esa-sceva/satcom-chunk-collection huggingface dataset:
# Role of Radio Telescopes in Space Debris Monitoring: Current Insights and Future Directions
## 3 Radio Telescope Measurements with Active Transmitters
### TIRA with the Effelsberg Telescope  
The Fraunhofer Institute for High-Frequency Physics and Radar Techniques (FHR), Germany, operates one of the largest radars in Europe, the Tracking and Imaging Radar (TIRA). It is capable of operating in both the L-band (1.33 GHz) as a narrow-band mono-pulse tracking radar and the Ku-band (16.7 GHz) as a high-range-resolution imaging radar [33]. In 1996, a space debris observation experiment was conducted using a bistatic radar formed with the TIRA system as the radar transmitter and the Effelsberg radio telescope as the radar receiver [8]. The Effelsberg radio telescope has a 100 m dish antenna and is operated by the Max Planck Institute for Radio Astronomy in Germany. Several experiments using TIRA as the transmitter/receiver and the Effelsberg radio telescope as a secondary receiver to lower the detection threshold have been performed since 1996 [34; 35; 36]. Figure 2a illustrates the geometry of the experiment, showing the TIRA transmitter and the Effelsberg radio telescope as the receiver.  
The initial COoperative BEAM-park mode (COBEAM) experiment was conducted over a 24-h period and successfully collected observational data on objects as small as 0.9 cm at a range of 1000 km [34]. Figure 4a illustrates the detection rate (detections per hour) against altitude (at 30 km intervals) for the TIRA L-band radar and Effelsberg. Effelsberg achieved a notably higher detection rate owing to its higher sensitivity, even though its beam width was only one-third that of the TIRA L-band beam. Figure 4b displays the detection rates compared with range rates over the \(-1\) to 1.8 km/s limits, segmented into 100 m/s increments. The distribution peaks at 0.1 km/s for data from TIRA, whereas the Effelsberg data predominate around 1 km/s. Following the first promising results of the COBEAM experiment, researchers have refined estimation and calibration procedures to further enhance system performance [37]. The authors in [38] showed a moderate improvement in the detection capability after revising the multidimensional maximum likelihood estimation for determining object trajectories and RCS between beam park campaigns in 2008 and 2009. Ongoing TIRA upgrades, including Ka-band polarimetric imaging and L-band tracking enhancements, will significantly boost space surveillance and support future bistatic measurement campaigns [33]. Expanding on the successful bistatic radar experiments involving TIRA-Effelsberg, which improved the detection of space debris in low Earth orbit, the German Experimental Space Surveillance and Tracking Radar (GESTRA) is set to further enhance these capabilities. Its phased-array design inherently supports bistatic and multistatic configurations, leading to improved detection accuracy and estimation performance [9].
## 3 Numerical Results and Convergence
This is an expected outcome, which is necessary to guarantee the convergence of the solution and has also been reported by other researchers, albeit for different configurations [52]. In addition, Figure 11a suggests that the truncation number does not depend strongly on the size of the inner cylinders, especially when they are rather small; the black and red curves almost coincide. However, by inspecting the curves of Figure 11b, one might conclude that the truncation number is strongly affected by the distance between the inner cylinders. For example, for \(D_{23}=0.6\lambda_{0}\) (black curve), 3-digit accuracy may be achieved with \(\text{N}_{r}\approx 7\), while 13 terms are necessary for the same accuracy if \(\text{D}_{23}=1.5\lambda_{0}\) (blue curve). The rather strong dependence of the truncation number on the separation between the inner cylinders may also be verified for the arbitrary polarization of the incident wave (Figure 12). It is evident from Figure 12 that the greater the distance between the rods, the greater the truncation number required in order to achieve a specific accuracy.
