esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Impact of Segmentation Errors on Spatial Features  
Spatial features were the most affected by incorrect stroke identification, particularly the minimum distance between strokes which had a propagated error of 99.3% as well as the minimum in-air time, a temporal feature, which similarly demonstrated a propagated error above 90%. This is unsurprising given that a single stroke would have closely adjacent x- and y-pixels compared to segmentations that yielded multiple strokes. Given the relationship between distance and time, it is also expected that minimum distance measurements would be positively correlated with minimum in-air time. Incidentally, spacing as a feature is commonly used in handwriting analysis to assess fine motor control in various populations [1; 2; 15; 17; 19], although the literature has not consistently associated this feature with neurodevelop writing capabilities. For example, it has been reported that children with dysgraphia [2] and non-proficient writing [19] tend to narrowly space their words. [PERSON] and colleagues found high variability in the inter-letter distance among younger children writers, and concluded that size and space parameters alone could discriminate between proficient and non-proficient handwriting [17]. In contrast, [PERSON] et al. found that spatial features, such as the distance between letter strokes, did not provide a clear distinction between children with and without handwriting difficulties [1]. The high possibility of errors in spatial features due to incorrect brushstroke segmentation may explain these inconsistent findings. From our data, segmenting strokes based solely on distance or pressure is vulnerable to error as variable speed and pauses while drawing could falsely emulate a pen lift. It is, thus, recommended that future studies consider the accuracy of their segmentation algorithm or exclude the measures of inter-stroke distance altogether.  
All other spatial features were moderately affected by incorrect segmentation. Brushstroke length measures exhibited an error rate between 11.1-28.8%. Again, this was also expected because the erroneous segmentation of one stroke as multiple brushstrokes would positively skew the measured segment length. Alternatively, quantifying brushstroke dimensions in height and width along the y- and x-axis, respectively, proved to be a more robust measurement as the measured errors were between 1.7-15.5%. This is likely due to the dependence of these measurements on the global image rather than individual segments. Some studies have reported correlations between spatial features, particularly those related to height, and writing proficiency [9; 17; 20]. However, given the sensitivity of these measures to segmentation, spatial features should be considered with caution as quantifiers of fine motor control.
## 4 Modeling a Solar-Hydrogen System in the University  
### Energy Management System Model  
The goal of creating the Microgrid EMS (Energy Management System) Optimization for Solar-Hydrogen (Figure 3) comprises a broad set of goals targeted at tackling current energy concerns and advancing sustainable solutions. The major goal is to incorporate solar and hydrogen technologies into a microgrid context. This integration (Figure 4) intends to maximize energy capture and storage by leveraging the complementary nature of these renewable sources. By maximizing energy storage, the concept attempts to improve the dependability and resilience of microgrid systems. The microgrid becomes more resilient in the face of external disruptions or variations by efficiently controlling the storage and delivery of solar and hydrogen-derived electricity.  
Designing a Hybrid Solar-Hydrogen model in Simulink that depicts the interactions between an electrolyzer, energy storage components, and solar photovoltaic (PV) system is the first step in designing a hybrid solar-hydrogen system. The PV system, including the panels, inverters, and controllers, can be represented using Simulink's graphical user interface. The electrolyzer stack and hydrogen storage can also be dynamically modeled. The integration of control systems and power electronics enhances system performance, and time-domain simulations evaluate the system's capabilities under different scenarios. Performance metrics and sensitivity analyses are used to assess overall system behavior, efficiency, and hydrogen generation rates. Results can be analyzed with the help of Simulink's visualization tools, and the best operating conditions can be found by applying optimization techniques.  
Figure 2: Elements of Solar Generation Monitoring System: (**a**) Inverter; (**b**) Home Dashboard; (**c**) Report; (**d**) Settings.
# Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
## 4 RL Based Selection of TRxPs for RA
### Design  
The architecture of the proposed neural network-based RL model is depicted in Figure 4. The agents running the deep RL algorithm interact with the RA environment by taking action from the action set. The RA environment in turn responds by returning a reward, which is a factor of the RA KPIs. The input to the network is the current state of the user from RRC_IDLE, RRC_INACTIVE or RRC_CONNECTED. To rearrange the dataset, we associate simple numerical values to the various RA states a node can occupy. We seek to obtain estimates of state-value action as an output, and the learning algorithm uses these metrics to select the best performing access point for an RA request.  
As with a typical RL design, we list the entities as follows.
# Coupled System of Dual-Axis Clinostat and Helmholtz Cage for Simulated Microgravity Experiments
## 3 Conclusions  
In this manuscript, a clinostat with a surrounding Helmholtz cage was presented. The system enables plant growth under simulated microgravity with the Earth magnetic field cancelled out or modified, respectively. Due to the impact of both factors on plant growth, simulations of plant cultivation in space can be performed very conveniently, without organizing special orbital missions. The first experiments with _Lepidium sativum_ and _Phaseolus occineus_ showed a significant difference between plants grown in simulated microgravity, i.e., in the clinostat, and under static conditions, i.e., in the usual Earth gravitational field. Future experiments will also be conducted in the presence of a magnetic field to show its impact on the dynamics of the growth of biological structures. Further research will also be conducted with microscopic objects, such as, for example, bacterial colonies. The authors hope that the information presented will also be useful for scientists who want to build their own similar experimental system.  
Conceptualization, T.B. and A.E.; methodology, T.B.; software, M.M.; validation, T.B.; formal analysis, M.M. and T.B.; investigation, M.M.; resources, T.B.; writing--original draft preparation, T.B. and A.E.; writing--review and editing, all authors; visualization, M.M. and T.B.; supervision, T.B.; funding acquisition, T.B. All authors have read and agreed to the published version of the manuscript.  
The process of designing and building was supported by the student research clubs GOLF and SAT at the Institute of Physics--Center for Science and Education, Silesian University of Technology in recent years. Further development of the system and the recent results were made possible thanks to the BKM-747/RIF2/2024 (14/020/BKM24/0051) grant obtained from Silesian University of Technology Program received by the first author (M.M.).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Study on the \"Two-Zone\" Heights in Lower Slice Mining Under Thick Al Alluvium and Thin Bedrock
## 2 Mining Situation and Geological Characteristics  
### Mining Layout of Coal Mine Working Face  
Zhaogu No. 1 Coal Mine is located in Jiaozuo City, Henan Province, to the east of Jiaozuo Coalfield. The coal mine's geographical location is illustrated in Figure 1. Its main mining product is Shanxi group no. 2-1 coal seam, with an average coal seam thickness of 5.29 m; the change is mostly between 5.50 m and 6.93 m, with the average buried depth exceeding 500 m. The mining methods predominantly employed are fully mechanized caving mining and layered mining. The coal seam at the 11092 working face is no. 2-1 coal seam. The roof elevation of the no. 2-1 coal seam ranges from \(-\)448.1 m to \(-\)491.6 m, while the corresponding ground elevation is between +79.9 m and +85.2 m. The III coal seam is situated at the bottom of the Shanxi group and is characterized by stable occurrence. The average thickness of the coal seam is 6.0 m, and it is divided into two layers.  
A total of 17 working faces have been arranged in the upper layer of the east panel of Zhaogu No. 1 Coal Mine, all of which have been mined. Currently, four working faces have been mined in the lower layer, namely the 11112, 11052, 11132, and 11072 working faces. The distribution of the working face and the characteristics of the overlying bedrock are illustrated in Figure 2. The lower-layer working face is positioned beneath the upper-layer working face, with the upper-layer coal pillar staggered by 10 m in the roadway; both of these are situated under the goaf.  
