esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Ecosystem Services across the Land-Sea Interface in Baltic Case Studies
## 4 Discussion
### Methodological Limitations and Perspective for Future Research  
To our best knowledge, this is the first integrated study that jointly assesses ES potentials and flows of a large variety of terrestrial, coastal, and marine habitats. Our application results provided a holistic overview of ES potentials, flows and development capacities in three Baltic CSS. However, our results are affected by some methodological limitations that need to be highlighted and shall be addressed in future studies:  
First, a lack of geospatial data partly affected the application of the spatial habitat typology in the CSS. To obtain spatial maps on sediments and submerged vegetation was most difficult. The lack of geospatial data on seabed sediments is likely a particularity of inner coastal waters, where areal mappings with traditional methods, like sediment echo sounders or side view sonars, are restricted to shallow water levels ([PERSON] et al., 2018). Furthermore, information of the distribution of submerged vegetation was largely lacking for all CSS. Thus, the best available data was used, which varied strongly among CSS. This affected the spatial mapping of submerged vegetation in particular. An application of satellite, aerial, and underwater drone imagery could be beneficial to support habitat mapping of coastal habitats and shallow water areas.  
Second, the definition of maximal flow values and assessment of CSS-specific flow values was based on literature and public databases, geoportals, and statistics. Due to language barriers the approach to define a maximal flow was partly adjusted and only based on data for Germany, which was available on a smaller spatial scale. In addition, some of the CSS flow values are highly affected by the available literature and data used. For instance, literature-based values were used to define flow values for the indicator 'nitrogen cleaning'. Due to the different scope of analyzed studies, indicated values are not reliable. Particularly for regulating ES, the potential use of modelling results should be further explored, to ensure better comparability.  
Third, for the purpose of this study, we multiplied the relative flow values with the ES potential values included in the Baltic ESP Matrix, to generate CSS-specific flow matrices. This is certainly a huge simplification. While it serves as a fast solution and provided comparable results for the purpose of this study, it has major weakness and should not be used for practical applications to support decision-making. In future studies, habitat-specific flow values need to be defined, for the maximal flow (or alternatively sustainable reference) and the flow values of the CSS.
# Green Care Achievement Based on Aquaponics Combined with Human-Computer Interaction
## 3 The Proposed System
### Security of SGCS
* Pseudocode for Authenticating Data Packets: The proposed SGCS will package the data packets to be transmitted according to the information and data security specifications, then make an API call to the API server on the cloud and pass the relevant parameters. The data packet verification process is as follows:\(RESTfulAPI\to CheckSignature(TK,SK,TS,DC,S)\)  
\(F\gets StringFormat(\"t\!s=\{TS\};d=\{DC\};sk=\{SK\}\")\)  
\(E\gets SHA512 Encrypt(DC,F)\)  
\(if:\;E=S\to True\)  
\(else:\;\to False\)  
The parameters are token key, call timestamp, API key signature, and data in sequence, which will be formatted according to the string before verification. SHA512 hash calculation is performed on the formatted \(F\) and \(DC\). Finally, the hashed \(E\) and \(S\) are compared. If \(E\) and \(S\) are the same, the verification is successful; otherwise, the verification fails.
# Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
## 3 Core Technologies in Vision-Based Collaborative Robotics
### SLAM and Spatial Awareness for Shared Workspaces  
Collaborative tasks often unfold in partially structured or dynamically changing spaces, necessitating robust spatial awareness. Simultaneous Localization and Mapping  
\begin{table}
\begin{tabular}{p{56.9 pt}|p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline  
(SLAM) allows cobots to build and update maps while localizing themselves within them, representing a cornerstone capability for safe and reliable autonomy [52; 53].  
Modern vision-based SLAM approaches, such as ORB-SLAM3 and Deep-SLAM, integrate monocular, stereo, and depth camera inputs to maintain situational awareness even in human-populated environments [54; 55; 56]. Semantic SLAM extends traditional SLAM by recognizing and excluding dynamic objects--this is crucial in HRC settings where humans move unpredictably. ORB-SLAM3, while not semantic by default, improves robustness via IMU integration and multi-map management. Experience with ORB-SLAM3, as shown in [57], shows that it handles dynamic occlusions by spawning sub-maps and maintaining inertial consistency, but it cannot explicitly exclude humans without external semantic masks. Methods like Dynamic-SLAM use segmentation (e.g., Mask R-CNN) to filter out moving people, reducing tracking errors and drift. For reliable HRC, best practice combines semantic segmentation to exclude moving humans with IMU-visual fusion for static mapping, Some examples of benchmarks on SLAM and drift corrections are seen in [58; 59; 60; 61; 62].  