## 3 Case Study  
### Study Area and Data Sources  
Jianye District is one of the six main urban areas of Nanjing, and the district boundary is determined as starting from Hanzhong Gate Avenue in the north, reaching the Qinhuai New River in the south, the External Qinhuai River in the east, and the Yangtze River midline in the west. The total area of the district is 80.87 km\({}^{2}\), and the topography is low in the south and high in the north, which is a Yangtze River floodplain landform unit [6]. Climatically, Jianye District belongs to the maritime climate zone with humid north subtropical climate zone and monsoon circulation, with significant monsoon, cold winter and hot summer, four distinct seasons, sufficient sunshine and abundant precipitation. Rainfall varies greatly between years and seasons, with obvious abundance and uneven rainfall distribution.  
According to the prediction objectives, the time series data collected in this paper mainly includes two categories: the first category is rainfall data, which are all the recorded original rainfall data from January 2015 to August 2018 at the rainfall stations of Olympic Sports Center and Jiangxinzhou. The time interval of the original rainfall data is 1 h. The second category is the ponding monitoring data, which is recorded at the ponding survey point near the gas station on Jiqing Gate Avenue in the same period as the rainfall records, with a data interval of 5 min. The two categories of original time series data are generally partially continuous, and the data are continuous in a certain period of time when rainfall events occur.
# Estimation and Healing of Coverage Hole in Hybrid Sensor Networks: A Simulation Approach
## 3 Preliminaries
### Communication and Sensing Models  
Each sensor has communication capacity and sensing capacity, \(R_{c}\) is defined as communication radius and \(R_{s}\) is defined as sensing radius, Figure 1. shows the communication and sensing models. If and only if the distance between two sensors is within \(R_{c}\), they can communicate with each other, otherwise the node is isolated. [PERSON] et al. (2017); [PERSON] et al. (2018); [PERSON] et al. (2019) proved that if a convex region is completely covered by a set of nodes, the communication graph consisting of these nodes is connected when \(R_{c}\geq 2R_{s}\).
# Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery
## 3 Methodology
### Geographically Weighted Regression
The GWR model is expressed as:  
\[Y_{k}=\beta_{0}(u_{i},v_{i})+\sum_{i=1}^{k}\beta_{i}(u_{i},v_{i})X_{i}+\varepsilon _{k} \tag{16}\]  
where \((ui,vi)\) denotes the spatial coordinates of point \(i\); \(X_{i}\) is the \(i\)th index of the independent variable; \(\beta_{0}(u_{i},v_{i})\) is the intercept value; \(\beta_{i}(u_{i},v_{i})\) represents the \(i\)th slope coefficient at point \(i\); and \(\varepsilon\) denotes the random error term. The weight is usually calculated by using a Gaussian function when correcting GWR. The Gaussian function is expressed by the following formula:  
\[W_{ij}=exp^{-\frac{1}{2}(\frac{4 ij}{b})^{2}} \tag{17}\]  
where \(dij\) denotes the Euclidean distance between location \(i\) and \(j\); and \(b\) is the fixed bandwidth for location \(i\). Before establishing regression models and analyzing, it should be judged as to whether there is a spatial correlation in the variables. The index commonly used is Moran's I. A scale of 2 km is suitable for studying the UBD--LST relationship in the main urban area of Hefei. The difference between the LST in different places depends mainly on the construction within 2 km of these places. Then, 2 km will also be a reasonable measure for calculating Moran's I. As shown in Figure 12, with a 2 km scale, the Moran's I value of the LST was 0.3948, the \(p\)-value was 0.0008, and the z score was 3.3532. This indicates that the LST in the study area is agglomerated and distributed, the spatial difference is vast, and there is spatial autocorrelation in the study area. Figure 12: Schematic diagram of [PERSON]’s I analysis of the LST in the study area.
# GPS Multipath Analysis Using Fresnel Zones
## 3 Identification of Diffracted Satellite Signals Using Fresnel Zones and OAEMs
### Measurement Concept  
The field test was performed on a cinder pitch, as shown in the left panel of Figure 14. The pitch has dimensions of 60 times 100 m and is surrounded by trees in several directions. Additionally, one building can be found in the Southern direction.  
