esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward a Comprehensive Understanding and Evaluation of the Sustainability of E-Health Solutions
## 5 E-Health Sustainability Model
### E-health Sustainability Model
#### 5.2.1 Technology  
The technology dimension contains three aspects: system quality, data quality, and hardware quality. All these aspects are concerned with the quality of the e-health solution. The system quality aspect refers to the soundness of the e-solution, which comprises the system's functional suitability, performance efficiency, usability, security, maintainability, compatibility, portability, flexibility, reliability, scalability, and integrability. The existence of these elements ensures that the system operates efficiently and therefore contributes positively to the solution sustainability. Similarly, the data quality aspect refers to the quality of the content information produced by the e-health solution. Elements of this aspect, such as data confidentiality, accuracy, and completeness are critical in the context of e-health solutions, as they deal with sensitive health data such as drug prescriptions and patients' information. The hardware quality aspect refers to the suitability of the hardware components of the e-health solution to the users' needs and comfort. Some elements of this aspect are the amount of electrical power consumed by e-health and the degree of mobility it provides to the user. This aspect is mostly applicable to e-health solutions that are related to m-health contexts, or in case of the existence of special hardware components required to operate the e-health (i.e., hardware components used directly by the users, such as a microphone to dictate medical reports). Although some unmentioned elements in the technology dimension can be used to determine the quality of a software product, the elements outlined in the model were found in the literature to significantly affect the sustainability of e-health solutions.  
Figure 4: The levels of the e-health sustainability model.
# Low-Cost Digitalization Solution through Scalable IIoT Prototypes
## 4 Case Study
### Digitization Target Analysis  
To start the broaching process the piston of the hydraulic cylinder is extracted and the broaching tool is mounted on its tip, as shown in Figure 2b. The pressure difference \(\Delta p=p-p_{0}\) between the cylinder chambers creates a force that retracts the piston and hence pulls the broaching tool through the tube. The teeth on the outer surface of the broaching tool cut material from the inner surface of the tube during this process. The most relevant performance indicator is the cutting force of the broaching tool. There are four main motives for the measurement of this force:* Recording of the cutting force progression during single cuts;
* Distinction between tools of different sizes used for tubes of different diameters;
* Comparison of a new and an old tool design used for tubes of the same diameter;
* Detection of cutting force increase for specific tools over time, due to edge wear.  
The measurement has been performed indirectly via the measurement of the cylinder pressure using a digital pressure transmitter.
## 3 Design Challenges of MAC Protocol for IBSN
### Requirements of MAC Protocol Design for IBSN
#### 3.1.3 Overhead  
Prior to the communication of useful data, the sensor nodes need to exchange the management packets between each other. The efficient management of packets will ensure that all sensor nodes are aware of other sensor nodes' resources, schedules of transmission and reception, channel availability information and general information about useful data packets that are to be transmitted. Management of packets not only creates overheads but also increases the active duty-cycle of the sensor nodes. IEEE 802.15.6 requires a large duty-cycle range: from 0.1% to 30%. Medium access mechanisms need to be adaptive in terms of their management, access, and communication strategies to meet the duty-cycle requirements of a variety of medical devices. It is important to note that the duty-cycle is set by the spectrum regulating institutions, which limits the the duty-cycle of the MICS band to 0.01% i.e., approximately 36 s of access to the medium in an hour [23]. Alternatively, the regulations allow the nodes to communicate with a _listen-before-talk_ mechanism, preventing the nodes from communicating if the physical medium is already occupied by some other device. In this aspect, the amount of overhead data required for link and network management should be minimal and not higher than the actual useful data to be transmitted or received.
## 4 Conclusions  
In this work, a performance comparison, in terms of the bit error rate versus distance of an IoT communication system, has been evaluated using four scenarios with three antennas each attached to the Tx and Rx RF modules of NRF24L01. For the evaluation, a commercial dipole of the brand Siretta, a previously designed frequency-scaled Landstorfer Yagi-Uda antenna (LaYUA), and a turnstile antenna (TuSA) were utilized.  
