esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Machine Learning in Electromagnetics: Mini-Review
## 2 Familiarization with ML
### Unsupervised Learning  
In unsupervised learning, there are no training data, and the algorithm finds patterns in data without any reference to labeled outcomes. The model itself discovers important features existing in a raw dataset. In most cases, unsupervised learning is used for classification problems or feature reduction. One of the simplest forms of unsupervised learning is K-means clustering. It is a classification method that determines a center and the samples neighboring the cluster. This determination of K-centers follows a pattern of choosing the farthest samples from each other. Moreover, Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are used to cluster a set of data with similar characteristics and distinguish unrelatable data from a given dataset without any supervision of the system or dataset. Figure 2 illustrates a simple block diagram of unsupervised learning.
# Long-Term Effects in Structures: Background and Recent Developments
## 2 Motivation
### The Civic Tower in Pavia, Italy  
The Civic Tower (city of Pavia, Italy) was built near Pavia Cathedral in 11 th century, from brickwork masonry (Figure 2). The tower had a rectangular footprint, and its height was 72 m.  
Figure 1: Age-dependent strain change in concrete.  
The Civic Tower suddenly collapsed on 17 March 1989 killing four and injuring fifteen people. All that was left of the tower was 8000 cubic meters of brick, sand and granite rubble (Figure 3) [7,8].  
The collapse stunned engineering community and officials. The tower was solidly built, there was not an indication of chemical-physical damage of the bricks (main building material), or the settlement. The collapse of the tower made the Italian government close the Leaning Tower of Pisa over concerns that the popular tourist site might also be at risk of collapse [7,8].  
To fully understand the cause of the collapse numerous experimental and numerical investigations were conducted, leading to several identified factors [7,8]:  
* The tower was built in several phases, starting from 1060. The steeple, built with granite blocks, was added 500-600 years later;
* The thick bearing walls (2.8 m) were made of two thin external faces in regular brickwork and the internal core was made (filled with) with some kind of soft concrete containing mixture of broken bricks and stone pebbles with thick layers of mortar;  
Figure 3: Remains of the Civic Tower in Pavia after the collapse [8].  
Figure 2: Civic Tower in Pavia, Italy, before the collapse [6].  
* The total estimated mass of the tower was 12,000 tons, with the steeple alone weighing 3000 tons.  
An investigation showed that the bottom part of the tower was under very high compression stresses caused by its own weight: maximum values of 1.7 to 2.0 MPa were found according to the FE elastic model, and between 1.8 and 3.5 MPa according to experimental measurements. These values are significantly high compared to the material's compressive strength [7; 8]. This likely resulted in the gradual development of tiny fissures, which, over centuries, might have contributed to the sudden collapse of the material without any obvious immediate cause or evident warning signs, such as significant cracks or spalling, even in the days leading up to the collapse. A careful review of the photographs captured by archaeologists for the Civic Museums in 1968 indicates that, even then, there were fine vertical cracks spread widely across the exterior surface of the tower wall. These cracks were exceptionally challenging to observe and resembled those observed on the specimens during testing [9].  
Identifying the sole cause of the collapse proved challenging, but the elevated compressive stress induced by the dead load of the tower was undoubtedly one of the main contributing factors to the disaster.
## 6 DRRF and Real-time Traffic
### Deficit Round Robin with Fragmentation (DRRF)  
Traditionally, DRR [9] is used to avoid packet fragmentation by scheduling only a full packet. If a packet will result in exceeding the fair share, the packet is not scheduled, and then the deficit (amount that would have been allocated) is stored. However, to fully utilize a Mobile WiMAX frame, we use a modified version of DRR, proposed in this paper (See Section 3), DRR with fragmentation, or DRRF. In general, if a packet meets the fair share limit, we schedule it in the current Mobile WiMAX frame, and if necessary, allow the part that will not fit in the current Mobile WiMAX frame to be scheduled in the next frame. This ensures that Mobile WiMAX frame capacity is not wasted.
## 1 Introduction
Compared to the 7 MHz band, the 14 MHz band not only offers a higher bandwidth but also benefits from its higher frequency. This allows electromagnetic waves to penetrate more low-altitude ionospheric layers and reflect off higher ionospheric points, enabling longer-distance communication [38]. As a result, it is a suitable choice for our model. To address the aforementioned issues, this study focuses on investigating skywave ionosphere communication channel characteristics at 14 MHz for hypersonic vehicles. In Section 2, a skywave ionosphere channel for hypersonic vehicles is defined. Based on the calculation method of electromagnetic wave propagation in ionospheric media, key dynamic parameters that affect channel characteristics, such as delays, Doppler shifts, and losses, are analyzed and derived. In Section 3, using the re-entry trajectory of the RAM-C II vehicle as an example, the IRI ionospheric model and the NRLMSIS-00 atmospheric background molecular model are applied to analyze and discuss the dynamic characteristics of multipath channels and possible propagation states in multiple seasons during the noon and midnight periods. Finally, a summary of the entire paper is provided, along with a brief discussion on future research that needs to be conducted.