For about 24 h, one Leica AS10 GNSS antenna in combination with a Leica GS25 receiver was mounted on a tripod. The antenna was located approximately in the middle of the pitch (c.f. red dot in Figure 14), and dual frequency GPS observations were logged at a sampling rate of 0.2 Hz. Furthermore, the Geodetic Post-Processing-Service (GPPS) of the German Satellite-Positioning-Service (SAPOS) ([[https://www.sapos.de](https://www.sapos.de)]([https://www.sapos.de](https://www.sapos.de))) were used to generate GNSS observations of a virtual reference station (VRS) for the complete duration of observations. To establish a short baseline between the GNSS antenna and the VRS, the VRS was located at the navigation solution of the antenna.  
Prior to the GPS measurements, a UAV flight was performed, and the area was captured with a Leica ScanStation P20 panoramic-type laser scanner. Both the processed UAV images and the TLS pint cloud were georeferenced using ground control points, which were determined via GPS-RTK.  
Finally, the georeferenced point cloud was used to generate the obstruction adaptive elevation mask for the test area following the procedure described in [25]. In the right panel of Figure 14, a skylpot including the OAEM and the GPS satellite tracks is shown.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
2-D & Two-dimensional \\ A-PTSM & Anisotropic PTSM \\ BA-PTSM & Bistatic A-PTSM \\ DDM & Delay-Doppler map \\ EIRP & Effective Isotropical Radiated Power \\ EM & Electromagnetic \\ GNSS & Global Navigation Satellite System \\ GNSS-R & GNSS-Reflectometry \\ GO & Geometrical Optics \\ HPBW & Half-power beamwidth \\ LHCP & Left-hand circular polarization \\ NRCS & Normalized radar cross section \\ PDF & Probability density function \\ PSD & Power Spectral Density \\ PTSM & Polarimetric TSM \\ RCS & Radar-cross section \\ RHCP & Right-hand circular polarization \\ RMS & Root-mean square \\ SAR & Synthetic aperture radar \\ SNR & Signal-to-noise ratio \\ SPM & Small Perturbation Method \\ SSA2 & Second-order small-slope approximation \\ TSM & Two-scale model \\ \end{tabular}
# Using Eye Tracking to Reveal Responses to the Built Environment and Its Constituents
## 4 Discussion
### Potential Shortcomings  
These studies, conducted online, on individual laptops, inevitably may not account for distractions, lighting changes, and other factors beyond the experimenters' control. Compared to eye-tracking in a dedicated laboratory setting with infrared eye trackers, webcam-based data can have lower accuracy and precision, and the framerate and resolution are dictated by the quality of the webcam. However, these shortcomings apply as a common denominator to all images and cannot favor one type over another; if anything, the introduction of inaccuracies might blunt potential differences between responses. Also, our studies relied on real buildings, and scenes photographed from a distance where details are harder to see. Another potential shortcoming is the bias introduced by the image positions: because of the left-to-right bias, which seems to exist not only in people whose native languages are written in this direction, but also in infants [35] and even in other species [36], the image on the left side would be at an advantage, especially regarding TTFF. Indeed, in some (but not all) cases, we noticed that this position correlated with a shorter TTFF and occasionally longer dwell time. To compensate for this, different images of each category were placed in both positions, so that, for example, in one pair the modern building was on the left, and in another pair it was on the right. In one sense, ideally all pairs should be presented in both configurations, to make sure that the effect is cancelled out for every pairing. But this would mean that in half of the cases, participants would be exposed to images they had already seen before, and this is something we wanted to avoid, as we were interested in first reactions. Also, to avoid any potential random bias within a study, the studies were not monothematic, so that the analysis of each category was not based on data from only one study.
