esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Comparison of Systems Discussed
### Comparative Discussion on Technologies and Techniques  
Numerous parameters have been used to compare IPSs, such as accuracy, type of location (2D or 3D), method (for example, triangulation or fingerprinting), algorithm, signal measurement (AoA, ToA, TDoA, RSSI), coverage, cost, etc. Based on these parameters, we consider that the most important ones when designing an indoor positioning system are precision (commonly indicated as an error value), scalability, the number of resources used in the studies in question, and the coverage area. These parameters were chosen because they are used in most projects and allow us to identify the benefits of using certain types of technology to develop an indoor positioning system.  
Accuracy refers to the difference between the estimated and current position, as indicated by the system. As this difference can change according to the conditions, it is a concept that works with statistics. It must be expressed in terms of parameters such as distance, margin of error, and percentage, such as \"error less than one meter in 90% of cases,\" although authors rarely express precision in these terms.  
Regarding the scalability criterion, the number of targets that the system can locate and navigate simultaneously in an indoor environment is discussed, observing how much its performance is affected.  
Figure 10: Indoor navigation system architecture.  
The resources identified as anchors in this review refer to the physical resources utilized in the installation and operation of an indoor positioning system. For this record, cases in which the resources were already available, making up the existing infrastructure (for example, lighting, ambient sound, terrestrial magnetic field, network nodes, etc.), were considered.  
Coverage is the extent of the area in which the system can locate users. Although some technologies can offer extensive coverage in an ideal environment, when used indoors, their coverage can be limited by numerous environmental factors. An IPS can locate persons within a range of meters or even find them on different floor levels within a building.  
Based on these parameters, we examined the scalability criterion, the number of physical resources used in the system, the coverage area or scope of the model tested, and the accuracy (described as the error value). These criteria were used because they were identified as those commonly used in other studies and allow us to indicate the gain in the adoption of one technology or approach over others in specific scenarios. The information for each system is shown in Table 1.  
Based on the parameters used by the various authors studied in this review, we maintained the criteria of accuracy, coverage, and cost, because we identified that these parameters are commonly used to indicate the advantage in choosing one technology or approach over others. For each technology presented, we combine their described accuracy, coverage, and cost to provide a macro view of the study. The information for each system is shown in Table 2.
# Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?
## 5 Methods for Congestion Learning
### Hidden Markov Model (HMM)  
It is a statistical model widely used for temporal series. There exists a set of internal states that cannot be directly observed, while they generate several observable variables. It is assumed that the internal states follow a first order Markov Chain. This algorithm has two main applications:  
* If the model is known, it is possible to ascertain the most likely state sequence that led to a given observable sequence.
* Given a sequence of observable samples, it is possible to estimate the transitions between states, as well as the model parameters. This problem is typically solved using the Baum-Welch algorithm, also known as Expectation-Maximization (EM).  
Congestion learning corresponds to the second use case, where given a sequence of metrics, we aim to estimate the state transitions (in terms of congestion) that caused it. The reader may refer to [45] for a succinct discussion of HMMs and their possible implementations.
# Advanced Progress of Optical Wireless Technologies for Power Industry: An Overview
## 3 Wireless Power Transfer of Power Industry Optical Wireless Communications
### Other Technologies of Wireless Power Transfer  
#### 3.3.1 Optical Power Transmission  
Optical power transmission (OPT) is one of the least studied technologies for wireless transmission of power to biomedical implants [107; 108; 109; 110; 111]. In the literature, there are few examples of OPT, even in other applications [88; 90; 101; 112]. An external T\({}_{\text{X}}\) unit in a complete OPT system uses an external laser to power the implant. The light emitted by the external laser is received by the CMOS PD array implanted under the skin and converted into electrical energy, using the power management unit. The advantage of this WPT method is to provide a compact solution, because the CMOS-based power receiving system can be smaller than other systems [100]. The authors suggest in [111] that implantable sensors could be used for short-term in vivo monitoring after surgery, and that the use of biodegradable materials could avoid secondary surgery to remove the devices. Therefore, OWPT is considered to be applied in human skin to complete communication with a broad prospect.  
