esa-sceva/satcom-chunk-collection huggingface dataset:
# Robotics Applications in the Hospital Domain:
## 4 Robotic Systems in Hospitals Settings
### Transportation and Logistics Robots  
In the hospital environment, the problem of transport and logistics, in general, relates to the efficient movement of goods and resources such as medical supplies, equipment, laboratory samples, non-medical materials, and patients. This challenge is critical in the sense that any delay or error in the delivery of these items can have serious consequences for the treatment and, consequently, the recovery of patients [42; 43]. The use of transport and logistics robots in hospitals, particularly for tasks such as the autonomous movement of goods, has shown significant success. These robots, known as AGVs, not only increase operational efficiency, but also open new possibilities for HRI thanks to their design to promote autonomy and accessibility [42]. The main features of hospital transport robots include load capacity, operational autonomy, precision in navigating hospital corridors, and integration with hospital systems for managing materials and supplies. The ability to operate in complex, high-demand environments such as hospitals is an important differentiator for these robots. Among the advantages of these robots are the ability to operate continuously, without the need for breaks, the precision and consistency of delivery of sensitive materials, and the reduced risk of contamination and cross-infection since they minimize human contact. They can also free operators from repetitive tasks so that they can focus solely on direct patient care [43; 44]. However, there are also disadvantages to be considered, such as the high initial cost of implementation, the need for specialized training, and the adaptation of hospital infrastructures to accommodate the new technology. Table 4 provides a comprehensive overview of the robots employed in logistics and transport systems [22; 44; 45].
# Evaluation of Land Subsidence during Groundwater Recovery
## 3 Methodology
### Subsoil Profile of Zone D  
The Bangkok plain in this study is an area that covers many provinces in the central part of Thailand, as shown in Figure 2. [PERSON] [20] described the soft soil thickness of Bangkok clay. Bangkok clay was separated into six zones designated with different colors. The zones were divided by their soft clay thickness. The dark red (F) area has a thickness of over 18 m. The red (E) area had a thickness of 14 to 18 m. The yellow (D) area had a thickness of 10 to 14 m. The blue (C) area had a thickness of 6 to 10 m. The green (B) area had a thickness of 3 to 6 m, and the gray (A) area had a thickness of less than 3 m. Land subsidence occurred around the Bangkok plain. This study considered only Zone D because this zone is a developed area and has data available, which we used for evaluating the behavior of the soil surface displacement using [PERSON]'s equation of consolidation. Therefore, the red marks in Figure 2 were chosen for hand calculation. The red marks in Figure 2 represent the 8 borehole locations of the Department of Public Works and Town & Country Planning [21]. All the borehole locations were chosen for our theoretical calculation.  
All data of each bored hole were interpreted to evaluate the soil parameters. Then, all soil parameters and soil profiles were verified with the groundwater level measurements  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Province**} & \multicolumn{3}{c}{**Groundwater Level Recovery Rate (2009)**} \\ \cline{2-4}  & **Elevation (m)** & \
\begin{tabular}{c} **Minimum Rate** \\ **(m/year)** \\ \end{tabular} &
\begin{tabular}{c} **Maximum Rate** \\ **(m/year)** \\ \end{tabular} \\ \hline Bangkok & 32.86 & 0.57 & 2.74 \\ Samutprakan & 29.47 & 1.07 & 2.45 \\ Pathumthani & 19.3 & 0.48 & 2.41 \\ Nonthaburi & 12.7 & 0.27 & 1.06 \\ Ayutthaya & 16.3 & 0.61 & 1.23 \\ Nakhonpathom & 19.18 & 0.60 & 1.59 \\ Samutskhon & 32.75 & 1.94 & 2.34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Groundwater level situation at each borehole.  
Figure 2: Locations of the studied holes.  
described in the report from the Department of Groundwater Resources. Following Figure 2, the green marks show the holes investigated by the Department of Groundwater Resources. The name and location of each hole are shown in Table 2.