## 4 Conclusions  
The objective of this work was to investigate the use of dedicated subcooling techniques in a CO\({}_{2}\) transcritical refrigeration cycle. The use of an external mechanical compression refrigeration cycle and an absorption chiller for subcooling the CO\({}_{2}\) after the gas cooler were examined for different operating scenarios. All the cases were optimized and the results were compared under the proper conditions. The analysis was conducted with developed models in Engineering Equations Solver which were validated with literature data. The most important conclusions of this work are summarized below:  
* The use of dedicated subcooling with the mechanical compression (M-SC) system leads to higher COP and to higher exergy efficiency compared to the reference (Ref) system.
* The use of dedicated subcooling with the absorption chiller (ACH-SC) system leads to lower COP and to higher exergy efficiency compared to the reference (Ref) system.
* The mean decrease in the work consumption is found to be 41.39% for the mechanical subcooling system and 53.83% with the absorption chiller subcooling system.
* The heat input in the absorption chiller ranged from 51 up to 137 kW. Generally, it is high for cases with high heat rejection temperature levels.
* The exergy efficiency of the ACH-SC system is a bit lower than the M-SC system, but the electricity savings are higher in the ACH-SC. These facts in combination with the environmental parameters have to be taken into consideration for the future design of the dedicated subcooling systems. Moreover, the availability of any heat source (e.g., solar systems, geothermal energy, or waste heat) is an extra parameter for the final selection.  
The authors of this work have the same contribution.  
This research was funded by \"Bodossaki Foundation\".  
Evangelos Bellos would like to thank \"Bodossaki Foundation\" for its financial support.  
The authors declare no conflict of interest.
## 4 Implementation
### Libp2p Host
#### 4.5.1 Negotiation  
Messages are delivered as UTF-8 byte strings, which are always followed by a newline character. In addition, each message is prefixed with its length in bytes (including the new line), which is encoded as an unsigned variable-length integer by the multi-format unsigned variant standard.  
For example, the text \"/multistream/1.0.0\" is delivered as the hexadecimal value 0x032F6D756C746973747265616D2F312E302E300A. The first byte is the variant-encoded length (0\(\times\)03), followed by the short name for the multistream selection protocol \"/multistream\" (0\(\times\)2F 0\(\times\)6D 0\(\times\)75 0\(\times\)6C 0\(\times\)74 0\(\times\)69 0\(\times\)73 0\(\times\)74 0\(\times\)72 0\(\times\)65 0\(\times\)61 0\(\times\)6D) and a version number \"/1.0.0\" (0\(\times\)2F 0\(\times\)31 0\(\times\)2E 0\(\times\)30 0\(\times\)2E 0\(\times\)30), and finally the new line (0\(\times\)0A).  
Multistream selection is a mechanism used in libp2p to handle peer connections. Its main purpose is to facilitate protocol negotiation over an existing connection in an efficient and flexible way. Such a mechanism is based on an exchange of messages using text strings to identify the available protocols. Each peer sends a message containing the name of the protocol it wishes to use. If the other peer supports that protocol, it responds with a confirmation.  
Figure 8 depicts the fundamental sequence for the \"multistream-select\" negotiation. The negotiation for establishing a connection between two nodes is as follows:  
1. The initiating peer establishes a channel with the destination peer; this channel could be a new connection or a new stream multiplexed over an existing connection.
2. Both peers will then broadcast the multistream protocol ID to indicate that they want to employ multistream selection. Both sides can deliver the first multistream protocol ID without waiting for data from the other side. If either side obtains anything other than the multistream protocol ID as the initial message, the negotiation process terminates.
3. After both peers have agreed to employ multistream selection, the initiator communicates the protocol ID they want to use (step 3A). If the recipient supports the protocol, it will answer by repeating the protocol ID to indicate its agreement (step 3B). If the protocol is not supported, the recipient will respond with the string \"na\", indicating that the requested protocol is unavailable (step 3C).