Compared with other methods, the OPT system for biomedical implant devices is still in the early stages of development, and the potential of this technology depends on the results of further research. In addition to the design and manufacturing challenges, more system-based implementation challenges need to be solved before considering using the system for complex implants. OPT requires a complete analysis of organizational security. Furthermore, misalignment and limited output power are important issues established by OPT in biomedical implant applications.  
OPT is the least researched WPT technique presently. To support most IMDs, PTE is much lower. Besides, any misalignment of Rx photodiodes with T\({}_{\text{X}}\) can significantly reduce PTE. Moreover, WPT technology still requires a rigorous organizational security analysis before its use in IMDs can be considered.  
The generation ratio of OWPT system with reflector is obtained when the reflector is 95% [106]. Figure 27b shows the power generation ratio of the system under a light source of 5 square mm. It can be seen that within the range of 150-650 cm, the power generation ratio can reach more than 0.7. Combined with Figure 27a, the result shows that the energy transfer efficiency above 0.7 can be achieved within a distance of 650 cm after the installation of the reflector, which causes the extension of the effective distance. Furthermore, when the distance is between 150 cm and 300 cm, the beam is transmitted more flexibly through direct irradiation of light source and indirect irradiation of mirror, even if the direct beam is blocked by obstacles. In this system, the reflection loss is a disadvantage because of the need of multi-plane mirror module.
### Parameters  
Table 1 presents our traffic simulator parameter settings. Vehicle numbers on roads within the map area were 1000, 1500, and 2000. Maximum vehicle speed was approximately 40 km/h for non-EV vehicles and 60 km/h for EVs. The starting point was Chang Gung Memorial Hospital; the two destinations had straight-line distances of 2000 and 2900 meters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Traffic simulator & SUMO 0.15.0 \\ Area of map & \(2300\times 2900\) m\({}^{2}\) \\ Number of vehicles & 1000, 1500 and 2000 \\ Max. speed & EV: 16.67 m/s (60 km/h) \\ Vehicles: 11.12 m/s (40 km/h) \\ TLS building options & default \\ Length of routes & Short route: 2000 m \\ Long route: 2900 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Traffic simulator parameter settings.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Network simulator & ns-2 v2.34 \\ Radio propagation model & Propagation/TwoRayGround \\ Network interface & Phy/Wireless \\ MAC protocol & IEEE 802.11 DCF \\ Max. transmission range & 250 m \\ Warning packet size & 256 bytes \\ Normal packet size & 512 bytes \\ Packets sent by vehicles & 1 per second \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network simulator parameter settings.  
Figure 9: Simulator screenshot (near).  
Parameter settings for the network simulator are shown in Table 2. A two-ray ground reflection model served as the radio propagation model [40]. This model considers both the weakness of straight-line signal propagation and ground signal reflection. The EV broadcast one emergency message per second. All data discussed from this point forward are averages from ten simulations.
## 3 Results
### Size and Protein Analysis of Isolated EVs  
According to the NTA, for BMECs without icarini intervention (control group), the average diameter of isolated EVs was 86.68 \(\pm\) 1.97 nm, while in the icarini group, the average diameter of EVs was 85.35 \(\pm\) 1.65 nm. We found that icarini treatment did not significantly alter the diameter of EVs derived from BMEC cells in the two groups (\(p\) < 0.01). The control group had a relatively low concentration of EVs (9.85 \(\pm\) 0.47 \(\times\) 10\({}^{6}\)/mL). After being treated with icarini, the concentration of EVs increased to 1.54 \(\pm\) 0.08 \(\times\) 10\({}^{7}\)/mL. The BCA results showed that the protein content of conditioned media was 132.63 \(\pm\) 3.33 \(\mu\)g/mL in the control group and 138.56 \(\pm\) 9.58 \(\mu\)g/mL in the icarini group. The difference in protein content of conditioned media between the two groups was not statistically significant (\(p\) < 0.01). CD9 and CD81, two representative EVs surface markers, were highly expressed in EVs isolated from cells of both the control group and the icarini group (Figure 2B), indicating that isolated particles were EVs. Furthermore, the expression of VEGF and TGF-\(\beta\)1 protein was elevated in EVs derived from cells in the icarini group compared with  
Figure 1: Obtaining of tissue sample, cell morphology observation, and purity identification. (**A**) Longitudinal section of the femoral head after femoral neck fracture; (**B**) bone particles for further isolation of cells; (**C**) three days after cell isolation, adherent cells evenly distributed in the bottom areas of the culture bottles, and were inconsistent in size and shape (100\(\times\) magnification); (**D**) isolated cells show cobblestone morphology after 7 days of culture (40\(\times\) magnification); (**E**,**F**) under scanning electron scope, cells were covered with microvilli and rich in organelles; (**G–I**) immunofluorescence staining shows high expression of CD31 (**G**) and vWF (**H**) and cells were negative for CD133 (**I**), red color indicates positive for CD31, green color indicates positive for vWF, and negative control images display cells stained with DAPI.  
