esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Detailed Analysis of Developments in Selected Specialties
#### 3.7.1 Telecardiology  
Over the past decade, telecardiology in Poland has evolved significantly, transitioning from feasibility studies to clinical trials, particularly for the management of heart failure. During the early phase from 2014 to 2016, prospective studies established the safety of home-based cardiac telerebabilization [49; 50; 51; 52; 53; 54]. This initial focus on telemedicine laid the groundwork for structured programs [52]. Between 2019 and 2021, studies validated portable electrocardiogram (ECG) devices against standard 12-lead ECG [39; 55]. The COVID-19 pandemic accelerated the adoption of telecardiology [56], while also highlighting its limitations [57]. An app was introduced to manage atrial fibrillation [43], and trials assessed the impact of hybrid telerehabilitation on heart failure patients [58; 59; 60; 61; 62; 63]. Selected trials developed interventions aimed at reducing hospitalizations [64], whereas studies examined telec consultations [39; 65; 66]. A cost-effectiveness analysis evaluated hy  
Figure 3: Changes in the number of publications on telemedicine and eHealth by specialization, comparison of two periods: 1995–2015 and 2014–2024. Significant progress has been noted in cardiology, psychiatry, and rehabilitation, where the number of scientific publications has increased significantly. Geriatric, pulmonology, and infectious diseases were almost absent before 2015; however, after 2015, they became very active, especially under the influence of the COVID-19 pandemic. Orthopedics, although very active, have seen relatively little growth in the last decade.  
brid telehehabilitation [67]. Reviews have analyzed telemedicine in cardiology and the impact of digital technologies [68; 69; 70; 71; 72], and AI applications have also been reviewed [73]. Natural language processing (NLP) algorithms have been developed to extract data from EHRs [74], and remote heart monitoring via VPG from drones has been explored [75]. Patient perspectives on telemedicine have also been evaluated [76; 77]. Trials have assessed post-myocardial interventions [78; 79; 80], and studies have supported the safety of telehehabilitation after acute myocardial infarction [81], demonstrating benefits for heart failure etiology [82]. Telemonitoring in cardiac resynchronization therapy has also been evaluated [83]. Overall, during the study period, Polish telecardiology advanced from telehehabilitation studies to comprehensive remote monitoring systems bolstered by clinical evidence and technological advancements.
## 2 Materials and Methods
### Method
#### 2.6.5 Vital Signs Monitoring Setup  
The configuration of vital signs monitoring is critical in implementing IoMT systems. Proper monitoring of vital signs allows for the accurate assessment of the patient's health and the early detection of medical problems. To configure the monitoring of vital signs in the IoMT system, the following steps have been established to guarantee the following results:  
* Selection of vital signs to monitor: the vital signs to be observed in the IoMT system must be selected. Common vital signs include heart rate, respiratory rate, body temperature, and blood pressure.
* The selection of monitoring devices may include blood pressure monitors, digital thermometers, heart rate sensors, and pulse oximeters.  
Figure 4: Google cloud platform architecture.  
* Configuration of the monitoring devices: the manufacturer's instructions must be followed to configure the monitoring devices and establish the necessary parameters for their correct operation in the IoMT system.
* Configuration of alert thresholds must be established for each monitored vital sign. If strong sign values exceed the set point, an alert will be sent to medical personnel for immediate action.
* The IoMT system must be scalable and adaptable for future upgrades and improvements.
* Integration with the cloud data analysis platform: the data from the monitoring devices must be integrated with the cloud data analysis platform to be analyzed and visualized in real-time.
* Functional tests must be carried out to verify that the vital signs monitoring is working correctly and that appropriate alerts and notifications are being generated in case the established thresholds are exceeded.  
Once the vital signs thresholds are set, continuous monitoring should be established to detect variations outside the specified limits. For this, a real-time monitoring tool can be used to visualize the values of vital signs and receive alerts in case an abnormality is detected [26]. In addition, an action plan must be established to handle situations where a variation in critical characters is seen outside the set limits. This may include immediate medical personnel notification or an emergency protocol activation. Finally, a record of the vital signs data must be kept to carry out a follow-up and subsequent analysis of the evolution of the patients. This will allow for the detection of patterns and trends in vital sign values and the making of necessary treatment adjustments.
