esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The presented work focused on the application of UAV-based photogrammetry in the design of blasthole patterns in a basalt quarry. The use of high-resolution aerial imagery and RTK-supported 3D modeling enabled precise reconstruction of the geometry of the bench face, including critical parameters, such as hole length, inclination, and actual burden distribution. Compared to traditional 2D approaches, the 3D analysis revealed significant spatial variations, especially in toe zones, directly impacting blast efficiency and flyrock risk.  
The method led to improved safety conditions, including a documented reduction in flyrock range by an average of 42% near protected structures. In addition, the photogrammetric approach allowed for a ten-fold reduction in the number of images and an 80% decrease in model processing time without compromising mapping accuracy. It should be noted that results may vary depending on specific mining conditions, and it is recommended that similar UAV-supported blast design approaches be further tested in other quarry environments. All UAV flights were conducted in accordance with Strayos software recommendations regarding image overlap, flight path, and camera orientation.  
Preliminary cost comparisons indicate that, over time, UAV-based methods offer a cost-effective and efficient alternative to traditional surveying techniques, particularly in operations requiring frequent and rapid terrain documentation.  
Despite operational constraints, such as weather sensitivity, licensing requirements, and surface reflectivity issues, UAV photogrammetry proved to be a robust and efficient solution for operational blast planning. Further integration with predictive models and machine learning may offer even greater control over risk assessment and blast outcome optimization.  
Conceptualization, J.P. and T.Z.; methodology, J.P. and T.Z.; formal analysis, T.Z.; investigation, J.P.; resources, J.P. and T.Z.; data curation, J.P. and T.Z.; writing--original draft preparation, J.P. and T.Z.; writing--review and editing, J.P. and T.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
Author [PERSON] was employed by Holcim Kruszywa Sp. z o.o. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Distributed Satellite Relay Cooperative Communication with Optimized Signal Space Dimension
## Abbreviations  
Abbreviation Comparison Table  
Amplify and Forward  
Decode and Forward  
Maximum Ratio Combining  
Selective Diversity Combining  
Network Coding  
High Altitude Platforms  
Unmanned Aerial Vehicles  
Common Channel Interference  
CCI  
Multiple Input Multiple Output  
Single Input Single Output  
SISO  
Degrees of Freedom  
DoF  
Contact Graph Routing  
CCR  
Multiple Access Channel  
MAC  
Broadcast Channel  
Receive Antenna Selection  
RAS  
Signal Space Alignment  
SSA  
Uniform Linear Array  
ULA  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline K & \(\mathbf{(d_{12},d_{13},d_{14},\ldots\ d_{42},d_{43})}\) & \(N\) & \(\mathbf{T_{proposed}}\) & \(\mathbf{T}\)[34] \\ \hline
3 & (2, 0, 0, 0, 2, 0, 0, 0, 0, 0) & 4 & 11 & 16 \\
4 & (1, 1, 0, 0, 1, 2, 0, 0, 1, 2, 0, 0) & 6 & 17 & 30 \\
4 & (3, 0, 0, 1, 2, 1, 1, 1, 0, 2, 0, 0) & 7 & 21 & 35 \\
4 & (3, 0, 0, 1, 3, 0, 1, 0, 3, 2, 0, 2) & 10 & 33 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Multicast transmission strategy based on ring planning.
## 5 Conclusions and Future Directions  
In this paper, we proposed a framework that integrates medical sensors, a sensor network, electronic patient records and web portal technology to allow remote monitoring of patient physiological status in the homecare conditions and to alert medical personnel when life-threatening events occur. Based on the architecture, we proposed an SMS-based medical diagnostic telemetry data transmission protocol for medical sensors. The standard SMS has several disadvantages such issues around delivery reliability, validity periods of messages, and the time-to-live for an SMS message, the delivery guarantees not made by the network, and the lack of formal delivery receipts must all be considered when examining potential applications for SMS. Furthermore, the security of information must be a concern in environments where personal or privileged data is being transferred. With the proposed extensions, it is possible to use SMS for the purposes of creating a formalised and highly structured communications protocol. This protocol, implemented within the confines of a single SMS message, but expandable into the realm of concatenated SMS messages for those messages which carry data, can be made sufficiently generic so as to be suitable for a wide variety of applications. We are currently studying the performance of a remote patient monitoring system using an OPNET based simulation model.