The LaYUA, with a directivity of about 9 dBi and horizontal polarization, was optimized with parameterized elements to enhance its performance, along with the TuSA, which features a directivity of about 3 dBi and circular polarization to provide consistent omnidirectional reception. The results illustrated that the combination of the LaYUA for the TxU and the TuSA for the RxU (Scenario 4) shows the best performance. At a distance of 60 m, this scenario maintained a bit error rate (BER) of approximately 10%, representing a 60% improvement over the baseline Scenario 1 (commercial dipole antennas), which exhibited a 90% BER. Even at 100 m, Scenario 4 kept the BER below 40%, significantly outperforming the baseline, which approached a 100% BER. This represents a substantial reduction in transmission errors, making it the most reliable and effective solution for long-range wireless communication. Therefore, the results suggest that high-performance antennas like those in Scenario 4 are essential for applications requiring extended transmission distances and reliable data integrity.  
The results demonstrated that using non-commercial antennas like the custom radiators designed herein significantly enhanced wireless communication performance, particularly by reducing bit error rates over longer distances. This approach highlighted the substantial benefits of employing specialized antennas to optimize wireless systems, particularly in environments where standard commercial antennas may be insufficient. Using the LaYUA in combination with the TuSA enables many IoT devices to function effectively in remote environments such as agriculture, energy grids, and environmental monitoring. This combination facilitates robust communication between nodes, reducing data loss and ensuring reliable monitoring over larger areas.  
Conceptualization, J.G.A. and M.V.B.; methodology, J.G.A., M.V.B. and A.R.N.; validation, J.G.A., M.V.B., and A.R.N.; investigation, J.G.A., M.V.B. and C.Y.V.; writing--original draft preparation, J.G.A., M.V.B. and C.Y.V.; writing--review and editing, A.R.N. and H.B.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Reflections on HM Remediation with Biochar/Biochar + HA  
The study confirms that the combined use of biochar and biochelator exhibits significant remediation effects on Mn, Cd, and Cu contamination in soil. Using 3D-EEM and PARAFAC techniques, it was discovered that the concurrent application of HA with biochar enhanced the stability of DOM and facilitated its humification process. This combination also increases the content of macromolecular structures, such as aromatic rings, enriches the functional group varieties within DOM, and notably improves the binding capacity between DOM and HM.  
However, molecular aggregation was found to occur during the quench titration when DOM was exposed to high concentrations of Cu contamination, which limited the ability of DOM to bind Cu. In addition, the results of 2D-COS showed that the addition of HA changes the reaction order of functional groups. Therefore, how to avoid the 'failure' of HM repair with a high concentration of HM and how to utilize the 'interference' effect of HA to promote the binding of HM become a question we need to think deeply. Perhaps we can try to modify the biochar with HA before extracting DOM to enhance the binding of HA to DOM more effectively through the formation of chemical bonds or surface chemisorption, thus optimizing the repair effect of HM.  
Furthermore, HA and biochar and their derivatives consist mainly of organic carbon. Applying them to soil is a strategy for carbon sequestration. It is of crucial importance to explore in depth how they affect the mineralization rate of soil organic carbon, whether they can effectively enhance soil fertility, reduce CO\({}_{2}\) emissions, and whether they can simultaneously achieve the dual goals of soil remediation and carbon sequestration.
## 2 Data  
The satellite IWV products used in this study are based on measurements from passive instruments onboard polar-orbiting satellites. However, the satellite instruments differ significantly in type of measurement, spatial resolution, revisit time over the region of interest, and retrieval limitations specific to instrument and algorithm design. High temporal resolution (15 min) IWV retrieved from a dense German GPS network with no limitations in respect to weather conditions and time of day serves as a reference dataset in this study. The GPS measurements were collocated in space and time with information on cloud coverage obtained from measurements from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the geostationary satellites MeteoSat Second Generation (MSG) [20] to also study the impact of cloud conditions on IWV distributions. A summary of GPS and satellite instrument characteristics, retrieval limitations and references is given in Table 1. Figure 1 shows for an example day the spatial sampling of the various satellite measurements as well as the spatial distribution of the GPS network. In the following more specifics on the various datasets are given.  