Additionally, semantic SLAM variants enrich maps with object-level annotations. These variants enhance the cobot's ability to reason about its environment in a task-relevant manner. SLAM Drift Correction, incorporating loop closure techniques, is vital for minimizing drift in long-duration SLAM deployments in dynamic, human-shared environments [63; 64]. Future directions include human-aware SLAM that not only filters but predicts human motion for safer, more adaptive collaboration.  
Figure 1 and Table 2 illustrate and summarize the core technologies in vision-based collaborative robotics.
## 2 Materials and Methods
### Air Pollution Data  
PM\({}_{10}\) mass concentrations from traffic and background stations were obtained from the Air Quality monitoring website [35]. The daily mean PM\({}_{10}\) values were further processed to assess the impact of traffic. To provide a more objective comparison and to highlight how the pandemic affected air quality, data from 1 January 2017 to 31 December 2022 were analyzed. The years 2017, 2018, 2019, 2021, and 2022 served as control years, and the same analytical methods used for 2020 were applied to these years, as well.  
During the COVID-19 pandemic in Romania, the government imposed various restrictions that impacted normal activities and pollution levels. Four distinct periods were identified in 2020: one without restrictions, termed Business as Usual (BAU) (I January-15 March), and three with varying levels of traffic and social activity restrictions based on government decisions. These periods were Lockdown (16 March-15 May), Alert status with traffic restrictions (Alert 1, 16 May-15 August), and Alert status with normal traffic but limited social activities (Alert 2, 16 August-13 December). These periods were used to segment and compare the PM\({}_{10}\) concentration time series.  
To accurately assess the impact of traffic restrictions on PM\({}_{10}\) concentrations, two approaches were employed: absolute and relative correction, following the methodology described by [PERSON] et al. [36]. Absolute correction (delta PM\({}_{10}\)) involves subtracting the background concentration from the mass concentration measured at a traffic station (Equation (1)), while relative correction (ratio PM\({}_{10}\)) refers to expressing the ratio of the mass concentration at a traffic station to the concentration at the background station (Equation (2)). These corrections help minimize the influence of background variations and eliminate the effects of fugitive PM\({}_{10}\) intrusions from distant sources, such as dust or smoke.  
\[\text{delta PM}_{10}=\text{PM}_{10}^{\text{traffic}}-\text{PM}_{10}^{\text{ background}} \tag{1}\]  
\[\text{ratio PM}_{10}=\frac{\text{PM}_{10}^{\text{traffic}}}{\text{PM}_{10}^{ \text{background}}} \tag{2}\]
# A Heart Rate Variability-Based Paroxysmal Atrial Fibrillation Prediction System
## 2 Materials and Methods  
### Data Collection  
Three databases, the PAF Prediction Challenge Database (accessed on 20 November 2019), MIT-BIH Atrial Fibrillation Database (accessed on 11 March 2021), and MIT-BIH Normal Sinus Rhythm Database (accessed on 11 March 2021) from PhysioNet, were em ployed to construct our PAF prediction system. Digitized ECG signals along with machine-generated beat annotation are available for download in the three databases. The PAF Prediction Challenge Database was issued in 2001 for competition and has been a benchmark database ever since. To compare with similar systems in the literature, we used it for training and validation, and used the other two databases for testing our system. The PAF Prediction Challenge Database consists of 200 30 min HRV sequences from 53 PAF patients and 47 normal subjects (two sequences per person). Those sequences were split into training and validation datasets with a ratio of 80:20 in such a way that the same numbers of PAF and non-PAF subjects were in the validation dataset, and the two HRV sequences from a person were allocated to the same dataset. We then extracted non-PAF HRV sequences from the MIT-BIH Normal Sinus Rhythm Database and PAF HRV sequences from the MIT-BIH Atrial Fibrillation Database to test the performance of our system. The MIT-BIH Normal Sinus Rhythm Database includes 24 h ECG recordings of 18 subjects without significant arrhythmias. On the other hand, the MIT-BIH Atrial Fibrillation Database contains 10 h ECG recordings of 25 patients with atrial fibrillation (mostly paroxysmal). We removed the top and the bottom one-hour data in each recording to avoid unstable data points.
