis of the manufacturer's guidelines. The instruments were set at either the measurement points A and B or C and D in Figure 2 at three different heights, in accordance with the tested traffic scenarios and vehicle movements. In each measurement, three microphones were set at a check point on each side of the road, respectively at a height of 1.2 m, 4.0 m and 6.5 m for the simulation of ground, first and second floor levels (see Figure 3). The sound pressure levels were recorded in terms of \(LA_{\text{eq(18)}}\). At the beginning, all instruments were started up 30 s to evaluate the background noise level. A total of two traffic scenarios were run for measurements, namely the \"non-stop\" and \"stop and go\" situations. In the former, the studied vehicles passed the check points at constant designated vehicular speeds at 30, 50 or 70 km/h. Table 1 shows the summary of the measurement information. For the latter, the studied vehicles decelerated from 50 km/h, stopped at the check points and then subsequently accelerated again. Figure 4 shows an example of sound level time history for a \"stop and go\" vehicle. In this scenario, there are three time sessions, namely deceleration \(\Delta t_{D}\), stop \(\Delta t_{S}\) and acceleration \(\Delta t_{A}\). The total measurement time is \(\Delta t_{DSA}=\Delta t_{D}+\Delta t_{S}+\Delta t_{A}\). The measurement heights are shown in Figure 3. In each scenario, the sound levels produced by the right-turning, left-turning or straight-going movements of the studied vehicles were recorded respectively.  
Figure 1: Sound measurements of a motor vehicle on road. (**a**) A van; (**b**) a coach.  
Figure 3: Measurement points in Figure 2 at three different heights.  
Figure 2: Measurement points A, B, C and D at Yau King Lane in Tai Po, Hong Kong.
# Identifying At-Risk Students for Early Intervention--A Probabilistic Machine Learning Approach
## 3 Materials and Methods
### Formulating a Logistic Regression Model  
A logistic regression model was implemented as a base model to compare model formulation and performance to the probabilistic logistic regression model. In logistic regression, the linear relationship between student data variables and binary outcome (at-risk or not at-risk) estimates were mediated by a sigmoid function to ensure the logistic regression model produces probabilities [16]. The logistic regression model was formulated as:  
\[lr_{s}=\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}+\ldots+\beta_{k}x_{k} \tag{8}\]  
\[p=\frac{1}{1+e^{-lr_{s}}} \tag{9}\]  
The logistic regression models the probability \(p\) that student \(i\) is at-risk based on \(k\) features at stage s, where \(lr\) is the logistic regression model for stage s, represented as \(lr_{s}\).
## 3 Materials and Methods
### Additive Manufacturing (AM)  
Advances in additive manufacturing (AM) technology have significantly spurred on the development of generative design algorithms. AM techniques enable the production of parts with complex geometries that are beyond the capabilities of conventional manufacturing methods. Notably, design concepts generated by data-driven generative design methods can incorporate complex geometries. However, the manufacturability of such intricate designs often poses challenges when using traditional manufacturing techniques. As a result, these advanced design concepts are typically only feasible through AM technologies, which provide the necessary flexibility and precision to accommodate their complexity.  
Within the scope of this research, in the last step of the generation and evaluation cycle, the concept designs were classified according to their weight and strength properties. Then, we aimed to manufacture the ten design concepts with the best performance. In this process, the defects (if any) on the parts were adjusted in the CAD environment. The voxel model was converted into the STL format and made suitable for printing. Concept designs can be manufactured by choosing printer settings according to the desired precision and print quality. Although the production of parts in layers is a familiar working principle in AM techniques, there are differences between the methods according to the materials and printing technology used [51; 52]. In this study, the performance of AM techniques in manufacturing parts generated by generative design was evaluated by the stereolithography (SLA) and fused deposition modeling (FDM) methods.  
SLA is a 3D printing method in which 3D objects are manufactured layer by layer using special photopolymer resins that solidify when exposed to ultraviolet light. In this method, the scanning system creates layers by directing the light to the surface to form a specific cross-section. With SLA, it is possible to produce exact and detailed parts. The other method, FDM, is one of the most commonly used methods in 3D-printing technology. This method involves creating a 3D object by layering material in filament form through a heated nozzle following a special design [53].
## 2 HSI Procedure
### Preprocessing  
Raw hyperspectral images are often unsuitable for direct use in models due to inherent noise that can distort both spectral and spatial features. Noise pollution, originating from factors such as image acquisition errors, camera sensitivity, and atmospheric interference, can significantly impact the accuracy of hyperspectral data analysis [35; 40]. Preprocessing techniques typically involve mathematical operations, applied on the spectral data of HSI images, that are essential to mitigate these issues, enhancing feature extraction and classification accuracy. Effective preprocessing copes with high-dimensional data and inter-class correlations, hence improving the model's performance [35].