Figure 1: Examples of spatial sampling for IASI, MIRS and MODIS for 24 April 2013 over a German domain, at overpass times of about 19:34 UTC, 20:30 UTC and 12:30 UTC, respectively. On the right are the GPS stations (circles) from the German GPS network used in this study and corresponding IWV values at the MODIS overpass time. For this day IWV products from 236 GPS stations were available. Areas in grey indicate that no (successful) IWV retrievals were performed or obtained.
## 2 Materials and Methods
### Design, Development, and Integration of SRGs  
The SRGs presented in this manuscript leverage a combination of multi-sensor data and SR exoskeleton actuation for sensorimotor rehabilitation of the human hand (Table 1) [31]. The emphasis on wearability, portability, and power autonomy is key to this effort, as it aligns with the practical needs of rehabilitation settings and the requirement for user-friendly therapies. The proposed system consists of three interrelated subsystems designed to synergistically facilitate rehabilitation and produce the best possible therapeutic outcomes: (a) The multi-sensor subsystem (MSS) encompasses a suite of sensors strategically integrated into the SRG, enabling data acquisition essential for monitoring hand movements and muscle activity for assessing the user's hand motor performance and physiological responses. Complementing this sensing capability is (b) the soft robotic pneumatic actuation subsystem (SRPAS), featuring a PneuNet-based [32] flexible soft-shell robotic exoskeleton engineered to apply targeted forces and stimuli to the user's hand [33]. Crucially, the SRPAS operates in tandem with a (c) microcontroller-based Communication Software Subsystem (ComSS), orchestrating near-real-time processing of the sensor data collected by the MSS, enabling precise control over the SRPAS movements and interactions with the user's hand. This approach has the potential to advance the area of neurorehabilitation by providing a dynamic and adaptable platform for tailored therapy interventions aimed at restoring and improving hand motor function.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subsystem** & **Components** & **Function** & **Interaction** \\ \hline \multirow{4}{*}{Multi-sensor subsystem (MSS)} & Piezoelectric pressure sensor, finger flexion sensors (across fingers), sEMG sensor, accelerometer & Measures piezoelectric pressure, finger flexion, muscle activity, and hand 3D position. Collects data on hand movements and muscle responses & Sends sensory data to communication software subsystem (ComSS) for processing. Data used to evaluate motor performance and physiological responses. \\ \hline \multirow{4}{*}{Soft robotic pneumatic actuation subsystem (SRPAS)} & PreuNet-based flexible soft robotic exoskeleton & Applies controlled forces to the hand for therapy and motor function & Receives control signals from ComSS. \\  & & & improvement \\ \hline \multirow{4}{*}{Communication software subsystem (ComSS)} & Microcontroller-based, control algorithms. Data collection software & Processes real-time sensor data. Sends commands to the SRPAS & Orchestrates the interaction between MSS and SRPAS. Ensures real-time response to hand movements and therapy stimuli. \\  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: SRG system architecture.
## 3 Theoretical Analysis
### Case Study: Transmitter Localization by LDWuR for a Mobile Network  
We propose two strategies to detect the transmitter's direction for mobile networks. One strategy is static antennas. The other strategy is dynamic antennas.
## 4 Experimentation and Results
### CNN Performance
Figure 16 shows the obtained results for accuracy and loss, where it is observed that \(\sim\)97% of accuracy is obtained during the fifth epoch. Figure 16: CNN training and validation: (**a**) Accuracy and (**b**) Loss. Figure 15: Obtained accuracy with the final CNN configuration for different minutes before the SCD. The dotted line indicates the mean value.