## 4 Optimization
### Updating Parameter Limits and Objective Function
Since the friction contact surface is the same as the extrusion contact surface mentioned above, the friction coefficient can be introduced into Equation (5), and the relationship between deformation and friction force can take the following form:  
\[F=\mu EA\epsilon \tag{6}\]  
where \(\mu\) is the friction coefficient and its value is about 0.1. As a result, the upper limit of \(k_{\rm H}\) can be determined by the upper limit of \(k_{\rm V}\). Due to the lack of sufficient theoretical basis, the lower limits cannot be set accurately; thus, the lower limits of \(k_{\rm H}\) and \(k_{\rm V}\) were taken as a smaller value relative to the value of their respective upper limits. The lower and upper limits of all updating parameters are listed in Table 8.  
In this work, the four-order natural frequencies obtained experimentally and those obtained numerically were used to construct the following objective function:  
\[F_{\rm obj}=\sqrt{\frac{\sum_{i=1}^{4}\omega_{i}\bigg{(}\frac{\ell_{i}^{\rm exp }-f_{i}^{\rm num}}{\ell_{i}^{\rm exp}}\bigg{)}^{2}}{\sum_{i=1}^{4}\omega_{i}}} \tag{7}\]  
where \(\omega_{i}\) is the weight coefficient of the natural frequency error of each order, \(f_{i}^{\rm exp}\) is the \(i\)-th order experimental natural frequency, and \(f_{i}^{\rm num}\) is the numerical natural frequency \begin{table}
\begin{tabular}{c c c} \hline \hline
**Updating Parameters** & **Lower Limit** & **Upper Limit** \\ \hline \(k_{\rm H}({\rm N}/{\rm m})\) & \(10^{4}\) & \(10^{8}\) \\ \(k_{\rm V}({\rm N}/{\rm m})\) & \(10^{5}\) & \(10^{9}\) \\ [PERSON]’s modulus (GPa) & 68.9 & 76 \\ Poisson’s ratio & 0.3 & 0.34 \\ Density (kg/m\({}^{3}\)) & 2680 & 2890 \\ Length (mm) & 296.81 & 303.10 \\ Thickness (mm) & 1.89 & 2.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Updating parameter limits. corresponding to \(f_{1}^{\text{exp}}\). Through this objective function, by increasing the value of \(\omega_{\text{i}}\), the i-th order experimental natural frequency error is easier to obtain a smaller value. In this work, \(\omega_{\text{i}}\) was set to the same value. The optimization model of the second model-updating approach can be expressed by Equation (8). \[ \begin{array}{ll}\text{Minimize}&F_{\text{obj}}=\sqrt{\frac{\sum_{i=1}^{4} \omega_{\text{i}}\left(\frac{\ell^{\text{exp}}-\ell^{\text{num}}_{\text{i}}}{f _{1}^{\text{exp}}}\right)^{2}}{\sum_{i=1}^{4}\omega_{\text{i}}}}\\ \text{S.T.}&k_{\text{H}}\leq k_{\text{H}}\leq k_{\text{H}}\\ &k_{\text{V}}\leq k_{\text{V}}\leq k_{\text{V}}\\ &\overline{E}\leq E\leq\overline{E}\\ &\underline{\mu}\leq\mu\leq\overline{\mu}\\ &\underline{\rho}\leq\rho\leq\overline{\rho}\\ &\underline{L}\leq L\leq\overline{L}\\ &\underline{T}\leq T\leq\overline{T}\end{array}  \tag{8}\]  
In this equation, the objective function is minimized, and the limits to which the problem is subject (S.T. ), consisting of the lower and upper limits of the updating parameters.