# A Systematic Review of Responsible Artificial Intelligence Principles and Practice
## 8 Finding 6: Responsible AI in Practice
### Responsible AI in Healthcare  
The integration of artificial intelligence in healthcare necessitates a careful balance between technological advancement and responsible implementation. Healthcare AI applications span diagnostic support, personalized treatment planning, clinical decision support systems, and virtual healthcare assistance [99]. AI applications in healthcare include clinical documentation improvement [100], population health management [99], and mental health support [99]. These applications demonstrate the potential for AI to enhance healthcare delivery while highlighting the need for responsible implementation. However, the critical nature of healthcare activities and the sensitivity of patient data demand rigorous adherence to responsible AI principles [41]. Healthcare organizations must establish robust frameworks for data privacy and security, ensuring compliance with regulations such as HIPAA and GDPR [99]. The interpretability and explainability of AI systems are essential, as healthcare professionals need to understand and validate AI-generated recommendations [41]. This transparency enables healthcare providers to maintain appropriate oversight and integrate AI-driven insights with clinical judgment [100]. Fairness and equality in healthcare AI systems need extra attention. AI algorithms must be designed and validated to avoid discriminatory effects on minority ethnic communities [41]. This includes careful consideration of training data representation and regular monitoring of system outputs for potential biases. Healthcare organizations implementing AI systems must establish clear accountability frameworks, defining responsibilities for AI development companies, healthcare professionals, and administrative staff [41]. The implementation of AI in clinical settings requires structured education programs for healthcare professionals. These programs should address the probabilistic nature of AI outputs and their limitations [98]. Healthcare providers need to understand both the capabilities and constraints of AI systems to make informed decisions about their integration into clinical workflows [98]. Security considerations extend beyond data protection to encompass the entire AI system lifecycle. Healthcare organizations must protect against potential misuse of development and validation data, software vulnerabilities, and unauthorized access [98]. This comprehensive security approach helps maintain the integrity of AI-driven healthcare services while protecting patient privacy. The successful integration of AI in healthcare depends on maintaining human oversight throughout the implementation process. Healthcare professionals must retain the ability to validate and override AI-generated recommendations when clinical judgment indicates alternative approaches [100]. This human-in-the-loop approach ensures that AI systems augment rather than replace clinical expertise while maintaining patient safety and care quality [98].
# PID Controller Parameter Tables for Time-Delayed Systems Optimized Using Hill-Climbing
## 2 PTn Systems and ITAE, IAE and ISE Criteria  
One method of representing time-delayed systems is with PTn systems. The control loop can be represented with a block diagram, as shown in Figure 1. The controller output is always limited in real systems, and the integrator of the controller is limited in most cases with an anti-windup.  
PTn systems consist of series-connected PT1 (first-order) elements. Such systems are very common, and they can be found in all engineering disciplines. The series connection of the elements leads to delayed step responses. Thus, the time delay can be approximated with linear models. The measurement of the step response of a controlled system is identified using Table 1. This is known from [21]. In many cases, one can simply measure the delay time Tu and the rise time Tg, according to Figure 2, by placing a tangent at the point of inflection. From this, one can identify the number n of PT1 elements connected in series and their identical time constants T1.  
In addition to this method, there are also other methods by which the PTn systems can be identified using measurements. This problem has been solved and has been dealt with several times, for example in [22].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PTn** & **PT2** & **PT3** & **PT4** & **PT5** & **PT6** \\ \hline Tg/Tu & 9.71 & 4.61 & 3.14 & 2.44 & 2.03 \\ Tg/T1 & 2.72 & 3.69 & 4.46 & 5.12 & 5.70 \\ Tu/T1 & 0.28 & 0.8 & 1.42 & 2.10 & 2.81 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table values, order n and T1 of the n PT1 systems, as a function of the delay time Tu and the rise time Tn [21].  
Figure 1: Block diagram with a PID-controlled PTn system.  
Figure 3 shows how the criteria are to be understood according to the step responses. The method uses the integral of the amount of the deviation of a step response from the unit step. IAE denotes 'integral of absolute error'. Here, the amount of the error is integrated. In the figure, this corresponds to the area highlighted in gray. ITAE denotes 'integral of time-multiplied absolute value of error'. In addition, the error is time-weighted in this case. This means that the integral grows faster over time. These criteria are also called the L1 criteria. The ISE criterion, the integral of the square value of the error, squares the error, and hence it increases more rapidly with larger errors. This criterion is also known as the L2 criterion.  