## 3 System Design and Implementation
### Proposed System
Since the central server at the Control Room is constantly coordinating and interacting with various departments involved in rescue operations to provide post-disaster management services, its actions can be mapped according to the user requests it receives. This can be seen in Table 2.  
The front-end Graphical User Interface (GUI) at the Control Room shows the map of the disaster area, the list of departments collaborating in the rescue activities, the log of all the operations underway, and the status of resources at disposal. This data visualization makes it easier for the authorities to make effective and timely decisions based on accurate and up-to-date information. The GUI of the server-side application is developed in Python using the T \begin{table}
\begin{tabular}{c l} \hline \hline
**Code** & **Action** \\ \hline
1 & Sends a message to the First-Aid team nearest to the location \\
2 & Sends a message to the Hospital service \\
3 & Sends a message to the Air-Base Control room \\
4 & Sends a message to the Food \& Accessories management head, along with location information \\
5 & Sends a message to the Transport service head, along with location information \\
6 & Sends a message to the Camping \& Shelter management head, along with location information \\
7 & Sends a message to the Volunteers management center asking for volunteers at the specified location \\
8 & Sends a message to the Food \& Accessories management head, along with location information \\
9 & Stores the information in database and displays it on the User Interface \\ A & Stores the information in database and displays it on the User Interface \\ B & Stores the information in database and displays it on the User Interface \\ C & Stores the information in database and displays it on the User Interface \\ D & No action is performed as this only acknowledges the end of the message \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of actions taken by the Server at the Control Room. Figure 2: An example of inserting data at the Client interface.
## WT1 Immunohistochemical Expression in Human Embryonal/Fetal and Neoplastic Tissues
### WT1 Expression in Neuroblastic Tumors  
Neuroblastic tumors arise in children and adolescents. They occur especially in the adrenal gland, retroperitoneum or, and more rarely, in the posterior mediastum. On the basis of a different degree of differentiation of immature neuroblasts in mature ganglionic cells, three variants of neuroblastic tumors are identified: (i) neuroblastoma in which the schwannian stroma component is poor, including undifferentiated, poorly differentiated and differentiating neuroblastomas; (ii) ganglioneuroblastoma in which the schwannian stroma component is dominant, but foci of neuroblasts are still present, including intermixed and nodular ganglioneuroblastomas; (iii) ganglioneuroma in which the schwannian stroma is predominant and neuroblasts are absent, including maturing and mature ganglioneuromas [147]. Neuroblastoma consist of small, round cells, with scant cytoplasm and round, hyperchromatic nuclei, arranged around a fibrillar area (neuropil). The neuroblastoma is classified as poorly differentiated if \(\leq\)5% of the tumor cells show ganglionic differentiation, while, if the neuroblastic component shows ganglionic differentiation in more than 5% of the tumor cells, the neuroblastoma is classified as differentiating; the neuroblastoma is classified as undifferentiated when ganglionic differentiation is almost or completely absent [147]. Ganglioneuroblastoma is a neuroblastic tumor with intermediate features of differentiation between neuroblastoma and ganglioneuroma. Histologically, two variants are recognized, intermixed and nodular. In the former, the ganglioneuromatous component is associated with a collection of immature ganglion cells that are interspersed among the schwannian stroma, while in the latter, in addition to the ganglioneuromatous component, there is a well-circumscribed area of neuroblastoma [147]. When the tumor is composed of mature or maturing ganglion cells surrounded by fascicles of Schwann cells, it is called ganglioneuroma [147]. This tumor model perfectly summarizes what happens during developmental stages of a normal peripheral sympathetic nervous system [148; 149; 150; 151; 152; 153]. In some studies, a focal and weak nuclear WT1 staining in neuroblastoma was reported [135; 145; 35]. [PERSON] et al. showed WT1 expression preferentially in ganglioneuroblastoma and ganglioneuroma [154].  
Figure 10: Embryonal rhabdomyosarcoma, bottyoid type (**A**) and alveolar rhabdomyosarcoma (**B**). Neoplastic cells showing strong and diffuse cytoplasmic WT1 (_N-terminus_) staining (**C**,**D**).  
In our experience, by using antibodies against the _N-terminal_ portion of the WT1 protein (clone WT6F-H2), a variable cytoplasmic WT1 staining was found only in the ganglion cell component of both ganglioneuroblastoma and ganglioneuroma, while the neuroblastic component was negative (Figure 11) [143].
# Clutter-Masked Waveform Design for LPI/LPD Radarcom Signal Encoding
## 6 Conclusions  
In this paper we have introduced a method for clutter decorrelation from UWB radar returns, provided motivation for using this approach to improve LPI/LPD characteristics of RSE for radarcom signal design, and tested the created clutter-masked RSE radarcom waveforms in simulations for their performance in communications and radar. Our clutter decorrelation and clutter model design approach were tested experimentally with a SDRS in indoor laboratory conditions. We simulated the performance of such signals in noise and found that a friendly receiver achieves BER below 10% for SNR values above approximately 15 dB with 300 retransmissions, each of which employed randomized signals, which would be interpreted as clutter returns by an unauthorized interceptor using MSE, NMSE, and/or NRMSE for intercept classification. Additionally, we showed the potential for these signals to be used in radar applications, such as SAR.  
