esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 4 Orthogonal Multiple Access (OMA)  
As described in Section 2.1, under OMA over the radio channel, one minislot every \(L_{U}\) is exclusively allocated to URLLC UEs, while eMBB UEs transmit over the remaining minislots. In this way, URLLC UEs never interfere with eMBB transmissions. If more than one URLLC packet is generated at a user between two URLLC transmission opportunities, only one of such packets (randomly selected) is transmitted, while the others are discarded, causing a blockage error. Due to the latency constraints, URLLC signals are digitized and decoded locally at the ENs, while theeMB signals are first mapped over the fronthaul lines, and then analogically forwarded to the BBU, as mathematically summarized in Figure 7. In this section, we derive the expressions for the eMBB and URLLC rates under OMA for a given URLLC access latency \(L_{U}\) and, in the case of URLLC, for a fixed URLLC target error probability \(\epsilon_{U}\).
# Identification, Control, and Characterization of Peristaltic Pumps in Hemodialysis Machines
## 2 Methodology
### Controller Design  
Classical procedures in automatic control are based on assumptions about the plant and the desired outcome. Furthermore, these procedures aim to obtain specific characteristics of the process, either analytically and/or visually. Subsequently, this knowledge is employed in the selection of the controller configuration. These strategies are appropriate for the preliminary phase, as they are computationally rapid and straightforward to implement. Nevertheless, attaining the anticipated outcomes from the controller configuration is not always feasible due to the underlying assumptions. In this article, the data obtained from experimentation are evaluated in the frequency domain. The Bode diagram is handy for elucidating how a system responds to disparate input frequencies. The magnitude plot presents the system's gain as a function of frequency on a logarithmic scale, while the phase plot shows the corresponding phase shift.  
In systems where the magnitude decreases at high frequencies, this indicates that the expected response acts as a low-pass filter, which is beneficial for attenuating high-frequency noise or disturbances. However, this behavior also indicates the potential for inadequate compensation at low frequencies, which can result in steady-state errors if appropriate integrative action is not introduced, as previously discussed in [18].  
In such instances, the P.I. controller regulates the system's behavior by addressing immediate errors through its proportional term and eliminating steady-state errors with its integral term.  
As shown in Figure 5, the Bode plot highlights the need for additional low-frequency compensation, addressed by the introduction of the P.I. controller. This controller improves the system's phase margin and stability, ensuring reliable performance. The enhanced stability is particularly significant for practical applications, where maintaining system reliability under dynamic conditions is critical.  
Figure 5: Bode plot of the proposed first-order plant. **Caption:** Frequency response analysis of the identified system, showing magnitude and phase at different frequencies. This analysis supported the selection of the P.I. controller to improve stability and reduce steady-state error.
# A Ku-Band GaAs Multifunction Transmitter and Receiver Chipset
## 3 Circuit Blocks of the Ku-Band Multifunction Chipset
### 5-Bit Digital Attenuator
Figure 4: Measurement results of the 5-bit digital attenuator: (**a**) insertion loss and matching performance in the reference state, (**b**) insertion loss for all 32 attenuation states, (**c**) input and output matching for all 32 attenuation states, (**d**) root mean square (RMS) attenuation and parasitic phase errors.
# Ion Channel Properties of a Cation Channelrhodopsin, \(Gt\_\)Ccr4
## 2 Materials and Methods
### Statistical Analysis  
All data in the text and figures are expressed as mean \(\pm\) SEM.  