# Study of Data Transfer in a Heterogeneous LoRa-Satellite Network for the Internet of Remote Things
## 4 Gateway Encoding and Packaging Method (GDEP)  
The GDEP method aims to optimize the amount of data transferred through a satellite link, which is the architectural bottleneck both in terms of speed and cost. The method consists of two connected steps (Figure 3). At the first step, a format conversion is performed resulting in reduction of overall data size, and at the second step, the packaging algorithm is additionally applied, which leads to a higher utilization rate of the satellite channel. Step 1. Data encoding  
To evaluate the effectiveness of the first step, we performed a network simulation taking into account the following constraints associated with network standards, protocols and hardware:  
* The maximum payload size of the LoRaWAN message is 222 bytes when sending messages through an intermediate device and 242 bytes when sending information directly between the endpoint device and the gateway [51];
* The maximum SBD payload size is 340 bytes [52; 53];  
Figure 3: Scheme of the Gateway Encoding and Packaging (GDEP) method. * The raw transmission delay in the LoRa network can vary from 60 to 1250 ms. The technical details of how JSON messages were encoded in Protobuf can be found on our GitHub project page [54]. One thousand LoRa messages from endpoint devices with different amounts of sensors--from 1 to 13--were generated, then we compared the amount of transmitted data using the JSON and Protobuf formats. The generation of events by endpoint devices was carried out independently of each other with a fixed average probability value of an event occurring, so we used the Poisson distribution to determine the number of messages from the endpoint devices in one message from the sensor [55]. We assumed that the values of the endpoint devices would obey the normal distribution law. The simulation results are shown in Table 2 below. Format conversion reduces the size of transmitted data by an average of 4.8 times. When using the Google Protobuf data presentation format, the size of the resulting IoT message on the terrestrial network varies from 70 to 126 bytes depending on the number of sensors (from 1 to 13). The maximum size of the SBD transmission is determined by the hardware, and in our case is 320 bytes; thus, it becomes possible to pack several Internet of things messages in one SBD transmission session. Upon receipt of the incoming messages, the LoRa-Iridium gateway buffers them and, when the buffer is larger than a specified size, packages the messages to perform transmission using the Iridium SBD communication channel. Step 2. Message packaging  
The packaging step can be defined as a standard optimization problem.
# PALANTIR: An NFV-Based Security-as-a-Service Approach for Automating Threat Mitigation
## 6 Validation
### Qualitative Evaluation
#### 6.2.5 Security Capability Prototype Implementation  
To create an SC, several steps are needed: (i) Define the Docker image (xNF) with the protection mechanism software already installed and correctly configured for the specific deployment; (ii) Implement the Juju charm (helped by a Python library) with the implementation of the available Day-0, Day-1 and Day-2 actions into the Docker image (deployed as _Podspec_ charm together with the Juju operator) and generate a.charm file with _charmcraft_ command; (iii) Define the xNF and NS descriptors, including the.charm file to be deployed with the SO; (iv) Instantiate the NS, which finally deploys three pods into K8s, namelya Juju model container, the Juju operator (triggering actions into the service container), and a main container that uses the Docker image initially created. These steps are used for atomic SCs, i.e., SCs with one Docker image containing the protection mechanism software. For SCs with more than one Docker image or container, it is necessary to use Helm. Helm allows complex deployments in K8s, and can be used by the SO to deploy SCs. For instance, this approach is used by the SIEM SC, composed of four Docker images. In this context, the first step is to define the Helm chart with the required resources, and the second step is to implement a changejJuju charm proxy to interact with the deployment created. This approach is transparent to the final user since the triggering of Day-0, Day-1 and Day-2 actions is performed in the same manner, disregarding the SC containing one container or more (thus, using Helm).  