Our future efforts will focus on rigorous modeling of the unauthorized receiver's classification of intercepted radarcom signals, improving ACF characteristics of the created  
Figure 4: Backprojection via synthetic aperture radar (SAR): (**a**) Target Scene; (**b**) Return image of LFM signal; (**c**) Return image of clutter-masked OFDM signals.  
signals, modeling clutter through ML techniques, and analyzing other potential clutter distributions for the purpose of applying them to radarcom signal design.  
Conceptualization, D.G. and S.M.; methodology, D.G., R.W., and S.M.; software, R.W. and B.B.; validation, R.W. and B.B.; formal analysis, D.G., R.W., B.B., and S.M.; investigation, R.W., B.B. and D.G.; resources, D.G. and S.M.; data curation, R.W. and B.B.; writing--original draft preparation, D.G. and R.W.; writing--review and editing, D.G., R.W. and S.M.; visualization, R.W.; supervision, D.G. and S.M.; project administration, D.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors acknowledge help and assistance of [PERSON] and [PERSON] (both formerly students at Miami University) in SDRS calibration and data collection.  
The authors declare no conflict of interest.
# RHLP-18T: A Radiation-Hardened 18T SRAM with Enhanced Read Stability and Low Power Consumption
## 1 Introduction
In contrast, proposed RHLP-18T structure uses its increased circuit complexity (18 transistors) to significantly enhance both data stability and SEU tolerance beyond what is typically achievable with lower-transistor-count designs. We strategically incorporated additional transistors not just for passive redundancy but also to actively strengthen feedback loops and effectively isolate sensitive internal nodes during upsets. By prioritizing major improvements in read stability and hold power--often critical metrics for overall system performance and energy budget--proposed design acknowledges and balances the inherent trade-offs between speed, power, noise margin, and radiation hardness in the SRAM design. This paper details the RHLP-18T architecture, analyzes its operational principles, presents simulation results demonstrating its performance and SEU robustness, and provides a comparative evaluation against relevant existing RHBD cell designs. The proposed integrated circuit was implemented using a 90 nm CMOS process, operating at a supply voltage of 1 V. A comparative evaluation was performed against existing RHBD SRAM cells under identical simulation settings: RHBD14T [11], HPFIS12T [12], NRHC14T [13], QCCS12T [14], RHMC12T [15], RHWC12T [16], SEA14T [17], SIMR18T [18], and SERSC16T [19].
## 3 Results and Discussion
The standardized data are then integrated, collated, and utilized by a common format and structure or keys; for instance, in this study, it is the animal traceability identification number. The cow traceability identification number links all relevant data sources regarding the specific individual cow, regardless of the differences in the data source or farms. The standardized data are loaded thereafter into the data-processing hub named ThinkDairy Hub in a table format, as shown in Figure 1. These offline and online data will then be run using classifier algorithms to identify the real-time condition of the cows and will create an animal performance score. The animal performance score will be the basis for the recommended action and feed rations to farmers. For instance, in dairy farms, cows are identified by a unique traceability identity number. In order to be able to compare all records concerning a specific cow, the offline and online records using the traceability identity number are selected in order to analyze and compare all records for the specific cow. Offline and real-time data from the sensors and cameras are identified by the unique traceability identity number, extracted and transformed into a usable data form, and relevant data is finally loaded into a table view as shown in the blue, yellow, and black table in Figure 2. The 21 relevant parameters such as the temperature, humidity, THI, body weight, BCS, body temperature, rumen temperature, rumen pH, respiration rate, ruminating, standing, resting, drinking, milk composition and milk yield, and feed intake were then classified by the k-nearest neighbor classifier to produce the animal performance score (orange table). According to the performance score of the specific cow, whether it is a numerical or verbal score, an action measure is displayed for the farmers. At the same time, if the suggested action identifies increase, decrease, or maintain feed ration, the data-processing hub will run modeling and simulation to compute feed rations specific for the requirements of the cow (green table). The part of modeling and simulation for the action and feed rations are only stated in this manuscript for the sake of discussing the overview of the whole function of the data-mining hub. In this study, the identification of the real-time condition, specifically heat stress and subclinical mastitisti of the dairy cows, are further discussed. If successfully integrated, these offline and online data can generate informative and accurate interpretations rather than giving disparate conclusions when analyzed independently. With the unceasing growth of the use of sensors and new and different technologies in dairy farms [13; 56], as well as in different industrial fields [57; 58], the ThinkDairy data-processing hub would be a useful tool to continuously access, stream, and integrate data information from dairy farms. The application of the proposed developed ThinkDairy data-mining hub is further discussed in the following sections of the manuscript.