# Detecting Traffic Incidents Using Persistence Diagrams
## 1 Introduction  
Traffic incidents are severely detrimental to society in terms of financial costs which are estimated in the U.S. by the National Highway Traffic Safety Administration [1]. Consequently, an important focus of data analysis concerns detecting incidents from traffic data for the management of response to accidents which can have significant benefits for society [2]. The type of data we consider is a time series of volumetric traffic counts. We propose a novel methodology for analyzing this data for the purpose of identifying traffic incidents.. Our approach is to view the identification of incidents as a problem of anomaly detection within the time-series data. Our method uses tools from statistical analysis-bootstrap aggregation or bagging-and from topological data analysis (TDA)-persistence diagrams-to form an ensemble of predictors to determine whether data points are normal or anomalous. To each data point and random bag, we associate two persistence diagrams, one reference and one modified. The predictors then consist of a score-the bottleneck distance between the diagrams-and for each data point, the set of scores are aggregated into several summary statistics. We then identify data points as being incidents or anomalies by percentile scores of the summary statistics.  
Our algorithm using randomized bagging of the data and resultant persistence diagrams as a feature extractor for anomaly detection can be viewed as a semi-supervised learning algorithm. Indeed, our method trains the summary statistics on a small amount of labeled data. Moreover, our algorithm can be applied to any vectorized data and, thus, can be adapted for many other data analytic problems.
# Experiencing LoRa Network Establishment on a Smart Energy Campus Testbed
## 1 Introduction
If this is the case, we can adjust the operational parameters by slowing down the collection frequency or reducing the number of nodes to resolve the collision problem. Installing more than one gateway can also mitigate the problem. To date, however, there are few experimental results showing the impacts of application parameters on LoRa performance, meaning that it is even unknown whether we need to change the parameters. These impacts must be investigated before deploying the gateway(s). This paper aims to resolve the technical challenges to help us understand how to deploy a LoRa network on the campus. The contributions of this paper are summarized as follows. * We build a LoRa testbed on the campus and run experiments to understand the properties of LoRa PHY. After deploying nodes outdoors only, the first experiment records the signal strength and computes the communication range, and the findings are compared with the results reported in the existing research to verify their accuracy. The next experiment also measures the signal strength from nodes that are installed inside and outside campus buildings. By using the experimental results, we establish a simplified path loss model for LoRa PHY and estimate the communication range in the combined environment (Section 4). * Given the signal properties of LoRa PHY, we conduct experiments to understand the properties of LoRa MAC via simulations. The first experiment adopts the application parameters of smart energy (data size and data period) and evaluates their impacts on the packet delivery ratio when setting different values of LoRa parameters. Experimental results reveal threshold values for the parameters to satisfy the given requirement for communication reliability (Sections 3 and 5). * The aim of the next experiment is to enumerate the impact of the LBT scheme on LoRa MAC performance. The experimental result leads to a recommendation for optimal sensing threshold values for different application parameter settings. We also apply a list of back-off algorithms to LoRa and compare their performance. Experiments find out an optimal algorithm and a corresponding contention window size (Section 5). * Taking into account all the experimental results, we present a strategy to establish a LoRa network. The strategy includes the number of nodes to deploy and how to set up LoRa parameters, such as the spreading factor (SF), in the nodes. The strategy also recommends the number of gateways to install on the campus to satisfy application requirements (Section 6). The following section starts with a brief review about LoRa technology, including a list of parameters on PHY and MAC layers and regional regulations.