Figure 1: Basic properties of _Gt_CCR4 and _Cr_ChR2_. (**A,B**) Each cation channelrhodopsin expressed in ND7/23 cells was stimulated by green (530 nm) or blue (470 nm) LED light (6.8 mW/mm\({}^{2}\)). Standard solutions were used. Membrane potentials were clamped from \(-\)60 mV to +60 mV in +20 mV steps. Fluorescence images were taken using a confocal microscope. (**C**) Current-voltage relationship (I-V plot) of _Gt_CCR4 (filled symbol) and _Cr_ChR2_ (empty symbol). Current peak component (square) and steady state amplitude (circle) of two channels are depicted. (**D**) Current-decay kinetics of _Gt_CCR4 and _Cr_ChR2_. \(\tau_{\text{off}}\) is plotted as a function of membrane voltage. Filled circle; _Gt_CCR4_, empty circle; _Cr_ChR2_. (**E,F**) Light power dependency of photocurrents from _Gt_CCR4 and _Cr_ChR2 at \(-\)60 mV. Each channel was stimulated by 530 nm (_Gt_CCR4_) and 470 nm (_Cr_ChR2_). Photocurrent values are normalized. Current peak component (filled circle) and steady state amplitude (empty circle) are depicted. (n = 4–8 cells).
# Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)
## 4 Healthcare Data Protection Legislation and Framework
### Major Healthcare Initiative
#### 4.1.1 Health Insurance Portability and Accountability Act (HIPAA)  
HIPAA was enacted by the US government to implement the security and privacy of healthcare data for American citizens. It has separate rule sets for security and privacy. The privacy rules enable the privacy of the health data to protect the data from disclosure. The security rules provide security of the individuals' health information by adopting advanced technologies to acquire more efficient means of patient care. The HIPAA security and privacy rules are implemented to healthcare and non-healthcare organizations that store, transmit, and process healthcare data of US citizens by any means.  
The privacy rule protects the following health information processed by concerned entities:  
* Common Identifier (e.g., name, address, birth date);
* Past, present, or future physical and mental health or condition;
* Past, present, or future payment provision for healthcare;
* Provision of healthcare to individuals.  
The mentioned entities are allowed to be processed or disclosed for research and public interest and should have the authorization to process or disclose health information except for treatment purposes, like payment and relevant health care operations.  
The Security Rule protects personal health information that has been created, received, transmitted, and managed electronically. These security rules have the following characteristics:  
* Making sure of the integrity and availability of personal healthcare information;
* Detects and protects against known threats to confidentiality, integrity, and availability;
* Protect against processes not permitted and the disclosure of information.  
The security rule requires the administrative protection of management processes personnel. Proper administrative controls should be maintained for devices and personnel. This rule also includes audit control, access control, integrity control, and transmission security.
## 6 Simulation Results and Discussion  
The simulation results are presented for three cases based on optimal capacity planning and flexible operation feasibility using BESS and PHES, with and without DR. The optimal size of system components is determined under each case at minimum investment costs and maximum supply reliability (minimum LPSP) while satisfying the system operational and flexibility requirements. The results of the considered three case simulation scenarios are outlined and discussed below:  
* Case 1: Comparing BESS and PHES without DRP consideration.
* Case 2: Comparing BESS and PHES with CPP DRP consideration.
* Case 3: Comparing BESS and PHES with TADP DRP consideration.
## 5 Results and Discussion
### Utility for Energy Efficiency Improvement  
In speed control, the optimal speed regulation time should be chosen for a good dynamic performance of the belt conveyor system. In this way, the risks, such as belt overloading, belt slippage, and belt tear or breaking can be prevented. Based on presented results, the dynamic model of a belt conveyor considering the non-uniform bulk material distribution would be very useful for optimization of the speed regulation time of the belt conveyor. It should be pointed out that this dynamic model is capable of refined calculating the belt tensions of all elements of the belt conveyor system with the bulk material distribution, especially the belt tensions around the drive pulley. With the dynamic model, simulations can be carried out to analyze the dynamic characteristics of the belt conveyor during the regulation operation if the initial regulation time is obtained. In this way, the risks can be predicted in advance. In addition, more simulations should be carried out to get the optimal regulation time to improve the regulation performance.  