# Prediction of Blast Crushing Lumpiness Based on CPO-BP Modeling
## 4 Engineering Applications
### Blast Block Size Prediction and Validation Based on CPO-BP Modeling  
The above 37 sets of blasting data were imported into the CPO-BP model for training, and the CPO-BP model and BP model output values are shown in Figure 6 and Table 2.  
To further validate the performance of the proposed model, its results were compared with those of the standard BP model under the same dataset and parameter settings. As shown in Table 2, compared to the BP model, the CPO-BP model reduces RMSE by over 85% and MAPE by nearly 85%, demonstrating a substantial improvement in prediction accuracy. Future work will include comparisons with additional hybrid models such as GA-BP [29] and PSO-BP [30] to strengthen benchmarking and generalization analysis.  
Figure 5: MIV calculation results.  
From Table 2, it can be seen that the average absolute error (MAE) of the CPO-BP model's prediction results is 0.014341, the mean square error (MSE) is 0.00025004, the root mean square error (RMSE) is 0.015813, and the mean absolute percentage error (MAPE) is 0.037595%. These values were obtained during the engineering validation phase, where the predicted block sizes were compared with actual measured results in a real blasting project. This demonstrates that the model achieves high prediction accuracy and is effective for guiding practical blasting operations. While the model achieved a low RMSE of 0.015813 on the testing set, it is important to note that this result is based on a relatively small sample size and a single testing partition. Additional validations on larger and more diverse datasets, or through k-fold cross-validation, are recommended to further assess the model's robustness.
# Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks
## 2 Related Work
### Traditional Methods  
[PERSON] et al. [10] have proposed a system for fall detection that is based on rules extracted from shape features. First, they convert the video frames into intensity images in which they detect the largest object and assume it to be the object of interest. Next, they apply a rule-based classification to classify the behavior as abnormal if a change in the shape of the interest object within the image is detected. A similar approach is proposed by [PERSON] et al. [11] for depth-sensor-based automatic fall detection. To improve the detection of falls in depth videos, they encode Curvature Scale Space (CSS) features with the Fisher Vector (FV) representation. First, they segment the human body and extract the silhouettes which are then used to calculate shape features. The features are then converted into FV representation. Next, they employ a one-vs-all SVM classifier to detect fall actions. [PERSON] et al. [15] proposed a system to detect the loitering of the elderly by tracking and analyzing their movement patterns. Their system monitors the individuals and applies a Generalized Sequential Patterns (GSP) algorithm to detect predefined patterns of repeated actions that characterize loitering. [PERSON] et al. [14] have proposed an approach that detects loitering based on pedestrian activity area fitting within three categories i.e., rectangle, ellipse, and sector loitering. The type of loitering is determined by trajectory maps and analyzing the suspicious target trajectory. To detect the moving object, they use Gaussian mixture models. If the activity stays constrained in an area for a certain period, they recognize it as loitering. For violence detection, following a Bag of Words (BoW) approach, the method in [18] has used MoSIFT [34] to model the movements of the tracked objects. It then performs a spatio-temporal analysis of the video to detect anomalies.
## 3 Results and Discuss
### Microscopic Mechanism of MICP-Treated Expansive Soil  
The MICP treatment was further investigated at the microscopic level to understand the mechanism by which it improves the properties of expansive soil. Scanning electron microscopy (SEM) was employed to examine the microstructural changes in the untreated soil (S-0) and the soil treated with a 1.0 M cementation solution (S-3).  
Figure 8a presents the SEM images of the untreated soil sample (S-0). The soil particles exhibit a dispersed and block structure, characteristic of expansive soils. The pore spaces between the particles are relatively large, allowing for the penetration of water and the subsequent swelling of the soil. In contrast, the SEM images of the MICP-treated soil sample (S-3) shown in Figure 8b reveal a markedly different microstructure. The soil particles are bound together by the precipitation of calcium carbonate, forming a denser and more agglomerated structure. The pore spaces between the particles are significantly reduced, with the CaCO\({}_{3}\) filling the voids and cementing the soil grains together. CaCO\({}_{3}\) enhances the soil stability by binding particles together and adsorbing onto expansive soil surfaces while also modifying their contact behavior.  