4. If the peers agree on a protocol, the task of selecting numerous streams is completed, and future communication across the channel will follow the agreed-upon protocol rules. If a peer receives a \"na\" answer to a proposed protocol ID, it may retry with an alternative protocol ID or terminate the channel.
# Resource Allocation of UAV-Assisted IoT Node Secure Communication System
## 2 System Model and Problem Formulation
### Problem Formulation  
In this work, a conjoint optimization of the UAV trajectory, transmission power allocations, and PSR is adopted to maximize the secrecy rate within a limited flight time \(T\). We assume that the average and maximum power constraints, mobility constraints, initial and final position constraints of the UAV, and energy harvesting constraints of each EN are satisfied. Let \(\mathbf{P}\overset{\Delta}{=}[p[1],\ldots p[N]]^{T},\mathbf{O}\overset{ \Delta}{=}[\mathbf{q}[1],\ldots\mathbf{q}[N]]^{T},\Xi\overset{\Delta}{=}[\xi[ 1],\ldots\xi[N]]^{T}\). Thus, the maximum of the secret rate of \(DN\) can be expressed as \[P_{1}:\max_{\mathbf{P},\mathbf{Q},\Xi}\sum_{n=1}^{N}\left[R_{SD}[n]- \max_{k\in K}R_{SE,k}[n]\right]^{+}\] \[\text{s.t. }\text{C1}:\frac{1}{N}\sum_{n=1}^{N}E_{EH,k}[n]\geq E_{ \min},\forall n\] \[\text{C2}:0\leq\xi[n]\leq 1\] \[\text{C3}:\mathbf{q}[1]=\mathbf{q}_{0},\mathbf{q}[N+1]=\mathbf{q }_{F}\] \[\text{C4}:\frac{1}{N}\sum_{n=1}^{N}p[n]\leq\bar{P},\forall n \tag{8}\] \[\text{C5}:0\leq p[n]\leq P_{\max},\forall n\] \[\text{C6}:\frac{1}{N}\sum_{n=1}^{N}E_{\text{S}D}[n]\geq E_{ \min},\forall n\] \[\text{C7}:\|\mathbf{q}[n+1]-\mathbf{q}[n]\|\leq S_{\max},n=\{1, \dots,N\}\] \[\text{C8}:\frac{1}{N}\sum_{n=1}^{N}E_{IT,k}[n]\leq E_{sen}\]  
Here, the constant \(E_{min}\) is the minimum average harvested energy. C1 and C6 have imposed basic constraints on Emin, which enable all nodes to collect energy. The constant \(E_{sen}\) is the minimum energy required for decoding. C8 prevents \(ENs\) from decoding the information transmitted to the main link (\(S-D\)). The information threshold \(E_{sen}\) is smaller than the power threshold \(E_{min}\). C2-C5 and C7 are the formulations defined in Section 2.1.
# Biochar for Wastewater Treatment--Conversion Technologies and Applications
## 2 Conventional Thermal Methods for the Conversion of Feedstock into Carbonaceous Materials
### Torrefaction  
Torrefaction is a conventional thermal pretreatment of biomass performed to improve the physicochemical and thermochemical properties of biomass; it enables the energy densification and the homogenization of biomass. The torrefaction is performed generally at slow heating rates under atmospheric pressure at temperatures ranging from 200 \({}^{\circ}\)C to 300 \({}^{\circ}\)C and without or with limited oxygen supplies [76]. Torrefaction is dominantly used for biofuel production [77], while pyrolysis is the dominant process to prepare biochar for water and wastewater depollution. Compared to biochar, torrefaction-char may contain higher amount of oxygen-containing functional groups, attributed to the much lower temperatures used during the thermal process [77]. Thus, torrefaction-char might be used as an efficient adsorbent. [PERSON] et al. [78] compared between the physico-chemical properties of char prepared by torrefaction at 200-300 \({}^{\circ}\)C and biochar prepared by pyrolysis at 500 \({}^{\circ}\)C and their removal efficiencies toward U(VI) and methylene blue. The results showed that the maximum adsorption capacities towards U(VI) and methylene blue were increased, respectively, from 56.2 and 192.7 mg/g for biochar to \(>\)100 and \(>\)350 mg/g for torrefaction-char, indicating that char from torrefaction is much more efficient than biochar from pyrolysis for water pollution control. Torrefaction-char from barley straw produced at 220 \({}^{\circ}\)C during 20 min was also used as an adsorbent and showed enhanced adsorption capacity towards methylene blue [79]. Due to the lower process temperature, simpler preparation method, and higher product yield, the torrefaction-char as an oxygen-rich-char could be considered as a promising adsorbent. More attention should be paid for its application for contaminants removal.