Generally, a series of influencing factors, such as external constraints (electricity consumption, electricity cost, greenhouse gas emissions, etc.), operation efficiency (throughput rate, average throughput, etc.), and system constraints (the belt, idler, driver system, etc.), should be considered in speed control strategies for the energy efficiency improvements. However, the relationship among them is hardly expressed by a mathematic function. Therefore, there are a couple of negative side effects of a speed control strategy that solely or primarily focus on equipment level or operational  
Figure 11: Dynamic characteristic curves of the belt conveyor under different starting times. (**a**) Starting time is 50 s. (**b**) Starting time 100 s. (**c**) Starting time 150 s.  
level. Most of the existing strategies make belt conveyors operate at unsuitable operating points since they do not consider the system constraints and external constraints. There must be a mechanism to compensate the inaccuracy caused by modelling uncertainties, external disturbances, and unexpected reactions of relevant components. However, during the process of conveying bulk materials on a belt conveyor, any belt speed changes will lead to nonlinear changes in the drive tension of the belt. In particular, if the non-uniform bulk material distribution is exerted a sudden dynamic shock on the belt, the tension of the belt will change greatly, which may lead to skidding, damage or even tearing. In actual situations, this may lead to misjudgment, affecting safety. We know that the speed optimization can play an important role in the energy efficiency improvement of the belt conveyor. Energy efficiency improvement brings not only high efficiency and low electricity consumption but also high safety. Therefore, developing a reasonable speed control strategy is the key for energy savings. The dynamic characteristics of the belt should be considered in the construction of the speed control strategy of the belt conveyor.
# Screen-Cam Robust Image Watermarking with Feature-Based Synchronization
## 5 Experimental Results and Analysis
### Robustness against Screen-Cam with Additional Common Attacks  
The scheme in [33] needs to record the four vertices, which means it needs to know the original size. Furthermore, the scheme in [21] cannot deal with the cropping attack. However, in a real-life scenario, images may under common image processing attacks caused by normal user operations. Therefore, we experimented with several hypothetical scenarios to verify the effectiveness of the proposed algorithm for screen-cam with additional common attacks. We designed four realistic application scenarios where method [21,33] are not applicable: (a) the Lena image is blocked by the window at 20 percent, which is equal to being cropped; (b) the Peppers image is rotated five degrees and cropped; (c) the Building image is scaled by 80%; (d) the Pentagon image is scaled by 80% and rotated 90 degrees counterclockwise. An example of the four scenarios is shown in Table 6. When doing  
Figure 16: Watermark detection results against screen-cam attack. (**a**) Lena. (**b**) Baboon. (**c**) Airplane (**d**) Peppers. (**e**) Building. (**f**) Pentagon. (**g**) White House. (**h**) Naval base.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Handhold Scenarios** & **Example 1** & **Example 2** & **Example 3** & **Example 4** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Examples of Handhold Shooting.  
the watermark detection, assume that we do not know the specifics of the attacks, which means we do not correct the image to its original scale or original orientation manually. The coordinate points that are used for perspective correction are denoted in Table 6 as red dots.  
Figure 17 shows the detection results of the four scenarios. The construction of Figure 17 is the same as Figure 16. Furthermore, due to the different sizes of the experimental images, the shooting distance was adjusted accordingly. Because Scenario (a) and Scenario (b) use the four corner points of the screen for perspective correction, the experiment shooting distance starts from 50 cm. In these two scenarios, the performance of watermark detection is the same as the detection results of the same host images in Section 5.2. In Scenario (c) and Scenario (d), because the images are scaled, the test starting shooting distance can be shortened, and the effective detection distance is also shortened. When the shooting angle is 15 and 30 degrees, the watermark information can be detected at all shooting distances in the experiments. As the shooting angle increases, the detectable shooting distance is substantially reduced. Watermark information can be detected within a shooting distance of 50 cm when the horizontal shooting angle is 60 degrees. Thus, the scaling of the images has a considerable influence on the watermark detection of the large angle captured image, but it can still meet the actual needs. These results verified the fact that the proposed scheme can handle screen-cam with common attacks.