We have implemented different SCs to detect and mitigate threats. Currently, four types of SCs have been deployed: IDS and DPI (Snort and Suricata [62]), FW (Iptables), DC (NetFlow and Zeek [62]), and SIEM (Wazuh [63]). All SCs implement the SEM element described in Section 4.6, which is finally implemented with two technologies: (i) Juju, in charge of triggering the different actions from the SO; (ii) Filebeat, a log collector that sends the logs generated in the SC to the Kafka broker in a specific Kafka topic. Finally, the current implementation provides a user-friendly approach that is transparent (abstracting low-level details) and adapted to the different deployment models.
# IoT-Based Medical Image Monitoring System Using HL7 in a Hospital Database
The advancements in fog computing, cloud computing, and IoT technologies are improving the healthcare industry and might save billions of lives by enabling quicker public access to facilities and services. Another report proposes an intelligent real-time patient monitoring system with pinpoint accuracy that detects any irregularity in the subject's vital data, such as temperature, and includes a fall detection model [11]. Their main objective is to design and construct a mobile IoT-based healthcare system that gathers patient data from different sensors and promptly notifies both the doctor and the patient's guardian via emails and SMS. It analyzes the patient's vital data remotely and detects ailments quickly [12]. Additionally, an IoT-based human health monitoring system has also been established which measures the user's blood pressure, pulse, body temperature, heart rate, physiological information, and other data about a patient's vital signs. After collecting long-term data, factors associated with a probable risk prediction should be investigated further in the future to expand the use of health monitoring systems [13]. In the future, this will provide a scientific and practical foundation for the prevention and treatment of chronic high-risk illnesses. Another study suggests a smart city health monitoring system. The proposed technique calculated a spectrum by using LPCC (Licensed Professional Clinical Counselors) to discriminate between individuals with normal and disturbed speech and detect the reason for this. Running speech is used to test the system's ability to grasp people's ordinary conversations and detect the existence of voice anomalies [14]. Perhaps this might be utilized in clinics to provide medical practitioners with extra information on the occurrence of voice anomalies. The recommended technology could also be placed on a soldier's body and use the Global Positioning System to track their health and current state. These data would be sent to the control room via the Internet of Things. The Gustier detection system, which employs emergency signaling sensors, would also be part of the envisioned system.
# BDS-3 Time Group Delay and Its Effect on Standard Point Positioning
## 2 Methodology
### Dual-Frequency Users  
For dual-frequency users, the first-order ionospheric delay will be removed by the dual-frequency ionospheric-free combination model. The higher-order ionospheric delay is not considered herein. Although the ionospheric delay is corrected by the ionospheric-free combination model, the TGD corrections still need to be considered for different frequency combinations.  
In the BeiDou navigation satellite system open service performance standard (version 2.0), the signals B1I or B1C are recommended for single-frequency services, while the signal combinations B1I/B3I and B1C/B2a are recommended for dual-frequency services [30]. When the B1I/B3I or B1C/B2a ionospheric-free model is used, the code biases \(B_{B1I/B3I}\) and \(B_{B1C/B2a}\) can be expressed as  
\[\left\{\begin{array}{l}B_{B1I/B3I}=\frac{f_{B1I}^{2}}{f_{B1I}^{2}-f_{B1I}^{ 2}}TGD_{1}\\ B_{B1C/B2a}=\frac{f_{B1C}^{2}TGD_{B1 Cp}-f_{B2a}^{2}TGD_{B2 ap}}{f_{B1C}^{2}-f_{B 2a}^{2}}\end{array}\right. \tag{4}\]
# Performance Analysis of Pile Group Installation in Saturated Clay
## 3 Two-Pile Group Analyses
### Internal Forces of Adjacent Pile  
In FLAC3D, while using solid elements to model pile foundations accurately reflects the actual cross-sectional shape and dimensions of the piles, it does not allow for the direct computation of internal forces such as axial force and bending moment. Therefore, this study introduces a structural element pile model to calculate the internal forces of adjacent piles. Specifically, the adjacent pile (the first pile) is modeled using solid elements to analyze compaction effects during its installation. Once the adjacent pile installation is complete, the structural element is established along the central axis of the pile. Subsequently, the installation of the subsequent pile (the second pile) is carried out, and the changes in axial force and bending moment in the adjacent pile during the process are recorded.  