## 2 Background on Computing Paradigms
### Fog-Related Aspects
In this section, an attempted explanation is detailed in an understanding manner, which describes the different components which make up the general architecture [38]. Generally, most of the research projects performed on Fog computing have mostly been represented as a three-layer model in its architecture [39], see Figure 2. Moreover, there is a detailed N-layer reference architecture [40], established by the OpenFog Consortium, being regarded as an improvement to the three-layer model. However, we will be looking at a three-layer architecture. Fog computing is considered to be non-trivial addition regarding Cloud computing based on Cloud-to-Things setup. In fact, it displays a middle layer (also known as the Fog layer), closing the gap between the local end devices and Cloud infrastructure [41]. Notably, and as in the Cloud, the Fog layer also uses local virtualization technologies. On the other hand, taking into consideration the available resources, it will be more adequate to implement virtualization with container-based solutions [38]. It should also be remembered that Fog nodes found in this layer are large in number. Based on OpenFog Consortium, Fog node is referred to as \"the physical and logical network element that implements Fog computing services\" [42]. Fog nodes have the capability of performing computation, transmission, and also storing data temporarily and are located in between the Cloud and end-user devices [43]. The essential pushes for the eminent migration from Cloud computing to Fog computing are caused by load from computations and bringing Cloud computing close to Edge. Several characteristics define Fog computing by the tremendous variety of applications and IoT design services [44]. The major one corresponds to the extreme heterogeneity of the ecosystem, which provides services between centralized Cloud and different devices found at the Edge, such as end-user applications via Fog. The heterogeneity of Fog computing servers comprises shared locations with hierarchically structured blocks. At the same time, the entire system is highly distributed geographically. Fog computing models consist of extensively shared deployments in actuality to offer a Quality of Service (QoS) regarding mobile and non-mobile user appliances [45]. The nodes and sensors of the Fog computing are geographically shared in the case of various stage environments, for instance, monitoring different aspects such as chemical vats, healthcare systems, sensors, and the climate. The ability to effectively react to the primary goal and objective can be called cognition. Customers' requirements are better alerted by analytics in a Fog-focused data gateway,  
Figure 2: Most commonly analyzed computing architectures. which helps give a good position to understand where to make a transmission, storage possibilities, and the control operations along the whole process from Cloud to the Internet of Things continuum. Customers enjoy the best experience due to applications' closeness to user devices and creating a better precision and reactiveness concerning the clients' needs [46].
# Review of Traffic Assignment and Future Challenges
## 2 Preview
### Problem Statement  
Traffic assignment deals with transportation traffic. It involves the following _input_ elements:  
* Traffic supply: Road network and/or public transportation services as well as their corresponding behaviors.
* Traffic demand: A flow matrix indicating the demand volume between each origin-destination (\(o\)-\(d\)) pair.  
The most extensively researched traffic assignment problem pertains to road traffic, aiming to estimate traffic demand for individual road segments and movements at intersections, including right and left turn movements. In the case of public transportation demand, the goal is to determine the number of passengers for each bus or rail line, along with estimates of boarding, alighting, and connecting passengers at each station. In other words, the _output_ of traffic assignment specifies the routes used for each \(o\)-\(d\) pair, with the corresponding volume.  
This problem was initially formulated for regular vehicles in 1920 by [PERSON] (1992). The author distinguishes between two classes of traffic assignment problems. The first is centralized, involving the optimal allocation of a fleet of vehicles on a transportation network. The entity responsible for the fleet's mission optimizes the vehicle assignment according to its specific objectives. The second class, presented in Section 3 of the document, is decentralized. The assignment results from the optimization that each user of the transportation network performs for their benefit. This second class of problems gained particular attention in the literature following [PERSON]'s contribution [PERSON] (1976), who meritoriously formulated the problem. In the following, unless stated otherwise, the term \"traffic assignment problem\" refers to the second class of problems.