## Abbreviations  
\begin{tabular}{l l l} S.No. & Abbreviation & Full Form \\
1 & AC & Alternating Current \\
2 & CPSs & Centralized Power Systems \\
3 & CT & Continuous Time \\
4 & DoS & Denial of Service \\
5 & EDP & Electronic Data Processing \\
6 & ETM & Event Triggered Mechanism \\
7 & GPS & Global Positioning System \\
8 & H\(\infty\) & H- infinity \\
9 & ICSs & Industrial Control Systems \\
10 & ILC & Interactive Learning Control \\
11 & L2 & Euclidean Norm \\
12 & LMI & Linear Matrix Inequality \\
13 & LT & Linear Time \\
14 & LQG & Linear Quadratic Gaussian \\
15 & LQR & Linear Quadratic Regulator \\
16 & LTI & Linear Time Invariant \\
17 & LTV & Linear Time Variant \\
18 & MBPNCSs & Model Based Predictive Networked Control Systems \\
19 & MIMO & Multiple Input Multiple Output \\
20 & NCS & Networked Control System \\
21 & NLTV & Nonlinear Time Varying \\
22 & OFOFC & Observer-based Fuzzy Output Feedback Controller \\
23 & PCA & Principal Component Analysis \\
24 & PID & Proportional Integral Derivative \\
25 & QCS & Quantized Control System \\
26 & QoS & Quality of Services \\
27 & RAMPC & Robust Approximation Based Model Predictive Control \\
28 & RFID & Radio-Frequency Identification \\
29 & RVSPC & Robust Variable Sampling Period Controller \\
30 & SMC & Sliding Mode Controller \\
31 & SSV & Structured Singular Value \\
32 & T-S & Takagi Sugeno \\
33 & UAVs & Unmanned Aerial Vehicles \\
34 & WSN & Wireless Sensor Networks \\
35 & ZOH & Zero Order Hold \\ \end{tabular}
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### Interference Mitigation in HetNets
#### 4.2.1 Carrier Aggregation  
CA, introduced in 3 GPP Rel-10 [180], allows UEs to utilize multiple component carriers across frequency bands. In HetNets, CA helps isolate macro-cell and SC transmissions by distributing traffic over primary and secondary component carriers, enhancing both SE and interference avoidance.
## 3 Role of Small RNA in Stress Response
### OxyS  
OxyS is a 117-nt sRNA in _Escherichia coli_ that plays a central role in the oxidative stress response [147]. Its expression is induced by the transcription factor OxyR under oxidative stress conditions. OxyS regulates the expression of approximately 40 genes [148] including genes involved in stress response, motility, DNA repair, and antibiotic resistance. One of the key functions of OxyS is to repress the transcriptional activator _fhlA_. Using Nuclear Magnetic Resonance (NMR), Small Angle X-ray Scattering, SAXS, and molecular modeling, [PERSON] et al. revealed that OxyS adopts a boomerang-like conformation consisting of four stem-loops (SL1-SL4). The stem-loops SL1 and SL3 are positioned at opposite ends whereas SL4 shortens the unstructured linker to adopt a confirmation that allows the repression of _fhlA_[149]. The chaperone protein Hfq enhances _oxyS-fhlA_ interactions by destabilizing SL2 and partially unfolding SL4, promoting more efficient target binding, although OxyS retains regulatory function in the absence of Hfq, possibly forming stable Watson-Crick base pairs with _fhlA_, with magnesium ions facilitating partial unfolding for enhanced base pairing. This structural plasticity likely underlies OxyS's ability to regulate multiple targets. OxyS also negatively regulates the expression of the alternative sigma factor RpoS, which is essential for general stress resistance in _E. coli_. While initial studies proposed direct base pairing between OxyS mRNA and the RpoS leader sequence, later research demonstrated that OxyS has low affinity for the _rpoS_ mRNA leader region despite its strong binding to Hfq [150]. This suggests an indirect mechanism, where OxyS sequesters Hfq, reducing its availability to other sRNAs, including DsrA, a known activator of _rpoS_.  