## 1 Introduction
The key contributions of this work are as follows:  
* We present ML-RASPF, a novel hybrid mist-edge-cloud framework for rate-adaptive and latency-aware IoT service provisioning in smart healthcare systems. * We formulate the service provisioning problem as a joint optimization model that integrates both latency constraints and service delivery rates. This formulation enables intelligent, QoS-aware resource allocation across heterogeneous IoT environments. * We propose a modular, ML-based algorithmic suite combining supervised learning for traffic prediction and RL for real-time service rate adaptation. * We evaluate the proposed framework within EdgeCloudSim, using realistic smart healthcare workloads and the results show that ML-RASPF significantly outperforms state-of-the-art rate-adaptive methods by reducing latency, energy consumption, and bandwidth utilization while improving service delivery rate. Figure 1: Illustration of latency-aware and rate-adaptive service flow across cloud, edge, and mist layers in a smart healthcare setting. RL and LSTM modules dynamically optimize traffic routing and service delivery. The remainder of this paper is organized as follows. Section 2 reviews the related work on IoT service provisioning, edge-cloud architectures, and ML applications in healthcare. Section 3 introduces the proposed rate-adaptive framework, detailing its layered architecture and operational workflow. Section 4 formulates the service provisioning problem as a joint optimization model and describes the ML-based algorithm used for resource allocation. Section 5 presents the experimental setup and performance evaluation results based on a smart healthcare scenario. Finally, Section 6 concludes the paper and outlines potential directions for future research.
# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 2 The Proposed Method
### The Transmission Tower Detection Based on the Improved YOLO
#### 2.2.3 Transmission Tower Detection Model Evaluation  
Object detection models are typically evaluated based on their inference time and accuracy. In this study, we chose mean Average Precision (\(mAP\)) to assess the accuracy of transmission tower detection. The \(mAP\) is derived from the average AP of all categories, indicating the overall detection performance of the model. The calculation formula for \(mAP\) is presented by Equation (6):  
\[P=\frac{TP}{TP+FP} \tag{3}\]  
\[R=\frac{TP}{TP+FN} \tag{4}\]  
\[AP=\int_{0}^{1}P(R)dR \tag{5}\]  
\[mAP=\sum_{i=1}^{N}\frac{AP_{i}}{N} \tag{6}\]  
where \(TP\) represents the number of true positives in the category, \(FP\) denotes the number of false positives in the category, \(FN\) is the number of false negatives in the category, \(AP_{i}\) represents the Average Precision for the \(i\)th category, \(N\) is the total number of categories, \(i\) represents the \(i\)th category, \(mAP\) represents the mean Average Precision.  
For object detection, \(IoU\) is the intersection ratio of two bounding boxes. In this paper, \(mAP@IoU\) is a comprehensive metric that considers the precision and recall of all detection categories under a given \(IoU\) value. Before calculating \(mAP@IoU\), it is necessary to first compute the \(AP@IoU\). \(AP@IoU\) represents the area under the \(PR\) curve formed by precision (\(P\)) and recall (\(R\)) under a given \(IoU\) value, reflecting the accuracy of a single category.
# Nanotechnology, Nanomedicine, and the Kidney
## 4 Nanotechnologies for Renal Imaging
### Magnetic Resonance Imaging  
Magnetic resonance imaging (MRI) is widely used in clinical diagnosis to differentiate lesions from healthy tissue. In the latest decade, NPs have been used in combination with MRI to improve the diagnosis and treatment of renal diseases. Recently, various superparamagnetic iron oxide nanoparticles (SPIONs) have been developed as transverse relaxation time T2-weighted contrast agents. SPIONs are composed of an iron oxide core and an outer layer that can be coated with drugs and antibodies for tissue-specific drug delivery and imaging. For example, the conjugation of SPOINs with an anti-MHC class II RT1 antibody improved the contrast in renal medullary inflammation imaging [46]. In another example, [PERSON] et al., introduced a novel activable dual-mode T1/T2-weighted contrast agent, an Fe\({}_{3}\)O\({}_{4}\) NP cross-linked to a cystamine dihydrochloride (Cys) to form Fe\({}_{3}\)O\({}_{4}\)/Cys cluster NPs. The authors demonstrated that Fe\({}_{3}\)O\({}_{4}\)/Cys NPs exhibit improved cellular uptake. More interestingly, the redox responsiveness of the Cys disulfide bond can be reduced under the acidic microenvironment of a tumor and caused the dissociation of Fe\({}_{3}\)O\({}_{4}\)/Cys clusters into single Fe\({}_{3}\)O\({}_{4}\) NPs, thereby changing its imaging properties and allowing tumor detection [47]. In magnetic resonance angiography (MRA), an MRI exam of the blood vessels, using gadolinium-based contrast agent NPs, has reported the correlation with nephrogenic systemic fibrosis (NSF) in advanced kidney failure patient [48]. More recently, Ferumoxytol, a semisynthetic carbohydrate encapsulated SPOIN, was used in MRA to treat iron-deficiency anemia caused by CKD. [PERSON] et al., used ferumoxytol-enhanced magnetic resonance angiography (FeMRA) to obtain arteriography and venography images to assist the assessment of patients with kidney failure before being wait-listed for kidney transplantation, with no observed nephrotoxicity [49]. Using FeMRA to detect early stage kidney allograft rejection in patients with clinically manifested acute rejection, [PERSON] et al., observed prolonged T2 values compared to patients without signs of rejection and infiltrating macrophages [50].