## 3 Nonlinear State-Feedback VRFT for Approximate ORM Tracking Control Using Neural Networks
This proves the claim. An NN can be used as a controller for nonlinear state-feedback control learning. Nonlinear VRFT is proposed in [41; 42] and successfully applied to NN controllers in [41; 43; 44; 45] but only for output feedback control and not for state-feedback control as in here. Notice that VRFT control does not need the entire extended state \(\widetilde{\mathbf{x}}_{k}^{E}\) for feedback (i.e. including the virtual states of the ORM), the process' initial states would suffice for this purpose. However, state extension is required for preserving the Markov property of the system (3) in order to ensure the correct collection of the transition samples; this is not possible otherwise without special collection design such as using a zero-order-hold for two-by-two consecutive time samples [43]. Correct transition samples collection is required for adaptive actor-critic tuning approach of the same NN controller that is initially tuned via VRFT. Notice that in the proposed state-feedback VRFT design, knowledge of the output function \(\mathbf{y}_{k}=\mathbf{g}(\mathbf{x}_{k})\) in Equation (1) is again not needed since \(\widetilde{\mathbf{y}}_{k}\) is used to calculate the virtual reference \(\widetilde{\mathbf{r}}_{k}\), while the controller only uses \(\widetilde{\mathbf{x}}_{k}^{E}\) for feedback purposes.
## 3 Methodology
### DInSAR Method  
The core principle of DInSAR involves processing a pair of SAR images acquired at different times over the same region. This process includes both coarse and fine registration, followed by phase interferometry to generate interferogram fringe patterns. Subsequently, external DEM data are employed to simulate and remove the topographic and flatland phases from the actual phases, isolating the deformation phase in the interferogram and ultimately transforming it into deformation signals [31; 32; 33]. This interferometric processing generates differential interferometric phase maps, encompassing the reference plane phase, topographic phase, deformation phase, and residual noise phase, thus providing deformation data of the area [10]. However, the interferometric phase in the interferogram may be affected by ongoing changes in the surface and atmospheric conditions. As such, any interferometric phase \(\varphi_{int}\) in the interferogram can be expressed as:  
\[\varphi_{int}=\varphi_{flat}+\varphi_{topo}+\varphi_{dis}+\varphi_{atm}+\varphi_{ noise} \tag{1}\]  
In this equation, \(\varphi_{flat}\) represents the flatland phase, \(\varphi_{topo}\) represents the topographic phase caused by uneven topography, \(\varphi_{dis}\) represents the displacement phase of the terrain, \(\varphi_{atm}\) represents the phase of atmospheric delay, and \(\varphi_{noise}\) represents the noise phase produced during observation.  
The primary use of DInSAR technology is to accurately obtain the deformation component \(\varphi_{dis}\) caused by surface deformation. The right side of the equation, \(\varphi_{flat}\) and \(\varphi_{topo}\), can be simulated and removed using external DEM, while \(\varphi_{atm}\) and \(\varphi_{noise}\) can be suppressed through filtering, multi-looking operations, or selecting ground control points to obtain the deformation phase \(\varphi_{dis}\). Finally, the ground target's deformation along the Line of Sight (LOS) direction is calculated:  
\[\Delta r=-\frac{4\pi}{\lambda}\varphi_{dis} \tag{2}\]  
In the equation, \(\Delta r\) represents the change in range (deformation) of the ground target along the LOS direction, \(\lambda\) denotes the wavelength of the radar signal used in the interferometric process, and \(\varphi_{dis}\) signifies the deformation phase component derived from the DInSAR analysis, representing the phase change caused by surface deformation.