The mining method is the strike longwall slicing comprehensive mechanized retreating mining method. Roof control is achieved through the use of the caving method, with the artificial false roof being extracted along the upper layer. The standard mining height is set at 2.2 m, with a general limit of 2.5 m under typical conditions, and a maximum threshold of 2.8 m.
## 4 Power Supply and Wireless Communication Technologies
Thus, LSK may not  
Figure 2: **(A)** Power extraction from cerebrospinal fluid by an implantable glucose fuel cell: plausible site of implantation within the subarachnoid space and a micrograph of one prototype, showing the metal layers of the anode (central electrode) and cathode contact (outer ring) patterned on a silicon wafer [21]. **(B)** A photovoltaic-driven energy-autonomous CMOS implantable sensor [103]. **(C)** An anatomically sized chip that harvests the energy of the electrochemical potential in the guinea pig cochlea to power a wireless transmitter: **(a)** plausible site of implantation within the mammalian ear; **(b)** cross-section of a typical cochlear half-turn, showing the endolymphatic space (yellow) bordered by tight junctions (red), the stria vascularis (green) and hair cells (blue), which are contacted by primary auditory neurons (orange) [96]. be appropriate for deep tissue implants at certain frequencies. This is not the case for those implants powered by the non-rechargeable batteries, where good transfer rate can be achieved at low transmission power. As the advancement of wireless sensor network continue to develop, new modulation techniques will need to be designed to address the needs of these complex systems. For example, an intra-body area network (implant BAN) are being considered to establish timely, reliable, and secure communication between indwelling devices, e.g., a cardiac implant, nerve sensor, and a drug delivery pumps; or a series of diverse injectable microdevices used for multi-site stimulation and sensing [104,105].
## 3 Results  
To quantitatively analyze the errors associated with the analysis methods described in the previous section, we have selected 3 wavelengths of light (\(\lambda_{1}=465\) nm, \(\lambda_{2}=525\) nm, and \(\lambda_{3}=615\) nm). The molar absorption coefficients for these selected wavelengths are given in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Wavelength (nm)**} & **HbAlc** & **HbO** & **HHb** \\  & **(M\({}^{-1}\)cm\({}^{-1}\))** & **(M\({}^{-1}\)cm\({}^{-1}\))** & **(M\({}^{-1}\)cm\({}^{-1}\))** \\ \hline
465 & 549,024.7353 & 38,440.2 & 18,701.6 \\
525 & 455,139.5677 & 30,882.8 & 35,170.8 \\
615 & 170,555.4218 & 1166.4 & 7553.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of molar absorption coefficients of HbA1c [22], HbO, and HHb [23] for selected wavelengths.
# Kinematic Analysis and Verification of a New 5-DOF Parallel Mechanism
## 2 Design and Verification of the Scheme  
### Design of the Primary Scheme  
At present, 6-DOF Stewart platform [31] is used as the prototype for 5-DOF parallel mechanism. The minimum degrees of freedom of the 3D printer structure that meets multi-directional printing is 5-3 movements and two rotations. In the case of satisfying the degree of freedom requirement, the less degree of freedom of the mechanism, the simpler the mechanical structure, and the lower the manufacturing and control costs. Therefore, we choose to remove one of the six active limbs in the Stewart platform, and then rearrange the spatial positions of the five active limbs. However, at this time, there are only five driving modules, while the number of degrees of freedom of the moving platform is six. The number of driving modules is less than the degrees of freedom of the mechanism, which will cause the motion of the moving platform to be uncertain. The redundant degree of freedom of the moving platform is limited by adding a restraint limb between the moving platform and the fixed platform. At this time, the number of degrees of freedom of the moving platform is five, which meets the requirement of the degree of freedom.  