those from the control group (\(p<0.01\)) (Figure 2B). By TEM, the morphology of exosomes displayed classic spherical shape(Figure 2C).
## 5 Experimental Evaluation
### Discussion  
The performances of Model V1 and Model V2 were evaluated under a limited field of view (FOV) of 70\({}^{\circ}\), with varying numbers of human obstacles present. A total of 50 trials were conducted for each configuration, and the results were recorded and analyzed.  
In Table 1, the metrics from model V1, which maneuver around dynamic obstacles, are compared to their respective baseline. The parameters are the dynamic collision rate, contact loss rate, timeout rate, success rate, and navigation time. In Table 2, the metrics from model V2, which maneuver around both static and dynamic obstacles, are compared to their respective baseline. It was found that as the number of humans in the scene increased, the success rate of the models decreased. The proposed model demonstrates superior collision rates when compared to the baseline models, with the exception of Model V2 when it was tested with 10 humans and static obstacles (as opposed to 2-7 humans and static obstacles in the baseline model). Despite achieving a better collision rate when navigating around dynamic objects, the collision rate for static objects was observed to be higher. This phenomenon can be attributed to the increased number of humans present in the environment, thereby highlighting the impact of human presence on collision rates. However, it is imperative to acknowledge that the contact loss rate is a parameter that solely pertains to our model and, hence, renders any comparison of a success rate with respect to baselines unfeasible. Therefore, to ensure a fair comparison, the success rate is considered as the subtraction of the sum of the object collision rate (dynamic for V1, dynamic and static for V2) and the timeout rate from 100.  
It is obvious from the experimental results that the success rate surpasses that of the baseline methods. There is the concept that contact loss refers to the scenario where the distance between the robot and the patient exceeds 25 cm from our reward function mentioned in Equations (5) and (6), which is less than the average arm length of an individual. This parameter has been established to create a secure environment for the patients, such that if they desire to abruptly halt the robot, it would instantaneously come to a stop while still being grasped by the patient's hands. Finally, Model V2 was implemented in the Gazebo simulation environment to assess its ability to assist patients and avoid obstacles, which is further discussed in the subsequent section of the research.
# Impact Wear Behavior of the Valve Cone Surface after Plasma Alloying Treatment
## 3 Results and Discussion
### Impact Wear  
#### 3.3.1 Wear Morphology  
The macroscopic morphology of the valve substrate before and after wear is shown in Figure 8a,b, and its microscopic morphology is depicted in Figure 8c-f. Initially, the valve surface exhibits a metallic luster after machining, characterized by minimal roughness and a conical area mirroring a polished surface. Following the impact wear, the valve loses its original luster whereby the neck turns dark blue and the conical surface becomes black brown. This alteration occurs due to the high-temperature oxidation throughout the testing, altering the material's structure and composition [23]. Figure 8c displays the flat wear surface of the substrate, with significant adhesion in the area near the neck of the substrate. Figure 8d-f reveal extensive grinding and adhesion on the surface alongside furrows and cracks. These phenomena result from the repeated high-load impacts inducing internal defects and microcracks. These cracks extend, connecting to the valve surface and leading to the material peel-off under the axial force, which causes adhesion between the valve and seat surfaces. After grinding, debris transform into hard particles at high temperatures, creating furrows on the surface of the substrate [24].  
Figure 8: Wear morphology of the valve substrate. (**a**) before wear; (**b**) after wear; (**c**) microscopic morphology of cone surface; (**d**,**e**) enlarged area in Figure 8c; (**f**) enlarged area in Figure 8e.  