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks
### Heterogeneous Networks  
The future of B5G wireless networks is forecasted to manage applications demanding massive data rates. One of the proposed solutions given by the third generation partnership project (3 GPP), Release 12, is to meet data rates demand to allow network densification through the deployment of SCs [45]. Such densification provides increased spectrum efficiency, increased network capacity and overall performance, cost-effective coverage expansion, and can even lower the mobile's power consumption because of communication with surrounding pico-cells [46]. This solution increases the coverage of the network dramatically. However, it necessitates invention in hardware miniaturizing and cost reduction in the construction of a small-cell base station (BS). These SC-BSs can be installed as low-power femto-cells for enterprise or residential installations or as higher-power pico-cells to improve a macro-cell's outdoor coverage. The synchronous operation of macro-, micro-, pico-, and femto-cells is referred to as HetNets [47]. Particularly, HetNet enables different types of SCs to cohabit with macro-cells by participating in the same resources of the spectrum, which can significantly enhance SE and decrease uncovered regions [48].
## 2 Modelling Methods
### Eulerian-Lagrangian Method  
The diffusion and drift of oil film were computed by the conservation law of mass and momentum based on the convection-diffusion equation to simplify the coupling of the particle motion, chemical dynamic and hydrodynamic equations [24].  
The ocean transport convection-diffusion equation was expressed in Lagrangian format as Equations (5)-(7). Differential dispersion was expressed by Equations (5)-(8) [25].  
\[\frac{DC}{D\mathrm{t}}\ =\ \frac{\partial}{\partial z}\bigg{(}k\frac{\partial C}{ \partial z}\bigg{)}+K_{H}\
abla^{2}C \tag{5}\]  
\[\
abla^{2}\ =\ \frac{\partial}{\partial x^{2}}+\frac{\partial}{\partial y^{2}} \tag{6}\]  
\[\frac{D}{D\mathrm{t}}\ =\ \frac{\partial}{\partial t}+u\frac{\partial}{ \partial x}+v\frac{\partial}{\partial y}+w\frac{\partial}{\partial z} \tag{7}\]  
\[\frac{DC}{D\mathrm{t}}\ =\ \frac{C_{Q}^{K+1}-C_{p}^{K}}{\Delta t} \tag{8}\]  
After the time \(\Delta t\), this micelle arrived at position \(Q\), and position \(P\) could be calculated by the velocity field along the trajectory of the substance micelle [26].  
The amount of oil spill was expressed as a release of particles. Thus, when the thickness of an oil film on the water surface reached a terminal value (the mass balance of oil spill), the oil layer could be regarded as a spatial mapping distribution of particles. Slick thickness \(h\) in grid cells was calculated by Equation (9).  
\[h\ =\ \sum_{i}\frac{V_{i}}{\Delta A} \tag{9}\]The Euler-Lagrangian method was then used to establish a drift-diffusion model of oil spill on the seawater surface. The equation of motion was expressed as Equation (10). The diffusion coefficient of the oil film was expressed as Equation (11).  
\[\frac{\partial h}{\partial t}+\frac{\partial h(u+\tau_{x}/f)}{\partial x}+\frac{ \partial h\big{(}v+\tau_{y}/f\big{)}}{\partial y}\ =\ \frac{\partial^{2}D_{s}h}{\partial x^{2}}+\frac{ \partial^{2}D_{s}h}{\partial y^{2}}+R_{h} \tag{10}\]  
\[D_{s}\ =\ gh^{2}(\rho_{w}-\rho_{o})\rho_{o}/\rho_{w}f \tag{11}\]