Figure 12a shows the variation in axial force of the adjacent pile with \(h\). The results indicate that the peak axial force is primarily located at the bottom of the pile, approximately 0.8-0.9 \(L\) from the top of the pile. Additionally, the peak axial force decreases with increasing \(h\). This is mainly because the axial force of the adjacent pile is largely related to the soil stress. After the adjacent pile is installed, there is significant geostress and additional stress at the bottom of the pile, resulting in a higher axial force at the bottom. As \(h\) increases, the geostress concentrates in the soil around the subsequent pile, leading to a decrease in the peak axial force of the adjacent pile. Since the bending moment of the adjacent pile is mainly related to the lateral pressure exerted by the subsequent pile, as \(h\) increases, the lateral pressure's effect on the adjacent pile is transmitted downward. Therefore, as shown in Figure 12b, the peak bending moment of the adjacent pile moves downwards along the pile with increasing \(h\).  
For subsequent analysis, normalization was performed using \(h/L\) (subsequent pile penetration depth/pile length). Figure 13 shows the variation in peak axial force and peak bending moment of the adjacent pile with \(S\). The results indicate that the peak axial force increases with the increase in \(S\), while the peak bending moment generally decreases with the increase in \(S\). Additionally, the peak axial force first decreases and then increases with the increase in \(h\), with the maximum peak axial force occurring around \(h/L=0.1\). The peak bending moment first increases and then decreases with the increase in \(h\), with the maximum peak bending moment occurring around \(h/L=0.7\). Figure 14 shows that the peak axial force and peak bending moment of the adjacent pile generally increase with the increase in \(L/D_{\text{e}}\). The maximum peak axial force occurs around \(h/L=0.1\), while the maximum peak bending moment occurs around \(h/L=0.7\) or \(h/L=0.8\).  
Figure 12: Internal forces of adjacent pile with different penetration depths of subsequent pile.
## 6 Response Analysis and Experimental Verification of Milling Cutter's Cutting Energy Efficiency  
### Response of Instantaneous Cutting Energy Efficiency of Milling Cutter  
To verify the effectiveness of the identification method, the structure parameters, initial process design posture, and cutting efficiency of the milling cutter in Table 1 were kept unchanged by changing the spindle speed, feed rate per tooth and cutter tooth error. The specific scheme is shown in Table 4.  
According the Table 4, the milling vibration acceleration signal of scheme 2 was obtained, as shown in Figure 19.  
Using Equations (39)-(41), the shear energy efficiency, specific shear energy and the specific tangential-force energy of scheme 2 were calculated, as shown in Figures 20 and 21.  
It can be seen from Figures 17 and 22 that the shear energy efficiency of the milling cutter in scheme 2 was improved and had good stability after changing the spindle speed and cutter tooth error. Compared with scheme 1, the specific shear energy and specific tangential-force energy of scheme 2 were both reduced. This represents that the cutting energy required for scheme 2 to remove the workpiece material per unit volume was less than that required for scheme 1. The above analysis further indicated that reducing the spindle speed and changing the cutter tooth error distribution could improve the level of the milling cutter's cutting energy efficiency.  
Figure 21: The distribution of specific cutting energy of cutter teeth (scheme 2). (**a**) The distribution of specific shear energy of cutter teeth; (**b**) the distribution of specific tangential-force energy of cutter tooth.  
Figure 22: Variation in instantaneous cutting energy efficiency of milling cutter (scheme 2).