The macroscopic and microscopic morphologies of the CrN coating before and after wear are shown in Figure 9a,b and Figure 9c-f, respectively. The roughness of the coating is higher than that of the valve substrate, presenting a slightly frosted appearance. As depicted in Figure 9c, the valve cone remains relatively smooth without notable gullies or fluctuations, yet it is covered with a large area of peeling pits, and there are adhesive characteristics in the area near the valve neck. Figure 9d-f indicate that these peeling pits are primarily round or oval and are small in size. This is attributed to the discontinuous contact of the surface during the prolonged high-temperature impact tests. Consequently, when the surface load and stress cycle reach a certain threshold, fatigue phenomena emerge on the contact surfaces of the valve or seat, leading to material cracks and peel-off, which subsequently induces the formation of abrasive chips [25].  
Figure 9: Wear morphology of CrN coating. (**a**) before wear; (**b**) after wear; (**c**) microscopic morphology of cone surface; (**d**,**e**) enlarged area in Figure 8c; (**f**) enlarged area in Figure 8e.
## 2 PE-SLAM System Framework  
PE-SLAM is an improvement of the ORB-SLAM2 framework, as shown in Figure 2, consisting of the following five modules: \"TRACKING\", \"LOCAL MAPPING\", \"LOOP CLOSING\", \"DENSE POINT CLOUD MAPPING\", and \"FULL BA\". \"TRACKING\" focuseson real-time computation of the camera's motion trajectory by matching features in adjacent image frames. \"LOCAL MAPPING\" optimizes local poses and constructs local maps in the local scene. \"LOOP CLOSING\" corrects trajectory drift caused by error accumulation by determining if the camera has returned to a previously visited location. \"DENSE POINT CLOUD MAPPING\" is used to build dense point cloud maps. \"FULL BA\" further enhances the system's accuracy and stability through optimization algorithms.  
PE-SLAM is based on the classic ORB-SLAM2 framework and combines particle swarm with the epipolar constraint feature matching algorithm (PE-match) to perform a mismatch removal operation on the feature matching completed by the motion model. At the same time, after generating keyframes in visual odometry, a dense point cloud map based on keyframes is established to compensate for the limitation of ORB-SLAM2 in only being able to generate sparse maps.
# Improving RF Fingerprinting Methods by Means of D2D Communication Protocol
## 4 Experiment and Results
### D2D Distance Effect  
To observe the effect of physical distance among the devices, in the proposed method three distance ranges were set-up: (1) 1 to 10 m; (2) 11 to 20 m, and (3) 21 to 30 m. In both scenarios, motion and stationary, the physical distance between UE and other devices did not put a significant impact. Figure 9 shows that, regardless of the distance range (denoted by \(d\)) among the devices, the positioning accuracy remains almost the same.  
Figure 8: CDF of the positioning errors provided by the proposed method based on D2D communications (motion and stationary scenario for the secondary devices) and traditional method based on k-NN.  
Figure 9: Effect of physical distance among the devices in the proposed method shown as the CDF of the positioning errors.  
Based on the outcomes of Figures 8 and 9, it can be stated that the accuracy level improves significantly if the RF fingerprint positioning information is verified with a true value. Moreover, some features of the reference device (distance, motion/stationary, etc.) do not affect the D2D collaborative positioning.