As for the drive part of parallel mechanism, fixed-length link drive and telescopic link drive are the two most widely used ways at present. The driving mode of the mobile pair with a fixed-length link can greatly reduce the structural dimension of the link and avoid interference during the movement [29]. Therefore, we choose the driving method of the mobile pair with a fixed-length link in this paper. The five mobile pairs are used as actuators, which are symmetrically distributed in the shape of regular pentagons in space. The moving platform also adopts the symmetrical structure of the positive pentagon. As shown in Figure 1, the mechanism consists of five PUS limbs and one UPU limb. Five PUS limbs are called driving limbs, where \(P_{1}\)\(\sim\)\(P_{5}\) are used as driving elements to represent the mobile pairs and are distributed on the corners of the positive pentagon, which are called sliders. \(U_{1}\)\(\sim\)\(U_{5}\) represent Hook joints, connecting five sliders and five connecting links. \(S_{1}\)\(\sim\)\(S_{5}\) represent the spherical hinges, connecting the moving platform and five connecting links. At the same time, the UPU limb is called the constrained limb and \(U_{6}\) and \(U_{6}^{\prime}\) represent Hook joints, which are fixed to the center of the regular pentagon and the center of the moving platform, respectively. \(P_{6}\) represents the mobile pair, connecting Hook joints \(U_{6}\) and \(U_{6}^{\prime}\).
### Background and Related Work
#### 1.1.2 Network Performance Simulations  
Different methods exist for obtaining channel information and estimating network performance. A ray-tracer defines propagation paths between two antennas using a geometric approach, i.e., reflections and transmissions are defined, and losses are calculated via Fresnel coefficients. A ray-launcher uses a fixed transmit antenna location, and launches rays in the azimuth and elevation field, defining interactions with the environment using electric properties of the materials. Both methods work well if we have an accurate description of the environment, i.e., both the object dimensions as well as the electrical properties are known. If such information is not known, we can use generic channel models or use statistical ray-tracing methods. In this approach, scattering objects are positioned randomly, and the number of clusters depends on statistical channel parameters, including angular spread and diffuse scattering.  
In [5], outdoor channel measurements are compared to the outcomes of a proprietary ray-tracer. The main drawback of this ray-tracer is that it relies on highly accurate light detection and ranging (LiDAR) point cloud data, and gathering such data is expensive. Statistical ray-tracing methods allow channel simulations when an accurate environmental layout is not available. In [7], it is found that QuaDRiGa, originally developed for sub-6 GHz systems, works well at 32 GHz and allows network performance evaluation for generic environments, based on statistical channel parameters such as the angular spread. Link budget estimation via ray-tracing for mmWave communication in outdoor environments is discussed in [42], but results are limited to a very simple environmental layout, with both antennas sandwiched between two parallel walls. In [43; 44], the indoor coverage of 5G mmWave networks is discussed using system-level simulations, and a radio resource mapping scheme is proposed.
## 3 Results
### Agro-Climatic Model Results in 2000 and 2050
#### 3.2.3 Regional Analysis  
Were the evolutions highlighted at the national scale consistent with a geographical study at the departmental scale?  
We analyzed the intensity and frequency of extreme droughts at the local level using DOWKI values computed at an 8 km \(\times\) 8 km scale and crop yield losses simulated at the departmental level.  
Figure 5: Average yield losses and percentile 10–90 on 100 simulations for the year 2000 and the year 2050 (RCP 8.5) for (**a**) soft winter wheat, (**b**) winter barley, and (**c**) grassland.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**10-Year Droughts** & \
\begin{tabular}{c} **Average Yield Losses at the** \\ **National Scale—Climate** \\ **in 2000** \\ \end{tabular} &
\begin{tabular}{c} **Average Yield Losses at the** \\ **National Scale—Climate** \\ **in 2050** \\ \end{tabular} \\ \hline Soft winter wheat & 4.2\% & 7.4\% \\ Winter barley & 5.3\% & 9.5\% \\ Grassland & 18.5\% & 25.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average yield losses at the national scale for soft winter wheat, winter barley, and grassland for 10-year droughts for the climate in 2000 and the climate in 2050.  