# A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
## 4 SQI for cPPG
### Rule-Based
#### 4.1.1 Criteria Based on Signal Features  
To address the different criteria reviewed in papers suggesting a rule-based SQI algorithm, we decided to group them within three scales. Signal scale criteria regroup conditions based on morphological and spectral statistics computed on an ensemble of successive pulses (see Table 4). Pulse scale criteria regroup conditions based on morphological and spectral features computed for each pulse. [PERSON] et al. [44; 45] suggested to evaluate the quality of a PPG pulsation based on the value taken by its morphological features (pulse width, rising time, pulse amplitude, area under the pulse, difference between autocorrelation of red and infrared signals). Each threshold separating good- from bad-quality signals has been designed from the literature and on statistical analysis. Among features computed on pulses, the skewness of the pulse is the most commonly used. This feature indicates the asymmetry of a PPG waveform and should be positive as the systolic peak is dominant in a normal pulse. [PERSON] demonstrated in [51] that skewness is the best indicator to separate excellent-quality from medium- and low-quality signals. The inter-beat scale regroups conditions based on the variation in features between successive beats. Within inter-beat features, the inter-beat interval (the time separating two successive pulse peaks) is the most commonly used feature and gives an idea of the stability of the signal from one beat to another. In Table 4, we only enumerate features extracted from a PPG signal. However, if the device used to measure the PPG signal offers measurement from other sensors, such as the acceleration from a smartwatch accelerometer [67], the use of features from other signals is often observed to assess its quality. For example, [PERSON] et al. [48] used the red and infrared PPG signals offered by their sensor to deduce the variation in oxygenated and deoxygenated blood and build criteria based on it (ratio between oxygenated and deoxygenated pulse). These criteria are not applicable for smartwatches measuring PPG from green light. Figure 6: Figure illustrating cPPG pulses and possible features extracted from them.
## 1 Introduction
It is worthy to clarify that III-V semiconductors play a crucial role in advancing solar cell technology due to their exceptional optoelectronic properties. It is well known that these materials exhibit high absorption coefficients, direct bandgaps, and superior carrier mobility, making them ideal for high-efficiency multi-junction solar cells. The continuous improvement in III-V semiconductor processing has demonstrated a record-breaking efficiency in multi-junction solar cells, reinforcing their importance in the future of photovoltaic energy. In [18], [PERSON] et al. developed a self-powered GaN-nanowall-network photodetector with bidirectional photocurrent generation, achieving responsivities of \(-2.5\) A W\({}^{-1}\) (266 nm) and \(1.7\) A W\({}^{-1}\) (355 nm) [18]. The same authors proved in [19] that the GaN hexagonal nano-spike photodetector further exhibited high sensitivity, reaching 2100 mA W\({}^{-1}\) responsivity under solar-blind UV light, highlighting its potential for self-powered optoelectronics. Advancements in heterostructure engineering emphasize the synergy between III-V materials, and emerging semiconductors were presented in some research studies, such as in [20], where [PERSON] et al. integrated monolayer MoS\({}_{2}\) with GaN to develop a self-powered, bidirectional photodetector with a responsivity of 631 mA W\({}^{-1}\) at 0 V bias, operating across UV and visible spectra. This heterostructure leverages the strengths of 2D materials and III-V compounds, similar to tandem solar cell designs. Additionally, the Sb\({}_{2}\)Se\({}_{3}\)/GaN heterojunction achieved ultra-high responsivity and maintained thermal stability up to 250 \({}^{\circ}\)C, demonstrating the robustness of III-V-based systems under extreme constraints [21]. This study focuses on the development of an InGaP/GaAs double-junction solar cell (DJSC) structure, integrating a buffer layer in the lower junction and a new back surface field (BSF) in the upper junction. The design was optimized using the Silvaco ATLAS simulator under the AM1.5G standard spectrum. It is found that by systematically adjusting the layer thickness, buffer layer carrier concentration, and BSF properties, the solar cell's performance was significantly enhanced compared to the baseline. Furthermore, the impact of operating temperature on key performance metrics was analyzed, and it was found that it can offer valuable insights for improving efficiency and reliability in advanced photovoltaic technologies.