\[\text{IAE}:\int\limits_{0}^{\infty}\lvert\text{e}(\text{t})-\text{e}(\infty) \rvert\text{dt};\;\text{ITAE}:\int\limits_{0}^{\infty}\lvert\text{e}(\text{t})- \text{e}(\infty)\rvert\cdot\text{t}\cdot\text{dt};\;\text{ISE}:\int\limits_{0 }^{\infty}\lvert\text{e}(\text{t})-\text{e}(\infty)\rvert^{2}\text{dt} \tag{1}\]
## 5 Periosteum-Enveloped Graft  
The tendon graft-bone tunnel junction is a critical element of the whole ACL reconstruct, prone to high pull-out forces and subsequent ACL reconstruction graft failure [30]. Two forms of osteotendinous junctions have been described: fibrous, where a tendon ataches to the bone directly via the fibrous connective tissue, and fibrocartilaginous, where a fibrocartilage and a calcified fibrocartilage are interposed between a tendon and a bone [31]. The fibrocartilaginous enthesis is considered to better disperse the stress concentration; hence, the re-creation of this form of enthesis at the tendon graft-bone tunnel junction during ACL reconstruction may lead to its superior durability [32]. Periosteum is known for its osteogenic and chondrogenic properties, and therefore, was speculated to strengthen the incorporation of the graft in the bone tunnel. This idea was investigated in several animal studies, where a tendon, enveloped in periosteum, was transplanted into the bone tunnel [33; 34; 35]. All of them confirmed the osteochondral differentiation of periosteum-derived stem cells and the formation of the fibrocartilage at the tendon-bone interface, similarly as in the fibrocartilaginous enthesis. Moreover, all studies also proved the significantly higher pull-out strength of the periosteum-enveloped graft compared to the non-enveloped control group [33; 34; 35]. Based on these findings, a technique of periosteum-enhanced ACL reconstruction was proposed [36]. The authors harvested periosteum from the medial side of tibia during the hamstring graft preparation, then wrapped and sutured it to the parts of the graft that were subsequently localized in the tibial and femoral tunnel (Figures 2 and 3). The follow-up
## 3 Results
### Biodegradation Assays  
In the hydrocarbon biodegradation assays, a rapid increase of the Oxitop values (i.e., a fast oxygen consumption) and a decrease of NO3\({}^{-}\) concentrations were verified in the aerobic and anoxic assays, respectively, during the first 30-40 days of incubation (data not shown). This was probably due to the oxidation of more biodegradable substrates from the sediment and inoculum sludge. The same inoculum already showed similar behavior in previous experiments [50]. Therefore, this initial period was not considered for the calculation of the hydrocarbon biodegradation rates.  
From day 32 and until the end of the test, the oxygen consumed increased gradually in the aerobic assays (Figure 3A), while in the anoxic tests NO3\({}^{-}\) concentrations decreased consistently after the first 40 days of incubation (Figure 3B), pointing to the occurrence of aerobic and denitifying hydrocarbon biodegradation activity. From the curves slope, the corresponding biodegradation rates were obtained and are depicted in the Figure 3. For the calculation of biodegradation rates, the stoichiometry of the chemical reactions presented in Table S1 was considered.  
Regarding the biodegradation rate in the presence of oxygen, a wide range of values have been reported by several authors, depending on the substrate and experimental conditions applied. For example, aerobic biodegradation rate of diesel could range from 0.074 d\({}^{-1}\) to 0.35 d\({}^{-1}\)[14], while other studies present values ranging from 0.587 d\({}^{-1}\) to 4.75 d\({}^{-1}\) for petroleum hydrocarbons and diesel [7; 51]. The value obtained in this work is in line with those from the literature. Regarding the values in the presence of nitrate, [PERSON] and [PERSON] [52] reported a higher rate (0.91 d\({}^{-1}\)) for hexadecane mineralisation in the presence of NaNO3, while [PERSON] et al. [53] reported values around 0.14 to 0.35 d\({}^{-1}\).  
Figure 2: Hexadecane partition between solid and liquid (aqueous and non-aqueous) phases. The results presented are the average and standard deviation for quadruplicate assays.