## 3 Experiment Results and Discussions
### Effect of Corrosion Amount on Tensile Shear Strength  
As the salt spray test increased, the tensile shear strength decreased in all welding conditions. In particular, compared with the weld-bonded specimen, more corrosion proceeded in Specim 1, a relatively large amount of red rust was observed, and the decrease in the area of the nugget diameter due to corrosion was thought to be the cause of the low tensile strength.  
In order to compare the amount of corrosion that progressed, the following equation was used:  
\[\text{Vc}=(\text{Ac/Awb})\times 100 \tag{1}\]  
where Vc is the total corrosion amount (%), Ac is the corrosion area, and Awb is the weld bonding area.  
Figure 8 shows the average amount of corrosion, calculated from Equation (1), for each specimen. From the results, it can be seen that the amount of corrosion was very small in the weld bond welding condition, compared with the conventional DeltaSpot welding condition (Specimen 1). This was due to the delay of galvanic corrosion; the corrosion solution did not penetrate through because of the adhesive on the faying interface.  
Figure 8: Measurement of the average corrosion amount at each welding condition.  
Figure 7: Comparison of surface roughness results of the (a) plain, as-received and (b) roughened conditions. The surface roughness was expressed in an arithmetic mean roughness (Ra) value for each specimen.  
Among the specimens, it can be seen that the corrosion progress of Specimen 4 was the slowest, and this is considered to be the reason that the shear tensile strength was maintained at a high level, even under a long-term corrosion environment.
# Defect Processes in Halogen Doped SnO2
## 4 Conclusions  
DFT calculations were performed for halogen doped SnO\({}_{2}\) and the effects of the exact kind of doping on the bandgap value and electronic properties were discussed. In this research, interstitial and substitutional halogen defects were examined. In all cases, it was observed that gap states were created from hybridization of O-2p with halogen 1s orbitals. Such energy states are beneficial for the oxide's photocatalytic activity as they significantly reduce the optical band gap concerning that of undoped SnO\({}_{2}\). They can also serve as charge transport paths in a certain type of optoelectronic device, whereas this might be detrimental for the application of halogen-doped oxide as electron transport material in other classes of photovoltaic devices. Iodine doped SnO\({}_{2}\) has the lowest bandgap value of 2.60 eV and it is one of the lowest values reported for SnO\({}_{2}\). Interestingly, when iodine resides in an oxygen site, it has a better refractive index and absorbance than the other halogen dopants. This makes it a suitable dopant candidate for SnO\({}_{2}\)-based energy harvesting devices.  
P.-P.F. and N.K. performed the DFT calculations, P.-F.F., N.K., M.V., A.C., wrote the paper, M.V., A.C. and D.D. contributed in the analysis and discussion of the results. All authors have read and agreed to the published version of the manuscript.  
P.-P.F.: [PERSON], D.D. and A.C. are grateful for LRF ICON funding from the Lloyd's Register Foundation charitable foundation helping to protect life and property by supporting engineering-related education, public engagement and the application of research. A.C. acknowledges support from European Union's H2020 Programme under Grant Agreement no 824072- HARVESTORE.  
Figure 7: The absorption for (**a**) intersitially doped SnO\({}_{2}\) for all the energy spectrum, (**b**) intersitially doped SnO\({}_{2}\) as a function of the wavelength, (**c**) substitutionally doped SnO\({}_{2}\) for all the energy spectrum, (**d**) substitutionally doped SnO\({}_{2}\) as a function of the wavelength.