The overexpression of OxyS inhibits swimming and swarming motility and reduces curli expression, linking it to biofilm formation [42]. Additionally, OxyS contributes to cellular stress adaptation by inducing temporary cell cycle arrest, allowing for DNA repair [151]. To achieve this, it represses the expression of transcription termination factor NusG, leading to the activation of the cryptic prophage gene _kilR_. KilR inhibits FtsZ, a key protein in bacterial cell division, slowing proliferation and allowing DNA repair to proceed effectively (Figure 2c). OxyS influences antibiotic susceptibility, particularly cephalothin resistance, by modulating target gene expression [152]. Transcriptomic and phenotypic analyses by [PERSON] et al. identified 27 differentially expressed OxyS-regulated genes, including _cycA_ (D-serine/alanine/glycine:H\({}^{+}\) symporter) and _cysH_ (phosphoadenosine phosphosulfate reductase). Knocking out 17 genes out of the 27 OxyS targets exhibited increased resistance. Nine were linked to the cAMP receptor protein (CRP), a global transcriptional regulator in _E. coli_. These findings suggest that OxyS modulates multiple pathways to confer stress response and may serve as a molecular marker or target for identifying and combating antibiotic resistant strains.
# Siberia: A Self-Sovereign Identity and Multi-Factor Authentication Framework for Industrial Access
## 1 Introduction
### Secure Voice Biometric Authentication
To meet these requirements, voiceprint protection strategies typically focus on three main principles: (1) unlinkability, ensuring that voiceprints cannot be correlated across different applications or databases; (2) irreversibility, guaranteeing that biometric samples or other personal traits cannot be reconstructed from the stored voiceprints; and (3) renewability, which mandates that multiple voiceprints generated for the same user are treated as independent and non-interchangeable [43]. To satisfy these principles, voiceprints must be stored and processed using robust encryption mechanisms. Although these voiceprints are usually stored as feature embeddings generated by neural networks, recent studies have shown that sensitive information such as gender, age, or even health-related biomarkers can still be inferred from these embeddings [43; 44]. Traditionally, voiceprints are encrypted only during storage (i.e., in the enrollment phase) but are decrypted during the authentication stage [45]. This decryption exposes the raw embeddings to the server and, potentially, to adversaries who may compromise the server, thereby creating privacy risks and enabling user tracking. To address this vulnerability, the processing of voiceprints should be carried out without decryption. This can be achieved using homomorphic encryption (HE), which allows mathematical operations to be performed directly on encrypted data. In this way, both verification and anti-spoofing scores can be computed in the encrypted domain, preserving the privacy of the holder's biometric data throughout the entire authentication process. SIBERIA implements a secure state-of-the-art SASV system for holder biometric authentication, composed of two independent modules: (1) a combined, cascaded ASV and anti-spoofing component that generates encrypted voiceprints and validates the user's identity, producing independent encrypted verification and anti-spoofing scores; (2) a key management component responsible for generating the cryptographic keys used to encrypt the holder's voiceprint and to decrypt the ASV and anti-spoofing scores. This module subsequently produces a final combined score that determines the holder's identity when accessing a specific service. Additional implementation details of these modules are provided in Section 2.2.
# Mapping with Pleiades--End-to-End Workflow
## 2 Related Work
### Sensor Modeling and Parameter Optimization  
For Pleiades images, a set of rational polynomial coefficients (RPC) is given, which define the rational polynomial sensor model and thus substitute a physical sensor model. This generic solution dates back to Reference [40] and for some sensors replaced the physical model. Many sensor models of optical VHR satellites are delivered with RPCs, such as Cartosat-2, CBERS-2, Deomis-2, EROS B, GaoFen-2, GeoEye-1, Ikonos, IRS-P5, Jilin-1, Kompsat-2, Kompsat-3, Kompsat-3A, QuickBird, Pleiades, RapidEye, SkySat, Spot-6, Spot-7, Spot-8, SuperView-1, TeLEOS-1, TripleSat, WorldView-3 or WorldView 4. Also some SAR sensor models, like for instance ERS-1, are supplied with RPCs. The main advantage of using the RPC model is that it presents a general solution for sensor modeling such that novel sensors are automatically supported by a given implementation. The disadvantage is that the coefficients are not directly related to physical properties like, for instance, sensor position or viewing direction. Therefore, in the adjustment procedure (cf. Reference [41]) it is hard to interpret which effect caused the change of coefficients. Mathematically, a non-linear equation system has to be solved to adjust the sensor parameters. This can be accomplished with numerical methods, such as [PERSON]'s method, Gauss-Newton algorithm, gradient descent or Levenberg-Marquardt algorithm. Depending on the number of released parameters and starting values the adjustment delivers robust solutions or gets stuck in a local minimum.  