The CaCO\({}_{3}\) precipitation has a twofold effect on the expansive soil properties. First, the cementation of the soil particles reduces the overall porosity and permeability of the soil, limiting the ingress of water and the subsequent swelling of the soil. Second, the CaCO\({}_{3}\) precipitates act as a bonding agent, improving the overall strength and stability of the soil structure. Therefore, the microscopic evidence provided by the SEM analysis  
Figure 8: SEM images of (**a**) the untreated soil sample (S-0); (**b**) the MICP-treated soil sample (S-3).  
supports the observed improvements in the swelling and mechanical properties of the MICP-treated expansive soil, as discussed in the previous sections.
# Structural Crack Detection and Recognition Based on Deep Learning
## 4 Crack Detection Based on YOLOv3
### Loss Function  
YOLOv3 uses the mean square and error as the loss function. It consists of three parts: coordinate error, IOU error, and classification error.  
\[Loss=\sum\limits_{i=0}^{s^{2}}coordErr+iouErr+clsErr \tag{5}\]  
In simple addition, the contribution rate of each loss should be considered. YOLOv3 sets the weight \(\lambda_{coord}=5\) for coordErr. When calculating the IOU error, the grid containing the object, and the grid without the object, the IOU error of the two contributes to the network loss, and the values are different. If the same weight is used, the confidence value of the grid that does not contain the object is approximately 0, which in disguise amplifies the influence of the confidence error of the grid that contains the object in calculating the gradient of the network parameters. To solve this problem, YOLOv3 uses \(\lambda_{noobj}=0.5\) to correct iouErr. For equal error values, the impact of large object errors on detection should be less than the impact of small object errors on detection. This is because the proportion of the same position deviation in large objects is much smaller than the proportion of the same deviation in small objects. YOLOv3 takes the square root of the object size information items (w and h) to improve this problem, but it cannot completely solve this problem.  
In summary, the loss calculation of YOLOv3 during training is as follows:  
\[\begin{array}{rl}Loss=&\lambda_{coord}\sum\limits_{i=0}^{s^{2}}\sum\limits _{j=0}^{B}I_{ij}^{obj}\Big{[}(x_{i}-\hat{x}_{i})^{2}+(y_{i}-\hat{y}_{i})^{2} \Big{]}+\lambda_{coord}\sum\limits_{i=0}^{s^{2}}\sum\limits_{j=0}^{B}I_{ij}^{ obj}\Bigg{[}(\sqrt{w_{i}}-\sqrt{\hat{w}_{i}})^{2}+\left(\sqrt{h_{i}}-\sqrt{h_{i}} \right)^{2}\Bigg{]}\\ &+\sum\limits_{i=0}^{s^{2}}\sum\limits_{j=0}^{B}I_{ij}^{obj}\big{(}C_{i}-\hat {C}_{i}\big{)}^{2}+\lambda_{noobj}\sum\limits_{i=0}^{s^{2}}\sum\limits_{j=0}^{ B}I_{ij}^{noobj}\big{(}C_{i}-\hat{C}_{i}\big{)}^{2}+\sum\limits_{i=0}^{s^{2}}I_{i}^{ obj}\sum\limits_{c\in classes}(p_{i}(c)-\hat{p}_{i}(c))^{2}\\ \\ &\qquad\qquad(x,y,w,h,C,p)\ \ \text{is the true value, and }\Big{(}\hat{x},\hat{y},\hat{w},\hat{h},\hat{C},\hat{p} \Big{)}\ \ \text{is the predicted value. }I_{ij}^{obj}\end{array}\] (6) indicates that the object falls into box \(j\) of grid \(i\). If there is no target in the box, the classification error is not back propagated. The loss function is used to back propagate and update the parameters of YOLOv3 until convergence.