## 4 Discussion  
The comprehensive and systematic real-time monitoring of PWV changes is necessary to research and predict extreme weather [37]. In this paper, real-time PPP technology was used to study the change in PWV in real-time, and RS PWV was used as a reference to evaluate the accuracy of real-time PPP PWV during rainfall and nonrainfall periods. The experimental results show that compared with RS PWV, real-time PPP PWV and RS PWV have good consistency during nonrainfall periods. The bias, STDEV, and RMS between the two sets of data at the HKSC station within one week are 0.93 mm, 1.21 mm, and 1.18 mm,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Station**} & \multirow{3}{*}{**Bias (mm)**} & \multicolumn{2}{c}{**STDEV**} & \multicolumn{2}{c}{**RMS**} & \multirow{3}{*}{**Bias (mm)**} & \multicolumn{2}{c}{**STDEV**} & \multicolumn{2}{c}{**RMS**} \\  & & **(mm)** & & & **(mm)** & & **(mm)** \\ \cline{2-6}  & & **During Rainfall** & \multicolumn{4}{c}{**During Nonrainfall**} \\ \hline HKCL & 1.89 & 2.87 & 2.43 & 1.4 & 2.3 & 1.81 \\ HKFN & 2.09 & 2.79 & 2.65 & 1.42 & 2.12 & 1.93 \\ HKKS & 1.93 & 2.51 & 2.5 & 1.47 & 2.1 & 1.9 \\ HKKT & 1.93 & 3.25 & 2.5 & 1.47 & 2.27 & 1.9 \\ HKLM & 1.89 & 2.78 & 2.4 & 1.33 & 1.93 & 1.79 \\ HKLT & 1.7 & 2.93 & 2.22 & 1.42 & 2.53 & 1.78 \\ HKMW & 2.03 & 2.73 & 2.62 & 1.34 & 1.81 & 1.72 \\ HKNP & 2.45 & 2.93 & 3.01 & 2.24 & 1.74 & 2.71 \\ HKOH & 1.59 & 2.19 & 2.04 & 1.42 & 1.77 & 1.83 \\ HKPC & 2.39 & 3.04 & 2.92 & 1.45 & 1.76 & 1.82 \\ HKQT & 1.82 & 2.64 & 2.32 & 1.24 & 1.57 & 1.56 \\ HKSL & 2.23 & 3.52 & 2.84 & 1.32 & 2.03 & 1.71 \\ HKSS & 1.89 & 2.68 & 2.37 & 1.33 & 1.85 & 1.77 \\ HKST & 2.06 & 2.95 & 2.64 & 1.35 & 2.06 & 1.79 \\ HKTK & 1.97 & 2.59 & 2.44 & 1.36 & 1.7 & 1.72 \\ HKWS & 2.16 & 3.48 & 2.75 & 1.73 & 2.44 & 2.31 \\ T430 & 2.21 & 3.05 & 2.79 & 1.53 & 1.89 & 2.04 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Bias, STDEV, and RMS between PPP PWV and NWP PWV.  
Figure 6: Comparison of PPP PWV between HKSC station and radiosonde station during rainfall.  
respectively. During rainfall periods, the bias, STDEV, and RMS between the two sets of data at the HKSC station within a week were 3.45 mm, 1.79 mm, and 3.85 mm, respectively. From the statistical results of Tables 6 and 8, it can be seen that when the ZTD and PWV inverted by the NWP model are used as a reference, the accuracy of ZTD inverted by real-time PPP and the accuracy of PWV inverted by real-time PPP are generally higher than those in rainfall periods. From Figures 5 and 6, it can be seen that the PWV during rainfall periods is higher than that during nonrainfall periods, and the PWV variation during rainfall periods is also higher than that during nonrainfall periods. The PWV change associated with DOY 174-175 during rainfall periods is the most significant. Studying the change in PWV with the change in precipitation and improving the accuracy of real-time PPP PWV during rainfall periods will be of great significance for the prediction of extreme weather.
# Towards Explainable Pedestrian Behavior Prediction:
## 5 Experimental Results and Discussion
### Experiment 1--Dataset Quantity  
In the context of ML, one of the most crucial decisions in model training is determining the optimal amount of data to use. The first experiment aimed to answer the following question: _Does increasing the amount of training data improve the prediction performance?_  
To investigate this, we designed a set of incremental data configurations for both PIE and JAAD, gradually increasing the number of data rows. Each row represents the state and pedestrian features for one frame.  
As can be observed in Table 3, the process began with 2000 rows (_J2K_ and _P2K_), evenly balanced between pedestrians who cross and those who do not. The dataset size was then progressively expanded to include 4000 rows (_J4K_ and _P4K_), 8000 rows (_J8K_ and _P8K_), 10,000 rows (_J10K_ and _P10K_), and then 14,000 rows (_J14K_ and _P14K_). Since the PIE dataset is larger, we conducted additional experiments with 20,000 rows (_P20K_) and 80,000 rows (_P80K_).  
By systematically expanding the dataset, we observed that increasing the amount of data within the same dataset can enhance the predictor's performance. However, this improvement does not necessarily translate to better results across different datasets. According to the accuracy and F1 score results, better performance is observed in configurations that do not include the maximum amount of data. This finding suggests that, for a pedestrian behavior predictor based on fuzzy logic, simply increasing the dataset size does not guarantee better generalization. Moreover, as shown in Table 3, a higher number of fuzzy rules does not always lead to better performance. Notably, one of the best-performing configurations over the PIE dataset, **P80K**, achieved optimal results with the fewest rules of only 36 among all tested configurations.  