There is also an ongoing discussion on which parameters to be optimized. For instance, only a shift is considered in References [42; 43] while linear coefficients are also considered in References [21; 40; 44; 45].  
The need to manually measure GCPs and their related image coordinates represent the main bottleneck of mapping. Therefore, automatic methods for GCP definition and transfer are of utter  
Figure 2: Proposed end-to-end 3D mapping flow graph for multi-view stereo Pleiades images. The four core tasks introduced in Figure 1 are depicted in detail.  
importance. One idea is to employ SAR images, like from the TerraSAR-X sensor. This sensor provides extremely high geo-location accuracy [46] and thus perspectives to retrieve highly accurate 3D point information via radargrammetric processing [47; 48; 49]. Then such GCPs can be transferred into the optical Pleiades image via multi-modal image matching as presented, for instance, in References [50; 51; 52].
# User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
## 5 Performance Evaluation
### Simulation Results
#### 5.3.4. Delays Experienced by Different Packet Types for Varying Number of UEs  
Next, we investigate the average data delivery delay experienced by different data packet types under an increased number of UEs, as shown in Figure 7. Since the increased number of UEs demands augmented transmission opportunities and injects more packets into the network, the general trend of increased delay is observed in all the studied cases. The _iRRM_ considers the repetition factor, time offset, and data rate for radio resource management, which performs better than _bRRM_ in terms of delay and which accounts for inter-carrier-interference only. Finally, the proposed PAUM system exhibits less delay compared to _bRRM_ and _iRRM_ for all data packets due to minimized interference and prioritized network access.  
Figure 6. Delays experienced by different packet types vs. data generation rate.
# Hybrid Power System Design for Multifunctional Mini-Loader Vehicles
## 2 Materials and Methods
### Design of the Hybrid Power System
#### 2.2.3 Battery Pack  
In the mild hybrid power system, there is little demand for the battery packs from the operation of vehicle functions, and therefore Li-ion batteries (26650, Lifecell Battery Fty., Guangzhou, Guangdong, China) were used, 15 battery cells were first connected in series, and then 6 groups were connected in parallel to make a battery pack of 48 V/12 Ah. The battery pack mainly supplies electricity to the motor, hydraulic pump, and the solenoid valve of the hydraulic valve. The parameters of a single battery cell are shown in Table 3.  
Table 4 shows the complete power platform specifications for the mild hybrid power system developed for the multifunctional mini-loader vehicle.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Data** \\ \hline Motor Structure & Inner rotor \\ \hline Motor Type & Permanent magnet synchronous motor \\ \hline Voltage & 39 to 58 V \\ \hline Cooling Method & Natural air cooling \\ \hline Weight & 6.3 kg \\ \hline Rated Output Power & 1.598 kW \\ \hline Rated Rotational Speed & 3300 rpm \\ \hline Rated Torque & 4.6 Nm \\ \hline Maximum Output Power & 4.0 kW \\ \hline Maximum Speed & 5380 rpm \\ \hline Maximum Torque & 16.56 Nm \\ \hline \hline \end{tabular}
\end{table}
Table 2: MC016 motor specifications.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Battery Type** & **LiFePO\({}_{4}\)** \\ \hline Rated Voltage (V) & 3.3 \\ \hline Rated Capacity (mAh) & 2300 \\ \hline Maximum Continuous Discharge Current (C/A) & 30/70 \\ \hline Maximum Impulse Discharge Current (C/A) & 50/115 \\ \hline Maximum Charging Voltage (V) & \(\leqq\) 3.75 \\ \hline Discharge Cutoff Voltage (V) & 2.0 \\ \hline Charging Current (C) & 5 \\ \hline Weight (g) & 70 \\ \hline Cycle Life (count) & 1500 to 2000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Battery cell parameters of A123 26650.