# Efficient Intrusion Detection Algorithms for Smart Cities-Based Wireless Sensing Technologies
## 5 Simulation and Analysis  
### Simulation Environment  
To simulate the previously mentioned algorithms and sensor networks, the CupCarbon simulator [34] is extended. CupCarbon provides a good library for analyzing the sensor's energy and providing sensor network performance evaluation. It also provides different types of sensors that can be programmed using simple script language. The simulator has been extended by adding the LEACH clustering, AODV routing protocol, ABC, S-LEACH, MS_LEACH, TBIDS, and MBIDS algorithms. The general network configuration is shown in Table 1.  
It is assumed that the compromised node is the one that has higher energy among the CHs. Besides, in this paper, the deployment process is done randomly; certainly, any type of deployment could be suitable to be used as well. Sensors select their CHs based on the LEACH algorithm. Additionally, _CHs_, as well as the sink node(s), are designed to use one of the IDS monitoring techniques (multi-path or threshold-based).  
The evaluation criteria are as follows:  
* Detection Accuracy: Based on the used algorithms, the detection accuracy is measured. This criterion has not been used as a measure in the previous algorithms, but it is believed that it will be beneficial to check on the detection accuracy in identifying the hacked node.
* Power Consumption: This is the average sum of the energy consumed by all nodes in transmitting messages at a certain time of operation. This could be measured by the following equation: \[\textit{Avg power Consumption}=\sum_{i=1}^{N}(\frac{\textit{sn}_{i}\textit{ consumed energy}}{N})\] (4)
* Network Lifetime: It is the time from the start of the simulation to the first node dies due to energy depletion.
* Packet Loss: The definition of the packet loss is a little bit different, where it represents the number of lost packets during the existence of the sinkhole attack.  
Experiments are designed to include different numbers of sensors, different number of sink nodes, and various locations of sinkhole nodes. In addition, our proposed methods are compared to three different algorithms--S-LEACH, MS-LEACH, and ABC algorithms--explained previously.
DIBH involves patients holding their breath during irradiation to temporarily remove the heart from the radiation field, reducing potential cardiac complications and those in other sensitive organs--organs at risk (OARs) [7; 9; 10; 11]. Comparative studies show significant differences in radiation dose to the heart between DIBH and free breathing (FB) techniques (mean heart dose (MHD) was reduced to \(<\)50%, the heart V25 Gy to \(<\)20%, the LAD mean dose to \(<\)40%, and the LAD maximum dose to about 50%), indicating a reduced cardiac risk with DIBH in breast cancer radiotherapy [12; 13]. Radiation therapy for patients with left-breast cancer increases the probability of death from cardiovascular causes (CVDs) [5]. The advantages of DIBH include the physical separation of breast tissue from the heart, resulting in a reduced cardiac radiation dose [5; 7; 8; 14]. Studies highlight the minimization of radiation dose to the lungs to prevent side effects, underscoring the potential of DIBH to reduce the risk of radiation-induced lung damage [9; 10]. Controlled deep inhalation allows for the precise targeting of the tumor while sparing healthy heart tissue and protecting other structures, which is particularly important for patients with potential cardiotoxicity undergoing systemic treatment [6; 7; 15]. However, successful implementation of DIBH depends on a variety of critical factors detailed in the recent literature [9; 16; 17; 18]. Challenges remain regarding patient comfort, the repeatability of breath-holding, and the need for specialized equipment [19; 20]. Eligibility criteria for DIBH in radiotherapy mainly focus on left-breast cancer due to its proximity to the heart [9; 21; 22; 23]. The purpose of this narrative review is to present the factors affecting the efficacy and side effects of DIBH in radiotherapy for left-breast cancer, serving to optimize its clinical application while minimizing side effects, which requires further research and prospective studies [13].