In addition, it is important to highlight that the **J14K** configuration, based on JAAD, demonstrated the best average performance across both datasets. This is particularly noteworthy because J14K contains 66,000 fewer records than the P80K configuration and provides similar performance over the PIE dataset. A possible explanation for this outcome  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Configuration**} & \multirow{2}{*}{**Rules**} & \multicolumn{2}{c}{\(\boldsymbol{JAAD}_{\boldsymbol{all}}\)} & \(\boldsymbol{JAAD}_{\boldsymbol{beh}}\) & \multicolumn{2}{c}{\(\boldsymbol{PIE}_{\boldsymbol{all}}\)} & \multicolumn{2}{c}{\(\boldsymbol{PIE}_{\boldsymbol{beh}}\)} \\ \cline{3-10}  & & **A** & **F** & **A** & **F** & **A** & **F** & **A** & **F** \\ \hline J2K & 55 & 0.68 & 0.70 & 0.67 & 0.70 & 0.73 & 0.44 & 0.74 & 0.48 \\ J4K  
is that JAAD videos contain a more diverse set of scenarios and pedestrian behaviors, enabling a better understanding of the factors influencing the crossing decision.
# Bi-Stability in Flexure Beams: Introducing a Setup for Experimental Characterization
## 3 Results and Discussion
### Characterization of the Flexure Beams under Cyclic Loading  
#### 3.3.1 Critical Forces  
To characterize the mechanical behavior of bi-stable flexure beams that would be used more than once in real-life applications, each sample was subjected to five loads under compression. The values of critical forces reached in each load are displayed in Figure 7. For samples c15, c20, c30, and c40, the first cycle has the highest critical force, and the following cycles show an input force reduction per load, indicating a loss of stress in the beam that suggests the elastic limit of the styrene stripe was exceeded. This behavior can be verified by the deformed shape of the flexure beams after the experiments (Figure 8), which will be discussed in Section 3.4.  
Figure 8: Image of the left side flexure beams after being subjected to compression five times. (**a**) c10 flexure beams (0.25 mm) (**b**) c15 flexure beams (0.38 mm) (**c**) c20 flexure beams (0.50 mm) (**d**) c30 flexure (0.75 mm) (**e**) c40 flexure beams (1.00 mm) (**f**) Compilation of samples of each thickness.  
Figure 7: (**a**) The c10 flexure beam presents variations in the CF due to the equipment capabilities and affectations of the manual assembly (**b–e**) The plots show a decrement in the critical force per load.  
For its part, the c10 flexure beam presents random variations in the results due to the equipment capabilities and affectations of the manual assembly. Small imperfections in fabrication and manual assembly have a higher influence on the thinnest beams. As the forces don't necessarily decrease per load and the shape of the flexure beams remains almost straight after the tests (Figure 8a), it is concluded that the elastic limit of the c10 samples is never surpassed.
# Additive Manufacturing for Surgical Planning and Education: A Review
## 4 Applications of 3D Printing in the Medical Field
### Drilling and Cutting Guides and Jigs  
Another application of AM in medical field is using the 3D-printed guides for the drilling of bone. Thoracic pedicles, unlike those in the lumbar spine, have smaller diameters and show greater variability in size among individuals and between segments. Additionally, the thoracic spine is closely related to vital organs like the lungs, esophagus, aorta, and large blood vessels, making surgeries in this area delicate. Therefore, inserting thoracic pedicle screws demands extreme precision, as any deviation from the optimal position can lead to poor patient outcomes. Proper insertion within the pedicle, ensuring accurate positioning and orientation, is crucial before advancing to the vertebral body to guarantee safety and secure anchoring. Particularly, placing screws in the mid- and upper-thoracic region remains technically challenging [106; 107]. Figure 16 illustrates the design of the navigational jig for the operation.  
To enhance the precision and safety of thoracic pedicle screw placement, several techniques have been devised and tested in both cadaveric and clinical studies. In this case, the application of templates designed for cervical spine anatomy had not been assessed for the more complex thoracic spine. To address this gap, a straightforward, secure, and efficient method was developed to assist in the insertion of thoracic pedicle screws. This technique leveraged the concepts of reverse engineering and rapid prototyping. The experimental results show that the operational accuracy increases from 75% to 95% by using drilling jigs [108].  
In another scenario, the use of a drilling jig is paramount to prevent potential damages. Drilling into the spine is inherently challenging, with significant risks of complications, including neurological and vascular issues due to improperly placed pedicle screws. Such misplacements could compromise the implants' pull-out strength and escalate the risk of implant failure [109; 110]. Therefore, ensuring accurate and safe screw placement within the pedicle is vital, especially in scoliosis surgery.  
Figure 15: Evaluation for anatomical mismatch of the pre-contoured plate. (**a**) Clinical photograph of the pre-contoured pelvic trim plate taped on the 3 DP model. (**b**) Corresponding postoperative 3D-reconstructed view. (**c**) Superimposed photograph of (**a**,**b**) demonstrates no evidence of a mismatch [105].