The results presented in Figure 6a illustrate that the increase in the water deficit will be more significant on average in the South of France (southwest and Mediterranean region). Overall, we observed a worsening water deficit of 30% to 50% throughout France and above 50% in the south.  
Brittany, Normandy, and the coastal northern regions showed the lowest evolution of drought index (<30%). In these areas, yield losses for grassland will increase by 30 to 50% on average.  
The translation of the DOWKI values in terms of yield losses showed the following results: the northeastern and southeastern parts of France will incur high yield loss increases for straw cereals. Depending on the department, Figure 6b,c shows that the yield losses will increase by 30% to 100% in 2050 for straw cereals. For grasslands, the whole of France will be affected by a significant increase in yield losses of between 30% and 75%.  
Figure 6: Annual average evolution between the climate in 2000 and in 2050 of (**a**) DOWKI values, (**b**) yield losses of soft winter wheat, (**c**) yield losses of winter barley, and (**d**) grassland.
# New Compact Wearable Metamaterials Circular Patch Antennas for IoT, Medical and 5G Applications
## 1 Introduction  
Small wearable sensors and antennas have been analyzed and presented in several papers and books in the last twenty years, see [1; 2; 3; 4; 5]. Patch, printed Slot, PIFA, Loop and other printed antennas are used in 5G communication, Internet of Things (IoT) and biomedical systems [2; 3; 4; 5; 6]. Meta material structures may be used to design small antennas with high efficiency for wearable medical and IoT systems, [7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. Meta material is a periodic artificial material that gain its electrical properties from the material structure rather than from its components. Periodic split ring resonators (SRRs) and metallic posts structures may be used to design materials with specific dielectric constant and permeability as presented in [7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. For example, artificial materials with negative dielectric permittivity were investigated in [7]. A quasi-analytical and self-consistent model to compute the polarizabilities of split ring resonators (SRR) is given in [8]. An experimental setup is also proposed for measuring the magnetic polarizability of SRR structures. Experimental data are compared with theoretical results by using the proposed model. The design of a compact microstrip patch antenna with a reduced size by using metamaterials technologies was presented in [11]. However, in [11] the antenna gain and bandwidth are the same as a standard patch antenna. A wideband transmission-line metamaterial antenna is developed in [12]. The antenna has two transmission line arms that resonate at two different frequencies. Each arm is a microstrip line loaded with five spiral inductors. The antenna radiationefficiency is 65.8% at 3.3 GHz. The antenna has 3% bandwidth. The antenna directivity is around 2.6 dBi. The measured peak gain is around 0.79 dBi. Small printed antennas such as printed dipole, PIFA and loop antennas have low efficiency [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28].  
Wearable systems were presented in [2; 3; 4] and in [29; 30; 31]. For example, a remote monitoring of patient in hospitals is presented in [29]. An adaptive thermal-aware routing protocol for wireless body area network is presented in [30]. A secure thermal-energy aware routing protocol in wireless body area network is presented in [31]. Active antennas and circular stacked patch antennas were presented in [32; 33; 34; 35; 36; 37; 38; 39; 40]. One of the main goals of wearable medical systems is to increase disease prevention. By using more wearable medical devices a person can handle and be aware of his private health. Sophistical analysis of continuously measured medical data of large number of medical centers patients may result in a better low-cost medical treatment.
## 3 Results
### Investigated Influencing Factors
#### 3.4.3 Temperature Changes  
The influence of temperature was reported in five studies. [PERSON] et al. [27] and [PERSON] et al. [32] assessed materials at varying temperatures, observing temperature-dependent variations in force variation and shape recovery, respectively. Three studies implemented thermocycling protocols between 5 \({}^{\circ}\)C and 55 \({}^{\circ}\)C, reporting reductions in flexural strength or force delivery [23; 28], and in one case, no significant effect [25].