# Computationally Efficient Soft Detection Schemes for Coded Massive MIMO Systems +
## 3 Proposed Method
### Soft-Symbol Value Estimation
\begin{split} E_{i}^{R}&=\sum_{\Re(a)\in O^{R}} \frac{\Re(a)^{2}}{2^{K/2}}\prod_{k\in\kappa^{R}}(1+\widetilde{x}_{i,k}\zeta_{i, k})-\Re(\widetilde{s}_{i})^{2}\\ &=\sum_{\Re(a)\in O^{R^{+}}}\frac{\Re(a)^{2}}{2^{K/2}}(1+\zeta_{i, k})\prod_{k\in\kappa^{R^{+}}}(1+\widetilde{x}_{i,k}\zeta_{i,k})\\ &\quad+\sum_{\Re(a)\in O^{R^{-}}}\frac{\Re(a)^{2}}{2^{K/2}}(1- \zeta_{i,\chi})\prod_{k\in\kappa^{R^{-}}}(1+\widetilde{x}_{i,k}\zeta_{i,k})- \Re(\widetilde{s}_{i})^{2}\\ &=\sum_{\Re(a)\in O^{R^{+}}}\frac{\Re(a)^{2}}{2^{K/2}}2\prod_{k\in \kappa^{R^{+}}}(1+\widetilde{x}_{i,k}\zeta_{i,k})-\Re(\widetilde{s}_{i})^{2} \\ &=\sum_{\Re(a)\in O^{R^{+}}}\frac{\Re(a)^{2}}{2^{K/2}-1}\prod_{k\in \kappa^{R^{+}}}(1+\widetilde{x}_{i,k}\zeta_{i,k})-\Re(\widetilde{s}_{i})^{2}, \end{split}  \tag{23}\]  
where \(O^{R^{+}}=\{a|a\) is a symbol projected on to the real axis, \(\Re(a)>0\ \}\), \(O^{R^{-}}=\{a|a\) is a symbol projected on to the real axis, \(\Re(a)<0\ \}\), \(\kappa^{R^{+}}=\kappa^{R^{-}}=\{k|k\in\kappa^{R},k\
eq\chi\}\), and \(\chi\) is the bit index that determines whether \(a\) is included in \(O^{R^{+}}\) or \(O^{R^{-}}\). Likewise,  
\[ \begin{split} E_{i}^{I}&=\sum_{\Im(a)\in O^{I}} \frac{\Im(a)^{2}}{2^{K/2}}\prod_{k\in\kappa^{I}}(1+\widetilde{x}_{i,k}\zeta_{ i,k})-\Im(\widetilde{s}_{i})^{2}\\ &=\sum_{\Im(a)\in O^{I^{+}}}\frac{\Im(a)^{2}}{2^{K/2}-1}\prod_{k \in\kappa^{I^{+}}}(1+\widetilde{x}_{i,k}\zeta_{i,k})-\Im(\widetilde{s}_{i})^{2 },\end{split}  \tag{24}\]  
where \(O^{I^{+}}=\{a|a\) is a symbol projected on to the imaginary axis, \(\Im(a)>0\ \}\), and \(\kappa^{I^{+}}=\{k|k\in\kappa^{I},k\
eq\chi\}\). By expanding (19)-(24) for a specific Gray coded QAM constellation, we could further reduce the number of computations. Table 1 shows the results of the expansions using (19)-(24) for a Gray coded 16, 64, and 256-QAM constellations in [17].
###### Abstract  
The Essential Climate Variable (ECV) Permafrost is currently undergoing strong changes due to rising ground and air temperatures. Surface movement, forming characteristic landforms such as rock glaciers, is one key indicator for mountain permafrost. Monitoring this movement can indicate ongoing changes in permafrost; therefore, rock glacier velocity (RGV) has recently been added as an ECV product. Despite the increased understanding of rock glacier dynamics in recent years, most observations are either limited in terms of the spatial coverage or temporal resolution. According to recent studies, Sentinel-1 (C-band) Differential SAR Interferometry (DInSAR) has potential for monitoring RSVs at high spatial and temporal resolutions. However, the suitability of DInSAR for the detection of heterogeneous small-scale spatial patterns of rock glacier velocities was never at the center of these studies. We address this shortcoming by generating and analyzing Sentinel-1 DInSAR time series over five years to detect small-scale displacement patterns of five high alpine permafrost environments located in the Central European Alps on a weekly basis at a range of a few millimeters. Our approach is based on a semi-automated procedure using open-source programs (SNAP, pyrate) and provides East-West displacement and elevation change with a ground sampling distance of 5 m. Comparison with annual movement derived from orthophotos and unpiloted aerial vehicle (UAV) data shows that DInSAR covers about one third of the total movement, which represents the proportion of the year suited for DInSAR, and shows good spatial agreement (Pearson R: 0.42-0.74, RMSE: 4.7-11.6 cm/a) except for areas with phase unwrapping errors. Moreover, the DInSAR time series unveils spatio-temporal variations and distinct seasonal movement dynamics related to different drivers and processes as well as internal structures. Combining our approach with in situ observations could help to achieve a more holistic understanding of rock glacier dynamics and to assess the future evolution of permafrost under changing climatic conditions.  
Sentinel-1; DInSAR; rock glaciers; seasonal dynamics; periglacial; feature tracking 1  
D 2023