###### Abstract  
The last-mile delivery crisis, exacerbated by the surge in e-commerce demands, continues to face persistent challenges. Logistics companies often overlook the possibility that recipients may not be at the designated delivery location during courier distribution, leading to interruptions in the delivery process and spatiotemporal mismatches between couriers and users. Parcel lockers (PLCs), as a contactless self-pickup solution, mitigate these mismatches but suffer from low utilization rates and user dissatisfaction caused by detour-heavy pickup paths. Existing PLC strategies prioritize operational costs over behavioral preferences, limiting their real-world applicability. To address this gap, we propose a user-centric path planning model that integrates spatiotemporal trajectory mining with kernel density estimation (KDE) to optimize PLC selection and conducted a small-scale experimental study. Our framework integrated user behavior and package characteristics elements: (1) Behavioral filtering: This extracted walking trajectories (speed of 4-5 km/h) from 1856 GPS tracks of four campus users, capturing daily mobility patterns. (2) Hotspot clustering: This identified 82% accuracy-aligned activity hotspots (50 m radius; \(\geq\)1 h stay) via spatiotemporal aggregation. (3) KDE-driven decision-making: This dynamically weighed parcel attributes (weight-volume-urgency ratio) and route regularity to minimize detour distances. Key results demonstrate the model's effectiveness: a 68% reduction in detour distance for User A was achieved, with similar improvements across all test subjects. This study enhances last-mile logistics by integrating user behavior analytics with operational optimization, providing a scalable tool for smart cities. The KDE-based framework has proven effective in campus environments. Its future potential for expansion to various urban settings, ranging from campuses to metropolitan hubs, supports carbon-neutral goals by reducing unnecessary travel, demonstrating its potential for application.  
s 2025 15 607 10.15 1
## 3 Methods
### Bibliometric Indicators
#### 3.2.3 Collaboration Analysis  
The third analysis centers on collaboration by constructing a network that connects various entities. Each link in the network quantifies the intensity of their collaborative interactions. In this study, the entities involved are: affiliations and countries. More specifically, a collaboration analysis is a graph, where each vertex \(V_{i}\) is an entity and each link \((V_{i},V_{j})\) between the pair of nodes \(V_{i}\) and \(V_{j}\) represents the fact that they have, at least, one paper in which they both appear. The weight of the link \(w_{ij}\), related to \((V_{i},V_{j})\), is the number of papers co-authored by \(V_{i}\) and \(V_{j}\). Let us note that the collaboration relationship is bidirectional, so the graph is undirected.  
According to the type of entity represented by \(V_{i}\) we have three different collaboration networks with different granularity:  
* The network with the smallest granularity, referred to as _co-authorship networks_, focuses on authors; therefore, \(V_{i}\) represents an author. This type of network is the most commonly used in many applications [35]. The graph obtained is a co-authorship network. A link in this network represents the fact that two researchers are co-authors of at least one paper. This type of collaboration falls outside the scope of this paper.
* The second type of network focus on affiliations and is referred to as _co-affiliation network_. In this case the entity, \(A_{i}\) is a research center such as a department, a laboratory, or a university. In the co-affiliation network, a link exists if (one or more) authors from the two affiliations collaborate on almost a publication. The weight, \(w_{ij}\) is the total number of papers which authors belong to \(A_{i}\) and \(A_{j}\). The density of the graph represents how much the different centers collaborate with each other. The number and size of connected components represent how fragmented the activity is, the degree of a node represents the number of collaborations, and the ratio between degree and weighted degree indicates the frequency of collaborations between centers \(A_{i}\) and \(A_{j}\).
* The third type of entity is the country, referred to as _co-nationalities network_; therefore, an entity represents a country, \(C_{i}\). Note that we will refer to the country where the affiliation of a researcher is located, not the nationality of the researcher. In this case, the link \((N_{i},N_{j})\) exists if there is a paper whose co-authors come from affiliations located in countries \(N_{i}\) and \(N_{j}\). The Weight degree of the link is the intensity of the collaboration between the two countries, while the degree of \(N_{i}\) represents how many countries \(N_{i}\) collaborates with. Finally, in this case, the ratio between the weighted degree and the degree measures the average intensity of collaborations of country \(N_{i}\).