## 3 Method
### Data Collection
\begin{table}
\begin{tabular}{c c} \hline \hline
**Country and Project** & **Project Information** \\ \hline UK: & Anglian Water is a large water and wastewater company. The collaborative @one Alliance was formed to design and build more than half of Anglian Waters capital investment program: around 800 schemes worth approximately GBP 1.2 billion between 2015 and 2020. Two projects were studied: Grafham WTW Resilience and Dalton Piercy WTW. \\ \hline UK: & High Speed 2 (HS2) is a high-speed railway under construction in the United Kingdom. The project studied is Phase 1 from London to Birmingham (176 km). HS2 is being developed by High Speed Two (HS2) Ltd, a NDPB (non-departmental public body) established by the Department of Transport. HS2 uses a two-stage early contractor involvement (ECI) approach for their main works civil contracts. Four contracts were signed in 2017 (Stage 1, design development) and 2020 (Stage 2, detailed design, and construction). Full operation is planned for 2029. \\ \hline USA: California High-Speed Rail & California High Speed Rail, CHSR, aims to connect the Californian state from North to South. The California High-Speed Rail Authority was formed in 2003 to oversee the planning, design, build, and operation of CHSR. The studied project is the fourth construction package of Phase 1, a 35-km railway stretch situated in Central Valley. Full operation of Phase 1 is planned for 2033. \\ \hline USA: & San Francisco International Airport (SFO) AirTrain Extension project comprises design and construction of an elevated guideway, long-term parking, and a new AirTrain station, to allow passengers to transit to the airport terminals. The AirTrain extension had an estimated budget of USD 207 million and was completed in 2021. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cont. criteria used in the projects for carbon emission reductions have been summarized and discussed in a previous article [19].
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 6 Implementations and Use Cases
### New Mobile Edge Applications
#### 6.2.2 Connected Vehicles  
5G mobile edge computing can enable new applications on a larger scale. Consider the coordination of increasingly ad-hoc networks from unmanned aerial vehicles (UAVs) and connected cars that must navigate new surroundings [117], offload computational tasks and download new information with the assistance of mobile edge computing stations nearby [118], all with low latency as vehicles move throughout their region. UAVs have strict memory and power-consumption restraints under which ML decision tasks must function, and, therefore, could benefit from distributed learning methods and computational offloading.
# Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach
## 3 Methodology
### Ref  
We used the RF ML algorithm, introduced by [PERSON] [17], to estimate SOM. It has been widely applied in precision farming, soil parameter measurement, and organic matter mapping [34; 35]. The RF algorithm can predict continuous response variables using classification and regression tree analysis [36]. Decision trees were used to model the data; each tree acted as a separate regression function and was trained using a distinct bootstrap sample of the training data. The final result was obtained by averaging the outputs of multiple trees [34]. The reliability of the decision trees was assessed using out-of-bag (OOB) samples, which were not included in the bootstrap sample, along with other model metrics, such as misclassification error and variable relevance [35].  
We optimized the RF parameters for two response variables, SOM and response variables (Landsat bands [1; 2; 3; 4; 5; 6; 7], bands 10 and 11, DEN, NDVI, SAVI, and BI), using the OOB error estimate. The optimization process focuses on two key parameters: three, which refers to the total number of trees generated by the algorithm, and mtry, which pertains to the number of random input parameters used for constructing each tree. We used the percentage increase in the MSE metric (%IncMSE) to determine the importance of each explanatory variable in model predictions. This metric measures the decline in model accuracy when a variable is removed [36]. By examining whether replacing a variable significantly affects %IncMSE, we can infer the significance of that variable [37].  
[PERSON] [PERSON] [38] developed the XGBoost algorithm to enhance the performance of gradient boosting machines, particularly regression trees [39]. This approach involves boosting, which creates a \"strong\" learner from a group of \"weak\" learners through additional training. The XGBoost method enhances the computation speed and reduces the frequency of overestimated events. In addition, it simplifies the objective functions and accelerates the computation by combining the estimation and adjustment terms. Furthermore, XGBoost automatically performs parallel computations of functions during the training phase [39]. In this study, we employed the R Project for statistical computing (Version 4.2) to develop an XGBoost model. The optimal model parameters were ascertained through meticuloustuning of pivotal hyperparameters, including the number of decision trees, the maximum count of nodes or leaves in a single decision tree, the maximum depth of the tree, and the learning rate, as outlined in [15]. Pertinent R packages utilized for this research encompassed xgboost [38], randomForest [40], caret [41], and raster [42].